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b/devdocs/python~3.12/about.html new file mode 100644 index 00000000..e6d786fd --- /dev/null +++ b/devdocs/python~3.12/about.html @@ -0,0 +1,6 @@ +

About these documents

These documents are generated from reStructuredText sources by Sphinx, a document processor specifically written for the Python documentation.

Development of the documentation and its toolchain is an entirely volunteer effort, just like Python itself. If you want to contribute, please take a look at the Dealing with Bugs page for information on how to do so. New volunteers are always welcome!

Many thanks go to:

Contributors to the Python Documentation

Many people have contributed to the Python language, the Python standard library, and the Python documentation. See Misc/ACKS in the Python source distribution for a partial list of contributors.

It is only with the input and contributions of the Python community that Python has such wonderful documentation – Thank You!

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/about.html +

+
diff --git a/devdocs/python~3.12/bugs.html b/devdocs/python~3.12/bugs.html new file mode 100644 index 00000000..1e5b1be8 --- /dev/null +++ b/devdocs/python~3.12/bugs.html @@ -0,0 +1,18 @@ +

Dealing with Bugs

Python is a mature programming language which has established a reputation for stability. In order to maintain this reputation, the developers would like to know of any deficiencies you find in Python.

It can be sometimes faster to fix bugs yourself and contribute patches to Python as it streamlines the process and involves less people. Learn how to contribute.

Documentation bugs

If you find a bug in this documentation or would like to propose an improvement, please submit a bug report on the tracker. If you have a suggestion on how to fix it, include that as well.

You can also open a discussion item on our Documentation Discourse forum.

If you’re short on time, you can also email documentation bug reports to docs@python.org (behavioral bugs can be sent to python-list@python.org). ‘docs@’ is a mailing list run by volunteers; your request will be noticed, though it may take a while to be processed.

See also

Documentation bugs
+
+

A list of documentation bugs that have been submitted to the Python issue tracker.

Issue Tracking
+
+

Overview of the process involved in reporting an improvement on the tracker.

Helping with Documentation
+
+

Comprehensive guide for individuals that are interested in contributing to Python documentation.

Documentation Translations
+
+

A list of GitHub pages for documentation translation and their primary contacts.

Using the Python issue tracker

Issue reports for Python itself should be submitted via the GitHub issues tracker (https://github.com/python/cpython/issues). The GitHub issues tracker offers a web form which allows pertinent information to be entered and submitted to the developers.

The first step in filing a report is to determine whether the problem has already been reported. The advantage in doing so, aside from saving the developers’ time, is that you learn what has been done to fix it; it may be that the problem has already been fixed for the next release, or additional information is needed (in which case you are welcome to provide it if you can!). To do this, search the tracker using the search box at the top of the page.

If the problem you’re reporting is not already in the list, log in to GitHub. If you don’t already have a GitHub account, create a new account using the “Sign up” link. It is not possible to submit a bug report anonymously.

Being now logged in, you can submit an issue. Click on the “New issue” button in the top bar to report a new issue.

The submission form has two fields, “Title” and “Comment”.

For the “Title” field, enter a very short description of the problem; fewer than ten words is good.

In the “Comment” field, describe the problem in detail, including what you expected to happen and what did happen. Be sure to include whether any extension modules were involved, and what hardware and software platform you were using (including version information as appropriate).

Each issue report will be reviewed by a developer who will determine what needs to be done to correct the problem. You will receive an update each time an action is taken on the issue.

See also

How to Report Bugs Effectively
+
+

Article which goes into some detail about how to create a useful bug report. This describes what kind of information is useful and why it is useful.

Bug Writing Guidelines
+
+

Information about writing a good bug report. Some of this is specific to the Mozilla project, but describes general good practices.

Getting started contributing to Python yourself

Beyond just reporting bugs that you find, you are also welcome to submit patches to fix them. You can find more information on how to get started patching Python in the Python Developer’s Guide. If you have questions, the core-mentorship mailing list is a friendly place to get answers to any and all questions pertaining to the process of fixing issues in Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/bugs.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fabstract.html b/devdocs/python~3.12/c-api%2Fabstract.html new file mode 100644 index 00000000..fd257b4e --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fabstract.html @@ -0,0 +1,11 @@ +

Abstract Objects Layer

The functions in this chapter interact with Python objects regardless of their type, or with wide classes of object types (e.g. all numerical types, or all sequence types). When used on object types for which they do not apply, they will raise a Python exception.

It is not possible to use these functions on objects that are not properly initialized, such as a list object that has been created by PyList_New(), but whose items have not been set to some non-NULL value yet.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/abstract.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fallocation.html b/devdocs/python~3.12/c-api%2Fallocation.html new file mode 100644 index 00000000..34e57881 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fallocation.html @@ -0,0 +1,32 @@ +

Allocating Objects on the Heap

+PyObject *_PyObject_New(PyTypeObject *type)
Return value: New reference.
+
+PyVarObject *_PyObject_NewVar(PyTypeObject *type, Py_ssize_t size)
Return value: New reference.
+
+PyObject *PyObject_Init(PyObject *op, PyTypeObject *type)
+Return value: Borrowed reference. Part of the Stable ABI.

Initialize a newly allocated object op with its type and initial reference. Returns the initialized object. If type indicates that the object participates in the cyclic garbage detector, it is added to the detector’s set of observed objects. Other fields of the object are not affected.

+
+PyVarObject *PyObject_InitVar(PyVarObject *op, PyTypeObject *type, Py_ssize_t size)
+Return value: Borrowed reference. Part of the Stable ABI.

This does everything PyObject_Init() does, and also initializes the length information for a variable-size object.

+
+PyObject_New(TYPE, typeobj)
+

Allocate a new Python object using the C structure type TYPE and the Python type object typeobj (PyTypeObject*). Fields not defined by the Python object header are not initialized. The caller will own the only reference to the object (i.e. its reference count will be one). The size of the memory allocation is determined from the tp_basicsize field of the type object.

+
+PyObject_NewVar(TYPE, typeobj, size)
+

Allocate a new Python object using the C structure type TYPE and the Python type object typeobj (PyTypeObject*). Fields not defined by the Python object header are not initialized. The allocated memory allows for the TYPE structure plus size (Py_ssize_t) fields of the size given by the tp_itemsize field of typeobj. This is useful for implementing objects like tuples, which are able to determine their size at construction time. Embedding the array of fields into the same allocation decreases the number of allocations, improving the memory management efficiency.

+
+void PyObject_Del(void *op)
+

Releases memory allocated to an object using PyObject_New or PyObject_NewVar. This is normally called from the tp_dealloc handler specified in the object’s type. The fields of the object should not be accessed after this call as the memory is no longer a valid Python object.

+
+PyObject _Py_NoneStruct
+

Object which is visible in Python as None. This should only be accessed using the Py_None macro, which evaluates to a pointer to this object.

+

See also

+ PyModule_Create() +
+
+

To allocate and create extension modules.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/allocation.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fapiabiversion.html b/devdocs/python~3.12/c-api%2Fapiabiversion.html new file mode 100644 index 00000000..7ef6a990 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fapiabiversion.html @@ -0,0 +1,33 @@ +

API and ABI Versioning

CPython exposes its version number in the following macros. Note that these correspond to the version code is built with, not necessarily the version used at run time.

See C API Stability for a discussion of API and ABI stability across versions.

+PY_MAJOR_VERSION
+

The 3 in 3.4.1a2.

+
+PY_MINOR_VERSION
+

The 4 in 3.4.1a2.

+
+PY_MICRO_VERSION
+

The 1 in 3.4.1a2.

+
+PY_RELEASE_LEVEL
+

The a in 3.4.1a2. This can be 0xA for alpha, 0xB for beta, 0xC for release candidate or 0xF for final.

+
+PY_RELEASE_SERIAL
+

The 2 in 3.4.1a2. Zero for final releases.

+
+PY_VERSION_HEX
+

The Python version number encoded in a single integer.

The underlying version information can be found by treating it as a 32 bit number in the following manner:

+ + + + + +

Bytes

Bits (big endian order)

Meaning

Value for 3.4.1a2

1

1-8

PY_MAJOR_VERSION

0x03

2

9-16

PY_MINOR_VERSION

0x04

3

17-24

PY_MICRO_VERSION

0x01

4

25-28

PY_RELEASE_LEVEL

0xA

29-32

PY_RELEASE_SERIAL

0x2

Thus 3.4.1a2 is hexversion 0x030401a2 and 3.10.0 is hexversion 0x030a00f0.

Use this for numeric comparisons, e.g. #if PY_VERSION_HEX >= ....

This version is also available via the symbol Py_Version.

+
+const unsigned long Py_Version
+ Part of the Stable ABI since version 3.11.

The Python runtime version number encoded in a single constant integer, with the same format as the PY_VERSION_HEX macro. This contains the Python version used at run time.

New in version 3.11.

+

All the given macros are defined in Include/patchlevel.h.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/apiabiversion.html +

+
diff --git a/devdocs/python~3.12/c-api%2Farg.html b/devdocs/python~3.12/c-api%2Farg.html new file mode 100644 index 00000000..fff2a626 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Farg.html @@ -0,0 +1,273 @@ +

Parsing arguments and building values

These functions are useful when creating your own extensions functions and methods. Additional information and examples are available in Extending and Embedding the Python Interpreter.

The first three of these functions described, PyArg_ParseTuple(), PyArg_ParseTupleAndKeywords(), and PyArg_Parse(), all use format strings which are used to tell the function about the expected arguments. The format strings use the same syntax for each of these functions.

Parsing arguments

A format string consists of zero or more “format units.” A format unit describes one Python object; it is usually a single character or a parenthesized sequence of format units. With a few exceptions, a format unit that is not a parenthesized sequence normally corresponds to a single address argument to these functions. In the following description, the quoted form is the format unit; the entry in (round) parentheses is the Python object type that matches the format unit; and the entry in [square] brackets is the type of the C variable(s) whose address should be passed.

Strings and buffers

These formats allow accessing an object as a contiguous chunk of memory. You don’t have to provide raw storage for the returned unicode or bytes area.

Unless otherwise stated, buffers are not NUL-terminated.

There are three ways strings and buffers can be converted to C:

  • Formats such as y* and s* fill a Py_buffer structure. This locks the underlying buffer so that the caller can subsequently use the buffer even inside a Py_BEGIN_ALLOW_THREADS block without the risk of mutable data being resized or destroyed. As a result, you have to call PyBuffer_Release() after you have finished processing the data (or in any early abort case).
  • The es, es#, et and et# formats allocate the result buffer. You have to call PyMem_Free() after you have finished processing the data (or in any early abort case).
  • +

    Other formats take a str or a read-only bytes-like object, such as bytes, and provide a const char * pointer to its buffer. In this case the buffer is “borrowed”: it is managed by the corresponding Python object, and shares the lifetime of this object. You won’t have to release any memory yourself.

    To ensure that the underlying buffer may be safely borrowed, the object’s PyBufferProcs.bf_releasebuffer field must be NULL. This disallows common mutable objects such as bytearray, but also some read-only objects such as memoryview of bytes.

    Besides this bf_releasebuffer requirement, there is no check to verify whether the input object is immutable (e.g. whether it would honor a request for a writable buffer, or whether another thread can mutate the data).

Note

For all # variants of formats (s#, y#, etc.), the macro PY_SSIZE_T_CLEAN must be defined before including Python.h. On Python 3.9 and older, the type of the length argument is Py_ssize_t if the PY_SSIZE_T_CLEAN macro is defined, or int otherwise.

+s (str) [const char *]
+
+

Convert a Unicode object to a C pointer to a character string. A pointer to an existing string is stored in the character pointer variable whose address you pass. The C string is NUL-terminated. The Python string must not contain embedded null code points; if it does, a ValueError exception is raised. Unicode objects are converted to C strings using 'utf-8' encoding. If this conversion fails, a UnicodeError is raised.

Note

This format does not accept bytes-like objects. If you want to accept filesystem paths and convert them to C character strings, it is preferable to use the O& format with PyUnicode_FSConverter() as converter.

Changed in version 3.5: Previously, TypeError was raised when embedded null code points were encountered in the Python string.

+s* (str or bytes-like object) [Py_buffer]
+
+

This format accepts Unicode objects as well as bytes-like objects. It fills a Py_buffer structure provided by the caller. In this case the resulting C string may contain embedded NUL bytes. Unicode objects are converted to C strings using 'utf-8' encoding.

+s# (str, read-only bytes-like object) [const char *, Py_ssize_t]
+
+

Like s*, except that it provides a borrowed buffer. The result is stored into two C variables, the first one a pointer to a C string, the second one its length. The string may contain embedded null bytes. Unicode objects are converted to C strings using 'utf-8' encoding.

+z (str or None) [const char *]
+
+

Like s, but the Python object may also be None, in which case the C pointer is set to NULL.

+z* (str, bytes-like object or None) [Py_buffer]
+
+

Like s*, but the Python object may also be None, in which case the buf member of the Py_buffer structure is set to NULL.

+z# (str, read-only bytes-like object or None) [const char *, Py_ssize_t]
+
+

Like s#, but the Python object may also be None, in which case the C pointer is set to NULL.

+y (read-only bytes-like object) [const char *]
+
+

This format converts a bytes-like object to a C pointer to a borrowed character string; it does not accept Unicode objects. The bytes buffer must not contain embedded null bytes; if it does, a ValueError exception is raised.

Changed in version 3.5: Previously, TypeError was raised when embedded null bytes were encountered in the bytes buffer.

+y* (bytes-like object) [Py_buffer]
+
+

This variant on s* doesn’t accept Unicode objects, only bytes-like objects. This is the recommended way to accept binary data.

+y# (read-only bytes-like object) [const char *, Py_ssize_t]
+
+

This variant on s# doesn’t accept Unicode objects, only bytes-like objects.

+S (bytes) [PyBytesObject *]
+
+

Requires that the Python object is a bytes object, without attempting any conversion. Raises TypeError if the object is not a bytes object. The C variable may also be declared as PyObject*.

+Y (bytearray) [PyByteArrayObject *]
+
+

Requires that the Python object is a bytearray object, without attempting any conversion. Raises TypeError if the object is not a bytearray object. The C variable may also be declared as PyObject*.

+U (str) [PyObject *]
+
+

Requires that the Python object is a Unicode object, without attempting any conversion. Raises TypeError if the object is not a Unicode object. The C variable may also be declared as PyObject*.

+w* (read-write bytes-like object) [Py_buffer]
+
+

This format accepts any object which implements the read-write buffer interface. It fills a Py_buffer structure provided by the caller. The buffer may contain embedded null bytes. The caller have to call PyBuffer_Release() when it is done with the buffer.

+es (str) [const char *encoding, char **buffer]
+
+

This variant on s is used for encoding Unicode into a character buffer. It only works for encoded data without embedded NUL bytes.

This format requires two arguments. The first is only used as input, and must be a const char* which points to the name of an encoding as a NUL-terminated string, or NULL, in which case 'utf-8' encoding is used. An exception is raised if the named encoding is not known to Python. The second argument must be a char**; the value of the pointer it references will be set to a buffer with the contents of the argument text. The text will be encoded in the encoding specified by the first argument.

PyArg_ParseTuple() will allocate a buffer of the needed size, copy the encoded data into this buffer and adjust *buffer to reference the newly allocated storage. The caller is responsible for calling PyMem_Free() to free the allocated buffer after use.

+et (str, bytes or bytearray) [const char *encoding, char **buffer]
+
+

Same as es except that byte string objects are passed through without recoding them. Instead, the implementation assumes that the byte string object uses the encoding passed in as parameter.

+es# (str) [const char *encoding, char **buffer, Py_ssize_t *buffer_length]
+
+

This variant on s# is used for encoding Unicode into a character buffer. Unlike the es format, this variant allows input data which contains NUL characters.

It requires three arguments. The first is only used as input, and must be a const char* which points to the name of an encoding as a NUL-terminated string, or NULL, in which case 'utf-8' encoding is used. An exception is raised if the named encoding is not known to Python. The second argument must be a char**; the value of the pointer it references will be set to a buffer with the contents of the argument text. The text will be encoded in the encoding specified by the first argument. The third argument must be a pointer to an integer; the referenced integer will be set to the number of bytes in the output buffer.

There are two modes of operation:

If *buffer points a NULL pointer, the function will allocate a buffer of the needed size, copy the encoded data into this buffer and set *buffer to reference the newly allocated storage. The caller is responsible for calling PyMem_Free() to free the allocated buffer after usage.

If *buffer points to a non-NULL pointer (an already allocated buffer), PyArg_ParseTuple() will use this location as the buffer and interpret the initial value of *buffer_length as the buffer size. It will then copy the encoded data into the buffer and NUL-terminate it. If the buffer is not large enough, a ValueError will be set.

In both cases, *buffer_length is set to the length of the encoded data without the trailing NUL byte.

+et# (str, bytes or bytearray) [const char *encoding, char **buffer, Py_ssize_t *buffer_length]
+
+

Same as es# except that byte string objects are passed through without recoding them. Instead, the implementation assumes that the byte string object uses the encoding passed in as parameter.

Changed in version 3.12: u, u#, Z, and Z# are removed because they used a legacy Py_UNICODE* representation.

Numbers

+b (int) [unsigned char]
+
+

Convert a nonnegative Python integer to an unsigned tiny int, stored in a C unsigned char.

+B (int) [unsigned char]
+
+

Convert a Python integer to a tiny int without overflow checking, stored in a C unsigned char.

+h (int) [short int]
+
+

Convert a Python integer to a C short int.

+H (int) [unsigned short int]
+
+

Convert a Python integer to a C unsigned short int, without overflow checking.

+i (int) [int]
+
+

Convert a Python integer to a plain C int.

+I (int) [unsigned int]
+
+

Convert a Python integer to a C unsigned int, without overflow checking.

+l (int) [long int]
+
+

Convert a Python integer to a C long int.

+k (int) [unsigned long]
+
+

Convert a Python integer to a C unsigned long without overflow checking.

+L (int) [long long]
+
+

Convert a Python integer to a C long long.

+K (int) [unsigned long long]
+
+

Convert a Python integer to a C unsigned long long without overflow checking.

+n (int) [Py_ssize_t]
+
+

Convert a Python integer to a C Py_ssize_t.

+c (bytes or bytearray of length 1) [char]
+
+

Convert a Python byte, represented as a bytes or bytearray object of length 1, to a C char.

Changed in version 3.3: Allow bytearray objects.

+C (str of length 1) [int]
+
+

Convert a Python character, represented as a str object of length 1, to a C int.

+f (float) [float]
+
+

Convert a Python floating point number to a C float.

+d (float) [double]
+
+

Convert a Python floating point number to a C double.

+D (complex) [Py_complex]
+
+

Convert a Python complex number to a C Py_complex structure.

Other objects

+O (object) [PyObject *]
+
+

Store a Python object (without any conversion) in a C object pointer. The C program thus receives the actual object that was passed. A new strong reference to the object is not created (i.e. its reference count is not increased). The pointer stored is not NULL.

+O! (object) [typeobject, PyObject *]
+
+

Store a Python object in a C object pointer. This is similar to O, but takes two C arguments: the first is the address of a Python type object, the second is the address of the C variable (of type PyObject*) into which the object pointer is stored. If the Python object does not have the required type, TypeError is raised.

+O& (object) [converter, anything]
+
+

Convert a Python object to a C variable through a converter function. This takes two arguments: the first is a function, the second is the address of a C variable (of arbitrary type), converted to void*. The converter function in turn is called as follows:

status = converter(object, address);
+

where object is the Python object to be converted and address is the void* argument that was passed to the PyArg_Parse* function. The returned status should be 1 for a successful conversion and 0 if the conversion has failed. When the conversion fails, the converter function should raise an exception and leave the content of address unmodified.

If the converter returns Py_CLEANUP_SUPPORTED, it may get called a second time if the argument parsing eventually fails, giving the converter a chance to release any memory that it had already allocated. In this second call, the object parameter will be NULL; address will have the same value as in the original call.

Changed in version 3.1: Py_CLEANUP_SUPPORTED was added.

+p (bool) [int]
+
+

Tests the value passed in for truth (a boolean predicate) and converts the result to its equivalent C true/false integer value. Sets the int to 1 if the expression was true and 0 if it was false. This accepts any valid Python value. See Truth Value Testing for more information about how Python tests values for truth.

New in version 3.3.

+(items) (tuple) [matching-items]
+
+

The object must be a Python sequence whose length is the number of format units in items. The C arguments must correspond to the individual format units in items. Format units for sequences may be nested.

It is possible to pass “long” integers (integers whose value exceeds the platform’s LONG_MAX) however no proper range checking is done — the most significant bits are silently truncated when the receiving field is too small to receive the value (actually, the semantics are inherited from downcasts in C — your mileage may vary).

A few other characters have a meaning in a format string. These may not occur inside nested parentheses. They are:

+|
+
+

Indicates that the remaining arguments in the Python argument list are optional. The C variables corresponding to optional arguments should be initialized to their default value — when an optional argument is not specified, PyArg_ParseTuple() does not touch the contents of the corresponding C variable(s).

+$
+
+

PyArg_ParseTupleAndKeywords() only: Indicates that the remaining arguments in the Python argument list are keyword-only. Currently, all keyword-only arguments must also be optional arguments, so | must always be specified before $ in the format string.

New in version 3.3.

+:
+
+

The list of format units ends here; the string after the colon is used as the function name in error messages (the “associated value” of the exception that PyArg_ParseTuple() raises).

+;
+
+

The list of format units ends here; the string after the semicolon is used as the error message instead of the default error message. : and ; mutually exclude each other.

Note that any Python object references which are provided to the caller are borrowed references; do not release them (i.e. do not decrement their reference count)!

Additional arguments passed to these functions must be addresses of variables whose type is determined by the format string; these are used to store values from the input tuple. There are a few cases, as described in the list of format units above, where these parameters are used as input values; they should match what is specified for the corresponding format unit in that case.

For the conversion to succeed, the arg object must match the format and the format must be exhausted. On success, the PyArg_Parse* functions return true, otherwise they return false and raise an appropriate exception. When the PyArg_Parse* functions fail due to conversion failure in one of the format units, the variables at the addresses corresponding to that and the following format units are left untouched.

API Functions

+int PyArg_ParseTuple(PyObject *args, const char *format, ...)
+ Part of the Stable ABI.

Parse the parameters of a function that takes only positional parameters into local variables. Returns true on success; on failure, it returns false and raises the appropriate exception.

+
+int PyArg_VaParse(PyObject *args, const char *format, va_list vargs)
+ Part of the Stable ABI.

Identical to PyArg_ParseTuple(), except that it accepts a va_list rather than a variable number of arguments.

+
+int PyArg_ParseTupleAndKeywords(PyObject *args, PyObject *kw, const char *format, char *keywords[], ...)
+ Part of the Stable ABI.

Parse the parameters of a function that takes both positional and keyword parameters into local variables. The keywords argument is a NULL-terminated array of keyword parameter names. Empty names denote positional-only parameters. Returns true on success; on failure, it returns false and raises the appropriate exception.

Changed in version 3.6: Added support for positional-only parameters.

+
+int PyArg_VaParseTupleAndKeywords(PyObject *args, PyObject *kw, const char *format, char *keywords[], va_list vargs)
+ Part of the Stable ABI.

Identical to PyArg_ParseTupleAndKeywords(), except that it accepts a va_list rather than a variable number of arguments.

+
+int PyArg_ValidateKeywordArguments(PyObject*)
+ Part of the Stable ABI.

Ensure that the keys in the keywords argument dictionary are strings. This is only needed if PyArg_ParseTupleAndKeywords() is not used, since the latter already does this check.

New in version 3.2.

+
+int PyArg_Parse(PyObject *args, const char *format, ...)
+ Part of the Stable ABI.

Function used to deconstruct the argument lists of “old-style” functions — these are functions which use the METH_OLDARGS parameter parsing method, which has been removed in Python 3. This is not recommended for use in parameter parsing in new code, and most code in the standard interpreter has been modified to no longer use this for that purpose. It does remain a convenient way to decompose other tuples, however, and may continue to be used for that purpose.

+
+int PyArg_UnpackTuple(PyObject *args, const char *name, Py_ssize_t min, Py_ssize_t max, ...)
+ Part of the Stable ABI.

A simpler form of parameter retrieval which does not use a format string to specify the types of the arguments. Functions which use this method to retrieve their parameters should be declared as METH_VARARGS in function or method tables. The tuple containing the actual parameters should be passed as args; it must actually be a tuple. The length of the tuple must be at least min and no more than max; min and max may be equal. Additional arguments must be passed to the function, each of which should be a pointer to a PyObject* variable; these will be filled in with the values from args; they will contain borrowed references. The variables which correspond to optional parameters not given by args will not be filled in; these should be initialized by the caller. This function returns true on success and false if args is not a tuple or contains the wrong number of elements; an exception will be set if there was a failure.

This is an example of the use of this function, taken from the sources for the _weakref helper module for weak references:

static PyObject *
+weakref_ref(PyObject *self, PyObject *args)
+{
+    PyObject *object;
+    PyObject *callback = NULL;
+    PyObject *result = NULL;
+
+    if (PyArg_UnpackTuple(args, "ref", 1, 2, &object, &callback)) {
+        result = PyWeakref_NewRef(object, callback);
+    }
+    return result;
+}
+

The call to PyArg_UnpackTuple() in this example is entirely equivalent to this call to PyArg_ParseTuple():

PyArg_ParseTuple(args, "O|O:ref", &object, &callback)
+
+

Building values

+PyObject *Py_BuildValue(const char *format, ...)
+Return value: New reference. Part of the Stable ABI.

Create a new value based on a format string similar to those accepted by the PyArg_Parse* family of functions and a sequence of values. Returns the value or NULL in the case of an error; an exception will be raised if NULL is returned.

Py_BuildValue() does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns None; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.

When memory buffers are passed as parameters to supply data to build objects, as for the s and s# formats, the required data is copied. Buffers provided by the caller are never referenced by the objects created by Py_BuildValue(). In other words, if your code invokes malloc() and passes the allocated memory to Py_BuildValue(), your code is responsible for calling free() for that memory once Py_BuildValue() returns.

In the following description, the quoted form is the format unit; the entry in (round) parentheses is the Python object type that the format unit will return; and the entry in [square] brackets is the type of the C value(s) to be passed.

The characters space, tab, colon and comma are ignored in format strings (but not within format units such as s#). This can be used to make long format strings a tad more readable.

+s (str or None) [const char *]
+
+

Convert a null-terminated C string to a Python str object using 'utf-8' encoding. If the C string pointer is NULL, None is used.

+s# (str or None) [const char *, Py_ssize_t]
+
+

Convert a C string and its length to a Python str object using 'utf-8' encoding. If the C string pointer is NULL, the length is ignored and None is returned.

+y (bytes) [const char *]
+
+

This converts a C string to a Python bytes object. If the C string pointer is NULL, None is returned.

+y# (bytes) [const char *, Py_ssize_t]
+
+

This converts a C string and its lengths to a Python object. If the C string pointer is NULL, None is returned.

+z (str or None) [const char *]
+
+

Same as s.

+z# (str or None) [const char *, Py_ssize_t]
+
+

Same as s#.

+u (str) [const wchar_t *]
+
+

Convert a null-terminated wchar_t buffer of Unicode (UTF-16 or UCS-4) data to a Python Unicode object. If the Unicode buffer pointer is NULL, None is returned.

+u# (str) [const wchar_t *, Py_ssize_t]
+
+

Convert a Unicode (UTF-16 or UCS-4) data buffer and its length to a Python Unicode object. If the Unicode buffer pointer is NULL, the length is ignored and None is returned.

+U (str or None) [const char *]
+
+

Same as s.

+U# (str or None) [const char *, Py_ssize_t]
+
+

Same as s#.

+i (int) [int]
+
+

Convert a plain C int to a Python integer object.

+b (int) [char]
+
+

Convert a plain C char to a Python integer object.

+h (int) [short int]
+
+

Convert a plain C short int to a Python integer object.

+l (int) [long int]
+
+

Convert a C long int to a Python integer object.

+B (int) [unsigned char]
+
+

Convert a C unsigned char to a Python integer object.

+H (int) [unsigned short int]
+
+

Convert a C unsigned short int to a Python integer object.

+I (int) [unsigned int]
+
+

Convert a C unsigned int to a Python integer object.

+k (int) [unsigned long]
+
+

Convert a C unsigned long to a Python integer object.

+L (int) [long long]
+
+

Convert a C long long to a Python integer object.

+K (int) [unsigned long long]
+
+

Convert a C unsigned long long to a Python integer object.

+n (int) [Py_ssize_t]
+
+

Convert a C Py_ssize_t to a Python integer.

+c (bytes of length 1) [char]
+
+

Convert a C int representing a byte to a Python bytes object of length 1.

+C (str of length 1) [int]
+
+

Convert a C int representing a character to Python str object of length 1.

+d (float) [double]
+
+

Convert a C double to a Python floating point number.

+f (float) [float]
+
+

Convert a C float to a Python floating point number.

+D (complex) [Py_complex *]
+
+

Convert a C Py_complex structure to a Python complex number.

+O (object) [PyObject *]
+
+

Pass a Python object untouched but create a new strong reference to it (i.e. its reference count is incremented by one). If the object passed in is a NULL pointer, it is assumed that this was caused because the call producing the argument found an error and set an exception. Therefore, Py_BuildValue() will return NULL but won’t raise an exception. If no exception has been raised yet, SystemError is set.

+S (object) [PyObject *]
+
+

Same as O.

+N (object) [PyObject *]
+
+

Same as O, except it doesn’t create a new strong reference. Useful when the object is created by a call to an object constructor in the argument list.

+O& (object) [converter, anything]
+
+

Convert anything to a Python object through a converter function. The function is called with anything (which should be compatible with void*) as its argument and should return a “new” Python object, or NULL if an error occurred.

+(items) (tuple) [matching-items]
+
+

Convert a sequence of C values to a Python tuple with the same number of items.

+[items] (list) [matching-items]
+
+

Convert a sequence of C values to a Python list with the same number of items.

+{items} (dict) [matching-items]
+
+

Convert a sequence of C values to a Python dictionary. Each pair of consecutive C values adds one item to the dictionary, serving as key and value, respectively.

If there is an error in the format string, the SystemError exception is set and NULL returned.

+
+PyObject *Py_VaBuildValue(const char *format, va_list vargs)
+Return value: New reference. Part of the Stable ABI.

Identical to Py_BuildValue(), except that it accepts a va_list rather than a variable number of arguments.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/arg.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fbool.html b/devdocs/python~3.12/c-api%2Fbool.html new file mode 100644 index 00000000..fefc0033 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fbool.html @@ -0,0 +1,27 @@ +

Boolean Objects

Booleans in Python are implemented as a subclass of integers. There are only two booleans, Py_False and Py_True. As such, the normal creation and deletion functions don’t apply to booleans. The following macros are available, however.

+PyTypeObject PyBool_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python boolean type; it is the same object as bool in the Python layer.

+
+int PyBool_Check(PyObject *o)
+

Return true if o is of type PyBool_Type. This function always succeeds.

+
+PyObject *Py_False
+

The Python False object. This object has no methods and is immortal.

+

Changed in version 3.12: Py_False is immortal.

+PyObject *Py_True
+

The Python True object. This object has no methods and is immortal.

+

Changed in version 3.12: Py_True is immortal.

+Py_RETURN_FALSE
+

Return Py_False from a function.

+
+Py_RETURN_TRUE
+

Return Py_True from a function.

+
+PyObject *PyBool_FromLong(long v)
+Return value: New reference. Part of the Stable ABI.

Return Py_True or Py_False, depending on the truth value of v.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/bool.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fbuffer.html b/devdocs/python~3.12/c-api%2Fbuffer.html new file mode 100644 index 00000000..f392187e --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fbuffer.html @@ -0,0 +1,187 @@ +

Buffer Protocol

Certain objects available in Python wrap access to an underlying memory array or buffer. Such objects include the built-in bytes and bytearray, and some extension types like array.array. Third-party libraries may define their own types for special purposes, such as image processing or numeric analysis.

While each of these types have their own semantics, they share the common characteristic of being backed by a possibly large memory buffer. It is then desirable, in some situations, to access that buffer directly and without intermediate copying.

Python provides such a facility at the C level in the form of the buffer protocol. This protocol has two sides:

Simple objects such as bytes and bytearray expose their underlying buffer in byte-oriented form. Other forms are possible; for example, the elements exposed by an array.array can be multi-byte values.

An example consumer of the buffer interface is the write() method of file objects: any object that can export a series of bytes through the buffer interface can be written to a file. While write() only needs read-only access to the internal contents of the object passed to it, other methods such as readinto() need write access to the contents of their argument. The buffer interface allows objects to selectively allow or reject exporting of read-write and read-only buffers.

There are two ways for a consumer of the buffer interface to acquire a buffer over a target object:

In both cases, PyBuffer_Release() must be called when the buffer isn’t needed anymore. Failure to do so could lead to various issues such as resource leaks.

Buffer structure

Buffer structures (or simply “buffers”) are useful as a way to expose the binary data from another object to the Python programmer. They can also be used as a zero-copy slicing mechanism. Using their ability to reference a block of memory, it is possible to expose any data to the Python programmer quite easily. The memory could be a large, constant array in a C extension, it could be a raw block of memory for manipulation before passing to an operating system library, or it could be used to pass around structured data in its native, in-memory format.

Contrary to most data types exposed by the Python interpreter, buffers are not PyObject pointers but rather simple C structures. This allows them to be created and copied very simply. When a generic wrapper around a buffer is needed, a memoryview object can be created.

For short instructions how to write an exporting object, see Buffer Object Structures. For obtaining a buffer, see PyObject_GetBuffer().

+type Py_buffer
+ Part of the Stable ABI (including all members) since version 3.11.
+void *buf
+

A pointer to the start of the logical structure described by the buffer fields. This can be any location within the underlying physical memory block of the exporter. For example, with negative strides the value may point to the end of the memory block.

For contiguous arrays, the value points to the beginning of the memory block.

+
+PyObject *obj
+

A new reference to the exporting object. The reference is owned by the consumer and automatically released (i.e. reference count decremented) and set to NULL by PyBuffer_Release(). The field is the equivalent of the return value of any standard C-API function.

As a special case, for temporary buffers that are wrapped by PyMemoryView_FromBuffer() or PyBuffer_FillInfo() this field is NULL. In general, exporting objects MUST NOT use this scheme.

+
+Py_ssize_t len
+

product(shape) * itemsize. For contiguous arrays, this is the length of the underlying memory block. For non-contiguous arrays, it is the length that the logical structure would have if it were copied to a contiguous representation.

Accessing ((char *)buf)[0] up to ((char *)buf)[len-1] is only valid if the buffer has been obtained by a request that guarantees contiguity. In most cases such a request will be PyBUF_SIMPLE or PyBUF_WRITABLE.

+
+int readonly
+

An indicator of whether the buffer is read-only. This field is controlled by the PyBUF_WRITABLE flag.

+
+Py_ssize_t itemsize
+

Item size in bytes of a single element. Same as the value of struct.calcsize() called on non-NULL format values.

Important exception: If a consumer requests a buffer without the PyBUF_FORMAT flag, format will be set to NULL, but itemsize still has the value for the original format.

If shape is present, the equality product(shape) * itemsize == len still holds and the consumer can use itemsize to navigate the buffer.

If shape is NULL as a result of a PyBUF_SIMPLE or a PyBUF_WRITABLE request, the consumer must disregard itemsize and assume itemsize == 1.

+
+const char *format
+

A NUL terminated string in struct module style syntax describing the contents of a single item. If this is NULL, "B" (unsigned bytes) is assumed.

This field is controlled by the PyBUF_FORMAT flag.

+
+int ndim
+

The number of dimensions the memory represents as an n-dimensional array. If it is 0, buf points to a single item representing a scalar. In this case, shape, strides and suboffsets MUST be NULL. The maximum number of dimensions is given by PyBUF_MAX_NDIM.

+
+Py_ssize_t *shape
+

An array of Py_ssize_t of length ndim indicating the shape of the memory as an n-dimensional array. Note that shape[0] * ... * shape[ndim-1] * itemsize MUST be equal to len.

Shape values are restricted to shape[n] >= 0. The case shape[n] == 0 requires special attention. See complex arrays for further information.

The shape array is read-only for the consumer.

+
+Py_ssize_t *strides
+

An array of Py_ssize_t of length ndim giving the number of bytes to skip to get to a new element in each dimension.

Stride values can be any integer. For regular arrays, strides are usually positive, but a consumer MUST be able to handle the case strides[n] <= 0. See complex arrays for further information.

The strides array is read-only for the consumer.

+
+Py_ssize_t *suboffsets
+

An array of Py_ssize_t of length ndim. If suboffsets[n] >= 0, the values stored along the nth dimension are pointers and the suboffset value dictates how many bytes to add to each pointer after de-referencing. A suboffset value that is negative indicates that no de-referencing should occur (striding in a contiguous memory block).

If all suboffsets are negative (i.e. no de-referencing is needed), then this field must be NULL (the default value).

This type of array representation is used by the Python Imaging Library (PIL). See complex arrays for further information how to access elements of such an array.

The suboffsets array is read-only for the consumer.

+
+void *internal
+

This is for use internally by the exporting object. For example, this might be re-cast as an integer by the exporter and used to store flags about whether or not the shape, strides, and suboffsets arrays must be freed when the buffer is released. The consumer MUST NOT alter this value.

+
+

Constants:

+PyBUF_MAX_NDIM
+

The maximum number of dimensions the memory represents. Exporters MUST respect this limit, consumers of multi-dimensional buffers SHOULD be able to handle up to PyBUF_MAX_NDIM dimensions. Currently set to 64.

+

Buffer request types

Buffers are usually obtained by sending a buffer request to an exporting object via PyObject_GetBuffer(). Since the complexity of the logical structure of the memory can vary drastically, the consumer uses the flags argument to specify the exact buffer type it can handle.

All Py_buffer fields are unambiguously defined by the request type.

request-independent fields

The following fields are not influenced by flags and must always be filled in with the correct values: obj, buf, len, itemsize, ndim.

readonly, format

+PyBUF_WRITABLE
+

Controls the readonly field. If set, the exporter MUST provide a writable buffer or else report failure. Otherwise, the exporter MAY provide either a read-only or writable buffer, but the choice MUST be consistent for all consumers.

+
+PyBUF_FORMAT
+

Controls the format field. If set, this field MUST be filled in correctly. Otherwise, this field MUST be NULL.

+

PyBUF_WRITABLE can be |’d to any of the flags in the next section. Since PyBUF_SIMPLE is defined as 0, PyBUF_WRITABLE can be used as a stand-alone flag to request a simple writable buffer.

PyBUF_FORMAT can be |’d to any of the flags except PyBUF_SIMPLE. The latter already implies format B (unsigned bytes).

shape, strides, suboffsets

The flags that control the logical structure of the memory are listed in decreasing order of complexity. Note that each flag contains all bits of the flags below it.

+ + + + +

Request

shape

strides

suboffsets
+
+PyBUF_INDIRECT
+

yes

yes

if needed
+
+PyBUF_STRIDES
+

yes

yes

NULL
+
+PyBUF_ND
+

yes

NULL

NULL
+
+PyBUF_SIMPLE
+

NULL

NULL

NULL

contiguity requests

C or Fortran contiguity can be explicitly requested, with and without stride information. Without stride information, the buffer must be C-contiguous.

+ + + + +

Request

shape

strides

suboffsets

contig
+
+PyBUF_C_CONTIGUOUS
+

yes

yes

NULL

C
+
+PyBUF_F_CONTIGUOUS
+

yes

yes

NULL

F
+
+PyBUF_ANY_CONTIGUOUS
+

yes

yes

NULL

C or F

PyBUF_ND

yes

NULL

NULL

C

compound requests

All possible requests are fully defined by some combination of the flags in the previous section. For convenience, the buffer protocol provides frequently used combinations as single flags.

In the following table U stands for undefined contiguity. The consumer would have to call PyBuffer_IsContiguous() to determine contiguity.

+ + + + + + + + +

Request

shape

strides

suboffsets

contig

readonly

format
+
+PyBUF_FULL
+

yes

yes

if needed

U

0

yes
+
+PyBUF_FULL_RO
+

yes

yes

if needed

U

1 or 0

yes
+
+PyBUF_RECORDS
+

yes

yes

NULL

U

0

yes
+
+PyBUF_RECORDS_RO
+

yes

yes

NULL

U

1 or 0

yes
+
+PyBUF_STRIDED
+

yes

yes

NULL

U

0

NULL
+
+PyBUF_STRIDED_RO
+

yes

yes

NULL

U

1 or 0

NULL
+
+PyBUF_CONTIG
+

yes

NULL

NULL

C

0

NULL
+
+PyBUF_CONTIG_RO
+

yes

NULL

NULL

C

1 or 0

NULL

Complex arrays

NumPy-style: shape and strides

The logical structure of NumPy-style arrays is defined by itemsize, ndim, shape and strides.

If ndim == 0, the memory location pointed to by buf is interpreted as a scalar of size itemsize. In that case, both shape and strides are NULL.

If strides is NULL, the array is interpreted as a standard n-dimensional C-array. Otherwise, the consumer must access an n-dimensional array as follows:

ptr = (char *)buf + indices[0] * strides[0] + ... + indices[n-1] * strides[n-1];
+item = *((typeof(item) *)ptr);
+

As noted above, buf can point to any location within the actual memory block. An exporter can check the validity of a buffer with this function:

def verify_structure(memlen, itemsize, ndim, shape, strides, offset):
+    """Verify that the parameters represent a valid array within
+       the bounds of the allocated memory:
+           char *mem: start of the physical memory block
+           memlen: length of the physical memory block
+           offset: (char *)buf - mem
+    """
+    if offset % itemsize:
+        return False
+    if offset < 0 or offset+itemsize > memlen:
+        return False
+    if any(v % itemsize for v in strides):
+        return False
+
+    if ndim <= 0:
+        return ndim == 0 and not shape and not strides
+    if 0 in shape:
+        return True
+
+    imin = sum(strides[j]*(shape[j]-1) for j in range(ndim)
+               if strides[j] <= 0)
+    imax = sum(strides[j]*(shape[j]-1) for j in range(ndim)
+               if strides[j] > 0)
+
+    return 0 <= offset+imin and offset+imax+itemsize <= memlen
+

PIL-style: shape, strides and suboffsets

In addition to the regular items, PIL-style arrays can contain pointers that must be followed in order to get to the next element in a dimension. For example, the regular three-dimensional C-array char v[2][2][3] can also be viewed as an array of 2 pointers to 2 two-dimensional arrays: char (*v[2])[2][3]. In suboffsets representation, those two pointers can be embedded at the start of buf, pointing to two char x[2][3] arrays that can be located anywhere in memory.

Here is a function that returns a pointer to the element in an N-D array pointed to by an N-dimensional index when there are both non-NULL strides and suboffsets:

void *get_item_pointer(int ndim, void *buf, Py_ssize_t *strides,
+                       Py_ssize_t *suboffsets, Py_ssize_t *indices) {
+    char *pointer = (char*)buf;
+    int i;
+    for (i = 0; i < ndim; i++) {
+        pointer += strides[i] * indices[i];
+        if (suboffsets[i] >=0 ) {
+            pointer = *((char**)pointer) + suboffsets[i];
+        }
+    }
+    return (void*)pointer;
+}
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/buffer.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fbytearray.html b/devdocs/python~3.12/c-api%2Fbytearray.html new file mode 100644 index 00000000..2cd5a634 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fbytearray.html @@ -0,0 +1,42 @@ +

Byte Array Objects

+type PyByteArrayObject
+

This subtype of PyObject represents a Python bytearray object.

+
+PyTypeObject PyByteArray_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python bytearray type; it is the same object as bytearray in the Python layer.

+

Type check macros

+int PyByteArray_Check(PyObject *o)
+

Return true if the object o is a bytearray object or an instance of a subtype of the bytearray type. This function always succeeds.

+
+int PyByteArray_CheckExact(PyObject *o)
+

Return true if the object o is a bytearray object, but not an instance of a subtype of the bytearray type. This function always succeeds.

+

Direct API functions

+PyObject *PyByteArray_FromObject(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Return a new bytearray object from any object, o, that implements the buffer protocol.

+
+PyObject *PyByteArray_FromStringAndSize(const char *string, Py_ssize_t len)
+Return value: New reference. Part of the Stable ABI.

Create a new bytearray object from string and its length, len. On failure, NULL is returned.

+
+PyObject *PyByteArray_Concat(PyObject *a, PyObject *b)
+Return value: New reference. Part of the Stable ABI.

Concat bytearrays a and b and return a new bytearray with the result.

+
+Py_ssize_t PyByteArray_Size(PyObject *bytearray)
+ Part of the Stable ABI.

Return the size of bytearray after checking for a NULL pointer.

+
+char *PyByteArray_AsString(PyObject *bytearray)
+ Part of the Stable ABI.

Return the contents of bytearray as a char array after checking for a NULL pointer. The returned array always has an extra null byte appended.

+
+int PyByteArray_Resize(PyObject *bytearray, Py_ssize_t len)
+ Part of the Stable ABI.

Resize the internal buffer of bytearray to len.

+

Macros

These macros trade safety for speed and they don’t check pointers.

+char *PyByteArray_AS_STRING(PyObject *bytearray)
+

Similar to PyByteArray_AsString(), but without error checking.

+
+Py_ssize_t PyByteArray_GET_SIZE(PyObject *bytearray)
+

Similar to PyByteArray_Size(), but without error checking.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/bytearray.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fbytes.html b/devdocs/python~3.12/c-api%2Fbytes.html new file mode 100644 index 00000000..d4f026b2 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fbytes.html @@ -0,0 +1,72 @@ +

Bytes Objects

These functions raise TypeError when expecting a bytes parameter and called with a non-bytes parameter.

+type PyBytesObject
+

This subtype of PyObject represents a Python bytes object.

+
+PyTypeObject PyBytes_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python bytes type; it is the same object as bytes in the Python layer.

+
+int PyBytes_Check(PyObject *o)
+

Return true if the object o is a bytes object or an instance of a subtype of the bytes type. This function always succeeds.

+
+int PyBytes_CheckExact(PyObject *o)
+

Return true if the object o is a bytes object, but not an instance of a subtype of the bytes type. This function always succeeds.

+
+PyObject *PyBytes_FromString(const char *v)
+Return value: New reference. Part of the Stable ABI.

Return a new bytes object with a copy of the string v as value on success, and NULL on failure. The parameter v must not be NULL; it will not be checked.

+
+PyObject *PyBytes_FromStringAndSize(const char *v, Py_ssize_t len)
+Return value: New reference. Part of the Stable ABI.

Return a new bytes object with a copy of the string v as value and length len on success, and NULL on failure. If v is NULL, the contents of the bytes object are uninitialized.

+
+PyObject *PyBytes_FromFormat(const char *format, ...)
+Return value: New reference. Part of the Stable ABI.

Take a C printf()-style format string and a variable number of arguments, calculate the size of the resulting Python bytes object and return a bytes object with the values formatted into it. The variable arguments must be C types and must correspond exactly to the format characters in the format string. The following format characters are allowed:

+ + + + + + + + + + + + +

Format Characters

Type

Comment

%%

n/a

The literal % character.

%c

int

A single byte, represented as a C int.

%d

int

Equivalent to printf("%d"). 1

%u

unsigned int

Equivalent to printf("%u"). 1

%ld

long

Equivalent to printf("%ld"). 1

%lu

unsigned long

Equivalent to printf("%lu"). 1

%zd

Py_ssize_t

Equivalent to printf("%zd"). 1

%zu

size_t

Equivalent to printf("%zu"). 1

%i

int

Equivalent to printf("%i"). 1

%x

int

Equivalent to printf("%x"). 1

%s

const char*

A null-terminated C character array.

%p

const void*

The hex representation of a C pointer. Mostly equivalent to printf("%p") except that it is guaranteed to start with the literal 0x regardless of what the platform’s printf yields.

An unrecognized format character causes all the rest of the format string to be copied as-is to the result object, and any extra arguments discarded.

+1(1,2,3,4,5,6,7,8)
+

For integer specifiers (d, u, ld, lu, zd, zu, i, x): the 0-conversion flag has effect even when a precision is given.

+
+PyObject *PyBytes_FromFormatV(const char *format, va_list vargs)
+Return value: New reference. Part of the Stable ABI.

Identical to PyBytes_FromFormat() except that it takes exactly two arguments.

+
+PyObject *PyBytes_FromObject(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Return the bytes representation of object o that implements the buffer protocol.

+
+Py_ssize_t PyBytes_Size(PyObject *o)
+ Part of the Stable ABI.

Return the length of the bytes in bytes object o.

+
+Py_ssize_t PyBytes_GET_SIZE(PyObject *o)
+

Similar to PyBytes_Size(), but without error checking.

+
+char *PyBytes_AsString(PyObject *o)
+ Part of the Stable ABI.

Return a pointer to the contents of o. The pointer refers to the internal buffer of o, which consists of len(o) + 1 bytes. The last byte in the buffer is always null, regardless of whether there are any other null bytes. The data must not be modified in any way, unless the object was just created using PyBytes_FromStringAndSize(NULL, size). It must not be deallocated. If o is not a bytes object at all, PyBytes_AsString() returns NULL and raises TypeError.

+
+char *PyBytes_AS_STRING(PyObject *string)
+

Similar to PyBytes_AsString(), but without error checking.

+
+int PyBytes_AsStringAndSize(PyObject *obj, char **buffer, Py_ssize_t *length)
+ Part of the Stable ABI.

Return the null-terminated contents of the object obj through the output variables buffer and length. Returns 0 on success.

If length is NULL, the bytes object may not contain embedded null bytes; if it does, the function returns -1 and a ValueError is raised.

The buffer refers to an internal buffer of obj, which includes an additional null byte at the end (not counted in length). The data must not be modified in any way, unless the object was just created using PyBytes_FromStringAndSize(NULL, size). It must not be deallocated. If obj is not a bytes object at all, PyBytes_AsStringAndSize() returns -1 and raises TypeError.

Changed in version 3.5: Previously, TypeError was raised when embedded null bytes were encountered in the bytes object.

+
+void PyBytes_Concat(PyObject **bytes, PyObject *newpart)
+ Part of the Stable ABI.

Create a new bytes object in *bytes containing the contents of newpart appended to bytes; the caller will own the new reference. The reference to the old value of bytes will be stolen. If the new object cannot be created, the old reference to bytes will still be discarded and the value of *bytes will be set to NULL; the appropriate exception will be set.

+
+void PyBytes_ConcatAndDel(PyObject **bytes, PyObject *newpart)
+ Part of the Stable ABI.

Create a new bytes object in *bytes containing the contents of newpart appended to bytes. This version releases the strong reference to newpart (i.e. decrements its reference count).

+
+int _PyBytes_Resize(PyObject **bytes, Py_ssize_t newsize)
+

A way to resize a bytes object even though it is “immutable”. Only use this to build up a brand new bytes object; don’t use this if the bytes may already be known in other parts of the code. It is an error to call this function if the refcount on the input bytes object is not one. Pass the address of an existing bytes object as an lvalue (it may be written into), and the new size desired. On success, *bytes holds the resized bytes object and 0 is returned; the address in *bytes may differ from its input value. If the reallocation fails, the original bytes object at *bytes is deallocated, *bytes is set to NULL, MemoryError is set, and -1 is returned.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/bytes.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcall.html b/devdocs/python~3.12/c-api%2Fcall.html new file mode 100644 index 00000000..748b9864 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcall.html @@ -0,0 +1,91 @@ +

Call Protocol

CPython supports two different calling protocols: tp_call and vectorcall.

The tp_call Protocol

Instances of classes that set tp_call are callable. The signature of the slot is:

PyObject *tp_call(PyObject *callable, PyObject *args, PyObject *kwargs);
+

A call is made using a tuple for the positional arguments and a dict for the keyword arguments, similarly to callable(*args, **kwargs) in Python code. args must be non-NULL (use an empty tuple if there are no arguments) but kwargs may be NULL if there are no keyword arguments.

This convention is not only used by tp_call: tp_new and tp_init also pass arguments this way.

To call an object, use PyObject_Call() or another call API.

The Vectorcall Protocol

New in version 3.9.

The vectorcall protocol was introduced in PEP 590 as an additional protocol for making calls more efficient.

As rule of thumb, CPython will prefer the vectorcall for internal calls if the callable supports it. However, this is not a hard rule. Additionally, some third-party extensions use tp_call directly (rather than using PyObject_Call()). Therefore, a class supporting vectorcall must also implement tp_call. Moreover, the callable must behave the same regardless of which protocol is used. The recommended way to achieve this is by setting tp_call to PyVectorcall_Call(). This bears repeating:

Warning

A class supporting vectorcall must also implement tp_call with the same semantics.

Changed in version 3.12: The Py_TPFLAGS_HAVE_VECTORCALL flag is now removed from a class when the class’s __call__() method is reassigned. (This internally sets tp_call only, and thus may make it behave differently than the vectorcall function.) In earlier Python versions, vectorcall should only be used with immutable or static types.

A class should not implement vectorcall if that would be slower than tp_call. For example, if the callee needs to convert the arguments to an args tuple and kwargs dict anyway, then there is no point in implementing vectorcall.

Classes can implement the vectorcall protocol by enabling the Py_TPFLAGS_HAVE_VECTORCALL flag and setting tp_vectorcall_offset to the offset inside the object structure where a vectorcallfunc appears. This is a pointer to a function with the following signature:

+typedef PyObject *(*vectorcallfunc)(PyObject *callable, PyObject *const *args, size_t nargsf, PyObject *kwnames)
Part of the Stable ABI since version 3.12.
+
+PY_VECTORCALL_ARGUMENTS_OFFSET
+ Part of the Stable ABI since version 3.12.

If this flag is set in a vectorcall nargsf argument, the callee is allowed to temporarily change args[-1]. In other words, args points to argument 1 (not 0) in the allocated vector. The callee must restore the value of args[-1] before returning.

For PyObject_VectorcallMethod(), this flag means instead that args[0] may be changed.

Whenever they can do so cheaply (without additional allocation), callers are encouraged to use PY_VECTORCALL_ARGUMENTS_OFFSET. Doing so will allow callables such as bound methods to make their onward calls (which include a prepended self argument) very efficiently.

New in version 3.8.

+

To call an object that implements vectorcall, use a call API function as with any other callable. PyObject_Vectorcall() will usually be most efficient.

Note

In CPython 3.8, the vectorcall API and related functions were available provisionally under names with a leading underscore: _PyObject_Vectorcall, _Py_TPFLAGS_HAVE_VECTORCALL, _PyObject_VectorcallMethod, _PyVectorcall_Function, _PyObject_CallOneArg, _PyObject_CallMethodNoArgs, _PyObject_CallMethodOneArg. Additionally, PyObject_VectorcallDict was available as _PyObject_FastCallDict. The old names are still defined as aliases of the new, non-underscored names.

Recursion Control

When using tp_call, callees do not need to worry about recursion: CPython uses Py_EnterRecursiveCall() and Py_LeaveRecursiveCall() for calls made using tp_call.

For efficiency, this is not the case for calls done using vectorcall: the callee should use Py_EnterRecursiveCall and Py_LeaveRecursiveCall if needed.

Vectorcall Support API

+Py_ssize_t PyVectorcall_NARGS(size_t nargsf)
+ Part of the Stable ABI since version 3.12.

Given a vectorcall nargsf argument, return the actual number of arguments. Currently equivalent to:

(Py_ssize_t)(nargsf & ~PY_VECTORCALL_ARGUMENTS_OFFSET)
+

However, the function PyVectorcall_NARGS should be used to allow for future extensions.

New in version 3.8.

+
+vectorcallfunc PyVectorcall_Function(PyObject *op)
+

If op does not support the vectorcall protocol (either because the type does not or because the specific instance does not), return NULL. Otherwise, return the vectorcall function pointer stored in op. This function never raises an exception.

This is mostly useful to check whether or not op supports vectorcall, which can be done by checking PyVectorcall_Function(op) != NULL.

New in version 3.9.

+
+PyObject *PyVectorcall_Call(PyObject *callable, PyObject *tuple, PyObject *dict)
+ Part of the Stable ABI since version 3.12.

Call callable’s vectorcallfunc with positional and keyword arguments given in a tuple and dict, respectively.

This is a specialized function, intended to be put in the tp_call slot or be used in an implementation of tp_call. It does not check the Py_TPFLAGS_HAVE_VECTORCALL flag and it does not fall back to tp_call.

New in version 3.8.

+

Object Calling API

Various functions are available for calling a Python object. Each converts its arguments to a convention supported by the called object – either tp_call or vectorcall. In order to do as little conversion as possible, pick one that best fits the format of data you have available.

The following table summarizes the available functions; please see individual documentation for details.

+ + + + + + + + + + + + + +

Function

callable

args

kwargs

PyObject_Call()

PyObject *

tuple

dict/NULL

PyObject_CallNoArgs()

PyObject *

—

—

PyObject_CallOneArg()

PyObject *

1 object

—

PyObject_CallObject()

PyObject *

tuple/NULL

—

PyObject_CallFunction()

PyObject *

format

—

PyObject_CallMethod()

obj + char*

format

—

PyObject_CallFunctionObjArgs()

PyObject *

variadic

—

PyObject_CallMethodObjArgs()

obj + name

variadic

—

PyObject_CallMethodNoArgs()

obj + name

—

—

PyObject_CallMethodOneArg()

obj + name

1 object

—

PyObject_Vectorcall()

PyObject *

vectorcall

vectorcall

PyObject_VectorcallDict()

PyObject *

vectorcall

dict/NULL

PyObject_VectorcallMethod()

arg + name

vectorcall

vectorcall
+PyObject *PyObject_Call(PyObject *callable, PyObject *args, PyObject *kwargs)
+Return value: New reference. Part of the Stable ABI.

Call a callable Python object callable, with arguments given by the tuple args, and named arguments given by the dictionary kwargs.

args must not be NULL; use an empty tuple if no arguments are needed. If no named arguments are needed, kwargs can be NULL.

Return the result of the call on success, or raise an exception and return NULL on failure.

This is the equivalent of the Python expression: callable(*args, **kwargs).

+
+PyObject *PyObject_CallNoArgs(PyObject *callable)
+ Part of the Stable ABI since version 3.10.

Call a callable Python object callable without any arguments. It is the most efficient way to call a callable Python object without any argument.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+
+PyObject *PyObject_CallOneArg(PyObject *callable, PyObject *arg)
+

Call a callable Python object callable with exactly 1 positional argument arg and no keyword arguments.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+
+PyObject *PyObject_CallObject(PyObject *callable, PyObject *args)
+Return value: New reference. Part of the Stable ABI.

Call a callable Python object callable, with arguments given by the tuple args. If no arguments are needed, then args can be NULL.

Return the result of the call on success, or raise an exception and return NULL on failure.

This is the equivalent of the Python expression: callable(*args).

+
+PyObject *PyObject_CallFunction(PyObject *callable, const char *format, ...)
+Return value: New reference. Part of the Stable ABI.

Call a callable Python object callable, with a variable number of C arguments. The C arguments are described using a Py_BuildValue() style format string. The format can be NULL, indicating that no arguments are provided.

Return the result of the call on success, or raise an exception and return NULL on failure.

This is the equivalent of the Python expression: callable(*args).

Note that if you only pass PyObject* args, PyObject_CallFunctionObjArgs() is a faster alternative.

Changed in version 3.4: The type of format was changed from char *.

+
+PyObject *PyObject_CallMethod(PyObject *obj, const char *name, const char *format, ...)
+Return value: New reference. Part of the Stable ABI.

Call the method named name of object obj with a variable number of C arguments. The C arguments are described by a Py_BuildValue() format string that should produce a tuple.

The format can be NULL, indicating that no arguments are provided.

Return the result of the call on success, or raise an exception and return NULL on failure.

This is the equivalent of the Python expression: obj.name(arg1, arg2, ...).

Note that if you only pass PyObject* args, PyObject_CallMethodObjArgs() is a faster alternative.

Changed in version 3.4: The types of name and format were changed from char *.

+
+PyObject *PyObject_CallFunctionObjArgs(PyObject *callable, ...)
+Return value: New reference. Part of the Stable ABI.

Call a callable Python object callable, with a variable number of PyObject* arguments. The arguments are provided as a variable number of parameters followed by NULL.

Return the result of the call on success, or raise an exception and return NULL on failure.

This is the equivalent of the Python expression: callable(arg1, arg2, ...).

+
+PyObject *PyObject_CallMethodObjArgs(PyObject *obj, PyObject *name, ...)
+Return value: New reference. Part of the Stable ABI.

Call a method of the Python object obj, where the name of the method is given as a Python string object in name. It is called with a variable number of PyObject* arguments. The arguments are provided as a variable number of parameters followed by NULL.

Return the result of the call on success, or raise an exception and return NULL on failure.

+
+PyObject *PyObject_CallMethodNoArgs(PyObject *obj, PyObject *name)
+

Call a method of the Python object obj without arguments, where the name of the method is given as a Python string object in name.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+
+PyObject *PyObject_CallMethodOneArg(PyObject *obj, PyObject *name, PyObject *arg)
+

Call a method of the Python object obj with a single positional argument arg, where the name of the method is given as a Python string object in name.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+
+PyObject *PyObject_Vectorcall(PyObject *callable, PyObject *const *args, size_t nargsf, PyObject *kwnames)
+ Part of the Stable ABI since version 3.12.

Call a callable Python object callable. The arguments are the same as for vectorcallfunc. If callable supports vectorcall, this directly calls the vectorcall function stored in callable.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+
+PyObject *PyObject_VectorcallDict(PyObject *callable, PyObject *const *args, size_t nargsf, PyObject *kwdict)
+

Call callable with positional arguments passed exactly as in the vectorcall protocol, but with keyword arguments passed as a dictionary kwdict. The args array contains only the positional arguments.

Regardless of which protocol is used internally, a conversion of arguments needs to be done. Therefore, this function should only be used if the caller already has a dictionary ready to use for the keyword arguments, but not a tuple for the positional arguments.

New in version 3.9.

+
+PyObject *PyObject_VectorcallMethod(PyObject *name, PyObject *const *args, size_t nargsf, PyObject *kwnames)
+ Part of the Stable ABI since version 3.12.

Call a method using the vectorcall calling convention. The name of the method is given as a Python string name. The object whose method is called is args[0], and the args array starting at args[1] represents the arguments of the call. There must be at least one positional argument. nargsf is the number of positional arguments including args[0], plus PY_VECTORCALL_ARGUMENTS_OFFSET if the value of args[0] may temporarily be changed. Keyword arguments can be passed just like in PyObject_Vectorcall().

If the object has the Py_TPFLAGS_METHOD_DESCRIPTOR feature, this will call the unbound method object with the full args vector as arguments.

Return the result of the call on success, or raise an exception and return NULL on failure.

New in version 3.9.

+

Call Support API

+int PyCallable_Check(PyObject *o)
+ Part of the Stable ABI.

Determine if the object o is callable. Return 1 if the object is callable and 0 otherwise. This function always succeeds.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/call.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcapsule.html b/devdocs/python~3.12/c-api%2Fcapsule.html new file mode 100644 index 00000000..83e0e1b8 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcapsule.html @@ -0,0 +1,49 @@ +

Capsules

Refer to Providing a C API for an Extension Module for more information on using these objects.

New in version 3.1.

+type PyCapsule
+

This subtype of PyObject represents an opaque value, useful for C extension modules who need to pass an opaque value (as a void* pointer) through Python code to other C code. It is often used to make a C function pointer defined in one module available to other modules, so the regular import mechanism can be used to access C APIs defined in dynamically loaded modules.

+
+type PyCapsule_Destructor
+ Part of the Stable ABI.

The type of a destructor callback for a capsule. Defined as:

typedef void (*PyCapsule_Destructor)(PyObject *);
+

See PyCapsule_New() for the semantics of PyCapsule_Destructor callbacks.

+
+int PyCapsule_CheckExact(PyObject *p)
+

Return true if its argument is a PyCapsule. This function always succeeds.

+
+PyObject *PyCapsule_New(void *pointer, const char *name, PyCapsule_Destructor destructor)
+Return value: New reference. Part of the Stable ABI.

Create a PyCapsule encapsulating the pointer. The pointer argument may not be NULL.

On failure, set an exception and return NULL.

The name string may either be NULL or a pointer to a valid C string. If non-NULL, this string must outlive the capsule. (Though it is permitted to free it inside the destructor.)

If the destructor argument is not NULL, it will be called with the capsule as its argument when it is destroyed.

If this capsule will be stored as an attribute of a module, the name should be specified as modulename.attributename. This will enable other modules to import the capsule using PyCapsule_Import().

+
+void *PyCapsule_GetPointer(PyObject *capsule, const char *name)
+ Part of the Stable ABI.

Retrieve the pointer stored in the capsule. On failure, set an exception and return NULL.

The name parameter must compare exactly to the name stored in the capsule. If the name stored in the capsule is NULL, the name passed in must also be NULL. Python uses the C function strcmp() to compare capsule names.

+
+PyCapsule_Destructor PyCapsule_GetDestructor(PyObject *capsule)
+ Part of the Stable ABI.

Return the current destructor stored in the capsule. On failure, set an exception and return NULL.

It is legal for a capsule to have a NULL destructor. This makes a NULL return code somewhat ambiguous; use PyCapsule_IsValid() or PyErr_Occurred() to disambiguate.

+
+void *PyCapsule_GetContext(PyObject *capsule)
+ Part of the Stable ABI.

Return the current context stored in the capsule. On failure, set an exception and return NULL.

It is legal for a capsule to have a NULL context. This makes a NULL return code somewhat ambiguous; use PyCapsule_IsValid() or PyErr_Occurred() to disambiguate.

+
+const char *PyCapsule_GetName(PyObject *capsule)
+ Part of the Stable ABI.

Return the current name stored in the capsule. On failure, set an exception and return NULL.

It is legal for a capsule to have a NULL name. This makes a NULL return code somewhat ambiguous; use PyCapsule_IsValid() or PyErr_Occurred() to disambiguate.

+
+void *PyCapsule_Import(const char *name, int no_block)
+ Part of the Stable ABI.

Import a pointer to a C object from a capsule attribute in a module. The name parameter should specify the full name to the attribute, as in module.attribute. The name stored in the capsule must match this string exactly.

Return the capsule’s internal pointer on success. On failure, set an exception and return NULL.

Changed in version 3.3: no_block has no effect anymore.

+
+int PyCapsule_IsValid(PyObject *capsule, const char *name)
+ Part of the Stable ABI.

Determines whether or not capsule is a valid capsule. A valid capsule is non-NULL, passes PyCapsule_CheckExact(), has a non-NULL pointer stored in it, and its internal name matches the name parameter. (See PyCapsule_GetPointer() for information on how capsule names are compared.)

In other words, if PyCapsule_IsValid() returns a true value, calls to any of the accessors (any function starting with PyCapsule_Get) are guaranteed to succeed.

Return a nonzero value if the object is valid and matches the name passed in. Return 0 otherwise. This function will not fail.

+
+int PyCapsule_SetContext(PyObject *capsule, void *context)
+ Part of the Stable ABI.

Set the context pointer inside capsule to context.

Return 0 on success. Return nonzero and set an exception on failure.

+
+int PyCapsule_SetDestructor(PyObject *capsule, PyCapsule_Destructor destructor)
+ Part of the Stable ABI.

Set the destructor inside capsule to destructor.

Return 0 on success. Return nonzero and set an exception on failure.

+
+int PyCapsule_SetName(PyObject *capsule, const char *name)
+ Part of the Stable ABI.

Set the name inside capsule to name. If non-NULL, the name must outlive the capsule. If the previous name stored in the capsule was not NULL, no attempt is made to free it.

Return 0 on success. Return nonzero and set an exception on failure.

+
+int PyCapsule_SetPointer(PyObject *capsule, void *pointer)
+ Part of the Stable ABI.

Set the void pointer inside capsule to pointer. The pointer may not be NULL.

Return 0 on success. Return nonzero and set an exception on failure.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/capsule.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcell.html b/devdocs/python~3.12/c-api%2Fcell.html new file mode 100644 index 00000000..23ab4f3b --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcell.html @@ -0,0 +1,30 @@ +

Cell Objects

“Cell” objects are used to implement variables referenced by multiple scopes. For each such variable, a cell object is created to store the value; the local variables of each stack frame that references the value contains a reference to the cells from outer scopes which also use that variable. When the value is accessed, the value contained in the cell is used instead of the cell object itself. This de-referencing of the cell object requires support from the generated byte-code; these are not automatically de-referenced when accessed. Cell objects are not likely to be useful elsewhere.

+type PyCellObject
+

The C structure used for cell objects.

+
+PyTypeObject PyCell_Type
+

The type object corresponding to cell objects.

+
+int PyCell_Check(PyObject *ob)
+

Return true if ob is a cell object; ob must not be NULL. This function always succeeds.

+
+PyObject *PyCell_New(PyObject *ob)
+Return value: New reference.

Create and return a new cell object containing the value ob. The parameter may be NULL.

+
+PyObject *PyCell_Get(PyObject *cell)
+Return value: New reference.

Return the contents of the cell cell.

+
+PyObject *PyCell_GET(PyObject *cell)
+Return value: Borrowed reference.

Return the contents of the cell cell, but without checking that cell is non-NULL and a cell object.

+
+int PyCell_Set(PyObject *cell, PyObject *value)
+

Set the contents of the cell object cell to value. This releases the reference to any current content of the cell. value may be NULL. cell must be non-NULL; if it is not a cell object, -1 will be returned. On success, 0 will be returned.

+
+void PyCell_SET(PyObject *cell, PyObject *value)
+

Sets the value of the cell object cell to value. No reference counts are adjusted, and no checks are made for safety; cell must be non-NULL and must be a cell object.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/cell.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcode.html b/devdocs/python~3.12/c-api%2Fcode.html new file mode 100644 index 00000000..9b0592d7 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcode.html @@ -0,0 +1,59 @@ +

Code Objects

Code objects are a low-level detail of the CPython implementation. Each one represents a chunk of executable code that hasn’t yet been bound into a function.

+type PyCodeObject
+

The C structure of the objects used to describe code objects. The fields of this type are subject to change at any time.

+
+PyTypeObject PyCode_Type
+

This is an instance of PyTypeObject representing the Python code type.

+
+int PyCode_Check(PyObject *co)
+

Return true if co is a code object. This function always succeeds.

+
+int PyCode_GetNumFree(PyCodeObject *co)
+

Return the number of free variables in co.

+
+PyCodeObject *PyUnstable_Code_New(int argcount, int kwonlyargcount, int nlocals, int stacksize, int flags, PyObject *code, PyObject *consts, PyObject *names, PyObject *varnames, PyObject *freevars, PyObject *cellvars, PyObject *filename, PyObject *name, PyObject *qualname, int firstlineno, PyObject *linetable, PyObject *exceptiontable)
+
This is Unstable API. It may change without warning in minor releases. +

Return a new code object. If you need a dummy code object to create a frame, use PyCode_NewEmpty() instead.

Since the definition of the bytecode changes often, calling PyUnstable_Code_New() directly can bind you to a precise Python version.

The many arguments of this function are inter-dependent in complex ways, meaning that subtle changes to values are likely to result in incorrect execution or VM crashes. Use this function only with extreme care.

Changed in version 3.11: Added qualname and exceptiontable parameters.

Changed in version 3.12: Renamed from PyCode_New as part of Unstable C API. The old name is deprecated, but will remain available until the signature changes again.

+
+PyCodeObject *PyUnstable_Code_NewWithPosOnlyArgs(int argcount, int posonlyargcount, int kwonlyargcount, int nlocals, int stacksize, int flags, PyObject *code, PyObject *consts, PyObject *names, PyObject *varnames, PyObject *freevars, PyObject *cellvars, PyObject *filename, PyObject *name, PyObject *qualname, int firstlineno, PyObject *linetable, PyObject *exceptiontable)
+
This is Unstable API. It may change without warning in minor releases. +

Similar to PyUnstable_Code_New(), but with an extra “posonlyargcount” for positional-only arguments. The same caveats that apply to PyUnstable_Code_New also apply to this function.

New in version 3.8: as PyCode_NewWithPosOnlyArgs

Changed in version 3.11: Added qualname and exceptiontable parameters.

Changed in version 3.12: Renamed to PyUnstable_Code_NewWithPosOnlyArgs. The old name is deprecated, but will remain available until the signature changes again.

+
+PyCodeObject *PyCode_NewEmpty(const char *filename, const char *funcname, int firstlineno)
+Return value: New reference.

Return a new empty code object with the specified filename, function name, and first line number. The resulting code object will raise an Exception if executed.

+
+int PyCode_Addr2Line(PyCodeObject *co, int byte_offset)
+

Return the line number of the instruction that occurs on or before byte_offset and ends after it. If you just need the line number of a frame, use PyFrame_GetLineNumber() instead.

For efficiently iterating over the line numbers in a code object, use the API described in PEP 626.

+
+int PyCode_Addr2Location(PyObject *co, int byte_offset, int *start_line, int *start_column, int *end_line, int *end_column)
+

Sets the passed int pointers to the source code line and column numbers for the instruction at byte_offset. Sets the value to 0 when information is not available for any particular element.

Returns 1 if the function succeeds and 0 otherwise.

New in version 3.11.

+
+PyObject *PyCode_GetCode(PyCodeObject *co)
+

Equivalent to the Python code getattr(co, 'co_code'). Returns a strong reference to a PyBytesObject representing the bytecode in a code object. On error, NULL is returned and an exception is raised.

This PyBytesObject may be created on-demand by the interpreter and does not necessarily represent the bytecode actually executed by CPython. The primary use case for this function is debuggers and profilers.

New in version 3.11.

+
+PyObject *PyCode_GetVarnames(PyCodeObject *co)
+

Equivalent to the Python code getattr(co, 'co_varnames'). Returns a new reference to a PyTupleObject containing the names of the local variables. On error, NULL is returned and an exception is raised.

New in version 3.11.

+
+PyObject *PyCode_GetCellvars(PyCodeObject *co)
+

Equivalent to the Python code getattr(co, 'co_cellvars'). Returns a new reference to a PyTupleObject containing the names of the local variables that are referenced by nested functions. On error, NULL is returned and an exception is raised.

New in version 3.11.

+
+PyObject *PyCode_GetFreevars(PyCodeObject *co)
+

Equivalent to the Python code getattr(co, 'co_freevars'). Returns a new reference to a PyTupleObject containing the names of the free variables. On error, NULL is returned and an exception is raised.

New in version 3.11.

+
+int PyCode_AddWatcher(PyCode_WatchCallback callback)
+

Register callback as a code object watcher for the current interpreter. Return an ID which may be passed to PyCode_ClearWatcher(). In case of error (e.g. no more watcher IDs available), return -1 and set an exception.

New in version 3.12.

+
+int PyCode_ClearWatcher(int watcher_id)
+

Clear watcher identified by watcher_id previously returned from PyCode_AddWatcher() for the current interpreter. Return 0 on success, or -1 and set an exception on error (e.g. if the given watcher_id was never registered.)

New in version 3.12.

+
+type PyCodeEvent
+

Enumeration of possible code object watcher events: - PY_CODE_EVENT_CREATE - PY_CODE_EVENT_DESTROY

New in version 3.12.

+
+typedef int (*PyCode_WatchCallback)(PyCodeEvent event, PyCodeObject *co)
+

Type of a code object watcher callback function.

If event is PY_CODE_EVENT_CREATE, then the callback is invoked after co has been fully initialized. Otherwise, the callback is invoked before the destruction of co takes place, so the prior state of co can be inspected.

If event is PY_CODE_EVENT_DESTROY, taking a reference in the callback to the about-to-be-destroyed code object will resurrect it and prevent it from being freed at this time. When the resurrected object is destroyed later, any watcher callbacks active at that time will be called again.

Users of this API should not rely on internal runtime implementation details. Such details may include, but are not limited to, the exact order and timing of creation and destruction of code objects. While changes in these details may result in differences observable by watchers (including whether a callback is invoked or not), it does not change the semantics of the Python code being executed.

If the callback sets an exception, it must return -1; this exception will be printed as an unraisable exception using PyErr_WriteUnraisable(). Otherwise it should return 0.

There may already be a pending exception set on entry to the callback. In this case, the callback should return 0 with the same exception still set. This means the callback may not call any other API that can set an exception unless it saves and clears the exception state first, and restores it before returning.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/code.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcodec.html b/devdocs/python~3.12/c-api%2Fcodec.html new file mode 100644 index 00000000..04490341 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcodec.html @@ -0,0 +1,63 @@ +

Codec registry and support functions

+int PyCodec_Register(PyObject *search_function)
+ Part of the Stable ABI.

Register a new codec search function.

As side effect, this tries to load the encodings package, if not yet done, to make sure that it is always first in the list of search functions.

+
+int PyCodec_Unregister(PyObject *search_function)
+ Part of the Stable ABI since version 3.10.

Unregister a codec search function and clear the registry’s cache. If the search function is not registered, do nothing. Return 0 on success. Raise an exception and return -1 on error.

New in version 3.10.

+
+int PyCodec_KnownEncoding(const char *encoding)
+ Part of the Stable ABI.

Return 1 or 0 depending on whether there is a registered codec for the given encoding. This function always succeeds.

+
+PyObject *PyCodec_Encode(PyObject *object, const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Generic codec based encoding API.

object is passed through the encoder function found for the given encoding using the error handling method defined by errors. errors may be NULL to use the default method defined for the codec. Raises a LookupError if no encoder can be found.

+
+PyObject *PyCodec_Decode(PyObject *object, const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Generic codec based decoding API.

object is passed through the decoder function found for the given encoding using the error handling method defined by errors. errors may be NULL to use the default method defined for the codec. Raises a LookupError if no encoder can be found.

+

Codec lookup API

In the following functions, the encoding string is looked up converted to all lower-case characters, which makes encodings looked up through this mechanism effectively case-insensitive. If no codec is found, a KeyError is set and NULL returned.

+PyObject *PyCodec_Encoder(const char *encoding)
+Return value: New reference. Part of the Stable ABI.

Get an encoder function for the given encoding.

+
+PyObject *PyCodec_Decoder(const char *encoding)
+Return value: New reference. Part of the Stable ABI.

Get a decoder function for the given encoding.

+
+PyObject *PyCodec_IncrementalEncoder(const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Get an IncrementalEncoder object for the given encoding.

+
+PyObject *PyCodec_IncrementalDecoder(const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Get an IncrementalDecoder object for the given encoding.

+
+PyObject *PyCodec_StreamReader(const char *encoding, PyObject *stream, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Get a StreamReader factory function for the given encoding.

+
+PyObject *PyCodec_StreamWriter(const char *encoding, PyObject *stream, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Get a StreamWriter factory function for the given encoding.

+

Registry API for Unicode encoding error handlers

+int PyCodec_RegisterError(const char *name, PyObject *error)
+ Part of the Stable ABI.

Register the error handling callback function error under the given name. This callback function will be called by a codec when it encounters unencodable characters/undecodable bytes and name is specified as the error parameter in the call to the encode/decode function.

The callback gets a single argument, an instance of UnicodeEncodeError, UnicodeDecodeError or UnicodeTranslateError that holds information about the problematic sequence of characters or bytes and their offset in the original string (see Unicode Exception Objects for functions to extract this information). The callback must either raise the given exception, or return a two-item tuple containing the replacement for the problematic sequence, and an integer giving the offset in the original string at which encoding/decoding should be resumed.

Return 0 on success, -1 on error.

+
+PyObject *PyCodec_LookupError(const char *name)
+Return value: New reference. Part of the Stable ABI.

Lookup the error handling callback function registered under name. As a special case NULL can be passed, in which case the error handling callback for “strict” will be returned.

+
+PyObject *PyCodec_StrictErrors(PyObject *exc)
+Return value: Always NULL. Part of the Stable ABI.

Raise exc as an exception.

+
+PyObject *PyCodec_IgnoreErrors(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Ignore the unicode error, skipping the faulty input.

+
+PyObject *PyCodec_ReplaceErrors(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Replace the unicode encode error with ? or U+FFFD.

+
+PyObject *PyCodec_XMLCharRefReplaceErrors(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Replace the unicode encode error with XML character references.

+
+PyObject *PyCodec_BackslashReplaceErrors(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Replace the unicode encode error with backslash escapes (\x, \u and \U).

+
+PyObject *PyCodec_NameReplaceErrors(PyObject *exc)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Replace the unicode encode error with \N{...} escapes.

New in version 3.5.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/codec.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcomplex.html b/devdocs/python~3.12/c-api%2Fcomplex.html new file mode 100644 index 00000000..a268d8e0 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcomplex.html @@ -0,0 +1,58 @@ +

Complex Number Objects

Python’s complex number objects are implemented as two distinct types when viewed from the C API: one is the Python object exposed to Python programs, and the other is a C structure which represents the actual complex number value. The API provides functions for working with both.

Complex Numbers as C Structures

Note that the functions which accept these structures as parameters and return them as results do so by value rather than dereferencing them through pointers. This is consistent throughout the API.

+type Py_complex
+

The C structure which corresponds to the value portion of a Python complex number object. Most of the functions for dealing with complex number objects use structures of this type as input or output values, as appropriate. It is defined as:

typedef struct {
+   double real;
+   double imag;
+} Py_complex;
+
+
+Py_complex _Py_c_sum(Py_complex left, Py_complex right)
+

Return the sum of two complex numbers, using the C Py_complex representation.

+
+Py_complex _Py_c_diff(Py_complex left, Py_complex right)
+

Return the difference between two complex numbers, using the C Py_complex representation.

+
+Py_complex _Py_c_neg(Py_complex num)
+

Return the negation of the complex number num, using the C Py_complex representation.

+
+Py_complex _Py_c_prod(Py_complex left, Py_complex right)
+

Return the product of two complex numbers, using the C Py_complex representation.

+
+Py_complex _Py_c_quot(Py_complex dividend, Py_complex divisor)
+

Return the quotient of two complex numbers, using the C Py_complex representation.

If divisor is null, this method returns zero and sets errno to EDOM.

+
+Py_complex _Py_c_pow(Py_complex num, Py_complex exp)
+

Return the exponentiation of num by exp, using the C Py_complex representation.

If num is null and exp is not a positive real number, this method returns zero and sets errno to EDOM.

+

Complex Numbers as Python Objects

+type PyComplexObject
+

This subtype of PyObject represents a Python complex number object.

+
+PyTypeObject PyComplex_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python complex number type. It is the same object as complex in the Python layer.

+
+int PyComplex_Check(PyObject *p)
+

Return true if its argument is a PyComplexObject or a subtype of PyComplexObject. This function always succeeds.

+
+int PyComplex_CheckExact(PyObject *p)
+

Return true if its argument is a PyComplexObject, but not a subtype of PyComplexObject. This function always succeeds.

+
+PyObject *PyComplex_FromCComplex(Py_complex v)
+Return value: New reference.

Create a new Python complex number object from a C Py_complex value.

+
+PyObject *PyComplex_FromDoubles(double real, double imag)
+Return value: New reference. Part of the Stable ABI.

Return a new PyComplexObject object from real and imag.

+
+double PyComplex_RealAsDouble(PyObject *op)
+ Part of the Stable ABI.

Return the real part of op as a C double.

+
+double PyComplex_ImagAsDouble(PyObject *op)
+ Part of the Stable ABI.

Return the imaginary part of op as a C double.

+
+Py_complex PyComplex_AsCComplex(PyObject *op)
+

Return the Py_complex value of the complex number op.

If op is not a Python complex number object but has a __complex__() method, this method will first be called to convert op to a Python complex number object. If __complex__() is not defined then it falls back to __float__(). If __float__() is not defined then it falls back to __index__(). Upon failure, this method returns -1.0 as a real value.

Changed in version 3.8: Use __index__() if available.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/complex.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fconcrete.html b/devdocs/python~3.12/c-api%2Fconcrete.html new file mode 100644 index 00000000..fffd3faa --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fconcrete.html @@ -0,0 +1,18 @@ +

Concrete Objects Layer

The functions in this chapter are specific to certain Python object types. Passing them an object of the wrong type is not a good idea; if you receive an object from a Python program and you are not sure that it has the right type, you must perform a type check first; for example, to check that an object is a dictionary, use PyDict_Check(). The chapter is structured like the “family tree” of Python object types.

Warning

While the functions described in this chapter carefully check the type of the objects which are passed in, many of them do not check for NULL being passed instead of a valid object. Allowing NULL to be passed in can cause memory access violations and immediate termination of the interpreter.

Fundamental Objects

This section describes Python type objects and the singleton object None.

Numeric Objects

Sequence Objects

Generic operations on sequence objects were discussed in the previous chapter; this section deals with the specific kinds of sequence objects that are intrinsic to the Python language.

Container Objects

Function Objects

Other Objects

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/concrete.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcontextvars.html b/devdocs/python~3.12/c-api%2Fcontextvars.html new file mode 100644 index 00000000..200b2e9f --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcontextvars.html @@ -0,0 +1,66 @@ +

Context Variables Objects

Changed in version 3.7.1:

Note

In Python 3.7.1 the signatures of all context variables C APIs were changed to use PyObject pointers instead of PyContext, PyContextVar, and PyContextToken, e.g.:

// in 3.7.0:
+PyContext *PyContext_New(void);
+
+// in 3.7.1+:
+PyObject *PyContext_New(void);
+

See bpo-34762 for more details.

New in version 3.7.

This section details the public C API for the contextvars module.

+type PyContext
+

The C structure used to represent a contextvars.Context object.

+
+type PyContextVar
+

The C structure used to represent a contextvars.ContextVar object.

+
+type PyContextToken
+

The C structure used to represent a contextvars.Token object.

+
+PyTypeObject PyContext_Type
+

The type object representing the context type.

+
+PyTypeObject PyContextVar_Type
+

The type object representing the context variable type.

+
+PyTypeObject PyContextToken_Type
+

The type object representing the context variable token type.

+

Type-check macros:

+int PyContext_CheckExact(PyObject *o)
+

Return true if o is of type PyContext_Type. o must not be NULL. This function always succeeds.

+
+int PyContextVar_CheckExact(PyObject *o)
+

Return true if o is of type PyContextVar_Type. o must not be NULL. This function always succeeds.

+
+int PyContextToken_CheckExact(PyObject *o)
+

Return true if o is of type PyContextToken_Type. o must not be NULL. This function always succeeds.

+

Context object management functions:

+PyObject *PyContext_New(void)
+Return value: New reference.

Create a new empty context object. Returns NULL if an error has occurred.

+
+PyObject *PyContext_Copy(PyObject *ctx)
+Return value: New reference.

Create a shallow copy of the passed ctx context object. Returns NULL if an error has occurred.

+
+PyObject *PyContext_CopyCurrent(void)
+Return value: New reference.

Create a shallow copy of the current thread context. Returns NULL if an error has occurred.

+
+int PyContext_Enter(PyObject *ctx)
+

Set ctx as the current context for the current thread. Returns 0 on success, and -1 on error.

+
+int PyContext_Exit(PyObject *ctx)
+

Deactivate the ctx context and restore the previous context as the current context for the current thread. Returns 0 on success, and -1 on error.

+

Context variable functions:

+PyObject *PyContextVar_New(const char *name, PyObject *def)
+Return value: New reference.

Create a new ContextVar object. The name parameter is used for introspection and debug purposes. The def parameter specifies a default value for the context variable, or NULL for no default. If an error has occurred, this function returns NULL.

+
+int PyContextVar_Get(PyObject *var, PyObject *default_value, PyObject **value)
+

Get the value of a context variable. Returns -1 if an error has occurred during lookup, and 0 if no error occurred, whether or not a value was found.

If the context variable was found, value will be a pointer to it. If the context variable was not found, value will point to:

  • +default_value, if not NULL;
  • the default value of var, if not NULL;
  • NULL

Except for NULL, the function returns a new reference.

+
+PyObject *PyContextVar_Set(PyObject *var, PyObject *value)
+Return value: New reference.

Set the value of var to value in the current context. Returns a new token object for this change, or NULL if an error has occurred.

+
+int PyContextVar_Reset(PyObject *var, PyObject *token)
+

Reset the state of the var context variable to that it was in before PyContextVar_Set() that returned the token was called. This function returns 0 on success and -1 on error.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/contextvars.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fconversion.html b/devdocs/python~3.12/c-api%2Fconversion.html new file mode 100644 index 00000000..2d974ed8 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fconversion.html @@ -0,0 +1,27 @@ +

String conversion and formatting

Functions for number conversion and formatted string output.

+int PyOS_snprintf(char *str, size_t size, const char *format, ...)
+ Part of the Stable ABI.

Output not more than size bytes to str according to the format string format and the extra arguments. See the Unix man page snprintf(3).

+
+int PyOS_vsnprintf(char *str, size_t size, const char *format, va_list va)
+ Part of the Stable ABI.

Output not more than size bytes to str according to the format string format and the variable argument list va. Unix man page vsnprintf(3).

+

PyOS_snprintf() and PyOS_vsnprintf() wrap the Standard C library functions snprintf() and vsnprintf(). Their purpose is to guarantee consistent behavior in corner cases, which the Standard C functions do not.

The wrappers ensure that str[size-1] is always '\0' upon return. They never write more than size bytes (including the trailing '\0') into str. Both functions require that str != NULL, size > 0, format != NULL and size < INT_MAX. Note that this means there is no equivalent to the C99 n = snprintf(NULL, 0, ...) which would determine the necessary buffer size.

The return value (rv) for these functions should be interpreted as follows:

The following functions provide locale-independent string to number conversions.

+double PyOS_string_to_double(const char *s, char **endptr, PyObject *overflow_exception)
+ Part of the Stable ABI.

Convert a string s to a double, raising a Python exception on failure. The set of accepted strings corresponds to the set of strings accepted by Python’s float() constructor, except that s must not have leading or trailing whitespace. The conversion is independent of the current locale.

If endptr is NULL, convert the whole string. Raise ValueError and return -1.0 if the string is not a valid representation of a floating-point number.

If endptr is not NULL, convert as much of the string as possible and set *endptr to point to the first unconverted character. If no initial segment of the string is the valid representation of a floating-point number, set *endptr to point to the beginning of the string, raise ValueError, and return -1.0.

If s represents a value that is too large to store in a float (for example, "1e500" is such a string on many platforms) then if overflow_exception is NULL return Py_HUGE_VAL (with an appropriate sign) and don’t set any exception. Otherwise, overflow_exception must point to a Python exception object; raise that exception and return -1.0. In both cases, set *endptr to point to the first character after the converted value.

If any other error occurs during the conversion (for example an out-of-memory error), set the appropriate Python exception and return -1.0.

New in version 3.1.

+
+char *PyOS_double_to_string(double val, char format_code, int precision, int flags, int *ptype)
+ Part of the Stable ABI.

Convert a double val to a string using supplied format_code, precision, and flags.

format_code must be one of 'e', 'E', 'f', 'F', 'g', 'G' or 'r'. For 'r', the supplied precision must be 0 and is ignored. The 'r' format code specifies the standard repr() format.

flags can be zero or more of the values Py_DTSF_SIGN, Py_DTSF_ADD_DOT_0, or Py_DTSF_ALT, or-ed together:

  • +Py_DTSF_SIGN means to always precede the returned string with a sign character, even if val is non-negative.
  • +Py_DTSF_ADD_DOT_0 means to ensure that the returned string will not look like an integer.
  • +Py_DTSF_ALT means to apply “alternate” formatting rules. See the documentation for the PyOS_snprintf() '#' specifier for details.

If ptype is non-NULL, then the value it points to will be set to one of Py_DTST_FINITE, Py_DTST_INFINITE, or Py_DTST_NAN, signifying that val is a finite number, an infinite number, or not a number, respectively.

The return value is a pointer to buffer with the converted string or NULL if the conversion failed. The caller is responsible for freeing the returned string by calling PyMem_Free().

New in version 3.1.

+
+int PyOS_stricmp(const char *s1, const char *s2)
+

Case insensitive comparison of strings. The function works almost identically to strcmp() except that it ignores the case.

+
+int PyOS_strnicmp(const char *s1, const char *s2, Py_ssize_t size)
+

Case insensitive comparison of strings. The function works almost identically to strncmp() except that it ignores the case.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/conversion.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fcoro.html b/devdocs/python~3.12/c-api%2Fcoro.html new file mode 100644 index 00000000..a95fa141 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fcoro.html @@ -0,0 +1,18 @@ +

Coroutine Objects

New in version 3.5.

Coroutine objects are what functions declared with an async keyword return.

+type PyCoroObject
+

The C structure used for coroutine objects.

+
+PyTypeObject PyCoro_Type
+

The type object corresponding to coroutine objects.

+
+int PyCoro_CheckExact(PyObject *ob)
+

Return true if ob’s type is PyCoro_Type; ob must not be NULL. This function always succeeds.

+
+PyObject *PyCoro_New(PyFrameObject *frame, PyObject *name, PyObject *qualname)
+Return value: New reference.

Create and return a new coroutine object based on the frame object, with __name__ and __qualname__ set to name and qualname. A reference to frame is stolen by this function. The frame argument must not be NULL.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/coro.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fdatetime.html b/devdocs/python~3.12/c-api%2Fdatetime.html new file mode 100644 index 00000000..ffd0abef --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fdatetime.html @@ -0,0 +1,150 @@ +

DateTime Objects

Various date and time objects are supplied by the datetime module. Before using any of these functions, the header file datetime.h must be included in your source (note that this is not included by Python.h), and the macro PyDateTime_IMPORT must be invoked, usually as part of the module initialisation function. The macro puts a pointer to a C structure into a static variable, PyDateTimeAPI, that is used by the following macros.

+type PyDateTime_Date
+

This subtype of PyObject represents a Python date object.

+
+type PyDateTime_DateTime
+

This subtype of PyObject represents a Python datetime object.

+
+type PyDateTime_Time
+

This subtype of PyObject represents a Python time object.

+
+type PyDateTime_Delta
+

This subtype of PyObject represents the difference between two datetime values.

+
+PyTypeObject PyDateTime_DateType
+

This instance of PyTypeObject represents the Python date type; it is the same object as datetime.date in the Python layer.

+
+PyTypeObject PyDateTime_DateTimeType
+

This instance of PyTypeObject represents the Python datetime type; it is the same object as datetime.datetime in the Python layer.

+
+PyTypeObject PyDateTime_TimeType
+

This instance of PyTypeObject represents the Python time type; it is the same object as datetime.time in the Python layer.

+
+PyTypeObject PyDateTime_DeltaType
+

This instance of PyTypeObject represents Python type for the difference between two datetime values; it is the same object as datetime.timedelta in the Python layer.

+
+PyTypeObject PyDateTime_TZInfoType
+

This instance of PyTypeObject represents the Python time zone info type; it is the same object as datetime.tzinfo in the Python layer.

+

Macro for access to the UTC singleton:

+PyObject *PyDateTime_TimeZone_UTC
+

Returns the time zone singleton representing UTC, the same object as datetime.timezone.utc.

New in version 3.7.

+

Type-check macros:

+int PyDate_Check(PyObject *ob)
+

Return true if ob is of type PyDateTime_DateType or a subtype of PyDateTime_DateType. ob must not be NULL. This function always succeeds.

+
+int PyDate_CheckExact(PyObject *ob)
+

Return true if ob is of type PyDateTime_DateType. ob must not be NULL. This function always succeeds.

+
+int PyDateTime_Check(PyObject *ob)
+

Return true if ob is of type PyDateTime_DateTimeType or a subtype of PyDateTime_DateTimeType. ob must not be NULL. This function always succeeds.

+
+int PyDateTime_CheckExact(PyObject *ob)
+

Return true if ob is of type PyDateTime_DateTimeType. ob must not be NULL. This function always succeeds.

+
+int PyTime_Check(PyObject *ob)
+

Return true if ob is of type PyDateTime_TimeType or a subtype of PyDateTime_TimeType. ob must not be NULL. This function always succeeds.

+
+int PyTime_CheckExact(PyObject *ob)
+

Return true if ob is of type PyDateTime_TimeType. ob must not be NULL. This function always succeeds.

+
+int PyDelta_Check(PyObject *ob)
+

Return true if ob is of type PyDateTime_DeltaType or a subtype of PyDateTime_DeltaType. ob must not be NULL. This function always succeeds.

+
+int PyDelta_CheckExact(PyObject *ob)
+

Return true if ob is of type PyDateTime_DeltaType. ob must not be NULL. This function always succeeds.

+
+int PyTZInfo_Check(PyObject *ob)
+

Return true if ob is of type PyDateTime_TZInfoType or a subtype of PyDateTime_TZInfoType. ob must not be NULL. This function always succeeds.

+
+int PyTZInfo_CheckExact(PyObject *ob)
+

Return true if ob is of type PyDateTime_TZInfoType. ob must not be NULL. This function always succeeds.

+

Macros to create objects:

+PyObject *PyDate_FromDate(int year, int month, int day)
+Return value: New reference.

Return a datetime.date object with the specified year, month and day.

+
+PyObject *PyDateTime_FromDateAndTime(int year, int month, int day, int hour, int minute, int second, int usecond)
+Return value: New reference.

Return a datetime.datetime object with the specified year, month, day, hour, minute, second and microsecond.

+
+PyObject *PyDateTime_FromDateAndTimeAndFold(int year, int month, int day, int hour, int minute, int second, int usecond, int fold)
+Return value: New reference.

Return a datetime.datetime object with the specified year, month, day, hour, minute, second, microsecond and fold.

New in version 3.6.

+
+PyObject *PyTime_FromTime(int hour, int minute, int second, int usecond)
+Return value: New reference.

Return a datetime.time object with the specified hour, minute, second and microsecond.

+
+PyObject *PyTime_FromTimeAndFold(int hour, int minute, int second, int usecond, int fold)
+Return value: New reference.

Return a datetime.time object with the specified hour, minute, second, microsecond and fold.

New in version 3.6.

+
+PyObject *PyDelta_FromDSU(int days, int seconds, int useconds)
+Return value: New reference.

Return a datetime.timedelta object representing the given number of days, seconds and microseconds. Normalization is performed so that the resulting number of microseconds and seconds lie in the ranges documented for datetime.timedelta objects.

+
+PyObject *PyTimeZone_FromOffset(PyObject *offset)
+Return value: New reference.

Return a datetime.timezone object with an unnamed fixed offset represented by the offset argument.

New in version 3.7.

+
+PyObject *PyTimeZone_FromOffsetAndName(PyObject *offset, PyObject *name)
+Return value: New reference.

Return a datetime.timezone object with a fixed offset represented by the offset argument and with tzname name.

New in version 3.7.

+

Macros to extract fields from date objects. The argument must be an instance of PyDateTime_Date, including subclasses (such as PyDateTime_DateTime). The argument must not be NULL, and the type is not checked:

+int PyDateTime_GET_YEAR(PyDateTime_Date *o)
+

Return the year, as a positive int.

+
+int PyDateTime_GET_MONTH(PyDateTime_Date *o)
+

Return the month, as an int from 1 through 12.

+
+int PyDateTime_GET_DAY(PyDateTime_Date *o)
+

Return the day, as an int from 1 through 31.

+

Macros to extract fields from datetime objects. The argument must be an instance of PyDateTime_DateTime, including subclasses. The argument must not be NULL, and the type is not checked:

+int PyDateTime_DATE_GET_HOUR(PyDateTime_DateTime *o)
+

Return the hour, as an int from 0 through 23.

+
+int PyDateTime_DATE_GET_MINUTE(PyDateTime_DateTime *o)
+

Return the minute, as an int from 0 through 59.

+
+int PyDateTime_DATE_GET_SECOND(PyDateTime_DateTime *o)
+

Return the second, as an int from 0 through 59.

+
+int PyDateTime_DATE_GET_MICROSECOND(PyDateTime_DateTime *o)
+

Return the microsecond, as an int from 0 through 999999.

+
+int PyDateTime_DATE_GET_FOLD(PyDateTime_DateTime *o)
+

Return the fold, as an int from 0 through 1.

New in version 3.6.

+
+PyObject *PyDateTime_DATE_GET_TZINFO(PyDateTime_DateTime *o)
+

Return the tzinfo (which may be None).

New in version 3.10.

+

Macros to extract fields from time objects. The argument must be an instance of PyDateTime_Time, including subclasses. The argument must not be NULL, and the type is not checked:

+int PyDateTime_TIME_GET_HOUR(PyDateTime_Time *o)
+

Return the hour, as an int from 0 through 23.

+
+int PyDateTime_TIME_GET_MINUTE(PyDateTime_Time *o)
+

Return the minute, as an int from 0 through 59.

+
+int PyDateTime_TIME_GET_SECOND(PyDateTime_Time *o)
+

Return the second, as an int from 0 through 59.

+
+int PyDateTime_TIME_GET_MICROSECOND(PyDateTime_Time *o)
+

Return the microsecond, as an int from 0 through 999999.

+
+int PyDateTime_TIME_GET_FOLD(PyDateTime_Time *o)
+

Return the fold, as an int from 0 through 1.

New in version 3.6.

+
+PyObject *PyDateTime_TIME_GET_TZINFO(PyDateTime_Time *o)
+

Return the tzinfo (which may be None).

New in version 3.10.

+

Macros to extract fields from time delta objects. The argument must be an instance of PyDateTime_Delta, including subclasses. The argument must not be NULL, and the type is not checked:

+int PyDateTime_DELTA_GET_DAYS(PyDateTime_Delta *o)
+

Return the number of days, as an int from -999999999 to 999999999.

New in version 3.3.

+
+int PyDateTime_DELTA_GET_SECONDS(PyDateTime_Delta *o)
+

Return the number of seconds, as an int from 0 through 86399.

New in version 3.3.

+
+int PyDateTime_DELTA_GET_MICROSECONDS(PyDateTime_Delta *o)
+

Return the number of microseconds, as an int from 0 through 999999.

New in version 3.3.

+

Macros for the convenience of modules implementing the DB API:

+PyObject *PyDateTime_FromTimestamp(PyObject *args)
+Return value: New reference.

Create and return a new datetime.datetime object given an argument tuple suitable for passing to datetime.datetime.fromtimestamp().

+
+PyObject *PyDate_FromTimestamp(PyObject *args)
+Return value: New reference.

Create and return a new datetime.date object given an argument tuple suitable for passing to datetime.date.fromtimestamp().

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/datetime.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fdescriptor.html b/devdocs/python~3.12/c-api%2Fdescriptor.html new file mode 100644 index 00000000..a6089a82 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fdescriptor.html @@ -0,0 +1,34 @@ +

Descriptor Objects

“Descriptors” are objects that describe some attribute of an object. They are found in the dictionary of type objects.

+PyTypeObject PyProperty_Type
+ Part of the Stable ABI.

The type object for the built-in descriptor types.

+
+PyObject *PyDescr_NewGetSet(PyTypeObject *type, struct PyGetSetDef *getset)
+Return value: New reference. Part of the Stable ABI. +
+
+PyObject *PyDescr_NewMember(PyTypeObject *type, struct PyMemberDef *meth)
+Return value: New reference. Part of the Stable ABI. +
+
+PyObject *PyDescr_NewMethod(PyTypeObject *type, struct PyMethodDef *meth)
+Return value: New reference. Part of the Stable ABI. +
+
+PyObject *PyDescr_NewWrapper(PyTypeObject *type, struct wrapperbase *wrapper, void *wrapped)
Return value: New reference.
+
+PyObject *PyDescr_NewClassMethod(PyTypeObject *type, PyMethodDef *method)
+Return value: New reference. Part of the Stable ABI. +
+
+int PyDescr_IsData(PyObject *descr)
+

Return non-zero if the descriptor objects descr describes a data attribute, or 0 if it describes a method. descr must be a descriptor object; there is no error checking.

+
+PyObject *PyWrapper_New(PyObject*, PyObject*)
+Return value: New reference. Part of the Stable ABI. +
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/descriptor.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fdict.html b/devdocs/python~3.12/c-api%2Fdict.html new file mode 100644 index 00000000..7fc4f910 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fdict.html @@ -0,0 +1,127 @@ +

Dictionary Objects

+type PyDictObject
+

This subtype of PyObject represents a Python dictionary object.

+
+PyTypeObject PyDict_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python dictionary type. This is the same object as dict in the Python layer.

+
+int PyDict_Check(PyObject *p)
+

Return true if p is a dict object or an instance of a subtype of the dict type. This function always succeeds.

+
+int PyDict_CheckExact(PyObject *p)
+

Return true if p is a dict object, but not an instance of a subtype of the dict type. This function always succeeds.

+
+PyObject *PyDict_New()
+Return value: New reference. Part of the Stable ABI.

Return a new empty dictionary, or NULL on failure.

+
+PyObject *PyDictProxy_New(PyObject *mapping)
+Return value: New reference. Part of the Stable ABI.

Return a types.MappingProxyType object for a mapping which enforces read-only behavior. This is normally used to create a view to prevent modification of the dictionary for non-dynamic class types.

+
+void PyDict_Clear(PyObject *p)
+ Part of the Stable ABI.

Empty an existing dictionary of all key-value pairs.

+
+int PyDict_Contains(PyObject *p, PyObject *key)
+ Part of the Stable ABI.

Determine if dictionary p contains key. If an item in p is matches key, return 1, otherwise return 0. On error, return -1. This is equivalent to the Python expression key in p.

+
+PyObject *PyDict_Copy(PyObject *p)
+Return value: New reference. Part of the Stable ABI.

Return a new dictionary that contains the same key-value pairs as p.

+
+int PyDict_SetItem(PyObject *p, PyObject *key, PyObject *val)
+ Part of the Stable ABI.

Insert val into the dictionary p with a key of key. key must be hashable; if it isn’t, TypeError will be raised. Return 0 on success or -1 on failure. This function does not steal a reference to val.

+
+int PyDict_SetItemString(PyObject *p, const char *key, PyObject *val)
+ Part of the Stable ABI.

This is the same as PyDict_SetItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+int PyDict_DelItem(PyObject *p, PyObject *key)
+ Part of the Stable ABI.

Remove the entry in dictionary p with key key. key must be hashable; if it isn’t, TypeError is raised. If key is not in the dictionary, KeyError is raised. Return 0 on success or -1 on failure.

+
+int PyDict_DelItemString(PyObject *p, const char *key)
+ Part of the Stable ABI.

This is the same as PyDict_DelItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+PyObject *PyDict_GetItem(PyObject *p, PyObject *key)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the object from dictionary p which has a key key. Return NULL if the key key is not present, but without setting an exception.

Note

Exceptions that occur while this calls __hash__() and __eq__() methods are silently ignored. Prefer the PyDict_GetItemWithError() function instead.

Changed in version 3.10: Calling this API without GIL held had been allowed for historical reason. It is no longer allowed.

+
+PyObject *PyDict_GetItemWithError(PyObject *p, PyObject *key)
+Return value: Borrowed reference. Part of the Stable ABI.

Variant of PyDict_GetItem() that does not suppress exceptions. Return NULL with an exception set if an exception occurred. Return NULL without an exception set if the key wasn’t present.

+
+PyObject *PyDict_GetItemString(PyObject *p, const char *key)
+Return value: Borrowed reference. Part of the Stable ABI.

This is the same as PyDict_GetItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

Note

Exceptions that occur while this calls __hash__() and __eq__() methods or while creating the temporary str object are silently ignored. Prefer using the PyDict_GetItemWithError() function with your own PyUnicode_FromString() key instead.

+
+PyObject *PyDict_SetDefault(PyObject *p, PyObject *key, PyObject *defaultobj)
+Return value: Borrowed reference.

This is the same as the Python-level dict.setdefault(). If present, it returns the value corresponding to key from the dictionary p. If the key is not in the dict, it is inserted with value defaultobj and defaultobj is returned. This function evaluates the hash function of key only once, instead of evaluating it independently for the lookup and the insertion.

New in version 3.4.

+
+PyObject *PyDict_Items(PyObject *p)
+Return value: New reference. Part of the Stable ABI.

Return a PyListObject containing all the items from the dictionary.

+
+PyObject *PyDict_Keys(PyObject *p)
+Return value: New reference. Part of the Stable ABI.

Return a PyListObject containing all the keys from the dictionary.

+
+PyObject *PyDict_Values(PyObject *p)
+Return value: New reference. Part of the Stable ABI.

Return a PyListObject containing all the values from the dictionary p.

+
+Py_ssize_t PyDict_Size(PyObject *p)
+ Part of the Stable ABI.

Return the number of items in the dictionary. This is equivalent to len(p) on a dictionary.

+
+int PyDict_Next(PyObject *p, Py_ssize_t *ppos, PyObject **pkey, PyObject **pvalue)
+ Part of the Stable ABI.

Iterate over all key-value pairs in the dictionary p. The Py_ssize_t referred to by ppos must be initialized to 0 prior to the first call to this function to start the iteration; the function returns true for each pair in the dictionary, and false once all pairs have been reported. The parameters pkey and pvalue should either point to PyObject* variables that will be filled in with each key and value, respectively, or may be NULL. Any references returned through them are borrowed. ppos should not be altered during iteration. Its value represents offsets within the internal dictionary structure, and since the structure is sparse, the offsets are not consecutive.

For example:

PyObject *key, *value;
+Py_ssize_t pos = 0;
+
+while (PyDict_Next(self->dict, &pos, &key, &value)) {
+    /* do something interesting with the values... */
+    ...
+}
+

The dictionary p should not be mutated during iteration. It is safe to modify the values of the keys as you iterate over the dictionary, but only so long as the set of keys does not change. For example:

PyObject *key, *value;
+Py_ssize_t pos = 0;
+
+while (PyDict_Next(self->dict, &pos, &key, &value)) {
+    long i = PyLong_AsLong(value);
+    if (i == -1 && PyErr_Occurred()) {
+        return -1;
+    }
+    PyObject *o = PyLong_FromLong(i + 1);
+    if (o == NULL)
+        return -1;
+    if (PyDict_SetItem(self->dict, key, o) < 0) {
+        Py_DECREF(o);
+        return -1;
+    }
+    Py_DECREF(o);
+}
+
+
+int PyDict_Merge(PyObject *a, PyObject *b, int override)
+ Part of the Stable ABI.

Iterate over mapping object b adding key-value pairs to dictionary a. b may be a dictionary, or any object supporting PyMapping_Keys() and PyObject_GetItem(). If override is true, existing pairs in a will be replaced if a matching key is found in b, otherwise pairs will only be added if there is not a matching key in a. Return 0 on success or -1 if an exception was raised.

+
+int PyDict_Update(PyObject *a, PyObject *b)
+ Part of the Stable ABI.

This is the same as PyDict_Merge(a, b, 1) in C, and is similar to a.update(b) in Python except that PyDict_Update() doesn’t fall back to the iterating over a sequence of key value pairs if the second argument has no “keys” attribute. Return 0 on success or -1 if an exception was raised.

+
+int PyDict_MergeFromSeq2(PyObject *a, PyObject *seq2, int override)
+ Part of the Stable ABI.

Update or merge into dictionary a, from the key-value pairs in seq2. seq2 must be an iterable object producing iterable objects of length 2, viewed as key-value pairs. In case of duplicate keys, the last wins if override is true, else the first wins. Return 0 on success or -1 if an exception was raised. Equivalent Python (except for the return value):

def PyDict_MergeFromSeq2(a, seq2, override):
+    for key, value in seq2:
+        if override or key not in a:
+            a[key] = value
+
+
+int PyDict_AddWatcher(PyDict_WatchCallback callback)
+

Register callback as a dictionary watcher. Return a non-negative integer id which must be passed to future calls to PyDict_Watch(). In case of error (e.g. no more watcher IDs available), return -1 and set an exception.

New in version 3.12.

+
+int PyDict_ClearWatcher(int watcher_id)
+

Clear watcher identified by watcher_id previously returned from PyDict_AddWatcher(). Return 0 on success, -1 on error (e.g. if the given watcher_id was never registered.)

New in version 3.12.

+
+int PyDict_Watch(int watcher_id, PyObject *dict)
+

Mark dictionary dict as watched. The callback granted watcher_id by PyDict_AddWatcher() will be called when dict is modified or deallocated. Return 0 on success or -1 on error.

New in version 3.12.

+
+int PyDict_Unwatch(int watcher_id, PyObject *dict)
+

Mark dictionary dict as no longer watched. The callback granted watcher_id by PyDict_AddWatcher() will no longer be called when dict is modified or deallocated. The dict must previously have been watched by this watcher. Return 0 on success or -1 on error.

New in version 3.12.

+
+type PyDict_WatchEvent
+

Enumeration of possible dictionary watcher events: PyDict_EVENT_ADDED, PyDict_EVENT_MODIFIED, PyDict_EVENT_DELETED, PyDict_EVENT_CLONED, PyDict_EVENT_CLEARED, or PyDict_EVENT_DEALLOCATED.

New in version 3.12.

+
+typedef int (*PyDict_WatchCallback)(PyDict_WatchEvent event, PyObject *dict, PyObject *key, PyObject *new_value)
+

Type of a dict watcher callback function.

If event is PyDict_EVENT_CLEARED or PyDict_EVENT_DEALLOCATED, both key and new_value will be NULL. If event is PyDict_EVENT_ADDED or PyDict_EVENT_MODIFIED, new_value will be the new value for key. If event is PyDict_EVENT_DELETED, key is being deleted from the dictionary and new_value will be NULL.

PyDict_EVENT_CLONED occurs when dict was previously empty and another dict is merged into it. To maintain efficiency of this operation, per-key PyDict_EVENT_ADDED events are not issued in this case; instead a single PyDict_EVENT_CLONED is issued, and key will be the source dictionary.

The callback may inspect but must not modify dict; doing so could have unpredictable effects, including infinite recursion. Do not trigger Python code execution in the callback, as it could modify the dict as a side effect.

If event is PyDict_EVENT_DEALLOCATED, taking a new reference in the callback to the about-to-be-destroyed dictionary will resurrect it and prevent it from being freed at this time. When the resurrected object is destroyed later, any watcher callbacks active at that time will be called again.

Callbacks occur before the notified modification to dict takes place, so the prior state of dict can be inspected.

If the callback sets an exception, it must return -1; this exception will be printed as an unraisable exception using PyErr_WriteUnraisable(). Otherwise it should return 0.

There may already be a pending exception set on entry to the callback. In this case, the callback should return 0 with the same exception still set. This means the callback may not call any other API that can set an exception unless it saves and clears the exception state first, and restores it before returning.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/dict.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fexceptions.html b/devdocs/python~3.12/c-api%2Fexceptions.html new file mode 100644 index 00000000..76675b01 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fexceptions.html @@ -0,0 +1,336 @@ +

Exception Handling

The functions described in this chapter will let you handle and raise Python exceptions. It is important to understand some of the basics of Python exception handling. It works somewhat like the POSIX errno variable: there is a global indicator (per thread) of the last error that occurred. Most C API functions don’t clear this on success, but will set it to indicate the cause of the error on failure. Most C API functions also return an error indicator, usually NULL if they are supposed to return a pointer, or -1 if they return an integer (exception: the PyArg_* functions return 1 for success and 0 for failure).

Concretely, the error indicator consists of three object pointers: the exception’s type, the exception’s value, and the traceback object. Any of those pointers can be NULL if non-set (although some combinations are forbidden, for example you can’t have a non-NULL traceback if the exception type is NULL).

When a function must fail because some function it called failed, it generally doesn’t set the error indicator; the function it called already set it. It is responsible for either handling the error and clearing the exception or returning after cleaning up any resources it holds (such as object references or memory allocations); it should not continue normally if it is not prepared to handle the error. If returning due to an error, it is important to indicate to the caller that an error has been set. If the error is not handled or carefully propagated, additional calls into the Python/C API may not behave as intended and may fail in mysterious ways.

Note

The error indicator is not the result of sys.exc_info(). The former corresponds to an exception that is not yet caught (and is therefore still propagating), while the latter returns an exception after it is caught (and has therefore stopped propagating).

Printing and clearing

+void PyErr_Clear()
+ Part of the Stable ABI.

Clear the error indicator. If the error indicator is not set, there is no effect.

+
+void PyErr_PrintEx(int set_sys_last_vars)
+ Part of the Stable ABI.

Print a standard traceback to sys.stderr and clear the error indicator. Unless the error is a SystemExit, in that case no traceback is printed and the Python process will exit with the error code specified by the SystemExit instance.

Call this function only when the error indicator is set. Otherwise it will cause a fatal error!

If set_sys_last_vars is nonzero, the variable sys.last_exc is set to the printed exception. For backwards compatibility, the deprecated variables sys.last_type, sys.last_value and sys.last_traceback are also set to the type, value and traceback of this exception, respectively.

Changed in version 3.12: The setting of sys.last_exc was added.

+
+void PyErr_Print()
+ Part of the Stable ABI.

Alias for PyErr_PrintEx(1).

+
+void PyErr_WriteUnraisable(PyObject *obj)
+ Part of the Stable ABI.

Call sys.unraisablehook() using the current exception and obj argument.

This utility function prints a warning message to sys.stderr when an exception has been set but it is impossible for the interpreter to actually raise the exception. It is used, for example, when an exception occurs in an __del__() method.

The function is called with a single argument obj that identifies the context in which the unraisable exception occurred. If possible, the repr of obj will be printed in the warning message. If obj is NULL, only the traceback is printed.

An exception must be set when calling this function.

Changed in version 3.4: Print a traceback. Print only traceback if obj is NULL.

Changed in version 3.8: Use sys.unraisablehook().

+
+void PyErr_DisplayException(PyObject *exc)
+ Part of the Stable ABI since version 3.12.

Print the standard traceback display of exc to sys.stderr, including chained exceptions and notes.

New in version 3.12.

+

Raising exceptions

These functions help you set the current thread’s error indicator. For convenience, some of these functions will always return a NULL pointer for use in a return statement.

+void PyErr_SetString(PyObject *type, const char *message)
+ Part of the Stable ABI.

This is the most common way to set the error indicator. The first argument specifies the exception type; it is normally one of the standard exceptions, e.g. PyExc_RuntimeError. You need not create a new strong reference to it (e.g. with Py_INCREF()). The second argument is an error message; it is decoded from 'utf-8'.

+
+void PyErr_SetObject(PyObject *type, PyObject *value)
+ Part of the Stable ABI.

This function is similar to PyErr_SetString() but lets you specify an arbitrary Python object for the “value” of the exception.

+
+PyObject *PyErr_Format(PyObject *exception, const char *format, ...)
+Return value: Always NULL. Part of the Stable ABI.

This function sets the error indicator and returns NULL. exception should be a Python exception class. The format and subsequent parameters help format the error message; they have the same meaning and values as in PyUnicode_FromFormat(). format is an ASCII-encoded string.

+
+PyObject *PyErr_FormatV(PyObject *exception, const char *format, va_list vargs)
+Return value: Always NULL. Part of the Stable ABI since version 3.5.

Same as PyErr_Format(), but taking a va_list argument rather than a variable number of arguments.

New in version 3.5.

+
+void PyErr_SetNone(PyObject *type)
+ Part of the Stable ABI.

This is a shorthand for PyErr_SetObject(type, Py_None).

+
+int PyErr_BadArgument()
+ Part of the Stable ABI.

This is a shorthand for PyErr_SetString(PyExc_TypeError, message), where message indicates that a built-in operation was invoked with an illegal argument. It is mostly for internal use.

+
+PyObject *PyErr_NoMemory()
+Return value: Always NULL. Part of the Stable ABI.

This is a shorthand for PyErr_SetNone(PyExc_MemoryError); it returns NULL so an object allocation function can write return PyErr_NoMemory(); when it runs out of memory.

+
+PyObject *PyErr_SetFromErrno(PyObject *type)
+Return value: Always NULL. Part of the Stable ABI.

This is a convenience function to raise an exception when a C library function has returned an error and set the C variable errno. It constructs a tuple object whose first item is the integer errno value and whose second item is the corresponding error message (gotten from strerror()), and then calls PyErr_SetObject(type, object). On Unix, when the errno value is EINTR, indicating an interrupted system call, this calls PyErr_CheckSignals(), and if that set the error indicator, leaves it set to that. The function always returns NULL, so a wrapper function around a system call can write return PyErr_SetFromErrno(type); when the system call returns an error.

+
+PyObject *PyErr_SetFromErrnoWithFilenameObject(PyObject *type, PyObject *filenameObject)
+Return value: Always NULL. Part of the Stable ABI.

Similar to PyErr_SetFromErrno(), with the additional behavior that if filenameObject is not NULL, it is passed to the constructor of type as a third parameter. In the case of OSError exception, this is used to define the filename attribute of the exception instance.

+
+PyObject *PyErr_SetFromErrnoWithFilenameObjects(PyObject *type, PyObject *filenameObject, PyObject *filenameObject2)
+Return value: Always NULL. Part of the Stable ABI since version 3.7.

Similar to PyErr_SetFromErrnoWithFilenameObject(), but takes a second filename object, for raising errors when a function that takes two filenames fails.

New in version 3.4.

+
+PyObject *PyErr_SetFromErrnoWithFilename(PyObject *type, const char *filename)
+Return value: Always NULL. Part of the Stable ABI.

Similar to PyErr_SetFromErrnoWithFilenameObject(), but the filename is given as a C string. filename is decoded from the filesystem encoding and error handler.

+
+PyObject *PyErr_SetFromWindowsErr(int ierr)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

This is a convenience function to raise WindowsError. If called with ierr of 0, the error code returned by a call to GetLastError() is used instead. It calls the Win32 function FormatMessage() to retrieve the Windows description of error code given by ierr or GetLastError(), then it constructs a tuple object whose first item is the ierr value and whose second item is the corresponding error message (gotten from FormatMessage()), and then calls PyErr_SetObject(PyExc_WindowsError, +object). This function always returns NULL.

Availability: Windows.

+
+PyObject *PyErr_SetExcFromWindowsErr(PyObject *type, int ierr)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

Similar to PyErr_SetFromWindowsErr(), with an additional parameter specifying the exception type to be raised.

Availability: Windows.

+
+PyObject *PyErr_SetFromWindowsErrWithFilename(int ierr, const char *filename)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

Similar to PyErr_SetFromWindowsErr(), with the additional behavior that if filename is not NULL, it is decoded from the filesystem encoding (os.fsdecode()) and passed to the constructor of OSError as a third parameter to be used to define the filename attribute of the exception instance.

Availability: Windows.

+
+PyObject *PyErr_SetExcFromWindowsErrWithFilenameObject(PyObject *type, int ierr, PyObject *filename)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

Similar to PyErr_SetExcFromWindowsErr(), with the additional behavior that if filename is not NULL, it is passed to the constructor of OSError as a third parameter to be used to define the filename attribute of the exception instance.

Availability: Windows.

+
+PyObject *PyErr_SetExcFromWindowsErrWithFilenameObjects(PyObject *type, int ierr, PyObject *filename, PyObject *filename2)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

Similar to PyErr_SetExcFromWindowsErrWithFilenameObject(), but accepts a second filename object.

Availability: Windows.

New in version 3.4.

+
+PyObject *PyErr_SetExcFromWindowsErrWithFilename(PyObject *type, int ierr, const char *filename)
+Return value: Always NULL. Part of the Stable ABI on Windows since version 3.7.

Similar to PyErr_SetFromWindowsErrWithFilename(), with an additional parameter specifying the exception type to be raised.

Availability: Windows.

+
+PyObject *PyErr_SetImportError(PyObject *msg, PyObject *name, PyObject *path)
+Return value: Always NULL. Part of the Stable ABI since version 3.7.

This is a convenience function to raise ImportError. msg will be set as the exception’s message string. name and path, both of which can be NULL, will be set as the ImportError’s respective name and path attributes.

New in version 3.3.

+
+PyObject *PyErr_SetImportErrorSubclass(PyObject *exception, PyObject *msg, PyObject *name, PyObject *path)
+Return value: Always NULL. Part of the Stable ABI since version 3.6.

Much like PyErr_SetImportError() but this function allows for specifying a subclass of ImportError to raise.

New in version 3.6.

+
+void PyErr_SyntaxLocationObject(PyObject *filename, int lineno, int col_offset)
+

Set file, line, and offset information for the current exception. If the current exception is not a SyntaxError, then it sets additional attributes, which make the exception printing subsystem think the exception is a SyntaxError.

New in version 3.4.

+
+void PyErr_SyntaxLocationEx(const char *filename, int lineno, int col_offset)
+ Part of the Stable ABI since version 3.7.

Like PyErr_SyntaxLocationObject(), but filename is a byte string decoded from the filesystem encoding and error handler.

New in version 3.2.

+
+void PyErr_SyntaxLocation(const char *filename, int lineno)
+ Part of the Stable ABI.

Like PyErr_SyntaxLocationEx(), but the col_offset parameter is omitted.

+
+void PyErr_BadInternalCall()
+ Part of the Stable ABI.

This is a shorthand for PyErr_SetString(PyExc_SystemError, message), where message indicates that an internal operation (e.g. a Python/C API function) was invoked with an illegal argument. It is mostly for internal use.

+

Issuing warnings

Use these functions to issue warnings from C code. They mirror similar functions exported by the Python warnings module. They normally print a warning message to sys.stderr; however, it is also possible that the user has specified that warnings are to be turned into errors, and in that case they will raise an exception. It is also possible that the functions raise an exception because of a problem with the warning machinery. The return value is 0 if no exception is raised, or -1 if an exception is raised. (It is not possible to determine whether a warning message is actually printed, nor what the reason is for the exception; this is intentional.) If an exception is raised, the caller should do its normal exception handling (for example, Py_DECREF() owned references and return an error value).

+int PyErr_WarnEx(PyObject *category, const char *message, Py_ssize_t stack_level)
+ Part of the Stable ABI.

Issue a warning message. The category argument is a warning category (see below) or NULL; the message argument is a UTF-8 encoded string. stack_level is a positive number giving a number of stack frames; the warning will be issued from the currently executing line of code in that stack frame. A stack_level of 1 is the function calling PyErr_WarnEx(), 2 is the function above that, and so forth.

Warning categories must be subclasses of PyExc_Warning; PyExc_Warning is a subclass of PyExc_Exception; the default warning category is PyExc_RuntimeWarning. The standard Python warning categories are available as global variables whose names are enumerated at Standard Warning Categories.

For information about warning control, see the documentation for the warnings module and the -W option in the command line documentation. There is no C API for warning control.

+
+int PyErr_WarnExplicitObject(PyObject *category, PyObject *message, PyObject *filename, int lineno, PyObject *module, PyObject *registry)
+

Issue a warning message with explicit control over all warning attributes. This is a straightforward wrapper around the Python function warnings.warn_explicit(); see there for more information. The module and registry arguments may be set to NULL to get the default effect described there.

New in version 3.4.

+
+int PyErr_WarnExplicit(PyObject *category, const char *message, const char *filename, int lineno, const char *module, PyObject *registry)
+ Part of the Stable ABI.

Similar to PyErr_WarnExplicitObject() except that message and module are UTF-8 encoded strings, and filename is decoded from the filesystem encoding and error handler.

+
+int PyErr_WarnFormat(PyObject *category, Py_ssize_t stack_level, const char *format, ...)
+ Part of the Stable ABI.

Function similar to PyErr_WarnEx(), but use PyUnicode_FromFormat() to format the warning message. format is an ASCII-encoded string.

New in version 3.2.

+
+int PyErr_ResourceWarning(PyObject *source, Py_ssize_t stack_level, const char *format, ...)
+ Part of the Stable ABI since version 3.6.

Function similar to PyErr_WarnFormat(), but category is ResourceWarning and it passes source to warnings.WarningMessage().

New in version 3.6.

+

Querying the error indicator

+PyObject *PyErr_Occurred()
+Return value: Borrowed reference. Part of the Stable ABI.

Test whether the error indicator is set. If set, return the exception type (the first argument to the last call to one of the PyErr_Set* functions or to PyErr_Restore()). If not set, return NULL. You do not own a reference to the return value, so you do not need to Py_DECREF() it.

The caller must hold the GIL.

Note

Do not compare the return value to a specific exception; use PyErr_ExceptionMatches() instead, shown below. (The comparison could easily fail since the exception may be an instance instead of a class, in the case of a class exception, or it may be a subclass of the expected exception.)

+
+int PyErr_ExceptionMatches(PyObject *exc)
+ Part of the Stable ABI.

Equivalent to PyErr_GivenExceptionMatches(PyErr_Occurred(), exc). This should only be called when an exception is actually set; a memory access violation will occur if no exception has been raised.

+
+int PyErr_GivenExceptionMatches(PyObject *given, PyObject *exc)
+ Part of the Stable ABI.

Return true if the given exception matches the exception type in exc. If exc is a class object, this also returns true when given is an instance of a subclass. If exc is a tuple, all exception types in the tuple (and recursively in subtuples) are searched for a match.

+
+PyObject *PyErr_GetRaisedException(void)
+Return value: New reference. Part of the Stable ABI since version 3.12.

Return the exception currently being raised, clearing the error indicator at the same time. Return NULL if the error indicator is not set.

This function is used by code that needs to catch exceptions, or code that needs to save and restore the error indicator temporarily.

For example:

{
+   PyObject *exc = PyErr_GetRaisedException();
+
+   /* ... code that might produce other errors ... */
+
+   PyErr_SetRaisedException(exc);
+}
+

See also

PyErr_GetHandledException(), to save the exception currently being handled.

New in version 3.12.

+
+void PyErr_SetRaisedException(PyObject *exc)
+ Part of the Stable ABI since version 3.12.

Set exc as the exception currently being raised, clearing the existing exception if one is set.

Warning

This call steals a reference to exc, which must be a valid exception.

New in version 3.12.

+
+void PyErr_Fetch(PyObject **ptype, PyObject **pvalue, PyObject **ptraceback)
+ Part of the Stable ABI.

Deprecated since version 3.12: Use PyErr_GetRaisedException() instead.

Retrieve the error indicator into three variables whose addresses are passed. If the error indicator is not set, set all three variables to NULL. If it is set, it will be cleared and you own a reference to each object retrieved. The value and traceback object may be NULL even when the type object is not.

Note

This function is normally only used by legacy code that needs to catch exceptions or save and restore the error indicator temporarily.

For example:

{
+   PyObject *type, *value, *traceback;
+   PyErr_Fetch(&type, &value, &traceback);
+
+   /* ... code that might produce other errors ... */
+
+   PyErr_Restore(type, value, traceback);
+}
+
+
+void PyErr_Restore(PyObject *type, PyObject *value, PyObject *traceback)
+ Part of the Stable ABI.

Deprecated since version 3.12: Use PyErr_SetRaisedException() instead.

Set the error indicator from the three objects, type, value, and traceback, clearing the existing exception if one is set. If the objects are NULL, the error indicator is cleared. Do not pass a NULL type and non-NULL value or traceback. The exception type should be a class. Do not pass an invalid exception type or value. (Violating these rules will cause subtle problems later.) This call takes away a reference to each object: you must own a reference to each object before the call and after the call you no longer own these references. (If you don’t understand this, don’t use this function. I warned you.)

Note

This function is normally only used by legacy code that needs to save and restore the error indicator temporarily. Use PyErr_Fetch() to save the current error indicator.

+
+void PyErr_NormalizeException(PyObject **exc, PyObject **val, PyObject **tb)
+ Part of the Stable ABI.

Deprecated since version 3.12: Use PyErr_GetRaisedException() instead, to avoid any possible de-normalization.

Under certain circumstances, the values returned by PyErr_Fetch() below can be “unnormalized”, meaning that *exc is a class object but *val is not an instance of the same class. This function can be used to instantiate the class in that case. If the values are already normalized, nothing happens. The delayed normalization is implemented to improve performance.

Note

This function does not implicitly set the __traceback__ attribute on the exception value. If setting the traceback appropriately is desired, the following additional snippet is needed:

if (tb != NULL) {
+  PyException_SetTraceback(val, tb);
+}
+
+
+PyObject *PyErr_GetHandledException(void)
+ Part of the Stable ABI since version 3.11.

Retrieve the active exception instance, as would be returned by sys.exception(). This refers to an exception that was already caught, not to an exception that was freshly raised. Returns a new reference to the exception or NULL. Does not modify the interpreter’s exception state.

Note

This function is not normally used by code that wants to handle exceptions. Rather, it can be used when code needs to save and restore the exception state temporarily. Use PyErr_SetHandledException() to restore or clear the exception state.

New in version 3.11.

+
+void PyErr_SetHandledException(PyObject *exc)
+ Part of the Stable ABI since version 3.11.

Set the active exception, as known from sys.exception(). This refers to an exception that was already caught, not to an exception that was freshly raised. To clear the exception state, pass NULL.

Note

This function is not normally used by code that wants to handle exceptions. Rather, it can be used when code needs to save and restore the exception state temporarily. Use PyErr_GetHandledException() to get the exception state.

New in version 3.11.

+
+void PyErr_GetExcInfo(PyObject **ptype, PyObject **pvalue, PyObject **ptraceback)
+ Part of the Stable ABI since version 3.7.

Retrieve the old-style representation of the exception info, as known from sys.exc_info(). This refers to an exception that was already caught, not to an exception that was freshly raised. Returns new references for the three objects, any of which may be NULL. Does not modify the exception info state. This function is kept for backwards compatibility. Prefer using PyErr_GetHandledException().

Note

This function is not normally used by code that wants to handle exceptions. Rather, it can be used when code needs to save and restore the exception state temporarily. Use PyErr_SetExcInfo() to restore or clear the exception state.

New in version 3.3.

+
+void PyErr_SetExcInfo(PyObject *type, PyObject *value, PyObject *traceback)
+ Part of the Stable ABI since version 3.7.

Set the exception info, as known from sys.exc_info(). This refers to an exception that was already caught, not to an exception that was freshly raised. This function steals the references of the arguments. To clear the exception state, pass NULL for all three arguments. This function is kept for backwards compatibility. Prefer using PyErr_SetHandledException().

Note

This function is not normally used by code that wants to handle exceptions. Rather, it can be used when code needs to save and restore the exception state temporarily. Use PyErr_GetExcInfo() to read the exception state.

New in version 3.3.

Changed in version 3.11: The type and traceback arguments are no longer used and can be NULL. The interpreter now derives them from the exception instance (the value argument). The function still steals references of all three arguments.

+

Signal Handling

+int PyErr_CheckSignals()
+ Part of the Stable ABI.

This function interacts with Python’s signal handling.

If the function is called from the main thread and under the main Python interpreter, it checks whether a signal has been sent to the processes and if so, invokes the corresponding signal handler. If the signal module is supported, this can invoke a signal handler written in Python.

The function attempts to handle all pending signals, and then returns 0. However, if a Python signal handler raises an exception, the error indicator is set and the function returns -1 immediately (such that other pending signals may not have been handled yet: they will be on the next PyErr_CheckSignals() invocation).

If the function is called from a non-main thread, or under a non-main Python interpreter, it does nothing and returns 0.

This function can be called by long-running C code that wants to be interruptible by user requests (such as by pressing Ctrl-C).

Note

The default Python signal handler for SIGINT raises the KeyboardInterrupt exception.

+
+void PyErr_SetInterrupt()
+ Part of the Stable ABI.

Simulate the effect of a SIGINT signal arriving. This is equivalent to PyErr_SetInterruptEx(SIGINT).

Note

This function is async-signal-safe. It can be called without the GIL and from a C signal handler.

+
+int PyErr_SetInterruptEx(int signum)
+ Part of the Stable ABI since version 3.10.

Simulate the effect of a signal arriving. The next time PyErr_CheckSignals() is called, the Python signal handler for the given signal number will be called.

This function can be called by C code that sets up its own signal handling and wants Python signal handlers to be invoked as expected when an interruption is requested (for example when the user presses Ctrl-C to interrupt an operation).

If the given signal isn’t handled by Python (it was set to signal.SIG_DFL or signal.SIG_IGN), it will be ignored.

If signum is outside of the allowed range of signal numbers, -1 is returned. Otherwise, 0 is returned. The error indicator is never changed by this function.

Note

This function is async-signal-safe. It can be called without the GIL and from a C signal handler.

New in version 3.10.

+
+int PySignal_SetWakeupFd(int fd)
+

This utility function specifies a file descriptor to which the signal number is written as a single byte whenever a signal is received. fd must be non-blocking. It returns the previous such file descriptor.

The value -1 disables the feature; this is the initial state. This is equivalent to signal.set_wakeup_fd() in Python, but without any error checking. fd should be a valid file descriptor. The function should only be called from the main thread.

Changed in version 3.5: On Windows, the function now also supports socket handles.

+

Exception Classes

+PyObject *PyErr_NewException(const char *name, PyObject *base, PyObject *dict)
+Return value: New reference. Part of the Stable ABI.

This utility function creates and returns a new exception class. The name argument must be the name of the new exception, a C string of the form module.classname. The base and dict arguments are normally NULL. This creates a class object derived from Exception (accessible in C as PyExc_Exception).

The __module__ attribute of the new class is set to the first part (up to the last dot) of the name argument, and the class name is set to the last part (after the last dot). The base argument can be used to specify alternate base classes; it can either be only one class or a tuple of classes. The dict argument can be used to specify a dictionary of class variables and methods.

+
+PyObject *PyErr_NewExceptionWithDoc(const char *name, const char *doc, PyObject *base, PyObject *dict)
+Return value: New reference. Part of the Stable ABI.

Same as PyErr_NewException(), except that the new exception class can easily be given a docstring: If doc is non-NULL, it will be used as the docstring for the exception class.

New in version 3.2.

+

Exception Objects

+PyObject *PyException_GetTraceback(PyObject *ex)
+Return value: New reference. Part of the Stable ABI.

Return the traceback associated with the exception as a new reference, as accessible from Python through the __traceback__ attribute. If there is no traceback associated, this returns NULL.

+
+int PyException_SetTraceback(PyObject *ex, PyObject *tb)
+ Part of the Stable ABI.

Set the traceback associated with the exception to tb. Use Py_None to clear it.

+
+PyObject *PyException_GetContext(PyObject *ex)
+Return value: New reference. Part of the Stable ABI.

Return the context (another exception instance during whose handling ex was raised) associated with the exception as a new reference, as accessible from Python through the __context__ attribute. If there is no context associated, this returns NULL.

+
+void PyException_SetContext(PyObject *ex, PyObject *ctx)
+ Part of the Stable ABI.

Set the context associated with the exception to ctx. Use NULL to clear it. There is no type check to make sure that ctx is an exception instance. This steals a reference to ctx.

+
+PyObject *PyException_GetCause(PyObject *ex)
+Return value: New reference. Part of the Stable ABI.

Return the cause (either an exception instance, or None, set by raise ... from ...) associated with the exception as a new reference, as accessible from Python through the __cause__ attribute.

+
+void PyException_SetCause(PyObject *ex, PyObject *cause)
+ Part of the Stable ABI.

Set the cause associated with the exception to cause. Use NULL to clear it. There is no type check to make sure that cause is either an exception instance or None. This steals a reference to cause.

The __suppress_context__ attribute is implicitly set to True by this function.

+
+PyObject *PyException_GetArgs(PyObject *ex)
+Return value: New reference. Part of the Stable ABI since version 3.12.

Return args of exception ex.

+
+void PyException_SetArgs(PyObject *ex, PyObject *args)
+ Part of the Stable ABI since version 3.12.

Set args of exception ex to args.

+
+PyObject *PyUnstable_Exc_PrepReraiseStar(PyObject *orig, PyObject *excs)
+
This is Unstable API. It may change without warning in minor releases. +

Implement part of the interpreter’s implementation of except*. orig is the original exception that was caught, and excs is the list of the exceptions that need to be raised. This list contains the unhandled part of orig, if any, as well as the exceptions that were raised from the except* clauses (so they have a different traceback from orig) and those that were reraised (and have the same traceback as orig). Return the ExceptionGroup that needs to be reraised in the end, or None if there is nothing to reraise.

New in version 3.12.

+

Unicode Exception Objects

The following functions are used to create and modify Unicode exceptions from C.

+PyObject *PyUnicodeDecodeError_Create(const char *encoding, const char *object, Py_ssize_t length, Py_ssize_t start, Py_ssize_t end, const char *reason)
+Return value: New reference. Part of the Stable ABI.

Create a UnicodeDecodeError object with the attributes encoding, object, length, start, end and reason. encoding and reason are UTF-8 encoded strings.

+
+PyObject *PyUnicodeDecodeError_GetEncoding(PyObject *exc)
+PyObject *PyUnicodeEncodeError_GetEncoding(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Return the encoding attribute of the given exception object.

+
+PyObject *PyUnicodeDecodeError_GetObject(PyObject *exc)
+PyObject *PyUnicodeEncodeError_GetObject(PyObject *exc)
+PyObject *PyUnicodeTranslateError_GetObject(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Return the object attribute of the given exception object.

+
+int PyUnicodeDecodeError_GetStart(PyObject *exc, Py_ssize_t *start)
+int PyUnicodeEncodeError_GetStart(PyObject *exc, Py_ssize_t *start)
+int PyUnicodeTranslateError_GetStart(PyObject *exc, Py_ssize_t *start)
+ Part of the Stable ABI.

Get the start attribute of the given exception object and place it into *start. start must not be NULL. Return 0 on success, -1 on failure.

+
+int PyUnicodeDecodeError_SetStart(PyObject *exc, Py_ssize_t start)
+int PyUnicodeEncodeError_SetStart(PyObject *exc, Py_ssize_t start)
+int PyUnicodeTranslateError_SetStart(PyObject *exc, Py_ssize_t start)
+ Part of the Stable ABI.

Set the start attribute of the given exception object to start. Return 0 on success, -1 on failure.

+
+int PyUnicodeDecodeError_GetEnd(PyObject *exc, Py_ssize_t *end)
+int PyUnicodeEncodeError_GetEnd(PyObject *exc, Py_ssize_t *end)
+int PyUnicodeTranslateError_GetEnd(PyObject *exc, Py_ssize_t *end)
+ Part of the Stable ABI.

Get the end attribute of the given exception object and place it into *end. end must not be NULL. Return 0 on success, -1 on failure.

+
+int PyUnicodeDecodeError_SetEnd(PyObject *exc, Py_ssize_t end)
+int PyUnicodeEncodeError_SetEnd(PyObject *exc, Py_ssize_t end)
+int PyUnicodeTranslateError_SetEnd(PyObject *exc, Py_ssize_t end)
+ Part of the Stable ABI.

Set the end attribute of the given exception object to end. Return 0 on success, -1 on failure.

+
+PyObject *PyUnicodeDecodeError_GetReason(PyObject *exc)
+PyObject *PyUnicodeEncodeError_GetReason(PyObject *exc)
+PyObject *PyUnicodeTranslateError_GetReason(PyObject *exc)
+Return value: New reference. Part of the Stable ABI.

Return the reason attribute of the given exception object.

+
+int PyUnicodeDecodeError_SetReason(PyObject *exc, const char *reason)
+int PyUnicodeEncodeError_SetReason(PyObject *exc, const char *reason)
+int PyUnicodeTranslateError_SetReason(PyObject *exc, const char *reason)
+ Part of the Stable ABI.

Set the reason attribute of the given exception object to reason. Return 0 on success, -1 on failure.

+

Recursion Control

These two functions provide a way to perform safe recursive calls at the C level, both in the core and in extension modules. They are needed if the recursive code does not necessarily invoke Python code (which tracks its recursion depth automatically). They are also not needed for tp_call implementations because the call protocol takes care of recursion handling.

+int Py_EnterRecursiveCall(const char *where)
+ Part of the Stable ABI since version 3.9.

Marks a point where a recursive C-level call is about to be performed.

If USE_STACKCHECK is defined, this function checks if the OS stack overflowed using PyOS_CheckStack(). In this is the case, it sets a MemoryError and returns a nonzero value.

The function then checks if the recursion limit is reached. If this is the case, a RecursionError is set and a nonzero value is returned. Otherwise, zero is returned.

where should be a UTF-8 encoded string such as " in instance check" to be concatenated to the RecursionError message caused by the recursion depth limit.

Changed in version 3.9: This function is now also available in the limited API.

+
+void Py_LeaveRecursiveCall(void)
+ Part of the Stable ABI since version 3.9.

Ends a Py_EnterRecursiveCall(). Must be called once for each successful invocation of Py_EnterRecursiveCall().

Changed in version 3.9: This function is now also available in the limited API.

+

Properly implementing tp_repr for container types requires special recursion handling. In addition to protecting the stack, tp_repr also needs to track objects to prevent cycles. The following two functions facilitate this functionality. Effectively, these are the C equivalent to reprlib.recursive_repr().

+int Py_ReprEnter(PyObject *object)
+ Part of the Stable ABI.

Called at the beginning of the tp_repr implementation to detect cycles.

If the object has already been processed, the function returns a positive integer. In that case the tp_repr implementation should return a string object indicating a cycle. As examples, dict objects return {...} and list objects return [...].

The function will return a negative integer if the recursion limit is reached. In that case the tp_repr implementation should typically return NULL.

Otherwise, the function returns zero and the tp_repr implementation can continue normally.

+
+void Py_ReprLeave(PyObject *object)
+ Part of the Stable ABI.

Ends a Py_ReprEnter(). Must be called once for each invocation of Py_ReprEnter() that returns zero.

+

Standard Exceptions

All standard Python exceptions are available as global variables whose names are PyExc_ followed by the Python exception name. These have the type PyObject*; they are all class objects. For completeness, here are all the variables:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

C Name

Python Name

Notes

PyExc_BaseException

BaseException

1

PyExc_Exception

Exception

1

PyExc_ArithmeticError

ArithmeticError

1

PyExc_AssertionError

AssertionError

PyExc_AttributeError

AttributeError

PyExc_BlockingIOError

BlockingIOError

PyExc_BrokenPipeError

BrokenPipeError

PyExc_BufferError

BufferError

PyExc_ChildProcessError

ChildProcessError

PyExc_ConnectionAbortedError

ConnectionAbortedError

PyExc_ConnectionError

ConnectionError

PyExc_ConnectionRefusedError

ConnectionRefusedError

PyExc_ConnectionResetError

ConnectionResetError

PyExc_EOFError

EOFError

PyExc_FileExistsError

FileExistsError

PyExc_FileNotFoundError

FileNotFoundError

PyExc_FloatingPointError

FloatingPointError

PyExc_GeneratorExit

GeneratorExit

PyExc_ImportError

ImportError

PyExc_IndentationError

IndentationError

PyExc_IndexError

IndexError

PyExc_InterruptedError

InterruptedError

PyExc_IsADirectoryError

IsADirectoryError

PyExc_KeyError

KeyError

PyExc_KeyboardInterrupt

KeyboardInterrupt

PyExc_LookupError

LookupError

1

PyExc_MemoryError

MemoryError

PyExc_ModuleNotFoundError

ModuleNotFoundError

PyExc_NameError

NameError

PyExc_NotADirectoryError

NotADirectoryError

PyExc_NotImplementedError

NotImplementedError

PyExc_OSError

OSError

1

PyExc_OverflowError

OverflowError

PyExc_PermissionError

PermissionError

PyExc_ProcessLookupError

ProcessLookupError

PyExc_RecursionError

RecursionError

PyExc_ReferenceError

ReferenceError

PyExc_RuntimeError

RuntimeError

PyExc_StopAsyncIteration

StopAsyncIteration

PyExc_StopIteration

StopIteration

PyExc_SyntaxError

SyntaxError

PyExc_SystemError

SystemError

PyExc_SystemExit

SystemExit

PyExc_TabError

TabError

PyExc_TimeoutError

TimeoutError

PyExc_TypeError

TypeError

PyExc_UnboundLocalError

UnboundLocalError

PyExc_UnicodeDecodeError

UnicodeDecodeError

PyExc_UnicodeEncodeError

UnicodeEncodeError

PyExc_UnicodeError

UnicodeError

PyExc_UnicodeTranslateError

UnicodeTranslateError

PyExc_ValueError

ValueError

PyExc_ZeroDivisionError

ZeroDivisionError

New in version 3.3: PyExc_BlockingIOError, PyExc_BrokenPipeError, PyExc_ChildProcessError, PyExc_ConnectionError, PyExc_ConnectionAbortedError, PyExc_ConnectionRefusedError, PyExc_ConnectionResetError, PyExc_FileExistsError, PyExc_FileNotFoundError, PyExc_InterruptedError, PyExc_IsADirectoryError, PyExc_NotADirectoryError, PyExc_PermissionError, PyExc_ProcessLookupError and PyExc_TimeoutError were introduced following PEP 3151.

New in version 3.5: PyExc_StopAsyncIteration and PyExc_RecursionError.

New in version 3.6: PyExc_ModuleNotFoundError.

These are compatibility aliases to PyExc_OSError:

+ + + +

C Name

Notes

PyExc_EnvironmentError

PyExc_IOError

PyExc_WindowsError

2

Changed in version 3.3: These aliases used to be separate exception types.

Notes:

+1(1,2,3,4,5)
+

This is a base class for other standard exceptions.

+2
+

Only defined on Windows; protect code that uses this by testing that the preprocessor macro MS_WINDOWS is defined.

Standard Warning Categories

All standard Python warning categories are available as global variables whose names are PyExc_ followed by the Python exception name. These have the type PyObject*; they are all class objects. For completeness, here are all the variables:

+ + + + + + + + + + + +

C Name

Python Name

Notes

PyExc_Warning

Warning

3

PyExc_BytesWarning

BytesWarning

PyExc_DeprecationWarning

DeprecationWarning

PyExc_FutureWarning

FutureWarning

PyExc_ImportWarning

ImportWarning

PyExc_PendingDeprecationWarning

PendingDeprecationWarning

PyExc_ResourceWarning

ResourceWarning

PyExc_RuntimeWarning

RuntimeWarning

PyExc_SyntaxWarning

SyntaxWarning

PyExc_UnicodeWarning

UnicodeWarning

PyExc_UserWarning

UserWarning

New in version 3.2: PyExc_ResourceWarning.

Notes:

+3
+

This is a base class for other standard warning categories.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/exceptions.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ffile.html b/devdocs/python~3.12/c-api%2Ffile.html new file mode 100644 index 00000000..acdf1a23 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ffile.html @@ -0,0 +1,24 @@ +

File Objects

These APIs are a minimal emulation of the Python 2 C API for built-in file objects, which used to rely on the buffered I/O (FILE*) support from the C standard library. In Python 3, files and streams use the new io module, which defines several layers over the low-level unbuffered I/O of the operating system. The functions described below are convenience C wrappers over these new APIs, and meant mostly for internal error reporting in the interpreter; third-party code is advised to access the io APIs instead.

+PyObject *PyFile_FromFd(int fd, const char *name, const char *mode, int buffering, const char *encoding, const char *errors, const char *newline, int closefd)
+Return value: New reference. Part of the Stable ABI.

Create a Python file object from the file descriptor of an already opened file fd. The arguments name, encoding, errors and newline can be NULL to use the defaults; buffering can be -1 to use the default. name is ignored and kept for backward compatibility. Return NULL on failure. For a more comprehensive description of the arguments, please refer to the io.open() function documentation.

Warning

Since Python streams have their own buffering layer, mixing them with OS-level file descriptors can produce various issues (such as unexpected ordering of data).

Changed in version 3.2: Ignore name attribute.

+
+int PyObject_AsFileDescriptor(PyObject *p)
+ Part of the Stable ABI.

Return the file descriptor associated with p as an int. If the object is an integer, its value is returned. If not, the object’s fileno() method is called if it exists; the method must return an integer, which is returned as the file descriptor value. Sets an exception and returns -1 on failure.

+
+PyObject *PyFile_GetLine(PyObject *p, int n)
+Return value: New reference. Part of the Stable ABI.

Equivalent to p.readline([n]), this function reads one line from the object p. p may be a file object or any object with a readline() method. If n is 0, exactly one line is read, regardless of the length of the line. If n is greater than 0, no more than n bytes will be read from the file; a partial line can be returned. In both cases, an empty string is returned if the end of the file is reached immediately. If n is less than 0, however, one line is read regardless of length, but EOFError is raised if the end of the file is reached immediately.

+
+int PyFile_SetOpenCodeHook(Py_OpenCodeHookFunction handler)
+

Overrides the normal behavior of io.open_code() to pass its parameter through the provided handler.

The handler is a function of type PyObject *(*)(PyObject *path, void *userData), where path is guaranteed to be PyUnicodeObject.

The userData pointer is passed into the hook function. Since hook functions may be called from different runtimes, this pointer should not refer directly to Python state.

As this hook is intentionally used during import, avoid importing new modules during its execution unless they are known to be frozen or available in sys.modules.

Once a hook has been set, it cannot be removed or replaced, and later calls to PyFile_SetOpenCodeHook() will fail. On failure, the function returns -1 and sets an exception if the interpreter has been initialized.

This function is safe to call before Py_Initialize().

Raises an auditing event setopencodehook with no arguments.

New in version 3.8.

+
+int PyFile_WriteObject(PyObject *obj, PyObject *p, int flags)
+ Part of the Stable ABI.

Write object obj to file object p. The only supported flag for flags is Py_PRINT_RAW; if given, the str() of the object is written instead of the repr(). Return 0 on success or -1 on failure; the appropriate exception will be set.

+
+int PyFile_WriteString(const char *s, PyObject *p)
+ Part of the Stable ABI.

Write string s to file object p. Return 0 on success or -1 on failure; the appropriate exception will be set.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/file.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ffloat.html b/devdocs/python~3.12/c-api%2Ffloat.html new file mode 100644 index 00000000..178b3ce8 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ffloat.html @@ -0,0 +1,58 @@ +

Floating Point Objects

+type PyFloatObject
+

This subtype of PyObject represents a Python floating point object.

+
+PyTypeObject PyFloat_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python floating point type. This is the same object as float in the Python layer.

+
+int PyFloat_Check(PyObject *p)
+

Return true if its argument is a PyFloatObject or a subtype of PyFloatObject. This function always succeeds.

+
+int PyFloat_CheckExact(PyObject *p)
+

Return true if its argument is a PyFloatObject, but not a subtype of PyFloatObject. This function always succeeds.

+
+PyObject *PyFloat_FromString(PyObject *str)
+Return value: New reference. Part of the Stable ABI.

Create a PyFloatObject object based on the string value in str, or NULL on failure.

+
+PyObject *PyFloat_FromDouble(double v)
+Return value: New reference. Part of the Stable ABI.

Create a PyFloatObject object from v, or NULL on failure.

+
+double PyFloat_AsDouble(PyObject *pyfloat)
+ Part of the Stable ABI.

Return a C double representation of the contents of pyfloat. If pyfloat is not a Python floating point object but has a __float__() method, this method will first be called to convert pyfloat into a float. If __float__() is not defined then it falls back to __index__(). This method returns -1.0 upon failure, so one should call PyErr_Occurred() to check for errors.

Changed in version 3.8: Use __index__() if available.

+
+double PyFloat_AS_DOUBLE(PyObject *pyfloat)
+

Return a C double representation of the contents of pyfloat, but without error checking.

+
+PyObject *PyFloat_GetInfo(void)
+Return value: New reference. Part of the Stable ABI.

Return a structseq instance which contains information about the precision, minimum and maximum values of a float. It’s a thin wrapper around the header file float.h.

+
+double PyFloat_GetMax()
+ Part of the Stable ABI.

Return the maximum representable finite float DBL_MAX as C double.

+
+double PyFloat_GetMin()
+ Part of the Stable ABI.

Return the minimum normalized positive float DBL_MIN as C double.

+

Pack and Unpack functions

The pack and unpack functions provide an efficient platform-independent way to store floating-point values as byte strings. The Pack routines produce a bytes string from a C double, and the Unpack routines produce a C double from such a bytes string. The suffix (2, 4 or 8) specifies the number of bytes in the bytes string.

On platforms that appear to use IEEE 754 formats these functions work by copying bits. On other platforms, the 2-byte format is identical to the IEEE 754 binary16 half-precision format, the 4-byte format (32-bit) is identical to the IEEE 754 binary32 single precision format, and the 8-byte format to the IEEE 754 binary64 double precision format, although the packing of INFs and NaNs (if such things exist on the platform) isn’t handled correctly, and attempting to unpack a bytes string containing an IEEE INF or NaN will raise an exception.

On non-IEEE platforms with more precision, or larger dynamic range, than IEEE 754 supports, not all values can be packed; on non-IEEE platforms with less precision, or smaller dynamic range, not all values can be unpacked. What happens in such cases is partly accidental (alas).

New in version 3.11.

Pack functions

The pack routines write 2, 4 or 8 bytes, starting at p. le is an int argument, non-zero if you want the bytes string in little-endian format (exponent last, at p+1, p+3, or p+6 p+7), zero if you want big-endian format (exponent first, at p). The PY_BIG_ENDIAN constant can be used to use the native endian: it is equal to 1 on big endian processor, or 0 on little endian processor.

Return value: 0 if all is OK, -1 if error (and an exception is set, most likely OverflowError).

There are two problems on non-IEEE platforms:

  • What this does is undefined if x is a NaN or infinity.
  • +-0.0 and +0.0 produce the same bytes string.
+int PyFloat_Pack2(double x, unsigned char *p, int le)
+

Pack a C double as the IEEE 754 binary16 half-precision format.

+
+int PyFloat_Pack4(double x, unsigned char *p, int le)
+

Pack a C double as the IEEE 754 binary32 single precision format.

+
+int PyFloat_Pack8(double x, unsigned char *p, int le)
+

Pack a C double as the IEEE 754 binary64 double precision format.

+

Unpack functions

The unpack routines read 2, 4 or 8 bytes, starting at p. le is an int argument, non-zero if the bytes string is in little-endian format (exponent last, at p+1, p+3 or p+6 and p+7), zero if big-endian (exponent first, at p). The PY_BIG_ENDIAN constant can be used to use the native endian: it is equal to 1 on big endian processor, or 0 on little endian processor.

Return value: The unpacked double. On error, this is -1.0 and PyErr_Occurred() is true (and an exception is set, most likely OverflowError).

Note that on a non-IEEE platform this will refuse to unpack a bytes string that represents a NaN or infinity.

+double PyFloat_Unpack2(const unsigned char *p, int le)
+

Unpack the IEEE 754 binary16 half-precision format as a C double.

+
+double PyFloat_Unpack4(const unsigned char *p, int le)
+

Unpack the IEEE 754 binary32 single precision format as a C double.

+
+double PyFloat_Unpack8(const unsigned char *p, int le)
+

Unpack the IEEE 754 binary64 double precision format as a C double.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/float.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fframe.html b/devdocs/python~3.12/c-api%2Fframe.html new file mode 100644 index 00000000..b842a9e6 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fframe.html @@ -0,0 +1,60 @@ +

Frame Objects

+type PyFrameObject
+ Part of the Limited API (as an opaque struct).

The C structure of the objects used to describe frame objects.

There are no public members in this structure.

Changed in version 3.11: The members of this structure were removed from the public C API. Refer to the What’s New entry for details.

+

The PyEval_GetFrame() and PyThreadState_GetFrame() functions can be used to get a frame object.

See also Reflection.

+PyTypeObject PyFrame_Type
+

The type of frame objects. It is the same object as types.FrameType in the Python layer.

Changed in version 3.11: Previously, this type was only available after including <frameobject.h>.

+
+int PyFrame_Check(PyObject *obj)
+

Return non-zero if obj is a frame object.

Changed in version 3.11: Previously, this function was only available after including <frameobject.h>.

+
+PyFrameObject *PyFrame_GetBack(PyFrameObject *frame)
+

Get the frame next outer frame.

Return a strong reference, or NULL if frame has no outer frame.

New in version 3.9.

+
+PyObject *PyFrame_GetBuiltins(PyFrameObject *frame)
+

Get the frame’s f_builtins attribute.

Return a strong reference. The result cannot be NULL.

New in version 3.11.

+
+PyCodeObject *PyFrame_GetCode(PyFrameObject *frame)
+ Part of the Stable ABI since version 3.10.

Get the frame code.

Return a strong reference.

The result (frame code) cannot be NULL.

New in version 3.9.

+
+PyObject *PyFrame_GetGenerator(PyFrameObject *frame)
+

Get the generator, coroutine, or async generator that owns this frame, or NULL if this frame is not owned by a generator. Does not raise an exception, even if the return value is NULL.

Return a strong reference, or NULL.

New in version 3.11.

+
+PyObject *PyFrame_GetGlobals(PyFrameObject *frame)
+

Get the frame’s f_globals attribute.

Return a strong reference. The result cannot be NULL.

New in version 3.11.

+
+int PyFrame_GetLasti(PyFrameObject *frame)
+

Get the frame’s f_lasti attribute.

Returns -1 if frame.f_lasti is None.

New in version 3.11.

+
+PyObject *PyFrame_GetVar(PyFrameObject *frame, PyObject *name)
+

Get the variable name of frame.

  • Return a strong reference to the variable value on success.
  • Raise NameError and return NULL if the variable does not exist.
  • Raise an exception and return NULL on error.

name type must be a str.

New in version 3.12.

+
+PyObject *PyFrame_GetVarString(PyFrameObject *frame, const char *name)
+

Similar to PyFrame_GetVar(), but the variable name is a C string encoded in UTF-8.

New in version 3.12.

+
+PyObject *PyFrame_GetLocals(PyFrameObject *frame)
+

Get the frame’s f_locals attribute (dict).

Return a strong reference.

New in version 3.11.

+
+int PyFrame_GetLineNumber(PyFrameObject *frame)
+ Part of the Stable ABI since version 3.10.

Return the line number that frame is currently executing.

+

Internal Frames

Unless using PEP 523, you will not need this.

+struct _PyInterpreterFrame
+

The interpreter’s internal frame representation.

New in version 3.11.

+
+PyObject *PyUnstable_InterpreterFrame_GetCode(struct _PyInterpreterFrame *frame);
+
This is Unstable API. It may change without warning in minor releases. +

Return a strong reference to the code object for the frame.

New in version 3.12.

+
+int PyUnstable_InterpreterFrame_GetLasti(struct _PyInterpreterFrame *frame);
+
This is Unstable API. It may change without warning in minor releases. +

Return the byte offset into the last executed instruction.

New in version 3.12.

+
+int PyUnstable_InterpreterFrame_GetLine(struct _PyInterpreterFrame *frame);
+
This is Unstable API. It may change without warning in minor releases. +

Return the currently executing line number, or -1 if there is no line number.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/frame.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ffunction.html b/devdocs/python~3.12/c-api%2Ffunction.html new file mode 100644 index 00000000..2e19709a --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ffunction.html @@ -0,0 +1,63 @@ +

Function Objects

There are a few functions specific to Python functions.

+type PyFunctionObject
+

The C structure used for functions.

+
+PyTypeObject PyFunction_Type
+

This is an instance of PyTypeObject and represents the Python function type. It is exposed to Python programmers as types.FunctionType.

+
+int PyFunction_Check(PyObject *o)
+

Return true if o is a function object (has type PyFunction_Type). The parameter must not be NULL. This function always succeeds.

+
+PyObject *PyFunction_New(PyObject *code, PyObject *globals)
+Return value: New reference.

Return a new function object associated with the code object code. globals must be a dictionary with the global variables accessible to the function.

The function’s docstring and name are retrieved from the code object. __module__ is retrieved from globals. The argument defaults, annotations and closure are set to NULL. __qualname__ is set to the same value as the code object’s co_qualname field.

+
+PyObject *PyFunction_NewWithQualName(PyObject *code, PyObject *globals, PyObject *qualname)
+Return value: New reference.

As PyFunction_New(), but also allows setting the function object’s __qualname__ attribute. qualname should be a unicode object or NULL; if NULL, the __qualname__ attribute is set to the same value as the code object’s co_qualname field.

New in version 3.3.

+
+PyObject *PyFunction_GetCode(PyObject *op)
+Return value: Borrowed reference.

Return the code object associated with the function object op.

+
+PyObject *PyFunction_GetGlobals(PyObject *op)
+Return value: Borrowed reference.

Return the globals dictionary associated with the function object op.

+
+PyObject *PyFunction_GetModule(PyObject *op)
+Return value: Borrowed reference.

Return a borrowed reference to the __module__ attribute of the function object op. It can be NULL.

This is normally a string containing the module name, but can be set to any other object by Python code.

+
+PyObject *PyFunction_GetDefaults(PyObject *op)
+Return value: Borrowed reference.

Return the argument default values of the function object op. This can be a tuple of arguments or NULL.

+
+int PyFunction_SetDefaults(PyObject *op, PyObject *defaults)
+

Set the argument default values for the function object op. defaults must be Py_None or a tuple.

Raises SystemError and returns -1 on failure.

+
+void PyFunction_SetVectorcall(PyFunctionObject *func, vectorcallfunc vectorcall)
+

Set the vectorcall field of a given function object func.

Warning: extensions using this API must preserve the behavior of the unaltered (default) vectorcall function!

New in version 3.12.

+
+PyObject *PyFunction_GetClosure(PyObject *op)
+Return value: Borrowed reference.

Return the closure associated with the function object op. This can be NULL or a tuple of cell objects.

+
+int PyFunction_SetClosure(PyObject *op, PyObject *closure)
+

Set the closure associated with the function object op. closure must be Py_None or a tuple of cell objects.

Raises SystemError and returns -1 on failure.

+
+PyObject *PyFunction_GetAnnotations(PyObject *op)
+Return value: Borrowed reference.

Return the annotations of the function object op. This can be a mutable dictionary or NULL.

+
+int PyFunction_SetAnnotations(PyObject *op, PyObject *annotations)
+

Set the annotations for the function object op. annotations must be a dictionary or Py_None.

Raises SystemError and returns -1 on failure.

+
+int PyFunction_AddWatcher(PyFunction_WatchCallback callback)
+

Register callback as a function watcher for the current interpreter. Return an ID which may be passed to PyFunction_ClearWatcher(). In case of error (e.g. no more watcher IDs available), return -1 and set an exception.

New in version 3.12.

+
+int PyFunction_ClearWatcher(int watcher_id)
+

Clear watcher identified by watcher_id previously returned from PyFunction_AddWatcher() for the current interpreter. Return 0 on success, or -1 and set an exception on error (e.g. if the given watcher_id was never registered.)

New in version 3.12.

+
+type PyFunction_WatchEvent
+

Enumeration of possible function watcher events: - PyFunction_EVENT_CREATE - PyFunction_EVENT_DESTROY - PyFunction_EVENT_MODIFY_CODE - PyFunction_EVENT_MODIFY_DEFAULTS - PyFunction_EVENT_MODIFY_KWDEFAULTS

New in version 3.12.

+
+typedef int (*PyFunction_WatchCallback)(PyFunction_WatchEvent event, PyFunctionObject *func, PyObject *new_value)
+

Type of a function watcher callback function.

If event is PyFunction_EVENT_CREATE or PyFunction_EVENT_DESTROY then new_value will be NULL. Otherwise, new_value will hold a borrowed reference to the new value that is about to be stored in func for the attribute that is being modified.

The callback may inspect but must not modify func; doing so could have unpredictable effects, including infinite recursion.

If event is PyFunction_EVENT_CREATE, then the callback is invoked after func has been fully initialized. Otherwise, the callback is invoked before the modification to func takes place, so the prior state of func can be inspected. The runtime is permitted to optimize away the creation of function objects when possible. In such cases no event will be emitted. Although this creates the possibility of an observable difference of runtime behavior depending on optimization decisions, it does not change the semantics of the Python code being executed.

If event is PyFunction_EVENT_DESTROY, Taking a reference in the callback to the about-to-be-destroyed function will resurrect it, preventing it from being freed at this time. When the resurrected object is destroyed later, any watcher callbacks active at that time will be called again.

If the callback sets an exception, it must return -1; this exception will be printed as an unraisable exception using PyErr_WriteUnraisable(). Otherwise it should return 0.

There may already be a pending exception set on entry to the callback. In this case, the callback should return 0 with the same exception still set. This means the callback may not call any other API that can set an exception unless it saves and clears the exception state first, and restores it before returning.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/function.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fgcsupport.html b/devdocs/python~3.12/c-api%2Fgcsupport.html new file mode 100644 index 00000000..67d90e9e --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fgcsupport.html @@ -0,0 +1,80 @@ +

Supporting Cyclic Garbage Collection

Python’s support for detecting and collecting garbage which involves circular references requires support from object types which are “containers” for other objects which may also be containers. Types which do not store references to other objects, or which only store references to atomic types (such as numbers or strings), do not need to provide any explicit support for garbage collection.

To create a container type, the tp_flags field of the type object must include the Py_TPFLAGS_HAVE_GC and provide an implementation of the tp_traverse handler. If instances of the type are mutable, a tp_clear implementation must also be provided.

+ Py_TPFLAGS_HAVE_GC +
+
+

Objects with a type with this flag set must conform with the rules documented here. For convenience these objects will be referred to as container objects.

Constructors for container types must conform to two rules:

  1. The memory for the object must be allocated using PyObject_GC_New or PyObject_GC_NewVar.
  2. Once all the fields which may contain references to other containers are initialized, it must call PyObject_GC_Track().

Similarly, the deallocator for the object must conform to a similar pair of rules:

  1. Before fields which refer to other containers are invalidated, PyObject_GC_UnTrack() must be called.
  2. +

    The object’s memory must be deallocated using PyObject_GC_Del().

    Warning

    If a type adds the Py_TPFLAGS_HAVE_GC, then it must implement at least a tp_traverse handler or explicitly use one from its subclass or subclasses.

    When calling PyType_Ready() or some of the APIs that indirectly call it like PyType_FromSpecWithBases() or PyType_FromSpec() the interpreter will automatically populate the tp_flags, tp_traverse and tp_clear fields if the type inherits from a class that implements the garbage collector protocol and the child class does not include the Py_TPFLAGS_HAVE_GC flag.

+PyObject_GC_New(TYPE, typeobj)
+

Analogous to PyObject_New but for container objects with the Py_TPFLAGS_HAVE_GC flag set.

+
+PyObject_GC_NewVar(TYPE, typeobj, size)
+

Analogous to PyObject_NewVar but for container objects with the Py_TPFLAGS_HAVE_GC flag set.

+
+PyObject *PyUnstable_Object_GC_NewWithExtraData(PyTypeObject *type, size_t extra_size)
+
This is Unstable API. It may change without warning in minor releases. +

Analogous to PyObject_GC_New but allocates extra_size bytes at the end of the object (at offset tp_basicsize). The allocated memory is initialized to zeros, except for the Python object header.

The extra data will be deallocated with the object, but otherwise it is not managed by Python.

Warning

The function is marked as unstable because the final mechanism for reserving extra data after an instance is not yet decided. For allocating a variable number of fields, prefer using PyVarObject and tp_itemsize instead.

New in version 3.12.

+
+TYPE *PyObject_GC_Resize(TYPE, PyVarObject *op, Py_ssize_t newsize)
+

Resize an object allocated by PyObject_NewVar. Returns the resized object or NULL on failure. op must not be tracked by the collector yet.

+
+void PyObject_GC_Track(PyObject *op)
+ Part of the Stable ABI.

Adds the object op to the set of container objects tracked by the collector. The collector can run at unexpected times so objects must be valid while being tracked. This should be called once all the fields followed by the tp_traverse handler become valid, usually near the end of the constructor.

+
+int PyObject_IS_GC(PyObject *obj)
+

Returns non-zero if the object implements the garbage collector protocol, otherwise returns 0.

The object cannot be tracked by the garbage collector if this function returns 0.

+
+int PyObject_GC_IsTracked(PyObject *op)
+ Part of the Stable ABI since version 3.9.

Returns 1 if the object type of op implements the GC protocol and op is being currently tracked by the garbage collector and 0 otherwise.

This is analogous to the Python function gc.is_tracked().

New in version 3.9.

+
+int PyObject_GC_IsFinalized(PyObject *op)
+ Part of the Stable ABI since version 3.9.

Returns 1 if the object type of op implements the GC protocol and op has been already finalized by the garbage collector and 0 otherwise.

This is analogous to the Python function gc.is_finalized().

New in version 3.9.

+
+void PyObject_GC_Del(void *op)
+ Part of the Stable ABI.

Releases memory allocated to an object using PyObject_GC_New or PyObject_GC_NewVar.

+
+void PyObject_GC_UnTrack(void *op)
+ Part of the Stable ABI.

Remove the object op from the set of container objects tracked by the collector. Note that PyObject_GC_Track() can be called again on this object to add it back to the set of tracked objects. The deallocator (tp_dealloc handler) should call this for the object before any of the fields used by the tp_traverse handler become invalid.

+

Changed in version 3.8: The _PyObject_GC_TRACK() and _PyObject_GC_UNTRACK() macros have been removed from the public C API.

The tp_traverse handler accepts a function parameter of this type:

+typedef int (*visitproc)(PyObject *object, void *arg)
+ Part of the Stable ABI.

Type of the visitor function passed to the tp_traverse handler. The function should be called with an object to traverse as object and the third parameter to the tp_traverse handler as arg. The Python core uses several visitor functions to implement cyclic garbage detection; it’s not expected that users will need to write their own visitor functions.

+

The tp_traverse handler must have the following type:

+typedef int (*traverseproc)(PyObject *self, visitproc visit, void *arg)
+ Part of the Stable ABI.

Traversal function for a container object. Implementations must call the visit function for each object directly contained by self, with the parameters to visit being the contained object and the arg value passed to the handler. The visit function must not be called with a NULL object argument. If visit returns a non-zero value that value should be returned immediately.

+

To simplify writing tp_traverse handlers, a Py_VISIT() macro is provided. In order to use this macro, the tp_traverse implementation must name its arguments exactly visit and arg:

+void Py_VISIT(PyObject *o)
+

If o is not NULL, call the visit callback, with arguments o and arg. If visit returns a non-zero value, then return it. Using this macro, tp_traverse handlers look like:

static int
+my_traverse(Noddy *self, visitproc visit, void *arg)
+{
+    Py_VISIT(self->foo);
+    Py_VISIT(self->bar);
+    return 0;
+}
+
+

The tp_clear handler must be of the inquiry type, or NULL if the object is immutable.

+typedef int (*inquiry)(PyObject *self)
+ Part of the Stable ABI.

Drop references that may have created reference cycles. Immutable objects do not have to define this method since they can never directly create reference cycles. Note that the object must still be valid after calling this method (don’t just call Py_DECREF() on a reference). The collector will call this method if it detects that this object is involved in a reference cycle.

+

Controlling the Garbage Collector State

The C-API provides the following functions for controlling garbage collection runs.

+Py_ssize_t PyGC_Collect(void)
+ Part of the Stable ABI.

Perform a full garbage collection, if the garbage collector is enabled. (Note that gc.collect() runs it unconditionally.)

Returns the number of collected + unreachable objects which cannot be collected. If the garbage collector is disabled or already collecting, returns 0 immediately. Errors during garbage collection are passed to sys.unraisablehook. This function does not raise exceptions.

+
+int PyGC_Enable(void)
+ Part of the Stable ABI since version 3.10.

Enable the garbage collector: similar to gc.enable(). Returns the previous state, 0 for disabled and 1 for enabled.

New in version 3.10.

+
+int PyGC_Disable(void)
+ Part of the Stable ABI since version 3.10.

Disable the garbage collector: similar to gc.disable(). Returns the previous state, 0 for disabled and 1 for enabled.

New in version 3.10.

+
+int PyGC_IsEnabled(void)
+ Part of the Stable ABI since version 3.10.

Query the state of the garbage collector: similar to gc.isenabled(). Returns the current state, 0 for disabled and 1 for enabled.

New in version 3.10.

+

Querying Garbage Collector State

The C-API provides the following interface for querying information about the garbage collector.

+void PyUnstable_GC_VisitObjects(gcvisitobjects_t callback, void *arg)
+
This is Unstable API. It may change without warning in minor releases. +

Run supplied callback on all live GC-capable objects. arg is passed through to all invocations of callback.

Warning

If new objects are (de)allocated by the callback it is undefined if they will be visited.

Garbage collection is disabled during operation. Explicitly running a collection in the callback may lead to undefined behaviour e.g. visiting the same objects multiple times or not at all.

New in version 3.12.

+
+typedef int (*gcvisitobjects_t)(PyObject *object, void *arg)
+

Type of the visitor function to be passed to PyUnstable_GC_VisitObjects(). arg is the same as the arg passed to PyUnstable_GC_VisitObjects. Return 0 to continue iteration, return 1 to stop iteration. Other return values are reserved for now so behavior on returning anything else is undefined.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/gcsupport.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fgen.html b/devdocs/python~3.12/c-api%2Fgen.html new file mode 100644 index 00000000..e345d932 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fgen.html @@ -0,0 +1,24 @@ +

Generator Objects

Generator objects are what Python uses to implement generator iterators. They are normally created by iterating over a function that yields values, rather than explicitly calling PyGen_New() or PyGen_NewWithQualName().

+type PyGenObject
+

The C structure used for generator objects.

+
+PyTypeObject PyGen_Type
+

The type object corresponding to generator objects.

+
+int PyGen_Check(PyObject *ob)
+

Return true if ob is a generator object; ob must not be NULL. This function always succeeds.

+
+int PyGen_CheckExact(PyObject *ob)
+

Return true if ob’s type is PyGen_Type; ob must not be NULL. This function always succeeds.

+
+PyObject *PyGen_New(PyFrameObject *frame)
+Return value: New reference.

Create and return a new generator object based on the frame object. A reference to frame is stolen by this function. The argument must not be NULL.

+
+PyObject *PyGen_NewWithQualName(PyFrameObject *frame, PyObject *name, PyObject *qualname)
+Return value: New reference.

Create and return a new generator object based on the frame object, with __name__ and __qualname__ set to name and qualname. A reference to frame is stolen by this function. The frame argument must not be NULL.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/gen.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fimport.html b/devdocs/python~3.12/c-api%2Fimport.html new file mode 100644 index 00000000..f9c041ba --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fimport.html @@ -0,0 +1,90 @@ +

Importing Modules

+PyObject *PyImport_ImportModule(const char *name)
+Return value: New reference. Part of the Stable ABI.

This is a simplified interface to PyImport_ImportModuleEx() below, leaving the globals and locals arguments set to NULL and level set to 0. When the name argument contains a dot (when it specifies a submodule of a package), the fromlist argument is set to the list ['*'] so that the return value is the named module rather than the top-level package containing it as would otherwise be the case. (Unfortunately, this has an additional side effect when name in fact specifies a subpackage instead of a submodule: the submodules specified in the package’s __all__ variable are loaded.) Return a new reference to the imported module, or NULL with an exception set on failure. A failing import of a module doesn’t leave the module in sys.modules.

This function always uses absolute imports.

+
+PyObject *PyImport_ImportModuleNoBlock(const char *name)
+Return value: New reference. Part of the Stable ABI.

This function is a deprecated alias of PyImport_ImportModule().

Changed in version 3.3: This function used to fail immediately when the import lock was held by another thread. In Python 3.3 though, the locking scheme switched to per-module locks for most purposes, so this function’s special behaviour isn’t needed anymore.

+
+PyObject *PyImport_ImportModuleEx(const char *name, PyObject *globals, PyObject *locals, PyObject *fromlist)
+Return value: New reference.

Import a module. This is best described by referring to the built-in Python function __import__().

The return value is a new reference to the imported module or top-level package, or NULL with an exception set on failure. Like for __import__(), the return value when a submodule of a package was requested is normally the top-level package, unless a non-empty fromlist was given.

Failing imports remove incomplete module objects, like with PyImport_ImportModule().

+
+PyObject *PyImport_ImportModuleLevelObject(PyObject *name, PyObject *globals, PyObject *locals, PyObject *fromlist, int level)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Import a module. This is best described by referring to the built-in Python function __import__(), as the standard __import__() function calls this function directly.

The return value is a new reference to the imported module or top-level package, or NULL with an exception set on failure. Like for __import__(), the return value when a submodule of a package was requested is normally the top-level package, unless a non-empty fromlist was given.

New in version 3.3.

+
+PyObject *PyImport_ImportModuleLevel(const char *name, PyObject *globals, PyObject *locals, PyObject *fromlist, int level)
+Return value: New reference. Part of the Stable ABI.

Similar to PyImport_ImportModuleLevelObject(), but the name is a UTF-8 encoded string instead of a Unicode object.

Changed in version 3.3: Negative values for level are no longer accepted.

+
+PyObject *PyImport_Import(PyObject *name)
+Return value: New reference. Part of the Stable ABI.

This is a higher-level interface that calls the current “import hook function” (with an explicit level of 0, meaning absolute import). It invokes the __import__() function from the __builtins__ of the current globals. This means that the import is done using whatever import hooks are installed in the current environment.

This function always uses absolute imports.

+
+PyObject *PyImport_ReloadModule(PyObject *m)
+Return value: New reference. Part of the Stable ABI.

Reload a module. Return a new reference to the reloaded module, or NULL with an exception set on failure (the module still exists in this case).

+
+PyObject *PyImport_AddModuleObject(PyObject *name)
+Return value: Borrowed reference. Part of the Stable ABI since version 3.7.

Return the module object corresponding to a module name. The name argument may be of the form package.module. First check the modules dictionary if there’s one there, and if not, create a new one and insert it in the modules dictionary. Return NULL with an exception set on failure.

Note

This function does not load or import the module; if the module wasn’t already loaded, you will get an empty module object. Use PyImport_ImportModule() or one of its variants to import a module. Package structures implied by a dotted name for name are not created if not already present.

New in version 3.3.

+
+PyObject *PyImport_AddModule(const char *name)
+Return value: Borrowed reference. Part of the Stable ABI.

Similar to PyImport_AddModuleObject(), but the name is a UTF-8 encoded string instead of a Unicode object.

+
+PyObject *PyImport_ExecCodeModule(const char *name, PyObject *co)
+Return value: New reference. Part of the Stable ABI.

Given a module name (possibly of the form package.module) and a code object read from a Python bytecode file or obtained from the built-in function compile(), load the module. Return a new reference to the module object, or NULL with an exception set if an error occurred. name is removed from sys.modules in error cases, even if name was already in sys.modules on entry to PyImport_ExecCodeModule(). Leaving incompletely initialized modules in sys.modules is dangerous, as imports of such modules have no way to know that the module object is an unknown (and probably damaged with respect to the module author’s intents) state.

The module’s __spec__ and __loader__ will be set, if not set already, with the appropriate values. The spec’s loader will be set to the module’s __loader__ (if set) and to an instance of SourceFileLoader otherwise.

The module’s __file__ attribute will be set to the code object’s co_filename. If applicable, __cached__ will also be set.

This function will reload the module if it was already imported. See PyImport_ReloadModule() for the intended way to reload a module.

If name points to a dotted name of the form package.module, any package structures not already created will still not be created.

See also PyImport_ExecCodeModuleEx() and PyImport_ExecCodeModuleWithPathnames().

Changed in version 3.12: The setting of __cached__ and __loader__ is deprecated. See ModuleSpec for alternatives.

+
+PyObject *PyImport_ExecCodeModuleEx(const char *name, PyObject *co, const char *pathname)
+Return value: New reference. Part of the Stable ABI.

Like PyImport_ExecCodeModule(), but the __file__ attribute of the module object is set to pathname if it is non-NULL.

See also PyImport_ExecCodeModuleWithPathnames().

+
+PyObject *PyImport_ExecCodeModuleObject(PyObject *name, PyObject *co, PyObject *pathname, PyObject *cpathname)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Like PyImport_ExecCodeModuleEx(), but the __cached__ attribute of the module object is set to cpathname if it is non-NULL. Of the three functions, this is the preferred one to use.

New in version 3.3.

Changed in version 3.12: Setting __cached__ is deprecated. See ModuleSpec for alternatives.

+
+PyObject *PyImport_ExecCodeModuleWithPathnames(const char *name, PyObject *co, const char *pathname, const char *cpathname)
+Return value: New reference. Part of the Stable ABI.

Like PyImport_ExecCodeModuleObject(), but name, pathname and cpathname are UTF-8 encoded strings. Attempts are also made to figure out what the value for pathname should be from cpathname if the former is set to NULL.

New in version 3.2.

Changed in version 3.3: Uses imp.source_from_cache() in calculating the source path if only the bytecode path is provided.

Changed in version 3.12: No longer uses the removed imp module.

+
+long PyImport_GetMagicNumber()
+ Part of the Stable ABI.

Return the magic number for Python bytecode files (a.k.a. .pyc file). The magic number should be present in the first four bytes of the bytecode file, in little-endian byte order. Returns -1 on error.

Changed in version 3.3: Return value of -1 upon failure.

+
+const char *PyImport_GetMagicTag()
+ Part of the Stable ABI.

Return the magic tag string for PEP 3147 format Python bytecode file names. Keep in mind that the value at sys.implementation.cache_tag is authoritative and should be used instead of this function.

New in version 3.2.

+
+PyObject *PyImport_GetModuleDict()
+Return value: Borrowed reference. Part of the Stable ABI.

Return the dictionary used for the module administration (a.k.a. sys.modules). Note that this is a per-interpreter variable.

+
+PyObject *PyImport_GetModule(PyObject *name)
+Return value: New reference. Part of the Stable ABI since version 3.8.

Return the already imported module with the given name. If the module has not been imported yet then returns NULL but does not set an error. Returns NULL and sets an error if the lookup failed.

New in version 3.7.

+
+PyObject *PyImport_GetImporter(PyObject *path)
+Return value: New reference. Part of the Stable ABI.

Return a finder object for a sys.path/pkg.__path__ item path, possibly by fetching it from the sys.path_importer_cache dict. If it wasn’t yet cached, traverse sys.path_hooks until a hook is found that can handle the path item. Return None if no hook could; this tells our caller that the path based finder could not find a finder for this path item. Cache the result in sys.path_importer_cache. Return a new reference to the finder object.

+
+int PyImport_ImportFrozenModuleObject(PyObject *name)
+ Part of the Stable ABI since version 3.7.

Load a frozen module named name. Return 1 for success, 0 if the module is not found, and -1 with an exception set if the initialization failed. To access the imported module on a successful load, use PyImport_ImportModule(). (Note the misnomer — this function would reload the module if it was already imported.)

New in version 3.3.

Changed in version 3.4: The __file__ attribute is no longer set on the module.

+
+int PyImport_ImportFrozenModule(const char *name)
+ Part of the Stable ABI.

Similar to PyImport_ImportFrozenModuleObject(), but the name is a UTF-8 encoded string instead of a Unicode object.

+
+struct _frozen
+

This is the structure type definition for frozen module descriptors, as generated by the freeze utility (see Tools/freeze/ in the Python source distribution). Its definition, found in Include/import.h, is:

struct _frozen {
+    const char *name;
+    const unsigned char *code;
+    int size;
+    bool is_package;
+};
+

Changed in version 3.11: The new is_package field indicates whether the module is a package or not. This replaces setting the size field to a negative value.

+
+const struct _frozen *PyImport_FrozenModules
+

This pointer is initialized to point to an array of _frozen records, terminated by one whose members are all NULL or zero. When a frozen module is imported, it is searched in this table. Third-party code could play tricks with this to provide a dynamically created collection of frozen modules.

+
+int PyImport_AppendInittab(const char *name, PyObject *(*initfunc)(void))
+ Part of the Stable ABI.

Add a single module to the existing table of built-in modules. This is a convenience wrapper around PyImport_ExtendInittab(), returning -1 if the table could not be extended. The new module can be imported by the name name, and uses the function initfunc as the initialization function called on the first attempted import. This should be called before Py_Initialize().

+
+struct _inittab
+

Structure describing a single entry in the list of built-in modules. Programs which embed Python may use an array of these structures in conjunction with PyImport_ExtendInittab() to provide additional built-in modules. The structure consists of two members:

+const char *name
+

The module name, as an ASCII encoded string.

+
+
+int PyImport_ExtendInittab(struct _inittab *newtab)
+

Add a collection of modules to the table of built-in modules. The newtab array must end with a sentinel entry which contains NULL for the name field; failure to provide the sentinel value can result in a memory fault. Returns 0 on success or -1 if insufficient memory could be allocated to extend the internal table. In the event of failure, no modules are added to the internal table. This must be called before Py_Initialize().

If Python is initialized multiple times, PyImport_AppendInittab() or PyImport_ExtendInittab() must be called before each Python initialization.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/import.html +

+
diff --git a/devdocs/python~3.12/c-api%2Findex.html b/devdocs/python~3.12/c-api%2Findex.html new file mode 100644 index 00000000..e894c006 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Findex.html @@ -0,0 +1,16 @@ +

Python/C API Reference Manual

This manual documents the API used by C and C++ programmers who want to write extension modules or embed Python. It is a companion to Extending and Embedding the Python Interpreter, which describes the general principles of extension writing but does not document the API functions in detail.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/index.html +

+
diff --git a/devdocs/python~3.12/c-api%2Finit.html b/devdocs/python~3.12/c-api%2Finit.html new file mode 100644 index 00000000..7c4be401 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Finit.html @@ -0,0 +1,436 @@ +

Initialization, Finalization, and Threads

See also Python Initialization Configuration.

Before Python Initialization

In an application embedding Python, the Py_Initialize() function must be called before using any other Python/C API functions; with the exception of a few functions and the global configuration variables.

The following functions can be safely called before Python is initialized:

Note

The following functions should not be called before Py_Initialize(): Py_EncodeLocale(), Py_GetPath(), Py_GetPrefix(), Py_GetExecPrefix(), Py_GetProgramFullPath(), Py_GetPythonHome(), Py_GetProgramName() and PyEval_InitThreads().

Global configuration variables

Python has variables for the global configuration to control different features and options. By default, these flags are controlled by command line options.

When a flag is set by an option, the value of the flag is the number of times that the option was set. For example, -b sets Py_BytesWarningFlag to 1 and -bb sets Py_BytesWarningFlag to 2.

+int Py_BytesWarningFlag
+

This API is kept for backward compatibility: setting PyConfig.bytes_warning should be used instead, see Python Initialization Configuration.

Issue a warning when comparing bytes or bytearray with str or bytes with int. Issue an error if greater or equal to 2.

Set by the -b option.

Deprecated since version 3.12.

+
+int Py_DebugFlag
+

This API is kept for backward compatibility: setting PyConfig.parser_debug should be used instead, see Python Initialization Configuration.

Turn on parser debugging output (for expert only, depending on compilation options).

Set by the -d option and the PYTHONDEBUG environment variable.

Deprecated since version 3.12.

+
+int Py_DontWriteBytecodeFlag
+

This API is kept for backward compatibility: setting PyConfig.write_bytecode should be used instead, see Python Initialization Configuration.

If set to non-zero, Python won’t try to write .pyc files on the import of source modules.

Set by the -B option and the PYTHONDONTWRITEBYTECODE environment variable.

Deprecated since version 3.12.

+
+int Py_FrozenFlag
+

This API is kept for backward compatibility: setting PyConfig.pathconfig_warnings should be used instead, see Python Initialization Configuration.

Suppress error messages when calculating the module search path in Py_GetPath().

Private flag used by _freeze_module and frozenmain programs.

Deprecated since version 3.12.

+
+int Py_HashRandomizationFlag
+

This API is kept for backward compatibility: setting PyConfig.hash_seed and PyConfig.use_hash_seed should be used instead, see Python Initialization Configuration.

Set to 1 if the PYTHONHASHSEED environment variable is set to a non-empty string.

If the flag is non-zero, read the PYTHONHASHSEED environment variable to initialize the secret hash seed.

Deprecated since version 3.12.

+
+int Py_IgnoreEnvironmentFlag
+

This API is kept for backward compatibility: setting PyConfig.use_environment should be used instead, see Python Initialization Configuration.

Ignore all PYTHON* environment variables, e.g. PYTHONPATH and PYTHONHOME, that might be set.

Set by the -E and -I options.

Deprecated since version 3.12.

+
+int Py_InspectFlag
+

This API is kept for backward compatibility: setting PyConfig.inspect should be used instead, see Python Initialization Configuration.

When a script is passed as first argument or the -c option is used, enter interactive mode after executing the script or the command, even when sys.stdin does not appear to be a terminal.

Set by the -i option and the PYTHONINSPECT environment variable.

Deprecated since version 3.12.

+
+int Py_InteractiveFlag
+

This API is kept for backward compatibility: setting PyConfig.interactive should be used instead, see Python Initialization Configuration.

Set by the -i option.

Deprecated since version 3.12.

+
+int Py_IsolatedFlag
+

This API is kept for backward compatibility: setting PyConfig.isolated should be used instead, see Python Initialization Configuration.

Run Python in isolated mode. In isolated mode sys.path contains neither the script’s directory nor the user’s site-packages directory.

Set by the -I option.

New in version 3.4.

Deprecated since version 3.12.

+
+int Py_LegacyWindowsFSEncodingFlag
+

This API is kept for backward compatibility: setting PyPreConfig.legacy_windows_fs_encoding should be used instead, see Python Initialization Configuration.

If the flag is non-zero, use the mbcs encoding with replace error handler, instead of the UTF-8 encoding with surrogatepass error handler, for the filesystem encoding and error handler.

Set to 1 if the PYTHONLEGACYWINDOWSFSENCODING environment variable is set to a non-empty string.

See PEP 529 for more details.

Availability: Windows.

Deprecated since version 3.12.

+
+int Py_LegacyWindowsStdioFlag
+

This API is kept for backward compatibility: setting PyConfig.legacy_windows_stdio should be used instead, see Python Initialization Configuration.

If the flag is non-zero, use io.FileIO instead of io._WindowsConsoleIO for sys standard streams.

Set to 1 if the PYTHONLEGACYWINDOWSSTDIO environment variable is set to a non-empty string.

See PEP 528 for more details.

Availability: Windows.

Deprecated since version 3.12.

+
+int Py_NoSiteFlag
+

This API is kept for backward compatibility: setting PyConfig.site_import should be used instead, see Python Initialization Configuration.

Disable the import of the module site and the site-dependent manipulations of sys.path that it entails. Also disable these manipulations if site is explicitly imported later (call site.main() if you want them to be triggered).

Set by the -S option.

Deprecated since version 3.12.

+
+int Py_NoUserSiteDirectory
+

This API is kept for backward compatibility: setting PyConfig.user_site_directory should be used instead, see Python Initialization Configuration.

Don’t add the user site-packages directory to sys.path.

Set by the -s and -I options, and the PYTHONNOUSERSITE environment variable.

Deprecated since version 3.12.

+
+int Py_OptimizeFlag
+

This API is kept for backward compatibility: setting PyConfig.optimization_level should be used instead, see Python Initialization Configuration.

Set by the -O option and the PYTHONOPTIMIZE environment variable.

Deprecated since version 3.12.

+
+int Py_QuietFlag
+

This API is kept for backward compatibility: setting PyConfig.quiet should be used instead, see Python Initialization Configuration.

Don’t display the copyright and version messages even in interactive mode.

Set by the -q option.

New in version 3.2.

Deprecated since version 3.12.

+
+int Py_UnbufferedStdioFlag
+

This API is kept for backward compatibility: setting PyConfig.buffered_stdio should be used instead, see Python Initialization Configuration.

Force the stdout and stderr streams to be unbuffered.

Set by the -u option and the PYTHONUNBUFFERED environment variable.

Deprecated since version 3.12.

+
+int Py_VerboseFlag
+

This API is kept for backward compatibility: setting PyConfig.verbose should be used instead, see Python Initialization Configuration.

Print a message each time a module is initialized, showing the place (filename or built-in module) from which it is loaded. If greater or equal to 2, print a message for each file that is checked for when searching for a module. Also provides information on module cleanup at exit.

Set by the -v option and the PYTHONVERBOSE environment variable.

Deprecated since version 3.12.

+

Initializing and finalizing the interpreter

+void Py_Initialize()
+ Part of the Stable ABI.

Initialize the Python interpreter. In an application embedding Python, this should be called before using any other Python/C API functions; see Before Python Initialization for the few exceptions.

This initializes the table of loaded modules (sys.modules), and creates the fundamental modules builtins, __main__ and sys. It also initializes the module search path (sys.path). It does not set sys.argv; use PySys_SetArgvEx() for that. This is a no-op when called for a second time (without calling Py_FinalizeEx() first). There is no return value; it is a fatal error if the initialization fails.

Use the Py_InitializeFromConfig() function to customize the Python Initialization Configuration.

Note

On Windows, changes the console mode from O_TEXT to O_BINARY, which will also affect non-Python uses of the console using the C Runtime.

+
+void Py_InitializeEx(int initsigs)
+ Part of the Stable ABI.

This function works like Py_Initialize() if initsigs is 1. If initsigs is 0, it skips initialization registration of signal handlers, which might be useful when Python is embedded.

Use the Py_InitializeFromConfig() function to customize the Python Initialization Configuration.

+
+int Py_IsInitialized()
+ Part of the Stable ABI.

Return true (nonzero) when the Python interpreter has been initialized, false (zero) if not. After Py_FinalizeEx() is called, this returns false until Py_Initialize() is called again.

+
+int Py_FinalizeEx()
+ Part of the Stable ABI since version 3.6.

Undo all initializations made by Py_Initialize() and subsequent use of Python/C API functions, and destroy all sub-interpreters (see Py_NewInterpreter() below) that were created and not yet destroyed since the last call to Py_Initialize(). Ideally, this frees all memory allocated by the Python interpreter. This is a no-op when called for a second time (without calling Py_Initialize() again first). Normally the return value is 0. If there were errors during finalization (flushing buffered data), -1 is returned.

This function is provided for a number of reasons. An embedding application might want to restart Python without having to restart the application itself. An application that has loaded the Python interpreter from a dynamically loadable library (or DLL) might want to free all memory allocated by Python before unloading the DLL. During a hunt for memory leaks in an application a developer might want to free all memory allocated by Python before exiting from the application.

Bugs and caveats: The destruction of modules and objects in modules is done in random order; this may cause destructors (__del__() methods) to fail when they depend on other objects (even functions) or modules. Dynamically loaded extension modules loaded by Python are not unloaded. Small amounts of memory allocated by the Python interpreter may not be freed (if you find a leak, please report it). Memory tied up in circular references between objects is not freed. Some memory allocated by extension modules may not be freed. Some extensions may not work properly if their initialization routine is called more than once; this can happen if an application calls Py_Initialize() and Py_FinalizeEx() more than once.

Raises an auditing event cpython._PySys_ClearAuditHooks with no arguments.

New in version 3.6.

+
+void Py_Finalize()
+ Part of the Stable ABI.

This is a backwards-compatible version of Py_FinalizeEx() that disregards the return value.

+

Process-wide parameters

+int Py_SetStandardStreamEncoding(const char *encoding, const char *errors)
+

This API is kept for backward compatibility: setting PyConfig.stdio_encoding and PyConfig.stdio_errors should be used instead, see Python Initialization Configuration.

This function should be called before Py_Initialize(), if it is called at all. It specifies which encoding and error handling to use with standard IO, with the same meanings as in str.encode().

It overrides PYTHONIOENCODING values, and allows embedding code to control IO encoding when the environment variable does not work.

encoding and/or errors may be NULL to use PYTHONIOENCODING and/or default values (depending on other settings).

Note that sys.stderr always uses the “backslashreplace” error handler, regardless of this (or any other) setting.

If Py_FinalizeEx() is called, this function will need to be called again in order to affect subsequent calls to Py_Initialize().

Returns 0 if successful, a nonzero value on error (e.g. calling after the interpreter has already been initialized).

New in version 3.4.

Deprecated since version 3.11.

+
+void Py_SetProgramName(const wchar_t *name)
+ Part of the Stable ABI.

This API is kept for backward compatibility: setting PyConfig.program_name should be used instead, see Python Initialization Configuration.

This function should be called before Py_Initialize() is called for the first time, if it is called at all. It tells the interpreter the value of the argv[0] argument to the main() function of the program (converted to wide characters). This is used by Py_GetPath() and some other functions below to find the Python run-time libraries relative to the interpreter executable. The default value is 'python'. The argument should point to a zero-terminated wide character string in static storage whose contents will not change for the duration of the program’s execution. No code in the Python interpreter will change the contents of this storage.

Use Py_DecodeLocale() to decode a bytes string to get a wchar_t* string.

Deprecated since version 3.11.

+
+wchar_t *Py_GetProgramName()
+ Part of the Stable ABI.

Return the program name set with Py_SetProgramName(), or the default. The returned string points into static storage; the caller should not modify its value.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+
+wchar_t *Py_GetPrefix()
+ Part of the Stable ABI.

Return the prefix for installed platform-independent files. This is derived through a number of complicated rules from the program name set with Py_SetProgramName() and some environment variables; for example, if the program name is '/usr/local/bin/python', the prefix is '/usr/local'. The returned string points into static storage; the caller should not modify its value. This corresponds to the prefix variable in the top-level Makefile and the --prefix argument to the configure script at build time. The value is available to Python code as sys.prefix. It is only useful on Unix. See also the next function.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+
+wchar_t *Py_GetExecPrefix()
+ Part of the Stable ABI.

Return the exec-prefix for installed platform-dependent files. This is derived through a number of complicated rules from the program name set with Py_SetProgramName() and some environment variables; for example, if the program name is '/usr/local/bin/python', the exec-prefix is '/usr/local'. The returned string points into static storage; the caller should not modify its value. This corresponds to the exec_prefix variable in the top-level Makefile and the --exec-prefix argument to the configure script at build time. The value is available to Python code as sys.exec_prefix. It is only useful on Unix.

Background: The exec-prefix differs from the prefix when platform dependent files (such as executables and shared libraries) are installed in a different directory tree. In a typical installation, platform dependent files may be installed in the /usr/local/plat subtree while platform independent may be installed in /usr/local.

Generally speaking, a platform is a combination of hardware and software families, e.g. Sparc machines running the Solaris 2.x operating system are considered the same platform, but Intel machines running Solaris 2.x are another platform, and Intel machines running Linux are yet another platform. Different major revisions of the same operating system generally also form different platforms. Non-Unix operating systems are a different story; the installation strategies on those systems are so different that the prefix and exec-prefix are meaningless, and set to the empty string. Note that compiled Python bytecode files are platform independent (but not independent from the Python version by which they were compiled!).

System administrators will know how to configure the mount or automount programs to share /usr/local between platforms while having /usr/local/plat be a different filesystem for each platform.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+
+wchar_t *Py_GetProgramFullPath()
+ Part of the Stable ABI.

Return the full program name of the Python executable; this is computed as a side-effect of deriving the default module search path from the program name (set by Py_SetProgramName() above). The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.executable.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+
+wchar_t *Py_GetPath()
+ Part of the Stable ABI.

Return the default module search path; this is computed from the program name (set by Py_SetProgramName() above) and some environment variables. The returned string consists of a series of directory names separated by a platform dependent delimiter character. The delimiter character is ':' on Unix and macOS, ';' on Windows. The returned string points into static storage; the caller should not modify its value. The list sys.path is initialized with this value on interpreter startup; it can be (and usually is) modified later to change the search path for loading modules.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+
+void Py_SetPath(const wchar_t*)
+ Part of the Stable ABI since version 3.7.

This API is kept for backward compatibility: setting PyConfig.module_search_paths and PyConfig.module_search_paths_set should be used instead, see Python Initialization Configuration.

Set the default module search path. If this function is called before Py_Initialize(), then Py_GetPath() won’t attempt to compute a default search path but uses the one provided instead. This is useful if Python is embedded by an application that has full knowledge of the location of all modules. The path components should be separated by the platform dependent delimiter character, which is ':' on Unix and macOS, ';' on Windows.

This also causes sys.executable to be set to the program full path (see Py_GetProgramFullPath()) and for sys.prefix and sys.exec_prefix to be empty. It is up to the caller to modify these if required after calling Py_Initialize().

Use Py_DecodeLocale() to decode a bytes string to get a wchar_* string.

The path argument is copied internally, so the caller may free it after the call completes.

Changed in version 3.8: The program full path is now used for sys.executable, instead of the program name.

Deprecated since version 3.11.

+
+const char *Py_GetVersion()
+ Part of the Stable ABI.

Return the version of this Python interpreter. This is a string that looks something like

"3.0a5+ (py3k:63103M, May 12 2008, 00:53:55) \n[GCC 4.2.3]"
+

The first word (up to the first space character) is the current Python version; the first characters are the major and minor version separated by a period. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.version.

See also the Py_Version constant.

+
+const char *Py_GetPlatform()
+ Part of the Stable ABI.

Return the platform identifier for the current platform. On Unix, this is formed from the “official” name of the operating system, converted to lower case, followed by the major revision number; e.g., for Solaris 2.x, which is also known as SunOS 5.x, the value is 'sunos5'. On macOS, it is 'darwin'. On Windows, it is 'win'. The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.platform.

+
+const char *Py_GetCopyright()
+ Part of the Stable ABI.

Return the official copyright string for the current Python version, for example

'Copyright 1991-1995 Stichting Mathematisch Centrum, Amsterdam'

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as sys.copyright.

+
+const char *Py_GetCompiler()
+ Part of the Stable ABI.

Return an indication of the compiler used to build the current Python version, in square brackets, for example:

"[GCC 2.7.2.2]"
+

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable sys.version.

+
+const char *Py_GetBuildInfo()
+ Part of the Stable ABI.

Return information about the sequence number and build date and time of the current Python interpreter instance, for example

"#67, Aug  1 1997, 22:34:28"
+

The returned string points into static storage; the caller should not modify its value. The value is available to Python code as part of the variable sys.version.

+
+void PySys_SetArgvEx(int argc, wchar_t **argv, int updatepath)
+ Part of the Stable ABI.

This API is kept for backward compatibility: setting PyConfig.argv, PyConfig.parse_argv and PyConfig.safe_path should be used instead, see Python Initialization Configuration.

Set sys.argv based on argc and argv. These parameters are similar to those passed to the program’s main() function with the difference that the first entry should refer to the script file to be executed rather than the executable hosting the Python interpreter. If there isn’t a script that will be run, the first entry in argv can be an empty string. If this function fails to initialize sys.argv, a fatal condition is signalled using Py_FatalError().

If updatepath is zero, this is all the function does. If updatepath is non-zero, the function also modifies sys.path according to the following algorithm:

  • If the name of an existing script is passed in argv[0], the absolute path of the directory where the script is located is prepended to sys.path.
  • Otherwise (that is, if argc is 0 or argv[0] doesn’t point to an existing file name), an empty string is prepended to sys.path, which is the same as prepending the current working directory (".").

Use Py_DecodeLocale() to decode a bytes string to get a wchar_* string.

See also PyConfig.orig_argv and PyConfig.argv members of the Python Initialization Configuration.

Note

It is recommended that applications embedding the Python interpreter for purposes other than executing a single script pass 0 as updatepath, and update sys.path themselves if desired. See CVE-2008-5983.

On versions before 3.1.3, you can achieve the same effect by manually popping the first sys.path element after having called PySys_SetArgv(), for example using:

PyRun_SimpleString("import sys; sys.path.pop(0)\n");
+

New in version 3.1.3.

Deprecated since version 3.11.

+
+void PySys_SetArgv(int argc, wchar_t **argv)
+ Part of the Stable ABI.

This API is kept for backward compatibility: setting PyConfig.argv and PyConfig.parse_argv should be used instead, see Python Initialization Configuration.

This function works like PySys_SetArgvEx() with updatepath set to 1 unless the python interpreter was started with the -I.

Use Py_DecodeLocale() to decode a bytes string to get a wchar_* string.

See also PyConfig.orig_argv and PyConfig.argv members of the Python Initialization Configuration.

Changed in version 3.4: The updatepath value depends on -I.

Deprecated since version 3.11.

+
+void Py_SetPythonHome(const wchar_t *home)
+ Part of the Stable ABI.

This API is kept for backward compatibility: setting PyConfig.home should be used instead, see Python Initialization Configuration.

Set the default “home” directory, that is, the location of the standard Python libraries. See PYTHONHOME for the meaning of the argument string.

The argument should point to a zero-terminated character string in static storage whose contents will not change for the duration of the program’s execution. No code in the Python interpreter will change the contents of this storage.

Use Py_DecodeLocale() to decode a bytes string to get a wchar_* string.

Deprecated since version 3.11.

+
+wchar_t *Py_GetPythonHome()
+ Part of the Stable ABI.

Return the default “home”, that is, the value set by a previous call to Py_SetPythonHome(), or the value of the PYTHONHOME environment variable if it is set.

This function should not be called before Py_Initialize(), otherwise it returns NULL.

Changed in version 3.10: It now returns NULL if called before Py_Initialize().

+

Thread State and the Global Interpreter Lock

The Python interpreter is not fully thread-safe. In order to support multi-threaded Python programs, there’s a global lock, called the global interpreter lock or GIL, that must be held by the current thread before it can safely access Python objects. Without the lock, even the simplest operations could cause problems in a multi-threaded program: for example, when two threads simultaneously increment the reference count of the same object, the reference count could end up being incremented only once instead of twice.

Therefore, the rule exists that only the thread that has acquired the GIL may operate on Python objects or call Python/C API functions. In order to emulate concurrency of execution, the interpreter regularly tries to switch threads (see sys.setswitchinterval()). The lock is also released around potentially blocking I/O operations like reading or writing a file, so that other Python threads can run in the meantime.

The Python interpreter keeps some thread-specific bookkeeping information inside a data structure called PyThreadState. There’s also one global variable pointing to the current PyThreadState: it can be retrieved using PyThreadState_Get().

Releasing the GIL from extension code

Most extension code manipulating the GIL has the following simple structure:

Save the thread state in a local variable.
+Release the global interpreter lock.
+... Do some blocking I/O operation ...
+Reacquire the global interpreter lock.
+Restore the thread state from the local variable.
+

This is so common that a pair of macros exists to simplify it:

Py_BEGIN_ALLOW_THREADS
+... Do some blocking I/O operation ...
+Py_END_ALLOW_THREADS
+

The Py_BEGIN_ALLOW_THREADS macro opens a new block and declares a hidden local variable; the Py_END_ALLOW_THREADS macro closes the block.

The block above expands to the following code:

PyThreadState *_save;
+
+_save = PyEval_SaveThread();
+... Do some blocking I/O operation ...
+PyEval_RestoreThread(_save);
+

Here is how these functions work: the global interpreter lock is used to protect the pointer to the current thread state. When releasing the lock and saving the thread state, the current thread state pointer must be retrieved before the lock is released (since another thread could immediately acquire the lock and store its own thread state in the global variable). Conversely, when acquiring the lock and restoring the thread state, the lock must be acquired before storing the thread state pointer.

Note

Calling system I/O functions is the most common use case for releasing the GIL, but it can also be useful before calling long-running computations which don’t need access to Python objects, such as compression or cryptographic functions operating over memory buffers. For example, the standard zlib and hashlib modules release the GIL when compressing or hashing data.

Non-Python created threads

When threads are created using the dedicated Python APIs (such as the threading module), a thread state is automatically associated to them and the code showed above is therefore correct. However, when threads are created from C (for example by a third-party library with its own thread management), they don’t hold the GIL, nor is there a thread state structure for them.

If you need to call Python code from these threads (often this will be part of a callback API provided by the aforementioned third-party library), you must first register these threads with the interpreter by creating a thread state data structure, then acquiring the GIL, and finally storing their thread state pointer, before you can start using the Python/C API. When you are done, you should reset the thread state pointer, release the GIL, and finally free the thread state data structure.

The PyGILState_Ensure() and PyGILState_Release() functions do all of the above automatically. The typical idiom for calling into Python from a C thread is:

PyGILState_STATE gstate;
+gstate = PyGILState_Ensure();
+
+/* Perform Python actions here. */
+result = CallSomeFunction();
+/* evaluate result or handle exception */
+
+/* Release the thread. No Python API allowed beyond this point. */
+PyGILState_Release(gstate);
+

Note that the PyGILState_* functions assume there is only one global interpreter (created automatically by Py_Initialize()). Python supports the creation of additional interpreters (using Py_NewInterpreter()), but mixing multiple interpreters and the PyGILState_* API is unsupported.

Cautions about fork()

Another important thing to note about threads is their behaviour in the face of the C fork() call. On most systems with fork(), after a process forks only the thread that issued the fork will exist. This has a concrete impact both on how locks must be handled and on all stored state in CPython’s runtime.

The fact that only the “current” thread remains means any locks held by other threads will never be released. Python solves this for os.fork() by acquiring the locks it uses internally before the fork, and releasing them afterwards. In addition, it resets any Lock Objects in the child. When extending or embedding Python, there is no way to inform Python of additional (non-Python) locks that need to be acquired before or reset after a fork. OS facilities such as pthread_atfork() would need to be used to accomplish the same thing. Additionally, when extending or embedding Python, calling fork() directly rather than through os.fork() (and returning to or calling into Python) may result in a deadlock by one of Python’s internal locks being held by a thread that is defunct after the fork. PyOS_AfterFork_Child() tries to reset the necessary locks, but is not always able to.

The fact that all other threads go away also means that CPython’s runtime state there must be cleaned up properly, which os.fork() does. This means finalizing all other PyThreadState objects belonging to the current interpreter and all other PyInterpreterState objects. Due to this and the special nature of the “main” interpreter, fork() should only be called in that interpreter’s “main” thread, where the CPython global runtime was originally initialized. The only exception is if exec() will be called immediately after.

High-level API

These are the most commonly used types and functions when writing C extension code, or when embedding the Python interpreter:

+type PyInterpreterState
+ Part of the Limited API (as an opaque struct).

This data structure represents the state shared by a number of cooperating threads. Threads belonging to the same interpreter share their module administration and a few other internal items. There are no public members in this structure.

Threads belonging to different interpreters initially share nothing, except process state like available memory, open file descriptors and such. The global interpreter lock is also shared by all threads, regardless of to which interpreter they belong.

+
+type PyThreadState
+ Part of the Limited API (as an opaque struct).

This data structure represents the state of a single thread. The only public data member is:

+PyInterpreterState *interp
+

This thread’s interpreter state.

+
+
+void PyEval_InitThreads()
+ Part of the Stable ABI.

Deprecated function which does nothing.

In Python 3.6 and older, this function created the GIL if it didn’t exist.

Changed in version 3.9: The function now does nothing.

Changed in version 3.7: This function is now called by Py_Initialize(), so you don’t have to call it yourself anymore.

Changed in version 3.2: This function cannot be called before Py_Initialize() anymore.

Deprecated since version 3.9.

+
+
+int PyEval_ThreadsInitialized()
+ Part of the Stable ABI.

Returns a non-zero value if PyEval_InitThreads() has been called. This function can be called without holding the GIL, and therefore can be used to avoid calls to the locking API when running single-threaded.

Changed in version 3.7: The GIL is now initialized by Py_Initialize().

Deprecated since version 3.9.

+
+PyThreadState *PyEval_SaveThread()
+ Part of the Stable ABI.

Release the global interpreter lock (if it has been created) and reset the thread state to NULL, returning the previous thread state (which is not NULL). If the lock has been created, the current thread must have acquired it.

+
+void PyEval_RestoreThread(PyThreadState *tstate)
+ Part of the Stable ABI.

Acquire the global interpreter lock (if it has been created) and set the thread state to tstate, which must not be NULL. If the lock has been created, the current thread must not have acquired it, otherwise deadlock ensues.

Note

Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use _Py_IsFinalizing() or sys.is_finalizing() to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.

+
+PyThreadState *PyThreadState_Get()
+ Part of the Stable ABI.

Return the current thread state. The global interpreter lock must be held. When the current thread state is NULL, this issues a fatal error (so that the caller needn’t check for NULL).

+
+PyThreadState *PyThreadState_Swap(PyThreadState *tstate)
+ Part of the Stable ABI.

Swap the current thread state with the thread state given by the argument tstate, which may be NULL. The global interpreter lock must be held and is not released.

+

The following functions use thread-local storage, and are not compatible with sub-interpreters:

+PyGILState_STATE PyGILState_Ensure()
+ Part of the Stable ABI.

Ensure that the current thread is ready to call the Python C API regardless of the current state of Python, or of the global interpreter lock. This may be called as many times as desired by a thread as long as each call is matched with a call to PyGILState_Release(). In general, other thread-related APIs may be used between PyGILState_Ensure() and PyGILState_Release() calls as long as the thread state is restored to its previous state before the Release(). For example, normal usage of the Py_BEGIN_ALLOW_THREADS and Py_END_ALLOW_THREADS macros is acceptable.

The return value is an opaque “handle” to the thread state when PyGILState_Ensure() was called, and must be passed to PyGILState_Release() to ensure Python is left in the same state. Even though recursive calls are allowed, these handles cannot be shared - each unique call to PyGILState_Ensure() must save the handle for its call to PyGILState_Release().

When the function returns, the current thread will hold the GIL and be able to call arbitrary Python code. Failure is a fatal error.

Note

Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use _Py_IsFinalizing() or sys.is_finalizing() to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.

+
+void PyGILState_Release(PyGILState_STATE)
+ Part of the Stable ABI.

Release any resources previously acquired. After this call, Python’s state will be the same as it was prior to the corresponding PyGILState_Ensure() call (but generally this state will be unknown to the caller, hence the use of the GILState API).

Every call to PyGILState_Ensure() must be matched by a call to PyGILState_Release() on the same thread.

+
+PyThreadState *PyGILState_GetThisThreadState()
+ Part of the Stable ABI.

Get the current thread state for this thread. May return NULL if no GILState API has been used on the current thread. Note that the main thread always has such a thread-state, even if no auto-thread-state call has been made on the main thread. This is mainly a helper/diagnostic function.

+
+int PyGILState_Check()
+

Return 1 if the current thread is holding the GIL and 0 otherwise. This function can be called from any thread at any time. Only if it has had its Python thread state initialized and currently is holding the GIL will it return 1. This is mainly a helper/diagnostic function. It can be useful for example in callback contexts or memory allocation functions when knowing that the GIL is locked can allow the caller to perform sensitive actions or otherwise behave differently.

New in version 3.4.

+

The following macros are normally used without a trailing semicolon; look for example usage in the Python source distribution.

+Py_BEGIN_ALLOW_THREADS
+ Part of the Stable ABI.

This macro expands to { PyThreadState *_save; _save = PyEval_SaveThread();. Note that it contains an opening brace; it must be matched with a following Py_END_ALLOW_THREADS macro. See above for further discussion of this macro.

+
+Py_END_ALLOW_THREADS
+ Part of the Stable ABI.

This macro expands to PyEval_RestoreThread(_save); }. Note that it contains a closing brace; it must be matched with an earlier Py_BEGIN_ALLOW_THREADS macro. See above for further discussion of this macro.

+
+Py_BLOCK_THREADS
+ Part of the Stable ABI.

This macro expands to PyEval_RestoreThread(_save);: it is equivalent to Py_END_ALLOW_THREADS without the closing brace.

+
+Py_UNBLOCK_THREADS
+ Part of the Stable ABI.

This macro expands to _save = PyEval_SaveThread();: it is equivalent to Py_BEGIN_ALLOW_THREADS without the opening brace and variable declaration.

+

Low-level API

All of the following functions must be called after Py_Initialize().

Changed in version 3.7: Py_Initialize() now initializes the GIL.

+PyInterpreterState *PyInterpreterState_New()
+ Part of the Stable ABI.

Create a new interpreter state object. The global interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.

Raises an auditing event cpython.PyInterpreterState_New with no arguments.

+
+void PyInterpreterState_Clear(PyInterpreterState *interp)
+ Part of the Stable ABI.

Reset all information in an interpreter state object. The global interpreter lock must be held.

Raises an auditing event cpython.PyInterpreterState_Clear with no arguments.

+
+void PyInterpreterState_Delete(PyInterpreterState *interp)
+ Part of the Stable ABI.

Destroy an interpreter state object. The global interpreter lock need not be held. The interpreter state must have been reset with a previous call to PyInterpreterState_Clear().

+
+PyThreadState *PyThreadState_New(PyInterpreterState *interp)
+ Part of the Stable ABI.

Create a new thread state object belonging to the given interpreter object. The global interpreter lock need not be held, but may be held if it is necessary to serialize calls to this function.

+
+void PyThreadState_Clear(PyThreadState *tstate)
+ Part of the Stable ABI.

Reset all information in a thread state object. The global interpreter lock must be held.

Changed in version 3.9: This function now calls the PyThreadState.on_delete callback. Previously, that happened in PyThreadState_Delete().

+
+void PyThreadState_Delete(PyThreadState *tstate)
+ Part of the Stable ABI.

Destroy a thread state object. The global interpreter lock need not be held. The thread state must have been reset with a previous call to PyThreadState_Clear().

+
+void PyThreadState_DeleteCurrent(void)
+

Destroy the current thread state and release the global interpreter lock. Like PyThreadState_Delete(), the global interpreter lock need not be held. The thread state must have been reset with a previous call to PyThreadState_Clear().

+
+PyFrameObject *PyThreadState_GetFrame(PyThreadState *tstate)
+ Part of the Stable ABI since version 3.10.

Get the current frame of the Python thread state tstate.

Return a strong reference. Return NULL if no frame is currently executing.

See also PyEval_GetFrame().

tstate must not be NULL.

New in version 3.9.

+
+uint64_t PyThreadState_GetID(PyThreadState *tstate)
+ Part of the Stable ABI since version 3.10.

Get the unique thread state identifier of the Python thread state tstate.

tstate must not be NULL.

New in version 3.9.

+
+PyInterpreterState *PyThreadState_GetInterpreter(PyThreadState *tstate)
+ Part of the Stable ABI since version 3.10.

Get the interpreter of the Python thread state tstate.

tstate must not be NULL.

New in version 3.9.

+
+void PyThreadState_EnterTracing(PyThreadState *tstate)
+

Suspend tracing and profiling in the Python thread state tstate.

Resume them using the PyThreadState_LeaveTracing() function.

New in version 3.11.

+
+void PyThreadState_LeaveTracing(PyThreadState *tstate)
+

Resume tracing and profiling in the Python thread state tstate suspended by the PyThreadState_EnterTracing() function.

See also PyEval_SetTrace() and PyEval_SetProfile() functions.

New in version 3.11.

+
+PyInterpreterState *PyInterpreterState_Get(void)
+ Part of the Stable ABI since version 3.9.

Get the current interpreter.

Issue a fatal error if there no current Python thread state or no current interpreter. It cannot return NULL.

The caller must hold the GIL.

New in version 3.9.

+
+int64_t PyInterpreterState_GetID(PyInterpreterState *interp)
+ Part of the Stable ABI since version 3.7.

Return the interpreter’s unique ID. If there was any error in doing so then -1 is returned and an error is set.

The caller must hold the GIL.

New in version 3.7.

+
+PyObject *PyInterpreterState_GetDict(PyInterpreterState *interp)
+ Part of the Stable ABI since version 3.8.

Return a dictionary in which interpreter-specific data may be stored. If this function returns NULL then no exception has been raised and the caller should assume no interpreter-specific dict is available.

This is not a replacement for PyModule_GetState(), which extensions should use to store interpreter-specific state information.

New in version 3.8.

+
+typedef PyObject *(*_PyFrameEvalFunction)(PyThreadState *tstate, _PyInterpreterFrame *frame, int throwflag)
+

Type of a frame evaluation function.

The throwflag parameter is used by the throw() method of generators: if non-zero, handle the current exception.

Changed in version 3.9: The function now takes a tstate parameter.

Changed in version 3.11: The frame parameter changed from PyFrameObject* to _PyInterpreterFrame*.

+
+_PyFrameEvalFunction _PyInterpreterState_GetEvalFrameFunc(PyInterpreterState *interp)
+

Get the frame evaluation function.

See the PEP 523 “Adding a frame evaluation API to CPython”.

New in version 3.9.

+
+void _PyInterpreterState_SetEvalFrameFunc(PyInterpreterState *interp, _PyFrameEvalFunction eval_frame)
+

Set the frame evaluation function.

See the PEP 523 “Adding a frame evaluation API to CPython”.

New in version 3.9.

+
+PyObject *PyThreadState_GetDict()
+Return value: Borrowed reference. Part of the Stable ABI.

Return a dictionary in which extensions can store thread-specific state information. Each extension should use a unique key to use to store state in the dictionary. It is okay to call this function when no current thread state is available. If this function returns NULL, no exception has been raised and the caller should assume no current thread state is available.

+
+int PyThreadState_SetAsyncExc(unsigned long id, PyObject *exc)
+ Part of the Stable ABI.

Asynchronously raise an exception in a thread. The id argument is the thread id of the target thread; exc is the exception object to be raised. This function does not steal any references to exc. To prevent naive misuse, you must write your own C extension to call this. Must be called with the GIL held. Returns the number of thread states modified; this is normally one, but will be zero if the thread id isn’t found. If exc is NULL, the pending exception (if any) for the thread is cleared. This raises no exceptions.

Changed in version 3.7: The type of the id parameter changed from long to unsigned long.

+
+void PyEval_AcquireThread(PyThreadState *tstate)
+ Part of the Stable ABI.

Acquire the global interpreter lock and set the current thread state to tstate, which must not be NULL. The lock must have been created earlier. If this thread already has the lock, deadlock ensues.

Note

Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use _Py_IsFinalizing() or sys.is_finalizing() to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.

Changed in version 3.8: Updated to be consistent with PyEval_RestoreThread(), Py_END_ALLOW_THREADS(), and PyGILState_Ensure(), and terminate the current thread if called while the interpreter is finalizing.

PyEval_RestoreThread() is a higher-level function which is always available (even when threads have not been initialized).

+
+void PyEval_ReleaseThread(PyThreadState *tstate)
+ Part of the Stable ABI.

Reset the current thread state to NULL and release the global interpreter lock. The lock must have been created earlier and must be held by the current thread. The tstate argument, which must not be NULL, is only used to check that it represents the current thread state — if it isn’t, a fatal error is reported.

PyEval_SaveThread() is a higher-level function which is always available (even when threads have not been initialized).

+
+void PyEval_AcquireLock()
+ Part of the Stable ABI.

Acquire the global interpreter lock. The lock must have been created earlier. If this thread already has the lock, a deadlock ensues.

Deprecated since version 3.2: This function does not update the current thread state. Please use PyEval_RestoreThread() or PyEval_AcquireThread() instead.

Note

Calling this function from a thread when the runtime is finalizing will terminate the thread, even if the thread was not created by Python. You can use _Py_IsFinalizing() or sys.is_finalizing() to check if the interpreter is in process of being finalized before calling this function to avoid unwanted termination.

Changed in version 3.8: Updated to be consistent with PyEval_RestoreThread(), Py_END_ALLOW_THREADS(), and PyGILState_Ensure(), and terminate the current thread if called while the interpreter is finalizing.

+
+void PyEval_ReleaseLock()
+ Part of the Stable ABI.

Release the global interpreter lock. The lock must have been created earlier.

Deprecated since version 3.2: This function does not update the current thread state. Please use PyEval_SaveThread() or PyEval_ReleaseThread() instead.

+

Sub-interpreter support

While in most uses, you will only embed a single Python interpreter, there are cases where you need to create several independent interpreters in the same process and perhaps even in the same thread. Sub-interpreters allow you to do that.

The “main” interpreter is the first one created when the runtime initializes. It is usually the only Python interpreter in a process. Unlike sub-interpreters, the main interpreter has unique process-global responsibilities like signal handling. It is also responsible for execution during runtime initialization and is usually the active interpreter during runtime finalization. The PyInterpreterState_Main() function returns a pointer to its state.

You can switch between sub-interpreters using the PyThreadState_Swap() function. You can create and destroy them using the following functions:

+type PyInterpreterConfig
+

Structure containing most parameters to configure a sub-interpreter. Its values are used only in Py_NewInterpreterFromConfig() and never modified by the runtime.

New in version 3.12.

Structure fields:

+int use_main_obmalloc
+

If this is 0 then the sub-interpreter will use its own “object” allocator state. Otherwise it will use (share) the main interpreter’s.

If this is 0 then check_multi_interp_extensions must be 1 (non-zero). If this is 1 then gil must not be PyInterpreterConfig_OWN_GIL.

+
+int allow_fork
+

If this is 0 then the runtime will not support forking the process in any thread where the sub-interpreter is currently active. Otherwise fork is unrestricted.

Note that the subprocess module still works when fork is disallowed.

+
+int allow_exec
+

If this is 0 then the runtime will not support replacing the current process via exec (e.g. os.execv()) in any thread where the sub-interpreter is currently active. Otherwise exec is unrestricted.

Note that the subprocess module still works when exec is disallowed.

+
+int allow_threads
+

If this is 0 then the sub-interpreter’s threading module won’t create threads. Otherwise threads are allowed.

+
+int allow_daemon_threads
+

If this is 0 then the sub-interpreter’s threading module won’t create daemon threads. Otherwise daemon threads are allowed (as long as allow_threads is non-zero).

+
+int check_multi_interp_extensions
+

If this is 0 then all extension modules may be imported, including legacy (single-phase init) modules, in any thread where the sub-interpreter is currently active. Otherwise only multi-phase init extension modules (see PEP 489) may be imported. (Also see Py_mod_multiple_interpreters.)

This must be 1 (non-zero) if use_main_obmalloc is 0.

+
+int gil
+

This determines the operation of the GIL for the sub-interpreter. It may be one of the following:

+PyInterpreterConfig_DEFAULT_GIL
+

Use the default selection (PyInterpreterConfig_SHARED_GIL).

+
+PyInterpreterConfig_SHARED_GIL
+

Use (share) the main interpreter’s GIL.

+
+PyInterpreterConfig_OWN_GIL
+

Use the sub-interpreter’s own GIL.

+

If this is PyInterpreterConfig_OWN_GIL then PyInterpreterConfig.use_main_obmalloc must be 0.

+
+
+PyStatus Py_NewInterpreterFromConfig(PyThreadState **tstate_p, const PyInterpreterConfig *config)
+

Create a new sub-interpreter. This is an (almost) totally separate environment for the execution of Python code. In particular, the new interpreter has separate, independent versions of all imported modules, including the fundamental modules builtins, __main__ and sys. The table of loaded modules (sys.modules) and the module search path (sys.path) are also separate. The new environment has no sys.argv variable. It has new standard I/O stream file objects sys.stdin, sys.stdout and sys.stderr (however these refer to the same underlying file descriptors).

The given config controls the options with which the interpreter is initialized.

Upon success, tstate_p will be set to the first thread state created in the new sub-interpreter. This thread state is made in the current thread state. Note that no actual thread is created; see the discussion of thread states below. If creation of the new interpreter is unsuccessful, tstate_p is set to NULL; no exception is set since the exception state is stored in the current thread state and there may not be a current thread state.

Like all other Python/C API functions, the global interpreter lock must be held before calling this function and is still held when it returns. Likewise a current thread state must be set on entry. On success, the returned thread state will be set as current. If the sub-interpreter is created with its own GIL then the GIL of the calling interpreter will be released. When the function returns, the new interpreter’s GIL will be held by the current thread and the previously interpreter’s GIL will remain released here.

New in version 3.12.

Sub-interpreters are most effective when isolated from each other, with certain functionality restricted:

PyInterpreterConfig config = {
+    .use_main_obmalloc = 0,
+    .allow_fork = 0,
+    .allow_exec = 0,
+    .allow_threads = 1,
+    .allow_daemon_threads = 0,
+    .check_multi_interp_extensions = 1,
+    .gil = PyInterpreterConfig_OWN_GIL,
+};
+PyThreadState *tstate = Py_NewInterpreterFromConfig(&config);
+

Note that the config is used only briefly and does not get modified. During initialization the config’s values are converted into various PyInterpreterState values. A read-only copy of the config may be stored internally on the PyInterpreterState.

Extension modules are shared between (sub-)interpreters as follows:

  • For modules using multi-phase initialization, e.g. PyModule_FromDefAndSpec(), a separate module object is created and initialized for each interpreter. Only C-level static and global variables are shared between these module objects.
  • +

    For modules using single-phase initialization, e.g. PyModule_Create(), the first time a particular extension is imported, it is initialized normally, and a (shallow) copy of its module’s dictionary is squirreled away. When the same extension is imported by another (sub-)interpreter, a new module is initialized and filled with the contents of this copy; the extension’s init function is not called. Objects in the module’s dictionary thus end up shared across (sub-)interpreters, which might cause unwanted behavior (see Bugs and caveats below).

    Note that this is different from what happens when an extension is imported after the interpreter has been completely re-initialized by calling Py_FinalizeEx() and Py_Initialize(); in that case, the extension’s initmodule function is called again. As with multi-phase initialization, this means that only C-level static and global variables are shared between these modules.

+
+
+PyThreadState *Py_NewInterpreter(void)
+ Part of the Stable ABI.

Create a new sub-interpreter. This is essentially just a wrapper around Py_NewInterpreterFromConfig() with a config that preserves the existing behavior. The result is an unisolated sub-interpreter that shares the main interpreter’s GIL, allows fork/exec, allows daemon threads, and allows single-phase init modules.

+
+void Py_EndInterpreter(PyThreadState *tstate)
+ Part of the Stable ABI.

Destroy the (sub-)interpreter represented by the given thread state. The given thread state must be the current thread state. See the discussion of thread states below. When the call returns, the current thread state is NULL. All thread states associated with this interpreter are destroyed. The global interpreter lock used by the target interpreter must be held before calling this function. No GIL is held when it returns.

Py_FinalizeEx() will destroy all sub-interpreters that haven’t been explicitly destroyed at that point.

+

A Per-Interpreter GIL

Using Py_NewInterpreterFromConfig() you can create a sub-interpreter that is completely isolated from other interpreters, including having its own GIL. The most important benefit of this isolation is that such an interpreter can execute Python code without being blocked by other interpreters or blocking any others. Thus a single Python process can truly take advantage of multiple CPU cores when running Python code. The isolation also encourages a different approach to concurrency than that of just using threads. (See PEP 554.)

Using an isolated interpreter requires vigilance in preserving that isolation. That especially means not sharing any objects or mutable state without guarantees about thread-safety. Even objects that are otherwise immutable (e.g. None, (1, 5)) can’t normally be shared because of the refcount. One simple but less-efficient approach around this is to use a global lock around all use of some state (or object). Alternately, effectively immutable objects (like integers or strings) can be made safe in spite of their refcounts by making them “immortal”. In fact, this has been done for the builtin singletons, small integers, and a number of other builtin objects.

If you preserve isolation then you will have access to proper multi-core computing without the complications that come with free-threading. Failure to preserve isolation will expose you to the full consequences of free-threading, including races and hard-to-debug crashes.

Aside from that, one of the main challenges of using multiple isolated interpreters is how to communicate between them safely (not break isolation) and efficiently. The runtime and stdlib do not provide any standard approach to this yet. A future stdlib module would help mitigate the effort of preserving isolation and expose effective tools for communicating (and sharing) data between interpreters.

New in version 3.12.

Bugs and caveats

Because sub-interpreters (and the main interpreter) are part of the same process, the insulation between them isn’t perfect — for example, using low-level file operations like os.close() they can (accidentally or maliciously) affect each other’s open files. Because of the way extensions are shared between (sub-)interpreters, some extensions may not work properly; this is especially likely when using single-phase initialization or (static) global variables. It is possible to insert objects created in one sub-interpreter into a namespace of another (sub-)interpreter; this should be avoided if possible.

Special care should be taken to avoid sharing user-defined functions, methods, instances or classes between sub-interpreters, since import operations executed by such objects may affect the wrong (sub-)interpreter’s dictionary of loaded modules. It is equally important to avoid sharing objects from which the above are reachable.

Also note that combining this functionality with PyGILState_* APIs is delicate, because these APIs assume a bijection between Python thread states and OS-level threads, an assumption broken by the presence of sub-interpreters. It is highly recommended that you don’t switch sub-interpreters between a pair of matching PyGILState_Ensure() and PyGILState_Release() calls. Furthermore, extensions (such as ctypes) using these APIs to allow calling of Python code from non-Python created threads will probably be broken when using sub-interpreters.

Asynchronous Notifications

A mechanism is provided to make asynchronous notifications to the main interpreter thread. These notifications take the form of a function pointer and a void pointer argument.

+int Py_AddPendingCall(int (*func)(void*), void *arg)
+ Part of the Stable ABI.

Schedule a function to be called from the main interpreter thread. On success, 0 is returned and func is queued for being called in the main thread. On failure, -1 is returned without setting any exception.

When successfully queued, func will be eventually called from the main interpreter thread with the argument arg. It will be called asynchronously with respect to normally running Python code, but with both these conditions met:

func must return 0 on success, or -1 on failure with an exception set. func won’t be interrupted to perform another asynchronous notification recursively, but it can still be interrupted to switch threads if the global interpreter lock is released.

This function doesn’t need a current thread state to run, and it doesn’t need the global interpreter lock.

To call this function in a subinterpreter, the caller must hold the GIL. Otherwise, the function func can be scheduled to be called from the wrong interpreter.

Warning

This is a low-level function, only useful for very special cases. There is no guarantee that func will be called as quick as possible. If the main thread is busy executing a system call, func won’t be called before the system call returns. This function is generally not suitable for calling Python code from arbitrary C threads. Instead, use the PyGILState API.

Changed in version 3.9: If this function is called in a subinterpreter, the function func is now scheduled to be called from the subinterpreter, rather than being called from the main interpreter. Each subinterpreter now has its own list of scheduled calls.

New in version 3.1.

+

Profiling and Tracing

The Python interpreter provides some low-level support for attaching profiling and execution tracing facilities. These are used for profiling, debugging, and coverage analysis tools.

This C interface allows the profiling or tracing code to avoid the overhead of calling through Python-level callable objects, making a direct C function call instead. The essential attributes of the facility have not changed; the interface allows trace functions to be installed per-thread, and the basic events reported to the trace function are the same as had been reported to the Python-level trace functions in previous versions.

+typedef int (*Py_tracefunc)(PyObject *obj, PyFrameObject *frame, int what, PyObject *arg)
+

The type of the trace function registered using PyEval_SetProfile() and PyEval_SetTrace(). The first parameter is the object passed to the registration function as obj, frame is the frame object to which the event pertains, what is one of the constants PyTrace_CALL, PyTrace_EXCEPTION, PyTrace_LINE, PyTrace_RETURN, PyTrace_C_CALL, PyTrace_C_EXCEPTION, PyTrace_C_RETURN, or PyTrace_OPCODE, and arg depends on the value of what:

+ + + + + + + + +

Value of what

Meaning of arg

PyTrace_CALL

Always Py_None.

PyTrace_EXCEPTION

Exception information as returned by sys.exc_info().

PyTrace_LINE

Always Py_None.

PyTrace_RETURN

Value being returned to the caller, or NULL if caused by an exception.

PyTrace_C_CALL

Function object being called.

PyTrace_C_EXCEPTION

Function object being called.

PyTrace_C_RETURN

Function object being called.

PyTrace_OPCODE

Always Py_None.
+
+int PyTrace_CALL
+

The value of the what parameter to a Py_tracefunc function when a new call to a function or method is being reported, or a new entry into a generator. Note that the creation of the iterator for a generator function is not reported as there is no control transfer to the Python bytecode in the corresponding frame.

+
+int PyTrace_EXCEPTION
+

The value of the what parameter to a Py_tracefunc function when an exception has been raised. The callback function is called with this value for what when after any bytecode is processed after which the exception becomes set within the frame being executed. The effect of this is that as exception propagation causes the Python stack to unwind, the callback is called upon return to each frame as the exception propagates. Only trace functions receives these events; they are not needed by the profiler.

+
+int PyTrace_LINE
+

The value passed as the what parameter to a Py_tracefunc function (but not a profiling function) when a line-number event is being reported. It may be disabled for a frame by setting f_trace_lines to 0 on that frame.

+
+int PyTrace_RETURN
+

The value for the what parameter to Py_tracefunc functions when a call is about to return.

+
+int PyTrace_C_CALL
+

The value for the what parameter to Py_tracefunc functions when a C function is about to be called.

+
+int PyTrace_C_EXCEPTION
+

The value for the what parameter to Py_tracefunc functions when a C function has raised an exception.

+
+int PyTrace_C_RETURN
+

The value for the what parameter to Py_tracefunc functions when a C function has returned.

+
+int PyTrace_OPCODE
+

The value for the what parameter to Py_tracefunc functions (but not profiling functions) when a new opcode is about to be executed. This event is not emitted by default: it must be explicitly requested by setting f_trace_opcodes to 1 on the frame.

+
+void PyEval_SetProfile(Py_tracefunc func, PyObject *obj)
+

Set the profiler function to func. The obj parameter is passed to the function as its first parameter, and may be any Python object, or NULL. If the profile function needs to maintain state, using a different value for obj for each thread provides a convenient and thread-safe place to store it. The profile function is called for all monitored events except PyTrace_LINE PyTrace_OPCODE and PyTrace_EXCEPTION.

See also the sys.setprofile() function.

The caller must hold the GIL.

+
+void PyEval_SetProfileAllThreads(Py_tracefunc func, PyObject *obj)
+

Like PyEval_SetProfile() but sets the profile function in all running threads belonging to the current interpreter instead of the setting it only on the current thread.

The caller must hold the GIL.

As PyEval_SetProfile(), this function ignores any exceptions raised while setting the profile functions in all threads.

+

New in version 3.12.

+void PyEval_SetTrace(Py_tracefunc func, PyObject *obj)
+

Set the tracing function to func. This is similar to PyEval_SetProfile(), except the tracing function does receive line-number events and per-opcode events, but does not receive any event related to C function objects being called. Any trace function registered using PyEval_SetTrace() will not receive PyTrace_C_CALL, PyTrace_C_EXCEPTION or PyTrace_C_RETURN as a value for the what parameter.

See also the sys.settrace() function.

The caller must hold the GIL.

+
+void PyEval_SetTraceAllThreads(Py_tracefunc func, PyObject *obj)
+

Like PyEval_SetTrace() but sets the tracing function in all running threads belonging to the current interpreter instead of the setting it only on the current thread.

The caller must hold the GIL.

As PyEval_SetTrace(), this function ignores any exceptions raised while setting the trace functions in all threads.

+

New in version 3.12.

Advanced Debugger Support

These functions are only intended to be used by advanced debugging tools.

+PyInterpreterState *PyInterpreterState_Head()
+

Return the interpreter state object at the head of the list of all such objects.

+
+PyInterpreterState *PyInterpreterState_Main()
+

Return the main interpreter state object.

+
+PyInterpreterState *PyInterpreterState_Next(PyInterpreterState *interp)
+

Return the next interpreter state object after interp from the list of all such objects.

+
+PyThreadState *PyInterpreterState_ThreadHead(PyInterpreterState *interp)
+

Return the pointer to the first PyThreadState object in the list of threads associated with the interpreter interp.

+
+PyThreadState *PyThreadState_Next(PyThreadState *tstate)
+

Return the next thread state object after tstate from the list of all such objects belonging to the same PyInterpreterState object.

+

Thread Local Storage Support

The Python interpreter provides low-level support for thread-local storage (TLS) which wraps the underlying native TLS implementation to support the Python-level thread local storage API (threading.local). The CPython C level APIs are similar to those offered by pthreads and Windows: use a thread key and functions to associate a void* value per thread.

The GIL does not need to be held when calling these functions; they supply their own locking.

Note that Python.h does not include the declaration of the TLS APIs, you need to include pythread.h to use thread-local storage.

Note

None of these API functions handle memory management on behalf of the void* values. You need to allocate and deallocate them yourself. If the void* values happen to be PyObject*, these functions don’t do refcount operations on them either.

Thread Specific Storage (TSS) API

TSS API is introduced to supersede the use of the existing TLS API within the CPython interpreter. This API uses a new type Py_tss_t instead of int to represent thread keys.

New in version 3.7.

See also

“A New C-API for Thread-Local Storage in CPython” (PEP 539)

+type Py_tss_t
+

This data structure represents the state of a thread key, the definition of which may depend on the underlying TLS implementation, and it has an internal field representing the key’s initialization state. There are no public members in this structure.

When Py_LIMITED_API is not defined, static allocation of this type by Py_tss_NEEDS_INIT is allowed.

+
+Py_tss_NEEDS_INIT
+

This macro expands to the initializer for Py_tss_t variables. Note that this macro won’t be defined with Py_LIMITED_API.

+

Dynamic Allocation

Dynamic allocation of the Py_tss_t, required in extension modules built with Py_LIMITED_API, where static allocation of this type is not possible due to its implementation being opaque at build time.

+Py_tss_t *PyThread_tss_alloc()
+ Part of the Stable ABI since version 3.7.

Return a value which is the same state as a value initialized with Py_tss_NEEDS_INIT, or NULL in the case of dynamic allocation failure.

+
+void PyThread_tss_free(Py_tss_t *key)
+ Part of the Stable ABI since version 3.7.

Free the given key allocated by PyThread_tss_alloc(), after first calling PyThread_tss_delete() to ensure any associated thread locals have been unassigned. This is a no-op if the key argument is NULL.

Note

A freed key becomes a dangling pointer. You should reset the key to NULL.

+

Methods

The parameter key of these functions must not be NULL. Moreover, the behaviors of PyThread_tss_set() and PyThread_tss_get() are undefined if the given Py_tss_t has not been initialized by PyThread_tss_create().

+int PyThread_tss_is_created(Py_tss_t *key)
+ Part of the Stable ABI since version 3.7.

Return a non-zero value if the given Py_tss_t has been initialized by PyThread_tss_create().

+
+int PyThread_tss_create(Py_tss_t *key)
+ Part of the Stable ABI since version 3.7.

Return a zero value on successful initialization of a TSS key. The behavior is undefined if the value pointed to by the key argument is not initialized by Py_tss_NEEDS_INIT. This function can be called repeatedly on the same key – calling it on an already initialized key is a no-op and immediately returns success.

+
+void PyThread_tss_delete(Py_tss_t *key)
+ Part of the Stable ABI since version 3.7.

Destroy a TSS key to forget the values associated with the key across all threads, and change the key’s initialization state to uninitialized. A destroyed key is able to be initialized again by PyThread_tss_create(). This function can be called repeatedly on the same key – calling it on an already destroyed key is a no-op.

+
+int PyThread_tss_set(Py_tss_t *key, void *value)
+ Part of the Stable ABI since version 3.7.

Return a zero value to indicate successfully associating a void* value with a TSS key in the current thread. Each thread has a distinct mapping of the key to a void* value.

+
+void *PyThread_tss_get(Py_tss_t *key)
+ Part of the Stable ABI since version 3.7.

Return the void* value associated with a TSS key in the current thread. This returns NULL if no value is associated with the key in the current thread.

+

Thread Local Storage (TLS) API

Deprecated since version 3.7: This API is superseded by Thread Specific Storage (TSS) API.

Note

This version of the API does not support platforms where the native TLS key is defined in a way that cannot be safely cast to int. On such platforms, PyThread_create_key() will return immediately with a failure status, and the other TLS functions will all be no-ops on such platforms.

Due to the compatibility problem noted above, this version of the API should not be used in new code.

+int PyThread_create_key()
Part of the Stable ABI.
+
+void PyThread_delete_key(int key)
Part of the Stable ABI.
+
+int PyThread_set_key_value(int key, void *value)
Part of the Stable ABI.
+
+void *PyThread_get_key_value(int key)
Part of the Stable ABI.
+
+void PyThread_delete_key_value(int key)
Part of the Stable ABI.
+
+void PyThread_ReInitTLS()
Part of the Stable ABI.
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/init.html +

+
diff --git a/devdocs/python~3.12/c-api%2Finit_config.html b/devdocs/python~3.12/c-api%2Finit_config.html new file mode 100644 index 00000000..dc81266c --- /dev/null +++ b/devdocs/python~3.12/c-api%2Finit_config.html @@ -0,0 +1,527 @@ +

Python Initialization Configuration

New in version 3.8.

Python can be initialized with Py_InitializeFromConfig() and the PyConfig structure. It can be preinitialized with Py_PreInitialize() and the PyPreConfig structure.

There are two kinds of configuration:

The Py_RunMain() function can be used to write a customized Python program.

See also Initialization, Finalization, and Threads.

See also

PEP 587 “Python Initialization Configuration”.

Example

Example of customized Python always running in isolated mode:

int main(int argc, char **argv)
+{
+    PyStatus status;
+
+    PyConfig config;
+    PyConfig_InitPythonConfig(&config);
+    config.isolated = 1;
+
+    /* Decode command line arguments.
+       Implicitly preinitialize Python (in isolated mode). */
+    status = PyConfig_SetBytesArgv(&config, argc, argv);
+    if (PyStatus_Exception(status)) {
+        goto exception;
+    }
+
+    status = Py_InitializeFromConfig(&config);
+    if (PyStatus_Exception(status)) {
+        goto exception;
+    }
+    PyConfig_Clear(&config);
+
+    return Py_RunMain();
+
+exception:
+    PyConfig_Clear(&config);
+    if (PyStatus_IsExit(status)) {
+        return status.exitcode;
+    }
+    /* Display the error message and exit the process with
+       non-zero exit code */
+    Py_ExitStatusException(status);
+}
+

PyWideStringList

+type PyWideStringList
+

List of wchar_t* strings.

If length is non-zero, items must be non-NULL and all strings must be non-NULL.

Methods:

+PyStatus PyWideStringList_Append(PyWideStringList *list, const wchar_t *item)
+

Append item to list.

Python must be preinitialized to call this function.

+
+PyStatus PyWideStringList_Insert(PyWideStringList *list, Py_ssize_t index, const wchar_t *item)
+

Insert item into list at index.

If index is greater than or equal to list length, append item to list.

index must be greater than or equal to 0.

Python must be preinitialized to call this function.

+

Structure fields:

+Py_ssize_t length
+

List length.

+
+wchar_t **items
+

List items.

+
+

PyStatus

+type PyStatus
+

Structure to store an initialization function status: success, error or exit.

For an error, it can store the C function name which created the error.

Structure fields:

+int exitcode
+

Exit code. Argument passed to exit().

+
+const char *err_msg
+

Error message.

+
+const char *func
+

Name of the function which created an error, can be NULL.

+

Functions to create a status:

+PyStatus PyStatus_Ok(void)
+

Success.

+
+PyStatus PyStatus_Error(const char *err_msg)
+

Initialization error with a message.

err_msg must not be NULL.

+
+PyStatus PyStatus_NoMemory(void)
+

Memory allocation failure (out of memory).

+
+PyStatus PyStatus_Exit(int exitcode)
+

Exit Python with the specified exit code.

+

Functions to handle a status:

+int PyStatus_Exception(PyStatus status)
+

Is the status an error or an exit? If true, the exception must be handled; by calling Py_ExitStatusException() for example.

+
+int PyStatus_IsError(PyStatus status)
+

Is the result an error?

+
+int PyStatus_IsExit(PyStatus status)
+

Is the result an exit?

+
+void Py_ExitStatusException(PyStatus status)
+

Call exit(exitcode) if status is an exit. Print the error message and exit with a non-zero exit code if status is an error. Must only be called if PyStatus_Exception(status) is non-zero.

+
+

Note

Internally, Python uses macros which set PyStatus.func, whereas functions to create a status set func to NULL.

Example:

PyStatus alloc(void **ptr, size_t size)
+{
+    *ptr = PyMem_RawMalloc(size);
+    if (*ptr == NULL) {
+        return PyStatus_NoMemory();
+    }
+    return PyStatus_Ok();
+}
+
+int main(int argc, char **argv)
+{
+    void *ptr;
+    PyStatus status = alloc(&ptr, 16);
+    if (PyStatus_Exception(status)) {
+        Py_ExitStatusException(status);
+    }
+    PyMem_Free(ptr);
+    return 0;
+}
+

PyPreConfig

+type PyPreConfig
+

Structure used to preinitialize Python.

Function to initialize a preconfiguration:

+void PyPreConfig_InitPythonConfig(PyPreConfig *preconfig)
+

Initialize the preconfiguration with Python Configuration.

+
+void PyPreConfig_InitIsolatedConfig(PyPreConfig *preconfig)
+

Initialize the preconfiguration with Isolated Configuration.

+

Structure fields:

+int allocator
+

Name of the Python memory allocators:

PYMEM_ALLOCATOR_PYMALLOC and PYMEM_ALLOCATOR_PYMALLOC_DEBUG are not supported if Python is configured using --without-pymalloc.

See Memory Management.

Default: PYMEM_ALLOCATOR_NOT_SET.

+
+int configure_locale
+

Set the LC_CTYPE locale to the user preferred locale.

If equals to 0, set coerce_c_locale and coerce_c_locale_warn members to 0.

See the locale encoding.

Default: 1 in Python config, 0 in isolated config.

+
+int coerce_c_locale
+

If equals to 2, coerce the C locale.

If equals to 1, read the LC_CTYPE locale to decide if it should be coerced.

See the locale encoding.

Default: -1 in Python config, 0 in isolated config.

+
+int coerce_c_locale_warn
+

If non-zero, emit a warning if the C locale is coerced.

Default: -1 in Python config, 0 in isolated config.

+
+int dev_mode
+

Python Development Mode: see PyConfig.dev_mode.

Default: -1 in Python mode, 0 in isolated mode.

+
+int isolated
+

Isolated mode: see PyConfig.isolated.

Default: 0 in Python mode, 1 in isolated mode.

+
+int legacy_windows_fs_encoding
+

If non-zero:

Initialized the from PYTHONLEGACYWINDOWSFSENCODING environment variable value.

Only available on Windows. #ifdef MS_WINDOWS macro can be used for Windows specific code.

Default: 0.

+
+int parse_argv
+

If non-zero, Py_PreInitializeFromArgs() and Py_PreInitializeFromBytesArgs() parse their argv argument the same way the regular Python parses command line arguments: see Command Line Arguments.

Default: 1 in Python config, 0 in isolated config.

+
+int use_environment
+

Use environment variables? See PyConfig.use_environment.

Default: 1 in Python config and 0 in isolated config.

+
+int utf8_mode
+

If non-zero, enable the Python UTF-8 Mode.

Set to 0 or 1 by the -X utf8 command line option and the PYTHONUTF8 environment variable.

Also set to 1 if the LC_CTYPE locale is C or POSIX.

Default: -1 in Python config and 0 in isolated config.

+
+

Preinitialize Python with PyPreConfig

The preinitialization of Python:

The current preconfiguration (PyPreConfig type) is stored in _PyRuntime.preconfig.

Functions to preinitialize Python:

+PyStatus Py_PreInitialize(const PyPreConfig *preconfig)
+

Preinitialize Python from preconfig preconfiguration.

preconfig must not be NULL.

+
+PyStatus Py_PreInitializeFromBytesArgs(const PyPreConfig *preconfig, int argc, char *const *argv)
+

Preinitialize Python from preconfig preconfiguration.

Parse argv command line arguments (bytes strings) if parse_argv of preconfig is non-zero.

preconfig must not be NULL.

+
+PyStatus Py_PreInitializeFromArgs(const PyPreConfig *preconfig, int argc, wchar_t *const *argv)
+

Preinitialize Python from preconfig preconfiguration.

Parse argv command line arguments (wide strings) if parse_argv of preconfig is non-zero.

preconfig must not be NULL.

+

The caller is responsible to handle exceptions (error or exit) using PyStatus_Exception() and Py_ExitStatusException().

For Python Configuration (PyPreConfig_InitPythonConfig()), if Python is initialized with command line arguments, the command line arguments must also be passed to preinitialize Python, since they have an effect on the pre-configuration like encodings. For example, the -X utf8 command line option enables the Python UTF-8 Mode.

PyMem_SetAllocator() can be called after Py_PreInitialize() and before Py_InitializeFromConfig() to install a custom memory allocator. It can be called before Py_PreInitialize() if PyPreConfig.allocator is set to PYMEM_ALLOCATOR_NOT_SET.

Python memory allocation functions like PyMem_RawMalloc() must not be used before the Python preinitialization, whereas calling directly malloc() and free() is always safe. Py_DecodeLocale() must not be called before the Python preinitialization.

Example using the preinitialization to enable the Python UTF-8 Mode:

PyStatus status;
+PyPreConfig preconfig;
+PyPreConfig_InitPythonConfig(&preconfig);
+
+preconfig.utf8_mode = 1;
+
+status = Py_PreInitialize(&preconfig);
+if (PyStatus_Exception(status)) {
+    Py_ExitStatusException(status);
+}
+
+/* at this point, Python speaks UTF-8 */
+
+Py_Initialize();
+/* ... use Python API here ... */
+Py_Finalize();
+

PyConfig

+type PyConfig
+

Structure containing most parameters to configure Python.

When done, the PyConfig_Clear() function must be used to release the configuration memory.

Structure methods:

+void PyConfig_InitPythonConfig(PyConfig *config)
+

Initialize configuration with the Python Configuration.

+
+void PyConfig_InitIsolatedConfig(PyConfig *config)
+

Initialize configuration with the Isolated Configuration.

+
+PyStatus PyConfig_SetString(PyConfig *config, wchar_t *const *config_str, const wchar_t *str)
+

Copy the wide character string str into *config_str.

Preinitialize Python if needed.

+
+PyStatus PyConfig_SetBytesString(PyConfig *config, wchar_t *const *config_str, const char *str)
+

Decode str using Py_DecodeLocale() and set the result into *config_str.

Preinitialize Python if needed.

+
+PyStatus PyConfig_SetArgv(PyConfig *config, int argc, wchar_t *const *argv)
+

Set command line arguments (argv member of config) from the argv list of wide character strings.

Preinitialize Python if needed.

+
+PyStatus PyConfig_SetBytesArgv(PyConfig *config, int argc, char *const *argv)
+

Set command line arguments (argv member of config) from the argv list of bytes strings. Decode bytes using Py_DecodeLocale().

Preinitialize Python if needed.

+
+PyStatus PyConfig_SetWideStringList(PyConfig *config, PyWideStringList *list, Py_ssize_t length, wchar_t **items)
+

Set the list of wide strings list to length and items.

Preinitialize Python if needed.

+
+PyStatus PyConfig_Read(PyConfig *config)
+

Read all Python configuration.

Fields which are already initialized are left unchanged.

Fields for path configuration are no longer calculated or modified when calling this function, as of Python 3.11.

The PyConfig_Read() function only parses PyConfig.argv arguments once: PyConfig.parse_argv is set to 2 after arguments are parsed. Since Python arguments are strippped from PyConfig.argv, parsing arguments twice would parse the application options as Python options.

Preinitialize Python if needed.

Changed in version 3.10: The PyConfig.argv arguments are now only parsed once, PyConfig.parse_argv is set to 2 after arguments are parsed, and arguments are only parsed if PyConfig.parse_argv equals 1.

Changed in version 3.11: PyConfig_Read() no longer calculates all paths, and so fields listed under Python Path Configuration may no longer be updated until Py_InitializeFromConfig() is called.

+
+void PyConfig_Clear(PyConfig *config)
+

Release configuration memory.

+

Most PyConfig methods preinitialize Python if needed. In that case, the Python preinitialization configuration (PyPreConfig) in based on the PyConfig. If configuration fields which are in common with PyPreConfig are tuned, they must be set before calling a PyConfig method:

Moreover, if PyConfig_SetArgv() or PyConfig_SetBytesArgv() is used, this method must be called before other methods, since the preinitialization configuration depends on command line arguments (if parse_argv is non-zero).

The caller of these methods is responsible to handle exceptions (error or exit) using PyStatus_Exception() and Py_ExitStatusException().

Structure fields:

+PyWideStringList argv
+

Command line arguments: sys.argv.

Set parse_argv to 1 to parse argv the same way the regular Python parses Python command line arguments and then to strip Python arguments from argv.

If argv is empty, an empty string is added to ensure that sys.argv always exists and is never empty.

Default: NULL.

See also the orig_argv member.

+
+int safe_path
+

If equals to zero, Py_RunMain() prepends a potentially unsafe path to sys.path at startup:

  • If argv[0] is equal to L"-m" (python -m module), prepend the current working directory.
  • If running a script (python script.py), prepend the script’s directory. If it’s a symbolic link, resolve symbolic links.
  • Otherwise (python -c code and python), prepend an empty string, which means the current working directory.

Set to 1 by the -P command line option and the PYTHONSAFEPATH environment variable.

Default: 0 in Python config, 1 in isolated config.

New in version 3.11.

+
+wchar_t *base_exec_prefix
+

sys.base_exec_prefix.

Default: NULL.

Part of the Python Path Configuration output.

+
+wchar_t *base_executable
+

Python base executable: sys._base_executable.

Set by the __PYVENV_LAUNCHER__ environment variable.

Set from PyConfig.executable if NULL.

Default: NULL.

Part of the Python Path Configuration output.

+
+wchar_t *base_prefix
+

sys.base_prefix.

Default: NULL.

Part of the Python Path Configuration output.

+
+int buffered_stdio
+

If equals to 0 and configure_c_stdio is non-zero, disable buffering on the C streams stdout and stderr.

Set to 0 by the -u command line option and the PYTHONUNBUFFERED environment variable.

stdin is always opened in buffered mode.

Default: 1.

+
+int bytes_warning
+

If equals to 1, issue a warning when comparing bytes or bytearray with str, or comparing bytes with int.

If equal or greater to 2, raise a BytesWarning exception in these cases.

Incremented by the -b command line option.

Default: 0.

+
+int warn_default_encoding
+

If non-zero, emit a EncodingWarning warning when io.TextIOWrapper uses its default encoding. See Opt-in EncodingWarning for details.

Default: 0.

New in version 3.10.

+
+int code_debug_ranges
+

If equals to 0, disables the inclusion of the end line and column mappings in code objects. Also disables traceback printing carets to specific error locations.

Set to 0 by the PYTHONNODEBUGRANGES environment variable and by the -X no_debug_ranges command line option.

Default: 1.

New in version 3.11.

+
+wchar_t *check_hash_pycs_mode
+

Control the validation behavior of hash-based .pyc files: value of the --check-hash-based-pycs command line option.

Valid values:

  • +L"always": Hash the source file for invalidation regardless of value of the ‘check_source’ flag.
  • +L"never": Assume that hash-based pycs always are valid.
  • +L"default": The ‘check_source’ flag in hash-based pycs determines invalidation.

Default: L"default".

See also PEP 552 “Deterministic pycs”.

+
+int configure_c_stdio
+

If non-zero, configure C standard streams:

  • On Windows, set the binary mode (O_BINARY) on stdin, stdout and stderr.
  • If buffered_stdio equals zero, disable buffering of stdin, stdout and stderr streams.
  • If interactive is non-zero, enable stream buffering on stdin and stdout (only stdout on Windows).

Default: 1 in Python config, 0 in isolated config.

+
+int dev_mode
+

If non-zero, enable the Python Development Mode.

Set to 1 by the -X dev option and the PYTHONDEVMODE environment variable.

Default: -1 in Python mode, 0 in isolated mode.

+
+int dump_refs
+

Dump Python references?

If non-zero, dump all objects which are still alive at exit.

Set to 1 by the PYTHONDUMPREFS environment variable.

Need a special build of Python with the Py_TRACE_REFS macro defined: see the configure --with-trace-refs option.

Default: 0.

+
+wchar_t *exec_prefix
+

The site-specific directory prefix where the platform-dependent Python files are installed: sys.exec_prefix.

Default: NULL.

Part of the Python Path Configuration output.

+
+wchar_t *executable
+

The absolute path of the executable binary for the Python interpreter: sys.executable.

Default: NULL.

Part of the Python Path Configuration output.

+
+int faulthandler
+

Enable faulthandler?

If non-zero, call faulthandler.enable() at startup.

Set to 1 by -X faulthandler and the PYTHONFAULTHANDLER environment variable.

Default: -1 in Python mode, 0 in isolated mode.

+
+wchar_t *filesystem_encoding
+

Filesystem encoding: sys.getfilesystemencoding().

On macOS, Android and VxWorks: use "utf-8" by default.

On Windows: use "utf-8" by default, or "mbcs" if legacy_windows_fs_encoding of PyPreConfig is non-zero.

Default encoding on other platforms:

  • +"utf-8" if PyPreConfig.utf8_mode is non-zero.
  • +"ascii" if Python detects that nl_langinfo(CODESET) announces the ASCII encoding, whereas the mbstowcs() function decodes from a different encoding (usually Latin1).
  • +"utf-8" if nl_langinfo(CODESET) returns an empty string.
  • Otherwise, use the locale encoding: nl_langinfo(CODESET) result.

At Python startup, the encoding name is normalized to the Python codec name. For example, "ANSI_X3.4-1968" is replaced with "ascii".

See also the filesystem_errors member.

+
+wchar_t *filesystem_errors
+

Filesystem error handler: sys.getfilesystemencodeerrors().

On Windows: use "surrogatepass" by default, or "replace" if legacy_windows_fs_encoding of PyPreConfig is non-zero.

On other platforms: use "surrogateescape" by default.

Supported error handlers:

  • "strict"
  • "surrogateescape"
  • +"surrogatepass" (only supported with the UTF-8 encoding)

See also the filesystem_encoding member.

+
+unsigned long hash_seed
+
+int use_hash_seed
+

Randomized hash function seed.

If use_hash_seed is zero, a seed is chosen randomly at Python startup, and hash_seed is ignored.

Set by the PYTHONHASHSEED environment variable.

Default use_hash_seed value: -1 in Python mode, 0 in isolated mode.

+
+wchar_t *home
+

Python home directory.

If Py_SetPythonHome() has been called, use its argument if it is not NULL.

Set by the PYTHONHOME environment variable.

Default: NULL.

Part of the Python Path Configuration input.

+
+int import_time
+

If non-zero, profile import time.

Set the 1 by the -X importtime option and the PYTHONPROFILEIMPORTTIME environment variable.

Default: 0.

+
+int inspect
+

Enter interactive mode after executing a script or a command.

If greater than 0, enable inspect: when a script is passed as first argument or the -c option is used, enter interactive mode after executing the script or the command, even when sys.stdin does not appear to be a terminal.

Incremented by the -i command line option. Set to 1 if the PYTHONINSPECT environment variable is non-empty.

Default: 0.

+
+int install_signal_handlers
+

Install Python signal handlers?

Default: 1 in Python mode, 0 in isolated mode.

+
+int interactive
+

If greater than 0, enable the interactive mode (REPL).

Incremented by the -i command line option.

Default: 0.

+
+int int_max_str_digits
+

Configures the integer string conversion length limitation. An initial value of -1 means the value will be taken from the command line or environment or otherwise default to 4300 (sys.int_info.default_max_str_digits). A value of 0 disables the limitation. Values greater than zero but less than 640 (sys.int_info.str_digits_check_threshold) are unsupported and will produce an error.

Configured by the -X int_max_str_digits command line flag or the PYTHONINTMAXSTRDIGITS environment variable.

Default: -1 in Python mode. 4300 (sys.int_info.default_max_str_digits) in isolated mode.

New in version 3.12.

+
+int isolated
+

If greater than 0, enable isolated mode:

  • Set safe_path to 1: don’t prepend a potentially unsafe path to sys.path at Python startup, such as the current directory, the script’s directory or an empty string.
  • Set use_environment to 0: ignore PYTHON environment variables.
  • Set user_site_directory to 0: don’t add the user site directory to sys.path.
  • Python REPL doesn’t import readline nor enable default readline configuration on interactive prompts.

Set to 1 by the -I command line option.

Default: 0 in Python mode, 1 in isolated mode.

See also the Isolated Configuration and PyPreConfig.isolated.

+
+int legacy_windows_stdio
+

If non-zero, use io.FileIO instead of io._WindowsConsoleIO for sys.stdin, sys.stdout and sys.stderr.

Set to 1 if the PYTHONLEGACYWINDOWSSTDIO environment variable is set to a non-empty string.

Only available on Windows. #ifdef MS_WINDOWS macro can be used for Windows specific code.

Default: 0.

See also the PEP 528 (Change Windows console encoding to UTF-8).

+
+int malloc_stats
+

If non-zero, dump statistics on Python pymalloc memory allocator at exit.

Set to 1 by the PYTHONMALLOCSTATS environment variable.

The option is ignored if Python is configured using +the --without-pymalloc option.

Default: 0.

+
+wchar_t *platlibdir
+

Platform library directory name: sys.platlibdir.

Set by the PYTHONPLATLIBDIR environment variable.

Default: value of the PLATLIBDIR macro which is set by the configure --with-platlibdir option (default: "lib", or "DLLs" on Windows).

Part of the Python Path Configuration input.

New in version 3.9.

Changed in version 3.11: This macro is now used on Windows to locate the standard library extension modules, typically under DLLs. However, for compatibility, note that this value is ignored for any non-standard layouts, including in-tree builds and virtual environments.

+
+wchar_t *pythonpath_env
+

Module search paths (sys.path) as a string separated by DELIM (os.pathsep).

Set by the PYTHONPATH environment variable.

Default: NULL.

Part of the Python Path Configuration input.

+
+PyWideStringList module_search_paths
+
+int module_search_paths_set
+

Module search paths: sys.path.

If module_search_paths_set is equal to 0, Py_InitializeFromConfig() will replace module_search_paths and sets module_search_paths_set to 1.

Default: empty list (module_search_paths) and 0 (module_search_paths_set).

Part of the Python Path Configuration output.

+
+int optimization_level
+

Compilation optimization level:

  • +0: Peephole optimizer, set __debug__ to True.
  • +1: Level 0, remove assertions, set __debug__ to False.
  • +2: Level 1, strip docstrings.

Incremented by the -O command line option. Set to the PYTHONOPTIMIZE environment variable value.

Default: 0.

+
+PyWideStringList orig_argv
+

The list of the original command line arguments passed to the Python executable: sys.orig_argv.

If orig_argv list is empty and argv is not a list only containing an empty string, PyConfig_Read() copies argv into orig_argv before modifying argv (if parse_argv is non-zero).

See also the argv member and the Py_GetArgcArgv() function.

Default: empty list.

New in version 3.10.

+
+int parse_argv
+

Parse command line arguments?

If equals to 1, parse argv the same way the regular Python parses command line arguments, and strip Python arguments from argv.

The PyConfig_Read() function only parses PyConfig.argv arguments once: PyConfig.parse_argv is set to 2 after arguments are parsed. Since Python arguments are strippped from PyConfig.argv, parsing arguments twice would parse the application options as Python options.

Default: 1 in Python mode, 0 in isolated mode.

Changed in version 3.10: The PyConfig.argv arguments are now only parsed if PyConfig.parse_argv equals to 1.

+
+int parser_debug
+

Parser debug mode. If greater than 0, turn on parser debugging output (for expert only, depending on compilation options).

Incremented by the -d command line option. Set to the PYTHONDEBUG environment variable value.

Need a debug build of Python (the Py_DEBUG macro must be defined).

Default: 0.

+
+int pathconfig_warnings
+

If non-zero, calculation of path configuration is allowed to log warnings into stderr. If equals to 0, suppress these warnings.

Default: 1 in Python mode, 0 in isolated mode.

Part of the Python Path Configuration input.

Changed in version 3.11: Now also applies on Windows.

+
+wchar_t *prefix
+

The site-specific directory prefix where the platform independent Python files are installed: sys.prefix.

Default: NULL.

Part of the Python Path Configuration output.

+
+wchar_t *program_name
+

Program name used to initialize executable and in early error messages during Python initialization.

  • If Py_SetProgramName() has been called, use its argument.
  • On macOS, use PYTHONEXECUTABLE environment variable if set.
  • If the WITH_NEXT_FRAMEWORK macro is defined, use __PYVENV_LAUNCHER__ environment variable if set.
  • Use argv[0] of argv if available and non-empty.
  • Otherwise, use L"python" on Windows, or L"python3" on other platforms.

Default: NULL.

Part of the Python Path Configuration input.

+
+wchar_t *pycache_prefix
+

Directory where cached .pyc files are written: sys.pycache_prefix.

Set by the -X pycache_prefix=PATH command line option and the PYTHONPYCACHEPREFIX environment variable.

If NULL, sys.pycache_prefix is set to None.

Default: NULL.

+
+int quiet
+

Quiet mode. If greater than 0, don’t display the copyright and version at Python startup in interactive mode.

Incremented by the -q command line option.

Default: 0.

+
+wchar_t *run_command
+

Value of the -c command line option.

Used by Py_RunMain().

Default: NULL.

+
+wchar_t *run_filename
+

Filename passed on the command line: trailing command line argument without -c or -m. It is used by the Py_RunMain() function.

For example, it is set to script.py by the python3 script.py arg command line.

See also the PyConfig.skip_source_first_line option.

Default: NULL.

+
+wchar_t *run_module
+

Value of the -m command line option.

Used by Py_RunMain().

Default: NULL.

+
+int show_ref_count
+

Show total reference count at exit (excluding immortal objects)?

Set to 1 by -X showrefcount command line option.

Need a debug build of Python (the Py_REF_DEBUG macro must be defined).

Default: 0.

+
+int site_import
+

Import the site module at startup?

If equal to zero, disable the import of the module site and the site-dependent manipulations of sys.path that it entails.

Also disable these manipulations if the site module is explicitly imported later (call site.main() if you want them to be triggered).

Set to 0 by the -S command line option.

sys.flags.no_site is set to the inverted value of site_import.

Default: 1.

+
+int skip_source_first_line
+

If non-zero, skip the first line of the PyConfig.run_filename source.

It allows the usage of non-Unix forms of #!cmd. This is intended for a DOS specific hack only.

Set to 1 by the -x command line option.

Default: 0.

+
+wchar_t *stdio_encoding
+
+wchar_t *stdio_errors
+

Encoding and encoding errors of sys.stdin, sys.stdout and sys.stderr (but sys.stderr always uses "backslashreplace" error handler).

If Py_SetStandardStreamEncoding() has been called, use its error and errors arguments if they are not NULL.

Use the PYTHONIOENCODING environment variable if it is non-empty.

Default encoding:

Default error handler:

  • On Windows: use "surrogateescape".
  • +"surrogateescape" if PyPreConfig.utf8_mode is non-zero, or if the LC_CTYPE locale is “C” or “POSIX”.
  • +"strict" otherwise.
+
+int tracemalloc
+

Enable tracemalloc?

If non-zero, call tracemalloc.start() at startup.

Set by -X tracemalloc=N command line option and by the PYTHONTRACEMALLOC environment variable.

Default: -1 in Python mode, 0 in isolated mode.

+
+int perf_profiling
+

Enable compatibility mode with the perf profiler?

If non-zero, initialize the perf trampoline. See Python support for the Linux perf profiler for more information.

Set by -X perf command line option and by the PYTHONPERFSUPPORT environment variable.

Default: -1.

New in version 3.12.

+
+int use_environment
+

Use environment variables?

If equals to zero, ignore the environment variables.

Set to 0 by the -E environment variable.

Default: 1 in Python config and 0 in isolated config.

+
+int user_site_directory
+

If non-zero, add the user site directory to sys.path.

Set to 0 by the -s and -I command line options.

Set to 0 by the PYTHONNOUSERSITE environment variable.

Default: 1 in Python mode, 0 in isolated mode.

+
+int verbose
+

Verbose mode. If greater than 0, print a message each time a module is imported, showing the place (filename or built-in module) from which it is loaded.

If greater than or equal to 2, print a message for each file that is checked for when searching for a module. Also provides information on module cleanup at exit.

Incremented by the -v command line option.

Set by the PYTHONVERBOSE environment variable value.

Default: 0.

+
+PyWideStringList warnoptions
+

Options of the warnings module to build warnings filters, lowest to highest priority: sys.warnoptions.

The warnings module adds sys.warnoptions in the reverse order: the last PyConfig.warnoptions item becomes the first item of warnings.filters which is checked first (highest priority).

The -W command line options adds its value to warnoptions, it can be used multiple times.

The PYTHONWARNINGS environment variable can also be used to add warning options. Multiple options can be specified, separated by commas (,).

Default: empty list.

+
+int write_bytecode
+

If equal to 0, Python won’t try to write .pyc files on the import of source modules.

Set to 0 by the -B command line option and the PYTHONDONTWRITEBYTECODE environment variable.

sys.dont_write_bytecode is initialized to the inverted value of write_bytecode.

Default: 1.

+
+PyWideStringList xoptions
+

Values of the -X command line options: sys._xoptions.

Default: empty list.

+
+

If parse_argv is non-zero, argv arguments are parsed the same way the regular Python parses command line arguments, and Python arguments are stripped from argv.

The xoptions options are parsed to set other options: see the -X command line option.

Changed in version 3.9: The show_alloc_count field has been removed.

Initialization with PyConfig

Function to initialize Python:

+PyStatus Py_InitializeFromConfig(const PyConfig *config)
+

Initialize Python from config configuration.

+

The caller is responsible to handle exceptions (error or exit) using PyStatus_Exception() and Py_ExitStatusException().

If PyImport_FrozenModules(), PyImport_AppendInittab() or PyImport_ExtendInittab() are used, they must be set or called after Python preinitialization and before the Python initialization. If Python is initialized multiple times, PyImport_AppendInittab() or PyImport_ExtendInittab() must be called before each Python initialization.

The current configuration (PyConfig type) is stored in PyInterpreterState.config.

Example setting the program name:

void init_python(void)
+{
+    PyStatus status;
+
+    PyConfig config;
+    PyConfig_InitPythonConfig(&config);
+
+    /* Set the program name. Implicitly preinitialize Python. */
+    status = PyConfig_SetString(&config, &config.program_name,
+                                L"/path/to/my_program");
+    if (PyStatus_Exception(status)) {
+        goto exception;
+    }
+
+    status = Py_InitializeFromConfig(&config);
+    if (PyStatus_Exception(status)) {
+        goto exception;
+    }
+    PyConfig_Clear(&config);
+    return;
+
+exception:
+    PyConfig_Clear(&config);
+    Py_ExitStatusException(status);
+}
+

More complete example modifying the default configuration, read the configuration, and then override some parameters. Note that since 3.11, many parameters are not calculated until initialization, and so values cannot be read from the configuration structure. Any values set before initialize is called will be left unchanged by initialization:

PyStatus init_python(const char *program_name)
+{
+    PyStatus status;
+
+    PyConfig config;
+    PyConfig_InitPythonConfig(&config);
+
+    /* Set the program name before reading the configuration
+       (decode byte string from the locale encoding).
+
+       Implicitly preinitialize Python. */
+    status = PyConfig_SetBytesString(&config, &config.program_name,
+                                     program_name);
+    if (PyStatus_Exception(status)) {
+        goto done;
+    }
+
+    /* Read all configuration at once */
+    status = PyConfig_Read(&config);
+    if (PyStatus_Exception(status)) {
+        goto done;
+    }
+
+    /* Specify sys.path explicitly */
+    /* If you want to modify the default set of paths, finish
+       initialization first and then use PySys_GetObject("path") */
+    config.module_search_paths_set = 1;
+    status = PyWideStringList_Append(&config.module_search_paths,
+                                     L"/path/to/stdlib");
+    if (PyStatus_Exception(status)) {
+        goto done;
+    }
+    status = PyWideStringList_Append(&config.module_search_paths,
+                                     L"/path/to/more/modules");
+    if (PyStatus_Exception(status)) {
+        goto done;
+    }
+
+    /* Override executable computed by PyConfig_Read() */
+    status = PyConfig_SetString(&config, &config.executable,
+                                L"/path/to/my_executable");
+    if (PyStatus_Exception(status)) {
+        goto done;
+    }
+
+    status = Py_InitializeFromConfig(&config);
+
+done:
+    PyConfig_Clear(&config);
+    return status;
+}
+

Isolated Configuration

PyPreConfig_InitIsolatedConfig() and PyConfig_InitIsolatedConfig() functions create a configuration to isolate Python from the system. For example, to embed Python into an application.

This configuration ignores global configuration variables, environment variables, command line arguments (PyConfig.argv is not parsed) and user site directory. The C standard streams (ex: stdout) and the LC_CTYPE locale are left unchanged. Signal handlers are not installed.

Configuration files are still used with this configuration to determine paths that are unspecified. Ensure PyConfig.home is specified to avoid computing the default path configuration.

Python Configuration

PyPreConfig_InitPythonConfig() and PyConfig_InitPythonConfig() functions create a configuration to build a customized Python which behaves as the regular Python.

Environments variables and command line arguments are used to configure Python, whereas global configuration variables are ignored.

This function enables C locale coercion (PEP 538) and Python UTF-8 Mode (PEP 540) depending on the LC_CTYPE locale, PYTHONUTF8 and PYTHONCOERCECLOCALE environment variables.

Python Path Configuration

PyConfig contains multiple fields for the path configuration:

If at least one “output field” is not set, Python calculates the path configuration to fill unset fields. If module_search_paths_set is equal to 0, module_search_paths is overridden and module_search_paths_set is set to 1.

It is possible to completely ignore the function calculating the default path configuration by setting explicitly all path configuration output fields listed above. A string is considered as set even if it is non-empty. module_search_paths is considered as set if module_search_paths_set is set to 1. In this case, module_search_paths will be used without modification.

Set pathconfig_warnings to 0 to suppress warnings when calculating the path configuration (Unix only, Windows does not log any warning).

If base_prefix or base_exec_prefix fields are not set, they inherit their value from prefix and exec_prefix respectively.

Py_RunMain() and Py_Main() modify sys.path:

If site_import is non-zero, sys.path can be modified by the site module. If user_site_directory is non-zero and the user’s site-package directory exists, the site module appends the user’s site-package directory to sys.path.

The following configuration files are used by the path configuration:

If a ._pth file is present:

The __PYVENV_LAUNCHER__ environment variable is used to set PyConfig.base_executable

Py_RunMain()

+int Py_RunMain(void)
+

Execute the command (PyConfig.run_command), the script (PyConfig.run_filename) or the module (PyConfig.run_module) specified on the command line or in the configuration.

By default and when if -i option is used, run the REPL.

Finally, finalizes Python and returns an exit status that can be passed to the exit() function.

+

See Python Configuration for an example of customized Python always running in isolated mode using Py_RunMain().

Py_GetArgcArgv()

+void Py_GetArgcArgv(int *argc, wchar_t ***argv)
+

Get the original command line arguments, before Python modified them.

See also PyConfig.orig_argv member.

+

Multi-Phase Initialization Private Provisional API

This section is a private provisional API introducing multi-phase initialization, the core feature of PEP 432:

Private provisional API:

+PyStatus _Py_InitializeMain(void)
+

Move to the “Main” initialization phase, finish the Python initialization.

+

No module is imported during the “Core” phase and the importlib module is not configured: the Path Configuration is only applied during the “Main” phase. It may allow to customize Python in Python to override or tune the Path Configuration, maybe install a custom sys.meta_path importer or an import hook, etc.

It may become possible to calculate the Path Configuration in Python, after the Core phase and before the Main phase, which is one of the PEP 432 motivation.

The “Core” phase is not properly defined: what should be and what should not be available at this phase is not specified yet. The API is marked as private and provisional: the API can be modified or even be removed anytime until a proper public API is designed.

Example running Python code between “Core” and “Main” initialization phases:

void init_python(void)
+{
+    PyStatus status;
+
+    PyConfig config;
+    PyConfig_InitPythonConfig(&config);
+    config._init_main = 0;
+
+    /* ... customize 'config' configuration ... */
+
+    status = Py_InitializeFromConfig(&config);
+    PyConfig_Clear(&config);
+    if (PyStatus_Exception(status)) {
+        Py_ExitStatusException(status);
+    }
+
+    /* Use sys.stderr because sys.stdout is only created
+       by _Py_InitializeMain() */
+    int res = PyRun_SimpleString(
+        "import sys; "
+        "print('Run Python code before _Py_InitializeMain', "
+               "file=sys.stderr)");
+    if (res < 0) {
+        exit(1);
+    }
+
+    /* ... put more configuration code here ... */
+
+    status = _Py_InitializeMain();
+    if (PyStatus_Exception(status)) {
+        Py_ExitStatusException(status);
+    }
+}
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/init_config.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fintro.html b/devdocs/python~3.12/c-api%2Fintro.html new file mode 100644 index 00000000..fdfedaec --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fintro.html @@ -0,0 +1,208 @@ +

Introduction

The Application Programmer’s Interface to Python gives C and C++ programmers access to the Python interpreter at a variety of levels. The API is equally usable from C++, but for brevity it is generally referred to as the Python/C API. There are two fundamentally different reasons for using the Python/C API. The first reason is to write extension modules for specific purposes; these are C modules that extend the Python interpreter. This is probably the most common use. The second reason is to use Python as a component in a larger application; this technique is generally referred to as embedding Python in an application.

Writing an extension module is a relatively well-understood process, where a “cookbook” approach works well. There are several tools that automate the process to some extent. While people have embedded Python in other applications since its early existence, the process of embedding Python is less straightforward than writing an extension.

Many API functions are useful independent of whether you’re embedding or extending Python; moreover, most applications that embed Python will need to provide a custom extension as well, so it’s probably a good idea to become familiar with writing an extension before attempting to embed Python in a real application.

Coding standards

If you’re writing C code for inclusion in CPython, you must follow the guidelines and standards defined in PEP 7. These guidelines apply regardless of the version of Python you are contributing to. Following these conventions is not necessary for your own third party extension modules, unless you eventually expect to contribute them to Python.

Include Files

All function, type and macro definitions needed to use the Python/C API are included in your code by the following line:

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+

This implies inclusion of the following standard headers: <stdio.h>, <string.h>, <errno.h>, <limits.h>, <assert.h> and <stdlib.h> (if available).

Note

Since Python may define some pre-processor definitions which affect the standard headers on some systems, you must include Python.h before any standard headers are included.

It is recommended to always define PY_SSIZE_T_CLEAN before including Python.h. See Parsing arguments and building values for a description of this macro.

All user visible names defined by Python.h (except those defined by the included standard headers) have one of the prefixes Py or _Py. Names beginning with _Py are for internal use by the Python implementation and should not be used by extension writers. Structure member names do not have a reserved prefix.

Note

User code should never define names that begin with Py or _Py. This confuses the reader, and jeopardizes the portability of the user code to future Python versions, which may define additional names beginning with one of these prefixes.

The header files are typically installed with Python. On Unix, these are located in the directories prefix/include/pythonversion/ and exec_prefix/include/pythonversion/, where prefix and exec_prefix are defined by the corresponding parameters to Python’s configure script and version is '%d.%d' % sys.version_info[:2]. On Windows, the headers are installed in prefix/include, where prefix is the installation directory specified to the installer.

To include the headers, place both directories (if different) on your compiler’s search path for includes. Do not place the parent directories on the search path and then use #include <pythonX.Y/Python.h>; this will break on multi-platform builds since the platform independent headers under prefix include the platform specific headers from exec_prefix.

C++ users should note that although the API is defined entirely using C, the header files properly declare the entry points to be extern "C". As a result, there is no need to do anything special to use the API from C++.

Useful macros

Several useful macros are defined in the Python header files. Many are defined closer to where they are useful (e.g. Py_RETURN_NONE). Others of a more general utility are defined here. This is not necessarily a complete listing.

+PyMODINIT_FUNC
+

Declare an extension module PyInit initialization function. The function return type is PyObject*. The macro declares any special linkage declarations required by the platform, and for C++ declares the function as extern "C".

The initialization function must be named PyInit_name, where name is the name of the module, and should be the only non-static item defined in the module file. Example:

static struct PyModuleDef spam_module = {
+    PyModuleDef_HEAD_INIT,
+    .m_name = "spam",
+    ...
+};
+
+PyMODINIT_FUNC
+PyInit_spam(void)
+{
+    return PyModule_Create(&spam_module);
+}
+
+
+Py_ABS(x)
+

Return the absolute value of x.

New in version 3.3.

+
+Py_ALWAYS_INLINE
+

Ask the compiler to always inline a static inline function. The compiler can ignore it and decides to not inline the function.

It can be used to inline performance critical static inline functions when building Python in debug mode with function inlining disabled. For example, MSC disables function inlining when building in debug mode.

Marking blindly a static inline function with Py_ALWAYS_INLINE can result in worse performances (due to increased code size for example). The compiler is usually smarter than the developer for the cost/benefit analysis.

If Python is built in debug mode (if the Py_DEBUG macro is defined), the Py_ALWAYS_INLINE macro does nothing.

It must be specified before the function return type. Usage:

static inline Py_ALWAYS_INLINE int random(void) { return 4; }
+

New in version 3.11.

+
+Py_CHARMASK(c)
+

Argument must be a character or an integer in the range [-128, 127] or [0, 255]. This macro returns c cast to an unsigned char.

+
+Py_DEPRECATED(version)
+

Use this for deprecated declarations. The macro must be placed before the symbol name.

Example:

Py_DEPRECATED(3.8) PyAPI_FUNC(int) Py_OldFunction(void);
+

Changed in version 3.8: MSVC support was added.

+
+Py_GETENV(s)
+

Like getenv(s), but returns NULL if -E was passed on the command line (see PyConfig.use_environment).

+
+Py_MAX(x, y)
+

Return the maximum value between x and y.

New in version 3.3.

+
+Py_MEMBER_SIZE(type, member)
+

Return the size of a structure (type) member in bytes.

New in version 3.6.

+
+Py_MIN(x, y)
+

Return the minimum value between x and y.

New in version 3.3.

+
+Py_NO_INLINE
+

Disable inlining on a function. For example, it reduces the C stack consumption: useful on LTO+PGO builds which heavily inline code (see bpo-33720).

Usage:

Py_NO_INLINE static int random(void) { return 4; }
+

New in version 3.11.

+
+Py_STRINGIFY(x)
+

Convert x to a C string. E.g. Py_STRINGIFY(123) returns "123".

New in version 3.4.

+
+Py_UNREACHABLE()
+

Use this when you have a code path that cannot be reached by design. For example, in the default: clause in a switch statement for which all possible values are covered in case statements. Use this in places where you might be tempted to put an assert(0) or abort() call.

In release mode, the macro helps the compiler to optimize the code, and avoids a warning about unreachable code. For example, the macro is implemented with __builtin_unreachable() on GCC in release mode.

A use for Py_UNREACHABLE() is following a call a function that never returns but that is not declared _Py_NO_RETURN.

If a code path is very unlikely code but can be reached under exceptional case, this macro must not be used. For example, under low memory condition or if a system call returns a value out of the expected range. In this case, it’s better to report the error to the caller. If the error cannot be reported to caller, Py_FatalError() can be used.

New in version 3.7.

+
+Py_UNUSED(arg)
+

Use this for unused arguments in a function definition to silence compiler warnings. Example: int func(int a, int Py_UNUSED(b)) { return a; }.

New in version 3.4.

+
+PyDoc_STRVAR(name, str)
+

Creates a variable with name name that can be used in docstrings. If Python is built without docstrings, the value will be empty.

Use PyDoc_STRVAR for docstrings to support building Python without docstrings, as specified in PEP 7.

Example:

PyDoc_STRVAR(pop_doc, "Remove and return the rightmost element.");
+
+static PyMethodDef deque_methods[] = {
+    // ...
+    {"pop", (PyCFunction)deque_pop, METH_NOARGS, pop_doc},
+    // ...
+}
+
+
+PyDoc_STR(str)
+

Creates a docstring for the given input string or an empty string if docstrings are disabled.

Use PyDoc_STR in specifying docstrings to support building Python without docstrings, as specified in PEP 7.

Example:

static PyMethodDef pysqlite_row_methods[] = {
+    {"keys", (PyCFunction)pysqlite_row_keys, METH_NOARGS,
+        PyDoc_STR("Returns the keys of the row.")},
+    {NULL, NULL}
+};
+
+

Objects, Types and Reference Counts

Most Python/C API functions have one or more arguments as well as a return value of type PyObject*. This type is a pointer to an opaque data type representing an arbitrary Python object. Since all Python object types are treated the same way by the Python language in most situations (e.g., assignments, scope rules, and argument passing), it is only fitting that they should be represented by a single C type. Almost all Python objects live on the heap: you never declare an automatic or static variable of type PyObject, only pointer variables of type PyObject* can be declared. The sole exception are the type objects; since these must never be deallocated, they are typically static PyTypeObject objects.

All Python objects (even Python integers) have a type and a reference count. An object’s type determines what kind of object it is (e.g., an integer, a list, or a user-defined function; there are many more as explained in The standard type hierarchy). For each of the well-known types there is a macro to check whether an object is of that type; for instance, PyList_Check(a) is true if (and only if) the object pointed to by a is a Python list.

Reference Counts

The reference count is important because today’s computers have a finite (and often severely limited) memory size; it counts how many different places there are that have a strong reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When the last strong reference to an object is released (i.e. its reference count becomes zero), the object is deallocated. If it contains references to other objects, those references are released. Those other objects may be deallocated in turn, if there are no more references to them, and so on. (There’s an obvious problem with objects that reference each other here; for now, the solution is “don’t do that.”)

Reference counts are always manipulated explicitly. The normal way is to use the macro Py_INCREF() to take a new reference to an object (i.e. increment its reference count by one), and Py_DECREF() to release that reference (i.e. decrement the reference count by one). The Py_DECREF() macro is considerably more complex than the incref one, since it must check whether the reference count becomes zero and then cause the object’s deallocator to be called. The deallocator is a function pointer contained in the object’s type structure. The type-specific deallocator takes care of releasing references for other objects contained in the object if this is a compound object type, such as a list, as well as performing any additional finalization that’s needed. There’s no chance that the reference count can overflow; at least as many bits are used to hold the reference count as there are distinct memory locations in virtual memory (assuming sizeof(Py_ssize_t) >= sizeof(void*)). Thus, the reference count increment is a simple operation.

It is not necessary to hold a strong reference (i.e. increment the reference count) for every local variable that contains a pointer to an object. In theory, the object’s reference count goes up by one when the variable is made to point to it and it goes down by one when the variable goes out of scope. However, these two cancel each other out, so at the end the reference count hasn’t changed. The only real reason to use the reference count is to prevent the object from being deallocated as long as our variable is pointing to it. If we know that there is at least one other reference to the object that lives at least as long as our variable, there is no need to take a new strong reference (i.e. increment the reference count) temporarily. An important situation where this arises is in objects that are passed as arguments to C functions in an extension module that are called from Python; the call mechanism guarantees to hold a reference to every argument for the duration of the call.

However, a common pitfall is to extract an object from a list and hold on to it for a while without taking a new reference. Some other operation might conceivably remove the object from the list, releasing that reference, and possibly deallocating it. The real danger is that innocent-looking operations may invoke arbitrary Python code which could do this; there is a code path which allows control to flow back to the user from a Py_DECREF(), so almost any operation is potentially dangerous.

A safe approach is to always use the generic operations (functions whose name begins with PyObject_, PyNumber_, PySequence_ or PyMapping_). These operations always create a new strong reference (i.e. increment the reference count) of the object they return. This leaves the caller with the responsibility to call Py_DECREF() when they are done with the result; this soon becomes second nature.

Reference Count Details

The reference count behavior of functions in the Python/C API is best explained in terms of ownership of references. Ownership pertains to references, never to objects (objects are not owned: they are always shared). “Owning a reference” means being responsible for calling Py_DECREF on it when the reference is no longer needed. Ownership can also be transferred, meaning that the code that receives ownership of the reference then becomes responsible for eventually releasing it by calling Py_DECREF() or Py_XDECREF() when it’s no longer needed—or passing on this responsibility (usually to its caller). When a function passes ownership of a reference on to its caller, the caller is said to receive a new reference. When no ownership is transferred, the caller is said to borrow the reference. Nothing needs to be done for a borrowed reference.

Conversely, when a calling function passes in a reference to an object, there are two possibilities: the function steals a reference to the object, or it does not. Stealing a reference means that when you pass a reference to a function, that function assumes that it now owns that reference, and you are not responsible for it any longer.

Few functions steal references; the two notable exceptions are PyList_SetItem() and PyTuple_SetItem(), which steal a reference to the item (but not to the tuple or list into which the item is put!). These functions were designed to steal a reference because of a common idiom for populating a tuple or list with newly created objects; for example, the code to create the tuple (1, 2, "three") could look like this (forgetting about error handling for the moment; a better way to code this is shown below):

PyObject *t;
+
+t = PyTuple_New(3);
+PyTuple_SetItem(t, 0, PyLong_FromLong(1L));
+PyTuple_SetItem(t, 1, PyLong_FromLong(2L));
+PyTuple_SetItem(t, 2, PyUnicode_FromString("three"));
+

Here, PyLong_FromLong() returns a new reference which is immediately stolen by PyTuple_SetItem(). When you want to keep using an object although the reference to it will be stolen, use Py_INCREF() to grab another reference before calling the reference-stealing function.

Incidentally, PyTuple_SetItem() is the only way to set tuple items; PySequence_SetItem() and PyObject_SetItem() refuse to do this since tuples are an immutable data type. You should only use PyTuple_SetItem() for tuples that you are creating yourself.

Equivalent code for populating a list can be written using PyList_New() and PyList_SetItem().

However, in practice, you will rarely use these ways of creating and populating a tuple or list. There’s a generic function, Py_BuildValue(), that can create most common objects from C values, directed by a format string. For example, the above two blocks of code could be replaced by the following (which also takes care of the error checking):

PyObject *tuple, *list;
+
+tuple = Py_BuildValue("(iis)", 1, 2, "three");
+list = Py_BuildValue("[iis]", 1, 2, "three");
+

It is much more common to use PyObject_SetItem() and friends with items whose references you are only borrowing, like arguments that were passed in to the function you are writing. In that case, their behaviour regarding references is much saner, since you don’t have to take a new reference just so you can give that reference away (“have it be stolen”). For example, this function sets all items of a list (actually, any mutable sequence) to a given item:

int
+set_all(PyObject *target, PyObject *item)
+{
+    Py_ssize_t i, n;
+
+    n = PyObject_Length(target);
+    if (n < 0)
+        return -1;
+    for (i = 0; i < n; i++) {
+        PyObject *index = PyLong_FromSsize_t(i);
+        if (!index)
+            return -1;
+        if (PyObject_SetItem(target, index, item) < 0) {
+            Py_DECREF(index);
+            return -1;
+        }
+        Py_DECREF(index);
+    }
+    return 0;
+}
+

The situation is slightly different for function return values. While passing a reference to most functions does not change your ownership responsibilities for that reference, many functions that return a reference to an object give you ownership of the reference. The reason is simple: in many cases, the returned object is created on the fly, and the reference you get is the only reference to the object. Therefore, the generic functions that return object references, like PyObject_GetItem() and PySequence_GetItem(), always return a new reference (the caller becomes the owner of the reference).

It is important to realize that whether you own a reference returned by a function depends on which function you call only — the plumage (the type of the object passed as an argument to the function) doesn’t enter into it! Thus, if you extract an item from a list using PyList_GetItem(), you don’t own the reference — but if you obtain the same item from the same list using PySequence_GetItem() (which happens to take exactly the same arguments), you do own a reference to the returned object.

Here is an example of how you could write a function that computes the sum of the items in a list of integers; once using PyList_GetItem(), and once using PySequence_GetItem().

long
+sum_list(PyObject *list)
+{
+    Py_ssize_t i, n;
+    long total = 0, value;
+    PyObject *item;
+
+    n = PyList_Size(list);
+    if (n < 0)
+        return -1; /* Not a list */
+    for (i = 0; i < n; i++) {
+        item = PyList_GetItem(list, i); /* Can't fail */
+        if (!PyLong_Check(item)) continue; /* Skip non-integers */
+        value = PyLong_AsLong(item);
+        if (value == -1 && PyErr_Occurred())
+            /* Integer too big to fit in a C long, bail out */
+            return -1;
+        total += value;
+    }
+    return total;
+}
+
long
+sum_sequence(PyObject *sequence)
+{
+    Py_ssize_t i, n;
+    long total = 0, value;
+    PyObject *item;
+    n = PySequence_Length(sequence);
+    if (n < 0)
+        return -1; /* Has no length */
+    for (i = 0; i < n; i++) {
+        item = PySequence_GetItem(sequence, i);
+        if (item == NULL)
+            return -1; /* Not a sequence, or other failure */
+        if (PyLong_Check(item)) {
+            value = PyLong_AsLong(item);
+            Py_DECREF(item);
+            if (value == -1 && PyErr_Occurred())
+                /* Integer too big to fit in a C long, bail out */
+                return -1;
+            total += value;
+        }
+        else {
+            Py_DECREF(item); /* Discard reference ownership */
+        }
+    }
+    return total;
+}
+

Types

There are few other data types that play a significant role in the Python/C API; most are simple C types such as int, long, double and char*. A few structure types are used to describe static tables used to list the functions exported by a module or the data attributes of a new object type, and another is used to describe the value of a complex number. These will be discussed together with the functions that use them.

+type Py_ssize_t
+ Part of the Stable ABI.

A signed integral type such that sizeof(Py_ssize_t) == sizeof(size_t). C99 doesn’t define such a thing directly (size_t is an unsigned integral type). See PEP 353 for details. PY_SSIZE_T_MAX is the largest positive value of type Py_ssize_t.

+

Exceptions

The Python programmer only needs to deal with exceptions if specific error handling is required; unhandled exceptions are automatically propagated to the caller, then to the caller’s caller, and so on, until they reach the top-level interpreter, where they are reported to the user accompanied by a stack traceback.

For C programmers, however, error checking always has to be explicit. All functions in the Python/C API can raise exceptions, unless an explicit claim is made otherwise in a function’s documentation. In general, when a function encounters an error, it sets an exception, discards any object references that it owns, and returns an error indicator. If not documented otherwise, this indicator is either NULL or -1, depending on the function’s return type. A few functions return a Boolean true/false result, with false indicating an error. Very few functions return no explicit error indicator or have an ambiguous return value, and require explicit testing for errors with PyErr_Occurred(). These exceptions are always explicitly documented.

Exception state is maintained in per-thread storage (this is equivalent to using global storage in an unthreaded application). A thread can be in one of two states: an exception has occurred, or not. The function PyErr_Occurred() can be used to check for this: it returns a borrowed reference to the exception type object when an exception has occurred, and NULL otherwise. There are a number of functions to set the exception state: PyErr_SetString() is the most common (though not the most general) function to set the exception state, and PyErr_Clear() clears the exception state.

The full exception state consists of three objects (all of which can be NULL): the exception type, the corresponding exception value, and the traceback. These have the same meanings as the Python result of sys.exc_info(); however, they are not the same: the Python objects represent the last exception being handled by a Python try … except statement, while the C level exception state only exists while an exception is being passed on between C functions until it reaches the Python bytecode interpreter’s main loop, which takes care of transferring it to sys.exc_info() and friends.

Note that starting with Python 1.5, the preferred, thread-safe way to access the exception state from Python code is to call the function sys.exc_info(), which returns the per-thread exception state for Python code. Also, the semantics of both ways to access the exception state have changed so that a function which catches an exception will save and restore its thread’s exception state so as to preserve the exception state of its caller. This prevents common bugs in exception handling code caused by an innocent-looking function overwriting the exception being handled; it also reduces the often unwanted lifetime extension for objects that are referenced by the stack frames in the traceback.

As a general principle, a function that calls another function to perform some task should check whether the called function raised an exception, and if so, pass the exception state on to its caller. It should discard any object references that it owns, and return an error indicator, but it should not set another exception — that would overwrite the exception that was just raised, and lose important information about the exact cause of the error.

A simple example of detecting exceptions and passing them on is shown in the sum_sequence() example above. It so happens that this example doesn’t need to clean up any owned references when it detects an error. The following example function shows some error cleanup. First, to remind you why you like Python, we show the equivalent Python code:

def incr_item(dict, key):
+    try:
+        item = dict[key]
+    except KeyError:
+        item = 0
+    dict[key] = item + 1
+

Here is the corresponding C code, in all its glory:

int
+incr_item(PyObject *dict, PyObject *key)
+{
+    /* Objects all initialized to NULL for Py_XDECREF */
+    PyObject *item = NULL, *const_one = NULL, *incremented_item = NULL;
+    int rv = -1; /* Return value initialized to -1 (failure) */
+
+    item = PyObject_GetItem(dict, key);
+    if (item == NULL) {
+        /* Handle KeyError only: */
+        if (!PyErr_ExceptionMatches(PyExc_KeyError))
+            goto error;
+
+        /* Clear the error and use zero: */
+        PyErr_Clear();
+        item = PyLong_FromLong(0L);
+        if (item == NULL)
+            goto error;
+    }
+    const_one = PyLong_FromLong(1L);
+    if (const_one == NULL)
+        goto error;
+
+    incremented_item = PyNumber_Add(item, const_one);
+    if (incremented_item == NULL)
+        goto error;
+
+    if (PyObject_SetItem(dict, key, incremented_item) < 0)
+        goto error;
+    rv = 0; /* Success */
+    /* Continue with cleanup code */
+
+ error:
+    /* Cleanup code, shared by success and failure path */
+
+    /* Use Py_XDECREF() to ignore NULL references */
+    Py_XDECREF(item);
+    Py_XDECREF(const_one);
+    Py_XDECREF(incremented_item);
+
+    return rv; /* -1 for error, 0 for success */
+}
+

This example represents an endorsed use of the goto statement in C! It illustrates the use of PyErr_ExceptionMatches() and PyErr_Clear() to handle specific exceptions, and the use of Py_XDECREF() to dispose of owned references that may be NULL (note the 'X' in the name; Py_DECREF() would crash when confronted with a NULL reference). It is important that the variables used to hold owned references are initialized to NULL for this to work; likewise, the proposed return value is initialized to -1 (failure) and only set to success after the final call made is successful.

Embedding Python

The one important task that only embedders (as opposed to extension writers) of the Python interpreter have to worry about is the initialization, and possibly the finalization, of the Python interpreter. Most functionality of the interpreter can only be used after the interpreter has been initialized.

The basic initialization function is Py_Initialize(). This initializes the table of loaded modules, and creates the fundamental modules builtins, __main__, and sys. It also initializes the module search path (sys.path).

Py_Initialize() does not set the “script argument list” (sys.argv). If this variable is needed by Python code that will be executed later, setting PyConfig.argv and PyConfig.parse_argv must be set: see Python Initialization Configuration.

On most systems (in particular, on Unix and Windows, although the details are slightly different), Py_Initialize() calculates the module search path based upon its best guess for the location of the standard Python interpreter executable, assuming that the Python library is found in a fixed location relative to the Python interpreter executable. In particular, it looks for a directory named lib/pythonX.Y relative to the parent directory where the executable named python is found on the shell command search path (the environment variable PATH).

For instance, if the Python executable is found in /usr/local/bin/python, it will assume that the libraries are in /usr/local/lib/pythonX.Y. (In fact, this particular path is also the “fallback” location, used when no executable file named python is found along PATH.) The user can override this behavior by setting the environment variable PYTHONHOME, or insert additional directories in front of the standard path by setting PYTHONPATH.

The embedding application can steer the search by calling Py_SetProgramName(file) before calling Py_Initialize(). Note that PYTHONHOME still overrides this and PYTHONPATH is still inserted in front of the standard path. An application that requires total control has to provide its own implementation of Py_GetPath(), Py_GetPrefix(), Py_GetExecPrefix(), and Py_GetProgramFullPath() (all defined in Modules/getpath.c).

Sometimes, it is desirable to “uninitialize” Python. For instance, the application may want to start over (make another call to Py_Initialize()) or the application is simply done with its use of Python and wants to free memory allocated by Python. This can be accomplished by calling Py_FinalizeEx(). The function Py_IsInitialized() returns true if Python is currently in the initialized state. More information about these functions is given in a later chapter. Notice that Py_FinalizeEx() does not free all memory allocated by the Python interpreter, e.g. memory allocated by extension modules currently cannot be released.

Debugging Builds

Python can be built with several macros to enable extra checks of the interpreter and extension modules. These checks tend to add a large amount of overhead to the runtime so they are not enabled by default.

A full list of the various types of debugging builds is in the file Misc/SpecialBuilds.txt in the Python source distribution. Builds are available that support tracing of reference counts, debugging the memory allocator, or low-level profiling of the main interpreter loop. Only the most frequently used builds will be described in the remainder of this section.

Compiling the interpreter with the Py_DEBUG macro defined produces what is generally meant by a debug build of Python. Py_DEBUG is enabled in the Unix build by adding --with-pydebug to the ./configure command. It is also implied by the presence of the not-Python-specific _DEBUG macro. When Py_DEBUG is enabled in the Unix build, compiler optimization is disabled.

In addition to the reference count debugging described below, extra checks are performed, see Python Debug Build.

Defining Py_TRACE_REFS enables reference tracing (see the configure --with-trace-refs option). When defined, a circular doubly linked list of active objects is maintained by adding two extra fields to every PyObject. Total allocations are tracked as well. Upon exit, all existing references are printed. (In interactive mode this happens after every statement run by the interpreter.)

Please refer to Misc/SpecialBuilds.txt in the Python source distribution for more detailed information.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/intro.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fiter.html b/devdocs/python~3.12/c-api%2Fiter.html new file mode 100644 index 00000000..9029c472 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fiter.html @@ -0,0 +1,46 @@ +

Iterator Protocol

There are two functions specifically for working with iterators.

+int PyIter_Check(PyObject *o)
+ Part of the Stable ABI since version 3.8.

Return non-zero if the object o can be safely passed to PyIter_Next(), and 0 otherwise. This function always succeeds.

+
+int PyAIter_Check(PyObject *o)
+ Part of the Stable ABI since version 3.10.

Return non-zero if the object o provides the AsyncIterator protocol, and 0 otherwise. This function always succeeds.

New in version 3.10.

+
+PyObject *PyIter_Next(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Return the next value from the iterator o. The object must be an iterator according to PyIter_Check() (it is up to the caller to check this). If there are no remaining values, returns NULL with no exception set. If an error occurs while retrieving the item, returns NULL and passes along the exception.

+

To write a loop which iterates over an iterator, the C code should look something like this:

PyObject *iterator = PyObject_GetIter(obj);
+PyObject *item;
+
+if (iterator == NULL) {
+    /* propagate error */
+}
+
+while ((item = PyIter_Next(iterator))) {
+    /* do something with item */
+    ...
+    /* release reference when done */
+    Py_DECREF(item);
+}
+
+Py_DECREF(iterator);
+
+if (PyErr_Occurred()) {
+    /* propagate error */
+}
+else {
+    /* continue doing useful work */
+}
+
+type PySendResult
+

The enum value used to represent different results of PyIter_Send().

New in version 3.10.

+
+PySendResult PyIter_Send(PyObject *iter, PyObject *arg, PyObject **presult)
+ Part of the Stable ABI since version 3.10.

Sends the arg value into the iterator iter. Returns:

  • +PYGEN_RETURN if iterator returns. Return value is returned via presult.
  • +PYGEN_NEXT if iterator yields. Yielded value is returned via presult.
  • +PYGEN_ERROR if iterator has raised and exception. presult is set to NULL.

New in version 3.10.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/iter.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fiterator.html b/devdocs/python~3.12/c-api%2Fiterator.html new file mode 100644 index 00000000..abe6b2e0 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fiterator.html @@ -0,0 +1,24 @@ +

Iterator Objects

Python provides two general-purpose iterator objects. The first, a sequence iterator, works with an arbitrary sequence supporting the __getitem__() method. The second works with a callable object and a sentinel value, calling the callable for each item in the sequence, and ending the iteration when the sentinel value is returned.

+PyTypeObject PySeqIter_Type
+ Part of the Stable ABI.

Type object for iterator objects returned by PySeqIter_New() and the one-argument form of the iter() built-in function for built-in sequence types.

+
+int PySeqIter_Check(PyObject *op)
+

Return true if the type of op is PySeqIter_Type. This function always succeeds.

+
+PyObject *PySeqIter_New(PyObject *seq)
+Return value: New reference. Part of the Stable ABI.

Return an iterator that works with a general sequence object, seq. The iteration ends when the sequence raises IndexError for the subscripting operation.

+
+PyTypeObject PyCallIter_Type
+ Part of the Stable ABI.

Type object for iterator objects returned by PyCallIter_New() and the two-argument form of the iter() built-in function.

+
+int PyCallIter_Check(PyObject *op)
+

Return true if the type of op is PyCallIter_Type. This function always succeeds.

+
+PyObject *PyCallIter_New(PyObject *callable, PyObject *sentinel)
+Return value: New reference. Part of the Stable ABI.

Return a new iterator. The first parameter, callable, can be any Python callable object that can be called with no parameters; each call to it should return the next item in the iteration. When callable returns a value equal to sentinel, the iteration will be terminated.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/iterator.html +

+
diff --git a/devdocs/python~3.12/c-api%2Flist.html b/devdocs/python~3.12/c-api%2Flist.html new file mode 100644 index 00000000..5d15a3f5 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Flist.html @@ -0,0 +1,60 @@ +

List Objects

+type PyListObject
+

This subtype of PyObject represents a Python list object.

+
+PyTypeObject PyList_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python list type. This is the same object as list in the Python layer.

+
+int PyList_Check(PyObject *p)
+

Return true if p is a list object or an instance of a subtype of the list type. This function always succeeds.

+
+int PyList_CheckExact(PyObject *p)
+

Return true if p is a list object, but not an instance of a subtype of the list type. This function always succeeds.

+
+PyObject *PyList_New(Py_ssize_t len)
+Return value: New reference. Part of the Stable ABI.

Return a new list of length len on success, or NULL on failure.

Note

If len is greater than zero, the returned list object’s items are set to NULL. Thus you cannot use abstract API functions such as PySequence_SetItem() or expose the object to Python code before setting all items to a real object with PyList_SetItem().

+
+Py_ssize_t PyList_Size(PyObject *list)
+ Part of the Stable ABI.

Return the length of the list object in list; this is equivalent to len(list) on a list object.

+
+Py_ssize_t PyList_GET_SIZE(PyObject *list)
+

Similar to PyList_Size(), but without error checking.

+
+PyObject *PyList_GetItem(PyObject *list, Py_ssize_t index)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the object at position index in the list pointed to by list. The position must be non-negative; indexing from the end of the list is not supported. If index is out of bounds (<0 or >=len(list)), return NULL and set an IndexError exception.

+
+PyObject *PyList_GET_ITEM(PyObject *list, Py_ssize_t i)
+Return value: Borrowed reference.

Similar to PyList_GetItem(), but without error checking.

+
+int PyList_SetItem(PyObject *list, Py_ssize_t index, PyObject *item)
+ Part of the Stable ABI.

Set the item at index index in list to item. Return 0 on success. If index is out of bounds, return -1 and set an IndexError exception.

Note

This function “steals” a reference to item and discards a reference to an item already in the list at the affected position.

+
+void PyList_SET_ITEM(PyObject *list, Py_ssize_t i, PyObject *o)
+

Macro form of PyList_SetItem() without error checking. This is normally only used to fill in new lists where there is no previous content.

Note

This macro “steals” a reference to item, and, unlike PyList_SetItem(), does not discard a reference to any item that is being replaced; any reference in list at position i will be leaked.

+
+int PyList_Insert(PyObject *list, Py_ssize_t index, PyObject *item)
+ Part of the Stable ABI.

Insert the item item into list list in front of index index. Return 0 if successful; return -1 and set an exception if unsuccessful. Analogous to list.insert(index, item).

+
+int PyList_Append(PyObject *list, PyObject *item)
+ Part of the Stable ABI.

Append the object item at the end of list list. Return 0 if successful; return -1 and set an exception if unsuccessful. Analogous to list.append(item).

+
+PyObject *PyList_GetSlice(PyObject *list, Py_ssize_t low, Py_ssize_t high)
+Return value: New reference. Part of the Stable ABI.

Return a list of the objects in list containing the objects between low and high. Return NULL and set an exception if unsuccessful. Analogous to list[low:high]. Indexing from the end of the list is not supported.

+
+int PyList_SetSlice(PyObject *list, Py_ssize_t low, Py_ssize_t high, PyObject *itemlist)
+ Part of the Stable ABI.

Set the slice of list between low and high to the contents of itemlist. Analogous to list[low:high] = itemlist. The itemlist may be NULL, indicating the assignment of an empty list (slice deletion). Return 0 on success, -1 on failure. Indexing from the end of the list is not supported.

+
+int PyList_Sort(PyObject *list)
+ Part of the Stable ABI.

Sort the items of list in place. Return 0 on success, -1 on failure. This is equivalent to list.sort().

+
+int PyList_Reverse(PyObject *list)
+ Part of the Stable ABI.

Reverse the items of list in place. Return 0 on success, -1 on failure. This is the equivalent of list.reverse().

+
+PyObject *PyList_AsTuple(PyObject *list)
+Return value: New reference. Part of the Stable ABI.

Return a new tuple object containing the contents of list; equivalent to tuple(list).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/list.html +

+
diff --git a/devdocs/python~3.12/c-api%2Flong.html b/devdocs/python~3.12/c-api%2Flong.html new file mode 100644 index 00000000..2619e512 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Flong.html @@ -0,0 +1,92 @@ +

Integer Objects

All integers are implemented as “long” integer objects of arbitrary size.

On error, most PyLong_As* APIs return (return type)-1 which cannot be distinguished from a number. Use PyErr_Occurred() to disambiguate.

+type PyLongObject
+ Part of the Limited API (as an opaque struct).

This subtype of PyObject represents a Python integer object.

+
+PyTypeObject PyLong_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python integer type. This is the same object as int in the Python layer.

+
+int PyLong_Check(PyObject *p)
+

Return true if its argument is a PyLongObject or a subtype of PyLongObject. This function always succeeds.

+
+int PyLong_CheckExact(PyObject *p)
+

Return true if its argument is a PyLongObject, but not a subtype of PyLongObject. This function always succeeds.

+
+PyObject *PyLong_FromLong(long v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from v, or NULL on failure.

The current implementation keeps an array of integer objects for all integers between -5 and 256. When you create an int in that range you actually just get back a reference to the existing object.

+
+PyObject *PyLong_FromUnsignedLong(unsigned long v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from a C unsigned long, or NULL on failure.

+
+PyObject *PyLong_FromSsize_t(Py_ssize_t v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from a C Py_ssize_t, or NULL on failure.

+
+PyObject *PyLong_FromSize_t(size_t v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from a C size_t, or NULL on failure.

+
+PyObject *PyLong_FromLongLong(long long v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from a C long long, or NULL on failure.

+
+PyObject *PyLong_FromUnsignedLongLong(unsigned long long v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from a C unsigned long long, or NULL on failure.

+
+PyObject *PyLong_FromDouble(double v)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject object from the integer part of v, or NULL on failure.

+
+PyObject *PyLong_FromString(const char *str, char **pend, int base)
+Return value: New reference. Part of the Stable ABI.

Return a new PyLongObject based on the string value in str, which is interpreted according to the radix in base, or NULL on failure. If pend is non-NULL, *pend will point to the end of str on success or to the first character that could not be processed on error. If base is 0, str is interpreted using the Integer literals definition; in this case, leading zeros in a non-zero decimal number raises a ValueError. If base is not 0, it must be between 2 and 36, inclusive. Leading and trailing whitespace and single underscores after a base specifier and between digits are ignored. If there are no digits or str is not NULL-terminated following the digits and trailing whitespace, ValueError will be raised.

See also

Python methods int.to_bytes() and int.from_bytes() to convert a PyLongObject to/from an array of bytes in base 256. You can call those from C using PyObject_CallMethod().

+
+PyObject *PyLong_FromUnicodeObject(PyObject *u, int base)
+Return value: New reference.

Convert a sequence of Unicode digits in the string u to a Python integer value.

New in version 3.3.

+
+PyObject *PyLong_FromVoidPtr(void *p)
+Return value: New reference. Part of the Stable ABI.

Create a Python integer from the pointer p. The pointer value can be retrieved from the resulting value using PyLong_AsVoidPtr().

+
+long PyLong_AsLong(PyObject *obj)
+ Part of the Stable ABI.

Return a C long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

Raise OverflowError if the value of obj is out of range for a long.

Returns -1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+long PyLong_AsLongAndOverflow(PyObject *obj, int *overflow)
+ Part of the Stable ABI.

Return a C long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

If the value of obj is greater than LONG_MAX or less than LONG_MIN, set *overflow to 1 or -1, respectively, and return -1; otherwise, set *overflow to 0. If any other exception occurs set *overflow to 0 and return -1 as usual.

Returns -1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+long long PyLong_AsLongLong(PyObject *obj)
+ Part of the Stable ABI.

Return a C long long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

Raise OverflowError if the value of obj is out of range for a long long.

Returns -1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+long long PyLong_AsLongLongAndOverflow(PyObject *obj, int *overflow)
+ Part of the Stable ABI.

Return a C long long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

If the value of obj is greater than LLONG_MAX or less than LLONG_MIN, set *overflow to 1 or -1, respectively, and return -1; otherwise, set *overflow to 0. If any other exception occurs set *overflow to 0 and return -1 as usual.

Returns -1 on error. Use PyErr_Occurred() to disambiguate.

New in version 3.2.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+Py_ssize_t PyLong_AsSsize_t(PyObject *pylong)
+ Part of the Stable ABI.

Return a C Py_ssize_t representation of pylong. pylong must be an instance of PyLongObject.

Raise OverflowError if the value of pylong is out of range for a Py_ssize_t.

Returns -1 on error. Use PyErr_Occurred() to disambiguate.

+
+unsigned long PyLong_AsUnsignedLong(PyObject *pylong)
+ Part of the Stable ABI.

Return a C unsigned long representation of pylong. pylong must be an instance of PyLongObject.

Raise OverflowError if the value of pylong is out of range for a unsigned long.

Returns (unsigned long)-1 on error. Use PyErr_Occurred() to disambiguate.

+
+size_t PyLong_AsSize_t(PyObject *pylong)
+ Part of the Stable ABI.

Return a C size_t representation of pylong. pylong must be an instance of PyLongObject.

Raise OverflowError if the value of pylong is out of range for a size_t.

Returns (size_t)-1 on error. Use PyErr_Occurred() to disambiguate.

+
+unsigned long long PyLong_AsUnsignedLongLong(PyObject *pylong)
+ Part of the Stable ABI.

Return a C unsigned long long representation of pylong. pylong must be an instance of PyLongObject.

Raise OverflowError if the value of pylong is out of range for an unsigned long long.

Returns (unsigned long long)-1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.1: A negative pylong now raises OverflowError, not TypeError.

+
+unsigned long PyLong_AsUnsignedLongMask(PyObject *obj)
+ Part of the Stable ABI.

Return a C unsigned long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

If the value of obj is out of range for an unsigned long, return the reduction of that value modulo ULONG_MAX + 1.

Returns (unsigned long)-1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+unsigned long long PyLong_AsUnsignedLongLongMask(PyObject *obj)
+ Part of the Stable ABI.

Return a C unsigned long long representation of obj. If obj is not an instance of PyLongObject, first call its __index__() method (if present) to convert it to a PyLongObject.

If the value of obj is out of range for an unsigned long long, return the reduction of that value modulo ULLONG_MAX + 1.

Returns (unsigned long long)-1 on error. Use PyErr_Occurred() to disambiguate.

Changed in version 3.8: Use __index__() if available.

Changed in version 3.10: This function will no longer use __int__().

+
+double PyLong_AsDouble(PyObject *pylong)
+ Part of the Stable ABI.

Return a C double representation of pylong. pylong must be an instance of PyLongObject.

Raise OverflowError if the value of pylong is out of range for a double.

Returns -1.0 on error. Use PyErr_Occurred() to disambiguate.

+
+void *PyLong_AsVoidPtr(PyObject *pylong)
+ Part of the Stable ABI.

Convert a Python integer pylong to a C void pointer. If pylong cannot be converted, an OverflowError will be raised. This is only assured to produce a usable void pointer for values created with PyLong_FromVoidPtr().

Returns NULL on error. Use PyErr_Occurred() to disambiguate.

+
+int PyUnstable_Long_IsCompact(const PyLongObject *op)
+
This is Unstable API. It may change without warning in minor releases. +

Return 1 if op is compact, 0 otherwise.

This function makes it possible for performance-critical code to implement a “fast path” for small integers. For compact values use PyUnstable_Long_CompactValue(); for others fall back to a PyLong_As* function or calling int.to_bytes().

The speedup is expected to be negligible for most users.

Exactly what values are considered compact is an implementation detail and is subject to change.

+
+Py_ssize_t PyUnstable_Long_CompactValue(const PyLongObject *op)
+
This is Unstable API. It may change without warning in minor releases. +

If op is compact, as determined by PyUnstable_Long_IsCompact(), return its value.

Otherwise, the return value is undefined.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/long.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmapping.html b/devdocs/python~3.12/c-api%2Fmapping.html new file mode 100644 index 00000000..62fa658f --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmapping.html @@ -0,0 +1,40 @@ +

Mapping Protocol

See also PyObject_GetItem(), PyObject_SetItem() and PyObject_DelItem().

+int PyMapping_Check(PyObject *o)
+ Part of the Stable ABI.

Return 1 if the object provides the mapping protocol or supports slicing, and 0 otherwise. Note that it returns 1 for Python classes with a __getitem__() method, since in general it is impossible to determine what type of keys the class supports. This function always succeeds.

+
+Py_ssize_t PyMapping_Size(PyObject *o)
+Py_ssize_t PyMapping_Length(PyObject *o)
+ Part of the Stable ABI.

Returns the number of keys in object o on success, and -1 on failure. This is equivalent to the Python expression len(o).

+
+PyObject *PyMapping_GetItemString(PyObject *o, const char *key)
+Return value: New reference. Part of the Stable ABI.

This is the same as PyObject_GetItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+int PyMapping_SetItemString(PyObject *o, const char *key, PyObject *v)
+ Part of the Stable ABI.

This is the same as PyObject_SetItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+int PyMapping_DelItem(PyObject *o, PyObject *key)
+

This is an alias of PyObject_DelItem().

+
+int PyMapping_DelItemString(PyObject *o, const char *key)
+

This is the same as PyObject_DelItem(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+int PyMapping_HasKey(PyObject *o, PyObject *key)
+ Part of the Stable ABI.

Return 1 if the mapping object has the key key and 0 otherwise. This is equivalent to the Python expression key in o. This function always succeeds.

Note

Exceptions which occur when this calls __getitem__() method are silently ignored. For proper error handling, use PyObject_GetItem() instead.

+
+int PyMapping_HasKeyString(PyObject *o, const char *key)
+ Part of the Stable ABI.

This is the same as PyMapping_HasKey(), but key is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

Note

Exceptions that occur when this calls __getitem__() method or while creating the temporary str object are silently ignored. For proper error handling, use PyMapping_GetItemString() instead.

+
+PyObject *PyMapping_Keys(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

On success, return a list of the keys in object o. On failure, return NULL.

Changed in version 3.7: Previously, the function returned a list or a tuple.

+
+PyObject *PyMapping_Values(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

On success, return a list of the values in object o. On failure, return NULL.

Changed in version 3.7: Previously, the function returned a list or a tuple.

+
+PyObject *PyMapping_Items(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

On success, return a list of the items in object o, where each item is a tuple containing a key-value pair. On failure, return NULL.

Changed in version 3.7: Previously, the function returned a list or a tuple.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/mapping.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmarshal.html b/devdocs/python~3.12/c-api%2Fmarshal.html new file mode 100644 index 00000000..c7ae2158 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmarshal.html @@ -0,0 +1,30 @@ +

Data marshalling support

These routines allow C code to work with serialized objects using the same data format as the marshal module. There are functions to write data into the serialization format, and additional functions that can be used to read the data back. Files used to store marshalled data must be opened in binary mode.

Numeric values are stored with the least significant byte first.

The module supports two versions of the data format: version 0 is the historical version, version 1 shares interned strings in the file, and upon unmarshalling. Version 2 uses a binary format for floating point numbers. Py_MARSHAL_VERSION indicates the current file format (currently 2).

+void PyMarshal_WriteLongToFile(long value, FILE *file, int version)
+

Marshal a long integer, value, to file. This will only write the least-significant 32 bits of value; regardless of the size of the native long type. version indicates the file format.

This function can fail, in which case it sets the error indicator. Use PyErr_Occurred() to check for that.

+
+void PyMarshal_WriteObjectToFile(PyObject *value, FILE *file, int version)
+

Marshal a Python object, value, to file. version indicates the file format.

This function can fail, in which case it sets the error indicator. Use PyErr_Occurred() to check for that.

+
+PyObject *PyMarshal_WriteObjectToString(PyObject *value, int version)
+Return value: New reference.

Return a bytes object containing the marshalled representation of value. version indicates the file format.

+

The following functions allow marshalled values to be read back in.

+long PyMarshal_ReadLongFromFile(FILE *file)
+

Return a C long from the data stream in a FILE* opened for reading. Only a 32-bit value can be read in using this function, regardless of the native size of long.

On error, sets the appropriate exception (EOFError) and returns -1.

+
+int PyMarshal_ReadShortFromFile(FILE *file)
+

Return a C short from the data stream in a FILE* opened for reading. Only a 16-bit value can be read in using this function, regardless of the native size of short.

On error, sets the appropriate exception (EOFError) and returns -1.

+
+PyObject *PyMarshal_ReadObjectFromFile(FILE *file)
+Return value: New reference.

Return a Python object from the data stream in a FILE* opened for reading.

On error, sets the appropriate exception (EOFError, ValueError or TypeError) and returns NULL.

+
+PyObject *PyMarshal_ReadLastObjectFromFile(FILE *file)
+Return value: New reference.

Return a Python object from the data stream in a FILE* opened for reading. Unlike PyMarshal_ReadObjectFromFile(), this function assumes that no further objects will be read from the file, allowing it to aggressively load file data into memory so that the de-serialization can operate from data in memory rather than reading a byte at a time from the file. Only use these variant if you are certain that you won’t be reading anything else from the file.

On error, sets the appropriate exception (EOFError, ValueError or TypeError) and returns NULL.

+
+PyObject *PyMarshal_ReadObjectFromString(const char *data, Py_ssize_t len)
+Return value: New reference.

Return a Python object from the data stream in a byte buffer containing len bytes pointed to by data.

On error, sets the appropriate exception (EOFError, ValueError or TypeError) and returns NULL.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/marshal.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmemory.html b/devdocs/python~3.12/c-api%2Fmemory.html new file mode 100644 index 00000000..436537ab --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmemory.html @@ -0,0 +1,167 @@ +

Memory Management

Overview

Memory management in Python involves a private heap containing all Python objects and data structures. The management of this private heap is ensured internally by the Python memory manager. The Python memory manager has different components which deal with various dynamic storage management aspects, like sharing, segmentation, preallocation or caching.

At the lowest level, a raw memory allocator ensures that there is enough room in the private heap for storing all Python-related data by interacting with the memory manager of the operating system. On top of the raw memory allocator, several object-specific allocators operate on the same heap and implement distinct memory management policies adapted to the peculiarities of every object type. For example, integer objects are managed differently within the heap than strings, tuples or dictionaries because integers imply different storage requirements and speed/space tradeoffs. The Python memory manager thus delegates some of the work to the object-specific allocators, but ensures that the latter operate within the bounds of the private heap.

It is important to understand that the management of the Python heap is performed by the interpreter itself and that the user has no control over it, even if they regularly manipulate object pointers to memory blocks inside that heap. The allocation of heap space for Python objects and other internal buffers is performed on demand by the Python memory manager through the Python/C API functions listed in this document.

To avoid memory corruption, extension writers should never try to operate on Python objects with the functions exported by the C library: malloc(), calloc(), realloc() and free(). This will result in mixed calls between the C allocator and the Python memory manager with fatal consequences, because they implement different algorithms and operate on different heaps. However, one may safely allocate and release memory blocks with the C library allocator for individual purposes, as shown in the following example:

PyObject *res;
+char *buf = (char *) malloc(BUFSIZ); /* for I/O */
+
+if (buf == NULL)
+    return PyErr_NoMemory();
+...Do some I/O operation involving buf...
+res = PyBytes_FromString(buf);
+free(buf); /* malloc'ed */
+return res;
+

In this example, the memory request for the I/O buffer is handled by the C library allocator. The Python memory manager is involved only in the allocation of the bytes object returned as a result.

In most situations, however, it is recommended to allocate memory from the Python heap specifically because the latter is under control of the Python memory manager. For example, this is required when the interpreter is extended with new object types written in C. Another reason for using the Python heap is the desire to inform the Python memory manager about the memory needs of the extension module. Even when the requested memory is used exclusively for internal, highly specific purposes, delegating all memory requests to the Python memory manager causes the interpreter to have a more accurate image of its memory footprint as a whole. Consequently, under certain circumstances, the Python memory manager may or may not trigger appropriate actions, like garbage collection, memory compaction or other preventive procedures. Note that by using the C library allocator as shown in the previous example, the allocated memory for the I/O buffer escapes completely the Python memory manager.

See also

The PYTHONMALLOC environment variable can be used to configure the memory allocators used by Python.

The PYTHONMALLOCSTATS environment variable can be used to print statistics of the pymalloc memory allocator every time a new pymalloc object arena is created, and on shutdown.

Allocator Domains

All allocating functions belong to one of three different “domains” (see also PyMemAllocatorDomain). These domains represent different allocation strategies and are optimized for different purposes. The specific details on how every domain allocates memory or what internal functions each domain calls is considered an implementation detail, but for debugging purposes a simplified table can be found at here. There is no hard requirement to use the memory returned by the allocation functions belonging to a given domain for only the purposes hinted by that domain (although this is the recommended practice). For example, one could use the memory returned by PyMem_RawMalloc() for allocating Python objects or the memory returned by PyObject_Malloc() for allocating memory for buffers.

The three allocation domains are:

When freeing memory previously allocated by the allocating functions belonging to a given domain,the matching specific deallocating functions must be used. For example, PyMem_Free() must be used to free memory allocated using PyMem_Malloc().

Raw Memory Interface

The following function sets are wrappers to the system allocator. These functions are thread-safe, the GIL does not need to be held.

The default raw memory allocator uses the following functions: malloc(), calloc(), realloc() and free(); call malloc(1) (or calloc(1, 1)) when requesting zero bytes.

New in version 3.4.

+void *PyMem_RawMalloc(size_t n)
+

Allocates n bytes and returns a pointer of type void* to the allocated memory, or NULL if the request fails.

Requesting zero bytes returns a distinct non-NULL pointer if possible, as if PyMem_RawMalloc(1) had been called instead. The memory will not have been initialized in any way.

+
+void *PyMem_RawCalloc(size_t nelem, size_t elsize)
+

Allocates nelem elements each whose size in bytes is elsize and returns a pointer of type void* to the allocated memory, or NULL if the request fails. The memory is initialized to zeros.

Requesting zero elements or elements of size zero bytes returns a distinct non-NULL pointer if possible, as if PyMem_RawCalloc(1, 1) had been called instead.

New in version 3.5.

+
+void *PyMem_RawRealloc(void *p, size_t n)
+

Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.

If p is NULL, the call is equivalent to PyMem_RawMalloc(n); else if n is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-NULL.

Unless p is NULL, it must have been returned by a previous call to PyMem_RawMalloc(), PyMem_RawRealloc() or PyMem_RawCalloc().

If the request fails, PyMem_RawRealloc() returns NULL and p remains a valid pointer to the previous memory area.

+
+void PyMem_RawFree(void *p)
+

Frees the memory block pointed to by p, which must have been returned by a previous call to PyMem_RawMalloc(), PyMem_RawRealloc() or PyMem_RawCalloc(). Otherwise, or if PyMem_RawFree(p) has been called before, undefined behavior occurs.

If p is NULL, no operation is performed.

+

Memory Interface

The following function sets, modeled after the ANSI C standard, but specifying behavior when requesting zero bytes, are available for allocating and releasing memory from the Python heap.

The default memory allocator uses the pymalloc memory allocator.

Warning

The GIL must be held when using these functions.

Changed in version 3.6: The default allocator is now pymalloc instead of system malloc().

+void *PyMem_Malloc(size_t n)
+ Part of the Stable ABI.

Allocates n bytes and returns a pointer of type void* to the allocated memory, or NULL if the request fails.

Requesting zero bytes returns a distinct non-NULL pointer if possible, as if PyMem_Malloc(1) had been called instead. The memory will not have been initialized in any way.

+
+void *PyMem_Calloc(size_t nelem, size_t elsize)
+ Part of the Stable ABI since version 3.7.

Allocates nelem elements each whose size in bytes is elsize and returns a pointer of type void* to the allocated memory, or NULL if the request fails. The memory is initialized to zeros.

Requesting zero elements or elements of size zero bytes returns a distinct non-NULL pointer if possible, as if PyMem_Calloc(1, 1) had been called instead.

New in version 3.5.

+
+void *PyMem_Realloc(void *p, size_t n)
+ Part of the Stable ABI.

Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.

If p is NULL, the call is equivalent to PyMem_Malloc(n); else if n is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-NULL.

Unless p is NULL, it must have been returned by a previous call to PyMem_Malloc(), PyMem_Realloc() or PyMem_Calloc().

If the request fails, PyMem_Realloc() returns NULL and p remains a valid pointer to the previous memory area.

+
+void PyMem_Free(void *p)
+ Part of the Stable ABI.

Frees the memory block pointed to by p, which must have been returned by a previous call to PyMem_Malloc(), PyMem_Realloc() or PyMem_Calloc(). Otherwise, or if PyMem_Free(p) has been called before, undefined behavior occurs.

If p is NULL, no operation is performed.

+

The following type-oriented macros are provided for convenience. Note that TYPE refers to any C type.

+PyMem_New(TYPE, n)
+

Same as PyMem_Malloc(), but allocates (n * sizeof(TYPE)) bytes of memory. Returns a pointer cast to TYPE*. The memory will not have been initialized in any way.

+
+PyMem_Resize(p, TYPE, n)
+

Same as PyMem_Realloc(), but the memory block is resized to (n * +sizeof(TYPE)) bytes. Returns a pointer cast to TYPE*. On return, p will be a pointer to the new memory area, or NULL in the event of failure.

This is a C preprocessor macro; p is always reassigned. Save the original value of p to avoid losing memory when handling errors.

+
+void PyMem_Del(void *p)
+

Same as PyMem_Free().

+

In addition, the following macro sets are provided for calling the Python memory allocator directly, without involving the C API functions listed above. However, note that their use does not preserve binary compatibility across Python versions and is therefore deprecated in extension modules.

Object allocators

The following function sets, modeled after the ANSI C standard, but specifying behavior when requesting zero bytes, are available for allocating and releasing memory from the Python heap.

Note

There is no guarantee that the memory returned by these allocators can be successfully cast to a Python object when intercepting the allocating functions in this domain by the methods described in the Customize Memory Allocators section.

The default object allocator uses the pymalloc memory allocator.

Warning

The GIL must be held when using these functions.

+void *PyObject_Malloc(size_t n)
+ Part of the Stable ABI.

Allocates n bytes and returns a pointer of type void* to the allocated memory, or NULL if the request fails.

Requesting zero bytes returns a distinct non-NULL pointer if possible, as if PyObject_Malloc(1) had been called instead. The memory will not have been initialized in any way.

+
+void *PyObject_Calloc(size_t nelem, size_t elsize)
+ Part of the Stable ABI since version 3.7.

Allocates nelem elements each whose size in bytes is elsize and returns a pointer of type void* to the allocated memory, or NULL if the request fails. The memory is initialized to zeros.

Requesting zero elements or elements of size zero bytes returns a distinct non-NULL pointer if possible, as if PyObject_Calloc(1, 1) had been called instead.

New in version 3.5.

+
+void *PyObject_Realloc(void *p, size_t n)
+ Part of the Stable ABI.

Resizes the memory block pointed to by p to n bytes. The contents will be unchanged to the minimum of the old and the new sizes.

If p is NULL, the call is equivalent to PyObject_Malloc(n); else if n is equal to zero, the memory block is resized but is not freed, and the returned pointer is non-NULL.

Unless p is NULL, it must have been returned by a previous call to PyObject_Malloc(), PyObject_Realloc() or PyObject_Calloc().

If the request fails, PyObject_Realloc() returns NULL and p remains a valid pointer to the previous memory area.

+
+void PyObject_Free(void *p)
+ Part of the Stable ABI.

Frees the memory block pointed to by p, which must have been returned by a previous call to PyObject_Malloc(), PyObject_Realloc() or PyObject_Calloc(). Otherwise, or if PyObject_Free(p) has been called before, undefined behavior occurs.

If p is NULL, no operation is performed.

+

Default Memory Allocators

Default memory allocators:

+ + + + +

Configuration

Name

PyMem_RawMalloc

PyMem_Malloc

PyObject_Malloc

Release build

"pymalloc"

malloc

pymalloc

pymalloc

Debug build

"pymalloc_debug"

malloc + debug

pymalloc + debug

pymalloc + debug

Release build, without pymalloc

"malloc"

malloc

malloc

malloc

Debug build, without pymalloc

"malloc_debug"

malloc + debug

malloc + debug

malloc + debug

Legend:

Customize Memory Allocators

New in version 3.4.

+type PyMemAllocatorEx
+

Structure used to describe a memory block allocator. The structure has the following fields:

+ + + + + +

Field

Meaning

void *ctx

user context passed as first argument

void* malloc(void *ctx, size_t size)

allocate a memory block

void* calloc(void *ctx, size_t nelem, size_t elsize)

allocate a memory block initialized with zeros

void* realloc(void *ctx, void *ptr, size_t new_size)

allocate or resize a memory block

void free(void *ctx, void *ptr)

free a memory block

Changed in version 3.5: The PyMemAllocator structure was renamed to PyMemAllocatorEx and a new calloc field was added.

+
+type PyMemAllocatorDomain
+

Enum used to identify an allocator domain. Domains:

+PYMEM_DOMAIN_RAW
+

Functions:

+
+PYMEM_DOMAIN_MEM
+

Functions:

+
+PYMEM_DOMAIN_OBJ
+

Functions:

+
+
+void PyMem_GetAllocator(PyMemAllocatorDomain domain, PyMemAllocatorEx *allocator)
+

Get the memory block allocator of the specified domain.

+
+void PyMem_SetAllocator(PyMemAllocatorDomain domain, PyMemAllocatorEx *allocator)
+

Set the memory block allocator of the specified domain.

The new allocator must return a distinct non-NULL pointer when requesting zero bytes.

For the PYMEM_DOMAIN_RAW domain, the allocator must be thread-safe: the GIL is not held when the allocator is called.

For the remaining domains, the allocator must also be thread-safe: the allocator may be called in different interpreters that do not share a GIL.

If the new allocator is not a hook (does not call the previous allocator), the PyMem_SetupDebugHooks() function must be called to reinstall the debug hooks on top on the new allocator.

See also PyPreConfig.allocator and Preinitialize Python with PyPreConfig.

Warning

PyMem_SetAllocator() does have the following contract:

  • It can be called after Py_PreInitialize() and before Py_InitializeFromConfig() to install a custom memory allocator. There are no restrictions over the installed allocator other than the ones imposed by the domain (for instance, the Raw Domain allows the allocator to be called without the GIL held). See the section on allocator domains for more information.
  • If called after Python has finish initializing (after Py_InitializeFromConfig() has been called) the allocator must wrap the existing allocator. Substituting the current allocator for some other arbitrary one is not supported.

Changed in version 3.12: All allocators must be thread-safe.

+
+void PyMem_SetupDebugHooks(void)
+

Setup debug hooks in the Python memory allocators to detect memory errors.

+

Debug hooks on the Python memory allocators

When Python is built in debug mode, the PyMem_SetupDebugHooks() function is called at the Python preinitialization to setup debug hooks on Python memory allocators to detect memory errors.

The PYTHONMALLOC environment variable can be used to install debug hooks on a Python compiled in release mode (ex: PYTHONMALLOC=debug).

The PyMem_SetupDebugHooks() function can be used to set debug hooks after calling PyMem_SetAllocator().

These debug hooks fill dynamically allocated memory blocks with special, recognizable bit patterns. Newly allocated memory is filled with the byte 0xCD (PYMEM_CLEANBYTE), freed memory is filled with the byte 0xDD (PYMEM_DEADBYTE). Memory blocks are surrounded by “forbidden bytes” filled with the byte 0xFD (PYMEM_FORBIDDENBYTE). Strings of these bytes are unlikely to be valid addresses, floats, or ASCII strings.

Runtime checks:

On error, the debug hooks use the tracemalloc module to get the traceback where a memory block was allocated. The traceback is only displayed if tracemalloc is tracing Python memory allocations and the memory block was traced.

Let S = sizeof(size_t). 2*S bytes are added at each end of each block of N bytes requested. The memory layout is like so, where p represents the address returned by a malloc-like or realloc-like function (p[i:j] means the slice of bytes from *(p+i) inclusive up to *(p+j) exclusive; note that the treatment of negative indices differs from a Python slice):

+p[-2*S:-S]
+
+

Number of bytes originally asked for. This is a size_t, big-endian (easier to read in a memory dump).

+p[-S]
+
+

API identifier (ASCII character):

+p[-S+1:0]
+
+

Copies of PYMEM_FORBIDDENBYTE. Used to catch under- writes and reads.

+p[0:N]
+
+

The requested memory, filled with copies of PYMEM_CLEANBYTE, used to catch reference to uninitialized memory. When a realloc-like function is called requesting a larger memory block, the new excess bytes are also filled with PYMEM_CLEANBYTE. When a free-like function is called, these are overwritten with PYMEM_DEADBYTE, to catch reference to freed memory. When a realloc- like function is called requesting a smaller memory block, the excess old bytes are also filled with PYMEM_DEADBYTE.

+p[N:N+S]
+
+

Copies of PYMEM_FORBIDDENBYTE. Used to catch over- writes and reads.

+p[N+S:N+2*S]
+
+

Only used if the PYMEM_DEBUG_SERIALNO macro is defined (not defined by default).

A serial number, incremented by 1 on each call to a malloc-like or realloc-like function. Big-endian size_t. If “bad memory” is detected later, the serial number gives an excellent way to set a breakpoint on the next run, to capture the instant at which this block was passed out. The static function bumpserialno() in obmalloc.c is the only place the serial number is incremented, and exists so you can set such a breakpoint easily.

A realloc-like or free-like function first checks that the PYMEM_FORBIDDENBYTE bytes at each end are intact. If they’ve been altered, diagnostic output is written to stderr, and the program is aborted via Py_FatalError(). The other main failure mode is provoking a memory error when a program reads up one of the special bit patterns and tries to use it as an address. If you get in a debugger then and look at the object, you’re likely to see that it’s entirely filled with PYMEM_DEADBYTE (meaning freed memory is getting used) or PYMEM_CLEANBYTE (meaning uninitialized memory is getting used).

Changed in version 3.6: The PyMem_SetupDebugHooks() function now also works on Python compiled in release mode. On error, the debug hooks now use tracemalloc to get the traceback where a memory block was allocated. The debug hooks now also check if the GIL is held when functions of PYMEM_DOMAIN_OBJ and PYMEM_DOMAIN_MEM domains are called.

Changed in version 3.8: Byte patterns 0xCB (PYMEM_CLEANBYTE), 0xDB (PYMEM_DEADBYTE) and 0xFB (PYMEM_FORBIDDENBYTE) have been replaced with 0xCD, 0xDD and 0xFD to use the same values than Windows CRT debug malloc() and free().

The pymalloc allocator

Python has a pymalloc allocator optimized for small objects (smaller or equal to 512 bytes) with a short lifetime. It uses memory mappings called “arenas” with a fixed size of either 256 KiB on 32-bit platforms or 1 MiB on 64-bit platforms. It falls back to PyMem_RawMalloc() and PyMem_RawRealloc() for allocations larger than 512 bytes.

pymalloc is the default allocator of the PYMEM_DOMAIN_MEM (ex: PyMem_Malloc()) and PYMEM_DOMAIN_OBJ (ex: PyObject_Malloc()) domains.

The arena allocator uses the following functions:

This allocator is disabled if Python is configured with the --without-pymalloc option. It can also be disabled at runtime using the PYTHONMALLOC environment variable (ex: PYTHONMALLOC=malloc).

Customize pymalloc Arena Allocator

New in version 3.4.

+type PyObjectArenaAllocator
+

Structure used to describe an arena allocator. The structure has three fields:

+ + + +

Field

Meaning

void *ctx

user context passed as first argument

void* alloc(void *ctx, size_t size)

allocate an arena of size bytes

void free(void *ctx, void *ptr, size_t size)

free an arena
+
+void PyObject_GetArenaAllocator(PyObjectArenaAllocator *allocator)
+

Get the arena allocator.

+
+void PyObject_SetArenaAllocator(PyObjectArenaAllocator *allocator)
+

Set the arena allocator.

+

tracemalloc C API

New in version 3.7.

+int PyTraceMalloc_Track(unsigned int domain, uintptr_t ptr, size_t size)
+

Track an allocated memory block in the tracemalloc module.

Return 0 on success, return -1 on error (failed to allocate memory to store the trace). Return -2 if tracemalloc is disabled.

If memory block is already tracked, update the existing trace.

+
+int PyTraceMalloc_Untrack(unsigned int domain, uintptr_t ptr)
+

Untrack an allocated memory block in the tracemalloc module. Do nothing if the block was not tracked.

Return -2 if tracemalloc is disabled, otherwise return 0.

+

Examples

Here is the example from section Overview, rewritten so that the I/O buffer is allocated from the Python heap by using the first function set:

PyObject *res;
+char *buf = (char *) PyMem_Malloc(BUFSIZ); /* for I/O */
+
+if (buf == NULL)
+    return PyErr_NoMemory();
+/* ...Do some I/O operation involving buf... */
+res = PyBytes_FromString(buf);
+PyMem_Free(buf); /* allocated with PyMem_Malloc */
+return res;
+

The same code using the type-oriented function set:

PyObject *res;
+char *buf = PyMem_New(char, BUFSIZ); /* for I/O */
+
+if (buf == NULL)
+    return PyErr_NoMemory();
+/* ...Do some I/O operation involving buf... */
+res = PyBytes_FromString(buf);
+PyMem_Del(buf); /* allocated with PyMem_New */
+return res;
+

Note that in the two examples above, the buffer is always manipulated via functions belonging to the same set. Indeed, it is required to use the same memory API family for a given memory block, so that the risk of mixing different allocators is reduced to a minimum. The following code sequence contains two errors, one of which is labeled as fatal because it mixes two different allocators operating on different heaps.

char *buf1 = PyMem_New(char, BUFSIZ);
+char *buf2 = (char *) malloc(BUFSIZ);
+char *buf3 = (char *) PyMem_Malloc(BUFSIZ);
+...
+PyMem_Del(buf3);  /* Wrong -- should be PyMem_Free() */
+free(buf2);       /* Right -- allocated via malloc() */
+free(buf1);       /* Fatal -- should be PyMem_Del()  */
+

In addition to the functions aimed at handling raw memory blocks from the Python heap, objects in Python are allocated and released with PyObject_New, PyObject_NewVar and PyObject_Del().

These will be explained in the next chapter on defining and implementing new object types in C.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/memory.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmemoryview.html b/devdocs/python~3.12/c-api%2Fmemoryview.html new file mode 100644 index 00000000..f5e83822 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmemoryview.html @@ -0,0 +1,27 @@ +

MemoryView objects

A memoryview object exposes the C level buffer interface as a Python object which can then be passed around like any other object.

+PyObject *PyMemoryView_FromObject(PyObject *obj)
+Return value: New reference. Part of the Stable ABI.

Create a memoryview object from an object that provides the buffer interface. If obj supports writable buffer exports, the memoryview object will be read/write, otherwise it may be either read-only or read/write at the discretion of the exporter.

+
+PyObject *PyMemoryView_FromMemory(char *mem, Py_ssize_t size, int flags)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Create a memoryview object using mem as the underlying buffer. flags can be one of PyBUF_READ or PyBUF_WRITE.

New in version 3.3.

+
+PyObject *PyMemoryView_FromBuffer(const Py_buffer *view)
+Return value: New reference. Part of the Stable ABI since version 3.11.

Create a memoryview object wrapping the given buffer structure view. For simple byte buffers, PyMemoryView_FromMemory() is the preferred function.

+
+PyObject *PyMemoryView_GetContiguous(PyObject *obj, int buffertype, char order)
+Return value: New reference. Part of the Stable ABI.

Create a memoryview object to a contiguous chunk of memory (in either ‘C’ or ‘F’ortran order) from an object that defines the buffer interface. If memory is contiguous, the memoryview object points to the original memory. Otherwise, a copy is made and the memoryview points to a new bytes object.

+
+int PyMemoryView_Check(PyObject *obj)
+

Return true if the object obj is a memoryview object. It is not currently allowed to create subclasses of memoryview. This function always succeeds.

+
+Py_buffer *PyMemoryView_GET_BUFFER(PyObject *mview)
+

Return a pointer to the memoryview’s private copy of the exporter’s buffer. mview must be a memoryview instance; this macro doesn’t check its type, you must do it yourself or you will risk crashes.

+
+PyObject *PyMemoryView_GET_BASE(PyObject *mview)
+

Return either a pointer to the exporting object that the memoryview is based on or NULL if the memoryview has been created by one of the functions PyMemoryView_FromMemory() or PyMemoryView_FromBuffer(). mview must be a memoryview instance.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/memoryview.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmethod.html b/devdocs/python~3.12/c-api%2Fmethod.html new file mode 100644 index 00000000..492a7ca1 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmethod.html @@ -0,0 +1,21 @@ +

Instance Method Objects

An instance method is a wrapper for a PyCFunction and the new way to bind a PyCFunction to a class object. It replaces the former call PyMethod_New(func, NULL, class).

+PyTypeObject PyInstanceMethod_Type
+

This instance of PyTypeObject represents the Python instance method type. It is not exposed to Python programs.

+
+int PyInstanceMethod_Check(PyObject *o)
+

Return true if o is an instance method object (has type PyInstanceMethod_Type). The parameter must not be NULL. This function always succeeds.

+
+PyObject *PyInstanceMethod_New(PyObject *func)
+Return value: New reference.

Return a new instance method object, with func being any callable object. func is the function that will be called when the instance method is called.

+
+PyObject *PyInstanceMethod_Function(PyObject *im)
+Return value: Borrowed reference.

Return the function object associated with the instance method im.

+
+PyObject *PyInstanceMethod_GET_FUNCTION(PyObject *im)
+Return value: Borrowed reference.

Macro version of PyInstanceMethod_Function() which avoids error checking.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/method.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fmodule.html b/devdocs/python~3.12/c-api%2Fmodule.html new file mode 100644 index 00000000..c60655b1 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fmodule.html @@ -0,0 +1,205 @@ +

Module Objects

+PyTypeObject PyModule_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python module type. This is exposed to Python programs as types.ModuleType.

+
+int PyModule_Check(PyObject *p)
+

Return true if p is a module object, or a subtype of a module object. This function always succeeds.

+
+int PyModule_CheckExact(PyObject *p)
+

Return true if p is a module object, but not a subtype of PyModule_Type. This function always succeeds.

+
+PyObject *PyModule_NewObject(PyObject *name)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Return a new module object with the __name__ attribute set to name. The module’s __name__, __doc__, __package__, and __loader__ attributes are filled in (all but __name__ are set to None); the caller is responsible for providing a __file__ attribute.

New in version 3.3.

Changed in version 3.4: __package__ and __loader__ are set to None.

+
+PyObject *PyModule_New(const char *name)
+Return value: New reference. Part of the Stable ABI.

Similar to PyModule_NewObject(), but the name is a UTF-8 encoded string instead of a Unicode object.

+
+PyObject *PyModule_GetDict(PyObject *module)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the dictionary object that implements module’s namespace; this object is the same as the __dict__ attribute of the module object. If module is not a module object (or a subtype of a module object), SystemError is raised and NULL is returned.

It is recommended extensions use other PyModule_* and PyObject_* functions rather than directly manipulate a module’s __dict__.

+
+PyObject *PyModule_GetNameObject(PyObject *module)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Return module’s __name__ value. If the module does not provide one, or if it is not a string, SystemError is raised and NULL is returned.

New in version 3.3.

+
+const char *PyModule_GetName(PyObject *module)
+ Part of the Stable ABI.

Similar to PyModule_GetNameObject() but return the name encoded to 'utf-8'.

+
+void *PyModule_GetState(PyObject *module)
+ Part of the Stable ABI.

Return the “state” of the module, that is, a pointer to the block of memory allocated at module creation time, or NULL. See PyModuleDef.m_size.

+
+PyModuleDef *PyModule_GetDef(PyObject *module)
+ Part of the Stable ABI.

Return a pointer to the PyModuleDef struct from which the module was created, or NULL if the module wasn’t created from a definition.

+
+PyObject *PyModule_GetFilenameObject(PyObject *module)
+Return value: New reference. Part of the Stable ABI.

Return the name of the file from which module was loaded using module’s __file__ attribute. If this is not defined, or if it is not a unicode string, raise SystemError and return NULL; otherwise return a reference to a Unicode object.

New in version 3.2.

+
+const char *PyModule_GetFilename(PyObject *module)
+ Part of the Stable ABI.

Similar to PyModule_GetFilenameObject() but return the filename encoded to ‘utf-8’.

Deprecated since version 3.2: PyModule_GetFilename() raises UnicodeEncodeError on unencodable filenames, use PyModule_GetFilenameObject() instead.

+

Initializing C modules

Modules objects are usually created from extension modules (shared libraries which export an initialization function), or compiled-in modules (where the initialization function is added using PyImport_AppendInittab()). See Building C and C++ Extensions or Extending Embedded Python for details.

The initialization function can either pass a module definition instance to PyModule_Create(), and return the resulting module object, or request “multi-phase initialization” by returning the definition struct itself.

+type PyModuleDef
+ Part of the Stable ABI (including all members).

The module definition struct, which holds all information needed to create a module object. There is usually only one statically initialized variable of this type for each module.

+PyModuleDef_Base m_base
+

Always initialize this member to PyModuleDef_HEAD_INIT.

+
+const char *m_name
+

Name for the new module.

+
+const char *m_doc
+

Docstring for the module; usually a docstring variable created with PyDoc_STRVAR is used.

+
+Py_ssize_t m_size
+

Module state may be kept in a per-module memory area that can be retrieved with PyModule_GetState(), rather than in static globals. This makes modules safe for use in multiple sub-interpreters.

This memory area is allocated based on m_size on module creation, and freed when the module object is deallocated, after the m_free function has been called, if present.

Setting m_size to -1 means that the module does not support sub-interpreters, because it has global state.

Setting it to a non-negative value means that the module can be re-initialized and specifies the additional amount of memory it requires for its state. Non-negative m_size is required for multi-phase initialization.

See PEP 3121 for more details.

+
+PyMethodDef *m_methods
+

A pointer to a table of module-level functions, described by PyMethodDef values. Can be NULL if no functions are present.

+
+PyModuleDef_Slot *m_slots
+

An array of slot definitions for multi-phase initialization, terminated by a {0, NULL} entry. When using single-phase initialization, m_slots must be NULL.

Changed in version 3.5: Prior to version 3.5, this member was always set to NULL, and was defined as:

+inquiry m_reload
+
+
+traverseproc m_traverse
+

A traversal function to call during GC traversal of the module object, or NULL if not needed.

This function is not called if the module state was requested but is not allocated yet. This is the case immediately after the module is created and before the module is executed (Py_mod_exec function). More precisely, this function is not called if m_size is greater than 0 and the module state (as returned by PyModule_GetState()) is NULL.

Changed in version 3.9: No longer called before the module state is allocated.

+
+inquiry m_clear
+

A clear function to call during GC clearing of the module object, or NULL if not needed.

This function is not called if the module state was requested but is not allocated yet. This is the case immediately after the module is created and before the module is executed (Py_mod_exec function). More precisely, this function is not called if m_size is greater than 0 and the module state (as returned by PyModule_GetState()) is NULL.

Like PyTypeObject.tp_clear, this function is not always called before a module is deallocated. For example, when reference counting is enough to determine that an object is no longer used, the cyclic garbage collector is not involved and m_free is called directly.

Changed in version 3.9: No longer called before the module state is allocated.

+
+freefunc m_free
+

A function to call during deallocation of the module object, or NULL if not needed.

This function is not called if the module state was requested but is not allocated yet. This is the case immediately after the module is created and before the module is executed (Py_mod_exec function). More precisely, this function is not called if m_size is greater than 0 and the module state (as returned by PyModule_GetState()) is NULL.

Changed in version 3.9: No longer called before the module state is allocated.

+
+

Single-phase initialization

The module initialization function may create and return the module object directly. This is referred to as “single-phase initialization”, and uses one of the following two module creation functions:

+PyObject *PyModule_Create(PyModuleDef *def)
+Return value: New reference.

Create a new module object, given the definition in def. This behaves like PyModule_Create2() with module_api_version set to PYTHON_API_VERSION.

+
+PyObject *PyModule_Create2(PyModuleDef *def, int module_api_version)
+Return value: New reference. Part of the Stable ABI.

Create a new module object, given the definition in def, assuming the API version module_api_version. If that version does not match the version of the running interpreter, a RuntimeWarning is emitted.

Note

Most uses of this function should be using PyModule_Create() instead; only use this if you are sure you need it.

+

Before it is returned from in the initialization function, the resulting module object is typically populated using functions like PyModule_AddObjectRef().

Multi-phase initialization

An alternate way to specify extensions is to request “multi-phase initialization”. Extension modules created this way behave more like Python modules: the initialization is split between the creation phase, when the module object is created, and the execution phase, when it is populated. The distinction is similar to the __new__() and __init__() methods of classes.

Unlike modules created using single-phase initialization, these modules are not singletons: if the sys.modules entry is removed and the module is re-imported, a new module object is created, and the old module is subject to normal garbage collection – as with Python modules. By default, multiple modules created from the same definition should be independent: changes to one should not affect the others. This means that all state should be specific to the module object (using e.g. using PyModule_GetState()), or its contents (such as the module’s __dict__ or individual classes created with PyType_FromSpec()).

All modules created using multi-phase initialization are expected to support sub-interpreters. Making sure multiple modules are independent is typically enough to achieve this.

To request multi-phase initialization, the initialization function (PyInit_modulename) returns a PyModuleDef instance with non-empty m_slots. Before it is returned, the PyModuleDef instance must be initialized with the following function:

+PyObject *PyModuleDef_Init(PyModuleDef *def)
+Return value: Borrowed reference. Part of the Stable ABI since version 3.5.

Ensures a module definition is a properly initialized Python object that correctly reports its type and reference count.

Returns def cast to PyObject*, or NULL if an error occurred.

New in version 3.5.

+

The m_slots member of the module definition must point to an array of PyModuleDef_Slot structures:

+type PyModuleDef_Slot
+
+int slot
+

A slot ID, chosen from the available values explained below.

+
+void *value
+

Value of the slot, whose meaning depends on the slot ID.

+

New in version 3.5.

+

The m_slots array must be terminated by a slot with id 0.

The available slot types are:

+Py_mod_create
+

Specifies a function that is called to create the module object itself. The value pointer of this slot must point to a function of the signature:

PyObject *create_module(PyObject *spec, PyModuleDef *def)
+
+

The function receives a ModuleSpec instance, as defined in PEP 451, and the module definition. It should return a new module object, or set an error and return NULL.

This function should be kept minimal. In particular, it should not call arbitrary Python code, as trying to import the same module again may result in an infinite loop.

Multiple Py_mod_create slots may not be specified in one module definition.

If Py_mod_create is not specified, the import machinery will create a normal module object using PyModule_New(). The name is taken from spec, not the definition, to allow extension modules to dynamically adjust to their place in the module hierarchy and be imported under different names through symlinks, all while sharing a single module definition.

There is no requirement for the returned object to be an instance of PyModule_Type. Any type can be used, as long as it supports setting and getting import-related attributes. However, only PyModule_Type instances may be returned if the PyModuleDef has non-NULL m_traverse, m_clear, m_free; non-zero m_size; or slots other than Py_mod_create.

+
+Py_mod_exec
+

Specifies a function that is called to execute the module. This is equivalent to executing the code of a Python module: typically, this function adds classes and constants to the module. The signature of the function is:

int exec_module(PyObject *module)
+
+

If multiple Py_mod_exec slots are specified, they are processed in the order they appear in the m_slots array.

+
+Py_mod_multiple_interpreters
+

Specifies one of the following values:

+Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED
+

The module does not support being imported in subinterpreters.

+
+Py_MOD_MULTIPLE_INTERPRETERS_SUPPORTED
+

The module supports being imported in subinterpreters, but only when they share the main interpreter’s GIL. (See Isolating Extension Modules.)

+
+Py_MOD_PER_INTERPRETER_GIL_SUPPORTED
+

The module supports being imported in subinterpreters, even when they have their own GIL. (See Isolating Extension Modules.)

+

This slot determines whether or not importing this module in a subinterpreter will fail.

Multiple Py_mod_multiple_interpreters slots may not be specified in one module definition.

If Py_mod_multiple_interpreters is not specified, the import machinery defaults to Py_MOD_MULTIPLE_INTERPRETERS_NOT_SUPPORTED.

New in version 3.12.

+

See PEP 489 for more details on multi-phase initialization.

Low-level module creation functions

The following functions are called under the hood when using multi-phase initialization. They can be used directly, for example when creating module objects dynamically. Note that both PyModule_FromDefAndSpec and PyModule_ExecDef must be called to fully initialize a module.

+PyObject *PyModule_FromDefAndSpec(PyModuleDef *def, PyObject *spec)
+Return value: New reference.

Create a new module object, given the definition in def and the ModuleSpec spec. This behaves like PyModule_FromDefAndSpec2() with module_api_version set to PYTHON_API_VERSION.

New in version 3.5.

+
+PyObject *PyModule_FromDefAndSpec2(PyModuleDef *def, PyObject *spec, int module_api_version)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Create a new module object, given the definition in def and the ModuleSpec spec, assuming the API version module_api_version. If that version does not match the version of the running interpreter, a RuntimeWarning is emitted.

Note

Most uses of this function should be using PyModule_FromDefAndSpec() instead; only use this if you are sure you need it.

New in version 3.5.

+
+int PyModule_ExecDef(PyObject *module, PyModuleDef *def)
+ Part of the Stable ABI since version 3.7.

Process any execution slots (Py_mod_exec) given in def.

New in version 3.5.

+
+int PyModule_SetDocString(PyObject *module, const char *docstring)
+ Part of the Stable ABI since version 3.7.

Set the docstring for module to docstring. This function is called automatically when creating a module from PyModuleDef, using either PyModule_Create or PyModule_FromDefAndSpec.

New in version 3.5.

+
+int PyModule_AddFunctions(PyObject *module, PyMethodDef *functions)
+ Part of the Stable ABI since version 3.7.

Add the functions from the NULL terminated functions array to module. Refer to the PyMethodDef documentation for details on individual entries (due to the lack of a shared module namespace, module level “functions” implemented in C typically receive the module as their first parameter, making them similar to instance methods on Python classes). This function is called automatically when creating a module from PyModuleDef, using either PyModule_Create or PyModule_FromDefAndSpec.

New in version 3.5.

+

Support functions

The module initialization function (if using single phase initialization) or a function called from a module execution slot (if using multi-phase initialization), can use the following functions to help initialize the module state:

+int PyModule_AddObjectRef(PyObject *module, const char *name, PyObject *value)
+ Part of the Stable ABI since version 3.10.

Add an object to module as name. This is a convenience function which can be used from the module’s initialization function.

On success, return 0. On error, raise an exception and return -1.

Return NULL if value is NULL. It must be called with an exception raised in this case.

Example usage:

static int
+add_spam(PyObject *module, int value)
+{
+    PyObject *obj = PyLong_FromLong(value);
+    if (obj == NULL) {
+        return -1;
+    }
+    int res = PyModule_AddObjectRef(module, "spam", obj);
+    Py_DECREF(obj);
+    return res;
+ }
+

The example can also be written without checking explicitly if obj is NULL:

static int
+add_spam(PyObject *module, int value)
+{
+    PyObject *obj = PyLong_FromLong(value);
+    int res = PyModule_AddObjectRef(module, "spam", obj);
+    Py_XDECREF(obj);
+    return res;
+ }
+

Note that Py_XDECREF() should be used instead of Py_DECREF() in this case, since obj can be NULL.

New in version 3.10.

+
+int PyModule_AddObject(PyObject *module, const char *name, PyObject *value)
+ Part of the Stable ABI.

Similar to PyModule_AddObjectRef(), but steals a reference to value on success (if it returns 0).

The new PyModule_AddObjectRef() function is recommended, since it is easy to introduce reference leaks by misusing the PyModule_AddObject() function.

Note

Unlike other functions that steal references, PyModule_AddObject() only releases the reference to value on success.

This means that its return value must be checked, and calling code must Py_DECREF() value manually on error.

Example usage:

static int
+add_spam(PyObject *module, int value)
+{
+    PyObject *obj = PyLong_FromLong(value);
+    if (obj == NULL) {
+        return -1;
+    }
+    if (PyModule_AddObject(module, "spam", obj) < 0) {
+        Py_DECREF(obj);
+        return -1;
+    }
+    // PyModule_AddObject() stole a reference to obj:
+    // Py_DECREF(obj) is not needed here
+    return 0;
+}
+

The example can also be written without checking explicitly if obj is NULL:

static int
+add_spam(PyObject *module, int value)
+{
+    PyObject *obj = PyLong_FromLong(value);
+    if (PyModule_AddObject(module, "spam", obj) < 0) {
+        Py_XDECREF(obj);
+        return -1;
+    }
+    // PyModule_AddObject() stole a reference to obj:
+    // Py_DECREF(obj) is not needed here
+    return 0;
+}
+

Note that Py_XDECREF() should be used instead of Py_DECREF() in this case, since obj can be NULL.

+
+int PyModule_AddIntConstant(PyObject *module, const char *name, long value)
+ Part of the Stable ABI.

Add an integer constant to module as name. This convenience function can be used from the module’s initialization function. Return -1 on error, 0 on success.

+
+int PyModule_AddStringConstant(PyObject *module, const char *name, const char *value)
+ Part of the Stable ABI.

Add a string constant to module as name. This convenience function can be used from the module’s initialization function. The string value must be NULL-terminated. Return -1 on error, 0 on success.

+
+PyModule_AddIntMacro(module, macro)
+

Add an int constant to module. The name and the value are taken from macro. For example PyModule_AddIntMacro(module, AF_INET) adds the int constant AF_INET with the value of AF_INET to module. Return -1 on error, 0 on success.

+
+PyModule_AddStringMacro(module, macro)
+

Add a string constant to module.

+
+int PyModule_AddType(PyObject *module, PyTypeObject *type)
+ Part of the Stable ABI since version 3.10.

Add a type object to module. The type object is finalized by calling internally PyType_Ready(). The name of the type object is taken from the last component of tp_name after dot. Return -1 on error, 0 on success.

New in version 3.9.

+

Module lookup

Single-phase initialization creates singleton modules that can be looked up in the context of the current interpreter. This allows the module object to be retrieved later with only a reference to the module definition.

These functions will not work on modules created using multi-phase initialization, since multiple such modules can be created from a single definition.

+PyObject *PyState_FindModule(PyModuleDef *def)
+Return value: Borrowed reference. Part of the Stable ABI.

Returns the module object that was created from def for the current interpreter. This method requires that the module object has been attached to the interpreter state with PyState_AddModule() beforehand. In case the corresponding module object is not found or has not been attached to the interpreter state yet, it returns NULL.

+
+int PyState_AddModule(PyObject *module, PyModuleDef *def)
+ Part of the Stable ABI since version 3.3.

Attaches the module object passed to the function to the interpreter state. This allows the module object to be accessible via PyState_FindModule().

Only effective on modules created using single-phase initialization.

Python calls PyState_AddModule automatically after importing a module, so it is unnecessary (but harmless) to call it from module initialization code. An explicit call is needed only if the module’s own init code subsequently calls PyState_FindModule. The function is mainly intended for implementing alternative import mechanisms (either by calling it directly, or by referring to its implementation for details of the required state updates).

The caller must hold the GIL.

Return 0 on success or -1 on failure.

New in version 3.3.

+
+int PyState_RemoveModule(PyModuleDef *def)
+ Part of the Stable ABI since version 3.3.

Removes the module object created from def from the interpreter state. Return 0 on success or -1 on failure.

The caller must hold the GIL.

New in version 3.3.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/module.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fnone.html b/devdocs/python~3.12/c-api%2Fnone.html new file mode 100644 index 00000000..e34a1fee --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fnone.html @@ -0,0 +1,12 @@ +

The None Object

Note that the PyTypeObject for None is not directly exposed in the Python/C API. Since None is a singleton, testing for object identity (using == in C) is sufficient. There is no PyNone_Check() function for the same reason.

+PyObject *Py_None
+

The Python None object, denoting lack of value. This object has no methods and is immortal.

+

Changed in version 3.12: Py_None is immortal.

+Py_RETURN_NONE
+

Return Py_None from a function.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/none.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fnumber.html b/devdocs/python~3.12/c-api%2Fnumber.html new file mode 100644 index 00000000..ffc69f79 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fnumber.html @@ -0,0 +1,120 @@ +

Number Protocol

+int PyNumber_Check(PyObject *o)
+ Part of the Stable ABI.

Returns 1 if the object o provides numeric protocols, and false otherwise. This function always succeeds.

Changed in version 3.8: Returns 1 if o is an index integer.

+
+PyObject *PyNumber_Add(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of adding o1 and o2, or NULL on failure. This is the equivalent of the Python expression o1 + o2.

+
+PyObject *PyNumber_Subtract(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of subtracting o2 from o1, or NULL on failure. This is the equivalent of the Python expression o1 - o2.

+
+PyObject *PyNumber_Multiply(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of multiplying o1 and o2, or NULL on failure. This is the equivalent of the Python expression o1 * o2.

+
+PyObject *PyNumber_MatrixMultiply(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Returns the result of matrix multiplication on o1 and o2, or NULL on failure. This is the equivalent of the Python expression o1 @ o2.

New in version 3.5.

+
+PyObject *PyNumber_FloorDivide(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Return the floor of o1 divided by o2, or NULL on failure. This is the equivalent of the Python expression o1 // o2.

+
+PyObject *PyNumber_TrueDivide(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Return a reasonable approximation for the mathematical value of o1 divided by o2, or NULL on failure. The return value is “approximate” because binary floating point numbers are approximate; it is not possible to represent all real numbers in base two. This function can return a floating point value when passed two integers. This is the equivalent of the Python expression o1 / o2.

+
+PyObject *PyNumber_Remainder(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the remainder of dividing o1 by o2, or NULL on failure. This is the equivalent of the Python expression o1 % o2.

+
+PyObject *PyNumber_Divmod(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

See the built-in function divmod(). Returns NULL on failure. This is the equivalent of the Python expression divmod(o1, o2).

+
+PyObject *PyNumber_Power(PyObject *o1, PyObject *o2, PyObject *o3)
+Return value: New reference. Part of the Stable ABI.

See the built-in function pow(). Returns NULL on failure. This is the equivalent of the Python expression pow(o1, o2, o3), where o3 is optional. If o3 is to be ignored, pass Py_None in its place (passing NULL for o3 would cause an illegal memory access).

+
+PyObject *PyNumber_Negative(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the negation of o on success, or NULL on failure. This is the equivalent of the Python expression -o.

+
+PyObject *PyNumber_Positive(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns o on success, or NULL on failure. This is the equivalent of the Python expression +o.

+
+PyObject *PyNumber_Absolute(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the absolute value of o, or NULL on failure. This is the equivalent of the Python expression abs(o).

+
+PyObject *PyNumber_Invert(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the bitwise negation of o on success, or NULL on failure. This is the equivalent of the Python expression ~o.

+
+PyObject *PyNumber_Lshift(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of left shifting o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1 << o2.

+
+PyObject *PyNumber_Rshift(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of right shifting o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1 >> o2.

+
+PyObject *PyNumber_And(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise and” of o1 and o2 on success and NULL on failure. This is the equivalent of the Python expression o1 & o2.

+
+PyObject *PyNumber_Xor(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise exclusive or” of o1 by o2 on success, or NULL on failure. This is the equivalent of the Python expression o1 ^ o2.

+
+PyObject *PyNumber_Or(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise or” of o1 and o2 on success, or NULL on failure. This is the equivalent of the Python expression o1 | o2.

+
+PyObject *PyNumber_InPlaceAdd(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of adding o1 and o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 += o2.

+
+PyObject *PyNumber_InPlaceSubtract(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of subtracting o2 from o1, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 -= o2.

+
+PyObject *PyNumber_InPlaceMultiply(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of multiplying o1 and o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 *= o2.

+
+PyObject *PyNumber_InPlaceMatrixMultiply(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Returns the result of matrix multiplication on o1 and o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 @= o2.

New in version 3.5.

+
+PyObject *PyNumber_InPlaceFloorDivide(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the mathematical floor of dividing o1 by o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 //= o2.

+
+PyObject *PyNumber_InPlaceTrueDivide(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Return a reasonable approximation for the mathematical value of o1 divided by o2, or NULL on failure. The return value is “approximate” because binary floating point numbers are approximate; it is not possible to represent all real numbers in base two. This function can return a floating point value when passed two integers. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 /= o2.

+
+PyObject *PyNumber_InPlaceRemainder(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the remainder of dividing o1 by o2, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 %= o2.

+
+PyObject *PyNumber_InPlacePower(PyObject *o1, PyObject *o2, PyObject *o3)
+Return value: New reference. Part of the Stable ABI.

See the built-in function pow(). Returns NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 **= o2 when o3 is Py_None, or an in-place variant of pow(o1, o2, o3) otherwise. If o3 is to be ignored, pass Py_None in its place (passing NULL for o3 would cause an illegal memory access).

+
+PyObject *PyNumber_InPlaceLshift(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of left shifting o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 <<= o2.

+
+PyObject *PyNumber_InPlaceRshift(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the result of right shifting o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 >>= o2.

+
+PyObject *PyNumber_InPlaceAnd(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise and” of o1 and o2 on success and NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 &= o2.

+
+PyObject *PyNumber_InPlaceXor(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise exclusive or” of o1 by o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 ^= o2.

+
+PyObject *PyNumber_InPlaceOr(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Returns the “bitwise or” of o1 and o2 on success, or NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python statement o1 |= o2.

+
+PyObject *PyNumber_Long(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the o converted to an integer object on success, or NULL on failure. This is the equivalent of the Python expression int(o).

+
+PyObject *PyNumber_Float(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the o converted to a float object on success, or NULL on failure. This is the equivalent of the Python expression float(o).

+
+PyObject *PyNumber_Index(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Returns the o converted to a Python int on success or NULL with a TypeError exception raised on failure.

Changed in version 3.10: The result always has exact type int. Previously, the result could have been an instance of a subclass of int.

+
+PyObject *PyNumber_ToBase(PyObject *n, int base)
+Return value: New reference. Part of the Stable ABI.

Returns the integer n converted to base base as a string. The base argument must be one of 2, 8, 10, or 16. For base 2, 8, or 16, the returned string is prefixed with a base marker of '0b', '0o', or '0x', respectively. If n is not a Python int, it is converted with PyNumber_Index() first.

+
+Py_ssize_t PyNumber_AsSsize_t(PyObject *o, PyObject *exc)
+ Part of the Stable ABI.

Returns o converted to a Py_ssize_t value if o can be interpreted as an integer. If the call fails, an exception is raised and -1 is returned.

If o can be converted to a Python int but the attempt to convert to a Py_ssize_t value would raise an OverflowError, then the exc argument is the type of exception that will be raised (usually IndexError or OverflowError). If exc is NULL, then the exception is cleared and the value is clipped to PY_SSIZE_T_MIN for a negative integer or PY_SSIZE_T_MAX for a positive integer.

+
+int PyIndex_Check(PyObject *o)
+ Part of the Stable ABI since version 3.8.

Returns 1 if o is an index integer (has the nb_index slot of the tp_as_number structure filled in), and 0 otherwise. This function always succeeds.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/number.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fobjbuffer.html b/devdocs/python~3.12/c-api%2Fobjbuffer.html new file mode 100644 index 00000000..951bbe2e --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fobjbuffer.html @@ -0,0 +1,18 @@ +

Old Buffer Protocol

Deprecated since version 3.0.

These functions were part of the “old buffer protocol” API in Python 2. In Python 3, this protocol doesn’t exist anymore but the functions are still exposed to ease porting 2.x code. They act as a compatibility wrapper around the new buffer protocol, but they don’t give you control over the lifetime of the resources acquired when a buffer is exported.

Therefore, it is recommended that you call PyObject_GetBuffer() (or the y* or w* format codes with the PyArg_ParseTuple() family of functions) to get a buffer view over an object, and PyBuffer_Release() when the buffer view can be released.

+int PyObject_AsCharBuffer(PyObject *obj, const char **buffer, Py_ssize_t *buffer_len)
+ Part of the Stable ABI.

Returns a pointer to a read-only memory location usable as character-based input. The obj argument must support the single-segment character buffer interface. On success, returns 0, sets buffer to the memory location and buffer_len to the buffer length. Returns -1 and sets a TypeError on error.

+
+int PyObject_AsReadBuffer(PyObject *obj, const void **buffer, Py_ssize_t *buffer_len)
+ Part of the Stable ABI.

Returns a pointer to a read-only memory location containing arbitrary data. The obj argument must support the single-segment readable buffer interface. On success, returns 0, sets buffer to the memory location and buffer_len to the buffer length. Returns -1 and sets a TypeError on error.

+
+int PyObject_CheckReadBuffer(PyObject *o)
+ Part of the Stable ABI.

Returns 1 if o supports the single-segment readable buffer interface. Otherwise returns 0. This function always succeeds.

Note that this function tries to get and release a buffer, and exceptions which occur while calling corresponding functions will get suppressed. To get error reporting use PyObject_GetBuffer() instead.

+
+int PyObject_AsWriteBuffer(PyObject *obj, void **buffer, Py_ssize_t *buffer_len)
+ Part of the Stable ABI.

Returns a pointer to a writable memory location. The obj argument must support the single-segment, character buffer interface. On success, returns 0, sets buffer to the memory location and buffer_len to the buffer length. Returns -1 and sets a TypeError on error.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/objbuffer.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fobject.html b/devdocs/python~3.12/c-api%2Fobject.html new file mode 100644 index 00000000..2fe37503 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fobject.html @@ -0,0 +1,134 @@ +

Object Protocol

+PyObject *Py_NotImplemented
+

The NotImplemented singleton, used to signal that an operation is not implemented for the given type combination.

+
+Py_RETURN_NOTIMPLEMENTED
+

Properly handle returning Py_NotImplemented from within a C function (that is, create a new strong reference to NotImplemented and return it).

+
+int PyObject_Print(PyObject *o, FILE *fp, int flags)
+

Print an object o, on file fp. Returns -1 on error. The flags argument is used to enable certain printing options. The only option currently supported is Py_PRINT_RAW; if given, the str() of the object is written instead of the repr().

+
+int PyObject_HasAttr(PyObject *o, PyObject *attr_name)
+ Part of the Stable ABI.

Returns 1 if o has the attribute attr_name, and 0 otherwise. This is equivalent to the Python expression hasattr(o, attr_name). This function always succeeds.

Note

Exceptions that occur when this calls __getattr__() and __getattribute__() methods are silently ignored. For proper error handling, use PyObject_GetAttr() instead.

+
+int PyObject_HasAttrString(PyObject *o, const char *attr_name)
+ Part of the Stable ABI.

This is the same as PyObject_HasAttr(), but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

Note

Exceptions that occur when this calls __getattr__() and __getattribute__() methods or while creating the temporary str object are silently ignored. For proper error handling, use PyObject_GetAttrString() instead.

+
+PyObject *PyObject_GetAttr(PyObject *o, PyObject *attr_name)
+Return value: New reference. Part of the Stable ABI.

Retrieve an attribute named attr_name from object o. Returns the attribute value on success, or NULL on failure. This is the equivalent of the Python expression o.attr_name.

+
+PyObject *PyObject_GetAttrString(PyObject *o, const char *attr_name)
+Return value: New reference. Part of the Stable ABI.

This is the same as PyObject_GetAttr(), but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+PyObject *PyObject_GenericGetAttr(PyObject *o, PyObject *name)
+Return value: New reference. Part of the Stable ABI.

Generic attribute getter function that is meant to be put into a type object’s tp_getattro slot. It looks for a descriptor in the dictionary of classes in the object’s MRO as well as an attribute in the object’s __dict__ (if present). As outlined in Implementing Descriptors, data descriptors take preference over instance attributes, while non-data descriptors don’t. Otherwise, an AttributeError is raised.

+
+int PyObject_SetAttr(PyObject *o, PyObject *attr_name, PyObject *v)
+ Part of the Stable ABI.

Set the value of the attribute named attr_name, for object o, to the value v. Raise an exception and return -1 on failure; return 0 on success. This is the equivalent of the Python statement o.attr_name = v.

If v is NULL, the attribute is deleted. This behaviour is deprecated in favour of using PyObject_DelAttr(), but there are currently no plans to remove it.

+
+int PyObject_SetAttrString(PyObject *o, const char *attr_name, PyObject *v)
+ Part of the Stable ABI.

This is the same as PyObject_SetAttr(), but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

If v is NULL, the attribute is deleted, but this feature is deprecated in favour of using PyObject_DelAttrString().

+
+int PyObject_GenericSetAttr(PyObject *o, PyObject *name, PyObject *value)
+ Part of the Stable ABI.

Generic attribute setter and deleter function that is meant to be put into a type object’s tp_setattro slot. It looks for a data descriptor in the dictionary of classes in the object’s MRO, and if found it takes preference over setting or deleting the attribute in the instance dictionary. Otherwise, the attribute is set or deleted in the object’s __dict__ (if present). On success, 0 is returned, otherwise an AttributeError is raised and -1 is returned.

+
+int PyObject_DelAttr(PyObject *o, PyObject *attr_name)
+

Delete attribute named attr_name, for object o. Returns -1 on failure. This is the equivalent of the Python statement del o.attr_name.

+
+int PyObject_DelAttrString(PyObject *o, const char *attr_name)
+

This is the same as PyObject_DelAttr(), but attr_name is specified as a const char* UTF-8 encoded bytes string, rather than a PyObject*.

+
+PyObject *PyObject_GenericGetDict(PyObject *o, void *context)
+Return value: New reference. Part of the Stable ABI since version 3.10.

A generic implementation for the getter of a __dict__ descriptor. It creates the dictionary if necessary.

This function may also be called to get the __dict__ of the object o. Pass NULL for context when calling it. Since this function may need to allocate memory for the dictionary, it may be more efficient to call PyObject_GetAttr() when accessing an attribute on the object.

On failure, returns NULL with an exception set.

New in version 3.3.

+
+int PyObject_GenericSetDict(PyObject *o, PyObject *value, void *context)
+ Part of the Stable ABI since version 3.7.

A generic implementation for the setter of a __dict__ descriptor. This implementation does not allow the dictionary to be deleted.

New in version 3.3.

+
+PyObject **_PyObject_GetDictPtr(PyObject *obj)
+

Return a pointer to __dict__ of the object obj. If there is no __dict__, return NULL without setting an exception.

This function may need to allocate memory for the dictionary, so it may be more efficient to call PyObject_GetAttr() when accessing an attribute on the object.

+
+PyObject *PyObject_RichCompare(PyObject *o1, PyObject *o2, int opid)
+Return value: New reference. Part of the Stable ABI.

Compare the values of o1 and o2 using the operation specified by opid, which must be one of Py_LT, Py_LE, Py_EQ, Py_NE, Py_GT, or Py_GE, corresponding to <, <=, ==, !=, >, or >= respectively. This is the equivalent of the Python expression o1 op o2, where op is the operator corresponding to opid. Returns the value of the comparison on success, or NULL on failure.

+
+int PyObject_RichCompareBool(PyObject *o1, PyObject *o2, int opid)
+ Part of the Stable ABI.

Compare the values of o1 and o2 using the operation specified by opid, which must be one of Py_LT, Py_LE, Py_EQ, Py_NE, Py_GT, or Py_GE, corresponding to <, <=, ==, !=, >, or >= respectively. Returns -1 on error, 0 if the result is false, 1 otherwise. This is the equivalent of the Python expression o1 op o2, where op is the operator corresponding to opid.

+

Note

If o1 and o2 are the same object, PyObject_RichCompareBool() will always return 1 for Py_EQ and 0 for Py_NE.

+PyObject *PyObject_Format(PyObject *obj, PyObject *format_spec)
+ Part of the Stable ABI.

Format obj using format_spec. This is equivalent to the Python expression format(obj, format_spec).

format_spec may be NULL. In this case the call is equivalent to format(obj). Returns the formatted string on success, NULL on failure.

+
+PyObject *PyObject_Repr(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Compute a string representation of object o. Returns the string representation on success, NULL on failure. This is the equivalent of the Python expression repr(o). Called by the repr() built-in function.

Changed in version 3.4: This function now includes a debug assertion to help ensure that it does not silently discard an active exception.

+
+PyObject *PyObject_ASCII(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

As PyObject_Repr(), compute a string representation of object o, but escape the non-ASCII characters in the string returned by PyObject_Repr() with \x, \u or \U escapes. This generates a string similar to that returned by PyObject_Repr() in Python 2. Called by the ascii() built-in function.

+
+
+PyObject *PyObject_Str(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Compute a string representation of object o. Returns the string representation on success, NULL on failure. This is the equivalent of the Python expression str(o). Called by the str() built-in function and, therefore, by the print() function.

Changed in version 3.4: This function now includes a debug assertion to help ensure that it does not silently discard an active exception.

+
+PyObject *PyObject_Bytes(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Compute a bytes representation of object o. NULL is returned on failure and a bytes object on success. This is equivalent to the Python expression bytes(o), when o is not an integer. Unlike bytes(o), a TypeError is raised when o is an integer instead of a zero-initialized bytes object.

+
+int PyObject_IsSubclass(PyObject *derived, PyObject *cls)
+ Part of the Stable ABI.

Return 1 if the class derived is identical to or derived from the class cls, otherwise return 0. In case of an error, return -1.

If cls is a tuple, the check will be done against every entry in cls. The result will be 1 when at least one of the checks returns 1, otherwise it will be 0.

If cls has a __subclasscheck__() method, it will be called to determine the subclass status as described in PEP 3119. Otherwise, derived is a subclass of cls if it is a direct or indirect subclass, i.e. contained in cls.__mro__.

Normally only class objects, i.e. instances of type or a derived class, are considered classes. However, objects can override this by having a __bases__ attribute (which must be a tuple of base classes).

+
+int PyObject_IsInstance(PyObject *inst, PyObject *cls)
+ Part of the Stable ABI.

Return 1 if inst is an instance of the class cls or a subclass of cls, or 0 if not. On error, returns -1 and sets an exception.

If cls is a tuple, the check will be done against every entry in cls. The result will be 1 when at least one of the checks returns 1, otherwise it will be 0.

If cls has a __instancecheck__() method, it will be called to determine the subclass status as described in PEP 3119. Otherwise, inst is an instance of cls if its class is a subclass of cls.

An instance inst can override what is considered its class by having a __class__ attribute.

An object cls can override if it is considered a class, and what its base classes are, by having a __bases__ attribute (which must be a tuple of base classes).

+
+Py_hash_t PyObject_Hash(PyObject *o)
+ Part of the Stable ABI.

Compute and return the hash value of an object o. On failure, return -1. This is the equivalent of the Python expression hash(o).

Changed in version 3.2: The return type is now Py_hash_t. This is a signed integer the same size as Py_ssize_t.

+
+Py_hash_t PyObject_HashNotImplemented(PyObject *o)
+ Part of the Stable ABI.

Set a TypeError indicating that type(o) is not hashable and return -1. This function receives special treatment when stored in a tp_hash slot, allowing a type to explicitly indicate to the interpreter that it is not hashable.

+
+int PyObject_IsTrue(PyObject *o)
+ Part of the Stable ABI.

Returns 1 if the object o is considered to be true, and 0 otherwise. This is equivalent to the Python expression not not o. On failure, return -1.

+
+int PyObject_Not(PyObject *o)
+ Part of the Stable ABI.

Returns 0 if the object o is considered to be true, and 1 otherwise. This is equivalent to the Python expression not o. On failure, return -1.

+
+PyObject *PyObject_Type(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

When o is non-NULL, returns a type object corresponding to the object type of object o. On failure, raises SystemError and returns NULL. This is equivalent to the Python expression type(o). This function creates a new strong reference to the return value. There’s really no reason to use this function instead of the Py_TYPE() function, which returns a pointer of type PyTypeObject*, except when a new strong reference is needed.

+
+int PyObject_TypeCheck(PyObject *o, PyTypeObject *type)
+

Return non-zero if the object o is of type type or a subtype of type, and 0 otherwise. Both parameters must be non-NULL.

+
+Py_ssize_t PyObject_Size(PyObject *o)
+Py_ssize_t PyObject_Length(PyObject *o)
+ Part of the Stable ABI.

Return the length of object o. If the object o provides either the sequence and mapping protocols, the sequence length is returned. On error, -1 is returned. This is the equivalent to the Python expression len(o).

+
+Py_ssize_t PyObject_LengthHint(PyObject *o, Py_ssize_t defaultvalue)
+

Return an estimated length for the object o. First try to return its actual length, then an estimate using __length_hint__(), and finally return the default value. On error return -1. This is the equivalent to the Python expression operator.length_hint(o, defaultvalue).

New in version 3.4.

+
+PyObject *PyObject_GetItem(PyObject *o, PyObject *key)
+Return value: New reference. Part of the Stable ABI.

Return element of o corresponding to the object key or NULL on failure. This is the equivalent of the Python expression o[key].

+
+int PyObject_SetItem(PyObject *o, PyObject *key, PyObject *v)
+ Part of the Stable ABI.

Map the object key to the value v. Raise an exception and return -1 on failure; return 0 on success. This is the equivalent of the Python statement o[key] = v. This function does not steal a reference to v.

+
+int PyObject_DelItem(PyObject *o, PyObject *key)
+ Part of the Stable ABI.

Remove the mapping for the object key from the object o. Return -1 on failure. This is equivalent to the Python statement del o[key].

+
+PyObject *PyObject_Dir(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

This is equivalent to the Python expression dir(o), returning a (possibly empty) list of strings appropriate for the object argument, or NULL if there was an error. If the argument is NULL, this is like the Python dir(), returning the names of the current locals; in this case, if no execution frame is active then NULL is returned but PyErr_Occurred() will return false.

+
+PyObject *PyObject_GetIter(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

This is equivalent to the Python expression iter(o). It returns a new iterator for the object argument, or the object itself if the object is already an iterator. Raises TypeError and returns NULL if the object cannot be iterated.

+
+PyObject *PyObject_GetAIter(PyObject *o)
+Return value: New reference. Part of the Stable ABI since version 3.10.

This is the equivalent to the Python expression aiter(o). Takes an AsyncIterable object and returns an AsyncIterator for it. This is typically a new iterator but if the argument is an AsyncIterator, this returns itself. Raises TypeError and returns NULL if the object cannot be iterated.

New in version 3.10.

+
+void *PyObject_GetTypeData(PyObject *o, PyTypeObject *cls)
+ Part of the Stable ABI since version 3.12.

Get a pointer to subclass-specific data reserved for cls.

The object o must be an instance of cls, and cls must have been created using negative PyType_Spec.basicsize. Python does not check this.

On error, set an exception and return NULL.

New in version 3.12.

+
+Py_ssize_t PyType_GetTypeDataSize(PyTypeObject *cls)
+ Part of the Stable ABI since version 3.12.

Return the size of the instance memory space reserved for cls, i.e. the size of the memory PyObject_GetTypeData() returns.

This may be larger than requested using -PyType_Spec.basicsize; it is safe to use this larger size (e.g. with memset()).

The type cls must have been created using negative PyType_Spec.basicsize. Python does not check this.

On error, set an exception and return a negative value.

New in version 3.12.

+
+void *PyObject_GetItemData(PyObject *o)
+

Get a pointer to per-item data for a class with Py_TPFLAGS_ITEMS_AT_END.

On error, set an exception and return NULL. TypeError is raised if o does not have Py_TPFLAGS_ITEMS_AT_END set.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/object.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fobjimpl.html b/devdocs/python~3.12/c-api%2Fobjimpl.html new file mode 100644 index 00000000..71a79822 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fobjimpl.html @@ -0,0 +1,11 @@ +

Object Implementation Support

This chapter describes the functions, types, and macros used when defining new object types.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/objimpl.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fperfmaps.html b/devdocs/python~3.12/c-api%2Fperfmaps.html new file mode 100644 index 00000000..ed48909c --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fperfmaps.html @@ -0,0 +1,20 @@ +

Support for Perf Maps

On supported platforms (as of this writing, only Linux), the runtime can take advantage of perf map files to make Python functions visible to an external profiling tool (such as perf). A running process may create a file in the /tmp directory, which contains entries that can map a section of executable code to a name. This interface is described in the documentation of the Linux Perf tool.

In Python, these helper APIs can be used by libraries and features that rely on generating machine code on the fly.

Note that holding the Global Interpreter Lock (GIL) is not required for these APIs.

+int PyUnstable_PerfMapState_Init(void)
+
This is Unstable API. It may change without warning in minor releases. +

Open the /tmp/perf-$pid.map file, unless it’s already opened, and create a lock to ensure thread-safe writes to the file (provided the writes are done through PyUnstable_WritePerfMapEntry()). Normally, there’s no need to call this explicitly; just use PyUnstable_WritePerfMapEntry() and it will initialize the state on first call.

Returns 0 on success, -1 on failure to create/open the perf map file, or -2 on failure to create a lock. Check errno for more information about the cause of a failure.

+
+int PyUnstable_WritePerfMapEntry(const void *code_addr, unsigned int code_size, const char *entry_name)
+
This is Unstable API. It may change without warning in minor releases. +

Write one single entry to the /tmp/perf-$pid.map file. This function is thread safe. Here is what an example entry looks like:

# address      size  name
+7f3529fcf759 b     py::bar:/run/t.py
+

Will call PyUnstable_PerfMapState_Init() before writing the entry, if the perf map file is not already opened. Returns 0 on success, or the same error codes as PyUnstable_PerfMapState_Init() on failure.

+
+void PyUnstable_PerfMapState_Fini(void)
+
This is Unstable API. It may change without warning in minor releases. +

Close the perf map file opened by PyUnstable_PerfMapState_Init(). This is called by the runtime itself during interpreter shut-down. In general, there shouldn’t be a reason to explicitly call this, except to handle specific scenarios such as forking.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/perfmaps.html +

+
diff --git a/devdocs/python~3.12/c-api%2Frefcounting.html b/devdocs/python~3.12/c-api%2Frefcounting.html new file mode 100644 index 00000000..38f2effe --- /dev/null +++ b/devdocs/python~3.12/c-api%2Frefcounting.html @@ -0,0 +1,51 @@ +

Reference Counting

The functions and macros in this section are used for managing reference counts of Python objects.

+Py_ssize_t Py_REFCNT(PyObject *o)
+

Get the reference count of the Python object o.

Note that the returned value may not actually reflect how many references to the object are actually held. For example, some objects are “immortal” and have a very high refcount that does not reflect the actual number of references. Consequently, do not rely on the returned value to be accurate, other than a value of 0 or 1.

Use the Py_SET_REFCNT() function to set an object reference count.

Changed in version 3.11: The parameter type is no longer const PyObject*.

Changed in version 3.10: Py_REFCNT() is changed to the inline static function.

+
+void Py_SET_REFCNT(PyObject *o, Py_ssize_t refcnt)
+

Set the object o reference counter to refcnt.

Note that this function has no effect on immortal objects.

New in version 3.9.

Changed in version 3.12: Immortal objects are not modified.

+
+void Py_INCREF(PyObject *o)
+

Indicate taking a new strong reference to object o, indicating it is in use and should not be destroyed.

This function is usually used to convert a borrowed reference to a strong reference in-place. The Py_NewRef() function can be used to create a new strong reference.

When done using the object, release it by calling Py_DECREF().

The object must not be NULL; if you aren’t sure that it isn’t NULL, use Py_XINCREF().

Do not expect this function to actually modify o in any way. For at least some objects, this function has no effect.

Changed in version 3.12: Immortal objects are not modified.

+
+void Py_XINCREF(PyObject *o)
+

Similar to Py_INCREF(), but the object o can be NULL, in which case this has no effect.

See also Py_XNewRef().

+
+PyObject *Py_NewRef(PyObject *o)
+ Part of the Stable ABI since version 3.10.

Create a new strong reference to an object: call Py_INCREF() on o and return the object o.

When the strong reference is no longer needed, Py_DECREF() should be called on it to release the reference.

The object o must not be NULL; use Py_XNewRef() if o can be NULL.

For example:

Py_INCREF(obj);
+self->attr = obj;
+

can be written as:

self->attr = Py_NewRef(obj);
+

See also Py_INCREF().

New in version 3.10.

+
+PyObject *Py_XNewRef(PyObject *o)
+ Part of the Stable ABI since version 3.10.

Similar to Py_NewRef(), but the object o can be NULL.

If the object o is NULL, the function just returns NULL.

New in version 3.10.

+
+void Py_DECREF(PyObject *o)
+

Release a strong reference to object o, indicating the reference is no longer used.

Once the last strong reference is released (i.e. the object’s reference count reaches 0), the object’s type’s deallocation function (which must not be NULL) is invoked.

This function is usually used to delete a strong reference before exiting its scope.

The object must not be NULL; if you aren’t sure that it isn’t NULL, use Py_XDECREF().

Do not expect this function to actually modify o in any way. For at least some objects, this function has no effect.

Warning

The deallocation function can cause arbitrary Python code to be invoked (e.g. when a class instance with a __del__() method is deallocated). While exceptions in such code are not propagated, the executed code has free access to all Python global variables. This means that any object that is reachable from a global variable should be in a consistent state before Py_DECREF() is invoked. For example, code to delete an object from a list should copy a reference to the deleted object in a temporary variable, update the list data structure, and then call Py_DECREF() for the temporary variable.

Changed in version 3.12: Immortal objects are not modified.

+
+void Py_XDECREF(PyObject *o)
+

Similar to Py_DECREF(), but the object o can be NULL, in which case this has no effect. The same warning from Py_DECREF() applies here as well.

+
+void Py_CLEAR(PyObject *o)
+

Release a strong reference for object o. The object may be NULL, in which case the macro has no effect; otherwise the effect is the same as for Py_DECREF(), except that the argument is also set to NULL. The warning for Py_DECREF() does not apply with respect to the object passed because the macro carefully uses a temporary variable and sets the argument to NULL before releasing the reference.

It is a good idea to use this macro whenever releasing a reference to an object that might be traversed during garbage collection.

Changed in version 3.12: The macro argument is now only evaluated once. If the argument has side effects, these are no longer duplicated.

+
+void Py_IncRef(PyObject *o)
+ Part of the Stable ABI.

Indicate taking a new strong reference to object o. A function version of Py_XINCREF(). It can be used for runtime dynamic embedding of Python.

+
+void Py_DecRef(PyObject *o)
+ Part of the Stable ABI.

Release a strong reference to object o. A function version of Py_XDECREF(). It can be used for runtime dynamic embedding of Python.

+
+Py_SETREF(dst, src)
+

Macro safely releasing a strong reference to object dst and setting dst to src.

As in case of Py_CLEAR(), “the obvious” code can be deadly:

Py_DECREF(dst);
+dst = src;
+

The safe way is:

Py_SETREF(dst, src);
+

That arranges to set dst to src _before_ releasing the reference to the old value of dst, so that any code triggered as a side-effect of dst getting torn down no longer believes dst points to a valid object.

New in version 3.6.

Changed in version 3.12: The macro arguments are now only evaluated once. If an argument has side effects, these are no longer duplicated.

+
+Py_XSETREF(dst, src)
+

Variant of Py_SETREF macro that uses Py_XDECREF() instead of Py_DECREF().

New in version 3.6.

Changed in version 3.12: The macro arguments are now only evaluated once. If an argument has side effects, these are no longer duplicated.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/refcounting.html +

+
diff --git a/devdocs/python~3.12/c-api%2Freflection.html b/devdocs/python~3.12/c-api%2Freflection.html new file mode 100644 index 00000000..4145a9d9 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Freflection.html @@ -0,0 +1,24 @@ +

Reflection

+PyObject *PyEval_GetBuiltins(void)
+Return value: Borrowed reference. Part of the Stable ABI.

Return a dictionary of the builtins in the current execution frame, or the interpreter of the thread state if no frame is currently executing.

+
+PyObject *PyEval_GetLocals(void)
+Return value: Borrowed reference. Part of the Stable ABI.

Return a dictionary of the local variables in the current execution frame, or NULL if no frame is currently executing.

+
+PyObject *PyEval_GetGlobals(void)
+Return value: Borrowed reference. Part of the Stable ABI.

Return a dictionary of the global variables in the current execution frame, or NULL if no frame is currently executing.

+
+PyFrameObject *PyEval_GetFrame(void)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the current thread state’s frame, which is NULL if no frame is currently executing.

See also PyThreadState_GetFrame().

+
+const char *PyEval_GetFuncName(PyObject *func)
+ Part of the Stable ABI.

Return the name of func if it is a function, class or instance object, else the name of funcs type.

+
+const char *PyEval_GetFuncDesc(PyObject *func)
+ Part of the Stable ABI.

Return a description string, depending on the type of func. Return values include “()” for functions and methods, ” constructor”, ” instance”, and ” object”. Concatenated with the result of PyEval_GetFuncName(), the result will be a description of func.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/reflection.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fsequence.html b/devdocs/python~3.12/c-api%2Fsequence.html new file mode 100644 index 00000000..88e65093 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fsequence.html @@ -0,0 +1,73 @@ +

Sequence Protocol

+int PySequence_Check(PyObject *o)
+ Part of the Stable ABI.

Return 1 if the object provides the sequence protocol, and 0 otherwise. Note that it returns 1 for Python classes with a __getitem__() method, unless they are dict subclasses, since in general it is impossible to determine what type of keys the class supports. This function always succeeds.

+
+Py_ssize_t PySequence_Size(PyObject *o)
+Py_ssize_t PySequence_Length(PyObject *o)
+ Part of the Stable ABI.

Returns the number of objects in sequence o on success, and -1 on failure. This is equivalent to the Python expression len(o).

+
+PyObject *PySequence_Concat(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Return the concatenation of o1 and o2 on success, and NULL on failure. This is the equivalent of the Python expression o1 + o2.

+
+PyObject *PySequence_Repeat(PyObject *o, Py_ssize_t count)
+Return value: New reference. Part of the Stable ABI.

Return the result of repeating sequence object o count times, or NULL on failure. This is the equivalent of the Python expression o * count.

+
+PyObject *PySequence_InPlaceConcat(PyObject *o1, PyObject *o2)
+Return value: New reference. Part of the Stable ABI.

Return the concatenation of o1 and o2 on success, and NULL on failure. The operation is done in-place when o1 supports it. This is the equivalent of the Python expression o1 += o2.

+
+PyObject *PySequence_InPlaceRepeat(PyObject *o, Py_ssize_t count)
+Return value: New reference. Part of the Stable ABI.

Return the result of repeating sequence object o count times, or NULL on failure. The operation is done in-place when o supports it. This is the equivalent of the Python expression o *= count.

+
+PyObject *PySequence_GetItem(PyObject *o, Py_ssize_t i)
+Return value: New reference. Part of the Stable ABI.

Return the ith element of o, or NULL on failure. This is the equivalent of the Python expression o[i].

+
+PyObject *PySequence_GetSlice(PyObject *o, Py_ssize_t i1, Py_ssize_t i2)
+Return value: New reference. Part of the Stable ABI.

Return the slice of sequence object o between i1 and i2, or NULL on failure. This is the equivalent of the Python expression o[i1:i2].

+
+int PySequence_SetItem(PyObject *o, Py_ssize_t i, PyObject *v)
+ Part of the Stable ABI.

Assign object v to the ith element of o. Raise an exception and return -1 on failure; return 0 on success. This is the equivalent of the Python statement o[i] = v. This function does not steal a reference to v.

If v is NULL, the element is deleted, but this feature is deprecated in favour of using PySequence_DelItem().

+
+int PySequence_DelItem(PyObject *o, Py_ssize_t i)
+ Part of the Stable ABI.

Delete the ith element of object o. Returns -1 on failure. This is the equivalent of the Python statement del o[i].

+
+int PySequence_SetSlice(PyObject *o, Py_ssize_t i1, Py_ssize_t i2, PyObject *v)
+ Part of the Stable ABI.

Assign the sequence object v to the slice in sequence object o from i1 to i2. This is the equivalent of the Python statement o[i1:i2] = v.

+
+int PySequence_DelSlice(PyObject *o, Py_ssize_t i1, Py_ssize_t i2)
+ Part of the Stable ABI.

Delete the slice in sequence object o from i1 to i2. Returns -1 on failure. This is the equivalent of the Python statement del o[i1:i2].

+
+Py_ssize_t PySequence_Count(PyObject *o, PyObject *value)
+ Part of the Stable ABI.

Return the number of occurrences of value in o, that is, return the number of keys for which o[key] == value. On failure, return -1. This is equivalent to the Python expression o.count(value).

+
+int PySequence_Contains(PyObject *o, PyObject *value)
+ Part of the Stable ABI.

Determine if o contains value. If an item in o is equal to value, return 1, otherwise return 0. On error, return -1. This is equivalent to the Python expression value in o.

+
+Py_ssize_t PySequence_Index(PyObject *o, PyObject *value)
+ Part of the Stable ABI.

Return the first index i for which o[i] == value. On error, return -1. This is equivalent to the Python expression o.index(value).

+
+PyObject *PySequence_List(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Return a list object with the same contents as the sequence or iterable o, or NULL on failure. The returned list is guaranteed to be new. This is equivalent to the Python expression list(o).

+
+PyObject *PySequence_Tuple(PyObject *o)
+Return value: New reference. Part of the Stable ABI.

Return a tuple object with the same contents as the sequence or iterable o, or NULL on failure. If o is a tuple, a new reference will be returned, otherwise a tuple will be constructed with the appropriate contents. This is equivalent to the Python expression tuple(o).

+
+PyObject *PySequence_Fast(PyObject *o, const char *m)
+Return value: New reference. Part of the Stable ABI.

Return the sequence or iterable o as an object usable by the other PySequence_Fast* family of functions. If the object is not a sequence or iterable, raises TypeError with m as the message text. Returns NULL on failure.

The PySequence_Fast* functions are thus named because they assume o is a PyTupleObject or a PyListObject and access the data fields of o directly.

As a CPython implementation detail, if o is already a sequence or list, it will be returned.

+
+Py_ssize_t PySequence_Fast_GET_SIZE(PyObject *o)
+

Returns the length of o, assuming that o was returned by PySequence_Fast() and that o is not NULL. The size can also be retrieved by calling PySequence_Size() on o, but PySequence_Fast_GET_SIZE() is faster because it can assume o is a list or tuple.

+
+PyObject *PySequence_Fast_GET_ITEM(PyObject *o, Py_ssize_t i)
+Return value: Borrowed reference.

Return the ith element of o, assuming that o was returned by PySequence_Fast(), o is not NULL, and that i is within bounds.

+
+PyObject **PySequence_Fast_ITEMS(PyObject *o)
+

Return the underlying array of PyObject pointers. Assumes that o was returned by PySequence_Fast() and o is not NULL.

Note, if a list gets resized, the reallocation may relocate the items array. So, only use the underlying array pointer in contexts where the sequence cannot change.

+
+PyObject *PySequence_ITEM(PyObject *o, Py_ssize_t i)
+Return value: New reference.

Return the ith element of o or NULL on failure. Faster form of PySequence_GetItem() but without checking that PySequence_Check() on o is true and without adjustment for negative indices.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/sequence.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fset.html b/devdocs/python~3.12/c-api%2Fset.html new file mode 100644 index 00000000..72a4937c --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fset.html @@ -0,0 +1,60 @@ +

Set Objects

This section details the public API for set and frozenset objects. Any functionality not listed below is best accessed using either the abstract object protocol (including PyObject_CallMethod(), PyObject_RichCompareBool(), PyObject_Hash(), PyObject_Repr(), PyObject_IsTrue(), PyObject_Print(), and PyObject_GetIter()) or the abstract number protocol (including PyNumber_And(), PyNumber_Subtract(), PyNumber_Or(), PyNumber_Xor(), PyNumber_InPlaceAnd(), PyNumber_InPlaceSubtract(), PyNumber_InPlaceOr(), and PyNumber_InPlaceXor()).

+type PySetObject
+

This subtype of PyObject is used to hold the internal data for both set and frozenset objects. It is like a PyDictObject in that it is a fixed size for small sets (much like tuple storage) and will point to a separate, variable sized block of memory for medium and large sized sets (much like list storage). None of the fields of this structure should be considered public and all are subject to change. All access should be done through the documented API rather than by manipulating the values in the structure.

+
+PyTypeObject PySet_Type
+ Part of the Stable ABI.

This is an instance of PyTypeObject representing the Python set type.

+
+PyTypeObject PyFrozenSet_Type
+ Part of the Stable ABI.

This is an instance of PyTypeObject representing the Python frozenset type.

+

The following type check macros work on pointers to any Python object. Likewise, the constructor functions work with any iterable Python object.

+int PySet_Check(PyObject *p)
+

Return true if p is a set object or an instance of a subtype. This function always succeeds.

+
+int PyFrozenSet_Check(PyObject *p)
+

Return true if p is a frozenset object or an instance of a subtype. This function always succeeds.

+
+int PyAnySet_Check(PyObject *p)
+

Return true if p is a set object, a frozenset object, or an instance of a subtype. This function always succeeds.

+
+int PySet_CheckExact(PyObject *p)
+

Return true if p is a set object but not an instance of a subtype. This function always succeeds.

New in version 3.10.

+
+int PyAnySet_CheckExact(PyObject *p)
+

Return true if p is a set object or a frozenset object but not an instance of a subtype. This function always succeeds.

+
+int PyFrozenSet_CheckExact(PyObject *p)
+

Return true if p is a frozenset object but not an instance of a subtype. This function always succeeds.

+
+PyObject *PySet_New(PyObject *iterable)
+Return value: New reference. Part of the Stable ABI.

Return a new set containing objects returned by the iterable. The iterable may be NULL to create a new empty set. Return the new set on success or NULL on failure. Raise TypeError if iterable is not actually iterable. The constructor is also useful for copying a set (c=set(s)).

+
+PyObject *PyFrozenSet_New(PyObject *iterable)
+Return value: New reference. Part of the Stable ABI.

Return a new frozenset containing objects returned by the iterable. The iterable may be NULL to create a new empty frozenset. Return the new set on success or NULL on failure. Raise TypeError if iterable is not actually iterable.

+

The following functions and macros are available for instances of set or frozenset or instances of their subtypes.

+Py_ssize_t PySet_Size(PyObject *anyset)
+ Part of the Stable ABI.

Return the length of a set or frozenset object. Equivalent to len(anyset). Raises a SystemError if anyset is not a set, frozenset, or an instance of a subtype.

+
+Py_ssize_t PySet_GET_SIZE(PyObject *anyset)
+

Macro form of PySet_Size() without error checking.

+
+int PySet_Contains(PyObject *anyset, PyObject *key)
+ Part of the Stable ABI.

Return 1 if found, 0 if not found, and -1 if an error is encountered. Unlike the Python __contains__() method, this function does not automatically convert unhashable sets into temporary frozensets. Raise a TypeError if the key is unhashable. Raise SystemError if anyset is not a set, frozenset, or an instance of a subtype.

+
+int PySet_Add(PyObject *set, PyObject *key)
+ Part of the Stable ABI.

Add key to a set instance. Also works with frozenset instances (like PyTuple_SetItem() it can be used to fill in the values of brand new frozensets before they are exposed to other code). Return 0 on success or -1 on failure. Raise a TypeError if the key is unhashable. Raise a MemoryError if there is no room to grow. Raise a SystemError if set is not an instance of set or its subtype.

+

The following functions are available for instances of set or its subtypes but not for instances of frozenset or its subtypes.

+int PySet_Discard(PyObject *set, PyObject *key)
+ Part of the Stable ABI.

Return 1 if found and removed, 0 if not found (no action taken), and -1 if an error is encountered. Does not raise KeyError for missing keys. Raise a TypeError if the key is unhashable. Unlike the Python discard() method, this function does not automatically convert unhashable sets into temporary frozensets. Raise SystemError if set is not an instance of set or its subtype.

+
+PyObject *PySet_Pop(PyObject *set)
+Return value: New reference. Part of the Stable ABI.

Return a new reference to an arbitrary object in the set, and removes the object from the set. Return NULL on failure. Raise KeyError if the set is empty. Raise a SystemError if set is not an instance of set or its subtype.

+
+int PySet_Clear(PyObject *set)
+ Part of the Stable ABI.

Empty an existing set of all elements. Return 0 on success. Return -1 and raise SystemError if set is not an instance of set or its subtype.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/set.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fslice.html b/devdocs/python~3.12/c-api%2Fslice.html new file mode 100644 index 00000000..a7a94407 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fslice.html @@ -0,0 +1,37 @@ +

Slice Objects

+PyTypeObject PySlice_Type
+ Part of the Stable ABI.

The type object for slice objects. This is the same as slice in the Python layer.

+
+int PySlice_Check(PyObject *ob)
+

Return true if ob is a slice object; ob must not be NULL. This function always succeeds.

+
+PyObject *PySlice_New(PyObject *start, PyObject *stop, PyObject *step)
+Return value: New reference. Part of the Stable ABI.

Return a new slice object with the given values. The start, stop, and step parameters are used as the values of the slice object attributes of the same names. Any of the values may be NULL, in which case the None will be used for the corresponding attribute. Return NULL if the new object could not be allocated.

+
+int PySlice_GetIndices(PyObject *slice, Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step)
+ Part of the Stable ABI.

Retrieve the start, stop and step indices from the slice object slice, assuming a sequence of length length. Treats indices greater than length as errors.

Returns 0 on success and -1 on error with no exception set (unless one of the indices was not None and failed to be converted to an integer, in which case -1 is returned with an exception set).

You probably do not want to use this function.

Changed in version 3.2: The parameter type for the slice parameter was PySliceObject* before.

+
+int PySlice_GetIndicesEx(PyObject *slice, Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step, Py_ssize_t *slicelength)
+ Part of the Stable ABI.

Usable replacement for PySlice_GetIndices(). Retrieve the start, stop, and step indices from the slice object slice assuming a sequence of length length, and store the length of the slice in slicelength. Out of bounds indices are clipped in a manner consistent with the handling of normal slices.

Returns 0 on success and -1 on error with exception set.

Note

This function is considered not safe for resizable sequences. Its invocation should be replaced by a combination of PySlice_Unpack() and PySlice_AdjustIndices() where

if (PySlice_GetIndicesEx(slice, length, &start, &stop, &step, &slicelength) < 0) {
+    // return error
+}
+

is replaced by

if (PySlice_Unpack(slice, &start, &stop, &step) < 0) {
+    // return error
+}
+slicelength = PySlice_AdjustIndices(length, &start, &stop, step);
+

Changed in version 3.2: The parameter type for the slice parameter was PySliceObject* before.

Changed in version 3.6.1: If Py_LIMITED_API is not set or set to the value between 0x03050400 and 0x03060000 (not including) or 0x03060100 or higher PySlice_GetIndicesEx() is implemented as a macro using PySlice_Unpack() and PySlice_AdjustIndices(). Arguments start, stop and step are evaluated more than once.

Deprecated since version 3.6.1: If Py_LIMITED_API is set to the value less than 0x03050400 or between 0x03060000 and 0x03060100 (not including) PySlice_GetIndicesEx() is a deprecated function.

+
+int PySlice_Unpack(PyObject *slice, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t *step)
+ Part of the Stable ABI since version 3.7.

Extract the start, stop and step data members from a slice object as C integers. Silently reduce values larger than PY_SSIZE_T_MAX to PY_SSIZE_T_MAX, silently boost the start and stop values less than PY_SSIZE_T_MIN to PY_SSIZE_T_MIN, and silently boost the step values less than -PY_SSIZE_T_MAX to -PY_SSIZE_T_MAX.

Return -1 on error, 0 on success.

New in version 3.6.1.

+
+Py_ssize_t PySlice_AdjustIndices(Py_ssize_t length, Py_ssize_t *start, Py_ssize_t *stop, Py_ssize_t step)
+ Part of the Stable ABI since version 3.7.

Adjust start/end slice indices assuming a sequence of the specified length. Out of bounds indices are clipped in a manner consistent with the handling of normal slices.

Return the length of the slice. Always successful. Doesn’t call Python code.

New in version 3.6.1.

+

Ellipsis Object

+PyObject *Py_Ellipsis
+

The Python Ellipsis object. This object has no methods. Like Py_None, it is an immortal. singleton object.

Changed in version 3.12: Py_Ellipsis is immortal.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/slice.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fstable.html b/devdocs/python~3.12/c-api%2Fstable.html new file mode 100644 index 00000000..35db60a6 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fstable.html @@ -0,0 +1,12 @@ +

C API Stability

Unless documented otherwise, Python’s C API is covered by the Backwards Compatibility Policy, PEP 387. Most changes to it are source-compatible (typically by only adding new API). Changing existing API or removing API is only done after a deprecation period or to fix serious issues.

CPython’s Application Binary Interface (ABI) is forward- and backwards-compatible across a minor release (if these are compiled the same way; see Platform Considerations below). So, code compiled for Python 3.10.0 will work on 3.10.8 and vice versa, but will need to be compiled separately for 3.9.x and 3.10.x.

There are two tiers of C API with different stability expectations:

These are discussed in more detail below.

Names prefixed by an underscore, such as _Py_InternalState, are private API that can change without notice even in patch releases. If you need to use this API, consider reaching out to CPython developers to discuss adding public API for your use case.

Unstable C API

Any API named with the PyUnstable prefix exposes CPython implementation details, and may change in every minor release (e.g. from 3.9 to 3.10) without any deprecation warnings. However, it will not change in a bugfix release (e.g. from 3.10.0 to 3.10.1).

It is generally intended for specialized, low-level tools like debuggers.

Projects that use this API are expected to follow CPython development and spend extra effort adjusting to changes.

Stable Application Binary Interface

For simplicity, this document talks about extensions, but the Limited API and Stable ABI work the same way for all uses of the API – for example, embedding Python.

Limited C API

Python 3.2 introduced the Limited API, a subset of Python’s C API. Extensions that only use the Limited API can be compiled once and work with multiple versions of Python. Contents of the Limited API are listed below.

+Py_LIMITED_API
+

Define this macro before including Python.h to opt in to only use the Limited API, and to select the Limited API version.

Define Py_LIMITED_API to the value of PY_VERSION_HEX corresponding to the lowest Python version your extension supports. The extension will work without recompilation with all Python 3 releases from the specified one onward, and can use Limited API introduced up to that version.

Rather than using the PY_VERSION_HEX macro directly, hardcode a minimum minor version (e.g. 0x030A0000 for Python 3.10) for stability when compiling with future Python versions.

You can also define Py_LIMITED_API to 3. This works the same as 0x03020000 (Python 3.2, the version that introduced Limited API).

+

Stable ABI

To enable this, Python provides a Stable ABI: a set of symbols that will remain compatible across Python 3.x versions.

The Stable ABI contains symbols exposed in the Limited API, but also other ones – for example, functions necessary to support older versions of the Limited API.

On Windows, extensions that use the Stable ABI should be linked against python3.dll rather than a version-specific library such as python39.dll.

On some platforms, Python will look for and load shared library files named with the abi3 tag (e.g. mymodule.abi3.so). It does not check if such extensions conform to a Stable ABI. The user (or their packaging tools) need to ensure that, for example, extensions built with the 3.10+ Limited API are not installed for lower versions of Python.

All functions in the Stable ABI are present as functions in Python’s shared library, not solely as macros. This makes them usable from languages that don’t use the C preprocessor.

Limited API Scope and Performance

The goal for the Limited API is to allow everything that is possible with the full C API, but possibly with a performance penalty.

For example, while PyList_GetItem() is available, its “unsafe” macro variant PyList_GET_ITEM() is not. The macro can be faster because it can rely on version-specific implementation details of the list object.

Without Py_LIMITED_API defined, some C API functions are inlined or replaced by macros. Defining Py_LIMITED_API disables this inlining, allowing stability as Python’s data structures are improved, but possibly reducing performance.

By leaving out the Py_LIMITED_API definition, it is possible to compile a Limited API extension with a version-specific ABI. This can improve performance for that Python version, but will limit compatibility. Compiling with Py_LIMITED_API will then yield an extension that can be distributed where a version-specific one is not available – for example, for prereleases of an upcoming Python version.

Limited API Caveats

Note that compiling with Py_LIMITED_API is not a complete guarantee that code conforms to the Limited API or the Stable ABI. Py_LIMITED_API only covers definitions, but an API also includes other issues, such as expected semantics.

One issue that Py_LIMITED_API does not guard against is calling a function with arguments that are invalid in a lower Python version. For example, consider a function that starts accepting NULL for an argument. In Python 3.9, NULL now selects a default behavior, but in Python 3.8, the argument will be used directly, causing a NULL dereference and crash. A similar argument works for fields of structs.

Another issue is that some struct fields are currently not hidden when Py_LIMITED_API is defined, even though they’re part of the Limited API.

For these reasons, we recommend testing an extension with all minor Python versions it supports, and preferably to build with the lowest such version.

We also recommend reviewing documentation of all used API to check if it is explicitly part of the Limited API. Even with Py_LIMITED_API defined, a few private declarations are exposed for technical reasons (or even unintentionally, as bugs).

Also note that the Limited API is not necessarily stable: compiling with Py_LIMITED_API with Python 3.8 means that the extension will run with Python 3.12, but it will not necessarily compile with Python 3.12. In particular, parts of the Limited API may be deprecated and removed, provided that the Stable ABI stays stable.

Platform Considerations

ABI stability depends not only on Python, but also on the compiler used, lower-level libraries and compiler options. For the purposes of the Stable ABI, these details define a “platform”. They usually depend on the OS type and processor architecture

It is the responsibility of each particular distributor of Python to ensure that all Python versions on a particular platform are built in a way that does not break the Stable ABI. This is the case with Windows and macOS releases from python.org and many third-party distributors.

Contents of Limited API

Currently, the Limited API includes the following items:

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/stable.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fstructures.html b/devdocs/python~3.12/c-api%2Fstructures.html new file mode 100644 index 00000000..ebaed672 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fstructures.html @@ -0,0 +1,274 @@ +

Common Object Structures

There are a large number of structures which are used in the definition of object types for Python. This section describes these structures and how they are used.

Base object types and macros

All Python objects ultimately share a small number of fields at the beginning of the object’s representation in memory. These are represented by the PyObject and PyVarObject types, which are defined, in turn, by the expansions of some macros also used, whether directly or indirectly, in the definition of all other Python objects. Additional macros can be found under reference counting.

+type PyObject
+ Part of the Limited API. (Only some members are part of the stable ABI.)

All object types are extensions of this type. This is a type which contains the information Python needs to treat a pointer to an object as an object. In a normal “release” build, it contains only the object’s reference count and a pointer to the corresponding type object. Nothing is actually declared to be a PyObject, but every pointer to a Python object can be cast to a PyObject*. Access to the members must be done by using the macros Py_REFCNT and Py_TYPE.

+
+type PyVarObject
+ Part of the Limited API. (Only some members are part of the stable ABI.)

This is an extension of PyObject that adds the ob_size field. This is only used for objects that have some notion of length. This type does not often appear in the Python/C API. Access to the members must be done by using the macros Py_REFCNT, Py_TYPE, and Py_SIZE.

+
+PyObject_HEAD
+

This is a macro used when declaring new types which represent objects without a varying length. The PyObject_HEAD macro expands to:

PyObject ob_base;
+

See documentation of PyObject above.

+
+PyObject_VAR_HEAD
+

This is a macro used when declaring new types which represent objects with a length that varies from instance to instance. The PyObject_VAR_HEAD macro expands to:

PyVarObject ob_base;
+

See documentation of PyVarObject above.

+
+int Py_Is(PyObject *x, PyObject *y)
+ Part of the Stable ABI since version 3.10.

Test if the x object is the y object, the same as x is y in Python.

New in version 3.10.

+
+int Py_IsNone(PyObject *x)
+ Part of the Stable ABI since version 3.10.

Test if an object is the None singleton, the same as x is None in Python.

New in version 3.10.

+
+int Py_IsTrue(PyObject *x)
+ Part of the Stable ABI since version 3.10.

Test if an object is the True singleton, the same as x is True in Python.

New in version 3.10.

+
+int Py_IsFalse(PyObject *x)
+ Part of the Stable ABI since version 3.10.

Test if an object is the False singleton, the same as x is False in Python.

New in version 3.10.

+
+PyTypeObject *Py_TYPE(PyObject *o)
+

Get the type of the Python object o.

Return a borrowed reference.

Use the Py_SET_TYPE() function to set an object type.

Changed in version 3.11: Py_TYPE() is changed to an inline static function. The parameter type is no longer const PyObject*.

+
+int Py_IS_TYPE(PyObject *o, PyTypeObject *type)
+

Return non-zero if the object o type is type. Return zero otherwise. Equivalent to: Py_TYPE(o) == type.

New in version 3.9.

+
+void Py_SET_TYPE(PyObject *o, PyTypeObject *type)
+

Set the object o type to type.

New in version 3.9.

+
+Py_ssize_t Py_SIZE(PyVarObject *o)
+

Get the size of the Python object o.

Use the Py_SET_SIZE() function to set an object size.

Changed in version 3.11: Py_SIZE() is changed to an inline static function. The parameter type is no longer const PyVarObject*.

+
+void Py_SET_SIZE(PyVarObject *o, Py_ssize_t size)
+

Set the object o size to size.

New in version 3.9.

+
+PyObject_HEAD_INIT(type)
+

This is a macro which expands to initialization values for a new PyObject type. This macro expands to:

_PyObject_EXTRA_INIT
+1, type,
+
+
+PyVarObject_HEAD_INIT(type, size)
+

This is a macro which expands to initialization values for a new PyVarObject type, including the ob_size field. This macro expands to:

_PyObject_EXTRA_INIT
+1, type, size,
+
+

Implementing functions and methods

+type PyCFunction
+ Part of the Stable ABI.

Type of the functions used to implement most Python callables in C. Functions of this type take two PyObject* parameters and return one such value. If the return value is NULL, an exception shall have been set. If not NULL, the return value is interpreted as the return value of the function as exposed in Python. The function must return a new reference.

The function signature is:

PyObject *PyCFunction(PyObject *self,
+                      PyObject *args);
+
+
+type PyCFunctionWithKeywords
+ Part of the Stable ABI.

Type of the functions used to implement Python callables in C with signature METH_VARARGS | METH_KEYWORDS. The function signature is:

PyObject *PyCFunctionWithKeywords(PyObject *self,
+                                  PyObject *args,
+                                  PyObject *kwargs);
+
+
+type _PyCFunctionFast
+

Type of the functions used to implement Python callables in C with signature METH_FASTCALL. The function signature is:

PyObject *_PyCFunctionFast(PyObject *self,
+                           PyObject *const *args,
+                           Py_ssize_t nargs);
+
+
+type _PyCFunctionFastWithKeywords
+

Type of the functions used to implement Python callables in C with signature METH_FASTCALL | METH_KEYWORDS. The function signature is:

PyObject *_PyCFunctionFastWithKeywords(PyObject *self,
+                                       PyObject *const *args,
+                                       Py_ssize_t nargs,
+                                       PyObject *kwnames);
+
+
+type PyCMethod
+

Type of the functions used to implement Python callables in C with signature METH_METHOD | METH_FASTCALL | METH_KEYWORDS. The function signature is:

PyObject *PyCMethod(PyObject *self,
+                    PyTypeObject *defining_class,
+                    PyObject *const *args,
+                    Py_ssize_t nargs,
+                    PyObject *kwnames)
+

New in version 3.9.

+
+type PyMethodDef
+ Part of the Stable ABI (including all members).

Structure used to describe a method of an extension type. This structure has four fields:

+const char *ml_name
+

Name of the method.

+
+PyCFunction ml_meth
+

Pointer to the C implementation.

+
+int ml_flags
+

Flags bits indicating how the call should be constructed.

+
+const char *ml_doc
+

Points to the contents of the docstring.

+
+

The ml_meth is a C function pointer. The functions may be of different types, but they always return PyObject*. If the function is not of the PyCFunction, the compiler will require a cast in the method table. Even though PyCFunction defines the first parameter as PyObject*, it is common that the method implementation uses the specific C type of the self object.

The ml_flags field is a bitfield which can include the following flags. The individual flags indicate either a calling convention or a binding convention.

There are these calling conventions:

+METH_VARARGS
+

This is the typical calling convention, where the methods have the type PyCFunction. The function expects two PyObject* values. The first one is the self object for methods; for module functions, it is the module object. The second parameter (often called args) is a tuple object representing all arguments. This parameter is typically processed using PyArg_ParseTuple() or PyArg_UnpackTuple().

+
+METH_KEYWORDS
+

Can only be used in certain combinations with other flags: METH_VARARGS | METH_KEYWORDS, METH_FASTCALL | METH_KEYWORDS and METH_METHOD | METH_FASTCALL | METH_KEYWORDS.

+
METH_VARARGS | METH_KEYWORDS
+
+

Methods with these flags must be of type PyCFunctionWithKeywords. The function expects three parameters: self, args, kwargs where kwargs is a dictionary of all the keyword arguments or possibly NULL if there are no keyword arguments. The parameters are typically processed using PyArg_ParseTupleAndKeywords().

+METH_FASTCALL
+

Fast calling convention supporting only positional arguments. The methods have the type _PyCFunctionFast. The first parameter is self, the second parameter is a C array of PyObject* values indicating the arguments and the third parameter is the number of arguments (the length of the array).

New in version 3.7.

Changed in version 3.10: METH_FASTCALL is now part of the stable ABI.

+
METH_FASTCALL | METH_KEYWORDS
+
+

Extension of METH_FASTCALL supporting also keyword arguments, with methods of type _PyCFunctionFastWithKeywords. Keyword arguments are passed the same way as in the vectorcall protocol: there is an additional fourth PyObject* parameter which is a tuple representing the names of the keyword arguments (which are guaranteed to be strings) or possibly NULL if there are no keywords. The values of the keyword arguments are stored in the args array, after the positional arguments.

New in version 3.7.

+METH_METHOD
+

Can only be used in the combination with other flags: METH_METHOD | METH_FASTCALL | METH_KEYWORDS.

+
METH_METHOD | METH_FASTCALL | METH_KEYWORDS
+
+

Extension of METH_FASTCALL | METH_KEYWORDS supporting the defining class, that is, the class that contains the method in question. The defining class might be a superclass of Py_TYPE(self).

The method needs to be of type PyCMethod, the same as for METH_FASTCALL | METH_KEYWORDS with defining_class argument added after self.

New in version 3.9.

+METH_NOARGS
+

Methods without parameters don’t need to check whether arguments are given if they are listed with the METH_NOARGS flag. They need to be of type PyCFunction. The first parameter is typically named self and will hold a reference to the module or object instance. In all cases the second parameter will be NULL.

The function must have 2 parameters. Since the second parameter is unused, Py_UNUSED can be used to prevent a compiler warning.

+
+METH_O
+

Methods with a single object argument can be listed with the METH_O flag, instead of invoking PyArg_ParseTuple() with a "O" argument. They have the type PyCFunction, with the self parameter, and a PyObject* parameter representing the single argument.

+

These two constants are not used to indicate the calling convention but the binding when use with methods of classes. These may not be used for functions defined for modules. At most one of these flags may be set for any given method.

+METH_CLASS
+

The method will be passed the type object as the first parameter rather than an instance of the type. This is used to create class methods, similar to what is created when using the classmethod() built-in function.

+
+METH_STATIC
+

The method will be passed NULL as the first parameter rather than an instance of the type. This is used to create static methods, similar to what is created when using the staticmethod() built-in function.

+

One other constant controls whether a method is loaded in place of another definition with the same method name.

+METH_COEXIST
+

The method will be loaded in place of existing definitions. Without METH_COEXIST, the default is to skip repeated definitions. Since slot wrappers are loaded before the method table, the existence of a sq_contains slot, for example, would generate a wrapped method named __contains__() and preclude the loading of a corresponding PyCFunction with the same name. With the flag defined, the PyCFunction will be loaded in place of the wrapper object and will co-exist with the slot. This is helpful because calls to PyCFunctions are optimized more than wrapper object calls.

+

Accessing attributes of extension types

+type PyMemberDef
+ Part of the Stable ABI (including all members).

Structure which describes an attribute of a type which corresponds to a C struct member. When defining a class, put a NULL-terminated array of these structures in the tp_members slot.

Its fields are, in order:

+const char *name
+

Name of the member. A NULL value marks the end of a PyMemberDef[] array.

The string should be static, no copy is made of it.

+
+int type
+

The type of the member in the C struct. See Member types for the possible values.

+
+Py_ssize_t offset
+

The offset in bytes that the member is located on the type’s object struct.

+
+int flags
+

Zero or more of the Member flags, combined using bitwise OR.

+
+const char *doc
+

The docstring, or NULL. The string should be static, no copy is made of it. Typically, it is defined using PyDoc_STR.

+

By default (when flags is 0), members allow both read and write access. Use the Py_READONLY flag for read-only access. Certain types, like Py_T_STRING, imply Py_READONLY. Only Py_T_OBJECT_EX (and legacy T_OBJECT) members can be deleted.

For heap-allocated types (created using PyType_FromSpec() or similar), PyMemberDef may contain a definition for the special member "__vectorcalloffset__", corresponding to tp_vectorcall_offset in type objects. These must be defined with Py_T_PYSSIZET and Py_READONLY, for example:

static PyMemberDef spam_type_members[] = {
+    {"__vectorcalloffset__", Py_T_PYSSIZET,
+     offsetof(Spam_object, vectorcall), Py_READONLY},
+    {NULL}  /* Sentinel */
+};
+

(You may need to #include <stddef.h> for offsetof().)

The legacy offsets tp_dictoffset and tp_weaklistoffset can be defined similarly using "__dictoffset__" and "__weaklistoffset__" members, but extensions are strongly encouraged to use Py_TPFLAGS_MANAGED_DICT and Py_TPFLAGS_MANAGED_WEAKREF instead.

Changed in version 3.12: PyMemberDef is always available. Previously, it required including "structmember.h".

+
+PyObject *PyMember_GetOne(const char *obj_addr, struct PyMemberDef *m)
+ Part of the Stable ABI.

Get an attribute belonging to the object at address obj_addr. The attribute is described by PyMemberDef m. Returns NULL on error.

Changed in version 3.12: PyMember_GetOne is always available. Previously, it required including "structmember.h".

+
+int PyMember_SetOne(char *obj_addr, struct PyMemberDef *m, PyObject *o)
+ Part of the Stable ABI.

Set an attribute belonging to the object at address obj_addr to object o. The attribute to set is described by PyMemberDef m. Returns 0 if successful and a negative value on failure.

Changed in version 3.12: PyMember_SetOne is always available. Previously, it required including "structmember.h".

+

Member flags

The following flags can be used with PyMemberDef.flags:

+Py_READONLY
+

Not writable.

+
+Py_AUDIT_READ
+

Emit an object.__getattr__ audit event before reading.

+
+Py_RELATIVE_OFFSET
+

Indicates that the offset of this PyMemberDef entry indicates an offset from the subclass-specific data, rather than from PyObject.

Can only be used as part of Py_tp_members slot when creating a class using negative basicsize. It is mandatory in that case.

This flag is only used in PyTypeSlot. When setting tp_members during class creation, Python clears it and sets PyMemberDef.offset to the offset from the PyObject struct.

+

Changed in version 3.10: The RESTRICTED, READ_RESTRICTED and WRITE_RESTRICTED macros available with #include "structmember.h" are deprecated. READ_RESTRICTED and RESTRICTED are equivalent to Py_AUDIT_READ; WRITE_RESTRICTED does nothing.

Changed in version 3.12: The READONLY macro was renamed to Py_READONLY. The PY_AUDIT_READ macro was renamed with the Py_ prefix. The new names are now always available. Previously, these required #include "structmember.h". The header is still available and it provides the old names.

Member types

PyMemberDef.type can be one of the following macros corresponding to various C types. When the member is accessed in Python, it will be converted to the equivalent Python type. When it is set from Python, it will be converted back to the C type. If that is not possible, an exception such as TypeError or ValueError is raised.

Unless marked (D), attributes defined this way cannot be deleted using e.g. del or delattr().

+ + + + + + + + + + + + + + + + + + +

Macro name

C type

Python type
+
+Py_T_BYTE
+

char

int
+
+Py_T_SHORT
+

short

int
+
+Py_T_INT
+

int

int
+
+Py_T_LONG
+

long

int
+
+Py_T_LONGLONG
+

long long

int
+
+Py_T_UBYTE
+

unsigned char

int
+
+Py_T_UINT
+

unsigned int

int
+
+Py_T_USHORT
+

unsigned short

int
+
+Py_T_ULONG
+

unsigned long

int
+
+Py_T_ULONGLONG
+

unsigned long long

int
+
+Py_T_PYSSIZET
+

Py_ssize_t

int
+
+Py_T_FLOAT
+

float

float
+
+Py_T_DOUBLE
+

double

float
+
+Py_T_BOOL
+

char (written as 0 or 1)

bool
+
+Py_T_STRING
+

const char* (*)

str (RO)
+
+Py_T_STRING_INPLACE
+

const char[] (*)

str (RO)
+
+Py_T_CHAR
+

char (0-127)

str (**)
+
+Py_T_OBJECT_EX
+

PyObject*

object (D)

(*): Zero-terminated, UTF8-encoded C string. With Py_T_STRING the C representation is a pointer; with Py_T_STRING_INPLACE the string is stored directly in the structure.

(**): String of length 1. Only ASCII is accepted.

(RO): Implies Py_READONLY.

(D): Can be deleted, in which case the pointer is set to NULL. Reading a NULL pointer raises AttributeError.

New in version 3.12: In previous versions, the macros were only available with #include "structmember.h" and were named without the Py_ prefix (e.g. as T_INT). The header is still available and contains the old names, along with the following deprecated types:

+T_OBJECT
+

Like Py_T_OBJECT_EX, but NULL is converted to None. This results in surprising behavior in Python: deleting the attribute effectively sets it to None.

+
+T_NONE
+

Always None. Must be used with Py_READONLY.

+

Defining Getters and Setters

+type PyGetSetDef
+ Part of the Stable ABI (including all members).

Structure to define property-like access for a type. See also description of the PyTypeObject.tp_getset slot.

+const char *name
+

attribute name

+
+getter get
+

C function to get the attribute.

+
+setter set
+

Optional C function to set or delete the attribute, if omitted the attribute is readonly.

+
+const char *doc
+

optional docstring

+
+void *closure
+

Optional function pointer, providing additional data for getter and setter.

+

The get function takes one PyObject* parameter (the instance) and a function pointer (the associated closure):

typedef PyObject *(*getter)(PyObject *, void *);
+

It should return a new reference on success or NULL with a set exception on failure.

set functions take two PyObject* parameters (the instance and the value to be set) and a function pointer (the associated closure):

typedef int (*setter)(PyObject *, PyObject *, void *);
+

In case the attribute should be deleted the second parameter is NULL. Should return 0 on success or -1 with a set exception on failure.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/structures.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fsys.html b/devdocs/python~3.12/c-api%2Fsys.html new file mode 100644 index 00000000..a0dba9bf --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fsys.html @@ -0,0 +1,39 @@ +

Operating System Utilities

+PyObject *PyOS_FSPath(PyObject *path)
+Return value: New reference. Part of the Stable ABI since version 3.6.

Return the file system representation for path. If the object is a str or bytes object, then a new strong reference is returned. If the object implements the os.PathLike interface, then __fspath__() is returned as long as it is a str or bytes object. Otherwise TypeError is raised and NULL is returned.

New in version 3.6.

+
+int Py_FdIsInteractive(FILE *fp, const char *filename)
+

Return true (nonzero) if the standard I/O file fp with name filename is deemed interactive. This is the case for files for which isatty(fileno(fp)) is true. If the PyConfig.interactive is non-zero, this function also returns true if the filename pointer is NULL or if the name is equal to one of the strings '<stdin>' or '???'.

This function must not be called before Python is initialized.

+
+void PyOS_BeforeFork()
+ Part of the Stable ABI on platforms with fork() since version 3.7.

Function to prepare some internal state before a process fork. This should be called before calling fork() or any similar function that clones the current process. Only available on systems where fork() is defined.

Warning

The C fork() call should only be made from the “main” thread (of the “main” interpreter). The same is true for PyOS_BeforeFork().

New in version 3.7.

+
+void PyOS_AfterFork_Parent()
+ Part of the Stable ABI on platforms with fork() since version 3.7.

Function to update some internal state after a process fork. This should be called from the parent process after calling fork() or any similar function that clones the current process, regardless of whether process cloning was successful. Only available on systems where fork() is defined.

Warning

The C fork() call should only be made from the “main” thread (of the “main” interpreter). The same is true for PyOS_AfterFork_Parent().

New in version 3.7.

+
+void PyOS_AfterFork_Child()
+ Part of the Stable ABI on platforms with fork() since version 3.7.

Function to update internal interpreter state after a process fork. This must be called from the child process after calling fork(), or any similar function that clones the current process, if there is any chance the process will call back into the Python interpreter. Only available on systems where fork() is defined.

Warning

The C fork() call should only be made from the “main” thread (of the “main” interpreter). The same is true for PyOS_AfterFork_Child().

New in version 3.7.

See also

os.register_at_fork() allows registering custom Python functions to be called by PyOS_BeforeFork(), PyOS_AfterFork_Parent() and PyOS_AfterFork_Child().

+
+void PyOS_AfterFork()
+ Part of the Stable ABI on platforms with fork().

Function to update some internal state after a process fork; this should be called in the new process if the Python interpreter will continue to be used. If a new executable is loaded into the new process, this function does not need to be called.

Deprecated since version 3.7: This function is superseded by PyOS_AfterFork_Child().

+
+int PyOS_CheckStack()
+ Part of the Stable ABI on platforms with USE_STACKCHECK since version 3.7.

Return true when the interpreter runs out of stack space. This is a reliable check, but is only available when USE_STACKCHECK is defined (currently on certain versions of Windows using the Microsoft Visual C++ compiler). USE_STACKCHECK will be defined automatically; you should never change the definition in your own code.

+
+PyOS_sighandler_t PyOS_getsig(int i)
+ Part of the Stable ABI.

Return the current signal handler for signal i. This is a thin wrapper around either sigaction() or signal(). Do not call those functions directly! PyOS_sighandler_t is a typedef alias for void (*)(int).

+
+PyOS_sighandler_t PyOS_setsig(int i, PyOS_sighandler_t h)
+ Part of the Stable ABI.

Set the signal handler for signal i to be h; return the old signal handler. This is a thin wrapper around either sigaction() or signal(). Do not call those functions directly! PyOS_sighandler_t is a typedef alias for void (*)(int).

+
+wchar_t *Py_DecodeLocale(const char *arg, size_t *size)
+ Part of the Stable ABI since version 3.7.

Warning

This function should not be called directly: use the PyConfig API with the PyConfig_SetBytesString() function which ensures that Python is preinitialized.

This function must not be called before Python is preinitialized and so that the LC_CTYPE locale is properly configured: see the Py_PreInitialize() function.

Decode a byte string from the filesystem encoding and error handler. If the error handler is surrogateescape error handler, undecodable bytes are decoded as characters in range U+DC80..U+DCFF; and if a byte sequence can be decoded as a surrogate character, the bytes are escaped using the surrogateescape error handler instead of decoding them.

Return a pointer to a newly allocated wide character string, use PyMem_RawFree() to free the memory. If size is not NULL, write the number of wide characters excluding the null character into *size

Return NULL on decoding error or memory allocation error. If size is not NULL, *size is set to (size_t)-1 on memory error or set to (size_t)-2 on decoding error.

The filesystem encoding and error handler are selected by PyConfig_Read(): see filesystem_encoding and filesystem_errors members of PyConfig.

Decoding errors should never happen, unless there is a bug in the C library.

Use the Py_EncodeLocale() function to encode the character string back to a byte string.

See also

The PyUnicode_DecodeFSDefaultAndSize() and PyUnicode_DecodeLocaleAndSize() functions.

New in version 3.5.

Changed in version 3.7: The function now uses the UTF-8 encoding in the Python UTF-8 Mode.

Changed in version 3.8: The function now uses the UTF-8 encoding on Windows if PyPreConfig.legacy_windows_fs_encoding is zero;

+
+char *Py_EncodeLocale(const wchar_t *text, size_t *error_pos)
+ Part of the Stable ABI since version 3.7.

Encode a wide character string to the filesystem encoding and error handler. If the error handler is surrogateescape error handler, surrogate characters in the range U+DC80..U+DCFF are converted to bytes 0x80..0xFF.

Return a pointer to a newly allocated byte string, use PyMem_Free() to free the memory. Return NULL on encoding error or memory allocation error.

If error_pos is not NULL, *error_pos is set to (size_t)-1 on success, or set to the index of the invalid character on encoding error.

The filesystem encoding and error handler are selected by PyConfig_Read(): see filesystem_encoding and filesystem_errors members of PyConfig.

Use the Py_DecodeLocale() function to decode the bytes string back to a wide character string.

Warning

This function must not be called before Python is preinitialized and so that the LC_CTYPE locale is properly configured: see the Py_PreInitialize() function.

See also

The PyUnicode_EncodeFSDefault() and PyUnicode_EncodeLocale() functions.

New in version 3.5.

Changed in version 3.7: The function now uses the UTF-8 encoding in the Python UTF-8 Mode.

Changed in version 3.8: The function now uses the UTF-8 encoding on Windows if PyPreConfig.legacy_windows_fs_encoding is zero.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/sys.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ftuple.html b/devdocs/python~3.12/c-api%2Ftuple.html new file mode 100644 index 00000000..b5905ec3 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ftuple.html @@ -0,0 +1,48 @@ +

Tuple Objects

+type PyTupleObject
+

This subtype of PyObject represents a Python tuple object.

+
+PyTypeObject PyTuple_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python tuple type; it is the same object as tuple in the Python layer.

+
+int PyTuple_Check(PyObject *p)
+

Return true if p is a tuple object or an instance of a subtype of the tuple type. This function always succeeds.

+
+int PyTuple_CheckExact(PyObject *p)
+

Return true if p is a tuple object, but not an instance of a subtype of the tuple type. This function always succeeds.

+
+PyObject *PyTuple_New(Py_ssize_t len)
+Return value: New reference. Part of the Stable ABI.

Return a new tuple object of size len, or NULL on failure.

+
+PyObject *PyTuple_Pack(Py_ssize_t n, ...)
+Return value: New reference. Part of the Stable ABI.

Return a new tuple object of size n, or NULL on failure. The tuple values are initialized to the subsequent n C arguments pointing to Python objects. PyTuple_Pack(2, a, b) is equivalent to Py_BuildValue("(OO)", a, b).

+
+Py_ssize_t PyTuple_Size(PyObject *p)
+ Part of the Stable ABI.

Take a pointer to a tuple object, and return the size of that tuple.

+
+Py_ssize_t PyTuple_GET_SIZE(PyObject *p)
+

Return the size of the tuple p, which must be non-NULL and point to a tuple; no error checking is performed.

+
+PyObject *PyTuple_GetItem(PyObject *p, Py_ssize_t pos)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the object at position pos in the tuple pointed to by p. If pos is negative or out of bounds, return NULL and set an IndexError exception.

+
+PyObject *PyTuple_GET_ITEM(PyObject *p, Py_ssize_t pos)
+Return value: Borrowed reference.

Like PyTuple_GetItem(), but does no checking of its arguments.

+
+PyObject *PyTuple_GetSlice(PyObject *p, Py_ssize_t low, Py_ssize_t high)
+Return value: New reference. Part of the Stable ABI.

Return the slice of the tuple pointed to by p between low and high, or NULL on failure. This is the equivalent of the Python expression p[low:high]. Indexing from the end of the tuple is not supported.

+
+int PyTuple_SetItem(PyObject *p, Py_ssize_t pos, PyObject *o)
+ Part of the Stable ABI.

Insert a reference to object o at position pos of the tuple pointed to by p. Return 0 on success. If pos is out of bounds, return -1 and set an IndexError exception.

Note

This function “steals” a reference to o and discards a reference to an item already in the tuple at the affected position.

+
+void PyTuple_SET_ITEM(PyObject *p, Py_ssize_t pos, PyObject *o)
+

Like PyTuple_SetItem(), but does no error checking, and should only be used to fill in brand new tuples.

Note

This function “steals” a reference to o, and, unlike PyTuple_SetItem(), does not discard a reference to any item that is being replaced; any reference in the tuple at position pos will be leaked.

+
+int _PyTuple_Resize(PyObject **p, Py_ssize_t newsize)
+

Can be used to resize a tuple. newsize will be the new length of the tuple. Because tuples are supposed to be immutable, this should only be used if there is only one reference to the object. Do not use this if the tuple may already be known to some other part of the code. The tuple will always grow or shrink at the end. Think of this as destroying the old tuple and creating a new one, only more efficiently. Returns 0 on success. Client code should never assume that the resulting value of *p will be the same as before calling this function. If the object referenced by *p is replaced, the original *p is destroyed. On failure, returns -1 and sets *p to NULL, and raises MemoryError or SystemError.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/tuple.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ftype.html b/devdocs/python~3.12/c-api%2Ftype.html new file mode 100644 index 00000000..1bb4c654 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ftype.html @@ -0,0 +1,134 @@ +

Type Objects

+type PyTypeObject
+ Part of the Limited API (as an opaque struct).

The C structure of the objects used to describe built-in types.

+
+PyTypeObject PyType_Type
+ Part of the Stable ABI.

This is the type object for type objects; it is the same object as type in the Python layer.

+
+int PyType_Check(PyObject *o)
+

Return non-zero if the object o is a type object, including instances of types derived from the standard type object. Return 0 in all other cases. This function always succeeds.

+
+int PyType_CheckExact(PyObject *o)
+

Return non-zero if the object o is a type object, but not a subtype of the standard type object. Return 0 in all other cases. This function always succeeds.

+
+unsigned int PyType_ClearCache()
+ Part of the Stable ABI.

Clear the internal lookup cache. Return the current version tag.

+
+unsigned long PyType_GetFlags(PyTypeObject *type)
+ Part of the Stable ABI.

Return the tp_flags member of type. This function is primarily meant for use with Py_LIMITED_API; the individual flag bits are guaranteed to be stable across Python releases, but access to tp_flags itself is not part of the limited API.

New in version 3.2.

Changed in version 3.4: The return type is now unsigned long rather than long.

+
+PyObject *PyType_GetDict(PyTypeObject *type)
+

Return the type object’s internal namespace, which is otherwise only exposed via a read-only proxy (cls.__dict__). This is a replacement for accessing tp_dict directly. The returned dictionary must be treated as read-only.

This function is meant for specific embedding and language-binding cases, where direct access to the dict is necessary and indirect access (e.g. via the proxy or PyObject_GetAttr()) isn’t adequate.

Extension modules should continue to use tp_dict, directly or indirectly, when setting up their own types.

New in version 3.12.

+
+void PyType_Modified(PyTypeObject *type)
+ Part of the Stable ABI.

Invalidate the internal lookup cache for the type and all of its subtypes. This function must be called after any manual modification of the attributes or base classes of the type.

+
+int PyType_AddWatcher(PyType_WatchCallback callback)
+

Register callback as a type watcher. Return a non-negative integer ID which must be passed to future calls to PyType_Watch(). In case of error (e.g. no more watcher IDs available), return -1 and set an exception.

New in version 3.12.

+
+int PyType_ClearWatcher(int watcher_id)
+

Clear watcher identified by watcher_id (previously returned from PyType_AddWatcher()). Return 0 on success, -1 on error (e.g. if watcher_id was never registered.)

An extension should never call PyType_ClearWatcher with a watcher_id that was not returned to it by a previous call to PyType_AddWatcher().

New in version 3.12.

+
+int PyType_Watch(int watcher_id, PyObject *type)
+

Mark type as watched. The callback granted watcher_id by PyType_AddWatcher() will be called whenever PyType_Modified() reports a change to type. (The callback may be called only once for a series of consecutive modifications to type, if _PyType_Lookup() is not called on type between the modifications; this is an implementation detail and subject to change.)

An extension should never call PyType_Watch with a watcher_id that was not returned to it by a previous call to PyType_AddWatcher().

New in version 3.12.

+
+typedef int (*PyType_WatchCallback)(PyObject *type)
+

Type of a type-watcher callback function.

The callback must not modify type or cause PyType_Modified() to be called on type or any type in its MRO; violating this rule could cause infinite recursion.

New in version 3.12.

+
+int PyType_HasFeature(PyTypeObject *o, int feature)
+

Return non-zero if the type object o sets the feature feature. Type features are denoted by single bit flags.

+
+int PyType_IS_GC(PyTypeObject *o)
+

Return true if the type object includes support for the cycle detector; this tests the type flag Py_TPFLAGS_HAVE_GC.

+
+int PyType_IsSubtype(PyTypeObject *a, PyTypeObject *b)
+ Part of the Stable ABI.

Return true if a is a subtype of b.

This function only checks for actual subtypes, which means that __subclasscheck__() is not called on b. Call PyObject_IsSubclass() to do the same check that issubclass() would do.

+
+PyObject *PyType_GenericAlloc(PyTypeObject *type, Py_ssize_t nitems)
+Return value: New reference. Part of the Stable ABI.

Generic handler for the tp_alloc slot of a type object. Use Python’s default memory allocation mechanism to allocate a new instance and initialize all its contents to NULL.

+
+PyObject *PyType_GenericNew(PyTypeObject *type, PyObject *args, PyObject *kwds)
+Return value: New reference. Part of the Stable ABI.

Generic handler for the tp_new slot of a type object. Create a new instance using the type’s tp_alloc slot.

+
+int PyType_Ready(PyTypeObject *type)
+ Part of the Stable ABI.

Finalize a type object. This should be called on all type objects to finish their initialization. This function is responsible for adding inherited slots from a type’s base class. Return 0 on success, or return -1 and sets an exception on error.

Note

If some of the base classes implements the GC protocol and the provided type does not include the Py_TPFLAGS_HAVE_GC in its flags, then the GC protocol will be automatically implemented from its parents. On the contrary, if the type being created does include Py_TPFLAGS_HAVE_GC in its flags then it must implement the GC protocol itself by at least implementing the tp_traverse handle.

+
+PyObject *PyType_GetName(PyTypeObject *type)
+Return value: New reference. Part of the Stable ABI since version 3.11.

Return the type’s name. Equivalent to getting the type’s __name__ attribute.

New in version 3.11.

+
+PyObject *PyType_GetQualName(PyTypeObject *type)
+Return value: New reference. Part of the Stable ABI since version 3.11.

Return the type’s qualified name. Equivalent to getting the type’s __qualname__ attribute.

New in version 3.11.

+
+void *PyType_GetSlot(PyTypeObject *type, int slot)
+ Part of the Stable ABI since version 3.4.

Return the function pointer stored in the given slot. If the result is NULL, this indicates that either the slot is NULL, or that the function was called with invalid parameters. Callers will typically cast the result pointer into the appropriate function type.

See PyType_Slot.slot for possible values of the slot argument.

New in version 3.4.

Changed in version 3.10: PyType_GetSlot() can now accept all types. Previously, it was limited to heap types.

+
+PyObject *PyType_GetModule(PyTypeObject *type)
+ Part of the Stable ABI since version 3.10.

Return the module object associated with the given type when the type was created using PyType_FromModuleAndSpec().

If no module is associated with the given type, sets TypeError and returns NULL.

This function is usually used to get the module in which a method is defined. Note that in such a method, PyType_GetModule(Py_TYPE(self)) may not return the intended result. Py_TYPE(self) may be a subclass of the intended class, and subclasses are not necessarily defined in the same module as their superclass. See PyCMethod to get the class that defines the method. See PyType_GetModuleByDef() for cases when PyCMethod cannot be used.

New in version 3.9.

+
+void *PyType_GetModuleState(PyTypeObject *type)
+ Part of the Stable ABI since version 3.10.

Return the state of the module object associated with the given type. This is a shortcut for calling PyModule_GetState() on the result of PyType_GetModule().

If no module is associated with the given type, sets TypeError and returns NULL.

If the type has an associated module but its state is NULL, returns NULL without setting an exception.

New in version 3.9.

+
+PyObject *PyType_GetModuleByDef(PyTypeObject *type, struct PyModuleDef *def)
+

Find the first superclass whose module was created from the given PyModuleDef def, and return that module.

If no module is found, raises a TypeError and returns NULL.

This function is intended to be used together with PyModule_GetState() to get module state from slot methods (such as tp_init or nb_add) and other places where a method’s defining class cannot be passed using the PyCMethod calling convention.

New in version 3.11.

+
+int PyUnstable_Type_AssignVersionTag(PyTypeObject *type)
+
This is Unstable API. It may change without warning in minor releases. +

Attempt to assign a version tag to the given type.

Returns 1 if the type already had a valid version tag or a new one was assigned, or 0 if a new tag could not be assigned.

New in version 3.12.

+

Creating Heap-Allocated Types

The following functions and structs are used to create heap types.

+PyObject *PyType_FromMetaclass(PyTypeObject *metaclass, PyObject *module, PyType_Spec *spec, PyObject *bases)
+ Part of the Stable ABI since version 3.12.

Create and return a heap type from the spec (see Py_TPFLAGS_HEAPTYPE).

The metaclass metaclass is used to construct the resulting type object. When metaclass is NULL, the metaclass is derived from bases (or Py_tp_base[s] slots if bases is NULL, see below).

Metaclasses that override tp_new are not supported, except if tp_new is NULL. (For backwards compatibility, other PyType_From* functions allow such metaclasses. They ignore tp_new, which may result in incomplete initialization. This is deprecated and in Python 3.14+ such metaclasses will not be supported.)

The bases argument can be used to specify base classes; it can either be only one class or a tuple of classes. If bases is NULL, the Py_tp_bases slot is used instead. If that also is NULL, the Py_tp_base slot is used instead. If that also is NULL, the new type derives from object.

The module argument can be used to record the module in which the new class is defined. It must be a module object or NULL. If not NULL, the module is associated with the new type and can later be retrieved with PyType_GetModule(). The associated module is not inherited by subclasses; it must be specified for each class individually.

This function calls PyType_Ready() on the new type.

Note that this function does not fully match the behavior of calling type() or using the class statement. With user-provided base types or metaclasses, prefer calling type (or the metaclass) over PyType_From* functions. Specifically:

New in version 3.12.

+
+PyObject *PyType_FromModuleAndSpec(PyObject *module, PyType_Spec *spec, PyObject *bases)
+Return value: New reference. Part of the Stable ABI since version 3.10.

Equivalent to PyType_FromMetaclass(NULL, module, spec, bases).

New in version 3.9.

Changed in version 3.10: The function now accepts a single class as the bases argument and NULL as the tp_doc slot.

Changed in version 3.12: The function now finds and uses a metaclass corresponding to the provided base classes. Previously, only type instances were returned.

The tp_new of the metaclass is ignored. which may result in incomplete initialization. Creating classes whose metaclass overrides tp_new is deprecated and in Python 3.14+ it will be no longer allowed.

+
+PyObject *PyType_FromSpecWithBases(PyType_Spec *spec, PyObject *bases)
+Return value: New reference. Part of the Stable ABI since version 3.3.

Equivalent to PyType_FromMetaclass(NULL, NULL, spec, bases).

New in version 3.3.

Changed in version 3.12: The function now finds and uses a metaclass corresponding to the provided base classes. Previously, only type instances were returned.

The tp_new of the metaclass is ignored. which may result in incomplete initialization. Creating classes whose metaclass overrides tp_new is deprecated and in Python 3.14+ it will be no longer allowed.

+
+PyObject *PyType_FromSpec(PyType_Spec *spec)
+Return value: New reference. Part of the Stable ABI.

Equivalent to PyType_FromMetaclass(NULL, NULL, spec, NULL).

Changed in version 3.12: The function now finds and uses a metaclass corresponding to the base classes provided in Py_tp_base[s] slots. Previously, only type instances were returned.

The tp_new of the metaclass is ignored. which may result in incomplete initialization. Creating classes whose metaclass overrides tp_new is deprecated and in Python 3.14+ it will be no longer allowed.

+
+type PyType_Spec
+ Part of the Stable ABI (including all members).

Structure defining a type’s behavior.

+const char *name
+

Name of the type, used to set PyTypeObject.tp_name.

+
+int basicsize
+

If positive, specifies the size of the instance in bytes. It is used to set PyTypeObject.tp_basicsize.

If zero, specifies that tp_basicsize should be inherited.

If negative, the absolute value specifies how much space instances of the class need in addition to the superclass. Use PyObject_GetTypeData() to get a pointer to subclass-specific memory reserved this way.

Changed in version 3.12: Previously, this field could not be negative.

+
+int itemsize
+

Size of one element of a variable-size type, in bytes. Used to set PyTypeObject.tp_itemsize. See tp_itemsize documentation for caveats.

If zero, tp_itemsize is inherited. Extending arbitrary variable-sized classes is dangerous, since some types use a fixed offset for variable-sized memory, which can then overlap fixed-sized memory used by a subclass. To help prevent mistakes, inheriting itemsize is only possible in the following situations:

+
+unsigned int flags
+

Type flags, used to set PyTypeObject.tp_flags.

If the Py_TPFLAGS_HEAPTYPE flag is not set, PyType_FromSpecWithBases() sets it automatically.

+
+PyType_Slot *slots
+

Array of PyType_Slot structures. Terminated by the special slot value {0, NULL}.

Each slot ID should be specified at most once.

+
+
+type PyType_Slot
+ Part of the Stable ABI (including all members).

Structure defining optional functionality of a type, containing a slot ID and a value pointer.

+int slot

A slot ID.

Slot IDs are named like the field names of the structures PyTypeObject, PyNumberMethods, PySequenceMethods, PyMappingMethods and PyAsyncMethods with an added Py_ prefix. For example, use:

The following “offset” fields cannot be set using PyType_Slot:

If it is not possible to switch to a MANAGED flag (for example, for vectorcall or to support Python older than 3.12), specify the offset in Py_tp_members. See PyMemberDef documentation for details.

The following fields cannot be set at all when creating a heap type:

Setting Py_tp_bases or Py_tp_base may be problematic on some platforms. To avoid issues, use the bases argument of PyType_FromSpecWithBases() instead.

Changed in version 3.9: Slots in PyBufferProcs may be set in the unlimited API.

Changed in version 3.11: bf_getbuffer and bf_releasebuffer are now available under the limited API.

+
+void *pfunc
+

The desired value of the slot. In most cases, this is a pointer to a function.

Slots other than Py_tp_doc may not be NULL.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/type.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ftypehints.html b/devdocs/python~3.12/c-api%2Ftypehints.html new file mode 100644 index 00000000..8ba681f1 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ftypehints.html @@ -0,0 +1,19 @@ +

Objects for Type Hinting

Various built-in types for type hinting are provided. Currently, two types exist – GenericAlias and Union. Only GenericAlias is exposed to C.

+PyObject *Py_GenericAlias(PyObject *origin, PyObject *args)
+ Part of the Stable ABI since version 3.9.

Create a GenericAlias object. Equivalent to calling the Python class types.GenericAlias. The origin and args arguments set the GenericAlias‘s __origin__ and __args__ attributes respectively. origin should be a PyTypeObject*, and args can be a PyTupleObject* or any PyObject*. If args passed is not a tuple, a 1-tuple is automatically constructed and __args__ is set to (args,). Minimal checking is done for the arguments, so the function will succeed even if origin is not a type. The GenericAlias‘s __parameters__ attribute is constructed lazily from __args__. On failure, an exception is raised and NULL is returned.

Here’s an example of how to make an extension type generic:

...
+static PyMethodDef my_obj_methods[] = {
+    // Other methods.
+    ...
+    {"__class_getitem__", Py_GenericAlias, METH_O|METH_CLASS, "See PEP 585"}
+    ...
+}
+

See also

The data model method __class_getitem__().

New in version 3.9.

+
+PyTypeObject Py_GenericAliasType
+ Part of the Stable ABI since version 3.9.

The C type of the object returned by Py_GenericAlias(). Equivalent to types.GenericAlias in Python.

New in version 3.9.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/typehints.html +

+
diff --git a/devdocs/python~3.12/c-api%2Ftypeobj.html b/devdocs/python~3.12/c-api%2Ftypeobj.html new file mode 100644 index 00000000..5be2e367 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Ftypeobj.html @@ -0,0 +1,561 @@ +

Type Objects

Perhaps one of the most important structures of the Python object system is the structure that defines a new type: the PyTypeObject structure. Type objects can be handled using any of the PyObject_* or PyType_* functions, but do not offer much that’s interesting to most Python applications. These objects are fundamental to how objects behave, so they are very important to the interpreter itself and to any extension module that implements new types.

Type objects are fairly large compared to most of the standard types. The reason for the size is that each type object stores a large number of values, mostly C function pointers, each of which implements a small part of the type’s functionality. The fields of the type object are examined in detail in this section. The fields will be described in the order in which they occur in the structure.

In addition to the following quick reference, the Examples section provides at-a-glance insight into the meaning and use of PyTypeObject.

Quick Reference

“tp slots”

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

PyTypeObject Slot 1

Type

special methods/attrs

Info 2

O

T

D

I

<R> tp_name

const char *

__name__

X

X

tp_basicsize

Py_ssize_t

X

X

X

tp_itemsize

Py_ssize_t

X

X

tp_dealloc

destructor

X

X

X

tp_vectorcall_offset

Py_ssize_t

X

X

(tp_getattr)

getattrfunc

__getattribute__, __getattr__

G

(tp_setattr)

setattrfunc

__setattr__, __delattr__

G

tp_as_async

PyAsyncMethods *

sub-slots

%

tp_repr

reprfunc

__repr__

X

X

X

tp_as_number

PyNumberMethods *

sub-slots

%

tp_as_sequence

PySequenceMethods *

sub-slots

%

tp_as_mapping

PyMappingMethods *

sub-slots

%

tp_hash

hashfunc

__hash__

X

G

tp_call

ternaryfunc

__call__

X

X

tp_str

reprfunc

__str__

X

X

tp_getattro

getattrofunc

__getattribute__, __getattr__

X

X

G

tp_setattro

setattrofunc

__setattr__, __delattr__

X

X

G

tp_as_buffer

PyBufferProcs *

%

tp_flags

unsigned long

X

X

?

tp_doc

const char *

__doc__

X

X

tp_traverse

traverseproc

X

G

tp_clear

inquiry

X

G

tp_richcompare

richcmpfunc

__lt__, __le__, __eq__, __ne__, __gt__, __ge__

X

G

(tp_weaklistoffset)

Py_ssize_t

X

?

tp_iter

getiterfunc

__iter__

X

tp_iternext

iternextfunc

__next__

X

tp_methods

PyMethodDef []

X

X

tp_members

PyMemberDef []

X

tp_getset

PyGetSetDef []

X

X

tp_base

PyTypeObject *

__base__

X

tp_dict

PyObject *

__dict__

?

tp_descr_get

descrgetfunc

__get__

X

tp_descr_set

descrsetfunc

__set__, __delete__

X

(tp_dictoffset)

Py_ssize_t

X

?

tp_init

initproc

__init__

X

X

X

tp_alloc

allocfunc

X

?

?

tp_new

newfunc

__new__

X

X

?

?

tp_free

freefunc

X

X

?

?

tp_is_gc

inquiry

X

X

<tp_bases>

PyObject *

__bases__

~

<tp_mro>

PyObject *

__mro__

~

[tp_cache]

PyObject *

[tp_subclasses]

void *

__subclasses__

[tp_weaklist]

PyObject *

(tp_del)

destructor

[tp_version_tag]

unsigned int

tp_finalize

destructor

__del__

X

tp_vectorcall

vectorcallfunc

[tp_watched]

unsigned char
+1
+

(): A slot name in parentheses indicates it is (effectively) deprecated.

<>: Names in angle brackets should be initially set to NULL and treated as read-only.

[]: Names in square brackets are for internal use only.

<R> (as a prefix) means the field is required (must be non-NULL).

+2
+

Columns:

“O”: set on PyBaseObject_Type

“T”: set on PyType_Type

“D”: default (if slot is set to NULL)

X - PyType_Ready sets this value if it is NULL
+~ - PyType_Ready always sets this value (it should be NULL)
+? - PyType_Ready may set this value depending on other slots
+
+Also see the inheritance column ("I").
+

“I”: inheritance

X - type slot is inherited via *PyType_Ready* if defined with a *NULL* value
+% - the slots of the sub-struct are inherited individually
+G - inherited, but only in combination with other slots; see the slot's description
+? - it's complicated; see the slot's description
+

Note that some slots are effectively inherited through the normal attribute lookup chain.

sub-slots

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Slot

Type

special methods

am_await

unaryfunc

__await__

am_aiter

unaryfunc

__aiter__

am_anext

unaryfunc

__anext__

am_send

sendfunc

nb_add

binaryfunc

__add__ __radd__

nb_inplace_add

binaryfunc

__iadd__

nb_subtract

binaryfunc

__sub__ __rsub__

nb_inplace_subtract

binaryfunc

__isub__

nb_multiply

binaryfunc

__mul__ __rmul__

nb_inplace_multiply

binaryfunc

__imul__

nb_remainder

binaryfunc

__mod__ __rmod__

nb_inplace_remainder

binaryfunc

__imod__

nb_divmod

binaryfunc

__divmod__ __rdivmod__

nb_power

ternaryfunc

__pow__ __rpow__

nb_inplace_power

ternaryfunc

__ipow__

nb_negative

unaryfunc

__neg__

nb_positive

unaryfunc

__pos__

nb_absolute

unaryfunc

__abs__

nb_bool

inquiry

__bool__

nb_invert

unaryfunc

__invert__

nb_lshift

binaryfunc

__lshift__ __rlshift__

nb_inplace_lshift

binaryfunc

__ilshift__

nb_rshift

binaryfunc

__rshift__ __rrshift__

nb_inplace_rshift

binaryfunc

__irshift__

nb_and

binaryfunc

__and__ __rand__

nb_inplace_and

binaryfunc

__iand__

nb_xor

binaryfunc

__xor__ __rxor__

nb_inplace_xor

binaryfunc

__ixor__

nb_or

binaryfunc

__or__ __ror__

nb_inplace_or

binaryfunc

__ior__

nb_int

unaryfunc

__int__

nb_reserved

void *

nb_float

unaryfunc

__float__

nb_floor_divide

binaryfunc

__floordiv__

nb_inplace_floor_divide

binaryfunc

__ifloordiv__

nb_true_divide

binaryfunc

__truediv__

nb_inplace_true_divide

binaryfunc

__itruediv__

nb_index

unaryfunc

__index__

nb_matrix_multiply

binaryfunc

__matmul__ __rmatmul__

nb_inplace_matrix_multiply

binaryfunc

__imatmul__

mp_length

lenfunc

__len__

mp_subscript

binaryfunc

__getitem__

mp_ass_subscript

objobjargproc

__setitem__, __delitem__

sq_length

lenfunc

__len__

sq_concat

binaryfunc

__add__

sq_repeat

ssizeargfunc

__mul__

sq_item

ssizeargfunc

__getitem__

sq_ass_item

ssizeobjargproc

__setitem__ __delitem__

sq_contains

objobjproc

__contains__

sq_inplace_concat

binaryfunc

__iadd__

sq_inplace_repeat

ssizeargfunc

__imul__

bf_getbuffer

getbufferproc()

bf_releasebuffer

releasebufferproc()

slot typedefs

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +

typedef

Parameter Types

Return Type

allocfunc

PyObject *

destructor

PyObject *

void

freefunc

void *

void

traverseproc

int

newfunc

PyObject *

initproc

int

reprfunc

PyObject *

PyObject *

getattrfunc

PyObject *

setattrfunc

int

getattrofunc

PyObject *

setattrofunc

int

descrgetfunc

PyObject *

descrsetfunc

int

hashfunc

PyObject *

Py_hash_t

richcmpfunc

PyObject *

getiterfunc

PyObject *

PyObject *

iternextfunc

PyObject *

PyObject *

lenfunc

PyObject *

Py_ssize_t

getbufferproc

int

releasebufferproc

void

inquiry

PyObject *

int

unaryfunc

PyObject *

binaryfunc

PyObject *

ternaryfunc

PyObject *

ssizeargfunc

PyObject *

ssizeobjargproc

int

objobjproc

int

objobjargproc

int

See Slot Type typedefs below for more detail.

PyTypeObject Definition

The structure definition for PyTypeObject can be found in Include/object.h. For convenience of reference, this repeats the definition found there:

typedef struct _typeobject {
+    PyObject_VAR_HEAD
+    const char *tp_name; /* For printing, in format "<module>.<name>" */
+    Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */
+
+    /* Methods to implement standard operations */
+
+    destructor tp_dealloc;
+    Py_ssize_t tp_vectorcall_offset;
+    getattrfunc tp_getattr;
+    setattrfunc tp_setattr;
+    PyAsyncMethods *tp_as_async; /* formerly known as tp_compare (Python 2)
+                                    or tp_reserved (Python 3) */
+    reprfunc tp_repr;
+
+    /* Method suites for standard classes */
+
+    PyNumberMethods *tp_as_number;
+    PySequenceMethods *tp_as_sequence;
+    PyMappingMethods *tp_as_mapping;
+
+    /* More standard operations (here for binary compatibility) */
+
+    hashfunc tp_hash;
+    ternaryfunc tp_call;
+    reprfunc tp_str;
+    getattrofunc tp_getattro;
+    setattrofunc tp_setattro;
+
+    /* Functions to access object as input/output buffer */
+    PyBufferProcs *tp_as_buffer;
+
+    /* Flags to define presence of optional/expanded features */
+    unsigned long tp_flags;
+
+    const char *tp_doc; /* Documentation string */
+
+    /* Assigned meaning in release 2.0 */
+    /* call function for all accessible objects */
+    traverseproc tp_traverse;
+
+    /* delete references to contained objects */
+    inquiry tp_clear;
+
+    /* Assigned meaning in release 2.1 */
+    /* rich comparisons */
+    richcmpfunc tp_richcompare;
+
+    /* weak reference enabler */
+    Py_ssize_t tp_weaklistoffset;
+
+    /* Iterators */
+    getiterfunc tp_iter;
+    iternextfunc tp_iternext;
+
+    /* Attribute descriptor and subclassing stuff */
+    struct PyMethodDef *tp_methods;
+    struct PyMemberDef *tp_members;
+    struct PyGetSetDef *tp_getset;
+    // Strong reference on a heap type, borrowed reference on a static type
+    struct _typeobject *tp_base;
+    PyObject *tp_dict;
+    descrgetfunc tp_descr_get;
+    descrsetfunc tp_descr_set;
+    Py_ssize_t tp_dictoffset;
+    initproc tp_init;
+    allocfunc tp_alloc;
+    newfunc tp_new;
+    freefunc tp_free; /* Low-level free-memory routine */
+    inquiry tp_is_gc; /* For PyObject_IS_GC */
+    PyObject *tp_bases;
+    PyObject *tp_mro; /* method resolution order */
+    PyObject *tp_cache;
+    PyObject *tp_subclasses;
+    PyObject *tp_weaklist;
+    destructor tp_del;
+
+    /* Type attribute cache version tag. Added in version 2.6 */
+    unsigned int tp_version_tag;
+
+    destructor tp_finalize;
+    vectorcallfunc tp_vectorcall;
+
+    /* bitset of which type-watchers care about this type */
+    unsigned char tp_watched;
+} PyTypeObject;
+

PyObject Slots

The type object structure extends the PyVarObject structure. The ob_size field is used for dynamic types (created by type_new(), usually called from a class statement). Note that PyType_Type (the metatype) initializes tp_itemsize, which means that its instances (i.e. type objects) must have the ob_size field.

+Py_ssize_t PyObject.ob_refcnt
+ Part of the Stable ABI.

This is the type object’s reference count, initialized to 1 by the PyObject_HEAD_INIT macro. Note that for statically allocated type objects, the type’s instances (objects whose ob_type points back to the type) do not count as references. But for dynamically allocated type objects, the instances do count as references.

Inheritance:

This field is not inherited by subtypes.

+
+PyTypeObject *PyObject.ob_type
+ Part of the Stable ABI.

This is the type’s type, in other words its metatype. It is initialized by the argument to the PyObject_HEAD_INIT macro, and its value should normally be &PyType_Type. However, for dynamically loadable extension modules that must be usable on Windows (at least), the compiler complains that this is not a valid initializer. Therefore, the convention is to pass NULL to the PyObject_HEAD_INIT macro and to initialize this field explicitly at the start of the module’s initialization function, before doing anything else. This is typically done like this:

Foo_Type.ob_type = &PyType_Type;
+

This should be done before any instances of the type are created. PyType_Ready() checks if ob_type is NULL, and if so, initializes it to the ob_type field of the base class. PyType_Ready() will not change this field if it is non-zero.

Inheritance:

This field is inherited by subtypes.

+
+PyObject *PyObject._ob_next
+PyObject *PyObject._ob_prev
+

These fields are only present when the macro Py_TRACE_REFS is defined (see the configure --with-trace-refs option).

Their initialization to NULL is taken care of by the PyObject_HEAD_INIT macro. For statically allocated objects, these fields always remain NULL. For dynamically allocated objects, these two fields are used to link the object into a doubly linked list of all live objects on the heap.

This could be used for various debugging purposes; currently the only uses are the sys.getobjects() function and to print the objects that are still alive at the end of a run when the environment variable PYTHONDUMPREFS is set.

Inheritance:

These fields are not inherited by subtypes.

+

PyVarObject Slots

+Py_ssize_t PyVarObject.ob_size
+ Part of the Stable ABI.

For statically allocated type objects, this should be initialized to zero. For dynamically allocated type objects, this field has a special internal meaning.

Inheritance:

This field is not inherited by subtypes.

+

PyTypeObject Slots

Each slot has a section describing inheritance. If PyType_Ready() may set a value when the field is set to NULL then there will also be a “Default” section. (Note that many fields set on PyBaseObject_Type and PyType_Type effectively act as defaults.)

+const char *PyTypeObject.tp_name
+

Pointer to a NUL-terminated string containing the name of the type. For types that are accessible as module globals, the string should be the full module name, followed by a dot, followed by the type name; for built-in types, it should be just the type name. If the module is a submodule of a package, the full package name is part of the full module name. For example, a type named T defined in module M in subpackage Q in package P should have the tp_name initializer "P.Q.M.T".

For dynamically allocated type objects, this should just be the type name, and the module name explicitly stored in the type dict as the value for key '__module__'.

For statically allocated type objects, the tp_name field should contain a dot. Everything before the last dot is made accessible as the __module__ attribute, and everything after the last dot is made accessible as the __name__ attribute.

If no dot is present, the entire tp_name field is made accessible as the __name__ attribute, and the __module__ attribute is undefined (unless explicitly set in the dictionary, as explained above). This means your type will be impossible to pickle. Additionally, it will not be listed in module documentations created with pydoc.

This field must not be NULL. It is the only required field in PyTypeObject() (other than potentially tp_itemsize).

Inheritance:

This field is not inherited by subtypes.

+
+Py_ssize_t PyTypeObject.tp_basicsize
+Py_ssize_t PyTypeObject.tp_itemsize
+

These fields allow calculating the size in bytes of instances of the type.

There are two kinds of types: types with fixed-length instances have a zero tp_itemsize field, types with variable-length instances have a non-zero tp_itemsize field. For a type with fixed-length instances, all instances have the same size, given in tp_basicsize.

For a type with variable-length instances, the instances must have an ob_size field, and the instance size is tp_basicsize plus N times tp_itemsize, where N is the “length” of the object. The value of N is typically stored in the instance’s ob_size field. There are exceptions: for example, ints use a negative ob_size to indicate a negative number, and N is abs(ob_size) there. Also, the presence of an ob_size field in the instance layout doesn’t mean that the instance structure is variable-length (for example, the structure for the list type has fixed-length instances, yet those instances have a meaningful ob_size field).

The basic size includes the fields in the instance declared by the macro PyObject_HEAD or PyObject_VAR_HEAD (whichever is used to declare the instance struct) and this in turn includes the _ob_prev and _ob_next fields if they are present. This means that the only correct way to get an initializer for the tp_basicsize is to use the sizeof operator on the struct used to declare the instance layout. The basic size does not include the GC header size.

A note about alignment: if the variable items require a particular alignment, this should be taken care of by the value of tp_basicsize. Example: suppose a type implements an array of double. tp_itemsize is sizeof(double). It is the programmer’s responsibility that tp_basicsize is a multiple of sizeof(double) (assuming this is the alignment requirement for double).

For any type with variable-length instances, this field must not be NULL.

Inheritance:

These fields are inherited separately by subtypes. If the base type has a non-zero tp_itemsize, it is generally not safe to set tp_itemsize to a different non-zero value in a subtype (though this depends on the implementation of the base type).

+
+destructor PyTypeObject.tp_dealloc
+

A pointer to the instance destructor function. This function must be defined unless the type guarantees that its instances will never be deallocated (as is the case for the singletons None and Ellipsis). The function signature is:

void tp_dealloc(PyObject *self);
+

The destructor function is called by the Py_DECREF() and Py_XDECREF() macros when the new reference count is zero. At this point, the instance is still in existence, but there are no references to it. The destructor function should free all references which the instance owns, free all memory buffers owned by the instance (using the freeing function corresponding to the allocation function used to allocate the buffer), and call the type’s tp_free function. If the type is not subtypable (doesn’t have the Py_TPFLAGS_BASETYPE flag bit set), it is permissible to call the object deallocator directly instead of via tp_free. The object deallocator should be the one used to allocate the instance; this is normally PyObject_Del() if the instance was allocated using PyObject_New or PyObject_NewVar, or PyObject_GC_Del() if the instance was allocated using PyObject_GC_New or PyObject_GC_NewVar.

If the type supports garbage collection (has the Py_TPFLAGS_HAVE_GC flag bit set), the destructor should call PyObject_GC_UnTrack() before clearing any member fields.

static void foo_dealloc(foo_object *self) {
+    PyObject_GC_UnTrack(self);
+    Py_CLEAR(self->ref);
+    Py_TYPE(self)->tp_free((PyObject *)self);
+}
+

Finally, if the type is heap allocated (Py_TPFLAGS_HEAPTYPE), the deallocator should release the owned reference to its type object (via Py_DECREF()) after calling the type deallocator. In order to avoid dangling pointers, the recommended way to achieve this is:

static void foo_dealloc(foo_object *self) {
+    PyTypeObject *tp = Py_TYPE(self);
+    // free references and buffers here
+    tp->tp_free(self);
+    Py_DECREF(tp);
+}
+

Inheritance:

This field is inherited by subtypes.

+
+Py_ssize_t PyTypeObject.tp_vectorcall_offset
+

An optional offset to a per-instance function that implements calling the object using the vectorcall protocol, a more efficient alternative of the simpler tp_call.

This field is only used if the flag Py_TPFLAGS_HAVE_VECTORCALL is set. If so, this must be a positive integer containing the offset in the instance of a vectorcallfunc pointer.

The vectorcallfunc pointer may be NULL, in which case the instance behaves as if Py_TPFLAGS_HAVE_VECTORCALL was not set: calling the instance falls back to tp_call.

Any class that sets Py_TPFLAGS_HAVE_VECTORCALL must also set tp_call and make sure its behaviour is consistent with the vectorcallfunc function. This can be done by setting tp_call to PyVectorcall_Call().

Changed in version 3.8: Before version 3.8, this slot was named tp_print. In Python 2.x, it was used for printing to a file. In Python 3.0 to 3.7, it was unused.

Changed in version 3.12: Before version 3.12, it was not recommended for mutable heap types to implement the vectorcall protocol. When a user sets __call__ in Python code, only tp_call is updated, likely making it inconsistent with the vectorcall function. Since 3.12, setting __call__ will disable vectorcall optimization by clearing the Py_TPFLAGS_HAVE_VECTORCALL flag.

Inheritance:

This field is always inherited. However, the Py_TPFLAGS_HAVE_VECTORCALL flag is not always inherited. If it’s not set, then the subclass won’t use vectorcall, except when PyVectorcall_Call() is explicitly called.

+
+getattrfunc PyTypeObject.tp_getattr
+

An optional pointer to the get-attribute-string function.

This field is deprecated. When it is defined, it should point to a function that acts the same as the tp_getattro function, but taking a C string instead of a Python string object to give the attribute name.

Inheritance:

Group: tp_getattr, tp_getattro

This field is inherited by subtypes together with tp_getattro: a subtype inherits both tp_getattr and tp_getattro from its base type when the subtype’s tp_getattr and tp_getattro are both NULL.

+
+setattrfunc PyTypeObject.tp_setattr
+

An optional pointer to the function for setting and deleting attributes.

This field is deprecated. When it is defined, it should point to a function that acts the same as the tp_setattro function, but taking a C string instead of a Python string object to give the attribute name.

Inheritance:

Group: tp_setattr, tp_setattro

This field is inherited by subtypes together with tp_setattro: a subtype inherits both tp_setattr and tp_setattro from its base type when the subtype’s tp_setattr and tp_setattro are both NULL.

+
+PyAsyncMethods *PyTypeObject.tp_as_async
+

Pointer to an additional structure that contains fields relevant only to objects which implement awaitable and asynchronous iterator protocols at the C-level. See Async Object Structures for details.

New in version 3.5: Formerly known as tp_compare and tp_reserved.

Inheritance:

The tp_as_async field is not inherited, but the contained fields are inherited individually.

+
+reprfunc PyTypeObject.tp_repr
+

An optional pointer to a function that implements the built-in function repr().

The signature is the same as for PyObject_Repr():

PyObject *tp_repr(PyObject *self);
+

The function must return a string or a Unicode object. Ideally, this function should return a string that, when passed to eval(), given a suitable environment, returns an object with the same value. If this is not feasible, it should return a string starting with '<' and ending with '>' from which both the type and the value of the object can be deduced.

Inheritance:

This field is inherited by subtypes.

Default:

When this field is not set, a string of the form <%s object at %p> is returned, where %s is replaced by the type name, and %p by the object’s memory address.

+
+PyNumberMethods *PyTypeObject.tp_as_number
+

Pointer to an additional structure that contains fields relevant only to objects which implement the number protocol. These fields are documented in Number Object Structures.

Inheritance:

The tp_as_number field is not inherited, but the contained fields are inherited individually.

+
+PySequenceMethods *PyTypeObject.tp_as_sequence
+

Pointer to an additional structure that contains fields relevant only to objects which implement the sequence protocol. These fields are documented in Sequence Object Structures.

Inheritance:

The tp_as_sequence field is not inherited, but the contained fields are inherited individually.

+
+PyMappingMethods *PyTypeObject.tp_as_mapping
+

Pointer to an additional structure that contains fields relevant only to objects which implement the mapping protocol. These fields are documented in Mapping Object Structures.

Inheritance:

The tp_as_mapping field is not inherited, but the contained fields are inherited individually.

+
+hashfunc PyTypeObject.tp_hash
+

An optional pointer to a function that implements the built-in function hash().

The signature is the same as for PyObject_Hash():

Py_hash_t tp_hash(PyObject *);
+

The value -1 should not be returned as a normal return value; when an error occurs during the computation of the hash value, the function should set an exception and return -1.

When this field is not set (and tp_richcompare is not set), an attempt to take the hash of the object raises TypeError. This is the same as setting it to PyObject_HashNotImplemented().

This field can be set explicitly to PyObject_HashNotImplemented() to block inheritance of the hash method from a parent type. This is interpreted as the equivalent of __hash__ = None at the Python level, causing isinstance(o, collections.Hashable) to correctly return False. Note that the converse is also true - setting __hash__ = None on a class at the Python level will result in the tp_hash slot being set to PyObject_HashNotImplemented().

Inheritance:

Group: tp_hash, tp_richcompare

This field is inherited by subtypes together with tp_richcompare: a subtype inherits both of tp_richcompare and tp_hash, when the subtype’s tp_richcompare and tp_hash are both NULL.

+
+ternaryfunc PyTypeObject.tp_call
+

An optional pointer to a function that implements calling the object. This should be NULL if the object is not callable. The signature is the same as for PyObject_Call():

PyObject *tp_call(PyObject *self, PyObject *args, PyObject *kwargs);
+

Inheritance:

This field is inherited by subtypes.

+
+reprfunc PyTypeObject.tp_str
+

An optional pointer to a function that implements the built-in operation str(). (Note that str is a type now, and str() calls the constructor for that type. This constructor calls PyObject_Str() to do the actual work, and PyObject_Str() will call this handler.)

The signature is the same as for PyObject_Str():

PyObject *tp_str(PyObject *self);
+

The function must return a string or a Unicode object. It should be a “friendly” string representation of the object, as this is the representation that will be used, among other things, by the print() function.

Inheritance:

This field is inherited by subtypes.

Default:

When this field is not set, PyObject_Repr() is called to return a string representation.

+
+getattrofunc PyTypeObject.tp_getattro
+

An optional pointer to the get-attribute function.

The signature is the same as for PyObject_GetAttr():

PyObject *tp_getattro(PyObject *self, PyObject *attr);
+

It is usually convenient to set this field to PyObject_GenericGetAttr(), which implements the normal way of looking for object attributes.

Inheritance:

Group: tp_getattr, tp_getattro

This field is inherited by subtypes together with tp_getattr: a subtype inherits both tp_getattr and tp_getattro from its base type when the subtype’s tp_getattr and tp_getattro are both NULL.

Default:

PyBaseObject_Type uses PyObject_GenericGetAttr().

+
+setattrofunc PyTypeObject.tp_setattro
+

An optional pointer to the function for setting and deleting attributes.

The signature is the same as for PyObject_SetAttr():

int tp_setattro(PyObject *self, PyObject *attr, PyObject *value);
+

In addition, setting value to NULL to delete an attribute must be supported. It is usually convenient to set this field to PyObject_GenericSetAttr(), which implements the normal way of setting object attributes.

Inheritance:

Group: tp_setattr, tp_setattro

This field is inherited by subtypes together with tp_setattr: a subtype inherits both tp_setattr and tp_setattro from its base type when the subtype’s tp_setattr and tp_setattro are both NULL.

Default:

PyBaseObject_Type uses PyObject_GenericSetAttr().

+
+PyBufferProcs *PyTypeObject.tp_as_buffer
+

Pointer to an additional structure that contains fields relevant only to objects which implement the buffer interface. These fields are documented in Buffer Object Structures.

Inheritance:

The tp_as_buffer field is not inherited, but the contained fields are inherited individually.

+
+unsigned long PyTypeObject.tp_flags
+

This field is a bit mask of various flags. Some flags indicate variant semantics for certain situations; others are used to indicate that certain fields in the type object (or in the extension structures referenced via tp_as_number, tp_as_sequence, tp_as_mapping, and tp_as_buffer) that were historically not always present are valid; if such a flag bit is clear, the type fields it guards must not be accessed and must be considered to have a zero or NULL value instead.

Inheritance:

Inheritance of this field is complicated. Most flag bits are inherited individually, i.e. if the base type has a flag bit set, the subtype inherits this flag bit. The flag bits that pertain to extension structures are strictly inherited if the extension structure is inherited, i.e. the base type’s value of the flag bit is copied into the subtype together with a pointer to the extension structure. The Py_TPFLAGS_HAVE_GC flag bit is inherited together with the tp_traverse and tp_clear fields, i.e. if the Py_TPFLAGS_HAVE_GC flag bit is clear in the subtype and the tp_traverse and tp_clear fields in the subtype exist and have NULL values. .. XXX are most flag bits really inherited individually?

Default:

PyBaseObject_Type uses Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE.

Bit Masks:

The following bit masks are currently defined; these can be ORed together using the | operator to form the value of the tp_flags field. The macro PyType_HasFeature() takes a type and a flags value, tp and f, and checks whether tp->tp_flags & f is non-zero.

+Py_TPFLAGS_HEAPTYPE
+

This bit is set when the type object itself is allocated on the heap, for example, types created dynamically using PyType_FromSpec(). In this case, the ob_type field of its instances is considered a reference to the type, and the type object is INCREF’ed when a new instance is created, and DECREF’ed when an instance is destroyed (this does not apply to instances of subtypes; only the type referenced by the instance’s ob_type gets INCREF’ed or DECREF’ed).

Inheritance:

???

+
+Py_TPFLAGS_BASETYPE
+

This bit is set when the type can be used as the base type of another type. If this bit is clear, the type cannot be subtyped (similar to a “final” class in Java).

Inheritance:

???

+
+Py_TPFLAGS_READY
+

This bit is set when the type object has been fully initialized by PyType_Ready().

Inheritance:

???

+
+Py_TPFLAGS_READYING
+

This bit is set while PyType_Ready() is in the process of initializing the type object.

Inheritance:

???

+
+Py_TPFLAGS_HAVE_GC
+

This bit is set when the object supports garbage collection. If this bit is set, instances must be created using PyObject_GC_New and destroyed using PyObject_GC_Del(). More information in section Supporting Cyclic Garbage Collection. This bit also implies that the GC-related fields tp_traverse and tp_clear are present in the type object.

Inheritance:

Group: Py_TPFLAGS_HAVE_GC, tp_traverse, tp_clear

The Py_TPFLAGS_HAVE_GC flag bit is inherited together with the tp_traverse and tp_clear fields, i.e. if the Py_TPFLAGS_HAVE_GC flag bit is clear in the subtype and the tp_traverse and tp_clear fields in the subtype exist and have NULL values.

+
+Py_TPFLAGS_DEFAULT
+

This is a bitmask of all the bits that pertain to the existence of certain fields in the type object and its extension structures. Currently, it includes the following bits: Py_TPFLAGS_HAVE_STACKLESS_EXTENSION.

Inheritance:

???

+
+Py_TPFLAGS_METHOD_DESCRIPTOR
+

This bit indicates that objects behave like unbound methods.

If this flag is set for type(meth), then:

  • +meth.__get__(obj, cls)(*args, **kwds) (with obj not None) must be equivalent to meth(obj, *args, **kwds).
  • +meth.__get__(None, cls)(*args, **kwds) must be equivalent to meth(*args, **kwds).

This flag enables an optimization for typical method calls like obj.meth(): it avoids creating a temporary “bound method” object for obj.meth.

New in version 3.8.

Inheritance:

This flag is never inherited by types without the Py_TPFLAGS_IMMUTABLETYPE flag set. For extension types, it is inherited whenever tp_descr_get is inherited.

+
+Py_TPFLAGS_MANAGED_DICT
+

This bit indicates that instances of the class have a __dict__ attribute, and that the space for the dictionary is managed by the VM.

If this flag is set, Py_TPFLAGS_HAVE_GC should also be set.

New in version 3.12.

Inheritance:

This flag is inherited unless the tp_dictoffset field is set in a superclass.

+
+Py_TPFLAGS_MANAGED_WEAKREF
+

This bit indicates that instances of the class should be weakly referenceable.

New in version 3.12.

Inheritance:

This flag is inherited unless the tp_weaklistoffset field is set in a superclass.

+
+Py_TPFLAGS_ITEMS_AT_END
+

Only usable with variable-size types, i.e. ones with non-zero tp_itemsize.

Indicates that the variable-sized portion of an instance of this type is at the end of the instance’s memory area, at an offset of Py_TYPE(obj)->tp_basicsize (which may be different in each subclass).

When setting this flag, be sure that all superclasses either use this memory layout, or are not variable-sized. Python does not check this.

New in version 3.12.

Inheritance:

This flag is inherited.

+
+Py_TPFLAGS_LONG_SUBCLASS
+
+Py_TPFLAGS_LIST_SUBCLASS
+
+Py_TPFLAGS_TUPLE_SUBCLASS
+
+Py_TPFLAGS_BYTES_SUBCLASS
+
+Py_TPFLAGS_UNICODE_SUBCLASS
+
+Py_TPFLAGS_DICT_SUBCLASS
+
+Py_TPFLAGS_BASE_EXC_SUBCLASS
+
+Py_TPFLAGS_TYPE_SUBCLASS
+

These flags are used by functions such as PyLong_Check() to quickly determine if a type is a subclass of a built-in type; such specific checks are faster than a generic check, like PyObject_IsInstance(). Custom types that inherit from built-ins should have their tp_flags set appropriately, or the code that interacts with such types will behave differently depending on what kind of check is used.

+
+Py_TPFLAGS_HAVE_FINALIZE
+

This bit is set when the tp_finalize slot is present in the type structure.

New in version 3.4.

Deprecated since version 3.8: This flag isn’t necessary anymore, as the interpreter assumes the tp_finalize slot is always present in the type structure.

+
+Py_TPFLAGS_HAVE_VECTORCALL
+

This bit is set when the class implements the vectorcall protocol. See tp_vectorcall_offset for details.

Inheritance:

This bit is inherited if tp_call is also inherited.

New in version 3.9.

Changed in version 3.12: This flag is now removed from a class when the class’s __call__() method is reassigned.

This flag can now be inherited by mutable classes.

+
+Py_TPFLAGS_IMMUTABLETYPE
+

This bit is set for type objects that are immutable: type attributes cannot be set nor deleted.

PyType_Ready() automatically applies this flag to static types.

Inheritance:

This flag is not inherited.

New in version 3.10.

+
+Py_TPFLAGS_DISALLOW_INSTANTIATION
+

Disallow creating instances of the type: set tp_new to NULL and don’t create the __new__ key in the type dictionary.

The flag must be set before creating the type, not after. For example, it must be set before PyType_Ready() is called on the type.

The flag is set automatically on static types if tp_base is NULL or &PyBaseObject_Type and tp_new is NULL.

Inheritance:

This flag is not inherited. However, subclasses will not be instantiable unless they provide a non-NULL tp_new (which is only possible via the C API).

Note

To disallow instantiating a class directly but allow instantiating its subclasses (e.g. for an abstract base class), do not use this flag. Instead, make tp_new only succeed for subclasses.

New in version 3.10.

+
+Py_TPFLAGS_MAPPING
+

This bit indicates that instances of the class may match mapping patterns when used as the subject of a match block. It is automatically set when registering or subclassing collections.abc.Mapping, and unset when registering collections.abc.Sequence.

Note

Py_TPFLAGS_MAPPING and Py_TPFLAGS_SEQUENCE are mutually exclusive; it is an error to enable both flags simultaneously.

Inheritance:

This flag is inherited by types that do not already set Py_TPFLAGS_SEQUENCE.

See also

PEP 634 – Structural Pattern Matching: Specification

New in version 3.10.

+
+Py_TPFLAGS_SEQUENCE
+

This bit indicates that instances of the class may match sequence patterns when used as the subject of a match block. It is automatically set when registering or subclassing collections.abc.Sequence, and unset when registering collections.abc.Mapping.

Note

Py_TPFLAGS_MAPPING and Py_TPFLAGS_SEQUENCE are mutually exclusive; it is an error to enable both flags simultaneously.

Inheritance:

This flag is inherited by types that do not already set Py_TPFLAGS_MAPPING.

See also

PEP 634 – Structural Pattern Matching: Specification

New in version 3.10.

+
+Py_TPFLAGS_VALID_VERSION_TAG
+

Internal. Do not set or unset this flag. To indicate that a class has changed call PyType_Modified()

Warning

This flag is present in header files, but is an internal feature and should not be used. It will be removed in a future version of CPython

+
+
+const char *PyTypeObject.tp_doc
+

An optional pointer to a NUL-terminated C string giving the docstring for this type object. This is exposed as the __doc__ attribute on the type and instances of the type.

Inheritance:

This field is not inherited by subtypes.

+
+traverseproc PyTypeObject.tp_traverse
+

An optional pointer to a traversal function for the garbage collector. This is only used if the Py_TPFLAGS_HAVE_GC flag bit is set. The signature is:

int tp_traverse(PyObject *self, visitproc visit, void *arg);
+

More information about Python’s garbage collection scheme can be found in section Supporting Cyclic Garbage Collection.

The tp_traverse pointer is used by the garbage collector to detect reference cycles. A typical implementation of a tp_traverse function simply calls Py_VISIT() on each of the instance’s members that are Python objects that the instance owns. For example, this is function local_traverse() from the _thread extension module:

static int
+local_traverse(localobject *self, visitproc visit, void *arg)
+{
+    Py_VISIT(self->args);
+    Py_VISIT(self->kw);
+    Py_VISIT(self->dict);
+    return 0;
+}
+

Note that Py_VISIT() is called only on those members that can participate in reference cycles. Although there is also a self->key member, it can only be NULL or a Python string and therefore cannot be part of a reference cycle.

On the other hand, even if you know a member can never be part of a cycle, as a debugging aid you may want to visit it anyway just so the gc module’s get_referents() function will include it.

Warning

When implementing tp_traverse, only the members that the instance owns (by having strong references to them) must be visited. For instance, if an object supports weak references via the tp_weaklist slot, the pointer supporting the linked list (what tp_weaklist points to) must not be visited as the instance does not directly own the weak references to itself (the weakreference list is there to support the weak reference machinery, but the instance has no strong reference to the elements inside it, as they are allowed to be removed even if the instance is still alive).

Note that Py_VISIT() requires the visit and arg parameters to local_traverse() to have these specific names; don’t name them just anything.

Instances of heap-allocated types hold a reference to their type. Their traversal function must therefore either visit Py_TYPE(self), or delegate this responsibility by calling tp_traverse of another heap-allocated type (such as a heap-allocated superclass). If they do not, the type object may not be garbage-collected.

Changed in version 3.9: Heap-allocated types are expected to visit Py_TYPE(self) in tp_traverse. In earlier versions of Python, due to bug 40217, doing this may lead to crashes in subclasses.

Inheritance:

Group: Py_TPFLAGS_HAVE_GC, tp_traverse, tp_clear

This field is inherited by subtypes together with tp_clear and the Py_TPFLAGS_HAVE_GC flag bit: the flag bit, tp_traverse, and tp_clear are all inherited from the base type if they are all zero in the subtype.

+
+inquiry PyTypeObject.tp_clear
+

An optional pointer to a clear function for the garbage collector. This is only used if the Py_TPFLAGS_HAVE_GC flag bit is set. The signature is:

int tp_clear(PyObject *);
+

The tp_clear member function is used to break reference cycles in cyclic garbage detected by the garbage collector. Taken together, all tp_clear functions in the system must combine to break all reference cycles. This is subtle, and if in any doubt supply a tp_clear function. For example, the tuple type does not implement a tp_clear function, because it’s possible to prove that no reference cycle can be composed entirely of tuples. Therefore the tp_clear functions of other types must be sufficient to break any cycle containing a tuple. This isn’t immediately obvious, and there’s rarely a good reason to avoid implementing tp_clear.

Implementations of tp_clear should drop the instance’s references to those of its members that may be Python objects, and set its pointers to those members to NULL, as in the following example:

static int
+local_clear(localobject *self)
+{
+    Py_CLEAR(self->key);
+    Py_CLEAR(self->args);
+    Py_CLEAR(self->kw);
+    Py_CLEAR(self->dict);
+    return 0;
+}
+

The Py_CLEAR() macro should be used, because clearing references is delicate: the reference to the contained object must not be released (via Py_DECREF()) until after the pointer to the contained object is set to NULL. This is because releasing the reference may cause the contained object to become trash, triggering a chain of reclamation activity that may include invoking arbitrary Python code (due to finalizers, or weakref callbacks, associated with the contained object). If it’s possible for such code to reference self again, it’s important that the pointer to the contained object be NULL at that time, so that self knows the contained object can no longer be used. The Py_CLEAR() macro performs the operations in a safe order.

Note that tp_clear is not always called before an instance is deallocated. For example, when reference counting is enough to determine that an object is no longer used, the cyclic garbage collector is not involved and tp_dealloc is called directly.

Because the goal of tp_clear functions is to break reference cycles, it’s not necessary to clear contained objects like Python strings or Python integers, which can’t participate in reference cycles. On the other hand, it may be convenient to clear all contained Python objects, and write the type’s tp_dealloc function to invoke tp_clear.

More information about Python’s garbage collection scheme can be found in section Supporting Cyclic Garbage Collection.

Inheritance:

Group: Py_TPFLAGS_HAVE_GC, tp_traverse, tp_clear

This field is inherited by subtypes together with tp_traverse and the Py_TPFLAGS_HAVE_GC flag bit: the flag bit, tp_traverse, and tp_clear are all inherited from the base type if they are all zero in the subtype.

+
+richcmpfunc PyTypeObject.tp_richcompare
+

An optional pointer to the rich comparison function, whose signature is:

PyObject *tp_richcompare(PyObject *self, PyObject *other, int op);
+

The first parameter is guaranteed to be an instance of the type that is defined by PyTypeObject.

The function should return the result of the comparison (usually Py_True or Py_False). If the comparison is undefined, it must return Py_NotImplemented, if another error occurred it must return NULL and set an exception condition.

The following constants are defined to be used as the third argument for tp_richcompare and for PyObject_RichCompare():

+ + + + + + +

Constant

Comparison
+
+Py_LT
+

<
+
+Py_LE
+

<=
+
+Py_EQ
+

==
+
+Py_NE
+

!=
+
+Py_GT
+

>
+
+Py_GE
+

>=

The following macro is defined to ease writing rich comparison functions:

+Py_RETURN_RICHCOMPARE(VAL_A, VAL_B, op)
+

Return Py_True or Py_False from the function, depending on the result of a comparison. VAL_A and VAL_B must be orderable by C comparison operators (for example, they may be C ints or floats). The third argument specifies the requested operation, as for PyObject_RichCompare().

The returned value is a new strong reference.

On error, sets an exception and returns NULL from the function.

New in version 3.7.

+

Inheritance:

Group: tp_hash, tp_richcompare

This field is inherited by subtypes together with tp_hash: a subtype inherits tp_richcompare and tp_hash when the subtype’s tp_richcompare and tp_hash are both NULL.

Default:

PyBaseObject_Type provides a tp_richcompare implementation, which may be inherited. However, if only tp_hash is defined, not even the inherited function is used and instances of the type will not be able to participate in any comparisons.

+
+Py_ssize_t PyTypeObject.tp_weaklistoffset
+

While this field is still supported, Py_TPFLAGS_MANAGED_WEAKREF should be used instead, if at all possible.

If the instances of this type are weakly referenceable, this field is greater than zero and contains the offset in the instance structure of the weak reference list head (ignoring the GC header, if present); this offset is used by PyObject_ClearWeakRefs() and the PyWeakref_* functions. The instance structure needs to include a field of type PyObject* which is initialized to NULL.

Do not confuse this field with tp_weaklist; that is the list head for weak references to the type object itself.

It is an error to set both the Py_TPFLAGS_MANAGED_WEAKREF bit and tp_weaklist.

Inheritance:

This field is inherited by subtypes, but see the rules listed below. A subtype may override this offset; this means that the subtype uses a different weak reference list head than the base type. Since the list head is always found via tp_weaklistoffset, this should not be a problem.

Default:

If the Py_TPFLAGS_MANAGED_WEAKREF bit is set in the tp_dict field, then tp_weaklistoffset will be set to a negative value, to indicate that it is unsafe to use this field.

+
+getiterfunc PyTypeObject.tp_iter
+

An optional pointer to a function that returns an iterator for the object. Its presence normally signals that the instances of this type are iterable (although sequences may be iterable without this function).

This function has the same signature as PyObject_GetIter():

PyObject *tp_iter(PyObject *self);
+

Inheritance:

This field is inherited by subtypes.

+
+iternextfunc PyTypeObject.tp_iternext
+

An optional pointer to a function that returns the next item in an iterator. The signature is:

PyObject *tp_iternext(PyObject *self);
+

When the iterator is exhausted, it must return NULL; a StopIteration exception may or may not be set. When another error occurs, it must return NULL too. Its presence signals that the instances of this type are iterators.

Iterator types should also define the tp_iter function, and that function should return the iterator instance itself (not a new iterator instance).

This function has the same signature as PyIter_Next().

Inheritance:

This field is inherited by subtypes.

+
+struct PyMethodDef *PyTypeObject.tp_methods
+

An optional pointer to a static NULL-terminated array of PyMethodDef structures, declaring regular methods of this type.

For each entry in the array, an entry is added to the type’s dictionary (see tp_dict below) containing a method descriptor.

Inheritance:

This field is not inherited by subtypes (methods are inherited through a different mechanism).

+
+struct PyMemberDef *PyTypeObject.tp_members
+

An optional pointer to a static NULL-terminated array of PyMemberDef structures, declaring regular data members (fields or slots) of instances of this type.

For each entry in the array, an entry is added to the type’s dictionary (see tp_dict below) containing a member descriptor.

Inheritance:

This field is not inherited by subtypes (members are inherited through a different mechanism).

+
+struct PyGetSetDef *PyTypeObject.tp_getset
+

An optional pointer to a static NULL-terminated array of PyGetSetDef structures, declaring computed attributes of instances of this type.

For each entry in the array, an entry is added to the type’s dictionary (see tp_dict below) containing a getset descriptor.

Inheritance:

This field is not inherited by subtypes (computed attributes are inherited through a different mechanism).

+
+PyTypeObject *PyTypeObject.tp_base
+

An optional pointer to a base type from which type properties are inherited. At this level, only single inheritance is supported; multiple inheritance require dynamically creating a type object by calling the metatype.

Note

Slot initialization is subject to the rules of initializing globals. C99 requires the initializers to be “address constants”. Function designators like PyType_GenericNew(), with implicit conversion to a pointer, are valid C99 address constants.

However, the unary ‘&’ operator applied to a non-static variable like PyBaseObject_Type is not required to produce an address constant. Compilers may support this (gcc does), MSVC does not. Both compilers are strictly standard conforming in this particular behavior.

Consequently, tp_base should be set in the extension module’s init function.

Inheritance:

This field is not inherited by subtypes (obviously).

Default:

This field defaults to &PyBaseObject_Type (which to Python programmers is known as the type object).

+
+PyObject *PyTypeObject.tp_dict
+

The type’s dictionary is stored here by PyType_Ready().

This field should normally be initialized to NULL before PyType_Ready is called; it may also be initialized to a dictionary containing initial attributes for the type. Once PyType_Ready() has initialized the type, extra attributes for the type may be added to this dictionary only if they don’t correspond to overloaded operations (like __add__()). Once initialization for the type has finished, this field should be treated as read-only.

Some types may not store their dictionary in this slot. Use PyType_GetDict() to retrieve the dictionary for an arbitrary type.

Changed in version 3.12: Internals detail: For static builtin types, this is always NULL. Instead, the dict for such types is stored on PyInterpreterState. Use PyType_GetDict() to get the dict for an arbitrary type.

Inheritance:

This field is not inherited by subtypes (though the attributes defined in here are inherited through a different mechanism).

Default:

If this field is NULL, PyType_Ready() will assign a new dictionary to it.

Warning

It is not safe to use PyDict_SetItem() on or otherwise modify tp_dict with the dictionary C-API.

+
+descrgetfunc PyTypeObject.tp_descr_get
+

An optional pointer to a “descriptor get” function.

The function signature is:

PyObject * tp_descr_get(PyObject *self, PyObject *obj, PyObject *type);
+

Inheritance:

This field is inherited by subtypes.

+
+descrsetfunc PyTypeObject.tp_descr_set
+

An optional pointer to a function for setting and deleting a descriptor’s value.

The function signature is:

int tp_descr_set(PyObject *self, PyObject *obj, PyObject *value);
+

The value argument is set to NULL to delete the value.

Inheritance:

This field is inherited by subtypes.

+
+Py_ssize_t PyTypeObject.tp_dictoffset
+

While this field is still supported, Py_TPFLAGS_MANAGED_DICT should be used instead, if at all possible.

If the instances of this type have a dictionary containing instance variables, this field is non-zero and contains the offset in the instances of the type of the instance variable dictionary; this offset is used by PyObject_GenericGetAttr().

Do not confuse this field with tp_dict; that is the dictionary for attributes of the type object itself.

The value specifies the offset of the dictionary from the start of the instance structure.

The tp_dictoffset should be regarded as write-only. To get the pointer to the dictionary call PyObject_GenericGetDict(). Calling PyObject_GenericGetDict() may need to allocate memory for the dictionary, so it is may be more efficient to call PyObject_GetAttr() when accessing an attribute on the object.

It is an error to set both the Py_TPFLAGS_MANAGED_WEAKREF bit and tp_dictoffset.

Inheritance:

This field is inherited by subtypes. A subtype should not override this offset; doing so could be unsafe, if C code tries to access the dictionary at the previous offset. To properly support inheritance, use Py_TPFLAGS_MANAGED_DICT.

Default:

This slot has no default. For static types, if the field is NULL then no __dict__ gets created for instances.

If the Py_TPFLAGS_MANAGED_DICT bit is set in the tp_dict field, then tp_dictoffset will be set to -1, to indicate that it is unsafe to use this field.

+
+initproc PyTypeObject.tp_init
+

An optional pointer to an instance initialization function.

This function corresponds to the __init__() method of classes. Like __init__(), it is possible to create an instance without calling __init__(), and it is possible to reinitialize an instance by calling its __init__() method again.

The function signature is:

int tp_init(PyObject *self, PyObject *args, PyObject *kwds);
+

The self argument is the instance to be initialized; the args and kwds arguments represent positional and keyword arguments of the call to __init__().

The tp_init function, if not NULL, is called when an instance is created normally by calling its type, after the type’s tp_new function has returned an instance of the type. If the tp_new function returns an instance of some other type that is not a subtype of the original type, no tp_init function is called; if tp_new returns an instance of a subtype of the original type, the subtype’s tp_init is called.

Returns 0 on success, -1 and sets an exception on error.

Inheritance:

This field is inherited by subtypes.

Default:

For static types this field does not have a default.

+
+allocfunc PyTypeObject.tp_alloc
+

An optional pointer to an instance allocation function.

The function signature is:

PyObject *tp_alloc(PyTypeObject *self, Py_ssize_t nitems);
+

Inheritance:

This field is inherited by static subtypes, but not by dynamic subtypes (subtypes created by a class statement).

Default:

For dynamic subtypes, this field is always set to PyType_GenericAlloc(), to force a standard heap allocation strategy.

For static subtypes, PyBaseObject_Type uses PyType_GenericAlloc(). That is the recommended value for all statically defined types.

+
+newfunc PyTypeObject.tp_new
+

An optional pointer to an instance creation function.

The function signature is:

PyObject *tp_new(PyTypeObject *subtype, PyObject *args, PyObject *kwds);
+

The subtype argument is the type of the object being created; the args and kwds arguments represent positional and keyword arguments of the call to the type. Note that subtype doesn’t have to equal the type whose tp_new function is called; it may be a subtype of that type (but not an unrelated type).

The tp_new function should call subtype->tp_alloc(subtype, nitems) to allocate space for the object, and then do only as much further initialization as is absolutely necessary. Initialization that can safely be ignored or repeated should be placed in the tp_init handler. A good rule of thumb is that for immutable types, all initialization should take place in tp_new, while for mutable types, most initialization should be deferred to tp_init.

Set the Py_TPFLAGS_DISALLOW_INSTANTIATION flag to disallow creating instances of the type in Python.

Inheritance:

This field is inherited by subtypes, except it is not inherited by static types whose tp_base is NULL or &PyBaseObject_Type.

Default:

For static types this field has no default. This means if the slot is defined as NULL, the type cannot be called to create new instances; presumably there is some other way to create instances, like a factory function.

+
+freefunc PyTypeObject.tp_free
+

An optional pointer to an instance deallocation function. Its signature is:

void tp_free(void *self);
+

An initializer that is compatible with this signature is PyObject_Free().

Inheritance:

This field is inherited by static subtypes, but not by dynamic subtypes (subtypes created by a class statement)

Default:

In dynamic subtypes, this field is set to a deallocator suitable to match PyType_GenericAlloc() and the value of the Py_TPFLAGS_HAVE_GC flag bit.

For static subtypes, PyBaseObject_Type uses PyObject_Del().

+
+inquiry PyTypeObject.tp_is_gc
+

An optional pointer to a function called by the garbage collector.

The garbage collector needs to know whether a particular object is collectible or not. Normally, it is sufficient to look at the object’s type’s tp_flags field, and check the Py_TPFLAGS_HAVE_GC flag bit. But some types have a mixture of statically and dynamically allocated instances, and the statically allocated instances are not collectible. Such types should define this function; it should return 1 for a collectible instance, and 0 for a non-collectible instance. The signature is:

int tp_is_gc(PyObject *self);
+

(The only example of this are types themselves. The metatype, PyType_Type, defines this function to distinguish between statically and dynamically allocated types.)

Inheritance:

This field is inherited by subtypes.

Default:

This slot has no default. If this field is NULL, Py_TPFLAGS_HAVE_GC is used as the functional equivalent.

+
+PyObject *PyTypeObject.tp_bases
+

Tuple of base types.

This field should be set to NULL and treated as read-only. Python will fill it in when the type is initialized.

For dynamically created classes, the Py_tp_bases slot can be used instead of the bases argument of PyType_FromSpecWithBases(). The argument form is preferred.

Warning

Multiple inheritance does not work well for statically defined types. If you set tp_bases to a tuple, Python will not raise an error, but some slots will only be inherited from the first base.

Inheritance:

This field is not inherited.

+
+PyObject *PyTypeObject.tp_mro
+

Tuple containing the expanded set of base types, starting with the type itself and ending with object, in Method Resolution Order.

This field should be set to NULL and treated as read-only. Python will fill it in when the type is initialized.

Inheritance:

This field is not inherited; it is calculated fresh by PyType_Ready().

+
+PyObject *PyTypeObject.tp_cache
+

Unused. Internal use only.

Inheritance:

This field is not inherited.

+
+void *PyTypeObject.tp_subclasses
+

A collection of subclasses. Internal use only. May be an invalid pointer.

To get a list of subclasses, call the Python method __subclasses__().

Changed in version 3.12: For some types, this field does not hold a valid PyObject*. The type was changed to void* to indicate this.

Inheritance:

This field is not inherited.

+
+PyObject *PyTypeObject.tp_weaklist
+

Weak reference list head, for weak references to this type object. Not inherited. Internal use only.

Changed in version 3.12: Internals detail: For the static builtin types this is always NULL, even if weakrefs are added. Instead, the weakrefs for each are stored on PyInterpreterState. Use the public C-API or the internal _PyObject_GET_WEAKREFS_LISTPTR() macro to avoid the distinction.

Inheritance:

This field is not inherited.

+
+destructor PyTypeObject.tp_del
+

This field is deprecated. Use tp_finalize instead.

+
+unsigned int PyTypeObject.tp_version_tag
+

Used to index into the method cache. Internal use only.

Inheritance:

This field is not inherited.

+
+destructor PyTypeObject.tp_finalize
+

An optional pointer to an instance finalization function. Its signature is:

void tp_finalize(PyObject *self);
+

If tp_finalize is set, the interpreter calls it once when finalizing an instance. It is called either from the garbage collector (if the instance is part of an isolated reference cycle) or just before the object is deallocated. Either way, it is guaranteed to be called before attempting to break reference cycles, ensuring that it finds the object in a sane state.

tp_finalize should not mutate the current exception status; therefore, a recommended way to write a non-trivial finalizer is:

static void
+local_finalize(PyObject *self)
+{
+    PyObject *error_type, *error_value, *error_traceback;
+
+    /* Save the current exception, if any. */
+    PyErr_Fetch(&error_type, &error_value, &error_traceback);
+
+    /* ... */
+
+    /* Restore the saved exception. */
+    PyErr_Restore(error_type, error_value, error_traceback);
+}
+

Also, note that, in a garbage collected Python, tp_dealloc may be called from any Python thread, not just the thread which created the object (if the object becomes part of a refcount cycle, that cycle might be collected by a garbage collection on any thread). This is not a problem for Python API calls, since the thread on which tp_dealloc is called will own the Global Interpreter Lock (GIL). However, if the object being destroyed in turn destroys objects from some other C or C++ library, care should be taken to ensure that destroying those objects on the thread which called tp_dealloc will not violate any assumptions of the library.

Inheritance:

This field is inherited by subtypes.

New in version 3.4.

Changed in version 3.8: Before version 3.8 it was necessary to set the Py_TPFLAGS_HAVE_FINALIZE flags bit in order for this field to be used. This is no longer required.

See also

“Safe object finalization” (PEP 442)

+
+vectorcallfunc PyTypeObject.tp_vectorcall
+

Vectorcall function to use for calls of this type object. In other words, it is used to implement vectorcall for type.__call__. If tp_vectorcall is NULL, the default call implementation using __new__() and __init__() is used.

Inheritance:

This field is never inherited.

New in version 3.9: (the field exists since 3.8 but it’s only used since 3.9)

+
+unsigned char PyTypeObject.tp_watched
+

Internal. Do not use.

New in version 3.12.

+

Static Types

Traditionally, types defined in C code are static, that is, a static PyTypeObject structure is defined directly in code and initialized using PyType_Ready().

This results in types that are limited relative to types defined in Python:

Also, since PyTypeObject is only part of the Limited API as an opaque struct, any extension modules using static types must be compiled for a specific Python minor version.

Heap Types

An alternative to static types is heap-allocated types, or heap types for short, which correspond closely to classes created by Python’s class statement. Heap types have the Py_TPFLAGS_HEAPTYPE flag set.

This is done by filling a PyType_Spec structure and calling PyType_FromSpec(), PyType_FromSpecWithBases(), PyType_FromModuleAndSpec(), or PyType_FromMetaclass().

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/typeobj.html +

+
diff --git a/devdocs/python~3.12/c-api%2Funicode.html b/devdocs/python~3.12/c-api%2Funicode.html new file mode 100644 index 00000000..2b11a1d7 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Funicode.html @@ -0,0 +1,382 @@ +

Unicode Objects and Codecs

Unicode Objects

Since the implementation of PEP 393 in Python 3.3, Unicode objects internally use a variety of representations, in order to allow handling the complete range of Unicode characters while staying memory efficient. There are special cases for strings where all code points are below 128, 256, or 65536; otherwise, code points must be below 1114112 (which is the full Unicode range).

UTF-8 representation is created on demand and cached in the Unicode object.

Note

The Py_UNICODE representation has been removed since Python 3.12 with deprecated APIs. See PEP 623 for more information.

Unicode Type

These are the basic Unicode object types used for the Unicode implementation in Python:

+type Py_UCS4
+type Py_UCS2
+type Py_UCS1
+ Part of the Stable ABI.

These types are typedefs for unsigned integer types wide enough to contain characters of 32 bits, 16 bits and 8 bits, respectively. When dealing with single Unicode characters, use Py_UCS4.

New in version 3.3.

+
+type Py_UNICODE
+

This is a typedef of wchar_t, which is a 16-bit type or 32-bit type depending on the platform.

Changed in version 3.3: In previous versions, this was a 16-bit type or a 32-bit type depending on whether you selected a “narrow” or “wide” Unicode version of Python at build time.

+
+type PyASCIIObject
+type PyCompactUnicodeObject
+type PyUnicodeObject
+

These subtypes of PyObject represent a Python Unicode object. In almost all cases, they shouldn’t be used directly, since all API functions that deal with Unicode objects take and return PyObject pointers.

New in version 3.3.

+
+PyTypeObject PyUnicode_Type
+ Part of the Stable ABI.

This instance of PyTypeObject represents the Python Unicode type. It is exposed to Python code as str.

+

The following APIs are C macros and static inlined functions for fast checks and access to internal read-only data of Unicode objects:

+int PyUnicode_Check(PyObject *obj)
+

Return true if the object obj is a Unicode object or an instance of a Unicode subtype. This function always succeeds.

+
+int PyUnicode_CheckExact(PyObject *obj)
+

Return true if the object obj is a Unicode object, but not an instance of a subtype. This function always succeeds.

+
+int PyUnicode_READY(PyObject *unicode)
+

Returns 0. This API is kept only for backward compatibility.

New in version 3.3.

Deprecated since version 3.10: This API does nothing since Python 3.12.

+
+Py_ssize_t PyUnicode_GET_LENGTH(PyObject *unicode)
+

Return the length of the Unicode string, in code points. unicode has to be a Unicode object in the “canonical” representation (not checked).

New in version 3.3.

+
+Py_UCS1 *PyUnicode_1BYTE_DATA(PyObject *unicode)
+Py_UCS2 *PyUnicode_2BYTE_DATA(PyObject *unicode)
+Py_UCS4 *PyUnicode_4BYTE_DATA(PyObject *unicode)
+

Return a pointer to the canonical representation cast to UCS1, UCS2 or UCS4 integer types for direct character access. No checks are performed if the canonical representation has the correct character size; use PyUnicode_KIND() to select the right function.

New in version 3.3.

+
+PyUnicode_1BYTE_KIND
+PyUnicode_2BYTE_KIND
+PyUnicode_4BYTE_KIND
+

Return values of the PyUnicode_KIND() macro.

New in version 3.3.

Changed in version 3.12: PyUnicode_WCHAR_KIND has been removed.

+
+int PyUnicode_KIND(PyObject *unicode)
+

Return one of the PyUnicode kind constants (see above) that indicate how many bytes per character this Unicode object uses to store its data. unicode has to be a Unicode object in the “canonical” representation (not checked).

New in version 3.3.

+
+void *PyUnicode_DATA(PyObject *unicode)
+

Return a void pointer to the raw Unicode buffer. unicode has to be a Unicode object in the “canonical” representation (not checked).

New in version 3.3.

+
+void PyUnicode_WRITE(int kind, void *data, Py_ssize_t index, Py_UCS4 value)
+

Write into a canonical representation data (as obtained with PyUnicode_DATA()). This function performs no sanity checks, and is intended for usage in loops. The caller should cache the kind value and data pointer as obtained from other calls. index is the index in the string (starts at 0) and value is the new code point value which should be written to that location.

New in version 3.3.

+
+Py_UCS4 PyUnicode_READ(int kind, void *data, Py_ssize_t index)
+

Read a code point from a canonical representation data (as obtained with PyUnicode_DATA()). No checks or ready calls are performed.

New in version 3.3.

+
+Py_UCS4 PyUnicode_READ_CHAR(PyObject *unicode, Py_ssize_t index)
+

Read a character from a Unicode object unicode, which must be in the “canonical” representation. This is less efficient than PyUnicode_READ() if you do multiple consecutive reads.

New in version 3.3.

+
+Py_UCS4 PyUnicode_MAX_CHAR_VALUE(PyObject *unicode)
+

Return the maximum code point that is suitable for creating another string based on unicode, which must be in the “canonical” representation. This is always an approximation but more efficient than iterating over the string.

New in version 3.3.

+
+int PyUnicode_IsIdentifier(PyObject *unicode)
+ Part of the Stable ABI.

Return 1 if the string is a valid identifier according to the language definition, section Identifiers and keywords. Return 0 otherwise.

Changed in version 3.9: The function does not call Py_FatalError() anymore if the string is not ready.

+

Unicode Character Properties

Unicode provides many different character properties. The most often needed ones are available through these macros which are mapped to C functions depending on the Python configuration.

+int Py_UNICODE_ISSPACE(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a whitespace character.

+
+int Py_UNICODE_ISLOWER(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a lowercase character.

+
+int Py_UNICODE_ISUPPER(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is an uppercase character.

+
+int Py_UNICODE_ISTITLE(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a titlecase character.

+
+int Py_UNICODE_ISLINEBREAK(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a linebreak character.

+
+int Py_UNICODE_ISDECIMAL(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a decimal character.

+
+int Py_UNICODE_ISDIGIT(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a digit character.

+
+int Py_UNICODE_ISNUMERIC(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a numeric character.

+
+int Py_UNICODE_ISALPHA(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is an alphabetic character.

+
+int Py_UNICODE_ISALNUM(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is an alphanumeric character.

+
+int Py_UNICODE_ISPRINTABLE(Py_UCS4 ch)
+

Return 1 or 0 depending on whether ch is a printable character. Nonprintable characters are those characters defined in the Unicode character database as “Other” or “Separator”, excepting the ASCII space (0x20) which is considered printable. (Note that printable characters in this context are those which should not be escaped when repr() is invoked on a string. It has no bearing on the handling of strings written to sys.stdout or sys.stderr.)

+

These APIs can be used for fast direct character conversions:

+Py_UCS4 Py_UNICODE_TOLOWER(Py_UCS4 ch)
+

Return the character ch converted to lower case.

Deprecated since version 3.3: This function uses simple case mappings.

+
+Py_UCS4 Py_UNICODE_TOUPPER(Py_UCS4 ch)
+

Return the character ch converted to upper case.

Deprecated since version 3.3: This function uses simple case mappings.

+
+Py_UCS4 Py_UNICODE_TOTITLE(Py_UCS4 ch)
+

Return the character ch converted to title case.

Deprecated since version 3.3: This function uses simple case mappings.

+
+int Py_UNICODE_TODECIMAL(Py_UCS4 ch)
+

Return the character ch converted to a decimal positive integer. Return -1 if this is not possible. This function does not raise exceptions.

+
+int Py_UNICODE_TODIGIT(Py_UCS4 ch)
+

Return the character ch converted to a single digit integer. Return -1 if this is not possible. This function does not raise exceptions.

+
+double Py_UNICODE_TONUMERIC(Py_UCS4 ch)
+

Return the character ch converted to a double. Return -1.0 if this is not possible. This function does not raise exceptions.

+

These APIs can be used to work with surrogates:

+int Py_UNICODE_IS_SURROGATE(Py_UCS4 ch)
+

Check if ch is a surrogate (0xD800 <= ch <= 0xDFFF).

+
+int Py_UNICODE_IS_HIGH_SURROGATE(Py_UCS4 ch)
+

Check if ch is a high surrogate (0xD800 <= ch <= 0xDBFF).

+
+int Py_UNICODE_IS_LOW_SURROGATE(Py_UCS4 ch)
+

Check if ch is a low surrogate (0xDC00 <= ch <= 0xDFFF).

+
+Py_UCS4 Py_UNICODE_JOIN_SURROGATES(Py_UCS4 high, Py_UCS4 low)
+

Join two surrogate characters and return a single Py_UCS4 value. high and low are respectively the leading and trailing surrogates in a surrogate pair. high must be in the range [0xD800; 0xDBFF] and low must be in the range [0xDC00; 0xDFFF].

+

Creating and accessing Unicode strings

To create Unicode objects and access their basic sequence properties, use these APIs:

+PyObject *PyUnicode_New(Py_ssize_t size, Py_UCS4 maxchar)
+Return value: New reference.

Create a new Unicode object. maxchar should be the true maximum code point to be placed in the string. As an approximation, it can be rounded up to the nearest value in the sequence 127, 255, 65535, 1114111.

This is the recommended way to allocate a new Unicode object. Objects created using this function are not resizable.

New in version 3.3.

+
+PyObject *PyUnicode_FromKindAndData(int kind, const void *buffer, Py_ssize_t size)
+Return value: New reference.

Create a new Unicode object with the given kind (possible values are PyUnicode_1BYTE_KIND etc., as returned by PyUnicode_KIND()). The buffer must point to an array of size units of 1, 2 or 4 bytes per character, as given by the kind.

If necessary, the input buffer is copied and transformed into the canonical representation. For example, if the buffer is a UCS4 string (PyUnicode_4BYTE_KIND) and it consists only of codepoints in the UCS1 range, it will be transformed into UCS1 (PyUnicode_1BYTE_KIND).

New in version 3.3.

+
+PyObject *PyUnicode_FromStringAndSize(const char *str, Py_ssize_t size)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object from the char buffer str. The bytes will be interpreted as being UTF-8 encoded. The buffer is copied into the new object. The return value might be a shared object, i.e. modification of the data is not allowed.

This function raises SystemError when:

  • +size < 0,
  • +str is NULL and size > 0

Changed in version 3.12: str == NULL with size > 0 is not allowed anymore.

+
+PyObject *PyUnicode_FromString(const char *str)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object from a UTF-8 encoded null-terminated char buffer str.

+
+PyObject *PyUnicode_FromFormat(const char *format, ...)
+Return value: New reference. Part of the Stable ABI.

Take a C printf()-style format string and a variable number of arguments, calculate the size of the resulting Python Unicode string and return a string with the values formatted into it. The variable arguments must be C types and must correspond exactly to the format characters in the format ASCII-encoded string.

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

  1. The '%' character, which marks the start of the specifier.
  2. Conversion flags (optional), which affect the result of some conversion types.
  3. Minimum field width (optional). If specified as an '*' (asterisk), the actual width is given in the next argument, which must be of type int, and the object to convert comes after the minimum field width and optional precision.
  4. Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is given in the next argument, which must be of type int, and the value to convert comes after the precision.
  5. Length modifier (optional).
  6. Conversion type.

The conversion flag characters are:

+ + +

Flag

Meaning

0

The conversion will be zero padded for numeric values.

-

The converted value is left adjusted (overrides the 0 flag if both are given).

The length modifiers for following integer conversions (d, i, o, u, x, or X) specify the type of the argument (int by default):

+ + + + + +

Modifier

Types

l

long or unsigned long

ll

long long or unsigned long long

j

intmax_t or uintmax_t

z

size_t or ssize_t

t

ptrdiff_t

The length modifier l for following conversions s or V specify that the type of the argument is const wchar_t*.

The conversion specifiers are:

+ + + + + + + + + + + + + + +

Conversion Specifier

Type

Comment

%

n/a

The literal % character.

d, i

Specified by the length modifier

The decimal representation of a signed C integer.

u

Specified by the length modifier

The decimal representation of an unsigned C integer.

o

Specified by the length modifier

The octal representation of an unsigned C integer.

x

Specified by the length modifier

The hexadecimal representation of an unsigned C integer (lowercase).

X

Specified by the length modifier

The hexadecimal representation of an unsigned C integer (uppercase).

c

int

A single character.

s

const char* or const wchar_t*

A null-terminated C character array.

p

const void*

The hex representation of a C pointer. Mostly equivalent to printf("%p") except that it is guaranteed to start with the literal 0x regardless of what the platform’s printf yields.

A

PyObject*

The result of calling ascii().

U

PyObject*

A Unicode object.

V

PyObject*, const char* or const wchar_t*

A Unicode object (which may be NULL) and a null-terminated C character array as a second parameter (which will be used, if the first parameter is NULL).

S

PyObject*

The result of calling PyObject_Str().

R

PyObject*

The result of calling PyObject_Repr().

Note

The width formatter unit is number of characters rather than bytes. The precision formatter unit is number of bytes or wchar_t items (if the length modifier l is used) for "%s" and "%V" (if the PyObject* argument is NULL), and a number of characters for "%A", "%U", "%S", "%R" and "%V" (if the PyObject* argument is not NULL).

Note

Unlike to C printf() the 0 flag has effect even when a precision is given for integer conversions (d, i, u, o, x, or X).

Changed in version 3.2: Support for "%lld" and "%llu" added.

Changed in version 3.3: Support for "%li", "%lli" and "%zi" added.

Changed in version 3.4: Support width and precision formatter for "%s", "%A", "%U", "%V", "%S", "%R" added.

Changed in version 3.12: Support for conversion specifiers o and X. Support for length modifiers j and t. Length modifiers are now applied to all integer conversions. Length modifier l is now applied to conversion specifiers s and V. Support for variable width and precision *. Support for flag -.

An unrecognized format character now sets a SystemError. In previous versions it caused all the rest of the format string to be copied as-is to the result string, and any extra arguments discarded.

+
+PyObject *PyUnicode_FromFormatV(const char *format, va_list vargs)
+Return value: New reference. Part of the Stable ABI.

Identical to PyUnicode_FromFormat() except that it takes exactly two arguments.

+
+PyObject *PyUnicode_FromObject(PyObject *obj)
+Return value: New reference. Part of the Stable ABI.

Copy an instance of a Unicode subtype to a new true Unicode object if necessary. If obj is already a true Unicode object (not a subtype), return a new strong reference to the object.

Objects other than Unicode or its subtypes will cause a TypeError.

+
+PyObject *PyUnicode_FromEncodedObject(PyObject *obj, const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Decode an encoded object obj to a Unicode object.

bytes, bytearray and other bytes-like objects are decoded according to the given encoding and using the error handling defined by errors. Both can be NULL to have the interface use the default values (see Built-in Codecs for details).

All other objects, including Unicode objects, cause a TypeError to be set.

The API returns NULL if there was an error. The caller is responsible for decref’ing the returned objects.

+
+Py_ssize_t PyUnicode_GetLength(PyObject *unicode)
+ Part of the Stable ABI since version 3.7.

Return the length of the Unicode object, in code points.

New in version 3.3.

+
+Py_ssize_t PyUnicode_CopyCharacters(PyObject *to, Py_ssize_t to_start, PyObject *from, Py_ssize_t from_start, Py_ssize_t how_many)
+

Copy characters from one Unicode object into another. This function performs character conversion when necessary and falls back to memcpy() if possible. Returns -1 and sets an exception on error, otherwise returns the number of copied characters.

New in version 3.3.

+
+Py_ssize_t PyUnicode_Fill(PyObject *unicode, Py_ssize_t start, Py_ssize_t length, Py_UCS4 fill_char)
+

Fill a string with a character: write fill_char into unicode[start:start+length].

Fail if fill_char is bigger than the string maximum character, or if the string has more than 1 reference.

Return the number of written character, or return -1 and raise an exception on error.

New in version 3.3.

+
+int PyUnicode_WriteChar(PyObject *unicode, Py_ssize_t index, Py_UCS4 character)
+ Part of the Stable ABI since version 3.7.

Write a character to a string. The string must have been created through PyUnicode_New(). Since Unicode strings are supposed to be immutable, the string must not be shared, or have been hashed yet.

This function checks that unicode is a Unicode object, that the index is not out of bounds, and that the object can be modified safely (i.e. that it its reference count is one).

New in version 3.3.

+
+Py_UCS4 PyUnicode_ReadChar(PyObject *unicode, Py_ssize_t index)
+ Part of the Stable ABI since version 3.7.

Read a character from a string. This function checks that unicode is a Unicode object and the index is not out of bounds, in contrast to PyUnicode_READ_CHAR(), which performs no error checking.

New in version 3.3.

+
+PyObject *PyUnicode_Substring(PyObject *unicode, Py_ssize_t start, Py_ssize_t end)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Return a substring of unicode, from character index start (included) to character index end (excluded). Negative indices are not supported.

New in version 3.3.

+
+Py_UCS4 *PyUnicode_AsUCS4(PyObject *unicode, Py_UCS4 *buffer, Py_ssize_t buflen, int copy_null)
+ Part of the Stable ABI since version 3.7.

Copy the string unicode into a UCS4 buffer, including a null character, if copy_null is set. Returns NULL and sets an exception on error (in particular, a SystemError if buflen is smaller than the length of unicode). buffer is returned on success.

New in version 3.3.

+
+Py_UCS4 *PyUnicode_AsUCS4Copy(PyObject *unicode)
+ Part of the Stable ABI since version 3.7.

Copy the string unicode into a new UCS4 buffer that is allocated using PyMem_Malloc(). If this fails, NULL is returned with a MemoryError set. The returned buffer always has an extra null code point appended.

New in version 3.3.

+

Locale Encoding

The current locale encoding can be used to decode text from the operating system.

+PyObject *PyUnicode_DecodeLocaleAndSize(const char *str, Py_ssize_t length, const char *errors)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Decode a string from UTF-8 on Android and VxWorks, or from the current locale encoding on other platforms. The supported error handlers are "strict" and "surrogateescape" (PEP 383). The decoder uses "strict" error handler if errors is NULL. str must end with a null character but cannot contain embedded null characters.

Use PyUnicode_DecodeFSDefaultAndSize() to decode a string from the filesystem encoding and error handler.

This function ignores the Python UTF-8 Mode.

See also

The Py_DecodeLocale() function.

New in version 3.3.

Changed in version 3.7: The function now also uses the current locale encoding for the surrogateescape error handler, except on Android. Previously, Py_DecodeLocale() was used for the surrogateescape, and the current locale encoding was used for strict.

+
+PyObject *PyUnicode_DecodeLocale(const char *str, const char *errors)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Similar to PyUnicode_DecodeLocaleAndSize(), but compute the string length using strlen().

New in version 3.3.

+
+PyObject *PyUnicode_EncodeLocale(PyObject *unicode, const char *errors)
+Return value: New reference. Part of the Stable ABI since version 3.7.

Encode a Unicode object to UTF-8 on Android and VxWorks, or to the current locale encoding on other platforms. The supported error handlers are "strict" and "surrogateescape" (PEP 383). The encoder uses "strict" error handler if errors is NULL. Return a bytes object. unicode cannot contain embedded null characters.

Use PyUnicode_EncodeFSDefault() to encode a string to the filesystem encoding and error handler.

This function ignores the Python UTF-8 Mode.

See also

The Py_EncodeLocale() function.

New in version 3.3.

Changed in version 3.7: The function now also uses the current locale encoding for the surrogateescape error handler, except on Android. Previously, Py_EncodeLocale() was used for the surrogateescape, and the current locale encoding was used for strict.

+

File System Encoding

Functions encoding to and decoding from the filesystem encoding and error handler (PEP 383 and PEP 529).

To encode file names to bytes during argument parsing, the "O&" converter should be used, passing PyUnicode_FSConverter() as the conversion function:

+int PyUnicode_FSConverter(PyObject *obj, void *result)
+ Part of the Stable ABI.

ParseTuple converter: encode str objects – obtained directly or through the os.PathLike interface – to bytes using PyUnicode_EncodeFSDefault(); bytes objects are output as-is. result must be a PyBytesObject* which must be released when it is no longer used.

New in version 3.1.

Changed in version 3.6: Accepts a path-like object.

+

To decode file names to str during argument parsing, the "O&" converter should be used, passing PyUnicode_FSDecoder() as the conversion function:

+int PyUnicode_FSDecoder(PyObject *obj, void *result)
+ Part of the Stable ABI.

ParseTuple converter: decode bytes objects – obtained either directly or indirectly through the os.PathLike interface – to str using PyUnicode_DecodeFSDefaultAndSize(); str objects are output as-is. result must be a PyUnicodeObject* which must be released when it is no longer used.

New in version 3.2.

Changed in version 3.6: Accepts a path-like object.

+
+PyObject *PyUnicode_DecodeFSDefaultAndSize(const char *str, Py_ssize_t size)
+Return value: New reference. Part of the Stable ABI.

Decode a string from the filesystem encoding and error handler.

If you need to decode a string from the current locale encoding, use PyUnicode_DecodeLocaleAndSize().

See also

The Py_DecodeLocale() function.

Changed in version 3.6: The filesystem error handler is now used.

+
+PyObject *PyUnicode_DecodeFSDefault(const char *str)
+Return value: New reference. Part of the Stable ABI.

Decode a null-terminated string from the filesystem encoding and error handler.

If the string length is known, use PyUnicode_DecodeFSDefaultAndSize().

Changed in version 3.6: The filesystem error handler is now used.

+
+PyObject *PyUnicode_EncodeFSDefault(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object to the filesystem encoding and error handler, and return bytes. Note that the resulting bytes object can contain null bytes.

If you need to encode a string to the current locale encoding, use PyUnicode_EncodeLocale().

See also

The Py_EncodeLocale() function.

New in version 3.2.

Changed in version 3.6: The filesystem error handler is now used.

+

wchar_t Support

wchar_t support for platforms which support it:

+PyObject *PyUnicode_FromWideChar(const wchar_t *wstr, Py_ssize_t size)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object from the wchar_t buffer wstr of the given size. Passing -1 as the size indicates that the function must itself compute the length, using wcslen(). Return NULL on failure.

+
+Py_ssize_t PyUnicode_AsWideChar(PyObject *unicode, wchar_t *wstr, Py_ssize_t size)
+ Part of the Stable ABI.

Copy the Unicode object contents into the wchar_t buffer wstr. At most size wchar_t characters are copied (excluding a possibly trailing null termination character). Return the number of wchar_t characters copied or -1 in case of an error. Note that the resulting wchar_t* string may or may not be null-terminated. It is the responsibility of the caller to make sure that the wchar_t* string is null-terminated in case this is required by the application. Also, note that the wchar_t* string might contain null characters, which would cause the string to be truncated when used with most C functions.

+
+wchar_t *PyUnicode_AsWideCharString(PyObject *unicode, Py_ssize_t *size)
+ Part of the Stable ABI since version 3.7.

Convert the Unicode object to a wide character string. The output string always ends with a null character. If size is not NULL, write the number of wide characters (excluding the trailing null termination character) into *size. Note that the resulting wchar_t string might contain null characters, which would cause the string to be truncated when used with most C functions. If size is NULL and the wchar_t* string contains null characters a ValueError is raised.

Returns a buffer allocated by PyMem_New (use PyMem_Free() to free it) on success. On error, returns NULL and *size is undefined. Raises a MemoryError if memory allocation is failed.

New in version 3.2.

Changed in version 3.7: Raises a ValueError if size is NULL and the wchar_t* string contains null characters.

+

Built-in Codecs

Python provides a set of built-in codecs which are written in C for speed. All of these codecs are directly usable via the following functions.

Many of the following APIs take two arguments encoding and errors, and they have the same semantics as the ones of the built-in str() string object constructor.

Setting encoding to NULL causes the default encoding to be used which is UTF-8. The file system calls should use PyUnicode_FSConverter() for encoding file names. This uses the filesystem encoding and error handler internally.

Error handling is set by errors which may also be set to NULL meaning to use the default handling defined for the codec. Default error handling for all built-in codecs is “strict” (ValueError is raised).

The codecs all use a similar interface. Only deviations from the following generic ones are documented for simplicity.

Generic Codecs

These are the generic codec APIs:

+PyObject *PyUnicode_Decode(const char *str, Py_ssize_t size, const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the encoded string str. encoding and errors have the same meaning as the parameters of the same name in the str() built-in function. The codec to be used is looked up using the Python codec registry. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_AsEncodedString(PyObject *unicode, const char *encoding, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object and return the result as Python bytes object. encoding and errors have the same meaning as the parameters of the same name in the Unicode encode() method. The codec to be used is looked up using the Python codec registry. Return NULL if an exception was raised by the codec.

+

UTF-8 Codecs

These are the UTF-8 codec APIs:

+PyObject *PyUnicode_DecodeUTF8(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the UTF-8 encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_DecodeUTF8Stateful(const char *str, Py_ssize_t size, const char *errors, Py_ssize_t *consumed)
+Return value: New reference. Part of the Stable ABI.

If consumed is NULL, behave like PyUnicode_DecodeUTF8(). If consumed is not NULL, trailing incomplete UTF-8 byte sequences will not be treated as an error. Those bytes will not be decoded and the number of bytes that have been decoded will be stored in consumed.

+
+PyObject *PyUnicode_AsUTF8String(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using UTF-8 and return the result as Python bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+
+const char *PyUnicode_AsUTF8AndSize(PyObject *unicode, Py_ssize_t *size)
+ Part of the Stable ABI since version 3.10.

Return a pointer to the UTF-8 encoding of the Unicode object, and store the size of the encoded representation (in bytes) in size. The size argument can be NULL; in this case no size will be stored. The returned buffer always has an extra null byte appended (not included in size), regardless of whether there are any other null code points.

In the case of an error, NULL is returned with an exception set and no size is stored.

This caches the UTF-8 representation of the string in the Unicode object, and subsequent calls will return a pointer to the same buffer. The caller is not responsible for deallocating the buffer. The buffer is deallocated and pointers to it become invalid when the Unicode object is garbage collected.

New in version 3.3.

Changed in version 3.7: The return type is now const char * rather of char *.

Changed in version 3.10: This function is a part of the limited API.

+
+const char *PyUnicode_AsUTF8(PyObject *unicode)
+

As PyUnicode_AsUTF8AndSize(), but does not store the size.

New in version 3.3.

Changed in version 3.7: The return type is now const char * rather of char *.

+

UTF-32 Codecs

These are the UTF-32 codec APIs:

+PyObject *PyUnicode_DecodeUTF32(const char *str, Py_ssize_t size, const char *errors, int *byteorder)
+Return value: New reference. Part of the Stable ABI.

Decode size bytes from a UTF-32 encoded buffer string and return the corresponding Unicode object. errors (if non-NULL) defines the error handling. It defaults to “strict”.

If byteorder is non-NULL, the decoder starts decoding using the given byte order:

*byteorder == -1: little endian
+*byteorder == 0:  native order
+*byteorder == 1:  big endian
+

If *byteorder is zero, and the first four bytes of the input data are a byte order mark (BOM), the decoder switches to this byte order and the BOM is not copied into the resulting Unicode string. If *byteorder is -1 or 1, any byte order mark is copied to the output.

After completion, *byteorder is set to the current byte order at the end of input data.

If byteorder is NULL, the codec starts in native order mode.

Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_DecodeUTF32Stateful(const char *str, Py_ssize_t size, const char *errors, int *byteorder, Py_ssize_t *consumed)
+Return value: New reference. Part of the Stable ABI.

If consumed is NULL, behave like PyUnicode_DecodeUTF32(). If consumed is not NULL, PyUnicode_DecodeUTF32Stateful() will not treat trailing incomplete UTF-32 byte sequences (such as a number of bytes not divisible by four) as an error. Those bytes will not be decoded and the number of bytes that have been decoded will be stored in consumed.

+
+PyObject *PyUnicode_AsUTF32String(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Return a Python byte string using the UTF-32 encoding in native byte order. The string always starts with a BOM mark. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

UTF-16 Codecs

These are the UTF-16 codec APIs:

+PyObject *PyUnicode_DecodeUTF16(const char *str, Py_ssize_t size, const char *errors, int *byteorder)
+Return value: New reference. Part of the Stable ABI.

Decode size bytes from a UTF-16 encoded buffer string and return the corresponding Unicode object. errors (if non-NULL) defines the error handling. It defaults to “strict”.

If byteorder is non-NULL, the decoder starts decoding using the given byte order:

*byteorder == -1: little endian
+*byteorder == 0:  native order
+*byteorder == 1:  big endian
+

If *byteorder is zero, and the first two bytes of the input data are a byte order mark (BOM), the decoder switches to this byte order and the BOM is not copied into the resulting Unicode string. If *byteorder is -1 or 1, any byte order mark is copied to the output (where it will result in either a \ufeff or a \ufffe character).

After completion, *byteorder is set to the current byte order at the end of input data.

If byteorder is NULL, the codec starts in native order mode.

Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_DecodeUTF16Stateful(const char *str, Py_ssize_t size, const char *errors, int *byteorder, Py_ssize_t *consumed)
+Return value: New reference. Part of the Stable ABI.

If consumed is NULL, behave like PyUnicode_DecodeUTF16(). If consumed is not NULL, PyUnicode_DecodeUTF16Stateful() will not treat trailing incomplete UTF-16 byte sequences (such as an odd number of bytes or a split surrogate pair) as an error. Those bytes will not be decoded and the number of bytes that have been decoded will be stored in consumed.

+
+PyObject *PyUnicode_AsUTF16String(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Return a Python byte string using the UTF-16 encoding in native byte order. The string always starts with a BOM mark. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

UTF-7 Codecs

These are the UTF-7 codec APIs:

+PyObject *PyUnicode_DecodeUTF7(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the UTF-7 encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_DecodeUTF7Stateful(const char *str, Py_ssize_t size, const char *errors, Py_ssize_t *consumed)
+Return value: New reference. Part of the Stable ABI.

If consumed is NULL, behave like PyUnicode_DecodeUTF7(). If consumed is not NULL, trailing incomplete UTF-7 base-64 sections will not be treated as an error. Those bytes will not be decoded and the number of bytes that have been decoded will be stored in consumed.

+

Unicode-Escape Codecs

These are the “Unicode Escape” codec APIs:

+PyObject *PyUnicode_DecodeUnicodeEscape(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the Unicode-Escape encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_AsUnicodeEscapeString(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using Unicode-Escape and return the result as a bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

Raw-Unicode-Escape Codecs

These are the “Raw Unicode Escape” codec APIs:

+PyObject *PyUnicode_DecodeRawUnicodeEscape(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the Raw-Unicode-Escape encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_AsRawUnicodeEscapeString(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using Raw-Unicode-Escape and return the result as a bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

Latin-1 Codecs

These are the Latin-1 codec APIs: Latin-1 corresponds to the first 256 Unicode ordinals and only these are accepted by the codecs during encoding.

+PyObject *PyUnicode_DecodeLatin1(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the Latin-1 encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_AsLatin1String(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using Latin-1 and return the result as Python bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

ASCII Codecs

These are the ASCII codec APIs. Only 7-bit ASCII data is accepted. All other codes generate errors.

+PyObject *PyUnicode_DecodeASCII(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the ASCII encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_AsASCIIString(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using ASCII and return the result as Python bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+

Character Map Codecs

This codec is special in that it can be used to implement many different codecs (and this is in fact what was done to obtain most of the standard codecs included in the encodings package). The codec uses mappings to encode and decode characters. The mapping objects provided must support the __getitem__() mapping interface; dictionaries and sequences work well.

These are the mapping codec APIs:

+PyObject *PyUnicode_DecodeCharmap(const char *str, Py_ssize_t length, PyObject *mapping, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Create a Unicode object by decoding size bytes of the encoded string str using the given mapping object. Return NULL if an exception was raised by the codec.

If mapping is NULL, Latin-1 decoding will be applied. Else mapping must map bytes ordinals (integers in the range from 0 to 255) to Unicode strings, integers (which are then interpreted as Unicode ordinals) or None. Unmapped data bytes – ones which cause a LookupError, as well as ones which get mapped to None, 0xFFFE or '\ufffe', are treated as undefined mappings and cause an error.

+
+PyObject *PyUnicode_AsCharmapString(PyObject *unicode, PyObject *mapping)
+Return value: New reference. Part of the Stable ABI.

Encode a Unicode object using the given mapping object and return the result as a bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

The mapping object must map Unicode ordinal integers to bytes objects, integers in the range from 0 to 255 or None. Unmapped character ordinals (ones which cause a LookupError) as well as mapped to None are treated as “undefined mapping” and cause an error.

+

The following codec API is special in that maps Unicode to Unicode.

+PyObject *PyUnicode_Translate(PyObject *unicode, PyObject *table, const char *errors)
+Return value: New reference. Part of the Stable ABI.

Translate a string by applying a character mapping table to it and return the resulting Unicode object. Return NULL if an exception was raised by the codec.

The mapping table must map Unicode ordinal integers to Unicode ordinal integers or None (causing deletion of the character).

Mapping tables need only provide the __getitem__() interface; dictionaries and sequences work well. Unmapped character ordinals (ones which cause a LookupError) are left untouched and are copied as-is.

errors has the usual meaning for codecs. It may be NULL which indicates to use the default error handling.

+

MBCS codecs for Windows

These are the MBCS codec APIs. They are currently only available on Windows and use the Win32 MBCS converters to implement the conversions. Note that MBCS (or DBCS) is a class of encodings, not just one. The target encoding is defined by the user settings on the machine running the codec.

+PyObject *PyUnicode_DecodeMBCS(const char *str, Py_ssize_t size, const char *errors)
+Return value: New reference. Part of the Stable ABI on Windows since version 3.7.

Create a Unicode object by decoding size bytes of the MBCS encoded string str. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_DecodeMBCSStateful(const char *str, Py_ssize_t size, const char *errors, Py_ssize_t *consumed)
+Return value: New reference. Part of the Stable ABI on Windows since version 3.7.

If consumed is NULL, behave like PyUnicode_DecodeMBCS(). If consumed is not NULL, PyUnicode_DecodeMBCSStateful() will not decode trailing lead byte and the number of bytes that have been decoded will be stored in consumed.

+
+PyObject *PyUnicode_AsMBCSString(PyObject *unicode)
+Return value: New reference. Part of the Stable ABI on Windows since version 3.7.

Encode a Unicode object using MBCS and return the result as Python bytes object. Error handling is “strict”. Return NULL if an exception was raised by the codec.

+
+PyObject *PyUnicode_EncodeCodePage(int code_page, PyObject *unicode, const char *errors)
+Return value: New reference. Part of the Stable ABI on Windows since version 3.7.

Encode the Unicode object using the specified code page and return a Python bytes object. Return NULL if an exception was raised by the codec. Use CP_ACP code page to get the MBCS encoder.

New in version 3.3.

+

Methods & Slots

Methods and Slot Functions

The following APIs are capable of handling Unicode objects and strings on input (we refer to them as strings in the descriptions) and return Unicode objects or integers as appropriate.

They all return NULL or -1 if an exception occurs.

+PyObject *PyUnicode_Concat(PyObject *left, PyObject *right)
+Return value: New reference. Part of the Stable ABI.

Concat two strings giving a new Unicode string.

+
+PyObject *PyUnicode_Split(PyObject *unicode, PyObject *sep, Py_ssize_t maxsplit)
+Return value: New reference. Part of the Stable ABI.

Split a string giving a list of Unicode strings. If sep is NULL, splitting will be done at all whitespace substrings. Otherwise, splits occur at the given separator. At most maxsplit splits will be done. If negative, no limit is set. Separators are not included in the resulting list.

+
+PyObject *PyUnicode_Splitlines(PyObject *unicode, int keepends)
+Return value: New reference. Part of the Stable ABI.

Split a Unicode string at line breaks, returning a list of Unicode strings. CRLF is considered to be one line break. If keepends is 0, the Line break characters are not included in the resulting strings.

+
+PyObject *PyUnicode_Join(PyObject *separator, PyObject *seq)
+Return value: New reference. Part of the Stable ABI.

Join a sequence of strings using the given separator and return the resulting Unicode string.

+
+Py_ssize_t PyUnicode_Tailmatch(PyObject *unicode, PyObject *substr, Py_ssize_t start, Py_ssize_t end, int direction)
+ Part of the Stable ABI.

Return 1 if substr matches unicode[start:end] at the given tail end (direction == -1 means to do a prefix match, direction == 1 a suffix match), 0 otherwise. Return -1 if an error occurred.

+
+Py_ssize_t PyUnicode_Find(PyObject *unicode, PyObject *substr, Py_ssize_t start, Py_ssize_t end, int direction)
+ Part of the Stable ABI.

Return the first position of substr in unicode[start:end] using the given direction (direction == 1 means to do a forward search, direction == -1 a backward search). The return value is the index of the first match; a value of -1 indicates that no match was found, and -2 indicates that an error occurred and an exception has been set.

+
+Py_ssize_t PyUnicode_FindChar(PyObject *unicode, Py_UCS4 ch, Py_ssize_t start, Py_ssize_t end, int direction)
+ Part of the Stable ABI since version 3.7.

Return the first position of the character ch in unicode[start:end] using the given direction (direction == 1 means to do a forward search, direction == -1 a backward search). The return value is the index of the first match; a value of -1 indicates that no match was found, and -2 indicates that an error occurred and an exception has been set.

New in version 3.3.

Changed in version 3.7: start and end are now adjusted to behave like unicode[start:end].

+
+Py_ssize_t PyUnicode_Count(PyObject *unicode, PyObject *substr, Py_ssize_t start, Py_ssize_t end)
+ Part of the Stable ABI.

Return the number of non-overlapping occurrences of substr in unicode[start:end]. Return -1 if an error occurred.

+
+PyObject *PyUnicode_Replace(PyObject *unicode, PyObject *substr, PyObject *replstr, Py_ssize_t maxcount)
+Return value: New reference. Part of the Stable ABI.

Replace at most maxcount occurrences of substr in unicode with replstr and return the resulting Unicode object. maxcount == -1 means replace all occurrences.

+
+int PyUnicode_Compare(PyObject *left, PyObject *right)
+ Part of the Stable ABI.

Compare two strings and return -1, 0, 1 for less than, equal, and greater than, respectively.

This function returns -1 upon failure, so one should call PyErr_Occurred() to check for errors.

+
+int PyUnicode_CompareWithASCIIString(PyObject *unicode, const char *string)
+ Part of the Stable ABI.

Compare a Unicode object, unicode, with string and return -1, 0, 1 for less than, equal, and greater than, respectively. It is best to pass only ASCII-encoded strings, but the function interprets the input string as ISO-8859-1 if it contains non-ASCII characters.

This function does not raise exceptions.

+
+PyObject *PyUnicode_RichCompare(PyObject *left, PyObject *right, int op)
+Return value: New reference. Part of the Stable ABI.

Rich compare two Unicode strings and return one of the following:

Possible values for op are Py_GT, Py_GE, Py_EQ, Py_NE, Py_LT, and Py_LE.

+
+PyObject *PyUnicode_Format(PyObject *format, PyObject *args)
+Return value: New reference. Part of the Stable ABI.

Return a new string object from format and args; this is analogous to format % args.

+
+int PyUnicode_Contains(PyObject *unicode, PyObject *substr)
+ Part of the Stable ABI.

Check whether substr is contained in unicode and return true or false accordingly.

substr has to coerce to a one element Unicode string. -1 is returned if there was an error.

+
+void PyUnicode_InternInPlace(PyObject **p_unicode)
+ Part of the Stable ABI.

Intern the argument *p_unicode in place. The argument must be the address of a pointer variable pointing to a Python Unicode string object. If there is an existing interned string that is the same as *p_unicode, it sets *p_unicode to it (releasing the reference to the old string object and creating a new strong reference to the interned string object), otherwise it leaves *p_unicode alone and interns it (creating a new strong reference). (Clarification: even though there is a lot of talk about references, think of this function as reference-neutral; you own the object after the call if and only if you owned it before the call.)

+
+PyObject *PyUnicode_InternFromString(const char *str)
+Return value: New reference. Part of the Stable ABI.

A combination of PyUnicode_FromString() and PyUnicode_InternInPlace(), returning either a new Unicode string object that has been interned, or a new (“owned”) reference to an earlier interned string object with the same value.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/unicode.html +

+
diff --git a/devdocs/python~3.12/c-api%2Futilities.html b/devdocs/python~3.12/c-api%2Futilities.html new file mode 100644 index 00000000..a792fe6d --- /dev/null +++ b/devdocs/python~3.12/c-api%2Futilities.html @@ -0,0 +1,9 @@ +

Utilities

The functions in this chapter perform various utility tasks, ranging from helping C code be more portable across platforms, using Python modules from C, and parsing function arguments and constructing Python values from C values.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/utilities.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fveryhigh.html b/devdocs/python~3.12/c-api%2Fveryhigh.html new file mode 100644 index 00000000..379983b5 --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fveryhigh.html @@ -0,0 +1,124 @@ +

The Very High Level Layer

The functions in this chapter will let you execute Python source code given in a file or a buffer, but they will not let you interact in a more detailed way with the interpreter.

Several of these functions accept a start symbol from the grammar as a parameter. The available start symbols are Py_eval_input, Py_file_input, and Py_single_input. These are described following the functions which accept them as parameters.

Note also that several of these functions take FILE* parameters. One particular issue which needs to be handled carefully is that the FILE structure for different C libraries can be different and incompatible. Under Windows (at least), it is possible for dynamically linked extensions to actually use different libraries, so care should be taken that FILE* parameters are only passed to these functions if it is certain that they were created by the same library that the Python runtime is using.

+int Py_Main(int argc, wchar_t **argv)
+ Part of the Stable ABI.

The main program for the standard interpreter. This is made available for programs which embed Python. The argc and argv parameters should be prepared exactly as those which are passed to a C program’s main() function (converted to wchar_t according to the user’s locale). It is important to note that the argument list may be modified (but the contents of the strings pointed to by the argument list are not). The return value will be 0 if the interpreter exits normally (i.e., without an exception), 1 if the interpreter exits due to an exception, or 2 if the parameter list does not represent a valid Python command line.

Note that if an otherwise unhandled SystemExit is raised, this function will not return 1, but exit the process, as long as PyConfig.inspect is zero.

+
+int Py_BytesMain(int argc, char **argv)
+ Part of the Stable ABI since version 3.8.

Similar to Py_Main() but argv is an array of bytes strings.

New in version 3.8.

+
+int PyRun_AnyFile(FILE *fp, const char *filename)
+

This is a simplified interface to PyRun_AnyFileExFlags() below, leaving closeit set to 0 and flags set to NULL.

+
+int PyRun_AnyFileFlags(FILE *fp, const char *filename, PyCompilerFlags *flags)
+

This is a simplified interface to PyRun_AnyFileExFlags() below, leaving the closeit argument set to 0.

+
+int PyRun_AnyFileEx(FILE *fp, const char *filename, int closeit)
+

This is a simplified interface to PyRun_AnyFileExFlags() below, leaving the flags argument set to NULL.

+
+int PyRun_AnyFileExFlags(FILE *fp, const char *filename, int closeit, PyCompilerFlags *flags)
+

If fp refers to a file associated with an interactive device (console or terminal input or Unix pseudo-terminal), return the value of PyRun_InteractiveLoop(), otherwise return the result of PyRun_SimpleFile(). filename is decoded from the filesystem encoding (sys.getfilesystemencoding()). If filename is NULL, this function uses "???" as the filename. If closeit is true, the file is closed before PyRun_SimpleFileExFlags() returns.

+
+int PyRun_SimpleString(const char *command)
+

This is a simplified interface to PyRun_SimpleStringFlags() below, leaving the PyCompilerFlags* argument set to NULL.

+
+int PyRun_SimpleStringFlags(const char *command, PyCompilerFlags *flags)
+

Executes the Python source code from command in the __main__ module according to the flags argument. If __main__ does not already exist, it is created. Returns 0 on success or -1 if an exception was raised. If there was an error, there is no way to get the exception information. For the meaning of flags, see below.

Note that if an otherwise unhandled SystemExit is raised, this function will not return -1, but exit the process, as long as PyConfig.inspect is zero.

+
+int PyRun_SimpleFile(FILE *fp, const char *filename)
+

This is a simplified interface to PyRun_SimpleFileExFlags() below, leaving closeit set to 0 and flags set to NULL.

+
+int PyRun_SimpleFileEx(FILE *fp, const char *filename, int closeit)
+

This is a simplified interface to PyRun_SimpleFileExFlags() below, leaving flags set to NULL.

+
+int PyRun_SimpleFileExFlags(FILE *fp, const char *filename, int closeit, PyCompilerFlags *flags)
+

Similar to PyRun_SimpleStringFlags(), but the Python source code is read from fp instead of an in-memory string. filename should be the name of the file, it is decoded from filesystem encoding and error handler. If closeit is true, the file is closed before PyRun_SimpleFileExFlags() returns.

Note

On Windows, fp should be opened as binary mode (e.g. fopen(filename, "rb")). Otherwise, Python may not handle script file with LF line ending correctly.

+
+int PyRun_InteractiveOne(FILE *fp, const char *filename)
+

This is a simplified interface to PyRun_InteractiveOneFlags() below, leaving flags set to NULL.

+
+int PyRun_InteractiveOneFlags(FILE *fp, const char *filename, PyCompilerFlags *flags)
+

Read and execute a single statement from a file associated with an interactive device according to the flags argument. The user will be prompted using sys.ps1 and sys.ps2. filename is decoded from the filesystem encoding and error handler.

Returns 0 when the input was executed successfully, -1 if there was an exception, or an error code from the errcode.h include file distributed as part of Python if there was a parse error. (Note that errcode.h is not included by Python.h, so must be included specifically if needed.)

+
+int PyRun_InteractiveLoop(FILE *fp, const char *filename)
+

This is a simplified interface to PyRun_InteractiveLoopFlags() below, leaving flags set to NULL.

+
+int PyRun_InteractiveLoopFlags(FILE *fp, const char *filename, PyCompilerFlags *flags)
+

Read and execute statements from a file associated with an interactive device until EOF is reached. The user will be prompted using sys.ps1 and sys.ps2. filename is decoded from the filesystem encoding and error handler. Returns 0 at EOF or a negative number upon failure.

+
+int (*PyOS_InputHook)(void)
+ Part of the Stable ABI.

Can be set to point to a function with the prototype int func(void). The function will be called when Python’s interpreter prompt is about to become idle and wait for user input from the terminal. The return value is ignored. Overriding this hook can be used to integrate the interpreter’s prompt with other event loops, as done in the Modules/_tkinter.c in the Python source code.

Changed in version 3.12: This function is only called from the main interpreter.

+
+char *(*PyOS_ReadlineFunctionPointer)(FILE*, FILE*, const char*)
+

Can be set to point to a function with the prototype char *func(FILE *stdin, FILE *stdout, char *prompt), overriding the default function used to read a single line of input at the interpreter’s prompt. The function is expected to output the string prompt if it’s not NULL, and then read a line of input from the provided standard input file, returning the resulting string. For example, The readline module sets this hook to provide line-editing and tab-completion features.

The result must be a string allocated by PyMem_RawMalloc() or PyMem_RawRealloc(), or NULL if an error occurred.

Changed in version 3.4: The result must be allocated by PyMem_RawMalloc() or PyMem_RawRealloc(), instead of being allocated by PyMem_Malloc() or PyMem_Realloc().

Changed in version 3.12: This function is only called from the main interpreter.

+
+PyObject *PyRun_String(const char *str, int start, PyObject *globals, PyObject *locals)
+Return value: New reference.

This is a simplified interface to PyRun_StringFlags() below, leaving flags set to NULL.

+
+PyObject *PyRun_StringFlags(const char *str, int start, PyObject *globals, PyObject *locals, PyCompilerFlags *flags)
+Return value: New reference.

Execute Python source code from str in the context specified by the objects globals and locals with the compiler flags specified by flags. globals must be a dictionary; locals can be any object that implements the mapping protocol. The parameter start specifies the start token that should be used to parse the source code.

Returns the result of executing the code as a Python object, or NULL if an exception was raised.

+
+PyObject *PyRun_File(FILE *fp, const char *filename, int start, PyObject *globals, PyObject *locals)
+Return value: New reference.

This is a simplified interface to PyRun_FileExFlags() below, leaving closeit set to 0 and flags set to NULL.

+
+PyObject *PyRun_FileEx(FILE *fp, const char *filename, int start, PyObject *globals, PyObject *locals, int closeit)
+Return value: New reference.

This is a simplified interface to PyRun_FileExFlags() below, leaving flags set to NULL.

+
+PyObject *PyRun_FileFlags(FILE *fp, const char *filename, int start, PyObject *globals, PyObject *locals, PyCompilerFlags *flags)
+Return value: New reference.

This is a simplified interface to PyRun_FileExFlags() below, leaving closeit set to 0.

+
+PyObject *PyRun_FileExFlags(FILE *fp, const char *filename, int start, PyObject *globals, PyObject *locals, int closeit, PyCompilerFlags *flags)
+Return value: New reference.

Similar to PyRun_StringFlags(), but the Python source code is read from fp instead of an in-memory string. filename should be the name of the file, it is decoded from the filesystem encoding and error handler. If closeit is true, the file is closed before PyRun_FileExFlags() returns.

+
+PyObject *Py_CompileString(const char *str, const char *filename, int start)
+Return value: New reference. Part of the Stable ABI.

This is a simplified interface to Py_CompileStringFlags() below, leaving flags set to NULL.

+
+PyObject *Py_CompileStringFlags(const char *str, const char *filename, int start, PyCompilerFlags *flags)
+Return value: New reference.

This is a simplified interface to Py_CompileStringExFlags() below, with optimize set to -1.

+
+PyObject *Py_CompileStringObject(const char *str, PyObject *filename, int start, PyCompilerFlags *flags, int optimize)
+Return value: New reference.

Parse and compile the Python source code in str, returning the resulting code object. The start token is given by start; this can be used to constrain the code which can be compiled and should be Py_eval_input, Py_file_input, or Py_single_input. The filename specified by filename is used to construct the code object and may appear in tracebacks or SyntaxError exception messages. This returns NULL if the code cannot be parsed or compiled.

The integer optimize specifies the optimization level of the compiler; a value of -1 selects the optimization level of the interpreter as given by -O options. Explicit levels are 0 (no optimization; __debug__ is true), 1 (asserts are removed, __debug__ is false) or 2 (docstrings are removed too).

New in version 3.4.

+
+PyObject *Py_CompileStringExFlags(const char *str, const char *filename, int start, PyCompilerFlags *flags, int optimize)
+Return value: New reference.

Like Py_CompileStringObject(), but filename is a byte string decoded from the filesystem encoding and error handler.

New in version 3.2.

+
+PyObject *PyEval_EvalCode(PyObject *co, PyObject *globals, PyObject *locals)
+Return value: New reference. Part of the Stable ABI.

This is a simplified interface to PyEval_EvalCodeEx(), with just the code object, and global and local variables. The other arguments are set to NULL.

+
+PyObject *PyEval_EvalCodeEx(PyObject *co, PyObject *globals, PyObject *locals, PyObject *const *args, int argcount, PyObject *const *kws, int kwcount, PyObject *const *defs, int defcount, PyObject *kwdefs, PyObject *closure)
+Return value: New reference. Part of the Stable ABI.

Evaluate a precompiled code object, given a particular environment for its evaluation. This environment consists of a dictionary of global variables, a mapping object of local variables, arrays of arguments, keywords and defaults, a dictionary of default values for keyword-only arguments and a closure tuple of cells.

+
+PyObject *PyEval_EvalFrame(PyFrameObject *f)
+Return value: New reference. Part of the Stable ABI.

Evaluate an execution frame. This is a simplified interface to PyEval_EvalFrameEx(), for backward compatibility.

+
+PyObject *PyEval_EvalFrameEx(PyFrameObject *f, int throwflag)
+Return value: New reference. Part of the Stable ABI.

This is the main, unvarnished function of Python interpretation. The code object associated with the execution frame f is executed, interpreting bytecode and executing calls as needed. The additional throwflag parameter can mostly be ignored - if true, then it causes an exception to immediately be thrown; this is used for the throw() methods of generator objects.

Changed in version 3.4: This function now includes a debug assertion to help ensure that it does not silently discard an active exception.

+
+int PyEval_MergeCompilerFlags(PyCompilerFlags *cf)
+

This function changes the flags of the current evaluation frame, and returns true on success, false on failure.

+
+int Py_eval_input
+

The start symbol from the Python grammar for isolated expressions; for use with Py_CompileString().

+
+int Py_file_input
+

The start symbol from the Python grammar for sequences of statements as read from a file or other source; for use with Py_CompileString(). This is the symbol to use when compiling arbitrarily long Python source code.

+
+int Py_single_input
+

The start symbol from the Python grammar for a single statement; for use with Py_CompileString(). This is the symbol used for the interactive interpreter loop.

+
+struct PyCompilerFlags
+

This is the structure used to hold compiler flags. In cases where code is only being compiled, it is passed as int flags, and in cases where code is being executed, it is passed as PyCompilerFlags *flags. In this case, from +__future__ import can modify flags.

Whenever PyCompilerFlags *flags is NULL, cf_flags is treated as equal to 0, and any modification due to from __future__ import is discarded.

+int cf_flags
+

Compiler flags.

+
+int cf_feature_version
+

cf_feature_version is the minor Python version. It should be initialized to PY_MINOR_VERSION.

The field is ignored by default, it is used if and only if PyCF_ONLY_AST flag is set in cf_flags.

+

Changed in version 3.8: Added cf_feature_version field.

+
+int CO_FUTURE_DIVISION
+

This bit can be set in flags to cause division operator / to be interpreted as “true division” according to PEP 238.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/veryhigh.html +

+
diff --git a/devdocs/python~3.12/c-api%2Fweakref.html b/devdocs/python~3.12/c-api%2Fweakref.html new file mode 100644 index 00000000..32e2a79a --- /dev/null +++ b/devdocs/python~3.12/c-api%2Fweakref.html @@ -0,0 +1,30 @@ +

Weak Reference Objects

Python supports weak references as first-class objects. There are two specific object types which directly implement weak references. The first is a simple reference object, and the second acts as a proxy for the original object as much as it can.

+int PyWeakref_Check(PyObject *ob)
+

Return true if ob is either a reference or proxy object. This function always succeeds.

+
+int PyWeakref_CheckRef(PyObject *ob)
+

Return true if ob is a reference object. This function always succeeds.

+
+int PyWeakref_CheckProxy(PyObject *ob)
+

Return true if ob is a proxy object. This function always succeeds.

+
+PyObject *PyWeakref_NewRef(PyObject *ob, PyObject *callback)
+Return value: New reference. Part of the Stable ABI.

Return a weak reference object for the object ob. This will always return a new reference, but is not guaranteed to create a new object; an existing reference object may be returned. The second parameter, callback, can be a callable object that receives notification when ob is garbage collected; it should accept a single parameter, which will be the weak reference object itself. callback may also be None or NULL. If ob is not a weakly referencable object, or if callback is not callable, None, or NULL, this will return NULL and raise TypeError.

+
+PyObject *PyWeakref_NewProxy(PyObject *ob, PyObject *callback)
+Return value: New reference. Part of the Stable ABI.

Return a weak reference proxy object for the object ob. This will always return a new reference, but is not guaranteed to create a new object; an existing proxy object may be returned. The second parameter, callback, can be a callable object that receives notification when ob is garbage collected; it should accept a single parameter, which will be the weak reference object itself. callback may also be None or NULL. If ob is not a weakly referencable object, or if callback is not callable, None, or NULL, this will return NULL and raise TypeError.

+
+PyObject *PyWeakref_GetObject(PyObject *ref)
+Return value: Borrowed reference. Part of the Stable ABI.

Return the referenced object from a weak reference, ref. If the referent is no longer live, returns Py_None.

Note

This function returns a borrowed reference to the referenced object. This means that you should always call Py_INCREF() on the object except when it cannot be destroyed before the last usage of the borrowed reference.

+
+PyObject *PyWeakref_GET_OBJECT(PyObject *ref)
+Return value: Borrowed reference.

Similar to PyWeakref_GetObject(), but does no error checking.

+
+void PyObject_ClearWeakRefs(PyObject *object)
+ Part of the Stable ABI.

This function is called by the tp_dealloc handler to clear weak references.

This iterates through the weak references for object and calls callbacks for those references which have one. It returns when all callbacks have been attempted.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/c-api/weakref.html +

+
diff --git a/devdocs/python~3.12/contents.html b/devdocs/python~3.12/contents.html new file mode 100644 index 00000000..b8457a99 --- /dev/null +++ b/devdocs/python~3.12/contents.html @@ -0,0 +1,911 @@ +

Python Documentation contents

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/contents.html +

+
diff --git a/devdocs/python~3.12/copyright.html b/devdocs/python~3.12/copyright.html new file mode 100644 index 00000000..b55e2392 --- /dev/null +++ b/devdocs/python~3.12/copyright.html @@ -0,0 +1,6 @@ +

Copyright

Python and this documentation is:

Copyright © 2001-2023 Python Software Foundation. All rights reserved.

Copyright © 2000 BeOpen.com. All rights reserved.

Copyright © 1995-2000 Corporation for National Research Initiatives. All rights reserved.

Copyright © 1991-1995 Stichting Mathematisch Centrum. All rights reserved.

See History and License for complete license and permissions information.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/copyright.html +

+
diff --git a/devdocs/python~3.12/distributing%2Findex.html b/devdocs/python~3.12/distributing%2Findex.html new file mode 100644 index 00000000..326a86f6 --- /dev/null +++ b/devdocs/python~3.12/distributing%2Findex.html @@ -0,0 +1,6 @@ +

Distributing Python Modules

Note

Information and guidance on distributing Python modules and packages has been moved to the Python Packaging User Guide, and the tutorial on packaging Python projects.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/distributing/index.html +

+
diff --git a/devdocs/python~3.12/download.html b/devdocs/python~3.12/download.html new file mode 100644 index 00000000..c481f71f --- /dev/null +++ b/devdocs/python~3.12/download.html @@ -0,0 +1,24 @@ +

Download Python 3.12.1 Documentation

Last updated on: Jan 05, 2024 (11:42 UTC).

To download an archive containing all the documents for this version of Python in one of various formats, follow one of links in this table.

+ + + + + + + + +
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These archives contain all the content in the documentation.

Unpacking

Unix users should download the .tar.bz2 archives; these are bzipped tar archives and can be handled in the usual way using tar and the bzip2 program. The Info-ZIP unzip program can be used to handle the ZIP archives if desired. The .tar.bz2 archives provide the best compression and fastest download times.

Windows users can use the ZIP archives since those are customary on that platform. These are created on Unix using the Info-ZIP zip program.

Problems

If you have comments or suggestions for the Python documentation, please send email to docs@python.org.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/download.html +

+
diff --git a/devdocs/python~3.12/extending%2Fbuilding.html b/devdocs/python~3.12/extending%2Fbuilding.html new file mode 100644 index 00000000..e63212e1 --- /dev/null +++ b/devdocs/python~3.12/extending%2Fbuilding.html @@ -0,0 +1,15 @@ +

Building C and C++ Extensions

A C extension for CPython is a shared library (e.g. a .so file on Linux, .pyd on Windows), which exports an initialization function.

To be importable, the shared library must be available on PYTHONPATH, and must be named after the module name, with an appropriate extension. When using setuptools, the correct filename is generated automatically.

The initialization function has the signature:

+PyObject *PyInit_modulename(void)
+

It returns either a fully initialized module, or a PyModuleDef instance. See Initializing C modules for details.

For modules with ASCII-only names, the function must be named PyInit_<modulename>, with <modulename> replaced by the name of the module. When using Multi-phase initialization, non-ASCII module names are allowed. In this case, the initialization function name is PyInitU_<modulename>, with <modulename> encoded using Python’s punycode encoding with hyphens replaced by underscores. In Python:

def initfunc_name(name):
+    try:
+        suffix = b'_' + name.encode('ascii')
+    except UnicodeEncodeError:
+        suffix = b'U_' + name.encode('punycode').replace(b'-', b'_')
+    return b'PyInit' + suffix
+

It is possible to export multiple modules from a single shared library by defining multiple initialization functions. However, importing them requires using symbolic links or a custom importer, because by default only the function corresponding to the filename is found. See the “Multiple modules in one library” section in PEP 489 for details.

+4.1. Building C and C++ Extensions with setuptools

Python 3.12 and newer no longer come with distutils. Please refer to the setuptools documentation at https://setuptools.readthedocs.io/en/latest/setuptools.html to learn more about how build and distribute C/C++ extensions with setuptools.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/building.html +

+
diff --git a/devdocs/python~3.12/extending%2Fembedding.html b/devdocs/python~3.12/extending%2Fembedding.html new file mode 100644 index 00000000..100bab0e --- /dev/null +++ b/devdocs/python~3.12/extending%2Fembedding.html @@ -0,0 +1,169 @@ +

Embedding Python in Another Application

The previous chapters discussed how to extend Python, that is, how to extend the functionality of Python by attaching a library of C functions to it. It is also possible to do it the other way around: enrich your C/C++ application by embedding Python in it. Embedding provides your application with the ability to implement some of the functionality of your application in Python rather than C or C++. This can be used for many purposes; one example would be to allow users to tailor the application to their needs by writing some scripts in Python. You can also use it yourself if some of the functionality can be written in Python more easily.

Embedding Python is similar to extending it, but not quite. The difference is that when you extend Python, the main program of the application is still the Python interpreter, while if you embed Python, the main program may have nothing to do with Python — instead, some parts of the application occasionally call the Python interpreter to run some Python code.

So if you are embedding Python, you are providing your own main program. One of the things this main program has to do is initialize the Python interpreter. At the very least, you have to call the function Py_Initialize(). There are optional calls to pass command line arguments to Python. Then later you can call the interpreter from any part of the application.

There are several different ways to call the interpreter: you can pass a string containing Python statements to PyRun_SimpleString(), or you can pass a stdio file pointer and a file name (for identification in error messages only) to PyRun_SimpleFile(). You can also call the lower-level operations described in the previous chapters to construct and use Python objects.

See also

Python/C API Reference Manual
+
+

The details of Python’s C interface are given in this manual. A great deal of necessary information can be found here.

+1.1. Very High Level Embedding

The simplest form of embedding Python is the use of the very high level interface. This interface is intended to execute a Python script without needing to interact with the application directly. This can for example be used to perform some operation on a file.

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
+int
+main(int argc, char *argv[])
+{
+    wchar_t *program = Py_DecodeLocale(argv[0], NULL);
+    if (program == NULL) {
+        fprintf(stderr, "Fatal error: cannot decode argv[0]\n");
+        exit(1);
+    }
+    Py_SetProgramName(program);  /* optional but recommended */
+    Py_Initialize();
+    PyRun_SimpleString("from time import time,ctime\n"
+                       "print('Today is', ctime(time()))\n");
+    if (Py_FinalizeEx() < 0) {
+        exit(120);
+    }
+    PyMem_RawFree(program);
+    return 0;
+}
+

The Py_SetProgramName() function should be called before Py_Initialize() to inform the interpreter about paths to Python run-time libraries. Next, the Python interpreter is initialized with Py_Initialize(), followed by the execution of a hard-coded Python script that prints the date and time. Afterwards, the Py_FinalizeEx() call shuts the interpreter down, followed by the end of the program. In a real program, you may want to get the Python script from another source, perhaps a text-editor routine, a file, or a database. Getting the Python code from a file can better be done by using the PyRun_SimpleFile() function, which saves you the trouble of allocating memory space and loading the file contents.

+1.2. Beyond Very High Level Embedding: An overview

The high level interface gives you the ability to execute arbitrary pieces of Python code from your application, but exchanging data values is quite cumbersome to say the least. If you want that, you should use lower level calls. At the cost of having to write more C code, you can achieve almost anything.

It should be noted that extending Python and embedding Python is quite the same activity, despite the different intent. Most topics discussed in the previous chapters are still valid. To show this, consider what the extension code from Python to C really does:

  1. Convert data values from Python to C,
  2. Perform a function call to a C routine using the converted values, and
  3. Convert the data values from the call from C to Python.

When embedding Python, the interface code does:

  1. Convert data values from C to Python,
  2. Perform a function call to a Python interface routine using the converted values, and
  3. Convert the data values from the call from Python to C.

As you can see, the data conversion steps are simply swapped to accommodate the different direction of the cross-language transfer. The only difference is the routine that you call between both data conversions. When extending, you call a C routine, when embedding, you call a Python routine.

This chapter will not discuss how to convert data from Python to C and vice versa. Also, proper use of references and dealing with errors is assumed to be understood. Since these aspects do not differ from extending the interpreter, you can refer to earlier chapters for the required information.

+1.3. Pure Embedding

The first program aims to execute a function in a Python script. Like in the section about the very high level interface, the Python interpreter does not directly interact with the application (but that will change in the next section).

The code to run a function defined in a Python script is:

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
+int
+main(int argc, char *argv[])
+{
+    PyObject *pName, *pModule, *pFunc;
+    PyObject *pArgs, *pValue;
+    int i;
+
+    if (argc < 3) {
+        fprintf(stderr,"Usage: call pythonfile funcname [args]\n");
+        return 1;
+    }
+
+    Py_Initialize();
+    pName = PyUnicode_DecodeFSDefault(argv[1]);
+    /* Error checking of pName left out */
+
+    pModule = PyImport_Import(pName);
+    Py_DECREF(pName);
+
+    if (pModule != NULL) {
+        pFunc = PyObject_GetAttrString(pModule, argv[2]);
+        /* pFunc is a new reference */
+
+        if (pFunc && PyCallable_Check(pFunc)) {
+            pArgs = PyTuple_New(argc - 3);
+            for (i = 0; i < argc - 3; ++i) {
+                pValue = PyLong_FromLong(atoi(argv[i + 3]));
+                if (!pValue) {
+                    Py_DECREF(pArgs);
+                    Py_DECREF(pModule);
+                    fprintf(stderr, "Cannot convert argument\n");
+                    return 1;
+                }
+                /* pValue reference stolen here: */
+                PyTuple_SetItem(pArgs, i, pValue);
+            }
+            pValue = PyObject_CallObject(pFunc, pArgs);
+            Py_DECREF(pArgs);
+            if (pValue != NULL) {
+                printf("Result of call: %ld\n", PyLong_AsLong(pValue));
+                Py_DECREF(pValue);
+            }
+            else {
+                Py_DECREF(pFunc);
+                Py_DECREF(pModule);
+                PyErr_Print();
+                fprintf(stderr,"Call failed\n");
+                return 1;
+            }
+        }
+        else {
+            if (PyErr_Occurred())
+                PyErr_Print();
+            fprintf(stderr, "Cannot find function \"%s\"\n", argv[2]);
+        }
+        Py_XDECREF(pFunc);
+        Py_DECREF(pModule);
+    }
+    else {
+        PyErr_Print();
+        fprintf(stderr, "Failed to load \"%s\"\n", argv[1]);
+        return 1;
+    }
+    if (Py_FinalizeEx() < 0) {
+        return 120;
+    }
+    return 0;
+}
+

This code loads a Python script using argv[1], and calls the function named in argv[2]. Its integer arguments are the other values of the argv array. If you compile and link this program (let’s call the finished executable call), and use it to execute a Python script, such as:

def multiply(a,b):
+    print("Will compute", a, "times", b)
+    c = 0
+    for i in range(0, a):
+        c = c + b
+    return c
+

then the result should be:

$ call multiply multiply 3 2
+Will compute 3 times 2
+Result of call: 6
+

Although the program is quite large for its functionality, most of the code is for data conversion between Python and C, and for error reporting. The interesting part with respect to embedding Python starts with

Py_Initialize();
+pName = PyUnicode_DecodeFSDefault(argv[1]);
+/* Error checking of pName left out */
+pModule = PyImport_Import(pName);
+

After initializing the interpreter, the script is loaded using PyImport_Import(). This routine needs a Python string as its argument, which is constructed using the PyUnicode_FromString() data conversion routine.

pFunc = PyObject_GetAttrString(pModule, argv[2]);
+/* pFunc is a new reference */
+
+if (pFunc && PyCallable_Check(pFunc)) {
+    ...
+}
+Py_XDECREF(pFunc);
+

Once the script is loaded, the name we’re looking for is retrieved using PyObject_GetAttrString(). If the name exists, and the object returned is callable, you can safely assume that it is a function. The program then proceeds by constructing a tuple of arguments as normal. The call to the Python function is then made with:

pValue = PyObject_CallObject(pFunc, pArgs);
+

Upon return of the function, pValue is either NULL or it contains a reference to the return value of the function. Be sure to release the reference after examining the value.

+1.4. Extending Embedded Python

Until now, the embedded Python interpreter had no access to functionality from the application itself. The Python API allows this by extending the embedded interpreter. That is, the embedded interpreter gets extended with routines provided by the application. While it sounds complex, it is not so bad. Simply forget for a while that the application starts the Python interpreter. Instead, consider the application to be a set of subroutines, and write some glue code that gives Python access to those routines, just like you would write a normal Python extension. For example:

static int numargs=0;
+
+/* Return the number of arguments of the application command line */
+static PyObject*
+emb_numargs(PyObject *self, PyObject *args)
+{
+    if(!PyArg_ParseTuple(args, ":numargs"))
+        return NULL;
+    return PyLong_FromLong(numargs);
+}
+
+static PyMethodDef EmbMethods[] = {
+    {"numargs", emb_numargs, METH_VARARGS,
+     "Return the number of arguments received by the process."},
+    {NULL, NULL, 0, NULL}
+};
+
+static PyModuleDef EmbModule = {
+    PyModuleDef_HEAD_INIT, "emb", NULL, -1, EmbMethods,
+    NULL, NULL, NULL, NULL
+};
+
+static PyObject*
+PyInit_emb(void)
+{
+    return PyModule_Create(&EmbModule);
+}
+

Insert the above code just above the main() function. Also, insert the following two statements before the call to Py_Initialize():

numargs = argc;
+PyImport_AppendInittab("emb", &PyInit_emb);
+

These two lines initialize the numargs variable, and make the emb.numargs() function accessible to the embedded Python interpreter. With these extensions, the Python script can do things like

import emb
+print("Number of arguments", emb.numargs())
+

In a real application, the methods will expose an API of the application to Python.

+1.5. Embedding Python in C++

It is also possible to embed Python in a C++ program; precisely how this is done will depend on the details of the C++ system used; in general you will need to write the main program in C++, and use the C++ compiler to compile and link your program. There is no need to recompile Python itself using C++.

+1.6. Compiling and Linking under Unix-like systems

It is not necessarily trivial to find the right flags to pass to your compiler (and linker) in order to embed the Python interpreter into your application, particularly because Python needs to load library modules implemented as C dynamic extensions (.so files) linked against it.

To find out the required compiler and linker flags, you can execute the pythonX.Y-config script which is generated as part of the installation process (a python3-config script may also be available). This script has several options, of which the following will be directly useful to you:

Note

To avoid confusion between several Python installations (and especially between the system Python and your own compiled Python), it is recommended that you use the absolute path to pythonX.Y-config, as in the above example.

If this procedure doesn’t work for you (it is not guaranteed to work for all Unix-like platforms; however, we welcome bug reports) you will have to read your system’s documentation about dynamic linking and/or examine Python’s Makefile (use sysconfig.get_makefile_filename() to find its location) and compilation options. In this case, the sysconfig module is a useful tool to programmatically extract the configuration values that you will want to combine together. For example:

>>> import sysconfig
+>>> sysconfig.get_config_var('LIBS')
+'-lpthread -ldl  -lutil'
+>>> sysconfig.get_config_var('LINKFORSHARED')
+'-Xlinker -export-dynamic'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/embedding.html +

+
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Extending Python with C or C++

It is quite easy to add new built-in modules to Python, if you know how to program in C. Such extension modules can do two things that can’t be done directly in Python: they can implement new built-in object types, and they can call C library functions and system calls.

To support extensions, the Python API (Application Programmers Interface) defines a set of functions, macros and variables that provide access to most aspects of the Python run-time system. The Python API is incorporated in a C source file by including the header "Python.h".

The compilation of an extension module depends on its intended use as well as on your system setup; details are given in later chapters.

Note

The C extension interface is specific to CPython, and extension modules do not work on other Python implementations. In many cases, it is possible to avoid writing C extensions and preserve portability to other implementations. For example, if your use case is calling C library functions or system calls, you should consider using the ctypes module or the cffi library rather than writing custom C code. These modules let you write Python code to interface with C code and are more portable between implementations of Python than writing and compiling a C extension module.

+1.1. A Simple Example

Let’s create an extension module called spam (the favorite food of Monty Python fans…) and let’s say we want to create a Python interface to the C library function system() 1. This function takes a null-terminated character string as argument and returns an integer. We want this function to be callable from Python as follows:

>>> import spam
+>>> status = spam.system("ls -l")
+

Begin by creating a file spammodule.c. (Historically, if a module is called spam, the C file containing its implementation is called spammodule.c; if the module name is very long, like spammify, the module name can be just spammify.c.)

The first two lines of our file can be:

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+

which pulls in the Python API (you can add a comment describing the purpose of the module and a copyright notice if you like).

Note

Since Python may define some pre-processor definitions which affect the standard headers on some systems, you must include Python.h before any standard headers are included.

It is recommended to always define PY_SSIZE_T_CLEAN before including Python.h. See Extracting Parameters in Extension Functions for a description of this macro.

All user-visible symbols defined by Python.h have a prefix of Py or PY, except those defined in standard header files. For convenience, and since they are used extensively by the Python interpreter, "Python.h" includes a few standard header files: <stdio.h>, <string.h>, <errno.h>, and <stdlib.h>. If the latter header file does not exist on your system, it declares the functions malloc(), free() and realloc() directly.

The next thing we add to our module file is the C function that will be called when the Python expression spam.system(string) is evaluated (we’ll see shortly how it ends up being called):

static PyObject *
+spam_system(PyObject *self, PyObject *args)
+{
+    const char *command;
+    int sts;
+
+    if (!PyArg_ParseTuple(args, "s", &command))
+        return NULL;
+    sts = system(command);
+    return PyLong_FromLong(sts);
+}
+

There is a straightforward translation from the argument list in Python (for example, the single expression "ls -l") to the arguments passed to the C function. The C function always has two arguments, conventionally named self and args.

The self argument points to the module object for module-level functions; for a method it would point to the object instance.

The args argument will be a pointer to a Python tuple object containing the arguments. Each item of the tuple corresponds to an argument in the call’s argument list. The arguments are Python objects — in order to do anything with them in our C function we have to convert them to C values. The function PyArg_ParseTuple() in the Python API checks the argument types and converts them to C values. It uses a template string to determine the required types of the arguments as well as the types of the C variables into which to store the converted values. More about this later.

PyArg_ParseTuple() returns true (nonzero) if all arguments have the right type and its components have been stored in the variables whose addresses are passed. It returns false (zero) if an invalid argument list was passed. In the latter case it also raises an appropriate exception so the calling function can return NULL immediately (as we saw in the example).

+1.2. Intermezzo: Errors and Exceptions

An important convention throughout the Python interpreter is the following: when a function fails, it should set an exception condition and return an error value (usually -1 or a NULL pointer). Exception information is stored in three members of the interpreter’s thread state. These are NULL if there is no exception. Otherwise they are the C equivalents of the members of the Python tuple returned by sys.exc_info(). These are the exception type, exception instance, and a traceback object. It is important to know about them to understand how errors are passed around.

The Python API defines a number of functions to set various types of exceptions.

The most common one is PyErr_SetString(). Its arguments are an exception object and a C string. The exception object is usually a predefined object like PyExc_ZeroDivisionError. The C string indicates the cause of the error and is converted to a Python string object and stored as the “associated value” of the exception.

Another useful function is PyErr_SetFromErrno(), which only takes an exception argument and constructs the associated value by inspection of the global variable errno. The most general function is PyErr_SetObject(), which takes two object arguments, the exception and its associated value. You don’t need to Py_INCREF() the objects passed to any of these functions.

You can test non-destructively whether an exception has been set with PyErr_Occurred(). This returns the current exception object, or NULL if no exception has occurred. You normally don’t need to call PyErr_Occurred() to see whether an error occurred in a function call, since you should be able to tell from the return value.

When a function f that calls another function g detects that the latter fails, f should itself return an error value (usually NULL or -1). It should not call one of the PyErr_* functions — one has already been called by g. f’s caller is then supposed to also return an error indication to its caller, again without calling PyErr_*, and so on — the most detailed cause of the error was already reported by the function that first detected it. Once the error reaches the Python interpreter’s main loop, this aborts the currently executing Python code and tries to find an exception handler specified by the Python programmer.

(There are situations where a module can actually give a more detailed error message by calling another PyErr_* function, and in such cases it is fine to do so. As a general rule, however, this is not necessary, and can cause information about the cause of the error to be lost: most operations can fail for a variety of reasons.)

To ignore an exception set by a function call that failed, the exception condition must be cleared explicitly by calling PyErr_Clear(). The only time C code should call PyErr_Clear() is if it doesn’t want to pass the error on to the interpreter but wants to handle it completely by itself (possibly by trying something else, or pretending nothing went wrong).

Every failing malloc() call must be turned into an exception — the direct caller of malloc() (or realloc()) must call PyErr_NoMemory() and return a failure indicator itself. All the object-creating functions (for example, PyLong_FromLong()) already do this, so this note is only relevant to those who call malloc() directly.

Also note that, with the important exception of PyArg_ParseTuple() and friends, functions that return an integer status usually return a positive value or zero for success and -1 for failure, like Unix system calls.

Finally, be careful to clean up garbage (by making Py_XDECREF() or Py_DECREF() calls for objects you have already created) when you return an error indicator!

The choice of which exception to raise is entirely yours. There are predeclared C objects corresponding to all built-in Python exceptions, such as PyExc_ZeroDivisionError, which you can use directly. Of course, you should choose exceptions wisely — don’t use PyExc_TypeError to mean that a file couldn’t be opened (that should probably be PyExc_OSError). If something’s wrong with the argument list, the PyArg_ParseTuple() function usually raises PyExc_TypeError. If you have an argument whose value must be in a particular range or must satisfy other conditions, PyExc_ValueError is appropriate.

You can also define a new exception that is unique to your module. For this, you usually declare a static object variable at the beginning of your file:

static PyObject *SpamError;
+

and initialize it in your module’s initialization function (PyInit_spam()) with an exception object:

PyMODINIT_FUNC
+PyInit_spam(void)
+{
+    PyObject *m;
+
+    m = PyModule_Create(&spammodule);
+    if (m == NULL)
+        return NULL;
+
+    SpamError = PyErr_NewException("spam.error", NULL, NULL);
+    Py_XINCREF(SpamError);
+    if (PyModule_AddObject(m, "error", SpamError) < 0) {
+        Py_XDECREF(SpamError);
+        Py_CLEAR(SpamError);
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

Note that the Python name for the exception object is spam.error. The PyErr_NewException() function may create a class with the base class being Exception (unless another class is passed in instead of NULL), described in Built-in Exceptions.

Note also that the SpamError variable retains a reference to the newly created exception class; this is intentional! Since the exception could be removed from the module by external code, an owned reference to the class is needed to ensure that it will not be discarded, causing SpamError to become a dangling pointer. Should it become a dangling pointer, C code which raises the exception could cause a core dump or other unintended side effects.

We discuss the use of PyMODINIT_FUNC as a function return type later in this sample.

The spam.error exception can be raised in your extension module using a call to PyErr_SetString() as shown below:

static PyObject *
+spam_system(PyObject *self, PyObject *args)
+{
+    const char *command;
+    int sts;
+
+    if (!PyArg_ParseTuple(args, "s", &command))
+        return NULL;
+    sts = system(command);
+    if (sts < 0) {
+        PyErr_SetString(SpamError, "System command failed");
+        return NULL;
+    }
+    return PyLong_FromLong(sts);
+}
+

+1.3. Back to the Example

Going back to our example function, you should now be able to understand this statement:

if (!PyArg_ParseTuple(args, "s", &command))
+    return NULL;
+

It returns NULL (the error indicator for functions returning object pointers) if an error is detected in the argument list, relying on the exception set by PyArg_ParseTuple(). Otherwise the string value of the argument has been copied to the local variable command. This is a pointer assignment and you are not supposed to modify the string to which it points (so in Standard C, the variable command should properly be declared as const char +*command).

The next statement is a call to the Unix function system(), passing it the string we just got from PyArg_ParseTuple():

sts = system(command);
+

Our spam.system() function must return the value of sts as a Python object. This is done using the function PyLong_FromLong().

return PyLong_FromLong(sts);
+

In this case, it will return an integer object. (Yes, even integers are objects on the heap in Python!)

If you have a C function that returns no useful argument (a function returning void), the corresponding Python function must return None. You need this idiom to do so (which is implemented by the Py_RETURN_NONE macro):

Py_INCREF(Py_None);
+return Py_None;
+

Py_None is the C name for the special Python object None. It is a genuine Python object rather than a NULL pointer, which means “error” in most contexts, as we have seen.

+1.4. The Module’s Method Table and Initialization Function

I promised to show how spam_system() is called from Python programs. First, we need to list its name and address in a “method table”:

static PyMethodDef SpamMethods[] = {
+    ...
+    {"system",  spam_system, METH_VARARGS,
+     "Execute a shell command."},
+    ...
+    {NULL, NULL, 0, NULL}        /* Sentinel */
+};
+

Note the third entry (METH_VARARGS). This is a flag telling the interpreter the calling convention to be used for the C function. It should normally always be METH_VARARGS or METH_VARARGS | METH_KEYWORDS; a value of 0 means that an obsolete variant of PyArg_ParseTuple() is used.

When using only METH_VARARGS, the function should expect the Python-level parameters to be passed in as a tuple acceptable for parsing via PyArg_ParseTuple(); more information on this function is provided below.

The METH_KEYWORDS bit may be set in the third field if keyword arguments should be passed to the function. In this case, the C function should accept a third PyObject * parameter which will be a dictionary of keywords. Use PyArg_ParseTupleAndKeywords() to parse the arguments to such a function.

The method table must be referenced in the module definition structure:

static struct PyModuleDef spammodule = {
+    PyModuleDef_HEAD_INIT,
+    "spam",   /* name of module */
+    spam_doc, /* module documentation, may be NULL */
+    -1,       /* size of per-interpreter state of the module,
+                 or -1 if the module keeps state in global variables. */
+    SpamMethods
+};
+

This structure, in turn, must be passed to the interpreter in the module’s initialization function. The initialization function must be named PyInit_name(), where name is the name of the module, and should be the only non-static item defined in the module file:

PyMODINIT_FUNC
+PyInit_spam(void)
+{
+    return PyModule_Create(&spammodule);
+}
+

Note that PyMODINIT_FUNC declares the function as PyObject * return type, declares any special linkage declarations required by the platform, and for C++ declares the function as extern "C".

When the Python program imports module spam for the first time, PyInit_spam() is called. (See below for comments about embedding Python.) It calls PyModule_Create(), which returns a module object, and inserts built-in function objects into the newly created module based upon the table (an array of PyMethodDef structures) found in the module definition. PyModule_Create() returns a pointer to the module object that it creates. It may abort with a fatal error for certain errors, or return NULL if the module could not be initialized satisfactorily. The init function must return the module object to its caller, so that it then gets inserted into sys.modules.

When embedding Python, the PyInit_spam() function is not called automatically unless there’s an entry in the PyImport_Inittab table. To add the module to the initialization table, use PyImport_AppendInittab(), optionally followed by an import of the module:

int
+main(int argc, char *argv[])
+{
+    wchar_t *program = Py_DecodeLocale(argv[0], NULL);
+    if (program == NULL) {
+        fprintf(stderr, "Fatal error: cannot decode argv[0]\n");
+        exit(1);
+    }
+
+    /* Add a built-in module, before Py_Initialize */
+    if (PyImport_AppendInittab("spam", PyInit_spam) == -1) {
+        fprintf(stderr, "Error: could not extend in-built modules table\n");
+        exit(1);
+    }
+
+    /* Pass argv[0] to the Python interpreter */
+    Py_SetProgramName(program);
+
+    /* Initialize the Python interpreter.  Required.
+       If this step fails, it will be a fatal error. */
+    Py_Initialize();
+
+    /* Optionally import the module; alternatively,
+       import can be deferred until the embedded script
+       imports it. */
+    PyObject *pmodule = PyImport_ImportModule("spam");
+    if (!pmodule) {
+        PyErr_Print();
+        fprintf(stderr, "Error: could not import module 'spam'\n");
+    }
+
+    ...
+
+    PyMem_RawFree(program);
+    return 0;
+}
+

Note

Removing entries from sys.modules or importing compiled modules into multiple interpreters within a process (or following a fork() without an intervening exec()) can create problems for some extension modules. Extension module authors should exercise caution when initializing internal data structures.

A more substantial example module is included in the Python source distribution as Modules/xxmodule.c. This file may be used as a template or simply read as an example.

Note

Unlike our spam example, xxmodule uses multi-phase initialization (new in Python 3.5), where a PyModuleDef structure is returned from PyInit_spam, and creation of the module is left to the import machinery. For details on multi-phase initialization, see PEP 489.

+1.5. Compilation and Linkage

There are two more things to do before you can use your new extension: compiling and linking it with the Python system. If you use dynamic loading, the details may depend on the style of dynamic loading your system uses; see the chapters about building extension modules (chapter Building C and C++ Extensions) and additional information that pertains only to building on Windows (chapter Building C and C++ Extensions on Windows) for more information about this.

If you can’t use dynamic loading, or if you want to make your module a permanent part of the Python interpreter, you will have to change the configuration setup and rebuild the interpreter. Luckily, this is very simple on Unix: just place your file (spammodule.c for example) in the Modules/ directory of an unpacked source distribution, add a line to the file Modules/Setup.local describing your file:

spam spammodule.o
+

and rebuild the interpreter by running make in the toplevel directory. You can also run make in the Modules/ subdirectory, but then you must first rebuild Makefile there by running ‘make Makefile’. (This is necessary each time you change the Setup file.)

If your module requires additional libraries to link with, these can be listed on the line in the configuration file as well, for instance:

spam spammodule.o -lX11
+

+1.6. Calling Python Functions from C

So far we have concentrated on making C functions callable from Python. The reverse is also useful: calling Python functions from C. This is especially the case for libraries that support so-called “callback” functions. If a C interface makes use of callbacks, the equivalent Python often needs to provide a callback mechanism to the Python programmer; the implementation will require calling the Python callback functions from a C callback. Other uses are also imaginable.

Fortunately, the Python interpreter is easily called recursively, and there is a standard interface to call a Python function. (I won’t dwell on how to call the Python parser with a particular string as input — if you’re interested, have a look at the implementation of the -c command line option in Modules/main.c from the Python source code.)

Calling a Python function is easy. First, the Python program must somehow pass you the Python function object. You should provide a function (or some other interface) to do this. When this function is called, save a pointer to the Python function object (be careful to Py_INCREF() it!) in a global variable — or wherever you see fit. For example, the following function might be part of a module definition:

static PyObject *my_callback = NULL;
+
+static PyObject *
+my_set_callback(PyObject *dummy, PyObject *args)
+{
+    PyObject *result = NULL;
+    PyObject *temp;
+
+    if (PyArg_ParseTuple(args, "O:set_callback", &temp)) {
+        if (!PyCallable_Check(temp)) {
+            PyErr_SetString(PyExc_TypeError, "parameter must be callable");
+            return NULL;
+        }
+        Py_XINCREF(temp);         /* Add a reference to new callback */
+        Py_XDECREF(my_callback);  /* Dispose of previous callback */
+        my_callback = temp;       /* Remember new callback */
+        /* Boilerplate to return "None" */
+        Py_INCREF(Py_None);
+        result = Py_None;
+    }
+    return result;
+}
+

This function must be registered with the interpreter using the METH_VARARGS flag; this is described in section The Module’s Method Table and Initialization Function. The PyArg_ParseTuple() function and its arguments are documented in section Extracting Parameters in Extension Functions.

The macros Py_XINCREF() and Py_XDECREF() increment/decrement the reference count of an object and are safe in the presence of NULL pointers (but note that temp will not be NULL in this context). More info on them in section Reference Counts.

Later, when it is time to call the function, you call the C function PyObject_CallObject(). This function has two arguments, both pointers to arbitrary Python objects: the Python function, and the argument list. The argument list must always be a tuple object, whose length is the number of arguments. To call the Python function with no arguments, pass in NULL, or an empty tuple; to call it with one argument, pass a singleton tuple. Py_BuildValue() returns a tuple when its format string consists of zero or more format codes between parentheses. For example:

int arg;
+PyObject *arglist;
+PyObject *result;
+...
+arg = 123;
+...
+/* Time to call the callback */
+arglist = Py_BuildValue("(i)", arg);
+result = PyObject_CallObject(my_callback, arglist);
+Py_DECREF(arglist);
+

PyObject_CallObject() returns a Python object pointer: this is the return value of the Python function. PyObject_CallObject() is “reference-count-neutral” with respect to its arguments. In the example a new tuple was created to serve as the argument list, which is Py_DECREF()-ed immediately after the PyObject_CallObject() call.

The return value of PyObject_CallObject() is “new”: either it is a brand new object, or it is an existing object whose reference count has been incremented. So, unless you want to save it in a global variable, you should somehow Py_DECREF() the result, even (especially!) if you are not interested in its value.

Before you do this, however, it is important to check that the return value isn’t NULL. If it is, the Python function terminated by raising an exception. If the C code that called PyObject_CallObject() is called from Python, it should now return an error indication to its Python caller, so the interpreter can print a stack trace, or the calling Python code can handle the exception. If this is not possible or desirable, the exception should be cleared by calling PyErr_Clear(). For example:

if (result == NULL)
+    return NULL; /* Pass error back */
+...use result...
+Py_DECREF(result);
+

Depending on the desired interface to the Python callback function, you may also have to provide an argument list to PyObject_CallObject(). In some cases the argument list is also provided by the Python program, through the same interface that specified the callback function. It can then be saved and used in the same manner as the function object. In other cases, you may have to construct a new tuple to pass as the argument list. The simplest way to do this is to call Py_BuildValue(). For example, if you want to pass an integral event code, you might use the following code:

PyObject *arglist;
+...
+arglist = Py_BuildValue("(l)", eventcode);
+result = PyObject_CallObject(my_callback, arglist);
+Py_DECREF(arglist);
+if (result == NULL)
+    return NULL; /* Pass error back */
+/* Here maybe use the result */
+Py_DECREF(result);
+

Note the placement of Py_DECREF(arglist) immediately after the call, before the error check! Also note that strictly speaking this code is not complete: Py_BuildValue() may run out of memory, and this should be checked.

You may also call a function with keyword arguments by using PyObject_Call(), which supports arguments and keyword arguments. As in the above example, we use Py_BuildValue() to construct the dictionary.

PyObject *dict;
+...
+dict = Py_BuildValue("{s:i}", "name", val);
+result = PyObject_Call(my_callback, NULL, dict);
+Py_DECREF(dict);
+if (result == NULL)
+    return NULL; /* Pass error back */
+/* Here maybe use the result */
+Py_DECREF(result);
+

+1.7. Extracting Parameters in Extension Functions

The PyArg_ParseTuple() function is declared as follows:

int PyArg_ParseTuple(PyObject *arg, const char *format, ...);
+

The arg argument must be a tuple object containing an argument list passed from Python to a C function. The format argument must be a format string, whose syntax is explained in Parsing arguments and building values in the Python/C API Reference Manual. The remaining arguments must be addresses of variables whose type is determined by the format string.

Note that while PyArg_ParseTuple() checks that the Python arguments have the required types, it cannot check the validity of the addresses of C variables passed to the call: if you make mistakes there, your code will probably crash or at least overwrite random bits in memory. So be careful!

Note that any Python object references which are provided to the caller are borrowed references; do not decrement their reference count!

Some example calls:

#define PY_SSIZE_T_CLEAN  /* Make "s#" use Py_ssize_t rather than int. */
+#include <Python.h>
+
int ok;
+int i, j;
+long k, l;
+const char *s;
+Py_ssize_t size;
+
+ok = PyArg_ParseTuple(args, ""); /* No arguments */
+    /* Python call: f() */
+
ok = PyArg_ParseTuple(args, "s", &s); /* A string */
+    /* Possible Python call: f('whoops!') */
+
ok = PyArg_ParseTuple(args, "lls", &k, &l, &s); /* Two longs and a string */
+    /* Possible Python call: f(1, 2, 'three') */
+
ok = PyArg_ParseTuple(args, "(ii)s#", &i, &j, &s, &size);
+    /* A pair of ints and a string, whose size is also returned */
+    /* Possible Python call: f((1, 2), 'three') */
+
{
+    const char *file;
+    const char *mode = "r";
+    int bufsize = 0;
+    ok = PyArg_ParseTuple(args, "s|si", &file, &mode, &bufsize);
+    /* A string, and optionally another string and an integer */
+    /* Possible Python calls:
+       f('spam')
+       f('spam', 'w')
+       f('spam', 'wb', 100000) */
+}
+
{
+    int left, top, right, bottom, h, v;
+    ok = PyArg_ParseTuple(args, "((ii)(ii))(ii)",
+             &left, &top, &right, &bottom, &h, &v);
+    /* A rectangle and a point */
+    /* Possible Python call:
+       f(((0, 0), (400, 300)), (10, 10)) */
+}
+
{
+    Py_complex c;
+    ok = PyArg_ParseTuple(args, "D:myfunction", &c);
+    /* a complex, also providing a function name for errors */
+    /* Possible Python call: myfunction(1+2j) */
+}
+

+1.8. Keyword Parameters for Extension Functions

The PyArg_ParseTupleAndKeywords() function is declared as follows:

int PyArg_ParseTupleAndKeywords(PyObject *arg, PyObject *kwdict,
+                                const char *format, char *kwlist[], ...);
+

The arg and format parameters are identical to those of the PyArg_ParseTuple() function. The kwdict parameter is the dictionary of keywords received as the third parameter from the Python runtime. The kwlist parameter is a NULL-terminated list of strings which identify the parameters; the names are matched with the type information from format from left to right. On success, PyArg_ParseTupleAndKeywords() returns true, otherwise it returns false and raises an appropriate exception.

Note

Nested tuples cannot be parsed when using keyword arguments! Keyword parameters passed in which are not present in the kwlist will cause TypeError to be raised.

Here is an example module which uses keywords, based on an example by Geoff Philbrick (philbrick@hks.com):

#define PY_SSIZE_T_CLEAN  /* Make "s#" use Py_ssize_t rather than int. */
+#include <Python.h>
+
+static PyObject *
+keywdarg_parrot(PyObject *self, PyObject *args, PyObject *keywds)
+{
+    int voltage;
+    const char *state = "a stiff";
+    const char *action = "voom";
+    const char *type = "Norwegian Blue";
+
+    static char *kwlist[] = {"voltage", "state", "action", "type", NULL};
+
+    if (!PyArg_ParseTupleAndKeywords(args, keywds, "i|sss", kwlist,
+                                     &voltage, &state, &action, &type))
+        return NULL;
+
+    printf("-- This parrot wouldn't %s if you put %i Volts through it.\n",
+           action, voltage);
+    printf("-- Lovely plumage, the %s -- It's %s!\n", type, state);
+
+    Py_RETURN_NONE;
+}
+
+static PyMethodDef keywdarg_methods[] = {
+    /* The cast of the function is necessary since PyCFunction values
+     * only take two PyObject* parameters, and keywdarg_parrot() takes
+     * three.
+     */
+    {"parrot", (PyCFunction)(void(*)(void))keywdarg_parrot, METH_VARARGS | METH_KEYWORDS,
+     "Print a lovely skit to standard output."},
+    {NULL, NULL, 0, NULL}   /* sentinel */
+};
+
+static struct PyModuleDef keywdargmodule = {
+    PyModuleDef_HEAD_INIT,
+    "keywdarg",
+    NULL,
+    -1,
+    keywdarg_methods
+};
+
+PyMODINIT_FUNC
+PyInit_keywdarg(void)
+{
+    return PyModule_Create(&keywdargmodule);
+}
+

+1.9. Building Arbitrary Values

This function is the counterpart to PyArg_ParseTuple(). It is declared as follows:

PyObject *Py_BuildValue(const char *format, ...);
+

It recognizes a set of format units similar to the ones recognized by PyArg_ParseTuple(), but the arguments (which are input to the function, not output) must not be pointers, just values. It returns a new Python object, suitable for returning from a C function called from Python.

One difference with PyArg_ParseTuple(): while the latter requires its first argument to be a tuple (since Python argument lists are always represented as tuples internally), Py_BuildValue() does not always build a tuple. It builds a tuple only if its format string contains two or more format units. If the format string is empty, it returns None; if it contains exactly one format unit, it returns whatever object is described by that format unit. To force it to return a tuple of size 0 or one, parenthesize the format string.

Examples (to the left the call, to the right the resulting Python value):

Py_BuildValue("")                        None
+Py_BuildValue("i", 123)                  123
+Py_BuildValue("iii", 123, 456, 789)      (123, 456, 789)
+Py_BuildValue("s", "hello")              'hello'
+Py_BuildValue("y", "hello")              b'hello'
+Py_BuildValue("ss", "hello", "world")    ('hello', 'world')
+Py_BuildValue("s#", "hello", 4)          'hell'
+Py_BuildValue("y#", "hello", 4)          b'hell'
+Py_BuildValue("()")                      ()
+Py_BuildValue("(i)", 123)                (123,)
+Py_BuildValue("(ii)", 123, 456)          (123, 456)
+Py_BuildValue("(i,i)", 123, 456)         (123, 456)
+Py_BuildValue("[i,i]", 123, 456)         [123, 456]
+Py_BuildValue("{s:i,s:i}",
+              "abc", 123, "def", 456)    {'abc': 123, 'def': 456}
+Py_BuildValue("((ii)(ii)) (ii)",
+              1, 2, 3, 4, 5, 6)          (((1, 2), (3, 4)), (5, 6))
+

+1.10. Reference Counts

In languages like C or C++, the programmer is responsible for dynamic allocation and deallocation of memory on the heap. In C, this is done using the functions malloc() and free(). In C++, the operators new and delete are used with essentially the same meaning and we’ll restrict the following discussion to the C case.

Every block of memory allocated with malloc() should eventually be returned to the pool of available memory by exactly one call to free(). It is important to call free() at the right time. If a block’s address is forgotten but free() is not called for it, the memory it occupies cannot be reused until the program terminates. This is called a memory leak. On the other hand, if a program calls free() for a block and then continues to use the block, it creates a conflict with re-use of the block through another malloc() call. This is called using freed memory. It has the same bad consequences as referencing uninitialized data — core dumps, wrong results, mysterious crashes.

Common causes of memory leaks are unusual paths through the code. For instance, a function may allocate a block of memory, do some calculation, and then free the block again. Now a change in the requirements for the function may add a test to the calculation that detects an error condition and can return prematurely from the function. It’s easy to forget to free the allocated memory block when taking this premature exit, especially when it is added later to the code. Such leaks, once introduced, often go undetected for a long time: the error exit is taken only in a small fraction of all calls, and most modern machines have plenty of virtual memory, so the leak only becomes apparent in a long-running process that uses the leaking function frequently. Therefore, it’s important to prevent leaks from happening by having a coding convention or strategy that minimizes this kind of errors.

Since Python makes heavy use of malloc() and free(), it needs a strategy to avoid memory leaks as well as the use of freed memory. The chosen method is called reference counting. The principle is simple: every object contains a counter, which is incremented when a reference to the object is stored somewhere, and which is decremented when a reference to it is deleted. When the counter reaches zero, the last reference to the object has been deleted and the object is freed.

An alternative strategy is called automatic garbage collection. (Sometimes, reference counting is also referred to as a garbage collection strategy, hence my use of “automatic” to distinguish the two.) The big advantage of automatic garbage collection is that the user doesn’t need to call free() explicitly. (Another claimed advantage is an improvement in speed or memory usage — this is no hard fact however.) The disadvantage is that for C, there is no truly portable automatic garbage collector, while reference counting can be implemented portably (as long as the functions malloc() and free() are available — which the C Standard guarantees). Maybe some day a sufficiently portable automatic garbage collector will be available for C. Until then, we’ll have to live with reference counts.

While Python uses the traditional reference counting implementation, it also offers a cycle detector that works to detect reference cycles. This allows applications to not worry about creating direct or indirect circular references; these are the weakness of garbage collection implemented using only reference counting. Reference cycles consist of objects which contain (possibly indirect) references to themselves, so that each object in the cycle has a reference count which is non-zero. Typical reference counting implementations are not able to reclaim the memory belonging to any objects in a reference cycle, or referenced from the objects in the cycle, even though there are no further references to the cycle itself.

The cycle detector is able to detect garbage cycles and can reclaim them. The gc module exposes a way to run the detector (the collect() function), as well as configuration interfaces and the ability to disable the detector at runtime.

+1.10.1. Reference Counting in Python

There are two macros, Py_INCREF(x) and Py_DECREF(x), which handle the incrementing and decrementing of the reference count. Py_DECREF() also frees the object when the count reaches zero. For flexibility, it doesn’t call free() directly — rather, it makes a call through a function pointer in the object’s type object. For this purpose (and others), every object also contains a pointer to its type object.

The big question now remains: when to use Py_INCREF(x) and Py_DECREF(x)? Let’s first introduce some terms. Nobody “owns” an object; however, you can own a reference to an object. An object’s reference count is now defined as the number of owned references to it. The owner of a reference is responsible for calling Py_DECREF() when the reference is no longer needed. Ownership of a reference can be transferred. There are three ways to dispose of an owned reference: pass it on, store it, or call Py_DECREF(). Forgetting to dispose of an owned reference creates a memory leak.

It is also possible to borrow 2 a reference to an object. The borrower of a reference should not call Py_DECREF(). The borrower must not hold on to the object longer than the owner from which it was borrowed. Using a borrowed reference after the owner has disposed of it risks using freed memory and should be avoided completely 3.

The advantage of borrowing over owning a reference is that you don’t need to take care of disposing of the reference on all possible paths through the code — in other words, with a borrowed reference you don’t run the risk of leaking when a premature exit is taken. The disadvantage of borrowing over owning is that there are some subtle situations where in seemingly correct code a borrowed reference can be used after the owner from which it was borrowed has in fact disposed of it.

A borrowed reference can be changed into an owned reference by calling Py_INCREF(). This does not affect the status of the owner from which the reference was borrowed — it creates a new owned reference, and gives full owner responsibilities (the new owner must dispose of the reference properly, as well as the previous owner).

+1.10.2. Ownership Rules

Whenever an object reference is passed into or out of a function, it is part of the function’s interface specification whether ownership is transferred with the reference or not.

Most functions that return a reference to an object pass on ownership with the reference. In particular, all functions whose function it is to create a new object, such as PyLong_FromLong() and Py_BuildValue(), pass ownership to the receiver. Even if the object is not actually new, you still receive ownership of a new reference to that object. For instance, PyLong_FromLong() maintains a cache of popular values and can return a reference to a cached item.

Many functions that extract objects from other objects also transfer ownership with the reference, for instance PyObject_GetAttrString(). The picture is less clear, here, however, since a few common routines are exceptions: PyTuple_GetItem(), PyList_GetItem(), PyDict_GetItem(), and PyDict_GetItemString() all return references that you borrow from the tuple, list or dictionary.

The function PyImport_AddModule() also returns a borrowed reference, even though it may actually create the object it returns: this is possible because an owned reference to the object is stored in sys.modules.

When you pass an object reference into another function, in general, the function borrows the reference from you — if it needs to store it, it will use Py_INCREF() to become an independent owner. There are exactly two important exceptions to this rule: PyTuple_SetItem() and PyList_SetItem(). These functions take over ownership of the item passed to them — even if they fail! (Note that PyDict_SetItem() and friends don’t take over ownership — they are “normal.”)

When a C function is called from Python, it borrows references to its arguments from the caller. The caller owns a reference to the object, so the borrowed reference’s lifetime is guaranteed until the function returns. Only when such a borrowed reference must be stored or passed on, it must be turned into an owned reference by calling Py_INCREF().

The object reference returned from a C function that is called from Python must be an owned reference — ownership is transferred from the function to its caller.

+1.10.3. Thin Ice

There are a few situations where seemingly harmless use of a borrowed reference can lead to problems. These all have to do with implicit invocations of the interpreter, which can cause the owner of a reference to dispose of it.

The first and most important case to know about is using Py_DECREF() on an unrelated object while borrowing a reference to a list item. For instance:

void
+bug(PyObject *list)
+{
+    PyObject *item = PyList_GetItem(list, 0);
+
+    PyList_SetItem(list, 1, PyLong_FromLong(0L));
+    PyObject_Print(item, stdout, 0); /* BUG! */
+}
+

This function first borrows a reference to list[0], then replaces list[1] with the value 0, and finally prints the borrowed reference. Looks harmless, right? But it’s not!

Let’s follow the control flow into PyList_SetItem(). The list owns references to all its items, so when item 1 is replaced, it has to dispose of the original item 1. Now let’s suppose the original item 1 was an instance of a user-defined class, and let’s further suppose that the class defined a __del__() method. If this class instance has a reference count of 1, disposing of it will call its __del__() method.

Since it is written in Python, the __del__() method can execute arbitrary Python code. Could it perhaps do something to invalidate the reference to item in bug()? You bet! Assuming that the list passed into bug() is accessible to the __del__() method, it could execute a statement to the effect of del list[0], and assuming this was the last reference to that object, it would free the memory associated with it, thereby invalidating item.

The solution, once you know the source of the problem, is easy: temporarily increment the reference count. The correct version of the function reads:

void
+no_bug(PyObject *list)
+{
+    PyObject *item = PyList_GetItem(list, 0);
+
+    Py_INCREF(item);
+    PyList_SetItem(list, 1, PyLong_FromLong(0L));
+    PyObject_Print(item, stdout, 0);
+    Py_DECREF(item);
+}
+

This is a true story. An older version of Python contained variants of this bug and someone spent a considerable amount of time in a C debugger to figure out why his __del__() methods would fail…

The second case of problems with a borrowed reference is a variant involving threads. Normally, multiple threads in the Python interpreter can’t get in each other’s way, because there is a global lock protecting Python’s entire object space. However, it is possible to temporarily release this lock using the macro Py_BEGIN_ALLOW_THREADS, and to re-acquire it using Py_END_ALLOW_THREADS. This is common around blocking I/O calls, to let other threads use the processor while waiting for the I/O to complete. Obviously, the following function has the same problem as the previous one:

void
+bug(PyObject *list)
+{
+    PyObject *item = PyList_GetItem(list, 0);
+    Py_BEGIN_ALLOW_THREADS
+    ...some blocking I/O call...
+    Py_END_ALLOW_THREADS
+    PyObject_Print(item, stdout, 0); /* BUG! */
+}
+

+1.10.4. NULL Pointers

In general, functions that take object references as arguments do not expect you to pass them NULL pointers, and will dump core (or cause later core dumps) if you do so. Functions that return object references generally return NULL only to indicate that an exception occurred. The reason for not testing for NULL arguments is that functions often pass the objects they receive on to other function — if each function were to test for NULL, there would be a lot of redundant tests and the code would run more slowly.

It is better to test for NULL only at the “source:” when a pointer that may be NULL is received, for example, from malloc() or from a function that may raise an exception.

The macros Py_INCREF() and Py_DECREF() do not check for NULL pointers — however, their variants Py_XINCREF() and Py_XDECREF() do.

The macros for checking for a particular object type (Pytype_Check()) don’t check for NULL pointers — again, there is much code that calls several of these in a row to test an object against various different expected types, and this would generate redundant tests. There are no variants with NULL checking.

The C function calling mechanism guarantees that the argument list passed to C functions (args in the examples) is never NULL — in fact it guarantees that it is always a tuple 4.

It is a severe error to ever let a NULL pointer “escape” to the Python user.

+1.11. Writing Extensions in C++

It is possible to write extension modules in C++. Some restrictions apply. If the main program (the Python interpreter) is compiled and linked by the C compiler, global or static objects with constructors cannot be used. This is not a problem if the main program is linked by the C++ compiler. Functions that will be called by the Python interpreter (in particular, module initialization functions) have to be declared using extern "C". It is unnecessary to enclose the Python header files in extern "C" {...} — they use this form already if the symbol __cplusplus is defined (all recent C++ compilers define this symbol).

+1.12. Providing a C API for an Extension Module

Many extension modules just provide new functions and types to be used from Python, but sometimes the code in an extension module can be useful for other extension modules. For example, an extension module could implement a type “collection” which works like lists without order. Just like the standard Python list type has a C API which permits extension modules to create and manipulate lists, this new collection type should have a set of C functions for direct manipulation from other extension modules.

At first sight this seems easy: just write the functions (without declaring them static, of course), provide an appropriate header file, and document the C API. And in fact this would work if all extension modules were always linked statically with the Python interpreter. When modules are used as shared libraries, however, the symbols defined in one module may not be visible to another module. The details of visibility depend on the operating system; some systems use one global namespace for the Python interpreter and all extension modules (Windows, for example), whereas others require an explicit list of imported symbols at module link time (AIX is one example), or offer a choice of different strategies (most Unices). And even if symbols are globally visible, the module whose functions one wishes to call might not have been loaded yet!

Portability therefore requires not to make any assumptions about symbol visibility. This means that all symbols in extension modules should be declared static, except for the module’s initialization function, in order to avoid name clashes with other extension modules (as discussed in section The Module’s Method Table and Initialization Function). And it means that symbols that should be accessible from other extension modules must be exported in a different way.

Python provides a special mechanism to pass C-level information (pointers) from one extension module to another one: Capsules. A Capsule is a Python data type which stores a pointer (void*). Capsules can only be created and accessed via their C API, but they can be passed around like any other Python object. In particular, they can be assigned to a name in an extension module’s namespace. Other extension modules can then import this module, retrieve the value of this name, and then retrieve the pointer from the Capsule.

There are many ways in which Capsules can be used to export the C API of an extension module. Each function could get its own Capsule, or all C API pointers could be stored in an array whose address is published in a Capsule. And the various tasks of storing and retrieving the pointers can be distributed in different ways between the module providing the code and the client modules.

Whichever method you choose, it’s important to name your Capsules properly. The function PyCapsule_New() takes a name parameter (const char*); you’re permitted to pass in a NULL name, but we strongly encourage you to specify a name. Properly named Capsules provide a degree of runtime type-safety; there is no feasible way to tell one unnamed Capsule from another.

In particular, Capsules used to expose C APIs should be given a name following this convention:

modulename.attributename
+

The convenience function PyCapsule_Import() makes it easy to load a C API provided via a Capsule, but only if the Capsule’s name matches this convention. This behavior gives C API users a high degree of certainty that the Capsule they load contains the correct C API.

The following example demonstrates an approach that puts most of the burden on the writer of the exporting module, which is appropriate for commonly used library modules. It stores all C API pointers (just one in the example!) in an array of void pointers which becomes the value of a Capsule. The header file corresponding to the module provides a macro that takes care of importing the module and retrieving its C API pointers; client modules only have to call this macro before accessing the C API.

The exporting module is a modification of the spam module from section A Simple Example. The function spam.system() does not call the C library function system() directly, but a function PySpam_System(), which would of course do something more complicated in reality (such as adding “spam” to every command). This function PySpam_System() is also exported to other extension modules.

The function PySpam_System() is a plain C function, declared static like everything else:

static int
+PySpam_System(const char *command)
+{
+    return system(command);
+}
+

The function spam_system() is modified in a trivial way:

static PyObject *
+spam_system(PyObject *self, PyObject *args)
+{
+    const char *command;
+    int sts;
+
+    if (!PyArg_ParseTuple(args, "s", &command))
+        return NULL;
+    sts = PySpam_System(command);
+    return PyLong_FromLong(sts);
+}
+

In the beginning of the module, right after the line

#include <Python.h>
+

two more lines must be added:

#define SPAM_MODULE
+#include "spammodule.h"
+

The #define is used to tell the header file that it is being included in the exporting module, not a client module. Finally, the module’s initialization function must take care of initializing the C API pointer array:

PyMODINIT_FUNC
+PyInit_spam(void)
+{
+    PyObject *m;
+    static void *PySpam_API[PySpam_API_pointers];
+    PyObject *c_api_object;
+
+    m = PyModule_Create(&spammodule);
+    if (m == NULL)
+        return NULL;
+
+    /* Initialize the C API pointer array */
+    PySpam_API[PySpam_System_NUM] = (void *)PySpam_System;
+
+    /* Create a Capsule containing the API pointer array's address */
+    c_api_object = PyCapsule_New((void *)PySpam_API, "spam._C_API", NULL);
+
+    if (PyModule_AddObject(m, "_C_API", c_api_object) < 0) {
+        Py_XDECREF(c_api_object);
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

Note that PySpam_API is declared static; otherwise the pointer array would disappear when PyInit_spam() terminates!

The bulk of the work is in the header file spammodule.h, which looks like this:

#ifndef Py_SPAMMODULE_H
+#define Py_SPAMMODULE_H
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+/* Header file for spammodule */
+
+/* C API functions */
+#define PySpam_System_NUM 0
+#define PySpam_System_RETURN int
+#define PySpam_System_PROTO (const char *command)
+
+/* Total number of C API pointers */
+#define PySpam_API_pointers 1
+
+
+#ifdef SPAM_MODULE
+/* This section is used when compiling spammodule.c */
+
+static PySpam_System_RETURN PySpam_System PySpam_System_PROTO;
+
+#else
+/* This section is used in modules that use spammodule's API */
+
+static void **PySpam_API;
+
+#define PySpam_System \
+ (*(PySpam_System_RETURN (*)PySpam_System_PROTO) PySpam_API[PySpam_System_NUM])
+
+/* Return -1 on error, 0 on success.
+ * PyCapsule_Import will set an exception if there's an error.
+ */
+static int
+import_spam(void)
+{
+    PySpam_API = (void **)PyCapsule_Import("spam._C_API", 0);
+    return (PySpam_API != NULL) ? 0 : -1;
+}
+
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+#endif /* !defined(Py_SPAMMODULE_H) */
+

All that a client module must do in order to have access to the function PySpam_System() is to call the function (or rather macro) import_spam() in its initialization function:

PyMODINIT_FUNC
+PyInit_client(void)
+{
+    PyObject *m;
+
+    m = PyModule_Create(&clientmodule);
+    if (m == NULL)
+        return NULL;
+    if (import_spam() < 0)
+        return NULL;
+    /* additional initialization can happen here */
+    return m;
+}
+

The main disadvantage of this approach is that the file spammodule.h is rather complicated. However, the basic structure is the same for each function that is exported, so it has to be learned only once.

Finally it should be mentioned that Capsules offer additional functionality, which is especially useful for memory allocation and deallocation of the pointer stored in a Capsule. The details are described in the Python/C API Reference Manual in the section Capsules and in the implementation of Capsules (files Include/pycapsule.h and Objects/pycapsule.c in the Python source code distribution).

Footnotes

+1
+

An interface for this function already exists in the standard module os — it was chosen as a simple and straightforward example.

+2
+

The metaphor of “borrowing” a reference is not completely correct: the owner still has a copy of the reference.

+3
+

Checking that the reference count is at least 1 does not work — the reference count itself could be in freed memory and may thus be reused for another object!

+4
+

These guarantees don’t hold when you use the “old” style calling convention — this is still found in much existing code.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/extending.html +

+
diff --git a/devdocs/python~3.12/extending%2Findex.html b/devdocs/python~3.12/extending%2Findex.html new file mode 100644 index 00000000..f748a326 --- /dev/null +++ b/devdocs/python~3.12/extending%2Findex.html @@ -0,0 +1,14 @@ +

Extending and Embedding the Python Interpreter

This document describes how to write modules in C or C++ to extend the Python interpreter with new modules. Those modules can not only define new functions but also new object types and their methods. The document also describes how to embed the Python interpreter in another application, for use as an extension language. Finally, it shows how to compile and link extension modules so that they can be loaded dynamically (at run time) into the interpreter, if the underlying operating system supports this feature.

This document assumes basic knowledge about Python. For an informal introduction to the language, see The Python Tutorial. The Python Language Reference gives a more formal definition of the language. The Python Standard Library documents the existing object types, functions and modules (both built-in and written in Python) that give the language its wide application range.

For a detailed description of the whole Python/C API, see the separate Python/C API Reference Manual.

Creating extensions without third party tools

This section of the guide covers creating C and C++ extensions without assistance from third party tools. It is intended primarily for creators of those tools, rather than being a recommended way to create your own C extensions.

Embedding the CPython runtime in a larger application

Sometimes, rather than creating an extension that runs inside the Python interpreter as the main application, it is desirable to instead embed the CPython runtime inside a larger application. This section covers some of the details involved in doing that successfully.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/index.html +

+
diff --git a/devdocs/python~3.12/extending%2Fnewtypes.html b/devdocs/python~3.12/extending%2Fnewtypes.html new file mode 100644 index 00000000..f8d2c6cd --- /dev/null +++ b/devdocs/python~3.12/extending%2Fnewtypes.html @@ -0,0 +1,281 @@ +

Defining Extension Types: Assorted Topics

This section aims to give a quick fly-by on the various type methods you can implement and what they do.

Here is the definition of PyTypeObject, with some fields only used in debug builds omitted:

typedef struct _typeobject {
+    PyObject_VAR_HEAD
+    const char *tp_name; /* For printing, in format "<module>.<name>" */
+    Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */
+
+    /* Methods to implement standard operations */
+
+    destructor tp_dealloc;
+    Py_ssize_t tp_vectorcall_offset;
+    getattrfunc tp_getattr;
+    setattrfunc tp_setattr;
+    PyAsyncMethods *tp_as_async; /* formerly known as tp_compare (Python 2)
+                                    or tp_reserved (Python 3) */
+    reprfunc tp_repr;
+
+    /* Method suites for standard classes */
+
+    PyNumberMethods *tp_as_number;
+    PySequenceMethods *tp_as_sequence;
+    PyMappingMethods *tp_as_mapping;
+
+    /* More standard operations (here for binary compatibility) */
+
+    hashfunc tp_hash;
+    ternaryfunc tp_call;
+    reprfunc tp_str;
+    getattrofunc tp_getattro;
+    setattrofunc tp_setattro;
+
+    /* Functions to access object as input/output buffer */
+    PyBufferProcs *tp_as_buffer;
+
+    /* Flags to define presence of optional/expanded features */
+    unsigned long tp_flags;
+
+    const char *tp_doc; /* Documentation string */
+
+    /* Assigned meaning in release 2.0 */
+    /* call function for all accessible objects */
+    traverseproc tp_traverse;
+
+    /* delete references to contained objects */
+    inquiry tp_clear;
+
+    /* Assigned meaning in release 2.1 */
+    /* rich comparisons */
+    richcmpfunc tp_richcompare;
+
+    /* weak reference enabler */
+    Py_ssize_t tp_weaklistoffset;
+
+    /* Iterators */
+    getiterfunc tp_iter;
+    iternextfunc tp_iternext;
+
+    /* Attribute descriptor and subclassing stuff */
+    struct PyMethodDef *tp_methods;
+    struct PyMemberDef *tp_members;
+    struct PyGetSetDef *tp_getset;
+    // Strong reference on a heap type, borrowed reference on a static type
+    struct _typeobject *tp_base;
+    PyObject *tp_dict;
+    descrgetfunc tp_descr_get;
+    descrsetfunc tp_descr_set;
+    Py_ssize_t tp_dictoffset;
+    initproc tp_init;
+    allocfunc tp_alloc;
+    newfunc tp_new;
+    freefunc tp_free; /* Low-level free-memory routine */
+    inquiry tp_is_gc; /* For PyObject_IS_GC */
+    PyObject *tp_bases;
+    PyObject *tp_mro; /* method resolution order */
+    PyObject *tp_cache;
+    PyObject *tp_subclasses;
+    PyObject *tp_weaklist;
+    destructor tp_del;
+
+    /* Type attribute cache version tag. Added in version 2.6 */
+    unsigned int tp_version_tag;
+
+    destructor tp_finalize;
+    vectorcallfunc tp_vectorcall;
+
+    /* bitset of which type-watchers care about this type */
+    unsigned char tp_watched;
+} PyTypeObject;
+

Now that’s a lot of methods. Don’t worry too much though – if you have a type you want to define, the chances are very good that you will only implement a handful of these.

As you probably expect by now, we’re going to go over this and give more information about the various handlers. We won’t go in the order they are defined in the structure, because there is a lot of historical baggage that impacts the ordering of the fields. It’s often easiest to find an example that includes the fields you need and then change the values to suit your new type.

const char *tp_name; /* For printing */
+

The name of the type – as mentioned in the previous chapter, this will appear in various places, almost entirely for diagnostic purposes. Try to choose something that will be helpful in such a situation!

Py_ssize_t tp_basicsize, tp_itemsize; /* For allocation */
+

These fields tell the runtime how much memory to allocate when new objects of this type are created. Python has some built-in support for variable length structures (think: strings, tuples) which is where the tp_itemsize field comes in. This will be dealt with later.

const char *tp_doc;
+

Here you can put a string (or its address) that you want returned when the Python script references obj.__doc__ to retrieve the doc string.

Now we come to the basic type methods – the ones most extension types will implement.

+3.1. Finalization and De-allocation

destructor tp_dealloc;
+

This function is called when the reference count of the instance of your type is reduced to zero and the Python interpreter wants to reclaim it. If your type has memory to free or other clean-up to perform, you can put it here. The object itself needs to be freed here as well. Here is an example of this function:

static void
+newdatatype_dealloc(newdatatypeobject *obj)
+{
+    free(obj->obj_UnderlyingDatatypePtr);
+    Py_TYPE(obj)->tp_free((PyObject *)obj);
+}
+

If your type supports garbage collection, the destructor should call PyObject_GC_UnTrack() before clearing any member fields:

static void
+newdatatype_dealloc(newdatatypeobject *obj)
+{
+    PyObject_GC_UnTrack(obj);
+    Py_CLEAR(obj->other_obj);
+    ...
+    Py_TYPE(obj)->tp_free((PyObject *)obj);
+}
+

One important requirement of the deallocator function is that it leaves any pending exceptions alone. This is important since deallocators are frequently called as the interpreter unwinds the Python stack; when the stack is unwound due to an exception (rather than normal returns), nothing is done to protect the deallocators from seeing that an exception has already been set. Any actions which a deallocator performs which may cause additional Python code to be executed may detect that an exception has been set. This can lead to misleading errors from the interpreter. The proper way to protect against this is to save a pending exception before performing the unsafe action, and restoring it when done. This can be done using the PyErr_Fetch() and PyErr_Restore() functions:

static void
+my_dealloc(PyObject *obj)
+{
+    MyObject *self = (MyObject *) obj;
+    PyObject *cbresult;
+
+    if (self->my_callback != NULL) {
+        PyObject *err_type, *err_value, *err_traceback;
+
+        /* This saves the current exception state */
+        PyErr_Fetch(&err_type, &err_value, &err_traceback);
+
+        cbresult = PyObject_CallNoArgs(self->my_callback);
+        if (cbresult == NULL)
+            PyErr_WriteUnraisable(self->my_callback);
+        else
+            Py_DECREF(cbresult);
+
+        /* This restores the saved exception state */
+        PyErr_Restore(err_type, err_value, err_traceback);
+
+        Py_DECREF(self->my_callback);
+    }
+    Py_TYPE(obj)->tp_free((PyObject*)self);
+}
+

Note

There are limitations to what you can safely do in a deallocator function. First, if your type supports garbage collection (using tp_traverse and/or tp_clear), some of the object’s members can have been cleared or finalized by the time tp_dealloc is called. Second, in tp_dealloc, your object is in an unstable state: its reference count is equal to zero. Any call to a non-trivial object or API (as in the example above) might end up calling tp_dealloc again, causing a double free and a crash.

Starting with Python 3.4, it is recommended not to put any complex finalization code in tp_dealloc, and instead use the new tp_finalize type method.

See also

PEP 442 explains the new finalization scheme.

+3.2. Object Presentation

In Python, there are two ways to generate a textual representation of an object: the repr() function, and the str() function. (The print() function just calls str().) These handlers are both optional.

reprfunc tp_repr;
+reprfunc tp_str;
+

The tp_repr handler should return a string object containing a representation of the instance for which it is called. Here is a simple example:

static PyObject *
+newdatatype_repr(newdatatypeobject *obj)
+{
+    return PyUnicode_FromFormat("Repr-ified_newdatatype{{size:%d}}",
+                                obj->obj_UnderlyingDatatypePtr->size);
+}
+

If no tp_repr handler is specified, the interpreter will supply a representation that uses the type’s tp_name and a uniquely identifying value for the object.

The tp_str handler is to str() what the tp_repr handler described above is to repr(); that is, it is called when Python code calls str() on an instance of your object. Its implementation is very similar to the tp_repr function, but the resulting string is intended for human consumption. If tp_str is not specified, the tp_repr handler is used instead.

Here is a simple example:

static PyObject *
+newdatatype_str(newdatatypeobject *obj)
+{
+    return PyUnicode_FromFormat("Stringified_newdatatype{{size:%d}}",
+                                obj->obj_UnderlyingDatatypePtr->size);
+}
+

+3.3. Attribute Management

For every object which can support attributes, the corresponding type must provide the functions that control how the attributes are resolved. There needs to be a function which can retrieve attributes (if any are defined), and another to set attributes (if setting attributes is allowed). Removing an attribute is a special case, for which the new value passed to the handler is NULL.

Python supports two pairs of attribute handlers; a type that supports attributes only needs to implement the functions for one pair. The difference is that one pair takes the name of the attribute as a char*, while the other accepts a PyObject*. Each type can use whichever pair makes more sense for the implementation’s convenience.

getattrfunc  tp_getattr;        /* char * version */
+setattrfunc  tp_setattr;
+/* ... */
+getattrofunc tp_getattro;       /* PyObject * version */
+setattrofunc tp_setattro;
+

If accessing attributes of an object is always a simple operation (this will be explained shortly), there are generic implementations which can be used to provide the PyObject* version of the attribute management functions. The actual need for type-specific attribute handlers almost completely disappeared starting with Python 2.2, though there are many examples which have not been updated to use some of the new generic mechanism that is available.

+3.3.1. Generic Attribute Management

Most extension types only use simple attributes. So, what makes the attributes simple? There are only a couple of conditions that must be met:

  1. The name of the attributes must be known when PyType_Ready() is called.
  2. No special processing is needed to record that an attribute was looked up or set, nor do actions need to be taken based on the value.

Note that this list does not place any restrictions on the values of the attributes, when the values are computed, or how relevant data is stored.

When PyType_Ready() is called, it uses three tables referenced by the type object to create descriptors which are placed in the dictionary of the type object. Each descriptor controls access to one attribute of the instance object. Each of the tables is optional; if all three are NULL, instances of the type will only have attributes that are inherited from their base type, and should leave the tp_getattro and tp_setattro fields NULL as well, allowing the base type to handle attributes.

The tables are declared as three fields of the type object:

struct PyMethodDef *tp_methods;
+struct PyMemberDef *tp_members;
+struct PyGetSetDef *tp_getset;
+

If tp_methods is not NULL, it must refer to an array of PyMethodDef structures. Each entry in the table is an instance of this structure:

typedef struct PyMethodDef {
+    const char  *ml_name;       /* method name */
+    PyCFunction  ml_meth;       /* implementation function */
+    int          ml_flags;      /* flags */
+    const char  *ml_doc;        /* docstring */
+} PyMethodDef;
+

One entry should be defined for each method provided by the type; no entries are needed for methods inherited from a base type. One additional entry is needed at the end; it is a sentinel that marks the end of the array. The ml_name field of the sentinel must be NULL.

The second table is used to define attributes which map directly to data stored in the instance. A variety of primitive C types are supported, and access may be read-only or read-write. The structures in the table are defined as:

typedef struct PyMemberDef {
+    const char *name;
+    int         type;
+    int         offset;
+    int         flags;
+    const char *doc;
+} PyMemberDef;
+

For each entry in the table, a descriptor will be constructed and added to the type which will be able to extract a value from the instance structure. The type field should contain a type code like Py_T_INT or Py_T_DOUBLE; the value will be used to determine how to convert Python values to and from C values. The flags field is used to store flags which control how the attribute can be accessed: you can set it to Py_READONLY to prevent Python code from setting it.

An interesting advantage of using the tp_members table to build descriptors that are used at runtime is that any attribute defined this way can have an associated doc string simply by providing the text in the table. An application can use the introspection API to retrieve the descriptor from the class object, and get the doc string using its __doc__ attribute.

As with the tp_methods table, a sentinel entry with a ml_name value of NULL is required.

+3.3.2. Type-specific Attribute Management

For simplicity, only the char* version will be demonstrated here; the type of the name parameter is the only difference between the char* and PyObject* flavors of the interface. This example effectively does the same thing as the generic example above, but does not use the generic support added in Python 2.2. It explains how the handler functions are called, so that if you do need to extend their functionality, you’ll understand what needs to be done.

The tp_getattr handler is called when the object requires an attribute look-up. It is called in the same situations where the __getattr__() method of a class would be called.

Here is an example:

static PyObject *
+newdatatype_getattr(newdatatypeobject *obj, char *name)
+{
+    if (strcmp(name, "data") == 0)
+    {
+        return PyLong_FromLong(obj->data);
+    }
+
+    PyErr_Format(PyExc_AttributeError,
+                 "'%.100s' object has no attribute '%.400s'",
+                 Py_TYPE(obj)->tp_name, name);
+    return NULL;
+}
+

The tp_setattr handler is called when the __setattr__() or __delattr__() method of a class instance would be called. When an attribute should be deleted, the third parameter will be NULL. Here is an example that simply raises an exception; if this were really all you wanted, the tp_setattr handler should be set to NULL.

static int
+newdatatype_setattr(newdatatypeobject *obj, char *name, PyObject *v)
+{
+    PyErr_Format(PyExc_RuntimeError, "Read-only attribute: %s", name);
+    return -1;
+}
+

+3.4. Object Comparison

richcmpfunc tp_richcompare;
+

The tp_richcompare handler is called when comparisons are needed. It is analogous to the rich comparison methods, like __lt__(), and also called by PyObject_RichCompare() and PyObject_RichCompareBool().

This function is called with two Python objects and the operator as arguments, where the operator is one of Py_EQ, Py_NE, Py_LE, Py_GE, Py_LT or Py_GT. It should compare the two objects with respect to the specified operator and return Py_True or Py_False if the comparison is successful, Py_NotImplemented to indicate that comparison is not implemented and the other object’s comparison method should be tried, or NULL if an exception was set.

Here is a sample implementation, for a datatype that is considered equal if the size of an internal pointer is equal:

static PyObject *
+newdatatype_richcmp(newdatatypeobject *obj1, newdatatypeobject *obj2, int op)
+{
+    PyObject *result;
+    int c, size1, size2;
+
+    /* code to make sure that both arguments are of type
+       newdatatype omitted */
+
+    size1 = obj1->obj_UnderlyingDatatypePtr->size;
+    size2 = obj2->obj_UnderlyingDatatypePtr->size;
+
+    switch (op) {
+    case Py_LT: c = size1 <  size2; break;
+    case Py_LE: c = size1 <= size2; break;
+    case Py_EQ: c = size1 == size2; break;
+    case Py_NE: c = size1 != size2; break;
+    case Py_GT: c = size1 >  size2; break;
+    case Py_GE: c = size1 >= size2; break;
+    }
+    result = c ? Py_True : Py_False;
+    Py_INCREF(result);
+    return result;
+ }
+

+3.5. Abstract Protocol Support

Python supports a variety of abstract ‘protocols;’ the specific interfaces provided to use these interfaces are documented in Abstract Objects Layer.

A number of these abstract interfaces were defined early in the development of the Python implementation. In particular, the number, mapping, and sequence protocols have been part of Python since the beginning. Other protocols have been added over time. For protocols which depend on several handler routines from the type implementation, the older protocols have been defined as optional blocks of handlers referenced by the type object. For newer protocols there are additional slots in the main type object, with a flag bit being set to indicate that the slots are present and should be checked by the interpreter. (The flag bit does not indicate that the slot values are non-NULL. The flag may be set to indicate the presence of a slot, but a slot may still be unfilled.)

PyNumberMethods   *tp_as_number;
+PySequenceMethods *tp_as_sequence;
+PyMappingMethods  *tp_as_mapping;
+

If you wish your object to be able to act like a number, a sequence, or a mapping object, then you place the address of a structure that implements the C type PyNumberMethods, PySequenceMethods, or PyMappingMethods, respectively. It is up to you to fill in this structure with appropriate values. You can find examples of the use of each of these in the Objects directory of the Python source distribution.

hashfunc tp_hash;
+

This function, if you choose to provide it, should return a hash number for an instance of your data type. Here is a simple example:

static Py_hash_t
+newdatatype_hash(newdatatypeobject *obj)
+{
+    Py_hash_t result;
+    result = obj->some_size + 32767 * obj->some_number;
+    if (result == -1)
+       result = -2;
+    return result;
+}
+

Py_hash_t is a signed integer type with a platform-varying width. Returning -1 from tp_hash indicates an error, which is why you should be careful to avoid returning it when hash computation is successful, as seen above.

ternaryfunc tp_call;
+

This function is called when an instance of your data type is “called”, for example, if obj1 is an instance of your data type and the Python script contains obj1('hello'), the tp_call handler is invoked.

This function takes three arguments:

  1. +self is the instance of the data type which is the subject of the call. If the call is obj1('hello'), then self is obj1.
  2. +args is a tuple containing the arguments to the call. You can use PyArg_ParseTuple() to extract the arguments.
  3. +kwds is a dictionary of keyword arguments that were passed. If this is non-NULL and you support keyword arguments, use PyArg_ParseTupleAndKeywords() to extract the arguments. If you do not want to support keyword arguments and this is non-NULL, raise a TypeError with a message saying that keyword arguments are not supported.

Here is a toy tp_call implementation:

static PyObject *
+newdatatype_call(newdatatypeobject *obj, PyObject *args, PyObject *kwds)
+{
+    PyObject *result;
+    const char *arg1;
+    const char *arg2;
+    const char *arg3;
+
+    if (!PyArg_ParseTuple(args, "sss:call", &arg1, &arg2, &arg3)) {
+        return NULL;
+    }
+    result = PyUnicode_FromFormat(
+        "Returning -- value: [%d] arg1: [%s] arg2: [%s] arg3: [%s]\n",
+        obj->obj_UnderlyingDatatypePtr->size,
+        arg1, arg2, arg3);
+    return result;
+}
+
/* Iterators */
+getiterfunc tp_iter;
+iternextfunc tp_iternext;
+

These functions provide support for the iterator protocol. Both handlers take exactly one parameter, the instance for which they are being called, and return a new reference. In the case of an error, they should set an exception and return NULL. tp_iter corresponds to the Python __iter__() method, while tp_iternext corresponds to the Python __next__() method.

Any iterable object must implement the tp_iter handler, which must return an iterator object. Here the same guidelines apply as for Python classes:

Any iterator object should implement both tp_iter and tp_iternext. An iterator’s tp_iter handler should return a new reference to the iterator. Its tp_iternext handler should return a new reference to the next object in the iteration, if there is one. If the iteration has reached the end, tp_iternext may return NULL without setting an exception, or it may set StopIteration in addition to returning NULL; avoiding the exception can yield slightly better performance. If an actual error occurs, tp_iternext should always set an exception and return NULL.

+3.6. Weak Reference Support

One of the goals of Python’s weak reference implementation is to allow any type to participate in the weak reference mechanism without incurring the overhead on performance-critical objects (such as numbers).

See also

Documentation for the weakref module.

For an object to be weakly referencable, the extension type must set the Py_TPFLAGS_MANAGED_WEAKREF bit of the tp_flags field. The legacy tp_weaklistoffset field should be left as zero.

Concretely, here is how the statically declared type object would look:

static PyTypeObject TrivialType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    /* ... other members omitted for brevity ... */
+    .tp_flags = Py_TPFLAGS_MANAGED_WEAKREF | ...,
+};
+

The only further addition is that tp_dealloc needs to clear any weak references (by calling PyObject_ClearWeakRefs()):

static void
+Trivial_dealloc(TrivialObject *self)
+{
+    /* Clear weakrefs first before calling any destructors */
+    PyObject_ClearWeakRefs((PyObject *) self);
+    /* ... remainder of destruction code omitted for brevity ... */
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+

+3.7. More Suggestions

In order to learn how to implement any specific method for your new data type, get the CPython source code. Go to the Objects directory, then search the C source files for tp_ plus the function you want (for example, tp_richcompare). You will find examples of the function you want to implement.

When you need to verify that an object is a concrete instance of the type you are implementing, use the PyObject_TypeCheck() function. A sample of its use might be something like the following:

if (!PyObject_TypeCheck(some_object, &MyType)) {
+    PyErr_SetString(PyExc_TypeError, "arg #1 not a mything");
+    return NULL;
+}
+

See also

Download CPython source releases.
+
+

https://www.python.org/downloads/source/

The CPython project on GitHub, where the CPython source code is developed.
+
+

https://github.com/python/cpython

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/newtypes.html +

+
diff --git a/devdocs/python~3.12/extending%2Fnewtypes_tutorial.html b/devdocs/python~3.12/extending%2Fnewtypes_tutorial.html new file mode 100644 index 00000000..d98d431c --- /dev/null +++ b/devdocs/python~3.12/extending%2Fnewtypes_tutorial.html @@ -0,0 +1,932 @@ +

Defining Extension Types: Tutorial

Python allows the writer of a C extension module to define new types that can be manipulated from Python code, much like the built-in str and list types. The code for all extension types follows a pattern, but there are some details that you need to understand before you can get started. This document is a gentle introduction to the topic.

+2.1. The Basics

The CPython runtime sees all Python objects as variables of type PyObject*, which serves as a “base type” for all Python objects. The PyObject structure itself only contains the object’s reference count and a pointer to the object’s “type object”. This is where the action is; the type object determines which (C) functions get called by the interpreter when, for instance, an attribute gets looked up on an object, a method called, or it is multiplied by another object. These C functions are called “type methods”.

So, if you want to define a new extension type, you need to create a new type object.

This sort of thing can only be explained by example, so here’s a minimal, but complete, module that defines a new type named Custom inside a C extension module custom:

Note

What we’re showing here is the traditional way of defining static extension types. It should be adequate for most uses. The C API also allows defining heap-allocated extension types using the PyType_FromSpec() function, which isn’t covered in this tutorial.

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
+typedef struct {
+    PyObject_HEAD
+    /* Type-specific fields go here. */
+} CustomObject;
+
+static PyTypeObject CustomType = {
+    .ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "custom.Custom",
+    .tp_doc = PyDoc_STR("Custom objects"),
+    .tp_basicsize = sizeof(CustomObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT,
+    .tp_new = PyType_GenericNew,
+};
+
+static PyModuleDef custommodule = {
+    .m_base = PyModuleDef_HEAD_INIT,
+    .m_name = "custom",
+    .m_doc = "Example module that creates an extension type.",
+    .m_size = -1,
+};
+
+PyMODINIT_FUNC
+PyInit_custom(void)
+{
+    PyObject *m;
+    if (PyType_Ready(&CustomType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&custommodule);
+    if (m == NULL)
+        return NULL;
+
+    Py_INCREF(&CustomType);
+    if (PyModule_AddObject(m, "Custom", (PyObject *) &CustomType) < 0) {
+        Py_DECREF(&CustomType);
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

Now that’s quite a bit to take in at once, but hopefully bits will seem familiar from the previous chapter. This file defines three things:

  1. What a Custom object contains: this is the CustomObject struct, which is allocated once for each Custom instance.
  2. How the Custom type behaves: this is the CustomType struct, which defines a set of flags and function pointers that the interpreter inspects when specific operations are requested.
  3. How to initialize the custom module: this is the PyInit_custom function and the associated custommodule struct.

The first bit is:

typedef struct {
+    PyObject_HEAD
+} CustomObject;
+

This is what a Custom object will contain. PyObject_HEAD is mandatory at the start of each object struct and defines a field called ob_base of type PyObject, containing a pointer to a type object and a reference count (these can be accessed using the macros Py_TYPE and Py_REFCNT respectively). The reason for the macro is to abstract away the layout and to enable additional fields in debug builds.

Note

There is no semicolon above after the PyObject_HEAD macro. Be wary of adding one by accident: some compilers will complain.

Of course, objects generally store additional data besides the standard PyObject_HEAD boilerplate; for example, here is the definition for standard Python floats:

typedef struct {
+    PyObject_HEAD
+    double ob_fval;
+} PyFloatObject;
+

The second bit is the definition of the type object.

static PyTypeObject CustomType = {
+    .ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "custom.Custom",
+    .tp_doc = PyDoc_STR("Custom objects"),
+    .tp_basicsize = sizeof(CustomObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT,
+    .tp_new = PyType_GenericNew,
+};
+

Note

We recommend using C99-style designated initializers as above, to avoid listing all the PyTypeObject fields that you don’t care about and also to avoid caring about the fields’ declaration order.

The actual definition of PyTypeObject in object.h has many more fields than the definition above. The remaining fields will be filled with zeros by the C compiler, and it’s common practice to not specify them explicitly unless you need them.

We’re going to pick it apart, one field at a time:

.ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+

This line is mandatory boilerplate to initialize the ob_base field mentioned above.

.tp_name = "custom.Custom",
+

The name of our type. This will appear in the default textual representation of our objects and in some error messages, for example:

>>> "" + custom.Custom()
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: can only concatenate str (not "custom.Custom") to str
+

Note that the name is a dotted name that includes both the module name and the name of the type within the module. The module in this case is custom and the type is Custom, so we set the type name to custom.Custom. Using the real dotted import path is important to make your type compatible with the pydoc and pickle modules.

.tp_basicsize = sizeof(CustomObject),
+.tp_itemsize = 0,
+

This is so that Python knows how much memory to allocate when creating new Custom instances. tp_itemsize is only used for variable-sized objects and should otherwise be zero.

Note

If you want your type to be subclassable from Python, and your type has the same tp_basicsize as its base type, you may have problems with multiple inheritance. A Python subclass of your type will have to list your type first in its __bases__, or else it will not be able to call your type’s __new__() method without getting an error. You can avoid this problem by ensuring that your type has a larger value for tp_basicsize than its base type does. Most of the time, this will be true anyway, because either your base type will be object, or else you will be adding data members to your base type, and therefore increasing its size.

We set the class flags to Py_TPFLAGS_DEFAULT.

.tp_flags = Py_TPFLAGS_DEFAULT,
+

All types should include this constant in their flags. It enables all of the members defined until at least Python 3.3. If you need further members, you will need to OR the corresponding flags.

We provide a doc string for the type in tp_doc.

.tp_doc = PyDoc_STR("Custom objects"),
+

To enable object creation, we have to provide a tp_new handler. This is the equivalent of the Python method __new__(), but has to be specified explicitly. In this case, we can just use the default implementation provided by the API function PyType_GenericNew().

.tp_new = PyType_GenericNew,
+

Everything else in the file should be familiar, except for some code in PyInit_custom():

if (PyType_Ready(&CustomType) < 0)
+    return;
+

This initializes the Custom type, filling in a number of members to the appropriate default values, including ob_type that we initially set to NULL.

Py_INCREF(&CustomType);
+if (PyModule_AddObject(m, "Custom", (PyObject *) &CustomType) < 0) {
+    Py_DECREF(&CustomType);
+    Py_DECREF(m);
+    return NULL;
+}
+

This adds the type to the module dictionary. This allows us to create Custom instances by calling the Custom class:

>>> import custom
+>>> mycustom = custom.Custom()
+

That’s it! All that remains is to build it; put the above code in a file called custom.c,

[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "custom"
+version = "1"
+

in a file called pyproject.toml, and

from setuptools import Extension, setup
+setup(ext_modules=[Extension("custom", ["custom.c"])])
+

in a file called setup.py; then typing

$ python -m pip install .
+

in a shell should produce a file custom.so in a subdirectory and install it; now fire up Python — you should be able to import custom and play around with Custom objects.

That wasn’t so hard, was it?

Of course, the current Custom type is pretty uninteresting. It has no data and doesn’t do anything. It can’t even be subclassed.

+2.2. Adding data and methods to the Basic example

Let’s extend the basic example to add some data and methods. Let’s also make the type usable as a base class. We’ll create a new module, custom2 that adds these capabilities:

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <stddef.h> /* for offsetof() */
+
+typedef struct {
+    PyObject_HEAD
+    PyObject *first; /* first name */
+    PyObject *last;  /* last name */
+    int number;
+} CustomObject;
+
+static void
+Custom_dealloc(CustomObject *self)
+{
+    Py_XDECREF(self->first);
+    Py_XDECREF(self->last);
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+
+static PyObject *
+Custom_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CustomObject *self;
+    self = (CustomObject *) type->tp_alloc(type, 0);
+    if (self != NULL) {
+        self->first = PyUnicode_FromString("");
+        if (self->first == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->last = PyUnicode_FromString("");
+        if (self->last == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->number = 0;
+    }
+    return (PyObject *) self;
+}
+
+static int
+Custom_init(CustomObject *self, PyObject *args, PyObject *kwds)
+{
+    static char *kwlist[] = {"first", "last", "number", NULL};
+    PyObject *first = NULL, *last = NULL;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOi", kwlist,
+                                     &first, &last,
+                                     &self->number))
+        return -1;
+
+    if (first) {
+        Py_XSETREF(self->first, Py_NewRef(first));
+    }
+    if (last) {
+        Py_XSETREF(self->last, Py_NewRef(last));
+    }
+    return 0;
+}
+
+static PyMemberDef Custom_members[] = {
+    {"first", Py_T_OBJECT_EX, offsetof(CustomObject, first), 0,
+     "first name"},
+    {"last", Py_T_OBJECT_EX, offsetof(CustomObject, last), 0,
+     "last name"},
+    {"number", Py_T_INT, offsetof(CustomObject, number), 0,
+     "custom number"},
+    {NULL}  /* Sentinel */
+};
+
+static PyObject *
+Custom_name(CustomObject *self, PyObject *Py_UNUSED(ignored))
+{
+    if (self->first == NULL) {
+        PyErr_SetString(PyExc_AttributeError, "first");
+        return NULL;
+    }
+    if (self->last == NULL) {
+        PyErr_SetString(PyExc_AttributeError, "last");
+        return NULL;
+    }
+    return PyUnicode_FromFormat("%S %S", self->first, self->last);
+}
+
+static PyMethodDef Custom_methods[] = {
+    {"name", (PyCFunction) Custom_name, METH_NOARGS,
+     "Return the name, combining the first and last name"
+    },
+    {NULL}  /* Sentinel */
+};
+
+static PyTypeObject CustomType = {
+    .ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "custom2.Custom",
+    .tp_doc = PyDoc_STR("Custom objects"),
+    .tp_basicsize = sizeof(CustomObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,
+    .tp_new = Custom_new,
+    .tp_init = (initproc) Custom_init,
+    .tp_dealloc = (destructor) Custom_dealloc,
+    .tp_members = Custom_members,
+    .tp_methods = Custom_methods,
+};
+
+static PyModuleDef custommodule = {
+    .m_base =PyModuleDef_HEAD_INIT,
+    .m_name = "custom2",
+    .m_doc = "Example module that creates an extension type.",
+    .m_size = -1,
+};
+
+PyMODINIT_FUNC
+PyInit_custom2(void)
+{
+    PyObject *m;
+    if (PyType_Ready(&CustomType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&custommodule);
+    if (m == NULL)
+        return NULL;
+
+    if (PyModule_AddObjectRef(m, "Custom", (PyObject *) &CustomType) < 0) {
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

This version of the module has a number of changes.

The Custom type now has three data attributes in its C struct, first, last, and number. The first and last variables are Python strings containing first and last names. The number attribute is a C integer.

The object structure is updated accordingly:

typedef struct {
+    PyObject_HEAD
+    PyObject *first; /* first name */
+    PyObject *last;  /* last name */
+    int number;
+} CustomObject;
+

Because we now have data to manage, we have to be more careful about object allocation and deallocation. At a minimum, we need a deallocation method:

static void
+Custom_dealloc(CustomObject *self)
+{
+    Py_XDECREF(self->first);
+    Py_XDECREF(self->last);
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+

which is assigned to the tp_dealloc member:

.tp_dealloc = (destructor) Custom_dealloc,
+

This method first clears the reference counts of the two Python attributes. Py_XDECREF() correctly handles the case where its argument is NULL (which might happen here if tp_new failed midway). It then calls the tp_free member of the object’s type (computed by Py_TYPE(self)) to free the object’s memory. Note that the object’s type might not be CustomType, because the object may be an instance of a subclass.

Note

The explicit cast to destructor above is needed because we defined Custom_dealloc to take a CustomObject * argument, but the tp_dealloc function pointer expects to receive a PyObject * argument. Otherwise, the compiler will emit a warning. This is object-oriented polymorphism, in C!

We want to make sure that the first and last names are initialized to empty strings, so we provide a tp_new implementation:

static PyObject *
+Custom_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CustomObject *self;
+    self = (CustomObject *) type->tp_alloc(type, 0);
+    if (self != NULL) {
+        self->first = PyUnicode_FromString("");
+        if (self->first == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->last = PyUnicode_FromString("");
+        if (self->last == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->number = 0;
+    }
+    return (PyObject *) self;
+}
+

and install it in the tp_new member:

.tp_new = Custom_new,
+

The tp_new handler is responsible for creating (as opposed to initializing) objects of the type. It is exposed in Python as the __new__() method. It is not required to define a tp_new member, and indeed many extension types will simply reuse PyType_GenericNew() as done in the first version of the Custom type above. In this case, we use the tp_new handler to initialize the first and last attributes to non-NULL default values.

tp_new is passed the type being instantiated (not necessarily CustomType, if a subclass is instantiated) and any arguments passed when the type was called, and is expected to return the instance created. tp_new handlers always accept positional and keyword arguments, but they often ignore the arguments, leaving the argument handling to initializer (a.k.a. tp_init in C or __init__ in Python) methods.

Note

tp_new shouldn’t call tp_init explicitly, as the interpreter will do it itself.

The tp_new implementation calls the tp_alloc slot to allocate memory:

self = (CustomObject *) type->tp_alloc(type, 0);
+

Since memory allocation may fail, we must check the tp_alloc result against NULL before proceeding.

Note

We didn’t fill the tp_alloc slot ourselves. Rather PyType_Ready() fills it for us by inheriting it from our base class, which is object by default. Most types use the default allocation strategy.

Note

If you are creating a co-operative tp_new (one that calls a base type’s tp_new or __new__()), you must not try to determine what method to call using method resolution order at runtime. Always statically determine what type you are going to call, and call its tp_new directly, or via type->tp_base->tp_new. If you do not do this, Python subclasses of your type that also inherit from other Python-defined classes may not work correctly. (Specifically, you may not be able to create instances of such subclasses without getting a TypeError.)

We also define an initialization function which accepts arguments to provide initial values for our instance:

static int
+Custom_init(CustomObject *self, PyObject *args, PyObject *kwds)
+{
+    static char *kwlist[] = {"first", "last", "number", NULL};
+    PyObject *first = NULL, *last = NULL, *tmp;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|OOi", kwlist,
+                                     &first, &last,
+                                     &self->number))
+        return -1;
+
+    if (first) {
+        tmp = self->first;
+        Py_INCREF(first);
+        self->first = first;
+        Py_XDECREF(tmp);
+    }
+    if (last) {
+        tmp = self->last;
+        Py_INCREF(last);
+        self->last = last;
+        Py_XDECREF(tmp);
+    }
+    return 0;
+}
+

by filling the tp_init slot.

.tp_init = (initproc) Custom_init,
+

The tp_init slot is exposed in Python as the __init__() method. It is used to initialize an object after it’s created. Initializers always accept positional and keyword arguments, and they should return either 0 on success or -1 on error.

Unlike the tp_new handler, there is no guarantee that tp_init is called at all (for example, the pickle module by default doesn’t call __init__() on unpickled instances). It can also be called multiple times. Anyone can call the __init__() method on our objects. For this reason, we have to be extra careful when assigning the new attribute values. We might be tempted, for example to assign the first member like this:

if (first) {
+    Py_XDECREF(self->first);
+    Py_INCREF(first);
+    self->first = first;
+}
+

But this would be risky. Our type doesn’t restrict the type of the first member, so it could be any kind of object. It could have a destructor that causes code to be executed that tries to access the first member; or that destructor could release the Global interpreter Lock and let arbitrary code run in other threads that accesses and modifies our object.

To be paranoid and protect ourselves against this possibility, we almost always reassign members before decrementing their reference counts. When don’t we have to do this?

We want to expose our instance variables as attributes. There are a number of ways to do that. The simplest way is to define member definitions:

static PyMemberDef Custom_members[] = {
+    {"first", Py_T_OBJECT_EX, offsetof(CustomObject, first), 0,
+     "first name"},
+    {"last", Py_T_OBJECT_EX, offsetof(CustomObject, last), 0,
+     "last name"},
+    {"number", Py_T_INT, offsetof(CustomObject, number), 0,
+     "custom number"},
+    {NULL}  /* Sentinel */
+};
+

and put the definitions in the tp_members slot:

.tp_members = Custom_members,
+

Each member definition has a member name, type, offset, access flags and documentation string. See the Generic Attribute Management section below for details.

A disadvantage of this approach is that it doesn’t provide a way to restrict the types of objects that can be assigned to the Python attributes. We expect the first and last names to be strings, but any Python objects can be assigned. Further, the attributes can be deleted, setting the C pointers to NULL. Even though we can make sure the members are initialized to non-NULL values, the members can be set to NULL if the attributes are deleted.

We define a single method, Custom.name(), that outputs the objects name as the concatenation of the first and last names.

static PyObject *
+Custom_name(CustomObject *self, PyObject *Py_UNUSED(ignored))
+{
+    if (self->first == NULL) {
+        PyErr_SetString(PyExc_AttributeError, "first");
+        return NULL;
+    }
+    if (self->last == NULL) {
+        PyErr_SetString(PyExc_AttributeError, "last");
+        return NULL;
+    }
+    return PyUnicode_FromFormat("%S %S", self->first, self->last);
+}
+

The method is implemented as a C function that takes a Custom (or Custom subclass) instance as the first argument. Methods always take an instance as the first argument. Methods often take positional and keyword arguments as well, but in this case we don’t take any and don’t need to accept a positional argument tuple or keyword argument dictionary. This method is equivalent to the Python method:

def name(self):
+    return "%s %s" % (self.first, self.last)
+

Note that we have to check for the possibility that our first and last members are NULL. This is because they can be deleted, in which case they are set to NULL. It would be better to prevent deletion of these attributes and to restrict the attribute values to be strings. We’ll see how to do that in the next section.

Now that we’ve defined the method, we need to create an array of method definitions:

static PyMethodDef Custom_methods[] = {
+    {"name", (PyCFunction) Custom_name, METH_NOARGS,
+     "Return the name, combining the first and last name"
+    },
+    {NULL}  /* Sentinel */
+};
+

(note that we used the METH_NOARGS flag to indicate that the method is expecting no arguments other than self)

and assign it to the tp_methods slot:

.tp_methods = Custom_methods,
+

Finally, we’ll make our type usable as a base class for subclassing. We’ve written our methods carefully so far so that they don’t make any assumptions about the type of the object being created or used, so all we need to do is to add the Py_TPFLAGS_BASETYPE to our class flag definition:

.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,
+

We rename PyInit_custom() to PyInit_custom2(), update the module name in the PyModuleDef struct, and update the full class name in the PyTypeObject struct.

Finally, we update our setup.py file to include the new module,

from setuptools import Extension, setup
+setup(ext_modules=[
+    Extension("custom", ["custom.c"]),
+    Extension("custom2", ["custom2.c"]),
+])
+

and then we re-install so that we can import custom2:

$ python -m pip install .
+

+2.3. Providing finer control over data attributes

In this section, we’ll provide finer control over how the first and last attributes are set in the Custom example. In the previous version of our module, the instance variables first and last could be set to non-string values or even deleted. We want to make sure that these attributes always contain strings.

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <stddef.h> /* for offsetof() */
+
+typedef struct {
+    PyObject_HEAD
+    PyObject *first; /* first name */
+    PyObject *last;  /* last name */
+    int number;
+} CustomObject;
+
+static void
+Custom_dealloc(CustomObject *self)
+{
+    Py_XDECREF(self->first);
+    Py_XDECREF(self->last);
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+
+static PyObject *
+Custom_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CustomObject *self;
+    self = (CustomObject *) type->tp_alloc(type, 0);
+    if (self != NULL) {
+        self->first = PyUnicode_FromString("");
+        if (self->first == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->last = PyUnicode_FromString("");
+        if (self->last == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->number = 0;
+    }
+    return (PyObject *) self;
+}
+
+static int
+Custom_init(CustomObject *self, PyObject *args, PyObject *kwds)
+{
+    static char *kwlist[] = {"first", "last", "number", NULL};
+    PyObject *first = NULL, *last = NULL;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|UUi", kwlist,
+                                     &first, &last,
+                                     &self->number))
+        return -1;
+
+    if (first) {
+        Py_SETREF(self->first, Py_NewRef(first));
+    }
+    if (last) {
+        Py_SETREF(self->last, Py_NewRef(last));
+    }
+    return 0;
+}
+
+static PyMemberDef Custom_members[] = {
+    {"number", Py_T_INT, offsetof(CustomObject, number), 0,
+     "custom number"},
+    {NULL}  /* Sentinel */
+};
+
+static PyObject *
+Custom_getfirst(CustomObject *self, void *closure)
+{
+    return Py_NewRef(self->first);
+}
+
+static int
+Custom_setfirst(CustomObject *self, PyObject *value, void *closure)
+{
+    if (value == NULL) {
+        PyErr_SetString(PyExc_TypeError, "Cannot delete the first attribute");
+        return -1;
+    }
+    if (!PyUnicode_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "The first attribute value must be a string");
+        return -1;
+    }
+    Py_SETREF(self->first, Py_NewRef(value));
+    return 0;
+}
+
+static PyObject *
+Custom_getlast(CustomObject *self, void *closure)
+{
+    return Py_NewRef(self->last);
+}
+
+static int
+Custom_setlast(CustomObject *self, PyObject *value, void *closure)
+{
+    if (value == NULL) {
+        PyErr_SetString(PyExc_TypeError, "Cannot delete the last attribute");
+        return -1;
+    }
+    if (!PyUnicode_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "The last attribute value must be a string");
+        return -1;
+    }
+    Py_SETREF(self->last, Py_NewRef(value));
+    return 0;
+}
+
+static PyGetSetDef Custom_getsetters[] = {
+    {"first", (getter) Custom_getfirst, (setter) Custom_setfirst,
+     "first name", NULL},
+    {"last", (getter) Custom_getlast, (setter) Custom_setlast,
+     "last name", NULL},
+    {NULL}  /* Sentinel */
+};
+
+static PyObject *
+Custom_name(CustomObject *self, PyObject *Py_UNUSED(ignored))
+{
+    return PyUnicode_FromFormat("%S %S", self->first, self->last);
+}
+
+static PyMethodDef Custom_methods[] = {
+    {"name", (PyCFunction) Custom_name, METH_NOARGS,
+     "Return the name, combining the first and last name"
+    },
+    {NULL}  /* Sentinel */
+};
+
+static PyTypeObject CustomType = {
+    .ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "custom3.Custom",
+    .tp_doc = PyDoc_STR("Custom objects"),
+    .tp_basicsize = sizeof(CustomObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,
+    .tp_new = Custom_new,
+    .tp_init = (initproc) Custom_init,
+    .tp_dealloc = (destructor) Custom_dealloc,
+    .tp_members = Custom_members,
+    .tp_methods = Custom_methods,
+    .tp_getset = Custom_getsetters,
+};
+
+static PyModuleDef custommodule = {
+    .m_base = PyModuleDef_HEAD_INIT,
+    .m_name = "custom3",
+    .m_doc = "Example module that creates an extension type.",
+    .m_size = -1,
+};
+
+PyMODINIT_FUNC
+PyInit_custom3(void)
+{
+    PyObject *m;
+    if (PyType_Ready(&CustomType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&custommodule);
+    if (m == NULL)
+        return NULL;
+
+    if (PyModule_AddObjectRef(m, "Custom", (PyObject *) &CustomType) < 0) {
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

To provide greater control, over the first and last attributes, we’ll use custom getter and setter functions. Here are the functions for getting and setting the first attribute:

static PyObject *
+Custom_getfirst(CustomObject *self, void *closure)
+{
+    Py_INCREF(self->first);
+    return self->first;
+}
+
+static int
+Custom_setfirst(CustomObject *self, PyObject *value, void *closure)
+{
+    PyObject *tmp;
+    if (value == NULL) {
+        PyErr_SetString(PyExc_TypeError, "Cannot delete the first attribute");
+        return -1;
+    }
+    if (!PyUnicode_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "The first attribute value must be a string");
+        return -1;
+    }
+    tmp = self->first;
+    Py_INCREF(value);
+    self->first = value;
+    Py_DECREF(tmp);
+    return 0;
+}
+

The getter function is passed a Custom object and a “closure”, which is a void pointer. In this case, the closure is ignored. (The closure supports an advanced usage in which definition data is passed to the getter and setter. This could, for example, be used to allow a single set of getter and setter functions that decide the attribute to get or set based on data in the closure.)

The setter function is passed the Custom object, the new value, and the closure. The new value may be NULL, in which case the attribute is being deleted. In our setter, we raise an error if the attribute is deleted or if its new value is not a string.

We create an array of PyGetSetDef structures:

static PyGetSetDef Custom_getsetters[] = {
+    {"first", (getter) Custom_getfirst, (setter) Custom_setfirst,
+     "first name", NULL},
+    {"last", (getter) Custom_getlast, (setter) Custom_setlast,
+     "last name", NULL},
+    {NULL}  /* Sentinel */
+};
+

and register it in the tp_getset slot:

.tp_getset = Custom_getsetters,
+

The last item in a PyGetSetDef structure is the “closure” mentioned above. In this case, we aren’t using a closure, so we just pass NULL.

We also remove the member definitions for these attributes:

static PyMemberDef Custom_members[] = {
+    {"number", Py_T_INT, offsetof(CustomObject, number), 0,
+     "custom number"},
+    {NULL}  /* Sentinel */
+};
+

We also need to update the tp_init handler to only allow strings 3 to be passed:

static int
+Custom_init(CustomObject *self, PyObject *args, PyObject *kwds)
+{
+    static char *kwlist[] = {"first", "last", "number", NULL};
+    PyObject *first = NULL, *last = NULL, *tmp;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|UUi", kwlist,
+                                     &first, &last,
+                                     &self->number))
+        return -1;
+
+    if (first) {
+        tmp = self->first;
+        Py_INCREF(first);
+        self->first = first;
+        Py_DECREF(tmp);
+    }
+    if (last) {
+        tmp = self->last;
+        Py_INCREF(last);
+        self->last = last;
+        Py_DECREF(tmp);
+    }
+    return 0;
+}
+

With these changes, we can assure that the first and last members are never NULL so we can remove checks for NULL values in almost all cases. This means that most of the Py_XDECREF() calls can be converted to Py_DECREF() calls. The only place we can’t change these calls is in the tp_dealloc implementation, where there is the possibility that the initialization of these members failed in tp_new.

We also rename the module initialization function and module name in the initialization function, as we did before, and we add an extra definition to the setup.py file.

+2.4. Supporting cyclic garbage collection

Python has a cyclic garbage collector (GC) that can identify unneeded objects even when their reference counts are not zero. This can happen when objects are involved in cycles. For example, consider:

>>> l = []
+>>> l.append(l)
+>>> del l
+

In this example, we create a list that contains itself. When we delete it, it still has a reference from itself. Its reference count doesn’t drop to zero. Fortunately, Python’s cyclic garbage collector will eventually figure out that the list is garbage and free it.

In the second version of the Custom example, we allowed any kind of object to be stored in the first or last attributes 4. Besides, in the second and third versions, we allowed subclassing Custom, and subclasses may add arbitrary attributes. For any of those two reasons, Custom objects can participate in cycles:

>>> import custom3
+>>> class Derived(custom3.Custom): pass
+...
+>>> n = Derived()
+>>> n.some_attribute = n
+

To allow a Custom instance participating in a reference cycle to be properly detected and collected by the cyclic GC, our Custom type needs to fill two additional slots and to enable a flag that enables these slots:

#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+#include <stddef.h> /* for offsetof() */
+
+typedef struct {
+    PyObject_HEAD
+    PyObject *first; /* first name */
+    PyObject *last;  /* last name */
+    int number;
+} CustomObject;
+
+static int
+Custom_traverse(CustomObject *self, visitproc visit, void *arg)
+{
+    Py_VISIT(self->first);
+    Py_VISIT(self->last);
+    return 0;
+}
+
+static int
+Custom_clear(CustomObject *self)
+{
+    Py_CLEAR(self->first);
+    Py_CLEAR(self->last);
+    return 0;
+}
+
+static void
+Custom_dealloc(CustomObject *self)
+{
+    PyObject_GC_UnTrack(self);
+    Custom_clear(self);
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+
+static PyObject *
+Custom_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+{
+    CustomObject *self;
+    self = (CustomObject *) type->tp_alloc(type, 0);
+    if (self != NULL) {
+        self->first = PyUnicode_FromString("");
+        if (self->first == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->last = PyUnicode_FromString("");
+        if (self->last == NULL) {
+            Py_DECREF(self);
+            return NULL;
+        }
+        self->number = 0;
+    }
+    return (PyObject *) self;
+}
+
+static int
+Custom_init(CustomObject *self, PyObject *args, PyObject *kwds)
+{
+    static char *kwlist[] = {"first", "last", "number", NULL};
+    PyObject *first = NULL, *last = NULL;
+
+    if (!PyArg_ParseTupleAndKeywords(args, kwds, "|UUi", kwlist,
+                                     &first, &last,
+                                     &self->number))
+        return -1;
+
+    if (first) {
+        Py_SETREF(self->first, Py_NewRef(first));
+    }
+    if (last) {
+        Py_SETREF(self->last, Py_NewRef(last));
+    }
+    return 0;
+}
+
+static PyMemberDef Custom_members[] = {
+    {"number", Py_T_INT, offsetof(CustomObject, number), 0,
+     "custom number"},
+    {NULL}  /* Sentinel */
+};
+
+static PyObject *
+Custom_getfirst(CustomObject *self, void *closure)
+{
+    return Py_NewRef(self->first);
+}
+
+static int
+Custom_setfirst(CustomObject *self, PyObject *value, void *closure)
+{
+    if (value == NULL) {
+        PyErr_SetString(PyExc_TypeError, "Cannot delete the first attribute");
+        return -1;
+    }
+    if (!PyUnicode_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "The first attribute value must be a string");
+        return -1;
+    }
+    Py_XSETREF(self->first, Py_NewRef(value));
+    return 0;
+}
+
+static PyObject *
+Custom_getlast(CustomObject *self, void *closure)
+{
+    return Py_NewRef(self->last);
+}
+
+static int
+Custom_setlast(CustomObject *self, PyObject *value, void *closure)
+{
+    if (value == NULL) {
+        PyErr_SetString(PyExc_TypeError, "Cannot delete the last attribute");
+        return -1;
+    }
+    if (!PyUnicode_Check(value)) {
+        PyErr_SetString(PyExc_TypeError,
+                        "The last attribute value must be a string");
+        return -1;
+    }
+    Py_XSETREF(self->last, Py_NewRef(value));
+    return 0;
+}
+
+static PyGetSetDef Custom_getsetters[] = {
+    {"first", (getter) Custom_getfirst, (setter) Custom_setfirst,
+     "first name", NULL},
+    {"last", (getter) Custom_getlast, (setter) Custom_setlast,
+     "last name", NULL},
+    {NULL}  /* Sentinel */
+};
+
+static PyObject *
+Custom_name(CustomObject *self, PyObject *Py_UNUSED(ignored))
+{
+    return PyUnicode_FromFormat("%S %S", self->first, self->last);
+}
+
+static PyMethodDef Custom_methods[] = {
+    {"name", (PyCFunction) Custom_name, METH_NOARGS,
+     "Return the name, combining the first and last name"
+    },
+    {NULL}  /* Sentinel */
+};
+
+static PyTypeObject CustomType = {
+    .ob_base = PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "custom4.Custom",
+    .tp_doc = PyDoc_STR("Custom objects"),
+    .tp_basicsize = sizeof(CustomObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC,
+    .tp_new = Custom_new,
+    .tp_init = (initproc) Custom_init,
+    .tp_dealloc = (destructor) Custom_dealloc,
+    .tp_traverse = (traverseproc) Custom_traverse,
+    .tp_clear = (inquiry) Custom_clear,
+    .tp_members = Custom_members,
+    .tp_methods = Custom_methods,
+    .tp_getset = Custom_getsetters,
+};
+
+static PyModuleDef custommodule = {
+    .m_base = PyModuleDef_HEAD_INIT,
+    .m_name = "custom4",
+    .m_doc = "Example module that creates an extension type.",
+    .m_size = -1,
+};
+
+PyMODINIT_FUNC
+PyInit_custom4(void)
+{
+    PyObject *m;
+    if (PyType_Ready(&CustomType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&custommodule);
+    if (m == NULL)
+        return NULL;
+
+    if (PyModule_AddObjectRef(m, "Custom", (PyObject *) &CustomType) < 0) {
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

First, the traversal method lets the cyclic GC know about subobjects that could participate in cycles:

static int
+Custom_traverse(CustomObject *self, visitproc visit, void *arg)
+{
+    int vret;
+    if (self->first) {
+        vret = visit(self->first, arg);
+        if (vret != 0)
+            return vret;
+    }
+    if (self->last) {
+        vret = visit(self->last, arg);
+        if (vret != 0)
+            return vret;
+    }
+    return 0;
+}
+

For each subobject that can participate in cycles, we need to call the visit() function, which is passed to the traversal method. The visit() function takes as arguments the subobject and the extra argument arg passed to the traversal method. It returns an integer value that must be returned if it is non-zero.

Python provides a Py_VISIT() macro that automates calling visit functions. With Py_VISIT(), we can minimize the amount of boilerplate in Custom_traverse:

static int
+Custom_traverse(CustomObject *self, visitproc visit, void *arg)
+{
+    Py_VISIT(self->first);
+    Py_VISIT(self->last);
+    return 0;
+}
+

Note

The tp_traverse implementation must name its arguments exactly visit and arg in order to use Py_VISIT().

Second, we need to provide a method for clearing any subobjects that can participate in cycles:

static int
+Custom_clear(CustomObject *self)
+{
+    Py_CLEAR(self->first);
+    Py_CLEAR(self->last);
+    return 0;
+}
+

Notice the use of the Py_CLEAR() macro. It is the recommended and safe way to clear data attributes of arbitrary types while decrementing their reference counts. If you were to call Py_XDECREF() instead on the attribute before setting it to NULL, there is a possibility that the attribute’s destructor would call back into code that reads the attribute again (especially if there is a reference cycle).

Note

You could emulate Py_CLEAR() by writing:

PyObject *tmp;
+tmp = self->first;
+self->first = NULL;
+Py_XDECREF(tmp);
+

Nevertheless, it is much easier and less error-prone to always use Py_CLEAR() when deleting an attribute. Don’t try to micro-optimize at the expense of robustness!

The deallocator Custom_dealloc may call arbitrary code when clearing attributes. It means the circular GC can be triggered inside the function. Since the GC assumes reference count is not zero, we need to untrack the object from the GC by calling PyObject_GC_UnTrack() before clearing members. Here is our reimplemented deallocator using PyObject_GC_UnTrack() and Custom_clear:

static void
+Custom_dealloc(CustomObject *self)
+{
+    PyObject_GC_UnTrack(self);
+    Custom_clear(self);
+    Py_TYPE(self)->tp_free((PyObject *) self);
+}
+

Finally, we add the Py_TPFLAGS_HAVE_GC flag to the class flags:

.tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE | Py_TPFLAGS_HAVE_GC,
+

That’s pretty much it. If we had written custom tp_alloc or tp_free handlers, we’d need to modify them for cyclic garbage collection. Most extensions will use the versions automatically provided.

+2.5. Subclassing other types

It is possible to create new extension types that are derived from existing types. It is easiest to inherit from the built in types, since an extension can easily use the PyTypeObject it needs. It can be difficult to share these PyTypeObject structures between extension modules.

In this example we will create a SubList type that inherits from the built-in list type. The new type will be completely compatible with regular lists, but will have an additional increment() method that increases an internal counter:

>>> import sublist
+>>> s = sublist.SubList(range(3))
+>>> s.extend(s)
+>>> print(len(s))
+6
+>>> print(s.increment())
+1
+>>> print(s.increment())
+2
+
#define PY_SSIZE_T_CLEAN
+#include <Python.h>
+
+typedef struct {
+    PyListObject list;
+    int state;
+} SubListObject;
+
+static PyObject *
+SubList_increment(SubListObject *self, PyObject *unused)
+{
+    self->state++;
+    return PyLong_FromLong(self->state);
+}
+
+static PyMethodDef SubList_methods[] = {
+    {"increment", (PyCFunction) SubList_increment, METH_NOARGS,
+     PyDoc_STR("increment state counter")},
+    {NULL},
+};
+
+static int
+SubList_init(SubListObject *self, PyObject *args, PyObject *kwds)
+{
+    if (PyList_Type.tp_init((PyObject *) self, args, kwds) < 0)
+        return -1;
+    self->state = 0;
+    return 0;
+}
+
+static PyTypeObject SubListType = {
+    PyVarObject_HEAD_INIT(NULL, 0)
+    .tp_name = "sublist.SubList",
+    .tp_doc = PyDoc_STR("SubList objects"),
+    .tp_basicsize = sizeof(SubListObject),
+    .tp_itemsize = 0,
+    .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_BASETYPE,
+    .tp_init = (initproc) SubList_init,
+    .tp_methods = SubList_methods,
+};
+
+static PyModuleDef sublistmodule = {
+    PyModuleDef_HEAD_INIT,
+    .m_name = "sublist",
+    .m_doc = "Example module that creates an extension type.",
+    .m_size = -1,
+};
+
+PyMODINIT_FUNC
+PyInit_sublist(void)
+{
+    PyObject *m;
+    SubListType.tp_base = &PyList_Type;
+    if (PyType_Ready(&SubListType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&sublistmodule);
+    if (m == NULL)
+        return NULL;
+
+    Py_INCREF(&SubListType);
+    if (PyModule_AddObject(m, "SubList", (PyObject *) &SubListType) < 0) {
+        Py_DECREF(&SubListType);
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

As you can see, the source code closely resembles the Custom examples in previous sections. We will break down the main differences between them.

typedef struct {
+    PyListObject list;
+    int state;
+} SubListObject;
+

The primary difference for derived type objects is that the base type’s object structure must be the first value. The base type will already include the PyObject_HEAD() at the beginning of its structure.

When a Python object is a SubList instance, its PyObject * pointer can be safely cast to both PyListObject * and SubListObject *:

static int
+SubList_init(SubListObject *self, PyObject *args, PyObject *kwds)
+{
+    if (PyList_Type.tp_init((PyObject *) self, args, kwds) < 0)
+        return -1;
+    self->state = 0;
+    return 0;
+}
+

We see above how to call through to the __init__() method of the base type.

This pattern is important when writing a type with custom tp_new and tp_dealloc members. The tp_new handler should not actually create the memory for the object with its tp_alloc, but let the base class handle it by calling its own tp_new.

The PyTypeObject struct supports a tp_base specifying the type’s concrete base class. Due to cross-platform compiler issues, you can’t fill that field directly with a reference to PyList_Type; it should be done later in the module initialization function:

PyMODINIT_FUNC
+PyInit_sublist(void)
+{
+    PyObject* m;
+    SubListType.tp_base = &PyList_Type;
+    if (PyType_Ready(&SubListType) < 0)
+        return NULL;
+
+    m = PyModule_Create(&sublistmodule);
+    if (m == NULL)
+        return NULL;
+
+    Py_INCREF(&SubListType);
+    if (PyModule_AddObject(m, "SubList", (PyObject *) &SubListType) < 0) {
+        Py_DECREF(&SubListType);
+        Py_DECREF(m);
+        return NULL;
+    }
+
+    return m;
+}
+

Before calling PyType_Ready(), the type structure must have the tp_base slot filled in. When we are deriving an existing type, it is not necessary to fill out the tp_alloc slot with PyType_GenericNew() – the allocation function from the base type will be inherited.

After that, calling PyType_Ready() and adding the type object to the module is the same as with the basic Custom examples.

Footnotes

+1
+

This is true when we know that the object is a basic type, like a string or a float.

+2
+

We relied on this in the tp_dealloc handler in this example, because our type doesn’t support garbage collection.

+3
+

We now know that the first and last members are strings, so perhaps we could be less careful about decrementing their reference counts, however, we accept instances of string subclasses. Even though deallocating normal strings won’t call back into our objects, we can’t guarantee that deallocating an instance of a string subclass won’t call back into our objects.

+4
+

Also, even with our attributes restricted to strings instances, the user could pass arbitrary str subclasses and therefore still create reference cycles.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/newtypes_tutorial.html +

+
diff --git a/devdocs/python~3.12/extending%2Fwindows.html b/devdocs/python~3.12/extending%2Fwindows.html new file mode 100644 index 00000000..f5221aab --- /dev/null +++ b/devdocs/python~3.12/extending%2Fwindows.html @@ -0,0 +1,11 @@ +

Building C and C++ Extensions on Windows

This chapter briefly explains how to create a Windows extension module for Python using Microsoft Visual C++, and follows with more detailed background information on how it works. The explanatory material is useful for both the Windows programmer learning to build Python extensions and the Unix programmer interested in producing software which can be successfully built on both Unix and Windows.

Module authors are encouraged to use the distutils approach for building extension modules, instead of the one described in this section. You will still need the C compiler that was used to build Python; typically Microsoft Visual C++.

Note

This chapter mentions a number of filenames that include an encoded Python version number. These filenames are represented with the version number shown as XY; in practice, 'X' will be the major version number and 'Y' will be the minor version number of the Python release you’re working with. For example, if you are using Python 2.2.1, XY will actually be 22.

+5.1. A Cookbook Approach

There are two approaches to building extension modules on Windows, just as there are on Unix: use the setuptools package to control the build process, or do things manually. The setuptools approach works well for most extensions; documentation on using setuptools to build and package extension modules is available in Building C and C++ Extensions with setuptools. If you find you really need to do things manually, it may be instructive to study the project file for the winsound standard library module.

+5.2. Differences Between Unix and Windows

Unix and Windows use completely different paradigms for run-time loading of code. Before you try to build a module that can be dynamically loaded, be aware of how your system works.

In Unix, a shared object (.so) file contains code to be used by the program, and also the names of functions and data that it expects to find in the program. When the file is joined to the program, all references to those functions and data in the file’s code are changed to point to the actual locations in the program where the functions and data are placed in memory. This is basically a link operation.

In Windows, a dynamic-link library (.dll) file has no dangling references. Instead, an access to functions or data goes through a lookup table. So the DLL code does not have to be fixed up at runtime to refer to the program’s memory; instead, the code already uses the DLL’s lookup table, and the lookup table is modified at runtime to point to the functions and data.

In Unix, there is only one type of library file (.a) which contains code from several object files (.o). During the link step to create a shared object file (.so), the linker may find that it doesn’t know where an identifier is defined. The linker will look for it in the object files in the libraries; if it finds it, it will include all the code from that object file.

In Windows, there are two types of library, a static library and an import library (both called .lib). A static library is like a Unix .a file; it contains code to be included as necessary. An import library is basically used only to reassure the linker that a certain identifier is legal, and will be present in the program when the DLL is loaded. So the linker uses the information from the import library to build the lookup table for using identifiers that are not included in the DLL. When an application or a DLL is linked, an import library may be generated, which will need to be used for all future DLLs that depend on the symbols in the application or DLL.

Suppose you are building two dynamic-load modules, B and C, which should share another block of code A. On Unix, you would not pass A.a to the linker for B.so and C.so; that would cause it to be included twice, so that B and C would each have their own copy. In Windows, building A.dll will also build A.lib. You do pass A.lib to the linker for B and C. A.lib does not contain code; it just contains information which will be used at runtime to access A’s code.

In Windows, using an import library is sort of like using import spam; it gives you access to spam’s names, but does not create a separate copy. On Unix, linking with a library is more like from spam import *; it does create a separate copy.

+5.3. Using DLLs in Practice

Windows Python is built in Microsoft Visual C++; using other compilers may or may not work. The rest of this section is MSVC++ specific.

When creating DLLs in Windows, you must pass pythonXY.lib to the linker. To build two DLLs, spam and ni (which uses C functions found in spam), you could use these commands:

cl /LD /I/python/include spam.c ../libs/pythonXY.lib
+cl /LD /I/python/include ni.c spam.lib ../libs/pythonXY.lib
+

The first command created three files: spam.obj, spam.dll and spam.lib. Spam.dll does not contain any Python functions (such as PyArg_ParseTuple()), but it does know how to find the Python code thanks to pythonXY.lib.

The second command created ni.dll (and .obj and .lib), which knows how to find the necessary functions from spam, and also from the Python executable.

Not every identifier is exported to the lookup table. If you want any other modules (including Python) to be able to see your identifiers, you have to say _declspec(dllexport), as in void _declspec(dllexport) initspam(void) or PyObject _declspec(dllexport) *NiGetSpamData(void).

Developer Studio will throw in a lot of import libraries that you do not really need, adding about 100K to your executable. To get rid of them, use the Project Settings dialog, Link tab, to specify ignore default libraries. Add the correct msvcrtxx.lib to the list of libraries.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/extending/windows.html +

+
diff --git a/devdocs/python~3.12/faq%2Fdesign.html b/devdocs/python~3.12/faq%2Fdesign.html new file mode 100644 index 00000000..12b1ca70 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fdesign.html @@ -0,0 +1,115 @@ +

Design and History FAQ

+

Why does Python use indentation for grouping of statements?

Guido van Rossum believes that using indentation for grouping is extremely elegant and contributes a lot to the clarity of the average Python program. Most people learn to love this feature after a while.

Since there are no begin/end brackets there cannot be a disagreement between grouping perceived by the parser and the human reader. Occasionally C programmers will encounter a fragment of code like this:

if (x <= y)
+        x++;
+        y--;
+z++;
+

Only the x++ statement is executed if the condition is true, but the indentation leads many to believe otherwise. Even experienced C programmers will sometimes stare at it a long time wondering as to why y is being decremented even for x > y.

Because there are no begin/end brackets, Python is much less prone to coding-style conflicts. In C there are many different ways to place the braces. After becoming used to reading and writing code using a particular style, it is normal to feel somewhat uneasy when reading (or being required to write) in a different one.

Many coding styles place begin/end brackets on a line by themselves. This makes programs considerably longer and wastes valuable screen space, making it harder to get a good overview of a program. Ideally, a function should fit on one screen (say, 20–30 lines). 20 lines of Python can do a lot more work than 20 lines of C. This is not solely due to the lack of begin/end brackets – the lack of declarations and the high-level data types are also responsible – but the indentation-based syntax certainly helps.

Why am I getting strange results with simple arithmetic operations?

See the next question.

Why are floating-point calculations so inaccurate?

Users are often surprised by results like this:

>>> 1.2 - 1.0
+0.19999999999999996
+

and think it is a bug in Python. It’s not. This has little to do with Python, and much more to do with how the underlying platform handles floating-point numbers.

The float type in CPython uses a C double for storage. A float object’s value is stored in binary floating-point with a fixed precision (typically 53 bits) and Python uses C operations, which in turn rely on the hardware implementation in the processor, to perform floating-point operations. This means that as far as floating-point operations are concerned, Python behaves like many popular languages including C and Java.

Many numbers that can be written easily in decimal notation cannot be expressed exactly in binary floating-point. For example, after:

>>> x = 1.2
+

the value stored for x is a (very good) approximation to the decimal value 1.2, but is not exactly equal to it. On a typical machine, the actual stored value is:

1.0011001100110011001100110011001100110011001100110011 (binary)
+

which is exactly:

1.1999999999999999555910790149937383830547332763671875 (decimal)
+

The typical precision of 53 bits provides Python floats with 15–16 decimal digits of accuracy.

For a fuller explanation, please see the floating point arithmetic chapter in the Python tutorial.

Why are Python strings immutable?

There are several advantages.

One is performance: knowing that a string is immutable means we can allocate space for it at creation time, and the storage requirements are fixed and unchanging. This is also one of the reasons for the distinction between tuples and lists.

Another advantage is that strings in Python are considered as “elemental” as numbers. No amount of activity will change the value 8 to anything else, and in Python, no amount of activity will change the string “eight” to anything else.

Why must ‘self’ be used explicitly in method definitions and calls?

The idea was borrowed from Modula-3. It turns out to be very useful, for a variety of reasons.

First, it’s more obvious that you are using a method or instance attribute instead of a local variable. Reading self.x or self.meth() makes it absolutely clear that an instance variable or method is used even if you don’t know the class definition by heart. In C++, you can sort of tell by the lack of a local variable declaration (assuming globals are rare or easily recognizable) – but in Python, there are no local variable declarations, so you’d have to look up the class definition to be sure. Some C++ and Java coding standards call for instance attributes to have an m_ prefix, so this explicitness is still useful in those languages, too.

Second, it means that no special syntax is necessary if you want to explicitly reference or call the method from a particular class. In C++, if you want to use a method from a base class which is overridden in a derived class, you have to use the :: operator – in Python you can write baseclass.methodname(self, <argument list>). This is particularly useful for __init__() methods, and in general in cases where a derived class method wants to extend the base class method of the same name and thus has to call the base class method somehow.

Finally, for instance variables it solves a syntactic problem with assignment: since local variables in Python are (by definition!) those variables to which a value is assigned in a function body (and that aren’t explicitly declared global), there has to be some way to tell the interpreter that an assignment was meant to assign to an instance variable instead of to a local variable, and it should preferably be syntactic (for efficiency reasons). C++ does this through declarations, but Python doesn’t have declarations and it would be a pity having to introduce them just for this purpose. Using the explicit self.var solves this nicely. Similarly, for using instance variables, having to write self.var means that references to unqualified names inside a method don’t have to search the instance’s directories. To put it another way, local variables and instance variables live in two different namespaces, and you need to tell Python which namespace to use.

Why can’t I use an assignment in an expression?

Starting in Python 3.8, you can!

Assignment expressions using the walrus operator := assign a variable in an expression:

while chunk := fp.read(200):
+   print(chunk)
+

See PEP 572 for more information.

Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))?

As Guido said:

(a) For some operations, prefix notation just reads better than postfix – prefix (and infix!) operations have a long tradition in mathematics which likes notations where the visuals help the mathematician thinking about a problem. Compare the easy with which we rewrite a formula like x*(a+b) into x*a + x*b to the clumsiness of doing the same thing using a raw OO notation.

(b) When I read code that says len(x) I know that it is asking for the length of something. This tells me two things: the result is an integer, and the argument is some kind of container. To the contrary, when I read x.len(), I have to already know that x is some kind of container implementing an interface or inheriting from a class that has a standard len(). Witness the confusion we occasionally have when a class that is not implementing a mapping has a get() or keys() method, or something that isn’t a file has a write() method.

—https://mail.python.org/pipermail/python-3000/2006-November/004643.html

Why is join() a string method instead of a list or tuple method?

Strings became much more like other standard types starting in Python 1.6, when methods were added which give the same functionality that has always been available using the functions of the string module. Most of these new methods have been widely accepted, but the one which appears to make some programmers feel uncomfortable is:

", ".join(['1', '2', '4', '8', '16'])
+

which gives the result:

"1, 2, 4, 8, 16"
+

There are two common arguments against this usage.

The first runs along the lines of: “It looks really ugly using a method of a string literal (string constant)”, to which the answer is that it might, but a string literal is just a fixed value. If the methods are to be allowed on names bound to strings there is no logical reason to make them unavailable on literals.

The second objection is typically cast as: “I am really telling a sequence to join its members together with a string constant”. Sadly, you aren’t. For some reason there seems to be much less difficulty with having split() as a string method, since in that case it is easy to see that

"1, 2, 4, 8, 16".split(", ")
+

is an instruction to a string literal to return the substrings delimited by the given separator (or, by default, arbitrary runs of white space).

join() is a string method because in using it you are telling the separator string to iterate over a sequence of strings and insert itself between adjacent elements. This method can be used with any argument which obeys the rules for sequence objects, including any new classes you might define yourself. Similar methods exist for bytes and bytearray objects.

How fast are exceptions?

A try/except block is extremely efficient if no exceptions are raised. Actually catching an exception is expensive. In versions of Python prior to 2.0 it was common to use this idiom:

try:
+    value = mydict[key]
+except KeyError:
+    mydict[key] = getvalue(key)
+    value = mydict[key]
+

This only made sense when you expected the dict to have the key almost all the time. If that wasn’t the case, you coded it like this:

if key in mydict:
+    value = mydict[key]
+else:
+    value = mydict[key] = getvalue(key)
+

For this specific case, you could also use value = dict.setdefault(key, +getvalue(key)), but only if the getvalue() call is cheap enough because it is evaluated in all cases.

Why isn’t there a switch or case statement in Python?

You can do this easily enough with a sequence of if... elif... elif... else. For literal values, or constants within a namespace, you can also use a match ... case statement.

For cases where you need to choose from a very large number of possibilities, you can create a dictionary mapping case values to functions to call. For example:

functions = {'a': function_1,
+             'b': function_2,
+             'c': self.method_1}
+
+func = functions[value]
+func()
+

For calling methods on objects, you can simplify yet further by using the getattr() built-in to retrieve methods with a particular name:

class MyVisitor:
+    def visit_a(self):
+        ...
+
+    def dispatch(self, value):
+        method_name = 'visit_' + str(value)
+        method = getattr(self, method_name)
+        method()
+

It’s suggested that you use a prefix for the method names, such as visit_ in this example. Without such a prefix, if values are coming from an untrusted source, an attacker would be able to call any method on your object.

Can’t you emulate threads in the interpreter instead of relying on an OS-specific thread implementation?

Answer 1: Unfortunately, the interpreter pushes at least one C stack frame for each Python stack frame. Also, extensions can call back into Python at almost random moments. Therefore, a complete threads implementation requires thread support for C.

Answer 2: Fortunately, there is Stackless Python, which has a completely redesigned interpreter loop that avoids the C stack.

Why can’t lambda expressions contain statements?

Python lambda expressions cannot contain statements because Python’s syntactic framework can’t handle statements nested inside expressions. However, in Python, this is not a serious problem. Unlike lambda forms in other languages, where they add functionality, Python lambdas are only a shorthand notation if you’re too lazy to define a function.

Functions are already first class objects in Python, and can be declared in a local scope. Therefore the only advantage of using a lambda instead of a locally defined function is that you don’t need to invent a name for the function – but that’s just a local variable to which the function object (which is exactly the same type of object that a lambda expression yields) is assigned!

Can Python be compiled to machine code, C or some other language?

Cython compiles a modified version of Python with optional annotations into C extensions. Nuitka is an up-and-coming compiler of Python into C++ code, aiming to support the full Python language.

How does Python manage memory?

The details of Python memory management depend on the implementation. The standard implementation of Python, CPython, uses reference counting to detect inaccessible objects, and another mechanism to collect reference cycles, periodically executing a cycle detection algorithm which looks for inaccessible cycles and deletes the objects involved. The gc module provides functions to perform a garbage collection, obtain debugging statistics, and tune the collector’s parameters.

Other implementations (such as Jython or PyPy), however, can rely on a different mechanism such as a full-blown garbage collector. This difference can cause some subtle porting problems if your Python code depends on the behavior of the reference counting implementation.

In some Python implementations, the following code (which is fine in CPython) will probably run out of file descriptors:

for file in very_long_list_of_files:
+    f = open(file)
+    c = f.read(1)
+

Indeed, using CPython’s reference counting and destructor scheme, each new assignment to f closes the previous file. With a traditional GC, however, those file objects will only get collected (and closed) at varying and possibly long intervals.

If you want to write code that will work with any Python implementation, you should explicitly close the file or use the with statement; this will work regardless of memory management scheme:

for file in very_long_list_of_files:
+    with open(file) as f:
+        c = f.read(1)
+

Why doesn’t CPython use a more traditional garbage collection scheme?

For one thing, this is not a C standard feature and hence it’s not portable. (Yes, we know about the Boehm GC library. It has bits of assembler code for most common platforms, not for all of them, and although it is mostly transparent, it isn’t completely transparent; patches are required to get Python to work with it.)

Traditional GC also becomes a problem when Python is embedded into other applications. While in a standalone Python it’s fine to replace the standard malloc() and free() with versions provided by the GC library, an application embedding Python may want to have its own substitute for malloc() and free(), and may not want Python’s. Right now, CPython works with anything that implements malloc() and free() properly.

Why isn’t all memory freed when CPython exits?

Objects referenced from the global namespaces of Python modules are not always deallocated when Python exits. This may happen if there are circular references. There are also certain bits of memory that are allocated by the C library that are impossible to free (e.g. a tool like Purify will complain about these). Python is, however, aggressive about cleaning up memory on exit and does try to destroy every single object.

If you want to force Python to delete certain things on deallocation use the atexit module to run a function that will force those deletions.

Why are there separate tuple and list data types?

Lists and tuples, while similar in many respects, are generally used in fundamentally different ways. Tuples can be thought of as being similar to Pascal records or C structs; they’re small collections of related data which may be of different types which are operated on as a group. For example, a Cartesian coordinate is appropriately represented as a tuple of two or three numbers.

Lists, on the other hand, are more like arrays in other languages. They tend to hold a varying number of objects all of which have the same type and which are operated on one-by-one. For example, os.listdir('.') returns a list of strings representing the files in the current directory. Functions which operate on this output would generally not break if you added another file or two to the directory.

Tuples are immutable, meaning that once a tuple has been created, you can’t replace any of its elements with a new value. Lists are mutable, meaning that you can always change a list’s elements. Only immutable elements can be used as dictionary keys, and hence only tuples and not lists can be used as keys.

How are lists implemented in CPython?

CPython’s lists are really variable-length arrays, not Lisp-style linked lists. The implementation uses a contiguous array of references to other objects, and keeps a pointer to this array and the array’s length in a list head structure.

This makes indexing a list a[i] an operation whose cost is independent of the size of the list or the value of the index.

When items are appended or inserted, the array of references is resized. Some cleverness is applied to improve the performance of appending items repeatedly; when the array must be grown, some extra space is allocated so the next few times don’t require an actual resize.

How are dictionaries implemented in CPython?

CPython’s dictionaries are implemented as resizable hash tables. Compared to B-trees, this gives better performance for lookup (the most common operation by far) under most circumstances, and the implementation is simpler.

Dictionaries work by computing a hash code for each key stored in the dictionary using the hash() built-in function. The hash code varies widely depending on the key and a per-process seed; for example, 'Python' could hash to -539294296 while 'python', a string that differs by a single bit, could hash to 1142331976. The hash code is then used to calculate a location in an internal array where the value will be stored. Assuming that you’re storing keys that all have different hash values, this means that dictionaries take constant time – O(1), in Big-O notation – to retrieve a key.

Why must dictionary keys be immutable?

The hash table implementation of dictionaries uses a hash value calculated from the key value to find the key. If the key were a mutable object, its value could change, and thus its hash could also change. But since whoever changes the key object can’t tell that it was being used as a dictionary key, it can’t move the entry around in the dictionary. Then, when you try to look up the same object in the dictionary it won’t be found because its hash value is different. If you tried to look up the old value it wouldn’t be found either, because the value of the object found in that hash bin would be different.

If you want a dictionary indexed with a list, simply convert the list to a tuple first; the function tuple(L) creates a tuple with the same entries as the list L. Tuples are immutable and can therefore be used as dictionary keys.

Some unacceptable solutions that have been proposed:

There is a trick to get around this if you need to, but use it at your own risk: You can wrap a mutable structure inside a class instance which has both a __eq__() and a __hash__() method. You must then make sure that the hash value for all such wrapper objects that reside in a dictionary (or other hash based structure), remain fixed while the object is in the dictionary (or other structure).

class ListWrapper:
+    def __init__(self, the_list):
+        self.the_list = the_list
+
+    def __eq__(self, other):
+        return self.the_list == other.the_list
+
+    def __hash__(self):
+        l = self.the_list
+        result = 98767 - len(l)*555
+        for i, el in enumerate(l):
+            try:
+                result = result + (hash(el) % 9999999) * 1001 + i
+            except Exception:
+                result = (result % 7777777) + i * 333
+        return result
+

Note that the hash computation is complicated by the possibility that some members of the list may be unhashable and also by the possibility of arithmetic overflow.

Furthermore it must always be the case that if o1 == o2 (ie o1.__eq__(o2) +is True) then hash(o1) == hash(o2) (ie, o1.__hash__() == o2.__hash__()), regardless of whether the object is in a dictionary or not. If you fail to meet these restrictions dictionaries and other hash based structures will misbehave.

In the case of ListWrapper, whenever the wrapper object is in a dictionary the wrapped list must not change to avoid anomalies. Don’t do this unless you are prepared to think hard about the requirements and the consequences of not meeting them correctly. Consider yourself warned.

Why doesn’t list.sort() return the sorted list?

In situations where performance matters, making a copy of the list just to sort it would be wasteful. Therefore, list.sort() sorts the list in place. In order to remind you of that fact, it does not return the sorted list. This way, you won’t be fooled into accidentally overwriting a list when you need a sorted copy but also need to keep the unsorted version around.

If you want to return a new list, use the built-in sorted() function instead. This function creates a new list from a provided iterable, sorts it and returns it. For example, here’s how to iterate over the keys of a dictionary in sorted order:

for key in sorted(mydict):
+    ...  # do whatever with mydict[key]...
+

How do you specify and enforce an interface spec in Python?

An interface specification for a module as provided by languages such as C++ and Java describes the prototypes for the methods and functions of the module. Many feel that compile-time enforcement of interface specifications helps in the construction of large programs.

Python 2.6 adds an abc module that lets you define Abstract Base Classes (ABCs). You can then use isinstance() and issubclass() to check whether an instance or a class implements a particular ABC. The collections.abc module defines a set of useful ABCs such as Iterable, Container, and MutableMapping.

For Python, many of the advantages of interface specifications can be obtained by an appropriate test discipline for components.

A good test suite for a module can both provide a regression test and serve as a module interface specification and a set of examples. Many Python modules can be run as a script to provide a simple “self test.” Even modules which use complex external interfaces can often be tested in isolation using trivial “stub” emulations of the external interface. The doctest and unittest modules or third-party test frameworks can be used to construct exhaustive test suites that exercise every line of code in a module.

An appropriate testing discipline can help build large complex applications in Python as well as having interface specifications would. In fact, it can be better because an interface specification cannot test certain properties of a program. For example, the list.append() method is expected to add new elements to the end of some internal list; an interface specification cannot test that your list.append() implementation will actually do this correctly, but it’s trivial to check this property in a test suite.

Writing test suites is very helpful, and you might want to design your code to make it easily tested. One increasingly popular technique, test-driven development, calls for writing parts of the test suite first, before you write any of the actual code. Of course Python allows you to be sloppy and not write test cases at all.

Why is there no goto?

In the 1970s people realized that unrestricted goto could lead to messy “spaghetti” code that was hard to understand and revise. In a high-level language, it is also unneeded as long as there are ways to branch (in Python, with if statements and or, and, and if/else expressions) and loop (with while and for statements, possibly containing continue and break).

One can also use exceptions to provide a “structured goto” that works even across function calls. Many feel that exceptions can conveniently emulate all reasonable uses of the go or goto constructs of C, Fortran, and other languages. For example:

class label(Exception): pass  # declare a label
+
+try:
+    ...
+    if condition: raise label()  # goto label
+    ...
+except label:  # where to goto
+    pass
+...
+

This doesn’t allow you to jump into the middle of a loop, but that’s usually considered an abuse of goto anyway. Use sparingly.

Why can’t raw strings (r-strings) end with a backslash?

More precisely, they can’t end with an odd number of backslashes: the unpaired backslash at the end escapes the closing quote character, leaving an unterminated string.

Raw strings were designed to ease creating input for processors (chiefly regular expression engines) that want to do their own backslash escape processing. Such processors consider an unmatched trailing backslash to be an error anyway, so raw strings disallow that. In return, they allow you to pass on the string quote character by escaping it with a backslash. These rules work well when r-strings are used for their intended purpose.

If you’re trying to build Windows pathnames, note that all Windows system calls accept forward slashes too:

f = open("/mydir/file.txt")  # works fine!
+

If you’re trying to build a pathname for a DOS command, try e.g. one of

dir = r"\this\is\my\dos\dir" "\\"
+dir = r"\this\is\my\dos\dir\ "[:-1]
+dir = "\\this\\is\\my\\dos\\dir\\"
+

Why doesn’t Python have a “with” statement for attribute assignments?

Python has a with statement that wraps the execution of a block, calling code on the entrance and exit from the block. Some languages have a construct that looks like this:

with obj:
+    a = 1               # equivalent to obj.a = 1
+    total = total + 1   # obj.total = obj.total + 1
+

In Python, such a construct would be ambiguous.

Other languages, such as Object Pascal, Delphi, and C++, use static types, so it’s possible to know, in an unambiguous way, what member is being assigned to. This is the main point of static typing – the compiler always knows the scope of every variable at compile time.

Python uses dynamic types. It is impossible to know in advance which attribute will be referenced at runtime. Member attributes may be added or removed from objects on the fly. This makes it impossible to know, from a simple reading, what attribute is being referenced: a local one, a global one, or a member attribute?

For instance, take the following incomplete snippet:

def foo(a):
+    with a:
+        print(x)
+

The snippet assumes that a must have a member attribute called x. However, there is nothing in Python that tells the interpreter this. What should happen if a is, let us say, an integer? If there is a global variable named x, will it be used inside the with block? As you see, the dynamic nature of Python makes such choices much harder.

The primary benefit of with and similar language features (reduction of code volume) can, however, easily be achieved in Python by assignment. Instead of:

function(args).mydict[index][index].a = 21
+function(args).mydict[index][index].b = 42
+function(args).mydict[index][index].c = 63
+

write this:

ref = function(args).mydict[index][index]
+ref.a = 21
+ref.b = 42
+ref.c = 63
+

This also has the side-effect of increasing execution speed because name bindings are resolved at run-time in Python, and the second version only needs to perform the resolution once.

Similar proposals that would introduce syntax to further reduce code volume, such as using a ‘leading dot’, have been rejected in favour of explicitness (see https://mail.python.org/pipermail/python-ideas/2016-May/040070.html).

Why don’t generators support the with statement?

For technical reasons, a generator used directly as a context manager would not work correctly. When, as is most common, a generator is used as an iterator run to completion, no closing is needed. When it is, wrap it as contextlib.closing(generator) in the with statement.

Why are colons required for the if/while/def/class statements?

The colon is required primarily to enhance readability (one of the results of the experimental ABC language). Consider this:

if a == b
+    print(a)
+

versus

if a == b:
+    print(a)
+

Notice how the second one is slightly easier to read. Notice further how a colon sets off the example in this FAQ answer; it’s a standard usage in English.

Another minor reason is that the colon makes it easier for editors with syntax highlighting; they can look for colons to decide when indentation needs to be increased instead of having to do a more elaborate parsing of the program text.

Why does Python allow commas at the end of lists and tuples?

Python lets you add a trailing comma at the end of lists, tuples, and dictionaries:

[1, 2, 3,]
+('a', 'b', 'c',)
+d = {
+    "A": [1, 5],
+    "B": [6, 7],  # last trailing comma is optional but good style
+}
+

There are several reasons to allow this.

When you have a literal value for a list, tuple, or dictionary spread across multiple lines, it’s easier to add more elements because you don’t have to remember to add a comma to the previous line. The lines can also be reordered without creating a syntax error.

Accidentally omitting the comma can lead to errors that are hard to diagnose. For example:

x = [
+  "fee",
+  "fie"
+  "foo",
+  "fum"
+]
+

This list looks like it has four elements, but it actually contains three: “fee”, “fiefoo” and “fum”. Always adding the comma avoids this source of error.

Allowing the trailing comma may also make programmatic code generation easier.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/design.html +

+
diff --git a/devdocs/python~3.12/faq%2Fextending.html b/devdocs/python~3.12/faq%2Fextending.html new file mode 100644 index 00000000..e55bafb5 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fextending.html @@ -0,0 +1,48 @@ +

Extending/Embedding FAQ

+

Can I create my own functions in C?

Yes, you can create built-in modules containing functions, variables, exceptions and even new types in C. This is explained in the document Extending and Embedding the Python Interpreter.

Most intermediate or advanced Python books will also cover this topic.

Can I create my own functions in C++?

Yes, using the C compatibility features found in C++. Place extern "C" { +... } around the Python include files and put extern "C" before each function that is going to be called by the Python interpreter. Global or static C++ objects with constructors are probably not a good idea.

Writing C is hard; are there any alternatives?

There are a number of alternatives to writing your own C extensions, depending on what you’re trying to do.

Cython and its relative Pyrex are compilers that accept a slightly modified form of Python and generate the corresponding C code. Cython and Pyrex make it possible to write an extension without having to learn Python’s C API.

If you need to interface to some C or C++ library for which no Python extension currently exists, you can try wrapping the library’s data types and functions with a tool such as SWIG. SIP, CXX Boost, or Weave are also alternatives for wrapping C++ libraries.

How can I execute arbitrary Python statements from C?

The highest-level function to do this is PyRun_SimpleString() which takes a single string argument to be executed in the context of the module __main__ and returns 0 for success and -1 when an exception occurred (including SyntaxError). If you want more control, use PyRun_String(); see the source for PyRun_SimpleString() in Python/pythonrun.c.

How can I evaluate an arbitrary Python expression from C?

Call the function PyRun_String() from the previous question with the start symbol Py_eval_input; it parses an expression, evaluates it and returns its value.

How do I extract C values from a Python object?

That depends on the object’s type. If it’s a tuple, PyTuple_Size() returns its length and PyTuple_GetItem() returns the item at a specified index. Lists have similar functions, PyList_Size() and PyList_GetItem().

For bytes, PyBytes_Size() returns its length and PyBytes_AsStringAndSize() provides a pointer to its value and its length. Note that Python bytes objects may contain null bytes so C’s strlen() should not be used.

To test the type of an object, first make sure it isn’t NULL, and then use PyBytes_Check(), PyTuple_Check(), PyList_Check(), etc.

There is also a high-level API to Python objects which is provided by the so-called ‘abstract’ interface – read Include/abstract.h for further details. It allows interfacing with any kind of Python sequence using calls like PySequence_Length(), PySequence_GetItem(), etc. as well as many other useful protocols such as numbers (PyNumber_Index() et al.) and mappings in the PyMapping APIs.

How do I use Py_BuildValue() to create a tuple of arbitrary length?

You can’t. Use PyTuple_Pack() instead.

How do I call an object’s method from C?

The PyObject_CallMethod() function can be used to call an arbitrary method of an object. The parameters are the object, the name of the method to call, a format string like that used with Py_BuildValue(), and the argument values:

PyObject *
+PyObject_CallMethod(PyObject *object, const char *method_name,
+                    const char *arg_format, ...);
+

This works for any object that has methods – whether built-in or user-defined. You are responsible for eventually Py_DECREF()‘ing the return value.

To call, e.g., a file object’s “seek” method with arguments 10, 0 (assuming the file object pointer is “f”):

res = PyObject_CallMethod(f, "seek", "(ii)", 10, 0);
+if (res == NULL) {
+        ... an exception occurred ...
+}
+else {
+        Py_DECREF(res);
+}
+

Note that since PyObject_CallObject() always wants a tuple for the argument list, to call a function without arguments, pass “()” for the format, and to call a function with one argument, surround the argument in parentheses, e.g. “(i)”.

How do I catch the output from PyErr_Print() (or anything that prints to stdout/stderr)?

In Python code, define an object that supports the write() method. Assign this object to sys.stdout and sys.stderr. Call print_error, or just allow the standard traceback mechanism to work. Then, the output will go wherever your write() method sends it.

The easiest way to do this is to use the io.StringIO class:

>>> import io, sys
+>>> sys.stdout = io.StringIO()
+>>> print('foo')
+>>> print('hello world!')
+>>> sys.stderr.write(sys.stdout.getvalue())
+foo
+hello world!
+

A custom object to do the same would look like this:

>>> import io, sys
+>>> class StdoutCatcher(io.TextIOBase):
+...     def __init__(self):
+...         self.data = []
+...     def write(self, stuff):
+...         self.data.append(stuff)
+...
+>>> import sys
+>>> sys.stdout = StdoutCatcher()
+>>> print('foo')
+>>> print('hello world!')
+>>> sys.stderr.write(''.join(sys.stdout.data))
+foo
+hello world!
+

How do I access a module written in Python from C?

You can get a pointer to the module object as follows:

module = PyImport_ImportModule("<modulename>");
+

If the module hasn’t been imported yet (i.e. it is not yet present in sys.modules), this initializes the module; otherwise it simply returns the value of sys.modules["<modulename>"]. Note that it doesn’t enter the module into any namespace – it only ensures it has been initialized and is stored in sys.modules.

You can then access the module’s attributes (i.e. any name defined in the module) as follows:

attr = PyObject_GetAttrString(module, "<attrname>");
+

Calling PyObject_SetAttrString() to assign to variables in the module also works.

How do I interface to C++ objects from Python?

Depending on your requirements, there are many approaches. To do this manually, begin by reading the “Extending and Embedding” document. Realize that for the Python run-time system, there isn’t a whole lot of difference between C and C++ – so the strategy of building a new Python type around a C structure (pointer) type will also work for C++ objects.

For C++ libraries, see Writing C is hard; are there any alternatives?.

I added a module using the Setup file and the make fails; why?

Setup must end in a newline, if there is no newline there, the build process fails. (Fixing this requires some ugly shell script hackery, and this bug is so minor that it doesn’t seem worth the effort.)

How do I debug an extension?

When using GDB with dynamically loaded extensions, you can’t set a breakpoint in your extension until your extension is loaded.

In your .gdbinit file (or interactively), add the command:

br _PyImport_LoadDynamicModule
+

Then, when you run GDB:

$ gdb /local/bin/python
+gdb) run myscript.py
+gdb) continue # repeat until your extension is loaded
+gdb) finish   # so that your extension is loaded
+gdb) br myfunction.c:50
+gdb) continue
+

I want to compile a Python module on my Linux system, but some files are missing. Why?

Most packaged versions of Python don’t include the /usr/lib/python2.x/config/ directory, which contains various files required for compiling Python extensions.

For Red Hat, install the python-devel RPM to get the necessary files.

For Debian, run apt-get install python-dev.

How do I tell “incomplete input” from “invalid input”?

Sometimes you want to emulate the Python interactive interpreter’s behavior, where it gives you a continuation prompt when the input is incomplete (e.g. you typed the start of an “if” statement or you didn’t close your parentheses or triple string quotes), but it gives you a syntax error message immediately when the input is invalid.

In Python you can use the codeop module, which approximates the parser’s behavior sufficiently. IDLE uses this, for example.

The easiest way to do it in C is to call PyRun_InteractiveLoop() (perhaps in a separate thread) and let the Python interpreter handle the input for you. You can also set the PyOS_ReadlineFunctionPointer() to point at your custom input function. See Modules/readline.c and Parser/myreadline.c for more hints.

How do I find undefined g++ symbols __builtin_new or __pure_virtual?

To dynamically load g++ extension modules, you must recompile Python, relink it using g++ (change LINKCC in the Python Modules Makefile), and link your extension module using g++ (e.g., g++ -shared -o mymodule.so mymodule.o).

Can I create an object class with some methods implemented in C and others in Python (e.g. through inheritance)?

Yes, you can inherit from built-in classes such as int, list, dict, etc.

The Boost Python Library (BPL, https://www.boost.org/libs/python/doc/index.html) provides a way of doing this from C++ (i.e. you can inherit from an extension class written in C++ using the BPL).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/extending.html +

+
diff --git a/devdocs/python~3.12/faq%2Fgeneral.html b/devdocs/python~3.12/faq%2Fgeneral.html new file mode 100644 index 00000000..be920360 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fgeneral.html @@ -0,0 +1,35 @@ +

General Python FAQ

+

General Information

What is Python?

Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes. It supports multiple programming paradigms beyond object-oriented programming, such as procedural and functional programming. Python combines remarkable power with very clear syntax. It has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++. It is also usable as an extension language for applications that need a programmable interface. Finally, Python is portable: it runs on many Unix variants including Linux and macOS, and on Windows.

To find out more, start with The Python Tutorial. The Beginner’s Guide to Python links to other introductory tutorials and resources for learning Python.

What is the Python Software Foundation?

The Python Software Foundation is an independent non-profit organization that holds the copyright on Python versions 2.1 and newer. The PSF’s mission is to advance open source technology related to the Python programming language and to publicize the use of Python. The PSF’s home page is at https://www.python.org/psf/.

Donations to the PSF are tax-exempt in the US. If you use Python and find it helpful, please contribute via the PSF donation page.

Why was Python created in the first place?

Here’s a very brief summary of what started it all, written by Guido van Rossum:

I had extensive experience with implementing an interpreted language in the ABC group at CWI, and from working with this group I had learned a lot about language design. This is the origin of many Python features, including the use of indentation for statement grouping and the inclusion of very-high-level data types (although the details are all different in Python).

I had a number of gripes about the ABC language, but also liked many of its features. It was impossible to extend the ABC language (or its implementation) to remedy my complaints – in fact its lack of extensibility was one of its biggest problems. I had some experience with using Modula-2+ and talked with the designers of Modula-3 and read the Modula-3 report. Modula-3 is the origin of the syntax and semantics used for exceptions, and some other Python features.

I was working in the Amoeba distributed operating system group at CWI. We needed a better way to do system administration than by writing either C programs or Bourne shell scripts, since Amoeba had its own system call interface which wasn’t easily accessible from the Bourne shell. My experience with error handling in Amoeba made me acutely aware of the importance of exceptions as a programming language feature.

It occurred to me that a scripting language with a syntax like ABC but with access to the Amoeba system calls would fill the need. I realized that it would be foolish to write an Amoeba-specific language, so I decided that I needed a language that was generally extensible.

During the 1989 Christmas holidays, I had a lot of time on my hand, so I decided to give it a try. During the next year, while still mostly working on it in my own time, Python was used in the Amoeba project with increasing success, and the feedback from colleagues made me add many early improvements.

In February 1991, after just over a year of development, I decided to post to USENET. The rest is in the Misc/HISTORY file.

What is Python good for?

Python is a high-level general-purpose programming language that can be applied to many different classes of problems.

The language comes with a large standard library that covers areas such as string processing (regular expressions, Unicode, calculating differences between files), internet protocols (HTTP, FTP, SMTP, XML-RPC, POP, IMAP), software engineering (unit testing, logging, profiling, parsing Python code), and operating system interfaces (system calls, filesystems, TCP/IP sockets). Look at the table of contents for The Python Standard Library to get an idea of what’s available. A wide variety of third-party extensions are also available. Consult the Python Package Index to find packages of interest to you.

How does the Python version numbering scheme work?

Python versions are numbered “A.B.C” or “A.B”:

  • +A is the major version number – it is only incremented for really major changes in the language.
  • +B is the minor version number – it is incremented for less earth-shattering changes.
  • +C is the micro version number – it is incremented for each bugfix release.

See PEP 6 for more information about bugfix releases.

Not all releases are bugfix releases. In the run-up to a new feature release, a series of development releases are made, denoted as alpha, beta, or release candidate. Alphas are early releases in which interfaces aren’t yet finalized; it’s not unexpected to see an interface change between two alpha releases. Betas are more stable, preserving existing interfaces but possibly adding new modules, and release candidates are frozen, making no changes except as needed to fix critical bugs.

Alpha, beta and release candidate versions have an additional suffix:

  • The suffix for an alpha version is “aN” for some small number N.
  • The suffix for a beta version is “bN” for some small number N.
  • The suffix for a release candidate version is “rcN” for some small number N.

In other words, all versions labeled 2.0aN precede the versions labeled 2.0bN, which precede versions labeled 2.0rcN, and those precede 2.0.

You may also find version numbers with a “+” suffix, e.g. “2.2+”. These are unreleased versions, built directly from the CPython development repository. In practice, after a final minor release is made, the version is incremented to the next minor version, which becomes the “a0” version, e.g. “2.4a0”.

See also the documentation for sys.version, sys.hexversion, and sys.version_info.

How do I obtain a copy of the Python source?

The latest Python source distribution is always available from python.org, at https://www.python.org/downloads/. The latest development sources can be obtained at https://github.com/python/cpython/.

The source distribution is a gzipped tar file containing the complete C source, Sphinx-formatted documentation, Python library modules, example programs, and several useful pieces of freely distributable software. The source will compile and run out of the box on most UNIX platforms.

Consult the Getting Started section of the Python Developer’s Guide for more information on getting the source code and compiling it.

How do I get documentation on Python?

The standard documentation for the current stable version of Python is available at https://docs.python.org/3/. PDF, plain text, and downloadable HTML versions are also available at https://docs.python.org/3/download.html.

The documentation is written in reStructuredText and processed by the Sphinx documentation tool. The reStructuredText source for the documentation is part of the Python source distribution.

I’ve never programmed before. Is there a Python tutorial?

There are numerous tutorials and books available. The standard documentation includes The Python Tutorial.

Consult the Beginner’s Guide to find information for beginning Python programmers, including lists of tutorials.

Is there a newsgroup or mailing list devoted to Python?

There is a newsgroup, comp.lang.python, and a mailing list, python-list. The newsgroup and mailing list are gatewayed into each other – if you can read news it’s unnecessary to subscribe to the mailing list. comp.lang.python is high-traffic, receiving hundreds of postings every day, and Usenet readers are often more able to cope with this volume.

Announcements of new software releases and events can be found in comp.lang.python.announce, a low-traffic moderated list that receives about five postings per day. It’s available as the python-announce mailing list.

More info about other mailing lists and newsgroups can be found at https://www.python.org/community/lists/.

How do I get a beta test version of Python?

Alpha and beta releases are available from https://www.python.org/downloads/. All releases are announced on the comp.lang.python and comp.lang.python.announce newsgroups and on the Python home page at https://www.python.org/; an RSS feed of news is available.

You can also access the development version of Python through Git. See The Python Developer’s Guide for details.

How do I submit bug reports and patches for Python?

To report a bug or submit a patch, use the issue tracker at https://github.com/python/cpython/issues.

For more information on how Python is developed, consult the Python Developer’s Guide.

Are there any published articles about Python that I can reference?

It’s probably best to cite your favorite book about Python.

The very first article about Python was written in 1991 and is now quite outdated.

Guido van Rossum and Jelke de Boer, “Interactively Testing Remote Servers Using the Python Programming Language”, CWI Quarterly, Volume 4, Issue 4 (December 1991), Amsterdam, pp 283–303.

Are there any books on Python?

Yes, there are many, and more are being published. See the python.org wiki at https://wiki.python.org/moin/PythonBooks for a list.

You can also search online bookstores for “Python” and filter out the Monty Python references; or perhaps search for “Python” and “language”.

Where in the world is www.python.org located?

The Python project’s infrastructure is located all over the world and is managed by the Python Infrastructure Team. Details here.

Why is it called Python?

When he began implementing Python, Guido van Rossum was also reading the published scripts from “Monty Python’s Flying Circus”, a BBC comedy series from the 1970s. Van Rossum thought he needed a name that was short, unique, and slightly mysterious, so he decided to call the language Python.

Do I have to like “Monty Python’s Flying Circus”?

No, but it helps. :)

Python in the real world

How stable is Python?

Very stable. New, stable releases have been coming out roughly every 6 to 18 months since 1991, and this seems likely to continue. As of version 3.9, Python will have a new feature release every 12 months (PEP 602).

The developers issue bugfix releases of older versions, so the stability of existing releases gradually improves. Bugfix releases, indicated by a third component of the version number (e.g. 3.5.3, 3.6.2), are managed for stability; only fixes for known problems are included in a bugfix release, and it’s guaranteed that interfaces will remain the same throughout a series of bugfix releases.

The latest stable releases can always be found on the Python download page. There are two production-ready versions of Python: 2.x and 3.x. The recommended version is 3.x, which is supported by most widely used libraries. Although 2.x is still widely used, it is not maintained anymore.

How many people are using Python?

There are probably millions of users, though it’s difficult to obtain an exact count.

Python is available for free download, so there are no sales figures, and it’s available from many different sites and packaged with many Linux distributions, so download statistics don’t tell the whole story either.

The comp.lang.python newsgroup is very active, but not all Python users post to the group or even read it.

Have any significant projects been done in Python?

See https://www.python.org/about/success for a list of projects that use Python. Consulting the proceedings for past Python conferences will reveal contributions from many different companies and organizations.

High-profile Python projects include the Mailman mailing list manager and the Zope application server. Several Linux distributions, most notably Red Hat, have written part or all of their installer and system administration software in Python. Companies that use Python internally include Google, Yahoo, and Lucasfilm Ltd.

What new developments are expected for Python in the future?

See https://peps.python.org/ for the Python Enhancement Proposals (PEPs). PEPs are design documents describing a suggested new feature for Python, providing a concise technical specification and a rationale. Look for a PEP titled “Python X.Y Release Schedule”, where X.Y is a version that hasn’t been publicly released yet.

New development is discussed on the python-dev mailing list.

Is it reasonable to propose incompatible changes to Python?

In general, no. There are already millions of lines of Python code around the world, so any change in the language that invalidates more than a very small fraction of existing programs has to be frowned upon. Even if you can provide a conversion program, there’s still the problem of updating all documentation; many books have been written about Python, and we don’t want to invalidate them all at a single stroke.

Providing a gradual upgrade path is necessary if a feature has to be changed. PEP 5 describes the procedure followed for introducing backward-incompatible changes while minimizing disruption for users.

Is Python a good language for beginning programmers?

Yes.

It is still common to start students with a procedural and statically typed language such as Pascal, C, or a subset of C++ or Java. Students may be better served by learning Python as their first language. Python has a very simple and consistent syntax and a large standard library and, most importantly, using Python in a beginning programming course lets students concentrate on important programming skills such as problem decomposition and data type design. With Python, students can be quickly introduced to basic concepts such as loops and procedures. They can probably even work with user-defined objects in their very first course.

For a student who has never programmed before, using a statically typed language seems unnatural. It presents additional complexity that the student must master and slows the pace of the course. The students are trying to learn to think like a computer, decompose problems, design consistent interfaces, and encapsulate data. While learning to use a statically typed language is important in the long term, it is not necessarily the best topic to address in the students’ first programming course.

Many other aspects of Python make it a good first language. Like Java, Python has a large standard library so that students can be assigned programming projects very early in the course that do something. Assignments aren’t restricted to the standard four-function calculator and check balancing programs. By using the standard library, students can gain the satisfaction of working on realistic applications as they learn the fundamentals of programming. Using the standard library also teaches students about code reuse. Third-party modules such as PyGame are also helpful in extending the students’ reach.

Python’s interactive interpreter enables students to test language features while they’re programming. They can keep a window with the interpreter running while they enter their program’s source in another window. If they can’t remember the methods for a list, they can do something like this:

>>> L = []
+>>> dir(L) 
+['__add__', '__class__', '__contains__', '__delattr__', '__delitem__',
+'__dir__', '__doc__', '__eq__', '__format__', '__ge__',
+'__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__',
+'__imul__', '__init__', '__iter__', '__le__', '__len__', '__lt__',
+'__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__',
+'__repr__', '__reversed__', '__rmul__', '__setattr__', '__setitem__',
+'__sizeof__', '__str__', '__subclasshook__', 'append', 'clear',
+'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove',
+'reverse', 'sort']
+>>> [d for d in dir(L) if '__' not in d]
+['append', 'clear', 'copy', 'count', 'extend', 'index', 'insert', 'pop', 'remove', 'reverse', 'sort']
+
+>>> help(L.append)
+Help on built-in function append:
+
+append(...)
+    L.append(object) -> None -- append object to end
+
+>>> L.append(1)
+>>> L
+[1]
+

With the interpreter, documentation is never far from the student as they are programming.

There are also good IDEs for Python. IDLE is a cross-platform IDE for Python that is written in Python using Tkinter. Emacs users will be happy to know that there is a very good Python mode for Emacs. All of these programming environments provide syntax highlighting, auto-indenting, and access to the interactive interpreter while coding. Consult the Python wiki for a full list of Python editing environments.

If you want to discuss Python’s use in education, you may be interested in joining the edu-sig mailing list.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/general.html +

+
diff --git a/devdocs/python~3.12/faq%2Fgui.html b/devdocs/python~3.12/faq%2Fgui.html new file mode 100644 index 00000000..f870fa98 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fgui.html @@ -0,0 +1,8 @@ +

Graphic User Interface FAQ

+

General GUI Questions

What GUI toolkits exist for Python?

Standard builds of Python include an object-oriented interface to the Tcl/Tk widget set, called tkinter. This is probably the easiest to install (since it comes included with most binary distributions of Python) and use. For more info about Tk, including pointers to the source, see the Tcl/Tk home page. Tcl/Tk is fully portable to the macOS, Windows, and Unix platforms.

Depending on what platform(s) you are aiming at, there are also several alternatives. A list of cross-platform and platform-specific GUI frameworks can be found on the python wiki.

Tkinter questions

How do I freeze Tkinter applications?

Freeze is a tool to create stand-alone applications. When freezing Tkinter applications, the applications will not be truly stand-alone, as the application will still need the Tcl and Tk libraries.

One solution is to ship the application with the Tcl and Tk libraries, and point to them at run-time using the TCL_LIBRARY and TK_LIBRARY environment variables.

To get truly stand-alone applications, the Tcl scripts that form the library have to be integrated into the application as well. One tool supporting that is SAM (stand-alone modules), which is part of the Tix distribution (https://tix.sourceforge.net/).

Build Tix with SAM enabled, perform the appropriate call to Tclsam_init(), etc. inside Python’s Modules/tkappinit.c, and link with libtclsam and libtksam (you might include the Tix libraries as well).

Can I have Tk events handled while waiting for I/O?

On platforms other than Windows, yes, and you don’t even need threads! But you’ll have to restructure your I/O code a bit. Tk has the equivalent of Xt’s XtAddInput() call, which allows you to register a callback function which will be called from the Tk mainloop when I/O is possible on a file descriptor. See File Handlers.

I can’t get key bindings to work in Tkinter: why?

An often-heard complaint is that event handlers bound to events with the bind() method don’t get handled even when the appropriate key is pressed.

The most common cause is that the widget to which the binding applies doesn’t have “keyboard focus”. Check out the Tk documentation for the focus command. Usually a widget is given the keyboard focus by clicking in it (but not for labels; see the takefocus option).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/gui.html +

+
diff --git a/devdocs/python~3.12/faq%2Findex.html b/devdocs/python~3.12/faq%2Findex.html new file mode 100644 index 00000000..782599ff --- /dev/null +++ b/devdocs/python~3.12/faq%2Findex.html @@ -0,0 +1,6 @@ +

Python Frequently Asked Questions

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/index.html +

+
diff --git a/devdocs/python~3.12/faq%2Finstalled.html b/devdocs/python~3.12/faq%2Finstalled.html new file mode 100644 index 00000000..2c9aca2c --- /dev/null +++ b/devdocs/python~3.12/faq%2Finstalled.html @@ -0,0 +1,6 @@ +

“Why is Python Installed on my Computer?” FAQ

What is Python?

Python is a programming language. It’s used for many different applications. It’s used in some high schools and colleges as an introductory programming language because Python is easy to learn, but it’s also used by professional software developers at places such as Google, NASA, and Lucasfilm Ltd.

If you wish to learn more about Python, start with the Beginner’s Guide to Python.

Why is Python installed on my machine?

If you find Python installed on your system but don’t remember installing it, there are several possible ways it could have gotten there.

Can I delete Python?

That depends on where Python came from.

If someone installed it deliberately, you can remove it without hurting anything. On Windows, use the Add/Remove Programs icon in the Control Panel.

If Python was installed by a third-party application, you can also remove it, but that application will no longer work. You should use that application’s uninstaller rather than removing Python directly.

If Python came with your operating system, removing it is not recommended. If you remove it, whatever tools were written in Python will no longer run, and some of them might be important to you. Reinstalling the whole system would then be required to fix things again.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/installed.html +

+
diff --git a/devdocs/python~3.12/faq%2Flibrary.html b/devdocs/python~3.12/faq%2Flibrary.html new file mode 100644 index 00000000..a62e9d0f --- /dev/null +++ b/devdocs/python~3.12/faq%2Flibrary.html @@ -0,0 +1,174 @@ +

Library and Extension FAQ

+

General Library Questions

How do I find a module or application to perform task X?

Check the Library Reference to see if there’s a relevant standard library module. (Eventually you’ll learn what’s in the standard library and will be able to skip this step.)

For third-party packages, search the Python Package Index or try Google or another web search engine. Searching for “Python” plus a keyword or two for your topic of interest will usually find something helpful.

Where is the math.py (socket.py, regex.py, etc.) source file?

If you can’t find a source file for a module it may be a built-in or dynamically loaded module implemented in C, C++ or other compiled language. In this case you may not have the source file or it may be something like mathmodule.c, somewhere in a C source directory (not on the Python Path).

There are (at least) three kinds of modules in Python:

  1. modules written in Python (.py);
  2. modules written in C and dynamically loaded (.dll, .pyd, .so, .sl, etc);
  3. +

    modules written in C and linked with the interpreter; to get a list of these, type:

    import sys
+print(sys.builtin_module_names)
+

How do I make a Python script executable on Unix?

You need to do two things: the script file’s mode must be executable and the first line must begin with #! followed by the path of the Python interpreter.

The first is done by executing chmod +x scriptfile or perhaps chmod 755 +scriptfile.

The second can be done in a number of ways. The most straightforward way is to write

#!/usr/local/bin/python
+

as the very first line of your file, using the pathname for where the Python interpreter is installed on your platform.

If you would like the script to be independent of where the Python interpreter lives, you can use the env program. Almost all Unix variants support the following, assuming the Python interpreter is in a directory on the user’s PATH:

#!/usr/bin/env python
+

Don’t do this for CGI scripts. The PATH variable for CGI scripts is often very minimal, so you need to use the actual absolute pathname of the interpreter.

Occasionally, a user’s environment is so full that the /usr/bin/env program fails; or there’s no env program at all. In that case, you can try the following hack (due to Alex Rezinsky):

#! /bin/sh
+""":"
+exec python $0 ${1+"$@"}
+"""
+

The minor disadvantage is that this defines the script’s __doc__ string. However, you can fix that by adding

__doc__ = """...Whatever..."""
+

Is there a curses/termcap package for Python?

For Unix variants: The standard Python source distribution comes with a curses module in the Modules subdirectory, though it’s not compiled by default. (Note that this is not available in the Windows distribution – there is no curses module for Windows.)

The curses module supports basic curses features as well as many additional functions from ncurses and SYSV curses such as colour, alternative character set support, pads, and mouse support. This means the module isn’t compatible with operating systems that only have BSD curses, but there don’t seem to be any currently maintained OSes that fall into this category.

Is there an equivalent to C’s onexit() in Python?

The atexit module provides a register function that is similar to C’s onexit().

Why don’t my signal handlers work?

The most common problem is that the signal handler is declared with the wrong argument list. It is called as

handler(signum, frame)
+

so it should be declared with two parameters:

def handler(signum, frame):
+    ...
+

Common tasks

How do I test a Python program or component?

Python comes with two testing frameworks. The doctest module finds examples in the docstrings for a module and runs them, comparing the output with the expected output given in the docstring.

The unittest module is a fancier testing framework modelled on Java and Smalltalk testing frameworks.

To make testing easier, you should use good modular design in your program. Your program should have almost all functionality encapsulated in either functions or class methods – and this sometimes has the surprising and delightful effect of making the program run faster (because local variable accesses are faster than global accesses). Furthermore the program should avoid depending on mutating global variables, since this makes testing much more difficult to do.

The “global main logic” of your program may be as simple as

if __name__ == "__main__":
+    main_logic()
+

at the bottom of the main module of your program.

Once your program is organized as a tractable collection of function and class behaviours, you should write test functions that exercise the behaviours. A test suite that automates a sequence of tests can be associated with each module. This sounds like a lot of work, but since Python is so terse and flexible it’s surprisingly easy. You can make coding much more pleasant and fun by writing your test functions in parallel with the “production code”, since this makes it easy to find bugs and even design flaws earlier.

“Support modules” that are not intended to be the main module of a program may include a self-test of the module.

if __name__ == "__main__":
+    self_test()
+

Even programs that interact with complex external interfaces may be tested when the external interfaces are unavailable by using “fake” interfaces implemented in Python.

How do I create documentation from doc strings?

The pydoc module can create HTML from the doc strings in your Python source code. An alternative for creating API documentation purely from docstrings is epydoc. Sphinx can also include docstring content.

How do I get a single keypress at a time?

For Unix variants there are several solutions. It’s straightforward to do this using curses, but curses is a fairly large module to learn.

Threads

How do I program using threads?

Be sure to use the threading module and not the _thread module. The threading module builds convenient abstractions on top of the low-level primitives provided by the _thread module.

None of my threads seem to run: why?

As soon as the main thread exits, all threads are killed. Your main thread is running too quickly, giving the threads no time to do any work.

A simple fix is to add a sleep to the end of the program that’s long enough for all the threads to finish:

import threading, time
+
+def thread_task(name, n):
+    for i in range(n):
+        print(name, i)
+
+for i in range(10):
+    T = threading.Thread(target=thread_task, args=(str(i), i))
+    T.start()
+
+time.sleep(10)  # <---------------------------!
+

But now (on many platforms) the threads don’t run in parallel, but appear to run sequentially, one at a time! The reason is that the OS thread scheduler doesn’t start a new thread until the previous thread is blocked.

A simple fix is to add a tiny sleep to the start of the run function:

def thread_task(name, n):
+    time.sleep(0.001)  # <--------------------!
+    for i in range(n):
+        print(name, i)
+
+for i in range(10):
+    T = threading.Thread(target=thread_task, args=(str(i), i))
+    T.start()
+
+time.sleep(10)
+

Instead of trying to guess a good delay value for time.sleep(), it’s better to use some kind of semaphore mechanism. One idea is to use the queue module to create a queue object, let each thread append a token to the queue when it finishes, and let the main thread read as many tokens from the queue as there are threads.

How do I parcel out work among a bunch of worker threads?

The easiest way is to use the concurrent.futures module, especially the ThreadPoolExecutor class.

Or, if you want fine control over the dispatching algorithm, you can write your own logic manually. Use the queue module to create a queue containing a list of jobs. The Queue class maintains a list of objects and has a .put(obj) method that adds items to the queue and a .get() method to return them. The class will take care of the locking necessary to ensure that each job is handed out exactly once.

Here’s a trivial example:

import threading, queue, time
+
+# The worker thread gets jobs off the queue.  When the queue is empty, it
+# assumes there will be no more work and exits.
+# (Realistically workers will run until terminated.)
+def worker():
+    print('Running worker')
+    time.sleep(0.1)
+    while True:
+        try:
+            arg = q.get(block=False)
+        except queue.Empty:
+            print('Worker', threading.current_thread(), end=' ')
+            print('queue empty')
+            break
+        else:
+            print('Worker', threading.current_thread(), end=' ')
+            print('running with argument', arg)
+            time.sleep(0.5)
+
+# Create queue
+q = queue.Queue()
+
+# Start a pool of 5 workers
+for i in range(5):
+    t = threading.Thread(target=worker, name='worker %i' % (i+1))
+    t.start()
+
+# Begin adding work to the queue
+for i in range(50):
+    q.put(i)
+
+# Give threads time to run
+print('Main thread sleeping')
+time.sleep(5)
+

When run, this will produce the following output:

Running worker
+Running worker
+Running worker
+Running worker
+Running worker
+Main thread sleeping
+Worker <Thread(worker 1, started 130283832797456)> running with argument 0
+Worker <Thread(worker 2, started 130283824404752)> running with argument 1
+Worker <Thread(worker 3, started 130283816012048)> running with argument 2
+Worker <Thread(worker 4, started 130283807619344)> running with argument 3
+Worker <Thread(worker 5, started 130283799226640)> running with argument 4
+Worker <Thread(worker 1, started 130283832797456)> running with argument 5
+...
+

Consult the module’s documentation for more details; the Queue class provides a featureful interface.

What kinds of global value mutation are thread-safe?

A global interpreter lock (GIL) is used internally to ensure that only one thread runs in the Python VM at a time. In general, Python offers to switch among threads only between bytecode instructions; how frequently it switches can be set via sys.setswitchinterval(). Each bytecode instruction and therefore all the C implementation code reached from each instruction is therefore atomic from the point of view of a Python program.

In theory, this means an exact accounting requires an exact understanding of the PVM bytecode implementation. In practice, it means that operations on shared variables of built-in data types (ints, lists, dicts, etc) that “look atomic” really are.

For example, the following operations are all atomic (L, L1, L2 are lists, D, D1, D2 are dicts, x, y are objects, i, j are ints):

L.append(x)
+L1.extend(L2)
+x = L[i]
+x = L.pop()
+L1[i:j] = L2
+L.sort()
+x = y
+x.field = y
+D[x] = y
+D1.update(D2)
+D.keys()
+

These aren’t:

i = i+1
+L.append(L[-1])
+L[i] = L[j]
+D[x] = D[x] + 1
+

Operations that replace other objects may invoke those other objects’ __del__() method when their reference count reaches zero, and that can affect things. This is especially true for the mass updates to dictionaries and lists. When in doubt, use a mutex!

Can’t we get rid of the Global Interpreter Lock?

The global interpreter lock (GIL) is often seen as a hindrance to Python’s deployment on high-end multiprocessor server machines, because a multi-threaded Python program effectively only uses one CPU, due to the insistence that (almost) all Python code can only run while the GIL is held.

Back in the days of Python 1.5, Greg Stein actually implemented a comprehensive patch set (the “free threading” patches) that removed the GIL and replaced it with fine-grained locking. Adam Olsen recently did a similar experiment in his python-safethread project. Unfortunately, both experiments exhibited a sharp drop in single-thread performance (at least 30% slower), due to the amount of fine-grained locking necessary to compensate for the removal of the GIL.

This doesn’t mean that you can’t make good use of Python on multi-CPU machines! You just have to be creative with dividing the work up between multiple processes rather than multiple threads. The ProcessPoolExecutor class in the new concurrent.futures module provides an easy way of doing so; the multiprocessing module provides a lower-level API in case you want more control over dispatching of tasks.

Judicious use of C extensions will also help; if you use a C extension to perform a time-consuming task, the extension can release the GIL while the thread of execution is in the C code and allow other threads to get some work done. Some standard library modules such as zlib and hashlib already do this.

It has been suggested that the GIL should be a per-interpreter-state lock rather than truly global; interpreters then wouldn’t be able to share objects. Unfortunately, this isn’t likely to happen either. It would be a tremendous amount of work, because many object implementations currently have global state. For example, small integers and short strings are cached; these caches would have to be moved to the interpreter state. Other object types have their own free list; these free lists would have to be moved to the interpreter state. And so on.

And I doubt that it can even be done in finite time, because the same problem exists for 3rd party extensions. It is likely that 3rd party extensions are being written at a faster rate than you can convert them to store all their global state in the interpreter state.

And finally, once you have multiple interpreters not sharing any state, what have you gained over running each interpreter in a separate process?

Input and Output

How do I delete a file? (And other file questions…)

Use os.remove(filename) or os.unlink(filename); for documentation, see the os module. The two functions are identical; unlink() is simply the name of the Unix system call for this function.

To remove a directory, use os.rmdir(); use os.mkdir() to create one. os.makedirs(path) will create any intermediate directories in path that don’t exist. os.removedirs(path) will remove intermediate directories as long as they’re empty; if you want to delete an entire directory tree and its contents, use shutil.rmtree().

To rename a file, use os.rename(old_path, new_path).

To truncate a file, open it using f = open(filename, "rb+"), and use f.truncate(offset); offset defaults to the current seek position. There’s also os.ftruncate(fd, offset) for files opened with os.open(), where fd is the file descriptor (a small integer).

The shutil module also contains a number of functions to work on files including copyfile(), copytree(), and rmtree().

How do I copy a file?

The shutil module contains a copyfile() function. Note that on Windows NTFS volumes, it does not copy alternate data streams nor resource forks on macOS HFS+ volumes, though both are now rarely used. It also doesn’t copy file permissions and metadata, though using shutil.copy2() instead will preserve most (though not all) of it.

How do I read (or write) binary data?

To read or write complex binary data formats, it’s best to use the struct module. It allows you to take a string containing binary data (usually numbers) and convert it to Python objects; and vice versa.

For example, the following code reads two 2-byte integers and one 4-byte integer in big-endian format from a file:

import struct
+
+with open(filename, "rb") as f:
+    s = f.read(8)
+    x, y, z = struct.unpack(">hhl", s)
+

The ‘>’ in the format string forces big-endian data; the letter ‘h’ reads one “short integer” (2 bytes), and ‘l’ reads one “long integer” (4 bytes) from the string.

For data that is more regular (e.g. a homogeneous list of ints or floats), you can also use the array module.

Note

To read and write binary data, it is mandatory to open the file in binary mode (here, passing "rb" to open()). If you use "r" instead (the default), the file will be open in text mode and f.read() will return str objects rather than bytes objects.

I can’t seem to use os.read() on a pipe created with os.popen(); why?

os.read() is a low-level function which takes a file descriptor, a small integer representing the opened file. os.popen() creates a high-level file object, the same type returned by the built-in open() function. Thus, to read n bytes from a pipe p created with os.popen(), you need to use p.read(n).

How do I access the serial (RS232) port?

For Win32, OSX, Linux, BSD, Jython, IronPython:

https://pypi.org/project/pyserial/

For Unix, see a Usenet post by Mitch Chapman:

https://groups.google.com/groups?selm=34A04430.CF9@ohioee.com

Why doesn’t closing sys.stdout (stdin, stderr) really close it?

Python file objects are a high-level layer of abstraction on low-level C file descriptors.

For most file objects you create in Python via the built-in open() function, f.close() marks the Python file object as being closed from Python’s point of view, and also arranges to close the underlying C file descriptor. This also happens automatically in f’s destructor, when f becomes garbage.

But stdin, stdout and stderr are treated specially by Python, because of the special status also given to them by C. Running sys.stdout.close() marks the Python-level file object as being closed, but does not close the associated C file descriptor.

To close the underlying C file descriptor for one of these three, you should first be sure that’s what you really want to do (e.g., you may confuse extension modules trying to do I/O). If it is, use os.close():

os.close(stdin.fileno())
+os.close(stdout.fileno())
+os.close(stderr.fileno())
+

Or you can use the numeric constants 0, 1 and 2, respectively.

Network/Internet Programming

What WWW tools are there for Python?

See the chapters titled Internet Protocols and Support and Internet Data Handling in the Library Reference Manual. Python has many modules that will help you build server-side and client-side web systems.

A summary of available frameworks is maintained by Paul Boddie at https://wiki.python.org/moin/WebProgramming.

Cameron Laird maintains a useful set of pages about Python web technologies at https://web.archive.org/web/20210224183619/http://phaseit.net/claird/comp.lang.python/web_python.

How can I mimic CGI form submission (METHOD=POST)?

I would like to retrieve web pages that are the result of POSTing a form. Is there existing code that would let me do this easily?

Yes. Here’s a simple example that uses urllib.request:

#!/usr/local/bin/python
+
+import urllib.request
+
+# build the query string
+qs = "First=Josephine&MI=Q&Last=Public"
+
+# connect and send the server a path
+req = urllib.request.urlopen('http://www.some-server.out-there'
+                             '/cgi-bin/some-cgi-script', data=qs)
+with req:
+    msg, hdrs = req.read(), req.info()
+

Note that in general for percent-encoded POST operations, query strings must be quoted using urllib.parse.urlencode(). For example, to send name=Guy Steele, Jr.:

>>> import urllib.parse
+>>> urllib.parse.urlencode({'name': 'Guy Steele, Jr.'})
+'name=Guy+Steele%2C+Jr.'
+

See also

HOWTO Fetch Internet Resources Using The urllib Package for extensive examples.

What module should I use to help with generating HTML?

You can find a collection of useful links on the Web Programming wiki page.

How do I send mail from a Python script?

Use the standard library module smtplib.

Here’s a very simple interactive mail sender that uses it. This method will work on any host that supports an SMTP listener.

import sys, smtplib
+
+fromaddr = input("From: ")
+toaddrs  = input("To: ").split(',')
+print("Enter message, end with ^D:")
+msg = ''
+while True:
+    line = sys.stdin.readline()
+    if not line:
+        break
+    msg += line
+
+# The actual mail send
+server = smtplib.SMTP('localhost')
+server.sendmail(fromaddr, toaddrs, msg)
+server.quit()
+

A Unix-only alternative uses sendmail. The location of the sendmail program varies between systems; sometimes it is /usr/lib/sendmail, sometimes /usr/sbin/sendmail. The sendmail manual page will help you out. Here’s some sample code:

import os
+
+SENDMAIL = "/usr/sbin/sendmail"  # sendmail location
+p = os.popen("%s -t -i" % SENDMAIL, "w")
+p.write("To: receiver@example.com\n")
+p.write("Subject: test\n")
+p.write("\n")  # blank line separating headers from body
+p.write("Some text\n")
+p.write("some more text\n")
+sts = p.close()
+if sts != 0:
+    print("Sendmail exit status", sts)
+

How do I avoid blocking in the connect() method of a socket?

The select module is commonly used to help with asynchronous I/O on sockets.

To prevent the TCP connect from blocking, you can set the socket to non-blocking mode. Then when you do the connect(), you will either connect immediately (unlikely) or get an exception that contains the error number as .errno. errno.EINPROGRESS indicates that the connection is in progress, but hasn’t finished yet. Different OSes will return different values, so you’re going to have to check what’s returned on your system.

You can use the connect_ex() method to avoid creating an exception. It will just return the errno value. To poll, you can call connect_ex() again later – 0 or errno.EISCONN indicate that you’re connected – or you can pass this socket to select.select() to check if it’s writable.

Note

The asyncio module provides a general purpose single-threaded and concurrent asynchronous library, which can be used for writing non-blocking network code. The third-party Twisted library is a popular and feature-rich alternative.

Databases

Are there any interfaces to database packages in Python?

Yes.

Interfaces to disk-based hashes such as DBM and GDBM are also included with standard Python. There is also the sqlite3 module, which provides a lightweight disk-based relational database.

Support for most relational databases is available. See the DatabaseProgramming wiki page for details.

How do you implement persistent objects in Python?

The pickle library module solves this in a very general way (though you still can’t store things like open files, sockets or windows), and the shelve library module uses pickle and (g)dbm to create persistent mappings containing arbitrary Python objects.

Mathematics and Numerics

How do I generate random numbers in Python?

The standard module random implements a random number generator. Usage is simple:

import random
+random.random()
+

This returns a random floating point number in the range [0, 1).

There are also many other specialized generators in this module, such as:

  • +randrange(a, b) chooses an integer in the range [a, b).
  • +uniform(a, b) chooses a floating point number in the range [a, b).
  • +normalvariate(mean, sdev) samples the normal (Gaussian) distribution.

Some higher-level functions operate on sequences directly, such as:

  • +choice(S) chooses a random element from a given sequence.
  • +shuffle(L) shuffles a list in-place, i.e. permutes it randomly.

There’s also a Random class you can instantiate to create independent multiple random number generators.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/library.html +

+
diff --git a/devdocs/python~3.12/faq%2Fprogramming.html b/devdocs/python~3.12/faq%2Fprogramming.html new file mode 100644 index 00000000..7a8ad9e5 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fprogramming.html @@ -0,0 +1,661 @@ +

Programming FAQ

+

General Questions

Is there a source code level debugger with breakpoints, single-stepping, etc.?

Yes.

Several debuggers for Python are described below, and the built-in function breakpoint() allows you to drop into any of them.

The pdb module is a simple but adequate console-mode debugger for Python. It is part of the standard Python library, and is documented in the Library +Reference Manual. You can also write your own debugger by using the code for pdb as an example.

The IDLE interactive development environment, which is part of the standard Python distribution (normally available as Tools/scripts/idle3), includes a graphical debugger.

PythonWin is a Python IDE that includes a GUI debugger based on pdb. The PythonWin debugger colors breakpoints and has quite a few cool features such as debugging non-PythonWin programs. PythonWin is available as part of pywin32 project and as a part of the ActivePython distribution.

Eric is an IDE built on PyQt and the Scintilla editing component.

trepan3k is a gdb-like debugger.

Visual Studio Code is an IDE with debugging tools that integrates with version-control software.

There are a number of commercial Python IDEs that include graphical debuggers. They include:

Are there tools to help find bugs or perform static analysis?

Yes.

Pylint and Pyflakes do basic checking that will help you catch bugs sooner.

Static type checkers such as Mypy, Pyre, and Pytype can check type hints in Python source code.

How can I create a stand-alone binary from a Python script?

You don’t need the ability to compile Python to C code if all you want is a stand-alone program that users can download and run without having to install the Python distribution first. There are a number of tools that determine the set of modules required by a program and bind these modules together with a Python binary to produce a single executable.

One is to use the freeze tool, which is included in the Python source tree as Tools/freeze. It converts Python byte code to C arrays; with a C compiler you can embed all your modules into a new program, which is then linked with the standard Python modules.

It works by scanning your source recursively for import statements (in both forms) and looking for the modules in the standard Python path as well as in the source directory (for built-in modules). It then turns the bytecode for modules written in Python into C code (array initializers that can be turned into code objects using the marshal module) and creates a custom-made config file that only contains those built-in modules which are actually used in the program. It then compiles the generated C code and links it with the rest of the Python interpreter to form a self-contained binary which acts exactly like your script.

The following packages can help with the creation of console and GUI executables:

Are there coding standards or a style guide for Python programs?

Yes. The coding style required for standard library modules is documented as PEP 8.

Core Language

Why am I getting an UnboundLocalError when the variable has a value?

It can be a surprise to get the UnboundLocalError in previously working code when it is modified by adding an assignment statement somewhere in the body of a function.

This code:

>>> x = 10
+>>> def bar():
+...     print(x)
+...
+>>> bar()
+10
+

works, but this code:

>>> x = 10
+>>> def foo():
+...     print(x)
+...     x += 1
+

results in an UnboundLocalError:

>>> foo()
+Traceback (most recent call last):
+  ...
+UnboundLocalError: local variable 'x' referenced before assignment
+

This is because when you make an assignment to a variable in a scope, that variable becomes local to that scope and shadows any similarly named variable in the outer scope. Since the last statement in foo assigns a new value to x, the compiler recognizes it as a local variable. Consequently when the earlier print(x) attempts to print the uninitialized local variable and an error results.

In the example above you can access the outer scope variable by declaring it global:

>>> x = 10
+>>> def foobar():
+...     global x
+...     print(x)
+...     x += 1
+...
+>>> foobar()
+10
+

This explicit declaration is required in order to remind you that (unlike the superficially analogous situation with class and instance variables) you are actually modifying the value of the variable in the outer scope:

>>> print(x)
+11
+

You can do a similar thing in a nested scope using the nonlocal keyword:

>>> def foo():
+...    x = 10
+...    def bar():
+...        nonlocal x
+...        print(x)
+...        x += 1
+...    bar()
+...    print(x)
+...
+>>> foo()
+10
+11
+

What are the rules for local and global variables in Python?

In Python, variables that are only referenced inside a function are implicitly global. If a variable is assigned a value anywhere within the function’s body, it’s assumed to be a local unless explicitly declared as global.

Though a bit surprising at first, a moment’s consideration explains this. On one hand, requiring global for assigned variables provides a bar against unintended side-effects. On the other hand, if global was required for all global references, you’d be using global all the time. You’d have to declare as global every reference to a built-in function or to a component of an imported module. This clutter would defeat the usefulness of the global declaration for identifying side-effects.

Why do lambdas defined in a loop with different values all return the same result?

Assume you use a for loop to define a few different lambdas (or even plain functions), e.g.:

>>> squares = []
+>>> for x in range(5):
+...     squares.append(lambda: x**2)
+

This gives you a list that contains 5 lambdas that calculate x**2. You might expect that, when called, they would return, respectively, 0, 1, 4, 9, and 16. However, when you actually try you will see that they all return 16:

>>> squares[2]()
+16
+>>> squares[4]()
+16
+

This happens because x is not local to the lambdas, but is defined in the outer scope, and it is accessed when the lambda is called — not when it is defined. At the end of the loop, the value of x is 4, so all the functions now return 4**2, i.e. 16. You can also verify this by changing the value of x and see how the results of the lambdas change:

>>> x = 8
+>>> squares[2]()
+64
+

In order to avoid this, you need to save the values in variables local to the lambdas, so that they don’t rely on the value of the global x:

>>> squares = []
+>>> for x in range(5):
+...     squares.append(lambda n=x: n**2)
+

Here, n=x creates a new variable n local to the lambda and computed when the lambda is defined so that it has the same value that x had at that point in the loop. This means that the value of n will be 0 in the first lambda, 1 in the second, 2 in the third, and so on. Therefore each lambda will now return the correct result:

>>> squares[2]()
+4
+>>> squares[4]()
+16
+

Note that this behaviour is not peculiar to lambdas, but applies to regular functions too.

How do I share global variables across modules?

The canonical way to share information across modules within a single program is to create a special module (often called config or cfg). Just import the config module in all modules of your application; the module then becomes available as a global name. Because there is only one instance of each module, any changes made to the module object get reflected everywhere. For example:

config.py:

x = 0   # Default value of the 'x' configuration setting
+

mod.py:

import config
+config.x = 1
+

main.py:

import config
+import mod
+print(config.x)
+

Note that using a module is also the basis for implementing the singleton design pattern, for the same reason.

What are the “best practices” for using import in a module?

In general, don’t use from modulename import *. Doing so clutters the importer’s namespace, and makes it much harder for linters to detect undefined names.

Import modules at the top of a file. Doing so makes it clear what other modules your code requires and avoids questions of whether the module name is in scope. Using one import per line makes it easy to add and delete module imports, but using multiple imports per line uses less screen space.

It’s good practice if you import modules in the following order:

  1. standard library modules – e.g. sys, os, argparse, re +
  2. third-party library modules (anything installed in Python’s site-packages directory) – e.g. dateutil, requests, PIL.Image +
  3. locally developed modules

It is sometimes necessary to move imports to a function or class to avoid problems with circular imports. Gordon McMillan says:

Circular imports are fine where both modules use the “import <module>” form of import. They fail when the 2nd module wants to grab a name out of the first (“from module import name”) and the import is at the top level. That’s because names in the 1st are not yet available, because the first module is busy importing the 2nd.

In this case, if the second module is only used in one function, then the import can easily be moved into that function. By the time the import is called, the first module will have finished initializing, and the second module can do its import.

It may also be necessary to move imports out of the top level of code if some of the modules are platform-specific. In that case, it may not even be possible to import all of the modules at the top of the file. In this case, importing the correct modules in the corresponding platform-specific code is a good option.

Only move imports into a local scope, such as inside a function definition, if it’s necessary to solve a problem such as avoiding a circular import or are trying to reduce the initialization time of a module. This technique is especially helpful if many of the imports are unnecessary depending on how the program executes. You may also want to move imports into a function if the modules are only ever used in that function. Note that loading a module the first time may be expensive because of the one time initialization of the module, but loading a module multiple times is virtually free, costing only a couple of dictionary lookups. Even if the module name has gone out of scope, the module is probably available in sys.modules.

Why are default values shared between objects?

This type of bug commonly bites neophyte programmers. Consider this function:

def foo(mydict={}):  # Danger: shared reference to one dict for all calls
+    ... compute something ...
+    mydict[key] = value
+    return mydict
+

The first time you call this function, mydict contains a single item. The second time, mydict contains two items because when foo() begins executing, mydict starts out with an item already in it.

It is often expected that a function call creates new objects for default values. This is not what happens. Default values are created exactly once, when the function is defined. If that object is changed, like the dictionary in this example, subsequent calls to the function will refer to this changed object.

By definition, immutable objects such as numbers, strings, tuples, and None, are safe from change. Changes to mutable objects such as dictionaries, lists, and class instances can lead to confusion.

Because of this feature, it is good programming practice to not use mutable objects as default values. Instead, use None as the default value and inside the function, check if the parameter is None and create a new list/dictionary/whatever if it is. For example, don’t write:

def foo(mydict={}):
+    ...
+

but:

def foo(mydict=None):
+    if mydict is None:
+        mydict = {}  # create a new dict for local namespace
+

This feature can be useful. When you have a function that’s time-consuming to compute, a common technique is to cache the parameters and the resulting value of each call to the function, and return the cached value if the same value is requested again. This is called “memoizing”, and can be implemented like this:

# Callers can only provide two parameters and optionally pass _cache by keyword
+def expensive(arg1, arg2, *, _cache={}):
+    if (arg1, arg2) in _cache:
+        return _cache[(arg1, arg2)]
+
+    # Calculate the value
+    result = ... expensive computation ...
+    _cache[(arg1, arg2)] = result           # Store result in the cache
+    return result
+

You could use a global variable containing a dictionary instead of the default value; it’s a matter of taste.

How can I pass optional or keyword parameters from one function to another?

Collect the arguments using the * and ** specifiers in the function’s parameter list; this gives you the positional arguments as a tuple and the keyword arguments as a dictionary. You can then pass these arguments when calling another function by using * and **:

def f(x, *args, **kwargs):
+    ...
+    kwargs['width'] = '14.3c'
+    ...
+    g(x, *args, **kwargs)
+

What is the difference between arguments and parameters?

Parameters are defined by the names that appear in a function definition, whereas arguments are the values actually passed to a function when calling it. Parameters define what kind of arguments a function can accept. For example, given the function definition:

def func(foo, bar=None, **kwargs):
+    pass
+

foo, bar and kwargs are parameters of func. However, when calling func, for example:

func(42, bar=314, extra=somevar)
+

the values 42, 314, and somevar are arguments.

Why did changing list ‘y’ also change list ‘x’?

If you wrote code like:

>>> x = []
+>>> y = x
+>>> y.append(10)
+>>> y
+[10]
+>>> x
+[10]
+

you might be wondering why appending an element to y changed x too.

There are two factors that produce this result:

  1. Variables are simply names that refer to objects. Doing y = x doesn’t create a copy of the list – it creates a new variable y that refers to the same object x refers to. This means that there is only one object (the list), and both x and y refer to it.
  2. Lists are mutable, which means that you can change their content.

After the call to append(), the content of the mutable object has changed from [] to [10]. Since both the variables refer to the same object, using either name accesses the modified value [10].

If we instead assign an immutable object to x:

>>> x = 5  # ints are immutable
+>>> y = x
+>>> x = x + 1  # 5 can't be mutated, we are creating a new object here
+>>> x
+6
+>>> y
+5
+

we can see that in this case x and y are not equal anymore. This is because integers are immutable, and when we do x = x + 1 we are not mutating the int 5 by incrementing its value; instead, we are creating a new object (the int 6) and assigning it to x (that is, changing which object x refers to). After this assignment we have two objects (the ints 6 and 5) and two variables that refer to them (x now refers to 6 but y still refers to 5).

Some operations (for example y.append(10) and y.sort()) mutate the object, whereas superficially similar operations (for example y = y + [10] and sorted(y)) create a new object. In general in Python (and in all cases in the standard library) a method that mutates an object will return None to help avoid getting the two types of operations confused. So if you mistakenly write y.sort() thinking it will give you a sorted copy of y, you’ll instead end up with None, which will likely cause your program to generate an easily diagnosed error.

However, there is one class of operations where the same operation sometimes has different behaviors with different types: the augmented assignment operators. For example, += mutates lists but not tuples or ints (a_list ++= [1, 2, 3] is equivalent to a_list.extend([1, 2, 3]) and mutates a_list, whereas some_tuple += (1, 2, 3) and some_int += 1 create new objects).

In other words:

  • If we have a mutable object (list, dict, set, etc.), we can use some specific operations to mutate it and all the variables that refer to it will see the change.
  • If we have an immutable object (str, int, tuple, etc.), all the variables that refer to it will always see the same value, but operations that transform that value into a new value always return a new object.

If you want to know if two variables refer to the same object or not, you can use the is operator, or the built-in function id().

How do I write a function with output parameters (call by reference)?

Remember that arguments are passed by assignment in Python. Since assignment just creates references to objects, there’s no alias between an argument name in the caller and callee, and so no call-by-reference per se. You can achieve the desired effect in a number of ways.

  1. +

    By returning a tuple of the results:

    >>> def func1(a, b):
+...     a = 'new-value'        # a and b are local names
+...     b = b + 1              # assigned to new objects
+...     return a, b            # return new values
+...
+>>> x, y = 'old-value', 99
+>>> func1(x, y)
+('new-value', 100)
+

    This is almost always the clearest solution.

  2. By using global variables. This isn’t thread-safe, and is not recommended.
  3. +

    By passing a mutable (changeable in-place) object:

    >>> def func2(a):
+...     a[0] = 'new-value'     # 'a' references a mutable list
+...     a[1] = a[1] + 1        # changes a shared object
+...
+>>> args = ['old-value', 99]
+>>> func2(args)
+>>> args
+['new-value', 100]
+
  4. +

    By passing in a dictionary that gets mutated:

    >>> def func3(args):
+...     args['a'] = 'new-value'     # args is a mutable dictionary
+...     args['b'] = args['b'] + 1   # change it in-place
+...
+>>> args = {'a': 'old-value', 'b': 99}
+>>> func3(args)
+>>> args
+{'a': 'new-value', 'b': 100}
+
  5. +

    Or bundle up values in a class instance:

    >>> class Namespace:
+...     def __init__(self, /, **args):
+...         for key, value in args.items():
+...             setattr(self, key, value)
+...
+>>> def func4(args):
+...     args.a = 'new-value'        # args is a mutable Namespace
+...     args.b = args.b + 1         # change object in-place
+...
+>>> args = Namespace(a='old-value', b=99)
+>>> func4(args)
+>>> vars(args)
+{'a': 'new-value', 'b': 100}
+

    There’s almost never a good reason to get this complicated.

Your best choice is to return a tuple containing the multiple results.

How do you make a higher order function in Python?

You have two choices: you can use nested scopes or you can use callable objects. For example, suppose you wanted to define linear(a,b) which returns a function f(x) that computes the value a*x+b. Using nested scopes:

def linear(a, b):
+    def result(x):
+        return a * x + b
+    return result
+

Or using a callable object:

class linear:
+
+    def __init__(self, a, b):
+        self.a, self.b = a, b
+
+    def __call__(self, x):
+        return self.a * x + self.b
+

In both cases,

taxes = linear(0.3, 2)
+

gives a callable object where taxes(10e6) == 0.3 * 10e6 + 2.

The callable object approach has the disadvantage that it is a bit slower and results in slightly longer code. However, note that a collection of callables can share their signature via inheritance:

class exponential(linear):
+    # __init__ inherited
+    def __call__(self, x):
+        return self.a * (x ** self.b)
+

Object can encapsulate state for several methods:

class counter:
+
+    value = 0
+
+    def set(self, x):
+        self.value = x
+
+    def up(self):
+        self.value = self.value + 1
+
+    def down(self):
+        self.value = self.value - 1
+
+count = counter()
+inc, dec, reset = count.up, count.down, count.set
+

Here inc(), dec() and reset() act like functions which share the same counting variable.

How do I copy an object in Python?

In general, try copy.copy() or copy.deepcopy() for the general case. Not all objects can be copied, but most can.

Some objects can be copied more easily. Dictionaries have a copy() method:

newdict = olddict.copy()
+

Sequences can be copied by slicing:

new_l = l[:]
+

How can I find the methods or attributes of an object?

For an instance x of a user-defined class, dir(x) returns an alphabetized list of the names containing the instance attributes and methods and attributes defined by its class.

How can my code discover the name of an object?

Generally speaking, it can’t, because objects don’t really have names. Essentially, assignment always binds a name to a value; the same is true of def and class statements, but in that case the value is a callable. Consider the following code:

>>> class A:
+...     pass
+...
+>>> B = A
+>>> a = B()
+>>> b = a
+>>> print(b)
+<__main__.A object at 0x16D07CC>
+>>> print(a)
+<__main__.A object at 0x16D07CC>
+

Arguably the class has a name: even though it is bound to two names and invoked through the name B the created instance is still reported as an instance of class A. However, it is impossible to say whether the instance’s name is a or b, since both names are bound to the same value.

Generally speaking it should not be necessary for your code to “know the names” of particular values. Unless you are deliberately writing introspective programs, this is usually an indication that a change of approach might be beneficial.

In comp.lang.python, Fredrik Lundh once gave an excellent analogy in answer to this question:

The same way as you get the name of that cat you found on your porch: the cat (object) itself cannot tell you its name, and it doesn’t really care – so the only way to find out what it’s called is to ask all your neighbours (namespaces) if it’s their cat (object)…

….and don’t be surprised if you’ll find that it’s known by many names, or no name at all!

What’s up with the comma operator’s precedence?

Comma is not an operator in Python. Consider this session:

>>> "a" in "b", "a"
+(False, 'a')
+

Since the comma is not an operator, but a separator between expressions the above is evaluated as if you had entered:

("a" in "b"), "a"
+

not:

"a" in ("b", "a")
+

The same is true of the various assignment operators (=, += etc). They are not truly operators but syntactic delimiters in assignment statements.

Is there an equivalent of C’s “?:” ternary operator?

Yes, there is. The syntax is as follows:

[on_true] if [expression] else [on_false]
+
+x, y = 50, 25
+small = x if x < y else y
+

Before this syntax was introduced in Python 2.5, a common idiom was to use logical operators:

[expression] and [on_true] or [on_false]
+

However, this idiom is unsafe, as it can give wrong results when on_true has a false boolean value. Therefore, it is always better to use the ... if ... else ... form.

Is it possible to write obfuscated one-liners in Python?

Yes. Usually this is done by nesting lambda within lambda. See the following three examples, slightly adapted from Ulf Bartelt:

from functools import reduce
+
+# Primes < 1000
+print(list(filter(None,map(lambda y:y*reduce(lambda x,y:x*y!=0,
+map(lambda x,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))))
+
+# First 10 Fibonacci numbers
+print(list(map(lambda x,f=lambda x,f:(f(x-1,f)+f(x-2,f)) if x>1 else 1:
+f(x,f), range(10))))
+
+# Mandelbrot set
+print((lambda Ru,Ro,Iu,Io,IM,Sx,Sy:reduce(lambda x,y:x+'\n'+y,map(lambda y,
+Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,Sy=Sy,L=lambda yc,Iu=Iu,Io=Io,Ru=Ru,Ro=Ro,i=IM,
+Sx=Sx,Sy=Sy:reduce(lambda x,y:x+y,map(lambda x,xc=Ru,yc=yc,Ru=Ru,Ro=Ro,
+i=i,Sx=Sx,F=lambda xc,yc,x,y,k,f=lambda xc,yc,x,y,k,f:(k<=0)or (x*x+y*y
+>=4.0) or 1+f(xc,yc,x*x-y*y+xc,2.0*x*y+yc,k-1,f):f(xc,yc,x,y,k,f):chr(
+64+F(Ru+x*(Ro-Ru)/Sx,yc,0,0,i)),range(Sx))):L(Iu+y*(Io-Iu)/Sy),range(Sy
+))))(-2.1, 0.7, -1.2, 1.2, 30, 80, 24))
+#    \___ ___/  \___ ___/  |   |   |__ lines on screen
+#        V          V      |   |______ columns on screen
+#        |          |      |__________ maximum of "iterations"
+#        |          |_________________ range on y axis
+#        |____________________________ range on x axis
+

Don’t try this at home, kids!

What does the slash(/) in the parameter list of a function mean?

A slash in the argument list of a function denotes that the parameters prior to it are positional-only. Positional-only parameters are the ones without an externally usable name. Upon calling a function that accepts positional-only parameters, arguments are mapped to parameters based solely on their position. For example, divmod() is a function that accepts positional-only parameters. Its documentation looks like this:

>>> help(divmod)
+Help on built-in function divmod in module builtins:
+
+divmod(x, y, /)
+    Return the tuple (x//y, x%y).  Invariant: div*y + mod == x.
+

The slash at the end of the parameter list means that both parameters are positional-only. Thus, calling divmod() with keyword arguments would lead to an error:

>>> divmod(x=3, y=4)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: divmod() takes no keyword arguments
+

Numbers and strings

How do I specify hexadecimal and octal integers?

To specify an octal digit, precede the octal value with a zero, and then a lower or uppercase “o”. For example, to set the variable “a” to the octal value “10” (8 in decimal), type:

>>> a = 0o10
+>>> a
+8
+

Hexadecimal is just as easy. Simply precede the hexadecimal number with a zero, and then a lower or uppercase “x”. Hexadecimal digits can be specified in lower or uppercase. For example, in the Python interpreter:

>>> a = 0xa5
+>>> a
+165
+>>> b = 0XB2
+>>> b
+178
+

Why does -22 // 10 return -3?

It’s primarily driven by the desire that i % j have the same sign as j. If you want that, and also want:

i == (i // j) * j + (i % j)
+

then integer division has to return the floor. C also requires that identity to hold, and then compilers that truncate i // j need to make i % j have the same sign as i.

There are few real use cases for i % j when j is negative. When j is positive, there are many, and in virtually all of them it’s more useful for i % j to be >= 0. If the clock says 10 now, what did it say 200 hours ago? -190 % 12 == 2 is useful; -190 % 12 == -10 is a bug waiting to bite.

How do I get int literal attribute instead of SyntaxError?

Trying to lookup an int literal attribute in the normal manner gives a SyntaxError because the period is seen as a decimal point:

>>> 1.__class__
+  File "<stdin>", line 1
+  1.__class__
+   ^
+SyntaxError: invalid decimal literal
+

The solution is to separate the literal from the period with either a space or parentheses.

>>> 1 .__class__
+<class 'int'>
+>>> (1).__class__
+<class 'int'>
+

How do I convert a string to a number?

For integers, use the built-in int() type constructor, e.g. int('144') +== 144. Similarly, float() converts to floating-point, e.g. float('144') == 144.0.

By default, these interpret the number as decimal, so that int('0144') == +144 holds true, and int('0x144') raises ValueError. int(string, +base) takes the base to convert from as a second optional argument, so int( +'0x144', 16) == 324. If the base is specified as 0, the number is interpreted using Python’s rules: a leading ‘0o’ indicates octal, and ‘0x’ indicates a hex number.

Do not use the built-in function eval() if all you need is to convert strings to numbers. eval() will be significantly slower and it presents a security risk: someone could pass you a Python expression that might have unwanted side effects. For example, someone could pass __import__('os').system("rm -rf $HOME") which would erase your home directory.

eval() also has the effect of interpreting numbers as Python expressions, so that e.g. eval('09') gives a syntax error because Python does not allow leading ‘0’ in a decimal number (except ‘0’).

How do I convert a number to a string?

To convert, e.g., the number 144 to the string '144', use the built-in type constructor str(). If you want a hexadecimal or octal representation, use the built-in functions hex() or oct(). For fancy formatting, see the f-strings and Format String Syntax sections, e.g. "{:04d}".format(144) yields '0144' and "{:.3f}".format(1.0/3.0) yields '0.333'.

How do I modify a string in place?

You can’t, because strings are immutable. In most situations, you should simply construct a new string from the various parts you want to assemble it from. However, if you need an object with the ability to modify in-place unicode data, try using an io.StringIO object or the array module:

>>> import io
+>>> s = "Hello, world"
+>>> sio = io.StringIO(s)
+>>> sio.getvalue()
+'Hello, world'
+>>> sio.seek(7)
+7
+>>> sio.write("there!")
+6
+>>> sio.getvalue()
+'Hello, there!'
+
+>>> import array
+>>> a = array.array('u', s)
+>>> print(a)
+array('u', 'Hello, world')
+>>> a[0] = 'y'
+>>> print(a)
+array('u', 'yello, world')
+>>> a.tounicode()
+'yello, world'
+

How do I use strings to call functions/methods?

There are various techniques.

  • +

    The best is to use a dictionary that maps strings to functions. The primary advantage of this technique is that the strings do not need to match the names of the functions. This is also the primary technique used to emulate a case construct:

    def a():
+    pass
+
+def b():
+    pass
+
+dispatch = {'go': a, 'stop': b}  # Note lack of parens for funcs
+
+dispatch[get_input()]()  # Note trailing parens to call function
+
  • +

    Use the built-in function getattr():

    import foo
+getattr(foo, 'bar')()
+

    Note that getattr() works on any object, including classes, class instances, modules, and so on.

    This is used in several places in the standard library, like this:

    class Foo:
+    def do_foo(self):
+        ...
+
+    def do_bar(self):
+        ...
+
+f = getattr(foo_instance, 'do_' + opname)
+f()
+
  • +

    Use locals() to resolve the function name:

    def myFunc():
+    print("hello")
+
+fname = "myFunc"
+
+f = locals()[fname]
+f()
+

Is there an equivalent to Perl’s chomp() for removing trailing newlines from strings?

You can use S.rstrip("\r\n") to remove all occurrences of any line terminator from the end of the string S without removing other trailing whitespace. If the string S represents more than one line, with several empty lines at the end, the line terminators for all the blank lines will be removed:

>>> lines = ("line 1 \r\n"
+...          "\r\n"
+...          "\r\n")
+>>> lines.rstrip("\n\r")
+'line 1 '
+

Since this is typically only desired when reading text one line at a time, using S.rstrip() this way works well.

Is there a scanf() or sscanf() equivalent?

Not as such.

For simple input parsing, the easiest approach is usually to split the line into whitespace-delimited words using the split() method of string objects and then convert decimal strings to numeric values using int() or float(). split() supports an optional “sep” parameter which is useful if the line uses something other than whitespace as a separator.

For more complicated input parsing, regular expressions are more powerful than C’s sscanf and better suited for the task.

What does ‘UnicodeDecodeError’ or ‘UnicodeEncodeError’ error mean?

See the Unicode HOWTO.

Can I end a raw string with an odd number of backslashes?

A raw string ending with an odd number of backslashes will escape the string’s quote:

>>> r'C:\this\will\not\work\'
+  File "<stdin>", line 1
+    r'C:\this\will\not\work\'
+         ^
+SyntaxError: unterminated string literal (detected at line 1)
+

There are several workarounds for this. One is to use regular strings and double the backslashes:

>>> 'C:\\this\\will\\work\\'
+'C:\\this\\will\\work\\'
+

Another is to concatenate a regular string containing an escaped backslash to the raw string:

>>> r'C:\this\will\work' '\\'
+'C:\\this\\will\\work\\'
+

It is also possible to use os.path.join() to append a backslash on Windows:

>>> os.path.join(r'C:\this\will\work', '')
+'C:\\this\\will\\work\\'
+

Note that while a backslash will “escape” a quote for the purposes of determining where the raw string ends, no escaping occurs when interpreting the value of the raw string. That is, the backslash remains present in the value of the raw string:

>>> r'backslash\'preserved'
+"backslash\\'preserved"
+

Also see the specification in the language reference.

Performance

My program is too slow. How do I speed it up?

That’s a tough one, in general. First, here are a list of things to remember before diving further:

  • Performance characteristics vary across Python implementations. This FAQ focuses on CPython.
  • Behaviour can vary across operating systems, especially when talking about I/O or multi-threading.
  • You should always find the hot spots in your program before attempting to optimize any code (see the profile module).
  • Writing benchmark scripts will allow you to iterate quickly when searching for improvements (see the timeit module).
  • It is highly recommended to have good code coverage (through unit testing or any other technique) before potentially introducing regressions hidden in sophisticated optimizations.

That being said, there are many tricks to speed up Python code. Here are some general principles which go a long way towards reaching acceptable performance levels:

  • Making your algorithms faster (or changing to faster ones) can yield much larger benefits than trying to sprinkle micro-optimization tricks all over your code.
  • Use the right data structures. Study documentation for the Built-in Types and the collections module.
  • When the standard library provides a primitive for doing something, it is likely (although not guaranteed) to be faster than any alternative you may come up with. This is doubly true for primitives written in C, such as builtins and some extension types. For example, be sure to use either the list.sort() built-in method or the related sorted() function to do sorting (and see the Sorting HOW TO for examples of moderately advanced usage).
  • Abstractions tend to create indirections and force the interpreter to work more. If the levels of indirection outweigh the amount of useful work done, your program will be slower. You should avoid excessive abstraction, especially under the form of tiny functions or methods (which are also often detrimental to readability).

If you have reached the limit of what pure Python can allow, there are tools to take you further away. For example, Cython can compile a slightly modified version of Python code into a C extension, and can be used on many different platforms. Cython can take advantage of compilation (and optional type annotations) to make your code significantly faster than when interpreted. If you are confident in your C programming skills, you can also write a C extension module yourself.

See also

The wiki page devoted to performance tips.

What is the most efficient way to concatenate many strings together?

str and bytes objects are immutable, therefore concatenating many strings together is inefficient as each concatenation creates a new object. In the general case, the total runtime cost is quadratic in the total string length.

To accumulate many str objects, the recommended idiom is to place them into a list and call str.join() at the end:

chunks = []
+for s in my_strings:
+    chunks.append(s)
+result = ''.join(chunks)
+

(another reasonably efficient idiom is to use io.StringIO)

To accumulate many bytes objects, the recommended idiom is to extend a bytearray object using in-place concatenation (the += operator):

result = bytearray()
+for b in my_bytes_objects:
+    result += b
+

Sequences (Tuples/Lists)

How do I convert between tuples and lists?

The type constructor tuple(seq) converts any sequence (actually, any iterable) into a tuple with the same items in the same order.

For example, tuple([1, 2, 3]) yields (1, 2, 3) and tuple('abc') yields ('a', 'b', 'c'). If the argument is a tuple, it does not make a copy but returns the same object, so it is cheap to call tuple() when you aren’t sure that an object is already a tuple.

The type constructor list(seq) converts any sequence or iterable into a list with the same items in the same order. For example, list((1, 2, 3)) yields [1, 2, 3] and list('abc') yields ['a', 'b', 'c']. If the argument is a list, it makes a copy just like seq[:] would.

What’s a negative index?

Python sequences are indexed with positive numbers and negative numbers. For positive numbers 0 is the first index 1 is the second index and so forth. For negative indices -1 is the last index and -2 is the penultimate (next to last) index and so forth. Think of seq[-n] as the same as seq[len(seq)-n].

Using negative indices can be very convenient. For example S[:-1] is all of the string except for its last character, which is useful for removing the trailing newline from a string.

How do I iterate over a sequence in reverse order?

Use the reversed() built-in function:

for x in reversed(sequence):
+    ...  # do something with x ...
+

This won’t touch your original sequence, but build a new copy with reversed order to iterate over.

How do you remove duplicates from a list?

See the Python Cookbook for a long discussion of many ways to do this:

https://code.activestate.com/recipes/52560/

If you don’t mind reordering the list, sort it and then scan from the end of the list, deleting duplicates as you go:

if mylist:
+    mylist.sort()
+    last = mylist[-1]
+    for i in range(len(mylist)-2, -1, -1):
+        if last == mylist[i]:
+            del mylist[i]
+        else:
+            last = mylist[i]
+

If all elements of the list may be used as set keys (i.e. they are all hashable) this is often faster

mylist = list(set(mylist))
+

This converts the list into a set, thereby removing duplicates, and then back into a list.

How do you remove multiple items from a list

As with removing duplicates, explicitly iterating in reverse with a delete condition is one possibility. However, it is easier and faster to use slice replacement with an implicit or explicit forward iteration. Here are three variations.:

mylist[:] = filter(keep_function, mylist)
+mylist[:] = (x for x in mylist if keep_condition)
+mylist[:] = [x for x in mylist if keep_condition]
+

The list comprehension may be fastest.

How do you make an array in Python?

Use a list:

["this", 1, "is", "an", "array"]
+

Lists are equivalent to C or Pascal arrays in their time complexity; the primary difference is that a Python list can contain objects of many different types.

The array module also provides methods for creating arrays of fixed types with compact representations, but they are slower to index than lists. Also note that NumPy and other third party packages define array-like structures with various characteristics as well.

To get Lisp-style linked lists, you can emulate cons cells using tuples:

lisp_list = ("like",  ("this",  ("example", None) ) )
+

If mutability is desired, you could use lists instead of tuples. Here the analogue of a Lisp car is lisp_list[0] and the analogue of cdr is lisp_list[1]. Only do this if you’re sure you really need to, because it’s usually a lot slower than using Python lists.

How do I create a multidimensional list?

You probably tried to make a multidimensional array like this:

>>> A = [[None] * 2] * 3
+

This looks correct if you print it:

>>> A
+[[None, None], [None, None], [None, None]]
+

But when you assign a value, it shows up in multiple places:

>>> A[0][0] = 5
+>>> A
+[[5, None], [5, None], [5, None]]
+

The reason is that replicating a list with * doesn’t create copies, it only creates references to the existing objects. The *3 creates a list containing 3 references to the same list of length two. Changes to one row will show in all rows, which is almost certainly not what you want.

The suggested approach is to create a list of the desired length first and then fill in each element with a newly created list:

A = [None] * 3
+for i in range(3):
+    A[i] = [None] * 2
+

This generates a list containing 3 different lists of length two. You can also use a list comprehension:

w, h = 2, 3
+A = [[None] * w for i in range(h)]
+

Or, you can use an extension that provides a matrix datatype; NumPy is the best known.

How do I apply a method or function to a sequence of objects?

To call a method or function and accumulate the return values is a list, a list comprehension is an elegant solution:

result = [obj.method() for obj in mylist]
+
+result = [function(obj) for obj in mylist]
+

To just run the method or function without saving the return values, a plain for loop will suffice:

for obj in mylist:
+    obj.method()
+
+for obj in mylist:
+    function(obj)
+

Why does a_tuple[i] += [‘item’] raise an exception when the addition works?

This is because of a combination of the fact that augmented assignment operators are assignment operators, and the difference between mutable and immutable objects in Python.

This discussion applies in general when augmented assignment operators are applied to elements of a tuple that point to mutable objects, but we’ll use a list and += as our exemplar.

If you wrote:

>>> a_tuple = (1, 2)
+>>> a_tuple[0] += 1
+Traceback (most recent call last):
+   ...
+TypeError: 'tuple' object does not support item assignment
+

The reason for the exception should be immediately clear: 1 is added to the object a_tuple[0] points to (1), producing the result object, 2, but when we attempt to assign the result of the computation, 2, to element 0 of the tuple, we get an error because we can’t change what an element of a tuple points to.

Under the covers, what this augmented assignment statement is doing is approximately this:

>>> result = a_tuple[0] + 1
+>>> a_tuple[0] = result
+Traceback (most recent call last):
+  ...
+TypeError: 'tuple' object does not support item assignment
+

It is the assignment part of the operation that produces the error, since a tuple is immutable.

When you write something like:

>>> a_tuple = (['foo'], 'bar')
+>>> a_tuple[0] += ['item']
+Traceback (most recent call last):
+  ...
+TypeError: 'tuple' object does not support item assignment
+

The exception is a bit more surprising, and even more surprising is the fact that even though there was an error, the append worked:

>>> a_tuple[0]
+['foo', 'item']
+

To see why this happens, you need to know that (a) if an object implements an __iadd__() magic method, it gets called when the += augmented assignment is executed, and its return value is what gets used in the assignment statement; and (b) for lists, __iadd__() is equivalent to calling extend() on the list and returning the list. That’s why we say that for lists, += is a “shorthand” for list.extend():

>>> a_list = []
+>>> a_list += [1]
+>>> a_list
+[1]
+

This is equivalent to:

>>> result = a_list.__iadd__([1])
+>>> a_list = result
+

The object pointed to by a_list has been mutated, and the pointer to the mutated object is assigned back to a_list. The end result of the assignment is a no-op, since it is a pointer to the same object that a_list was previously pointing to, but the assignment still happens.

Thus, in our tuple example what is happening is equivalent to:

>>> result = a_tuple[0].__iadd__(['item'])
+>>> a_tuple[0] = result
+Traceback (most recent call last):
+  ...
+TypeError: 'tuple' object does not support item assignment
+

The __iadd__() succeeds, and thus the list is extended, but even though result points to the same object that a_tuple[0] already points to, that final assignment still results in an error, because tuples are immutable.

I want to do a complicated sort: can you do a Schwartzian Transform in Python?

The technique, attributed to Randal Schwartz of the Perl community, sorts the elements of a list by a metric which maps each element to its “sort value”. In Python, use the key argument for the list.sort() method:

Isorted = L[:]
+Isorted.sort(key=lambda s: int(s[10:15]))
+

How can I sort one list by values from another list?

Merge them into an iterator of tuples, sort the resulting list, and then pick out the element you want.

>>> list1 = ["what", "I'm", "sorting", "by"]
+>>> list2 = ["something", "else", "to", "sort"]
+>>> pairs = zip(list1, list2)
+>>> pairs = sorted(pairs)
+>>> pairs
+[("I'm", 'else'), ('by', 'sort'), ('sorting', 'to'), ('what', 'something')]
+>>> result = [x[1] for x in pairs]
+>>> result
+['else', 'sort', 'to', 'something']
+

Objects

What is a class?

A class is the particular object type created by executing a class statement. Class objects are used as templates to create instance objects, which embody both the data (attributes) and code (methods) specific to a datatype.

A class can be based on one or more other classes, called its base class(es). It then inherits the attributes and methods of its base classes. This allows an object model to be successively refined by inheritance. You might have a generic Mailbox class that provides basic accessor methods for a mailbox, and subclasses such as MboxMailbox, MaildirMailbox, OutlookMailbox that handle various specific mailbox formats.

What is a method?

A method is a function on some object x that you normally call as x.name(arguments...). Methods are defined as functions inside the class definition:

class C:
+    def meth(self, arg):
+        return arg * 2 + self.attribute
+

What is self?

Self is merely a conventional name for the first argument of a method. A method defined as meth(self, a, b, c) should be called as x.meth(a, b, c) for some instance x of the class in which the definition occurs; the called method will think it is called as meth(x, a, b, c).

See also Why must ‘self’ be used explicitly in method definitions and calls?.

How do I check if an object is an instance of a given class or of a subclass of it?

Use the built-in function isinstance(obj, cls). You can check if an object is an instance of any of a number of classes by providing a tuple instead of a single class, e.g. isinstance(obj, (class1, class2, ...)), and can also check whether an object is one of Python’s built-in types, e.g. isinstance(obj, str) or isinstance(obj, (int, float, complex)).

Note that isinstance() also checks for virtual inheritance from an abstract base class. So, the test will return True for a registered class even if hasn’t directly or indirectly inherited from it. To test for “true inheritance”, scan the MRO of the class:

from collections.abc import Mapping
+
+class P:
+     pass
+
+class C(P):
+    pass
+
+Mapping.register(P)
+
>>> c = C()
+>>> isinstance(c, C)        # direct
+True
+>>> isinstance(c, P)        # indirect
+True
+>>> isinstance(c, Mapping)  # virtual
+True
+
+# Actual inheritance chain
+>>> type(c).__mro__
+(<class 'C'>, <class 'P'>, <class 'object'>)
+
+# Test for "true inheritance"
+>>> Mapping in type(c).__mro__
+False
+

Note that most programs do not use isinstance() on user-defined classes very often. If you are developing the classes yourself, a more proper object-oriented style is to define methods on the classes that encapsulate a particular behaviour, instead of checking the object’s class and doing a different thing based on what class it is. For example, if you have a function that does something:

def search(obj):
+    if isinstance(obj, Mailbox):
+        ...  # code to search a mailbox
+    elif isinstance(obj, Document):
+        ...  # code to search a document
+    elif ...
+

A better approach is to define a search() method on all the classes and just call it:

class Mailbox:
+    def search(self):
+        ...  # code to search a mailbox
+
+class Document:
+    def search(self):
+        ...  # code to search a document
+
+obj.search()
+

What is delegation?

Delegation is an object oriented technique (also called a design pattern). Let’s say you have an object x and want to change the behaviour of just one of its methods. You can create a new class that provides a new implementation of the method you’re interested in changing and delegates all other methods to the corresponding method of x.

Python programmers can easily implement delegation. For example, the following class implements a class that behaves like a file but converts all written data to uppercase:

class UpperOut:
+
+    def __init__(self, outfile):
+        self._outfile = outfile
+
+    def write(self, s):
+        self._outfile.write(s.upper())
+
+    def __getattr__(self, name):
+        return getattr(self._outfile, name)
+

Here the UpperOut class redefines the write() method to convert the argument string to uppercase before calling the underlying self._outfile.write() method. All other methods are delegated to the underlying self._outfile object. The delegation is accomplished via the __getattr__() method; consult the language reference for more information about controlling attribute access.

Note that for more general cases delegation can get trickier. When attributes must be set as well as retrieved, the class must define a __setattr__() method too, and it must do so carefully. The basic implementation of __setattr__() is roughly equivalent to the following:

class X:
+    ...
+    def __setattr__(self, name, value):
+        self.__dict__[name] = value
+    ...
+

Most __setattr__() implementations must modify self.__dict__ to store local state for self without causing an infinite recursion.

How do I call a method defined in a base class from a derived class that extends it?

Use the built-in super() function:

class Derived(Base):
+    def meth(self):
+        super().meth()  # calls Base.meth
+

In the example, super() will automatically determine the instance from which it was called (the self value), look up the method resolution order (MRO) with type(self).__mro__, and return the next in line after Derived in the MRO: Base.

How can I organize my code to make it easier to change the base class?

You could assign the base class to an alias and derive from the alias. Then all you have to change is the value assigned to the alias. Incidentally, this trick is also handy if you want to decide dynamically (e.g. depending on availability of resources) which base class to use. Example:

class Base:
+    ...
+
+BaseAlias = Base
+
+class Derived(BaseAlias):
+    ...
+

How do I create static class data and static class methods?

Both static data and static methods (in the sense of C++ or Java) are supported in Python.

For static data, simply define a class attribute. To assign a new value to the attribute, you have to explicitly use the class name in the assignment:

class C:
+    count = 0   # number of times C.__init__ called
+
+    def __init__(self):
+        C.count = C.count + 1
+
+    def getcount(self):
+        return C.count  # or return self.count
+

c.count also refers to C.count for any c such that isinstance(c, +C) holds, unless overridden by c itself or by some class on the base-class search path from c.__class__ back to C.

Caution: within a method of C, an assignment like self.count = 42 creates a new and unrelated instance named “count” in self’s own dict. Rebinding of a class-static data name must always specify the class whether inside a method or not:

C.count = 314
+

Static methods are possible:

class C:
+    @staticmethod
+    def static(arg1, arg2, arg3):
+        # No 'self' parameter!
+        ...
+

However, a far more straightforward way to get the effect of a static method is via a simple module-level function:

def getcount():
+    return C.count
+

If your code is structured so as to define one class (or tightly related class hierarchy) per module, this supplies the desired encapsulation.

How can I overload constructors (or methods) in Python?

This answer actually applies to all methods, but the question usually comes up first in the context of constructors.

In C++ you’d write

class C {
+    C() { cout << "No arguments\n"; }
+    C(int i) { cout << "Argument is " << i << "\n"; }
+}
+

In Python you have to write a single constructor that catches all cases using default arguments. For example:

class C:
+    def __init__(self, i=None):
+        if i is None:
+            print("No arguments")
+        else:
+            print("Argument is", i)
+

This is not entirely equivalent, but close enough in practice.

You could also try a variable-length argument list, e.g.

def __init__(self, *args):
+    ...
+

The same approach works for all method definitions.

I try to use __spam and I get an error about _SomeClassName__spam.

Variable names with double leading underscores are “mangled” to provide a simple but effective way to define class private variables. Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with any leading underscores stripped.

This doesn’t guarantee privacy: an outside user can still deliberately access the “_classname__spam” attribute, and private values are visible in the object’s __dict__. Many Python programmers never bother to use private variable names at all.

My class defines __del__ but it is not called when I delete the object.

There are several possible reasons for this.

The del statement does not necessarily call __del__() – it simply decrements the object’s reference count, and if this reaches zero __del__() is called.

If your data structures contain circular links (e.g. a tree where each child has a parent reference and each parent has a list of children) the reference counts will never go back to zero. Once in a while Python runs an algorithm to detect such cycles, but the garbage collector might run some time after the last reference to your data structure vanishes, so your __del__() method may be called at an inconvenient and random time. This is inconvenient if you’re trying to reproduce a problem. Worse, the order in which object’s __del__() methods are executed is arbitrary. You can run gc.collect() to force a collection, but there are pathological cases where objects will never be collected.

Despite the cycle collector, it’s still a good idea to define an explicit close() method on objects to be called whenever you’re done with them. The close() method can then remove attributes that refer to subobjects. Don’t call __del__() directly – __del__() should call close() and close() should make sure that it can be called more than once for the same object.

Another way to avoid cyclical references is to use the weakref module, which allows you to point to objects without incrementing their reference count. Tree data structures, for instance, should use weak references for their parent and sibling references (if they need them!).

Finally, if your __del__() method raises an exception, a warning message is printed to sys.stderr.

How do I get a list of all instances of a given class?

Python does not keep track of all instances of a class (or of a built-in type). You can program the class’s constructor to keep track of all instances by keeping a list of weak references to each instance.

Why does the result of id() appear to be not unique?

The id() builtin returns an integer that is guaranteed to be unique during the lifetime of the object. Since in CPython, this is the object’s memory address, it happens frequently that after an object is deleted from memory, the next freshly created object is allocated at the same position in memory. This is illustrated by this example:

>>> id(1000) 
+13901272
+>>> id(2000) 
+13901272
+

The two ids belong to different integer objects that are created before, and deleted immediately after execution of the id() call. To be sure that objects whose id you want to examine are still alive, create another reference to the object:

>>> a = 1000; b = 2000
+>>> id(a) 
+13901272
+>>> id(b) 
+13891296
+

When can I rely on identity tests with the is operator?

The is operator tests for object identity. The test a is b is equivalent to id(a) == id(b).

The most important property of an identity test is that an object is always identical to itself, a is a always returns True. Identity tests are usually faster than equality tests. And unlike equality tests, identity tests are guaranteed to return a boolean True or False.

However, identity tests can only be substituted for equality tests when object identity is assured. Generally, there are three circumstances where identity is guaranteed:

1) Assignments create new names but do not change object identity. After the assignment new = old, it is guaranteed that new is old.

2) Putting an object in a container that stores object references does not change object identity. After the list assignment s[0] = x, it is guaranteed that s[0] is x.

3) If an object is a singleton, it means that only one instance of that object can exist. After the assignments a = None and b = None, it is guaranteed that a is b because None is a singleton.

In most other circumstances, identity tests are inadvisable and equality tests are preferred. In particular, identity tests should not be used to check constants such as int and str which aren’t guaranteed to be singletons:

>>> a = 1000
+>>> b = 500
+>>> c = b + 500
+>>> a is c
+False
+
+>>> a = 'Python'
+>>> b = 'Py'
+>>> c = b + 'thon'
+>>> a is c
+False
+

Likewise, new instances of mutable containers are never identical:

>>> a = []
+>>> b = []
+>>> a is b
+False
+

In the standard library code, you will see several common patterns for correctly using identity tests:

1) As recommended by PEP 8, an identity test is the preferred way to check for None. This reads like plain English in code and avoids confusion with other objects that may have boolean values that evaluate to false.

2) Detecting optional arguments can be tricky when None is a valid input value. In those situations, you can create a singleton sentinel object guaranteed to be distinct from other objects. For example, here is how to implement a method that behaves like dict.pop():

_sentinel = object()
+
+def pop(self, key, default=_sentinel):
+    if key in self:
+        value = self[key]
+        del self[key]
+        return value
+    if default is _sentinel:
+        raise KeyError(key)
+    return default
+

3) Container implementations sometimes need to augment equality tests with identity tests. This prevents the code from being confused by objects such as float('NaN') that are not equal to themselves.

For example, here is the implementation of collections.abc.Sequence.__contains__():

def __contains__(self, value):
+    for v in self:
+        if v is value or v == value:
+            return True
+    return False
+

How can a subclass control what data is stored in an immutable instance?

When subclassing an immutable type, override the __new__() method instead of the __init__() method. The latter only runs after an instance is created, which is too late to alter data in an immutable instance.

All of these immutable classes have a different signature than their parent class:

from datetime import date
+
+class FirstOfMonthDate(date):
+    "Always choose the first day of the month"
+    def __new__(cls, year, month, day):
+        return super().__new__(cls, year, month, 1)
+
+class NamedInt(int):
+    "Allow text names for some numbers"
+    xlat = {'zero': 0, 'one': 1, 'ten': 10}
+    def __new__(cls, value):
+        value = cls.xlat.get(value, value)
+        return super().__new__(cls, value)
+
+class TitleStr(str):
+    "Convert str to name suitable for a URL path"
+    def __new__(cls, s):
+        s = s.lower().replace(' ', '-')
+        s = ''.join([c for c in s if c.isalnum() or c == '-'])
+        return super().__new__(cls, s)
+

The classes can be used like this:

>>> FirstOfMonthDate(2012, 2, 14)
+FirstOfMonthDate(2012, 2, 1)
+>>> NamedInt('ten')
+10
+>>> NamedInt(20)
+20
+>>> TitleStr('Blog: Why Python Rocks')
+'blog-why-python-rocks'
+

How do I cache method calls?

The two principal tools for caching methods are functools.cached_property() and functools.lru_cache(). The former stores results at the instance level and the latter at the class level.

The cached_property approach only works with methods that do not take any arguments. It does not create a reference to the instance. The cached method result will be kept only as long as the instance is alive.

The advantage is that when an instance is no longer used, the cached method result will be released right away. The disadvantage is that if instances accumulate, so too will the accumulated method results. They can grow without bound.

The lru_cache approach works with methods that have hashable arguments. It creates a reference to the instance unless special efforts are made to pass in weak references.

The advantage of the least recently used algorithm is that the cache is bounded by the specified maxsize. The disadvantage is that instances are kept alive until they age out of the cache or until the cache is cleared.

This example shows the various techniques:

class Weather:
+    "Lookup weather information on a government website"
+
+    def __init__(self, station_id):
+        self._station_id = station_id
+        # The _station_id is private and immutable
+
+    def current_temperature(self):
+        "Latest hourly observation"
+        # Do not cache this because old results
+        # can be out of date.
+
+    @cached_property
+    def location(self):
+        "Return the longitude/latitude coordinates of the station"
+        # Result only depends on the station_id
+
+    @lru_cache(maxsize=20)
+    def historic_rainfall(self, date, units='mm'):
+        "Rainfall on a given date"
+        # Depends on the station_id, date, and units.
+

The above example assumes that the station_id never changes. If the relevant instance attributes are mutable, the cached_property approach can’t be made to work because it cannot detect changes to the attributes.

To make the lru_cache approach work when the station_id is mutable, the class needs to define the __eq__() and __hash__() methods so that the cache can detect relevant attribute updates:

class Weather:
+    "Example with a mutable station identifier"
+
+    def __init__(self, station_id):
+        self.station_id = station_id
+
+    def change_station(self, station_id):
+        self.station_id = station_id
+
+    def __eq__(self, other):
+        return self.station_id == other.station_id
+
+    def __hash__(self):
+        return hash(self.station_id)
+
+    @lru_cache(maxsize=20)
+    def historic_rainfall(self, date, units='cm'):
+        'Rainfall on a given date'
+        # Depends on the station_id, date, and units.
+

Modules

How do I create a .pyc file?

When a module is imported for the first time (or when the source file has changed since the current compiled file was created) a .pyc file containing the compiled code should be created in a __pycache__ subdirectory of the directory containing the .py file. The .pyc file will have a filename that starts with the same name as the .py file, and ends with .pyc, with a middle component that depends on the particular python binary that created it. (See PEP 3147 for details.)

One reason that a .pyc file may not be created is a permissions problem with the directory containing the source file, meaning that the __pycache__ subdirectory cannot be created. This can happen, for example, if you develop as one user but run as another, such as if you are testing with a web server.

Unless the PYTHONDONTWRITEBYTECODE environment variable is set, creation of a .pyc file is automatic if you’re importing a module and Python has the ability (permissions, free space, etc…) to create a __pycache__ subdirectory and write the compiled module to that subdirectory.

Running Python on a top level script is not considered an import and no .pyc will be created. For example, if you have a top-level module foo.py that imports another module xyz.py, when you run foo (by typing python foo.py as a shell command), a .pyc will be created for xyz because xyz is imported, but no .pyc file will be created for foo since foo.py isn’t being imported.

If you need to create a .pyc file for foo – that is, to create a .pyc file for a module that is not imported – you can, using the py_compile and compileall modules.

The py_compile module can manually compile any module. One way is to use the compile() function in that module interactively:

>>> import py_compile
+>>> py_compile.compile('foo.py')                 
+

This will write the .pyc to a __pycache__ subdirectory in the same location as foo.py (or you can override that with the optional parameter cfile).

You can also automatically compile all files in a directory or directories using the compileall module. You can do it from the shell prompt by running compileall.py and providing the path of a directory containing Python files to compile:

python -m compileall .
+

How do I find the current module name?

A module can find out its own module name by looking at the predefined global variable __name__. If this has the value '__main__', the program is running as a script. Many modules that are usually used by importing them also provide a command-line interface or a self-test, and only execute this code after checking __name__:

def main():
+    print('Running test...')
+    ...
+
+if __name__ == '__main__':
+    main()
+

How can I have modules that mutually import each other?

Suppose you have the following modules:

foo.py:

from bar import bar_var
+foo_var = 1
+

bar.py:

from foo import foo_var
+bar_var = 2
+

The problem is that the interpreter will perform the following steps:

  • main imports foo +
  • Empty globals for foo are created
  • +foo is compiled and starts executing
  • +foo imports bar +
  • Empty globals for bar are created
  • +bar is compiled and starts executing
  • +bar imports foo (which is a no-op since there already is a module named foo)
  • The import mechanism tries to read foo_var from foo globals, to set bar.foo_var = foo.foo_var +

The last step fails, because Python isn’t done with interpreting foo yet and the global symbol dictionary for foo is still empty.

The same thing happens when you use import foo, and then try to access foo.foo_var in global code.

There are (at least) three possible workarounds for this problem.

Guido van Rossum recommends avoiding all uses of from <module> import ..., and placing all code inside functions. Initializations of global variables and class variables should use constants or built-in functions only. This means everything from an imported module is referenced as <module>.<name>.

Jim Roskind suggests performing steps in the following order in each module:

  • exports (globals, functions, and classes that don’t need imported base classes)
  • +import statements
  • active code (including globals that are initialized from imported values).

Van Rossum doesn’t like this approach much because the imports appear in a strange place, but it does work.

Matthias Urlichs recommends restructuring your code so that the recursive import is not necessary in the first place.

These solutions are not mutually exclusive.

__import__(‘x.y.z’) returns <module ‘x’>; how do I get z?

Consider using the convenience function import_module() from importlib instead:

z = importlib.import_module('x.y.z')
+

When I edit an imported module and reimport it, the changes don’t show up. Why does this happen?

For reasons of efficiency as well as consistency, Python only reads the module file on the first time a module is imported. If it didn’t, in a program consisting of many modules where each one imports the same basic module, the basic module would be parsed and re-parsed many times. To force re-reading of a changed module, do this:

import importlib
+import modname
+importlib.reload(modname)
+

Warning: this technique is not 100% fool-proof. In particular, modules containing statements like

from modname import some_objects
+

will continue to work with the old version of the imported objects. If the module contains class definitions, existing class instances will not be updated to use the new class definition. This can result in the following paradoxical behaviour:

>>> import importlib
+>>> import cls
+>>> c = cls.C()                # Create an instance of C
+>>> importlib.reload(cls)
+<module 'cls' from 'cls.py'>
+>>> isinstance(c, cls.C)       # isinstance is false?!?
+False
+

The nature of the problem is made clear if you print out the “identity” of the class objects:

>>> hex(id(c.__class__))
+'0x7352a0'
+>>> hex(id(cls.C))
+'0x4198d0'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/programming.html +

+
diff --git a/devdocs/python~3.12/faq%2Fwindows.html b/devdocs/python~3.12/faq%2Fwindows.html new file mode 100644 index 00000000..ac263f29 --- /dev/null +++ b/devdocs/python~3.12/faq%2Fwindows.html @@ -0,0 +1,34 @@ +

Python on Windows FAQ

+

How do I run a Python program under Windows?

This is not necessarily a straightforward question. If you are already familiar with running programs from the Windows command line then everything will seem obvious; otherwise, you might need a little more guidance.

Unless you use some sort of integrated development environment, you will end up typing Windows commands into what is referred to as a “Command prompt window”. Usually you can create such a window from your search bar by searching for cmd. You should be able to recognize when you have started such a window because you will see a Windows “command prompt”, which usually looks like this:

C:\>
+

The letter may be different, and there might be other things after it, so you might just as easily see something like:

D:\YourName\Projects\Python>
+

depending on how your computer has been set up and what else you have recently done with it. Once you have started such a window, you are well on the way to running Python programs.

You need to realize that your Python scripts have to be processed by another program called the Python interpreter. The interpreter reads your script, compiles it into bytecodes, and then executes the bytecodes to run your program. So, how do you arrange for the interpreter to handle your Python?

First, you need to make sure that your command window recognises the word “py” as an instruction to start the interpreter. If you have opened a command window, you should try entering the command py and hitting return:

C:\Users\YourName> py
+

You should then see something like:

Python 3.6.4 (v3.6.4:d48eceb, Dec 19 2017, 06:04:45) [MSC v.1900 32 bit (Intel)] on win32
+Type "help", "copyright", "credits" or "license" for more information.
+>>>
+

You have started the interpreter in “interactive mode”. That means you can enter Python statements or expressions interactively and have them executed or evaluated while you wait. This is one of Python’s strongest features. Check it by entering a few expressions of your choice and seeing the results:

>>> print("Hello")
+Hello
+>>> "Hello" * 3
+'HelloHelloHello'
+

Many people use the interactive mode as a convenient yet highly programmable calculator. When you want to end your interactive Python session, call the exit() function or hold the Ctrl key down while you enter a Z, then hit the “Enter” key to get back to your Windows command prompt.

You may also find that you have a Start-menu entry such as Start ‣ Programs ‣ Python 3.x ‣ Python (command line) that results in you seeing the >>> prompt in a new window. If so, the window will disappear after you call the exit() function or enter the Ctrl-Z character; Windows is running a single “python” command in the window, and closes it when you terminate the interpreter.

Now that we know the py command is recognized, you can give your Python script to it. You’ll have to give either an absolute or a relative path to the Python script. Let’s say your Python script is located in your desktop and is named hello.py, and your command prompt is nicely opened in your home directory so you’re seeing something similar to:

C:\Users\YourName>
+

So now you’ll ask the py command to give your script to Python by typing py followed by your script path:

C:\Users\YourName> py Desktop\hello.py
+hello
+

How do I make Python scripts executable?

On Windows, the standard Python installer already associates the .py extension with a file type (Python.File) and gives that file type an open command that runs the interpreter (D:\Program Files\Python\python.exe "%1" +%*). This is enough to make scripts executable from the command prompt as ‘foo.py’. If you’d rather be able to execute the script by simple typing ‘foo’ with no extension you need to add .py to the PATHEXT environment variable.

Why does Python sometimes take so long to start?

Usually Python starts very quickly on Windows, but occasionally there are bug reports that Python suddenly begins to take a long time to start up. This is made even more puzzling because Python will work fine on other Windows systems which appear to be configured identically.

The problem may be caused by a misconfiguration of virus checking software on the problem machine. Some virus scanners have been known to introduce startup overhead of two orders of magnitude when the scanner is configured to monitor all reads from the filesystem. Try checking the configuration of virus scanning software on your systems to ensure that they are indeed configured identically. McAfee, when configured to scan all file system read activity, is a particular offender.

How do I make an executable from a Python script?

See How can I create a stand-alone binary from a Python script? for a list of tools that can be used to make executables.

Is a *.pyd file the same as a DLL?

Yes, .pyd files are dll’s, but there are a few differences. If you have a DLL named foo.pyd, then it must have a function PyInit_foo(). You can then write Python “import foo”, and Python will search for foo.pyd (as well as foo.py, foo.pyc) and if it finds it, will attempt to call PyInit_foo() to initialize it. You do not link your .exe with foo.lib, as that would cause Windows to require the DLL to be present.

Note that the search path for foo.pyd is PYTHONPATH, not the same as the path that Windows uses to search for foo.dll. Also, foo.pyd need not be present to run your program, whereas if you linked your program with a dll, the dll is required. Of course, foo.pyd is required if you want to say import foo. In a DLL, linkage is declared in the source code with __declspec(dllexport). In a .pyd, linkage is defined in a list of available functions.

How can I embed Python into a Windows application?

Embedding the Python interpreter in a Windows app can be summarized as follows:

  1. +

    Do not build Python into your .exe file directly. On Windows, Python must be a DLL to handle importing modules that are themselves DLL’s. (This is the first key undocumented fact.) Instead, link to pythonNN.dll; it is typically installed in C:\Windows\System. NN is the Python version, a number such as “33” for Python 3.3.

    You can link to Python in two different ways. Load-time linking means linking against pythonNN.lib, while run-time linking means linking against pythonNN.dll. (General note: pythonNN.lib is the so-called “import lib” corresponding to pythonNN.dll. It merely defines symbols for the linker.)

    Run-time linking greatly simplifies link options; everything happens at run time. Your code must load pythonNN.dll using the Windows LoadLibraryEx() routine. The code must also use access routines and data in pythonNN.dll (that is, Python’s C API’s) using pointers obtained by the Windows GetProcAddress() routine. Macros can make using these pointers transparent to any C code that calls routines in Python’s C API.

  2. If you use SWIG, it is easy to create a Python “extension module” that will make the app’s data and methods available to Python. SWIG will handle just about all the grungy details for you. The result is C code that you link into your .exe file (!) You do not have to create a DLL file, and this also simplifies linking.
  3. +

    SWIG will create an init function (a C function) whose name depends on the name of the extension module. For example, if the name of the module is leo, the init function will be called initleo(). If you use SWIG shadow classes, as you should, the init function will be called initleoc(). This initializes a mostly hidden helper class used by the shadow class.

    The reason you can link the C code in step 2 into your .exe file is that calling the initialization function is equivalent to importing the module into Python! (This is the second key undocumented fact.)

  4. +

    In short, you can use the following code to initialize the Python interpreter with your extension module.

    #include <Python.h>
+...
+Py_Initialize();  // Initialize Python.
+initmyAppc();  // Initialize (import) the helper class.
+PyRun_SimpleString("import myApp");  // Import the shadow class.
+
  5. +

    There are two problems with Python’s C API which will become apparent if you use a compiler other than MSVC, the compiler used to build pythonNN.dll.

    Problem 1: The so-called “Very High Level” functions that take FILE * arguments will not work in a multi-compiler environment because each compiler’s notion of a struct FILE will be different. From an implementation standpoint these are very low level functions.

    Problem 2: SWIG generates the following code when generating wrappers to void functions:

    Py_INCREF(Py_None);
+_resultobj = Py_None;
+return _resultobj;
+

    Alas, Py_None is a macro that expands to a reference to a complex data structure called _Py_NoneStruct inside pythonNN.dll. Again, this code will fail in a mult-compiler environment. Replace such code by:

    return Py_BuildValue("");
+

    It may be possible to use SWIG’s %typemap command to make the change automatically, though I have not been able to get this to work (I’m a complete SWIG newbie).

  6. Using a Python shell script to put up a Python interpreter window from inside your Windows app is not a good idea; the resulting window will be independent of your app’s windowing system. Rather, you (or the wxPythonWindow class) should create a “native” interpreter window. It is easy to connect that window to the Python interpreter. You can redirect Python’s i/o to _any_ object that supports read and write, so all you need is a Python object (defined in your extension module) that contains read() and write() methods.

How do I keep editors from inserting tabs into my Python source?

The FAQ does not recommend using tabs, and the Python style guide, PEP 8, recommends 4 spaces for distributed Python code; this is also the Emacs python-mode default.

Under any editor, mixing tabs and spaces is a bad idea. MSVC is no different in this respect, and is easily configured to use spaces: Take Tools ‣ Options ‣ Tabs, and for file type “Default” set “Tab size” and “Indent size” to 4, and select the “Insert spaces” radio button.

Python raises IndentationError or TabError if mixed tabs and spaces are causing problems in leading whitespace. You may also run the tabnanny module to check a directory tree in batch mode.

How do I check for a keypress without blocking?

Use the msvcrt module. This is a standard Windows-specific extension module. It defines a function kbhit() which checks whether a keyboard hit is present, and getch() which gets one character without echoing it.

How do I solve the missing api-ms-win-crt-runtime-l1-1-0.dll error?

This can occur on Python 3.5 and later when using Windows 8.1 or earlier without all updates having been installed. First ensure your operating system is supported and is up to date, and if that does not resolve the issue, visit the Microsoft support page for guidance on manually installing the C Runtime update.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/faq/windows.html +

+
diff --git a/devdocs/python~3.12/genindex-all.html b/devdocs/python~3.12/genindex-all.html new file mode 100644 index 00000000..6c865086 --- /dev/null +++ b/devdocs/python~3.12/genindex-all.html @@ -0,0 +1,16965 @@ +

Index

Symbols | _ | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z

Symbols

_

A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

R

S

T

U

V

W

X

Y

Z

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/genindex-all.html +

+
diff --git a/devdocs/python~3.12/glossary.html b/devdocs/python~3.12/glossary.html new file mode 100644 index 00000000..1806337a --- /dev/null +++ b/devdocs/python~3.12/glossary.html @@ -0,0 +1,460 @@ +

Glossary

+>>>
+
+

The default Python prompt of the interactive shell. Often seen for code examples which can be executed interactively in the interpreter.

+...
+
+

Can refer to:

  • The default Python prompt of the interactive shell when entering the code for an indented code block, when within a pair of matching left and right delimiters (parentheses, square brackets, curly braces or triple quotes), or after specifying a decorator.
  • The Ellipsis built-in constant.
+2to3
+
+

A tool that tries to convert Python 2.x code to Python 3.x code by handling most of the incompatibilities which can be detected by parsing the source and traversing the parse tree.

2to3 is available in the standard library as lib2to3; a standalone entry point is provided as Tools/scripts/2to3. See 2to3 — Automated Python 2 to 3 code translation.

+abstract base class
+
+

Abstract base classes complement duck-typing by providing a way to define interfaces when other techniques like hasattr() would be clumsy or subtly wrong (for example with magic methods). ABCs introduce virtual subclasses, which are classes that don’t inherit from a class but are still recognized by isinstance() and issubclass(); see the abc module documentation. Python comes with many built-in ABCs for data structures (in the collections.abc module), numbers (in the numbers module), streams (in the io module), import finders and loaders (in the importlib.abc module). You can create your own ABCs with the abc module.

+annotation
+
+

A label associated with a variable, a class attribute or a function parameter or return value, used by convention as a type hint.

Annotations of local variables cannot be accessed at runtime, but annotations of global variables, class attributes, and functions are stored in the __annotations__ special attribute of modules, classes, and functions, respectively.

See variable annotation, function annotation, PEP 484 and PEP 526, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.

+argument
+
+

A value passed to a function (or method) when calling the function. There are two kinds of argument:

  • +

    keyword argument: an argument preceded by an identifier (e.g. name=) in a function call or passed as a value in a dictionary preceded by **. For example, 3 and 5 are both keyword arguments in the following calls to complex():

    complex(real=3, imag=5)
+complex(**{'real': 3, 'imag': 5})
+
  • +

    positional argument: an argument that is not a keyword argument. Positional arguments can appear at the beginning of an argument list and/or be passed as elements of an iterable preceded by *. For example, 3 and 5 are both positional arguments in the following calls:

    complex(3, 5)
+complex(*(3, 5))
+

Arguments are assigned to the named local variables in a function body. See the Calls section for the rules governing this assignment. Syntactically, any expression can be used to represent an argument; the evaluated value is assigned to the local variable.

See also the parameter glossary entry, the FAQ question on the difference between arguments and parameters, and PEP 362.

+asynchronous context manager
+
+

An object which controls the environment seen in an async with statement by defining __aenter__() and __aexit__() methods. Introduced by PEP 492.

+asynchronous generator
+
+

A function which returns an asynchronous generator iterator. It looks like a coroutine function defined with async def except that it contains yield expressions for producing a series of values usable in an async for loop.

Usually refers to an asynchronous generator function, but may refer to an asynchronous generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity.

An asynchronous generator function may contain await expressions as well as async for, and async with statements.

+asynchronous generator iterator
+
+

An object created by a asynchronous generator function.

This is an asynchronous iterator which when called using the __anext__() method returns an awaitable object which will execute the body of the asynchronous generator function until the next yield expression.

Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements). When the asynchronous generator iterator effectively resumes with another awaitable returned by __anext__(), it picks up where it left off. See PEP 492 and PEP 525.

+asynchronous iterable
+
+

An object, that can be used in an async for statement. Must return an asynchronous iterator from its __aiter__() method. Introduced by PEP 492.

+asynchronous iterator
+
+

An object that implements the __aiter__() and __anext__() methods. __anext__() must return an awaitable object. async for resolves the awaitables returned by an asynchronous iterator’s __anext__() method until it raises a StopAsyncIteration exception. Introduced by PEP 492.

+attribute
+
+

A value associated with an object which is usually referenced by name using dotted expressions. For example, if an object o has an attribute a it would be referenced as o.a.

It is possible to give an object an attribute whose name is not an identifier as defined by Identifiers and keywords, for example using setattr(), if the object allows it. Such an attribute will not be accessible using a dotted expression, and would instead need to be retrieved with getattr().

+awaitable
+
+

An object that can be used in an await expression. Can be a coroutine or an object with an __await__() method. See also PEP 492.

+BDFL
+
+

Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python’s creator.

+binary file
+
+

A file object able to read and write bytes-like objects. Examples of binary files are files opened in binary mode ('rb', 'wb' or 'rb+'), sys.stdin.buffer, sys.stdout.buffer, and instances of io.BytesIO and gzip.GzipFile.

See also text file for a file object able to read and write str objects.

+borrowed reference
+
+

In Python’s C API, a borrowed reference is a reference to an object, where the code using the object does not own the reference. It becomes a dangling pointer if the object is destroyed. For example, a garbage collection can remove the last strong reference to the object and so destroy it.

Calling Py_INCREF() on the borrowed reference is recommended to convert it to a strong reference in-place, except when the object cannot be destroyed before the last usage of the borrowed reference. The Py_NewRef() function can be used to create a new strong reference.

+bytes-like object
+
+

An object that supports the Buffer Protocol and can export a C-contiguous buffer. This includes all bytes, bytearray, and array.array objects, as well as many common memoryview objects. Bytes-like objects can be used for various operations that work with binary data; these include compression, saving to a binary file, and sending over a socket.

Some operations need the binary data to be mutable. The documentation often refers to these as “read-write bytes-like objects”. Example mutable buffer objects include bytearray and a memoryview of a bytearray. Other operations require the binary data to be stored in immutable objects (“read-only bytes-like objects”); examples of these include bytes and a memoryview of a bytes object.

+bytecode
+
+

Python source code is compiled into bytecode, the internal representation of a Python program in the CPython interpreter. The bytecode is also cached in .pyc files so that executing the same file is faster the second time (recompilation from source to bytecode can be avoided). This “intermediate language” is said to run on a virtual machine that executes the machine code corresponding to each bytecode. Do note that bytecodes are not expected to work between different Python virtual machines, nor to be stable between Python releases.

A list of bytecode instructions can be found in the documentation for the dis module.

+callable
+
+

A callable is an object that can be called, possibly with a set of arguments (see argument), with the following syntax:

callable(argument1, argument2, argumentN)
+

A function, and by extension a method, is a callable. An instance of a class that implements the __call__() method is also a callable.

+callback
+
+

A subroutine function which is passed as an argument to be executed at some point in the future.

+class
+
+

A template for creating user-defined objects. Class definitions normally contain method definitions which operate on instances of the class.

+class variable
+
+

A variable defined in a class and intended to be modified only at class level (i.e., not in an instance of the class).

+complex number
+
+

An extension of the familiar real number system in which all numbers are expressed as a sum of a real part and an imaginary part. Imaginary numbers are real multiples of the imaginary unit (the square root of -1), often written i in mathematics or j in engineering. Python has built-in support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j. To get access to complex equivalents of the math module, use cmath. Use of complex numbers is a fairly advanced mathematical feature. If you’re not aware of a need for them, it’s almost certain you can safely ignore them.

+context manager
+
+

An object which controls the environment seen in a with statement by defining __enter__() and __exit__() methods. See PEP 343.

+context variable
+
+

A variable which can have different values depending on its context. This is similar to Thread-Local Storage in which each execution thread may have a different value for a variable. However, with context variables, there may be several contexts in one execution thread and the main usage for context variables is to keep track of variables in concurrent asynchronous tasks. See contextvars.

+contiguous
+
+

A buffer is considered contiguous exactly if it is either C-contiguous or Fortran contiguous. Zero-dimensional buffers are C and Fortran contiguous. In one-dimensional arrays, the items must be laid out in memory next to each other, in order of increasing indexes starting from zero. In multidimensional C-contiguous arrays, the last index varies the fastest when visiting items in order of memory address. However, in Fortran contiguous arrays, the first index varies the fastest.

+coroutine
+
+

Coroutines are a more generalized form of subroutines. Subroutines are entered at one point and exited at another point. Coroutines can be entered, exited, and resumed at many different points. They can be implemented with the async def statement. See also PEP 492.

+coroutine function
+
+

A function which returns a coroutine object. A coroutine function may be defined with the async def statement, and may contain await, async for, and async with keywords. These were introduced by PEP 492.

+CPython
+
+

The canonical implementation of the Python programming language, as distributed on python.org. The term “CPython” is used when necessary to distinguish this implementation from others such as Jython or IronPython.

+decorator
+
+

A function returning another function, usually applied as a function transformation using the @wrapper syntax. Common examples for decorators are classmethod() and staticmethod().

The decorator syntax is merely syntactic sugar, the following two function definitions are semantically equivalent:

def f(arg):
+    ...
+f = staticmethod(f)
+
+@staticmethod
+def f(arg):
+    ...
+

The same concept exists for classes, but is less commonly used there. See the documentation for function definitions and class definitions for more about decorators.

+descriptor
+
+

Any object which defines the methods __get__(), __set__(), or __delete__(). When a class attribute is a descriptor, its special binding behavior is triggered upon attribute lookup. Normally, using a.b to get, set or delete an attribute looks up the object named b in the class dictionary for a, but if b is a descriptor, the respective descriptor method gets called. Understanding descriptors is a key to a deep understanding of Python because they are the basis for many features including functions, methods, properties, class methods, static methods, and reference to super classes.

For more information about descriptors’ methods, see Implementing Descriptors or the Descriptor How To Guide.

+dictionary
+
+

An associative array, where arbitrary keys are mapped to values. The keys can be any object with __hash__() and __eq__() methods. Called a hash in Perl.

+dictionary comprehension
+
+

A compact way to process all or part of the elements in an iterable and return a dictionary with the results. results = {n: n ** 2 for n in +range(10)} generates a dictionary containing key n mapped to value n ** 2. See Displays for lists, sets and dictionaries.

+dictionary view
+
+

The objects returned from dict.keys(), dict.values(), and dict.items() are called dictionary views. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes. To force the dictionary view to become a full list use list(dictview). See Dictionary view objects.

+docstring
+
+

A string literal which appears as the first expression in a class, function or module. While ignored when the suite is executed, it is recognized by the compiler and put into the __doc__ attribute of the enclosing class, function or module. Since it is available via introspection, it is the canonical place for documentation of the object.

+duck-typing
+
+

A programming style which does not look at an object’s type to determine if it has the right interface; instead, the method or attribute is simply called or used (“If it looks like a duck and quacks like a duck, it must be a duck.”) By emphasizing interfaces rather than specific types, well-designed code improves its flexibility by allowing polymorphic substitution. Duck-typing avoids tests using type() or isinstance(). (Note, however, that duck-typing can be complemented with abstract base classes.) Instead, it typically employs hasattr() tests or EAFP programming.

+EAFP
+
+

Easier to ask for forgiveness than permission. This common Python coding style assumes the existence of valid keys or attributes and catches exceptions if the assumption proves false. This clean and fast style is characterized by the presence of many try and except statements. The technique contrasts with the LBYL style common to many other languages such as C.

+expression
+
+

A piece of syntax which can be evaluated to some value. In other words, an expression is an accumulation of expression elements like literals, names, attribute access, operators or function calls which all return a value. In contrast to many other languages, not all language constructs are expressions. There are also statements which cannot be used as expressions, such as while. Assignments are also statements, not expressions.

+extension module
+
+

A module written in C or C++, using Python’s C API to interact with the core and with user code.

+f-string
+
+

String literals prefixed with 'f' or 'F' are commonly called “f-strings” which is short for formatted string literals. See also PEP 498.

+file object
+
+

An object exposing a file-oriented API (with methods such as read() or write()) to an underlying resource. Depending on the way it was created, a file object can mediate access to a real on-disk file or to another type of storage or communication device (for example standard input/output, in-memory buffers, sockets, pipes, etc.). File objects are also called file-like objects or streams.

There are actually three categories of file objects: raw binary files, buffered binary files and text files. Their interfaces are defined in the io module. The canonical way to create a file object is by using the open() function.

+file-like object
+
+

A synonym for file object.

+filesystem encoding and error handler
+
+

Encoding and error handler used by Python to decode bytes from the operating system and encode Unicode to the operating system.

The filesystem encoding must guarantee to successfully decode all bytes below 128. If the file system encoding fails to provide this guarantee, API functions can raise UnicodeError.

The sys.getfilesystemencoding() and sys.getfilesystemencodeerrors() functions can be used to get the filesystem encoding and error handler.

The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig.

See also the locale encoding.

+finder
+
+

An object that tries to find the loader for a module that is being imported.

Since Python 3.3, there are two types of finder: meta path finders for use with sys.meta_path, and path entry finders for use with sys.path_hooks.

See PEP 302, PEP 420 and PEP 451 for much more detail.

+floor division
+
+

Mathematical division that rounds down to nearest integer. The floor division operator is //. For example, the expression 11 // 4 evaluates to 2 in contrast to the 2.75 returned by float true division. Note that (-11) // 4 is -3 because that is -2.75 rounded downward. See PEP 238.

+function
+
+

A series of statements which returns some value to a caller. It can also be passed zero or more arguments which may be used in the execution of the body. See also parameter, method, and the Function definitions section.

+function annotation
+
+

An annotation of a function parameter or return value.

Function annotations are usually used for type hints: for example, this function is expected to take two int arguments and is also expected to have an int return value:

def sum_two_numbers(a: int, b: int) -> int:
+   return a + b
+

Function annotation syntax is explained in section Function definitions.

See variable annotation and PEP 484, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.

+__future__
+
+

A future statement, from __future__ import <feature>, directs the compiler to compile the current module using syntax or semantics that will become standard in a future release of Python. The __future__ module documents the possible values of feature. By importing this module and evaluating its variables, you can see when a new feature was first added to the language and when it will (or did) become the default:

>>> import __future__
+>>> __future__.division
+_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
+
+garbage collection
+
+

The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles. The garbage collector can be controlled using the gc module.

+generator
+
+

A function which returns a generator iterator. It looks like a normal function except that it contains yield expressions for producing a series of values usable in a for-loop or that can be retrieved one at a time with the next() function.

Usually refers to a generator function, but may refer to a generator iterator in some contexts. In cases where the intended meaning isn’t clear, using the full terms avoids ambiguity.

+generator iterator
+
+

An object created by a generator function.

Each yield temporarily suspends processing, remembering the location execution state (including local variables and pending try-statements). When the generator iterator resumes, it picks up where it left off (in contrast to functions which start fresh on every invocation).

+generator expression
+
+

An expression that returns an iterator. It looks like a normal expression followed by a for clause defining a loop variable, range, and an optional if clause. The combined expression generates values for an enclosing function:

>>> sum(i*i for i in range(10))         # sum of squares 0, 1, 4, ... 81
+285
+
+generic function
+
+

A function composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the dispatch algorithm.

See also the single dispatch glossary entry, the functools.singledispatch() decorator, and PEP 443.

+generic type
+
+

A type that can be parameterized; typically a container class such as list or dict. Used for type hints and annotations.

For more details, see generic alias types, PEP 483, PEP 484, PEP 585, and the typing module.

+GIL
+
+

See global interpreter lock.

+global interpreter lock
+
+

The mechanism used by the CPython interpreter to assure that only one thread executes Python bytecode at a time. This simplifies the CPython implementation by making the object model (including critical built-in types such as dict) implicitly safe against concurrent access. Locking the entire interpreter makes it easier for the interpreter to be multi-threaded, at the expense of much of the parallelism afforded by multi-processor machines.

However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

Past efforts to create a “free-threaded” interpreter (one which locks shared data at a much finer granularity) have not been successful because performance suffered in the common single-processor case. It is believed that overcoming this performance issue would make the implementation much more complicated and therefore costlier to maintain.

+hash-based pyc
+
+

A bytecode cache file that uses the hash rather than the last-modified time of the corresponding source file to determine its validity. See Cached bytecode invalidation.

+hashable
+
+

An object is hashable if it has a hash value which never changes during its lifetime (it needs a __hash__() method), and can be compared to other objects (it needs an __eq__() method). Hashable objects which compare equal must have the same hash value.

Hashability makes an object usable as a dictionary key and a set member, because these data structures use the hash value internally.

Most of Python’s immutable built-in objects are hashable; mutable containers (such as lists or dictionaries) are not; immutable containers (such as tuples and frozensets) are only hashable if their elements are hashable. Objects which are instances of user-defined classes are hashable by default. They all compare unequal (except with themselves), and their hash value is derived from their id().

+IDLE
+
+

An Integrated Development and Learning Environment for Python. IDLE is a basic editor and interpreter environment which ships with the standard distribution of Python.

+immutable
+
+

An object with a fixed value. Immutable objects include numbers, strings and tuples. Such an object cannot be altered. A new object has to be created if a different value has to be stored. They play an important role in places where a constant hash value is needed, for example as a key in a dictionary.

+import path
+
+

A list of locations (or path entries) that are searched by the path based finder for modules to import. During import, this list of locations usually comes from sys.path, but for subpackages it may also come from the parent package’s __path__ attribute.

+importing
+
+

The process by which Python code in one module is made available to Python code in another module.

+importer
+
+

An object that both finds and loads a module; both a finder and loader object.

+interactive
+
+

Python has an interactive interpreter which means you can enter statements and expressions at the interpreter prompt, immediately execute them and see their results. Just launch python with no arguments (possibly by selecting it from your computer’s main menu). It is a very powerful way to test out new ideas or inspect modules and packages (remember help(x)).

+interpreted
+
+

Python is an interpreted language, as opposed to a compiled one, though the distinction can be blurry because of the presence of the bytecode compiler. This means that source files can be run directly without explicitly creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive.

+interpreter shutdown
+
+

When asked to shut down, the Python interpreter enters a special phase where it gradually releases all allocated resources, such as modules and various critical internal structures. It also makes several calls to the garbage collector. This can trigger the execution of code in user-defined destructors or weakref callbacks. Code executed during the shutdown phase can encounter various exceptions as the resources it relies on may not function anymore (common examples are library modules or the warnings machinery).

The main reason for interpreter shutdown is that the __main__ module or the script being run has finished executing.

+iterable
+
+

An object capable of returning its members one at a time. Examples of iterables include all sequence types (such as list, str, and tuple) and some non-sequence types like dict, file objects, and objects of any classes you define with an __iter__() method or with a __getitem__() method that implements sequence semantics.

Iterables can be used in a for loop and in many other places where a sequence is needed (zip(), map(), …). When an iterable object is passed as an argument to the built-in function iter(), it returns an iterator for the object. This iterator is good for one pass over the set of values. When using iterables, it is usually not necessary to call iter() or deal with iterator objects yourself. The for statement does that automatically for you, creating a temporary unnamed variable to hold the iterator for the duration of the loop. See also iterator, sequence, and generator.

+iterator
+
+

An object representing a stream of data. Repeated calls to the iterator’s __next__() method (or passing it to the built-in function next()) return successive items in the stream. When no more data are available a StopIteration exception is raised instead. At this point, the iterator object is exhausted and any further calls to its __next__() method just raise StopIteration again. Iterators are required to have an __iter__() method that returns the iterator object itself so every iterator is also iterable and may be used in most places where other iterables are accepted. One notable exception is code which attempts multiple iteration passes. A container object (such as a list) produces a fresh new iterator each time you pass it to the iter() function or use it in a for loop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container.

More information can be found in Iterator Types.

CPython implementation detail: CPython does not consistently apply the requirement that an iterator define __iter__().

+key function
+
+

A key function or collation function is a callable that returns a value used for sorting or ordering. For example, locale.strxfrm() is used to produce a sort key that is aware of locale specific sort conventions.

A number of tools in Python accept key functions to control how elements are ordered or grouped. They include min(), max(), sorted(), list.sort(), heapq.merge(), heapq.nsmallest(), heapq.nlargest(), and itertools.groupby().

There are several ways to create a key function. For example. the str.lower() method can serve as a key function for case insensitive sorts. Alternatively, a key function can be built from a lambda expression such as lambda r: (r[0], r[2]). Also, operator.attrgetter(), operator.itemgetter(), and operator.methodcaller() are three key function constructors. See the Sorting HOW TO for examples of how to create and use key functions.

+keyword argument
+
+

See argument.

+lambda
+
+

An anonymous inline function consisting of a single expression which is evaluated when the function is called. The syntax to create a lambda function is lambda [parameters]: expression

+LBYL
+
+

Look before you leap. This coding style explicitly tests for pre-conditions before making calls or lookups. This style contrasts with the EAFP approach and is characterized by the presence of many if statements.

In a multi-threaded environment, the LBYL approach can risk introducing a race condition between “the looking” and “the leaping”. For example, the code, if key in mapping: return mapping[key] can fail if another thread removes key from mapping after the test, but before the lookup. This issue can be solved with locks or by using the EAFP approach.

+locale encoding
+
+

On Unix, it is the encoding of the LC_CTYPE locale. It can be set with locale.setlocale(locale.LC_CTYPE, new_locale).

On Windows, it is the ANSI code page (ex: "cp1252").

On Android and VxWorks, Python uses "utf-8" as the locale encoding.

locale.getencoding() can be used to get the locale encoding.

See also the filesystem encoding and error handler.

+list
+
+

A built-in Python sequence. Despite its name it is more akin to an array in other languages than to a linked list since access to elements is O(1).

+list comprehension
+
+

A compact way to process all or part of the elements in a sequence and return a list with the results. result = ['{:#04x}'.format(x) for x in +range(256) if x % 2 == 0] generates a list of strings containing even hex numbers (0x..) in the range from 0 to 255. The if clause is optional. If omitted, all elements in range(256) are processed.

+loader
+
+

An object that loads a module. It must define a method named load_module(). A loader is typically returned by a finder. See PEP 302 for details and importlib.abc.Loader for an abstract base class.

+magic method
+
+

An informal synonym for special method.

+mapping
+
+

A container object that supports arbitrary key lookups and implements the methods specified in the collections.abc.Mapping or collections.abc.MutableMapping abstract base classes. Examples include dict, collections.defaultdict, collections.OrderedDict and collections.Counter.

+meta path finder
+
+

A finder returned by a search of sys.meta_path. Meta path finders are related to, but different from path entry finders.

See importlib.abc.MetaPathFinder for the methods that meta path finders implement.

+metaclass
+
+

The class of a class. Class definitions create a class name, a class dictionary, and a list of base classes. The metaclass is responsible for taking those three arguments and creating the class. Most object oriented programming languages provide a default implementation. What makes Python special is that it is possible to create custom metaclasses. Most users never need this tool, but when the need arises, metaclasses can provide powerful, elegant solutions. They have been used for logging attribute access, adding thread-safety, tracking object creation, implementing singletons, and many other tasks.

More information can be found in Metaclasses.

+method
+
+

A function which is defined inside a class body. If called as an attribute of an instance of that class, the method will get the instance object as its first argument (which is usually called self). See function and nested scope.

+method resolution order
+
+

Method Resolution Order is the order in which base classes are searched for a member during lookup. See The Python 2.3 Method Resolution Order for details of the algorithm used by the Python interpreter since the 2.3 release.

+module
+
+

An object that serves as an organizational unit of Python code. Modules have a namespace containing arbitrary Python objects. Modules are loaded into Python by the process of importing.

See also package.

+module spec
+
+

A namespace containing the import-related information used to load a module. An instance of importlib.machinery.ModuleSpec.

+MRO
+
+

See method resolution order.

+mutable
+
+

Mutable objects can change their value but keep their id(). See also immutable.

+named tuple
+
+

The term “named tuple” applies to any type or class that inherits from tuple and whose indexable elements are also accessible using named attributes. The type or class may have other features as well.

Several built-in types are named tuples, including the values returned by time.localtime() and os.stat(). Another example is sys.float_info:

>>> sys.float_info[1]                   # indexed access
+1024
+>>> sys.float_info.max_exp              # named field access
+1024
+>>> isinstance(sys.float_info, tuple)   # kind of tuple
+True
+

Some named tuples are built-in types (such as the above examples). Alternatively, a named tuple can be created from a regular class definition that inherits from tuple and that defines named fields. Such a class can be written by hand or it can be created with the factory function collections.namedtuple(). The latter technique also adds some extra methods that may not be found in hand-written or built-in named tuples.

+namespace
+
+

The place where a variable is stored. Namespaces are implemented as dictionaries. There are the local, global and built-in namespaces as well as nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, the functions builtins.open and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing random.seed() or itertools.islice() makes it clear that those functions are implemented by the random and itertools modules, respectively.

+namespace package
+
+

A PEP 420 package which serves only as a container for subpackages. Namespace packages may have no physical representation, and specifically are not like a regular package because they have no __init__.py file.

See also module.

+nested scope
+
+

The ability to refer to a variable in an enclosing definition. For instance, a function defined inside another function can refer to variables in the outer function. Note that nested scopes by default work only for reference and not for assignment. Local variables both read and write in the innermost scope. Likewise, global variables read and write to the global namespace. The nonlocal allows writing to outer scopes.

+new-style class
+
+

Old name for the flavor of classes now used for all class objects. In earlier Python versions, only new-style classes could use Python’s newer, versatile features like __slots__, descriptors, properties, __getattribute__(), class methods, and static methods.

+object
+
+

Any data with state (attributes or value) and defined behavior (methods). Also the ultimate base class of any new-style class.

+package
+
+

A Python module which can contain submodules or recursively, subpackages. Technically, a package is a Python module with a __path__ attribute.

See also regular package and namespace package.

+parameter
+
+

A named entity in a function (or method) definition that specifies an argument (or in some cases, arguments) that the function can accept. There are five kinds of parameter:

  • +

    positional-or-keyword: specifies an argument that can be passed either positionally or as a keyword argument. This is the default kind of parameter, for example foo and bar in the following:

    def func(foo, bar=None): ...
+
  • +

    positional-only: specifies an argument that can be supplied only by position. Positional-only parameters can be defined by including a / character in the parameter list of the function definition after them, for example posonly1 and posonly2 in the following:

    def func(posonly1, posonly2, /, positional_or_keyword): ...
+
  • +

    keyword-only: specifies an argument that can be supplied only by keyword. Keyword-only parameters can be defined by including a single var-positional parameter or bare * in the parameter list of the function definition before them, for example kw_only1 and kw_only2 in the following:

    def func(arg, *, kw_only1, kw_only2): ...
+
  • +

    var-positional: specifies that an arbitrary sequence of positional arguments can be provided (in addition to any positional arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with *, for example args in the following:

    def func(*args, **kwargs): ...
+
  • +var-keyword: specifies that arbitrarily many keyword arguments can be provided (in addition to any keyword arguments already accepted by other parameters). Such a parameter can be defined by prepending the parameter name with **, for example kwargs in the example above.

Parameters can specify both optional and required arguments, as well as default values for some optional arguments.

See also the argument glossary entry, the FAQ question on the difference between arguments and parameters, the inspect.Parameter class, the Function definitions section, and PEP 362.

+path entry
+
+

A single location on the import path which the path based finder consults to find modules for importing.

+path entry finder
+
+

A finder returned by a callable on sys.path_hooks (i.e. a path entry hook) which knows how to locate modules given a path entry.

See importlib.abc.PathEntryFinder for the methods that path entry finders implement.

+path entry hook
+
+

A callable on the sys.path_hooks list which returns a path entry finder if it knows how to find modules on a specific path entry.

+path based finder
+
+

One of the default meta path finders which searches an import path for modules.

+path-like object
+
+

An object representing a file system path. A path-like object is either a str or bytes object representing a path, or an object implementing the os.PathLike protocol. An object that supports the os.PathLike protocol can be converted to a str or bytes file system path by calling the os.fspath() function; os.fsdecode() and os.fsencode() can be used to guarantee a str or bytes result instead, respectively. Introduced by PEP 519.

+PEP
+
+

Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python or its processes or environment. PEPs should provide a concise technical specification and a rationale for proposed features.

PEPs are intended to be the primary mechanisms for proposing major new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions.

See PEP 1.

+portion
+
+

A set of files in a single directory (possibly stored in a zip file) that contribute to a namespace package, as defined in PEP 420.

+positional argument
+
+

See argument.

+provisional API
+
+

A provisional API is one which has been deliberately excluded from the standard library’s backwards compatibility guarantees. While major changes to such interfaces are not expected, as long as they are marked provisional, backwards incompatible changes (up to and including removal of the interface) may occur if deemed necessary by core developers. Such changes will not be made gratuitously – they will occur only if serious fundamental flaws are uncovered that were missed prior to the inclusion of the API.

Even for provisional APIs, backwards incompatible changes are seen as a “solution of last resort” - every attempt will still be made to find a backwards compatible resolution to any identified problems.

This process allows the standard library to continue to evolve over time, without locking in problematic design errors for extended periods of time. See PEP 411 for more details.

+provisional package
+
+

See provisional API.

+Python 3000
+
+

Nickname for the Python 3.x release line (coined long ago when the release of version 3 was something in the distant future.) This is also abbreviated “Py3k”.

+Pythonic
+
+

An idea or piece of code which closely follows the most common idioms of the Python language, rather than implementing code using concepts common to other languages. For example, a common idiom in Python is to loop over all elements of an iterable using a for statement. Many other languages don’t have this type of construct, so people unfamiliar with Python sometimes use a numerical counter instead:

for i in range(len(food)):
+    print(food[i])
+

As opposed to the cleaner, Pythonic method:

for piece in food:
+    print(piece)
+
+qualified name
+
+

A dotted name showing the “path” from a module’s global scope to a class, function or method defined in that module, as defined in PEP 3155. For top-level functions and classes, the qualified name is the same as the object’s name:

>>> class C:
+...     class D:
+...         def meth(self):
+...             pass
+...
+>>> C.__qualname__
+'C'
+>>> C.D.__qualname__
+'C.D'
+>>> C.D.meth.__qualname__
+'C.D.meth'
+

When used to refer to modules, the fully qualified name means the entire dotted path to the module, including any parent packages, e.g. email.mime.text:

>>> import email.mime.text
+>>> email.mime.text.__name__
+'email.mime.text'
+
+reference count
+
+

The number of references to an object. When the reference count of an object drops to zero, it is deallocated. Some objects are “immortal” and have reference counts that are never modified, and therefore the objects are never deallocated. Reference counting is generally not visible to Python code, but it is a key element of the CPython implementation. Programmers can call the sys.getrefcount() function to return the reference count for a particular object.

+regular package
+
+

A traditional package, such as a directory containing an __init__.py file.

See also namespace package.

+__slots__
+
+

A declaration inside a class that saves memory by pre-declaring space for instance attributes and eliminating instance dictionaries. Though popular, the technique is somewhat tricky to get right and is best reserved for rare cases where there are large numbers of instances in a memory-critical application.

+sequence
+
+

An iterable which supports efficient element access using integer indices via the __getitem__() special method and defines a __len__() method that returns the length of the sequence. Some built-in sequence types are list, str, tuple, and bytes. Note that dict also supports __getitem__() and __len__(), but is considered a mapping rather than a sequence because the lookups use arbitrary immutable keys rather than integers.

The collections.abc.Sequence abstract base class defines a much richer interface that goes beyond just __getitem__() and __len__(), adding count(), index(), __contains__(), and __reversed__(). Types that implement this expanded interface can be registered explicitly using register().

+set comprehension
+
+

A compact way to process all or part of the elements in an iterable and return a set with the results. results = {c for c in 'abracadabra' if +c not in 'abc'} generates the set of strings {'r', 'd'}. See Displays for lists, sets and dictionaries.

+single dispatch
+
+

A form of generic function dispatch where the implementation is chosen based on the type of a single argument.

+slice
+
+

An object usually containing a portion of a sequence. A slice is created using the subscript notation, [] with colons between numbers when several are given, such as in variable_name[1:3:5]. The bracket (subscript) notation uses slice objects internally.

+special method
+
+

A method that is called implicitly by Python to execute a certain operation on a type, such as addition. Such methods have names starting and ending with double underscores. Special methods are documented in Special method names.

+statement
+
+

A statement is part of a suite (a “block” of code). A statement is either an expression or one of several constructs with a keyword, such as if, while or for.

+static type checker
+
+

An external tool that reads Python code and analyzes it, looking for issues such as incorrect types. See also type hints and the typing module.

+strong reference
+
+

In Python’s C API, a strong reference is a reference to an object which is owned by the code holding the reference. The strong reference is taken by calling Py_INCREF() when the reference is created and released with Py_DECREF() when the reference is deleted.

The Py_NewRef() function can be used to create a strong reference to an object. Usually, the Py_DECREF() function must be called on the strong reference before exiting the scope of the strong reference, to avoid leaking one reference.

See also borrowed reference.

+text encoding
+
+

A string in Python is a sequence of Unicode code points (in range U+0000–U+10FFFF). To store or transfer a string, it needs to be serialized as a sequence of bytes.

Serializing a string into a sequence of bytes is known as “encoding”, and recreating the string from the sequence of bytes is known as “decoding”.

There are a variety of different text serialization codecs, which are collectively referred to as “text encodings”.

+text file
+
+

A file object able to read and write str objects. Often, a text file actually accesses a byte-oriented datastream and handles the text encoding automatically. Examples of text files are files opened in text mode ('r' or 'w'), sys.stdin, sys.stdout, and instances of io.StringIO.

See also binary file for a file object able to read and write bytes-like objects.

+triple-quoted string
+
+

A string which is bound by three instances of either a quotation mark (”) or an apostrophe (‘). While they don’t provide any functionality not available with single-quoted strings, they are useful for a number of reasons. They allow you to include unescaped single and double quotes within a string and they can span multiple lines without the use of the continuation character, making them especially useful when writing docstrings.

+type
+
+

The type of a Python object determines what kind of object it is; every object has a type. An object’s type is accessible as its __class__ attribute or can be retrieved with type(obj).

+type alias
+
+

A synonym for a type, created by assigning the type to an identifier.

Type aliases are useful for simplifying type hints. For example:

def remove_gray_shades(
+        colors: list[tuple[int, int, int]]) -> list[tuple[int, int, int]]:
+    pass
+

could be made more readable like this:

Color = tuple[int, int, int]
+
+def remove_gray_shades(colors: list[Color]) -> list[Color]:
+    pass
+

See typing and PEP 484, which describe this functionality.

+type hint
+
+

An annotation that specifies the expected type for a variable, a class attribute, or a function parameter or return value.

Type hints are optional and are not enforced by Python but they are useful to static type checkers. They can also aid IDEs with code completion and refactoring.

Type hints of global variables, class attributes, and functions, but not local variables, can be accessed using typing.get_type_hints().

See typing and PEP 484, which describe this functionality.

+universal newlines
+
+

A manner of interpreting text streams in which all of the following are recognized as ending a line: the Unix end-of-line convention '\n', the Windows convention '\r\n', and the old Macintosh convention '\r'. See PEP 278 and PEP 3116, as well as bytes.splitlines() for an additional use.

+variable annotation
+
+

An annotation of a variable or a class attribute.

When annotating a variable or a class attribute, assignment is optional:

class C:
+    field: 'annotation'
+

Variable annotations are usually used for type hints: for example this variable is expected to take int values:

count: int = 0
+

Variable annotation syntax is explained in section Annotated assignment statements.

See function annotation, PEP 484 and PEP 526, which describe this functionality. Also see Annotations Best Practices for best practices on working with annotations.

+virtual environment
+
+

A cooperatively isolated runtime environment that allows Python users and applications to install and upgrade Python distribution packages without interfering with the behaviour of other Python applications running on the same system.

See also venv.

+virtual machine
+
+

A computer defined entirely in software. Python’s virtual machine executes the bytecode emitted by the bytecode compiler.

+Zen of Python
+
+

Listing of Python design principles and philosophies that are helpful in understanding and using the language. The listing can be found by typing “import this” at the interactive prompt.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/glossary.html +

+
diff --git a/devdocs/python~3.12/howto%2Fannotations.html b/devdocs/python~3.12/howto%2Fannotations.html new file mode 100644 index 00000000..6689684a --- /dev/null +++ b/devdocs/python~3.12/howto%2Fannotations.html @@ -0,0 +1,25 @@ +

Annotations Best Practices

author
+

Larry Hastings

Abstract

This document is designed to encapsulate the best practices for working with annotations dicts. If you write Python code that examines __annotations__ on Python objects, we encourage you to follow the guidelines described below.

The document is organized into four sections: best practices for accessing the annotations of an object in Python versions 3.10 and newer, best practices for accessing the annotations of an object in Python versions 3.9 and older, other best practices for __annotations__ that apply to any Python version, and quirks of __annotations__.

Note that this document is specifically about working with __annotations__, not uses for annotations. If you’re looking for information on how to use “type hints” in your code, please see the typing module.

Accessing The Annotations Dict Of An Object In Python 3.10 And Newer

Python 3.10 adds a new function to the standard library: inspect.get_annotations(). In Python versions 3.10 and newer, calling this function is the best practice for accessing the annotations dict of any object that supports annotations. This function can also “un-stringize” stringized annotations for you.

If for some reason inspect.get_annotations() isn’t viable for your use case, you may access the __annotations__ data member manually. Best practice for this changed in Python 3.10 as well: as of Python 3.10, o.__annotations__ is guaranteed to always work on Python functions, classes, and modules. If you’re certain the object you’re examining is one of these three specific objects, you may simply use o.__annotations__ to get at the object’s annotations dict.

However, other types of callables–for example, callables created by functools.partial()–may not have an __annotations__ attribute defined. When accessing the __annotations__ of a possibly unknown object, best practice in Python versions 3.10 and newer is to call getattr() with three arguments, for example getattr(o, '__annotations__', None).

Before Python 3.10, accessing __annotations__ on a class that defines no annotations but that has a parent class with annotations would return the parent’s __annotations__. In Python 3.10 and newer, the child class’s annotations will be an empty dict instead.

Accessing The Annotations Dict Of An Object In Python 3.9 And Older

In Python 3.9 and older, accessing the annotations dict of an object is much more complicated than in newer versions. The problem is a design flaw in these older versions of Python, specifically to do with class annotations.

Best practice for accessing the annotations dict of other objects–functions, other callables, and modules–is the same as best practice for 3.10, assuming you aren’t calling inspect.get_annotations(): you should use three-argument getattr() to access the object’s __annotations__ attribute.

Unfortunately, this isn’t best practice for classes. The problem is that, since __annotations__ is optional on classes, and because classes can inherit attributes from their base classes, accessing the __annotations__ attribute of a class may inadvertently return the annotations dict of a base class. As an example:

class Base:
+    a: int = 3
+    b: str = 'abc'
+
+class Derived(Base):
+    pass
+
+print(Derived.__annotations__)
+

This will print the annotations dict from Base, not Derived.

Your code will have to have a separate code path if the object you’re examining is a class (isinstance(o, type)). In that case, best practice relies on an implementation detail of Python 3.9 and before: if a class has annotations defined, they are stored in the class’s __dict__ dictionary. Since the class may or may not have annotations defined, best practice is to call the get method on the class dict.

To put it all together, here is some sample code that safely accesses the __annotations__ attribute on an arbitrary object in Python 3.9 and before:

if isinstance(o, type):
+    ann = o.__dict__.get('__annotations__', None)
+else:
+    ann = getattr(o, '__annotations__', None)
+

After running this code, ann should be either a dictionary or None. You’re encouraged to double-check the type of ann using isinstance() before further examination.

Note that some exotic or malformed type objects may not have a __dict__ attribute, so for extra safety you may also wish to use getattr() to access __dict__.

Manually Un-Stringizing Stringized Annotations

In situations where some annotations may be “stringized”, and you wish to evaluate those strings to produce the Python values they represent, it really is best to call inspect.get_annotations() to do this work for you.

If you’re using Python 3.9 or older, or if for some reason you can’t use inspect.get_annotations(), you’ll need to duplicate its logic. You’re encouraged to examine the implementation of inspect.get_annotations() in the current Python version and follow a similar approach.

In a nutshell, if you wish to evaluate a stringized annotation on an arbitrary object o:

However, not all string values used as annotations can be successfully turned into Python values by eval(). String values could theoretically contain any valid string, and in practice there are valid use cases for type hints that require annotating with string values that specifically can’t be evaluated. For example:

If eval() attempts to evaluate such values, it will fail and raise an exception. So, when designing a library API that works with annotations, it’s recommended to only attempt to evaluate string values when explicitly requested to by the caller.

Best Practices For __annotations__ In Any Python Version

+__annotations__ Quirks

In all versions of Python 3, function objects lazy-create an annotations dict if no annotations are defined on that object. You can delete the __annotations__ attribute using del fn.__annotations__, but if you then access fn.__annotations__ the object will create a new empty dict that it will store and return as its annotations. Deleting the annotations on a function before it has lazily created its annotations dict will throw an AttributeError; using del fn.__annotations__ twice in a row is guaranteed to always throw an AttributeError.

Everything in the above paragraph also applies to class and module objects in Python 3.10 and newer.

In all versions of Python 3, you can set __annotations__ on a function object to None. However, subsequently accessing the annotations on that object using fn.__annotations__ will lazy-create an empty dictionary as per the first paragraph of this section. This is not true of modules and classes, in any Python version; those objects permit setting __annotations__ to any Python value, and will retain whatever value is set.

If Python stringizes your annotations for you (using from __future__ import annotations), and you specify a string as an annotation, the string will itself be quoted. In effect the annotation is quoted twice. For example:

from __future__ import annotations
+def foo(a: "str"): pass
+
+print(foo.__annotations__)
+

This prints {'a': "'str'"}. This shouldn’t really be considered a “quirk”; it’s mentioned here simply because it might be surprising.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/annotations.html +

+
diff --git a/devdocs/python~3.12/howto%2Fargparse.html b/devdocs/python~3.12/howto%2Fargparse.html new file mode 100644 index 00000000..0f175cbc --- /dev/null +++ b/devdocs/python~3.12/howto%2Fargparse.html @@ -0,0 +1,432 @@ +

Argparse Tutorial

author
+

Tshepang Mbambo

This tutorial is intended to be a gentle introduction to argparse, the recommended command-line parsing module in the Python standard library.

Note

There are two other modules that fulfill the same task, namely getopt (an equivalent for getopt() from the C language) and the deprecated optparse. Note also that argparse is based on optparse, and therefore very similar in terms of usage.

Concepts

Let’s show the sort of functionality that we are going to explore in this introductory tutorial by making use of the ls command:

$ ls
+cpython  devguide  prog.py  pypy  rm-unused-function.patch
+$ ls pypy
+ctypes_configure  demo  dotviewer  include  lib_pypy  lib-python ...
+$ ls -l
+total 20
+drwxr-xr-x 19 wena wena 4096 Feb 18 18:51 cpython
+drwxr-xr-x  4 wena wena 4096 Feb  8 12:04 devguide
+-rwxr-xr-x  1 wena wena  535 Feb 19 00:05 prog.py
+drwxr-xr-x 14 wena wena 4096 Feb  7 00:59 pypy
+-rw-r--r--  1 wena wena  741 Feb 18 01:01 rm-unused-function.patch
+$ ls --help
+Usage: ls [OPTION]... [FILE]...
+List information about the FILEs (the current directory by default).
+Sort entries alphabetically if none of -cftuvSUX nor --sort is specified.
+...
+

A few concepts we can learn from the four commands:

The basics

Let us start with a very simple example which does (almost) nothing:

import argparse
+parser = argparse.ArgumentParser()
+parser.parse_args()
+

Following is a result of running the code:

$ python prog.py
+$ python prog.py --help
+usage: prog.py [-h]
+
+options:
+  -h, --help  show this help message and exit
+$ python prog.py --verbose
+usage: prog.py [-h]
+prog.py: error: unrecognized arguments: --verbose
+$ python prog.py foo
+usage: prog.py [-h]
+prog.py: error: unrecognized arguments: foo
+

Here is what is happening:

Introducing Positional arguments

An example:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("echo")
+args = parser.parse_args()
+print(args.echo)
+

And running the code:

$ python prog.py
+usage: prog.py [-h] echo
+prog.py: error: the following arguments are required: echo
+$ python prog.py --help
+usage: prog.py [-h] echo
+
+positional arguments:
+  echo
+
+options:
+  -h, --help  show this help message and exit
+$ python prog.py foo
+foo
+

Here is what’s happening:

Note however that, although the help display looks nice and all, it currently is not as helpful as it can be. For example we see that we got echo as a positional argument, but we don’t know what it does, other than by guessing or by reading the source code. So, let’s make it a bit more useful:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("echo", help="echo the string you use here")
+args = parser.parse_args()
+print(args.echo)
+

And we get:

$ python prog.py -h
+usage: prog.py [-h] echo
+
+positional arguments:
+  echo        echo the string you use here
+
+options:
+  -h, --help  show this help message and exit
+

Now, how about doing something even more useful:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", help="display a square of a given number")
+args = parser.parse_args()
+print(args.square**2)
+

Following is a result of running the code:

$ python prog.py 4
+Traceback (most recent call last):
+  File "prog.py", line 5, in <module>
+    print(args.square**2)
+TypeError: unsupported operand type(s) for ** or pow(): 'str' and 'int'
+

That didn’t go so well. That’s because argparse treats the options we give it as strings, unless we tell it otherwise. So, let’s tell argparse to treat that input as an integer:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", help="display a square of a given number",
+                    type=int)
+args = parser.parse_args()
+print(args.square**2)
+

Following is a result of running the code:

$ python prog.py 4
+16
+$ python prog.py four
+usage: prog.py [-h] square
+prog.py: error: argument square: invalid int value: 'four'
+

That went well. The program now even helpfully quits on bad illegal input before proceeding.

Introducing Optional arguments

So far we have been playing with positional arguments. Let us have a look on how to add optional ones:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--verbosity", help="increase output verbosity")
+args = parser.parse_args()
+if args.verbosity:
+    print("verbosity turned on")
+

And the output:

$ python prog.py --verbosity 1
+verbosity turned on
+$ python prog.py
+$ python prog.py --help
+usage: prog.py [-h] [--verbosity VERBOSITY]
+
+options:
+  -h, --help            show this help message and exit
+  --verbosity VERBOSITY
+                        increase output verbosity
+$ python prog.py --verbosity
+usage: prog.py [-h] [--verbosity VERBOSITY]
+prog.py: error: argument --verbosity: expected one argument
+

Here is what is happening:

The above example accepts arbitrary integer values for --verbosity, but for our simple program, only two values are actually useful, True or False. Let’s modify the code accordingly:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("--verbose", help="increase output verbosity",
+                    action="store_true")
+args = parser.parse_args()
+if args.verbose:
+    print("verbosity turned on")
+

And the output:

$ python prog.py --verbose
+verbosity turned on
+$ python prog.py --verbose 1
+usage: prog.py [-h] [--verbose]
+prog.py: error: unrecognized arguments: 1
+$ python prog.py --help
+usage: prog.py [-h] [--verbose]
+
+options:
+  -h, --help  show this help message and exit
+  --verbose   increase output verbosity
+

Here is what is happening:

Short options

If you are familiar with command line usage, you will notice that I haven’t yet touched on the topic of short versions of the options. It’s quite simple:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("-v", "--verbose", help="increase output verbosity",
+                    action="store_true")
+args = parser.parse_args()
+if args.verbose:
+    print("verbosity turned on")
+

And here goes:

$ python prog.py -v
+verbosity turned on
+$ python prog.py --help
+usage: prog.py [-h] [-v]
+
+options:
+  -h, --help     show this help message and exit
+  -v, --verbose  increase output verbosity
+

Note that the new ability is also reflected in the help text.

Combining Positional and Optional arguments

Our program keeps growing in complexity:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display a square of a given number")
+parser.add_argument("-v", "--verbose", action="store_true",
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+if args.verbose:
+    print(f"the square of {args.square} equals {answer}")
+else:
+    print(answer)
+

And now the output:

$ python prog.py
+usage: prog.py [-h] [-v] square
+prog.py: error: the following arguments are required: square
+$ python prog.py 4
+16
+$ python prog.py 4 --verbose
+the square of 4 equals 16
+$ python prog.py --verbose 4
+the square of 4 equals 16
+

How about we give this program of ours back the ability to have multiple verbosity values, and actually get to use them:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display a square of a given number")
+parser.add_argument("-v", "--verbosity", type=int,
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+if args.verbosity == 2:
+    print(f"the square of {args.square} equals {answer}")
+elif args.verbosity == 1:
+    print(f"{args.square}^2 == {answer}")
+else:
+    print(answer)
+

And the output:

$ python prog.py 4
+16
+$ python prog.py 4 -v
+usage: prog.py [-h] [-v VERBOSITY] square
+prog.py: error: argument -v/--verbosity: expected one argument
+$ python prog.py 4 -v 1
+4^2 == 16
+$ python prog.py 4 -v 2
+the square of 4 equals 16
+$ python prog.py 4 -v 3
+16
+

These all look good except the last one, which exposes a bug in our program. Let’s fix it by restricting the values the --verbosity option can accept:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display a square of a given number")
+parser.add_argument("-v", "--verbosity", type=int, choices=[0, 1, 2],
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+if args.verbosity == 2:
+    print(f"the square of {args.square} equals {answer}")
+elif args.verbosity == 1:
+    print(f"{args.square}^2 == {answer}")
+else:
+    print(answer)
+

And the output:

$ python prog.py 4 -v 3
+usage: prog.py [-h] [-v {0,1,2}] square
+prog.py: error: argument -v/--verbosity: invalid choice: 3 (choose from 0, 1, 2)
+$ python prog.py 4 -h
+usage: prog.py [-h] [-v {0,1,2}] square
+
+positional arguments:
+  square                display a square of a given number
+
+options:
+  -h, --help            show this help message and exit
+  -v {0,1,2}, --verbosity {0,1,2}
+                        increase output verbosity
+

Note that the change also reflects both in the error message as well as the help string.

Now, let’s use a different approach of playing with verbosity, which is pretty common. It also matches the way the CPython executable handles its own verbosity argument (check the output of python --help):

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display the square of a given number")
+parser.add_argument("-v", "--verbosity", action="count",
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+if args.verbosity == 2:
+    print(f"the square of {args.square} equals {answer}")
+elif args.verbosity == 1:
+    print(f"{args.square}^2 == {answer}")
+else:
+    print(answer)
+

We have introduced another action, “count”, to count the number of occurrences of specific options.

$ python prog.py 4
+16
+$ python prog.py 4 -v
+4^2 == 16
+$ python prog.py 4 -vv
+the square of 4 equals 16
+$ python prog.py 4 --verbosity --verbosity
+the square of 4 equals 16
+$ python prog.py 4 -v 1
+usage: prog.py [-h] [-v] square
+prog.py: error: unrecognized arguments: 1
+$ python prog.py 4 -h
+usage: prog.py [-h] [-v] square
+
+positional arguments:
+  square           display a square of a given number
+
+options:
+  -h, --help       show this help message and exit
+  -v, --verbosity  increase output verbosity
+$ python prog.py 4 -vvv
+16
+

Let’s fix:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display a square of a given number")
+parser.add_argument("-v", "--verbosity", action="count",
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+
+# bugfix: replace == with >=
+if args.verbosity >= 2:
+    print(f"the square of {args.square} equals {answer}")
+elif args.verbosity >= 1:
+    print(f"{args.square}^2 == {answer}")
+else:
+    print(answer)
+

And this is what it gives:

$ python prog.py 4 -vvv
+the square of 4 equals 16
+$ python prog.py 4 -vvvv
+the square of 4 equals 16
+$ python prog.py 4
+Traceback (most recent call last):
+  File "prog.py", line 11, in <module>
+    if args.verbosity >= 2:
+TypeError: '>=' not supported between instances of 'NoneType' and 'int'
+

Let’s fix that bug:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("square", type=int,
+                    help="display a square of a given number")
+parser.add_argument("-v", "--verbosity", action="count", default=0,
+                    help="increase output verbosity")
+args = parser.parse_args()
+answer = args.square**2
+if args.verbosity >= 2:
+    print(f"the square of {args.square} equals {answer}")
+elif args.verbosity >= 1:
+    print(f"{args.square}^2 == {answer}")
+else:
+    print(answer)
+

We’ve just introduced yet another keyword, default. We’ve set it to 0 in order to make it comparable to the other int values. Remember that by default, if an optional argument isn’t specified, it gets the None value, and that cannot be compared to an int value (hence the TypeError exception).

And:

$ python prog.py 4
+16
+

You can go quite far just with what we’ve learned so far, and we have only scratched the surface. The argparse module is very powerful, and we’ll explore a bit more of it before we end this tutorial.

Getting a little more advanced

What if we wanted to expand our tiny program to perform other powers, not just squares:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("x", type=int, help="the base")
+parser.add_argument("y", type=int, help="the exponent")
+parser.add_argument("-v", "--verbosity", action="count", default=0)
+args = parser.parse_args()
+answer = args.x**args.y
+if args.verbosity >= 2:
+    print(f"{args.x} to the power {args.y} equals {answer}")
+elif args.verbosity >= 1:
+    print(f"{args.x}^{args.y} == {answer}")
+else:
+    print(answer)
+

Output:

$ python prog.py
+usage: prog.py [-h] [-v] x y
+prog.py: error: the following arguments are required: x, y
+$ python prog.py -h
+usage: prog.py [-h] [-v] x y
+
+positional arguments:
+  x                the base
+  y                the exponent
+
+options:
+  -h, --help       show this help message and exit
+  -v, --verbosity
+$ python prog.py 4 2 -v
+4^2 == 16
+

Notice that so far we’ve been using verbosity level to change the text that gets displayed. The following example instead uses verbosity level to display more text instead:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument("x", type=int, help="the base")
+parser.add_argument("y", type=int, help="the exponent")
+parser.add_argument("-v", "--verbosity", action="count", default=0)
+args = parser.parse_args()
+answer = args.x**args.y
+if args.verbosity >= 2:
+    print(f"Running '{__file__}'")
+if args.verbosity >= 1:
+    print(f"{args.x}^{args.y} == ", end="")
+print(answer)
+

Output:

$ python prog.py 4 2
+16
+$ python prog.py 4 2 -v
+4^2 == 16
+$ python prog.py 4 2 -vv
+Running 'prog.py'
+4^2 == 16
+

Specifying ambiguous arguments

When there is ambiguity in deciding whether an argument is positional or for an argument, -- can be used to tell parse_args() that everything after that is a positional argument:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-n', nargs='+')
+>>> parser.add_argument('args', nargs='*')
+
+>>> # ambiguous, so parse_args assumes it's an option
+>>> parser.parse_args(['-f'])
+usage: PROG [-h] [-n N [N ...]] [args ...]
+PROG: error: unrecognized arguments: -f
+
+>>> parser.parse_args(['--', '-f'])
+Namespace(args=['-f'], n=None)
+
+>>> # ambiguous, so the -n option greedily accepts arguments
+>>> parser.parse_args(['-n', '1', '2', '3'])
+Namespace(args=[], n=['1', '2', '3'])
+
+>>> parser.parse_args(['-n', '1', '--', '2', '3'])
+Namespace(args=['2', '3'], n=['1'])
+

Conflicting options

So far, we have been working with two methods of an argparse.ArgumentParser instance. Let’s introduce a third one, add_mutually_exclusive_group(). It allows for us to specify options that conflict with each other. Let’s also change the rest of the program so that the new functionality makes more sense: we’ll introduce the --quiet option, which will be the opposite of the --verbose one:

import argparse
+
+parser = argparse.ArgumentParser()
+group = parser.add_mutually_exclusive_group()
+group.add_argument("-v", "--verbose", action="store_true")
+group.add_argument("-q", "--quiet", action="store_true")
+parser.add_argument("x", type=int, help="the base")
+parser.add_argument("y", type=int, help="the exponent")
+args = parser.parse_args()
+answer = args.x**args.y
+
+if args.quiet:
+    print(answer)
+elif args.verbose:
+    print(f"{args.x} to the power {args.y} equals {answer}")
+else:
+    print(f"{args.x}^{args.y} == {answer}")
+

Our program is now simpler, and we’ve lost some functionality for the sake of demonstration. Anyways, here’s the output:

$ python prog.py 4 2
+4^2 == 16
+$ python prog.py 4 2 -q
+16
+$ python prog.py 4 2 -v
+4 to the power 2 equals 16
+$ python prog.py 4 2 -vq
+usage: prog.py [-h] [-v | -q] x y
+prog.py: error: argument -q/--quiet: not allowed with argument -v/--verbose
+$ python prog.py 4 2 -v --quiet
+usage: prog.py [-h] [-v | -q] x y
+prog.py: error: argument -q/--quiet: not allowed with argument -v/--verbose
+

That should be easy to follow. I’ve added that last output so you can see the sort of flexibility you get, i.e. mixing long form options with short form ones.

Before we conclude, you probably want to tell your users the main purpose of your program, just in case they don’t know:

import argparse
+
+parser = argparse.ArgumentParser(description="calculate X to the power of Y")
+group = parser.add_mutually_exclusive_group()
+group.add_argument("-v", "--verbose", action="store_true")
+group.add_argument("-q", "--quiet", action="store_true")
+parser.add_argument("x", type=int, help="the base")
+parser.add_argument("y", type=int, help="the exponent")
+args = parser.parse_args()
+answer = args.x**args.y
+
+if args.quiet:
+    print(answer)
+elif args.verbose:
+    print(f"{args.x} to the power {args.y} equals {answer}")
+else:
+    print(f"{args.x}^{args.y} == {answer}")
+

Note that slight difference in the usage text. Note the [-v | -q], which tells us that we can either use -v or -q, but not both at the same time:

$ python prog.py --help
+usage: prog.py [-h] [-v | -q] x y
+
+calculate X to the power of Y
+
+positional arguments:
+  x              the base
+  y              the exponent
+
+options:
+  -h, --help     show this help message and exit
+  -v, --verbose
+  -q, --quiet
+

How to translate the argparse output

The output of the argparse module such as its help text and error messages are all made translatable using the gettext module. This allows applications to easily localize messages produced by argparse. See also Internationalizing your programs and modules.

For instance, in this argparse output:

$ python prog.py --help
+usage: prog.py [-h] [-v | -q] x y
+
+calculate X to the power of Y
+
+positional arguments:
+  x              the base
+  y              the exponent
+
+options:
+  -h, --help     show this help message and exit
+  -v, --verbose
+  -q, --quiet
+

The strings usage:, positional arguments:, options: and show this help message and exit are all translatable.

In order to translate these strings, they must first be extracted into a .po file. For example, using Babel, run this command:

$ pybabel extract -o messages.po /usr/lib/python3.12/argparse.py
+

This command will extract all translatable strings from the argparse module and output them into a file named messages.po. This command assumes that your Python installation is in /usr/lib.

You can find out the location of the argparse module on your system using this script:

import argparse
+print(argparse.__file__)
+

Once the messages in the .po file are translated and the translations are installed using gettext, argparse will be able to display the translated messages.

To translate your own strings in the argparse output, use gettext.

Conclusion

The argparse module offers a lot more than shown here. Its docs are quite detailed and thorough, and full of examples. Having gone through this tutorial, you should easily digest them without feeling overwhelmed.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/argparse.html +

+
diff --git a/devdocs/python~3.12/howto%2Fcporting.html b/devdocs/python~3.12/howto%2Fcporting.html new file mode 100644 index 00000000..d7a23e4c --- /dev/null +++ b/devdocs/python~3.12/howto%2Fcporting.html @@ -0,0 +1,6 @@ +

Porting Extension Modules to Python 3

We recommend the following resources for porting extension modules to Python 3:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/cporting.html +

+
diff --git a/devdocs/python~3.12/howto%2Fcurses.html b/devdocs/python~3.12/howto%2Fcurses.html new file mode 100644 index 00000000..653036e5 --- /dev/null +++ b/devdocs/python~3.12/howto%2Fcurses.html @@ -0,0 +1,102 @@ +

Curses Programming with Python

Author
+

A.M. Kuchling, Eric S. Raymond

Release
+

2.04

Abstract

This document describes how to use the curses extension module to control text-mode displays.

What is curses?

The curses library supplies a terminal-independent screen-painting and keyboard-handling facility for text-based terminals; such terminals include VT100s, the Linux console, and the simulated terminal provided by various programs. Display terminals support various control codes to perform common operations such as moving the cursor, scrolling the screen, and erasing areas. Different terminals use widely differing codes, and often have their own minor quirks.

In a world of graphical displays, one might ask “why bother”? It’s true that character-cell display terminals are an obsolete technology, but there are niches in which being able to do fancy things with them are still valuable. One niche is on small-footprint or embedded Unixes that don’t run an X server. Another is tools such as OS installers and kernel configurators that may have to run before any graphical support is available.

The curses library provides fairly basic functionality, providing the programmer with an abstraction of a display containing multiple non-overlapping windows of text. The contents of a window can be changed in various ways—adding text, erasing it, changing its appearance—and the curses library will figure out what control codes need to be sent to the terminal to produce the right output. curses doesn’t provide many user-interface concepts such as buttons, checkboxes, or dialogs; if you need such features, consider a user interface library such as Urwid.

The curses library was originally written for BSD Unix; the later System V versions of Unix from AT&T added many enhancements and new functions. BSD curses is no longer maintained, having been replaced by ncurses, which is an open-source implementation of the AT&T interface. If you’re using an open-source Unix such as Linux or FreeBSD, your system almost certainly uses ncurses. Since most current commercial Unix versions are based on System V code, all the functions described here will probably be available. The older versions of curses carried by some proprietary Unixes may not support everything, though.

The Windows version of Python doesn’t include the curses module. A ported version called UniCurses is available.

The Python curses module

The Python module is a fairly simple wrapper over the C functions provided by curses; if you’re already familiar with curses programming in C, it’s really easy to transfer that knowledge to Python. The biggest difference is that the Python interface makes things simpler by merging different C functions such as addstr(), mvaddstr(), and mvwaddstr() into a single addstr() method. You’ll see this covered in more detail later.

This HOWTO is an introduction to writing text-mode programs with curses and Python. It doesn’t attempt to be a complete guide to the curses API; for that, see the Python library guide’s section on ncurses, and the C manual pages for ncurses. It will, however, give you the basic ideas.

Starting and ending a curses application

Before doing anything, curses must be initialized. This is done by calling the initscr() function, which will determine the terminal type, send any required setup codes to the terminal, and create various internal data structures. If successful, initscr() returns a window object representing the entire screen; this is usually called stdscr after the name of the corresponding C variable.

import curses
+stdscr = curses.initscr()
+

Usually curses applications turn off automatic echoing of keys to the screen, in order to be able to read keys and only display them under certain circumstances. This requires calling the noecho() function.

curses.noecho()
+

Applications will also commonly need to react to keys instantly, without requiring the Enter key to be pressed; this is called cbreak mode, as opposed to the usual buffered input mode.

curses.cbreak()
+

Terminals usually return special keys, such as the cursor keys or navigation keys such as Page Up and Home, as a multibyte escape sequence. While you could write your application to expect such sequences and process them accordingly, curses can do it for you, returning a special value such as curses.KEY_LEFT. To get curses to do the job, you’ll have to enable keypad mode.

stdscr.keypad(True)
+

Terminating a curses application is much easier than starting one. You’ll need to call:

curses.nocbreak()
+stdscr.keypad(False)
+curses.echo()
+

to reverse the curses-friendly terminal settings. Then call the endwin() function to restore the terminal to its original operating mode.

curses.endwin()
+

A common problem when debugging a curses application is to get your terminal messed up when the application dies without restoring the terminal to its previous state. In Python this commonly happens when your code is buggy and raises an uncaught exception. Keys are no longer echoed to the screen when you type them, for example, which makes using the shell difficult.

In Python you can avoid these complications and make debugging much easier by importing the curses.wrapper() function and using it like this:

from curses import wrapper
+
+def main(stdscr):
+    # Clear screen
+    stdscr.clear()
+
+    # This raises ZeroDivisionError when i == 10.
+    for i in range(0, 11):
+        v = i-10
+        stdscr.addstr(i, 0, '10 divided by {} is {}'.format(v, 10/v))
+
+    stdscr.refresh()
+    stdscr.getkey()
+
+wrapper(main)
+

The wrapper() function takes a callable object and does the initializations described above, also initializing colors if color support is present. wrapper() then runs your provided callable. Once the callable returns, wrapper() will restore the original state of the terminal. The callable is called inside a try…except that catches exceptions, restores the state of the terminal, and then re-raises the exception. Therefore your terminal won’t be left in a funny state on exception and you’ll be able to read the exception’s message and traceback.

Windows and Pads

Windows are the basic abstraction in curses. A window object represents a rectangular area of the screen, and supports methods to display text, erase it, allow the user to input strings, and so forth.

The stdscr object returned by the initscr() function is a window object that covers the entire screen. Many programs may need only this single window, but you might wish to divide the screen into smaller windows, in order to redraw or clear them separately. The newwin() function creates a new window of a given size, returning the new window object.

begin_x = 20; begin_y = 7
+height = 5; width = 40
+win = curses.newwin(height, width, begin_y, begin_x)
+

Note that the coordinate system used in curses is unusual. Coordinates are always passed in the order y,x, and the top-left corner of a window is coordinate (0,0). This breaks the normal convention for handling coordinates where the x coordinate comes first. This is an unfortunate difference from most other computer applications, but it’s been part of curses since it was first written, and it’s too late to change things now.

Your application can determine the size of the screen by using the curses.LINES and curses.COLS variables to obtain the y and x sizes. Legal coordinates will then extend from (0,0) to (curses.LINES - 1, curses.COLS - 1).

When you call a method to display or erase text, the effect doesn’t immediately show up on the display. Instead you must call the refresh() method of window objects to update the screen.

This is because curses was originally written with slow 300-baud terminal connections in mind; with these terminals, minimizing the time required to redraw the screen was very important. Instead curses accumulates changes to the screen and displays them in the most efficient manner when you call refresh(). For example, if your program displays some text in a window and then clears the window, there’s no need to send the original text because they’re never visible.

In practice, explicitly telling curses to redraw a window doesn’t really complicate programming with curses much. Most programs go into a flurry of activity, and then pause waiting for a keypress or some other action on the part of the user. All you have to do is to be sure that the screen has been redrawn before pausing to wait for user input, by first calling stdscr.refresh() or the refresh() method of some other relevant window.

A pad is a special case of a window; it can be larger than the actual display screen, and only a portion of the pad displayed at a time. Creating a pad requires the pad’s height and width, while refreshing a pad requires giving the coordinates of the on-screen area where a subsection of the pad will be displayed.

pad = curses.newpad(100, 100)
+# These loops fill the pad with letters; addch() is
+# explained in the next section
+for y in range(0, 99):
+    for x in range(0, 99):
+        pad.addch(y,x, ord('a') + (x*x+y*y) % 26)
+
+# Displays a section of the pad in the middle of the screen.
+# (0,0) : coordinate of upper-left corner of pad area to display.
+# (5,5) : coordinate of upper-left corner of window area to be filled
+#         with pad content.
+# (20, 75) : coordinate of lower-right corner of window area to be
+#          : filled with pad content.
+pad.refresh( 0,0, 5,5, 20,75)
+

The refresh() call displays a section of the pad in the rectangle extending from coordinate (5,5) to coordinate (20,75) on the screen; the upper left corner of the displayed section is coordinate (0,0) on the pad. Beyond that difference, pads are exactly like ordinary windows and support the same methods.

If you have multiple windows and pads on screen there is a more efficient way to update the screen and prevent annoying screen flicker as each part of the screen gets updated. refresh() actually does two things:

  1. Calls the noutrefresh() method of each window to update an underlying data structure representing the desired state of the screen.
  2. Calls the function doupdate() function to change the physical screen to match the desired state recorded in the data structure.

Instead you can call noutrefresh() on a number of windows to update the data structure, and then call doupdate() to update the screen.

Displaying Text

From a C programmer’s point of view, curses may sometimes look like a twisty maze of functions, all subtly different. For example, addstr() displays a string at the current cursor location in the stdscr window, while mvaddstr() moves to a given y,x coordinate first before displaying the string. waddstr() is just like addstr(), but allows specifying a window to use instead of using stdscr by default. mvwaddstr() allows specifying both a window and a coordinate.

Fortunately the Python interface hides all these details. stdscr is a window object like any other, and methods such as addstr() accept multiple argument forms. Usually there are four different forms.

+ + + + +

Form

Description

str or ch

Display the string str or character ch at the current position

str or ch, attr

Display the string str or character ch, using attribute attr at the current position

y, x, str or ch

Move to position y,x within the window, and display str or ch

y, x, str or ch, attr

Move to position y,x within the window, and display str or ch, using attribute attr

Attributes allow displaying text in highlighted forms such as boldface, underline, reverse code, or in color. They’ll be explained in more detail in the next subsection.

The addstr() method takes a Python string or bytestring as the value to be displayed. The contents of bytestrings are sent to the terminal as-is. Strings are encoded to bytes using the value of the window’s encoding attribute; this defaults to the default system encoding as returned by locale.getencoding().

The addch() methods take a character, which can be either a string of length 1, a bytestring of length 1, or an integer.

Constants are provided for extension characters; these constants are integers greater than 255. For example, ACS_PLMINUS is a +/- symbol, and ACS_ULCORNER is the upper left corner of a box (handy for drawing borders). You can also use the appropriate Unicode character.

Windows remember where the cursor was left after the last operation, so if you leave out the y,x coordinates, the string or character will be displayed wherever the last operation left off. You can also move the cursor with the move(y,x) method. Because some terminals always display a flashing cursor, you may want to ensure that the cursor is positioned in some location where it won’t be distracting; it can be confusing to have the cursor blinking at some apparently random location.

If your application doesn’t need a blinking cursor at all, you can call curs_set(False) to make it invisible. For compatibility with older curses versions, there’s a leaveok(bool) function that’s a synonym for curs_set(). When bool is true, the curses library will attempt to suppress the flashing cursor, and you won’t need to worry about leaving it in odd locations.

Attributes and Color

Characters can be displayed in different ways. Status lines in a text-based application are commonly shown in reverse video, or a text viewer may need to highlight certain words. curses supports this by allowing you to specify an attribute for each cell on the screen.

An attribute is an integer, each bit representing a different attribute. You can try to display text with multiple attribute bits set, but curses doesn’t guarantee that all the possible combinations are available, or that they’re all visually distinct. That depends on the ability of the terminal being used, so it’s safest to stick to the most commonly available attributes, listed here.

+ + + + + + +

Attribute

Description

A_BLINK

Blinking text

A_BOLD

Extra bright or bold text

A_DIM

Half bright text

A_REVERSE

Reverse-video text

A_STANDOUT

The best highlighting mode available

A_UNDERLINE

Underlined text

So, to display a reverse-video status line on the top line of the screen, you could code:

stdscr.addstr(0, 0, "Current mode: Typing mode",
+              curses.A_REVERSE)
+stdscr.refresh()
+

The curses library also supports color on those terminals that provide it. The most common such terminal is probably the Linux console, followed by color xterms.

To use color, you must call the start_color() function soon after calling initscr(), to initialize the default color set (the curses.wrapper() function does this automatically). Once that’s done, the has_colors() function returns TRUE if the terminal in use can actually display color. (Note: curses uses the American spelling ‘color’, instead of the Canadian/British spelling ‘colour’. If you’re used to the British spelling, you’ll have to resign yourself to misspelling it for the sake of these functions.)

The curses library maintains a finite number of color pairs, containing a foreground (or text) color and a background color. You can get the attribute value corresponding to a color pair with the color_pair() function; this can be bitwise-OR’ed with other attributes such as A_REVERSE, but again, such combinations are not guaranteed to work on all terminals.

An example, which displays a line of text using color pair 1:

stdscr.addstr("Pretty text", curses.color_pair(1))
+stdscr.refresh()
+

As I said before, a color pair consists of a foreground and background color. The init_pair(n, f, b) function changes the definition of color pair n, to foreground color f and background color b. Color pair 0 is hard-wired to white on black, and cannot be changed.

Colors are numbered, and start_color() initializes 8 basic colors when it activates color mode. They are: 0:black, 1:red, 2:green, 3:yellow, 4:blue, 5:magenta, 6:cyan, and 7:white. The curses module defines named constants for each of these colors: curses.COLOR_BLACK, curses.COLOR_RED, and so forth.

Let’s put all this together. To change color 1 to red text on a white background, you would call:

curses.init_pair(1, curses.COLOR_RED, curses.COLOR_WHITE)
+

When you change a color pair, any text already displayed using that color pair will change to the new colors. You can also display new text in this color with:

stdscr.addstr(0,0, "RED ALERT!", curses.color_pair(1))
+

Very fancy terminals can change the definitions of the actual colors to a given RGB value. This lets you change color 1, which is usually red, to purple or blue or any other color you like. Unfortunately, the Linux console doesn’t support this, so I’m unable to try it out, and can’t provide any examples. You can check if your terminal can do this by calling can_change_color(), which returns True if the capability is there. If you’re lucky enough to have such a talented terminal, consult your system’s man pages for more information.

User Input

The C curses library offers only very simple input mechanisms. Python’s curses module adds a basic text-input widget. (Other libraries such as Urwid have more extensive collections of widgets.)

There are two methods for getting input from a window:

It’s possible to not wait for the user using the nodelay() window method. After nodelay(True), getch() and getkey() for the window become non-blocking. To signal that no input is ready, getch() returns curses.ERR (a value of -1) and getkey() raises an exception. There’s also a halfdelay() function, which can be used to (in effect) set a timer on each getch(); if no input becomes available within a specified delay (measured in tenths of a second), curses raises an exception.

The getch() method returns an integer; if it’s between 0 and 255, it represents the ASCII code of the key pressed. Values greater than 255 are special keys such as Page Up, Home, or the cursor keys. You can compare the value returned to constants such as curses.KEY_PPAGE, curses.KEY_HOME, or curses.KEY_LEFT. The main loop of your program may look something like this:

while True:
+    c = stdscr.getch()
+    if c == ord('p'):
+        PrintDocument()
+    elif c == ord('q'):
+        break  # Exit the while loop
+    elif c == curses.KEY_HOME:
+        x = y = 0
+

The curses.ascii module supplies ASCII class membership functions that take either integer or 1-character string arguments; these may be useful in writing more readable tests for such loops. It also supplies conversion functions that take either integer or 1-character-string arguments and return the same type. For example, curses.ascii.ctrl() returns the control character corresponding to its argument.

There’s also a method to retrieve an entire string, getstr(). It isn’t used very often, because its functionality is quite limited; the only editing keys available are the backspace key and the Enter key, which terminates the string. It can optionally be limited to a fixed number of characters.

curses.echo()            # Enable echoing of characters
+
+# Get a 15-character string, with the cursor on the top line
+s = stdscr.getstr(0,0, 15)
+

The curses.textpad module supplies a text box that supports an Emacs-like set of keybindings. Various methods of the Textbox class support editing with input validation and gathering the edit results either with or without trailing spaces. Here’s an example:

import curses
+from curses.textpad import Textbox, rectangle
+
+def main(stdscr):
+    stdscr.addstr(0, 0, "Enter IM message: (hit Ctrl-G to send)")
+
+    editwin = curses.newwin(5,30, 2,1)
+    rectangle(stdscr, 1,0, 1+5+1, 1+30+1)
+    stdscr.refresh()
+
+    box = Textbox(editwin)
+
+    # Let the user edit until Ctrl-G is struck.
+    box.edit()
+
+    # Get resulting contents
+    message = box.gather()
+

See the library documentation on curses.textpad for more details.

For More Information

This HOWTO doesn’t cover some advanced topics, such as reading the contents of the screen or capturing mouse events from an xterm instance, but the Python library page for the curses module is now reasonably complete. You should browse it next.

If you’re in doubt about the detailed behavior of the curses functions, consult the manual pages for your curses implementation, whether it’s ncurses or a proprietary Unix vendor’s. The manual pages will document any quirks, and provide complete lists of all the functions, attributes, and ACS_* characters available to you.

Because the curses API is so large, some functions aren’t supported in the Python interface. Often this isn’t because they’re difficult to implement, but because no one has needed them yet. Also, Python doesn’t yet support the menu library associated with ncurses. Patches adding support for these would be welcome; see the Python Developer’s Guide to learn more about submitting patches to Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/curses.html +

+
diff --git a/devdocs/python~3.12/howto%2Fdescriptor.html b/devdocs/python~3.12/howto%2Fdescriptor.html new file mode 100644 index 00000000..94257f03 --- /dev/null +++ b/devdocs/python~3.12/howto%2Fdescriptor.html @@ -0,0 +1,631 @@ +

Descriptor HowTo Guide

Author
+

Raymond Hettinger

Contact
+

<python at rcn dot com>

+

Descriptors let objects customize attribute lookup, storage, and deletion.

This guide has four major sections:

  1. The “primer” gives a basic overview, moving gently from simple examples, adding one feature at a time. Start here if you’re new to descriptors.
  2. The second section shows a complete, practical descriptor example. If you already know the basics, start there.
  3. The third section provides a more technical tutorial that goes into the detailed mechanics of how descriptors work. Most people don’t need this level of detail.
  4. The last section has pure Python equivalents for built-in descriptors that are written in C. Read this if you’re curious about how functions turn into bound methods or about the implementation of common tools like classmethod(), staticmethod(), property(), and __slots__.

Primer

In this primer, we start with the most basic possible example and then we’ll add new capabilities one by one.

Simple example: A descriptor that returns a constant

The Ten class is a descriptor whose __get__() method always returns the constant 10:

class Ten:
+    def __get__(self, obj, objtype=None):
+        return 10
+

To use the descriptor, it must be stored as a class variable in another class:

class A:
+    x = 5                       # Regular class attribute
+    y = Ten()                   # Descriptor instance
+

An interactive session shows the difference between normal attribute lookup and descriptor lookup:

>>> a = A()                     # Make an instance of class A
+>>> a.x                         # Normal attribute lookup
+5
+>>> a.y                         # Descriptor lookup
+10
+

In the a.x attribute lookup, the dot operator finds 'x': 5 in the class dictionary. In the a.y lookup, the dot operator finds a descriptor instance, recognized by its __get__ method. Calling that method returns 10.

Note that the value 10 is not stored in either the class dictionary or the instance dictionary. Instead, the value 10 is computed on demand.

This example shows how a simple descriptor works, but it isn’t very useful. For retrieving constants, normal attribute lookup would be better.

In the next section, we’ll create something more useful, a dynamic lookup.

Dynamic lookups

Interesting descriptors typically run computations instead of returning constants:

import os
+
+class DirectorySize:
+
+    def __get__(self, obj, objtype=None):
+        return len(os.listdir(obj.dirname))
+
+class Directory:
+
+    size = DirectorySize()              # Descriptor instance
+
+    def __init__(self, dirname):
+        self.dirname = dirname          # Regular instance attribute
+

An interactive session shows that the lookup is dynamic — it computes different, updated answers each time:

>>> s = Directory('songs')
+>>> g = Directory('games')
+>>> s.size                              # The songs directory has twenty files
+20
+>>> g.size                              # The games directory has three files
+3
+>>> os.remove('games/chess')            # Delete a game
+>>> g.size                              # File count is automatically updated
+2
+

Besides showing how descriptors can run computations, this example also reveals the purpose of the parameters to __get__(). The self parameter is size, an instance of DirectorySize. The obj parameter is either g or s, an instance of Directory. It is the obj parameter that lets the __get__() method learn the target directory. The objtype parameter is the class Directory.

Managed attributes

A popular use for descriptors is managing access to instance data. The descriptor is assigned to a public attribute in the class dictionary while the actual data is stored as a private attribute in the instance dictionary. The descriptor’s __get__() and __set__() methods are triggered when the public attribute is accessed.

In the following example, age is the public attribute and _age is the private attribute. When the public attribute is accessed, the descriptor logs the lookup or update:

import logging
+
+logging.basicConfig(level=logging.INFO)
+
+class LoggedAgeAccess:
+
+    def __get__(self, obj, objtype=None):
+        value = obj._age
+        logging.info('Accessing %r giving %r', 'age', value)
+        return value
+
+    def __set__(self, obj, value):
+        logging.info('Updating %r to %r', 'age', value)
+        obj._age = value
+
+class Person:
+
+    age = LoggedAgeAccess()             # Descriptor instance
+
+    def __init__(self, name, age):
+        self.name = name                # Regular instance attribute
+        self.age = age                  # Calls __set__()
+
+    def birthday(self):
+        self.age += 1                   # Calls both __get__() and __set__()
+

An interactive session shows that all access to the managed attribute age is logged, but that the regular attribute name is not logged:

>>> mary = Person('Mary M', 30)         # The initial age update is logged
+INFO:root:Updating 'age' to 30
+>>> dave = Person('David D', 40)
+INFO:root:Updating 'age' to 40
+
+>>> vars(mary)                          # The actual data is in a private attribute
+{'name': 'Mary M', '_age': 30}
+>>> vars(dave)
+{'name': 'David D', '_age': 40}
+
+>>> mary.age                            # Access the data and log the lookup
+INFO:root:Accessing 'age' giving 30
+30
+>>> mary.birthday()                     # Updates are logged as well
+INFO:root:Accessing 'age' giving 30
+INFO:root:Updating 'age' to 31
+
+>>> dave.name                           # Regular attribute lookup isn't logged
+'David D'
+>>> dave.age                            # Only the managed attribute is logged
+INFO:root:Accessing 'age' giving 40
+40
+

One major issue with this example is that the private name _age is hardwired in the LoggedAgeAccess class. That means that each instance can only have one logged attribute and that its name is unchangeable. In the next example, we’ll fix that problem.

Customized names

When a class uses descriptors, it can inform each descriptor about which variable name was used.

In this example, the Person class has two descriptor instances, name and age. When the Person class is defined, it makes a callback to __set_name__() in LoggedAccess so that the field names can be recorded, giving each descriptor its own public_name and private_name:

import logging
+
+logging.basicConfig(level=logging.INFO)
+
+class LoggedAccess:
+
+    def __set_name__(self, owner, name):
+        self.public_name = name
+        self.private_name = '_' + name
+
+    def __get__(self, obj, objtype=None):
+        value = getattr(obj, self.private_name)
+        logging.info('Accessing %r giving %r', self.public_name, value)
+        return value
+
+    def __set__(self, obj, value):
+        logging.info('Updating %r to %r', self.public_name, value)
+        setattr(obj, self.private_name, value)
+
+class Person:
+
+    name = LoggedAccess()                # First descriptor instance
+    age = LoggedAccess()                 # Second descriptor instance
+
+    def __init__(self, name, age):
+        self.name = name                 # Calls the first descriptor
+        self.age = age                   # Calls the second descriptor
+
+    def birthday(self):
+        self.age += 1
+

An interactive session shows that the Person class has called __set_name__() so that the field names would be recorded. Here we call vars() to look up the descriptor without triggering it:

>>> vars(vars(Person)['name'])
+{'public_name': 'name', 'private_name': '_name'}
+>>> vars(vars(Person)['age'])
+{'public_name': 'age', 'private_name': '_age'}
+

The new class now logs access to both name and age:

>>> pete = Person('Peter P', 10)
+INFO:root:Updating 'name' to 'Peter P'
+INFO:root:Updating 'age' to 10
+>>> kate = Person('Catherine C', 20)
+INFO:root:Updating 'name' to 'Catherine C'
+INFO:root:Updating 'age' to 20
+

The two Person instances contain only the private names:

>>> vars(pete)
+{'_name': 'Peter P', '_age': 10}
+>>> vars(kate)
+{'_name': 'Catherine C', '_age': 20}
+

Closing thoughts

A descriptor is what we call any object that defines __get__(), __set__(), or __delete__().

Optionally, descriptors can have a __set_name__() method. This is only used in cases where a descriptor needs to know either the class where it was created or the name of class variable it was assigned to. (This method, if present, is called even if the class is not a descriptor.)

Descriptors get invoked by the dot operator during attribute lookup. If a descriptor is accessed indirectly with vars(some_class)[descriptor_name], the descriptor instance is returned without invoking it.

Descriptors only work when used as class variables. When put in instances, they have no effect.

The main motivation for descriptors is to provide a hook allowing objects stored in class variables to control what happens during attribute lookup.

Traditionally, the calling class controls what happens during lookup. Descriptors invert that relationship and allow the data being looked-up to have a say in the matter.

Descriptors are used throughout the language. It is how functions turn into bound methods. Common tools like classmethod(), staticmethod(), property(), and functools.cached_property() are all implemented as descriptors.

Complete Practical Example

In this example, we create a practical and powerful tool for locating notoriously hard to find data corruption bugs.

Validator class

A validator is a descriptor for managed attribute access. Prior to storing any data, it verifies that the new value meets various type and range restrictions. If those restrictions aren’t met, it raises an exception to prevent data corruption at its source.

This Validator class is both an abstract base class and a managed attribute descriptor:

from abc import ABC, abstractmethod
+
+class Validator(ABC):
+
+    def __set_name__(self, owner, name):
+        self.private_name = '_' + name
+
+    def __get__(self, obj, objtype=None):
+        return getattr(obj, self.private_name)
+
+    def __set__(self, obj, value):
+        self.validate(value)
+        setattr(obj, self.private_name, value)
+
+    @abstractmethod
+    def validate(self, value):
+        pass
+

Custom validators need to inherit from Validator and must supply a validate() method to test various restrictions as needed.

Custom validators

Here are three practical data validation utilities:

  1. +OneOf verifies that a value is one of a restricted set of options.
  2. +Number verifies that a value is either an int or float. Optionally, it verifies that a value is between a given minimum or maximum.
  3. +String verifies that a value is a str. Optionally, it validates a given minimum or maximum length. It can validate a user-defined predicate as well.
class OneOf(Validator):
+
+    def __init__(self, *options):
+        self.options = set(options)
+
+    def validate(self, value):
+        if value not in self.options:
+            raise ValueError(f'Expected {value!r} to be one of {self.options!r}')
+
+class Number(Validator):
+
+    def __init__(self, minvalue=None, maxvalue=None):
+        self.minvalue = minvalue
+        self.maxvalue = maxvalue
+
+    def validate(self, value):
+        if not isinstance(value, (int, float)):
+            raise TypeError(f'Expected {value!r} to be an int or float')
+        if self.minvalue is not None and value < self.minvalue:
+            raise ValueError(
+                f'Expected {value!r} to be at least {self.minvalue!r}'
+            )
+        if self.maxvalue is not None and value > self.maxvalue:
+            raise ValueError(
+                f'Expected {value!r} to be no more than {self.maxvalue!r}'
+            )
+
+class String(Validator):
+
+    def __init__(self, minsize=None, maxsize=None, predicate=None):
+        self.minsize = minsize
+        self.maxsize = maxsize
+        self.predicate = predicate
+
+    def validate(self, value):
+        if not isinstance(value, str):
+            raise TypeError(f'Expected {value!r} to be an str')
+        if self.minsize is not None and len(value) < self.minsize:
+            raise ValueError(
+                f'Expected {value!r} to be no smaller than {self.minsize!r}'
+            )
+        if self.maxsize is not None and len(value) > self.maxsize:
+            raise ValueError(
+                f'Expected {value!r} to be no bigger than {self.maxsize!r}'
+            )
+        if self.predicate is not None and not self.predicate(value):
+            raise ValueError(
+                f'Expected {self.predicate} to be true for {value!r}'
+            )
+

Practical application

Here’s how the data validators can be used in a real class:

class Component:
+
+    name = String(minsize=3, maxsize=10, predicate=str.isupper)
+    kind = OneOf('wood', 'metal', 'plastic')
+    quantity = Number(minvalue=0)
+
+    def __init__(self, name, kind, quantity):
+        self.name = name
+        self.kind = kind
+        self.quantity = quantity
+

The descriptors prevent invalid instances from being created:

>>> Component('Widget', 'metal', 5)      # Blocked: 'Widget' is not all uppercase
+Traceback (most recent call last):
+    ...
+ValueError: Expected <method 'isupper' of 'str' objects> to be true for 'Widget'
+
+>>> Component('WIDGET', 'metle', 5)      # Blocked: 'metle' is misspelled
+Traceback (most recent call last):
+    ...
+ValueError: Expected 'metle' to be one of {'metal', 'plastic', 'wood'}
+
+>>> Component('WIDGET', 'metal', -5)     # Blocked: -5 is negative
+Traceback (most recent call last):
+    ...
+ValueError: Expected -5 to be at least 0
+>>> Component('WIDGET', 'metal', 'V')    # Blocked: 'V' isn't a number
+Traceback (most recent call last):
+    ...
+TypeError: Expected 'V' to be an int or float
+
+>>> c = Component('WIDGET', 'metal', 5)  # Allowed:  The inputs are valid
+

Technical Tutorial

What follows is a more technical tutorial for the mechanics and details of how descriptors work.

Abstract

Defines descriptors, summarizes the protocol, and shows how descriptors are called. Provides an example showing how object relational mappings work.

Learning about descriptors not only provides access to a larger toolset, it creates a deeper understanding of how Python works.

Definition and introduction

In general, a descriptor is an attribute value that has one of the methods in the descriptor protocol. Those methods are __get__(), __set__(), and __delete__(). If any of those methods are defined for an attribute, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, a.x has a lookup chain starting with a.__dict__['x'], then type(a).__dict__['x'], and continuing through the method resolution order of type(a). If the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined.

Descriptors are a powerful, general purpose protocol. They are the mechanism behind properties, methods, static methods, class methods, and super(). They are used throughout Python itself. Descriptors simplify the underlying C code and offer a flexible set of new tools for everyday Python programs.

Descriptor protocol

descr.__get__(self, obj, type=None)

descr.__set__(self, obj, value)

descr.__delete__(self, obj)

That is all there is to it. Define any of these methods and an object is considered a descriptor and can override default behavior upon being looked up as an attribute.

If an object defines __set__() or __delete__(), it is considered a data descriptor. Descriptors that only define __get__() are called non-data descriptors (they are often used for methods but other uses are possible).

Data and non-data descriptors differ in how overrides are calculated with respect to entries in an instance’s dictionary. If an instance’s dictionary has an entry with the same name as a data descriptor, the data descriptor takes precedence. If an instance’s dictionary has an entry with the same name as a non-data descriptor, the dictionary entry takes precedence.

To make a read-only data descriptor, define both __get__() and __set__() with the __set__() raising an AttributeError when called. Defining the __set__() method with an exception raising placeholder is enough to make it a data descriptor.

Overview of descriptor invocation

A descriptor can be called directly with desc.__get__(obj) or desc.__get__(None, cls).

But it is more common for a descriptor to be invoked automatically from attribute access.

The expression obj.x looks up the attribute x in the chain of namespaces for obj. If the search finds a descriptor outside of the instance __dict__, its __get__() method is invoked according to the precedence rules listed below.

The details of invocation depend on whether obj is an object, class, or instance of super.

Invocation from an instance

Instance lookup scans through a chain of namespaces giving data descriptors the highest priority, followed by instance variables, then non-data descriptors, then class variables, and lastly __getattr__() if it is provided.

If a descriptor is found for a.x, then it is invoked with: desc.__get__(a, type(a)).

The logic for a dotted lookup is in object.__getattribute__(). Here is a pure Python equivalent:

def find_name_in_mro(cls, name, default):
+    "Emulate _PyType_Lookup() in Objects/typeobject.c"
+    for base in cls.__mro__:
+        if name in vars(base):
+            return vars(base)[name]
+    return default
+
+def object_getattribute(obj, name):
+    "Emulate PyObject_GenericGetAttr() in Objects/object.c"
+    null = object()
+    objtype = type(obj)
+    cls_var = find_name_in_mro(objtype, name, null)
+    descr_get = getattr(type(cls_var), '__get__', null)
+    if descr_get is not null:
+        if (hasattr(type(cls_var), '__set__')
+            or hasattr(type(cls_var), '__delete__')):
+            return descr_get(cls_var, obj, objtype)     # data descriptor
+    if hasattr(obj, '__dict__') and name in vars(obj):
+        return vars(obj)[name]                          # instance variable
+    if descr_get is not null:
+        return descr_get(cls_var, obj, objtype)         # non-data descriptor
+    if cls_var is not null:
+        return cls_var                                  # class variable
+    raise AttributeError(name)
+

Note, there is no __getattr__() hook in the __getattribute__() code. That is why calling __getattribute__() directly or with super().__getattribute__ will bypass __getattr__() entirely.

Instead, it is the dot operator and the getattr() function that are responsible for invoking __getattr__() whenever __getattribute__() raises an AttributeError. Their logic is encapsulated in a helper function:

def getattr_hook(obj, name):
+    "Emulate slot_tp_getattr_hook() in Objects/typeobject.c"
+    try:
+        return obj.__getattribute__(name)
+    except AttributeError:
+        if not hasattr(type(obj), '__getattr__'):
+            raise
+    return type(obj).__getattr__(obj, name)             # __getattr__
+

Invocation from a class

The logic for a dotted lookup such as A.x is in type.__getattribute__(). The steps are similar to those for object.__getattribute__() but the instance dictionary lookup is replaced by a search through the class’s method resolution order.

If a descriptor is found, it is invoked with desc.__get__(None, A).

The full C implementation can be found in type_getattro() and _PyType_Lookup() in Objects/typeobject.c.

Invocation from super

The logic for super’s dotted lookup is in the __getattribute__() method for object returned by super().

A dotted lookup such as super(A, obj).m searches obj.__class__.__mro__ for the base class B immediately following A and then returns B.__dict__['m'].__get__(obj, A). If not a descriptor, m is returned unchanged.

The full C implementation can be found in super_getattro() in Objects/typeobject.c. A pure Python equivalent can be found in Guido’s Tutorial.

Summary of invocation logic

The mechanism for descriptors is embedded in the __getattribute__() methods for object, type, and super().

The important points to remember are:

  • Descriptors are invoked by the __getattribute__() method.
  • Classes inherit this machinery from object, type, or super().
  • Overriding __getattribute__() prevents automatic descriptor calls because all the descriptor logic is in that method.
  • +object.__getattribute__() and type.__getattribute__() make different calls to __get__(). The first includes the instance and may include the class. The second puts in None for the instance and always includes the class.
  • Data descriptors always override instance dictionaries.
  • Non-data descriptors may be overridden by instance dictionaries.

Automatic name notification

Sometimes it is desirable for a descriptor to know what class variable name it was assigned to. When a new class is created, the type metaclass scans the dictionary of the new class. If any of the entries are descriptors and if they define __set_name__(), that method is called with two arguments. The owner is the class where the descriptor is used, and the name is the class variable the descriptor was assigned to.

The implementation details are in type_new() and set_names() in Objects/typeobject.c.

Since the update logic is in type.__new__(), notifications only take place at the time of class creation. If descriptors are added to the class afterwards, __set_name__() will need to be called manually.

ORM example

The following code is a simplified skeleton showing how data descriptors could be used to implement an object relational mapping.

The essential idea is that the data is stored in an external database. The Python instances only hold keys to the database’s tables. Descriptors take care of lookups or updates:

class Field:
+
+    def __set_name__(self, owner, name):
+        self.fetch = f'SELECT {name} FROM {owner.table} WHERE {owner.key}=?;'
+        self.store = f'UPDATE {owner.table} SET {name}=? WHERE {owner.key}=?;'
+
+    def __get__(self, obj, objtype=None):
+        return conn.execute(self.fetch, [obj.key]).fetchone()[0]
+
+    def __set__(self, obj, value):
+        conn.execute(self.store, [value, obj.key])
+        conn.commit()
+

We can use the Field class to define models that describe the schema for each table in a database:

class Movie:
+    table = 'Movies'                    # Table name
+    key = 'title'                       # Primary key
+    director = Field()
+    year = Field()
+
+    def __init__(self, key):
+        self.key = key
+
+class Song:
+    table = 'Music'
+    key = 'title'
+    artist = Field()
+    year = Field()
+    genre = Field()
+
+    def __init__(self, key):
+        self.key = key
+

To use the models, first connect to the database:

>>> import sqlite3
+>>> conn = sqlite3.connect('entertainment.db')
+

An interactive session shows how data is retrieved from the database and how it can be updated:

>>> Movie('Star Wars').director
+'George Lucas'
+>>> jaws = Movie('Jaws')
+>>> f'Released in {jaws.year} by {jaws.director}'
+'Released in 1975 by Steven Spielberg'
+
+>>> Song('Country Roads').artist
+'John Denver'
+
+>>> Movie('Star Wars').director = 'J.J. Abrams'
+>>> Movie('Star Wars').director
+'J.J. Abrams'
+

Pure Python Equivalents

The descriptor protocol is simple and offers exciting possibilities. Several use cases are so common that they have been prepackaged into built-in tools. Properties, bound methods, static methods, class methods, and __slots__ are all based on the descriptor protocol.

Properties

Calling property() is a succinct way of building a data descriptor that triggers a function call upon access to an attribute. Its signature is:

property(fget=None, fset=None, fdel=None, doc=None) -> property
+

The documentation shows a typical use to define a managed attribute x:

class C:
+    def getx(self): return self.__x
+    def setx(self, value): self.__x = value
+    def delx(self): del self.__x
+    x = property(getx, setx, delx, "I'm the 'x' property.")
+

To see how property() is implemented in terms of the descriptor protocol, here is a pure Python equivalent:

class Property:
+    "Emulate PyProperty_Type() in Objects/descrobject.c"
+
+    def __init__(self, fget=None, fset=None, fdel=None, doc=None):
+        self.fget = fget
+        self.fset = fset
+        self.fdel = fdel
+        if doc is None and fget is not None:
+            doc = fget.__doc__
+        self.__doc__ = doc
+        self._name = ''
+
+    def __set_name__(self, owner, name):
+        self._name = name
+
+    def __get__(self, obj, objtype=None):
+        if obj is None:
+            return self
+        if self.fget is None:
+            raise AttributeError(
+                f'property {self._name!r} of {type(obj).__name__!r} object has no getter'
+             )
+        return self.fget(obj)
+
+    def __set__(self, obj, value):
+        if self.fset is None:
+            raise AttributeError(
+                f'property {self._name!r} of {type(obj).__name__!r} object has no setter'
+             )
+        self.fset(obj, value)
+
+    def __delete__(self, obj):
+        if self.fdel is None:
+            raise AttributeError(
+                f'property {self._name!r} of {type(obj).__name__!r} object has no deleter'
+             )
+        self.fdel(obj)
+
+    def getter(self, fget):
+        prop = type(self)(fget, self.fset, self.fdel, self.__doc__)
+        prop._name = self._name
+        return prop
+
+    def setter(self, fset):
+        prop = type(self)(self.fget, fset, self.fdel, self.__doc__)
+        prop._name = self._name
+        return prop
+
+    def deleter(self, fdel):
+        prop = type(self)(self.fget, self.fset, fdel, self.__doc__)
+        prop._name = self._name
+        return prop
+

The property() builtin helps whenever a user interface has granted attribute access and then subsequent changes require the intervention of a method.

For instance, a spreadsheet class may grant access to a cell value through Cell('b10').value. Subsequent improvements to the program require the cell to be recalculated on every access; however, the programmer does not want to affect existing client code accessing the attribute directly. The solution is to wrap access to the value attribute in a property data descriptor:

class Cell:
+    ...
+
+    @property
+    def value(self):
+        "Recalculate the cell before returning value"
+        self.recalc()
+        return self._value
+

Either the built-in property() or our Property() equivalent would work in this example.

Functions and methods

Python’s object oriented features are built upon a function based environment. Using non-data descriptors, the two are merged seamlessly.

Functions stored in class dictionaries get turned into methods when invoked. Methods only differ from regular functions in that the object instance is prepended to the other arguments. By convention, the instance is called self but could be called this or any other variable name.

Methods can be created manually with types.MethodType which is roughly equivalent to:

class MethodType:
+    "Emulate PyMethod_Type in Objects/classobject.c"
+
+    def __init__(self, func, obj):
+        self.__func__ = func
+        self.__self__ = obj
+
+    def __call__(self, *args, **kwargs):
+        func = self.__func__
+        obj = self.__self__
+        return func(obj, *args, **kwargs)
+

To support automatic creation of methods, functions include the __get__() method for binding methods during attribute access. This means that functions are non-data descriptors that return bound methods during dotted lookup from an instance. Here’s how it works:

class Function:
+    ...
+
+    def __get__(self, obj, objtype=None):
+        "Simulate func_descr_get() in Objects/funcobject.c"
+        if obj is None:
+            return self
+        return MethodType(self, obj)
+

Running the following class in the interpreter shows how the function descriptor works in practice:

class D:
+    def f(self, x):
+         return x
+

The function has a qualified name attribute to support introspection:

>>> D.f.__qualname__
+'D.f'
+

Accessing the function through the class dictionary does not invoke __get__(). Instead, it just returns the underlying function object:

>>> D.__dict__['f']
+<function D.f at 0x00C45070>
+

Dotted access from a class calls __get__() which just returns the underlying function unchanged:

>>> D.f
+<function D.f at 0x00C45070>
+

The interesting behavior occurs during dotted access from an instance. The dotted lookup calls __get__() which returns a bound method object:

>>> d = D()
+>>> d.f
+<bound method D.f of <__main__.D object at 0x00B18C90>>
+

Internally, the bound method stores the underlying function and the bound instance:

>>> d.f.__func__
+<function D.f at 0x00C45070>
+
+>>> d.f.__self__
+<__main__.D object at 0x1012e1f98>
+

If you have ever wondered where self comes from in regular methods or where cls comes from in class methods, this is it!

Kinds of methods

Non-data descriptors provide a simple mechanism for variations on the usual patterns of binding functions into methods.

To recap, functions have a __get__() method so that they can be converted to a method when accessed as attributes. The non-data descriptor transforms an obj.f(*args) call into f(obj, *args). Calling cls.f(*args) becomes f(*args).

This chart summarizes the binding and its two most useful variants:

+ + + +

Transformation

Called from an object

Called from a class

function

f(obj, *args)

f(*args)

staticmethod

f(*args)

f(*args)

classmethod

f(type(obj), *args)

f(cls, *args)

Static methods

Static methods return the underlying function without changes. Calling either c.f or C.f is the equivalent of a direct lookup into object.__getattribute__(c, "f") or object.__getattribute__(C, "f"). As a result, the function becomes identically accessible from either an object or a class.

Good candidates for static methods are methods that do not reference the self variable.

For instance, a statistics package may include a container class for experimental data. The class provides normal methods for computing the average, mean, median, and other descriptive statistics that depend on the data. However, there may be useful functions which are conceptually related but do not depend on the data. For instance, erf(x) is handy conversion routine that comes up in statistical work but does not directly depend on a particular dataset. It can be called either from an object or the class: s.erf(1.5) --> .9332 or Sample.erf(1.5) --> .9332.

Since static methods return the underlying function with no changes, the example calls are unexciting:

class E:
+    @staticmethod
+    def f(x):
+        return x * 10
+
>>> E.f(3)
+30
+>>> E().f(3)
+30
+

Using the non-data descriptor protocol, a pure Python version of staticmethod() would look like this:

import functools
+
+class StaticMethod:
+    "Emulate PyStaticMethod_Type() in Objects/funcobject.c"
+
+    def __init__(self, f):
+        self.f = f
+        functools.update_wrapper(self, f)
+
+    def __get__(self, obj, objtype=None):
+        return self.f
+
+    def __call__(self, *args, **kwds):
+        return self.f(*args, **kwds)
+

The functools.update_wrapper() call adds a __wrapped__ attribute that refers to the underlying function. Also it carries forward the attributes necessary to make the wrapper look like the wrapped function: __name__, __qualname__, __doc__, and __annotations__.

Class methods

Unlike static methods, class methods prepend the class reference to the argument list before calling the function. This format is the same for whether the caller is an object or a class:

class F:
+    @classmethod
+    def f(cls, x):
+        return cls.__name__, x
+
>>> F.f(3)
+('F', 3)
+>>> F().f(3)
+('F', 3)
+

This behavior is useful whenever the method only needs to have a class reference and does not rely on data stored in a specific instance. One use for class methods is to create alternate class constructors. For example, the classmethod dict.fromkeys() creates a new dictionary from a list of keys. The pure Python equivalent is:

class Dict(dict):
+    @classmethod
+    def fromkeys(cls, iterable, value=None):
+        "Emulate dict_fromkeys() in Objects/dictobject.c"
+        d = cls()
+        for key in iterable:
+            d[key] = value
+        return d
+

Now a new dictionary of unique keys can be constructed like this:

>>> d = Dict.fromkeys('abracadabra')
+>>> type(d) is Dict
+True
+>>> d
+{'a': None, 'b': None, 'r': None, 'c': None, 'd': None}
+

Using the non-data descriptor protocol, a pure Python version of classmethod() would look like this:

import functools
+
+class ClassMethod:
+    "Emulate PyClassMethod_Type() in Objects/funcobject.c"
+
+    def __init__(self, f):
+        self.f = f
+        functools.update_wrapper(self, f)
+
+    def __get__(self, obj, cls=None):
+        if cls is None:
+            cls = type(obj)
+        if hasattr(type(self.f), '__get__'):
+            # This code path was added in Python 3.9
+            # and was deprecated in Python 3.11.
+            return self.f.__get__(cls, cls)
+        return MethodType(self.f, cls)
+

The code path for hasattr(type(self.f), '__get__') was added in Python 3.9 and makes it possible for classmethod() to support chained decorators. For example, a classmethod and property could be chained together. In Python 3.11, this functionality was deprecated.

class G:
+    @classmethod
+    @property
+    def __doc__(cls):
+        return f'A doc for {cls.__name__!r}'
+
>>> G.__doc__
+"A doc for 'G'"
+

The functools.update_wrapper() call in ClassMethod adds a __wrapped__ attribute that refers to the underlying function. Also it carries forward the attributes necessary to make the wrapper look like the wrapped function: __name__, __qualname__, __doc__, and __annotations__.

Member objects and __slots__

When a class defines __slots__, it replaces instance dictionaries with a fixed-length array of slot values. From a user point of view that has several effects:

1. Provides immediate detection of bugs due to misspelled attribute assignments. Only attribute names specified in __slots__ are allowed:

class Vehicle:
+    __slots__ = ('id_number', 'make', 'model')
+
>>> auto = Vehicle()
+>>> auto.id_nubmer = 'VYE483814LQEX'
+Traceback (most recent call last):
+    ...
+AttributeError: 'Vehicle' object has no attribute 'id_nubmer'
+

2. Helps create immutable objects where descriptors manage access to private attributes stored in __slots__:

class Immutable:
+
+    __slots__ = ('_dept', '_name')          # Replace the instance dictionary
+
+    def __init__(self, dept, name):
+        self._dept = dept                   # Store to private attribute
+        self._name = name                   # Store to private attribute
+
+    @property                               # Read-only descriptor
+    def dept(self):
+        return self._dept
+
+    @property
+    def name(self):                         # Read-only descriptor
+        return self._name
+
>>> mark = Immutable('Botany', 'Mark Watney')
+>>> mark.dept
+'Botany'
+>>> mark.dept = 'Space Pirate'
+Traceback (most recent call last):
+    ...
+AttributeError: property 'dept' of 'Immutable' object has no setter
+>>> mark.location = 'Mars'
+Traceback (most recent call last):
+    ...
+AttributeError: 'Immutable' object has no attribute 'location'
+

3. Saves memory. On a 64-bit Linux build, an instance with two attributes takes 48 bytes with __slots__ and 152 bytes without. This flyweight design pattern likely only matters when a large number of instances are going to be created.

4. Improves speed. Reading instance variables is 35% faster with __slots__ (as measured with Python 3.10 on an Apple M1 processor).

5. Blocks tools like functools.cached_property() which require an instance dictionary to function correctly:

from functools import cached_property
+
+class CP:
+    __slots__ = ()                          # Eliminates the instance dict
+
+    @cached_property                        # Requires an instance dict
+    def pi(self):
+        return 4 * sum((-1.0)**n / (2.0*n + 1.0)
+                       for n in reversed(range(100_000)))
+
>>> CP().pi
+Traceback (most recent call last):
+  ...
+TypeError: No '__dict__' attribute on 'CP' instance to cache 'pi' property.
+

It is not possible to create an exact drop-in pure Python version of __slots__ because it requires direct access to C structures and control over object memory allocation. However, we can build a mostly faithful simulation where the actual C structure for slots is emulated by a private _slotvalues list. Reads and writes to that private structure are managed by member descriptors:

null = object()
+
+class Member:
+
+    def __init__(self, name, clsname, offset):
+        'Emulate PyMemberDef in Include/structmember.h'
+        # Also see descr_new() in Objects/descrobject.c
+        self.name = name
+        self.clsname = clsname
+        self.offset = offset
+
+    def __get__(self, obj, objtype=None):
+        'Emulate member_get() in Objects/descrobject.c'
+        # Also see PyMember_GetOne() in Python/structmember.c
+        if obj is None:
+            return self
+        value = obj._slotvalues[self.offset]
+        if value is null:
+            raise AttributeError(self.name)
+        return value
+
+    def __set__(self, obj, value):
+        'Emulate member_set() in Objects/descrobject.c'
+        obj._slotvalues[self.offset] = value
+
+    def __delete__(self, obj):
+        'Emulate member_delete() in Objects/descrobject.c'
+        value = obj._slotvalues[self.offset]
+        if value is null:
+            raise AttributeError(self.name)
+        obj._slotvalues[self.offset] = null
+
+    def __repr__(self):
+        'Emulate member_repr() in Objects/descrobject.c'
+        return f'<Member {self.name!r} of {self.clsname!r}>'
+

The type.__new__() method takes care of adding member objects to class variables:

class Type(type):
+    'Simulate how the type metaclass adds member objects for slots'
+
+    def __new__(mcls, clsname, bases, mapping, **kwargs):
+        'Emulate type_new() in Objects/typeobject.c'
+        # type_new() calls PyTypeReady() which calls add_methods()
+        slot_names = mapping.get('slot_names', [])
+        for offset, name in enumerate(slot_names):
+            mapping[name] = Member(name, clsname, offset)
+        return type.__new__(mcls, clsname, bases, mapping, **kwargs)
+

The object.__new__() method takes care of creating instances that have slots instead of an instance dictionary. Here is a rough simulation in pure Python:

class Object:
+    'Simulate how object.__new__() allocates memory for __slots__'
+
+    def __new__(cls, *args, **kwargs):
+        'Emulate object_new() in Objects/typeobject.c'
+        inst = super().__new__(cls)
+        if hasattr(cls, 'slot_names'):
+            empty_slots = [null] * len(cls.slot_names)
+            object.__setattr__(inst, '_slotvalues', empty_slots)
+        return inst
+
+    def __setattr__(self, name, value):
+        'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
+        cls = type(self)
+        if hasattr(cls, 'slot_names') and name not in cls.slot_names:
+            raise AttributeError(
+                f'{cls.__name__!r} object has no attribute {name!r}'
+            )
+        super().__setattr__(name, value)
+
+    def __delattr__(self, name):
+        'Emulate _PyObject_GenericSetAttrWithDict() Objects/object.c'
+        cls = type(self)
+        if hasattr(cls, 'slot_names') and name not in cls.slot_names:
+            raise AttributeError(
+                f'{cls.__name__!r} object has no attribute {name!r}'
+            )
+        super().__delattr__(name)
+

To use the simulation in a real class, just inherit from Object and set the metaclass to Type:

class H(Object, metaclass=Type):
+    'Instance variables stored in slots'
+
+    slot_names = ['x', 'y']
+
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+

At this point, the metaclass has loaded member objects for x and y:

>>> from pprint import pp
+>>> pp(dict(vars(H)))
+{'__module__': '__main__',
+ '__doc__': 'Instance variables stored in slots',
+ 'slot_names': ['x', 'y'],
+ '__init__': <function H.__init__ at 0x7fb5d302f9d0>,
+ 'x': <Member 'x' of 'H'>,
+ 'y': <Member 'y' of 'H'>}
+

When instances are created, they have a slot_values list where the attributes are stored:

>>> h = H(10, 20)
+>>> vars(h)
+{'_slotvalues': [10, 20]}
+>>> h.x = 55
+>>> vars(h)
+{'_slotvalues': [55, 20]}
+

Misspelled or unassigned attributes will raise an exception:

>>> h.xz
+Traceback (most recent call last):
+    ...
+AttributeError: 'H' object has no attribute 'xz'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/descriptor.html +

+
diff --git a/devdocs/python~3.12/howto%2Fenum.html b/devdocs/python~3.12/howto%2Fenum.html new file mode 100644 index 00000000..0abc0df6 --- /dev/null +++ b/devdocs/python~3.12/howto%2Fenum.html @@ -0,0 +1,641 @@ +

Enum HOWTO

An Enum is a set of symbolic names bound to unique values. They are similar to global variables, but they offer a more useful repr(), grouping, type-safety, and a few other features.

They are most useful when you have a variable that can take one of a limited selection of values. For example, the days of the week:

>>> from enum import Enum
+>>> class Weekday(Enum):
+...     MONDAY = 1
+...     TUESDAY = 2
+...     WEDNESDAY = 3
+...     THURSDAY = 4
+...     FRIDAY = 5
+...     SATURDAY = 6
+...     SUNDAY = 7
+

Or perhaps the RGB primary colors:

>>> from enum import Enum
+>>> class Color(Enum):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 3
+

As you can see, creating an Enum is as simple as writing a class that inherits from Enum itself.

Note

Case of Enum Members

Because Enums are used to represent constants, and to help avoid issues with name clashes between mixin-class methods/attributes and enum names, we strongly recommend using UPPER_CASE names for members, and will be using that style in our examples.

Depending on the nature of the enum a member’s value may or may not be important, but either way that value can be used to get the corresponding member:

>>> Weekday(3)
+<Weekday.WEDNESDAY: 3>
+

As you can see, the repr() of a member shows the enum name, the member name, and the value. The str() of a member shows only the enum name and member name:

>>> print(Weekday.THURSDAY)
+Weekday.THURSDAY
+

The type of an enumeration member is the enum it belongs to:

>>> type(Weekday.MONDAY)
+<enum 'Weekday'>
+>>> isinstance(Weekday.FRIDAY, Weekday)
+True
+

Enum members have an attribute that contains just their name:

>>> print(Weekday.TUESDAY.name)
+TUESDAY
+

Likewise, they have an attribute for their value:

>>> Weekday.WEDNESDAY.value
+3
+

Unlike many languages that treat enumerations solely as name/value pairs, Python Enums can have behavior added. For example, datetime.date has two methods for returning the weekday: weekday() and isoweekday(). The difference is that one of them counts from 0-6 and the other from 1-7. Rather than keep track of that ourselves we can add a method to the Weekday enum to extract the day from the date instance and return the matching enum member:

@classmethod
+def from_date(cls, date):
+    return cls(date.isoweekday())
+

The complete Weekday enum now looks like this:

>>> class Weekday(Enum):
+...     MONDAY = 1
+...     TUESDAY = 2
+...     WEDNESDAY = 3
+...     THURSDAY = 4
+...     FRIDAY = 5
+...     SATURDAY = 6
+...     SUNDAY = 7
+...     #
+...     @classmethod
+...     def from_date(cls, date):
+...         return cls(date.isoweekday())
+

Now we can find out what today is! Observe:

>>> from datetime import date
+>>> Weekday.from_date(date.today())     
+<Weekday.TUESDAY: 2>
+

Of course, if you’re reading this on some other day, you’ll see that day instead.

This Weekday enum is great if our variable only needs one day, but what if we need several? Maybe we’re writing a function to plot chores during a week, and don’t want to use a list – we could use a different type of Enum:

>>> from enum import Flag
+>>> class Weekday(Flag):
+...     MONDAY = 1
+...     TUESDAY = 2
+...     WEDNESDAY = 4
+...     THURSDAY = 8
+...     FRIDAY = 16
+...     SATURDAY = 32
+...     SUNDAY = 64
+

We’ve changed two things: we’re inherited from Flag, and the values are all powers of 2.

Just like the original Weekday enum above, we can have a single selection:

>>> first_week_day = Weekday.MONDAY
+>>> first_week_day
+<Weekday.MONDAY: 1>
+

But Flag also allows us to combine several members into a single variable:

>>> weekend = Weekday.SATURDAY | Weekday.SUNDAY
+>>> weekend
+<Weekday.SATURDAY|SUNDAY: 96>
+

You can even iterate over a Flag variable:

>>> for day in weekend:
+...     print(day)
+Weekday.SATURDAY
+Weekday.SUNDAY
+

Okay, let’s get some chores set up:

>>> chores_for_ethan = {
+...     'feed the cat': Weekday.MONDAY | Weekday.WEDNESDAY | Weekday.FRIDAY,
+...     'do the dishes': Weekday.TUESDAY | Weekday.THURSDAY,
+...     'answer SO questions': Weekday.SATURDAY,
+...     }
+

And a function to display the chores for a given day:

>>> def show_chores(chores, day):
+...     for chore, days in chores.items():
+...         if day in days:
+...             print(chore)
+...
+>>> show_chores(chores_for_ethan, Weekday.SATURDAY)
+answer SO questions
+

In cases where the actual values of the members do not matter, you can save yourself some work and use auto() for the values:

>>> from enum import auto
+>>> class Weekday(Flag):
+...     MONDAY = auto()
+...     TUESDAY = auto()
+...     WEDNESDAY = auto()
+...     THURSDAY = auto()
+...     FRIDAY = auto()
+...     SATURDAY = auto()
+...     SUNDAY = auto()
+...     WEEKEND = SATURDAY | SUNDAY
+

Programmatic access to enumeration members and their attributes

Sometimes it’s useful to access members in enumerations programmatically (i.e. situations where Color.RED won’t do because the exact color is not known at program-writing time). Enum allows such access:

>>> Color(1)
+<Color.RED: 1>
+>>> Color(3)
+<Color.BLUE: 3>
+

If you want to access enum members by name, use item access:

>>> Color['RED']
+<Color.RED: 1>
+>>> Color['GREEN']
+<Color.GREEN: 2>
+

If you have an enum member and need its name or value:

>>> member = Color.RED
+>>> member.name
+'RED'
+>>> member.value
+1
+

Duplicating enum members and values

Having two enum members with the same name is invalid:

>>> class Shape(Enum):
+...     SQUARE = 2
+...     SQUARE = 3
+...
+Traceback (most recent call last):
+...
+TypeError: 'SQUARE' already defined as 2
+

However, an enum member can have other names associated with it. Given two entries A and B with the same value (and A defined first), B is an alias for the member A. By-value lookup of the value of A will return the member A. By-name lookup of A will return the member A. By-name lookup of B will also return the member A:

>>> class Shape(Enum):
+...     SQUARE = 2
+...     DIAMOND = 1
+...     CIRCLE = 3
+...     ALIAS_FOR_SQUARE = 2
+...
+>>> Shape.SQUARE
+<Shape.SQUARE: 2>
+>>> Shape.ALIAS_FOR_SQUARE
+<Shape.SQUARE: 2>
+>>> Shape(2)
+<Shape.SQUARE: 2>
+

Note

Attempting to create a member with the same name as an already defined attribute (another member, a method, etc.) or attempting to create an attribute with the same name as a member is not allowed.

Ensuring unique enumeration values

By default, enumerations allow multiple names as aliases for the same value. When this behavior isn’t desired, you can use the unique() decorator:

>>> from enum import Enum, unique
+>>> @unique
+... class Mistake(Enum):
+...     ONE = 1
+...     TWO = 2
+...     THREE = 3
+...     FOUR = 3
+...
+Traceback (most recent call last):
+...
+ValueError: duplicate values found in <enum 'Mistake'>: FOUR -> THREE
+

Using automatic values

If the exact value is unimportant you can use auto:

>>> from enum import Enum, auto
+>>> class Color(Enum):
+...     RED = auto()
+...     BLUE = auto()
+...     GREEN = auto()
+...
+>>> [member.value for member in Color]
+[1, 2, 3]
+

The values are chosen by _generate_next_value_(), which can be overridden:

>>> class AutoName(Enum):
+...     @staticmethod
+...     def _generate_next_value_(name, start, count, last_values):
+...         return name
+...
+>>> class Ordinal(AutoName):
+...     NORTH = auto()
+...     SOUTH = auto()
+...     EAST = auto()
+...     WEST = auto()
+...
+>>> [member.value for member in Ordinal]
+['NORTH', 'SOUTH', 'EAST', 'WEST']
+

Note

The _generate_next_value_() method must be defined before any members.

Iteration

Iterating over the members of an enum does not provide the aliases:

>>> list(Shape)
+[<Shape.SQUARE: 2>, <Shape.DIAMOND: 1>, <Shape.CIRCLE: 3>]
+>>> list(Weekday)
+[<Weekday.MONDAY: 1>, <Weekday.TUESDAY: 2>, <Weekday.WEDNESDAY: 4>, <Weekday.THURSDAY: 8>, <Weekday.FRIDAY: 16>, <Weekday.SATURDAY: 32>, <Weekday.SUNDAY: 64>]
+

Note that the aliases Shape.ALIAS_FOR_SQUARE and Weekday.WEEKEND aren’t shown.

The special attribute __members__ is a read-only ordered mapping of names to members. It includes all names defined in the enumeration, including the aliases:

>>> for name, member in Shape.__members__.items():
+...     name, member
+...
+('SQUARE', <Shape.SQUARE: 2>)
+('DIAMOND', <Shape.DIAMOND: 1>)
+('CIRCLE', <Shape.CIRCLE: 3>)
+('ALIAS_FOR_SQUARE', <Shape.SQUARE: 2>)
+

The __members__ attribute can be used for detailed programmatic access to the enumeration members. For example, finding all the aliases:

>>> [name for name, member in Shape.__members__.items() if member.name != name]
+['ALIAS_FOR_SQUARE']
+

Note

Aliases for flags include values with multiple flags set, such as 3, and no flags set, i.e. 0.

Comparisons

Enumeration members are compared by identity:

>>> Color.RED is Color.RED
+True
+>>> Color.RED is Color.BLUE
+False
+>>> Color.RED is not Color.BLUE
+True
+

Ordered comparisons between enumeration values are not supported. Enum members are not integers (but see IntEnum below):

>>> Color.RED < Color.BLUE
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: '<' not supported between instances of 'Color' and 'Color'
+

Equality comparisons are defined though:

>>> Color.BLUE == Color.RED
+False
+>>> Color.BLUE != Color.RED
+True
+>>> Color.BLUE == Color.BLUE
+True
+

Comparisons against non-enumeration values will always compare not equal (again, IntEnum was explicitly designed to behave differently, see below):

>>> Color.BLUE == 2
+False
+

Warning

It is possible to reload modules – if a reloaded module contains enums, they will be recreated, and the new members may not compare identical/equal to the original members.

Allowed members and attributes of enumerations

Most of the examples above use integers for enumeration values. Using integers is short and handy (and provided by default by the Functional API), but not strictly enforced. In the vast majority of use-cases, one doesn’t care what the actual value of an enumeration is. But if the value is important, enumerations can have arbitrary values.

Enumerations are Python classes, and can have methods and special methods as usual. If we have this enumeration:

>>> class Mood(Enum):
+...     FUNKY = 1
+...     HAPPY = 3
+...
+...     def describe(self):
+...         # self is the member here
+...         return self.name, self.value
+...
+...     def __str__(self):
+...         return 'my custom str! {0}'.format(self.value)
+...
+...     @classmethod
+...     def favorite_mood(cls):
+...         # cls here is the enumeration
+...         return cls.HAPPY
+...
+

Then:

>>> Mood.favorite_mood()
+<Mood.HAPPY: 3>
+>>> Mood.HAPPY.describe()
+('HAPPY', 3)
+>>> str(Mood.FUNKY)
+'my custom str! 1'
+

The rules for what is allowed are as follows: names that start and end with a single underscore are reserved by enum and cannot be used; all other attributes defined within an enumeration will become members of this enumeration, with the exception of special methods (__str__(), __add__(), etc.), descriptors (methods are also descriptors), and variable names listed in _ignore_.

Note: if your enumeration defines __new__() and/or __init__(), any value(s) given to the enum member will be passed into those methods. See Planet for an example.

Note

The __new__() method, if defined, is used during creation of the Enum members; it is then replaced by Enum’s __new__() which is used after class creation for lookup of existing members. See When to use __new__() vs. __init__() for more details.

Restricted Enum subclassing

A new Enum class must have one base enum class, up to one concrete data type, and as many object-based mixin classes as needed. The order of these base classes is:

class EnumName([mix-in, ...,] [data-type,] base-enum):
+    pass
+

Also, subclassing an enumeration is allowed only if the enumeration does not define any members. So this is forbidden:

>>> class MoreColor(Color):
+...     PINK = 17
+...
+Traceback (most recent call last):
+...
+TypeError: <enum 'MoreColor'> cannot extend <enum 'Color'>
+

But this is allowed:

>>> class Foo(Enum):
+...     def some_behavior(self):
+...         pass
+...
+>>> class Bar(Foo):
+...     HAPPY = 1
+...     SAD = 2
+...
+

Allowing subclassing of enums that define members would lead to a violation of some important invariants of types and instances. On the other hand, it makes sense to allow sharing some common behavior between a group of enumerations. (See OrderedEnum for an example.)

Dataclass support

When inheriting from a dataclass, the __repr__() omits the inherited class’ name. For example:

>>> from dataclasses import dataclass, field
+>>> @dataclass
+... class CreatureDataMixin:
+...     size: str
+...     legs: int
+...     tail: bool = field(repr=False, default=True)
+...
+>>> class Creature(CreatureDataMixin, Enum):
+...     BEETLE = 'small', 6
+...     DOG = 'medium', 4
+...
+>>> Creature.DOG
+<Creature.DOG: size='medium', legs=4>
+

Use the dataclass() argument repr=False to use the standard repr().

Changed in version 3.12: Only the dataclass fields are shown in the value area, not the dataclass’ name.

Pickling

Enumerations can be pickled and unpickled:

>>> from test.test_enum import Fruit
+>>> from pickle import dumps, loads
+>>> Fruit.TOMATO is loads(dumps(Fruit.TOMATO))
+True
+

The usual restrictions for pickling apply: picklable enums must be defined in the top level of a module, since unpickling requires them to be importable from that module.

Note

With pickle protocol version 4 it is possible to easily pickle enums nested in other classes.

It is possible to modify how enum members are pickled/unpickled by defining __reduce_ex__() in the enumeration class. The default method is by-value, but enums with complicated values may want to use by-name:

>>> import enum
+>>> class MyEnum(enum.Enum):
+...     __reduce_ex__ = enum.pickle_by_enum_name
+

Note

Using by-name for flags is not recommended, as unnamed aliases will not unpickle.

Functional API

The Enum class is callable, providing the following functional API:

>>> Animal = Enum('Animal', 'ANT BEE CAT DOG')
+>>> Animal
+<enum 'Animal'>
+>>> Animal.ANT
+<Animal.ANT: 1>
+>>> list(Animal)
+[<Animal.ANT: 1>, <Animal.BEE: 2>, <Animal.CAT: 3>, <Animal.DOG: 4>]
+

The semantics of this API resemble namedtuple. The first argument of the call to Enum is the name of the enumeration.

The second argument is the source of enumeration member names. It can be a whitespace-separated string of names, a sequence of names, a sequence of 2-tuples with key/value pairs, or a mapping (e.g. dictionary) of names to values. The last two options enable assigning arbitrary values to enumerations; the others auto-assign increasing integers starting with 1 (use the start parameter to specify a different starting value). A new class derived from Enum is returned. In other words, the above assignment to Animal is equivalent to:

>>> class Animal(Enum):
+...     ANT = 1
+...     BEE = 2
+...     CAT = 3
+...     DOG = 4
+...
+

The reason for defaulting to 1 as the starting number and not 0 is that 0 is False in a boolean sense, but by default enum members all evaluate to True.

Pickling enums created with the functional API can be tricky as frame stack implementation details are used to try and figure out which module the enumeration is being created in (e.g. it will fail if you use a utility function in a separate module, and also may not work on IronPython or Jython). The solution is to specify the module name explicitly as follows:

>>> Animal = Enum('Animal', 'ANT BEE CAT DOG', module=__name__)
+

Warning

If module is not supplied, and Enum cannot determine what it is, the new Enum members will not be unpicklable; to keep errors closer to the source, pickling will be disabled.

The new pickle protocol 4 also, in some circumstances, relies on __qualname__ being set to the location where pickle will be able to find the class. For example, if the class was made available in class SomeData in the global scope:

>>> Animal = Enum('Animal', 'ANT BEE CAT DOG', qualname='SomeData.Animal')
+

The complete signature is:

Enum(
+    value='NewEnumName',
+    names=<...>,
+    *,
+    module='...',
+    qualname='...',
+    type=<mixed-in class>,
+    start=1,
+    )
+

Changed in version 3.5: The start parameter was added.

Derived Enumerations

IntEnum

The first variation of Enum that is provided is also a subclass of int. Members of an IntEnum can be compared to integers; by extension, integer enumerations of different types can also be compared to each other:

>>> from enum import IntEnum
+>>> class Shape(IntEnum):
+...     CIRCLE = 1
+...     SQUARE = 2
+...
+>>> class Request(IntEnum):
+...     POST = 1
+...     GET = 2
+...
+>>> Shape == 1
+False
+>>> Shape.CIRCLE == 1
+True
+>>> Shape.CIRCLE == Request.POST
+True
+

However, they still can’t be compared to standard Enum enumerations:

>>> class Shape(IntEnum):
+...     CIRCLE = 1
+...     SQUARE = 2
+...
+>>> class Color(Enum):
+...     RED = 1
+...     GREEN = 2
+...
+>>> Shape.CIRCLE == Color.RED
+False
+

IntEnum values behave like integers in other ways you’d expect:

>>> int(Shape.CIRCLE)
+1
+>>> ['a', 'b', 'c'][Shape.CIRCLE]
+'b'
+>>> [i for i in range(Shape.SQUARE)]
+[0, 1]
+

StrEnum

The second variation of Enum that is provided is also a subclass of str. Members of a StrEnum can be compared to strings; by extension, string enumerations of different types can also be compared to each other.

New in version 3.11.

IntFlag

The next variation of Enum provided, IntFlag, is also based on int. The difference being IntFlag members can be combined using the bitwise operators (&, |, ^, ~) and the result is still an IntFlag member, if possible. Like IntEnum, IntFlag members are also integers and can be used wherever an int is used.

Note

Any operation on an IntFlag member besides the bit-wise operations will lose the IntFlag membership.

Bit-wise operations that result in invalid IntFlag values will lose the IntFlag membership. See FlagBoundary for details.

New in version 3.6.

Changed in version 3.11.

Sample IntFlag class:

>>> from enum import IntFlag
+>>> class Perm(IntFlag):
+...     R = 4
+...     W = 2
+...     X = 1
+...
+>>> Perm.R | Perm.W
+<Perm.R|W: 6>
+>>> Perm.R + Perm.W
+6
+>>> RW = Perm.R | Perm.W
+>>> Perm.R in RW
+True
+

It is also possible to name the combinations:

>>> class Perm(IntFlag):
+...     R = 4
+...     W = 2
+...     X = 1
+...     RWX = 7
+...
+>>> Perm.RWX
+<Perm.RWX: 7>
+>>> ~Perm.RWX
+<Perm: 0>
+>>> Perm(7)
+<Perm.RWX: 7>
+

Note

Named combinations are considered aliases. Aliases do not show up during iteration, but can be returned from by-value lookups.

Changed in version 3.11.

Another important difference between IntFlag and Enum is that if no flags are set (the value is 0), its boolean evaluation is False:

>>> Perm.R & Perm.X
+<Perm: 0>
+>>> bool(Perm.R & Perm.X)
+False
+

Because IntFlag members are also subclasses of int they can be combined with them (but may lose IntFlag membership:

>>> Perm.X | 4
+<Perm.R|X: 5>
+
+>>> Perm.X + 8
+9
+

Note

The negation operator, ~, always returns an IntFlag member with a positive value:

>>> (~Perm.X).value == (Perm.R|Perm.W).value == 6
+True
+

IntFlag members can also be iterated over:

>>> list(RW)
+[<Perm.R: 4>, <Perm.W: 2>]
+

New in version 3.11.

Flag

The last variation is Flag. Like IntFlag, Flag members can be combined using the bitwise operators (&, |, ^, ~). Unlike IntFlag, they cannot be combined with, nor compared against, any other Flag enumeration, nor int. While it is possible to specify the values directly it is recommended to use auto as the value and let Flag select an appropriate value.

New in version 3.6.

Like IntFlag, if a combination of Flag members results in no flags being set, the boolean evaluation is False:

>>> from enum import Flag, auto
+>>> class Color(Flag):
+...     RED = auto()
+...     BLUE = auto()
+...     GREEN = auto()
+...
+>>> Color.RED & Color.GREEN
+<Color: 0>
+>>> bool(Color.RED & Color.GREEN)
+False
+

Individual flags should have values that are powers of two (1, 2, 4, 8, …), while combinations of flags will not:

>>> class Color(Flag):
+...     RED = auto()
+...     BLUE = auto()
+...     GREEN = auto()
+...     WHITE = RED | BLUE | GREEN
+...
+>>> Color.WHITE
+<Color.WHITE: 7>
+

Giving a name to the “no flags set” condition does not change its boolean value:

>>> class Color(Flag):
+...     BLACK = 0
+...     RED = auto()
+...     BLUE = auto()
+...     GREEN = auto()
+...
+>>> Color.BLACK
+<Color.BLACK: 0>
+>>> bool(Color.BLACK)
+False
+

Flag members can also be iterated over:

>>> purple = Color.RED | Color.BLUE
+>>> list(purple)
+[<Color.RED: 1>, <Color.BLUE: 2>]
+

New in version 3.11.

Note

For the majority of new code, Enum and Flag are strongly recommended, since IntEnum and IntFlag break some semantic promises of an enumeration (by being comparable to integers, and thus by transitivity to other unrelated enumerations). IntEnum and IntFlag should be used only in cases where Enum and Flag will not do; for example, when integer constants are replaced with enumerations, or for interoperability with other systems.

Others

While IntEnum is part of the enum module, it would be very simple to implement independently:

class IntEnum(int, Enum):
+    pass
+

This demonstrates how similar derived enumerations can be defined; for example a FloatEnum that mixes in float instead of int.

Some rules:

  1. When subclassing Enum, mix-in types must appear before Enum itself in the sequence of bases, as in the IntEnum example above.
  2. Mix-in types must be subclassable. For example, bool and range are not subclassable and will throw an error during Enum creation if used as the mix-in type.
  3. While Enum can have members of any type, once you mix in an additional type, all the members must have values of that type, e.g. int above. This restriction does not apply to mix-ins which only add methods and don’t specify another type.
  4. When another data type is mixed in, the value attribute is not the same as the enum member itself, although it is equivalent and will compare equal.
  5. A data type is a mixin that defines __new__(), or a dataclass +
  6. %-style formatting: %s and %r call the Enum class’s __str__() and __repr__() respectively; other codes (such as %i or %h for IntEnum) treat the enum member as its mixed-in type.
  7. +Formatted string literals, str.format(), and format() will use the enum’s __str__() method.

Note

Because IntEnum, IntFlag, and StrEnum are designed to be drop-in replacements for existing constants, their __str__() method has been reset to their data types’ __str__() method.

When to use __new__() vs. __init__() +

__new__() must be used whenever you want to customize the actual value of the Enum member. Any other modifications may go in either __new__() or __init__(), with __init__() being preferred.

For example, if you want to pass several items to the constructor, but only want one of them to be the value:

>>> class Coordinate(bytes, Enum):
+...     """
+...     Coordinate with binary codes that can be indexed by the int code.
+...     """
+...     def __new__(cls, value, label, unit):
+...         obj = bytes.__new__(cls, [value])
+...         obj._value_ = value
+...         obj.label = label
+...         obj.unit = unit
+...         return obj
+...     PX = (0, 'P.X', 'km')
+...     PY = (1, 'P.Y', 'km')
+...     VX = (2, 'V.X', 'km/s')
+...     VY = (3, 'V.Y', 'km/s')
+...
+
+>>> print(Coordinate['PY'])
+Coordinate.PY
+
+>>> print(Coordinate(3))
+Coordinate.VY
+

Warning

Do not call super().__new__(), as the lookup-only __new__ is the one that is found; instead, use the data type directly.

Finer Points

Supported __dunder__ names

__members__ is a read-only ordered mapping of member_name:member items. It is only available on the class.

__new__(), if specified, must create and return the enum members; it is also a very good idea to set the member’s _value_ appropriately. Once all the members are created it is no longer used.

Supported _sunder_ names

  • +_name_ – name of the member
  • +_value_ – value of the member; can be set / modified in __new__ +
  • +_missing_ – a lookup function used when a value is not found; may be overridden
  • +_ignore_ – a list of names, either as a list or a str, that will not be transformed into members, and will be removed from the final class
  • +_order_ – used in Python 2/3 code to ensure member order is consistent (class attribute, removed during class creation)
  • +_generate_next_value_ – used by the Functional API and by auto to get an appropriate value for an enum member; may be overridden

Note

For standard Enum classes the next value chosen is the last value seen incremented by one.

For Flag classes the next value chosen will be the next highest power-of-two, regardless of the last value seen.

New in version 3.6: _missing_, _order_, _generate_next_value_

New in version 3.7: _ignore_

To help keep Python 2 / Python 3 code in sync an _order_ attribute can be provided. It will be checked against the actual order of the enumeration and raise an error if the two do not match:

>>> class Color(Enum):
+...     _order_ = 'RED GREEN BLUE'
+...     RED = 1
+...     BLUE = 3
+...     GREEN = 2
+...
+Traceback (most recent call last):
+...
+TypeError: member order does not match _order_:
+  ['RED', 'BLUE', 'GREEN']
+  ['RED', 'GREEN', 'BLUE']
+

Note

In Python 2 code the _order_ attribute is necessary as definition order is lost before it can be recorded.

_Private__names

Private names are not converted to enum members, but remain normal attributes.

Changed in version 3.11.

+Enum member type

Enum members are instances of their enum class, and are normally accessed as EnumClass.member. In certain situations, such as writing custom enum behavior, being able to access one member directly from another is useful, and is supported; however, in order to avoid name clashes between member names and attributes/methods from mixed-in classes, upper-case names are strongly recommended.

Changed in version 3.5.

Creating members that are mixed with other data types

When subclassing other data types, such as int or str, with an Enum, all values after the = are passed to that data type’s constructor. For example:

>>> class MyEnum(IntEnum):      # help(int) -> int(x, base=10) -> integer
+...     example = '11', 16      # so x='11' and base=16
+...
+>>> MyEnum.example.value        # and hex(11) is...
+17
+

Boolean value of Enum classes and members

Enum classes that are mixed with non-Enum types (such as int, str, etc.) are evaluated according to the mixed-in type’s rules; otherwise, all members evaluate as True. To make your own enum’s boolean evaluation depend on the member’s value add the following to your class:

def __bool__(self):
+    return bool(self.value)
+

Plain Enum classes always evaluate as True.

+Enum classes with methods

If you give your enum subclass extra methods, like the Planet class below, those methods will show up in a dir() of the member, but not of the class:

>>> dir(Planet)                         
+['EARTH', 'JUPITER', 'MARS', 'MERCURY', 'NEPTUNE', 'SATURN', 'URANUS', 'VENUS', '__class__', '__doc__', '__members__', '__module__']
+>>> dir(Planet.EARTH)                   
+['__class__', '__doc__', '__module__', 'mass', 'name', 'radius', 'surface_gravity', 'value']
+

Combining members of Flag +

Iterating over a combination of Flag members will only return the members that are comprised of a single bit:

>>> class Color(Flag):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...     MAGENTA = RED | BLUE
+...     YELLOW = RED | GREEN
+...     CYAN = GREEN | BLUE
+...
+>>> Color(3)  # named combination
+<Color.YELLOW: 3>
+>>> Color(7)      # not named combination
+<Color.RED|GREEN|BLUE: 7>
+

+Flag and IntFlag minutia

Using the following snippet for our examples:

>>> class Color(IntFlag):
+...     BLACK = 0
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 4
+...     PURPLE = RED | BLUE
+...     WHITE = RED | GREEN | BLUE
+...
+

the following are true:

  • single-bit flags are canonical
  • multi-bit and zero-bit flags are aliases
  • +

    only canonical flags are returned during iteration:

    >>> list(Color.WHITE)
+[<Color.RED: 1>, <Color.GREEN: 2>, <Color.BLUE: 4>]
+
  • +

    negating a flag or flag set returns a new flag/flag set with the corresponding positive integer value:

    >>> Color.BLUE
+<Color.BLUE: 4>
+
+>>> ~Color.BLUE
+<Color.RED|GREEN: 3>
+
  • +

    names of pseudo-flags are constructed from their members’ names:

    >>> (Color.RED | Color.GREEN).name
+'RED|GREEN'
+
  • +

    multi-bit flags, aka aliases, can be returned from operations:

    >>> Color.RED | Color.BLUE
+<Color.PURPLE: 5>
+
+>>> Color(7)  # or Color(-1)
+<Color.WHITE: 7>
+
+>>> Color(0)
+<Color.BLACK: 0>
+
  • +

    membership / containment checking: zero-valued flags are always considered to be contained:

    >>> Color.BLACK in Color.WHITE
+True
+

    otherwise, only if all bits of one flag are in the other flag will True be returned:

    >>> Color.PURPLE in Color.WHITE
+True
+
+>>> Color.GREEN in Color.PURPLE
+False
+

There is a new boundary mechanism that controls how out-of-range / invalid bits are handled: STRICT, CONFORM, EJECT, and KEEP:

  • STRICT –> raises an exception when presented with invalid values
  • CONFORM –> discards any invalid bits
  • EJECT –> lose Flag status and become a normal int with the given value
  • +

    KEEP –> keep the extra bits

    • keeps Flag status and extra bits
    • extra bits do not show up in iteration
    • extra bits do show up in repr() and str()

The default for Flag is STRICT, the default for IntFlag is EJECT, and the default for _convert_ is KEEP (see ssl.Options for an example of when KEEP is needed).

How are Enums and Flags different?

Enums have a custom metaclass that affects many aspects of both derived Enum classes and their instances (members).

Enum Classes

The EnumType metaclass is responsible for providing the __contains__(), __dir__(), __iter__() and other methods that allow one to do things with an Enum class that fail on a typical class, such as list(Color) or some_enum_var in Color. EnumType is responsible for ensuring that various other methods on the final Enum class are correct (such as __new__(), __getnewargs__(), __str__() and __repr__()).

Flag Classes

Flags have an expanded view of aliasing: to be canonical, the value of a flag needs to be a power-of-two value, and not a duplicate name. So, in addition to the Enum definition of alias, a flag with no value (a.k.a. 0) or with more than one power-of-two value (e.g. 3) is considered an alias.

Enum Members (aka instances)

The most interesting thing about enum members is that they are singletons. EnumType creates them all while it is creating the enum class itself, and then puts a custom __new__() in place to ensure that no new ones are ever instantiated by returning only the existing member instances.

Flag Members

Flag members can be iterated over just like the Flag class, and only the canonical members will be returned. For example:

>>> list(Color)
+[<Color.RED: 1>, <Color.GREEN: 2>, <Color.BLUE: 4>]
+

(Note that BLACK, PURPLE, and WHITE do not show up.)

Inverting a flag member returns the corresponding positive value, rather than a negative value — for example:

>>> ~Color.RED
+<Color.GREEN|BLUE: 6>
+

Flag members have a length corresponding to the number of power-of-two values they contain. For example:

>>> len(Color.PURPLE)
+2
+

Enum Cookbook

While Enum, IntEnum, StrEnum, Flag, and IntFlag are expected to cover the majority of use-cases, they cannot cover them all. Here are recipes for some different types of enumerations that can be used directly, or as examples for creating one’s own.

Omitting values

In many use-cases, one doesn’t care what the actual value of an enumeration is. There are several ways to define this type of simple enumeration:

  • use instances of auto for the value
  • use instances of object as the value
  • use a descriptive string as the value
  • use a tuple as the value and a custom __new__() to replace the tuple with an int value

Using any of these methods signifies to the user that these values are not important, and also enables one to add, remove, or reorder members without having to renumber the remaining members.

Using auto +

Using auto would look like:

>>> class Color(Enum):
+...     RED = auto()
+...     BLUE = auto()
+...     GREEN = auto()
+...
+>>> Color.GREEN
+<Color.GREEN: 3>
+

Using object +

Using object would look like:

>>> class Color(Enum):
+...     RED = object()
+...     GREEN = object()
+...     BLUE = object()
+...
+>>> Color.GREEN                         
+<Color.GREEN: <object object at 0x...>>
+

This is also a good example of why you might want to write your own __repr__():

>>> class Color(Enum):
+...     RED = object()
+...     GREEN = object()
+...     BLUE = object()
+...     def __repr__(self):
+...         return "<%s.%s>" % (self.__class__.__name__, self._name_)
+...
+>>> Color.GREEN
+<Color.GREEN>
+

Using a descriptive string

Using a string as the value would look like:

>>> class Color(Enum):
+...     RED = 'stop'
+...     GREEN = 'go'
+...     BLUE = 'too fast!'
+...
+>>> Color.GREEN
+<Color.GREEN: 'go'>
+

Using a custom __new__() +

Using an auto-numbering __new__() would look like:

>>> class AutoNumber(Enum):
+...     def __new__(cls):
+...         value = len(cls.__members__) + 1
+...         obj = object.__new__(cls)
+...         obj._value_ = value
+...         return obj
+...
+>>> class Color(AutoNumber):
+...     RED = ()
+...     GREEN = ()
+...     BLUE = ()
+...
+>>> Color.GREEN
+<Color.GREEN: 2>
+

To make a more general purpose AutoNumber, add *args to the signature:

>>> class AutoNumber(Enum):
+...     def __new__(cls, *args):      # this is the only change from above
+...         value = len(cls.__members__) + 1
+...         obj = object.__new__(cls)
+...         obj._value_ = value
+...         return obj
+...
+

Then when you inherit from AutoNumber you can write your own __init__ to handle any extra arguments:

>>> class Swatch(AutoNumber):
+...     def __init__(self, pantone='unknown'):
+...         self.pantone = pantone
+...     AUBURN = '3497'
+...     SEA_GREEN = '1246'
+...     BLEACHED_CORAL = () # New color, no Pantone code yet!
+...
+>>> Swatch.SEA_GREEN
+<Swatch.SEA_GREEN: 2>
+>>> Swatch.SEA_GREEN.pantone
+'1246'
+>>> Swatch.BLEACHED_CORAL.pantone
+'unknown'
+

Note

The __new__() method, if defined, is used during creation of the Enum members; it is then replaced by Enum’s __new__() which is used after class creation for lookup of existing members.

Warning

Do not call super().__new__(), as the lookup-only __new__ is the one that is found; instead, use the data type directly – e.g.:

obj = int.__new__(cls, value)
+

OrderedEnum

An ordered enumeration that is not based on IntEnum and so maintains the normal Enum invariants (such as not being comparable to other enumerations):

>>> class OrderedEnum(Enum):
+...     def __ge__(self, other):
+...         if self.__class__ is other.__class__:
+...             return self.value >= other.value
+...         return NotImplemented
+...     def __gt__(self, other):
+...         if self.__class__ is other.__class__:
+...             return self.value > other.value
+...         return NotImplemented
+...     def __le__(self, other):
+...         if self.__class__ is other.__class__:
+...             return self.value <= other.value
+...         return NotImplemented
+...     def __lt__(self, other):
+...         if self.__class__ is other.__class__:
+...             return self.value < other.value
+...         return NotImplemented
+...
+>>> class Grade(OrderedEnum):
+...     A = 5
+...     B = 4
+...     C = 3
+...     D = 2
+...     F = 1
+...
+>>> Grade.C < Grade.A
+True
+

DuplicateFreeEnum

Raises an error if a duplicate member value is found instead of creating an alias:

>>> class DuplicateFreeEnum(Enum):
+...     def __init__(self, *args):
+...         cls = self.__class__
+...         if any(self.value == e.value for e in cls):
+...             a = self.name
+...             e = cls(self.value).name
+...             raise ValueError(
+...                 "aliases not allowed in DuplicateFreeEnum:  %r --> %r"
+...                 % (a, e))
+...
+>>> class Color(DuplicateFreeEnum):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 3
+...     GRENE = 2
+...
+Traceback (most recent call last):
+  ...
+ValueError: aliases not allowed in DuplicateFreeEnum:  'GRENE' --> 'GREEN'
+

Note

This is a useful example for subclassing Enum to add or change other behaviors as well as disallowing aliases. If the only desired change is disallowing aliases, the unique() decorator can be used instead.

Planet

If __new__() or __init__() is defined, the value of the enum member will be passed to those methods:

>>> class Planet(Enum):
+...     MERCURY = (3.303e+23, 2.4397e6)
+...     VENUS   = (4.869e+24, 6.0518e6)
+...     EARTH   = (5.976e+24, 6.37814e6)
+...     MARS    = (6.421e+23, 3.3972e6)
+...     JUPITER = (1.9e+27,   7.1492e7)
+...     SATURN  = (5.688e+26, 6.0268e7)
+...     URANUS  = (8.686e+25, 2.5559e7)
+...     NEPTUNE = (1.024e+26, 2.4746e7)
+...     def __init__(self, mass, radius):
+...         self.mass = mass       # in kilograms
+...         self.radius = radius   # in meters
+...     @property
+...     def surface_gravity(self):
+...         # universal gravitational constant  (m3 kg-1 s-2)
+...         G = 6.67300E-11
+...         return G * self.mass / (self.radius * self.radius)
+...
+>>> Planet.EARTH.value
+(5.976e+24, 6378140.0)
+>>> Planet.EARTH.surface_gravity
+9.802652743337129
+

TimePeriod

An example to show the _ignore_ attribute in use:

>>> from datetime import timedelta
+>>> class Period(timedelta, Enum):
+...     "different lengths of time"
+...     _ignore_ = 'Period i'
+...     Period = vars()
+...     for i in range(367):
+...         Period['day_%d' % i] = i
+...
+>>> list(Period)[:2]
+[<Period.day_0: datetime.timedelta(0)>, <Period.day_1: datetime.timedelta(days=1)>]
+>>> list(Period)[-2:]
+[<Period.day_365: datetime.timedelta(days=365)>, <Period.day_366: datetime.timedelta(days=366)>]
+

Subclassing EnumType

While most enum needs can be met by customizing Enum subclasses, either with class decorators or custom functions, EnumType can be subclassed to provide a different Enum experience.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/enum.html +

+
diff --git a/devdocs/python~3.12/howto%2Ffunctional.html b/devdocs/python~3.12/howto%2Ffunctional.html new file mode 100644 index 00000000..b8b2c56f --- /dev/null +++ b/devdocs/python~3.12/howto%2Ffunctional.html @@ -0,0 +1,379 @@ +

Functional Programming HOWTO

Author
+

A. M. Kuchling

Release
+

0.32

In this document, we’ll take a tour of Python’s features suitable for implementing programs in a functional style. After an introduction to the concepts of functional programming, we’ll look at language features such as iterators and generators and relevant library modules such as itertools and functools.

Introduction

This section explains the basic concept of functional programming; if you’re just interested in learning about Python language features, skip to the next section on Iterators.

Programming languages support decomposing problems in several different ways:

The designers of some computer languages choose to emphasize one particular approach to programming. This often makes it difficult to write programs that use a different approach. Other languages are multi-paradigm languages that support several different approaches. Lisp, C++, and Python are multi-paradigm; you can write programs or libraries that are largely procedural, object-oriented, or functional in all of these languages. In a large program, different sections might be written using different approaches; the GUI might be object-oriented while the processing logic is procedural or functional, for example.

In a functional program, input flows through a set of functions. Each function operates on its input and produces some output. Functional style discourages functions with side effects that modify internal state or make other changes that aren’t visible in the function’s return value. Functions that have no side effects at all are called purely functional. Avoiding side effects means not using data structures that get updated as a program runs; every function’s output must only depend on its input.

Some languages are very strict about purity and don’t even have assignment statements such as a=3 or c = a + b, but it’s difficult to avoid all side effects, such as printing to the screen or writing to a disk file. Another example is a call to the print() or time.sleep() function, neither of which returns a useful value. Both are called only for their side effects of sending some text to the screen or pausing execution for a second.

Python programs written in functional style usually won’t go to the extreme of avoiding all I/O or all assignments; instead, they’ll provide a functional-appearing interface but will use non-functional features internally. For example, the implementation of a function will still use assignments to local variables, but won’t modify global variables or have other side effects.

Functional programming can be considered the opposite of object-oriented programming. Objects are little capsules containing some internal state along with a collection of method calls that let you modify this state, and programs consist of making the right set of state changes. Functional programming wants to avoid state changes as much as possible and works with data flowing between functions. In Python you might combine the two approaches by writing functions that take and return instances representing objects in your application (e-mail messages, transactions, etc.).

Functional design may seem like an odd constraint to work under. Why should you avoid objects and side effects? There are theoretical and practical advantages to the functional style:

Formal provability

A theoretical benefit is that it’s easier to construct a mathematical proof that a functional program is correct.

For a long time researchers have been interested in finding ways to mathematically prove programs correct. This is different from testing a program on numerous inputs and concluding that its output is usually correct, or reading a program’s source code and concluding that the code looks right; the goal is instead a rigorous proof that a program produces the right result for all possible inputs.

The technique used to prove programs correct is to write down invariants, properties of the input data and of the program’s variables that are always true. For each line of code, you then show that if invariants X and Y are true before the line is executed, the slightly different invariants X’ and Y’ are true after the line is executed. This continues until you reach the end of the program, at which point the invariants should match the desired conditions on the program’s output.

Functional programming’s avoidance of assignments arose because assignments are difficult to handle with this technique; assignments can break invariants that were true before the assignment without producing any new invariants that can be propagated onward.

Unfortunately, proving programs correct is largely impractical and not relevant to Python software. Even trivial programs require proofs that are several pages long; the proof of correctness for a moderately complicated program would be enormous, and few or none of the programs you use daily (the Python interpreter, your XML parser, your web browser) could be proven correct. Even if you wrote down or generated a proof, there would then be the question of verifying the proof; maybe there’s an error in it, and you wrongly believe you’ve proved the program correct.

Modularity

A more practical benefit of functional programming is that it forces you to break apart your problem into small pieces. Programs are more modular as a result. It’s easier to specify and write a small function that does one thing than a large function that performs a complicated transformation. Small functions are also easier to read and to check for errors.

Ease of debugging and testing

Testing and debugging a functional-style program is easier.

Debugging is simplified because functions are generally small and clearly specified. When a program doesn’t work, each function is an interface point where you can check that the data are correct. You can look at the intermediate inputs and outputs to quickly isolate the function that’s responsible for a bug.

Testing is easier because each function is a potential subject for a unit test. Functions don’t depend on system state that needs to be replicated before running a test; instead you only have to synthesize the right input and then check that the output matches expectations.

Composability

As you work on a functional-style program, you’ll write a number of functions with varying inputs and outputs. Some of these functions will be unavoidably specialized to a particular application, but others will be useful in a wide variety of programs. For example, a function that takes a directory path and returns all the XML files in the directory, or a function that takes a filename and returns its contents, can be applied to many different situations.

Over time you’ll form a personal library of utilities. Often you’ll assemble new programs by arranging existing functions in a new configuration and writing a few functions specialized for the current task.

Iterators

I’ll start by looking at a Python language feature that’s an important foundation for writing functional-style programs: iterators.

An iterator is an object representing a stream of data; this object returns the data one element at a time. A Python iterator must support a method called __next__() that takes no arguments and always returns the next element of the stream. If there are no more elements in the stream, __next__() must raise the StopIteration exception. Iterators don’t have to be finite, though; it’s perfectly reasonable to write an iterator that produces an infinite stream of data.

The built-in iter() function takes an arbitrary object and tries to return an iterator that will return the object’s contents or elements, raising TypeError if the object doesn’t support iteration. Several of Python’s built-in data types support iteration, the most common being lists and dictionaries. An object is called iterable if you can get an iterator for it.

You can experiment with the iteration interface manually:

>>> L = [1, 2, 3]
+>>> it = iter(L)
+>>> it  
+<...iterator object at ...>
+>>> it.__next__()  # same as next(it)
+1
+>>> next(it)
+2
+>>> next(it)
+3
+>>> next(it)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+StopIteration
+>>>
+

Python expects iterable objects in several different contexts, the most important being the for statement. In the statement for X in Y, Y must be an iterator or some object for which iter() can create an iterator. These two statements are equivalent:

for i in iter(obj):
+    print(i)
+
+for i in obj:
+    print(i)
+

Iterators can be materialized as lists or tuples by using the list() or tuple() constructor functions:

>>> L = [1, 2, 3]
+>>> iterator = iter(L)
+>>> t = tuple(iterator)
+>>> t
+(1, 2, 3)
+

Sequence unpacking also supports iterators: if you know an iterator will return N elements, you can unpack them into an N-tuple:

>>> L = [1, 2, 3]
+>>> iterator = iter(L)
+>>> a, b, c = iterator
+>>> a, b, c
+(1, 2, 3)
+

Built-in functions such as max() and min() can take a single iterator argument and will return the largest or smallest element. The "in" and "not in" operators also support iterators: X in iterator is true if X is found in the stream returned by the iterator. You’ll run into obvious problems if the iterator is infinite; max(), min() will never return, and if the element X never appears in the stream, the "in" and "not in" operators won’t return either.

Note that you can only go forward in an iterator; there’s no way to get the previous element, reset the iterator, or make a copy of it. Iterator objects can optionally provide these additional capabilities, but the iterator protocol only specifies the __next__() method. Functions may therefore consume all of the iterator’s output, and if you need to do something different with the same stream, you’ll have to create a new iterator.

Data Types That Support Iterators

We’ve already seen how lists and tuples support iterators. In fact, any Python sequence type, such as strings, will automatically support creation of an iterator.

Calling iter() on a dictionary returns an iterator that will loop over the dictionary’s keys:

>>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,
+...      'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
+>>> for key in m:
+...     print(key, m[key])
+Jan 1
+Feb 2
+Mar 3
+Apr 4
+May 5
+Jun 6
+Jul 7
+Aug 8
+Sep 9
+Oct 10
+Nov 11
+Dec 12
+

Note that starting with Python 3.7, dictionary iteration order is guaranteed to be the same as the insertion order. In earlier versions, the behaviour was unspecified and could vary between implementations.

Applying iter() to a dictionary always loops over the keys, but dictionaries have methods that return other iterators. If you want to iterate over values or key/value pairs, you can explicitly call the values() or items() methods to get an appropriate iterator.

The dict() constructor can accept an iterator that returns a finite stream of (key, value) tuples:

>>> L = [('Italy', 'Rome'), ('France', 'Paris'), ('US', 'Washington DC')]
+>>> dict(iter(L))
+{'Italy': 'Rome', 'France': 'Paris', 'US': 'Washington DC'}
+

Files also support iteration by calling the readline() method until there are no more lines in the file. This means you can read each line of a file like this:

for line in file:
+    # do something for each line
+    ...
+

Sets can take their contents from an iterable and let you iterate over the set’s elements:

>>> S = {2, 3, 5, 7, 11, 13}
+>>> for i in S:
+...     print(i)
+2
+3
+5
+7
+11
+13
+

Generator expressions and list comprehensions

Two common operations on an iterator’s output are 1) performing some operation for every element, 2) selecting a subset of elements that meet some condition. For example, given a list of strings, you might want to strip off trailing whitespace from each line or extract all the strings containing a given substring.

List comprehensions and generator expressions (short form: “listcomps” and “genexps”) are a concise notation for such operations, borrowed from the functional programming language Haskell (https://www.haskell.org/). You can strip all the whitespace from a stream of strings with the following code:

>>> line_list = ['  line 1\n', 'line 2  \n', ' \n', '']
+
+>>> # Generator expression -- returns iterator
+>>> stripped_iter = (line.strip() for line in line_list)
+
+>>> # List comprehension -- returns list
+>>> stripped_list = [line.strip() for line in line_list]
+

You can select only certain elements by adding an "if" condition:

>>> stripped_list = [line.strip() for line in line_list
+...                  if line != ""]
+

With a list comprehension, you get back a Python list; stripped_list is a list containing the resulting lines, not an iterator. Generator expressions return an iterator that computes the values as necessary, not needing to materialize all the values at once. This means that list comprehensions aren’t useful if you’re working with iterators that return an infinite stream or a very large amount of data. Generator expressions are preferable in these situations.

Generator expressions are surrounded by parentheses (“()”) and list comprehensions are surrounded by square brackets (“[]”). Generator expressions have the form:

( expression for expr in sequence1
+             if condition1
+             for expr2 in sequence2
+             if condition2
+             for expr3 in sequence3
+             ...
+             if condition3
+             for exprN in sequenceN
+             if conditionN )
+

Again, for a list comprehension only the outside brackets are different (square brackets instead of parentheses).

The elements of the generated output will be the successive values of expression. The if clauses are all optional; if present, expression is only evaluated and added to the result when condition is true.

Generator expressions always have to be written inside parentheses, but the parentheses signalling a function call also count. If you want to create an iterator that will be immediately passed to a function you can write:

obj_total = sum(obj.count for obj in list_all_objects())
+

The for...in clauses contain the sequences to be iterated over. The sequences do not have to be the same length, because they are iterated over from left to right, not in parallel. For each element in sequence1, sequence2 is looped over from the beginning. sequence3 is then looped over for each resulting pair of elements from sequence1 and sequence2.

To put it another way, a list comprehension or generator expression is equivalent to the following Python code:

for expr1 in sequence1:
+    if not (condition1):
+        continue   # Skip this element
+    for expr2 in sequence2:
+        if not (condition2):
+            continue   # Skip this element
+        ...
+        for exprN in sequenceN:
+            if not (conditionN):
+                continue   # Skip this element
+
+            # Output the value of
+            # the expression.
+

This means that when there are multiple for...in clauses but no if clauses, the length of the resulting output will be equal to the product of the lengths of all the sequences. If you have two lists of length 3, the output list is 9 elements long:

>>> seq1 = 'abc'
+>>> seq2 = (1, 2, 3)
+>>> [(x, y) for x in seq1 for y in seq2]  
+[('a', 1), ('a', 2), ('a', 3),
+ ('b', 1), ('b', 2), ('b', 3),
+ ('c', 1), ('c', 2), ('c', 3)]
+

To avoid introducing an ambiguity into Python’s grammar, if expression is creating a tuple, it must be surrounded with parentheses. The first list comprehension below is a syntax error, while the second one is correct:

# Syntax error
+[x, y for x in seq1 for y in seq2]
+# Correct
+[(x, y) for x in seq1 for y in seq2]
+

Generators

Generators are a special class of functions that simplify the task of writing iterators. Regular functions compute a value and return it, but generators return an iterator that returns a stream of values.

You’re doubtless familiar with how regular function calls work in Python or C. When you call a function, it gets a private namespace where its local variables are created. When the function reaches a return statement, the local variables are destroyed and the value is returned to the caller. A later call to the same function creates a new private namespace and a fresh set of local variables. But, what if the local variables weren’t thrown away on exiting a function? What if you could later resume the function where it left off? This is what generators provide; they can be thought of as resumable functions.

Here’s the simplest example of a generator function:

>>> def generate_ints(N):
+...    for i in range(N):
+...        yield i
+

Any function containing a yield keyword is a generator function; this is detected by Python’s bytecode compiler which compiles the function specially as a result.

When you call a generator function, it doesn’t return a single value; instead it returns a generator object that supports the iterator protocol. On executing the yield expression, the generator outputs the value of i, similar to a return statement. The big difference between yield and a return statement is that on reaching a yield the generator’s state of execution is suspended and local variables are preserved. On the next call to the generator’s __next__() method, the function will resume executing.

Here’s a sample usage of the generate_ints() generator:

>>> gen = generate_ints(3)
+>>> gen  
+<generator object generate_ints at ...>
+>>> next(gen)
+0
+>>> next(gen)
+1
+>>> next(gen)
+2
+>>> next(gen)
+Traceback (most recent call last):
+  File "stdin", line 1, in <module>
+  File "stdin", line 2, in generate_ints
+StopIteration
+

You could equally write for i in generate_ints(5), or a, b, c = +generate_ints(3).

Inside a generator function, return value causes StopIteration(value) to be raised from the __next__() method. Once this happens, or the bottom of the function is reached, the procession of values ends and the generator cannot yield any further values.

You could achieve the effect of generators manually by writing your own class and storing all the local variables of the generator as instance variables. For example, returning a list of integers could be done by setting self.count to 0, and having the __next__() method increment self.count and return it. However, for a moderately complicated generator, writing a corresponding class can be much messier.

The test suite included with Python’s library, Lib/test/test_generators.py, contains a number of more interesting examples. Here’s one generator that implements an in-order traversal of a tree using generators recursively.

# A recursive generator that generates Tree leaves in in-order.
+def inorder(t):
+    if t:
+        for x in inorder(t.left):
+            yield x
+
+        yield t.label
+
+        for x in inorder(t.right):
+            yield x
+

Two other examples in test_generators.py produce solutions for the N-Queens problem (placing N queens on an NxN chess board so that no queen threatens another) and the Knight’s Tour (finding a route that takes a knight to every square of an NxN chessboard without visiting any square twice).

Passing values into a generator

In Python 2.4 and earlier, generators only produced output. Once a generator’s code was invoked to create an iterator, there was no way to pass any new information into the function when its execution is resumed. You could hack together this ability by making the generator look at a global variable or by passing in some mutable object that callers then modify, but these approaches are messy.

In Python 2.5 there’s a simple way to pass values into a generator. yield became an expression, returning a value that can be assigned to a variable or otherwise operated on:

val = (yield i)
+

I recommend that you always put parentheses around a yield expression when you’re doing something with the returned value, as in the above example. The parentheses aren’t always necessary, but it’s easier to always add them instead of having to remember when they’re needed.

(PEP 342 explains the exact rules, which are that a yield-expression must always be parenthesized except when it occurs at the top-level expression on the right-hand side of an assignment. This means you can write val = yield i but have to use parentheses when there’s an operation, as in val = (yield i) ++ 12.)

Values are sent into a generator by calling its send(value) method. This method resumes the generator’s code and the yield expression returns the specified value. If the regular __next__() method is called, the yield returns None.

Here’s a simple counter that increments by 1 and allows changing the value of the internal counter.

def counter(maximum):
+    i = 0
+    while i < maximum:
+        val = (yield i)
+        # If value provided, change counter
+        if val is not None:
+            i = val
+        else:
+            i += 1
+

And here’s an example of changing the counter:

>>> it = counter(10)  
+>>> next(it)  
+0
+>>> next(it)  
+1
+>>> it.send(8)  
+8
+>>> next(it)  
+9
+>>> next(it)  
+Traceback (most recent call last):
+  File "t.py", line 15, in <module>
+    it.next()
+StopIteration
+

Because yield will often be returning None, you should always check for this case. Don’t just use its value in expressions unless you’re sure that the send() method will be the only method used to resume your generator function.

In addition to send(), there are two other methods on generators:

  • +throw(value) is used to raise an exception inside the generator; the exception is raised by the yield expression where the generator’s execution is paused.
  • +

    close() raises a GeneratorExit exception inside the generator to terminate the iteration. On receiving this exception, the generator’s code must either raise GeneratorExit or StopIteration; catching the exception and doing anything else is illegal and will trigger a RuntimeError. close() will also be called by Python’s garbage collector when the generator is garbage-collected.

    If you need to run cleanup code when a GeneratorExit occurs, I suggest using a try: ... finally: suite instead of catching GeneratorExit.

The cumulative effect of these changes is to turn generators from one-way producers of information into both producers and consumers.

Generators also become coroutines, a more generalized form of subroutines. Subroutines are entered at one point and exited at another point (the top of the function, and a return statement), but coroutines can be entered, exited, and resumed at many different points (the yield statements).

Built-in functions

Let’s look in more detail at built-in functions often used with iterators.

Two of Python’s built-in functions, map() and filter() duplicate the features of generator expressions:

+map(f, iterA, iterB, ...) returns an iterator over the sequence
+
+

f(iterA[0], iterB[0]), f(iterA[1], iterB[1]), f(iterA[2], iterB[2]), ....

>>> def upper(s):
+...     return s.upper()
+
>>> list(map(upper, ['sentence', 'fragment']))
+['SENTENCE', 'FRAGMENT']
+>>> [upper(s) for s in ['sentence', 'fragment']]
+['SENTENCE', 'FRAGMENT']
+

You can of course achieve the same effect with a list comprehension.

filter(predicate, iter) returns an iterator over all the sequence elements that meet a certain condition, and is similarly duplicated by list comprehensions. A predicate is a function that returns the truth value of some condition; for use with filter(), the predicate must take a single value.

>>> def is_even(x):
+...     return (x % 2) == 0
+
>>> list(filter(is_even, range(10)))
+[0, 2, 4, 6, 8]
+

This can also be written as a list comprehension:

>>> list(x for x in range(10) if is_even(x))
+[0, 2, 4, 6, 8]
+

enumerate(iter, start=0) counts off the elements in the iterable returning 2-tuples containing the count (from start) and each element.

>>> for item in enumerate(['subject', 'verb', 'object']):
+...     print(item)
+(0, 'subject')
+(1, 'verb')
+(2, 'object')
+

enumerate() is often used when looping through a list and recording the indexes at which certain conditions are met:

f = open('data.txt', 'r')
+for i, line in enumerate(f):
+    if line.strip() == '':
+        print('Blank line at line #%i' % i)
+

sorted(iterable, key=None, reverse=False) collects all the elements of the iterable into a list, sorts the list, and returns the sorted result. The key and reverse arguments are passed through to the constructed list’s sort() method.

>>> import random
+>>> # Generate 8 random numbers between [0, 10000)
+>>> rand_list = random.sample(range(10000), 8)
+>>> rand_list  
+[769, 7953, 9828, 6431, 8442, 9878, 6213, 2207]
+>>> sorted(rand_list)  
+[769, 2207, 6213, 6431, 7953, 8442, 9828, 9878]
+>>> sorted(rand_list, reverse=True)  
+[9878, 9828, 8442, 7953, 6431, 6213, 2207, 769]
+

(For a more detailed discussion of sorting, see the Sorting HOW TO.)

The any(iter) and all(iter) built-ins look at the truth values of an iterable’s contents. any() returns True if any element in the iterable is a true value, and all() returns True if all of the elements are true values:

>>> any([0, 1, 0])
+True
+>>> any([0, 0, 0])
+False
+>>> any([1, 1, 1])
+True
+>>> all([0, 1, 0])
+False
+>>> all([0, 0, 0])
+False
+>>> all([1, 1, 1])
+True
+

zip(iterA, iterB, ...) takes one element from each iterable and returns them in a tuple:

zip(['a', 'b', 'c'], (1, 2, 3)) =>
+  ('a', 1), ('b', 2), ('c', 3)
+

It doesn’t construct an in-memory list and exhaust all the input iterators before returning; instead tuples are constructed and returned only if they’re requested. (The technical term for this behaviour is lazy evaluation.)

This iterator is intended to be used with iterables that are all of the same length. If the iterables are of different lengths, the resulting stream will be the same length as the shortest iterable.

zip(['a', 'b'], (1, 2, 3)) =>
+  ('a', 1), ('b', 2)
+

You should avoid doing this, though, because an element may be taken from the longer iterators and discarded. This means you can’t go on to use the iterators further because you risk skipping a discarded element.

The itertools module

The itertools module contains a number of commonly used iterators as well as functions for combining several iterators. This section will introduce the module’s contents by showing small examples.

The module’s functions fall into a few broad classes:

Creating new iterators

itertools.count(start, step) returns an infinite stream of evenly spaced values. You can optionally supply the starting number, which defaults to 0, and the interval between numbers, which defaults to 1:

itertools.count() =>
+  0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+itertools.count(10) =>
+  10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
+itertools.count(10, 5) =>
+  10, 15, 20, 25, 30, 35, 40, 45, 50, 55, ...
+

itertools.cycle(iter) saves a copy of the contents of a provided iterable and returns a new iterator that returns its elements from first to last. The new iterator will repeat these elements infinitely.

itertools.cycle([1, 2, 3, 4, 5]) =>
+  1, 2, 3, 4, 5, 1, 2, 3, 4, 5, ...
+

itertools.repeat(elem, [n]) returns the provided element n times, or returns the element endlessly if n is not provided.

itertools.repeat('abc') =>
+  abc, abc, abc, abc, abc, abc, abc, abc, abc, abc, ...
+itertools.repeat('abc', 5) =>
+  abc, abc, abc, abc, abc
+

itertools.chain(iterA, iterB, ...) takes an arbitrary number of iterables as input, and returns all the elements of the first iterator, then all the elements of the second, and so on, until all of the iterables have been exhausted.

itertools.chain(['a', 'b', 'c'], (1, 2, 3)) =>
+  a, b, c, 1, 2, 3
+

itertools.islice(iter, [start], stop, [step]) returns a stream that’s a slice of the iterator. With a single stop argument, it will return the first stop elements. If you supply a starting index, you’ll get stop-start elements, and if you supply a value for step, elements will be skipped accordingly. Unlike Python’s string and list slicing, you can’t use negative values for start, stop, or step.

itertools.islice(range(10), 8) =>
+  0, 1, 2, 3, 4, 5, 6, 7
+itertools.islice(range(10), 2, 8) =>
+  2, 3, 4, 5, 6, 7
+itertools.islice(range(10), 2, 8, 2) =>
+  2, 4, 6
+

itertools.tee(iter, [n]) replicates an iterator; it returns n independent iterators that will all return the contents of the source iterator. If you don’t supply a value for n, the default is 2. Replicating iterators requires saving some of the contents of the source iterator, so this can consume significant memory if the iterator is large and one of the new iterators is consumed more than the others.

itertools.tee( itertools.count() ) =>
+   iterA, iterB
+
+where iterA ->
+   0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+
+and   iterB ->
+   0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...
+

Calling functions on elements

The operator module contains a set of functions corresponding to Python’s operators. Some examples are operator.add(a, b) (adds two values), operator.ne(a, b) (same as a != b), and operator.attrgetter('id') (returns a callable that fetches the .id attribute).

itertools.starmap(func, iter) assumes that the iterable will return a stream of tuples, and calls func using these tuples as the arguments:

itertools.starmap(os.path.join,
+                  [('/bin', 'python'), ('/usr', 'bin', 'java'),
+                   ('/usr', 'bin', 'perl'), ('/usr', 'bin', 'ruby')])
+=>
+  /bin/python, /usr/bin/java, /usr/bin/perl, /usr/bin/ruby
+

Selecting elements

Another group of functions chooses a subset of an iterator’s elements based on a predicate.

itertools.filterfalse(predicate, iter) is the opposite of filter(), returning all elements for which the predicate returns false:

itertools.filterfalse(is_even, itertools.count()) =>
+  1, 3, 5, 7, 9, 11, 13, 15, ...
+

itertools.takewhile(predicate, iter) returns elements for as long as the predicate returns true. Once the predicate returns false, the iterator will signal the end of its results.

def less_than_10(x):
+    return x < 10
+
+itertools.takewhile(less_than_10, itertools.count()) =>
+  0, 1, 2, 3, 4, 5, 6, 7, 8, 9
+
+itertools.takewhile(is_even, itertools.count()) =>
+  0
+

itertools.dropwhile(predicate, iter) discards elements while the predicate returns true, and then returns the rest of the iterable’s results.

itertools.dropwhile(less_than_10, itertools.count()) =>
+  10, 11, 12, 13, 14, 15, 16, 17, 18, 19, ...
+
+itertools.dropwhile(is_even, itertools.count()) =>
+  1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...
+

itertools.compress(data, selectors) takes two iterators and returns only those elements of data for which the corresponding element of selectors is true, stopping whenever either one is exhausted:

itertools.compress([1, 2, 3, 4, 5], [True, True, False, False, True]) =>
+   1, 2, 5
+

Combinatoric functions

The itertools.combinations(iterable, r) returns an iterator giving all possible r-tuple combinations of the elements contained in iterable.

itertools.combinations([1, 2, 3, 4, 5], 2) =>
+  (1, 2), (1, 3), (1, 4), (1, 5),
+  (2, 3), (2, 4), (2, 5),
+  (3, 4), (3, 5),
+  (4, 5)
+
+itertools.combinations([1, 2, 3, 4, 5], 3) =>
+  (1, 2, 3), (1, 2, 4), (1, 2, 5), (1, 3, 4), (1, 3, 5), (1, 4, 5),
+  (2, 3, 4), (2, 3, 5), (2, 4, 5),
+  (3, 4, 5)
+

The elements within each tuple remain in the same order as iterable returned them. For example, the number 1 is always before 2, 3, 4, or 5 in the examples above. A similar function, itertools.permutations(iterable, r=None), removes this constraint on the order, returning all possible arrangements of length r:

itertools.permutations([1, 2, 3, 4, 5], 2) =>
+  (1, 2), (1, 3), (1, 4), (1, 5),
+  (2, 1), (2, 3), (2, 4), (2, 5),
+  (3, 1), (3, 2), (3, 4), (3, 5),
+  (4, 1), (4, 2), (4, 3), (4, 5),
+  (5, 1), (5, 2), (5, 3), (5, 4)
+
+itertools.permutations([1, 2, 3, 4, 5]) =>
+  (1, 2, 3, 4, 5), (1, 2, 3, 5, 4), (1, 2, 4, 3, 5),
+  ...
+  (5, 4, 3, 2, 1)
+

If you don’t supply a value for r the length of the iterable is used, meaning that all the elements are permuted.

Note that these functions produce all of the possible combinations by position and don’t require that the contents of iterable are unique:

itertools.permutations('aba', 3) =>
+  ('a', 'b', 'a'), ('a', 'a', 'b'), ('b', 'a', 'a'),
+  ('b', 'a', 'a'), ('a', 'a', 'b'), ('a', 'b', 'a')
+

The identical tuple ('a', 'a', 'b') occurs twice, but the two ‘a’ strings came from different positions.

The itertools.combinations_with_replacement(iterable, r) function relaxes a different constraint: elements can be repeated within a single tuple. Conceptually an element is selected for the first position of each tuple and then is replaced before the second element is selected.

itertools.combinations_with_replacement([1, 2, 3, 4, 5], 2) =>
+  (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
+  (2, 2), (2, 3), (2, 4), (2, 5),
+  (3, 3), (3, 4), (3, 5),
+  (4, 4), (4, 5),
+  (5, 5)
+

Grouping elements

The last function I’ll discuss, itertools.groupby(iter, key_func=None), is the most complicated. key_func(elem) is a function that can compute a key value for each element returned by the iterable. If you don’t supply a key function, the key is simply each element itself.

groupby() collects all the consecutive elements from the underlying iterable that have the same key value, and returns a stream of 2-tuples containing a key value and an iterator for the elements with that key.

city_list = [('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL'),
+             ('Anchorage', 'AK'), ('Nome', 'AK'),
+             ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ'),
+             ...
+            ]
+
+def get_state(city_state):
+    return city_state[1]
+
+itertools.groupby(city_list, get_state) =>
+  ('AL', iterator-1),
+  ('AK', iterator-2),
+  ('AZ', iterator-3), ...
+
+where
+iterator-1 =>
+  ('Decatur', 'AL'), ('Huntsville', 'AL'), ('Selma', 'AL')
+iterator-2 =>
+  ('Anchorage', 'AK'), ('Nome', 'AK')
+iterator-3 =>
+  ('Flagstaff', 'AZ'), ('Phoenix', 'AZ'), ('Tucson', 'AZ')
+

groupby() assumes that the underlying iterable’s contents will already be sorted based on the key. Note that the returned iterators also use the underlying iterable, so you have to consume the results of iterator-1 before requesting iterator-2 and its corresponding key.

The functools module

The functools module contains some higher-order functions. A higher-order function takes one or more functions as input and returns a new function. The most useful tool in this module is the functools.partial() function.

For programs written in a functional style, you’ll sometimes want to construct variants of existing functions that have some of the parameters filled in. Consider a Python function f(a, b, c); you may wish to create a new function g(b, c) that’s equivalent to f(1, b, c); you’re filling in a value for one of f()’s parameters. This is called “partial function application”.

The constructor for partial() takes the arguments (function, arg1, arg2, ..., kwarg1=value1, kwarg2=value2). The resulting object is callable, so you can just call it to invoke function with the filled-in arguments.

Here’s a small but realistic example:

import functools
+
+def log(message, subsystem):
+    """Write the contents of 'message' to the specified subsystem."""
+    print('%s: %s' % (subsystem, message))
+    ...
+
+server_log = functools.partial(log, subsystem='server')
+server_log('Unable to open socket')
+

functools.reduce(func, iter, [initial_value]) cumulatively performs an operation on all the iterable’s elements and, therefore, can’t be applied to infinite iterables. func must be a function that takes two elements and returns a single value. functools.reduce() takes the first two elements A and B returned by the iterator and calculates func(A, B). It then requests the third element, C, calculates func(func(A, B), C), combines this result with the fourth element returned, and continues until the iterable is exhausted. If the iterable returns no values at all, a TypeError exception is raised. If the initial value is supplied, it’s used as a starting point and func(initial_value, A) is the first calculation.

>>> import operator, functools
+>>> functools.reduce(operator.concat, ['A', 'BB', 'C'])
+'ABBC'
+>>> functools.reduce(operator.concat, [])
+Traceback (most recent call last):
+  ...
+TypeError: reduce() of empty sequence with no initial value
+>>> functools.reduce(operator.mul, [1, 2, 3], 1)
+6
+>>> functools.reduce(operator.mul, [], 1)
+1
+

If you use operator.add() with functools.reduce(), you’ll add up all the elements of the iterable. This case is so common that there’s a special built-in called sum() to compute it:

>>> import functools, operator
+>>> functools.reduce(operator.add, [1, 2, 3, 4], 0)
+10
+>>> sum([1, 2, 3, 4])
+10
+>>> sum([])
+0
+

For many uses of functools.reduce(), though, it can be clearer to just write the obvious for loop:

import functools
+# Instead of:
+product = functools.reduce(operator.mul, [1, 2, 3], 1)
+
+# You can write:
+product = 1
+for i in [1, 2, 3]:
+    product *= i
+

A related function is itertools.accumulate(iterable, func=operator.add). It performs the same calculation, but instead of returning only the final result, accumulate() returns an iterator that also yields each partial result:

itertools.accumulate([1, 2, 3, 4, 5]) =>
+  1, 3, 6, 10, 15
+
+itertools.accumulate([1, 2, 3, 4, 5], operator.mul) =>
+  1, 2, 6, 24, 120
+

The operator module

The operator module was mentioned earlier. It contains a set of functions corresponding to Python’s operators. These functions are often useful in functional-style code because they save you from writing trivial functions that perform a single operation.

Some of the functions in this module are:

  • Math operations: add(), sub(), mul(), floordiv(), abs(), …
  • Logical operations: not_(), truth().
  • Bitwise operations: and_(), or_(), invert().
  • Comparisons: eq(), ne(), lt(), le(), gt(), and ge().
  • Object identity: is_(), is_not().

Consult the operator module’s documentation for a complete list.

Small functions and the lambda expression

When writing functional-style programs, you’ll often need little functions that act as predicates or that combine elements in some way.

If there’s a Python built-in or a module function that’s suitable, you don’t need to define a new function at all:

stripped_lines = [line.strip() for line in lines]
+existing_files = filter(os.path.exists, file_list)
+

If the function you need doesn’t exist, you need to write it. One way to write small functions is to use the lambda expression. lambda takes a number of parameters and an expression combining these parameters, and creates an anonymous function that returns the value of the expression:

adder = lambda x, y: x+y
+
+print_assign = lambda name, value: name + '=' + str(value)
+

An alternative is to just use the def statement and define a function in the usual way:

def adder(x, y):
+    return x + y
+
+def print_assign(name, value):
+    return name + '=' + str(value)
+

Which alternative is preferable? That’s a style question; my usual course is to avoid using lambda.

One reason for my preference is that lambda is quite limited in the functions it can define. The result has to be computable as a single expression, which means you can’t have multiway if... elif... else comparisons or try... except statements. If you try to do too much in a lambda statement, you’ll end up with an overly complicated expression that’s hard to read. Quick, what’s the following code doing?

import functools
+total = functools.reduce(lambda a, b: (0, a[1] + b[1]), items)[1]
+

You can figure it out, but it takes time to disentangle the expression to figure out what’s going on. Using a short nested def statements makes things a little bit better:

import functools
+def combine(a, b):
+    return 0, a[1] + b[1]
+
+total = functools.reduce(combine, items)[1]
+

But it would be best of all if I had simply used a for loop:

total = 0
+for a, b in items:
+    total += b
+

Or the sum() built-in and a generator expression:

total = sum(b for a, b in items)
+

Many uses of functools.reduce() are clearer when written as for loops.

Fredrik Lundh once suggested the following set of rules for refactoring uses of lambda:

  1. Write a lambda function.
  2. Write a comment explaining what the heck that lambda does.
  3. Study the comment for a while, and think of a name that captures the essence of the comment.
  4. Convert the lambda to a def statement, using that name.
  5. Remove the comment.

I really like these rules, but you’re free to disagree about whether this lambda-free style is better.

Revision History and Acknowledgements

The author would like to thank the following people for offering suggestions, corrections and assistance with various drafts of this article: Ian Bicking, Nick Coghlan, Nick Efford, Raymond Hettinger, Jim Jewett, Mike Krell, Leandro Lameiro, Jussi Salmela, Collin Winter, Blake Winton.

Version 0.1: posted June 30 2006.

Version 0.11: posted July 1 2006. Typo fixes.

Version 0.2: posted July 10 2006. Merged genexp and listcomp sections into one. Typo fixes.

Version 0.21: Added more references suggested on the tutor mailing list.

Version 0.30: Adds a section on the functional module written by Collin Winter; adds short section on the operator module; a few other edits.

References

General

Structure and Interpretation of Computer Programs, by Harold Abelson and Gerald Jay Sussman with Julie Sussman. The book can be found at https://mitpress.mit.edu/sicp. In this classic textbook of computer science, chapters 2 and 3 discuss the use of sequences and streams to organize the data flow inside a program. The book uses Scheme for its examples, but many of the design approaches described in these chapters are applicable to functional-style Python code.

https://www.defmacro.org/ramblings/fp.html: A general introduction to functional programming that uses Java examples and has a lengthy historical introduction.

https://en.wikipedia.org/wiki/Functional_programming: General Wikipedia entry describing functional programming.

https://en.wikipedia.org/wiki/Coroutine: Entry for coroutines.

https://en.wikipedia.org/wiki/Partial_application: Entry for the concept of partial function application.

https://en.wikipedia.org/wiki/Currying: Entry for the concept of currying.

Python-specific

https://gnosis.cx/TPiP/: The first chapter of David Mertz’s book Text Processing in Python discusses functional programming for text processing, in the section titled “Utilizing Higher-Order Functions in Text Processing”.

Mertz also wrote a 3-part series of articles on functional programming for IBM’s DeveloperWorks site; see part 1, part 2, and part 3,

Python documentation

Documentation for the itertools module.

Documentation for the functools module.

Documentation for the operator module.

PEP 289: “Generator Expressions”

PEP 342: “Coroutines via Enhanced Generators” describes the new generator features in Python 2.5.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/functional.html +

+
diff --git a/devdocs/python~3.12/howto%2Findex.html b/devdocs/python~3.12/howto%2Findex.html new file mode 100644 index 00000000..8068b211 --- /dev/null +++ b/devdocs/python~3.12/howto%2Findex.html @@ -0,0 +1,6 @@ +

Python HOWTOs

Python HOWTOs are documents that cover a single, specific topic, and attempt to cover it fairly completely. Modelled on the Linux Documentation Project’s HOWTO collection, this collection is an effort to foster documentation that’s more detailed than the Python Library Reference.

Currently, the HOWTOs are:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/index.html +

+
diff --git a/devdocs/python~3.12/howto%2Finstrumentation.html b/devdocs/python~3.12/howto%2Finstrumentation.html new file mode 100644 index 00000000..6b66b2f1 --- /dev/null +++ b/devdocs/python~3.12/howto%2Finstrumentation.html @@ -0,0 +1,221 @@ +

Instrumenting CPython with DTrace and SystemTap

author
+

David Malcolm

author
+

Łukasz Langa

DTrace and SystemTap are monitoring tools, each providing a way to inspect what the processes on a computer system are doing. They both use domain-specific languages allowing a user to write scripts which:

As of Python 3.6, CPython can be built with embedded “markers”, also known as “probes”, that can be observed by a DTrace or SystemTap script, making it easier to monitor what the CPython processes on a system are doing.

CPython implementation detail: DTrace markers are implementation details of the CPython interpreter. No guarantees are made about probe compatibility between versions of CPython. DTrace scripts can stop working or work incorrectly without warning when changing CPython versions.

Enabling the static markers

macOS comes with built-in support for DTrace. On Linux, in order to build CPython with the embedded markers for SystemTap, the SystemTap development tools must be installed.

On a Linux machine, this can be done via:

$ yum install systemtap-sdt-devel
+

or:

$ sudo apt-get install systemtap-sdt-dev
+

CPython must then be configured with the --with-dtrace option:

checking for --with-dtrace... yes
+

On macOS, you can list available DTrace probes by running a Python process in the background and listing all probes made available by the Python provider:

$ python3.6 -q &
+$ sudo dtrace -l -P python$!  # or: dtrace -l -m python3.6
+
+   ID   PROVIDER            MODULE                          FUNCTION NAME
+29564 python18035        python3.6          _PyEval_EvalFrameDefault function-entry
+29565 python18035        python3.6             dtrace_function_entry function-entry
+29566 python18035        python3.6          _PyEval_EvalFrameDefault function-return
+29567 python18035        python3.6            dtrace_function_return function-return
+29568 python18035        python3.6                           collect gc-done
+29569 python18035        python3.6                           collect gc-start
+29570 python18035        python3.6          _PyEval_EvalFrameDefault line
+29571 python18035        python3.6                 maybe_dtrace_line line
+

On Linux, you can verify if the SystemTap static markers are present in the built binary by seeing if it contains a “.note.stapsdt” section.

$ readelf -S ./python | grep .note.stapsdt
+[30] .note.stapsdt        NOTE         0000000000000000 00308d78
+

If you’ve built Python as a shared library (with the --enable-shared configure option), you need to look instead within the shared library. For example:

$ readelf -S libpython3.3dm.so.1.0 | grep .note.stapsdt
+[29] .note.stapsdt        NOTE         0000000000000000 00365b68
+

Sufficiently modern readelf can print the metadata:

$ readelf -n ./python
+
+Displaying notes found at file offset 0x00000254 with length 0x00000020:
+    Owner                 Data size          Description
+    GNU                  0x00000010          NT_GNU_ABI_TAG (ABI version tag)
+        OS: Linux, ABI: 2.6.32
+
+Displaying notes found at file offset 0x00000274 with length 0x00000024:
+    Owner                 Data size          Description
+    GNU                  0x00000014          NT_GNU_BUILD_ID (unique build ID bitstring)
+        Build ID: df924a2b08a7e89f6e11251d4602022977af2670
+
+Displaying notes found at file offset 0x002d6c30 with length 0x00000144:
+    Owner                 Data size          Description
+    stapsdt              0x00000031          NT_STAPSDT (SystemTap probe descriptors)
+        Provider: python
+        Name: gc__start
+        Location: 0x00000000004371c3, Base: 0x0000000000630ce2, Semaphore: 0x00000000008d6bf6
+        Arguments: -4@%ebx
+    stapsdt              0x00000030          NT_STAPSDT (SystemTap probe descriptors)
+        Provider: python
+        Name: gc__done
+        Location: 0x00000000004374e1, Base: 0x0000000000630ce2, Semaphore: 0x00000000008d6bf8
+        Arguments: -8@%rax
+    stapsdt              0x00000045          NT_STAPSDT (SystemTap probe descriptors)
+        Provider: python
+        Name: function__entry
+        Location: 0x000000000053db6c, Base: 0x0000000000630ce2, Semaphore: 0x00000000008d6be8
+        Arguments: 8@%rbp 8@%r12 -4@%eax
+    stapsdt              0x00000046          NT_STAPSDT (SystemTap probe descriptors)
+        Provider: python
+        Name: function__return
+        Location: 0x000000000053dba8, Base: 0x0000000000630ce2, Semaphore: 0x00000000008d6bea
+        Arguments: 8@%rbp 8@%r12 -4@%eax
+

The above metadata contains information for SystemTap describing how it can patch strategically placed machine code instructions to enable the tracing hooks used by a SystemTap script.

Static DTrace probes

The following example DTrace script can be used to show the call/return hierarchy of a Python script, only tracing within the invocation of a function called “start”. In other words, import-time function invocations are not going to be listed:

self int indent;
+
+python$target:::function-entry
+/copyinstr(arg1) == "start"/
+{
+        self->trace = 1;
+}
+
+python$target:::function-entry
+/self->trace/
+{
+        printf("%d\t%*s:", timestamp, 15, probename);
+        printf("%*s", self->indent, "");
+        printf("%s:%s:%d\n", basename(copyinstr(arg0)), copyinstr(arg1), arg2);
+        self->indent++;
+}
+
+python$target:::function-return
+/self->trace/
+{
+        self->indent--;
+        printf("%d\t%*s:", timestamp, 15, probename);
+        printf("%*s", self->indent, "");
+        printf("%s:%s:%d\n", basename(copyinstr(arg0)), copyinstr(arg1), arg2);
+}
+
+python$target:::function-return
+/copyinstr(arg1) == "start"/
+{
+        self->trace = 0;
+}
+

It can be invoked like this:

$ sudo dtrace -q -s call_stack.d -c "python3.6 script.py"
+

The output looks like this:

156641360502280  function-entry:call_stack.py:start:23
+156641360518804  function-entry: call_stack.py:function_1:1
+156641360532797  function-entry:  call_stack.py:function_3:9
+156641360546807 function-return:  call_stack.py:function_3:10
+156641360563367 function-return: call_stack.py:function_1:2
+156641360578365  function-entry: call_stack.py:function_2:5
+156641360591757  function-entry:  call_stack.py:function_1:1
+156641360605556  function-entry:   call_stack.py:function_3:9
+156641360617482 function-return:   call_stack.py:function_3:10
+156641360629814 function-return:  call_stack.py:function_1:2
+156641360642285 function-return: call_stack.py:function_2:6
+156641360656770  function-entry: call_stack.py:function_3:9
+156641360669707 function-return: call_stack.py:function_3:10
+156641360687853  function-entry: call_stack.py:function_4:13
+156641360700719 function-return: call_stack.py:function_4:14
+156641360719640  function-entry: call_stack.py:function_5:18
+156641360732567 function-return: call_stack.py:function_5:21
+156641360747370 function-return:call_stack.py:start:28
+

Static SystemTap markers

The low-level way to use the SystemTap integration is to use the static markers directly. This requires you to explicitly state the binary file containing them.

For example, this SystemTap script can be used to show the call/return hierarchy of a Python script:

probe process("python").mark("function__entry") {
+     filename = user_string($arg1);
+     funcname = user_string($arg2);
+     lineno = $arg3;
+
+     printf("%s => %s in %s:%d\\n",
+            thread_indent(1), funcname, filename, lineno);
+}
+
+probe process("python").mark("function__return") {
+    filename = user_string($arg1);
+    funcname = user_string($arg2);
+    lineno = $arg3;
+
+    printf("%s <= %s in %s:%d\\n",
+           thread_indent(-1), funcname, filename, lineno);
+}
+

It can be invoked like this:

$ stap \
+  show-call-hierarchy.stp \
+  -c "./python test.py"
+

The output looks like this:

11408 python(8274):        => __contains__ in Lib/_abcoll.py:362
+11414 python(8274):         => __getitem__ in Lib/os.py:425
+11418 python(8274):          => encode in Lib/os.py:490
+11424 python(8274):          <= encode in Lib/os.py:493
+11428 python(8274):         <= __getitem__ in Lib/os.py:426
+11433 python(8274):        <= __contains__ in Lib/_abcoll.py:366
+

where the columns are:

and the remainder indicates the call/return hierarchy as the script executes.

For a --enable-shared build of CPython, the markers are contained within the libpython shared library, and the probe’s dotted path needs to reflect this. For example, this line from the above example:

probe process("python").mark("function__entry") {
+

should instead read:

probe process("python").library("libpython3.6dm.so.1.0").mark("function__entry") {
+

(assuming a debug build of CPython 3.6)

Available static markers

function__entry(str filename, str funcname, int lineno) +
+

This marker indicates that execution of a Python function has begun. It is only triggered for pure-Python (bytecode) functions.

The filename, function name, and line number are provided back to the tracing script as positional arguments, which must be accessed using $arg1, $arg2, $arg3:

  • +$arg1 : (const char *) filename, accessible using user_string($arg1) +
  • +$arg2 : (const char *) function name, accessible using user_string($arg2) +
  • +$arg3 : int line number
+
function__return(str filename, str funcname, int lineno) +
+

This marker is the converse of function__entry(), and indicates that execution of a Python function has ended (either via return, or via an exception). It is only triggered for pure-Python (bytecode) functions.

The arguments are the same as for function__entry()

+
line(str filename, str funcname, int lineno) +
+

This marker indicates a Python line is about to be executed. It is the equivalent of line-by-line tracing with a Python profiler. It is not triggered within C functions.

The arguments are the same as for function__entry().

+
gc__start(int generation) +
+

Fires when the Python interpreter starts a garbage collection cycle. arg0 is the generation to scan, like gc.collect().

+
gc__done(long collected) +
+

Fires when the Python interpreter finishes a garbage collection cycle. arg0 is the number of collected objects.

+
import__find__load__start(str modulename) +
+

Fires before importlib attempts to find and load the module. arg0 is the module name.

New in version 3.7.

+
import__find__load__done(str modulename, int found) +
+

Fires after importlib’s find_and_load function is called. arg0 is the module name, arg1 indicates if module was successfully loaded.

New in version 3.7.

+
audit(str event, void *tuple) +
+

Fires when sys.audit() or PySys_Audit() is called. arg0 is the event name as C string, arg1 is a PyObject pointer to a tuple object.

New in version 3.8.

+

SystemTap Tapsets

The higher-level way to use the SystemTap integration is to use a “tapset”: SystemTap’s equivalent of a library, which hides some of the lower-level details of the static markers.

Here is a tapset file, based on a non-shared build of CPython:

/*
+   Provide a higher-level wrapping around the function__entry and
+   function__return markers:
+ \*/
+probe python.function.entry = process("python").mark("function__entry")
+{
+    filename = user_string($arg1);
+    funcname = user_string($arg2);
+    lineno = $arg3;
+    frameptr = $arg4
+}
+probe python.function.return = process("python").mark("function__return")
+{
+    filename = user_string($arg1);
+    funcname = user_string($arg2);
+    lineno = $arg3;
+    frameptr = $arg4
+}
+

If this file is installed in SystemTap’s tapset directory (e.g. /usr/share/systemtap/tapset), then these additional probepoints become available:

python.function.entry(str filename, str funcname, int lineno, frameptr) +
+

This probe point indicates that execution of a Python function has begun. It is only triggered for pure-Python (bytecode) functions.

+
python.function.return(str filename, str funcname, int lineno, frameptr) +
+

This probe point is the converse of python.function.return, and indicates that execution of a Python function has ended (either via return, or via an exception). It is only triggered for pure-Python (bytecode) functions.

+

Examples

This SystemTap script uses the tapset above to more cleanly implement the example given above of tracing the Python function-call hierarchy, without needing to directly name the static markers:

probe python.function.entry
+{
+  printf("%s => %s in %s:%d\n",
+         thread_indent(1), funcname, filename, lineno);
+}
+
+probe python.function.return
+{
+  printf("%s <= %s in %s:%d\n",
+         thread_indent(-1), funcname, filename, lineno);
+}
+

The following script uses the tapset above to provide a top-like view of all running CPython code, showing the top 20 most frequently entered bytecode frames, each second, across the whole system:

global fn_calls;
+
+probe python.function.entry
+{
+    fn_calls[pid(), filename, funcname, lineno] += 1;
+}
+
+probe timer.ms(1000) {
+    printf("\033[2J\033[1;1H") /* clear screen \*/
+    printf("%6s %80s %6s %30s %6s\n",
+           "PID", "FILENAME", "LINE", "FUNCTION", "CALLS")
+    foreach ([pid, filename, funcname, lineno] in fn_calls- limit 20) {
+        printf("%6d %80s %6d %30s %6d\n",
+            pid, filename, lineno, funcname,
+            fn_calls[pid, filename, funcname, lineno]);
+    }
+    delete fn_calls;
+}
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/instrumentation.html +

+
diff --git a/devdocs/python~3.12/howto%2Fipaddress.html b/devdocs/python~3.12/howto%2Fipaddress.html new file mode 100644 index 00000000..da202bde --- /dev/null +++ b/devdocs/python~3.12/howto%2Fipaddress.html @@ -0,0 +1,130 @@ +

An introduction to the ipaddress module

author
+

Peter Moody

author
+

Nick Coghlan

Overview

This document aims to provide a gentle introduction to the ipaddress module. It is aimed primarily at users that aren’t already familiar with IP networking terminology, but may also be useful to network engineers wanting an overview of how ipaddress represents IP network addressing concepts.

Creating Address/Network/Interface objects

Since ipaddress is a module for inspecting and manipulating IP addresses, the first thing you’ll want to do is create some objects. You can use ipaddress to create objects from strings and integers.

A Note on IP Versions

For readers that aren’t particularly familiar with IP addressing, it’s important to know that the Internet Protocol (IP) is currently in the process of moving from version 4 of the protocol to version 6. This transition is occurring largely because version 4 of the protocol doesn’t provide enough addresses to handle the needs of the whole world, especially given the increasing number of devices with direct connections to the internet.

Explaining the details of the differences between the two versions of the protocol is beyond the scope of this introduction, but readers need to at least be aware that these two versions exist, and it will sometimes be necessary to force the use of one version or the other.

IP Host Addresses

Addresses, often referred to as “host addresses” are the most basic unit when working with IP addressing. The simplest way to create addresses is to use the ipaddress.ip_address() factory function, which automatically determines whether to create an IPv4 or IPv6 address based on the passed in value:

>>> ipaddress.ip_address('192.0.2.1')
+IPv4Address('192.0.2.1')
+>>> ipaddress.ip_address('2001:DB8::1')
+IPv6Address('2001:db8::1')
+

Addresses can also be created directly from integers. Values that will fit within 32 bits are assumed to be IPv4 addresses:

>>> ipaddress.ip_address(3221225985)
+IPv4Address('192.0.2.1')
+>>> ipaddress.ip_address(42540766411282592856903984951653826561)
+IPv6Address('2001:db8::1')
+

To force the use of IPv4 or IPv6 addresses, the relevant classes can be invoked directly. This is particularly useful to force creation of IPv6 addresses for small integers:

>>> ipaddress.ip_address(1)
+IPv4Address('0.0.0.1')
+>>> ipaddress.IPv4Address(1)
+IPv4Address('0.0.0.1')
+>>> ipaddress.IPv6Address(1)
+IPv6Address('::1')
+

Defining Networks

Host addresses are usually grouped together into IP networks, so ipaddress provides a way to create, inspect and manipulate network definitions. IP network objects are constructed from strings that define the range of host addresses that are part of that network. The simplest form for that information is a “network address/network prefix” pair, where the prefix defines the number of leading bits that are compared to determine whether or not an address is part of the network and the network address defines the expected value of those bits.

As for addresses, a factory function is provided that determines the correct IP version automatically:

>>> ipaddress.ip_network('192.0.2.0/24')
+IPv4Network('192.0.2.0/24')
+>>> ipaddress.ip_network('2001:db8::0/96')
+IPv6Network('2001:db8::/96')
+

Network objects cannot have any host bits set. The practical effect of this is that 192.0.2.1/24 does not describe a network. Such definitions are referred to as interface objects since the ip-on-a-network notation is commonly used to describe network interfaces of a computer on a given network and are described further in the next section.

By default, attempting to create a network object with host bits set will result in ValueError being raised. To request that the additional bits instead be coerced to zero, the flag strict=False can be passed to the constructor:

>>> ipaddress.ip_network('192.0.2.1/24')
+Traceback (most recent call last):
+   ...
+ValueError: 192.0.2.1/24 has host bits set
+>>> ipaddress.ip_network('192.0.2.1/24', strict=False)
+IPv4Network('192.0.2.0/24')
+

While the string form offers significantly more flexibility, networks can also be defined with integers, just like host addresses. In this case, the network is considered to contain only the single address identified by the integer, so the network prefix includes the entire network address:

>>> ipaddress.ip_network(3221225984)
+IPv4Network('192.0.2.0/32')
+>>> ipaddress.ip_network(42540766411282592856903984951653826560)
+IPv6Network('2001:db8::/128')
+

As with addresses, creation of a particular kind of network can be forced by calling the class constructor directly instead of using the factory function.

Host Interfaces

As mentioned just above, if you need to describe an address on a particular network, neither the address nor the network classes are sufficient. Notation like 192.0.2.1/24 is commonly used by network engineers and the people who write tools for firewalls and routers as shorthand for “the host 192.0.2.1 on the network 192.0.2.0/24”, Accordingly, ipaddress provides a set of hybrid classes that associate an address with a particular network. The interface for creation is identical to that for defining network objects, except that the address portion isn’t constrained to being a network address.

>>> ipaddress.ip_interface('192.0.2.1/24')
+IPv4Interface('192.0.2.1/24')
+>>> ipaddress.ip_interface('2001:db8::1/96')
+IPv6Interface('2001:db8::1/96')
+

Integer inputs are accepted (as with networks), and use of a particular IP version can be forced by calling the relevant constructor directly.

Inspecting Address/Network/Interface Objects

You’ve gone to the trouble of creating an IPv(4|6)(Address|Network|Interface) object, so you probably want to get information about it. ipaddress tries to make doing this easy and intuitive.

Extracting the IP version:

>>> addr4 = ipaddress.ip_address('192.0.2.1')
+>>> addr6 = ipaddress.ip_address('2001:db8::1')
+>>> addr6.version
+6
+>>> addr4.version
+4
+

Obtaining the network from an interface:

>>> host4 = ipaddress.ip_interface('192.0.2.1/24')
+>>> host4.network
+IPv4Network('192.0.2.0/24')
+>>> host6 = ipaddress.ip_interface('2001:db8::1/96')
+>>> host6.network
+IPv6Network('2001:db8::/96')
+

Finding out how many individual addresses are in a network:

>>> net4 = ipaddress.ip_network('192.0.2.0/24')
+>>> net4.num_addresses
+256
+>>> net6 = ipaddress.ip_network('2001:db8::0/96')
+>>> net6.num_addresses
+4294967296
+

Iterating through the “usable” addresses on a network:

>>> net4 = ipaddress.ip_network('192.0.2.0/24')
+>>> for x in net4.hosts():
+...     print(x)  
+192.0.2.1
+192.0.2.2
+192.0.2.3
+192.0.2.4
+...
+192.0.2.252
+192.0.2.253
+192.0.2.254
+

Obtaining the netmask (i.e. set bits corresponding to the network prefix) or the hostmask (any bits that are not part of the netmask):

>>> net4 = ipaddress.ip_network('192.0.2.0/24')
+>>> net4.netmask
+IPv4Address('255.255.255.0')
+>>> net4.hostmask
+IPv4Address('0.0.0.255')
+>>> net6 = ipaddress.ip_network('2001:db8::0/96')
+>>> net6.netmask
+IPv6Address('ffff:ffff:ffff:ffff:ffff:ffff::')
+>>> net6.hostmask
+IPv6Address('::ffff:ffff')
+

Exploding or compressing the address:

>>> addr6.exploded
+'2001:0db8:0000:0000:0000:0000:0000:0001'
+>>> addr6.compressed
+'2001:db8::1'
+>>> net6.exploded
+'2001:0db8:0000:0000:0000:0000:0000:0000/96'
+>>> net6.compressed
+'2001:db8::/96'
+

While IPv4 doesn’t support explosion or compression, the associated objects still provide the relevant properties so that version neutral code can easily ensure the most concise or most verbose form is used for IPv6 addresses while still correctly handling IPv4 addresses.

Networks as lists of Addresses

It’s sometimes useful to treat networks as lists. This means it is possible to index them like this:

>>> net4[1]
+IPv4Address('192.0.2.1')
+>>> net4[-1]
+IPv4Address('192.0.2.255')
+>>> net6[1]
+IPv6Address('2001:db8::1')
+>>> net6[-1]
+IPv6Address('2001:db8::ffff:ffff')
+

It also means that network objects lend themselves to using the list membership test syntax like this:

if address in network:
+    # do something
+

Containment testing is done efficiently based on the network prefix:

>>> addr4 = ipaddress.ip_address('192.0.2.1')
+>>> addr4 in ipaddress.ip_network('192.0.2.0/24')
+True
+>>> addr4 in ipaddress.ip_network('192.0.3.0/24')
+False
+

Comparisons

ipaddress provides some simple, hopefully intuitive ways to compare objects, where it makes sense:

>>> ipaddress.ip_address('192.0.2.1') < ipaddress.ip_address('192.0.2.2')
+True
+

A TypeError exception is raised if you try to compare objects of different versions or different types.

Using IP Addresses with other modules

Other modules that use IP addresses (such as socket) usually won’t accept objects from this module directly. Instead, they must be coerced to an integer or string that the other module will accept:

>>> addr4 = ipaddress.ip_address('192.0.2.1')
+>>> str(addr4)
+'192.0.2.1'
+>>> int(addr4)
+3221225985
+

Getting more detail when instance creation fails

When creating address/network/interface objects using the version-agnostic factory functions, any errors will be reported as ValueError with a generic error message that simply says the passed in value was not recognized as an object of that type. The lack of a specific error is because it’s necessary to know whether the value is supposed to be IPv4 or IPv6 in order to provide more detail on why it has been rejected.

To support use cases where it is useful to have access to this additional detail, the individual class constructors actually raise the ValueError subclasses ipaddress.AddressValueError and ipaddress.NetmaskValueError to indicate exactly which part of the definition failed to parse correctly.

The error messages are significantly more detailed when using the class constructors directly. For example:

>>> ipaddress.ip_address("192.168.0.256")
+Traceback (most recent call last):
+  ...
+ValueError: '192.168.0.256' does not appear to be an IPv4 or IPv6 address
+>>> ipaddress.IPv4Address("192.168.0.256")
+Traceback (most recent call last):
+  ...
+ipaddress.AddressValueError: Octet 256 (> 255) not permitted in '192.168.0.256'
+
+>>> ipaddress.ip_network("192.168.0.1/64")
+Traceback (most recent call last):
+  ...
+ValueError: '192.168.0.1/64' does not appear to be an IPv4 or IPv6 network
+>>> ipaddress.IPv4Network("192.168.0.1/64")
+Traceback (most recent call last):
+  ...
+ipaddress.NetmaskValueError: '64' is not a valid netmask
+

However, both of the module specific exceptions have ValueError as their parent class, so if you’re not concerned with the particular type of error, you can still write code like the following:

try:
+    network = ipaddress.IPv4Network(address)
+except ValueError:
+    print('address/netmask is invalid for IPv4:', address)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/ipaddress.html +

+
diff --git a/devdocs/python~3.12/howto%2Fisolating-extensions.html b/devdocs/python~3.12/howto%2Fisolating-extensions.html new file mode 100644 index 00000000..e9d8d15c --- /dev/null +++ b/devdocs/python~3.12/howto%2Fisolating-extensions.html @@ -0,0 +1,128 @@ +

Isolating Extension Modules

Abstract

Traditionally, state belonging to Python extension modules was kept in C static variables, which have process-wide scope. This document describes problems of such per-process state and shows a safer way: per-module state.

The document also describes how to switch to per-module state where possible. This transition involves allocating space for that state, potentially switching from static types to heap types, and—perhaps most importantly—accessing per-module state from code.

Who should read this

This guide is written for maintainers of C-API extensions who would like to make that extension safer to use in applications where Python itself is used as a library.

Background

An interpreter is the context in which Python code runs. It contains configuration (e.g. the import path) and runtime state (e.g. the set of imported modules).

Python supports running multiple interpreters in one process. There are two cases to think about—users may run interpreters:

Both cases (and combinations of them) would be most useful when embedding Python within a library. Libraries generally shouldn’t make assumptions about the application that uses them, which include assuming a process-wide “main Python interpreter”.

Historically, Python extension modules don’t handle this use case well. Many extension modules (and even some stdlib modules) use per-process global state, because C static variables are extremely easy to use. Thus, data that should be specific to an interpreter ends up being shared between interpreters. Unless the extension developer is careful, it is very easy to introduce edge cases that lead to crashes when a module is loaded in more than one interpreter in the same process.

Unfortunately, per-interpreter state is not easy to achieve. Extension authors tend to not keep multiple interpreters in mind when developing, and it is currently cumbersome to test the behavior.

Enter Per-Module State

Instead of focusing on per-interpreter state, Python’s C API is evolving to better support the more granular per-module state. This means that C-level data should be attached to a module object. Each interpreter creates its own module object, keeping the data separate. For testing the isolation, multiple module objects corresponding to a single extension can even be loaded in a single interpreter.

Per-module state provides an easy way to think about lifetime and resource ownership: the extension module will initialize when a module object is created, and clean up when it’s freed. In this regard, a module is just like any other PyObject*; there are no “on interpreter shutdown” hooks to think—or forget—about.

Note that there are use cases for different kinds of “globals”: per-process, per-interpreter, per-thread or per-task state. With per-module state as the default, these are still possible, but you should treat them as exceptional cases: if you need them, you should give them additional care and testing. (Note that this guide does not cover them.)

Isolated Module Objects

The key point to keep in mind when developing an extension module is that several module objects can be created from a single shared library. For example:

>>> import sys
+>>> import binascii
+>>> old_binascii = binascii
+>>> del sys.modules['binascii']
+>>> import binascii  # create a new module object
+>>> old_binascii == binascii
+False
+

As a rule of thumb, the two modules should be completely independent. All objects and state specific to the module should be encapsulated within the module object, not shared with other module objects, and cleaned up when the module object is deallocated. Since this just is a rule of thumb, exceptions are possible (see Managing Global State), but they will need more thought and attention to edge cases.

While some modules could do with less stringent restrictions, isolated modules make it easier to set clear expectations and guidelines that work across a variety of use cases.

Surprising Edge Cases

Note that isolated modules do create some surprising edge cases. Most notably, each module object will typically not share its classes and exceptions with other similar modules. Continuing from the example above, note that old_binascii.Error and binascii.Error are separate objects. In the following code, the exception is not caught:

>>> old_binascii.Error == binascii.Error
+False
+>>> try:
+...     old_binascii.unhexlify(b'qwertyuiop')
+... except binascii.Error:
+...     print('boo')
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+binascii.Error: Non-hexadecimal digit found
+

This is expected. Notice that pure-Python modules behave the same way: it is a part of how Python works.

The goal is to make extension modules safe at the C level, not to make hacks behave intuitively. Mutating sys.modules “manually” counts as a hack.

Making Modules Safe with Multiple Interpreters

Managing Global State

Sometimes, the state associated with a Python module is not specific to that module, but to the entire process (or something else “more global” than a module). For example:

  • The readline module manages the terminal.
  • A module running on a circuit board wants to control the on-board LED.

In these cases, the Python module should provide access to the global state, rather than own it. If possible, write the module so that multiple copies of it can access the state independently (along with other libraries, whether for Python or other languages). If that is not possible, consider explicit locking.

If it is necessary to use process-global state, the simplest way to avoid issues with multiple interpreters is to explicitly prevent a module from being loaded more than once per process—see Opt-Out: Limiting to One Module Object per Process.

Managing Per-Module State

To use per-module state, use multi-phase extension module initialization. This signals that your module supports multiple interpreters correctly.

Set PyModuleDef.m_size to a positive number to request that many bytes of storage local to the module. Usually, this will be set to the size of some module-specific struct, which can store all of the module’s C-level state. In particular, it is where you should put pointers to classes (including exceptions, but excluding static types) and settings (e.g. csv’s field_size_limit) which the C code needs to function.

Note

Another option is to store state in the module’s __dict__, but you must avoid crashing when users modify __dict__ from Python code. This usually means error- and type-checking at the C level, which is easy to get wrong and hard to test sufficiently.

However, if module state is not needed in C code, storing it in __dict__ only is a good idea.

If the module state includes PyObject pointers, the module object must hold references to those objects and implement the module-level hooks m_traverse, m_clear and m_free. These work like tp_traverse, tp_clear and tp_free of a class. Adding them will require some work and make the code longer; this is the price for modules which can be unloaded cleanly.

An example of a module with per-module state is currently available as xxlimited; example module initialization shown at the bottom of the file.

Opt-Out: Limiting to One Module Object per Process

A non-negative PyModuleDef.m_size signals that a module supports multiple interpreters correctly. If this is not yet the case for your module, you can explicitly make your module loadable only once per process. For example:

static int loaded = 0;
+
+static int
+exec_module(PyObject* module)
+{
+    if (loaded) {
+        PyErr_SetString(PyExc_ImportError,
+                        "cannot load module more than once per process");
+        return -1;
+    }
+    loaded = 1;
+    // ... rest of initialization
+}
+

Module State Access from Functions

Accessing the state from module-level functions is straightforward. Functions get the module object as their first argument; for extracting the state, you can use PyModule_GetState:

static PyObject *
+func(PyObject *module, PyObject *args)
+{
+    my_struct *state = (my_struct*)PyModule_GetState(module);
+    if (state == NULL) {
+        return NULL;
+    }
+    // ... rest of logic
+}
+

Note

PyModule_GetState may return NULL without setting an exception if there is no module state, i.e. PyModuleDef.m_size was zero. In your own module, you’re in control of m_size, so this is easy to prevent.

Heap Types

Traditionally, types defined in C code are static; that is, static PyTypeObject structures defined directly in code and initialized using PyType_Ready().

Such types are necessarily shared across the process. Sharing them between module objects requires paying attention to any state they own or access. To limit the possible issues, static types are immutable at the Python level: for example, you can’t set str.myattribute = 123.

CPython implementation detail: Sharing truly immutable objects between interpreters is fine, as long as they don’t provide access to mutable objects. However, in CPython, every Python object has a mutable implementation detail: the reference count. Changes to the refcount are guarded by the GIL. Thus, code that shares any Python objects across interpreters implicitly depends on CPython’s current, process-wide GIL.

Because they are immutable and process-global, static types cannot access “their” module state. If any method of such a type requires access to module state, the type must be converted to a heap-allocated type, or heap type for short. These correspond more closely to classes created by Python’s class statement.

For new modules, using heap types by default is a good rule of thumb.

Changing Static Types to Heap Types

Static types can be converted to heap types, but note that the heap type API was not designed for “lossless” conversion from static types—that is, creating a type that works exactly like a given static type. So, when rewriting the class definition in a new API, you are likely to unintentionally change a few details (e.g. pickleability or inherited slots). Always test the details that are important to you.

Watch out for the following two points in particular (but note that this is not a comprehensive list):

Defining Heap Types

Heap types can be created by filling a PyType_Spec structure, a description or “blueprint” of a class, and calling PyType_FromModuleAndSpec() to construct a new class object.

Note

Other functions, like PyType_FromSpec(), can also create heap types, but PyType_FromModuleAndSpec() associates the module with the class, allowing access to the module state from methods.

The class should generally be stored in both the module state (for safe access from C) and the module’s __dict__ (for access from Python code).

Garbage-Collection Protocol

Instances of heap types hold a reference to their type. This ensures that the type isn’t destroyed before all its instances are, but may result in reference cycles that need to be broken by the garbage collector.

To avoid memory leaks, instances of heap types must implement the garbage collection protocol. That is, heap types should:

  • Have the Py_TPFLAGS_HAVE_GC flag.
  • Define a traverse function using Py_tp_traverse, which visits the type (e.g. using Py_VISIT(Py_TYPE(self))).

Please refer to the the documentation of Py_TPFLAGS_HAVE_GC and tp_traverse for additional considerations.

The API for defining heap types grew organically, leaving it somewhat awkward to use in its current state. The following sections will guide you through common issues.

+tp_traverse in Python 3.8 and lower

The requirement to visit the type from tp_traverse was added in Python 3.9. If you support Python 3.8 and lower, the traverse function must not visit the type, so it must be more complicated:

static int my_traverse(PyObject *self, visitproc visit, void *arg)
+{
+    if (Py_Version >= 0x03090000) {
+        Py_VISIT(Py_TYPE(self));
+    }
+    return 0;
+}
+

Unfortunately, Py_Version was only added in Python 3.11. As a replacement, use:

Delegating tp_traverse +

If your traverse function delegates to the tp_traverse of its base class (or another type), ensure that Py_TYPE(self) is visited only once. Note that only heap type are expected to visit the type in tp_traverse.

For example, if your traverse function includes:

base->tp_traverse(self, visit, arg)
+

…and base may be a static type, then it should also include:

if (base->tp_flags & Py_TPFLAGS_HEAPTYPE) {
+    // a heap type's tp_traverse already visited Py_TYPE(self)
+} else {
+    if (Py_Version >= 0x03090000) {
+        Py_VISIT(Py_TYPE(self));
+    }
+}
+

It is not necessary to handle the type’s reference count in tp_new and tp_clear.

Defining tp_dealloc +

If your type has a custom tp_dealloc function, it needs to:

  • call PyObject_GC_UnTrack() before any fields are invalidated, and
  • decrement the reference count of the type.

To keep the type valid while tp_free is called, the type’s refcount needs to be decremented after the instance is deallocated. For example:

static void my_dealloc(PyObject *self)
+{
+    PyObject_GC_UnTrack(self);
+    ...
+    PyTypeObject *type = Py_TYPE(self);
+    type->tp_free(self);
+    Py_DECREF(type);
+}
+

The default tp_dealloc function does this, so if your type does not override tp_dealloc you don’t need to add it.

Not overriding tp_free +

The tp_free slot of a heap type must be set to PyObject_GC_Del(). This is the default; do not override it.

Avoiding PyObject_New +

GC-tracked objects need to be allocated using GC-aware functions.

If you use use PyObject_New() or PyObject_NewVar():

  • +

    Get and call type’s tp_alloc slot, if possible. That is, replace TYPE *o = PyObject_New(TYPE, typeobj) with:

    TYPE *o = typeobj->tp_alloc(typeobj, 0);
+

    Replace o = PyObject_NewVar(TYPE, typeobj, size) with the same, but use size instead of the 0.

  • +

    If the above is not possible (e.g. inside a custom tp_alloc), call PyObject_GC_New() or PyObject_GC_NewVar():

    TYPE *o = PyObject_GC_New(TYPE, typeobj);
+
+TYPE *o = PyObject_GC_NewVar(TYPE, typeobj, size);
+

Module State Access from Classes

If you have a type object defined with PyType_FromModuleAndSpec(), you can call PyType_GetModule() to get the associated module, and then PyModule_GetState() to get the module’s state.

To save a some tedious error-handling boilerplate code, you can combine these two steps with PyType_GetModuleState(), resulting in:

my_struct *state = (my_struct*)PyType_GetModuleState(type);
+if (state == NULL) {
+    return NULL;
+}
+

Module State Access from Regular Methods

Accessing the module-level state from methods of a class is somewhat more complicated, but is possible thanks to API introduced in Python 3.9. To get the state, you need to first get the defining class, and then get the module state from it.

The largest roadblock is getting the class a method was defined in, or that method’s “defining class” for short. The defining class can have a reference to the module it is part of.

Do not confuse the defining class with Py_TYPE(self). If the method is called on a subclass of your type, Py_TYPE(self) will refer to that subclass, which may be defined in different module than yours.

Note

The following Python code can illustrate the concept. Base.get_defining_class returns Base even if type(self) == Sub:

class Base:
+    def get_type_of_self(self):
+        return type(self)
+
+    def get_defining_class(self):
+        return __class__
+
+class Sub(Base):
+    pass
+

For a method to get its “defining class”, it must use the METH_METHOD | METH_FASTCALL | METH_KEYWORDS calling convention and the corresponding PyCMethod signature:

PyObject *PyCMethod(
+    PyObject *self,               // object the method was called on
+    PyTypeObject *defining_class, // defining class
+    PyObject *const *args,        // C array of arguments
+    Py_ssize_t nargs,             // length of "args"
+    PyObject *kwnames)            // NULL, or dict of keyword arguments
+

Once you have the defining class, call PyType_GetModuleState() to get the state of its associated module.

For example:

static PyObject *
+example_method(PyObject *self,
+        PyTypeObject *defining_class,
+        PyObject *const *args,
+        Py_ssize_t nargs,
+        PyObject *kwnames)
+{
+    my_struct *state = (my_struct*)PyType_GetModuleState(defining_class);
+    if (state == NULL) {
+        return NULL;
+    }
+    ... // rest of logic
+}
+
+PyDoc_STRVAR(example_method_doc, "...");
+
+static PyMethodDef my_methods[] = {
+    {"example_method",
+      (PyCFunction)(void(*)(void))example_method,
+      METH_METHOD|METH_FASTCALL|METH_KEYWORDS,
+      example_method_doc}
+    {NULL},
+}
+

Module State Access from Slot Methods, Getters and Setters

Note

This is new in Python 3.11.

Slot methods—the fast C equivalents for special methods, such as nb_add for __add__ or tp_new for initialization—have a very simple API that doesn’t allow passing in the defining class, unlike with PyCMethod. The same goes for getters and setters defined with PyGetSetDef.

To access the module state in these cases, use the PyType_GetModuleByDef() function, and pass in the module definition. Once you have the module, call PyModule_GetState() to get the state:

PyObject *module = PyType_GetModuleByDef(Py_TYPE(self), &module_def);
+my_struct *state = (my_struct*)PyModule_GetState(module);
+if (state == NULL) {
+    return NULL;
+}
+

PyType_GetModuleByDef() works by searching the method resolution order (i.e. all superclasses) for the first superclass that has a corresponding module.

Note

In very exotic cases (inheritance chains spanning multiple modules created from the same definition), PyType_GetModuleByDef() might not return the module of the true defining class. However, it will always return a module with the same definition, ensuring a compatible C memory layout.

Lifetime of the Module State

When a module object is garbage-collected, its module state is freed. For each pointer to (a part of) the module state, you must hold a reference to the module object.

Usually this is not an issue, because types created with PyType_FromModuleAndSpec(), and their instances, hold a reference to the module. However, you must be careful in reference counting when you reference module state from other places, such as callbacks for external libraries.

Open Issues

Several issues around per-module state and heap types are still open.

Discussions about improving the situation are best held on the capi-sig mailing list.

Per-Class Scope

It is currently (as of Python 3.11) not possible to attach state to individual types without relying on CPython implementation details (which may change in the future—perhaps, ironically, to allow a proper solution for per-class scope).

Lossless Conversion to Heap Types

The heap type API was not designed for “lossless” conversion from static types; that is, creating a type that works exactly like a given static type.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/isolating-extensions.html +

+
diff --git a/devdocs/python~3.12/howto%2Flogging-cookbook.html b/devdocs/python~3.12/howto%2Flogging-cookbook.html new file mode 100644 index 00000000..2b5b0130 --- /dev/null +++ b/devdocs/python~3.12/howto%2Flogging-cookbook.html @@ -0,0 +1,2436 @@ +

Logging Cookbook

Author
+

Vinay Sajip <vinay_sajip at red-dove dot com>

This page contains a number of recipes related to logging, which have been found useful in the past. For links to tutorial and reference information, please see Other resources.

Using logging in multiple modules

Multiple calls to logging.getLogger('someLogger') return a reference to the same logger object. This is true not only within the same module, but also across modules as long as it is in the same Python interpreter process. It is true for references to the same object; additionally, application code can define and configure a parent logger in one module and create (but not configure) a child logger in a separate module, and all logger calls to the child will pass up to the parent. Here is a main module:

import logging
+import auxiliary_module
+
+# create logger with 'spam_application'
+logger = logging.getLogger('spam_application')
+logger.setLevel(logging.DEBUG)
+# create file handler which logs even debug messages
+fh = logging.FileHandler('spam.log')
+fh.setLevel(logging.DEBUG)
+# create console handler with a higher log level
+ch = logging.StreamHandler()
+ch.setLevel(logging.ERROR)
+# create formatter and add it to the handlers
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+fh.setFormatter(formatter)
+ch.setFormatter(formatter)
+# add the handlers to the logger
+logger.addHandler(fh)
+logger.addHandler(ch)
+
+logger.info('creating an instance of auxiliary_module.Auxiliary')
+a = auxiliary_module.Auxiliary()
+logger.info('created an instance of auxiliary_module.Auxiliary')
+logger.info('calling auxiliary_module.Auxiliary.do_something')
+a.do_something()
+logger.info('finished auxiliary_module.Auxiliary.do_something')
+logger.info('calling auxiliary_module.some_function()')
+auxiliary_module.some_function()
+logger.info('done with auxiliary_module.some_function()')
+

Here is the auxiliary module:

import logging
+
+# create logger
+module_logger = logging.getLogger('spam_application.auxiliary')
+
+class Auxiliary:
+    def __init__(self):
+        self.logger = logging.getLogger('spam_application.auxiliary.Auxiliary')
+        self.logger.info('creating an instance of Auxiliary')
+
+    def do_something(self):
+        self.logger.info('doing something')
+        a = 1 + 1
+        self.logger.info('done doing something')
+
+def some_function():
+    module_logger.info('received a call to "some_function"')
+

The output looks like this:

2005-03-23 23:47:11,663 - spam_application - INFO -
+   creating an instance of auxiliary_module.Auxiliary
+2005-03-23 23:47:11,665 - spam_application.auxiliary.Auxiliary - INFO -
+   creating an instance of Auxiliary
+2005-03-23 23:47:11,665 - spam_application - INFO -
+   created an instance of auxiliary_module.Auxiliary
+2005-03-23 23:47:11,668 - spam_application - INFO -
+   calling auxiliary_module.Auxiliary.do_something
+2005-03-23 23:47:11,668 - spam_application.auxiliary.Auxiliary - INFO -
+   doing something
+2005-03-23 23:47:11,669 - spam_application.auxiliary.Auxiliary - INFO -
+   done doing something
+2005-03-23 23:47:11,670 - spam_application - INFO -
+   finished auxiliary_module.Auxiliary.do_something
+2005-03-23 23:47:11,671 - spam_application - INFO -
+   calling auxiliary_module.some_function()
+2005-03-23 23:47:11,672 - spam_application.auxiliary - INFO -
+   received a call to 'some_function'
+2005-03-23 23:47:11,673 - spam_application - INFO -
+   done with auxiliary_module.some_function()
+

Logging from multiple threads

Logging from multiple threads requires no special effort. The following example shows logging from the main (initial) thread and another thread:

import logging
+import threading
+import time
+
+def worker(arg):
+    while not arg['stop']:
+        logging.debug('Hi from myfunc')
+        time.sleep(0.5)
+
+def main():
+    logging.basicConfig(level=logging.DEBUG, format='%(relativeCreated)6d %(threadName)s %(message)s')
+    info = {'stop': False}
+    thread = threading.Thread(target=worker, args=(info,))
+    thread.start()
+    while True:
+        try:
+            logging.debug('Hello from main')
+            time.sleep(0.75)
+        except KeyboardInterrupt:
+            info['stop'] = True
+            break
+    thread.join()
+
+if __name__ == '__main__':
+    main()
+

When run, the script should print something like the following:

   0 Thread-1 Hi from myfunc
+   3 MainThread Hello from main
+ 505 Thread-1 Hi from myfunc
+ 755 MainThread Hello from main
+1007 Thread-1 Hi from myfunc
+1507 MainThread Hello from main
+1508 Thread-1 Hi from myfunc
+2010 Thread-1 Hi from myfunc
+2258 MainThread Hello from main
+2512 Thread-1 Hi from myfunc
+3009 MainThread Hello from main
+3013 Thread-1 Hi from myfunc
+3515 Thread-1 Hi from myfunc
+3761 MainThread Hello from main
+4017 Thread-1 Hi from myfunc
+4513 MainThread Hello from main
+4518 Thread-1 Hi from myfunc
+

This shows the logging output interspersed as one might expect. This approach works for more threads than shown here, of course.

Multiple handlers and formatters

Loggers are plain Python objects. The addHandler() method has no minimum or maximum quota for the number of handlers you may add. Sometimes it will be beneficial for an application to log all messages of all severities to a text file while simultaneously logging errors or above to the console. To set this up, simply configure the appropriate handlers. The logging calls in the application code will remain unchanged. Here is a slight modification to the previous simple module-based configuration example:

import logging
+
+logger = logging.getLogger('simple_example')
+logger.setLevel(logging.DEBUG)
+# create file handler which logs even debug messages
+fh = logging.FileHandler('spam.log')
+fh.setLevel(logging.DEBUG)
+# create console handler with a higher log level
+ch = logging.StreamHandler()
+ch.setLevel(logging.ERROR)
+# create formatter and add it to the handlers
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+ch.setFormatter(formatter)
+fh.setFormatter(formatter)
+# add the handlers to logger
+logger.addHandler(ch)
+logger.addHandler(fh)
+
+# 'application' code
+logger.debug('debug message')
+logger.info('info message')
+logger.warning('warn message')
+logger.error('error message')
+logger.critical('critical message')
+

Notice that the ‘application’ code does not care about multiple handlers. All that changed was the addition and configuration of a new handler named fh.

The ability to create new handlers with higher- or lower-severity filters can be very helpful when writing and testing an application. Instead of using many print statements for debugging, use logger.debug: Unlike the print statements, which you will have to delete or comment out later, the logger.debug statements can remain intact in the source code and remain dormant until you need them again. At that time, the only change that needs to happen is to modify the severity level of the logger and/or handler to debug.

Logging to multiple destinations

Let’s say you want to log to console and file with different message formats and in differing circumstances. Say you want to log messages with levels of DEBUG and higher to file, and those messages at level INFO and higher to the console. Let’s also assume that the file should contain timestamps, but the console messages should not. Here’s how you can achieve this:

import logging
+
+# set up logging to file - see previous section for more details
+logging.basicConfig(level=logging.DEBUG,
+                    format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
+                    datefmt='%m-%d %H:%M',
+                    filename='/tmp/myapp.log',
+                    filemode='w')
+# define a Handler which writes INFO messages or higher to the sys.stderr
+console = logging.StreamHandler()
+console.setLevel(logging.INFO)
+# set a format which is simpler for console use
+formatter = logging.Formatter('%(name)-12s: %(levelname)-8s %(message)s')
+# tell the handler to use this format
+console.setFormatter(formatter)
+# add the handler to the root logger
+logging.getLogger('').addHandler(console)
+
+# Now, we can log to the root logger, or any other logger. First the root...
+logging.info('Jackdaws love my big sphinx of quartz.')
+
+# Now, define a couple of other loggers which might represent areas in your
+# application:
+
+logger1 = logging.getLogger('myapp.area1')
+logger2 = logging.getLogger('myapp.area2')
+
+logger1.debug('Quick zephyrs blow, vexing daft Jim.')
+logger1.info('How quickly daft jumping zebras vex.')
+logger2.warning('Jail zesty vixen who grabbed pay from quack.')
+logger2.error('The five boxing wizards jump quickly.')
+

When you run this, on the console you will see

root        : INFO     Jackdaws love my big sphinx of quartz.
+myapp.area1 : INFO     How quickly daft jumping zebras vex.
+myapp.area2 : WARNING  Jail zesty vixen who grabbed pay from quack.
+myapp.area2 : ERROR    The five boxing wizards jump quickly.
+

and in the file you will see something like

10-22 22:19 root         INFO     Jackdaws love my big sphinx of quartz.
+10-22 22:19 myapp.area1  DEBUG    Quick zephyrs blow, vexing daft Jim.
+10-22 22:19 myapp.area1  INFO     How quickly daft jumping zebras vex.
+10-22 22:19 myapp.area2  WARNING  Jail zesty vixen who grabbed pay from quack.
+10-22 22:19 myapp.area2  ERROR    The five boxing wizards jump quickly.
+

As you can see, the DEBUG message only shows up in the file. The other messages are sent to both destinations.

This example uses console and file handlers, but you can use any number and combination of handlers you choose.

Note that the above choice of log filename /tmp/myapp.log implies use of a standard location for temporary files on POSIX systems. On Windows, you may need to choose a different directory name for the log - just ensure that the directory exists and that you have the permissions to create and update files in it.

Custom handling of levels

Sometimes, you might want to do something slightly different from the standard handling of levels in handlers, where all levels above a threshold get processed by a handler. To do this, you need to use filters. Let’s look at a scenario where you want to arrange things as follows:

Suppose you configure logging with the following JSON:

{
+    "version": 1,
+    "disable_existing_loggers": false,
+    "formatters": {
+        "simple": {
+            "format": "%(levelname)-8s - %(message)s"
+        }
+    },
+    "handlers": {
+        "stdout": {
+            "class": "logging.StreamHandler",
+            "level": "INFO",
+            "formatter": "simple",
+            "stream": "ext://sys.stdout"
+        },
+        "stderr": {
+            "class": "logging.StreamHandler",
+            "level": "ERROR",
+            "formatter": "simple",
+            "stream": "ext://sys.stderr"
+        },
+        "file": {
+            "class": "logging.FileHandler",
+            "formatter": "simple",
+            "filename": "app.log",
+            "mode": "w"
+        }
+    },
+    "root": {
+        "level": "DEBUG",
+        "handlers": [
+            "stderr",
+            "stdout",
+            "file"
+        ]
+    }
+}
+

This configuration does almost what we want, except that sys.stdout would show messages of severity ERROR and above as well as INFO and WARNING messages. To prevent this, we can set up a filter which excludes those messages and add it to the relevant handler. This can be configured by adding a filters section parallel to formatters and handlers:

{
+    "filters": {
+        "warnings_and_below": {
+            "()" : "__main__.filter_maker",
+            "level": "WARNING"
+        }
+    }
+}
+

and changing the section on the stdout handler to add it:

{
+    "stdout": {
+        "class": "logging.StreamHandler",
+        "level": "INFO",
+        "formatter": "simple",
+        "stream": "ext://sys.stdout",
+        "filters": ["warnings_and_below"]
+    }
+}
+

A filter is just a function, so we can define the filter_maker (a factory function) as follows:

def filter_maker(level):
+    level = getattr(logging, level)
+
+    def filter(record):
+        return record.levelno <= level
+
+    return filter
+

This converts the string argument passed in to a numeric level, and returns a function which only returns True if the level of the passed in record is at or below the specified level. Note that in this example I have defined the filter_maker in a test script main.py that I run from the command line, so its module will be __main__ - hence the __main__.filter_maker in the filter configuration. You will need to change that if you define it in a different module.

With the filter added, we can run main.py, which in full is:

import json
+import logging
+import logging.config
+
+CONFIG = '''
+{
+    "version": 1,
+    "disable_existing_loggers": false,
+    "formatters": {
+        "simple": {
+            "format": "%(levelname)-8s - %(message)s"
+        }
+    },
+    "filters": {
+        "warnings_and_below": {
+            "()" : "__main__.filter_maker",
+            "level": "WARNING"
+        }
+    },
+    "handlers": {
+        "stdout": {
+            "class": "logging.StreamHandler",
+            "level": "INFO",
+            "formatter": "simple",
+            "stream": "ext://sys.stdout",
+            "filters": ["warnings_and_below"]
+        },
+        "stderr": {
+            "class": "logging.StreamHandler",
+            "level": "ERROR",
+            "formatter": "simple",
+            "stream": "ext://sys.stderr"
+        },
+        "file": {
+            "class": "logging.FileHandler",
+            "formatter": "simple",
+            "filename": "app.log",
+            "mode": "w"
+        }
+    },
+    "root": {
+        "level": "DEBUG",
+        "handlers": [
+            "stderr",
+            "stdout",
+            "file"
+        ]
+    }
+}
+'''
+
+def filter_maker(level):
+    level = getattr(logging, level)
+
+    def filter(record):
+        return record.levelno <= level
+
+    return filter
+
+logging.config.dictConfig(json.loads(CONFIG))
+logging.debug('A DEBUG message')
+logging.info('An INFO message')
+logging.warning('A WARNING message')
+logging.error('An ERROR message')
+logging.critical('A CRITICAL message')
+

And after running it like this:

python main.py 2>stderr.log >stdout.log
+

We can see the results are as expected:

$ more *.log
+::::::::::::::
+app.log
+::::::::::::::
+DEBUG    - A DEBUG message
+INFO     - An INFO message
+WARNING  - A WARNING message
+ERROR    - An ERROR message
+CRITICAL - A CRITICAL message
+::::::::::::::
+stderr.log
+::::::::::::::
+ERROR    - An ERROR message
+CRITICAL - A CRITICAL message
+::::::::::::::
+stdout.log
+::::::::::::::
+INFO     - An INFO message
+WARNING  - A WARNING message
+

Configuration server example

Here is an example of a module using the logging configuration server:

import logging
+import logging.config
+import time
+import os
+
+# read initial config file
+logging.config.fileConfig('logging.conf')
+
+# create and start listener on port 9999
+t = logging.config.listen(9999)
+t.start()
+
+logger = logging.getLogger('simpleExample')
+
+try:
+    # loop through logging calls to see the difference
+    # new configurations make, until Ctrl+C is pressed
+    while True:
+        logger.debug('debug message')
+        logger.info('info message')
+        logger.warning('warn message')
+        logger.error('error message')
+        logger.critical('critical message')
+        time.sleep(5)
+except KeyboardInterrupt:
+    # cleanup
+    logging.config.stopListening()
+    t.join()
+

And here is a script that takes a filename and sends that file to the server, properly preceded with the binary-encoded length, as the new logging configuration:

#!/usr/bin/env python
+import socket, sys, struct
+
+with open(sys.argv[1], 'rb') as f:
+    data_to_send = f.read()
+
+HOST = 'localhost'
+PORT = 9999
+s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+print('connecting...')
+s.connect((HOST, PORT))
+print('sending config...')
+s.send(struct.pack('>L', len(data_to_send)))
+s.send(data_to_send)
+s.close()
+print('complete')
+

Dealing with handlers that block

Sometimes you have to get your logging handlers to do their work without blocking the thread you’re logging from. This is common in web applications, though of course it also occurs in other scenarios.

A common culprit which demonstrates sluggish behaviour is the SMTPHandler: sending emails can take a long time, for a number of reasons outside the developer’s control (for example, a poorly performing mail or network infrastructure). But almost any network-based handler can block: Even a SocketHandler operation may do a DNS query under the hood which is too slow (and this query can be deep in the socket library code, below the Python layer, and outside your control).

One solution is to use a two-part approach. For the first part, attach only a QueueHandler to those loggers which are accessed from performance-critical threads. They simply write to their queue, which can be sized to a large enough capacity or initialized with no upper bound to their size. The write to the queue will typically be accepted quickly, though you will probably need to catch the queue.Full exception as a precaution in your code. If you are a library developer who has performance-critical threads in their code, be sure to document this (together with a suggestion to attach only QueueHandlers to your loggers) for the benefit of other developers who will use your code.

The second part of the solution is QueueListener, which has been designed as the counterpart to QueueHandler. A QueueListener is very simple: it’s passed a queue and some handlers, and it fires up an internal thread which listens to its queue for LogRecords sent from QueueHandlers (or any other source of LogRecords, for that matter). The LogRecords are removed from the queue and passed to the handlers for processing.

The advantage of having a separate QueueListener class is that you can use the same instance to service multiple QueueHandlers. This is more resource-friendly than, say, having threaded versions of the existing handler classes, which would eat up one thread per handler for no particular benefit.

An example of using these two classes follows (imports omitted):

que = queue.Queue(-1)  # no limit on size
+queue_handler = QueueHandler(que)
+handler = logging.StreamHandler()
+listener = QueueListener(que, handler)
+root = logging.getLogger()
+root.addHandler(queue_handler)
+formatter = logging.Formatter('%(threadName)s: %(message)s')
+handler.setFormatter(formatter)
+listener.start()
+# The log output will display the thread which generated
+# the event (the main thread) rather than the internal
+# thread which monitors the internal queue. This is what
+# you want to happen.
+root.warning('Look out!')
+listener.stop()
+

which, when run, will produce:

MainThread: Look out!
+

Note

Although the earlier discussion wasn’t specifically talking about async code, but rather about slow logging handlers, it should be noted that when logging from async code, network and even file handlers could lead to problems (blocking the event loop) because some logging is done from asyncio internals. It might be best, if any async code is used in an application, to use the above approach for logging, so that any blocking code runs only in the QueueListener thread.

Changed in version 3.5: Prior to Python 3.5, the QueueListener always passed every message received from the queue to every handler it was initialized with. (This was because it was assumed that level filtering was all done on the other side, where the queue is filled.) From 3.5 onwards, this behaviour can be changed by passing a keyword argument respect_handler_level=True to the listener’s constructor. When this is done, the listener compares the level of each message with the handler’s level, and only passes a message to a handler if it’s appropriate to do so.

Sending and receiving logging events across a network

Let’s say you want to send logging events across a network, and handle them at the receiving end. A simple way of doing this is attaching a SocketHandler instance to the root logger at the sending end:

import logging, logging.handlers
+
+rootLogger = logging.getLogger('')
+rootLogger.setLevel(logging.DEBUG)
+socketHandler = logging.handlers.SocketHandler('localhost',
+                    logging.handlers.DEFAULT_TCP_LOGGING_PORT)
+# don't bother with a formatter, since a socket handler sends the event as
+# an unformatted pickle
+rootLogger.addHandler(socketHandler)
+
+# Now, we can log to the root logger, or any other logger. First the root...
+logging.info('Jackdaws love my big sphinx of quartz.')
+
+# Now, define a couple of other loggers which might represent areas in your
+# application:
+
+logger1 = logging.getLogger('myapp.area1')
+logger2 = logging.getLogger('myapp.area2')
+
+logger1.debug('Quick zephyrs blow, vexing daft Jim.')
+logger1.info('How quickly daft jumping zebras vex.')
+logger2.warning('Jail zesty vixen who grabbed pay from quack.')
+logger2.error('The five boxing wizards jump quickly.')
+

At the receiving end, you can set up a receiver using the socketserver module. Here is a basic working example:

import pickle
+import logging
+import logging.handlers
+import socketserver
+import struct
+
+
+class LogRecordStreamHandler(socketserver.StreamRequestHandler):
+    """Handler for a streaming logging request.
+
+    This basically logs the record using whatever logging policy is
+    configured locally.
+    """
+
+    def handle(self):
+        """
+        Handle multiple requests - each expected to be a 4-byte length,
+        followed by the LogRecord in pickle format. Logs the record
+        according to whatever policy is configured locally.
+        """
+        while True:
+            chunk = self.connection.recv(4)
+            if len(chunk) < 4:
+                break
+            slen = struct.unpack('>L', chunk)[0]
+            chunk = self.connection.recv(slen)
+            while len(chunk) < slen:
+                chunk = chunk + self.connection.recv(slen - len(chunk))
+            obj = self.unPickle(chunk)
+            record = logging.makeLogRecord(obj)
+            self.handleLogRecord(record)
+
+    def unPickle(self, data):
+        return pickle.loads(data)
+
+    def handleLogRecord(self, record):
+        # if a name is specified, we use the named logger rather than the one
+        # implied by the record.
+        if self.server.logname is not None:
+            name = self.server.logname
+        else:
+            name = record.name
+        logger = logging.getLogger(name)
+        # N.B. EVERY record gets logged. This is because Logger.handle
+        # is normally called AFTER logger-level filtering. If you want
+        # to do filtering, do it at the client end to save wasting
+        # cycles and network bandwidth!
+        logger.handle(record)
+
+class LogRecordSocketReceiver(socketserver.ThreadingTCPServer):
+    """
+    Simple TCP socket-based logging receiver suitable for testing.
+    """
+
+    allow_reuse_address = True
+
+    def __init__(self, host='localhost',
+                 port=logging.handlers.DEFAULT_TCP_LOGGING_PORT,
+                 handler=LogRecordStreamHandler):
+        socketserver.ThreadingTCPServer.__init__(self, (host, port), handler)
+        self.abort = 0
+        self.timeout = 1
+        self.logname = None
+
+    def serve_until_stopped(self):
+        import select
+        abort = 0
+        while not abort:
+            rd, wr, ex = select.select([self.socket.fileno()],
+                                       [], [],
+                                       self.timeout)
+            if rd:
+                self.handle_request()
+            abort = self.abort
+
+def main():
+    logging.basicConfig(
+        format='%(relativeCreated)5d %(name)-15s %(levelname)-8s %(message)s')
+    tcpserver = LogRecordSocketReceiver()
+    print('About to start TCP server...')
+    tcpserver.serve_until_stopped()
+
+if __name__ == '__main__':
+    main()
+

First run the server, and then the client. On the client side, nothing is printed on the console; on the server side, you should see something like:

About to start TCP server...
+   59 root            INFO     Jackdaws love my big sphinx of quartz.
+   59 myapp.area1     DEBUG    Quick zephyrs blow, vexing daft Jim.
+   69 myapp.area1     INFO     How quickly daft jumping zebras vex.
+   69 myapp.area2     WARNING  Jail zesty vixen who grabbed pay from quack.
+   69 myapp.area2     ERROR    The five boxing wizards jump quickly.
+

Note that there are some security issues with pickle in some scenarios. If these affect you, you can use an alternative serialization scheme by overriding the makePickle() method and implementing your alternative there, as well as adapting the above script to use your alternative serialization.

Running a logging socket listener in production

To run a logging listener in production, you may need to use a process-management tool such as Supervisor. Here is a Gist which provides the bare-bones files to run the above functionality using Supervisor. It consists of the following files:

+ + + + + + + +

File

Purpose

prepare.sh

A Bash script to prepare the environment for testing

supervisor.conf

The Supervisor configuration file, which has entries for the listener and a multi-process web application

ensure_app.sh

A Bash script to ensure that Supervisor is running with the above configuration

log_listener.py

The socket listener program which receives log events and records them to a file

main.py

A simple web application which performs logging via a socket connected to the listener

webapp.json

A JSON configuration file for the web application

client.py

A Python script to exercise the web application

The web application uses Gunicorn, which is a popular web application server that starts multiple worker processes to handle requests. This example setup shows how the workers can write to the same log file without conflicting with one another — they all go through the socket listener.

To test these files, do the following in a POSIX environment:

  1. Download the Gist as a ZIP archive using the Download ZIP button.
  2. Unzip the above files from the archive into a scratch directory.
  3. In the scratch directory, run bash prepare.sh to get things ready. This creates a run subdirectory to contain Supervisor-related and log files, and a venv subdirectory to contain a virtual environment into which bottle, gunicorn and supervisor are installed.
  4. Run bash ensure_app.sh to ensure that Supervisor is running with the above configuration.
  5. Run venv/bin/python client.py to exercise the web application, which will lead to records being written to the log.
  6. Inspect the log files in the run subdirectory. You should see the most recent log lines in files matching the pattern app.log*. They won’t be in any particular order, since they have been handled concurrently by different worker processes in a non-deterministic way.
  7. You can shut down the listener and the web application by running venv/bin/supervisorctl -c supervisor.conf shutdown.

You may need to tweak the configuration files in the unlikely event that the configured ports clash with something else in your test environment.

Adding contextual information to your logging output

Sometimes you want logging output to contain contextual information in addition to the parameters passed to the logging call. For example, in a networked application, it may be desirable to log client-specific information in the log (e.g. remote client’s username, or IP address). Although you could use the extra parameter to achieve this, it’s not always convenient to pass the information in this way. While it might be tempting to create Logger instances on a per-connection basis, this is not a good idea because these instances are not garbage collected. While this is not a problem in practice, when the number of Logger instances is dependent on the level of granularity you want to use in logging an application, it could be hard to manage if the number of Logger instances becomes effectively unbounded.

Using LoggerAdapters to impart contextual information

An easy way in which you can pass contextual information to be output along with logging event information is to use the LoggerAdapter class. This class is designed to look like a Logger, so that you can call debug(), info(), warning(), error(), exception(), critical() and log(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably.

When you create an instance of LoggerAdapter, you pass it a Logger instance and a dict-like object which contains your contextual information. When you call one of the logging methods on an instance of LoggerAdapter, it delegates the call to the underlying instance of Logger passed to its constructor, and arranges to pass the contextual information in the delegated call. Here’s a snippet from the code of LoggerAdapter:

def debug(self, msg, /, *args, **kwargs):
+    """
+    Delegate a debug call to the underlying logger, after adding
+    contextual information from this adapter instance.
+    """
+    msg, kwargs = self.process(msg, kwargs)
+    self.logger.debug(msg, *args, **kwargs)
+

The process() method of LoggerAdapter is where the contextual information is added to the logging output. It’s passed the message and keyword arguments of the logging call, and it passes back (potentially) modified versions of these to use in the call to the underlying logger. The default implementation of this method leaves the message alone, but inserts an ‘extra’ key in the keyword argument whose value is the dict-like object passed to the constructor. Of course, if you had passed an ‘extra’ keyword argument in the call to the adapter, it will be silently overwritten.

The advantage of using ‘extra’ is that the values in the dict-like object are merged into the LogRecord instance’s __dict__, allowing you to use customized strings with your Formatter instances which know about the keys of the dict-like object. If you need a different method, e.g. if you want to prepend or append the contextual information to the message string, you just need to subclass LoggerAdapter and override process() to do what you need. Here is a simple example:

class CustomAdapter(logging.LoggerAdapter):
+    """
+    This example adapter expects the passed in dict-like object to have a
+    'connid' key, whose value in brackets is prepended to the log message.
+    """
+    def process(self, msg, kwargs):
+        return '[%s] %s' % (self.extra['connid'], msg), kwargs
+

which you can use like this:

logger = logging.getLogger(__name__)
+adapter = CustomAdapter(logger, {'connid': some_conn_id})
+

Then any events that you log to the adapter will have the value of some_conn_id prepended to the log messages.

Using objects other than dicts to pass contextual information

You don’t need to pass an actual dict to a LoggerAdapter - you could pass an instance of a class which implements __getitem__ and __iter__ so that it looks like a dict to logging. This would be useful if you want to generate values dynamically (whereas the values in a dict would be constant).

Using Filters to impart contextual information

You can also add contextual information to log output using a user-defined Filter. Filter instances are allowed to modify the LogRecords passed to them, including adding additional attributes which can then be output using a suitable format string, or if needed a custom Formatter.

For example in a web application, the request being processed (or at least, the interesting parts of it) can be stored in a threadlocal (threading.local) variable, and then accessed from a Filter to add, say, information from the request - say, the remote IP address and remote user’s username - to the LogRecord, using the attribute names ‘ip’ and ‘user’ as in the LoggerAdapter example above. In that case, the same format string can be used to get similar output to that shown above. Here’s an example script:

import logging
+from random import choice
+
+class ContextFilter(logging.Filter):
+    """
+    This is a filter which injects contextual information into the log.
+
+    Rather than use actual contextual information, we just use random
+    data in this demo.
+    """
+
+    USERS = ['jim', 'fred', 'sheila']
+    IPS = ['123.231.231.123', '127.0.0.1', '192.168.0.1']
+
+    def filter(self, record):
+
+        record.ip = choice(ContextFilter.IPS)
+        record.user = choice(ContextFilter.USERS)
+        return True
+
+if __name__ == '__main__':
+    levels = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR, logging.CRITICAL)
+    logging.basicConfig(level=logging.DEBUG,
+                        format='%(asctime)-15s %(name)-5s %(levelname)-8s IP: %(ip)-15s User: %(user)-8s %(message)s')
+    a1 = logging.getLogger('a.b.c')
+    a2 = logging.getLogger('d.e.f')
+
+    f = ContextFilter()
+    a1.addFilter(f)
+    a2.addFilter(f)
+    a1.debug('A debug message')
+    a1.info('An info message with %s', 'some parameters')
+    for x in range(10):
+        lvl = choice(levels)
+        lvlname = logging.getLevelName(lvl)
+        a2.log(lvl, 'A message at %s level with %d %s', lvlname, 2, 'parameters')
+

which, when run, produces something like:

2010-09-06 22:38:15,292 a.b.c DEBUG    IP: 123.231.231.123 User: fred     A debug message
+2010-09-06 22:38:15,300 a.b.c INFO     IP: 192.168.0.1     User: sheila   An info message with some parameters
+2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1       User: sheila   A message at CRITICAL level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f ERROR    IP: 127.0.0.1       User: jim      A message at ERROR level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f DEBUG    IP: 127.0.0.1       User: sheila   A message at DEBUG level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f ERROR    IP: 123.231.231.123 User: fred     A message at ERROR level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 192.168.0.1     User: jim      A message at CRITICAL level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f CRITICAL IP: 127.0.0.1       User: sheila   A message at CRITICAL level with 2 parameters
+2010-09-06 22:38:15,300 d.e.f DEBUG    IP: 192.168.0.1     User: jim      A message at DEBUG level with 2 parameters
+2010-09-06 22:38:15,301 d.e.f ERROR    IP: 127.0.0.1       User: sheila   A message at ERROR level with 2 parameters
+2010-09-06 22:38:15,301 d.e.f DEBUG    IP: 123.231.231.123 User: fred     A message at DEBUG level with 2 parameters
+2010-09-06 22:38:15,301 d.e.f INFO     IP: 123.231.231.123 User: fred     A message at INFO level with 2 parameters
+

Use of contextvars +

Since Python 3.7, the contextvars module has provided context-local storage which works for both threading and asyncio processing needs. This type of storage may thus be generally preferable to thread-locals. The following example shows how, in a multi-threaded environment, logs can populated with contextual information such as, for example, request attributes handled by web applications.

For the purposes of illustration, say that you have different web applications, each independent of the other but running in the same Python process and using a library common to them. How can each of these applications have their own log, where all logging messages from the library (and other request processing code) are directed to the appropriate application’s log file, while including in the log additional contextual information such as client IP, HTTP request method and client username?

Let’s assume that the library can be simulated by the following code:

# webapplib.py
+import logging
+import time
+
+logger = logging.getLogger(__name__)
+
+def useful():
+    # Just a representative event logged from the library
+    logger.debug('Hello from webapplib!')
+    # Just sleep for a bit so other threads get to run
+    time.sleep(0.01)
+

We can simulate the multiple web applications by means of two simple classes, Request and WebApp. These simulate how real threaded web applications work - each request is handled by a thread:

# main.py
+import argparse
+from contextvars import ContextVar
+import logging
+import os
+from random import choice
+import threading
+import webapplib
+
+logger = logging.getLogger(__name__)
+root = logging.getLogger()
+root.setLevel(logging.DEBUG)
+
+class Request:
+    """
+    A simple dummy request class which just holds dummy HTTP request method,
+    client IP address and client username
+    """
+    def __init__(self, method, ip, user):
+        self.method = method
+        self.ip = ip
+        self.user = user
+
+# A dummy set of requests which will be used in the simulation - we'll just pick
+# from this list randomly. Note that all GET requests are from 192.168.2.XXX
+# addresses, whereas POST requests are from 192.16.3.XXX addresses. Three users
+# are represented in the sample requests.
+
+REQUESTS = [
+    Request('GET', '192.168.2.20', 'jim'),
+    Request('POST', '192.168.3.20', 'fred'),
+    Request('GET', '192.168.2.21', 'sheila'),
+    Request('POST', '192.168.3.21', 'jim'),
+    Request('GET', '192.168.2.22', 'fred'),
+    Request('POST', '192.168.3.22', 'sheila'),
+]
+
+# Note that the format string includes references to request context information
+# such as HTTP method, client IP and username
+
+formatter = logging.Formatter('%(threadName)-11s %(appName)s %(name)-9s %(user)-6s %(ip)s %(method)-4s %(message)s')
+
+# Create our context variables. These will be filled at the start of request
+# processing, and used in the logging that happens during that processing
+
+ctx_request = ContextVar('request')
+ctx_appname = ContextVar('appname')
+
+class InjectingFilter(logging.Filter):
+    """
+    A filter which injects context-specific information into logs and ensures
+    that only information for a specific webapp is included in its log
+    """
+    def __init__(self, app):
+        self.app = app
+
+    def filter(self, record):
+        request = ctx_request.get()
+        record.method = request.method
+        record.ip = request.ip
+        record.user = request.user
+        record.appName = appName = ctx_appname.get()
+        return appName == self.app.name
+
+class WebApp:
+    """
+    A dummy web application class which has its own handler and filter for a
+    webapp-specific log.
+    """
+    def __init__(self, name):
+        self.name = name
+        handler = logging.FileHandler(name + '.log', 'w')
+        f = InjectingFilter(self)
+        handler.setFormatter(formatter)
+        handler.addFilter(f)
+        root.addHandler(handler)
+        self.num_requests = 0
+
+    def process_request(self, request):
+        """
+        This is the dummy method for processing a request. It's called on a
+        different thread for every request. We store the context information into
+        the context vars before doing anything else.
+        """
+        ctx_request.set(request)
+        ctx_appname.set(self.name)
+        self.num_requests += 1
+        logger.debug('Request processing started')
+        webapplib.useful()
+        logger.debug('Request processing finished')
+
+def main():
+    fn = os.path.splitext(os.path.basename(__file__))[0]
+    adhf = argparse.ArgumentDefaultsHelpFormatter
+    ap = argparse.ArgumentParser(formatter_class=adhf, prog=fn,
+                                 description='Simulate a couple of web '
+                                             'applications handling some '
+                                             'requests, showing how request '
+                                             'context can be used to '
+                                             'populate logs')
+    aa = ap.add_argument
+    aa('--count', '-c', type=int, default=100, help='How many requests to simulate')
+    options = ap.parse_args()
+
+    # Create the dummy webapps and put them in a list which we can use to select
+    # from randomly
+    app1 = WebApp('app1')
+    app2 = WebApp('app2')
+    apps = [app1, app2]
+    threads = []
+    # Add a common handler which will capture all events
+    handler = logging.FileHandler('app.log', 'w')
+    handler.setFormatter(formatter)
+    root.addHandler(handler)
+
+    # Generate calls to process requests
+    for i in range(options.count):
+        try:
+            # Pick an app at random and a request for it to process
+            app = choice(apps)
+            request = choice(REQUESTS)
+            # Process the request in its own thread
+            t = threading.Thread(target=app.process_request, args=(request,))
+            threads.append(t)
+            t.start()
+        except KeyboardInterrupt:
+            break
+
+    # Wait for the threads to terminate
+    for t in threads:
+        t.join()
+
+    for app in apps:
+        print('%s processed %s requests' % (app.name, app.num_requests))
+
+if __name__ == '__main__':
+    main()
+

If you run the above, you should find that roughly half the requests go into app1.log and the rest into app2.log, and the all the requests are logged to app.log. Each webapp-specific log will contain only log entries for only that webapp, and the request information will be displayed consistently in the log (i.e. the information in each dummy request will always appear together in a log line). This is illustrated by the following shell output:

~/logging-contextual-webapp$ python main.py
+app1 processed 51 requests
+app2 processed 49 requests
+~/logging-contextual-webapp$ wc -l *.log
+  153 app1.log
+  147 app2.log
+  300 app.log
+  600 total
+~/logging-contextual-webapp$ head -3 app1.log
+Thread-3 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
+Thread-3 (process_request) app1 webapplib jim    192.168.3.21 POST Hello from webapplib!
+Thread-5 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
+~/logging-contextual-webapp$ head -3 app2.log
+Thread-1 (process_request) app2 __main__  sheila 192.168.2.21 GET  Request processing started
+Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET  Hello from webapplib!
+Thread-2 (process_request) app2 __main__  jim    192.168.2.20 GET  Request processing started
+~/logging-contextual-webapp$ head app.log
+Thread-1 (process_request) app2 __main__  sheila 192.168.2.21 GET  Request processing started
+Thread-1 (process_request) app2 webapplib sheila 192.168.2.21 GET  Hello from webapplib!
+Thread-2 (process_request) app2 __main__  jim    192.168.2.20 GET  Request processing started
+Thread-3 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
+Thread-2 (process_request) app2 webapplib jim    192.168.2.20 GET  Hello from webapplib!
+Thread-3 (process_request) app1 webapplib jim    192.168.3.21 POST Hello from webapplib!
+Thread-4 (process_request) app2 __main__  fred   192.168.2.22 GET  Request processing started
+Thread-5 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
+Thread-4 (process_request) app2 webapplib fred   192.168.2.22 GET  Hello from webapplib!
+Thread-6 (process_request) app1 __main__  jim    192.168.3.21 POST Request processing started
+~/logging-contextual-webapp$ grep app1 app1.log | wc -l
+153
+~/logging-contextual-webapp$ grep app2 app2.log | wc -l
+147
+~/logging-contextual-webapp$ grep app1 app.log | wc -l
+153
+~/logging-contextual-webapp$ grep app2 app.log | wc -l
+147
+

Imparting contextual information in handlers

Each Handler has its own chain of filters. If you want to add contextual information to a LogRecord without leaking it to other handlers, you can use a filter that returns a new LogRecord instead of modifying it in-place, as shown in the following script:

import copy
+import logging
+
+def filter(record: logging.LogRecord):
+    record = copy.copy(record)
+    record.user = 'jim'
+    return record
+
+if __name__ == '__main__':
+    logger = logging.getLogger()
+    logger.setLevel(logging.INFO)
+    handler = logging.StreamHandler()
+    formatter = logging.Formatter('%(message)s from %(user)-8s')
+    handler.setFormatter(formatter)
+    handler.addFilter(filter)
+    logger.addHandler(handler)
+
+    logger.info('A log message')
+

Logging to a single file from multiple processes

Although logging is thread-safe, and logging to a single file from multiple threads in a single process is supported, logging to a single file from multiple processes is not supported, because there is no standard way to serialize access to a single file across multiple processes in Python. If you need to log to a single file from multiple processes, one way of doing this is to have all the processes log to a SocketHandler, and have a separate process which implements a socket server which reads from the socket and logs to file. (If you prefer, you can dedicate one thread in one of the existing processes to perform this function.) This section documents this approach in more detail and includes a working socket receiver which can be used as a starting point for you to adapt in your own applications.

You could also write your own handler which uses the Lock class from the multiprocessing module to serialize access to the file from your processes. The existing FileHandler and subclasses do not make use of multiprocessing at present, though they may do so in the future. Note that at present, the multiprocessing module does not provide working lock functionality on all platforms (see https://bugs.python.org/issue3770).

Alternatively, you can use a Queue and a QueueHandler to send all logging events to one of the processes in your multi-process application. The following example script demonstrates how you can do this; in the example a separate listener process listens for events sent by other processes and logs them according to its own logging configuration. Although the example only demonstrates one way of doing it (for example, you may want to use a listener thread rather than a separate listener process – the implementation would be analogous) it does allow for completely different logging configurations for the listener and the other processes in your application, and can be used as the basis for code meeting your own specific requirements:

# You'll need these imports in your own code
+import logging
+import logging.handlers
+import multiprocessing
+
+# Next two import lines for this demo only
+from random import choice, random
+import time
+
+#
+# Because you'll want to define the logging configurations for listener and workers, the
+# listener and worker process functions take a configurer parameter which is a callable
+# for configuring logging for that process. These functions are also passed the queue,
+# which they use for communication.
+#
+# In practice, you can configure the listener however you want, but note that in this
+# simple example, the listener does not apply level or filter logic to received records.
+# In practice, you would probably want to do this logic in the worker processes, to avoid
+# sending events which would be filtered out between processes.
+#
+# The size of the rotated files is made small so you can see the results easily.
+def listener_configurer():
+    root = logging.getLogger()
+    h = logging.handlers.RotatingFileHandler('mptest.log', 'a', 300, 10)
+    f = logging.Formatter('%(asctime)s %(processName)-10s %(name)s %(levelname)-8s %(message)s')
+    h.setFormatter(f)
+    root.addHandler(h)
+
+# This is the listener process top-level loop: wait for logging events
+# (LogRecords)on the queue and handle them, quit when you get a None for a
+# LogRecord.
+def listener_process(queue, configurer):
+    configurer()
+    while True:
+        try:
+            record = queue.get()
+            if record is None:  # We send this as a sentinel to tell the listener to quit.
+                break
+            logger = logging.getLogger(record.name)
+            logger.handle(record)  # No level or filter logic applied - just do it!
+        except Exception:
+            import sys, traceback
+            print('Whoops! Problem:', file=sys.stderr)
+            traceback.print_exc(file=sys.stderr)
+
+# Arrays used for random selections in this demo
+
+LEVELS = [logging.DEBUG, logging.INFO, logging.WARNING,
+          logging.ERROR, logging.CRITICAL]
+
+LOGGERS = ['a.b.c', 'd.e.f']
+
+MESSAGES = [
+    'Random message #1',
+    'Random message #2',
+    'Random message #3',
+]
+
+# The worker configuration is done at the start of the worker process run.
+# Note that on Windows you can't rely on fork semantics, so each process
+# will run the logging configuration code when it starts.
+def worker_configurer(queue):
+    h = logging.handlers.QueueHandler(queue)  # Just the one handler needed
+    root = logging.getLogger()
+    root.addHandler(h)
+    # send all messages, for demo; no other level or filter logic applied.
+    root.setLevel(logging.DEBUG)
+
+# This is the worker process top-level loop, which just logs ten events with
+# random intervening delays before terminating.
+# The print messages are just so you know it's doing something!
+def worker_process(queue, configurer):
+    configurer(queue)
+    name = multiprocessing.current_process().name
+    print('Worker started: %s' % name)
+    for i in range(10):
+        time.sleep(random())
+        logger = logging.getLogger(choice(LOGGERS))
+        level = choice(LEVELS)
+        message = choice(MESSAGES)
+        logger.log(level, message)
+    print('Worker finished: %s' % name)
+
+# Here's where the demo gets orchestrated. Create the queue, create and start
+# the listener, create ten workers and start them, wait for them to finish,
+# then send a None to the queue to tell the listener to finish.
+def main():
+    queue = multiprocessing.Queue(-1)
+    listener = multiprocessing.Process(target=listener_process,
+                                       args=(queue, listener_configurer))
+    listener.start()
+    workers = []
+    for i in range(10):
+        worker = multiprocessing.Process(target=worker_process,
+                                         args=(queue, worker_configurer))
+        workers.append(worker)
+        worker.start()
+    for w in workers:
+        w.join()
+    queue.put_nowait(None)
+    listener.join()
+
+if __name__ == '__main__':
+    main()
+

A variant of the above script keeps the logging in the main process, in a separate thread:

import logging
+import logging.config
+import logging.handlers
+from multiprocessing import Process, Queue
+import random
+import threading
+import time
+
+def logger_thread(q):
+    while True:
+        record = q.get()
+        if record is None:
+            break
+        logger = logging.getLogger(record.name)
+        logger.handle(record)
+
+
+def worker_process(q):
+    qh = logging.handlers.QueueHandler(q)
+    root = logging.getLogger()
+    root.setLevel(logging.DEBUG)
+    root.addHandler(qh)
+    levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
+              logging.CRITICAL]
+    loggers = ['foo', 'foo.bar', 'foo.bar.baz',
+               'spam', 'spam.ham', 'spam.ham.eggs']
+    for i in range(100):
+        lvl = random.choice(levels)
+        logger = logging.getLogger(random.choice(loggers))
+        logger.log(lvl, 'Message no. %d', i)
+
+if __name__ == '__main__':
+    q = Queue()
+    d = {
+        'version': 1,
+        'formatters': {
+            'detailed': {
+                'class': 'logging.Formatter',
+                'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
+            }
+        },
+        'handlers': {
+            'console': {
+                'class': 'logging.StreamHandler',
+                'level': 'INFO',
+            },
+            'file': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog.log',
+                'mode': 'w',
+                'formatter': 'detailed',
+            },
+            'foofile': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog-foo.log',
+                'mode': 'w',
+                'formatter': 'detailed',
+            },
+            'errors': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog-errors.log',
+                'mode': 'w',
+                'level': 'ERROR',
+                'formatter': 'detailed',
+            },
+        },
+        'loggers': {
+            'foo': {
+                'handlers': ['foofile']
+            }
+        },
+        'root': {
+            'level': 'DEBUG',
+            'handlers': ['console', 'file', 'errors']
+        },
+    }
+    workers = []
+    for i in range(5):
+        wp = Process(target=worker_process, name='worker %d' % (i + 1), args=(q,))
+        workers.append(wp)
+        wp.start()
+    logging.config.dictConfig(d)
+    lp = threading.Thread(target=logger_thread, args=(q,))
+    lp.start()
+    # At this point, the main process could do some useful work of its own
+    # Once it's done that, it can wait for the workers to terminate...
+    for wp in workers:
+        wp.join()
+    # And now tell the logging thread to finish up, too
+    q.put(None)
+    lp.join()
+

This variant shows how you can e.g. apply configuration for particular loggers - e.g. the foo logger has a special handler which stores all events in the foo subsystem in a file mplog-foo.log. This will be used by the logging machinery in the main process (even though the logging events are generated in the worker processes) to direct the messages to the appropriate destinations.

Using concurrent.futures.ProcessPoolExecutor

If you want to use concurrent.futures.ProcessPoolExecutor to start your worker processes, you need to create the queue slightly differently. Instead of

queue = multiprocessing.Queue(-1)
+

you should use

queue = multiprocessing.Manager().Queue(-1)  # also works with the examples above
+

and you can then replace the worker creation from this:

workers = []
+for i in range(10):
+    worker = multiprocessing.Process(target=worker_process,
+                                     args=(queue, worker_configurer))
+    workers.append(worker)
+    worker.start()
+for w in workers:
+    w.join()
+

to this (remembering to first import concurrent.futures):

with concurrent.futures.ProcessPoolExecutor(max_workers=10) as executor:
+    for i in range(10):
+        executor.submit(worker_process, queue, worker_configurer)
+

Deploying Web applications using Gunicorn and uWSGI

When deploying Web applications using Gunicorn or uWSGI (or similar), multiple worker processes are created to handle client requests. In such environments, avoid creating file-based handlers directly in your web application. Instead, use a SocketHandler to log from the web application to a listener in a separate process. This can be set up using a process management tool such as Supervisor - see Running a logging socket listener in production for more details.

Using file rotation

Sometimes you want to let a log file grow to a certain size, then open a new file and log to that. You may want to keep a certain number of these files, and when that many files have been created, rotate the files so that the number of files and the size of the files both remain bounded. For this usage pattern, the logging package provides a RotatingFileHandler:

import glob
+import logging
+import logging.handlers
+
+LOG_FILENAME = 'logging_rotatingfile_example.out'
+
+# Set up a specific logger with our desired output level
+my_logger = logging.getLogger('MyLogger')
+my_logger.setLevel(logging.DEBUG)
+
+# Add the log message handler to the logger
+handler = logging.handlers.RotatingFileHandler(
+              LOG_FILENAME, maxBytes=20, backupCount=5)
+
+my_logger.addHandler(handler)
+
+# Log some messages
+for i in range(20):
+    my_logger.debug('i = %d' % i)
+
+# See what files are created
+logfiles = glob.glob('%s*' % LOG_FILENAME)
+
+for filename in logfiles:
+    print(filename)
+

The result should be 6 separate files, each with part of the log history for the application:

logging_rotatingfile_example.out
+logging_rotatingfile_example.out.1
+logging_rotatingfile_example.out.2
+logging_rotatingfile_example.out.3
+logging_rotatingfile_example.out.4
+logging_rotatingfile_example.out.5
+

The most current file is always logging_rotatingfile_example.out, and each time it reaches the size limit it is renamed with the suffix .1. Each of the existing backup files is renamed to increment the suffix (.1 becomes .2, etc.) and the .6 file is erased.

Obviously this example sets the log length much too small as an extreme example. You would want to set maxBytes to an appropriate value.

Use of alternative formatting styles

When logging was added to the Python standard library, the only way of formatting messages with variable content was to use the %-formatting method. Since then, Python has gained two new formatting approaches: string.Template (added in Python 2.4) and str.format() (added in Python 2.6).

Logging (as of 3.2) provides improved support for these two additional formatting styles. The Formatter class been enhanced to take an additional, optional keyword parameter named style. This defaults to '%', but other possible values are '{' and '$', which correspond to the other two formatting styles. Backwards compatibility is maintained by default (as you would expect), but by explicitly specifying a style parameter, you get the ability to specify format strings which work with str.format() or string.Template. Here’s an example console session to show the possibilities:

>>> import logging
+>>> root = logging.getLogger()
+>>> root.setLevel(logging.DEBUG)
+>>> handler = logging.StreamHandler()
+>>> bf = logging.Formatter('{asctime} {name} {levelname:8s} {message}',
+...                        style='{')
+>>> handler.setFormatter(bf)
+>>> root.addHandler(handler)
+>>> logger = logging.getLogger('foo.bar')
+>>> logger.debug('This is a DEBUG message')
+2010-10-28 15:11:55,341 foo.bar DEBUG    This is a DEBUG message
+>>> logger.critical('This is a CRITICAL message')
+2010-10-28 15:12:11,526 foo.bar CRITICAL This is a CRITICAL message
+>>> df = logging.Formatter('$asctime $name ${levelname} $message',
+...                        style='$')
+>>> handler.setFormatter(df)
+>>> logger.debug('This is a DEBUG message')
+2010-10-28 15:13:06,924 foo.bar DEBUG This is a DEBUG message
+>>> logger.critical('This is a CRITICAL message')
+2010-10-28 15:13:11,494 foo.bar CRITICAL This is a CRITICAL message
+>>>
+

Note that the formatting of logging messages for final output to logs is completely independent of how an individual logging message is constructed. That can still use %-formatting, as shown here:

>>> logger.error('This is an%s %s %s', 'other,', 'ERROR,', 'message')
+2010-10-28 15:19:29,833 foo.bar ERROR This is another, ERROR, message
+>>>
+

Logging calls (logger.debug(), logger.info() etc.) only take positional parameters for the actual logging message itself, with keyword parameters used only for determining options for how to handle the actual logging call (e.g. the exc_info keyword parameter to indicate that traceback information should be logged, or the extra keyword parameter to indicate additional contextual information to be added to the log). So you cannot directly make logging calls using str.format() or string.Template syntax, because internally the logging package uses %-formatting to merge the format string and the variable arguments. There would be no changing this while preserving backward compatibility, since all logging calls which are out there in existing code will be using %-format strings.

There is, however, a way that you can use {}- and $- formatting to construct your individual log messages. Recall that for a message you can use an arbitrary object as a message format string, and that the logging package will call str() on that object to get the actual format string. Consider the following two classes:

class BraceMessage:
+    def __init__(self, fmt, /, *args, **kwargs):
+        self.fmt = fmt
+        self.args = args
+        self.kwargs = kwargs
+
+    def __str__(self):
+        return self.fmt.format(*self.args, **self.kwargs)
+
+class DollarMessage:
+    def __init__(self, fmt, /, **kwargs):
+        self.fmt = fmt
+        self.kwargs = kwargs
+
+    def __str__(self):
+        from string import Template
+        return Template(self.fmt).substitute(**self.kwargs)
+

Either of these can be used in place of a format string, to allow {}- or $-formatting to be used to build the actual “message” part which appears in the formatted log output in place of “%(message)s” or “{message}” or “$message”. It’s a little unwieldy to use the class names whenever you want to log something, but it’s quite palatable if you use an alias such as __ (double underscore — not to be confused with _, the single underscore used as a synonym/alias for gettext.gettext() or its brethren).

The above classes are not included in Python, though they’re easy enough to copy and paste into your own code. They can be used as follows (assuming that they’re declared in a module called wherever):

>>> from wherever import BraceMessage as __
+>>> print(__('Message with {0} {name}', 2, name='placeholders'))
+Message with 2 placeholders
+>>> class Point: pass
+...
+>>> p = Point()
+>>> p.x = 0.5
+>>> p.y = 0.5
+>>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})',
+...       point=p))
+Message with coordinates: (0.50, 0.50)
+>>> from wherever import DollarMessage as __
+>>> print(__('Message with $num $what', num=2, what='placeholders'))
+Message with 2 placeholders
+>>>
+

While the above examples use print() to show how the formatting works, you would of course use logger.debug() or similar to actually log using this approach.

One thing to note is that you pay no significant performance penalty with this approach: the actual formatting happens not when you make the logging call, but when (and if) the logged message is actually about to be output to a log by a handler. So the only slightly unusual thing which might trip you up is that the parentheses go around the format string and the arguments, not just the format string. That’s because the __ notation is just syntax sugar for a constructor call to one of the XXXMessage classes.

If you prefer, you can use a LoggerAdapter to achieve a similar effect to the above, as in the following example:

import logging
+
+class Message:
+    def __init__(self, fmt, args):
+        self.fmt = fmt
+        self.args = args
+
+    def __str__(self):
+        return self.fmt.format(*self.args)
+
+class StyleAdapter(logging.LoggerAdapter):
+    def __init__(self, logger, extra=None):
+        super().__init__(logger, extra or {})
+
+    def log(self, level, msg, /, *args, **kwargs):
+        if self.isEnabledFor(level):
+            msg, kwargs = self.process(msg, kwargs)
+            self.logger._log(level, Message(msg, args), (), **kwargs)
+
+logger = StyleAdapter(logging.getLogger(__name__))
+
+def main():
+    logger.debug('Hello, {}', 'world!')
+
+if __name__ == '__main__':
+    logging.basicConfig(level=logging.DEBUG)
+    main()
+

The above script should log the message Hello, world! when run with Python 3.2 or later.

Customizing LogRecord +

Every logging event is represented by a LogRecord instance. When an event is logged and not filtered out by a logger’s level, a LogRecord is created, populated with information about the event and then passed to the handlers for that logger (and its ancestors, up to and including the logger where further propagation up the hierarchy is disabled). Before Python 3.2, there were only two places where this creation was done:

This has usually meant that if you need to do anything special with a LogRecord, you’ve had to do one of the following.

The first approach would be a little unwieldy in the scenario where (say) several different libraries wanted to do different things. Each would attempt to set its own Logger subclass, and the one which did this last would win.

The second approach works reasonably well for many cases, but does not allow you to e.g. use a specialized subclass of LogRecord. Library developers can set a suitable filter on their loggers, but they would have to remember to do this every time they introduced a new logger (which they would do simply by adding new packages or modules and doing

logger = logging.getLogger(__name__)
+

at module level). It’s probably one too many things to think about. Developers could also add the filter to a NullHandler attached to their top-level logger, but this would not be invoked if an application developer attached a handler to a lower-level library logger — so output from that handler would not reflect the intentions of the library developer.

In Python 3.2 and later, LogRecord creation is done through a factory, which you can specify. The factory is just a callable you can set with setLogRecordFactory(), and interrogate with getLogRecordFactory(). The factory is invoked with the same signature as the LogRecord constructor, as LogRecord is the default setting for the factory.

This approach allows a custom factory to control all aspects of LogRecord creation. For example, you could return a subclass, or just add some additional attributes to the record once created, using a pattern similar to this:

old_factory = logging.getLogRecordFactory()
+
+def record_factory(*args, **kwargs):
+    record = old_factory(*args, **kwargs)
+    record.custom_attribute = 0xdecafbad
+    return record
+
+logging.setLogRecordFactory(record_factory)
+

This pattern allows different libraries to chain factories together, and as long as they don’t overwrite each other’s attributes or unintentionally overwrite the attributes provided as standard, there should be no surprises. However, it should be borne in mind that each link in the chain adds run-time overhead to all logging operations, and the technique should only be used when the use of a Filter does not provide the desired result.

Subclassing QueueHandler - a ZeroMQ example

You can use a QueueHandler subclass to send messages to other kinds of queues, for example a ZeroMQ ‘publish’ socket. In the example below,the socket is created separately and passed to the handler (as its ‘queue’):

import zmq   # using pyzmq, the Python binding for ZeroMQ
+import json  # for serializing records portably
+
+ctx = zmq.Context()
+sock = zmq.Socket(ctx, zmq.PUB)  # or zmq.PUSH, or other suitable value
+sock.bind('tcp://*:5556')        # or wherever
+
+class ZeroMQSocketHandler(QueueHandler):
+    def enqueue(self, record):
+        self.queue.send_json(record.__dict__)
+
+
+handler = ZeroMQSocketHandler(sock)
+

Of course there are other ways of organizing this, for example passing in the data needed by the handler to create the socket:

class ZeroMQSocketHandler(QueueHandler):
+    def __init__(self, uri, socktype=zmq.PUB, ctx=None):
+        self.ctx = ctx or zmq.Context()
+        socket = zmq.Socket(self.ctx, socktype)
+        socket.bind(uri)
+        super().__init__(socket)
+
+    def enqueue(self, record):
+        self.queue.send_json(record.__dict__)
+
+    def close(self):
+        self.queue.close()
+

Subclassing QueueListener - a ZeroMQ example

You can also subclass QueueListener to get messages from other kinds of queues, for example a ZeroMQ ‘subscribe’ socket. Here’s an example:

class ZeroMQSocketListener(QueueListener):
+    def __init__(self, uri, /, *handlers, **kwargs):
+        self.ctx = kwargs.get('ctx') or zmq.Context()
+        socket = zmq.Socket(self.ctx, zmq.SUB)
+        socket.setsockopt_string(zmq.SUBSCRIBE, '')  # subscribe to everything
+        socket.connect(uri)
+        super().__init__(socket, *handlers, **kwargs)
+
+    def dequeue(self):
+        msg = self.queue.recv_json()
+        return logging.makeLogRecord(msg)
+

See also

+Module logging +
+
+

API reference for the logging module.

+Module logging.config +
+
+

Configuration API for the logging module.

+Module logging.handlers +
+
+

Useful handlers included with the logging module.

A basic logging tutorial

A more advanced logging tutorial

An example dictionary-based configuration

Below is an example of a logging configuration dictionary - it’s taken from the documentation on the Django project. This dictionary is passed to dictConfig() to put the configuration into effect:

LOGGING = {
+    'version': 1,
+    'disable_existing_loggers': True,
+    'formatters': {
+        'verbose': {
+            'format': '%(levelname)s %(asctime)s %(module)s %(process)d %(thread)d %(message)s'
+        },
+        'simple': {
+            'format': '%(levelname)s %(message)s'
+        },
+    },
+    'filters': {
+        'special': {
+            '()': 'project.logging.SpecialFilter',
+            'foo': 'bar',
+        }
+    },
+    'handlers': {
+        'null': {
+            'level':'DEBUG',
+            'class':'django.utils.log.NullHandler',
+        },
+        'console':{
+            'level':'DEBUG',
+            'class':'logging.StreamHandler',
+            'formatter': 'simple'
+        },
+        'mail_admins': {
+            'level': 'ERROR',
+            'class': 'django.utils.log.AdminEmailHandler',
+            'filters': ['special']
+        }
+    },
+    'loggers': {
+        'django': {
+            'handlers':['null'],
+            'propagate': True,
+            'level':'INFO',
+        },
+        'django.request': {
+            'handlers': ['mail_admins'],
+            'level': 'ERROR',
+            'propagate': False,
+        },
+        'myproject.custom': {
+            'handlers': ['console', 'mail_admins'],
+            'level': 'INFO',
+            'filters': ['special']
+        }
+    }
+}
+

For more information about this configuration, you can see the relevant section of the Django documentation.

Using a rotator and namer to customize log rotation processing

An example of how you can define a namer and rotator is given in the following runnable script, which shows gzip compression of the log file:

import gzip
+import logging
+import logging.handlers
+import os
+import shutil
+
+def namer(name):
+    return name + ".gz"
+
+def rotator(source, dest):
+    with open(source, 'rb') as f_in:
+        with gzip.open(dest, 'wb') as f_out:
+            shutil.copyfileobj(f_in, f_out)
+    os.remove(source)
+
+
+rh = logging.handlers.RotatingFileHandler('rotated.log', maxBytes=128, backupCount=5)
+rh.rotator = rotator
+rh.namer = namer
+
+root = logging.getLogger()
+root.setLevel(logging.INFO)
+root.addHandler(rh)
+f = logging.Formatter('%(asctime)s %(message)s')
+rh.setFormatter(f)
+for i in range(1000):
+    root.info(f'Message no. {i + 1}')
+

After running this, you will see six new files, five of which are compressed:

$ ls rotated.log*
+rotated.log       rotated.log.2.gz  rotated.log.4.gz
+rotated.log.1.gz  rotated.log.3.gz  rotated.log.5.gz
+$ zcat rotated.log.1.gz
+2023-01-20 02:28:17,767 Message no. 996
+2023-01-20 02:28:17,767 Message no. 997
+2023-01-20 02:28:17,767 Message no. 998
+

A more elaborate multiprocessing example

The following working example shows how logging can be used with multiprocessing using configuration files. The configurations are fairly simple, but serve to illustrate how more complex ones could be implemented in a real multiprocessing scenario.

In the example, the main process spawns a listener process and some worker processes. Each of the main process, the listener and the workers have three separate configurations (the workers all share the same configuration). We can see logging in the main process, how the workers log to a QueueHandler and how the listener implements a QueueListener and a more complex logging configuration, and arranges to dispatch events received via the queue to the handlers specified in the configuration. Note that these configurations are purely illustrative, but you should be able to adapt this example to your own scenario.

Here’s the script - the docstrings and the comments hopefully explain how it works:

import logging
+import logging.config
+import logging.handlers
+from multiprocessing import Process, Queue, Event, current_process
+import os
+import random
+import time
+
+class MyHandler:
+    """
+    A simple handler for logging events. It runs in the listener process and
+    dispatches events to loggers based on the name in the received record,
+    which then get dispatched, by the logging system, to the handlers
+    configured for those loggers.
+    """
+
+    def handle(self, record):
+        if record.name == "root":
+            logger = logging.getLogger()
+        else:
+            logger = logging.getLogger(record.name)
+
+        if logger.isEnabledFor(record.levelno):
+            # The process name is transformed just to show that it's the listener
+            # doing the logging to files and console
+            record.processName = '%s (for %s)' % (current_process().name, record.processName)
+            logger.handle(record)
+
+def listener_process(q, stop_event, config):
+    """
+    This could be done in the main process, but is just done in a separate
+    process for illustrative purposes.
+
+    This initialises logging according to the specified configuration,
+    starts the listener and waits for the main process to signal completion
+    via the event. The listener is then stopped, and the process exits.
+    """
+    logging.config.dictConfig(config)
+    listener = logging.handlers.QueueListener(q, MyHandler())
+    listener.start()
+    if os.name == 'posix':
+        # On POSIX, the setup logger will have been configured in the
+        # parent process, but should have been disabled following the
+        # dictConfig call.
+        # On Windows, since fork isn't used, the setup logger won't
+        # exist in the child, so it would be created and the message
+        # would appear - hence the "if posix" clause.
+        logger = logging.getLogger('setup')
+        logger.critical('Should not appear, because of disabled logger ...')
+    stop_event.wait()
+    listener.stop()
+
+def worker_process(config):
+    """
+    A number of these are spawned for the purpose of illustration. In
+    practice, they could be a heterogeneous bunch of processes rather than
+    ones which are identical to each other.
+
+    This initialises logging according to the specified configuration,
+    and logs a hundred messages with random levels to randomly selected
+    loggers.
+
+    A small sleep is added to allow other processes a chance to run. This
+    is not strictly needed, but it mixes the output from the different
+    processes a bit more than if it's left out.
+    """
+    logging.config.dictConfig(config)
+    levels = [logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
+              logging.CRITICAL]
+    loggers = ['foo', 'foo.bar', 'foo.bar.baz',
+               'spam', 'spam.ham', 'spam.ham.eggs']
+    if os.name == 'posix':
+        # On POSIX, the setup logger will have been configured in the
+        # parent process, but should have been disabled following the
+        # dictConfig call.
+        # On Windows, since fork isn't used, the setup logger won't
+        # exist in the child, so it would be created and the message
+        # would appear - hence the "if posix" clause.
+        logger = logging.getLogger('setup')
+        logger.critical('Should not appear, because of disabled logger ...')
+    for i in range(100):
+        lvl = random.choice(levels)
+        logger = logging.getLogger(random.choice(loggers))
+        logger.log(lvl, 'Message no. %d', i)
+        time.sleep(0.01)
+
+def main():
+    q = Queue()
+    # The main process gets a simple configuration which prints to the console.
+    config_initial = {
+        'version': 1,
+        'handlers': {
+            'console': {
+                'class': 'logging.StreamHandler',
+                'level': 'INFO'
+            }
+        },
+        'root': {
+            'handlers': ['console'],
+            'level': 'DEBUG'
+        }
+    }
+    # The worker process configuration is just a QueueHandler attached to the
+    # root logger, which allows all messages to be sent to the queue.
+    # We disable existing loggers to disable the "setup" logger used in the
+    # parent process. This is needed on POSIX because the logger will
+    # be there in the child following a fork().
+    config_worker = {
+        'version': 1,
+        'disable_existing_loggers': True,
+        'handlers': {
+            'queue': {
+                'class': 'logging.handlers.QueueHandler',
+                'queue': q
+            }
+        },
+        'root': {
+            'handlers': ['queue'],
+            'level': 'DEBUG'
+        }
+    }
+    # The listener process configuration shows that the full flexibility of
+    # logging configuration is available to dispatch events to handlers however
+    # you want.
+    # We disable existing loggers to disable the "setup" logger used in the
+    # parent process. This is needed on POSIX because the logger will
+    # be there in the child following a fork().
+    config_listener = {
+        'version': 1,
+        'disable_existing_loggers': True,
+        'formatters': {
+            'detailed': {
+                'class': 'logging.Formatter',
+                'format': '%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
+            },
+            'simple': {
+                'class': 'logging.Formatter',
+                'format': '%(name)-15s %(levelname)-8s %(processName)-10s %(message)s'
+            }
+        },
+        'handlers': {
+            'console': {
+                'class': 'logging.StreamHandler',
+                'formatter': 'simple',
+                'level': 'INFO'
+            },
+            'file': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog.log',
+                'mode': 'w',
+                'formatter': 'detailed'
+            },
+            'foofile': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog-foo.log',
+                'mode': 'w',
+                'formatter': 'detailed'
+            },
+            'errors': {
+                'class': 'logging.FileHandler',
+                'filename': 'mplog-errors.log',
+                'mode': 'w',
+                'formatter': 'detailed',
+                'level': 'ERROR'
+            }
+        },
+        'loggers': {
+            'foo': {
+                'handlers': ['foofile']
+            }
+        },
+        'root': {
+            'handlers': ['console', 'file', 'errors'],
+            'level': 'DEBUG'
+        }
+    }
+    # Log some initial events, just to show that logging in the parent works
+    # normally.
+    logging.config.dictConfig(config_initial)
+    logger = logging.getLogger('setup')
+    logger.info('About to create workers ...')
+    workers = []
+    for i in range(5):
+        wp = Process(target=worker_process, name='worker %d' % (i + 1),
+                     args=(config_worker,))
+        workers.append(wp)
+        wp.start()
+        logger.info('Started worker: %s', wp.name)
+    logger.info('About to create listener ...')
+    stop_event = Event()
+    lp = Process(target=listener_process, name='listener',
+                 args=(q, stop_event, config_listener))
+    lp.start()
+    logger.info('Started listener')
+    # We now hang around for the workers to finish their work.
+    for wp in workers:
+        wp.join()
+    # Workers all done, listening can now stop.
+    # Logging in the parent still works normally.
+    logger.info('Telling listener to stop ...')
+    stop_event.set()
+    lp.join()
+    logger.info('All done.')
+
+if __name__ == '__main__':
+    main()
+

Inserting a BOM into messages sent to a SysLogHandler

RFC 5424 requires that a Unicode message be sent to a syslog daemon as a set of bytes which have the following structure: an optional pure-ASCII component, followed by a UTF-8 Byte Order Mark (BOM), followed by Unicode encoded using UTF-8. (See the relevant section of the specification.)

In Python 3.1, code was added to SysLogHandler to insert a BOM into the message, but unfortunately, it was implemented incorrectly, with the BOM appearing at the beginning of the message and hence not allowing any pure-ASCII component to appear before it.

As this behaviour is broken, the incorrect BOM insertion code is being removed from Python 3.2.4 and later. However, it is not being replaced, and if you want to produce RFC 5424-compliant messages which include a BOM, an optional pure-ASCII sequence before it and arbitrary Unicode after it, encoded using UTF-8, then you need to do the following:

  1. +

    Attach a Formatter instance to your SysLogHandler instance, with a format string such as:

    'ASCII section\ufeffUnicode section'
+

    The Unicode code point U+FEFF, when encoded using UTF-8, will be encoded as a UTF-8 BOM – the byte-string b'\xef\xbb\xbf'.

  2. Replace the ASCII section with whatever placeholders you like, but make sure that the data that appears in there after substitution is always ASCII (that way, it will remain unchanged after UTF-8 encoding).
  3. Replace the Unicode section with whatever placeholders you like; if the data which appears there after substitution contains characters outside the ASCII range, that’s fine – it will be encoded using UTF-8.

The formatted message will be encoded using UTF-8 encoding by SysLogHandler. If you follow the above rules, you should be able to produce RFC 5424-compliant messages. If you don’t, logging may not complain, but your messages will not be RFC 5424-compliant, and your syslog daemon may complain.

Implementing structured logging

Although most logging messages are intended for reading by humans, and thus not readily machine-parseable, there might be circumstances where you want to output messages in a structured format which is capable of being parsed by a program (without needing complex regular expressions to parse the log message). This is straightforward to achieve using the logging package. There are a number of ways in which this could be achieved, but the following is a simple approach which uses JSON to serialise the event in a machine-parseable manner:

import json
+import logging
+
+class StructuredMessage:
+    def __init__(self, message, /, **kwargs):
+        self.message = message
+        self.kwargs = kwargs
+
+    def __str__(self):
+        return '%s >>> %s' % (self.message, json.dumps(self.kwargs))
+
+_ = StructuredMessage   # optional, to improve readability
+
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+logging.info(_('message 1', foo='bar', bar='baz', num=123, fnum=123.456))
+

If the above script is run, it prints:

message 1 >>> {"fnum": 123.456, "num": 123, "bar": "baz", "foo": "bar"}
+

Note that the order of items might be different according to the version of Python used.

If you need more specialised processing, you can use a custom JSON encoder, as in the following complete example:

import json
+import logging
+
+
+class Encoder(json.JSONEncoder):
+    def default(self, o):
+        if isinstance(o, set):
+            return tuple(o)
+        elif isinstance(o, str):
+            return o.encode('unicode_escape').decode('ascii')
+        return super().default(o)
+
+class StructuredMessage:
+    def __init__(self, message, /, **kwargs):
+        self.message = message
+        self.kwargs = kwargs
+
+    def __str__(self):
+        s = Encoder().encode(self.kwargs)
+        return '%s >>> %s' % (self.message, s)
+
+_ = StructuredMessage   # optional, to improve readability
+
+def main():
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+    logging.info(_('message 1', set_value={1, 2, 3}, snowman='\u2603'))
+
+if __name__ == '__main__':
+    main()
+

When the above script is run, it prints:

message 1 >>> {"snowman": "\u2603", "set_value": [1, 2, 3]}
+

Note that the order of items might be different according to the version of Python used.

Customizing handlers with dictConfig()

There are times when you want to customize logging handlers in particular ways, and if you use dictConfig() you may be able to do this without subclassing. As an example, consider that you may want to set the ownership of a log file. On POSIX, this is easily done using shutil.chown(), but the file handlers in the stdlib don’t offer built-in support. You can customize handler creation using a plain function such as:

def owned_file_handler(filename, mode='a', encoding=None, owner=None):
+    if owner:
+        if not os.path.exists(filename):
+            open(filename, 'a').close()
+        shutil.chown(filename, *owner)
+    return logging.FileHandler(filename, mode, encoding)
+

You can then specify, in a logging configuration passed to dictConfig(), that a logging handler be created by calling this function:

LOGGING = {
+    'version': 1,
+    'disable_existing_loggers': False,
+    'formatters': {
+        'default': {
+            'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
+        },
+    },
+    'handlers': {
+        'file':{
+            # The values below are popped from this dictionary and
+            # used to create the handler, set the handler's level and
+            # its formatter.
+            '()': owned_file_handler,
+            'level':'DEBUG',
+            'formatter': 'default',
+            # The values below are passed to the handler creator callable
+            # as keyword arguments.
+            'owner': ['pulse', 'pulse'],
+            'filename': 'chowntest.log',
+            'mode': 'w',
+            'encoding': 'utf-8',
+        },
+    },
+    'root': {
+        'handlers': ['file'],
+        'level': 'DEBUG',
+    },
+}
+

In this example I am setting the ownership using the pulse user and group, just for the purposes of illustration. Putting it together into a working script, chowntest.py:

import logging, logging.config, os, shutil
+
+def owned_file_handler(filename, mode='a', encoding=None, owner=None):
+    if owner:
+        if not os.path.exists(filename):
+            open(filename, 'a').close()
+        shutil.chown(filename, *owner)
+    return logging.FileHandler(filename, mode, encoding)
+
+LOGGING = {
+    'version': 1,
+    'disable_existing_loggers': False,
+    'formatters': {
+        'default': {
+            'format': '%(asctime)s %(levelname)s %(name)s %(message)s'
+        },
+    },
+    'handlers': {
+        'file':{
+            # The values below are popped from this dictionary and
+            # used to create the handler, set the handler's level and
+            # its formatter.
+            '()': owned_file_handler,
+            'level':'DEBUG',
+            'formatter': 'default',
+            # The values below are passed to the handler creator callable
+            # as keyword arguments.
+            'owner': ['pulse', 'pulse'],
+            'filename': 'chowntest.log',
+            'mode': 'w',
+            'encoding': 'utf-8',
+        },
+    },
+    'root': {
+        'handlers': ['file'],
+        'level': 'DEBUG',
+    },
+}
+
+logging.config.dictConfig(LOGGING)
+logger = logging.getLogger('mylogger')
+logger.debug('A debug message')
+

To run this, you will probably need to run as root:

$ sudo python3.3 chowntest.py
+$ cat chowntest.log
+2013-11-05 09:34:51,128 DEBUG mylogger A debug message
+$ ls -l chowntest.log
+-rw-r--r-- 1 pulse pulse 55 2013-11-05 09:34 chowntest.log
+

Note that this example uses Python 3.3 because that’s where shutil.chown() makes an appearance. This approach should work with any Python version that supports dictConfig() - namely, Python 2.7, 3.2 or later. With pre-3.3 versions, you would need to implement the actual ownership change using e.g. os.chown().

In practice, the handler-creating function may be in a utility module somewhere in your project. Instead of the line in the configuration:

'()': owned_file_handler,
+

you could use e.g.:

'()': 'ext://project.util.owned_file_handler',
+

where project.util can be replaced with the actual name of the package where the function resides. In the above working script, using 'ext://__main__.owned_file_handler' should work. Here, the actual callable is resolved by dictConfig() from the ext:// specification.

This example hopefully also points the way to how you could implement other types of file change - e.g. setting specific POSIX permission bits - in the same way, using os.chmod().

Of course, the approach could also be extended to types of handler other than a FileHandler - for example, one of the rotating file handlers, or a different type of handler altogether.

Using particular formatting styles throughout your application

In Python 3.2, the Formatter gained a style keyword parameter which, while defaulting to % for backward compatibility, allowed the specification of { or $ to support the formatting approaches supported by str.format() and string.Template. Note that this governs the formatting of logging messages for final output to logs, and is completely orthogonal to how an individual logging message is constructed.

Logging calls (debug(), info() etc.) only take positional parameters for the actual logging message itself, with keyword parameters used only for determining options for how to handle the logging call (e.g. the exc_info keyword parameter to indicate that traceback information should be logged, or the extra keyword parameter to indicate additional contextual information to be added to the log). So you cannot directly make logging calls using str.format() or string.Template syntax, because internally the logging package uses %-formatting to merge the format string and the variable arguments. There would be no changing this while preserving backward compatibility, since all logging calls which are out there in existing code will be using %-format strings.

There have been suggestions to associate format styles with specific loggers, but that approach also runs into backward compatibility problems because any existing code could be using a given logger name and using %-formatting.

For logging to work interoperably between any third-party libraries and your code, decisions about formatting need to be made at the level of the individual logging call. This opens up a couple of ways in which alternative formatting styles can be accommodated.

Using LogRecord factories

In Python 3.2, along with the Formatter changes mentioned above, the logging package gained the ability to allow users to set their own LogRecord subclasses, using the setLogRecordFactory() function. You can use this to set your own subclass of LogRecord, which does the Right Thing by overriding the getMessage() method. The base class implementation of this method is where the msg % args formatting happens, and where you can substitute your alternate formatting; however, you should be careful to support all formatting styles and allow %-formatting as the default, to ensure interoperability with other code. Care should also be taken to call str(self.msg), just as the base implementation does.

Refer to the reference documentation on setLogRecordFactory() and LogRecord for more information.

Using custom message objects

There is another, perhaps simpler way that you can use {}- and $- formatting to construct your individual log messages. You may recall (from Using arbitrary objects as messages) that when logging you can use an arbitrary object as a message format string, and that the logging package will call str() on that object to get the actual format string. Consider the following two classes:

class BraceMessage:
+    def __init__(self, fmt, /, *args, **kwargs):
+        self.fmt = fmt
+        self.args = args
+        self.kwargs = kwargs
+
+    def __str__(self):
+        return self.fmt.format(*self.args, **self.kwargs)
+
+class DollarMessage:
+    def __init__(self, fmt, /, **kwargs):
+        self.fmt = fmt
+        self.kwargs = kwargs
+
+    def __str__(self):
+        from string import Template
+        return Template(self.fmt).substitute(**self.kwargs)
+

Either of these can be used in place of a format string, to allow {}- or $-formatting to be used to build the actual “message” part which appears in the formatted log output in place of “%(message)s” or “{message}” or “$message”. If you find it a little unwieldy to use the class names whenever you want to log something, you can make it more palatable if you use an alias such as M or _ for the message (or perhaps __, if you are using _ for localization).

Examples of this approach are given below. Firstly, formatting with str.format():

>>> __ = BraceMessage
+>>> print(__('Message with {0} {1}', 2, 'placeholders'))
+Message with 2 placeholders
+>>> class Point: pass
+...
+>>> p = Point()
+>>> p.x = 0.5
+>>> p.y = 0.5
+>>> print(__('Message with coordinates: ({point.x:.2f}, {point.y:.2f})', point=p))
+Message with coordinates: (0.50, 0.50)
+

Secondly, formatting with string.Template:

>>> __ = DollarMessage
+>>> print(__('Message with $num $what', num=2, what='placeholders'))
+Message with 2 placeholders
+>>>
+

One thing to note is that you pay no significant performance penalty with this approach: the actual formatting happens not when you make the logging call, but when (and if) the logged message is actually about to be output to a log by a handler. So the only slightly unusual thing which might trip you up is that the parentheses go around the format string and the arguments, not just the format string. That’s because the __ notation is just syntax sugar for a constructor call to one of the XXXMessage classes shown above.

Configuring filters with dictConfig()

You can configure filters using dictConfig(), though it might not be obvious at first glance how to do it (hence this recipe). Since Filter is the only filter class included in the standard library, and it is unlikely to cater to many requirements (it’s only there as a base class), you will typically need to define your own Filter subclass with an overridden filter() method. To do this, specify the () key in the configuration dictionary for the filter, specifying a callable which will be used to create the filter (a class is the most obvious, but you can provide any callable which returns a Filter instance). Here is a complete example:

import logging
+import logging.config
+import sys
+
+class MyFilter(logging.Filter):
+    def __init__(self, param=None):
+        self.param = param
+
+    def filter(self, record):
+        if self.param is None:
+            allow = True
+        else:
+            allow = self.param not in record.msg
+        if allow:
+            record.msg = 'changed: ' + record.msg
+        return allow
+
+LOGGING = {
+    'version': 1,
+    'filters': {
+        'myfilter': {
+            '()': MyFilter,
+            'param': 'noshow',
+        }
+    },
+    'handlers': {
+        'console': {
+            'class': 'logging.StreamHandler',
+            'filters': ['myfilter']
+        }
+    },
+    'root': {
+        'level': 'DEBUG',
+        'handlers': ['console']
+    },
+}
+
+if __name__ == '__main__':
+    logging.config.dictConfig(LOGGING)
+    logging.debug('hello')
+    logging.debug('hello - noshow')
+

This example shows how you can pass configuration data to the callable which constructs the instance, in the form of keyword parameters. When run, the above script will print:

changed: hello
+

which shows that the filter is working as configured.

A couple of extra points to note:

Customized exception formatting

There might be times when you want to do customized exception formatting - for argument’s sake, let’s say you want exactly one line per logged event, even when exception information is present. You can do this with a custom formatter class, as shown in the following example:

import logging
+
+class OneLineExceptionFormatter(logging.Formatter):
+    def formatException(self, exc_info):
+        """
+        Format an exception so that it prints on a single line.
+        """
+        result = super().formatException(exc_info)
+        return repr(result)  # or format into one line however you want to
+
+    def format(self, record):
+        s = super().format(record)
+        if record.exc_text:
+            s = s.replace('\n', '') + '|'
+        return s
+
+def configure_logging():
+    fh = logging.FileHandler('output.txt', 'w')
+    f = OneLineExceptionFormatter('%(asctime)s|%(levelname)s|%(message)s|',
+                                  '%d/%m/%Y %H:%M:%S')
+    fh.setFormatter(f)
+    root = logging.getLogger()
+    root.setLevel(logging.DEBUG)
+    root.addHandler(fh)
+
+def main():
+    configure_logging()
+    logging.info('Sample message')
+    try:
+        x = 1 / 0
+    except ZeroDivisionError as e:
+        logging.exception('ZeroDivisionError: %s', e)
+
+if __name__ == '__main__':
+    main()
+

When run, this produces a file with exactly two lines:

28/01/2015 07:21:23|INFO|Sample message|
+28/01/2015 07:21:23|ERROR|ZeroDivisionError: integer division or modulo by zero|'Traceback (most recent call last):\n  File "logtest7.py", line 30, in main\n    x = 1 / 0\nZeroDivisionError: integer division or modulo by zero'|
+

While the above treatment is simplistic, it points the way to how exception information can be formatted to your liking. The traceback module may be helpful for more specialized needs.

Speaking logging messages

There might be situations when it is desirable to have logging messages rendered in an audible rather than a visible format. This is easy to do if you have text-to-speech (TTS) functionality available in your system, even if it doesn’t have a Python binding. Most TTS systems have a command line program you can run, and this can be invoked from a handler using subprocess. It’s assumed here that TTS command line programs won’t expect to interact with users or take a long time to complete, and that the frequency of logged messages will be not so high as to swamp the user with messages, and that it’s acceptable to have the messages spoken one at a time rather than concurrently, The example implementation below waits for one message to be spoken before the next is processed, and this might cause other handlers to be kept waiting. Here is a short example showing the approach, which assumes that the espeak TTS package is available:

import logging
+import subprocess
+import sys
+
+class TTSHandler(logging.Handler):
+    def emit(self, record):
+        msg = self.format(record)
+        # Speak slowly in a female English voice
+        cmd = ['espeak', '-s150', '-ven+f3', msg]
+        p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
+                             stderr=subprocess.STDOUT)
+        # wait for the program to finish
+        p.communicate()
+
+def configure_logging():
+    h = TTSHandler()
+    root = logging.getLogger()
+    root.addHandler(h)
+    # the default formatter just returns the message
+    root.setLevel(logging.DEBUG)
+
+def main():
+    logging.info('Hello')
+    logging.debug('Goodbye')
+
+if __name__ == '__main__':
+    configure_logging()
+    sys.exit(main())
+

When run, this script should say “Hello” and then “Goodbye” in a female voice.

The above approach can, of course, be adapted to other TTS systems and even other systems altogether which can process messages via external programs run from a command line.

Buffering logging messages and outputting them conditionally

There might be situations where you want to log messages in a temporary area and only output them if a certain condition occurs. For example, you may want to start logging debug events in a function, and if the function completes without errors, you don’t want to clutter the log with the collected debug information, but if there is an error, you want all the debug information to be output as well as the error.

Here is an example which shows how you could do this using a decorator for your functions where you want logging to behave this way. It makes use of the logging.handlers.MemoryHandler, which allows buffering of logged events until some condition occurs, at which point the buffered events are flushed - passed to another handler (the target handler) for processing. By default, the MemoryHandler flushed when its buffer gets filled up or an event whose level is greater than or equal to a specified threshold is seen. You can use this recipe with a more specialised subclass of MemoryHandler if you want custom flushing behavior.

The example script has a simple function, foo, which just cycles through all the logging levels, writing to sys.stderr to say what level it’s about to log at, and then actually logging a message at that level. You can pass a parameter to foo which, if true, will log at ERROR and CRITICAL levels - otherwise, it only logs at DEBUG, INFO and WARNING levels.

The script just arranges to decorate foo with a decorator which will do the conditional logging that’s required. The decorator takes a logger as a parameter and attaches a memory handler for the duration of the call to the decorated function. The decorator can be additionally parameterised using a target handler, a level at which flushing should occur, and a capacity for the buffer (number of records buffered). These default to a StreamHandler which writes to sys.stderr, logging.ERROR and 100 respectively.

Here’s the script:

import logging
+from logging.handlers import MemoryHandler
+import sys
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+def log_if_errors(logger, target_handler=None, flush_level=None, capacity=None):
+    if target_handler is None:
+        target_handler = logging.StreamHandler()
+    if flush_level is None:
+        flush_level = logging.ERROR
+    if capacity is None:
+        capacity = 100
+    handler = MemoryHandler(capacity, flushLevel=flush_level, target=target_handler)
+
+    def decorator(fn):
+        def wrapper(*args, **kwargs):
+            logger.addHandler(handler)
+            try:
+                return fn(*args, **kwargs)
+            except Exception:
+                logger.exception('call failed')
+                raise
+            finally:
+                super(MemoryHandler, handler).flush()
+                logger.removeHandler(handler)
+        return wrapper
+
+    return decorator
+
+def write_line(s):
+    sys.stderr.write('%s\n' % s)
+
+def foo(fail=False):
+    write_line('about to log at DEBUG ...')
+    logger.debug('Actually logged at DEBUG')
+    write_line('about to log at INFO ...')
+    logger.info('Actually logged at INFO')
+    write_line('about to log at WARNING ...')
+    logger.warning('Actually logged at WARNING')
+    if fail:
+        write_line('about to log at ERROR ...')
+        logger.error('Actually logged at ERROR')
+        write_line('about to log at CRITICAL ...')
+        logger.critical('Actually logged at CRITICAL')
+    return fail
+
+decorated_foo = log_if_errors(logger)(foo)
+
+if __name__ == '__main__':
+    logger.setLevel(logging.DEBUG)
+    write_line('Calling undecorated foo with False')
+    assert not foo(False)
+    write_line('Calling undecorated foo with True')
+    assert foo(True)
+    write_line('Calling decorated foo with False')
+    assert not decorated_foo(False)
+    write_line('Calling decorated foo with True')
+    assert decorated_foo(True)
+

When this script is run, the following output should be observed:

Calling undecorated foo with False
+about to log at DEBUG ...
+about to log at INFO ...
+about to log at WARNING ...
+Calling undecorated foo with True
+about to log at DEBUG ...
+about to log at INFO ...
+about to log at WARNING ...
+about to log at ERROR ...
+about to log at CRITICAL ...
+Calling decorated foo with False
+about to log at DEBUG ...
+about to log at INFO ...
+about to log at WARNING ...
+Calling decorated foo with True
+about to log at DEBUG ...
+about to log at INFO ...
+about to log at WARNING ...
+about to log at ERROR ...
+Actually logged at DEBUG
+Actually logged at INFO
+Actually logged at WARNING
+Actually logged at ERROR
+about to log at CRITICAL ...
+Actually logged at CRITICAL
+

As you can see, actual logging output only occurs when an event is logged whose severity is ERROR or greater, but in that case, any previous events at lower severities are also logged.

You can of course use the conventional means of decoration:

@log_if_errors(logger)
+def foo(fail=False):
+    ...
+

Sending logging messages to email, with buffering

To illustrate how you can send log messages via email, so that a set number of messages are sent per email, you can subclass BufferingHandler. In the following example, which you can adapt to suit your specific needs, a simple test harness is provided which allows you to run the script with command line arguments specifying what you typically need to send things via SMTP. (Run the downloaded script with the -h argument to see the required and optional arguments.)

import logging
+import logging.handlers
+import smtplib
+
+class BufferingSMTPHandler(logging.handlers.BufferingHandler):
+    def __init__(self, mailhost, port, username, password, fromaddr, toaddrs,
+                 subject, capacity):
+        logging.handlers.BufferingHandler.__init__(self, capacity)
+        self.mailhost = mailhost
+        self.mailport = port
+        self.username = username
+        self.password = password
+        self.fromaddr = fromaddr
+        if isinstance(toaddrs, str):
+            toaddrs = [toaddrs]
+        self.toaddrs = toaddrs
+        self.subject = subject
+        self.setFormatter(logging.Formatter("%(asctime)s %(levelname)-5s %(message)s"))
+
+    def flush(self):
+        if len(self.buffer) > 0:
+            try:
+                smtp = smtplib.SMTP(self.mailhost, self.mailport)
+                smtp.starttls()
+                smtp.login(self.username, self.password)
+                msg = "From: %s\r\nTo: %s\r\nSubject: %s\r\n\r\n" % (self.fromaddr, ','.join(self.toaddrs), self.subject)
+                for record in self.buffer:
+                    s = self.format(record)
+                    msg = msg + s + "\r\n"
+                smtp.sendmail(self.fromaddr, self.toaddrs, msg)
+                smtp.quit()
+            except Exception:
+                if logging.raiseExceptions:
+                    raise
+            self.buffer = []
+
+if __name__ == '__main__':
+    import argparse
+
+    ap = argparse.ArgumentParser()
+    aa = ap.add_argument
+    aa('host', metavar='HOST', help='SMTP server')
+    aa('--port', '-p', type=int, default=587, help='SMTP port')
+    aa('user', metavar='USER', help='SMTP username')
+    aa('password', metavar='PASSWORD', help='SMTP password')
+    aa('to', metavar='TO', help='Addressee for emails')
+    aa('sender', metavar='SENDER', help='Sender email address')
+    aa('--subject', '-s',
+       default='Test Logging email from Python logging module (buffering)',
+       help='Subject of email')
+    options = ap.parse_args()
+    logger = logging.getLogger()
+    logger.setLevel(logging.DEBUG)
+    h = BufferingSMTPHandler(options.host, options.port, options.user,
+                             options.password, options.sender,
+                             options.to, options.subject, 10)
+    logger.addHandler(h)
+    for i in range(102):
+        logger.info("Info index = %d", i)
+    h.flush()
+    h.close()
+

If you run this script and your SMTP server is correctly set up, you should find that it sends eleven emails to the addressee you specify. The first ten emails will each have ten log messages, and the eleventh will have two messages. That makes up 102 messages as specified in the script.

Formatting times using UTC (GMT) via configuration

Sometimes you want to format times using UTC, which can be done using a class such as UTCFormatter, shown below:

import logging
+import time
+
+class UTCFormatter(logging.Formatter):
+    converter = time.gmtime
+

and you can then use the UTCFormatter in your code instead of Formatter. If you want to do that via configuration, you can use the dictConfig() API with an approach illustrated by the following complete example:

import logging
+import logging.config
+import time
+
+class UTCFormatter(logging.Formatter):
+    converter = time.gmtime
+
+LOGGING = {
+    'version': 1,
+    'disable_existing_loggers': False,
+    'formatters': {
+        'utc': {
+            '()': UTCFormatter,
+            'format': '%(asctime)s %(message)s',
+        },
+        'local': {
+            'format': '%(asctime)s %(message)s',
+        }
+    },
+    'handlers': {
+        'console1': {
+            'class': 'logging.StreamHandler',
+            'formatter': 'utc',
+        },
+        'console2': {
+            'class': 'logging.StreamHandler',
+            'formatter': 'local',
+        },
+    },
+    'root': {
+        'handlers': ['console1', 'console2'],
+   }
+}
+
+if __name__ == '__main__':
+    logging.config.dictConfig(LOGGING)
+    logging.warning('The local time is %s', time.asctime())
+

When this script is run, it should print something like:

2015-10-17 12:53:29,501 The local time is Sat Oct 17 13:53:29 2015
+2015-10-17 13:53:29,501 The local time is Sat Oct 17 13:53:29 2015
+

showing how the time is formatted both as local time and UTC, one for each handler.

Using a context manager for selective logging

There are times when it would be useful to temporarily change the logging configuration and revert it back after doing something. For this, a context manager is the most obvious way of saving and restoring the logging context. Here is a simple example of such a context manager, which allows you to optionally change the logging level and add a logging handler purely in the scope of the context manager:

import logging
+import sys
+
+class LoggingContext:
+    def __init__(self, logger, level=None, handler=None, close=True):
+        self.logger = logger
+        self.level = level
+        self.handler = handler
+        self.close = close
+
+    def __enter__(self):
+        if self.level is not None:
+            self.old_level = self.logger.level
+            self.logger.setLevel(self.level)
+        if self.handler:
+            self.logger.addHandler(self.handler)
+
+    def __exit__(self, et, ev, tb):
+        if self.level is not None:
+            self.logger.setLevel(self.old_level)
+        if self.handler:
+            self.logger.removeHandler(self.handler)
+        if self.handler and self.close:
+            self.handler.close()
+        # implicit return of None => don't swallow exceptions
+

If you specify a level value, the logger’s level is set to that value in the scope of the with block covered by the context manager. If you specify a handler, it is added to the logger on entry to the block and removed on exit from the block. You can also ask the manager to close the handler for you on block exit - you could do this if you don’t need the handler any more.

To illustrate how it works, we can add the following block of code to the above:

if __name__ == '__main__':
+    logger = logging.getLogger('foo')
+    logger.addHandler(logging.StreamHandler())
+    logger.setLevel(logging.INFO)
+    logger.info('1. This should appear just once on stderr.')
+    logger.debug('2. This should not appear.')
+    with LoggingContext(logger, level=logging.DEBUG):
+        logger.debug('3. This should appear once on stderr.')
+    logger.debug('4. This should not appear.')
+    h = logging.StreamHandler(sys.stdout)
+    with LoggingContext(logger, level=logging.DEBUG, handler=h, close=True):
+        logger.debug('5. This should appear twice - once on stderr and once on stdout.')
+    logger.info('6. This should appear just once on stderr.')
+    logger.debug('7. This should not appear.')
+

We initially set the logger’s level to INFO, so message #1 appears and message #2 doesn’t. We then change the level to DEBUG temporarily in the following with block, and so message #3 appears. After the block exits, the logger’s level is restored to INFO and so message #4 doesn’t appear. In the next with block, we set the level to DEBUG again but also add a handler writing to sys.stdout. Thus, message #5 appears twice on the console (once via stderr and once via stdout). After the with statement’s completion, the status is as it was before so message #6 appears (like message #1) whereas message #7 doesn’t (just like message #2).

If we run the resulting script, the result is as follows:

$ python logctx.py
+1. This should appear just once on stderr.
+3. This should appear once on stderr.
+5. This should appear twice - once on stderr and once on stdout.
+5. This should appear twice - once on stderr and once on stdout.
+6. This should appear just once on stderr.
+

If we run it again, but pipe stderr to /dev/null, we see the following, which is the only message written to stdout:

$ python logctx.py 2>/dev/null
+5. This should appear twice - once on stderr and once on stdout.
+

Once again, but piping stdout to /dev/null, we get:

$ python logctx.py >/dev/null
+1. This should appear just once on stderr.
+3. This should appear once on stderr.
+5. This should appear twice - once on stderr and once on stdout.
+6. This should appear just once on stderr.
+

In this case, the message #5 printed to stdout doesn’t appear, as expected.

Of course, the approach described here can be generalised, for example to attach logging filters temporarily. Note that the above code works in Python 2 as well as Python 3.

A CLI application starter template

Here’s an example which shows how you can:

Suppose we have a command-line application whose job is to stop, start or restart some services. This could be organised for the purposes of illustration as a file app.py that is the main script for the application, with individual commands implemented in start.py, stop.py and restart.py. Suppose further that we want to control the verbosity of the application via a command-line argument, defaulting to logging.INFO. Here’s one way that app.py could be written:

import argparse
+import importlib
+import logging
+import os
+import sys
+
+def main(args=None):
+    scriptname = os.path.basename(__file__)
+    parser = argparse.ArgumentParser(scriptname)
+    levels = ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL')
+    parser.add_argument('--log-level', default='INFO', choices=levels)
+    subparsers = parser.add_subparsers(dest='command',
+                                       help='Available commands:')
+    start_cmd = subparsers.add_parser('start', help='Start a service')
+    start_cmd.add_argument('name', metavar='NAME',
+                           help='Name of service to start')
+    stop_cmd = subparsers.add_parser('stop',
+                                     help='Stop one or more services')
+    stop_cmd.add_argument('names', metavar='NAME', nargs='+',
+                          help='Name of service to stop')
+    restart_cmd = subparsers.add_parser('restart',
+                                        help='Restart one or more services')
+    restart_cmd.add_argument('names', metavar='NAME', nargs='+',
+                             help='Name of service to restart')
+    options = parser.parse_args()
+    # the code to dispatch commands could all be in this file. For the purposes
+    # of illustration only, we implement each command in a separate module.
+    try:
+        mod = importlib.import_module(options.command)
+        cmd = getattr(mod, 'command')
+    except (ImportError, AttributeError):
+        print('Unable to find the code for command \'%s\'' % options.command)
+        return 1
+    # Could get fancy here and load configuration from file or dictionary
+    logging.basicConfig(level=options.log_level,
+                        format='%(levelname)s %(name)s %(message)s')
+    cmd(options)
+
+if __name__ == '__main__':
+    sys.exit(main())
+

And the start, stop and restart commands can be implemented in separate modules, like so for starting:

# start.py
+import logging
+
+logger = logging.getLogger(__name__)
+
+def command(options):
+    logger.debug('About to start %s', options.name)
+    # actually do the command processing here ...
+    logger.info('Started the \'%s\' service.', options.name)
+

and thus for stopping:

# stop.py
+import logging
+
+logger = logging.getLogger(__name__)
+
+def command(options):
+    n = len(options.names)
+    if n == 1:
+        plural = ''
+        services = '\'%s\'' % options.names[0]
+    else:
+        plural = 's'
+        services = ', '.join('\'%s\'' % name for name in options.names)
+        i = services.rfind(', ')
+        services = services[:i] + ' and ' + services[i + 2:]
+    logger.debug('About to stop %s', services)
+    # actually do the command processing here ...
+    logger.info('Stopped the %s service%s.', services, plural)
+

and similarly for restarting:

# restart.py
+import logging
+
+logger = logging.getLogger(__name__)
+
+def command(options):
+    n = len(options.names)
+    if n == 1:
+        plural = ''
+        services = '\'%s\'' % options.names[0]
+    else:
+        plural = 's'
+        services = ', '.join('\'%s\'' % name for name in options.names)
+        i = services.rfind(', ')
+        services = services[:i] + ' and ' + services[i + 2:]
+    logger.debug('About to restart %s', services)
+    # actually do the command processing here ...
+    logger.info('Restarted the %s service%s.', services, plural)
+

If we run this application with the default log level, we get output like this:

$ python app.py start foo
+INFO start Started the 'foo' service.
+
+$ python app.py stop foo bar
+INFO stop Stopped the 'foo' and 'bar' services.
+
+$ python app.py restart foo bar baz
+INFO restart Restarted the 'foo', 'bar' and 'baz' services.
+

The first word is the logging level, and the second word is the module or package name of the place where the event was logged.

If we change the logging level, then we can change the information sent to the log. For example, if we want more information:

$ python app.py --log-level DEBUG start foo
+DEBUG start About to start foo
+INFO start Started the 'foo' service.
+
+$ python app.py --log-level DEBUG stop foo bar
+DEBUG stop About to stop 'foo' and 'bar'
+INFO stop Stopped the 'foo' and 'bar' services.
+
+$ python app.py --log-level DEBUG restart foo bar baz
+DEBUG restart About to restart 'foo', 'bar' and 'baz'
+INFO restart Restarted the 'foo', 'bar' and 'baz' services.
+

And if we want less:

$ python app.py --log-level WARNING start foo
+$ python app.py --log-level WARNING stop foo bar
+$ python app.py --log-level WARNING restart foo bar baz
+

In this case, the commands don’t print anything to the console, since nothing at WARNING level or above is logged by them.

A Qt GUI for logging

A question that comes up from time to time is about how to log to a GUI application. The Qt framework is a popular cross-platform UI framework with Python bindings using PySide2 or PyQt5 libraries.

The following example shows how to log to a Qt GUI. This introduces a simple QtHandler class which takes a callable, which should be a slot in the main thread that does GUI updates. A worker thread is also created to show how you can log to the GUI from both the UI itself (via a button for manual logging) as well as a worker thread doing work in the background (here, just logging messages at random levels with random short delays in between).

The worker thread is implemented using Qt’s QThread class rather than the threading module, as there are circumstances where one has to use QThread, which offers better integration with other Qt components.

The code should work with recent releases of either PySide2 or PyQt5. You should be able to adapt the approach to earlier versions of Qt. Please refer to the comments in the code snippet for more detailed information.

import datetime
+import logging
+import random
+import sys
+import time
+
+# Deal with minor differences between PySide2 and PyQt5
+try:
+    from PySide2 import QtCore, QtGui, QtWidgets
+    Signal = QtCore.Signal
+    Slot = QtCore.Slot
+except ImportError:
+    from PyQt5 import QtCore, QtGui, QtWidgets
+    Signal = QtCore.pyqtSignal
+    Slot = QtCore.pyqtSlot
+
+
+logger = logging.getLogger(__name__)
+
+
+#
+# Signals need to be contained in a QObject or subclass in order to be correctly
+# initialized.
+#
+class Signaller(QtCore.QObject):
+    signal = Signal(str, logging.LogRecord)
+
+#
+# Output to a Qt GUI is only supposed to happen on the main thread. So, this
+# handler is designed to take a slot function which is set up to run in the main
+# thread. In this example, the function takes a string argument which is a
+# formatted log message, and the log record which generated it. The formatted
+# string is just a convenience - you could format a string for output any way
+# you like in the slot function itself.
+#
+# You specify the slot function to do whatever GUI updates you want. The handler
+# doesn't know or care about specific UI elements.
+#
+class QtHandler(logging.Handler):
+    def __init__(self, slotfunc, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.signaller = Signaller()
+        self.signaller.signal.connect(slotfunc)
+
+    def emit(self, record):
+        s = self.format(record)
+        self.signaller.signal.emit(s, record)
+
+#
+# This example uses QThreads, which means that the threads at the Python level
+# are named something like "Dummy-1". The function below gets the Qt name of the
+# current thread.
+#
+def ctname():
+    return QtCore.QThread.currentThread().objectName()
+
+
+#
+# Used to generate random levels for logging.
+#
+LEVELS = (logging.DEBUG, logging.INFO, logging.WARNING, logging.ERROR,
+          logging.CRITICAL)
+
+#
+# This worker class represents work that is done in a thread separate to the
+# main thread. The way the thread is kicked off to do work is via a button press
+# that connects to a slot in the worker.
+#
+# Because the default threadName value in the LogRecord isn't much use, we add
+# a qThreadName which contains the QThread name as computed above, and pass that
+# value in an "extra" dictionary which is used to update the LogRecord with the
+# QThread name.
+#
+# This example worker just outputs messages sequentially, interspersed with
+# random delays of the order of a few seconds.
+#
+class Worker(QtCore.QObject):
+    @Slot()
+    def start(self):
+        extra = {'qThreadName': ctname() }
+        logger.debug('Started work', extra=extra)
+        i = 1
+        # Let the thread run until interrupted. This allows reasonably clean
+        # thread termination.
+        while not QtCore.QThread.currentThread().isInterruptionRequested():
+            delay = 0.5 + random.random() * 2
+            time.sleep(delay)
+            level = random.choice(LEVELS)
+            logger.log(level, 'Message after delay of %3.1f: %d', delay, i, extra=extra)
+            i += 1
+
+#
+# Implement a simple UI for this cookbook example. This contains:
+#
+# * A read-only text edit window which holds formatted log messages
+# * A button to start work and log stuff in a separate thread
+# * A button to log something from the main thread
+# * A button to clear the log window
+#
+class Window(QtWidgets.QWidget):
+
+    COLORS = {
+        logging.DEBUG: 'black',
+        logging.INFO: 'blue',
+        logging.WARNING: 'orange',
+        logging.ERROR: 'red',
+        logging.CRITICAL: 'purple',
+    }
+
+    def __init__(self, app):
+        super().__init__()
+        self.app = app
+        self.textedit = te = QtWidgets.QPlainTextEdit(self)
+        # Set whatever the default monospace font is for the platform
+        f = QtGui.QFont('nosuchfont')
+        f.setStyleHint(f.Monospace)
+        te.setFont(f)
+        te.setReadOnly(True)
+        PB = QtWidgets.QPushButton
+        self.work_button = PB('Start background work', self)
+        self.log_button = PB('Log a message at a random level', self)
+        self.clear_button = PB('Clear log window', self)
+        self.handler = h = QtHandler(self.update_status)
+        # Remember to use qThreadName rather than threadName in the format string.
+        fs = '%(asctime)s %(qThreadName)-12s %(levelname)-8s %(message)s'
+        formatter = logging.Formatter(fs)
+        h.setFormatter(formatter)
+        logger.addHandler(h)
+        # Set up to terminate the QThread when we exit
+        app.aboutToQuit.connect(self.force_quit)
+
+        # Lay out all the widgets
+        layout = QtWidgets.QVBoxLayout(self)
+        layout.addWidget(te)
+        layout.addWidget(self.work_button)
+        layout.addWidget(self.log_button)
+        layout.addWidget(self.clear_button)
+        self.setFixedSize(900, 400)
+
+        # Connect the non-worker slots and signals
+        self.log_button.clicked.connect(self.manual_update)
+        self.clear_button.clicked.connect(self.clear_display)
+
+        # Start a new worker thread and connect the slots for the worker
+        self.start_thread()
+        self.work_button.clicked.connect(self.worker.start)
+        # Once started, the button should be disabled
+        self.work_button.clicked.connect(lambda : self.work_button.setEnabled(False))
+
+    def start_thread(self):
+        self.worker = Worker()
+        self.worker_thread = QtCore.QThread()
+        self.worker.setObjectName('Worker')
+        self.worker_thread.setObjectName('WorkerThread')  # for qThreadName
+        self.worker.moveToThread(self.worker_thread)
+        # This will start an event loop in the worker thread
+        self.worker_thread.start()
+
+    def kill_thread(self):
+        # Just tell the worker to stop, then tell it to quit and wait for that
+        # to happen
+        self.worker_thread.requestInterruption()
+        if self.worker_thread.isRunning():
+            self.worker_thread.quit()
+            self.worker_thread.wait()
+        else:
+            print('worker has already exited.')
+
+    def force_quit(self):
+        # For use when the window is closed
+        if self.worker_thread.isRunning():
+            self.kill_thread()
+
+    # The functions below update the UI and run in the main thread because
+    # that's where the slots are set up
+
+    @Slot(str, logging.LogRecord)
+    def update_status(self, status, record):
+        color = self.COLORS.get(record.levelno, 'black')
+        s = '<pre><font color="%s">%s</font></pre>' % (color, status)
+        self.textedit.appendHtml(s)
+
+    @Slot()
+    def manual_update(self):
+        # This function uses the formatted message passed in, but also uses
+        # information from the record to format the message in an appropriate
+        # color according to its severity (level).
+        level = random.choice(LEVELS)
+        extra = {'qThreadName': ctname() }
+        logger.log(level, 'Manually logged!', extra=extra)
+
+    @Slot()
+    def clear_display(self):
+        self.textedit.clear()
+
+
+def main():
+    QtCore.QThread.currentThread().setObjectName('MainThread')
+    logging.getLogger().setLevel(logging.DEBUG)
+    app = QtWidgets.QApplication(sys.argv)
+    example = Window(app)
+    example.show()
+    sys.exit(app.exec_())
+
+if __name__=='__main__':
+    main()
+

Logging to syslog with RFC5424 support

Although RFC 5424 dates from 2009, most syslog servers are configured by default to use the older RFC 3164, which hails from 2001. When logging was added to Python in 2003, it supported the earlier (and only existing) protocol at the time. Since RFC5424 came out, as there has not been widespread deployment of it in syslog servers, the SysLogHandler functionality has not been updated.

RFC 5424 contains some useful features such as support for structured data, and if you need to be able to log to a syslog server with support for it, you can do so with a subclassed handler which looks something like this:

import datetime
+import logging.handlers
+import re
+import socket
+import time
+
+class SysLogHandler5424(logging.handlers.SysLogHandler):
+
+    tz_offset = re.compile(r'([+-]\d{2})(\d{2})$')
+    escaped = re.compile(r'([\]"\\])')
+
+    def __init__(self, *args, **kwargs):
+        self.msgid = kwargs.pop('msgid', None)
+        self.appname = kwargs.pop('appname', None)
+        super().__init__(*args, **kwargs)
+
+    def format(self, record):
+        version = 1
+        asctime = datetime.datetime.fromtimestamp(record.created).isoformat()
+        m = self.tz_offset.match(time.strftime('%z'))
+        has_offset = False
+        if m and time.timezone:
+            hrs, mins = m.groups()
+            if int(hrs) or int(mins):
+                has_offset = True
+        if not has_offset:
+            asctime += 'Z'
+        else:
+            asctime += f'{hrs}:{mins}'
+        try:
+            hostname = socket.gethostname()
+        except Exception:
+            hostname = '-'
+        appname = self.appname or '-'
+        procid = record.process
+        msgid = '-'
+        msg = super().format(record)
+        sdata = '-'
+        if hasattr(record, 'structured_data'):
+            sd = record.structured_data
+            # This should be a dict where the keys are SD-ID and the value is a
+            # dict mapping PARAM-NAME to PARAM-VALUE (refer to the RFC for what these
+            # mean)
+            # There's no error checking here - it's purely for illustration, and you
+            # can adapt this code for use in production environments
+            parts = []
+
+            def replacer(m):
+                g = m.groups()
+                return '\\' + g[0]
+
+            for sdid, dv in sd.items():
+                part = f'[{sdid}'
+                for k, v in dv.items():
+                    s = str(v)
+                    s = self.escaped.sub(replacer, s)
+                    part += f' {k}="{s}"'
+                part += ']'
+                parts.append(part)
+            sdata = ''.join(parts)
+        return f'{version} {asctime} {hostname} {appname} {procid} {msgid} {sdata} {msg}'
+

You’ll need to be familiar with RFC 5424 to fully understand the above code, and it may be that you have slightly different needs (e.g. for how you pass structural data to the log). Nevertheless, the above should be adaptable to your speciric needs. With the above handler, you’d pass structured data using something like this:

sd = {
+    'foo@12345': {'bar': 'baz', 'baz': 'bozz', 'fizz': r'buzz'},
+    'foo@54321': {'rab': 'baz', 'zab': 'bozz', 'zzif': r'buzz'}
+}
+extra = {'structured_data': sd}
+i = 1
+logger.debug('Message %d', i, extra=extra)
+

How to treat a logger like an output stream

Sometimes, you need to interface to a third-party API which expects a file-like object to write to, but you want to direct the API’s output to a logger. You can do this using a class which wraps a logger with a file-like API. Here’s a short script illustrating such a class:

import logging
+
+class LoggerWriter:
+    def __init__(self, logger, level):
+        self.logger = logger
+        self.level = level
+
+    def write(self, message):
+        if message != '\n':  # avoid printing bare newlines, if you like
+            self.logger.log(self.level, message)
+
+    def flush(self):
+        # doesn't actually do anything, but might be expected of a file-like
+        # object - so optional depending on your situation
+        pass
+
+    def close(self):
+        # doesn't actually do anything, but might be expected of a file-like
+        # object - so optional depending on your situation. You might want
+        # to set a flag so that later calls to write raise an exception
+        pass
+
+def main():
+    logging.basicConfig(level=logging.DEBUG)
+    logger = logging.getLogger('demo')
+    info_fp = LoggerWriter(logger, logging.INFO)
+    debug_fp = LoggerWriter(logger, logging.DEBUG)
+    print('An INFO message', file=info_fp)
+    print('A DEBUG message', file=debug_fp)
+
+if __name__ == "__main__":
+    main()
+

When this script is run, it prints

INFO:demo:An INFO message
+DEBUG:demo:A DEBUG message
+

You could also use LoggerWriter to redirect sys.stdout and sys.stderr by doing something like this:

import sys
+
+sys.stdout = LoggerWriter(logger, logging.INFO)
+sys.stderr = LoggerWriter(logger, logging.WARNING)
+

You should do this after configuring logging for your needs. In the above example, the basicConfig() call does this (using the sys.stderr value before it is overwritten by a LoggerWriter instance). Then, you’d get this kind of result:

>>> print('Foo')
+INFO:demo:Foo
+>>> print('Bar', file=sys.stderr)
+WARNING:demo:Bar
+>>>
+

Of course, the examples above show output according to the format used by basicConfig(), but you can use a different formatter when you configure logging.

Note that with the above scheme, you are somewhat at the mercy of buffering and the sequence of write calls which you are intercepting. For example, with the definition of LoggerWriter above, if you have the snippet

sys.stderr = LoggerWriter(logger, logging.WARNING)
+1 / 0
+

then running the script results in

WARNING:demo:Traceback (most recent call last):
+
+WARNING:demo:  File "/home/runner/cookbook-loggerwriter/test.py", line 53, in <module>
+
+WARNING:demo:
+WARNING:demo:main()
+WARNING:demo:  File "/home/runner/cookbook-loggerwriter/test.py", line 49, in main
+
+WARNING:demo:
+WARNING:demo:1 / 0
+WARNING:demo:ZeroDivisionError
+WARNING:demo::
+WARNING:demo:division by zero
+

As you can see, this output isn’t ideal. That’s because the underlying code which writes to sys.stderr makes multiple writes, each of which results in a separate logged line (for example, the last three lines above). To get around this problem, you need to buffer things and only output log lines when newlines are seen. Let’s use a slghtly better implementation of LoggerWriter:

class BufferingLoggerWriter(LoggerWriter):
+    def __init__(self, logger, level):
+        super().__init__(logger, level)
+        self.buffer = ''
+
+    def write(self, message):
+        if '\n' not in message:
+            self.buffer += message
+        else:
+            parts = message.split('\n')
+            if self.buffer:
+                s = self.buffer + parts.pop(0)
+                self.logger.log(self.level, s)
+            self.buffer = parts.pop()
+            for part in parts:
+                self.logger.log(self.level, part)
+

This just buffers up stuff until a newline is seen, and then logs complete lines. With this approach, you get better output:

WARNING:demo:Traceback (most recent call last):
+WARNING:demo:  File "/home/runner/cookbook-loggerwriter/main.py", line 55, in <module>
+WARNING:demo:    main()
+WARNING:demo:  File "/home/runner/cookbook-loggerwriter/main.py", line 52, in main
+WARNING:demo:    1/0
+WARNING:demo:ZeroDivisionError: division by zero
+

Patterns to avoid

Although the preceding sections have described ways of doing things you might need to do or deal with, it is worth mentioning some usage patterns which are unhelpful, and which should therefore be avoided in most cases. The following sections are in no particular order.

Opening the same log file multiple times

On Windows, you will generally not be able to open the same file multiple times as this will lead to a “file is in use by another process” error. However, on POSIX platforms you’ll not get any errors if you open the same file multiple times. This could be done accidentally, for example by:

  • Adding a file handler more than once which references the same file (e.g. by a copy/paste/forget-to-change error).
  • Opening two files that look different, as they have different names, but are the same because one is a symbolic link to the other.
  • Forking a process, following which both parent and child have a reference to the same file. This might be through use of the multiprocessing module, for example.

Opening a file multiple times might appear to work most of the time, but can lead to a number of problems in practice:

  • Logging output can be garbled because multiple threads or processes try to write to the same file. Although logging guards against concurrent use of the same handler instance by multiple threads, there is no such protection if concurrent writes are attempted by two different threads using two different handler instances which happen to point to the same file.
  • An attempt to delete a file (e.g. during file rotation) silently fails, because there is another reference pointing to it. This can lead to confusion and wasted debugging time - log entries end up in unexpected places, or are lost altogether. Or a file that was supposed to be moved remains in place, and grows in size unexpectedly despite size-based rotation being supposedly in place.

Use the techniques outlined in Logging to a single file from multiple processes to circumvent such issues.

Using loggers as attributes in a class or passing them as parameters

While there might be unusual cases where you’ll need to do this, in general there is no point because loggers are singletons. Code can always access a given logger instance by name using logging.getLogger(name), so passing instances around and holding them as instance attributes is pointless. Note that in other languages such as Java and C#, loggers are often static class attributes. However, this pattern doesn’t make sense in Python, where the module (and not the class) is the unit of software decomposition.

Adding handlers other than NullHandler to a logger in a library

Configuring logging by adding handlers, formatters and filters is the responsibility of the application developer, not the library developer. If you are maintaining a library, ensure that you don’t add handlers to any of your loggers other than a NullHandler instance.

Creating a lot of loggers

Loggers are singletons that are never freed during a script execution, and so creating lots of loggers will use up memory which can’t then be freed. Rather than create a logger per e.g. file processed or network connection made, use the existing mechanisms for passing contextual information into your logs and restrict the loggers created to those describing areas within your application (generally modules, but occasionally slightly more fine-grained than that).

Other resources

See also

+Module logging +
+
+

API reference for the logging module.

+Module logging.config +
+
+

Configuration API for the logging module.

+Module logging.handlers +
+
+

Useful handlers included with the logging module.

Basic Tutorial

Advanced Tutorial

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/logging-cookbook.html +

+
diff --git a/devdocs/python~3.12/howto%2Flogging.html b/devdocs/python~3.12/howto%2Flogging.html new file mode 100644 index 00000000..ac5c2d73 --- /dev/null +++ b/devdocs/python~3.12/howto%2Flogging.html @@ -0,0 +1,238 @@ +

Logging HOWTO

Author
+

Vinay Sajip <vinay_sajip at red-dove dot com>

This page contains tutorial information. For links to reference information and a logging cookbook, please see Other resources.

Basic Logging Tutorial

Logging is a means of tracking events that happen when some software runs. The software’s developer adds logging calls to their code to indicate that certain events have occurred. An event is described by a descriptive message which can optionally contain variable data (i.e. data that is potentially different for each occurrence of the event). Events also have an importance which the developer ascribes to the event; the importance can also be called the level or severity.

When to use logging

Logging provides a set of convenience functions for simple logging usage. These are debug(), info(), warning(), error() and critical(). To determine when to use logging, see the table below, which states, for each of a set of common tasks, the best tool to use for it.

+ + + + + +

Task you want to perform

The best tool for the task

Display console output for ordinary usage of a command line script or program

print()

Report events that occur during normal operation of a program (e.g. for status monitoring or fault investigation)

logging.info() (or logging.debug() for very detailed output for diagnostic purposes)

Issue a warning regarding a particular runtime event

 +

warnings.warn() in library code if the issue is avoidable and the client application should be modified to eliminate the warning

logging.warning() if there is nothing the client application can do about the situation, but the event should still be noted

Report an error regarding a particular runtime event

Raise an exception

Report suppression of an error without raising an exception (e.g. error handler in a long-running server process)

logging.error(), logging.exception() or logging.critical() as appropriate for the specific error and application domain

The logging functions are named after the level or severity of the events they are used to track. The standard levels and their applicability are described below (in increasing order of severity):

+ + + + + +

Level

When it’s used

DEBUG

Detailed information, typically of interest only when diagnosing problems.

INFO

Confirmation that things are working as expected.

WARNING

An indication that something unexpected happened, or indicative of some problem in the near future (e.g. ‘disk space low’). The software is still working as expected.

ERROR

Due to a more serious problem, the software has not been able to perform some function.

CRITICAL

A serious error, indicating that the program itself may be unable to continue running.

The default level is WARNING, which means that only events of this level and above will be tracked, unless the logging package is configured to do otherwise.

Events that are tracked can be handled in different ways. The simplest way of handling tracked events is to print them to the console. Another common way is to write them to a disk file.

A simple example

A very simple example is:

import logging
+logging.warning('Watch out!')  # will print a message to the console
+logging.info('I told you so')  # will not print anything
+

If you type these lines into a script and run it, you’ll see:

WARNING:root:Watch out!
+

printed out on the console. The INFO message doesn’t appear because the default level is WARNING. The printed message includes the indication of the level and the description of the event provided in the logging call, i.e. ‘Watch out!’. Don’t worry about the ‘root’ part for now: it will be explained later. The actual output can be formatted quite flexibly if you need that; formatting options will also be explained later.

Logging to a file

A very common situation is that of recording logging events in a file, so let’s look at that next. Be sure to try the following in a newly started Python interpreter, and don’t just continue from the session described above:

import logging
+logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.DEBUG)
+logging.debug('This message should go to the log file')
+logging.info('So should this')
+logging.warning('And this, too')
+logging.error('And non-ASCII stuff, too, like Øresund and Malmö')
+

Changed in version 3.9: The encoding argument was added. In earlier Python versions, or if not specified, the encoding used is the default value used by open(). While not shown in the above example, an errors argument can also now be passed, which determines how encoding errors are handled. For available values and the default, see the documentation for open().

And now if we open the file and look at what we have, we should find the log messages:

DEBUG:root:This message should go to the log file
+INFO:root:So should this
+WARNING:root:And this, too
+ERROR:root:And non-ASCII stuff, too, like Øresund and Malmö
+

This example also shows how you can set the logging level which acts as the threshold for tracking. In this case, because we set the threshold to DEBUG, all of the messages were printed.

If you want to set the logging level from a command-line option such as:

--log=INFO
+

and you have the value of the parameter passed for --log in some variable loglevel, you can use:

getattr(logging, loglevel.upper())
+

to get the value which you’ll pass to basicConfig() via the level argument. You may want to error check any user input value, perhaps as in the following example:

# assuming loglevel is bound to the string value obtained from the
+# command line argument. Convert to upper case to allow the user to
+# specify --log=DEBUG or --log=debug
+numeric_level = getattr(logging, loglevel.upper(), None)
+if not isinstance(numeric_level, int):
+    raise ValueError('Invalid log level: %s' % loglevel)
+logging.basicConfig(level=numeric_level, ...)
+

The call to basicConfig() should come before any calls to debug(), info(), etc. Otherwise, those functions will call basicConfig() for you with the default options. As it’s intended as a one-off simple configuration facility, only the first call will actually do anything: subsequent calls are effectively no-ops.

If you run the above script several times, the messages from successive runs are appended to the file example.log. If you want each run to start afresh, not remembering the messages from earlier runs, you can specify the filemode argument, by changing the call in the above example to:

logging.basicConfig(filename='example.log', filemode='w', level=logging.DEBUG)
+

The output will be the same as before, but the log file is no longer appended to, so the messages from earlier runs are lost.

Logging from multiple modules

If your program consists of multiple modules, here’s an example of how you could organize logging in it:

# myapp.py
+import logging
+import mylib
+
+def main():
+    logging.basicConfig(filename='myapp.log', level=logging.INFO)
+    logging.info('Started')
+    mylib.do_something()
+    logging.info('Finished')
+
+if __name__ == '__main__':
+    main()
+
# mylib.py
+import logging
+
+def do_something():
+    logging.info('Doing something')
+

If you run myapp.py, you should see this in myapp.log:

INFO:root:Started
+INFO:root:Doing something
+INFO:root:Finished
+

which is hopefully what you were expecting to see. You can generalize this to multiple modules, using the pattern in mylib.py. Note that for this simple usage pattern, you won’t know, by looking in the log file, where in your application your messages came from, apart from looking at the event description. If you want to track the location of your messages, you’ll need to refer to the documentation beyond the tutorial level – see Advanced Logging Tutorial.

Logging variable data

To log variable data, use a format string for the event description message and append the variable data as arguments. For example:

import logging
+logging.warning('%s before you %s', 'Look', 'leap!')
+

will display:

WARNING:root:Look before you leap!
+

As you can see, merging of variable data into the event description message uses the old, %-style of string formatting. This is for backwards compatibility: the logging package pre-dates newer formatting options such as str.format() and string.Template. These newer formatting options are supported, but exploring them is outside the scope of this tutorial: see Using particular formatting styles throughout your application for more information.

Changing the format of displayed messages

To change the format which is used to display messages, you need to specify the format you want to use:

import logging
+logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
+logging.debug('This message should appear on the console')
+logging.info('So should this')
+logging.warning('And this, too')
+

which would print:

DEBUG:This message should appear on the console
+INFO:So should this
+WARNING:And this, too
+

Notice that the ‘root’ which appeared in earlier examples has disappeared. For a full set of things that can appear in format strings, you can refer to the documentation for LogRecord attributes, but for simple usage, you just need the levelname (severity), message (event description, including variable data) and perhaps to display when the event occurred. This is described in the next section.

Displaying the date/time in messages

To display the date and time of an event, you would place ‘%(asctime)s’ in your format string:

import logging
+logging.basicConfig(format='%(asctime)s %(message)s')
+logging.warning('is when this event was logged.')
+

which should print something like this:

2010-12-12 11:41:42,612 is when this event was logged.
+

The default format for date/time display (shown above) is like ISO8601 or RFC 3339. If you need more control over the formatting of the date/time, provide a datefmt argument to basicConfig, as in this example:

import logging
+logging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')
+logging.warning('is when this event was logged.')
+

which would display something like this:

12/12/2010 11:46:36 AM is when this event was logged.
+

The format of the datefmt argument is the same as supported by time.strftime().

Next Steps

That concludes the basic tutorial. It should be enough to get you up and running with logging. There’s a lot more that the logging package offers, but to get the best out of it, you’ll need to invest a little more of your time in reading the following sections. If you’re ready for that, grab some of your favourite beverage and carry on.

If your logging needs are simple, then use the above examples to incorporate logging into your own scripts, and if you run into problems or don’t understand something, please post a question on the comp.lang.python Usenet group (available at https://groups.google.com/g/comp.lang.python) and you should receive help before too long.

Still here? You can carry on reading the next few sections, which provide a slightly more advanced/in-depth tutorial than the basic one above. After that, you can take a look at the Logging Cookbook.

Advanced Logging Tutorial

The logging library takes a modular approach and offers several categories of components: loggers, handlers, filters, and formatters.

Log event information is passed between loggers, handlers, filters and formatters in a LogRecord instance.

Logging is performed by calling methods on instances of the Logger class (hereafter called loggers). Each instance has a name, and they are conceptually arranged in a namespace hierarchy using dots (periods) as separators. For example, a logger named ‘scan’ is the parent of loggers ‘scan.text’, ‘scan.html’ and ‘scan.pdf’. Logger names can be anything you want, and indicate the area of an application in which a logged message originates.

A good convention to use when naming loggers is to use a module-level logger, in each module which uses logging, named as follows:

logger = logging.getLogger(__name__)
+

This means that logger names track the package/module hierarchy, and it’s intuitively obvious where events are logged just from the logger name.

The root of the hierarchy of loggers is called the root logger. That’s the logger used by the functions debug(), info(), warning(), error() and critical(), which just call the same-named method of the root logger. The functions and the methods have the same signatures. The root logger’s name is printed as ‘root’ in the logged output.

It is, of course, possible to log messages to different destinations. Support is included in the package for writing log messages to files, HTTP GET/POST locations, email via SMTP, generic sockets, queues, or OS-specific logging mechanisms such as syslog or the Windows NT event log. Destinations are served by handler classes. You can create your own log destination class if you have special requirements not met by any of the built-in handler classes.

By default, no destination is set for any logging messages. You can specify a destination (such as console or file) by using basicConfig() as in the tutorial examples. If you call the functions debug(), info(), warning(), error() and critical(), they will check to see if no destination is set; and if one is not set, they will set a destination of the console (sys.stderr) and a default format for the displayed message before delegating to the root logger to do the actual message output.

The default format set by basicConfig() for messages is:

severity:logger name:message
+

You can change this by passing a format string to basicConfig() with the format keyword argument. For all options regarding how a format string is constructed, see Formatter Objects.

Logging Flow

The flow of log event information in loggers and handlers is illustrated in the following diagram.

../_images/logging_flow.png

Loggers

Logger objects have a threefold job. First, they expose several methods to application code so that applications can log messages at runtime. Second, logger objects determine which log messages to act upon based upon severity (the default filtering facility) or filter objects. Third, logger objects pass along relevant log messages to all interested log handlers.

The most widely used methods on logger objects fall into two categories: configuration and message sending.

These are the most common configuration methods:

  • +Logger.setLevel() specifies the lowest-severity log message a logger will handle, where debug is the lowest built-in severity level and critical is the highest built-in severity. For example, if the severity level is INFO, the logger will handle only INFO, WARNING, ERROR, and CRITICAL messages and will ignore DEBUG messages.
  • +Logger.addHandler() and Logger.removeHandler() add and remove handler objects from the logger object. Handlers are covered in more detail in Handlers.
  • +Logger.addFilter() and Logger.removeFilter() add and remove filter objects from the logger object. Filters are covered in more detail in Filter Objects.

You don’t need to always call these methods on every logger you create. See the last two paragraphs in this section.

With the logger object configured, the following methods create log messages:

  • +Logger.debug(), Logger.info(), Logger.warning(), Logger.error(), and Logger.critical() all create log records with a message and a level that corresponds to their respective method names. The message is actually a format string, which may contain the standard string substitution syntax of %s, %d, %f, and so on. The rest of their arguments is a list of objects that correspond with the substitution fields in the message. With regard to **kwargs, the logging methods care only about a keyword of exc_info and use it to determine whether to log exception information.
  • +Logger.exception() creates a log message similar to Logger.error(). The difference is that Logger.exception() dumps a stack trace along with it. Call this method only from an exception handler.
  • +Logger.log() takes a log level as an explicit argument. This is a little more verbose for logging messages than using the log level convenience methods listed above, but this is how to log at custom log levels.

getLogger() returns a reference to a logger instance with the specified name if it is provided, or root if not. The names are period-separated hierarchical structures. Multiple calls to getLogger() with the same name will return a reference to the same logger object. Loggers that are further down in the hierarchical list are children of loggers higher up in the list. For example, given a logger with a name of foo, loggers with names of foo.bar, foo.bar.baz, and foo.bam are all descendants of foo.

Loggers have a concept of effective level. If a level is not explicitly set on a logger, the level of its parent is used instead as its effective level. If the parent has no explicit level set, its parent is examined, and so on - all ancestors are searched until an explicitly set level is found. The root logger always has an explicit level set (WARNING by default). When deciding whether to process an event, the effective level of the logger is used to determine whether the event is passed to the logger’s handlers.

Child loggers propagate messages up to the handlers associated with their ancestor loggers. Because of this, it is unnecessary to define and configure handlers for all the loggers an application uses. It is sufficient to configure handlers for a top-level logger and create child loggers as needed. (You can, however, turn off propagation by setting the propagate attribute of a logger to False.)

Handlers

Handler objects are responsible for dispatching the appropriate log messages (based on the log messages’ severity) to the handler’s specified destination. Logger objects can add zero or more handler objects to themselves with an addHandler() method. As an example scenario, an application may want to send all log messages to a log file, all log messages of error or higher to stdout, and all messages of critical to an email address. This scenario requires three individual handlers where each handler is responsible for sending messages of a specific severity to a specific location.

The standard library includes quite a few handler types (see Useful Handlers); the tutorials use mainly StreamHandler and FileHandler in its examples.

There are very few methods in a handler for application developers to concern themselves with. The only handler methods that seem relevant for application developers who are using the built-in handler objects (that is, not creating custom handlers) are the following configuration methods:

  • The setLevel() method, just as in logger objects, specifies the lowest severity that will be dispatched to the appropriate destination. Why are there two setLevel() methods? The level set in the logger determines which severity of messages it will pass to its handlers. The level set in each handler determines which messages that handler will send on.
  • +setFormatter() selects a Formatter object for this handler to use.
  • +addFilter() and removeFilter() respectively configure and deconfigure filter objects on handlers.

Application code should not directly instantiate and use instances of Handler. Instead, the Handler class is a base class that defines the interface that all handlers should have and establishes some default behavior that child classes can use (or override).

Formatters

Formatter objects configure the final order, structure, and contents of the log message. Unlike the base logging.Handler class, application code may instantiate formatter classes, although you could likely subclass the formatter if your application needs special behavior. The constructor takes three optional arguments – a message format string, a date format string and a style indicator.

+logging.Formatter.__init__(fmt=None, datefmt=None, style='%')
+

If there is no message format string, the default is to use the raw message. If there is no date format string, the default date format is:

%Y-%m-%d %H:%M:%S
+

with the milliseconds tacked on at the end. The style is one of '%', '{', or '$'. If one of these is not specified, then '%' will be used.

If the style is '%', the message format string uses %(<dictionary key>)s styled string substitution; the possible keys are documented in LogRecord attributes. If the style is '{', the message format string is assumed to be compatible with str.format() (using keyword arguments), while if the style is '$' then the message format string should conform to what is expected by string.Template.substitute().

Changed in version 3.2: Added the style parameter.

The following message format string will log the time in a human-readable format, the severity of the message, and the contents of the message, in that order:

'%(asctime)s - %(levelname)s - %(message)s'
+

Formatters use a user-configurable function to convert the creation time of a record to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute of the instance to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class (to time.gmtime for GMT display).

Configuring Logging

Programmers can configure logging in three ways:

  1. Creating loggers, handlers, and formatters explicitly using Python code that calls the configuration methods listed above.
  2. Creating a logging config file and reading it using the fileConfig() function.
  3. Creating a dictionary of configuration information and passing it to the dictConfig() function.

For the reference documentation on the last two options, see Configuration functions. The following example configures a very simple logger, a console handler, and a simple formatter using Python code:

import logging
+
+# create logger
+logger = logging.getLogger('simple_example')
+logger.setLevel(logging.DEBUG)
+
+# create console handler and set level to debug
+ch = logging.StreamHandler()
+ch.setLevel(logging.DEBUG)
+
+# create formatter
+formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+# add formatter to ch
+ch.setFormatter(formatter)
+
+# add ch to logger
+logger.addHandler(ch)
+
+# 'application' code
+logger.debug('debug message')
+logger.info('info message')
+logger.warning('warn message')
+logger.error('error message')
+logger.critical('critical message')
+

Running this module from the command line produces the following output:

$ python simple_logging_module.py
+2005-03-19 15:10:26,618 - simple_example - DEBUG - debug message
+2005-03-19 15:10:26,620 - simple_example - INFO - info message
+2005-03-19 15:10:26,695 - simple_example - WARNING - warn message
+2005-03-19 15:10:26,697 - simple_example - ERROR - error message
+2005-03-19 15:10:26,773 - simple_example - CRITICAL - critical message
+

The following Python module creates a logger, handler, and formatter nearly identical to those in the example listed above, with the only difference being the names of the objects:

import logging
+import logging.config
+
+logging.config.fileConfig('logging.conf')
+
+# create logger
+logger = logging.getLogger('simpleExample')
+
+# 'application' code
+logger.debug('debug message')
+logger.info('info message')
+logger.warning('warn message')
+logger.error('error message')
+logger.critical('critical message')
+

Here is the logging.conf file:

[loggers]
+keys=root,simpleExample
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=simpleFormatter
+
+[logger_root]
+level=DEBUG
+handlers=consoleHandler
+
+[logger_simpleExample]
+level=DEBUG
+handlers=consoleHandler
+qualname=simpleExample
+propagate=0
+
+[handler_consoleHandler]
+class=StreamHandler
+level=DEBUG
+formatter=simpleFormatter
+args=(sys.stdout,)
+
+[formatter_simpleFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
+

The output is nearly identical to that of the non-config-file-based example:

$ python simple_logging_config.py
+2005-03-19 15:38:55,977 - simpleExample - DEBUG - debug message
+2005-03-19 15:38:55,979 - simpleExample - INFO - info message
+2005-03-19 15:38:56,054 - simpleExample - WARNING - warn message
+2005-03-19 15:38:56,055 - simpleExample - ERROR - error message
+2005-03-19 15:38:56,130 - simpleExample - CRITICAL - critical message
+

You can see that the config file approach has a few advantages over the Python code approach, mainly separation of configuration and code and the ability of noncoders to easily modify the logging properties.

Warning

The fileConfig() function takes a default parameter, disable_existing_loggers, which defaults to True for reasons of backward compatibility. This may or may not be what you want, since it will cause any non-root loggers existing before the fileConfig() call to be disabled unless they (or an ancestor) are explicitly named in the configuration. Please refer to the reference documentation for more information, and specify False for this parameter if you wish.

The dictionary passed to dictConfig() can also specify a Boolean value with key disable_existing_loggers, which if not specified explicitly in the dictionary also defaults to being interpreted as True. This leads to the logger-disabling behaviour described above, which may not be what you want - in which case, provide the key explicitly with a value of False.

Note that the class names referenced in config files need to be either relative to the logging module, or absolute values which can be resolved using normal import mechanisms. Thus, you could use either WatchedFileHandler (relative to the logging module) or mypackage.mymodule.MyHandler (for a class defined in package mypackage and module mymodule, where mypackage is available on the Python import path).

In Python 3.2, a new means of configuring logging has been introduced, using dictionaries to hold configuration information. This provides a superset of the functionality of the config-file-based approach outlined above, and is the recommended configuration method for new applications and deployments. Because a Python dictionary is used to hold configuration information, and since you can populate that dictionary using different means, you have more options for configuration. For example, you can use a configuration file in JSON format, or, if you have access to YAML processing functionality, a file in YAML format, to populate the configuration dictionary. Or, of course, you can construct the dictionary in Python code, receive it in pickled form over a socket, or use whatever approach makes sense for your application.

Here’s an example of the same configuration as above, in YAML format for the new dictionary-based approach:

version: 1
+formatters:
+  simple:
+    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+handlers:
+  console:
+    class: logging.StreamHandler
+    level: DEBUG
+    formatter: simple
+    stream: ext://sys.stdout
+loggers:
+  simpleExample:
+    level: DEBUG
+    handlers: [console]
+    propagate: no
+root:
+  level: DEBUG
+  handlers: [console]
+

For more information about logging using a dictionary, see Configuration functions.

What happens if no configuration is provided

If no logging configuration is provided, it is possible to have a situation where a logging event needs to be output, but no handlers can be found to output the event. The behaviour of the logging package in these circumstances is dependent on the Python version.

For versions of Python prior to 3.2, the behaviour is as follows:

  • If logging.raiseExceptions is False (production mode), the event is silently dropped.
  • If logging.raiseExceptions is True (development mode), a message ‘No handlers could be found for logger X.Y.Z’ is printed once.

In Python 3.2 and later, the behaviour is as follows:

  • The event is output using a ‘handler of last resort’, stored in logging.lastResort. This internal handler is not associated with any logger, and acts like a StreamHandler which writes the event description message to the current value of sys.stderr (therefore respecting any redirections which may be in effect). No formatting is done on the message - just the bare event description message is printed. The handler’s level is set to WARNING, so all events at this and greater severities will be output.

To obtain the pre-3.2 behaviour, logging.lastResort can be set to None.

Configuring Logging for a Library

When developing a library which uses logging, you should take care to document how the library uses logging - for example, the names of loggers used. Some consideration also needs to be given to its logging configuration. If the using application does not use logging, and library code makes logging calls, then (as described in the previous section) events of severity WARNING and greater will be printed to sys.stderr. This is regarded as the best default behaviour.

If for some reason you don’t want these messages printed in the absence of any logging configuration, you can attach a do-nothing handler to the top-level logger for your library. This avoids the message being printed, since a handler will always be found for the library’s events: it just doesn’t produce any output. If the library user configures logging for application use, presumably that configuration will add some handlers, and if levels are suitably configured then logging calls made in library code will send output to those handlers, as normal.

A do-nothing handler is included in the logging package: NullHandler (since Python 3.1). An instance of this handler could be added to the top-level logger of the logging namespace used by the library (if you want to prevent your library’s logged events being output to sys.stderr in the absence of logging configuration). If all logging by a library foo is done using loggers with names matching ‘foo.x’, ‘foo.x.y’, etc. then the code:

import logging
+logging.getLogger('foo').addHandler(logging.NullHandler())
+

should have the desired effect. If an organisation produces a number of libraries, then the logger name specified can be ‘orgname.foo’ rather than just ‘foo’.

Note

It is strongly advised that you do not log to the root logger in your library. Instead, use a logger with a unique and easily identifiable name, such as the __name__ for your library’s top-level package or module. Logging to the root logger will make it difficult or impossible for the application developer to configure the logging verbosity or handlers of your library as they wish.

Note

It is strongly advised that you do not add any handlers other than NullHandler to your library’s loggers. This is because the configuration of handlers is the prerogative of the application developer who uses your library. The application developer knows their target audience and what handlers are most appropriate for their application: if you add handlers ‘under the hood’, you might well interfere with their ability to carry out unit tests and deliver logs which suit their requirements.

Logging Levels

The numeric values of logging levels are given in the following table. These are primarily of interest if you want to define your own levels, and need them to have specific values relative to the predefined levels. If you define a level with the same numeric value, it overwrites the predefined value; the predefined name is lost.

+ + + + + + +

Level

Numeric value

CRITICAL

50

ERROR

40

WARNING

30

INFO

20

DEBUG

10

NOTSET

0

Levels can also be associated with loggers, being set either by the developer or through loading a saved logging configuration. When a logging method is called on a logger, the logger compares its own level with the level associated with the method call. If the logger’s level is higher than the method call’s, no logging message is actually generated. This is the basic mechanism controlling the verbosity of logging output.

Logging messages are encoded as instances of the LogRecord class. When a logger decides to actually log an event, a LogRecord instance is created from the logging message.

Logging messages are subjected to a dispatch mechanism through the use of handlers, which are instances of subclasses of the Handler class. Handlers are responsible for ensuring that a logged message (in the form of a LogRecord) ends up in a particular location (or set of locations) which is useful for the target audience for that message (such as end users, support desk staff, system administrators, developers). Handlers are passed LogRecord instances intended for particular destinations. Each logger can have zero, one or more handlers associated with it (via the addHandler() method of Logger). In addition to any handlers directly associated with a logger, all handlers associated with all ancestors of the logger are called to dispatch the message (unless the propagate flag for a logger is set to a false value, at which point the passing to ancestor handlers stops).

Just as for loggers, handlers can have levels associated with them. A handler’s level acts as a filter in the same way as a logger’s level does. If a handler decides to actually dispatch an event, the emit() method is used to send the message to its destination. Most user-defined subclasses of Handler will need to override this emit().

Custom Levels

Defining your own levels is possible, but should not be necessary, as the existing levels have been chosen on the basis of practical experience. However, if you are convinced that you need custom levels, great care should be exercised when doing this, and it is possibly a very bad idea to define custom levels if you are developing a library. That’s because if multiple library authors all define their own custom levels, there is a chance that the logging output from such multiple libraries used together will be difficult for the using developer to control and/or interpret, because a given numeric value might mean different things for different libraries.

Useful Handlers

In addition to the base Handler class, many useful subclasses are provided:

  1. +StreamHandler instances send messages to streams (file-like objects).
  2. +FileHandler instances send messages to disk files.
  3. +BaseRotatingHandler is the base class for handlers that rotate log files at a certain point. It is not meant to be instantiated directly. Instead, use RotatingFileHandler or TimedRotatingFileHandler.
  4. +RotatingFileHandler instances send messages to disk files, with support for maximum log file sizes and log file rotation.
  5. +TimedRotatingFileHandler instances send messages to disk files, rotating the log file at certain timed intervals.
  6. +SocketHandler instances send messages to TCP/IP sockets. Since 3.4, Unix domain sockets are also supported.
  7. +DatagramHandler instances send messages to UDP sockets. Since 3.4, Unix domain sockets are also supported.
  8. +SMTPHandler instances send messages to a designated email address.
  9. +SysLogHandler instances send messages to a Unix syslog daemon, possibly on a remote machine.
  10. +NTEventLogHandler instances send messages to a Windows NT/2000/XP event log.
  11. +MemoryHandler instances send messages to a buffer in memory, which is flushed whenever specific criteria are met.
  12. +HTTPHandler instances send messages to an HTTP server using either GET or POST semantics.
  13. +WatchedFileHandler instances watch the file they are logging to. If the file changes, it is closed and reopened using the file name. This handler is only useful on Unix-like systems; Windows does not support the underlying mechanism used.
  14. +QueueHandler instances send messages to a queue, such as those implemented in the queue or multiprocessing modules.
  15. +NullHandler instances do nothing with error messages. They are used by library developers who want to use logging, but want to avoid the ‘No handlers could be found for logger XXX’ message which can be displayed if the library user has not configured logging. See Configuring Logging for a Library for more information.

New in version 3.1: The NullHandler class.

New in version 3.2: The QueueHandler class.

The NullHandler, StreamHandler and FileHandler classes are defined in the core logging package. The other handlers are defined in a sub-module, logging.handlers. (There is also another sub-module, logging.config, for configuration functionality.)

Logged messages are formatted for presentation through instances of the Formatter class. They are initialized with a format string suitable for use with the % operator and a dictionary.

For formatting multiple messages in a batch, instances of BufferingFormatter can be used. In addition to the format string (which is applied to each message in the batch), there is provision for header and trailer format strings.

When filtering based on logger level and/or handler level is not enough, instances of Filter can be added to both Logger and Handler instances (through their addFilter() method). Before deciding to process a message further, both loggers and handlers consult all their filters for permission. If any filter returns a false value, the message is not processed further.

The basic Filter functionality allows filtering by specific logger name. If this feature is used, messages sent to the named logger and its children are allowed through the filter, and all others dropped.

Exceptions raised during logging

The logging package is designed to swallow exceptions which occur while logging in production. This is so that errors which occur while handling logging events - such as logging misconfiguration, network or other similar errors - do not cause the application using logging to terminate prematurely.

SystemExit and KeyboardInterrupt exceptions are never swallowed. Other exceptions which occur during the emit() method of a Handler subclass are passed to its handleError() method.

The default implementation of handleError() in Handler checks to see if a module-level variable, raiseExceptions, is set. If set, a traceback is printed to sys.stderr. If not set, the exception is swallowed.

Note

The default value of raiseExceptions is True. This is because during development, you typically want to be notified of any exceptions that occur. It’s advised that you set raiseExceptions to False for production usage.

Using arbitrary objects as messages

In the preceding sections and examples, it has been assumed that the message passed when logging the event is a string. However, this is not the only possibility. You can pass an arbitrary object as a message, and its __str__() method will be called when the logging system needs to convert it to a string representation. In fact, if you want to, you can avoid computing a string representation altogether - for example, the SocketHandler emits an event by pickling it and sending it over the wire.

Optimization

Formatting of message arguments is deferred until it cannot be avoided. However, computing the arguments passed to the logging method can also be expensive, and you may want to avoid doing it if the logger will just throw away your event. To decide what to do, you can call the isEnabledFor() method which takes a level argument and returns true if the event would be created by the Logger for that level of call. You can write code like this:

if logger.isEnabledFor(logging.DEBUG):
+    logger.debug('Message with %s, %s', expensive_func1(),
+                                        expensive_func2())
+

so that if the logger’s threshold is set above DEBUG, the calls to expensive_func1() and expensive_func2() are never made.

Note

In some cases, isEnabledFor() can itself be more expensive than you’d like (e.g. for deeply nested loggers where an explicit level is only set high up in the logger hierarchy). In such cases (or if you want to avoid calling a method in tight loops), you can cache the result of a call to isEnabledFor() in a local or instance variable, and use that instead of calling the method each time. Such a cached value would only need to be recomputed when the logging configuration changes dynamically while the application is running (which is not all that common).

There are other optimizations which can be made for specific applications which need more precise control over what logging information is collected. Here’s a list of things you can do to avoid processing during logging which you don’t need:

+ + + + + +

What you don’t want to collect

How to avoid collecting it

Information about where calls were made from.

Set logging._srcfile to None. This avoids calling sys._getframe(), which may help to speed up your code in environments like PyPy (which can’t speed up code that uses sys._getframe()).

Threading information.

Set logging.logThreads to False.

Current process ID (os.getpid())

Set logging.logProcesses to False.

Current process name when using multiprocessing to manage multiple processes.

Set logging.logMultiprocessing to False.

Current asyncio.Task name when using asyncio.

Set logging.logAsyncioTasks to False.

Also note that the core logging module only includes the basic handlers. If you don’t import logging.handlers and logging.config, they won’t take up any memory.

Other resources

See also

+Module logging +
+
+

API reference for the logging module.

+Module logging.config +
+
+

Configuration API for the logging module.

+Module logging.handlers +
+
+

Useful handlers included with the logging module.

A logging cookbook

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/logging.html +

+
diff --git a/devdocs/python~3.12/howto%2Fperf_profiling.html b/devdocs/python~3.12/howto%2Fperf_profiling.html new file mode 100644 index 00000000..0535f7dd --- /dev/null +++ b/devdocs/python~3.12/howto%2Fperf_profiling.html @@ -0,0 +1,111 @@ +

Python support for the Linux perf profiler

author
+

Pablo Galindo

The Linux perf profiler is a very powerful tool that allows you to profile and obtain information about the performance of your application. perf also has a very vibrant ecosystem of tools that aid with the analysis of the data that it produces.

The main problem with using the perf profiler with Python applications is that perf only gets information about native symbols, that is, the names of functions and procedures written in C. This means that the names and file names of Python functions in your code will not appear in the output of perf.

Since Python 3.12, the interpreter can run in a special mode that allows Python functions to appear in the output of the perf profiler. When this mode is enabled, the interpreter will interpose a small piece of code compiled on the fly before the execution of every Python function and it will teach perf the relationship between this piece of code and the associated Python function using perf map files.

Note

Support for the perf profiler is currently only available for Linux on select architectures. Check the output of the configure build step or check the output of python -m sysconfig | grep HAVE_PERF_TRAMPOLINE to see if your system is supported.

For example, consider the following script:

def foo(n):
+    result = 0
+    for _ in range(n):
+        result += 1
+    return result
+
+def bar(n):
+    foo(n)
+
+def baz(n):
+    bar(n)
+
+if __name__ == "__main__":
+    baz(1000000)
+

We can run perf to sample CPU stack traces at 9999 hertz:

$ perf record -F 9999 -g -o perf.data python my_script.py
+

Then we can use perf report to analyze the data:

$ perf report --stdio -n -g
+
+# Children      Self       Samples  Command     Shared Object       Symbol
+# ........  ........  ............  ..........  ..................  ..........................................
+#
+    91.08%     0.00%             0  python.exe  python.exe          [.] _start
+            |
+            ---_start
+            |
+                --90.71%--__libc_start_main
+                        Py_BytesMain
+                        |
+                        |--56.88%--pymain_run_python.constprop.0
+                        |          |
+                        |          |--56.13%--_PyRun_AnyFileObject
+                        |          |          _PyRun_SimpleFileObject
+                        |          |          |
+                        |          |          |--55.02%--run_mod
+                        |          |          |          |
+                        |          |          |           --54.65%--PyEval_EvalCode
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     |
+                        |          |          |                     |--51.67%--_PyEval_EvalFrameDefault
+                        |          |          |                     |          |
+                        |          |          |                     |          |--11.52%--_PyLong_Add
+                        |          |          |                     |          |          |
+                        |          |          |                     |          |          |--2.97%--_PyObject_Malloc
+...
+

As you can see, the Python functions are not shown in the output, only _PyEval_EvalFrameDefault (the function that evaluates the Python bytecode) shows up. Unfortunately that’s not very useful because all Python functions use the same C function to evaluate bytecode so we cannot know which Python function corresponds to which bytecode-evaluating function.

Instead, if we run the same experiment with perf support enabled we get:

$ perf report --stdio -n -g
+
+# Children      Self       Samples  Command     Shared Object       Symbol
+# ........  ........  ............  ..........  ..................  .....................................................................
+#
+    90.58%     0.36%             1  python.exe  python.exe          [.] _start
+            |
+            ---_start
+            |
+                --89.86%--__libc_start_main
+                        Py_BytesMain
+                        |
+                        |--55.43%--pymain_run_python.constprop.0
+                        |          |
+                        |          |--54.71%--_PyRun_AnyFileObject
+                        |          |          _PyRun_SimpleFileObject
+                        |          |          |
+                        |          |          |--53.62%--run_mod
+                        |          |          |          |
+                        |          |          |           --53.26%--PyEval_EvalCode
+                        |          |          |                     py::<module>:/src/script.py
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     py::baz:/src/script.py
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     py::bar:/src/script.py
+                        |          |          |                     _PyEval_EvalFrameDefault
+                        |          |          |                     PyObject_Vectorcall
+                        |          |          |                     _PyEval_Vector
+                        |          |          |                     py::foo:/src/script.py
+                        |          |          |                     |
+                        |          |          |                     |--51.81%--_PyEval_EvalFrameDefault
+                        |          |          |                     |          |
+                        |          |          |                     |          |--13.77%--_PyLong_Add
+                        |          |          |                     |          |          |
+                        |          |          |                     |          |          |--3.26%--_PyObject_Malloc
+

How to enable perf profiling support

perf profiling support can be enabled either from the start using the environment variable PYTHONPERFSUPPORT or the -X perf option, or dynamically using sys.activate_stack_trampoline() and sys.deactivate_stack_trampoline().

The sys functions take precedence over the -X option, the -X option takes precedence over the environment variable.

Example, using the environment variable:

$ PYTHONPERFSUPPORT=1 python script.py
+$ perf report -g -i perf.data
+

Example, using the -X option:

$ python -X perf script.py
+$ perf report -g -i perf.data
+

Example, using the sys APIs in file example.py:

import sys
+
+sys.activate_stack_trampoline("perf")
+do_profiled_stuff()
+sys.deactivate_stack_trampoline()
+
+non_profiled_stuff()
+

…then:

$ python ./example.py
+$ perf report -g -i perf.data
+

How to obtain the best results

For best results, Python should be compiled with CFLAGS="-fno-omit-frame-pointer -mno-omit-leaf-frame-pointer" as this allows profilers to unwind using only the frame pointer and not on DWARF debug information. This is because as the code that is interposed to allow perf support is dynamically generated it doesn’t have any DWARF debugging information available.

You can check if your system has been compiled with this flag by running:

$ python -m sysconfig | grep 'no-omit-frame-pointer'
+

If you don’t see any output it means that your interpreter has not been compiled with frame pointers and therefore it may not be able to show Python functions in the output of perf.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/perf_profiling.html +

+
diff --git a/devdocs/python~3.12/howto%2Fpyporting.html b/devdocs/python~3.12/howto%2Fpyporting.html new file mode 100644 index 00000000..0fa1f11d --- /dev/null +++ b/devdocs/python~3.12/howto%2Fpyporting.html @@ -0,0 +1,32 @@ +

How to port Python 2 Code to Python 3

author
+

Brett Cannon

Abstract

Python 2 reached its official end-of-life at the start of 2020. This means that no new bug reports, fixes, or changes will be made to Python 2 - it’s no longer supported.

This guide is intended to provide you with a path to Python 3 for your code, that includes compatibility with Python 2 as a first step.

If you are looking to port an extension module instead of pure Python code, please see Porting Extension Modules to Python 3.

The archived python-porting mailing list may contain some useful guidance.

The Short Explanation

To achieve Python 2/3 compatibility in a single code base, the basic steps are:

  1. Only worry about supporting Python 2.7
  2. Make sure you have good test coverage (coverage.py can help; python -m pip install coverage)
  3. Learn the differences between Python 2 and 3
  4. Use Futurize (or Modernize) to update your code (e.g. python -m pip install future)
  5. Use Pylint to help make sure you don’t regress on your Python 3 support (python -m pip install pylint)
  6. Use caniusepython3 to find out which of your dependencies are blocking your use of Python 3 (python -m pip install caniusepython3)
  7. Once your dependencies are no longer blocking you, use continuous integration to make sure you stay compatible with Python 2 and 3 (tox can help test against multiple versions of Python; python -m pip install tox)
  8. Consider using optional static type checking to make sure your type usage works in both Python 2 and 3 (e.g. use mypy to check your typing under both Python 2 and Python 3; python -m pip install mypy).

Note

Note: Using python -m pip install guarantees that the pip you invoke is the one installed for the Python currently in use, whether it be a system-wide pip or one installed within a virtual environment.

Details

Even if other factors - say, dependencies over which you have no control - still require you to support Python 2, that does not prevent you taking the step of including Python 3 support.

Most changes required to support Python 3 lead to cleaner code using newer practices even in Python 2 code.

Different versions of Python 2

Ideally, your code should be compatible with Python 2.7, which was the last supported version of Python 2.

Some of the tools mentioned in this guide will not work with Python 2.6.

If absolutely necessary, the six project can help you support Python 2.5 and 3 simultaneously. Do realize, though, that nearly all the projects listed in this guide will not be available to you.

If you are able to skip Python 2.5 and older, the required changes to your code will be minimal. At worst you will have to use a function instead of a method in some instances or have to import a function instead of using a built-in one.

Make sure you specify the proper version support in your setup.py file

In your setup.py file you should have the proper trove classifier specifying what versions of Python you support. As your project does not support Python 3 yet you should at least have Programming Language :: Python :: 2 :: Only specified. Ideally you should also specify each major/minor version of Python that you do support, e.g. Programming Language :: Python :: 2.7.

Have good test coverage

Once you have your code supporting the oldest version of Python 2 you want it to, you will want to make sure your test suite has good coverage. A good rule of thumb is that if you want to be confident enough in your test suite that any failures that appear after having tools rewrite your code are actual bugs in the tools and not in your code. If you want a number to aim for, try to get over 80% coverage (and don’t feel bad if you find it hard to get better than 90% coverage). If you don’t already have a tool to measure test coverage then coverage.py is recommended.

Be aware of the differences between Python 2 and 3

Once you have your code well-tested you are ready to begin porting your code to Python 3! But to fully understand how your code is going to change and what you want to look out for while you code, you will want to learn what changes Python 3 makes in terms of Python 2.

Some resources for understanding the differences and their implications for you code:

Update your code

There are tools available that can port your code automatically.

Futurize does its best to make Python 3 idioms and practices exist in Python 2, e.g. backporting the bytes type from Python 3 so that you have semantic parity between the major versions of Python. This is the better approach for most cases.

Modernize, on the other hand, is more conservative and targets a Python 2/3 subset of Python, directly relying on six to help provide compatibility.

A good approach is to run the tool over your test suite first and visually inspect the diff to make sure the transformation is accurate. After you have transformed your test suite and verified that all the tests still pass as expected, then you can transform your application code knowing that any tests which fail is a translation failure.

Unfortunately the tools can’t automate everything to make your code work under Python 3, and you will also need to read the tools’ documentation in case some options you need are turned off by default.

Key issues to be aware of and check for:

Division

In Python 3, 5 / 2 == 2.5 and not 2 as it was in Python 2; all division between int values result in a float. This change has actually been planned since Python 2.2 which was released in 2002. Since then users have been encouraged to add from __future__ import division to any and all files which use the / and // operators or to be running the interpreter with the -Q flag. If you have not been doing this then you will need to go through your code and do two things:

  1. Add from __future__ import division to your files
  2. Update any division operator as necessary to either use // to use floor division or continue using / and expect a float

The reason that / isn’t simply translated to // automatically is that if an object defines a __truediv__ method but not __floordiv__ then your code would begin to fail (e.g. a user-defined class that uses / to signify some operation but not // for the same thing or at all).

Text versus binary data

In Python 2 you could use the str type for both text and binary data. Unfortunately this confluence of two different concepts could lead to brittle code which sometimes worked for either kind of data, sometimes not. It also could lead to confusing APIs if people didn’t explicitly state that something that accepted str accepted either text or binary data instead of one specific type. This complicated the situation especially for anyone supporting multiple languages as APIs wouldn’t bother explicitly supporting unicode when they claimed text data support.

Python 3 made text and binary data distinct types that cannot simply be mixed together. For any code that deals only with text or only binary data, this separation doesn’t pose an issue. But for code that has to deal with both, it does mean you might have to now care about when you are using text compared to binary data, which is why this cannot be entirely automated.

Decide which APIs take text and which take binary (it is highly recommended you don’t design APIs that can take both due to the difficulty of keeping the code working; as stated earlier it is difficult to do well). In Python 2 this means making sure the APIs that take text can work with unicode and those that work with binary data work with the bytes type from Python 3 (which is a subset of str in Python 2 and acts as an alias for bytes type in Python 2). Usually the biggest issue is realizing which methods exist on which types in Python 2 and 3 simultaneously (for text that’s unicode in Python 2 and str in Python 3, for binary that’s str/bytes in Python 2 and bytes in Python 3).

The following table lists the unique methods of each data type across Python 2 and 3 (e.g., the decode() method is usable on the equivalent binary data type in either Python 2 or 3, but it can’t be used by the textual data type consistently between Python 2 and 3 because str in Python 3 doesn’t have the method). Do note that as of Python 3.5 the __mod__ method was added to the bytes type.

+ + + + + +

Text data

Binary data

decode

encode

format

isdecimal

isnumeric

Making the distinction easier to handle can be accomplished by encoding and decoding between binary data and text at the edge of your code. This means that when you receive text in binary data, you should immediately decode it. And if your code needs to send text as binary data then encode it as late as possible. This allows your code to work with only text internally and thus eliminates having to keep track of what type of data you are working with.

The next issue is making sure you know whether the string literals in your code represent text or binary data. You should add a b prefix to any literal that presents binary data. For text you should add a u prefix to the text literal. (There is a __future__ import to force all unspecified literals to be Unicode, but usage has shown it isn’t as effective as adding a b or u prefix to all literals explicitly)

You also need to be careful about opening files. Possibly you have not always bothered to add the b mode when opening a binary file (e.g., rb for binary reading). Under Python 3, binary files and text files are clearly distinct and mutually incompatible; see the io module for details. Therefore, you must make a decision of whether a file will be used for binary access (allowing binary data to be read and/or written) or textual access (allowing text data to be read and/or written). You should also use io.open() for opening files instead of the built-in open() function as the io module is consistent from Python 2 to 3 while the built-in open() function is not (in Python 3 it’s actually io.open()). Do not bother with the outdated practice of using codecs.open() as that’s only necessary for keeping compatibility with Python 2.5.

The constructors of both str and bytes have different semantics for the same arguments between Python 2 and 3. Passing an integer to bytes in Python 2 will give you the string representation of the integer: bytes(3) == '3'. But in Python 3, an integer argument to bytes will give you a bytes object as long as the integer specified, filled with null bytes: bytes(3) == b'\x00\x00\x00'. A similar worry is necessary when passing a bytes object to str. In Python 2 you just get the bytes object back: str(b'3') == b'3'. But in Python 3 you get the string representation of the bytes object: str(b'3') == "b'3'".

Finally, the indexing of binary data requires careful handling (slicing does not require any special handling). In Python 2, b'123'[1] == b'2' while in Python 3 b'123'[1] == 50. Because binary data is simply a collection of binary numbers, Python 3 returns the integer value for the byte you index on. But in Python 2 because bytes == str, indexing returns a one-item slice of bytes. The six project has a function named six.indexbytes() which will return an integer like in Python 3: six.indexbytes(b'123', 1).

To summarize:

  1. Decide which of your APIs take text and which take binary data
  2. Make sure that your code that works with text also works with unicode and code for binary data works with bytes in Python 2 (see the table above for what methods you cannot use for each type)
  3. Mark all binary literals with a b prefix, textual literals with a u prefix
  4. Decode binary data to text as soon as possible, encode text as binary data as late as possible
  5. Open files using io.open() and make sure to specify the b mode when appropriate
  6. Be careful when indexing into binary data

Use feature detection instead of version detection

Inevitably you will have code that has to choose what to do based on what version of Python is running. The best way to do this is with feature detection of whether the version of Python you’re running under supports what you need. If for some reason that doesn’t work then you should make the version check be against Python 2 and not Python 3. To help explain this, let’s look at an example.

Let’s pretend that you need access to a feature of importlib that is available in Python’s standard library since Python 3.3 and available for Python 2 through importlib2 on PyPI. You might be tempted to write code to access e.g. the importlib.abc module by doing the following:

import sys
+
+if sys.version_info[0] == 3:
+    from importlib import abc
+else:
+    from importlib2 import abc
+

The problem with this code is what happens when Python 4 comes out? It would be better to treat Python 2 as the exceptional case instead of Python 3 and assume that future Python versions will be more compatible with Python 3 than Python 2:

import sys
+
+if sys.version_info[0] > 2:
+    from importlib import abc
+else:
+    from importlib2 import abc
+

The best solution, though, is to do no version detection at all and instead rely on feature detection. That avoids any potential issues of getting the version detection wrong and helps keep you future-compatible:

try:
+    from importlib import abc
+except ImportError:
+    from importlib2 import abc
+

Prevent compatibility regressions

Once you have fully translated your code to be compatible with Python 3, you will want to make sure your code doesn’t regress and stop working under Python 3. This is especially true if you have a dependency which is blocking you from actually running under Python 3 at the moment.

To help with staying compatible, any new modules you create should have at least the following block of code at the top of it:

from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+

You can also run Python 2 with the -3 flag to be warned about various compatibility issues your code triggers during execution. If you turn warnings into errors with -Werror then you can make sure that you don’t accidentally miss a warning.

You can also use the Pylint project and its --py3k flag to lint your code to receive warnings when your code begins to deviate from Python 3 compatibility. This also prevents you from having to run Modernize or Futurize over your code regularly to catch compatibility regressions. This does require you only support Python 2.7 and Python 3.4 or newer as that is Pylint’s minimum Python version support.

Check which dependencies block your transition

After you have made your code compatible with Python 3 you should begin to care about whether your dependencies have also been ported. The caniusepython3 project was created to help you determine which projects – directly or indirectly – are blocking you from supporting Python 3. There is both a command-line tool as well as a web interface at https://caniusepython3.com.

The project also provides code which you can integrate into your test suite so that you will have a failing test when you no longer have dependencies blocking you from using Python 3. This allows you to avoid having to manually check your dependencies and to be notified quickly when you can start running on Python 3.

Update your setup.py file to denote Python 3 compatibility

Once your code works under Python 3, you should update the classifiers in your setup.py to contain Programming Language :: Python :: 3 and to not specify sole Python 2 support. This will tell anyone using your code that you support Python 2 and 3. Ideally you will also want to add classifiers for each major/minor version of Python you now support.

Use continuous integration to stay compatible

Once you are able to fully run under Python 3 you will want to make sure your code always works under both Python 2 and 3. Probably the best tool for running your tests under multiple Python interpreters is tox. You can then integrate tox with your continuous integration system so that you never accidentally break Python 2 or 3 support.

You may also want to use the -bb flag with the Python 3 interpreter to trigger an exception when you are comparing bytes to strings or bytes to an int (the latter is available starting in Python 3.5). By default type-differing comparisons simply return False, but if you made a mistake in your separation of text/binary data handling or indexing on bytes you wouldn’t easily find the mistake. This flag will raise an exception when these kinds of comparisons occur, making the mistake much easier to track down.

Consider using optional static type checking

Another way to help port your code is to use a static type checker like mypy or pytype on your code. These tools can be used to analyze your code as if it’s being run under Python 2, then you can run the tool a second time as if your code is running under Python 3. By running a static type checker twice like this you can discover if you’re e.g. misusing binary data type in one version of Python compared to another. If you add optional type hints to your code you can also explicitly state whether your APIs use textual or binary data, helping to make sure everything functions as expected in both versions of Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/pyporting.html +

+
diff --git a/devdocs/python~3.12/howto%2Fregex.html b/devdocs/python~3.12/howto%2Fregex.html new file mode 100644 index 00000000..da9b1f3a --- /dev/null +++ b/devdocs/python~3.12/howto%2Fregex.html @@ -0,0 +1,333 @@ +

Regular Expression HOWTO

Author
+

A.M. Kuchling <amk@amk.ca>

Abstract

This document is an introductory tutorial to using regular expressions in Python with the re module. It provides a gentler introduction than the corresponding section in the Library Reference.

Introduction

Regular expressions (called REs, or regexes, or regex patterns) are essentially a tiny, highly specialized programming language embedded inside Python and made available through the re module. Using this little language, you specify the rules for the set of possible strings that you want to match; this set might contain English sentences, or e-mail addresses, or TeX commands, or anything you like. You can then ask questions such as “Does this string match the pattern?”, or “Is there a match for the pattern anywhere in this string?”. You can also use REs to modify a string or to split it apart in various ways.

Regular expression patterns are compiled into a series of bytecodes which are then executed by a matching engine written in C. For advanced use, it may be necessary to pay careful attention to how the engine will execute a given RE, and write the RE in a certain way in order to produce bytecode that runs faster. Optimization isn’t covered in this document, because it requires that you have a good understanding of the matching engine’s internals.

The regular expression language is relatively small and restricted, so not all possible string processing tasks can be done using regular expressions. There are also tasks that can be done with regular expressions, but the expressions turn out to be very complicated. In these cases, you may be better off writing Python code to do the processing; while Python code will be slower than an elaborate regular expression, it will also probably be more understandable.

Simple Patterns

We’ll start by learning about the simplest possible regular expressions. Since regular expressions are used to operate on strings, we’ll begin with the most common task: matching characters.

For a detailed explanation of the computer science underlying regular expressions (deterministic and non-deterministic finite automata), you can refer to almost any textbook on writing compilers.

Matching Characters

Most letters and characters will simply match themselves. For example, the regular expression test will match the string test exactly. (You can enable a case-insensitive mode that would let this RE match Test or TEST as well; more about this later.)

There are exceptions to this rule; some characters are special metacharacters, and don’t match themselves. Instead, they signal that some out-of-the-ordinary thing should be matched, or they affect other portions of the RE by repeating them or changing their meaning. Much of this document is devoted to discussing various metacharacters and what they do.

Here’s a complete list of the metacharacters; their meanings will be discussed in the rest of this HOWTO.

. ^ $ * + ? { } [ ] \ | ( )
+

The first metacharacters we’ll look at are [ and ]. They’re used for specifying a character class, which is a set of characters that you wish to match. Characters can be listed individually, or a range of characters can be indicated by giving two characters and separating them by a '-'. For example, [abc] will match any of the characters a, b, or c; this is the same as [a-c], which uses a range to express the same set of characters. If you wanted to match only lowercase letters, your RE would be [a-z].

Metacharacters (except \) are not active inside classes. For example, [akm$] will match any of the characters 'a', 'k', 'm', or '$'; '$' is usually a metacharacter, but inside a character class it’s stripped of its special nature.

You can match the characters not listed within the class by complementing the set. This is indicated by including a '^' as the first character of the class. For example, [^5] will match any character except '5'. If the caret appears elsewhere in a character class, it does not have special meaning. For example: [5^] will match either a '5' or a '^'.

Perhaps the most important metacharacter is the backslash, \. As in Python string literals, the backslash can be followed by various characters to signal various special sequences. It’s also used to escape all the metacharacters so you can still match them in patterns; for example, if you need to match a [ or \, you can precede them with a backslash to remove their special meaning: \[ or \\.

Some of the special sequences beginning with '\' represent predefined sets of characters that are often useful, such as the set of digits, the set of letters, or the set of anything that isn’t whitespace.

Let’s take an example: \w matches any alphanumeric character. If the regex pattern is expressed in bytes, this is equivalent to the class [a-zA-Z0-9_]. If the regex pattern is a string, \w will match all the characters marked as letters in the Unicode database provided by the unicodedata module. You can use the more restricted definition of \w in a string pattern by supplying the re.ASCII flag when compiling the regular expression.

The following list of special sequences isn’t complete. For a complete list of sequences and expanded class definitions for Unicode string patterns, see the last part of Regular Expression Syntax in the Standard Library reference. In general, the Unicode versions match any character that’s in the appropriate category in the Unicode database.

+\d
+
+

Matches any decimal digit; this is equivalent to the class [0-9].

+\D
+
+

Matches any non-digit character; this is equivalent to the class [^0-9].

+\s
+
+

Matches any whitespace character; this is equivalent to the class [ +\t\n\r\f\v].

+\S
+
+

Matches any non-whitespace character; this is equivalent to the class [^ +\t\n\r\f\v].

+\w
+
+

Matches any alphanumeric character; this is equivalent to the class [a-zA-Z0-9_].

+\W
+
+

Matches any non-alphanumeric character; this is equivalent to the class [^a-zA-Z0-9_].

These sequences can be included inside a character class. For example, [\s,.] is a character class that will match any whitespace character, or ',' or '.'.

The final metacharacter in this section is .. It matches anything except a newline character, and there’s an alternate mode (re.DOTALL) where it will match even a newline. . is often used where you want to match “any character”.

Repeating Things

Being able to match varying sets of characters is the first thing regular expressions can do that isn’t already possible with the methods available on strings. However, if that was the only additional capability of regexes, they wouldn’t be much of an advance. Another capability is that you can specify that portions of the RE must be repeated a certain number of times.

The first metacharacter for repeating things that we’ll look at is *. * doesn’t match the literal character '*'; instead, it specifies that the previous character can be matched zero or more times, instead of exactly once.

For example, ca*t will match 'ct' (0 'a' characters), 'cat' (1 'a'), 'caaat' (3 'a' characters), and so forth.

Repetitions such as * are greedy; when repeating a RE, the matching engine will try to repeat it as many times as possible. If later portions of the pattern don’t match, the matching engine will then back up and try again with fewer repetitions.

A step-by-step example will make this more obvious. Let’s consider the expression a[bcd]*b. This matches the letter 'a', zero or more letters from the class [bcd], and finally ends with a 'b'. Now imagine matching this RE against the string 'abcbd'.

+ + + + + + + +

Step

Matched

Explanation

1

a

The a in the RE matches.

2

abcbd

The engine matches [bcd]*, going as far as it can, which is to the end of the string.

3

Failure

The engine tries to match b, but the current position is at the end of the string, so it fails.

4

abcb

Back up, so that [bcd]* matches one less character.

5

Failure

Try b again, but the current position is at the last character, which is a 'd'.

6

abc

Back up again, so that [bcd]* is only matching bc.

6

abcb

Try b again. This time the character at the current position is 'b', so it succeeds.

The end of the RE has now been reached, and it has matched 'abcb'. This demonstrates how the matching engine goes as far as it can at first, and if no match is found it will then progressively back up and retry the rest of the RE again and again. It will back up until it has tried zero matches for [bcd]*, and if that subsequently fails, the engine will conclude that the string doesn’t match the RE at all.

Another repeating metacharacter is +, which matches one or more times. Pay careful attention to the difference between * and +; * matches zero or more times, so whatever’s being repeated may not be present at all, while + requires at least one occurrence. To use a similar example, ca+t will match 'cat' (1 'a'), 'caaat' (3 'a's), but won’t match 'ct'.

There are two more repeating operators or quantifiers. The question mark character, ?, matches either once or zero times; you can think of it as marking something as being optional. For example, home-?brew matches either 'homebrew' or 'home-brew'.

The most complicated quantifier is {m,n}, where m and n are decimal integers. This quantifier means there must be at least m repetitions, and at most n. For example, a/{1,3}b will match 'a/b', 'a//b', and 'a///b'. It won’t match 'ab', which has no slashes, or 'a////b', which has four.

You can omit either m or n; in that case, a reasonable value is assumed for the missing value. Omitting m is interpreted as a lower limit of 0, while omitting n results in an upper bound of infinity.

The simplest case {m} matches the preceding item exactly m times. For example, a/{2}b will only match 'a//b'.

Readers of a reductionist bent may notice that the three other quantifiers can all be expressed using this notation. {0,} is the same as *, {1,} is equivalent to +, and {0,1} is the same as ?. It’s better to use *, +, or ? when you can, simply because they’re shorter and easier to read.

Using Regular Expressions

Now that we’ve looked at some simple regular expressions, how do we actually use them in Python? The re module provides an interface to the regular expression engine, allowing you to compile REs into objects and then perform matches with them.

Compiling Regular Expressions

Regular expressions are compiled into pattern objects, which have methods for various operations such as searching for pattern matches or performing string substitutions.

>>> import re
+>>> p = re.compile('ab*')
+>>> p
+re.compile('ab*')
+

re.compile() also accepts an optional flags argument, used to enable various special features and syntax variations. We’ll go over the available settings later, but for now a single example will do:

>>> p = re.compile('ab*', re.IGNORECASE)
+

The RE is passed to re.compile() as a string. REs are handled as strings because regular expressions aren’t part of the core Python language, and no special syntax was created for expressing them. (There are applications that don’t need REs at all, so there’s no need to bloat the language specification by including them.) Instead, the re module is simply a C extension module included with Python, just like the socket or zlib modules.

Putting REs in strings keeps the Python language simpler, but has one disadvantage which is the topic of the next section.

The Backslash Plague

As stated earlier, regular expressions use the backslash character ('\') to indicate special forms or to allow special characters to be used without invoking their special meaning. This conflicts with Python’s usage of the same character for the same purpose in string literals.

Let’s say you want to write a RE that matches the string \section, which might be found in a LaTeX file. To figure out what to write in the program code, start with the desired string to be matched. Next, you must escape any backslashes and other metacharacters by preceding them with a backslash, resulting in the string \\section. The resulting string that must be passed to re.compile() must be \\section. However, to express this as a Python string literal, both backslashes must be escaped again.

+ + + +

Characters

Stage

\section

Text string to be matched

\\section

Escaped backslash for re.compile()

"\\\\section"

Escaped backslashes for a string literal

In short, to match a literal backslash, one has to write '\\\\' as the RE string, because the regular expression must be \\, and each backslash must be expressed as \\ inside a regular Python string literal. In REs that feature backslashes repeatedly, this leads to lots of repeated backslashes and makes the resulting strings difficult to understand.

The solution is to use Python’s raw string notation for regular expressions; backslashes are not handled in any special way in a string literal prefixed with 'r', so r"\n" is a two-character string containing '\' and 'n', while "\n" is a one-character string containing a newline. Regular expressions will often be written in Python code using this raw string notation.

In addition, special escape sequences that are valid in regular expressions, but not valid as Python string literals, now result in a DeprecationWarning and will eventually become a SyntaxError, which means the sequences will be invalid if raw string notation or escaping the backslashes isn’t used.

+ + + +

Regular String

Raw string

"ab*"

r"ab*"

"\\\\section"

r"\\section"

"\\w+\\s+\\1"

r"\w+\s+\1"

Performing Matches

Once you have an object representing a compiled regular expression, what do you do with it? Pattern objects have several methods and attributes. Only the most significant ones will be covered here; consult the re docs for a complete listing.

+ + + + +

Method/Attribute

Purpose

match()

Determine if the RE matches at the beginning of the string.

search()

Scan through a string, looking for any location where this RE matches.

findall()

Find all substrings where the RE matches, and returns them as a list.

finditer()

Find all substrings where the RE matches, and returns them as an iterator.

match() and search() return None if no match can be found. If they’re successful, a match object instance is returned, containing information about the match: where it starts and ends, the substring it matched, and more.

You can learn about this by interactively experimenting with the re module.

This HOWTO uses the standard Python interpreter for its examples. First, run the Python interpreter, import the re module, and compile a RE:

>>> import re
+>>> p = re.compile('[a-z]+')
+>>> p
+re.compile('[a-z]+')
+

Now, you can try matching various strings against the RE [a-z]+. An empty string shouldn’t match at all, since + means ‘one or more repetitions’. match() should return None in this case, which will cause the interpreter to print no output. You can explicitly print the result of match() to make this clear.

>>> p.match("")
+>>> print(p.match(""))
+None
+

Now, let’s try it on a string that it should match, such as tempo. In this case, match() will return a match object, so you should store the result in a variable for later use.

>>> m = p.match('tempo')
+>>> m
+<re.Match object; span=(0, 5), match='tempo'>
+

Now you can query the match object for information about the matching string. Match object instances also have several methods and attributes; the most important ones are:

+ + + + +

Method/Attribute

Purpose

group()

Return the string matched by the RE

start()

Return the starting position of the match

end()

Return the ending position of the match

span()

Return a tuple containing the (start, end) positions of the match

Trying these methods will soon clarify their meaning:

>>> m.group()
+'tempo'
+>>> m.start(), m.end()
+(0, 5)
+>>> m.span()
+(0, 5)
+

group() returns the substring that was matched by the RE. start() and end() return the starting and ending index of the match. span() returns both start and end indexes in a single tuple. Since the match() method only checks if the RE matches at the start of a string, start() will always be zero. However, the search() method of patterns scans through the string, so the match may not start at zero in that case.

>>> print(p.match('::: message'))
+None
+>>> m = p.search('::: message'); print(m)
+<re.Match object; span=(4, 11), match='message'>
+>>> m.group()
+'message'
+>>> m.span()
+(4, 11)
+

In actual programs, the most common style is to store the match object in a variable, and then check if it was None. This usually looks like:

p = re.compile( ... )
+m = p.match( 'string goes here' )
+if m:
+    print('Match found: ', m.group())
+else:
+    print('No match')
+

Two pattern methods return all of the matches for a pattern. findall() returns a list of matching strings:

>>> p = re.compile(r'\d+')
+>>> p.findall('12 drummers drumming, 11 pipers piping, 10 lords a-leaping')
+['12', '11', '10']
+

The r prefix, making the literal a raw string literal, is needed in this example because escape sequences in a normal “cooked” string literal that are not recognized by Python, as opposed to regular expressions, now result in a DeprecationWarning and will eventually become a SyntaxError. See The Backslash Plague.

findall() has to create the entire list before it can be returned as the result. The finditer() method returns a sequence of match object instances as an iterator:

>>> iterator = p.finditer('12 drummers drumming, 11 ... 10 ...')
+>>> iterator  
+<callable_iterator object at 0x...>
+>>> for match in iterator:
+...     print(match.span())
+...
+(0, 2)
+(22, 24)
+(29, 31)
+

Module-Level Functions

You don’t have to create a pattern object and call its methods; the re module also provides top-level functions called match(), search(), findall(), sub(), and so forth. These functions take the same arguments as the corresponding pattern method with the RE string added as the first argument, and still return either None or a match object instance.

>>> print(re.match(r'From\s+', 'Fromage amk'))
+None
+>>> re.match(r'From\s+', 'From amk Thu May 14 19:12:10 1998')  
+<re.Match object; span=(0, 5), match='From '>
+

Under the hood, these functions simply create a pattern object for you and call the appropriate method on it. They also store the compiled object in a cache, so future calls using the same RE won’t need to parse the pattern again and again.

Should you use these module-level functions, or should you get the pattern and call its methods yourself? If you’re accessing a regex within a loop, pre-compiling it will save a few function calls. Outside of loops, there’s not much difference thanks to the internal cache.

Compilation Flags

Compilation flags let you modify some aspects of how regular expressions work. Flags are available in the re module under two names, a long name such as IGNORECASE and a short, one-letter form such as I. (If you’re familiar with Perl’s pattern modifiers, the one-letter forms use the same letters; the short form of re.VERBOSE is re.X, for example.) Multiple flags can be specified by bitwise OR-ing them; re.I | re.M sets both the I and M flags, for example.

Here’s a table of the available flags, followed by a more detailed explanation of each one.

+ + + + + + +

Flag

Meaning

ASCII, A

Makes several escapes like \w, \b, \s and \d match only on ASCII characters with the respective property.

DOTALL, S

Make . match any character, including newlines.

IGNORECASE, I

Do case-insensitive matches.

LOCALE, L

Do a locale-aware match.

MULTILINE, M

Multi-line matching, affecting ^ and $.

VERBOSE, X (for ‘extended’)

Enable verbose REs, which can be organized more cleanly and understandably.
re.I +
re.IGNORECASE +
+

Perform case-insensitive matching; character class and literal strings will match letters by ignoring case. For example, [A-Z] will match lowercase letters, too. Full Unicode matching also works unless the ASCII flag is used to disable non-ASCII matches. When the Unicode patterns [a-z] or [A-Z] are used in combination with the IGNORECASE flag, they will match the 52 ASCII letters and 4 additional non-ASCII letters: ‘İ’ (U+0130, Latin capital letter I with dot above), ‘ı’ (U+0131, Latin small letter dotless i), ‘ſ’ (U+017F, Latin small letter long s) and ‘K’ (U+212A, Kelvin sign). Spam will match 'Spam', 'spam', 'spAM', or 'ſpam' (the latter is matched only in Unicode mode). This lowercasing doesn’t take the current locale into account; it will if you also set the LOCALE flag.

+
re.L +
re.LOCALE +
+

Make \w, \W, \b, \B and case-insensitive matching dependent on the current locale instead of the Unicode database.

Locales are a feature of the C library intended to help in writing programs that take account of language differences. For example, if you’re processing encoded French text, you’d want to be able to write \w+ to match words, but \w only matches the character class [A-Za-z] in bytes patterns; it won’t match bytes corresponding to é or ç. If your system is configured properly and a French locale is selected, certain C functions will tell the program that the byte corresponding to é should also be considered a letter. Setting the LOCALE flag when compiling a regular expression will cause the resulting compiled object to use these C functions for \w; this is slower, but also enables \w+ to match French words as you’d expect. The use of this flag is discouraged in Python 3 as the locale mechanism is very unreliable, it only handles one “culture” at a time, and it only works with 8-bit locales. Unicode matching is already enabled by default in Python 3 for Unicode (str) patterns, and it is able to handle different locales/languages.

+
re.M +
re.MULTILINE +
+

(^ and $ haven’t been explained yet; they’ll be introduced in section More Metacharacters.)

Usually ^ matches only at the beginning of the string, and $ matches only at the end of the string and immediately before the newline (if any) at the end of the string. When this flag is specified, ^ matches at the beginning of the string and at the beginning of each line within the string, immediately following each newline. Similarly, the $ metacharacter matches either at the end of the string and at the end of each line (immediately preceding each newline).

+
re.S +
re.DOTALL +
+

Makes the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline.

+
re.A +
re.ASCII +
+

Make \w, \W, \b, \B, \s and \S perform ASCII-only matching instead of full Unicode matching. This is only meaningful for Unicode patterns, and is ignored for byte patterns.

+
re.X +
re.VERBOSE +
+

This flag allows you to write regular expressions that are more readable by granting you more flexibility in how you can format them. When this flag has been specified, whitespace within the RE string is ignored, except when the whitespace is in a character class or preceded by an unescaped backslash; this lets you organize and indent the RE more clearly. This flag also lets you put comments within a RE that will be ignored by the engine; comments are marked by a '#' that’s neither in a character class or preceded by an unescaped backslash.

For example, here’s a RE that uses re.VERBOSE; see how much easier it is to read?

charref = re.compile(r"""
+ &[#]                # Start of a numeric entity reference
+ (
+     0[0-7]+         # Octal form
+   | [0-9]+          # Decimal form
+   | x[0-9a-fA-F]+   # Hexadecimal form
+ )
+ ;                   # Trailing semicolon
+""", re.VERBOSE)
+

Without the verbose setting, the RE would look like this:

charref = re.compile("&#(0[0-7]+"
+                     "|[0-9]+"
+                     "|x[0-9a-fA-F]+);")
+

In the above example, Python’s automatic concatenation of string literals has been used to break up the RE into smaller pieces, but it’s still more difficult to understand than the version using re.VERBOSE.

+

More Pattern Power

So far we’ve only covered a part of the features of regular expressions. In this section, we’ll cover some new metacharacters, and how to use groups to retrieve portions of the text that was matched.

More Metacharacters

There are some metacharacters that we haven’t covered yet. Most of them will be covered in this section.

Some of the remaining metacharacters to be discussed are zero-width assertions. They don’t cause the engine to advance through the string; instead, they consume no characters at all, and simply succeed or fail. For example, \b is an assertion that the current position is located at a word boundary; the position isn’t changed by the \b at all. This means that zero-width assertions should never be repeated, because if they match once at a given location, they can obviously be matched an infinite number of times.

+|
+
+

Alternation, or the “or” operator. If A and B are regular expressions, A|B will match any string that matches either A or B. | has very low precedence in order to make it work reasonably when you’re alternating multi-character strings. Crow|Servo will match either 'Crow' or 'Servo', not 'Cro', a 'w' or an 'S', and 'ervo'.

To match a literal '|', use \|, or enclose it inside a character class, as in [|].

+^
+
+

Matches at the beginning of lines. Unless the MULTILINE flag has been set, this will only match at the beginning of the string. In MULTILINE mode, this also matches immediately after each newline within the string.

For example, if you wish to match the word From only at the beginning of a line, the RE to use is ^From.

>>> print(re.search('^From', 'From Here to Eternity'))  
+<re.Match object; span=(0, 4), match='From'>
+>>> print(re.search('^From', 'Reciting From Memory'))
+None
+

To match a literal '^', use \^.

+$
+
+

Matches at the end of a line, which is defined as either the end of the string, or any location followed by a newline character.

>>> print(re.search('}$', '{block}'))  
+<re.Match object; span=(6, 7), match='}'>
+>>> print(re.search('}$', '{block} '))
+None
+>>> print(re.search('}$', '{block}\n'))  
+<re.Match object; span=(6, 7), match='}'>
+

To match a literal '$', use \$ or enclose it inside a character class, as in [$].

+\A
+
+

Matches only at the start of the string. When not in MULTILINE mode, \A and ^ are effectively the same. In MULTILINE mode, they’re different: \A still matches only at the beginning of the string, but ^ may match at any location inside the string that follows a newline character.

+\Z
+
+

Matches only at the end of the string.

+\b
+
+

Word boundary. This is a zero-width assertion that matches only at the beginning or end of a word. A word is defined as a sequence of alphanumeric characters, so the end of a word is indicated by whitespace or a non-alphanumeric character.

The following example matches class only when it’s a complete word; it won’t match when it’s contained inside another word.

>>> p = re.compile(r'\bclass\b')
+>>> print(p.search('no class at all'))
+<re.Match object; span=(3, 8), match='class'>
+>>> print(p.search('the declassified algorithm'))
+None
+>>> print(p.search('one subclass is'))
+None
+

There are two subtleties you should remember when using this special sequence. First, this is the worst collision between Python’s string literals and regular expression sequences. In Python’s string literals, \b is the backspace character, ASCII value 8. If you’re not using raw strings, then Python will convert the \b to a backspace, and your RE won’t match as you expect it to. The following example looks the same as our previous RE, but omits the 'r' in front of the RE string.

>>> p = re.compile('\bclass\b')
+>>> print(p.search('no class at all'))
+None
+>>> print(p.search('\b' + 'class' + '\b'))
+<re.Match object; span=(0, 7), match='\x08class\x08'>
+

Second, inside a character class, where there’s no use for this assertion, \b represents the backspace character, for compatibility with Python’s string literals.

+\B
+
+

Another zero-width assertion, this is the opposite of \b, only matching when the current position is not at a word boundary.

Grouping

Frequently you need to obtain more information than just whether the RE matched or not. Regular expressions are often used to dissect strings by writing a RE divided into several subgroups which match different components of interest. For example, an RFC-822 header line is divided into a header name and a value, separated by a ':', like this:

From: author@example.com
+User-Agent: Thunderbird 1.5.0.9 (X11/20061227)
+MIME-Version: 1.0
+To: editor@example.com
+

This can be handled by writing a regular expression which matches an entire header line, and has one group which matches the header name, and another group which matches the header’s value.

Groups are marked by the '(', ')' metacharacters. '(' and ')' have much the same meaning as they do in mathematical expressions; they group together the expressions contained inside them, and you can repeat the contents of a group with a quantifier, such as *, +, ?, or {m,n}. For example, (ab)* will match zero or more repetitions of ab.

>>> p = re.compile('(ab)*')
+>>> print(p.match('ababababab').span())
+(0, 10)
+

Groups indicated with '(', ')' also capture the starting and ending index of the text that they match; this can be retrieved by passing an argument to group(), start(), end(), and span(). Groups are numbered starting with 0. Group 0 is always present; it’s the whole RE, so match object methods all have group 0 as their default argument. Later we’ll see how to express groups that don’t capture the span of text that they match.

>>> p = re.compile('(a)b')
+>>> m = p.match('ab')
+>>> m.group()
+'ab'
+>>> m.group(0)
+'ab'
+

Subgroups are numbered from left to right, from 1 upward. Groups can be nested; to determine the number, just count the opening parenthesis characters, going from left to right.

>>> p = re.compile('(a(b)c)d')
+>>> m = p.match('abcd')
+>>> m.group(0)
+'abcd'
+>>> m.group(1)
+'abc'
+>>> m.group(2)
+'b'
+

group() can be passed multiple group numbers at a time, in which case it will return a tuple containing the corresponding values for those groups.

>>> m.group(2,1,2)
+('b', 'abc', 'b')
+

The groups() method returns a tuple containing the strings for all the subgroups, from 1 up to however many there are.

>>> m.groups()
+('abc', 'b')
+

Backreferences in a pattern allow you to specify that the contents of an earlier capturing group must also be found at the current location in the string. For example, \1 will succeed if the exact contents of group 1 can be found at the current position, and fails otherwise. Remember that Python’s string literals also use a backslash followed by numbers to allow including arbitrary characters in a string, so be sure to use a raw string when incorporating backreferences in a RE.

For example, the following RE detects doubled words in a string.

>>> p = re.compile(r'\b(\w+)\s+\1\b')
+>>> p.search('Paris in the the spring').group()
+'the the'
+

Backreferences like this aren’t often useful for just searching through a string — there are few text formats which repeat data in this way — but you’ll soon find out that they’re very useful when performing string substitutions.

Non-capturing and Named Groups

Elaborate REs may use many groups, both to capture substrings of interest, and to group and structure the RE itself. In complex REs, it becomes difficult to keep track of the group numbers. There are two features which help with this problem. Both of them use a common syntax for regular expression extensions, so we’ll look at that first.

Perl 5 is well known for its powerful additions to standard regular expressions. For these new features the Perl developers couldn’t choose new single-keystroke metacharacters or new special sequences beginning with \ without making Perl’s regular expressions confusingly different from standard REs. If they chose & as a new metacharacter, for example, old expressions would be assuming that & was a regular character and wouldn’t have escaped it by writing \& or [&].

The solution chosen by the Perl developers was to use (?...) as the extension syntax. ? immediately after a parenthesis was a syntax error because the ? would have nothing to repeat, so this didn’t introduce any compatibility problems. The characters immediately after the ? indicate what extension is being used, so (?=foo) is one thing (a positive lookahead assertion) and (?:foo) is something else (a non-capturing group containing the subexpression foo).

Python supports several of Perl’s extensions and adds an extension syntax to Perl’s extension syntax. If the first character after the question mark is a P, you know that it’s an extension that’s specific to Python.

Now that we’ve looked at the general extension syntax, we can return to the features that simplify working with groups in complex REs.

Sometimes you’ll want to use a group to denote a part of a regular expression, but aren’t interested in retrieving the group’s contents. You can make this fact explicit by using a non-capturing group: (?:...), where you can replace the ... with any other regular expression.

>>> m = re.match("([abc])+", "abc")
+>>> m.groups()
+('c',)
+>>> m = re.match("(?:[abc])+", "abc")
+>>> m.groups()
+()
+

Except for the fact that you can’t retrieve the contents of what the group matched, a non-capturing group behaves exactly the same as a capturing group; you can put anything inside it, repeat it with a repetition metacharacter such as *, and nest it within other groups (capturing or non-capturing). (?:...) is particularly useful when modifying an existing pattern, since you can add new groups without changing how all the other groups are numbered. It should be mentioned that there’s no performance difference in searching between capturing and non-capturing groups; neither form is any faster than the other.

A more significant feature is named groups: instead of referring to them by numbers, groups can be referenced by a name.

The syntax for a named group is one of the Python-specific extensions: (?P<name>...). name is, obviously, the name of the group. Named groups behave exactly like capturing groups, and additionally associate a name with a group. The match object methods that deal with capturing groups all accept either integers that refer to the group by number or strings that contain the desired group’s name. Named groups are still given numbers, so you can retrieve information about a group in two ways:

>>> p = re.compile(r'(?P<word>\b\w+\b)')
+>>> m = p.search( '(((( Lots of punctuation )))' )
+>>> m.group('word')
+'Lots'
+>>> m.group(1)
+'Lots'
+

Additionally, you can retrieve named groups as a dictionary with groupdict():

>>> m = re.match(r'(?P<first>\w+) (?P<last>\w+)', 'Jane Doe')
+>>> m.groupdict()
+{'first': 'Jane', 'last': 'Doe'}
+

Named groups are handy because they let you use easily remembered names, instead of having to remember numbers. Here’s an example RE from the imaplib module:

InternalDate = re.compile(r'INTERNALDATE "'
+        r'(?P<day>[ 123][0-9])-(?P<mon>[A-Z][a-z][a-z])-'
+        r'(?P<year>[0-9][0-9][0-9][0-9])'
+        r' (?P<hour>[0-9][0-9]):(?P<min>[0-9][0-9]):(?P<sec>[0-9][0-9])'
+        r' (?P<zonen>[-+])(?P<zoneh>[0-9][0-9])(?P<zonem>[0-9][0-9])'
+        r'"')
+

It’s obviously much easier to retrieve m.group('zonem'), instead of having to remember to retrieve group 9.

The syntax for backreferences in an expression such as (...)\1 refers to the number of the group. There’s naturally a variant that uses the group name instead of the number. This is another Python extension: (?P=name) indicates that the contents of the group called name should again be matched at the current point. The regular expression for finding doubled words, \b(\w+)\s+\1\b can also be written as \b(?P<word>\w+)\s+(?P=word)\b:

>>> p = re.compile(r'\b(?P<word>\w+)\s+(?P=word)\b')
+>>> p.search('Paris in the the spring').group()
+'the the'
+

Lookahead Assertions

Another zero-width assertion is the lookahead assertion. Lookahead assertions are available in both positive and negative form, and look like this:

+(?=...)
+
+

Positive lookahead assertion. This succeeds if the contained regular expression, represented here by ..., successfully matches at the current location, and fails otherwise. But, once the contained expression has been tried, the matching engine doesn’t advance at all; the rest of the pattern is tried right where the assertion started.

+(?!...)
+
+

Negative lookahead assertion. This is the opposite of the positive assertion; it succeeds if the contained expression doesn’t match at the current position in the string.

To make this concrete, let’s look at a case where a lookahead is useful. Consider a simple pattern to match a filename and split it apart into a base name and an extension, separated by a .. For example, in news.rc, news is the base name, and rc is the filename’s extension.

The pattern to match this is quite simple:

.*[.].*$

Notice that the . needs to be treated specially because it’s a metacharacter, so it’s inside a character class to only match that specific character. Also notice the trailing $; this is added to ensure that all the rest of the string must be included in the extension. This regular expression matches foo.bar and autoexec.bat and sendmail.cf and printers.conf.

Now, consider complicating the problem a bit; what if you want to match filenames where the extension is not bat? Some incorrect attempts:

.*[.][^b].*$ The first attempt above tries to exclude bat by requiring that the first character of the extension is not a b. This is wrong, because the pattern also doesn’t match foo.bar.

.*[.]([^b]..|.[^a].|..[^t])$

The expression gets messier when you try to patch up the first solution by requiring one of the following cases to match: the first character of the extension isn’t b; the second character isn’t a; or the third character isn’t t. This accepts foo.bar and rejects autoexec.bat, but it requires a three-letter extension and won’t accept a filename with a two-letter extension such as sendmail.cf. We’ll complicate the pattern again in an effort to fix it.

.*[.]([^b].?.?|.[^a]?.?|..?[^t]?)$

In the third attempt, the second and third letters are all made optional in order to allow matching extensions shorter than three characters, such as sendmail.cf.

The pattern’s getting really complicated now, which makes it hard to read and understand. Worse, if the problem changes and you want to exclude both bat and exe as extensions, the pattern would get even more complicated and confusing.

A negative lookahead cuts through all this confusion:

.*[.](?!bat$)[^.]*$ The negative lookahead means: if the expression bat doesn’t match at this point, try the rest of the pattern; if bat$ does match, the whole pattern will fail. The trailing $ is required to ensure that something like sample.batch, where the extension only starts with bat, will be allowed. The [^.]* makes sure that the pattern works when there are multiple dots in the filename.

Excluding another filename extension is now easy; simply add it as an alternative inside the assertion. The following pattern excludes filenames that end in either bat or exe:

.*[.](?!bat$|exe$)[^.]*$

Modifying Strings

Up to this point, we’ve simply performed searches against a static string. Regular expressions are also commonly used to modify strings in various ways, using the following pattern methods:

+ + + +

Method/Attribute

Purpose

split()

Split the string into a list, splitting it wherever the RE matches

sub()

Find all substrings where the RE matches, and replace them with a different string

subn()

Does the same thing as sub(), but returns the new string and the number of replacements

Splitting Strings

The split() method of a pattern splits a string apart wherever the RE matches, returning a list of the pieces. It’s similar to the split() method of strings but provides much more generality in the delimiters that you can split by; string split() only supports splitting by whitespace or by a fixed string. As you’d expect, there’s a module-level re.split() function, too.

.split(string[, maxsplit=0]) +
+

Split string by the matches of the regular expression. If capturing parentheses are used in the RE, then their contents will also be returned as part of the resulting list. If maxsplit is nonzero, at most maxsplit splits are performed.

+

You can limit the number of splits made, by passing a value for maxsplit. When maxsplit is nonzero, at most maxsplit splits will be made, and the remainder of the string is returned as the final element of the list. In the following example, the delimiter is any sequence of non-alphanumeric characters.

>>> p = re.compile(r'\W+')
+>>> p.split('This is a test, short and sweet, of split().')
+['This', 'is', 'a', 'test', 'short', 'and', 'sweet', 'of', 'split', '']
+>>> p.split('This is a test, short and sweet, of split().', 3)
+['This', 'is', 'a', 'test, short and sweet, of split().']
+

Sometimes you’re not only interested in what the text between delimiters is, but also need to know what the delimiter was. If capturing parentheses are used in the RE, then their values are also returned as part of the list. Compare the following calls:

>>> p = re.compile(r'\W+')
+>>> p2 = re.compile(r'(\W+)')
+>>> p.split('This... is a test.')
+['This', 'is', 'a', 'test', '']
+>>> p2.split('This... is a test.')
+['This', '... ', 'is', ' ', 'a', ' ', 'test', '.', '']
+

The module-level function re.split() adds the RE to be used as the first argument, but is otherwise the same.

>>> re.split(r'[\W]+', 'Words, words, words.')
+['Words', 'words', 'words', '']
+>>> re.split(r'([\W]+)', 'Words, words, words.')
+['Words', ', ', 'words', ', ', 'words', '.', '']
+>>> re.split(r'[\W]+', 'Words, words, words.', 1)
+['Words', 'words, words.']
+

Search and Replace

Another common task is to find all the matches for a pattern, and replace them with a different string. The sub() method takes a replacement value, which can be either a string or a function, and the string to be processed.

.sub(replacement, string[, count=0]) +
+

Returns the string obtained by replacing the leftmost non-overlapping occurrences of the RE in string by the replacement replacement. If the pattern isn’t found, string is returned unchanged.

The optional argument count is the maximum number of pattern occurrences to be replaced; count must be a non-negative integer. The default value of 0 means to replace all occurrences.

+

Here’s a simple example of using the sub() method. It replaces colour names with the word colour:

>>> p = re.compile('(blue|white|red)')
+>>> p.sub('colour', 'blue socks and red shoes')
+'colour socks and colour shoes'
+>>> p.sub('colour', 'blue socks and red shoes', count=1)
+'colour socks and red shoes'
+

The subn() method does the same work, but returns a 2-tuple containing the new string value and the number of replacements that were performed:

>>> p = re.compile('(blue|white|red)')
+>>> p.subn('colour', 'blue socks and red shoes')
+('colour socks and colour shoes', 2)
+>>> p.subn('colour', 'no colours at all')
+('no colours at all', 0)
+

Empty matches are replaced only when they’re not adjacent to a previous empty match.

>>> p = re.compile('x*')
+>>> p.sub('-', 'abxd')
+'-a-b--d-'
+

If replacement is a string, any backslash escapes in it are processed. That is, \n is converted to a single newline character, \r is converted to a carriage return, and so forth. Unknown escapes such as \& are left alone. Backreferences, such as \6, are replaced with the substring matched by the corresponding group in the RE. This lets you incorporate portions of the original text in the resulting replacement string.

This example matches the word section followed by a string enclosed in {, }, and changes section to subsection:

>>> p = re.compile('section{ ( [^}]* ) }', re.VERBOSE)
+>>> p.sub(r'subsection{\1}','section{First} section{second}')
+'subsection{First} subsection{second}'
+

There’s also a syntax for referring to named groups as defined by the (?P<name>...) syntax. \g<name> will use the substring matched by the group named name, and \g<number> uses the corresponding group number. \g<2> is therefore equivalent to \2, but isn’t ambiguous in a replacement string such as \g<2>0. (\20 would be interpreted as a reference to group 20, not a reference to group 2 followed by the literal character '0'.) The following substitutions are all equivalent, but use all three variations of the replacement string.

>>> p = re.compile('section{ (?P<name> [^}]* ) }', re.VERBOSE)
+>>> p.sub(r'subsection{\1}','section{First}')
+'subsection{First}'
+>>> p.sub(r'subsection{\g<1>}','section{First}')
+'subsection{First}'
+>>> p.sub(r'subsection{\g<name>}','section{First}')
+'subsection{First}'
+

replacement can also be a function, which gives you even more control. If replacement is a function, the function is called for every non-overlapping occurrence of pattern. On each call, the function is passed a match object argument for the match and can use this information to compute the desired replacement string and return it.

In the following example, the replacement function translates decimals into hexadecimal:

>>> def hexrepl(match):
+...     "Return the hex string for a decimal number"
+...     value = int(match.group())
+...     return hex(value)
+...
+>>> p = re.compile(r'\d+')
+>>> p.sub(hexrepl, 'Call 65490 for printing, 49152 for user code.')
+'Call 0xffd2 for printing, 0xc000 for user code.'
+

When using the module-level re.sub() function, the pattern is passed as the first argument. The pattern may be provided as an object or as a string; if you need to specify regular expression flags, you must either use a pattern object as the first parameter, or use embedded modifiers in the pattern string, e.g. sub("(?i)b+", "x", "bbbb BBBB") returns 'x x'.

Common Problems

Regular expressions are a powerful tool for some applications, but in some ways their behaviour isn’t intuitive and at times they don’t behave the way you may expect them to. This section will point out some of the most common pitfalls.

Use String Methods

Sometimes using the re module is a mistake. If you’re matching a fixed string, or a single character class, and you’re not using any re features such as the IGNORECASE flag, then the full power of regular expressions may not be required. Strings have several methods for performing operations with fixed strings and they’re usually much faster, because the implementation is a single small C loop that’s been optimized for the purpose, instead of the large, more generalized regular expression engine.

One example might be replacing a single fixed string with another one; for example, you might replace word with deed. re.sub() seems like the function to use for this, but consider the replace() method. Note that replace() will also replace word inside words, turning swordfish into sdeedfish, but the naive RE word would have done that, too. (To avoid performing the substitution on parts of words, the pattern would have to be \bword\b, in order to require that word have a word boundary on either side. This takes the job beyond replace()’s abilities.)

Another common task is deleting every occurrence of a single character from a string or replacing it with another single character. You might do this with something like re.sub('\n', ' ', S), but translate() is capable of doing both tasks and will be faster than any regular expression operation can be.

In short, before turning to the re module, consider whether your problem can be solved with a faster and simpler string method.

Greedy versus Non-Greedy

When repeating a regular expression, as in a*, the resulting action is to consume as much of the pattern as possible. This fact often bites you when you’re trying to match a pair of balanced delimiters, such as the angle brackets surrounding an HTML tag. The naive pattern for matching a single HTML tag doesn’t work because of the greedy nature of .*.

>>> s = '<html><head><title>Title</title>'
+>>> len(s)
+32
+>>> print(re.match('<.*>', s).span())
+(0, 32)
+>>> print(re.match('<.*>', s).group())
+<html><head><title>Title</title>
+

The RE matches the '<' in '<html>', and the .* consumes the rest of the string. There’s still more left in the RE, though, and the > can’t match at the end of the string, so the regular expression engine has to backtrack character by character until it finds a match for the >. The final match extends from the '<' in '<html>' to the '>' in '</title>', which isn’t what you want.

In this case, the solution is to use the non-greedy quantifiers *?, +?, ??, or {m,n}?, which match as little text as possible. In the above example, the '>' is tried immediately after the first '<' matches, and when it fails, the engine advances a character at a time, retrying the '>' at every step. This produces just the right result:

>>> print(re.match('<.*?>', s).group())
+<html>
+

(Note that parsing HTML or XML with regular expressions is painful. Quick-and-dirty patterns will handle common cases, but HTML and XML have special cases that will break the obvious regular expression; by the time you’ve written a regular expression that handles all of the possible cases, the patterns will be very complicated. Use an HTML or XML parser module for such tasks.)

Using re.VERBOSE

By now you’ve probably noticed that regular expressions are a very compact notation, but they’re not terribly readable. REs of moderate complexity can become lengthy collections of backslashes, parentheses, and metacharacters, making them difficult to read and understand.

For such REs, specifying the re.VERBOSE flag when compiling the regular expression can be helpful, because it allows you to format the regular expression more clearly.

The re.VERBOSE flag has several effects. Whitespace in the regular expression that isn’t inside a character class is ignored. This means that an expression such as dog | cat is equivalent to the less readable dog|cat, but [a b] will still match the characters 'a', 'b', or a space. In addition, you can also put comments inside a RE; comments extend from a # character to the next newline. When used with triple-quoted strings, this enables REs to be formatted more neatly:

pat = re.compile(r"""
+ \s*                 # Skip leading whitespace
+ (?P<header>[^:]+)   # Header name
+ \s* :               # Whitespace, and a colon
+ (?P<value>.*?)      # The header's value -- *? used to
+                     # lose the following trailing whitespace
+ \s*$                # Trailing whitespace to end-of-line
+""", re.VERBOSE)
+

This is far more readable than:

pat = re.compile(r"\s*(?P<header>[^:]+)\s*:(?P<value>.*?)\s*$")
+

Feedback

Regular expressions are a complicated topic. Did this document help you understand them? Were there parts that were unclear, or Problems you encountered that weren’t covered here? If so, please send suggestions for improvements to the author.

The most complete book on regular expressions is almost certainly Jeffrey Friedl’s Mastering Regular Expressions, published by O’Reilly. Unfortunately, it exclusively concentrates on Perl and Java’s flavours of regular expressions, and doesn’t contain any Python material at all, so it won’t be useful as a reference for programming in Python. (The first edition covered Python’s now-removed regex module, which won’t help you much.) Consider checking it out from your library.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/regex.html +

+
diff --git a/devdocs/python~3.12/howto%2Fsockets.html b/devdocs/python~3.12/howto%2Fsockets.html new file mode 100644 index 00000000..b082869e --- /dev/null +++ b/devdocs/python~3.12/howto%2Fsockets.html @@ -0,0 +1,66 @@ +

Socket Programming HOWTO

Author
+

Gordon McMillan

Abstract

Sockets are used nearly everywhere, but are one of the most severely misunderstood technologies around. This is a 10,000 foot overview of sockets. It’s not really a tutorial - you’ll still have work to do in getting things operational. It doesn’t cover the fine points (and there are a lot of them), but I hope it will give you enough background to begin using them decently.

Sockets

I’m only going to talk about INET (i.e. IPv4) sockets, but they account for at least 99% of the sockets in use. And I’ll only talk about STREAM (i.e. TCP) sockets - unless you really know what you’re doing (in which case this HOWTO isn’t for you!), you’ll get better behavior and performance from a STREAM socket than anything else. I will try to clear up the mystery of what a socket is, as well as some hints on how to work with blocking and non-blocking sockets. But I’ll start by talking about blocking sockets. You’ll need to know how they work before dealing with non-blocking sockets.

Part of the trouble with understanding these things is that “socket” can mean a number of subtly different things, depending on context. So first, let’s make a distinction between a “client” socket - an endpoint of a conversation, and a “server” socket, which is more like a switchboard operator. The client application (your browser, for example) uses “client” sockets exclusively; the web server it’s talking to uses both “server” sockets and “client” sockets.

History

Of the various forms of IPC, sockets are by far the most popular. On any given platform, there are likely to be other forms of IPC that are faster, but for cross-platform communication, sockets are about the only game in town.

They were invented in Berkeley as part of the BSD flavor of Unix. They spread like wildfire with the internet. With good reason — the combination of sockets with INET makes talking to arbitrary machines around the world unbelievably easy (at least compared to other schemes).

Creating a Socket

Roughly speaking, when you clicked on the link that brought you to this page, your browser did something like the following:

# create an INET, STREAMing socket
+s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+# now connect to the web server on port 80 - the normal http port
+s.connect(("www.python.org", 80))
+

When the connect completes, the socket s can be used to send in a request for the text of the page. The same socket will read the reply, and then be destroyed. That’s right, destroyed. Client sockets are normally only used for one exchange (or a small set of sequential exchanges).

What happens in the web server is a bit more complex. First, the web server creates a “server socket”:

# create an INET, STREAMing socket
+serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+# bind the socket to a public host, and a well-known port
+serversocket.bind((socket.gethostname(), 80))
+# become a server socket
+serversocket.listen(5)
+

A couple things to notice: we used socket.gethostname() so that the socket would be visible to the outside world. If we had used s.bind(('localhost', +80)) or s.bind(('127.0.0.1', 80)) we would still have a “server” socket, but one that was only visible within the same machine. s.bind(('', 80)) specifies that the socket is reachable by any address the machine happens to have.

A second thing to note: low number ports are usually reserved for “well known” services (HTTP, SNMP etc). If you’re playing around, use a nice high number (4 digits).

Finally, the argument to listen tells the socket library that we want it to queue up as many as 5 connect requests (the normal max) before refusing outside connections. If the rest of the code is written properly, that should be plenty.

Now that we have a “server” socket, listening on port 80, we can enter the mainloop of the web server:

while True:
+    # accept connections from outside
+    (clientsocket, address) = serversocket.accept()
+    # now do something with the clientsocket
+    # in this case, we'll pretend this is a threaded server
+    ct = client_thread(clientsocket)
+    ct.run()
+

There’s actually 3 general ways in which this loop could work - dispatching a thread to handle clientsocket, create a new process to handle clientsocket, or restructure this app to use non-blocking sockets, and multiplex between our “server” socket and any active clientsockets using select. More about that later. The important thing to understand now is this: this is all a “server” socket does. It doesn’t send any data. It doesn’t receive any data. It just produces “client” sockets. Each clientsocket is created in response to some other “client” socket doing a connect() to the host and port we’re bound to. As soon as we’ve created that clientsocket, we go back to listening for more connections. The two “clients” are free to chat it up - they are using some dynamically allocated port which will be recycled when the conversation ends.

IPC

If you need fast IPC between two processes on one machine, you should look into pipes or shared memory. If you do decide to use AF_INET sockets, bind the “server” socket to 'localhost'. On most platforms, this will take a shortcut around a couple of layers of network code and be quite a bit faster.

See also

The multiprocessing integrates cross-platform IPC into a higher-level API.

Using a Socket

The first thing to note, is that the web browser’s “client” socket and the web server’s “client” socket are identical beasts. That is, this is a “peer to peer” conversation. Or to put it another way, as the designer, you will have to decide what the rules of etiquette are for a conversation. Normally, the connecting socket starts the conversation, by sending in a request, or perhaps a signon. But that’s a design decision - it’s not a rule of sockets.

Now there are two sets of verbs to use for communication. You can use send and recv, or you can transform your client socket into a file-like beast and use read and write. The latter is the way Java presents its sockets. I’m not going to talk about it here, except to warn you that you need to use flush on sockets. These are buffered “files”, and a common mistake is to write something, and then read for a reply. Without a flush in there, you may wait forever for the reply, because the request may still be in your output buffer.

Now we come to the major stumbling block of sockets - send and recv operate on the network buffers. They do not necessarily handle all the bytes you hand them (or expect from them), because their major focus is handling the network buffers. In general, they return when the associated network buffers have been filled (send) or emptied (recv). They then tell you how many bytes they handled. It is your responsibility to call them again until your message has been completely dealt with.

When a recv returns 0 bytes, it means the other side has closed (or is in the process of closing) the connection. You will not receive any more data on this connection. Ever. You may be able to send data successfully; I’ll talk more about this later.

A protocol like HTTP uses a socket for only one transfer. The client sends a request, then reads a reply. That’s it. The socket is discarded. This means that a client can detect the end of the reply by receiving 0 bytes.

But if you plan to reuse your socket for further transfers, you need to realize that there is no EOT on a socket. I repeat: if a socket send or recv returns after handling 0 bytes, the connection has been broken. If the connection has not been broken, you may wait on a recv forever, because the socket will not tell you that there’s nothing more to read (for now). Now if you think about that a bit, you’ll come to realize a fundamental truth of sockets: messages must either be fixed length (yuck), or be delimited (shrug), or indicate how long they are (much better), or end by shutting down the connection. The choice is entirely yours, (but some ways are righter than others).

Assuming you don’t want to end the connection, the simplest solution is a fixed length message:

class MySocket:
+    """demonstration class only
+      - coded for clarity, not efficiency
+    """
+
+    def __init__(self, sock=None):
+        if sock is None:
+            self.sock = socket.socket(
+                            socket.AF_INET, socket.SOCK_STREAM)
+        else:
+            self.sock = sock
+
+    def connect(self, host, port):
+        self.sock.connect((host, port))
+
+    def mysend(self, msg):
+        totalsent = 0
+        while totalsent < MSGLEN:
+            sent = self.sock.send(msg[totalsent:])
+            if sent == 0:
+                raise RuntimeError("socket connection broken")
+            totalsent = totalsent + sent
+
+    def myreceive(self):
+        chunks = []
+        bytes_recd = 0
+        while bytes_recd < MSGLEN:
+            chunk = self.sock.recv(min(MSGLEN - bytes_recd, 2048))
+            if chunk == b'':
+                raise RuntimeError("socket connection broken")
+            chunks.append(chunk)
+            bytes_recd = bytes_recd + len(chunk)
+        return b''.join(chunks)
+

The sending code here is usable for almost any messaging scheme - in Python you send strings, and you can use len() to determine its length (even if it has embedded \0 characters). It’s mostly the receiving code that gets more complex. (And in C, it’s not much worse, except you can’t use strlen if the message has embedded \0s.)

The easiest enhancement is to make the first character of the message an indicator of message type, and have the type determine the length. Now you have two recvs - the first to get (at least) that first character so you can look up the length, and the second in a loop to get the rest. If you decide to go the delimited route, you’ll be receiving in some arbitrary chunk size, (4096 or 8192 is frequently a good match for network buffer sizes), and scanning what you’ve received for a delimiter.

One complication to be aware of: if your conversational protocol allows multiple messages to be sent back to back (without some kind of reply), and you pass recv an arbitrary chunk size, you may end up reading the start of a following message. You’ll need to put that aside and hold onto it, until it’s needed.

Prefixing the message with its length (say, as 5 numeric characters) gets more complex, because (believe it or not), you may not get all 5 characters in one recv. In playing around, you’ll get away with it; but in high network loads, your code will very quickly break unless you use two recv loops - the first to determine the length, the second to get the data part of the message. Nasty. This is also when you’ll discover that send does not always manage to get rid of everything in one pass. And despite having read this, you will eventually get bit by it!

In the interests of space, building your character, (and preserving my competitive position), these enhancements are left as an exercise for the reader. Lets move on to cleaning up.

Binary Data

It is perfectly possible to send binary data over a socket. The major problem is that not all machines use the same formats for binary data. For example, network byte order is big-endian, with the most significant byte first, so a 16 bit integer with the value 1 would be the two hex bytes 00 01. However, most common processors (x86/AMD64, ARM, RISC-V), are little-endian, with the least significant byte first - that same 1 would be 01 00.

Socket libraries have calls for converting 16 and 32 bit integers - ntohl, +htonl, ntohs, htons where “n” means network and “h” means host, “s” means short and “l” means long. Where network order is host order, these do nothing, but where the machine is byte-reversed, these swap the bytes around appropriately.

In these days of 64-bit machines, the ASCII representation of binary data is frequently smaller than the binary representation. That’s because a surprising amount of the time, most integers have the value 0, or maybe 1. The string "0" would be two bytes, while a full 64-bit integer would be 8. Of course, this doesn’t fit well with fixed-length messages. Decisions, decisions.

Disconnecting

Strictly speaking, you’re supposed to use shutdown on a socket before you close it. The shutdown is an advisory to the socket at the other end. Depending on the argument you pass it, it can mean “I’m not going to send anymore, but I’ll still listen”, or “I’m not listening, good riddance!”. Most socket libraries, however, are so used to programmers neglecting to use this piece of etiquette that normally a close is the same as shutdown(); +close(). So in most situations, an explicit shutdown is not needed.

One way to use shutdown effectively is in an HTTP-like exchange. The client sends a request and then does a shutdown(1). This tells the server “This client is done sending, but can still receive.” The server can detect “EOF” by a receive of 0 bytes. It can assume it has the complete request. The server sends a reply. If the send completes successfully then, indeed, the client was still receiving.

Python takes the automatic shutdown a step further, and says that when a socket is garbage collected, it will automatically do a close if it’s needed. But relying on this is a very bad habit. If your socket just disappears without doing a close, the socket at the other end may hang indefinitely, thinking you’re just being slow. Please close your sockets when you’re done.

When Sockets Die

Probably the worst thing about using blocking sockets is what happens when the other side comes down hard (without doing a close). Your socket is likely to hang. TCP is a reliable protocol, and it will wait a long, long time before giving up on a connection. If you’re using threads, the entire thread is essentially dead. There’s not much you can do about it. As long as you aren’t doing something dumb, like holding a lock while doing a blocking read, the thread isn’t really consuming much in the way of resources. Do not try to kill the thread - part of the reason that threads are more efficient than processes is that they avoid the overhead associated with the automatic recycling of resources. In other words, if you do manage to kill the thread, your whole process is likely to be screwed up.

Non-blocking Sockets

If you’ve understood the preceding, you already know most of what you need to know about the mechanics of using sockets. You’ll still use the same calls, in much the same ways. It’s just that, if you do it right, your app will be almost inside-out.

In Python, you use socket.setblocking(False) to make it non-blocking. In C, it’s more complex, (for one thing, you’ll need to choose between the BSD flavor O_NONBLOCK and the almost indistinguishable POSIX flavor O_NDELAY, which is completely different from TCP_NODELAY), but it’s the exact same idea. You do this after creating the socket, but before using it. (Actually, if you’re nuts, you can switch back and forth.)

The major mechanical difference is that send, recv, connect and accept can return without having done anything. You have (of course) a number of choices. You can check return code and error codes and generally drive yourself crazy. If you don’t believe me, try it sometime. Your app will grow large, buggy and suck CPU. So let’s skip the brain-dead solutions and do it right.

Use select.

In C, coding select is fairly complex. In Python, it’s a piece of cake, but it’s close enough to the C version that if you understand select in Python, you’ll have little trouble with it in C:

ready_to_read, ready_to_write, in_error = \
+               select.select(
+                  potential_readers,
+                  potential_writers,
+                  potential_errs,
+                  timeout)
+

You pass select three lists: the first contains all sockets that you might want to try reading; the second all the sockets you might want to try writing to, and the last (normally left empty) those that you want to check for errors. You should note that a socket can go into more than one list. The select call is blocking, but you can give it a timeout. This is generally a sensible thing to do - give it a nice long timeout (say a minute) unless you have good reason to do otherwise.

In return, you will get three lists. They contain the sockets that are actually readable, writable and in error. Each of these lists is a subset (possibly empty) of the corresponding list you passed in.

If a socket is in the output readable list, you can be as-close-to-certain-as-we-ever-get-in-this-business that a recv on that socket will return something. Same idea for the writable list. You’ll be able to send something. Maybe not all you want to, but something is better than nothing. (Actually, any reasonably healthy socket will return as writable - it just means outbound network buffer space is available.)

If you have a “server” socket, put it in the potential_readers list. If it comes out in the readable list, your accept will (almost certainly) work. If you have created a new socket to connect to someone else, put it in the potential_writers list. If it shows up in the writable list, you have a decent chance that it has connected.

Actually, select can be handy even with blocking sockets. It’s one way of determining whether you will block - the socket returns as readable when there’s something in the buffers. However, this still doesn’t help with the problem of determining whether the other end is done, or just busy with something else.

Portability alert: On Unix, select works both with the sockets and files. Don’t try this on Windows. On Windows, select works with sockets only. Also note that in C, many of the more advanced socket options are done differently on Windows. In fact, on Windows I usually use threads (which work very, very well) with my sockets.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/sockets.html +

+
diff --git a/devdocs/python~3.12/howto%2Fsorting.html b/devdocs/python~3.12/howto%2Fsorting.html new file mode 100644 index 00000000..6a18caa2 --- /dev/null +++ b/devdocs/python~3.12/howto%2Fsorting.html @@ -0,0 +1,91 @@ +

Sorting HOW TO

Author
+

Andrew Dalke and Raymond Hettinger

Release
+

0.1

Python lists have a built-in list.sort() method that modifies the list in-place. There is also a sorted() built-in function that builds a new sorted list from an iterable.

In this document, we explore the various techniques for sorting data using Python.

Sorting Basics

A simple ascending sort is very easy: just call the sorted() function. It returns a new sorted list:

>>> sorted([5, 2, 3, 1, 4])
+[1, 2, 3, 4, 5]
+

You can also use the list.sort() method. It modifies the list in-place (and returns None to avoid confusion). Usually it’s less convenient than sorted() - but if you don’t need the original list, it’s slightly more efficient.

>>> a = [5, 2, 3, 1, 4]
+>>> a.sort()
+>>> a
+[1, 2, 3, 4, 5]
+

Another difference is that the list.sort() method is only defined for lists. In contrast, the sorted() function accepts any iterable.

>>> sorted({1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'})
+[1, 2, 3, 4, 5]
+

Key Functions

Both list.sort() and sorted() have a key parameter to specify a function (or other callable) to be called on each list element prior to making comparisons.

For example, here’s a case-insensitive string comparison:

>>> sorted("This is a test string from Andrew".split(), key=str.lower)
+['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
+

The value of the key parameter should be a function (or other callable) that takes a single argument and returns a key to use for sorting purposes. This technique is fast because the key function is called exactly once for each input record.

A common pattern is to sort complex objects using some of the object’s indices as keys. For example:

>>> student_tuples = [
+...     ('john', 'A', 15),
+...     ('jane', 'B', 12),
+...     ('dave', 'B', 10),
+... ]
+>>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+

The same technique works for objects with named attributes. For example:

>>> class Student:
+...     def __init__(self, name, grade, age):
+...         self.name = name
+...         self.grade = grade
+...         self.age = age
+...     def __repr__(self):
+...         return repr((self.name, self.grade, self.age))
+
+>>> student_objects = [
+...     Student('john', 'A', 15),
+...     Student('jane', 'B', 12),
+...     Student('dave', 'B', 10),
+... ]
+>>> sorted(student_objects, key=lambda student: student.age)   # sort by age
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+

Operator Module Functions

The key-function patterns shown above are very common, so Python provides convenience functions to make accessor functions easier and faster. The operator module has itemgetter(), attrgetter(), and a methodcaller() function.

Using those functions, the above examples become simpler and faster:

>>> from operator import itemgetter, attrgetter
+
+>>> sorted(student_tuples, key=itemgetter(2))
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+
+>>> sorted(student_objects, key=attrgetter('age'))
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+

The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age:

>>> sorted(student_tuples, key=itemgetter(1,2))
+[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
+
+>>> sorted(student_objects, key=attrgetter('grade', 'age'))
+[('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
+

Ascending and Descending

Both list.sort() and sorted() accept a reverse parameter with a boolean value. This is used to flag descending sorts. For example, to get the student data in reverse age order:

>>> sorted(student_tuples, key=itemgetter(2), reverse=True)
+[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
+
+>>> sorted(student_objects, key=attrgetter('age'), reverse=True)
+[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
+

Sort Stability and Complex Sorts

Sorts are guaranteed to be stable. That means that when multiple records have the same key, their original order is preserved.

>>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)]
+>>> sorted(data, key=itemgetter(0))
+[('blue', 1), ('blue', 2), ('red', 1), ('red', 2)]
+

Notice how the two records for blue retain their original order so that ('blue', 1) is guaranteed to precede ('blue', 2).

This wonderful property lets you build complex sorts in a series of sorting steps. For example, to sort the student data by descending grade and then ascending age, do the age sort first and then sort again using grade:

>>> s = sorted(student_objects, key=attrgetter('age'))     # sort on secondary key
+>>> sorted(s, key=attrgetter('grade'), reverse=True)       # now sort on primary key, descending
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+

This can be abstracted out into a wrapper function that can take a list and tuples of field and order to sort them on multiple passes.

>>> def multisort(xs, specs):
+...     for key, reverse in reversed(specs):
+...         xs.sort(key=attrgetter(key), reverse=reverse)
+...     return xs
+
+>>> multisort(list(student_objects), (('grade', True), ('age', False)))
+[('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
+

The Timsort algorithm used in Python does multiple sorts efficiently because it can take advantage of any ordering already present in a dataset.

Decorate-Sort-Undecorate

This idiom is called Decorate-Sort-Undecorate after its three steps:

For example, to sort the student data by grade using the DSU approach:

>>> decorated = [(student.grade, i, student) for i, student in enumerate(student_objects)]
+>>> decorated.sort()
+>>> [student for grade, i, student in decorated]               # undecorate
+[('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
+

This idiom works because tuples are compared lexicographically; the first items are compared; if they are the same then the second items are compared, and so on.

It is not strictly necessary in all cases to include the index i in the decorated list, but including it gives two benefits:

Another name for this idiom is Schwartzian transform, after Randal L. Schwartz, who popularized it among Perl programmers.

Now that Python sorting provides key-functions, this technique is not often needed.

Comparison Functions

Unlike key functions that return an absolute value for sorting, a comparison function computes the relative ordering for two inputs.

For example, a balance scale compares two samples giving a relative ordering: lighter, equal, or heavier. Likewise, a comparison function such as cmp(a, b) will return a negative value for less-than, zero if the inputs are equal, or a positive value for greater-than.

It is common to encounter comparison functions when translating algorithms from other languages. Also, some libraries provide comparison functions as part of their API. For example, locale.strcoll() is a comparison function.

To accommodate those situations, Python provides functools.cmp_to_key to wrap the comparison function to make it usable as a key function:

sorted(words, key=cmp_to_key(strcoll))  # locale-aware sort order
+

Odds and Ends

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/sorting.html +

+
diff --git a/devdocs/python~3.12/howto%2Funicode.html b/devdocs/python~3.12/howto%2Funicode.html new file mode 100644 index 00000000..9d841664 --- /dev/null +++ b/devdocs/python~3.12/howto%2Funicode.html @@ -0,0 +1,177 @@ +

Unicode HOWTO

Release
+

1.12

This HOWTO discusses Python’s support for the Unicode specification for representing textual data, and explains various problems that people commonly encounter when trying to work with Unicode.

Introduction to Unicode

Definitions

Today’s programs need to be able to handle a wide variety of characters. Applications are often internationalized to display messages and output in a variety of user-selectable languages; the same program might need to output an error message in English, French, Japanese, Hebrew, or Russian. Web content can be written in any of these languages and can also include a variety of emoji symbols. Python’s string type uses the Unicode Standard for representing characters, which lets Python programs work with all these different possible characters.

Unicode (https://www.unicode.org/) is a specification that aims to list every character used by human languages and give each character its own unique code. The Unicode specifications are continually revised and updated to add new languages and symbols.

A character is the smallest possible component of a text. ‘A’, ‘B’, ‘C’, etc., are all different characters. So are ‘È’ and ‘Í’. Characters vary depending on the language or context you’re talking about. For example, there’s a character for “Roman Numeral One”, ‘Ⅰ’, that’s separate from the uppercase letter ‘I’. They’ll usually look the same, but these are two different characters that have different meanings.

The Unicode standard describes how characters are represented by code points. A code point value is an integer in the range 0 to 0x10FFFF (about 1.1 million values, the actual number assigned is less than that). In the standard and in this document, a code point is written using the notation U+265E to mean the character with value 0x265e (9,822 in decimal).

The Unicode standard contains a lot of tables listing characters and their corresponding code points:

0061    'a'; LATIN SMALL LETTER A
+0062    'b'; LATIN SMALL LETTER B
+0063    'c'; LATIN SMALL LETTER C
+...
+007B    '{'; LEFT CURLY BRACKET
+...
+2167    'Ⅷ'; ROMAN NUMERAL EIGHT
+2168    'Ⅸ'; ROMAN NUMERAL NINE
+...
+265E    '♞'; BLACK CHESS KNIGHT
+265F    '♟'; BLACK CHESS PAWN
+...
+1F600   '😀'; GRINNING FACE
+1F609   '😉'; WINKING FACE
+...
+

Strictly, these definitions imply that it’s meaningless to say ‘this is character U+265E’. U+265E is a code point, which represents some particular character; in this case, it represents the character ‘BLACK CHESS KNIGHT’, ‘♞’. In informal contexts, this distinction between code points and characters will sometimes be forgotten.

A character is represented on a screen or on paper by a set of graphical elements that’s called a glyph. The glyph for an uppercase A, for example, is two diagonal strokes and a horizontal stroke, though the exact details will depend on the font being used. Most Python code doesn’t need to worry about glyphs; figuring out the correct glyph to display is generally the job of a GUI toolkit or a terminal’s font renderer.

Encodings

To summarize the previous section: a Unicode string is a sequence of code points, which are numbers from 0 through 0x10FFFF (1,114,111 decimal). This sequence of code points needs to be represented in memory as a set of code units, and code units are then mapped to 8-bit bytes. The rules for translating a Unicode string into a sequence of bytes are called a character encoding, or just an encoding.

The first encoding you might think of is using 32-bit integers as the code unit, and then using the CPU’s representation of 32-bit integers. In this representation, the string “Python” might look like this:

   P           y           t           h           o           n
+0x50 00 00 00 79 00 00 00 74 00 00 00 68 00 00 00 6f 00 00 00 6e 00 00 00
+   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
+

This representation is straightforward but using it presents a number of problems.

  1. It’s not portable; different processors order the bytes differently.
  2. It’s very wasteful of space. In most texts, the majority of the code points are less than 127, or less than 255, so a lot of space is occupied by 0x00 bytes. The above string takes 24 bytes compared to the 6 bytes needed for an ASCII representation. Increased RAM usage doesn’t matter too much (desktop computers have gigabytes of RAM, and strings aren’t usually that large), but expanding our usage of disk and network bandwidth by a factor of 4 is intolerable.
  3. It’s not compatible with existing C functions such as strlen(), so a new family of wide string functions would need to be used.

Therefore this encoding isn’t used very much, and people instead choose other encodings that are more efficient and convenient, such as UTF-8.

UTF-8 is one of the most commonly used encodings, and Python often defaults to using it. UTF stands for “Unicode Transformation Format”, and the ‘8’ means that 8-bit values are used in the encoding. (There are also UTF-16 and UTF-32 encodings, but they are less frequently used than UTF-8.) UTF-8 uses the following rules:

  1. If the code point is < 128, it’s represented by the corresponding byte value.
  2. If the code point is >= 128, it’s turned into a sequence of two, three, or four bytes, where each byte of the sequence is between 128 and 255.

UTF-8 has several convenient properties:

  1. It can handle any Unicode code point.
  2. A Unicode string is turned into a sequence of bytes that contains embedded zero bytes only where they represent the null character (U+0000). This means that UTF-8 strings can be processed by C functions such as strcpy() and sent through protocols that can’t handle zero bytes for anything other than end-of-string markers.
  3. A string of ASCII text is also valid UTF-8 text.
  4. UTF-8 is fairly compact; the majority of commonly used characters can be represented with one or two bytes.
  5. If bytes are corrupted or lost, it’s possible to determine the start of the next UTF-8-encoded code point and resynchronize. It’s also unlikely that random 8-bit data will look like valid UTF-8.
  6. UTF-8 is a byte oriented encoding. The encoding specifies that each character is represented by a specific sequence of one or more bytes. This avoids the byte-ordering issues that can occur with integer and word oriented encodings, like UTF-16 and UTF-32, where the sequence of bytes varies depending on the hardware on which the string was encoded.

References

The Unicode Consortium site has character charts, a glossary, and PDF versions of the Unicode specification. Be prepared for some difficult reading. A chronology of the origin and development of Unicode is also available on the site.

On the Computerphile Youtube channel, Tom Scott briefly discusses the history of Unicode and UTF-8 (9 minutes 36 seconds).

To help understand the standard, Jukka Korpela has written an introductory guide to reading the Unicode character tables.

Another good introductory article was written by Joel Spolsky. If this introduction didn’t make things clear to you, you should try reading this alternate article before continuing.

Wikipedia entries are often helpful; see the entries for “character encoding” and UTF-8, for example.

Python’s Unicode Support

Now that you’ve learned the rudiments of Unicode, we can look at Python’s Unicode features.

The String Type

Since Python 3.0, the language’s str type contains Unicode characters, meaning any string created using "unicode rocks!", 'unicode +rocks!', or the triple-quoted string syntax is stored as Unicode.

The default encoding for Python source code is UTF-8, so you can simply include a Unicode character in a string literal:

try:
+    with open('/tmp/input.txt', 'r') as f:
+        ...
+except OSError:
+    # 'File not found' error message.
+    print("Fichier non trouvé")
+

Side note: Python 3 also supports using Unicode characters in identifiers:

répertoire = "/tmp/records.log"
+with open(répertoire, "w") as f:
+    f.write("test\n")
+

If you can’t enter a particular character in your editor or want to keep the source code ASCII-only for some reason, you can also use escape sequences in string literals. (Depending on your system, you may see the actual capital-delta glyph instead of a u escape.)

>>> "\N{GREEK CAPITAL LETTER DELTA}"  # Using the character name
+'\u0394'
+>>> "\u0394"                          # Using a 16-bit hex value
+'\u0394'
+>>> "\U00000394"                      # Using a 32-bit hex value
+'\u0394'
+

In addition, one can create a string using the decode() method of bytes. This method takes an encoding argument, such as UTF-8, and optionally an errors argument.

The errors argument specifies the response when the input string can’t be converted according to the encoding’s rules. Legal values for this argument are 'strict' (raise a UnicodeDecodeError exception), 'replace' (use U+FFFD, REPLACEMENT CHARACTER), 'ignore' (just leave the character out of the Unicode result), or 'backslashreplace' (inserts a \xNN escape sequence). The following examples show the differences:

>>> b'\x80abc'.decode("utf-8", "strict")  
+Traceback (most recent call last):
+    ...
+UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0:
+  invalid start byte
+>>> b'\x80abc'.decode("utf-8", "replace")
+'\ufffdabc'
+>>> b'\x80abc'.decode("utf-8", "backslashreplace")
+'\\x80abc'
+>>> b'\x80abc'.decode("utf-8", "ignore")
+'abc'
+

Encodings are specified as strings containing the encoding’s name. Python comes with roughly 100 different encodings; see the Python Library Reference at Standard Encodings for a list. Some encodings have multiple names; for example, 'latin-1', 'iso_8859_1' and '8859’ are all synonyms for the same encoding.

One-character Unicode strings can also be created with the chr() built-in function, which takes integers and returns a Unicode string of length 1 that contains the corresponding code point. The reverse operation is the built-in ord() function that takes a one-character Unicode string and returns the code point value:

>>> chr(57344)
+'\ue000'
+>>> ord('\ue000')
+57344
+

Converting to Bytes

The opposite method of bytes.decode() is str.encode(), which returns a bytes representation of the Unicode string, encoded in the requested encoding.

The errors parameter is the same as the parameter of the decode() method but supports a few more possible handlers. As well as 'strict', 'ignore', and 'replace' (which in this case inserts a question mark instead of the unencodable character), there is also 'xmlcharrefreplace' (inserts an XML character reference), backslashreplace (inserts a \uNNNN escape sequence) and namereplace (inserts a \N{...} escape sequence).

The following example shows the different results:

>>> u = chr(40960) + 'abcd' + chr(1972)
+>>> u.encode('utf-8')
+b'\xea\x80\x80abcd\xde\xb4'
+>>> u.encode('ascii')  
+Traceback (most recent call last):
+    ...
+UnicodeEncodeError: 'ascii' codec can't encode character '\ua000' in
+  position 0: ordinal not in range(128)
+>>> u.encode('ascii', 'ignore')
+b'abcd'
+>>> u.encode('ascii', 'replace')
+b'?abcd?'
+>>> u.encode('ascii', 'xmlcharrefreplace')
+b'&#40960;abcd&#1972;'
+>>> u.encode('ascii', 'backslashreplace')
+b'\\ua000abcd\\u07b4'
+>>> u.encode('ascii', 'namereplace')
+b'\\N{YI SYLLABLE IT}abcd\\u07b4'
+

The low-level routines for registering and accessing the available encodings are found in the codecs module. Implementing new encodings also requires understanding the codecs module. However, the encoding and decoding functions returned by this module are usually more low-level than is comfortable, and writing new encodings is a specialized task, so the module won’t be covered in this HOWTO.

Unicode Literals in Python Source Code

In Python source code, specific Unicode code points can be written using the \u escape sequence, which is followed by four hex digits giving the code point. The \U escape sequence is similar, but expects eight hex digits, not four:

>>> s = "a\xac\u1234\u20ac\U00008000"
+... #     ^^^^ two-digit hex escape
+... #         ^^^^^^ four-digit Unicode escape
+... #                     ^^^^^^^^^^ eight-digit Unicode escape
+>>> [ord(c) for c in s]
+[97, 172, 4660, 8364, 32768]
+

Using escape sequences for code points greater than 127 is fine in small doses, but becomes an annoyance if you’re using many accented characters, as you would in a program with messages in French or some other accent-using language. You can also assemble strings using the chr() built-in function, but this is even more tedious.

Ideally, you’d want to be able to write literals in your language’s natural encoding. You could then edit Python source code with your favorite editor which would display the accented characters naturally, and have the right characters used at runtime.

Python supports writing source code in UTF-8 by default, but you can use almost any encoding if you declare the encoding being used. This is done by including a special comment as either the first or second line of the source file:

#!/usr/bin/env python
+# -*- coding: latin-1 -*-
+
+u = 'abcdé'
+print(ord(u[-1]))
+

The syntax is inspired by Emacs’s notation for specifying variables local to a file. Emacs supports many different variables, but Python only supports ‘coding’. The -*- symbols indicate to Emacs that the comment is special; they have no significance to Python but are a convention. Python looks for coding: name or coding=name in the comment.

If you don’t include such a comment, the default encoding used will be UTF-8 as already mentioned. See also PEP 263 for more information.

Unicode Properties

The Unicode specification includes a database of information about code points. For each defined code point, the information includes the character’s name, its category, the numeric value if applicable (for characters representing numeric concepts such as the Roman numerals, fractions such as one-third and four-fifths, etc.). There are also display-related properties, such as how to use the code point in bidirectional text.

The following program displays some information about several characters, and prints the numeric value of one particular character:

import unicodedata
+
+u = chr(233) + chr(0x0bf2) + chr(3972) + chr(6000) + chr(13231)
+
+for i, c in enumerate(u):
+    print(i, '%04x' % ord(c), unicodedata.category(c), end=" ")
+    print(unicodedata.name(c))
+
+# Get numeric value of second character
+print(unicodedata.numeric(u[1]))
+

When run, this prints:

0 00e9 Ll LATIN SMALL LETTER E WITH ACUTE
+1 0bf2 No TAMIL NUMBER ONE THOUSAND
+2 0f84 Mn TIBETAN MARK HALANTA
+3 1770 Lo TAGBANWA LETTER SA
+4 33af So SQUARE RAD OVER S SQUARED
+1000.0
+

The category codes are abbreviations describing the nature of the character. These are grouped into categories such as “Letter”, “Number”, “Punctuation”, or “Symbol”, which in turn are broken up into subcategories. To take the codes from the above output, 'Ll' means ‘Letter, lowercase’, 'No' means “Number, other”, 'Mn' is “Mark, nonspacing”, and 'So' is “Symbol, other”. See the General Category Values section of the Unicode Character Database documentation for a list of category codes.

Comparing Strings

Unicode adds some complication to comparing strings, because the same set of characters can be represented by different sequences of code points. For example, a letter like ‘ê’ can be represented as a single code point U+00EA, or as U+0065 U+0302, which is the code point for ‘e’ followed by a code point for ‘COMBINING CIRCUMFLEX ACCENT’. These will produce the same output when printed, but one is a string of length 1 and the other is of length 2.

One tool for a case-insensitive comparison is the casefold() string method that converts a string to a case-insensitive form following an algorithm described by the Unicode Standard. This algorithm has special handling for characters such as the German letter ‘ß’ (code point U+00DF), which becomes the pair of lowercase letters ‘ss’.

>>> street = 'Gürzenichstraße'
+>>> street.casefold()
+'gürzenichstrasse'
+

A second tool is the unicodedata module’s normalize() function that converts strings to one of several normal forms, where letters followed by a combining character are replaced with single characters. normalize() can be used to perform string comparisons that won’t falsely report inequality if two strings use combining characters differently:

import unicodedata
+
+def compare_strs(s1, s2):
+    def NFD(s):
+        return unicodedata.normalize('NFD', s)
+
+    return NFD(s1) == NFD(s2)
+
+single_char = 'ê'
+multiple_chars = '\N{LATIN SMALL LETTER E}\N{COMBINING CIRCUMFLEX ACCENT}'
+print('length of first string=', len(single_char))
+print('length of second string=', len(multiple_chars))
+print(compare_strs(single_char, multiple_chars))
+

When run, this outputs:

$ python compare-strs.py
+length of first string= 1
+length of second string= 2
+True
+

The first argument to the normalize() function is a string giving the desired normalization form, which can be one of ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’.

The Unicode Standard also specifies how to do caseless comparisons:

import unicodedata
+
+def compare_caseless(s1, s2):
+    def NFD(s):
+        return unicodedata.normalize('NFD', s)
+
+    return NFD(NFD(s1).casefold()) == NFD(NFD(s2).casefold())
+
+# Example usage
+single_char = 'ê'
+multiple_chars = '\N{LATIN CAPITAL LETTER E}\N{COMBINING CIRCUMFLEX ACCENT}'
+
+print(compare_caseless(single_char, multiple_chars))
+

This will print True. (Why is NFD() invoked twice? Because there are a few characters that make casefold() return a non-normalized string, so the result needs to be normalized again. See section 3.13 of the Unicode Standard for a discussion and an example.)

Unicode Regular Expressions

The regular expressions supported by the re module can be provided either as bytes or strings. Some of the special character sequences such as \d and \w have different meanings depending on whether the pattern is supplied as bytes or a string. For example, \d will match the characters [0-9] in bytes but in strings will match any character that’s in the 'Nd' category.

The string in this example has the number 57 written in both Thai and Arabic numerals:

import re
+p = re.compile(r'\d+')
+
+s = "Over \u0e55\u0e57 57 flavours"
+m = p.search(s)
+print(repr(m.group()))
+

When executed, \d+ will match the Thai numerals and print them out. If you supply the re.ASCII flag to compile(), \d+ will match the substring “57” instead.

Similarly, \w matches a wide variety of Unicode characters but only [a-zA-Z0-9_] in bytes or if re.ASCII is supplied, and \s will match either Unicode whitespace characters or [ \t\n\r\f\v].

References

Some good alternative discussions of Python’s Unicode support are:

The str type is described in the Python library reference at Text Sequence Type — str.

The documentation for the unicodedata module.

The documentation for the codecs module.

Marc-André Lemburg gave a presentation titled “Python and Unicode” (PDF slides) at EuroPython 2002. The slides are an excellent overview of the design of Python 2’s Unicode features (where the Unicode string type is called unicode and literals start with u).

Reading and Writing Unicode Data

Once you’ve written some code that works with Unicode data, the next problem is input/output. How do you get Unicode strings into your program, and how do you convert Unicode into a form suitable for storage or transmission?

It’s possible that you may not need to do anything depending on your input sources and output destinations; you should check whether the libraries used in your application support Unicode natively. XML parsers often return Unicode data, for example. Many relational databases also support Unicode-valued columns and can return Unicode values from an SQL query.

Unicode data is usually converted to a particular encoding before it gets written to disk or sent over a socket. It’s possible to do all the work yourself: open a file, read an 8-bit bytes object from it, and convert the bytes with bytes.decode(encoding). However, the manual approach is not recommended.

One problem is the multi-byte nature of encodings; one Unicode character can be represented by several bytes. If you want to read the file in arbitrary-sized chunks (say, 1024 or 4096 bytes), you need to write error-handling code to catch the case where only part of the bytes encoding a single Unicode character are read at the end of a chunk. One solution would be to read the entire file into memory and then perform the decoding, but that prevents you from working with files that are extremely large; if you need to read a 2 GiB file, you need 2 GiB of RAM. (More, really, since for at least a moment you’d need to have both the encoded string and its Unicode version in memory.)

The solution would be to use the low-level decoding interface to catch the case of partial coding sequences. The work of implementing this has already been done for you: the built-in open() function can return a file-like object that assumes the file’s contents are in a specified encoding and accepts Unicode parameters for methods such as read() and write(). This works through open()'s encoding and errors parameters which are interpreted just like those in str.encode() and bytes.decode().

Reading Unicode from a file is therefore simple:

with open('unicode.txt', encoding='utf-8') as f:
+    for line in f:
+        print(repr(line))
+

It’s also possible to open files in update mode, allowing both reading and writing:

with open('test', encoding='utf-8', mode='w+') as f:
+    f.write('\u4500 blah blah blah\n')
+    f.seek(0)
+    print(repr(f.readline()[:1]))
+

The Unicode character U+FEFF is used as a byte-order mark (BOM), and is often written as the first character of a file in order to assist with autodetection of the file’s byte ordering. Some encodings, such as UTF-16, expect a BOM to be present at the start of a file; when such an encoding is used, the BOM will be automatically written as the first character and will be silently dropped when the file is read. There are variants of these encodings, such as ‘utf-16-le’ and ‘utf-16-be’ for little-endian and big-endian encodings, that specify one particular byte ordering and don’t skip the BOM.

In some areas, it is also convention to use a “BOM” at the start of UTF-8 encoded files; the name is misleading since UTF-8 is not byte-order dependent. The mark simply announces that the file is encoded in UTF-8. For reading such files, use the ‘utf-8-sig’ codec to automatically skip the mark if present.

Unicode filenames

Most of the operating systems in common use today support filenames that contain arbitrary Unicode characters. Usually this is implemented by converting the Unicode string into some encoding that varies depending on the system. Today Python is converging on using UTF-8: Python on MacOS has used UTF-8 for several versions, and Python 3.6 switched to using UTF-8 on Windows as well. On Unix systems, there will only be a filesystem encoding. if you’ve set the LANG or LC_CTYPE environment variables; if you haven’t, the default encoding is again UTF-8.

The sys.getfilesystemencoding() function returns the encoding to use on your current system, in case you want to do the encoding manually, but there’s not much reason to bother. When opening a file for reading or writing, you can usually just provide the Unicode string as the filename, and it will be automatically converted to the right encoding for you:

filename = 'filename\u4500abc'
+with open(filename, 'w') as f:
+    f.write('blah\n')
+

Functions in the os module such as os.stat() will also accept Unicode filenames.

The os.listdir() function returns filenames, which raises an issue: should it return the Unicode version of filenames, or should it return bytes containing the encoded versions? os.listdir() can do both, depending on whether you provided the directory path as bytes or a Unicode string. If you pass a Unicode string as the path, filenames will be decoded using the filesystem’s encoding and a list of Unicode strings will be returned, while passing a byte path will return the filenames as bytes. For example, assuming the default filesystem encoding is UTF-8, running the following program:

fn = 'filename\u4500abc'
+f = open(fn, 'w')
+f.close()
+
+import os
+print(os.listdir(b'.'))
+print(os.listdir('.'))
+

will produce the following output:

$ python listdir-test.py
+[b'filename\xe4\x94\x80abc', ...]
+['filename\u4500abc', ...]
+

The first list contains UTF-8-encoded filenames, and the second list contains the Unicode versions.

Note that on most occasions, you should can just stick with using Unicode with these APIs. The bytes APIs should only be used on systems where undecodable file names can be present; that’s pretty much only Unix systems now.

Tips for Writing Unicode-aware Programs

This section provides some suggestions on writing software that deals with Unicode.

The most important tip is:

Software should only work with Unicode strings internally, decoding the input data as soon as possible and encoding the output only at the end.

If you attempt to write processing functions that accept both Unicode and byte strings, you will find your program vulnerable to bugs wherever you combine the two different kinds of strings. There is no automatic encoding or decoding: if you do e.g. str + bytes, a TypeError will be raised.

When using data coming from a web browser or some other untrusted source, a common technique is to check for illegal characters in a string before using the string in a generated command line or storing it in a database. If you’re doing this, be careful to check the decoded string, not the encoded bytes data; some encodings may have interesting properties, such as not being bijective or not being fully ASCII-compatible. This is especially true if the input data also specifies the encoding, since the attacker can then choose a clever way to hide malicious text in the encoded bytestream.

Converting Between File Encodings

The StreamRecoder class can transparently convert between encodings, taking a stream that returns data in encoding #1 and behaving like a stream returning data in encoding #2.

For example, if you have an input file f that’s in Latin-1, you can wrap it with a StreamRecoder to return bytes encoded in UTF-8:

new_f = codecs.StreamRecoder(f,
+    # en/decoder: used by read() to encode its results and
+    # by write() to decode its input.
+    codecs.getencoder('utf-8'), codecs.getdecoder('utf-8'),
+
+    # reader/writer: used to read and write to the stream.
+    codecs.getreader('latin-1'), codecs.getwriter('latin-1') )
+

Files in an Unknown Encoding

What can you do if you need to make a change to a file, but don’t know the file’s encoding? If you know the encoding is ASCII-compatible and only want to examine or modify the ASCII parts, you can open the file with the surrogateescape error handler:

with open(fname, 'r', encoding="ascii", errors="surrogateescape") as f:
+    data = f.read()
+
+# make changes to the string 'data'
+
+with open(fname + '.new', 'w',
+          encoding="ascii", errors="surrogateescape") as f:
+    f.write(data)
+

The surrogateescape error handler will decode any non-ASCII bytes as code points in a special range running from U+DC80 to U+DCFF. These code points will then turn back into the same bytes when the surrogateescape error handler is used to encode the data and write it back out.

References

One section of Mastering Python 3 Input/Output, a PyCon 2010 talk by David Beazley, discusses text processing and binary data handling.

The PDF slides for Marc-André Lemburg’s presentation “Writing Unicode-aware Applications in Python” discuss questions of character encodings as well as how to internationalize and localize an application. These slides cover Python 2.x only.

The Guts of Unicode in Python is a PyCon 2013 talk by Benjamin Peterson that discusses the internal Unicode representation in Python 3.3.

Acknowledgements

The initial draft of this document was written by Andrew Kuchling. It has since been revised further by Alexander Belopolsky, Georg Brandl, Andrew Kuchling, and Ezio Melotti.

Thanks to the following people who have noted errors or offered suggestions on this article: Éric Araujo, Nicholas Bastin, Nick Coghlan, Marius Gedminas, Kent Johnson, Ken Krugler, Marc-André Lemburg, Martin von Löwis, Terry J. Reedy, Serhiy Storchaka, Eryk Sun, Chad Whitacre, Graham Wideman.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/unicode.html +

+
diff --git a/devdocs/python~3.12/howto%2Furllib2.html b/devdocs/python~3.12/howto%2Furllib2.html new file mode 100644 index 00000000..b2ca01f3 --- /dev/null +++ b/devdocs/python~3.12/howto%2Furllib2.html @@ -0,0 +1,227 @@ +

HOWTO Fetch Internet Resources Using The urllib Package

Author
+

Michael Foord

Introduction

urllib.request is a Python module for fetching URLs (Uniform Resource Locators). It offers a very simple interface, in the form of the urlopen function. This is capable of fetching URLs using a variety of different protocols. It also offers a slightly more complex interface for handling common situations - like basic authentication, cookies, proxies and so on. These are provided by objects called handlers and openers.

urllib.request supports fetching URLs for many “URL schemes” (identified by the string before the ":" in URL - for example "ftp" is the URL scheme of "ftp://python.org/") using their associated network protocols (e.g. FTP, HTTP). This tutorial focuses on the most common case, HTTP.

For straightforward situations urlopen is very easy to use. But as soon as you encounter errors or non-trivial cases when opening HTTP URLs, you will need some understanding of the HyperText Transfer Protocol. The most comprehensive and authoritative reference to HTTP is RFC 2616. This is a technical document and not intended to be easy to read. This HOWTO aims to illustrate using urllib, with enough detail about HTTP to help you through. It is not intended to replace the urllib.request docs, but is supplementary to them.

Fetching URLs

The simplest way to use urllib.request is as follows:

import urllib.request
+with urllib.request.urlopen('http://python.org/') as response:
+   html = response.read()
+

If you wish to retrieve a resource via URL and store it in a temporary location, you can do so via the shutil.copyfileobj() and tempfile.NamedTemporaryFile() functions:

import shutil
+import tempfile
+import urllib.request
+
+with urllib.request.urlopen('http://python.org/') as response:
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        shutil.copyfileobj(response, tmp_file)
+
+with open(tmp_file.name) as html:
+    pass
+

Many uses of urllib will be that simple (note that instead of an ‘http:’ URL we could have used a URL starting with ‘ftp:’, ‘file:’, etc.). However, it’s the purpose of this tutorial to explain the more complicated cases, concentrating on HTTP.

HTTP is based on requests and responses - the client makes requests and servers send responses. urllib.request mirrors this with a Request object which represents the HTTP request you are making. In its simplest form you create a Request object that specifies the URL you want to fetch. Calling urlopen with this Request object returns a response object for the URL requested. This response is a file-like object, which means you can for example call .read() on the response:

import urllib.request
+
+req = urllib.request.Request('http://python.org/')
+with urllib.request.urlopen(req) as response:
+   the_page = response.read()
+

Note that urllib.request makes use of the same Request interface to handle all URL schemes. For example, you can make an FTP request like so:

req = urllib.request.Request('ftp://example.com/')
+

In the case of HTTP, there are two extra things that Request objects allow you to do: First, you can pass data to be sent to the server. Second, you can pass extra information (“metadata”) about the data or about the request itself, to the server - this information is sent as HTTP “headers”. Let’s look at each of these in turn.

Data

Sometimes you want to send data to a URL (often the URL will refer to a CGI (Common Gateway Interface) script or other web application). With HTTP, this is often done using what’s known as a POST request. This is often what your browser does when you submit a HTML form that you filled in on the web. Not all POSTs have to come from forms: you can use a POST to transmit arbitrary data to your own application. In the common case of HTML forms, the data needs to be encoded in a standard way, and then passed to the Request object as the data argument. The encoding is done using a function from the urllib.parse library.

import urllib.parse
+import urllib.request
+
+url = 'http://www.someserver.com/cgi-bin/register.cgi'
+values = {'name' : 'Michael Foord',
+          'location' : 'Northampton',
+          'language' : 'Python' }
+
+data = urllib.parse.urlencode(values)
+data = data.encode('ascii') # data should be bytes
+req = urllib.request.Request(url, data)
+with urllib.request.urlopen(req) as response:
+   the_page = response.read()
+

Note that other encodings are sometimes required (e.g. for file upload from HTML forms - see HTML Specification, Form Submission for more details).

If you do not pass the data argument, urllib uses a GET request. One way in which GET and POST requests differ is that POST requests often have “side-effects”: they change the state of the system in some way (for example by placing an order with the website for a hundredweight of tinned spam to be delivered to your door). Though the HTTP standard makes it clear that POSTs are intended to always cause side-effects, and GET requests never to cause side-effects, nothing prevents a GET request from having side-effects, nor a POST requests from having no side-effects. Data can also be passed in an HTTP GET request by encoding it in the URL itself.

This is done as follows:

>>> import urllib.request
+>>> import urllib.parse
+>>> data = {}
+>>> data['name'] = 'Somebody Here'
+>>> data['location'] = 'Northampton'
+>>> data['language'] = 'Python'
+>>> url_values = urllib.parse.urlencode(data)
+>>> print(url_values)  # The order may differ from below.  
+name=Somebody+Here&language=Python&location=Northampton
+>>> url = 'http://www.example.com/example.cgi'
+>>> full_url = url + '?' + url_values
+>>> data = urllib.request.urlopen(full_url)
+

Notice that the full URL is created by adding a ? to the URL, followed by the encoded values.

Headers

We’ll discuss here one particular HTTP header, to illustrate how to add headers to your HTTP request.

Some websites 1 dislike being browsed by programs, or send different versions to different browsers 2. By default urllib identifies itself as Python-urllib/x.y (where x and y are the major and minor version numbers of the Python release, e.g. Python-urllib/2.5), which may confuse the site, or just plain not work. The way a browser identifies itself is through the User-Agent header 3. When you create a Request object you can pass a dictionary of headers in. The following example makes the same request as above, but identifies itself as a version of Internet Explorer 4.

import urllib.parse
+import urllib.request
+
+url = 'http://www.someserver.com/cgi-bin/register.cgi'
+user_agent = 'Mozilla/5.0 (Windows NT 6.1; Win64; x64)'
+values = {'name': 'Michael Foord',
+          'location': 'Northampton',
+          'language': 'Python' }
+headers = {'User-Agent': user_agent}
+
+data = urllib.parse.urlencode(values)
+data = data.encode('ascii')
+req = urllib.request.Request(url, data, headers)
+with urllib.request.urlopen(req) as response:
+   the_page = response.read()
+

The response also has two useful methods. See the section on info and geturl which comes after we have a look at what happens when things go wrong.

Handling Exceptions

urlopen raises URLError when it cannot handle a response (though as usual with Python APIs, built-in exceptions such as ValueError, TypeError etc. may also be raised).

HTTPError is the subclass of URLError raised in the specific case of HTTP URLs.

The exception classes are exported from the urllib.error module.

URLError

Often, URLError is raised because there is no network connection (no route to the specified server), or the specified server doesn’t exist. In this case, the exception raised will have a ‘reason’ attribute, which is a tuple containing an error code and a text error message.

e.g.

>>> req = urllib.request.Request('http://www.pretend_server.org')
+>>> try: urllib.request.urlopen(req)
+... except urllib.error.URLError as e:
+...     print(e.reason)      
+...
+(4, 'getaddrinfo failed')
+

HTTPError

Every HTTP response from the server contains a numeric “status code”. Sometimes the status code indicates that the server is unable to fulfil the request. The default handlers will handle some of these responses for you (for example, if the response is a “redirection” that requests the client fetch the document from a different URL, urllib will handle that for you). For those it can’t handle, urlopen will raise an HTTPError. Typical errors include ‘404’ (page not found), ‘403’ (request forbidden), and ‘401’ (authentication required).

See section 10 of RFC 2616 for a reference on all the HTTP error codes.

The HTTPError instance raised will have an integer ‘code’ attribute, which corresponds to the error sent by the server.

Error Codes

Because the default handlers handle redirects (codes in the 300 range), and codes in the 100–299 range indicate success, you will usually only see error codes in the 400–599 range.

http.server.BaseHTTPRequestHandler.responses is a useful dictionary of response codes in that shows all the response codes used by RFC 2616. The dictionary is reproduced here for convenience

# Table mapping response codes to messages; entries have the
+# form {code: (shortmessage, longmessage)}.
+responses = {
+    100: ('Continue', 'Request received, please continue'),
+    101: ('Switching Protocols',
+          'Switching to new protocol; obey Upgrade header'),
+
+    200: ('OK', 'Request fulfilled, document follows'),
+    201: ('Created', 'Document created, URL follows'),
+    202: ('Accepted',
+          'Request accepted, processing continues off-line'),
+    203: ('Non-Authoritative Information', 'Request fulfilled from cache'),
+    204: ('No Content', 'Request fulfilled, nothing follows'),
+    205: ('Reset Content', 'Clear input form for further input.'),
+    206: ('Partial Content', 'Partial content follows.'),
+
+    300: ('Multiple Choices',
+          'Object has several resources -- see URI list'),
+    301: ('Moved Permanently', 'Object moved permanently -- see URI list'),
+    302: ('Found', 'Object moved temporarily -- see URI list'),
+    303: ('See Other', 'Object moved -- see Method and URL list'),
+    304: ('Not Modified',
+          'Document has not changed since given time'),
+    305: ('Use Proxy',
+          'You must use proxy specified in Location to access this '
+          'resource.'),
+    307: ('Temporary Redirect',
+          'Object moved temporarily -- see URI list'),
+
+    400: ('Bad Request',
+          'Bad request syntax or unsupported method'),
+    401: ('Unauthorized',
+          'No permission -- see authorization schemes'),
+    402: ('Payment Required',
+          'No payment -- see charging schemes'),
+    403: ('Forbidden',
+          'Request forbidden -- authorization will not help'),
+    404: ('Not Found', 'Nothing matches the given URI'),
+    405: ('Method Not Allowed',
+          'Specified method is invalid for this server.'),
+    406: ('Not Acceptable', 'URI not available in preferred format.'),
+    407: ('Proxy Authentication Required', 'You must authenticate with '
+          'this proxy before proceeding.'),
+    408: ('Request Timeout', 'Request timed out; try again later.'),
+    409: ('Conflict', 'Request conflict.'),
+    410: ('Gone',
+          'URI no longer exists and has been permanently removed.'),
+    411: ('Length Required', 'Client must specify Content-Length.'),
+    412: ('Precondition Failed', 'Precondition in headers is false.'),
+    413: ('Request Entity Too Large', 'Entity is too large.'),
+    414: ('Request-URI Too Long', 'URI is too long.'),
+    415: ('Unsupported Media Type', 'Entity body in unsupported format.'),
+    416: ('Requested Range Not Satisfiable',
+          'Cannot satisfy request range.'),
+    417: ('Expectation Failed',
+          'Expect condition could not be satisfied.'),
+
+    500: ('Internal Server Error', 'Server got itself in trouble'),
+    501: ('Not Implemented',
+          'Server does not support this operation'),
+    502: ('Bad Gateway', 'Invalid responses from another server/proxy.'),
+    503: ('Service Unavailable',
+          'The server cannot process the request due to a high load'),
+    504: ('Gateway Timeout',
+          'The gateway server did not receive a timely response'),
+    505: ('HTTP Version Not Supported', 'Cannot fulfill request.'),
+    }
+

When an error is raised the server responds by returning an HTTP error code and an error page. You can use the HTTPError instance as a response on the page returned. This means that as well as the code attribute, it also has read, geturl, and info, methods as returned by the urllib.response module:

>>> req = urllib.request.Request('http://www.python.org/fish.html')
+>>> try:
+...     urllib.request.urlopen(req)
+... except urllib.error.HTTPError as e:
+...     print(e.code)
+...     print(e.read())  
+...
+404
+b'<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">\n\n\n<html
+  ...
+  <title>Page Not Found</title>\n
+  ...
+

Wrapping it Up

So if you want to be prepared for HTTPError or URLError there are two basic approaches. I prefer the second approach.

Number 1

from urllib.request import Request, urlopen
+from urllib.error import URLError, HTTPError
+req = Request(someurl)
+try:
+    response = urlopen(req)
+except HTTPError as e:
+    print('The server couldn\'t fulfill the request.')
+    print('Error code: ', e.code)
+except URLError as e:
+    print('We failed to reach a server.')
+    print('Reason: ', e.reason)
+else:
+    # everything is fine
+

Note

The except HTTPError must come first, otherwise except URLError will also catch an HTTPError.

Number 2

from urllib.request import Request, urlopen
+from urllib.error import URLError
+req = Request(someurl)
+try:
+    response = urlopen(req)
+except URLError as e:
+    if hasattr(e, 'reason'):
+        print('We failed to reach a server.')
+        print('Reason: ', e.reason)
+    elif hasattr(e, 'code'):
+        print('The server couldn\'t fulfill the request.')
+        print('Error code: ', e.code)
+else:
+    # everything is fine
+

info and geturl

The response returned by urlopen (or the HTTPError instance) has two useful methods info() and geturl() and is defined in the module urllib.response..

geturl - this returns the real URL of the page fetched. This is useful because urlopen (or the opener object used) may have followed a redirect. The URL of the page fetched may not be the same as the URL requested.

info - this returns a dictionary-like object that describes the page fetched, particularly the headers sent by the server. It is currently an http.client.HTTPMessage instance.

Typical headers include ‘Content-length’, ‘Content-type’, and so on. See the Quick Reference to HTTP Headers for a useful listing of HTTP headers with brief explanations of their meaning and use.

Openers and Handlers

When you fetch a URL you use an opener (an instance of the perhaps confusingly named urllib.request.OpenerDirector). Normally we have been using the default opener - via urlopen - but you can create custom openers. Openers use handlers. All the “heavy lifting” is done by the handlers. Each handler knows how to open URLs for a particular URL scheme (http, ftp, etc.), or how to handle an aspect of URL opening, for example HTTP redirections or HTTP cookies.

You will want to create openers if you want to fetch URLs with specific handlers installed, for example to get an opener that handles cookies, or to get an opener that does not handle redirections.

To create an opener, instantiate an OpenerDirector, and then call .add_handler(some_handler_instance) repeatedly.

Alternatively, you can use build_opener, which is a convenience function for creating opener objects with a single function call. build_opener adds several handlers by default, but provides a quick way to add more and/or override the default handlers.

Other sorts of handlers you might want to can handle proxies, authentication, and other common but slightly specialised situations.

install_opener can be used to make an opener object the (global) default opener. This means that calls to urlopen will use the opener you have installed.

Opener objects have an open method, which can be called directly to fetch urls in the same way as the urlopen function: there’s no need to call install_opener, except as a convenience.

Basic Authentication

To illustrate creating and installing a handler we will use the HTTPBasicAuthHandler. For a more detailed discussion of this subject – including an explanation of how Basic Authentication works - see the Basic Authentication Tutorial.

When authentication is required, the server sends a header (as well as the 401 error code) requesting authentication. This specifies the authentication scheme and a ‘realm’. The header looks like: WWW-Authenticate: SCHEME +realm="REALM".

e.g.

WWW-Authenticate: Basic realm="cPanel Users"
+

The client should then retry the request with the appropriate name and password for the realm included as a header in the request. This is ‘basic authentication’. In order to simplify this process we can create an instance of HTTPBasicAuthHandler and an opener to use this handler.

The HTTPBasicAuthHandler uses an object called a password manager to handle the mapping of URLs and realms to passwords and usernames. If you know what the realm is (from the authentication header sent by the server), then you can use a HTTPPasswordMgr. Frequently one doesn’t care what the realm is. In that case, it is convenient to use HTTPPasswordMgrWithDefaultRealm. This allows you to specify a default username and password for a URL. This will be supplied in the absence of you providing an alternative combination for a specific realm. We indicate this by providing None as the realm argument to the add_password method.

The top-level URL is the first URL that requires authentication. URLs “deeper” than the URL you pass to .add_password() will also match.

# create a password manager
+password_mgr = urllib.request.HTTPPasswordMgrWithDefaultRealm()
+
+# Add the username and password.
+# If we knew the realm, we could use it instead of None.
+top_level_url = "http://example.com/foo/"
+password_mgr.add_password(None, top_level_url, username, password)
+
+handler = urllib.request.HTTPBasicAuthHandler(password_mgr)
+
+# create "opener" (OpenerDirector instance)
+opener = urllib.request.build_opener(handler)
+
+# use the opener to fetch a URL
+opener.open(a_url)
+
+# Install the opener.
+# Now all calls to urllib.request.urlopen use our opener.
+urllib.request.install_opener(opener)
+

Note

In the above example we only supplied our HTTPBasicAuthHandler to build_opener. By default openers have the handlers for normal situations – ProxyHandler (if a proxy setting such as an http_proxy environment variable is set), UnknownHandler, HTTPHandler, HTTPDefaultErrorHandler, HTTPRedirectHandler, FTPHandler, FileHandler, DataHandler, HTTPErrorProcessor.

top_level_url is in fact either a full URL (including the ‘http:’ scheme component and the hostname and optionally the port number) e.g. "http://example.com/" or an “authority” (i.e. the hostname, optionally including the port number) e.g. "example.com" or "example.com:8080" (the latter example includes a port number). The authority, if present, must NOT contain the “userinfo” component - for example "joe:password@example.com" is not correct.

Proxies

urllib will auto-detect your proxy settings and use those. This is through the ProxyHandler, which is part of the normal handler chain when a proxy setting is detected. Normally that’s a good thing, but there are occasions when it may not be helpful 5. One way to do this is to setup our own ProxyHandler, with no proxies defined. This is done using similar steps to setting up a Basic Authentication handler:

>>> proxy_support = urllib.request.ProxyHandler({})
+>>> opener = urllib.request.build_opener(proxy_support)
+>>> urllib.request.install_opener(opener)
+

Note

Currently urllib.request does not support fetching of https locations through a proxy. However, this can be enabled by extending urllib.request as shown in the recipe 6.

Note

HTTP_PROXY will be ignored if a variable REQUEST_METHOD is set; see the documentation on getproxies().

Sockets and Layers

The Python support for fetching resources from the web is layered. urllib uses the http.client library, which in turn uses the socket library.

As of Python 2.3 you can specify how long a socket should wait for a response before timing out. This can be useful in applications which have to fetch web pages. By default the socket module has no timeout and can hang. Currently, the socket timeout is not exposed at the http.client or urllib.request levels. However, you can set the default timeout globally for all sockets using

import socket
+import urllib.request
+
+# timeout in seconds
+timeout = 10
+socket.setdefaulttimeout(timeout)
+
+# this call to urllib.request.urlopen now uses the default timeout
+# we have set in the socket module
+req = urllib.request.Request('http://www.voidspace.org.uk')
+response = urllib.request.urlopen(req)
+

Footnotes

This document was reviewed and revised by John Lee.

+1
+

Google for example.

+2
+

Browser sniffing is a very bad practice for website design - building sites using web standards is much more sensible. Unfortunately a lot of sites still send different versions to different browsers.

+3
+

The user agent for MSIE 6 is ‘Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)’

+4
+

For details of more HTTP request headers, see Quick Reference to HTTP Headers.

+5
+

In my case I have to use a proxy to access the internet at work. If you attempt to fetch localhost URLs through this proxy it blocks them. IE is set to use the proxy, which urllib picks up on. In order to test scripts with a localhost server, I have to prevent urllib from using the proxy.

+6
+

urllib opener for SSL proxy (CONNECT method): ASPN Cookbook Recipe.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/howto/urllib2.html +

+
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"Unix") (count . 82) (slug . "unix"))])) \ No newline at end of file diff --git a/devdocs/python~3.12/index.html b/devdocs/python~3.12/index.html new file mode 100644 index 00000000..2933c8c1 --- /dev/null +++ b/devdocs/python~3.12/index.html @@ -0,0 +1,15 @@ +

Python

Welcome! This is the official documentation for Python 3.12.1.

Parts of the documentation:

+ + +

What's new in Python 3.12?
or all "What's new" documents since 2.0

Tutorial
start here

Library Reference
keep this under your pillow

Language Reference
describes syntax and language elements

Python Setup and Usage
how to use Python on different platforms

Python HOWTOs
in-depth documents on specific topics

Installing Python Modules
installing from the Python Package Index & other sources

Distributing Python Modules
publishing modules for installation by others

Extending and Embedding
tutorial for C/C++ programmers

Python/C API
reference for C/C++ programmers

FAQs
frequently asked questions (with answers!)

Indices and tables:

+ + +

Global Module Index
quick access to all modules

General Index
all functions, classes, terms

Glossary
the most important terms explained

Search page
search this documentation

Complete Table of Contents
lists all sections and subsections

Meta information:

+ + +

Reporting bugs

Contributing to Docs

About the documentation

History and License of Python

Copyright

Download the documentation

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/index.html +

+
diff --git a/devdocs/python~3.12/installing%2Findex.html b/devdocs/python~3.12/installing%2Findex.html new file mode 100644 index 00000000..9f533e0e --- /dev/null +++ b/devdocs/python~3.12/installing%2Findex.html @@ -0,0 +1,24 @@ +

Installing Python Modules

Email
+

distutils-sig@python.org

As a popular open source development project, Python has an active supporting community of contributors and users that also make their software available for other Python developers to use under open source license terms.

This allows Python users to share and collaborate effectively, benefiting from the solutions others have already created to common (and sometimes even rare!) problems, as well as potentially contributing their own solutions to the common pool.

This guide covers the installation part of the process. For a guide to creating and sharing your own Python projects, refer to the Python packaging user guide.

Note

For corporate and other institutional users, be aware that many organisations have their own policies around using and contributing to open source software. Please take such policies into account when making use of the distribution and installation tools provided with Python.

Key terms

  • +pip is the preferred installer program. Starting with Python 3.4, it is included by default with the Python binary installers.
  • A virtual environment is a semi-isolated Python environment that allows packages to be installed for use by a particular application, rather than being installed system wide.
  • +venv is the standard tool for creating virtual environments, and has been part of Python since Python 3.3. Starting with Python 3.4, it defaults to installing pip into all created virtual environments.
  • +virtualenv is a third party alternative (and predecessor) to venv. It allows virtual environments to be used on versions of Python prior to 3.4, which either don’t provide venv at all, or aren’t able to automatically install pip into created environments.
  • The Python Package Index is a public repository of open source licensed packages made available for use by other Python users.
  • the Python Packaging Authority is the group of developers and documentation authors responsible for the maintenance and evolution of the standard packaging tools and the associated metadata and file format standards. They maintain a variety of tools, documentation, and issue trackers on GitHub.
  • +distutils is the original build and distribution system first added to the Python standard library in 1998. While direct use of distutils is being phased out, it still laid the foundation for the current packaging and distribution infrastructure, and it not only remains part of the standard library, but its name lives on in other ways (such as the name of the mailing list used to coordinate Python packaging standards development).

Changed in version 3.5: The use of venv is now recommended for creating virtual environments.

See also

Python Packaging User Guide: Creating and using virtual environments

Basic usage

The standard packaging tools are all designed to be used from the command line.

The following command will install the latest version of a module and its dependencies from the Python Package Index:

python -m pip install SomePackage
+

Note

For POSIX users (including macOS and Linux users), the examples in this guide assume the use of a virtual environment.

For Windows users, the examples in this guide assume that the option to adjust the system PATH environment variable was selected when installing Python.

It’s also possible to specify an exact or minimum version directly on the command line. When using comparator operators such as >, < or some other special character which get interpreted by shell, the package name and the version should be enclosed within double quotes:

python -m pip install SomePackage==1.0.4    # specific version
+python -m pip install "SomePackage>=1.0.4"  # minimum version
+

Normally, if a suitable module is already installed, attempting to install it again will have no effect. Upgrading existing modules must be requested explicitly:

python -m pip install --upgrade SomePackage
+

More information and resources regarding pip and its capabilities can be found in the Python Packaging User Guide.

Creation of virtual environments is done through the venv module. Installing packages into an active virtual environment uses the commands shown above.

See also

Python Packaging User Guide: Installing Python Distribution Packages

How do I …?

These are quick answers or links for some common tasks.

… install pip in versions of Python prior to Python 3.4?

Python only started bundling pip with Python 3.4. For earlier versions, pip needs to be “bootstrapped” as described in the Python Packaging User Guide.

See also

Python Packaging User Guide: Requirements for Installing Packages

… install packages just for the current user?

Passing the --user option to python -m pip install will install a package just for the current user, rather than for all users of the system.

… install scientific Python packages?

A number of scientific Python packages have complex binary dependencies, and aren’t currently easy to install using pip directly. At this point in time, it will often be easier for users to install these packages by other means rather than attempting to install them with pip.

See also

Python Packaging User Guide: Installing Scientific Packages

… work with multiple versions of Python installed in parallel?

On Linux, macOS, and other POSIX systems, use the versioned Python commands in combination with the -m switch to run the appropriate copy of pip:

python2   -m pip install SomePackage  # default Python 2
+python2.7 -m pip install SomePackage  # specifically Python 2.7
+python3   -m pip install SomePackage  # default Python 3
+python3.4 -m pip install SomePackage  # specifically Python 3.4
+

Appropriately versioned pip commands may also be available.

On Windows, use the py Python launcher in combination with the -m switch:

py -2   -m pip install SomePackage  # default Python 2
+py -2.7 -m pip install SomePackage  # specifically Python 2.7
+py -3   -m pip install SomePackage  # default Python 3
+py -3.4 -m pip install SomePackage  # specifically Python 3.4
+

Common installation issues

Installing into the system Python on Linux

On Linux systems, a Python installation will typically be included as part of the distribution. Installing into this Python installation requires root access to the system, and may interfere with the operation of the system package manager and other components of the system if a component is unexpectedly upgraded using pip.

On such systems, it is often better to use a virtual environment or a per-user installation when installing packages with pip.

Pip not installed

It is possible that pip does not get installed by default. One potential fix is:

python -m ensurepip --default-pip
+

There are also additional resources for installing pip.

Installing binary extensions

Python has typically relied heavily on source based distribution, with end users being expected to compile extension modules from source as part of the installation process.

With the introduction of support for the binary wheel format, and the ability to publish wheels for at least Windows and macOS through the Python Package Index, this problem is expected to diminish over time, as users are more regularly able to install pre-built extensions rather than needing to build them themselves.

Some of the solutions for installing scientific software that are not yet available as pre-built wheel files may also help with obtaining other binary extensions without needing to build them locally.

See also

Python Packaging User Guide: Binary Extensions

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/installing/index.html +

+
diff --git a/devdocs/python~3.12/library%2F__future__.html b/devdocs/python~3.12/library%2F__future__.html new file mode 100644 index 00000000..39cf6cfe --- /dev/null +++ b/devdocs/python~3.12/library%2F__future__.html @@ -0,0 +1,40 @@ +

__future__ — Future statement definitions

Source code: Lib/__future__.py

__future__ is a real module, and serves three purposes:

+class __future__._Feature
+

Each statement in __future__.py is of the form:

FeatureName = _Feature(OptionalRelease, MandatoryRelease,
+                       CompilerFlag)
+

where, normally, OptionalRelease is less than MandatoryRelease, and both are 5-tuples of the same form as sys.version_info:

(PY_MAJOR_VERSION, # the 2 in 2.1.0a3; an int
+ PY_MINOR_VERSION, # the 1; an int
+ PY_MICRO_VERSION, # the 0; an int
+ PY_RELEASE_LEVEL, # "alpha", "beta", "candidate" or "final"; string
+ PY_RELEASE_SERIAL # the 3; an int
+)
+
+
+_Feature.getOptionalRelease()
+

OptionalRelease records the first release in which the feature was accepted.

+
+_Feature.getMandatoryRelease()
+

In the case of a MandatoryRelease that has not yet occurred, MandatoryRelease predicts the release in which the feature will become part of the language.

Else MandatoryRelease records when the feature became part of the language; in releases at or after that, modules no longer need a future statement to use the feature in question, but may continue to use such imports.

MandatoryRelease may also be None, meaning that a planned feature got dropped or that it is not yet decided.

+
+_Feature.compiler_flag
+

CompilerFlag is the (bitfield) flag that should be passed in the fourth argument to the built-in function compile() to enable the feature in dynamically compiled code. This flag is stored in the _Feature.compiler_flag attribute on _Feature instances.

+

No feature description will ever be deleted from __future__. Since its introduction in Python 2.1 the following features have found their way into the language using this mechanism:

+ + + + + + + + + +

feature

optional in

mandatory in

effect

nested_scopes

2.1.0b1

2.2

PEP 227: Statically Nested Scopes

generators

2.2.0a1

2.3

PEP 255: Simple Generators

division

2.2.0a2

3.0

PEP 238: Changing the Division Operator

absolute_import

2.5.0a1

3.0

PEP 328: Imports: Multi-Line and Absolute/Relative

with_statement

2.5.0a1

2.6

PEP 343: The “with” Statement

print_function

2.6.0a2

3.0

PEP 3105: Make print a function

unicode_literals

2.6.0a2

3.0

PEP 3112: Bytes literals in Python 3000

generator_stop

3.5.0b1

3.7

PEP 479: StopIteration handling inside generators

annotations

3.7.0b1

TBD 1

PEP 563: Postponed evaluation of annotations
+1
+

from __future__ import annotations was previously scheduled to become mandatory in Python 3.10, but the Python Steering Council twice decided to delay the change (announcement for Python 3.10; announcement for Python 3.11). No final decision has been made yet. See also PEP 563 and PEP 649.

See also

Future statements
+
+

How the compiler treats future imports.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/__future__.html +

+
diff --git a/devdocs/python~3.12/library%2F__main__.html b/devdocs/python~3.12/library%2F__main__.html new file mode 100644 index 00000000..a612efc3 --- /dev/null +++ b/devdocs/python~3.12/library%2F__main__.html @@ -0,0 +1,122 @@ +

__main__ — Top-level code environment

In Python, the special name __main__ is used for two important constructs:

  1. the name of the top-level environment of the program, which can be checked using the __name__ == '__main__' expression; and
  2. the __main__.py file in Python packages.

Both of these mechanisms are related to Python modules; how users interact with them and how they interact with each other. They are explained in detail below. If you’re new to Python modules, see the tutorial section Modules for an introduction.

__name__ == '__main__'

When a Python module or package is imported, __name__ is set to the module’s name. Usually, this is the name of the Python file itself without the .py extension:

>>> import configparser
+>>> configparser.__name__
+'configparser'
+

If the file is part of a package, __name__ will also include the parent package’s path:

>>> from concurrent.futures import process
+>>> process.__name__
+'concurrent.futures.process'
+

However, if the module is executed in the top-level code environment, its __name__ is set to the string '__main__'.

What is the “top-level code environment”?

__main__ is the name of the environment where top-level code is run. “Top-level code” is the first user-specified Python module that starts running. It’s “top-level” because it imports all other modules that the program needs. Sometimes “top-level code” is called an entry point to the application.

The top-level code environment can be:

  • +

    the scope of an interactive prompt:

    >>> __name__
+'__main__'
+
  • +

    the Python module passed to the Python interpreter as a file argument:

    $ python helloworld.py
+Hello, world!
+
  • +

    the Python module or package passed to the Python interpreter with the -m argument:

    $ python -m tarfile
+usage: tarfile.py [-h] [-v] (...)
+
  • +

    Python code read by the Python interpreter from standard input:

    $ echo "import this" | python
+The Zen of Python, by Tim Peters
+
+Beautiful is better than ugly.
+Explicit is better than implicit.
+...
+
  • +

    Python code passed to the Python interpreter with the -c argument:

    $ python -c "import this"
+The Zen of Python, by Tim Peters
+
+Beautiful is better than ugly.
+Explicit is better than implicit.
+...
+

In each of these situations, the top-level module’s __name__ is set to '__main__'.

As a result, a module can discover whether or not it is running in the top-level environment by checking its own __name__, which allows a common idiom for conditionally executing code when the module is not initialized from an import statement:

if __name__ == '__main__':
+    # Execute when the module is not initialized from an import statement.
+    ...
+

See also

For a more detailed look at how __name__ is set in all situations, see the tutorial section Modules.

Idiomatic Usage

Some modules contain code that is intended for script use only, like parsing command-line arguments or fetching data from standard input. If a module like this was imported from a different module, for example to unit test it, the script code would unintentionally execute as well.

This is where using the if __name__ == '__main__' code block comes in handy. Code within this block won’t run unless the module is executed in the top-level environment.

Putting as few statements as possible in the block below if __name__ == +'__main__' can improve code clarity and correctness. Most often, a function named main encapsulates the program’s primary behavior:

# echo.py
+
+import shlex
+import sys
+
+def echo(phrase: str) -> None:
+   """A dummy wrapper around print."""
+   # for demonstration purposes, you can imagine that there is some
+   # valuable and reusable logic inside this function
+   print(phrase)
+
+def main() -> int:
+    """Echo the input arguments to standard output"""
+    phrase = shlex.join(sys.argv)
+    echo(phrase)
+    return 0
+
+if __name__ == '__main__':
+    sys.exit(main())  # next section explains the use of sys.exit
+

Note that if the module didn’t encapsulate code inside the main function but instead put it directly within the if __name__ == '__main__' block, the phrase variable would be global to the entire module. This is error-prone as other functions within the module could be unintentionally using the global variable instead of a local name. A main function solves this problem.

Using a main function has the added benefit of the echo function itself being isolated and importable elsewhere. When echo.py is imported, the echo and main functions will be defined, but neither of them will be called, because __name__ != '__main__'.

Packaging Considerations

main functions are often used to create command-line tools by specifying them as entry points for console scripts. When this is done, pip inserts the function call into a template script, where the return value of main is passed into sys.exit(). For example:

sys.exit(main())
+

Since the call to main is wrapped in sys.exit(), the expectation is that your function will return some value acceptable as an input to sys.exit(); typically, an integer or None (which is implicitly returned if your function does not have a return statement).

By proactively following this convention ourselves, our module will have the same behavior when run directly (i.e. python echo.py) as it will have if we later package it as a console script entry-point in a pip-installable package.

In particular, be careful about returning strings from your main function. sys.exit() will interpret a string argument as a failure message, so your program will have an exit code of 1, indicating failure, and the string will be written to sys.stderr. The echo.py example from earlier exemplifies using the sys.exit(main()) convention.

See also

Python Packaging User Guide contains a collection of tutorials and references on how to distribute and install Python packages with modern tools.

+__main__.py in Python Packages

If you are not familiar with Python packages, see section Packages of the tutorial. Most commonly, the __main__.py file is used to provide a command-line interface for a package. Consider the following hypothetical package, “bandclass”:

bandclass
+  ├── __init__.py
+  ├── __main__.py
+  └── student.py
+

__main__.py will be executed when the package itself is invoked directly from the command line using the -m flag. For example:

$ python -m bandclass
+

This command will cause __main__.py to run. How you utilize this mechanism will depend on the nature of the package you are writing, but in this hypothetical case, it might make sense to allow the teacher to search for students:

# bandclass/__main__.py
+
+import sys
+from .student import search_students
+
+student_name = sys.argv[1] if len(sys.argv) >= 2 else ''
+print(f'Found student: {search_students(student_name)}')
+

Note that from .student import search_students is an example of a relative import. This import style can be used when referencing modules within a package. For more details, see Intra-package References in the Modules section of the tutorial.

Idiomatic Usage

The content of __main__.py typically isn’t fenced with an if __name__ == '__main__' block. Instead, those files are kept short and import functions to execute from other modules. Those other modules can then be easily unit-tested and are properly reusable.

If used, an if __name__ == '__main__' block will still work as expected for a __main__.py file within a package, because its __name__ attribute will include the package’s path if imported:

>>> import asyncio.__main__
+>>> asyncio.__main__.__name__
+'asyncio.__main__'
+

This won’t work for __main__.py files in the root directory of a .zip file though. Hence, for consistency, minimal __main__.py like the venv one mentioned below are preferred.

See also

See venv for an example of a package with a minimal __main__.py in the standard library. It doesn’t contain a if __name__ == '__main__' block. You can invoke it with python -m venv [directory].

See runpy for more details on the -m flag to the interpreter executable.

See zipapp for how to run applications packaged as .zip files. In this case Python looks for a __main__.py file in the root directory of the archive.

import __main__

Regardless of which module a Python program was started with, other modules running within that same program can import the top-level environment’s scope (namespace) by importing the __main__ module. This doesn’t import a __main__.py file but rather whichever module that received the special name '__main__'.

Here is an example module that consumes the __main__ namespace:

# namely.py
+
+import __main__
+
+def did_user_define_their_name():
+    return 'my_name' in dir(__main__)
+
+def print_user_name():
+    if not did_user_define_their_name():
+        raise ValueError('Define the variable `my_name`!')
+
+    if '__file__' in dir(__main__):
+        print(__main__.my_name, "found in file", __main__.__file__)
+    else:
+        print(__main__.my_name)
+

Example usage of this module could be as follows:

# start.py
+
+import sys
+
+from namely import print_user_name
+
+# my_name = "Dinsdale"
+
+def main():
+    try:
+        print_user_name()
+    except ValueError as ve:
+        return str(ve)
+
+if __name__ == "__main__":
+    sys.exit(main())
+

Now, if we started our program, the result would look like this:

$ python start.py
+Define the variable `my_name`!
+

The exit code of the program would be 1, indicating an error. Uncommenting the line with my_name = "Dinsdale" fixes the program and now it exits with status code 0, indicating success:

$ python start.py
+Dinsdale found in file /path/to/start.py
+

Note that importing __main__ doesn’t cause any issues with unintentionally running top-level code meant for script use which is put in the if __name__ == "__main__" block of the start module. Why does this work?

Python inserts an empty __main__ module in sys.modules at interpreter startup, and populates it by running top-level code. In our example this is the start module which runs line by line and imports namely. In turn, namely imports __main__ (which is really start). That’s an import cycle! Fortunately, since the partially populated __main__ module is present in sys.modules, Python passes that to namely. See Special considerations for __main__ in the import system’s reference for details on how this works.

The Python REPL is another example of a “top-level environment”, so anything defined in the REPL becomes part of the __main__ scope:

>>> import namely
+>>> namely.did_user_define_their_name()
+False
+>>> namely.print_user_name()
+Traceback (most recent call last):
+...
+ValueError: Define the variable `my_name`!
+>>> my_name = 'Jabberwocky'
+>>> namely.did_user_define_their_name()
+True
+>>> namely.print_user_name()
+Jabberwocky
+

Note that in this case the __main__ scope doesn’t contain a __file__ attribute as it’s interactive.

The __main__ scope is used in the implementation of pdb and rlcompleter.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/__main__.html +

+
diff --git a/devdocs/python~3.12/library%2F_thread.html b/devdocs/python~3.12/library%2F_thread.html new file mode 100644 index 00000000..20831b53 --- /dev/null +++ b/devdocs/python~3.12/library%2F_thread.html @@ -0,0 +1,51 @@ +

_thread — Low-level threading API

This module provides low-level primitives for working with multiple threads (also called light-weight processes or tasks) — multiple threads of control sharing their global data space. For synchronization, simple locks (also called mutexes or binary semaphores) are provided. The threading module provides an easier to use and higher-level threading API built on top of this module.

Changed in version 3.7: This module used to be optional, it is now always available.

This module defines the following constants and functions:

+exception _thread.error
+

Raised on thread-specific errors.

Changed in version 3.3: This is now a synonym of the built-in RuntimeError.

+
+_thread.LockType
+

This is the type of lock objects.

+
+_thread.start_new_thread(function, args[, kwargs])
+

Start a new thread and return its identifier. The thread executes the function function with the argument list args (which must be a tuple). The optional kwargs argument specifies a dictionary of keyword arguments.

When the function returns, the thread silently exits.

When the function terminates with an unhandled exception, sys.unraisablehook() is called to handle the exception. The object attribute of the hook argument is function. By default, a stack trace is printed and then the thread exits (but other threads continue to run).

When the function raises a SystemExit exception, it is silently ignored.

Raises an auditing event _thread.start_new_thread with arguments function, args, kwargs.

Changed in version 3.8: sys.unraisablehook() is now used to handle unhandled exceptions.

+
+_thread.interrupt_main(signum=signal.SIGINT, /)
+

Simulate the effect of a signal arriving in the main thread. A thread can use this function to interrupt the main thread, though there is no guarantee that the interruption will happen immediately.

If given, signum is the number of the signal to simulate. If signum is not given, signal.SIGINT is simulated.

If the given signal isn’t handled by Python (it was set to signal.SIG_DFL or signal.SIG_IGN), this function does nothing.

Changed in version 3.10: The signum argument is added to customize the signal number.

Note

This does not emit the corresponding signal but schedules a call to the associated handler (if it exists). If you want to truly emit the signal, use signal.raise_signal().

+
+_thread.exit()
+

Raise the SystemExit exception. When not caught, this will cause the thread to exit silently.

+
+_thread.allocate_lock()
+

Return a new lock object. Methods of locks are described below. The lock is initially unlocked.

+
+_thread.get_ident()
+

Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.

+
+_thread.get_native_id()
+

Return the native integral Thread ID of the current thread assigned by the kernel. This is a non-negative integer. Its value may be used to uniquely identify this particular thread system-wide (until the thread terminates, after which the value may be recycled by the OS).

Availability: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD.

New in version 3.8.

+
+_thread.stack_size([size])
+

Return the thread stack size used when creating new threads. The optional size argument specifies the stack size to be used for subsequently created threads, and must be 0 (use platform or configured default) or a positive integer value of at least 32,768 (32 KiB). If size is not specified, 0 is used. If changing the thread stack size is unsupported, a RuntimeError is raised. If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified. 32 KiB is currently the minimum supported stack size value to guarantee sufficient stack space for the interpreter itself. Note that some platforms may have particular restrictions on values for the stack size, such as requiring a minimum stack size > 32 KiB or requiring allocation in multiples of the system memory page size - platform documentation should be referred to for more information (4 KiB pages are common; using multiples of 4096 for the stack size is the suggested approach in the absence of more specific information).

Availability: Windows, pthreads.

Unix platforms with POSIX threads support.

+
+_thread.TIMEOUT_MAX
+

The maximum value allowed for the timeout parameter of Lock.acquire. Specifying a timeout greater than this value will raise an OverflowError.

New in version 3.2.

+

Lock objects have the following methods:

+lock.acquire(blocking=True, timeout=- 1)
+

Without any optional argument, this method acquires the lock unconditionally, if necessary waiting until it is released by another thread (only one thread at a time can acquire a lock — that’s their reason for existence).

If the blocking argument is present, the action depends on its value: if it is False, the lock is only acquired if it can be acquired immediately without waiting, while if it is True, the lock is acquired unconditionally as above.

If the floating-point timeout argument is present and positive, it specifies the maximum wait time in seconds before returning. A negative timeout argument specifies an unbounded wait. You cannot specify a timeout if blocking is False.

The return value is True if the lock is acquired successfully, False if not.

Changed in version 3.2: The timeout parameter is new.

Changed in version 3.2: Lock acquires can now be interrupted by signals on POSIX.

+
+lock.release()
+

Releases the lock. The lock must have been acquired earlier, but not necessarily by the same thread.

+
+lock.locked()
+

Return the status of the lock: True if it has been acquired by some thread, False if not.

+

In addition to these methods, lock objects can also be used via the with statement, e.g.:

import _thread
+
+a_lock = _thread.allocate_lock()
+
+with a_lock:
+    print("a_lock is locked while this executes")
+

Caveats:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/_thread.html +

+
diff --git a/devdocs/python~3.12/library%2Fabc.html b/devdocs/python~3.12/library%2Fabc.html new file mode 100644 index 00000000..a8ddfaf6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fabc.html @@ -0,0 +1,144 @@ +

abc — Abstract Base Classes

Source code: Lib/abc.py

This module provides the infrastructure for defining abstract base classes (ABCs) in Python, as outlined in PEP 3119; see the PEP for why this was added to Python. (See also PEP 3141 and the numbers module regarding a type hierarchy for numbers based on ABCs.)

The collections module has some concrete classes that derive from ABCs; these can, of course, be further derived. In addition, the collections.abc submodule has some ABCs that can be used to test whether a class or instance provides a particular interface, for example, if it is hashable or if it is a mapping.

This module provides the metaclass ABCMeta for defining ABCs and a helper class ABC to alternatively define ABCs through inheritance:

+class abc.ABC
+

A helper class that has ABCMeta as its metaclass. With this class, an abstract base class can be created by simply deriving from ABC avoiding sometimes confusing metaclass usage, for example:

from abc import ABC
+
+class MyABC(ABC):
+    pass
+

Note that the type of ABC is still ABCMeta, therefore inheriting from ABC requires the usual precautions regarding metaclass usage, as multiple inheritance may lead to metaclass conflicts. One may also define an abstract base class by passing the metaclass keyword and using ABCMeta directly, for example:

from abc import ABCMeta
+
+class MyABC(metaclass=ABCMeta):
+    pass
+

New in version 3.4.

+
+class abc.ABCMeta
+

Metaclass for defining Abstract Base Classes (ABCs).

Use this metaclass to create an ABC. An ABC can be subclassed directly, and then acts as a mix-in class. You can also register unrelated concrete classes (even built-in classes) and unrelated ABCs as “virtual subclasses” – these and their descendants will be considered subclasses of the registering ABC by the built-in issubclass() function, but the registering ABC won’t show up in their MRO (Method Resolution Order) nor will method implementations defined by the registering ABC be callable (not even via super()). 1

Classes created with a metaclass of ABCMeta have the following method:

+register(subclass)
+

Register subclass as a “virtual subclass” of this ABC. For example:

from abc import ABC
+
+class MyABC(ABC):
+    pass
+
+MyABC.register(tuple)
+
+assert issubclass(tuple, MyABC)
+assert isinstance((), MyABC)
+

Changed in version 3.3: Returns the registered subclass, to allow usage as a class decorator.

Changed in version 3.4: To detect calls to register(), you can use the get_cache_token() function.

+

You can also override this method in an abstract base class:

+__subclasshook__(subclass)
+

(Must be defined as a class method.)

Check whether subclass is considered a subclass of this ABC. This means that you can customize the behavior of issubclass() further without the need to call register() on every class you want to consider a subclass of the ABC. (This class method is called from the __subclasscheck__() method of the ABC.)

This method should return True, False or NotImplemented. If it returns True, the subclass is considered a subclass of this ABC. If it returns False, the subclass is not considered a subclass of this ABC, even if it would normally be one. If it returns NotImplemented, the subclass check is continued with the usual mechanism.

+

For a demonstration of these concepts, look at this example ABC definition:

class Foo:
+    def __getitem__(self, index):
+        ...
+    def __len__(self):
+        ...
+    def get_iterator(self):
+        return iter(self)
+
+class MyIterable(ABC):
+
+    @abstractmethod
+    def __iter__(self):
+        while False:
+            yield None
+
+    def get_iterator(self):
+        return self.__iter__()
+
+    @classmethod
+    def __subclasshook__(cls, C):
+        if cls is MyIterable:
+            if any("__iter__" in B.__dict__ for B in C.__mro__):
+                return True
+        return NotImplemented
+
+MyIterable.register(Foo)
+

The ABC MyIterable defines the standard iterable method, __iter__(), as an abstract method. The implementation given here can still be called from subclasses. The get_iterator() method is also part of the MyIterable abstract base class, but it does not have to be overridden in non-abstract derived classes.

The __subclasshook__() class method defined here says that any class that has an __iter__() method in its __dict__ (or in that of one of its base classes, accessed via the __mro__ list) is considered a MyIterable too.

Finally, the last line makes Foo a virtual subclass of MyIterable, even though it does not define an __iter__() method (it uses the old-style iterable protocol, defined in terms of __len__() and __getitem__()). Note that this will not make get_iterator available as a method of Foo, so it is provided separately.

+

The abc module also provides the following decorator:

+@abc.abstractmethod
+

A decorator indicating abstract methods.

Using this decorator requires that the class’s metaclass is ABCMeta or is derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods and properties are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Dynamically adding abstract methods to a class, or attempting to modify the abstraction status of a method or class once it is created, are only supported using the update_abstractmethods() function. The abstractmethod() only affects subclasses derived using regular inheritance; “virtual subclasses” registered with the ABC’s register() method are not affected.

When abstractmethod() is applied in combination with other method descriptors, it should be applied as the innermost decorator, as shown in the following usage examples:

class C(ABC):
+    @abstractmethod
+    def my_abstract_method(self, arg1):
+        ...
+    @classmethod
+    @abstractmethod
+    def my_abstract_classmethod(cls, arg2):
+        ...
+    @staticmethod
+    @abstractmethod
+    def my_abstract_staticmethod(arg3):
+        ...
+
+    @property
+    @abstractmethod
+    def my_abstract_property(self):
+        ...
+    @my_abstract_property.setter
+    @abstractmethod
+    def my_abstract_property(self, val):
+        ...
+
+    @abstractmethod
+    def _get_x(self):
+        ...
+    @abstractmethod
+    def _set_x(self, val):
+        ...
+    x = property(_get_x, _set_x)
+

In order to correctly interoperate with the abstract base class machinery, the descriptor must identify itself as abstract using __isabstractmethod__. In general, this attribute should be True if any of the methods used to compose the descriptor are abstract. For example, Python’s built-in property does the equivalent of:

class Descriptor:
+    ...
+    @property
+    def __isabstractmethod__(self):
+        return any(getattr(f, '__isabstractmethod__', False) for
+                   f in (self._fget, self._fset, self._fdel))
+

Note

Unlike Java abstract methods, these abstract methods may have an implementation. This implementation can be called via the super() mechanism from the class that overrides it. This could be useful as an end-point for a super-call in a framework that uses cooperative multiple-inheritance.

+

The abc module also supports the following legacy decorators:

+@abc.abstractclassmethod
+

New in version 3.2.

Deprecated since version 3.3: It is now possible to use classmethod with abstractmethod(), making this decorator redundant.

A subclass of the built-in classmethod(), indicating an abstract classmethod. Otherwise it is similar to abstractmethod().

This special case is deprecated, as the classmethod() decorator is now correctly identified as abstract when applied to an abstract method:

class C(ABC):
+    @classmethod
+    @abstractmethod
+    def my_abstract_classmethod(cls, arg):
+        ...
+
+
+@abc.abstractstaticmethod
+

New in version 3.2.

Deprecated since version 3.3: It is now possible to use staticmethod with abstractmethod(), making this decorator redundant.

A subclass of the built-in staticmethod(), indicating an abstract staticmethod. Otherwise it is similar to abstractmethod().

This special case is deprecated, as the staticmethod() decorator is now correctly identified as abstract when applied to an abstract method:

class C(ABC):
+    @staticmethod
+    @abstractmethod
+    def my_abstract_staticmethod(arg):
+        ...
+
+
+@abc.abstractproperty
+

Deprecated since version 3.3: It is now possible to use property, property.getter(), property.setter() and property.deleter() with abstractmethod(), making this decorator redundant.

A subclass of the built-in property(), indicating an abstract property.

This special case is deprecated, as the property() decorator is now correctly identified as abstract when applied to an abstract method:

class C(ABC):
+    @property
+    @abstractmethod
+    def my_abstract_property(self):
+        ...
+

The above example defines a read-only property; you can also define a read-write abstract property by appropriately marking one or more of the underlying methods as abstract:

class C(ABC):
+    @property
+    def x(self):
+        ...
+
+    @x.setter
+    @abstractmethod
+    def x(self, val):
+        ...
+

If only some components are abstract, only those components need to be updated to create a concrete property in a subclass:

class D(C):
+    @C.x.setter
+    def x(self, val):
+        ...
+
+

The abc module also provides the following functions:

+abc.get_cache_token()
+

Returns the current abstract base class cache token.

The token is an opaque object (that supports equality testing) identifying the current version of the abstract base class cache for virtual subclasses. The token changes with every call to ABCMeta.register() on any ABC.

New in version 3.4.

+
+abc.update_abstractmethods(cls)
+

A function to recalculate an abstract class’s abstraction status. This function should be called if a class’s abstract methods have been implemented or changed after it was created. Usually, this function should be called from within a class decorator.

Returns cls, to allow usage as a class decorator.

If cls is not an instance of ABCMeta, does nothing.

Note

This function assumes that cls’s superclasses are already updated. It does not update any subclasses.

New in version 3.10.

+

Footnotes

+1
+

C++ programmers should note that Python’s virtual base class concept is not the same as C++’s.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/abc.html +

+
diff --git a/devdocs/python~3.12/library%2Fallos.html b/devdocs/python~3.12/library%2Fallos.html new file mode 100644 index 00000000..4a21744a --- /dev/null +++ b/devdocs/python~3.12/library%2Fallos.html @@ -0,0 +1,32 @@ +

Generic Operating System Services

The modules described in this chapter provide interfaces to operating system features that are available on (almost) all operating systems, such as files and a clock. The interfaces are generally modeled after the Unix or C interfaces, but they are available on most other systems as well. Here’s an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/allos.html +

+
diff --git a/devdocs/python~3.12/library%2Farchiving.html b/devdocs/python~3.12/library%2Farchiving.html new file mode 100644 index 00000000..d143fb24 --- /dev/null +++ b/devdocs/python~3.12/library%2Farchiving.html @@ -0,0 +1,16 @@ +

Data Compression and Archiving

The modules described in this chapter support data compression with the zlib, gzip, bzip2 and lzma algorithms, and the creation of ZIP- and tar-format archives. See also Archiving operations provided by the shutil module.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/archiving.html +

+
diff --git a/devdocs/python~3.12/library%2Fargparse.html b/devdocs/python~3.12/library%2Fargparse.html new file mode 100644 index 00000000..f490f9ea --- /dev/null +++ b/devdocs/python~3.12/library%2Fargparse.html @@ -0,0 +1,990 @@ +

argparse — Parser for command-line options, arguments and sub-commands

New in version 3.2.

Source code: Lib/argparse.py

The argparse module makes it easy to write user-friendly command-line interfaces. The program defines what arguments it requires, and argparse will figure out how to parse those out of sys.argv. The argparse module also automatically generates help and usage messages. The module will also issue errors when users give the program invalid arguments.

Core Functionality

The argparse module’s support for command-line interfaces is built around an instance of argparse.ArgumentParser. It is a container for argument specifications and has options that apply to the parser as whole:

parser = argparse.ArgumentParser(
+                    prog='ProgramName',
+                    description='What the program does',
+                    epilog='Text at the bottom of help')
+

The ArgumentParser.add_argument() method attaches individual argument specifications to the parser. It supports positional arguments, options that accept values, and on/off flags:

parser.add_argument('filename')           # positional argument
+parser.add_argument('-c', '--count')      # option that takes a value
+parser.add_argument('-v', '--verbose',
+                    action='store_true')  # on/off flag
+

The ArgumentParser.parse_args() method runs the parser and places the extracted data in a argparse.Namespace object:

args = parser.parse_args()
+print(args.filename, args.count, args.verbose)
+

Example

The following code is a Python program that takes a list of integers and produces either the sum or the max:

import argparse
+
+parser = argparse.ArgumentParser(description='Process some integers.')
+parser.add_argument('integers', metavar='N', type=int, nargs='+',
+                    help='an integer for the accumulator')
+parser.add_argument('--sum', dest='accumulate', action='store_const',
+                    const=sum, default=max,
+                    help='sum the integers (default: find the max)')
+
+args = parser.parse_args()
+print(args.accumulate(args.integers))
+

Assuming the above Python code is saved into a file called prog.py, it can be run at the command line and it provides useful help messages:

$ python prog.py -h
+usage: prog.py [-h] [--sum] N [N ...]
+
+Process some integers.
+
+positional arguments:
+ N           an integer for the accumulator
+
+options:
+ -h, --help  show this help message and exit
+ --sum       sum the integers (default: find the max)
+

When run with the appropriate arguments, it prints either the sum or the max of the command-line integers:

$ python prog.py 1 2 3 4
+4
+
+$ python prog.py 1 2 3 4 --sum
+10
+

If invalid arguments are passed in, an error will be displayed:

$ python prog.py a b c
+usage: prog.py [-h] [--sum] N [N ...]
+prog.py: error: argument N: invalid int value: 'a'
+

The following sections walk you through this example.

Creating a parser

The first step in using the argparse is creating an ArgumentParser object:

>>> parser = argparse.ArgumentParser(description='Process some integers.')
+

The ArgumentParser object will hold all the information necessary to parse the command line into Python data types.

Adding arguments

Filling an ArgumentParser with information about program arguments is done by making calls to the add_argument() method. Generally, these calls tell the ArgumentParser how to take the strings on the command line and turn them into objects. This information is stored and used when parse_args() is called. For example:

>>> parser.add_argument('integers', metavar='N', type=int, nargs='+',
+...                     help='an integer for the accumulator')
+>>> parser.add_argument('--sum', dest='accumulate', action='store_const',
+...                     const=sum, default=max,
+...                     help='sum the integers (default: find the max)')
+

Later, calling parse_args() will return an object with two attributes, integers and accumulate. The integers attribute will be a list of one or more integers, and the accumulate attribute will be either the sum() function, if --sum was specified at the command line, or the max() function if it was not.

Parsing arguments

ArgumentParser parses arguments through the parse_args() method. This will inspect the command line, convert each argument to the appropriate type and then invoke the appropriate action. In most cases, this means a simple Namespace object will be built up from attributes parsed out of the command line:

>>> parser.parse_args(['--sum', '7', '-1', '42'])
+Namespace(accumulate=<built-in function sum>, integers=[7, -1, 42])
+

In a script, parse_args() will typically be called with no arguments, and the ArgumentParser will automatically determine the command-line arguments from sys.argv.

ArgumentParser objects

+class argparse.ArgumentParser(prog=None, usage=None, description=None, epilog=None, parents=[], formatter_class=argparse.HelpFormatter, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='error', add_help=True, allow_abbrev=True, exit_on_error=True)
+

Create a new ArgumentParser object. All parameters should be passed as keyword arguments. Each parameter has its own more detailed description below, but in short they are:

  • +prog - The name of the program (default: os.path.basename(sys.argv[0]))
  • +usage - The string describing the program usage (default: generated from arguments added to parser)
  • +description - Text to display before the argument help (by default, no text)
  • +epilog - Text to display after the argument help (by default, no text)
  • +parents - A list of ArgumentParser objects whose arguments should also be included
  • +formatter_class - A class for customizing the help output
  • +prefix_chars - The set of characters that prefix optional arguments (default: ‘-‘)
  • +fromfile_prefix_chars - The set of characters that prefix files from which additional arguments should be read (default: None)
  • +argument_default - The global default value for arguments (default: None)
  • +conflict_handler - The strategy for resolving conflicting optionals (usually unnecessary)
  • +add_help - Add a -h/--help option to the parser (default: True)
  • +allow_abbrev - Allows long options to be abbreviated if the abbreviation is unambiguous. (default: True)
  • +exit_on_error - Determines whether or not ArgumentParser exits with error info when an error occurs. (default: True)

Changed in version 3.5: allow_abbrev parameter was added.

Changed in version 3.8: In previous versions, allow_abbrev also disabled grouping of short flags such as -vv to mean -v -v.

Changed in version 3.9: exit_on_error parameter was added.

+

The following sections describe how each of these are used.

prog

By default, ArgumentParser objects use sys.argv[0] to determine how to display the name of the program in help messages. This default is almost always desirable because it will make the help messages match how the program was invoked on the command line. For example, consider a file named myprogram.py with the following code:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--foo', help='foo help')
+args = parser.parse_args()
+

The help for this program will display myprogram.py as the program name (regardless of where the program was invoked from):

$ python myprogram.py --help
+usage: myprogram.py [-h] [--foo FOO]
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO   foo help
+$ cd ..
+$ python subdir/myprogram.py --help
+usage: myprogram.py [-h] [--foo FOO]
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO   foo help
+

To change this default behavior, another value can be supplied using the prog= argument to ArgumentParser:

>>> parser = argparse.ArgumentParser(prog='myprogram')
+>>> parser.print_help()
+usage: myprogram [-h]
+
+options:
+ -h, --help  show this help message and exit
+

Note that the program name, whether determined from sys.argv[0] or from the prog= argument, is available to help messages using the %(prog)s format specifier.

>>> parser = argparse.ArgumentParser(prog='myprogram')
+>>> parser.add_argument('--foo', help='foo of the %(prog)s program')
+>>> parser.print_help()
+usage: myprogram [-h] [--foo FOO]
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO   foo of the myprogram program
+

usage

By default, ArgumentParser calculates the usage message from the arguments it contains:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('--foo', nargs='?', help='foo help')
+>>> parser.add_argument('bar', nargs='+', help='bar help')
+>>> parser.print_help()
+usage: PROG [-h] [--foo [FOO]] bar [bar ...]
+
+positional arguments:
+ bar          bar help
+
+options:
+ -h, --help   show this help message and exit
+ --foo [FOO]  foo help
+

The default message can be overridden with the usage= keyword argument:

>>> parser = argparse.ArgumentParser(prog='PROG', usage='%(prog)s [options]')
+>>> parser.add_argument('--foo', nargs='?', help='foo help')
+>>> parser.add_argument('bar', nargs='+', help='bar help')
+>>> parser.print_help()
+usage: PROG [options]
+
+positional arguments:
+ bar          bar help
+
+options:
+ -h, --help   show this help message and exit
+ --foo [FOO]  foo help
+

The %(prog)s format specifier is available to fill in the program name in your usage messages.

description

Most calls to the ArgumentParser constructor will use the description= keyword argument. This argument gives a brief description of what the program does and how it works. In help messages, the description is displayed between the command-line usage string and the help messages for the various arguments:

>>> parser = argparse.ArgumentParser(description='A foo that bars')
+>>> parser.print_help()
+usage: argparse.py [-h]
+
+A foo that bars
+
+options:
+ -h, --help  show this help message and exit
+

By default, the description will be line-wrapped so that it fits within the given space. To change this behavior, see the formatter_class argument.

epilog

Some programs like to display additional description of the program after the description of the arguments. Such text can be specified using the epilog= argument to ArgumentParser:

>>> parser = argparse.ArgumentParser(
+...     description='A foo that bars',
+...     epilog="And that's how you'd foo a bar")
+>>> parser.print_help()
+usage: argparse.py [-h]
+
+A foo that bars
+
+options:
+ -h, --help  show this help message and exit
+
+And that's how you'd foo a bar
+

As with the description argument, the epilog= text is by default line-wrapped, but this behavior can be adjusted with the formatter_class argument to ArgumentParser.

parents

Sometimes, several parsers share a common set of arguments. Rather than repeating the definitions of these arguments, a single parser with all the shared arguments and passed to parents= argument to ArgumentParser can be used. The parents= argument takes a list of ArgumentParser objects, collects all the positional and optional actions from them, and adds these actions to the ArgumentParser object being constructed:

>>> parent_parser = argparse.ArgumentParser(add_help=False)
+>>> parent_parser.add_argument('--parent', type=int)
+
+>>> foo_parser = argparse.ArgumentParser(parents=[parent_parser])
+>>> foo_parser.add_argument('foo')
+>>> foo_parser.parse_args(['--parent', '2', 'XXX'])
+Namespace(foo='XXX', parent=2)
+
+>>> bar_parser = argparse.ArgumentParser(parents=[parent_parser])
+>>> bar_parser.add_argument('--bar')
+>>> bar_parser.parse_args(['--bar', 'YYY'])
+Namespace(bar='YYY', parent=None)
+

Note that most parent parsers will specify add_help=False. Otherwise, the ArgumentParser will see two -h/--help options (one in the parent and one in the child) and raise an error.

Note

You must fully initialize the parsers before passing them via parents=. If you change the parent parsers after the child parser, those changes will not be reflected in the child.

formatter_class

ArgumentParser objects allow the help formatting to be customized by specifying an alternate formatting class. Currently, there are four such classes:

+class argparse.RawDescriptionHelpFormatter
+class argparse.RawTextHelpFormatter
+class argparse.ArgumentDefaultsHelpFormatter
+class argparse.MetavarTypeHelpFormatter
+

RawDescriptionHelpFormatter and RawTextHelpFormatter give more control over how textual descriptions are displayed. By default, ArgumentParser objects line-wrap the description and epilog texts in command-line help messages:

>>> parser = argparse.ArgumentParser(
+...     prog='PROG',
+...     description='''this description
+...         was indented weird
+...             but that is okay''',
+...     epilog='''
+...             likewise for this epilog whose whitespace will
+...         be cleaned up and whose words will be wrapped
+...         across a couple lines''')
+>>> parser.print_help()
+usage: PROG [-h]
+
+this description was indented weird but that is okay
+
+options:
+ -h, --help  show this help message and exit
+
+likewise for this epilog whose whitespace will be cleaned up and whose words
+will be wrapped across a couple lines
+

Passing RawDescriptionHelpFormatter as formatter_class= indicates that description and epilog are already correctly formatted and should not be line-wrapped:

>>> parser = argparse.ArgumentParser(
+...     prog='PROG',
+...     formatter_class=argparse.RawDescriptionHelpFormatter,
+...     description=textwrap.dedent('''\
+...         Please do not mess up this text!
+...         --------------------------------
+...             I have indented it
+...             exactly the way
+...             I want it
+...         '''))
+>>> parser.print_help()
+usage: PROG [-h]
+
+Please do not mess up this text!
+--------------------------------
+   I have indented it
+   exactly the way
+   I want it
+
+options:
+ -h, --help  show this help message and exit
+

RawTextHelpFormatter maintains whitespace for all sorts of help text, including argument descriptions. However, multiple new lines are replaced with one. If you wish to preserve multiple blank lines, add spaces between the newlines.

ArgumentDefaultsHelpFormatter automatically adds information about default values to each of the argument help messages:

>>> parser = argparse.ArgumentParser(
+...     prog='PROG',
+...     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+>>> parser.add_argument('--foo', type=int, default=42, help='FOO!')
+>>> parser.add_argument('bar', nargs='*', default=[1, 2, 3], help='BAR!')
+>>> parser.print_help()
+usage: PROG [-h] [--foo FOO] [bar ...]
+
+positional arguments:
+ bar         BAR! (default: [1, 2, 3])
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO   FOO! (default: 42)
+

MetavarTypeHelpFormatter uses the name of the type argument for each argument as the display name for its values (rather than using the dest as the regular formatter does):

>>> parser = argparse.ArgumentParser(
+...     prog='PROG',
+...     formatter_class=argparse.MetavarTypeHelpFormatter)
+>>> parser.add_argument('--foo', type=int)
+>>> parser.add_argument('bar', type=float)
+>>> parser.print_help()
+usage: PROG [-h] [--foo int] float
+
+positional arguments:
+  float
+
+options:
+  -h, --help  show this help message and exit
+  --foo int
+

prefix_chars

Most command-line options will use - as the prefix, e.g. -f/--foo. Parsers that need to support different or additional prefix characters, e.g. for options like +f or /foo, may specify them using the prefix_chars= argument to the ArgumentParser constructor:

>>> parser = argparse.ArgumentParser(prog='PROG', prefix_chars='-+')
+>>> parser.add_argument('+f')
+>>> parser.add_argument('++bar')
+>>> parser.parse_args('+f X ++bar Y'.split())
+Namespace(bar='Y', f='X')
+

The prefix_chars= argument defaults to '-'. Supplying a set of characters that does not include - will cause -f/--foo options to be disallowed.

fromfile_prefix_chars

Sometimes, when dealing with a particularly long argument list, it may make sense to keep the list of arguments in a file rather than typing it out at the command line. If the fromfile_prefix_chars= argument is given to the ArgumentParser constructor, then arguments that start with any of the specified characters will be treated as files, and will be replaced by the arguments they contain. For example:

>>> with open('args.txt', 'w', encoding=sys.getfilesystemencoding()) as fp:
+...     fp.write('-f\nbar')
+...
+>>> parser = argparse.ArgumentParser(fromfile_prefix_chars='@')
+>>> parser.add_argument('-f')
+>>> parser.parse_args(['-f', 'foo', '@args.txt'])
+Namespace(f='bar')
+

Arguments read from a file must by default be one per line (but see also convert_arg_line_to_args()) and are treated as if they were in the same place as the original file referencing argument on the command line. So in the example above, the expression ['-f', 'foo', '@args.txt'] is considered equivalent to the expression ['-f', 'foo', '-f', 'bar'].

ArgumentParser uses filesystem encoding and error handler to read the file containing arguments.

The fromfile_prefix_chars= argument defaults to None, meaning that arguments will never be treated as file references.

Changed in version 3.12: ArgumentParser changed encoding and errors to read arguments files from default (e.g. locale.getpreferredencoding(False) and "strict") to filesystem encoding and error handler. Arguments file should be encoded in UTF-8 instead of ANSI Codepage on Windows.

argument_default

Generally, argument defaults are specified either by passing a default to add_argument() or by calling the set_defaults() methods with a specific set of name-value pairs. Sometimes however, it may be useful to specify a single parser-wide default for arguments. This can be accomplished by passing the argument_default= keyword argument to ArgumentParser. For example, to globally suppress attribute creation on parse_args() calls, we supply argument_default=SUPPRESS:

>>> parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS)
+>>> parser.add_argument('--foo')
+>>> parser.add_argument('bar', nargs='?')
+>>> parser.parse_args(['--foo', '1', 'BAR'])
+Namespace(bar='BAR', foo='1')
+>>> parser.parse_args([])
+Namespace()
+

allow_abbrev

Normally, when you pass an argument list to the parse_args() method of an ArgumentParser, it recognizes abbreviations of long options.

This feature can be disabled by setting allow_abbrev to False:

>>> parser = argparse.ArgumentParser(prog='PROG', allow_abbrev=False)
+>>> parser.add_argument('--foobar', action='store_true')
+>>> parser.add_argument('--foonley', action='store_false')
+>>> parser.parse_args(['--foon'])
+usage: PROG [-h] [--foobar] [--foonley]
+PROG: error: unrecognized arguments: --foon
+

New in version 3.5.

conflict_handler

ArgumentParser objects do not allow two actions with the same option string. By default, ArgumentParser objects raise an exception if an attempt is made to create an argument with an option string that is already in use:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-f', '--foo', help='old foo help')
+>>> parser.add_argument('--foo', help='new foo help')
+Traceback (most recent call last):
+ ..
+ArgumentError: argument --foo: conflicting option string(s): --foo
+

Sometimes (e.g. when using parents) it may be useful to simply override any older arguments with the same option string. To get this behavior, the value 'resolve' can be supplied to the conflict_handler= argument of ArgumentParser:

>>> parser = argparse.ArgumentParser(prog='PROG', conflict_handler='resolve')
+>>> parser.add_argument('-f', '--foo', help='old foo help')
+>>> parser.add_argument('--foo', help='new foo help')
+>>> parser.print_help()
+usage: PROG [-h] [-f FOO] [--foo FOO]
+
+options:
+ -h, --help  show this help message and exit
+ -f FOO      old foo help
+ --foo FOO   new foo help
+

Note that ArgumentParser objects only remove an action if all of its option strings are overridden. So, in the example above, the old -f/--foo action is retained as the -f action, because only the --foo option string was overridden.

add_help

By default, ArgumentParser objects add an option which simply displays the parser’s help message. For example, consider a file named myprogram.py containing the following code:

import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--foo', help='foo help')
+args = parser.parse_args()
+

If -h or --help is supplied at the command line, the ArgumentParser help will be printed:

$ python myprogram.py --help
+usage: myprogram.py [-h] [--foo FOO]
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO   foo help
+

Occasionally, it may be useful to disable the addition of this help option. This can be achieved by passing False as the add_help= argument to ArgumentParser:

>>> parser = argparse.ArgumentParser(prog='PROG', add_help=False)
+>>> parser.add_argument('--foo', help='foo help')
+>>> parser.print_help()
+usage: PROG [--foo FOO]
+
+options:
+ --foo FOO  foo help
+

The help option is typically -h/--help. The exception to this is if the prefix_chars= is specified and does not include -, in which case -h and --help are not valid options. In this case, the first character in prefix_chars is used to prefix the help options:

>>> parser = argparse.ArgumentParser(prog='PROG', prefix_chars='+/')
+>>> parser.print_help()
+usage: PROG [+h]
+
+options:
+  +h, ++help  show this help message and exit
+

exit_on_error

Normally, when you pass an invalid argument list to the parse_args() method of an ArgumentParser, it will exit with error info.

If the user would like to catch errors manually, the feature can be enabled by setting exit_on_error to False:

>>> parser = argparse.ArgumentParser(exit_on_error=False)
+>>> parser.add_argument('--integers', type=int)
+_StoreAction(option_strings=['--integers'], dest='integers', nargs=None, const=None, default=None, type=<class 'int'>, choices=None, help=None, metavar=None)
+>>> try:
+...     parser.parse_args('--integers a'.split())
+... except argparse.ArgumentError:
+...     print('Catching an argumentError')
+...
+Catching an argumentError
+

New in version 3.9.

The add_argument() method

+ArgumentParser.add_argument(name or flags...[, action][, nargs][, const][, default][, type][, choices][, required][, help][, metavar][, dest])
+

Define how a single command-line argument should be parsed. Each parameter has its own more detailed description below, but in short they are:

  • +name or flags - Either a name or a list of option strings, e.g. foo or -f, --foo.
  • +action - The basic type of action to be taken when this argument is encountered at the command line.
  • +nargs - The number of command-line arguments that should be consumed.
  • +const - A constant value required by some action and nargs selections.
  • +default - The value produced if the argument is absent from the command line and if it is absent from the namespace object.
  • +type - The type to which the command-line argument should be converted.
  • +choices - A sequence of the allowable values for the argument.
  • +required - Whether or not the command-line option may be omitted (optionals only).
  • +help - A brief description of what the argument does.
  • +metavar - A name for the argument in usage messages.
  • +dest - The name of the attribute to be added to the object returned by parse_args().
+

The following sections describe how each of these are used.

name or flags

The add_argument() method must know whether an optional argument, like -f or --foo, or a positional argument, like a list of filenames, is expected. The first arguments passed to add_argument() must therefore be either a series of flags, or a simple argument name.

For example, an optional argument could be created like:

>>> parser.add_argument('-f', '--foo')
+

while a positional argument could be created like:

>>> parser.add_argument('bar')
+

When parse_args() is called, optional arguments will be identified by the - prefix, and the remaining arguments will be assumed to be positional:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-f', '--foo')
+>>> parser.add_argument('bar')
+>>> parser.parse_args(['BAR'])
+Namespace(bar='BAR', foo=None)
+>>> parser.parse_args(['BAR', '--foo', 'FOO'])
+Namespace(bar='BAR', foo='FOO')
+>>> parser.parse_args(['--foo', 'FOO'])
+usage: PROG [-h] [-f FOO] bar
+PROG: error: the following arguments are required: bar
+

action

ArgumentParser objects associate command-line arguments with actions. These actions can do just about anything with the command-line arguments associated with them, though most actions simply add an attribute to the object returned by parse_args(). The action keyword argument specifies how the command-line arguments should be handled. The supplied actions are:

  • +

    'store' - This just stores the argument’s value. This is the default action. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo')
+>>> parser.parse_args('--foo 1'.split())
+Namespace(foo='1')
+
  • +

    'store_const' - This stores the value specified by the const keyword argument; note that the const keyword argument defaults to None. The 'store_const' action is most commonly used with optional arguments that specify some sort of flag. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action='store_const', const=42)
+>>> parser.parse_args(['--foo'])
+Namespace(foo=42)
+
  • +

    'store_true' and 'store_false' - These are special cases of 'store_const' used for storing the values True and False respectively. In addition, they create default values of False and True respectively. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action='store_true')
+>>> parser.add_argument('--bar', action='store_false')
+>>> parser.add_argument('--baz', action='store_false')
+>>> parser.parse_args('--foo --bar'.split())
+Namespace(foo=True, bar=False, baz=True)
+
  • +

    'append' - This stores a list, and appends each argument value to the list. It is useful to allow an option to be specified multiple times. If the default value is non-empty, the default elements will be present in the parsed value for the option, with any values from the command line appended after those default values. Example usage:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action='append')
+>>> parser.parse_args('--foo 1 --foo 2'.split())
+Namespace(foo=['1', '2'])
+
  • +

    'append_const' - This stores a list, and appends the value specified by the const keyword argument to the list; note that the const keyword argument defaults to None. The 'append_const' action is typically useful when multiple arguments need to store constants to the same list. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--str', dest='types', action='append_const', const=str)
+>>> parser.add_argument('--int', dest='types', action='append_const', const=int)
+>>> parser.parse_args('--str --int'.split())
+Namespace(types=[<class 'str'>, <class 'int'>])
+
  • +

    'count' - This counts the number of times a keyword argument occurs. For example, this is useful for increasing verbosity levels:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--verbose', '-v', action='count', default=0)
+>>> parser.parse_args(['-vvv'])
+Namespace(verbose=3)
+

    Note, the default will be None unless explicitly set to 0.

  • +'help' - This prints a complete help message for all the options in the current parser and then exits. By default a help action is automatically added to the parser. See ArgumentParser for details of how the output is created.
  • +

    'version' - This expects a version= keyword argument in the add_argument() call, and prints version information and exits when invoked:

    >>> import argparse
+>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('--version', action='version', version='%(prog)s 2.0')
+>>> parser.parse_args(['--version'])
+PROG 2.0
+
  • +

    'extend' - This stores a list, and extends each argument value to the list. Example usage:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument("--foo", action="extend", nargs="+", type=str)
+>>> parser.parse_args(["--foo", "f1", "--foo", "f2", "f3", "f4"])
+Namespace(foo=['f1', 'f2', 'f3', 'f4'])
+

    New in version 3.8.

You may also specify an arbitrary action by passing an Action subclass or other object that implements the same interface. The BooleanOptionalAction is available in argparse and adds support for boolean actions such as --foo and --no-foo:

>>> import argparse
+>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action=argparse.BooleanOptionalAction)
+>>> parser.parse_args(['--no-foo'])
+Namespace(foo=False)
+

New in version 3.9.

The recommended way to create a custom action is to extend Action, overriding the __call__ method and optionally the __init__ and format_usage methods.

An example of a custom action:

>>> class FooAction(argparse.Action):
+...     def __init__(self, option_strings, dest, nargs=None, **kwargs):
+...         if nargs is not None:
+...             raise ValueError("nargs not allowed")
+...         super().__init__(option_strings, dest, **kwargs)
+...     def __call__(self, parser, namespace, values, option_string=None):
+...         print('%r %r %r' % (namespace, values, option_string))
+...         setattr(namespace, self.dest, values)
+...
+>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action=FooAction)
+>>> parser.add_argument('bar', action=FooAction)
+>>> args = parser.parse_args('1 --foo 2'.split())
+Namespace(bar=None, foo=None) '1' None
+Namespace(bar='1', foo=None) '2' '--foo'
+>>> args
+Namespace(bar='1', foo='2')
+

For more details, see Action.

nargs

ArgumentParser objects usually associate a single command-line argument with a single action to be taken. The nargs keyword argument associates a different number of command-line arguments with a single action. See also Specifying ambiguous arguments. The supported values are:

  • +

    N (an integer). N arguments from the command line will be gathered together into a list. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', nargs=2)
+>>> parser.add_argument('bar', nargs=1)
+>>> parser.parse_args('c --foo a b'.split())
+Namespace(bar=['c'], foo=['a', 'b'])
+

    Note that nargs=1 produces a list of one item. This is different from the default, in which the item is produced by itself.

  • +

    '?'. One argument will be consumed from the command line if possible, and produced as a single item. If no command-line argument is present, the value from default will be produced. Note that for optional arguments, there is an additional case - the option string is present but not followed by a command-line argument. In this case the value from const will be produced. Some examples to illustrate this:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', nargs='?', const='c', default='d')
+>>> parser.add_argument('bar', nargs='?', default='d')
+>>> parser.parse_args(['XX', '--foo', 'YY'])
+Namespace(bar='XX', foo='YY')
+>>> parser.parse_args(['XX', '--foo'])
+Namespace(bar='XX', foo='c')
+>>> parser.parse_args([])
+Namespace(bar='d', foo='d')
+

    One of the more common uses of nargs='?' is to allow optional input and output files:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('infile', nargs='?', type=argparse.FileType('r'),
+...                     default=sys.stdin)
+>>> parser.add_argument('outfile', nargs='?', type=argparse.FileType('w'),
+...                     default=sys.stdout)
+>>> parser.parse_args(['input.txt', 'output.txt'])
+Namespace(infile=<_io.TextIOWrapper name='input.txt' encoding='UTF-8'>,
+          outfile=<_io.TextIOWrapper name='output.txt' encoding='UTF-8'>)
+>>> parser.parse_args([])
+Namespace(infile=<_io.TextIOWrapper name='<stdin>' encoding='UTF-8'>,
+          outfile=<_io.TextIOWrapper name='<stdout>' encoding='UTF-8'>)
+
  • +

    '*'. All command-line arguments present are gathered into a list. Note that it generally doesn’t make much sense to have more than one positional argument with nargs='*', but multiple optional arguments with nargs='*' is possible. For example:

    >>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', nargs='*')
+>>> parser.add_argument('--bar', nargs='*')
+>>> parser.add_argument('baz', nargs='*')
+>>> parser.parse_args('a b --foo x y --bar 1 2'.split())
+Namespace(bar=['1', '2'], baz=['a', 'b'], foo=['x', 'y'])
+
  • +

    '+'. Just like '*', all command-line args present are gathered into a list. Additionally, an error message will be generated if there wasn’t at least one command-line argument present. For example:

    >>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('foo', nargs='+')
+>>> parser.parse_args(['a', 'b'])
+Namespace(foo=['a', 'b'])
+>>> parser.parse_args([])
+usage: PROG [-h] foo [foo ...]
+PROG: error: the following arguments are required: foo
+

If the nargs keyword argument is not provided, the number of arguments consumed is determined by the action. Generally this means a single command-line argument will be consumed and a single item (not a list) will be produced.

const

The const argument of add_argument() is used to hold constant values that are not read from the command line but are required for the various ArgumentParser actions. The two most common uses of it are:

  • When add_argument() is called with action='store_const' or action='append_const'. These actions add the const value to one of the attributes of the object returned by parse_args(). See the action description for examples. If const is not provided to add_argument(), it will receive a default value of None.
  • When add_argument() is called with option strings (like -f or --foo) and nargs='?'. This creates an optional argument that can be followed by zero or one command-line arguments. When parsing the command line, if the option string is encountered with no command-line argument following it, the value of const will be assumed to be None instead. See the nargs description for examples.

Changed in version 3.11: const=None by default, including when action='append_const' or action='store_const'.

default

All optional arguments and some positional arguments may be omitted at the command line. The default keyword argument of add_argument(), whose value defaults to None, specifies what value should be used if the command-line argument is not present. For optional arguments, the default value is used when the option string was not present at the command line:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', default=42)
+>>> parser.parse_args(['--foo', '2'])
+Namespace(foo='2')
+>>> parser.parse_args([])
+Namespace(foo=42)
+

If the target namespace already has an attribute set, the action default will not over write it:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', default=42)
+>>> parser.parse_args([], namespace=argparse.Namespace(foo=101))
+Namespace(foo=101)
+

If the default value is a string, the parser parses the value as if it were a command-line argument. In particular, the parser applies any type conversion argument, if provided, before setting the attribute on the Namespace return value. Otherwise, the parser uses the value as is:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--length', default='10', type=int)
+>>> parser.add_argument('--width', default=10.5, type=int)
+>>> parser.parse_args()
+Namespace(length=10, width=10.5)
+

For positional arguments with nargs equal to ? or *, the default value is used when no command-line argument was present:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('foo', nargs='?', default=42)
+>>> parser.parse_args(['a'])
+Namespace(foo='a')
+>>> parser.parse_args([])
+Namespace(foo=42)
+

Providing default=argparse.SUPPRESS causes no attribute to be added if the command-line argument was not present:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', default=argparse.SUPPRESS)
+>>> parser.parse_args([])
+Namespace()
+>>> parser.parse_args(['--foo', '1'])
+Namespace(foo='1')
+

type

By default, the parser reads command-line arguments in as simple strings. However, quite often the command-line string should instead be interpreted as another type, such as a float or int. The type keyword for add_argument() allows any necessary type-checking and type conversions to be performed.

If the type keyword is used with the default keyword, the type converter is only applied if the default is a string.

The argument to type can be any callable that accepts a single string. If the function raises ArgumentTypeError, TypeError, or ValueError, the exception is caught and a nicely formatted error message is displayed. No other exception types are handled.

Common built-in types and functions can be used as type converters:

import argparse
+import pathlib
+
+parser = argparse.ArgumentParser()
+parser.add_argument('count', type=int)
+parser.add_argument('distance', type=float)
+parser.add_argument('street', type=ascii)
+parser.add_argument('code_point', type=ord)
+parser.add_argument('source_file', type=open)
+parser.add_argument('dest_file', type=argparse.FileType('w', encoding='latin-1'))
+parser.add_argument('datapath', type=pathlib.Path)
+

User defined functions can be used as well:

>>> def hyphenated(string):
+...     return '-'.join([word[:4] for word in string.casefold().split()])
+...
+>>> parser = argparse.ArgumentParser()
+>>> _ = parser.add_argument('short_title', type=hyphenated)
+>>> parser.parse_args(['"The Tale of Two Cities"'])
+Namespace(short_title='"the-tale-of-two-citi')
+

The bool() function is not recommended as a type converter. All it does is convert empty strings to False and non-empty strings to True. This is usually not what is desired.

In general, the type keyword is a convenience that should only be used for simple conversions that can only raise one of the three supported exceptions. Anything with more interesting error-handling or resource management should be done downstream after the arguments are parsed.

For example, JSON or YAML conversions have complex error cases that require better reporting than can be given by the type keyword. A JSONDecodeError would not be well formatted and a FileNotFoundError exception would not be handled at all.

Even FileType has its limitations for use with the type keyword. If one argument uses FileType and then a subsequent argument fails, an error is reported but the file is not automatically closed. In this case, it would be better to wait until after the parser has run and then use the with-statement to manage the files.

For type checkers that simply check against a fixed set of values, consider using the choices keyword instead.

choices

Some command-line arguments should be selected from a restricted set of values. These can be handled by passing a sequence object as the choices keyword argument to add_argument(). When the command line is parsed, argument values will be checked, and an error message will be displayed if the argument was not one of the acceptable values:

>>> parser = argparse.ArgumentParser(prog='game.py')
+>>> parser.add_argument('move', choices=['rock', 'paper', 'scissors'])
+>>> parser.parse_args(['rock'])
+Namespace(move='rock')
+>>> parser.parse_args(['fire'])
+usage: game.py [-h] {rock,paper,scissors}
+game.py: error: argument move: invalid choice: 'fire' (choose from 'rock',
+'paper', 'scissors')
+

Note that inclusion in the choices sequence is checked after any type conversions have been performed, so the type of the objects in the choices sequence should match the type specified:

>>> parser = argparse.ArgumentParser(prog='doors.py')
+>>> parser.add_argument('door', type=int, choices=range(1, 4))
+>>> print(parser.parse_args(['3']))
+Namespace(door=3)
+>>> parser.parse_args(['4'])
+usage: doors.py [-h] {1,2,3}
+doors.py: error: argument door: invalid choice: 4 (choose from 1, 2, 3)
+

Any sequence can be passed as the choices value, so list objects, tuple objects, and custom sequences are all supported.

Use of enum.Enum is not recommended because it is difficult to control its appearance in usage, help, and error messages.

Formatted choices override the default metavar which is normally derived from dest. This is usually what you want because the user never sees the dest parameter. If this display isn’t desirable (perhaps because there are many choices), just specify an explicit metavar.

required

In general, the argparse module assumes that flags like -f and --bar indicate optional arguments, which can always be omitted at the command line. To make an option required, True can be specified for the required= keyword argument to add_argument():

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', required=True)
+>>> parser.parse_args(['--foo', 'BAR'])
+Namespace(foo='BAR')
+>>> parser.parse_args([])
+usage: [-h] --foo FOO
+: error: the following arguments are required: --foo
+

As the example shows, if an option is marked as required, parse_args() will report an error if that option is not present at the command line.

Note

Required options are generally considered bad form because users expect options to be optional, and thus they should be avoided when possible.

help

The help value is a string containing a brief description of the argument. When a user requests help (usually by using -h or --help at the command line), these help descriptions will be displayed with each argument:

>>> parser = argparse.ArgumentParser(prog='frobble')
+>>> parser.add_argument('--foo', action='store_true',
+...                     help='foo the bars before frobbling')
+>>> parser.add_argument('bar', nargs='+',
+...                     help='one of the bars to be frobbled')
+>>> parser.parse_args(['-h'])
+usage: frobble [-h] [--foo] bar [bar ...]
+
+positional arguments:
+ bar     one of the bars to be frobbled
+
+options:
+ -h, --help  show this help message and exit
+ --foo   foo the bars before frobbling
+

The help strings can include various format specifiers to avoid repetition of things like the program name or the argument default. The available specifiers include the program name, %(prog)s and most keyword arguments to add_argument(), e.g. %(default)s, %(type)s, etc.:

>>> parser = argparse.ArgumentParser(prog='frobble')
+>>> parser.add_argument('bar', nargs='?', type=int, default=42,
+...                     help='the bar to %(prog)s (default: %(default)s)')
+>>> parser.print_help()
+usage: frobble [-h] [bar]
+
+positional arguments:
+ bar     the bar to frobble (default: 42)
+
+options:
+ -h, --help  show this help message and exit
+

As the help string supports %-formatting, if you want a literal % to appear in the help string, you must escape it as %%.

argparse supports silencing the help entry for certain options, by setting the help value to argparse.SUPPRESS:

>>> parser = argparse.ArgumentParser(prog='frobble')
+>>> parser.add_argument('--foo', help=argparse.SUPPRESS)
+>>> parser.print_help()
+usage: frobble [-h]
+
+options:
+  -h, --help  show this help message and exit
+

metavar

When ArgumentParser generates help messages, it needs some way to refer to each expected argument. By default, ArgumentParser objects use the dest value as the “name” of each object. By default, for positional argument actions, the dest value is used directly, and for optional argument actions, the dest value is uppercased. So, a single positional argument with dest='bar' will be referred to as bar. A single optional argument --foo that should be followed by a single command-line argument will be referred to as FOO. An example:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo')
+>>> parser.add_argument('bar')
+>>> parser.parse_args('X --foo Y'.split())
+Namespace(bar='X', foo='Y')
+>>> parser.print_help()
+usage:  [-h] [--foo FOO] bar
+
+positional arguments:
+ bar
+
+options:
+ -h, --help  show this help message and exit
+ --foo FOO
+

An alternative name can be specified with metavar:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', metavar='YYY')
+>>> parser.add_argument('bar', metavar='XXX')
+>>> parser.parse_args('X --foo Y'.split())
+Namespace(bar='X', foo='Y')
+>>> parser.print_help()
+usage:  [-h] [--foo YYY] XXX
+
+positional arguments:
+ XXX
+
+options:
+ -h, --help  show this help message and exit
+ --foo YYY
+

Note that metavar only changes the displayed name - the name of the attribute on the parse_args() object is still determined by the dest value.

Different values of nargs may cause the metavar to be used multiple times. Providing a tuple to metavar specifies a different display for each of the arguments:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-x', nargs=2)
+>>> parser.add_argument('--foo', nargs=2, metavar=('bar', 'baz'))
+>>> parser.print_help()
+usage: PROG [-h] [-x X X] [--foo bar baz]
+
+options:
+ -h, --help     show this help message and exit
+ -x X X
+ --foo bar baz
+

dest

Most ArgumentParser actions add some value as an attribute of the object returned by parse_args(). The name of this attribute is determined by the dest keyword argument of add_argument(). For positional argument actions, dest is normally supplied as the first argument to add_argument():

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('bar')
+>>> parser.parse_args(['XXX'])
+Namespace(bar='XXX')
+

For optional argument actions, the value of dest is normally inferred from the option strings. ArgumentParser generates the value of dest by taking the first long option string and stripping away the initial -- string. If no long option strings were supplied, dest will be derived from the first short option string by stripping the initial - character. Any internal - characters will be converted to _ characters to make sure the string is a valid attribute name. The examples below illustrate this behavior:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('-f', '--foo-bar', '--foo')
+>>> parser.add_argument('-x', '-y')
+>>> parser.parse_args('-f 1 -x 2'.split())
+Namespace(foo_bar='1', x='2')
+>>> parser.parse_args('--foo 1 -y 2'.split())
+Namespace(foo_bar='1', x='2')
+

dest allows a custom attribute name to be provided:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', dest='bar')
+>>> parser.parse_args('--foo XXX'.split())
+Namespace(bar='XXX')
+

Action classes

Action classes implement the Action API, a callable which returns a callable which processes arguments from the command-line. Any object which follows this API may be passed as the action parameter to add_argument().

+class argparse.Action(option_strings, dest, nargs=None, const=None, default=None, type=None, choices=None, required=False, help=None, metavar=None)
+

Action objects are used by an ArgumentParser to represent the information needed to parse a single argument from one or more strings from the command line. The Action class must accept the two positional arguments plus any keyword arguments passed to ArgumentParser.add_argument() except for the action itself.

Instances of Action (or return value of any callable to the action parameter) should have attributes “dest”, “option_strings”, “default”, “type”, “required”, “help”, etc. defined. The easiest way to ensure these attributes are defined is to call Action.__init__.

Action instances should be callable, so subclasses must override the __call__ method, which should accept four parameters:

  • +parser - The ArgumentParser object which contains this action.
  • +namespace - The Namespace object that will be returned by parse_args(). Most actions add an attribute to this object using setattr().
  • +values - The associated command-line arguments, with any type conversions applied. Type conversions are specified with the type keyword argument to add_argument().
  • +option_string - The option string that was used to invoke this action. The option_string argument is optional, and will be absent if the action is associated with a positional argument.

The __call__ method may perform arbitrary actions, but will typically set attributes on the namespace based on dest and values.

Action subclasses can define a format_usage method that takes no argument and return a string which will be used when printing the usage of the program. If such method is not provided, a sensible default will be used.

The parse_args() method

+ArgumentParser.parse_args(args=None, namespace=None)
+

Convert argument strings to objects and assign them as attributes of the namespace. Return the populated namespace.

Previous calls to add_argument() determine exactly what objects are created and how they are assigned. See the documentation for add_argument() for details.

  • +args - List of strings to parse. The default is taken from sys.argv.
  • +namespace - An object to take the attributes. The default is a new empty Namespace object.
+

Option value syntax

The parse_args() method supports several ways of specifying the value of an option (if it takes one). In the simplest case, the option and its value are passed as two separate arguments:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-x')
+>>> parser.add_argument('--foo')
+>>> parser.parse_args(['-x', 'X'])
+Namespace(foo=None, x='X')
+>>> parser.parse_args(['--foo', 'FOO'])
+Namespace(foo='FOO', x=None)
+

For long options (options with names longer than a single character), the option and value can also be passed as a single command-line argument, using = to separate them:

>>> parser.parse_args(['--foo=FOO'])
+Namespace(foo='FOO', x=None)
+

For short options (options only one character long), the option and its value can be concatenated:

>>> parser.parse_args(['-xX'])
+Namespace(foo=None, x='X')
+

Several short options can be joined together, using only a single - prefix, as long as only the last option (or none of them) requires a value:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-x', action='store_true')
+>>> parser.add_argument('-y', action='store_true')
+>>> parser.add_argument('-z')
+>>> parser.parse_args(['-xyzZ'])
+Namespace(x=True, y=True, z='Z')
+

Invalid arguments

While parsing the command line, parse_args() checks for a variety of errors, including ambiguous options, invalid types, invalid options, wrong number of positional arguments, etc. When it encounters such an error, it exits and prints the error along with a usage message:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('--foo', type=int)
+>>> parser.add_argument('bar', nargs='?')
+
+>>> # invalid type
+>>> parser.parse_args(['--foo', 'spam'])
+usage: PROG [-h] [--foo FOO] [bar]
+PROG: error: argument --foo: invalid int value: 'spam'
+
+>>> # invalid option
+>>> parser.parse_args(['--bar'])
+usage: PROG [-h] [--foo FOO] [bar]
+PROG: error: no such option: --bar
+
+>>> # wrong number of arguments
+>>> parser.parse_args(['spam', 'badger'])
+usage: PROG [-h] [--foo FOO] [bar]
+PROG: error: extra arguments found: badger
+

Arguments containing - +

The parse_args() method attempts to give errors whenever the user has clearly made a mistake, but some situations are inherently ambiguous. For example, the command-line argument -1 could either be an attempt to specify an option or an attempt to provide a positional argument. The parse_args() method is cautious here: positional arguments may only begin with - if they look like negative numbers and there are no options in the parser that look like negative numbers:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-x')
+>>> parser.add_argument('foo', nargs='?')
+
+>>> # no negative number options, so -1 is a positional argument
+>>> parser.parse_args(['-x', '-1'])
+Namespace(foo=None, x='-1')
+
+>>> # no negative number options, so -1 and -5 are positional arguments
+>>> parser.parse_args(['-x', '-1', '-5'])
+Namespace(foo='-5', x='-1')
+
+>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-1', dest='one')
+>>> parser.add_argument('foo', nargs='?')
+
+>>> # negative number options present, so -1 is an option
+>>> parser.parse_args(['-1', 'X'])
+Namespace(foo=None, one='X')
+
+>>> # negative number options present, so -2 is an option
+>>> parser.parse_args(['-2'])
+usage: PROG [-h] [-1 ONE] [foo]
+PROG: error: no such option: -2
+
+>>> # negative number options present, so both -1s are options
+>>> parser.parse_args(['-1', '-1'])
+usage: PROG [-h] [-1 ONE] [foo]
+PROG: error: argument -1: expected one argument
+

If you have positional arguments that must begin with - and don’t look like negative numbers, you can insert the pseudo-argument '--' which tells parse_args() that everything after that is a positional argument:

>>> parser.parse_args(['--', '-f'])
+Namespace(foo='-f', one=None)
+

See also the argparse howto on ambiguous arguments for more details.

Argument abbreviations (prefix matching)

The parse_args() method by default allows long options to be abbreviated to a prefix, if the abbreviation is unambiguous (the prefix matches a unique option):

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('-bacon')
+>>> parser.add_argument('-badger')
+>>> parser.parse_args('-bac MMM'.split())
+Namespace(bacon='MMM', badger=None)
+>>> parser.parse_args('-bad WOOD'.split())
+Namespace(bacon=None, badger='WOOD')
+>>> parser.parse_args('-ba BA'.split())
+usage: PROG [-h] [-bacon BACON] [-badger BADGER]
+PROG: error: ambiguous option: -ba could match -badger, -bacon
+

An error is produced for arguments that could produce more than one options. This feature can be disabled by setting allow_abbrev to False.

Beyond sys.argv +

Sometimes it may be useful to have an ArgumentParser parse arguments other than those of sys.argv. This can be accomplished by passing a list of strings to parse_args(). This is useful for testing at the interactive prompt:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument(
+...     'integers', metavar='int', type=int, choices=range(10),
+...     nargs='+', help='an integer in the range 0..9')
+>>> parser.add_argument(
+...     '--sum', dest='accumulate', action='store_const', const=sum,
+...     default=max, help='sum the integers (default: find the max)')
+>>> parser.parse_args(['1', '2', '3', '4'])
+Namespace(accumulate=<built-in function max>, integers=[1, 2, 3, 4])
+>>> parser.parse_args(['1', '2', '3', '4', '--sum'])
+Namespace(accumulate=<built-in function sum>, integers=[1, 2, 3, 4])
+

The Namespace object

+class argparse.Namespace
+

Simple class used by default by parse_args() to create an object holding attributes and return it.

+

This class is deliberately simple, just an object subclass with a readable string representation. If you prefer to have dict-like view of the attributes, you can use the standard Python idiom, vars():

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo')
+>>> args = parser.parse_args(['--foo', 'BAR'])
+>>> vars(args)
+{'foo': 'BAR'}
+

It may also be useful to have an ArgumentParser assign attributes to an already existing object, rather than a new Namespace object. This can be achieved by specifying the namespace= keyword argument:

>>> class C:
+...     pass
+...
+>>> c = C()
+>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo')
+>>> parser.parse_args(args=['--foo', 'BAR'], namespace=c)
+>>> c.foo
+'BAR'
+

Other utilities

Sub-commands

+ArgumentParser.add_subparsers([title][, description][, prog][, parser_class][, action][, option_strings][, dest][, required][, help][, metavar])
+

Many programs split up their functionality into a number of sub-commands, for example, the svn program can invoke sub-commands like svn +checkout, svn update, and svn commit. Splitting up functionality this way can be a particularly good idea when a program performs several different functions which require different kinds of command-line arguments. ArgumentParser supports the creation of such sub-commands with the add_subparsers() method. The add_subparsers() method is normally called with no arguments and returns a special action object. This object has a single method, add_parser(), which takes a command name and any ArgumentParser constructor arguments, and returns an ArgumentParser object that can be modified as usual.

Description of parameters:

  • title - title for the sub-parser group in help output; by default “subcommands” if description is provided, otherwise uses title for positional arguments
  • description - description for the sub-parser group in help output, by default None +
  • prog - usage information that will be displayed with sub-command help, by default the name of the program and any positional arguments before the subparser argument
  • parser_class - class which will be used to create sub-parser instances, by default the class of the current parser (e.g. ArgumentParser)
  • +action - the basic type of action to be taken when this argument is encountered at the command line
  • +dest - name of the attribute under which sub-command name will be stored; by default None and no value is stored
  • +required - Whether or not a subcommand must be provided, by default False (added in 3.7)
  • +help - help for sub-parser group in help output, by default None +
  • +metavar - string presenting available sub-commands in help; by default it is None and presents sub-commands in form {cmd1, cmd2, ..}

Some example usage:

>>> # create the top-level parser
+>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> parser.add_argument('--foo', action='store_true', help='foo help')
+>>> subparsers = parser.add_subparsers(help='sub-command help')
+>>>
+>>> # create the parser for the "a" command
+>>> parser_a = subparsers.add_parser('a', help='a help')
+>>> parser_a.add_argument('bar', type=int, help='bar help')
+>>>
+>>> # create the parser for the "b" command
+>>> parser_b = subparsers.add_parser('b', help='b help')
+>>> parser_b.add_argument('--baz', choices='XYZ', help='baz help')
+>>>
+>>> # parse some argument lists
+>>> parser.parse_args(['a', '12'])
+Namespace(bar=12, foo=False)
+>>> parser.parse_args(['--foo', 'b', '--baz', 'Z'])
+Namespace(baz='Z', foo=True)
+

Note that the object returned by parse_args() will only contain attributes for the main parser and the subparser that was selected by the command line (and not any other subparsers). So in the example above, when the a command is specified, only the foo and bar attributes are present, and when the b command is specified, only the foo and baz attributes are present.

Similarly, when a help message is requested from a subparser, only the help for that particular parser will be printed. The help message will not include parent parser or sibling parser messages. (A help message for each subparser command, however, can be given by supplying the help= argument to add_parser() as above.)

>>> parser.parse_args(['--help'])
+usage: PROG [-h] [--foo] {a,b} ...
+
+positional arguments:
+  {a,b}   sub-command help
+    a     a help
+    b     b help
+
+options:
+  -h, --help  show this help message and exit
+  --foo   foo help
+
+>>> parser.parse_args(['a', '--help'])
+usage: PROG a [-h] bar
+
+positional arguments:
+  bar     bar help
+
+options:
+  -h, --help  show this help message and exit
+
+>>> parser.parse_args(['b', '--help'])
+usage: PROG b [-h] [--baz {X,Y,Z}]
+
+options:
+  -h, --help     show this help message and exit
+  --baz {X,Y,Z}  baz help
+

The add_subparsers() method also supports title and description keyword arguments. When either is present, the subparser’s commands will appear in their own group in the help output. For example:

>>> parser = argparse.ArgumentParser()
+>>> subparsers = parser.add_subparsers(title='subcommands',
+...                                    description='valid subcommands',
+...                                    help='additional help')
+>>> subparsers.add_parser('foo')
+>>> subparsers.add_parser('bar')
+>>> parser.parse_args(['-h'])
+usage:  [-h] {foo,bar} ...
+
+options:
+  -h, --help  show this help message and exit
+
+subcommands:
+  valid subcommands
+
+  {foo,bar}   additional help
+

Furthermore, add_parser supports an additional aliases argument, which allows multiple strings to refer to the same subparser. This example, like svn, aliases co as a shorthand for checkout:

>>> parser = argparse.ArgumentParser()
+>>> subparsers = parser.add_subparsers()
+>>> checkout = subparsers.add_parser('checkout', aliases=['co'])
+>>> checkout.add_argument('foo')
+>>> parser.parse_args(['co', 'bar'])
+Namespace(foo='bar')
+

One particularly effective way of handling sub-commands is to combine the use of the add_subparsers() method with calls to set_defaults() so that each subparser knows which Python function it should execute. For example:

>>> # sub-command functions
+>>> def foo(args):
+...     print(args.x * args.y)
+...
+>>> def bar(args):
+...     print('((%s))' % args.z)
+...
+>>> # create the top-level parser
+>>> parser = argparse.ArgumentParser()
+>>> subparsers = parser.add_subparsers(required=True)
+>>>
+>>> # create the parser for the "foo" command
+>>> parser_foo = subparsers.add_parser('foo')
+>>> parser_foo.add_argument('-x', type=int, default=1)
+>>> parser_foo.add_argument('y', type=float)
+>>> parser_foo.set_defaults(func=foo)
+>>>
+>>> # create the parser for the "bar" command
+>>> parser_bar = subparsers.add_parser('bar')
+>>> parser_bar.add_argument('z')
+>>> parser_bar.set_defaults(func=bar)
+>>>
+>>> # parse the args and call whatever function was selected
+>>> args = parser.parse_args('foo 1 -x 2'.split())
+>>> args.func(args)
+2.0
+>>>
+>>> # parse the args and call whatever function was selected
+>>> args = parser.parse_args('bar XYZYX'.split())
+>>> args.func(args)
+((XYZYX))
+

This way, you can let parse_args() do the job of calling the appropriate function after argument parsing is complete. Associating functions with actions like this is typically the easiest way to handle the different actions for each of your subparsers. However, if it is necessary to check the name of the subparser that was invoked, the dest keyword argument to the add_subparsers() call will work:

>>> parser = argparse.ArgumentParser()
+>>> subparsers = parser.add_subparsers(dest='subparser_name')
+>>> subparser1 = subparsers.add_parser('1')
+>>> subparser1.add_argument('-x')
+>>> subparser2 = subparsers.add_parser('2')
+>>> subparser2.add_argument('y')
+>>> parser.parse_args(['2', 'frobble'])
+Namespace(subparser_name='2', y='frobble')
+

Changed in version 3.7: New required keyword argument.

+

FileType objects

+class argparse.FileType(mode='r', bufsize=- 1, encoding=None, errors=None)
+

The FileType factory creates objects that can be passed to the type argument of ArgumentParser.add_argument(). Arguments that have FileType objects as their type will open command-line arguments as files with the requested modes, buffer sizes, encodings and error handling (see the open() function for more details):

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--raw', type=argparse.FileType('wb', 0))
+>>> parser.add_argument('out', type=argparse.FileType('w', encoding='UTF-8'))
+>>> parser.parse_args(['--raw', 'raw.dat', 'file.txt'])
+Namespace(out=<_io.TextIOWrapper name='file.txt' mode='w' encoding='UTF-8'>, raw=<_io.FileIO name='raw.dat' mode='wb'>)
+

FileType objects understand the pseudo-argument '-' and automatically convert this into sys.stdin for readable FileType objects and sys.stdout for writable FileType objects:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('infile', type=argparse.FileType('r'))
+>>> parser.parse_args(['-'])
+Namespace(infile=<_io.TextIOWrapper name='<stdin>' encoding='UTF-8'>)
+

New in version 3.4: The encodings and errors keyword arguments.

+

Argument groups

+ArgumentParser.add_argument_group(title=None, description=None)
+

By default, ArgumentParser groups command-line arguments into “positional arguments” and “options” when displaying help messages. When there is a better conceptual grouping of arguments than this default one, appropriate groups can be created using the add_argument_group() method:

>>> parser = argparse.ArgumentParser(prog='PROG', add_help=False)
+>>> group = parser.add_argument_group('group')
+>>> group.add_argument('--foo', help='foo help')
+>>> group.add_argument('bar', help='bar help')
+>>> parser.print_help()
+usage: PROG [--foo FOO] bar
+
+group:
+  bar    bar help
+  --foo FOO  foo help
+

The add_argument_group() method returns an argument group object which has an add_argument() method just like a regular ArgumentParser. When an argument is added to the group, the parser treats it just like a normal argument, but displays the argument in a separate group for help messages. The add_argument_group() method accepts title and description arguments which can be used to customize this display:

>>> parser = argparse.ArgumentParser(prog='PROG', add_help=False)
+>>> group1 = parser.add_argument_group('group1', 'group1 description')
+>>> group1.add_argument('foo', help='foo help')
+>>> group2 = parser.add_argument_group('group2', 'group2 description')
+>>> group2.add_argument('--bar', help='bar help')
+>>> parser.print_help()
+usage: PROG [--bar BAR] foo
+
+group1:
+  group1 description
+
+  foo    foo help
+
+group2:
+  group2 description
+
+  --bar BAR  bar help
+

Note that any arguments not in your user-defined groups will end up back in the usual “positional arguments” and “optional arguments” sections.

Changed in version 3.11: Calling add_argument_group() on an argument group is deprecated. This feature was never supported and does not always work correctly. The function exists on the API by accident through inheritance and will be removed in the future.

+

Mutual exclusion

+ArgumentParser.add_mutually_exclusive_group(required=False)
+

Create a mutually exclusive group. argparse will make sure that only one of the arguments in the mutually exclusive group was present on the command line:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> group = parser.add_mutually_exclusive_group()
+>>> group.add_argument('--foo', action='store_true')
+>>> group.add_argument('--bar', action='store_false')
+>>> parser.parse_args(['--foo'])
+Namespace(bar=True, foo=True)
+>>> parser.parse_args(['--bar'])
+Namespace(bar=False, foo=False)
+>>> parser.parse_args(['--foo', '--bar'])
+usage: PROG [-h] [--foo | --bar]
+PROG: error: argument --bar: not allowed with argument --foo
+

The add_mutually_exclusive_group() method also accepts a required argument, to indicate that at least one of the mutually exclusive arguments is required:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> group = parser.add_mutually_exclusive_group(required=True)
+>>> group.add_argument('--foo', action='store_true')
+>>> group.add_argument('--bar', action='store_false')
+>>> parser.parse_args([])
+usage: PROG [-h] (--foo | --bar)
+PROG: error: one of the arguments --foo --bar is required
+

Note that currently mutually exclusive argument groups do not support the title and description arguments of add_argument_group(). However, a mutually exclusive group can be added to an argument group that has a title and description. For example:

>>> parser = argparse.ArgumentParser(prog='PROG')
+>>> group = parser.add_argument_group('Group title', 'Group description')
+>>> exclusive_group = group.add_mutually_exclusive_group(required=True)
+>>> exclusive_group.add_argument('--foo', help='foo help')
+>>> exclusive_group.add_argument('--bar', help='bar help')
+>>> parser.print_help()
+usage: PROG [-h] (--foo FOO | --bar BAR)
+
+options:
+  -h, --help  show this help message and exit
+
+Group title:
+  Group description
+
+  --foo FOO   foo help
+  --bar BAR   bar help
+

Changed in version 3.11: Calling add_argument_group() or add_mutually_exclusive_group() on a mutually exclusive group is deprecated. These features were never supported and do not always work correctly. The functions exist on the API by accident through inheritance and will be removed in the future.

+

Parser defaults

+ArgumentParser.set_defaults(**kwargs)
+

Most of the time, the attributes of the object returned by parse_args() will be fully determined by inspecting the command-line arguments and the argument actions. set_defaults() allows some additional attributes that are determined without any inspection of the command line to be added:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('foo', type=int)
+>>> parser.set_defaults(bar=42, baz='badger')
+>>> parser.parse_args(['736'])
+Namespace(bar=42, baz='badger', foo=736)
+

Note that parser-level defaults always override argument-level defaults:

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', default='bar')
+>>> parser.set_defaults(foo='spam')
+>>> parser.parse_args([])
+Namespace(foo='spam')
+

Parser-level defaults can be particularly useful when working with multiple parsers. See the add_subparsers() method for an example of this type.

+
+ArgumentParser.get_default(dest)
+

Get the default value for a namespace attribute, as set by either add_argument() or by set_defaults():

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', default='badger')
+>>> parser.get_default('foo')
+'badger'
+
+

Printing help

In most typical applications, parse_args() will take care of formatting and printing any usage or error messages. However, several formatting methods are available:

+ArgumentParser.print_usage(file=None)
+

Print a brief description of how the ArgumentParser should be invoked on the command line. If file is None, sys.stdout is assumed.

+
+ArgumentParser.print_help(file=None)
+

Print a help message, including the program usage and information about the arguments registered with the ArgumentParser. If file is None, sys.stdout is assumed.

+

There are also variants of these methods that simply return a string instead of printing it:

+ArgumentParser.format_usage()
+

Return a string containing a brief description of how the ArgumentParser should be invoked on the command line.

+
+ArgumentParser.format_help()
+

Return a string containing a help message, including the program usage and information about the arguments registered with the ArgumentParser.

+

Partial parsing

+ArgumentParser.parse_known_args(args=None, namespace=None)
+

Sometimes a script may only parse a few of the command-line arguments, passing the remaining arguments on to another script or program. In these cases, the parse_known_args() method can be useful. It works much like parse_args() except that it does not produce an error when extra arguments are present. Instead, it returns a two item tuple containing the populated namespace and the list of remaining argument strings.

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo', action='store_true')
+>>> parser.add_argument('bar')
+>>> parser.parse_known_args(['--foo', '--badger', 'BAR', 'spam'])
+(Namespace(bar='BAR', foo=True), ['--badger', 'spam'])
+

Warning

Prefix matching rules apply to parse_known_args(). The parser may consume an option even if it’s just a prefix of one of its known options, instead of leaving it in the remaining arguments list.

Customizing file parsing

+ArgumentParser.convert_arg_line_to_args(arg_line)
+

Arguments that are read from a file (see the fromfile_prefix_chars keyword argument to the ArgumentParser constructor) are read one argument per line. convert_arg_line_to_args() can be overridden for fancier reading.

This method takes a single argument arg_line which is a string read from the argument file. It returns a list of arguments parsed from this string. The method is called once per line read from the argument file, in order.

A useful override of this method is one that treats each space-separated word as an argument. The following example demonstrates how to do this:

class MyArgumentParser(argparse.ArgumentParser):
+    def convert_arg_line_to_args(self, arg_line):
+        return arg_line.split()
+
+

Exiting methods

+ArgumentParser.exit(status=0, message=None)
+

This method terminates the program, exiting with the specified status and, if given, it prints a message before that. The user can override this method to handle these steps differently:

class ErrorCatchingArgumentParser(argparse.ArgumentParser):
+    def exit(self, status=0, message=None):
+        if status:
+            raise Exception(f'Exiting because of an error: {message}')
+        exit(status)
+
+
+ArgumentParser.error(message)
+

This method prints a usage message including the message to the standard error and terminates the program with a status code of 2.

+

Intermixed parsing

+ArgumentParser.parse_intermixed_args(args=None, namespace=None)
+
+ArgumentParser.parse_known_intermixed_args(args=None, namespace=None)
+

A number of Unix commands allow the user to intermix optional arguments with positional arguments. The parse_intermixed_args() and parse_known_intermixed_args() methods support this parsing style.

These parsers do not support all the argparse features, and will raise exceptions if unsupported features are used. In particular, subparsers, and mutually exclusive groups that include both optionals and positionals are not supported.

The following example shows the difference between parse_known_args() and parse_intermixed_args(): the former returns ['2', +'3'] as unparsed arguments, while the latter collects all the positionals into rest.

>>> parser = argparse.ArgumentParser()
+>>> parser.add_argument('--foo')
+>>> parser.add_argument('cmd')
+>>> parser.add_argument('rest', nargs='*', type=int)
+>>> parser.parse_known_args('doit 1 --foo bar 2 3'.split())
+(Namespace(cmd='doit', foo='bar', rest=[1]), ['2', '3'])
+>>> parser.parse_intermixed_args('doit 1 --foo bar 2 3'.split())
+Namespace(cmd='doit', foo='bar', rest=[1, 2, 3])
+

parse_known_intermixed_args() returns a two item tuple containing the populated namespace and the list of remaining argument strings. parse_intermixed_args() raises an error if there are any remaining unparsed argument strings.

New in version 3.7.

Upgrading optparse code

Originally, the argparse module had attempted to maintain compatibility with optparse. However, optparse was difficult to extend transparently, particularly with the changes required to support the new nargs= specifiers and better usage messages. When most everything in optparse had either been copy-pasted over or monkey-patched, it no longer seemed practical to try to maintain the backwards compatibility.

The argparse module improves on the standard library optparse module in a number of ways including:

  • Handling positional arguments.
  • Supporting sub-commands.
  • Allowing alternative option prefixes like + and /.
  • Handling zero-or-more and one-or-more style arguments.
  • Producing more informative usage messages.
  • Providing a much simpler interface for custom type and action.

A partial upgrade path from optparse to argparse:

Exceptions

+exception argparse.ArgumentError
+

An error from creating or using an argument (optional or positional).

The string value of this exception is the message, augmented with information about the argument that caused it.

+
+exception argparse.ArgumentTypeError
+

Raised when something goes wrong converting a command line string to a type.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/argparse.html +

+
diff --git a/devdocs/python~3.12/library%2Farray.html b/devdocs/python~3.12/library%2Farray.html new file mode 100644 index 00000000..9bed74e8 --- /dev/null +++ b/devdocs/python~3.12/library%2Farray.html @@ -0,0 +1,103 @@ +

array — Efficient arrays of numeric values

This module defines an object type which can compactly represent an array of basic values: characters, integers, floating point numbers. Arrays are sequence types and behave very much like lists, except that the type of objects stored in them is constrained. The type is specified at object creation time by using a type code, which is a single character. The following type codes are defined:

+ + + + + + + + + + + + + +

Type code

C Type

Python Type

Minimum size in bytes

Notes

'b'

signed char

int

1

'B'

unsigned char

int

1

'u'

wchar_t

Unicode character

2

(1)

'h'

signed short

int

2

'H'

unsigned short

int

2

'i'

signed int

int

2

'I'

unsigned int

int

2

'l'

signed long

int

4

'L'

unsigned long

int

4

'q'

signed long long

int

8

'Q'

unsigned long long

int

8

'f'

float

float

4

'd'

double

float

8

Notes:

  1. +

    It can be 16 bits or 32 bits depending on the platform.

    Changed in version 3.9: array('u') now uses wchar_t as C type instead of deprecated Py_UNICODE. This change doesn’t affect its behavior because Py_UNICODE is alias of wchar_t since Python 3.3.

    Deprecated since version 3.3, will be removed in version 4.0.

The actual representation of values is determined by the machine architecture (strictly speaking, by the C implementation). The actual size can be accessed through the array.itemsize attribute.

The module defines the following item:

+array.typecodes
+

A string with all available type codes.

+

The module defines the following type:

+class array.array(typecode[, initializer])
+

A new array whose items are restricted by typecode, and initialized from the optional initializer value, which must be a list, a bytes-like object, or iterable over elements of the appropriate type.

If given a list or string, the initializer is passed to the new array’s fromlist(), frombytes(), or fromunicode() method (see below) to add initial items to the array. Otherwise, the iterable initializer is passed to the extend() method.

Array objects support the ordinary sequence operations of indexing, slicing, concatenation, and multiplication. When using slice assignment, the assigned value must be an array object with the same type code; in all other cases, TypeError is raised. Array objects also implement the buffer interface, and may be used wherever bytes-like objects are supported.

Raises an auditing event array.__new__ with arguments typecode, initializer.

+typecode
+

The typecode character used to create the array.

+
+itemsize
+

The length in bytes of one array item in the internal representation.

+
+append(x)
+

Append a new item with value x to the end of the array.

+
+buffer_info()
+

Return a tuple (address, length) giving the current memory address and the length in elements of the buffer used to hold array’s contents. The size of the memory buffer in bytes can be computed as array.buffer_info()[1] * +array.itemsize. This is occasionally useful when working with low-level (and inherently unsafe) I/O interfaces that require memory addresses, such as certain ioctl() operations. The returned numbers are valid as long as the array exists and no length-changing operations are applied to it.

Note

When using array objects from code written in C or C++ (the only way to effectively make use of this information), it makes more sense to use the buffer interface supported by array objects. This method is maintained for backward compatibility and should be avoided in new code. The buffer interface is documented in Buffer Protocol.

+
+byteswap()
+

“Byteswap” all items of the array. This is only supported for values which are 1, 2, 4, or 8 bytes in size; for other types of values, RuntimeError is raised. It is useful when reading data from a file written on a machine with a different byte order.

+
+count(x)
+

Return the number of occurrences of x in the array.

+
+extend(iterable)
+

Append items from iterable to the end of the array. If iterable is another array, it must have exactly the same type code; if not, TypeError will be raised. If iterable is not an array, it must be iterable and its elements must be the right type to be appended to the array.

+
+frombytes(s)
+

Appends items from the string, interpreting the string as an array of machine values (as if it had been read from a file using the fromfile() method).

New in version 3.2: fromstring() is renamed to frombytes() for clarity.

+
+fromfile(f, n)
+

Read n items (as machine values) from the file object f and append them to the end of the array. If less than n items are available, EOFError is raised, but the items that were available are still inserted into the array.

+
+fromlist(list)
+

Append items from the list. This is equivalent to for x in list: +a.append(x) except that if there is a type error, the array is unchanged.

+
+fromunicode(s)
+

Extends this array with data from the given unicode string. The array must be a type 'u' array; otherwise a ValueError is raised. Use array.frombytes(unicodestring.encode(enc)) to append Unicode data to an array of some other type.

+
+index(x[, start[, stop]])
+

Return the smallest i such that i is the index of the first occurrence of x in the array. The optional arguments start and stop can be specified to search for x within a subsection of the array. Raise ValueError if x is not found.

Changed in version 3.10: Added optional start and stop parameters.

+
+insert(i, x)
+

Insert a new item with value x in the array before position i. Negative values are treated as being relative to the end of the array.

+
+pop([i])
+

Removes the item with the index i from the array and returns it. The optional argument defaults to -1, so that by default the last item is removed and returned.

+
+remove(x)
+

Remove the first occurrence of x from the array.

+
+reverse()
+

Reverse the order of the items in the array.

+
+tobytes()
+

Convert the array to an array of machine values and return the bytes representation (the same sequence of bytes that would be written to a file by the tofile() method.)

New in version 3.2: tostring() is renamed to tobytes() for clarity.

+
+tofile(f)
+

Write all items (as machine values) to the file object f.

+
+tolist()
+

Convert the array to an ordinary list with the same items.

+
+tounicode()
+

Convert the array to a unicode string. The array must be a type 'u' array; otherwise a ValueError is raised. Use array.tobytes().decode(enc) to obtain a unicode string from an array of some other type.

+
+

When an array object is printed or converted to a string, it is represented as array(typecode, initializer). The initializer is omitted if the array is empty, otherwise it is a string if the typecode is 'u', otherwise it is a list of numbers. The string is guaranteed to be able to be converted back to an array with the same type and value using eval(), so long as the array class has been imported using from array import array. Examples:

array('l')
+array('u', 'hello \u2641')
+array('l', [1, 2, 3, 4, 5])
+array('d', [1.0, 2.0, 3.14])
+

See also

+Module struct +
+
+

Packing and unpacking of heterogeneous binary data.

+Module xdrlib +
+
+

Packing and unpacking of External Data Representation (XDR) data as used in some remote procedure call systems.

NumPy
+
+

The NumPy package defines another array type.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/array.html +

+
diff --git a/devdocs/python~3.12/library%2Fast.html b/devdocs/python~3.12/library%2Fast.html new file mode 100644 index 00000000..afc73575 --- /dev/null +++ b/devdocs/python~3.12/library%2Fast.html @@ -0,0 +1,1630 @@ +

ast — Abstract Syntax Trees

Source code: Lib/ast.py

The ast module helps Python applications to process trees of the Python abstract syntax grammar. The abstract syntax itself might change with each Python release; this module helps to find out programmatically what the current grammar looks like.

An abstract syntax tree can be generated by passing ast.PyCF_ONLY_AST as a flag to the compile() built-in function, or using the parse() helper provided in this module. The result will be a tree of objects whose classes all inherit from ast.AST. An abstract syntax tree can be compiled into a Python code object using the built-in compile() function.

Abstract Grammar

The abstract grammar is currently defined as follows:

-- ASDL's 4 builtin types are:
+-- identifier, int, string, constant
+
+module Python
+{
+    mod = Module(stmt* body, type_ignore* type_ignores)
+        | Interactive(stmt* body)
+        | Expression(expr body)
+        | FunctionType(expr* argtypes, expr returns)
+
+    stmt = FunctionDef(identifier name, arguments args,
+                       stmt* body, expr* decorator_list, expr? returns,
+                       string? type_comment, type_param* type_params)
+          | AsyncFunctionDef(identifier name, arguments args,
+                             stmt* body, expr* decorator_list, expr? returns,
+                             string? type_comment, type_param* type_params)
+
+          | ClassDef(identifier name,
+             expr* bases,
+             keyword* keywords,
+             stmt* body,
+             expr* decorator_list,
+             type_param* type_params)
+          | Return(expr? value)
+
+          | Delete(expr* targets)
+          | Assign(expr* targets, expr value, string? type_comment)
+          | TypeAlias(expr name, type_param* type_params, expr value)
+          | AugAssign(expr target, operator op, expr value)
+          -- 'simple' indicates that we annotate simple name without parens
+          | AnnAssign(expr target, expr annotation, expr? value, int simple)
+
+          -- use 'orelse' because else is a keyword in target languages
+          | For(expr target, expr iter, stmt* body, stmt* orelse, string? type_comment)
+          | AsyncFor(expr target, expr iter, stmt* body, stmt* orelse, string? type_comment)
+          | While(expr test, stmt* body, stmt* orelse)
+          | If(expr test, stmt* body, stmt* orelse)
+          | With(withitem* items, stmt* body, string? type_comment)
+          | AsyncWith(withitem* items, stmt* body, string? type_comment)
+
+          | Match(expr subject, match_case* cases)
+
+          | Raise(expr? exc, expr? cause)
+          | Try(stmt* body, excepthandler* handlers, stmt* orelse, stmt* finalbody)
+          | TryStar(stmt* body, excepthandler* handlers, stmt* orelse, stmt* finalbody)
+          | Assert(expr test, expr? msg)
+
+          | Import(alias* names)
+          | ImportFrom(identifier? module, alias* names, int? level)
+
+          | Global(identifier* names)
+          | Nonlocal(identifier* names)
+          | Expr(expr value)
+          | Pass | Break | Continue
+
+          -- col_offset is the byte offset in the utf8 string the parser uses
+          attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+          -- BoolOp() can use left & right?
+    expr = BoolOp(boolop op, expr* values)
+         | NamedExpr(expr target, expr value)
+         | BinOp(expr left, operator op, expr right)
+         | UnaryOp(unaryop op, expr operand)
+         | Lambda(arguments args, expr body)
+         | IfExp(expr test, expr body, expr orelse)
+         | Dict(expr* keys, expr* values)
+         | Set(expr* elts)
+         | ListComp(expr elt, comprehension* generators)
+         | SetComp(expr elt, comprehension* generators)
+         | DictComp(expr key, expr value, comprehension* generators)
+         | GeneratorExp(expr elt, comprehension* generators)
+         -- the grammar constrains where yield expressions can occur
+         | Await(expr value)
+         | Yield(expr? value)
+         | YieldFrom(expr value)
+         -- need sequences for compare to distinguish between
+         -- x < 4 < 3 and (x < 4) < 3
+         | Compare(expr left, cmpop* ops, expr* comparators)
+         | Call(expr func, expr* args, keyword* keywords)
+         | FormattedValue(expr value, int conversion, expr? format_spec)
+         | JoinedStr(expr* values)
+         | Constant(constant value, string? kind)
+
+         -- the following expression can appear in assignment context
+         | Attribute(expr value, identifier attr, expr_context ctx)
+         | Subscript(expr value, expr slice, expr_context ctx)
+         | Starred(expr value, expr_context ctx)
+         | Name(identifier id, expr_context ctx)
+         | List(expr* elts, expr_context ctx)
+         | Tuple(expr* elts, expr_context ctx)
+
+         -- can appear only in Subscript
+         | Slice(expr? lower, expr? upper, expr? step)
+
+          -- col_offset is the byte offset in the utf8 string the parser uses
+          attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+    expr_context = Load | Store | Del
+
+    boolop = And | Or
+
+    operator = Add | Sub | Mult | MatMult | Div | Mod | Pow | LShift
+                 | RShift | BitOr | BitXor | BitAnd | FloorDiv
+
+    unaryop = Invert | Not | UAdd | USub
+
+    cmpop = Eq | NotEq | Lt | LtE | Gt | GtE | Is | IsNot | In | NotIn
+
+    comprehension = (expr target, expr iter, expr* ifs, int is_async)
+
+    excepthandler = ExceptHandler(expr? type, identifier? name, stmt* body)
+                    attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+    arguments = (arg* posonlyargs, arg* args, arg? vararg, arg* kwonlyargs,
+                 expr* kw_defaults, arg? kwarg, expr* defaults)
+
+    arg = (identifier arg, expr? annotation, string? type_comment)
+           attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+    -- keyword arguments supplied to call (NULL identifier for **kwargs)
+    keyword = (identifier? arg, expr value)
+               attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+    -- import name with optional 'as' alias.
+    alias = (identifier name, identifier? asname)
+             attributes (int lineno, int col_offset, int? end_lineno, int? end_col_offset)
+
+    withitem = (expr context_expr, expr? optional_vars)
+
+    match_case = (pattern pattern, expr? guard, stmt* body)
+
+    pattern = MatchValue(expr value)
+            | MatchSingleton(constant value)
+            | MatchSequence(pattern* patterns)
+            | MatchMapping(expr* keys, pattern* patterns, identifier? rest)
+            | MatchClass(expr cls, pattern* patterns, identifier* kwd_attrs, pattern* kwd_patterns)
+
+            | MatchStar(identifier? name)
+            -- The optional "rest" MatchMapping parameter handles capturing extra mapping keys
+
+            | MatchAs(pattern? pattern, identifier? name)
+            | MatchOr(pattern* patterns)
+
+             attributes (int lineno, int col_offset, int end_lineno, int end_col_offset)
+
+    type_ignore = TypeIgnore(int lineno, string tag)
+
+    type_param = TypeVar(identifier name, expr? bound)
+               | ParamSpec(identifier name)
+               | TypeVarTuple(identifier name)
+               attributes (int lineno, int col_offset, int end_lineno, int end_col_offset)
+}
+

Node classes

+class ast.AST
+

This is the base of all AST node classes. The actual node classes are derived from the Parser/Python.asdl file, which is reproduced above. They are defined in the _ast C module and re-exported in ast.

There is one class defined for each left-hand side symbol in the abstract grammar (for example, ast.stmt or ast.expr). In addition, there is one class defined for each constructor on the right-hand side; these classes inherit from the classes for the left-hand side trees. For example, ast.BinOp inherits from ast.expr. For production rules with alternatives (aka “sums”), the left-hand side class is abstract: only instances of specific constructor nodes are ever created.

+_fields
+

Each concrete class has an attribute _fields which gives the names of all child nodes.

Each instance of a concrete class has one attribute for each child node, of the type as defined in the grammar. For example, ast.BinOp instances have an attribute left of type ast.expr.

If these attributes are marked as optional in the grammar (using a question mark), the value might be None. If the attributes can have zero-or-more values (marked with an asterisk), the values are represented as Python lists. All possible attributes must be present and have valid values when compiling an AST with compile().

+
+lineno
+col_offset
+end_lineno
+end_col_offset
+

Instances of ast.expr and ast.stmt subclasses have lineno, col_offset, end_lineno, and end_col_offset attributes. The lineno and end_lineno are the first and last line numbers of source text span (1-indexed so the first line is line 1) and the col_offset and end_col_offset are the corresponding UTF-8 byte offsets of the first and last tokens that generated the node. The UTF-8 offset is recorded because the parser uses UTF-8 internally.

Note that the end positions are not required by the compiler and are therefore optional. The end offset is after the last symbol, for example one can get the source segment of a one-line expression node using source_line[node.col_offset : node.end_col_offset].

+

The constructor of a class ast.T parses its arguments as follows:

  • If there are positional arguments, there must be as many as there are items in T._fields; they will be assigned as attributes of these names.
  • If there are keyword arguments, they will set the attributes of the same names to the given values.

For example, to create and populate an ast.UnaryOp node, you could use

node = ast.UnaryOp()
+node.op = ast.USub()
+node.operand = ast.Constant()
+node.operand.value = 5
+node.operand.lineno = 0
+node.operand.col_offset = 0
+node.lineno = 0
+node.col_offset = 0
+

or the more compact

node = ast.UnaryOp(ast.USub(), ast.Constant(5, lineno=0, col_offset=0),
+                   lineno=0, col_offset=0)
+
+

Changed in version 3.8: Class ast.Constant is now used for all constants.

Changed in version 3.9: Simple indices are represented by their value, extended slices are represented as tuples.

Deprecated since version 3.8: Old classes ast.Num, ast.Str, ast.Bytes, ast.NameConstant and ast.Ellipsis are still available, but they will be removed in future Python releases. In the meantime, instantiating them will return an instance of a different class.

Deprecated since version 3.9: Old classes ast.Index and ast.ExtSlice are still available, but they will be removed in future Python releases. In the meantime, instantiating them will return an instance of a different class.

Note

The descriptions of the specific node classes displayed here were initially adapted from the fantastic Green Tree Snakes project and all its contributors.

Root nodes

+class ast.Module(body, type_ignores)
+

A Python module, as with file input. Node type generated by ast.parse() in the default "exec" mode.

body is a list of the module’s Statements.

type_ignores is a list of the module’s type ignore comments; see ast.parse() for more details.

>>> print(ast.dump(ast.parse('x = 1'), indent=4))
+Module(
+    body=[
+        Assign(
+            targets=[
+                Name(id='x', ctx=Store())],
+            value=Constant(value=1))],
+    type_ignores=[])
+
+
+class ast.Expression(body)
+

A single Python expression input. Node type generated by ast.parse() when mode is "eval".

body is a single node, one of the expression types.

>>> print(ast.dump(ast.parse('123', mode='eval'), indent=4))
+Expression(
+    body=Constant(value=123))
+
+
+class ast.Interactive(body)
+

A single interactive input, like in Interactive Mode. Node type generated by ast.parse() when mode is "single".

body is a list of statement nodes.

>>> print(ast.dump(ast.parse('x = 1; y = 2', mode='single'), indent=4))
+Interactive(
+    body=[
+        Assign(
+            targets=[
+                Name(id='x', ctx=Store())],
+            value=Constant(value=1)),
+        Assign(
+            targets=[
+                Name(id='y', ctx=Store())],
+            value=Constant(value=2))])
+
+
+class ast.FunctionType(argtypes, returns)
+

A representation of an old-style type comments for functions, as Python versions prior to 3.5 didn’t support PEP 484 annotations. Node type generated by ast.parse() when mode is "func_type".

Such type comments would look like this:

def sum_two_number(a, b):
+    # type: (int, int) -> int
+    return a + b
+

argtypes is a list of expression nodes.

returns is a single expression node.

>>> print(ast.dump(ast.parse('(int, str) -> List[int]', mode='func_type'), indent=4))
+FunctionType(
+    argtypes=[
+        Name(id='int', ctx=Load()),
+        Name(id='str', ctx=Load())],
+    returns=Subscript(
+        value=Name(id='List', ctx=Load()),
+        slice=Name(id='int', ctx=Load()),
+        ctx=Load()))
+

New in version 3.8.

+

Literals

+class ast.Constant(value)
+

A constant value. The value attribute of the Constant literal contains the Python object it represents. The values represented can be simple types such as a number, string or None, but also immutable container types (tuples and frozensets) if all of their elements are constant.

>>> print(ast.dump(ast.parse('123', mode='eval'), indent=4))
+Expression(
+    body=Constant(value=123))
+
+
+class ast.FormattedValue(value, conversion, format_spec)
+

Node representing a single formatting field in an f-string. If the string contains a single formatting field and nothing else the node can be isolated otherwise it appears in JoinedStr.

  • +value is any expression node (such as a literal, a variable, or a function call).
  • +

    conversion is an integer:

    • -1: no formatting
    • 115: !s string formatting
    • 114: !r repr formatting
    • 97: !a ascii formatting
  • +format_spec is a JoinedStr node representing the formatting of the value, or None if no format was specified. Both conversion and format_spec can be set at the same time.
+
+class ast.JoinedStr(values)
+

An f-string, comprising a series of FormattedValue and Constant nodes.

>>> print(ast.dump(ast.parse('f"sin({a}) is {sin(a):.3}"', mode='eval'), indent=4))
+Expression(
+    body=JoinedStr(
+        values=[
+            Constant(value='sin('),
+            FormattedValue(
+                value=Name(id='a', ctx=Load()),
+                conversion=-1),
+            Constant(value=') is '),
+            FormattedValue(
+                value=Call(
+                    func=Name(id='sin', ctx=Load()),
+                    args=[
+                        Name(id='a', ctx=Load())],
+                    keywords=[]),
+                conversion=-1,
+                format_spec=JoinedStr(
+                    values=[
+                        Constant(value='.3')]))]))
+
+
+class ast.List(elts, ctx)
+class ast.Tuple(elts, ctx)
+

A list or tuple. elts holds a list of nodes representing the elements. ctx is Store if the container is an assignment target (i.e. (x,y)=something), and Load otherwise.

>>> print(ast.dump(ast.parse('[1, 2, 3]', mode='eval'), indent=4))
+Expression(
+    body=List(
+        elts=[
+            Constant(value=1),
+            Constant(value=2),
+            Constant(value=3)],
+        ctx=Load()))
+>>> print(ast.dump(ast.parse('(1, 2, 3)', mode='eval'), indent=4))
+Expression(
+    body=Tuple(
+        elts=[
+            Constant(value=1),
+            Constant(value=2),
+            Constant(value=3)],
+        ctx=Load()))
+
+
+class ast.Set(elts)
+

A set. elts holds a list of nodes representing the set’s elements.

>>> print(ast.dump(ast.parse('{1, 2, 3}', mode='eval'), indent=4))
+Expression(
+    body=Set(
+        elts=[
+            Constant(value=1),
+            Constant(value=2),
+            Constant(value=3)]))
+
+
+class ast.Dict(keys, values)
+

A dictionary. keys and values hold lists of nodes representing the keys and the values respectively, in matching order (what would be returned when calling dictionary.keys() and dictionary.values()).

When doing dictionary unpacking using dictionary literals the expression to be expanded goes in the values list, with a None at the corresponding position in keys.

>>> print(ast.dump(ast.parse('{"a":1, **d}', mode='eval'), indent=4))
+Expression(
+    body=Dict(
+        keys=[
+            Constant(value='a'),
+            None],
+        values=[
+            Constant(value=1),
+            Name(id='d', ctx=Load())]))
+
+

Variables

+class ast.Name(id, ctx)
+

A variable name. id holds the name as a string, and ctx is one of the following types.

+
+class ast.Load
+class ast.Store
+class ast.Del
+

Variable references can be used to load the value of a variable, to assign a new value to it, or to delete it. Variable references are given a context to distinguish these cases.

>>> print(ast.dump(ast.parse('a'), indent=4))
+Module(
+    body=[
+        Expr(
+            value=Name(id='a', ctx=Load()))],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('a = 1'), indent=4))
+Module(
+    body=[
+        Assign(
+            targets=[
+                Name(id='a', ctx=Store())],
+            value=Constant(value=1))],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('del a'), indent=4))
+Module(
+    body=[
+        Delete(
+            targets=[
+                Name(id='a', ctx=Del())])],
+    type_ignores=[])
+
+
+class ast.Starred(value, ctx)
+

A *var variable reference. value holds the variable, typically a Name node. This type must be used when building a Call node with *args.

>>> print(ast.dump(ast.parse('a, *b = it'), indent=4))
+Module(
+    body=[
+        Assign(
+            targets=[
+                Tuple(
+                    elts=[
+                        Name(id='a', ctx=Store()),
+                        Starred(
+                            value=Name(id='b', ctx=Store()),
+                            ctx=Store())],
+                    ctx=Store())],
+            value=Name(id='it', ctx=Load()))],
+    type_ignores=[])
+
+

Expressions

+class ast.Expr(value)
+

When an expression, such as a function call, appears as a statement by itself with its return value not used or stored, it is wrapped in this container. value holds one of the other nodes in this section, a Constant, a Name, a Lambda, a Yield or YieldFrom node.

>>> print(ast.dump(ast.parse('-a'), indent=4))
+Module(
+    body=[
+        Expr(
+            value=UnaryOp(
+                op=USub(),
+                operand=Name(id='a', ctx=Load())))],
+    type_ignores=[])
+
+
+class ast.UnaryOp(op, operand)
+

A unary operation. op is the operator, and operand any expression node.

+
+class ast.UAdd
+class ast.USub
+class ast.Not
+class ast.Invert
+

Unary operator tokens. Not is the not keyword, Invert is the ~ operator.

>>> print(ast.dump(ast.parse('not x', mode='eval'), indent=4))
+Expression(
+    body=UnaryOp(
+        op=Not(),
+        operand=Name(id='x', ctx=Load())))
+
+
+class ast.BinOp(left, op, right)
+

A binary operation (like addition or division). op is the operator, and left and right are any expression nodes.

>>> print(ast.dump(ast.parse('x + y', mode='eval'), indent=4))
+Expression(
+    body=BinOp(
+        left=Name(id='x', ctx=Load()),
+        op=Add(),
+        right=Name(id='y', ctx=Load())))
+
+
+class ast.Add
+class ast.Sub
+class ast.Mult
+class ast.Div
+class ast.FloorDiv
+class ast.Mod
+class ast.Pow
+class ast.LShift
+class ast.RShift
+class ast.BitOr
+class ast.BitXor
+class ast.BitAnd
+class ast.MatMult
+

Binary operator tokens.

+
+class ast.BoolOp(op, values)
+

A boolean operation, ‘or’ or ‘and’. op is Or or And. values are the values involved. Consecutive operations with the same operator, such as a or b or c, are collapsed into one node with several values.

This doesn’t include not, which is a UnaryOp.

>>> print(ast.dump(ast.parse('x or y', mode='eval'), indent=4))
+Expression(
+    body=BoolOp(
+        op=Or(),
+        values=[
+            Name(id='x', ctx=Load()),
+            Name(id='y', ctx=Load())]))
+
+
+class ast.And
+class ast.Or
+

Boolean operator tokens.

+
+class ast.Compare(left, ops, comparators)
+

A comparison of two or more values. left is the first value in the comparison, ops the list of operators, and comparators the list of values after the first element in the comparison.

>>> print(ast.dump(ast.parse('1 <= a < 10', mode='eval'), indent=4))
+Expression(
+    body=Compare(
+        left=Constant(value=1),
+        ops=[
+            LtE(),
+            Lt()],
+        comparators=[
+            Name(id='a', ctx=Load()),
+            Constant(value=10)]))
+
+
+class ast.Eq
+class ast.NotEq
+class ast.Lt
+class ast.LtE
+class ast.Gt
+class ast.GtE
+class ast.Is
+class ast.IsNot
+class ast.In
+class ast.NotIn
+

Comparison operator tokens.

+
+class ast.Call(func, args, keywords)
+

A function call. func is the function, which will often be a Name or Attribute object. Of the arguments:

  • +args holds a list of the arguments passed by position.
  • +keywords holds a list of keyword objects representing arguments passed by keyword.

When creating a Call node, args and keywords are required, but they can be empty lists.

>>> print(ast.dump(ast.parse('func(a, b=c, *d, **e)', mode='eval'), indent=4))
+Expression(
+    body=Call(
+        func=Name(id='func', ctx=Load()),
+        args=[
+            Name(id='a', ctx=Load()),
+            Starred(
+                value=Name(id='d', ctx=Load()),
+                ctx=Load())],
+        keywords=[
+            keyword(
+                arg='b',
+                value=Name(id='c', ctx=Load())),
+            keyword(
+                value=Name(id='e', ctx=Load()))]))
+
+
+class ast.keyword(arg, value)
+

A keyword argument to a function call or class definition. arg is a raw string of the parameter name, value is a node to pass in.

+
+class ast.IfExp(test, body, orelse)
+

An expression such as a if b else c. Each field holds a single node, so in the following example, all three are Name nodes.

>>> print(ast.dump(ast.parse('a if b else c', mode='eval'), indent=4))
+Expression(
+    body=IfExp(
+        test=Name(id='b', ctx=Load()),
+        body=Name(id='a', ctx=Load()),
+        orelse=Name(id='c', ctx=Load())))
+
+
+class ast.Attribute(value, attr, ctx)
+

Attribute access, e.g. d.keys. value is a node, typically a Name. attr is a bare string giving the name of the attribute, and ctx is Load, Store or Del according to how the attribute is acted on.

>>> print(ast.dump(ast.parse('snake.colour', mode='eval'), indent=4))
+Expression(
+    body=Attribute(
+        value=Name(id='snake', ctx=Load()),
+        attr='colour',
+        ctx=Load()))
+
+
+class ast.NamedExpr(target, value)
+

A named expression. This AST node is produced by the assignment expressions operator (also known as the walrus operator). As opposed to the Assign node in which the first argument can be multiple nodes, in this case both target and value must be single nodes.

>>> print(ast.dump(ast.parse('(x := 4)', mode='eval'), indent=4))
+Expression(
+    body=NamedExpr(
+        target=Name(id='x', ctx=Store()),
+        value=Constant(value=4)))
+

New in version 3.8.

+

Subscripting

+class ast.Subscript(value, slice, ctx)
+

A subscript, such as l[1]. value is the subscripted object (usually sequence or mapping). slice is an index, slice or key. It can be a Tuple and contain a Slice. ctx is Load, Store or Del according to the action performed with the subscript.

>>> print(ast.dump(ast.parse('l[1:2, 3]', mode='eval'), indent=4))
+Expression(
+    body=Subscript(
+        value=Name(id='l', ctx=Load()),
+        slice=Tuple(
+            elts=[
+                Slice(
+                    lower=Constant(value=1),
+                    upper=Constant(value=2)),
+                Constant(value=3)],
+            ctx=Load()),
+        ctx=Load()))
+
+
+class ast.Slice(lower, upper, step)
+

Regular slicing (on the form lower:upper or lower:upper:step). Can occur only inside the slice field of Subscript, either directly or as an element of Tuple.

>>> print(ast.dump(ast.parse('l[1:2]', mode='eval'), indent=4))
+Expression(
+    body=Subscript(
+        value=Name(id='l', ctx=Load()),
+        slice=Slice(
+            lower=Constant(value=1),
+            upper=Constant(value=2)),
+        ctx=Load()))
+
+

Comprehensions

+class ast.ListComp(elt, generators)
+class ast.SetComp(elt, generators)
+class ast.GeneratorExp(elt, generators)
+class ast.DictComp(key, value, generators)
+

List and set comprehensions, generator expressions, and dictionary comprehensions. elt (or key and value) is a single node representing the part that will be evaluated for each item.

generators is a list of comprehension nodes.

>>> print(ast.dump(ast.parse('[x for x in numbers]', mode='eval'), indent=4))
+Expression(
+    body=ListComp(
+        elt=Name(id='x', ctx=Load()),
+        generators=[
+            comprehension(
+                target=Name(id='x', ctx=Store()),
+                iter=Name(id='numbers', ctx=Load()),
+                ifs=[],
+                is_async=0)]))
+>>> print(ast.dump(ast.parse('{x: x**2 for x in numbers}', mode='eval'), indent=4))
+Expression(
+    body=DictComp(
+        key=Name(id='x', ctx=Load()),
+        value=BinOp(
+            left=Name(id='x', ctx=Load()),
+            op=Pow(),
+            right=Constant(value=2)),
+        generators=[
+            comprehension(
+                target=Name(id='x', ctx=Store()),
+                iter=Name(id='numbers', ctx=Load()),
+                ifs=[],
+                is_async=0)]))
+>>> print(ast.dump(ast.parse('{x for x in numbers}', mode='eval'), indent=4))
+Expression(
+    body=SetComp(
+        elt=Name(id='x', ctx=Load()),
+        generators=[
+            comprehension(
+                target=Name(id='x', ctx=Store()),
+                iter=Name(id='numbers', ctx=Load()),
+                ifs=[],
+                is_async=0)]))
+
+
+class ast.comprehension(target, iter, ifs, is_async)
+

One for clause in a comprehension. target is the reference to use for each element - typically a Name or Tuple node. iter is the object to iterate over. ifs is a list of test expressions: each for clause can have multiple ifs.

is_async indicates a comprehension is asynchronous (using an async for instead of for). The value is an integer (0 or 1).

>>> print(ast.dump(ast.parse('[ord(c) for line in file for c in line]', mode='eval'),
+...                indent=4)) # Multiple comprehensions in one.
+Expression(
+    body=ListComp(
+        elt=Call(
+            func=Name(id='ord', ctx=Load()),
+            args=[
+                Name(id='c', ctx=Load())],
+            keywords=[]),
+        generators=[
+            comprehension(
+                target=Name(id='line', ctx=Store()),
+                iter=Name(id='file', ctx=Load()),
+                ifs=[],
+                is_async=0),
+            comprehension(
+                target=Name(id='c', ctx=Store()),
+                iter=Name(id='line', ctx=Load()),
+                ifs=[],
+                is_async=0)]))
+
+>>> print(ast.dump(ast.parse('(n**2 for n in it if n>5 if n<10)', mode='eval'),
+...                indent=4)) # generator comprehension
+Expression(
+    body=GeneratorExp(
+        elt=BinOp(
+            left=Name(id='n', ctx=Load()),
+            op=Pow(),
+            right=Constant(value=2)),
+        generators=[
+            comprehension(
+                target=Name(id='n', ctx=Store()),
+                iter=Name(id='it', ctx=Load()),
+                ifs=[
+                    Compare(
+                        left=Name(id='n', ctx=Load()),
+                        ops=[
+                            Gt()],
+                        comparators=[
+                            Constant(value=5)]),
+                    Compare(
+                        left=Name(id='n', ctx=Load()),
+                        ops=[
+                            Lt()],
+                        comparators=[
+                            Constant(value=10)])],
+                is_async=0)]))
+
+>>> print(ast.dump(ast.parse('[i async for i in soc]', mode='eval'),
+...                indent=4)) # Async comprehension
+Expression(
+    body=ListComp(
+        elt=Name(id='i', ctx=Load()),
+        generators=[
+            comprehension(
+                target=Name(id='i', ctx=Store()),
+                iter=Name(id='soc', ctx=Load()),
+                ifs=[],
+                is_async=1)]))
+
+

Statements

+class ast.Assign(targets, value, type_comment)
+

An assignment. targets is a list of nodes, and value is a single node.

Multiple nodes in targets represents assigning the same value to each. Unpacking is represented by putting a Tuple or List within targets.

+type_comment
+

type_comment is an optional string with the type annotation as a comment.

+
>>> print(ast.dump(ast.parse('a = b = 1'), indent=4)) # Multiple assignment
+Module(
+    body=[
+        Assign(
+            targets=[
+                Name(id='a', ctx=Store()),
+                Name(id='b', ctx=Store())],
+            value=Constant(value=1))],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('a,b = c'), indent=4)) # Unpacking
+Module(
+    body=[
+        Assign(
+            targets=[
+                Tuple(
+                    elts=[
+                        Name(id='a', ctx=Store()),
+                        Name(id='b', ctx=Store())],
+                    ctx=Store())],
+            value=Name(id='c', ctx=Load()))],
+    type_ignores=[])
+
+
+class ast.AnnAssign(target, annotation, value, simple)
+

An assignment with a type annotation. target is a single node and can be a Name, a Attribute or a Subscript. annotation is the annotation, such as a Constant or Name node. value is a single optional node. simple is a boolean integer set to True for a Name node in target that do not appear in between parenthesis and are hence pure names and not expressions.

>>> print(ast.dump(ast.parse('c: int'), indent=4))
+Module(
+    body=[
+        AnnAssign(
+            target=Name(id='c', ctx=Store()),
+            annotation=Name(id='int', ctx=Load()),
+            simple=1)],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('(a): int = 1'), indent=4)) # Annotation with parenthesis
+Module(
+    body=[
+        AnnAssign(
+            target=Name(id='a', ctx=Store()),
+            annotation=Name(id='int', ctx=Load()),
+            value=Constant(value=1),
+            simple=0)],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('a.b: int'), indent=4)) # Attribute annotation
+Module(
+    body=[
+        AnnAssign(
+            target=Attribute(
+                value=Name(id='a', ctx=Load()),
+                attr='b',
+                ctx=Store()),
+            annotation=Name(id='int', ctx=Load()),
+            simple=0)],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('a[1]: int'), indent=4)) # Subscript annotation
+Module(
+    body=[
+        AnnAssign(
+            target=Subscript(
+                value=Name(id='a', ctx=Load()),
+                slice=Constant(value=1),
+                ctx=Store()),
+            annotation=Name(id='int', ctx=Load()),
+            simple=0)],
+    type_ignores=[])
+
+
+class ast.AugAssign(target, op, value)
+

Augmented assignment, such as a += 1. In the following example, target is a Name node for x (with the Store context), op is Add, and value is a Constant with value for 1.

The target attribute cannot be of class Tuple or List, unlike the targets of Assign.

>>> print(ast.dump(ast.parse('x += 2'), indent=4))
+Module(
+    body=[
+        AugAssign(
+            target=Name(id='x', ctx=Store()),
+            op=Add(),
+            value=Constant(value=2))],
+    type_ignores=[])
+
+
+class ast.Raise(exc, cause)
+

A raise statement. exc is the exception object to be raised, normally a Call or Name, or None for a standalone raise. cause is the optional part for y in raise x from y.

>>> print(ast.dump(ast.parse('raise x from y'), indent=4))
+Module(
+    body=[
+        Raise(
+            exc=Name(id='x', ctx=Load()),
+            cause=Name(id='y', ctx=Load()))],
+    type_ignores=[])
+
+
+class ast.Assert(test, msg)
+

An assertion. test holds the condition, such as a Compare node. msg holds the failure message.

>>> print(ast.dump(ast.parse('assert x,y'), indent=4))
+Module(
+    body=[
+        Assert(
+            test=Name(id='x', ctx=Load()),
+            msg=Name(id='y', ctx=Load()))],
+    type_ignores=[])
+
+
+class ast.Delete(targets)
+

Represents a del statement. targets is a list of nodes, such as Name, Attribute or Subscript nodes.

>>> print(ast.dump(ast.parse('del x,y,z'), indent=4))
+Module(
+    body=[
+        Delete(
+            targets=[
+                Name(id='x', ctx=Del()),
+                Name(id='y', ctx=Del()),
+                Name(id='z', ctx=Del())])],
+    type_ignores=[])
+
+
+class ast.Pass
+

A pass statement.

>>> print(ast.dump(ast.parse('pass'), indent=4))
+Module(
+    body=[
+        Pass()],
+    type_ignores=[])
+
+
+class ast.TypeAlias(name, type_params, value)
+

A type alias created through the type statement. name is the name of the alias, type_params is a list of type parameters, and value is the value of the type alias.

>>> print(ast.dump(ast.parse('type Alias = int'), indent=4))
+Module(
+    body=[
+        TypeAlias(
+            name=Name(id='Alias', ctx=Store()),
+            type_params=[],
+            value=Name(id='int', ctx=Load()))],
+    type_ignores=[])
+

New in version 3.12.

+

Other statements which are only applicable inside functions or loops are described in other sections.

Imports

+class ast.Import(names)
+

An import statement. names is a list of alias nodes.

>>> print(ast.dump(ast.parse('import x,y,z'), indent=4))
+Module(
+    body=[
+        Import(
+            names=[
+                alias(name='x'),
+                alias(name='y'),
+                alias(name='z')])],
+    type_ignores=[])
+
+
+class ast.ImportFrom(module, names, level)
+

Represents from x import y. module is a raw string of the ‘from’ name, without any leading dots, or None for statements such as from . import foo. level is an integer holding the level of the relative import (0 means absolute import).

>>> print(ast.dump(ast.parse('from y import x,y,z'), indent=4))
+Module(
+    body=[
+        ImportFrom(
+            module='y',
+            names=[
+                alias(name='x'),
+                alias(name='y'),
+                alias(name='z')],
+            level=0)],
+    type_ignores=[])
+
+
+class ast.alias(name, asname)
+

Both parameters are raw strings of the names. asname can be None if the regular name is to be used.

>>> print(ast.dump(ast.parse('from ..foo.bar import a as b, c'), indent=4))
+Module(
+    body=[
+        ImportFrom(
+            module='foo.bar',
+            names=[
+                alias(name='a', asname='b'),
+                alias(name='c')],
+            level=2)],
+    type_ignores=[])
+
+

Control flow

Note

Optional clauses such as else are stored as an empty list if they’re not present.

+class ast.If(test, body, orelse)
+

An if statement. test holds a single node, such as a Compare node. body and orelse each hold a list of nodes.

elif clauses don’t have a special representation in the AST, but rather appear as extra If nodes within the orelse section of the previous one.

>>> print(ast.dump(ast.parse("""
+... if x:
+...    ...
+... elif y:
+...    ...
+... else:
+...    ...
+... """), indent=4))
+Module(
+    body=[
+        If(
+            test=Name(id='x', ctx=Load()),
+            body=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            orelse=[
+                If(
+                    test=Name(id='y', ctx=Load()),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))],
+                    orelse=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+
+
+class ast.For(target, iter, body, orelse, type_comment)
+

A for loop. target holds the variable(s) the loop assigns to, as a single Name, Tuple, List, Attribute or Subscript node. iter holds the item to be looped over, again as a single node. body and orelse contain lists of nodes to execute. Those in orelse are executed if the loop finishes normally, rather than via a break statement.

+type_comment
+

type_comment is an optional string with the type annotation as a comment.

+
>>> print(ast.dump(ast.parse("""
+... for x in y:
+...     ...
+... else:
+...     ...
+... """), indent=4))
+Module(
+    body=[
+        For(
+            target=Name(id='x', ctx=Store()),
+            iter=Name(id='y', ctx=Load()),
+            body=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            orelse=[
+                Expr(
+                    value=Constant(value=Ellipsis))])],
+    type_ignores=[])
+
+
+class ast.While(test, body, orelse)
+

A while loop. test holds the condition, such as a Compare node.

>> print(ast.dump(ast.parse("""
+... while x:
+...    ...
+... else:
+...    ...
+... """), indent=4))
+Module(
+    body=[
+        While(
+            test=Name(id='x', ctx=Load()),
+            body=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            orelse=[
+                Expr(
+                    value=Constant(value=Ellipsis))])],
+    type_ignores=[])
+
+
+class ast.Break
+class ast.Continue
+

The break and continue statements.

>>> print(ast.dump(ast.parse("""\
+... for a in b:
+...     if a > 5:
+...         break
+...     else:
+...         continue
+...
+... """), indent=4))
+Module(
+    body=[
+        For(
+            target=Name(id='a', ctx=Store()),
+            iter=Name(id='b', ctx=Load()),
+            body=[
+                If(
+                    test=Compare(
+                        left=Name(id='a', ctx=Load()),
+                        ops=[
+                            Gt()],
+                        comparators=[
+                            Constant(value=5)]),
+                    body=[
+                        Break()],
+                    orelse=[
+                        Continue()])],
+            orelse=[])],
+    type_ignores=[])
+
+
+class ast.Try(body, handlers, orelse, finalbody)
+

try blocks. All attributes are list of nodes to execute, except for handlers, which is a list of ExceptHandler nodes.

>>> print(ast.dump(ast.parse("""
+... try:
+...    ...
+... except Exception:
+...    ...
+... except OtherException as e:
+...    ...
+... else:
+...    ...
+... finally:
+...    ...
+... """), indent=4))
+Module(
+    body=[
+        Try(
+            body=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            handlers=[
+                ExceptHandler(
+                    type=Name(id='Exception', ctx=Load()),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                ExceptHandler(
+                    type=Name(id='OtherException', ctx=Load()),
+                    name='e',
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])],
+            orelse=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            finalbody=[
+                Expr(
+                    value=Constant(value=Ellipsis))])],
+    type_ignores=[])
+
+
+class ast.TryStar(body, handlers, orelse, finalbody)
+

try blocks which are followed by except* clauses. The attributes are the same as for Try but the ExceptHandler nodes in handlers are interpreted as except* blocks rather then except.

>>> print(ast.dump(ast.parse("""
+... try:
+...    ...
+... except* Exception:
+...    ...
+... """), indent=4))
+Module(
+    body=[
+        TryStar(
+            body=[
+                Expr(
+                    value=Constant(value=Ellipsis))],
+            handlers=[
+                ExceptHandler(
+                    type=Name(id='Exception', ctx=Load()),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])],
+            orelse=[],
+            finalbody=[])],
+    type_ignores=[])
+

New in version 3.11.

+
+class ast.ExceptHandler(type, name, body)
+

A single except clause. type is the exception type it will match, typically a Name node (or None for a catch-all except: clause). name is a raw string for the name to hold the exception, or None if the clause doesn’t have as foo. body is a list of nodes.

>>> print(ast.dump(ast.parse("""\
+... try:
+...     a + 1
+... except TypeError:
+...     pass
+... """), indent=4))
+Module(
+    body=[
+        Try(
+            body=[
+                Expr(
+                    value=BinOp(
+                        left=Name(id='a', ctx=Load()),
+                        op=Add(),
+                        right=Constant(value=1)))],
+            handlers=[
+                ExceptHandler(
+                    type=Name(id='TypeError', ctx=Load()),
+                    body=[
+                        Pass()])],
+            orelse=[],
+            finalbody=[])],
+    type_ignores=[])
+
+
+class ast.With(items, body, type_comment)
+

A with block. items is a list of withitem nodes representing the context managers, and body is the indented block inside the context.

+type_comment
+

type_comment is an optional string with the type annotation as a comment.

+
+
+class ast.withitem(context_expr, optional_vars)
+

A single context manager in a with block. context_expr is the context manager, often a Call node. optional_vars is a Name, Tuple or List for the as foo part, or None if that isn’t used.

>>> print(ast.dump(ast.parse("""\
+... with a as b, c as d:
+...    something(b, d)
+... """), indent=4))
+Module(
+    body=[
+        With(
+            items=[
+                withitem(
+                    context_expr=Name(id='a', ctx=Load()),
+                    optional_vars=Name(id='b', ctx=Store())),
+                withitem(
+                    context_expr=Name(id='c', ctx=Load()),
+                    optional_vars=Name(id='d', ctx=Store()))],
+            body=[
+                Expr(
+                    value=Call(
+                        func=Name(id='something', ctx=Load()),
+                        args=[
+                            Name(id='b', ctx=Load()),
+                            Name(id='d', ctx=Load())],
+                        keywords=[]))])],
+    type_ignores=[])
+
+

Pattern matching

+class ast.Match(subject, cases)
+

A match statement. subject holds the subject of the match (the object that is being matched against the cases) and cases contains an iterable of match_case nodes with the different cases.

New in version 3.10.

+
+class ast.match_case(pattern, guard, body)
+

A single case pattern in a match statement. pattern contains the match pattern that the subject will be matched against. Note that the AST nodes produced for patterns differ from those produced for expressions, even when they share the same syntax.

The guard attribute contains an expression that will be evaluated if the pattern matches the subject.

body contains a list of nodes to execute if the pattern matches and the result of evaluating the guard expression is true.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case [x] if x>0:
+...         ...
+...     case tuple():
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchSequence(
+                        patterns=[
+                            MatchAs(name='x')]),
+                    guard=Compare(
+                        left=Name(id='x', ctx=Load()),
+                        ops=[
+                            Gt()],
+                        comparators=[
+                            Constant(value=0)]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                match_case(
+                    pattern=MatchClass(
+                        cls=Name(id='tuple', ctx=Load()),
+                        patterns=[],
+                        kwd_attrs=[],
+                        kwd_patterns=[]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchValue(value)
+

A match literal or value pattern that compares by equality. value is an expression node. Permitted value nodes are restricted as described in the match statement documentation. This pattern succeeds if the match subject is equal to the evaluated value.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case "Relevant":
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchValue(
+                        value=Constant(value='Relevant')),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchSingleton(value)
+

A match literal pattern that compares by identity. value is the singleton to be compared against: None, True, or False. This pattern succeeds if the match subject is the given constant.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case None:
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchSingleton(value=None),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchSequence(patterns)
+

A match sequence pattern. patterns contains the patterns to be matched against the subject elements if the subject is a sequence. Matches a variable length sequence if one of the subpatterns is a MatchStar node, otherwise matches a fixed length sequence.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case [1, 2]:
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchSequence(
+                        patterns=[
+                            MatchValue(
+                                value=Constant(value=1)),
+                            MatchValue(
+                                value=Constant(value=2))]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchStar(name)
+

Matches the rest of the sequence in a variable length match sequence pattern. If name is not None, a list containing the remaining sequence elements is bound to that name if the overall sequence pattern is successful.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case [1, 2, *rest]:
+...         ...
+...     case [*_]:
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchSequence(
+                        patterns=[
+                            MatchValue(
+                                value=Constant(value=1)),
+                            MatchValue(
+                                value=Constant(value=2)),
+                            MatchStar(name='rest')]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                match_case(
+                    pattern=MatchSequence(
+                        patterns=[
+                            MatchStar()]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchMapping(keys, patterns, rest)
+

A match mapping pattern. keys is a sequence of expression nodes. patterns is a corresponding sequence of pattern nodes. rest is an optional name that can be specified to capture the remaining mapping elements. Permitted key expressions are restricted as described in the match statement documentation.

This pattern succeeds if the subject is a mapping, all evaluated key expressions are present in the mapping, and the value corresponding to each key matches the corresponding subpattern. If rest is not None, a dict containing the remaining mapping elements is bound to that name if the overall mapping pattern is successful.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case {1: _, 2: _}:
+...         ...
+...     case {**rest}:
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchMapping(
+                        keys=[
+                            Constant(value=1),
+                            Constant(value=2)],
+                        patterns=[
+                            MatchAs(),
+                            MatchAs()]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                match_case(
+                    pattern=MatchMapping(keys=[], patterns=[], rest='rest'),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchClass(cls, patterns, kwd_attrs, kwd_patterns)
+

A match class pattern. cls is an expression giving the nominal class to be matched. patterns is a sequence of pattern nodes to be matched against the class defined sequence of pattern matching attributes. kwd_attrs is a sequence of additional attributes to be matched (specified as keyword arguments in the class pattern), kwd_patterns are the corresponding patterns (specified as keyword values in the class pattern).

This pattern succeeds if the subject is an instance of the nominated class, all positional patterns match the corresponding class-defined attributes, and any specified keyword attributes match their corresponding pattern.

Note: classes may define a property that returns self in order to match a pattern node against the instance being matched. Several builtin types are also matched that way, as described in the match statement documentation.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case Point2D(0, 0):
+...         ...
+...     case Point3D(x=0, y=0, z=0):
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchClass(
+                        cls=Name(id='Point2D', ctx=Load()),
+                        patterns=[
+                            MatchValue(
+                                value=Constant(value=0)),
+                            MatchValue(
+                                value=Constant(value=0))],
+                        kwd_attrs=[],
+                        kwd_patterns=[]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                match_case(
+                    pattern=MatchClass(
+                        cls=Name(id='Point3D', ctx=Load()),
+                        patterns=[],
+                        kwd_attrs=[
+                            'x',
+                            'y',
+                            'z'],
+                        kwd_patterns=[
+                            MatchValue(
+                                value=Constant(value=0)),
+                            MatchValue(
+                                value=Constant(value=0)),
+                            MatchValue(
+                                value=Constant(value=0))]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchAs(pattern, name)
+

A match “as-pattern”, capture pattern or wildcard pattern. pattern contains the match pattern that the subject will be matched against. If the pattern is None, the node represents a capture pattern (i.e a bare name) and will always succeed.

The name attribute contains the name that will be bound if the pattern is successful. If name is None, pattern must also be None and the node represents the wildcard pattern.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case [x] as y:
+...         ...
+...     case _:
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchAs(
+                        pattern=MatchSequence(
+                            patterns=[
+                                MatchAs(name='x')]),
+                        name='y'),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))]),
+                match_case(
+                    pattern=MatchAs(),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+
+class ast.MatchOr(patterns)
+

A match “or-pattern”. An or-pattern matches each of its subpatterns in turn to the subject, until one succeeds. The or-pattern is then deemed to succeed. If none of the subpatterns succeed the or-pattern fails. The patterns attribute contains a list of match pattern nodes that will be matched against the subject.

>>> print(ast.dump(ast.parse("""
+... match x:
+...     case [x] | (y):
+...         ...
+... """), indent=4))
+Module(
+    body=[
+        Match(
+            subject=Name(id='x', ctx=Load()),
+            cases=[
+                match_case(
+                    pattern=MatchOr(
+                        patterns=[
+                            MatchSequence(
+                                patterns=[
+                                    MatchAs(name='x')]),
+                            MatchAs(name='y')]),
+                    body=[
+                        Expr(
+                            value=Constant(value=Ellipsis))])])],
+    type_ignores=[])
+

New in version 3.10.

+

Type parameters

Type parameters can exist on classes, functions, and type aliases.

+class ast.TypeVar(name, bound)
+

A typing.TypeVar. name is the name of the type variable. bound is the bound or constraints, if any. If bound is a Tuple, it represents constraints; otherwise it represents the bound.

>>> print(ast.dump(ast.parse("type Alias[T: int] = list[T]"), indent=4))
+Module(
+    body=[
+        TypeAlias(
+            name=Name(id='Alias', ctx=Store()),
+            type_params=[
+                TypeVar(
+                    name='T',
+                    bound=Name(id='int', ctx=Load()))],
+            value=Subscript(
+                value=Name(id='list', ctx=Load()),
+                slice=Name(id='T', ctx=Load()),
+                ctx=Load()))],
+    type_ignores=[])
+

New in version 3.12.

+
+class ast.ParamSpec(name)
+

A typing.ParamSpec. name is the name of the parameter specification.

>>> print(ast.dump(ast.parse("type Alias[**P] = Callable[P, int]"), indent=4))
+Module(
+    body=[
+        TypeAlias(
+            name=Name(id='Alias', ctx=Store()),
+            type_params=[
+                ParamSpec(name='P')],
+            value=Subscript(
+                value=Name(id='Callable', ctx=Load()),
+                slice=Tuple(
+                    elts=[
+                        Name(id='P', ctx=Load()),
+                        Name(id='int', ctx=Load())],
+                    ctx=Load()),
+                ctx=Load()))],
+    type_ignores=[])
+

New in version 3.12.

+
+class ast.TypeVarTuple(name)
+

A typing.TypeVarTuple. name is the name of the type variable tuple.

>>> print(ast.dump(ast.parse("type Alias[*Ts] = tuple[*Ts]"), indent=4))
+Module(
+    body=[
+        TypeAlias(
+            name=Name(id='Alias', ctx=Store()),
+            type_params=[
+                TypeVarTuple(name='Ts')],
+            value=Subscript(
+                value=Name(id='tuple', ctx=Load()),
+                slice=Tuple(
+                    elts=[
+                        Starred(
+                            value=Name(id='Ts', ctx=Load()),
+                            ctx=Load())],
+                    ctx=Load()),
+                ctx=Load()))],
+    type_ignores=[])
+

New in version 3.12.

+

Function and class definitions

+class ast.FunctionDef(name, args, body, decorator_list, returns, type_comment, type_params)
+

A function definition.

  • +name is a raw string of the function name.
  • +args is an arguments node.
  • +body is the list of nodes inside the function.
  • +decorator_list is the list of decorators to be applied, stored outermost first (i.e. the first in the list will be applied last).
  • +returns is the return annotation.
  • +type_params is a list of type parameters.
+type_comment
+

type_comment is an optional string with the type annotation as a comment.

+

Changed in version 3.12: Added type_params.

+
+class ast.Lambda(args, body)
+

lambda is a minimal function definition that can be used inside an expression. Unlike FunctionDef, body holds a single node.

>>> print(ast.dump(ast.parse('lambda x,y: ...'), indent=4))
+Module(
+    body=[
+        Expr(
+            value=Lambda(
+                args=arguments(
+                    posonlyargs=[],
+                    args=[
+                        arg(arg='x'),
+                        arg(arg='y')],
+                    kwonlyargs=[],
+                    kw_defaults=[],
+                    defaults=[]),
+                body=Constant(value=Ellipsis)))],
+    type_ignores=[])
+
+
+class ast.arguments(posonlyargs, args, vararg, kwonlyargs, kw_defaults, kwarg, defaults)
+

The arguments for a function.

  • +posonlyargs, args and kwonlyargs are lists of arg nodes.
  • +vararg and kwarg are single arg nodes, referring to the *args, **kwargs parameters.
  • +kw_defaults is a list of default values for keyword-only arguments. If one is None, the corresponding argument is required.
  • +defaults is a list of default values for arguments that can be passed positionally. If there are fewer defaults, they correspond to the last n arguments.
+
+class ast.arg(arg, annotation, type_comment)
+

A single argument in a list. arg is a raw string of the argument name; annotation is its annotation, such as a Name node.

+type_comment
+

type_comment is an optional string with the type annotation as a comment

+
>>> print(ast.dump(ast.parse("""\
+... @decorator1
+... @decorator2
+... def f(a: 'annotation', b=1, c=2, *d, e, f=3, **g) -> 'return annotation':
+...     pass
+... """), indent=4))
+Module(
+    body=[
+        FunctionDef(
+            name='f',
+            args=arguments(
+                posonlyargs=[],
+                args=[
+                    arg(
+                        arg='a',
+                        annotation=Constant(value='annotation')),
+                    arg(arg='b'),
+                    arg(arg='c')],
+                vararg=arg(arg='d'),
+                kwonlyargs=[
+                    arg(arg='e'),
+                    arg(arg='f')],
+                kw_defaults=[
+                    None,
+                    Constant(value=3)],
+                kwarg=arg(arg='g'),
+                defaults=[
+                    Constant(value=1),
+                    Constant(value=2)]),
+            body=[
+                Pass()],
+            decorator_list=[
+                Name(id='decorator1', ctx=Load()),
+                Name(id='decorator2', ctx=Load())],
+            returns=Constant(value='return annotation'),
+            type_params=[])],
+    type_ignores=[])
+
+
+class ast.Return(value)
+

A return statement.

>>> print(ast.dump(ast.parse('return 4'), indent=4))
+Module(
+    body=[
+        Return(
+            value=Constant(value=4))],
+    type_ignores=[])
+
+
+class ast.Yield(value)
+class ast.YieldFrom(value)
+

A yield or yield from expression. Because these are expressions, they must be wrapped in a Expr node if the value sent back is not used.

>>> print(ast.dump(ast.parse('yield x'), indent=4))
+Module(
+    body=[
+        Expr(
+            value=Yield(
+                value=Name(id='x', ctx=Load())))],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('yield from x'), indent=4))
+Module(
+    body=[
+        Expr(
+            value=YieldFrom(
+                value=Name(id='x', ctx=Load())))],
+    type_ignores=[])
+
+
+class ast.Global(names)
+class ast.Nonlocal(names)
+

global and nonlocal statements. names is a list of raw strings.

>>> print(ast.dump(ast.parse('global x,y,z'), indent=4))
+Module(
+    body=[
+        Global(
+            names=[
+                'x',
+                'y',
+                'z'])],
+    type_ignores=[])
+
+>>> print(ast.dump(ast.parse('nonlocal x,y,z'), indent=4))
+Module(
+    body=[
+        Nonlocal(
+            names=[
+                'x',
+                'y',
+                'z'])],
+    type_ignores=[])
+
+
+class ast.ClassDef(name, bases, keywords, body, decorator_list, type_params)
+

A class definition.

  • +name is a raw string for the class name
  • +bases is a list of nodes for explicitly specified base classes.
  • +keywords is a list of keyword nodes, principally for ‘metaclass’. Other keywords will be passed to the metaclass, as per PEP-3115.
  • +body is a list of nodes representing the code within the class definition.
  • +decorator_list is a list of nodes, as in FunctionDef.
  • +type_params is a list of type parameters.
>>> print(ast.dump(ast.parse("""\
+... @decorator1
+... @decorator2
+... class Foo(base1, base2, metaclass=meta):
+...     pass
+... """), indent=4))
+Module(
+    body=[
+        ClassDef(
+            name='Foo',
+            bases=[
+                Name(id='base1', ctx=Load()),
+                Name(id='base2', ctx=Load())],
+            keywords=[
+                keyword(
+                    arg='metaclass',
+                    value=Name(id='meta', ctx=Load()))],
+            body=[
+                Pass()],
+            decorator_list=[
+                Name(id='decorator1', ctx=Load()),
+                Name(id='decorator2', ctx=Load())],
+            type_params=[])],
+    type_ignores=[])
+

Changed in version 3.12: Added type_params.

+

Async and await

+class ast.AsyncFunctionDef(name, args, body, decorator_list, returns, type_comment, type_params)
+

An async def function definition. Has the same fields as FunctionDef.

Changed in version 3.12: Added type_params.

+
+class ast.Await(value)
+

An await expression. value is what it waits for. Only valid in the body of an AsyncFunctionDef.

+
>>> print(ast.dump(ast.parse("""\
+... async def f():
+...     await other_func()
+... """), indent=4))
+Module(
+    body=[
+        AsyncFunctionDef(
+            name='f',
+            args=arguments(
+                posonlyargs=[],
+                args=[],
+                kwonlyargs=[],
+                kw_defaults=[],
+                defaults=[]),
+            body=[
+                Expr(
+                    value=Await(
+                        value=Call(
+                            func=Name(id='other_func', ctx=Load()),
+                            args=[],
+                            keywords=[])))],
+            decorator_list=[],
+            type_params=[])],
+    type_ignores=[])
+
+class ast.AsyncFor(target, iter, body, orelse, type_comment)
+class ast.AsyncWith(items, body, type_comment)
+

async for loops and async with context managers. They have the same fields as For and With, respectively. Only valid in the body of an AsyncFunctionDef.

+

Note

When a string is parsed by ast.parse(), operator nodes (subclasses of ast.operator, ast.unaryop, ast.cmpop, ast.boolop and ast.expr_context) on the returned tree will be singletons. Changes to one will be reflected in all other occurrences of the same value (e.g. ast.Add).

ast Helpers

Apart from the node classes, the ast module defines these utility functions and classes for traversing abstract syntax trees:

+ast.parse(source, filename='<unknown>', mode='exec', *, type_comments=False, feature_version=None)
+

Parse the source into an AST node. Equivalent to compile(source, +filename, mode, ast.PyCF_ONLY_AST).

If type_comments=True is given, the parser is modified to check and return type comments as specified by PEP 484 and PEP 526. This is equivalent to adding ast.PyCF_TYPE_COMMENTS to the flags passed to compile(). This will report syntax errors for misplaced type comments. Without this flag, type comments will be ignored, and the type_comment field on selected AST nodes will always be None. In addition, the locations of # type: +ignore comments will be returned as the type_ignores attribute of Module (otherwise it is always an empty list).

In addition, if mode is 'func_type', the input syntax is modified to correspond to PEP 484 “signature type comments”, e.g. (str, int) -> List[str].

Also, setting feature_version to a tuple (major, minor) will attempt to parse using that Python version’s grammar. Currently major must equal to 3. For example, setting feature_version=(3, 4) will allow the use of async and await as variable names. The lowest supported version is (3, 4); the highest is sys.version_info[0:2].

If source contains a null character (’0’), ValueError is raised.

Warning

Note that successfully parsing source code into an AST object doesn’t guarantee that the source code provided is valid Python code that can be executed as the compilation step can raise further SyntaxError exceptions. For instance, the source return 42 generates a valid AST node for a return statement, but it cannot be compiled alone (it needs to be inside a function node).

In particular, ast.parse() won’t do any scoping checks, which the compilation step does.

Warning

It is possible to crash the Python interpreter with a sufficiently large/complex string due to stack depth limitations in Python’s AST compiler.

Changed in version 3.8: Added type_comments, mode='func_type' and feature_version.

+
+ast.unparse(ast_obj)
+

Unparse an ast.AST object and generate a string with code that would produce an equivalent ast.AST object if parsed back with ast.parse().

Warning

The produced code string will not necessarily be equal to the original code that generated the ast.AST object (without any compiler optimizations, such as constant tuples/frozensets).

Warning

Trying to unparse a highly complex expression would result with RecursionError.

New in version 3.9.

+
+ast.literal_eval(node_or_string)
+

Evaluate an expression node or a string containing only a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, sets, booleans, None and Ellipsis.

This can be used for evaluating strings containing Python values without the need to parse the values oneself. It is not capable of evaluating arbitrarily complex expressions, for example involving operators or indexing.

This function had been documented as “safe” in the past without defining what that meant. That was misleading. This is specifically designed not to execute Python code, unlike the more general eval(). There is no namespace, no name lookups, or ability to call out. But it is not free from attack: A relatively small input can lead to memory exhaustion or to C stack exhaustion, crashing the process. There is also the possibility for excessive CPU consumption denial of service on some inputs. Calling it on untrusted data is thus not recommended.

Warning

It is possible to crash the Python interpreter due to stack depth limitations in Python’s AST compiler.

It can raise ValueError, TypeError, SyntaxError, MemoryError and RecursionError depending on the malformed input.

Changed in version 3.2: Now allows bytes and set literals.

Changed in version 3.9: Now supports creating empty sets with 'set()'.

Changed in version 3.10: For string inputs, leading spaces and tabs are now stripped.

+
+ast.get_docstring(node, clean=True)
+

Return the docstring of the given node (which must be a FunctionDef, AsyncFunctionDef, ClassDef, or Module node), or None if it has no docstring. If clean is true, clean up the docstring’s indentation with inspect.cleandoc().

Changed in version 3.5: AsyncFunctionDef is now supported.

+
+ast.get_source_segment(source, node, *, padded=False)
+

Get source code segment of the source that generated node. If some location information (lineno, end_lineno, col_offset, or end_col_offset) is missing, return None.

If padded is True, the first line of a multi-line statement will be padded with spaces to match its original position.

New in version 3.8.

+
+ast.fix_missing_locations(node)
+

When you compile a node tree with compile(), the compiler expects lineno and col_offset attributes for every node that supports them. This is rather tedious to fill in for generated nodes, so this helper adds these attributes recursively where not already set, by setting them to the values of the parent node. It works recursively starting at node.

+
+ast.increment_lineno(node, n=1)
+

Increment the line number and end line number of each node in the tree starting at node by n. This is useful to “move code” to a different location in a file.

+
+ast.copy_location(new_node, old_node)
+

Copy source location (lineno, col_offset, end_lineno, and end_col_offset) from old_node to new_node if possible, and return new_node.

+
+ast.iter_fields(node)
+

Yield a tuple of (fieldname, value) for each field in node._fields that is present on node.

+
+ast.iter_child_nodes(node)
+

Yield all direct child nodes of node, that is, all fields that are nodes and all items of fields that are lists of nodes.

+
+ast.walk(node)
+

Recursively yield all descendant nodes in the tree starting at node (including node itself), in no specified order. This is useful if you only want to modify nodes in place and don’t care about the context.

+
+class ast.NodeVisitor
+

A node visitor base class that walks the abstract syntax tree and calls a visitor function for every node found. This function may return a value which is forwarded by the visit() method.

This class is meant to be subclassed, with the subclass adding visitor methods.

+visit(node)
+

Visit a node. The default implementation calls the method called self.visit_classname where classname is the name of the node class, or generic_visit() if that method doesn’t exist.

+
+generic_visit(node)
+

This visitor calls visit() on all children of the node.

Note that child nodes of nodes that have a custom visitor method won’t be visited unless the visitor calls generic_visit() or visits them itself.

+
+visit_Constant(node)
+

Handles all constant nodes.

+

Don’t use the NodeVisitor if you want to apply changes to nodes during traversal. For this a special visitor exists (NodeTransformer) that allows modifications.

Deprecated since version 3.8: Methods visit_Num(), visit_Str(), visit_Bytes(), visit_NameConstant() and visit_Ellipsis() are deprecated now and will not be called in future Python versions. Add the visit_Constant() method to handle all constant nodes.

+
+class ast.NodeTransformer
+

A NodeVisitor subclass that walks the abstract syntax tree and allows modification of nodes.

The NodeTransformer will walk the AST and use the return value of the visitor methods to replace or remove the old node. If the return value of the visitor method is None, the node will be removed from its location, otherwise it is replaced with the return value. The return value may be the original node in which case no replacement takes place.

Here is an example transformer that rewrites all occurrences of name lookups (foo) to data['foo']:

class RewriteName(NodeTransformer):
+
+    def visit_Name(self, node):
+        return Subscript(
+            value=Name(id='data', ctx=Load()),
+            slice=Constant(value=node.id),
+            ctx=node.ctx
+        )
+

Keep in mind that if the node you’re operating on has child nodes you must either transform the child nodes yourself or call the generic_visit() method for the node first.

For nodes that were part of a collection of statements (that applies to all statement nodes), the visitor may also return a list of nodes rather than just a single node.

If NodeTransformer introduces new nodes (that weren’t part of original tree) without giving them location information (such as lineno), fix_missing_locations() should be called with the new sub-tree to recalculate the location information:

tree = ast.parse('foo', mode='eval')
+new_tree = fix_missing_locations(RewriteName().visit(tree))
+

Usually you use the transformer like this:

node = YourTransformer().visit(node)
+
+
+ast.dump(node, annotate_fields=True, include_attributes=False, *, indent=None)
+

Return a formatted dump of the tree in node. This is mainly useful for debugging purposes. If annotate_fields is true (by default), the returned string will show the names and the values for fields. If annotate_fields is false, the result string will be more compact by omitting unambiguous field names. Attributes such as line numbers and column offsets are not dumped by default. If this is wanted, include_attributes can be set to true.

If indent is a non-negative integer or string, then the tree will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the single line representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level.

Changed in version 3.9: Added the indent option.

+

Compiler Flags

The following flags may be passed to compile() in order to change effects on the compilation of a program:

+ast.PyCF_ALLOW_TOP_LEVEL_AWAIT
+

Enables support for top-level await, async for, async with and async comprehensions.

New in version 3.8.

+
+ast.PyCF_ONLY_AST
+

Generates and returns an abstract syntax tree instead of returning a compiled code object.

+
+ast.PyCF_TYPE_COMMENTS
+

Enables support for PEP 484 and PEP 526 style type comments (# type: <type>, # type: ignore <stuff>).

New in version 3.8.

+

Command-Line Usage

New in version 3.9.

The ast module can be executed as a script from the command line. It is as simple as:

python -m ast [-m <mode>] [-a] [infile]
+

The following options are accepted:

+-h, --help
+

Show the help message and exit.

+
+-m <mode>
+--mode <mode>
+

Specify what kind of code must be compiled, like the mode argument in parse().

+
+--no-type-comments
+

Don’t parse type comments.

+
+-a, --include-attributes
+

Include attributes such as line numbers and column offsets.

+
+-i <indent>
+--indent <indent>
+

Indentation of nodes in AST (number of spaces).

+

If infile is specified its contents are parsed to AST and dumped to stdout. Otherwise, the content is read from stdin.

See also

Green Tree Snakes, an external documentation resource, has good details on working with Python ASTs.

ASTTokens annotates Python ASTs with the positions of tokens and text in the source code that generated them. This is helpful for tools that make source code transformations.

leoAst.py unifies the token-based and parse-tree-based views of python programs by inserting two-way links between tokens and ast nodes.

LibCST parses code as a Concrete Syntax Tree that looks like an ast tree and keeps all formatting details. It’s useful for building automated refactoring (codemod) applications and linters.

Parso is a Python parser that supports error recovery and round-trip parsing for different Python versions (in multiple Python versions). Parso is also able to list multiple syntax errors in your python file.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ast.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-api-index.html b/devdocs/python~3.12/library%2Fasyncio-api-index.html new file mode 100644 index 00000000..b916c2b8 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-api-index.html @@ -0,0 +1,51 @@ +

High-level API Index

This page lists all high-level async/await enabled asyncio APIs.

Tasks

Utilities to run asyncio programs, create Tasks, and await on multiple things with timeouts.

+ + + + + + + + + + + + + + + +

run()

Create event loop, run a coroutine, close the loop.

Runner

A context manager that simplifies multiple async function calls.

Task

Task object.

TaskGroup

A context manager that holds a group of tasks. Provides a convenient and reliable way to wait for all tasks in the group to finish.

create_task()

Start an asyncio Task, then returns it.

current_task()

Return the current Task.

all_tasks()

Return all tasks that are not yet finished for an event loop.

await sleep()

Sleep for a number of seconds.

await gather()

Schedule and wait for things concurrently.

await wait_for()

Run with a timeout.

await shield()

Shield from cancellation.

await wait()

Monitor for completion.

timeout()

Run with a timeout. Useful in cases when wait_for is not suitable.

to_thread()

Asynchronously run a function in a separate OS thread.

run_coroutine_threadsafe()

Schedule a coroutine from another OS thread.

for in as_completed()

Monitor for completion with a for loop.

Examples

Queues

Queues should be used to distribute work amongst multiple asyncio Tasks, implement connection pools, and pub/sub patterns.

+ + +

Queue

A FIFO queue.

PriorityQueue

A priority queue.

LifoQueue

A LIFO queue.

Examples

Subprocesses

Utilities to spawn subprocesses and run shell commands.

+ +

await create_subprocess_exec()

Create a subprocess.

await create_subprocess_shell()

Run a shell command.

Examples

Streams

High-level APIs to work with network IO.

+ + + + + +

await open_connection()

Establish a TCP connection.

await open_unix_connection()

Establish a Unix socket connection.

await start_server()

Start a TCP server.

await start_unix_server()

Start a Unix socket server.

StreamReader

High-level async/await object to receive network data.

StreamWriter

High-level async/await object to send network data.

Examples

Synchronization

Threading-like synchronization primitives that can be used in Tasks.

+ + + + + +

Lock

A mutex lock.

Event

An event object.

Condition

A condition object.

Semaphore

A semaphore.

BoundedSemaphore

A bounded semaphore.

Barrier

A barrier object.

Examples

Exceptions

+ +

asyncio.CancelledError

Raised when a Task is cancelled. See also Task.cancel().

asyncio.BrokenBarrierError

Raised when a Barrier is broken. See also Barrier.wait().

Examples

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-api-index.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-dev.html b/devdocs/python~3.12/library%2Fasyncio-dev.html new file mode 100644 index 00000000..7737e2ab --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-dev.html @@ -0,0 +1,74 @@ +

Developing with asyncio

Asynchronous programming is different from classic “sequential” programming.

This page lists common mistakes and traps and explains how to avoid them.

Debug Mode

By default asyncio runs in production mode. In order to ease the development asyncio has a debug mode.

There are several ways to enable asyncio debug mode:

In addition to enabling the debug mode, consider also:

  • +

    setting the log level of the asyncio logger to logging.DEBUG, for example the following snippet of code can be run at startup of the application:

    logging.basicConfig(level=logging.DEBUG)
+
  • configuring the warnings module to display ResourceWarning warnings. One way of doing that is by using the -W default command line option.

When the debug mode is enabled:

  • asyncio checks for coroutines that were not awaited and logs them; this mitigates the “forgotten await” pitfall.
  • Many non-threadsafe asyncio APIs (such as loop.call_soon() and loop.call_at() methods) raise an exception if they are called from a wrong thread.
  • The execution time of the I/O selector is logged if it takes too long to perform an I/O operation.
  • Callbacks taking longer than 100 milliseconds are logged. The loop.slow_callback_duration attribute can be used to set the minimum execution duration in seconds that is considered “slow”.

Concurrency and Multithreading

An event loop runs in a thread (typically the main thread) and executes all callbacks and Tasks in its thread. While a Task is running in the event loop, no other Tasks can run in the same thread. When a Task executes an await expression, the running Task gets suspended, and the event loop executes the next Task.

To schedule a callback from another OS thread, the loop.call_soon_threadsafe() method should be used. Example:

loop.call_soon_threadsafe(callback, *args)
+

Almost all asyncio objects are not thread safe, which is typically not a problem unless there is code that works with them from outside of a Task or a callback. If there’s a need for such code to call a low-level asyncio API, the loop.call_soon_threadsafe() method should be used, e.g.:

loop.call_soon_threadsafe(fut.cancel)
+

To schedule a coroutine object from a different OS thread, the run_coroutine_threadsafe() function should be used. It returns a concurrent.futures.Future to access the result:

async def coro_func():
+     return await asyncio.sleep(1, 42)
+
+# Later in another OS thread:
+
+future = asyncio.run_coroutine_threadsafe(coro_func(), loop)
+# Wait for the result:
+result = future.result()
+

To handle signals the event loop must be run in the main thread.

The loop.run_in_executor() method can be used with a concurrent.futures.ThreadPoolExecutor to execute blocking code in a different OS thread without blocking the OS thread that the event loop runs in.

There is currently no way to schedule coroutines or callbacks directly from a different process (such as one started with multiprocessing). The Event Loop Methods section lists APIs that can read from pipes and watch file descriptors without blocking the event loop. In addition, asyncio’s Subprocess APIs provide a way to start a process and communicate with it from the event loop. Lastly, the aforementioned loop.run_in_executor() method can also be used with a concurrent.futures.ProcessPoolExecutor to execute code in a different process.

Running Blocking Code

Blocking (CPU-bound) code should not be called directly. For example, if a function performs a CPU-intensive calculation for 1 second, all concurrent asyncio Tasks and IO operations would be delayed by 1 second.

An executor can be used to run a task in a different thread or even in a different process to avoid blocking the OS thread with the event loop. See the loop.run_in_executor() method for more details.

Logging

asyncio uses the logging module and all logging is performed via the "asyncio" logger.

The default log level is logging.INFO, which can be easily adjusted:

logging.getLogger("asyncio").setLevel(logging.WARNING)
+

Network logging can block the event loop. It is recommended to use a separate thread for handling logs or use non-blocking IO. For example, see Dealing with handlers that block.

Detect never-awaited coroutines

When a coroutine function is called, but not awaited (e.g. coro() instead of await coro()) or the coroutine is not scheduled with asyncio.create_task(), asyncio will emit a RuntimeWarning:

import asyncio
+
+async def test():
+    print("never scheduled")
+
+async def main():
+    test()
+
+asyncio.run(main())
+

Output:

test.py:7: RuntimeWarning: coroutine 'test' was never awaited
+  test()
+

Output in debug mode:

test.py:7: RuntimeWarning: coroutine 'test' was never awaited
+Coroutine created at (most recent call last)
+  File "../t.py", line 9, in <module>
+    asyncio.run(main(), debug=True)
+
+  < .. >
+
+  File "../t.py", line 7, in main
+    test()
+  test()
+

The usual fix is to either await the coroutine or call the asyncio.create_task() function:

async def main():
+    await test()
+

Detect never-retrieved exceptions

If a Future.set_exception() is called but the Future object is never awaited on, the exception would never be propagated to the user code. In this case, asyncio would emit a log message when the Future object is garbage collected.

Example of an unhandled exception:

import asyncio
+
+async def bug():
+    raise Exception("not consumed")
+
+async def main():
+    asyncio.create_task(bug())
+
+asyncio.run(main())
+

Output:

Task exception was never retrieved
+future: <Task finished coro=<bug() done, defined at test.py:3>
+  exception=Exception('not consumed')>
+
+Traceback (most recent call last):
+  File "test.py", line 4, in bug
+    raise Exception("not consumed")
+Exception: not consumed
+

Enable the debug mode to get the traceback where the task was created:

asyncio.run(main(), debug=True)
+

Output in debug mode:

Task exception was never retrieved
+future: <Task finished coro=<bug() done, defined at test.py:3>
+    exception=Exception('not consumed') created at asyncio/tasks.py:321>
+
+source_traceback: Object created at (most recent call last):
+  File "../t.py", line 9, in <module>
+    asyncio.run(main(), debug=True)
+
+< .. >
+
+Traceback (most recent call last):
+  File "../t.py", line 4, in bug
+    raise Exception("not consumed")
+Exception: not consumed
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-dev.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-eventloop.html b/devdocs/python~3.12/library%2Fasyncio-eventloop.html new file mode 100644 index 00000000..a06dce1c --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-eventloop.html @@ -0,0 +1,449 @@ +

Event Loop

Source code: Lib/asyncio/events.py, Lib/asyncio/base_events.py

Preface

The event loop is the core of every asyncio application. Event loops run asynchronous tasks and callbacks, perform network IO operations, and run subprocesses.

Application developers should typically use the high-level asyncio functions, such as asyncio.run(), and should rarely need to reference the loop object or call its methods. This section is intended mostly for authors of lower-level code, libraries, and frameworks, who need finer control over the event loop behavior.

Obtaining the Event Loop

The following low-level functions can be used to get, set, or create an event loop:

+asyncio.get_running_loop()
+

Return the running event loop in the current OS thread.

Raise a RuntimeError if there is no running event loop.

This function can only be called from a coroutine or a callback.

New in version 3.7.

+
+asyncio.get_event_loop()
+

Get the current event loop.

When called from a coroutine or a callback (e.g. scheduled with call_soon or similar API), this function will always return the running event loop.

If there is no running event loop set, the function will return the result of the get_event_loop_policy().get_event_loop() call.

Because this function has rather complex behavior (especially when custom event loop policies are in use), using the get_running_loop() function is preferred to get_event_loop() in coroutines and callbacks.

As noted above, consider using the higher-level asyncio.run() function, instead of using these lower level functions to manually create and close an event loop.

Deprecated since version 3.12: Deprecation warning is emitted if there is no current event loop. In some future Python release this will become an error.

+
+asyncio.set_event_loop(loop)
+

Set loop as the current event loop for the current OS thread.

+
+asyncio.new_event_loop()
+

Create and return a new event loop object.

+

Note that the behaviour of get_event_loop(), set_event_loop(), and new_event_loop() functions can be altered by setting a custom event loop policy.

Contents

This documentation page contains the following sections:

Event Loop Methods

Event loops have low-level APIs for the following:

Running and stopping the loop

+loop.run_until_complete(future)
+

Run until the future (an instance of Future) has completed.

If the argument is a coroutine object it is implicitly scheduled to run as a asyncio.Task.

Return the Future’s result or raise its exception.

+
+loop.run_forever()
+

Run the event loop until stop() is called.

If stop() is called before run_forever() is called, the loop will poll the I/O selector once with a timeout of zero, run all callbacks scheduled in response to I/O events (and those that were already scheduled), and then exit.

If stop() is called while run_forever() is running, the loop will run the current batch of callbacks and then exit. Note that new callbacks scheduled by callbacks will not run in this case; instead, they will run the next time run_forever() or run_until_complete() is called.

+
+loop.stop()
+

Stop the event loop.

+
+loop.is_running()
+

Return True if the event loop is currently running.

+
+loop.is_closed()
+

Return True if the event loop was closed.

+
+loop.close()
+

Close the event loop.

The loop must not be running when this function is called. Any pending callbacks will be discarded.

This method clears all queues and shuts down the executor, but does not wait for the executor to finish.

This method is idempotent and irreversible. No other methods should be called after the event loop is closed.

+
+coroutine loop.shutdown_asyncgens()
+

Schedule all currently open asynchronous generator objects to close with an aclose() call. After calling this method, the event loop will issue a warning if a new asynchronous generator is iterated. This should be used to reliably finalize all scheduled asynchronous generators.

Note that there is no need to call this function when asyncio.run() is used.

Example:

try:
+    loop.run_forever()
+finally:
+    loop.run_until_complete(loop.shutdown_asyncgens())
+    loop.close()
+

New in version 3.6.

+
+coroutine loop.shutdown_default_executor(timeout=None)
+

Schedule the closure of the default executor and wait for it to join all of the threads in the ThreadPoolExecutor. Once this method has been called, using the default executor with loop.run_in_executor() will raise a RuntimeError.

The timeout parameter specifies the amount of time (in float seconds) the executor will be given to finish joining. With the default, None, the executor is allowed an unlimited amount of time.

If the timeout is reached, a RuntimeWarning is emitted and the default executor is terminated without waiting for its threads to finish joining.

Note

Do not call this method when using asyncio.run(), as the latter handles default executor shutdown automatically.

New in version 3.9.

Changed in version 3.12: Added the timeout parameter.

+

Scheduling callbacks

+loop.call_soon(callback, *args, context=None)
+

Schedule the callback callback to be called with args arguments at the next iteration of the event loop.

Return an instance of asyncio.Handle, which can be used later to cancel the callback.

Callbacks are called in the order in which they are registered. Each callback will be called exactly once.

The optional keyword-only context argument specifies a custom contextvars.Context for the callback to run in. Callbacks use the current context when no context is provided.

Unlike call_soon_threadsafe(), this method is not thread-safe.

+
+loop.call_soon_threadsafe(callback, *args, context=None)
+

A thread-safe variant of call_soon(). When scheduling callbacks from another thread, this function must be used, since call_soon() is not thread-safe.

Raises RuntimeError if called on a loop that’s been closed. This can happen on a secondary thread when the main application is shutting down.

See the concurrency and multithreading section of the documentation.

Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details.

+

Note

Most asyncio scheduling functions don’t allow passing keyword arguments. To do that, use functools.partial():

# will schedule "print("Hello", flush=True)"
+loop.call_soon(
+    functools.partial(print, "Hello", flush=True))
+

Using partial objects is usually more convenient than using lambdas, as asyncio can render partial objects better in debug and error messages.

Scheduling delayed callbacks

Event loop provides mechanisms to schedule callback functions to be called at some point in the future. Event loop uses monotonic clocks to track time.

+loop.call_later(delay, callback, *args, context=None)
+

Schedule callback to be called after the given delay number of seconds (can be either an int or a float).

An instance of asyncio.TimerHandle is returned which can be used to cancel the callback.

callback will be called exactly once. If two callbacks are scheduled for exactly the same time, the order in which they are called is undefined.

The optional positional args will be passed to the callback when it is called. If you want the callback to be called with keyword arguments use functools.partial().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the callback to run in. The current context is used when no context is provided.

Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details.

Changed in version 3.8: In Python 3.7 and earlier with the default event loop implementation, the delay could not exceed one day. This has been fixed in Python 3.8.

+
+loop.call_at(when, callback, *args, context=None)
+

Schedule callback to be called at the given absolute timestamp when (an int or a float), using the same time reference as loop.time().

This method’s behavior is the same as call_later().

An instance of asyncio.TimerHandle is returned which can be used to cancel the callback.

Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details.

Changed in version 3.8: In Python 3.7 and earlier with the default event loop implementation, the difference between when and the current time could not exceed one day. This has been fixed in Python 3.8.

+
+loop.time()
+

Return the current time, as a float value, according to the event loop’s internal monotonic clock.

+

Note

Changed in version 3.8: In Python 3.7 and earlier timeouts (relative delay or absolute when) should not exceed one day. This has been fixed in Python 3.8.

See also

The asyncio.sleep() function.

Creating Futures and Tasks

+loop.create_future()
+

Create an asyncio.Future object attached to the event loop.

This is the preferred way to create Futures in asyncio. This lets third-party event loops provide alternative implementations of the Future object (with better performance or instrumentation).

New in version 3.5.2.

+
+loop.create_task(coro, *, name=None, context=None)
+

Schedule the execution of coroutine coro. Return a Task object.

Third-party event loops can use their own subclass of Task for interoperability. In this case, the result type is a subclass of Task.

If the name argument is provided and not None, it is set as the name of the task using Task.set_name().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the coro to run in. The current context copy is created when no context is provided.

Changed in version 3.8: Added the name parameter.

Changed in version 3.11: Added the context parameter.

+
+loop.set_task_factory(factory)
+

Set a task factory that will be used by loop.create_task().

If factory is None the default task factory will be set. Otherwise, factory must be a callable with the signature matching (loop, coro, context=None), where loop is a reference to the active event loop, and coro is a coroutine object. The callable must return a asyncio.Future-compatible object.

+
+loop.get_task_factory()
+

Return a task factory or None if the default one is in use.

+

Opening network connections

+coroutine loop.create_connection(protocol_factory, host=None, port=None, *, ssl=None, family=0, proto=0, flags=0, sock=None, local_addr=None, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, happy_eyeballs_delay=None, interleave=None, all_errors=False)
+

Open a streaming transport connection to a given address specified by host and port.

The socket family can be either AF_INET or AF_INET6 depending on host (or the family argument, if provided).

The socket type will be SOCK_STREAM.

protocol_factory must be a callable returning an asyncio protocol implementation.

This method will try to establish the connection in the background. When successful, it returns a (transport, protocol) pair.

The chronological synopsis of the underlying operation is as follows:

  1. The connection is established and a transport is created for it.
  2. +protocol_factory is called without arguments and is expected to return a protocol instance.
  3. The protocol instance is coupled with the transport by calling its connection_made() method.
  4. A (transport, protocol) tuple is returned on success.

The created transport is an implementation-dependent bidirectional stream.

Other arguments:

  • +

    ssl: if given and not false, a SSL/TLS transport is created (by default a plain TCP transport is created). If ssl is a ssl.SSLContext object, this context is used to create the transport; if ssl is True, a default context returned from ssl.create_default_context() is used.

    See also

    SSL/TLS security considerations

  • +server_hostname sets or overrides the hostname that the target server’s certificate will be matched against. Should only be passed if ssl is not None. By default the value of the host argument is used. If host is empty, there is no default and you must pass a value for server_hostname. If server_hostname is an empty string, hostname matching is disabled (which is a serious security risk, allowing for potential man-in-the-middle attacks).
  • +family, proto, flags are the optional address family, protocol and flags to be passed through to getaddrinfo() for host resolution. If given, these should all be integers from the corresponding socket module constants.
  • +happy_eyeballs_delay, if given, enables Happy Eyeballs for this connection. It should be a floating-point number representing the amount of time in seconds to wait for a connection attempt to complete, before starting the next attempt in parallel. This is the “Connection Attempt Delay” as defined in RFC 8305. A sensible default value recommended by the RFC is 0.25 (250 milliseconds).
  • +interleave controls address reordering when a host name resolves to multiple IP addresses. If 0 or unspecified, no reordering is done, and addresses are tried in the order returned by getaddrinfo(). If a positive integer is specified, the addresses are interleaved by address family, and the given integer is interpreted as “First Address Family Count” as defined in RFC 8305. The default is 0 if happy_eyeballs_delay is not specified, and 1 if it is.
  • +

    sock, if given, should be an existing, already connected socket.socket object to be used by the transport. If sock is given, none of host, port, family, proto, flags, happy_eyeballs_delay, interleave and local_addr should be specified.

    Note

    The sock argument transfers ownership of the socket to the transport created. To close the socket, call the transport’s close() method.

  • +local_addr, if given, is a (local_host, local_port) tuple used to bind the socket locally. The local_host and local_port are looked up using getaddrinfo(), similarly to host and port.
  • +ssl_handshake_timeout is (for a TLS connection) the time in seconds to wait for the TLS handshake to complete before aborting the connection. 60.0 seconds if None (default).
  • +ssl_shutdown_timeout is the time in seconds to wait for the SSL shutdown to complete before aborting the connection. 30.0 seconds if None (default).
  • +all_errors determines what exceptions are raised when a connection cannot be created. By default, only a single Exception is raised: the first exception if there is only one or all errors have same message, or a single OSError with the error messages combined. When all_errors is True, an ExceptionGroup will be raised containing all exceptions (even if there is only one).

Changed in version 3.5: Added support for SSL/TLS in ProactorEventLoop.

Changed in version 3.6: The socket option socket.TCP_NODELAY is set by default for all TCP connections.

Changed in version 3.7: Added the ssl_handshake_timeout parameter.

Changed in version 3.8: Added the happy_eyeballs_delay and interleave parameters.

Happy Eyeballs Algorithm: Success with Dual-Stack Hosts. When a server’s IPv4 path and protocol are working, but the server’s IPv6 path and protocol are not working, a dual-stack client application experiences significant connection delay compared to an IPv4-only client. This is undesirable because it causes the dual-stack client to have a worse user experience. This document specifies requirements for algorithms that reduce this user-visible delay and provides an algorithm.

For more information: https://datatracker.ietf.org/doc/html/rfc6555

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

Changed in version 3.12: all_errors was added.

See also

The open_connection() function is a high-level alternative API. It returns a pair of (StreamReader, StreamWriter) that can be used directly in async/await code.

+
+coroutine loop.create_datagram_endpoint(protocol_factory, local_addr=None, remote_addr=None, *, family=0, proto=0, flags=0, reuse_port=None, allow_broadcast=None, sock=None)
+

Create a datagram connection.

The socket family can be either AF_INET, AF_INET6, or AF_UNIX, depending on host (or the family argument, if provided).

The socket type will be SOCK_DGRAM.

protocol_factory must be a callable returning a protocol implementation.

A tuple of (transport, protocol) is returned on success.

Other arguments:

  • +local_addr, if given, is a (local_host, local_port) tuple used to bind the socket locally. The local_host and local_port are looked up using getaddrinfo().
  • +remote_addr, if given, is a (remote_host, remote_port) tuple used to connect the socket to a remote address. The remote_host and remote_port are looked up using getaddrinfo().
  • +family, proto, flags are the optional address family, protocol and flags to be passed through to getaddrinfo() for host resolution. If given, these should all be integers from the corresponding socket module constants.
  • +reuse_port tells the kernel to allow this endpoint to be bound to the same port as other existing endpoints are bound to, so long as they all set this flag when being created. This option is not supported on Windows and some Unixes. If the socket.SO_REUSEPORT constant is not defined then this capability is unsupported.
  • +allow_broadcast tells the kernel to allow this endpoint to send messages to the broadcast address.
  • +

    sock can optionally be specified in order to use a preexisting, already connected, socket.socket object to be used by the transport. If specified, local_addr and remote_addr should be omitted (must be None).

    Note

    The sock argument transfers ownership of the socket to the transport created. To close the socket, call the transport’s close() method.

See UDP echo client protocol and UDP echo server protocol examples.

Changed in version 3.4.4: The family, proto, flags, reuse_address, reuse_port, allow_broadcast, and sock parameters were added.

Changed in version 3.8.1: The reuse_address parameter is no longer supported, as using socket.SO_REUSEADDR poses a significant security concern for UDP. Explicitly passing reuse_address=True will raise an exception.

When multiple processes with differing UIDs assign sockets to an identical UDP socket address with SO_REUSEADDR, incoming packets can become randomly distributed among the sockets.

For supported platforms, reuse_port can be used as a replacement for similar functionality. With reuse_port, socket.SO_REUSEPORT is used instead, which specifically prevents processes with differing UIDs from assigning sockets to the same socket address.

Changed in version 3.8: Added support for Windows.

Changed in version 3.11: The reuse_address parameter, disabled since Python 3.9.0, 3.8.1, 3.7.6 and 3.6.10, has been entirely removed.

+
+coroutine loop.create_unix_connection(protocol_factory, path=None, *, ssl=None, sock=None, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None)
+

Create a Unix connection.

The socket family will be AF_UNIX; socket type will be SOCK_STREAM.

A tuple of (transport, protocol) is returned on success.

path is the name of a Unix domain socket and is required, unless a sock parameter is specified. Abstract Unix sockets, str, bytes, and Path paths are supported.

See the documentation of the loop.create_connection() method for information about arguments to this method.

Availability: Unix.

Changed in version 3.7: Added the ssl_handshake_timeout parameter. The path parameter can now be a path-like object.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+

Creating network servers

+coroutine loop.create_server(protocol_factory, host=None, port=None, *, family=socket.AF_UNSPEC, flags=socket.AI_PASSIVE, sock=None, backlog=100, ssl=None, reuse_address=None, reuse_port=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, start_serving=True)
+

Create a TCP server (socket type SOCK_STREAM) listening on port of the host address.

Returns a Server object.

Arguments:

  • +protocol_factory must be a callable returning a protocol implementation.
  • +

    The host parameter can be set to several types which determine where the server would be listening:

    • If host is a string, the TCP server is bound to a single network interface specified by host.
    • If host is a sequence of strings, the TCP server is bound to all network interfaces specified by the sequence.
    • If host is an empty string or None, all interfaces are assumed and a list of multiple sockets will be returned (most likely one for IPv4 and another one for IPv6).
  • The port parameter can be set to specify which port the server should listen on. If 0 or None (the default), a random unused port will be selected (note that if host resolves to multiple network interfaces, a different random port will be selected for each interface).
  • +family can be set to either socket.AF_INET or AF_INET6 to force the socket to use IPv4 or IPv6. If not set, the family will be determined from host name (defaults to AF_UNSPEC).
  • +flags is a bitmask for getaddrinfo().
  • +

    sock can optionally be specified in order to use a preexisting socket object. If specified, host and port must not be specified.

    Note

    The sock argument transfers ownership of the socket to the server created. To close the socket, call the server’s close() method.

  • +backlog is the maximum number of queued connections passed to listen() (defaults to 100).
  • +ssl can be set to an SSLContext instance to enable TLS over the accepted connections.
  • +reuse_address tells the kernel to reuse a local socket in TIME_WAIT state, without waiting for its natural timeout to expire. If not specified will automatically be set to True on Unix.
  • +reuse_port tells the kernel to allow this endpoint to be bound to the same port as other existing endpoints are bound to, so long as they all set this flag when being created. This option is not supported on Windows.
  • +ssl_handshake_timeout is (for a TLS server) the time in seconds to wait for the TLS handshake to complete before aborting the connection. 60.0 seconds if None (default).
  • +ssl_shutdown_timeout is the time in seconds to wait for the SSL shutdown to complete before aborting the connection. 30.0 seconds if None (default).
  • +start_serving set to True (the default) causes the created server to start accepting connections immediately. When set to False, the user should await on Server.start_serving() or Server.serve_forever() to make the server to start accepting connections.

Changed in version 3.5: Added support for SSL/TLS in ProactorEventLoop.

Changed in version 3.5.1: The host parameter can be a sequence of strings.

Changed in version 3.6: Added ssl_handshake_timeout and start_serving parameters. The socket option socket.TCP_NODELAY is set by default for all TCP connections.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

See also

The start_server() function is a higher-level alternative API that returns a pair of StreamReader and StreamWriter that can be used in an async/await code.

+
+coroutine loop.create_unix_server(protocol_factory, path=None, *, sock=None, backlog=100, ssl=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, start_serving=True)
+

Similar to loop.create_server() but works with the AF_UNIX socket family.

path is the name of a Unix domain socket, and is required, unless a sock argument is provided. Abstract Unix sockets, str, bytes, and Path paths are supported.

See the documentation of the loop.create_server() method for information about arguments to this method.

Availability: Unix.

Changed in version 3.7: Added the ssl_handshake_timeout and start_serving parameters. The path parameter can now be a Path object.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+
+coroutine loop.connect_accepted_socket(protocol_factory, sock, *, ssl=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None)
+

Wrap an already accepted connection into a transport/protocol pair.

This method can be used by servers that accept connections outside of asyncio but that use asyncio to handle them.

Parameters:

  • +protocol_factory must be a callable returning a protocol implementation.
  • +

    sock is a preexisting socket object returned from socket.accept.

    Note

    The sock argument transfers ownership of the socket to the transport created. To close the socket, call the transport’s close() method.

  • +ssl can be set to an SSLContext to enable SSL over the accepted connections.
  • +ssl_handshake_timeout is (for an SSL connection) the time in seconds to wait for the SSL handshake to complete before aborting the connection. 60.0 seconds if None (default).
  • +ssl_shutdown_timeout is the time in seconds to wait for the SSL shutdown to complete before aborting the connection. 30.0 seconds if None (default).

Returns a (transport, protocol) pair.

New in version 3.5.3.

Changed in version 3.7: Added the ssl_handshake_timeout parameter.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+

Transferring files

+coroutine loop.sendfile(transport, file, offset=0, count=None, *, fallback=True)
+

Send a file over a transport. Return the total number of bytes sent.

The method uses high-performance os.sendfile() if available.

file must be a regular file object opened in binary mode.

offset tells from where to start reading the file. If specified, count is the total number of bytes to transmit as opposed to sending the file until EOF is reached. File position is always updated, even when this method raises an error, and file.tell() can be used to obtain the actual number of bytes sent.

fallback set to True makes asyncio to manually read and send the file when the platform does not support the sendfile system call (e.g. Windows or SSL socket on Unix).

Raise SendfileNotAvailableError if the system does not support the sendfile syscall and fallback is False.

New in version 3.7.

+

TLS Upgrade

+coroutine loop.start_tls(transport, protocol, sslcontext, *, server_side=False, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None)
+

Upgrade an existing transport-based connection to TLS.

Create a TLS coder/decoder instance and insert it between the transport and the protocol. The coder/decoder implements both transport-facing protocol and protocol-facing transport.

Return the created two-interface instance. After await, the protocol must stop using the original transport and communicate with the returned object only because the coder caches protocol-side data and sporadically exchanges extra TLS session packets with transport.

In some situations (e.g. when the passed transport is already closing) this may return None.

Parameters:

  • +transport and protocol instances that methods like create_server() and create_connection() return.
  • +sslcontext: a configured instance of SSLContext.
  • +server_side pass True when a server-side connection is being upgraded (like the one created by create_server()).
  • +server_hostname: sets or overrides the host name that the target server’s certificate will be matched against.
  • +ssl_handshake_timeout is (for a TLS connection) the time in seconds to wait for the TLS handshake to complete before aborting the connection. 60.0 seconds if None (default).
  • +ssl_shutdown_timeout is the time in seconds to wait for the SSL shutdown to complete before aborting the connection. 30.0 seconds if None (default).

New in version 3.7.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+

Watching file descriptors

+loop.add_reader(fd, callback, *args)
+

Start monitoring the fd file descriptor for read availability and invoke callback with the specified arguments once fd is available for reading.

+
+loop.remove_reader(fd)
+

Stop monitoring the fd file descriptor for read availability. Returns True if fd was previously being monitored for reads.

+
+loop.add_writer(fd, callback, *args)
+

Start monitoring the fd file descriptor for write availability and invoke callback with the specified arguments once fd is available for writing.

Use functools.partial() to pass keyword arguments to callback.

+
+loop.remove_writer(fd)
+

Stop monitoring the fd file descriptor for write availability. Returns True if fd was previously being monitored for writes.

+

See also Platform Support section for some limitations of these methods.

Working with socket objects directly

In general, protocol implementations that use transport-based APIs such as loop.create_connection() and loop.create_server() are faster than implementations that work with sockets directly. However, there are some use cases when performance is not critical, and working with socket objects directly is more convenient.

+coroutine loop.sock_recv(sock, nbytes)
+

Receive up to nbytes from sock. Asynchronous version of socket.recv().

Return the received data as a bytes object.

sock must be a non-blocking socket.

Changed in version 3.7: Even though this method was always documented as a coroutine method, releases before Python 3.7 returned a Future. Since Python 3.7 this is an async def method.

+
+coroutine loop.sock_recv_into(sock, buf)
+

Receive data from sock into the buf buffer. Modeled after the blocking socket.recv_into() method.

Return the number of bytes written to the buffer.

sock must be a non-blocking socket.

New in version 3.7.

+
+coroutine loop.sock_recvfrom(sock, bufsize)
+

Receive a datagram of up to bufsize from sock. Asynchronous version of socket.recvfrom().

Return a tuple of (received data, remote address).

sock must be a non-blocking socket.

New in version 3.11.

+
+coroutine loop.sock_recvfrom_into(sock, buf, nbytes=0)
+

Receive a datagram of up to nbytes from sock into buf. Asynchronous version of socket.recvfrom_into().

Return a tuple of (number of bytes received, remote address).

sock must be a non-blocking socket.

New in version 3.11.

+
+coroutine loop.sock_sendall(sock, data)
+

Send data to the sock socket. Asynchronous version of socket.sendall().

This method continues to send to the socket until either all data in data has been sent or an error occurs. None is returned on success. On error, an exception is raised. Additionally, there is no way to determine how much data, if any, was successfully processed by the receiving end of the connection.

sock must be a non-blocking socket.

Changed in version 3.7: Even though the method was always documented as a coroutine method, before Python 3.7 it returned a Future. Since Python 3.7, this is an async def method.

+
+coroutine loop.sock_sendto(sock, data, address)
+

Send a datagram from sock to address. Asynchronous version of socket.sendto().

Return the number of bytes sent.

sock must be a non-blocking socket.

New in version 3.11.

+
+coroutine loop.sock_connect(sock, address)
+

Connect sock to a remote socket at address.

Asynchronous version of socket.connect().

sock must be a non-blocking socket.

Changed in version 3.5.2: address no longer needs to be resolved. sock_connect will try to check if the address is already resolved by calling socket.inet_pton(). If not, loop.getaddrinfo() will be used to resolve the address.

See also

loop.create_connection() and asyncio.open_connection().

+
+coroutine loop.sock_accept(sock)
+

Accept a connection. Modeled after the blocking socket.accept() method.

The socket must be bound to an address and listening for connections. The return value is a pair (conn, address) where conn is a new socket object usable to send and receive data on the connection, and address is the address bound to the socket on the other end of the connection.

sock must be a non-blocking socket.

Changed in version 3.7: Even though the method was always documented as a coroutine method, before Python 3.7 it returned a Future. Since Python 3.7, this is an async def method.

See also

loop.create_server() and start_server().

+
+coroutine loop.sock_sendfile(sock, file, offset=0, count=None, *, fallback=True)
+

Send a file using high-performance os.sendfile if possible. Return the total number of bytes sent.

Asynchronous version of socket.sendfile().

sock must be a non-blocking socket.SOCK_STREAM socket.

file must be a regular file object open in binary mode.

offset tells from where to start reading the file. If specified, count is the total number of bytes to transmit as opposed to sending the file until EOF is reached. File position is always updated, even when this method raises an error, and file.tell() can be used to obtain the actual number of bytes sent.

fallback, when set to True, makes asyncio manually read and send the file when the platform does not support the sendfile syscall (e.g. Windows or SSL socket on Unix).

Raise SendfileNotAvailableError if the system does not support sendfile syscall and fallback is False.

sock must be a non-blocking socket.

New in version 3.7.

+

DNS

+coroutine loop.getaddrinfo(host, port, *, family=0, type=0, proto=0, flags=0)
+

Asynchronous version of socket.getaddrinfo().

+
+coroutine loop.getnameinfo(sockaddr, flags=0)
+

Asynchronous version of socket.getnameinfo().

+

Changed in version 3.7: Both getaddrinfo and getnameinfo methods were always documented to return a coroutine, but prior to Python 3.7 they were, in fact, returning asyncio.Future objects. Starting with Python 3.7 both methods are coroutines.

Working with pipes

+coroutine loop.connect_read_pipe(protocol_factory, pipe)
+

Register the read end of pipe in the event loop.

protocol_factory must be a callable returning an asyncio protocol implementation.

pipe is a file-like object.

Return pair (transport, protocol), where transport supports the ReadTransport interface and protocol is an object instantiated by the protocol_factory.

With SelectorEventLoop event loop, the pipe is set to non-blocking mode.

+
+coroutine loop.connect_write_pipe(protocol_factory, pipe)
+

Register the write end of pipe in the event loop.

protocol_factory must be a callable returning an asyncio protocol implementation.

pipe is file-like object.

Return pair (transport, protocol), where transport supports WriteTransport interface and protocol is an object instantiated by the protocol_factory.

With SelectorEventLoop event loop, the pipe is set to non-blocking mode.

+

Note

SelectorEventLoop does not support the above methods on Windows. Use ProactorEventLoop instead for Windows.

See also

The loop.subprocess_exec() and loop.subprocess_shell() methods.

Unix signals

+loop.add_signal_handler(signum, callback, *args)
+

Set callback as the handler for the signum signal.

The callback will be invoked by loop, along with other queued callbacks and runnable coroutines of that event loop. Unlike signal handlers registered using signal.signal(), a callback registered with this function is allowed to interact with the event loop.

Raise ValueError if the signal number is invalid or uncatchable. Raise RuntimeError if there is a problem setting up the handler.

Use functools.partial() to pass keyword arguments to callback.

Like signal.signal(), this function must be invoked in the main thread.

+
+loop.remove_signal_handler(sig)
+

Remove the handler for the sig signal.

Return True if the signal handler was removed, or False if no handler was set for the given signal.

Availability: Unix.

+

See also

The signal module.

Executing code in thread or process pools

+awaitable loop.run_in_executor(executor, func, *args)
+

Arrange for func to be called in the specified executor.

The executor argument should be an concurrent.futures.Executor instance. The default executor is used if executor is None.

Example:

import asyncio
+import concurrent.futures
+
+def blocking_io():
+    # File operations (such as logging) can block the
+    # event loop: run them in a thread pool.
+    with open('/dev/urandom', 'rb') as f:
+        return f.read(100)
+
+def cpu_bound():
+    # CPU-bound operations will block the event loop:
+    # in general it is preferable to run them in a
+    # process pool.
+    return sum(i * i for i in range(10 ** 7))
+
+async def main():
+    loop = asyncio.get_running_loop()
+
+    ## Options:
+
+    # 1. Run in the default loop's executor:
+    result = await loop.run_in_executor(
+        None, blocking_io)
+    print('default thread pool', result)
+
+    # 2. Run in a custom thread pool:
+    with concurrent.futures.ThreadPoolExecutor() as pool:
+        result = await loop.run_in_executor(
+            pool, blocking_io)
+        print('custom thread pool', result)
+
+    # 3. Run in a custom process pool:
+    with concurrent.futures.ProcessPoolExecutor() as pool:
+        result = await loop.run_in_executor(
+            pool, cpu_bound)
+        print('custom process pool', result)
+
+if __name__ == '__main__':
+    asyncio.run(main())
+

Note that the entry point guard (if __name__ == '__main__') is required for option 3 due to the peculiarities of multiprocessing, which is used by ProcessPoolExecutor. See Safe importing of main module.

This method returns a asyncio.Future object.

Use functools.partial() to pass keyword arguments to func.

Changed in version 3.5.3: loop.run_in_executor() no longer configures the max_workers of the thread pool executor it creates, instead leaving it up to the thread pool executor (ThreadPoolExecutor) to set the default.

+
+loop.set_default_executor(executor)
+

Set executor as the default executor used by run_in_executor(). executor must be an instance of ThreadPoolExecutor.

Changed in version 3.11: executor must be an instance of ThreadPoolExecutor.

+

Error Handling API

Allows customizing how exceptions are handled in the event loop.

+loop.set_exception_handler(handler)
+

Set handler as the new event loop exception handler.

If handler is None, the default exception handler will be set. Otherwise, handler must be a callable with the signature matching (loop, context), where loop is a reference to the active event loop, and context is a dict object containing the details of the exception (see call_exception_handler() documentation for details about context).

If the handler is called on behalf of a Task or Handle, it is run in the contextvars.Context of that task or callback handle.

Changed in version 3.12: The handler may be called in the Context of the task or handle where the exception originated.

+
+loop.get_exception_handler()
+

Return the current exception handler, or None if no custom exception handler was set.

New in version 3.5.2.

+
+loop.default_exception_handler(context)
+

Default exception handler.

This is called when an exception occurs and no exception handler is set. This can be called by a custom exception handler that wants to defer to the default handler behavior.

context parameter has the same meaning as in call_exception_handler().

+
+loop.call_exception_handler(context)
+

Call the current event loop exception handler.

context is a dict object containing the following keys (new keys may be introduced in future Python versions):

  • ‘message’: Error message;
  • ‘exception’ (optional): Exception object;
  • ‘future’ (optional): asyncio.Future instance;
  • ‘task’ (optional): asyncio.Task instance;
  • ‘handle’ (optional): asyncio.Handle instance;
  • ‘protocol’ (optional): Protocol instance;
  • ‘transport’ (optional): Transport instance;
  • ‘socket’ (optional): socket.socket instance;
  • +
    ‘asyncgen’ (optional): Asynchronous generator that caused
    +
    +

    the exception.

Note

This method should not be overloaded in subclassed event loops. For custom exception handling, use the set_exception_handler() method.

+

Enabling debug mode

+loop.get_debug()
+

Get the debug mode (bool) of the event loop.

The default value is True if the environment variable PYTHONASYNCIODEBUG is set to a non-empty string, False otherwise.

+
+loop.set_debug(enabled: bool)
+

Set the debug mode of the event loop.

Changed in version 3.7: The new Python Development Mode can now also be used to enable the debug mode.

+
+loop.slow_callback_duration
+

This attribute can be used to set the minimum execution duration in seconds that is considered “slow”. When debug mode is enabled, “slow” callbacks are logged.

Default value is 100 milliseconds.

+

See also

The debug mode of asyncio.

Running Subprocesses

Methods described in this subsections are low-level. In regular async/await code consider using the high-level asyncio.create_subprocess_shell() and asyncio.create_subprocess_exec() convenience functions instead.

Note

On Windows, the default event loop ProactorEventLoop supports subprocesses, whereas SelectorEventLoop does not. See Subprocess Support on Windows for details.

+coroutine loop.subprocess_exec(protocol_factory, *args, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)
+

Create a subprocess from one or more string arguments specified by args.

args must be a list of strings represented by:

The first string specifies the program executable, and the remaining strings specify the arguments. Together, string arguments form the argv of the program.

This is similar to the standard library subprocess.Popen class called with shell=False and the list of strings passed as the first argument; however, where Popen takes a single argument which is list of strings, subprocess_exec takes multiple string arguments.

The protocol_factory must be a callable returning a subclass of the asyncio.SubprocessProtocol class.

Other parameters:

  • +

    stdin can be any of these:

    • a file-like object
    • an existing file descriptor (a positive integer), for example those created with os.pipe() +
    • the subprocess.PIPE constant (default) which will create a new pipe and connect it,
    • the value None which will make the subprocess inherit the file descriptor from this process
    • the subprocess.DEVNULL constant which indicates that the special os.devnull file will be used
  • +

    stdout can be any of these:

    • a file-like object
    • the subprocess.PIPE constant (default) which will create a new pipe and connect it,
    • the value None which will make the subprocess inherit the file descriptor from this process
    • the subprocess.DEVNULL constant which indicates that the special os.devnull file will be used
  • +

    stderr can be any of these:

    • a file-like object
    • the subprocess.PIPE constant (default) which will create a new pipe and connect it,
    • the value None which will make the subprocess inherit the file descriptor from this process
    • the subprocess.DEVNULL constant which indicates that the special os.devnull file will be used
    • the subprocess.STDOUT constant which will connect the standard error stream to the process’ standard output stream
  • +

    All other keyword arguments are passed to subprocess.Popen without interpretation, except for bufsize, universal_newlines, shell, text, encoding and errors, which should not be specified at all.

    The asyncio subprocess API does not support decoding the streams as text. bytes.decode() can be used to convert the bytes returned from the stream to text.

If a file-like object passed as stdin, stdout or stderr represents a pipe, then the other side of this pipe should be registered with connect_write_pipe() or connect_read_pipe() for use with the event loop.

See the constructor of the subprocess.Popen class for documentation on other arguments.

Returns a pair of (transport, protocol), where transport conforms to the asyncio.SubprocessTransport base class and protocol is an object instantiated by the protocol_factory.

+
+coroutine loop.subprocess_shell(protocol_factory, cmd, *, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs)
+

Create a subprocess from cmd, which can be a str or a bytes string encoded to the filesystem encoding, using the platform’s “shell” syntax.

This is similar to the standard library subprocess.Popen class called with shell=True.

The protocol_factory must be a callable returning a subclass of the SubprocessProtocol class.

See subprocess_exec() for more details about the remaining arguments.

Returns a pair of (transport, protocol), where transport conforms to the SubprocessTransport base class and protocol is an object instantiated by the protocol_factory.

+

Note

It is the application’s responsibility to ensure that all whitespace and special characters are quoted appropriately to avoid shell injection vulnerabilities. The shlex.quote() function can be used to properly escape whitespace and special characters in strings that are going to be used to construct shell commands.

Callback Handles

+class asyncio.Handle
+

A callback wrapper object returned by loop.call_soon(), loop.call_soon_threadsafe().

+get_context()
+

Return the contextvars.Context object associated with the handle.

New in version 3.12.

+
+cancel()
+

Cancel the callback. If the callback has already been canceled or executed, this method has no effect.

+
+cancelled()
+

Return True if the callback was cancelled.

New in version 3.7.

+
+
+class asyncio.TimerHandle
+

A callback wrapper object returned by loop.call_later(), and loop.call_at().

This class is a subclass of Handle.

+when()
+

Return a scheduled callback time as float seconds.

The time is an absolute timestamp, using the same time reference as loop.time().

New in version 3.7.

+
+

Server Objects

Server objects are created by loop.create_server(), loop.create_unix_server(), start_server(), and start_unix_server() functions.

Do not instantiate the Server class directly.

+class asyncio.Server
+

Server objects are asynchronous context managers. When used in an async with statement, it’s guaranteed that the Server object is closed and not accepting new connections when the async with statement is completed:

srv = await loop.create_server(...)
+
+async with srv:
+    # some code
+
+# At this point, srv is closed and no longer accepts new connections.
+

Changed in version 3.7: Server object is an asynchronous context manager since Python 3.7.

Changed in version 3.11: This class was exposed publicly as asyncio.Server in Python 3.9.11, 3.10.3 and 3.11.

+close()
+

Stop serving: close listening sockets and set the sockets attribute to None.

The sockets that represent existing incoming client connections are left open.

The server is closed asynchronously; use the wait_closed() coroutine to wait until the server is closed (and no more connections are active).

+
+get_loop()
+

Return the event loop associated with the server object.

New in version 3.7.

+
+coroutine start_serving()
+

Start accepting connections.

This method is idempotent, so it can be called when the server is already serving.

The start_serving keyword-only parameter to loop.create_server() and asyncio.start_server() allows creating a Server object that is not accepting connections initially. In this case Server.start_serving(), or Server.serve_forever() can be used to make the Server start accepting connections.

New in version 3.7.

+
+coroutine serve_forever()
+

Start accepting connections until the coroutine is cancelled. Cancellation of serve_forever task causes the server to be closed.

This method can be called if the server is already accepting connections. Only one serve_forever task can exist per one Server object.

Example:

async def client_connected(reader, writer):
+    # Communicate with the client with
+    # reader/writer streams.  For example:
+    await reader.readline()
+
+async def main(host, port):
+    srv = await asyncio.start_server(
+        client_connected, host, port)
+    await srv.serve_forever()
+
+asyncio.run(main('127.0.0.1', 0))
+

New in version 3.7.

+
+is_serving()
+

Return True if the server is accepting new connections.

New in version 3.7.

+
+coroutine wait_closed()
+

Wait until the close() method completes and all active connections have finished.

+
+sockets
+

List of socket-like objects, asyncio.trsock.TransportSocket, which the server is listening on.

Changed in version 3.7: Prior to Python 3.7 Server.sockets used to return an internal list of server sockets directly. In 3.7 a copy of that list is returned.

+
+

Event Loop Implementations

asyncio ships with two different event loop implementations: SelectorEventLoop and ProactorEventLoop.

By default asyncio is configured to use SelectorEventLoop on Unix and ProactorEventLoop on Windows.

+class asyncio.SelectorEventLoop
+

An event loop based on the selectors module.

Uses the most efficient selector available for the given platform. It is also possible to manually configure the exact selector implementation to be used:

import asyncio
+import selectors
+
+class MyPolicy(asyncio.DefaultEventLoopPolicy):
+   def new_event_loop(self):
+      selector = selectors.SelectSelector()
+      return asyncio.SelectorEventLoop(selector)
+
+asyncio.set_event_loop_policy(MyPolicy())
+

Availability: Unix, Windows.

+
+class asyncio.ProactorEventLoop
+

An event loop for Windows that uses “I/O Completion Ports” (IOCP).

Availability: Windows.

See also

MSDN documentation on I/O Completion Ports.

+
+class asyncio.AbstractEventLoop
+

Abstract base class for asyncio-compliant event loops.

The Event Loop Methods section lists all methods that an alternative implementation of AbstractEventLoop should have defined.

+

Examples

Note that all examples in this section purposefully show how to use the low-level event loop APIs, such as loop.run_forever() and loop.call_soon(). Modern asyncio applications rarely need to be written this way; consider using the high-level functions like asyncio.run().

Hello World with call_soon()

An example using the loop.call_soon() method to schedule a callback. The callback displays "Hello World" and then stops the event loop:

import asyncio
+
+def hello_world(loop):
+    """A callback to print 'Hello World' and stop the event loop"""
+    print('Hello World')
+    loop.stop()
+
+loop = asyncio.new_event_loop()
+
+# Schedule a call to hello_world()
+loop.call_soon(hello_world, loop)
+
+# Blocking call interrupted by loop.stop()
+try:
+    loop.run_forever()
+finally:
+    loop.close()
+

See also

A similar Hello World example created with a coroutine and the run() function.

Display the current date with call_later()

An example of a callback displaying the current date every second. The callback uses the loop.call_later() method to reschedule itself after 5 seconds, and then stops the event loop:

import asyncio
+import datetime
+
+def display_date(end_time, loop):
+    print(datetime.datetime.now())
+    if (loop.time() + 1.0) < end_time:
+        loop.call_later(1, display_date, end_time, loop)
+    else:
+        loop.stop()
+
+loop = asyncio.new_event_loop()
+
+# Schedule the first call to display_date()
+end_time = loop.time() + 5.0
+loop.call_soon(display_date, end_time, loop)
+
+# Blocking call interrupted by loop.stop()
+try:
+    loop.run_forever()
+finally:
+    loop.close()
+

See also

A similar current date example created with a coroutine and the run() function.

Watch a file descriptor for read events

Wait until a file descriptor received some data using the loop.add_reader() method and then close the event loop:

import asyncio
+from socket import socketpair
+
+# Create a pair of connected file descriptors
+rsock, wsock = socketpair()
+
+loop = asyncio.new_event_loop()
+
+def reader():
+    data = rsock.recv(100)
+    print("Received:", data.decode())
+
+    # We are done: unregister the file descriptor
+    loop.remove_reader(rsock)
+
+    # Stop the event loop
+    loop.stop()
+
+# Register the file descriptor for read event
+loop.add_reader(rsock, reader)
+
+# Simulate the reception of data from the network
+loop.call_soon(wsock.send, 'abc'.encode())
+
+try:
+    # Run the event loop
+    loop.run_forever()
+finally:
+    # We are done. Close sockets and the event loop.
+    rsock.close()
+    wsock.close()
+    loop.close()
+

See also

Set signal handlers for SIGINT and SIGTERM

(This signals example only works on Unix.)

Register handlers for signals SIGINT and SIGTERM using the loop.add_signal_handler() method:

import asyncio
+import functools
+import os
+import signal
+
+def ask_exit(signame, loop):
+    print("got signal %s: exit" % signame)
+    loop.stop()
+
+async def main():
+    loop = asyncio.get_running_loop()
+
+    for signame in {'SIGINT', 'SIGTERM'}:
+        loop.add_signal_handler(
+            getattr(signal, signame),
+            functools.partial(ask_exit, signame, loop))
+
+    await asyncio.sleep(3600)
+
+print("Event loop running for 1 hour, press Ctrl+C to interrupt.")
+print(f"pid {os.getpid()}: send SIGINT or SIGTERM to exit.")
+
+asyncio.run(main())
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-eventloop.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-exceptions.html b/devdocs/python~3.12/library%2Fasyncio-exceptions.html new file mode 100644 index 00000000..1c4f05ce --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-exceptions.html @@ -0,0 +1,33 @@ +

Exceptions

Source code: Lib/asyncio/exceptions.py

+exception asyncio.TimeoutError
+

A deprecated alias of TimeoutError, raised when the operation has exceeded the given deadline.

Changed in version 3.11: This class was made an alias of TimeoutError.

+
+exception asyncio.CancelledError
+

The operation has been cancelled.

This exception can be caught to perform custom operations when asyncio Tasks are cancelled. In almost all situations the exception must be re-raised.

Changed in version 3.8: CancelledError is now a subclass of BaseException rather than Exception.

+
+exception asyncio.InvalidStateError
+

Invalid internal state of Task or Future.

Can be raised in situations like setting a result value for a Future object that already has a result value set.

+
+exception asyncio.SendfileNotAvailableError
+

The “sendfile” syscall is not available for the given socket or file type.

A subclass of RuntimeError.

+
+exception asyncio.IncompleteReadError
+

The requested read operation did not complete fully.

Raised by the asyncio stream APIs.

This exception is a subclass of EOFError.

+expected
+

The total number (int) of expected bytes.

+
+partial
+

A string of bytes read before the end of stream was reached.

+
+
+exception asyncio.LimitOverrunError
+

Reached the buffer size limit while looking for a separator.

Raised by the asyncio stream APIs.

+consumed
+

The total number of to be consumed bytes.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-exceptions.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-extending.html b/devdocs/python~3.12/library%2Fasyncio-extending.html new file mode 100644 index 00000000..d34d7230 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-extending.html @@ -0,0 +1,24 @@ +

Extending

The main direction for asyncio extending is writing custom event loop classes. Asyncio has helpers that could be used to simplify this task.

Note

Third-parties should reuse existing asyncio code with caution, a new Python version is free to break backward compatibility in internal part of API.

Writing a Custom Event Loop

asyncio.AbstractEventLoop declares very many methods. Implementing all them from scratch is a tedious job.

A loop can get many common methods implementation for free by inheriting from asyncio.BaseEventLoop.

In turn, the successor should implement a bunch of private methods declared but not implemented in asyncio.BaseEventLoop.

For example, loop.create_connection() checks arguments, resolves DNS addresses, and calls loop._make_socket_transport() that should be implemented by inherited class. The _make_socket_transport() method is not documented and is considered as an internal API.

Future and Task private constructors

asyncio.Future and asyncio.Task should be never created directly, please use corresponding loop.create_future() and loop.create_task(), or asyncio.create_task() factories instead.

However, third-party event loops may reuse built-in future and task implementations for the sake of getting a complex and highly optimized code for free.

For this purpose the following, private constructors are listed:

+Future.__init__(*, loop=None)
+

Create a built-in future instance.

loop is an optional event loop instance.

+
+Task.__init__(coro, *, loop=None, name=None, context=None)
+

Create a built-in task instance.

loop is an optional event loop instance. The rest of arguments are described in loop.create_task() description.

Changed in version 3.11: context argument is added.

+

Task lifetime support

A third party task implementation should call the following functions to keep a task visible by asyncio.all_tasks() and asyncio.current_task():

+asyncio._register_task(task)
+

Register a new task as managed by asyncio.

Call the function from a task constructor.

+
+asyncio._unregister_task(task)
+

Unregister a task from asyncio internal structures.

The function should be called when a task is about to finish.

+
+asyncio._enter_task(loop, task)
+

Switch the current task to the task argument.

Call the function just before executing a portion of embedded coroutine (coroutine.send() or coroutine.throw()).

+
+asyncio._leave_task(loop, task)
+

Switch the current task back from task to None.

Call the function just after coroutine.send() or coroutine.throw() execution.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-extending.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-future.html b/devdocs/python~3.12/library%2Fasyncio-future.html new file mode 100644 index 00000000..2eb0cc8e --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-future.html @@ -0,0 +1,84 @@ +

Futures

Source code: Lib/asyncio/futures.py, Lib/asyncio/base_futures.py

Future objects are used to bridge low-level callback-based code with high-level async/await code.

Future Functions

+asyncio.isfuture(obj)
+

Return True if obj is either of:

New in version 3.5.

+
+asyncio.ensure_future(obj, *, loop=None)
+

Return:

  • +obj argument as is, if obj is a Future, a Task, or a Future-like object (isfuture() is used for the test.)
  • a Task object wrapping obj, if obj is a coroutine (iscoroutine() is used for the test); in this case the coroutine will be scheduled by ensure_future().
  • a Task object that would await on obj, if obj is an awaitable (inspect.isawaitable() is used for the test.)

If obj is neither of the above a TypeError is raised.

Important

See also the create_task() function which is the preferred way for creating new Tasks.

Save a reference to the result of this function, to avoid a task disappearing mid-execution.

Changed in version 3.5.1: The function accepts any awaitable object.

Deprecated since version 3.10: Deprecation warning is emitted if obj is not a Future-like object and loop is not specified and there is no running event loop.

+
+asyncio.wrap_future(future, *, loop=None)
+

Wrap a concurrent.futures.Future object in a asyncio.Future object.

Deprecated since version 3.10: Deprecation warning is emitted if future is not a Future-like object and loop is not specified and there is no running event loop.

+

Future Object

+class asyncio.Future(*, loop=None)
+

A Future represents an eventual result of an asynchronous operation. Not thread-safe.

Future is an awaitable object. Coroutines can await on Future objects until they either have a result or an exception set, or until they are cancelled. A Future can be awaited multiple times and the result is same.

Typically Futures are used to enable low-level callback-based code (e.g. in protocols implemented using asyncio transports) to interoperate with high-level async/await code.

The rule of thumb is to never expose Future objects in user-facing APIs, and the recommended way to create a Future object is to call loop.create_future(). This way alternative event loop implementations can inject their own optimized implementations of a Future object.

Changed in version 3.7: Added support for the contextvars module.

Deprecated since version 3.10: Deprecation warning is emitted if loop is not specified and there is no running event loop.

+result()
+

Return the result of the Future.

If the Future is done and has a result set by the set_result() method, the result value is returned.

If the Future is done and has an exception set by the set_exception() method, this method raises the exception.

If the Future has been cancelled, this method raises a CancelledError exception.

If the Future’s result isn’t yet available, this method raises a InvalidStateError exception.

+
+set_result(result)
+

Mark the Future as done and set its result.

Raises a InvalidStateError error if the Future is already done.

+
+set_exception(exception)
+

Mark the Future as done and set an exception.

Raises a InvalidStateError error if the Future is already done.

+
+done()
+

Return True if the Future is done.

A Future is done if it was cancelled or if it has a result or an exception set with set_result() or set_exception() calls.

+
+cancelled()
+

Return True if the Future was cancelled.

The method is usually used to check if a Future is not cancelled before setting a result or an exception for it:

if not fut.cancelled():
+    fut.set_result(42)
+
+
+add_done_callback(callback, *, context=None)
+

Add a callback to be run when the Future is done.

The callback is called with the Future object as its only argument.

If the Future is already done when this method is called, the callback is scheduled with loop.call_soon().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the callback to run in. The current context is used when no context is provided.

functools.partial() can be used to pass parameters to the callback, e.g.:

# Call 'print("Future:", fut)' when "fut" is done.
+fut.add_done_callback(
+    functools.partial(print, "Future:"))
+

Changed in version 3.7: The context keyword-only parameter was added. See PEP 567 for more details.

+
+remove_done_callback(callback)
+

Remove callback from the callbacks list.

Returns the number of callbacks removed, which is typically 1, unless a callback was added more than once.

+
+cancel(msg=None)
+

Cancel the Future and schedule callbacks.

If the Future is already done or cancelled, return False. Otherwise, change the Future’s state to cancelled, schedule the callbacks, and return True.

Changed in version 3.9: Added the msg parameter.

+
+exception()
+

Return the exception that was set on this Future.

The exception (or None if no exception was set) is returned only if the Future is done.

If the Future has been cancelled, this method raises a CancelledError exception.

If the Future isn’t done yet, this method raises an InvalidStateError exception.

+
+get_loop()
+

Return the event loop the Future object is bound to.

New in version 3.7.

+
+

This example creates a Future object, creates and schedules an asynchronous Task to set result for the Future, and waits until the Future has a result:

async def set_after(fut, delay, value):
+    # Sleep for *delay* seconds.
+    await asyncio.sleep(delay)
+
+    # Set *value* as a result of *fut* Future.
+    fut.set_result(value)
+
+async def main():
+    # Get the current event loop.
+    loop = asyncio.get_running_loop()
+
+    # Create a new Future object.
+    fut = loop.create_future()
+
+    # Run "set_after()" coroutine in a parallel Task.
+    # We are using the low-level "loop.create_task()" API here because
+    # we already have a reference to the event loop at hand.
+    # Otherwise we could have just used "asyncio.create_task()".
+    loop.create_task(
+        set_after(fut, 1, '... world'))
+
+    print('hello ...')
+
+    # Wait until *fut* has a result (1 second) and print it.
+    print(await fut)
+
+asyncio.run(main())
+

Important

The Future object was designed to mimic concurrent.futures.Future. Key differences include:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-future.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-llapi-index.html b/devdocs/python~3.12/library%2Fasyncio-llapi-index.html new file mode 100644 index 00000000..0c6bb291 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-llapi-index.html @@ -0,0 +1,110 @@ +

Low-level API Index

This page lists all low-level asyncio APIs.

Obtaining the Event Loop

+ + + +

asyncio.get_running_loop()

The preferred function to get the running event loop.

asyncio.get_event_loop()

Get an event loop instance (running or current via the current policy).

asyncio.set_event_loop()

Set the event loop as current via the current policy.

asyncio.new_event_loop()

Create a new event loop.

Examples

Event Loop Methods

See also the main documentation section about the Event Loop Methods.

Lifecycle

+ + + + + + +

loop.run_until_complete()

Run a Future/Task/awaitable until complete.

loop.run_forever()

Run the event loop forever.

loop.stop()

Stop the event loop.

loop.close()

Close the event loop.

loop.is_running()

Return True if the event loop is running.

loop.is_closed()

Return True if the event loop is closed.

await loop.shutdown_asyncgens()

Close asynchronous generators.

Debugging

+ +

loop.set_debug()

Enable or disable the debug mode.

loop.get_debug()

Get the current debug mode.

Scheduling Callbacks

+ + + +

loop.call_soon()

Invoke a callback soon.

loop.call_soon_threadsafe()

A thread-safe variant of loop.call_soon().

loop.call_later()

Invoke a callback after the given time.

loop.call_at()

Invoke a callback at the given time.

Thread/Process Pool

+ +

await loop.run_in_executor()

Run a CPU-bound or other blocking function in a concurrent.futures executor.

loop.set_default_executor()

Set the default executor for loop.run_in_executor().

Tasks and Futures

+ + + +

loop.create_future()

Create a Future object.

loop.create_task()

Schedule coroutine as a Task.

loop.set_task_factory()

Set a factory used by loop.create_task() to create Tasks.

loop.get_task_factory()

Get the factory loop.create_task() uses to create Tasks.

DNS

+ +

await loop.getaddrinfo()

Asynchronous version of socket.getaddrinfo().

await loop.getnameinfo()

Asynchronous version of socket.getnameinfo().

Networking and IPC

+ + + + + + + + + +

await loop.create_connection()

Open a TCP connection.

await loop.create_server()

Create a TCP server.

await loop.create_unix_connection()

Open a Unix socket connection.

await loop.create_unix_server()

Create a Unix socket server.

await loop.connect_accepted_socket()

Wrap a socket into a (transport, protocol) pair.

await loop.create_datagram_endpoint()

Open a datagram (UDP) connection.

await loop.sendfile()

Send a file over a transport.

await loop.start_tls()

Upgrade an existing connection to TLS.

await loop.connect_read_pipe()

Wrap a read end of a pipe into a (transport, protocol) pair.

await loop.connect_write_pipe()

Wrap a write end of a pipe into a (transport, protocol) pair.

Sockets

+ + + + + + + + + + + + +

await loop.sock_recv()

Receive data from the socket.

await loop.sock_recv_into()

Receive data from the socket into a buffer.

await loop.sock_recvfrom()

Receive a datagram from the socket.

await loop.sock_recvfrom_into()

Receive a datagram from the socket into a buffer.

await loop.sock_sendall()

Send data to the socket.

await loop.sock_sendto()

Send a datagram via the socket to the given address.

await loop.sock_connect()

Connect the socket.

await loop.sock_accept()

Accept a socket connection.

await loop.sock_sendfile()

Send a file over the socket.

loop.add_reader()

Start watching a file descriptor for read availability.

loop.remove_reader()

Stop watching a file descriptor for read availability.

loop.add_writer()

Start watching a file descriptor for write availability.

loop.remove_writer()

Stop watching a file descriptor for write availability.

Unix Signals

+ +

loop.add_signal_handler()

Add a handler for a signal.

loop.remove_signal_handler()

Remove a handler for a signal.

Subprocesses

+ +

loop.subprocess_exec()

Spawn a subprocess.

loop.subprocess_shell()

Spawn a subprocess from a shell command.

Error Handling

+ + + +

loop.call_exception_handler()

Call the exception handler.

loop.set_exception_handler()

Set a new exception handler.

loop.get_exception_handler()

Get the current exception handler.

loop.default_exception_handler()

The default exception handler implementation.

Examples

Transports

All transports implement the following methods:

+ + + + +

transport.close()

Close the transport.

transport.is_closing()

Return True if the transport is closing or is closed.

transport.get_extra_info()

Request for information about the transport.

transport.set_protocol()

Set a new protocol.

transport.get_protocol()

Return the current protocol.

Transports that can receive data (TCP and Unix connections, pipes, etc). Returned from methods like loop.create_connection(), loop.create_unix_connection(), loop.connect_read_pipe(), etc:

Read Transports

+ + +

transport.is_reading()

Return True if the transport is receiving.

transport.pause_reading()

Pause receiving.

transport.resume_reading()

Resume receiving.

Transports that can Send data (TCP and Unix connections, pipes, etc). Returned from methods like loop.create_connection(), loop.create_unix_connection(), loop.connect_write_pipe(), etc:

Write Transports

+ + + + + + + +

transport.write()

Write data to the transport.

transport.writelines()

Write buffers to the transport.

transport.can_write_eof()

Return True if the transport supports sending EOF.

transport.write_eof()

Close and send EOF after flushing buffered data.

transport.abort()

Close the transport immediately.

transport.get_write_buffer_size()

Return the current size of the output buffer.

transport.get_write_buffer_limits()

Return high and low water marks for write flow control.

transport.set_write_buffer_limits()

Set new high and low water marks for write flow control.

Transports returned by loop.create_datagram_endpoint():

Datagram Transports

+ +

transport.sendto()

Send data to the remote peer.

transport.abort()

Close the transport immediately.

Low-level transport abstraction over subprocesses. Returned by loop.subprocess_exec() and loop.subprocess_shell():

Subprocess Transports

+ + + + + + +

transport.get_pid()

Return the subprocess process id.

transport.get_pipe_transport()

Return the transport for the requested communication pipe (stdin, stdout, or stderr).

transport.get_returncode()

Return the subprocess return code.

transport.kill()

Kill the subprocess.

transport.send_signal()

Send a signal to the subprocess.

transport.terminate()

Stop the subprocess.

transport.close()

Kill the subprocess and close all pipes.

Protocols

Protocol classes can implement the following callback methods:

+ + + +

callback connection_made()

Called when a connection is made.

callback connection_lost()

Called when the connection is lost or closed.

callback pause_writing()

Called when the transport’s buffer goes over the high water mark.

callback resume_writing()

Called when the transport’s buffer drains below the low water mark.

Streaming Protocols (TCP, Unix Sockets, Pipes)

+ +

callback data_received()

Called when some data is received.

callback eof_received()

Called when an EOF is received.

Buffered Streaming Protocols

+ + +

callback get_buffer()

Called to allocate a new receive buffer.

callback buffer_updated()

Called when the buffer was updated with the received data.

callback eof_received()

Called when an EOF is received.

Datagram Protocols

+ +

callback datagram_received()

Called when a datagram is received.

callback error_received()

Called when a previous send or receive operation raises an OSError.

Subprocess Protocols

+ + +

callback pipe_data_received()

Called when the child process writes data into its stdout or stderr pipe.

callback pipe_connection_lost()

Called when one of the pipes communicating with the child process is closed.

callback process_exited()

Called when the child process has exited. It can be called before pipe_data_received() and pipe_connection_lost() methods.

Event Loop Policies

Policies is a low-level mechanism to alter the behavior of functions like asyncio.get_event_loop(). See also the main policies section for more details.

Accessing Policies

+ + +

asyncio.get_event_loop_policy()

Return the current process-wide policy.

asyncio.set_event_loop_policy()

Set a new process-wide policy.

AbstractEventLoopPolicy

Base class for policy objects.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-llapi-index.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-platforms.html b/devdocs/python~3.12/library%2Fasyncio-platforms.html new file mode 100644 index 00000000..5705c6ff --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-platforms.html @@ -0,0 +1,18 @@ +

Platform Support

The asyncio module is designed to be portable, but some platforms have subtle differences and limitations due to the platforms’ underlying architecture and capabilities.

All Platforms

Windows

Source code: Lib/asyncio/proactor_events.py, Lib/asyncio/windows_events.py, Lib/asyncio/windows_utils.py

Changed in version 3.8: On Windows, ProactorEventLoop is now the default event loop.

All event loops on Windows do not support the following methods:

SelectorEventLoop has the following limitations:

ProactorEventLoop has the following limitations:

The resolution of the monotonic clock on Windows is usually around 15.6 milliseconds. The best resolution is 0.5 milliseconds. The resolution depends on the hardware (availability of HPET) and on the Windows configuration.

Subprocess Support on Windows

On Windows, the default event loop ProactorEventLoop supports subprocesses, whereas SelectorEventLoop does not.

The policy.set_child_watcher() function is also not supported, as ProactorEventLoop has a different mechanism to watch child processes.

macOS

Modern macOS versions are fully supported.

macOS <= 10.8

On macOS 10.6, 10.7 and 10.8, the default event loop uses selectors.KqueueSelector, which does not support character devices on these versions. The SelectorEventLoop can be manually configured to use SelectSelector or PollSelector to support character devices on these older versions of macOS. Example:

import asyncio
+import selectors
+
+selector = selectors.SelectSelector()
+loop = asyncio.SelectorEventLoop(selector)
+asyncio.set_event_loop(loop)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-platforms.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-policy.html b/devdocs/python~3.12/library%2Fasyncio-policy.html new file mode 100644 index 00000000..c0154e4c --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-policy.html @@ -0,0 +1,90 @@ +

Policies

An event loop policy is a global object used to get and set the current event loop, as well as create new event loops. The default policy can be replaced with built-in alternatives to use different event loop implementations, or substituted by a custom policy that can override these behaviors.

The policy object gets and sets a separate event loop per context. This is per-thread by default, though custom policies could define context differently.

Custom event loop policies can control the behavior of get_event_loop(), set_event_loop(), and new_event_loop().

Policy objects should implement the APIs defined in the AbstractEventLoopPolicy abstract base class.

Getting and Setting the Policy

The following functions can be used to get and set the policy for the current process:

+asyncio.get_event_loop_policy()
+

Return the current process-wide policy.

+
+asyncio.set_event_loop_policy(policy)
+

Set the current process-wide policy to policy.

If policy is set to None, the default policy is restored.

+

Policy Objects

The abstract event loop policy base class is defined as follows:

+class asyncio.AbstractEventLoopPolicy
+

An abstract base class for asyncio policies.

+get_event_loop()
+

Get the event loop for the current context.

Return an event loop object implementing the AbstractEventLoop interface.

This method should never return None.

Changed in version 3.6.

+
+set_event_loop(loop)
+

Set the event loop for the current context to loop.

+
+new_event_loop()
+

Create and return a new event loop object.

This method should never return None.

+
+get_child_watcher()
+

Get a child process watcher object.

Return a watcher object implementing the AbstractChildWatcher interface.

This function is Unix specific.

Deprecated since version 3.12.

+
+set_child_watcher(watcher)
+

Set the current child process watcher to watcher.

This function is Unix specific.

Deprecated since version 3.12.

+
+

asyncio ships with the following built-in policies:

+class asyncio.DefaultEventLoopPolicy
+

The default asyncio policy. Uses SelectorEventLoop on Unix and ProactorEventLoop on Windows.

There is no need to install the default policy manually. asyncio is configured to use the default policy automatically.

Changed in version 3.8: On Windows, ProactorEventLoop is now used by default.

Deprecated since version 3.12: The get_event_loop() method of the default asyncio policy now emits a DeprecationWarning if there is no current event loop set and it decides to create one. In some future Python release this will become an error.

+
+class asyncio.WindowsSelectorEventLoopPolicy
+

An alternative event loop policy that uses the SelectorEventLoop event loop implementation.

Availability: Windows.

+
+class asyncio.WindowsProactorEventLoopPolicy
+

An alternative event loop policy that uses the ProactorEventLoop event loop implementation.

Availability: Windows.

+

Process Watchers

A process watcher allows customization of how an event loop monitors child processes on Unix. Specifically, the event loop needs to know when a child process has exited.

In asyncio, child processes are created with create_subprocess_exec() and loop.subprocess_exec() functions.

asyncio defines the AbstractChildWatcher abstract base class, which child watchers should implement, and has four different implementations: ThreadedChildWatcher (configured to be used by default), MultiLoopChildWatcher, SafeChildWatcher, and FastChildWatcher.

See also the Subprocess and Threads section.

The following two functions can be used to customize the child process watcher implementation used by the asyncio event loop:

+asyncio.get_child_watcher()
+

Return the current child watcher for the current policy.

Deprecated since version 3.12.

+
+asyncio.set_child_watcher(watcher)
+

Set the current child watcher to watcher for the current policy. watcher must implement methods defined in the AbstractChildWatcher base class.

Deprecated since version 3.12.

+

Note

Third-party event loops implementations might not support custom child watchers. For such event loops, using set_child_watcher() might be prohibited or have no effect.

+class asyncio.AbstractChildWatcher
+
+add_child_handler(pid, callback, *args)
+

Register a new child handler.

Arrange for callback(pid, returncode, *args) to be called when a process with PID equal to pid terminates. Specifying another callback for the same process replaces the previous handler.

The callback callable must be thread-safe.

+
+remove_child_handler(pid)
+

Removes the handler for process with PID equal to pid.

The function returns True if the handler was successfully removed, False if there was nothing to remove.

+
+attach_loop(loop)
+

Attach the watcher to an event loop.

If the watcher was previously attached to an event loop, then it is first detached before attaching to the new loop.

Note: loop may be None.

+
+is_active()
+

Return True if the watcher is ready to use.

Spawning a subprocess with inactive current child watcher raises RuntimeError.

New in version 3.8.

+
+close()
+

Close the watcher.

This method has to be called to ensure that underlying resources are cleaned-up.

+

Deprecated since version 3.12.

+
+class asyncio.ThreadedChildWatcher
+

This implementation starts a new waiting thread for every subprocess spawn.

It works reliably even when the asyncio event loop is run in a non-main OS thread.

There is no noticeable overhead when handling a big number of children (O(1) each time a child terminates), but starting a thread per process requires extra memory.

This watcher is used by default.

New in version 3.8.

+
+class asyncio.MultiLoopChildWatcher
+

This implementation registers a SIGCHLD signal handler on instantiation. That can break third-party code that installs a custom handler for SIGCHLD signal.

The watcher avoids disrupting other code spawning processes by polling every process explicitly on a SIGCHLD signal.

There is no limitation for running subprocesses from different threads once the watcher is installed.

The solution is safe but it has a significant overhead when handling a big number of processes (O(n) each time a SIGCHLD is received).

New in version 3.8.

Deprecated since version 3.12.

+
+class asyncio.SafeChildWatcher
+

This implementation uses active event loop from the main thread to handle SIGCHLD signal. If the main thread has no running event loop another thread cannot spawn a subprocess (RuntimeError is raised).

The watcher avoids disrupting other code spawning processes by polling every process explicitly on a SIGCHLD signal.

This solution is as safe as MultiLoopChildWatcher and has the same O(N) complexity but requires a running event loop in the main thread to work.

Deprecated since version 3.12.

+
+class asyncio.FastChildWatcher
+

This implementation reaps every terminated processes by calling os.waitpid(-1) directly, possibly breaking other code spawning processes and waiting for their termination.

There is no noticeable overhead when handling a big number of children (O(1) each time a child terminates).

This solution requires a running event loop in the main thread to work, as SafeChildWatcher.

Deprecated since version 3.12.

+
+class asyncio.PidfdChildWatcher
+

This implementation polls process file descriptors (pidfds) to await child process termination. In some respects, PidfdChildWatcher is a “Goldilocks” child watcher implementation. It doesn’t require signals or threads, doesn’t interfere with any processes launched outside the event loop, and scales linearly with the number of subprocesses launched by the event loop. The main disadvantage is that pidfds are specific to Linux, and only work on recent (5.3+) kernels.

New in version 3.9.

+

Custom Policies

To implement a new event loop policy, it is recommended to subclass DefaultEventLoopPolicy and override the methods for which custom behavior is wanted, e.g.:

class MyEventLoopPolicy(asyncio.DefaultEventLoopPolicy):
+
+    def get_event_loop(self):
+        """Get the event loop.
+
+        This may be None or an instance of EventLoop.
+        """
+        loop = super().get_event_loop()
+        # Do something with loop ...
+        return loop
+
+asyncio.set_event_loop_policy(MyEventLoopPolicy())
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-policy.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-protocol.html b/devdocs/python~3.12/library%2Fasyncio-protocol.html new file mode 100644 index 00000000..c0fedefe --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-protocol.html @@ -0,0 +1,455 @@ +

Transports and Protocols

Preface

Transports and Protocols are used by the low-level event loop APIs such as loop.create_connection(). They use callback-based programming style and enable high-performance implementations of network or IPC protocols (e.g. HTTP).

Essentially, transports and protocols should only be used in libraries and frameworks and never in high-level asyncio applications.

This documentation page covers both Transports and Protocols.

Introduction

At the highest level, the transport is concerned with how bytes are transmitted, while the protocol determines which bytes to transmit (and to some extent when).

A different way of saying the same thing: a transport is an abstraction for a socket (or similar I/O endpoint) while a protocol is an abstraction for an application, from the transport’s point of view.

Yet another view is the transport and protocol interfaces together define an abstract interface for using network I/O and interprocess I/O.

There is always a 1:1 relationship between transport and protocol objects: the protocol calls transport methods to send data, while the transport calls protocol methods to pass it data that has been received.

Most of connection oriented event loop methods (such as loop.create_connection()) usually accept a protocol_factory argument used to create a Protocol object for an accepted connection, represented by a Transport object. Such methods usually return a tuple of (transport, protocol).

Contents

This documentation page contains the following sections:

Transports

Source code: Lib/asyncio/transports.py

Transports are classes provided by asyncio in order to abstract various kinds of communication channels.

Transport objects are always instantiated by an asyncio event loop.

asyncio implements transports for TCP, UDP, SSL, and subprocess pipes. The methods available on a transport depend on the transport’s kind.

The transport classes are not thread safe.

Transports Hierarchy

+class asyncio.BaseTransport
+

Base class for all transports. Contains methods that all asyncio transports share.

+
+class asyncio.WriteTransport(BaseTransport)
+

A base transport for write-only connections.

Instances of the WriteTransport class are returned from the loop.connect_write_pipe() event loop method and are also used by subprocess-related methods like loop.subprocess_exec().

+
+class asyncio.ReadTransport(BaseTransport)
+

A base transport for read-only connections.

Instances of the ReadTransport class are returned from the loop.connect_read_pipe() event loop method and are also used by subprocess-related methods like loop.subprocess_exec().

+
+class asyncio.Transport(WriteTransport, ReadTransport)
+

Interface representing a bidirectional transport, such as a TCP connection.

The user does not instantiate a transport directly; they call a utility function, passing it a protocol factory and other information necessary to create the transport and protocol.

Instances of the Transport class are returned from or used by event loop methods like loop.create_connection(), loop.create_unix_connection(), loop.create_server(), loop.sendfile(), etc.

+
+class asyncio.DatagramTransport(BaseTransport)
+

A transport for datagram (UDP) connections.

Instances of the DatagramTransport class are returned from the loop.create_datagram_endpoint() event loop method.

+
+class asyncio.SubprocessTransport(BaseTransport)
+

An abstraction to represent a connection between a parent and its child OS process.

Instances of the SubprocessTransport class are returned from event loop methods loop.subprocess_shell() and loop.subprocess_exec().

+

Base Transport

+BaseTransport.close()
+

Close the transport.

If the transport has a buffer for outgoing data, buffered data will be flushed asynchronously. No more data will be received. After all buffered data is flushed, the protocol’s protocol.connection_lost() method will be called with None as its argument. The transport should not be used once it is closed.

+
+BaseTransport.is_closing()
+

Return True if the transport is closing or is closed.

+
+BaseTransport.get_extra_info(name, default=None)
+

Return information about the transport or underlying resources it uses.

name is a string representing the piece of transport-specific information to get.

default is the value to return if the information is not available, or if the transport does not support querying it with the given third-party event loop implementation or on the current platform.

For example, the following code attempts to get the underlying socket object of the transport:

sock = transport.get_extra_info('socket')
+if sock is not None:
+    print(sock.getsockopt(...))
+

Categories of information that can be queried on some transports:

+
+BaseTransport.set_protocol(protocol)
+

Set a new protocol.

Switching protocol should only be done when both protocols are documented to support the switch.

+
+BaseTransport.get_protocol()
+

Return the current protocol.

+

Read-only Transports

+ReadTransport.is_reading()
+

Return True if the transport is receiving new data.

New in version 3.7.

+
+ReadTransport.pause_reading()
+

Pause the receiving end of the transport. No data will be passed to the protocol’s protocol.data_received() method until resume_reading() is called.

Changed in version 3.7: The method is idempotent, i.e. it can be called when the transport is already paused or closed.

+
+ReadTransport.resume_reading()
+

Resume the receiving end. The protocol’s protocol.data_received() method will be called once again if some data is available for reading.

Changed in version 3.7: The method is idempotent, i.e. it can be called when the transport is already reading.

+

Write-only Transports

+WriteTransport.abort()
+

Close the transport immediately, without waiting for pending operations to complete. Buffered data will be lost. No more data will be received. The protocol’s protocol.connection_lost() method will eventually be called with None as its argument.

+
+WriteTransport.can_write_eof()
+

Return True if the transport supports write_eof(), False if not.

+
+WriteTransport.get_write_buffer_size()
+

Return the current size of the output buffer used by the transport.

+
+WriteTransport.get_write_buffer_limits()
+

Get the high and low watermarks for write flow control. Return a tuple (low, high) where low and high are positive number of bytes.

Use set_write_buffer_limits() to set the limits.

New in version 3.4.2.

+
+WriteTransport.set_write_buffer_limits(high=None, low=None)
+

Set the high and low watermarks for write flow control.

These two values (measured in number of bytes) control when the protocol’s protocol.pause_writing() and protocol.resume_writing() methods are called. If specified, the low watermark must be less than or equal to the high watermark. Neither high nor low can be negative.

pause_writing() is called when the buffer size becomes greater than or equal to the high value. If writing has been paused, resume_writing() is called when the buffer size becomes less than or equal to the low value.

The defaults are implementation-specific. If only the high watermark is given, the low watermark defaults to an implementation-specific value less than or equal to the high watermark. Setting high to zero forces low to zero as well, and causes pause_writing() to be called whenever the buffer becomes non-empty. Setting low to zero causes resume_writing() to be called only once the buffer is empty. Use of zero for either limit is generally sub-optimal as it reduces opportunities for doing I/O and computation concurrently.

Use get_write_buffer_limits() to get the limits.

+
+WriteTransport.write(data)
+

Write some data bytes to the transport.

This method does not block; it buffers the data and arranges for it to be sent out asynchronously.

+
+WriteTransport.writelines(list_of_data)
+

Write a list (or any iterable) of data bytes to the transport. This is functionally equivalent to calling write() on each element yielded by the iterable, but may be implemented more efficiently.

+
+WriteTransport.write_eof()
+

Close the write end of the transport after flushing all buffered data. Data may still be received.

This method can raise NotImplementedError if the transport (e.g. SSL) doesn’t support half-closed connections.

+

Datagram Transports

+DatagramTransport.sendto(data, addr=None)
+

Send the data bytes to the remote peer given by addr (a transport-dependent target address). If addr is None, the data is sent to the target address given on transport creation.

This method does not block; it buffers the data and arranges for it to be sent out asynchronously.

+
+DatagramTransport.abort()
+

Close the transport immediately, without waiting for pending operations to complete. Buffered data will be lost. No more data will be received. The protocol’s protocol.connection_lost() method will eventually be called with None as its argument.

+

Subprocess Transports

+SubprocessTransport.get_pid()
+

Return the subprocess process id as an integer.

+
+SubprocessTransport.get_pipe_transport(fd)
+

Return the transport for the communication pipe corresponding to the integer file descriptor fd:

  • +0: readable streaming transport of the standard input (stdin), or None if the subprocess was not created with stdin=PIPE +
  • +1: writable streaming transport of the standard output (stdout), or None if the subprocess was not created with stdout=PIPE +
  • +2: writable streaming transport of the standard error (stderr), or None if the subprocess was not created with stderr=PIPE +
  • other fd: None +
+
+SubprocessTransport.get_returncode()
+

Return the subprocess return code as an integer or None if it hasn’t returned, which is similar to the subprocess.Popen.returncode attribute.

+
+SubprocessTransport.kill()
+

Kill the subprocess.

On POSIX systems, the function sends SIGKILL to the subprocess. On Windows, this method is an alias for terminate().

See also subprocess.Popen.kill().

+
+SubprocessTransport.send_signal(signal)
+

Send the signal number to the subprocess, as in subprocess.Popen.send_signal().

+
+SubprocessTransport.terminate()
+

Stop the subprocess.

On POSIX systems, this method sends SIGTERM to the subprocess. On Windows, the Windows API function TerminateProcess() is called to stop the subprocess.

See also subprocess.Popen.terminate().

+
+SubprocessTransport.close()
+

Kill the subprocess by calling the kill() method.

If the subprocess hasn’t returned yet, and close transports of stdin, stdout, and stderr pipes.

+

Protocols

Source code: Lib/asyncio/protocols.py

asyncio provides a set of abstract base classes that should be used to implement network protocols. Those classes are meant to be used together with transports.

Subclasses of abstract base protocol classes may implement some or all methods. All these methods are callbacks: they are called by transports on certain events, for example when some data is received. A base protocol method should be called by the corresponding transport.

Base Protocols

+class asyncio.BaseProtocol
+

Base protocol with methods that all protocols share.

+
+class asyncio.Protocol(BaseProtocol)
+

The base class for implementing streaming protocols (TCP, Unix sockets, etc).

+
+class asyncio.BufferedProtocol(BaseProtocol)
+

A base class for implementing streaming protocols with manual control of the receive buffer.

+
+class asyncio.DatagramProtocol(BaseProtocol)
+

The base class for implementing datagram (UDP) protocols.

+
+class asyncio.SubprocessProtocol(BaseProtocol)
+

The base class for implementing protocols communicating with child processes (unidirectional pipes).

+

Base Protocol

All asyncio protocols can implement Base Protocol callbacks.

Connection Callbacks

Connection callbacks are called on all protocols, exactly once per a successful connection. All other protocol callbacks can only be called between those two methods.

+BaseProtocol.connection_made(transport)
+

Called when a connection is made.

The transport argument is the transport representing the connection. The protocol is responsible for storing the reference to its transport.

+
+BaseProtocol.connection_lost(exc)
+

Called when the connection is lost or closed.

The argument is either an exception object or None. The latter means a regular EOF is received, or the connection was aborted or closed by this side of the connection.

+

Flow Control Callbacks

Flow control callbacks can be called by transports to pause or resume writing performed by the protocol.

See the documentation of the set_write_buffer_limits() method for more details.

+BaseProtocol.pause_writing()
+

Called when the transport’s buffer goes over the high watermark.

+
+BaseProtocol.resume_writing()
+

Called when the transport’s buffer drains below the low watermark.

+

If the buffer size equals the high watermark, pause_writing() is not called: the buffer size must go strictly over.

Conversely, resume_writing() is called when the buffer size is equal or lower than the low watermark. These end conditions are important to ensure that things go as expected when either mark is zero.

Streaming Protocols

Event methods, such as loop.create_server(), loop.create_unix_server(), loop.create_connection(), loop.create_unix_connection(), loop.connect_accepted_socket(), loop.connect_read_pipe(), and loop.connect_write_pipe() accept factories that return streaming protocols.

+Protocol.data_received(data)
+

Called when some data is received. data is a non-empty bytes object containing the incoming data.

Whether the data is buffered, chunked or reassembled depends on the transport. In general, you shouldn’t rely on specific semantics and instead make your parsing generic and flexible. However, data is always received in the correct order.

The method can be called an arbitrary number of times while a connection is open.

However, protocol.eof_received() is called at most once. Once eof_received() is called, data_received() is not called anymore.

+
+Protocol.eof_received()
+

Called when the other end signals it won’t send any more data (for example by calling transport.write_eof(), if the other end also uses asyncio).

This method may return a false value (including None), in which case the transport will close itself. Conversely, if this method returns a true value, the protocol used determines whether to close the transport. Since the default implementation returns None, it implicitly closes the connection.

Some transports, including SSL, don’t support half-closed connections, in which case returning true from this method will result in the connection being closed.

+

State machine:

start -> connection_made
+    [-> data_received]*
+    [-> eof_received]?
+-> connection_lost -> end
+

Buffered Streaming Protocols

New in version 3.7.

Buffered Protocols can be used with any event loop method that supports Streaming Protocols.

BufferedProtocol implementations allow explicit manual allocation and control of the receive buffer. Event loops can then use the buffer provided by the protocol to avoid unnecessary data copies. This can result in noticeable performance improvement for protocols that receive big amounts of data. Sophisticated protocol implementations can significantly reduce the number of buffer allocations.

The following callbacks are called on BufferedProtocol instances:

+BufferedProtocol.get_buffer(sizehint)
+

Called to allocate a new receive buffer.

sizehint is the recommended minimum size for the returned buffer. It is acceptable to return smaller or larger buffers than what sizehint suggests. When set to -1, the buffer size can be arbitrary. It is an error to return a buffer with a zero size.

get_buffer() must return an object implementing the buffer protocol.

+
+BufferedProtocol.buffer_updated(nbytes)
+

Called when the buffer was updated with the received data.

nbytes is the total number of bytes that were written to the buffer.

+
+BufferedProtocol.eof_received()
+

See the documentation of the protocol.eof_received() method.

+

get_buffer() can be called an arbitrary number of times during a connection. However, protocol.eof_received() is called at most once and, if called, get_buffer() and buffer_updated() won’t be called after it.

State machine:

start -> connection_made
+    [-> get_buffer
+        [-> buffer_updated]?
+    ]*
+    [-> eof_received]?
+-> connection_lost -> end
+

Datagram Protocols

Datagram Protocol instances should be constructed by protocol factories passed to the loop.create_datagram_endpoint() method.

+DatagramProtocol.datagram_received(data, addr)
+

Called when a datagram is received. data is a bytes object containing the incoming data. addr is the address of the peer sending the data; the exact format depends on the transport.

+
+DatagramProtocol.error_received(exc)
+

Called when a previous send or receive operation raises an OSError. exc is the OSError instance.

This method is called in rare conditions, when the transport (e.g. UDP) detects that a datagram could not be delivered to its recipient. In many conditions though, undeliverable datagrams will be silently dropped.

+

Note

On BSD systems (macOS, FreeBSD, etc.) flow control is not supported for datagram protocols, because there is no reliable way to detect send failures caused by writing too many packets.

The socket always appears ‘ready’ and excess packets are dropped. An OSError with errno set to errno.ENOBUFS may or may not be raised; if it is raised, it will be reported to DatagramProtocol.error_received() but otherwise ignored.

Subprocess Protocols

Subprocess Protocol instances should be constructed by protocol factories passed to the loop.subprocess_exec() and loop.subprocess_shell() methods.

+SubprocessProtocol.pipe_data_received(fd, data)
+

Called when the child process writes data into its stdout or stderr pipe.

fd is the integer file descriptor of the pipe.

data is a non-empty bytes object containing the received data.

+
+SubprocessProtocol.pipe_connection_lost(fd, exc)
+

Called when one of the pipes communicating with the child process is closed.

fd is the integer file descriptor that was closed.

+
+SubprocessProtocol.process_exited()
+

Called when the child process has exited.

It can be called before pipe_data_received() and pipe_connection_lost() methods.

+

Examples

TCP Echo Server

Create a TCP echo server using the loop.create_server() method, send back received data, and close the connection:

import asyncio
+
+
+class EchoServerProtocol(asyncio.Protocol):
+    def connection_made(self, transport):
+        peername = transport.get_extra_info('peername')
+        print('Connection from {}'.format(peername))
+        self.transport = transport
+
+    def data_received(self, data):
+        message = data.decode()
+        print('Data received: {!r}'.format(message))
+
+        print('Send: {!r}'.format(message))
+        self.transport.write(data)
+
+        print('Close the client socket')
+        self.transport.close()
+
+
+async def main():
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    server = await loop.create_server(
+        lambda: EchoServerProtocol(),
+        '127.0.0.1', 8888)
+
+    async with server:
+        await server.serve_forever()
+
+
+asyncio.run(main())
+

See also

The TCP echo server using streams example uses the high-level asyncio.start_server() function.

TCP Echo Client

A TCP echo client using the loop.create_connection() method, sends data, and waits until the connection is closed:

import asyncio
+
+
+class EchoClientProtocol(asyncio.Protocol):
+    def __init__(self, message, on_con_lost):
+        self.message = message
+        self.on_con_lost = on_con_lost
+
+    def connection_made(self, transport):
+        transport.write(self.message.encode())
+        print('Data sent: {!r}'.format(self.message))
+
+    def data_received(self, data):
+        print('Data received: {!r}'.format(data.decode()))
+
+    def connection_lost(self, exc):
+        print('The server closed the connection')
+        self.on_con_lost.set_result(True)
+
+
+async def main():
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    on_con_lost = loop.create_future()
+    message = 'Hello World!'
+
+    transport, protocol = await loop.create_connection(
+        lambda: EchoClientProtocol(message, on_con_lost),
+        '127.0.0.1', 8888)
+
+    # Wait until the protocol signals that the connection
+    # is lost and close the transport.
+    try:
+        await on_con_lost
+    finally:
+        transport.close()
+
+
+asyncio.run(main())
+

See also

The TCP echo client using streams example uses the high-level asyncio.open_connection() function.

UDP Echo Server

A UDP echo server, using the loop.create_datagram_endpoint() method, sends back received data:

import asyncio
+
+
+class EchoServerProtocol:
+    def connection_made(self, transport):
+        self.transport = transport
+
+    def datagram_received(self, data, addr):
+        message = data.decode()
+        print('Received %r from %s' % (message, addr))
+        print('Send %r to %s' % (message, addr))
+        self.transport.sendto(data, addr)
+
+
+async def main():
+    print("Starting UDP server")
+
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    # One protocol instance will be created to serve all
+    # client requests.
+    transport, protocol = await loop.create_datagram_endpoint(
+        lambda: EchoServerProtocol(),
+        local_addr=('127.0.0.1', 9999))
+
+    try:
+        await asyncio.sleep(3600)  # Serve for 1 hour.
+    finally:
+        transport.close()
+
+
+asyncio.run(main())
+

UDP Echo Client

A UDP echo client, using the loop.create_datagram_endpoint() method, sends data and closes the transport when it receives the answer:

import asyncio
+
+
+class EchoClientProtocol:
+    def __init__(self, message, on_con_lost):
+        self.message = message
+        self.on_con_lost = on_con_lost
+        self.transport = None
+
+    def connection_made(self, transport):
+        self.transport = transport
+        print('Send:', self.message)
+        self.transport.sendto(self.message.encode())
+
+    def datagram_received(self, data, addr):
+        print("Received:", data.decode())
+
+        print("Close the socket")
+        self.transport.close()
+
+    def error_received(self, exc):
+        print('Error received:', exc)
+
+    def connection_lost(self, exc):
+        print("Connection closed")
+        self.on_con_lost.set_result(True)
+
+
+async def main():
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    on_con_lost = loop.create_future()
+    message = "Hello World!"
+
+    transport, protocol = await loop.create_datagram_endpoint(
+        lambda: EchoClientProtocol(message, on_con_lost),
+        remote_addr=('127.0.0.1', 9999))
+
+    try:
+        await on_con_lost
+    finally:
+        transport.close()
+
+
+asyncio.run(main())
+

Connecting Existing Sockets

Wait until a socket receives data using the loop.create_connection() method with a protocol:

import asyncio
+import socket
+
+
+class MyProtocol(asyncio.Protocol):
+
+    def __init__(self, on_con_lost):
+        self.transport = None
+        self.on_con_lost = on_con_lost
+
+    def connection_made(self, transport):
+        self.transport = transport
+
+    def data_received(self, data):
+        print("Received:", data.decode())
+
+        # We are done: close the transport;
+        # connection_lost() will be called automatically.
+        self.transport.close()
+
+    def connection_lost(self, exc):
+        # The socket has been closed
+        self.on_con_lost.set_result(True)
+
+
+async def main():
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+    on_con_lost = loop.create_future()
+
+    # Create a pair of connected sockets
+    rsock, wsock = socket.socketpair()
+
+    # Register the socket to wait for data.
+    transport, protocol = await loop.create_connection(
+        lambda: MyProtocol(on_con_lost), sock=rsock)
+
+    # Simulate the reception of data from the network.
+    loop.call_soon(wsock.send, 'abc'.encode())
+
+    try:
+        await protocol.on_con_lost
+    finally:
+        transport.close()
+        wsock.close()
+
+asyncio.run(main())
+

See also

The watch a file descriptor for read events example uses the low-level loop.add_reader() method to register an FD.

The register an open socket to wait for data using streams example uses high-level streams created by the open_connection() function in a coroutine.

loop.subprocess_exec() and SubprocessProtocol

An example of a subprocess protocol used to get the output of a subprocess and to wait for the subprocess exit.

The subprocess is created by the loop.subprocess_exec() method:

import asyncio
+import sys
+
+class DateProtocol(asyncio.SubprocessProtocol):
+    def __init__(self, exit_future):
+        self.exit_future = exit_future
+        self.output = bytearray()
+        self.pipe_closed = False
+        self.exited = False
+
+    def pipe_connection_lost(self, fd, exc):
+        self.pipe_closed = True
+        self.check_for_exit()
+
+    def pipe_data_received(self, fd, data):
+        self.output.extend(data)
+
+    def process_exited(self):
+        self.exited = True
+        # process_exited() method can be called before
+        # pipe_connection_lost() method: wait until both methods are
+        # called.
+        self.check_for_exit()
+
+    def check_for_exit(self):
+        if self.pipe_closed and self.exited:
+            self.exit_future.set_result(True)
+
+async def get_date():
+    # Get a reference to the event loop as we plan to use
+    # low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    code = 'import datetime; print(datetime.datetime.now())'
+    exit_future = asyncio.Future(loop=loop)
+
+    # Create the subprocess controlled by DateProtocol;
+    # redirect the standard output into a pipe.
+    transport, protocol = await loop.subprocess_exec(
+        lambda: DateProtocol(exit_future),
+        sys.executable, '-c', code,
+        stdin=None, stderr=None)
+
+    # Wait for the subprocess exit using the process_exited()
+    # method of the protocol.
+    await exit_future
+
+    # Close the stdout pipe.
+    transport.close()
+
+    # Read the output which was collected by the
+    # pipe_data_received() method of the protocol.
+    data = bytes(protocol.output)
+    return data.decode('ascii').rstrip()
+
+date = asyncio.run(get_date())
+print(f"Current date: {date}")
+

See also the same example written using high-level APIs.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-protocol.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-queue.html b/devdocs/python~3.12/library%2Fasyncio-queue.html new file mode 100644 index 00000000..757550e6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-queue.html @@ -0,0 +1,104 @@ +

Queues

Source code: Lib/asyncio/queues.py

asyncio queues are designed to be similar to classes of the queue module. Although asyncio queues are not thread-safe, they are designed to be used specifically in async/await code.

Note that methods of asyncio queues don’t have a timeout parameter; use asyncio.wait_for() function to do queue operations with a timeout.

See also the Examples section below.

Queue

+class asyncio.Queue(maxsize=0)
+

A first in, first out (FIFO) queue.

If maxsize is less than or equal to zero, the queue size is infinite. If it is an integer greater than 0, then await put() blocks when the queue reaches maxsize until an item is removed by get().

Unlike the standard library threading queue, the size of the queue is always known and can be returned by calling the qsize() method.

Changed in version 3.10: Removed the loop parameter.

This class is not thread safe.

+maxsize
+

Number of items allowed in the queue.

+
+empty()
+

Return True if the queue is empty, False otherwise.

+
+full()
+

Return True if there are maxsize items in the queue.

If the queue was initialized with maxsize=0 (the default), then full() never returns True.

+
+coroutine get()
+

Remove and return an item from the queue. If queue is empty, wait until an item is available.

+
+get_nowait()
+

Return an item if one is immediately available, else raise QueueEmpty.

+
+coroutine join()
+

Block until all items in the queue have been received and processed.

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer coroutine calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.

+
+coroutine put(item)
+

Put an item into the queue. If the queue is full, wait until a free slot is available before adding the item.

+
+put_nowait(item)
+

Put an item into the queue without blocking.

If no free slot is immediately available, raise QueueFull.

+
+qsize()
+

Return the number of items in the queue.

+
+task_done()
+

Indicate that a formerly enqueued task is complete.

Used by queue consumers. For each get() used to fetch a task, a subsequent call to task_done() tells the queue that the processing on the task is complete.

If a join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

Raises ValueError if called more times than there were items placed in the queue.

+
+

Priority Queue

+class asyncio.PriorityQueue
+

A variant of Queue; retrieves entries in priority order (lowest first).

Entries are typically tuples of the form (priority_number, data).

+

LIFO Queue

+class asyncio.LifoQueue
+

A variant of Queue that retrieves most recently added entries first (last in, first out).

+

Exceptions

+exception asyncio.QueueEmpty
+

This exception is raised when the get_nowait() method is called on an empty queue.

+
+exception asyncio.QueueFull
+

Exception raised when the put_nowait() method is called on a queue that has reached its maxsize.

+

Examples

Queues can be used to distribute workload between several concurrent tasks:

import asyncio
+import random
+import time
+
+
+async def worker(name, queue):
+    while True:
+        # Get a "work item" out of the queue.
+        sleep_for = await queue.get()
+
+        # Sleep for the "sleep_for" seconds.
+        await asyncio.sleep(sleep_for)
+
+        # Notify the queue that the "work item" has been processed.
+        queue.task_done()
+
+        print(f'{name} has slept for {sleep_for:.2f} seconds')
+
+
+async def main():
+    # Create a queue that we will use to store our "workload".
+    queue = asyncio.Queue()
+
+    # Generate random timings and put them into the queue.
+    total_sleep_time = 0
+    for _ in range(20):
+        sleep_for = random.uniform(0.05, 1.0)
+        total_sleep_time += sleep_for
+        queue.put_nowait(sleep_for)
+
+    # Create three worker tasks to process the queue concurrently.
+    tasks = []
+    for i in range(3):
+        task = asyncio.create_task(worker(f'worker-{i}', queue))
+        tasks.append(task)
+
+    # Wait until the queue is fully processed.
+    started_at = time.monotonic()
+    await queue.join()
+    total_slept_for = time.monotonic() - started_at
+
+    # Cancel our worker tasks.
+    for task in tasks:
+        task.cancel()
+    # Wait until all worker tasks are cancelled.
+    await asyncio.gather(*tasks, return_exceptions=True)
+
+    print('====')
+    print(f'3 workers slept in parallel for {total_slept_for:.2f} seconds')
+    print(f'total expected sleep time: {total_sleep_time:.2f} seconds')
+
+
+asyncio.run(main())
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-queue.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-runner.html b/devdocs/python~3.12/library%2Fasyncio-runner.html new file mode 100644 index 00000000..f0a7c008 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-runner.html @@ -0,0 +1,33 @@ +

Runners

Source code: Lib/asyncio/runners.py

This section outlines high-level asyncio primitives to run asyncio code.

They are built on top of an event loop with the aim to simplify async code usage for common wide-spread scenarios.

Running an asyncio Program

+asyncio.run(coro, *, debug=None, loop_factory=None)
+

Execute the coroutine coro and return the result.

This function runs the passed coroutine, taking care of managing the asyncio event loop, finalizing asynchronous generators, and closing the executor.

This function cannot be called when another asyncio event loop is running in the same thread.

If debug is True, the event loop will be run in debug mode. False disables debug mode explicitly. None is used to respect the global Debug Mode settings.

If loop_factory is not None, it is used to create a new event loop; otherwise asyncio.new_event_loop() is used. The loop is closed at the end. This function should be used as a main entry point for asyncio programs, and should ideally only be called once. It is recommended to use loop_factory to configure the event loop instead of policies.

The executor is given a timeout duration of 5 minutes to shutdown. If the executor hasn’t finished within that duration, a warning is emitted and the executor is closed.

Example:

async def main():
+    await asyncio.sleep(1)
+    print('hello')
+
+asyncio.run(main())
+

New in version 3.7.

Changed in version 3.9: Updated to use loop.shutdown_default_executor().

Changed in version 3.10: debug is None by default to respect the global debug mode settings.

Changed in version 3.12: Added loop_factory parameter.

+

Runner context manager

+class asyncio.Runner(*, debug=None, loop_factory=None)
+

A context manager that simplifies multiple async function calls in the same context.

Sometimes several top-level async functions should be called in the same event loop and contextvars.Context.

If debug is True, the event loop will be run in debug mode. False disables debug mode explicitly. None is used to respect the global Debug Mode settings.

loop_factory could be used for overriding the loop creation. It is the responsibility of the loop_factory to set the created loop as the current one. By default asyncio.new_event_loop() is used and set as current event loop with asyncio.set_event_loop() if loop_factory is None.

Basically, asyncio.run() example can be rewritten with the runner usage:

async def main():
+    await asyncio.sleep(1)
+    print('hello')
+
+with asyncio.Runner() as runner:
+    runner.run(main())
+

New in version 3.11.

+run(coro, *, context=None)
+

Run a coroutine coro in the embedded loop.

Return the coroutine’s result or raise its exception.

An optional keyword-only context argument allows specifying a custom contextvars.Context for the coro to run in. The runner’s default context is used if None.

This function cannot be called when another asyncio event loop is running in the same thread.

+
+close()
+

Close the runner.

Finalize asynchronous generators, shutdown default executor, close the event loop and release embedded contextvars.Context.

+
+get_loop()
+

Return the event loop associated with the runner instance.

+

Note

Runner uses the lazy initialization strategy, its constructor doesn’t initialize underlying low-level structures.

Embedded loop and context are created at the with body entering or the first call of run() or get_loop().

+

Handling Keyboard Interruption

New in version 3.11.

When signal.SIGINT is raised by Ctrl-C, KeyboardInterrupt exception is raised in the main thread by default. However this doesn’t work with asyncio because it can interrupt asyncio internals and can hang the program from exiting.

To mitigate this issue, asyncio handles signal.SIGINT as follows:

  1. +asyncio.Runner.run() installs a custom signal.SIGINT handler before any user code is executed and removes it when exiting from the function.
  2. The Runner creates the main task for the passed coroutine for its execution.
  3. When signal.SIGINT is raised by Ctrl-C, the custom signal handler cancels the main task by calling asyncio.Task.cancel() which raises asyncio.CancelledError inside the main task. This causes the Python stack to unwind, try/except and try/finally blocks can be used for resource cleanup. After the main task is cancelled, asyncio.Runner.run() raises KeyboardInterrupt.
  4. A user could write a tight loop which cannot be interrupted by asyncio.Task.cancel(), in which case the second following Ctrl-C immediately raises the KeyboardInterrupt without cancelling the main task.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-runner.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-stream.html b/devdocs/python~3.12/library%2Fasyncio-stream.html new file mode 100644 index 00000000..e81896ca --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-stream.html @@ -0,0 +1,216 @@ +

Streams

Source code: Lib/asyncio/streams.py

Streams are high-level async/await-ready primitives to work with network connections. Streams allow sending and receiving data without using callbacks or low-level protocols and transports.

Here is an example of a TCP echo client written using asyncio streams:

import asyncio
+
+async def tcp_echo_client(message):
+    reader, writer = await asyncio.open_connection(
+        '127.0.0.1', 8888)
+
+    print(f'Send: {message!r}')
+    writer.write(message.encode())
+    await writer.drain()
+
+    data = await reader.read(100)
+    print(f'Received: {data.decode()!r}')
+
+    print('Close the connection')
+    writer.close()
+    await writer.wait_closed()
+
+asyncio.run(tcp_echo_client('Hello World!'))
+

See also the Examples section below.

Stream Functions

The following top-level asyncio functions can be used to create and work with streams:

+coroutine asyncio.open_connection(host=None, port=None, *, limit=None, ssl=None, family=0, proto=0, flags=0, sock=None, local_addr=None, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, happy_eyeballs_delay=None, interleave=None)
+

Establish a network connection and return a pair of (reader, writer) objects.

The returned reader and writer objects are instances of StreamReader and StreamWriter classes.

limit determines the buffer size limit used by the returned StreamReader instance. By default the limit is set to 64 KiB.

The rest of the arguments are passed directly to loop.create_connection().

Note

The sock argument transfers ownership of the socket to the StreamWriter created. To close the socket, call its close() method.

Changed in version 3.7: Added the ssl_handshake_timeout parameter.

New in version 3.8: Added happy_eyeballs_delay and interleave parameters.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+
+coroutine asyncio.start_server(client_connected_cb, host=None, port=None, *, limit=None, family=socket.AF_UNSPEC, flags=socket.AI_PASSIVE, sock=None, backlog=100, ssl=None, reuse_address=None, reuse_port=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, start_serving=True)
+

Start a socket server.

The client_connected_cb callback is called whenever a new client connection is established. It receives a (reader, writer) pair as two arguments, instances of the StreamReader and StreamWriter classes.

client_connected_cb can be a plain callable or a coroutine function; if it is a coroutine function, it will be automatically scheduled as a Task.

limit determines the buffer size limit used by the returned StreamReader instance. By default the limit is set to 64 KiB.

The rest of the arguments are passed directly to loop.create_server().

Note

The sock argument transfers ownership of the socket to the server created. To close the socket, call the server’s close() method.

Changed in version 3.7: Added the ssl_handshake_timeout and start_serving parameters.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+

Unix Sockets

+coroutine asyncio.open_unix_connection(path=None, *, limit=None, ssl=None, sock=None, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None)

Establish a Unix socket connection and return a pair of (reader, writer).

Similar to open_connection() but operates on Unix sockets.

See also the documentation of loop.create_unix_connection().

Note

The sock argument transfers ownership of the socket to the StreamWriter created. To close the socket, call its close() method.

Availability: Unix.

Changed in version 3.7: Added the ssl_handshake_timeout parameter. The path parameter can now be a path-like object

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+
+coroutine asyncio.start_unix_server(client_connected_cb, path=None, *, limit=None, sock=None, backlog=100, ssl=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None, start_serving=True)
+

Start a Unix socket server.

Similar to start_server() but works with Unix sockets.

See also the documentation of loop.create_unix_server().

Note

The sock argument transfers ownership of the socket to the server created. To close the socket, call the server’s close() method.

Availability: Unix.

Changed in version 3.7: Added the ssl_handshake_timeout and start_serving parameters. The path parameter can now be a path-like object.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Added the ssl_shutdown_timeout parameter.

+

StreamReader

+class asyncio.StreamReader
+

Represents a reader object that provides APIs to read data from the IO stream. As an asynchronous iterable, the object supports the async for statement.

It is not recommended to instantiate StreamReader objects directly; use open_connection() and start_server() instead.

+feed_eof()
+

Acknowledge the EOF.

+
+coroutine read(n=- 1)
+

Read up to n bytes from the stream.

If n is not provided or set to -1, read until EOF, then return all read bytes. If EOF was received and the internal buffer is empty, return an empty bytes object.

If n is 0, return an empty bytes object immediately.

If n is positive, return at most n available bytes as soon as at least 1 byte is available in the internal buffer. If EOF is received before any byte is read, return an empty bytes object.

+
+coroutine readline()
+

Read one line, where “line” is a sequence of bytes ending with \n.

If EOF is received and \n was not found, the method returns partially read data.

If EOF is received and the internal buffer is empty, return an empty bytes object.

+
+coroutine readexactly(n)
+

Read exactly n bytes.

Raise an IncompleteReadError if EOF is reached before n can be read. Use the IncompleteReadError.partial attribute to get the partially read data.

+
+coroutine readuntil(separator=b'\n')
+

Read data from the stream until separator is found.

On success, the data and separator will be removed from the internal buffer (consumed). Returned data will include the separator at the end.

If the amount of data read exceeds the configured stream limit, a LimitOverrunError exception is raised, and the data is left in the internal buffer and can be read again.

If EOF is reached before the complete separator is found, an IncompleteReadError exception is raised, and the internal buffer is reset. The IncompleteReadError.partial attribute may contain a portion of the separator.

New in version 3.5.2.

+
+at_eof()
+

Return True if the buffer is empty and feed_eof() was called.

+
+

StreamWriter

+class asyncio.StreamWriter
+

Represents a writer object that provides APIs to write data to the IO stream.

It is not recommended to instantiate StreamWriter objects directly; use open_connection() and start_server() instead.

+write(data)
+

The method attempts to write the data to the underlying socket immediately. If that fails, the data is queued in an internal write buffer until it can be sent.

The method should be used along with the drain() method:

stream.write(data)
+await stream.drain()
+
+
+writelines(data)
+

The method writes a list (or any iterable) of bytes to the underlying socket immediately. If that fails, the data is queued in an internal write buffer until it can be sent.

The method should be used along with the drain() method:

stream.writelines(lines)
+await stream.drain()
+
+
+close()
+

The method closes the stream and the underlying socket.

The method should be used, though not mandatory, along with the wait_closed() method:

stream.close()
+await stream.wait_closed()
+
+
+can_write_eof()
+

Return True if the underlying transport supports the write_eof() method, False otherwise.

+
+write_eof()
+

Close the write end of the stream after the buffered write data is flushed.

+
+transport
+

Return the underlying asyncio transport.

+
+get_extra_info(name, default=None)
+

Access optional transport information; see BaseTransport.get_extra_info() for details.

+
+coroutine drain()
+

Wait until it is appropriate to resume writing to the stream. Example:

writer.write(data)
+await writer.drain()
+

This is a flow control method that interacts with the underlying IO write buffer. When the size of the buffer reaches the high watermark, drain() blocks until the size of the buffer is drained down to the low watermark and writing can be resumed. When there is nothing to wait for, the drain() returns immediately.

+
+coroutine start_tls(sslcontext, \*, server_hostname=None, ssl_handshake_timeout=None, ssl_shutdown_timeout=None)
+

Upgrade an existing stream-based connection to TLS.

Parameters:

  • +sslcontext: a configured instance of SSLContext.
  • +server_hostname: sets or overrides the host name that the target server’s certificate will be matched against.
  • +ssl_handshake_timeout is the time in seconds to wait for the TLS handshake to complete before aborting the connection. 60.0 seconds if None (default).
  • +ssl_shutdown_timeout is the time in seconds to wait for the SSL shutdown to complete before aborting the connection. 30.0 seconds if None (default).

New in version 3.11.

Changed in version 3.12: Added the ssl_shutdown_timeout parameter.

+
+is_closing()
+

Return True if the stream is closed or in the process of being closed.

New in version 3.7.

+
+coroutine wait_closed()
+

Wait until the stream is closed.

Should be called after close() to wait until the underlying connection is closed, ensuring that all data has been flushed before e.g. exiting the program.

New in version 3.7.

+
+

Examples

TCP echo client using streams

TCP echo client using the asyncio.open_connection() function:

import asyncio
+
+async def tcp_echo_client(message):
+    reader, writer = await asyncio.open_connection(
+        '127.0.0.1', 8888)
+
+    print(f'Send: {message!r}')
+    writer.write(message.encode())
+    await writer.drain()
+
+    data = await reader.read(100)
+    print(f'Received: {data.decode()!r}')
+
+    print('Close the connection')
+    writer.close()
+    await writer.wait_closed()
+
+asyncio.run(tcp_echo_client('Hello World!'))
+

See also

The TCP echo client protocol example uses the low-level loop.create_connection() method.

TCP echo server using streams

TCP echo server using the asyncio.start_server() function:

import asyncio
+
+async def handle_echo(reader, writer):
+    data = await reader.read(100)
+    message = data.decode()
+    addr = writer.get_extra_info('peername')
+
+    print(f"Received {message!r} from {addr!r}")
+
+    print(f"Send: {message!r}")
+    writer.write(data)
+    await writer.drain()
+
+    print("Close the connection")
+    writer.close()
+    await writer.wait_closed()
+
+async def main():
+    server = await asyncio.start_server(
+        handle_echo, '127.0.0.1', 8888)
+
+    addrs = ', '.join(str(sock.getsockname()) for sock in server.sockets)
+    print(f'Serving on {addrs}')
+
+    async with server:
+        await server.serve_forever()
+
+asyncio.run(main())
+

See also

The TCP echo server protocol example uses the loop.create_server() method.

Get HTTP headers

Simple example querying HTTP headers of the URL passed on the command line:

import asyncio
+import urllib.parse
+import sys
+
+async def print_http_headers(url):
+    url = urllib.parse.urlsplit(url)
+    if url.scheme == 'https':
+        reader, writer = await asyncio.open_connection(
+            url.hostname, 443, ssl=True)
+    else:
+        reader, writer = await asyncio.open_connection(
+            url.hostname, 80)
+
+    query = (
+        f"HEAD {url.path or '/'} HTTP/1.0\r\n"
+        f"Host: {url.hostname}\r\n"
+        f"\r\n"
+    )
+
+    writer.write(query.encode('latin-1'))
+    while True:
+        line = await reader.readline()
+        if not line:
+            break
+
+        line = line.decode('latin1').rstrip()
+        if line:
+            print(f'HTTP header> {line}')
+
+    # Ignore the body, close the socket
+    writer.close()
+    await writer.wait_closed()
+
+url = sys.argv[1]
+asyncio.run(print_http_headers(url))
+

Usage:

python example.py http://example.com/path/page.html
+

or with HTTPS:

python example.py https://example.com/path/page.html
+

Register an open socket to wait for data using streams

Coroutine waiting until a socket receives data using the open_connection() function:

import asyncio
+import socket
+
+async def wait_for_data():
+    # Get a reference to the current event loop because
+    # we want to access low-level APIs.
+    loop = asyncio.get_running_loop()
+
+    # Create a pair of connected sockets.
+    rsock, wsock = socket.socketpair()
+
+    # Register the open socket to wait for data.
+    reader, writer = await asyncio.open_connection(sock=rsock)
+
+    # Simulate the reception of data from the network
+    loop.call_soon(wsock.send, 'abc'.encode())
+
+    # Wait for data
+    data = await reader.read(100)
+
+    # Got data, we are done: close the socket
+    print("Received:", data.decode())
+    writer.close()
+    await writer.wait_closed()
+
+    # Close the second socket
+    wsock.close()
+
+asyncio.run(wait_for_data())
+

See also

The register an open socket to wait for data using a protocol example uses a low-level protocol and the loop.create_connection() method.

The watch a file descriptor for read events example uses the low-level loop.add_reader() method to watch a file descriptor.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-stream.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-subprocess.html b/devdocs/python~3.12/library%2Fasyncio-subprocess.html new file mode 100644 index 00000000..f3b26006 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-subprocess.html @@ -0,0 +1,102 @@ +

Subprocesses

Source code: Lib/asyncio/subprocess.py, Lib/asyncio/base_subprocess.py

This section describes high-level async/await asyncio APIs to create and manage subprocesses.

Here’s an example of how asyncio can run a shell command and obtain its result:

import asyncio
+
+async def run(cmd):
+    proc = await asyncio.create_subprocess_shell(
+        cmd,
+        stdout=asyncio.subprocess.PIPE,
+        stderr=asyncio.subprocess.PIPE)
+
+    stdout, stderr = await proc.communicate()
+
+    print(f'[{cmd!r} exited with {proc.returncode}]')
+    if stdout:
+        print(f'[stdout]\n{stdout.decode()}')
+    if stderr:
+        print(f'[stderr]\n{stderr.decode()}')
+
+asyncio.run(run('ls /zzz'))
+

will print:

['ls /zzz' exited with 1]
+[stderr]
+ls: /zzz: No such file or directory
+

Because all asyncio subprocess functions are asynchronous and asyncio provides many tools to work with such functions, it is easy to execute and monitor multiple subprocesses in parallel. It is indeed trivial to modify the above example to run several commands simultaneously:

async def main():
+    await asyncio.gather(
+        run('ls /zzz'),
+        run('sleep 1; echo "hello"'))
+
+asyncio.run(main())
+

See also the Examples subsection.

Creating Subprocesses

+coroutine asyncio.create_subprocess_exec(program, *args, stdin=None, stdout=None, stderr=None, limit=None, **kwds)
+

Create a subprocess.

The limit argument sets the buffer limit for StreamReader wrappers for Process.stdout and Process.stderr (if subprocess.PIPE is passed to stdout and stderr arguments).

Return a Process instance.

See the documentation of loop.subprocess_exec() for other parameters.

Changed in version 3.10: Removed the loop parameter.

+
+coroutine asyncio.create_subprocess_shell(cmd, stdin=None, stdout=None, stderr=None, limit=None, **kwds)
+

Run the cmd shell command.

The limit argument sets the buffer limit for StreamReader wrappers for Process.stdout and Process.stderr (if subprocess.PIPE is passed to stdout and stderr arguments).

Return a Process instance.

See the documentation of loop.subprocess_shell() for other parameters.

Important

It is the application’s responsibility to ensure that all whitespace and special characters are quoted appropriately to avoid shell injection vulnerabilities. The shlex.quote() function can be used to properly escape whitespace and special shell characters in strings that are going to be used to construct shell commands.

Changed in version 3.10: Removed the loop parameter.

+

Note

Subprocesses are available for Windows if a ProactorEventLoop is used. See Subprocess Support on Windows for details.

See also

asyncio also has the following low-level APIs to work with subprocesses: loop.subprocess_exec(), loop.subprocess_shell(), loop.connect_read_pipe(), loop.connect_write_pipe(), as well as the Subprocess Transports and Subprocess Protocols.

Constants

+asyncio.subprocess.PIPE
+

Can be passed to the stdin, stdout or stderr parameters.

If PIPE is passed to stdin argument, the Process.stdin attribute will point to a StreamWriter instance.

If PIPE is passed to stdout or stderr arguments, the Process.stdout and Process.stderr attributes will point to StreamReader instances.

+
+asyncio.subprocess.STDOUT
+

Special value that can be used as the stderr argument and indicates that standard error should be redirected into standard output.

+
+asyncio.subprocess.DEVNULL
+

Special value that can be used as the stdin, stdout or stderr argument to process creation functions. It indicates that the special file os.devnull will be used for the corresponding subprocess stream.

+

Interacting with Subprocesses

Both create_subprocess_exec() and create_subprocess_shell() functions return instances of the Process class. Process is a high-level wrapper that allows communicating with subprocesses and watching for their completion.

+class asyncio.subprocess.Process
+

An object that wraps OS processes created by the create_subprocess_exec() and create_subprocess_shell() functions.

This class is designed to have a similar API to the subprocess.Popen class, but there are some notable differences:

This class is not thread safe.

See also the Subprocess and Threads section.

+coroutine wait()
+

Wait for the child process to terminate.

Set and return the returncode attribute.

Note

This method can deadlock when using stdout=PIPE or stderr=PIPE and the child process generates so much output that it blocks waiting for the OS pipe buffer to accept more data. Use the communicate() method when using pipes to avoid this condition.

+
+coroutine communicate(input=None)
+

Interact with process:

  1. send data to stdin (if input is not None);
  2. closes stdin;
  3. read data from stdout and stderr, until EOF is reached;
  4. wait for process to terminate.

The optional input argument is the data (bytes object) that will be sent to the child process.

Return a tuple (stdout_data, stderr_data).

If either BrokenPipeError or ConnectionResetError exception is raised when writing input into stdin, the exception is ignored. This condition occurs when the process exits before all data are written into stdin.

If it is desired to send data to the process’ stdin, the process needs to be created with stdin=PIPE. Similarly, to get anything other than None in the result tuple, the process has to be created with stdout=PIPE and/or stderr=PIPE arguments.

Note, that the data read is buffered in memory, so do not use this method if the data size is large or unlimited.

Changed in version 3.12: stdin gets closed when input=None too.

+
+send_signal(signal)
+

Sends the signal signal to the child process.

Note

On Windows, SIGTERM is an alias for terminate(). CTRL_C_EVENT and CTRL_BREAK_EVENT can be sent to processes started with a creationflags parameter which includes CREATE_NEW_PROCESS_GROUP.

+
+terminate()
+

Stop the child process.

On POSIX systems this method sends signal.SIGTERM to the child process.

On Windows the Win32 API function TerminateProcess() is called to stop the child process.

+
+kill()
+

Kill the child process.

On POSIX systems this method sends SIGKILL to the child process.

On Windows this method is an alias for terminate().

+
+stdin
+

Standard input stream (StreamWriter) or None if the process was created with stdin=None.

+
+stdout
+

Standard output stream (StreamReader) or None if the process was created with stdout=None.

+
+stderr
+

Standard error stream (StreamReader) or None if the process was created with stderr=None.

+

Warning

Use the communicate() method rather than process.stdin.write(), await process.stdout.read() or await process.stderr.read(). This avoids deadlocks due to streams pausing reading or writing and blocking the child process.

+pid
+

Process identification number (PID).

Note that for processes created by the create_subprocess_shell() function, this attribute is the PID of the spawned shell.

+
+returncode
+

Return code of the process when it exits.

A None value indicates that the process has not terminated yet.

A negative value -N indicates that the child was terminated by signal N (POSIX only).

+
+

Subprocess and Threads

Standard asyncio event loop supports running subprocesses from different threads by default.

On Windows subprocesses are provided by ProactorEventLoop only (default), SelectorEventLoop has no subprocess support.

On UNIX child watchers are used for subprocess finish waiting, see Process Watchers for more info.

Changed in version 3.8: UNIX switched to use ThreadedChildWatcher for spawning subprocesses from different threads without any limitation.

Spawning a subprocess with inactive current child watcher raises RuntimeError.

Note that alternative event loop implementations might have own limitations; please refer to their documentation.

See also

The Concurrency and multithreading in asyncio section.

Examples

An example using the Process class to control a subprocess and the StreamReader class to read from its standard output.

The subprocess is created by the create_subprocess_exec() function:

import asyncio
+import sys
+
+async def get_date():
+    code = 'import datetime; print(datetime.datetime.now())'
+
+    # Create the subprocess; redirect the standard output
+    # into a pipe.
+    proc = await asyncio.create_subprocess_exec(
+        sys.executable, '-c', code,
+        stdout=asyncio.subprocess.PIPE)
+
+    # Read one line of output.
+    data = await proc.stdout.readline()
+    line = data.decode('ascii').rstrip()
+
+    # Wait for the subprocess exit.
+    await proc.wait()
+    return line
+
+date = asyncio.run(get_date())
+print(f"Current date: {date}")
+

See also the same example written using low-level APIs.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-subprocess.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-sync.html b/devdocs/python~3.12/library%2Fasyncio-sync.html new file mode 100644 index 00000000..e32b08f5 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-sync.html @@ -0,0 +1,185 @@ +

Synchronization Primitives

Source code: Lib/asyncio/locks.py

asyncio synchronization primitives are designed to be similar to those of the threading module with two important caveats:

asyncio has the following basic synchronization primitives:

Lock

+class asyncio.Lock
+

Implements a mutex lock for asyncio tasks. Not thread-safe.

An asyncio lock can be used to guarantee exclusive access to a shared resource.

The preferred way to use a Lock is an async with statement:

lock = asyncio.Lock()
+
+# ... later
+async with lock:
+    # access shared state
+

which is equivalent to:

lock = asyncio.Lock()
+
+# ... later
+await lock.acquire()
+try:
+    # access shared state
+finally:
+    lock.release()
+

Changed in version 3.10: Removed the loop parameter.

+coroutine acquire()
+

Acquire the lock.

This method waits until the lock is unlocked, sets it to locked and returns True.

When more than one coroutine is blocked in acquire() waiting for the lock to be unlocked, only one coroutine eventually proceeds.

Acquiring a lock is fair: the coroutine that proceeds will be the first coroutine that started waiting on the lock.

+
+release()
+

Release the lock.

When the lock is locked, reset it to unlocked and return.

If the lock is unlocked, a RuntimeError is raised.

+
+locked()
+

Return True if the lock is locked.

+
+

Event

+class asyncio.Event
+

An event object. Not thread-safe.

An asyncio event can be used to notify multiple asyncio tasks that some event has happened.

An Event object manages an internal flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is set to true. The flag is set to false initially.

Changed in version 3.10: Removed the loop parameter.

Example:

async def waiter(event):
+    print('waiting for it ...')
+    await event.wait()
+    print('... got it!')
+
+async def main():
+    # Create an Event object.
+    event = asyncio.Event()
+
+    # Spawn a Task to wait until 'event' is set.
+    waiter_task = asyncio.create_task(waiter(event))
+
+    # Sleep for 1 second and set the event.
+    await asyncio.sleep(1)
+    event.set()
+
+    # Wait until the waiter task is finished.
+    await waiter_task
+
+asyncio.run(main())
+
+coroutine wait()
+

Wait until the event is set.

If the event is set, return True immediately. Otherwise block until another task calls set().

+
+set()
+

Set the event.

All tasks waiting for event to be set will be immediately awakened.

+
+clear()
+

Clear (unset) the event.

Tasks awaiting on wait() will now block until the set() method is called again.

+
+is_set()
+

Return True if the event is set.

+
+

Condition

+class asyncio.Condition(lock=None)
+

A Condition object. Not thread-safe.

An asyncio condition primitive can be used by a task to wait for some event to happen and then get exclusive access to a shared resource.

In essence, a Condition object combines the functionality of an Event and a Lock. It is possible to have multiple Condition objects share one Lock, which allows coordinating exclusive access to a shared resource between different tasks interested in particular states of that shared resource.

The optional lock argument must be a Lock object or None. In the latter case a new Lock object is created automatically.

Changed in version 3.10: Removed the loop parameter.

The preferred way to use a Condition is an async with statement:

cond = asyncio.Condition()
+
+# ... later
+async with cond:
+    await cond.wait()
+

which is equivalent to:

cond = asyncio.Condition()
+
+# ... later
+await cond.acquire()
+try:
+    await cond.wait()
+finally:
+    cond.release()
+
+coroutine acquire()
+

Acquire the underlying lock.

This method waits until the underlying lock is unlocked, sets it to locked and returns True.

+
+notify(n=1)
+

Wake up at most n tasks (1 by default) waiting on this condition. The method is no-op if no tasks are waiting.

The lock must be acquired before this method is called and released shortly after. If called with an unlocked lock a RuntimeError error is raised.

+
+locked()
+

Return True if the underlying lock is acquired.

+
+notify_all()
+

Wake up all tasks waiting on this condition.

This method acts like notify(), but wakes up all waiting tasks.

The lock must be acquired before this method is called and released shortly after. If called with an unlocked lock a RuntimeError error is raised.

+
+release()
+

Release the underlying lock.

When invoked on an unlocked lock, a RuntimeError is raised.

+
+coroutine wait()
+

Wait until notified.

If the calling task has not acquired the lock when this method is called, a RuntimeError is raised.

This method releases the underlying lock, and then blocks until it is awakened by a notify() or notify_all() call. Once awakened, the Condition re-acquires its lock and this method returns True.

+
+coroutine wait_for(predicate)
+

Wait until a predicate becomes true.

The predicate must be a callable which result will be interpreted as a boolean value. The final value is the return value.

+
+

Semaphore

+class asyncio.Semaphore(value=1)
+

A Semaphore object. Not thread-safe.

A semaphore manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The counter can never go below zero; when acquire() finds that it is zero, it blocks, waiting until some task calls release().

The optional value argument gives the initial value for the internal counter (1 by default). If the given value is less than 0 a ValueError is raised.

Changed in version 3.10: Removed the loop parameter.

The preferred way to use a Semaphore is an async with statement:

sem = asyncio.Semaphore(10)
+
+# ... later
+async with sem:
+    # work with shared resource
+

which is equivalent to:

sem = asyncio.Semaphore(10)
+
+# ... later
+await sem.acquire()
+try:
+    # work with shared resource
+finally:
+    sem.release()
+
+coroutine acquire()
+

Acquire a semaphore.

If the internal counter is greater than zero, decrement it by one and return True immediately. If it is zero, wait until a release() is called and return True.

+
+locked()
+

Returns True if semaphore can not be acquired immediately.

+
+release()
+

Release a semaphore, incrementing the internal counter by one. Can wake up a task waiting to acquire the semaphore.

Unlike BoundedSemaphore, Semaphore allows making more release() calls than acquire() calls.

+
+

BoundedSemaphore

+class asyncio.BoundedSemaphore(value=1)
+

A bounded semaphore object. Not thread-safe.

Bounded Semaphore is a version of Semaphore that raises a ValueError in release() if it increases the internal counter above the initial value.

Changed in version 3.10: Removed the loop parameter.

+

Barrier

+class asyncio.Barrier(parties)
+

A barrier object. Not thread-safe.

A barrier is a simple synchronization primitive that allows to block until parties number of tasks are waiting on it. Tasks can wait on the wait() method and would be blocked until the specified number of tasks end up waiting on wait(). At that point all of the waiting tasks would unblock simultaneously.

async with can be used as an alternative to awaiting on wait().

The barrier can be reused any number of times.

Example:

async def example_barrier():
+   # barrier with 3 parties
+   b = asyncio.Barrier(3)
+
+   # create 2 new waiting tasks
+   asyncio.create_task(b.wait())
+   asyncio.create_task(b.wait())
+
+   await asyncio.sleep(0)
+   print(b)
+
+   # The third .wait() call passes the barrier
+   await b.wait()
+   print(b)
+   print("barrier passed")
+
+   await asyncio.sleep(0)
+   print(b)
+
+asyncio.run(example_barrier())
+

Result of this example is:

<asyncio.locks.Barrier object at 0x... [filling, waiters:2/3]>
+<asyncio.locks.Barrier object at 0x... [draining, waiters:0/3]>
+barrier passed
+<asyncio.locks.Barrier object at 0x... [filling, waiters:0/3]>
+

New in version 3.11.

+coroutine wait()
+

Pass the barrier. When all the tasks party to the barrier have called this function, they are all unblocked simultaneously.

When a waiting or blocked task in the barrier is cancelled, this task exits the barrier which stays in the same state. If the state of the barrier is “filling”, the number of waiting task decreases by 1.

The return value is an integer in the range of 0 to parties-1, different for each task. This can be used to select a task to do some special housekeeping, e.g.:

...
+async with barrier as position:
+   if position == 0:
+      # Only one task prints this
+      print('End of *draining phase*')
+

This method may raise a BrokenBarrierError exception if the barrier is broken or reset while a task is waiting. It could raise a CancelledError if a task is cancelled.

+
+coroutine reset()
+

Return the barrier to the default, empty state. Any tasks waiting on it will receive the BrokenBarrierError exception.

If a barrier is broken it may be better to just leave it and create a new one.

+
+coroutine abort()
+

Put the barrier into a broken state. This causes any active or future calls to wait() to fail with the BrokenBarrierError. Use this for example if one of the tasks needs to abort, to avoid infinite waiting tasks.

+
+parties
+

The number of tasks required to pass the barrier.

+
+n_waiting
+

The number of tasks currently waiting in the barrier while filling.

+
+broken
+

A boolean that is True if the barrier is in the broken state.

+
+
+exception asyncio.BrokenBarrierError
+

This exception, a subclass of RuntimeError, is raised when the Barrier object is reset or broken.

+

Changed in version 3.9: Acquiring a lock using await lock or yield from lock and/or with statement (with await lock, with (yield from +lock)) was removed. Use async with lock instead.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-sync.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio-task.html b/devdocs/python~3.12/library%2Fasyncio-task.html new file mode 100644 index 00000000..3c025762 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio-task.html @@ -0,0 +1,442 @@ +

Coroutines and Tasks

This section outlines high-level asyncio APIs to work with coroutines and Tasks.

Coroutines

Source code: Lib/asyncio/coroutines.py

Coroutines declared with the async/await syntax is the preferred way of writing asyncio applications. For example, the following snippet of code prints “hello”, waits 1 second, and then prints “world”:

>>> import asyncio
+
+>>> async def main():
+...     print('hello')
+...     await asyncio.sleep(1)
+...     print('world')
+
+>>> asyncio.run(main())
+hello
+world
+

Note that simply calling a coroutine will not schedule it to be executed:

>>> main()
+<coroutine object main at 0x1053bb7c8>
+

To actually run a coroutine, asyncio provides the following mechanisms:

  • The asyncio.run() function to run the top-level entry point “main()” function (see the above example.)
  • +

    Awaiting on a coroutine. The following snippet of code will print “hello” after waiting for 1 second, and then print “world” after waiting for another 2 seconds:

    import asyncio
+import time
+
+async def say_after(delay, what):
+    await asyncio.sleep(delay)
+    print(what)
+
+async def main():
+    print(f"started at {time.strftime('%X')}")
+
+    await say_after(1, 'hello')
+    await say_after(2, 'world')
+
+    print(f"finished at {time.strftime('%X')}")
+
+asyncio.run(main())
+

    Expected output:

    started at 17:13:52
+hello
+world
+finished at 17:13:55
+
  • +

    The asyncio.create_task() function to run coroutines concurrently as asyncio Tasks.

    Let’s modify the above example and run two say_after coroutines concurrently:

    async def main():
+    task1 = asyncio.create_task(
+        say_after(1, 'hello'))
+
+    task2 = asyncio.create_task(
+        say_after(2, 'world'))
+
+    print(f"started at {time.strftime('%X')}")
+
+    # Wait until both tasks are completed (should take
+    # around 2 seconds.)
+    await task1
+    await task2
+
+    print(f"finished at {time.strftime('%X')}")
+

    Note that expected output now shows that the snippet runs 1 second faster than before:

    started at 17:14:32
+hello
+world
+finished at 17:14:34
+
  • +

    The asyncio.TaskGroup class provides a more modern alternative to create_task(). Using this API, the last example becomes:

    async def main():
+    async with asyncio.TaskGroup() as tg:
+        task1 = tg.create_task(
+            say_after(1, 'hello'))
+
+        task2 = tg.create_task(
+            say_after(2, 'world'))
+
+        print(f"started at {time.strftime('%X')}")
+
+    # The await is implicit when the context manager exits.
+
+    print(f"finished at {time.strftime('%X')}")
+

    The timing and output should be the same as for the previous version.

    New in version 3.11: asyncio.TaskGroup.

Awaitables

We say that an object is an awaitable object if it can be used in an await expression. Many asyncio APIs are designed to accept awaitables.

There are three main types of awaitable objects: coroutines, Tasks, and Futures.

Coroutines

Python coroutines are awaitables and therefore can be awaited from other coroutines:

import asyncio
+
+async def nested():
+    return 42
+
+async def main():
+    # Nothing happens if we just call "nested()".
+    # A coroutine object is created but not awaited,
+    # so it *won't run at all*.
+    nested()
+
+    # Let's do it differently now and await it:
+    print(await nested())  # will print "42".
+
+asyncio.run(main())
+

Important

In this documentation the term “coroutine” can be used for two closely related concepts:

  • a coroutine function: an async def function;
  • a coroutine object: an object returned by calling a coroutine function.

Tasks

Tasks are used to schedule coroutines concurrently.

When a coroutine is wrapped into a Task with functions like asyncio.create_task() the coroutine is automatically scheduled to run soon:

import asyncio
+
+async def nested():
+    return 42
+
+async def main():
+    # Schedule nested() to run soon concurrently
+    # with "main()".
+    task = asyncio.create_task(nested())
+
+    # "task" can now be used to cancel "nested()", or
+    # can simply be awaited to wait until it is complete:
+    await task
+
+asyncio.run(main())
+

Futures

A Future is a special low-level awaitable object that represents an eventual result of an asynchronous operation.

When a Future object is awaited it means that the coroutine will wait until the Future is resolved in some other place.

Future objects in asyncio are needed to allow callback-based code to be used with async/await.

Normally there is no need to create Future objects at the application level code.

Future objects, sometimes exposed by libraries and some asyncio APIs, can be awaited:

async def main():
+    await function_that_returns_a_future_object()
+
+    # this is also valid:
+    await asyncio.gather(
+        function_that_returns_a_future_object(),
+        some_python_coroutine()
+    )
+

A good example of a low-level function that returns a Future object is loop.run_in_executor().

Creating Tasks

Source code: Lib/asyncio/tasks.py

+asyncio.create_task(coro, *, name=None, context=None)
+

Wrap the coro coroutine into a Task and schedule its execution. Return the Task object.

If name is not None, it is set as the name of the task using Task.set_name().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the coro to run in. The current context copy is created when no context is provided.

The task is executed in the loop returned by get_running_loop(), RuntimeError is raised if there is no running loop in current thread.

Note

asyncio.TaskGroup.create_task() is a new alternative leveraging structural concurrency; it allows for waiting for a group of related tasks with strong safety guarantees.

Important

Save a reference to the result of this function, to avoid a task disappearing mid-execution. The event loop only keeps weak references to tasks. A task that isn’t referenced elsewhere may get garbage collected at any time, even before it’s done. For reliable “fire-and-forget” background tasks, gather them in a collection:

background_tasks = set()
+
+for i in range(10):
+    task = asyncio.create_task(some_coro(param=i))
+
+    # Add task to the set. This creates a strong reference.
+    background_tasks.add(task)
+
+    # To prevent keeping references to finished tasks forever,
+    # make each task remove its own reference from the set after
+    # completion:
+    task.add_done_callback(background_tasks.discard)
+

New in version 3.7.

Changed in version 3.8: Added the name parameter.

Changed in version 3.11: Added the context parameter.

+

Task Cancellation

Tasks can easily and safely be cancelled. When a task is cancelled, asyncio.CancelledError will be raised in the task at the next opportunity.

It is recommended that coroutines use try/finally blocks to robustly perform clean-up logic. In case asyncio.CancelledError is explicitly caught, it should generally be propagated when clean-up is complete. asyncio.CancelledError directly subclasses BaseException so most code will not need to be aware of it.

The asyncio components that enable structured concurrency, like asyncio.TaskGroup and asyncio.timeout(), are implemented using cancellation internally and might misbehave if a coroutine swallows asyncio.CancelledError. Similarly, user code should not generally call uncancel. However, in cases when suppressing asyncio.CancelledError is truly desired, it is necessary to also call uncancel() to completely remove the cancellation state.

Task Groups

Task groups combine a task creation API with a convenient and reliable way to wait for all tasks in the group to finish.

+class asyncio.TaskGroup
+

An asynchronous context manager holding a group of tasks. Tasks can be added to the group using create_task(). All tasks are awaited when the context manager exits.

New in version 3.11.

+create_task(coro, *, name=None, context=None)
+

Create a task in this task group. The signature matches that of asyncio.create_task().

+
+

Example:

async def main():
+    async with asyncio.TaskGroup() as tg:
+        task1 = tg.create_task(some_coro(...))
+        task2 = tg.create_task(another_coro(...))
+    print(f"Both tasks have completed now: {task1.result()}, {task2.result()}")
+

The async with statement will wait for all tasks in the group to finish. While waiting, new tasks may still be added to the group (for example, by passing tg into one of the coroutines and calling tg.create_task() in that coroutine). Once the last task has finished and the async with block is exited, no new tasks may be added to the group.

The first time any of the tasks belonging to the group fails with an exception other than asyncio.CancelledError, the remaining tasks in the group are cancelled. No further tasks can then be added to the group. At this point, if the body of the async with statement is still active (i.e., __aexit__() hasn’t been called yet), the task directly containing the async with statement is also cancelled. The resulting asyncio.CancelledError will interrupt an await, but it will not bubble out of the containing async with statement.

Once all tasks have finished, if any tasks have failed with an exception other than asyncio.CancelledError, those exceptions are combined in an ExceptionGroup or BaseExceptionGroup (as appropriate; see their documentation) which is then raised.

Two base exceptions are treated specially: If any task fails with KeyboardInterrupt or SystemExit, the task group still cancels the remaining tasks and waits for them, but then the initial KeyboardInterrupt or SystemExit is re-raised instead of ExceptionGroup or BaseExceptionGroup.

If the body of the async with statement exits with an exception (so __aexit__() is called with an exception set), this is treated the same as if one of the tasks failed: the remaining tasks are cancelled and then waited for, and non-cancellation exceptions are grouped into an exception group and raised. The exception passed into __aexit__(), unless it is asyncio.CancelledError, is also included in the exception group. The same special case is made for KeyboardInterrupt and SystemExit as in the previous paragraph.

Sleeping

+coroutine asyncio.sleep(delay, result=None)
+

Block for delay seconds.

If result is provided, it is returned to the caller when the coroutine completes.

sleep() always suspends the current task, allowing other tasks to run.

Setting the delay to 0 provides an optimized path to allow other tasks to run. This can be used by long-running functions to avoid blocking the event loop for the full duration of the function call.

Example of coroutine displaying the current date every second for 5 seconds:

import asyncio
+import datetime
+
+async def display_date():
+    loop = asyncio.get_running_loop()
+    end_time = loop.time() + 5.0
+    while True:
+        print(datetime.datetime.now())
+        if (loop.time() + 1.0) >= end_time:
+            break
+        await asyncio.sleep(1)
+
+asyncio.run(display_date())
+

Changed in version 3.10: Removed the loop parameter.

+

Running Tasks Concurrently

+awaitable asyncio.gather(*aws, return_exceptions=False)
+

Run awaitable objects in the aws sequence concurrently.

If any awaitable in aws is a coroutine, it is automatically scheduled as a Task.

If all awaitables are completed successfully, the result is an aggregate list of returned values. The order of result values corresponds to the order of awaitables in aws.

If return_exceptions is False (default), the first raised exception is immediately propagated to the task that awaits on gather(). Other awaitables in the aws sequence won’t be cancelled and will continue to run.

If return_exceptions is True, exceptions are treated the same as successful results, and aggregated in the result list.

If gather() is cancelled, all submitted awaitables (that have not completed yet) are also cancelled.

If any Task or Future from the aws sequence is cancelled, it is treated as if it raised CancelledError – the gather() call is not cancelled in this case. This is to prevent the cancellation of one submitted Task/Future to cause other Tasks/Futures to be cancelled.

Note

A new alternative to create and run tasks concurrently and wait for their completion is asyncio.TaskGroup. TaskGroup provides stronger safety guarantees than gather for scheduling a nesting of subtasks: if a task (or a subtask, a task scheduled by a task) raises an exception, TaskGroup will, while gather will not, cancel the remaining scheduled tasks).

Example:

import asyncio
+
+async def factorial(name, number):
+    f = 1
+    for i in range(2, number + 1):
+        print(f"Task {name}: Compute factorial({number}), currently i={i}...")
+        await asyncio.sleep(1)
+        f *= i
+    print(f"Task {name}: factorial({number}) = {f}")
+    return f
+
+async def main():
+    # Schedule three calls *concurrently*:
+    L = await asyncio.gather(
+        factorial("A", 2),
+        factorial("B", 3),
+        factorial("C", 4),
+    )
+    print(L)
+
+asyncio.run(main())
+
+# Expected output:
+#
+#     Task A: Compute factorial(2), currently i=2...
+#     Task B: Compute factorial(3), currently i=2...
+#     Task C: Compute factorial(4), currently i=2...
+#     Task A: factorial(2) = 2
+#     Task B: Compute factorial(3), currently i=3...
+#     Task C: Compute factorial(4), currently i=3...
+#     Task B: factorial(3) = 6
+#     Task C: Compute factorial(4), currently i=4...
+#     Task C: factorial(4) = 24
+#     [2, 6, 24]
+

Note

If return_exceptions is False, cancelling gather() after it has been marked done won’t cancel any submitted awaitables. For instance, gather can be marked done after propagating an exception to the caller, therefore, calling gather.cancel() after catching an exception (raised by one of the awaitables) from gather won’t cancel any other awaitables.

Changed in version 3.7: If the gather itself is cancelled, the cancellation is propagated regardless of return_exceptions.

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if no positional arguments are provided or not all positional arguments are Future-like objects and there is no running event loop.

+

Eager Task Factory

+asyncio.eager_task_factory(loop, coro, *, name=None, context=None)
+

A task factory for eager task execution.

When using this factory (via loop.set_task_factory(asyncio.eager_task_factory)), coroutines begin execution synchronously during Task construction. Tasks are only scheduled on the event loop if they block. This can be a performance improvement as the overhead of loop scheduling is avoided for coroutines that complete synchronously.

A common example where this is beneficial is coroutines which employ caching or memoization to avoid actual I/O when possible.

Note

Immediate execution of the coroutine is a semantic change. If the coroutine returns or raises, the task is never scheduled to the event loop. If the coroutine execution blocks, the task is scheduled to the event loop. This change may introduce behavior changes to existing applications. For example, the application’s task execution order is likely to change.

New in version 3.12.

+
+asyncio.create_eager_task_factory(custom_task_constructor)
+

Create an eager task factory, similar to eager_task_factory(), using the provided custom_task_constructor when creating a new task instead of the default Task.

custom_task_constructor must be a callable with the signature matching the signature of Task.__init__. The callable must return a asyncio.Task-compatible object.

This function returns a callable intended to be used as a task factory of an event loop via loop.set_task_factory(factory)).

New in version 3.12.

+

Shielding From Cancellation

+awaitable asyncio.shield(aw)
+

Protect an awaitable object from being cancelled.

If aw is a coroutine it is automatically scheduled as a Task.

The statement:

task = asyncio.create_task(something())
+res = await shield(task)
+

is equivalent to:

res = await something()
+

except that if the coroutine containing it is cancelled, the Task running in something() is not cancelled. From the point of view of something(), the cancellation did not happen. Although its caller is still cancelled, so the “await” expression still raises a CancelledError.

If something() is cancelled by other means (i.e. from within itself) that would also cancel shield().

If it is desired to completely ignore cancellation (not recommended) the shield() function should be combined with a try/except clause, as follows:

task = asyncio.create_task(something())
+try:
+    res = await shield(task)
+except CancelledError:
+    res = None
+

Important

Save a reference to tasks passed to this function, to avoid a task disappearing mid-execution. The event loop only keeps weak references to tasks. A task that isn’t referenced elsewhere may get garbage collected at any time, even before it’s done.

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if aw is not Future-like object and there is no running event loop.

+

Timeouts

+asyncio.timeout(delay)
+

Return an asynchronous context manager that can be used to limit the amount of time spent waiting on something.

delay can either be None, or a float/int number of seconds to wait. If delay is None, no time limit will be applied; this can be useful if the delay is unknown when the context manager is created.

In either case, the context manager can be rescheduled after creation using Timeout.reschedule().

Example:

async def main():
+    async with asyncio.timeout(10):
+        await long_running_task()
+

If long_running_task takes more than 10 seconds to complete, the context manager will cancel the current task and handle the resulting asyncio.CancelledError internally, transforming it into a TimeoutError which can be caught and handled.

Note

The asyncio.timeout() context manager is what transforms the asyncio.CancelledError into a TimeoutError, which means the TimeoutError can only be caught outside of the context manager.

Example of catching TimeoutError:

async def main():
+    try:
+        async with asyncio.timeout(10):
+            await long_running_task()
+    except TimeoutError:
+        print("The long operation timed out, but we've handled it.")
+
+    print("This statement will run regardless.")
+

The context manager produced by asyncio.timeout() can be rescheduled to a different deadline and inspected.

+class asyncio.Timeout(when)
+

An asynchronous context manager for cancelling overdue coroutines.

when should be an absolute time at which the context should time out, as measured by the event loop’s clock:

  • If when is None, the timeout will never trigger.
  • If when < loop.time(), the timeout will trigger on the next iteration of the event loop.
+when() → float | None
+

Return the current deadline, or None if the current deadline is not set.

+
+reschedule(when: float | None)
+

Reschedule the timeout.

+
+expired() → bool
+

Return whether the context manager has exceeded its deadline (expired).

+
+

Example:

async def main():
+    try:
+        # We do not know the timeout when starting, so we pass ``None``.
+        async with asyncio.timeout(None) as cm:
+            # We know the timeout now, so we reschedule it.
+            new_deadline = get_running_loop().time() + 10
+            cm.reschedule(new_deadline)
+
+            await long_running_task()
+    except TimeoutError:
+        pass
+
+    if cm.expired():
+        print("Looks like we haven't finished on time.")
+

Timeout context managers can be safely nested.

New in version 3.11.

+
+asyncio.timeout_at(when)
+

Similar to asyncio.timeout(), except when is the absolute time to stop waiting, or None.

Example:

async def main():
+    loop = get_running_loop()
+    deadline = loop.time() + 20
+    try:
+        async with asyncio.timeout_at(deadline):
+            await long_running_task()
+    except TimeoutError:
+        print("The long operation timed out, but we've handled it.")
+
+    print("This statement will run regardless.")
+

New in version 3.11.

+
+coroutine asyncio.wait_for(aw, timeout)
+

Wait for the aw awaitable to complete with a timeout.

If aw is a coroutine it is automatically scheduled as a Task.

timeout can either be None or a float or int number of seconds to wait for. If timeout is None, block until the future completes.

If a timeout occurs, it cancels the task and raises TimeoutError.

To avoid the task cancellation, wrap it in shield().

The function will wait until the future is actually cancelled, so the total wait time may exceed the timeout. If an exception happens during cancellation, it is propagated.

If the wait is cancelled, the future aw is also cancelled.

Example:

async def eternity():
+    # Sleep for one hour
+    await asyncio.sleep(3600)
+    print('yay!')
+
+async def main():
+    # Wait for at most 1 second
+    try:
+        await asyncio.wait_for(eternity(), timeout=1.0)
+    except TimeoutError:
+        print('timeout!')
+
+asyncio.run(main())
+
+# Expected output:
+#
+#     timeout!
+

Changed in version 3.7: When aw is cancelled due to a timeout, wait_for waits for aw to be cancelled. Previously, it raised TimeoutError immediately.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Raises TimeoutError instead of asyncio.TimeoutError.

+

Waiting Primitives

+coroutine asyncio.wait(aws, *, timeout=None, return_when=ALL_COMPLETED)
+

Run Future and Task instances in the aws iterable concurrently and block until the condition specified by return_when.

The aws iterable must not be empty.

Returns two sets of Tasks/Futures: (done, pending).

Usage:

done, pending = await asyncio.wait(aws)
+

timeout (a float or int), if specified, can be used to control the maximum number of seconds to wait before returning.

Note that this function does not raise TimeoutError. Futures or Tasks that aren’t done when the timeout occurs are simply returned in the second set.

return_when indicates when this function should return. It must be one of the following constants:

+ + + +

Constant

Description

FIRST_COMPLETED

The function will return when any future finishes or is cancelled.

FIRST_EXCEPTION

The function will return when any future finishes by raising an exception. If no future raises an exception then it is equivalent to ALL_COMPLETED.

ALL_COMPLETED

The function will return when all futures finish or are cancelled.

Unlike wait_for(), wait() does not cancel the futures when a timeout occurs.

Changed in version 3.10: Removed the loop parameter.

Changed in version 3.11: Passing coroutine objects to wait() directly is forbidden.

Changed in version 3.12: Added support for generators yielding tasks.

+
+asyncio.as_completed(aws, *, timeout=None)
+

Run awaitable objects in the aws iterable concurrently. Return an iterator of coroutines. Each coroutine returned can be awaited to get the earliest next result from the iterable of the remaining awaitables.

Raises TimeoutError if the timeout occurs before all Futures are done.

Example:

for coro in as_completed(aws):
+    earliest_result = await coro
+    # ...
+

Changed in version 3.10: Removed the loop parameter.

Deprecated since version 3.10: Deprecation warning is emitted if not all awaitable objects in the aws iterable are Future-like objects and there is no running event loop.

Changed in version 3.12: Added support for generators yielding tasks.

+

Running in Threads

+coroutine asyncio.to_thread(func, /, *args, **kwargs)
+

Asynchronously run function func in a separate thread.

Any *args and **kwargs supplied for this function are directly passed to func. Also, the current contextvars.Context is propagated, allowing context variables from the event loop thread to be accessed in the separate thread.

Return a coroutine that can be awaited to get the eventual result of func.

This coroutine function is primarily intended to be used for executing IO-bound functions/methods that would otherwise block the event loop if they were run in the main thread. For example:

def blocking_io():
+    print(f"start blocking_io at {time.strftime('%X')}")
+    # Note that time.sleep() can be replaced with any blocking
+    # IO-bound operation, such as file operations.
+    time.sleep(1)
+    print(f"blocking_io complete at {time.strftime('%X')}")
+
+async def main():
+    print(f"started main at {time.strftime('%X')}")
+
+    await asyncio.gather(
+        asyncio.to_thread(blocking_io),
+        asyncio.sleep(1))
+
+    print(f"finished main at {time.strftime('%X')}")
+
+
+asyncio.run(main())
+
+# Expected output:
+#
+# started main at 19:50:53
+# start blocking_io at 19:50:53
+# blocking_io complete at 19:50:54
+# finished main at 19:50:54
+

Directly calling blocking_io() in any coroutine would block the event loop for its duration, resulting in an additional 1 second of run time. Instead, by using asyncio.to_thread(), we can run it in a separate thread without blocking the event loop.

Note

Due to the GIL, asyncio.to_thread() can typically only be used to make IO-bound functions non-blocking. However, for extension modules that release the GIL or alternative Python implementations that don’t have one, asyncio.to_thread() can also be used for CPU-bound functions.

New in version 3.9.

+

Scheduling From Other Threads

+asyncio.run_coroutine_threadsafe(coro, loop)
+

Submit a coroutine to the given event loop. Thread-safe.

Return a concurrent.futures.Future to wait for the result from another OS thread.

This function is meant to be called from a different OS thread than the one where the event loop is running. Example:

# Create a coroutine
+coro = asyncio.sleep(1, result=3)
+
+# Submit the coroutine to a given loop
+future = asyncio.run_coroutine_threadsafe(coro, loop)
+
+# Wait for the result with an optional timeout argument
+assert future.result(timeout) == 3
+

If an exception is raised in the coroutine, the returned Future will be notified. It can also be used to cancel the task in the event loop:

try:
+    result = future.result(timeout)
+except TimeoutError:
+    print('The coroutine took too long, cancelling the task...')
+    future.cancel()
+except Exception as exc:
+    print(f'The coroutine raised an exception: {exc!r}')
+else:
+    print(f'The coroutine returned: {result!r}')
+

See the concurrency and multithreading section of the documentation.

Unlike other asyncio functions this function requires the loop argument to be passed explicitly.

New in version 3.5.1.

+

Introspection

+asyncio.current_task(loop=None)
+

Return the currently running Task instance, or None if no task is running.

If loop is None get_running_loop() is used to get the current loop.

New in version 3.7.

+
+asyncio.all_tasks(loop=None)
+

Return a set of not yet finished Task objects run by the loop.

If loop is None, get_running_loop() is used for getting current loop.

New in version 3.7.

+
+asyncio.iscoroutine(obj)
+

Return True if obj is a coroutine object.

New in version 3.4.

+

Task Object

+class asyncio.Task(coro, *, loop=None, name=None, context=None, eager_start=False)
+

A Future-like object that runs a Python coroutine. Not thread-safe.

Tasks are used to run coroutines in event loops. If a coroutine awaits on a Future, the Task suspends the execution of the coroutine and waits for the completion of the Future. When the Future is done, the execution of the wrapped coroutine resumes.

Event loops use cooperative scheduling: an event loop runs one Task at a time. While a Task awaits for the completion of a Future, the event loop runs other Tasks, callbacks, or performs IO operations.

Use the high-level asyncio.create_task() function to create Tasks, or the low-level loop.create_task() or ensure_future() functions. Manual instantiation of Tasks is discouraged.

To cancel a running Task use the cancel() method. Calling it will cause the Task to throw a CancelledError exception into the wrapped coroutine. If a coroutine is awaiting on a Future object during cancellation, the Future object will be cancelled.

cancelled() can be used to check if the Task was cancelled. The method returns True if the wrapped coroutine did not suppress the CancelledError exception and was actually cancelled.

asyncio.Task inherits from Future all of its APIs except Future.set_result() and Future.set_exception().

An optional keyword-only context argument allows specifying a custom contextvars.Context for the coro to run in. If no context is provided, the Task copies the current context and later runs its coroutine in the copied context.

An optional keyword-only eager_start argument allows eagerly starting the execution of the asyncio.Task at task creation time. If set to True and the event loop is running, the task will start executing the coroutine immediately, until the first time the coroutine blocks. If the coroutine returns or raises without blocking, the task will be finished eagerly and will skip scheduling to the event loop.

Changed in version 3.7: Added support for the contextvars module.

Changed in version 3.8: Added the name parameter.

Deprecated since version 3.10: Deprecation warning is emitted if loop is not specified and there is no running event loop.

Changed in version 3.11: Added the context parameter.

Changed in version 3.12: Added the eager_start parameter.

+done()
+

Return True if the Task is done.

A Task is done when the wrapped coroutine either returned a value, raised an exception, or the Task was cancelled.

+
+result()
+

Return the result of the Task.

If the Task is done, the result of the wrapped coroutine is returned (or if the coroutine raised an exception, that exception is re-raised.)

If the Task has been cancelled, this method raises a CancelledError exception.

If the Task’s result isn’t yet available, this method raises a InvalidStateError exception.

+
+exception()
+

Return the exception of the Task.

If the wrapped coroutine raised an exception that exception is returned. If the wrapped coroutine returned normally this method returns None.

If the Task has been cancelled, this method raises a CancelledError exception.

If the Task isn’t done yet, this method raises an InvalidStateError exception.

+
+add_done_callback(callback, *, context=None)
+

Add a callback to be run when the Task is done.

This method should only be used in low-level callback-based code.

See the documentation of Future.add_done_callback() for more details.

+
+remove_done_callback(callback)
+

Remove callback from the callbacks list.

This method should only be used in low-level callback-based code.

See the documentation of Future.remove_done_callback() for more details.

+
+get_stack(*, limit=None)
+

Return the list of stack frames for this Task.

If the wrapped coroutine is not done, this returns the stack where it is suspended. If the coroutine has completed successfully or was cancelled, this returns an empty list. If the coroutine was terminated by an exception, this returns the list of traceback frames.

The frames are always ordered from oldest to newest.

Only one stack frame is returned for a suspended coroutine.

The optional limit argument sets the maximum number of frames to return; by default all available frames are returned. The ordering of the returned list differs depending on whether a stack or a traceback is returned: the newest frames of a stack are returned, but the oldest frames of a traceback are returned. (This matches the behavior of the traceback module.)

+
+print_stack(*, limit=None, file=None)
+

Print the stack or traceback for this Task.

This produces output similar to that of the traceback module for the frames retrieved by get_stack().

The limit argument is passed to get_stack() directly.

The file argument is an I/O stream to which the output is written; by default output is written to sys.stdout.

+
+get_coro()
+

Return the coroutine object wrapped by the Task.

Note

This will return None for Tasks which have already completed eagerly. See the Eager Task Factory.

New in version 3.8.

Changed in version 3.12: Newly added eager task execution means result may be None.

+
+get_context()
+

Return the contextvars.Context object associated with the task.

New in version 3.12.

+
+get_name()
+

Return the name of the Task.

If no name has been explicitly assigned to the Task, the default asyncio Task implementation generates a default name during instantiation.

New in version 3.8.

+
+set_name(value)
+

Set the name of the Task.

The value argument can be any object, which is then converted to a string.

In the default Task implementation, the name will be visible in the repr() output of a task object.

New in version 3.8.

+
+cancel(msg=None)
+

Request the Task to be cancelled.

This arranges for a CancelledError exception to be thrown into the wrapped coroutine on the next cycle of the event loop.

The coroutine then has a chance to clean up or even deny the request by suppressing the exception with a try … … except CancelledError … finally block. Therefore, unlike Future.cancel(), Task.cancel() does not guarantee that the Task will be cancelled, although suppressing cancellation completely is not common and is actively discouraged. Should the coroutine nevertheless decide to suppress the cancellation, it needs to call Task.uncancel() in addition to catching the exception.

Changed in version 3.9: Added the msg parameter.

Changed in version 3.11: The msg parameter is propagated from cancelled task to its awaiter.

The following example illustrates how coroutines can intercept the cancellation request:

async def cancel_me():
+    print('cancel_me(): before sleep')
+
+    try:
+        # Wait for 1 hour
+        await asyncio.sleep(3600)
+    except asyncio.CancelledError:
+        print('cancel_me(): cancel sleep')
+        raise
+    finally:
+        print('cancel_me(): after sleep')
+
+async def main():
+    # Create a "cancel_me" Task
+    task = asyncio.create_task(cancel_me())
+
+    # Wait for 1 second
+    await asyncio.sleep(1)
+
+    task.cancel()
+    try:
+        await task
+    except asyncio.CancelledError:
+        print("main(): cancel_me is cancelled now")
+
+asyncio.run(main())
+
+# Expected output:
+#
+#     cancel_me(): before sleep
+#     cancel_me(): cancel sleep
+#     cancel_me(): after sleep
+#     main(): cancel_me is cancelled now
+
+
+cancelled()
+

Return True if the Task is cancelled.

The Task is cancelled when the cancellation was requested with cancel() and the wrapped coroutine propagated the CancelledError exception thrown into it.

+
+uncancel()
+

Decrement the count of cancellation requests to this Task.

Returns the remaining number of cancellation requests.

Note that once execution of a cancelled task completed, further calls to uncancel() are ineffective.

New in version 3.11.

This method is used by asyncio’s internals and isn’t expected to be used by end-user code. In particular, if a Task gets successfully uncancelled, this allows for elements of structured concurrency like Task Groups and asyncio.timeout() to continue running, isolating cancellation to the respective structured block. For example:

async def make_request_with_timeout():
+    try:
+        async with asyncio.timeout(1):
+            # Structured block affected by the timeout:
+            await make_request()
+            await make_another_request()
+    except TimeoutError:
+        log("There was a timeout")
+    # Outer code not affected by the timeout:
+    await unrelated_code()
+

While the block with make_request() and make_another_request() might get cancelled due to the timeout, unrelated_code() should continue running even in case of the timeout. This is implemented with uncancel(). TaskGroup context managers use uncancel() in a similar fashion.

If end-user code is, for some reason, suppresing cancellation by catching CancelledError, it needs to call this method to remove the cancellation state.

+
+cancelling()
+

Return the number of pending cancellation requests to this Task, i.e., the number of calls to cancel() less the number of uncancel() calls.

Note that if this number is greater than zero but the Task is still executing, cancelled() will still return False. This is because this number can be lowered by calling uncancel(), which can lead to the task not being cancelled after all if the cancellation requests go down to zero.

This method is used by asyncio’s internals and isn’t expected to be used by end-user code. See uncancel() for more details.

New in version 3.11.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio-task.html +

+
diff --git a/devdocs/python~3.12/library%2Fasyncio.html b/devdocs/python~3.12/library%2Fasyncio.html new file mode 100644 index 00000000..f6aa0f06 --- /dev/null +++ b/devdocs/python~3.12/library%2Fasyncio.html @@ -0,0 +1,24 @@ +

asyncio — Asynchronous I/O

asyncio is a library to write concurrent code using the async/await syntax.

asyncio is used as a foundation for multiple Python asynchronous frameworks that provide high-performance network and web-servers, database connection libraries, distributed task queues, etc.

asyncio is often a perfect fit for IO-bound and high-level structured network code.

asyncio provides a set of high-level APIs to:

Additionally, there are low-level APIs for library and framework developers to:

You can experiment with an asyncio concurrent context in the REPL:

$ python -m asyncio
+asyncio REPL ...
+Use "await" directly instead of "asyncio.run()".
+Type "help", "copyright", "credits" or "license" for more information.
+>>> import asyncio
+>>> await asyncio.sleep(10, result='hello')
+'hello'
+

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Reference

High-level APIs

Low-level APIs

Guides and Tutorials

Note

The source code for asyncio can be found in Lib/asyncio/.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/asyncio.html +

+
diff --git a/devdocs/python~3.12/library%2Fatexit.html b/devdocs/python~3.12/library%2Fatexit.html new file mode 100644 index 00000000..982040cf --- /dev/null +++ b/devdocs/python~3.12/library%2Fatexit.html @@ -0,0 +1,46 @@ +

atexit — Exit handlers

The atexit module defines functions to register and unregister cleanup functions. Functions thus registered are automatically executed upon normal interpreter termination. atexit runs these functions in the reverse order in which they were registered; if you register A, B, and C, at interpreter termination time they will be run in the order C, B, A.

Note: The functions registered via this module are not called when the program is killed by a signal not handled by Python, when a Python fatal internal error is detected, or when os._exit() is called.

Note: The effect of registering or unregistering functions from within a cleanup function is undefined.

Changed in version 3.7: When used with C-API subinterpreters, registered functions are local to the interpreter they were registered in.

+atexit.register(func, *args, **kwargs)
+

Register func as a function to be executed at termination. Any optional arguments that are to be passed to func must be passed as arguments to register(). It is possible to register the same function and arguments more than once.

At normal program termination (for instance, if sys.exit() is called or the main module’s execution completes), all functions registered are called in last in, first out order. The assumption is that lower level modules will normally be imported before higher level modules and thus must be cleaned up later.

If an exception is raised during execution of the exit handlers, a traceback is printed (unless SystemExit is raised) and the exception information is saved. After all exit handlers have had a chance to run, the last exception to be raised is re-raised.

This function returns func, which makes it possible to use it as a decorator.

Warning

Starting new threads or calling os.fork() from a registered function can lead to race condition between the main Python runtime thread freeing thread states while internal threading routines or the new process try to use that state. This can lead to crashes rather than clean shutdown.

Changed in version 3.12: Attempts to start a new thread or os.fork() a new process in a registered function now leads to RuntimeError.

+
+atexit.unregister(func)
+

Remove func from the list of functions to be run at interpreter shutdown. unregister() silently does nothing if func was not previously registered. If func has been registered more than once, every occurrence of that function in the atexit call stack will be removed. Equality comparisons (==) are used internally during unregistration, so function references do not need to have matching identities.

+

See also

+Module readline +
+
+

Useful example of atexit to read and write readline history files.

atexit Example

The following simple example demonstrates how a module can initialize a counter from a file when it is imported and save the counter’s updated value automatically when the program terminates without relying on the application making an explicit call into this module at termination.

try:
+    with open('counterfile') as infile:
+        _count = int(infile.read())
+except FileNotFoundError:
+    _count = 0
+
+def incrcounter(n):
+    global _count
+    _count = _count + n
+
+def savecounter():
+    with open('counterfile', 'w') as outfile:
+        outfile.write('%d' % _count)
+
+import atexit
+
+atexit.register(savecounter)
+

Positional and keyword arguments may also be passed to register() to be passed along to the registered function when it is called:

def goodbye(name, adjective):
+    print('Goodbye %s, it was %s to meet you.' % (name, adjective))
+
+import atexit
+
+atexit.register(goodbye, 'Donny', 'nice')
+# or:
+atexit.register(goodbye, adjective='nice', name='Donny')
+

Usage as a decorator:

import atexit
+
+@atexit.register
+def goodbye():
+    print('You are now leaving the Python sector.')
+

This only works with functions that can be called without arguments.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/atexit.html +

+
diff --git a/devdocs/python~3.12/library%2Faudit_events.html b/devdocs/python~3.12/library%2Faudit_events.html new file mode 100644 index 00000000..5691b815 --- /dev/null +++ b/devdocs/python~3.12/library%2Faudit_events.html @@ -0,0 +1,197 @@ +

Audit events table

This table contains all events raised by sys.audit() or PySys_Audit() calls throughout the CPython runtime and the standard library. These calls were added in 3.8.0 or later (see PEP 578).

See sys.addaudithook() and PySys_AddAuditHook() for information on handling these events.

CPython implementation detail: This table is generated from the CPython documentation, and may not represent events raised by other implementations. See your runtime specific documentation for actual events raised.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Audit event

Arguments

References

_thread.start_new_thread

function, args, kwargs

[1]

array.__new__

typecode, initializer

[1]

builtins.breakpoint

breakpointhook

[1]

builtins.id

id

[1]

builtins.input

prompt

[1]

builtins.input/result

result

[1]

code.__new__

code, filename, name, argcount, posonlyargcount, kwonlyargcount, nlocals, stacksize, flags

[1]

compile

source, filename

[1]

cpython.PyInterpreterState_Clear

[1]

cpython.PyInterpreterState_New

[1]

cpython._PySys_ClearAuditHooks

[1]

cpython.run_command

command

[1]

cpython.run_file

filename

[1]

cpython.run_interactivehook

hook

[1]

cpython.run_module

module-name

[1]

cpython.run_startup

filename

[1]

cpython.run_stdin

[1]

ctypes.addressof

obj

[1]

ctypes.call_function

func_pointer, arguments

[1]

ctypes.cdata

address

[1]

ctypes.cdata/buffer

pointer, size, offset

[1][2]

ctypes.create_string_buffer

init, size

[1]

ctypes.create_unicode_buffer

init, size

[1]

ctypes.dlopen

name

[1]

ctypes.dlsym

library, name

[1]

ctypes.dlsym/handle

handle, name

[1]

ctypes.get_errno

[1]

ctypes.get_last_error

[1]

ctypes.set_errno

errno

[1]

ctypes.set_exception

code

[1]

ctypes.set_last_error

error

[1]

ctypes.string_at

address, size

[1]

ctypes.wstring_at

address, size

[1]

ensurepip.bootstrap

root

[1]

exec

code_object

[1][2]

fcntl.fcntl

fd, cmd, arg

[1]

fcntl.flock

fd, operation

[1]

fcntl.ioctl

fd, request, arg

[1]

fcntl.lockf

fd, cmd, len, start, whence

[1]

ftplib.connect

self, host, port

[1]

ftplib.sendcmd

self, cmd

[1][2]

function.__new__

code

[1]

gc.get_objects

generation

[1]

gc.get_referents

objs

[1]

gc.get_referrers

objs

[1]

glob.glob

pathname, recursive

[1][2]

glob.glob/2

pathname, recursive, root_dir, dir_fd

[1][2]

http.client.connect

self, host, port

[1]

http.client.send

self, data

[1]

imaplib.open

self, host, port

[1]

imaplib.send

self, data

[1]

import

module, filename, sys.path, sys.meta_path, sys.path_hooks

[1]

marshal.dumps

value, version

[1]

marshal.load

[1]

marshal.loads

bytes

[1]

mmap.__new__

fileno, length, access, offset

[1]

msvcrt.get_osfhandle

fd

[1]

msvcrt.locking

fd, mode, nbytes

[1]

msvcrt.open_osfhandle

handle, flags

[1]

nntplib.connect

self, host, port

[1][2]

nntplib.putline

self, line

[1][2]

object.__delattr__

obj, name

[1]

object.__getattr__

obj, name

[1]

object.__setattr__

obj, name, value

[1]

open

path, mode, flags

[1][2][3]

os.add_dll_directory

path

[1]

os.chdir

path

[1][2]

os.chflags

path, flags

[1][2]

os.chmod

path, mode, dir_fd

[1][2][3]

os.chown

path, uid, gid, dir_fd

[1][2][3]

os.exec

path, args, env

[1]

os.fork

[1]

os.forkpty

[1]

os.fwalk

top, topdown, onerror, follow_symlinks, dir_fd

[1]

os.getxattr

path, attribute

[1]

os.kill

pid, sig

[1]

os.killpg

pgid, sig

[1]

os.link

src, dst, src_dir_fd, dst_dir_fd

[1]

os.listdir

path

[1]

os.listdrives

[1]

os.listmounts

volume

[1]

os.listvolumes

[1]

os.listxattr

path

[1]

os.lockf

fd, cmd, len

[1]

os.mkdir

path, mode, dir_fd

[1][2]

os.posix_spawn

path, argv, env

[1][2]

os.putenv

key, value

[1]

os.remove

path, dir_fd

[1][2][3]

os.removexattr

path, attribute

[1]

os.rename

src, dst, src_dir_fd, dst_dir_fd

[1][2][3]

os.rmdir

path, dir_fd

[1]

os.scandir

path

[1]

os.setxattr

path, attribute, value, flags

[1]

os.spawn

mode, path, args, env

[1]

os.startfile

path, operation

[1]

os.startfile/2

path, operation, arguments, cwd, show_cmd

[1]

os.symlink

src, dst, dir_fd

[1]

os.system

command

[1]

os.truncate

fd, length

[1][2]

os.unsetenv

key

[1]

os.utime

path, times, ns, dir_fd

[1]

os.walk

top, topdown, onerror, followlinks

[1]

pathlib.Path.glob

self, pattern

[1]

pathlib.Path.rglob

self, pattern

[1]

pdb.Pdb

[1]

pickle.find_class

module, name

[1]

poplib.connect

self, host, port

[1][2]

poplib.putline

self, line

[1][2]

pty.spawn

argv

[1]

resource.prlimit

pid, resource, limits

[1]

resource.setrlimit

resource, limits

[1]

setopencodehook

[1]

shutil.chown

path, user, group

[1]

shutil.copyfile

src, dst

[1][2][3]

shutil.copymode

src, dst

[1][2]

shutil.copystat

src, dst

[1][2]

shutil.copytree

src, dst

[1]

shutil.make_archive

base_name, format, root_dir, base_dir

[1]

shutil.move

src, dst

[1]

shutil.rmtree

path, dir_fd

[1]

shutil.unpack_archive

filename, extract_dir, format

[1]

signal.pthread_kill

thread_id, signalnum

[1]

smtplib.connect

self, host, port

[1]

smtplib.send

self, data

[1]

socket.__new__

self, family, type, protocol

[1]

socket.bind

self, address

[1]

socket.connect

self, address

[1][2]

socket.getaddrinfo

host, port, family, type, protocol

[1]

socket.gethostbyaddr

ip_address

[1]

socket.gethostbyname

hostname

[1][2]

socket.gethostname

[1]

socket.getnameinfo

sockaddr

[1]

socket.getservbyname

servicename, protocolname

[1]

socket.getservbyport

port, protocolname

[1]

socket.sendmsg

self, address

[1]

socket.sendto

self, address

[1]

socket.sethostname

name

[1]

sqlite3.connect

database

[1]

sqlite3.connect/handle

connection_handle

[1]

sqlite3.enable_load_extension

connection, enabled

[1]

sqlite3.load_extension

connection, path

[1]

subprocess.Popen

executable, args, cwd, env

[1]

sys._current_exceptions

[1]

sys._current_frames

[1]

sys._getframe

frame

[1]

sys._getframemodulename

depth

[1]

sys.addaudithook

[1][2]

sys.excepthook

hook, type, value, traceback

[1]

sys.set_asyncgen_hooks_finalizer

[1]

sys.set_asyncgen_hooks_firstiter

[1]

sys.setprofile

[1]

sys.settrace

[1]

sys.unraisablehook

hook, unraisable

[1]

syslog.closelog

[1]

syslog.openlog

ident, logoption, facility

[1]

syslog.setlogmask

maskpri

[1]

syslog.syslog

priority, message

[1]

telnetlib.Telnet.open

self, host, port

[1]

telnetlib.Telnet.write

self, buffer

[1]

tempfile.mkdtemp

fullpath

[1][2]

tempfile.mkstemp

fullpath

[1][2][3]

urllib.Request

fullurl, data, headers, method

[1]

webbrowser.open

url

[1]

winreg.ConnectRegistry

computer_name, key

[1]

winreg.CreateKey

key, sub_key, access

[1][2]

winreg.DeleteKey

key, sub_key, access

[1][2]

winreg.DeleteValue

key, value

[1]

winreg.DisableReflectionKey

key

[1]

winreg.EnableReflectionKey

key

[1]

winreg.EnumKey

key, index

[1]

winreg.EnumValue

key, index

[1]

winreg.ExpandEnvironmentStrings

str

[1]

winreg.LoadKey

key, sub_key, file_name

[1]

winreg.OpenKey

key, sub_key, access

[1]

winreg.OpenKey/result

key

[1][2][3]

winreg.PyHKEY.Detach

key

[1]

winreg.QueryInfoKey

key

[1]

winreg.QueryReflectionKey

key

[1]

winreg.QueryValue

key, sub_key, value_name

[1][2]

winreg.SaveKey

key, file_name

[1]

winreg.SetValue

key, sub_key, type, value

[1][2]

The following events are raised internally and do not correspond to any public API of CPython:

+ + + + + + + + +

Audit event

Arguments

_winapi.CreateFile

file_name, desired_access, share_mode, creation_disposition, flags_and_attributes

_winapi.CreateJunction

src_path, dst_path

_winapi.CreateNamedPipe

name, open_mode, pipe_mode

_winapi.CreatePipe

_winapi.CreateProcess

application_name, command_line, current_directory

_winapi.OpenProcess

process_id, desired_access

_winapi.TerminateProcess

handle, exit_code

ctypes.PyObj_FromPtr

obj
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/audit_events.html +

+
diff --git a/devdocs/python~3.12/library%2Fbase64.html b/devdocs/python~3.12/library%2Fbase64.html new file mode 100644 index 00000000..61f8156e --- /dev/null +++ b/devdocs/python~3.12/library%2Fbase64.html @@ -0,0 +1,80 @@ +

base64 — Base16, Base32, Base64, Base85 Data Encodings

Source code: Lib/base64.py

This module provides functions for encoding binary data to printable ASCII characters and decoding such encodings back to binary data. It provides encoding and decoding functions for the encodings specified in RFC 4648, which defines the Base16, Base32, and Base64 algorithms, and for the de-facto standard Ascii85 and Base85 encodings.

The RFC 4648 encodings are suitable for encoding binary data so that it can be safely sent by email, used as parts of URLs, or included as part of an HTTP POST request. The encoding algorithm is not the same as the uuencode program.

There are two interfaces provided by this module. The modern interface supports encoding bytes-like objects to ASCII bytes, and decoding bytes-like objects or strings containing ASCII to bytes. Both base-64 alphabets defined in RFC 4648 (normal, and URL- and filesystem-safe) are supported.

The legacy interface does not support decoding from strings, but it does provide functions for encoding and decoding to and from file objects. It only supports the Base64 standard alphabet, and it adds newlines every 76 characters as per RFC 2045. Note that if you are looking for RFC 2045 support you probably want to be looking at the email package instead.

Changed in version 3.3: ASCII-only Unicode strings are now accepted by the decoding functions of the modern interface.

Changed in version 3.4: Any bytes-like objects are now accepted by all encoding and decoding functions in this module. Ascii85/Base85 support added.

The modern interface provides:

+base64.b64encode(s, altchars=None)
+

Encode the bytes-like object s using Base64 and return the encoded bytes.

Optional altchars must be a bytes-like object of length 2 which specifies an alternative alphabet for the + and / characters. This allows an application to e.g. generate URL or filesystem safe Base64 strings. The default is None, for which the standard Base64 alphabet is used.

May assert or raise a ValueError if the length of altchars is not 2. Raises a TypeError if altchars is not a bytes-like object.

+
+base64.b64decode(s, altchars=None, validate=False)
+

Decode the Base64 encoded bytes-like object or ASCII string s and return the decoded bytes.

Optional altchars must be a bytes-like object or ASCII string of length 2 which specifies the alternative alphabet used instead of the + and / characters.

A binascii.Error exception is raised if s is incorrectly padded.

If validate is False (the default), characters that are neither in the normal base-64 alphabet nor the alternative alphabet are discarded prior to the padding check. If validate is True, these non-alphabet characters in the input result in a binascii.Error.

For more information about the strict base64 check, see binascii.a2b_base64()

May assert or raise a ValueError if the length of altchars is not 2.

+
+base64.standard_b64encode(s)
+

Encode bytes-like object s using the standard Base64 alphabet and return the encoded bytes.

+
+base64.standard_b64decode(s)
+

Decode bytes-like object or ASCII string s using the standard Base64 alphabet and return the decoded bytes.

+
+base64.urlsafe_b64encode(s)
+

Encode bytes-like object s using the URL- and filesystem-safe alphabet, which substitutes - instead of + and _ instead of / in the standard Base64 alphabet, and return the encoded bytes. The result can still contain =.

+
+base64.urlsafe_b64decode(s)
+

Decode bytes-like object or ASCII string s using the URL- and filesystem-safe alphabet, which substitutes - instead of + and _ instead of / in the standard Base64 alphabet, and return the decoded bytes.

+
+base64.b32encode(s)
+

Encode the bytes-like object s using Base32 and return the encoded bytes.

+
+base64.b32decode(s, casefold=False, map01=None)
+

Decode the Base32 encoded bytes-like object or ASCII string s and return the decoded bytes.

Optional casefold is a flag specifying whether a lowercase alphabet is acceptable as input. For security purposes, the default is False.

RFC 4648 allows for optional mapping of the digit 0 (zero) to the letter O (oh), and for optional mapping of the digit 1 (one) to either the letter I (eye) or letter L (el). The optional argument map01 when not None, specifies which letter the digit 1 should be mapped to (when map01 is not None, the digit 0 is always mapped to the letter O). For security purposes the default is None, so that 0 and 1 are not allowed in the input.

A binascii.Error is raised if s is incorrectly padded or if there are non-alphabet characters present in the input.

+
+base64.b32hexencode(s)
+

Similar to b32encode() but uses the Extended Hex Alphabet, as defined in RFC 4648.

New in version 3.10.

+
+base64.b32hexdecode(s, casefold=False)
+

Similar to b32decode() but uses the Extended Hex Alphabet, as defined in RFC 4648.

This version does not allow the digit 0 (zero) to the letter O (oh) and digit 1 (one) to either the letter I (eye) or letter L (el) mappings, all these characters are included in the Extended Hex Alphabet and are not interchangeable.

New in version 3.10.

+
+base64.b16encode(s)
+

Encode the bytes-like object s using Base16 and return the encoded bytes.

+
+base64.b16decode(s, casefold=False)
+

Decode the Base16 encoded bytes-like object or ASCII string s and return the decoded bytes.

Optional casefold is a flag specifying whether a lowercase alphabet is acceptable as input. For security purposes, the default is False.

A binascii.Error is raised if s is incorrectly padded or if there are non-alphabet characters present in the input.

+
+base64.a85encode(b, *, foldspaces=False, wrapcol=0, pad=False, adobe=False)
+

Encode the bytes-like object b using Ascii85 and return the encoded bytes.

foldspaces is an optional flag that uses the special short sequence ‘y’ instead of 4 consecutive spaces (ASCII 0x20) as supported by ‘btoa’. This feature is not supported by the “standard” Ascii85 encoding.

wrapcol controls whether the output should have newline (b'\n') characters added to it. If this is non-zero, each output line will be at most this many characters long.

pad controls whether the input is padded to a multiple of 4 before encoding. Note that the btoa implementation always pads.

adobe controls whether the encoded byte sequence is framed with <~ and ~>, which is used by the Adobe implementation.

New in version 3.4.

+
+base64.a85decode(b, *, foldspaces=False, adobe=False, ignorechars=b' \t\n\r\x0b')
+

Decode the Ascii85 encoded bytes-like object or ASCII string b and return the decoded bytes.

foldspaces is a flag that specifies whether the ‘y’ short sequence should be accepted as shorthand for 4 consecutive spaces (ASCII 0x20). This feature is not supported by the “standard” Ascii85 encoding.

adobe controls whether the input sequence is in Adobe Ascii85 format (i.e. is framed with <~ and ~>).

ignorechars should be a bytes-like object or ASCII string containing characters to ignore from the input. This should only contain whitespace characters, and by default contains all whitespace characters in ASCII.

New in version 3.4.

+
+base64.b85encode(b, pad=False)
+

Encode the bytes-like object b using base85 (as used in e.g. git-style binary diffs) and return the encoded bytes.

If pad is true, the input is padded with b'\0' so its length is a multiple of 4 bytes before encoding.

New in version 3.4.

+
+base64.b85decode(b)
+

Decode the base85-encoded bytes-like object or ASCII string b and return the decoded bytes. Padding is implicitly removed, if necessary.

New in version 3.4.

+

The legacy interface:

+base64.decode(input, output)
+

Decode the contents of the binary input file and write the resulting binary data to the output file. input and output must be file objects. input will be read until input.readline() returns an empty bytes object.

+
+base64.decodebytes(s)
+

Decode the bytes-like object s, which must contain one or more lines of base64 encoded data, and return the decoded bytes.

New in version 3.1.

+
+base64.encode(input, output)
+

Encode the contents of the binary input file and write the resulting base64 encoded data to the output file. input and output must be file objects. input will be read until input.read() returns an empty bytes object. encode() inserts a newline character (b'\n') after every 76 bytes of the output, as well as ensuring that the output always ends with a newline, as per RFC 2045 (MIME).

+
+base64.encodebytes(s)
+

Encode the bytes-like object s, which can contain arbitrary binary data, and return bytes containing the base64-encoded data, with newlines (b'\n') inserted after every 76 bytes of output, and ensuring that there is a trailing newline, as per RFC 2045 (MIME).

New in version 3.1.

+

An example usage of the module:

>>> import base64
+>>> encoded = base64.b64encode(b'data to be encoded')
+>>> encoded
+b'ZGF0YSB0byBiZSBlbmNvZGVk'
+>>> data = base64.b64decode(encoded)
+>>> data
+b'data to be encoded'
+

Security Considerations

A new security considerations section was added to RFC 4648 (section 12); it’s recommended to review the security section for any code deployed to production.

See also

+Module binascii +
+
+

Support module containing ASCII-to-binary and binary-to-ASCII conversions.

+RFC 1521 - MIME (Multipurpose Internet Mail Extensions) Part One: Mechanisms for Specifying and Describing the Format of Internet Message Bodies
+
+

Section 5.2, “Base64 Content-Transfer-Encoding,” provides the definition of the base64 encoding.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/base64.html +

+
diff --git a/devdocs/python~3.12/library%2Fbdb.html b/devdocs/python~3.12/library%2Fbdb.html new file mode 100644 index 00000000..676f8506 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbdb.html @@ -0,0 +1,193 @@ +

bdb — Debugger framework

Source code: Lib/bdb.py

The bdb module handles basic debugger functions, like setting breakpoints or managing execution via the debugger.

The following exception is defined:

+exception bdb.BdbQuit
+

Exception raised by the Bdb class for quitting the debugger.

+

The bdb module also defines two classes:

+class bdb.Breakpoint(self, file, line, temporary=False, cond=None, funcname=None)
+

This class implements temporary breakpoints, ignore counts, disabling and (re-)enabling, and conditionals.

Breakpoints are indexed by number through a list called bpbynumber and by (file, line) pairs through bplist. The former points to a single instance of class Breakpoint. The latter points to a list of such instances since there may be more than one breakpoint per line.

When creating a breakpoint, its associated file name should be in canonical form. If a funcname is defined, a breakpoint hit will be counted when the first line of that function is executed. A conditional breakpoint always counts a hit.

Breakpoint instances have the following methods:

+deleteMe()
+

Delete the breakpoint from the list associated to a file/line. If it is the last breakpoint in that position, it also deletes the entry for the file/line.

+
+enable()
+

Mark the breakpoint as enabled.

+
+disable()
+

Mark the breakpoint as disabled.

+
+bpformat()
+

Return a string with all the information about the breakpoint, nicely formatted:

  • Breakpoint number.
  • Temporary status (del or keep).
  • File/line position.
  • Break condition.
  • Number of times to ignore.
  • Number of times hit.

New in version 3.2.

+
+bpprint(out=None)
+

Print the output of bpformat() to the file out, or if it is None, to standard output.

+

Breakpoint instances have the following attributes:

+file
+

File name of the Breakpoint.

+
+line
+

Line number of the Breakpoint within file.

+
+temporary
+

True if a Breakpoint at (file, line) is temporary.

+
+cond
+

Condition for evaluating a Breakpoint at (file, line).

+
+funcname
+

Function name that defines whether a Breakpoint is hit upon entering the function.

+
+enabled
+

True if Breakpoint is enabled.

+
+bpbynumber
+

Numeric index for a single instance of a Breakpoint.

+
+bplist
+

Dictionary of Breakpoint instances indexed by (file, line) tuples.

+
+ignore
+

Number of times to ignore a Breakpoint.

+
+hits
+

Count of the number of times a Breakpoint has been hit.

+
+
+class bdb.Bdb(skip=None)
+

The Bdb class acts as a generic Python debugger base class.

This class takes care of the details of the trace facility; a derived class should implement user interaction. The standard debugger class (pdb.Pdb) is an example.

The skip argument, if given, must be an iterable of glob-style module name patterns. The debugger will not step into frames that originate in a module that matches one of these patterns. Whether a frame is considered to originate in a certain module is determined by the __name__ in the frame globals.

New in version 3.1: The skip argument.

The following methods of Bdb normally don’t need to be overridden.

+canonic(filename)
+

Return canonical form of filename.

For real file names, the canonical form is an operating-system-dependent, case-normalized absolute path. A filename with angle brackets, such as "<stdin>" generated in interactive mode, is returned unchanged.

+
+reset()
+

Set the botframe, stopframe, returnframe and quitting attributes with values ready to start debugging.

+
+trace_dispatch(frame, event, arg)
+

This function is installed as the trace function of debugged frames. Its return value is the new trace function (in most cases, that is, itself).

The default implementation decides how to dispatch a frame, depending on the type of event (passed as a string) that is about to be executed. event can be one of the following:

  • +"line": A new line of code is going to be executed.
  • +"call": A function is about to be called, or another code block entered.
  • +"return": A function or other code block is about to return.
  • +"exception": An exception has occurred.
  • +"c_call": A C function is about to be called.
  • +"c_return": A C function has returned.
  • +"c_exception": A C function has raised an exception.

For the Python events, specialized functions (see below) are called. For the C events, no action is taken.

The arg parameter depends on the previous event.

See the documentation for sys.settrace() for more information on the trace function. For more information on code and frame objects, refer to The standard type hierarchy.

+
+dispatch_line(frame)
+

If the debugger should stop on the current line, invoke the user_line() method (which should be overridden in subclasses). Raise a BdbQuit exception if the Bdb.quitting flag is set (which can be set from user_line()). Return a reference to the trace_dispatch() method for further tracing in that scope.

+
+dispatch_call(frame, arg)
+

If the debugger should stop on this function call, invoke the user_call() method (which should be overridden in subclasses). Raise a BdbQuit exception if the Bdb.quitting flag is set (which can be set from user_call()). Return a reference to the trace_dispatch() method for further tracing in that scope.

+
+dispatch_return(frame, arg)
+

If the debugger should stop on this function return, invoke the user_return() method (which should be overridden in subclasses). Raise a BdbQuit exception if the Bdb.quitting flag is set (which can be set from user_return()). Return a reference to the trace_dispatch() method for further tracing in that scope.

+
+dispatch_exception(frame, arg)
+

If the debugger should stop at this exception, invokes the user_exception() method (which should be overridden in subclasses). Raise a BdbQuit exception if the Bdb.quitting flag is set (which can be set from user_exception()). Return a reference to the trace_dispatch() method for further tracing in that scope.

+

Normally derived classes don’t override the following methods, but they may if they want to redefine the definition of stopping and breakpoints.

+is_skipped_line(module_name)
+

Return True if module_name matches any skip pattern.

+
+stop_here(frame)
+

Return True if frame is below the starting frame in the stack.

+
+break_here(frame)
+

Return True if there is an effective breakpoint for this line.

Check whether a line or function breakpoint exists and is in effect. Delete temporary breakpoints based on information from effective().

+
+break_anywhere(frame)
+

Return True if any breakpoint exists for frame’s filename.

+

Derived classes should override these methods to gain control over debugger operation.

+user_call(frame, argument_list)
+

Called from dispatch_call() if a break might stop inside the called function.

+
+user_line(frame)
+

Called from dispatch_line() when either stop_here() or break_here() returns True.

+
+user_return(frame, return_value)
+

Called from dispatch_return() when stop_here() returns True.

+
+user_exception(frame, exc_info)
+

Called from dispatch_exception() when stop_here() returns True.

+
+do_clear(arg)
+

Handle how a breakpoint must be removed when it is a temporary one.

This method must be implemented by derived classes.

+

Derived classes and clients can call the following methods to affect the stepping state.

+set_step()
+

Stop after one line of code.

+
+set_next(frame)
+

Stop on the next line in or below the given frame.

+
+set_return(frame)
+

Stop when returning from the given frame.

+
+set_until(frame, lineno=None)
+

Stop when the line with the lineno greater than the current one is reached or when returning from current frame.

+
+set_trace([frame])
+

Start debugging from frame. If frame is not specified, debugging starts from caller’s frame.

+
+set_continue()
+

Stop only at breakpoints or when finished. If there are no breakpoints, set the system trace function to None.

+
+set_quit()
+

Set the quitting attribute to True. This raises BdbQuit in the next call to one of the dispatch_*() methods.

+

Derived classes and clients can call the following methods to manipulate breakpoints. These methods return a string containing an error message if something went wrong, or None if all is well.

+set_break(filename, lineno, temporary=False, cond=None, funcname=None)
+

Set a new breakpoint. If the lineno line doesn’t exist for the filename passed as argument, return an error message. The filename should be in canonical form, as described in the canonic() method.

+
+clear_break(filename, lineno)
+

Delete the breakpoints in filename and lineno. If none were set, return an error message.

+
+clear_bpbynumber(arg)
+

Delete the breakpoint which has the index arg in the Breakpoint.bpbynumber. If arg is not numeric or out of range, return an error message.

+
+clear_all_file_breaks(filename)
+

Delete all breakpoints in filename. If none were set, return an error message.

+
+clear_all_breaks()
+

Delete all existing breakpoints. If none were set, return an error message.

+
+get_bpbynumber(arg)
+

Return a breakpoint specified by the given number. If arg is a string, it will be converted to a number. If arg is a non-numeric string, if the given breakpoint never existed or has been deleted, a ValueError is raised.

New in version 3.2.

+
+get_break(filename, lineno)
+

Return True if there is a breakpoint for lineno in filename.

+
+get_breaks(filename, lineno)
+

Return all breakpoints for lineno in filename, or an empty list if none are set.

+
+get_file_breaks(filename)
+

Return all breakpoints in filename, or an empty list if none are set.

+
+get_all_breaks()
+

Return all breakpoints that are set.

+

Derived classes and clients can call the following methods to get a data structure representing a stack trace.

+get_stack(f, t)
+

Return a list of (frame, lineno) tuples in a stack trace, and a size.

The most recently called frame is last in the list. The size is the number of frames below the frame where the debugger was invoked.

+
+format_stack_entry(frame_lineno, lprefix=': ')
+

Return a string with information about a stack entry, which is a (frame, lineno) tuple. The return string contains:

  • The canonical filename which contains the frame.
  • The function name or "<lambda>".
  • The input arguments.
  • The return value.
  • The line of code (if it exists).
+

The following two methods can be called by clients to use a debugger to debug a statement, given as a string.

+run(cmd, globals=None, locals=None)
+

Debug a statement executed via the exec() function. globals defaults to __main__.__dict__, locals defaults to globals.

+
+runeval(expr, globals=None, locals=None)
+

Debug an expression executed via the eval() function. globals and locals have the same meaning as in run().

+
+runctx(cmd, globals, locals)
+

For backwards compatibility. Calls the run() method.

+
+runcall(func, /, *args, **kwds)
+

Debug a single function call, and return its result.

+
+

Finally, the module defines the following functions:

+bdb.checkfuncname(b, frame)
+

Return True if we should break here, depending on the way the Breakpoint b was set.

If it was set via line number, it checks if b.line is the same as the one in frame. If the breakpoint was set via function name, we have to check we are in the right frame (the right function) and if we are on its first executable line.

+
+bdb.effective(file, line, frame)
+

Return (active breakpoint, delete temporary flag) or (None, None) as the breakpoint to act upon.

The active breakpoint is the first entry in bplist for the (file, line) (which must exist) that is enabled, for which checkfuncname() is True, and that has neither a False condition nor positive ignore count. The flag, meaning that a temporary breakpoint should be deleted, is False only when the cond cannot be evaluated (in which case, ignore count is ignored).

If no such entry exists, then (None, None) is returned.

+
+bdb.set_trace()
+

Start debugging with a Bdb instance from caller’s frame.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/bdb.html +

+
diff --git a/devdocs/python~3.12/library%2Fbinary.html b/devdocs/python~3.12/library%2Fbinary.html new file mode 100644 index 00000000..74c186f1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbinary.html @@ -0,0 +1,14 @@ +

Binary Data Services

The modules described in this chapter provide some basic services operations for manipulation of binary data. Other operations on binary data, specifically in relation to file formats and network protocols, are described in the relevant sections.

Some libraries described under Text Processing Services also work with either ASCII-compatible binary formats (for example, re) or all binary data (for example, difflib).

In addition, see the documentation for Python’s built-in binary data types in Binary Sequence Types — bytes, bytearray, memoryview.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/binary.html +

+
diff --git a/devdocs/python~3.12/library%2Fbinascii.html b/devdocs/python~3.12/library%2Fbinascii.html new file mode 100644 index 00000000..d50bc961 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbinascii.html @@ -0,0 +1,70 @@ +

binascii — Convert between binary and ASCII

The binascii module contains a number of methods to convert between binary and various ASCII-encoded binary representations. Normally, you will not use these functions directly but use wrapper modules like uu or base64 instead. The binascii module contains low-level functions written in C for greater speed that are used by the higher-level modules.

Note

a2b_* functions accept Unicode strings containing only ASCII characters. Other functions only accept bytes-like objects (such as bytes, bytearray and other objects that support the buffer protocol).

Changed in version 3.3: ASCII-only unicode strings are now accepted by the a2b_* functions.

The binascii module defines the following functions:

+binascii.a2b_uu(string)
+

Convert a single line of uuencoded data back to binary and return the binary data. Lines normally contain 45 (binary) bytes, except for the last line. Line data may be followed by whitespace.

+
+binascii.b2a_uu(data, *, backtick=False)
+

Convert binary data to a line of ASCII characters, the return value is the converted line, including a newline char. The length of data should be at most 45. If backtick is true, zeros are represented by '`' instead of spaces.

Changed in version 3.7: Added the backtick parameter.

+
+binascii.a2b_base64(string, /, *, strict_mode=False)
+

Convert a block of base64 data back to binary and return the binary data. More than one line may be passed at a time.

If strict_mode is true, only valid base64 data will be converted. Invalid base64 data will raise binascii.Error.

Valid base64:

  • Conforms to RFC 3548.
  • Contains only characters from the base64 alphabet.
  • Contains no excess data after padding (including excess padding, newlines, etc.).
  • Does not start with a padding.

Changed in version 3.11: Added the strict_mode parameter.

+
+binascii.b2a_base64(data, *, newline=True)
+

Convert binary data to a line of ASCII characters in base64 coding. The return value is the converted line, including a newline char if newline is true. The output of this function conforms to RFC 3548.

Changed in version 3.6: Added the newline parameter.

+
+binascii.a2b_qp(data, header=False)
+

Convert a block of quoted-printable data back to binary and return the binary data. More than one line may be passed at a time. If the optional argument header is present and true, underscores will be decoded as spaces.

+
+binascii.b2a_qp(data, quotetabs=False, istext=True, header=False)
+

Convert binary data to a line(s) of ASCII characters in quoted-printable encoding. The return value is the converted line(s). If the optional argument quotetabs is present and true, all tabs and spaces will be encoded. If the optional argument istext is present and true, newlines are not encoded but trailing whitespace will be encoded. If the optional argument header is present and true, spaces will be encoded as underscores per RFC 1522. If the optional argument header is present and false, newline characters will be encoded as well; otherwise linefeed conversion might corrupt the binary data stream.

+
+binascii.crc_hqx(data, value)
+

Compute a 16-bit CRC value of data, starting with value as the initial CRC, and return the result. This uses the CRC-CCITT polynomial x16 + x12 + x5 + 1, often represented as 0x1021. This CRC is used in the binhex4 format.

+
+binascii.crc32(data[, value])
+

Compute CRC-32, the unsigned 32-bit checksum of data, starting with an initial CRC of value. The default initial CRC is zero. The algorithm is consistent with the ZIP file checksum. Since the algorithm is designed for use as a checksum algorithm, it is not suitable for use as a general hash algorithm. Use as follows:

print(binascii.crc32(b"hello world"))
+# Or, in two pieces:
+crc = binascii.crc32(b"hello")
+crc = binascii.crc32(b" world", crc)
+print('crc32 = {:#010x}'.format(crc))
+

Changed in version 3.0: The result is always unsigned.

+
+binascii.b2a_hex(data[, sep[, bytes_per_sep=1]])
+binascii.hexlify(data[, sep[, bytes_per_sep=1]])
+

Return the hexadecimal representation of the binary data. Every byte of data is converted into the corresponding 2-digit hex representation. The returned bytes object is therefore twice as long as the length of data.

Similar functionality (but returning a text string) is also conveniently accessible using the bytes.hex() method.

If sep is specified, it must be a single character str or bytes object. It will be inserted in the output after every bytes_per_sep input bytes. Separator placement is counted from the right end of the output by default, if you wish to count from the left, supply a negative bytes_per_sep value.

>>> import binascii
+>>> binascii.b2a_hex(b'\xb9\x01\xef')
+b'b901ef'
+>>> binascii.hexlify(b'\xb9\x01\xef', '-')
+b'b9-01-ef'
+>>> binascii.b2a_hex(b'\xb9\x01\xef', b'_', 2)
+b'b9_01ef'
+>>> binascii.b2a_hex(b'\xb9\x01\xef', b' ', -2)
+b'b901 ef'
+

Changed in version 3.8: The sep and bytes_per_sep parameters were added.

+
+binascii.a2b_hex(hexstr)
+binascii.unhexlify(hexstr)
+

Return the binary data represented by the hexadecimal string hexstr. This function is the inverse of b2a_hex(). hexstr must contain an even number of hexadecimal digits (which can be upper or lower case), otherwise an Error exception is raised.

Similar functionality (accepting only text string arguments, but more liberal towards whitespace) is also accessible using the bytes.fromhex() class method.

+
+exception binascii.Error
+

Exception raised on errors. These are usually programming errors.

+
+exception binascii.Incomplete
+

Exception raised on incomplete data. These are usually not programming errors, but may be handled by reading a little more data and trying again.

+

See also

+Module base64 +
+
+

Support for RFC compliant base64-style encoding in base 16, 32, 64, and 85.

+Module uu +
+
+

Support for UU encoding used on Unix.

+Module quopri +
+
+

Support for quoted-printable encoding used in MIME email messages.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/binascii.html +

+
diff --git a/devdocs/python~3.12/library%2Fbisect.html b/devdocs/python~3.12/library%2Fbisect.html new file mode 100644 index 00000000..467c1bf0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbisect.html @@ -0,0 +1,101 @@ +

bisect — Array bisection algorithm

Source code: Lib/bisect.py

This module provides support for maintaining a list in sorted order without having to sort the list after each insertion. For long lists of items with expensive comparison operations, this can be an improvement over linear searches or frequent resorting.

The module is called bisect because it uses a basic bisection algorithm to do its work. Unlike other bisection tools that search for a specific value, the functions in this module are designed to locate an insertion point. Accordingly, the functions never call an __eq__() method to determine whether a value has been found. Instead, the functions only call the __lt__() method and will return an insertion point between values in an array.

The following functions are provided:

+bisect.bisect_left(a, x, lo=0, hi=len(a), *, key=None)
+

Locate the insertion point for x in a to maintain sorted order. The parameters lo and hi may be used to specify a subset of the list which should be considered; by default the entire list is used. If x is already present in a, the insertion point will be before (to the left of) any existing entries. The return value is suitable for use as the first parameter to list.insert() assuming that a is already sorted.

The returned insertion point ip partitions the array a into two slices such that all(elem < x for elem in a[lo : ip]) is true for the left slice and all(elem >= x for elem in a[ip : hi]) is true for the right slice.

key specifies a key function of one argument that is used to extract a comparison key from each element in the array. To support searching complex records, the key function is not applied to the x value.

If key is None, the elements are compared directly and no key function is called.

Changed in version 3.10: Added the key parameter.

+
+bisect.bisect_right(a, x, lo=0, hi=len(a), *, key=None)
+bisect.bisect(a, x, lo=0, hi=len(a), *, key=None)
+

Similar to bisect_left(), but returns an insertion point which comes after (to the right of) any existing entries of x in a.

The returned insertion point ip partitions the array a into two slices such that all(elem <= x for elem in a[lo : ip]) is true for the left slice and all(elem > x for elem in a[ip : hi]) is true for the right slice.

Changed in version 3.10: Added the key parameter.

+
+bisect.insort_left(a, x, lo=0, hi=len(a), *, key=None)
+

Insert x in a in sorted order.

This function first runs bisect_left() to locate an insertion point. Next, it runs the insert() method on a to insert x at the appropriate position to maintain sort order.

To support inserting records in a table, the key function (if any) is applied to x for the search step but not for the insertion step.

Keep in mind that the O(log n) search is dominated by the slow O(n) insertion step.

Changed in version 3.10: Added the key parameter.

+
+bisect.insort_right(a, x, lo=0, hi=len(a), *, key=None)
+bisect.insort(a, x, lo=0, hi=len(a), *, key=None)
+

Similar to insort_left(), but inserting x in a after any existing entries of x.

This function first runs bisect_right() to locate an insertion point. Next, it runs the insert() method on a to insert x at the appropriate position to maintain sort order.

To support inserting records in a table, the key function (if any) is applied to x for the search step but not for the insertion step.

Keep in mind that the O(log n) search is dominated by the slow O(n) insertion step.

Changed in version 3.10: Added the key parameter.

+

Performance Notes

When writing time sensitive code using bisect() and insort(), keep these thoughts in mind:

  • Bisection is effective for searching ranges of values. For locating specific values, dictionaries are more performant.
  • The insort() functions are O(n) because the logarithmic search step is dominated by the linear time insertion step.
  • The search functions are stateless and discard key function results after they are used. Consequently, if the search functions are used in a loop, the key function may be called again and again on the same array elements. If the key function isn’t fast, consider wrapping it with functools.cache() to avoid duplicate computations. Alternatively, consider searching an array of precomputed keys to locate the insertion point (as shown in the examples section below).

See also

  • +Sorted Collections is a high performance module that uses bisect to managed sorted collections of data.
  • The SortedCollection recipe uses bisect to build a full-featured collection class with straight-forward search methods and support for a key-function. The keys are precomputed to save unnecessary calls to the key function during searches.

Searching Sorted Lists

The above bisect functions are useful for finding insertion points but can be tricky or awkward to use for common searching tasks. The following five functions show how to transform them into the standard lookups for sorted lists:

def index(a, x):
+    'Locate the leftmost value exactly equal to x'
+    i = bisect_left(a, x)
+    if i != len(a) and a[i] == x:
+        return i
+    raise ValueError
+
+def find_lt(a, x):
+    'Find rightmost value less than x'
+    i = bisect_left(a, x)
+    if i:
+        return a[i-1]
+    raise ValueError
+
+def find_le(a, x):
+    'Find rightmost value less than or equal to x'
+    i = bisect_right(a, x)
+    if i:
+        return a[i-1]
+    raise ValueError
+
+def find_gt(a, x):
+    'Find leftmost value greater than x'
+    i = bisect_right(a, x)
+    if i != len(a):
+        return a[i]
+    raise ValueError
+
+def find_ge(a, x):
+    'Find leftmost item greater than or equal to x'
+    i = bisect_left(a, x)
+    if i != len(a):
+        return a[i]
+    raise ValueError
+

Examples

The bisect() function can be useful for numeric table lookups. This example uses bisect() to look up a letter grade for an exam score (say) based on a set of ordered numeric breakpoints: 90 and up is an ‘A’, 80 to 89 is a ‘B’, and so on:

>>> def grade(score, breakpoints=[60, 70, 80, 90], grades='FDCBA'):
+...     i = bisect(breakpoints, score)
+...     return grades[i]
+...
+>>> [grade(score) for score in [33, 99, 77, 70, 89, 90, 100]]
+['F', 'A', 'C', 'C', 'B', 'A', 'A']
+

The bisect() and insort() functions also work with lists of tuples. The key argument can serve to extract the field used for ordering records in a table:

>>> from collections import namedtuple
+>>> from operator import attrgetter
+>>> from bisect import bisect, insort
+>>> from pprint import pprint
+
+>>> Movie = namedtuple('Movie', ('name', 'released', 'director'))
+
+>>> movies = [
+...     Movie('Jaws', 1975, 'Spielberg'),
+...     Movie('Titanic', 1997, 'Cameron'),
+...     Movie('The Birds', 1963, 'Hitchcock'),
+...     Movie('Aliens', 1986, 'Cameron')
+... ]
+
+>>> # Find the first movie released after 1960
+>>> by_year = attrgetter('released')
+>>> movies.sort(key=by_year)
+>>> movies[bisect(movies, 1960, key=by_year)]
+Movie(name='The Birds', released=1963, director='Hitchcock')
+
+>>> # Insert a movie while maintaining sort order
+>>> romance = Movie('Love Story', 1970, 'Hiller')
+>>> insort(movies, romance, key=by_year)
+>>> pprint(movies)
+[Movie(name='The Birds', released=1963, director='Hitchcock'),
+ Movie(name='Love Story', released=1970, director='Hiller'),
+ Movie(name='Jaws', released=1975, director='Spielberg'),
+ Movie(name='Aliens', released=1986, director='Cameron'),
+ Movie(name='Titanic', released=1997, director='Cameron')]
+

If the key function is expensive, it is possible to avoid repeated function calls by searching a list of precomputed keys to find the index of a record:

>>> data = [('red', 5), ('blue', 1), ('yellow', 8), ('black', 0)]
+>>> data.sort(key=lambda r: r[1])       # Or use operator.itemgetter(1).
+>>> keys = [r[1] for r in data]         # Precompute a list of keys.
+>>> data[bisect_left(keys, 0)]
+('black', 0)
+>>> data[bisect_left(keys, 1)]
+('blue', 1)
+>>> data[bisect_left(keys, 5)]
+('red', 5)
+>>> data[bisect_left(keys, 8)]
+('yellow', 8)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/bisect.html +

+
diff --git a/devdocs/python~3.12/library%2Fbuiltins.html b/devdocs/python~3.12/library%2Fbuiltins.html new file mode 100644 index 00000000..779799b3 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbuiltins.html @@ -0,0 +1,22 @@ +

builtins — Built-in objects

This module provides direct access to all ‘built-in’ identifiers of Python; for example, builtins.open is the full name for the built-in function open(). See Built-in Functions and Built-in Constants for documentation.

This module is not normally accessed explicitly by most applications, but can be useful in modules that provide objects with the same name as a built-in value, but in which the built-in of that name is also needed. For example, in a module that wants to implement an open() function that wraps the built-in open(), this module can be used directly:

import builtins
+
+def open(path):
+    f = builtins.open(path, 'r')
+    return UpperCaser(f)
+
+class UpperCaser:
+    '''Wrapper around a file that converts output to uppercase.'''
+
+    def __init__(self, f):
+        self._f = f
+
+    def read(self, count=-1):
+        return self._f.read(count).upper()
+
+    # ...
+

As an implementation detail, most modules have the name __builtins__ made available as part of their globals. The value of __builtins__ is normally either this module or the value of this module’s __dict__ attribute. Since this is an implementation detail, it may not be used by alternate implementations of Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/builtins.html +

+
diff --git a/devdocs/python~3.12/library%2Fbz2.html b/devdocs/python~3.12/library%2Fbz2.html new file mode 100644 index 00000000..5fe62e77 --- /dev/null +++ b/devdocs/python~3.12/library%2Fbz2.html @@ -0,0 +1,112 @@ +

bz2 — Support for bzip2 compression

Source code: Lib/bz2.py

This module provides a comprehensive interface for compressing and decompressing data using the bzip2 compression algorithm.

The bz2 module contains:

(De)compression of files

+bz2.open(filename, mode='rb', compresslevel=9, encoding=None, errors=None, newline=None)
+

Open a bzip2-compressed file in binary or text mode, returning a file object.

As with the constructor for BZ2File, the filename argument can be an actual filename (a str or bytes object), or an existing file object to read from or write to.

The mode argument can be any of 'r', 'rb', 'w', 'wb', 'x', 'xb', 'a' or 'ab' for binary mode, or 'rt', 'wt', 'xt', or 'at' for text mode. The default is 'rb'.

The compresslevel argument is an integer from 1 to 9, as for the BZ2File constructor.

For binary mode, this function is equivalent to the BZ2File constructor: BZ2File(filename, mode, compresslevel=compresslevel). In this case, the encoding, errors and newline arguments must not be provided.

For text mode, a BZ2File object is created, and wrapped in an io.TextIOWrapper instance with the specified encoding, error handling behavior, and line ending(s).

New in version 3.3.

Changed in version 3.4: The 'x' (exclusive creation) mode was added.

Changed in version 3.6: Accepts a path-like object.

+
+class bz2.BZ2File(filename, mode='r', *, compresslevel=9)
+

Open a bzip2-compressed file in binary mode.

If filename is a str or bytes object, open the named file directly. Otherwise, filename should be a file object, which will be used to read or write the compressed data.

The mode argument can be either 'r' for reading (default), 'w' for overwriting, 'x' for exclusive creation, or 'a' for appending. These can equivalently be given as 'rb', 'wb', 'xb' and 'ab' respectively.

If filename is a file object (rather than an actual file name), a mode of 'w' does not truncate the file, and is instead equivalent to 'a'.

If mode is 'w' or 'a', compresslevel can be an integer between 1 and 9 specifying the level of compression: 1 produces the least compression, and 9 (default) produces the most compression.

If mode is 'r', the input file may be the concatenation of multiple compressed streams.

BZ2File provides all of the members specified by the io.BufferedIOBase, except for detach() and truncate(). Iteration and the with statement are supported.

BZ2File also provides the following methods:

+peek([n])
+

Return buffered data without advancing the file position. At least one byte of data will be returned (unless at EOF). The exact number of bytes returned is unspecified.

Note

While calling peek() does not change the file position of the BZ2File, it may change the position of the underlying file object (e.g. if the BZ2File was constructed by passing a file object for filename).

New in version 3.3.

+
+fileno()
+

Return the file descriptor for the underlying file.

New in version 3.3.

+
+readable()
+

Return whether the file was opened for reading.

New in version 3.3.

+
+seekable()
+

Return whether the file supports seeking.

New in version 3.3.

+
+writable()
+

Return whether the file was opened for writing.

New in version 3.3.

+
+read1(size=- 1)
+

Read up to size uncompressed bytes, while trying to avoid making multiple reads from the underlying stream. Reads up to a buffer’s worth of data if size is negative.

Returns b'' if the file is at EOF.

New in version 3.3.

+
+readinto(b)
+

Read bytes into b.

Returns the number of bytes read (0 for EOF).

New in version 3.3.

+

Changed in version 3.1: Support for the with statement was added.

Changed in version 3.3: Support was added for filename being a file object instead of an actual filename.

Changed in version 3.3: The 'a' (append) mode was added, along with support for reading multi-stream files.

Changed in version 3.4: The 'x' (exclusive creation) mode was added.

Changed in version 3.5: The read() method now accepts an argument of None.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.9: The buffering parameter has been removed. It was ignored and deprecated since Python 3.0. Pass an open file object to control how the file is opened.

The compresslevel parameter became keyword-only.

Changed in version 3.10: This class is thread unsafe in the face of multiple simultaneous readers or writers, just like its equivalent classes in gzip and lzma have always been.

+

Incremental (de)compression

+class bz2.BZ2Compressor(compresslevel=9)
+

Create a new compressor object. This object may be used to compress data incrementally. For one-shot compression, use the compress() function instead.

compresslevel, if given, must be an integer between 1 and 9. The default is 9.

+compress(data)
+

Provide data to the compressor object. Returns a chunk of compressed data if possible, or an empty byte string otherwise.

When you have finished providing data to the compressor, call the flush() method to finish the compression process.

+
+flush()
+

Finish the compression process. Returns the compressed data left in internal buffers.

The compressor object may not be used after this method has been called.

+
+
+class bz2.BZ2Decompressor
+

Create a new decompressor object. This object may be used to decompress data incrementally. For one-shot compression, use the decompress() function instead.

Note

This class does not transparently handle inputs containing multiple compressed streams, unlike decompress() and BZ2File. If you need to decompress a multi-stream input with BZ2Decompressor, you must use a new decompressor for each stream.

+decompress(data, max_length=- 1)
+

Decompress data (a bytes-like object), returning uncompressed data as bytes. Some of data may be buffered internally, for use in later calls to decompress(). The returned data should be concatenated with the output of any previous calls to decompress().

If max_length is nonnegative, returns at most max_length bytes of decompressed data. If this limit is reached and further output can be produced, the needs_input attribute will be set to False. In this case, the next call to decompress() may provide data as b'' to obtain more of the output.

If all of the input data was decompressed and returned (either because this was less than max_length bytes, or because max_length was negative), the needs_input attribute will be set to True.

Attempting to decompress data after the end of stream is reached raises an EOFError. Any data found after the end of the stream is ignored and saved in the unused_data attribute.

Changed in version 3.5: Added the max_length parameter.

+
+eof
+

True if the end-of-stream marker has been reached.

New in version 3.3.

+
+unused_data
+

Data found after the end of the compressed stream.

If this attribute is accessed before the end of the stream has been reached, its value will be b''.

+
+needs_input
+

False if the decompress() method can provide more decompressed data before requiring new uncompressed input.

New in version 3.5.

+
+

One-shot (de)compression

+bz2.compress(data, compresslevel=9)
+

Compress data, a bytes-like object.

compresslevel, if given, must be an integer between 1 and 9. The default is 9.

For incremental compression, use a BZ2Compressor instead.

+
+bz2.decompress(data)
+

Decompress data, a bytes-like object.

If data is the concatenation of multiple compressed streams, decompress all of the streams.

For incremental decompression, use a BZ2Decompressor instead.

Changed in version 3.3: Support for multi-stream inputs was added.

+

Examples of usage

Below are some examples of typical usage of the bz2 module.

Using compress() and decompress() to demonstrate round-trip compression:

>>> import bz2
+>>> data = b"""\
+... Donec rhoncus quis sapien sit amet molestie. Fusce scelerisque vel augue
+... nec ullamcorper. Nam rutrum pretium placerat. Aliquam vel tristique lorem,
+... sit amet cursus ante. In interdum laoreet mi, sit amet ultrices purus
+... pulvinar a. Nam gravida euismod magna, non varius justo tincidunt feugiat.
+... Aliquam pharetra lacus non risus vehicula rutrum. Maecenas aliquam leo
+... felis. Pellentesque semper nunc sit amet nibh ullamcorper, ac elementum
+... dolor luctus. Curabitur lacinia mi ornare consectetur vestibulum."""
+>>> c = bz2.compress(data)
+>>> len(data) / len(c)  # Data compression ratio
+1.513595166163142
+>>> d = bz2.decompress(c)
+>>> data == d  # Check equality to original object after round-trip
+True
+

Using BZ2Compressor for incremental compression:

>>> import bz2
+>>> def gen_data(chunks=10, chunksize=1000):
+...     """Yield incremental blocks of chunksize bytes."""
+...     for _ in range(chunks):
+...         yield b"z" * chunksize
+...
+>>> comp = bz2.BZ2Compressor()
+>>> out = b""
+>>> for chunk in gen_data():
+...     # Provide data to the compressor object
+...     out = out + comp.compress(chunk)
+...
+>>> # Finish the compression process.  Call this once you have
+>>> # finished providing data to the compressor.
+>>> out = out + comp.flush()
+

The example above uses a very “nonrandom” stream of data (a stream of b"z" chunks). Random data tends to compress poorly, while ordered, repetitive data usually yields a high compression ratio.

Writing and reading a bzip2-compressed file in binary mode:

>>> import bz2
+>>> data = b"""\
+... Donec rhoncus quis sapien sit amet molestie. Fusce scelerisque vel augue
+... nec ullamcorper. Nam rutrum pretium placerat. Aliquam vel tristique lorem,
+... sit amet cursus ante. In interdum laoreet mi, sit amet ultrices purus
+... pulvinar a. Nam gravida euismod magna, non varius justo tincidunt feugiat.
+... Aliquam pharetra lacus non risus vehicula rutrum. Maecenas aliquam leo
+... felis. Pellentesque semper nunc sit amet nibh ullamcorper, ac elementum
+... dolor luctus. Curabitur lacinia mi ornare consectetur vestibulum."""
+>>> with bz2.open("myfile.bz2", "wb") as f:
+...     # Write compressed data to file
+...     unused = f.write(data)
+...
+>>> with bz2.open("myfile.bz2", "rb") as f:
+...     # Decompress data from file
+...     content = f.read()
+...
+>>> content == data  # Check equality to original object after round-trip
+True
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/bz2.html +

+
diff --git a/devdocs/python~3.12/library%2Fcalendar.html b/devdocs/python~3.12/library%2Fcalendar.html new file mode 100644 index 00000000..c131f578 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcalendar.html @@ -0,0 +1,286 @@ +

calendar — General calendar-related functions

Source code: Lib/calendar.py

This module allows you to output calendars like the Unix cal program, and provides additional useful functions related to the calendar. By default, these calendars have Monday as the first day of the week, and Sunday as the last (the European convention). Use setfirstweekday() to set the first day of the week to Sunday (6) or to any other weekday. Parameters that specify dates are given as integers. For related functionality, see also the datetime and time modules.

The functions and classes defined in this module use an idealized calendar, the current Gregorian calendar extended indefinitely in both directions. This matches the definition of the “proleptic Gregorian” calendar in Dershowitz and Reingold’s book “Calendrical Calculations”, where it’s the base calendar for all computations. Zero and negative years are interpreted as prescribed by the ISO 8601 standard. Year 0 is 1 BC, year -1 is 2 BC, and so on.

+class calendar.Calendar(firstweekday=0)
+

Creates a Calendar object. firstweekday is an integer specifying the first day of the week. MONDAY is 0 (the default), SUNDAY is 6.

A Calendar object provides several methods that can be used for preparing the calendar data for formatting. This class doesn’t do any formatting itself. This is the job of subclasses.

Calendar instances have the following methods:

+iterweekdays()
+

Return an iterator for the week day numbers that will be used for one week. The first value from the iterator will be the same as the value of the firstweekday property.

+
+itermonthdates(year, month)
+

Return an iterator for the month month (1–12) in the year year. This iterator will return all days (as datetime.date objects) for the month and all days before the start of the month or after the end of the month that are required to get a complete week.

+
+itermonthdays(year, month)
+

Return an iterator for the month month in the year year similar to itermonthdates(), but not restricted by the datetime.date range. Days returned will simply be day of the month numbers. For the days outside of the specified month, the day number is 0.

+
+itermonthdays2(year, month)
+

Return an iterator for the month month in the year year similar to itermonthdates(), but not restricted by the datetime.date range. Days returned will be tuples consisting of a day of the month number and a week day number.

+
+itermonthdays3(year, month)
+

Return an iterator for the month month in the year year similar to itermonthdates(), but not restricted by the datetime.date range. Days returned will be tuples consisting of a year, a month and a day of the month numbers.

New in version 3.7.

+
+itermonthdays4(year, month)
+

Return an iterator for the month month in the year year similar to itermonthdates(), but not restricted by the datetime.date range. Days returned will be tuples consisting of a year, a month, a day of the month, and a day of the week numbers.

New in version 3.7.

+
+monthdatescalendar(year, month)
+

Return a list of the weeks in the month month of the year as full weeks. Weeks are lists of seven datetime.date objects.

+
+monthdays2calendar(year, month)
+

Return a list of the weeks in the month month of the year as full weeks. Weeks are lists of seven tuples of day numbers and weekday numbers.

+
+monthdayscalendar(year, month)
+

Return a list of the weeks in the month month of the year as full weeks. Weeks are lists of seven day numbers.

+
+yeardatescalendar(year, width=3)
+

Return the data for the specified year ready for formatting. The return value is a list of month rows. Each month row contains up to width months (defaulting to 3). Each month contains between 4 and 6 weeks and each week contains 1–7 days. Days are datetime.date objects.

+
+yeardays2calendar(year, width=3)
+

Return the data for the specified year ready for formatting (similar to yeardatescalendar()). Entries in the week lists are tuples of day numbers and weekday numbers. Day numbers outside this month are zero.

+
+yeardayscalendar(year, width=3)
+

Return the data for the specified year ready for formatting (similar to yeardatescalendar()). Entries in the week lists are day numbers. Day numbers outside this month are zero.

+
+
+class calendar.TextCalendar(firstweekday=0)
+

This class can be used to generate plain text calendars.

TextCalendar instances have the following methods:

+formatmonth(theyear, themonth, w=0, l=0)
+

Return a month’s calendar in a multi-line string. If w is provided, it specifies the width of the date columns, which are centered. If l is given, it specifies the number of lines that each week will use. Depends on the first weekday as specified in the constructor or set by the setfirstweekday() method.

+
+prmonth(theyear, themonth, w=0, l=0)
+

Print a month’s calendar as returned by formatmonth().

+
+formatyear(theyear, w=2, l=1, c=6, m=3)
+

Return a m-column calendar for an entire year as a multi-line string. Optional parameters w, l, and c are for date column width, lines per week, and number of spaces between month columns, respectively. Depends on the first weekday as specified in the constructor or set by the setfirstweekday() method. The earliest year for which a calendar can be generated is platform-dependent.

+
+pryear(theyear, w=2, l=1, c=6, m=3)
+

Print the calendar for an entire year as returned by formatyear().

+
+
+class calendar.HTMLCalendar(firstweekday=0)
+

This class can be used to generate HTML calendars.

HTMLCalendar instances have the following methods:

+formatmonth(theyear, themonth, withyear=True)
+

Return a month’s calendar as an HTML table. If withyear is true the year will be included in the header, otherwise just the month name will be used.

+
+formatyear(theyear, width=3)
+

Return a year’s calendar as an HTML table. width (defaulting to 3) specifies the number of months per row.

+
+formatyearpage(theyear, width=3, css='calendar.css', encoding=None)
+

Return a year’s calendar as a complete HTML page. width (defaulting to 3) specifies the number of months per row. css is the name for the cascading style sheet to be used. None can be passed if no style sheet should be used. encoding specifies the encoding to be used for the output (defaulting to the system default encoding).

+
+formatmonthname(theyear, themonth, withyear=True)
+

Return a month name as an HTML table row. If withyear is true the year will be included in the row, otherwise just the month name will be used.

+

HTMLCalendar has the following attributes you can override to customize the CSS classes used by the calendar:

+cssclasses
+

A list of CSS classes used for each weekday. The default class list is:

cssclasses = ["mon", "tue", "wed", "thu", "fri", "sat", "sun"]
+

more styles can be added for each day:

cssclasses = ["mon text-bold", "tue", "wed", "thu", "fri", "sat", "sun red"]
+

Note that the length of this list must be seven items.

+
+cssclass_noday
+

The CSS class for a weekday occurring in the previous or coming month.

New in version 3.7.

+
+cssclasses_weekday_head
+

A list of CSS classes used for weekday names in the header row. The default is the same as cssclasses.

New in version 3.7.

+
+cssclass_month_head
+

The month’s head CSS class (used by formatmonthname()). The default value is "month".

New in version 3.7.

+
+cssclass_month
+

The CSS class for the whole month’s table (used by formatmonth()). The default value is "month".

New in version 3.7.

+
+cssclass_year
+

The CSS class for the whole year’s table of tables (used by formatyear()). The default value is "year".

New in version 3.7.

+
+cssclass_year_head
+

The CSS class for the table head for the whole year (used by formatyear()). The default value is "year".

New in version 3.7.

+

Note that although the naming for the above described class attributes is singular (e.g. cssclass_month cssclass_noday), one can replace the single CSS class with a space separated list of CSS classes, for example:

"text-bold text-red"
+

Here is an example how HTMLCalendar can be customized:

class CustomHTMLCal(calendar.HTMLCalendar):
+    cssclasses = [style + " text-nowrap" for style in
+                  calendar.HTMLCalendar.cssclasses]
+    cssclass_month_head = "text-center month-head"
+    cssclass_month = "text-center month"
+    cssclass_year = "text-italic lead"
+
+
+class calendar.LocaleTextCalendar(firstweekday=0, locale=None)
+

This subclass of TextCalendar can be passed a locale name in the constructor and will return month and weekday names in the specified locale.

+
+class calendar.LocaleHTMLCalendar(firstweekday=0, locale=None)
+

This subclass of HTMLCalendar can be passed a locale name in the constructor and will return month and weekday names in the specified locale.

+

Note

The constructor, formatweekday() and formatmonthname() methods of these two classes temporarily change the LC_TIME locale to the given locale. Because the current locale is a process-wide setting, they are not thread-safe.

For simple text calendars this module provides the following functions.

+calendar.setfirstweekday(weekday)
+

Sets the weekday (0 is Monday, 6 is Sunday) to start each week. The values MONDAY, TUESDAY, WEDNESDAY, THURSDAY, FRIDAY, SATURDAY, and SUNDAY are provided for convenience. For example, to set the first weekday to Sunday:

import calendar
+calendar.setfirstweekday(calendar.SUNDAY)
+
+
+calendar.firstweekday()
+

Returns the current setting for the weekday to start each week.

+
+calendar.isleap(year)
+

Returns True if year is a leap year, otherwise False.

+
+calendar.leapdays(y1, y2)
+

Returns the number of leap years in the range from y1 to y2 (exclusive), where y1 and y2 are years.

This function works for ranges spanning a century change.

+
+calendar.weekday(year, month, day)
+

Returns the day of the week (0 is Monday) for year (1970–…), month (1–12), day (1–31).

+
+calendar.weekheader(n)
+

Return a header containing abbreviated weekday names. n specifies the width in characters for one weekday.

+
+calendar.monthrange(year, month)
+

Returns weekday of first day of the month and number of days in month, for the specified year and month.

+
+calendar.monthcalendar(year, month)
+

Returns a matrix representing a month’s calendar. Each row represents a week; days outside of the month are represented by zeros. Each week begins with Monday unless set by setfirstweekday().

+
+calendar.prmonth(theyear, themonth, w=0, l=0)
+

Prints a month’s calendar as returned by month().

+
+calendar.month(theyear, themonth, w=0, l=0)
+

Returns a month’s calendar in a multi-line string using the formatmonth() of the TextCalendar class.

+
+calendar.prcal(year, w=0, l=0, c=6, m=3)
+

Prints the calendar for an entire year as returned by calendar().

+
+calendar.calendar(year, w=2, l=1, c=6, m=3)
+

Returns a 3-column calendar for an entire year as a multi-line string using the formatyear() of the TextCalendar class.

+
+calendar.timegm(tuple)
+

An unrelated but handy function that takes a time tuple such as returned by the gmtime() function in the time module, and returns the corresponding Unix timestamp value, assuming an epoch of 1970, and the POSIX encoding. In fact, time.gmtime() and timegm() are each others’ inverse.

+

The calendar module exports the following data attributes:

+calendar.day_name
+

An array that represents the days of the week in the current locale.

+
+calendar.day_abbr
+

An array that represents the abbreviated days of the week in the current locale.

+
+calendar.MONDAY
+calendar.TUESDAY
+calendar.WEDNESDAY
+calendar.THURSDAY
+calendar.FRIDAY
+calendar.SATURDAY
+calendar.SUNDAY
+

Aliases for the days of the week, where MONDAY is 0 and SUNDAY is 6.

New in version 3.12.

+
+class calendar.Day
+

Enumeration defining days of the week as integer constants. The members of this enumeration are exported to the module scope as MONDAY through SUNDAY.

New in version 3.12.

+
+calendar.month_name
+

An array that represents the months of the year in the current locale. This follows normal convention of January being month number 1, so it has a length of 13 and month_name[0] is the empty string.

+
+calendar.month_abbr
+

An array that represents the abbreviated months of the year in the current locale. This follows normal convention of January being month number 1, so it has a length of 13 and month_abbr[0] is the empty string.

+
+calendar.JANUARY
+calendar.FEBRUARY
+calendar.MARCH
+calendar.APRIL
+calendar.MAY
+calendar.JUNE
+calendar.JULY
+calendar.AUGUST
+calendar.SEPTEMBER
+calendar.OCTOBER
+calendar.NOVEMBER
+calendar.DECEMBER
+

Aliases for the months of the year, where JANUARY is 1 and DECEMBER is 12.

New in version 3.12.

+
+class calendar.Month
+

Enumeration defining months of the year as integer constants. The members of this enumeration are exported to the module scope as JANUARY through DECEMBER.

New in version 3.12.

+

The calendar module defines the following exceptions:

+exception calendar.IllegalMonthError(month)
+

A subclass of ValueError, raised when the given month number is outside of the range 1-12 (inclusive).

+month
+

The invalid month number.

+
+
+exception calendar.IllegalWeekdayError(weekday)
+

A subclass of ValueError, raised when the given weekday number is outside of the range 0-6 (inclusive).

+weekday
+

The invalid weekday number.

+
+

See also

+Module datetime +
+
+

Object-oriented interface to dates and times with similar functionality to the time module.

+Module time +
+
+

Low-level time related functions.

Command-Line Usage

New in version 2.5.

The calendar module can be executed as a script from the command line to interactively print a calendar.

python -m calendar [-h] [-L LOCALE] [-e ENCODING] [-t {text,html}]
+                   [-w WIDTH] [-l LINES] [-s SPACING] [-m MONTHS] [-c CSS]
+                   [year] [month]
+

For example, to print a calendar for the year 2000:

$ python -m calendar 2000
+                                  2000
+
+      January                   February                   March
+Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
+                1  2          1  2  3  4  5  6             1  2  3  4  5
+ 3  4  5  6  7  8  9       7  8  9 10 11 12 13       6  7  8  9 10 11 12
+10 11 12 13 14 15 16      14 15 16 17 18 19 20      13 14 15 16 17 18 19
+17 18 19 20 21 22 23      21 22 23 24 25 26 27      20 21 22 23 24 25 26
+24 25 26 27 28 29 30      28 29                     27 28 29 30 31
+31
+
+       April                      May                       June
+Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
+                1  2       1  2  3  4  5  6  7                1  2  3  4
+ 3  4  5  6  7  8  9       8  9 10 11 12 13 14       5  6  7  8  9 10 11
+10 11 12 13 14 15 16      15 16 17 18 19 20 21      12 13 14 15 16 17 18
+17 18 19 20 21 22 23      22 23 24 25 26 27 28      19 20 21 22 23 24 25
+24 25 26 27 28 29 30      29 30 31                  26 27 28 29 30
+
+        July                     August                  September
+Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
+                1  2          1  2  3  4  5  6                   1  2  3
+ 3  4  5  6  7  8  9       7  8  9 10 11 12 13       4  5  6  7  8  9 10
+10 11 12 13 14 15 16      14 15 16 17 18 19 20      11 12 13 14 15 16 17
+17 18 19 20 21 22 23      21 22 23 24 25 26 27      18 19 20 21 22 23 24
+24 25 26 27 28 29 30      28 29 30 31               25 26 27 28 29 30
+31
+
+      October                   November                  December
+Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su      Mo Tu We Th Fr Sa Su
+                   1             1  2  3  4  5                   1  2  3
+ 2  3  4  5  6  7  8       6  7  8  9 10 11 12       4  5  6  7  8  9 10
+ 9 10 11 12 13 14 15      13 14 15 16 17 18 19      11 12 13 14 15 16 17
+16 17 18 19 20 21 22      20 21 22 23 24 25 26      18 19 20 21 22 23 24
+23 24 25 26 27 28 29      27 28 29 30               25 26 27 28 29 30 31
+30 31
+

The following options are accepted:

+--help, -h
+

Show the help message and exit.

+
+--locale LOCALE, -L LOCALE
+

The locale to use for month and weekday names. Defaults to English.

+
+--encoding ENCODING, -e ENCODING
+

The encoding to use for output. --encoding is required if --locale is set.

+
+--type {text,html}, -t {text,html}
+

Print the calendar to the terminal as text, or as an HTML document.

+
+year
+

The year to print the calendar for. Must be a number between 1 and 9999. Defaults to the current year.

+
+month
+

The month of the specified year to print the calendar for. Must be a number between 1 and 12, and may only be used in text mode. Defaults to printing a calendar for the full year.

+

Text-mode options:

+--width WIDTH, -w WIDTH
+

The width of the date column in terminal columns. The date is printed centred in the column. Any value lower than 2 is ignored. Defaults to 2.

+
+--lines LINES, -l LINES
+

The number of lines for each week in terminal rows. The date is printed top-aligned. Any value lower than 1 is ignored. Defaults to 1.

+
+--spacing SPACING, -s SPACING
+

The space between months in columns. Any value lower than 2 is ignored. Defaults to 6.

+
+--months MONTHS, -m MONTHS
+

The number of months printed per row. Defaults to 3.

+

HTML-mode options:

+--css CSS, -c CSS
+

The path of a CSS stylesheet to use for the calendar. This must either be relative to the generated HTML, or an absolute HTTP or file:/// URL.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/calendar.html +

+
diff --git a/devdocs/python~3.12/library%2Fcmath.html b/devdocs/python~3.12/library%2Fcmath.html new file mode 100644 index 00000000..21c1e1b6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcmath.html @@ -0,0 +1,106 @@ +

cmath — Mathematical functions for complex numbers

This module provides access to mathematical functions for complex numbers. The functions in this module accept integers, floating-point numbers or complex numbers as arguments. They will also accept any Python object that has either a __complex__() or a __float__() method: these methods are used to convert the object to a complex or floating-point number, respectively, and the function is then applied to the result of the conversion.

Note

For functions involving branch cuts, we have the problem of deciding how to define those functions on the cut itself. Following Kahan’s “Branch cuts for complex elementary functions” paper, as well as Annex G of C99 and later C standards, we use the sign of zero to distinguish one side of the branch cut from the other: for a branch cut along (a portion of) the real axis we look at the sign of the imaginary part, while for a branch cut along the imaginary axis we look at the sign of the real part.

For example, the cmath.sqrt() function has a branch cut along the negative real axis. An argument of complex(-2.0, -0.0) is treated as though it lies below the branch cut, and so gives a result on the negative imaginary axis:

>>> cmath.sqrt(complex(-2.0, -0.0))
+-1.4142135623730951j
+

But an argument of complex(-2.0, 0.0) is treated as though it lies above the branch cut:

>>> cmath.sqrt(complex(-2.0, 0.0))
+1.4142135623730951j
+

Conversions to and from polar coordinates

A Python complex number z is stored internally using rectangular or Cartesian coordinates. It is completely determined by its real part z.real and its imaginary part z.imag. In other words:

z == z.real + z.imag*1j
+

Polar coordinates give an alternative way to represent a complex number. In polar coordinates, a complex number z is defined by the modulus r and the phase angle phi. The modulus r is the distance from z to the origin, while the phase phi is the counterclockwise angle, measured in radians, from the positive x-axis to the line segment that joins the origin to z.

The following functions can be used to convert from the native rectangular coordinates to polar coordinates and back.

+cmath.phase(x)
+

Return the phase of x (also known as the argument of x), as a float. phase(x) is equivalent to math.atan2(x.imag, x.real). The result lies in the range [-π, π], and the branch cut for this operation lies along the negative real axis. The sign of the result is the same as the sign of x.imag, even when x.imag is zero:

>>> phase(complex(-1.0, 0.0))
+3.141592653589793
+>>> phase(complex(-1.0, -0.0))
+-3.141592653589793
+
+

Note

The modulus (absolute value) of a complex number x can be computed using the built-in abs() function. There is no separate cmath module function for this operation.

+cmath.polar(x)
+

Return the representation of x in polar coordinates. Returns a pair (r, phi) where r is the modulus of x and phi is the phase of x. polar(x) is equivalent to (abs(x), +phase(x)).

+
+cmath.rect(r, phi)
+

Return the complex number x with polar coordinates r and phi. Equivalent to r * (math.cos(phi) + math.sin(phi)*1j).

+

Power and logarithmic functions

+cmath.exp(x)
+

Return e raised to the power x, where e is the base of natural logarithms.

+
+cmath.log(x[, base])
+

Returns the logarithm of x to the given base. If the base is not specified, returns the natural logarithm of x. There is one branch cut, from 0 along the negative real axis to -∞.

+
+cmath.log10(x)
+

Return the base-10 logarithm of x. This has the same branch cut as log().

+
+cmath.sqrt(x)
+

Return the square root of x. This has the same branch cut as log().

+

Trigonometric functions

+cmath.acos(x)
+

Return the arc cosine of x. There are two branch cuts: One extends right from 1 along the real axis to ∞. The other extends left from -1 along the real axis to -∞.

+
+cmath.asin(x)
+

Return the arc sine of x. This has the same branch cuts as acos().

+
+cmath.atan(x)
+

Return the arc tangent of x. There are two branch cuts: One extends from 1j along the imaginary axis to ∞j. The other extends from -1j along the imaginary axis to -∞j.

+
+cmath.cos(x)
+

Return the cosine of x.

+
+cmath.sin(x)
+

Return the sine of x.

+
+cmath.tan(x)
+

Return the tangent of x.

+

Hyperbolic functions

+cmath.acosh(x)
+

Return the inverse hyperbolic cosine of x. There is one branch cut, extending left from 1 along the real axis to -∞.

+
+cmath.asinh(x)
+

Return the inverse hyperbolic sine of x. There are two branch cuts: One extends from 1j along the imaginary axis to ∞j. The other extends from -1j along the imaginary axis to -∞j.

+
+cmath.atanh(x)
+

Return the inverse hyperbolic tangent of x. There are two branch cuts: One extends from 1 along the real axis to ∞. The other extends from -1 along the real axis to -∞.

+
+cmath.cosh(x)
+

Return the hyperbolic cosine of x.

+
+cmath.sinh(x)
+

Return the hyperbolic sine of x.

+
+cmath.tanh(x)
+

Return the hyperbolic tangent of x.

+

Classification functions

+cmath.isfinite(x)
+

Return True if both the real and imaginary parts of x are finite, and False otherwise.

New in version 3.2.

+
+cmath.isinf(x)
+

Return True if either the real or the imaginary part of x is an infinity, and False otherwise.

+
+cmath.isnan(x)
+

Return True if either the real or the imaginary part of x is a NaN, and False otherwise.

+
+cmath.isclose(a, b, *, rel_tol=1e-09, abs_tol=0.0)
+

Return True if the values a and b are close to each other and False otherwise.

Whether or not two values are considered close is determined according to given absolute and relative tolerances.

rel_tol is the relative tolerance – it is the maximum allowed difference between a and b, relative to the larger absolute value of a or b. For example, to set a tolerance of 5%, pass rel_tol=0.05. The default tolerance is 1e-09, which assures that the two values are the same within about 9 decimal digits. rel_tol must be greater than zero.

abs_tol is the minimum absolute tolerance – useful for comparisons near zero. abs_tol must be at least zero.

If no errors occur, the result will be: abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol).

The IEEE 754 special values of NaN, inf, and -inf will be handled according to IEEE rules. Specifically, NaN is not considered close to any other value, including NaN. inf and -inf are only considered close to themselves.

New in version 3.5.

See also

PEP 485 – A function for testing approximate equality

+

Constants

+cmath.pi
+

The mathematical constant π, as a float.

+
+cmath.e
+

The mathematical constant e, as a float.

+
+cmath.tau
+

The mathematical constant τ, as a float.

New in version 3.6.

+
+cmath.inf
+

Floating-point positive infinity. Equivalent to float('inf').

New in version 3.6.

+
+cmath.infj
+

Complex number with zero real part and positive infinity imaginary part. Equivalent to complex(0.0, float('inf')).

New in version 3.6.

+
+cmath.nan
+

A floating-point “not a number” (NaN) value. Equivalent to float('nan').

New in version 3.6.

+
+cmath.nanj
+

Complex number with zero real part and NaN imaginary part. Equivalent to complex(0.0, float('nan')).

New in version 3.6.

+

Note that the selection of functions is similar, but not identical, to that in module math. The reason for having two modules is that some users aren’t interested in complex numbers, and perhaps don’t even know what they are. They would rather have math.sqrt(-1) raise an exception than return a complex number. Also note that the functions defined in cmath always return a complex number, even if the answer can be expressed as a real number (in which case the complex number has an imaginary part of zero).

A note on branch cuts: They are curves along which the given function fails to be continuous. They are a necessary feature of many complex functions. It is assumed that if you need to compute with complex functions, you will understand about branch cuts. Consult almost any (not too elementary) book on complex variables for enlightenment. For information of the proper choice of branch cuts for numerical purposes, a good reference should be the following:

See also

Kahan, W: Branch cuts for complex elementary functions; or, Much ado about nothing’s sign bit. In Iserles, A., and Powell, M. (eds.), The state of the art in numerical analysis. Clarendon Press (1987) pp165–211.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/cmath.html +

+
diff --git a/devdocs/python~3.12/library%2Fcmd.html b/devdocs/python~3.12/library%2Fcmd.html new file mode 100644 index 00000000..cb29a143 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcmd.html @@ -0,0 +1,202 @@ +

cmd — Support for line-oriented command interpreters

Source code: Lib/cmd.py

The Cmd class provides a simple framework for writing line-oriented command interpreters. These are often useful for test harnesses, administrative tools, and prototypes that will later be wrapped in a more sophisticated interface.

+class cmd.Cmd(completekey='tab', stdin=None, stdout=None)
+

A Cmd instance or subclass instance is a line-oriented interpreter framework. There is no good reason to instantiate Cmd itself; rather, it’s useful as a superclass of an interpreter class you define yourself in order to inherit Cmd’s methods and encapsulate action methods.

The optional argument completekey is the readline name of a completion key; it defaults to Tab. If completekey is not None and readline is available, command completion is done automatically.

The optional arguments stdin and stdout specify the input and output file objects that the Cmd instance or subclass instance will use for input and output. If not specified, they will default to sys.stdin and sys.stdout.

If you want a given stdin to be used, make sure to set the instance’s use_rawinput attribute to False, otherwise stdin will be ignored.

+

Cmd Objects

A Cmd instance has the following methods:

+Cmd.cmdloop(intro=None)
+

Repeatedly issue a prompt, accept input, parse an initial prefix off the received input, and dispatch to action methods, passing them the remainder of the line as argument.

The optional argument is a banner or intro string to be issued before the first prompt (this overrides the intro class attribute).

If the readline module is loaded, input will automatically inherit bash-like history-list editing (e.g. Control-P scrolls back to the last command, Control-N forward to the next one, Control-F moves the cursor to the right non-destructively, Control-B moves the cursor to the left non-destructively, etc.).

An end-of-file on input is passed back as the string 'EOF'.

An interpreter instance will recognize a command name foo if and only if it has a method do_foo(). As a special case, a line beginning with the character '?' is dispatched to the method do_help(). As another special case, a line beginning with the character '!' is dispatched to the method do_shell() (if such a method is defined).

This method will return when the postcmd() method returns a true value. The stop argument to postcmd() is the return value from the command’s corresponding do_*() method.

If completion is enabled, completing commands will be done automatically, and completing of commands args is done by calling complete_foo() with arguments text, line, begidx, and endidx. text is the string prefix we are attempting to match: all returned matches must begin with it. line is the current input line with leading whitespace removed, begidx and endidx are the beginning and ending indexes of the prefix text, which could be used to provide different completion depending upon which position the argument is in.

+
+Cmd.do_help(arg)
+

All subclasses of Cmd inherit a predefined do_help(). This method, called with an argument 'bar', invokes the corresponding method help_bar(), and if that is not present, prints the docstring of do_bar(), if available. With no argument, do_help() lists all available help topics (that is, all commands with corresponding help_*() methods or commands that have docstrings), and also lists any undocumented commands.

+
+Cmd.onecmd(str)
+

Interpret the argument as though it had been typed in response to the prompt. This may be overridden, but should not normally need to be; see the precmd() and postcmd() methods for useful execution hooks. The return value is a flag indicating whether interpretation of commands by the interpreter should stop. If there is a do_*() method for the command str, the return value of that method is returned, otherwise the return value from the default() method is returned.

+
+Cmd.emptyline()
+

Method called when an empty line is entered in response to the prompt. If this method is not overridden, it repeats the last nonempty command entered.

+
+Cmd.default(line)
+

Method called on an input line when the command prefix is not recognized. If this method is not overridden, it prints an error message and returns.

+
+Cmd.completedefault(text, line, begidx, endidx)
+

Method called to complete an input line when no command-specific complete_*() method is available. By default, it returns an empty list.

+
+Cmd.columnize(list, displaywidth=80)
+

Method called to display a list of strings as a compact set of columns. Each column is only as wide as necessary. Columns are separated by two spaces for readability.

+
+Cmd.precmd(line)
+

Hook method executed just before the command line line is interpreted, but after the input prompt is generated and issued. This method is a stub in Cmd; it exists to be overridden by subclasses. The return value is used as the command which will be executed by the onecmd() method; the precmd() implementation may re-write the command or simply return line unchanged.

+
+Cmd.postcmd(stop, line)
+

Hook method executed just after a command dispatch is finished. This method is a stub in Cmd; it exists to be overridden by subclasses. line is the command line which was executed, and stop is a flag which indicates whether execution will be terminated after the call to postcmd(); this will be the return value of the onecmd() method. The return value of this method will be used as the new value for the internal flag which corresponds to stop; returning false will cause interpretation to continue.

+
+Cmd.preloop()
+

Hook method executed once when cmdloop() is called. This method is a stub in Cmd; it exists to be overridden by subclasses.

+
+Cmd.postloop()
+

Hook method executed once when cmdloop() is about to return. This method is a stub in Cmd; it exists to be overridden by subclasses.

+

Instances of Cmd subclasses have some public instance variables:

+Cmd.prompt
+

The prompt issued to solicit input.

+
+Cmd.identchars
+

The string of characters accepted for the command prefix.

+
+Cmd.lastcmd
+

The last nonempty command prefix seen.

+
+Cmd.cmdqueue
+

A list of queued input lines. The cmdqueue list is checked in cmdloop() when new input is needed; if it is nonempty, its elements will be processed in order, as if entered at the prompt.

+
+Cmd.intro
+

A string to issue as an intro or banner. May be overridden by giving the cmdloop() method an argument.

+
+Cmd.doc_header
+

The header to issue if the help output has a section for documented commands.

+
+Cmd.misc_header
+

The header to issue if the help output has a section for miscellaneous help topics (that is, there are help_*() methods without corresponding do_*() methods).

+
+Cmd.undoc_header
+

The header to issue if the help output has a section for undocumented commands (that is, there are do_*() methods without corresponding help_*() methods).

+
+Cmd.ruler
+

The character used to draw separator lines under the help-message headers. If empty, no ruler line is drawn. It defaults to '='.

+
+Cmd.use_rawinput
+

A flag, defaulting to true. If true, cmdloop() uses input() to display a prompt and read the next command; if false, sys.stdout.write() and sys.stdin.readline() are used. (This means that by importing readline, on systems that support it, the interpreter will automatically support Emacs-like line editing and command-history keystrokes.)

+

Cmd Example

The cmd module is mainly useful for building custom shells that let a user work with a program interactively.

This section presents a simple example of how to build a shell around a few of the commands in the turtle module.

Basic turtle commands such as forward() are added to a Cmd subclass with method named do_forward(). The argument is converted to a number and dispatched to the turtle module. The docstring is used in the help utility provided by the shell.

The example also includes a basic record and playback facility implemented with the precmd() method which is responsible for converting the input to lowercase and writing the commands to a file. The do_playback() method reads the file and adds the recorded commands to the cmdqueue for immediate playback:

import cmd, sys
+from turtle import *
+
+class TurtleShell(cmd.Cmd):
+    intro = 'Welcome to the turtle shell.   Type help or ? to list commands.\n'
+    prompt = '(turtle) '
+    file = None
+
+    # ----- basic turtle commands -----
+    def do_forward(self, arg):
+        'Move the turtle forward by the specified distance:  FORWARD 10'
+        forward(*parse(arg))
+    def do_right(self, arg):
+        'Turn turtle right by given number of degrees:  RIGHT 20'
+        right(*parse(arg))
+    def do_left(self, arg):
+        'Turn turtle left by given number of degrees:  LEFT 90'
+        left(*parse(arg))
+    def do_goto(self, arg):
+        'Move turtle to an absolute position with changing orientation.  GOTO 100 200'
+        goto(*parse(arg))
+    def do_home(self, arg):
+        'Return turtle to the home position:  HOME'
+        home()
+    def do_circle(self, arg):
+        'Draw circle with given radius an options extent and steps:  CIRCLE 50'
+        circle(*parse(arg))
+    def do_position(self, arg):
+        'Print the current turtle position:  POSITION'
+        print('Current position is %d %d\n' % position())
+    def do_heading(self, arg):
+        'Print the current turtle heading in degrees:  HEADING'
+        print('Current heading is %d\n' % (heading(),))
+    def do_color(self, arg):
+        'Set the color:  COLOR BLUE'
+        color(arg.lower())
+    def do_undo(self, arg):
+        'Undo (repeatedly) the last turtle action(s):  UNDO'
+    def do_reset(self, arg):
+        'Clear the screen and return turtle to center:  RESET'
+        reset()
+    def do_bye(self, arg):
+        'Stop recording, close the turtle window, and exit:  BYE'
+        print('Thank you for using Turtle')
+        self.close()
+        bye()
+        return True
+
+    # ----- record and playback -----
+    def do_record(self, arg):
+        'Save future commands to filename:  RECORD rose.cmd'
+        self.file = open(arg, 'w')
+    def do_playback(self, arg):
+        'Playback commands from a file:  PLAYBACK rose.cmd'
+        self.close()
+        with open(arg) as f:
+            self.cmdqueue.extend(f.read().splitlines())
+    def precmd(self, line):
+        line = line.lower()
+        if self.file and 'playback' not in line:
+            print(line, file=self.file)
+        return line
+    def close(self):
+        if self.file:
+            self.file.close()
+            self.file = None
+
+def parse(arg):
+    'Convert a series of zero or more numbers to an argument tuple'
+    return tuple(map(int, arg.split()))
+
+if __name__ == '__main__':
+    TurtleShell().cmdloop()
+

Here is a sample session with the turtle shell showing the help functions, using blank lines to repeat commands, and the simple record and playback facility:

Welcome to the turtle shell.   Type help or ? to list commands.
+
+(turtle) ?
+
+Documented commands (type help <topic>):
+========================================
+bye     color    goto     home  playback  record  right
+circle  forward  heading  left  position  reset   undo
+
+(turtle) help forward
+Move the turtle forward by the specified distance:  FORWARD 10
+(turtle) record spiral.cmd
+(turtle) position
+Current position is 0 0
+
+(turtle) heading
+Current heading is 0
+
+(turtle) reset
+(turtle) circle 20
+(turtle) right 30
+(turtle) circle 40
+(turtle) right 30
+(turtle) circle 60
+(turtle) right 30
+(turtle) circle 80
+(turtle) right 30
+(turtle) circle 100
+(turtle) right 30
+(turtle) circle 120
+(turtle) right 30
+(turtle) circle 120
+(turtle) heading
+Current heading is 180
+
+(turtle) forward 100
+(turtle)
+(turtle) right 90
+(turtle) forward 100
+(turtle)
+(turtle) right 90
+(turtle) forward 400
+(turtle) right 90
+(turtle) forward 500
+(turtle) right 90
+(turtle) forward 400
+(turtle) right 90
+(turtle) forward 300
+(turtle) playback spiral.cmd
+Current position is 0 0
+
+Current heading is 0
+
+Current heading is 180
+
+(turtle) bye
+Thank you for using Turtle
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/cmd.html +

+
diff --git a/devdocs/python~3.12/library%2Fcmdline.html b/devdocs/python~3.12/library%2Fcmdline.html new file mode 100644 index 00000000..8e6492d7 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcmdline.html @@ -0,0 +1,8 @@ +

Modules command-line interface (CLI)

The following modules have a command-line interface.

See also the Python command-line interface.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/cmdline.html +

+
diff --git a/devdocs/python~3.12/library%2Fcode.html b/devdocs/python~3.12/library%2Fcode.html new file mode 100644 index 00000000..18948921 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcode.html @@ -0,0 +1,45 @@ +

code — Interpreter base classes

Source code: Lib/code.py

The code module provides facilities to implement read-eval-print loops in Python. Two classes and convenience functions are included which can be used to build applications which provide an interactive interpreter prompt.

+class code.InteractiveInterpreter(locals=None)
+

This class deals with parsing and interpreter state (the user’s namespace); it does not deal with input buffering or prompting or input file naming (the filename is always passed in explicitly). The optional locals argument specifies the dictionary in which code will be executed; it defaults to a newly created dictionary with key '__name__' set to '__console__' and key '__doc__' set to None.

+
+class code.InteractiveConsole(locals=None, filename='<console>')
+

Closely emulate the behavior of the interactive Python interpreter. This class builds on InteractiveInterpreter and adds prompting using the familiar sys.ps1 and sys.ps2, and input buffering.

+
+code.interact(banner=None, readfunc=None, local=None, exitmsg=None)
+

Convenience function to run a read-eval-print loop. This creates a new instance of InteractiveConsole and sets readfunc to be used as the InteractiveConsole.raw_input() method, if provided. If local is provided, it is passed to the InteractiveConsole constructor for use as the default namespace for the interpreter loop. The interact() method of the instance is then run with banner and exitmsg passed as the banner and exit message to use, if provided. The console object is discarded after use.

Changed in version 3.6: Added exitmsg parameter.

+
+code.compile_command(source, filename='<input>', symbol='single')
+

This function is useful for programs that want to emulate Python’s interpreter main loop (a.k.a. the read-eval-print loop). The tricky part is to determine when the user has entered an incomplete command that can be completed by entering more text (as opposed to a complete command or a syntax error). This function almost always makes the same decision as the real interpreter main loop.

source is the source string; filename is the optional filename from which source was read, defaulting to '<input>'; and symbol is the optional grammar start symbol, which should be 'single' (the default), 'eval' or 'exec'.

Returns a code object (the same as compile(source, filename, symbol)) if the command is complete and valid; None if the command is incomplete; raises SyntaxError if the command is complete and contains a syntax error, or raises OverflowError or ValueError if the command contains an invalid literal.

+

Interactive Interpreter Objects

+InteractiveInterpreter.runsource(source, filename='<input>', symbol='single')
+

Compile and run some source in the interpreter. Arguments are the same as for compile_command(); the default for filename is '<input>', and for symbol is 'single'. One of several things can happen:

The return value can be used to decide whether to use sys.ps1 or sys.ps2 to prompt the next line.

+
+InteractiveInterpreter.runcode(code)
+

Execute a code object. When an exception occurs, showtraceback() is called to display a traceback. All exceptions are caught except SystemExit, which is allowed to propagate.

A note about KeyboardInterrupt: this exception may occur elsewhere in this code, and may not always be caught. The caller should be prepared to deal with it.

+
+InteractiveInterpreter.showsyntaxerror(filename=None)
+

Display the syntax error that just occurred. This does not display a stack trace because there isn’t one for syntax errors. If filename is given, it is stuffed into the exception instead of the default filename provided by Python’s parser, because it always uses '<string>' when reading from a string. The output is written by the write() method.

+
+InteractiveInterpreter.showtraceback()
+

Display the exception that just occurred. We remove the first stack item because it is within the interpreter object implementation. The output is written by the write() method.

Changed in version 3.5: The full chained traceback is displayed instead of just the primary traceback.

+
+InteractiveInterpreter.write(data)
+

Write a string to the standard error stream (sys.stderr). Derived classes should override this to provide the appropriate output handling as needed.

+

Interactive Console Objects

The InteractiveConsole class is a subclass of InteractiveInterpreter, and so offers all the methods of the interpreter objects as well as the following additions.

+InteractiveConsole.interact(banner=None, exitmsg=None)
+

Closely emulate the interactive Python console. The optional banner argument specify the banner to print before the first interaction; by default it prints a banner similar to the one printed by the standard Python interpreter, followed by the class name of the console object in parentheses (so as not to confuse this with the real interpreter – since it’s so close!).

The optional exitmsg argument specifies an exit message printed when exiting. Pass the empty string to suppress the exit message. If exitmsg is not given or None, a default message is printed.

Changed in version 3.4: To suppress printing any banner, pass an empty string.

Changed in version 3.6: Print an exit message when exiting.

+
+InteractiveConsole.push(line)
+

Push a line of source text to the interpreter. The line should not have a trailing newline; it may have internal newlines. The line is appended to a buffer and the interpreter’s runsource() method is called with the concatenated contents of the buffer as source. If this indicates that the command was executed or invalid, the buffer is reset; otherwise, the command is incomplete, and the buffer is left as it was after the line was appended. The return value is True if more input is required, False if the line was dealt with in some way (this is the same as runsource()).

+
+InteractiveConsole.resetbuffer()
+

Remove any unhandled source text from the input buffer.

+
+InteractiveConsole.raw_input(prompt='')
+

Write a prompt and read a line. The returned line does not include the trailing newline. When the user enters the EOF key sequence, EOFError is raised. The base implementation reads from sys.stdin; a subclass may replace this with a different implementation.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/code.html +

+
diff --git a/devdocs/python~3.12/library%2Fcodecs.html b/devdocs/python~3.12/library%2Fcodecs.html new file mode 100644 index 00000000..132404bb --- /dev/null +++ b/devdocs/python~3.12/library%2Fcodecs.html @@ -0,0 +1,333 @@ +

codecs — Codec registry and base classes

Source code: Lib/codecs.py

This module defines base classes for standard Python codecs (encoders and decoders) and provides access to the internal Python codec registry, which manages the codec and error handling lookup process. Most standard codecs are text encodings, which encode text to bytes (and decode bytes to text), but there are also codecs provided that encode text to text, and bytes to bytes. Custom codecs may encode and decode between arbitrary types, but some module features are restricted to be used specifically with text encodings or with codecs that encode to bytes.

The module defines the following functions for encoding and decoding with any codec:

+codecs.encode(obj, encoding='utf-8', errors='strict')
+

Encodes obj using the codec registered for encoding.

Errors may be given to set the desired error handling scheme. The default error handler is 'strict' meaning that encoding errors raise ValueError (or a more codec specific subclass, such as UnicodeEncodeError). Refer to Codec Base Classes for more information on codec error handling.

+
+codecs.decode(obj, encoding='utf-8', errors='strict')
+

Decodes obj using the codec registered for encoding.

Errors may be given to set the desired error handling scheme. The default error handler is 'strict' meaning that decoding errors raise ValueError (or a more codec specific subclass, such as UnicodeDecodeError). Refer to Codec Base Classes for more information on codec error handling.

+

The full details for each codec can also be looked up directly:

+codecs.lookup(encoding)
+

Looks up the codec info in the Python codec registry and returns a CodecInfo object as defined below.

Encodings are first looked up in the registry’s cache. If not found, the list of registered search functions is scanned. If no CodecInfo object is found, a LookupError is raised. Otherwise, the CodecInfo object is stored in the cache and returned to the caller.

+
+class codecs.CodecInfo(encode, decode, streamreader=None, streamwriter=None, incrementalencoder=None, incrementaldecoder=None, name=None)
+

Codec details when looking up the codec registry. The constructor arguments are stored in attributes of the same name:

+name
+

The name of the encoding.

+
+encode
+decode
+

The stateless encoding and decoding functions. These must be functions or methods which have the same interface as the encode() and decode() methods of Codec instances (see Codec Interface). The functions or methods are expected to work in a stateless mode.

+
+incrementalencoder
+incrementaldecoder
+

Incremental encoder and decoder classes or factory functions. These have to provide the interface defined by the base classes IncrementalEncoder and IncrementalDecoder, respectively. Incremental codecs can maintain state.

+
+streamwriter
+streamreader
+

Stream writer and reader classes or factory functions. These have to provide the interface defined by the base classes StreamWriter and StreamReader, respectively. Stream codecs can maintain state.

+
+

To simplify access to the various codec components, the module provides these additional functions which use lookup() for the codec lookup:

+codecs.getencoder(encoding)
+

Look up the codec for the given encoding and return its encoder function.

Raises a LookupError in case the encoding cannot be found.

+
+codecs.getdecoder(encoding)
+

Look up the codec for the given encoding and return its decoder function.

Raises a LookupError in case the encoding cannot be found.

+
+codecs.getincrementalencoder(encoding)
+

Look up the codec for the given encoding and return its incremental encoder class or factory function.

Raises a LookupError in case the encoding cannot be found or the codec doesn’t support an incremental encoder.

+
+codecs.getincrementaldecoder(encoding)
+

Look up the codec for the given encoding and return its incremental decoder class or factory function.

Raises a LookupError in case the encoding cannot be found or the codec doesn’t support an incremental decoder.

+
+codecs.getreader(encoding)
+

Look up the codec for the given encoding and return its StreamReader class or factory function.

Raises a LookupError in case the encoding cannot be found.

+
+codecs.getwriter(encoding)
+

Look up the codec for the given encoding and return its StreamWriter class or factory function.

Raises a LookupError in case the encoding cannot be found.

+

Custom codecs are made available by registering a suitable codec search function:

+codecs.register(search_function)
+

Register a codec search function. Search functions are expected to take one argument, being the encoding name in all lower case letters with hyphens and spaces converted to underscores, and return a CodecInfo object. In case a search function cannot find a given encoding, it should return None.

Changed in version 3.9: Hyphens and spaces are converted to underscore.

+
+codecs.unregister(search_function)
+

Unregister a codec search function and clear the registry’s cache. If the search function is not registered, do nothing.

New in version 3.10.

+

While the builtin open() and the associated io module are the recommended approach for working with encoded text files, this module provides additional utility functions and classes that allow the use of a wider range of codecs when working with binary files:

+codecs.open(filename, mode='r', encoding=None, errors='strict', buffering=- 1)
+

Open an encoded file using the given mode and return an instance of StreamReaderWriter, providing transparent encoding/decoding. The default file mode is 'r', meaning to open the file in read mode.

Note

If encoding is not None, then the underlying encoded files are always opened in binary mode. No automatic conversion of '\n' is done on reading and writing. The mode argument may be any binary mode acceptable to the built-in open() function; the 'b' is automatically added.

encoding specifies the encoding which is to be used for the file. Any encoding that encodes to and decodes from bytes is allowed, and the data types supported by the file methods depend on the codec used.

errors may be given to define the error handling. It defaults to 'strict' which causes a ValueError to be raised in case an encoding error occurs.

buffering has the same meaning as for the built-in open() function. It defaults to -1 which means that the default buffer size will be used.

Changed in version 3.11: The 'U' mode has been removed.

+
+codecs.EncodedFile(file, data_encoding, file_encoding=None, errors='strict')
+

Return a StreamRecoder instance, a wrapped version of file which provides transparent transcoding. The original file is closed when the wrapped version is closed.

Data written to the wrapped file is decoded according to the given data_encoding and then written to the original file as bytes using file_encoding. Bytes read from the original file are decoded according to file_encoding, and the result is encoded using data_encoding.

If file_encoding is not given, it defaults to data_encoding.

errors may be given to define the error handling. It defaults to 'strict', which causes ValueError to be raised in case an encoding error occurs.

+
+codecs.iterencode(iterator, encoding, errors='strict', **kwargs)
+

Uses an incremental encoder to iteratively encode the input provided by iterator. This function is a generator. The errors argument (as well as any other keyword argument) is passed through to the incremental encoder.

This function requires that the codec accept text str objects to encode. Therefore it does not support bytes-to-bytes encoders such as base64_codec.

+
+codecs.iterdecode(iterator, encoding, errors='strict', **kwargs)
+

Uses an incremental decoder to iteratively decode the input provided by iterator. This function is a generator. The errors argument (as well as any other keyword argument) is passed through to the incremental decoder.

This function requires that the codec accept bytes objects to decode. Therefore it does not support text-to-text encoders such as rot_13, although rot_13 may be used equivalently with iterencode().

+

The module also provides the following constants which are useful for reading and writing to platform dependent files:

+codecs.BOM
+codecs.BOM_BE
+codecs.BOM_LE
+codecs.BOM_UTF8
+codecs.BOM_UTF16
+codecs.BOM_UTF16_BE
+codecs.BOM_UTF16_LE
+codecs.BOM_UTF32
+codecs.BOM_UTF32_BE
+codecs.BOM_UTF32_LE
+

These constants define various byte sequences, being Unicode byte order marks (BOMs) for several encodings. They are used in UTF-16 and UTF-32 data streams to indicate the byte order used, and in UTF-8 as a Unicode signature. BOM_UTF16 is either BOM_UTF16_BE or BOM_UTF16_LE depending on the platform’s native byte order, BOM is an alias for BOM_UTF16, BOM_LE for BOM_UTF16_LE and BOM_BE for BOM_UTF16_BE. The others represent the BOM in UTF-8 and UTF-32 encodings.

+

Codec Base Classes

The codecs module defines a set of base classes which define the interfaces for working with codec objects, and can also be used as the basis for custom codec implementations.

Each codec has to define four interfaces to make it usable as codec in Python: stateless encoder, stateless decoder, stream reader and stream writer. The stream reader and writers typically reuse the stateless encoder/decoder to implement the file protocols. Codec authors also need to define how the codec will handle encoding and decoding errors.

Error Handlers

To simplify and standardize error handling, codecs may implement different error handling schemes by accepting the errors string argument:

>>> 'German ß, ♬'.encode(encoding='ascii', errors='backslashreplace')
+b'German \\xdf, \\u266c'
+>>> 'German ß, ♬'.encode(encoding='ascii', errors='xmlcharrefreplace')
+b'German &#223;, &#9836;'
+

The following error handlers can be used with all Python Standard Encodings codecs:

+ + + + + +

Value

Meaning

'strict'

Raise UnicodeError (or a subclass), this is the default. Implemented in strict_errors().

'ignore'

Ignore the malformed data and continue without further notice. Implemented in ignore_errors().

'replace'

Replace with a replacement marker. On encoding, use ? (ASCII character). On decoding, use � (U+FFFD, the official REPLACEMENT CHARACTER). Implemented in replace_errors().

'backslashreplace'

Replace with backslashed escape sequences. On encoding, use hexadecimal form of Unicode code point with formats \xhh \uxxxx \Uxxxxxxxx. On decoding, use hexadecimal form of byte value with format \xhh. Implemented in backslashreplace_errors().

'surrogateescape'

On decoding, replace byte with individual surrogate code ranging from U+DC80 to U+DCFF. This code will then be turned back into the same byte when the 'surrogateescape' error handler is used when encoding the data. (See PEP 383 for more.)

The following error handlers are only applicable to encoding (within text encodings):

+ + +

Value

Meaning

'xmlcharrefreplace'

Replace with XML/HTML numeric character reference, which is a decimal form of Unicode code point with format &#num;. Implemented in xmlcharrefreplace_errors().

'namereplace'

Replace with \N{...} escape sequences, what appears in the braces is the Name property from Unicode Character Database. Implemented in namereplace_errors().

In addition, the following error handler is specific to the given codecs:

+ +

Value

Codecs

Meaning

'surrogatepass'

utf-8, utf-16, utf-32, utf-16-be, utf-16-le, utf-32-be, utf-32-le

Allow encoding and decoding surrogate code point (U+D800 - U+DFFF) as normal code point. Otherwise these codecs treat the presence of surrogate code point in str as an error.

New in version 3.1: The 'surrogateescape' and 'surrogatepass' error handlers.

Changed in version 3.4: The 'surrogatepass' error handler now works with utf-16* and utf-32* codecs.

New in version 3.5: The 'namereplace' error handler.

Changed in version 3.5: The 'backslashreplace' error handler now works with decoding and translating.

The set of allowed values can be extended by registering a new named error handler:

+codecs.register_error(name, error_handler)
+

Register the error handling function error_handler under the name name. The error_handler argument will be called during encoding and decoding in case of an error, when name is specified as the errors parameter.

For encoding, error_handler will be called with a UnicodeEncodeError instance, which contains information about the location of the error. The error handler must either raise this or a different exception, or return a tuple with a replacement for the unencodable part of the input and a position where encoding should continue. The replacement may be either str or bytes. If the replacement is bytes, the encoder will simply copy them into the output buffer. If the replacement is a string, the encoder will encode the replacement. Encoding continues on original input at the specified position. Negative position values will be treated as being relative to the end of the input string. If the resulting position is out of bound an IndexError will be raised.

Decoding and translating works similarly, except UnicodeDecodeError or UnicodeTranslateError will be passed to the handler and that the replacement from the error handler will be put into the output directly.

+

Previously registered error handlers (including the standard error handlers) can be looked up by name:

+codecs.lookup_error(name)
+

Return the error handler previously registered under the name name.

Raises a LookupError in case the handler cannot be found.

+

The following standard error handlers are also made available as module level functions:

+codecs.strict_errors(exception)
+

Implements the 'strict' error handling.

Each encoding or decoding error raises a UnicodeError.

+
+codecs.ignore_errors(exception)
+

Implements the 'ignore' error handling.

Malformed data is ignored; encoding or decoding is continued without further notice.

+
+codecs.replace_errors(exception)
+

Implements the 'replace' error handling.

Substitutes ? (ASCII character) for encoding errors or � (U+FFFD, the official REPLACEMENT CHARACTER) for decoding errors.

+
+codecs.backslashreplace_errors(exception)
+

Implements the 'backslashreplace' error handling.

Malformed data is replaced by a backslashed escape sequence. On encoding, use the hexadecimal form of Unicode code point with formats \xhh \uxxxx \Uxxxxxxxx. On decoding, use the hexadecimal form of byte value with format \xhh.

Changed in version 3.5: Works with decoding and translating.

+
+codecs.xmlcharrefreplace_errors(exception)
+

Implements the 'xmlcharrefreplace' error handling (for encoding within text encoding only).

The unencodable character is replaced by an appropriate XML/HTML numeric character reference, which is a decimal form of Unicode code point with format &#num; .

+
+codecs.namereplace_errors(exception)
+

Implements the 'namereplace' error handling (for encoding within text encoding only).

The unencodable character is replaced by a \N{...} escape sequence. The set of characters that appear in the braces is the Name property from Unicode Character Database. For example, the German lowercase letter 'ß' will be converted to byte sequence \N{LATIN SMALL LETTER SHARP S} .

New in version 3.5.

+

Stateless Encoding and Decoding

The base Codec class defines these methods which also define the function interfaces of the stateless encoder and decoder:

+class codecs.Codec
+
+encode(input, errors='strict')
+

Encodes the object input and returns a tuple (output object, length consumed). For instance, text encoding converts a string object to a bytes object using a particular character set encoding (e.g., cp1252 or iso-8859-1).

The errors argument defines the error handling to apply. It defaults to 'strict' handling.

The method may not store state in the Codec instance. Use StreamWriter for codecs which have to keep state in order to make encoding efficient.

The encoder must be able to handle zero length input and return an empty object of the output object type in this situation.

+
+decode(input, errors='strict')
+

Decodes the object input and returns a tuple (output object, length consumed). For instance, for a text encoding, decoding converts a bytes object encoded using a particular character set encoding to a string object.

For text encodings and bytes-to-bytes codecs, input must be a bytes object or one which provides the read-only buffer interface – for example, buffer objects and memory mapped files.

The errors argument defines the error handling to apply. It defaults to 'strict' handling.

The method may not store state in the Codec instance. Use StreamReader for codecs which have to keep state in order to make decoding efficient.

The decoder must be able to handle zero length input and return an empty object of the output object type in this situation.

+
+

Incremental Encoding and Decoding

The IncrementalEncoder and IncrementalDecoder classes provide the basic interface for incremental encoding and decoding. Encoding/decoding the input isn’t done with one call to the stateless encoder/decoder function, but with multiple calls to the encode()/decode() method of the incremental encoder/decoder. The incremental encoder/decoder keeps track of the encoding/decoding process during method calls.

The joined output of calls to the encode()/decode() method is the same as if all the single inputs were joined into one, and this input was encoded/decoded with the stateless encoder/decoder.

IncrementalEncoder Objects

The IncrementalEncoder class is used for encoding an input in multiple steps. It defines the following methods which every incremental encoder must define in order to be compatible with the Python codec registry.

+class codecs.IncrementalEncoder(errors='strict')
+

Constructor for an IncrementalEncoder instance.

All incremental encoders must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry.

The IncrementalEncoder may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for possible values.

The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the IncrementalEncoder object.

+encode(object, final=False)
+

Encodes object (taking the current state of the encoder into account) and returns the resulting encoded object. If this is the last call to encode() final must be true (the default is false).

+
+reset()
+

Reset the encoder to the initial state. The output is discarded: call .encode(object, final=True), passing an empty byte or text string if necessary, to reset the encoder and to get the output.

+
+getstate()
+

Return the current state of the encoder which must be an integer. The implementation should make sure that 0 is the most common state. (States that are more complicated than integers can be converted into an integer by marshaling/pickling the state and encoding the bytes of the resulting string into an integer.)

+
+setstate(state)
+

Set the state of the encoder to state. state must be an encoder state returned by getstate().

+
+

IncrementalDecoder Objects

The IncrementalDecoder class is used for decoding an input in multiple steps. It defines the following methods which every incremental decoder must define in order to be compatible with the Python codec registry.

+class codecs.IncrementalDecoder(errors='strict')
+

Constructor for an IncrementalDecoder instance.

All incremental decoders must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry.

The IncrementalDecoder may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for possible values.

The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the IncrementalDecoder object.

+decode(object, final=False)
+

Decodes object (taking the current state of the decoder into account) and returns the resulting decoded object. If this is the last call to decode() final must be true (the default is false). If final is true the decoder must decode the input completely and must flush all buffers. If this isn’t possible (e.g. because of incomplete byte sequences at the end of the input) it must initiate error handling just like in the stateless case (which might raise an exception).

+
+reset()
+

Reset the decoder to the initial state.

+
+getstate()
+

Return the current state of the decoder. This must be a tuple with two items, the first must be the buffer containing the still undecoded input. The second must be an integer and can be additional state info. (The implementation should make sure that 0 is the most common additional state info.) If this additional state info is 0 it must be possible to set the decoder to the state which has no input buffered and 0 as the additional state info, so that feeding the previously buffered input to the decoder returns it to the previous state without producing any output. (Additional state info that is more complicated than integers can be converted into an integer by marshaling/pickling the info and encoding the bytes of the resulting string into an integer.)

+
+setstate(state)
+

Set the state of the decoder to state. state must be a decoder state returned by getstate().

+
+

Stream Encoding and Decoding

The StreamWriter and StreamReader classes provide generic working interfaces which can be used to implement new encoding submodules very easily. See encodings.utf_8 for an example of how this is done.

StreamWriter Objects

The StreamWriter class is a subclass of Codec and defines the following methods which every stream writer must define in order to be compatible with the Python codec registry.

+class codecs.StreamWriter(stream, errors='strict')
+

Constructor for a StreamWriter instance.

All stream writers must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry.

The stream argument must be a file-like object open for writing text or binary data, as appropriate for the specific codec.

The StreamWriter may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for the standard error handlers the underlying stream codec may support.

The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the StreamWriter object.

+write(object)
+

Writes the object’s contents encoded to the stream.

+
+writelines(list)
+

Writes the concatenated iterable of strings to the stream (possibly by reusing the write() method). Infinite or very large iterables are not supported. The standard bytes-to-bytes codecs do not support this method.

+
+reset()
+

Resets the codec buffers used for keeping internal state.

Calling this method should ensure that the data on the output is put into a clean state that allows appending of new fresh data without having to rescan the whole stream to recover state.

+
+

In addition to the above methods, the StreamWriter must also inherit all other methods and attributes from the underlying stream.

StreamReader Objects

The StreamReader class is a subclass of Codec and defines the following methods which every stream reader must define in order to be compatible with the Python codec registry.

+class codecs.StreamReader(stream, errors='strict')
+

Constructor for a StreamReader instance.

All stream readers must provide this constructor interface. They are free to add additional keyword arguments, but only the ones defined here are used by the Python codec registry.

The stream argument must be a file-like object open for reading text or binary data, as appropriate for the specific codec.

The StreamReader may implement different error handling schemes by providing the errors keyword argument. See Error Handlers for the standard error handlers the underlying stream codec may support.

The errors argument will be assigned to an attribute of the same name. Assigning to this attribute makes it possible to switch between different error handling strategies during the lifetime of the StreamReader object.

The set of allowed values for the errors argument can be extended with register_error().

+read(size=- 1, chars=- 1, firstline=False)
+

Decodes data from the stream and returns the resulting object.

The chars argument indicates the number of decoded code points or bytes to return. The read() method will never return more data than requested, but it might return less, if there is not enough available.

The size argument indicates the approximate maximum number of encoded bytes or code points to read for decoding. The decoder can modify this setting as appropriate. The default value -1 indicates to read and decode as much as possible. This parameter is intended to prevent having to decode huge files in one step.

The firstline flag indicates that it would be sufficient to only return the first line, if there are decoding errors on later lines.

The method should use a greedy read strategy meaning that it should read as much data as is allowed within the definition of the encoding and the given size, e.g. if optional encoding endings or state markers are available on the stream, these should be read too.

+
+readline(size=None, keepends=True)
+

Read one line from the input stream and return the decoded data.

size, if given, is passed as size argument to the stream’s read() method.

If keepends is false line-endings will be stripped from the lines returned.

+
+readlines(sizehint=None, keepends=True)
+

Read all lines available on the input stream and return them as a list of lines.

Line-endings are implemented using the codec’s decode() method and are included in the list entries if keepends is true.

sizehint, if given, is passed as the size argument to the stream’s read() method.

+
+reset()
+

Resets the codec buffers used for keeping internal state.

Note that no stream repositioning should take place. This method is primarily intended to be able to recover from decoding errors.

+
+

In addition to the above methods, the StreamReader must also inherit all other methods and attributes from the underlying stream.

StreamReaderWriter Objects

The StreamReaderWriter is a convenience class that allows wrapping streams which work in both read and write modes.

The design is such that one can use the factory functions returned by the lookup() function to construct the instance.

+class codecs.StreamReaderWriter(stream, Reader, Writer, errors='strict')
+

Creates a StreamReaderWriter instance. stream must be a file-like object. Reader and Writer must be factory functions or classes providing the StreamReader and StreamWriter interface resp. Error handling is done in the same way as defined for the stream readers and writers.

+

StreamReaderWriter instances define the combined interfaces of StreamReader and StreamWriter classes. They inherit all other methods and attributes from the underlying stream.

StreamRecoder Objects

The StreamRecoder translates data from one encoding to another, which is sometimes useful when dealing with different encoding environments.

The design is such that one can use the factory functions returned by the lookup() function to construct the instance.

+class codecs.StreamRecoder(stream, encode, decode, Reader, Writer, errors='strict')
+

Creates a StreamRecoder instance which implements a two-way conversion: encode and decode work on the frontend — the data visible to code calling read() and write(), while Reader and Writer work on the backend — the data in stream.

You can use these objects to do transparent transcodings, e.g., from Latin-1 to UTF-8 and back.

The stream argument must be a file-like object.

The encode and decode arguments must adhere to the Codec interface. Reader and Writer must be factory functions or classes providing objects of the StreamReader and StreamWriter interface respectively.

Error handling is done in the same way as defined for the stream readers and writers.

+

StreamRecoder instances define the combined interfaces of StreamReader and StreamWriter classes. They inherit all other methods and attributes from the underlying stream.

Encodings and Unicode

Strings are stored internally as sequences of code points in range U+0000–U+10FFFF. (See PEP 393 for more details about the implementation.) Once a string object is used outside of CPU and memory, endianness and how these arrays are stored as bytes become an issue. As with other codecs, serialising a string into a sequence of bytes is known as encoding, and recreating the string from the sequence of bytes is known as decoding. There are a variety of different text serialisation codecs, which are collectivity referred to as text encodings.

The simplest text encoding (called 'latin-1' or 'iso-8859-1') maps the code points 0–255 to the bytes 0x0–0xff, which means that a string object that contains code points above U+00FF can’t be encoded with this codec. Doing so will raise a UnicodeEncodeError that looks like the following (although the details of the error message may differ): UnicodeEncodeError: 'latin-1' codec can't encode character '\u1234' in +position 3: ordinal not in range(256).

There’s another group of encodings (the so called charmap encodings) that choose a different subset of all Unicode code points and how these code points are mapped to the bytes 0x0–0xff. To see how this is done simply open e.g. encodings/cp1252.py (which is an encoding that is used primarily on Windows). There’s a string constant with 256 characters that shows you which character is mapped to which byte value.

All of these encodings can only encode 256 of the 1114112 code points defined in Unicode. A simple and straightforward way that can store each Unicode code point, is to store each code point as four consecutive bytes. There are two possibilities: store the bytes in big endian or in little endian order. These two encodings are called UTF-32-BE and UTF-32-LE respectively. Their disadvantage is that if e.g. you use UTF-32-BE on a little endian machine you will always have to swap bytes on encoding and decoding. UTF-32 avoids this problem: bytes will always be in natural endianness. When these bytes are read by a CPU with a different endianness, then bytes have to be swapped though. To be able to detect the endianness of a UTF-16 or UTF-32 byte sequence, there’s the so called BOM (“Byte Order Mark”). This is the Unicode character U+FEFF. This character can be prepended to every UTF-16 or UTF-32 byte sequence. The byte swapped version of this character (0xFFFE) is an illegal character that may not appear in a Unicode text. So when the first character in a UTF-16 or UTF-32 byte sequence appears to be a U+FFFE the bytes have to be swapped on decoding. Unfortunately the character U+FEFF had a second purpose as a ZERO WIDTH NO-BREAK SPACE: a character that has no width and doesn’t allow a word to be split. It can e.g. be used to give hints to a ligature algorithm. With Unicode 4.0 using U+FEFF as a ZERO WIDTH NO-BREAK SPACE has been deprecated (with U+2060 (WORD JOINER) assuming this role). Nevertheless Unicode software still must be able to handle U+FEFF in both roles: as a BOM it’s a device to determine the storage layout of the encoded bytes, and vanishes once the byte sequence has been decoded into a string; as a ZERO WIDTH +NO-BREAK SPACE it’s a normal character that will be decoded like any other.

There’s another encoding that is able to encode the full range of Unicode characters: UTF-8. UTF-8 is an 8-bit encoding, which means there are no issues with byte order in UTF-8. Each byte in a UTF-8 byte sequence consists of two parts: marker bits (the most significant bits) and payload bits. The marker bits are a sequence of zero to four 1 bits followed by a 0 bit. Unicode characters are encoded like this (with x being payload bits, which when concatenated give the Unicode character):

+ + + + +

Range

Encoding

U-00000000 … U-0000007F

0xxxxxxx

U-00000080 … U-000007FF

110xxxxx 10xxxxxx

U-00000800 … U-0000FFFF

1110xxxx 10xxxxxx 10xxxxxx

U-00010000 … U-0010FFFF

11110xxx 10xxxxxx 10xxxxxx 10xxxxxx

The least significant bit of the Unicode character is the rightmost x bit.

As UTF-8 is an 8-bit encoding no BOM is required and any U+FEFF character in the decoded string (even if it’s the first character) is treated as a ZERO +WIDTH NO-BREAK SPACE.

Without external information it’s impossible to reliably determine which encoding was used for encoding a string. Each charmap encoding can decode any random byte sequence. However that’s not possible with UTF-8, as UTF-8 byte sequences have a structure that doesn’t allow arbitrary byte sequences. To increase the reliability with which a UTF-8 encoding can be detected, Microsoft invented a variant of UTF-8 (that Python calls "utf-8-sig") for its Notepad program: Before any of the Unicode characters is written to the file, a UTF-8 encoded BOM (which looks like this as a byte sequence: 0xef, 0xbb, 0xbf) is written. As it’s rather improbable that any charmap encoded file starts with these byte values (which would e.g. map to

in iso-8859-1), this increases the probability that a utf-8-sig encoding can be correctly guessed from the byte sequence. So here the BOM is not used to be able to determine the byte order used for generating the byte sequence, but as a signature that helps in guessing the encoding. On encoding the utf-8-sig codec will write 0xef, 0xbb, 0xbf as the first three bytes to the file. On decoding utf-8-sig will skip those three bytes if they appear as the first three bytes in the file. In UTF-8, the use of the BOM is discouraged and should generally be avoided.

Standard Encodings

Python comes with a number of codecs built-in, either implemented as C functions or with dictionaries as mapping tables. The following table lists the codecs by name, together with a few common aliases, and the languages for which the encoding is likely used. Neither the list of aliases nor the list of languages is meant to be exhaustive. Notice that spelling alternatives that only differ in case or use a hyphen instead of an underscore are also valid aliases; therefore, e.g. 'utf-8' is a valid alias for the 'utf_8' codec.

CPython implementation detail: Some common encodings can bypass the codecs lookup machinery to improve performance. These optimization opportunities are only recognized by CPython for a limited set of (case insensitive) aliases: utf-8, utf8, latin-1, latin1, iso-8859-1, iso8859-1, mbcs (Windows only), ascii, us-ascii, utf-16, utf16, utf-32, utf32, and the same using underscores instead of dashes. Using alternative aliases for these encodings may result in slower execution.

Changed in version 3.6: Optimization opportunity recognized for us-ascii.

Many of the character sets support the same languages. They vary in individual characters (e.g. whether the EURO SIGN is supported or not), and in the assignment of characters to code positions. For the European languages in particular, the following variants typically exist:

  • an ISO 8859 codeset
  • a Microsoft Windows code page, which is typically derived from an 8859 codeset, but replaces control characters with additional graphic characters
  • an IBM EBCDIC code page
  • an IBM PC code page, which is ASCII compatible
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Codec

Aliases

Languages

ascii

646, us-ascii

English

big5

big5-tw, csbig5

Traditional Chinese

big5hkscs

big5-hkscs, hkscs

Traditional Chinese

cp037

IBM037, IBM039

English

cp273

273, IBM273, csIBM273

 +

German

New in version 3.4.

cp424

EBCDIC-CP-HE, IBM424

Hebrew

cp437

437, IBM437

English

cp500

EBCDIC-CP-BE, EBCDIC-CP-CH, IBM500

Western Europe

cp720

Arabic

cp737

Greek

cp775

IBM775

Baltic languages

cp850

850, IBM850

Western Europe

cp852

852, IBM852

Central and Eastern Europe

cp855

855, IBM855

Bulgarian, Byelorussian, Macedonian, Russian, Serbian

cp856

Hebrew

cp857

857, IBM857

Turkish

cp858

858, IBM858

Western Europe

cp860

860, IBM860

Portuguese

cp861

861, CP-IS, IBM861

Icelandic

cp862

862, IBM862

Hebrew

cp863

863, IBM863

Canadian

cp864

IBM864

Arabic

cp865

865, IBM865

Danish, Norwegian

cp866

866, IBM866

Russian

cp869

869, CP-GR, IBM869

Greek

cp874

Thai

cp875

Greek

cp932

932, ms932, mskanji, ms-kanji

Japanese

cp949

949, ms949, uhc

Korean

cp950

950, ms950

Traditional Chinese

cp1006

Urdu

cp1026

ibm1026

Turkish

cp1125

1125, ibm1125, cp866u, ruscii

 +

Ukrainian

New in version 3.4.

cp1140

ibm1140

Western Europe

cp1250

windows-1250

Central and Eastern Europe

cp1251

windows-1251

Bulgarian, Byelorussian, Macedonian, Russian, Serbian

cp1252

windows-1252

Western Europe

cp1253

windows-1253

Greek

cp1254

windows-1254

Turkish

cp1255

windows-1255

Hebrew

cp1256

windows-1256

Arabic

cp1257

windows-1257

Baltic languages

cp1258

windows-1258

Vietnamese

euc_jp

eucjp, ujis, u-jis

Japanese

euc_jis_2004

jisx0213, eucjis2004

Japanese

euc_jisx0213

eucjisx0213

Japanese

euc_kr

euckr, korean, ksc5601, ks_c-5601, ks_c-5601-1987, ksx1001, ks_x-1001

Korean

gb2312

chinese, csiso58gb231280, euc-cn, euccn, eucgb2312-cn, gb2312-1980, gb2312-80, iso-ir-58

Simplified Chinese

gbk

936, cp936, ms936

Unified Chinese

gb18030

gb18030-2000

Unified Chinese

hz

hzgb, hz-gb, hz-gb-2312

Simplified Chinese

iso2022_jp

csiso2022jp, iso2022jp, iso-2022-jp

Japanese

iso2022_jp_1

iso2022jp-1, iso-2022-jp-1

Japanese

iso2022_jp_2

iso2022jp-2, iso-2022-jp-2

Japanese, Korean, Simplified Chinese, Western Europe, Greek

iso2022_jp_2004

iso2022jp-2004, iso-2022-jp-2004

Japanese

iso2022_jp_3

iso2022jp-3, iso-2022-jp-3

Japanese

iso2022_jp_ext

iso2022jp-ext, iso-2022-jp-ext

Japanese

iso2022_kr

csiso2022kr, iso2022kr, iso-2022-kr

Korean

latin_1

iso-8859-1, iso8859-1, 8859, cp819, latin, latin1, L1

Western Europe

iso8859_2

iso-8859-2, latin2, L2

Central and Eastern Europe

iso8859_3

iso-8859-3, latin3, L3

Esperanto, Maltese

iso8859_4

iso-8859-4, latin4, L4

Baltic languages

iso8859_5

iso-8859-5, cyrillic

Bulgarian, Byelorussian, Macedonian, Russian, Serbian

iso8859_6

iso-8859-6, arabic

Arabic

iso8859_7

iso-8859-7, greek, greek8

Greek

iso8859_8

iso-8859-8, hebrew

Hebrew

iso8859_9

iso-8859-9, latin5, L5

Turkish

iso8859_10

iso-8859-10, latin6, L6

Nordic languages

iso8859_11

iso-8859-11, thai

Thai languages

iso8859_13

iso-8859-13, latin7, L7

Baltic languages

iso8859_14

iso-8859-14, latin8, L8

Celtic languages

iso8859_15

iso-8859-15, latin9, L9

Western Europe

iso8859_16

iso-8859-16, latin10, L10

South-Eastern Europe

johab

cp1361, ms1361

Korean

koi8_r

Russian

koi8_t

 +

Tajik

New in version 3.5.

koi8_u

Ukrainian

kz1048

kz_1048, strk1048_2002, rk1048

 +

Kazakh

New in version 3.5.

mac_cyrillic

maccyrillic

Bulgarian, Byelorussian, Macedonian, Russian, Serbian

mac_greek

macgreek

Greek

mac_iceland

maciceland

Icelandic

mac_latin2

maclatin2, maccentraleurope, mac_centeuro

Central and Eastern Europe

mac_roman

macroman, macintosh

Western Europe

mac_turkish

macturkish

Turkish

ptcp154

csptcp154, pt154, cp154, cyrillic-asian

Kazakh

shift_jis

csshiftjis, shiftjis, sjis, s_jis

Japanese

shift_jis_2004

shiftjis2004, sjis_2004, sjis2004

Japanese

shift_jisx0213

shiftjisx0213, sjisx0213, s_jisx0213

Japanese

utf_32

U32, utf32

all languages

utf_32_be

UTF-32BE

all languages

utf_32_le

UTF-32LE

all languages

utf_16

U16, utf16

all languages

utf_16_be

UTF-16BE

all languages

utf_16_le

UTF-16LE

all languages

utf_7

U7, unicode-1-1-utf-7

all languages

utf_8

U8, UTF, utf8, cp65001

all languages

utf_8_sig

all languages

Changed in version 3.4: The utf-16* and utf-32* encoders no longer allow surrogate code points (U+D800–U+DFFF) to be encoded. The utf-32* decoders no longer decode byte sequences that correspond to surrogate code points.

Changed in version 3.8: cp65001 is now an alias to utf_8.

Python Specific Encodings

A number of predefined codecs are specific to Python, so their codec names have no meaning outside Python. These are listed in the tables below based on the expected input and output types (note that while text encodings are the most common use case for codecs, the underlying codec infrastructure supports arbitrary data transforms rather than just text encodings). For asymmetric codecs, the stated meaning describes the encoding direction.

Text Encodings

The following codecs provide str to bytes encoding and bytes-like object to str decoding, similar to the Unicode text encodings.

+ + + + + + + + +

Codec

Aliases

Meaning

idna

Implement RFC 3490, see also encodings.idna. Only errors='strict' is supported.

mbcs

ansi, dbcs

Windows only: Encode the operand according to the ANSI codepage (CP_ACP).

oem

 +

Windows only: Encode the operand according to the OEM codepage (CP_OEMCP).

New in version 3.6.

palmos

Encoding of PalmOS 3.5.

punycode

Implement RFC 3492. Stateful codecs are not supported.

raw_unicode_escape

Latin-1 encoding with \uXXXX and \UXXXXXXXX for other code points. Existing backslashes are not escaped in any way. It is used in the Python pickle protocol.

undefined

Raise an exception for all conversions, even empty strings. The error handler is ignored.

unicode_escape

Encoding suitable as the contents of a Unicode literal in ASCII-encoded Python source code, except that quotes are not escaped. Decode from Latin-1 source code. Beware that Python source code actually uses UTF-8 by default.

Changed in version 3.8: “unicode_internal” codec is removed.

Binary Transforms

The following codecs provide binary transforms: bytes-like object to bytes mappings. They are not supported by bytes.decode() (which only produces str output).

+ + + + + + +

Codec

Aliases

Meaning

Encoder / decoder

base64_codec 1

base64, base_64

 +

Convert the operand to multiline MIME base64 (the result always includes a trailing '\n').

Changed in version 3.4: accepts any bytes-like object as input for encoding and decoding

base64.encodebytes() / base64.decodebytes()

bz2_codec

bz2

Compress the operand using bz2.

bz2.compress() / bz2.decompress()

hex_codec

hex

Convert the operand to hexadecimal representation, with two digits per byte.

binascii.b2a_hex() / binascii.a2b_hex()

quopri_codec

quopri, quotedprintable, quoted_printable

Convert the operand to MIME quoted printable.

quopri.encode() with quotetabs=True / quopri.decode()

uu_codec

uu

Convert the operand using uuencode.

uu.encode() / uu.decode() (Note: uu is deprecated.)

zlib_codec

zip, zlib

Compress the operand using gzip.

zlib.compress() / zlib.decompress()
+1
+

In addition to bytes-like objects, 'base64_codec' also accepts ASCII-only instances of str for decoding

New in version 3.2: Restoration of the binary transforms.

Changed in version 3.4: Restoration of the aliases for the binary transforms.

Text Transforms

The following codec provides a text transform: a str to str mapping. It is not supported by str.encode() (which only produces bytes output).

+ +

Codec

Aliases

Meaning

rot_13

rot13

Return the Caesar-cypher encryption of the operand.

New in version 3.2: Restoration of the rot_13 text transform.

Changed in version 3.4: Restoration of the rot13 alias.

encodings.idna — Internationalized Domain Names in Applications

This module implements RFC 3490 (Internationalized Domain Names in Applications) and RFC 3492 (Nameprep: A Stringprep Profile for Internationalized Domain Names (IDN)). It builds upon the punycode encoding and stringprep.

If you need the IDNA 2008 standard from RFC 5891 and RFC 5895, use the third-party idna module.

These RFCs together define a protocol to support non-ASCII characters in domain names. A domain name containing non-ASCII characters (such as www.Alliancefrançaise.nu) is converted into an ASCII-compatible encoding (ACE, such as www.xn--alliancefranaise-npb.nu). The ACE form of the domain name is then used in all places where arbitrary characters are not allowed by the protocol, such as DNS queries, HTTP Host fields, and so on. This conversion is carried out in the application; if possible invisible to the user: The application should transparently convert Unicode domain labels to IDNA on the wire, and convert back ACE labels to Unicode before presenting them to the user.

Python supports this conversion in several ways: the idna codec performs conversion between Unicode and ACE, separating an input string into labels based on the separator characters defined in section 3.1 of RFC 3490 and converting each label to ACE as required, and conversely separating an input byte string into labels based on the . separator and converting any ACE labels found into unicode. Furthermore, the socket module transparently converts Unicode host names to ACE, so that applications need not be concerned about converting host names themselves when they pass them to the socket module. On top of that, modules that have host names as function parameters, such as http.client and ftplib, accept Unicode host names (http.client then also transparently sends an IDNA hostname in the Host field if it sends that field at all).

When receiving host names from the wire (such as in reverse name lookup), no automatic conversion to Unicode is performed: applications wishing to present such host names to the user should decode them to Unicode.

The module encodings.idna also implements the nameprep procedure, which performs certain normalizations on host names, to achieve case-insensitivity of international domain names, and to unify similar characters. The nameprep functions can be used directly if desired.

+encodings.idna.nameprep(label)
+

Return the nameprepped version of label. The implementation currently assumes query strings, so AllowUnassigned is true.

+
+encodings.idna.ToASCII(label)
+

Convert a label to ASCII, as specified in RFC 3490. UseSTD3ASCIIRules is assumed to be false.

+
+encodings.idna.ToUnicode(label)
+

Convert a label to Unicode, as specified in RFC 3490.

+

encodings.mbcs — Windows ANSI codepage

This module implements the ANSI codepage (CP_ACP).

Availability: Windows.

Changed in version 3.3: Support any error handler.

Changed in version 3.2: Before 3.2, the errors argument was ignored; 'replace' was always used to encode, and 'ignore' to decode.

encodings.utf_8_sig — UTF-8 codec with BOM signature

This module implements a variant of the UTF-8 codec. On encoding, a UTF-8 encoded BOM will be prepended to the UTF-8 encoded bytes. For the stateful encoder this is only done once (on the first write to the byte stream). On decoding, an optional UTF-8 encoded BOM at the start of the data will be skipped.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/codecs.html +

+
diff --git a/devdocs/python~3.12/library%2Fcodeop.html b/devdocs/python~3.12/library%2Fcodeop.html new file mode 100644 index 00000000..557ace14 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcodeop.html @@ -0,0 +1,15 @@ +

codeop — Compile Python code

Source code: Lib/codeop.py

The codeop module provides utilities upon which the Python read-eval-print loop can be emulated, as is done in the code module. As a result, you probably don’t want to use the module directly; if you want to include such a loop in your program you probably want to use the code module instead.

There are two parts to this job:

  1. Being able to tell if a line of input completes a Python statement: in short, telling whether to print ‘>>>’ or ‘...’ next.
  2. Remembering which future statements the user has entered, so subsequent input can be compiled with these in effect.

The codeop module provides a way of doing each of these things, and a way of doing them both.

To do just the former:

+codeop.compile_command(source, filename='<input>', symbol='single')
+

Tries to compile source, which should be a string of Python code and return a code object if source is valid Python code. In that case, the filename attribute of the code object will be filename, which defaults to '<input>'. Returns None if source is not valid Python code, but is a prefix of valid Python code.

If there is a problem with source, an exception will be raised. SyntaxError is raised if there is invalid Python syntax, and OverflowError or ValueError if there is an invalid literal.

The symbol argument determines whether source is compiled as a statement ('single', the default), as a sequence of statement ('exec') or as an expression ('eval'). Any other value will cause ValueError to be raised.

Note

It is possible (but not likely) that the parser stops parsing with a successful outcome before reaching the end of the source; in this case, trailing symbols may be ignored instead of causing an error. For example, a backslash followed by two newlines may be followed by arbitrary garbage. This will be fixed once the API for the parser is better.

+
+class codeop.Compile
+

Instances of this class have __call__() methods identical in signature to the built-in function compile(), but with the difference that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.

+
+class codeop.CommandCompiler
+

Instances of this class have __call__() methods identical in signature to compile_command(); the difference is that if the instance compiles program text containing a __future__ statement, the instance ‘remembers’ and compiles all subsequent program texts with the statement in force.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/codeop.html +

+
diff --git a/devdocs/python~3.12/library%2Fcollections.abc.html b/devdocs/python~3.12/library%2Fcollections.abc.html new file mode 100644 index 00000000..1af80012 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcollections.abc.html @@ -0,0 +1,154 @@ +

collections.abc — Abstract Base Classes for Containers

New in version 3.3: Formerly, this module was part of the collections module.

Source code: Lib/_collections_abc.py

This module provides abstract base classes that can be used to test whether a class provides a particular interface; for example, whether it is hashable or whether it is a mapping.

An issubclass() or isinstance() test for an interface works in one of three ways.

1) A newly written class can inherit directly from one of the abstract base classes. The class must supply the required abstract methods. The remaining mixin methods come from inheritance and can be overridden if desired. Other methods may be added as needed:

class C(Sequence):                      # Direct inheritance
+    def __init__(self): ...             # Extra method not required by the ABC
+    def __getitem__(self, index):  ...  # Required abstract method
+    def __len__(self):  ...             # Required abstract method
+    def count(self, value): ...         # Optionally override a mixin method
+
>>> issubclass(C, Sequence)
+True
+>>> isinstance(C(), Sequence)
+True
+

2) Existing classes and built-in classes can be registered as “virtual subclasses” of the ABCs. Those classes should define the full API including all of the abstract methods and all of the mixin methods. This lets users rely on issubclass() or isinstance() tests to determine whether the full interface is supported. The exception to this rule is for methods that are automatically inferred from the rest of the API:

class D:                                 # No inheritance
+    def __init__(self): ...              # Extra method not required by the ABC
+    def __getitem__(self, index):  ...   # Abstract method
+    def __len__(self):  ...              # Abstract method
+    def count(self, value): ...          # Mixin method
+    def index(self, value): ...          # Mixin method
+
+Sequence.register(D)                     # Register instead of inherit
+
>>> issubclass(D, Sequence)
+True
+>>> isinstance(D(), Sequence)
+True
+

In this example, class D does not need to define __contains__, __iter__, and __reversed__ because the in-operator, the iteration logic, and the reversed() function automatically fall back to using __getitem__ and __len__.

3) Some simple interfaces are directly recognizable by the presence of the required methods (unless those methods have been set to None):

class E:
+    def __iter__(self): ...
+    def __next__(self): ...
+
>>> issubclass(E, Iterable)
+True
+>>> isinstance(E(), Iterable)
+True
+

Complex interfaces do not support this last technique because an interface is more than just the presence of method names. Interfaces specify semantics and relationships between methods that cannot be inferred solely from the presence of specific method names. For example, knowing that a class supplies __getitem__, __len__, and __iter__ is insufficient for distinguishing a Sequence from a Mapping.

New in version 3.9: These abstract classes now support []. See Generic Alias Type and PEP 585.

Collections Abstract Base Classes

The collections module offers the following ABCs:

+ + + + + + + + + + + + + + + + + + + + + + + + + + +

ABC

Inherits from

Abstract Methods

Mixin Methods

Container 1

__contains__

Hashable 1

__hash__

Iterable 1 2

__iter__

Iterator 1

Iterable

__next__

__iter__

Reversible 1

Iterable

__reversed__

Generator 1

Iterator

send, throw

close, __iter__, __next__

Sized 1

__len__

Callable 1

__call__

Collection 1

Sized, Iterable, Container

__contains__, __iter__, __len__

Sequence

Reversible, Collection

__getitem__, __len__

__contains__, __iter__, __reversed__, index, and count

MutableSequence

Sequence

__getitem__, __setitem__, __delitem__, __len__, insert

Inherited Sequence methods and append, reverse, extend, pop, remove, and __iadd__

ByteString

Sequence

__getitem__, __len__

Inherited Sequence methods

Set

Collection

__contains__, __iter__, __len__

__le__, __lt__, __eq__, __ne__, __gt__, __ge__, __and__, __or__, __sub__, __xor__, and isdisjoint

MutableSet

Set

__contains__, __iter__, __len__, add, discard

Inherited Set methods and clear, pop, remove, __ior__, __iand__, __ixor__, and __isub__

Mapping

Collection

__getitem__, __iter__, __len__

__contains__, keys, items, values, get, __eq__, and __ne__

MutableMapping

Mapping

__getitem__, __setitem__, __delitem__, __iter__, __len__

Inherited Mapping methods and pop, popitem, clear, update, and setdefault

MappingView

Sized

__len__

ItemsView

MappingView, Set

__contains__, __iter__

KeysView

MappingView, Set

__contains__, __iter__

ValuesView

MappingView, Collection

__contains__, __iter__

Awaitable 1

__await__

Coroutine 1

Awaitable

send, throw

close

AsyncIterable 1

__aiter__

AsyncIterator 1

AsyncIterable

__anext__

__aiter__

AsyncGenerator 1

AsyncIterator

asend, athrow

aclose, __aiter__, __anext__

Buffer 1

__buffer__

Footnotes

+1(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15)
+

These ABCs override __subclasshook__() to support testing an interface by verifying the required methods are present and have not been set to None. This only works for simple interfaces. More complex interfaces require registration or direct subclassing.

+2
+

Checking isinstance(obj, Iterable) detects classes that are registered as Iterable or that have an __iter__() method, but it does not detect classes that iterate with the __getitem__() method. The only reliable way to determine whether an object is iterable is to call iter(obj).

Collections Abstract Base Classes – Detailed Descriptions

+class collections.abc.Container
+

ABC for classes that provide the __contains__() method.

+
+class collections.abc.Hashable
+

ABC for classes that provide the __hash__() method.

+
+class collections.abc.Sized
+

ABC for classes that provide the __len__() method.

+
+class collections.abc.Callable
+

ABC for classes that provide the __call__() method.

+
+class collections.abc.Iterable
+

ABC for classes that provide the __iter__() method.

Checking isinstance(obj, Iterable) detects classes that are registered as Iterable or that have an __iter__() method, but it does not detect classes that iterate with the __getitem__() method. The only reliable way to determine whether an object is iterable is to call iter(obj).

+
+class collections.abc.Collection
+

ABC for sized iterable container classes.

New in version 3.6.

+
+class collections.abc.Iterator
+

ABC for classes that provide the __iter__() and __next__() methods. See also the definition of iterator.

+
+class collections.abc.Reversible
+

ABC for iterable classes that also provide the __reversed__() method.

New in version 3.6.

+
+class collections.abc.Generator
+

ABC for generator classes that implement the protocol defined in PEP 342 that extends iterators with the send(), throw() and close() methods.

New in version 3.5.

+
+class collections.abc.Sequence
+class collections.abc.MutableSequence
+class collections.abc.ByteString
+

ABCs for read-only and mutable sequences.

Implementation note: Some of the mixin methods, such as __iter__(), __reversed__() and index(), make repeated calls to the underlying __getitem__() method. Consequently, if __getitem__() is implemented with constant access speed, the mixin methods will have linear performance; however, if the underlying method is linear (as it would be with a linked list), the mixins will have quadratic performance and will likely need to be overridden.

Changed in version 3.5: The index() method added support for stop and start arguments.

Deprecated since version 3.12, will be removed in version 3.14: The ByteString ABC has been deprecated. For use in typing, prefer a union, like bytes | bytearray, or collections.abc.Buffer. For use as an ABC, prefer Sequence or collections.abc.Buffer.

+
+class collections.abc.Set
+class collections.abc.MutableSet
+

ABCs for read-only and mutable sets.

+
+class collections.abc.Mapping
+class collections.abc.MutableMapping
+

ABCs for read-only and mutable mappings.

+
+class collections.abc.MappingView
+class collections.abc.ItemsView
+class collections.abc.KeysView
+class collections.abc.ValuesView
+

ABCs for mapping, items, keys, and values views.

+
+class collections.abc.Awaitable
+

ABC for awaitable objects, which can be used in await expressions. Custom implementations must provide the __await__() method.

Coroutine objects and instances of the Coroutine ABC are all instances of this ABC.

Note

In CPython, generator-based coroutines (generators decorated with @types.coroutine) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Awaitable) for them will return False. Use inspect.isawaitable() to detect them.

New in version 3.5.

+
+class collections.abc.Coroutine
+

ABC for coroutine compatible classes. These implement the following methods, defined in Coroutine Objects: send(), throw(), and close(). Custom implementations must also implement __await__(). All Coroutine instances are also instances of Awaitable.

Note

In CPython, generator-based coroutines (generators decorated with @types.coroutine) are awaitables, even though they do not have an __await__() method. Using isinstance(gencoro, Coroutine) for them will return False. Use inspect.isawaitable() to detect them.

New in version 3.5.

+
+class collections.abc.AsyncIterable
+

ABC for classes that provide an __aiter__ method. See also the definition of asynchronous iterable.

New in version 3.5.

+
+class collections.abc.AsyncIterator
+

ABC for classes that provide __aiter__ and __anext__ methods. See also the definition of asynchronous iterator.

New in version 3.5.

+
+class collections.abc.AsyncGenerator
+

ABC for asynchronous generator classes that implement the protocol defined in PEP 525 and PEP 492.

New in version 3.6.

+
+class collections.abc.Buffer
+

ABC for classes that provide the __buffer__() method, implementing the buffer protocol. See PEP 688.

New in version 3.12.

+

Examples and Recipes

ABCs allow us to ask classes or instances if they provide particular functionality, for example:

size = None
+if isinstance(myvar, collections.abc.Sized):
+    size = len(myvar)
+

Several of the ABCs are also useful as mixins that make it easier to develop classes supporting container APIs. For example, to write a class supporting the full Set API, it is only necessary to supply the three underlying abstract methods: __contains__(), __iter__(), and __len__(). The ABC supplies the remaining methods such as __and__() and isdisjoint():

class ListBasedSet(collections.abc.Set):
+    ''' Alternate set implementation favoring space over speed
+        and not requiring the set elements to be hashable. '''
+    def __init__(self, iterable):
+        self.elements = lst = []
+        for value in iterable:
+            if value not in lst:
+                lst.append(value)
+
+    def __iter__(self):
+        return iter(self.elements)
+
+    def __contains__(self, value):
+        return value in self.elements
+
+    def __len__(self):
+        return len(self.elements)
+
+s1 = ListBasedSet('abcdef')
+s2 = ListBasedSet('defghi')
+overlap = s1 & s2            # The __and__() method is supported automatically
+

Notes on using Set and MutableSet as a mixin:

  1. Since some set operations create new sets, the default mixin methods need a way to create new instances from an iterable. The class constructor is assumed to have a signature in the form ClassName(iterable). That assumption is factored-out to an internal classmethod called _from_iterable() which calls cls(iterable) to produce a new set. If the Set mixin is being used in a class with a different constructor signature, you will need to override _from_iterable() with a classmethod or regular method that can construct new instances from an iterable argument.
  2. To override the comparisons (presumably for speed, as the semantics are fixed), redefine __le__() and __ge__(), then the other operations will automatically follow suit.
  3. The Set mixin provides a _hash() method to compute a hash value for the set; however, __hash__() is not defined because not all sets are hashable or immutable. To add set hashability using mixins, inherit from both Set() and Hashable(), then define __hash__ = Set._hash.

See also

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/collections.abc.html +

+
diff --git a/devdocs/python~3.12/library%2Fcollections.html b/devdocs/python~3.12/library%2Fcollections.html new file mode 100644 index 00000000..345017f6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcollections.html @@ -0,0 +1,546 @@ +

collections — Container datatypes

Source code: Lib/collections/__init__.py

This module implements specialized container datatypes providing alternatives to Python’s general purpose built-in containers, dict, list, set, and tuple.

+ + + + + + + + +

namedtuple()

factory function for creating tuple subclasses with named fields

deque

list-like container with fast appends and pops on either end

ChainMap

dict-like class for creating a single view of multiple mappings

Counter

dict subclass for counting hashable objects

OrderedDict

dict subclass that remembers the order entries were added

defaultdict

dict subclass that calls a factory function to supply missing values

UserDict

wrapper around dictionary objects for easier dict subclassing

UserList

wrapper around list objects for easier list subclassing

UserString

wrapper around string objects for easier string subclassing

ChainMap objects

New in version 3.3.

A ChainMap class is provided for quickly linking a number of mappings so they can be treated as a single unit. It is often much faster than creating a new dictionary and running multiple update() calls.

The class can be used to simulate nested scopes and is useful in templating.

+class collections.ChainMap(*maps)
+

A ChainMap groups multiple dicts or other mappings together to create a single, updateable view. If no maps are specified, a single empty dictionary is provided so that a new chain always has at least one mapping.

The underlying mappings are stored in a list. That list is public and can be accessed or updated using the maps attribute. There is no other state.

Lookups search the underlying mappings successively until a key is found. In contrast, writes, updates, and deletions only operate on the first mapping.

A ChainMap incorporates the underlying mappings by reference. So, if one of the underlying mappings gets updated, those changes will be reflected in ChainMap.

All of the usual dictionary methods are supported. In addition, there is a maps attribute, a method for creating new subcontexts, and a property for accessing all but the first mapping:

+maps
+

A user updateable list of mappings. The list is ordered from first-searched to last-searched. It is the only stored state and can be modified to change which mappings are searched. The list should always contain at least one mapping.

+
+new_child(m=None, **kwargs)
+

Returns a new ChainMap containing a new map followed by all of the maps in the current instance. If m is specified, it becomes the new map at the front of the list of mappings; if not specified, an empty dict is used, so that a call to d.new_child() is equivalent to: ChainMap({}, *d.maps). If any keyword arguments are specified, they update passed map or new empty dict. This method is used for creating subcontexts that can be updated without altering values in any of the parent mappings.

Changed in version 3.4: The optional m parameter was added.

Changed in version 3.10: Keyword arguments support was added.

+
+parents
+

Property returning a new ChainMap containing all of the maps in the current instance except the first one. This is useful for skipping the first map in the search. Use cases are similar to those for the nonlocal keyword used in nested scopes. The use cases also parallel those for the built-in super() function. A reference to d.parents is equivalent to: ChainMap(*d.maps[1:]).

+

Note, the iteration order of a ChainMap() is determined by scanning the mappings last to first:

>>> baseline = {'music': 'bach', 'art': 'rembrandt'}
+>>> adjustments = {'art': 'van gogh', 'opera': 'carmen'}
+>>> list(ChainMap(adjustments, baseline))
+['music', 'art', 'opera']
+

This gives the same ordering as a series of dict.update() calls starting with the last mapping:

>>> combined = baseline.copy()
+>>> combined.update(adjustments)
+>>> list(combined)
+['music', 'art', 'opera']
+

Changed in version 3.9: Added support for | and |= operators, specified in PEP 584.

+

See also

+ChainMap Examples and Recipes

This section shows various approaches to working with chained maps.

Example of simulating Python’s internal lookup chain:

import builtins
+pylookup = ChainMap(locals(), globals(), vars(builtins))
+

Example of letting user specified command-line arguments take precedence over environment variables which in turn take precedence over default values:

import os, argparse
+
+defaults = {'color': 'red', 'user': 'guest'}
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-u', '--user')
+parser.add_argument('-c', '--color')
+namespace = parser.parse_args()
+command_line_args = {k: v for k, v in vars(namespace).items() if v is not None}
+
+combined = ChainMap(command_line_args, os.environ, defaults)
+print(combined['color'])
+print(combined['user'])
+

Example patterns for using the ChainMap class to simulate nested contexts:

c = ChainMap()        # Create root context
+d = c.new_child()     # Create nested child context
+e = c.new_child()     # Child of c, independent from d
+e.maps[0]             # Current context dictionary -- like Python's locals()
+e.maps[-1]            # Root context -- like Python's globals()
+e.parents             # Enclosing context chain -- like Python's nonlocals
+
+d['x'] = 1            # Set value in current context
+d['x']                # Get first key in the chain of contexts
+del d['x']            # Delete from current context
+list(d)               # All nested values
+k in d                # Check all nested values
+len(d)                # Number of nested values
+d.items()             # All nested items
+dict(d)               # Flatten into a regular dictionary
+

The ChainMap class only makes updates (writes and deletions) to the first mapping in the chain while lookups will search the full chain. However, if deep writes and deletions are desired, it is easy to make a subclass that updates keys found deeper in the chain:

class DeepChainMap(ChainMap):
+    'Variant of ChainMap that allows direct updates to inner scopes'
+
+    def __setitem__(self, key, value):
+        for mapping in self.maps:
+            if key in mapping:
+                mapping[key] = value
+                return
+        self.maps[0][key] = value
+
+    def __delitem__(self, key):
+        for mapping in self.maps:
+            if key in mapping:
+                del mapping[key]
+                return
+        raise KeyError(key)
+
+>>> d = DeepChainMap({'zebra': 'black'}, {'elephant': 'blue'}, {'lion': 'yellow'})
+>>> d['lion'] = 'orange'         # update an existing key two levels down
+>>> d['snake'] = 'red'           # new keys get added to the topmost dict
+>>> del d['elephant']            # remove an existing key one level down
+>>> d                            # display result
+DeepChainMap({'zebra': 'black', 'snake': 'red'}, {}, {'lion': 'orange'})
+

Counter objects

A counter tool is provided to support convenient and rapid tallies. For example:

>>> # Tally occurrences of words in a list
+>>> cnt = Counter()
+>>> for word in ['red', 'blue', 'red', 'green', 'blue', 'blue']:
+...     cnt[word] += 1
+...
+>>> cnt
+Counter({'blue': 3, 'red': 2, 'green': 1})
+
+>>> # Find the ten most common words in Hamlet
+>>> import re
+>>> words = re.findall(r'\w+', open('hamlet.txt').read().lower())
+>>> Counter(words).most_common(10)
+[('the', 1143), ('and', 966), ('to', 762), ('of', 669), ('i', 631),
+ ('you', 554),  ('a', 546), ('my', 514), ('hamlet', 471), ('in', 451)]
+
+class collections.Counter([iterable-or-mapping])
+

A Counter is a dict subclass for counting hashable objects. It is a collection where elements are stored as dictionary keys and their counts are stored as dictionary values. Counts are allowed to be any integer value including zero or negative counts. The Counter class is similar to bags or multisets in other languages.

Elements are counted from an iterable or initialized from another mapping (or counter):

>>> c = Counter()                           # a new, empty counter
+>>> c = Counter('gallahad')                 # a new counter from an iterable
+>>> c = Counter({'red': 4, 'blue': 2})      # a new counter from a mapping
+>>> c = Counter(cats=4, dogs=8)             # a new counter from keyword args
+

Counter objects have a dictionary interface except that they return a zero count for missing items instead of raising a KeyError:

>>> c = Counter(['eggs', 'ham'])
+>>> c['bacon']                              # count of a missing element is zero
+0
+

Setting a count to zero does not remove an element from a counter. Use del to remove it entirely:

>>> c['sausage'] = 0                        # counter entry with a zero count
+>>> del c['sausage']                        # del actually removes the entry
+

New in version 3.1.

Changed in version 3.7: As a dict subclass, Counter inherited the capability to remember insertion order. Math operations on Counter objects also preserve order. Results are ordered according to when an element is first encountered in the left operand and then by the order encountered in the right operand.

Counter objects support additional methods beyond those available for all dictionaries:

+elements()
+

Return an iterator over elements repeating each as many times as its count. Elements are returned in the order first encountered. If an element’s count is less than one, elements() will ignore it.

>>> c = Counter(a=4, b=2, c=0, d=-2)
+>>> sorted(c.elements())
+['a', 'a', 'a', 'a', 'b', 'b']
+
+
+most_common([n])
+

Return a list of the n most common elements and their counts from the most common to the least. If n is omitted or None, most_common() returns all elements in the counter. Elements with equal counts are ordered in the order first encountered:

>>> Counter('abracadabra').most_common(3)
+[('a', 5), ('b', 2), ('r', 2)]
+
+
+subtract([iterable-or-mapping])
+

Elements are subtracted from an iterable or from another mapping (or counter). Like dict.update() but subtracts counts instead of replacing them. Both inputs and outputs may be zero or negative.

>>> c = Counter(a=4, b=2, c=0, d=-2)
+>>> d = Counter(a=1, b=2, c=3, d=4)
+>>> c.subtract(d)
+>>> c
+Counter({'a': 3, 'b': 0, 'c': -3, 'd': -6})
+

New in version 3.2.

+
+total()
+

Compute the sum of the counts.

>>> c = Counter(a=10, b=5, c=0)
+>>> c.total()
+15
+

New in version 3.10.

+

The usual dictionary methods are available for Counter objects except for two which work differently for counters.

+fromkeys(iterable)
+

This class method is not implemented for Counter objects.

+
+update([iterable-or-mapping])
+

Elements are counted from an iterable or added-in from another mapping (or counter). Like dict.update() but adds counts instead of replacing them. Also, the iterable is expected to be a sequence of elements, not a sequence of (key, value) pairs.

+
+

Counters support rich comparison operators for equality, subset, and superset relationships: ==, !=, <, <=, >, >=. All of those tests treat missing elements as having zero counts so that Counter(a=1) == Counter(a=1, b=0) returns true.

New in version 3.10: Rich comparison operations were added.

Changed in version 3.10: In equality tests, missing elements are treated as having zero counts. Formerly, Counter(a=3) and Counter(a=3, b=0) were considered distinct.

Common patterns for working with Counter objects:

c.total()                       # total of all counts
+c.clear()                       # reset all counts
+list(c)                         # list unique elements
+set(c)                          # convert to a set
+dict(c)                         # convert to a regular dictionary
+c.items()                       # convert to a list of (elem, cnt) pairs
+Counter(dict(list_of_pairs))    # convert from a list of (elem, cnt) pairs
+c.most_common()[:-n-1:-1]       # n least common elements
++c                              # remove zero and negative counts
+

Several mathematical operations are provided for combining Counter objects to produce multisets (counters that have counts greater than zero). Addition and subtraction combine counters by adding or subtracting the counts of corresponding elements. Intersection and union return the minimum and maximum of corresponding counts. Equality and inclusion compare corresponding counts. Each operation can accept inputs with signed counts, but the output will exclude results with counts of zero or less.

>>> c = Counter(a=3, b=1)
+>>> d = Counter(a=1, b=2)
+>>> c + d                       # add two counters together:  c[x] + d[x]
+Counter({'a': 4, 'b': 3})
+>>> c - d                       # subtract (keeping only positive counts)
+Counter({'a': 2})
+>>> c & d                       # intersection:  min(c[x], d[x])
+Counter({'a': 1, 'b': 1})
+>>> c | d                       # union:  max(c[x], d[x])
+Counter({'a': 3, 'b': 2})
+>>> c == d                      # equality:  c[x] == d[x]
+False
+>>> c <= d                      # inclusion:  c[x] <= d[x]
+False
+

Unary addition and subtraction are shortcuts for adding an empty counter or subtracting from an empty counter.

>>> c = Counter(a=2, b=-4)
+>>> +c
+Counter({'a': 2})
+>>> -c
+Counter({'b': 4})
+

New in version 3.3: Added support for unary plus, unary minus, and in-place multiset operations.

Note

Counters were primarily designed to work with positive integers to represent running counts; however, care was taken to not unnecessarily preclude use cases needing other types or negative values. To help with those use cases, this section documents the minimum range and type restrictions.

  • The Counter class itself is a dictionary subclass with no restrictions on its keys and values. The values are intended to be numbers representing counts, but you could store anything in the value field.
  • The most_common() method requires only that the values be orderable.
  • For in-place operations such as c[key] += 1, the value type need only support addition and subtraction. So fractions, floats, and decimals would work and negative values are supported. The same is also true for update() and subtract() which allow negative and zero values for both inputs and outputs.
  • The multiset methods are designed only for use cases with positive values. The inputs may be negative or zero, but only outputs with positive values are created. There are no type restrictions, but the value type needs to support addition, subtraction, and comparison.
  • The elements() method requires integer counts. It ignores zero and negative counts.

See also

  • +Bag class in Smalltalk.
  • Wikipedia entry for Multisets.
  • +C++ multisets tutorial with examples.
  • For mathematical operations on multisets and their use cases, see Knuth, Donald. The Art of Computer Programming Volume II, Section 4.6.3, Exercise 19.
  • +

    To enumerate all distinct multisets of a given size over a given set of elements, see itertools.combinations_with_replacement():

    map(Counter, combinations_with_replacement('ABC', 2)) # --> AA AB AC BB BC CC
+

deque objects

+class collections.deque([iterable[, maxlen]])
+

Returns a new deque object initialized left-to-right (using append()) with data from iterable. If iterable is not specified, the new deque is empty.

Deques are a generalization of stacks and queues (the name is pronounced “deck” and is short for “double-ended queue”). Deques support thread-safe, memory efficient appends and pops from either side of the deque with approximately the same O(1) performance in either direction.

Though list objects support similar operations, they are optimized for fast fixed-length operations and incur O(n) memory movement costs for pop(0) and insert(0, v) operations which change both the size and position of the underlying data representation.

If maxlen is not specified or is None, deques may grow to an arbitrary length. Otherwise, the deque is bounded to the specified maximum length. Once a bounded length deque is full, when new items are added, a corresponding number of items are discarded from the opposite end. Bounded length deques provide functionality similar to the tail filter in Unix. They are also useful for tracking transactions and other pools of data where only the most recent activity is of interest.

Deque objects support the following methods:

+append(x)
+

Add x to the right side of the deque.

+
+appendleft(x)
+

Add x to the left side of the deque.

+
+clear()
+

Remove all elements from the deque leaving it with length 0.

+
+copy()
+

Create a shallow copy of the deque.

New in version 3.5.

+
+count(x)
+

Count the number of deque elements equal to x.

New in version 3.2.

+
+extend(iterable)
+

Extend the right side of the deque by appending elements from the iterable argument.

+
+extendleft(iterable)
+

Extend the left side of the deque by appending elements from iterable. Note, the series of left appends results in reversing the order of elements in the iterable argument.

+
+index(x[, start[, stop]])
+

Return the position of x in the deque (at or after index start and before index stop). Returns the first match or raises ValueError if not found.

New in version 3.5.

+
+insert(i, x)
+

Insert x into the deque at position i.

If the insertion would cause a bounded deque to grow beyond maxlen, an IndexError is raised.

New in version 3.5.

+
+pop()
+

Remove and return an element from the right side of the deque. If no elements are present, raises an IndexError.

+
+popleft()
+

Remove and return an element from the left side of the deque. If no elements are present, raises an IndexError.

+
+remove(value)
+

Remove the first occurrence of value. If not found, raises a ValueError.

+
+reverse()
+

Reverse the elements of the deque in-place and then return None.

New in version 3.2.

+
+rotate(n=1)
+

Rotate the deque n steps to the right. If n is negative, rotate to the left.

When the deque is not empty, rotating one step to the right is equivalent to d.appendleft(d.pop()), and rotating one step to the left is equivalent to d.append(d.popleft()).

+

Deque objects also provide one read-only attribute:

+maxlen
+

Maximum size of a deque or None if unbounded.

New in version 3.1.

+
+

In addition to the above, deques support iteration, pickling, len(d), reversed(d), copy.copy(d), copy.deepcopy(d), membership testing with the in operator, and subscript references such as d[0] to access the first element. Indexed access is O(1) at both ends but slows to O(n) in the middle. For fast random access, use lists instead.

Starting in version 3.5, deques support __add__(), __mul__(), and __imul__().

Example:

>>> from collections import deque
+>>> d = deque('ghi')                 # make a new deque with three items
+>>> for elem in d:                   # iterate over the deque's elements
+...     print(elem.upper())
+G
+H
+I
+
+>>> d.append('j')                    # add a new entry to the right side
+>>> d.appendleft('f')                # add a new entry to the left side
+>>> d                                # show the representation of the deque
+deque(['f', 'g', 'h', 'i', 'j'])
+
+>>> d.pop()                          # return and remove the rightmost item
+'j'
+>>> d.popleft()                      # return and remove the leftmost item
+'f'
+>>> list(d)                          # list the contents of the deque
+['g', 'h', 'i']
+>>> d[0]                             # peek at leftmost item
+'g'
+>>> d[-1]                            # peek at rightmost item
+'i'
+
+>>> list(reversed(d))                # list the contents of a deque in reverse
+['i', 'h', 'g']
+>>> 'h' in d                         # search the deque
+True
+>>> d.extend('jkl')                  # add multiple elements at once
+>>> d
+deque(['g', 'h', 'i', 'j', 'k', 'l'])
+>>> d.rotate(1)                      # right rotation
+>>> d
+deque(['l', 'g', 'h', 'i', 'j', 'k'])
+>>> d.rotate(-1)                     # left rotation
+>>> d
+deque(['g', 'h', 'i', 'j', 'k', 'l'])
+
+>>> deque(reversed(d))               # make a new deque in reverse order
+deque(['l', 'k', 'j', 'i', 'h', 'g'])
+>>> d.clear()                        # empty the deque
+>>> d.pop()                          # cannot pop from an empty deque
+Traceback (most recent call last):
+    File "<pyshell#6>", line 1, in -toplevel-
+        d.pop()
+IndexError: pop from an empty deque
+
+>>> d.extendleft('abc')              # extendleft() reverses the input order
+>>> d
+deque(['c', 'b', 'a'])
+

+deque Recipes

This section shows various approaches to working with deques.

Bounded length deques provide functionality similar to the tail filter in Unix:

def tail(filename, n=10):
+    'Return the last n lines of a file'
+    with open(filename) as f:
+        return deque(f, n)
+

Another approach to using deques is to maintain a sequence of recently added elements by appending to the right and popping to the left:

def moving_average(iterable, n=3):
+    # moving_average([40, 30, 50, 46, 39, 44]) --> 40.0 42.0 45.0 43.0
+    # https://en.wikipedia.org/wiki/Moving_average
+    it = iter(iterable)
+    d = deque(itertools.islice(it, n-1))
+    d.appendleft(0)
+    s = sum(d)
+    for elem in it:
+        s += elem - d.popleft()
+        d.append(elem)
+        yield s / n
+

A round-robin scheduler can be implemented with input iterators stored in a deque. Values are yielded from the active iterator in position zero. If that iterator is exhausted, it can be removed with popleft(); otherwise, it can be cycled back to the end with the rotate() method:

def roundrobin(*iterables):
+    "roundrobin('ABC', 'D', 'EF') --> A D E B F C"
+    iterators = deque(map(iter, iterables))
+    while iterators:
+        try:
+            while True:
+                yield next(iterators[0])
+                iterators.rotate(-1)
+        except StopIteration:
+            # Remove an exhausted iterator.
+            iterators.popleft()
+

The rotate() method provides a way to implement deque slicing and deletion. For example, a pure Python implementation of del d[n] relies on the rotate() method to position elements to be popped:

def delete_nth(d, n):
+    d.rotate(-n)
+    d.popleft()
+    d.rotate(n)
+

To implement deque slicing, use a similar approach applying rotate() to bring a target element to the left side of the deque. Remove old entries with popleft(), add new entries with extend(), and then reverse the rotation. With minor variations on that approach, it is easy to implement Forth style stack manipulations such as dup, drop, swap, over, pick, rot, and roll.

defaultdict objects

+class collections.defaultdict(default_factory=None, /[, ...])
+

Return a new dictionary-like object. defaultdict is a subclass of the built-in dict class. It overrides one method and adds one writable instance variable. The remaining functionality is the same as for the dict class and is not documented here.

The first argument provides the initial value for the default_factory attribute; it defaults to None. All remaining arguments are treated the same as if they were passed to the dict constructor, including keyword arguments.

defaultdict objects support the following method in addition to the standard dict operations:

+__missing__(key)
+

If the default_factory attribute is None, this raises a KeyError exception with the key as argument.

If default_factory is not None, it is called without arguments to provide a default value for the given key, this value is inserted in the dictionary for the key, and returned.

If calling default_factory raises an exception this exception is propagated unchanged.

This method is called by the __getitem__() method of the dict class when the requested key is not found; whatever it returns or raises is then returned or raised by __getitem__().

Note that __missing__() is not called for any operations besides __getitem__(). This means that get() will, like normal dictionaries, return None as a default rather than using default_factory.

+

defaultdict objects support the following instance variable:

+default_factory
+

This attribute is used by the __missing__() method; it is initialized from the first argument to the constructor, if present, or to None, if absent.

+

Changed in version 3.9: Added merge (|) and update (|=) operators, specified in PEP 584.

+

+defaultdict Examples

Using list as the default_factory, it is easy to group a sequence of key-value pairs into a dictionary of lists:

>>> s = [('yellow', 1), ('blue', 2), ('yellow', 3), ('blue', 4), ('red', 1)]
+>>> d = defaultdict(list)
+>>> for k, v in s:
+...     d[k].append(v)
+...
+>>> sorted(d.items())
+[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
+

When each key is encountered for the first time, it is not already in the mapping; so an entry is automatically created using the default_factory function which returns an empty list. The list.append() operation then attaches the value to the new list. When keys are encountered again, the look-up proceeds normally (returning the list for that key) and the list.append() operation adds another value to the list. This technique is simpler and faster than an equivalent technique using dict.setdefault():

>>> d = {}
+>>> for k, v in s:
+...     d.setdefault(k, []).append(v)
+...
+>>> sorted(d.items())
+[('blue', [2, 4]), ('red', [1]), ('yellow', [1, 3])]
+

Setting the default_factory to int makes the defaultdict useful for counting (like a bag or multiset in other languages):

>>> s = 'mississippi'
+>>> d = defaultdict(int)
+>>> for k in s:
+...     d[k] += 1
+...
+>>> sorted(d.items())
+[('i', 4), ('m', 1), ('p', 2), ('s', 4)]
+

When a letter is first encountered, it is missing from the mapping, so the default_factory function calls int() to supply a default count of zero. The increment operation then builds up the count for each letter.

The function int() which always returns zero is just a special case of constant functions. A faster and more flexible way to create constant functions is to use a lambda function which can supply any constant value (not just zero):

>>> def constant_factory(value):
+...     return lambda: value
+...
+>>> d = defaultdict(constant_factory('<missing>'))
+>>> d.update(name='John', action='ran')
+>>> '%(name)s %(action)s to %(object)s' % d
+'John ran to <missing>'
+

Setting the default_factory to set makes the defaultdict useful for building a dictionary of sets:

>>> s = [('red', 1), ('blue', 2), ('red', 3), ('blue', 4), ('red', 1), ('blue', 4)]
+>>> d = defaultdict(set)
+>>> for k, v in s:
+...     d[k].add(v)
+...
+>>> sorted(d.items())
+[('blue', {2, 4}), ('red', {1, 3})]
+

namedtuple() Factory Function for Tuples with Named Fields

Named tuples assign meaning to each position in a tuple and allow for more readable, self-documenting code. They can be used wherever regular tuples are used, and they add the ability to access fields by name instead of position index.

+collections.namedtuple(typename, field_names, *, rename=False, defaults=None, module=None)
+

Returns a new tuple subclass named typename. The new subclass is used to create tuple-like objects that have fields accessible by attribute lookup as well as being indexable and iterable. Instances of the subclass also have a helpful docstring (with typename and field_names) and a helpful __repr__() method which lists the tuple contents in a name=value format.

The field_names are a sequence of strings such as ['x', 'y']. Alternatively, field_names can be a single string with each fieldname separated by whitespace and/or commas, for example 'x y' or 'x, y'.

Any valid Python identifier may be used for a fieldname except for names starting with an underscore. Valid identifiers consist of letters, digits, and underscores but do not start with a digit or underscore and cannot be a keyword such as class, for, return, global, pass, or raise.

If rename is true, invalid fieldnames are automatically replaced with positional names. For example, ['abc', 'def', 'ghi', 'abc'] is converted to ['abc', '_1', 'ghi', '_3'], eliminating the keyword def and the duplicate fieldname abc.

defaults can be None or an iterable of default values. Since fields with a default value must come after any fields without a default, the defaults are applied to the rightmost parameters. For example, if the fieldnames are ['x', 'y', 'z'] and the defaults are (1, 2), then x will be a required argument, y will default to 1, and z will default to 2.

If module is defined, the __module__ attribute of the named tuple is set to that value.

Named tuple instances do not have per-instance dictionaries, so they are lightweight and require no more memory than regular tuples.

To support pickling, the named tuple class should be assigned to a variable that matches typename.

Changed in version 3.1: Added support for rename.

Changed in version 3.6: The verbose and rename parameters became keyword-only arguments.

Changed in version 3.6: Added the module parameter.

Changed in version 3.7: Removed the verbose parameter and the _source attribute.

Changed in version 3.7: Added the defaults parameter and the _field_defaults attribute.

+
>>> # Basic example
+>>> Point = namedtuple('Point', ['x', 'y'])
+>>> p = Point(11, y=22)     # instantiate with positional or keyword arguments
+>>> p[0] + p[1]             # indexable like the plain tuple (11, 22)
+33
+>>> x, y = p                # unpack like a regular tuple
+>>> x, y
+(11, 22)
+>>> p.x + p.y               # fields also accessible by name
+33
+>>> p                       # readable __repr__ with a name=value style
+Point(x=11, y=22)
+

Named tuples are especially useful for assigning field names to result tuples returned by the csv or sqlite3 modules:

EmployeeRecord = namedtuple('EmployeeRecord', 'name, age, title, department, paygrade')
+
+import csv
+for emp in map(EmployeeRecord._make, csv.reader(open("employees.csv", "rb"))):
+    print(emp.name, emp.title)
+
+import sqlite3
+conn = sqlite3.connect('/companydata')
+cursor = conn.cursor()
+cursor.execute('SELECT name, age, title, department, paygrade FROM employees')
+for emp in map(EmployeeRecord._make, cursor.fetchall()):
+    print(emp.name, emp.title)
+

In addition to the methods inherited from tuples, named tuples support three additional methods and two attributes. To prevent conflicts with field names, the method and attribute names start with an underscore.

+classmethod somenamedtuple._make(iterable)
+

Class method that makes a new instance from an existing sequence or iterable.

>>> t = [11, 22]
+>>> Point._make(t)
+Point(x=11, y=22)
+
+
+somenamedtuple._asdict()
+

Return a new dict which maps field names to their corresponding values:

>>> p = Point(x=11, y=22)
+>>> p._asdict()
+{'x': 11, 'y': 22}
+

Changed in version 3.1: Returns an OrderedDict instead of a regular dict.

Changed in version 3.8: Returns a regular dict instead of an OrderedDict. As of Python 3.7, regular dicts are guaranteed to be ordered. If the extra features of OrderedDict are required, the suggested remediation is to cast the result to the desired type: OrderedDict(nt._asdict()).

+
+somenamedtuple._replace(**kwargs)
+

Return a new instance of the named tuple replacing specified fields with new values:

>>> p = Point(x=11, y=22)
+>>> p._replace(x=33)
+Point(x=33, y=22)
+
+>>> for partnum, record in inventory.items():
+...     inventory[partnum] = record._replace(price=newprices[partnum], timestamp=time.now())
+
+
+somenamedtuple._fields
+

Tuple of strings listing the field names. Useful for introspection and for creating new named tuple types from existing named tuples.

>>> p._fields            # view the field names
+('x', 'y')
+
+>>> Color = namedtuple('Color', 'red green blue')
+>>> Pixel = namedtuple('Pixel', Point._fields + Color._fields)
+>>> Pixel(11, 22, 128, 255, 0)
+Pixel(x=11, y=22, red=128, green=255, blue=0)
+
+
+somenamedtuple._field_defaults
+

Dictionary mapping field names to default values.

>>> Account = namedtuple('Account', ['type', 'balance'], defaults=[0])
+>>> Account._field_defaults
+{'balance': 0}
+>>> Account('premium')
+Account(type='premium', balance=0)
+
+

To retrieve a field whose name is stored in a string, use the getattr() function:

>>> getattr(p, 'x')
+11
+

To convert a dictionary to a named tuple, use the double-star-operator (as described in Unpacking Argument Lists):

>>> d = {'x': 11, 'y': 22}
+>>> Point(**d)
+Point(x=11, y=22)
+

Since a named tuple is a regular Python class, it is easy to add or change functionality with a subclass. Here is how to add a calculated field and a fixed-width print format:

>>> class Point(namedtuple('Point', ['x', 'y'])):
+...     __slots__ = ()
+...     @property
+...     def hypot(self):
+...         return (self.x ** 2 + self.y ** 2) ** 0.5
+...     def __str__(self):
+...         return 'Point: x=%6.3f  y=%6.3f  hypot=%6.3f' % (self.x, self.y, self.hypot)
+
+>>> for p in Point(3, 4), Point(14, 5/7):
+...     print(p)
+Point: x= 3.000  y= 4.000  hypot= 5.000
+Point: x=14.000  y= 0.714  hypot=14.018
+

The subclass shown above sets __slots__ to an empty tuple. This helps keep memory requirements low by preventing the creation of instance dictionaries.

Subclassing is not useful for adding new, stored fields. Instead, simply create a new named tuple type from the _fields attribute:

>>> Point3D = namedtuple('Point3D', Point._fields + ('z',))
+

Docstrings can be customized by making direct assignments to the __doc__ fields:

>>> Book = namedtuple('Book', ['id', 'title', 'authors'])
+>>> Book.__doc__ += ': Hardcover book in active collection'
+>>> Book.id.__doc__ = '13-digit ISBN'
+>>> Book.title.__doc__ = 'Title of first printing'
+>>> Book.authors.__doc__ = 'List of authors sorted by last name'
+

Changed in version 3.5: Property docstrings became writeable.

See also

  • +

    See typing.NamedTuple for a way to add type hints for named tuples. It also provides an elegant notation using the class keyword:

    class Component(NamedTuple):
+    part_number: int
+    weight: float
+    description: Optional[str] = None
+
  • See types.SimpleNamespace() for a mutable namespace based on an underlying dictionary instead of a tuple.
  • The dataclasses module provides a decorator and functions for automatically adding generated special methods to user-defined classes.

OrderedDict objects

Ordered dictionaries are just like regular dictionaries but have some extra capabilities relating to ordering operations. They have become less important now that the built-in dict class gained the ability to remember insertion order (this new behavior became guaranteed in Python 3.7).

Some differences from dict still remain:

  • The regular dict was designed to be very good at mapping operations. Tracking insertion order was secondary.
  • The OrderedDict was designed to be good at reordering operations. Space efficiency, iteration speed, and the performance of update operations were secondary.
  • The OrderedDict algorithm can handle frequent reordering operations better than dict. As shown in the recipes below, this makes it suitable for implementing various kinds of LRU caches.
  • +

    The equality operation for OrderedDict checks for matching order.

    A regular dict can emulate the order sensitive equality test with p == q and all(k1 == k2 for k1, k2 in zip(p, q)).

  • +

    The popitem() method of OrderedDict has a different signature. It accepts an optional argument to specify which item is popped.

    A regular dict can emulate OrderedDict’s od.popitem(last=True) with d.popitem() which is guaranteed to pop the rightmost (last) item.

    A regular dict can emulate OrderedDict’s od.popitem(last=False) with (k := next(iter(d)), d.pop(k)) which will return and remove the leftmost (first) item if it exists.

  • +

    OrderedDict has a move_to_end() method to efficiently reposition an element to an endpoint.

    A regular dict can emulate OrderedDict’s od.move_to_end(k, +last=True) with d[k] = d.pop(k) which will move the key and its associated value to the rightmost (last) position.

    A regular dict does not have an efficient equivalent for OrderedDict’s od.move_to_end(k, last=False) which moves the key and its associated value to the leftmost (first) position.

  • Until Python 3.8, dict lacked a __reversed__() method.
+class collections.OrderedDict([items])
+

Return an instance of a dict subclass that has methods specialized for rearranging dictionary order.

New in version 3.1.

+popitem(last=True)
+

The popitem() method for ordered dictionaries returns and removes a (key, value) pair. The pairs are returned in LIFO order if last is true or FIFO order if false.

+
+move_to_end(key, last=True)
+

Move an existing key to either end of an ordered dictionary. The item is moved to the right end if last is true (the default) or to the beginning if last is false. Raises KeyError if the key does not exist:

>>> d = OrderedDict.fromkeys('abcde')
+>>> d.move_to_end('b')
+>>> ''.join(d)
+'acdeb'
+>>> d.move_to_end('b', last=False)
+>>> ''.join(d)
+'bacde'
+

New in version 3.2.

+
+

In addition to the usual mapping methods, ordered dictionaries also support reverse iteration using reversed().

Equality tests between OrderedDict objects are order-sensitive and are implemented as list(od1.items())==list(od2.items()). Equality tests between OrderedDict objects and other Mapping objects are order-insensitive like regular dictionaries. This allows OrderedDict objects to be substituted anywhere a regular dictionary is used.

Changed in version 3.5: The items, keys, and values views of OrderedDict now support reverse iteration using reversed().

Changed in version 3.6: With the acceptance of PEP 468, order is retained for keyword arguments passed to the OrderedDict constructor and its update() method.

Changed in version 3.9: Added merge (|) and update (|=) operators, specified in PEP 584.

+OrderedDict Examples and Recipes

It is straightforward to create an ordered dictionary variant that remembers the order the keys were last inserted. If a new entry overwrites an existing entry, the original insertion position is changed and moved to the end:

class LastUpdatedOrderedDict(OrderedDict):
+    'Store items in the order the keys were last added'
+
+    def __setitem__(self, key, value):
+        super().__setitem__(key, value)
+        self.move_to_end(key)
+

An OrderedDict would also be useful for implementing variants of functools.lru_cache():

from collections import OrderedDict
+from time import time
+
+class TimeBoundedLRU:
+    "LRU Cache that invalidates and refreshes old entries."
+
+    def __init__(self, func, maxsize=128, maxage=30):
+        self.cache = OrderedDict()      # { args : (timestamp, result)}
+        self.func = func
+        self.maxsize = maxsize
+        self.maxage = maxage
+
+    def __call__(self, *args):
+        if args in self.cache:
+            self.cache.move_to_end(args)
+            timestamp, result = self.cache[args]
+            if time() - timestamp <= self.maxage:
+                return result
+        result = self.func(*args)
+        self.cache[args] = time(), result
+        if len(self.cache) > self.maxsize:
+            self.cache.popitem(0)
+        return result
+
class MultiHitLRUCache:
+    """ LRU cache that defers caching a result until
+        it has been requested multiple times.
+
+        To avoid flushing the LRU cache with one-time requests,
+        we don't cache until a request has been made more than once.
+
+    """
+
+    def __init__(self, func, maxsize=128, maxrequests=4096, cache_after=1):
+        self.requests = OrderedDict()   # { uncached_key : request_count }
+        self.cache = OrderedDict()      # { cached_key : function_result }
+        self.func = func
+        self.maxrequests = maxrequests  # max number of uncached requests
+        self.maxsize = maxsize          # max number of stored return values
+        self.cache_after = cache_after
+
+    def __call__(self, *args):
+        if args in self.cache:
+            self.cache.move_to_end(args)
+            return self.cache[args]
+        result = self.func(*args)
+        self.requests[args] = self.requests.get(args, 0) + 1
+        if self.requests[args] <= self.cache_after:
+            self.requests.move_to_end(args)
+            if len(self.requests) > self.maxrequests:
+                self.requests.popitem(0)
+        else:
+            self.requests.pop(args, None)
+            self.cache[args] = result
+            if len(self.cache) > self.maxsize:
+                self.cache.popitem(0)
+        return result
+

UserDict objects

The class, UserDict acts as a wrapper around dictionary objects. The need for this class has been partially supplanted by the ability to subclass directly from dict; however, this class can be easier to work with because the underlying dictionary is accessible as an attribute.

+class collections.UserDict([initialdata])
+

Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the data attribute of UserDict instances. If initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it to be used for other purposes.

In addition to supporting the methods and operations of mappings, UserDict instances provide the following attribute:

+data
+

A real dictionary used to store the contents of the UserDict class.

+
+

UserList objects

This class acts as a wrapper around list objects. It is a useful base class for your own list-like classes which can inherit from them and override existing methods or add new ones. In this way, one can add new behaviors to lists.

The need for this class has been partially supplanted by the ability to subclass directly from list; however, this class can be easier to work with because the underlying list is accessible as an attribute.

+class collections.UserList([list])
+

Class that simulates a list. The instance’s contents are kept in a regular list, which is accessible via the data attribute of UserList instances. The instance’s contents are initially set to a copy of list, defaulting to the empty list []. list can be any iterable, for example a real Python list or a UserList object.

In addition to supporting the methods and operations of mutable sequences, UserList instances provide the following attribute:

+data
+

A real list object used to store the contents of the UserList class.

+
+

Subclassing requirements: Subclasses of UserList are expected to offer a constructor which can be called with either no arguments or one argument. List operations which return a new sequence attempt to create an instance of the actual implementation class. To do so, it assumes that the constructor can be called with a single parameter, which is a sequence object used as a data source.

If a derived class does not wish to comply with this requirement, all of the special methods supported by this class will need to be overridden; please consult the sources for information about the methods which need to be provided in that case.

UserString objects

The class, UserString acts as a wrapper around string objects. The need for this class has been partially supplanted by the ability to subclass directly from str; however, this class can be easier to work with because the underlying string is accessible as an attribute.

+class collections.UserString(seq)
+

Class that simulates a string object. The instance’s content is kept in a regular string object, which is accessible via the data attribute of UserString instances. The instance’s contents are initially set to a copy of seq. The seq argument can be any object which can be converted into a string using the built-in str() function.

In addition to supporting the methods and operations of strings, UserString instances provide the following attribute:

+data
+

A real str object used to store the contents of the UserString class.

+

Changed in version 3.5: New methods __getnewargs__, __rmod__, casefold, format_map, isprintable, and maketrans.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/collections.html +

+
diff --git a/devdocs/python~3.12/library%2Fcolorsys.html b/devdocs/python~3.12/library%2Fcolorsys.html new file mode 100644 index 00000000..ed442b59 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcolorsys.html @@ -0,0 +1,29 @@ +

colorsys — Conversions between color systems

Source code: Lib/colorsys.py

The colorsys module defines bidirectional conversions of color values between colors expressed in the RGB (Red Green Blue) color space used in computer monitors and three other coordinate systems: YIQ, HLS (Hue Lightness Saturation) and HSV (Hue Saturation Value). Coordinates in all of these color spaces are floating point values. In the YIQ space, the Y coordinate is between 0 and 1, but the I and Q coordinates can be positive or negative. In all other spaces, the coordinates are all between 0 and 1.

See also

More information about color spaces can be found at https://poynton.ca/ColorFAQ.html and https://www.cambridgeincolour.com/tutorials/color-spaces.htm.

The colorsys module defines the following functions:

+colorsys.rgb_to_yiq(r, g, b)
+

Convert the color from RGB coordinates to YIQ coordinates.

+
+colorsys.yiq_to_rgb(y, i, q)
+

Convert the color from YIQ coordinates to RGB coordinates.

+
+colorsys.rgb_to_hls(r, g, b)
+

Convert the color from RGB coordinates to HLS coordinates.

+
+colorsys.hls_to_rgb(h, l, s)
+

Convert the color from HLS coordinates to RGB coordinates.

+
+colorsys.rgb_to_hsv(r, g, b)
+

Convert the color from RGB coordinates to HSV coordinates.

+
+colorsys.hsv_to_rgb(h, s, v)
+

Convert the color from HSV coordinates to RGB coordinates.

+

Example:

>>> import colorsys
+>>> colorsys.rgb_to_hsv(0.2, 0.4, 0.4)
+(0.5, 0.5, 0.4)
+>>> colorsys.hsv_to_rgb(0.5, 0.5, 0.4)
+(0.2, 0.4, 0.4)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/colorsys.html +

+
diff --git a/devdocs/python~3.12/library%2Fcompileall.html b/devdocs/python~3.12/library%2Fcompileall.html new file mode 100644 index 00000000..d37531a0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcompileall.html @@ -0,0 +1,78 @@ +

compileall — Byte-compile Python libraries

Source code: Lib/compileall.py

This module provides some utility functions to support installing Python libraries. These functions compile Python source files in a directory tree. This module can be used to create the cached byte-code files at library installation time, which makes them available for use even by users who don’t have write permission to the library directories.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Command-line use

This module can work as a script (using python -m compileall) to compile Python sources.

+directory ...
+file ...
+

Positional arguments are files to compile or directories that contain source files, traversed recursively. If no argument is given, behave as if the command line was -l <directories from sys.path>.

+
+-l
+

Do not recurse into subdirectories, only compile source code files directly contained in the named or implied directories.

+
+-f
+

Force rebuild even if timestamps are up-to-date.

+
+-q
+

Do not print the list of files compiled. If passed once, error messages will still be printed. If passed twice (-qq), all output is suppressed.

+
+-d destdir
+

Directory prepended to the path to each file being compiled. This will appear in compilation time tracebacks, and is also compiled in to the byte-code file, where it will be used in tracebacks and other messages in cases where the source file does not exist at the time the byte-code file is executed.

+
+-s strip_prefix
+
+-p prepend_prefix
+

Remove (-s) or append (-p) the given prefix of paths recorded in the .pyc files. Cannot be combined with -d.

+
+-x regex
+

regex is used to search the full path to each file considered for compilation, and if the regex produces a match, the file is skipped.

+
+-i list
+

Read the file list and add each line that it contains to the list of files and directories to compile. If list is -, read lines from stdin.

+
+-b
+

Write the byte-code files to their legacy locations and names, which may overwrite byte-code files created by another version of Python. The default is to write files to their PEP 3147 locations and names, which allows byte-code files from multiple versions of Python to coexist.

+
+-r
+

Control the maximum recursion level for subdirectories. If this is given, then -l option will not be taken into account. python -m compileall <directory> -r 0 is equivalent to python -m compileall <directory> -l.

+
+-j N
+

Use N workers to compile the files within the given directory. If 0 is used, then the result of os.cpu_count() will be used.

+
+--invalidation-mode [timestamp|checked-hash|unchecked-hash]
+

Control how the generated byte-code files are invalidated at runtime. The timestamp value, means that .pyc files with the source timestamp and size embedded will be generated. The checked-hash and unchecked-hash values cause hash-based pycs to be generated. Hash-based pycs embed a hash of the source file contents rather than a timestamp. See Cached bytecode invalidation for more information on how Python validates bytecode cache files at runtime. The default is timestamp if the SOURCE_DATE_EPOCH environment variable is not set, and checked-hash if the SOURCE_DATE_EPOCH environment variable is set.

+
+-o level
+

Compile with the given optimization level. May be used multiple times to compile for multiple levels at a time (for example, compileall -o 1 -o 2).

+
+-e dir
+

Ignore symlinks pointing outside the given directory.

+
+

If two .pyc files with different optimization level have the same content, use hard links to consolidate duplicate files.

+

Changed in version 3.2: Added the -i, -b and -h options.

Changed in version 3.5: Added the -j, -r, and -qq options. -q option was changed to a multilevel value. -b will always produce a byte-code file ending in .pyc, never .pyo.

Changed in version 3.7: Added the --invalidation-mode option.

Changed in version 3.9: Added the -s, -p, -e and --hardlink-dupes options. Raised the default recursion limit from 10 to sys.getrecursionlimit(). Added the possibility to specify the -o option multiple times.

There is no command-line option to control the optimization level used by the compile() function, because the Python interpreter itself already provides the option: python -O -m compileall.

Similarly, the compile() function respects the sys.pycache_prefix setting. The generated bytecode cache will only be useful if compile() is run with the same sys.pycache_prefix (if any) that will be used at runtime.

Public functions

+compileall.compile_dir(dir, maxlevels=sys.getrecursionlimit(), ddir=None, force=False, rx=None, quiet=0, legacy=False, optimize=- 1, workers=1, invalidation_mode=None, *, stripdir=None, prependdir=None, limit_sl_dest=None, hardlink_dupes=False)
+

Recursively descend the directory tree named by dir, compiling all .py files along the way. Return a true value if all the files compiled successfully, and a false value otherwise.

The maxlevels parameter is used to limit the depth of the recursion; it defaults to sys.getrecursionlimit().

If ddir is given, it is prepended to the path to each file being compiled for use in compilation time tracebacks, and is also compiled in to the byte-code file, where it will be used in tracebacks and other messages in cases where the source file does not exist at the time the byte-code file is executed.

If force is true, modules are re-compiled even if the timestamps are up to date.

If rx is given, its search method is called on the complete path to each file considered for compilation, and if it returns a true value, the file is skipped. This can be used to exclude files matching a regular expression, given as a re.Pattern object.

If quiet is False or 0 (the default), the filenames and other information are printed to standard out. Set to 1, only errors are printed. Set to 2, all output is suppressed.

If legacy is true, byte-code files are written to their legacy locations and names, which may overwrite byte-code files created by another version of Python. The default is to write files to their PEP 3147 locations and names, which allows byte-code files from multiple versions of Python to coexist.

optimize specifies the optimization level for the compiler. It is passed to the built-in compile() function. Accepts also a sequence of optimization levels which lead to multiple compilations of one .py file in one call.

The argument workers specifies how many workers are used to compile files in parallel. The default is to not use multiple workers. If the platform can’t use multiple workers and workers argument is given, then sequential compilation will be used as a fallback. If workers is 0, the number of cores in the system is used. If workers is lower than 0, a ValueError will be raised.

invalidation_mode should be a member of the py_compile.PycInvalidationMode enum and controls how the generated pycs are invalidated at runtime.

The stripdir, prependdir and limit_sl_dest arguments correspond to the -s, -p and -e options described above. They may be specified as str or os.PathLike.

If hardlink_dupes is true and two .pyc files with different optimization level have the same content, use hard links to consolidate duplicate files.

Changed in version 3.2: Added the legacy and optimize parameter.

Changed in version 3.5: Added the workers parameter.

Changed in version 3.5: quiet parameter was changed to a multilevel value.

Changed in version 3.5: The legacy parameter only writes out .pyc files, not .pyo files no matter what the value of optimize is.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.7: The invalidation_mode parameter was added.

Changed in version 3.7.2: The invalidation_mode parameter’s default value is updated to None.

Changed in version 3.8: Setting workers to 0 now chooses the optimal number of cores.

Changed in version 3.9: Added stripdir, prependdir, limit_sl_dest and hardlink_dupes arguments. Default value of maxlevels was changed from 10 to sys.getrecursionlimit()

+
+compileall.compile_file(fullname, ddir=None, force=False, rx=None, quiet=0, legacy=False, optimize=- 1, invalidation_mode=None, *, stripdir=None, prependdir=None, limit_sl_dest=None, hardlink_dupes=False)
+

Compile the file with path fullname. Return a true value if the file compiled successfully, and a false value otherwise.

If ddir is given, it is prepended to the path to the file being compiled for use in compilation time tracebacks, and is also compiled in to the byte-code file, where it will be used in tracebacks and other messages in cases where the source file does not exist at the time the byte-code file is executed.

If rx is given, its search method is passed the full path name to the file being compiled, and if it returns a true value, the file is not compiled and True is returned. This can be used to exclude files matching a regular expression, given as a re.Pattern object.

If quiet is False or 0 (the default), the filenames and other information are printed to standard out. Set to 1, only errors are printed. Set to 2, all output is suppressed.

If legacy is true, byte-code files are written to their legacy locations and names, which may overwrite byte-code files created by another version of Python. The default is to write files to their PEP 3147 locations and names, which allows byte-code files from multiple versions of Python to coexist.

optimize specifies the optimization level for the compiler. It is passed to the built-in compile() function. Accepts also a sequence of optimization levels which lead to multiple compilations of one .py file in one call.

invalidation_mode should be a member of the py_compile.PycInvalidationMode enum and controls how the generated pycs are invalidated at runtime.

The stripdir, prependdir and limit_sl_dest arguments correspond to the -s, -p and -e options described above. They may be specified as str or os.PathLike.

If hardlink_dupes is true and two .pyc files with different optimization level have the same content, use hard links to consolidate duplicate files.

New in version 3.2.

Changed in version 3.5: quiet parameter was changed to a multilevel value.

Changed in version 3.5: The legacy parameter only writes out .pyc files, not .pyo files no matter what the value of optimize is.

Changed in version 3.7: The invalidation_mode parameter was added.

Changed in version 3.7.2: The invalidation_mode parameter’s default value is updated to None.

Changed in version 3.9: Added stripdir, prependdir, limit_sl_dest and hardlink_dupes arguments.

+
+compileall.compile_path(skip_curdir=True, maxlevels=0, force=False, quiet=0, legacy=False, optimize=- 1, invalidation_mode=None)
+

Byte-compile all the .py files found along sys.path. Return a true value if all the files compiled successfully, and a false value otherwise.

If skip_curdir is true (the default), the current directory is not included in the search. All other parameters are passed to the compile_dir() function. Note that unlike the other compile functions, maxlevels defaults to 0.

Changed in version 3.2: Added the legacy and optimize parameter.

Changed in version 3.5: quiet parameter was changed to a multilevel value.

Changed in version 3.5: The legacy parameter only writes out .pyc files, not .pyo files no matter what the value of optimize is.

Changed in version 3.7: The invalidation_mode parameter was added.

Changed in version 3.7.2: The invalidation_mode parameter’s default value is updated to None.

+

To force a recompile of all the .py files in the Lib/ subdirectory and all its subdirectories:

import compileall
+
+compileall.compile_dir('Lib/', force=True)
+
+# Perform same compilation, excluding files in .svn directories.
+import re
+compileall.compile_dir('Lib/', rx=re.compile(r'[/\\][.]svn'), force=True)
+
+# pathlib.Path objects can also be used.
+import pathlib
+compileall.compile_dir(pathlib.Path('Lib/'), force=True)
+

See also

+Module py_compile +
+
+

Byte-compile a single source file.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/compileall.html +

+
diff --git a/devdocs/python~3.12/library%2Fconcurrency.html b/devdocs/python~3.12/library%2Fconcurrency.html new file mode 100644 index 00000000..de5a929d --- /dev/null +++ b/devdocs/python~3.12/library%2Fconcurrency.html @@ -0,0 +1,27 @@ +

Concurrent Execution

The modules described in this chapter provide support for concurrent execution of code. The appropriate choice of tool will depend on the task to be executed (CPU bound vs IO bound) and preferred style of development (event driven cooperative multitasking vs preemptive multitasking). Here’s an overview:

The following are support modules for some of the above services:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/concurrency.html +

+
diff --git a/devdocs/python~3.12/library%2Fconcurrent.futures.html b/devdocs/python~3.12/library%2Fconcurrent.futures.html new file mode 100644 index 00000000..92f20a7f --- /dev/null +++ b/devdocs/python~3.12/library%2Fconcurrent.futures.html @@ -0,0 +1,179 @@ +

concurrent.futures — Launching parallel tasks

New in version 3.2.

Source code: Lib/concurrent/futures/thread.py and Lib/concurrent/futures/process.py

The concurrent.futures module provides a high-level interface for asynchronously executing callables.

The asynchronous execution can be performed with threads, using ThreadPoolExecutor, or separate processes, using ProcessPoolExecutor. Both implement the same interface, which is defined by the abstract Executor class.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Executor Objects

+class concurrent.futures.Executor
+

An abstract class that provides methods to execute calls asynchronously. It should not be used directly, but through its concrete subclasses.

+submit(fn, /, *args, **kwargs)
+

Schedules the callable, fn, to be executed as fn(*args, **kwargs) and returns a Future object representing the execution of the callable.

with ThreadPoolExecutor(max_workers=1) as executor:
+    future = executor.submit(pow, 323, 1235)
+    print(future.result())
+
+
+map(func, *iterables, timeout=None, chunksize=1)
+

Similar to map(func, *iterables) except:

  • the iterables are collected immediately rather than lazily;
  • +func is executed asynchronously and several calls to func may be made concurrently.

The returned iterator raises a TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to Executor.map(). timeout can be an int or a float. If timeout is not specified or None, there is no limit to the wait time.

If a func call raises an exception, then that exception will be raised when its value is retrieved from the iterator.

When using ProcessPoolExecutor, this method chops iterables into a number of chunks which it submits to the pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer. For very long iterables, using a large value for chunksize can significantly improve performance compared to the default size of 1. With ThreadPoolExecutor, chunksize has no effect.

Changed in version 3.5: Added the chunksize argument.

+
+shutdown(wait=True, *, cancel_futures=False)
+

Signal the executor that it should free any resources that it is using when the currently pending futures are done executing. Calls to Executor.submit() and Executor.map() made after shutdown will raise RuntimeError.

If wait is True then this method will not return until all the pending futures are done executing and the resources associated with the executor have been freed. If wait is False then this method will return immediately and the resources associated with the executor will be freed when all pending futures are done executing. Regardless of the value of wait, the entire Python program will not exit until all pending futures are done executing.

If cancel_futures is True, this method will cancel all pending futures that the executor has not started running. Any futures that are completed or running won’t be cancelled, regardless of the value of cancel_futures.

If both cancel_futures and wait are True, all futures that the executor has started running will be completed prior to this method returning. The remaining futures are cancelled.

You can avoid having to call this method explicitly if you use the with statement, which will shutdown the Executor (waiting as if Executor.shutdown() were called with wait set to True):

import shutil
+with ThreadPoolExecutor(max_workers=4) as e:
+    e.submit(shutil.copy, 'src1.txt', 'dest1.txt')
+    e.submit(shutil.copy, 'src2.txt', 'dest2.txt')
+    e.submit(shutil.copy, 'src3.txt', 'dest3.txt')
+    e.submit(shutil.copy, 'src4.txt', 'dest4.txt')
+

Changed in version 3.9: Added cancel_futures.

+
+

ThreadPoolExecutor

ThreadPoolExecutor is an Executor subclass that uses a pool of threads to execute calls asynchronously.

Deadlocks can occur when the callable associated with a Future waits on the results of another Future. For example:

import time
+def wait_on_b():
+    time.sleep(5)
+    print(b.result())  # b will never complete because it is waiting on a.
+    return 5
+
+def wait_on_a():
+    time.sleep(5)
+    print(a.result())  # a will never complete because it is waiting on b.
+    return 6
+
+
+executor = ThreadPoolExecutor(max_workers=2)
+a = executor.submit(wait_on_b)
+b = executor.submit(wait_on_a)
+

And:

def wait_on_future():
+    f = executor.submit(pow, 5, 2)
+    # This will never complete because there is only one worker thread and
+    # it is executing this function.
+    print(f.result())
+
+executor = ThreadPoolExecutor(max_workers=1)
+executor.submit(wait_on_future)
+
+class concurrent.futures.ThreadPoolExecutor(max_workers=None, thread_name_prefix='', initializer=None, initargs=())
+

An Executor subclass that uses a pool of at most max_workers threads to execute calls asynchronously.

All threads enqueued to ThreadPoolExecutor will be joined before the interpreter can exit. Note that the exit handler which does this is executed before any exit handlers added using atexit. This means exceptions in the main thread must be caught and handled in order to signal threads to exit gracefully. For this reason, it is recommended that ThreadPoolExecutor not be used for long-running tasks.

initializer is an optional callable that is called at the start of each worker thread; initargs is a tuple of arguments passed to the initializer. Should initializer raise an exception, all currently pending jobs will raise a BrokenThreadPool, as well as any attempt to submit more jobs to the pool.

Changed in version 3.5: If max_workers is None or not given, it will default to the number of processors on the machine, multiplied by 5, assuming that ThreadPoolExecutor is often used to overlap I/O instead of CPU work and the number of workers should be higher than the number of workers for ProcessPoolExecutor.

New in version 3.6: The thread_name_prefix argument was added to allow users to control the threading.Thread names for worker threads created by the pool for easier debugging.

Changed in version 3.7: Added the initializer and initargs arguments.

Changed in version 3.8: Default value of max_workers is changed to min(32, os.cpu_count() + 4). This default value preserves at least 5 workers for I/O bound tasks. It utilizes at most 32 CPU cores for CPU bound tasks which release the GIL. And it avoids using very large resources implicitly on many-core machines.

ThreadPoolExecutor now reuses idle worker threads before starting max_workers worker threads too.

+

ThreadPoolExecutor Example

import concurrent.futures
+import urllib.request
+
+URLS = ['http://www.foxnews.com/',
+        'http://www.cnn.com/',
+        'http://europe.wsj.com/',
+        'http://www.bbc.co.uk/',
+        'http://nonexistant-subdomain.python.org/']
+
+# Retrieve a single page and report the URL and contents
+def load_url(url, timeout):
+    with urllib.request.urlopen(url, timeout=timeout) as conn:
+        return conn.read()
+
+# We can use a with statement to ensure threads are cleaned up promptly
+with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
+    # Start the load operations and mark each future with its URL
+    future_to_url = {executor.submit(load_url, url, 60): url for url in URLS}
+    for future in concurrent.futures.as_completed(future_to_url):
+        url = future_to_url[future]
+        try:
+            data = future.result()
+        except Exception as exc:
+            print('%r generated an exception: %s' % (url, exc))
+        else:
+            print('%r page is %d bytes' % (url, len(data)))
+

ProcessPoolExecutor

The ProcessPoolExecutor class is an Executor subclass that uses a pool of processes to execute calls asynchronously. ProcessPoolExecutor uses the multiprocessing module, which allows it to side-step the Global Interpreter Lock but also means that only picklable objects can be executed and returned.

The __main__ module must be importable by worker subprocesses. This means that ProcessPoolExecutor will not work in the interactive interpreter.

Calling Executor or Future methods from a callable submitted to a ProcessPoolExecutor will result in deadlock.

+class concurrent.futures.ProcessPoolExecutor(max_workers=None, mp_context=None, initializer=None, initargs=(), max_tasks_per_child=None)
+

An Executor subclass that executes calls asynchronously using a pool of at most max_workers processes. If max_workers is None or not given, it will default to the number of processors on the machine. If max_workers is less than or equal to 0, then a ValueError will be raised. On Windows, max_workers must be less than or equal to 61. If it is not then ValueError will be raised. If max_workers is None, then the default chosen will be at most 61, even if more processors are available. mp_context can be a multiprocessing context or None. It will be used to launch the workers. If mp_context is None or not given, the default multiprocessing context is used. See Contexts and start methods.

initializer is an optional callable that is called at the start of each worker process; initargs is a tuple of arguments passed to the initializer. Should initializer raise an exception, all currently pending jobs will raise a BrokenProcessPool, as well as any attempt to submit more jobs to the pool.

max_tasks_per_child is an optional argument that specifies the maximum number of tasks a single process can execute before it will exit and be replaced with a fresh worker process. By default max_tasks_per_child is None which means worker processes will live as long as the pool. When a max is specified, the “spawn” multiprocessing start method will be used by default in absence of a mp_context parameter. This feature is incompatible with the “fork” start method.

Changed in version 3.3: When one of the worker processes terminates abruptly, a BrokenProcessPool error is now raised. Previously, behaviour was undefined but operations on the executor or its futures would often freeze or deadlock.

Changed in version 3.7: The mp_context argument was added to allow users to control the start_method for worker processes created by the pool.

Added the initializer and initargs arguments.

Note

The default multiprocessing start method (see Contexts and start methods) will change away from fork in Python 3.14. Code that requires fork be used for their ProcessPoolExecutor should explicitly specify that by passing a mp_context=multiprocessing.get_context("fork") parameter.

Changed in version 3.11: The max_tasks_per_child argument was added to allow users to control the lifetime of workers in the pool.

Changed in version 3.12: On POSIX systems, if your application has multiple threads and the multiprocessing context uses the "fork" start method: The os.fork() function called internally to spawn workers may raise a DeprecationWarning. Pass a mp_context configured to use a different start method. See the os.fork() documentation for further explanation.

+

ProcessPoolExecutor Example

import concurrent.futures
+import math
+
+PRIMES = [
+    112272535095293,
+    112582705942171,
+    112272535095293,
+    115280095190773,
+    115797848077099,
+    1099726899285419]
+
+def is_prime(n):
+    if n < 2:
+        return False
+    if n == 2:
+        return True
+    if n % 2 == 0:
+        return False
+
+    sqrt_n = int(math.floor(math.sqrt(n)))
+    for i in range(3, sqrt_n + 1, 2):
+        if n % i == 0:
+            return False
+    return True
+
+def main():
+    with concurrent.futures.ProcessPoolExecutor() as executor:
+        for number, prime in zip(PRIMES, executor.map(is_prime, PRIMES)):
+            print('%d is prime: %s' % (number, prime))
+
+if __name__ == '__main__':
+    main()
+

Future Objects

The Future class encapsulates the asynchronous execution of a callable. Future instances are created by Executor.submit().

+class concurrent.futures.Future
+

Encapsulates the asynchronous execution of a callable. Future instances are created by Executor.submit() and should not be created directly except for testing.

+cancel()
+

Attempt to cancel the call. If the call is currently being executed or finished running and cannot be cancelled then the method will return False, otherwise the call will be cancelled and the method will return True.

+
+cancelled()
+

Return True if the call was successfully cancelled.

+
+running()
+

Return True if the call is currently being executed and cannot be cancelled.

+
+done()
+

Return True if the call was successfully cancelled or finished running.

+
+result(timeout=None)
+

Return the value returned by the call. If the call hasn’t yet completed then this method will wait up to timeout seconds. If the call hasn’t completed in timeout seconds, then a TimeoutError will be raised. timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time.

If the future is cancelled before completing then CancelledError will be raised.

If the call raised an exception, this method will raise the same exception.

+
+exception(timeout=None)
+

Return the exception raised by the call. If the call hasn’t yet completed then this method will wait up to timeout seconds. If the call hasn’t completed in timeout seconds, then a TimeoutError will be raised. timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time.

If the future is cancelled before completing then CancelledError will be raised.

If the call completed without raising, None is returned.

+
+add_done_callback(fn)
+

Attaches the callable fn to the future. fn will be called, with the future as its only argument, when the future is cancelled or finishes running.

Added callables are called in the order that they were added and are always called in a thread belonging to the process that added them. If the callable raises an Exception subclass, it will be logged and ignored. If the callable raises a BaseException subclass, the behavior is undefined.

If the future has already completed or been cancelled, fn will be called immediately.

+

The following Future methods are meant for use in unit tests and Executor implementations.

+set_running_or_notify_cancel()
+

This method should only be called by Executor implementations before executing the work associated with the Future and by unit tests.

If the method returns False then the Future was cancelled, i.e. Future.cancel() was called and returned True. Any threads waiting on the Future completing (i.e. through as_completed() or wait()) will be woken up.

If the method returns True then the Future was not cancelled and has been put in the running state, i.e. calls to Future.running() will return True.

This method can only be called once and cannot be called after Future.set_result() or Future.set_exception() have been called.

+
+set_result(result)
+

Sets the result of the work associated with the Future to result.

This method should only be used by Executor implementations and unit tests.

Changed in version 3.8: This method raises concurrent.futures.InvalidStateError if the Future is already done.

+
+set_exception(exception)
+

Sets the result of the work associated with the Future to the Exception exception.

This method should only be used by Executor implementations and unit tests.

Changed in version 3.8: This method raises concurrent.futures.InvalidStateError if the Future is already done.

+
+

Module Functions

+concurrent.futures.wait(fs, timeout=None, return_when=ALL_COMPLETED)
+

Wait for the Future instances (possibly created by different Executor instances) given by fs to complete. Duplicate futures given to fs are removed and will be returned only once. Returns a named 2-tuple of sets. The first set, named done, contains the futures that completed (finished or cancelled futures) before the wait completed. The second set, named not_done, contains the futures that did not complete (pending or running futures).

timeout can be used to control the maximum number of seconds to wait before returning. timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time.

return_when indicates when this function should return. It must be one of the following constants:

+ + + +

Constant

Description

FIRST_COMPLETED

The function will return when any future finishes or is cancelled.

FIRST_EXCEPTION

The function will return when any future finishes by raising an exception. If no future raises an exception then it is equivalent to ALL_COMPLETED.

ALL_COMPLETED

The function will return when all futures finish or are cancelled.
+
+concurrent.futures.as_completed(fs, timeout=None)
+

Returns an iterator over the Future instances (possibly created by different Executor instances) given by fs that yields futures as they complete (finished or cancelled futures). Any futures given by fs that are duplicated will be returned once. Any futures that completed before as_completed() is called will be yielded first. The returned iterator raises a TimeoutError if __next__() is called and the result isn’t available after timeout seconds from the original call to as_completed(). timeout can be an int or float. If timeout is not specified or None, there is no limit to the wait time.

+

See also

+PEP 3148 – futures - execute computations asynchronously
+
+

The proposal which described this feature for inclusion in the Python standard library.

Exception classes

+exception concurrent.futures.CancelledError
+

Raised when a future is cancelled.

+
+exception concurrent.futures.TimeoutError
+

A deprecated alias of TimeoutError, raised when a future operation exceeds the given timeout.

Changed in version 3.11: This class was made an alias of TimeoutError.

+
+exception concurrent.futures.BrokenExecutor
+

Derived from RuntimeError, this exception class is raised when an executor is broken for some reason, and cannot be used to submit or execute new tasks.

New in version 3.7.

+
+exception concurrent.futures.InvalidStateError
+

Raised when an operation is performed on a future that is not allowed in the current state.

New in version 3.8.

+
+exception concurrent.futures.thread.BrokenThreadPool
+

Derived from BrokenExecutor, this exception class is raised when one of the workers of a ThreadPoolExecutor has failed initializing.

New in version 3.7.

+
+exception concurrent.futures.process.BrokenProcessPool
+

Derived from BrokenExecutor (formerly RuntimeError), this exception class is raised when one of the workers of a ProcessPoolExecutor has terminated in a non-clean fashion (for example, if it was killed from the outside).

New in version 3.3.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/concurrent.futures.html +

+
diff --git a/devdocs/python~3.12/library%2Fconcurrent.html b/devdocs/python~3.12/library%2Fconcurrent.html new file mode 100644 index 00000000..96634ab5 --- /dev/null +++ b/devdocs/python~3.12/library%2Fconcurrent.html @@ -0,0 +1,7 @@ +

The concurrent package

Currently, there is only one module in this package:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/concurrent.html +

+
diff --git a/devdocs/python~3.12/library%2Fconfigparser.html b/devdocs/python~3.12/library%2Fconfigparser.html new file mode 100644 index 00000000..8b380112 --- /dev/null +++ b/devdocs/python~3.12/library%2Fconfigparser.html @@ -0,0 +1,551 @@ +

configparser — Configuration file parser

Source code: Lib/configparser.py

This module provides the ConfigParser class which implements a basic configuration language which provides a structure similar to what’s found in Microsoft Windows INI files. You can use this to write Python programs which can be customized by end users easily.

Note

This library does not interpret or write the value-type prefixes used in the Windows Registry extended version of INI syntax.

See also

+Module tomllib +
+
+

TOML is a well-specified format for application configuration files. It is specifically designed to be an improved version of INI.

+Module shlex +
+
+

Support for creating Unix shell-like mini-languages which can also be used for application configuration files.

+Module json +
+
+

The json module implements a subset of JavaScript syntax which is sometimes used for configuration, but does not support comments.

Quick Start

Let’s take a very basic configuration file that looks like this:

[DEFAULT]
+ServerAliveInterval = 45
+Compression = yes
+CompressionLevel = 9
+ForwardX11 = yes
+
+[forge.example]
+User = hg
+
+[topsecret.server.example]
+Port = 50022
+ForwardX11 = no
+

The structure of INI files is described in the following section. Essentially, the file consists of sections, each of which contains keys with values. configparser classes can read and write such files. Let’s start by creating the above configuration file programmatically.

>>> import configparser
+>>> config = configparser.ConfigParser()
+>>> config['DEFAULT'] = {'ServerAliveInterval': '45',
+...                      'Compression': 'yes',
+...                      'CompressionLevel': '9'}
+>>> config['forge.example'] = {}
+>>> config['forge.example']['User'] = 'hg'
+>>> config['topsecret.server.example'] = {}
+>>> topsecret = config['topsecret.server.example']
+>>> topsecret['Port'] = '50022'     # mutates the parser
+>>> topsecret['ForwardX11'] = 'no'  # same here
+>>> config['DEFAULT']['ForwardX11'] = 'yes'
+>>> with open('example.ini', 'w') as configfile:
+...   config.write(configfile)
+...
+

As you can see, we can treat a config parser much like a dictionary. There are differences, outlined later, but the behavior is very close to what you would expect from a dictionary.

Now that we have created and saved a configuration file, let’s read it back and explore the data it holds.

>>> config = configparser.ConfigParser()
+>>> config.sections()
+[]
+>>> config.read('example.ini')
+['example.ini']
+>>> config.sections()
+['forge.example', 'topsecret.server.example']
+>>> 'forge.example' in config
+True
+>>> 'python.org' in config
+False
+>>> config['forge.example']['User']
+'hg'
+>>> config['DEFAULT']['Compression']
+'yes'
+>>> topsecret = config['topsecret.server.example']
+>>> topsecret['ForwardX11']
+'no'
+>>> topsecret['Port']
+'50022'
+>>> for key in config['forge.example']:  
+...     print(key)
+user
+compressionlevel
+serveraliveinterval
+compression
+forwardx11
+>>> config['forge.example']['ForwardX11']
+'yes'
+

As we can see above, the API is pretty straightforward. The only bit of magic involves the DEFAULT section which provides default values for all other sections 1. Note also that keys in sections are case-insensitive and stored in lowercase 1.

It is possible to read several configurations into a single ConfigParser, where the most recently added configuration has the highest priority. Any conflicting keys are taken from the more recent configuration while the previously existing keys are retained.

>>> another_config = configparser.ConfigParser()
+>>> another_config.read('example.ini')
+['example.ini']
+>>> another_config['topsecret.server.example']['Port']
+'50022'
+>>> another_config.read_string("[topsecret.server.example]\nPort=48484")
+>>> another_config['topsecret.server.example']['Port']
+'48484'
+>>> another_config.read_dict({"topsecret.server.example": {"Port": 21212}})
+>>> another_config['topsecret.server.example']['Port']
+'21212'
+>>> another_config['topsecret.server.example']['ForwardX11']
+'no'
+

This behaviour is equivalent to a ConfigParser.read() call with several files passed to the filenames parameter.

Supported Datatypes

Config parsers do not guess datatypes of values in configuration files, always storing them internally as strings. This means that if you need other datatypes, you should convert on your own:

>>> int(topsecret['Port'])
+50022
+>>> float(topsecret['CompressionLevel'])
+9.0
+

Since this task is so common, config parsers provide a range of handy getter methods to handle integers, floats and booleans. The last one is the most interesting because simply passing the value to bool() would do no good since bool('False') is still True. This is why config parsers also provide getboolean(). This method is case-insensitive and recognizes Boolean values from 'yes'/'no', 'on'/'off', 'true'/'false' and '1'/'0' 1. For example:

>>> topsecret.getboolean('ForwardX11')
+False
+>>> config['forge.example'].getboolean('ForwardX11')
+True
+>>> config.getboolean('forge.example', 'Compression')
+True
+

Apart from getboolean(), config parsers also provide equivalent getint() and getfloat() methods. You can register your own converters and customize the provided ones. 1

Fallback Values

As with a dictionary, you can use a section’s get() method to provide fallback values:

>>> topsecret.get('Port')
+'50022'
+>>> topsecret.get('CompressionLevel')
+'9'
+>>> topsecret.get('Cipher')
+>>> topsecret.get('Cipher', '3des-cbc')
+'3des-cbc'
+

Please note that default values have precedence over fallback values. For instance, in our example the 'CompressionLevel' key was specified only in the 'DEFAULT' section. If we try to get it from the section 'topsecret.server.example', we will always get the default, even if we specify a fallback:

>>> topsecret.get('CompressionLevel', '3')
+'9'
+

One more thing to be aware of is that the parser-level get() method provides a custom, more complex interface, maintained for backwards compatibility. When using this method, a fallback value can be provided via the fallback keyword-only argument:

>>> config.get('forge.example', 'monster',
+...            fallback='No such things as monsters')
+'No such things as monsters'
+

The same fallback argument can be used with the getint(), getfloat() and getboolean() methods, for example:

>>> 'BatchMode' in topsecret
+False
+>>> topsecret.getboolean('BatchMode', fallback=True)
+True
+>>> config['DEFAULT']['BatchMode'] = 'no'
+>>> topsecret.getboolean('BatchMode', fallback=True)
+False
+

Supported INI File Structure

A configuration file consists of sections, each led by a [section] header, followed by key/value entries separated by a specific string (= or : by default 1). By default, section names are case sensitive but keys are not 1. Leading and trailing whitespace is removed from keys and values. Values can be omitted if the parser is configured to allow it 1, in which case the key/value delimiter may also be left out. Values can also span multiple lines, as long as they are indented deeper than the first line of the value. Depending on the parser’s mode, blank lines may be treated as parts of multiline values or ignored.

By default, a valid section name can be any string that does not contain ‘\n’. To change this, see ConfigParser.SECTCRE.

Configuration files may include comments, prefixed by specific characters (# and ; by default 1). Comments may appear on their own on an otherwise empty line, possibly indented. 1

For example:

[Simple Values]
+key=value
+spaces in keys=allowed
+spaces in values=allowed as well
+spaces around the delimiter = obviously
+you can also use : to delimit keys from values
+
+[All Values Are Strings]
+values like this: 1000000
+or this: 3.14159265359
+are they treated as numbers? : no
+integers, floats and booleans are held as: strings
+can use the API to get converted values directly: true
+
+[Multiline Values]
+chorus: I'm a lumberjack, and I'm okay
+    I sleep all night and I work all day
+
+[No Values]
+key_without_value
+empty string value here =
+
+[You can use comments]
+# like this
+; or this
+
+# By default only in an empty line.
+# Inline comments can be harmful because they prevent users
+# from using the delimiting characters as parts of values.
+# That being said, this can be customized.
+
+    [Sections Can Be Indented]
+        can_values_be_as_well = True
+        does_that_mean_anything_special = False
+        purpose = formatting for readability
+        multiline_values = are
+            handled just fine as
+            long as they are indented
+            deeper than the first line
+            of a value
+        # Did I mention we can indent comments, too?
+

Interpolation of values

On top of the core functionality, ConfigParser supports interpolation. This means values can be preprocessed before returning them from get() calls.

+class configparser.BasicInterpolation
+

The default implementation used by ConfigParser. It enables values to contain format strings which refer to other values in the same section, or values in the special default section 1. Additional default values can be provided on initialization.

For example:

[Paths]
+home_dir: /Users
+my_dir: %(home_dir)s/lumberjack
+my_pictures: %(my_dir)s/Pictures
+
+[Escape]
+# use a %% to escape the % sign (% is the only character that needs to be escaped):
+gain: 80%%
+

In the example above, ConfigParser with interpolation set to BasicInterpolation() would resolve %(home_dir)s to the value of home_dir (/Users in this case). %(my_dir)s in effect would resolve to /Users/lumberjack. All interpolations are done on demand so keys used in the chain of references do not have to be specified in any specific order in the configuration file.

With interpolation set to None, the parser would simply return %(my_dir)s/Pictures as the value of my_pictures and %(home_dir)s/lumberjack as the value of my_dir.

+
+class configparser.ExtendedInterpolation
+

An alternative handler for interpolation which implements a more advanced syntax, used for instance in zc.buildout. Extended interpolation is using ${section:option} to denote a value from a foreign section. Interpolation can span multiple levels. For convenience, if the section: part is omitted, interpolation defaults to the current section (and possibly the default values from the special section).

For example, the configuration specified above with basic interpolation, would look like this with extended interpolation:

[Paths]
+home_dir: /Users
+my_dir: ${home_dir}/lumberjack
+my_pictures: ${my_dir}/Pictures
+
+[Escape]
+# use a $$ to escape the $ sign ($ is the only character that needs to be escaped):
+cost: $$80
+

Values from other sections can be fetched as well:

[Common]
+home_dir: /Users
+library_dir: /Library
+system_dir: /System
+macports_dir: /opt/local
+
+[Frameworks]
+Python: 3.2
+path: ${Common:system_dir}/Library/Frameworks/
+
+[Arthur]
+nickname: Two Sheds
+last_name: Jackson
+my_dir: ${Common:home_dir}/twosheds
+my_pictures: ${my_dir}/Pictures
+python_dir: ${Frameworks:path}/Python/Versions/${Frameworks:Python}
+
+

Mapping Protocol Access

New in version 3.2.

Mapping protocol access is a generic name for functionality that enables using custom objects as if they were dictionaries. In case of configparser, the mapping interface implementation is using the parser['section']['option'] notation.

parser['section'] in particular returns a proxy for the section’s data in the parser. This means that the values are not copied but they are taken from the original parser on demand. What’s even more important is that when values are changed on a section proxy, they are actually mutated in the original parser.

configparser objects behave as close to actual dictionaries as possible. The mapping interface is complete and adheres to the MutableMapping ABC. However, there are a few differences that should be taken into account:

  • +

    By default, all keys in sections are accessible in a case-insensitive manner 1. E.g. for option in parser["section"] yields only optionxform’ed option key names. This means lowercased keys by default. At the same time, for a section that holds the key 'a', both expressions return True:

    "a" in parser["section"]
+"A" in parser["section"]
+
  • All sections include DEFAULTSECT values as well which means that .clear() on a section may not leave the section visibly empty. This is because default values cannot be deleted from the section (because technically they are not there). If they are overridden in the section, deleting causes the default value to be visible again. Trying to delete a default value causes a KeyError.
  • +

    DEFAULTSECT cannot be removed from the parser:

    • trying to delete it raises ValueError,
    • +parser.clear() leaves it intact,
    • +parser.popitem() never returns it.
  • +parser.get(section, option, **kwargs) - the second argument is not a fallback value. Note however that the section-level get() methods are compatible both with the mapping protocol and the classic configparser API.
  • +parser.items() is compatible with the mapping protocol (returns a list of section_name, section_proxy pairs including the DEFAULTSECT). However, this method can also be invoked with arguments: parser.items(section, raw, +vars). The latter call returns a list of option, value pairs for a specified section, with all interpolations expanded (unless raw=True is provided).

The mapping protocol is implemented on top of the existing legacy API so that subclasses overriding the original interface still should have mappings working as expected.

Customizing Parser Behaviour

There are nearly as many INI format variants as there are applications using it. configparser goes a long way to provide support for the largest sensible set of INI styles available. The default functionality is mainly dictated by historical background and it’s very likely that you will want to customize some of the features.

The most common way to change the way a specific config parser works is to use the __init__() options:

  • +

    defaults, default value: None

    This option accepts a dictionary of key-value pairs which will be initially put in the DEFAULT section. This makes for an elegant way to support concise configuration files that don’t specify values which are the same as the documented default.

    Hint: if you want to specify default values for a specific section, use read_dict() before you read the actual file.

  • +

    dict_type, default value: dict

    This option has a major impact on how the mapping protocol will behave and how the written configuration files look. With the standard dictionary, every section is stored in the order they were added to the parser. Same goes for options within sections.

    An alternative dictionary type can be used for example to sort sections and options on write-back.

    Please note: there are ways to add a set of key-value pairs in a single operation. When you use a regular dictionary in those operations, the order of the keys will be ordered. For example:

    >>> parser = configparser.ConfigParser()
+>>> parser.read_dict({'section1': {'key1': 'value1',
+...                                'key2': 'value2',
+...                                'key3': 'value3'},
+...                   'section2': {'keyA': 'valueA',
+...                                'keyB': 'valueB',
+...                                'keyC': 'valueC'},
+...                   'section3': {'foo': 'x',
+...                                'bar': 'y',
+...                                'baz': 'z'}
+... })
+>>> parser.sections()
+['section1', 'section2', 'section3']
+>>> [option for option in parser['section3']]
+['foo', 'bar', 'baz']
+
  • +

    allow_no_value, default value: False

    Some configuration files are known to include settings without values, but which otherwise conform to the syntax supported by configparser. The allow_no_value parameter to the constructor can be used to indicate that such values should be accepted:

    >>> import configparser
+
+>>> sample_config = """
+... [mysqld]
+...   user = mysql
+...   pid-file = /var/run/mysqld/mysqld.pid
+...   skip-external-locking
+...   old_passwords = 1
+...   skip-bdb
+...   # we don't need ACID today
+...   skip-innodb
+... """
+>>> config = configparser.ConfigParser(allow_no_value=True)
+>>> config.read_string(sample_config)
+
+>>> # Settings with values are treated as before:
+>>> config["mysqld"]["user"]
+'mysql'
+
+>>> # Settings without values provide None:
+>>> config["mysqld"]["skip-bdb"]
+
+>>> # Settings which aren't specified still raise an error:
+>>> config["mysqld"]["does-not-exist"]
+Traceback (most recent call last):
+  ...
+KeyError: 'does-not-exist'
+
  • +

    delimiters, default value: ('=', ':')

    Delimiters are substrings that delimit keys from values within a section. The first occurrence of a delimiting substring on a line is considered a delimiter. This means values (but not keys) can contain the delimiters.

    See also the space_around_delimiters argument to ConfigParser.write().

  • +comment_prefixes, default value: ('#', ';') +
  • +

    inline_comment_prefixes, default value: None

    Comment prefixes are strings that indicate the start of a valid comment within a config file. comment_prefixes are used only on otherwise empty lines (optionally indented) whereas inline_comment_prefixes can be used after every valid value (e.g. section names, options and empty lines as well). By default inline comments are disabled and '#' and ';' are used as prefixes for whole line comments.

    Changed in version 3.2: In previous versions of configparser behaviour matched comment_prefixes=('#',';') and inline_comment_prefixes=(';',).

    Please note that config parsers don’t support escaping of comment prefixes so using inline_comment_prefixes may prevent users from specifying option values with characters used as comment prefixes. When in doubt, avoid setting inline_comment_prefixes. In any circumstances, the only way of storing comment prefix characters at the beginning of a line in multiline values is to interpolate the prefix, for example:

    >>> from configparser import ConfigParser, ExtendedInterpolation
+>>> parser = ConfigParser(interpolation=ExtendedInterpolation())
+>>> # the default BasicInterpolation could be used as well
+>>> parser.read_string("""
+... [DEFAULT]
+... hash = #
+...
+... [hashes]
+... shebang =
+...   ${hash}!/usr/bin/env python
+...   ${hash} -*- coding: utf-8 -*-
+...
+... extensions =
+...   enabled_extension
+...   another_extension
+...   #disabled_by_comment
+...   yet_another_extension
+...
+... interpolation not necessary = if # is not at line start
+... even in multiline values = line #1
+...   line #2
+...   line #3
+... """)
+>>> print(parser['hashes']['shebang'])
+
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+>>> print(parser['hashes']['extensions'])
+
+enabled_extension
+another_extension
+yet_another_extension
+>>> print(parser['hashes']['interpolation not necessary'])
+if # is not at line start
+>>> print(parser['hashes']['even in multiline values'])
+line #1
+line #2
+line #3
+
  • +

    strict, default value: True

    When set to True, the parser will not allow for any section or option duplicates while reading from a single source (using read_file(), read_string() or read_dict()). It is recommended to use strict parsers in new applications.

    Changed in version 3.2: In previous versions of configparser behaviour matched strict=False.

  • +

    empty_lines_in_values, default value: True

    In config parsers, values can span multiple lines as long as they are indented more than the key that holds them. By default parsers also let empty lines to be parts of values. At the same time, keys can be arbitrarily indented themselves to improve readability. In consequence, when configuration files get big and complex, it is easy for the user to lose track of the file structure. Take for instance:

    [Section]
+key = multiline
+  value with a gotcha
+
+ this = is still a part of the multiline value of 'key'
+

    This can be especially problematic for the user to see if she’s using a proportional font to edit the file. That is why when your application does not need values with empty lines, you should consider disallowing them. This will make empty lines split keys every time. In the example above, it would produce two keys, key and this.

  • +

    default_section, default value: configparser.DEFAULTSECT (that is: "DEFAULT")

    The convention of allowing a special section of default values for other sections or interpolation purposes is a powerful concept of this library, letting users create complex declarative configurations. This section is normally called "DEFAULT" but this can be customized to point to any other valid section name. Some typical values include: "general" or "common". The name provided is used for recognizing default sections when reading from any source and is used when writing configuration back to a file. Its current value can be retrieved using the parser_instance.default_section attribute and may be modified at runtime (i.e. to convert files from one format to another).

  • +

    interpolation, default value: configparser.BasicInterpolation

    Interpolation behaviour may be customized by providing a custom handler through the interpolation argument. None can be used to turn off interpolation completely, ExtendedInterpolation() provides a more advanced variant inspired by zc.buildout. More on the subject in the dedicated documentation section. RawConfigParser has a default value of None.

  • +

    converters, default value: not set

    Config parsers provide option value getters that perform type conversion. By default getint(), getfloat(), and getboolean() are implemented. Should other getters be desirable, users may define them in a subclass or pass a dictionary where each key is a name of the converter and each value is a callable implementing said conversion. For instance, passing {'decimal': decimal.Decimal} would add getdecimal() on both the parser object and all section proxies. In other words, it will be possible to write both parser_instance.getdecimal('section', 'key', fallback=0) and parser_instance['section'].getdecimal('key', 0).

    If the converter needs to access the state of the parser, it can be implemented as a method on a config parser subclass. If the name of this method starts with get, it will be available on all section proxies, in the dict-compatible form (see the getdecimal() example above).

More advanced customization may be achieved by overriding default values of these parser attributes. The defaults are defined on the classes, so they may be overridden by subclasses or by attribute assignment.

+ConfigParser.BOOLEAN_STATES
+

By default when using getboolean(), config parsers consider the following values True: '1', 'yes', 'true', 'on' and the following values False: '0', 'no', 'false', 'off'. You can override this by specifying a custom dictionary of strings and their Boolean outcomes. For example:

>>> custom = configparser.ConfigParser()
+>>> custom['section1'] = {'funky': 'nope'}
+>>> custom['section1'].getboolean('funky')
+Traceback (most recent call last):
+...
+ValueError: Not a boolean: nope
+>>> custom.BOOLEAN_STATES = {'sure': True, 'nope': False}
+>>> custom['section1'].getboolean('funky')
+False
+

Other typical Boolean pairs include accept/reject or enabled/disabled.

+
ConfigParser.optionxform(option) +
+

This method transforms option names on every read, get, or set operation. The default converts the name to lowercase. This also means that when a configuration file gets written, all keys will be lowercase. Override this method if that’s unsuitable. For example:

>>> config = """
+... [Section1]
+... Key = Value
+...
+... [Section2]
+... AnotherKey = Value
+... """
+>>> typical = configparser.ConfigParser()
+>>> typical.read_string(config)
+>>> list(typical['Section1'].keys())
+['key']
+>>> list(typical['Section2'].keys())
+['anotherkey']
+>>> custom = configparser.RawConfigParser()
+>>> custom.optionxform = lambda option: option
+>>> custom.read_string(config)
+>>> list(custom['Section1'].keys())
+['Key']
+>>> list(custom['Section2'].keys())
+['AnotherKey']
+

Note

The optionxform function transforms option names to a canonical form. This should be an idempotent function: if the name is already in canonical form, it should be returned unchanged.

+
+ConfigParser.SECTCRE
+

A compiled regular expression used to parse section headers. The default matches [section] to the name "section". Whitespace is considered part of the section name, thus [  larch  ] will be read as a section of name "  larch  ". Override this attribute if that’s unsuitable. For example:

>>> import re
+>>> config = """
+... [Section 1]
+... option = value
+...
+... [  Section 2  ]
+... another = val
+... """
+>>> typical = configparser.ConfigParser()
+>>> typical.read_string(config)
+>>> typical.sections()
+['Section 1', '  Section 2  ']
+>>> custom = configparser.ConfigParser()
+>>> custom.SECTCRE = re.compile(r"\[ *(?P<header>[^]]+?) *\]")
+>>> custom.read_string(config)
+>>> custom.sections()
+['Section 1', 'Section 2']
+

Note

While ConfigParser objects also use an OPTCRE attribute for recognizing option lines, it’s not recommended to override it because that would interfere with constructor options allow_no_value and delimiters.

+

Legacy API Examples

Mainly because of backwards compatibility concerns, configparser provides also a legacy API with explicit get/set methods. While there are valid use cases for the methods outlined below, mapping protocol access is preferred for new projects. The legacy API is at times more advanced, low-level and downright counterintuitive.

An example of writing to a configuration file:

import configparser
+
+config = configparser.RawConfigParser()
+
+# Please note that using RawConfigParser's set functions, you can assign
+# non-string values to keys internally, but will receive an error when
+# attempting to write to a file or when you get it in non-raw mode. Setting
+# values using the mapping protocol or ConfigParser's set() does not allow
+# such assignments to take place.
+config.add_section('Section1')
+config.set('Section1', 'an_int', '15')
+config.set('Section1', 'a_bool', 'true')
+config.set('Section1', 'a_float', '3.1415')
+config.set('Section1', 'baz', 'fun')
+config.set('Section1', 'bar', 'Python')
+config.set('Section1', 'foo', '%(bar)s is %(baz)s!')
+
+# Writing our configuration file to 'example.cfg'
+with open('example.cfg', 'w') as configfile:
+    config.write(configfile)
+

An example of reading the configuration file again:

import configparser
+
+config = configparser.RawConfigParser()
+config.read('example.cfg')
+
+# getfloat() raises an exception if the value is not a float
+# getint() and getboolean() also do this for their respective types
+a_float = config.getfloat('Section1', 'a_float')
+an_int = config.getint('Section1', 'an_int')
+print(a_float + an_int)
+
+# Notice that the next output does not interpolate '%(bar)s' or '%(baz)s'.
+# This is because we are using a RawConfigParser().
+if config.getboolean('Section1', 'a_bool'):
+    print(config.get('Section1', 'foo'))
+

To get interpolation, use ConfigParser:

import configparser
+
+cfg = configparser.ConfigParser()
+cfg.read('example.cfg')
+
+# Set the optional *raw* argument of get() to True if you wish to disable
+# interpolation in a single get operation.
+print(cfg.get('Section1', 'foo', raw=False))  # -> "Python is fun!"
+print(cfg.get('Section1', 'foo', raw=True))   # -> "%(bar)s is %(baz)s!"
+
+# The optional *vars* argument is a dict with members that will take
+# precedence in interpolation.
+print(cfg.get('Section1', 'foo', vars={'bar': 'Documentation',
+                                       'baz': 'evil'}))
+
+# The optional *fallback* argument can be used to provide a fallback value
+print(cfg.get('Section1', 'foo'))
+      # -> "Python is fun!"
+
+print(cfg.get('Section1', 'foo', fallback='Monty is not.'))
+      # -> "Python is fun!"
+
+print(cfg.get('Section1', 'monster', fallback='No such things as monsters.'))
+      # -> "No such things as monsters."
+
+# A bare print(cfg.get('Section1', 'monster')) would raise NoOptionError
+# but we can also use:
+
+print(cfg.get('Section1', 'monster', fallback=None))
+      # -> None
+

Default values are available in both types of ConfigParsers. They are used in interpolation if an option used is not defined elsewhere.

import configparser
+
+# New instance with 'bar' and 'baz' defaulting to 'Life' and 'hard' each
+config = configparser.ConfigParser({'bar': 'Life', 'baz': 'hard'})
+config.read('example.cfg')
+
+print(config.get('Section1', 'foo'))     # -> "Python is fun!"
+config.remove_option('Section1', 'bar')
+config.remove_option('Section1', 'baz')
+print(config.get('Section1', 'foo'))     # -> "Life is hard!"
+

ConfigParser Objects

+class configparser.ConfigParser(defaults=None, dict_type=dict, allow_no_value=False, delimiters=('=', ':'), comment_prefixes=('#', ';'), inline_comment_prefixes=None, strict=True, empty_lines_in_values=True, default_section=configparser.DEFAULTSECT, interpolation=BasicInterpolation(), converters={})
+

The main configuration parser. When defaults is given, it is initialized into the dictionary of intrinsic defaults. When dict_type is given, it will be used to create the dictionary objects for the list of sections, for the options within a section, and for the default values.

When delimiters is given, it is used as the set of substrings that divide keys from values. When comment_prefixes is given, it will be used as the set of substrings that prefix comments in otherwise empty lines. Comments can be indented. When inline_comment_prefixes is given, it will be used as the set of substrings that prefix comments in non-empty lines.

When strict is True (the default), the parser won’t allow for any section or option duplicates while reading from a single source (file, string or dictionary), raising DuplicateSectionError or DuplicateOptionError. When empty_lines_in_values is False (default: True), each empty line marks the end of an option. Otherwise, internal empty lines of a multiline option are kept as part of the value. When allow_no_value is True (default: False), options without values are accepted; the value held for these is None and they are serialized without the trailing delimiter.

When default_section is given, it specifies the name for the special section holding default values for other sections and interpolation purposes (normally named "DEFAULT"). This value can be retrieved and changed at runtime using the default_section instance attribute. This won’t re-evaluate an already parsed config file, but will be used when writing parsed settings to a new config file.

Interpolation behaviour may be customized by providing a custom handler through the interpolation argument. None can be used to turn off interpolation completely, ExtendedInterpolation() provides a more advanced variant inspired by zc.buildout. More on the subject in the dedicated documentation section.

All option names used in interpolation will be passed through the optionxform() method just like any other option name reference. For example, using the default implementation of optionxform() (which converts option names to lower case), the values foo %(bar)s and foo +%(BAR)s are equivalent.

When converters is given, it should be a dictionary where each key represents the name of a type converter and each value is a callable implementing the conversion from string to the desired datatype. Every converter gets its own corresponding get*() method on the parser object and section proxies.

Changed in version 3.1: The default dict_type is collections.OrderedDict.

Changed in version 3.2: allow_no_value, delimiters, comment_prefixes, strict, empty_lines_in_values, default_section and interpolation were added.

Changed in version 3.5: The converters argument was added.

Changed in version 3.7: The defaults argument is read with read_dict(), providing consistent behavior across the parser: non-string keys and values are implicitly converted to strings.

Changed in version 3.8: The default dict_type is dict, since it now preserves insertion order.

+defaults()
+

Return a dictionary containing the instance-wide defaults.

+
+sections()
+

Return a list of the sections available; the default section is not included in the list.

+
+add_section(section)
+

Add a section named section to the instance. If a section by the given name already exists, DuplicateSectionError is raised. If the default section name is passed, ValueError is raised. The name of the section must be a string; if not, TypeError is raised.

Changed in version 3.2: Non-string section names raise TypeError.

+
+has_section(section)
+

Indicates whether the named section is present in the configuration. The default section is not acknowledged.

+
+options(section)
+

Return a list of options available in the specified section.

+
+has_option(section, option)
+

If the given section exists, and contains the given option, return True; otherwise return False. If the specified section is None or an empty string, DEFAULT is assumed.

+
+read(filenames, encoding=None)
+

Attempt to read and parse an iterable of filenames, returning a list of filenames which were successfully parsed.

If filenames is a string, a bytes object or a path-like object, it is treated as a single filename. If a file named in filenames cannot be opened, that file will be ignored. This is designed so that you can specify an iterable of potential configuration file locations (for example, the current directory, the user’s home directory, and some system-wide directory), and all existing configuration files in the iterable will be read.

If none of the named files exist, the ConfigParser instance will contain an empty dataset. An application which requires initial values to be loaded from a file should load the required file or files using read_file() before calling read() for any optional files:

import configparser, os
+
+config = configparser.ConfigParser()
+config.read_file(open('defaults.cfg'))
+config.read(['site.cfg', os.path.expanduser('~/.myapp.cfg')],
+            encoding='cp1250')
+

New in version 3.2: The encoding parameter. Previously, all files were read using the default encoding for open().

New in version 3.6.1: The filenames parameter accepts a path-like object.

New in version 3.7: The filenames parameter accepts a bytes object.

+
+read_file(f, source=None)
+

Read and parse configuration data from f which must be an iterable yielding Unicode strings (for example files opened in text mode).

Optional argument source specifies the name of the file being read. If not given and f has a name attribute, that is used for source; the default is '<???>'.

New in version 3.2: Replaces readfp().

+
+read_string(string, source='<string>')
+

Parse configuration data from a string.

Optional argument source specifies a context-specific name of the string passed. If not given, '<string>' is used. This should commonly be a filesystem path or a URL.

New in version 3.2.

+
+read_dict(dictionary, source='<dict>')
+

Load configuration from any object that provides a dict-like items() method. Keys are section names, values are dictionaries with keys and values that should be present in the section. If the used dictionary type preserves order, sections and their keys will be added in order. Values are automatically converted to strings.

Optional argument source specifies a context-specific name of the dictionary passed. If not given, <dict> is used.

This method can be used to copy state between parsers.

New in version 3.2.

+
+get(section, option, *, raw=False, vars=None[, fallback])
+

Get an option value for the named section. If vars is provided, it must be a dictionary. The option is looked up in vars (if provided), section, and in DEFAULTSECT in that order. If the key is not found and fallback is provided, it is used as a fallback value. None can be provided as a fallback value.

All the '%' interpolations are expanded in the return values, unless the raw argument is true. Values for interpolation keys are looked up in the same manner as the option.

Changed in version 3.2: Arguments raw, vars and fallback are keyword only to protect users from trying to use the third argument as the fallback fallback (especially when using the mapping protocol).

+
+getint(section, option, *, raw=False, vars=None[, fallback])
+

A convenience method which coerces the option in the specified section to an integer. See get() for explanation of raw, vars and fallback.

+
+getfloat(section, option, *, raw=False, vars=None[, fallback])
+

A convenience method which coerces the option in the specified section to a floating point number. See get() for explanation of raw, vars and fallback.

+
+getboolean(section, option, *, raw=False, vars=None[, fallback])
+

A convenience method which coerces the option in the specified section to a Boolean value. Note that the accepted values for the option are '1', 'yes', 'true', and 'on', which cause this method to return True, and '0', 'no', 'false', and 'off', which cause it to return False. These string values are checked in a case-insensitive manner. Any other value will cause it to raise ValueError. See get() for explanation of raw, vars and fallback.

+
+items(raw=False, vars=None)
items(section, raw=False, vars=None) +
+

When section is not given, return a list of section_name, section_proxy pairs, including DEFAULTSECT.

Otherwise, return a list of name, value pairs for the options in the given section. Optional arguments have the same meaning as for the get() method.

Changed in version 3.8: Items present in vars no longer appear in the result. The previous behaviour mixed actual parser options with variables provided for interpolation.

+
+set(section, option, value)
+

If the given section exists, set the given option to the specified value; otherwise raise NoSectionError. option and value must be strings; if not, TypeError is raised.

+
+write(fileobject, space_around_delimiters=True)
+

Write a representation of the configuration to the specified file object, which must be opened in text mode (accepting strings). This representation can be parsed by a future read() call. If space_around_delimiters is true, delimiters between keys and values are surrounded by spaces.

+

Note

Comments in the original configuration file are not preserved when writing the configuration back. What is considered a comment, depends on the given values for comment_prefix and inline_comment_prefix.

+remove_option(section, option)
+

Remove the specified option from the specified section. If the section does not exist, raise NoSectionError. If the option existed to be removed, return True; otherwise return False.

+
+remove_section(section)
+

Remove the specified section from the configuration. If the section in fact existed, return True. Otherwise return False.

+
+optionxform(option)
+

Transforms the option name option as found in an input file or as passed in by client code to the form that should be used in the internal structures. The default implementation returns a lower-case version of option; subclasses may override this or client code can set an attribute of this name on instances to affect this behavior.

You don’t need to subclass the parser to use this method, you can also set it on an instance, to a function that takes a string argument and returns a string. Setting it to str, for example, would make option names case sensitive:

cfgparser = ConfigParser()
+cfgparser.optionxform = str
+

Note that when reading configuration files, whitespace around the option names is stripped before optionxform() is called.

+
+
+configparser.MAX_INTERPOLATION_DEPTH
+

The maximum depth for recursive interpolation for get() when the raw parameter is false. This is relevant only when the default interpolation is used.

+

RawConfigParser Objects

+class configparser.RawConfigParser(defaults=None, dict_type=dict, allow_no_value=False, *, delimiters=('=', ':'), comment_prefixes=('#', ';'), inline_comment_prefixes=None, strict=True, empty_lines_in_values=True, default_section=configparser.DEFAULTSECT[, interpolation])
+

Legacy variant of the ConfigParser. It has interpolation disabled by default and allows for non-string section names, option names, and values via its unsafe add_section and set methods, as well as the legacy defaults= keyword argument handling.

Changed in version 3.8: The default dict_type is dict, since it now preserves insertion order.

Note

Consider using ConfigParser instead which checks types of the values to be stored internally. If you don’t want interpolation, you can use ConfigParser(interpolation=None).

+add_section(section)
+

Add a section named section to the instance. If a section by the given name already exists, DuplicateSectionError is raised. If the default section name is passed, ValueError is raised.

Type of section is not checked which lets users create non-string named sections. This behaviour is unsupported and may cause internal errors.

+
+set(section, option, value)
+

If the given section exists, set the given option to the specified value; otherwise raise NoSectionError. While it is possible to use RawConfigParser (or ConfigParser with raw parameters set to true) for internal storage of non-string values, full functionality (including interpolation and output to files) can only be achieved using string values.

This method lets users assign non-string values to keys internally. This behaviour is unsupported and will cause errors when attempting to write to a file or get it in non-raw mode. Use the mapping protocol API which does not allow such assignments to take place.

+
+

Exceptions

+exception configparser.Error
+

Base class for all other configparser exceptions.

+
+exception configparser.NoSectionError
+

Exception raised when a specified section is not found.

+
+exception configparser.DuplicateSectionError
+

Exception raised if add_section() is called with the name of a section that is already present or in strict parsers when a section if found more than once in a single input file, string or dictionary.

New in version 3.2: Optional source and lineno attributes and arguments to __init__() were added.

+
+exception configparser.DuplicateOptionError
+

Exception raised by strict parsers if a single option appears twice during reading from a single file, string or dictionary. This catches misspellings and case sensitivity-related errors, e.g. a dictionary may have two keys representing the same case-insensitive configuration key.

+
+exception configparser.NoOptionError
+

Exception raised when a specified option is not found in the specified section.

+
+exception configparser.InterpolationError
+

Base class for exceptions raised when problems occur performing string interpolation.

+
+exception configparser.InterpolationDepthError
+

Exception raised when string interpolation cannot be completed because the number of iterations exceeds MAX_INTERPOLATION_DEPTH. Subclass of InterpolationError.

+
+exception configparser.InterpolationMissingOptionError
+

Exception raised when an option referenced from a value does not exist. Subclass of InterpolationError.

+
+exception configparser.InterpolationSyntaxError
+

Exception raised when the source text into which substitutions are made does not conform to the required syntax. Subclass of InterpolationError.

+
+exception configparser.MissingSectionHeaderError
+

Exception raised when attempting to parse a file which has no section headers.

+
+exception configparser.ParsingError
+

Exception raised when errors occur attempting to parse a file.

Changed in version 3.12: The filename attribute and __init__() constructor argument were removed. They have been available using the name source since 3.2.

+

Footnotes

+1(1,2,3,4,5,6,7,8,9,10,11)
+

Config parsers allow for heavy customization. If you are interested in changing the behaviour outlined by the footnote reference, consult the Customizing Parser Behaviour section.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/configparser.html +

+
diff --git a/devdocs/python~3.12/library%2Fconstants.html b/devdocs/python~3.12/library%2Fconstants.html new file mode 100644 index 00000000..1ba45341 --- /dev/null +++ b/devdocs/python~3.12/library%2Fconstants.html @@ -0,0 +1,35 @@ +

Built-in Constants

A small number of constants live in the built-in namespace. They are:

+False
+

The false value of the bool type. Assignments to False are illegal and raise a SyntaxError.

+
+True
+

The true value of the bool type. Assignments to True are illegal and raise a SyntaxError.

+
+None
+

An object frequently used to represent the absence of a value, as when default arguments are not passed to a function. Assignments to None are illegal and raise a SyntaxError. None is the sole instance of the NoneType type.

+
+NotImplemented
+

A special value which should be returned by the binary special methods (e.g. __eq__(), __lt__(), __add__(), __rsub__(), etc.) to indicate that the operation is not implemented with respect to the other type; may be returned by the in-place binary special methods (e.g. __imul__(), __iand__(), etc.) for the same purpose. It should not be evaluated in a boolean context. NotImplemented is the sole instance of the types.NotImplementedType type.

Note

When a binary (or in-place) method returns NotImplemented the interpreter will try the reflected operation on the other type (or some other fallback, depending on the operator). If all attempts return NotImplemented, the interpreter will raise an appropriate exception. Incorrectly returning NotImplemented will result in a misleading error message or the NotImplemented value being returned to Python code.

See Implementing the arithmetic operations for examples.

Note

NotImplementedError and NotImplemented are not interchangeable, even though they have similar names and purposes. See NotImplementedError for details on when to use it.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning. It will raise a TypeError in a future version of Python.

+
+Ellipsis
+

The same as the ellipsis literal “...”. Special value used mostly in conjunction with extended slicing syntax for user-defined container data types. Ellipsis is the sole instance of the types.EllipsisType type.

+
+__debug__
+

This constant is true if Python was not started with an -O option. See also the assert statement.

+

Note

The names None, False, True and __debug__ cannot be reassigned (assignments to them, even as an attribute name, raise SyntaxError), so they can be considered “true” constants.

Constants added by the site module

The site module (which is imported automatically during startup, except if the -S command-line option is given) adds several constants to the built-in namespace. They are useful for the interactive interpreter shell and should not be used in programs.

+quit(code=None)
+exit(code=None)
+

Objects that when printed, print a message like “Use quit() or Ctrl-D (i.e. EOF) to exit”, and when called, raise SystemExit with the specified exit code.

+
+credits
+

Objects that when printed or called, print the text of copyright or credits, respectively.

+
+license
+

Object that when printed, prints the message “Type license() to see the full license text”, and when called, displays the full license text in a pager-like fashion (one screen at a time).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/constants.html +

+
diff --git a/devdocs/python~3.12/library%2Fcontextlib.html b/devdocs/python~3.12/library%2Fcontextlib.html new file mode 100644 index 00000000..25f4c403 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcontextlib.html @@ -0,0 +1,465 @@ +

contextlib — Utilities for with-statement contexts

Source code: Lib/contextlib.py

This module provides utilities for common tasks involving the with statement. For more information see also Context Manager Types and With Statement Context Managers.

Utilities

Functions and classes provided:

+class contextlib.AbstractContextManager
+

An abstract base class for classes that implement object.__enter__() and object.__exit__(). A default implementation for object.__enter__() is provided which returns self while object.__exit__() is an abstract method which by default returns None. See also the definition of Context Manager Types.

New in version 3.6.

+
+class contextlib.AbstractAsyncContextManager
+

An abstract base class for classes that implement object.__aenter__() and object.__aexit__(). A default implementation for object.__aenter__() is provided which returns self while object.__aexit__() is an abstract method which by default returns None. See also the definition of Asynchronous Context Managers.

New in version 3.7.

+
+@contextlib.contextmanager
+

This function is a decorator that can be used to define a factory function for with statement context managers, without needing to create a class or separate __enter__() and __exit__() methods.

While many objects natively support use in with statements, sometimes a resource needs to be managed that isn’t a context manager in its own right, and doesn’t implement a close() method for use with contextlib.closing

An abstract example would be the following to ensure correct resource management:

from contextlib import contextmanager
+
+@contextmanager
+def managed_resource(*args, **kwds):
+    # Code to acquire resource, e.g.:
+    resource = acquire_resource(*args, **kwds)
+    try:
+        yield resource
+    finally:
+        # Code to release resource, e.g.:
+        release_resource(resource)
+

The function can then be used like this:

>>> with managed_resource(timeout=3600) as resource:
+...     # Resource is released at the end of this block,
+...     # even if code in the block raises an exception
+

The function being decorated must return a generator-iterator when called. This iterator must yield exactly one value, which will be bound to the targets in the with statement’s as clause, if any.

At the point where the generator yields, the block nested in the with statement is executed. The generator is then resumed after the block is exited. If an unhandled exception occurs in the block, it is reraised inside the generator at the point where the yield occurred. Thus, you can use a try…except…finally statement to trap the error (if any), or ensure that some cleanup takes place. If an exception is trapped merely in order to log it or to perform some action (rather than to suppress it entirely), the generator must reraise that exception. Otherwise the generator context manager will indicate to the with statement that the exception has been handled, and execution will resume with the statement immediately following the with statement.

contextmanager() uses ContextDecorator so the context managers it creates can be used as decorators as well as in with statements. When used as a decorator, a new generator instance is implicitly created on each function call (this allows the otherwise “one-shot” context managers created by contextmanager() to meet the requirement that context managers support multiple invocations in order to be used as decorators).

Changed in version 3.2: Use of ContextDecorator.

+
+@contextlib.asynccontextmanager

Similar to contextmanager(), but creates an asynchronous context manager.

This function is a decorator that can be used to define a factory function for async with statement asynchronous context managers, without needing to create a class or separate __aenter__() and __aexit__() methods. It must be applied to an asynchronous generator function.

A simple example:

from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def get_connection():
+    conn = await acquire_db_connection()
+    try:
+        yield conn
+    finally:
+        await release_db_connection(conn)
+
+async def get_all_users():
+    async with get_connection() as conn:
+        return conn.query('SELECT ...')
+

New in version 3.7.

Context managers defined with asynccontextmanager() can be used either as decorators or with async with statements:

import time
+from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def timeit():
+    now = time.monotonic()
+    try:
+        yield
+    finally:
+        print(f'it took {time.monotonic() - now}s to run')
+
+@timeit()
+async def main():
+    # ... async code ...
+

When used as a decorator, a new generator instance is implicitly created on each function call. This allows the otherwise “one-shot” context managers created by asynccontextmanager() to meet the requirement that context managers support multiple invocations in order to be used as decorators.

Changed in version 3.10: Async context managers created with asynccontextmanager() can be used as decorators.

+
+contextlib.closing(thing)
+

Return a context manager that closes thing upon completion of the block. This is basically equivalent to:

from contextlib import contextmanager
+
+@contextmanager
+def closing(thing):
+    try:
+        yield thing
+    finally:
+        thing.close()
+

And lets you write code like this:

from contextlib import closing
+from urllib.request import urlopen
+
+with closing(urlopen('https://www.python.org')) as page:
+    for line in page:
+        print(line)
+

without needing to explicitly close page. Even if an error occurs, page.close() will be called when the with block is exited.

+
+contextlib.aclosing(thing)
+

Return an async context manager that calls the aclose() method of thing upon completion of the block. This is basically equivalent to:

from contextlib import asynccontextmanager
+
+@asynccontextmanager
+async def aclosing(thing):
+    try:
+        yield thing
+    finally:
+        await thing.aclose()
+

Significantly, aclosing() supports deterministic cleanup of async generators when they happen to exit early by break or an exception. For example:

from contextlib import aclosing
+
+async with aclosing(my_generator()) as values:
+    async for value in values:
+        if value == 42:
+            break
+

This pattern ensures that the generator’s async exit code is executed in the same context as its iterations (so that exceptions and context variables work as expected, and the exit code isn’t run after the lifetime of some task it depends on).

New in version 3.10.

+
+contextlib.nullcontext(enter_result=None)
+

Return a context manager that returns enter_result from __enter__, but otherwise does nothing. It is intended to be used as a stand-in for an optional context manager, for example:

def myfunction(arg, ignore_exceptions=False):
+    if ignore_exceptions:
+        # Use suppress to ignore all exceptions.
+        cm = contextlib.suppress(Exception)
+    else:
+        # Do not ignore any exceptions, cm has no effect.
+        cm = contextlib.nullcontext()
+    with cm:
+        # Do something
+

An example using enter_result:

def process_file(file_or_path):
+    if isinstance(file_or_path, str):
+        # If string, open file
+        cm = open(file_or_path)
+    else:
+        # Caller is responsible for closing file
+        cm = nullcontext(file_or_path)
+
+    with cm as file:
+        # Perform processing on the file
+

It can also be used as a stand-in for asynchronous context managers:

async def send_http(session=None):
+    if not session:
+        # If no http session, create it with aiohttp
+        cm = aiohttp.ClientSession()
+    else:
+        # Caller is responsible for closing the session
+        cm = nullcontext(session)
+
+    async with cm as session:
+        # Send http requests with session
+

New in version 3.7.

Changed in version 3.10: asynchronous context manager support was added.

+
+contextlib.suppress(*exceptions)
+

Return a context manager that suppresses any of the specified exceptions if they occur in the body of a with statement and then resumes execution with the first statement following the end of the with statement.

As with any other mechanism that completely suppresses exceptions, this context manager should be used only to cover very specific errors where silently continuing with program execution is known to be the right thing to do.

For example:

from contextlib import suppress
+
+with suppress(FileNotFoundError):
+    os.remove('somefile.tmp')
+
+with suppress(FileNotFoundError):
+    os.remove('someotherfile.tmp')
+

This code is equivalent to:

try:
+    os.remove('somefile.tmp')
+except FileNotFoundError:
+    pass
+
+try:
+    os.remove('someotherfile.tmp')
+except FileNotFoundError:
+    pass
+

This context manager is reentrant.

If the code within the with block raises a BaseExceptionGroup, suppressed exceptions are removed from the group. If any exceptions in the group are not suppressed, a group containing them is re-raised.

New in version 3.4.

Changed in version 3.12: suppress now supports suppressing exceptions raised as part of an BaseExceptionGroup.

+
+contextlib.redirect_stdout(new_target)
+

Context manager for temporarily redirecting sys.stdout to another file or file-like object.

This tool adds flexibility to existing functions or classes whose output is hardwired to stdout.

For example, the output of help() normally is sent to sys.stdout. You can capture that output in a string by redirecting the output to an io.StringIO object. The replacement stream is returned from the __enter__ method and so is available as the target of the with statement:

with redirect_stdout(io.StringIO()) as f:
+    help(pow)
+s = f.getvalue()
+

To send the output of help() to a file on disk, redirect the output to a regular file:

with open('help.txt', 'w') as f:
+    with redirect_stdout(f):
+        help(pow)
+

To send the output of help() to sys.stderr:

with redirect_stdout(sys.stderr):
+    help(pow)
+

Note that the global side effect on sys.stdout means that this context manager is not suitable for use in library code and most threaded applications. It also has no effect on the output of subprocesses. However, it is still a useful approach for many utility scripts.

This context manager is reentrant.

New in version 3.4.

+
+contextlib.redirect_stderr(new_target)
+

Similar to redirect_stdout() but redirecting sys.stderr to another file or file-like object.

This context manager is reentrant.

New in version 3.5.

+
+contextlib.chdir(path)
+

Non parallel-safe context manager to change the current working directory. As this changes a global state, the working directory, it is not suitable for use in most threaded or async contexts. It is also not suitable for most non-linear code execution, like generators, where the program execution is temporarily relinquished – unless explicitly desired, you should not yield when this context manager is active.

This is a simple wrapper around chdir(), it changes the current working directory upon entering and restores the old one on exit.

This context manager is reentrant.

New in version 3.11.

+
+class contextlib.ContextDecorator
+

A base class that enables a context manager to also be used as a decorator.

Context managers inheriting from ContextDecorator have to implement __enter__ and __exit__ as normal. __exit__ retains its optional exception handling even when used as a decorator.

ContextDecorator is used by contextmanager(), so you get this functionality automatically.

Example of ContextDecorator:

from contextlib import ContextDecorator
+
+class mycontext(ContextDecorator):
+    def __enter__(self):
+        print('Starting')
+        return self
+
+    def __exit__(self, *exc):
+        print('Finishing')
+        return False
+

The class can then be used like this:

>>> @mycontext()
+... def function():
+...     print('The bit in the middle')
+...
+>>> function()
+Starting
+The bit in the middle
+Finishing
+
+>>> with mycontext():
+...     print('The bit in the middle')
+...
+Starting
+The bit in the middle
+Finishing
+

This change is just syntactic sugar for any construct of the following form:

def f():
+    with cm():
+        # Do stuff
+

ContextDecorator lets you instead write:

@cm()
+def f():
+    # Do stuff
+

It makes it clear that the cm applies to the whole function, rather than just a piece of it (and saving an indentation level is nice, too).

Existing context managers that already have a base class can be extended by using ContextDecorator as a mixin class:

from contextlib import ContextDecorator
+
+class mycontext(ContextBaseClass, ContextDecorator):
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *exc):
+        return False
+

Note

As the decorated function must be able to be called multiple times, the underlying context manager must support use in multiple with statements. If this is not the case, then the original construct with the explicit with statement inside the function should be used.

New in version 3.2.

+
+class contextlib.AsyncContextDecorator
+

Similar to ContextDecorator but only for asynchronous functions.

Example of AsyncContextDecorator:

from asyncio import run
+from contextlib import AsyncContextDecorator
+
+class mycontext(AsyncContextDecorator):
+    async def __aenter__(self):
+        print('Starting')
+        return self
+
+    async def __aexit__(self, *exc):
+        print('Finishing')
+        return False
+

The class can then be used like this:

>>> @mycontext()
+... async def function():
+...     print('The bit in the middle')
+...
+>>> run(function())
+Starting
+The bit in the middle
+Finishing
+
+>>> async def function():
+...    async with mycontext():
+...         print('The bit in the middle')
+...
+>>> run(function())
+Starting
+The bit in the middle
+Finishing
+

New in version 3.10.

+
+class contextlib.ExitStack
+

A context manager that is designed to make it easy to programmatically combine other context managers and cleanup functions, especially those that are optional or otherwise driven by input data.

For example, a set of files may easily be handled in a single with statement as follows:

with ExitStack() as stack:
+    files = [stack.enter_context(open(fname)) for fname in filenames]
+    # All opened files will automatically be closed at the end of
+    # the with statement, even if attempts to open files later
+    # in the list raise an exception
+

The __enter__() method returns the ExitStack instance, and performs no additional operations.

Each instance maintains a stack of registered callbacks that are called in reverse order when the instance is closed (either explicitly or implicitly at the end of a with statement). Note that callbacks are not invoked implicitly when the context stack instance is garbage collected.

This stack model is used so that context managers that acquire their resources in their __init__ method (such as file objects) can be handled correctly.

Since registered callbacks are invoked in the reverse order of registration, this ends up behaving as if multiple nested with statements had been used with the registered set of callbacks. This even extends to exception handling - if an inner callback suppresses or replaces an exception, then outer callbacks will be passed arguments based on that updated state.

This is a relatively low level API that takes care of the details of correctly unwinding the stack of exit callbacks. It provides a suitable foundation for higher level context managers that manipulate the exit stack in application specific ways.

New in version 3.3.

+enter_context(cm)
+

Enters a new context manager and adds its __exit__() method to the callback stack. The return value is the result of the context manager’s own __enter__() method.

These context managers may suppress exceptions just as they normally would if used directly as part of a with statement.

Changed in version 3.11: Raises TypeError instead of AttributeError if cm is not a context manager.

+
+push(exit)
+

Adds a context manager’s __exit__() method to the callback stack.

As __enter__ is not invoked, this method can be used to cover part of an __enter__() implementation with a context manager’s own __exit__() method.

If passed an object that is not a context manager, this method assumes it is a callback with the same signature as a context manager’s __exit__() method and adds it directly to the callback stack.

By returning true values, these callbacks can suppress exceptions the same way context manager __exit__() methods can.

The passed in object is returned from the function, allowing this method to be used as a function decorator.

+
+callback(callback, /, *args, **kwds)
+

Accepts an arbitrary callback function and arguments and adds it to the callback stack.

Unlike the other methods, callbacks added this way cannot suppress exceptions (as they are never passed the exception details).

The passed in callback is returned from the function, allowing this method to be used as a function decorator.

+
+pop_all()
+

Transfers the callback stack to a fresh ExitStack instance and returns it. No callbacks are invoked by this operation - instead, they will now be invoked when the new stack is closed (either explicitly or implicitly at the end of a with statement).

For example, a group of files can be opened as an “all or nothing” operation as follows:

with ExitStack() as stack:
+    files = [stack.enter_context(open(fname)) for fname in filenames]
+    # Hold onto the close method, but don't call it yet.
+    close_files = stack.pop_all().close
+    # If opening any file fails, all previously opened files will be
+    # closed automatically. If all files are opened successfully,
+    # they will remain open even after the with statement ends.
+    # close_files() can then be invoked explicitly to close them all.
+
+
+close()
+

Immediately unwinds the callback stack, invoking callbacks in the reverse order of registration. For any context managers and exit callbacks registered, the arguments passed in will indicate that no exception occurred.

+
+
+class contextlib.AsyncExitStack
+

An asynchronous context manager, similar to ExitStack, that supports combining both synchronous and asynchronous context managers, as well as having coroutines for cleanup logic.

The close() method is not implemented; aclose() must be used instead.

+coroutine enter_async_context(cm)
+

Similar to ExitStack.enter_context() but expects an asynchronous context manager.

Changed in version 3.11: Raises TypeError instead of AttributeError if cm is not an asynchronous context manager.

+
+push_async_exit(exit)
+

Similar to ExitStack.push() but expects either an asynchronous context manager or a coroutine function.

+
+push_async_callback(callback, /, *args, **kwds)
+

Similar to ExitStack.callback() but expects a coroutine function.

+
+coroutine aclose()
+

Similar to ExitStack.close() but properly handles awaitables.

+

Continuing the example for asynccontextmanager():

async with AsyncExitStack() as stack:
+    connections = [await stack.enter_async_context(get_connection())
+        for i in range(5)]
+    # All opened connections will automatically be released at the end of
+    # the async with statement, even if attempts to open a connection
+    # later in the list raise an exception.
+

New in version 3.7.

+

Examples and Recipes

This section describes some examples and recipes for making effective use of the tools provided by contextlib.

Supporting a variable number of context managers

The primary use case for ExitStack is the one given in the class documentation: supporting a variable number of context managers and other cleanup operations in a single with statement. The variability may come from the number of context managers needed being driven by user input (such as opening a user specified collection of files), or from some of the context managers being optional:

with ExitStack() as stack:
+    for resource in resources:
+        stack.enter_context(resource)
+    if need_special_resource():
+        special = acquire_special_resource()
+        stack.callback(release_special_resource, special)
+    # Perform operations that use the acquired resources
+

As shown, ExitStack also makes it quite easy to use with statements to manage arbitrary resources that don’t natively support the context management protocol.

Catching exceptions from __enter__ methods

It is occasionally desirable to catch exceptions from an __enter__ method implementation, without inadvertently catching exceptions from the with statement body or the context manager’s __exit__ method. By using ExitStack the steps in the context management protocol can be separated slightly in order to allow this:

stack = ExitStack()
+try:
+    x = stack.enter_context(cm)
+except Exception:
+    # handle __enter__ exception
+else:
+    with stack:
+        # Handle normal case
+

Actually needing to do this is likely to indicate that the underlying API should be providing a direct resource management interface for use with try/except/finally statements, but not all APIs are well designed in that regard. When a context manager is the only resource management API provided, then ExitStack can make it easier to handle various situations that can’t be handled directly in a with statement.

Cleaning up in an __enter__ implementation

As noted in the documentation of ExitStack.push(), this method can be useful in cleaning up an already allocated resource if later steps in the __enter__() implementation fail.

Here’s an example of doing this for a context manager that accepts resource acquisition and release functions, along with an optional validation function, and maps them to the context management protocol:

from contextlib import contextmanager, AbstractContextManager, ExitStack
+
+class ResourceManager(AbstractContextManager):
+
+    def __init__(self, acquire_resource, release_resource, check_resource_ok=None):
+        self.acquire_resource = acquire_resource
+        self.release_resource = release_resource
+        if check_resource_ok is None:
+            def check_resource_ok(resource):
+                return True
+        self.check_resource_ok = check_resource_ok
+
+    @contextmanager
+    def _cleanup_on_error(self):
+        with ExitStack() as stack:
+            stack.push(self)
+            yield
+            # The validation check passed and didn't raise an exception
+            # Accordingly, we want to keep the resource, and pass it
+            # back to our caller
+            stack.pop_all()
+
+    def __enter__(self):
+        resource = self.acquire_resource()
+        with self._cleanup_on_error():
+            if not self.check_resource_ok(resource):
+                msg = "Failed validation for {!r}"
+                raise RuntimeError(msg.format(resource))
+        return resource
+
+    def __exit__(self, *exc_details):
+        # We don't need to duplicate any of our resource release logic
+        self.release_resource()
+

Replacing any use of try-finally and flag variables

A pattern you will sometimes see is a try-finally statement with a flag variable to indicate whether or not the body of the finally clause should be executed. In its simplest form (that can’t already be handled just by using an except clause instead), it looks something like this:

cleanup_needed = True
+try:
+    result = perform_operation()
+    if result:
+        cleanup_needed = False
+finally:
+    if cleanup_needed:
+        cleanup_resources()
+

As with any try statement based code, this can cause problems for development and review, because the setup code and the cleanup code can end up being separated by arbitrarily long sections of code.

ExitStack makes it possible to instead register a callback for execution at the end of a with statement, and then later decide to skip executing that callback:

from contextlib import ExitStack
+
+with ExitStack() as stack:
+    stack.callback(cleanup_resources)
+    result = perform_operation()
+    if result:
+        stack.pop_all()
+

This allows the intended cleanup up behaviour to be made explicit up front, rather than requiring a separate flag variable.

If a particular application uses this pattern a lot, it can be simplified even further by means of a small helper class:

from contextlib import ExitStack
+
+class Callback(ExitStack):
+    def __init__(self, callback, /, *args, **kwds):
+        super().__init__()
+        self.callback(callback, *args, **kwds)
+
+    def cancel(self):
+        self.pop_all()
+
+with Callback(cleanup_resources) as cb:
+    result = perform_operation()
+    if result:
+        cb.cancel()
+

If the resource cleanup isn’t already neatly bundled into a standalone function, then it is still possible to use the decorator form of ExitStack.callback() to declare the resource cleanup in advance:

from contextlib import ExitStack
+
+with ExitStack() as stack:
+    @stack.callback
+    def cleanup_resources():
+        ...
+    result = perform_operation()
+    if result:
+        stack.pop_all()
+

Due to the way the decorator protocol works, a callback function declared this way cannot take any parameters. Instead, any resources to be released must be accessed as closure variables.

Using a context manager as a function decorator

ContextDecorator makes it possible to use a context manager in both an ordinary with statement and also as a function decorator.

For example, it is sometimes useful to wrap functions or groups of statements with a logger that can track the time of entry and time of exit. Rather than writing both a function decorator and a context manager for the task, inheriting from ContextDecorator provides both capabilities in a single definition:

from contextlib import ContextDecorator
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+class track_entry_and_exit(ContextDecorator):
+    def __init__(self, name):
+        self.name = name
+
+    def __enter__(self):
+        logging.info('Entering: %s', self.name)
+
+    def __exit__(self, exc_type, exc, exc_tb):
+        logging.info('Exiting: %s', self.name)
+

Instances of this class can be used as both a context manager:

with track_entry_and_exit('widget loader'):
+    print('Some time consuming activity goes here')
+    load_widget()
+

And also as a function decorator:

@track_entry_and_exit('widget loader')
+def activity():
+    print('Some time consuming activity goes here')
+    load_widget()
+

Note that there is one additional limitation when using context managers as function decorators: there’s no way to access the return value of __enter__(). If that value is needed, then it is still necessary to use an explicit with statement.

See also

+PEP 343 - The “with” statement
+
+

The specification, background, and examples for the Python with statement.

Single use, reusable and reentrant context managers

Most context managers are written in a way that means they can only be used effectively in a with statement once. These single use context managers must be created afresh each time they’re used - attempting to use them a second time will trigger an exception or otherwise not work correctly.

This common limitation means that it is generally advisable to create context managers directly in the header of the with statement where they are used (as shown in all of the usage examples above).

Files are an example of effectively single use context managers, since the first with statement will close the file, preventing any further IO operations using that file object.

Context managers created using contextmanager() are also single use context managers, and will complain about the underlying generator failing to yield if an attempt is made to use them a second time:

>>> from contextlib import contextmanager
+>>> @contextmanager
+... def singleuse():
+...     print("Before")
+...     yield
+...     print("After")
+...
+>>> cm = singleuse()
+>>> with cm:
+...     pass
+...
+Before
+After
+>>> with cm:
+...     pass
+...
+Traceback (most recent call last):
+    ...
+RuntimeError: generator didn't yield
+

Reentrant context managers

More sophisticated context managers may be “reentrant”. These context managers can not only be used in multiple with statements, but may also be used inside a with statement that is already using the same context manager.

threading.RLock is an example of a reentrant context manager, as are suppress(), redirect_stdout(), and chdir(). Here’s a very simple example of reentrant use:

>>> from contextlib import redirect_stdout
+>>> from io import StringIO
+>>> stream = StringIO()
+>>> write_to_stream = redirect_stdout(stream)
+>>> with write_to_stream:
+...     print("This is written to the stream rather than stdout")
+...     with write_to_stream:
+...         print("This is also written to the stream")
+...
+>>> print("This is written directly to stdout")
+This is written directly to stdout
+>>> print(stream.getvalue())
+This is written to the stream rather than stdout
+This is also written to the stream
+

Real world examples of reentrancy are more likely to involve multiple functions calling each other and hence be far more complicated than this example.

Note also that being reentrant is not the same thing as being thread safe. redirect_stdout(), for example, is definitely not thread safe, as it makes a global modification to the system state by binding sys.stdout to a different stream.

Reusable context managers

Distinct from both single use and reentrant context managers are “reusable” context managers (or, to be completely explicit, “reusable, but not reentrant” context managers, since reentrant context managers are also reusable). These context managers support being used multiple times, but will fail (or otherwise not work correctly) if the specific context manager instance has already been used in a containing with statement.

threading.Lock is an example of a reusable, but not reentrant, context manager (for a reentrant lock, it is necessary to use threading.RLock instead).

Another example of a reusable, but not reentrant, context manager is ExitStack, as it invokes all currently registered callbacks when leaving any with statement, regardless of where those callbacks were added:

>>> from contextlib import ExitStack
+>>> stack = ExitStack()
+>>> with stack:
+...     stack.callback(print, "Callback: from first context")
+...     print("Leaving first context")
+...
+Leaving first context
+Callback: from first context
+>>> with stack:
+...     stack.callback(print, "Callback: from second context")
+...     print("Leaving second context")
+...
+Leaving second context
+Callback: from second context
+>>> with stack:
+...     stack.callback(print, "Callback: from outer context")
+...     with stack:
+...         stack.callback(print, "Callback: from inner context")
+...         print("Leaving inner context")
+...     print("Leaving outer context")
+...
+Leaving inner context
+Callback: from inner context
+Callback: from outer context
+Leaving outer context
+

As the output from the example shows, reusing a single stack object across multiple with statements works correctly, but attempting to nest them will cause the stack to be cleared at the end of the innermost with statement, which is unlikely to be desirable behaviour.

Using separate ExitStack instances instead of reusing a single instance avoids that problem:

>>> from contextlib import ExitStack
+>>> with ExitStack() as outer_stack:
+...     outer_stack.callback(print, "Callback: from outer context")
+...     with ExitStack() as inner_stack:
+...         inner_stack.callback(print, "Callback: from inner context")
+...         print("Leaving inner context")
+...     print("Leaving outer context")
+...
+Leaving inner context
+Callback: from inner context
+Leaving outer context
+Callback: from outer context
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/contextlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fcontextvars.html b/devdocs/python~3.12/library%2Fcontextvars.html new file mode 100644 index 00000000..d96744eb --- /dev/null +++ b/devdocs/python~3.12/library%2Fcontextvars.html @@ -0,0 +1,145 @@ +

contextvars — Context Variables

This module provides APIs to manage, store, and access context-local state. The ContextVar class is used to declare and work with Context Variables. The copy_context() function and the Context class should be used to manage the current context in asynchronous frameworks.

Context managers that have state should use Context Variables instead of threading.local() to prevent their state from bleeding to other code unexpectedly, when used in concurrent code.

See also PEP 567 for additional details.

New in version 3.7.

Context Variables

+class contextvars.ContextVar(name[, *, default])
+

This class is used to declare a new Context Variable, e.g.:

var: ContextVar[int] = ContextVar('var', default=42)
+

The required name parameter is used for introspection and debug purposes.

The optional keyword-only default parameter is returned by ContextVar.get() when no value for the variable is found in the current context.

Important: Context Variables should be created at the top module level and never in closures. Context objects hold strong references to context variables which prevents context variables from being properly garbage collected.

+name
+

The name of the variable. This is a read-only property.

New in version 3.7.1.

+
+get([default])
+

Return a value for the context variable for the current context.

If there is no value for the variable in the current context, the method will:

  • return the value of the default argument of the method, if provided; or
  • return the default value for the context variable, if it was created with one; or
  • raise a LookupError.
+
+set(value)
+

Call to set a new value for the context variable in the current context.

The required value argument is the new value for the context variable.

Returns a Token object that can be used to restore the variable to its previous value via the ContextVar.reset() method.

+
+reset(token)
+

Reset the context variable to the value it had before the ContextVar.set() that created the token was used.

For example:

var = ContextVar('var')
+
+token = var.set('new value')
+# code that uses 'var'; var.get() returns 'new value'.
+var.reset(token)
+
+# After the reset call the var has no value again, so
+# var.get() would raise a LookupError.
+
+
+
+class contextvars.Token
+

Token objects are returned by the ContextVar.set() method. They can be passed to the ContextVar.reset() method to revert the value of the variable to what it was before the corresponding set.

+var
+

A read-only property. Points to the ContextVar object that created the token.

+
+old_value
+

A read-only property. Set to the value the variable had before the ContextVar.set() method call that created the token. It points to Token.MISSING if the variable was not set before the call.

+
+MISSING
+

A marker object used by Token.old_value.

+
+

Manual Context Management

+contextvars.copy_context()
+

Returns a copy of the current Context object.

The following snippet gets a copy of the current context and prints all variables and their values that are set in it:

ctx: Context = copy_context()
+print(list(ctx.items()))
+

The function has an O(1) complexity, i.e. works equally fast for contexts with a few context variables and for contexts that have a lot of them.

+
+class contextvars.Context
+

A mapping of ContextVars to their values.

Context() creates an empty context with no values in it. To get a copy of the current context use the copy_context() function.

Every thread will have a different top-level Context object. This means that a ContextVar object behaves in a similar fashion to threading.local() when values are assigned in different threads.

Context implements the collections.abc.Mapping interface.

+run(callable, *args, **kwargs)
+

Execute callable(*args, **kwargs) code in the context object the run method is called on. Return the result of the execution or propagate an exception if one occurred.

Any changes to any context variables that callable makes will be contained in the context object:

var = ContextVar('var')
+var.set('spam')
+
+def main():
+    # 'var' was set to 'spam' before
+    # calling 'copy_context()' and 'ctx.run(main)', so:
+    # var.get() == ctx[var] == 'spam'
+
+    var.set('ham')
+
+    # Now, after setting 'var' to 'ham':
+    # var.get() == ctx[var] == 'ham'
+
+ctx = copy_context()
+
+# Any changes that the 'main' function makes to 'var'
+# will be contained in 'ctx'.
+ctx.run(main)
+
+# The 'main()' function was run in the 'ctx' context,
+# so changes to 'var' are contained in it:
+# ctx[var] == 'ham'
+
+# However, outside of 'ctx', 'var' is still set to 'spam':
+# var.get() == 'spam'
+

The method raises a RuntimeError when called on the same context object from more than one OS thread, or when called recursively.

+
+copy()
+

Return a shallow copy of the context object.

+
var in context +
+

Return True if the context has a value for var set; return False otherwise.

+
context[var] +
+

Return the value of the var ContextVar variable. If the variable is not set in the context object, a KeyError is raised.

+
+get(var[, default])
+

Return the value for var if var has the value in the context object. Return default otherwise. If default is not given, return None.

+
iter(context) +
+

Return an iterator over the variables stored in the context object.

+
len(proxy) +
+

Return the number of variables set in the context object.

+
+keys()
+

Return a list of all variables in the context object.

+
+values()
+

Return a list of all variables’ values in the context object.

+
+items()
+

Return a list of 2-tuples containing all variables and their values in the context object.

+
+

asyncio support

Context variables are natively supported in asyncio and are ready to be used without any extra configuration. For example, here is a simple echo server, that uses a context variable to make the address of a remote client available in the Task that handles that client:

import asyncio
+import contextvars
+
+client_addr_var = contextvars.ContextVar('client_addr')
+
+def render_goodbye():
+    # The address of the currently handled client can be accessed
+    # without passing it explicitly to this function.
+
+    client_addr = client_addr_var.get()
+    return f'Good bye, client @ {client_addr}\n'.encode()
+
+async def handle_request(reader, writer):
+    addr = writer.transport.get_extra_info('socket').getpeername()
+    client_addr_var.set(addr)
+
+    # In any code that we call is now possible to get
+    # client's address by calling 'client_addr_var.get()'.
+
+    while True:
+        line = await reader.readline()
+        print(line)
+        if not line.strip():
+            break
+        writer.write(line)
+
+    writer.write(render_goodbye())
+    writer.close()
+
+async def main():
+    srv = await asyncio.start_server(
+        handle_request, '127.0.0.1', 8081)
+
+    async with srv:
+        await srv.serve_forever()
+
+asyncio.run(main())
+
+# To test it you can use telnet:
+#     telnet 127.0.0.1 8081
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/contextvars.html +

+
diff --git a/devdocs/python~3.12/library%2Fcopy.html b/devdocs/python~3.12/library%2Fcopy.html new file mode 100644 index 00000000..f6c1328e --- /dev/null +++ b/devdocs/python~3.12/library%2Fcopy.html @@ -0,0 +1,19 @@ +

copy — Shallow and deep copy operations

Source code: Lib/copy.py

Assignment statements in Python do not copy objects, they create bindings between a target and an object. For collections that are mutable or contain mutable items, a copy is sometimes needed so one can change one copy without changing the other. This module provides generic shallow and deep copy operations (explained below).

Interface summary:

+copy.copy(x)
+

Return a shallow copy of x.

+
+copy.deepcopy(x[, memo])
+

Return a deep copy of x.

+
+exception copy.Error
+

Raised for module specific errors.

+

The difference between shallow and deep copying is only relevant for compound objects (objects that contain other objects, like lists or class instances):

Two problems often exist with deep copy operations that don’t exist with shallow copy operations:

The deepcopy() function avoids these problems by:

This module does not copy types like module, method, stack trace, stack frame, file, socket, window, or any similar types. It does “copy” functions and classes (shallow and deeply), by returning the original object unchanged; this is compatible with the way these are treated by the pickle module.

Shallow copies of dictionaries can be made using dict.copy(), and of lists by assigning a slice of the entire list, for example, copied_list = original_list[:].

Classes can use the same interfaces to control copying that they use to control pickling. See the description of module pickle for information on these methods. In fact, the copy module uses the registered pickle functions from the copyreg module.

In order for a class to define its own copy implementation, it can define special methods __copy__() and __deepcopy__(). The former is called to implement the shallow copy operation; no additional arguments are passed. The latter is called to implement the deep copy operation; it is passed one argument, the memo dictionary. If the __deepcopy__() implementation needs to make a deep copy of a component, it should call the deepcopy() function with the component as first argument and the memo dictionary as second argument. The memo dictionary should be treated as an opaque object.

See also

+Module pickle +
+
+

Discussion of the special methods used to support object state retrieval and restoration.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/copy.html +

+
diff --git a/devdocs/python~3.12/library%2Fcopyreg.html b/devdocs/python~3.12/library%2Fcopyreg.html new file mode 100644 index 00000000..42c6898c --- /dev/null +++ b/devdocs/python~3.12/library%2Fcopyreg.html @@ -0,0 +1,27 @@ +

copyreg — Register pickle support functions

Source code: Lib/copyreg.py

The copyreg module offers a way to define functions used while pickling specific objects. The pickle and copy modules use those functions when pickling/copying those objects. The module provides configuration information about object constructors which are not classes. Such constructors may be factory functions or class instances.

+copyreg.constructor(object)
+

Declares object to be a valid constructor. If object is not callable (and hence not valid as a constructor), raises TypeError.

+
+copyreg.pickle(type, function, constructor_ob=None)
+

Declares that function should be used as a “reduction” function for objects of type type. function must return either a string or a tuple containing between two and six elements. See the dispatch_table for more details on the interface of function.

The constructor_ob parameter is a legacy feature and is now ignored, but if passed it must be a callable.

Note that the dispatch_table attribute of a pickler object or subclass of pickle.Pickler can also be used for declaring reduction functions.

+

Example

The example below would like to show how to register a pickle function and how it will be used:

>>> import copyreg, copy, pickle
+>>> class C:
+...     def __init__(self, a):
+...         self.a = a
+...
+>>> def pickle_c(c):
+...     print("pickling a C instance...")
+...     return C, (c.a,)
+...
+>>> copyreg.pickle(C, pickle_c)
+>>> c = C(1)
+>>> d = copy.copy(c)  
+pickling a C instance...
+>>> p = pickle.dumps(c)  
+pickling a C instance...
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/copyreg.html +

+
diff --git a/devdocs/python~3.12/library%2Fcrypto.html b/devdocs/python~3.12/library%2Fcrypto.html new file mode 100644 index 00000000..1f909b27 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcrypto.html @@ -0,0 +1,11 @@ +

Cryptographic Services

The modules described in this chapter implement various algorithms of a cryptographic nature. They are available at the discretion of the installation. On Unix systems, the crypt module may also be available. Here’s an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/crypto.html +

+
diff --git a/devdocs/python~3.12/library%2Fcsv.html b/devdocs/python~3.12/library%2Fcsv.html new file mode 100644 index 00000000..af9d13c1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcsv.html @@ -0,0 +1,207 @@ +

csv — CSV File Reading and Writing

Source code: Lib/csv.py

The so-called CSV (Comma Separated Values) format is the most common import and export format for spreadsheets and databases. CSV format was used for many years prior to attempts to describe the format in a standardized way in RFC 4180. The lack of a well-defined standard means that subtle differences often exist in the data produced and consumed by different applications. These differences can make it annoying to process CSV files from multiple sources. Still, while the delimiters and quoting characters vary, the overall format is similar enough that it is possible to write a single module which can efficiently manipulate such data, hiding the details of reading and writing the data from the programmer.

The csv module implements classes to read and write tabular data in CSV format. It allows programmers to say, “write this data in the format preferred by Excel,” or “read data from this file which was generated by Excel,” without knowing the precise details of the CSV format used by Excel. Programmers can also describe the CSV formats understood by other applications or define their own special-purpose CSV formats.

The csv module’s reader and writer objects read and write sequences. Programmers can also read and write data in dictionary form using the DictReader and DictWriter classes.

See also

+PEP 305 - CSV File API
+
+

The Python Enhancement Proposal which proposed this addition to Python.

Module Contents

The csv module defines the following functions:

+csv.reader(csvfile, dialect='excel', **fmtparams)
+

Return a reader object that will process lines from the given csvfile. A csvfile must be an iterable of strings, each in the reader’s defined csv format. A csvfile is most commonly a file-like object or list. If csvfile is a file object, it should be opened with newline=''. 1 An optional dialect parameter can be given which is used to define a set of parameters specific to a particular CSV dialect. It may be an instance of a subclass of the Dialect class or one of the strings returned by the list_dialects() function. The other optional fmtparams keyword arguments can be given to override individual formatting parameters in the current dialect. For full details about the dialect and formatting parameters, see section Dialects and Formatting Parameters.

Each row read from the csv file is returned as a list of strings. No automatic data type conversion is performed unless the QUOTE_NONNUMERIC format option is specified (in which case unquoted fields are transformed into floats).

A short usage example:

>>> import csv
+>>> with open('eggs.csv', newline='') as csvfile:
+...     spamreader = csv.reader(csvfile, delimiter=' ', quotechar='|')
+...     for row in spamreader:
+...         print(', '.join(row))
+Spam, Spam, Spam, Spam, Spam, Baked Beans
+Spam, Lovely Spam, Wonderful Spam
+
+
+csv.writer(csvfile, dialect='excel', **fmtparams)
+

Return a writer object responsible for converting the user’s data into delimited strings on the given file-like object. csvfile can be any object with a write() method. If csvfile is a file object, it should be opened with newline='' 1. An optional dialect parameter can be given which is used to define a set of parameters specific to a particular CSV dialect. It may be an instance of a subclass of the Dialect class or one of the strings returned by the list_dialects() function. The other optional fmtparams keyword arguments can be given to override individual formatting parameters in the current dialect. For full details about dialects and formatting parameters, see the Dialects and Formatting Parameters section. To make it as easy as possible to interface with modules which implement the DB API, the value None is written as the empty string. While this isn’t a reversible transformation, it makes it easier to dump SQL NULL data values to CSV files without preprocessing the data returned from a cursor.fetch* call. All other non-string data are stringified with str() before being written.

A short usage example:

import csv
+with open('eggs.csv', 'w', newline='') as csvfile:
+    spamwriter = csv.writer(csvfile, delimiter=' ',
+                            quotechar='|', quoting=csv.QUOTE_MINIMAL)
+    spamwriter.writerow(['Spam'] * 5 + ['Baked Beans'])
+    spamwriter.writerow(['Spam', 'Lovely Spam', 'Wonderful Spam'])
+
+
+csv.register_dialect(name[, dialect[, **fmtparams]])
+

Associate dialect with name. name must be a string. The dialect can be specified either by passing a sub-class of Dialect, or by fmtparams keyword arguments, or both, with keyword arguments overriding parameters of the dialect. For full details about dialects and formatting parameters, see section Dialects and Formatting Parameters.

+
+csv.unregister_dialect(name)
+

Delete the dialect associated with name from the dialect registry. An Error is raised if name is not a registered dialect name.

+
+csv.get_dialect(name)
+

Return the dialect associated with name. An Error is raised if name is not a registered dialect name. This function returns an immutable Dialect.

+
+csv.list_dialects()
+

Return the names of all registered dialects.

+
+csv.field_size_limit([new_limit])
+

Returns the current maximum field size allowed by the parser. If new_limit is given, this becomes the new limit.

+

The csv module defines the following classes:

+class csv.DictReader(f, fieldnames=None, restkey=None, restval=None, dialect='excel', *args, **kwds)
+

Create an object that operates like a regular reader but maps the information in each row to a dict whose keys are given by the optional fieldnames parameter.

The fieldnames parameter is a sequence. If fieldnames is omitted, the values in the first row of file f will be used as the fieldnames. Regardless of how the fieldnames are determined, the dictionary preserves their original ordering.

If a row has more fields than fieldnames, the remaining data is put in a list and stored with the fieldname specified by restkey (which defaults to None). If a non-blank row has fewer fields than fieldnames, the missing values are filled-in with the value of restval (which defaults to None).

All other optional or keyword arguments are passed to the underlying reader instance.

If the argument passed to fieldnames is an iterator, it will be coerced to a list.

Changed in version 3.6: Returned rows are now of type OrderedDict.

Changed in version 3.8: Returned rows are now of type dict.

A short usage example:

>>> import csv
+>>> with open('names.csv', newline='') as csvfile:
+...     reader = csv.DictReader(csvfile)
+...     for row in reader:
+...         print(row['first_name'], row['last_name'])
+...
+Eric Idle
+John Cleese
+
+>>> print(row)
+{'first_name': 'John', 'last_name': 'Cleese'}
+
+
+class csv.DictWriter(f, fieldnames, restval='', extrasaction='raise', dialect='excel', *args, **kwds)
+

Create an object which operates like a regular writer but maps dictionaries onto output rows. The fieldnames parameter is a sequence of keys that identify the order in which values in the dictionary passed to the writerow() method are written to file f. The optional restval parameter specifies the value to be written if the dictionary is missing a key in fieldnames. If the dictionary passed to the writerow() method contains a key not found in fieldnames, the optional extrasaction parameter indicates what action to take. If it is set to 'raise', the default value, a ValueError is raised. If it is set to 'ignore', extra values in the dictionary are ignored. Any other optional or keyword arguments are passed to the underlying writer instance.

Note that unlike the DictReader class, the fieldnames parameter of the DictWriter class is not optional.

If the argument passed to fieldnames is an iterator, it will be coerced to a list.

A short usage example:

import csv
+
+with open('names.csv', 'w', newline='') as csvfile:
+    fieldnames = ['first_name', 'last_name']
+    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+
+    writer.writeheader()
+    writer.writerow({'first_name': 'Baked', 'last_name': 'Beans'})
+    writer.writerow({'first_name': 'Lovely', 'last_name': 'Spam'})
+    writer.writerow({'first_name': 'Wonderful', 'last_name': 'Spam'})
+
+
+class csv.Dialect
+

The Dialect class is a container class whose attributes contain information for how to handle doublequotes, whitespace, delimiters, etc. Due to the lack of a strict CSV specification, different applications produce subtly different CSV data. Dialect instances define how reader and writer instances behave.

All available Dialect names are returned by list_dialects(), and they can be registered with specific reader and writer classes through their initializer (__init__) functions like this:

import csv
+
+with open('students.csv', 'w', newline='') as csvfile:
+    writer = csv.writer(csvfile, dialect='unix')
+                                 ^^^^^^^^^^^^^^
+
+
+class csv.excel
+

The excel class defines the usual properties of an Excel-generated CSV file. It is registered with the dialect name 'excel'.

+
+class csv.excel_tab
+

The excel_tab class defines the usual properties of an Excel-generated TAB-delimited file. It is registered with the dialect name 'excel-tab'.

+
+class csv.unix_dialect
+

The unix_dialect class defines the usual properties of a CSV file generated on UNIX systems, i.e. using '\n' as line terminator and quoting all fields. It is registered with the dialect name 'unix'.

New in version 3.2.

+
+class csv.Sniffer
+

The Sniffer class is used to deduce the format of a CSV file.

The Sniffer class provides two methods:

+sniff(sample, delimiters=None)
+

Analyze the given sample and return a Dialect subclass reflecting the parameters found. If the optional delimiters parameter is given, it is interpreted as a string containing possible valid delimiter characters.

+
+has_header(sample)
+

Analyze the sample text (presumed to be in CSV format) and return True if the first row appears to be a series of column headers. Inspecting each column, one of two key criteria will be considered to estimate if the sample contains a header:

  • the second through n-th rows contain numeric values
  • the second through n-th rows contain strings where at least one value’s length differs from that of the putative header of that column.

Twenty rows after the first row are sampled; if more than half of columns + rows meet the criteria, True is returned.

+

Note

This method is a rough heuristic and may produce both false positives and negatives.

+

An example for Sniffer use:

with open('example.csv', newline='') as csvfile:
+    dialect = csv.Sniffer().sniff(csvfile.read(1024))
+    csvfile.seek(0)
+    reader = csv.reader(csvfile, dialect)
+    # ... process CSV file contents here ...
+

The csv module defines the following constants:

+csv.QUOTE_ALL
+

Instructs writer objects to quote all fields.

+
+csv.QUOTE_MINIMAL
+

Instructs writer objects to only quote those fields which contain special characters such as delimiter, quotechar or any of the characters in lineterminator.

+
+csv.QUOTE_NONNUMERIC
+

Instructs writer objects to quote all non-numeric fields.

Instructs reader objects to convert all non-quoted fields to type float.

+
+csv.QUOTE_NONE
+

Instructs writer objects to never quote fields. When the current delimiter occurs in output data it is preceded by the current escapechar character. If escapechar is not set, the writer will raise Error if any characters that require escaping are encountered.

Instructs reader objects to perform no special processing of quote characters.

+
+csv.QUOTE_NOTNULL
+

Instructs writer objects to quote all fields which are not None. This is similar to QUOTE_ALL, except that if a field value is None an empty (unquoted) string is written.

Instructs reader objects to interpret an empty (unquoted) field as None and to otherwise behave as QUOTE_ALL.

+
+csv.QUOTE_STRINGS
+

Instructs writer objects to always place quotes around fields which are strings. This is similar to QUOTE_NONNUMERIC, except that if a field value is None an empty (unquoted) string is written.

Instructs reader objects to interpret an empty (unquoted) string as None and to otherwise behave as QUOTE_NONNUMERIC.

+

The csv module defines the following exception:

+exception csv.Error
+

Raised by any of the functions when an error is detected.

+

Dialects and Formatting Parameters

To make it easier to specify the format of input and output records, specific formatting parameters are grouped together into dialects. A dialect is a subclass of the Dialect class having a set of specific methods and a single validate() method. When creating reader or writer objects, the programmer can specify a string or a subclass of the Dialect class as the dialect parameter. In addition to, or instead of, the dialect parameter, the programmer can also specify individual formatting parameters, which have the same names as the attributes defined below for the Dialect class.

Dialects support the following attributes:

+Dialect.delimiter
+

A one-character string used to separate fields. It defaults to ','.

+
+Dialect.doublequote
+

Controls how instances of quotechar appearing inside a field should themselves be quoted. When True, the character is doubled. When False, the escapechar is used as a prefix to the quotechar. It defaults to True.

On output, if doublequote is False and no escapechar is set, Error is raised if a quotechar is found in a field.

+
+Dialect.escapechar
+

A one-character string used by the writer to escape the delimiter if quoting is set to QUOTE_NONE and the quotechar if doublequote is False. On reading, the escapechar removes any special meaning from the following character. It defaults to None, which disables escaping.

Changed in version 3.11: An empty escapechar is not allowed.

+
+Dialect.lineterminator
+

The string used to terminate lines produced by the writer. It defaults to '\r\n'.

Note

The reader is hard-coded to recognise either '\r' or '\n' as end-of-line, and ignores lineterminator. This behavior may change in the future.

+
+Dialect.quotechar
+

A one-character string used to quote fields containing special characters, such as the delimiter or quotechar, or which contain new-line characters. It defaults to '"'.

Changed in version 3.11: An empty quotechar is not allowed.

+
+Dialect.quoting
+

Controls when quotes should be generated by the writer and recognised by the reader. It can take on any of the QUOTE_* constants and defaults to QUOTE_MINIMAL.

+
+Dialect.skipinitialspace
+

When True, spaces immediately following the delimiter are ignored. The default is False.

+
+Dialect.strict
+

When True, raise exception Error on bad CSV input. The default is False.

+

Reader Objects

Reader objects (DictReader instances and objects returned by the reader() function) have the following public methods:

+csvreader.__next__()
+

Return the next row of the reader’s iterable object as a list (if the object was returned from reader()) or a dict (if it is a DictReader instance), parsed according to the current Dialect. Usually you should call this as next(reader).

+

Reader objects have the following public attributes:

+csvreader.dialect
+

A read-only description of the dialect in use by the parser.

+
+csvreader.line_num
+

The number of lines read from the source iterator. This is not the same as the number of records returned, as records can span multiple lines.

+

DictReader objects have the following public attribute:

+DictReader.fieldnames
+

If not passed as a parameter when creating the object, this attribute is initialized upon first access or when the first record is read from the file.

+

Writer Objects

Writer objects (DictWriter instances and objects returned by the writer() function) have the following public methods. A row must be an iterable of strings or numbers for Writer objects and a dictionary mapping fieldnames to strings or numbers (by passing them through str() first) for DictWriter objects. Note that complex numbers are written out surrounded by parens. This may cause some problems for other programs which read CSV files (assuming they support complex numbers at all).

+csvwriter.writerow(row)
+

Write the row parameter to the writer’s file object, formatted according to the current Dialect. Return the return value of the call to the write method of the underlying file object.

Changed in version 3.5: Added support of arbitrary iterables.

+
+csvwriter.writerows(rows)
+

Write all elements in rows (an iterable of row objects as described above) to the writer’s file object, formatted according to the current dialect.

+

Writer objects have the following public attribute:

+csvwriter.dialect
+

A read-only description of the dialect in use by the writer.

+

DictWriter objects have the following public method:

+DictWriter.writeheader()
+

Write a row with the field names (as specified in the constructor) to the writer’s file object, formatted according to the current dialect. Return the return value of the csvwriter.writerow() call used internally.

New in version 3.2.

Changed in version 3.8: writeheader() now also returns the value returned by the csvwriter.writerow() method it uses internally.

+

Examples

The simplest example of reading a CSV file:

import csv
+with open('some.csv', newline='') as f:
+    reader = csv.reader(f)
+    for row in reader:
+        print(row)
+

Reading a file with an alternate format:

import csv
+with open('passwd', newline='') as f:
+    reader = csv.reader(f, delimiter=':', quoting=csv.QUOTE_NONE)
+    for row in reader:
+        print(row)
+

The corresponding simplest possible writing example is:

import csv
+with open('some.csv', 'w', newline='') as f:
+    writer = csv.writer(f)
+    writer.writerows(someiterable)
+

Since open() is used to open a CSV file for reading, the file will by default be decoded into unicode using the system default encoding (see locale.getencoding()). To decode a file using a different encoding, use the encoding argument of open:

import csv
+with open('some.csv', newline='', encoding='utf-8') as f:
+    reader = csv.reader(f)
+    for row in reader:
+        print(row)
+

The same applies to writing in something other than the system default encoding: specify the encoding argument when opening the output file.

Registering a new dialect:

import csv
+csv.register_dialect('unixpwd', delimiter=':', quoting=csv.QUOTE_NONE)
+with open('passwd', newline='') as f:
+    reader = csv.reader(f, 'unixpwd')
+

A slightly more advanced use of the reader — catching and reporting errors:

import csv, sys
+filename = 'some.csv'
+with open(filename, newline='') as f:
+    reader = csv.reader(f)
+    try:
+        for row in reader:
+            print(row)
+    except csv.Error as e:
+        sys.exit('file {}, line {}: {}'.format(filename, reader.line_num, e))
+

And while the module doesn’t directly support parsing strings, it can easily be done:

import csv
+for row in csv.reader(['one,two,three']):
+    print(row)
+

Footnotes

+1(1,2)
+

If newline='' is not specified, newlines embedded inside quoted fields will not be interpreted correctly, and on platforms that use \r\n linendings on write an extra \r will be added. It should always be safe to specify newline='', since the csv module does its own (universal) newline handling.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/csv.html +

+
diff --git a/devdocs/python~3.12/library%2Fctypes.html b/devdocs/python~3.12/library%2Fctypes.html new file mode 100644 index 00000000..67615f59 --- /dev/null +++ b/devdocs/python~3.12/library%2Fctypes.html @@ -0,0 +1,968 @@ +

ctypes — A foreign function library for Python

Source code: Lib/ctypes

ctypes is a foreign function library for Python. It provides C compatible data types, and allows calling functions in DLLs or shared libraries. It can be used to wrap these libraries in pure Python.

ctypes tutorial

Note: The code samples in this tutorial use doctest to make sure that they actually work. Since some code samples behave differently under Linux, Windows, or macOS, they contain doctest directives in comments.

Note: Some code samples reference the ctypes c_int type. On platforms where sizeof(long) == sizeof(int) it is an alias to c_long. So, you should not be confused if c_long is printed if you would expect c_int — they are actually the same type.

Accessing functions from loaded dlls

Functions are accessed as attributes of dll objects:

>>> from ctypes import *
+>>> libc.printf
+<_FuncPtr object at 0x...>
+>>> print(windll.kernel32.GetModuleHandleA)  
+<_FuncPtr object at 0x...>
+>>> print(windll.kernel32.MyOwnFunction)     
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "ctypes.py", line 239, in __getattr__
+    func = _StdcallFuncPtr(name, self)
+AttributeError: function 'MyOwnFunction' not found
+>>>
+

Note that win32 system dlls like kernel32 and user32 often export ANSI as well as UNICODE versions of a function. The UNICODE version is exported with an W appended to the name, while the ANSI version is exported with an A appended to the name. The win32 GetModuleHandle function, which returns a module handle for a given module name, has the following C prototype, and a macro is used to expose one of them as GetModuleHandle depending on whether UNICODE is defined or not:

/* ANSI version */
+HMODULE GetModuleHandleA(LPCSTR lpModuleName);
+/* UNICODE version */
+HMODULE GetModuleHandleW(LPCWSTR lpModuleName);
+

windll does not try to select one of them by magic, you must access the version you need by specifying GetModuleHandleA or GetModuleHandleW explicitly, and then call it with bytes or string objects respectively.

Sometimes, dlls export functions with names which aren’t valid Python identifiers, like "??2@YAPAXI@Z". In this case you have to use getattr() to retrieve the function:

>>> getattr(cdll.msvcrt, "??2@YAPAXI@Z")  
+<_FuncPtr object at 0x...>
+>>>
+

On Windows, some dlls export functions not by name but by ordinal. These functions can be accessed by indexing the dll object with the ordinal number:

>>> cdll.kernel32[1]  
+<_FuncPtr object at 0x...>
+>>> cdll.kernel32[0]  
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "ctypes.py", line 310, in __getitem__
+    func = _StdcallFuncPtr(name, self)
+AttributeError: function ordinal 0 not found
+>>>
+

Calling functions

You can call these functions like any other Python callable. This example uses the rand() function, which takes no arguments and returns a pseudo-random integer:

>>> print(libc.rand())  
+1804289383
+

On Windows, you can call the GetModuleHandleA() function, which returns a win32 module handle (passing None as single argument to call it with a NULL pointer):

>>> print(hex(windll.kernel32.GetModuleHandleA(None)))  
+0x1d000000
+>>>
+

ValueError is raised when you call an stdcall function with the cdecl calling convention, or vice versa:

>>> cdll.kernel32.GetModuleHandleA(None)  
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: Procedure probably called with not enough arguments (4 bytes missing)
+>>>
+
+>>> windll.msvcrt.printf(b"spam")  
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: Procedure probably called with too many arguments (4 bytes in excess)
+>>>
+

To find out the correct calling convention you have to look into the C header file or the documentation for the function you want to call.

On Windows, ctypes uses win32 structured exception handling to prevent crashes from general protection faults when functions are called with invalid argument values:

>>> windll.kernel32.GetModuleHandleA(32)  
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+OSError: exception: access violation reading 0x00000020
+>>>
+

There are, however, enough ways to crash Python with ctypes, so you should be careful anyway. The faulthandler module can be helpful in debugging crashes (e.g. from segmentation faults produced by erroneous C library calls).

None, integers, bytes objects and (unicode) strings are the only native Python objects that can directly be used as parameters in these function calls. None is passed as a C NULL pointer, bytes objects and strings are passed as pointer to the memory block that contains their data (char* or wchar_t*). Python integers are passed as the platforms default C int type, their value is masked to fit into the C type.

Before we move on calling functions with other parameter types, we have to learn more about ctypes data types.

Fundamental data types

ctypes defines a number of primitive C compatible data types:

+ + + + + + + + + + + + + + + + + + + + + + +

ctypes type

C type

Python type

c_bool

_Bool

bool (1)

c_char

char

1-character bytes object

c_wchar

wchar_t

1-character string

c_byte

char

int

c_ubyte

unsigned char

int

c_short

short

int

c_ushort

unsigned short

int

c_int

int

int

c_uint

unsigned int

int

c_long

long

int

c_ulong

unsigned long

int

c_longlong

__int64 or long long

int

c_ulonglong

unsigned __int64 or unsigned long long

int

c_size_t

size_t

int

c_ssize_t

ssize_t or Py_ssize_t

int

c_time_t

time_t

int

c_float

float

float

c_double

double

float

c_longdouble

long double

float

c_char_p

char* (NUL terminated)

bytes object or None

c_wchar_p

wchar_t* (NUL terminated)

string or None

c_void_p

void*

int or None
  1. The constructor accepts any object with a truth value.

All these types can be created by calling them with an optional initializer of the correct type and value:

>>> c_int()
+c_long(0)
+>>> c_wchar_p("Hello, World")
+c_wchar_p(140018365411392)
+>>> c_ushort(-3)
+c_ushort(65533)
+>>>
+

Since these types are mutable, their value can also be changed afterwards:

>>> i = c_int(42)
+>>> print(i)
+c_long(42)
+>>> print(i.value)
+42
+>>> i.value = -99
+>>> print(i.value)
+-99
+>>>
+

Assigning a new value to instances of the pointer types c_char_p, c_wchar_p, and c_void_p changes the memory location they point to, not the contents of the memory block (of course not, because Python bytes objects are immutable):

>>> s = "Hello, World"
+>>> c_s = c_wchar_p(s)
+>>> print(c_s)
+c_wchar_p(139966785747344)
+>>> print(c_s.value)
+Hello World
+>>> c_s.value = "Hi, there"
+>>> print(c_s)              # the memory location has changed
+c_wchar_p(139966783348904)
+>>> print(c_s.value)
+Hi, there
+>>> print(s)                # first object is unchanged
+Hello, World
+>>>
+

You should be careful, however, not to pass them to functions expecting pointers to mutable memory. If you need mutable memory blocks, ctypes has a create_string_buffer() function which creates these in various ways. The current memory block contents can be accessed (or changed) with the raw property; if you want to access it as NUL terminated string, use the value property:

>>> from ctypes import *
+>>> p = create_string_buffer(3)            # create a 3 byte buffer, initialized to NUL bytes
+>>> print(sizeof(p), repr(p.raw))
+3 b'\x00\x00\x00'
+>>> p = create_string_buffer(b"Hello")     # create a buffer containing a NUL terminated string
+>>> print(sizeof(p), repr(p.raw))
+6 b'Hello\x00'
+>>> print(repr(p.value))
+b'Hello'
+>>> p = create_string_buffer(b"Hello", 10) # create a 10 byte buffer
+>>> print(sizeof(p), repr(p.raw))
+10 b'Hello\x00\x00\x00\x00\x00'
+>>> p.value = b"Hi"
+>>> print(sizeof(p), repr(p.raw))
+10 b'Hi\x00lo\x00\x00\x00\x00\x00'
+>>>
+

The create_string_buffer() function replaces the old c_buffer() function (which is still available as an alias). To create a mutable memory block containing unicode characters of the C type wchar_t, use the create_unicode_buffer() function.

Calling functions, continued

Note that printf prints to the real standard output channel, not to sys.stdout, so these examples will only work at the console prompt, not from within IDLE or PythonWin:

>>> printf = libc.printf
+>>> printf(b"Hello, %s\n", b"World!")
+Hello, World!
+14
+>>> printf(b"Hello, %S\n", "World!")
+Hello, World!
+14
+>>> printf(b"%d bottles of beer\n", 42)
+42 bottles of beer
+19
+>>> printf(b"%f bottles of beer\n", 42.5)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ArgumentError: argument 2: TypeError: Don't know how to convert parameter 2
+>>>
+

As has been mentioned before, all Python types except integers, strings, and bytes objects have to be wrapped in their corresponding ctypes type, so that they can be converted to the required C data type:

>>> printf(b"An int %d, a double %f\n", 1234, c_double(3.14))
+An int 1234, a double 3.140000
+31
+>>>
+

Calling variadic functions

On a lot of platforms calling variadic functions through ctypes is exactly the same as calling functions with a fixed number of parameters. On some platforms, and in particular ARM64 for Apple Platforms, the calling convention for variadic functions is different than that for regular functions.

On those platforms it is required to specify the argtypes attribute for the regular, non-variadic, function arguments:

libc.printf.argtypes = [ctypes.c_char_p]
+

Because specifying the attribute does not inhibit portability it is advised to always specify argtypes for all variadic functions.

Calling functions with your own custom data types

You can also customize ctypes argument conversion to allow instances of your own classes be used as function arguments. ctypes looks for an _as_parameter_ attribute and uses this as the function argument. The attribute must be an integer, string, bytes, a ctypes instance, or an object with an _as_parameter_ attribute:

>>> class Bottles:
+...     def __init__(self, number):
+...         self._as_parameter_ = number
+...
+>>> bottles = Bottles(42)
+>>> printf(b"%d bottles of beer\n", bottles)
+42 bottles of beer
+19
+>>>
+

If you don’t want to store the instance’s data in the _as_parameter_ instance variable, you could define a property which makes the attribute available on request.

Specifying the required argument types (function prototypes)

It is possible to specify the required argument types of functions exported from DLLs by setting the argtypes attribute.

argtypes must be a sequence of C data types (the printf() function is probably not a good example here, because it takes a variable number and different types of parameters depending on the format string, on the other hand this is quite handy to experiment with this feature):

>>> printf.argtypes = [c_char_p, c_char_p, c_int, c_double]
+>>> printf(b"String '%s', Int %d, Double %f\n", b"Hi", 10, 2.2)
+String 'Hi', Int 10, Double 2.200000
+37
+>>>
+

Specifying a format protects against incompatible argument types (just as a prototype for a C function), and tries to convert the arguments to valid types:

>>> printf(b"%d %d %d", 1, 2, 3)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ArgumentError: argument 2: TypeError: wrong type
+>>> printf(b"%s %d %f\n", b"X", 2, 3)
+X 2 3.000000
+13
+>>>
+

If you have defined your own classes which you pass to function calls, you have to implement a from_param() class method for them to be able to use them in the argtypes sequence. The from_param() class method receives the Python object passed to the function call, it should do a typecheck or whatever is needed to make sure this object is acceptable, and then return the object itself, its _as_parameter_ attribute, or whatever you want to pass as the C function argument in this case. Again, the result should be an integer, string, bytes, a ctypes instance, or an object with an _as_parameter_ attribute.

Return types

By default functions are assumed to return the C int type. Other return types can be specified by setting the restype attribute of the function object.

The C prototype of time() is time_t time(time_t *). Because time_t might be of a different type than the default return type int, you should specify the restype attribute:

>>> libc.time.restype = c_time_t
+

The argument types can be specified using argtypes:

>>> libc.time.argtypes = (POINTER(c_time_t),)
+

To call the function with a NULL pointer as first argument, use None:

>>> print(libc.time(None))  
+1150640792
+

Here is a more advanced example, it uses the strchr() function, which expects a string pointer and a char, and returns a pointer to a string:

>>> strchr = libc.strchr
+>>> strchr(b"abcdef", ord("d"))  
+8059983
+>>> strchr.restype = c_char_p    # c_char_p is a pointer to a string
+>>> strchr(b"abcdef", ord("d"))
+b'def'
+>>> print(strchr(b"abcdef", ord("x")))
+None
+>>>
+

If you want to avoid the ord("x") calls above, you can set the argtypes attribute, and the second argument will be converted from a single character Python bytes object into a C char:

>>> strchr.restype = c_char_p
+>>> strchr.argtypes = [c_char_p, c_char]
+>>> strchr(b"abcdef", b"d")
+b'def'
+>>> strchr(b"abcdef", b"def")
+Traceback (most recent call last):
+ctypes.ArgumentError: argument 2: TypeError: one character bytes, bytearray or integer expected
+>>> print(strchr(b"abcdef", b"x"))
+None
+>>> strchr(b"abcdef", b"d")
+b'def'
+>>>
+

You can also use a callable Python object (a function or a class for example) as the restype attribute, if the foreign function returns an integer. The callable will be called with the integer the C function returns, and the result of this call will be used as the result of your function call. This is useful to check for error return values and automatically raise an exception:

>>> GetModuleHandle = windll.kernel32.GetModuleHandleA  
+>>> def ValidHandle(value):
+...     if value == 0:
+...         raise WinError()
+...     return value
+...
+>>>
+>>> GetModuleHandle.restype = ValidHandle  
+>>> GetModuleHandle(None)  
+486539264
+>>> GetModuleHandle("something silly")  
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "<stdin>", line 3, in ValidHandle
+OSError: [Errno 126] The specified module could not be found.
+>>>
+

WinError is a function which will call Windows FormatMessage() api to get the string representation of an error code, and returns an exception. WinError takes an optional error code parameter, if no one is used, it calls GetLastError() to retrieve it.

Please note that a much more powerful error checking mechanism is available through the errcheck attribute; see the reference manual for details.

Passing pointers (or: passing parameters by reference)

Sometimes a C api function expects a pointer to a data type as parameter, probably to write into the corresponding location, or if the data is too large to be passed by value. This is also known as passing parameters by reference.

ctypes exports the byref() function which is used to pass parameters by reference. The same effect can be achieved with the pointer() function, although pointer() does a lot more work since it constructs a real pointer object, so it is faster to use byref() if you don’t need the pointer object in Python itself:

>>> i = c_int()
+>>> f = c_float()
+>>> s = create_string_buffer(b'\000' * 32)
+>>> print(i.value, f.value, repr(s.value))
+0 0.0 b''
+>>> libc.sscanf(b"1 3.14 Hello", b"%d %f %s",
+...             byref(i), byref(f), s)
+3
+>>> print(i.value, f.value, repr(s.value))
+1 3.1400001049 b'Hello'
+>>>
+

Structures and unions

Structures and unions must derive from the Structure and Union base classes which are defined in the ctypes module. Each subclass must define a _fields_ attribute. _fields_ must be a list of 2-tuples, containing a field name and a field type.

The field type must be a ctypes type like c_int, or any other derived ctypes type: structure, union, array, pointer.

Here is a simple example of a POINT structure, which contains two integers named x and y, and also shows how to initialize a structure in the constructor:

>>> from ctypes import *
+>>> class POINT(Structure):
+...     _fields_ = [("x", c_int),
+...                 ("y", c_int)]
+...
+>>> point = POINT(10, 20)
+>>> print(point.x, point.y)
+10 20
+>>> point = POINT(y=5)
+>>> print(point.x, point.y)
+0 5
+>>> POINT(1, 2, 3)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: too many initializers
+>>>
+

You can, however, build much more complicated structures. A structure can itself contain other structures by using a structure as a field type.

Here is a RECT structure which contains two POINTs named upperleft and lowerright:

>>> class RECT(Structure):
+...     _fields_ = [("upperleft", POINT),
+...                 ("lowerright", POINT)]
+...
+>>> rc = RECT(point)
+>>> print(rc.upperleft.x, rc.upperleft.y)
+0 5
+>>> print(rc.lowerright.x, rc.lowerright.y)
+0 0
+>>>
+

Nested structures can also be initialized in the constructor in several ways:

>>> r = RECT(POINT(1, 2), POINT(3, 4))
+>>> r = RECT((1, 2), (3, 4))
+

Field descriptors can be retrieved from the class, they are useful for debugging because they can provide useful information:

>>> print(POINT.x)
+<Field type=c_long, ofs=0, size=4>
+>>> print(POINT.y)
+<Field type=c_long, ofs=4, size=4>
+>>>
+

Warning

ctypes does not support passing unions or structures with bit-fields to functions by value. While this may work on 32-bit x86, it’s not guaranteed by the library to work in the general case. Unions and structures with bit-fields should always be passed to functions by pointer.

Structure/union alignment and byte order

By default, Structure and Union fields are aligned in the same way the C compiler does it. It is possible to override this behavior by specifying a _pack_ class attribute in the subclass definition. This must be set to a positive integer and specifies the maximum alignment for the fields. This is what #pragma pack(n) also does in MSVC.

ctypes uses the native byte order for Structures and Unions. To build structures with non-native byte order, you can use one of the BigEndianStructure, LittleEndianStructure, BigEndianUnion, and LittleEndianUnion base classes. These classes cannot contain pointer fields.

Bit fields in structures and unions

It is possible to create structures and unions containing bit fields. Bit fields are only possible for integer fields, the bit width is specified as the third item in the _fields_ tuples:

>>> class Int(Structure):
+...     _fields_ = [("first_16", c_int, 16),
+...                 ("second_16", c_int, 16)]
+...
+>>> print(Int.first_16)
+<Field type=c_long, ofs=0:0, bits=16>
+>>> print(Int.second_16)
+<Field type=c_long, ofs=0:16, bits=16>
+>>>
+

Arrays

Arrays are sequences, containing a fixed number of instances of the same type.

The recommended way to create array types is by multiplying a data type with a positive integer:

TenPointsArrayType = POINT * 10
+

Here is an example of a somewhat artificial data type, a structure containing 4 POINTs among other stuff:

>>> from ctypes import *
+>>> class POINT(Structure):
+...     _fields_ = ("x", c_int), ("y", c_int)
+...
+>>> class MyStruct(Structure):
+...     _fields_ = [("a", c_int),
+...                 ("b", c_float),
+...                 ("point_array", POINT * 4)]
+>>>
+>>> print(len(MyStruct().point_array))
+4
+>>>
+

Instances are created in the usual way, by calling the class:

arr = TenPointsArrayType()
+for pt in arr:
+    print(pt.x, pt.y)
+

The above code print a series of 0 0 lines, because the array contents is initialized to zeros.

Initializers of the correct type can also be specified:

>>> from ctypes import *
+>>> TenIntegers = c_int * 10
+>>> ii = TenIntegers(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
+>>> print(ii)
+<c_long_Array_10 object at 0x...>
+>>> for i in ii: print(i, end=" ")
+...
+1 2 3 4 5 6 7 8 9 10
+>>>
+

Pointers

Pointer instances are created by calling the pointer() function on a ctypes type:

>>> from ctypes import *
+>>> i = c_int(42)
+>>> pi = pointer(i)
+>>>
+

Pointer instances have a contents attribute which returns the object to which the pointer points, the i object above:

>>> pi.contents
+c_long(42)
+>>>
+

Note that ctypes does not have OOR (original object return), it constructs a new, equivalent object each time you retrieve an attribute:

>>> pi.contents is i
+False
+>>> pi.contents is pi.contents
+False
+>>>
+

Assigning another c_int instance to the pointer’s contents attribute would cause the pointer to point to the memory location where this is stored:

>>> i = c_int(99)
+>>> pi.contents = i
+>>> pi.contents
+c_long(99)
+>>>
+

Pointer instances can also be indexed with integers:

>>> pi[0]
+99
+>>>
+

Assigning to an integer index changes the pointed to value:

>>> print(i)
+c_long(99)
+>>> pi[0] = 22
+>>> print(i)
+c_long(22)
+>>>
+

It is also possible to use indexes different from 0, but you must know what you’re doing, just as in C: You can access or change arbitrary memory locations. Generally you only use this feature if you receive a pointer from a C function, and you know that the pointer actually points to an array instead of a single item.

Behind the scenes, the pointer() function does more than simply create pointer instances, it has to create pointer types first. This is done with the POINTER() function, which accepts any ctypes type, and returns a new type:

>>> PI = POINTER(c_int)
+>>> PI
+<class 'ctypes.LP_c_long'>
+>>> PI(42)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: expected c_long instead of int
+>>> PI(c_int(42))
+<ctypes.LP_c_long object at 0x...>
+>>>
+

Calling the pointer type without an argument creates a NULL pointer. NULL pointers have a False boolean value:

>>> null_ptr = POINTER(c_int)()
+>>> print(bool(null_ptr))
+False
+>>>
+

ctypes checks for NULL when dereferencing pointers (but dereferencing invalid non-NULL pointers would crash Python):

>>> null_ptr[0]
+Traceback (most recent call last):
+    ....
+ValueError: NULL pointer access
+>>>
+
+>>> null_ptr[0] = 1234
+Traceback (most recent call last):
+    ....
+ValueError: NULL pointer access
+>>>
+

Type conversions

Usually, ctypes does strict type checking. This means, if you have POINTER(c_int) in the argtypes list of a function or as the type of a member field in a structure definition, only instances of exactly the same type are accepted. There are some exceptions to this rule, where ctypes accepts other objects. For example, you can pass compatible array instances instead of pointer types. So, for POINTER(c_int), ctypes accepts an array of c_int:

>>> class Bar(Structure):
+...     _fields_ = [("count", c_int), ("values", POINTER(c_int))]
+...
+>>> bar = Bar()
+>>> bar.values = (c_int * 3)(1, 2, 3)
+>>> bar.count = 3
+>>> for i in range(bar.count):
+...     print(bar.values[i])
+...
+1
+2
+3
+>>>
+

In addition, if a function argument is explicitly declared to be a pointer type (such as POINTER(c_int)) in argtypes, an object of the pointed type (c_int in this case) can be passed to the function. ctypes will apply the required byref() conversion in this case automatically.

To set a POINTER type field to NULL, you can assign None:

>>> bar.values = None
+>>>
+

Sometimes you have instances of incompatible types. In C, you can cast one type into another type. ctypes provides a cast() function which can be used in the same way. The Bar structure defined above accepts POINTER(c_int) pointers or c_int arrays for its values field, but not instances of other types:

>>> bar.values = (c_byte * 4)()
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: incompatible types, c_byte_Array_4 instance instead of LP_c_long instance
+>>>
+

For these cases, the cast() function is handy.

The cast() function can be used to cast a ctypes instance into a pointer to a different ctypes data type. cast() takes two parameters, a ctypes object that is or can be converted to a pointer of some kind, and a ctypes pointer type. It returns an instance of the second argument, which references the same memory block as the first argument:

>>> a = (c_byte * 4)()
+>>> cast(a, POINTER(c_int))
+<ctypes.LP_c_long object at ...>
+>>>
+

So, cast() can be used to assign to the values field of Bar the structure:

>>> bar = Bar()
+>>> bar.values = cast((c_byte * 4)(), POINTER(c_int))
+>>> print(bar.values[0])
+0
+>>>
+

Incomplete Types

Incomplete Types are structures, unions or arrays whose members are not yet specified. In C, they are specified by forward declarations, which are defined later:

struct cell; /* forward declaration */
+
+struct cell {
+    char *name;
+    struct cell *next;
+};
+

The straightforward translation into ctypes code would be this, but it does not work:

>>> class cell(Structure):
+...     _fields_ = [("name", c_char_p),
+...                 ("next", POINTER(cell))]
+...
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "<stdin>", line 2, in cell
+NameError: name 'cell' is not defined
+>>>
+

because the new class cell is not available in the class statement itself. In ctypes, we can define the cell class and set the _fields_ attribute later, after the class statement:

>>> from ctypes import *
+>>> class cell(Structure):
+...     pass
+...
+>>> cell._fields_ = [("name", c_char_p),
+...                  ("next", POINTER(cell))]
+>>>
+

Let’s try it. We create two instances of cell, and let them point to each other, and finally follow the pointer chain a few times:

>>> c1 = cell()
+>>> c1.name = b"foo"
+>>> c2 = cell()
+>>> c2.name = b"bar"
+>>> c1.next = pointer(c2)
+>>> c2.next = pointer(c1)
+>>> p = c1
+>>> for i in range(8):
+...     print(p.name, end=" ")
+...     p = p.next[0]
+...
+foo bar foo bar foo bar foo bar
+>>>
+

Callback functions

ctypes allows creating C callable function pointers from Python callables. These are sometimes called callback functions.

First, you must create a class for the callback function. The class knows the calling convention, the return type, and the number and types of arguments this function will receive.

The CFUNCTYPE() factory function creates types for callback functions using the cdecl calling convention. On Windows, the WINFUNCTYPE() factory function creates types for callback functions using the stdcall calling convention.

Both of these factory functions are called with the result type as first argument, and the callback functions expected argument types as the remaining arguments.

I will present an example here which uses the standard C library’s qsort() function, that is used to sort items with the help of a callback function. qsort() will be used to sort an array of integers:

>>> IntArray5 = c_int * 5
+>>> ia = IntArray5(5, 1, 7, 33, 99)
+>>> qsort = libc.qsort
+>>> qsort.restype = None
+>>>
+

qsort() must be called with a pointer to the data to sort, the number of items in the data array, the size of one item, and a pointer to the comparison function, the callback. The callback will then be called with two pointers to items, and it must return a negative integer if the first item is smaller than the second, a zero if they are equal, and a positive integer otherwise.

So our callback function receives pointers to integers, and must return an integer. First we create the type for the callback function:

>>> CMPFUNC = CFUNCTYPE(c_int, POINTER(c_int), POINTER(c_int))
+>>>
+

To get started, here is a simple callback that shows the values it gets passed:

>>> def py_cmp_func(a, b):
+...     print("py_cmp_func", a[0], b[0])
+...     return 0
+...
+>>> cmp_func = CMPFUNC(py_cmp_func)
+>>>
+

The result:

>>> qsort(ia, len(ia), sizeof(c_int), cmp_func)  
+py_cmp_func 5 1
+py_cmp_func 33 99
+py_cmp_func 7 33
+py_cmp_func 5 7
+py_cmp_func 1 7
+>>>
+

Now we can actually compare the two items and return a useful result:

>>> def py_cmp_func(a, b):
+...     print("py_cmp_func", a[0], b[0])
+...     return a[0] - b[0]
+...
+>>>
+>>> qsort(ia, len(ia), sizeof(c_int), CMPFUNC(py_cmp_func)) 
+py_cmp_func 5 1
+py_cmp_func 33 99
+py_cmp_func 7 33
+py_cmp_func 1 7
+py_cmp_func 5 7
+>>>
+

As we can easily check, our array is sorted now:

>>> for i in ia: print(i, end=" ")
+...
+1 5 7 33 99
+>>>
+

The function factories can be used as decorator factories, so we may as well write:

>>> @CFUNCTYPE(c_int, POINTER(c_int), POINTER(c_int))
+... def py_cmp_func(a, b):
+...     print("py_cmp_func", a[0], b[0])
+...     return a[0] - b[0]
+...
+>>> qsort(ia, len(ia), sizeof(c_int), py_cmp_func)
+py_cmp_func 5 1
+py_cmp_func 33 99
+py_cmp_func 7 33
+py_cmp_func 1 7
+py_cmp_func 5 7
+>>>
+

Note

Make sure you keep references to CFUNCTYPE() objects as long as they are used from C code. ctypes doesn’t, and if you don’t, they may be garbage collected, crashing your program when a callback is made.

Also, note that if the callback function is called in a thread created outside of Python’s control (e.g. by the foreign code that calls the callback), ctypes creates a new dummy Python thread on every invocation. This behavior is correct for most purposes, but it means that values stored with threading.local will not survive across different callbacks, even when those calls are made from the same C thread.

Accessing values exported from dlls

Some shared libraries not only export functions, they also export variables. An example in the Python library itself is the Py_Version, Python runtime version number encoded in a single constant integer.

ctypes can access values like this with the in_dll() class methods of the type. pythonapi is a predefined symbol giving access to the Python C api:

>>> version = ctypes.c_int.in_dll(ctypes.pythonapi, "Py_Version")
+>>> print(hex(version.value))
+0x30c00a0
+

If the interpreter would have been started with -O, the sample would have printed c_long(1), or c_long(2) if -OO would have been specified.

An extended example which also demonstrates the use of pointers accesses the PyImport_FrozenModules pointer exported by Python.

Quoting the docs for that value:

This pointer is initialized to point to an array of _frozen records, terminated by one whose members are all NULL or zero. When a frozen module is imported, it is searched in this table. Third-party code could play tricks with this to provide a dynamically created collection of frozen modules.

So manipulating this pointer could even prove useful. To restrict the example size, we show only how this table can be read with ctypes:

>>> from ctypes import *
+>>>
+>>> class struct_frozen(Structure):
+...     _fields_ = [("name", c_char_p),
+...                 ("code", POINTER(c_ubyte)),
+...                 ("size", c_int),
+...                 ("get_code", POINTER(c_ubyte)),  # Function pointer
+...                ]
+...
+>>>
+

We have defined the _frozen data type, so we can get the pointer to the table:

>>> FrozenTable = POINTER(struct_frozen)
+>>> table = FrozenTable.in_dll(pythonapi, "_PyImport_FrozenBootstrap")
+>>>
+

Since table is a pointer to the array of struct_frozen records, we can iterate over it, but we just have to make sure that our loop terminates, because pointers have no size. Sooner or later it would probably crash with an access violation or whatever, so it’s better to break out of the loop when we hit the NULL entry:

>>> for item in table:
+...     if item.name is None:
+...         break
+...     print(item.name.decode("ascii"), item.size)
+...
+_frozen_importlib 31764
+_frozen_importlib_external 41499
+zipimport 12345
+>>>
+

The fact that standard Python has a frozen module and a frozen package (indicated by the negative size member) is not well known, it is only used for testing. Try it out with import __hello__ for example.

Surprises

There are some edges in ctypes where you might expect something other than what actually happens.

Consider the following example:

>>> from ctypes import *
+>>> class POINT(Structure):
+...     _fields_ = ("x", c_int), ("y", c_int)
+...
+>>> class RECT(Structure):
+...     _fields_ = ("a", POINT), ("b", POINT)
+...
+>>> p1 = POINT(1, 2)
+>>> p2 = POINT(3, 4)
+>>> rc = RECT(p1, p2)
+>>> print(rc.a.x, rc.a.y, rc.b.x, rc.b.y)
+1 2 3 4
+>>> # now swap the two points
+>>> rc.a, rc.b = rc.b, rc.a
+>>> print(rc.a.x, rc.a.y, rc.b.x, rc.b.y)
+3 4 3 4
+>>>
+

Hm. We certainly expected the last statement to print 3 4 1 2. What happened? Here are the steps of the rc.a, rc.b = rc.b, rc.a line above:

>>> temp0, temp1 = rc.b, rc.a
+>>> rc.a = temp0
+>>> rc.b = temp1
+>>>
+

Note that temp0 and temp1 are objects still using the internal buffer of the rc object above. So executing rc.a = temp0 copies the buffer contents of temp0 into rc ‘s buffer. This, in turn, changes the contents of temp1. So, the last assignment rc.b = temp1, doesn’t have the expected effect.

Keep in mind that retrieving sub-objects from Structure, Unions, and Arrays doesn’t copy the sub-object, instead it retrieves a wrapper object accessing the root-object’s underlying buffer.

Another example that may behave differently from what one would expect is this:

>>> s = c_char_p()
+>>> s.value = b"abc def ghi"
+>>> s.value
+b'abc def ghi'
+>>> s.value is s.value
+False
+>>>
+

Note

Objects instantiated from c_char_p can only have their value set to bytes or integers.

Why is it printing False? ctypes instances are objects containing a memory block plus some descriptors accessing the contents of the memory. Storing a Python object in the memory block does not store the object itself, instead the contents of the object is stored. Accessing the contents again constructs a new Python object each time!

Variable-sized data types

ctypes provides some support for variable-sized arrays and structures.

The resize() function can be used to resize the memory buffer of an existing ctypes object. The function takes the object as first argument, and the requested size in bytes as the second argument. The memory block cannot be made smaller than the natural memory block specified by the objects type, a ValueError is raised if this is tried:

>>> short_array = (c_short * 4)()
+>>> print(sizeof(short_array))
+8
+>>> resize(short_array, 4)
+Traceback (most recent call last):
+    ...
+ValueError: minimum size is 8
+>>> resize(short_array, 32)
+>>> sizeof(short_array)
+32
+>>> sizeof(type(short_array))
+8
+>>>
+

This is nice and fine, but how would one access the additional elements contained in this array? Since the type still only knows about 4 elements, we get errors accessing other elements:

>>> short_array[:]
+[0, 0, 0, 0]
+>>> short_array[7]
+Traceback (most recent call last):
+    ...
+IndexError: invalid index
+>>>
+

Another way to use variable-sized data types with ctypes is to use the dynamic nature of Python, and (re-)define the data type after the required size is already known, on a case by case basis.

ctypes reference

Finding shared libraries

When programming in a compiled language, shared libraries are accessed when compiling/linking a program, and when the program is run.

The purpose of the find_library() function is to locate a library in a way similar to what the compiler or runtime loader does (on platforms with several versions of a shared library the most recent should be loaded), while the ctypes library loaders act like when a program is run, and call the runtime loader directly.

The ctypes.util module provides a function which can help to determine the library to load.

ctypes.util.find_library(name) +
+

Try to find a library and return a pathname. name is the library name without any prefix like lib, suffix like .so, .dylib or version number (this is the form used for the posix linker option -l). If no library can be found, returns None.

+

The exact functionality is system dependent.

On Linux, find_library() tries to run external programs (/sbin/ldconfig, gcc, objdump and ld) to find the library file. It returns the filename of the library file.

Changed in version 3.6: On Linux, the value of the environment variable LD_LIBRARY_PATH is used when searching for libraries, if a library cannot be found by any other means.

Here are some examples:

>>> from ctypes.util import find_library
+>>> find_library("m")
+'libm.so.6'
+>>> find_library("c")
+'libc.so.6'
+>>> find_library("bz2")
+'libbz2.so.1.0'
+>>>
+

On macOS, find_library() tries several predefined naming schemes and paths to locate the library, and returns a full pathname if successful:

>>> from ctypes.util import find_library
+>>> find_library("c")
+'/usr/lib/libc.dylib'
+>>> find_library("m")
+'/usr/lib/libm.dylib'
+>>> find_library("bz2")
+'/usr/lib/libbz2.dylib'
+>>> find_library("AGL")
+'/System/Library/Frameworks/AGL.framework/AGL'
+>>>
+

On Windows, find_library() searches along the system search path, and returns the full pathname, but since there is no predefined naming scheme a call like find_library("c") will fail and return None.

If wrapping a shared library with ctypes, it may be better to determine the shared library name at development time, and hardcode that into the wrapper module instead of using find_library() to locate the library at runtime.

Loading shared libraries

There are several ways to load shared libraries into the Python process. One way is to instantiate one of the following classes:

+class ctypes.CDLL(name, mode=DEFAULT_MODE, handle=None, use_errno=False, use_last_error=False, winmode=None)
+

Instances of this class represent loaded shared libraries. Functions in these libraries use the standard C calling convention, and are assumed to return int.

On Windows creating a CDLL instance may fail even if the DLL name exists. When a dependent DLL of the loaded DLL is not found, a OSError error is raised with the message “[WinError 126] The specified module could not be found”. This error message does not contain the name of the missing DLL because the Windows API does not return this information making this error hard to diagnose. To resolve this error and determine which DLL is not found, you need to find the list of dependent DLLs and determine which one is not found using Windows debugging and tracing tools.

Changed in version 3.12: The name parameter can now be a path-like object.

+

See also

Microsoft DUMPBIN tool – A tool to find DLL dependents.

+class ctypes.OleDLL(name, mode=DEFAULT_MODE, handle=None, use_errno=False, use_last_error=False, winmode=None)
+

Windows only: Instances of this class represent loaded shared libraries, functions in these libraries use the stdcall calling convention, and are assumed to return the windows specific HRESULT code. HRESULT values contain information specifying whether the function call failed or succeeded, together with additional error code. If the return value signals a failure, an OSError is automatically raised.

Changed in version 3.3: WindowsError used to be raised, which is now an alias of OSError.

Changed in version 3.12: The name parameter can now be a path-like object.

+
+class ctypes.WinDLL(name, mode=DEFAULT_MODE, handle=None, use_errno=False, use_last_error=False, winmode=None)
+

Windows only: Instances of this class represent loaded shared libraries, functions in these libraries use the stdcall calling convention, and are assumed to return int by default.

Changed in version 3.12: The name parameter can now be a path-like object.

+

The Python global interpreter lock is released before calling any function exported by these libraries, and reacquired afterwards.

+class ctypes.PyDLL(name, mode=DEFAULT_MODE, handle=None)
+

Instances of this class behave like CDLL instances, except that the Python GIL is not released during the function call, and after the function execution the Python error flag is checked. If the error flag is set, a Python exception is raised.

Thus, this is only useful to call Python C api functions directly.

Changed in version 3.12: The name parameter can now be a path-like object.

+

All these classes can be instantiated by calling them with at least one argument, the pathname of the shared library. If you have an existing handle to an already loaded shared library, it can be passed as the handle named parameter, otherwise the underlying platforms dlopen() or LoadLibrary() function is used to load the library into the process, and to get a handle to it.

The mode parameter can be used to specify how the library is loaded. For details, consult the dlopen(3) manpage. On Windows, mode is ignored. On posix systems, RTLD_NOW is always added, and is not configurable.

The use_errno parameter, when set to true, enables a ctypes mechanism that allows accessing the system errno error number in a safe way. ctypes maintains a thread-local copy of the systems errno variable; if you call foreign functions created with use_errno=True then the errno value before the function call is swapped with the ctypes private copy, the same happens immediately after the function call.

The function ctypes.get_errno() returns the value of the ctypes private copy, and the function ctypes.set_errno() changes the ctypes private copy to a new value and returns the former value.

The use_last_error parameter, when set to true, enables the same mechanism for the Windows error code which is managed by the GetLastError() and SetLastError() Windows API functions; ctypes.get_last_error() and ctypes.set_last_error() are used to request and change the ctypes private copy of the windows error code.

The winmode parameter is used on Windows to specify how the library is loaded (since mode is ignored). It takes any value that is valid for the Win32 API LoadLibraryEx flags parameter. When omitted, the default is to use the flags that result in the most secure DLL load, which avoids issues such as DLL hijacking. Passing the full path to the DLL is the safest way to ensure the correct library and dependencies are loaded.

Changed in version 3.8: Added winmode parameter.

ctypes.RTLD_GLOBAL +
+

Flag to use as mode parameter. On platforms where this flag is not available, it is defined as the integer zero.

+
ctypes.RTLD_LOCAL +
+

Flag to use as mode parameter. On platforms where this is not available, it is the same as RTLD_GLOBAL.

+
ctypes.DEFAULT_MODE +
+

The default mode which is used to load shared libraries. On OSX 10.3, this is RTLD_GLOBAL, otherwise it is the same as RTLD_LOCAL.

+

Instances of these classes have no public methods. Functions exported by the shared library can be accessed as attributes or by index. Please note that accessing the function through an attribute caches the result and therefore accessing it repeatedly returns the same object each time. On the other hand, accessing it through an index returns a new object each time:

>>> from ctypes import CDLL
+>>> libc = CDLL("libc.so.6")  # On Linux
+>>> libc.time == libc.time
+True
+>>> libc['time'] == libc['time']
+False
+

The following public attributes are available, their name starts with an underscore to not clash with exported function names:

+PyDLL._handle
+

The system handle used to access the library.

+
+PyDLL._name
+

The name of the library passed in the constructor.

+

Shared libraries can also be loaded by using one of the prefabricated objects, which are instances of the LibraryLoader class, either by calling the LoadLibrary() method, or by retrieving the library as attribute of the loader instance.

+class ctypes.LibraryLoader(dlltype)
+

Class which loads shared libraries. dlltype should be one of the CDLL, PyDLL, WinDLL, or OleDLL types.

__getattr__() has special behavior: It allows loading a shared library by accessing it as attribute of a library loader instance. The result is cached, so repeated attribute accesses return the same library each time.

+LoadLibrary(name)
+

Load a shared library into the process and return it. This method always returns a new instance of the library.

+
+

These prefabricated library loaders are available:

ctypes.cdll +
+

Creates CDLL instances.

+
ctypes.windll +
+

Windows only: Creates WinDLL instances.

+
ctypes.oledll +
+

Windows only: Creates OleDLL instances.

+
ctypes.pydll +
+

Creates PyDLL instances.

+

For accessing the C Python api directly, a ready-to-use Python shared library object is available:

ctypes.pythonapi +
+

An instance of PyDLL that exposes Python C API functions as attributes. Note that all these functions are assumed to return C int, which is of course not always the truth, so you have to assign the correct restype attribute to use these functions.

+

+

Loading a library through any of these objects raises an auditing event ctypes.dlopen with string argument name, the name used to load the library.

+

Accessing a function on a loaded library raises an auditing event ctypes.dlsym with arguments library (the library object) and name (the symbol’s name as a string or integer).

+

In cases when only the library handle is available rather than the object, accessing a function raises an auditing event ctypes.dlsym/handle with arguments handle (the raw library handle) and name.

Foreign functions

As explained in the previous section, foreign functions can be accessed as attributes of loaded shared libraries. The function objects created in this way by default accept any number of arguments, accept any ctypes data instances as arguments, and return the default result type specified by the library loader. They are instances of a private class:

+class ctypes._FuncPtr
+

Base class for C callable foreign functions.

Instances of foreign functions are also C compatible data types; they represent C function pointers.

This behavior can be customized by assigning to special attributes of the foreign function object.

+restype
+

Assign a ctypes type to specify the result type of the foreign function. Use None for void, a function not returning anything.

It is possible to assign a callable Python object that is not a ctypes type, in this case the function is assumed to return a C int, and the callable will be called with this integer, allowing further processing or error checking. Using this is deprecated, for more flexible post processing or error checking use a ctypes data type as restype and assign a callable to the errcheck attribute.

+
+argtypes
+

Assign a tuple of ctypes types to specify the argument types that the function accepts. Functions using the stdcall calling convention can only be called with the same number of arguments as the length of this tuple; functions using the C calling convention accept additional, unspecified arguments as well.

When a foreign function is called, each actual argument is passed to the from_param() class method of the items in the argtypes tuple, this method allows adapting the actual argument to an object that the foreign function accepts. For example, a c_char_p item in the argtypes tuple will convert a string passed as argument into a bytes object using ctypes conversion rules.

New: It is now possible to put items in argtypes which are not ctypes types, but each item must have a from_param() method which returns a value usable as argument (integer, string, ctypes instance). This allows defining adapters that can adapt custom objects as function parameters.

+
+errcheck
+

Assign a Python function or another callable to this attribute. The callable will be called with three or more arguments:

callable(result, func, arguments) +
+

result is what the foreign function returns, as specified by the restype attribute.

func is the foreign function object itself, this allows reusing the same callable object to check or post process the results of several functions.

arguments is a tuple containing the parameters originally passed to the function call, this allows specializing the behavior on the arguments used.

+

The object that this function returns will be returned from the foreign function call, but it can also check the result value and raise an exception if the foreign function call failed.

+
+
+exception ctypes.ArgumentError
+

This exception is raised when a foreign function call cannot convert one of the passed arguments.

+

+

On Windows, when a foreign function call raises a system exception (for example, due to an access violation), it will be captured and replaced with a suitable Python exception. Further, an auditing event ctypes.set_exception with argument code will be raised, allowing an audit hook to replace the exception with its own.

+

Some ways to invoke foreign function calls may raise an auditing event ctypes.call_function with arguments function pointer and arguments.

Function prototypes

Foreign functions can also be created by instantiating function prototypes. Function prototypes are similar to function prototypes in C; they describe a function (return type, argument types, calling convention) without defining an implementation. The factory functions must be called with the desired result type and the argument types of the function, and can be used as decorator factories, and as such, be applied to functions through the @wrapper syntax. See Callback functions for examples.

+ctypes.CFUNCTYPE(restype, *argtypes, use_errno=False, use_last_error=False)
+

The returned function prototype creates functions that use the standard C calling convention. The function will release the GIL during the call. If use_errno is set to true, the ctypes private copy of the system errno variable is exchanged with the real errno value before and after the call; use_last_error does the same for the Windows error code.

+
+ctypes.WINFUNCTYPE(restype, *argtypes, use_errno=False, use_last_error=False)
+

Windows only: The returned function prototype creates functions that use the stdcall calling convention. The function will release the GIL during the call. use_errno and use_last_error have the same meaning as above.

+
+ctypes.PYFUNCTYPE(restype, *argtypes)
+

The returned function prototype creates functions that use the Python calling convention. The function will not release the GIL during the call.

+

Function prototypes created by these factory functions can be instantiated in different ways, depending on the type and number of the parameters in the call:

prototype(address) +
+

Returns a foreign function at the specified address which must be an integer.

+
prototype(callable) +
+

Create a C callable function (a callback function) from a Python callable.

+
prototype(func_spec[, paramflags]) +
+

Returns a foreign function exported by a shared library. func_spec must be a 2-tuple (name_or_ordinal, library). The first item is the name of the exported function as string, or the ordinal of the exported function as small integer. The second item is the shared library instance.

+
prototype(vtbl_index, name[, paramflags[, iid]]) +
+

Returns a foreign function that will call a COM method. vtbl_index is the index into the virtual function table, a small non-negative integer. name is name of the COM method. iid is an optional pointer to the interface identifier which is used in extended error reporting.

COM methods use a special calling convention: They require a pointer to the COM interface as first argument, in addition to those parameters that are specified in the argtypes tuple.

+

The optional paramflags parameter creates foreign function wrappers with much more functionality than the features described above.

paramflags must be a tuple of the same length as argtypes.

Each item in this tuple contains further information about a parameter, it must be a tuple containing one, two, or three items.

The first item is an integer containing a combination of direction flags for the parameter:

1
+
+

Specifies an input parameter to the function.

2
+
+

Output parameter. The foreign function fills in a value.

4
+
+

Input parameter which defaults to the integer zero.

The optional second item is the parameter name as string. If this is specified, the foreign function can be called with named parameters.

The optional third item is the default value for this parameter.

The following example demonstrates how to wrap the Windows MessageBoxW function so that it supports default parameters and named arguments. The C declaration from the windows header file is this:

WINUSERAPI int WINAPI
+MessageBoxW(
+    HWND hWnd,
+    LPCWSTR lpText,
+    LPCWSTR lpCaption,
+    UINT uType);
+

Here is the wrapping with ctypes:

>>> from ctypes import c_int, WINFUNCTYPE, windll
+>>> from ctypes.wintypes import HWND, LPCWSTR, UINT
+>>> prototype = WINFUNCTYPE(c_int, HWND, LPCWSTR, LPCWSTR, UINT)
+>>> paramflags = (1, "hwnd", 0), (1, "text", "Hi"), (1, "caption", "Hello from ctypes"), (1, "flags", 0)
+>>> MessageBox = prototype(("MessageBoxW", windll.user32), paramflags)
+

The MessageBox foreign function can now be called in these ways:

>>> MessageBox()
+>>> MessageBox(text="Spam, spam, spam")
+>>> MessageBox(flags=2, text="foo bar")
+

A second example demonstrates output parameters. The win32 GetWindowRect function retrieves the dimensions of a specified window by copying them into RECT structure that the caller has to supply. Here is the C declaration:

WINUSERAPI BOOL WINAPI
+GetWindowRect(
+     HWND hWnd,
+     LPRECT lpRect);
+

Here is the wrapping with ctypes:

>>> from ctypes import POINTER, WINFUNCTYPE, windll, WinError
+>>> from ctypes.wintypes import BOOL, HWND, RECT
+>>> prototype = WINFUNCTYPE(BOOL, HWND, POINTER(RECT))
+>>> paramflags = (1, "hwnd"), (2, "lprect")
+>>> GetWindowRect = prototype(("GetWindowRect", windll.user32), paramflags)
+>>>
+

Functions with output parameters will automatically return the output parameter value if there is a single one, or a tuple containing the output parameter values when there are more than one, so the GetWindowRect function now returns a RECT instance, when called.

Output parameters can be combined with the errcheck protocol to do further output processing and error checking. The win32 GetWindowRect api function returns a BOOL to signal success or failure, so this function could do the error checking, and raises an exception when the api call failed:

>>> def errcheck(result, func, args):
+...     if not result:
+...         raise WinError()
+...     return args
+...
+>>> GetWindowRect.errcheck = errcheck
+>>>
+

If the errcheck function returns the argument tuple it receives unchanged, ctypes continues the normal processing it does on the output parameters. If you want to return a tuple of window coordinates instead of a RECT instance, you can retrieve the fields in the function and return them instead, the normal processing will no longer take place:

>>> def errcheck(result, func, args):
+...     if not result:
+...         raise WinError()
+...     rc = args[1]
+...     return rc.left, rc.top, rc.bottom, rc.right
+...
+>>> GetWindowRect.errcheck = errcheck
+>>>
+

Utility functions

+ctypes.addressof(obj)
+

Returns the address of the memory buffer as integer. obj must be an instance of a ctypes type.

Raises an auditing event ctypes.addressof with argument obj.

+
+ctypes.alignment(obj_or_type)
+

Returns the alignment requirements of a ctypes type. obj_or_type must be a ctypes type or instance.

+
+ctypes.byref(obj[, offset])
+

Returns a light-weight pointer to obj, which must be an instance of a ctypes type. offset defaults to zero, and must be an integer that will be added to the internal pointer value.

byref(obj, offset) corresponds to this C code:

(((char *)&obj) + offset)
+

The returned object can only be used as a foreign function call parameter. It behaves similar to pointer(obj), but the construction is a lot faster.

+
+ctypes.cast(obj, type)
+

This function is similar to the cast operator in C. It returns a new instance of type which points to the same memory block as obj. type must be a pointer type, and obj must be an object that can be interpreted as a pointer.

+
+ctypes.create_string_buffer(init_or_size, size=None)
+

This function creates a mutable character buffer. The returned object is a ctypes array of c_char.

init_or_size must be an integer which specifies the size of the array, or a bytes object which will be used to initialize the array items.

If a bytes object is specified as first argument, the buffer is made one item larger than its length so that the last element in the array is a NUL termination character. An integer can be passed as second argument which allows specifying the size of the array if the length of the bytes should not be used.

Raises an auditing event ctypes.create_string_buffer with arguments init, size.

+
+ctypes.create_unicode_buffer(init_or_size, size=None)
+

This function creates a mutable unicode character buffer. The returned object is a ctypes array of c_wchar.

init_or_size must be an integer which specifies the size of the array, or a string which will be used to initialize the array items.

If a string is specified as first argument, the buffer is made one item larger than the length of the string so that the last element in the array is a NUL termination character. An integer can be passed as second argument which allows specifying the size of the array if the length of the string should not be used.

Raises an auditing event ctypes.create_unicode_buffer with arguments init, size.

+
+ctypes.DllCanUnloadNow()
+

Windows only: This function is a hook which allows implementing in-process COM servers with ctypes. It is called from the DllCanUnloadNow function that the _ctypes extension dll exports.

+
+ctypes.DllGetClassObject()
+

Windows only: This function is a hook which allows implementing in-process COM servers with ctypes. It is called from the DllGetClassObject function that the _ctypes extension dll exports.

+
+ctypes.util.find_library(name)
+

Try to find a library and return a pathname. name is the library name without any prefix like lib, suffix like .so, .dylib or version number (this is the form used for the posix linker option -l). If no library can be found, returns None.

The exact functionality is system dependent.

+
+ctypes.util.find_msvcrt()
+

Windows only: return the filename of the VC runtime library used by Python, and by the extension modules. If the name of the library cannot be determined, None is returned.

If you need to free memory, for example, allocated by an extension module with a call to the free(void *), it is important that you use the function in the same library that allocated the memory.

+
+ctypes.FormatError([code])
+

Windows only: Returns a textual description of the error code code. If no error code is specified, the last error code is used by calling the Windows api function GetLastError.

+
+ctypes.GetLastError()
+

Windows only: Returns the last error code set by Windows in the calling thread. This function calls the Windows GetLastError() function directly, it does not return the ctypes-private copy of the error code.

+
+ctypes.get_errno()
+

Returns the current value of the ctypes-private copy of the system errno variable in the calling thread.

Raises an auditing event ctypes.get_errno with no arguments.

+
+ctypes.get_last_error()
+

Windows only: returns the current value of the ctypes-private copy of the system LastError variable in the calling thread.

Raises an auditing event ctypes.get_last_error with no arguments.

+
+ctypes.memmove(dst, src, count)
+

Same as the standard C memmove library function: copies count bytes from src to dst. dst and src must be integers or ctypes instances that can be converted to pointers.

+
+ctypes.memset(dst, c, count)
+

Same as the standard C memset library function: fills the memory block at address dst with count bytes of value c. dst must be an integer specifying an address, or a ctypes instance.

+
+ctypes.POINTER(type, /)
+

Create and return a new ctypes pointer type. Pointer types are cached and reused internally, so calling this function repeatedly is cheap. type must be a ctypes type.

+
+ctypes.pointer(obj, /)
+

Create a new pointer instance, pointing to obj. The returned object is of the type POINTER(type(obj)).

Note: If you just want to pass a pointer to an object to a foreign function call, you should use byref(obj) which is much faster.

+
+ctypes.resize(obj, size)
+

This function resizes the internal memory buffer of obj, which must be an instance of a ctypes type. It is not possible to make the buffer smaller than the native size of the objects type, as given by sizeof(type(obj)), but it is possible to enlarge the buffer.

+
+ctypes.set_errno(value)
+

Set the current value of the ctypes-private copy of the system errno variable in the calling thread to value and return the previous value.

Raises an auditing event ctypes.set_errno with argument errno.

+
+ctypes.set_last_error(value)
+

Windows only: set the current value of the ctypes-private copy of the system LastError variable in the calling thread to value and return the previous value.

Raises an auditing event ctypes.set_last_error with argument error.

+
+ctypes.sizeof(obj_or_type)
+

Returns the size in bytes of a ctypes type or instance memory buffer. Does the same as the C sizeof operator.

+
+ctypes.string_at(address, size=- 1)
+

This function returns the C string starting at memory address address as a bytes object. If size is specified, it is used as size, otherwise the string is assumed to be zero-terminated.

Raises an auditing event ctypes.string_at with arguments address, size.

+
+ctypes.WinError(code=None, descr=None)
+

Windows only: this function is probably the worst-named thing in ctypes. It creates an instance of OSError. If code is not specified, GetLastError is called to determine the error code. If descr is not specified, FormatError() is called to get a textual description of the error.

Changed in version 3.3: An instance of WindowsError used to be created, which is now an alias of OSError.

+
+ctypes.wstring_at(address, size=- 1)
+

This function returns the wide character string starting at memory address address as a string. If size is specified, it is used as the number of characters of the string, otherwise the string is assumed to be zero-terminated.

Raises an auditing event ctypes.wstring_at with arguments address, size.

+

Data types

+class ctypes._CData
+

This non-public class is the common base class of all ctypes data types. Among other things, all ctypes type instances contain a memory block that hold C compatible data; the address of the memory block is returned by the addressof() helper function. Another instance variable is exposed as _objects; this contains other Python objects that need to be kept alive in case the memory block contains pointers.

Common methods of ctypes data types, these are all class methods (to be exact, they are methods of the metaclass):

+from_buffer(source[, offset])
+

This method returns a ctypes instance that shares the buffer of the source object. The source object must support the writeable buffer interface. The optional offset parameter specifies an offset into the source buffer in bytes; the default is zero. If the source buffer is not large enough a ValueError is raised.

Raises an auditing event ctypes.cdata/buffer with arguments pointer, size, offset.

+
+from_buffer_copy(source[, offset])
+

This method creates a ctypes instance, copying the buffer from the source object buffer which must be readable. The optional offset parameter specifies an offset into the source buffer in bytes; the default is zero. If the source buffer is not large enough a ValueError is raised.

Raises an auditing event ctypes.cdata/buffer with arguments pointer, size, offset.

+
+from_address(address)
+

This method returns a ctypes type instance using the memory specified by address which must be an integer.

+

This method, and others that indirectly call this method, raises an auditing event ctypes.cdata with argument address.

+
+from_param(obj)
+

This method adapts obj to a ctypes type. It is called with the actual object used in a foreign function call when the type is present in the foreign function’s argtypes tuple; it must return an object that can be used as a function call parameter.

All ctypes data types have a default implementation of this classmethod that normally returns obj if that is an instance of the type. Some types accept other objects as well.

+
+in_dll(library, name)
+

This method returns a ctypes type instance exported by a shared library. name is the name of the symbol that exports the data, library is the loaded shared library.

+

Common instance variables of ctypes data types:

+_b_base_
+

Sometimes ctypes data instances do not own the memory block they contain, instead they share part of the memory block of a base object. The _b_base_ read-only member is the root ctypes object that owns the memory block.

+
+_b_needsfree_
+

This read-only variable is true when the ctypes data instance has allocated the memory block itself, false otherwise.

+
+_objects
+

This member is either None or a dictionary containing Python objects that need to be kept alive so that the memory block contents is kept valid. This object is only exposed for debugging; never modify the contents of this dictionary.

+
+

Fundamental data types

+class ctypes._SimpleCData
+

This non-public class is the base class of all fundamental ctypes data types. It is mentioned here because it contains the common attributes of the fundamental ctypes data types. _SimpleCData is a subclass of _CData, so it inherits their methods and attributes. ctypes data types that are not and do not contain pointers can now be pickled.

Instances have a single attribute:

+value
+

This attribute contains the actual value of the instance. For integer and pointer types, it is an integer, for character types, it is a single character bytes object or string, for character pointer types it is a Python bytes object or string.

When the value attribute is retrieved from a ctypes instance, usually a new object is returned each time. ctypes does not implement original object return, always a new object is constructed. The same is true for all other ctypes object instances.

+
+

Fundamental data types, when returned as foreign function call results, or, for example, by retrieving structure field members or array items, are transparently converted to native Python types. In other words, if a foreign function has a restype of c_char_p, you will always receive a Python bytes object, not a c_char_p instance.

Subclasses of fundamental data types do not inherit this behavior. So, if a foreign functions restype is a subclass of c_void_p, you will receive an instance of this subclass from the function call. Of course, you can get the value of the pointer by accessing the value attribute.

These are the fundamental ctypes data types:

+class ctypes.c_byte
+

Represents the C signed char datatype, and interprets the value as small integer. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_char
+

Represents the C char datatype, and interprets the value as a single character. The constructor accepts an optional string initializer, the length of the string must be exactly one character.

+
+class ctypes.c_char_p
+

Represents the C char* datatype when it points to a zero-terminated string. For a general character pointer that may also point to binary data, POINTER(c_char) must be used. The constructor accepts an integer address, or a bytes object.

+
+class ctypes.c_double
+

Represents the C double datatype. The constructor accepts an optional float initializer.

+
+class ctypes.c_longdouble
+

Represents the C long double datatype. The constructor accepts an optional float initializer. On platforms where sizeof(long double) == +sizeof(double) it is an alias to c_double.

+
+class ctypes.c_float
+

Represents the C float datatype. The constructor accepts an optional float initializer.

+
+class ctypes.c_int
+

Represents the C signed int datatype. The constructor accepts an optional integer initializer; no overflow checking is done. On platforms where sizeof(int) == sizeof(long) it is an alias to c_long.

+
+class ctypes.c_int8
+

Represents the C 8-bit signed int datatype. Usually an alias for c_byte.

+
+class ctypes.c_int16
+

Represents the C 16-bit signed int datatype. Usually an alias for c_short.

+
+class ctypes.c_int32
+

Represents the C 32-bit signed int datatype. Usually an alias for c_int.

+
+class ctypes.c_int64
+

Represents the C 64-bit signed int datatype. Usually an alias for c_longlong.

+
+class ctypes.c_long
+

Represents the C signed long datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_longlong
+

Represents the C signed long long datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_short
+

Represents the C signed short datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_size_t
+

Represents the C size_t datatype.

+
+class ctypes.c_ssize_t
+

Represents the C ssize_t datatype.

New in version 3.2.

+
+class ctypes.c_time_t
+

Represents the C time_t datatype.

New in version 3.12.

+
+class ctypes.c_ubyte
+

Represents the C unsigned char datatype, it interprets the value as small integer. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_uint
+

Represents the C unsigned int datatype. The constructor accepts an optional integer initializer; no overflow checking is done. On platforms where sizeof(int) == sizeof(long) it is an alias for c_ulong.

+
+class ctypes.c_uint8
+

Represents the C 8-bit unsigned int datatype. Usually an alias for c_ubyte.

+
+class ctypes.c_uint16
+

Represents the C 16-bit unsigned int datatype. Usually an alias for c_ushort.

+
+class ctypes.c_uint32
+

Represents the C 32-bit unsigned int datatype. Usually an alias for c_uint.

+
+class ctypes.c_uint64
+

Represents the C 64-bit unsigned int datatype. Usually an alias for c_ulonglong.

+
+class ctypes.c_ulong
+

Represents the C unsigned long datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_ulonglong
+

Represents the C unsigned long long datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_ushort
+

Represents the C unsigned short datatype. The constructor accepts an optional integer initializer; no overflow checking is done.

+
+class ctypes.c_void_p
+

Represents the C void* type. The value is represented as integer. The constructor accepts an optional integer initializer.

+
+class ctypes.c_wchar
+

Represents the C wchar_t datatype, and interprets the value as a single character unicode string. The constructor accepts an optional string initializer, the length of the string must be exactly one character.

+
+class ctypes.c_wchar_p
+

Represents the C wchar_t* datatype, which must be a pointer to a zero-terminated wide character string. The constructor accepts an integer address, or a string.

+
+class ctypes.c_bool
+

Represent the C bool datatype (more accurately, _Bool from C99). Its value can be True or False, and the constructor accepts any object that has a truth value.

+
+class ctypes.HRESULT
+

Windows only: Represents a HRESULT value, which contains success or error information for a function or method call.

+
+class ctypes.py_object
+

Represents the C PyObject* datatype. Calling this without an argument creates a NULL PyObject* pointer.

+

The ctypes.wintypes module provides quite some other Windows specific data types, for example HWND, WPARAM, or DWORD. Some useful structures like MSG or RECT are also defined.

Structured data types

+class ctypes.Union(*args, **kw)
+

Abstract base class for unions in native byte order.

+
+class ctypes.BigEndianUnion(*args, **kw)
+

Abstract base class for unions in big endian byte order.

New in version 3.11.

+
+class ctypes.LittleEndianUnion(*args, **kw)
+

Abstract base class for unions in little endian byte order.

New in version 3.11.

+
+class ctypes.BigEndianStructure(*args, **kw)
+

Abstract base class for structures in big endian byte order.

+
+class ctypes.LittleEndianStructure(*args, **kw)
+

Abstract base class for structures in little endian byte order.

+

Structures and unions with non-native byte order cannot contain pointer type fields, or any other data types containing pointer type fields.

+class ctypes.Structure(*args, **kw)
+

Abstract base class for structures in native byte order.

Concrete structure and union types must be created by subclassing one of these types, and at least define a _fields_ class variable. ctypes will create descriptors which allow reading and writing the fields by direct attribute accesses. These are the

+_fields_
+

A sequence defining the structure fields. The items must be 2-tuples or 3-tuples. The first item is the name of the field, the second item specifies the type of the field; it can be any ctypes data type.

For integer type fields like c_int, a third optional item can be given. It must be a small positive integer defining the bit width of the field.

Field names must be unique within one structure or union. This is not checked, only one field can be accessed when names are repeated.

It is possible to define the _fields_ class variable after the class statement that defines the Structure subclass, this allows creating data types that directly or indirectly reference themselves:

class List(Structure):
+    pass
+List._fields_ = [("pnext", POINTER(List)),
+                 ...
+                ]
+

The _fields_ class variable must, however, be defined before the type is first used (an instance is created, sizeof() is called on it, and so on). Later assignments to the _fields_ class variable will raise an AttributeError.

It is possible to define sub-subclasses of structure types, they inherit the fields of the base class plus the _fields_ defined in the sub-subclass, if any.

+
+_pack_
+

An optional small integer that allows overriding the alignment of structure fields in the instance. _pack_ must already be defined when _fields_ is assigned, otherwise it will have no effect. Setting this attribute to 0 is the same as not setting it at all.

+
+_anonymous_
+

An optional sequence that lists the names of unnamed (anonymous) fields. _anonymous_ must be already defined when _fields_ is assigned, otherwise it will have no effect.

The fields listed in this variable must be structure or union type fields. ctypes will create descriptors in the structure type that allows accessing the nested fields directly, without the need to create the structure or union field.

Here is an example type (Windows):

class _U(Union):
+    _fields_ = [("lptdesc", POINTER(TYPEDESC)),
+                ("lpadesc", POINTER(ARRAYDESC)),
+                ("hreftype", HREFTYPE)]
+
+class TYPEDESC(Structure):
+    _anonymous_ = ("u",)
+    _fields_ = [("u", _U),
+                ("vt", VARTYPE)]
+

The TYPEDESC structure describes a COM data type, the vt field specifies which one of the union fields is valid. Since the u field is defined as anonymous field, it is now possible to access the members directly off the TYPEDESC instance. td.lptdesc and td.u.lptdesc are equivalent, but the former is faster since it does not need to create a temporary union instance:

td = TYPEDESC()
+td.vt = VT_PTR
+td.lptdesc = POINTER(some_type)
+td.u.lptdesc = POINTER(some_type)
+
+

It is possible to define sub-subclasses of structures, they inherit the fields of the base class. If the subclass definition has a separate _fields_ variable, the fields specified in this are appended to the fields of the base class.

Structure and union constructors accept both positional and keyword arguments. Positional arguments are used to initialize member fields in the same order as they are appear in _fields_. Keyword arguments in the constructor are interpreted as attribute assignments, so they will initialize _fields_ with the same name, or create new attributes for names not present in _fields_.

+

Arrays and pointers

+class ctypes.Array(*args)
+

Abstract base class for arrays.

The recommended way to create concrete array types is by multiplying any ctypes data type with a non-negative integer. Alternatively, you can subclass this type and define _length_ and _type_ class variables. Array elements can be read and written using standard subscript and slice accesses; for slice reads, the resulting object is not itself an Array.

+_length_
+

A positive integer specifying the number of elements in the array. Out-of-range subscripts result in an IndexError. Will be returned by len().

+
+_type_
+

Specifies the type of each element in the array.

+

Array subclass constructors accept positional arguments, used to initialize the elements in order.

+
+class ctypes._Pointer
+

Private, abstract base class for pointers.

Concrete pointer types are created by calling POINTER() with the type that will be pointed to; this is done automatically by pointer().

If a pointer points to an array, its elements can be read and written using standard subscript and slice accesses. Pointer objects have no size, so len() will raise TypeError. Negative subscripts will read from the memory before the pointer (as in C), and out-of-range subscripts will probably crash with an access violation (if you’re lucky).

+_type_
+

Specifies the type pointed to.

+
+contents
+

Returns the object to which to pointer points. Assigning to this attribute changes the pointer to point to the assigned object.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ctypes.html +

+
diff --git a/devdocs/python~3.12/library%2Fcurses.ascii.html b/devdocs/python~3.12/library%2Fcurses.ascii.html new file mode 100644 index 00000000..6ca98fc5 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcurses.ascii.html @@ -0,0 +1,214 @@ +

curses.ascii — Utilities for ASCII characters

Source code: Lib/curses/ascii.py

The curses.ascii module supplies name constants for ASCII characters and functions to test membership in various ASCII character classes. The constants supplied are names for control characters as follows:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Name

Meaning
+
+curses.ascii.NUL
+
+
+curses.ascii.SOH
+

Start of heading, console interrupt
+
+curses.ascii.STX
+

Start of text
+
+curses.ascii.ETX
+

End of text
+
+curses.ascii.EOT
+

End of transmission
+
+curses.ascii.ENQ
+

Enquiry, goes with ACK flow control
+
+curses.ascii.ACK
+

Acknowledgement
+
+curses.ascii.BEL
+

Bell
+
+curses.ascii.BS
+

Backspace
+
+curses.ascii.TAB
+

Tab
+
+curses.ascii.HT
+

Alias for TAB: “Horizontal tab”
+
+curses.ascii.LF
+

Line feed
+
+curses.ascii.NL
+

Alias for LF: “New line”
+
+curses.ascii.VT
+

Vertical tab
+
+curses.ascii.FF
+

Form feed
+
+curses.ascii.CR
+

Carriage return
+
+curses.ascii.SO
+

Shift-out, begin alternate character set
+
+curses.ascii.SI
+

Shift-in, resume default character set
+
+curses.ascii.DLE
+

Data-link escape
+
+curses.ascii.DC1
+

XON, for flow control
+
+curses.ascii.DC2
+

Device control 2, block-mode flow control
+
+curses.ascii.DC3
+

XOFF, for flow control
+
+curses.ascii.DC4
+

Device control 4
+
+curses.ascii.NAK
+

Negative acknowledgement
+
+curses.ascii.SYN
+

Synchronous idle
+
+curses.ascii.ETB
+

End transmission block
+
+curses.ascii.CAN
+

Cancel
+
+curses.ascii.EM
+

End of medium
+
+curses.ascii.SUB
+

Substitute
+
+curses.ascii.ESC
+

Escape
+
+curses.ascii.FS
+

File separator
+
+curses.ascii.GS
+

Group separator
+
+curses.ascii.RS
+

Record separator, block-mode terminator
+
+curses.ascii.US
+

Unit separator
+
+curses.ascii.SP
+

Space
+
+curses.ascii.DEL
+

Delete

Note that many of these have little practical significance in modern usage. The mnemonics derive from teleprinter conventions that predate digital computers.

The module supplies the following functions, patterned on those in the standard C library:

+curses.ascii.isalnum(c)
+

Checks for an ASCII alphanumeric character; it is equivalent to isalpha(c) or +isdigit(c).

+
+curses.ascii.isalpha(c)
+

Checks for an ASCII alphabetic character; it is equivalent to isupper(c) or +islower(c).

+
+curses.ascii.isascii(c)
+

Checks for a character value that fits in the 7-bit ASCII set.

+
+curses.ascii.isblank(c)
+

Checks for an ASCII whitespace character; space or horizontal tab.

+
+curses.ascii.iscntrl(c)
+

Checks for an ASCII control character (in the range 0x00 to 0x1f or 0x7f).

+
+curses.ascii.isdigit(c)
+

Checks for an ASCII decimal digit, '0' through '9'. This is equivalent to c in string.digits.

+
+curses.ascii.isgraph(c)
+

Checks for ASCII any printable character except space.

+
+curses.ascii.islower(c)
+

Checks for an ASCII lower-case character.

+
+curses.ascii.isprint(c)
+

Checks for any ASCII printable character including space.

+
+curses.ascii.ispunct(c)
+

Checks for any printable ASCII character which is not a space or an alphanumeric character.

+
+curses.ascii.isspace(c)
+

Checks for ASCII white-space characters; space, line feed, carriage return, form feed, horizontal tab, vertical tab.

+
+curses.ascii.isupper(c)
+

Checks for an ASCII uppercase letter.

+
+curses.ascii.isxdigit(c)
+

Checks for an ASCII hexadecimal digit. This is equivalent to c in +string.hexdigits.

+
+curses.ascii.isctrl(c)
+

Checks for an ASCII control character (ordinal values 0 to 31).

+
+curses.ascii.ismeta(c)
+

Checks for a non-ASCII character (ordinal values 0x80 and above).

+

These functions accept either integers or single-character strings; when the argument is a string, it is first converted using the built-in function ord().

Note that all these functions check ordinal bit values derived from the character of the string you pass in; they do not actually know anything about the host machine’s character encoding.

The following two functions take either a single-character string or integer byte value; they return a value of the same type.

+curses.ascii.ascii(c)
+

Return the ASCII value corresponding to the low 7 bits of c.

+
+curses.ascii.ctrl(c)
+

Return the control character corresponding to the given character (the character bit value is bitwise-anded with 0x1f).

+
+curses.ascii.alt(c)
+

Return the 8-bit character corresponding to the given ASCII character (the character bit value is bitwise-ored with 0x80).

+

The following function takes either a single-character string or integer value; it returns a string.

+curses.ascii.unctrl(c)
+

Return a string representation of the ASCII character c. If c is printable, this string is the character itself. If the character is a control character (0x00–0x1f) the string consists of a caret ('^') followed by the corresponding uppercase letter. If the character is an ASCII delete (0x7f) the string is '^?'. If the character has its meta bit (0x80) set, the meta bit is stripped, the preceding rules applied, and '!' prepended to the result.

+
+curses.ascii.controlnames
+

A 33-element string array that contains the ASCII mnemonics for the thirty-two ASCII control characters from 0 (NUL) to 0x1f (US), in order, plus the mnemonic SP for the space character.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/curses.ascii.html +

+
diff --git a/devdocs/python~3.12/library%2Fcurses.html b/devdocs/python~3.12/library%2Fcurses.html new file mode 100644 index 00000000..8eb1a7c1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcurses.html @@ -0,0 +1,1246 @@ +

curses — Terminal handling for character-cell displays

Source code: Lib/curses

The curses module provides an interface to the curses library, the de-facto standard for portable advanced terminal handling.

While curses is most widely used in the Unix environment, versions are available for Windows, DOS, and possibly other systems as well. This extension module is designed to match the API of ncurses, an open-source curses library hosted on Linux and the BSD variants of Unix.

Note

Whenever the documentation mentions a character it can be specified as an integer, a one-character Unicode string or a one-byte byte string.

Whenever the documentation mentions a character string it can be specified as a Unicode string or a byte string.

See also

+Module curses.ascii +
+
+

Utilities for working with ASCII characters, regardless of your locale settings.

+Module curses.panel +
+
+

A panel stack extension that adds depth to curses windows.

+Module curses.textpad +
+
+

Editable text widget for curses supporting Emacs-like bindings.

Curses Programming with Python
+
+

Tutorial material on using curses with Python, by Andrew Kuchling and Eric Raymond.

Functions

The module curses defines the following exception:

+exception curses.error
+

Exception raised when a curses library function returns an error.

+

Note

Whenever x or y arguments to a function or a method are optional, they default to the current cursor location. Whenever attr is optional, it defaults to A_NORMAL.

The module curses defines the following functions:

+curses.baudrate()
+

Return the output speed of the terminal in bits per second. On software terminal emulators it will have a fixed high value. Included for historical reasons; in former times, it was used to write output loops for time delays and occasionally to change interfaces depending on the line speed.

+
+curses.beep()
+

Emit a short attention sound.

+
+curses.can_change_color()
+

Return True or False, depending on whether the programmer can change the colors displayed by the terminal.

+
+curses.cbreak()
+

Enter cbreak mode. In cbreak mode (sometimes called “rare” mode) normal tty line buffering is turned off and characters are available to be read one by one. However, unlike raw mode, special characters (interrupt, quit, suspend, and flow control) retain their effects on the tty driver and calling program. Calling first raw() then cbreak() leaves the terminal in cbreak mode.

+
+curses.color_content(color_number)
+

Return the intensity of the red, green, and blue (RGB) components in the color color_number, which must be between 0 and COLORS - 1. Return a 3-tuple, containing the R,G,B values for the given color, which will be between 0 (no component) and 1000 (maximum amount of component).

+
+curses.color_pair(pair_number)
+

Return the attribute value for displaying text in the specified color pair. Only the first 256 color pairs are supported. This attribute value can be combined with A_STANDOUT, A_REVERSE, and the other A_* attributes. pair_number() is the counterpart to this function.

+
+curses.curs_set(visibility)
+

Set the cursor state. visibility can be set to 0, 1, or 2, for invisible, normal, or very visible. If the terminal supports the visibility requested, return the previous cursor state; otherwise raise an exception. On many terminals, the “visible” mode is an underline cursor and the “very visible” mode is a block cursor.

+
+curses.def_prog_mode()
+

Save the current terminal mode as the “program” mode, the mode when the running program is using curses. (Its counterpart is the “shell” mode, for when the program is not in curses.) Subsequent calls to reset_prog_mode() will restore this mode.

+
+curses.def_shell_mode()
+

Save the current terminal mode as the “shell” mode, the mode when the running program is not using curses. (Its counterpart is the “program” mode, when the program is using curses capabilities.) Subsequent calls to reset_shell_mode() will restore this mode.

+
+curses.delay_output(ms)
+

Insert an ms millisecond pause in output.

+
+curses.doupdate()
+

Update the physical screen. The curses library keeps two data structures, one representing the current physical screen contents and a virtual screen representing the desired next state. The doupdate() ground updates the physical screen to match the virtual screen.

The virtual screen may be updated by a noutrefresh() call after write operations such as addstr() have been performed on a window. The normal refresh() call is simply noutrefresh() followed by doupdate(); if you have to update multiple windows, you can speed performance and perhaps reduce screen flicker by issuing noutrefresh() calls on all windows, followed by a single doupdate().

+
+curses.echo()
+

Enter echo mode. In echo mode, each character input is echoed to the screen as it is entered.

+
+curses.endwin()
+

De-initialize the library, and return terminal to normal status.

+
+curses.erasechar()
+

Return the user’s current erase character as a one-byte bytes object. Under Unix operating systems this is a property of the controlling tty of the curses program, and is not set by the curses library itself.

+
+curses.filter()
+

The filter() routine, if used, must be called before initscr() is called. The effect is that, during those calls, LINES is set to 1; the capabilities clear, cup, cud, cud1, cuu1, cuu, vpa are disabled; and the home string is set to the value of cr. The effect is that the cursor is confined to the current line, and so are screen updates. This may be used for enabling character-at-a-time line editing without touching the rest of the screen.

+
+curses.flash()
+

Flash the screen. That is, change it to reverse-video and then change it back in a short interval. Some people prefer such as ‘visible bell’ to the audible attention signal produced by beep().

+
+curses.flushinp()
+

Flush all input buffers. This throws away any typeahead that has been typed by the user and has not yet been processed by the program.

+
+curses.getmouse()
+

After getch() returns KEY_MOUSE to signal a mouse event, this method should be called to retrieve the queued mouse event, represented as a 5-tuple (id, x, y, z, bstate). id is an ID value used to distinguish multiple devices, and x, y, z are the event’s coordinates. (z is currently unused.) bstate is an integer value whose bits will be set to indicate the type of event, and will be the bitwise OR of one or more of the following constants, where n is the button number from 1 to 5: BUTTONn_PRESSED, BUTTONn_RELEASED, BUTTONn_CLICKED, BUTTONn_DOUBLE_CLICKED, BUTTONn_TRIPLE_CLICKED, BUTTON_SHIFT, BUTTON_CTRL, BUTTON_ALT.

Changed in version 3.10: The BUTTON5_* constants are now exposed if they are provided by the underlying curses library.

+
+curses.getsyx()
+

Return the current coordinates of the virtual screen cursor as a tuple (y, x). If leaveok is currently True, then return (-1, -1).

+
+curses.getwin(file)
+

Read window related data stored in the file by an earlier window.putwin() call. The routine then creates and initializes a new window using that data, returning the new window object.

+
+curses.has_colors()
+

Return True if the terminal can display colors; otherwise, return False.

+
+curses.has_extended_color_support()
+

Return True if the module supports extended colors; otherwise, return False. Extended color support allows more than 256 color pairs for terminals that support more than 16 colors (e.g. xterm-256color).

Extended color support requires ncurses version 6.1 or later.

New in version 3.10.

+
+curses.has_ic()
+

Return True if the terminal has insert- and delete-character capabilities. This function is included for historical reasons only, as all modern software terminal emulators have such capabilities.

+
+curses.has_il()
+

Return True if the terminal has insert- and delete-line capabilities, or can simulate them using scrolling regions. This function is included for historical reasons only, as all modern software terminal emulators have such capabilities.

+
+curses.has_key(ch)
+

Take a key value ch, and return True if the current terminal type recognizes a key with that value.

+
+curses.halfdelay(tenths)
+

Used for half-delay mode, which is similar to cbreak mode in that characters typed by the user are immediately available to the program. However, after blocking for tenths tenths of seconds, raise an exception if nothing has been typed. The value of tenths must be a number between 1 and 255. Use nocbreak() to leave half-delay mode.

+
+curses.init_color(color_number, r, g, b)
+

Change the definition of a color, taking the number of the color to be changed followed by three RGB values (for the amounts of red, green, and blue components). The value of color_number must be between 0 and COLORS - 1. Each of r, g, b, must be a value between 0 and 1000. When init_color() is used, all occurrences of that color on the screen immediately change to the new definition. This function is a no-op on most terminals; it is active only if can_change_color() returns True.

+
+curses.init_pair(pair_number, fg, bg)
+

Change the definition of a color-pair. It takes three arguments: the number of the color-pair to be changed, the foreground color number, and the background color number. The value of pair_number must be between 1 and COLOR_PAIRS - 1 (the 0 color pair is wired to white on black and cannot be changed). The value of fg and bg arguments must be between 0 and COLORS - 1, or, after calling use_default_colors(), -1. If the color-pair was previously initialized, the screen is refreshed and all occurrences of that color-pair are changed to the new definition.

+
+curses.initscr()
+

Initialize the library. Return a window object which represents the whole screen.

Note

If there is an error opening the terminal, the underlying curses library may cause the interpreter to exit.

+
+curses.is_term_resized(nlines, ncols)
+

Return True if resize_term() would modify the window structure, False otherwise.

+
+curses.isendwin()
+

Return True if endwin() has been called (that is, the curses library has been deinitialized).

+
+curses.keyname(k)
+

Return the name of the key numbered k as a bytes object. The name of a key generating printable ASCII character is the key’s character. The name of a control-key combination is a two-byte bytes object consisting of a caret (b'^') followed by the corresponding printable ASCII character. The name of an alt-key combination (128–255) is a bytes object consisting of the prefix b'M-' followed by the name of the corresponding ASCII character.

+
+curses.killchar()
+

Return the user’s current line kill character as a one-byte bytes object. Under Unix operating systems this is a property of the controlling tty of the curses program, and is not set by the curses library itself.

+
+curses.longname()
+

Return a bytes object containing the terminfo long name field describing the current terminal. The maximum length of a verbose description is 128 characters. It is defined only after the call to initscr().

+
+curses.meta(flag)
+

If flag is True, allow 8-bit characters to be input. If flag is False, allow only 7-bit chars.

+
+curses.mouseinterval(interval)
+

Set the maximum time in milliseconds that can elapse between press and release events in order for them to be recognized as a click, and return the previous interval value. The default value is 200 milliseconds, or one fifth of a second.

+
+curses.mousemask(mousemask)
+

Set the mouse events to be reported, and return a tuple (availmask, +oldmask). availmask indicates which of the specified mouse events can be reported; on complete failure it returns 0. oldmask is the previous value of the given window’s mouse event mask. If this function is never called, no mouse events are ever reported.

+
+curses.napms(ms)
+

Sleep for ms milliseconds.

+
+curses.newpad(nlines, ncols)
+

Create and return a pointer to a new pad data structure with the given number of lines and columns. Return a pad as a window object.

A pad is like a window, except that it is not restricted by the screen size, and is not necessarily associated with a particular part of the screen. Pads can be used when a large window is needed, and only a part of the window will be on the screen at one time. Automatic refreshes of pads (such as from scrolling or echoing of input) do not occur. The refresh() and noutrefresh() methods of a pad require 6 arguments to specify the part of the pad to be displayed and the location on the screen to be used for the display. The arguments are pminrow, pmincol, sminrow, smincol, smaxrow, smaxcol; the p arguments refer to the upper left corner of the pad region to be displayed and the s arguments define a clipping box on the screen within which the pad region is to be displayed.

+
+curses.newwin(nlines, ncols)
curses.newwin(nlines, ncols, begin_y, begin_x) +
+

Return a new window, whose left-upper corner is at (begin_y, begin_x), and whose height/width is nlines/ncols.

By default, the window will extend from the specified position to the lower right corner of the screen.

+
+curses.nl()
+

Enter newline mode. This mode translates the return key into newline on input, and translates newline into return and line-feed on output. Newline mode is initially on.

+
+curses.nocbreak()
+

Leave cbreak mode. Return to normal “cooked” mode with line buffering.

+
+curses.noecho()
+

Leave echo mode. Echoing of input characters is turned off.

+
+curses.nonl()
+

Leave newline mode. Disable translation of return into newline on input, and disable low-level translation of newline into newline/return on output (but this does not change the behavior of addch('\n'), which always does the equivalent of return and line feed on the virtual screen). With translation off, curses can sometimes speed up vertical motion a little; also, it will be able to detect the return key on input.

+
+curses.noqiflush()
+

When the noqiflush() routine is used, normal flush of input and output queues associated with the INTR, QUIT and SUSP characters will not be done. You may want to call noqiflush() in a signal handler if you want output to continue as though the interrupt had not occurred, after the handler exits.

+
+curses.noraw()
+

Leave raw mode. Return to normal “cooked” mode with line buffering.

+
+curses.pair_content(pair_number)
+

Return a tuple (fg, bg) containing the colors for the requested color pair. The value of pair_number must be between 0 and COLOR_PAIRS - 1.

+
+curses.pair_number(attr)
+

Return the number of the color-pair set by the attribute value attr. color_pair() is the counterpart to this function.

+
+curses.putp(str)
+

Equivalent to tputs(str, 1, putchar); emit the value of a specified terminfo capability for the current terminal. Note that the output of putp() always goes to standard output.

+
+curses.qiflush([flag])
+

If flag is False, the effect is the same as calling noqiflush(). If flag is True, or no argument is provided, the queues will be flushed when these control characters are read.

+
+curses.raw()
+

Enter raw mode. In raw mode, normal line buffering and processing of interrupt, quit, suspend, and flow control keys are turned off; characters are presented to curses input functions one by one.

+
+curses.reset_prog_mode()
+

Restore the terminal to “program” mode, as previously saved by def_prog_mode().

+
+curses.reset_shell_mode()
+

Restore the terminal to “shell” mode, as previously saved by def_shell_mode().

+
+curses.resetty()
+

Restore the state of the terminal modes to what it was at the last call to savetty().

+
+curses.resize_term(nlines, ncols)
+

Backend function used by resizeterm(), performing most of the work; when resizing the windows, resize_term() blank-fills the areas that are extended. The calling application should fill in these areas with appropriate data. The resize_term() function attempts to resize all windows. However, due to the calling convention of pads, it is not possible to resize these without additional interaction with the application.

+
+curses.resizeterm(nlines, ncols)
+

Resize the standard and current windows to the specified dimensions, and adjusts other bookkeeping data used by the curses library that record the window dimensions (in particular the SIGWINCH handler).

+
+curses.savetty()
+

Save the current state of the terminal modes in a buffer, usable by resetty().

+
+curses.get_escdelay()
+

Retrieves the value set by set_escdelay().

New in version 3.9.

+
+curses.set_escdelay(ms)
+

Sets the number of milliseconds to wait after reading an escape character, to distinguish between an individual escape character entered on the keyboard from escape sequences sent by cursor and function keys.

New in version 3.9.

+
+curses.get_tabsize()
+

Retrieves the value set by set_tabsize().

New in version 3.9.

+
+curses.set_tabsize(size)
+

Sets the number of columns used by the curses library when converting a tab character to spaces as it adds the tab to a window.

New in version 3.9.

+
+curses.setsyx(y, x)
+

Set the virtual screen cursor to y, x. If y and x are both -1, then leaveok is set True.

+
+curses.setupterm(term=None, fd=- 1)
+

Initialize the terminal. term is a string giving the terminal name, or None; if omitted or None, the value of the TERM environment variable will be used. fd is the file descriptor to which any initialization sequences will be sent; if not supplied or -1, the file descriptor for sys.stdout will be used.

+
+curses.start_color()
+

Must be called if the programmer wants to use colors, and before any other color manipulation routine is called. It is good practice to call this routine right after initscr().

start_color() initializes eight basic colors (black, red, green, yellow, blue, magenta, cyan, and white), and two global variables in the curses module, COLORS and COLOR_PAIRS, containing the maximum number of colors and color-pairs the terminal can support. It also restores the colors on the terminal to the values they had when the terminal was just turned on.

+
+curses.termattrs()
+

Return a logical OR of all video attributes supported by the terminal. This information is useful when a curses program needs complete control over the appearance of the screen.

+
+curses.termname()
+

Return the value of the environment variable TERM, as a bytes object, truncated to 14 characters.

+
+curses.tigetflag(capname)
+

Return the value of the Boolean capability corresponding to the terminfo capability name capname as an integer. Return the value -1 if capname is not a Boolean capability, or 0 if it is canceled or absent from the terminal description.

+
+curses.tigetnum(capname)
+

Return the value of the numeric capability corresponding to the terminfo capability name capname as an integer. Return the value -2 if capname is not a numeric capability, or -1 if it is canceled or absent from the terminal description.

+
+curses.tigetstr(capname)
+

Return the value of the string capability corresponding to the terminfo capability name capname as a bytes object. Return None if capname is not a terminfo “string capability”, or is canceled or absent from the terminal description.

+
+curses.tparm(str[, ...])
+

Instantiate the bytes object str with the supplied parameters, where str should be a parameterized string obtained from the terminfo database. E.g. tparm(tigetstr("cup"), 5, 3) could result in b'\033[6;4H', the exact result depending on terminal type.

+
+curses.typeahead(fd)
+

Specify that the file descriptor fd be used for typeahead checking. If fd is -1, then no typeahead checking is done.

The curses library does “line-breakout optimization” by looking for typeahead periodically while updating the screen. If input is found, and it is coming from a tty, the current update is postponed until refresh or doupdate is called again, allowing faster response to commands typed in advance. This function allows specifying a different file descriptor for typeahead checking.

+
+curses.unctrl(ch)
+

Return a bytes object which is a printable representation of the character ch. Control characters are represented as a caret followed by the character, for example as b'^C'. Printing characters are left as they are.

+
+curses.ungetch(ch)
+

Push ch so the next getch() will return it.

Note

Only one ch can be pushed before getch() is called.

+
+curses.update_lines_cols()
+

Update the LINES and COLS module variables. Useful for detecting manual screen resize.

New in version 3.5.

+
+curses.unget_wch(ch)
+

Push ch so the next get_wch() will return it.

Note

Only one ch can be pushed before get_wch() is called.

New in version 3.3.

+
+curses.ungetmouse(id, x, y, z, bstate)
+

Push a KEY_MOUSE event onto the input queue, associating the given state data with it.

+
+curses.use_env(flag)
+

If used, this function should be called before initscr() or newterm are called. When flag is False, the values of lines and columns specified in the terminfo database will be used, even if environment variables LINES and COLUMNS (used by default) are set, or if curses is running in a window (in which case default behavior would be to use the window size if LINES and COLUMNS are not set).

+
+curses.use_default_colors()
+

Allow use of default values for colors on terminals supporting this feature. Use this to support transparency in your application. The default color is assigned to the color number -1. After calling this function, init_pair(x, +curses.COLOR_RED, -1) initializes, for instance, color pair x to a red foreground color on the default background.

+
+curses.wrapper(func, /, *args, **kwargs)
+

Initialize curses and call another callable object, func, which should be the rest of your curses-using application. If the application raises an exception, this function will restore the terminal to a sane state before re-raising the exception and generating a traceback. The callable object func is then passed the main window ‘stdscr’ as its first argument, followed by any other arguments passed to wrapper(). Before calling func, wrapper() turns on cbreak mode, turns off echo, enables the terminal keypad, and initializes colors if the terminal has color support. On exit (whether normally or by exception) it restores cooked mode, turns on echo, and disables the terminal keypad.

+

Window Objects

Window objects, as returned by initscr() and newwin() above, have the following methods and attributes:

+window.addch(ch[, attr])
window.addch(y, x, ch[, attr]) +
+

Paint character ch at (y, x) with attributes attr, overwriting any character previously painted at that location. By default, the character position and attributes are the current settings for the window object.

Note

Writing outside the window, subwindow, or pad raises a curses.error. Attempting to write to the lower right corner of a window, subwindow, or pad will cause an exception to be raised after the character is printed.

+
+window.addnstr(str, n[, attr])
window.addnstr(y, x, str, n[, attr]) +
+

Paint at most n characters of the character string str at (y, x) with attributes attr, overwriting anything previously on the display.

+
+window.addstr(str[, attr])
window.addstr(y, x, str[, attr]) +
+

Paint the character string str at (y, x) with attributes attr, overwriting anything previously on the display.

Note

  • Writing outside the window, subwindow, or pad raises curses.error. Attempting to write to the lower right corner of a window, subwindow, or pad will cause an exception to be raised after the string is printed.
  • A bug in ncurses, the backend for this Python module, can cause SegFaults when resizing windows. This is fixed in ncurses-6.1-20190511. If you are stuck with an earlier ncurses, you can avoid triggering this if you do not call addstr() with a str that has embedded newlines. Instead, call addstr() separately for each line.
+
+window.attroff(attr)
+

Remove attribute attr from the “background” set applied to all writes to the current window.

+
+window.attron(attr)
+

Add attribute attr from the “background” set applied to all writes to the current window.

+
+window.attrset(attr)
+

Set the “background” set of attributes to attr. This set is initially 0 (no attributes).

+
+window.bkgd(ch[, attr])
+

Set the background property of the window to the character ch, with attributes attr. The change is then applied to every character position in that window:

  • The attribute of every character in the window is changed to the new background attribute.
  • Wherever the former background character appears, it is changed to the new background character.
+
+window.bkgdset(ch[, attr])
+

Set the window’s background. A window’s background consists of a character and any combination of attributes. The attribute part of the background is combined (OR’ed) with all non-blank characters that are written into the window. Both the character and attribute parts of the background are combined with the blank characters. The background becomes a property of the character and moves with the character through any scrolling and insert/delete line/character operations.

+
+window.border([ls[, rs[, ts[, bs[, tl[, tr[, bl[, br]]]]]]]])
+

Draw a border around the edges of the window. Each parameter specifies the character to use for a specific part of the border; see the table below for more details.

Note

A 0 value for any parameter will cause the default character to be used for that parameter. Keyword parameters can not be used. The defaults are listed in this table:

+ + + + + + + + +

Parameter

Description

Default value

ls

Left side

ACS_VLINE

rs

Right side

ACS_VLINE

ts

Top

ACS_HLINE

bs

Bottom

ACS_HLINE

tl

Upper-left corner

ACS_ULCORNER

tr

Upper-right corner

ACS_URCORNER

bl

Bottom-left corner

ACS_LLCORNER

br

Bottom-right corner

ACS_LRCORNER
+
+window.box([vertch, horch])
+

Similar to border(), but both ls and rs are vertch and both ts and bs are horch. The default corner characters are always used by this function.

+
+window.chgat(attr)
window.chgat(num, attr) +
window.chgat(y, x, attr) +
window.chgat(y, x, num, attr) +
+

Set the attributes of num characters at the current cursor position, or at position (y, x) if supplied. If num is not given or is -1, the attribute will be set on all the characters to the end of the line. This function moves cursor to position (y, x) if supplied. The changed line will be touched using the touchline() method so that the contents will be redisplayed by the next window refresh.

+
+window.clear()
+

Like erase(), but also cause the whole window to be repainted upon next call to refresh().

+
+window.clearok(flag)
+

If flag is True, the next call to refresh() will clear the window completely.

+
+window.clrtobot()
+

Erase from cursor to the end of the window: all lines below the cursor are deleted, and then the equivalent of clrtoeol() is performed.

+
+window.clrtoeol()
+

Erase from cursor to the end of the line.

+
+window.cursyncup()
+

Update the current cursor position of all the ancestors of the window to reflect the current cursor position of the window.

+
+window.delch([y, x])
+

Delete any character at (y, x).

+
+window.deleteln()
+

Delete the line under the cursor. All following lines are moved up by one line.

+
+window.derwin(begin_y, begin_x)
window.derwin(nlines, ncols, begin_y, begin_x) +
+

An abbreviation for “derive window”, derwin() is the same as calling subwin(), except that begin_y and begin_x are relative to the origin of the window, rather than relative to the entire screen. Return a window object for the derived window.

+
+window.echochar(ch[, attr])
+

Add character ch with attribute attr, and immediately call refresh() on the window.

+
+window.enclose(y, x)
+

Test whether the given pair of screen-relative character-cell coordinates are enclosed by the given window, returning True or False. It is useful for determining what subset of the screen windows enclose the location of a mouse event.

Changed in version 3.10: Previously it returned 1 or 0 instead of True or False.

+
+window.encoding
+

Encoding used to encode method arguments (Unicode strings and characters). The encoding attribute is inherited from the parent window when a subwindow is created, for example with window.subwin(). By default, current locale encoding is used (see locale.getencoding()).

New in version 3.3.

+
+window.erase()
+

Clear the window.

+
+window.getbegyx()
+

Return a tuple (y, x) of co-ordinates of upper-left corner.

+
+window.getbkgd()
+

Return the given window’s current background character/attribute pair.

+
+window.getch([y, x])
+

Get a character. Note that the integer returned does not have to be in ASCII range: function keys, keypad keys and so on are represented by numbers higher than 255. In no-delay mode, return -1 if there is no input, otherwise wait until a key is pressed.

+
+window.get_wch([y, x])
+

Get a wide character. Return a character for most keys, or an integer for function keys, keypad keys, and other special keys. In no-delay mode, raise an exception if there is no input.

New in version 3.3.

+
+window.getkey([y, x])
+

Get a character, returning a string instead of an integer, as getch() does. Function keys, keypad keys and other special keys return a multibyte string containing the key name. In no-delay mode, raise an exception if there is no input.

+
+window.getmaxyx()
+

Return a tuple (y, x) of the height and width of the window.

+
+window.getparyx()
+

Return the beginning coordinates of this window relative to its parent window as a tuple (y, x). Return (-1, -1) if this window has no parent.

+
+window.getstr()
window.getstr(n) +
window.getstr(y, x) +
window.getstr(y, x, n) +
+

Read a bytes object from the user, with primitive line editing capacity.

+
+window.getyx()
+

Return a tuple (y, x) of current cursor position relative to the window’s upper-left corner.

+
+window.hline(ch, n)
window.hline(y, x, ch, n) +
+

Display a horizontal line starting at (y, x) with length n consisting of the character ch.

+
+window.idcok(flag)
+

If flag is False, curses no longer considers using the hardware insert/delete character feature of the terminal; if flag is True, use of character insertion and deletion is enabled. When curses is first initialized, use of character insert/delete is enabled by default.

+
+window.idlok(flag)
+

If flag is True, curses will try and use hardware line editing facilities. Otherwise, line insertion/deletion are disabled.

+
+window.immedok(flag)
+

If flag is True, any change in the window image automatically causes the window to be refreshed; you no longer have to call refresh() yourself. However, it may degrade performance considerably, due to repeated calls to wrefresh. This option is disabled by default.

+
+window.inch([y, x])
+

Return the character at the given position in the window. The bottom 8 bits are the character proper, and upper bits are the attributes.

+
+window.insch(ch[, attr])
window.insch(y, x, ch[, attr]) +
+

Paint character ch at (y, x) with attributes attr, moving the line from position x right by one character.

+
+window.insdelln(nlines)
+

Insert nlines lines into the specified window above the current line. The nlines bottom lines are lost. For negative nlines, delete nlines lines starting with the one under the cursor, and move the remaining lines up. The bottom nlines lines are cleared. The current cursor position remains the same.

+
+window.insertln()
+

Insert a blank line under the cursor. All following lines are moved down by one line.

+
+window.insnstr(str, n[, attr])
window.insnstr(y, x, str, n[, attr]) +
+

Insert a character string (as many characters as will fit on the line) before the character under the cursor, up to n characters. If n is zero or negative, the entire string is inserted. All characters to the right of the cursor are shifted right, with the rightmost characters on the line being lost. The cursor position does not change (after moving to y, x, if specified).

+
+window.insstr(str[, attr])
window.insstr(y, x, str[, attr]) +
+

Insert a character string (as many characters as will fit on the line) before the character under the cursor. All characters to the right of the cursor are shifted right, with the rightmost characters on the line being lost. The cursor position does not change (after moving to y, x, if specified).

+
+window.instr([n])
window.instr(y, x[, n]) +
+

Return a bytes object of characters, extracted from the window starting at the current cursor position, or at y, x if specified. Attributes are stripped from the characters. If n is specified, instr() returns a string at most n characters long (exclusive of the trailing NUL).

+
+window.is_linetouched(line)
+

Return True if the specified line was modified since the last call to refresh(); otherwise return False. Raise a curses.error exception if line is not valid for the given window.

+
+window.is_wintouched()
+

Return True if the specified window was modified since the last call to refresh(); otherwise return False.

+
+window.keypad(flag)
+

If flag is True, escape sequences generated by some keys (keypad, function keys) will be interpreted by curses. If flag is False, escape sequences will be left as is in the input stream.

+
+window.leaveok(flag)
+

If flag is True, cursor is left where it is on update, instead of being at “cursor position.” This reduces cursor movement where possible. If possible the cursor will be made invisible.

If flag is False, cursor will always be at “cursor position” after an update.

+
+window.move(new_y, new_x)
+

Move cursor to (new_y, new_x).

+
+window.mvderwin(y, x)
+

Move the window inside its parent window. The screen-relative parameters of the window are not changed. This routine is used to display different parts of the parent window at the same physical position on the screen.

+
+window.mvwin(new_y, new_x)
+

Move the window so its upper-left corner is at (new_y, new_x).

+
+window.nodelay(flag)
+

If flag is True, getch() will be non-blocking.

+
+window.notimeout(flag)
+

If flag is True, escape sequences will not be timed out.

If flag is False, after a few milliseconds, an escape sequence will not be interpreted, and will be left in the input stream as is.

+
+window.noutrefresh()
+

Mark for refresh but wait. This function updates the data structure representing the desired state of the window, but does not force an update of the physical screen. To accomplish that, call doupdate().

+
+window.overlay(destwin[, sminrow, smincol, dminrow, dmincol, dmaxrow, dmaxcol])
+

Overlay the window on top of destwin. The windows need not be the same size, only the overlapping region is copied. This copy is non-destructive, which means that the current background character does not overwrite the old contents of destwin.

To get fine-grained control over the copied region, the second form of overlay() can be used. sminrow and smincol are the upper-left coordinates of the source window, and the other variables mark a rectangle in the destination window.

+
+window.overwrite(destwin[, sminrow, smincol, dminrow, dmincol, dmaxrow, dmaxcol])
+

Overwrite the window on top of destwin. The windows need not be the same size, in which case only the overlapping region is copied. This copy is destructive, which means that the current background character overwrites the old contents of destwin.

To get fine-grained control over the copied region, the second form of overwrite() can be used. sminrow and smincol are the upper-left coordinates of the source window, the other variables mark a rectangle in the destination window.

+
+window.putwin(file)
+

Write all data associated with the window into the provided file object. This information can be later retrieved using the getwin() function.

+
+window.redrawln(beg, num)
+

Indicate that the num screen lines, starting at line beg, are corrupted and should be completely redrawn on the next refresh() call.

+
+window.redrawwin()
+

Touch the entire window, causing it to be completely redrawn on the next refresh() call.

+
+window.refresh([pminrow, pmincol, sminrow, smincol, smaxrow, smaxcol])
+

Update the display immediately (sync actual screen with previous drawing/deleting methods).

The 6 optional arguments can only be specified when the window is a pad created with newpad(). The additional parameters are needed to indicate what part of the pad and screen are involved. pminrow and pmincol specify the upper left-hand corner of the rectangle to be displayed in the pad. sminrow, smincol, smaxrow, and smaxcol specify the edges of the rectangle to be displayed on the screen. The lower right-hand corner of the rectangle to be displayed in the pad is calculated from the screen coordinates, since the rectangles must be the same size. Both rectangles must be entirely contained within their respective structures. Negative values of pminrow, pmincol, sminrow, or smincol are treated as if they were zero.

+
+window.resize(nlines, ncols)
+

Reallocate storage for a curses window to adjust its dimensions to the specified values. If either dimension is larger than the current values, the window’s data is filled with blanks that have the current background rendition (as set by bkgdset()) merged into them.

+
+window.scroll([lines=1])
+

Scroll the screen or scrolling region upward by lines lines.

+
+window.scrollok(flag)
+

Control what happens when the cursor of a window is moved off the edge of the window or scrolling region, either as a result of a newline action on the bottom line, or typing the last character of the last line. If flag is False, the cursor is left on the bottom line. If flag is True, the window is scrolled up one line. Note that in order to get the physical scrolling effect on the terminal, it is also necessary to call idlok().

+
+window.setscrreg(top, bottom)
+

Set the scrolling region from line top to line bottom. All scrolling actions will take place in this region.

+
+window.standend()
+

Turn off the standout attribute. On some terminals this has the side effect of turning off all attributes.

+
+window.standout()
+

Turn on attribute A_STANDOUT.

+
+window.subpad(begin_y, begin_x)
window.subpad(nlines, ncols, begin_y, begin_x) +
+

Return a sub-window, whose upper-left corner is at (begin_y, begin_x), and whose width/height is ncols/nlines.

+
+window.subwin(begin_y, begin_x)
window.subwin(nlines, ncols, begin_y, begin_x) +
+

Return a sub-window, whose upper-left corner is at (begin_y, begin_x), and whose width/height is ncols/nlines.

By default, the sub-window will extend from the specified position to the lower right corner of the window.

+
+window.syncdown()
+

Touch each location in the window that has been touched in any of its ancestor windows. This routine is called by refresh(), so it should almost never be necessary to call it manually.

+
+window.syncok(flag)
+

If flag is True, then syncup() is called automatically whenever there is a change in the window.

+
+window.syncup()
+

Touch all locations in ancestors of the window that have been changed in the window.

+
+window.timeout(delay)
+

Set blocking or non-blocking read behavior for the window. If delay is negative, blocking read is used (which will wait indefinitely for input). If delay is zero, then non-blocking read is used, and getch() will return -1 if no input is waiting. If delay is positive, then getch() will block for delay milliseconds, and return -1 if there is still no input at the end of that time.

+
+window.touchline(start, count[, changed])
+

Pretend count lines have been changed, starting with line start. If changed is supplied, it specifies whether the affected lines are marked as having been changed (changed=True) or unchanged (changed=False).

+
+window.touchwin()
+

Pretend the whole window has been changed, for purposes of drawing optimizations.

+
+window.untouchwin()
+

Mark all lines in the window as unchanged since the last call to refresh().

+
+window.vline(ch, n[, attr])
window.vline(y, x, ch, n[, attr]) +
+

Display a vertical line starting at (y, x) with length n consisting of the character ch with attributes attr.

+

Constants

The curses module defines the following data members:

+curses.ERR
+

Some curses routines that return an integer, such as getch(), return ERR upon failure.

+
+curses.OK
+

Some curses routines that return an integer, such as napms(), return OK upon success.

+
+curses.version
+
+curses.__version__
+

A bytes object representing the current version of the module.

+
+curses.ncurses_version
+

A named tuple containing the three components of the ncurses library version: major, minor, and patch. All values are integers. The components can also be accessed by name, so curses.ncurses_version[0] is equivalent to curses.ncurses_version.major and so on.

Availability: if the ncurses library is used.

New in version 3.8.

+
+curses.COLORS
+

The maximum number of colors the terminal can support. It is defined only after the call to start_color().

+
+curses.COLOR_PAIRS
+

The maximum number of color pairs the terminal can support. It is defined only after the call to start_color().

+
+curses.COLS
+

The width of the screen, i.e., the number of columns. It is defined only after the call to initscr(). Updated by update_lines_cols(), resizeterm() and resize_term().

+
+curses.LINES
+

The height of the screen, i.e., the number of lines. It is defined only after the call to initscr(). Updated by update_lines_cols(), resizeterm() and resize_term().

+

Some constants are available to specify character cell attributes. The exact constants available are system dependent.

+ + + + + + + + + + + + + + + + + +

Attribute

Meaning
+
+curses.A_ALTCHARSET
+

Alternate character set mode
+
+

Blink mode
+
+curses.A_BOLD
+

Bold mode
+
+curses.A_DIM
+

Dim mode
+
+curses.A_INVIS
+

Invisible or blank mode
+
+curses.A_ITALIC
+

Italic mode
+
+curses.A_NORMAL
+

Normal attribute
+
+curses.A_PROTECT
+

Protected mode
+
+curses.A_REVERSE
+

Reverse background and foreground colors
+
+curses.A_STANDOUT
+

Standout mode
+
+curses.A_UNDERLINE
+

Underline mode
+
+curses.A_HORIZONTAL
+

Horizontal highlight
+
+curses.A_LEFT
+

Left highlight
+
+curses.A_LOW
+

Low highlight
+
+curses.A_RIGHT
+

Right highlight
+
+curses.A_TOP
+

Top highlight
+
+curses.A_VERTICAL
+

Vertical highlight

New in version 3.7: A_ITALIC was added.

Several constants are available to extract corresponding attributes returned by some methods.

+ + + +

Bit-mask

Meaning
+
+curses.A_ATTRIBUTES
+

Bit-mask to extract attributes
+
+curses.A_CHARTEXT
+

Bit-mask to extract a character
+
+curses.A_COLOR
+

Bit-mask to extract color-pair field information

Keys are referred to by integer constants with names starting with KEY_. The exact keycaps available are system dependent.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Key constant

Key
+
+curses.KEY_MIN
+

Minimum key value
+
+curses.KEY_BREAK
+

Break key (unreliable)
+
+curses.KEY_DOWN
+

Down-arrow
+
+curses.KEY_UP
+

Up-arrow
+
+curses.KEY_LEFT
+

Left-arrow
+
+curses.KEY_RIGHT
+

Right-arrow
+
+curses.KEY_HOME
+

Home key (upward+left arrow)
+
+curses.KEY_BACKSPACE
+

Backspace (unreliable)
+
+curses.KEY_F0
+

Function keys. Up to 64 function keys are supported.
+
+curses.KEY_Fn
+

Value of function key n
+
+curses.KEY_DL
+

Delete line
+
+curses.KEY_IL
+

Insert line
+
+curses.KEY_DC
+

Delete character
+
+curses.KEY_IC
+

Insert char or enter insert mode
+
+curses.KEY_EIC
+

Exit insert char mode
+
+curses.KEY_CLEAR
+

Clear screen
+
+curses.KEY_EOS
+

Clear to end of screen
+
+curses.KEY_EOL
+

Clear to end of line
+
+curses.KEY_SF
+

Scroll 1 line forward
+
+curses.KEY_SR
+

Scroll 1 line backward (reverse)
+
+curses.KEY_NPAGE
+

Next page
+
+curses.KEY_PPAGE
+

Previous page
+
+curses.KEY_STAB
+

Set tab
+
+curses.KEY_CTAB
+

Clear tab
+
+curses.KEY_CATAB
+

Clear all tabs
+
+curses.KEY_ENTER
+

Enter or send (unreliable)
+
+curses.KEY_SRESET
+

Soft (partial) reset (unreliable)
+
+curses.KEY_RESET
+

Reset or hard reset (unreliable)
+
+curses.KEY_PRINT
+

Print
+
+curses.KEY_LL
+

Home down or bottom (lower left)
+
+curses.KEY_A1
+

Upper left of keypad
+
+curses.KEY_A3
+

Upper right of keypad
+
+curses.KEY_B2
+

Center of keypad
+
+curses.KEY_C1
+

Lower left of keypad
+
+curses.KEY_C3
+

Lower right of keypad
+
+curses.KEY_BTAB
+

Back tab
+
+curses.KEY_BEG
+

Beg (beginning)
+
+curses.KEY_CANCEL
+

Cancel
+
+curses.KEY_CLOSE
+

Close
+
+curses.KEY_COMMAND
+

Cmd (command)
+
+curses.KEY_COPY
+

Copy
+
+curses.KEY_CREATE
+

Create
+
+curses.KEY_END
+

End
+
+curses.KEY_EXIT
+

Exit
+
+curses.KEY_FIND
+

Find
+
+curses.KEY_HELP
+

Help
+
+curses.KEY_MARK
+

Mark
+
+curses.KEY_MESSAGE
+

Message
+
+curses.KEY_MOVE
+

Move
+
+curses.KEY_NEXT
+

Next
+
+curses.KEY_OPEN
+

Open
+
+curses.KEY_OPTIONS
+

Options
+
+curses.KEY_PREVIOUS
+

Prev (previous)
+
+curses.KEY_REDO
+

Redo
+
+curses.KEY_REFERENCE
+

Ref (reference)
+
+curses.KEY_REFRESH
+

Refresh
+
+curses.KEY_REPLACE
+

Replace
+
+curses.KEY_RESTART
+

Restart
+
+curses.KEY_RESUME
+

Resume
+
+curses.KEY_SAVE
+

Save
+
+curses.KEY_SBEG
+

Shifted Beg (beginning)
+
+curses.KEY_SCANCEL
+

Shifted Cancel
+
+curses.KEY_SCOMMAND
+

Shifted Command
+
+curses.KEY_SCOPY
+

Shifted Copy
+
+curses.KEY_SCREATE
+

Shifted Create
+
+curses.KEY_SDC
+

Shifted Delete char
+
+curses.KEY_SDL
+

Shifted Delete line
+
+curses.KEY_SELECT
+

Select
+
+curses.KEY_SEND
+

Shifted End
+
+curses.KEY_SEOL
+

Shifted Clear line
+
+curses.KEY_SEXIT
+

Shifted Exit
+
+curses.KEY_SFIND
+

Shifted Find
+
+curses.KEY_SHELP
+

Shifted Help
+
+curses.KEY_SHOME
+

Shifted Home
+
+curses.KEY_SIC
+

Shifted Input
+
+curses.KEY_SLEFT
+

Shifted Left arrow
+
+curses.KEY_SMESSAGE
+

Shifted Message
+
+curses.KEY_SMOVE
+

Shifted Move
+
+curses.KEY_SNEXT
+

Shifted Next
+
+curses.KEY_SOPTIONS
+

Shifted Options
+
+curses.KEY_SPREVIOUS
+

Shifted Prev
+
+curses.KEY_SPRINT
+

Shifted Print
+
+curses.KEY_SREDO
+

Shifted Redo
+
+curses.KEY_SREPLACE
+

Shifted Replace
+
+curses.KEY_SRIGHT
+

Shifted Right arrow
+
+curses.KEY_SRSUME
+

Shifted Resume
+
+curses.KEY_SSAVE
+

Shifted Save
+
+curses.KEY_SSUSPEND
+

Shifted Suspend
+
+curses.KEY_SUNDO
+

Shifted Undo
+
+curses.KEY_SUSPEND
+

Suspend
+
+curses.KEY_UNDO
+

Undo
+
+curses.KEY_MOUSE
+

Mouse event has occurred
+
+curses.KEY_RESIZE
+

Terminal resize event
+
+curses.KEY_MAX
+

Maximum key value

On VT100s and their software emulations, such as X terminal emulators, there are normally at least four function keys (KEY_F1, KEY_F2, KEY_F3, KEY_F4) available, and the arrow keys mapped to KEY_UP, KEY_DOWN, KEY_LEFT and KEY_RIGHT in the obvious way. If your machine has a PC keyboard, it is safe to expect arrow keys and twelve function keys (older PC keyboards may have only ten function keys); also, the following keypad mappings are standard:

+ + + + + + +

Keycap

Constant

Insert

KEY_IC

Delete

KEY_DC

Home

KEY_HOME

End

KEY_END

Page Up

KEY_PPAGE

Page Down

KEY_NPAGE

The following table lists characters from the alternate character set. These are inherited from the VT100 terminal, and will generally be available on software emulations such as X terminals. When there is no graphic available, curses falls back on a crude printable ASCII approximation.

Note

These are available only after initscr() has been called.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

ACS code

Meaning
+
+curses.ACS_BBSS
+

alternate name for upper right corner
+
+curses.ACS_BLOCK
+

solid square block
+
+curses.ACS_BOARD
+

board of squares
+
+curses.ACS_BSBS
+

alternate name for horizontal line
+
+curses.ACS_BSSB
+

alternate name for upper left corner
+
+curses.ACS_BSSS
+

alternate name for top tee
+
+curses.ACS_BTEE
+

bottom tee
+
+curses.ACS_BULLET
+

bullet
+
+curses.ACS_CKBOARD
+

checker board (stipple)
+
+curses.ACS_DARROW
+

arrow pointing down
+
+curses.ACS_DEGREE
+

degree symbol
+
+curses.ACS_DIAMOND
+

diamond
+
+curses.ACS_GEQUAL
+

greater-than-or-equal-to
+
+curses.ACS_HLINE
+

horizontal line
+
+curses.ACS_LANTERN
+

lantern symbol
+
+curses.ACS_LARROW
+

left arrow
+
+curses.ACS_LEQUAL
+

less-than-or-equal-to
+
+curses.ACS_LLCORNER
+

lower left-hand corner
+
+curses.ACS_LRCORNER
+

lower right-hand corner
+
+curses.ACS_LTEE
+

left tee
+
+curses.ACS_NEQUAL
+

not-equal sign
+
+curses.ACS_PI
+

letter pi
+
+curses.ACS_PLMINUS
+

plus-or-minus sign
+
+curses.ACS_PLUS
+

big plus sign
+
+curses.ACS_RARROW
+

right arrow
+
+curses.ACS_RTEE
+

right tee
+
+curses.ACS_S1
+

scan line 1
+
+curses.ACS_S3
+

scan line 3
+
+curses.ACS_S7
+

scan line 7
+
+curses.ACS_S9
+

scan line 9
+
+curses.ACS_SBBS
+

alternate name for lower right corner
+
+curses.ACS_SBSB
+

alternate name for vertical line
+
+curses.ACS_SBSS
+

alternate name for right tee
+
+curses.ACS_SSBB
+

alternate name for lower left corner
+
+curses.ACS_SSBS
+

alternate name for bottom tee
+
+curses.ACS_SSSB
+

alternate name for left tee
+
+curses.ACS_SSSS
+

alternate name for crossover or big plus
+
+curses.ACS_STERLING
+

pound sterling
+
+curses.ACS_TTEE
+

top tee
+
+curses.ACS_UARROW
+

up arrow
+
+curses.ACS_ULCORNER
+

upper left corner
+
+curses.ACS_URCORNER
+

upper right corner
+
+curses.ACS_VLINE
+

vertical line

The following table lists mouse button constants used by getmouse():

+ + + + + + + + +

Mouse button constant

Meaning
+
+curses.BUTTONn_PRESSED
+

Mouse button n pressed
+
+curses.BUTTONn_RELEASED
+

Mouse button n released
+
+curses.BUTTONn_CLICKED
+

Mouse button n clicked
+
+curses.BUTTONn_DOUBLE_CLICKED
+

Mouse button n double clicked
+
+curses.BUTTONn_TRIPLE_CLICKED
+

Mouse button n triple clicked
+
+curses.BUTTON_SHIFT
+

Shift was down during button state change
+
+curses.BUTTON_CTRL
+

Control was down during button state change
+
+curses.BUTTON_ALT
+

Control was down during button state change

Changed in version 3.10: The BUTTON5_* constants are now exposed if they are provided by the underlying curses library.

The following table lists the predefined colors:

+ + + + + + + + +

Constant

Color
+
+curses.COLOR_BLACK
+

Black
+
+curses.COLOR_BLUE
+

Blue
+
+curses.COLOR_CYAN
+

Cyan (light greenish blue)
+
+curses.COLOR_GREEN
+

Green
+
+curses.COLOR_MAGENTA
+

Magenta (purplish red)
+
+curses.COLOR_RED
+

Red
+
+curses.COLOR_WHITE
+

White
+
+curses.COLOR_YELLOW
+

Yellow
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/curses.html +

+
diff --git a/devdocs/python~3.12/library%2Fcurses.panel.html b/devdocs/python~3.12/library%2Fcurses.panel.html new file mode 100644 index 00000000..5943364b --- /dev/null +++ b/devdocs/python~3.12/library%2Fcurses.panel.html @@ -0,0 +1,54 @@ +

curses.panel — A panel stack extension for curses

Panels are windows with the added feature of depth, so they can be stacked on top of each other, and only the visible portions of each window will be displayed. Panels can be added, moved up or down in the stack, and removed.

Functions

The module curses.panel defines the following functions:

+curses.panel.bottom_panel()
+

Returns the bottom panel in the panel stack.

+
+curses.panel.new_panel(win)
+

Returns a panel object, associating it with the given window win. Be aware that you need to keep the returned panel object referenced explicitly. If you don’t, the panel object is garbage collected and removed from the panel stack.

+
+curses.panel.top_panel()
+

Returns the top panel in the panel stack.

+
+curses.panel.update_panels()
+

Updates the virtual screen after changes in the panel stack. This does not call curses.doupdate(), so you’ll have to do this yourself.

+

Panel Objects

Panel objects, as returned by new_panel() above, are windows with a stacking order. There’s always a window associated with a panel which determines the content, while the panel methods are responsible for the window’s depth in the panel stack.

Panel objects have the following methods:

+Panel.above()
+

Returns the panel above the current panel.

+
+Panel.below()
+

Returns the panel below the current panel.

+
+Panel.bottom()
+

Push the panel to the bottom of the stack.

+
+Panel.hidden()
+

Returns True if the panel is hidden (not visible), False otherwise.

+
+Panel.hide()
+

Hide the panel. This does not delete the object, it just makes the window on screen invisible.

+
+Panel.move(y, x)
+

Move the panel to the screen coordinates (y, x).

+
+Panel.replace(win)
+

Change the window associated with the panel to the window win.

+
+Panel.set_userptr(obj)
+

Set the panel’s user pointer to obj. This is used to associate an arbitrary piece of data with the panel, and can be any Python object.

+
+Panel.show()
+

Display the panel (which might have been hidden).

+
+Panel.top()
+

Push panel to the top of the stack.

+
+Panel.userptr()
+

Returns the user pointer for the panel. This might be any Python object.

+
+Panel.window()
+

Returns the window object associated with the panel.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/curses.panel.html +

+
diff --git a/devdocs/python~3.12/library%2Fcustominterp.html b/devdocs/python~3.12/library%2Fcustominterp.html new file mode 100644 index 00000000..53c3a8c8 --- /dev/null +++ b/devdocs/python~3.12/library%2Fcustominterp.html @@ -0,0 +1,7 @@ +

Custom Python Interpreters

The modules described in this chapter allow writing interfaces similar to Python’s interactive interpreter. If you want a Python interpreter that supports some special feature in addition to the Python language, you should look at the code module. (The codeop module is lower-level, used to support compiling a possibly incomplete chunk of Python code.)

The full list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/custominterp.html +

+
diff --git a/devdocs/python~3.12/library%2Fdataclasses.html b/devdocs/python~3.12/library%2Fdataclasses.html new file mode 100644 index 00000000..117fccd4 --- /dev/null +++ b/devdocs/python~3.12/library%2Fdataclasses.html @@ -0,0 +1,258 @@ +

dataclasses — Data Classes

Source code: Lib/dataclasses.py

This module provides a decorator and functions for automatically adding generated special methods such as __init__() and __repr__() to user-defined classes. It was originally described in PEP 557.

The member variables to use in these generated methods are defined using PEP 526 type annotations. For example, this code:

from dataclasses import dataclass
+
+@dataclass
+class InventoryItem:
+    """Class for keeping track of an item in inventory."""
+    name: str
+    unit_price: float
+    quantity_on_hand: int = 0
+
+    def total_cost(self) -> float:
+        return self.unit_price * self.quantity_on_hand
+

will add, among other things, a __init__() that looks like:

def __init__(self, name: str, unit_price: float, quantity_on_hand: int = 0):
+    self.name = name
+    self.unit_price = unit_price
+    self.quantity_on_hand = quantity_on_hand
+

Note that this method is automatically added to the class: it is not directly specified in the InventoryItem definition shown above.

New in version 3.7.

Module contents

+@dataclasses.dataclass(*, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False)
+

This function is a decorator that is used to add generated special methods to classes, as described below.

The dataclass() decorator examines the class to find fields. A field is defined as a class variable that has a type annotation. With two exceptions described below, nothing in dataclass() examines the type specified in the variable annotation.

The order of the fields in all of the generated methods is the order in which they appear in the class definition.

The dataclass() decorator will add various “dunder” methods to the class, described below. If any of the added methods already exist in the class, the behavior depends on the parameter, as documented below. The decorator returns the same class that it is called on; no new class is created.

If dataclass() is used just as a simple decorator with no parameters, it acts as if it has the default values documented in this signature. That is, these three uses of dataclass() are equivalent:

@dataclass
+class C:
+    ...
+
+@dataclass()
+class C:
+    ...
+
+@dataclass(init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False,
+           match_args=True, kw_only=False, slots=False, weakref_slot=False)
+class C:
+    ...
+

The parameters to dataclass() are:

  • +

    init: If true (the default), a __init__() method will be generated.

    If the class already defines __init__(), this parameter is ignored.

  • +

    repr: If true (the default), a __repr__() method will be generated. The generated repr string will have the class name and the name and repr of each field, in the order they are defined in the class. Fields that are marked as being excluded from the repr are not included. For example: InventoryItem(name='widget', unit_price=3.0, quantity_on_hand=10).

    If the class already defines __repr__(), this parameter is ignored.

  • +

    eq: If true (the default), an __eq__() method will be generated. This method compares the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type.

    If the class already defines __eq__(), this parameter is ignored.

  • +

    order: If true (the default is False), __lt__(), __le__(), __gt__(), and __ge__() methods will be generated. These compare the class as if it were a tuple of its fields, in order. Both instances in the comparison must be of the identical type. If order is true and eq is false, a ValueError is raised.

    If the class already defines any of __lt__(), __le__(), __gt__(), or __ge__(), then TypeError is raised.

  • +

    unsafe_hash: If False (the default), a __hash__() method is generated according to how eq and frozen are set.

    __hash__() is used by built-in hash(), and when objects are added to hashed collections such as dictionaries and sets. Having a __hash__() implies that instances of the class are immutable. Mutability is a complicated property that depends on the programmer’s intent, the existence and behavior of __eq__(), and the values of the eq and frozen flags in the dataclass() decorator.

    By default, dataclass() will not implicitly add a __hash__() method unless it is safe to do so. Neither will it add or change an existing explicitly defined __hash__() method. Setting the class attribute __hash__ = None has a specific meaning to Python, as described in the __hash__() documentation.

    If __hash__() is not explicitly defined, or if it is set to None, then dataclass() may add an implicit __hash__() method. Although not recommended, you can force dataclass() to create a __hash__() method with unsafe_hash=True. This might be the case if your class is logically immutable but can nonetheless be mutated. This is a specialized use case and should be considered carefully.

    Here are the rules governing implicit creation of a __hash__() method. Note that you cannot both have an explicit __hash__() method in your dataclass and set unsafe_hash=True; this will result in a TypeError.

    If eq and frozen are both true, by default dataclass() will generate a __hash__() method for you. If eq is true and frozen is false, __hash__() will be set to None, marking it unhashable (which it is, since it is mutable). If eq is false, __hash__() will be left untouched meaning the __hash__() method of the superclass will be used (if the superclass is object, this means it will fall back to id-based hashing).

  • +frozen: If true (the default is False), assigning to fields will generate an exception. This emulates read-only frozen instances. If __setattr__() or __delattr__() is defined in the class, then TypeError is raised. See the discussion below.
  • +match_args: If true (the default is True), the __match_args__ tuple will be created from the list of parameters to the generated __init__() method (even if __init__() is not generated, see above). If false, or if __match_args__ is already defined in the class, then __match_args__ will not be generated.

New in version 3.10.

  • +kw_only: If true (the default value is False), then all fields will be marked as keyword-only. If a field is marked as keyword-only, then the only effect is that the __init__() parameter generated from a keyword-only field must be specified with a keyword when __init__() is called. There is no effect on any other aspect of dataclasses. See the parameter glossary entry for details. Also see the KW_ONLY section.

New in version 3.10.

  • +slots: If true (the default is False), __slots__ attribute will be generated and new class will be returned instead of the original one. If __slots__ is already defined in the class, then TypeError is raised.

New in version 3.10.

Changed in version 3.11: If a field name is already included in the __slots__ of a base class, it will not be included in the generated __slots__ to prevent overriding them. Therefore, do not use __slots__ to retrieve the field names of a dataclass. Use fields() instead. To be able to determine inherited slots, base class __slots__ may be any iterable, but not an iterator.

  • +weakref_slot: If true (the default is False), add a slot named “__weakref__”, which is required to make an instance weakref-able. It is an error to specify weakref_slot=True without also specifying slots=True.

New in version 3.11.

fields may optionally specify a default value, using normal Python syntax:

@dataclass
+class C:
+    a: int       # 'a' has no default value
+    b: int = 0   # assign a default value for 'b'
+

In this example, both a and b will be included in the added __init__() method, which will be defined as:

def __init__(self, a: int, b: int = 0):
+

TypeError will be raised if a field without a default value follows a field with a default value. This is true whether this occurs in a single class, or as a result of class inheritance.

+
+dataclasses.field(*, default=MISSING, default_factory=MISSING, init=True, repr=True, hash=None, compare=True, metadata=None, kw_only=MISSING)
+

For common and simple use cases, no other functionality is required. There are, however, some dataclass features that require additional per-field information. To satisfy this need for additional information, you can replace the default field value with a call to the provided field() function. For example:

@dataclass
+class C:
+    mylist: list[int] = field(default_factory=list)
+
+c = C()
+c.mylist += [1, 2, 3]
+

As shown above, the MISSING value is a sentinel object used to detect if some parameters are provided by the user. This sentinel is used because None is a valid value for some parameters with a distinct meaning. No code should directly use the MISSING value.

The parameters to field() are:

  • +default: If provided, this will be the default value for this field. This is needed because the field() call itself replaces the normal position of the default value.
  • +default_factory: If provided, it must be a zero-argument callable that will be called when a default value is needed for this field. Among other purposes, this can be used to specify fields with mutable default values, as discussed below. It is an error to specify both default and default_factory.
  • +init: If true (the default), this field is included as a parameter to the generated __init__() method.
  • +repr: If true (the default), this field is included in the string returned by the generated __repr__() method.
  • +

    hash: This can be a bool or None. If true, this field is included in the generated __hash__() method. If None (the default), use the value of compare: this would normally be the expected behavior. A field should be considered in the hash if it’s used for comparisons. Setting this value to anything other than None is discouraged.

    One possible reason to set hash=False but compare=True would be if a field is expensive to compute a hash value for, that field is needed for equality testing, and there are other fields that contribute to the type’s hash value. Even if a field is excluded from the hash, it will still be used for comparisons.

  • +compare: If true (the default), this field is included in the generated equality and comparison methods (__eq__(), __gt__(), et al.).
  • +metadata: This can be a mapping or None. None is treated as an empty dict. This value is wrapped in MappingProxyType() to make it read-only, and exposed on the Field object. It is not used at all by Data Classes, and is provided as a third-party extension mechanism. Multiple third-parties can each have their own key, to use as a namespace in the metadata.
  • +kw_only: If true, this field will be marked as keyword-only. This is used when the generated __init__() method’s parameters are computed.

New in version 3.10.

If the default value of a field is specified by a call to field(), then the class attribute for this field will be replaced by the specified default value. If no default is provided, then the class attribute will be deleted. The intent is that after the dataclass() decorator runs, the class attributes will all contain the default values for the fields, just as if the default value itself were specified. For example, after:

@dataclass
+class C:
+    x: int
+    y: int = field(repr=False)
+    z: int = field(repr=False, default=10)
+    t: int = 20
+

The class attribute C.z will be 10, the class attribute C.t will be 20, and the class attributes C.x and C.y will not be set.

+
+class dataclasses.Field
+

Field objects describe each defined field. These objects are created internally, and are returned by the fields() module-level method (see below). Users should never instantiate a Field object directly. Its documented attributes are:

  • +name: The name of the field.
  • +type: The type of the field.
  • +default, default_factory, init, repr, hash, compare, metadata, and kw_only have the identical meaning and values as they do in the field() function.

Other attributes may exist, but they are private and must not be inspected or relied on.

+
+dataclasses.fields(class_or_instance)
+

Returns a tuple of Field objects that define the fields for this dataclass. Accepts either a dataclass, or an instance of a dataclass. Raises TypeError if not passed a dataclass or instance of one. Does not return pseudo-fields which are ClassVar or InitVar.

+
+dataclasses.asdict(obj, *, dict_factory=dict)
+

Converts the dataclass obj to a dict (by using the factory function dict_factory). Each dataclass is converted to a dict of its fields, as name: value pairs. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with copy.deepcopy().

Example of using asdict() on nested dataclasses:

@dataclass
+class Point:
+     x: int
+     y: int
+
+@dataclass
+class C:
+     mylist: list[Point]
+
+p = Point(10, 20)
+assert asdict(p) == {'x': 10, 'y': 20}
+
+c = C([Point(0, 0), Point(10, 4)])
+assert asdict(c) == {'mylist': [{'x': 0, 'y': 0}, {'x': 10, 'y': 4}]}
+

To create a shallow copy, the following workaround may be used:

dict((field.name, getattr(obj, field.name)) for field in fields(obj))
+

asdict() raises TypeError if obj is not a dataclass instance.

+
+dataclasses.astuple(obj, *, tuple_factory=tuple)
+

Converts the dataclass obj to a tuple (by using the factory function tuple_factory). Each dataclass is converted to a tuple of its field values. dataclasses, dicts, lists, and tuples are recursed into. Other objects are copied with copy.deepcopy().

Continuing from the previous example:

assert astuple(p) == (10, 20)
+assert astuple(c) == ([(0, 0), (10, 4)],)
+

To create a shallow copy, the following workaround may be used:

tuple(getattr(obj, field.name) for field in dataclasses.fields(obj))
+

astuple() raises TypeError if obj is not a dataclass instance.

+
+dataclasses.make_dataclass(cls_name, fields, *, bases=(), namespace=None, init=True, repr=True, eq=True, order=False, unsafe_hash=False, frozen=False, match_args=True, kw_only=False, slots=False, weakref_slot=False, module=None)
+

Creates a new dataclass with name cls_name, fields as defined in fields, base classes as given in bases, and initialized with a namespace as given in namespace. fields is an iterable whose elements are each either name, (name, type), or (name, type, Field). If just name is supplied, typing.Any is used for type. The values of init, repr, eq, order, unsafe_hash, frozen, match_args, kw_only, slots, and weakref_slot have the same meaning as they do in dataclass().

If module is defined, the __module__ attribute of the dataclass is set to that value. By default, it is set to the module name of the caller.

This function is not strictly required, because any Python mechanism for creating a new class with __annotations__ can then apply the dataclass() function to convert that class to a dataclass. This function is provided as a convenience. For example:

C = make_dataclass('C',
+                   [('x', int),
+                     'y',
+                    ('z', int, field(default=5))],
+                   namespace={'add_one': lambda self: self.x + 1})
+

Is equivalent to:

@dataclass
+class C:
+    x: int
+    y: 'typing.Any'
+    z: int = 5
+
+    def add_one(self):
+        return self.x + 1
+
+
+dataclasses.replace(obj, /, **changes)
+

Creates a new object of the same type as obj, replacing fields with values from changes. If obj is not a Data Class, raises TypeError. If values in changes do not specify fields, raises TypeError.

The newly returned object is created by calling the __init__() method of the dataclass. This ensures that __post_init__(), if present, is also called.

Init-only variables without default values, if any exist, must be specified on the call to replace() so that they can be passed to __init__() and __post_init__().

It is an error for changes to contain any fields that are defined as having init=False. A ValueError will be raised in this case.

Be forewarned about how init=False fields work during a call to replace(). They are not copied from the source object, but rather are initialized in __post_init__(), if they’re initialized at all. It is expected that init=False fields will be rarely and judiciously used. If they are used, it might be wise to have alternate class constructors, or perhaps a custom replace() (or similarly named) method which handles instance copying.

+
+dataclasses.is_dataclass(obj)
+

Return True if its parameter is a dataclass or an instance of one, otherwise return False.

If you need to know if a class is an instance of a dataclass (and not a dataclass itself), then add a further check for not +isinstance(obj, type):

def is_dataclass_instance(obj):
+    return is_dataclass(obj) and not isinstance(obj, type)
+
+
+dataclasses.MISSING
+

A sentinel value signifying a missing default or default_factory.

+
+dataclasses.KW_ONLY
+

A sentinel value used as a type annotation. Any fields after a pseudo-field with the type of KW_ONLY are marked as keyword-only fields. Note that a pseudo-field of type KW_ONLY is otherwise completely ignored. This includes the name of such a field. By convention, a name of _ is used for a KW_ONLY field. Keyword-only fields signify __init__() parameters that must be specified as keywords when the class is instantiated.

In this example, the fields y and z will be marked as keyword-only fields:

@dataclass
+class Point:
+    x: float
+    _: KW_ONLY
+    y: float
+    z: float
+
+p = Point(0, y=1.5, z=2.0)
+

In a single dataclass, it is an error to specify more than one field whose type is KW_ONLY.

New in version 3.10.

+
+exception dataclasses.FrozenInstanceError
+

Raised when an implicitly defined __setattr__() or __delattr__() is called on a dataclass which was defined with frozen=True. It is a subclass of AttributeError.

+

Post-init processing

+dataclasses.__post_init__()
+

When defined on the class, it will be called by the generated __init__(), normally as self.__post_init__(). However, if any InitVar fields are defined, they will also be passed to __post_init__() in the order they were defined in the class. If no __init__() method is generated, then __post_init__() will not automatically be called.

Among other uses, this allows for initializing field values that depend on one or more other fields. For example:

@dataclass
+class C:
+    a: float
+    b: float
+    c: float = field(init=False)
+
+    def __post_init__(self):
+        self.c = self.a + self.b
+
+

The __init__() method generated by dataclass() does not call base class __init__() methods. If the base class has an __init__() method that has to be called, it is common to call this method in a __post_init__() method:

@dataclass
+class Rectangle:
+    height: float
+    width: float
+
+@dataclass
+class Square(Rectangle):
+    side: float
+
+    def __post_init__(self):
+        super().__init__(self.side, self.side)
+

Note, however, that in general the dataclass-generated __init__() methods don’t need to be called, since the derived dataclass will take care of initializing all fields of any base class that is a dataclass itself.

See the section below on init-only variables for ways to pass parameters to __post_init__(). Also see the warning about how replace() handles init=False fields.

Class variables

One of the few places where dataclass() actually inspects the type of a field is to determine if a field is a class variable as defined in PEP 526. It does this by checking if the type of the field is typing.ClassVar. If a field is a ClassVar, it is excluded from consideration as a field and is ignored by the dataclass mechanisms. Such ClassVar pseudo-fields are not returned by the module-level fields() function.

Init-only variables

Another place where dataclass() inspects a type annotation is to determine if a field is an init-only variable. It does this by seeing if the type of a field is of type dataclasses.InitVar. If a field is an InitVar, it is considered a pseudo-field called an init-only field. As it is not a true field, it is not returned by the module-level fields() function. Init-only fields are added as parameters to the generated __init__() method, and are passed to the optional __post_init__() method. They are not otherwise used by dataclasses.

For example, suppose a field will be initialized from a database, if a value is not provided when creating the class:

@dataclass
+class C:
+    i: int
+    j: int | None = None
+    database: InitVar[DatabaseType | None] = None
+
+    def __post_init__(self, database):
+        if self.j is None and database is not None:
+            self.j = database.lookup('j')
+
+c = C(10, database=my_database)
+

In this case, fields() will return Field objects for i and j, but not for database.

Frozen instances

It is not possible to create truly immutable Python objects. However, by passing frozen=True to the dataclass() decorator you can emulate immutability. In that case, dataclasses will add __setattr__() and __delattr__() methods to the class. These methods will raise a FrozenInstanceError when invoked.

There is a tiny performance penalty when using frozen=True: __init__() cannot use simple assignment to initialize fields, and must use object.__setattr__().

Inheritance

When the dataclass is being created by the dataclass() decorator, it looks through all of the class’s base classes in reverse MRO (that is, starting at object) and, for each dataclass that it finds, adds the fields from that base class to an ordered mapping of fields. After all of the base class fields are added, it adds its own fields to the ordered mapping. All of the generated methods will use this combined, calculated ordered mapping of fields. Because the fields are in insertion order, derived classes override base classes. An example:

@dataclass
+class Base:
+    x: Any = 15.0
+    y: int = 0
+
+@dataclass
+class C(Base):
+    z: int = 10
+    x: int = 15
+

The final list of fields is, in order, x, y, z. The final type of x is int, as specified in class C.

The generated __init__() method for C will look like:

def __init__(self, x: int = 15, y: int = 0, z: int = 10):
+

Re-ordering of keyword-only parameters in __init__()

After the parameters needed for __init__() are computed, any keyword-only parameters are moved to come after all regular (non-keyword-only) parameters. This is a requirement of how keyword-only parameters are implemented in Python: they must come after non-keyword-only parameters.

In this example, Base.y, Base.w, and D.t are keyword-only fields, and Base.x and D.z are regular fields:

@dataclass
+class Base:
+    x: Any = 15.0
+    _: KW_ONLY
+    y: int = 0
+    w: int = 1
+
+@dataclass
+class D(Base):
+    z: int = 10
+    t: int = field(kw_only=True, default=0)
+

The generated __init__() method for D will look like:

def __init__(self, x: Any = 15.0, z: int = 10, *, y: int = 0, w: int = 1, t: int = 0):
+

Note that the parameters have been re-ordered from how they appear in the list of fields: parameters derived from regular fields are followed by parameters derived from keyword-only fields.

The relative ordering of keyword-only parameters is maintained in the re-ordered __init__() parameter list.

Default factory functions

If a field() specifies a default_factory, it is called with zero arguments when a default value for the field is needed. For example, to create a new instance of a list, use:

mylist: list = field(default_factory=list)
+

If a field is excluded from __init__() (using init=False) and the field also specifies default_factory, then the default factory function will always be called from the generated __init__() function. This happens because there is no other way to give the field an initial value.

Mutable default values

Python stores default member variable values in class attributes. Consider this example, not using dataclasses:

class C:
+    x = []
+    def add(self, element):
+        self.x.append(element)
+
+o1 = C()
+o2 = C()
+o1.add(1)
+o2.add(2)
+assert o1.x == [1, 2]
+assert o1.x is o2.x
+

Note that the two instances of class C share the same class variable x, as expected.

Using dataclasses, if this code was valid:

@dataclass
+class D:
+    x: list = []      # This code raises ValueError
+    def add(self, element):
+        self.x += element
+

it would generate code similar to:

class D:
+    x = []
+    def __init__(self, x=x):
+        self.x = x
+    def add(self, element):
+        self.x += element
+
+assert D().x is D().x
+

This has the same issue as the original example using class C. That is, two instances of class D that do not specify a value for x when creating a class instance will share the same copy of x. Because dataclasses just use normal Python class creation they also share this behavior. There is no general way for Data Classes to detect this condition. Instead, the dataclass() decorator will raise a ValueError if it detects an unhashable default parameter. The assumption is that if a value is unhashable, it is mutable. This is a partial solution, but it does protect against many common errors.

Using default factory functions is a way to create new instances of mutable types as default values for fields:

@dataclass
+class D:
+    x: list = field(default_factory=list)
+
+assert D().x is not D().x
+

Changed in version 3.11: Instead of looking for and disallowing objects of type list, dict, or set, unhashable objects are now not allowed as default values. Unhashability is used to approximate mutability.

Descriptor-typed fields

Fields that are assigned descriptor objects as their default value have the following special behaviors:

  • The value for the field passed to the dataclass’s __init__ method is passed to the descriptor’s __set__ method rather than overwriting the descriptor object.
  • Similarly, when getting or setting the field, the descriptor’s __get__ or __set__ method is called rather than returning or overwriting the descriptor object.
  • To determine whether a field contains a default value, dataclasses will call the descriptor’s __get__ method using its class access form (i.e. descriptor.__get__(obj=None, type=cls). If the descriptor returns a value in this case, it will be used as the field’s default. On the other hand, if the descriptor raises AttributeError in this situation, no default value will be provided for the field.
class IntConversionDescriptor:
+    def __init__(self, *, default):
+        self._default = default
+
+    def __set_name__(self, owner, name):
+        self._name = "_" + name
+
+    def __get__(self, obj, type):
+        if obj is None:
+            return self._default
+
+        return getattr(obj, self._name, self._default)
+
+    def __set__(self, obj, value):
+        setattr(obj, self._name, int(value))
+
+@dataclass
+class InventoryItem:
+    quantity_on_hand: IntConversionDescriptor = IntConversionDescriptor(default=100)
+
+i = InventoryItem()
+print(i.quantity_on_hand)   # 100
+i.quantity_on_hand = 2.5    # calls __set__ with 2.5
+print(i.quantity_on_hand)   # 2
+

Note that if a field is annotated with a descriptor type, but is not assigned a descriptor object as its default value, the field will act like a normal field.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/dataclasses.html +

+
diff --git a/devdocs/python~3.12/library%2Fdatatypes.html b/devdocs/python~3.12/library%2Fdatatypes.html new file mode 100644 index 00000000..4912150e --- /dev/null +++ b/devdocs/python~3.12/library%2Fdatatypes.html @@ -0,0 +1,33 @@ +

Data Types

The modules described in this chapter provide a variety of specialized data types such as dates and times, fixed-type arrays, heap queues, double-ended queues, and enumerations.

Python also provides some built-in data types, in particular, dict, list, set and frozenset, and tuple. The str class is used to hold Unicode strings, and the bytes and bytearray classes are used to hold binary data.

The following modules are documented in this chapter:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/datatypes.html +

+
diff --git a/devdocs/python~3.12/library%2Fdatetime.html b/devdocs/python~3.12/library%2Fdatetime.html new file mode 100644 index 00000000..9fcc6aee --- /dev/null +++ b/devdocs/python~3.12/library%2Fdatetime.html @@ -0,0 +1,1111 @@ +

datetime — Basic date and time types

Source code: Lib/datetime.py

The datetime module supplies classes for manipulating dates and times.

While date and time arithmetic is supported, the focus of the implementation is on efficient attribute extraction for output formatting and manipulation.

Tip

Skip to the format codes.

See also

+Module calendar +
+
+

General calendar related functions.

+Module time +
+
+

Time access and conversions.

+Module zoneinfo +
+
+

Concrete time zones representing the IANA time zone database.

Package dateutil +
+
+

Third-party library with expanded time zone and parsing support.

Package DateType +
+
+

Third-party library that introduces distinct static types to e.g. allow static type checkers to differentiate between naive and aware datetimes.

Aware and Naive Objects

Date and time objects may be categorized as “aware” or “naive” depending on whether or not they include timezone information.

With sufficient knowledge of applicable algorithmic and political time adjustments, such as time zone and daylight saving time information, an aware object can locate itself relative to other aware objects. An aware object represents a specific moment in time that is not open to interpretation. 1

A naive object does not contain enough information to unambiguously locate itself relative to other date/time objects. Whether a naive object represents Coordinated Universal Time (UTC), local time, or time in some other timezone is purely up to the program, just like it is up to the program whether a particular number represents metres, miles, or mass. Naive objects are easy to understand and to work with, at the cost of ignoring some aspects of reality.

For applications requiring aware objects, datetime and time objects have an optional time zone information attribute, tzinfo, that can be set to an instance of a subclass of the abstract tzinfo class. These tzinfo objects capture information about the offset from UTC time, the time zone name, and whether daylight saving time is in effect.

Only one concrete tzinfo class, the timezone class, is supplied by the datetime module. The timezone class can represent simple timezones with fixed offsets from UTC, such as UTC itself or North American EST and EDT timezones. Supporting timezones at deeper levels of detail is up to the application. The rules for time adjustment across the world are more political than rational, change frequently, and there is no standard suitable for every application aside from UTC.

Constants

The datetime module exports the following constants:

+datetime.MINYEAR
+

The smallest year number allowed in a date or datetime object. MINYEAR is 1.

+
+datetime.MAXYEAR
+

The largest year number allowed in a date or datetime object. MAXYEAR is 9999.

+
+datetime.UTC
+

Alias for the UTC timezone singleton datetime.timezone.utc.

New in version 3.11.

+

Available Types

class datetime.date +
+

An idealized naive date, assuming the current Gregorian calendar always was, and always will be, in effect. Attributes: year, month, and day.

+
class datetime.time +
+

An idealized time, independent of any particular day, assuming that every day has exactly 24*60*60 seconds. (There is no notion of “leap seconds” here.) Attributes: hour, minute, second, microsecond, and tzinfo.

+
class datetime.datetime +
+

A combination of a date and a time. Attributes: year, month, day, hour, minute, second, microsecond, and tzinfo.

+
class datetime.timedelta +
+

A duration expressing the difference between two date, time, or datetime instances to microsecond resolution.

+
class datetime.tzinfo +
+

An abstract base class for time zone information objects. These are used by the datetime and time classes to provide a customizable notion of time adjustment (for example, to account for time zone and/or daylight saving time).

+
class datetime.timezone +
+

A class that implements the tzinfo abstract base class as a fixed offset from the UTC.

New in version 3.2.

+

Objects of these types are immutable.

Subclass relationships:

object
+    timedelta
+    tzinfo
+        timezone
+    time
+    date
+        datetime
+

Common Properties

The date, datetime, time, and timezone types share these common features:

  • Objects of these types are immutable.
  • Objects of these types are hashable, meaning that they can be used as dictionary keys.
  • Objects of these types support efficient pickling via the pickle module.

Determining if an Object is Aware or Naive

Objects of the date type are always naive.

An object of type time or datetime may be aware or naive.

A datetime object d is aware if both of the following hold:

  1. +d.tzinfo is not None +
  2. +d.tzinfo.utcoffset(d) does not return None +

Otherwise, d is naive.

A time object t is aware if both of the following hold:

  1. +t.tzinfo is not None +
  2. +t.tzinfo.utcoffset(None) does not return None.

Otherwise, t is naive.

The distinction between aware and naive doesn’t apply to timedelta objects.

timedelta Objects

A timedelta object represents a duration, the difference between two dates or times.

+class datetime.timedelta(days=0, seconds=0, microseconds=0, milliseconds=0, minutes=0, hours=0, weeks=0)
+

All arguments are optional and default to 0. Arguments may be integers or floats, and may be positive or negative.

Only days, seconds and microseconds are stored internally. Arguments are converted to those units:

  • A millisecond is converted to 1000 microseconds.
  • A minute is converted to 60 seconds.
  • An hour is converted to 3600 seconds.
  • A week is converted to 7 days.

and days, seconds and microseconds are then normalized so that the representation is unique, with

  • 0 <= microseconds < 1000000
  • +0 <= seconds < 3600*24 (the number of seconds in one day)
  • -999999999 <= days <= 999999999

The following example illustrates how any arguments besides days, seconds and microseconds are “merged” and normalized into those three resulting attributes:

>>> from datetime import timedelta
+>>> delta = timedelta(
+...     days=50,
+...     seconds=27,
+...     microseconds=10,
+...     milliseconds=29000,
+...     minutes=5,
+...     hours=8,
+...     weeks=2
+... )
+>>> # Only days, seconds, and microseconds remain
+>>> delta
+datetime.timedelta(days=64, seconds=29156, microseconds=10)
+

If any argument is a float and there are fractional microseconds, the fractional microseconds left over from all arguments are combined and their sum is rounded to the nearest microsecond using round-half-to-even tiebreaker. If no argument is a float, the conversion and normalization processes are exact (no information is lost).

If the normalized value of days lies outside the indicated range, OverflowError is raised.

Note that normalization of negative values may be surprising at first. For example:

>>> from datetime import timedelta
+>>> d = timedelta(microseconds=-1)
+>>> (d.days, d.seconds, d.microseconds)
+(-1, 86399, 999999)
+
+

Class attributes:

+timedelta.min
+

The most negative timedelta object, timedelta(-999999999).

+
+timedelta.max
+

The most positive timedelta object, timedelta(days=999999999, +hours=23, minutes=59, seconds=59, microseconds=999999).

+
+timedelta.resolution
+

The smallest possible difference between non-equal timedelta objects, timedelta(microseconds=1).

+

Note that, because of normalization, timedelta.max > -timedelta.min. -timedelta.max is not representable as a timedelta object.

Instance attributes (read-only):

+ + + +

Attribute

Value

days

Between -999999999 and 999999999 inclusive

seconds

Between 0 and 86399 inclusive

microseconds

Between 0 and 999999 inclusive

Supported operations:

+ + + + + + + + + + + + + + + +

Operation

Result

t1 = t2 + t3

Sum of t2 and t3. Afterwards t1-t2 == t3 and t1-t3 == t2 are true. (1)

t1 = t2 - t3

Difference of t2 and t3. Afterwards t1 == t2 - t3 and t2 == t1 + t3 are true. (1)(6)

t1 = t2 * i or t1 = i * t2

Delta multiplied by an integer. Afterwards t1 // i == t2 is true, provided i != 0.

In general, t1 * i == t1 * (i-1) + t1 is true. (1)

t1 = t2 * f or t1 = f * t2

Delta multiplied by a float. The result is rounded to the nearest multiple of timedelta.resolution using round-half-to-even.

f = t2 / t3

Division (3) of overall duration t2 by interval unit t3. Returns a float object.

t1 = t2 / f or t1 = t2 / i

Delta divided by a float or an int. The result is rounded to the nearest multiple of timedelta.resolution using round-half-to-even.

t1 = t2 // i or t1 = t2 // t3

The floor is computed and the remainder (if any) is thrown away. In the second case, an integer is returned. (3)

t1 = t2 % t3

The remainder is computed as a timedelta object. (3)

q, r = divmod(t1, t2)

Computes the quotient and the remainder: q = t1 // t2 (3) and r = t1 % t2. q is an integer and r is a timedelta object.

+t1

Returns a timedelta object with the same value. (2)

-t1

equivalent to timedelta(-t1.days, -t1.seconds, -t1.microseconds), and to t1* -1. (1)(4)

abs(t)

equivalent to +t when t.days >= 0, and to -t when t.days < 0. (2)

str(t)

Returns a string in the form [D day[s], ][H]H:MM:SS[.UUUUUU], where D is negative for negative t. (5)

repr(t)

Returns a string representation of the timedelta object as a constructor call with canonical attribute values.

Notes:

  1. This is exact but may overflow.
  2. This is exact and cannot overflow.
  3. Division by 0 raises ZeroDivisionError.
  4. -timedelta.max is not representable as a timedelta object.
  5. +

    String representations of timedelta objects are normalized similarly to their internal representation. This leads to somewhat unusual results for negative timedeltas. For example:

    >>> timedelta(hours=-5)
+datetime.timedelta(days=-1, seconds=68400)
+>>> print(_)
+-1 day, 19:00:00
+
  6. The expression t2 - t3 will always be equal to the expression t2 + (-t3) except when t3 is equal to timedelta.max; in that case the former will produce a result while the latter will overflow.

In addition to the operations listed above, timedelta objects support certain additions and subtractions with date and datetime objects (see below).

Changed in version 3.2: Floor division and true division of a timedelta object by another timedelta object are now supported, as are remainder operations and the divmod() function. True division and multiplication of a timedelta object by a float object are now supported.

Comparisons of timedelta objects are supported, with some caveats.

The comparisons == or != always return a bool, no matter the type of the compared object:

>>> from datetime import timedelta
+>>> delta1 = timedelta(seconds=57)
+>>> delta2 = timedelta(hours=25, seconds=2)
+>>> delta2 != delta1
+True
+>>> delta2 == 5
+False
+

For all other comparisons (such as < and >), when a timedelta object is compared to an object of a different type, TypeError is raised:

>>> delta2 > delta1
+True
+>>> delta2 > 5
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: '>' not supported between instances of 'datetime.timedelta' and 'int'
+

In Boolean contexts, a timedelta object is considered to be true if and only if it isn’t equal to timedelta(0).

Instance methods:

+timedelta.total_seconds()
+

Return the total number of seconds contained in the duration. Equivalent to td / timedelta(seconds=1). For interval units other than seconds, use the division form directly (e.g. td / timedelta(microseconds=1)).

Note that for very large time intervals (greater than 270 years on most platforms) this method will lose microsecond accuracy.

New in version 3.2.

+

Examples of usage: timedelta +

An additional example of normalization:

>>> # Components of another_year add up to exactly 365 days
+>>> from datetime import timedelta
+>>> year = timedelta(days=365)
+>>> another_year = timedelta(weeks=40, days=84, hours=23,
+...                          minutes=50, seconds=600)
+>>> year == another_year
+True
+>>> year.total_seconds()
+31536000.0
+

Examples of timedelta arithmetic:

>>> from datetime import timedelta
+>>> year = timedelta(days=365)
+>>> ten_years = 10 * year
+>>> ten_years
+datetime.timedelta(days=3650)
+>>> ten_years.days // 365
+10
+>>> nine_years = ten_years - year
+>>> nine_years
+datetime.timedelta(days=3285)
+>>> three_years = nine_years // 3
+>>> three_years, three_years.days // 365
+(datetime.timedelta(days=1095), 3)
+

date Objects

A date object represents a date (year, month and day) in an idealized calendar, the current Gregorian calendar indefinitely extended in both directions.

January 1 of year 1 is called day number 1, January 2 of year 1 is called day number 2, and so on. 2

+class datetime.date(year, month, day)
+

All arguments are required. Arguments must be integers, in the following ranges:

  • MINYEAR <= year <= MAXYEAR
  • 1 <= month <= 12
  • 1 <= day <= number of days in the given month and year

If an argument outside those ranges is given, ValueError is raised.

+

Other constructors, all class methods:

+classmethod date.today()
+

Return the current local date.

This is equivalent to date.fromtimestamp(time.time()).

+
+classmethod date.fromtimestamp(timestamp)
+

Return the local date corresponding to the POSIX timestamp, such as is returned by time.time().

This may raise OverflowError, if the timestamp is out of the range of values supported by the platform C localtime() function, and OSError on localtime() failure. It’s common for this to be restricted to years from 1970 through 2038. Note that on non-POSIX systems that include leap seconds in their notion of a timestamp, leap seconds are ignored by fromtimestamp().

Changed in version 3.3: Raise OverflowError instead of ValueError if the timestamp is out of the range of values supported by the platform C localtime() function. Raise OSError instead of ValueError on localtime() failure.

+
+classmethod date.fromordinal(ordinal)
+

Return the date corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1.

ValueError is raised unless 1 <= ordinal <= +date.max.toordinal(). For any date d, date.fromordinal(d.toordinal()) == d.

+
+classmethod date.fromisoformat(date_string)
+

Return a date corresponding to a date_string given in any valid ISO 8601 format, except ordinal dates (e.g. YYYY-DDD):

>>> from datetime import date
+>>> date.fromisoformat('2019-12-04')
+datetime.date(2019, 12, 4)
+>>> date.fromisoformat('20191204')
+datetime.date(2019, 12, 4)
+>>> date.fromisoformat('2021-W01-1')
+datetime.date(2021, 1, 4)
+

New in version 3.7.

Changed in version 3.11: Previously, this method only supported the format YYYY-MM-DD.

+
+classmethod date.fromisocalendar(year, week, day)
+

Return a date corresponding to the ISO calendar date specified by year, week and day. This is the inverse of the function date.isocalendar().

New in version 3.8.

+

Class attributes:

+date.min
+

The earliest representable date, date(MINYEAR, 1, 1).

+
+date.max
+

The latest representable date, date(MAXYEAR, 12, 31).

+
+date.resolution
+

The smallest possible difference between non-equal date objects, timedelta(days=1).

+

Instance attributes (read-only):

+date.year
+

Between MINYEAR and MAXYEAR inclusive.

+
+date.month
+

Between 1 and 12 inclusive.

+
+date.day
+

Between 1 and the number of days in the given month of the given year.

+

Supported operations:

+ + + + +

Operation

Result

date2 = date1 + timedelta

date2 will be timedelta.days days after date1. (1)

date2 = date1 - timedelta

Computes date2 such that date2 + +timedelta == date1. (2)

timedelta = date1 - date2

(3)

date1 < date2

date1 is considered less than date2 when date1 precedes date2 in time. (4)

Notes:

  1. +date2 is moved forward in time if timedelta.days > 0, or backward if timedelta.days < 0. Afterward date2 - date1 == timedelta.days. timedelta.seconds and timedelta.microseconds are ignored. OverflowError is raised if date2.year would be smaller than MINYEAR or larger than MAXYEAR.
  2. +timedelta.seconds and timedelta.microseconds are ignored.
  3. This is exact, and cannot overflow. timedelta.seconds and timedelta.microseconds are 0, and date2 + timedelta == date1 after.
  4. In other words, date1 < date2 if and only if date1.toordinal() < +date2.toordinal(). Date comparison raises TypeError if the other comparand isn’t also a date object. However, NotImplemented is returned instead if the other comparand has a timetuple() attribute. This hook gives other kinds of date objects a chance at implementing mixed-type comparison. If not, when a date object is compared to an object of a different type, TypeError is raised unless the comparison is == or !=. The latter cases return False or True, respectively.

In Boolean contexts, all date objects are considered to be true.

Instance methods:

+date.replace(year=self.year, month=self.month, day=self.day)
+

Return a date with the same value, except for those parameters given new values by whichever keyword arguments are specified.

Example:

>>> from datetime import date
+>>> d = date(2002, 12, 31)
+>>> d.replace(day=26)
+datetime.date(2002, 12, 26)
+
+
+date.timetuple()
+

Return a time.struct_time such as returned by time.localtime().

The hours, minutes and seconds are 0, and the DST flag is -1.

d.timetuple() is equivalent to:

time.struct_time((d.year, d.month, d.day, 0, 0, 0, d.weekday(), yday, -1))
+

where yday = d.toordinal() - date(d.year, 1, 1).toordinal() + 1 is the day number within the current year starting with 1 for January 1st.

+
+date.toordinal()
+

Return the proleptic Gregorian ordinal of the date, where January 1 of year 1 has ordinal 1. For any date object d, date.fromordinal(d.toordinal()) == d.

+
+date.weekday()
+

Return the day of the week as an integer, where Monday is 0 and Sunday is 6. For example, date(2002, 12, 4).weekday() == 2, a Wednesday. See also isoweekday().

+
+date.isoweekday()
+

Return the day of the week as an integer, where Monday is 1 and Sunday is 7. For example, date(2002, 12, 4).isoweekday() == 3, a Wednesday. See also weekday(), isocalendar().

+
+date.isocalendar()
+

Return a named tuple object with three components: year, week and weekday.

The ISO calendar is a widely used variant of the Gregorian calendar. 3

The ISO year consists of 52 or 53 full weeks, and where a week starts on a Monday and ends on a Sunday. The first week of an ISO year is the first (Gregorian) calendar week of a year containing a Thursday. This is called week number 1, and the ISO year of that Thursday is the same as its Gregorian year.

For example, 2004 begins on a Thursday, so the first week of ISO year 2004 begins on Monday, 29 Dec 2003 and ends on Sunday, 4 Jan 2004:

>>> from datetime import date
+>>> date(2003, 12, 29).isocalendar()
+datetime.IsoCalendarDate(year=2004, week=1, weekday=1)
+>>> date(2004, 1, 4).isocalendar()
+datetime.IsoCalendarDate(year=2004, week=1, weekday=7)
+

Changed in version 3.9: Result changed from a tuple to a named tuple.

+
+date.isoformat()
+

Return a string representing the date in ISO 8601 format, YYYY-MM-DD:

>>> from datetime import date
+>>> date(2002, 12, 4).isoformat()
+'2002-12-04'
+
+
+date.__str__()
+

For a date d, str(d) is equivalent to d.isoformat().

+
+date.ctime()
+

Return a string representing the date:

>>> from datetime import date
+>>> date(2002, 12, 4).ctime()
+'Wed Dec  4 00:00:00 2002'
+

d.ctime() is equivalent to:

time.ctime(time.mktime(d.timetuple()))
+

on platforms where the native C ctime() function (which time.ctime() invokes, but which date.ctime() does not invoke) conforms to the C standard.

+
+date.strftime(format)
+

Return a string representing the date, controlled by an explicit format string. Format codes referring to hours, minutes or seconds will see 0 values. See also strftime() and strptime() Behavior and date.isoformat().

+
+date.__format__(format)
+

Same as date.strftime(). This makes it possible to specify a format string for a date object in formatted string literals and when using str.format(). See also strftime() and strptime() Behavior and date.isoformat().

+

Examples of Usage: date +

Example of counting days to an event:

>>> import time
+>>> from datetime import date
+>>> today = date.today()
+>>> today
+datetime.date(2007, 12, 5)
+>>> today == date.fromtimestamp(time.time())
+True
+>>> my_birthday = date(today.year, 6, 24)
+>>> if my_birthday < today:
+...     my_birthday = my_birthday.replace(year=today.year + 1)
+...
+>>> my_birthday
+datetime.date(2008, 6, 24)
+>>> time_to_birthday = abs(my_birthday - today)
+>>> time_to_birthday.days
+202
+

More examples of working with date:

>>> from datetime import date
+>>> d = date.fromordinal(730920) # 730920th day after 1. 1. 0001
+>>> d
+datetime.date(2002, 3, 11)
+
+>>> # Methods related to formatting string output
+>>> d.isoformat()
+'2002-03-11'
+>>> d.strftime("%d/%m/%y")
+'11/03/02'
+>>> d.strftime("%A %d. %B %Y")
+'Monday 11. March 2002'
+>>> d.ctime()
+'Mon Mar 11 00:00:00 2002'
+>>> 'The {1} is {0:%d}, the {2} is {0:%B}.'.format(d, "day", "month")
+'The day is 11, the month is March.'
+
+>>> # Methods for to extracting 'components' under different calendars
+>>> t = d.timetuple()
+>>> for i in t:     
+...     print(i)
+2002                # year
+3                   # month
+11                  # day
+0
+0
+0
+0                   # weekday (0 = Monday)
+70                  # 70th day in the year
+-1
+>>> ic = d.isocalendar()
+>>> for i in ic:    
+...     print(i)
+2002                # ISO year
+11                  # ISO week number
+1                   # ISO day number ( 1 = Monday )
+
+>>> # A date object is immutable; all operations produce a new object
+>>> d.replace(year=2005)
+datetime.date(2005, 3, 11)
+

datetime Objects

A datetime object is a single object containing all the information from a date object and a time object.

Like a date object, datetime assumes the current Gregorian calendar extended in both directions; like a time object, datetime assumes there are exactly 3600*24 seconds in every day.

Constructor:

+class datetime.datetime(year, month, day, hour=0, minute=0, second=0, microsecond=0, tzinfo=None, *, fold=0)
+

The year, month and day arguments are required. tzinfo may be None, or an instance of a tzinfo subclass. The remaining arguments must be integers in the following ranges:

  • +MINYEAR <= year <= MAXYEAR,
  • +1 <= month <= 12,
  • +1 <= day <= number of days in the given month and year,
  • +0 <= hour < 24,
  • +0 <= minute < 60,
  • +0 <= second < 60,
  • +0 <= microsecond < 1000000,
  • +fold in [0, 1].

If an argument outside those ranges is given, ValueError is raised.

New in version 3.6: Added the fold argument.

+

Other constructors, all class methods:

+classmethod datetime.today()
+

Return the current local datetime, with tzinfo None.

Equivalent to:

datetime.fromtimestamp(time.time())
+

See also now(), fromtimestamp().

This method is functionally equivalent to now(), but without a tz parameter.

+
+classmethod datetime.now(tz=None)
+

Return the current local date and time.

If optional argument tz is None or not specified, this is like today(), but, if possible, supplies more precision than can be gotten from going through a time.time() timestamp (for example, this may be possible on platforms supplying the C gettimeofday() function).

If tz is not None, it must be an instance of a tzinfo subclass, and the current date and time are converted to tz’s time zone.

This function is preferred over today() and utcnow().

+
+classmethod datetime.utcnow()
+

Return the current UTC date and time, with tzinfo None.

This is like now(), but returns the current UTC date and time, as a naive datetime object. An aware current UTC datetime can be obtained by calling datetime.now(timezone.utc). See also now().

Warning

Because naive datetime objects are treated by many datetime methods as local times, it is preferred to use aware datetimes to represent times in UTC. As such, the recommended way to create an object representing the current time in UTC is by calling datetime.now(timezone.utc).

Deprecated since version 3.12: Use datetime.now() with UTC instead.

+
+classmethod datetime.fromtimestamp(timestamp, tz=None)
+

Return the local date and time corresponding to the POSIX timestamp, such as is returned by time.time(). If optional argument tz is None or not specified, the timestamp is converted to the platform’s local date and time, and the returned datetime object is naive.

If tz is not None, it must be an instance of a tzinfo subclass, and the timestamp is converted to tz’s time zone.

fromtimestamp() may raise OverflowError, if the timestamp is out of the range of values supported by the platform C localtime() or gmtime() functions, and OSError on localtime() or gmtime() failure. It’s common for this to be restricted to years in 1970 through 2038. Note that on non-POSIX systems that include leap seconds in their notion of a timestamp, leap seconds are ignored by fromtimestamp(), and then it’s possible to have two timestamps differing by a second that yield identical datetime objects. This method is preferred over utcfromtimestamp().

Changed in version 3.3: Raise OverflowError instead of ValueError if the timestamp is out of the range of values supported by the platform C localtime() or gmtime() functions. Raise OSError instead of ValueError on localtime() or gmtime() failure.

Changed in version 3.6: fromtimestamp() may return instances with fold set to 1.

+
+classmethod datetime.utcfromtimestamp(timestamp)
+

Return the UTC datetime corresponding to the POSIX timestamp, with tzinfo None. (The resulting object is naive.)

This may raise OverflowError, if the timestamp is out of the range of values supported by the platform C gmtime() function, and OSError on gmtime() failure. It’s common for this to be restricted to years in 1970 through 2038.

To get an aware datetime object, call fromtimestamp():

datetime.fromtimestamp(timestamp, timezone.utc)
+

On the POSIX compliant platforms, it is equivalent to the following expression:

datetime(1970, 1, 1, tzinfo=timezone.utc) + timedelta(seconds=timestamp)
+

except the latter formula always supports the full years range: between MINYEAR and MAXYEAR inclusive.

Warning

Because naive datetime objects are treated by many datetime methods as local times, it is preferred to use aware datetimes to represent times in UTC. As such, the recommended way to create an object representing a specific timestamp in UTC is by calling datetime.fromtimestamp(timestamp, tz=timezone.utc).

Changed in version 3.3: Raise OverflowError instead of ValueError if the timestamp is out of the range of values supported by the platform C gmtime() function. Raise OSError instead of ValueError on gmtime() failure.

Deprecated since version 3.12: Use datetime.fromtimestamp() with UTC instead.

+
+classmethod datetime.fromordinal(ordinal)
+

Return the datetime corresponding to the proleptic Gregorian ordinal, where January 1 of year 1 has ordinal 1. ValueError is raised unless 1 +<= ordinal <= datetime.max.toordinal(). The hour, minute, second and microsecond of the result are all 0, and tzinfo is None.

+
+classmethod datetime.combine(date, time, tzinfo=time.tzinfo)
+

Return a new datetime object whose date components are equal to the given date object’s, and whose time components are equal to the given time object’s. If the tzinfo argument is provided, its value is used to set the tzinfo attribute of the result, otherwise the tzinfo attribute of the time argument is used. If the date argument is a datetime object, its time components and tzinfo attributes are ignored.

For any datetime object d, d == datetime.combine(d.date(), d.time(), d.tzinfo).

Changed in version 3.6: Added the tzinfo argument.

+
+classmethod datetime.fromisoformat(date_string)
+

Return a datetime corresponding to a date_string in any valid ISO 8601 format, with the following exceptions:

  1. Time zone offsets may have fractional seconds.
  2. The T separator may be replaced by any single unicode character.
  3. Ordinal dates are not currently supported.
  4. Fractional hours and minutes are not supported.

Examples:

>>> from datetime import datetime
+>>> datetime.fromisoformat('2011-11-04')
+datetime.datetime(2011, 11, 4, 0, 0)
+>>> datetime.fromisoformat('20111104')
+datetime.datetime(2011, 11, 4, 0, 0)
+>>> datetime.fromisoformat('2011-11-04T00:05:23')
+datetime.datetime(2011, 11, 4, 0, 5, 23)
+>>> datetime.fromisoformat('2011-11-04T00:05:23Z')
+datetime.datetime(2011, 11, 4, 0, 5, 23, tzinfo=datetime.timezone.utc)
+>>> datetime.fromisoformat('20111104T000523')
+datetime.datetime(2011, 11, 4, 0, 5, 23)
+>>> datetime.fromisoformat('2011-W01-2T00:05:23.283')
+datetime.datetime(2011, 1, 4, 0, 5, 23, 283000)
+>>> datetime.fromisoformat('2011-11-04 00:05:23.283')
+datetime.datetime(2011, 11, 4, 0, 5, 23, 283000)
+>>> datetime.fromisoformat('2011-11-04 00:05:23.283+00:00')
+datetime.datetime(2011, 11, 4, 0, 5, 23, 283000, tzinfo=datetime.timezone.utc)
+>>> datetime.fromisoformat('2011-11-04T00:05:23+04:00')   
+datetime.datetime(2011, 11, 4, 0, 5, 23,
+    tzinfo=datetime.timezone(datetime.timedelta(seconds=14400)))
+

New in version 3.7.

Changed in version 3.11: Previously, this method only supported formats that could be emitted by date.isoformat() or datetime.isoformat().

+
+classmethod datetime.fromisocalendar(year, week, day)
+

Return a datetime corresponding to the ISO calendar date specified by year, week and day. The non-date components of the datetime are populated with their normal default values. This is the inverse of the function datetime.isocalendar().

New in version 3.8.

+
+classmethod datetime.strptime(date_string, format)
+

Return a datetime corresponding to date_string, parsed according to format.

If format does not contain microseconds or timezone information, this is equivalent to:

datetime(*(time.strptime(date_string, format)[0:6]))
+

ValueError is raised if the date_string and format can’t be parsed by time.strptime() or if it returns a value which isn’t a time tuple. See also strftime() and strptime() Behavior and datetime.fromisoformat().

+

Class attributes:

+datetime.min
+

The earliest representable datetime, datetime(MINYEAR, 1, 1, +tzinfo=None).

+
+datetime.max
+

The latest representable datetime, datetime(MAXYEAR, 12, 31, 23, 59, +59, 999999, tzinfo=None).

+
+datetime.resolution
+

The smallest possible difference between non-equal datetime objects, timedelta(microseconds=1).

+

Instance attributes (read-only):

+datetime.year
+

Between MINYEAR and MAXYEAR inclusive.

+
+datetime.month
+

Between 1 and 12 inclusive.

+
+datetime.day
+

Between 1 and the number of days in the given month of the given year.

+
+datetime.hour
+

In range(24).

+
+datetime.minute
+

In range(60).

+
+datetime.second
+

In range(60).

+
+datetime.microsecond
+

In range(1000000).

+
+datetime.tzinfo
+

The object passed as the tzinfo argument to the datetime constructor, or None if none was passed.

+
+datetime.fold
+

In [0, 1]. Used to disambiguate wall times during a repeated interval. (A repeated interval occurs when clocks are rolled back at the end of daylight saving time or when the UTC offset for the current zone is decreased for political reasons.) The value 0 (1) represents the earlier (later) of the two moments with the same wall time representation.

New in version 3.6.

+

Supported operations:

+ + + + +

Operation

Result

datetime2 = datetime1 + timedelta

(1)

datetime2 = datetime1 - timedelta

(2)

timedelta = datetime1 - datetime2

(3)

datetime1 < datetime2

Compares datetime to datetime. (4)
  1. datetime2 is a duration of timedelta removed from datetime1, moving forward in time if timedelta.days > 0, or backward if timedelta.days < 0. The result has the same tzinfo attribute as the input datetime, and datetime2 - datetime1 == timedelta after. OverflowError is raised if datetime2.year would be smaller than MINYEAR or larger than MAXYEAR. Note that no time zone adjustments are done even if the input is an aware object.
  2. Computes the datetime2 such that datetime2 + timedelta == datetime1. As for addition, the result has the same tzinfo attribute as the input datetime, and no time zone adjustments are done even if the input is aware.
  3. +

    Subtraction of a datetime from a datetime is defined only if both operands are naive, or if both are aware. If one is aware and the other is naive, TypeError is raised.

    If both are naive, or both are aware and have the same tzinfo attribute, the tzinfo attributes are ignored, and the result is a timedelta object t such that datetime2 + t == datetime1. No time zone adjustments are done in this case.

    If both are aware and have different tzinfo attributes, a-b acts as if a and b were first converted to naive UTC datetimes first. The result is (a.replace(tzinfo=None) - a.utcoffset()) - (b.replace(tzinfo=None) +- b.utcoffset()) except that the implementation never overflows.

  4. +

    datetime1 is considered less than datetime2 when datetime1 precedes datetime2 in time.

    If one comparand is naive and the other is aware, TypeError is raised if an order comparison is attempted. For equality comparisons, naive instances are never equal to aware instances.

    If both comparands are aware, and have the same tzinfo attribute, the common tzinfo attribute is ignored and the base datetimes are compared. If both comparands are aware and have different tzinfo attributes, the comparands are first adjusted by subtracting their UTC offsets (obtained from self.utcoffset()).

    Changed in version 3.3: Equality comparisons between aware and naive datetime instances don’t raise TypeError.

    Note

    In order to stop comparison from falling back to the default scheme of comparing object addresses, datetime comparison normally raises TypeError if the other comparand isn’t also a datetime object. However, NotImplemented is returned instead if the other comparand has a timetuple() attribute. This hook gives other kinds of date objects a chance at implementing mixed-type comparison. If not, when a datetime object is compared to an object of a different type, TypeError is raised unless the comparison is == or !=. The latter cases return False or True, respectively.

Instance methods:

+datetime.date()
+

Return date object with same year, month and day.

+
+datetime.time()
+

Return time object with same hour, minute, second, microsecond and fold. tzinfo is None. See also method timetz().

Changed in version 3.6: The fold value is copied to the returned time object.

+
+datetime.timetz()
+

Return time object with same hour, minute, second, microsecond, fold, and tzinfo attributes. See also method time().

Changed in version 3.6: The fold value is copied to the returned time object.

+
+datetime.replace(year=self.year, month=self.month, day=self.day, hour=self.hour, minute=self.minute, second=self.second, microsecond=self.microsecond, tzinfo=self.tzinfo, *, fold=0)
+

Return a datetime with the same attributes, except for those attributes given new values by whichever keyword arguments are specified. Note that tzinfo=None can be specified to create a naive datetime from an aware datetime with no conversion of date and time data.

New in version 3.6: Added the fold argument.

+
+datetime.astimezone(tz=None)
+

Return a datetime object with new tzinfo attribute tz, adjusting the date and time data so the result is the same UTC time as self, but in tz’s local time.

If provided, tz must be an instance of a tzinfo subclass, and its utcoffset() and dst() methods must not return None. If self is naive, it is presumed to represent time in the system timezone.

If called without arguments (or with tz=None) the system local timezone is assumed for the target timezone. The .tzinfo attribute of the converted datetime instance will be set to an instance of timezone with the zone name and offset obtained from the OS.

If self.tzinfo is tz, self.astimezone(tz) is equal to self: no adjustment of date or time data is performed. Else the result is local time in the timezone tz, representing the same UTC time as self: after astz = dt.astimezone(tz), astz - astz.utcoffset() will have the same date and time data as dt - dt.utcoffset().

If you merely want to attach a time zone object tz to a datetime dt without adjustment of date and time data, use dt.replace(tzinfo=tz). If you merely want to remove the time zone object from an aware datetime dt without conversion of date and time data, use dt.replace(tzinfo=None).

Note that the default tzinfo.fromutc() method can be overridden in a tzinfo subclass to affect the result returned by astimezone(). Ignoring error cases, astimezone() acts like:

def astimezone(self, tz):
+    if self.tzinfo is tz:
+        return self
+    # Convert self to UTC, and attach the new time zone object.
+    utc = (self - self.utcoffset()).replace(tzinfo=tz)
+    # Convert from UTC to tz's local time.
+    return tz.fromutc(utc)
+

Changed in version 3.3: tz now can be omitted.

Changed in version 3.6: The astimezone() method can now be called on naive instances that are presumed to represent system local time.

+
+datetime.utcoffset()
+

If tzinfo is None, returns None, else returns self.tzinfo.utcoffset(self), and raises an exception if the latter doesn’t return None or a timedelta object with magnitude less than one day.

Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

+
+datetime.dst()
+

If tzinfo is None, returns None, else returns self.tzinfo.dst(self), and raises an exception if the latter doesn’t return None or a timedelta object with magnitude less than one day.

Changed in version 3.7: The DST offset is not restricted to a whole number of minutes.

+
+datetime.tzname()
+

If tzinfo is None, returns None, else returns self.tzinfo.tzname(self), raises an exception if the latter doesn’t return None or a string object,

+
+datetime.timetuple()
+

Return a time.struct_time such as returned by time.localtime().

d.timetuple() is equivalent to:

time.struct_time((d.year, d.month, d.day,
+                  d.hour, d.minute, d.second,
+                  d.weekday(), yday, dst))
+

where yday = d.toordinal() - date(d.year, 1, 1).toordinal() + 1 is the day number within the current year starting with 1 for January 1st. The tm_isdst flag of the result is set according to the dst() method: tzinfo is None or dst() returns None, tm_isdst is set to -1; else if dst() returns a non-zero value, tm_isdst is set to 1; else tm_isdst is set to 0.

+
+datetime.utctimetuple()
+

If datetime instance d is naive, this is the same as d.timetuple() except that tm_isdst is forced to 0 regardless of what d.dst() returns. DST is never in effect for a UTC time.

If d is aware, d is normalized to UTC time, by subtracting d.utcoffset(), and a time.struct_time for the normalized time is returned. tm_isdst is forced to 0. Note that an OverflowError may be raised if d.year was MINYEAR or MAXYEAR and UTC adjustment spills over a year boundary.

Warning

Because naive datetime objects are treated by many datetime methods as local times, it is preferred to use aware datetimes to represent times in UTC; as a result, using datetime.utctimetuple() may give misleading results. If you have a naive datetime representing UTC, use datetime.replace(tzinfo=timezone.utc) to make it aware, at which point you can use datetime.timetuple().

+
+datetime.toordinal()
+

Return the proleptic Gregorian ordinal of the date. The same as self.date().toordinal().

+
+datetime.timestamp()
+

Return POSIX timestamp corresponding to the datetime instance. The return value is a float similar to that returned by time.time().

Naive datetime instances are assumed to represent local time and this method relies on the platform C mktime() function to perform the conversion. Since datetime supports wider range of values than mktime() on many platforms, this method may raise OverflowError or OSError for times far in the past or far in the future.

For aware datetime instances, the return value is computed as:

(dt - datetime(1970, 1, 1, tzinfo=timezone.utc)).total_seconds()
+

New in version 3.3.

Changed in version 3.6: The timestamp() method uses the fold attribute to disambiguate the times during a repeated interval.

Note

There is no method to obtain the POSIX timestamp directly from a naive datetime instance representing UTC time. If your application uses this convention and your system timezone is not set to UTC, you can obtain the POSIX timestamp by supplying tzinfo=timezone.utc:

timestamp = dt.replace(tzinfo=timezone.utc).timestamp()
+

or by calculating the timestamp directly:

timestamp = (dt - datetime(1970, 1, 1)) / timedelta(seconds=1)
+
+
+datetime.weekday()
+

Return the day of the week as an integer, where Monday is 0 and Sunday is 6. The same as self.date().weekday(). See also isoweekday().

+
+datetime.isoweekday()
+

Return the day of the week as an integer, where Monday is 1 and Sunday is 7. The same as self.date().isoweekday(). See also weekday(), isocalendar().

+
+datetime.isocalendar()
+

Return a named tuple with three components: year, week and weekday. The same as self.date().isocalendar().

+
+datetime.isoformat(sep='T', timespec='auto')
+

Return a string representing the date and time in ISO 8601 format:

If utcoffset() does not return None, a string is appended, giving the UTC offset:

  • +YYYY-MM-DDTHH:MM:SS.ffffff+HH:MM[:SS[.ffffff]], if microsecond is not 0
  • +YYYY-MM-DDTHH:MM:SS+HH:MM[:SS[.ffffff]], if microsecond is 0

Examples:

>>> from datetime import datetime, timezone
+>>> datetime(2019, 5, 18, 15, 17, 8, 132263).isoformat()
+'2019-05-18T15:17:08.132263'
+>>> datetime(2019, 5, 18, 15, 17, tzinfo=timezone.utc).isoformat()
+'2019-05-18T15:17:00+00:00'
+

The optional argument sep (default 'T') is a one-character separator, placed between the date and time portions of the result. For example:

>>> from datetime import tzinfo, timedelta, datetime
+>>> class TZ(tzinfo):
+...     """A time zone with an arbitrary, constant -06:39 offset."""
+...     def utcoffset(self, dt):
+...         return timedelta(hours=-6, minutes=-39)
+...
+>>> datetime(2002, 12, 25, tzinfo=TZ()).isoformat(' ')
+'2002-12-25 00:00:00-06:39'
+>>> datetime(2009, 11, 27, microsecond=100, tzinfo=TZ()).isoformat()
+'2009-11-27T00:00:00.000100-06:39'
+

The optional argument timespec specifies the number of additional components of the time to include (the default is 'auto'). It can be one of the following:

  • +'auto': Same as 'seconds' if microsecond is 0, same as 'microseconds' otherwise.
  • +'hours': Include the hour in the two-digit HH format.
  • +'minutes': Include hour and minute in HH:MM format.
  • +'seconds': Include hour, minute, and second in HH:MM:SS format.
  • +'milliseconds': Include full time, but truncate fractional second part to milliseconds. HH:MM:SS.sss format.
  • +'microseconds': Include full time in HH:MM:SS.ffffff format.

Note

Excluded time components are truncated, not rounded.

ValueError will be raised on an invalid timespec argument:

>>> from datetime import datetime
+>>> datetime.now().isoformat(timespec='minutes')   
+'2002-12-25T00:00'
+>>> dt = datetime(2015, 1, 1, 12, 30, 59, 0)
+>>> dt.isoformat(timespec='microseconds')
+'2015-01-01T12:30:59.000000'
+

New in version 3.6: Added the timespec argument.

+
+datetime.__str__()
+

For a datetime instance d, str(d) is equivalent to d.isoformat(' ').

+
+datetime.ctime()
+

Return a string representing the date and time:

>>> from datetime import datetime
+>>> datetime(2002, 12, 4, 20, 30, 40).ctime()
+'Wed Dec  4 20:30:40 2002'
+

The output string will not include time zone information, regardless of whether the input is aware or naive.

d.ctime() is equivalent to:

time.ctime(time.mktime(d.timetuple()))
+

on platforms where the native C ctime() function (which time.ctime() invokes, but which datetime.ctime() does not invoke) conforms to the C standard.

+
+datetime.strftime(format)
+

Return a string representing the date and time, controlled by an explicit format string. See also strftime() and strptime() Behavior and datetime.isoformat().

+
+datetime.__format__(format)
+

Same as datetime.strftime(). This makes it possible to specify a format string for a datetime object in formatted string literals and when using str.format(). See also strftime() and strptime() Behavior and datetime.isoformat().

+

Examples of Usage: datetime +

Examples of working with datetime objects:

>>> from datetime import datetime, date, time, timezone
+
+>>> # Using datetime.combine()
+>>> d = date(2005, 7, 14)
+>>> t = time(12, 30)
+>>> datetime.combine(d, t)
+datetime.datetime(2005, 7, 14, 12, 30)
+
+>>> # Using datetime.now()
+>>> datetime.now()   
+datetime.datetime(2007, 12, 6, 16, 29, 43, 79043)   # GMT +1
+>>> datetime.now(timezone.utc)   
+datetime.datetime(2007, 12, 6, 15, 29, 43, 79060, tzinfo=datetime.timezone.utc)
+
+>>> # Using datetime.strptime()
+>>> dt = datetime.strptime("21/11/06 16:30", "%d/%m/%y %H:%M")
+>>> dt
+datetime.datetime(2006, 11, 21, 16, 30)
+
+>>> # Using datetime.timetuple() to get tuple of all attributes
+>>> tt = dt.timetuple()
+>>> for it in tt:   
+...     print(it)
+...
+2006    # year
+11      # month
+21      # day
+16      # hour
+30      # minute
+0       # second
+1       # weekday (0 = Monday)
+325     # number of days since 1st January
+-1      # dst - method tzinfo.dst() returned None
+
+>>> # Date in ISO format
+>>> ic = dt.isocalendar()
+>>> for it in ic:   
+...     print(it)
+...
+2006    # ISO year
+47      # ISO week
+2       # ISO weekday
+
+>>> # Formatting a datetime
+>>> dt.strftime("%A, %d. %B %Y %I:%M%p")
+'Tuesday, 21. November 2006 04:30PM'
+>>> 'The {1} is {0:%d}, the {2} is {0:%B}, the {3} is {0:%I:%M%p}.'.format(dt, "day", "month", "time")
+'The day is 21, the month is November, the time is 04:30PM.'
+

The example below defines a tzinfo subclass capturing time zone information for Kabul, Afghanistan, which used +4 UTC until 1945 and then +4:30 UTC thereafter:

from datetime import timedelta, datetime, tzinfo, timezone
+
+class KabulTz(tzinfo):
+    # Kabul used +4 until 1945, when they moved to +4:30
+    UTC_MOVE_DATE = datetime(1944, 12, 31, 20, tzinfo=timezone.utc)
+
+    def utcoffset(self, dt):
+        if dt.year < 1945:
+            return timedelta(hours=4)
+        elif (1945, 1, 1, 0, 0) <= dt.timetuple()[:5] < (1945, 1, 1, 0, 30):
+            # An ambiguous ("imaginary") half-hour range representing
+            # a 'fold' in time due to the shift from +4 to +4:30.
+            # If dt falls in the imaginary range, use fold to decide how
+            # to resolve. See PEP495.
+            return timedelta(hours=4, minutes=(30 if dt.fold else 0))
+        else:
+            return timedelta(hours=4, minutes=30)
+
+    def fromutc(self, dt):
+        # Follow same validations as in datetime.tzinfo
+        if not isinstance(dt, datetime):
+            raise TypeError("fromutc() requires a datetime argument")
+        if dt.tzinfo is not self:
+            raise ValueError("dt.tzinfo is not self")
+
+        # A custom implementation is required for fromutc as
+        # the input to this function is a datetime with utc values
+        # but with a tzinfo set to self.
+        # See datetime.astimezone or fromtimestamp.
+        if dt.replace(tzinfo=timezone.utc) >= self.UTC_MOVE_DATE:
+            return dt + timedelta(hours=4, minutes=30)
+        else:
+            return dt + timedelta(hours=4)
+
+    def dst(self, dt):
+        # Kabul does not observe daylight saving time.
+        return timedelta(0)
+
+    def tzname(self, dt):
+        if dt >= self.UTC_MOVE_DATE:
+            return "+04:30"
+        return "+04"
+

Usage of KabulTz from above:

>>> tz1 = KabulTz()
+
+>>> # Datetime before the change
+>>> dt1 = datetime(1900, 11, 21, 16, 30, tzinfo=tz1)
+>>> print(dt1.utcoffset())
+4:00:00
+
+>>> # Datetime after the change
+>>> dt2 = datetime(2006, 6, 14, 13, 0, tzinfo=tz1)
+>>> print(dt2.utcoffset())
+4:30:00
+
+>>> # Convert datetime to another time zone
+>>> dt3 = dt2.astimezone(timezone.utc)
+>>> dt3
+datetime.datetime(2006, 6, 14, 8, 30, tzinfo=datetime.timezone.utc)
+>>> dt2
+datetime.datetime(2006, 6, 14, 13, 0, tzinfo=KabulTz())
+>>> dt2 == dt3
+True
+

time Objects

A time object represents a (local) time of day, independent of any particular day, and subject to adjustment via a tzinfo object.

+class datetime.time(hour=0, minute=0, second=0, microsecond=0, tzinfo=None, *, fold=0)
+

All arguments are optional. tzinfo may be None, or an instance of a tzinfo subclass. The remaining arguments must be integers in the following ranges:

  • +0 <= hour < 24,
  • +0 <= minute < 60,
  • +0 <= second < 60,
  • +0 <= microsecond < 1000000,
  • +fold in [0, 1].

If an argument outside those ranges is given, ValueError is raised. All default to 0 except tzinfo, which defaults to None.

+

Class attributes:

+time.min
+

The earliest representable time, time(0, 0, 0, 0).

+
+time.max
+

The latest representable time, time(23, 59, 59, 999999).

+
+time.resolution
+

The smallest possible difference between non-equal time objects, timedelta(microseconds=1), although note that arithmetic on time objects is not supported.

+

Instance attributes (read-only):

+time.hour
+

In range(24).

+
+time.minute
+

In range(60).

+
+time.second
+

In range(60).

+
+time.microsecond
+

In range(1000000).

+
+time.tzinfo
+

The object passed as the tzinfo argument to the time constructor, or None if none was passed.

+
+time.fold
+

In [0, 1]. Used to disambiguate wall times during a repeated interval. (A repeated interval occurs when clocks are rolled back at the end of daylight saving time or when the UTC offset for the current zone is decreased for political reasons.) The value 0 (1) represents the earlier (later) of the two moments with the same wall time representation.

New in version 3.6.

+

time objects support comparison of time to time, where a is considered less than b when a precedes b in time. If one comparand is naive and the other is aware, TypeError is raised if an order comparison is attempted. For equality comparisons, naive instances are never equal to aware instances.

If both comparands are aware, and have the same tzinfo attribute, the common tzinfo attribute is ignored and the base times are compared. If both comparands are aware and have different tzinfo attributes, the comparands are first adjusted by subtracting their UTC offsets (obtained from self.utcoffset()). In order to stop mixed-type comparisons from falling back to the default comparison by object address, when a time object is compared to an object of a different type, TypeError is raised unless the comparison is == or !=. The latter cases return False or True, respectively.

Changed in version 3.3: Equality comparisons between aware and naive time instances don’t raise TypeError.

In Boolean contexts, a time object is always considered to be true.

Changed in version 3.5: Before Python 3.5, a time object was considered to be false if it represented midnight in UTC. This behavior was considered obscure and error-prone and has been removed in Python 3.5. See bpo-13936 for full details.

Other constructor:

+classmethod time.fromisoformat(time_string)
+

Return a time corresponding to a time_string in any valid ISO 8601 format, with the following exceptions:

  1. Time zone offsets may have fractional seconds.
  2. The leading T, normally required in cases where there may be ambiguity between a date and a time, is not required.
  3. Fractional seconds may have any number of digits (anything beyond 6 will be truncated).
  4. Fractional hours and minutes are not supported.

Examples:

.. doctest::
+
>>> from datetime import time
+>>> time.fromisoformat('04:23:01')
+datetime.time(4, 23, 1)
+>>> time.fromisoformat('T04:23:01')
+datetime.time(4, 23, 1)
+>>> time.fromisoformat('T042301')
+datetime.time(4, 23, 1)
+>>> time.fromisoformat('04:23:01.000384')
+datetime.time(4, 23, 1, 384)
+>>> time.fromisoformat('04:23:01,000384')
+datetime.time(4, 23, 1, 384)
+>>> time.fromisoformat('04:23:01+04:00')
+datetime.time(4, 23, 1, tzinfo=datetime.timezone(datetime.timedelta(seconds=14400)))
+>>> time.fromisoformat('04:23:01Z')
+datetime.time(4, 23, 1, tzinfo=datetime.timezone.utc)
+>>> time.fromisoformat('04:23:01+00:00')
+datetime.time(4, 23, 1, tzinfo=datetime.timezone.utc)
+

New in version 3.7.

Changed in version 3.11: Previously, this method only supported formats that could be emitted by time.isoformat().

+

Instance methods:

+time.replace(hour=self.hour, minute=self.minute, second=self.second, microsecond=self.microsecond, tzinfo=self.tzinfo, *, fold=0)
+

Return a time with the same value, except for those attributes given new values by whichever keyword arguments are specified. Note that tzinfo=None can be specified to create a naive time from an aware time, without conversion of the time data.

New in version 3.6: Added the fold argument.

+
+time.isoformat(timespec='auto')
+

Return a string representing the time in ISO 8601 format, one of:

The optional argument timespec specifies the number of additional components of the time to include (the default is 'auto'). It can be one of the following:

  • +'auto': Same as 'seconds' if microsecond is 0, same as 'microseconds' otherwise.
  • +'hours': Include the hour in the two-digit HH format.
  • +'minutes': Include hour and minute in HH:MM format.
  • +'seconds': Include hour, minute, and second in HH:MM:SS format.
  • +'milliseconds': Include full time, but truncate fractional second part to milliseconds. HH:MM:SS.sss format.
  • +'microseconds': Include full time in HH:MM:SS.ffffff format.

Note

Excluded time components are truncated, not rounded.

ValueError will be raised on an invalid timespec argument.

Example:

>>> from datetime import time
+>>> time(hour=12, minute=34, second=56, microsecond=123456).isoformat(timespec='minutes')
+'12:34'
+>>> dt = time(hour=12, minute=34, second=56, microsecond=0)
+>>> dt.isoformat(timespec='microseconds')
+'12:34:56.000000'
+>>> dt.isoformat(timespec='auto')
+'12:34:56'
+

New in version 3.6: Added the timespec argument.

+
+time.__str__()
+

For a time t, str(t) is equivalent to t.isoformat().

+
+time.strftime(format)
+

Return a string representing the time, controlled by an explicit format string. See also strftime() and strptime() Behavior and time.isoformat().

+
+time.__format__(format)
+

Same as time.strftime(). This makes it possible to specify a format string for a time object in formatted string literals and when using str.format(). See also strftime() and strptime() Behavior and time.isoformat().

+
+time.utcoffset()
+

If tzinfo is None, returns None, else returns self.tzinfo.utcoffset(None), and raises an exception if the latter doesn’t return None or a timedelta object with magnitude less than one day.

Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

+
+time.dst()
+

If tzinfo is None, returns None, else returns self.tzinfo.dst(None), and raises an exception if the latter doesn’t return None, or a timedelta object with magnitude less than one day.

Changed in version 3.7: The DST offset is not restricted to a whole number of minutes.

+
+time.tzname()
+

If tzinfo is None, returns None, else returns self.tzinfo.tzname(None), or raises an exception if the latter doesn’t return None or a string object.

+

Examples of Usage: time +

Examples of working with a time object:

>>> from datetime import time, tzinfo, timedelta
+>>> class TZ1(tzinfo):
+...     def utcoffset(self, dt):
+...         return timedelta(hours=1)
+...     def dst(self, dt):
+...         return timedelta(0)
+...     def tzname(self,dt):
+...         return "+01:00"
+...     def  __repr__(self):
+...         return f"{self.__class__.__name__}()"
+...
+>>> t = time(12, 10, 30, tzinfo=TZ1())
+>>> t
+datetime.time(12, 10, 30, tzinfo=TZ1())
+>>> t.isoformat()
+'12:10:30+01:00'
+>>> t.dst()
+datetime.timedelta(0)
+>>> t.tzname()
+'+01:00'
+>>> t.strftime("%H:%M:%S %Z")
+'12:10:30 +01:00'
+>>> 'The {} is {:%H:%M}.'.format("time", t)
+'The time is 12:10.'
+

tzinfo Objects

+class datetime.tzinfo
+

This is an abstract base class, meaning that this class should not be instantiated directly. Define a subclass of tzinfo to capture information about a particular time zone.

An instance of (a concrete subclass of) tzinfo can be passed to the constructors for datetime and time objects. The latter objects view their attributes as being in local time, and the tzinfo object supports methods revealing offset of local time from UTC, the name of the time zone, and DST offset, all relative to a date or time object passed to them.

You need to derive a concrete subclass, and (at least) supply implementations of the standard tzinfo methods needed by the datetime methods you use. The datetime module provides timezone, a simple concrete subclass of tzinfo which can represent timezones with fixed offset from UTC such as UTC itself or North American EST and EDT.

Special requirement for pickling: A tzinfo subclass must have an __init__() method that can be called with no arguments, otherwise it can be pickled but possibly not unpickled again. This is a technical requirement that may be relaxed in the future.

A concrete subclass of tzinfo may need to implement the following methods. Exactly which methods are needed depends on the uses made of aware datetime objects. If in doubt, simply implement all of them.

+
+tzinfo.utcoffset(dt)
+

Return offset of local time from UTC, as a timedelta object that is positive east of UTC. If local time is west of UTC, this should be negative.

This represents the total offset from UTC; for example, if a tzinfo object represents both time zone and DST adjustments, utcoffset() should return their sum. If the UTC offset isn’t known, return None. Else the value returned must be a timedelta object strictly between -timedelta(hours=24) and timedelta(hours=24) (the magnitude of the offset must be less than one day). Most implementations of utcoffset() will probably look like one of these two:

return CONSTANT                 # fixed-offset class
+return CONSTANT + self.dst(dt)  # daylight-aware class
+

If utcoffset() does not return None, dst() should not return None either.

The default implementation of utcoffset() raises NotImplementedError.

Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

+
+tzinfo.dst(dt)
+

Return the daylight saving time (DST) adjustment, as a timedelta object or None if DST information isn’t known.

Return timedelta(0) if DST is not in effect. If DST is in effect, return the offset as a timedelta object (see utcoffset() for details). Note that DST offset, if applicable, has already been added to the UTC offset returned by utcoffset(), so there’s no need to consult dst() unless you’re interested in obtaining DST info separately. For example, datetime.timetuple() calls its tzinfo attribute’s dst() method to determine how the tm_isdst flag should be set, and tzinfo.fromutc() calls dst() to account for DST changes when crossing time zones.

An instance tz of a tzinfo subclass that models both standard and daylight times must be consistent in this sense:

tz.utcoffset(dt) - tz.dst(dt)

must return the same result for every datetime dt with dt.tzinfo == +tz For sane tzinfo subclasses, this expression yields the time zone’s “standard offset”, which should not depend on the date or the time, but only on geographic location. The implementation of datetime.astimezone() relies on this, but cannot detect violations; it’s the programmer’s responsibility to ensure it. If a tzinfo subclass cannot guarantee this, it may be able to override the default implementation of tzinfo.fromutc() to work correctly with astimezone() regardless.

Most implementations of dst() will probably look like one of these two:

def dst(self, dt):
+    # a fixed-offset class:  doesn't account for DST
+    return timedelta(0)
+

or:

def dst(self, dt):
+    # Code to set dston and dstoff to the time zone's DST
+    # transition times based on the input dt.year, and expressed
+    # in standard local time.
+
+    if dston <= dt.replace(tzinfo=None) < dstoff:
+        return timedelta(hours=1)
+    else:
+        return timedelta(0)
+

The default implementation of dst() raises NotImplementedError.

Changed in version 3.7: The DST offset is not restricted to a whole number of minutes.

+
+tzinfo.tzname(dt)
+

Return the time zone name corresponding to the datetime object dt, as a string. Nothing about string names is defined by the datetime module, and there’s no requirement that it mean anything in particular. For example, “GMT”, “UTC”, “-500”, “-5:00”, “EDT”, “US/Eastern”, “America/New York” are all valid replies. Return None if a string name isn’t known. Note that this is a method rather than a fixed string primarily because some tzinfo subclasses will wish to return different names depending on the specific value of dt passed, especially if the tzinfo class is accounting for daylight time.

The default implementation of tzname() raises NotImplementedError.

+

These methods are called by a datetime or time object, in response to their methods of the same names. A datetime object passes itself as the argument, and a time object passes None as the argument. A tzinfo subclass’s methods should therefore be prepared to accept a dt argument of None, or of class datetime.

When None is passed, it’s up to the class designer to decide the best response. For example, returning None is appropriate if the class wishes to say that time objects don’t participate in the tzinfo protocols. It may be more useful for utcoffset(None) to return the standard UTC offset, as there is no other convention for discovering the standard offset.

When a datetime object is passed in response to a datetime method, dt.tzinfo is the same object as self. tzinfo methods can rely on this, unless user code calls tzinfo methods directly. The intent is that the tzinfo methods interpret dt as being in local time, and not need worry about objects in other timezones.

There is one more tzinfo method that a subclass may wish to override:

+tzinfo.fromutc(dt)
+

This is called from the default datetime.astimezone() implementation. When called from that, dt.tzinfo is self, and dt’s date and time data are to be viewed as expressing a UTC time. The purpose of fromutc() is to adjust the date and time data, returning an equivalent datetime in self’s local time.

Most tzinfo subclasses should be able to inherit the default fromutc() implementation without problems. It’s strong enough to handle fixed-offset time zones, and time zones accounting for both standard and daylight time, and the latter even if the DST transition times differ in different years. An example of a time zone the default fromutc() implementation may not handle correctly in all cases is one where the standard offset (from UTC) depends on the specific date and time passed, which can happen for political reasons. The default implementations of astimezone() and fromutc() may not produce the result you want if the result is one of the hours straddling the moment the standard offset changes.

Skipping code for error cases, the default fromutc() implementation acts like:

def fromutc(self, dt):
+    # raise ValueError error if dt.tzinfo is not self
+    dtoff = dt.utcoffset()
+    dtdst = dt.dst()
+    # raise ValueError if dtoff is None or dtdst is None
+    delta = dtoff - dtdst  # this is self's standard offset
+    if delta:
+        dt += delta   # convert to standard local time
+        dtdst = dt.dst()
+        # raise ValueError if dtdst is None
+    if dtdst:
+        return dt + dtdst
+    else:
+        return dt
+
+

In the following tzinfo_examples.py file there are some examples of tzinfo classes:

from datetime import tzinfo, timedelta, datetime
+
+ZERO = timedelta(0)
+HOUR = timedelta(hours=1)
+SECOND = timedelta(seconds=1)
+
+# A class capturing the platform's idea of local time.
+# (May result in wrong values on historical times in
+#  timezones where UTC offset and/or the DST rules had
+#  changed in the past.)
+import time as _time
+
+STDOFFSET = timedelta(seconds = -_time.timezone)
+if _time.daylight:
+    DSTOFFSET = timedelta(seconds = -_time.altzone)
+else:
+    DSTOFFSET = STDOFFSET
+
+DSTDIFF = DSTOFFSET - STDOFFSET
+
+class LocalTimezone(tzinfo):
+
+    def fromutc(self, dt):
+        assert dt.tzinfo is self
+        stamp = (dt - datetime(1970, 1, 1, tzinfo=self)) // SECOND
+        args = _time.localtime(stamp)[:6]
+        dst_diff = DSTDIFF // SECOND
+        # Detect fold
+        fold = (args == _time.localtime(stamp - dst_diff))
+        return datetime(*args, microsecond=dt.microsecond,
+                        tzinfo=self, fold=fold)
+
+    def utcoffset(self, dt):
+        if self._isdst(dt):
+            return DSTOFFSET
+        else:
+            return STDOFFSET
+
+    def dst(self, dt):
+        if self._isdst(dt):
+            return DSTDIFF
+        else:
+            return ZERO
+
+    def tzname(self, dt):
+        return _time.tzname[self._isdst(dt)]
+
+    def _isdst(self, dt):
+        tt = (dt.year, dt.month, dt.day,
+              dt.hour, dt.minute, dt.second,
+              dt.weekday(), 0, 0)
+        stamp = _time.mktime(tt)
+        tt = _time.localtime(stamp)
+        return tt.tm_isdst > 0
+
+Local = LocalTimezone()
+
+
+# A complete implementation of current DST rules for major US time zones.
+
+def first_sunday_on_or_after(dt):
+    days_to_go = 6 - dt.weekday()
+    if days_to_go:
+        dt += timedelta(days_to_go)
+    return dt
+
+
+# US DST Rules
+#
+# This is a simplified (i.e., wrong for a few cases) set of rules for US
+# DST start and end times. For a complete and up-to-date set of DST rules
+# and timezone definitions, visit the Olson Database (or try pytz):
+# http://www.twinsun.com/tz/tz-link.htm
+# https://sourceforge.net/projects/pytz/ (might not be up-to-date)
+#
+# In the US, since 2007, DST starts at 2am (standard time) on the second
+# Sunday in March, which is the first Sunday on or after Mar 8.
+DSTSTART_2007 = datetime(1, 3, 8, 2)
+# and ends at 2am (DST time) on the first Sunday of Nov.
+DSTEND_2007 = datetime(1, 11, 1, 2)
+# From 1987 to 2006, DST used to start at 2am (standard time) on the first
+# Sunday in April and to end at 2am (DST time) on the last
+# Sunday of October, which is the first Sunday on or after Oct 25.
+DSTSTART_1987_2006 = datetime(1, 4, 1, 2)
+DSTEND_1987_2006 = datetime(1, 10, 25, 2)
+# From 1967 to 1986, DST used to start at 2am (standard time) on the last
+# Sunday in April (the one on or after April 24) and to end at 2am (DST time)
+# on the last Sunday of October, which is the first Sunday
+# on or after Oct 25.
+DSTSTART_1967_1986 = datetime(1, 4, 24, 2)
+DSTEND_1967_1986 = DSTEND_1987_2006
+
+def us_dst_range(year):
+    # Find start and end times for US DST. For years before 1967, return
+    # start = end for no DST.
+    if 2006 < year:
+        dststart, dstend = DSTSTART_2007, DSTEND_2007
+    elif 1986 < year < 2007:
+        dststart, dstend = DSTSTART_1987_2006, DSTEND_1987_2006
+    elif 1966 < year < 1987:
+        dststart, dstend = DSTSTART_1967_1986, DSTEND_1967_1986
+    else:
+        return (datetime(year, 1, 1), ) * 2
+
+    start = first_sunday_on_or_after(dststart.replace(year=year))
+    end = first_sunday_on_or_after(dstend.replace(year=year))
+    return start, end
+
+
+class USTimeZone(tzinfo):
+
+    def __init__(self, hours, reprname, stdname, dstname):
+        self.stdoffset = timedelta(hours=hours)
+        self.reprname = reprname
+        self.stdname = stdname
+        self.dstname = dstname
+
+    def __repr__(self):
+        return self.reprname
+
+    def tzname(self, dt):
+        if self.dst(dt):
+            return self.dstname
+        else:
+            return self.stdname
+
+    def utcoffset(self, dt):
+        return self.stdoffset + self.dst(dt)
+
+    def dst(self, dt):
+        if dt is None or dt.tzinfo is None:
+            # An exception may be sensible here, in one or both cases.
+            # It depends on how you want to treat them.  The default
+            # fromutc() implementation (called by the default astimezone()
+            # implementation) passes a datetime with dt.tzinfo is self.
+            return ZERO
+        assert dt.tzinfo is self
+        start, end = us_dst_range(dt.year)
+        # Can't compare naive to aware objects, so strip the timezone from
+        # dt first.
+        dt = dt.replace(tzinfo=None)
+        if start + HOUR <= dt < end - HOUR:
+            # DST is in effect.
+            return HOUR
+        if end - HOUR <= dt < end:
+            # Fold (an ambiguous hour): use dt.fold to disambiguate.
+            return ZERO if dt.fold else HOUR
+        if start <= dt < start + HOUR:
+            # Gap (a non-existent hour): reverse the fold rule.
+            return HOUR if dt.fold else ZERO
+        # DST is off.
+        return ZERO
+
+    def fromutc(self, dt):
+        assert dt.tzinfo is self
+        start, end = us_dst_range(dt.year)
+        start = start.replace(tzinfo=self)
+        end = end.replace(tzinfo=self)
+        std_time = dt + self.stdoffset
+        dst_time = std_time + HOUR
+        if end <= dst_time < end + HOUR:
+            # Repeated hour
+            return std_time.replace(fold=1)
+        if std_time < start or dst_time >= end:
+            # Standard time
+            return std_time
+        if start <= std_time < end - HOUR:
+            # Daylight saving time
+            return dst_time
+
+
+Eastern  = USTimeZone(-5, "Eastern",  "EST", "EDT")
+Central  = USTimeZone(-6, "Central",  "CST", "CDT")
+Mountain = USTimeZone(-7, "Mountain", "MST", "MDT")
+Pacific  = USTimeZone(-8, "Pacific",  "PST", "PDT")
+

Note that there are unavoidable subtleties twice per year in a tzinfo subclass accounting for both standard and daylight time, at the DST transition points. For concreteness, consider US Eastern (UTC -0500), where EDT begins the minute after 1:59 (EST) on the second Sunday in March, and ends the minute after 1:59 (EDT) on the first Sunday in November:

  UTC   3:MM  4:MM  5:MM  6:MM  7:MM  8:MM
+  EST  22:MM 23:MM  0:MM  1:MM  2:MM  3:MM
+  EDT  23:MM  0:MM  1:MM  2:MM  3:MM  4:MM
+
+start  22:MM 23:MM  0:MM  1:MM  3:MM  4:MM
+
+  end  23:MM  0:MM  1:MM  1:MM  2:MM  3:MM
+

When DST starts (the “start” line), the local wall clock leaps from 1:59 to 3:00. A wall time of the form 2:MM doesn’t really make sense on that day, so astimezone(Eastern) won’t deliver a result with hour == 2 on the day DST begins. For example, at the Spring forward transition of 2016, we get:

>>> from datetime import datetime, timezone
+>>> from tzinfo_examples import HOUR, Eastern
+>>> u0 = datetime(2016, 3, 13, 5, tzinfo=timezone.utc)
+>>> for i in range(4):
+...     u = u0 + i*HOUR
+...     t = u.astimezone(Eastern)
+...     print(u.time(), 'UTC =', t.time(), t.tzname())
+...
+05:00:00 UTC = 00:00:00 EST
+06:00:00 UTC = 01:00:00 EST
+07:00:00 UTC = 03:00:00 EDT
+08:00:00 UTC = 04:00:00 EDT
+

When DST ends (the “end” line), there’s a potentially worse problem: there’s an hour that can’t be spelled unambiguously in local wall time: the last hour of daylight time. In Eastern, that’s times of the form 5:MM UTC on the day daylight time ends. The local wall clock leaps from 1:59 (daylight time) back to 1:00 (standard time) again. Local times of the form 1:MM are ambiguous. astimezone() mimics the local clock’s behavior by mapping two adjacent UTC hours into the same local hour then. In the Eastern example, UTC times of the form 5:MM and 6:MM both map to 1:MM when converted to Eastern, but earlier times have the fold attribute set to 0 and the later times have it set to 1. For example, at the Fall back transition of 2016, we get:

>>> u0 = datetime(2016, 11, 6, 4, tzinfo=timezone.utc)
+>>> for i in range(4):
+...     u = u0 + i*HOUR
+...     t = u.astimezone(Eastern)
+...     print(u.time(), 'UTC =', t.time(), t.tzname(), t.fold)
+...
+04:00:00 UTC = 00:00:00 EDT 0
+05:00:00 UTC = 01:00:00 EDT 0
+06:00:00 UTC = 01:00:00 EST 1
+07:00:00 UTC = 02:00:00 EST 0
+

Note that the datetime instances that differ only by the value of the fold attribute are considered equal in comparisons.

Applications that can’t bear wall-time ambiguities should explicitly check the value of the fold attribute or avoid using hybrid tzinfo subclasses; there are no ambiguities when using timezone, or any other fixed-offset tzinfo subclass (such as a class representing only EST (fixed offset -5 hours), or only EDT (fixed offset -4 hours)).

See also

+ zoneinfo +
+
+

The datetime module has a basic timezone class (for handling arbitrary fixed offsets from UTC) and its timezone.utc attribute (a UTC timezone instance).

zoneinfo brings the IANA timezone database (also known as the Olson database) to Python, and its usage is recommended.

IANA timezone database
+
+

The Time Zone Database (often called tz, tzdata or zoneinfo) contains code and data that represent the history of local time for many representative locations around the globe. It is updated periodically to reflect changes made by political bodies to time zone boundaries, UTC offsets, and daylight-saving rules.

timezone Objects

The timezone class is a subclass of tzinfo, each instance of which represents a timezone defined by a fixed offset from UTC.

Objects of this class cannot be used to represent timezone information in the locations where different offsets are used in different days of the year or where historical changes have been made to civil time.

+class datetime.timezone(offset, name=None)
+

The offset argument must be specified as a timedelta object representing the difference between the local time and UTC. It must be strictly between -timedelta(hours=24) and timedelta(hours=24), otherwise ValueError is raised.

The name argument is optional. If specified it must be a string that will be used as the value returned by the datetime.tzname() method.

New in version 3.2.

Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

+
+timezone.utcoffset(dt)
+

Return the fixed value specified when the timezone instance is constructed.

The dt argument is ignored. The return value is a timedelta instance equal to the difference between the local time and UTC.

Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

+
+timezone.tzname(dt)
+

Return the fixed value specified when the timezone instance is constructed.

If name is not provided in the constructor, the name returned by tzname(dt) is generated from the value of the offset as follows. If offset is timedelta(0), the name is “UTC”, otherwise it is a string in the format UTC±HH:MM, where ± is the sign of offset, HH and MM are two digits of offset.hours and offset.minutes respectively.

Changed in version 3.6: Name generated from offset=timedelta(0) is now plain 'UTC', not 'UTC+00:00'.

+
+timezone.dst(dt)
+

Always returns None.

+
+timezone.fromutc(dt)
+

Return dt + offset. The dt argument must be an aware datetime instance, with tzinfo set to self.

+

Class attributes:

+timezone.utc
+

The UTC timezone, timezone(timedelta(0)).

+

+strftime() and strptime() Behavior

date, datetime, and time objects all support a strftime(format) method, to create a string representing the time under the control of an explicit format string.

Conversely, the datetime.strptime() class method creates a datetime object from a string representing a date and time and a corresponding format string.

The table below provides a high-level comparison of strftime() versus strptime():

+ + + + +

strftime

strptime

Usage

Convert object to a string according to a given format

Parse a string into a datetime object given a corresponding format

Type of method

Instance method

Class method

Method of

date; datetime; time

datetime

Signature

strftime(format)

strptime(date_string, format)

+strftime() and strptime() Format Codes

These methods accept format codes that can be used to parse and format dates:

>>> datetime.strptime('31/01/22 23:59:59.999999',
+...                   '%d/%m/%y %H:%M:%S.%f')
+datetime.datetime(2022, 1, 31, 23, 59, 59, 999999)
+>>> _.strftime('%a %d %b %Y, %I:%M%p')
+'Mon 31 Jan 2022, 11:59PM'
+

The following is a list of all the format codes that the 1989 C standard requires, and these work on all platforms with a standard C implementation.

+ + + + + + + + + + + + + + + + + + + + + + + + +

Directive

Meaning

Example

Notes

%a

Weekday as locale’s abbreviated name.

(1)

%A

Weekday as locale’s full name.

(1)

%w

Weekday as a decimal number, where 0 is Sunday and 6 is Saturday.

0, 1, …, 6

%d

Day of the month as a zero-padded decimal number.

01, 02, …, 31

(9)

%b

Month as locale’s abbreviated name.

(1)

%B

Month as locale’s full name.

(1)

%m

Month as a zero-padded decimal number.

01, 02, …, 12

(9)

%y

Year without century as a zero-padded decimal number.

00, 01, …, 99

(9)

%Y

Year with century as a decimal number.

0001, 0002, …, 2013, 2014, …, 9998, 9999

(2)

%H

Hour (24-hour clock) as a zero-padded decimal number.

00, 01, …, 23

(9)

%I

Hour (12-hour clock) as a zero-padded decimal number.

01, 02, …, 12

(9)

%p

Locale’s equivalent of either AM or PM.

(1), (3)

%M

Minute as a zero-padded decimal number.

00, 01, …, 59

(9)

%S

Second as a zero-padded decimal number.

00, 01, …, 59

(4), (9)

%f

Microsecond as a decimal number, zero-padded to 6 digits.

000000, 000001, …, 999999

(5)

%z

UTC offset in the form ±HHMM[SS[.ffffff]] (empty string if the object is naive).

(empty), +0000, -0400, +1030, +063415, -030712.345216

(6)

%Z

Time zone name (empty string if the object is naive).

(empty), UTC, GMT

(6)

%j

Day of the year as a zero-padded decimal number.

001, 002, …, 366

(9)

%U

Week number of the year (Sunday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Sunday are considered to be in week 0.

00, 01, …, 53

(7), (9)

%W

Week number of the year (Monday as the first day of the week) as a zero-padded decimal number. All days in a new year preceding the first Monday are considered to be in week 0.

00, 01, …, 53

(7), (9)

%c

Locale’s appropriate date and time representation.

(1)

%x

Locale’s appropriate date representation.

(1)

%X

Locale’s appropriate time representation.

(1)

%%

A literal '%' character.

%

Several additional directives not required by the C89 standard are included for convenience. These parameters all correspond to ISO 8601 date values.

+ + + + +

Directive

Meaning

Example

Notes

%G

ISO 8601 year with century representing the year that contains the greater part of the ISO week (%V).

0001, 0002, …, 2013, 2014, …, 9998, 9999

(8)

%u

ISO 8601 weekday as a decimal number where 1 is Monday.

1, 2, …, 7

%V

ISO 8601 week as a decimal number with Monday as the first day of the week. Week 01 is the week containing Jan 4.

01, 02, …, 53

(8), (9)

%:z

UTC offset in the form ±HH:MM[:SS[.ffffff]] (empty string if the object is naive).

(empty), +00:00, -04:00, +10:30, +06:34:15, -03:07:12.345216

(6)

These may not be available on all platforms when used with the strftime() method. The ISO 8601 year and ISO 8601 week directives are not interchangeable with the year and week number directives above. Calling strptime() with incomplete or ambiguous ISO 8601 directives will raise a ValueError.

The full set of format codes supported varies across platforms, because Python calls the platform C library’s strftime() function, and platform variations are common. To see the full set of format codes supported on your platform, consult the strftime(3) documentation. There are also differences between platforms in handling of unsupported format specifiers.

New in version 3.6: %G, %u and %V were added.

New in version 3.12: %:z was added.

Technical Detail

Broadly speaking, d.strftime(fmt) acts like the time module’s time.strftime(fmt, d.timetuple()) although not all objects support a timetuple() method.

For the datetime.strptime() class method, the default value is 1900-01-01T00:00:00.000: any components not specified in the format string will be pulled from the default value. 4

Using datetime.strptime(date_string, format) is equivalent to:

datetime(*(time.strptime(date_string, format)[0:6]))
+

except when the format includes sub-second components or timezone offset information, which are supported in datetime.strptime but are discarded by time.strptime.

For time objects, the format codes for year, month, and day should not be used, as time objects have no such values. If they’re used anyway, 1900 is substituted for the year, and 1 for the month and day.

For date objects, the format codes for hours, minutes, seconds, and microseconds should not be used, as date objects have no such values. If they’re used anyway, 0 is substituted for them.

For the same reason, handling of format strings containing Unicode code points that can’t be represented in the charset of the current locale is also platform-dependent. On some platforms such code points are preserved intact in the output, while on others strftime may raise UnicodeError or return an empty string instead.

Notes:

  1. Because the format depends on the current locale, care should be taken when making assumptions about the output value. Field orderings will vary (for example, “month/day/year” versus “day/month/year”), and the output may contain non-ASCII characters.
  2. +

    The strptime() method can parse years in the full [1, 9999] range, but years < 1000 must be zero-filled to 4-digit width.

    Changed in version 3.2: In previous versions, strftime() method was restricted to years >= 1900.

    Changed in version 3.3: In version 3.2, strftime() method was restricted to years >= 1000.

  3. When used with the strptime() method, the %p directive only affects the output hour field if the %I directive is used to parse the hour.
  4. Unlike the time module, the datetime module does not support leap seconds.
  5. When used with the strptime() method, the %f directive accepts from one to six digits and zero pads on the right. %f is an extension to the set of format characters in the C standard (but implemented separately in datetime objects, and therefore always available).
  6. +

    For a naive object, the %z, %:z and %Z format codes are replaced by empty strings.

    For an aware object:

    +%z
    +
    +

    utcoffset() is transformed into a string of the form ±HHMM[SS[.ffffff]], where HH is a 2-digit string giving the number of UTC offset hours, MM is a 2-digit string giving the number of UTC offset minutes, SS is a 2-digit string giving the number of UTC offset seconds and ffffff is a 6-digit string giving the number of UTC offset microseconds. The ffffff part is omitted when the offset is a whole number of seconds and both the ffffff and the SS part is omitted when the offset is a whole number of minutes. For example, if utcoffset() returns timedelta(hours=-3, minutes=-30), %z is replaced with the string '-0330'.

    Changed in version 3.7: The UTC offset is not restricted to a whole number of minutes.

    Changed in version 3.7: When the %z directive is provided to the strptime() method, the UTC offsets can have a colon as a separator between hours, minutes and seconds. For example, '+01:00:00' will be parsed as an offset of one hour. In addition, providing 'Z' is identical to '+00:00'.

    +%:z
    +
    +

    Behaves exactly as %z, but has a colon separator added between hours, minutes and seconds.

    +%Z
    +
    +

    In strftime(), %Z is replaced by an empty string if tzname() returns None; otherwise %Z is replaced by the returned value, which must be a string.

    strptime() only accepts certain values for %Z:

    1. any value in time.tzname for your machine’s locale
    2. the hard-coded values UTC and GMT +

    So someone living in Japan may have JST, UTC, and GMT as valid values, but probably not EST. It will raise ValueError for invalid values.

    Changed in version 3.2: When the %z directive is provided to the strptime() method, an aware datetime object will be produced. The tzinfo of the result will be set to a timezone instance.

  7. When used with the strptime() method, %U and %W are only used in calculations when the day of the week and the calendar year (%Y) are specified.
  8. Similar to %U and %W, %V is only used in calculations when the day of the week and the ISO year (%G) are specified in a strptime() format string. Also note that %G and %Y are not interchangeable.
  9. When used with the strptime() method, the leading zero is optional for formats %d, %m, %H, %I, %M, %S, %j, %U, %W, and %V. Format %y does require a leading zero.

Footnotes

+1
+

If, that is, we ignore the effects of Relativity

+2
+

This matches the definition of the “proleptic Gregorian” calendar in Dershowitz and Reingold’s book Calendrical Calculations, where it’s the base calendar for all computations. See the book for algorithms for converting between proleptic Gregorian ordinals and many other calendar systems.

+3
+

See R. H. van Gent’s guide to the mathematics of the ISO 8601 calendar for a good explanation.

+4
+

Passing datetime.strptime('Feb 29', '%b %d') will fail since 1900 is not a leap year.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/datetime.html +

+
diff --git a/devdocs/python~3.12/library%2Fdbm.html b/devdocs/python~3.12/library%2Fdbm.html new file mode 100644 index 00000000..1b8fb6fd --- /dev/null +++ b/devdocs/python~3.12/library%2Fdbm.html @@ -0,0 +1,115 @@ +

dbm — Interfaces to Unix “databases”

Source code: Lib/dbm/__init__.py

dbm is a generic interface to variants of the DBM database — dbm.gnu or dbm.ndbm. If none of these modules is installed, the slow-but-simple implementation in module dbm.dumb will be used. There is a third party interface to the Oracle Berkeley DB.

+exception dbm.error
+

A tuple containing the exceptions that can be raised by each of the supported modules, with a unique exception also named dbm.error as the first item — the latter is used when dbm.error is raised.

+
+dbm.whichdb(filename)
+

This function attempts to guess which of the several simple database modules available — dbm.gnu, dbm.ndbm or dbm.dumb — should be used to open a given file.

Returns one of the following values: None if the file can’t be opened because it’s unreadable or doesn’t exist; the empty string ('') if the file’s format can’t be guessed; or a string containing the required module name, such as 'dbm.ndbm' or 'dbm.gnu'.

+

Changed in version 3.11: Accepts path-like object for filename.

+dbm.open(file, flag='r', mode=0o666)
+

Open the database file file and return a corresponding object.

If the database file already exists, the whichdb() function is used to determine its type and the appropriate module is used; if it does not exist, the first module listed above that can be imported is used.

The optional flag argument can be:

+ + + + +

Value

Meaning

'r'

Open existing database for reading only (default)

'w'

Open existing database for reading and writing

'c'

Open database for reading and writing, creating it if it doesn’t exist

'n'

Always create a new, empty database, open for reading and writing

The optional mode argument is the Unix mode of the file, used only when the database has to be created. It defaults to octal 0o666 (and will be modified by the prevailing umask).

+

The object returned by open() supports the same basic functionality as dictionaries; keys and their corresponding values can be stored, retrieved, and deleted, and the in operator and the keys() method are available, as well as get() and setdefault().

Changed in version 3.2: get() and setdefault() are now available in all database modules.

Changed in version 3.8: Deleting a key from a read-only database raises database module specific error instead of KeyError.

Changed in version 3.11: Accepts path-like object for file.

Key and values are always stored as bytes. This means that when strings are used they are implicitly converted to the default encoding before being stored.

These objects also support being used in a with statement, which will automatically close them when done.

Changed in version 3.4: Added native support for the context management protocol to the objects returned by open().

The following example records some hostnames and a corresponding title, and then prints out the contents of the database:

import dbm
+
+# Open database, creating it if necessary.
+with dbm.open('cache', 'c') as db:
+
+    # Record some values
+    db[b'hello'] = b'there'
+    db['www.python.org'] = 'Python Website'
+    db['www.cnn.com'] = 'Cable News Network'
+
+    # Note that the keys are considered bytes now.
+    assert db[b'www.python.org'] == b'Python Website'
+    # Notice how the value is now in bytes.
+    assert db['www.cnn.com'] == b'Cable News Network'
+
+    # Often-used methods of the dict interface work too.
+    print(db.get('python.org', b'not present'))
+
+    # Storing a non-string key or value will raise an exception (most
+    # likely a TypeError).
+    db['www.yahoo.com'] = 4
+
+# db is automatically closed when leaving the with statement.
+

See also

+Module shelve +
+
+

Persistence module which stores non-string data.

The individual submodules are described in the following sections.

dbm.gnu — GNU’s reinterpretation of dbm

Source code: Lib/dbm/gnu.py

This module is quite similar to the dbm module, but uses the GNU library gdbm instead to provide some additional functionality. Please note that the file formats created by dbm.gnu and dbm.ndbm are incompatible.

The dbm.gnu module provides an interface to the GNU DBM library. dbm.gnu.gdbm objects behave like mappings (dictionaries), except that keys and values are always converted to bytes before storing. Printing a gdbm object doesn’t print the keys and values, and the items() and values() methods are not supported.

+exception dbm.gnu.error
+

Raised on dbm.gnu-specific errors, such as I/O errors. KeyError is raised for general mapping errors like specifying an incorrect key.

+
+dbm.gnu.open(filename[, flag[, mode]])
+

Open a gdbm database and return a gdbm object. The filename argument is the name of the database file.

The optional flag argument can be:

+ + + + +

Value

Meaning

'r'

Open existing database for reading only (default)

'w'

Open existing database for reading and writing

'c'

Open database for reading and writing, creating it if it doesn’t exist

'n'

Always create a new, empty database, open for reading and writing

The following additional characters may be appended to the flag to control how the database is opened:

+ + + +

Value

Meaning

'f'

Open the database in fast mode. Writes to the database will not be synchronized.

's'

Synchronized mode. This will cause changes to the database to be immediately written to the file.

'u'

Do not lock database.

Not all flags are valid for all versions of gdbm. The module constant open_flags is a string of supported flag characters. The exception error is raised if an invalid flag is specified.

The optional mode argument is the Unix mode of the file, used only when the database has to be created. It defaults to octal 0o666.

In addition to the dictionary-like methods, gdbm objects have the following methods:

Changed in version 3.11: Accepts path-like object for filename.

+gdbm.firstkey()
+

It’s possible to loop over every key in the database using this method and the nextkey() method. The traversal is ordered by gdbm’s internal hash values, and won’t be sorted by the key values. This method returns the starting key.

+
+gdbm.nextkey(key)
+

Returns the key that follows key in the traversal. The following code prints every key in the database db, without having to create a list in memory that contains them all:

k = db.firstkey()
+while k is not None:
+    print(k)
+    k = db.nextkey(k)
+
+
+gdbm.reorganize()
+

If you have carried out a lot of deletions and would like to shrink the space used by the gdbm file, this routine will reorganize the database. gdbm objects will not shorten the length of a database file except by using this reorganization; otherwise, deleted file space will be kept and reused as new (key, value) pairs are added.

+
+gdbm.sync()
+

When the database has been opened in fast mode, this method forces any unwritten data to be written to the disk.

+
+gdbm.close()
+

Close the gdbm database.

+
+

dbm.ndbm — Interface based on ndbm

Source code: Lib/dbm/ndbm.py

The dbm.ndbm module provides an interface to the Unix “(n)dbm” library. Dbm objects behave like mappings (dictionaries), except that keys and values are always stored as bytes. Printing a dbm object doesn’t print the keys and values, and the items() and values() methods are not supported.

This module can be used with the “classic” ndbm interface or the GNU GDBM compatibility interface. On Unix, the configure script will attempt to locate the appropriate header file to simplify building this module.

Warning

The ndbm library shipped as part of macOS has an undocumented limitation on the size of values, which can result in corrupted database files when storing values larger than this limit. Reading such corrupted files can result in a hard crash (segmentation fault).

+exception dbm.ndbm.error
+

Raised on dbm.ndbm-specific errors, such as I/O errors. KeyError is raised for general mapping errors like specifying an incorrect key.

+
+dbm.ndbm.library
+

Name of the ndbm implementation library used.

+
+dbm.ndbm.open(filename[, flag[, mode]])
+

Open a dbm database and return a ndbm object. The filename argument is the name of the database file (without the .dir or .pag extensions).

The optional flag argument must be one of these values:

+ + + + +

Value

Meaning

'r'

Open existing database for reading only (default)

'w'

Open existing database for reading and writing

'c'

Open database for reading and writing, creating it if it doesn’t exist

'n'

Always create a new, empty database, open for reading and writing

The optional mode argument is the Unix mode of the file, used only when the database has to be created. It defaults to octal 0o666 (and will be modified by the prevailing umask).

In addition to the dictionary-like methods, ndbm objects provide the following method:

Changed in version 3.11: Accepts path-like object for filename.

+ndbm.close()
+

Close the ndbm database.

+
+

dbm.dumb — Portable DBM implementation

Source code: Lib/dbm/dumb.py

Note

The dbm.dumb module is intended as a last resort fallback for the dbm module when a more robust module is not available. The dbm.dumb module is not written for speed and is not nearly as heavily used as the other database modules.

The dbm.dumb module provides a persistent dictionary-like interface which is written entirely in Python. Unlike other modules such as dbm.gnu no external library is required. As with other persistent mappings, the keys and values are always stored as bytes.

The module defines the following:

+exception dbm.dumb.error
+

Raised on dbm.dumb-specific errors, such as I/O errors. KeyError is raised for general mapping errors like specifying an incorrect key.

+
+dbm.dumb.open(filename[, flag[, mode]])
+

Open a dumbdbm database and return a dumbdbm object. The filename argument is the basename of the database file (without any specific extensions). When a dumbdbm database is created, files with .dat and .dir extensions are created.

The optional flag argument can be:

+ + + + +

Value

Meaning

'r'

Open existing database for reading only (default)

'w'

Open existing database for reading and writing

'c'

Open database for reading and writing, creating it if it doesn’t exist

'n'

Always create a new, empty database, open for reading and writing

The optional mode argument is the Unix mode of the file, used only when the database has to be created. It defaults to octal 0o666 (and will be modified by the prevailing umask).

Warning

It is possible to crash the Python interpreter when loading a database with a sufficiently large/complex entry due to stack depth limitations in Python’s AST compiler.

Changed in version 3.5: open() always creates a new database when the flag has the value 'n'.

Changed in version 3.8: A database opened with flags 'r' is now read-only. Opening with flags 'r' and 'w' no longer creates a database if it does not exist.

Changed in version 3.11: Accepts path-like object for filename.

In addition to the methods provided by the collections.abc.MutableMapping class, dumbdbm objects provide the following methods:

+dumbdbm.sync()
+

Synchronize the on-disk directory and data files. This method is called by the Shelve.sync() method.

+
+dumbdbm.close()
+

Close the dumbdbm database.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/dbm.html +

+
diff --git a/devdocs/python~3.12/library%2Fdebug.html b/devdocs/python~3.12/library%2Fdebug.html new file mode 100644 index 00000000..343a4d8c --- /dev/null +++ b/devdocs/python~3.12/library%2Fdebug.html @@ -0,0 +1,16 @@ +

Debugging and Profiling

These libraries help you with Python development: the debugger enables you to step through code, analyze stack frames and set breakpoints etc., and the profilers run code and give you a detailed breakdown of execution times, allowing you to identify bottlenecks in your programs. Auditing events provide visibility into runtime behaviors that would otherwise require intrusive debugging or patching.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/debug.html +

+
diff --git a/devdocs/python~3.12/library%2Fdecimal.html b/devdocs/python~3.12/library%2Fdecimal.html new file mode 100644 index 00000000..67373c1b --- /dev/null +++ b/devdocs/python~3.12/library%2Fdecimal.html @@ -0,0 +1,989 @@ +

decimal — Decimal fixed point and floating point arithmetic

Source code: Lib/decimal.py

The decimal module provides support for fast correctly rounded decimal floating point arithmetic. It offers several advantages over the float datatype:

The module design is centered around three concepts: the decimal number, the context for arithmetic, and signals.

A decimal number is immutable. It has a sign, coefficient digits, and an exponent. To preserve significance, the coefficient digits do not truncate trailing zeros. Decimals also include special values such as Infinity, -Infinity, and NaN. The standard also differentiates -0 from +0.

The context for arithmetic is an environment specifying precision, rounding rules, limits on exponents, flags indicating the results of operations, and trap enablers which determine whether signals are treated as exceptions. Rounding options include ROUND_CEILING, ROUND_DOWN, ROUND_FLOOR, ROUND_HALF_DOWN, ROUND_HALF_EVEN, ROUND_HALF_UP, ROUND_UP, and ROUND_05UP.

Signals are groups of exceptional conditions arising during the course of computation. Depending on the needs of the application, signals may be ignored, considered as informational, or treated as exceptions. The signals in the decimal module are: Clamped, InvalidOperation, DivisionByZero, Inexact, Rounded, Subnormal, Overflow, Underflow and FloatOperation.

For each signal there is a flag and a trap enabler. When a signal is encountered, its flag is set to one, then, if the trap enabler is set to one, an exception is raised. Flags are sticky, so the user needs to reset them before monitoring a calculation.

See also

Quick-start Tutorial

The usual start to using decimals is importing the module, viewing the current context with getcontext() and, if necessary, setting new values for precision, rounding, or enabled traps:

>>> from decimal import *
+>>> getcontext()
+Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
+        capitals=1, clamp=0, flags=[], traps=[Overflow, DivisionByZero,
+        InvalidOperation])
+
+>>> getcontext().prec = 7       # Set a new precision
+

Decimal instances can be constructed from integers, strings, floats, or tuples. Construction from an integer or a float performs an exact conversion of the value of that integer or float. Decimal numbers include special values such as NaN which stands for “Not a number”, positive and negative Infinity, and -0:

>>> getcontext().prec = 28
+>>> Decimal(10)
+Decimal('10')
+>>> Decimal('3.14')
+Decimal('3.14')
+>>> Decimal(3.14)
+Decimal('3.140000000000000124344978758017532527446746826171875')
+>>> Decimal((0, (3, 1, 4), -2))
+Decimal('3.14')
+>>> Decimal(str(2.0 ** 0.5))
+Decimal('1.4142135623730951')
+>>> Decimal(2) ** Decimal('0.5')
+Decimal('1.414213562373095048801688724')
+>>> Decimal('NaN')
+Decimal('NaN')
+>>> Decimal('-Infinity')
+Decimal('-Infinity')
+

If the FloatOperation signal is trapped, accidental mixing of decimals and floats in constructors or ordering comparisons raises an exception:

>>> c = getcontext()
+>>> c.traps[FloatOperation] = True
+>>> Decimal(3.14)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+decimal.FloatOperation: [<class 'decimal.FloatOperation'>]
+>>> Decimal('3.5') < 3.7
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+decimal.FloatOperation: [<class 'decimal.FloatOperation'>]
+>>> Decimal('3.5') == 3.5
+True
+

New in version 3.3.

The significance of a new Decimal is determined solely by the number of digits input. Context precision and rounding only come into play during arithmetic operations.

>>> getcontext().prec = 6
+>>> Decimal('3.0')
+Decimal('3.0')
+>>> Decimal('3.1415926535')
+Decimal('3.1415926535')
+>>> Decimal('3.1415926535') + Decimal('2.7182818285')
+Decimal('5.85987')
+>>> getcontext().rounding = ROUND_UP
+>>> Decimal('3.1415926535') + Decimal('2.7182818285')
+Decimal('5.85988')
+

If the internal limits of the C version are exceeded, constructing a decimal raises InvalidOperation:

>>> Decimal("1e9999999999999999999")
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+decimal.InvalidOperation: [<class 'decimal.InvalidOperation'>]
+

Changed in version 3.3.

Decimals interact well with much of the rest of Python. Here is a small decimal floating point flying circus:

>>> data = list(map(Decimal, '1.34 1.87 3.45 2.35 1.00 0.03 9.25'.split()))
+>>> max(data)
+Decimal('9.25')
+>>> min(data)
+Decimal('0.03')
+>>> sorted(data)
+[Decimal('0.03'), Decimal('1.00'), Decimal('1.34'), Decimal('1.87'),
+ Decimal('2.35'), Decimal('3.45'), Decimal('9.25')]
+>>> sum(data)
+Decimal('19.29')
+>>> a,b,c = data[:3]
+>>> str(a)
+'1.34'
+>>> float(a)
+1.34
+>>> round(a, 1)
+Decimal('1.3')
+>>> int(a)
+1
+>>> a * 5
+Decimal('6.70')
+>>> a * b
+Decimal('2.5058')
+>>> c % a
+Decimal('0.77')
+

And some mathematical functions are also available to Decimal:

>>> getcontext().prec = 28
+>>> Decimal(2).sqrt()
+Decimal('1.414213562373095048801688724')
+>>> Decimal(1).exp()
+Decimal('2.718281828459045235360287471')
+>>> Decimal('10').ln()
+Decimal('2.302585092994045684017991455')
+>>> Decimal('10').log10()
+Decimal('1')
+

The quantize() method rounds a number to a fixed exponent. This method is useful for monetary applications that often round results to a fixed number of places:

>>> Decimal('7.325').quantize(Decimal('.01'), rounding=ROUND_DOWN)
+Decimal('7.32')
+>>> Decimal('7.325').quantize(Decimal('1.'), rounding=ROUND_UP)
+Decimal('8')
+

As shown above, the getcontext() function accesses the current context and allows the settings to be changed. This approach meets the needs of most applications.

For more advanced work, it may be useful to create alternate contexts using the Context() constructor. To make an alternate active, use the setcontext() function.

In accordance with the standard, the decimal module provides two ready to use standard contexts, BasicContext and ExtendedContext. The former is especially useful for debugging because many of the traps are enabled:

>>> myothercontext = Context(prec=60, rounding=ROUND_HALF_DOWN)
+>>> setcontext(myothercontext)
+>>> Decimal(1) / Decimal(7)
+Decimal('0.142857142857142857142857142857142857142857142857142857142857')
+
+>>> ExtendedContext
+Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
+        capitals=1, clamp=0, flags=[], traps=[])
+>>> setcontext(ExtendedContext)
+>>> Decimal(1) / Decimal(7)
+Decimal('0.142857143')
+>>> Decimal(42) / Decimal(0)
+Decimal('Infinity')
+
+>>> setcontext(BasicContext)
+>>> Decimal(42) / Decimal(0)
+Traceback (most recent call last):
+  File "<pyshell#143>", line 1, in -toplevel-
+    Decimal(42) / Decimal(0)
+DivisionByZero: x / 0
+

Contexts also have signal flags for monitoring exceptional conditions encountered during computations. The flags remain set until explicitly cleared, so it is best to clear the flags before each set of monitored computations by using the clear_flags() method.

>>> setcontext(ExtendedContext)
+>>> getcontext().clear_flags()
+>>> Decimal(355) / Decimal(113)
+Decimal('3.14159292')
+>>> getcontext()
+Context(prec=9, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999,
+        capitals=1, clamp=0, flags=[Inexact, Rounded], traps=[])
+

The flags entry shows that the rational approximation to pi was rounded (digits beyond the context precision were thrown away) and that the result is inexact (some of the discarded digits were non-zero).

Individual traps are set using the dictionary in the traps attribute of a context:

>>> setcontext(ExtendedContext)
+>>> Decimal(1) / Decimal(0)
+Decimal('Infinity')
+>>> getcontext().traps[DivisionByZero] = 1
+>>> Decimal(1) / Decimal(0)
+Traceback (most recent call last):
+  File "<pyshell#112>", line 1, in -toplevel-
+    Decimal(1) / Decimal(0)
+DivisionByZero: x / 0
+

Most programs adjust the current context only once, at the beginning of the program. And, in many applications, data is converted to Decimal with a single cast inside a loop. With context set and decimals created, the bulk of the program manipulates the data no differently than with other Python numeric types.

Decimal objects

+class decimal.Decimal(value='0', context=None)
+

Construct a new Decimal object based from value.

value can be an integer, string, tuple, float, or another Decimal object. If no value is given, returns Decimal('0'). If value is a string, it should conform to the decimal numeric string syntax after leading and trailing whitespace characters, as well as underscores throughout, are removed:

sign           ::=  '+' | '-'
+digit          ::=  '0' | '1' | '2' | '3' | '4' | '5' | '6' | '7' | '8' | '9'
+indicator      ::=  'e' | 'E'
+digits         ::=  digit [digit]...
+decimal-part   ::=  digits '.' [digits] | ['.'] digits
+exponent-part  ::=  indicator [sign] digits
+infinity       ::=  'Infinity' | 'Inf'
+nan            ::=  'NaN' [digits] | 'sNaN' [digits]
+numeric-value  ::=  decimal-part [exponent-part] | infinity
+numeric-string ::=  [sign] numeric-value | [sign] nan
+

Other Unicode decimal digits are also permitted where digit appears above. These include decimal digits from various other alphabets (for example, Arabic-Indic and Devanāgarī digits) along with the fullwidth digits '\uff10' through '\uff19'.

If value is a tuple, it should have three components, a sign (0 for positive or 1 for negative), a tuple of digits, and an integer exponent. For example, Decimal((0, (1, 4, 1, 4), -3)) returns Decimal('1.414').

If value is a float, the binary floating point value is losslessly converted to its exact decimal equivalent. This conversion can often require 53 or more digits of precision. For example, Decimal(float('1.1')) converts to Decimal('1.100000000000000088817841970012523233890533447265625').

The context precision does not affect how many digits are stored. That is determined exclusively by the number of digits in value. For example, Decimal('3.00000') records all five zeros even if the context precision is only three.

The purpose of the context argument is determining what to do if value is a malformed string. If the context traps InvalidOperation, an exception is raised; otherwise, the constructor returns a new Decimal with the value of NaN.

Once constructed, Decimal objects are immutable.

Changed in version 3.2: The argument to the constructor is now permitted to be a float instance.

Changed in version 3.3: float arguments raise an exception if the FloatOperation trap is set. By default the trap is off.

Changed in version 3.6: Underscores are allowed for grouping, as with integral and floating-point literals in code.

Decimal floating point objects share many properties with the other built-in numeric types such as float and int. All of the usual math operations and special methods apply. Likewise, decimal objects can be copied, pickled, printed, used as dictionary keys, used as set elements, compared, sorted, and coerced to another type (such as float or int).

There are some small differences between arithmetic on Decimal objects and arithmetic on integers and floats. When the remainder operator % is applied to Decimal objects, the sign of the result is the sign of the dividend rather than the sign of the divisor:

>>> (-7) % 4
+1
+>>> Decimal(-7) % Decimal(4)
+Decimal('-3')
+

The integer division operator // behaves analogously, returning the integer part of the true quotient (truncating towards zero) rather than its floor, so as to preserve the usual identity x == (x // y) * y + x % y:

>>> -7 // 4
+-2
+>>> Decimal(-7) // Decimal(4)
+Decimal('-1')
+

The % and // operators implement the remainder and divide-integer operations (respectively) as described in the specification.

Decimal objects cannot generally be combined with floats or instances of fractions.Fraction in arithmetic operations: an attempt to add a Decimal to a float, for example, will raise a TypeError. However, it is possible to use Python’s comparison operators to compare a Decimal instance x with another number y. This avoids confusing results when doing equality comparisons between numbers of different types.

Changed in version 3.2: Mixed-type comparisons between Decimal instances and other numeric types are now fully supported.

In addition to the standard numeric properties, decimal floating point objects also have a number of specialized methods:

+adjusted()
+

Return the adjusted exponent after shifting out the coefficient’s rightmost digits until only the lead digit remains: Decimal('321e+5').adjusted() returns seven. Used for determining the position of the most significant digit with respect to the decimal point.

+
+as_integer_ratio()
+

Return a pair (n, d) of integers that represent the given Decimal instance as a fraction, in lowest terms and with a positive denominator:

>>> Decimal('-3.14').as_integer_ratio()
+(-157, 50)
+

The conversion is exact. Raise OverflowError on infinities and ValueError on NaNs.

+

New in version 3.6.

+as_tuple()
+

Return a named tuple representation of the number: DecimalTuple(sign, digits, exponent).

+
+canonical()
+

Return the canonical encoding of the argument. Currently, the encoding of a Decimal instance is always canonical, so this operation returns its argument unchanged.

+
+compare(other, context=None)
+

Compare the values of two Decimal instances. compare() returns a Decimal instance, and if either operand is a NaN then the result is a NaN:

a or b is a NaN  ==> Decimal('NaN')
+a < b            ==> Decimal('-1')
+a == b           ==> Decimal('0')
+a > b            ==> Decimal('1')
+
+
+compare_signal(other, context=None)
+

This operation is identical to the compare() method, except that all NaNs signal. That is, if neither operand is a signaling NaN then any quiet NaN operand is treated as though it were a signaling NaN.

+
+compare_total(other, context=None)
+

Compare two operands using their abstract representation rather than their numerical value. Similar to the compare() method, but the result gives a total ordering on Decimal instances. Two Decimal instances with the same numeric value but different representations compare unequal in this ordering:

>>> Decimal('12.0').compare_total(Decimal('12'))
+Decimal('-1')
+

Quiet and signaling NaNs are also included in the total ordering. The result of this function is Decimal('0') if both operands have the same representation, Decimal('-1') if the first operand is lower in the total order than the second, and Decimal('1') if the first operand is higher in the total order than the second operand. See the specification for details of the total order.

This operation is unaffected by context and is quiet: no flags are changed and no rounding is performed. As an exception, the C version may raise InvalidOperation if the second operand cannot be converted exactly.

+
+compare_total_mag(other, context=None)
+

Compare two operands using their abstract representation rather than their value as in compare_total(), but ignoring the sign of each operand. x.compare_total_mag(y) is equivalent to x.copy_abs().compare_total(y.copy_abs()).

This operation is unaffected by context and is quiet: no flags are changed and no rounding is performed. As an exception, the C version may raise InvalidOperation if the second operand cannot be converted exactly.

+
+conjugate()
+

Just returns self, this method is only to comply with the Decimal Specification.

+
+copy_abs()
+

Return the absolute value of the argument. This operation is unaffected by the context and is quiet: no flags are changed and no rounding is performed.

+
+copy_negate()
+

Return the negation of the argument. This operation is unaffected by the context and is quiet: no flags are changed and no rounding is performed.

+
+copy_sign(other, context=None)
+

Return a copy of the first operand with the sign set to be the same as the sign of the second operand. For example:

>>> Decimal('2.3').copy_sign(Decimal('-1.5'))
+Decimal('-2.3')
+

This operation is unaffected by context and is quiet: no flags are changed and no rounding is performed. As an exception, the C version may raise InvalidOperation if the second operand cannot be converted exactly.

+
+exp(context=None)
+

Return the value of the (natural) exponential function e**x at the given number. The result is correctly rounded using the ROUND_HALF_EVEN rounding mode.

>>> Decimal(1).exp()
+Decimal('2.718281828459045235360287471')
+>>> Decimal(321).exp()
+Decimal('2.561702493119680037517373933E+139')
+
+
+classmethod from_float(f)
+

Alternative constructor that only accepts instances of float or int.

Note Decimal.from_float(0.1) is not the same as Decimal('0.1'). Since 0.1 is not exactly representable in binary floating point, the value is stored as the nearest representable value which is 0x1.999999999999ap-4. That equivalent value in decimal is 0.1000000000000000055511151231257827021181583404541015625.

Note

From Python 3.2 onwards, a Decimal instance can also be constructed directly from a float.

>>> Decimal.from_float(0.1)
+Decimal('0.1000000000000000055511151231257827021181583404541015625')
+>>> Decimal.from_float(float('nan'))
+Decimal('NaN')
+>>> Decimal.from_float(float('inf'))
+Decimal('Infinity')
+>>> Decimal.from_float(float('-inf'))
+Decimal('-Infinity')
+

New in version 3.1.

+
+fma(other, third, context=None)
+

Fused multiply-add. Return self*other+third with no rounding of the intermediate product self*other.

>>> Decimal(2).fma(3, 5)
+Decimal('11')
+
+
+is_canonical()
+

Return True if the argument is canonical and False otherwise. Currently, a Decimal instance is always canonical, so this operation always returns True.

+
+is_finite()
+

Return True if the argument is a finite number, and False if the argument is an infinity or a NaN.

+
+is_infinite()
+

Return True if the argument is either positive or negative infinity and False otherwise.

+
+is_nan()
+

Return True if the argument is a (quiet or signaling) NaN and False otherwise.

+
+is_normal(context=None)
+

Return True if the argument is a normal finite number. Return False if the argument is zero, subnormal, infinite or a NaN.

+
+is_qnan()
+

Return True if the argument is a quiet NaN, and False otherwise.

+
+is_signed()
+

Return True if the argument has a negative sign and False otherwise. Note that zeros and NaNs can both carry signs.

+
+is_snan()
+

Return True if the argument is a signaling NaN and False otherwise.

+
+is_subnormal(context=None)
+

Return True if the argument is subnormal, and False otherwise.

+
+is_zero()
+

Return True if the argument is a (positive or negative) zero and False otherwise.

+
+ln(context=None)
+

Return the natural (base e) logarithm of the operand. The result is correctly rounded using the ROUND_HALF_EVEN rounding mode.

+
+log10(context=None)
+

Return the base ten logarithm of the operand. The result is correctly rounded using the ROUND_HALF_EVEN rounding mode.

+
+logb(context=None)
+

For a nonzero number, return the adjusted exponent of its operand as a Decimal instance. If the operand is a zero then Decimal('-Infinity') is returned and the DivisionByZero flag is raised. If the operand is an infinity then Decimal('Infinity') is returned.

+
+logical_and(other, context=None)
+

logical_and() is a logical operation which takes two logical operands (see Logical operands). The result is the digit-wise and of the two operands.

+
+logical_invert(context=None)
+

logical_invert() is a logical operation. The result is the digit-wise inversion of the operand.

+
+logical_or(other, context=None)
+

logical_or() is a logical operation which takes two logical operands (see Logical operands). The result is the digit-wise or of the two operands.

+
+logical_xor(other, context=None)
+

logical_xor() is a logical operation which takes two logical operands (see Logical operands). The result is the digit-wise exclusive or of the two operands.

+
+max(other, context=None)
+

Like max(self, other) except that the context rounding rule is applied before returning and that NaN values are either signaled or ignored (depending on the context and whether they are signaling or quiet).

+
+max_mag(other, context=None)
+

Similar to the max() method, but the comparison is done using the absolute values of the operands.

+
+min(other, context=None)
+

Like min(self, other) except that the context rounding rule is applied before returning and that NaN values are either signaled or ignored (depending on the context and whether they are signaling or quiet).

+
+min_mag(other, context=None)
+

Similar to the min() method, but the comparison is done using the absolute values of the operands.

+
+next_minus(context=None)
+

Return the largest number representable in the given context (or in the current thread’s context if no context is given) that is smaller than the given operand.

+
+next_plus(context=None)
+

Return the smallest number representable in the given context (or in the current thread’s context if no context is given) that is larger than the given operand.

+
+next_toward(other, context=None)
+

If the two operands are unequal, return the number closest to the first operand in the direction of the second operand. If both operands are numerically equal, return a copy of the first operand with the sign set to be the same as the sign of the second operand.

+
+normalize(context=None)
+

Used for producing canonical values of an equivalence class within either the current context or the specified context.

This has the same semantics as the unary plus operation, except that if the final result is finite it is reduced to its simplest form, with all trailing zeros removed and its sign preserved. That is, while the coefficient is non-zero and a multiple of ten the coefficient is divided by ten and the exponent is incremented by 1. Otherwise (the coefficient is zero) the exponent is set to 0. In all cases the sign is unchanged.

For example, Decimal('32.100') and Decimal('0.321000e+2') both normalize to the equivalent value Decimal('32.1').

Note that rounding is applied before reducing to simplest form.

In the latest versions of the specification, this operation is also known as reduce.

+
+number_class(context=None)
+

Return a string describing the class of the operand. The returned value is one of the following ten strings.

  • +"-Infinity", indicating that the operand is negative infinity.
  • +"-Normal", indicating that the operand is a negative normal number.
  • +"-Subnormal", indicating that the operand is negative and subnormal.
  • +"-Zero", indicating that the operand is a negative zero.
  • +"+Zero", indicating that the operand is a positive zero.
  • +"+Subnormal", indicating that the operand is positive and subnormal.
  • +"+Normal", indicating that the operand is a positive normal number.
  • +"+Infinity", indicating that the operand is positive infinity.
  • +"NaN", indicating that the operand is a quiet NaN (Not a Number).
  • +"sNaN", indicating that the operand is a signaling NaN.
+
+quantize(exp, rounding=None, context=None)
+

Return a value equal to the first operand after rounding and having the exponent of the second operand.

>>> Decimal('1.41421356').quantize(Decimal('1.000'))
+Decimal('1.414')
+

Unlike other operations, if the length of the coefficient after the quantize operation would be greater than precision, then an InvalidOperation is signaled. This guarantees that, unless there is an error condition, the quantized exponent is always equal to that of the right-hand operand.

Also unlike other operations, quantize never signals Underflow, even if the result is subnormal and inexact.

If the exponent of the second operand is larger than that of the first then rounding may be necessary. In this case, the rounding mode is determined by the rounding argument if given, else by the given context argument; if neither argument is given the rounding mode of the current thread’s context is used.

An error is returned whenever the resulting exponent is greater than Emax or less than Etiny().

+
+radix()
+

Return Decimal(10), the radix (base) in which the Decimal class does all its arithmetic. Included for compatibility with the specification.

+
+remainder_near(other, context=None)
+

Return the remainder from dividing self by other. This differs from self % other in that the sign of the remainder is chosen so as to minimize its absolute value. More precisely, the return value is self - n * other where n is the integer nearest to the exact value of self / other, and if two integers are equally near then the even one is chosen.

If the result is zero then its sign will be the sign of self.

>>> Decimal(18).remainder_near(Decimal(10))
+Decimal('-2')
+>>> Decimal(25).remainder_near(Decimal(10))
+Decimal('5')
+>>> Decimal(35).remainder_near(Decimal(10))
+Decimal('-5')
+
+
+rotate(other, context=None)
+

Return the result of rotating the digits of the first operand by an amount specified by the second operand. The second operand must be an integer in the range -precision through precision. The absolute value of the second operand gives the number of places to rotate. If the second operand is positive then rotation is to the left; otherwise rotation is to the right. The coefficient of the first operand is padded on the left with zeros to length precision if necessary. The sign and exponent of the first operand are unchanged.

+
+same_quantum(other, context=None)
+

Test whether self and other have the same exponent or whether both are NaN.

This operation is unaffected by context and is quiet: no flags are changed and no rounding is performed. As an exception, the C version may raise InvalidOperation if the second operand cannot be converted exactly.

+
+scaleb(other, context=None)
+

Return the first operand with exponent adjusted by the second. Equivalently, return the first operand multiplied by 10**other. The second operand must be an integer.

+
+shift(other, context=None)
+

Return the result of shifting the digits of the first operand by an amount specified by the second operand. The second operand must be an integer in the range -precision through precision. The absolute value of the second operand gives the number of places to shift. If the second operand is positive then the shift is to the left; otherwise the shift is to the right. Digits shifted into the coefficient are zeros. The sign and exponent of the first operand are unchanged.

+
+sqrt(context=None)
+

Return the square root of the argument to full precision.

+
+to_eng_string(context=None)
+

Convert to a string, using engineering notation if an exponent is needed.

Engineering notation has an exponent which is a multiple of 3. This can leave up to 3 digits to the left of the decimal place and may require the addition of either one or two trailing zeros.

For example, this converts Decimal('123E+1') to Decimal('1.23E+3').

+
+to_integral(rounding=None, context=None)
+

Identical to the to_integral_value() method. The to_integral name has been kept for compatibility with older versions.

+
+to_integral_exact(rounding=None, context=None)
+

Round to the nearest integer, signaling Inexact or Rounded as appropriate if rounding occurs. The rounding mode is determined by the rounding parameter if given, else by the given context. If neither parameter is given then the rounding mode of the current context is used.

+
+to_integral_value(rounding=None, context=None)
+

Round to the nearest integer without signaling Inexact or Rounded. If given, applies rounding; otherwise, uses the rounding method in either the supplied context or the current context.

+
+

Logical operands

The logical_and(), logical_invert(), logical_or(), and logical_xor() methods expect their arguments to be logical operands. A logical operand is a Decimal instance whose exponent and sign are both zero, and whose digits are all either 0 or 1.

Context objects

Contexts are environments for arithmetic operations. They govern precision, set rules for rounding, determine which signals are treated as exceptions, and limit the range for exponents.

Each thread has its own current context which is accessed or changed using the getcontext() and setcontext() functions:

+decimal.getcontext()
+

Return the current context for the active thread.

+
+decimal.setcontext(c)
+

Set the current context for the active thread to c.

+

You can also use the with statement and the localcontext() function to temporarily change the active context.

+decimal.localcontext(ctx=None, **kwargs)
+

Return a context manager that will set the current context for the active thread to a copy of ctx on entry to the with-statement and restore the previous context when exiting the with-statement. If no context is specified, a copy of the current context is used. The kwargs argument is used to set the attributes of the new context.

For example, the following code sets the current decimal precision to 42 places, performs a calculation, and then automatically restores the previous context:

from decimal import localcontext
+
+with localcontext() as ctx:
+    ctx.prec = 42   # Perform a high precision calculation
+    s = calculate_something()
+s = +s  # Round the final result back to the default precision
+

Using keyword arguments, the code would be the following:

from decimal import localcontext
+
+with localcontext(prec=42) as ctx:
+    s = calculate_something()
+s = +s
+

Raises TypeError if kwargs supplies an attribute that Context doesn’t support. Raises either TypeError or ValueError if kwargs supplies an invalid value for an attribute.

Changed in version 3.11: localcontext() now supports setting context attributes through the use of keyword arguments.

+

New contexts can also be created using the Context constructor described below. In addition, the module provides three pre-made contexts:

+class decimal.BasicContext
+

This is a standard context defined by the General Decimal Arithmetic Specification. Precision is set to nine. Rounding is set to ROUND_HALF_UP. All flags are cleared. All traps are enabled (treated as exceptions) except Inexact, Rounded, and Subnormal.

Because many of the traps are enabled, this context is useful for debugging.

+
+class decimal.ExtendedContext
+

This is a standard context defined by the General Decimal Arithmetic Specification. Precision is set to nine. Rounding is set to ROUND_HALF_EVEN. All flags are cleared. No traps are enabled (so that exceptions are not raised during computations).

Because the traps are disabled, this context is useful for applications that prefer to have result value of NaN or Infinity instead of raising exceptions. This allows an application to complete a run in the presence of conditions that would otherwise halt the program.

+
+class decimal.DefaultContext
+

This context is used by the Context constructor as a prototype for new contexts. Changing a field (such a precision) has the effect of changing the default for new contexts created by the Context constructor.

This context is most useful in multi-threaded environments. Changing one of the fields before threads are started has the effect of setting system-wide defaults. Changing the fields after threads have started is not recommended as it would require thread synchronization to prevent race conditions.

In single threaded environments, it is preferable to not use this context at all. Instead, simply create contexts explicitly as described below.

The default values are Context.prec=28, Context.rounding=ROUND_HALF_EVEN, and enabled traps for Overflow, InvalidOperation, and DivisionByZero.

+

In addition to the three supplied contexts, new contexts can be created with the Context constructor.

+class decimal.Context(prec=None, rounding=None, Emin=None, Emax=None, capitals=None, clamp=None, flags=None, traps=None)
+

Creates a new context. If a field is not specified or is None, the default values are copied from the DefaultContext. If the flags field is not specified or is None, all flags are cleared.

prec is an integer in the range [1, MAX_PREC] that sets the precision for arithmetic operations in the context.

The rounding option is one of the constants listed in the section Rounding Modes.

The traps and flags fields list any signals to be set. Generally, new contexts should only set traps and leave the flags clear.

The Emin and Emax fields are integers specifying the outer limits allowable for exponents. Emin must be in the range [MIN_EMIN, 0], Emax in the range [0, MAX_EMAX].

The capitals field is either 0 or 1 (the default). If set to 1, exponents are printed with a capital E; otherwise, a lowercase e is used: Decimal('6.02e+23').

The clamp field is either 0 (the default) or 1. If set to 1, the exponent e of a Decimal instance representable in this context is strictly limited to the range Emin - prec + 1 <= e <= Emax - prec + 1. If clamp is 0 then a weaker condition holds: the adjusted exponent of the Decimal instance is at most Emax. When clamp is 1, a large normal number will, where possible, have its exponent reduced and a corresponding number of zeros added to its coefficient, in order to fit the exponent constraints; this preserves the value of the number but loses information about significant trailing zeros. For example:

>>> Context(prec=6, Emax=999, clamp=1).create_decimal('1.23e999')
+Decimal('1.23000E+999')
+

A clamp value of 1 allows compatibility with the fixed-width decimal interchange formats specified in IEEE 754.

The Context class defines several general purpose methods as well as a large number of methods for doing arithmetic directly in a given context. In addition, for each of the Decimal methods described above (with the exception of the adjusted() and as_tuple() methods) there is a corresponding Context method. For example, for a Context instance C and Decimal instance x, C.exp(x) is equivalent to x.exp(context=C). Each Context method accepts a Python integer (an instance of int) anywhere that a Decimal instance is accepted.

+clear_flags()
+

Resets all of the flags to 0.

+
+clear_traps()
+

Resets all of the traps to 0.

New in version 3.3.

+
+copy()
+

Return a duplicate of the context.

+
+copy_decimal(num)
+

Return a copy of the Decimal instance num.

+
+create_decimal(num)
+

Creates a new Decimal instance from num but using self as context. Unlike the Decimal constructor, the context precision, rounding method, flags, and traps are applied to the conversion.

This is useful because constants are often given to a greater precision than is needed by the application. Another benefit is that rounding immediately eliminates unintended effects from digits beyond the current precision. In the following example, using unrounded inputs means that adding zero to a sum can change the result:

>>> getcontext().prec = 3
+>>> Decimal('3.4445') + Decimal('1.0023')
+Decimal('4.45')
+>>> Decimal('3.4445') + Decimal(0) + Decimal('1.0023')
+Decimal('4.44')
+

This method implements the to-number operation of the IBM specification. If the argument is a string, no leading or trailing whitespace or underscores are permitted.

+
+create_decimal_from_float(f)
+

Creates a new Decimal instance from a float f but rounding using self as the context. Unlike the Decimal.from_float() class method, the context precision, rounding method, flags, and traps are applied to the conversion.

>>> context = Context(prec=5, rounding=ROUND_DOWN)
+>>> context.create_decimal_from_float(math.pi)
+Decimal('3.1415')
+>>> context = Context(prec=5, traps=[Inexact])
+>>> context.create_decimal_from_float(math.pi)
+Traceback (most recent call last):
+    ...
+decimal.Inexact: None
+

New in version 3.1.

+
+Etiny()
+

Returns a value equal to Emin - prec + 1 which is the minimum exponent value for subnormal results. When underflow occurs, the exponent is set to Etiny.

+
+Etop()
+

Returns a value equal to Emax - prec + 1.

+

The usual approach to working with decimals is to create Decimal instances and then apply arithmetic operations which take place within the current context for the active thread. An alternative approach is to use context methods for calculating within a specific context. The methods are similar to those for the Decimal class and are only briefly recounted here.

+abs(x)
+

Returns the absolute value of x.

+
+add(x, y)
+

Return the sum of x and y.

+
+canonical(x)
+

Returns the same Decimal object x.

+
+compare(x, y)
+

Compares x and y numerically.

+
+compare_signal(x, y)
+

Compares the values of the two operands numerically.

+
+compare_total(x, y)
+

Compares two operands using their abstract representation.

+
+compare_total_mag(x, y)
+

Compares two operands using their abstract representation, ignoring sign.

+
+copy_abs(x)
+

Returns a copy of x with the sign set to 0.

+
+copy_negate(x)
+

Returns a copy of x with the sign inverted.

+
+copy_sign(x, y)
+

Copies the sign from y to x.

+
+divide(x, y)
+

Return x divided by y.

+
+divide_int(x, y)
+

Return x divided by y, truncated to an integer.

+
+divmod(x, y)
+

Divides two numbers and returns the integer part of the result.

+
+exp(x)
+

Returns e ** x.

+
+fma(x, y, z)
+

Returns x multiplied by y, plus z.

+
+is_canonical(x)
+

Returns True if x is canonical; otherwise returns False.

+
+is_finite(x)
+

Returns True if x is finite; otherwise returns False.

+
+is_infinite(x)
+

Returns True if x is infinite; otherwise returns False.

+
+is_nan(x)
+

Returns True if x is a qNaN or sNaN; otherwise returns False.

+
+is_normal(x)
+

Returns True if x is a normal number; otherwise returns False.

+
+is_qnan(x)
+

Returns True if x is a quiet NaN; otherwise returns False.

+
+is_signed(x)
+

Returns True if x is negative; otherwise returns False.

+
+is_snan(x)
+

Returns True if x is a signaling NaN; otherwise returns False.

+
+is_subnormal(x)
+

Returns True if x is subnormal; otherwise returns False.

+
+is_zero(x)
+

Returns True if x is a zero; otherwise returns False.

+
+ln(x)
+

Returns the natural (base e) logarithm of x.

+
+log10(x)
+

Returns the base 10 logarithm of x.

+
+logb(x)
+

Returns the exponent of the magnitude of the operand’s MSD.

+
+logical_and(x, y)
+

Applies the logical operation and between each operand’s digits.

+
+logical_invert(x)
+

Invert all the digits in x.

+
+logical_or(x, y)
+

Applies the logical operation or between each operand’s digits.

+
+logical_xor(x, y)
+

Applies the logical operation xor between each operand’s digits.

+
+max(x, y)
+

Compares two values numerically and returns the maximum.

+
+max_mag(x, y)
+

Compares the values numerically with their sign ignored.

+
+min(x, y)
+

Compares two values numerically and returns the minimum.

+
+min_mag(x, y)
+

Compares the values numerically with their sign ignored.

+
+minus(x)
+

Minus corresponds to the unary prefix minus operator in Python.

+
+multiply(x, y)
+

Return the product of x and y.

+
+next_minus(x)
+

Returns the largest representable number smaller than x.

+
+next_plus(x)
+

Returns the smallest representable number larger than x.

+
+next_toward(x, y)
+

Returns the number closest to x, in direction towards y.

+
+normalize(x)
+

Reduces x to its simplest form.

+
+number_class(x)
+

Returns an indication of the class of x.

+
+plus(x)
+

Plus corresponds to the unary prefix plus operator in Python. This operation applies the context precision and rounding, so it is not an identity operation.

+
+power(x, y, modulo=None)
+

Return x to the power of y, reduced modulo modulo if given.

With two arguments, compute x**y. If x is negative then y must be integral. The result will be inexact unless y is integral and the result is finite and can be expressed exactly in ‘precision’ digits. The rounding mode of the context is used. Results are always correctly rounded in the Python version.

Decimal(0) ** Decimal(0) results in InvalidOperation, and if InvalidOperation is not trapped, then results in Decimal('NaN').

Changed in version 3.3: The C module computes power() in terms of the correctly rounded exp() and ln() functions. The result is well-defined but only “almost always correctly rounded”.

With three arguments, compute (x**y) % modulo. For the three argument form, the following restrictions on the arguments hold:

  • all three arguments must be integral
  • +y must be nonnegative
  • at least one of x or y must be nonzero
  • +modulo must be nonzero and have at most ‘precision’ digits

The value resulting from Context.power(x, y, modulo) is equal to the value that would be obtained by computing (x**y) +% modulo with unbounded precision, but is computed more efficiently. The exponent of the result is zero, regardless of the exponents of x, y and modulo. The result is always exact.

+
+quantize(x, y)
+

Returns a value equal to x (rounded), having the exponent of y.

+
+radix()
+

Just returns 10, as this is Decimal, :)

+
+remainder(x, y)
+

Returns the remainder from integer division.

The sign of the result, if non-zero, is the same as that of the original dividend.

+
+remainder_near(x, y)
+

Returns x - y * n, where n is the integer nearest the exact value of x / y (if the result is 0 then its sign will be the sign of x).

+
+rotate(x, y)
+

Returns a rotated copy of x, y times.

+
+same_quantum(x, y)
+

Returns True if the two operands have the same exponent.

+
+scaleb(x, y)
+

Returns the first operand after adding the second value its exp.

+
+shift(x, y)
+

Returns a shifted copy of x, y times.

+
+sqrt(x)
+

Square root of a non-negative number to context precision.

+
+subtract(x, y)
+

Return the difference between x and y.

+
+to_eng_string(x)
+

Convert to a string, using engineering notation if an exponent is needed.

Engineering notation has an exponent which is a multiple of 3. This can leave up to 3 digits to the left of the decimal place and may require the addition of either one or two trailing zeros.

+
+to_integral_exact(x)
+

Rounds to an integer.

+
+to_sci_string(x)
+

Converts a number to a string using scientific notation.

+
+

Constants

The constants in this section are only relevant for the C module. They are also included in the pure Python version for compatibility.

+ + + + +

32-bit

64-bit
+
+decimal.MAX_PREC
+

425000000

999999999999999999
+
+decimal.MAX_EMAX
+

425000000

999999999999999999
+
+decimal.MIN_EMIN
+

-425000000

-999999999999999999
+
+decimal.MIN_ETINY
+

-849999999

-1999999999999999997
+decimal.HAVE_THREADS
+

The value is True. Deprecated, because Python now always has threads.

+

Deprecated since version 3.9.

+decimal.HAVE_CONTEXTVAR
+

The default value is True. If Python is configured using +the --without-decimal-contextvar option, the C version uses a thread-local rather than a coroutine-local context and the value is False. This is slightly faster in some nested context scenarios.

+

New in version 3.9: backported to 3.7 and 3.8.

Rounding modes

+decimal.ROUND_CEILING
+

Round towards Infinity.

+
+decimal.ROUND_DOWN
+

Round towards zero.

+
+decimal.ROUND_FLOOR
+

Round towards -Infinity.

+
+decimal.ROUND_HALF_DOWN
+

Round to nearest with ties going towards zero.

+
+decimal.ROUND_HALF_EVEN
+

Round to nearest with ties going to nearest even integer.

+
+decimal.ROUND_HALF_UP
+

Round to nearest with ties going away from zero.

+
+decimal.ROUND_UP
+

Round away from zero.

+
+decimal.ROUND_05UP
+

Round away from zero if last digit after rounding towards zero would have been 0 or 5; otherwise round towards zero.

+

Signals

Signals represent conditions that arise during computation. Each corresponds to one context flag and one context trap enabler.

The context flag is set whenever the condition is encountered. After the computation, flags may be checked for informational purposes (for instance, to determine whether a computation was exact). After checking the flags, be sure to clear all flags before starting the next computation.

If the context’s trap enabler is set for the signal, then the condition causes a Python exception to be raised. For example, if the DivisionByZero trap is set, then a DivisionByZero exception is raised upon encountering the condition.

+class decimal.Clamped
+

Altered an exponent to fit representation constraints.

Typically, clamping occurs when an exponent falls outside the context’s Emin and Emax limits. If possible, the exponent is reduced to fit by adding zeros to the coefficient.

+
+class decimal.DecimalException
+

Base class for other signals and a subclass of ArithmeticError.

+
+class decimal.DivisionByZero
+

Signals the division of a non-infinite number by zero.

Can occur with division, modulo division, or when raising a number to a negative power. If this signal is not trapped, returns Infinity or -Infinity with the sign determined by the inputs to the calculation.

+
+class decimal.Inexact
+

Indicates that rounding occurred and the result is not exact.

Signals when non-zero digits were discarded during rounding. The rounded result is returned. The signal flag or trap is used to detect when results are inexact.

+
+class decimal.InvalidOperation
+

An invalid operation was performed.

Indicates that an operation was requested that does not make sense. If not trapped, returns NaN. Possible causes include:

Infinity - Infinity
+0 * Infinity
+Infinity / Infinity
+x % 0
+Infinity % x
+sqrt(-x) and x > 0
+0 ** 0
+x ** (non-integer)
+x ** Infinity
+
+
+class decimal.Overflow
+

Numerical overflow.

Indicates the exponent is larger than Context.Emax after rounding has occurred. If not trapped, the result depends on the rounding mode, either pulling inward to the largest representable finite number or rounding outward to Infinity. In either case, Inexact and Rounded are also signaled.

+
+class decimal.Rounded
+

Rounding occurred though possibly no information was lost.

Signaled whenever rounding discards digits; even if those digits are zero (such as rounding 5.00 to 5.0). If not trapped, returns the result unchanged. This signal is used to detect loss of significant digits.

+
+class decimal.Subnormal
+

Exponent was lower than Emin prior to rounding.

Occurs when an operation result is subnormal (the exponent is too small). If not trapped, returns the result unchanged.

+
+class decimal.Underflow
+

Numerical underflow with result rounded to zero.

Occurs when a subnormal result is pushed to zero by rounding. Inexact and Subnormal are also signaled.

+
+class decimal.FloatOperation
+

Enable stricter semantics for mixing floats and Decimals.

If the signal is not trapped (default), mixing floats and Decimals is permitted in the Decimal constructor, create_decimal() and all comparison operators. Both conversion and comparisons are exact. Any occurrence of a mixed operation is silently recorded by setting FloatOperation in the context flags. Explicit conversions with from_float() or create_decimal_from_float() do not set the flag.

Otherwise (the signal is trapped), only equality comparisons and explicit conversions are silent. All other mixed operations raise FloatOperation.

+

The following table summarizes the hierarchy of signals:

exceptions.ArithmeticError(exceptions.Exception)
+    DecimalException
+        Clamped
+        DivisionByZero(DecimalException, exceptions.ZeroDivisionError)
+        Inexact
+            Overflow(Inexact, Rounded)
+            Underflow(Inexact, Rounded, Subnormal)
+        InvalidOperation
+        Rounded
+        Subnormal
+        FloatOperation(DecimalException, exceptions.TypeError)
+

Floating Point Notes

Mitigating round-off error with increased precision

The use of decimal floating point eliminates decimal representation error (making it possible to represent 0.1 exactly); however, some operations can still incur round-off error when non-zero digits exceed the fixed precision.

The effects of round-off error can be amplified by the addition or subtraction of nearly offsetting quantities resulting in loss of significance. Knuth provides two instructive examples where rounded floating point arithmetic with insufficient precision causes the breakdown of the associative and distributive properties of addition:

# Examples from Seminumerical Algorithms, Section 4.2.2.
+>>> from decimal import Decimal, getcontext
+>>> getcontext().prec = 8
+
+>>> u, v, w = Decimal(11111113), Decimal(-11111111), Decimal('7.51111111')
+>>> (u + v) + w
+Decimal('9.5111111')
+>>> u + (v + w)
+Decimal('10')
+
+>>> u, v, w = Decimal(20000), Decimal(-6), Decimal('6.0000003')
+>>> (u*v) + (u*w)
+Decimal('0.01')
+>>> u * (v+w)
+Decimal('0.0060000')
+

The decimal module makes it possible to restore the identities by expanding the precision sufficiently to avoid loss of significance:

>>> getcontext().prec = 20
+>>> u, v, w = Decimal(11111113), Decimal(-11111111), Decimal('7.51111111')
+>>> (u + v) + w
+Decimal('9.51111111')
+>>> u + (v + w)
+Decimal('9.51111111')
+>>>
+>>> u, v, w = Decimal(20000), Decimal(-6), Decimal('6.0000003')
+>>> (u*v) + (u*w)
+Decimal('0.0060000')
+>>> u * (v+w)
+Decimal('0.0060000')
+

Special values

The number system for the decimal module provides special values including NaN, sNaN, -Infinity, Infinity, and two zeros, +0 and -0.

Infinities can be constructed directly with: Decimal('Infinity'). Also, they can arise from dividing by zero when the DivisionByZero signal is not trapped. Likewise, when the Overflow signal is not trapped, infinity can result from rounding beyond the limits of the largest representable number.

The infinities are signed (affine) and can be used in arithmetic operations where they get treated as very large, indeterminate numbers. For instance, adding a constant to infinity gives another infinite result.

Some operations are indeterminate and return NaN, or if the InvalidOperation signal is trapped, raise an exception. For example, 0/0 returns NaN which means “not a number”. This variety of NaN is quiet and, once created, will flow through other computations always resulting in another NaN. This behavior can be useful for a series of computations that occasionally have missing inputs — it allows the calculation to proceed while flagging specific results as invalid.

A variant is sNaN which signals rather than remaining quiet after every operation. This is a useful return value when an invalid result needs to interrupt a calculation for special handling.

The behavior of Python’s comparison operators can be a little surprising where a NaN is involved. A test for equality where one of the operands is a quiet or signaling NaN always returns False (even when doing Decimal('NaN')==Decimal('NaN')), while a test for inequality always returns True. An attempt to compare two Decimals using any of the <, <=, > or >= operators will raise the InvalidOperation signal if either operand is a NaN, and return False if this signal is not trapped. Note that the General Decimal Arithmetic specification does not specify the behavior of direct comparisons; these rules for comparisons involving a NaN were taken from the IEEE 854 standard (see Table 3 in section 5.7). To ensure strict standards-compliance, use the compare() and compare_signal() methods instead.

The signed zeros can result from calculations that underflow. They keep the sign that would have resulted if the calculation had been carried out to greater precision. Since their magnitude is zero, both positive and negative zeros are treated as equal and their sign is informational.

In addition to the two signed zeros which are distinct yet equal, there are various representations of zero with differing precisions yet equivalent in value. This takes a bit of getting used to. For an eye accustomed to normalized floating point representations, it is not immediately obvious that the following calculation returns a value equal to zero:

>>> 1 / Decimal('Infinity')
+Decimal('0E-1000026')
+

Working with threads

The getcontext() function accesses a different Context object for each thread. Having separate thread contexts means that threads may make changes (such as getcontext().prec=10) without interfering with other threads.

Likewise, the setcontext() function automatically assigns its target to the current thread.

If setcontext() has not been called before getcontext(), then getcontext() will automatically create a new context for use in the current thread.

The new context is copied from a prototype context called DefaultContext. To control the defaults so that each thread will use the same values throughout the application, directly modify the DefaultContext object. This should be done before any threads are started so that there won’t be a race condition between threads calling getcontext(). For example:

# Set applicationwide defaults for all threads about to be launched
+DefaultContext.prec = 12
+DefaultContext.rounding = ROUND_DOWN
+DefaultContext.traps = ExtendedContext.traps.copy()
+DefaultContext.traps[InvalidOperation] = 1
+setcontext(DefaultContext)
+
+# Afterwards, the threads can be started
+t1.start()
+t2.start()
+t3.start()
+ . . .
+

Recipes

Here are a few recipes that serve as utility functions and that demonstrate ways to work with the Decimal class:

def moneyfmt(value, places=2, curr='', sep=',', dp='.',
+             pos='', neg='-', trailneg=''):
+    """Convert Decimal to a money formatted string.
+
+    places:  required number of places after the decimal point
+    curr:    optional currency symbol before the sign (may be blank)
+    sep:     optional grouping separator (comma, period, space, or blank)
+    dp:      decimal point indicator (comma or period)
+             only specify as blank when places is zero
+    pos:     optional sign for positive numbers: '+', space or blank
+    neg:     optional sign for negative numbers: '-', '(', space or blank
+    trailneg:optional trailing minus indicator:  '-', ')', space or blank
+
+    >>> d = Decimal('-1234567.8901')
+    >>> moneyfmt(d, curr='$')
+    '-$1,234,567.89'
+    >>> moneyfmt(d, places=0, sep='.', dp='', neg='', trailneg='-')
+    '1.234.568-'
+    >>> moneyfmt(d, curr='$', neg='(', trailneg=')')
+    '($1,234,567.89)'
+    >>> moneyfmt(Decimal(123456789), sep=' ')
+    '123 456 789.00'
+    >>> moneyfmt(Decimal('-0.02'), neg='<', trailneg='>')
+    '<0.02>'
+
+    """
+    q = Decimal(10) ** -places      # 2 places --> '0.01'
+    sign, digits, exp = value.quantize(q).as_tuple()
+    result = []
+    digits = list(map(str, digits))
+    build, next = result.append, digits.pop
+    if sign:
+        build(trailneg)
+    for i in range(places):
+        build(next() if digits else '0')
+    if places:
+        build(dp)
+    if not digits:
+        build('0')
+    i = 0
+    while digits:
+        build(next())
+        i += 1
+        if i == 3 and digits:
+            i = 0
+            build(sep)
+    build(curr)
+    build(neg if sign else pos)
+    return ''.join(reversed(result))
+
+def pi():
+    """Compute Pi to the current precision.
+
+    >>> print(pi())
+    3.141592653589793238462643383
+
+    """
+    getcontext().prec += 2  # extra digits for intermediate steps
+    three = Decimal(3)      # substitute "three=3.0" for regular floats
+    lasts, t, s, n, na, d, da = 0, three, 3, 1, 0, 0, 24
+    while s != lasts:
+        lasts = s
+        n, na = n+na, na+8
+        d, da = d+da, da+32
+        t = (t * n) / d
+        s += t
+    getcontext().prec -= 2
+    return +s               # unary plus applies the new precision
+
+def exp(x):
+    """Return e raised to the power of x.  Result type matches input type.
+
+    >>> print(exp(Decimal(1)))
+    2.718281828459045235360287471
+    >>> print(exp(Decimal(2)))
+    7.389056098930650227230427461
+    >>> print(exp(2.0))
+    7.38905609893
+    >>> print(exp(2+0j))
+    (7.38905609893+0j)
+
+    """
+    getcontext().prec += 2
+    i, lasts, s, fact, num = 0, 0, 1, 1, 1
+    while s != lasts:
+        lasts = s
+        i += 1
+        fact *= i
+        num *= x
+        s += num / fact
+    getcontext().prec -= 2
+    return +s
+
+def cos(x):
+    """Return the cosine of x as measured in radians.
+
+    The Taylor series approximation works best for a small value of x.
+    For larger values, first compute x = x % (2 * pi).
+
+    >>> print(cos(Decimal('0.5')))
+    0.8775825618903727161162815826
+    >>> print(cos(0.5))
+    0.87758256189
+    >>> print(cos(0.5+0j))
+    (0.87758256189+0j)
+
+    """
+    getcontext().prec += 2
+    i, lasts, s, fact, num, sign = 0, 0, 1, 1, 1, 1
+    while s != lasts:
+        lasts = s
+        i += 2
+        fact *= i * (i-1)
+        num *= x * x
+        sign *= -1
+        s += num / fact * sign
+    getcontext().prec -= 2
+    return +s
+
+def sin(x):
+    """Return the sine of x as measured in radians.
+
+    The Taylor series approximation works best for a small value of x.
+    For larger values, first compute x = x % (2 * pi).
+
+    >>> print(sin(Decimal('0.5')))
+    0.4794255386042030002732879352
+    >>> print(sin(0.5))
+    0.479425538604
+    >>> print(sin(0.5+0j))
+    (0.479425538604+0j)
+
+    """
+    getcontext().prec += 2
+    i, lasts, s, fact, num, sign = 1, 0, x, 1, x, 1
+    while s != lasts:
+        lasts = s
+        i += 2
+        fact *= i * (i-1)
+        num *= x * x
+        sign *= -1
+        s += num / fact * sign
+    getcontext().prec -= 2
+    return +s
+

Decimal FAQ

Q. It is cumbersome to type decimal.Decimal('1234.5'). Is there a way to minimize typing when using the interactive interpreter?

A. Some users abbreviate the constructor to just a single letter:

>>> D = decimal.Decimal
+>>> D('1.23') + D('3.45')
+Decimal('4.68')
+

Q. In a fixed-point application with two decimal places, some inputs have many places and need to be rounded. Others are not supposed to have excess digits and need to be validated. What methods should be used?

A. The quantize() method rounds to a fixed number of decimal places. If the Inexact trap is set, it is also useful for validation:

>>> TWOPLACES = Decimal(10) ** -2       # same as Decimal('0.01')
+
>>> # Round to two places
+>>> Decimal('3.214').quantize(TWOPLACES)
+Decimal('3.21')
+
>>> # Validate that a number does not exceed two places
+>>> Decimal('3.21').quantize(TWOPLACES, context=Context(traps=[Inexact]))
+Decimal('3.21')
+
>>> Decimal('3.214').quantize(TWOPLACES, context=Context(traps=[Inexact]))
+Traceback (most recent call last):
+   ...
+Inexact: None
+

Q. Once I have valid two place inputs, how do I maintain that invariant throughout an application?

A. Some operations like addition, subtraction, and multiplication by an integer will automatically preserve fixed point. Others operations, like division and non-integer multiplication, will change the number of decimal places and need to be followed-up with a quantize() step:

>>> a = Decimal('102.72')           # Initial fixed-point values
+>>> b = Decimal('3.17')
+>>> a + b                           # Addition preserves fixed-point
+Decimal('105.89')
+>>> a - b
+Decimal('99.55')
+>>> a * 42                          # So does integer multiplication
+Decimal('4314.24')
+>>> (a * b).quantize(TWOPLACES)     # Must quantize non-integer multiplication
+Decimal('325.62')
+>>> (b / a).quantize(TWOPLACES)     # And quantize division
+Decimal('0.03')
+

In developing fixed-point applications, it is convenient to define functions to handle the quantize() step:

>>> def mul(x, y, fp=TWOPLACES):
+...     return (x * y).quantize(fp)
+...
+>>> def div(x, y, fp=TWOPLACES):
+...     return (x / y).quantize(fp)
+
>>> mul(a, b)                       # Automatically preserve fixed-point
+Decimal('325.62')
+>>> div(b, a)
+Decimal('0.03')
+

Q. There are many ways to express the same value. The numbers 200, 200.000, 2E2, and .02E+4 all have the same value at various precisions. Is there a way to transform them to a single recognizable canonical value?

A. The normalize() method maps all equivalent values to a single representative:

>>> values = map(Decimal, '200 200.000 2E2 .02E+4'.split())
+>>> [v.normalize() for v in values]
+[Decimal('2E+2'), Decimal('2E+2'), Decimal('2E+2'), Decimal('2E+2')]
+

Q. When does rounding occur in a computation?

A. It occurs after the computation. The philosophy of the decimal specification is that numbers are considered exact and are created independent of the current context. They can even have greater precision than current context. Computations process with those exact inputs and then rounding (or other context operations) is applied to the result of the computation:

>>> getcontext().prec = 5
+>>> pi = Decimal('3.1415926535')   # More than 5 digits
+>>> pi                             # All digits are retained
+Decimal('3.1415926535')
+>>> pi + 0                         # Rounded after an addition
+Decimal('3.1416')
+>>> pi - Decimal('0.00005')        # Subtract unrounded numbers, then round
+Decimal('3.1415')
+>>> pi + 0 - Decimal('0.00005').   # Intermediate values are rounded
+Decimal('3.1416')
+

Q. Some decimal values always print with exponential notation. Is there a way to get a non-exponential representation?

A. For some values, exponential notation is the only way to express the number of significant places in the coefficient. For example, expressing 5.0E+3 as 5000 keeps the value constant but cannot show the original’s two-place significance.

If an application does not care about tracking significance, it is easy to remove the exponent and trailing zeroes, losing significance, but keeping the value unchanged:

>>> def remove_exponent(d):
+...     return d.quantize(Decimal(1)) if d == d.to_integral() else d.normalize()
+
>>> remove_exponent(Decimal('5E+3'))
+Decimal('5000')
+

Q. Is there a way to convert a regular float to a Decimal?

A. Yes, any binary floating point number can be exactly expressed as a Decimal though an exact conversion may take more precision than intuition would suggest:

>>> Decimal(math.pi)
+Decimal('3.141592653589793115997963468544185161590576171875')
+

Q. Within a complex calculation, how can I make sure that I haven’t gotten a spurious result because of insufficient precision or rounding anomalies.

A. The decimal module makes it easy to test results. A best practice is to re-run calculations using greater precision and with various rounding modes. Widely differing results indicate insufficient precision, rounding mode issues, ill-conditioned inputs, or a numerically unstable algorithm.

Q. I noticed that context precision is applied to the results of operations but not to the inputs. Is there anything to watch out for when mixing values of different precisions?

A. Yes. The principle is that all values are considered to be exact and so is the arithmetic on those values. Only the results are rounded. The advantage for inputs is that “what you type is what you get”. A disadvantage is that the results can look odd if you forget that the inputs haven’t been rounded:

>>> getcontext().prec = 3
+>>> Decimal('3.104') + Decimal('2.104')
+Decimal('5.21')
+>>> Decimal('3.104') + Decimal('0.000') + Decimal('2.104')
+Decimal('5.20')
+

The solution is either to increase precision or to force rounding of inputs using the unary plus operation:

>>> getcontext().prec = 3
+>>> +Decimal('1.23456789')      # unary plus triggers rounding
+Decimal('1.23')
+

Alternatively, inputs can be rounded upon creation using the Context.create_decimal() method:

>>> Context(prec=5, rounding=ROUND_DOWN).create_decimal('1.2345678')
+Decimal('1.2345')
+

Q. Is the CPython implementation fast for large numbers?

A. Yes. In the CPython and PyPy3 implementations, the C/CFFI versions of the decimal module integrate the high speed libmpdec library for arbitrary precision correctly rounded decimal floating point arithmetic 1. libmpdec uses Karatsuba multiplication for medium-sized numbers and the Number Theoretic Transform for very large numbers.

The context must be adapted for exact arbitrary precision arithmetic. Emin and Emax should always be set to the maximum values, clamp should always be 0 (the default). Setting prec requires some care.

The easiest approach for trying out bignum arithmetic is to use the maximum value for prec as well 2:

>>> setcontext(Context(prec=MAX_PREC, Emax=MAX_EMAX, Emin=MIN_EMIN))
+>>> x = Decimal(2) ** 256
+>>> x / 128
+Decimal('904625697166532776746648320380374280103671755200316906558262375061821325312')
+

For inexact results, MAX_PREC is far too large on 64-bit platforms and the available memory will be insufficient:

>>> Decimal(1) / 3
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+MemoryError
+

On systems with overallocation (e.g. Linux), a more sophisticated approach is to adjust prec to the amount of available RAM. Suppose that you have 8GB of RAM and expect 10 simultaneous operands using a maximum of 500MB each:

>>> import sys
+>>>
+>>> # Maximum number of digits for a single operand using 500MB in 8-byte words
+>>> # with 19 digits per word (4-byte and 9 digits for the 32-bit build):
+>>> maxdigits = 19 * ((500 * 1024**2) // 8)
+>>>
+>>> # Check that this works:
+>>> c = Context(prec=maxdigits, Emax=MAX_EMAX, Emin=MIN_EMIN)
+>>> c.traps[Inexact] = True
+>>> setcontext(c)
+>>>
+>>> # Fill the available precision with nines:
+>>> x = Decimal(0).logical_invert() * 9
+>>> sys.getsizeof(x)
+524288112
+>>> x + 2
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  decimal.Inexact: [<class 'decimal.Inexact'>]
+

In general (and especially on systems without overallocation), it is recommended to estimate even tighter bounds and set the Inexact trap if all calculations are expected to be exact.

+1
+

New in version 3.3.

+2
+

Changed in version 3.9: This approach now works for all exact results except for non-integer powers.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/decimal.html +

+
diff --git a/devdocs/python~3.12/library%2Fdevelopment.html b/devdocs/python~3.12/library%2Fdevelopment.html new file mode 100644 index 00000000..0a0ddebd --- /dev/null +++ b/devdocs/python~3.12/library%2Fdevelopment.html @@ -0,0 +1,29 @@ +

Development Tools

The modules described in this chapter help you write software. For example, the pydoc module takes a module and generates documentation based on the module’s contents. The doctest and unittest modules contains frameworks for writing unit tests that automatically exercise code and verify that the expected output is produced. 2to3 can translate Python 2.x source code into valid Python 3.x code.

The list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/development.html +

+
diff --git a/devdocs/python~3.12/library%2Fdevmode.html b/devdocs/python~3.12/library%2Fdevmode.html new file mode 100644 index 00000000..1f32a201 --- /dev/null +++ b/devdocs/python~3.12/library%2Fdevmode.html @@ -0,0 +1,66 @@ +

Python Development Mode

New in version 3.7.

The Python Development Mode introduces additional runtime checks that are too expensive to be enabled by default. It should not be more verbose than the default if the code is correct; new warnings are only emitted when an issue is detected.

It can be enabled using the -X dev command line option or by setting the PYTHONDEVMODE environment variable to 1.

See also Python debug build.

Effects of the Python Development Mode

Enabling the Python Development Mode is similar to the following command, but with additional effects described below:

PYTHONMALLOC=debug PYTHONASYNCIODEBUG=1 python -W default -X faulthandler
+

Effects of the Python Development Mode:

The Python Development Mode does not enable the tracemalloc module by default, because the overhead cost (to performance and memory) would be too large. Enabling the tracemalloc module provides additional information on the origin of some errors. For example, ResourceWarning logs the traceback where the resource was allocated, and a buffer overflow error logs the traceback where the memory block was allocated.

The Python Development Mode does not prevent the -O command line option from removing assert statements nor from setting __debug__ to False.

The Python Development Mode can only be enabled at the Python startup. Its value can be read from sys.flags.dev_mode.

Changed in version 3.8: The io.IOBase destructor now logs close() exceptions.

Changed in version 3.9: The encoding and errors arguments are now checked for string encoding and decoding operations.

ResourceWarning Example

Example of a script counting the number of lines of the text file specified in the command line:

import sys
+
+def main():
+    fp = open(sys.argv[1])
+    nlines = len(fp.readlines())
+    print(nlines)
+    # The file is closed implicitly
+
+if __name__ == "__main__":
+    main()
+

The script does not close the file explicitly. By default, Python does not emit any warning. Example using README.txt, which has 269 lines:

$ python script.py README.txt
+269
+

Enabling the Python Development Mode displays a ResourceWarning warning:

$ python -X dev script.py README.txt
+269
+script.py:10: ResourceWarning: unclosed file <_io.TextIOWrapper name='README.rst' mode='r' encoding='UTF-8'>
+  main()
+ResourceWarning: Enable tracemalloc to get the object allocation traceback
+

In addition, enabling tracemalloc shows the line where the file was opened:

$ python -X dev -X tracemalloc=5 script.py README.rst
+269
+script.py:10: ResourceWarning: unclosed file <_io.TextIOWrapper name='README.rst' mode='r' encoding='UTF-8'>
+  main()
+Object allocated at (most recent call last):
+  File "script.py", lineno 10
+    main()
+  File "script.py", lineno 4
+    fp = open(sys.argv[1])
+

The fix is to close explicitly the file. Example using a context manager:

def main():
+    # Close the file explicitly when exiting the with block
+    with open(sys.argv[1]) as fp:
+        nlines = len(fp.readlines())
+    print(nlines)
+

Not closing a resource explicitly can leave a resource open for way longer than expected; it can cause severe issues upon exiting Python. It is bad in CPython, but it is even worse in PyPy. Closing resources explicitly makes an application more deterministic and more reliable.

Bad file descriptor error example

Script displaying the first line of itself:

import os
+
+def main():
+    fp = open(__file__)
+    firstline = fp.readline()
+    print(firstline.rstrip())
+    os.close(fp.fileno())
+    # The file is closed implicitly
+
+main()
+

By default, Python does not emit any warning:

$ python script.py
+import os
+

The Python Development Mode shows a ResourceWarning and logs a “Bad file descriptor” error when finalizing the file object:

$ python -X dev script.py
+import os
+script.py:10: ResourceWarning: unclosed file <_io.TextIOWrapper name='script.py' mode='r' encoding='UTF-8'>
+  main()
+ResourceWarning: Enable tracemalloc to get the object allocation traceback
+Exception ignored in: <_io.TextIOWrapper name='script.py' mode='r' encoding='UTF-8'>
+Traceback (most recent call last):
+  File "script.py", line 10, in <module>
+    main()
+OSError: [Errno 9] Bad file descriptor
+

os.close(fp.fileno()) closes the file descriptor. When the file object finalizer tries to close the file descriptor again, it fails with the Bad +file descriptor error. A file descriptor must be closed only once. In the worst case scenario, closing it twice can lead to a crash (see bpo-18748 for an example).

The fix is to remove the os.close(fp.fileno()) line, or open the file with closefd=False.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/devmode.html +

+
diff --git a/devdocs/python~3.12/library%2Fdialog.html b/devdocs/python~3.12/library%2Fdialog.html new file mode 100644 index 00000000..1801278a --- /dev/null +++ b/devdocs/python~3.12/library%2Fdialog.html @@ -0,0 +1,103 @@ +

Tkinter Dialogs

tkinter.simpledialog — Standard Tkinter input dialogs

Source code: Lib/tkinter/simpledialog.py

The tkinter.simpledialog module contains convenience classes and functions for creating simple modal dialogs to get a value from the user.

+tkinter.simpledialog.askfloat(title, prompt, **kw)
+tkinter.simpledialog.askinteger(title, prompt, **kw)
+tkinter.simpledialog.askstring(title, prompt, **kw)
+

The above three functions provide dialogs that prompt the user to enter a value of the desired type.

+
+class tkinter.simpledialog.Dialog(parent, title=None)
+

The base class for custom dialogs.

+body(master)
+

Override to construct the dialog’s interface and return the widget that should have initial focus.

+
+buttonbox()
+

Default behaviour adds OK and Cancel buttons. Override for custom button layouts.

+
+

tkinter.filedialog — File selection dialogs

Source code: Lib/tkinter/filedialog.py

The tkinter.filedialog module provides classes and factory functions for creating file/directory selection windows.

Native Load/Save Dialogs

The following classes and functions provide file dialog windows that combine a native look-and-feel with configuration options to customize behaviour. The following keyword arguments are applicable to the classes and functions listed below:

Static factory functions

The below functions when called create a modal, native look-and-feel dialog, wait for the user’s selection, then return the selected value(s) or None to the caller.

+tkinter.filedialog.askopenfile(mode='r', **options)
+tkinter.filedialog.askopenfiles(mode='r', **options)
+

The above two functions create an Open dialog and return the opened file object(s) in read-only mode.

+
+tkinter.filedialog.asksaveasfile(mode='w', **options)
+

Create a SaveAs dialog and return a file object opened in write-only mode.

+
+tkinter.filedialog.askopenfilename(**options)
+tkinter.filedialog.askopenfilenames(**options)
+

The above two functions create an Open dialog and return the selected filename(s) that correspond to existing file(s).

+
+tkinter.filedialog.asksaveasfilename(**options)
+

Create a SaveAs dialog and return the selected filename.

+
+tkinter.filedialog.askdirectory(**options)
+
+class tkinter.filedialog.Open(master=None, **options)
+class tkinter.filedialog.SaveAs(master=None, **options)
+

The above two classes provide native dialog windows for saving and loading files.

+

Convenience classes

The below classes are used for creating file/directory windows from scratch. These do not emulate the native look-and-feel of the platform.

+class tkinter.filedialog.Directory(master=None, **options)
+

Create a dialog prompting the user to select a directory.

+

Note

The FileDialog class should be subclassed for custom event handling and behaviour.

+class tkinter.filedialog.FileDialog(master, title=None)
+

Create a basic file selection dialog.

+cancel_command(event=None)
+

Trigger the termination of the dialog window.

+
+dirs_double_event(event)
+

Event handler for double-click event on directory.

+
+dirs_select_event(event)
+

Event handler for click event on directory.

+
+files_double_event(event)
+

Event handler for double-click event on file.

+
+files_select_event(event)
+

Event handler for single-click event on file.

+
+filter_command(event=None)
+

Filter the files by directory.

+
+get_filter()
+

Retrieve the file filter currently in use.

+
+get_selection()
+

Retrieve the currently selected item.

+
+go(dir_or_file=os.curdir, pattern='*', default='', key=None)
+

Render dialog and start event loop.

+
+ok_event(event)
+

Exit dialog returning current selection.

+
+quit(how=None)
+

Exit dialog returning filename, if any.

+
+set_filter(dir, pat)
+

Set the file filter.

+
+set_selection(file)
+

Update the current file selection to file.

+
+
+class tkinter.filedialog.LoadFileDialog(master, title=None)
+

A subclass of FileDialog that creates a dialog window for selecting an existing file.

+ok_command()
+

Test that a file is provided and that the selection indicates an already existing file.

+
+
+class tkinter.filedialog.SaveFileDialog(master, title=None)
+

A subclass of FileDialog that creates a dialog window for selecting a destination file.

+ok_command()
+

Test whether or not the selection points to a valid file that is not a directory. Confirmation is required if an already existing file is selected.

+
+

tkinter.commondialog — Dialog window templates

Source code: Lib/tkinter/commondialog.py

The tkinter.commondialog module provides the Dialog class that is the base class for dialogs defined in other supporting modules.

+class tkinter.commondialog.Dialog(master=None, **options)
+
+show(color=None, **options)
+

Render the Dialog window.

+
+

See also

Modules tkinter.messagebox, Reading and Writing Files

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/dialog.html +

+
diff --git a/devdocs/python~3.12/library%2Fdifflib.html b/devdocs/python~3.12/library%2Fdifflib.html new file mode 100644 index 00000000..ac44dc56 --- /dev/null +++ b/devdocs/python~3.12/library%2Fdifflib.html @@ -0,0 +1,426 @@ +

difflib — Helpers for computing deltas

Source code: Lib/difflib.py

This module provides classes and functions for comparing sequences. It can be used for example, for comparing files, and can produce information about file differences in various formats, including HTML and context and unified diffs. For comparing directories and files, see also, the filecmp module.

class difflib.SequenceMatcher +
+

This is a flexible class for comparing pairs of sequences of any type, so long as the sequence elements are hashable. The basic algorithm predates, and is a little fancier than, an algorithm published in the late 1980’s by Ratcliff and Obershelp under the hyperbolic name “gestalt pattern matching.” The idea is to find the longest contiguous matching subsequence that contains no “junk” elements; these “junk” elements are ones that are uninteresting in some sense, such as blank lines or whitespace. (Handling junk is an extension to the Ratcliff and Obershelp algorithm.) The same idea is then applied recursively to the pieces of the sequences to the left and to the right of the matching subsequence. This does not yield minimal edit sequences, but does tend to yield matches that “look right” to people.

Timing: The basic Ratcliff-Obershelp algorithm is cubic time in the worst case and quadratic time in the expected case. SequenceMatcher is quadratic time for the worst case and has expected-case behavior dependent in a complicated way on how many elements the sequences have in common; best case time is linear.

Automatic junk heuristic: SequenceMatcher supports a heuristic that automatically treats certain sequence items as junk. The heuristic counts how many times each individual item appears in the sequence. If an item’s duplicates (after the first one) account for more than 1% of the sequence and the sequence is at least 200 items long, this item is marked as “popular” and is treated as junk for the purpose of sequence matching. This heuristic can be turned off by setting the autojunk argument to False when creating the SequenceMatcher.

New in version 3.2: The autojunk parameter.

+
+class difflib.Differ
+

This is a class for comparing sequences of lines of text, and producing human-readable differences or deltas. Differ uses SequenceMatcher both to compare sequences of lines, and to compare sequences of characters within similar (near-matching) lines.

Each line of a Differ delta begins with a two-letter code:

+ + + + +

Code

Meaning

'- '

line unique to sequence 1

'+ '

line unique to sequence 2

'  '

line common to both sequences

'? '

line not present in either input sequence

Lines beginning with ‘?’ attempt to guide the eye to intraline differences, and were not present in either input sequence. These lines can be confusing if the sequences contain whitespace characters, such as spaces, tabs or line breaks.

+
+class difflib.HtmlDiff
+

This class can be used to create an HTML table (or a complete HTML file containing the table) showing a side by side, line by line comparison of text with inter-line and intra-line change highlights. The table can be generated in either full or contextual difference mode.

The constructor for this class is:

+__init__(tabsize=8, wrapcolumn=None, linejunk=None, charjunk=IS_CHARACTER_JUNK)
+

Initializes instance of HtmlDiff.

tabsize is an optional keyword argument to specify tab stop spacing and defaults to 8.

wrapcolumn is an optional keyword to specify column number where lines are broken and wrapped, defaults to None where lines are not wrapped.

linejunk and charjunk are optional keyword arguments passed into ndiff() (used by HtmlDiff to generate the side by side HTML differences). See ndiff() documentation for argument default values and descriptions.

+

The following methods are public:

+make_file(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5, *, charset='utf-8')
+

Compares fromlines and tolines (lists of strings) and returns a string which is a complete HTML file containing a table showing line by line differences with inter-line and intra-line changes highlighted.

fromdesc and todesc are optional keyword arguments to specify from/to file column header strings (both default to an empty string).

context and numlines are both optional keyword arguments. Set context to True when contextual differences are to be shown, else the default is False to show the full files. numlines defaults to 5. When context is True numlines controls the number of context lines which surround the difference highlights. When context is False numlines controls the number of lines which are shown before a difference highlight when using the “next” hyperlinks (setting to zero would cause the “next” hyperlinks to place the next difference highlight at the top of the browser without any leading context).

Note

fromdesc and todesc are interpreted as unescaped HTML and should be properly escaped while receiving input from untrusted sources.

Changed in version 3.5: charset keyword-only argument was added. The default charset of HTML document changed from 'ISO-8859-1' to 'utf-8'.

+
+make_table(fromlines, tolines, fromdesc='', todesc='', context=False, numlines=5)
+

Compares fromlines and tolines (lists of strings) and returns a string which is a complete HTML table showing line by line differences with inter-line and intra-line changes highlighted.

The arguments for this method are the same as those for the make_file() method.

+
+
+difflib.context_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')
+

Compare a and b (lists of strings); return a delta (a generator generating the delta lines) in context diff format.

Context diffs are a compact way of showing just the lines that have changed plus a few lines of context. The changes are shown in a before/after style. The number of context lines is set by n which defaults to three.

By default, the diff control lines (those with *** or ---) are created with a trailing newline. This is helpful so that inputs created from io.IOBase.readlines() result in diffs that are suitable for use with io.IOBase.writelines() since both the inputs and outputs have trailing newlines.

For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free.

The context diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for fromfile, tofile, fromfiledate, and tofiledate. The modification times are normally expressed in the ISO 8601 format. If not specified, the strings default to blanks.

>>> import sys
+>>> from difflib import *
+>>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
+>>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
+>>> sys.stdout.writelines(context_diff(s1, s2, fromfile='before.py',
+...                        tofile='after.py'))
+*** before.py
+--- after.py
+***************
+*** 1,4 ****
+! bacon
+! eggs
+! ham
+  guido
+--- 1,4 ----
+! python
+! eggy
+! hamster
+  guido
+

See A command-line interface to difflib for a more detailed example.

+
+difflib.get_close_matches(word, possibilities, n=3, cutoff=0.6)
+

Return a list of the best “good enough” matches. word is a sequence for which close matches are desired (typically a string), and possibilities is a list of sequences against which to match word (typically a list of strings).

Optional argument n (default 3) is the maximum number of close matches to return; n must be greater than 0.

Optional argument cutoff (default 0.6) is a float in the range [0, 1]. Possibilities that don’t score at least that similar to word are ignored.

The best (no more than n) matches among the possibilities are returned in a list, sorted by similarity score, most similar first.

>>> get_close_matches('appel', ['ape', 'apple', 'peach', 'puppy'])
+['apple', 'ape']
+>>> import keyword
+>>> get_close_matches('wheel', keyword.kwlist)
+['while']
+>>> get_close_matches('pineapple', keyword.kwlist)
+[]
+>>> get_close_matches('accept', keyword.kwlist)
+['except']
+
+
+difflib.ndiff(a, b, linejunk=None, charjunk=IS_CHARACTER_JUNK)
+

Compare a and b (lists of strings); return a Differ-style delta (a generator generating the delta lines).

Optional keyword parameters linejunk and charjunk are filtering functions (or None):

linejunk: A function that accepts a single string argument, and returns true if the string is junk, or false if not. The default is None. There is also a module-level function IS_LINE_JUNK(), which filters out lines without visible characters, except for at most one pound character ('#') – however the underlying SequenceMatcher class does a dynamic analysis of which lines are so frequent as to constitute noise, and this usually works better than using this function.

charjunk: A function that accepts a character (a string of length 1), and returns if the character is junk, or false if not. The default is module-level function IS_CHARACTER_JUNK(), which filters out whitespace characters (a blank or tab; it’s a bad idea to include newline in this!).

>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+>>> print(''.join(diff), end="")
+- one
+?  ^
++ ore
+?  ^
+- two
+- three
+?  -
++ tree
++ emu
+
+
+difflib.restore(sequence, which)
+

Return one of the two sequences that generated a delta.

Given a sequence produced by Differ.compare() or ndiff(), extract lines originating from file 1 or 2 (parameter which), stripping off line prefixes.

Example:

>>> diff = ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
+...              'ore\ntree\nemu\n'.splitlines(keepends=True))
+>>> diff = list(diff) # materialize the generated delta into a list
+>>> print(''.join(restore(diff, 1)), end="")
+one
+two
+three
+>>> print(''.join(restore(diff, 2)), end="")
+ore
+tree
+emu
+
+
+difflib.unified_diff(a, b, fromfile='', tofile='', fromfiledate='', tofiledate='', n=3, lineterm='\n')
+

Compare a and b (lists of strings); return a delta (a generator generating the delta lines) in unified diff format.

Unified diffs are a compact way of showing just the lines that have changed plus a few lines of context. The changes are shown in an inline style (instead of separate before/after blocks). The number of context lines is set by n which defaults to three.

By default, the diff control lines (those with ---, +++, or @@) are created with a trailing newline. This is helpful so that inputs created from io.IOBase.readlines() result in diffs that are suitable for use with io.IOBase.writelines() since both the inputs and outputs have trailing newlines.

For inputs that do not have trailing newlines, set the lineterm argument to "" so that the output will be uniformly newline free.

The unified diff format normally has a header for filenames and modification times. Any or all of these may be specified using strings for fromfile, tofile, fromfiledate, and tofiledate. The modification times are normally expressed in the ISO 8601 format. If not specified, the strings default to blanks.

>>> s1 = ['bacon\n', 'eggs\n', 'ham\n', 'guido\n']
+>>> s2 = ['python\n', 'eggy\n', 'hamster\n', 'guido\n']
+>>> sys.stdout.writelines(unified_diff(s1, s2, fromfile='before.py', tofile='after.py'))
+--- before.py
++++ after.py
+@@ -1,4 +1,4 @@
+-bacon
+-eggs
+-ham
++python
++eggy
++hamster
+ guido
+

See A command-line interface to difflib for a more detailed example.

+
+difflib.diff_bytes(dfunc, a, b, fromfile=b'', tofile=b'', fromfiledate=b'', tofiledate=b'', n=3, lineterm=b'\n')
+

Compare a and b (lists of bytes objects) using dfunc; yield a sequence of delta lines (also bytes) in the format returned by dfunc. dfunc must be a callable, typically either unified_diff() or context_diff().

Allows you to compare data with unknown or inconsistent encoding. All inputs except n must be bytes objects, not str. Works by losslessly converting all inputs (except n) to str, and calling dfunc(a, b, +fromfile, tofile, fromfiledate, tofiledate, n, lineterm). The output of dfunc is then converted back to bytes, so the delta lines that you receive have the same unknown/inconsistent encodings as a and b.

New in version 3.5.

+
+difflib.IS_LINE_JUNK(line)
+

Return True for ignorable lines. The line line is ignorable if line is blank or contains a single '#', otherwise it is not ignorable. Used as a default for parameter linejunk in ndiff() in older versions.

+
+difflib.IS_CHARACTER_JUNK(ch)
+

Return True for ignorable characters. The character ch is ignorable if ch is a space or tab, otherwise it is not ignorable. Used as a default for parameter charjunk in ndiff().

+

See also

Pattern Matching: The Gestalt Approach
+
+

Discussion of a similar algorithm by John W. Ratcliff and D. E. Metzener. This was published in Dr. Dobb’s Journal in July, 1988.

SequenceMatcher Objects

The SequenceMatcher class has this constructor:

+class difflib.SequenceMatcher(isjunk=None, a='', b='', autojunk=True)
+

Optional argument isjunk must be None (the default) or a one-argument function that takes a sequence element and returns true if and only if the element is “junk” and should be ignored. Passing None for isjunk is equivalent to passing lambda x: False; in other words, no elements are ignored. For example, pass:

lambda x: x in " \t"
+

if you’re comparing lines as sequences of characters, and don’t want to synch up on blanks or hard tabs.

The optional arguments a and b are sequences to be compared; both default to empty strings. The elements of both sequences must be hashable.

The optional argument autojunk can be used to disable the automatic junk heuristic.

New in version 3.2: The autojunk parameter.

SequenceMatcher objects get three data attributes: bjunk is the set of elements of b for which isjunk is True; bpopular is the set of non-junk elements considered popular by the heuristic (if it is not disabled); b2j is a dict mapping the remaining elements of b to a list of positions where they occur. All three are reset whenever b is reset with set_seqs() or set_seq2().

New in version 3.2: The bjunk and bpopular attributes.

SequenceMatcher objects have the following methods:

+set_seqs(a, b)
+

Set the two sequences to be compared.

+

SequenceMatcher computes and caches detailed information about the second sequence, so if you want to compare one sequence against many sequences, use set_seq2() to set the commonly used sequence once and call set_seq1() repeatedly, once for each of the other sequences.

+set_seq1(a)
+

Set the first sequence to be compared. The second sequence to be compared is not changed.

+
+set_seq2(b)
+

Set the second sequence to be compared. The first sequence to be compared is not changed.

+
+find_longest_match(alo=0, ahi=None, blo=0, bhi=None)
+

Find longest matching block in a[alo:ahi] and b[blo:bhi].

If isjunk was omitted or None, find_longest_match() returns (i, j, k) such that a[i:i+k] is equal to b[j:j+k], where alo +<= i <= i+k <= ahi and blo <= j <= j+k <= bhi. For all (i', j', +k') meeting those conditions, the additional conditions k >= k', i +<= i', and if i == i', j <= j' are also met. In other words, of all maximal matching blocks, return one that starts earliest in a, and of all those maximal matching blocks that start earliest in a, return the one that starts earliest in b.

>>> s = SequenceMatcher(None, " abcd", "abcd abcd")
+>>> s.find_longest_match(0, 5, 0, 9)
+Match(a=0, b=4, size=5)
+

If isjunk was provided, first the longest matching block is determined as above, but with the additional restriction that no junk element appears in the block. Then that block is extended as far as possible by matching (only) junk elements on both sides. So the resulting block never matches on junk except as identical junk happens to be adjacent to an interesting match.

Here’s the same example as before, but considering blanks to be junk. That prevents ' abcd' from matching the ' abcd' at the tail end of the second sequence directly. Instead only the 'abcd' can match, and matches the leftmost 'abcd' in the second sequence:

>>> s = SequenceMatcher(lambda x: x==" ", " abcd", "abcd abcd")
+>>> s.find_longest_match(0, 5, 0, 9)
+Match(a=1, b=0, size=4)
+

If no blocks match, this returns (alo, blo, 0).

This method returns a named tuple Match(a, b, size).

Changed in version 3.9: Added default arguments.

+
+get_matching_blocks()
+

Return list of triples describing non-overlapping matching subsequences. Each triple is of the form (i, j, n), and means that a[i:i+n] == b[j:j+n]. The triples are monotonically increasing in i and j.

The last triple is a dummy, and has the value (len(a), len(b), 0). It is the only triple with n == 0. If (i, j, n) and (i', j', n') are adjacent triples in the list, and the second is not the last triple in the list, then i+n < i' or j+n < j'; in other words, adjacent triples always describe non-adjacent equal blocks.

>>> s = SequenceMatcher(None, "abxcd", "abcd")
+>>> s.get_matching_blocks()
+[Match(a=0, b=0, size=2), Match(a=3, b=2, size=2), Match(a=5, b=4, size=0)]
+
+
+get_opcodes()
+

Return list of 5-tuples describing how to turn a into b. Each tuple is of the form (tag, i1, i2, j1, j2). The first tuple has i1 == j1 == +0, and remaining tuples have i1 equal to the i2 from the preceding tuple, and, likewise, j1 equal to the previous j2.

The tag values are strings, with these meanings:

+ + + + +

Value

Meaning

'replace'

a[i1:i2] should be replaced by b[j1:j2].

'delete'

a[i1:i2] should be deleted. Note that j1 == j2 in this case.

'insert'

b[j1:j2] should be inserted at a[i1:i1]. Note that i1 == i2 in this case.

'equal'

a[i1:i2] == b[j1:j2] (the sub-sequences are equal).

For example:

>>> a = "qabxcd"
+>>> b = "abycdf"
+>>> s = SequenceMatcher(None, a, b)
+>>> for tag, i1, i2, j1, j2 in s.get_opcodes():
+...     print('{:7}   a[{}:{}] --> b[{}:{}] {!r:>8} --> {!r}'.format(
+...         tag, i1, i2, j1, j2, a[i1:i2], b[j1:j2]))
+delete    a[0:1] --> b[0:0]      'q' --> ''
+equal     a[1:3] --> b[0:2]     'ab' --> 'ab'
+replace   a[3:4] --> b[2:3]      'x' --> 'y'
+equal     a[4:6] --> b[3:5]     'cd' --> 'cd'
+insert    a[6:6] --> b[5:6]       '' --> 'f'
+
+
+get_grouped_opcodes(n=3)
+

Return a generator of groups with up to n lines of context.

Starting with the groups returned by get_opcodes(), this method splits out smaller change clusters and eliminates intervening ranges which have no changes.

The groups are returned in the same format as get_opcodes().

+
+ratio()
+

Return a measure of the sequences’ similarity as a float in the range [0, 1].

Where T is the total number of elements in both sequences, and M is the number of matches, this is 2.0*M / T. Note that this is 1.0 if the sequences are identical, and 0.0 if they have nothing in common.

This is expensive to compute if get_matching_blocks() or get_opcodes() hasn’t already been called, in which case you may want to try quick_ratio() or real_quick_ratio() first to get an upper bound.

Note

Caution: The result of a ratio() call may depend on the order of the arguments. For instance:

>>> SequenceMatcher(None, 'tide', 'diet').ratio()
+0.25
+>>> SequenceMatcher(None, 'diet', 'tide').ratio()
+0.5
+
+
+quick_ratio()
+

Return an upper bound on ratio() relatively quickly.

+
+real_quick_ratio()
+

Return an upper bound on ratio() very quickly.

+
+

The three methods that return the ratio of matching to total characters can give different results due to differing levels of approximation, although quick_ratio() and real_quick_ratio() are always at least as large as ratio():

>>> s = SequenceMatcher(None, "abcd", "bcde")
+>>> s.ratio()
+0.75
+>>> s.quick_ratio()
+0.75
+>>> s.real_quick_ratio()
+1.0
+

SequenceMatcher Examples

This example compares two strings, considering blanks to be “junk”:

>>> s = SequenceMatcher(lambda x: x == " ",
+...                     "private Thread currentThread;",
+...                     "private volatile Thread currentThread;")
+

ratio() returns a float in [0, 1], measuring the similarity of the sequences. As a rule of thumb, a ratio() value over 0.6 means the sequences are close matches:

>>> print(round(s.ratio(), 3))
+0.866
+

If you’re only interested in where the sequences match, get_matching_blocks() is handy:

>>> for block in s.get_matching_blocks():
+...     print("a[%d] and b[%d] match for %d elements" % block)
+a[0] and b[0] match for 8 elements
+a[8] and b[17] match for 21 elements
+a[29] and b[38] match for 0 elements
+

Note that the last tuple returned by get_matching_blocks() is always a dummy, (len(a), len(b), 0), and this is the only case in which the last tuple element (number of elements matched) is 0.

If you want to know how to change the first sequence into the second, use get_opcodes():

>>> for opcode in s.get_opcodes():
+...     print("%6s a[%d:%d] b[%d:%d]" % opcode)
+ equal a[0:8] b[0:8]
+insert a[8:8] b[8:17]
+ equal a[8:29] b[17:38]
+

See also

Differ Objects

Note that Differ-generated deltas make no claim to be minimal diffs. To the contrary, minimal diffs are often counter-intuitive, because they synch up anywhere possible, sometimes accidental matches 100 pages apart. Restricting synch points to contiguous matches preserves some notion of locality, at the occasional cost of producing a longer diff.

The Differ class has this constructor:

class difflib.Differ(linejunk=None, charjunk=None) +
+

Optional keyword parameters linejunk and charjunk are for filter functions (or None):

linejunk: A function that accepts a single string argument, and returns true if the string is junk. The default is None, meaning that no line is considered junk.

charjunk: A function that accepts a single character argument (a string of length 1), and returns true if the character is junk. The default is None, meaning that no character is considered junk.

These junk-filtering functions speed up matching to find differences and do not cause any differing lines or characters to be ignored. Read the description of the find_longest_match() method’s isjunk parameter for an explanation.

Differ objects are used (deltas generated) via a single method:

+compare(a, b)
+

Compare two sequences of lines, and generate the delta (a sequence of lines).

Each sequence must contain individual single-line strings ending with newlines. Such sequences can be obtained from the readlines() method of file-like objects. The delta generated also consists of newline-terminated strings, ready to be printed as-is via the writelines() method of a file-like object.

+
+

Differ Example

This example compares two texts. First we set up the texts, sequences of individual single-line strings ending with newlines (such sequences can also be obtained from the readlines() method of file-like objects):

>>> text1 = '''  1. Beautiful is better than ugly.
+...   2. Explicit is better than implicit.
+...   3. Simple is better than complex.
+...   4. Complex is better than complicated.
+... '''.splitlines(keepends=True)
+>>> len(text1)
+4
+>>> text1[0][-1]
+'\n'
+>>> text2 = '''  1. Beautiful is better than ugly.
+...   3.   Simple is better than complex.
+...   4. Complicated is better than complex.
+...   5. Flat is better than nested.
+... '''.splitlines(keepends=True)
+

Next we instantiate a Differ object:

>>> d = Differ()
+

Note that when instantiating a Differ object we may pass functions to filter out line and character “junk.” See the Differ() constructor for details.

Finally, we compare the two:

>>> result = list(d.compare(text1, text2))
+

result is a list of strings, so let’s pretty-print it:

>>> from pprint import pprint
+>>> pprint(result)
+['    1. Beautiful is better than ugly.\n',
+ '-   2. Explicit is better than implicit.\n',
+ '-   3. Simple is better than complex.\n',
+ '+   3.   Simple is better than complex.\n',
+ '?     ++\n',
+ '-   4. Complex is better than complicated.\n',
+ '?            ^                     ---- ^\n',
+ '+   4. Complicated is better than complex.\n',
+ '?           ++++ ^                      ^\n',
+ '+   5. Flat is better than nested.\n']
+

As a single multi-line string it looks like this:

>>> import sys
+>>> sys.stdout.writelines(result)
+    1. Beautiful is better than ugly.
+-   2. Explicit is better than implicit.
+-   3. Simple is better than complex.
++   3.   Simple is better than complex.
+?     ++
+-   4. Complex is better than complicated.
+?            ^                     ---- ^
++   4. Complicated is better than complex.
+?           ++++ ^                      ^
++   5. Flat is better than nested.
+

A command-line interface to difflib

This example shows how to use difflib to create a diff-like utility.

""" Command line interface to difflib.py providing diffs in four formats:
+
+* ndiff:    lists every line and highlights interline changes.
+* context:  highlights clusters of changes in a before/after format.
+* unified:  highlights clusters of changes in an inline format.
+* html:     generates side by side comparison with change highlights.
+
+"""
+
+import sys, os, difflib, argparse
+from datetime import datetime, timezone
+
+def file_mtime(path):
+    t = datetime.fromtimestamp(os.stat(path).st_mtime,
+                               timezone.utc)
+    return t.astimezone().isoformat()
+
+def main():
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', action='store_true', default=False,
+                        help='Produce a context format diff (default)')
+    parser.add_argument('-u', action='store_true', default=False,
+                        help='Produce a unified format diff')
+    parser.add_argument('-m', action='store_true', default=False,
+                        help='Produce HTML side by side diff '
+                             '(can use -c and -l in conjunction)')
+    parser.add_argument('-n', action='store_true', default=False,
+                        help='Produce a ndiff format diff')
+    parser.add_argument('-l', '--lines', type=int, default=3,
+                        help='Set number of context lines (default 3)')
+    parser.add_argument('fromfile')
+    parser.add_argument('tofile')
+    options = parser.parse_args()
+
+    n = options.lines
+    fromfile = options.fromfile
+    tofile = options.tofile
+
+    fromdate = file_mtime(fromfile)
+    todate = file_mtime(tofile)
+    with open(fromfile) as ff:
+        fromlines = ff.readlines()
+    with open(tofile) as tf:
+        tolines = tf.readlines()
+
+    if options.u:
+        diff = difflib.unified_diff(fromlines, tolines, fromfile, tofile, fromdate, todate, n=n)
+    elif options.n:
+        diff = difflib.ndiff(fromlines, tolines)
+    elif options.m:
+        diff = difflib.HtmlDiff().make_file(fromlines,tolines,fromfile,tofile,context=options.c,numlines=n)
+    else:
+        diff = difflib.context_diff(fromlines, tolines, fromfile, tofile, fromdate, todate, n=n)
+
+    sys.stdout.writelines(diff)
+
+if __name__ == '__main__':
+    main()
+

ndiff example

This example shows how to use difflib.ndiff().

"""ndiff [-q] file1 file2
+    or
+ndiff (-r1 | -r2) < ndiff_output > file1_or_file2
+
+Print a human-friendly file difference report to stdout.  Both inter-
+and intra-line differences are noted.  In the second form, recreate file1
+(-r1) or file2 (-r2) on stdout, from an ndiff report on stdin.
+
+In the first form, if -q ("quiet") is not specified, the first two lines
+of output are
+
+-: file1
++: file2
+
+Each remaining line begins with a two-letter code:
+
+    "- "    line unique to file1
+    "+ "    line unique to file2
+    "  "    line common to both files
+    "? "    line not present in either input file
+
+Lines beginning with "? " attempt to guide the eye to intraline
+differences, and were not present in either input file.  These lines can be
+confusing if the source files contain tab characters.
+
+The first file can be recovered by retaining only lines that begin with
+"  " or "- ", and deleting those 2-character prefixes; use ndiff with -r1.
+
+The second file can be recovered similarly, but by retaining only "  " and
+"+ " lines; use ndiff with -r2; or, on Unix, the second file can be
+recovered by piping the output through
+
+    sed -n '/^[+ ] /s/^..//p'
+"""
+
+__version__ = 1, 7, 0
+
+import difflib, sys
+
+def fail(msg):
+    out = sys.stderr.write
+    out(msg + "\n\n")
+    out(__doc__)
+    return 0
+
+# open a file & return the file object; gripe and return 0 if it
+# couldn't be opened
+def fopen(fname):
+    try:
+        return open(fname)
+    except IOError as detail:
+        return fail("couldn't open " + fname + ": " + str(detail))
+
+# open two files & spray the diff to stdout; return false iff a problem
+def fcompare(f1name, f2name):
+    f1 = fopen(f1name)
+    f2 = fopen(f2name)
+    if not f1 or not f2:
+        return 0
+
+    a = f1.readlines(); f1.close()
+    b = f2.readlines(); f2.close()
+    for line in difflib.ndiff(a, b):
+        print(line, end=' ')
+
+    return 1
+
+# crack args (sys.argv[1:] is normal) & compare;
+# return false iff a problem
+
+def main(args):
+    import getopt
+    try:
+        opts, args = getopt.getopt(args, "qr:")
+    except getopt.error as detail:
+        return fail(str(detail))
+    noisy = 1
+    qseen = rseen = 0
+    for opt, val in opts:
+        if opt == "-q":
+            qseen = 1
+            noisy = 0
+        elif opt == "-r":
+            rseen = 1
+            whichfile = val
+    if qseen and rseen:
+        return fail("can't specify both -q and -r")
+    if rseen:
+        if args:
+            return fail("no args allowed with -r option")
+        if whichfile in ("1", "2"):
+            restore(whichfile)
+            return 1
+        return fail("-r value must be 1 or 2")
+    if len(args) != 2:
+        return fail("need 2 filename args")
+    f1name, f2name = args
+    if noisy:
+        print('-:', f1name)
+        print('+:', f2name)
+    return fcompare(f1name, f2name)
+
+# read ndiff output from stdin, and print file1 (which=='1') or
+# file2 (which=='2') to stdout
+
+def restore(which):
+    restored = difflib.restore(sys.stdin.readlines(), which)
+    sys.stdout.writelines(restored)
+
+if __name__ == '__main__':
+    main(sys.argv[1:])
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/difflib.html +

+
diff --git a/devdocs/python~3.12/library%2Fdis.html b/devdocs/python~3.12/library%2Fdis.html new file mode 100644 index 00000000..4754fc94 --- /dev/null +++ b/devdocs/python~3.12/library%2Fdis.html @@ -0,0 +1,598 @@ +

dis — Disassembler for Python bytecode

Source code: Lib/dis.py

The dis module supports the analysis of CPython bytecode by disassembling it. The CPython bytecode which this module takes as an input is defined in the file Include/opcode.h and used by the compiler and the interpreter.

CPython implementation detail: Bytecode is an implementation detail of the CPython interpreter. No guarantees are made that bytecode will not be added, removed, or changed between versions of Python. Use of this module should not be considered to work across Python VMs or Python releases.

Changed in version 3.6: Use 2 bytes for each instruction. Previously the number of bytes varied by instruction.

Changed in version 3.10: The argument of jump, exception handling and loop instructions is now the instruction offset rather than the byte offset.

Changed in version 3.11: Some instructions are accompanied by one or more inline cache entries, which take the form of CACHE instructions. These instructions are hidden by default, but can be shown by passing show_caches=True to any dis utility. Furthermore, the interpreter now adapts the bytecode to specialize it for different runtime conditions. The adaptive bytecode can be shown by passing adaptive=True.

Changed in version 3.12: The argument of a jump is the offset of the target instruction relative to the instruction that appears immediately after the jump instruction’s CACHE entries.

As a consequence, the presence of the CACHE instructions is transparent for forward jumps but needs to be taken into account when reasoning about backward jumps.

Example: Given the function myfunc():

def myfunc(alist):
+    return len(alist)
+

the following command can be used to display the disassembly of myfunc():

>>> dis.dis(myfunc)
+  2           0 RESUME                   0
+
+  3           2 LOAD_GLOBAL              1 (NULL + len)
+             12 LOAD_FAST                0 (alist)
+             14 CALL                     1
+             22 RETURN_VALUE
+

(The “2” is a line number).

Command-line interface

The dis module can be invoked as a script from the command line:

python -m dis [-h] [infile]
+

The following options are accepted:

+-h, --help
+

Display usage and exit.

+

If infile is specified, its disassembled code will be written to stdout. Otherwise, disassembly is performed on compiled source code recieved from stdin.

Bytecode analysis

New in version 3.4.

The bytecode analysis API allows pieces of Python code to be wrapped in a Bytecode object that provides easy access to details of the compiled code.

+class dis.Bytecode(x, *, first_line=None, current_offset=None, show_caches=False, adaptive=False)
+

Analyse the bytecode corresponding to a function, generator, asynchronous generator, coroutine, method, string of source code, or a code object (as returned by compile()).

This is a convenience wrapper around many of the functions listed below, most notably get_instructions(), as iterating over a Bytecode instance yields the bytecode operations as Instruction instances.

If first_line is not None, it indicates the line number that should be reported for the first source line in the disassembled code. Otherwise, the source line information (if any) is taken directly from the disassembled code object.

If current_offset is not None, it refers to an instruction offset in the disassembled code. Setting this means dis() will display a “current instruction” marker against the specified opcode.

If show_caches is True, dis() will display inline cache entries used by the interpreter to specialize the bytecode.

If adaptive is True, dis() will display specialized bytecode that may be different from the original bytecode.

+classmethod from_traceback(tb, *, show_caches=False)
+

Construct a Bytecode instance from the given traceback, setting current_offset to the instruction responsible for the exception.

+
+codeobj
+

The compiled code object.

+
+first_line
+

The first source line of the code object (if available)

+
+dis()
+

Return a formatted view of the bytecode operations (the same as printed by dis.dis(), but returned as a multi-line string).

+
+info()
+

Return a formatted multi-line string with detailed information about the code object, like code_info().

+

Changed in version 3.7: This can now handle coroutine and asynchronous generator objects.

Changed in version 3.11: Added the show_caches and adaptive parameters.

+

Example:

>>> bytecode = dis.Bytecode(myfunc)
+>>> for instr in bytecode:
+...     print(instr.opname)
+...
+RESUME
+LOAD_GLOBAL
+LOAD_FAST
+CALL
+RETURN_VALUE
+

Analysis functions

The dis module also defines the following analysis functions that convert the input directly to the desired output. They can be useful if only a single operation is being performed, so the intermediate analysis object isn’t useful:

+dis.code_info(x)
+

Return a formatted multi-line string with detailed code object information for the supplied function, generator, asynchronous generator, coroutine, method, source code string or code object.

Note that the exact contents of code info strings are highly implementation dependent and they may change arbitrarily across Python VMs or Python releases.

New in version 3.2.

Changed in version 3.7: This can now handle coroutine and asynchronous generator objects.

+
+dis.show_code(x, *, file=None)
+

Print detailed code object information for the supplied function, method, source code string or code object to file (or sys.stdout if file is not specified).

This is a convenient shorthand for print(code_info(x), file=file), intended for interactive exploration at the interpreter prompt.

New in version 3.2.

Changed in version 3.4: Added file parameter.

+
+dis.dis(x=None, *, file=None, depth=None, show_caches=False, adaptive=False)
+

Disassemble the x object. x can denote either a module, a class, a method, a function, a generator, an asynchronous generator, a coroutine, a code object, a string of source code or a byte sequence of raw bytecode. For a module, it disassembles all functions. For a class, it disassembles all methods (including class and static methods). For a code object or sequence of raw bytecode, it prints one line per bytecode instruction. It also recursively disassembles nested code objects. These can include generator expressions, nested functions, the bodies of nested classes, and the code objects used for annotation scopes. Strings are first compiled to code objects with the compile() built-in function before being disassembled. If no object is provided, this function disassembles the last traceback.

The disassembly is written as text to the supplied file argument if provided and to sys.stdout otherwise.

The maximal depth of recursion is limited by depth unless it is None. depth=0 means no recursion.

If show_caches is True, this function will display inline cache entries used by the interpreter to specialize the bytecode.

If adaptive is True, this function will display specialized bytecode that may be different from the original bytecode.

Changed in version 3.4: Added file parameter.

Changed in version 3.7: Implemented recursive disassembling and added depth parameter.

Changed in version 3.7: This can now handle coroutine and asynchronous generator objects.

Changed in version 3.11: Added the show_caches and adaptive parameters.

+
+dis.distb(tb=None, *, file=None, show_caches=False, adaptive=False)
+

Disassemble the top-of-stack function of a traceback, using the last traceback if none was passed. The instruction causing the exception is indicated.

The disassembly is written as text to the supplied file argument if provided and to sys.stdout otherwise.

Changed in version 3.4: Added file parameter.

Changed in version 3.11: Added the show_caches and adaptive parameters.

+
+dis.disassemble(code, lasti=- 1, *, file=None, show_caches=False, adaptive=False)
+dis.disco(code, lasti=- 1, *, file=None, show_caches=False, adaptive=False)
+

Disassemble a code object, indicating the last instruction if lasti was provided. The output is divided in the following columns:

  1. the line number, for the first instruction of each line
  2. the current instruction, indicated as -->,
  3. a labelled instruction, indicated with >>,
  4. the address of the instruction,
  5. the operation code name,
  6. operation parameters, and
  7. interpretation of the parameters in parentheses.

The parameter interpretation recognizes local and global variable names, constant values, branch targets, and compare operators.

The disassembly is written as text to the supplied file argument if provided and to sys.stdout otherwise.

Changed in version 3.4: Added file parameter.

Changed in version 3.11: Added the show_caches and adaptive parameters.

+
+dis.get_instructions(x, *, first_line=None, show_caches=False, adaptive=False)
+

Return an iterator over the instructions in the supplied function, method, source code string or code object.

The iterator generates a series of Instruction named tuples giving the details of each operation in the supplied code.

If first_line is not None, it indicates the line number that should be reported for the first source line in the disassembled code. Otherwise, the source line information (if any) is taken directly from the disassembled code object.

The show_caches and adaptive parameters work as they do in dis().

New in version 3.4.

Changed in version 3.11: Added the show_caches and adaptive parameters.

+
+dis.findlinestarts(code)
+

This generator function uses the co_lines() method of the code object code to find the offsets which are starts of lines in the source code. They are generated as (offset, lineno) pairs.

Changed in version 3.6: Line numbers can be decreasing. Before, they were always increasing.

Changed in version 3.10: The PEP 626 co_lines() method is used instead of the co_firstlineno and co_lnotab attributes of the code object.

+
+dis.findlabels(code)
+

Detect all offsets in the raw compiled bytecode string code which are jump targets, and return a list of these offsets.

+
+dis.stack_effect(opcode, oparg=None, *, jump=None)
+

Compute the stack effect of opcode with argument oparg.

If the code has a jump target and jump is True, stack_effect() will return the stack effect of jumping. If jump is False, it will return the stack effect of not jumping. And if jump is None (default), it will return the maximal stack effect of both cases.

New in version 3.4.

Changed in version 3.8: Added jump parameter.

+

Python Bytecode Instructions

The get_instructions() function and Bytecode class provide details of bytecode instructions as Instruction instances:

+class dis.Instruction
+

Details for a bytecode operation

+opcode
+

numeric code for operation, corresponding to the opcode values listed below and the bytecode values in the Opcode collections.

+
+opname
+

human readable name for operation

+
+arg
+

numeric argument to operation (if any), otherwise None

+
+argval
+

resolved arg value (if any), otherwise None

+
+argrepr
+

human readable description of operation argument (if any), otherwise an empty string.

+
+offset
+

start index of operation within bytecode sequence

+
+starts_line
+

line started by this opcode (if any), otherwise None

+
+is_jump_target
+

True if other code jumps to here, otherwise False

+
+positions
+

dis.Positions object holding the start and end locations that are covered by this instruction.

+

New in version 3.4.

Changed in version 3.11: Field positions is added.

+
+class dis.Positions
+

In case the information is not available, some fields might be None.

+lineno
+
+end_lineno
+
+col_offset
+
+end_col_offset
+

New in version 3.11.

+

The Python compiler currently generates the following bytecode instructions.

General instructions

In the following, We will refer to the interpreter stack as STACK and describe operations on it as if it was a Python list. The top of the stack corresponds to STACK[-1] in this language.

+NOP
+

Do nothing code. Used as a placeholder by the bytecode optimizer, and to generate line tracing events.

+
+POP_TOP
+

Removes the top-of-stack item:

STACK.pop()
+
+
+END_FOR
+

Removes the top two values from the stack. Equivalent to POP_TOP; POP_TOP. Used to clean up at the end of loops, hence the name.

New in version 3.12.

+
+END_SEND
+

Implements del STACK[-2]. Used to clean up when a generator exits.

New in version 3.12.

+
+COPY(i)
+

Push the i-th item to the top of the stack without removing it from its original location:

assert i > 0
+STACK.append(STACK[-i])
+

New in version 3.11.

+
+SWAP(i)
+

Swap the top of the stack with the i-th element:

STACK[-i], STACK[-1] = stack[-1], STACK[-i]
+

New in version 3.11.

+
+CACHE
+

Rather than being an actual instruction, this opcode is used to mark extra space for the interpreter to cache useful data directly in the bytecode itself. It is automatically hidden by all dis utilities, but can be viewed with show_caches=True.

Logically, this space is part of the preceding instruction. Many opcodes expect to be followed by an exact number of caches, and will instruct the interpreter to skip over them at runtime.

Populated caches can look like arbitrary instructions, so great care should be taken when reading or modifying raw, adaptive bytecode containing quickened data.

New in version 3.11.

+

Unary operations

Unary operations take the top of the stack, apply the operation, and push the result back on the stack.

+UNARY_NEGATIVE
+

Implements STACK[-1] = -STACK[-1].

+
+UNARY_NOT
+

Implements STACK[-1] = not STACK[-1].

+
+UNARY_INVERT
+

Implements STACK[-1] = ~STACK[-1].

+
+GET_ITER
+

Implements STACK[-1] = iter(STACK[-1]).

+
+GET_YIELD_FROM_ITER
+

If STACK[-1] is a generator iterator or coroutine object it is left as is. Otherwise, implements STACK[-1] = iter(STACK[-1]).

New in version 3.5.

+

Binary and in-place operations

Binary operations remove the top two items from the stack (STACK[-1] and STACK[-2]). They perform the operation, then put the result back on the stack.

In-place operations are like binary operations, but the operation is done in-place when STACK[-2] supports it, and the resulting STACK[-1] may be (but does not have to be) the original STACK[-2].

+BINARY_OP(op)
+

Implements the binary and in-place operators (depending on the value of op):

rhs = STACK.pop()
+lhs = STACK.pop()
+STACK.append(lhs op rhs)
+

New in version 3.11.

+
+BINARY_SUBSCR
+

Implements:

key = STACK.pop()
+container = STACK.pop()
+STACK.append(container[key])
+
+
+STORE_SUBSCR
+

Implements:

key = STACK.pop()
+container = STACK.pop()
+value = STACK.pop()
+container[key] = value
+
+
+DELETE_SUBSCR
+

Implements:

key = STACK.pop()
+container = STACK.pop()
+del container[key]
+
+
+BINARY_SLICE
+

Implements:

end = STACK.pop()
+start = STACK.pop()
+container = STACK.pop()
+STACK.append(container[start:end])
+

New in version 3.12.

+
+STORE_SLICE
+

Implements:

end = STACK.pop()
+start = STACK.pop()
+container = STACK.pop()
+values = STACK.pop()
+container[start:end] = value
+

New in version 3.12.

+

Coroutine opcodes

+GET_AWAITABLE(where)
+

Implements STACK[-1] = get_awaitable(STACK[-1]), where get_awaitable(o) returns o if o is a coroutine object or a generator object with the CO_ITERABLE_COROUTINE flag, or resolves o.__await__.

If the where operand is nonzero, it indicates where the instruction occurs:

  • +1: After a call to __aenter__ +
  • +2: After a call to __aexit__ +

New in version 3.5.

Changed in version 3.11: Previously, this instruction did not have an oparg.

+
+GET_AITER
+

Implements STACK[-1] = STACK[-1].__aiter__().

New in version 3.5.

Changed in version 3.7: Returning awaitable objects from __aiter__ is no longer supported.

+
+GET_ANEXT
+

Implement STACK.append(get_awaitable(STACK[-1].__anext__())) to the stack. See GET_AWAITABLE for details about get_awaitable.

New in version 3.5.

+
+END_ASYNC_FOR
+

Terminates an async for loop. Handles an exception raised when awaiting a next item. The stack contains the async iterable in STACK[-2] and the raised exception in STACK[-1]. Both are popped. If the exception is not StopAsyncIteration, it is re-raised.

New in version 3.8.

Changed in version 3.11: Exception representation on the stack now consist of one, not three, items.

+
+CLEANUP_THROW
+

Handles an exception raised during a throw() or close() call through the current frame. If STACK[-1] is an instance of StopIteration, pop three values from the stack and push its value member. Otherwise, re-raise STACK[-1].

New in version 3.12.

+
+BEFORE_ASYNC_WITH
+

Resolves __aenter__ and __aexit__ from STACK[-1]. Pushes __aexit__ and result of __aenter__() to the stack:

STACK.extend((__aexit__, __aenter__())
+

New in version 3.5.

+

Miscellaneous opcodes

+SET_ADD(i)
+

Implements:

item = STACK.pop()
+set.add(STACK[-i], item)
+

Used to implement set comprehensions.

+
+LIST_APPEND(i)
+

Implements:

item = STACK.pop()
+list.append(STACK[-i], item)
+

Used to implement list comprehensions.

+
+MAP_ADD(i)
+

Implements:

value = STACK.pop()
+key = STACK.pop()
+dict.__setitem__(STACK[-i], key, value)
+

Used to implement dict comprehensions.

New in version 3.1.

Changed in version 3.8: Map value is STACK[-1] and map key is STACK[-2]. Before, those were reversed.

+

For all of the SET_ADD, LIST_APPEND and MAP_ADD instructions, while the added value or key/value pair is popped off, the container object remains on the stack so that it is available for further iterations of the loop.

+RETURN_VALUE
+

Returns with STACK[-1] to the caller of the function.

+
+RETURN_CONST(consti)
+

Returns with co_consts[consti] to the caller of the function.

New in version 3.12.

+
+YIELD_VALUE
+

Yields STACK.pop() from a generator.

Changed in version 3.11: oparg set to be the stack depth.

Changed in version 3.12: oparg set to be the exception block depth, for efficient closing of generators.

+
+SETUP_ANNOTATIONS
+

Checks whether __annotations__ is defined in locals(), if not it is set up to an empty dict. This opcode is only emitted if a class or module body contains variable annotations statically.

New in version 3.6.

+
+POP_EXCEPT
+

Pops a value from the stack, which is used to restore the exception state.

Changed in version 3.11: Exception representation on the stack now consist of one, not three, items.

+
+RERAISE
+

Re-raises the exception currently on top of the stack. If oparg is non-zero, pops an additional value from the stack which is used to set f_lasti of the current frame.

New in version 3.9.

Changed in version 3.11: Exception representation on the stack now consist of one, not three, items.

+
+PUSH_EXC_INFO
+

Pops a value from the stack. Pushes the current exception to the top of the stack. Pushes the value originally popped back to the stack. Used in exception handlers.

New in version 3.11.

+
+CHECK_EXC_MATCH
+

Performs exception matching for except. Tests whether the STACK[-2] is an exception matching STACK[-1]. Pops STACK[-1] and pushes the boolean result of the test.

New in version 3.11.

+
+CHECK_EG_MATCH
+

Performs exception matching for except*. Applies split(STACK[-1]) on the exception group representing STACK[-2].

In case of a match, pops two items from the stack and pushes the non-matching subgroup (None in case of full match) followed by the matching subgroup. When there is no match, pops one item (the match type) and pushes None.

New in version 3.11.

+
+WITH_EXCEPT_START
+

Calls the function in position 4 on the stack with arguments (type, val, tb) representing the exception at the top of the stack. Used to implement the call context_manager.__exit__(*exc_info()) when an exception has occurred in a with statement.

New in version 3.9.

Changed in version 3.11: The __exit__ function is in position 4 of the stack rather than 7. Exception representation on the stack now consist of one, not three, items.

+
+LOAD_ASSERTION_ERROR
+

Pushes AssertionError onto the stack. Used by the assert statement.

New in version 3.9.

+
+LOAD_BUILD_CLASS
+

Pushes builtins.__build_class__() onto the stack. It is later called to construct a class.

+
+BEFORE_WITH
+

This opcode performs several operations before a with block starts. First, it loads __exit__() from the context manager and pushes it onto the stack for later use by WITH_EXCEPT_START. Then, __enter__() is called. Finally, the result of calling the __enter__() method is pushed onto the stack.

New in version 3.11.

+
+GET_LEN
+

Perform STACK.append(len(STACK[-1])).

New in version 3.10.

+
+MATCH_MAPPING
+

If STACK[-1] is an instance of collections.abc.Mapping (or, more technically: if it has the Py_TPFLAGS_MAPPING flag set in its tp_flags), push True onto the stack. Otherwise, push False.

New in version 3.10.

+
+MATCH_SEQUENCE
+

If STACK[-1] is an instance of collections.abc.Sequence and is not an instance of str/bytes/bytearray (or, more technically: if it has the Py_TPFLAGS_SEQUENCE flag set in its tp_flags), push True onto the stack. Otherwise, push False.

New in version 3.10.

+
+MATCH_KEYS
+

STACK[-1] is a tuple of mapping keys, and STACK[-2] is the match subject. If STACK[-2] contains all of the keys in STACK[-1], push a tuple containing the corresponding values. Otherwise, push None.

New in version 3.10.

Changed in version 3.11: Previously, this instruction also pushed a boolean value indicating success (True) or failure (False).

+
+STORE_NAME(namei)
+

Implements name = STACK.pop(). namei is the index of name in the attribute co_names of the code object. The compiler tries to use STORE_FAST or STORE_GLOBAL if possible.

+
+DELETE_NAME(namei)
+

Implements del name, where namei is the index into co_names attribute of the code object.

+
+UNPACK_SEQUENCE(count)
+

Unpacks STACK[-1] into count individual values, which are put onto the stack right-to-left. Require there to be exactly count values.:

assert(len(STACK[-1]) == count)
+STACK.extend(STACK.pop()[:-count-1:-1])
+
+
+UNPACK_EX(counts)
+

Implements assignment with a starred target: Unpacks an iterable in STACK[-1] into individual values, where the total number of values can be smaller than the number of items in the iterable: one of the new values will be a list of all leftover items.

The number of values before and after the list value is limited to 255.

The number of values before the list value is encoded in the argument of the opcode. The number of values after the list if any is encoded using an EXTENDED_ARG. As a consequence, the argument can be seen as a two bytes values where the low byte of counts is the number of values before the list value, the high byte of counts the number of values after it.

The extracted values are put onto the stack right-to-left, i.e. a, *b, c = d will be stored after execution as STACK.extend((a, b, c)).

+
+STORE_ATTR(namei)
+

Implements:

obj = STACK.pop()
+value = STACK.pop()
+obj.name = value
+

where namei is the index of name in co_names of the code object.

+
+DELETE_ATTR(namei)
+

Implements:

obj = STACK.pop()
+del obj.name
+

where namei is the index of name into co_names of the code object.

+
+STORE_GLOBAL(namei)
+

Works as STORE_NAME, but stores the name as a global.

+
+DELETE_GLOBAL(namei)
+

Works as DELETE_NAME, but deletes a global name.

+
+LOAD_CONST(consti)
+

Pushes co_consts[consti] onto the stack.

+
+LOAD_NAME(namei)
+

Pushes the value associated with co_names[namei] onto the stack. The name is looked up within the locals, then the globals, then the builtins.

+
+LOAD_LOCALS
+

Pushes a reference to the locals dictionary onto the stack. This is used to prepare namespace dictionaries for LOAD_FROM_DICT_OR_DEREF and LOAD_FROM_DICT_OR_GLOBALS.

New in version 3.12.

+
+LOAD_FROM_DICT_OR_GLOBALS(i)
+

Pops a mapping off the stack and looks up the value for co_names[namei]. If the name is not found there, looks it up in the globals and then the builtins, similar to LOAD_GLOBAL. This is used for loading global variables in annotation scopes within class bodies.

New in version 3.12.

+
+BUILD_TUPLE(count)
+

Creates a tuple consuming count items from the stack, and pushes the resulting tuple onto the stack.:

assert count > 0
+STACK, values = STACK[:-count], STACK[-count:]
+STACK.append(tuple(values))
+
+
+BUILD_LIST(count)
+

Works as BUILD_TUPLE, but creates a list.

+
+BUILD_SET(count)
+

Works as BUILD_TUPLE, but creates a set.

+
+BUILD_MAP(count)
+

Pushes a new dictionary object onto the stack. Pops 2 * count items so that the dictionary holds count entries: {..., STACK[-4]: STACK[-3], STACK[-2]: STACK[-1]}.

Changed in version 3.5: The dictionary is created from stack items instead of creating an empty dictionary pre-sized to hold count items.

+
+BUILD_CONST_KEY_MAP(count)
+

The version of BUILD_MAP specialized for constant keys. Pops the top element on the stack which contains a tuple of keys, then starting from STACK[-2], pops count values to form values in the built dictionary.

New in version 3.6.

+
+BUILD_STRING(count)
+

Concatenates count strings from the stack and pushes the resulting string onto the stack.

New in version 3.6.

+
+LIST_EXTEND(i)
+

Implements:

seq = STACK.pop()
+list.extend(STACK[-i], seq)
+

Used to build lists.

New in version 3.9.

+
+SET_UPDATE(i)
+

Implements:

seq = STACK.pop()
+set.update(STACK[-i], seq)
+

Used to build sets.

New in version 3.9.

+
+DICT_UPDATE(i)
+

Implements:

map = STACK.pop()
+dict.update(STACK[-i], map)
+

Used to build dicts.

New in version 3.9.

+
+DICT_MERGE(i)
+

Like DICT_UPDATE but raises an exception for duplicate keys.

New in version 3.9.

+
+LOAD_ATTR(namei)
+

If the low bit of namei is not set, this replaces STACK[-1] with getattr(STACK[-1], co_names[namei>>1]).

If the low bit of namei is set, this will attempt to load a method named co_names[namei>>1] from the STACK[-1] object. STACK[-1] is popped. This bytecode distinguishes two cases: if STACK[-1] has a method with the correct name, the bytecode pushes the unbound method and STACK[-1]. STACK[-1] will be used as the first argument (self) by CALL when calling the unbound method. Otherwise, NULL and the object returned by the attribute lookup are pushed.

Changed in version 3.12: If the low bit of namei is set, then a NULL or self is pushed to the stack before the attribute or unbound method respectively.

+
+LOAD_SUPER_ATTR(namei)
+

This opcode implements super(), both in its zero-argument and two-argument forms (e.g. super().method(), super().attr and super(cls, self).method(), super(cls, self).attr).

It pops three values from the stack (from top of stack down): - self: the first argument to the current method - cls: the class within which the current method was defined - the global super

With respect to its argument, it works similarly to LOAD_ATTR, except that namei is shifted left by 2 bits instead of 1.

The low bit of namei signals to attempt a method load, as with LOAD_ATTR, which results in pushing None and the loaded method. When it is unset a single value is pushed to the stack.

The second-low bit of namei, if set, means that this was a two-argument call to super() (unset means zero-argument).

New in version 3.12.

+
+COMPARE_OP(opname)
+

Performs a Boolean operation. The operation name can be found in cmp_op[opname].

+
+IS_OP(invert)
+

Performs is comparison, or is not if invert is 1.

New in version 3.9.

+
+CONTAINS_OP(invert)
+

Performs in comparison, or not in if invert is 1.

New in version 3.9.

+
+IMPORT_NAME(namei)
+

Imports the module co_names[namei]. STACK[-1] and STACK[-2] are popped and provide the fromlist and level arguments of __import__(). The module object is pushed onto the stack. The current namespace is not affected: for a proper import statement, a subsequent STORE_FAST instruction modifies the namespace.

+
+IMPORT_FROM(namei)
+

Loads the attribute co_names[namei] from the module found in STACK[-1]. The resulting object is pushed onto the stack, to be subsequently stored by a STORE_FAST instruction.

+
+JUMP_FORWARD(delta)
+

Increments bytecode counter by delta.

+
+JUMP_BACKWARD(delta)
+

Decrements bytecode counter by delta. Checks for interrupts.

New in version 3.11.

+
+JUMP_BACKWARD_NO_INTERRUPT(delta)
+

Decrements bytecode counter by delta. Does not check for interrupts.

New in version 3.11.

+
+POP_JUMP_IF_TRUE(delta)
+

If STACK[-1] is true, increments the bytecode counter by delta. STACK[-1] is popped.

Changed in version 3.11: The oparg is now a relative delta rather than an absolute target. This opcode is a pseudo-instruction, replaced in final bytecode by the directed versions (forward/backward).

Changed in version 3.12: This is no longer a pseudo-instruction.

+
+POP_JUMP_IF_FALSE(delta)
+

If STACK[-1] is false, increments the bytecode counter by delta. STACK[-1] is popped.

Changed in version 3.11: The oparg is now a relative delta rather than an absolute target. This opcode is a pseudo-instruction, replaced in final bytecode by the directed versions (forward/backward).

Changed in version 3.12: This is no longer a pseudo-instruction.

+
+POP_JUMP_IF_NOT_NONE(delta)
+

If STACK[-1] is not None, increments the bytecode counter by delta. STACK[-1] is popped.

This opcode is a pseudo-instruction, replaced in final bytecode by the directed versions (forward/backward).

New in version 3.11.

Changed in version 3.12: This is no longer a pseudo-instruction.

+
+POP_JUMP_IF_NONE(delta)
+

If STACK[-1] is None, increments the bytecode counter by delta. STACK[-1] is popped.

This opcode is a pseudo-instruction, replaced in final bytecode by the directed versions (forward/backward).

New in version 3.11.

Changed in version 3.12: This is no longer a pseudo-instruction.

+
+FOR_ITER(delta)
+

STACK[-1] is an iterator. Call its __next__() method. If this yields a new value, push it on the stack (leaving the iterator below it). If the iterator indicates it is exhausted then the byte code counter is incremented by delta.

Changed in version 3.12: Up until 3.11 the iterator was popped when it was exhausted.

+
+LOAD_GLOBAL(namei)
+

Loads the global named co_names[namei>>1] onto the stack.

Changed in version 3.11: If the low bit of namei is set, then a NULL is pushed to the stack before the global variable.

+
+LOAD_FAST(var_num)
+

Pushes a reference to the local co_varnames[var_num] onto the stack.

Changed in version 3.12: This opcode is now only used in situations where the local variable is guaranteed to be initialized. It cannot raise UnboundLocalError.

+
+LOAD_FAST_CHECK(var_num)
+

Pushes a reference to the local co_varnames[var_num] onto the stack, raising an UnboundLocalError if the local variable has not been initialized.

New in version 3.12.

+
+LOAD_FAST_AND_CLEAR(var_num)
+

Pushes a reference to the local co_varnames[var_num] onto the stack (or pushes NULL onto the stack if the local variable has not been initialized) and sets co_varnames[var_num] to NULL.

New in version 3.12.

+
+STORE_FAST(var_num)
+

Stores STACK.pop() into the local co_varnames[var_num].

+
+DELETE_FAST(var_num)
+

Deletes local co_varnames[var_num].

+
+MAKE_CELL(i)
+

Creates a new cell in slot i. If that slot is nonempty then that value is stored into the new cell.

New in version 3.11.

+
+LOAD_CLOSURE(i)
+

Pushes a reference to the cell contained in slot i of the “fast locals” storage. The name of the variable is co_fastlocalnames[i].

Note that LOAD_CLOSURE is effectively an alias for LOAD_FAST. It exists to keep bytecode a little more readable.

Changed in version 3.11: i is no longer offset by the length of co_varnames.

+
+LOAD_DEREF(i)
+

Loads the cell contained in slot i of the “fast locals” storage. Pushes a reference to the object the cell contains on the stack.

Changed in version 3.11: i is no longer offset by the length of co_varnames.

+
+LOAD_FROM_DICT_OR_DEREF(i)
+

Pops a mapping off the stack and looks up the name associated with slot i of the “fast locals” storage in this mapping. If the name is not found there, loads it from the cell contained in slot i, similar to LOAD_DEREF. This is used for loading free variables in class bodies (which previously used LOAD_CLASSDEREF) and in annotation scopes within class bodies.

New in version 3.12.

+
+STORE_DEREF(i)
+

Stores STACK.pop() into the cell contained in slot i of the “fast locals” storage.

Changed in version 3.11: i is no longer offset by the length of co_varnames.

+
+DELETE_DEREF(i)
+

Empties the cell contained in slot i of the “fast locals” storage. Used by the del statement.

New in version 3.2.

Changed in version 3.11: i is no longer offset by the length of co_varnames.

+
+COPY_FREE_VARS(n)
+

Copies the n free variables from the closure into the frame. Removes the need for special code on the caller’s side when calling closures.

New in version 3.11.

+
+RAISE_VARARGS(argc)
+

Raises an exception using one of the 3 forms of the raise statement, depending on the value of argc:

  • 0: raise (re-raise previous exception)
  • 1: raise STACK[-1] (raise exception instance or type at STACK[-1])
  • 2: raise STACK[-2] from STACK[-1] (raise exception instance or type at STACK[-2] with __cause__ set to STACK[-1])
+
+CALL(argc)
+

Calls a callable object with the number of arguments specified by argc, including the named arguments specified by the preceding KW_NAMES, if any. On the stack are (in ascending order), either:

  • NULL
  • The callable
  • The positional arguments
  • The named arguments

or:

  • The callable
  • self
  • The remaining positional arguments
  • The named arguments

argc is the total of the positional and named arguments, excluding self when a NULL is not present.

CALL pops all arguments and the callable object off the stack, calls the callable object with those arguments, and pushes the return value returned by the callable object.

New in version 3.11.

+
+CALL_FUNCTION_EX(flags)
+

Calls a callable object with variable set of positional and keyword arguments. If the lowest bit of flags is set, the top of the stack contains a mapping object containing additional keyword arguments. Before the callable is called, the mapping object and iterable object are each “unpacked” and their contents passed in as keyword and positional arguments respectively. CALL_FUNCTION_EX pops all arguments and the callable object off the stack, calls the callable object with those arguments, and pushes the return value returned by the callable object.

New in version 3.6.

+
+PUSH_NULL
+

Pushes a NULL to the stack. Used in the call sequence to match the NULL pushed by LOAD_METHOD for non-method calls.

New in version 3.11.

+
+KW_NAMES(consti)
+

Prefixes CALL. Stores a reference to co_consts[consti] into an internal variable for use by CALL. co_consts[consti] must be a tuple of strings.

New in version 3.11.

+
+MAKE_FUNCTION(flags)
+

Pushes a new function object on the stack. From bottom to top, the consumed stack must consist of values if the argument carries a specified flag value

  • +0x01 a tuple of default values for positional-only and positional-or-keyword parameters in positional order
  • +0x02 a dictionary of keyword-only parameters’ default values
  • +0x04 a tuple of strings containing parameters’ annotations
  • +0x08 a tuple containing cells for free variables, making a closure
  • the code associated with the function (at STACK[-1])

Changed in version 3.10: Flag value 0x04 is a tuple of strings instead of dictionary

Changed in version 3.11: Qualified name at STACK[-1] was removed.

+
+BUILD_SLICE(argc)
+

Pushes a slice object on the stack. argc must be 2 or 3. If it is 2, implements:

end = STACK.pop()
+start = STACK.pop()
+STACK.append(slice(start, stop))
+

if it is 3, implements:

step = STACK.pop()
+end = STACK.pop()
+start = STACK.pop()
+STACK.append(slice(start, end, step))
+

See the slice() built-in function for more information.

+
+EXTENDED_ARG(ext)
+

Prefixes any opcode which has an argument too big to fit into the default one byte. ext holds an additional byte which act as higher bits in the argument. For each opcode, at most three prefixal EXTENDED_ARG are allowed, forming an argument from two-byte to four-byte.

+
+FORMAT_VALUE(flags)
+

Used for implementing formatted literal strings (f-strings). Pops an optional fmt_spec from the stack, then a required value. flags is interpreted as follows:

  • +(flags & 0x03) == 0x00: value is formatted as-is.
  • +(flags & 0x03) == 0x01: call str() on value before formatting it.
  • +(flags & 0x03) == 0x02: call repr() on value before formatting it.
  • +(flags & 0x03) == 0x03: call ascii() on value before formatting it.
  • +(flags & 0x04) == 0x04: pop fmt_spec from the stack and use it, else use an empty fmt_spec.

Formatting is performed using PyObject_Format(). The result is pushed on the stack.

New in version 3.6.

+
+MATCH_CLASS(count)
+

STACK[-1] is a tuple of keyword attribute names, STACK[-2] is the class being matched against, and STACK[-3] is the match subject. count is the number of positional sub-patterns.

Pop STACK[-1], STACK[-2], and STACK[-3]. If STACK[-3] is an instance of STACK[-2] and has the positional and keyword attributes required by count and STACK[-1], push a tuple of extracted attributes. Otherwise, push None.

New in version 3.10.

Changed in version 3.11: Previously, this instruction also pushed a boolean value indicating success (True) or failure (False).

+
+RESUME(where)
+

A no-op. Performs internal tracing, debugging and optimization checks.

The where operand marks where the RESUME occurs:

  • +0 The start of a function, which is neither a generator, coroutine nor an async generator
  • +1 After a yield expression
  • +2 After a yield from expression
  • +3 After an await expression

New in version 3.11.

+
+RETURN_GENERATOR
+

Create a generator, coroutine, or async generator from the current frame. Used as first opcode of in code object for the above mentioned callables. Clear the current frame and return the newly created generator.

New in version 3.11.

+
+SEND(delta)
+

Equivalent to STACK[-1] = STACK[-2].send(STACK[-1]). Used in yield from and await statements.

If the call raises StopIteration, pop the top value from the stack, push the exception’s value attribute, and increment the bytecode counter by delta.

New in version 3.11.

+
+HAVE_ARGUMENT
+

This is not really an opcode. It identifies the dividing line between opcodes in the range [0,255] which don’t use their argument and those that do (< HAVE_ARGUMENT and >= HAVE_ARGUMENT, respectively).

If your application uses pseudo instructions, use the hasarg collection instead.

Changed in version 3.6: Now every instruction has an argument, but opcodes < HAVE_ARGUMENT ignore it. Before, only opcodes >= HAVE_ARGUMENT had an argument.

Changed in version 3.12: Pseudo instructions were added to the dis module, and for them it is not true that comparison with HAVE_ARGUMENT indicates whether they use their arg.

+
+CALL_INTRINSIC_1
+

Calls an intrinsic function with one argument. Passes STACK[-1] as the argument and sets STACK[-1] to the result. Used to implement functionality that is not performance critical.

The operand determines which intrinsic function is called:

+ + + + + + + + + + + + +

Operand

Description

INTRINSIC_1_INVALID

Not valid

INTRINSIC_PRINT

Prints the argument to standard out. Used in the REPL.

INTRINSIC_IMPORT_STAR

Performs import * for the named module.

INTRINSIC_STOPITERATION_ERROR

Extracts the return value from a StopIteration exception.

INTRINSIC_ASYNC_GEN_WRAP

Wraps an aync generator value

INTRINSIC_UNARY_POSITIVE

Performs the unary + operation

INTRINSIC_LIST_TO_TUPLE

Converts a list to a tuple

INTRINSIC_TYPEVAR

Creates a typing.TypeVar

INTRINSIC_PARAMSPEC

Creates a typing.ParamSpec

INTRINSIC_TYPEVARTUPLE

Creates a typing.TypeVarTuple

INTRINSIC_SUBSCRIPT_GENERIC

Returns typing.Generic subscripted with the argument

INTRINSIC_TYPEALIAS

Creates a typing.TypeAliasType; used in the type statement. The argument is a tuple of the type alias’s name, type parameters, and value.

New in version 3.12.

+
+CALL_INTRINSIC_2
+

Calls an intrinsic function with two arguments. Used to implement functionality that is not performance critical:

arg2 = STACK.pop()
+arg1 = STACK.pop()
+result = intrinsic2(arg1, arg2)
+STACK.push(result)
+

The operand determines which intrinsic function is called:

+ + + + + +

Operand

Description

INTRINSIC_2_INVALID

Not valid

INTRINSIC_PREP_RERAISE_STAR

Calculates the ExceptionGroup to raise from a try-except*.

INTRINSIC_TYPEVAR_WITH_BOUND

Creates a typing.TypeVar with a bound.

INTRINSIC_TYPEVAR_WITH_CONSTRAINTS

Creates a typing.TypeVar with constraints.

INTRINSIC_SET_FUNCTION_TYPE_PARAMS

Sets the __type_params__ attribute of a function.

New in version 3.12.

+

Pseudo-instructions

These opcodes do not appear in Python bytecode. They are used by the compiler but are replaced by real opcodes or removed before bytecode is generated.

+SETUP_FINALLY(target)
+

Set up an exception handler for the following code block. If an exception occurs, the value stack level is restored to its current state and control is transferred to the exception handler at target.

+
+SETUP_CLEANUP(target)
+

Like SETUP_FINALLY, but in case of an exception also pushes the last instruction (lasti) to the stack so that RERAISE can restore it. If an exception occurs, the value stack level and the last instruction on the frame are restored to their current state, and control is transferred to the exception handler at target.

+
+SETUP_WITH(target)
+

Like SETUP_CLEANUP, but in case of an exception one more item is popped from the stack before control is transferred to the exception handler at target.

This variant is used in with and async with constructs, which push the return value of the context manager’s __enter__() or __aenter__() to the stack.

+
+POP_BLOCK
+

Marks the end of the code block associated with the last SETUP_FINALLY, SETUP_CLEANUP or SETUP_WITH.

+
+JUMP
+
+JUMP_NO_INTERRUPT
+

Undirected relative jump instructions which are replaced by their directed (forward/backward) counterparts by the assembler.

+
+LOAD_METHOD
+

Optimized unbound method lookup. Emitted as a LOAD_ATTR opcode with a flag set in the arg.

+

Opcode collections

These collections are provided for automatic introspection of bytecode instructions:

Changed in version 3.12: The collections now contain pseudo instructions and instrumented instructions as well. These are opcodes with values >= MIN_PSEUDO_OPCODE and >= MIN_INSTRUMENTED_OPCODE.

+dis.opname
+

Sequence of operation names, indexable using the bytecode.

+
+dis.opmap
+

Dictionary mapping operation names to bytecodes.

+
+dis.cmp_op
+

Sequence of all compare operation names.

+
+dis.hasarg
+

Sequence of bytecodes that use their argument.

New in version 3.12.

+
+dis.hasconst
+

Sequence of bytecodes that access a constant.

+
+dis.hasfree
+

Sequence of bytecodes that access a free variable. ‘free’ in this context refers to names in the current scope that are referenced by inner scopes or names in outer scopes that are referenced from this scope. It does not include references to global or builtin scopes.

+
+dis.hasname
+

Sequence of bytecodes that access an attribute by name.

+
+dis.hasjrel
+

Sequence of bytecodes that have a relative jump target.

+
+dis.hasjabs
+

Sequence of bytecodes that have an absolute jump target.

+
+dis.haslocal
+

Sequence of bytecodes that access a local variable.

+
+dis.hascompare
+

Sequence of bytecodes of Boolean operations.

+
+dis.hasexc
+

Sequence of bytecodes that set an exception handler.

New in version 3.12.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/dis.html +

+
diff --git a/devdocs/python~3.12/library%2Fdistribution.html b/devdocs/python~3.12/library%2Fdistribution.html new file mode 100644 index 00000000..15cc437e --- /dev/null +++ b/devdocs/python~3.12/library%2Fdistribution.html @@ -0,0 +1,10 @@ +

Software Packaging and Distribution

These libraries help you with publishing and installing Python software. While these modules are designed to work in conjunction with the Python Package Index, they can also be used with a local index server, or without any index server at all.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/distribution.html +

+
diff --git a/devdocs/python~3.12/library%2Fdoctest.html b/devdocs/python~3.12/library%2Fdoctest.html new file mode 100644 index 00000000..3a5c3e9c --- /dev/null +++ b/devdocs/python~3.12/library%2Fdoctest.html @@ -0,0 +1,529 @@ +

doctest — Test interactive Python examples

Source code: Lib/doctest.py

The doctest module searches for pieces of text that look like interactive Python sessions, and then executes those sessions to verify that they work exactly as shown. There are several common ways to use doctest:

Here’s a complete but small example module:

"""
+This is the "example" module.
+
+The example module supplies one function, factorial().  For example,
+
+>>> factorial(5)
+120
+"""
+
+def factorial(n):
+    """Return the factorial of n, an exact integer >= 0.
+
+    >>> [factorial(n) for n in range(6)]
+    [1, 1, 2, 6, 24, 120]
+    >>> factorial(30)
+    265252859812191058636308480000000
+    >>> factorial(-1)
+    Traceback (most recent call last):
+        ...
+    ValueError: n must be >= 0
+
+    Factorials of floats are OK, but the float must be an exact integer:
+    >>> factorial(30.1)
+    Traceback (most recent call last):
+        ...
+    ValueError: n must be exact integer
+    >>> factorial(30.0)
+    265252859812191058636308480000000
+
+    It must also not be ridiculously large:
+    >>> factorial(1e100)
+    Traceback (most recent call last):
+        ...
+    OverflowError: n too large
+    """
+
+    import math
+    if not n >= 0:
+        raise ValueError("n must be >= 0")
+    if math.floor(n) != n:
+        raise ValueError("n must be exact integer")
+    if n+1 == n:  # catch a value like 1e300
+        raise OverflowError("n too large")
+    result = 1
+    factor = 2
+    while factor <= n:
+        result *= factor
+        factor += 1
+    return result
+
+
+if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
+

If you run example.py directly from the command line, doctest works its magic:

$ python example.py
+$
+

There’s no output! That’s normal, and it means all the examples worked. Pass -v to the script, and doctest prints a detailed log of what it’s trying, and prints a summary at the end:

$ python example.py -v
+Trying:
+    factorial(5)
+Expecting:
+    120
+ok
+Trying:
+    [factorial(n) for n in range(6)]
+Expecting:
+    [1, 1, 2, 6, 24, 120]
+ok
+

And so on, eventually ending with:

Trying:
+    factorial(1e100)
+Expecting:
+    Traceback (most recent call last):
+        ...
+    OverflowError: n too large
+ok
+2 items passed all tests:
+   1 tests in __main__
+   8 tests in __main__.factorial
+9 tests in 2 items.
+9 passed and 0 failed.
+Test passed.
+$
+

That’s all you need to know to start making productive use of doctest! Jump in. The following sections provide full details. Note that there are many examples of doctests in the standard Python test suite and libraries. Especially useful examples can be found in the standard test file Lib/test/test_doctest.py.

Simple Usage: Checking Examples in Docstrings

The simplest way to start using doctest (but not necessarily the way you’ll continue to do it) is to end each module M with:

if __name__ == "__main__":
+    import doctest
+    doctest.testmod()
+

doctest then examines docstrings in module M.

Running the module as a script causes the examples in the docstrings to get executed and verified:

python M.py
+

This won’t display anything unless an example fails, in which case the failing example(s) and the cause(s) of the failure(s) are printed to stdout, and the final line of output is ***Test Failed*** N failures., where N is the number of examples that failed.

Run it with the -v switch instead:

python M.py -v
+

and a detailed report of all examples tried is printed to standard output, along with assorted summaries at the end.

You can force verbose mode by passing verbose=True to testmod(), or prohibit it by passing verbose=False. In either of those cases, sys.argv is not examined by testmod() (so passing -v or not has no effect).

There is also a command line shortcut for running testmod(). You can instruct the Python interpreter to run the doctest module directly from the standard library and pass the module name(s) on the command line:

python -m doctest -v example.py
+

This will import example.py as a standalone module and run testmod() on it. Note that this may not work correctly if the file is part of a package and imports other submodules from that package.

For more information on testmod(), see section Basic API.

Simple Usage: Checking Examples in a Text File

Another simple application of doctest is testing interactive examples in a text file. This can be done with the testfile() function:

import doctest
+doctest.testfile("example.txt")
+

That short script executes and verifies any interactive Python examples contained in the file example.txt. The file content is treated as if it were a single giant docstring; the file doesn’t need to contain a Python program! For example, perhaps example.txt contains this:

The ``example`` module
+======================
+
+Using ``factorial``
+-------------------
+
+This is an example text file in reStructuredText format.  First import
+``factorial`` from the ``example`` module:
+
+    >>> from example import factorial
+
+Now use it:
+
+    >>> factorial(6)
+    120
+

Running doctest.testfile("example.txt") then finds the error in this documentation:

File "./example.txt", line 14, in example.txt
+Failed example:
+    factorial(6)
+Expected:
+    120
+Got:
+    720
+

As with testmod(), testfile() won’t display anything unless an example fails. If an example does fail, then the failing example(s) and the cause(s) of the failure(s) are printed to stdout, using the same format as testmod().

By default, testfile() looks for files in the calling module’s directory. See section Basic API for a description of the optional arguments that can be used to tell it to look for files in other locations.

Like testmod(), testfile()’s verbosity can be set with the -v command-line switch or with the optional keyword argument verbose.

There is also a command line shortcut for running testfile(). You can instruct the Python interpreter to run the doctest module directly from the standard library and pass the file name(s) on the command line:

python -m doctest -v example.txt
+

Because the file name does not end with .py, doctest infers that it must be run with testfile(), not testmod().

For more information on testfile(), see section Basic API.

How It Works

This section examines in detail how doctest works: which docstrings it looks at, how it finds interactive examples, what execution context it uses, how it handles exceptions, and how option flags can be used to control its behavior. This is the information that you need to know to write doctest examples; for information about actually running doctest on these examples, see the following sections.

Which Docstrings Are Examined?

The module docstring, and all function, class and method docstrings are searched. Objects imported into the module are not searched.

In addition, there are cases when you want tests to be part of a module but not part of the help text, which requires that the tests not be included in the docstring. Doctest looks for a module-level variable called __test__ and uses it to locate other tests. If M.__test__ exists and is truthy, it must be a dict, and each entry maps a (string) name to a function object, class object, or string. Function and class object docstrings found from M.__test__ are searched, and strings are treated as if they were docstrings. In output, a key K in M.__test__ appears with name M.__test__.K.

For example, place this block of code at the top of example.py:

__test__ = {
+    'numbers': """
+>>> factorial(6)
+720
+
+>>> [factorial(n) for n in range(6)]
+[1, 1, 2, 6, 24, 120]
+"""
+}
+

The value of example.__test__["numbers"] will be treated as a docstring and all the tests inside it will be run. It is important to note that the value can be mapped to a function, class object, or module; if so, doctest searches them recursively for docstrings, which are then scanned for tests.

Any classes found are recursively searched similarly, to test docstrings in their contained methods and nested classes.

How are Docstring Examples Recognized?

In most cases a copy-and-paste of an interactive console session works fine, but doctest isn’t trying to do an exact emulation of any specific Python shell.

>>> # comments are ignored
+>>> x = 12
+>>> x
+12
+>>> if x == 13:
+...     print("yes")
+... else:
+...     print("no")
+...     print("NO")
+...     print("NO!!!")
+...
+no
+NO
+NO!!!
+>>>
+

Any expected output must immediately follow the final '>>> ' or '... ' line containing the code, and the expected output (if any) extends to the next '>>> ' or all-whitespace line.

The fine print:

  • Expected output cannot contain an all-whitespace line, since such a line is taken to signal the end of expected output. If expected output does contain a blank line, put <BLANKLINE> in your doctest example each place a blank line is expected.
  • All hard tab characters are expanded to spaces, using 8-column tab stops. Tabs in output generated by the tested code are not modified. Because any hard tabs in the sample output are expanded, this means that if the code output includes hard tabs, the only way the doctest can pass is if the NORMALIZE_WHITESPACE option or directive is in effect. Alternatively, the test can be rewritten to capture the output and compare it to an expected value as part of the test. This handling of tabs in the source was arrived at through trial and error, and has proven to be the least error prone way of handling them. It is possible to use a different algorithm for handling tabs by writing a custom DocTestParser class.
  • Output to stdout is captured, but not output to stderr (exception tracebacks are captured via a different means).
  • +

    If you continue a line via backslashing in an interactive session, or for any other reason use a backslash, you should use a raw docstring, which will preserve your backslashes exactly as you type them:

    >>> def f(x):
+...     r'''Backslashes in a raw docstring: m\n'''
+...
+>>> print(f.__doc__)
+Backslashes in a raw docstring: m\n
+

    Otherwise, the backslash will be interpreted as part of the string. For example, the \n above would be interpreted as a newline character. Alternatively, you can double each backslash in the doctest version (and not use a raw string):

    >>> def f(x):
+...     '''Backslashes in a raw docstring: m\\n'''
+...
+>>> print(f.__doc__)
+Backslashes in a raw docstring: m\n
+
  • +

    The starting column doesn’t matter:

    >>> assert "Easy!"
+      >>> import math
+          >>> math.floor(1.9)
+          1
+

    and as many leading whitespace characters are stripped from the expected output as appeared in the initial '>>> ' line that started the example.

What’s the Execution Context?

By default, each time doctest finds a docstring to test, it uses a shallow copy of M’s globals, so that running tests doesn’t change the module’s real globals, and so that one test in M can’t leave behind crumbs that accidentally allow another test to work. This means examples can freely use any names defined at top-level in M, and names defined earlier in the docstring being run. Examples cannot see names defined in other docstrings.

You can force use of your own dict as the execution context by passing globs=your_dict to testmod() or testfile() instead.

What About Exceptions?

No problem, provided that the traceback is the only output produced by the example: just paste in the traceback. 1 Since tracebacks contain details that are likely to change rapidly (for example, exact file paths and line numbers), this is one case where doctest works hard to be flexible in what it accepts.

Simple example:

>>> [1, 2, 3].remove(42)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: list.remove(x): x not in list
+

That doctest succeeds if ValueError is raised, with the list.remove(x): +x not in list detail as shown.

The expected output for an exception must start with a traceback header, which may be either of the following two lines, indented the same as the first line of the example:

Traceback (most recent call last):
+Traceback (innermost last):
+

The traceback header is followed by an optional traceback stack, whose contents are ignored by doctest. The traceback stack is typically omitted, or copied verbatim from an interactive session.

The traceback stack is followed by the most interesting part: the line(s) containing the exception type and detail. This is usually the last line of a traceback, but can extend across multiple lines if the exception has a multi-line detail:

>>> raise ValueError('multi\n    line\ndetail')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: multi
+    line
+detail
+

The last three lines (starting with ValueError) are compared against the exception’s type and detail, and the rest are ignored.

Best practice is to omit the traceback stack, unless it adds significant documentation value to the example. So the last example is probably better as:

>>> raise ValueError('multi\n    line\ndetail')
+Traceback (most recent call last):
+    ...
+ValueError: multi
+    line
+detail
+

Note that tracebacks are treated very specially. In particular, in the rewritten example, the use of ... is independent of doctest’s ELLIPSIS option. The ellipsis in that example could be left out, or could just as well be three (or three hundred) commas or digits, or an indented transcript of a Monty Python skit.

Some details you should read once, but won’t need to remember:

  • Doctest can’t guess whether your expected output came from an exception traceback or from ordinary printing. So, e.g., an example that expects ValueError: 42 is prime will pass whether ValueError is actually raised or if the example merely prints that traceback text. In practice, ordinary output rarely begins with a traceback header line, so this doesn’t create real problems.
  • Each line of the traceback stack (if present) must be indented further than the first line of the example, or start with a non-alphanumeric character. The first line following the traceback header indented the same and starting with an alphanumeric is taken to be the start of the exception detail. Of course this does the right thing for genuine tracebacks.
  • When the IGNORE_EXCEPTION_DETAIL doctest option is specified, everything following the leftmost colon and any module information in the exception name is ignored.
  • The interactive shell omits the traceback header line for some SyntaxErrors. But doctest uses the traceback header line to distinguish exceptions from non-exceptions. So in the rare case where you need to test a SyntaxError that omits the traceback header, you will need to manually add the traceback header line to your test example.
  • +

    For some exceptions, Python displays the position of the error using ^ markers and tildes:

    >>> 1 + None
+  File "<stdin>", line 1
+    1 + None
+    ~~^~~~~~
+TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
+

    Since the lines showing the position of the error come before the exception type and detail, they are not checked by doctest. For example, the following test would pass, even though it puts the ^ marker in the wrong location:

    >>> 1 + None
+  File "<stdin>", line 1
+    1 + None
+    ^~~~~~~~
+TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
+

Option Flags

A number of option flags control various aspects of doctest’s behavior. Symbolic names for the flags are supplied as module constants, which can be bitwise ORed together and passed to various functions. The names can also be used in doctest directives, and may be passed to the doctest command line interface via the -o option.

New in version 3.4: The -o command line option.

The first group of options define test semantics, controlling aspects of how doctest decides whether actual output matches an example’s expected output:

+doctest.DONT_ACCEPT_TRUE_FOR_1
+

By default, if an expected output block contains just 1, an actual output block containing just 1 or just True is considered to be a match, and similarly for 0 versus False. When DONT_ACCEPT_TRUE_FOR_1 is specified, neither substitution is allowed. The default behavior caters to that Python changed the return type of many functions from integer to boolean; doctests expecting “little integer” output still work in these cases. This option will probably go away, but not for several years.

+
+doctest.DONT_ACCEPT_BLANKLINE
+

By default, if an expected output block contains a line containing only the string <BLANKLINE>, then that line will match a blank line in the actual output. Because a genuinely blank line delimits the expected output, this is the only way to communicate that a blank line is expected. When DONT_ACCEPT_BLANKLINE is specified, this substitution is not allowed.

+
+doctest.NORMALIZE_WHITESPACE
+

When specified, all sequences of whitespace (blanks and newlines) are treated as equal. Any sequence of whitespace within the expected output will match any sequence of whitespace within the actual output. By default, whitespace must match exactly. NORMALIZE_WHITESPACE is especially useful when a line of expected output is very long, and you want to wrap it across multiple lines in your source.

+
+doctest.ELLIPSIS
+

When specified, an ellipsis marker (...) in the expected output can match any substring in the actual output. This includes substrings that span line boundaries, and empty substrings, so it’s best to keep usage of this simple. Complicated uses can lead to the same kinds of “oops, it matched too much!” surprises that .* is prone to in regular expressions.

+
+doctest.IGNORE_EXCEPTION_DETAIL
+

When specified, doctests expecting exceptions pass so long as an exception of the expected type is raised, even if the details (message and fully qualified exception name) don’t match.

For example, an example expecting ValueError: 42 will pass if the actual exception raised is ValueError: 3*14, but will fail if, say, a TypeError is raised instead. It will also ignore any fully qualified name included before the exception class, which can vary between implementations and versions of Python and the code/libraries in use. Hence, all three of these variations will work with the flag specified:

>>> raise Exception('message')
+Traceback (most recent call last):
+Exception: message
+
+>>> raise Exception('message')
+Traceback (most recent call last):
+builtins.Exception: message
+
+>>> raise Exception('message')
+Traceback (most recent call last):
+__main__.Exception: message
+

Note that ELLIPSIS can also be used to ignore the details of the exception message, but such a test may still fail based on whether the module name is present or matches exactly.

Changed in version 3.2: IGNORE_EXCEPTION_DETAIL now also ignores any information relating to the module containing the exception under test.

+
+doctest.SKIP
+

When specified, do not run the example at all. This can be useful in contexts where doctest examples serve as both documentation and test cases, and an example should be included for documentation purposes, but should not be checked. E.g., the example’s output might be random; or the example might depend on resources which would be unavailable to the test driver.

The SKIP flag can also be used for temporarily “commenting out” examples.

+
+doctest.COMPARISON_FLAGS
+

A bitmask or’ing together all the comparison flags above.

+

The second group of options controls how test failures are reported:

+doctest.REPORT_UDIFF
+

When specified, failures that involve multi-line expected and actual outputs are displayed using a unified diff.

+
+doctest.REPORT_CDIFF
+

When specified, failures that involve multi-line expected and actual outputs will be displayed using a context diff.

+
+doctest.REPORT_NDIFF
+

When specified, differences are computed by difflib.Differ, using the same algorithm as the popular ndiff.py utility. This is the only method that marks differences within lines as well as across lines. For example, if a line of expected output contains digit 1 where actual output contains letter l, a line is inserted with a caret marking the mismatching column positions.

+
+doctest.REPORT_ONLY_FIRST_FAILURE
+

When specified, display the first failing example in each doctest, but suppress output for all remaining examples. This will prevent doctest from reporting correct examples that break because of earlier failures; but it might also hide incorrect examples that fail independently of the first failure. When REPORT_ONLY_FIRST_FAILURE is specified, the remaining examples are still run, and still count towards the total number of failures reported; only the output is suppressed.

+
+doctest.FAIL_FAST
+

When specified, exit after the first failing example and don’t attempt to run the remaining examples. Thus, the number of failures reported will be at most 1. This flag may be useful during debugging, since examples after the first failure won’t even produce debugging output.

The doctest command line accepts the option -f as a shorthand for -o +FAIL_FAST.

New in version 3.4.

+
+doctest.REPORTING_FLAGS
+

A bitmask or’ing together all the reporting flags above.

+

There is also a way to register new option flag names, though this isn’t useful unless you intend to extend doctest internals via subclassing:

+doctest.register_optionflag(name)
+

Create a new option flag with a given name, and return the new flag’s integer value. register_optionflag() can be used when subclassing OutputChecker or DocTestRunner to create new options that are supported by your subclasses. register_optionflag() should always be called using the following idiom:

MY_FLAG = register_optionflag('MY_FLAG')
+
+

Directives

Doctest directives may be used to modify the option flags for an individual example. Doctest directives are special Python comments following an example’s source code:

+directive             ::=  "#" "doctest:" directive_options
+directive_options     ::=  directive_option ("," directive_option)*
+directive_option      ::=  on_or_off directive_option_name
+on_or_off             ::=  "+" | "-"
+directive_option_name ::=  "DONT_ACCEPT_BLANKLINE" | "NORMALIZE_WHITESPACE" | ...
+

Whitespace is not allowed between the + or - and the directive option name. The directive option name can be any of the option flag names explained above.

An example’s doctest directives modify doctest’s behavior for that single example. Use + to enable the named behavior, or - to disable it.

For example, this test passes:

>>> print(list(range(20)))  # doctest: +NORMALIZE_WHITESPACE
+[0,   1,  2,  3,  4,  5,  6,  7,  8,  9,
+10,  11, 12, 13, 14, 15, 16, 17, 18, 19]
+

Without the directive it would fail, both because the actual output doesn’t have two blanks before the single-digit list elements, and because the actual output is on a single line. This test also passes, and also requires a directive to do so:

>>> print(list(range(20)))  # doctest: +ELLIPSIS
+[0, 1, ..., 18, 19]
+

Multiple directives can be used on a single physical line, separated by commas:

>>> print(list(range(20)))  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
+[0,    1, ...,   18,    19]
+

If multiple directive comments are used for a single example, then they are combined:

>>> print(list(range(20)))  # doctest: +ELLIPSIS
+...                         # doctest: +NORMALIZE_WHITESPACE
+[0,    1, ...,   18,    19]
+

As the previous example shows, you can add ... lines to your example containing only directives. This can be useful when an example is too long for a directive to comfortably fit on the same line:

>>> print(list(range(5)) + list(range(10, 20)) + list(range(30, 40)))
+... # doctest: +ELLIPSIS
+[0, ..., 4, 10, ..., 19, 30, ..., 39]
+

Note that since all options are disabled by default, and directives apply only to the example they appear in, enabling options (via + in a directive) is usually the only meaningful choice. However, option flags can also be passed to functions that run doctests, establishing different defaults. In such cases, disabling an option via - in a directive can be useful.

Warnings

doctest is serious about requiring exact matches in expected output. If even a single character doesn’t match, the test fails. This will probably surprise you a few times, as you learn exactly what Python does and doesn’t guarantee about output. For example, when printing a set, Python doesn’t guarantee that the element is printed in any particular order, so a test like

>>> foo()
+{"Hermione", "Harry"}
+

is vulnerable! One workaround is to do

>>> foo() == {"Hermione", "Harry"}
+True
+

instead. Another is to do

>>> d = sorted(foo())
+>>> d
+['Harry', 'Hermione']
+

There are others, but you get the idea.

Another bad idea is to print things that embed an object address, like

>>> id(1.0)  # certain to fail some of the time  
+7948648
+>>> class C: pass
+>>> C()  # the default repr() for instances embeds an address   
+<C object at 0x00AC18F0>
+

The ELLIPSIS directive gives a nice approach for the last example:

>>> C()  # doctest: +ELLIPSIS
+<C object at 0x...>
+

Floating-point numbers are also subject to small output variations across platforms, because Python defers to the platform C library for float formatting, and C libraries vary widely in quality here.

>>> 1./7  # risky
+0.14285714285714285
+>>> print(1./7) # safer
+0.142857142857
+>>> print(round(1./7, 6)) # much safer
+0.142857
+

Numbers of the form I/2.**J are safe across all platforms, and I often contrive doctest examples to produce numbers of that form:

>>> 3./4  # utterly safe
+0.75
+

Simple fractions are also easier for people to understand, and that makes for better documentation.

Basic API

The functions testmod() and testfile() provide a simple interface to doctest that should be sufficient for most basic uses. For a less formal introduction to these two functions, see sections Simple Usage: Checking Examples in Docstrings and Simple Usage: Checking Examples in a Text File.

+doctest.testfile(filename, module_relative=True, name=None, package=None, globs=None, verbose=None, report=True, optionflags=0, extraglobs=None, raise_on_error=False, parser=DocTestParser(), encoding=None)
+

All arguments except filename are optional, and should be specified in keyword form.

Test examples in the file named filename. Return (failure_count, +test_count).

Optional argument module_relative specifies how the filename should be interpreted:

  • If module_relative is True (the default), then filename specifies an OS-independent module-relative path. By default, this path is relative to the calling module’s directory; but if the package argument is specified, then it is relative to that package. To ensure OS-independence, filename should use / characters to separate path segments, and may not be an absolute path (i.e., it may not begin with /).
  • If module_relative is False, then filename specifies an OS-specific path. The path may be absolute or relative; relative paths are resolved with respect to the current working directory.

Optional argument name gives the name of the test; by default, or if None, os.path.basename(filename) is used.

Optional argument package is a Python package or the name of a Python package whose directory should be used as the base directory for a module-relative filename. If no package is specified, then the calling module’s directory is used as the base directory for module-relative filenames. It is an error to specify package if module_relative is False.

Optional argument globs gives a dict to be used as the globals when executing examples. A new shallow copy of this dict is created for the doctest, so its examples start with a clean slate. By default, or if None, a new empty dict is used.

Optional argument extraglobs gives a dict merged into the globals used to execute examples. This works like dict.update(): if globs and extraglobs have a common key, the associated value in extraglobs appears in the combined dict. By default, or if None, no extra globals are used. This is an advanced feature that allows parameterization of doctests. For example, a doctest can be written for a base class, using a generic name for the class, then reused to test any number of subclasses by passing an extraglobs dict mapping the generic name to the subclass to be tested.

Optional argument verbose prints lots of stuff if true, and prints only failures if false; by default, or if None, it’s true if and only if '-v' is in sys.argv.

Optional argument report prints a summary at the end when true, else prints nothing at the end. In verbose mode, the summary is detailed, else the summary is very brief (in fact, empty if all tests passed).

Optional argument optionflags (default value 0) takes the bitwise OR of option flags. See section Option Flags.

Optional argument raise_on_error defaults to false. If true, an exception is raised upon the first failure or unexpected exception in an example. This allows failures to be post-mortem debugged. Default behavior is to continue running examples.

Optional argument parser specifies a DocTestParser (or subclass) that should be used to extract tests from the files. It defaults to a normal parser (i.e., DocTestParser()).

Optional argument encoding specifies an encoding that should be used to convert the file to unicode.

+
+doctest.testmod(m=None, name=None, globs=None, verbose=None, report=True, optionflags=0, extraglobs=None, raise_on_error=False, exclude_empty=False)
+

All arguments are optional, and all except for m should be specified in keyword form.

Test examples in docstrings in functions and classes reachable from module m (or module __main__ if m is not supplied or is None), starting with m.__doc__.

Also test examples reachable from dict m.__test__, if it exists and is not None. m.__test__ maps names (strings) to functions, classes and strings; function and class docstrings are searched for examples; strings are searched directly, as if they were docstrings.

Only docstrings attached to objects belonging to module m are searched.

Return (failure_count, test_count).

Optional argument name gives the name of the module; by default, or if None, m.__name__ is used.

Optional argument exclude_empty defaults to false. If true, objects for which no doctests are found are excluded from consideration. The default is a backward compatibility hack, so that code still using doctest.master.summarize in conjunction with testmod() continues to get output for objects with no tests. The exclude_empty argument to the newer DocTestFinder constructor defaults to true.

Optional arguments extraglobs, verbose, report, optionflags, raise_on_error, and globs are the same as for function testfile() above, except that globs defaults to m.__dict__.

+
+doctest.run_docstring_examples(f, globs, verbose=False, name='NoName', compileflags=None, optionflags=0)
+

Test examples associated with object f; for example, f may be a string, a module, a function, or a class object.

A shallow copy of dictionary argument globs is used for the execution context.

Optional argument name is used in failure messages, and defaults to "NoName".

If optional argument verbose is true, output is generated even if there are no failures. By default, output is generated only in case of an example failure.

Optional argument compileflags gives the set of flags that should be used by the Python compiler when running the examples. By default, or if None, flags are deduced corresponding to the set of future features found in globs.

Optional argument optionflags works as for function testfile() above.

+

Unittest API

As your collection of doctest’ed modules grows, you’ll want a way to run all their doctests systematically. doctest provides two functions that can be used to create unittest test suites from modules and text files containing doctests. To integrate with unittest test discovery, include a load_tests function in your test module:

import unittest
+import doctest
+import my_module_with_doctests
+
+def load_tests(loader, tests, ignore):
+    tests.addTests(doctest.DocTestSuite(my_module_with_doctests))
+    return tests
+

There are two main functions for creating unittest.TestSuite instances from text files and modules with doctests:

+doctest.DocFileSuite(*paths, module_relative=True, package=None, setUp=None, tearDown=None, globs=None, optionflags=0, parser=DocTestParser(), encoding=None)
+

Convert doctest tests from one or more text files to a unittest.TestSuite.

The returned unittest.TestSuite is to be run by the unittest framework and runs the interactive examples in each file. If an example in any file fails, then the synthesized unit test fails, and a failureException exception is raised showing the name of the file containing the test and a (sometimes approximate) line number.

Pass one or more paths (as strings) to text files to be examined.

Options may be provided as keyword arguments:

Optional argument module_relative specifies how the filenames in paths should be interpreted:

  • If module_relative is True (the default), then each filename in paths specifies an OS-independent module-relative path. By default, this path is relative to the calling module’s directory; but if the package argument is specified, then it is relative to that package. To ensure OS-independence, each filename should use / characters to separate path segments, and may not be an absolute path (i.e., it may not begin with /).
  • If module_relative is False, then each filename in paths specifies an OS-specific path. The path may be absolute or relative; relative paths are resolved with respect to the current working directory.

Optional argument package is a Python package or the name of a Python package whose directory should be used as the base directory for module-relative filenames in paths. If no package is specified, then the calling module’s directory is used as the base directory for module-relative filenames. It is an error to specify package if module_relative is False.

Optional argument setUp specifies a set-up function for the test suite. This is called before running the tests in each file. The setUp function will be passed a DocTest object. The setUp function can access the test globals as the globs attribute of the test passed.

Optional argument tearDown specifies a tear-down function for the test suite. This is called after running the tests in each file. The tearDown function will be passed a DocTest object. The setUp function can access the test globals as the globs attribute of the test passed.

Optional argument globs is a dictionary containing the initial global variables for the tests. A new copy of this dictionary is created for each test. By default, globs is a new empty dictionary.

Optional argument optionflags specifies the default doctest options for the tests, created by or-ing together individual option flags. See section Option Flags. See function set_unittest_reportflags() below for a better way to set reporting options.

Optional argument parser specifies a DocTestParser (or subclass) that should be used to extract tests from the files. It defaults to a normal parser (i.e., DocTestParser()).

Optional argument encoding specifies an encoding that should be used to convert the file to unicode.

The global __file__ is added to the globals provided to doctests loaded from a text file using DocFileSuite().

+
+doctest.DocTestSuite(module=None, globs=None, extraglobs=None, test_finder=None, setUp=None, tearDown=None, optionflags=0, checker=None)
+

Convert doctest tests for a module to a unittest.TestSuite.

The returned unittest.TestSuite is to be run by the unittest framework and runs each doctest in the module. If any of the doctests fail, then the synthesized unit test fails, and a failureException exception is raised showing the name of the file containing the test and a (sometimes approximate) line number.

Optional argument module provides the module to be tested. It can be a module object or a (possibly dotted) module name. If not specified, the module calling this function is used.

Optional argument globs is a dictionary containing the initial global variables for the tests. A new copy of this dictionary is created for each test. By default, globs is a new empty dictionary.

Optional argument extraglobs specifies an extra set of global variables, which is merged into globs. By default, no extra globals are used.

Optional argument test_finder is the DocTestFinder object (or a drop-in replacement) that is used to extract doctests from the module.

Optional arguments setUp, tearDown, and optionflags are the same as for function DocFileSuite() above.

This function uses the same search technique as testmod().

Changed in version 3.5: DocTestSuite() returns an empty unittest.TestSuite if module contains no docstrings instead of raising ValueError.

+
+exception doctest.failureException
+

When doctests which have been converted to unit tests by DocFileSuite() or DocTestSuite() fail, this exception is raised showing the name of the file containing the test and a (sometimes approximate) line number.

+

Under the covers, DocTestSuite() creates a unittest.TestSuite out of doctest.DocTestCase instances, and DocTestCase is a subclass of unittest.TestCase. DocTestCase isn’t documented here (it’s an internal detail), but studying its code can answer questions about the exact details of unittest integration.

Similarly, DocFileSuite() creates a unittest.TestSuite out of doctest.DocFileCase instances, and DocFileCase is a subclass of DocTestCase.

So both ways of creating a unittest.TestSuite run instances of DocTestCase. This is important for a subtle reason: when you run doctest functions yourself, you can control the doctest options in use directly, by passing option flags to doctest functions. However, if you’re writing a unittest framework, unittest ultimately controls when and how tests get run. The framework author typically wants to control doctest reporting options (perhaps, e.g., specified by command line options), but there’s no way to pass options through unittest to doctest test runners.

For this reason, doctest also supports a notion of doctest reporting flags specific to unittest support, via this function:

+doctest.set_unittest_reportflags(flags)
+

Set the doctest reporting flags to use.

Argument flags takes the bitwise OR of option flags. See section Option Flags. Only “reporting flags” can be used.

This is a module-global setting, and affects all future doctests run by module unittest: the runTest() method of DocTestCase looks at the option flags specified for the test case when the DocTestCase instance was constructed. If no reporting flags were specified (which is the typical and expected case), doctest’s unittest reporting flags are bitwise ORed into the option flags, and the option flags so augmented are passed to the DocTestRunner instance created to run the doctest. If any reporting flags were specified when the DocTestCase instance was constructed, doctest’s unittest reporting flags are ignored.

The value of the unittest reporting flags in effect before the function was called is returned by the function.

+

Advanced API

The basic API is a simple wrapper that’s intended to make doctest easy to use. It is fairly flexible, and should meet most users’ needs; however, if you require more fine-grained control over testing, or wish to extend doctest’s capabilities, then you should use the advanced API.

The advanced API revolves around two container classes, which are used to store the interactive examples extracted from doctest cases:

  • +Example: A single Python statement, paired with its expected output.
  • +DocTest: A collection of Examples, typically extracted from a single docstring or text file.

Additional processing classes are defined to find, parse, and run, and check doctest examples:

The relationships among these processing classes are summarized in the following diagram:

                            list of:
++------+                   +---------+
+|module| --DocTestFinder-> | DocTest | --DocTestRunner-> results
++------+    |        ^     +---------+     |       ^    (printed)
+            |        |     | Example |     |       |
+            v        |     |   ...   |     v       |
+           DocTestParser   | Example |   OutputChecker
+                           +---------+
+

DocTest Objects

+class doctest.DocTest(examples, globs, name, filename, lineno, docstring)
+

A collection of doctest examples that should be run in a single namespace. The constructor arguments are used to initialize the attributes of the same names.

DocTest defines the following attributes. They are initialized by the constructor, and should not be modified directly.

+examples
+

A list of Example objects encoding the individual interactive Python examples that should be run by this test.

+
+globs
+

The namespace (aka globals) that the examples should be run in. This is a dictionary mapping names to values. Any changes to the namespace made by the examples (such as binding new variables) will be reflected in globs after the test is run.

+
+name
+

A string name identifying the DocTest. Typically, this is the name of the object or file that the test was extracted from.

+
+filename
+

The name of the file that this DocTest was extracted from; or None if the filename is unknown, or if the DocTest was not extracted from a file.

+
+lineno
+

The line number within filename where this DocTest begins, or None if the line number is unavailable. This line number is zero-based with respect to the beginning of the file.

+
+docstring
+

The string that the test was extracted from, or None if the string is unavailable, or if the test was not extracted from a string.

+
+

Example Objects

+class doctest.Example(source, want, exc_msg=None, lineno=0, indent=0, options=None)
+

A single interactive example, consisting of a Python statement and its expected output. The constructor arguments are used to initialize the attributes of the same names.

Example defines the following attributes. They are initialized by the constructor, and should not be modified directly.

+source
+

A string containing the example’s source code. This source code consists of a single Python statement, and always ends with a newline; the constructor adds a newline when necessary.

+
+want
+

The expected output from running the example’s source code (either from stdout, or a traceback in case of exception). want ends with a newline unless no output is expected, in which case it’s an empty string. The constructor adds a newline when necessary.

+
+exc_msg
+

The exception message generated by the example, if the example is expected to generate an exception; or None if it is not expected to generate an exception. This exception message is compared against the return value of traceback.format_exception_only(). exc_msg ends with a newline unless it’s None. The constructor adds a newline if needed.

+
+lineno
+

The line number within the string containing this example where the example begins. This line number is zero-based with respect to the beginning of the containing string.

+
+indent
+

The example’s indentation in the containing string, i.e., the number of space characters that precede the example’s first prompt.

+
+options
+

A dictionary mapping from option flags to True or False, which is used to override default options for this example. Any option flags not contained in this dictionary are left at their default value (as specified by the DocTestRunner’s optionflags). By default, no options are set.

+
+

DocTestFinder objects

+class doctest.DocTestFinder(verbose=False, parser=DocTestParser(), recurse=True, exclude_empty=True)
+

A processing class used to extract the DocTests that are relevant to a given object, from its docstring and the docstrings of its contained objects. DocTests can be extracted from modules, classes, functions, methods, staticmethods, classmethods, and properties.

The optional argument verbose can be used to display the objects searched by the finder. It defaults to False (no output).

The optional argument parser specifies the DocTestParser object (or a drop-in replacement) that is used to extract doctests from docstrings.

If the optional argument recurse is false, then DocTestFinder.find() will only examine the given object, and not any contained objects.

If the optional argument exclude_empty is false, then DocTestFinder.find() will include tests for objects with empty docstrings.

DocTestFinder defines the following method:

+find(obj[, name][, module][, globs][, extraglobs])
+

Return a list of the DocTests that are defined by obj’s docstring, or by any of its contained objects’ docstrings.

The optional argument name specifies the object’s name; this name will be used to construct names for the returned DocTests. If name is not specified, then obj.__name__ is used.

The optional parameter module is the module that contains the given object. If the module is not specified or is None, then the test finder will attempt to automatically determine the correct module. The object’s module is used:

  • As a default namespace, if globs is not specified.
  • To prevent the DocTestFinder from extracting DocTests from objects that are imported from other modules. (Contained objects with modules other than module are ignored.)
  • To find the name of the file containing the object.
  • To help find the line number of the object within its file.

If module is False, no attempt to find the module will be made. This is obscure, of use mostly in testing doctest itself: if module is False, or is None but cannot be found automatically, then all objects are considered to belong to the (non-existent) module, so all contained objects will (recursively) be searched for doctests.

The globals for each DocTest is formed by combining globs and extraglobs (bindings in extraglobs override bindings in globs). A new shallow copy of the globals dictionary is created for each DocTest. If globs is not specified, then it defaults to the module’s __dict__, if specified, or {} otherwise. If extraglobs is not specified, then it defaults to {}.

+
+

DocTestParser objects

+class doctest.DocTestParser
+

A processing class used to extract interactive examples from a string, and use them to create a DocTest object.

DocTestParser defines the following methods:

+get_doctest(string, globs, name, filename, lineno)
+

Extract all doctest examples from the given string, and collect them into a DocTest object.

globs, name, filename, and lineno are attributes for the new DocTest object. See the documentation for DocTest for more information.

+
+get_examples(string, name='<string>')
+

Extract all doctest examples from the given string, and return them as a list of Example objects. Line numbers are 0-based. The optional argument name is a name identifying this string, and is only used for error messages.

+
+parse(string, name='<string>')
+

Divide the given string into examples and intervening text, and return them as a list of alternating Examples and strings. Line numbers for the Examples are 0-based. The optional argument name is a name identifying this string, and is only used for error messages.

+
+

DocTestRunner objects

+class doctest.DocTestRunner(checker=None, verbose=None, optionflags=0)
+

A processing class used to execute and verify the interactive examples in a DocTest.

The comparison between expected outputs and actual outputs is done by an OutputChecker. This comparison may be customized with a number of option flags; see section Option Flags for more information. If the option flags are insufficient, then the comparison may also be customized by passing a subclass of OutputChecker to the constructor.

The test runner’s display output can be controlled in two ways. First, an output function can be passed to run(); this function will be called with strings that should be displayed. It defaults to sys.stdout.write. If capturing the output is not sufficient, then the display output can be also customized by subclassing DocTestRunner, and overriding the methods report_start(), report_success(), report_unexpected_exception(), and report_failure().

The optional keyword argument checker specifies the OutputChecker object (or drop-in replacement) that should be used to compare the expected outputs to the actual outputs of doctest examples.

The optional keyword argument verbose controls the DocTestRunner’s verbosity. If verbose is True, then information is printed about each example, as it is run. If verbose is False, then only failures are printed. If verbose is unspecified, or None, then verbose output is used iff the command-line switch -v is used.

The optional keyword argument optionflags can be used to control how the test runner compares expected output to actual output, and how it displays failures. For more information, see section Option Flags.

DocTestRunner defines the following methods:

+report_start(out, test, example)
+

Report that the test runner is about to process the given example. This method is provided to allow subclasses of DocTestRunner to customize their output; it should not be called directly.

example is the example about to be processed. test is the test containing example. out is the output function that was passed to DocTestRunner.run().

+
+report_success(out, test, example, got)
+

Report that the given example ran successfully. This method is provided to allow subclasses of DocTestRunner to customize their output; it should not be called directly.

example is the example about to be processed. got is the actual output from the example. test is the test containing example. out is the output function that was passed to DocTestRunner.run().

+
+report_failure(out, test, example, got)
+

Report that the given example failed. This method is provided to allow subclasses of DocTestRunner to customize their output; it should not be called directly.

example is the example about to be processed. got is the actual output from the example. test is the test containing example. out is the output function that was passed to DocTestRunner.run().

+
+report_unexpected_exception(out, test, example, exc_info)
+

Report that the given example raised an unexpected exception. This method is provided to allow subclasses of DocTestRunner to customize their output; it should not be called directly.

example is the example about to be processed. exc_info is a tuple containing information about the unexpected exception (as returned by sys.exc_info()). test is the test containing example. out is the output function that was passed to DocTestRunner.run().

+
+run(test, compileflags=None, out=None, clear_globs=True)
+

Run the examples in test (a DocTest object), and display the results using the writer function out.

The examples are run in the namespace test.globs. If clear_globs is true (the default), then this namespace will be cleared after the test runs, to help with garbage collection. If you would like to examine the namespace after the test completes, then use clear_globs=False.

compileflags gives the set of flags that should be used by the Python compiler when running the examples. If not specified, then it will default to the set of future-import flags that apply to globs.

The output of each example is checked using the DocTestRunner’s output checker, and the results are formatted by the DocTestRunner.report_*() methods.

+
+summarize(verbose=None)
+

Print a summary of all the test cases that have been run by this DocTestRunner, and return a named tuple TestResults(failed, attempted).

The optional verbose argument controls how detailed the summary is. If the verbosity is not specified, then the DocTestRunner’s verbosity is used.

+
+

OutputChecker objects

+class doctest.OutputChecker
+

A class used to check the whether the actual output from a doctest example matches the expected output. OutputChecker defines two methods: check_output(), which compares a given pair of outputs, and returns True if they match; and output_difference(), which returns a string describing the differences between two outputs.

OutputChecker defines the following methods:

+check_output(want, got, optionflags)
+

Return True iff the actual output from an example (got) matches the expected output (want). These strings are always considered to match if they are identical; but depending on what option flags the test runner is using, several non-exact match types are also possible. See section Option Flags for more information about option flags.

+
+output_difference(example, got, optionflags)
+

Return a string describing the differences between the expected output for a given example (example) and the actual output (got). optionflags is the set of option flags used to compare want and got.

+
+

Debugging

Doctest provides several mechanisms for debugging doctest examples:

  • Several functions convert doctests to executable Python programs, which can be run under the Python debugger, pdb.
  • The DebugRunner class is a subclass of DocTestRunner that raises an exception for the first failing example, containing information about that example. This information can be used to perform post-mortem debugging on the example.
  • The unittest cases generated by DocTestSuite() support the debug() method defined by unittest.TestCase.
  • +

    You can add a call to pdb.set_trace() in a doctest example, and you’ll drop into the Python debugger when that line is executed. Then you can inspect current values of variables, and so on. For example, suppose a.py contains just this module docstring:

    """
+>>> def f(x):
+...     g(x*2)
+>>> def g(x):
+...     print(x+3)
+...     import pdb; pdb.set_trace()
+>>> f(3)
+9
+"""
+

    Then an interactive Python session may look like this:

    >>> import a, doctest
+>>> doctest.testmod(a)
+--Return--
+> <doctest a[1]>(3)g()->None
+-> import pdb; pdb.set_trace()
+(Pdb) list
+  1     def g(x):
+  2         print(x+3)
+  3  ->     import pdb; pdb.set_trace()
+[EOF]
+(Pdb) p x
+6
+(Pdb) step
+--Return--
+> <doctest a[0]>(2)f()->None
+-> g(x*2)
+(Pdb) list
+  1     def f(x):
+  2  ->     g(x*2)
+[EOF]
+(Pdb) p x
+3
+(Pdb) step
+--Return--
+> <doctest a[2]>(1)?()->None
+-> f(3)
+(Pdb) cont
+(0, 3)
+>>>
+

Functions that convert doctests to Python code, and possibly run the synthesized code under the debugger:

+doctest.script_from_examples(s)
+

Convert text with examples to a script.

Argument s is a string containing doctest examples. The string is converted to a Python script, where doctest examples in s are converted to regular code, and everything else is converted to Python comments. The generated script is returned as a string. For example,

import doctest
+print(doctest.script_from_examples(r"""
+    Set x and y to 1 and 2.
+    >>> x, y = 1, 2
+
+    Print their sum:
+    >>> print(x+y)
+    3
+"""))
+

displays:

# Set x and y to 1 and 2.
+x, y = 1, 2
+#
+# Print their sum:
+print(x+y)
+# Expected:
+## 3
+

This function is used internally by other functions (see below), but can also be useful when you want to transform an interactive Python session into a Python script.

+
+doctest.testsource(module, name)
+

Convert the doctest for an object to a script.

Argument module is a module object, or dotted name of a module, containing the object whose doctests are of interest. Argument name is the name (within the module) of the object with the doctests of interest. The result is a string, containing the object’s docstring converted to a Python script, as described for script_from_examples() above. For example, if module a.py contains a top-level function f(), then

import a, doctest
+print(doctest.testsource(a, "a.f"))
+

prints a script version of function f()’s docstring, with doctests converted to code, and the rest placed in comments.

+
+doctest.debug(module, name, pm=False)
+

Debug the doctests for an object.

The module and name arguments are the same as for function testsource() above. The synthesized Python script for the named object’s docstring is written to a temporary file, and then that file is run under the control of the Python debugger, pdb.

A shallow copy of module.__dict__ is used for both local and global execution context.

Optional argument pm controls whether post-mortem debugging is used. If pm has a true value, the script file is run directly, and the debugger gets involved only if the script terminates via raising an unhandled exception. If it does, then post-mortem debugging is invoked, via pdb.post_mortem(), passing the traceback object from the unhandled exception. If pm is not specified, or is false, the script is run under the debugger from the start, via passing an appropriate exec() call to pdb.run().

+
+doctest.debug_src(src, pm=False, globs=None)
+

Debug the doctests in a string.

This is like function debug() above, except that a string containing doctest examples is specified directly, via the src argument.

Optional argument pm has the same meaning as in function debug() above.

Optional argument globs gives a dictionary to use as both local and global execution context. If not specified, or None, an empty dictionary is used. If specified, a shallow copy of the dictionary is used.

+

The DebugRunner class, and the special exceptions it may raise, are of most interest to testing framework authors, and will only be sketched here. See the source code, and especially DebugRunner’s docstring (which is a doctest!) for more details:

+class doctest.DebugRunner(checker=None, verbose=None, optionflags=0)
+

A subclass of DocTestRunner that raises an exception as soon as a failure is encountered. If an unexpected exception occurs, an UnexpectedException exception is raised, containing the test, the example, and the original exception. If the output doesn’t match, then a DocTestFailure exception is raised, containing the test, the example, and the actual output.

For information about the constructor parameters and methods, see the documentation for DocTestRunner in section Advanced API.

+

There are two exceptions that may be raised by DebugRunner instances:

+exception doctest.DocTestFailure(test, example, got)
+

An exception raised by DocTestRunner to signal that a doctest example’s actual output did not match its expected output. The constructor arguments are used to initialize the attributes of the same names.

+

DocTestFailure defines the following attributes:

+DocTestFailure.test
+

The DocTest object that was being run when the example failed.

+
+DocTestFailure.example
+

The Example that failed.

+
+DocTestFailure.got
+

The example’s actual output.

+
+exception doctest.UnexpectedException(test, example, exc_info)
+

An exception raised by DocTestRunner to signal that a doctest example raised an unexpected exception. The constructor arguments are used to initialize the attributes of the same names.

+

UnexpectedException defines the following attributes:

+UnexpectedException.test
+

The DocTest object that was being run when the example failed.

+
+UnexpectedException.example
+

The Example that failed.

+
+UnexpectedException.exc_info
+

A tuple containing information about the unexpected exception, as returned by sys.exc_info().

+

Soapbox

As mentioned in the introduction, doctest has grown to have three primary uses:

  1. Checking examples in docstrings.
  2. Regression testing.
  3. Executable documentation / literate testing.

These uses have different requirements, and it is important to distinguish them. In particular, filling your docstrings with obscure test cases makes for bad documentation.

When writing a docstring, choose docstring examples with care. There’s an art to this that needs to be learned—it may not be natural at first. Examples should add genuine value to the documentation. A good example can often be worth many words. If done with care, the examples will be invaluable for your users, and will pay back the time it takes to collect them many times over as the years go by and things change. I’m still amazed at how often one of my doctest examples stops working after a “harmless” change.

Doctest also makes an excellent tool for regression testing, especially if you don’t skimp on explanatory text. By interleaving prose and examples, it becomes much easier to keep track of what’s actually being tested, and why. When a test fails, good prose can make it much easier to figure out what the problem is, and how it should be fixed. It’s true that you could write extensive comments in code-based testing, but few programmers do. Many have found that using doctest approaches instead leads to much clearer tests. Perhaps this is simply because doctest makes writing prose a little easier than writing code, while writing comments in code is a little harder. I think it goes deeper than just that: the natural attitude when writing a doctest-based test is that you want to explain the fine points of your software, and illustrate them with examples. This in turn naturally leads to test files that start with the simplest features, and logically progress to complications and edge cases. A coherent narrative is the result, instead of a collection of isolated functions that test isolated bits of functionality seemingly at random. It’s a different attitude, and produces different results, blurring the distinction between testing and explaining.

Regression testing is best confined to dedicated objects or files. There are several options for organizing tests:

  • Write text files containing test cases as interactive examples, and test the files using testfile() or DocFileSuite(). This is recommended, although is easiest to do for new projects, designed from the start to use doctest.
  • Define functions named _regrtest_topic that consist of single docstrings, containing test cases for the named topics. These functions can be included in the same file as the module, or separated out into a separate test file.
  • Define a __test__ dictionary mapping from regression test topics to docstrings containing test cases.

When you have placed your tests in a module, the module can itself be the test runner. When a test fails, you can arrange for your test runner to re-run only the failing doctest while you debug the problem. Here is a minimal example of such a test runner:

if __name__ == '__main__':
+    import doctest
+    flags = doctest.REPORT_NDIFF|doctest.FAIL_FAST
+    if len(sys.argv) > 1:
+        name = sys.argv[1]
+        if name in globals():
+            obj = globals()[name]
+        else:
+            obj = __test__[name]
+        doctest.run_docstring_examples(obj, globals(), name=name,
+                                       optionflags=flags)
+    else:
+        fail, total = doctest.testmod(optionflags=flags)
+        print("{} failures out of {} tests".format(fail, total))
+

Footnotes

+1
+

Examples containing both expected output and an exception are not supported. Trying to guess where one ends and the other begins is too error-prone, and that also makes for a confusing test.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/doctest.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.charset.html b/devdocs/python~3.12/library%2Femail.charset.html new file mode 100644 index 00000000..486a267e --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.charset.html @@ -0,0 +1,60 @@ +

email.charset: Representing character sets

Source code: Lib/email/charset.py

This module is part of the legacy (Compat32) email API. In the new API only the aliases table is used.

The remaining text in this section is the original documentation of the module.

This module provides a class Charset for representing character sets and character set conversions in email messages, as well as a character set registry and several convenience methods for manipulating this registry. Instances of Charset are used in several other modules within the email package.

Import this class from the email.charset module.

+class email.charset.Charset(input_charset=DEFAULT_CHARSET)
+

Map character sets to their email properties.

This class provides information about the requirements imposed on email for a specific character set. It also provides convenience routines for converting between character sets, given the availability of the applicable codecs. Given a character set, it will do its best to provide information on how to use that character set in an email message in an RFC-compliant way.

Certain character sets must be encoded with quoted-printable or base64 when used in email headers or bodies. Certain character sets must be converted outright, and are not allowed in email.

Optional input_charset is as described below; it is always coerced to lower case. After being alias normalized it is also used as a lookup into the registry of character sets to find out the header encoding, body encoding, and output conversion codec to be used for the character set. For example, if input_charset is iso-8859-1, then headers and bodies will be encoded using quoted-printable and no output conversion codec is necessary. If input_charset is euc-jp, then headers will be encoded with base64, bodies will not be encoded, but output text will be converted from the euc-jp character set to the iso-2022-jp character set.

Charset instances have the following data attributes:

+input_charset
+

The initial character set specified. Common aliases are converted to their official email names (e.g. latin_1 is converted to iso-8859-1). Defaults to 7-bit us-ascii.

+
+header_encoding
+

If the character set must be encoded before it can be used in an email header, this attribute will be set to charset.QP (for quoted-printable), charset.BASE64 (for base64 encoding), or charset.SHORTEST for the shortest of QP or BASE64 encoding. Otherwise, it will be None.

+
+body_encoding
+

Same as header_encoding, but describes the encoding for the mail message’s body, which indeed may be different than the header encoding. charset.SHORTEST is not allowed for body_encoding.

+
+output_charset
+

Some character sets must be converted before they can be used in email headers or bodies. If the input_charset is one of them, this attribute will contain the name of the character set output will be converted to. Otherwise, it will be None.

+
+input_codec
+

The name of the Python codec used to convert the input_charset to Unicode. If no conversion codec is necessary, this attribute will be None.

+
+output_codec
+

The name of the Python codec used to convert Unicode to the output_charset. If no conversion codec is necessary, this attribute will have the same value as the input_codec.

+

Charset instances also have the following methods:

+get_body_encoding()
+

Return the content transfer encoding used for body encoding.

This is either the string quoted-printable or base64 depending on the encoding used, or it is a function, in which case you should call the function with a single argument, the Message object being encoded. The function should then set the Content-Transfer-Encoding header itself to whatever is appropriate.

Returns the string quoted-printable if body_encoding is QP, returns the string base64 if body_encoding is BASE64, and returns the string 7bit otherwise.

+
+get_output_charset()
+

Return the output character set.

This is the output_charset attribute if that is not None, otherwise it is input_charset.

+
+header_encode(string)
+

Header-encode the string string.

The type of encoding (base64 or quoted-printable) will be based on the header_encoding attribute.

+
+header_encode_lines(string, maxlengths)
+

Header-encode a string by converting it first to bytes.

This is similar to header_encode() except that the string is fit into maximum line lengths as given by the argument maxlengths, which must be an iterator: each element returned from this iterator will provide the next maximum line length.

+
+body_encode(string)
+

Body-encode the string string.

The type of encoding (base64 or quoted-printable) will be based on the body_encoding attribute.

+

The Charset class also provides a number of methods to support standard operations and built-in functions.

+__str__()
+

Returns input_charset as a string coerced to lower case. __repr__() is an alias for __str__().

+
+__eq__(other)
+

This method allows you to compare two Charset instances for equality.

+
+__ne__(other)
+

This method allows you to compare two Charset instances for inequality.

+
+

The email.charset module also provides the following functions for adding new entries to the global character set, alias, and codec registries:

+email.charset.add_charset(charset, header_enc=None, body_enc=None, output_charset=None)
+

Add character properties to the global registry.

charset is the input character set, and must be the canonical name of a character set.

Optional header_enc and body_enc is either charset.QP for quoted-printable, charset.BASE64 for base64 encoding, charset.SHORTEST for the shortest of quoted-printable or base64 encoding, or None for no encoding. SHORTEST is only valid for header_enc. The default is None for no encoding.

Optional output_charset is the character set that the output should be in. Conversions will proceed from input charset, to Unicode, to the output charset when the method Charset.convert() is called. The default is to output in the same character set as the input.

Both input_charset and output_charset must have Unicode codec entries in the module’s character set-to-codec mapping; use add_codec() to add codecs the module does not know about. See the codecs module’s documentation for more information.

The global character set registry is kept in the module global dictionary CHARSETS.

+
+email.charset.add_alias(alias, canonical)
+

Add a character set alias. alias is the alias name, e.g. latin-1. canonical is the character set’s canonical name, e.g. iso-8859-1.

The global charset alias registry is kept in the module global dictionary ALIASES.

+
+email.charset.add_codec(charset, codecname)
+

Add a codec that map characters in the given character set to and from Unicode.

charset is the canonical name of a character set. codecname is the name of a Python codec, as appropriate for the second argument to the str’s encode() method.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.charset.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.compat32-message.html b/devdocs/python~3.12/library%2Femail.compat32-message.html new file mode 100644 index 00000000..7e047e46 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.compat32-message.html @@ -0,0 +1,191 @@ +

email.message.Message: Representing an email message using the compat32 API

The Message class is very similar to the EmailMessage class, without the methods added by that class, and with the default behavior of certain other methods being slightly different. We also document here some methods that, while supported by the EmailMessage class, are not recommended unless you are dealing with legacy code.

The philosophy and structure of the two classes is otherwise the same.

This document describes the behavior under the default (for Message) policy Compat32. If you are going to use another policy, you should be using the EmailMessage class instead.

An email message consists of headers and a payload. Headers must be RFC 5322 style names and values, where the field name and value are separated by a colon. The colon is not part of either the field name or the field value. The payload may be a simple text message, or a binary object, or a structured sequence of sub-messages each with their own set of headers and their own payload. The latter type of payload is indicated by the message having a MIME type such as multipart/* or message/rfc822.

The conceptual model provided by a Message object is that of an ordered dictionary of headers with additional methods for accessing both specialized information from the headers, for accessing the payload, for generating a serialized version of the message, and for recursively walking over the object tree. Note that duplicate headers are supported but special methods must be used to access them.

The Message pseudo-dictionary is indexed by the header names, which must be ASCII values. The values of the dictionary are strings that are supposed to contain only ASCII characters; there is some special handling for non-ASCII input, but it doesn’t always produce the correct results. Headers are stored and returned in case-preserving form, but field names are matched case-insensitively. There may also be a single envelope header, also known as the Unix-From header or the From_ header. The payload is either a string or bytes, in the case of simple message objects, or a list of Message objects, for MIME container documents (e.g. multipart/* and message/rfc822).

Here are the methods of the Message class:

+class email.message.Message(policy=compat32)
+

If policy is specified (it must be an instance of a policy class) use the rules it specifies to update and serialize the representation of the message. If policy is not set, use the compat32 policy, which maintains backward compatibility with the Python 3.2 version of the email package. For more information see the policy documentation.

Changed in version 3.3: The policy keyword argument was added.

+as_string(unixfrom=False, maxheaderlen=0, policy=None)
+

Return the entire message flattened as a string. When optional unixfrom is true, the envelope header is included in the returned string. unixfrom defaults to False. For backward compatibility reasons, maxheaderlen defaults to 0, so if you want a different value you must override it explicitly (the value specified for max_line_length in the policy will be ignored by this method). The policy argument may be used to override the default policy obtained from the message instance. This can be used to control some of the formatting produced by the method, since the specified policy will be passed to the Generator.

Flattening the message may trigger changes to the Message if defaults need to be filled in to complete the transformation to a string (for example, MIME boundaries may be generated or modified).

Note that this method is provided as a convenience and may not always format the message the way you want. For example, by default it does not do the mangling of lines that begin with From that is required by the Unix mbox format. For more flexibility, instantiate a Generator instance and use its flatten() method directly. For example:

from io import StringIO
+from email.generator import Generator
+fp = StringIO()
+g = Generator(fp, mangle_from_=True, maxheaderlen=60)
+g.flatten(msg)
+text = fp.getvalue()
+

If the message object contains binary data that is not encoded according to RFC standards, the non-compliant data will be replaced by unicode “unknown character” code points. (See also as_bytes() and BytesGenerator.)

Changed in version 3.4: the policy keyword argument was added.

+
+__str__()
+

Equivalent to as_string(). Allows str(msg) to produce a string containing the formatted message.

+
+as_bytes(unixfrom=False, policy=None)
+

Return the entire message flattened as a bytes object. When optional unixfrom is true, the envelope header is included in the returned string. unixfrom defaults to False. The policy argument may be used to override the default policy obtained from the message instance. This can be used to control some of the formatting produced by the method, since the specified policy will be passed to the BytesGenerator.

Flattening the message may trigger changes to the Message if defaults need to be filled in to complete the transformation to a string (for example, MIME boundaries may be generated or modified).

Note that this method is provided as a convenience and may not always format the message the way you want. For example, by default it does not do the mangling of lines that begin with From that is required by the Unix mbox format. For more flexibility, instantiate a BytesGenerator instance and use its flatten() method directly. For example:

from io import BytesIO
+from email.generator import BytesGenerator
+fp = BytesIO()
+g = BytesGenerator(fp, mangle_from_=True, maxheaderlen=60)
+g.flatten(msg)
+text = fp.getvalue()
+

New in version 3.4.

+
+__bytes__()
+

Equivalent to as_bytes(). Allows bytes(msg) to produce a bytes object containing the formatted message.

New in version 3.4.

+
+is_multipart()
+

Return True if the message’s payload is a list of sub-Message objects, otherwise return False. When is_multipart() returns False, the payload should be a string object (which might be a CTE encoded binary payload). (Note that is_multipart() returning True does not necessarily mean that “msg.get_content_maintype() == ‘multipart’” will return the True. For example, is_multipart will return True when the Message is of type message/rfc822.)

+
+set_unixfrom(unixfrom)
+

Set the message’s envelope header to unixfrom, which should be a string.

+
+get_unixfrom()
+

Return the message’s envelope header. Defaults to None if the envelope header was never set.

+
+attach(payload)
+

Add the given payload to the current payload, which must be None or a list of Message objects before the call. After the call, the payload will always be a list of Message objects. If you want to set the payload to a scalar object (e.g. a string), use set_payload() instead.

This is a legacy method. On the EmailMessage class its functionality is replaced by set_content() and the related make and add methods.

+
+get_payload(i=None, decode=False)
+

Return the current payload, which will be a list of Message objects when is_multipart() is True, or a string when is_multipart() is False. If the payload is a list and you mutate the list object, you modify the message’s payload in place.

With optional argument i, get_payload() will return the i-th element of the payload, counting from zero, if is_multipart() is True. An IndexError will be raised if i is less than 0 or greater than or equal to the number of items in the payload. If the payload is a string (i.e. is_multipart() is False) and i is given, a TypeError is raised.

Optional decode is a flag indicating whether the payload should be decoded or not, according to the Content-Transfer-Encoding header. When True and the message is not a multipart, the payload will be decoded if this header’s value is quoted-printable or base64. If some other encoding is used, or Content-Transfer-Encoding header is missing, the payload is returned as-is (undecoded). In all cases the returned value is binary data. If the message is a multipart and the decode flag is True, then None is returned. If the payload is base64 and it was not perfectly formed (missing padding, characters outside the base64 alphabet), then an appropriate defect will be added to the message’s defect property (InvalidBase64PaddingDefect or InvalidBase64CharactersDefect, respectively).

When decode is False (the default) the body is returned as a string without decoding the Content-Transfer-Encoding. However, for a Content-Transfer-Encoding of 8bit, an attempt is made to decode the original bytes using the charset specified by the Content-Type header, using the replace error handler. If no charset is specified, or if the charset given is not recognized by the email package, the body is decoded using the default ASCII charset.

This is a legacy method. On the EmailMessage class its functionality is replaced by get_content() and iter_parts().

+
+set_payload(payload, charset=None)
+

Set the entire message object’s payload to payload. It is the client’s responsibility to ensure the payload invariants. Optional charset sets the message’s default character set; see set_charset() for details.

This is a legacy method. On the EmailMessage class its functionality is replaced by set_content().

+
+set_charset(charset)
+

Set the character set of the payload to charset, which can either be a Charset instance (see email.charset), a string naming a character set, or None. If it is a string, it will be converted to a Charset instance. If charset is None, the charset parameter will be removed from the Content-Type header (the message will not be otherwise modified). Anything else will generate a TypeError.

If there is no existing MIME-Version header one will be added. If there is no existing Content-Type header, one will be added with a value of text/plain. Whether the Content-Type header already exists or not, its charset parameter will be set to charset.output_charset. If charset.input_charset and charset.output_charset differ, the payload will be re-encoded to the output_charset. If there is no existing Content-Transfer-Encoding header, then the payload will be transfer-encoded, if needed, using the specified Charset, and a header with the appropriate value will be added. If a Content-Transfer-Encoding header already exists, the payload is assumed to already be correctly encoded using that Content-Transfer-Encoding and is not modified.

This is a legacy method. On the EmailMessage class its functionality is replaced by the charset parameter of the email.emailmessage.EmailMessage.set_content() method.

+
+get_charset()
+

Return the Charset instance associated with the message’s payload.

This is a legacy method. On the EmailMessage class it always returns None.

+

The following methods implement a mapping-like interface for accessing the message’s RFC 2822 headers. Note that there are some semantic differences between these methods and a normal mapping (i.e. dictionary) interface. For example, in a dictionary there are no duplicate keys, but here there may be duplicate message headers. Also, in dictionaries there is no guaranteed order to the keys returned by keys(), but in a Message object, headers are always returned in the order they appeared in the original message, or were added to the message later. Any header deleted and then re-added are always appended to the end of the header list.

These semantic differences are intentional and are biased toward maximal convenience.

Note that in all cases, any envelope header present in the message is not included in the mapping interface.

In a model generated from bytes, any header values that (in contravention of the RFCs) contain non-ASCII bytes will, when retrieved through this interface, be represented as Header objects with a charset of unknown-8bit.

+__len__()
+

Return the total number of headers, including duplicates.

+
+__contains__(name)
+

Return True if the message object has a field named name. Matching is done case-insensitively and name should not include the trailing colon. Used for the in operator, e.g.:

if 'message-id' in myMessage:
+   print('Message-ID:', myMessage['message-id'])
+
+
+__getitem__(name)
+

Return the value of the named header field. name should not include the colon field separator. If the header is missing, None is returned; a KeyError is never raised.

Note that if the named field appears more than once in the message’s headers, exactly which of those field values will be returned is undefined. Use the get_all() method to get the values of all the extant named headers.

+
+__setitem__(name, val)
+

Add a header to the message with field name name and value val. The field is appended to the end of the message’s existing fields.

Note that this does not overwrite or delete any existing header with the same name. If you want to ensure that the new header is the only one present in the message with field name name, delete the field first, e.g.:

del msg['subject']
+msg['subject'] = 'Python roolz!'
+
+
+__delitem__(name)
+

Delete all occurrences of the field with name name from the message’s headers. No exception is raised if the named field isn’t present in the headers.

+
+keys()
+

Return a list of all the message’s header field names.

+
+values()
+

Return a list of all the message’s field values.

+
+items()
+

Return a list of 2-tuples containing all the message’s field headers and values.

+
+get(name, failobj=None)
+

Return the value of the named header field. This is identical to __getitem__() except that optional failobj is returned if the named header is missing (defaults to None).

+

Here are some additional useful methods:

+get_all(name, failobj=None)
+

Return a list of all the values for the field named name. If there are no such named headers in the message, failobj is returned (defaults to None).

+
+add_header(_name, _value, **_params)
+

Extended header setting. This method is similar to __setitem__() except that additional header parameters can be provided as keyword arguments. _name is the header field to add and _value is the primary value for the header.

For each item in the keyword argument dictionary _params, the key is taken as the parameter name, with underscores converted to dashes (since dashes are illegal in Python identifiers). Normally, the parameter will be added as key="value" unless the value is None, in which case only the key will be added. If the value contains non-ASCII characters, it can be specified as a three tuple in the format (CHARSET, LANGUAGE, VALUE), where CHARSET is a string naming the charset to be used to encode the value, LANGUAGE can usually be set to None or the empty string (see RFC 2231 for other possibilities), and VALUE is the string value containing non-ASCII code points. If a three tuple is not passed and the value contains non-ASCII characters, it is automatically encoded in RFC 2231 format using a CHARSET of utf-8 and a LANGUAGE of None.

Here’s an example:

msg.add_header('Content-Disposition', 'attachment', filename='bud.gif')
+

This will add a header that looks like

Content-Disposition: attachment; filename="bud.gif"
+

An example with non-ASCII characters:

msg.add_header('Content-Disposition', 'attachment',
+               filename=('iso-8859-1', '', 'Fußballer.ppt'))
+

Which produces

Content-Disposition: attachment; filename*="iso-8859-1''Fu%DFballer.ppt"
+
+
+replace_header(_name, _value)
+

Replace a header. Replace the first header found in the message that matches _name, retaining header order and field name case. If no matching header was found, a KeyError is raised.

+
+get_content_type()
+

Return the message’s content type. The returned string is coerced to lower case of the form maintype/subtype. If there was no Content-Type header in the message the default type as given by get_default_type() will be returned. Since according to RFC 2045, messages always have a default type, get_content_type() will always return a value.

RFC 2045 defines a message’s default type to be text/plain unless it appears inside a multipart/digest container, in which case it would be message/rfc822. If the Content-Type header has an invalid type specification, RFC 2045 mandates that the default type be text/plain.

+
+get_content_maintype()
+

Return the message’s main content type. This is the maintype part of the string returned by get_content_type().

+
+get_content_subtype()
+

Return the message’s sub-content type. This is the subtype part of the string returned by get_content_type().

+
+get_default_type()
+

Return the default content type. Most messages have a default content type of text/plain, except for messages that are subparts of multipart/digest containers. Such subparts have a default content type of message/rfc822.

+
+set_default_type(ctype)
+

Set the default content type. ctype should either be text/plain or message/rfc822, although this is not enforced. The default content type is not stored in the Content-Type header.

+
+get_params(failobj=None, header='content-type', unquote=True)
+

Return the message’s Content-Type parameters, as a list. The elements of the returned list are 2-tuples of key/value pairs, as split on the '=' sign. The left hand side of the '=' is the key, while the right hand side is the value. If there is no '=' sign in the parameter the value is the empty string, otherwise the value is as described in get_param() and is unquoted if optional unquote is True (the default).

Optional failobj is the object to return if there is no Content-Type header. Optional header is the header to search instead of Content-Type.

This is a legacy method. On the EmailMessage class its functionality is replaced by the params property of the individual header objects returned by the header access methods.

+
+get_param(param, failobj=None, header='content-type', unquote=True)
+

Return the value of the Content-Type header’s parameter param as a string. If the message has no Content-Type header or if there is no such parameter, then failobj is returned (defaults to None).

Optional header if given, specifies the message header to use instead of Content-Type.

Parameter keys are always compared case insensitively. The return value can either be a string, or a 3-tuple if the parameter was RFC 2231 encoded. When it’s a 3-tuple, the elements of the value are of the form (CHARSET, LANGUAGE, VALUE). Note that both CHARSET and LANGUAGE can be None, in which case you should consider VALUE to be encoded in the us-ascii charset. You can usually ignore LANGUAGE.

If your application doesn’t care whether the parameter was encoded as in RFC 2231, you can collapse the parameter value by calling email.utils.collapse_rfc2231_value(), passing in the return value from get_param(). This will return a suitably decoded Unicode string when the value is a tuple, or the original string unquoted if it isn’t. For example:

rawparam = msg.get_param('foo')
+param = email.utils.collapse_rfc2231_value(rawparam)
+

In any case, the parameter value (either the returned string, or the VALUE item in the 3-tuple) is always unquoted, unless unquote is set to False.

This is a legacy method. On the EmailMessage class its functionality is replaced by the params property of the individual header objects returned by the header access methods.

+
+set_param(param, value, header='Content-Type', requote=True, charset=None, language='', replace=False)
+

Set a parameter in the Content-Type header. If the parameter already exists in the header, its value will be replaced with value. If the Content-Type header as not yet been defined for this message, it will be set to text/plain and the new parameter value will be appended as per RFC 2045.

Optional header specifies an alternative header to Content-Type, and all parameters will be quoted as necessary unless optional requote is False (the default is True).

If optional charset is specified, the parameter will be encoded according to RFC 2231. Optional language specifies the RFC 2231 language, defaulting to the empty string. Both charset and language should be strings.

If replace is False (the default) the header is moved to the end of the list of headers. If replace is True, the header will be updated in place.

Changed in version 3.4: replace keyword was added.

+
+del_param(param, header='content-type', requote=True)
+

Remove the given parameter completely from the Content-Type header. The header will be re-written in place without the parameter or its value. All values will be quoted as necessary unless requote is False (the default is True). Optional header specifies an alternative to Content-Type.

+
+set_type(type, header='Content-Type', requote=True)
+

Set the main type and subtype for the Content-Type header. type must be a string in the form maintype/subtype, otherwise a ValueError is raised.

This method replaces the Content-Type header, keeping all the parameters in place. If requote is False, this leaves the existing header’s quoting as is, otherwise the parameters will be quoted (the default).

An alternative header can be specified in the header argument. When the Content-Type header is set a MIME-Version header is also added.

This is a legacy method. On the EmailMessage class its functionality is replaced by the make_ and add_ methods.

+
+get_filename(failobj=None)
+

Return the value of the filename parameter of the Content-Disposition header of the message. If the header does not have a filename parameter, this method falls back to looking for the name parameter on the Content-Type header. If neither is found, or the header is missing, then failobj is returned. The returned string will always be unquoted as per email.utils.unquote().

+
+get_boundary(failobj=None)
+

Return the value of the boundary parameter of the Content-Type header of the message, or failobj if either the header is missing, or has no boundary parameter. The returned string will always be unquoted as per email.utils.unquote().

+
+set_boundary(boundary)
+

Set the boundary parameter of the Content-Type header to boundary. set_boundary() will always quote boundary if necessary. A HeaderParseError is raised if the message object has no Content-Type header.

Note that using this method is subtly different than deleting the old Content-Type header and adding a new one with the new boundary via add_header(), because set_boundary() preserves the order of the Content-Type header in the list of headers. However, it does not preserve any continuation lines which may have been present in the original Content-Type header.

+
+get_content_charset(failobj=None)
+

Return the charset parameter of the Content-Type header, coerced to lower case. If there is no Content-Type header, or if that header has no charset parameter, failobj is returned.

Note that this method differs from get_charset() which returns the Charset instance for the default encoding of the message body.

+
+get_charsets(failobj=None)
+

Return a list containing the character set names in the message. If the message is a multipart, then the list will contain one element for each subpart in the payload, otherwise, it will be a list of length 1.

Each item in the list will be a string which is the value of the charset parameter in the Content-Type header for the represented subpart. However, if the subpart has no Content-Type header, no charset parameter, or is not of the text main MIME type, then that item in the returned list will be failobj.

+
+get_content_disposition()
+

Return the lowercased value (without parameters) of the message’s Content-Disposition header if it has one, or None. The possible values for this method are inline, attachment or None if the message follows RFC 2183.

New in version 3.5.

+
+walk()
+

The walk() method is an all-purpose generator which can be used to iterate over all the parts and subparts of a message object tree, in depth-first traversal order. You will typically use walk() as the iterator in a for loop; each iteration returns the next subpart.

Here’s an example that prints the MIME type of every part of a multipart message structure:

>>> for part in msg.walk():
+...     print(part.get_content_type())
+multipart/report
+text/plain
+message/delivery-status
+text/plain
+text/plain
+message/rfc822
+text/plain
+

walk iterates over the subparts of any part where is_multipart() returns True, even though msg.get_content_maintype() == 'multipart' may return False. We can see this in our example by making use of the _structure debug helper function:

>>> for part in msg.walk():
+...     print(part.get_content_maintype() == 'multipart',
+...           part.is_multipart())
+True True
+False False
+False True
+False False
+False False
+False True
+False False
+>>> _structure(msg)
+multipart/report
+    text/plain
+    message/delivery-status
+        text/plain
+        text/plain
+    message/rfc822
+        text/plain
+

Here the message parts are not multiparts, but they do contain subparts. is_multipart() returns True and walk descends into the subparts.

+

Message objects can also optionally contain two instance attributes, which can be used when generating the plain text of a MIME message.

+preamble
+

The format of a MIME document allows for some text between the blank line following the headers, and the first multipart boundary string. Normally, this text is never visible in a MIME-aware mail reader because it falls outside the standard MIME armor. However, when viewing the raw text of the message, or when viewing the message in a non-MIME aware reader, this text can become visible.

The preamble attribute contains this leading extra-armor text for MIME documents. When the Parser discovers some text after the headers but before the first boundary string, it assigns this text to the message’s preamble attribute. When the Generator is writing out the plain text representation of a MIME message, and it finds the message has a preamble attribute, it will write this text in the area between the headers and the first boundary. See email.parser and email.generator for details.

Note that if the message object has no preamble, the preamble attribute will be None.

+
+epilogue
+

The epilogue attribute acts the same way as the preamble attribute, except that it contains text that appears between the last boundary and the end of the message.

You do not need to set the epilogue to the empty string in order for the Generator to print a newline at the end of the file.

+
+defects
+

The defects attribute contains a list of all the problems found when parsing this message. See email.errors for a detailed description of the possible parsing defects.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.compat32-message.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.contentmanager.html b/devdocs/python~3.12/library%2Femail.contentmanager.html new file mode 100644 index 00000000..303a49fb --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.contentmanager.html @@ -0,0 +1,36 @@ +

email.contentmanager: Managing MIME Content

Source code: Lib/email/contentmanager.py

New in version 3.6: 1

+class email.contentmanager.ContentManager
+

Base class for content managers. Provides the standard registry mechanisms to register converters between MIME content and other representations, as well as the get_content and set_content dispatch methods.

+get_content(msg, *args, **kw)
+

Look up a handler function based on the mimetype of msg (see next paragraph), call it, passing through all arguments, and return the result of the call. The expectation is that the handler will extract the payload from msg and return an object that encodes information about the extracted data.

To find the handler, look for the following keys in the registry, stopping with the first one found:

  • the string representing the full MIME type (maintype/subtype)
  • the string representing the maintype +
  • the empty string

If none of these keys produce a handler, raise a KeyError for the full MIME type.

+
+set_content(msg, obj, *args, **kw)
+

If the maintype is multipart, raise a TypeError; otherwise look up a handler function based on the type of obj (see next paragraph), call clear_content() on the msg, and call the handler function, passing through all arguments. The expectation is that the handler will transform and store obj into msg, possibly making other changes to msg as well, such as adding various MIME headers to encode information needed to interpret the stored data.

To find the handler, obtain the type of obj (typ = type(obj)), and look for the following keys in the registry, stopping with the first one found:

  • the type itself (typ)
  • the type’s fully qualified name (typ.__module__ + '.' + +typ.__qualname__).
  • the type’s qualname (typ.__qualname__)
  • the type’s name (typ.__name__).

If none of the above match, repeat all of the checks above for each of the types in the MRO (typ.__mro__). Finally, if no other key yields a handler, check for a handler for the key None. If there is no handler for None, raise a KeyError for the fully qualified name of the type.

Also add a MIME-Version header if one is not present (see also MIMEPart).

+
+add_get_handler(key, handler)
+

Record the function handler as the handler for key. For the possible values of key, see get_content().

+
+add_set_handler(typekey, handler)
+

Record handler as the function to call when an object of a type matching typekey is passed to set_content(). For the possible values of typekey, see set_content().

+
+

Content Manager Instances

Currently the email package provides only one concrete content manager, raw_data_manager, although more may be added in the future. raw_data_manager is the content_manager provided by EmailPolicy and its derivatives.

+email.contentmanager.raw_data_manager
+

This content manager provides only a minimum interface beyond that provided by Message itself: it deals only with text, raw byte strings, and Message objects. Nevertheless, it provides significant advantages compared to the base API: get_content on a text part will return a unicode string without the application needing to manually decode it, set_content provides a rich set of options for controlling the headers added to a part and controlling the content transfer encoding, and it enables the use of the various add_ methods, thereby simplifying the creation of multipart messages.

+email.contentmanager.get_content(msg, errors='replace')
+

Return the payload of the part as either a string (for text parts), an EmailMessage object (for message/rfc822 parts), or a bytes object (for all other non-multipart types). Raise a KeyError if called on a multipart. If the part is a text part and errors is specified, use it as the error handler when decoding the payload to unicode. The default error handler is replace.

+
+email.contentmanager.set_content(msg, <'str'>, subtype="plain", charset='utf-8', cte=None, disposition=None, filename=None, cid=None, params=None, headers=None)
email.contentmanager.set_content(msg, <'bytes'>, maintype, subtype, cte="base64", disposition=None, filename=None, cid=None, params=None, headers=None) +
email.contentmanager.set_content(msg, <'EmailMessage'>, cte=None, disposition=None, filename=None, cid=None, params=None, headers=None) +
+

Add headers and payload to msg:

Add a Content-Type header with a maintype/subtype value.

  • For str, set the MIME maintype to text, and set the subtype to subtype if it is specified, or plain if it is not.
  • For bytes, use the specified maintype and subtype, or raise a TypeError if they are not specified.
  • For EmailMessage objects, set the maintype to message, and set the subtype to subtype if it is specified or rfc822 if it is not. If subtype is partial, raise an error (bytes objects must be used to construct message/partial parts).

If charset is provided (which is valid only for str), encode the string to bytes using the specified character set. The default is utf-8. If the specified charset is a known alias for a standard MIME charset name, use the standard charset instead.

If cte is set, encode the payload using the specified content transfer encoding, and set the Content-Transfer-Encoding header to that value. Possible values for cte are quoted-printable, base64, 7bit, 8bit, and binary. If the input cannot be encoded in the specified encoding (for example, specifying a cte of 7bit for an input that contains non-ASCII values), raise a ValueError.

  • For str objects, if cte is not set use heuristics to determine the most compact encoding.
  • For EmailMessage, per RFC 2046, raise an error if a cte of quoted-printable or base64 is requested for subtype rfc822, and for any cte other than 7bit for subtype external-body. For message/rfc822, use 8bit if cte is not specified. For all other values of subtype, use 7bit.

Note

A cte of binary does not actually work correctly yet. The EmailMessage object as modified by set_content is correct, but BytesGenerator does not serialize it correctly.

If disposition is set, use it as the value of the Content-Disposition header. If not specified, and filename is specified, add the header with the value attachment. If disposition is not specified and filename is also not specified, do not add the header. The only valid values for disposition are attachment and inline.

If filename is specified, use it as the value of the filename parameter of the Content-Disposition header.

If cid is specified, add a Content-ID header with cid as its value.

If params is specified, iterate its items method and use the resulting (key, value) pairs to set additional parameters on the Content-Type header.

If headers is specified and is a list of strings of the form headername: headervalue or a list of header objects (distinguished from strings by having a name attribute), add the headers to msg.

+
+

Footnotes

+1
+

Originally added in 3.4 as a provisional module

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.contentmanager.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.encoders.html b/devdocs/python~3.12/library%2Femail.encoders.html new file mode 100644 index 00000000..a92e2cc3 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.encoders.html @@ -0,0 +1,20 @@ +

email.encoders: Encoders

Source code: Lib/email/encoders.py

This module is part of the legacy (Compat32) email API. In the new API the functionality is provided by the cte parameter of the set_content() method.

This module is deprecated in Python 3. The functions provided here should not be called explicitly since the MIMEText class sets the content type and CTE header using the _subtype and _charset values passed during the instantiation of that class.

The remaining text in this section is the original documentation of the module.

When creating Message objects from scratch, you often need to encode the payloads for transport through compliant mail servers. This is especially true for image/* and text/* type messages containing binary data.

The email package provides some convenient encoders in its encoders module. These encoders are actually used by the MIMEAudio and MIMEImage class constructors to provide default encodings. All encoder functions take exactly one argument, the message object to encode. They usually extract the payload, encode it, and reset the payload to this newly encoded value. They should also set the Content-Transfer-Encoding header as appropriate.

Note that these functions are not meaningful for a multipart message. They must be applied to individual subparts instead, and will raise a TypeError if passed a message whose type is multipart.

Here are the encoding functions provided:

+email.encoders.encode_quopri(msg)
+

Encodes the payload into quoted-printable form and sets the Content-Transfer-Encoding header to quoted-printable 1. This is a good encoding to use when most of your payload is normal printable data, but contains a few unprintable characters.

+
+email.encoders.encode_base64(msg)
+

Encodes the payload into base64 form and sets the Content-Transfer-Encoding header to base64. This is a good encoding to use when most of your payload is unprintable data since it is a more compact form than quoted-printable. The drawback of base64 encoding is that it renders the text non-human readable.

+
+email.encoders.encode_7or8bit(msg)
+

This doesn’t actually modify the message’s payload, but it does set the Content-Transfer-Encoding header to either 7bit or 8bit as appropriate, based on the payload data.

+
+email.encoders.encode_noop(msg)
+

This does nothing; it doesn’t even set the Content-Transfer-Encoding header.

+

Footnotes

+1
+

Note that encoding with encode_quopri() also encodes all tabs and space characters in the data.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.encoders.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.errors.html b/devdocs/python~3.12/library%2Femail.errors.html new file mode 100644 index 00000000..d9f65906 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.errors.html @@ -0,0 +1,39 @@ +

email.errors: Exception and Defect classes

Source code: Lib/email/errors.py

The following exception classes are defined in the email.errors module:

+exception email.errors.MessageError
+

This is the base class for all exceptions that the email package can raise. It is derived from the standard Exception class and defines no additional methods.

+
+exception email.errors.MessageParseError
+

This is the base class for exceptions raised by the Parser class. It is derived from MessageError. This class is also used internally by the parser used by headerregistry.

+
+exception email.errors.HeaderParseError
+

Raised under some error conditions when parsing the RFC 5322 headers of a message, this class is derived from MessageParseError. The set_boundary() method will raise this error if the content type is unknown when the method is called. Header may raise this error for certain base64 decoding errors, and when an attempt is made to create a header that appears to contain an embedded header (that is, there is what is supposed to be a continuation line that has no leading whitespace and looks like a header).

+
+exception email.errors.BoundaryError
+

Deprecated and no longer used.

+
+exception email.errors.MultipartConversionError
+

Raised when a payload is added to a Message object using add_payload(), but the payload is already a scalar and the message’s Content-Type main type is not either multipart or missing. MultipartConversionError multiply inherits from MessageError and the built-in TypeError.

Since Message.add_payload() is deprecated, this exception is rarely raised in practice. However the exception may also be raised if the attach() method is called on an instance of a class derived from MIMENonMultipart (e.g. MIMEImage).

+
+exception email.errors.MessageDefect
+

This is the base class for all defects found when parsing email messages. It is derived from ValueError.

+
+exception email.errors.HeaderDefect
+

This is the base class for all defects found when parsing email headers. It is derived from MessageDefect.

+

Here is the list of the defects that the FeedParser can find while parsing messages. Note that the defects are added to the message where the problem was found, so for example, if a message nested inside a multipart/alternative had a malformed header, that nested message object would have a defect, but the containing messages would not.

All defect classes are subclassed from email.errors.MessageDefect.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.errors.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.examples.html b/devdocs/python~3.12/library%2Femail.examples.html new file mode 100644 index 00000000..d19e8fde --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.examples.html @@ -0,0 +1,353 @@ +

email: Examples

Here are a few examples of how to use the email package to read, write, and send simple email messages, as well as more complex MIME messages.

First, let’s see how to create and send a simple text message (both the text content and the addresses may contain unicode characters):

# Import smtplib for the actual sending function
+import smtplib
+
+# Import the email modules we'll need
+from email.message import EmailMessage
+
+# Open the plain text file whose name is in textfile for reading.
+with open(textfile) as fp:
+    # Create a text/plain message
+    msg = EmailMessage()
+    msg.set_content(fp.read())
+
+# me == the sender's email address
+# you == the recipient's email address
+msg['Subject'] = f'The contents of {textfile}'
+msg['From'] = me
+msg['To'] = you
+
+# Send the message via our own SMTP server.
+s = smtplib.SMTP('localhost')
+s.send_message(msg)
+s.quit()
+

Parsing RFC 822 headers can easily be done by the using the classes from the parser module:

# Import the email modules we'll need
+#from email.parser import BytesParser
+from email.parser import Parser
+from email.policy import default
+
+# If the e-mail headers are in a file, uncomment these two lines:
+# with open(messagefile, 'rb') as fp:
+#     headers = BytesParser(policy=default).parse(fp)
+
+#  Or for parsing headers in a string (this is an uncommon operation), use:
+headers = Parser(policy=default).parsestr(
+        'From: Foo Bar <user@example.com>\n'
+        'To: <someone_else@example.com>\n'
+        'Subject: Test message\n'
+        '\n'
+        'Body would go here\n')
+
+#  Now the header items can be accessed as a dictionary:
+print('To: {}'.format(headers['to']))
+print('From: {}'.format(headers['from']))
+print('Subject: {}'.format(headers['subject']))
+
+# You can also access the parts of the addresses:
+print('Recipient username: {}'.format(headers['to'].addresses[0].username))
+print('Sender name: {}'.format(headers['from'].addresses[0].display_name))
+

Here’s an example of how to send a MIME message containing a bunch of family pictures that may be residing in a directory:

# Import smtplib for the actual sending function.
+import smtplib
+
+# Here are the email package modules we'll need.
+from email.message import EmailMessage
+
+# Create the container email message.
+msg = EmailMessage()
+msg['Subject'] = 'Our family reunion'
+# me == the sender's email address
+# family = the list of all recipients' email addresses
+msg['From'] = me
+msg['To'] = ', '.join(family)
+msg.preamble = 'You will not see this in a MIME-aware mail reader.\n'
+
+# Open the files in binary mode.  You can also omit the subtype
+# if you want MIMEImage to guess it.
+for file in pngfiles:
+    with open(file, 'rb') as fp:
+        img_data = fp.read()
+    msg.add_attachment(img_data, maintype='image',
+                                 subtype='png')
+
+# Send the email via our own SMTP server.
+with smtplib.SMTP('localhost') as s:
+    s.send_message(msg)
+

Here’s an example of how to send the entire contents of a directory as an email message: 1

#!/usr/bin/env python3
+
+"""Send the contents of a directory as a MIME message."""
+
+import os
+import smtplib
+# For guessing MIME type based on file name extension
+import mimetypes
+
+from argparse import ArgumentParser
+
+from email.message import EmailMessage
+from email.policy import SMTP
+
+
+def main():
+    parser = ArgumentParser(description="""\
+Send the contents of a directory as a MIME message.
+Unless the -o option is given, the email is sent by forwarding to your local
+SMTP server, which then does the normal delivery process.  Your local machine
+must be running an SMTP server.
+""")
+    parser.add_argument('-d', '--directory',
+                        help="""Mail the contents of the specified directory,
+                        otherwise use the current directory.  Only the regular
+                        files in the directory are sent, and we don't recurse to
+                        subdirectories.""")
+    parser.add_argument('-o', '--output',
+                        metavar='FILE',
+                        help="""Print the composed message to FILE instead of
+                        sending the message to the SMTP server.""")
+    parser.add_argument('-s', '--sender', required=True,
+                        help='The value of the From: header (required)')
+    parser.add_argument('-r', '--recipient', required=True,
+                        action='append', metavar='RECIPIENT',
+                        default=[], dest='recipients',
+                        help='A To: header value (at least one required)')
+    args = parser.parse_args()
+    directory = args.directory
+    if not directory:
+        directory = '.'
+    # Create the message
+    msg = EmailMessage()
+    msg['Subject'] = f'Contents of directory {os.path.abspath(directory)}'
+    msg['To'] = ', '.join(args.recipients)
+    msg['From'] = args.sender
+    msg.preamble = 'You will not see this in a MIME-aware mail reader.\n'
+
+    for filename in os.listdir(directory):
+        path = os.path.join(directory, filename)
+        if not os.path.isfile(path):
+            continue
+        # Guess the content type based on the file's extension.  Encoding
+        # will be ignored, although we should check for simple things like
+        # gzip'd or compressed files.
+        ctype, encoding = mimetypes.guess_type(path)
+        if ctype is None or encoding is not None:
+            # No guess could be made, or the file is encoded (compressed), so
+            # use a generic bag-of-bits type.
+            ctype = 'application/octet-stream'
+        maintype, subtype = ctype.split('/', 1)
+        with open(path, 'rb') as fp:
+            msg.add_attachment(fp.read(),
+                               maintype=maintype,
+                               subtype=subtype,
+                               filename=filename)
+    # Now send or store the message
+    if args.output:
+        with open(args.output, 'wb') as fp:
+            fp.write(msg.as_bytes(policy=SMTP))
+    else:
+        with smtplib.SMTP('localhost') as s:
+            s.send_message(msg)
+
+
+if __name__ == '__main__':
+    main()
+

Here’s an example of how to unpack a MIME message like the one above, into a directory of files:

#!/usr/bin/env python3
+
+"""Unpack a MIME message into a directory of files."""
+
+import os
+import email
+import mimetypes
+
+from email.policy import default
+
+from argparse import ArgumentParser
+
+
+def main():
+    parser = ArgumentParser(description="""\
+Unpack a MIME message into a directory of files.
+""")
+    parser.add_argument('-d', '--directory', required=True,
+                        help="""Unpack the MIME message into the named
+                        directory, which will be created if it doesn't already
+                        exist.""")
+    parser.add_argument('msgfile')
+    args = parser.parse_args()
+
+    with open(args.msgfile, 'rb') as fp:
+        msg = email.message_from_binary_file(fp, policy=default)
+
+    try:
+        os.mkdir(args.directory)
+    except FileExistsError:
+        pass
+
+    counter = 1
+    for part in msg.walk():
+        # multipart/* are just containers
+        if part.get_content_maintype() == 'multipart':
+            continue
+        # Applications should really sanitize the given filename so that an
+        # email message can't be used to overwrite important files
+        filename = part.get_filename()
+        if not filename:
+            ext = mimetypes.guess_extension(part.get_content_type())
+            if not ext:
+                # Use a generic bag-of-bits extension
+                ext = '.bin'
+            filename = f'part-{counter:03d}{ext}'
+        counter += 1
+        with open(os.path.join(args.directory, filename), 'wb') as fp:
+            fp.write(part.get_payload(decode=True))
+
+
+if __name__ == '__main__':
+    main()
+

Here’s an example of how to create an HTML message with an alternative plain text version. To make things a bit more interesting, we include a related image in the html part, and we save a copy of what we are going to send to disk, as well as sending it.

#!/usr/bin/env python3
+
+import smtplib
+
+from email.message import EmailMessage
+from email.headerregistry import Address
+from email.utils import make_msgid
+
+# Create the base text message.
+msg = EmailMessage()
+msg['Subject'] = "Ayons asperges pour le déjeuner"
+msg['From'] = Address("Pepé Le Pew", "pepe", "example.com")
+msg['To'] = (Address("Penelope Pussycat", "penelope", "example.com"),
+             Address("Fabrette Pussycat", "fabrette", "example.com"))
+msg.set_content("""\
+Salut!
+
+Cela ressemble à un excellent recipie[1] déjeuner.
+
+[1] http://www.yummly.com/recipe/Roasted-Asparagus-Epicurious-203718
+
+--Pepé
+""")
+
+# Add the html version.  This converts the message into a multipart/alternative
+# container, with the original text message as the first part and the new html
+# message as the second part.
+asparagus_cid = make_msgid()
+msg.add_alternative("""\
+<html>
+  <head></head>
+  <body>
+    <p>Salut!</p>
+    <p>Cela ressemble à un excellent
+        <a href="http://www.yummly.com/recipe/Roasted-Asparagus-Epicurious-203718">
+            recipie
+        </a> déjeuner.
+    </p>
+    <img src="cid:{asparagus_cid}" />
+  </body>
+</html>
+""".format(asparagus_cid=asparagus_cid[1:-1]), subtype='html')
+# note that we needed to peel the <> off the msgid for use in the html.
+
+# Now add the related image to the html part.
+with open("roasted-asparagus.jpg", 'rb') as img:
+    msg.get_payload()[1].add_related(img.read(), 'image', 'jpeg',
+                                     cid=asparagus_cid)
+
+# Make a local copy of what we are going to send.
+with open('outgoing.msg', 'wb') as f:
+    f.write(bytes(msg))
+
+# Send the message via local SMTP server.
+with smtplib.SMTP('localhost') as s:
+    s.send_message(msg)
+

If we were sent the message from the last example, here is one way we could process it:

import os
+import sys
+import tempfile
+import mimetypes
+import webbrowser
+
+# Import the email modules we'll need
+from email import policy
+from email.parser import BytesParser
+
+
+def magic_html_parser(html_text, partfiles):
+    """Return safety-sanitized html linked to partfiles.
+
+    Rewrite the href="cid:...." attributes to point to the filenames in partfiles.
+    Though not trivial, this should be possible using html.parser.
+    """
+    raise NotImplementedError("Add the magic needed")
+
+
+# In a real program you'd get the filename from the arguments.
+with open('outgoing.msg', 'rb') as fp:
+    msg = BytesParser(policy=policy.default).parse(fp)
+
+# Now the header items can be accessed as a dictionary, and any non-ASCII will
+# be converted to unicode:
+print('To:', msg['to'])
+print('From:', msg['from'])
+print('Subject:', msg['subject'])
+
+# If we want to print a preview of the message content, we can extract whatever
+# the least formatted payload is and print the first three lines.  Of course,
+# if the message has no plain text part printing the first three lines of html
+# is probably useless, but this is just a conceptual example.
+simplest = msg.get_body(preferencelist=('plain', 'html'))
+print()
+print(''.join(simplest.get_content().splitlines(keepends=True)[:3]))
+
+ans = input("View full message?")
+if ans.lower()[0] == 'n':
+    sys.exit()
+
+# We can extract the richest alternative in order to display it:
+richest = msg.get_body()
+partfiles = {}
+if richest['content-type'].maintype == 'text':
+    if richest['content-type'].subtype == 'plain':
+        for line in richest.get_content().splitlines():
+            print(line)
+        sys.exit()
+    elif richest['content-type'].subtype == 'html':
+        body = richest
+    else:
+        print("Don't know how to display {}".format(richest.get_content_type()))
+        sys.exit()
+elif richest['content-type'].content_type == 'multipart/related':
+    body = richest.get_body(preferencelist=('html'))
+    for part in richest.iter_attachments():
+        fn = part.get_filename()
+        if fn:
+            extension = os.path.splitext(part.get_filename())[1]
+        else:
+            extension = mimetypes.guess_extension(part.get_content_type())
+        with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as f:
+            f.write(part.get_content())
+            # again strip the <> to go from email form of cid to html form.
+            partfiles[part['content-id'][1:-1]] = f.name
+else:
+    print("Don't know how to display {}".format(richest.get_content_type()))
+    sys.exit()
+with tempfile.NamedTemporaryFile(mode='w', delete=False) as f:
+    f.write(magic_html_parser(body.get_content(), partfiles))
+webbrowser.open(f.name)
+os.remove(f.name)
+for fn in partfiles.values():
+    os.remove(fn)
+
+# Of course, there are lots of email messages that could break this simple
+# minded program, but it will handle the most common ones.
+

Up to the prompt, the output from the above is:

To: Penelope Pussycat <penelope@example.com>, Fabrette Pussycat <fabrette@example.com>
+From: Pepé Le Pew <pepe@example.com>
+Subject: Ayons asperges pour le déjeuner
+
+Salut!
+
+Cela ressemble à un excellent recipie[1] déjeuner.
+

Footnotes

+1
+

Thanks to Matthew Dixon Cowles for the original inspiration and examples.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.examples.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.generator.html b/devdocs/python~3.12/library%2Femail.generator.html new file mode 100644 index 00000000..a2bcf2e4 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.generator.html @@ -0,0 +1,41 @@ +

email.generator: Generating MIME documents

Source code: Lib/email/generator.py

One of the most common tasks is to generate the flat (serialized) version of the email message represented by a message object structure. You will need to do this if you want to send your message via smtplib.SMTP.sendmail() or the nntplib module, or print the message on the console. Taking a message object structure and producing a serialized representation is the job of the generator classes.

As with the email.parser module, you aren’t limited to the functionality of the bundled generator; you could write one from scratch yourself. However the bundled generator knows how to generate most email in a standards-compliant way, should handle MIME and non-MIME email messages just fine, and is designed so that the bytes-oriented parsing and generation operations are inverses, assuming the same non-transforming policy is used for both. That is, parsing the serialized byte stream via the BytesParser class and then regenerating the serialized byte stream using BytesGenerator should produce output identical to the input 1. (On the other hand, using the generator on an EmailMessage constructed by program may result in changes to the EmailMessage object as defaults are filled in.)

The Generator class can be used to flatten a message into a text (as opposed to binary) serialized representation, but since Unicode cannot represent binary data directly, the message is of necessity transformed into something that contains only ASCII characters, using the standard email RFC Content Transfer Encoding techniques for encoding email messages for transport over channels that are not “8 bit clean”.

To accommodate reproducible processing of SMIME-signed messages Generator disables header folding for message parts of type multipart/signed and all subparts.

+class email.generator.BytesGenerator(outfp, mangle_from_=None, maxheaderlen=None, *, policy=None)
+

Return a BytesGenerator object that will write any message provided to the flatten() method, or any surrogateescape encoded text provided to the write() method, to the file-like object outfp. outfp must support a write method that accepts binary data.

If optional mangle_from_ is True, put a > character in front of any line in the body that starts with the exact string "From ", that is From followed by a space at the beginning of a line. mangle_from_ defaults to the value of the mangle_from_ setting of the policy (which is True for the compat32 policy and False for all others). mangle_from_ is intended for use when messages are stored in Unix mbox format (see mailbox and WHY THE CONTENT-LENGTH FORMAT IS BAD).

If maxheaderlen is not None, refold any header lines that are longer than maxheaderlen, or if 0, do not rewrap any headers. If manheaderlen is None (the default), wrap headers and other message lines according to the policy settings.

If policy is specified, use that policy to control message generation. If policy is None (the default), use the policy associated with the Message or EmailMessage object passed to flatten to control the message generation. See email.policy for details on what policy controls.

New in version 3.2.

Changed in version 3.3: Added the policy keyword.

Changed in version 3.6: The default behavior of the mangle_from_ and maxheaderlen parameters is to follow the policy.

+flatten(msg, unixfrom=False, linesep=None)
+

Print the textual representation of the message object structure rooted at msg to the output file specified when the BytesGenerator instance was created.

If the policy option cte_type is 8bit (the default), copy any headers in the original parsed message that have not been modified to the output with any bytes with the high bit set reproduced as in the original, and preserve the non-ASCII Content-Transfer-Encoding of any body parts that have them. If cte_type is 7bit, convert the bytes with the high bit set as needed using an ASCII-compatible Content-Transfer-Encoding. That is, transform parts with non-ASCII Content-Transfer-Encoding (Content-Transfer-Encoding: 8bit) to an ASCII compatible Content-Transfer-Encoding, and encode RFC-invalid non-ASCII bytes in headers using the MIME unknown-8bit character set, thus rendering them RFC-compliant.

If unixfrom is True, print the envelope header delimiter used by the Unix mailbox format (see mailbox) before the first of the RFC 5322 headers of the root message object. If the root object has no envelope header, craft a standard one. The default is False. Note that for subparts, no envelope header is ever printed.

If linesep is not None, use it as the separator character between all the lines of the flattened message. If linesep is None (the default), use the value specified in the policy.

+
+clone(fp)
+

Return an independent clone of this BytesGenerator instance with the exact same option settings, and fp as the new outfp.

+
+write(s)
+

Encode s using the ASCII codec and the surrogateescape error handler, and pass it to the write method of the outfp passed to the BytesGenerator’s constructor.

+
+

As a convenience, EmailMessage provides the methods as_bytes() and bytes(aMessage) (a.k.a. __bytes__()), which simplify the generation of a serialized binary representation of a message object. For more detail, see email.message.

Because strings cannot represent binary data, the Generator class must convert any binary data in any message it flattens to an ASCII compatible format, by converting them to an ASCII compatible Content-Transfer_Encoding. Using the terminology of the email RFCs, you can think of this as Generator serializing to an I/O stream that is not “8 bit clean”. In other words, most applications will want to be using BytesGenerator, and not Generator.

+class email.generator.Generator(outfp, mangle_from_=None, maxheaderlen=None, *, policy=None)
+

Return a Generator object that will write any message provided to the flatten() method, or any text provided to the write() method, to the file-like object outfp. outfp must support a write method that accepts string data.

If optional mangle_from_ is True, put a > character in front of any line in the body that starts with the exact string "From ", that is From followed by a space at the beginning of a line. mangle_from_ defaults to the value of the mangle_from_ setting of the policy (which is True for the compat32 policy and False for all others). mangle_from_ is intended for use when messages are stored in Unix mbox format (see mailbox and WHY THE CONTENT-LENGTH FORMAT IS BAD).

If maxheaderlen is not None, refold any header lines that are longer than maxheaderlen, or if 0, do not rewrap any headers. If manheaderlen is None (the default), wrap headers and other message lines according to the policy settings.

If policy is specified, use that policy to control message generation. If policy is None (the default), use the policy associated with the Message or EmailMessage object passed to flatten to control the message generation. See email.policy for details on what policy controls.

Changed in version 3.3: Added the policy keyword.

Changed in version 3.6: The default behavior of the mangle_from_ and maxheaderlen parameters is to follow the policy.

+flatten(msg, unixfrom=False, linesep=None)
+

Print the textual representation of the message object structure rooted at msg to the output file specified when the Generator instance was created.

If the policy option cte_type is 8bit, generate the message as if the option were set to 7bit. (This is required because strings cannot represent non-ASCII bytes.) Convert any bytes with the high bit set as needed using an ASCII-compatible Content-Transfer-Encoding. That is, transform parts with non-ASCII Content-Transfer-Encoding (Content-Transfer-Encoding: 8bit) to an ASCII compatible Content-Transfer-Encoding, and encode RFC-invalid non-ASCII bytes in headers using the MIME unknown-8bit character set, thus rendering them RFC-compliant.

If unixfrom is True, print the envelope header delimiter used by the Unix mailbox format (see mailbox) before the first of the RFC 5322 headers of the root message object. If the root object has no envelope header, craft a standard one. The default is False. Note that for subparts, no envelope header is ever printed.

If linesep is not None, use it as the separator character between all the lines of the flattened message. If linesep is None (the default), use the value specified in the policy.

Changed in version 3.2: Added support for re-encoding 8bit message bodies, and the linesep argument.

+
+clone(fp)
+

Return an independent clone of this Generator instance with the exact same options, and fp as the new outfp.

+
+write(s)
+

Write s to the write method of the outfp passed to the Generator’s constructor. This provides just enough file-like API for Generator instances to be used in the print() function.

+
+

As a convenience, EmailMessage provides the methods as_string() and str(aMessage) (a.k.a. __str__()), which simplify the generation of a formatted string representation of a message object. For more detail, see email.message.

The email.generator module also provides a derived class, DecodedGenerator, which is like the Generator base class, except that non-text parts are not serialized, but are instead represented in the output stream by a string derived from a template filled in with information about the part.

+class email.generator.DecodedGenerator(outfp, mangle_from_=None, maxheaderlen=None, fmt=None, *, policy=None)
+

Act like Generator, except that for any subpart of the message passed to Generator.flatten(), if the subpart is of main type text, print the decoded payload of the subpart, and if the main type is not text, instead of printing it fill in the string fmt using information from the part and print the resulting filled-in string.

To fill in fmt, execute fmt % part_info, where part_info is a dictionary composed of the following keys and values:

  • +type – Full MIME type of the non-text part
  • +maintype – Main MIME type of the non-text part
  • +subtype – Sub-MIME type of the non-text part
  • +filename – Filename of the non-text part
  • +description – Description associated with the non-text part
  • +encoding – Content transfer encoding of the non-text part

If fmt is None, use the following default fmt:

“[Non-text (%(type)s) part of message omitted, filename %(filename)s]”

Optional _mangle_from_ and maxheaderlen are as with the Generator base class.

+

Footnotes

+1
+

This statement assumes that you use the appropriate setting for unixfrom, and that there are no email.policy settings calling for automatic adjustments (for example, refold_source must be none, which is not the default). It is also not 100% true, since if the message does not conform to the RFC standards occasionally information about the exact original text is lost during parsing error recovery. It is a goal to fix these latter edge cases when possible.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.generator.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.header.html b/devdocs/python~3.12/library%2Femail.header.html new file mode 100644 index 00000000..1faadc59 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.header.html @@ -0,0 +1,40 @@ +

email.header: Internationalized headers

Source code: Lib/email/header.py

This module is part of the legacy (Compat32) email API. In the current API encoding and decoding of headers is handled transparently by the dictionary-like API of the EmailMessage class. In addition to uses in legacy code, this module can be useful in applications that need to completely control the character sets used when encoding headers.

The remaining text in this section is the original documentation of the module.

RFC 2822 is the base standard that describes the format of email messages. It derives from the older RFC 822 standard which came into widespread use at a time when most email was composed of ASCII characters only. RFC 2822 is a specification written assuming email contains only 7-bit ASCII characters.

Of course, as email has been deployed worldwide, it has become internationalized, such that language specific character sets can now be used in email messages. The base standard still requires email messages to be transferred using only 7-bit ASCII characters, so a slew of RFCs have been written describing how to encode email containing non-ASCII characters into RFC 2822-compliant format. These RFCs include RFC 2045, RFC 2046, RFC 2047, and RFC 2231. The email package supports these standards in its email.header and email.charset modules.

If you want to include non-ASCII characters in your email headers, say in the Subject or To fields, you should use the Header class and assign the field in the Message object to an instance of Header instead of using a string for the header value. Import the Header class from the email.header module. For example:

>>> from email.message import Message
+>>> from email.header import Header
+>>> msg = Message()
+>>> h = Header('p\xf6stal', 'iso-8859-1')
+>>> msg['Subject'] = h
+>>> msg.as_string()
+'Subject: =?iso-8859-1?q?p=F6stal?=\n\n'
+

Notice here how we wanted the Subject field to contain a non-ASCII character? We did this by creating a Header instance and passing in the character set that the byte string was encoded in. When the subsequent Message instance was flattened, the Subject field was properly RFC 2047 encoded. MIME-aware mail readers would show this header using the embedded ISO-8859-1 character.

Here is the Header class description:

+class email.header.Header(s=None, charset=None, maxlinelen=None, header_name=None, continuation_ws=' ', errors='strict')
+

Create a MIME-compliant header that can contain strings in different character sets.

Optional s is the initial header value. If None (the default), the initial header value is not set. You can later append to the header with append() method calls. s may be an instance of bytes or str, but see the append() documentation for semantics.

Optional charset serves two purposes: it has the same meaning as the charset argument to the append() method. It also sets the default character set for all subsequent append() calls that omit the charset argument. If charset is not provided in the constructor (the default), the us-ascii character set is used both as s’s initial charset and as the default for subsequent append() calls.

The maximum line length can be specified explicitly via maxlinelen. For splitting the first line to a shorter value (to account for the field header which isn’t included in s, e.g. Subject) pass in the name of the field in header_name. The default maxlinelen is 76, and the default value for header_name is None, meaning it is not taken into account for the first line of a long, split header.

Optional continuation_ws must be RFC 2822-compliant folding whitespace, and is usually either a space or a hard tab character. This character will be prepended to continuation lines. continuation_ws defaults to a single space character.

Optional errors is passed straight through to the append() method.

+append(s, charset=None, errors='strict')
+

Append the string s to the MIME header.

Optional charset, if given, should be a Charset instance (see email.charset) or the name of a character set, which will be converted to a Charset instance. A value of None (the default) means that the charset given in the constructor is used.

s may be an instance of bytes or str. If it is an instance of bytes, then charset is the encoding of that byte string, and a UnicodeError will be raised if the string cannot be decoded with that character set.

If s is an instance of str, then charset is a hint specifying the character set of the characters in the string.

In either case, when producing an RFC 2822-compliant header using RFC 2047 rules, the string will be encoded using the output codec of the charset. If the string cannot be encoded using the output codec, a UnicodeError will be raised.

Optional errors is passed as the errors argument to the decode call if s is a byte string.

+
+encode(splitchars=';, \t', maxlinelen=None, linesep='\n')
+

Encode a message header into an RFC-compliant format, possibly wrapping long lines and encapsulating non-ASCII parts in base64 or quoted-printable encodings.

Optional splitchars is a string containing characters which should be given extra weight by the splitting algorithm during normal header wrapping. This is in very rough support of RFC 2822's ‘higher level syntactic breaks’: split points preceded by a splitchar are preferred during line splitting, with the characters preferred in the order in which they appear in the string. Space and tab may be included in the string to indicate whether preference should be given to one over the other as a split point when other split chars do not appear in the line being split. Splitchars does not affect RFC 2047 encoded lines.

maxlinelen, if given, overrides the instance’s value for the maximum line length.

linesep specifies the characters used to separate the lines of the folded header. It defaults to the most useful value for Python application code (\n), but \r\n can be specified in order to produce headers with RFC-compliant line separators.

Changed in version 3.2: Added the linesep argument.

+

The Header class also provides a number of methods to support standard operators and built-in functions.

+__str__()
+

Returns an approximation of the Header as a string, using an unlimited line length. All pieces are converted to unicode using the specified encoding and joined together appropriately. Any pieces with a charset of 'unknown-8bit' are decoded as ASCII using the 'replace' error handler.

Changed in version 3.2: Added handling for the 'unknown-8bit' charset.

+
+__eq__(other)
+

This method allows you to compare two Header instances for equality.

+
+__ne__(other)
+

This method allows you to compare two Header instances for inequality.

+
+

The email.header module also provides the following convenient functions.

+email.header.decode_header(header)
+

Decode a message header value without converting the character set. The header value is in header.

This function returns a list of (decoded_string, charset) pairs containing each of the decoded parts of the header. charset is None for non-encoded parts of the header, otherwise a lower case string containing the name of the character set specified in the encoded string.

Here’s an example:

>>> from email.header import decode_header
+>>> decode_header('=?iso-8859-1?q?p=F6stal?=')
+[(b'p\xf6stal', 'iso-8859-1')]
+
+
+email.header.make_header(decoded_seq, maxlinelen=None, header_name=None, continuation_ws=' ')
+

Create a Header instance from a sequence of pairs as returned by decode_header().

decode_header() takes a header value string and returns a sequence of pairs of the format (decoded_string, charset) where charset is the name of the character set.

This function takes one of those sequence of pairs and returns a Header instance. Optional maxlinelen, header_name, and continuation_ws are as in the Header constructor.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.header.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.headerregistry.html b/devdocs/python~3.12/library%2Femail.headerregistry.html new file mode 100644 index 00000000..68eae25e --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.headerregistry.html @@ -0,0 +1,160 @@ +

email.headerregistry: Custom Header Objects

Source code: Lib/email/headerregistry.py

New in version 3.6: 1

Headers are represented by customized subclasses of str. The particular class used to represent a given header is determined by the header_factory of the policy in effect when the headers are created. This section documents the particular header_factory implemented by the email package for handling RFC 5322 compliant email messages, which not only provides customized header objects for various header types, but also provides an extension mechanism for applications to add their own custom header types.

When using any of the policy objects derived from EmailPolicy, all headers are produced by HeaderRegistry and have BaseHeader as their last base class. Each header class has an additional base class that is determined by the type of the header. For example, many headers have the class UnstructuredHeader as their other base class. The specialized second class for a header is determined by the name of the header, using a lookup table stored in the HeaderRegistry. All of this is managed transparently for the typical application program, but interfaces are provided for modifying the default behavior for use by more complex applications.

The sections below first document the header base classes and their attributes, followed by the API for modifying the behavior of HeaderRegistry, and finally the support classes used to represent the data parsed from structured headers.

+class email.headerregistry.BaseHeader(name, value)
+

name and value are passed to BaseHeader from the header_factory call. The string value of any header object is the value fully decoded to unicode.

This base class defines the following read-only properties:

+name
+

The name of the header (the portion of the field before the ‘:’). This is exactly the value passed in the header_factory call for name; that is, case is preserved.

+
+defects
+

A tuple of HeaderDefect instances reporting any RFC compliance problems found during parsing. The email package tries to be complete about detecting compliance issues. See the errors module for a discussion of the types of defects that may be reported.

+
+max_count
+

The maximum number of headers of this type that can have the same name. A value of None means unlimited. The BaseHeader value for this attribute is None; it is expected that specialized header classes will override this value as needed.

+

BaseHeader also provides the following method, which is called by the email library code and should not in general be called by application programs:

+fold(*, policy)
+

Return a string containing linesep characters as required to correctly fold the header according to policy. A cte_type of 8bit will be treated as if it were 7bit, since headers may not contain arbitrary binary data. If utf8 is False, non-ASCII data will be RFC 2047 encoded.

+

BaseHeader by itself cannot be used to create a header object. It defines a protocol that each specialized header cooperates with in order to produce the header object. Specifically, BaseHeader requires that the specialized class provide a classmethod() named parse. This method is called as follows:

parse(string, kwds)
+

kwds is a dictionary containing one pre-initialized key, defects. defects is an empty list. The parse method should append any detected defects to this list. On return, the kwds dictionary must contain values for at least the keys decoded and defects. decoded should be the string value for the header (that is, the header value fully decoded to unicode). The parse method should assume that string may contain content-transfer-encoded parts, but should correctly handle all valid unicode characters as well so that it can parse un-encoded header values.

BaseHeader’s __new__ then creates the header instance, and calls its init method. The specialized class only needs to provide an init method if it wishes to set additional attributes beyond those provided by BaseHeader itself. Such an init method should look like this:

def init(self, /, *args, **kw):
+    self._myattr = kw.pop('myattr')
+    super().init(*args, **kw)
+

That is, anything extra that the specialized class puts in to the kwds dictionary should be removed and handled, and the remaining contents of kw (and args) passed to the BaseHeader init method.

+
+class email.headerregistry.UnstructuredHeader
+

An “unstructured” header is the default type of header in RFC 5322. Any header that does not have a specified syntax is treated as unstructured. The classic example of an unstructured header is the Subject header.

In RFC 5322, an unstructured header is a run of arbitrary text in the ASCII character set. RFC 2047, however, has an RFC 5322 compatible mechanism for encoding non-ASCII text as ASCII characters within a header value. When a value containing encoded words is passed to the constructor, the UnstructuredHeader parser converts such encoded words into unicode, following the RFC 2047 rules for unstructured text. The parser uses heuristics to attempt to decode certain non-compliant encoded words. Defects are registered in such cases, as well as defects for issues such as invalid characters within the encoded words or the non-encoded text.

This header type provides no additional attributes.

+
+class email.headerregistry.DateHeader
+

RFC 5322 specifies a very specific format for dates within email headers. The DateHeader parser recognizes that date format, as well as recognizing a number of variant forms that are sometimes found “in the wild”.

This header type provides the following additional attributes:

+datetime
+

If the header value can be recognized as a valid date of one form or another, this attribute will contain a datetime instance representing that date. If the timezone of the input date is specified as -0000 (indicating it is in UTC but contains no information about the source timezone), then datetime will be a naive datetime. If a specific timezone offset is found (including +0000), then datetime will contain an aware datetime that uses datetime.timezone to record the timezone offset.

+

The decoded value of the header is determined by formatting the datetime according to the RFC 5322 rules; that is, it is set to:

email.utils.format_datetime(self.datetime)
+

When creating a DateHeader, value may be datetime instance. This means, for example, that the following code is valid and does what one would expect:

msg['Date'] = datetime(2011, 7, 15, 21)
+

Because this is a naive datetime it will be interpreted as a UTC timestamp, and the resulting value will have a timezone of -0000. Much more useful is to use the localtime() function from the utils module:

msg['Date'] = utils.localtime()
+

This example sets the date header to the current time and date using the current timezone offset.

+
+class email.headerregistry.AddressHeader
+

Address headers are one of the most complex structured header types. The AddressHeader class provides a generic interface to any address header.

This header type provides the following additional attributes:

+groups
+

A tuple of Group objects encoding the addresses and groups found in the header value. Addresses that are not part of a group are represented in this list as single-address Groups whose display_name is None.

+
+addresses
+

A tuple of Address objects encoding all of the individual addresses from the header value. If the header value contains any groups, the individual addresses from the group are included in the list at the point where the group occurs in the value (that is, the list of addresses is “flattened” into a one dimensional list).

+

The decoded value of the header will have all encoded words decoded to unicode. idna encoded domain names are also decoded to unicode. The decoded value is set by joining the str value of the elements of the groups attribute with ', +'.

A list of Address and Group objects in any combination may be used to set the value of an address header. Group objects whose display_name is None will be interpreted as single addresses, which allows an address list to be copied with groups intact by using the list obtained from the groups attribute of the source header.

+
+class email.headerregistry.SingleAddressHeader
+

A subclass of AddressHeader that adds one additional attribute:

+address
+

The single address encoded by the header value. If the header value actually contains more than one address (which would be a violation of the RFC under the default policy), accessing this attribute will result in a ValueError.

+
+

Many of the above classes also have a Unique variant (for example, UniqueUnstructuredHeader). The only difference is that in the Unique variant, max_count is set to 1.

+class email.headerregistry.MIMEVersionHeader
+

There is really only one valid value for the MIME-Version header, and that is 1.0. For future proofing, this header class supports other valid version numbers. If a version number has a valid value per RFC 2045, then the header object will have non-None values for the following attributes:

+version
+

The version number as a string, with any whitespace and/or comments removed.

+
+major
+

The major version number as an integer

+
+minor
+

The minor version number as an integer

+
+
+class email.headerregistry.ParameterizedMIMEHeader
+

MIME headers all start with the prefix ‘Content-’. Each specific header has a certain value, described under the class for that header. Some can also take a list of supplemental parameters, which have a common format. This class serves as a base for all the MIME headers that take parameters.

+params
+

A dictionary mapping parameter names to parameter values.

+
+
+class email.headerregistry.ContentTypeHeader
+

A ParameterizedMIMEHeader class that handles the Content-Type header.

+content_type
+

The content type string, in the form maintype/subtype.

+
+maintype
+
+subtype
+
+
+class email.headerregistry.ContentDispositionHeader
+

A ParameterizedMIMEHeader class that handles the Content-Disposition header.

+content_disposition
+

inline and attachment are the only valid values in common use.

+
+
+class email.headerregistry.ContentTransferEncoding
+

Handles the Content-Transfer-Encoding header.

+cte
+

Valid values are 7bit, 8bit, base64, and quoted-printable. See RFC 2045 for more information.

+
+
+class email.headerregistry.HeaderRegistry(base_class=BaseHeader, default_class=UnstructuredHeader, use_default_map=True)
+

This is the factory used by EmailPolicy by default. HeaderRegistry builds the class used to create a header instance dynamically, using base_class and a specialized class retrieved from a registry that it holds. When a given header name does not appear in the registry, the class specified by default_class is used as the specialized class. When use_default_map is True (the default), the standard mapping of header names to classes is copied in to the registry during initialization. base_class is always the last class in the generated class’s __bases__ list.

The default mappings are:

subject
+

UniqueUnstructuredHeader

date
+

UniqueDateHeader

resent-date
+

DateHeader

orig-date
+

UniqueDateHeader

sender
+

UniqueSingleAddressHeader

resent-sender
+

SingleAddressHeader

to
+

UniqueAddressHeader

resent-to
+

AddressHeader

cc
+

UniqueAddressHeader

resent-cc
+

AddressHeader

bcc
+

UniqueAddressHeader

resent-bcc
+

AddressHeader

from
+

UniqueAddressHeader

resent-from
+

AddressHeader

reply-to
+

UniqueAddressHeader

mime-version
+

MIMEVersionHeader

content-type
+

ContentTypeHeader

content-disposition
+

ContentDispositionHeader

content-transfer-encoding
+

ContentTransferEncodingHeader

message-id
+

MessageIDHeader

HeaderRegistry has the following methods:

+map_to_type(self, name, cls)
+

name is the name of the header to be mapped. It will be converted to lower case in the registry. cls is the specialized class to be used, along with base_class, to create the class used to instantiate headers that match name.

+
+__getitem__(name)
+

Construct and return a class to handle creating a name header.

+
+__call__(name, value)
+

Retrieves the specialized header associated with name from the registry (using default_class if name does not appear in the registry) and composes it with base_class to produce a class, calls the constructed class’s constructor, passing it the same argument list, and finally returns the class instance created thereby.

+
+

The following classes are the classes used to represent data parsed from structured headers and can, in general, be used by an application program to construct structured values to assign to specific headers.

+class email.headerregistry.Address(display_name='', username='', domain='', addr_spec=None)
+

The class used to represent an email address. The general form of an address is:

[display_name] <username@domain>
+

or:

username@domain
+

where each part must conform to specific syntax rules spelled out in RFC 5322.

As a convenience addr_spec can be specified instead of username and domain, in which case username and domain will be parsed from the addr_spec. An addr_spec must be a properly RFC quoted string; if it is not Address will raise an error. Unicode characters are allowed and will be property encoded when serialized. However, per the RFCs, unicode is not allowed in the username portion of the address.

+display_name
+

The display name portion of the address, if any, with all quoting removed. If the address does not have a display name, this attribute will be an empty string.

+
+username
+

The username portion of the address, with all quoting removed.

+
+domain
+

The domain portion of the address.

+
+addr_spec
+

The username@domain portion of the address, correctly quoted for use as a bare address (the second form shown above). This attribute is not mutable.

+
+__str__()
+

The str value of the object is the address quoted according to RFC 5322 rules, but with no Content Transfer Encoding of any non-ASCII characters.

+

To support SMTP (RFC 5321), Address handles one special case: if username and domain are both the empty string (or None), then the string value of the Address is <>.

+
+class email.headerregistry.Group(display_name=None, addresses=None)
+

The class used to represent an address group. The general form of an address group is:

display_name: [address-list];
+

As a convenience for processing lists of addresses that consist of a mixture of groups and single addresses, a Group may also be used to represent single addresses that are not part of a group by setting display_name to None and providing a list of the single address as addresses.

+display_name
+

The display_name of the group. If it is None and there is exactly one Address in addresses, then the Group represents a single address that is not in a group.

+
+addresses
+

A possibly empty tuple of Address objects representing the addresses in the group.

+
+__str__()
+

The str value of a Group is formatted according to RFC 5322, but with no Content Transfer Encoding of any non-ASCII characters. If display_name is none and there is a single Address in the addresses list, the str value will be the same as the str of that single Address.

+
+

Footnotes

+1
+

Originally added in 3.3 as a provisional module

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.headerregistry.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.html b/devdocs/python~3.12/library%2Femail.html new file mode 100644 index 00000000..bfb78fa0 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.html @@ -0,0 +1,28 @@ +

email — An email and MIME handling package

Source code: Lib/email/__init__.py

The email package is a library for managing email messages. It is specifically not designed to do any sending of email messages to SMTP (RFC 2821), NNTP, or other servers; those are functions of modules such as smtplib and nntplib. The email package attempts to be as RFC-compliant as possible, supporting RFC 5322 and RFC 6532, as well as such MIME-related RFCs as RFC 2045, RFC 2046, RFC 2047, RFC 2183, and RFC 2231.

The overall structure of the email package can be divided into three major components, plus a fourth component that controls the behavior of the other components.

The central component of the package is an “object model” that represents email messages. An application interacts with the package primarily through the object model interface defined in the message sub-module. The application can use this API to ask questions about an existing email, to construct a new email, or to add or remove email subcomponents that themselves use the same object model interface. That is, following the nature of email messages and their MIME subcomponents, the email object model is a tree structure of objects that all provide the EmailMessage API.

The other two major components of the package are the parser and the generator. The parser takes the serialized version of an email message (a stream of bytes) and converts it into a tree of EmailMessage objects. The generator takes an EmailMessage and turns it back into a serialized byte stream. (The parser and generator also handle streams of text characters, but this usage is discouraged as it is too easy to end up with messages that are not valid in one way or another.)

The control component is the policy module. Every EmailMessage, every generator, and every parser has an associated policy object that controls its behavior. Usually an application only needs to specify the policy when an EmailMessage is created, either by directly instantiating an EmailMessage to create a new email, or by parsing an input stream using a parser. But the policy can be changed when the message is serialized using a generator. This allows, for example, a generic email message to be parsed from disk, but to serialize it using standard SMTP settings when sending it to an email server.

The email package does its best to hide the details of the various governing RFCs from the application. Conceptually the application should be able to treat the email message as a structured tree of unicode text and binary attachments, without having to worry about how these are represented when serialized. In practice, however, it is often necessary to be aware of at least some of the rules governing MIME messages and their structure, specifically the names and nature of the MIME “content types” and how they identify multipart documents. For the most part this knowledge should only be required for more complex applications, and even then it should only be the high level structure in question, and not the details of how those structures are represented. Since MIME content types are used widely in modern internet software (not just email), this will be a familiar concept to many programmers.

The following sections describe the functionality of the email package. We start with the message object model, which is the primary interface an application will use, and follow that with the parser and generator components. Then we cover the policy controls, which completes the treatment of the main components of the library.

The next three sections cover the exceptions the package may raise and the defects (non-compliance with the RFCs) that the parser may detect. Then we cover the headerregistry and the contentmanager sub-components, which provide tools for doing more detailed manipulation of headers and payloads, respectively. Both of these components contain features relevant to consuming and producing non-trivial messages, but also document their extensibility APIs, which will be of interest to advanced applications.

Following those is a set of examples of using the fundamental parts of the APIs covered in the preceding sections.

The foregoing represent the modern (unicode friendly) API of the email package. The remaining sections, starting with the Message class, cover the legacy compat32 API that deals much more directly with the details of how email messages are represented. The compat32 API does not hide the details of the RFCs from the application, but for applications that need to operate at that level, they can be useful tools. This documentation is also relevant for applications that are still using the compat32 API for backward compatibility reasons.

Changed in version 3.6: Docs reorganized and rewritten to promote the new EmailMessage/EmailPolicy API.

Contents of the email package documentation:

Legacy API:

See also

+Module smtplib +
+
+

SMTP (Simple Mail Transport Protocol) client

+Module poplib +
+
+

POP (Post Office Protocol) client

+Module imaplib +
+
+

IMAP (Internet Message Access Protocol) client

+Module nntplib +
+
+

NNTP (Net News Transport Protocol) client

+Module mailbox +
+
+

Tools for creating, reading, and managing collections of messages on disk using a variety standard formats.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.iterators.html b/devdocs/python~3.12/library%2Femail.iterators.html new file mode 100644 index 00000000..162db283 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.iterators.html @@ -0,0 +1,32 @@ +

email.iterators: Iterators

Source code: Lib/email/iterators.py

Iterating over a message object tree is fairly easy with the Message.walk method. The email.iterators module provides some useful higher level iterations over message object trees.

+email.iterators.body_line_iterator(msg, decode=False)
+

This iterates over all the payloads in all the subparts of msg, returning the string payloads line-by-line. It skips over all the subpart headers, and it skips over any subpart with a payload that isn’t a Python string. This is somewhat equivalent to reading the flat text representation of the message from a file using readline(), skipping over all the intervening headers.

Optional decode is passed through to Message.get_payload.

+
+email.iterators.typed_subpart_iterator(msg, maintype='text', subtype=None)
+

This iterates over all the subparts of msg, returning only those subparts that match the MIME type specified by maintype and subtype.

Note that subtype is optional; if omitted, then subpart MIME type matching is done only with the main type. maintype is optional too; it defaults to text.

Thus, by default typed_subpart_iterator() returns each subpart that has a MIME type of text/*.

+

The following function has been added as a useful debugging tool. It should not be considered part of the supported public interface for the package.

+email.iterators._structure(msg, fp=None, level=0, include_default=False)
+

Prints an indented representation of the content types of the message object structure. For example:

>>> msg = email.message_from_file(somefile)
+>>> _structure(msg)
+multipart/mixed
+    text/plain
+    text/plain
+    multipart/digest
+        message/rfc822
+            text/plain
+        message/rfc822
+            text/plain
+        message/rfc822
+            text/plain
+        message/rfc822
+            text/plain
+        message/rfc822
+            text/plain
+    text/plain
+

Optional fp is a file-like object to print the output to. It must be suitable for Python’s print() function. level is used internally. include_default, if true, prints the default type as well.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.iterators.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.message.html b/devdocs/python~3.12/library%2Femail.message.html new file mode 100644 index 00000000..17eab6b0 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.message.html @@ -0,0 +1,200 @@ +

email.message: Representing an email message

Source code: Lib/email/message.py

New in version 3.6: 1

The central class in the email package is the EmailMessage class, imported from the email.message module. It is the base class for the email object model. EmailMessage provides the core functionality for setting and querying header fields, for accessing message bodies, and for creating or modifying structured messages.

An email message consists of headers and a payload (which is also referred to as the content). Headers are RFC 5322 or RFC 6532 style field names and values, where the field name and value are separated by a colon. The colon is not part of either the field name or the field value. The payload may be a simple text message, or a binary object, or a structured sequence of sub-messages each with their own set of headers and their own payload. The latter type of payload is indicated by the message having a MIME type such as multipart/* or message/rfc822.

The conceptual model provided by an EmailMessage object is that of an ordered dictionary of headers coupled with a payload that represents the RFC 5322 body of the message, which might be a list of sub-EmailMessage objects. In addition to the normal dictionary methods for accessing the header names and values, there are methods for accessing specialized information from the headers (for example the MIME content type), for operating on the payload, for generating a serialized version of the message, and for recursively walking over the object tree.

The EmailMessage dictionary-like interface is indexed by the header names, which must be ASCII values. The values of the dictionary are strings with some extra methods. Headers are stored and returned in case-preserving form, but field names are matched case-insensitively. Unlike a real dict, there is an ordering to the keys, and there can be duplicate keys. Additional methods are provided for working with headers that have duplicate keys.

The payload is either a string or bytes object, in the case of simple message objects, or a list of EmailMessage objects, for MIME container documents such as multipart/* and message/rfc822 message objects.

+class email.message.EmailMessage(policy=default)
+

If policy is specified use the rules it specifies to update and serialize the representation of the message. If policy is not set, use the default policy, which follows the rules of the email RFCs except for line endings (instead of the RFC mandated \r\n, it uses the Python standard \n line endings). For more information see the policy documentation.

+as_string(unixfrom=False, maxheaderlen=None, policy=None)
+

Return the entire message flattened as a string. When optional unixfrom is true, the envelope header is included in the returned string. unixfrom defaults to False. For backward compatibility with the base Message class maxheaderlen is accepted, but defaults to None, which means that by default the line length is controlled by the max_line_length of the policy. The policy argument may be used to override the default policy obtained from the message instance. This can be used to control some of the formatting produced by the method, since the specified policy will be passed to the Generator.

Flattening the message may trigger changes to the EmailMessage if defaults need to be filled in to complete the transformation to a string (for example, MIME boundaries may be generated or modified).

Note that this method is provided as a convenience and may not be the most useful way to serialize messages in your application, especially if you are dealing with multiple messages. See email.generator.Generator for a more flexible API for serializing messages. Note also that this method is restricted to producing messages serialized as “7 bit clean” when utf8 is False, which is the default.

Changed in version 3.6: the default behavior when maxheaderlen is not specified was changed from defaulting to 0 to defaulting to the value of max_line_length from the policy.

+
+__str__()
+

Equivalent to as_string(policy=self.policy.clone(utf8=True)). Allows str(msg) to produce a string containing the serialized message in a readable format.

Changed in version 3.4: the method was changed to use utf8=True, thus producing an RFC 6531-like message representation, instead of being a direct alias for as_string().

+
+as_bytes(unixfrom=False, policy=None)
+

Return the entire message flattened as a bytes object. When optional unixfrom is true, the envelope header is included in the returned string. unixfrom defaults to False. The policy argument may be used to override the default policy obtained from the message instance. This can be used to control some of the formatting produced by the method, since the specified policy will be passed to the BytesGenerator.

Flattening the message may trigger changes to the EmailMessage if defaults need to be filled in to complete the transformation to a string (for example, MIME boundaries may be generated or modified).

Note that this method is provided as a convenience and may not be the most useful way to serialize messages in your application, especially if you are dealing with multiple messages. See email.generator.BytesGenerator for a more flexible API for serializing messages.

+
+__bytes__()
+

Equivalent to as_bytes(). Allows bytes(msg) to produce a bytes object containing the serialized message.

+
+is_multipart()
+

Return True if the message’s payload is a list of sub-EmailMessage objects, otherwise return False. When is_multipart() returns False, the payload should be a string object (which might be a CTE encoded binary payload). Note that is_multipart() returning True does not necessarily mean that “msg.get_content_maintype() == ‘multipart’” will return the True. For example, is_multipart will return True when the EmailMessage is of type message/rfc822.

+
+set_unixfrom(unixfrom)
+

Set the message’s envelope header to unixfrom, which should be a string. (See mboxMessage for a brief description of this header.)

+
+get_unixfrom()
+

Return the message’s envelope header. Defaults to None if the envelope header was never set.

+

The following methods implement the mapping-like interface for accessing the message’s headers. Note that there are some semantic differences between these methods and a normal mapping (i.e. dictionary) interface. For example, in a dictionary there are no duplicate keys, but here there may be duplicate message headers. Also, in dictionaries there is no guaranteed order to the keys returned by keys(), but in an EmailMessage object, headers are always returned in the order they appeared in the original message, or in which they were added to the message later. Any header deleted and then re-added is always appended to the end of the header list.

These semantic differences are intentional and are biased toward convenience in the most common use cases.

Note that in all cases, any envelope header present in the message is not included in the mapping interface.

+__len__()
+

Return the total number of headers, including duplicates.

+
+__contains__(name)
+

Return True if the message object has a field named name. Matching is done without regard to case and name does not include the trailing colon. Used for the in operator. For example:

if 'message-id' in myMessage:
+   print('Message-ID:', myMessage['message-id'])
+
+
+__getitem__(name)
+

Return the value of the named header field. name does not include the colon field separator. If the header is missing, None is returned; a KeyError is never raised.

Note that if the named field appears more than once in the message’s headers, exactly which of those field values will be returned is undefined. Use the get_all() method to get the values of all the extant headers named name.

Using the standard (non-compat32) policies, the returned value is an instance of a subclass of email.headerregistry.BaseHeader.

+
+__setitem__(name, val)
+

Add a header to the message with field name name and value val. The field is appended to the end of the message’s existing headers.

Note that this does not overwrite or delete any existing header with the same name. If you want to ensure that the new header is the only one present in the message with field name name, delete the field first, e.g.:

del msg['subject']
+msg['subject'] = 'Python roolz!'
+

If the policy defines certain headers to be unique (as the standard policies do), this method may raise a ValueError when an attempt is made to assign a value to such a header when one already exists. This behavior is intentional for consistency’s sake, but do not depend on it as we may choose to make such assignments do an automatic deletion of the existing header in the future.

+
+__delitem__(name)
+

Delete all occurrences of the field with name name from the message’s headers. No exception is raised if the named field isn’t present in the headers.

+
+keys()
+

Return a list of all the message’s header field names.

+
+values()
+

Return a list of all the message’s field values.

+
+items()
+

Return a list of 2-tuples containing all the message’s field headers and values.

+
+get(name, failobj=None)
+

Return the value of the named header field. This is identical to __getitem__() except that optional failobj is returned if the named header is missing (failobj defaults to None).

+

Here are some additional useful header related methods:

+get_all(name, failobj=None)
+

Return a list of all the values for the field named name. If there are no such named headers in the message, failobj is returned (defaults to None).

+
+add_header(_name, _value, **_params)
+

Extended header setting. This method is similar to __setitem__() except that additional header parameters can be provided as keyword arguments. _name is the header field to add and _value is the primary value for the header.

For each item in the keyword argument dictionary _params, the key is taken as the parameter name, with underscores converted to dashes (since dashes are illegal in Python identifiers). Normally, the parameter will be added as key="value" unless the value is None, in which case only the key will be added.

If the value contains non-ASCII characters, the charset and language may be explicitly controlled by specifying the value as a three tuple in the format (CHARSET, LANGUAGE, VALUE), where CHARSET is a string naming the charset to be used to encode the value, LANGUAGE can usually be set to None or the empty string (see RFC 2231 for other possibilities), and VALUE is the string value containing non-ASCII code points. If a three tuple is not passed and the value contains non-ASCII characters, it is automatically encoded in RFC 2231 format using a CHARSET of utf-8 and a LANGUAGE of None.

Here is an example:

msg.add_header('Content-Disposition', 'attachment', filename='bud.gif')
+

This will add a header that looks like

Content-Disposition: attachment; filename="bud.gif"
+

An example of the extended interface with non-ASCII characters:

msg.add_header('Content-Disposition', 'attachment',
+               filename=('iso-8859-1', '', 'Fußballer.ppt'))
+
+
+replace_header(_name, _value)
+

Replace a header. Replace the first header found in the message that matches _name, retaining header order and field name case of the original header. If no matching header is found, raise a KeyError.

+
+get_content_type()
+

Return the message’s content type, coerced to lower case of the form maintype/subtype. If there is no Content-Type header in the message return the value returned by get_default_type(). If the Content-Type header is invalid, return text/plain.

(According to RFC 2045, messages always have a default type, get_content_type() will always return a value. RFC 2045 defines a message’s default type to be text/plain unless it appears inside a multipart/digest container, in which case it would be message/rfc822. If the Content-Type header has an invalid type specification, RFC 2045 mandates that the default type be text/plain.)

+
+get_content_maintype()
+

Return the message’s main content type. This is the maintype part of the string returned by get_content_type().

+
+get_content_subtype()
+

Return the message’s sub-content type. This is the subtype part of the string returned by get_content_type().

+
+get_default_type()
+

Return the default content type. Most messages have a default content type of text/plain, except for messages that are subparts of multipart/digest containers. Such subparts have a default content type of message/rfc822.

+
+set_default_type(ctype)
+

Set the default content type. ctype should either be text/plain or message/rfc822, although this is not enforced. The default content type is not stored in the Content-Type header, so it only affects the return value of the get_content_type methods when no Content-Type header is present in the message.

+
+set_param(param, value, header='Content-Type', requote=True, charset=None, language='', replace=False)
+

Set a parameter in the Content-Type header. If the parameter already exists in the header, replace its value with value. When header is Content-Type (the default) and the header does not yet exist in the message, add it, set its value to text/plain, and append the new parameter value. Optional header specifies an alternative header to Content-Type.

If the value contains non-ASCII characters, the charset and language may be explicitly specified using the optional charset and language parameters. Optional language specifies the RFC 2231 language, defaulting to the empty string. Both charset and language should be strings. The default is to use the utf8 charset and None for the language.

If replace is False (the default) the header is moved to the end of the list of headers. If replace is True, the header will be updated in place.

Use of the requote parameter with EmailMessage objects is deprecated.

Note that existing parameter values of headers may be accessed through the params attribute of the header value (for example, msg['Content-Type'].params['charset']).

Changed in version 3.4: replace keyword was added.

+
+del_param(param, header='content-type', requote=True)
+

Remove the given parameter completely from the Content-Type header. The header will be re-written in place without the parameter or its value. Optional header specifies an alternative to Content-Type.

Use of the requote parameter with EmailMessage objects is deprecated.

+
+get_filename(failobj=None)
+

Return the value of the filename parameter of the Content-Disposition header of the message. If the header does not have a filename parameter, this method falls back to looking for the name parameter on the Content-Type header. If neither is found, or the header is missing, then failobj is returned. The returned string will always be unquoted as per email.utils.unquote().

+
+get_boundary(failobj=None)
+

Return the value of the boundary parameter of the Content-Type header of the message, or failobj if either the header is missing, or has no boundary parameter. The returned string will always be unquoted as per email.utils.unquote().

+
+set_boundary(boundary)
+

Set the boundary parameter of the Content-Type header to boundary. set_boundary() will always quote boundary if necessary. A HeaderParseError is raised if the message object has no Content-Type header.

Note that using this method is subtly different from deleting the old Content-Type header and adding a new one with the new boundary via add_header(), because set_boundary() preserves the order of the Content-Type header in the list of headers.

+
+get_content_charset(failobj=None)
+

Return the charset parameter of the Content-Type header, coerced to lower case. If there is no Content-Type header, or if that header has no charset parameter, failobj is returned.

+
+get_charsets(failobj=None)
+

Return a list containing the character set names in the message. If the message is a multipart, then the list will contain one element for each subpart in the payload, otherwise, it will be a list of length 1.

Each item in the list will be a string which is the value of the charset parameter in the Content-Type header for the represented subpart. If the subpart has no Content-Type header, no charset parameter, or is not of the text main MIME type, then that item in the returned list will be failobj.

+
+is_attachment()
+

Return True if there is a Content-Disposition header and its (case insensitive) value is attachment, False otherwise.

Changed in version 3.4.2: is_attachment is now a method instead of a property, for consistency with is_multipart().

+
+get_content_disposition()
+

Return the lowercased value (without parameters) of the message’s Content-Disposition header if it has one, or None. The possible values for this method are inline, attachment or None if the message follows RFC 2183.

New in version 3.5.

+

The following methods relate to interrogating and manipulating the content (payload) of the message.

+walk()
+

The walk() method is an all-purpose generator which can be used to iterate over all the parts and subparts of a message object tree, in depth-first traversal order. You will typically use walk() as the iterator in a for loop; each iteration returns the next subpart.

Here’s an example that prints the MIME type of every part of a multipart message structure:

>>> for part in msg.walk():
+...     print(part.get_content_type())
+multipart/report
+text/plain
+message/delivery-status
+text/plain
+text/plain
+message/rfc822
+text/plain
+

walk iterates over the subparts of any part where is_multipart() returns True, even though msg.get_content_maintype() == 'multipart' may return False. We can see this in our example by making use of the _structure debug helper function:

>>> from email.iterators import _structure
+>>> for part in msg.walk():
+...     print(part.get_content_maintype() == 'multipart',
+...           part.is_multipart())
+True True
+False False
+False True
+False False
+False False
+False True
+False False
+>>> _structure(msg)
+multipart/report
+    text/plain
+    message/delivery-status
+        text/plain
+        text/plain
+    message/rfc822
+        text/plain
+

Here the message parts are not multiparts, but they do contain subparts. is_multipart() returns True and walk descends into the subparts.

+
+get_body(preferencelist=('related', 'html', 'plain'))
+

Return the MIME part that is the best candidate to be the “body” of the message.

preferencelist must be a sequence of strings from the set related, html, and plain, and indicates the order of preference for the content type of the part returned.

Start looking for candidate matches with the object on which the get_body method is called.

If related is not included in preferencelist, consider the root part (or subpart of the root part) of any related encountered as a candidate if the (sub-)part matches a preference.

When encountering a multipart/related, check the start parameter and if a part with a matching Content-ID is found, consider only it when looking for candidate matches. Otherwise consider only the first (default root) part of the multipart/related.

If a part has a Content-Disposition header, only consider the part a candidate match if the value of the header is inline.

If none of the candidates matches any of the preferences in preferencelist, return None.

Notes: (1) For most applications the only preferencelist combinations that really make sense are ('plain',), ('html', 'plain'), and the default ('related', 'html', 'plain'). (2) Because matching starts with the object on which get_body is called, calling get_body on a multipart/related will return the object itself unless preferencelist has a non-default value. (3) Messages (or message parts) that do not specify a Content-Type or whose Content-Type header is invalid will be treated as if they are of type text/plain, which may occasionally cause get_body to return unexpected results.

+
+iter_attachments()
+

Return an iterator over all of the immediate sub-parts of the message that are not candidate “body” parts. That is, skip the first occurrence of each of text/plain, text/html, multipart/related, or multipart/alternative (unless they are explicitly marked as attachments via Content-Disposition: attachment), and return all remaining parts. When applied directly to a multipart/related, return an iterator over the all the related parts except the root part (ie: the part pointed to by the start parameter, or the first part if there is no start parameter or the start parameter doesn’t match the Content-ID of any of the parts). When applied directly to a multipart/alternative or a non-multipart, return an empty iterator.

+
+iter_parts()
+

Return an iterator over all of the immediate sub-parts of the message, which will be empty for a non-multipart. (See also walk().)

+
+get_content(*args, content_manager=None, **kw)
+

Call the get_content() method of the content_manager, passing self as the message object, and passing along any other arguments or keywords as additional arguments. If content_manager is not specified, use the content_manager specified by the current policy.

+
+set_content(*args, content_manager=None, **kw)
+

Call the set_content() method of the content_manager, passing self as the message object, and passing along any other arguments or keywords as additional arguments. If content_manager is not specified, use the content_manager specified by the current policy.

+
+

Convert a non-multipart message into a multipart/related message, moving any existing Content- headers and payload into a (new) first part of the multipart. If boundary is specified, use it as the boundary string in the multipart, otherwise leave the boundary to be automatically created when it is needed (for example, when the message is serialized).

+
+make_alternative(boundary=None)
+

Convert a non-multipart or a multipart/related into a multipart/alternative, moving any existing Content- headers and payload into a (new) first part of the multipart. If boundary is specified, use it as the boundary string in the multipart, otherwise leave the boundary to be automatically created when it is needed (for example, when the message is serialized).

+
+make_mixed(boundary=None)
+

Convert a non-multipart, a multipart/related, or a multipart-alternative into a multipart/mixed, moving any existing Content- headers and payload into a (new) first part of the multipart. If boundary is specified, use it as the boundary string in the multipart, otherwise leave the boundary to be automatically created when it is needed (for example, when the message is serialized).

+
+

If the message is a multipart/related, create a new message object, pass all of the arguments to its set_content() method, and attach() it to the multipart. If the message is a non-multipart, call make_related() and then proceed as above. If the message is any other type of multipart, raise a TypeError. If content_manager is not specified, use the content_manager specified by the current policy. If the added part has no Content-Disposition header, add one with the value inline.

+
+add_alternative(*args, content_manager=None, **kw)
+

If the message is a multipart/alternative, create a new message object, pass all of the arguments to its set_content() method, and attach() it to the multipart. If the message is a non-multipart or multipart/related, call make_alternative() and then proceed as above. If the message is any other type of multipart, raise a TypeError. If content_manager is not specified, use the content_manager specified by the current policy.

+
+add_attachment(*args, content_manager=None, **kw)
+

If the message is a multipart/mixed, create a new message object, pass all of the arguments to its set_content() method, and attach() it to the multipart. If the message is a non-multipart, multipart/related, or multipart/alternative, call make_mixed() and then proceed as above. If content_manager is not specified, use the content_manager specified by the current policy. If the added part has no Content-Disposition header, add one with the value attachment. This method can be used both for explicit attachments (Content-Disposition: attachment) and inline attachments (Content-Disposition: inline), by passing appropriate options to the content_manager.

+
+clear()
+

Remove the payload and all of the headers.

+
+clear_content()
+

Remove the payload and all of the !Content- headers, leaving all other headers intact and in their original order.

+

EmailMessage objects have the following instance attributes:

+preamble
+

The format of a MIME document allows for some text between the blank line following the headers, and the first multipart boundary string. Normally, this text is never visible in a MIME-aware mail reader because it falls outside the standard MIME armor. However, when viewing the raw text of the message, or when viewing the message in a non-MIME aware reader, this text can become visible.

The preamble attribute contains this leading extra-armor text for MIME documents. When the Parser discovers some text after the headers but before the first boundary string, it assigns this text to the message’s preamble attribute. When the Generator is writing out the plain text representation of a MIME message, and it finds the message has a preamble attribute, it will write this text in the area between the headers and the first boundary. See email.parser and email.generator for details.

Note that if the message object has no preamble, the preamble attribute will be None.

+
+epilogue
+

The epilogue attribute acts the same way as the preamble attribute, except that it contains text that appears between the last boundary and the end of the message. As with the preamble, if there is no epilog text this attribute will be None.

+
+defects
+

The defects attribute contains a list of all the problems found when parsing this message. See email.errors for a detailed description of the possible parsing defects.

+
+
+class email.message.MIMEPart(policy=default)
+

This class represents a subpart of a MIME message. It is identical to EmailMessage, except that no MIME-Version headers are added when set_content() is called, since sub-parts do not need their own MIME-Version headers.

+

Footnotes

+1
+

Originally added in 3.4 as a provisional module. Docs for legacy message class moved to email.message.Message: Representing an email message using the compat32 API.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.message.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.mime.html b/devdocs/python~3.12/library%2Femail.mime.html new file mode 100644 index 00000000..404f9607 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.mime.html @@ -0,0 +1,30 @@ +

email.mime: Creating email and MIME objects from scratch

Source code: Lib/email/mime/

This module is part of the legacy (Compat32) email API. Its functionality is partially replaced by the contentmanager in the new API, but in certain applications these classes may still be useful, even in non-legacy code.

Ordinarily, you get a message object structure by passing a file or some text to a parser, which parses the text and returns the root message object. However you can also build a complete message structure from scratch, or even individual Message objects by hand. In fact, you can also take an existing structure and add new Message objects, move them around, etc. This makes a very convenient interface for slicing-and-dicing MIME messages.

You can create a new object structure by creating Message instances, adding attachments and all the appropriate headers manually. For MIME messages though, the email package provides some convenient subclasses to make things easier.

Here are the classes:

+class email.mime.base.MIMEBase(_maintype, _subtype, *, policy=compat32, **_params)
+

Module: email.mime.base

This is the base class for all the MIME-specific subclasses of Message. Ordinarily you won’t create instances specifically of MIMEBase, although you could. MIMEBase is provided primarily as a convenient base class for more specific MIME-aware subclasses.

_maintype is the Content-Type major type (e.g. text or image), and _subtype is the Content-Type minor type (e.g. plain or gif). _params is a parameter key/value dictionary and is passed directly to Message.add_header.

If policy is specified, (defaults to the compat32 policy) it will be passed to Message.

The MIMEBase class always adds a Content-Type header (based on _maintype, _subtype, and _params), and a MIME-Version header (always set to 1.0).

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.nonmultipart.MIMENonMultipart
+

Module: email.mime.nonmultipart

A subclass of MIMEBase, this is an intermediate base class for MIME messages that are not multipart. The primary purpose of this class is to prevent the use of the attach() method, which only makes sense for multipart messages. If attach() is called, a MultipartConversionError exception is raised.

+
+class email.mime.multipart.MIMEMultipart(_subtype='mixed', boundary=None, _subparts=None, *, policy=compat32, **_params)
+

Module: email.mime.multipart

A subclass of MIMEBase, this is an intermediate base class for MIME messages that are multipart. Optional _subtype defaults to mixed, but can be used to specify the subtype of the message. A Content-Type header of multipart/_subtype will be added to the message object. A MIME-Version header will also be added.

Optional boundary is the multipart boundary string. When None (the default), the boundary is calculated when needed (for example, when the message is serialized).

_subparts is a sequence of initial subparts for the payload. It must be possible to convert this sequence to a list. You can always attach new subparts to the message by using the Message.attach method.

Optional policy argument defaults to compat32.

Additional parameters for the Content-Type header are taken from the keyword arguments, or passed into the _params argument, which is a keyword dictionary.

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.application.MIMEApplication(_data, _subtype='octet-stream', _encoder=email.encoders.encode_base64, *, policy=compat32, **_params)
+

Module: email.mime.application

A subclass of MIMENonMultipart, the MIMEApplication class is used to represent MIME message objects of major type application. _data contains the bytes for the raw application data. Optional _subtype specifies the MIME subtype and defaults to octet-stream.

Optional _encoder is a callable (i.e. function) which will perform the actual encoding of the data for transport. This callable takes one argument, which is the MIMEApplication instance. It should use get_payload() and set_payload() to change the payload to encoded form. It should also add any Content-Transfer-Encoding or other headers to the message object as necessary. The default encoding is base64. See the email.encoders module for a list of the built-in encoders.

Optional policy argument defaults to compat32.

_params are passed straight through to the base class constructor.

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.audio.MIMEAudio(_audiodata, _subtype=None, _encoder=email.encoders.encode_base64, *, policy=compat32, **_params)
+

Module: email.mime.audio

A subclass of MIMENonMultipart, the MIMEAudio class is used to create MIME message objects of major type audio. _audiodata contains the bytes for the raw audio data. If this data can be decoded as au, wav, aiff, or aifc, then the subtype will be automatically included in the Content-Type header. Otherwise you can explicitly specify the audio subtype via the _subtype argument. If the minor type could not be guessed and _subtype was not given, then TypeError is raised.

Optional _encoder is a callable (i.e. function) which will perform the actual encoding of the audio data for transport. This callable takes one argument, which is the MIMEAudio instance. It should use get_payload() and set_payload() to change the payload to encoded form. It should also add any Content-Transfer-Encoding or other headers to the message object as necessary. The default encoding is base64. See the email.encoders module for a list of the built-in encoders.

Optional policy argument defaults to compat32.

_params are passed straight through to the base class constructor.

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.image.MIMEImage(_imagedata, _subtype=None, _encoder=email.encoders.encode_base64, *, policy=compat32, **_params)
+

Module: email.mime.image

A subclass of MIMENonMultipart, the MIMEImage class is used to create MIME message objects of major type image. _imagedata contains the bytes for the raw image data. If this data type can be detected (jpeg, png, gif, tiff, rgb, pbm, pgm, ppm, rast, xbm, bmp, webp, and exr attempted), then the subtype will be automatically included in the Content-Type header. Otherwise you can explicitly specify the image subtype via the _subtype argument. If the minor type could not be guessed and _subtype was not given, then TypeError is raised.

Optional _encoder is a callable (i.e. function) which will perform the actual encoding of the image data for transport. This callable takes one argument, which is the MIMEImage instance. It should use get_payload() and set_payload() to change the payload to encoded form. It should also add any Content-Transfer-Encoding or other headers to the message object as necessary. The default encoding is base64. See the email.encoders module for a list of the built-in encoders.

Optional policy argument defaults to compat32.

_params are passed straight through to the MIMEBase constructor.

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.message.MIMEMessage(_msg, _subtype='rfc822', *, policy=compat32)
+

Module: email.mime.message

A subclass of MIMENonMultipart, the MIMEMessage class is used to create MIME objects of main type message. _msg is used as the payload, and must be an instance of class Message (or a subclass thereof), otherwise a TypeError is raised.

Optional _subtype sets the subtype of the message; it defaults to rfc822.

Optional policy argument defaults to compat32.

Changed in version 3.6: Added policy keyword-only parameter.

+
+class email.mime.text.MIMEText(_text, _subtype='plain', _charset=None, *, policy=compat32)
+

Module: email.mime.text

A subclass of MIMENonMultipart, the MIMEText class is used to create MIME objects of major type text. _text is the string for the payload. _subtype is the minor type and defaults to plain. _charset is the character set of the text and is passed as an argument to the MIMENonMultipart constructor; it defaults to us-ascii if the string contains only ascii code points, and utf-8 otherwise. The _charset parameter accepts either a string or a Charset instance.

Unless the _charset argument is explicitly set to None, the MIMEText object created will have both a Content-Type header with a charset parameter, and a Content-Transfer-Encoding header. This means that a subsequent set_payload call will not result in an encoded payload, even if a charset is passed in the set_payload command. You can “reset” this behavior by deleting the Content-Transfer-Encoding header, after which a set_payload call will automatically encode the new payload (and add a new Content-Transfer-Encoding header).

Optional policy argument defaults to compat32.

Changed in version 3.5: _charset also accepts Charset instances.

Changed in version 3.6: Added policy keyword-only parameter.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.mime.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.parser.html b/devdocs/python~3.12/library%2Femail.parser.html new file mode 100644 index 00000000..de673231 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.parser.html @@ -0,0 +1,56 @@ +

email.parser: Parsing email messages

Source code: Lib/email/parser.py

Message object structures can be created in one of two ways: they can be created from whole cloth by creating an EmailMessage object, adding headers using the dictionary interface, and adding payload(s) using set_content() and related methods, or they can be created by parsing a serialized representation of the email message.

The email package provides a standard parser that understands most email document structures, including MIME documents. You can pass the parser a bytes, string or file object, and the parser will return to you the root EmailMessage instance of the object structure. For simple, non-MIME messages the payload of this root object will likely be a string containing the text of the message. For MIME messages, the root object will return True from its is_multipart() method, and the subparts can be accessed via the payload manipulation methods, such as get_body(), iter_parts(), and walk().

There are actually two parser interfaces available for use, the Parser API and the incremental FeedParser API. The Parser API is most useful if you have the entire text of the message in memory, or if the entire message lives in a file on the file system. FeedParser is more appropriate when you are reading the message from a stream which might block waiting for more input (such as reading an email message from a socket). The FeedParser can consume and parse the message incrementally, and only returns the root object when you close the parser.

Note that the parser can be extended in limited ways, and of course you can implement your own parser completely from scratch. All of the logic that connects the email package’s bundled parser and the EmailMessage class is embodied in the Policy class, so a custom parser can create message object trees any way it finds necessary by implementing custom versions of the appropriate Policy methods.

FeedParser API

The BytesFeedParser, imported from the email.feedparser module, provides an API that is conducive to incremental parsing of email messages, such as would be necessary when reading the text of an email message from a source that can block (such as a socket). The BytesFeedParser can of course be used to parse an email message fully contained in a bytes-like object, string, or file, but the BytesParser API may be more convenient for such use cases. The semantics and results of the two parser APIs are identical.

The BytesFeedParser’s API is simple; you create an instance, feed it a bunch of bytes until there’s no more to feed it, then close the parser to retrieve the root message object. The BytesFeedParser is extremely accurate when parsing standards-compliant messages, and it does a very good job of parsing non-compliant messages, providing information about how a message was deemed broken. It will populate a message object’s defects attribute with a list of any problems it found in a message. See the email.errors module for the list of defects that it can find.

Here is the API for the BytesFeedParser:

+class email.parser.BytesFeedParser(_factory=None, *, policy=policy.compat32)
+

Create a BytesFeedParser instance. Optional _factory is a no-argument callable; if not specified use the message_factory from the policy. Call _factory whenever a new message object is needed.

If policy is specified use the rules it specifies to update the representation of the message. If policy is not set, use the compat32 policy, which maintains backward compatibility with the Python 3.2 version of the email package and provides Message as the default factory. All other policies provide EmailMessage as the default _factory. For more information on what else policy controls, see the policy documentation.

Note: The policy keyword should always be specified; The default will change to email.policy.default in a future version of Python.

New in version 3.2.

Changed in version 3.3: Added the policy keyword.

Changed in version 3.6: _factory defaults to the policy message_factory.

+feed(data)
+

Feed the parser some more data. data should be a bytes-like object containing one or more lines. The lines can be partial and the parser will stitch such partial lines together properly. The lines can have any of the three common line endings: carriage return, newline, or carriage return and newline (they can even be mixed).

+
+close()
+

Complete the parsing of all previously fed data and return the root message object. It is undefined what happens if feed() is called after this method has been called.

+
+
+class email.parser.FeedParser(_factory=None, *, policy=policy.compat32)
+

Works like BytesFeedParser except that the input to the feed() method must be a string. This is of limited utility, since the only way for such a message to be valid is for it to contain only ASCII text or, if utf8 is True, no binary attachments.

Changed in version 3.3: Added the policy keyword.

+

Parser API

The BytesParser class, imported from the email.parser module, provides an API that can be used to parse a message when the complete contents of the message are available in a bytes-like object or file. The email.parser module also provides Parser for parsing strings, and header-only parsers, BytesHeaderParser and HeaderParser, which can be used if you’re only interested in the headers of the message. BytesHeaderParser and HeaderParser can be much faster in these situations, since they do not attempt to parse the message body, instead setting the payload to the raw body.

+class email.parser.BytesParser(_class=None, *, policy=policy.compat32)
+

Create a BytesParser instance. The _class and policy arguments have the same meaning and semantics as the _factory and policy arguments of BytesFeedParser.

Note: The policy keyword should always be specified; The default will change to email.policy.default in a future version of Python.

Changed in version 3.3: Removed the strict argument that was deprecated in 2.4. Added the policy keyword.

Changed in version 3.6: _class defaults to the policy message_factory.

+parse(fp, headersonly=False)
+

Read all the data from the binary file-like object fp, parse the resulting bytes, and return the message object. fp must support both the readline() and the read() methods.

The bytes contained in fp must be formatted as a block of RFC 5322 (or, if utf8 is True, RFC 6532) style headers and header continuation lines, optionally preceded by an envelope header. The header block is terminated either by the end of the data or by a blank line. Following the header block is the body of the message (which may contain MIME-encoded subparts, including subparts with a Content-Transfer-Encoding of 8bit).

Optional headersonly is a flag specifying whether to stop parsing after reading the headers or not. The default is False, meaning it parses the entire contents of the file.

+
+parsebytes(bytes, headersonly=False)
+

Similar to the parse() method, except it takes a bytes-like object instead of a file-like object. Calling this method on a bytes-like object is equivalent to wrapping bytes in a BytesIO instance first and calling parse().

Optional headersonly is as with the parse() method.

+

New in version 3.2.

+
+class email.parser.BytesHeaderParser(_class=None, *, policy=policy.compat32)
+

Exactly like BytesParser, except that headersonly defaults to True.

New in version 3.3.

+
+class email.parser.Parser(_class=None, *, policy=policy.compat32)
+

This class is parallel to BytesParser, but handles string input.

Changed in version 3.3: Removed the strict argument. Added the policy keyword.

Changed in version 3.6: _class defaults to the policy message_factory.

+parse(fp, headersonly=False)
+

Read all the data from the text-mode file-like object fp, parse the resulting text, and return the root message object. fp must support both the readline() and the read() methods on file-like objects.

Other than the text mode requirement, this method operates like BytesParser.parse().

+
+parsestr(text, headersonly=False)
+

Similar to the parse() method, except it takes a string object instead of a file-like object. Calling this method on a string is equivalent to wrapping text in a StringIO instance first and calling parse().

Optional headersonly is as with the parse() method.

+
+
+class email.parser.HeaderParser(_class=None, *, policy=policy.compat32)
+

Exactly like Parser, except that headersonly defaults to True.

+

Since creating a message object structure from a string or a file object is such a common task, four functions are provided as a convenience. They are available in the top-level email package namespace.

+email.message_from_bytes(s, _class=None, *, policy=policy.compat32)
+

Return a message object structure from a bytes-like object. This is equivalent to BytesParser().parsebytes(s). Optional _class and policy are interpreted as with the BytesParser class constructor.

New in version 3.2.

Changed in version 3.3: Removed the strict argument. Added the policy keyword.

+
+email.message_from_binary_file(fp, _class=None, *, policy=policy.compat32)
+

Return a message object structure tree from an open binary file object. This is equivalent to BytesParser().parse(fp). _class and policy are interpreted as with the BytesParser class constructor.

New in version 3.2.

Changed in version 3.3: Removed the strict argument. Added the policy keyword.

+
+email.message_from_string(s, _class=None, *, policy=policy.compat32)
+

Return a message object structure from a string. This is equivalent to Parser().parsestr(s). _class and policy are interpreted as with the Parser class constructor.

Changed in version 3.3: Removed the strict argument. Added the policy keyword.

+
+email.message_from_file(fp, _class=None, *, policy=policy.compat32)
+

Return a message object structure tree from an open file object. This is equivalent to Parser().parse(fp). _class and policy are interpreted as with the Parser class constructor.

Changed in version 3.3: Removed the strict argument. Added the policy keyword.

Changed in version 3.6: _class defaults to the policy message_factory.

+

Here’s an example of how you might use message_from_bytes() at an interactive Python prompt:

>>> import email
+>>> msg = email.message_from_bytes(myBytes)  
+

Additional notes

Here are some notes on the parsing semantics:

  • Most non-multipart type messages are parsed as a single message object with a string payload. These objects will return False for is_multipart(), and iter_parts() will yield an empty list.
  • All multipart type messages will be parsed as a container message object with a list of sub-message objects for their payload. The outer container message will return True for is_multipart(), and iter_parts() will yield a list of subparts.
  • Most messages with a content type of message/* (such as message/delivery-status and message/rfc822) will also be parsed as container object containing a list payload of length 1. Their is_multipart() method will return True. The single element yielded by iter_parts() will be a sub-message object.
  • Some non-standards-compliant messages may not be internally consistent about their multipart-edness. Such messages may have a Content-Type header of type multipart, but their is_multipart() method may return False. If such messages were parsed with the FeedParser, they will have an instance of the MultipartInvariantViolationDefect class in their defects attribute list. See email.errors for details.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.parser.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.policy.html b/devdocs/python~3.12/library%2Femail.policy.html new file mode 100644 index 00000000..86e331a4 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.policy.html @@ -0,0 +1,161 @@ +

email.policy: Policy Objects

New in version 3.3.

Source code: Lib/email/policy.py

The email package’s prime focus is the handling of email messages as described by the various email and MIME RFCs. However, the general format of email messages (a block of header fields each consisting of a name followed by a colon followed by a value, the whole block followed by a blank line and an arbitrary ‘body’), is a format that has found utility outside of the realm of email. Some of these uses conform fairly closely to the main email RFCs, some do not. Even when working with email, there are times when it is desirable to break strict compliance with the RFCs, such as generating emails that interoperate with email servers that do not themselves follow the standards, or that implement extensions you want to use in ways that violate the standards.

Policy objects give the email package the flexibility to handle all these disparate use cases.

A Policy object encapsulates a set of attributes and methods that control the behavior of various components of the email package during use. Policy instances can be passed to various classes and methods in the email package to alter the default behavior. The settable values and their defaults are described below.

There is a default policy used by all classes in the email package. For all of the parser classes and the related convenience functions, and for the Message class, this is the Compat32 policy, via its corresponding pre-defined instance compat32. This policy provides for complete backward compatibility (in some cases, including bug compatibility) with the pre-Python3.3 version of the email package.

This default value for the policy keyword to EmailMessage is the EmailPolicy policy, via its pre-defined instance default.

When a Message or EmailMessage object is created, it acquires a policy. If the message is created by a parser, a policy passed to the parser will be the policy used by the message it creates. If the message is created by the program, then the policy can be specified when it is created. When a message is passed to a generator, the generator uses the policy from the message by default, but you can also pass a specific policy to the generator that will override the one stored on the message object.

The default value for the policy keyword for the email.parser classes and the parser convenience functions will be changing in a future version of Python. Therefore you should always specify explicitly which policy you want to use when calling any of the classes and functions described in the parser module.

The first part of this documentation covers the features of Policy, an abstract base class that defines the features that are common to all policy objects, including compat32. This includes certain hook methods that are called internally by the email package, which a custom policy could override to obtain different behavior. The second part describes the concrete classes EmailPolicy and Compat32, which implement the hooks that provide the standard behavior and the backward compatible behavior and features, respectively.

Policy instances are immutable, but they can be cloned, accepting the same keyword arguments as the class constructor and returning a new Policy instance that is a copy of the original but with the specified attributes values changed.

As an example, the following code could be used to read an email message from a file on disk and pass it to the system sendmail program on a Unix system:

>>> from email import message_from_binary_file
+>>> from email.generator import BytesGenerator
+>>> from email import policy
+>>> from subprocess import Popen, PIPE
+>>> with open('mymsg.txt', 'rb') as f:
+...     msg = message_from_binary_file(f, policy=policy.default)
+...
+>>> p = Popen(['sendmail', msg['To'].addresses[0]], stdin=PIPE)
+>>> g = BytesGenerator(p.stdin, policy=msg.policy.clone(linesep='\r\n'))
+>>> g.flatten(msg)
+>>> p.stdin.close()
+>>> rc = p.wait()
+

Here we are telling BytesGenerator to use the RFC correct line separator characters when creating the binary string to feed into sendmail's stdin, where the default policy would use \n line separators.

Some email package methods accept a policy keyword argument, allowing the policy to be overridden for that method. For example, the following code uses the as_bytes() method of the msg object from the previous example and writes the message to a file using the native line separators for the platform on which it is running:

>>> import os
+>>> with open('converted.txt', 'wb') as f:
+...     f.write(msg.as_bytes(policy=msg.policy.clone(linesep=os.linesep)))
+17
+

Policy objects can also be combined using the addition operator, producing a policy object whose settings are a combination of the non-default values of the summed objects:

>>> compat_SMTP = policy.compat32.clone(linesep='\r\n')
+>>> compat_strict = policy.compat32.clone(raise_on_defect=True)
+>>> compat_strict_SMTP = compat_SMTP + compat_strict
+

This operation is not commutative; that is, the order in which the objects are added matters. To illustrate:

>>> policy100 = policy.compat32.clone(max_line_length=100)
+>>> policy80 = policy.compat32.clone(max_line_length=80)
+>>> apolicy = policy100 + policy80
+>>> apolicy.max_line_length
+80
+>>> apolicy = policy80 + policy100
+>>> apolicy.max_line_length
+100
+
+class email.policy.Policy(**kw)
+

This is the abstract base class for all policy classes. It provides default implementations for a couple of trivial methods, as well as the implementation of the immutability property, the clone() method, and the constructor semantics.

The constructor of a policy class can be passed various keyword arguments. The arguments that may be specified are any non-method properties on this class, plus any additional non-method properties on the concrete class. A value specified in the constructor will override the default value for the corresponding attribute.

This class defines the following properties, and thus values for the following may be passed in the constructor of any policy class:

+max_line_length
+

The maximum length of any line in the serialized output, not counting the end of line character(s). Default is 78, per RFC 5322. A value of 0 or None indicates that no line wrapping should be done at all.

+
+linesep
+

The string to be used to terminate lines in serialized output. The default is \n because that’s the internal end-of-line discipline used by Python, though \r\n is required by the RFCs.

+
+cte_type
+

Controls the type of Content Transfer Encodings that may be or are required to be used. The possible values are:

+ +

7bit

all data must be “7 bit clean” (ASCII-only). This means that where necessary data will be encoded using either quoted-printable or base64 encoding.

8bit

data is not constrained to be 7 bit clean. Data in headers is still required to be ASCII-only and so will be encoded (see fold_binary() and utf8 below for exceptions), but body parts may use the 8bit CTE.

A cte_type value of 8bit only works with BytesGenerator, not Generator, because strings cannot contain binary data. If a Generator is operating under a policy that specifies cte_type=8bit, it will act as if cte_type is 7bit.

+
+raise_on_defect
+

If True, any defects encountered will be raised as errors. If False (the default), defects will be passed to the register_defect() method.

+
+mangle_from_
+

If True, lines starting with “From “ in the body are escaped by putting a > in front of them. This parameter is used when the message is being serialized by a generator. Default: False.

New in version 3.5: The mangle_from_ parameter.

+
+message_factory
+

A factory function for constructing a new empty message object. Used by the parser when building messages. Defaults to None, in which case Message is used.

New in version 3.6.

+

The following Policy method is intended to be called by code using the email library to create policy instances with custom settings:

+clone(**kw)
+

Return a new Policy instance whose attributes have the same values as the current instance, except where those attributes are given new values by the keyword arguments.

+

The remaining Policy methods are called by the email package code, and are not intended to be called by an application using the email package. A custom policy must implement all of these methods.

+handle_defect(obj, defect)
+

Handle a defect found on obj. When the email package calls this method, defect will always be a subclass of Defect.

The default implementation checks the raise_on_defect flag. If it is True, defect is raised as an exception. If it is False (the default), obj and defect are passed to register_defect().

+
+register_defect(obj, defect)
+

Register a defect on obj. In the email package, defect will always be a subclass of Defect.

The default implementation calls the append method of the defects attribute of obj. When the email package calls handle_defect, obj will normally have a defects attribute that has an append method. Custom object types used with the email package (for example, custom Message objects) should also provide such an attribute, otherwise defects in parsed messages will raise unexpected errors.

+
+header_max_count(name)
+

Return the maximum allowed number of headers named name.

Called when a header is added to an EmailMessage or Message object. If the returned value is not 0 or None, and there are already a number of headers with the name name greater than or equal to the value returned, a ValueError is raised.

Because the default behavior of Message.__setitem__ is to append the value to the list of headers, it is easy to create duplicate headers without realizing it. This method allows certain headers to be limited in the number of instances of that header that may be added to a Message programmatically. (The limit is not observed by the parser, which will faithfully produce as many headers as exist in the message being parsed.)

The default implementation returns None for all header names.

+
+header_source_parse(sourcelines)
+

The email package calls this method with a list of strings, each string ending with the line separation characters found in the source being parsed. The first line includes the field header name and separator. All whitespace in the source is preserved. The method should return the (name, value) tuple that is to be stored in the Message to represent the parsed header.

If an implementation wishes to retain compatibility with the existing email package policies, name should be the case preserved name (all characters up to the ‘:’ separator), while value should be the unfolded value (all line separator characters removed, but whitespace kept intact), stripped of leading whitespace.

sourcelines may contain surrogateescaped binary data.

There is no default implementation

+
+header_store_parse(name, value)
+

The email package calls this method with the name and value provided by the application program when the application program is modifying a Message programmatically (as opposed to a Message created by a parser). The method should return the (name, value) tuple that is to be stored in the Message to represent the header.

If an implementation wishes to retain compatibility with the existing email package policies, the name and value should be strings or string subclasses that do not change the content of the passed in arguments.

There is no default implementation

+
+header_fetch_parse(name, value)
+

The email package calls this method with the name and value currently stored in the Message when that header is requested by the application program, and whatever the method returns is what is passed back to the application as the value of the header being retrieved. Note that there may be more than one header with the same name stored in the Message; the method is passed the specific name and value of the header destined to be returned to the application.

value may contain surrogateescaped binary data. There should be no surrogateescaped binary data in the value returned by the method.

There is no default implementation

+
+fold(name, value)
+

The email package calls this method with the name and value currently stored in the Message for a given header. The method should return a string that represents that header “folded” correctly (according to the policy settings) by composing the name with the value and inserting linesep characters at the appropriate places. See RFC 5322 for a discussion of the rules for folding email headers.

value may contain surrogateescaped binary data. There should be no surrogateescaped binary data in the string returned by the method.

+
+fold_binary(name, value)
+

The same as fold(), except that the returned value should be a bytes object rather than a string.

value may contain surrogateescaped binary data. These could be converted back into binary data in the returned bytes object.

+
+
+class email.policy.EmailPolicy(**kw)
+

This concrete Policy provides behavior that is intended to be fully compliant with the current email RFCs. These include (but are not limited to) RFC 5322, RFC 2047, and the current MIME RFCs.

This policy adds new header parsing and folding algorithms. Instead of simple strings, headers are str subclasses with attributes that depend on the type of the field. The parsing and folding algorithm fully implement RFC 2047 and RFC 5322.

The default value for the message_factory attribute is EmailMessage.

In addition to the settable attributes listed above that apply to all policies, this policy adds the following additional attributes:

New in version 3.6: 1

+utf8
+

If False, follow RFC 5322, supporting non-ASCII characters in headers by encoding them as “encoded words”. If True, follow RFC 6532 and use utf-8 encoding for headers. Messages formatted in this way may be passed to SMTP servers that support the SMTPUTF8 extension (RFC 6531).

+
+refold_source
+

If the value for a header in the Message object originated from a parser (as opposed to being set by a program), this attribute indicates whether or not a generator should refold that value when transforming the message back into serialized form. The possible values are:

+ + +

none

all source values use original folding

long

source values that have any line that is longer than max_line_length will be refolded

all

all values are refolded.

The default is long.

+
+header_factory
+

A callable that takes two arguments, name and value, where name is a header field name and value is an unfolded header field value, and returns a string subclass that represents that header. A default header_factory (see headerregistry) is provided that supports custom parsing for the various address and date RFC 5322 header field types, and the major MIME header field stypes. Support for additional custom parsing will be added in the future.

+
+content_manager
+

An object with at least two methods: get_content and set_content. When the get_content() or set_content() method of an EmailMessage object is called, it calls the corresponding method of this object, passing it the message object as its first argument, and any arguments or keywords that were passed to it as additional arguments. By default content_manager is set to raw_data_manager.

New in version 3.4.

+

The class provides the following concrete implementations of the abstract methods of Policy:

+header_max_count(name)
+

Returns the value of the max_count attribute of the specialized class used to represent the header with the given name.

+
+header_source_parse(sourcelines)
+

The name is parsed as everything up to the ‘:’ and returned unmodified. The value is determined by stripping leading whitespace off the remainder of the first line, joining all subsequent lines together, and stripping any trailing carriage return or linefeed characters.

+
+header_store_parse(name, value)
+

The name is returned unchanged. If the input value has a name attribute and it matches name ignoring case, the value is returned unchanged. Otherwise the name and value are passed to header_factory, and the resulting header object is returned as the value. In this case a ValueError is raised if the input value contains CR or LF characters.

+
+header_fetch_parse(name, value)
+

If the value has a name attribute, it is returned to unmodified. Otherwise the name, and the value with any CR or LF characters removed, are passed to the header_factory, and the resulting header object is returned. Any surrogateescaped bytes get turned into the unicode unknown-character glyph.

+
+fold(name, value)
+

Header folding is controlled by the refold_source policy setting. A value is considered to be a ‘source value’ if and only if it does not have a name attribute (having a name attribute means it is a header object of some sort). If a source value needs to be refolded according to the policy, it is converted into a header object by passing the name and the value with any CR and LF characters removed to the header_factory. Folding of a header object is done by calling its fold method with the current policy.

Source values are split into lines using splitlines(). If the value is not to be refolded, the lines are rejoined using the linesep from the policy and returned. The exception is lines containing non-ascii binary data. In that case the value is refolded regardless of the refold_source setting, which causes the binary data to be CTE encoded using the unknown-8bit charset.

+
+fold_binary(name, value)
+

The same as fold() if cte_type is 7bit, except that the returned value is bytes.

If cte_type is 8bit, non-ASCII binary data is converted back into bytes. Headers with binary data are not refolded, regardless of the refold_header setting, since there is no way to know whether the binary data consists of single byte characters or multibyte characters.

+
+

The following instances of EmailPolicy provide defaults suitable for specific application domains. Note that in the future the behavior of these instances (in particular the HTTP instance) may be adjusted to conform even more closely to the RFCs relevant to their domains.

+email.policy.default
+

An instance of EmailPolicy with all defaults unchanged. This policy uses the standard Python \n line endings rather than the RFC-correct \r\n.

+
+email.policy.SMTP
+

Suitable for serializing messages in conformance with the email RFCs. Like default, but with linesep set to \r\n, which is RFC compliant.

+
+email.policy.SMTPUTF8
+

The same as SMTP except that utf8 is True. Useful for serializing messages to a message store without using encoded words in the headers. Should only be used for SMTP transmission if the sender or recipient addresses have non-ASCII characters (the smtplib.SMTP.send_message() method handles this automatically).

+
+email.policy.HTTP
+

Suitable for serializing headers with for use in HTTP traffic. Like SMTP except that max_line_length is set to None (unlimited).

+
+email.policy.strict
+

Convenience instance. The same as default except that raise_on_defect is set to True. This allows any policy to be made strict by writing:

somepolicy + policy.strict
+
+

With all of these EmailPolicies, the effective API of the email package is changed from the Python 3.2 API in the following ways:

From the application view, this means that any header obtained through the EmailMessage is a header object with extra attributes, whose string value is the fully decoded unicode value of the header. Likewise, a header may be assigned a new value, or a new header created, using a unicode string, and the policy will take care of converting the unicode string into the correct RFC encoded form.

The header objects and their attributes are described in headerregistry.

+class email.policy.Compat32(**kw)
+

This concrete Policy is the backward compatibility policy. It replicates the behavior of the email package in Python 3.2. The policy module also defines an instance of this class, compat32, that is used as the default policy. Thus the default behavior of the email package is to maintain compatibility with Python 3.2.

The following attributes have values that are different from the Policy default:

+mangle_from_
+

The default is True.

+

The class provides the following concrete implementations of the abstract methods of Policy:

+header_source_parse(sourcelines)
+

The name is parsed as everything up to the ‘:’ and returned unmodified. The value is determined by stripping leading whitespace off the remainder of the first line, joining all subsequent lines together, and stripping any trailing carriage return or linefeed characters.

+
+header_store_parse(name, value)
+

The name and value are returned unmodified.

+
+header_fetch_parse(name, value)
+

If the value contains binary data, it is converted into a Header object using the unknown-8bit charset. Otherwise it is returned unmodified.

+
+fold(name, value)
+

Headers are folded using the Header folding algorithm, which preserves existing line breaks in the value, and wraps each resulting line to the max_line_length. Non-ASCII binary data are CTE encoded using the unknown-8bit charset.

+
+fold_binary(name, value)
+

Headers are folded using the Header folding algorithm, which preserves existing line breaks in the value, and wraps each resulting line to the max_line_length. If cte_type is 7bit, non-ascii binary data is CTE encoded using the unknown-8bit charset. Otherwise the original source header is used, with its existing line breaks and any (RFC invalid) binary data it may contain.

+
+
+email.policy.compat32
+

An instance of Compat32, providing backward compatibility with the behavior of the email package in Python 3.2.

+

Footnotes

+1
+

Originally added in 3.3 as a provisional feature.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.policy.html +

+
diff --git a/devdocs/python~3.12/library%2Femail.utils.html b/devdocs/python~3.12/library%2Femail.utils.html new file mode 100644 index 00000000..6d8f47f2 --- /dev/null +++ b/devdocs/python~3.12/library%2Femail.utils.html @@ -0,0 +1,68 @@ +

email.utils: Miscellaneous utilities

Source code: Lib/email/utils.py

There are a couple of useful utilities provided in the email.utils module:

+email.utils.localtime(dt=None)
+

Return local time as an aware datetime object. If called without arguments, return current time. Otherwise dt argument should be a datetime instance, and it is converted to the local time zone according to the system time zone database. If dt is naive (that is, dt.tzinfo is None), it is assumed to be in local time. The isdst parameter is ignored.

New in version 3.3.

Deprecated since version 3.12, will be removed in version 3.14: The isdst parameter.

+
+email.utils.make_msgid(idstring=None, domain=None)
+

Returns a string suitable for an RFC 2822-compliant Message-ID header. Optional idstring if given, is a string used to strengthen the uniqueness of the message id. Optional domain if given provides the portion of the msgid after the ‘@’. The default is the local hostname. It is not normally necessary to override this default, but may be useful certain cases, such as a constructing distributed system that uses a consistent domain name across multiple hosts.

Changed in version 3.2: Added the domain keyword.

+

The remaining functions are part of the legacy (Compat32) email API. There is no need to directly use these with the new API, since the parsing and formatting they provide is done automatically by the header parsing machinery of the new API.

+email.utils.quote(str)
+

Return a new string with backslashes in str replaced by two backslashes, and double quotes replaced by backslash-double quote.

+
+email.utils.unquote(str)
+

Return a new string which is an unquoted version of str. If str ends and begins with double quotes, they are stripped off. Likewise if str ends and begins with angle brackets, they are stripped off.

+
+email.utils.parseaddr(address)
+

Parse address – which should be the value of some address-containing field such as To or Cc – into its constituent realname and email address parts. Returns a tuple of that information, unless the parse fails, in which case a 2-tuple of ('', '') is returned.

+
+email.utils.formataddr(pair, charset='utf-8')
+

The inverse of parseaddr(), this takes a 2-tuple of the form (realname, +email_address) and returns the string value suitable for a To or Cc header. If the first element of pair is false, then the second element is returned unmodified.

Optional charset is the character set that will be used in the RFC 2047 encoding of the realname if the realname contains non-ASCII characters. Can be an instance of str or a Charset. Defaults to utf-8.

Changed in version 3.3: Added the charset option.

+
+email.utils.getaddresses(fieldvalues)
+

This method returns a list of 2-tuples of the form returned by parseaddr(). fieldvalues is a sequence of header field values as might be returned by Message.get_all. Here’s a simple example that gets all the recipients of a message:

from email.utils import getaddresses
+
+tos = msg.get_all('to', [])
+ccs = msg.get_all('cc', [])
+resent_tos = msg.get_all('resent-to', [])
+resent_ccs = msg.get_all('resent-cc', [])
+all_recipients = getaddresses(tos + ccs + resent_tos + resent_ccs)
+
+
+email.utils.parsedate(date)
+

Attempts to parse a date according to the rules in RFC 2822. however, some mailers don’t follow that format as specified, so parsedate() tries to guess correctly in such cases. date is a string containing an RFC 2822 date, such as "Mon, 20 Nov 1995 19:12:08 -0500". If it succeeds in parsing the date, parsedate() returns a 9-tuple that can be passed directly to time.mktime(); otherwise None will be returned. Note that indexes 6, 7, and 8 of the result tuple are not usable.

+
+email.utils.parsedate_tz(date)
+

Performs the same function as parsedate(), but returns either None or a 10-tuple; the first 9 elements make up a tuple that can be passed directly to time.mktime(), and the tenth is the offset of the date’s timezone from UTC (which is the official term for Greenwich Mean Time) 1. If the input string has no timezone, the last element of the tuple returned is 0, which represents UTC. Note that indexes 6, 7, and 8 of the result tuple are not usable.

+
+email.utils.parsedate_to_datetime(date)
+

The inverse of format_datetime(). Performs the same function as parsedate(), but on success returns a datetime; otherwise ValueError is raised if date contains an invalid value such as an hour greater than 23 or a timezone offset not between -24 and 24 hours. If the input date has a timezone of -0000, the datetime will be a naive datetime, and if the date is conforming to the RFCs it will represent a time in UTC but with no indication of the actual source timezone of the message the date comes from. If the input date has any other valid timezone offset, the datetime will be an aware datetime with the corresponding a timezone tzinfo.

New in version 3.3.

+
+email.utils.mktime_tz(tuple)
+

Turn a 10-tuple as returned by parsedate_tz() into a UTC timestamp (seconds since the Epoch). If the timezone item in the tuple is None, assume local time.

+
+email.utils.formatdate(timeval=None, localtime=False, usegmt=False)
+

Returns a date string as per RFC 2822, e.g.:

Fri, 09 Nov 2001 01:08:47 -0000
+

Optional timeval if given is a floating point time value as accepted by time.gmtime() and time.localtime(), otherwise the current time is used.

Optional localtime is a flag that when True, interprets timeval, and returns a date relative to the local timezone instead of UTC, properly taking daylight savings time into account. The default is False meaning UTC is used.

Optional usegmt is a flag that when True, outputs a date string with the timezone as an ascii string GMT, rather than a numeric -0000. This is needed for some protocols (such as HTTP). This only applies when localtime is False. The default is False.

+
+email.utils.format_datetime(dt, usegmt=False)
+

Like formatdate, but the input is a datetime instance. If it is a naive datetime, it is assumed to be “UTC with no information about the source timezone”, and the conventional -0000 is used for the timezone. If it is an aware datetime, then the numeric timezone offset is used. If it is an aware timezone with offset zero, then usegmt may be set to True, in which case the string GMT is used instead of the numeric timezone offset. This provides a way to generate standards conformant HTTP date headers.

New in version 3.3.

+
+email.utils.decode_rfc2231(s)
+

Decode the string s according to RFC 2231.

+
+email.utils.encode_rfc2231(s, charset=None, language=None)
+

Encode the string s according to RFC 2231. Optional charset and language, if given is the character set name and language name to use. If neither is given, s is returned as-is. If charset is given but language is not, the string is encoded using the empty string for language.

+
+email.utils.collapse_rfc2231_value(value, errors='replace', fallback_charset='us-ascii')
+

When a header parameter is encoded in RFC 2231 format, Message.get_param may return a 3-tuple containing the character set, language, and value. collapse_rfc2231_value() turns this into a unicode string. Optional errors is passed to the errors argument of str’s encode() method; it defaults to 'replace'. Optional fallback_charset specifies the character set to use if the one in the RFC 2231 header is not known by Python; it defaults to 'us-ascii'.

For convenience, if the value passed to collapse_rfc2231_value() is not a tuple, it should be a string and it is returned unquoted.

+
+email.utils.decode_params(params)
+

Decode parameters list according to RFC 2231. params is a sequence of 2-tuples containing elements of the form (content-type, string-value).

+

Footnotes

+1
+

Note that the sign of the timezone offset is the opposite of the sign of the time.timezone variable for the same timezone; the latter variable follows the POSIX standard while this module follows RFC 2822.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/email.utils.html +

+
diff --git a/devdocs/python~3.12/library%2Fensurepip.html b/devdocs/python~3.12/library%2Fensurepip.html new file mode 100644 index 00000000..3cef526c --- /dev/null +++ b/devdocs/python~3.12/library%2Fensurepip.html @@ -0,0 +1,23 @@ +

ensurepip — Bootstrapping the pip installer

New in version 3.4.

Source code: Lib/ensurepip

The ensurepip package provides support for bootstrapping the pip installer into an existing Python installation or virtual environment. This bootstrapping approach reflects the fact that pip is an independent project with its own release cycle, and the latest available stable version is bundled with maintenance and feature releases of the CPython reference interpreter.

In most cases, end users of Python shouldn’t need to invoke this module directly (as pip should be bootstrapped by default), but it may be needed if installing pip was skipped when installing Python (or when creating a virtual environment) or after explicitly uninstalling pip.

Note

This module does not access the internet. All of the components needed to bootstrap pip are included as internal parts of the package.

See also

Installing Python Modules
+
+

The end user guide for installing Python packages

+PEP 453: Explicit bootstrapping of pip in Python installations
+
+

The original rationale and specification for this module.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Command line interface

The command line interface is invoked using the interpreter’s -m switch.

The simplest possible invocation is:

python -m ensurepip
+

This invocation will install pip if it is not already installed, but otherwise does nothing. To ensure the installed version of pip is at least as recent as the one available in ensurepip, pass the --upgrade option:

python -m ensurepip --upgrade
+

By default, pip is installed into the current virtual environment (if one is active) or into the system site packages (if there is no active virtual environment). The installation location can be controlled through two additional command line options:

  • +--root dir: Installs pip relative to the given root directory rather than the root of the currently active virtual environment (if any) or the default root for the current Python installation.
  • +--user: Installs pip into the user site packages directory rather than globally for the current Python installation (this option is not permitted inside an active virtual environment).

By default, the scripts pipX and pipX.Y will be installed (where X.Y stands for the version of Python used to invoke ensurepip). The scripts installed can be controlled through two additional command line options:

  • +--altinstall: if an alternate installation is requested, the pipX script will not be installed.
  • +--default-pip: if a “default pip” installation is requested, the pip script will be installed in addition to the two regular scripts.

Providing both of the script selection options will trigger an exception.

Module API

ensurepip exposes two functions for programmatic use:

+ensurepip.version()
+

Returns a string specifying the available version of pip that will be installed when bootstrapping an environment.

+
+ensurepip.bootstrap(root=None, upgrade=False, user=False, altinstall=False, default_pip=False, verbosity=0)
+

Bootstraps pip into the current or designated environment.

root specifies an alternative root directory to install relative to. If root is None, then installation uses the default install location for the current environment.

upgrade indicates whether or not to upgrade an existing installation of an earlier version of pip to the available version.

user indicates whether to use the user scheme rather than installing globally.

By default, the scripts pipX and pipX.Y will be installed (where X.Y stands for the current version of Python).

If altinstall is set, then pipX will not be installed.

If default_pip is set, then pip will be installed in addition to the two regular scripts.

Setting both altinstall and default_pip will trigger ValueError.

verbosity controls the level of output to sys.stdout from the bootstrapping operation.

Raises an auditing event ensurepip.bootstrap with argument root.

Note

The bootstrapping process has side effects on both sys.path and os.environ. Invoking the command line interface in a subprocess instead allows these side effects to be avoided.

Note

The bootstrapping process may install additional modules required by pip, but other software should not assume those dependencies will always be present by default (as the dependencies may be removed in a future version of pip).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ensurepip.html +

+
diff --git a/devdocs/python~3.12/library%2Fenum.html b/devdocs/python~3.12/library%2Fenum.html new file mode 100644 index 00000000..7ca1c485 --- /dev/null +++ b/devdocs/python~3.12/library%2Fenum.html @@ -0,0 +1,448 @@ +

enum — Support for enumerations

New in version 3.4.

Source code: Lib/enum.py

An enumeration:

Enumerations are created either by using class syntax, or by using function-call syntax:

>>> from enum import Enum
+
+>>> # class syntax
+>>> class Color(Enum):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 3
+
+>>> # functional syntax
+>>> Color = Enum('Color', ['RED', 'GREEN', 'BLUE'])
+

Even though we can use class syntax to create Enums, Enums are not normal Python classes. See How are Enums different? for more details.

Note

Nomenclature

  • The class Color is an enumeration (or enum)
  • The attributes Color.RED, Color.GREEN, etc., are enumeration members (or members) and are functionally constants.
  • The enum members have names and values (the name of Color.RED is RED, the value of Color.BLUE is 3, etc.)

Module Contents

EnumType

The type for Enum and its subclasses.

Enum

Base class for creating enumerated constants.

IntEnum

Base class for creating enumerated constants that are also subclasses of int. (Notes)

StrEnum

Base class for creating enumerated constants that are also subclasses of str. (Notes)

Flag

Base class for creating enumerated constants that can be combined using the bitwise operations without losing their Flag membership.

IntFlag

Base class for creating enumerated constants that can be combined using the bitwise operators without losing their IntFlag membership. IntFlag members are also subclasses of int. (Notes)

ReprEnum

Used by IntEnum, StrEnum, and IntFlag to keep the str() of the mixed-in type.

EnumCheck

An enumeration with the values CONTINUOUS, NAMED_FLAGS, and UNIQUE, for use with verify() to ensure various constraints are met by a given enumeration.

FlagBoundary

An enumeration with the values STRICT, CONFORM, EJECT, and KEEP which allows for more fine-grained control over how invalid values are dealt with in an enumeration.

auto

Instances are replaced with an appropriate value for Enum members. StrEnum defaults to the lower-cased version of the member name, while other Enums default to 1 and increase from there.

property()

Allows Enum members to have attributes without conflicting with member names. The value and name attributes are implemented this way.

unique()

Enum class decorator that ensures only one name is bound to any one value.

verify()

Enum class decorator that checks user-selectable constraints on an enumeration.

member()

Make obj a member. Can be used as a decorator.

nonmember()

Do not make obj a member. Can be used as a decorator.

global_enum()

Modify the str() and repr() of an enum to show its members as belonging to the module instead of its class, and export the enum members to the global namespace.

show_flag_values()

Return a list of all power-of-two integers contained in a flag.

New in version 3.6: Flag, IntFlag, auto

New in version 3.11: StrEnum, EnumCheck, ReprEnum, FlagBoundary, property, member, nonmember, global_enum, show_flag_values

Data Types

+class enum.EnumType
+

EnumType is the metaclass for enum enumerations. It is possible to subclass EnumType – see Subclassing EnumType for details.

EnumType is responsible for setting the correct __repr__(), __str__(), __format__(), and __reduce__() methods on the final enum, as well as creating the enum members, properly handling duplicates, providing iteration over the enum class, etc.

+__call__(cls, value, names=None, \*, module=None, qualname=None, type=None, start=1, boundary=None)
+

This method is called in two different ways:

  • +

    to look up an existing member:

    cls
    +

    The enum class being called.

    value
    +

    The value to lookup.

  • +

    to use the cls enum to create a new enum (only if the existing enum does not have any members):

    cls
    +

    The enum class being called.

    value
    +

    The name of the new Enum to create.

    names
    +

    The names/values of the members for the new Enum.

    module
    +

    The name of the module the new Enum is created in.

    qualname
    +

    The actual location in the module where this Enum can be found.

    type
    +

    A mix-in type for the new Enum.

    start
    +

    The first integer value for the Enum (used by auto).

    boundary
    +

    How to handle out-of-range values from bit operations (Flag only).

+
+__contains__(cls, member)
+

Returns True if member belongs to the cls:

>>> some_var = Color.RED
+>>> some_var in Color
+True
+>>> Color.RED.value in Color
+True
+
+

Changed in version 3.12: Before Python 3.12, a TypeError is raised if a non-Enum-member is used in a containment check.

+__dir__(cls)
+

Returns ['__class__', '__doc__', '__members__', '__module__'] and the names of the members in cls:

>>> dir(Color)
+['BLUE', 'GREEN', 'RED', '__class__', '__contains__', '__doc__', '__getitem__', '__init_subclass__', '__iter__', '__len__', '__members__', '__module__', '__name__', '__qualname__']
+
+
+__getitem__(cls, name)
+

Returns the Enum member in cls matching name, or raises a KeyError:

>>> Color['BLUE']
+<Color.BLUE: 3>
+
+
+__iter__(cls)
+

Returns each member in cls in definition order:

>>> list(Color)
+[<Color.RED: 1>, <Color.GREEN: 2>, <Color.BLUE: 3>]
+
+
+__len__(cls)
+

Returns the number of member in cls:

>>> len(Color)
+3
+
+
+__reversed__(cls)
+

Returns each member in cls in reverse definition order:

>>> list(reversed(Color))
+[<Color.BLUE: 3>, <Color.GREEN: 2>, <Color.RED: 1>]
+
+

New in version 3.11: Before 3.11 enum used EnumMeta type, which is kept as an alias.

+
+class enum.Enum
+

Enum is the base class for all enum enumerations.

+name
+

The name used to define the Enum member:

>>> Color.BLUE.name
+'BLUE'
+
+
+value
+

The value given to the Enum member:

>>> Color.RED.value
+1
+

Note

Enum member values

Member values can be anything: int, str, etc. If the exact value is unimportant you may use auto instances and an appropriate value will be chosen for you. See auto for the details.

+
+_ignore_
+

_ignore_ is only used during creation and is removed from the enumeration once creation is complete.

_ignore_ is a list of names that will not become members, and whose names will also be removed from the completed enumeration. See TimePeriod for an example.

+
+__dir__(self)
+

Returns ['__class__', '__doc__', '__module__', 'name', 'value'] and any public methods defined on self.__class__:

>>> from datetime import date
+>>> class Weekday(Enum):
+...     MONDAY = 1
+...     TUESDAY = 2
+...     WEDNESDAY = 3
+...     THURSDAY = 4
+...     FRIDAY = 5
+...     SATURDAY = 6
+...     SUNDAY = 7
+...     @classmethod
+...     def today(cls):
+...         print('today is %s' % cls(date.today().isoweekday()).name)
+...
+>>> dir(Weekday.SATURDAY)
+['__class__', '__doc__', '__eq__', '__hash__', '__module__', 'name', 'today', 'value']
+
+
+_generate_next_value_(name, start, count, last_values)
name
+

The name of the member being defined (e.g. ‘RED’).

start
+

The start value for the Enum; the default is 1.

count
+

The number of members currently defined, not including this one.

last_values
+

A list of the previous values.

A staticmethod that is used to determine the next value returned by auto:

>>> from enum import auto
+>>> class PowersOfThree(Enum):
+...     @staticmethod
+...     def _generate_next_value_(name, start, count, last_values):
+...         return 3 ** (count + 1)
+...     FIRST = auto()
+...     SECOND = auto()
+...
+>>> PowersOfThree.SECOND.value
+9
+
+
+__init_subclass__(cls, \**kwds)
+

A classmethod that is used to further configure subsequent subclasses. By default, does nothing.

+
+_missing_(cls, value)
+

A classmethod for looking up values not found in cls. By default it does nothing, but can be overridden to implement custom search behavior:

>>> from enum import StrEnum
+>>> class Build(StrEnum):
+...     DEBUG = auto()
+...     OPTIMIZED = auto()
+...     @classmethod
+...     def _missing_(cls, value):
+...         value = value.lower()
+...         for member in cls:
+...             if member.value == value:
+...                 return member
+...         return None
+...
+>>> Build.DEBUG.value
+'debug'
+>>> Build('deBUG')
+<Build.DEBUG: 'debug'>
+
+
+__repr__(self)
+

Returns the string used for repr() calls. By default, returns the Enum name, member name, and value, but can be overridden:

>>> class OtherStyle(Enum):
+...     ALTERNATE = auto()
+...     OTHER = auto()
+...     SOMETHING_ELSE = auto()
+...     def __repr__(self):
+...         cls_name = self.__class__.__name__
+...         return f'{cls_name}.{self.name}'
+...
+>>> OtherStyle.ALTERNATE, str(OtherStyle.ALTERNATE), f"{OtherStyle.ALTERNATE}"
+(OtherStyle.ALTERNATE, 'OtherStyle.ALTERNATE', 'OtherStyle.ALTERNATE')
+
+
+__str__(self)
+

Returns the string used for str() calls. By default, returns the Enum name and member name, but can be overridden:

>>> class OtherStyle(Enum):
+...     ALTERNATE = auto()
+...     OTHER = auto()
+...     SOMETHING_ELSE = auto()
+...     def __str__(self):
+...         return f'{self.name}'
+...
+>>> OtherStyle.ALTERNATE, str(OtherStyle.ALTERNATE), f"{OtherStyle.ALTERNATE}"
+(<OtherStyle.ALTERNATE: 1>, 'ALTERNATE', 'ALTERNATE')
+
+
+__format__(self)
+

Returns the string used for format() and f-string calls. By default, returns __str__() return value, but can be overridden:

>>> class OtherStyle(Enum):
+...     ALTERNATE = auto()
+...     OTHER = auto()
+...     SOMETHING_ELSE = auto()
+...     def __format__(self, spec):
+...         return f'{self.name}'
+...
+>>> OtherStyle.ALTERNATE, str(OtherStyle.ALTERNATE), f"{OtherStyle.ALTERNATE}"
+(<OtherStyle.ALTERNATE: 1>, 'OtherStyle.ALTERNATE', 'ALTERNATE')
+
+

Note

Using auto with Enum results in integers of increasing value, starting with 1.

Changed in version 3.12: Added Dataclass support

+
+class enum.IntEnum
+

IntEnum is the same as Enum, but its members are also integers and can be used anywhere that an integer can be used. If any integer operation is performed with an IntEnum member, the resulting value loses its enumeration status.

>>> from enum import IntEnum
+>>> class Number(IntEnum):
+...     ONE = 1
+...     TWO = 2
+...     THREE = 3
+...
+>>> Number.THREE
+<Number.THREE: 3>
+>>> Number.ONE + Number.TWO
+3
+>>> Number.THREE + 5
+8
+>>> Number.THREE == 3
+True
+

Note

Using auto with IntEnum results in integers of increasing value, starting with 1.

Changed in version 3.11: __str__() is now int.__str__() to better support the replacement of existing constants use-case. __format__() was already int.__format__() for that same reason.

+
+class enum.StrEnum
+

StrEnum is the same as Enum, but its members are also strings and can be used in most of the same places that a string can be used. The result of any string operation performed on or with a StrEnum member is not part of the enumeration.

Note

There are places in the stdlib that check for an exact str instead of a str subclass (i.e. type(unknown) == str instead of isinstance(unknown, str)), and in those locations you will need to use str(StrEnum.member).

Note

Using auto with StrEnum results in the lower-cased member name as the value.

Note

__str__() is str.__str__() to better support the replacement of existing constants use-case. __format__() is likewise str.__format__() for that same reason.

New in version 3.11.

+
+class enum.Flag
+

Flag members support the bitwise operators & (AND), | (OR), ^ (XOR), and ~ (INVERT); the results of those operators are members of the enumeration.

+__contains__(self, value)
+

Returns True if value is in self:

>>> from enum import Flag, auto
+>>> class Color(Flag):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> purple = Color.RED | Color.BLUE
+>>> white = Color.RED | Color.GREEN | Color.BLUE
+>>> Color.GREEN in purple
+False
+>>> Color.GREEN in white
+True
+>>> purple in white
+True
+>>> white in purple
+False
+
+
__iter__(self): +
+

Returns all contained non-alias members:

>>> list(Color.RED)
+[<Color.RED: 1>]
+>>> list(purple)
+[<Color.RED: 1>, <Color.BLUE: 4>]
+

Changed in version 3.11: Aliases are no longer returned during iteration.

+
__len__(self): +
+

Returns number of members in flag:

>>> len(Color.GREEN)
+1
+>>> len(white)
+3
+
+
__bool__(self): +
+

Returns True if any members in flag, False otherwise:

>>> bool(Color.GREEN)
+True
+>>> bool(white)
+True
+>>> black = Color(0)
+>>> bool(black)
+False
+
+
+__or__(self, other)
+

Returns current flag binary or’ed with other:

>>> Color.RED | Color.GREEN
+<Color.RED|GREEN: 3>
+
+
+__and__(self, other)
+

Returns current flag binary and’ed with other:

>>> purple & white
+<Color.RED|BLUE: 5>
+>>> purple & Color.GREEN
+<Color: 0>
+
+
+__xor__(self, other)
+

Returns current flag binary xor’ed with other:

>>> purple ^ white
+<Color.GREEN: 2>
+>>> purple ^ Color.GREEN
+<Color.RED|GREEN|BLUE: 7>
+
+
__invert__(self): +
+

Returns all the flags in type(self) that are not in self:

>>> ~white
+<Color: 0>
+>>> ~purple
+<Color.GREEN: 2>
+>>> ~Color.RED
+<Color.GREEN|BLUE: 6>
+
+
+_numeric_repr_()
+

Function used to format any remaining unnamed numeric values. Default is the value’s repr; common choices are hex() and oct().

+

Note

Using auto with Flag results in integers that are powers of two, starting with 1.

Changed in version 3.11: The repr() of zero-valued flags has changed. It is now::

>>> Color(0) 
+<Color: 0>
+
+
+class enum.IntFlag
+

IntFlag is the same as Flag, but its members are also integers and can be used anywhere that an integer can be used.

>>> from enum import IntFlag, auto
+>>> class Color(IntFlag):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> Color.RED & 2
+<Color: 0>
+>>> Color.RED | 2
+<Color.RED|GREEN: 3>
+

If any integer operation is performed with an IntFlag member, the result is not an IntFlag:

>>> Color.RED + 2
+3
+

If a Flag operation is performed with an IntFlag member and:

  • the result is a valid IntFlag: an IntFlag is returned
  • the result is not a valid IntFlag: the result depends on the FlagBoundary setting

The repr() of unnamed zero-valued flags has changed. It is now:

>>> Color(0)
+<Color: 0>
+

Note

Using auto with IntFlag results in integers that are powers of two, starting with 1.

Changed in version 3.11: __str__() is now int.__str__() to better support the replacement of existing constants use-case. __format__() was already int.__format__() for that same reason.

Inversion of an IntFlag now returns a positive value that is the union of all flags not in the given flag, rather than a negative value. This matches the existing Flag behavior.

+
+class enum.ReprEnum
+

ReprEnum uses the repr() of Enum, but the str() of the mixed-in data type:

Inherit from ReprEnum to keep the str() / format() of the mixed-in data type instead of using the Enum-default str().

New in version 3.11.

+
+class enum.EnumCheck
+

EnumCheck contains the options used by the verify() decorator to ensure various constraints; failed constraints result in a ValueError.

+UNIQUE
+

Ensure that each value has only one name:

>>> from enum import Enum, verify, UNIQUE
+>>> @verify(UNIQUE)
+... class Color(Enum):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 3
+...     CRIMSON = 1
+Traceback (most recent call last):
+...
+ValueError: aliases found in <enum 'Color'>: CRIMSON -> RED
+
+
+CONTINUOUS
+

Ensure that there are no missing values between the lowest-valued member and the highest-valued member:

>>> from enum import Enum, verify, CONTINUOUS
+>>> @verify(CONTINUOUS)
+... class Color(Enum):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 5
+Traceback (most recent call last):
+...
+ValueError: invalid enum 'Color': missing values 3, 4
+
+
+NAMED_FLAGS
+

Ensure that any flag groups/masks contain only named flags – useful when values are specified instead of being generated by auto():

>>> from enum import Flag, verify, NAMED_FLAGS
+>>> @verify(NAMED_FLAGS)
+... class Color(Flag):
+...     RED = 1
+...     GREEN = 2
+...     BLUE = 4
+...     WHITE = 15
+...     NEON = 31
+Traceback (most recent call last):
+...
+ValueError: invalid Flag 'Color': aliases WHITE and NEON are missing combined values of 0x18 [use enum.show_flag_values(value) for details]
+
+

Note

CONTINUOUS and NAMED_FLAGS are designed to work with integer-valued members.

New in version 3.11.

+
+class enum.FlagBoundary
+

FlagBoundary controls how out-of-range values are handled in Flag and its subclasses.

+STRICT
+

Out-of-range values cause a ValueError to be raised. This is the default for Flag:

>>> from enum import Flag, STRICT, auto
+>>> class StrictFlag(Flag, boundary=STRICT):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> StrictFlag(2**2 + 2**4)
+Traceback (most recent call last):
+...
+ValueError: <flag 'StrictFlag'> invalid value 20
+    given 0b0 10100
+  allowed 0b0 00111
+
+
+CONFORM
+

Out-of-range values have invalid values removed, leaving a valid Flag value:

>>> from enum import Flag, CONFORM, auto
+>>> class ConformFlag(Flag, boundary=CONFORM):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> ConformFlag(2**2 + 2**4)
+<ConformFlag.BLUE: 4>
+
+
+EJECT
+

Out-of-range values lose their Flag membership and revert to int.

>>> from enum import Flag, EJECT, auto
+>>> class EjectFlag(Flag, boundary=EJECT):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> EjectFlag(2**2 + 2**4)
+20
+
+
+KEEP
+

Out-of-range values are kept, and the Flag membership is kept. This is the default for IntFlag:

>>> from enum import Flag, KEEP, auto
+>>> class KeepFlag(Flag, boundary=KEEP):
+...     RED = auto()
+...     GREEN = auto()
+...     BLUE = auto()
+...
+>>> KeepFlag(2**2 + 2**4)
+<KeepFlag.BLUE|16: 20>
+
+
+

New in version 3.11.

Supported __dunder__ names

__members__ is a read-only ordered mapping of member_name:member items. It is only available on the class.

__new__(), if specified, must create and return the enum members; it is also a very good idea to set the member’s _value_ appropriately. Once all the members are created it is no longer used.

Supported _sunder_ names

  • +_name_ – name of the member
  • +_value_ – value of the member; can be set / modified in __new__ +
  • +_missing_ – a lookup function used when a value is not found; may be overridden
  • +_ignore_ – a list of names, either as a list or a str, that will not be transformed into members, and will be removed from the final class
  • +_order_ – used in Python 2/3 code to ensure member order is consistent (class attribute, removed during class creation)
  • +

    _generate_next_value_ – used to get an appropriate value for an enum member; may be overridden

    Note

    For standard Enum classes the next value chosen is the last value seen incremented by one.

    For Flag classes the next value chosen will be the next highest power-of-two, regardless of the last value seen.

New in version 3.6: _missing_, _order_, _generate_next_value_

New in version 3.7: _ignore_

Utilities and Decorators

+class enum.auto
+

auto can be used in place of a value. If used, the Enum machinery will call an Enum’s _generate_next_value_() to get an appropriate value. For Enum and IntEnum that appropriate value will be the last value plus one; for Flag and IntFlag it will be the first power-of-two greater than the highest value; for StrEnum it will be the lower-cased version of the member’s name. Care must be taken if mixing auto() with manually specified values.

auto instances are only resolved when at the top level of an assignment:

  • +FIRST = auto() will work (auto() is replaced with 1);
  • +
    +SECOND = auto(), -2 will work (auto is replaced with 2, so 2, -2 is
    +
    +

    used to create the SECOND enum member;

  • +THREE = [auto(), -3] will not work (<auto instance>, -3 is used to create the THREE enum member)

Changed in version 3.11.1: In prior versions, auto() had to be the only thing on the assignment line to work properly.

_generate_next_value_ can be overridden to customize the values used by auto.

Note

in 3.13 the default _generate_next_value_ will always return the highest member value incremented by 1, and will fail if any member is an incompatible type.

+
+@enum.property
+

A decorator similar to the built-in property, but specifically for enumerations. It allows member attributes to have the same names as members themselves.

Note

the property and the member must be defined in separate classes; for example, the value and name attributes are defined in the Enum class, and Enum subclasses can define members with the names value and name.

New in version 3.11.

+
+@enum.unique
+

A class decorator specifically for enumerations. It searches an enumeration’s __members__, gathering any aliases it finds; if any are found ValueError is raised with the details:

>>> from enum import Enum, unique
+>>> @unique
+... class Mistake(Enum):
+...     ONE = 1
+...     TWO = 2
+...     THREE = 3
+...     FOUR = 3
+...
+Traceback (most recent call last):
+...
+ValueError: duplicate values found in <enum 'Mistake'>: FOUR -> THREE
+
+
+@enum.verify
+

A class decorator specifically for enumerations. Members from EnumCheck are used to specify which constraints should be checked on the decorated enumeration.

New in version 3.11.

+
+@enum.member
+

A decorator for use in enums: its target will become a member.

New in version 3.11.

+
+@enum.nonmember
+

A decorator for use in enums: its target will not become a member.

New in version 3.11.

+
+@enum.global_enum
+

A decorator to change the str() and repr() of an enum to show its members as belonging to the module instead of its class. Should only be used when the enum members are exported to the module global namespace (see re.RegexFlag for an example).

New in version 3.11.

+
+enum.show_flag_values(value)
+

Return a list of all power-of-two integers contained in a flag value.

New in version 3.11.

+

Notes

IntEnum, StrEnum, and IntFlag

These three enum types are designed to be drop-in replacements for existing integer- and string-based values; as such, they have extra limitations:

  • +__str__ uses the value and not the name of the enum member
  • +__format__, because it uses __str__, will also use the value of the enum member instead of its name

If you do not need/want those limitations, you can either create your own base class by mixing in the int or str type yourself:

>>> from enum import Enum
+>>> class MyIntEnum(int, Enum):
+...     pass
+

or you can reassign the appropriate str(), etc., in your enum:

>>> from enum import Enum, IntEnum
+>>> class MyIntEnum(IntEnum):
+...     __str__ = Enum.__str__
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/enum.html +

+
diff --git a/devdocs/python~3.12/library%2Ferrno.html b/devdocs/python~3.12/library%2Ferrno.html new file mode 100644 index 00000000..14b9dab1 --- /dev/null +++ b/devdocs/python~3.12/library%2Ferrno.html @@ -0,0 +1,393 @@ +

errno — Standard errno system symbols

This module makes available standard errno system symbols. The value of each symbol is the corresponding integer value. The names and descriptions are borrowed from linux/include/errno.h, which should be all-inclusive.

+errno.errorcode
+

Dictionary providing a mapping from the errno value to the string name in the underlying system. For instance, errno.errorcode[errno.EPERM] maps to 'EPERM'.

+

To translate a numeric error code to an error message, use os.strerror().

Of the following list, symbols that are not used on the current platform are not defined by the module. The specific list of defined symbols is available as errno.errorcode.keys(). Symbols available can include:

+errno.EPERM
+

Operation not permitted. This error is mapped to the exception PermissionError.

+
+errno.ENOENT
+

No such file or directory. This error is mapped to the exception FileNotFoundError.

+
+errno.ESRCH
+

No such process. This error is mapped to the exception ProcessLookupError.

+
+errno.EINTR
+

Interrupted system call. This error is mapped to the exception InterruptedError.

+
+errno.EIO
+

I/O error

+
+errno.ENXIO
+

No such device or address

+
+errno.E2BIG
+

Arg list too long

+
+errno.ENOEXEC
+

Exec format error

+
+errno.EBADF
+

Bad file number

+
+errno.ECHILD
+

No child processes. This error is mapped to the exception ChildProcessError.

+
+errno.EAGAIN
+

Try again. This error is mapped to the exception BlockingIOError.

+
+errno.ENOMEM
+

Out of memory

+
+errno.EACCES
+

Permission denied. This error is mapped to the exception PermissionError.

+
+errno.EFAULT
+

Bad address

+
+errno.ENOTBLK
+

Block device required

+
+errno.EBUSY
+

Device or resource busy

+
+errno.EEXIST
+

File exists. This error is mapped to the exception FileExistsError.

+
+errno.EXDEV
+

Cross-device link

+
+errno.ENODEV
+

No such device

+
+errno.ENOTDIR
+

Not a directory. This error is mapped to the exception NotADirectoryError.

+
+errno.EISDIR
+

Is a directory. This error is mapped to the exception IsADirectoryError.

+
+errno.EINVAL
+

Invalid argument

+
+errno.ENFILE
+

File table overflow

+
+errno.EMFILE
+

Too many open files

+
+errno.ENOTTY
+

Not a typewriter

+
+errno.ETXTBSY
+

Text file busy

+
+errno.EFBIG
+

File too large

+
+errno.ENOSPC
+

No space left on device

+
+errno.ESPIPE
+

Illegal seek

+
+errno.EROFS
+

Read-only file system

+
+

Too many links

+
+errno.EPIPE
+

Broken pipe. This error is mapped to the exception BrokenPipeError.

+
+errno.EDOM
+

Math argument out of domain of func

+
+errno.ERANGE
+

Math result not representable

+
+errno.EDEADLK
+

Resource deadlock would occur

+
+errno.ENAMETOOLONG
+

File name too long

+
+errno.ENOLCK
+

No record locks available

+
+errno.ENOSYS
+

Function not implemented

+
+errno.ENOTEMPTY
+

Directory not empty

+
+errno.ELOOP
+

Too many symbolic links encountered

+
+errno.EWOULDBLOCK
+

Operation would block. This error is mapped to the exception BlockingIOError.

+
+errno.ENOMSG
+

No message of desired type

+
+errno.EIDRM
+

Identifier removed

+
+errno.ECHRNG
+

Channel number out of range

+
+errno.EL2NSYNC
+

Level 2 not synchronized

+
+errno.EL3HLT
+

Level 3 halted

+
+errno.EL3RST
+

Level 3 reset

+
+errno.ELNRNG
+

Link number out of range

+
+errno.EUNATCH
+

Protocol driver not attached

+
+errno.ENOCSI
+

No CSI structure available

+
+errno.EL2HLT
+

Level 2 halted

+
+errno.EBADE
+

Invalid exchange

+
+errno.EBADR
+

Invalid request descriptor

+
+errno.EXFULL
+

Exchange full

+
+errno.ENOANO
+

No anode

+
+errno.EBADRQC
+

Invalid request code

+
+errno.EBADSLT
+

Invalid slot

+
+errno.EDEADLOCK
+

File locking deadlock error

+
+errno.EBFONT
+

Bad font file format

+
+errno.ENOSTR
+

Device not a stream

+
+errno.ENODATA
+

No data available

+
+errno.ETIME
+

Timer expired

+
+errno.ENOSR
+

Out of streams resources

+
+errno.ENONET
+

Machine is not on the network

+
+errno.ENOPKG
+

Package not installed

+
+errno.EREMOTE
+

Object is remote

+
+

Link has been severed

+
+errno.EADV
+

Advertise error

+
+errno.ESRMNT
+

Srmount error

+
+errno.ECOMM
+

Communication error on send

+
+errno.EPROTO
+

Protocol error

+
+errno.EMULTIHOP
+

Multihop attempted

+
+errno.EDOTDOT
+

RFS specific error

+
+errno.EBADMSG
+

Not a data message

+
+errno.EOVERFLOW
+

Value too large for defined data type

+
+errno.ENOTUNIQ
+

Name not unique on network

+
+errno.EBADFD
+

File descriptor in bad state

+
+errno.EREMCHG
+

Remote address changed

+
+errno.ELIBACC
+

Can not access a needed shared library

+
+errno.ELIBBAD
+

Accessing a corrupted shared library

+
+errno.ELIBSCN
+

.lib section in a.out corrupted

+
+errno.ELIBMAX
+

Attempting to link in too many shared libraries

+
+errno.ELIBEXEC
+

Cannot exec a shared library directly

+
+errno.EILSEQ
+

Illegal byte sequence

+
+errno.ERESTART
+

Interrupted system call should be restarted

+
+errno.ESTRPIPE
+

Streams pipe error

+
+errno.EUSERS
+

Too many users

+
+errno.ENOTSOCK
+

Socket operation on non-socket

+
+errno.EDESTADDRREQ
+

Destination address required

+
+errno.EMSGSIZE
+

Message too long

+
+errno.EPROTOTYPE
+

Protocol wrong type for socket

+
+errno.ENOPROTOOPT
+

Protocol not available

+
+errno.EPROTONOSUPPORT
+

Protocol not supported

+
+errno.ESOCKTNOSUPPORT
+

Socket type not supported

+
+errno.EOPNOTSUPP
+

Operation not supported on transport endpoint

+
+errno.ENOTSUP
+

Operation not supported

New in version 3.2.

+
+errno.EPFNOSUPPORT
+

Protocol family not supported

+
+errno.EAFNOSUPPORT
+

Address family not supported by protocol

+
+errno.EADDRINUSE
+

Address already in use

+
+errno.EADDRNOTAVAIL
+

Cannot assign requested address

+
+errno.ENETDOWN
+

Network is down

+
+errno.ENETUNREACH
+

Network is unreachable

+
+errno.ENETRESET
+

Network dropped connection because of reset

+
+errno.ECONNABORTED
+

Software caused connection abort. This error is mapped to the exception ConnectionAbortedError.

+
+errno.ECONNRESET
+

Connection reset by peer. This error is mapped to the exception ConnectionResetError.

+
+errno.ENOBUFS
+

No buffer space available

+
+errno.EISCONN
+

Transport endpoint is already connected

+
+errno.ENOTCONN
+

Transport endpoint is not connected

+
+errno.ESHUTDOWN
+

Cannot send after transport endpoint shutdown. This error is mapped to the exception BrokenPipeError.

+
+errno.ETOOMANYREFS
+

Too many references: cannot splice

+
+errno.ETIMEDOUT
+

Connection timed out. This error is mapped to the exception TimeoutError.

+
+errno.ECONNREFUSED
+

Connection refused. This error is mapped to the exception ConnectionRefusedError.

+
+errno.EHOSTDOWN
+

Host is down

+
+errno.EHOSTUNREACH
+

No route to host

+
+errno.EALREADY
+

Operation already in progress. This error is mapped to the exception BlockingIOError.

+
+errno.EINPROGRESS
+

Operation now in progress. This error is mapped to the exception BlockingIOError.

+
+errno.ESTALE
+

Stale NFS file handle

+
+errno.EUCLEAN
+

Structure needs cleaning

+
+errno.ENOTNAM
+

Not a XENIX named type file

+
+errno.ENAVAIL
+

No XENIX semaphores available

+
+errno.EISNAM
+

Is a named type file

+
+errno.EREMOTEIO
+

Remote I/O error

+
+errno.EDQUOT
+

Quota exceeded

+
+errno.EQFULL
+

Interface output queue is full

New in version 3.11.

+
+errno.ENOTCAPABLE
+

Capabilities insufficient. This error is mapped to the exception PermissionError.

Availability: WASI, FreeBSD

New in version 3.11.1.

+
+errno.ECANCELED
+

Operation canceled

New in version 3.2.

+
+errno.EOWNERDEAD
+

Owner died

New in version 3.2.

+
+errno.ENOTRECOVERABLE
+

State not recoverable

New in version 3.2.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/errno.html +

+
diff --git a/devdocs/python~3.12/library%2Fexceptions.html b/devdocs/python~3.12/library%2Fexceptions.html new file mode 100644 index 00000000..257a7b9c --- /dev/null +++ b/devdocs/python~3.12/library%2Fexceptions.html @@ -0,0 +1,413 @@ +

Built-in Exceptions

In Python, all exceptions must be instances of a class that derives from BaseException. In a try statement with an except clause that mentions a particular class, that clause also handles any exception classes derived from that class (but not exception classes from which it is derived). Two exception classes that are not related via subclassing are never equivalent, even if they have the same name.

The built-in exceptions listed below can be generated by the interpreter or built-in functions. Except where mentioned, they have an “associated value” indicating the detailed cause of the error. This may be a string or a tuple of several items of information (e.g., an error code and a string explaining the code). The associated value is usually passed as arguments to the exception class’s constructor.

User code can raise built-in exceptions. This can be used to test an exception handler or to report an error condition “just like” the situation in which the interpreter raises the same exception; but beware that there is nothing to prevent user code from raising an inappropriate error.

The built-in exception classes can be subclassed to define new exceptions; programmers are encouraged to derive new exceptions from the Exception class or one of its subclasses, and not from BaseException. More information on defining exceptions is available in the Python Tutorial under User-defined Exceptions.

Exception context

Three attributes on exception objects provide information about the context in which the exception was raised:

+BaseException.__context__
+BaseException.__cause__
+BaseException.__suppress_context__
+

When raising a new exception while another exception is already being handled, the new exception’s __context__ attribute is automatically set to the handled exception. An exception may be handled when an except or finally clause, or a with statement, is used.

This implicit exception context can be supplemented with an explicit cause by using from with raise:

raise new_exc from original_exc
+

The expression following from must be an exception or None. It will be set as __cause__ on the raised exception. Setting __cause__ also implicitly sets the __suppress_context__ attribute to True, so that using raise new_exc from None effectively replaces the old exception with the new one for display purposes (e.g. converting KeyError to AttributeError), while leaving the old exception available in __context__ for introspection when debugging.

The default traceback display code shows these chained exceptions in addition to the traceback for the exception itself. An explicitly chained exception in __cause__ is always shown when present. An implicitly chained exception in __context__ is shown only if __cause__ is None and __suppress_context__ is false.

In either case, the exception itself is always shown after any chained exceptions so that the final line of the traceback always shows the last exception that was raised.

+

Inheriting from built-in exceptions

User code can create subclasses that inherit from an exception type. It’s recommended to only subclass one exception type at a time to avoid any possible conflicts between how the bases handle the args attribute, as well as due to possible memory layout incompatibilities.

CPython implementation detail: Most built-in exceptions are implemented in C for efficiency, see: Objects/exceptions.c. Some have custom memory layouts which makes it impossible to create a subclass that inherits from multiple exception types. The memory layout of a type is an implementation detail and might change between Python versions, leading to new conflicts in the future. Therefore, it’s recommended to avoid subclassing multiple exception types altogether.

Base classes

The following exceptions are used mostly as base classes for other exceptions.

+exception BaseException
+

The base class for all built-in exceptions. It is not meant to be directly inherited by user-defined classes (for that, use Exception). If str() is called on an instance of this class, the representation of the argument(s) to the instance are returned, or the empty string when there were no arguments.

+args
+

The tuple of arguments given to the exception constructor. Some built-in exceptions (like OSError) expect a certain number of arguments and assign a special meaning to the elements of this tuple, while others are usually called only with a single string giving an error message.

+
+with_traceback(tb)
+

This method sets tb as the new traceback for the exception and returns the exception object. It was more commonly used before the exception chaining features of PEP 3134 became available. The following example shows how we can convert an instance of SomeException into an instance of OtherException while preserving the traceback. Once raised, the current frame is pushed onto the traceback of the OtherException, as would have happened to the traceback of the original SomeException had we allowed it to propagate to the caller.

try:
+    ...
+except SomeException:
+    tb = sys.exception().__traceback__
+    raise OtherException(...).with_traceback(tb)
+
+
+__traceback__
+

A writable field that holds the traceback object associated with this exception. See also: The raise statement.

+
+add_note(note)
+

Add the string note to the exception’s notes which appear in the standard traceback after the exception string. A TypeError is raised if note is not a string.

New in version 3.11.

+
+__notes__
+

A list of the notes of this exception, which were added with add_note(). This attribute is created when add_note() is called.

New in version 3.11.

+
+
+exception Exception
+

All built-in, non-system-exiting exceptions are derived from this class. All user-defined exceptions should also be derived from this class.

+
+exception ArithmeticError
+

The base class for those built-in exceptions that are raised for various arithmetic errors: OverflowError, ZeroDivisionError, FloatingPointError.

+
+exception BufferError
+

Raised when a buffer related operation cannot be performed.

+
+exception LookupError
+

The base class for the exceptions that are raised when a key or index used on a mapping or sequence is invalid: IndexError, KeyError. This can be raised directly by codecs.lookup().

+

Concrete exceptions

The following exceptions are the exceptions that are usually raised.

+exception AssertionError
+

Raised when an assert statement fails.

+
+exception AttributeError
+

Raised when an attribute reference (see Attribute references) or assignment fails. (When an object does not support attribute references or attribute assignments at all, TypeError is raised.)

The name and obj attributes can be set using keyword-only arguments to the constructor. When set they represent the name of the attribute that was attempted to be accessed and the object that was accessed for said attribute, respectively.

Changed in version 3.10: Added the name and obj attributes.

+
+exception EOFError
+

Raised when the input() function hits an end-of-file condition (EOF) without reading any data. (N.B.: the io.IOBase.read() and io.IOBase.readline() methods return an empty string when they hit EOF.)

+
+exception FloatingPointError
+

Not currently used.

+
+exception GeneratorExit
+

Raised when a generator or coroutine is closed; see generator.close() and coroutine.close(). It directly inherits from BaseException instead of Exception since it is technically not an error.

+
+exception ImportError
+

Raised when the import statement has troubles trying to load a module. Also raised when the “from list” in from ... import has a name that cannot be found.

The optional name and path keyword-only arguments set the corresponding attributes:

+name
+

The name of the module that was attempted to be imported.

+
+path
+

The path to any file which triggered the exception.

+

Changed in version 3.3: Added the name and path attributes.

+
+exception ModuleNotFoundError
+

A subclass of ImportError which is raised by import when a module could not be located. It is also raised when None is found in sys.modules.

New in version 3.6.

+
+exception IndexError
+

Raised when a sequence subscript is out of range. (Slice indices are silently truncated to fall in the allowed range; if an index is not an integer, TypeError is raised.)

+
+exception KeyError
+

Raised when a mapping (dictionary) key is not found in the set of existing keys.

+
+exception KeyboardInterrupt
+

Raised when the user hits the interrupt key (normally Control-C or Delete). During execution, a check for interrupts is made regularly. The exception inherits from BaseException so as to not be accidentally caught by code that catches Exception and thus prevent the interpreter from exiting.

Note

Catching a KeyboardInterrupt requires special consideration. Because it can be raised at unpredictable points, it may, in some circumstances, leave the running program in an inconsistent state. It is generally best to allow KeyboardInterrupt to end the program as quickly as possible or avoid raising it entirely. (See Note on Signal Handlers and Exceptions.)

+
+exception MemoryError
+

Raised when an operation runs out of memory but the situation may still be rescued (by deleting some objects). The associated value is a string indicating what kind of (internal) operation ran out of memory. Note that because of the underlying memory management architecture (C’s malloc() function), the interpreter may not always be able to completely recover from this situation; it nevertheless raises an exception so that a stack traceback can be printed, in case a run-away program was the cause.

+
+exception NameError
+

Raised when a local or global name is not found. This applies only to unqualified names. The associated value is an error message that includes the name that could not be found.

The name attribute can be set using a keyword-only argument to the constructor. When set it represent the name of the variable that was attempted to be accessed.

Changed in version 3.10: Added the name attribute.

+
+exception NotImplementedError
+

This exception is derived from RuntimeError. In user defined base classes, abstract methods should raise this exception when they require derived classes to override the method, or while the class is being developed to indicate that the real implementation still needs to be added.

Note

It should not be used to indicate that an operator or method is not meant to be supported at all – in that case either leave the operator / method undefined or, if a subclass, set it to None.

Note

NotImplementedError and NotImplemented are not interchangeable, even though they have similar names and purposes. See NotImplemented for details on when to use it.

+
+exception OSError([arg])
exception OSError(errno, strerror[, filename[, winerror[, filename2]]]) +
+

This exception is raised when a system function returns a system-related error, including I/O failures such as “file not found” or “disk full” (not for illegal argument types or other incidental errors).

The second form of the constructor sets the corresponding attributes, described below. The attributes default to None if not specified. For backwards compatibility, if three arguments are passed, the args attribute contains only a 2-tuple of the first two constructor arguments.

The constructor often actually returns a subclass of OSError, as described in OS exceptions below. The particular subclass depends on the final errno value. This behaviour only occurs when constructing OSError directly or via an alias, and is not inherited when subclassing.

+errno
+

A numeric error code from the C variable errno.

+
+winerror
+

Under Windows, this gives you the native Windows error code. The errno attribute is then an approximate translation, in POSIX terms, of that native error code.

Under Windows, if the winerror constructor argument is an integer, the errno attribute is determined from the Windows error code, and the errno argument is ignored. On other platforms, the winerror argument is ignored, and the winerror attribute does not exist.

+
+strerror
+

The corresponding error message, as provided by the operating system. It is formatted by the C functions perror() under POSIX, and FormatMessage() under Windows.

+
+filename
+filename2
+

For exceptions that involve a file system path (such as open() or os.unlink()), filename is the file name passed to the function. For functions that involve two file system paths (such as os.rename()), filename2 corresponds to the second file name passed to the function.

+

Changed in version 3.3: EnvironmentError, IOError, WindowsError, socket.error, select.error and mmap.error have been merged into OSError, and the constructor may return a subclass.

Changed in version 3.4: The filename attribute is now the original file name passed to the function, instead of the name encoded to or decoded from the filesystem encoding and error handler. Also, the filename2 constructor argument and attribute was added.

+
+exception OverflowError
+

Raised when the result of an arithmetic operation is too large to be represented. This cannot occur for integers (which would rather raise MemoryError than give up). However, for historical reasons, OverflowError is sometimes raised for integers that are outside a required range. Because of the lack of standardization of floating point exception handling in C, most floating point operations are not checked.

+
+exception RecursionError
+

This exception is derived from RuntimeError. It is raised when the interpreter detects that the maximum recursion depth (see sys.getrecursionlimit()) is exceeded.

New in version 3.5: Previously, a plain RuntimeError was raised.

+
+exception ReferenceError
+

This exception is raised when a weak reference proxy, created by the weakref.proxy() function, is used to access an attribute of the referent after it has been garbage collected. For more information on weak references, see the weakref module.

+
+exception RuntimeError
+

Raised when an error is detected that doesn’t fall in any of the other categories. The associated value is a string indicating what precisely went wrong.

+
+exception StopIteration
+

Raised by built-in function next() and an iterator's __next__() method to signal that there are no further items produced by the iterator.

+value
+

The exception object has a single attribute value, which is given as an argument when constructing the exception, and defaults to None.

+

When a generator or coroutine function returns, a new StopIteration instance is raised, and the value returned by the function is used as the value parameter to the constructor of the exception.

If a generator code directly or indirectly raises StopIteration, it is converted into a RuntimeError (retaining the StopIteration as the new exception’s cause).

Changed in version 3.3: Added value attribute and the ability for generator functions to use it to return a value.

Changed in version 3.5: Introduced the RuntimeError transformation via from __future__ import generator_stop, see PEP 479.

Changed in version 3.7: Enable PEP 479 for all code by default: a StopIteration error raised in a generator is transformed into a RuntimeError.

+
+exception StopAsyncIteration
+

Must be raised by __anext__() method of an asynchronous iterator object to stop the iteration.

New in version 3.5.

+
+exception SyntaxError(message, details)
+

Raised when the parser encounters a syntax error. This may occur in an import statement, in a call to the built-in functions compile(), exec(), or eval(), or when reading the initial script or standard input (also interactively).

The str() of the exception instance returns only the error message. Details is a tuple whose members are also available as separate attributes.

+filename
+

The name of the file the syntax error occurred in.

+
+lineno
+

Which line number in the file the error occurred in. This is 1-indexed: the first line in the file has a lineno of 1.

+
+offset
+

The column in the line where the error occurred. This is 1-indexed: the first character in the line has an offset of 1.

+
+text
+

The source code text involved in the error.

+
+end_lineno
+

Which line number in the file the error occurred ends in. This is 1-indexed: the first line in the file has a lineno of 1.

+
+end_offset
+

The column in the end line where the error occurred finishes. This is 1-indexed: the first character in the line has an offset of 1.

+

For errors in f-string fields, the message is prefixed by “f-string: ” and the offsets are offsets in a text constructed from the replacement expression. For example, compiling f’Bad {a b} field’ results in this args attribute: (‘f-string: …’, (‘’, 1, 2, ‘(a b)n’, 1, 5)).

Changed in version 3.10: Added the end_lineno and end_offset attributes.

+
+exception IndentationError
+

Base class for syntax errors related to incorrect indentation. This is a subclass of SyntaxError.

+
+exception TabError
+

Raised when indentation contains an inconsistent use of tabs and spaces. This is a subclass of IndentationError.

+
+exception SystemError
+

Raised when the interpreter finds an internal error, but the situation does not look so serious to cause it to abandon all hope. The associated value is a string indicating what went wrong (in low-level terms).

You should report this to the author or maintainer of your Python interpreter. Be sure to report the version of the Python interpreter (sys.version; it is also printed at the start of an interactive Python session), the exact error message (the exception’s associated value) and if possible the source of the program that triggered the error.

+
+exception SystemExit
+

This exception is raised by the sys.exit() function. It inherits from BaseException instead of Exception so that it is not accidentally caught by code that catches Exception. This allows the exception to properly propagate up and cause the interpreter to exit. When it is not handled, the Python interpreter exits; no stack traceback is printed. The constructor accepts the same optional argument passed to sys.exit(). If the value is an integer, it specifies the system exit status (passed to C’s exit() function); if it is None, the exit status is zero; if it has another type (such as a string), the object’s value is printed and the exit status is one.

A call to sys.exit() is translated into an exception so that clean-up handlers (finally clauses of try statements) can be executed, and so that a debugger can execute a script without running the risk of losing control. The os._exit() function can be used if it is absolutely positively necessary to exit immediately (for example, in the child process after a call to os.fork()).

+code
+

The exit status or error message that is passed to the constructor. (Defaults to None.)

+
+
+exception TypeError
+

Raised when an operation or function is applied to an object of inappropriate type. The associated value is a string giving details about the type mismatch.

This exception may be raised by user code to indicate that an attempted operation on an object is not supported, and is not meant to be. If an object is meant to support a given operation but has not yet provided an implementation, NotImplementedError is the proper exception to raise.

Passing arguments of the wrong type (e.g. passing a list when an int is expected) should result in a TypeError, but passing arguments with the wrong value (e.g. a number outside expected boundaries) should result in a ValueError.

+
+exception UnboundLocalError
+

Raised when a reference is made to a local variable in a function or method, but no value has been bound to that variable. This is a subclass of NameError.

+
+exception UnicodeError
+

Raised when a Unicode-related encoding or decoding error occurs. It is a subclass of ValueError.

UnicodeError has attributes that describe the encoding or decoding error. For example, err.object[err.start:err.end] gives the particular invalid input that the codec failed on.

+encoding
+

The name of the encoding that raised the error.

+
+reason
+

A string describing the specific codec error.

+
+object
+

The object the codec was attempting to encode or decode.

+
+start
+

The first index of invalid data in object.

+
+end
+

The index after the last invalid data in object.

+
+
+exception UnicodeEncodeError
+

Raised when a Unicode-related error occurs during encoding. It is a subclass of UnicodeError.

+
+exception UnicodeDecodeError
+

Raised when a Unicode-related error occurs during decoding. It is a subclass of UnicodeError.

+
+exception UnicodeTranslateError
+

Raised when a Unicode-related error occurs during translating. It is a subclass of UnicodeError.

+
+exception ValueError
+

Raised when an operation or function receives an argument that has the right type but an inappropriate value, and the situation is not described by a more precise exception such as IndexError.

+
+exception ZeroDivisionError
+

Raised when the second argument of a division or modulo operation is zero. The associated value is a string indicating the type of the operands and the operation.

+

The following exceptions are kept for compatibility with previous versions; starting from Python 3.3, they are aliases of OSError.

+exception EnvironmentError
+
+exception IOError
+
+exception WindowsError
+

Only available on Windows.

+

OS exceptions

The following exceptions are subclasses of OSError, they get raised depending on the system error code.

+exception BlockingIOError
+

Raised when an operation would block on an object (e.g. socket) set for non-blocking operation. Corresponds to errno EAGAIN, EALREADY, EWOULDBLOCK and EINPROGRESS.

In addition to those of OSError, BlockingIOError can have one more attribute:

+characters_written
+

An integer containing the number of characters written to the stream before it blocked. This attribute is available when using the buffered I/O classes from the io module.

+
+
+exception ChildProcessError
+

Raised when an operation on a child process failed. Corresponds to errno ECHILD.

+
+exception ConnectionError
+

A base class for connection-related issues.

Subclasses are BrokenPipeError, ConnectionAbortedError, ConnectionRefusedError and ConnectionResetError.

+
+exception BrokenPipeError
+

A subclass of ConnectionError, raised when trying to write on a pipe while the other end has been closed, or trying to write on a socket which has been shutdown for writing. Corresponds to errno EPIPE and ESHUTDOWN.

+
+exception ConnectionAbortedError
+

A subclass of ConnectionError, raised when a connection attempt is aborted by the peer. Corresponds to errno ECONNABORTED.

+
+exception ConnectionRefusedError
+

A subclass of ConnectionError, raised when a connection attempt is refused by the peer. Corresponds to errno ECONNREFUSED.

+
+exception ConnectionResetError
+

A subclass of ConnectionError, raised when a connection is reset by the peer. Corresponds to errno ECONNRESET.

+
+exception FileExistsError
+

Raised when trying to create a file or directory which already exists. Corresponds to errno EEXIST.

+
+exception FileNotFoundError
+

Raised when a file or directory is requested but doesn’t exist. Corresponds to errno ENOENT.

+
+exception InterruptedError
+

Raised when a system call is interrupted by an incoming signal. Corresponds to errno EINTR.

Changed in version 3.5: Python now retries system calls when a syscall is interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+
+exception IsADirectoryError
+

Raised when a file operation (such as os.remove()) is requested on a directory. Corresponds to errno EISDIR.

+
+exception NotADirectoryError
+

Raised when a directory operation (such as os.listdir()) is requested on something which is not a directory. On most POSIX platforms, it may also be raised if an operation attempts to open or traverse a non-directory file as if it were a directory. Corresponds to errno ENOTDIR.

+
+exception PermissionError
+

Raised when trying to run an operation without the adequate access rights - for example filesystem permissions. Corresponds to errno EACCES, EPERM, and ENOTCAPABLE.

Changed in version 3.11.1: WASI’s ENOTCAPABLE is now mapped to PermissionError.

+
+exception ProcessLookupError
+

Raised when a given process doesn’t exist. Corresponds to errno ESRCH.

+
+exception TimeoutError
+

Raised when a system function timed out at the system level. Corresponds to errno ETIMEDOUT.

+

New in version 3.3: All the above OSError subclasses were added.

See also

PEP 3151 - Reworking the OS and IO exception hierarchy

Warnings

The following exceptions are used as warning categories; see the Warning Categories documentation for more details.

+exception Warning
+

Base class for warning categories.

+
+exception UserWarning
+

Base class for warnings generated by user code.

+
+exception DeprecationWarning
+

Base class for warnings about deprecated features when those warnings are intended for other Python developers.

Ignored by the default warning filters, except in the __main__ module (PEP 565). Enabling the Python Development Mode shows this warning.

The deprecation policy is described in PEP 387.

+
+exception PendingDeprecationWarning
+

Base class for warnings about features which are obsolete and expected to be deprecated in the future, but are not deprecated at the moment.

This class is rarely used as emitting a warning about a possible upcoming deprecation is unusual, and DeprecationWarning is preferred for already active deprecations.

Ignored by the default warning filters. Enabling the Python Development Mode shows this warning.

The deprecation policy is described in PEP 387.

+
+exception SyntaxWarning
+

Base class for warnings about dubious syntax.

+
+exception RuntimeWarning
+

Base class for warnings about dubious runtime behavior.

+
+exception FutureWarning
+

Base class for warnings about deprecated features when those warnings are intended for end users of applications that are written in Python.

+
+exception ImportWarning
+

Base class for warnings about probable mistakes in module imports.

Ignored by the default warning filters. Enabling the Python Development Mode shows this warning.

+
+exception UnicodeWarning
+

Base class for warnings related to Unicode.

+
+exception EncodingWarning
+

Base class for warnings related to encodings.

See Opt-in EncodingWarning for details.

New in version 3.10.

+
+exception BytesWarning
+

Base class for warnings related to bytes and bytearray.

+
+exception ResourceWarning
+

Base class for warnings related to resource usage.

Ignored by the default warning filters. Enabling the Python Development Mode shows this warning.

New in version 3.2.

+

Exception groups

The following are used when it is necessary to raise multiple unrelated exceptions. They are part of the exception hierarchy so they can be handled with except like all other exceptions. In addition, they are recognised by except*, which matches their subgroups based on the types of the contained exceptions.

+exception ExceptionGroup(msg, excs)
+
+exception BaseExceptionGroup(msg, excs)
+

Both of these exception types wrap the exceptions in the sequence excs. The msg parameter must be a string. The difference between the two classes is that BaseExceptionGroup extends BaseException and it can wrap any exception, while ExceptionGroup extends Exception and it can only wrap subclasses of Exception. This design is so that except Exception catches an ExceptionGroup but not BaseExceptionGroup.

The BaseExceptionGroup constructor returns an ExceptionGroup rather than a BaseExceptionGroup if all contained exceptions are Exception instances, so it can be used to make the selection automatic. The ExceptionGroup constructor, on the other hand, raises a TypeError if any contained exception is not an Exception subclass.

+message
+

The msg argument to the constructor. This is a read-only attribute.

+
+exceptions
+

A tuple of the exceptions in the excs sequence given to the constructor. This is a read-only attribute.

+
+subgroup(condition)
+

Returns an exception group that contains only the exceptions from the current group that match condition, or None if the result is empty.

The condition can be either a function that accepts an exception and returns true for those that should be in the subgroup, or it can be an exception type or a tuple of exception types, which is used to check for a match using the same check that is used in an except clause.

The nesting structure of the current exception is preserved in the result, as are the values of its message, __traceback__, __cause__, __context__ and __notes__ fields. Empty nested groups are omitted from the result.

The condition is checked for all exceptions in the nested exception group, including the top-level and any nested exception groups. If the condition is true for such an exception group, it is included in the result in full.

+
+split(condition)
+

Like subgroup(), but returns the pair (match, rest) where match is subgroup(condition) and rest is the remaining non-matching part.

+
+derive(excs)
+

Returns an exception group with the same message, but which wraps the exceptions in excs.

This method is used by subgroup() and split(). A subclass needs to override it in order to make subgroup() and split() return instances of the subclass rather than ExceptionGroup.

subgroup() and split() copy the __traceback__, __cause__, __context__ and __notes__ fields from the original exception group to the one returned by derive(), so these fields do not need to be updated by derive().

>>> class MyGroup(ExceptionGroup):
+...     def derive(self, excs):
+...         return MyGroup(self.message, excs)
+...
+>>> e = MyGroup("eg", [ValueError(1), TypeError(2)])
+>>> e.add_note("a note")
+>>> e.__context__ = Exception("context")
+>>> e.__cause__ = Exception("cause")
+>>> try:
+...    raise e
+... except Exception as e:
+...    exc = e
+...
+>>> match, rest = exc.split(ValueError)
+>>> exc, exc.__context__, exc.__cause__, exc.__notes__
+(MyGroup('eg', [ValueError(1), TypeError(2)]), Exception('context'), Exception('cause'), ['a note'])
+>>> match, match.__context__, match.__cause__, match.__notes__
+(MyGroup('eg', [ValueError(1)]), Exception('context'), Exception('cause'), ['a note'])
+>>> rest, rest.__context__, rest.__cause__, rest.__notes__
+(MyGroup('eg', [TypeError(2)]), Exception('context'), Exception('cause'), ['a note'])
+>>> exc.__traceback__ is match.__traceback__ is rest.__traceback__
+True
+
+

Note that BaseExceptionGroup defines __new__(), so subclasses that need a different constructor signature need to override that rather than __init__(). For example, the following defines an exception group subclass which accepts an exit_code and and constructs the group’s message from it.

class Errors(ExceptionGroup):
+   def __new__(cls, errors, exit_code):
+      self = super().__new__(Errors, f"exit code: {exit_code}", errors)
+      self.exit_code = exit_code
+      return self
+
+   def derive(self, excs):
+      return Errors(excs, self.exit_code)
+

Like ExceptionGroup, any subclass of BaseExceptionGroup which is also a subclass of Exception can only wrap instances of Exception.

New in version 3.11.

+

Exception hierarchy

The class hierarchy for built-in exceptions is:

BaseException
+ ├── BaseExceptionGroup
+ ├── GeneratorExit
+ ├── KeyboardInterrupt
+ ├── SystemExit
+ └── Exception
+      ├── ArithmeticError
+      │    ├── FloatingPointError
+      │    ├── OverflowError
+      │    └── ZeroDivisionError
+      ├── AssertionError
+      ├── AttributeError
+      ├── BufferError
+      ├── EOFError
+      ├── ExceptionGroup [BaseExceptionGroup]
+      ├── ImportError
+      │    └── ModuleNotFoundError
+      ├── LookupError
+      │    ├── IndexError
+      │    └── KeyError
+      ├── MemoryError
+      ├── NameError
+      │    └── UnboundLocalError
+      ├── OSError
+      │    ├── BlockingIOError
+      │    ├── ChildProcessError
+      │    ├── ConnectionError
+      │    │    ├── BrokenPipeError
+      │    │    ├── ConnectionAbortedError
+      │    │    ├── ConnectionRefusedError
+      │    │    └── ConnectionResetError
+      │    ├── FileExistsError
+      │    ├── FileNotFoundError
+      │    ├── InterruptedError
+      │    ├── IsADirectoryError
+      │    ├── NotADirectoryError
+      │    ├── PermissionError
+      │    ├── ProcessLookupError
+      │    └── TimeoutError
+      ├── ReferenceError
+      ├── RuntimeError
+      │    ├── NotImplementedError
+      │    └── RecursionError
+      ├── StopAsyncIteration
+      ├── StopIteration
+      ├── SyntaxError
+      │    └── IndentationError
+      │         └── TabError
+      ├── SystemError
+      ├── TypeError
+      ├── ValueError
+      │    └── UnicodeError
+      │         ├── UnicodeDecodeError
+      │         ├── UnicodeEncodeError
+      │         └── UnicodeTranslateError
+      └── Warning
+           ├── BytesWarning
+           ├── DeprecationWarning
+           ├── EncodingWarning
+           ├── FutureWarning
+           ├── ImportWarning
+           ├── PendingDeprecationWarning
+           ├── ResourceWarning
+           ├── RuntimeWarning
+           ├── SyntaxWarning
+           ├── UnicodeWarning
+           └── UserWarning
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/exceptions.html +

+
diff --git a/devdocs/python~3.12/library%2Ffaulthandler.html b/devdocs/python~3.12/library%2Ffaulthandler.html new file mode 100644 index 00000000..2e8ce8ae --- /dev/null +++ b/devdocs/python~3.12/library%2Ffaulthandler.html @@ -0,0 +1,50 @@ +

faulthandler — Dump the Python traceback

New in version 3.3.

This module contains functions to dump Python tracebacks explicitly, on a fault, after a timeout, or on a user signal. Call faulthandler.enable() to install fault handlers for the SIGSEGV, SIGFPE, SIGABRT, SIGBUS, and SIGILL signals. You can also enable them at startup by setting the PYTHONFAULTHANDLER environment variable or by using the -X faulthandler command line option.

The fault handler is compatible with system fault handlers like Apport or the Windows fault handler. The module uses an alternative stack for signal handlers if the sigaltstack() function is available. This allows it to dump the traceback even on a stack overflow.

The fault handler is called on catastrophic cases and therefore can only use signal-safe functions (e.g. it cannot allocate memory on the heap). Because of this limitation traceback dumping is minimal compared to normal Python tracebacks:

By default, the Python traceback is written to sys.stderr. To see tracebacks, applications must be run in the terminal. A log file can alternatively be passed to faulthandler.enable().

The module is implemented in C, so tracebacks can be dumped on a crash or when Python is deadlocked.

The Python Development Mode calls faulthandler.enable() at Python startup.

See also

+Module pdb +
+
+

Interactive source code debugger for Python programs.

+Module traceback +
+
+

Standard interface to extract, format and print stack traces of Python programs.

Dumping the traceback

+faulthandler.dump_traceback(file=sys.stderr, all_threads=True)
+

Dump the tracebacks of all threads into file. If all_threads is False, dump only the current thread.

See also

traceback.print_tb(), which can be used to print a traceback object.

Changed in version 3.5: Added support for passing file descriptor to this function.

+

Fault handler state

+faulthandler.enable(file=sys.stderr, all_threads=True)
+

Enable the fault handler: install handlers for the SIGSEGV, SIGFPE, SIGABRT, SIGBUS and SIGILL signals to dump the Python traceback. If all_threads is True, produce tracebacks for every running thread. Otherwise, dump only the current thread.

The file must be kept open until the fault handler is disabled: see issue with file descriptors.

Changed in version 3.5: Added support for passing file descriptor to this function.

Changed in version 3.6: On Windows, a handler for Windows exception is also installed.

Changed in version 3.10: The dump now mentions if a garbage collector collection is running if all_threads is true.

+
+faulthandler.disable()
+

Disable the fault handler: uninstall the signal handlers installed by enable().

+
+faulthandler.is_enabled()
+

Check if the fault handler is enabled.

+

Dumping the tracebacks after a timeout

+faulthandler.dump_traceback_later(timeout, repeat=False, file=sys.stderr, exit=False)
+

Dump the tracebacks of all threads, after a timeout of timeout seconds, or every timeout seconds if repeat is True. If exit is True, call _exit() with status=1 after dumping the tracebacks. (Note _exit() exits the process immediately, which means it doesn’t do any cleanup like flushing file buffers.) If the function is called twice, the new call replaces previous parameters and resets the timeout. The timer has a sub-second resolution.

The file must be kept open until the traceback is dumped or cancel_dump_traceback_later() is called: see issue with file descriptors.

This function is implemented using a watchdog thread.

Changed in version 3.7: This function is now always available.

Changed in version 3.5: Added support for passing file descriptor to this function.

+
+faulthandler.cancel_dump_traceback_later()
+

Cancel the last call to dump_traceback_later().

+

Dumping the traceback on a user signal

+faulthandler.register(signum, file=sys.stderr, all_threads=True, chain=False)
+

Register a user signal: install a handler for the signum signal to dump the traceback of all threads, or of the current thread if all_threads is False, into file. Call the previous handler if chain is True.

The file must be kept open until the signal is unregistered by unregister(): see issue with file descriptors.

Not available on Windows.

Changed in version 3.5: Added support for passing file descriptor to this function.

+
+faulthandler.unregister(signum)
+

Unregister a user signal: uninstall the handler of the signum signal installed by register(). Return True if the signal was registered, False otherwise.

Not available on Windows.

+

Issue with file descriptors

enable(), dump_traceback_later() and register() keep the file descriptor of their file argument. If the file is closed and its file descriptor is reused by a new file, or if os.dup2() is used to replace the file descriptor, the traceback will be written into a different file. Call these functions again each time that the file is replaced.

Example

Example of a segmentation fault on Linux with and without enabling the fault handler:

$ python -c "import ctypes; ctypes.string_at(0)"
+Segmentation fault
+
+$ python -q -X faulthandler
+>>> import ctypes
+>>> ctypes.string_at(0)
+Fatal Python error: Segmentation fault
+
+Current thread 0x00007fb899f39700 (most recent call first):
+  File "/home/python/cpython/Lib/ctypes/__init__.py", line 486 in string_at
+  File "<stdin>", line 1 in <module>
+Segmentation fault
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/faulthandler.html +

+
diff --git a/devdocs/python~3.12/library%2Ffcntl.html b/devdocs/python~3.12/library%2Ffcntl.html new file mode 100644 index 00000000..e261333e --- /dev/null +++ b/devdocs/python~3.12/library%2Ffcntl.html @@ -0,0 +1,45 @@ +

fcntl — The fcntl and ioctl system calls

This module performs file control and I/O control on file descriptors. It is an interface to the fcntl() and ioctl() Unix routines. For a complete description of these calls, see fcntl(2) and ioctl(2) Unix manual pages.

Availability: Unix, not Emscripten, not WASI.

All functions in this module take a file descriptor fd as their first argument. This can be an integer file descriptor, such as returned by sys.stdin.fileno(), or an io.IOBase object, such as sys.stdin itself, which provides a fileno() that returns a genuine file descriptor.

Changed in version 3.3: Operations in this module used to raise an IOError where they now raise an OSError.

Changed in version 3.8: The fcntl module now contains F_ADD_SEALS, F_GET_SEALS, and F_SEAL_* constants for sealing of os.memfd_create() file descriptors.

Changed in version 3.9: On macOS, the fcntl module exposes the F_GETPATH constant, which obtains the path of a file from a file descriptor. On Linux(>=3.15), the fcntl module exposes the F_OFD_GETLK, F_OFD_SETLK and F_OFD_SETLKW constants, which are used when working with open file description locks.

Changed in version 3.10: On Linux >= 2.6.11, the fcntl module exposes the F_GETPIPE_SZ and F_SETPIPE_SZ constants, which allow to check and modify a pipe’s size respectively.

Changed in version 3.11: On FreeBSD, the fcntl module exposes the F_DUP2FD and F_DUP2FD_CLOEXEC constants, which allow to duplicate a file descriptor, the latter setting FD_CLOEXEC flag in addition.

Changed in version 3.12: On Linux >= 4.5, the fcntl module exposes the FICLONE and FICLONERANGE constants, which allow to share some data of one file with another file by reflinking on some filesystems (e.g., btrfs, OCFS2, and XFS). This behavior is commonly referred to as “copy-on-write”.

The module defines the following functions:

+fcntl.fcntl(fd, cmd, arg=0)
+

Perform the operation cmd on file descriptor fd (file objects providing a fileno() method are accepted as well). The values used for cmd are operating system dependent, and are available as constants in the fcntl module, using the same names as used in the relevant C header files. The argument arg can either be an integer value, or a bytes object. With an integer value, the return value of this function is the integer return value of the C fcntl() call. When the argument is bytes it represents a binary structure, e.g. created by struct.pack(). The binary data is copied to a buffer whose address is passed to the C fcntl() call. The return value after a successful call is the contents of the buffer, converted to a bytes object. The length of the returned object will be the same as the length of the arg argument. This is limited to 1024 bytes. If the information returned in the buffer by the operating system is larger than 1024 bytes, this is most likely to result in a segmentation violation or a more subtle data corruption.

If the fcntl() fails, an OSError is raised.

Raises an auditing event fcntl.fcntl with arguments fd, cmd, arg.

+
+fcntl.ioctl(fd, request, arg=0, mutate_flag=True)
+

This function is identical to the fcntl() function, except that the argument handling is even more complicated.

The request parameter is limited to values that can fit in 32-bits. Additional constants of interest for use as the request argument can be found in the termios module, under the same names as used in the relevant C header files.

The parameter arg can be one of an integer, an object supporting the read-only buffer interface (like bytes) or an object supporting the read-write buffer interface (like bytearray).

In all but the last case, behaviour is as for the fcntl() function.

If a mutable buffer is passed, then the behaviour is determined by the value of the mutate_flag parameter.

If it is false, the buffer’s mutability is ignored and behaviour is as for a read-only buffer, except that the 1024 byte limit mentioned above is avoided – so long as the buffer you pass is at least as long as what the operating system wants to put there, things should work.

If mutate_flag is true (the default), then the buffer is (in effect) passed to the underlying ioctl() system call, the latter’s return code is passed back to the calling Python, and the buffer’s new contents reflect the action of the ioctl(). This is a slight simplification, because if the supplied buffer is less than 1024 bytes long it is first copied into a static buffer 1024 bytes long which is then passed to ioctl() and copied back into the supplied buffer.

If the ioctl() fails, an OSError exception is raised.

An example:

>>> import array, fcntl, struct, termios, os
+>>> os.getpgrp()
+13341
+>>> struct.unpack('h', fcntl.ioctl(0, termios.TIOCGPGRP, "  "))[0]
+13341
+>>> buf = array.array('h', [0])
+>>> fcntl.ioctl(0, termios.TIOCGPGRP, buf, 1)
+0
+>>> buf
+array('h', [13341])
+

Raises an auditing event fcntl.ioctl with arguments fd, request, arg.

+
+fcntl.flock(fd, operation)
+

Perform the lock operation operation on file descriptor fd (file objects providing a fileno() method are accepted as well). See the Unix manual flock(2) for details. (On some systems, this function is emulated using fcntl().)

If the flock() fails, an OSError exception is raised.

Raises an auditing event fcntl.flock with arguments fd, operation.

+
+fcntl.lockf(fd, cmd, len=0, start=0, whence=0)
+

This is essentially a wrapper around the fcntl() locking calls. fd is the file descriptor (file objects providing a fileno() method are accepted as well) of the file to lock or unlock, and cmd is one of the following values:

  • +LOCK_UN – unlock
  • +LOCK_SH – acquire a shared lock
  • +LOCK_EX – acquire an exclusive lock

When cmd is LOCK_SH or LOCK_EX, it can also be bitwise ORed with LOCK_NB to avoid blocking on lock acquisition. If LOCK_NB is used and the lock cannot be acquired, an OSError will be raised and the exception will have an errno attribute set to EACCES or EAGAIN (depending on the operating system; for portability, check for both values). On at least some systems, LOCK_EX can only be used if the file descriptor refers to a file opened for writing.

len is the number of bytes to lock, start is the byte offset at which the lock starts, relative to whence, and whence is as with io.IOBase.seek(), specifically:

  • +0 – relative to the start of the file (os.SEEK_SET)
  • +1 – relative to the current buffer position (os.SEEK_CUR)
  • +2 – relative to the end of the file (os.SEEK_END)

The default for start is 0, which means to start at the beginning of the file. The default for len is 0 which means to lock to the end of the file. The default for whence is also 0.

Raises an auditing event fcntl.lockf with arguments fd, cmd, len, start, whence.

+

Examples (all on a SVR4 compliant system):

import struct, fcntl, os
+
+f = open(...)
+rv = fcntl.fcntl(f, fcntl.F_SETFL, os.O_NDELAY)
+
+lockdata = struct.pack('hhllhh', fcntl.F_WRLCK, 0, 0, 0, 0, 0)
+rv = fcntl.fcntl(f, fcntl.F_SETLKW, lockdata)
+

Note that in the first example the return value variable rv will hold an integer value; in the second example it will hold a bytes object. The structure lay-out for the lockdata variable is system dependent — therefore using the flock() call may be better.

See also

+Module os +
+
+

If the locking flags O_SHLOCK and O_EXLOCK are present in the os module (on BSD only), the os.open() function provides an alternative to the lockf() and flock() functions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/fcntl.html +

+
diff --git a/devdocs/python~3.12/library%2Ffilecmp.html b/devdocs/python~3.12/library%2Ffilecmp.html new file mode 100644 index 00000000..a9af2ebd --- /dev/null +++ b/devdocs/python~3.12/library%2Ffilecmp.html @@ -0,0 +1,82 @@ +

filecmp — File and Directory Comparisons

Source code: Lib/filecmp.py

The filecmp module defines functions to compare files and directories, with various optional time/correctness trade-offs. For comparing files, see also the difflib module.

The filecmp module defines the following functions:

+filecmp.cmp(f1, f2, shallow=True)
+

Compare the files named f1 and f2, returning True if they seem equal, False otherwise.

If shallow is true and the os.stat() signatures (file type, size, and modification time) of both files are identical, the files are taken to be equal.

Otherwise, the files are treated as different if their sizes or contents differ.

Note that no external programs are called from this function, giving it portability and efficiency.

This function uses a cache for past comparisons and the results, with cache entries invalidated if the os.stat() information for the file changes. The entire cache may be cleared using clear_cache().

+
+filecmp.cmpfiles(dir1, dir2, common, shallow=True)
+

Compare the files in the two directories dir1 and dir2 whose names are given by common.

Returns three lists of file names: match, mismatch, errors. match contains the list of files that match, mismatch contains the names of those that don’t, and errors lists the names of files which could not be compared. Files are listed in errors if they don’t exist in one of the directories, the user lacks permission to read them or if the comparison could not be done for some other reason.

The shallow parameter has the same meaning and default value as for filecmp.cmp().

For example, cmpfiles('a', 'b', ['c', 'd/e']) will compare a/c with b/c and a/d/e with b/d/e. 'c' and 'd/e' will each be in one of the three returned lists.

+
+filecmp.clear_cache()
+

Clear the filecmp cache. This may be useful if a file is compared so quickly after it is modified that it is within the mtime resolution of the underlying filesystem.

New in version 3.4.

+

The dircmp class

+class filecmp.dircmp(a, b, ignore=None, hide=None)
+

Construct a new directory comparison object, to compare the directories a and b. ignore is a list of names to ignore, and defaults to filecmp.DEFAULT_IGNORES. hide is a list of names to hide, and defaults to [os.curdir, os.pardir].

The dircmp class compares files by doing shallow comparisons as described for filecmp.cmp().

The dircmp class provides the following methods:

+report()
+

Print (to sys.stdout) a comparison between a and b.

+
+report_partial_closure()
+

Print a comparison between a and b and common immediate subdirectories.

+
+report_full_closure()
+

Print a comparison between a and b and common subdirectories (recursively).

+

The dircmp class offers a number of interesting attributes that may be used to get various bits of information about the directory trees being compared.

Note that via __getattr__() hooks, all attributes are computed lazily, so there is no speed penalty if only those attributes which are lightweight to compute are used.

+left
+

The directory a.

+
+right
+

The directory b.

+
+left_list
+

Files and subdirectories in a, filtered by hide and ignore.

+
+right_list
+

Files and subdirectories in b, filtered by hide and ignore.

+
+common
+

Files and subdirectories in both a and b.

+
+left_only
+

Files and subdirectories only in a.

+
+right_only
+

Files and subdirectories only in b.

+
+common_dirs
+

Subdirectories in both a and b.

+
+common_files
+

Files in both a and b.

+
+common_funny
+

Names in both a and b, such that the type differs between the directories, or names for which os.stat() reports an error.

+
+same_files
+

Files which are identical in both a and b, using the class’s file comparison operator.

+
+diff_files
+

Files which are in both a and b, whose contents differ according to the class’s file comparison operator.

+
+funny_files
+

Files which are in both a and b, but could not be compared.

+
+subdirs
+

A dictionary mapping names in common_dirs to dircmp instances (or MyDirCmp instances if this instance is of type MyDirCmp, a subclass of dircmp).

Changed in version 3.10: Previously entries were always dircmp instances. Now entries are the same type as self, if self is a subclass of dircmp.

+
+
+filecmp.DEFAULT_IGNORES
+

New in version 3.4.

List of directories ignored by dircmp by default.

+

Here is a simplified example of using the subdirs attribute to search recursively through two directories to show common different files:

>>> from filecmp import dircmp
+>>> def print_diff_files(dcmp):
+...     for name in dcmp.diff_files:
+...         print("diff_file %s found in %s and %s" % (name, dcmp.left,
+...               dcmp.right))
+...     for sub_dcmp in dcmp.subdirs.values():
+...         print_diff_files(sub_dcmp)
+...
+>>> dcmp = dircmp('dir1', 'dir2') 
+>>> print_diff_files(dcmp) 
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/filecmp.html +

+
diff --git a/devdocs/python~3.12/library%2Ffileformats.html b/devdocs/python~3.12/library%2Ffileformats.html new file mode 100644 index 00000000..0a0c78be --- /dev/null +++ b/devdocs/python~3.12/library%2Ffileformats.html @@ -0,0 +1,11 @@ +

File Formats

The modules described in this chapter parse various miscellaneous file formats that aren’t markup languages and are not related to e-mail.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/fileformats.html +

+
diff --git a/devdocs/python~3.12/library%2Ffileinput.html b/devdocs/python~3.12/library%2Ffileinput.html new file mode 100644 index 00000000..d65be7c0 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffileinput.html @@ -0,0 +1,52 @@ +

fileinput — Iterate over lines from multiple input streams

Source code: Lib/fileinput.py

This module implements a helper class and functions to quickly write a loop over standard input or a list of files. If you just want to read or write one file see open().

The typical use is:

import fileinput
+for line in fileinput.input(encoding="utf-8"):
+    process(line)
+

This iterates over the lines of all files listed in sys.argv[1:], defaulting to sys.stdin if the list is empty. If a filename is '-', it is also replaced by sys.stdin and the optional arguments mode and openhook are ignored. To specify an alternative list of filenames, pass it as the first argument to input(). A single file name is also allowed.

All files are opened in text mode by default, but you can override this by specifying the mode parameter in the call to input() or FileInput. If an I/O error occurs during opening or reading a file, OSError is raised.

Changed in version 3.3: IOError used to be raised; it is now an alias of OSError.

If sys.stdin is used more than once, the second and further use will return no lines, except perhaps for interactive use, or if it has been explicitly reset (e.g. using sys.stdin.seek(0)).

Empty files are opened and immediately closed; the only time their presence in the list of filenames is noticeable at all is when the last file opened is empty.

Lines are returned with any newlines intact, which means that the last line in a file may not have one.

You can control how files are opened by providing an opening hook via the openhook parameter to fileinput.input() or FileInput(). The hook must be a function that takes two arguments, filename and mode, and returns an accordingly opened file-like object. If encoding and/or errors are specified, they will be passed to the hook as additional keyword arguments. This module provides a hook_compressed() to support compressed files.

The following function is the primary interface of this module:

+fileinput.input(files=None, inplace=False, backup='', *, mode='r', openhook=None, encoding=None, errors=None)
+

Create an instance of the FileInput class. The instance will be used as global state for the functions of this module, and is also returned to use during iteration. The parameters to this function will be passed along to the constructor of the FileInput class.

The FileInput instance can be used as a context manager in the with statement. In this example, input is closed after the with statement is exited, even if an exception occurs:

with fileinput.input(files=('spam.txt', 'eggs.txt'), encoding="utf-8") as f:
+    for line in f:
+        process(line)
+

Changed in version 3.2: Can be used as a context manager.

Changed in version 3.8: The keyword parameters mode and openhook are now keyword-only.

Changed in version 3.10: The keyword-only parameter encoding and errors are added.

+

The following functions use the global state created by fileinput.input(); if there is no active state, RuntimeError is raised.

+fileinput.filename()
+

Return the name of the file currently being read. Before the first line has been read, returns None.

+
+fileinput.fileno()
+

Return the integer “file descriptor” for the current file. When no file is opened (before the first line and between files), returns -1.

+
+fileinput.lineno()
+

Return the cumulative line number of the line that has just been read. Before the first line has been read, returns 0. After the last line of the last file has been read, returns the line number of that line.

+
+fileinput.filelineno()
+

Return the line number in the current file. Before the first line has been read, returns 0. After the last line of the last file has been read, returns the line number of that line within the file.

+
+fileinput.isfirstline()
+

Return True if the line just read is the first line of its file, otherwise return False.

+
+fileinput.isstdin()
+

Return True if the last line was read from sys.stdin, otherwise return False.

+
+fileinput.nextfile()
+

Close the current file so that the next iteration will read the first line from the next file (if any); lines not read from the file will not count towards the cumulative line count. The filename is not changed until after the first line of the next file has been read. Before the first line has been read, this function has no effect; it cannot be used to skip the first file. After the last line of the last file has been read, this function has no effect.

+
+fileinput.close()
+

Close the sequence.

+

The class which implements the sequence behavior provided by the module is available for subclassing as well:

+class fileinput.FileInput(files=None, inplace=False, backup='', *, mode='r', openhook=None, encoding=None, errors=None)
+

Class FileInput is the implementation; its methods filename(), fileno(), lineno(), filelineno(), isfirstline(), isstdin(), nextfile() and close() correspond to the functions of the same name in the module. In addition it is iterable and has a readline() method which returns the next input line. The sequence must be accessed in strictly sequential order; random access and readline() cannot be mixed.

With mode you can specify which file mode will be passed to open(). It must be one of 'r' and 'rb'.

The openhook, when given, must be a function that takes two arguments, filename and mode, and returns an accordingly opened file-like object. You cannot use inplace and openhook together.

You can specify encoding and errors that is passed to open() or openhook.

A FileInput instance can be used as a context manager in the with statement. In this example, input is closed after the with statement is exited, even if an exception occurs:

with FileInput(files=('spam.txt', 'eggs.txt')) as input:
+    process(input)
+

Changed in version 3.2: Can be used as a context manager.

Changed in version 3.8: The keyword parameter mode and openhook are now keyword-only.

Changed in version 3.10: The keyword-only parameter encoding and errors are added.

Changed in version 3.11: The 'rU' and 'U' modes and the __getitem__() method have been removed.

+

Optional in-place filtering: if the keyword argument inplace=True is passed to fileinput.input() or to the FileInput constructor, the file is moved to a backup file and standard output is directed to the input file (if a file of the same name as the backup file already exists, it will be replaced silently). This makes it possible to write a filter that rewrites its input file in place. If the backup parameter is given (typically as backup='.<some extension>'), it specifies the extension for the backup file, and the backup file remains around; by default, the extension is '.bak' and it is deleted when the output file is closed. In-place filtering is disabled when standard input is read.

The two following opening hooks are provided by this module:

+fileinput.hook_compressed(filename, mode, *, encoding=None, errors=None)
+

Transparently opens files compressed with gzip and bzip2 (recognized by the extensions '.gz' and '.bz2') using the gzip and bz2 modules. If the filename extension is not '.gz' or '.bz2', the file is opened normally (ie, using open() without any decompression).

The encoding and errors values are passed to io.TextIOWrapper for compressed files and open for normal files.

Usage example: fi = fileinput.FileInput(openhook=fileinput.hook_compressed, encoding="utf-8")

Changed in version 3.10: The keyword-only parameter encoding and errors are added.

+
+fileinput.hook_encoded(encoding, errors=None)
+

Returns a hook which opens each file with open(), using the given encoding and errors to read the file.

Usage example: fi = +fileinput.FileInput(openhook=fileinput.hook_encoded("utf-8", +"surrogateescape"))

Changed in version 3.6: Added the optional errors parameter.

Deprecated since version 3.10: This function is deprecated since fileinput.input() and FileInput now have encoding and errors parameters.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/fileinput.html +

+
diff --git a/devdocs/python~3.12/library%2Ffilesys.html b/devdocs/python~3.12/library%2Ffilesys.html new file mode 100644 index 00000000..01f42560 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffilesys.html @@ -0,0 +1,26 @@ +

File and Directory Access

The modules described in this chapter deal with disk files and directories. For example, there are modules for reading the properties of files, manipulating paths in a portable way, and creating temporary files. The full list of modules in this chapter is:

See also

+Module os +
+
+

Operating system interfaces, including functions to work with files at a lower level than Python file objects.

+Module io +
+
+

Python’s built-in I/O library, including both abstract classes and some concrete classes such as file I/O.

+Built-in function open() +
+
+

The standard way to open files for reading and writing with Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/filesys.html +

+
diff --git a/devdocs/python~3.12/library%2Ffnmatch.html b/devdocs/python~3.12/library%2Ffnmatch.html new file mode 100644 index 00000000..e736db27 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffnmatch.html @@ -0,0 +1,41 @@ +

fnmatch — Unix filename pattern matching

Source code: Lib/fnmatch.py

This module provides support for Unix shell-style wildcards, which are not the same as regular expressions (which are documented in the re module). The special characters used in shell-style wildcards are:

+ + + + +

Pattern

Meaning

*

matches everything

?

matches any single character

[seq]

matches any character in seq

[!seq]

matches any character not in seq

For a literal match, wrap the meta-characters in brackets. For example, '[?]' matches the character '?'.

Note that the filename separator ('/' on Unix) is not special to this module. See module glob for pathname expansion (glob uses filter() to match pathname segments). Similarly, filenames starting with a period are not special for this module, and are matched by the * and ? patterns.

Also note that functools.lru_cache() with the maxsize of 32768 is used to cache the compiled regex patterns in the following functions: fnmatch(), fnmatchcase(), filter().

+fnmatch.fnmatch(filename, pattern)
+

Test whether the filename string matches the pattern string, returning True or False. Both parameters are case-normalized using os.path.normcase(). fnmatchcase() can be used to perform a case-sensitive comparison, regardless of whether that’s standard for the operating system.

This example will print all file names in the current directory with the extension .txt:

import fnmatch
+import os
+
+for file in os.listdir('.'):
+    if fnmatch.fnmatch(file, '*.txt'):
+        print(file)
+
+
+fnmatch.fnmatchcase(filename, pattern)
+

Test whether filename matches pattern, returning True or False; the comparison is case-sensitive and does not apply os.path.normcase().

+
+fnmatch.filter(names, pattern)
+

Construct a list from those elements of the iterable names that match pattern. It is the same as [n for n in names if fnmatch(n, pattern)], but implemented more efficiently.

+
+fnmatch.translate(pattern)
+

Return the shell-style pattern converted to a regular expression for using with re.match().

Example:

>>> import fnmatch, re
+>>>
+>>> regex = fnmatch.translate('*.txt')
+>>> regex
+'(?s:.*\\.txt)\\Z'
+>>> reobj = re.compile(regex)
+>>> reobj.match('foobar.txt')
+<re.Match object; span=(0, 10), match='foobar.txt'>
+
+

See also

+Module glob +
+
+

Unix shell-style path expansion.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/fnmatch.html +

+
diff --git a/devdocs/python~3.12/library%2Ffractions.html b/devdocs/python~3.12/library%2Ffractions.html new file mode 100644 index 00000000..1c160c4e --- /dev/null +++ b/devdocs/python~3.12/library%2Ffractions.html @@ -0,0 +1,99 @@ +

fractions — Rational numbers

Source code: Lib/fractions.py

The fractions module provides support for rational number arithmetic.

A Fraction instance can be constructed from a pair of integers, from another rational number, or from a string.

+class fractions.Fraction(numerator=0, denominator=1)
class fractions.Fraction(other_fraction) +
class fractions.Fraction(float) +
class fractions.Fraction(decimal) +
class fractions.Fraction(string) +
+

The first version requires that numerator and denominator are instances of numbers.Rational and returns a new Fraction instance with value numerator/denominator. If denominator is 0, it raises a ZeroDivisionError. The second version requires that other_fraction is an instance of numbers.Rational and returns a Fraction instance with the same value. The next two versions accept either a float or a decimal.Decimal instance, and return a Fraction instance with exactly the same value. Note that due to the usual issues with binary floating-point (see Floating Point Arithmetic: Issues and Limitations), the argument to Fraction(1.1) is not exactly equal to 11/10, and so Fraction(1.1) does not return Fraction(11, 10) as one might expect. (But see the documentation for the limit_denominator() method below.) The last version of the constructor expects a string or unicode instance. The usual form for this instance is:

[sign] numerator ['/' denominator]
+

where the optional sign may be either ‘+’ or ‘-’ and numerator and denominator (if present) are strings of decimal digits (underscores may be used to delimit digits as with integral literals in code). In addition, any string that represents a finite value and is accepted by the float constructor is also accepted by the Fraction constructor. In either form the input string may also have leading and/or trailing whitespace. Here are some examples:

>>> from fractions import Fraction
+>>> Fraction(16, -10)
+Fraction(-8, 5)
+>>> Fraction(123)
+Fraction(123, 1)
+>>> Fraction()
+Fraction(0, 1)
+>>> Fraction('3/7')
+Fraction(3, 7)
+>>> Fraction(' -3/7 ')
+Fraction(-3, 7)
+>>> Fraction('1.414213 \t\n')
+Fraction(1414213, 1000000)
+>>> Fraction('-.125')
+Fraction(-1, 8)
+>>> Fraction('7e-6')
+Fraction(7, 1000000)
+>>> Fraction(2.25)
+Fraction(9, 4)
+>>> Fraction(1.1)
+Fraction(2476979795053773, 2251799813685248)
+>>> from decimal import Decimal
+>>> Fraction(Decimal('1.1'))
+Fraction(11, 10)
+

The Fraction class inherits from the abstract base class numbers.Rational, and implements all of the methods and operations from that class. Fraction instances are hashable, and should be treated as immutable. In addition, Fraction has the following properties and methods:

Changed in version 3.2: The Fraction constructor now accepts float and decimal.Decimal instances.

Changed in version 3.9: The math.gcd() function is now used to normalize the numerator and denominator. math.gcd() always return a int type. Previously, the GCD type depended on numerator and denominator.

Changed in version 3.11: Underscores are now permitted when creating a Fraction instance from a string, following PEP 515 rules.

Changed in version 3.11: Fraction implements __int__ now to satisfy typing.SupportsInt instance checks.

Changed in version 3.12: Space is allowed around the slash for string inputs: Fraction('2 / 3').

Changed in version 3.12: Fraction instances now support float-style formatting, with presentation types "e", "E", "f", "F", "g", "G" and "%"".

+numerator
+

Numerator of the Fraction in lowest term.

+
+denominator
+

Denominator of the Fraction in lowest term.

+
+as_integer_ratio()
+

Return a tuple of two integers, whose ratio is equal to the original Fraction. The ratio is in lowest terms and has a positive denominator.

New in version 3.8.

+
+is_integer()
+

Return True if the Fraction is an integer.

New in version 3.12.

+
+classmethod from_float(flt)
+

Alternative constructor which only accepts instances of float or numbers.Integral. Beware that Fraction.from_float(0.3) is not the same value as Fraction(3, 10).

Note

From Python 3.2 onwards, you can also construct a Fraction instance directly from a float.

+
+classmethod from_decimal(dec)
+

Alternative constructor which only accepts instances of decimal.Decimal or numbers.Integral.

Note

From Python 3.2 onwards, you can also construct a Fraction instance directly from a decimal.Decimal instance.

+
+limit_denominator(max_denominator=1000000)
+

Finds and returns the closest Fraction to self that has denominator at most max_denominator. This method is useful for finding rational approximations to a given floating-point number:

>>> from fractions import Fraction
+>>> Fraction('3.1415926535897932').limit_denominator(1000)
+Fraction(355, 113)
+

or for recovering a rational number that’s represented as a float:

>>> from math import pi, cos
+>>> Fraction(cos(pi/3))
+Fraction(4503599627370497, 9007199254740992)
+>>> Fraction(cos(pi/3)).limit_denominator()
+Fraction(1, 2)
+>>> Fraction(1.1).limit_denominator()
+Fraction(11, 10)
+
+
+__floor__()
+

Returns the greatest int <= self. This method can also be accessed through the math.floor() function:

>>> from math import floor
+>>> floor(Fraction(355, 113))
+3
+
+
+__ceil__()
+

Returns the least int >= self. This method can also be accessed through the math.ceil() function.

+
+__round__()
__round__(ndigits) +
+

The first version returns the nearest int to self, rounding half to even. The second version rounds self to the nearest multiple of Fraction(1, 10**ndigits) (logically, if ndigits is negative), again rounding half toward even. This method can also be accessed through the round() function.

+
+__format__(format_spec, /)
+

Provides support for float-style formatting of Fraction instances via the str.format() method, the format() built-in function, or Formatted string literals. The presentation types "e", "E", "f", "F", "g", "G" and "%" are supported. For these presentation types, formatting for a Fraction object x follows the rules outlined for the float type in the Format Specification Mini-Language section.

Here are some examples:

>>> from fractions import Fraction
+>>> format(Fraction(1, 7), '.40g')
+'0.1428571428571428571428571428571428571429'
+>>> format(Fraction('1234567.855'), '_.2f')
+'1_234_567.86'
+>>> f"{Fraction(355, 113):*>20.6e}"
+'********3.141593e+00'
+>>> old_price, new_price = 499, 672
+>>> "{:.2%} price increase".format(Fraction(new_price, old_price) - 1)
+'34.67% price increase'
+
+
+

See also

+Module numbers +
+
+

The abstract base classes making up the numeric tower.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/fractions.html +

+
diff --git a/devdocs/python~3.12/library%2Fframeworks.html b/devdocs/python~3.12/library%2Fframeworks.html new file mode 100644 index 00000000..bb19cbc2 --- /dev/null +++ b/devdocs/python~3.12/library%2Fframeworks.html @@ -0,0 +1,18 @@ +

Program Frameworks

The modules described in this chapter are frameworks that will largely dictate the structure of your program. Currently the modules described here are all oriented toward writing command-line interfaces.

The full list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/frameworks.html +

+
diff --git a/devdocs/python~3.12/library%2Fftplib.html b/devdocs/python~3.12/library%2Fftplib.html new file mode 100644 index 00000000..6c90c34c --- /dev/null +++ b/devdocs/python~3.12/library%2Fftplib.html @@ -0,0 +1,161 @@ +

ftplib — FTP protocol client

Source code: Lib/ftplib.py

This module defines the class FTP and a few related items. The FTP class implements the client side of the FTP protocol. You can use this to write Python programs that perform a variety of automated FTP jobs, such as mirroring other FTP servers. It is also used by the module urllib.request to handle URLs that use FTP. For more information on FTP (File Transfer Protocol), see internet RFC 959.

The default encoding is UTF-8, following RFC 2640.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Here’s a sample session using the ftplib module:

>>> from ftplib import FTP
+>>> ftp = FTP('ftp.us.debian.org')  # connect to host, default port
+>>> ftp.login()                     # user anonymous, passwd anonymous@
+'230 Login successful.'
+>>> ftp.cwd('debian')               # change into "debian" directory
+'250 Directory successfully changed.'
+>>> ftp.retrlines('LIST')           # list directory contents
+-rw-rw-r--    1 1176     1176         1063 Jun 15 10:18 README
+...
+drwxr-sr-x    5 1176     1176         4096 Dec 19  2000 pool
+drwxr-sr-x    4 1176     1176         4096 Nov 17  2008 project
+drwxr-xr-x    3 1176     1176         4096 Oct 10  2012 tools
+'226 Directory send OK.'
+>>> with open('README', 'wb') as fp:
+>>>     ftp.retrbinary('RETR README', fp.write)
+'226 Transfer complete.'
+>>> ftp.quit()
+'221 Goodbye.'
+

The module defines the following items:

+class ftplib.FTP(host='', user='', passwd='', acct='', timeout=None, source_address=None, *, encoding='utf-8')
+

Return a new instance of the FTP class. When host is given, the method call connect(host) is made. When user is given, additionally the method call login(user, passwd, acct) is made (where passwd and acct default to the empty string when not given). The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if is not specified, the global default timeout setting will be used). source_address is a 2-tuple (host, port) for the socket to bind to as its source address before connecting. The encoding parameter specifies the encoding for directories and filenames.

The FTP class supports the with statement, e.g.:

>>> from ftplib import FTP
+>>> with FTP("ftp1.at.proftpd.org") as ftp:
+...     ftp.login()
+...     ftp.dir()
+... 
+'230 Anonymous login ok, restrictions apply.'
+dr-xr-xr-x   9 ftp      ftp           154 May  6 10:43 .
+dr-xr-xr-x   9 ftp      ftp           154 May  6 10:43 ..
+dr-xr-xr-x   5 ftp      ftp          4096 May  6 10:43 CentOS
+dr-xr-xr-x   3 ftp      ftp            18 Jul 10  2008 Fedora
+>>>
+

Changed in version 3.2: Support for the with statement was added.

Changed in version 3.3: source_address parameter was added.

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket. The encoding parameter was added, and the default was changed from Latin-1 to UTF-8 to follow RFC 2640.

+
FTP_TLS(host='', user='', passwd='', acct='', *, context=None, +
timeout=None, source_address=None, encoding='utf-8') +
+

A FTP subclass which adds TLS support to FTP as described in RFC 4217. Connect as usual to port 21 implicitly securing the FTP control connection before authenticating. Securing the data connection requires the user to explicitly ask for it by calling the prot_p() method. context is a ssl.SSLContext object which allows bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure. Please read Security considerations for best practices.

New in version 3.2.

Changed in version 3.3: source_address parameter was added.

Changed in version 3.4: The class now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket. The encoding parameter was added, and the default was changed from Latin-1 to UTF-8 to follow RFC 2640.

Changed in version 3.12: The deprecated keyfile and certfile parameters have been removed.

Here’s a sample session using the FTP_TLS class:

>>> ftps = FTP_TLS('ftp.pureftpd.org')
+>>> ftps.login()
+'230 Anonymous user logged in'
+>>> ftps.prot_p()
+'200 Data protection level set to "private"'
+>>> ftps.nlst()
+['6jack', 'OpenBSD', 'antilink', 'blogbench', 'bsdcam', 'clockspeed', 'djbdns-jedi', 'docs', 'eaccelerator-jedi', 'favicon.ico', 'francotone', 'fugu', 'ignore', 'libpuzzle', 'metalog', 'minidentd', 'misc', 'mysql-udf-global-user-variables', 'php-jenkins-hash', 'php-skein-hash', 'php-webdav', 'phpaudit', 'phpbench', 'pincaster', 'ping', 'posto', 'pub', 'public', 'public_keys', 'pure-ftpd', 'qscan', 'qtc', 'sharedance', 'skycache', 'sound', 'tmp', 'ucarp']
+
+
+exception ftplib.error_reply
+

Exception raised when an unexpected reply is received from the server.

+
+exception ftplib.error_temp
+

Exception raised when an error code signifying a temporary error (response codes in the range 400–499) is received.

+
+exception ftplib.error_perm
+

Exception raised when an error code signifying a permanent error (response codes in the range 500–599) is received.

+
+exception ftplib.error_proto
+

Exception raised when a reply is received from the server that does not fit the response specifications of the File Transfer Protocol, i.e. begin with a digit in the range 1–5.

+
+ftplib.all_errors
+

The set of all exceptions (as a tuple) that methods of FTP instances may raise as a result of problems with the FTP connection (as opposed to programming errors made by the caller). This set includes the four exceptions listed above as well as OSError and EOFError.

+

See also

+Module netrc +
+
+

Parser for the .netrc file format. The file .netrc is typically used by FTP clients to load user authentication information before prompting the user.

FTP Objects

Several methods are available in two flavors: one for handling text files and another for binary files. These are named for the command which is used followed by lines for the text version or binary for the binary version.

FTP instances have the following methods:

+FTP.set_debuglevel(level)
+

Set the instance’s debugging level. This controls the amount of debugging output printed. The default, 0, produces no debugging output. A value of 1 produces a moderate amount of debugging output, generally a single line per request. A value of 2 or higher produces the maximum amount of debugging output, logging each line sent and received on the control connection.

+
+FTP.connect(host='', port=0, timeout=None, source_address=None)
+

Connect to the given host and port. The default port number is 21, as specified by the FTP protocol specification. It is rarely needed to specify a different port number. This function should be called only once for each instance; it should not be called at all if a host was given when the instance was created. All other methods can only be used after a connection has been made. The optional timeout parameter specifies a timeout in seconds for the connection attempt. If no timeout is passed, the global default timeout setting will be used. source_address is a 2-tuple (host, port) for the socket to bind to as its source address before connecting.

Raises an auditing event ftplib.connect with arguments self, host, port.

Changed in version 3.3: source_address parameter was added.

+
+FTP.getwelcome()
+

Return the welcome message sent by the server in reply to the initial connection. (This message sometimes contains disclaimers or help information that may be relevant to the user.)

+
+FTP.login(user='anonymous', passwd='', acct='')
+

Log in as the given user. The passwd and acct parameters are optional and default to the empty string. If no user is specified, it defaults to 'anonymous'. If user is 'anonymous', the default passwd is 'anonymous@'. This function should be called only once for each instance, after a connection has been established; it should not be called at all if a host and user were given when the instance was created. Most FTP commands are only allowed after the client has logged in. The acct parameter supplies “accounting information”; few systems implement this.

+
+FTP.abort()
+

Abort a file transfer that is in progress. Using this does not always work, but it’s worth a try.

+
+FTP.sendcmd(cmd)
+

Send a simple command string to the server and return the response string.

Raises an auditing event ftplib.sendcmd with arguments self, cmd.

+
+FTP.voidcmd(cmd)
+

Send a simple command string to the server and handle the response. Return nothing if a response code corresponding to success (codes in the range 200–299) is received. Raise error_reply otherwise.

Raises an auditing event ftplib.sendcmd with arguments self, cmd.

+
+FTP.retrbinary(cmd, callback, blocksize=8192, rest=None)
+

Retrieve a file in binary transfer mode. cmd should be an appropriate RETR command: 'RETR filename'. The callback function is called for each block of data received, with a single bytes argument giving the data block. The optional blocksize argument specifies the maximum chunk size to read on the low-level socket object created to do the actual transfer (which will also be the largest size of the data blocks passed to callback). A reasonable default is chosen. rest means the same thing as in the transfercmd() method.

+
+FTP.retrlines(cmd, callback=None)
+

Retrieve a file or directory listing in the encoding specified by the encoding parameter at initialization. cmd should be an appropriate RETR command (see retrbinary()) or a command such as LIST or NLST (usually just the string 'LIST'). LIST retrieves a list of files and information about those files. NLST retrieves a list of file names. The callback function is called for each line with a string argument containing the line with the trailing CRLF stripped. The default callback prints the line to sys.stdout.

+
+FTP.set_pasv(val)
+

Enable “passive” mode if val is true, otherwise disable passive mode. Passive mode is on by default.

+
+FTP.storbinary(cmd, fp, blocksize=8192, callback=None, rest=None)
+

Store a file in binary transfer mode. cmd should be an appropriate STOR command: "STOR filename". fp is a file object (opened in binary mode) which is read until EOF using its read() method in blocks of size blocksize to provide the data to be stored. The blocksize argument defaults to 8192. callback is an optional single parameter callable that is called on each block of data after it is sent. rest means the same thing as in the transfercmd() method.

Changed in version 3.2: rest parameter added.

+
+FTP.storlines(cmd, fp, callback=None)
+

Store a file in line mode. cmd should be an appropriate STOR command (see storbinary()). Lines are read until EOF from the file object fp (opened in binary mode) using its readline() method to provide the data to be stored. callback is an optional single parameter callable that is called on each line after it is sent.

+
+FTP.transfercmd(cmd, rest=None)
+

Initiate a transfer over the data connection. If the transfer is active, send an EPRT or PORT command and the transfer command specified by cmd, and accept the connection. If the server is passive, send an EPSV or PASV command, connect to it, and start the transfer command. Either way, return the socket for the connection.

If optional rest is given, a REST command is sent to the server, passing rest as an argument. rest is usually a byte offset into the requested file, telling the server to restart sending the file’s bytes at the requested offset, skipping over the initial bytes. Note however that the transfercmd() method converts rest to a string with the encoding parameter specified at initialization, but no check is performed on the string’s contents. If the server does not recognize the REST command, an error_reply exception will be raised. If this happens, simply call transfercmd() without a rest argument.

+
+FTP.ntransfercmd(cmd, rest=None)
+

Like transfercmd(), but returns a tuple of the data connection and the expected size of the data. If the expected size could not be computed, None will be returned as the expected size. cmd and rest means the same thing as in transfercmd().

+
+FTP.mlsd(path='', facts=[])
+

List a directory in a standardized format by using MLSD command (RFC 3659). If path is omitted the current directory is assumed. facts is a list of strings representing the type of information desired (e.g. ["type", "size", "perm"]). Return a generator object yielding a tuple of two elements for every file found in path. First element is the file name, the second one is a dictionary containing facts about the file name. Content of this dictionary might be limited by the facts argument but server is not guaranteed to return all requested facts.

New in version 3.3.

+
+FTP.nlst(argument[, ...])
+

Return a list of file names as returned by the NLST command. The optional argument is a directory to list (default is the current server directory). Multiple arguments can be used to pass non-standard options to the NLST command.

Note

If your server supports the command, mlsd() offers a better API.

+
+FTP.dir(argument[, ...])
+

Produce a directory listing as returned by the LIST command, printing it to standard output. The optional argument is a directory to list (default is the current server directory). Multiple arguments can be used to pass non-standard options to the LIST command. If the last argument is a function, it is used as a callback function as for retrlines(); the default prints to sys.stdout. This method returns None.

Note

If your server supports the command, mlsd() offers a better API.

+
+FTP.rename(fromname, toname)
+

Rename file fromname on the server to toname.

+
+FTP.delete(filename)
+

Remove the file named filename from the server. If successful, returns the text of the response, otherwise raises error_perm on permission errors or error_reply on other errors.

+
+FTP.cwd(pathname)
+

Set the current directory on the server.

+
+FTP.mkd(pathname)
+

Create a new directory on the server.

+
+FTP.pwd()
+

Return the pathname of the current directory on the server.

+
+FTP.rmd(dirname)
+

Remove the directory named dirname on the server.

+
+FTP.size(filename)
+

Request the size of the file named filename on the server. On success, the size of the file is returned as an integer, otherwise None is returned. Note that the SIZE command is not standardized, but is supported by many common server implementations.

+
+FTP.quit()
+

Send a QUIT command to the server and close the connection. This is the “polite” way to close a connection, but it may raise an exception if the server responds with an error to the QUIT command. This implies a call to the close() method which renders the FTP instance useless for subsequent calls (see below).

+
+FTP.close()
+

Close the connection unilaterally. This should not be applied to an already closed connection such as after a successful call to quit(). After this call the FTP instance should not be used any more (after a call to close() or quit() you cannot reopen the connection by issuing another login() method).

+

FTP_TLS Objects

FTP_TLS class inherits from FTP, defining these additional objects:

+FTP_TLS.ssl_version
+

The SSL version to use (defaults to ssl.PROTOCOL_SSLv23).

+
+FTP_TLS.auth()
+

Set up a secure control connection by using TLS or SSL, depending on what is specified in the ssl_version attribute.

Changed in version 3.4: The method now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

+
+FTP_TLS.ccc()
+

Revert control channel back to plaintext. This can be useful to take advantage of firewalls that know how to handle NAT with non-secure FTP without opening fixed ports.

New in version 3.3.

+
+FTP_TLS.prot_p()
+

Set up secure data connection.

+
+FTP_TLS.prot_c()
+

Set up clear text data connection.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ftplib.html +

+
diff --git a/devdocs/python~3.12/library%2Ffunctional.html b/devdocs/python~3.12/library%2Ffunctional.html new file mode 100644 index 00000000..b9448da5 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffunctional.html @@ -0,0 +1,9 @@ +

Functional Programming Modules

The modules described in this chapter provide functions and classes that support a functional programming style, and general operations on callables.

The following modules are documented in this chapter:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/functional.html +

+
diff --git a/devdocs/python~3.12/library%2Ffunctions.html b/devdocs/python~3.12/library%2Ffunctions.html new file mode 100644 index 00000000..9e7ce083 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffunctions.html @@ -0,0 +1,488 @@ +

Built-in Functions

The Python interpreter has a number of functions and types built into it that are always available. They are listed here in alphabetical order.

+ +

Built-in Functions
+abs(x)
+

Return the absolute value of a number. The argument may be an integer, a floating point number, or an object implementing __abs__(). If the argument is a complex number, its magnitude is returned.

+
+aiter(async_iterable)
+

Return an asynchronous iterator for an asynchronous iterable. Equivalent to calling x.__aiter__().

Note: Unlike iter(), aiter() has no 2-argument variant.

New in version 3.10.

+
+all(iterable)
+

Return True if all elements of the iterable are true (or if the iterable is empty). Equivalent to:

def all(iterable):
+    for element in iterable:
+        if not element:
+            return False
+    return True
+
+
+awaitable anext(async_iterator)
awaitable anext(async_iterator, default) +
+

When awaited, return the next item from the given asynchronous iterator, or default if given and the iterator is exhausted.

This is the async variant of the next() builtin, and behaves similarly.

This calls the __anext__() method of async_iterator, returning an awaitable. Awaiting this returns the next value of the iterator. If default is given, it is returned if the iterator is exhausted, otherwise StopAsyncIteration is raised.

New in version 3.10.

+
+any(iterable)
+

Return True if any element of the iterable is true. If the iterable is empty, return False. Equivalent to:

def any(iterable):
+    for element in iterable:
+        if element:
+            return True
+    return False
+
+
+ascii(object)
+

As repr(), return a string containing a printable representation of an object, but escape the non-ASCII characters in the string returned by repr() using \x, \u, or \U escapes. This generates a string similar to that returned by repr() in Python 2.

+
+bin(x)
+

Convert an integer number to a binary string prefixed with “0b”. The result is a valid Python expression. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples:

>>> bin(3)
+'0b11'
+>>> bin(-10)
+'-0b1010'
+

If the prefix “0b” is desired or not, you can use either of the following ways.

>>> format(14, '#b'), format(14, 'b')
+('0b1110', '1110')
+>>> f'{14:#b}', f'{14:b}'
+('0b1110', '1110')
+

See also format() for more information.

+
+class bool(x=False)
+

Return a Boolean value, i.e. one of True or False. x is converted using the standard truth testing procedure. If x is false or omitted, this returns False; otherwise, it returns True. The bool class is a subclass of int (see Numeric Types — int, float, complex). It cannot be subclassed further. Its only instances are False and True (see Boolean Type - bool).

Changed in version 3.7: x is now a positional-only parameter.

+
+breakpoint(*args, **kws)
+

This function drops you into the debugger at the call site. Specifically, it calls sys.breakpointhook(), passing args and kws straight through. By default, sys.breakpointhook() calls pdb.set_trace() expecting no arguments. In this case, it is purely a convenience function so you don’t have to explicitly import pdb or type as much code to enter the debugger. However, sys.breakpointhook() can be set to some other function and breakpoint() will automatically call that, allowing you to drop into the debugger of choice. If sys.breakpointhook() is not accessible, this function will raise RuntimeError.

By default, the behavior of breakpoint() can be changed with the PYTHONBREAKPOINT environment variable. See sys.breakpointhook() for usage details.

Note that this is not guaranteed if sys.breakpointhook() has been replaced.

Raises an auditing event builtins.breakpoint with argument breakpointhook.

New in version 3.7.

+
class bytearray(source=b'') +
class bytearray(source, encoding) +
class bytearray(source, encoding, errors) +
+

Return a new array of bytes. The bytearray class is a mutable sequence of integers in the range 0 <= x < 256. It has most of the usual methods of mutable sequences, described in Mutable Sequence Types, as well as most methods that the bytes type has, see Bytes and Bytearray Operations.

The optional source parameter can be used to initialize the array in a few different ways:

  • If it is a string, you must also give the encoding (and optionally, errors) parameters; bytearray() then converts the string to bytes using str.encode().
  • If it is an integer, the array will have that size and will be initialized with null bytes.
  • If it is an object conforming to the buffer interface, a read-only buffer of the object will be used to initialize the bytes array.
  • If it is an iterable, it must be an iterable of integers in the range 0 <= x < 256, which are used as the initial contents of the array.

Without an argument, an array of size 0 is created.

See also Binary Sequence Types — bytes, bytearray, memoryview and Bytearray Objects.

+
class bytes(source=b'') +
class bytes(source, encoding) +
class bytes(source, encoding, errors) +
+

Return a new “bytes” object which is an immutable sequence of integers in the range 0 <= x < 256. bytes is an immutable version of bytearray – it has the same non-mutating methods and the same indexing and slicing behavior.

Accordingly, constructor arguments are interpreted as for bytearray().

Bytes objects can also be created with literals, see String and Bytes literals.

See also Binary Sequence Types — bytes, bytearray, memoryview, Bytes Objects, and Bytes and Bytearray Operations.

+
+callable(object)
+

Return True if the object argument appears callable, False if not. If this returns True, it is still possible that a call fails, but if it is False, calling object will never succeed. Note that classes are callable (calling a class returns a new instance); instances are callable if their class has a __call__() method.

New in version 3.2: This function was first removed in Python 3.0 and then brought back in Python 3.2.

+
+chr(i)
+

Return the string representing a character whose Unicode code point is the integer i. For example, chr(97) returns the string 'a', while chr(8364) returns the string '€'. This is the inverse of ord().

The valid range for the argument is from 0 through 1,114,111 (0x10FFFF in base 16). ValueError will be raised if i is outside that range.

+
+@classmethod
+

Transform a method into a class method.

A class method receives the class as an implicit first argument, just like an instance method receives the instance. To declare a class method, use this idiom:

class C:
+    @classmethod
+    def f(cls, arg1, arg2): ...
+

The @classmethod form is a function decorator – see Function definitions for details.

A class method can be called either on the class (such as C.f()) or on an instance (such as C().f()). The instance is ignored except for its class. If a class method is called for a derived class, the derived class object is passed as the implied first argument.

Class methods are different than C++ or Java static methods. If you want those, see staticmethod() in this section. For more information on class methods, see The standard type hierarchy.

Changed in version 3.9: Class methods can now wrap other descriptors such as property().

Changed in version 3.10: Class methods now inherit the method attributes (__module__, __name__, __qualname__, __doc__ and __annotations__) and have a new __wrapped__ attribute.

Changed in version 3.11: Class methods can no longer wrap other descriptors such as property().

+
+compile(source, filename, mode, flags=0, dont_inherit=False, optimize=- 1)
+

Compile the source into a code or AST object. Code objects can be executed by exec() or eval(). source can either be a normal string, a byte string, or an AST object. Refer to the ast module documentation for information on how to work with AST objects.

The filename argument should give the file from which the code was read; pass some recognizable value if it wasn’t read from a file ('<string>' is commonly used).

The mode argument specifies what kind of code must be compiled; it can be 'exec' if source consists of a sequence of statements, 'eval' if it consists of a single expression, or 'single' if it consists of a single interactive statement (in the latter case, expression statements that evaluate to something other than None will be printed).

The optional arguments flags and dont_inherit control which compiler options should be activated and which future features should be allowed. If neither is present (or both are zero) the code is compiled with the same flags that affect the code that is calling compile(). If the flags argument is given and dont_inherit is not (or is zero) then the compiler options and the future statements specified by the flags argument are used in addition to those that would be used anyway. If dont_inherit is a non-zero integer then the flags argument is it – the flags (future features and compiler options) in the surrounding code are ignored.

Compiler options and future statements are specified by bits which can be bitwise ORed together to specify multiple options. The bitfield required to specify a given future feature can be found as the compiler_flag attribute on the _Feature instance in the __future__ module. Compiler flags can be found in ast module, with PyCF_ prefix.

The argument optimize specifies the optimization level of the compiler; the default value of -1 selects the optimization level of the interpreter as given by -O options. Explicit levels are 0 (no optimization; __debug__ is true), 1 (asserts are removed, __debug__ is false) or 2 (docstrings are removed too).

This function raises SyntaxError if the compiled source is invalid, and ValueError if the source contains null bytes.

If you want to parse Python code into its AST representation, see ast.parse().

+

Raises an auditing event compile with arguments source and filename. This event may also be raised by implicit compilation.

Note

When compiling a string with multi-line code in 'single' or 'eval' mode, input must be terminated by at least one newline character. This is to facilitate detection of incomplete and complete statements in the code module.

Warning

It is possible to crash the Python interpreter with a sufficiently large/complex string when compiling to an AST object due to stack depth limitations in Python’s AST compiler.

Changed in version 3.2: Allowed use of Windows and Mac newlines. Also, input in 'exec' mode does not have to end in a newline anymore. Added the optimize parameter.

Changed in version 3.5: Previously, TypeError was raised when null bytes were encountered in source.

New in version 3.8: ast.PyCF_ALLOW_TOP_LEVEL_AWAIT can now be passed in flags to enable support for top-level await, async for, and async with.

+
+class complex(real=0, imag=0)
class complex(string) +
+

Return a complex number with the value real + imag*1j or convert a string or number to a complex number. If the first parameter is a string, it will be interpreted as a complex number and the function must be called without a second parameter. The second parameter can never be a string. Each argument may be any numeric type (including complex). If imag is omitted, it defaults to zero and the constructor serves as a numeric conversion like int and float. If both arguments are omitted, returns 0j.

For a general Python object x, complex(x) delegates to x.__complex__(). If __complex__() is not defined then it falls back to __float__(). If __float__() is not defined then it falls back to __index__().

Note

When converting from a string, the string must not contain whitespace around the central + or - operator. For example, complex('1+2j') is fine, but complex('1 + 2j') raises ValueError.

The complex type is described in Numeric Types — int, float, complex.

Changed in version 3.6: Grouping digits with underscores as in code literals is allowed.

Changed in version 3.8: Falls back to __index__() if __complex__() and __float__() are not defined.

+
+delattr(object, name)
+

This is a relative of setattr(). The arguments are an object and a string. The string must be the name of one of the object’s attributes. The function deletes the named attribute, provided the object allows it. For example, delattr(x, 'foobar') is equivalent to del x.foobar. name need not be a Python identifier (see setattr()).

+
class dict(**kwarg) +
class dict(mapping, **kwarg) +
class dict(iterable, **kwarg) +
+

Create a new dictionary. The dict object is the dictionary class. See dict and Mapping Types — dict for documentation about this class.

For other containers see the built-in list, set, and tuple classes, as well as the collections module.

+
+dir()
dir(object) +
+

Without arguments, return the list of names in the current local scope. With an argument, attempt to return a list of valid attributes for that object.

If the object has a method named __dir__(), this method will be called and must return the list of attributes. This allows objects that implement a custom __getattr__() or __getattribute__() function to customize the way dir() reports their attributes.

If the object does not provide __dir__(), the function tries its best to gather information from the object’s __dict__ attribute, if defined, and from its type object. The resulting list is not necessarily complete and may be inaccurate when the object has a custom __getattr__().

The default dir() mechanism behaves differently with different types of objects, as it attempts to produce the most relevant, rather than complete, information:

  • If the object is a module object, the list contains the names of the module’s attributes.
  • If the object is a type or class object, the list contains the names of its attributes, and recursively of the attributes of its bases.
  • Otherwise, the list contains the object’s attributes’ names, the names of its class’s attributes, and recursively of the attributes of its class’s base classes.

The resulting list is sorted alphabetically. For example:

>>> import struct
+>>> dir()   # show the names in the module namespace  
+['__builtins__', '__name__', 'struct']
+>>> dir(struct)   # show the names in the struct module 
+['Struct', '__all__', '__builtins__', '__cached__', '__doc__', '__file__',
+ '__initializing__', '__loader__', '__name__', '__package__',
+ '_clearcache', 'calcsize', 'error', 'pack', 'pack_into',
+ 'unpack', 'unpack_from']
+>>> class Shape:
+...     def __dir__(self):
+...         return ['area', 'perimeter', 'location']
+...
+>>> s = Shape()
+>>> dir(s)
+['area', 'location', 'perimeter']
+

Note

Because dir() is supplied primarily as a convenience for use at an interactive prompt, it tries to supply an interesting set of names more than it tries to supply a rigorously or consistently defined set of names, and its detailed behavior may change across releases. For example, metaclass attributes are not in the result list when the argument is a class.

+
+divmod(a, b)
+

Take two (non-complex) numbers as arguments and return a pair of numbers consisting of their quotient and remainder when using integer division. With mixed operand types, the rules for binary arithmetic operators apply. For integers, the result is the same as (a // b, a % b). For floating point numbers the result is (q, a % b), where q is usually math.floor(a / +b) but may be 1 less than that. In any case q * b + a % b is very close to a, if a % b is non-zero it has the same sign as b, and 0 +<= abs(a % b) < abs(b).

+
+enumerate(iterable, start=0)
+

Return an enumerate object. iterable must be a sequence, an iterator, or some other object which supports iteration. The __next__() method of the iterator returned by enumerate() returns a tuple containing a count (from start which defaults to 0) and the values obtained from iterating over iterable.

>>> seasons = ['Spring', 'Summer', 'Fall', 'Winter']
+>>> list(enumerate(seasons))
+[(0, 'Spring'), (1, 'Summer'), (2, 'Fall'), (3, 'Winter')]
+>>> list(enumerate(seasons, start=1))
+[(1, 'Spring'), (2, 'Summer'), (3, 'Fall'), (4, 'Winter')]
+

Equivalent to:

def enumerate(iterable, start=0):
+    n = start
+    for elem in iterable:
+        yield n, elem
+        n += 1
+
+
+eval(expression, globals=None, locals=None)
+

The arguments are a string and optional globals and locals. If provided, globals must be a dictionary. If provided, locals can be any mapping object.

The expression argument is parsed and evaluated as a Python expression (technically speaking, a condition list) using the globals and locals dictionaries as global and local namespace. If the globals dictionary is present and does not contain a value for the key __builtins__, a reference to the dictionary of the built-in module builtins is inserted under that key before expression is parsed. That way you can control what builtins are available to the executed code by inserting your own __builtins__ dictionary into globals before passing it to eval(). If the locals dictionary is omitted it defaults to the globals dictionary. If both dictionaries are omitted, the expression is executed with the globals and locals in the environment where eval() is called. Note, eval() does not have access to the nested scopes (non-locals) in the enclosing environment.

The return value is the result of the evaluated expression. Syntax errors are reported as exceptions. Example:

>>> x = 1
+>>> eval('x+1')
+2
+

This function can also be used to execute arbitrary code objects (such as those created by compile()). In this case, pass a code object instead of a string. If the code object has been compiled with 'exec' as the mode argument, eval()'s return value will be None.

Hints: dynamic execution of statements is supported by the exec() function. The globals() and locals() functions return the current global and local dictionary, respectively, which may be useful to pass around for use by eval() or exec().

If the given source is a string, then leading and trailing spaces and tabs are stripped.

See ast.literal_eval() for a function that can safely evaluate strings with expressions containing only literals.

+

Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised.

+
+exec(object, globals=None, locals=None, /, *, closure=None)
+

This function supports dynamic execution of Python code. object must be either a string or a code object. If it is a string, the string is parsed as a suite of Python statements which is then executed (unless a syntax error occurs). 1 If it is a code object, it is simply executed. In all cases, the code that’s executed is expected to be valid as file input (see the section File input in the Reference Manual). Be aware that the nonlocal, yield, and return statements may not be used outside of function definitions even within the context of code passed to the exec() function. The return value is None.

In all cases, if the optional parts are omitted, the code is executed in the current scope. If only globals is provided, it must be a dictionary (and not a subclass of dictionary), which will be used for both the global and the local variables. If globals and locals are given, they are used for the global and local variables, respectively. If provided, locals can be any mapping object. Remember that at the module level, globals and locals are the same dictionary. If exec gets two separate objects as globals and locals, the code will be executed as if it were embedded in a class definition.

If the globals dictionary does not contain a value for the key __builtins__, a reference to the dictionary of the built-in module builtins is inserted under that key. That way you can control what builtins are available to the executed code by inserting your own __builtins__ dictionary into globals before passing it to exec().

The closure argument specifies a closure–a tuple of cellvars. It’s only valid when the object is a code object containing free variables. The length of the tuple must exactly match the number of free variables referenced by the code object.

+

Raises an auditing event exec with the code object as the argument. Code compilation events may also be raised.

Note

The built-in functions globals() and locals() return the current global and local dictionary, respectively, which may be useful to pass around for use as the second and third argument to exec().

Note

The default locals act as described for function locals() below: modifications to the default locals dictionary should not be attempted. Pass an explicit locals dictionary if you need to see effects of the code on locals after function exec() returns.

Changed in version 3.11: Added the closure parameter.

+
+filter(function, iterable)
+

Construct an iterator from those elements of iterable for which function is true. iterable may be either a sequence, a container which supports iteration, or an iterator. If function is None, the identity function is assumed, that is, all elements of iterable that are false are removed.

Note that filter(function, iterable) is equivalent to the generator expression (item for item in iterable if function(item)) if function is not None and (item for item in iterable if item) if function is None.

See itertools.filterfalse() for the complementary function that returns elements of iterable for which function is false.

+
+class float(x=0.0)
+

Return a floating point number constructed from a number or string x.

If the argument is a string, it should contain a decimal number, optionally preceded by a sign, and optionally embedded in whitespace. The optional sign may be '+' or '-'; a '+' sign has no effect on the value produced. The argument may also be a string representing a NaN (not-a-number), or positive or negative infinity. More precisely, the input must conform to the floatvalue production rule in the following grammar, after leading and trailing whitespace characters are removed:

+sign        ::=  "+" | "-"
+infinity    ::=  "Infinity" | "inf"
+nan         ::=  "nan"
+digitpart   ::=  `!digit` (["_"] `!digit`)*
+number      ::=  [digitpart] "." digitpart | digitpart ["."]
+exponent    ::=  ("e" | "E") ["+" | "-"] digitpart
+floatnumber ::=  number [exponent]
+floatvalue  ::=  [sign] (floatnumber | infinity | nan)
+

Here digit is a Unicode decimal digit (character in the Unicode general category Nd). Case is not significant, so, for example, “inf”, “Inf”, “INFINITY”, and “iNfINity” are all acceptable spellings for positive infinity.

Otherwise, if the argument is an integer or a floating point number, a floating point number with the same value (within Python’s floating point precision) is returned. If the argument is outside the range of a Python float, an OverflowError will be raised.

For a general Python object x, float(x) delegates to x.__float__(). If __float__() is not defined then it falls back to __index__().

If no argument is given, 0.0 is returned.

Examples:

>>> float('+1.23')
+1.23
+>>> float('   -12345\n')
+-12345.0
+>>> float('1e-003')
+0.001
+>>> float('+1E6')
+1000000.0
+>>> float('-Infinity')
+-inf
+

The float type is described in Numeric Types — int, float, complex.

Changed in version 3.6: Grouping digits with underscores as in code literals is allowed.

Changed in version 3.7: x is now a positional-only parameter.

Changed in version 3.8: Falls back to __index__() if __float__() is not defined.

+
+format(value, format_spec='')
+

Convert a value to a “formatted” representation, as controlled by format_spec. The interpretation of format_spec will depend on the type of the value argument; however, there is a standard formatting syntax that is used by most built-in types: Format Specification Mini-Language.

The default format_spec is an empty string which usually gives the same effect as calling str(value).

A call to format(value, format_spec) is translated to type(value).__format__(value, format_spec) which bypasses the instance dictionary when searching for the value’s __format__() method. A TypeError exception is raised if the method search reaches object and the format_spec is non-empty, or if either the format_spec or the return value are not strings.

Changed in version 3.4: object().__format__(format_spec) raises TypeError if format_spec is not an empty string.

+
class frozenset(iterable=set()) +
+

Return a new frozenset object, optionally with elements taken from iterable. frozenset is a built-in class. See frozenset and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in set, list, tuple, and dict classes, as well as the collections module.

+
+getattr(object, name)
getattr(object, name, default) +
+

Return the value of the named attribute of object. name must be a string. If the string is the name of one of the object’s attributes, the result is the value of that attribute. For example, getattr(x, 'foobar') is equivalent to x.foobar. If the named attribute does not exist, default is returned if provided, otherwise AttributeError is raised. name need not be a Python identifier (see setattr()).

Note

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to retrieve it with getattr().

+
+globals()
+

Return the dictionary implementing the current module namespace. For code within functions, this is set when the function is defined and remains the same regardless of where the function is called.

+
+hasattr(object, name)
+

The arguments are an object and a string. The result is True if the string is the name of one of the object’s attributes, False if not. (This is implemented by calling getattr(object, name) and seeing whether it raises an AttributeError or not.)

+
+hash(object)
+

Return the hash value of the object (if it has one). Hash values are integers. They are used to quickly compare dictionary keys during a dictionary lookup. Numeric values that compare equal have the same hash value (even if they are of different types, as is the case for 1 and 1.0).

Note

For objects with custom __hash__() methods, note that hash() truncates the return value based on the bit width of the host machine.

+
+help()
help(request) +
+

Invoke the built-in help system. (This function is intended for interactive use.) If no argument is given, the interactive help system starts on the interpreter console. If the argument is a string, then the string is looked up as the name of a module, function, class, method, keyword, or documentation topic, and a help page is printed on the console. If the argument is any other kind of object, a help page on the object is generated.

Note that if a slash(/) appears in the parameter list of a function when invoking help(), it means that the parameters prior to the slash are positional-only. For more info, see the FAQ entry on positional-only parameters.

This function is added to the built-in namespace by the site module.

Changed in version 3.4: Changes to pydoc and inspect mean that the reported signatures for callables are now more comprehensive and consistent.

+
+hex(x)
+

Convert an integer number to a lowercase hexadecimal string prefixed with “0x”. If x is not a Python int object, it has to define an __index__() method that returns an integer. Some examples:

>>> hex(255)
+'0xff'
+>>> hex(-42)
+'-0x2a'
+

If you want to convert an integer number to an uppercase or lower hexadecimal string with prefix or not, you can use either of the following ways:

>>> '%#x' % 255, '%x' % 255, '%X' % 255
+('0xff', 'ff', 'FF')
+>>> format(255, '#x'), format(255, 'x'), format(255, 'X')
+('0xff', 'ff', 'FF')
+>>> f'{255:#x}', f'{255:x}', f'{255:X}'
+('0xff', 'ff', 'FF')
+

See also format() for more information.

See also int() for converting a hexadecimal string to an integer using a base of 16.

Note

To obtain a hexadecimal string representation for a float, use the float.hex() method.

+
+id(object)
+

Return the “identity” of an object. This is an integer which is guaranteed to be unique and constant for this object during its lifetime. Two objects with non-overlapping lifetimes may have the same id() value.

CPython implementation detail: This is the address of the object in memory.

Raises an auditing event builtins.id with argument id.

+
+input()
input(prompt) +
+

If the prompt argument is present, it is written to standard output without a trailing newline. The function then reads a line from input, converts it to a string (stripping a trailing newline), and returns that. When EOF is read, EOFError is raised. Example:

>>> s = input('--> ')  
+--> Monty Python's Flying Circus
+>>> s  
+"Monty Python's Flying Circus"
+

If the readline module was loaded, then input() will use it to provide elaborate line editing and history features.

+

Raises an auditing event builtins.input with argument prompt before reading input

+

Raises an auditing event builtins.input/result with the result after successfully reading input.

+
+class int(x=0)
class int(x, base=10) +
+

Return an integer object constructed from a number or string x, or return 0 if no arguments are given. If x defines __int__(), int(x) returns x.__int__(). If x defines __index__(), it returns x.__index__(). If x defines __trunc__(), it returns x.__trunc__(). For floating point numbers, this truncates towards zero.

If x is not a number or if base is given, then x must be a string, bytes, or bytearray instance representing an integer in radix base. Optionally, the string can be preceded by + or - (with no space in between), have leading zeros, be surrounded by whitespace, and have single underscores interspersed between digits.

A base-n integer string contains digits, each representing a value from 0 to n-1. The values 0–9 can be represented by any Unicode decimal digit. The values 10–35 can be represented by a to z (or A to Z). The default base is 10. The allowed bases are 0 and 2–36. Base-2, -8, and -16 strings can be optionally prefixed with 0b/0B, 0o/0O, or 0x/0X, as with integer literals in code. For base 0, the string is interpreted in a similar way to an integer literal in code, in that the actual base is 2, 8, 10, or 16 as determined by the prefix. Base 0 also disallows leading zeros: int('010', 0) is not legal, while int('010') and int('010', 8) are.

The integer type is described in Numeric Types — int, float, complex.

Changed in version 3.4: If base is not an instance of int and the base object has a base.__index__ method, that method is called to obtain an integer for the base. Previous versions used base.__int__ instead of base.__index__.

Changed in version 3.6: Grouping digits with underscores as in code literals is allowed.

Changed in version 3.7: x is now a positional-only parameter.

Changed in version 3.8: Falls back to __index__() if __int__() is not defined.

Changed in version 3.11: The delegation to __trunc__() is deprecated.

Changed in version 3.11: int string inputs and string representations can be limited to help avoid denial of service attacks. A ValueError is raised when the limit is exceeded while converting a string x to an int or when converting an int into a string would exceed the limit. See the integer string conversion length limitation documentation.

+
+isinstance(object, classinfo)
+

Return True if the object argument is an instance of the classinfo argument, or of a (direct, indirect, or virtual) subclass thereof. If object is not an object of the given type, the function always returns False. If classinfo is a tuple of type objects (or recursively, other such tuples) or a Union Type of multiple types, return True if object is an instance of any of the types. If classinfo is not a type or tuple of types and such tuples, a TypeError exception is raised. TypeError may not be raised for an invalid type if an earlier check succeeds.

Changed in version 3.10: classinfo can be a Union Type.

+
+issubclass(class, classinfo)
+

Return True if class is a subclass (direct, indirect, or virtual) of classinfo. A class is considered a subclass of itself. classinfo may be a tuple of class objects (or recursively, other such tuples) or a Union Type, in which case return True if class is a subclass of any entry in classinfo. In any other case, a TypeError exception is raised.

Changed in version 3.10: classinfo can be a Union Type.

+
+iter(object)
iter(object, sentinel) +
+

Return an iterator object. The first argument is interpreted very differently depending on the presence of the second argument. Without a second argument, object must be a collection object which supports the iterable protocol (the __iter__() method), or it must support the sequence protocol (the __getitem__() method with integer arguments starting at 0). If it does not support either of those protocols, TypeError is raised. If the second argument, sentinel, is given, then object must be a callable object. The iterator created in this case will call object with no arguments for each call to its __next__() method; if the value returned is equal to sentinel, StopIteration will be raised, otherwise the value will be returned.

See also Iterator Types.

One useful application of the second form of iter() is to build a block-reader. For example, reading fixed-width blocks from a binary database file until the end of file is reached:

from functools import partial
+with open('mydata.db', 'rb') as f:
+    for block in iter(partial(f.read, 64), b''):
+        process_block(block)
+
+
+len(s)
+

Return the length (the number of items) of an object. The argument may be a sequence (such as a string, bytes, tuple, list, or range) or a collection (such as a dictionary, set, or frozen set).

CPython implementation detail: len raises OverflowError on lengths larger than sys.maxsize, such as range(2 ** 100).

+
class list +
class list(iterable) +
+

Rather than being a function, list is actually a mutable sequence type, as documented in Lists and Sequence Types — list, tuple, range.

+
+locals()
+

Update and return a dictionary representing the current local symbol table. Free variables are returned by locals() when it is called in function blocks, but not in class blocks. Note that at the module level, locals() and globals() are the same dictionary.

Note

The contents of this dictionary should not be modified; changes may not affect the values of local and free variables used by the interpreter.

+
+map(function, iterable, *iterables)
+

Return an iterator that applies function to every item of iterable, yielding the results. If additional iterables arguments are passed, function must take that many arguments and is applied to the items from all iterables in parallel. With multiple iterables, the iterator stops when the shortest iterable is exhausted. For cases where the function inputs are already arranged into argument tuples, see itertools.starmap().

+
+max(iterable, *, key=None)
max(iterable, *, default, key=None) +
max(arg1, arg2, *args, key=None) +
+

Return the largest item in an iterable or the largest of two or more arguments.

If one positional argument is provided, it should be an iterable. The largest item in the iterable is returned. If two or more positional arguments are provided, the largest of the positional arguments is returned.

There are two optional keyword-only arguments. The key argument specifies a one-argument ordering function like that used for list.sort(). The default argument specifies an object to return if the provided iterable is empty. If the iterable is empty and default is not provided, a ValueError is raised.

If multiple items are maximal, the function returns the first one encountered. This is consistent with other sort-stability preserving tools such as sorted(iterable, key=keyfunc, reverse=True)[0] and heapq.nlargest(1, iterable, key=keyfunc).

New in version 3.4: The default keyword-only argument.

Changed in version 3.8: The key can be None.

+
class memoryview(object) +
+

Return a “memory view” object created from the given argument. See Memory Views for more information.

+
+min(iterable, *, key=None)
min(iterable, *, default, key=None) +
min(arg1, arg2, *args, key=None) +
+

Return the smallest item in an iterable or the smallest of two or more arguments.

If one positional argument is provided, it should be an iterable. The smallest item in the iterable is returned. If two or more positional arguments are provided, the smallest of the positional arguments is returned.

There are two optional keyword-only arguments. The key argument specifies a one-argument ordering function like that used for list.sort(). The default argument specifies an object to return if the provided iterable is empty. If the iterable is empty and default is not provided, a ValueError is raised.

If multiple items are minimal, the function returns the first one encountered. This is consistent with other sort-stability preserving tools such as sorted(iterable, key=keyfunc)[0] and heapq.nsmallest(1, +iterable, key=keyfunc).

New in version 3.4: The default keyword-only argument.

Changed in version 3.8: The key can be None.

+
+next(iterator)
next(iterator, default) +
+

Retrieve the next item from the iterator by calling its __next__() method. If default is given, it is returned if the iterator is exhausted, otherwise StopIteration is raised.

+
+class object
+

Return a new featureless object. object is a base for all classes. It has methods that are common to all instances of Python classes. This function does not accept any arguments.

Note

object does not have a __dict__, so you can’t assign arbitrary attributes to an instance of the object class.

+
+oct(x)
+

Convert an integer number to an octal string prefixed with “0o”. The result is a valid Python expression. If x is not a Python int object, it has to define an __index__() method that returns an integer. For example:

>>> oct(8)
+'0o10'
+>>> oct(-56)
+'-0o70'
+

If you want to convert an integer number to an octal string either with the prefix “0o” or not, you can use either of the following ways.

>>> '%#o' % 10, '%o' % 10
+('0o12', '12')
+>>> format(10, '#o'), format(10, 'o')
+('0o12', '12')
+>>> f'{10:#o}', f'{10:o}'
+('0o12', '12')
+

See also format() for more information.

+
+open(file, mode='r', buffering=- 1, encoding=None, errors=None, newline=None, closefd=True, opener=None)
+

Open file and return a corresponding file object. If the file cannot be opened, an OSError is raised. See Reading and Writing Files for more examples of how to use this function.

file is a path-like object giving the pathname (absolute or relative to the current working directory) of the file to be opened or an integer file descriptor of the file to be wrapped. (If a file descriptor is given, it is closed when the returned I/O object is closed unless closefd is set to False.)

mode is an optional string that specifies the mode in which the file is opened. It defaults to 'r' which means open for reading in text mode. Other common values are 'w' for writing (truncating the file if it already exists), 'x' for exclusive creation, and 'a' for appending (which on some Unix systems, means that all writes append to the end of the file regardless of the current seek position). In text mode, if encoding is not specified the encoding used is platform-dependent: locale.getencoding() is called to get the current locale encoding. (For reading and writing raw bytes use binary mode and leave encoding unspecified.) The available modes are:

+ + + + + + + +

Character

Meaning

'r'

open for reading (default)

'w'

open for writing, truncating the file first

'x'

open for exclusive creation, failing if the file already exists

'a'

open for writing, appending to the end of file if it exists

'b'

binary mode

't'

text mode (default)

'+'

open for updating (reading and writing)

The default mode is 'r' (open for reading text, a synonym of 'rt'). Modes 'w+' and 'w+b' open and truncate the file. Modes 'r+' and 'r+b' open the file with no truncation.

As mentioned in the Overview, Python distinguishes between binary and text I/O. Files opened in binary mode (including 'b' in the mode argument) return contents as bytes objects without any decoding. In text mode (the default, or when 't' is included in the mode argument), the contents of the file are returned as str, the bytes having been first decoded using a platform-dependent encoding or using the specified encoding if given.

Note

Python doesn’t depend on the underlying operating system’s notion of text files; all the processing is done by Python itself, and is therefore platform-independent.

buffering is an optional integer used to set the buffering policy. Pass 0 to switch buffering off (only allowed in binary mode), 1 to select line buffering (only usable when writing in text mode), and an integer > 1 to indicate the size in bytes of a fixed-size chunk buffer. Note that specifying a buffer size this way applies for binary buffered I/O, but TextIOWrapper (i.e., files opened with mode='r+') would have another buffering. To disable buffering in TextIOWrapper, consider using the write_through flag for io.TextIOWrapper.reconfigure(). When no buffering argument is given, the default buffering policy works as follows:

  • Binary files are buffered in fixed-size chunks; the size of the buffer is chosen using a heuristic trying to determine the underlying device’s “block size” and falling back on io.DEFAULT_BUFFER_SIZE. On many systems, the buffer will typically be 4096 or 8192 bytes long.
  • “Interactive” text files (files for which isatty() returns True) use line buffering. Other text files use the policy described above for binary files.

encoding is the name of the encoding used to decode or encode the file. This should only be used in text mode. The default encoding is platform dependent (whatever locale.getencoding() returns), but any text encoding supported by Python can be used. See the codecs module for the list of supported encodings.

errors is an optional string that specifies how encoding and decoding errors are to be handled—this cannot be used in binary mode. A variety of standard error handlers are available (listed under Error Handlers), though any error handling name that has been registered with codecs.register_error() is also valid. The standard names include:

  • +'strict' to raise a ValueError exception if there is an encoding error. The default value of None has the same effect.
  • +'ignore' ignores errors. Note that ignoring encoding errors can lead to data loss.
  • +'replace' causes a replacement marker (such as '?') to be inserted where there is malformed data.
  • +'surrogateescape' will represent any incorrect bytes as low surrogate code units ranging from U+DC80 to U+DCFF. These surrogate code units will then be turned back into the same bytes when the surrogateescape error handler is used when writing data. This is useful for processing files in an unknown encoding.
  • +'xmlcharrefreplace' is only supported when writing to a file. Characters not supported by the encoding are replaced with the appropriate XML character reference &#nnn;.
  • +'backslashreplace' replaces malformed data by Python’s backslashed escape sequences.
  • +'namereplace' (also only supported when writing) replaces unsupported characters with \N{...} escape sequences.

newline determines how to parse newline characters from the stream. It can be None, '', '\n', '\r', and '\r\n'. It works as follows:

  • When reading input from the stream, if newline is None, universal newlines mode is enabled. Lines in the input can end in '\n', '\r', or '\r\n', and these are translated into '\n' before being returned to the caller. If it is '', universal newlines mode is enabled, but line endings are returned to the caller untranslated. If it has any of the other legal values, input lines are only terminated by the given string, and the line ending is returned to the caller untranslated.
  • When writing output to the stream, if newline is None, any '\n' characters written are translated to the system default line separator, os.linesep. If newline is '' or '\n', no translation takes place. If newline is any of the other legal values, any '\n' characters written are translated to the given string.

If closefd is False and a file descriptor rather than a filename was given, the underlying file descriptor will be kept open when the file is closed. If a filename is given closefd must be True (the default); otherwise, an error will be raised.

A custom opener can be used by passing a callable as opener. The underlying file descriptor for the file object is then obtained by calling opener with (file, flags). opener must return an open file descriptor (passing os.open as opener results in functionality similar to passing None).

The newly created file is non-inheritable.

The following example uses the dir_fd parameter of the os.open() function to open a file relative to a given directory:

>>> import os
+>>> dir_fd = os.open('somedir', os.O_RDONLY)
+>>> def opener(path, flags):
+...     return os.open(path, flags, dir_fd=dir_fd)
+...
+>>> with open('spamspam.txt', 'w', opener=opener) as f:
+...     print('This will be written to somedir/spamspam.txt', file=f)
+...
+>>> os.close(dir_fd)  # don't leak a file descriptor
+

The type of file object returned by the open() function depends on the mode. When open() is used to open a file in a text mode ('w', 'r', 'wt', 'rt', etc.), it returns a subclass of io.TextIOBase (specifically io.TextIOWrapper). When used to open a file in a binary mode with buffering, the returned class is a subclass of io.BufferedIOBase. The exact class varies: in read binary mode, it returns an io.BufferedReader; in write binary and append binary modes, it returns an io.BufferedWriter, and in read/write mode, it returns an io.BufferedRandom. When buffering is disabled, the raw stream, a subclass of io.RawIOBase, io.FileIO, is returned.

See also the file handling modules, such as fileinput, io (where open() is declared), os, os.path, tempfile, and shutil.

Raises an auditing event open with arguments file, mode, flags.

The mode and flags arguments may have been modified or inferred from the original call.

Changed in version 3.3:

  • The opener parameter was added.
  • The 'x' mode was added.
  • +IOError used to be raised, it is now an alias of OSError.
  • +FileExistsError is now raised if the file opened in exclusive creation mode ('x') already exists.

Changed in version 3.4:

  • The file is now non-inheritable.

Changed in version 3.5:

  • If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).
  • The 'namereplace' error handler was added.

Changed in version 3.6:

Changed in version 3.11: The 'U' mode has been removed.

+
+ord(c)
+

Given a string representing one Unicode character, return an integer representing the Unicode code point of that character. For example, ord('a') returns the integer 97 and ord('€') (Euro sign) returns 8364. This is the inverse of chr().

+
+pow(base, exp, mod=None)
+

Return base to the power exp; if mod is present, return base to the power exp, modulo mod (computed more efficiently than pow(base, exp) % mod). The two-argument form pow(base, exp) is equivalent to using the power operator: base**exp.

The arguments must have numeric types. With mixed operand types, the coercion rules for binary arithmetic operators apply. For int operands, the result has the same type as the operands (after coercion) unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, pow(10, 2) returns 100, but pow(10, -2) returns 0.01. For a negative base of type int or float and a non-integral exponent, a complex result is delivered. For example, pow(-9, 0.5) returns a value close to 3j.

For int operands base and exp, if mod is present, mod must also be of integer type and mod must be nonzero. If mod is present and exp is negative, base must be relatively prime to mod. In that case, pow(inv_base, -exp, mod) is returned, where inv_base is an inverse to base modulo mod.

Here’s an example of computing an inverse for 38 modulo 97:

>>> pow(38, -1, mod=97)
+23
+>>> 23 * 38 % 97 == 1
+True
+

Changed in version 3.8: For int operands, the three-argument form of pow now allows the second argument to be negative, permitting computation of modular inverses.

Changed in version 3.8: Allow keyword arguments. Formerly, only positional arguments were supported.

+
+print(*objects, sep=' ', end='\n', file=None, flush=False)
+

Print objects to the text stream file, separated by sep and followed by end. sep, end, file, and flush, if present, must be given as keyword arguments.

All non-keyword arguments are converted to strings like str() does and written to the stream, separated by sep and followed by end. Both sep and end must be strings; they can also be None, which means to use the default values. If no objects are given, print() will just write end.

The file argument must be an object with a write(string) method; if it is not present or None, sys.stdout will be used. Since printed arguments are converted to text strings, print() cannot be used with binary mode file objects. For these, use file.write(...) instead.

Output buffering is usually determined by file. However, if flush is true, the stream is forcibly flushed.

Changed in version 3.3: Added the flush keyword argument.

+
+class property(fget=None, fset=None, fdel=None, doc=None)
+

Return a property attribute.

fget is a function for getting an attribute value. fset is a function for setting an attribute value. fdel is a function for deleting an attribute value. And doc creates a docstring for the attribute.

A typical use is to define a managed attribute x:

class C:
+    def __init__(self):
+        self._x = None
+
+    def getx(self):
+        return self._x
+
+    def setx(self, value):
+        self._x = value
+
+    def delx(self):
+        del self._x
+
+    x = property(getx, setx, delx, "I'm the 'x' property.")
+

If c is an instance of C, c.x will invoke the getter, c.x = value will invoke the setter, and del c.x the deleter.

If given, doc will be the docstring of the property attribute. Otherwise, the property will copy fget’s docstring (if it exists). This makes it possible to create read-only properties easily using property() as a decorator:

class Parrot:
+    def __init__(self):
+        self._voltage = 100000
+
+    @property
+    def voltage(self):
+        """Get the current voltage."""
+        return self._voltage
+

The @property decorator turns the voltage() method into a “getter” for a read-only attribute with the same name, and it sets the docstring for voltage to “Get the current voltage.”

+@getter
+
+@setter
+
+@deleter
+

A property object has getter, setter, and deleter methods usable as decorators that create a copy of the property with the corresponding accessor function set to the decorated function. This is best explained with an example:

class C:
+    def __init__(self):
+        self._x = None
+
+    @property
+    def x(self):
+        """I'm the 'x' property."""
+        return self._x
+
+    @x.setter
+    def x(self, value):
+        self._x = value
+
+    @x.deleter
+    def x(self):
+        del self._x
+

This code is exactly equivalent to the first example. Be sure to give the additional functions the same name as the original property (x in this case.)

The returned property object also has the attributes fget, fset, and fdel corresponding to the constructor arguments.

+

Changed in version 3.5: The docstrings of property objects are now writeable.

+
class range(stop) +
class range(start, stop, step=1) +
+

Rather than being a function, range is actually an immutable sequence type, as documented in Ranges and Sequence Types — list, tuple, range.

+
+repr(object)
+

Return a string containing a printable representation of an object. For many types, this function makes an attempt to return a string that would yield an object with the same value when passed to eval(); otherwise, the representation is a string enclosed in angle brackets that contains the name of the type of the object together with additional information often including the name and address of the object. A class can control what this function returns for its instances by defining a __repr__() method. If sys.displayhook() is not accessible, this function will raise RuntimeError.

+
+reversed(seq)
+

Return a reverse iterator. seq must be an object which has a __reversed__() method or supports the sequence protocol (the __len__() method and the __getitem__() method with integer arguments starting at 0).

+
+round(number, ndigits=None)
+

Return number rounded to ndigits precision after the decimal point. If ndigits is omitted or is None, it returns the nearest integer to its input.

For the built-in types supporting round(), values are rounded to the closest multiple of 10 to the power minus ndigits; if two multiples are equally close, rounding is done toward the even choice (so, for example, both round(0.5) and round(-0.5) are 0, and round(1.5) is 2). Any integer value is valid for ndigits (positive, zero, or negative). The return value is an integer if ndigits is omitted or None. Otherwise, the return value has the same type as number.

For a general Python object number, round delegates to number.__round__.

Note

The behavior of round() for floats can be surprising: for example, round(2.675, 2) gives 2.67 instead of the expected 2.68. This is not a bug: it’s a result of the fact that most decimal fractions can’t be represented exactly as a float. See Floating Point Arithmetic: Issues and Limitations for more information.

+
class set +
class set(iterable) +
+

Return a new set object, optionally with elements taken from iterable. set is a built-in class. See set and Set Types — set, frozenset for documentation about this class.

For other containers see the built-in frozenset, list, tuple, and dict classes, as well as the collections module.

+
+setattr(object, name, value)
+

This is the counterpart of getattr(). The arguments are an object, a string, and an arbitrary value. The string may name an existing attribute or a new attribute. The function assigns the value to the attribute, provided the object allows it. For example, setattr(x, 'foobar', 123) is equivalent to x.foobar = 123.

name need not be a Python identifier as defined in Identifiers and keywords unless the object chooses to enforce that, for example in a custom __getattribute__() or via __slots__. An attribute whose name is not an identifier will not be accessible using the dot notation, but is accessible through getattr() etc..

Note

Since private name mangling happens at compilation time, one must manually mangle a private attribute’s (attributes with two leading underscores) name in order to set it with setattr().

+
+class slice(stop)
class slice(start, stop, step=None) +
+

Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None.

+start
+
+stop
+
+step
+

Slice objects have read-only data attributes start, stop, and step which merely return the argument values (or their default). They have no other explicit functionality; however, they are used by NumPy and other third-party packages.

+

Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i]. See itertools.islice() for an alternate version that returns an iterator.

Changed in version 3.12: Slice objects are now hashable (provided start, stop, and step are hashable).

+
+sorted(iterable, /, *, key=None, reverse=False)
+

Return a new sorted list from the items in iterable.

Has two optional arguments which must be specified as keyword arguments.

key specifies a function of one argument that is used to extract a comparison key from each element in iterable (for example, key=str.lower). The default value is None (compare the elements directly).

reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed.

Use functools.cmp_to_key() to convert an old-style cmp function to a key function.

The built-in sorted() function is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade).

The sort algorithm uses only < comparisons between items. While defining an __lt__() method will suffice for sorting, PEP 8 recommends that all six rich comparisons be implemented. This will help avoid bugs when using the same data with other ordering tools such as max() that rely on a different underlying method. Implementing all six comparisons also helps avoid confusion for mixed type comparisons which can call reflected the __gt__() method.

For sorting examples and a brief sorting tutorial, see Sorting HOW TO.

+
+@staticmethod
+

Transform a method into a static method.

A static method does not receive an implicit first argument. To declare a static method, use this idiom:

class C:
+    @staticmethod
+    def f(arg1, arg2, argN): ...
+

The @staticmethod form is a function decorator – see Function definitions for details.

A static method can be called either on the class (such as C.f()) or on an instance (such as C().f()). Moreover, they can be called as regular functions (such as f()).

Static methods in Python are similar to those found in Java or C++. Also, see classmethod() for a variant that is useful for creating alternate class constructors.

Like all decorators, it is also possible to call staticmethod as a regular function and do something with its result. This is needed in some cases where you need a reference to a function from a class body and you want to avoid the automatic transformation to instance method. For these cases, use this idiom:

def regular_function():
+    ...
+
+class C:
+    method = staticmethod(regular_function)
+

For more information on static methods, see The standard type hierarchy.

Changed in version 3.10: Static methods now inherit the method attributes (__module__, __name__, __qualname__, __doc__ and __annotations__), have a new __wrapped__ attribute, and are now callable as regular functions.

+
class str(object='') +
class str(object=b'', encoding='utf-8', errors='strict') +
+

Return a str version of object. See str() for details.

str is the built-in string class. For general information about strings, see Text Sequence Type — str.

+
+sum(iterable, /, start=0)
+

Sums start and the items of an iterable from left to right and returns the total. The iterable’s items are normally numbers, and the start value is not allowed to be a string.

For some use cases, there are good alternatives to sum(). The preferred, fast way to concatenate a sequence of strings is by calling ''.join(sequence). To add floating point values with extended precision, see math.fsum(). To concatenate a series of iterables, consider using itertools.chain().

Changed in version 3.8: The start parameter can be specified as a keyword argument.

Changed in version 3.12: Summation of floats switched to an algorithm that gives higher accuracy on most builds.

+
+class super
class super(type, object_or_type=None) +
+

Return a proxy object that delegates method calls to a parent or sibling class of type. This is useful for accessing inherited methods that have been overridden in a class.

The object_or_type determines the method resolution order to be searched. The search starts from the class right after the type.

For example, if __mro__ of object_or_type is D -> B -> C -> A -> object and the value of type is B, then super() searches C -> A -> object.

The __mro__ attribute of the object_or_type lists the method resolution search order used by both getattr() and super(). The attribute is dynamic and can change whenever the inheritance hierarchy is updated.

If the second argument is omitted, the super object returned is unbound. If the second argument is an object, isinstance(obj, type) must be true. If the second argument is a type, issubclass(type2, type) must be true (this is useful for classmethods).

There are two typical use cases for super. In a class hierarchy with single inheritance, super can be used to refer to parent classes without naming them explicitly, thus making the code more maintainable. This use closely parallels the use of super in other programming languages.

The second use case is to support cooperative multiple inheritance in a dynamic execution environment. This use case is unique to Python and is not found in statically compiled languages or languages that only support single inheritance. This makes it possible to implement “diamond diagrams” where multiple base classes implement the same method. Good design dictates that such implementations have the same calling signature in every case (because the order of calls is determined at runtime, because that order adapts to changes in the class hierarchy, and because that order can include sibling classes that are unknown prior to runtime).

For both use cases, a typical superclass call looks like this:

class C(B):
+    def method(self, arg):
+        super().method(arg)    # This does the same thing as:
+                               # super(C, self).method(arg)
+

In addition to method lookups, super() also works for attribute lookups. One possible use case for this is calling descriptors in a parent or sibling class.

Note that super() is implemented as part of the binding process for explicit dotted attribute lookups such as super().__getitem__(name). It does so by implementing its own __getattribute__() method for searching classes in a predictable order that supports cooperative multiple inheritance. Accordingly, super() is undefined for implicit lookups using statements or operators such as super()[name].

Also note that, aside from the zero argument form, super() is not limited to use inside methods. The two argument form specifies the arguments exactly and makes the appropriate references. The zero argument form only works inside a class definition, as the compiler fills in the necessary details to correctly retrieve the class being defined, as well as accessing the current instance for ordinary methods.

For practical suggestions on how to design cooperative classes using super(), see guide to using super().

+
class tuple +
class tuple(iterable) +
+

Rather than being a function, tuple is actually an immutable sequence type, as documented in Tuples and Sequence Types — list, tuple, range.

+
+class type(object)
class type(name, bases, dict, **kwds) +
+

With one argument, return the type of an object. The return value is a type object and generally the same object as returned by object.__class__.

The isinstance() built-in function is recommended for testing the type of an object, because it takes subclasses into account.

With three arguments, return a new type object. This is essentially a dynamic form of the class statement. The name string is the class name and becomes the __name__ attribute. The bases tuple contains the base classes and becomes the __bases__ attribute; if empty, object, the ultimate base of all classes, is added. The dict dictionary contains attribute and method definitions for the class body; it may be copied or wrapped before becoming the __dict__ attribute. The following two statements create identical type objects:

>>> class X:
+...     a = 1
+...
+>>> X = type('X', (), dict(a=1))
+

See also Type Objects.

Keyword arguments provided to the three argument form are passed to the appropriate metaclass machinery (usually __init_subclass__()) in the same way that keywords in a class definition (besides metaclass) would.

See also Customizing class creation.

Changed in version 3.6: Subclasses of type which don’t override type.__new__ may no longer use the one-argument form to get the type of an object.

+
+vars()
vars(object) +
+

Return the __dict__ attribute for a module, class, instance, or any other object with a __dict__ attribute.

Objects such as modules and instances have an updateable __dict__ attribute; however, other objects may have write restrictions on their __dict__ attributes (for example, classes use a types.MappingProxyType to prevent direct dictionary updates).

Without an argument, vars() acts like locals(). Note, the locals dictionary is only useful for reads since updates to the locals dictionary are ignored.

A TypeError exception is raised if an object is specified but it doesn’t have a __dict__ attribute (for example, if its class defines the __slots__ attribute).

+
+zip(*iterables, strict=False)
+

Iterate over several iterables in parallel, producing tuples with an item from each one.

Example:

>>> for item in zip([1, 2, 3], ['sugar', 'spice', 'everything nice']):
+...     print(item)
+...
+(1, 'sugar')
+(2, 'spice')
+(3, 'everything nice')
+

More formally: zip() returns an iterator of tuples, where the i-th tuple contains the i-th element from each of the argument iterables.

Another way to think of zip() is that it turns rows into columns, and columns into rows. This is similar to transposing a matrix.

zip() is lazy: The elements won’t be processed until the iterable is iterated on, e.g. by a for loop or by wrapping in a list.

One thing to consider is that the iterables passed to zip() could have different lengths; sometimes by design, and sometimes because of a bug in the code that prepared these iterables. Python offers three different approaches to dealing with this issue:

  • +

    By default, zip() stops when the shortest iterable is exhausted. It will ignore the remaining items in the longer iterables, cutting off the result to the length of the shortest iterable:

    >>> list(zip(range(3), ['fee', 'fi', 'fo', 'fum']))
+[(0, 'fee'), (1, 'fi'), (2, 'fo')]
+
  • +

    zip() is often used in cases where the iterables are assumed to be of equal length. In such cases, it’s recommended to use the strict=True option. Its output is the same as regular zip():

    >>> list(zip(('a', 'b', 'c'), (1, 2, 3), strict=True))
+[('a', 1), ('b', 2), ('c', 3)]
+

    Unlike the default behavior, it raises a ValueError if one iterable is exhausted before the others:

    >>> for item in zip(range(3), ['fee', 'fi', 'fo', 'fum'], strict=True):  
+...     print(item)
+...
+(0, 'fee')
+(1, 'fi')
+(2, 'fo')
+Traceback (most recent call last):
+  ...
+ValueError: zip() argument 2 is longer than argument 1
+

    Without the strict=True argument, any bug that results in iterables of different lengths will be silenced, possibly manifesting as a hard-to-find bug in another part of the program.

  • Shorter iterables can be padded with a constant value to make all the iterables have the same length. This is done by itertools.zip_longest().

Edge cases: With a single iterable argument, zip() returns an iterator of 1-tuples. With no arguments, it returns an empty iterator.

Tips and tricks:

  • The left-to-right evaluation order of the iterables is guaranteed. This makes possible an idiom for clustering a data series into n-length groups using zip(*[iter(s)]*n, strict=True). This repeats the same iterator n times so that each output tuple has the result of n calls to the iterator. This has the effect of dividing the input into n-length chunks.
  • +

    zip() in conjunction with the * operator can be used to unzip a list:

    >>> x = [1, 2, 3]
+>>> y = [4, 5, 6]
+>>> list(zip(x, y))
+[(1, 4), (2, 5), (3, 6)]
+>>> x2, y2 = zip(*zip(x, y))
+>>> x == list(x2) and y == list(y2)
+True
+

Changed in version 3.10: Added the strict argument.

+
+__import__(name, globals=None, locals=None, fromlist=(), level=0)
+

Note

This is an advanced function that is not needed in everyday Python programming, unlike importlib.import_module().

This function is invoked by the import statement. It can be replaced (by importing the builtins module and assigning to builtins.__import__) in order to change semantics of the import statement, but doing so is strongly discouraged as it is usually simpler to use import hooks (see PEP 302) to attain the same goals and does not cause issues with code which assumes the default import implementation is in use. Direct use of __import__() is also discouraged in favor of importlib.import_module().

The function imports the module name, potentially using the given globals and locals to determine how to interpret the name in a package context. The fromlist gives the names of objects or submodules that should be imported from the module given by name. The standard implementation does not use its locals argument at all and uses its globals only to determine the package context of the import statement.

level specifies whether to use absolute or relative imports. 0 (the default) means only perform absolute imports. Positive values for level indicate the number of parent directories to search relative to the directory of the module calling __import__() (see PEP 328 for the details).

When the name variable is of the form package.module, normally, the top-level package (the name up till the first dot) is returned, not the module named by name. However, when a non-empty fromlist argument is given, the module named by name is returned.

For example, the statement import spam results in bytecode resembling the following code:

spam = __import__('spam', globals(), locals(), [], 0)
+

The statement import spam.ham results in this call:

spam = __import__('spam.ham', globals(), locals(), [], 0)
+

Note how __import__() returns the toplevel module here because this is the object that is bound to a name by the import statement.

On the other hand, the statement from spam.ham import eggs, sausage as +saus results in

_temp = __import__('spam.ham', globals(), locals(), ['eggs', 'sausage'], 0)
+eggs = _temp.eggs
+saus = _temp.sausage
+

Here, the spam.ham module is returned from __import__(). From this object, the names to import are retrieved and assigned to their respective names.

If you simply want to import a module (potentially within a package) by name, use importlib.import_module().

Changed in version 3.3: Negative values for level are no longer supported (which also changes the default value to 0).

Changed in version 3.9: When the command line options -E or -I are being used, the environment variable PYTHONCASEOK is now ignored.

+

Footnotes

+1
+

Note that the parser only accepts the Unix-style end of line convention. If you are reading the code from a file, make sure to use newline conversion mode to convert Windows or Mac-style newlines.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/functions.html +

+
diff --git a/devdocs/python~3.12/library%2Ffunctools.html b/devdocs/python~3.12/library%2Ffunctools.html new file mode 100644 index 00000000..361a1b85 --- /dev/null +++ b/devdocs/python~3.12/library%2Ffunctools.html @@ -0,0 +1,291 @@ +

functools — Higher-order functions and operations on callable objects

Source code: Lib/functools.py

The functools module is for higher-order functions: functions that act on or return other functions. In general, any callable object can be treated as a function for the purposes of this module.

The functools module defines the following functions:

+@functools.cache(user_function)
+

Simple lightweight unbounded function cache. Sometimes called “memoize”.

Returns the same as lru_cache(maxsize=None), creating a thin wrapper around a dictionary lookup for the function arguments. Because it never needs to evict old values, this is smaller and faster than lru_cache() with a size limit.

For example:

@cache
+def factorial(n):
+    return n * factorial(n-1) if n else 1
+
+>>> factorial(10)      # no previously cached result, makes 11 recursive calls
+3628800
+>>> factorial(5)       # just looks up cached value result
+120
+>>> factorial(12)      # makes two new recursive calls, the other 10 are cached
+479001600
+

The cache is threadsafe so that the wrapped function can be used in multiple threads. This means that the underlying data structure will remain coherent during concurrent updates.

It is possible for the wrapped function to be called more than once if another thread makes an additional call before the initial call has been completed and cached.

New in version 3.9.

+
+@functools.cached_property(func)
+

Transform a method of a class into a property whose value is computed once and then cached as a normal attribute for the life of the instance. Similar to property(), with the addition of caching. Useful for expensive computed properties of instances that are otherwise effectively immutable.

Example:

class DataSet:
+
+    def __init__(self, sequence_of_numbers):
+        self._data = tuple(sequence_of_numbers)
+
+    @cached_property
+    def stdev(self):
+        return statistics.stdev(self._data)
+

The mechanics of cached_property() are somewhat different from property(). A regular property blocks attribute writes unless a setter is defined. In contrast, a cached_property allows writes.

The cached_property decorator only runs on lookups and only when an attribute of the same name doesn’t exist. When it does run, the cached_property writes to the attribute with the same name. Subsequent attribute reads and writes take precedence over the cached_property method and it works like a normal attribute.

The cached value can be cleared by deleting the attribute. This allows the cached_property method to run again.

The cached_property does not prevent a possible race condition in multi-threaded usage. The getter function could run more than once on the same instance, with the latest run setting the cached value. If the cached property is idempotent or otherwise not harmful to run more than once on an instance, this is fine. If synchronization is needed, implement the necessary locking inside the decorated getter function or around the cached property access.

Note, this decorator interferes with the operation of PEP 412 key-sharing dictionaries. This means that instance dictionaries can take more space than usual.

Also, this decorator requires that the __dict__ attribute on each instance be a mutable mapping. This means it will not work with some types, such as metaclasses (since the __dict__ attributes on type instances are read-only proxies for the class namespace), and those that specify __slots__ without including __dict__ as one of the defined slots (as such classes don’t provide a __dict__ attribute at all).

If a mutable mapping is not available or if space-efficient key sharing is desired, an effect similar to cached_property() can also be achieved by stacking property() on top of lru_cache(). See How do I cache method calls? for more details on how this differs from cached_property().

New in version 3.8.

Changed in version 3.12: Prior to Python 3.12, cached_property included an undocumented lock to ensure that in multi-threaded usage the getter function was guaranteed to run only once per instance. However, the lock was per-property, not per-instance, which could result in unacceptably high lock contention. In Python 3.12+ this locking is removed.

+
+functools.cmp_to_key(func)
+

Transform an old-style comparison function to a key function. Used with tools that accept key functions (such as sorted(), min(), max(), heapq.nlargest(), heapq.nsmallest(), itertools.groupby()). This function is primarily used as a transition tool for programs being converted from Python 2 which supported the use of comparison functions.

A comparison function is any callable that accepts two arguments, compares them, and returns a negative number for less-than, zero for equality, or a positive number for greater-than. A key function is a callable that accepts one argument and returns another value to be used as the sort key.

Example:

sorted(iterable, key=cmp_to_key(locale.strcoll))  # locale-aware sort order
+

For sorting examples and a brief sorting tutorial, see Sorting HOW TO.

New in version 3.2.

+
+@functools.lru_cache(user_function)
@functools.lru_cache(maxsize=128, typed=False) +
+

Decorator to wrap a function with a memoizing callable that saves up to the maxsize most recent calls. It can save time when an expensive or I/O bound function is periodically called with the same arguments.

The cache is threadsafe so that the wrapped function can be used in multiple threads. This means that the underlying data structure will remain coherent during concurrent updates.

It is possible for the wrapped function to be called more than once if another thread makes an additional call before the initial call has been completed and cached.

Since a dictionary is used to cache results, the positional and keyword arguments to the function must be hashable.

Distinct argument patterns may be considered to be distinct calls with separate cache entries. For example, f(a=1, b=2) and f(b=2, a=1) differ in their keyword argument order and may have two separate cache entries.

If user_function is specified, it must be a callable. This allows the lru_cache decorator to be applied directly to a user function, leaving the maxsize at its default value of 128:

@lru_cache
+def count_vowels(sentence):
+    return sum(sentence.count(vowel) for vowel in 'AEIOUaeiou')
+

If maxsize is set to None, the LRU feature is disabled and the cache can grow without bound.

If typed is set to true, function arguments of different types will be cached separately. If typed is false, the implementation will usually regard them as equivalent calls and only cache a single result. (Some types such as str and int may be cached separately even when typed is false.)

Note, type specificity applies only to the function’s immediate arguments rather than their contents. The scalar arguments, Decimal(42) and Fraction(42) are be treated as distinct calls with distinct results. In contrast, the tuple arguments ('answer', Decimal(42)) and ('answer', Fraction(42)) are treated as equivalent.

The wrapped function is instrumented with a cache_parameters() function that returns a new dict showing the values for maxsize and typed. This is for information purposes only. Mutating the values has no effect.

To help measure the effectiveness of the cache and tune the maxsize parameter, the wrapped function is instrumented with a cache_info() function that returns a named tuple showing hits, misses, maxsize and currsize.

The decorator also provides a cache_clear() function for clearing or invalidating the cache.

The original underlying function is accessible through the __wrapped__ attribute. This is useful for introspection, for bypassing the cache, or for rewrapping the function with a different cache.

The cache keeps references to the arguments and return values until they age out of the cache or until the cache is cleared.

If a method is cached, the self instance argument is included in the cache. See How do I cache method calls?

An LRU (least recently used) cache works best when the most recent calls are the best predictors of upcoming calls (for example, the most popular articles on a news server tend to change each day). The cache’s size limit assures that the cache does not grow without bound on long-running processes such as web servers.

In general, the LRU cache should only be used when you want to reuse previously computed values. Accordingly, it doesn’t make sense to cache functions with side-effects, functions that need to create distinct mutable objects on each call (such as generators and async functions), or impure functions such as time() or random().

Example of an LRU cache for static web content:

@lru_cache(maxsize=32)
+def get_pep(num):
+    'Retrieve text of a Python Enhancement Proposal'
+    resource = f'https://peps.python.org/pep-{num:04d}'
+    try:
+        with urllib.request.urlopen(resource) as s:
+            return s.read()
+    except urllib.error.HTTPError:
+        return 'Not Found'
+
+>>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
+...     pep = get_pep(n)
+...     print(n, len(pep))
+
+>>> get_pep.cache_info()
+CacheInfo(hits=3, misses=8, maxsize=32, currsize=8)
+

Example of efficiently computing Fibonacci numbers using a cache to implement a dynamic programming technique:

@lru_cache(maxsize=None)
+def fib(n):
+    if n < 2:
+        return n
+    return fib(n-1) + fib(n-2)
+
+>>> [fib(n) for n in range(16)]
+[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]
+
+>>> fib.cache_info()
+CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)
+

New in version 3.2.

Changed in version 3.3: Added the typed option.

Changed in version 3.8: Added the user_function option.

New in version 3.9: Added the function cache_parameters()

+
+@functools.total_ordering
+

Given a class defining one or more rich comparison ordering methods, this class decorator supplies the rest. This simplifies the effort involved in specifying all of the possible rich comparison operations:

The class must define one of __lt__(), __le__(), __gt__(), or __ge__(). In addition, the class should supply an __eq__() method.

For example:

@total_ordering
+class Student:
+    def _is_valid_operand(self, other):
+        return (hasattr(other, "lastname") and
+                hasattr(other, "firstname"))
+    def __eq__(self, other):
+        if not self._is_valid_operand(other):
+            return NotImplemented
+        return ((self.lastname.lower(), self.firstname.lower()) ==
+                (other.lastname.lower(), other.firstname.lower()))
+    def __lt__(self, other):
+        if not self._is_valid_operand(other):
+            return NotImplemented
+        return ((self.lastname.lower(), self.firstname.lower()) <
+                (other.lastname.lower(), other.firstname.lower()))
+

Note

While this decorator makes it easy to create well behaved totally ordered types, it does come at the cost of slower execution and more complex stack traces for the derived comparison methods. If performance benchmarking indicates this is a bottleneck for a given application, implementing all six rich comparison methods instead is likely to provide an easy speed boost.

Note

This decorator makes no attempt to override methods that have been declared in the class or its superclasses. Meaning that if a superclass defines a comparison operator, total_ordering will not implement it again, even if the original method is abstract.

New in version 3.2.

Changed in version 3.4: Returning NotImplemented from the underlying comparison function for unrecognised types is now supported.

+
+functools.partial(func, /, *args, **keywords)
+

Return a new partial object which when called will behave like func called with the positional arguments args and keyword arguments keywords. If more arguments are supplied to the call, they are appended to args. If additional keyword arguments are supplied, they extend and override keywords. Roughly equivalent to:

def partial(func, /, *args, **keywords):
+    def newfunc(*fargs, **fkeywords):
+        newkeywords = {**keywords, **fkeywords}
+        return func(*args, *fargs, **newkeywords)
+    newfunc.func = func
+    newfunc.args = args
+    newfunc.keywords = keywords
+    return newfunc
+

The partial() is used for partial function application which “freezes” some portion of a function’s arguments and/or keywords resulting in a new object with a simplified signature. For example, partial() can be used to create a callable that behaves like the int() function where the base argument defaults to two:

>>> from functools import partial
+>>> basetwo = partial(int, base=2)
+>>> basetwo.__doc__ = 'Convert base 2 string to an int.'
+>>> basetwo('10010')
+18
+
+
+class functools.partialmethod(func, /, *args, **keywords)
+

Return a new partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable.

func must be a descriptor or a callable (objects which are both, like normal functions, are handled as descriptors).

When func is a descriptor (such as a normal Python function, classmethod(), staticmethod(), abstractmethod() or another instance of partialmethod), calls to __get__ are delegated to the underlying descriptor, and an appropriate partial object returned as the result.

When func is a non-descriptor callable, an appropriate bound method is created dynamically. This behaves like a normal Python function when used as a method: the self argument will be inserted as the first positional argument, even before the args and keywords supplied to the partialmethod constructor.

Example:

>>> class Cell:
+...     def __init__(self):
+...         self._alive = False
+...     @property
+...     def alive(self):
+...         return self._alive
+...     def set_state(self, state):
+...         self._alive = bool(state)
+...     set_alive = partialmethod(set_state, True)
+...     set_dead = partialmethod(set_state, False)
+...
+>>> c = Cell()
+>>> c.alive
+False
+>>> c.set_alive()
+>>> c.alive
+True
+

New in version 3.4.

+
+functools.reduce(function, iterable[, initializer])
+

Apply function of two arguments cumulatively to the items of iterable, from left to right, so as to reduce the iterable to a single value. For example, reduce(lambda x, y: x+y, [1, 2, 3, 4, 5]) calculates ((((1+2)+3)+4)+5). The left argument, x, is the accumulated value and the right argument, y, is the update value from the iterable. If the optional initializer is present, it is placed before the items of the iterable in the calculation, and serves as a default when the iterable is empty. If initializer is not given and iterable contains only one item, the first item is returned.

Roughly equivalent to:

def reduce(function, iterable, initializer=None):
+    it = iter(iterable)
+    if initializer is None:
+        value = next(it)
+    else:
+        value = initializer
+    for element in it:
+        value = function(value, element)
+    return value
+

See itertools.accumulate() for an iterator that yields all intermediate values.

+
+@functools.singledispatch
+

Transform a function into a single-dispatch generic function.

To define a generic function, decorate it with the @singledispatch decorator. When defining a function using @singledispatch, note that the dispatch happens on the type of the first argument:

>>> from functools import singledispatch
+>>> @singledispatch
+... def fun(arg, verbose=False):
+...     if verbose:
+...         print("Let me just say,", end=" ")
+...     print(arg)
+

To add overloaded implementations to the function, use the register() attribute of the generic function, which can be used as a decorator. For functions annotated with types, the decorator will infer the type of the first argument automatically:

>>> @fun.register
+... def _(arg: int, verbose=False):
+...     if verbose:
+...         print("Strength in numbers, eh?", end=" ")
+...     print(arg)
+...
+>>> @fun.register
+... def _(arg: list, verbose=False):
+...     if verbose:
+...         print("Enumerate this:")
+...     for i, elem in enumerate(arg):
+...         print(i, elem)
+

types.UnionType and typing.Union can also be used:

>>> @fun.register
+... def _(arg: int | float, verbose=False):
+...     if verbose:
+...         print("Strength in numbers, eh?", end=" ")
+...     print(arg)
+...
+>>> from typing import Union
+>>> @fun.register
+... def _(arg: Union[list, set], verbose=False):
+...     if verbose:
+...         print("Enumerate this:")
+...     for i, elem in enumerate(arg):
+...         print(i, elem)
+...
+

For code which doesn’t use type annotations, the appropriate type argument can be passed explicitly to the decorator itself:

>>> @fun.register(complex)
+... def _(arg, verbose=False):
+...     if verbose:
+...         print("Better than complicated.", end=" ")
+...     print(arg.real, arg.imag)
+...
+

To enable registering lambdas and pre-existing functions, the register() attribute can also be used in a functional form:

>>> def nothing(arg, verbose=False):
+...     print("Nothing.")
+...
+>>> fun.register(type(None), nothing)
+

The register() attribute returns the undecorated function. This enables decorator stacking, pickling, and the creation of unit tests for each variant independently:

>>> @fun.register(float)
+... @fun.register(Decimal)
+... def fun_num(arg, verbose=False):
+...     if verbose:
+...         print("Half of your number:", end=" ")
+...     print(arg / 2)
+...
+>>> fun_num is fun
+False
+

When called, the generic function dispatches on the type of the first argument:

>>> fun("Hello, world.")
+Hello, world.
+>>> fun("test.", verbose=True)
+Let me just say, test.
+>>> fun(42, verbose=True)
+Strength in numbers, eh? 42
+>>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
+Enumerate this:
+0 spam
+1 spam
+2 eggs
+3 spam
+>>> fun(None)
+Nothing.
+>>> fun(1.23)
+0.615
+

Where there is no registered implementation for a specific type, its method resolution order is used to find a more generic implementation. The original function decorated with @singledispatch is registered for the base object type, which means it is used if no better implementation is found.

If an implementation is registered to an abstract base class, virtual subclasses of the base class will be dispatched to that implementation:

>>> from collections.abc import Mapping
+>>> @fun.register
+... def _(arg: Mapping, verbose=False):
+...     if verbose:
+...         print("Keys & Values")
+...     for key, value in arg.items():
+...         print(key, "=>", value)
+...
+>>> fun({"a": "b"})
+a => b
+

To check which implementation the generic function will choose for a given type, use the dispatch() attribute:

>>> fun.dispatch(float)
+<function fun_num at 0x1035a2840>
+>>> fun.dispatch(dict)    # note: default implementation
+<function fun at 0x103fe0000>
+

To access all registered implementations, use the read-only registry attribute:

>>> fun.registry.keys()
+dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
+          <class 'decimal.Decimal'>, <class 'list'>,
+          <class 'float'>])
+>>> fun.registry[float]
+<function fun_num at 0x1035a2840>
+>>> fun.registry[object]
+<function fun at 0x103fe0000>
+

New in version 3.4.

Changed in version 3.7: The register() attribute now supports using type annotations.

Changed in version 3.11: The register() attribute now supports types.UnionType and typing.Union as type annotations.

+
+class functools.singledispatchmethod(func)
+

Transform a method into a single-dispatch generic function.

To define a generic method, decorate it with the @singledispatchmethod decorator. When defining a function using @singledispatchmethod, note that the dispatch happens on the type of the first non-self or non-cls argument:

class Negator:
+    @singledispatchmethod
+    def neg(self, arg):
+        raise NotImplementedError("Cannot negate a")
+
+    @neg.register
+    def _(self, arg: int):
+        return -arg
+
+    @neg.register
+    def _(self, arg: bool):
+        return not arg
+

@singledispatchmethod supports nesting with other decorators such as @classmethod. Note that to allow for dispatcher.register, singledispatchmethod must be the outer most decorator. Here is the Negator class with the neg methods bound to the class, rather than an instance of the class:

class Negator:
+    @singledispatchmethod
+    @classmethod
+    def neg(cls, arg):
+        raise NotImplementedError("Cannot negate a")
+
+    @neg.register
+    @classmethod
+    def _(cls, arg: int):
+        return -arg
+
+    @neg.register
+    @classmethod
+    def _(cls, arg: bool):
+        return not arg
+

The same pattern can be used for other similar decorators: @staticmethod, @abstractmethod, and others.

New in version 3.8.

+
+functools.update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
+

Update a wrapper function to look like the wrapped function. The optional arguments are tuples to specify which attributes of the original function are assigned directly to the matching attributes on the wrapper function and which attributes of the wrapper function are updated with the corresponding attributes from the original function. The default values for these arguments are the module level constants WRAPPER_ASSIGNMENTS (which assigns to the wrapper function’s __module__, __name__, __qualname__, __annotations__ and __doc__, the documentation string) and WRAPPER_UPDATES (which updates the wrapper function’s __dict__, i.e. the instance dictionary).

To allow access to the original function for introspection and other purposes (e.g. bypassing a caching decorator such as lru_cache()), this function automatically adds a __wrapped__ attribute to the wrapper that refers to the function being wrapped.

The main intended use for this function is in decorator functions which wrap the decorated function and return the wrapper. If the wrapper function is not updated, the metadata of the returned function will reflect the wrapper definition rather than the original function definition, which is typically less than helpful.

update_wrapper() may be used with callables other than functions. Any attributes named in assigned or updated that are missing from the object being wrapped are ignored (i.e. this function will not attempt to set them on the wrapper function). AttributeError is still raised if the wrapper function itself is missing any attributes named in updated.

New in version 3.2: Automatic addition of the __wrapped__ attribute.

New in version 3.2: Copying of the __annotations__ attribute by default.

Changed in version 3.2: Missing attributes no longer trigger an AttributeError.

Changed in version 3.4: The __wrapped__ attribute now always refers to the wrapped function, even if that function defined a __wrapped__ attribute. (see bpo-17482)

+
+@functools.wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
+

This is a convenience function for invoking update_wrapper() as a function decorator when defining a wrapper function. It is equivalent to partial(update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated). For example:

>>> from functools import wraps
+>>> def my_decorator(f):
+...     @wraps(f)
+...     def wrapper(*args, **kwds):
+...         print('Calling decorated function')
+...         return f(*args, **kwds)
+...     return wrapper
+...
+>>> @my_decorator
+... def example():
+...     """Docstring"""
+...     print('Called example function')
+...
+>>> example()
+Calling decorated function
+Called example function
+>>> example.__name__
+'example'
+>>> example.__doc__
+'Docstring'
+

Without the use of this decorator factory, the name of the example function would have been 'wrapper', and the docstring of the original example() would have been lost.

+

partial Objects

partial objects are callable objects created by partial(). They have three read-only attributes:

+partial.func
+

A callable object or function. Calls to the partial object will be forwarded to func with new arguments and keywords.

+
+partial.args
+

The leftmost positional arguments that will be prepended to the positional arguments provided to a partial object call.

+
+partial.keywords
+

The keyword arguments that will be supplied when the partial object is called.

+

partial objects are like function objects in that they are callable, weak referenceable, and can have attributes. There are some important differences. For instance, the __name__ and __doc__ attributes are not created automatically. Also, partial objects defined in classes behave like static methods and do not transform into bound methods during instance attribute look-up.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/functools.html +

+
diff --git a/devdocs/python~3.12/library%2Fgc.html b/devdocs/python~3.12/library%2Fgc.html new file mode 100644 index 00000000..17d4c135 --- /dev/null +++ b/devdocs/python~3.12/library%2Fgc.html @@ -0,0 +1,111 @@ +

gc — Garbage Collector interface

This module provides an interface to the optional garbage collector. It provides the ability to disable the collector, tune the collection frequency, and set debugging options. It also provides access to unreachable objects that the collector found but cannot free. Since the collector supplements the reference counting already used in Python, you can disable the collector if you are sure your program does not create reference cycles. Automatic collection can be disabled by calling gc.disable(). To debug a leaking program call gc.set_debug(gc.DEBUG_LEAK). Notice that this includes gc.DEBUG_SAVEALL, causing garbage-collected objects to be saved in gc.garbage for inspection.

The gc module provides the following functions:

+gc.enable()
+

Enable automatic garbage collection.

+
+gc.disable()
+

Disable automatic garbage collection.

+
+gc.isenabled()
+

Return True if automatic collection is enabled.

+
+gc.collect(generation=2)
+

With no arguments, run a full collection. The optional argument generation may be an integer specifying which generation to collect (from 0 to 2). A ValueError is raised if the generation number is invalid. The number of unreachable objects found is returned.

The free lists maintained for a number of built-in types are cleared whenever a full collection or collection of the highest generation (2) is run. Not all items in some free lists may be freed due to the particular implementation, in particular float.

The effect of calling gc.collect() while the interpreter is already performing a collection is undefined.

+
+gc.set_debug(flags)
+

Set the garbage collection debugging flags. Debugging information will be written to sys.stderr. See below for a list of debugging flags which can be combined using bit operations to control debugging.

+
+gc.get_debug()
+

Return the debugging flags currently set.

+
+gc.get_objects(generation=None)
+

Returns a list of all objects tracked by the collector, excluding the list returned. If generation is not None, return only the objects tracked by the collector that are in that generation.

Changed in version 3.8: New generation parameter.

Raises an auditing event gc.get_objects with argument generation.

+
+gc.get_stats()
+

Return a list of three per-generation dictionaries containing collection statistics since interpreter start. The number of keys may change in the future, but currently each dictionary will contain the following items:

  • +collections is the number of times this generation was collected;
  • +collected is the total number of objects collected inside this generation;
  • +uncollectable is the total number of objects which were found to be uncollectable (and were therefore moved to the garbage list) inside this generation.

New in version 3.4.

+
+gc.set_threshold(threshold0[, threshold1[, threshold2]])
+

Set the garbage collection thresholds (the collection frequency). Setting threshold0 to zero disables collection.

The GC classifies objects into three generations depending on how many collection sweeps they have survived. New objects are placed in the youngest generation (generation 0). If an object survives a collection it is moved into the next older generation. Since generation 2 is the oldest generation, objects in that generation remain there after a collection. In order to decide when to run, the collector keeps track of the number object allocations and deallocations since the last collection. When the number of allocations minus the number of deallocations exceeds threshold0, collection starts. Initially only generation 0 is examined. If generation 0 has been examined more than threshold1 times since generation 1 has been examined, then generation 1 is examined as well. With the third generation, things are a bit more complicated, see Collecting the oldest generation for more information.

+
+gc.get_count()
+

Return the current collection counts as a tuple of (count0, count1, +count2).

+
+gc.get_threshold()
+

Return the current collection thresholds as a tuple of (threshold0, +threshold1, threshold2).

+
+gc.get_referrers(*objs)
+

Return the list of objects that directly refer to any of objs. This function will only locate those containers which support garbage collection; extension types which do refer to other objects but do not support garbage collection will not be found.

Note that objects which have already been dereferenced, but which live in cycles and have not yet been collected by the garbage collector can be listed among the resulting referrers. To get only currently live objects, call collect() before calling get_referrers().

Warning

Care must be taken when using objects returned by get_referrers() because some of them could still be under construction and hence in a temporarily invalid state. Avoid using get_referrers() for any purpose other than debugging.

Raises an auditing event gc.get_referrers with argument objs.

+
+gc.get_referents(*objs)
+

Return a list of objects directly referred to by any of the arguments. The referents returned are those objects visited by the arguments’ C-level tp_traverse methods (if any), and may not be all objects actually directly reachable. tp_traverse methods are supported only by objects that support garbage collection, and are only required to visit objects that may be involved in a cycle. So, for example, if an integer is directly reachable from an argument, that integer object may or may not appear in the result list.

Raises an auditing event gc.get_referents with argument objs.

+
+gc.is_tracked(obj)
+

Returns True if the object is currently tracked by the garbage collector, False otherwise. As a general rule, instances of atomic types aren’t tracked and instances of non-atomic types (containers, user-defined objects…) are. However, some type-specific optimizations can be present in order to suppress the garbage collector footprint of simple instances (e.g. dicts containing only atomic keys and values):

>>> gc.is_tracked(0)
+False
+>>> gc.is_tracked("a")
+False
+>>> gc.is_tracked([])
+True
+>>> gc.is_tracked({})
+False
+>>> gc.is_tracked({"a": 1})
+False
+>>> gc.is_tracked({"a": []})
+True
+

New in version 3.1.

+
+gc.is_finalized(obj)
+

Returns True if the given object has been finalized by the garbage collector, False otherwise.

>>> x = None
+>>> class Lazarus:
+...     def __del__(self):
+...         global x
+...         x = self
+...
+>>> lazarus = Lazarus()
+>>> gc.is_finalized(lazarus)
+False
+>>> del lazarus
+>>> gc.is_finalized(x)
+True
+

New in version 3.9.

+
+gc.freeze()
+

Freeze all the objects tracked by the garbage collector; move them to a permanent generation and ignore them in all the future collections.

If a process will fork() without exec(), avoiding unnecessary copy-on-write in child processes will maximize memory sharing and reduce overall memory usage. This requires both avoiding creation of freed “holes” in memory pages in the parent process and ensuring that GC collections in child processes won’t touch the gc_refs counter of long-lived objects originating in the parent process. To accomplish both, call gc.disable() early in the parent process, gc.freeze() right before fork(), and gc.enable() early in child processes.

New in version 3.7.

+
+gc.unfreeze()
+

Unfreeze the objects in the permanent generation, put them back into the oldest generation.

New in version 3.7.

+
+gc.get_freeze_count()
+

Return the number of objects in the permanent generation.

New in version 3.7.

+

The following variables are provided for read-only access (you can mutate the values but should not rebind them):

+gc.garbage
+

A list of objects which the collector found to be unreachable but could not be freed (uncollectable objects). Starting with Python 3.4, this list should be empty most of the time, except when using instances of C extension types with a non-NULL tp_del slot.

If DEBUG_SAVEALL is set, then all unreachable objects will be added to this list rather than freed.

Changed in version 3.2: If this list is non-empty at interpreter shutdown, a ResourceWarning is emitted, which is silent by default. If DEBUG_UNCOLLECTABLE is set, in addition all uncollectable objects are printed.

Changed in version 3.4: Following PEP 442, objects with a __del__() method don’t end up in gc.garbage anymore.

+
+gc.callbacks
+

A list of callbacks that will be invoked by the garbage collector before and after collection. The callbacks will be called with two arguments, phase and info.

phase can be one of two values:

“start”: The garbage collection is about to start.

“stop”: The garbage collection has finished.

info is a dict providing more information for the callback. The following keys are currently defined:

“generation”: The oldest generation being collected.

“collected”: When phase is “stop”, the number of objects successfully collected.

“uncollectable”: When phase is “stop”, the number of objects that could not be collected and were put in garbage.

Applications can add their own callbacks to this list. The primary use cases are:

Gathering statistics about garbage collection, such as how often various generations are collected, and how long the collection takes.

Allowing applications to identify and clear their own uncollectable types when they appear in garbage.

New in version 3.3.

+

The following constants are provided for use with set_debug():

+gc.DEBUG_STATS
+

Print statistics during collection. This information can be useful when tuning the collection frequency.

+
+gc.DEBUG_COLLECTABLE
+

Print information on collectable objects found.

+
+gc.DEBUG_UNCOLLECTABLE
+

Print information of uncollectable objects found (objects which are not reachable but cannot be freed by the collector). These objects will be added to the garbage list.

Changed in version 3.2: Also print the contents of the garbage list at interpreter shutdown, if it isn’t empty.

+
+gc.DEBUG_SAVEALL
+

When set, all unreachable objects found will be appended to garbage rather than being freed. This can be useful for debugging a leaking program.

+
+gc.DEBUG_LEAK
+

The debugging flags necessary for the collector to print information about a leaking program (equal to DEBUG_COLLECTABLE | DEBUG_UNCOLLECTABLE | +DEBUG_SAVEALL).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/gc.html +

+
diff --git a/devdocs/python~3.12/library%2Fgetopt.html b/devdocs/python~3.12/library%2Fgetopt.html new file mode 100644 index 00000000..931eb25c --- /dev/null +++ b/devdocs/python~3.12/library%2Fgetopt.html @@ -0,0 +1,77 @@ +

getopt — C-style parser for command line options

Source code: Lib/getopt.py

Note

The getopt module is a parser for command line options whose API is designed to be familiar to users of the C getopt() function. Users who are unfamiliar with the C getopt() function or who would like to write less code and get better help and error messages should consider using the argparse module instead.

This module helps scripts to parse the command line arguments in sys.argv. It supports the same conventions as the Unix getopt() function (including the special meanings of arguments of the form ‘-’ and ‘--‘). Long options similar to those supported by GNU software may be used as well via an optional third argument.

This module provides two functions and an exception:

+getopt.getopt(args, shortopts, longopts=[])
+

Parses command line options and parameter list. args is the argument list to be parsed, without the leading reference to the running program. Typically, this means sys.argv[1:]. shortopts is the string of option letters that the script wants to recognize, with options that require an argument followed by a colon (':'; i.e., the same format that Unix getopt() uses).

Note

Unlike GNU getopt(), after a non-option argument, all further arguments are considered also non-options. This is similar to the way non-GNU Unix systems work.

longopts, if specified, must be a list of strings with the names of the long options which should be supported. The leading '--' characters should not be included in the option name. Long options which require an argument should be followed by an equal sign ('='). Optional arguments are not supported. To accept only long options, shortopts should be an empty string. Long options on the command line can be recognized so long as they provide a prefix of the option name that matches exactly one of the accepted options. For example, if longopts is ['foo', 'frob'], the option --fo will match as --foo, but --f will not match uniquely, so GetoptError will be raised.

The return value consists of two elements: the first is a list of (option, +value) pairs; the second is the list of program arguments left after the option list was stripped (this is a trailing slice of args). Each option-and-value pair returned has the option as its first element, prefixed with a hyphen for short options (e.g., '-x') or two hyphens for long options (e.g., '--long-option'), and the option argument as its second element, or an empty string if the option has no argument. The options occur in the list in the same order in which they were found, thus allowing multiple occurrences. Long and short options may be mixed.

+
+getopt.gnu_getopt(args, shortopts, longopts=[])
+

This function works like getopt(), except that GNU style scanning mode is used by default. This means that option and non-option arguments may be intermixed. The getopt() function stops processing options as soon as a non-option argument is encountered.

If the first character of the option string is '+', or if the environment variable POSIXLY_CORRECT is set, then option processing stops as soon as a non-option argument is encountered.

+
+exception getopt.GetoptError
+

This is raised when an unrecognized option is found in the argument list or when an option requiring an argument is given none. The argument to the exception is a string indicating the cause of the error. For long options, an argument given to an option which does not require one will also cause this exception to be raised. The attributes msg and opt give the error message and related option; if there is no specific option to which the exception relates, opt is an empty string.

+
+exception getopt.error
+

Alias for GetoptError; for backward compatibility.

+

An example using only Unix style options:

>>> import getopt
+>>> args = '-a -b -cfoo -d bar a1 a2'.split()
+>>> args
+['-a', '-b', '-cfoo', '-d', 'bar', 'a1', 'a2']
+>>> optlist, args = getopt.getopt(args, 'abc:d:')
+>>> optlist
+[('-a', ''), ('-b', ''), ('-c', 'foo'), ('-d', 'bar')]
+>>> args
+['a1', 'a2']
+

Using long option names is equally easy:

>>> s = '--condition=foo --testing --output-file abc.def -x a1 a2'
+>>> args = s.split()
+>>> args
+['--condition=foo', '--testing', '--output-file', 'abc.def', '-x', 'a1', 'a2']
+>>> optlist, args = getopt.getopt(args, 'x', [
+...     'condition=', 'output-file=', 'testing'])
+>>> optlist
+[('--condition', 'foo'), ('--testing', ''), ('--output-file', 'abc.def'), ('-x', '')]
+>>> args
+['a1', 'a2']
+

In a script, typical usage is something like this:

import getopt, sys
+
+def main():
+    try:
+        opts, args = getopt.getopt(sys.argv[1:], "ho:v", ["help", "output="])
+    except getopt.GetoptError as err:
+        # print help information and exit:
+        print(err)  # will print something like "option -a not recognized"
+        usage()
+        sys.exit(2)
+    output = None
+    verbose = False
+    for o, a in opts:
+        if o == "-v":
+            verbose = True
+        elif o in ("-h", "--help"):
+            usage()
+            sys.exit()
+        elif o in ("-o", "--output"):
+            output = a
+        else:
+            assert False, "unhandled option"
+    # ...
+
+if __name__ == "__main__":
+    main()
+

Note that an equivalent command line interface could be produced with less code and more informative help and error messages by using the argparse module:

import argparse
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-o', '--output')
+    parser.add_argument('-v', dest='verbose', action='store_true')
+    args = parser.parse_args()
+    # ... do something with args.output ...
+    # ... do something with args.verbose ..
+

See also

+Module argparse +
+
+

Alternative command line option and argument parsing library.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/getopt.html +

+
diff --git a/devdocs/python~3.12/library%2Fgetpass.html b/devdocs/python~3.12/library%2Fgetpass.html new file mode 100644 index 00000000..faf0f46b --- /dev/null +++ b/devdocs/python~3.12/library%2Fgetpass.html @@ -0,0 +1,15 @@ +

getpass — Portable password input

Source code: Lib/getpass.py

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The getpass module provides two functions:

+getpass.getpass(prompt='Password: ', stream=None)
+

Prompt the user for a password without echoing. The user is prompted using the string prompt, which defaults to 'Password: '. On Unix, the prompt is written to the file-like object stream using the replace error handler if needed. stream defaults to the controlling terminal (/dev/tty) or if that is unavailable to sys.stderr (this argument is ignored on Windows).

If echo free input is unavailable getpass() falls back to printing a warning message to stream and reading from sys.stdin and issuing a GetPassWarning.

Note

If you call getpass from within IDLE, the input may be done in the terminal you launched IDLE from rather than the idle window itself.

+
+exception getpass.GetPassWarning
+

A UserWarning subclass issued when password input may be echoed.

+
+getpass.getuser()
+

Return the “login name” of the user.

This function checks the environment variables LOGNAME, USER, LNAME and USERNAME, in order, and returns the value of the first one which is set to a non-empty string. If none are set, the login name from the password database is returned on systems which support the pwd module, otherwise, an exception is raised.

In general, this function should be preferred over os.getlogin().

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/getpass.html +

+
diff --git a/devdocs/python~3.12/library%2Fgettext.html b/devdocs/python~3.12/library%2Fgettext.html new file mode 100644 index 00000000..27fedf1c --- /dev/null +++ b/devdocs/python~3.12/library%2Fgettext.html @@ -0,0 +1,169 @@ +

gettext — Multilingual internationalization services

Source code: Lib/gettext.py

The gettext module provides internationalization (I18N) and localization (L10N) services for your Python modules and applications. It supports both the GNU gettext message catalog API and a higher level, class-based API that may be more appropriate for Python files. The interface described below allows you to write your module and application messages in one natural language, and provide a catalog of translated messages for running under different natural languages.

Some hints on localizing your Python modules and applications are also given.

GNU gettext API

The gettext module defines the following API, which is very similar to the GNU gettext API. If you use this API you will affect the translation of your entire application globally. Often this is what you want if your application is monolingual, with the choice of language dependent on the locale of your user. If you are localizing a Python module, or if your application needs to switch languages on the fly, you probably want to use the class-based API instead.

+gettext.bindtextdomain(domain, localedir=None)
+

Bind the domain to the locale directory localedir. More concretely, gettext will look for binary .mo files for the given domain using the path (on Unix): localedir/language/LC_MESSAGES/domain.mo, where language is searched for in the environment variables LANGUAGE, LC_ALL, LC_MESSAGES, and LANG respectively.

If localedir is omitted or None, then the current binding for domain is returned. 1

+
+gettext.textdomain(domain=None)
+

Change or query the current global domain. If domain is None, then the current global domain is returned, otherwise the global domain is set to domain, which is returned.

+
+gettext.gettext(message)
+

Return the localized translation of message, based on the current global domain, language, and locale directory. This function is usually aliased as _() in the local namespace (see examples below).

+
+gettext.dgettext(domain, message)
+

Like gettext(), but look the message up in the specified domain.

+
+gettext.ngettext(singular, plural, n)
+

Like gettext(), but consider plural forms. If a translation is found, apply the plural formula to n, and return the resulting message (some languages have more than two plural forms). If no translation is found, return singular if n is 1; return plural otherwise.

The Plural formula is taken from the catalog header. It is a C or Python expression that has a free variable n; the expression evaluates to the index of the plural in the catalog. See the GNU gettext documentation for the precise syntax to be used in .po files and the formulas for a variety of languages.

+
+gettext.dngettext(domain, singular, plural, n)
+

Like ngettext(), but look the message up in the specified domain.

+
+gettext.pgettext(context, message)
+
+gettext.dpgettext(domain, context, message)
+
+gettext.npgettext(context, singular, plural, n)
+
+gettext.dnpgettext(domain, context, singular, plural, n)
+

Similar to the corresponding functions without the p in the prefix (that is, gettext(), dgettext(), ngettext(), dngettext()), but the translation is restricted to the given message context.

New in version 3.8.

+

Note that GNU gettext also defines a dcgettext() method, but this was deemed not useful and so it is currently unimplemented.

Here’s an example of typical usage for this API:

import gettext
+gettext.bindtextdomain('myapplication', '/path/to/my/language/directory')
+gettext.textdomain('myapplication')
+_ = gettext.gettext
+# ...
+print(_('This is a translatable string.'))
+

Class-based API

The class-based API of the gettext module gives you more flexibility and greater convenience than the GNU gettext API. It is the recommended way of localizing your Python applications and modules. gettext defines a GNUTranslations class which implements the parsing of GNU .mo format files, and has methods for returning strings. Instances of this class can also install themselves in the built-in namespace as the function _().

+gettext.find(domain, localedir=None, languages=None, all=False)
+

This function implements the standard .mo file search algorithm. It takes a domain, identical to what textdomain() takes. Optional localedir is as in bindtextdomain(). Optional languages is a list of strings, where each string is a language code.

If localedir is not given, then the default system locale directory is used. 2 If languages is not given, then the following environment variables are searched: LANGUAGE, LC_ALL, LC_MESSAGES, and LANG. The first one returning a non-empty value is used for the languages variable. The environment variables should contain a colon separated list of languages, which will be split on the colon to produce the expected list of language code strings.

find() then expands and normalizes the languages, and then iterates through them, searching for an existing file built of these components:

localedir/language/LC_MESSAGES/domain.mo

The first such file name that exists is returned by find(). If no such file is found, then None is returned. If all is given, it returns a list of all file names, in the order in which they appear in the languages list or the environment variables.

+
+gettext.translation(domain, localedir=None, languages=None, class_=None, fallback=False)
+

Return a *Translations instance based on the domain, localedir, and languages, which are first passed to find() to get a list of the associated .mo file paths. Instances with identical .mo file names are cached. The actual class instantiated is class_ if provided, otherwise GNUTranslations. The class’s constructor must take a single file object argument.

If multiple files are found, later files are used as fallbacks for earlier ones. To allow setting the fallback, copy.copy() is used to clone each translation object from the cache; the actual instance data is still shared with the cache.

If no .mo file is found, this function raises OSError if fallback is false (which is the default), and returns a NullTranslations instance if fallback is true.

Changed in version 3.3: IOError used to be raised, it is now an alias of OSError.

Changed in version 3.11: codeset parameter is removed.

+
+gettext.install(domain, localedir=None, *, names=None)
+

This installs the function _() in Python’s builtins namespace, based on domain and localedir which are passed to the function translation().

For the names parameter, please see the description of the translation object’s install() method.

As seen below, you usually mark the strings in your application that are candidates for translation, by wrapping them in a call to the _() function, like this:

print(_('This string will be translated.'))
+

For convenience, you want the _() function to be installed in Python’s builtins namespace, so it is easily accessible in all modules of your application.

Changed in version 3.11: names is now a keyword-only parameter.

+

The NullTranslations class

Translation classes are what actually implement the translation of original source file message strings to translated message strings. The base class used by all translation classes is NullTranslations; this provides the basic interface you can use to write your own specialized translation classes. Here are the methods of NullTranslations:

+class gettext.NullTranslations(fp=None)
+

Takes an optional file object fp, which is ignored by the base class. Initializes “protected” instance variables _info and _charset which are set by derived classes, as well as _fallback, which is set through add_fallback(). It then calls self._parse(fp) if fp is not None.

+_parse(fp)
+

No-op in the base class, this method takes file object fp, and reads the data from the file, initializing its message catalog. If you have an unsupported message catalog file format, you should override this method to parse your format.

+
+add_fallback(fallback)
+

Add fallback as the fallback object for the current translation object. A translation object should consult the fallback if it cannot provide a translation for a given message.

+
+gettext(message)
+

If a fallback has been set, forward gettext() to the fallback. Otherwise, return message. Overridden in derived classes.

+
+ngettext(singular, plural, n)
+

If a fallback has been set, forward ngettext() to the fallback. Otherwise, return singular if n is 1; return plural otherwise. Overridden in derived classes.

+
+pgettext(context, message)
+

If a fallback has been set, forward pgettext() to the fallback. Otherwise, return the translated message. Overridden in derived classes.

New in version 3.8.

+
+npgettext(context, singular, plural, n)
+

If a fallback has been set, forward npgettext() to the fallback. Otherwise, return the translated message. Overridden in derived classes.

New in version 3.8.

+
+info()
+

Return a dictionary containing the metadata found in the message catalog file.

+
+charset()
+

Return the encoding of the message catalog file.

+
+install(names=None)
+

This method installs gettext() into the built-in namespace, binding it to _.

If the names parameter is given, it must be a sequence containing the names of functions you want to install in the builtins namespace in addition to _(). Supported names are 'gettext', 'ngettext', 'pgettext', and 'npgettext'.

Note that this is only one way, albeit the most convenient way, to make the _() function available to your application. Because it affects the entire application globally, and specifically the built-in namespace, localized modules should never install _(). Instead, they should use this code to make _() available to their module:

import gettext
+t = gettext.translation('mymodule', ...)
+_ = t.gettext
+

This puts _() only in the module’s global namespace and so only affects calls within this module.

Changed in version 3.8: Added 'pgettext' and 'npgettext'.

+
+

The GNUTranslations class

The gettext module provides one additional class derived from NullTranslations: GNUTranslations. This class overrides _parse() to enable reading GNU gettext format .mo files in both big-endian and little-endian format.

GNUTranslations parses optional metadata out of the translation catalog. It is convention with GNU gettext to include metadata as the translation for the empty string. This metadata is in RFC 822-style key: value pairs, and should contain the Project-Id-Version key. If the key Content-Type is found, then the charset property is used to initialize the “protected” _charset instance variable, defaulting to None if not found. If the charset encoding is specified, then all message ids and message strings read from the catalog are converted to Unicode using this encoding, else ASCII is assumed.

Since message ids are read as Unicode strings too, all *gettext() methods will assume message ids as Unicode strings, not byte strings.

The entire set of key/value pairs are placed into a dictionary and set as the “protected” _info instance variable.

If the .mo file’s magic number is invalid, the major version number is unexpected, or if other problems occur while reading the file, instantiating a GNUTranslations class can raise OSError.

+class gettext.GNUTranslations
+

The following methods are overridden from the base class implementation:

+gettext(message)
+

Look up the message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id, and a fallback has been set, the look up is forwarded to the fallback’s gettext() method. Otherwise, the message id is returned.

+
+ngettext(singular, plural, n)
+

Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use. The returned message string is a Unicode string.

If the message id is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s ngettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases.

Here is an example:

n = len(os.listdir('.'))
+cat = GNUTranslations(somefile)
+message = cat.ngettext(
+    'There is %(num)d file in this directory',
+    'There are %(num)d files in this directory',
+    n) % {'num': n}
+
+
+pgettext(context, message)
+

Look up the context and message id in the catalog and return the corresponding message string, as a Unicode string. If there is no entry in the catalog for the message id and context, and a fallback has been set, the look up is forwarded to the fallback’s pgettext() method. Otherwise, the message id is returned.

New in version 3.8.

+
+npgettext(context, singular, plural, n)
+

Do a plural-forms lookup of a message id. singular is used as the message id for purposes of lookup in the catalog, while n is used to determine which plural form to use.

If the message id for context is not found in the catalog, and a fallback is specified, the request is forwarded to the fallback’s npgettext() method. Otherwise, when n is 1 singular is returned, and plural is returned in all other cases.

New in version 3.8.

+
+

Solaris message catalog support

The Solaris operating system defines its own binary .mo file format, but since no documentation can be found on this format, it is not supported at this time.

The Catalog constructor

GNOME uses a version of the gettext module by James Henstridge, but this version has a slightly different API. Its documented usage was:

import gettext
+cat = gettext.Catalog(domain, localedir)
+_ = cat.gettext
+print(_('hello world'))
+

For compatibility with this older module, the function Catalog() is an alias for the translation() function described above.

One difference between this module and Henstridge’s: his catalog objects supported access through a mapping API, but this appears to be unused and so is not currently supported.

Internationalizing your programs and modules

Internationalization (I18N) refers to the operation by which a program is made aware of multiple languages. Localization (L10N) refers to the adaptation of your program, once internationalized, to the local language and cultural habits. In order to provide multilingual messages for your Python programs, you need to take the following steps:

  1. prepare your program or module by specially marking translatable strings
  2. run a suite of tools over your marked files to generate raw messages catalogs
  3. create language-specific translations of the message catalogs
  4. use the gettext module so that message strings are properly translated

In order to prepare your code for I18N, you need to look at all the strings in your files. Any string that needs to be translated should be marked by wrapping it in _('...') — that is, a call to the function _. For example:

filename = 'mylog.txt'
+message = _('writing a log message')
+with open(filename, 'w') as fp:
+    fp.write(message)
+

In this example, the string 'writing a log message' is marked as a candidate for translation, while the strings 'mylog.txt' and 'w' are not.

There are a few tools to extract the strings meant for translation. The original GNU gettext only supported C or C++ source code but its extended version xgettext scans code written in a number of languages, including Python, to find strings marked as translatable. Babel is a Python internationalization library that includes a pybabel script to extract and compile message catalogs. François Pinard’s program called xpot does a similar job and is available as part of his po-utils package.

(Python also includes pure-Python versions of these programs, called pygettext.py and msgfmt.py; some Python distributions will install them for you. pygettext.py is similar to xgettext, but only understands Python source code and cannot handle other programming languages such as C or C++. pygettext.py supports a command-line interface similar to xgettext; for details on its use, run pygettext.py +--help. msgfmt.py is binary compatible with GNU msgfmt. With these two programs, you may not need the GNU gettext package to internationalize your Python applications.)

xgettext, pygettext, and similar tools generate .po files that are message catalogs. They are structured human-readable files that contain every marked string in the source code, along with a placeholder for the translated versions of these strings.

Copies of these .po files are then handed over to the individual human translators who write translations for every supported natural language. They send back the completed language-specific versions as a <language-name>.po file that’s compiled into a machine-readable .mo binary catalog file using the msgfmt program. The .mo files are used by the gettext module for the actual translation processing at run-time.

How you use the gettext module in your code depends on whether you are internationalizing a single module or your entire application. The next two sections will discuss each case.

Localizing your module

If you are localizing your module, you must take care not to make global changes, e.g. to the built-in namespace. You should not use the GNU gettext API but instead the class-based API.

Let’s say your module is called “spam” and the module’s various natural language translation .mo files reside in /usr/share/locale in GNU gettext format. Here’s what you would put at the top of your module:

import gettext
+t = gettext.translation('spam', '/usr/share/locale')
+_ = t.gettext
+

Localizing your application

If you are localizing your application, you can install the _() function globally into the built-in namespace, usually in the main driver file of your application. This will let all your application-specific files just use _('...') without having to explicitly install it in each file.

In the simple case then, you need only add the following bit of code to the main driver file of your application:

import gettext
+gettext.install('myapplication')
+

If you need to set the locale directory, you can pass it into the install() function:

import gettext
+gettext.install('myapplication', '/usr/share/locale')
+

Changing languages on the fly

If your program needs to support many languages at the same time, you may want to create multiple translation instances and then switch between them explicitly, like so:

import gettext
+
+lang1 = gettext.translation('myapplication', languages=['en'])
+lang2 = gettext.translation('myapplication', languages=['fr'])
+lang3 = gettext.translation('myapplication', languages=['de'])
+
+# start by using language1
+lang1.install()
+
+# ... time goes by, user selects language 2
+lang2.install()
+
+# ... more time goes by, user selects language 3
+lang3.install()
+

Deferred translations

In most coding situations, strings are translated where they are coded. Occasionally however, you need to mark strings for translation, but defer actual translation until later. A classic example is:

animals = ['mollusk',
+           'albatross',
+           'rat',
+           'penguin',
+           'python', ]
+# ...
+for a in animals:
+    print(a)
+

Here, you want to mark the strings in the animals list as being translatable, but you don’t actually want to translate them until they are printed.

Here is one way you can handle this situation:

def _(message): return message
+
+animals = [_('mollusk'),
+           _('albatross'),
+           _('rat'),
+           _('penguin'),
+           _('python'), ]
+
+del _
+
+# ...
+for a in animals:
+    print(_(a))
+

This works because the dummy definition of _() simply returns the string unchanged. And this dummy definition will temporarily override any definition of _() in the built-in namespace (until the del command). Take care, though if you have a previous definition of _() in the local namespace.

Note that the second use of _() will not identify “a” as being translatable to the gettext program, because the parameter is not a string literal.

Another way to handle this is with the following example:

def N_(message): return message
+
+animals = [N_('mollusk'),
+           N_('albatross'),
+           N_('rat'),
+           N_('penguin'),
+           N_('python'), ]
+
+# ...
+for a in animals:
+    print(_(a))
+

In this case, you are marking translatable strings with the function N_(), which won’t conflict with any definition of _(). However, you will need to teach your message extraction program to look for translatable strings marked with N_(). xgettext, pygettext, pybabel extract, and xpot all support this through the use of the -k command-line switch. The choice of N_() here is totally arbitrary; it could have just as easily been MarkThisStringForTranslation().

Acknowledgements

The following people contributed code, feedback, design suggestions, previous implementations, and valuable experience to the creation of this module:

  • Peter Funk
  • James Henstridge
  • Juan David Ibáñez Palomar
  • Marc-André Lemburg
  • Martin von Löwis
  • François Pinard
  • Barry Warsaw
  • Gustavo Niemeyer

Footnotes

+1
+

The default locale directory is system dependent; for example, on Red Hat Linux it is /usr/share/locale, but on Solaris it is /usr/lib/locale. The gettext module does not try to support these system dependent defaults; instead its default is sys.base_prefix/share/locale (see sys.base_prefix). For this reason, it is always best to call bindtextdomain() with an explicit absolute path at the start of your application.

+2
+

See the footnote for bindtextdomain() above.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/gettext.html +

+
diff --git a/devdocs/python~3.12/library%2Fglob.html b/devdocs/python~3.12/library%2Fglob.html new file mode 100644 index 00000000..f02f8f7a --- /dev/null +++ b/devdocs/python~3.12/library%2Fglob.html @@ -0,0 +1,35 @@ +

glob — Unix style pathname pattern expansion

Source code: Lib/glob.py

The glob module finds all the pathnames matching a specified pattern according to the rules used by the Unix shell, although results are returned in arbitrary order. No tilde expansion is done, but *, ?, and character ranges expressed with [] will be correctly matched. This is done by using the os.scandir() and fnmatch.fnmatch() functions in concert, and not by actually invoking a subshell.

Note that files beginning with a dot (.) can only be matched by patterns that also start with a dot, unlike fnmatch.fnmatch() or pathlib.Path.glob(). (For tilde and shell variable expansion, use os.path.expanduser() and os.path.expandvars().)

For a literal match, wrap the meta-characters in brackets. For example, '[?]' matches the character '?'.

See also

The pathlib module offers high-level path objects.

+glob.glob(pathname, *, root_dir=None, dir_fd=None, recursive=False, include_hidden=False)
+

Return a possibly empty list of path names that match pathname, which must be a string containing a path specification. pathname can be either absolute (like /usr/src/Python-1.5/Makefile) or relative (like ../../Tools/*/*.gif), and can contain shell-style wildcards. Broken symlinks are included in the results (as in the shell). Whether or not the results are sorted depends on the file system. If a file that satisfies conditions is removed or added during the call of this function, whether a path name for that file will be included is unspecified.

If root_dir is not None, it should be a path-like object specifying the root directory for searching. It has the same effect on glob() as changing the current directory before calling it. If pathname is relative, the result will contain paths relative to root_dir.

This function can support paths relative to directory descriptors with the dir_fd parameter.

If recursive is true, the pattern “**” will match any files and zero or more directories, subdirectories and symbolic links to directories. If the pattern is followed by an os.sep or os.altsep then files will not match.

If include_hidden is true, “**” pattern will match hidden directories.

Raises an auditing event glob.glob with arguments pathname, recursive.

Raises an auditing event glob.glob/2 with arguments pathname, recursive, root_dir, dir_fd.

Note

Using the “**” pattern in large directory trees may consume an inordinate amount of time.

Changed in version 3.5: Support for recursive globs using “**”.

Changed in version 3.10: Added the root_dir and dir_fd parameters.

Changed in version 3.11: Added the include_hidden parameter.

+
+glob.iglob(pathname, *, root_dir=None, dir_fd=None, recursive=False, include_hidden=False)
+

Return an iterator which yields the same values as glob() without actually storing them all simultaneously.

Raises an auditing event glob.glob with arguments pathname, recursive.

Raises an auditing event glob.glob/2 with arguments pathname, recursive, root_dir, dir_fd.

Changed in version 3.5: Support for recursive globs using “**”.

Changed in version 3.10: Added the root_dir and dir_fd parameters.

Changed in version 3.11: Added the include_hidden parameter.

+
+glob.escape(pathname)
+

Escape all special characters ('?', '*' and '['). This is useful if you want to match an arbitrary literal string that may have special characters in it. Special characters in drive/UNC sharepoints are not escaped, e.g. on Windows escape('//?/c:/Quo vadis?.txt') returns '//?/c:/Quo vadis[?].txt'.

New in version 3.4.

+

For example, consider a directory containing the following files: 1.gif, 2.txt, card.gif and a subdirectory sub which contains only the file 3.txt. glob() will produce the following results. Notice how any leading components of the path are preserved.

>>> import glob
+>>> glob.glob('./[0-9].*')
+['./1.gif', './2.txt']
+>>> glob.glob('*.gif')
+['1.gif', 'card.gif']
+>>> glob.glob('?.gif')
+['1.gif']
+>>> glob.glob('**/*.txt', recursive=True)
+['2.txt', 'sub/3.txt']
+>>> glob.glob('./**/', recursive=True)
+['./', './sub/']
+

If the directory contains files starting with . they won’t be matched by default. For example, consider a directory containing card.gif and .card.gif:

>>> import glob
+>>> glob.glob('*.gif')
+['card.gif']
+>>> glob.glob('.c*')
+['.card.gif']
+

See also

+Module fnmatch +
+
+

Shell-style filename (not path) expansion

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/glob.html +

+
diff --git a/devdocs/python~3.12/library%2Fgraphlib.html b/devdocs/python~3.12/library%2Fgraphlib.html new file mode 100644 index 00000000..ef17078a --- /dev/null +++ b/devdocs/python~3.12/library%2Fgraphlib.html @@ -0,0 +1,76 @@ +

graphlib — Functionality to operate with graph-like structures

Source code: Lib/graphlib.py

+class graphlib.TopologicalSorter(graph=None)
+

Provides functionality to topologically sort a graph of hashable nodes.

A topological order is a linear ordering of the vertices in a graph such that for every directed edge u -> v from vertex u to vertex v, vertex u comes before vertex v in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this example, a topological ordering is just a valid sequence for the tasks. A complete topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph.

If the optional graph argument is provided it must be a dictionary representing a directed acyclic graph where the keys are nodes and the values are iterables of all predecessors of that node in the graph (the nodes that have edges that point to the value in the key). Additional nodes can be added to the graph using the add() method.

In the general case, the steps required to perform the sorting of a given graph are as follows:

  • Create an instance of the TopologicalSorter with an optional initial graph.
  • Add additional nodes to the graph.
  • Call prepare() on the graph.
  • While is_active() is True, iterate over the nodes returned by get_ready() and process them. Call done() on each node as it finishes processing.

In case just an immediate sorting of the nodes in the graph is required and no parallelism is involved, the convenience method TopologicalSorter.static_order() can be used directly:

>>> graph = {"D": {"B", "C"}, "C": {"A"}, "B": {"A"}}
+>>> ts = TopologicalSorter(graph)
+>>> tuple(ts.static_order())
+('A', 'C', 'B', 'D')
+

The class is designed to easily support parallel processing of the nodes as they become ready. For instance:

topological_sorter = TopologicalSorter()
+
+# Add nodes to 'topological_sorter'...
+
+topological_sorter.prepare()
+while topological_sorter.is_active():
+    for node in topological_sorter.get_ready():
+        # Worker threads or processes take nodes to work on off the
+        # 'task_queue' queue.
+        task_queue.put(node)
+
+    # When the work for a node is done, workers put the node in
+    # 'finalized_tasks_queue' so we can get more nodes to work on.
+    # The definition of 'is_active()' guarantees that, at this point, at
+    # least one node has been placed on 'task_queue' that hasn't yet
+    # been passed to 'done()', so this blocking 'get()' must (eventually)
+    # succeed.  After calling 'done()', we loop back to call 'get_ready()'
+    # again, so put newly freed nodes on 'task_queue' as soon as
+    # logically possible.
+    node = finalized_tasks_queue.get()
+    topological_sorter.done(node)
+
+add(node, *predecessors)
+

Add a new node and its predecessors to the graph. Both the node and all elements in predecessors must be hashable.

If called multiple times with the same node argument, the set of dependencies will be the union of all dependencies passed in.

It is possible to add a node with no dependencies (predecessors is not provided) or to provide a dependency twice. If a node that has not been provided before is included among predecessors it will be automatically added to the graph with no predecessors of its own.

Raises ValueError if called after prepare().

+
+prepare()
+

Mark the graph as finished and check for cycles in the graph. If any cycle is detected, CycleError will be raised, but get_ready() can still be used to obtain as many nodes as possible until cycles block more progress. After a call to this function, the graph cannot be modified, and therefore no more nodes can be added using add().

+
+is_active()
+

Returns True if more progress can be made and False otherwise. Progress can be made if cycles do not block the resolution and either there are still nodes ready that haven’t yet been returned by TopologicalSorter.get_ready() or the number of nodes marked TopologicalSorter.done() is less than the number that have been returned by TopologicalSorter.get_ready().

The __bool__() method of this class defers to this function, so instead of:

if ts.is_active():
+    ...
+

it is possible to simply do:

if ts:
+    ...
+

Raises ValueError if called without calling prepare() previously.

+
+done(*nodes)
+

Marks a set of nodes returned by TopologicalSorter.get_ready() as processed, unblocking any successor of each node in nodes for being returned in the future by a call to TopologicalSorter.get_ready().

Raises ValueError if any node in nodes has already been marked as processed by a previous call to this method or if a node was not added to the graph by using TopologicalSorter.add(), if called without calling prepare() or if node has not yet been returned by get_ready().

+
+get_ready()
+

Returns a tuple with all the nodes that are ready. Initially it returns all nodes with no predecessors, and once those are marked as processed by calling TopologicalSorter.done(), further calls will return all new nodes that have all their predecessors already processed. Once no more progress can be made, empty tuples are returned.

Raises ValueError if called without calling prepare() previously.

+
+static_order()
+

Returns an iterator object which will iterate over nodes in a topological order. When using this method, prepare() and done() should not be called. This method is equivalent to:

def static_order(self):
+    self.prepare()
+    while self.is_active():
+        node_group = self.get_ready()
+        yield from node_group
+        self.done(*node_group)
+

The particular order that is returned may depend on the specific order in which the items were inserted in the graph. For example:

>>> ts = TopologicalSorter()
+>>> ts.add(3, 2, 1)
+>>> ts.add(1, 0)
+>>> print([*ts.static_order()])
+[2, 0, 1, 3]
+
+>>> ts2 = TopologicalSorter()
+>>> ts2.add(1, 0)
+>>> ts2.add(3, 2, 1)
+>>> print([*ts2.static_order()])
+[0, 2, 1, 3]
+

This is due to the fact that “0” and “2” are in the same level in the graph (they would have been returned in the same call to get_ready()) and the order between them is determined by the order of insertion.

If any cycle is detected, CycleError will be raised.

+

New in version 3.9.

+

Exceptions

The graphlib module defines the following exception classes:

+exception graphlib.CycleError
+

Subclass of ValueError raised by TopologicalSorter.prepare() if cycles exist in the working graph. If multiple cycles exist, only one undefined choice among them will be reported and included in the exception.

The detected cycle can be accessed via the second element in the args attribute of the exception instance and consists in a list of nodes, such that each node is, in the graph, an immediate predecessor of the next node in the list. In the reported list, the first and the last node will be the same, to make it clear that it is cyclic.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/graphlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fgrp.html b/devdocs/python~3.12/library%2Fgrp.html new file mode 100644 index 00000000..a196dbc7 --- /dev/null +++ b/devdocs/python~3.12/library%2Fgrp.html @@ -0,0 +1,28 @@ +

grp — The group database

This module provides access to the Unix group database. It is available on all Unix versions.

Availability: Unix, not Emscripten, not WASI.

Group database entries are reported as a tuple-like object, whose attributes correspond to the members of the group structure (Attribute field below, see <grp.h>):

+ + + + +

Index

Attribute

Meaning

0

gr_name

the name of the group

1

gr_passwd

the (encrypted) group password; often empty

2

gr_gid

the numerical group ID

3

gr_mem

all the group member’s user names

The gid is an integer, name and password are strings, and the member list is a list of strings. (Note that most users are not explicitly listed as members of the group they are in according to the password database. Check both databases to get complete membership information. Also note that a gr_name that starts with a + or - is likely to be a YP/NIS reference and may not be accessible via getgrnam() or getgrgid().)

It defines the following items:

+grp.getgrgid(id)
+

Return the group database entry for the given numeric group ID. KeyError is raised if the entry asked for cannot be found.

Changed in version 3.10: TypeError is raised for non-integer arguments like floats or strings.

+
+grp.getgrnam(name)
+

Return the group database entry for the given group name. KeyError is raised if the entry asked for cannot be found.

+
+grp.getgrall()
+

Return a list of all available group entries, in arbitrary order.

+

See also

+Module pwd +
+
+

An interface to the user database, similar to this.

+Module spwd +
+
+

An interface to the shadow password database, similar to this.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/grp.html +

+
diff --git a/devdocs/python~3.12/library%2Fgzip.html b/devdocs/python~3.12/library%2Fgzip.html new file mode 100644 index 00000000..efb81e26 --- /dev/null +++ b/devdocs/python~3.12/library%2Fgzip.html @@ -0,0 +1,64 @@ +

gzip — Support for gzip files

Source code: Lib/gzip.py

This module provides a simple interface to compress and decompress files just like the GNU programs gzip and gunzip would.

The data compression is provided by the zlib module.

The gzip module provides the GzipFile class, as well as the open(), compress() and decompress() convenience functions. The GzipFile class reads and writes gzip-format files, automatically compressing or decompressing the data so that it looks like an ordinary file object.

Note that additional file formats which can be decompressed by the gzip and gunzip programs, such as those produced by compress and pack, are not supported by this module.

The module defines the following items:

+gzip.open(filename, mode='rb', compresslevel=9, encoding=None, errors=None, newline=None)
+

Open a gzip-compressed file in binary or text mode, returning a file object.

The filename argument can be an actual filename (a str or bytes object), or an existing file object to read from or write to.

The mode argument can be any of 'r', 'rb', 'a', 'ab', 'w', 'wb', 'x' or 'xb' for binary mode, or 'rt', 'at', 'wt', or 'xt' for text mode. The default is 'rb'.

The compresslevel argument is an integer from 0 to 9, as for the GzipFile constructor.

For binary mode, this function is equivalent to the GzipFile constructor: GzipFile(filename, mode, compresslevel). In this case, the encoding, errors and newline arguments must not be provided.

For text mode, a GzipFile object is created, and wrapped in an io.TextIOWrapper instance with the specified encoding, error handling behavior, and line ending(s).

Changed in version 3.3: Added support for filename being a file object, support for text mode, and the encoding, errors and newline arguments.

Changed in version 3.4: Added support for the 'x', 'xb' and 'xt' modes.

Changed in version 3.6: Accepts a path-like object.

+
+exception gzip.BadGzipFile
+

An exception raised for invalid gzip files. It inherits OSError. EOFError and zlib.error can also be raised for invalid gzip files.

New in version 3.8.

+
+class gzip.GzipFile(filename=None, mode=None, compresslevel=9, fileobj=None, mtime=None)
+

Constructor for the GzipFile class, which simulates most of the methods of a file object, with the exception of the truncate() method. At least one of fileobj and filename must be given a non-trivial value.

The new class instance is based on fileobj, which can be a regular file, an io.BytesIO object, or any other object which simulates a file. It defaults to None, in which case filename is opened to provide a file object.

When fileobj is not None, the filename argument is only used to be included in the gzip file header, which may include the original filename of the uncompressed file. It defaults to the filename of fileobj, if discernible; otherwise, it defaults to the empty string, and in this case the original filename is not included in the header.

The mode argument can be any of 'r', 'rb', 'a', 'ab', 'w', 'wb', 'x', or 'xb', depending on whether the file will be read or written. The default is the mode of fileobj if discernible; otherwise, the default is 'rb'. In future Python releases the mode of fileobj will not be used. It is better to always specify mode for writing.

Note that the file is always opened in binary mode. To open a compressed file in text mode, use open() (or wrap your GzipFile with an io.TextIOWrapper).

The compresslevel argument is an integer from 0 to 9 controlling the level of compression; 1 is fastest and produces the least compression, and 9 is slowest and produces the most compression. 0 is no compression. The default is 9.

The mtime argument is an optional numeric timestamp to be written to the last modification time field in the stream when compressing. It should only be provided in compression mode. If omitted or None, the current time is used. See the mtime attribute for more details.

Calling a GzipFile object’s close() method does not close fileobj, since you might wish to append more material after the compressed data. This also allows you to pass an io.BytesIO object opened for writing as fileobj, and retrieve the resulting memory buffer using the io.BytesIO object’s getvalue() method.

GzipFile supports the io.BufferedIOBase interface, including iteration and the with statement. Only the truncate() method isn’t implemented.

GzipFile also provides the following method and attribute:

+peek(n)
+

Read n uncompressed bytes without advancing the file position. At most one single read on the compressed stream is done to satisfy the call. The number of bytes returned may be more or less than requested.

Note

While calling peek() does not change the file position of the GzipFile, it may change the position of the underlying file object (e.g. if the GzipFile was constructed with the fileobj parameter).

New in version 3.2.

+
+mtime
+

When decompressing, the value of the last modification time field in the most recently read header may be read from this attribute, as an integer. The initial value before reading any headers is None.

All gzip compressed streams are required to contain this timestamp field. Some programs, such as gunzip, make use of the timestamp. The format is the same as the return value of time.time() and the st_mtime attribute of the object returned by os.stat().

+
+name
+

The path to the gzip file on disk, as a str or bytes. Equivalent to the output of os.fspath() on the original input path, with no other normalization, resolution or expansion.

+

Changed in version 3.1: Support for the with statement was added, along with the mtime constructor argument and mtime attribute.

Changed in version 3.2: Support for zero-padded and unseekable files was added.

Changed in version 3.3: The io.BufferedIOBase.read1() method is now implemented.

Changed in version 3.4: Added support for the 'x' and 'xb' modes.

Changed in version 3.5: Added support for writing arbitrary bytes-like objects. The read() method now accepts an argument of None.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.12: Remove the filename attribute, use the name attribute instead.

Deprecated since version 3.9: Opening GzipFile for writing without specifying the mode argument is deprecated.

+
+gzip.compress(data, compresslevel=9, *, mtime=None)
+

Compress the data, returning a bytes object containing the compressed data. compresslevel and mtime have the same meaning as in the GzipFile constructor above. When mtime is set to 0, this function is equivalent to zlib.compress() with wbits set to 31. The zlib function is faster.

New in version 3.2.

Changed in version 3.8: Added the mtime parameter for reproducible output.

Changed in version 3.11: Speed is improved by compressing all data at once instead of in a streamed fashion. Calls with mtime set to 0 are delegated to zlib.compress() for better speed.

+
+gzip.decompress(data)
+

Decompress the data, returning a bytes object containing the uncompressed data. This function is capable of decompressing multi-member gzip data (multiple gzip blocks concatenated together). When the data is certain to contain only one member the zlib.decompress() function with wbits set to 31 is faster.

New in version 3.2.

Changed in version 3.11: Speed is improved by decompressing members at once in memory instead of in a streamed fashion.

+

Examples of usage

Example of how to read a compressed file:

import gzip
+with gzip.open('/home/joe/file.txt.gz', 'rb') as f:
+    file_content = f.read()
+

Example of how to create a compressed GZIP file:

import gzip
+content = b"Lots of content here"
+with gzip.open('/home/joe/file.txt.gz', 'wb') as f:
+    f.write(content)
+

Example of how to GZIP compress an existing file:

import gzip
+import shutil
+with open('/home/joe/file.txt', 'rb') as f_in:
+    with gzip.open('/home/joe/file.txt.gz', 'wb') as f_out:
+        shutil.copyfileobj(f_in, f_out)
+

Example of how to GZIP compress a binary string:

import gzip
+s_in = b"Lots of content here"
+s_out = gzip.compress(s_in)
+

See also

+Module zlib +
+
+

The basic data compression module needed to support the gzip file format.

Command Line Interface

The gzip module provides a simple command line interface to compress or decompress files.

Once executed the gzip module keeps the input file(s).

Changed in version 3.8: Add a new command line interface with a usage. By default, when you will execute the CLI, the default compression level is 6.

Command line options

+file
+

If file is not specified, read from sys.stdin.

+
+--fast
+

Indicates the fastest compression method (less compression).

+
+--best
+

Indicates the slowest compression method (best compression).

+
+-d, --decompress
+

Decompress the given file.

+
+-h, --help
+

Show the help message.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/gzip.html +

+
diff --git a/devdocs/python~3.12/library%2Fhashlib.html b/devdocs/python~3.12/library%2Fhashlib.html new file mode 100644 index 00000000..56edf496 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhashlib.html @@ -0,0 +1,300 @@ +

hashlib — Secure hashes and message digests

Source code: Lib/hashlib.py

This module implements a common interface to many different secure hash and message digest algorithms. Included are the FIPS secure hash algorithms SHA1, SHA224, SHA256, SHA384, SHA512, (defined in the FIPS 180-4 standard), the SHA-3 series (defined in the FIPS 202 standard) as well as RSA’s MD5 algorithm (defined in internet RFC 1321). The terms “secure hash” and “message digest” are interchangeable. Older algorithms were called message digests. The modern term is secure hash.

Note

If you want the adler32 or crc32 hash functions, they are available in the zlib module.

Hash algorithms

There is one constructor method named for each type of hash. All return a hash object with the same simple interface. For example: use sha256() to create a SHA-256 hash object. You can now feed this object with bytes-like objects (normally bytes) using the update method. At any point you can ask it for the digest of the concatenation of the data fed to it so far using the digest() or hexdigest() methods.

To allow multithreading, the Python GIL is released while computing a hash supplied more than 2047 bytes of data at once in its constructor or .update method.

Constructors for hash algorithms that are always present in this module are sha1(), sha224(), sha256(), sha384(), sha512(), sha3_224(), sha3_256(), sha3_384(), sha3_512(), shake_128(), shake_256(), blake2b(), and blake2s(). md5() is normally available as well, though it may be missing or blocked if you are using a rare “FIPS compliant” build of Python. These correspond to algorithms_guaranteed.

Additional algorithms may also be available if your Python distribution’s hashlib was linked against a build of OpenSSL that provides others. Others are not guaranteed available on all installations and will only be accessible by name via new(). See algorithms_available.

Warning

Some algorithms have known hash collision weaknesses (including MD5 and SHA1). Refer to Attacks on cryptographic hash algorithms and the hashlib-seealso section at the end of this document.

New in version 3.6: SHA3 (Keccak) and SHAKE constructors sha3_224(), sha3_256(), sha3_384(), sha3_512(), shake_128(), shake_256() were added.

New in version 3.6: blake2b() and blake2s() were added.

Changed in version 3.9: All hashlib constructors take a keyword-only argument usedforsecurity with default value True. A false value allows the use of insecure and blocked hashing algorithms in restricted environments. False indicates that the hashing algorithm is not used in a security context, e.g. as a non-cryptographic one-way compression function.

Changed in version 3.9: Hashlib now uses SHA3 and SHAKE from OpenSSL if it provides it.

Changed in version 3.12: For any of the MD5, SHA1, SHA2, or SHA3 algorithms that the linked OpenSSL does not provide we fall back to a verified implementation from the HACL* project.

Usage

To obtain the digest of the byte string b"Nobody inspects the spammish +repetition":

>>> import hashlib
+>>> m = hashlib.sha256()
+>>> m.update(b"Nobody inspects")
+>>> m.update(b" the spammish repetition")
+>>> m.digest()
+b'\x03\x1e\xdd}Ae\x15\x93\xc5\xfe\\\x00o\xa5u+7\xfd\xdf\xf7\xbcN\x84:\xa6\xaf\x0c\x95\x0fK\x94\x06'
+>>> m.hexdigest()
+'031edd7d41651593c5fe5c006fa5752b37fddff7bc4e843aa6af0c950f4b9406'
+

More condensed:

>>> hashlib.sha256(b"Nobody inspects the spammish repetition").hexdigest()
+'031edd7d41651593c5fe5c006fa5752b37fddff7bc4e843aa6af0c950f4b9406'
+

Constructors

+hashlib.new(name, [data, ]\*, usedforsecurity=True)
+

Is a generic constructor that takes the string name of the desired algorithm as its first parameter. It also exists to allow access to the above listed hashes as well as any other algorithms that your OpenSSL library may offer.

+

Using new() with an algorithm name:

>>> h = hashlib.new('sha256')
+>>> h.update(b"Nobody inspects the spammish repetition")
+>>> h.hexdigest()
+'031edd7d41651593c5fe5c006fa5752b37fddff7bc4e843aa6af0c950f4b9406'
+
+hashlib.md5([data, ]*, usedforsecurity=True)
+
+hashlib.sha1([data, ]*, usedforsecurity=True)
+
+hashlib.sha224([data, ]*, usedforsecurity=True)
+
+hashlib.sha256([data, ]*, usedforsecurity=True)
+
+hashlib.sha384([data, ]*, usedforsecurity=True)
+
+hashlib.sha512([data, ]*, usedforsecurity=True)
+
+hashlib.sha3_224([data, ]*, usedforsecurity=True)
+
+hashlib.sha3_256([data, ]*, usedforsecurity=True)
+
+hashlib.sha3_384([data, ]*, usedforsecurity=True)
+
+hashlib.sha3_512([data, ]*, usedforsecurity=True)
+

Named constructors such as these are faster than passing an algorithm name to new().

Attributes

Hashlib provides the following constant module attributes:

+hashlib.algorithms_guaranteed
+

A set containing the names of the hash algorithms guaranteed to be supported by this module on all platforms. Note that ‘md5’ is in this list despite some upstream vendors offering an odd “FIPS compliant” Python build that excludes it.

New in version 3.2.

+
+hashlib.algorithms_available
+

A set containing the names of the hash algorithms that are available in the running Python interpreter. These names will be recognized when passed to new(). algorithms_guaranteed will always be a subset. The same algorithm may appear multiple times in this set under different names (thanks to OpenSSL).

New in version 3.2.

+

Hash Objects

The following values are provided as constant attributes of the hash objects returned by the constructors:

+hash.digest_size
+

The size of the resulting hash in bytes.

+
+hash.block_size
+

The internal block size of the hash algorithm in bytes.

+

A hash object has the following attributes:

+hash.name
+

The canonical name of this hash, always lowercase and always suitable as a parameter to new() to create another hash of this type.

Changed in version 3.4: The name attribute has been present in CPython since its inception, but until Python 3.4 was not formally specified, so may not exist on some platforms.

+

A hash object has the following methods:

+hash.update(data)
+

Update the hash object with the bytes-like object. Repeated calls are equivalent to a single call with the concatenation of all the arguments: m.update(a); m.update(b) is equivalent to m.update(a+b).

+
+hash.digest()
+

Return the digest of the data passed to the update() method so far. This is a bytes object of size digest_size which may contain bytes in the whole range from 0 to 255.

+
+hash.hexdigest()
+

Like digest() except the digest is returned as a string object of double length, containing only hexadecimal digits. This may be used to exchange the value safely in email or other non-binary environments.

+
+hash.copy()
+

Return a copy (“clone”) of the hash object. This can be used to efficiently compute the digests of data sharing a common initial substring.

+

SHAKE variable length digests

+hashlib.shake_128([data, ]*, usedforsecurity=True)
+
+hashlib.shake_256([data, ]*, usedforsecurity=True)
+

The shake_128() and shake_256() algorithms provide variable length digests with length_in_bits//2 up to 128 or 256 bits of security. As such, their digest methods require a length. Maximum length is not limited by the SHAKE algorithm.

+shake.digest(length)
+

Return the digest of the data passed to the update() method so far. This is a bytes object of size length which may contain bytes in the whole range from 0 to 255.

+
+shake.hexdigest(length)
+

Like digest() except the digest is returned as a string object of double length, containing only hexadecimal digits. This may be used to exchange the value in email or other non-binary environments.

+

Example use:

>>> h = hashlib.shake_256(b'Nobody inspects the spammish repetition')
+>>> h.hexdigest(20)
+'44709d6fcb83d92a76dcb0b668c98e1b1d3dafe7'
+

File hashing

The hashlib module provides a helper function for efficient hashing of a file or file-like object.

+hashlib.file_digest(fileobj, digest, /)
+

Return a digest object that has been updated with contents of file object.

fileobj must be a file-like object opened for reading in binary mode. It accepts file objects from builtin open(), BytesIO instances, SocketIO objects from socket.socket.makefile(), and similar. The function may bypass Python’s I/O and use the file descriptor from fileno() directly. fileobj must be assumed to be in an unknown state after this function returns or raises. It is up to the caller to close fileobj.

digest must either be a hash algorithm name as a str, a hash constructor, or a callable that returns a hash object.

Example:

>>> import io, hashlib, hmac
+>>> with open(hashlib.__file__, "rb") as f:
+...     digest = hashlib.file_digest(f, "sha256")
+...
+>>> digest.hexdigest()  
+'...'
+
>>> buf = io.BytesIO(b"somedata")
+>>> mac1 = hmac.HMAC(b"key", digestmod=hashlib.sha512)
+>>> digest = hashlib.file_digest(buf, lambda: mac1)
+
>>> digest is mac1
+True
+>>> mac2 = hmac.HMAC(b"key", b"somedata", digestmod=hashlib.sha512)
+>>> mac1.digest() == mac2.digest()
+True
+

New in version 3.11.

+

Key derivation

Key derivation and key stretching algorithms are designed for secure password hashing. Naive algorithms such as sha1(password) are not resistant against brute-force attacks. A good password hashing function must be tunable, slow, and include a salt.

+hashlib.pbkdf2_hmac(hash_name, password, salt, iterations, dklen=None)
+

The function provides PKCS#5 password-based key derivation function 2. It uses HMAC as pseudorandom function.

The string hash_name is the desired name of the hash digest algorithm for HMAC, e.g. ‘sha1’ or ‘sha256’. password and salt are interpreted as buffers of bytes. Applications and libraries should limit password to a sensible length (e.g. 1024). salt should be about 16 or more bytes from a proper source, e.g. os.urandom().

The number of iterations should be chosen based on the hash algorithm and computing power. As of 2022, hundreds of thousands of iterations of SHA-256 are suggested. For rationale as to why and how to choose what is best for your application, read Appendix A.2.2 of NIST-SP-800-132. The answers on the stackexchange pbkdf2 iterations question explain in detail.

dklen is the length of the derived key. If dklen is None then the digest size of the hash algorithm hash_name is used, e.g. 64 for SHA-512.

>>> from hashlib import pbkdf2_hmac
+>>> our_app_iters = 500_000  # Application specific, read above.
+>>> dk = pbkdf2_hmac('sha256', b'password', b'bad salt' * 2, our_app_iters)
+>>> dk.hex()
+'15530bba69924174860db778f2c6f8104d3aaf9d26241840c8c4a641c8d000a9'
+

Function only available when Python is compiled with OpenSSL.

New in version 3.4.

Changed in version 3.12: Function now only available when Python is built with OpenSSL. The slow pure Python implementation has been removed.

+
+hashlib.scrypt(password, *, salt, n, r, p, maxmem=0, dklen=64)
+

The function provides scrypt password-based key derivation function as defined in RFC 7914.

password and salt must be bytes-like objects. Applications and libraries should limit password to a sensible length (e.g. 1024). salt should be about 16 or more bytes from a proper source, e.g. os.urandom().

n is the CPU/Memory cost factor, r the block size, p parallelization factor and maxmem limits memory (OpenSSL 1.1.0 defaults to 32 MiB). dklen is the length of the derived key.

New in version 3.6.

+

BLAKE2

BLAKE2 is a cryptographic hash function defined in RFC 7693 that comes in two flavors:

  • +BLAKE2b, optimized for 64-bit platforms and produces digests of any size between 1 and 64 bytes,
  • +BLAKE2s, optimized for 8- to 32-bit platforms and produces digests of any size between 1 and 32 bytes.

BLAKE2 supports keyed mode (a faster and simpler replacement for HMAC), salted hashing, personalization, and tree hashing.

Hash objects from this module follow the API of standard library’s hashlib objects.

Creating hash objects

New hash objects are created by calling constructor functions:

+hashlib.blake2b(data=b'', *, digest_size=64, key=b'', salt=b'', person=b'', fanout=1, depth=1, leaf_size=0, node_offset=0, node_depth=0, inner_size=0, last_node=False, usedforsecurity=True)
+
+hashlib.blake2s(data=b'', *, digest_size=32, key=b'', salt=b'', person=b'', fanout=1, depth=1, leaf_size=0, node_offset=0, node_depth=0, inner_size=0, last_node=False, usedforsecurity=True)
+

These functions return the corresponding hash objects for calculating BLAKE2b or BLAKE2s. They optionally take these general parameters:

  • +data: initial chunk of data to hash, which must be bytes-like object. It can be passed only as positional argument.
  • +digest_size: size of output digest in bytes.
  • +key: key for keyed hashing (up to 64 bytes for BLAKE2b, up to 32 bytes for BLAKE2s).
  • +salt: salt for randomized hashing (up to 16 bytes for BLAKE2b, up to 8 bytes for BLAKE2s).
  • +person: personalization string (up to 16 bytes for BLAKE2b, up to 8 bytes for BLAKE2s).

The following table shows limits for general parameters (in bytes):

+ + +

Hash

digest_size

len(key)

len(salt)

len(person)

BLAKE2b

64

64

16

16

BLAKE2s

32

32

8

8

Note

BLAKE2 specification defines constant lengths for salt and personalization parameters, however, for convenience, this implementation accepts byte strings of any size up to the specified length. If the length of the parameter is less than specified, it is padded with zeros, thus, for example, b'salt' and b'salt\x00' is the same value. (This is not the case for key.)

These sizes are available as module constants described below.

Constructor functions also accept the following tree hashing parameters:

  • +fanout: fanout (0 to 255, 0 if unlimited, 1 in sequential mode).
  • +depth: maximal depth of tree (1 to 255, 255 if unlimited, 1 in sequential mode).
  • +leaf_size: maximal byte length of leaf (0 to 2**32-1, 0 if unlimited or in sequential mode).
  • +node_offset: node offset (0 to 2**64-1 for BLAKE2b, 0 to 2**48-1 for BLAKE2s, 0 for the first, leftmost, leaf, or in sequential mode).
  • +node_depth: node depth (0 to 255, 0 for leaves, or in sequential mode).
  • +inner_size: inner digest size (0 to 64 for BLAKE2b, 0 to 32 for BLAKE2s, 0 in sequential mode).
  • +last_node: boolean indicating whether the processed node is the last one (False for sequential mode).
Explanation of tree mode parameters.

See section 2.10 in BLAKE2 specification for comprehensive review of tree hashing.

Constants

+blake2b.SALT_SIZE
+
+blake2s.SALT_SIZE
+

Salt length (maximum length accepted by constructors).

+blake2b.PERSON_SIZE
+
+blake2s.PERSON_SIZE
+

Personalization string length (maximum length accepted by constructors).

+blake2b.MAX_KEY_SIZE
+
+blake2s.MAX_KEY_SIZE
+

Maximum key size.

+blake2b.MAX_DIGEST_SIZE
+
+blake2s.MAX_DIGEST_SIZE
+

Maximum digest size that the hash function can output.

Examples

Simple hashing

To calculate hash of some data, you should first construct a hash object by calling the appropriate constructor function (blake2b() or blake2s()), then update it with the data by calling update() on the object, and, finally, get the digest out of the object by calling digest() (or hexdigest() for hex-encoded string).

>>> from hashlib import blake2b
+>>> h = blake2b()
+>>> h.update(b'Hello world')
+>>> h.hexdigest()
+'6ff843ba685842aa82031d3f53c48b66326df7639a63d128974c5c14f31a0f33343a8c65551134ed1ae0f2b0dd2bb495dc81039e3eeb0aa1bb0388bbeac29183'
+

As a shortcut, you can pass the first chunk of data to update directly to the constructor as the positional argument:

>>> from hashlib import blake2b
+>>> blake2b(b'Hello world').hexdigest()
+'6ff843ba685842aa82031d3f53c48b66326df7639a63d128974c5c14f31a0f33343a8c65551134ed1ae0f2b0dd2bb495dc81039e3eeb0aa1bb0388bbeac29183'
+

You can call hash.update() as many times as you need to iteratively update the hash:

>>> from hashlib import blake2b
+>>> items = [b'Hello', b' ', b'world']
+>>> h = blake2b()
+>>> for item in items:
+...     h.update(item)
+...
+>>> h.hexdigest()
+'6ff843ba685842aa82031d3f53c48b66326df7639a63d128974c5c14f31a0f33343a8c65551134ed1ae0f2b0dd2bb495dc81039e3eeb0aa1bb0388bbeac29183'
+

Using different digest sizes

BLAKE2 has configurable size of digests up to 64 bytes for BLAKE2b and up to 32 bytes for BLAKE2s. For example, to replace SHA-1 with BLAKE2b without changing the size of output, we can tell BLAKE2b to produce 20-byte digests:

>>> from hashlib import blake2b
+>>> h = blake2b(digest_size=20)
+>>> h.update(b'Replacing SHA1 with the more secure function')
+>>> h.hexdigest()
+'d24f26cf8de66472d58d4e1b1774b4c9158b1f4c'
+>>> h.digest_size
+20
+>>> len(h.digest())
+20
+

Hash objects with different digest sizes have completely different outputs (shorter hashes are not prefixes of longer hashes); BLAKE2b and BLAKE2s produce different outputs even if the output length is the same:

>>> from hashlib import blake2b, blake2s
+>>> blake2b(digest_size=10).hexdigest()
+'6fa1d8fcfd719046d762'
+>>> blake2b(digest_size=11).hexdigest()
+'eb6ec15daf9546254f0809'
+>>> blake2s(digest_size=10).hexdigest()
+'1bf21a98c78a1c376ae9'
+>>> blake2s(digest_size=11).hexdigest()
+'567004bf96e4a25773ebf4'
+

Keyed hashing

Keyed hashing can be used for authentication as a faster and simpler replacement for Hash-based message authentication code (HMAC). BLAKE2 can be securely used in prefix-MAC mode thanks to the indifferentiability property inherited from BLAKE.

This example shows how to get a (hex-encoded) 128-bit authentication code for message b'message data' with key b'pseudorandom key':

>>> from hashlib import blake2b
+>>> h = blake2b(key=b'pseudorandom key', digest_size=16)
+>>> h.update(b'message data')
+>>> h.hexdigest()
+'3d363ff7401e02026f4a4687d4863ced'
+

As a practical example, a web application can symmetrically sign cookies sent to users and later verify them to make sure they weren’t tampered with:

>>> from hashlib import blake2b
+>>> from hmac import compare_digest
+>>>
+>>> SECRET_KEY = b'pseudorandomly generated server secret key'
+>>> AUTH_SIZE = 16
+>>>
+>>> def sign(cookie):
+...     h = blake2b(digest_size=AUTH_SIZE, key=SECRET_KEY)
+...     h.update(cookie)
+...     return h.hexdigest().encode('utf-8')
+>>>
+>>> def verify(cookie, sig):
+...     good_sig = sign(cookie)
+...     return compare_digest(good_sig, sig)
+>>>
+>>> cookie = b'user-alice'
+>>> sig = sign(cookie)
+>>> print("{0},{1}".format(cookie.decode('utf-8'), sig))
+user-alice,b'43b3c982cf697e0c5ab22172d1ca7421'
+>>> verify(cookie, sig)
+True
+>>> verify(b'user-bob', sig)
+False
+>>> verify(cookie, b'0102030405060708090a0b0c0d0e0f00')
+False
+

Even though there’s a native keyed hashing mode, BLAKE2 can, of course, be used in HMAC construction with hmac module:

>>> import hmac, hashlib
+>>> m = hmac.new(b'secret key', digestmod=hashlib.blake2s)
+>>> m.update(b'message')
+>>> m.hexdigest()
+'e3c8102868d28b5ff85fc35dda07329970d1a01e273c37481326fe0c861c8142'
+

Randomized hashing

By setting salt parameter users can introduce randomization to the hash function. Randomized hashing is useful for protecting against collision attacks on the hash function used in digital signatures.

Randomized hashing is designed for situations where one party, the message preparer, generates all or part of a message to be signed by a second party, the message signer. If the message preparer is able to find cryptographic hash function collisions (i.e., two messages producing the same hash value), then they might prepare meaningful versions of the message that would produce the same hash value and digital signature, but with different results (e.g., transferring $1,000,000 to an account, rather than $10). Cryptographic hash functions have been designed with collision resistance as a major goal, but the current concentration on attacking cryptographic hash functions may result in a given cryptographic hash function providing less collision resistance than expected. Randomized hashing offers the signer additional protection by reducing the likelihood that a preparer can generate two or more messages that ultimately yield the same hash value during the digital signature generation process — even if it is practical to find collisions for the hash function. However, the use of randomized hashing may reduce the amount of security provided by a digital signature when all portions of the message are prepared by the signer.

(NIST SP-800-106 “Randomized Hashing for Digital Signatures”)

In BLAKE2 the salt is processed as a one-time input to the hash function during initialization, rather than as an input to each compression function.

Warning

Salted hashing (or just hashing) with BLAKE2 or any other general-purpose cryptographic hash function, such as SHA-256, is not suitable for hashing passwords. See BLAKE2 FAQ for more information.

>>> import os
+>>> from hashlib import blake2b
+>>> msg = b'some message'
+>>> # Calculate the first hash with a random salt.
+>>> salt1 = os.urandom(blake2b.SALT_SIZE)
+>>> h1 = blake2b(salt=salt1)
+>>> h1.update(msg)
+>>> # Calculate the second hash with a different random salt.
+>>> salt2 = os.urandom(blake2b.SALT_SIZE)
+>>> h2 = blake2b(salt=salt2)
+>>> h2.update(msg)
+>>> # The digests are different.
+>>> h1.digest() != h2.digest()
+True
+

Personalization

Sometimes it is useful to force hash function to produce different digests for the same input for different purposes. Quoting the authors of the Skein hash function:

We recommend that all application designers seriously consider doing this; we have seen many protocols where a hash that is computed in one part of the protocol can be used in an entirely different part because two hash computations were done on similar or related data, and the attacker can force the application to make the hash inputs the same. Personalizing each hash function used in the protocol summarily stops this type of attack.

(The Skein Hash Function Family, p. 21)

BLAKE2 can be personalized by passing bytes to the person argument:

>>> from hashlib import blake2b
+>>> FILES_HASH_PERSON = b'MyApp Files Hash'
+>>> BLOCK_HASH_PERSON = b'MyApp Block Hash'
+>>> h = blake2b(digest_size=32, person=FILES_HASH_PERSON)
+>>> h.update(b'the same content')
+>>> h.hexdigest()
+'20d9cd024d4fb086aae819a1432dd2466de12947831b75c5a30cf2676095d3b4'
+>>> h = blake2b(digest_size=32, person=BLOCK_HASH_PERSON)
+>>> h.update(b'the same content')
+>>> h.hexdigest()
+'cf68fb5761b9c44e7878bfb2c4c9aea52264a80b75005e65619778de59f383a3'
+

Personalization together with the keyed mode can also be used to derive different keys from a single one.

>>> from hashlib import blake2s
+>>> from base64 import b64decode, b64encode
+>>> orig_key = b64decode(b'Rm5EPJai72qcK3RGBpW3vPNfZy5OZothY+kHY6h21KM=')
+>>> enc_key = blake2s(key=orig_key, person=b'kEncrypt').digest()
+>>> mac_key = blake2s(key=orig_key, person=b'kMAC').digest()
+>>> print(b64encode(enc_key).decode('utf-8'))
+rbPb15S/Z9t+agffno5wuhB77VbRi6F9Iv2qIxU7WHw=
+>>> print(b64encode(mac_key).decode('utf-8'))
+G9GtHFE1YluXY1zWPlYk1e/nWfu0WSEb0KRcjhDeP/o=
+

Tree mode

Here’s an example of hashing a minimal tree with two leaf nodes:

  10
+ /  \
+00  01
+

This example uses 64-byte internal digests, and returns the 32-byte final digest:

>>> from hashlib import blake2b
+>>>
+>>> FANOUT = 2
+>>> DEPTH = 2
+>>> LEAF_SIZE = 4096
+>>> INNER_SIZE = 64
+>>>
+>>> buf = bytearray(6000)
+>>>
+>>> # Left leaf
+... h00 = blake2b(buf[0:LEAF_SIZE], fanout=FANOUT, depth=DEPTH,
+...               leaf_size=LEAF_SIZE, inner_size=INNER_SIZE,
+...               node_offset=0, node_depth=0, last_node=False)
+>>> # Right leaf
+... h01 = blake2b(buf[LEAF_SIZE:], fanout=FANOUT, depth=DEPTH,
+...               leaf_size=LEAF_SIZE, inner_size=INNER_SIZE,
+...               node_offset=1, node_depth=0, last_node=True)
+>>> # Root node
+... h10 = blake2b(digest_size=32, fanout=FANOUT, depth=DEPTH,
+...               leaf_size=LEAF_SIZE, inner_size=INNER_SIZE,
+...               node_offset=0, node_depth=1, last_node=True)
+>>> h10.update(h00.digest())
+>>> h10.update(h01.digest())
+>>> h10.hexdigest()
+'3ad2a9b37c6070e374c7a8c508fe20ca86b6ed54e286e93a0318e95e881db5aa'
+

Credits

BLAKE2 was designed by Jean-Philippe Aumasson, Samuel Neves, Zooko Wilcox-O’Hearn, and Christian Winnerlein based on SHA-3 finalist BLAKE created by Jean-Philippe Aumasson, Luca Henzen, Willi Meier, and Raphael C.-W. Phan.

It uses core algorithm from ChaCha cipher designed by Daniel J. Bernstein.

The stdlib implementation is based on pyblake2 module. It was written by Dmitry Chestnykh based on C implementation written by Samuel Neves. The documentation was copied from pyblake2 and written by Dmitry Chestnykh.

The C code was partly rewritten for Python by Christian Heimes.

The following public domain dedication applies for both C hash function implementation, extension code, and this documentation:

To the extent possible under law, the author(s) have dedicated all copyright and related and neighboring rights to this software to the public domain worldwide. This software is distributed without any warranty.

You should have received a copy of the CC0 Public Domain Dedication along with this software. If not, see https://creativecommons.org/publicdomain/zero/1.0/.

The following people have helped with development or contributed their changes to the project and the public domain according to the Creative Commons Public Domain Dedication 1.0 Universal:

  • Alexandr Sokolovskiy

See also

+Module hmac +
+
+

A module to generate message authentication codes using hashes.

+Module base64 +
+
+

Another way to encode binary hashes for non-binary environments.

https://nvlpubs.nist.gov/nistpubs/fips/nist.fips.180-4.pdf
+
+

The FIPS 180-4 publication on Secure Hash Algorithms.

https://csrc.nist.gov/publications/detail/fips/202/final
+
+

The FIPS 202 publication on the SHA-3 Standard.

https://www.blake2.net/
+
+

Official BLAKE2 website.

https://en.wikipedia.org/wiki/Cryptographic_hash_function
+
+

Wikipedia article with information on which algorithms have known issues and what that means regarding their use.

https://www.ietf.org/rfc/rfc8018.txt
+
+

PKCS #5: Password-Based Cryptography Specification Version 2.1

https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-132.pdf
+
+

NIST Recommendation for Password-Based Key Derivation.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/hashlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fheapq.html b/devdocs/python~3.12/library%2Fheapq.html new file mode 100644 index 00000000..343684f4 --- /dev/null +++ b/devdocs/python~3.12/library%2Fheapq.html @@ -0,0 +1,91 @@ +

heapq — Heap queue algorithm

Source code: Lib/heapq.py

This module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm.

Heaps are binary trees for which every parent node has a value less than or equal to any of its children. This implementation uses arrays for which heap[k] <= heap[2*k+1] and heap[k] <= heap[2*k+2] for all k, counting elements from zero. For the sake of comparison, non-existing elements are considered to be infinite. The interesting property of a heap is that its smallest element is always the root, heap[0].

The API below differs from textbook heap algorithms in two aspects: (a) We use zero-based indexing. This makes the relationship between the index for a node and the indexes for its children slightly less obvious, but is more suitable since Python uses zero-based indexing. (b) Our pop method returns the smallest item, not the largest (called a “min heap” in textbooks; a “max heap” is more common in texts because of its suitability for in-place sorting).

These two make it possible to view the heap as a regular Python list without surprises: heap[0] is the smallest item, and heap.sort() maintains the heap invariant!

To create a heap, use a list initialized to [], or you can transform a populated list into a heap via function heapify().

The following functions are provided:

+heapq.heappush(heap, item)
+

Push the value item onto the heap, maintaining the heap invariant.

+
+heapq.heappop(heap)
+

Pop and return the smallest item from the heap, maintaining the heap invariant. If the heap is empty, IndexError is raised. To access the smallest item without popping it, use heap[0].

+
+heapq.heappushpop(heap, item)
+

Push item on the heap, then pop and return the smallest item from the heap. The combined action runs more efficiently than heappush() followed by a separate call to heappop().

+
+heapq.heapify(x)
+

Transform list x into a heap, in-place, in linear time.

+
+heapq.heapreplace(heap, item)
+

Pop and return the smallest item from the heap, and also push the new item. The heap size doesn’t change. If the heap is empty, IndexError is raised.

This one step operation is more efficient than a heappop() followed by heappush() and can be more appropriate when using a fixed-size heap. The pop/push combination always returns an element from the heap and replaces it with item.

The value returned may be larger than the item added. If that isn’t desired, consider using heappushpop() instead. Its push/pop combination returns the smaller of the two values, leaving the larger value on the heap.

+

The module also offers three general purpose functions based on heaps.

+heapq.merge(*iterables, key=None, reverse=False)
+

Merge multiple sorted inputs into a single sorted output (for example, merge timestamped entries from multiple log files). Returns an iterator over the sorted values.

Similar to sorted(itertools.chain(*iterables)) but returns an iterable, does not pull the data into memory all at once, and assumes that each of the input streams is already sorted (smallest to largest).

Has two optional arguments which must be specified as keyword arguments.

key specifies a key function of one argument that is used to extract a comparison key from each input element. The default value is None (compare the elements directly).

reverse is a boolean value. If set to True, then the input elements are merged as if each comparison were reversed. To achieve behavior similar to sorted(itertools.chain(*iterables), reverse=True), all iterables must be sorted from largest to smallest.

Changed in version 3.5: Added the optional key and reverse parameters.

+
+heapq.nlargest(n, iterable, key=None)
+

Return a list with the n largest elements from the dataset defined by iterable. key, if provided, specifies a function of one argument that is used to extract a comparison key from each element in iterable (for example, key=str.lower). Equivalent to: sorted(iterable, key=key, +reverse=True)[:n].

+
+heapq.nsmallest(n, iterable, key=None)
+

Return a list with the n smallest elements from the dataset defined by iterable. key, if provided, specifies a function of one argument that is used to extract a comparison key from each element in iterable (for example, key=str.lower). Equivalent to: sorted(iterable, key=key)[:n].

+

The latter two functions perform best for smaller values of n. For larger values, it is more efficient to use the sorted() function. Also, when n==1, it is more efficient to use the built-in min() and max() functions. If repeated usage of these functions is required, consider turning the iterable into an actual heap.

Basic Examples

A heapsort can be implemented by pushing all values onto a heap and then popping off the smallest values one at a time:

>>> def heapsort(iterable):
+...     h = []
+...     for value in iterable:
+...         heappush(h, value)
+...     return [heappop(h) for i in range(len(h))]
+...
+>>> heapsort([1, 3, 5, 7, 9, 2, 4, 6, 8, 0])
+[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+

This is similar to sorted(iterable), but unlike sorted(), this implementation is not stable.

Heap elements can be tuples. This is useful for assigning comparison values (such as task priorities) alongside the main record being tracked:

>>> h = []
+>>> heappush(h, (5, 'write code'))
+>>> heappush(h, (7, 'release product'))
+>>> heappush(h, (1, 'write spec'))
+>>> heappush(h, (3, 'create tests'))
+>>> heappop(h)
+(1, 'write spec')
+

Priority Queue Implementation Notes

A priority queue is common use for a heap, and it presents several implementation challenges:

  • Sort stability: how do you get two tasks with equal priorities to be returned in the order they were originally added?
  • Tuple comparison breaks for (priority, task) pairs if the priorities are equal and the tasks do not have a default comparison order.
  • If the priority of a task changes, how do you move it to a new position in the heap?
  • Or if a pending task needs to be deleted, how do you find it and remove it from the queue?

A solution to the first two challenges is to store entries as 3-element list including the priority, an entry count, and the task. The entry count serves as a tie-breaker so that two tasks with the same priority are returned in the order they were added. And since no two entry counts are the same, the tuple comparison will never attempt to directly compare two tasks.

Another solution to the problem of non-comparable tasks is to create a wrapper class that ignores the task item and only compares the priority field:

from dataclasses import dataclass, field
+from typing import Any
+
+@dataclass(order=True)
+class PrioritizedItem:
+    priority: int
+    item: Any=field(compare=False)
+

The remaining challenges revolve around finding a pending task and making changes to its priority or removing it entirely. Finding a task can be done with a dictionary pointing to an entry in the queue.

Removing the entry or changing its priority is more difficult because it would break the heap structure invariants. So, a possible solution is to mark the entry as removed and add a new entry with the revised priority:

pq = []                         # list of entries arranged in a heap
+entry_finder = {}               # mapping of tasks to entries
+REMOVED = '<removed-task>'      # placeholder for a removed task
+counter = itertools.count()     # unique sequence count
+
+def add_task(task, priority=0):
+    'Add a new task or update the priority of an existing task'
+    if task in entry_finder:
+        remove_task(task)
+    count = next(counter)
+    entry = [priority, count, task]
+    entry_finder[task] = entry
+    heappush(pq, entry)
+
+def remove_task(task):
+    'Mark an existing task as REMOVED.  Raise KeyError if not found.'
+    entry = entry_finder.pop(task)
+    entry[-1] = REMOVED
+
+def pop_task():
+    'Remove and return the lowest priority task. Raise KeyError if empty.'
+    while pq:
+        priority, count, task = heappop(pq)
+        if task is not REMOVED:
+            del entry_finder[task]
+            return task
+    raise KeyError('pop from an empty priority queue')
+

Theory

Heaps are arrays for which a[k] <= a[2*k+1] and a[k] <= a[2*k+2] for all k, counting elements from 0. For the sake of comparison, non-existing elements are considered to be infinite. The interesting property of a heap is that a[0] is always its smallest element.

The strange invariant above is meant to be an efficient memory representation for a tournament. The numbers below are k, not a[k]:

                               0
+
+              1                                 2
+
+      3               4                5               6
+
+  7       8       9       10      11      12      13      14
+
+15 16   17 18   19 20   21 22   23 24   25 26   27 28   29 30
+

In the tree above, each cell k is topping 2*k+1 and 2*k+2. In a usual binary tournament we see in sports, each cell is the winner over the two cells it tops, and we can trace the winner down the tree to see all opponents s/he had. However, in many computer applications of such tournaments, we do not need to trace the history of a winner. To be more memory efficient, when a winner is promoted, we try to replace it by something else at a lower level, and the rule becomes that a cell and the two cells it tops contain three different items, but the top cell “wins” over the two topped cells.

If this heap invariant is protected at all time, index 0 is clearly the overall winner. The simplest algorithmic way to remove it and find the “next” winner is to move some loser (let’s say cell 30 in the diagram above) into the 0 position, and then percolate this new 0 down the tree, exchanging values, until the invariant is re-established. This is clearly logarithmic on the total number of items in the tree. By iterating over all items, you get an O(n log n) sort.

A nice feature of this sort is that you can efficiently insert new items while the sort is going on, provided that the inserted items are not “better” than the last 0’th element you extracted. This is especially useful in simulation contexts, where the tree holds all incoming events, and the “win” condition means the smallest scheduled time. When an event schedules other events for execution, they are scheduled into the future, so they can easily go into the heap. So, a heap is a good structure for implementing schedulers (this is what I used for my MIDI sequencer :-).

Various structures for implementing schedulers have been extensively studied, and heaps are good for this, as they are reasonably speedy, the speed is almost constant, and the worst case is not much different than the average case. However, there are other representations which are more efficient overall, yet the worst cases might be terrible.

Heaps are also very useful in big disk sorts. You most probably all know that a big sort implies producing “runs” (which are pre-sorted sequences, whose size is usually related to the amount of CPU memory), followed by a merging passes for these runs, which merging is often very cleverly organised 1. It is very important that the initial sort produces the longest runs possible. Tournaments are a good way to achieve that. If, using all the memory available to hold a tournament, you replace and percolate items that happen to fit the current run, you’ll produce runs which are twice the size of the memory for random input, and much better for input fuzzily ordered.

Moreover, if you output the 0’th item on disk and get an input which may not fit in the current tournament (because the value “wins” over the last output value), it cannot fit in the heap, so the size of the heap decreases. The freed memory could be cleverly reused immediately for progressively building a second heap, which grows at exactly the same rate the first heap is melting. When the first heap completely vanishes, you switch heaps and start a new run. Clever and quite effective!

In a word, heaps are useful memory structures to know. I use them in a few applications, and I think it is good to keep a ‘heap’ module around. :-)

Footnotes

+1
+

The disk balancing algorithms which are current, nowadays, are more annoying than clever, and this is a consequence of the seeking capabilities of the disks. On devices which cannot seek, like big tape drives, the story was quite different, and one had to be very clever to ensure (far in advance) that each tape movement will be the most effective possible (that is, will best participate at “progressing” the merge). Some tapes were even able to read backwards, and this was also used to avoid the rewinding time. Believe me, real good tape sorts were quite spectacular to watch! From all times, sorting has always been a Great Art! :-)

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/heapq.html +

+
diff --git a/devdocs/python~3.12/library%2Fhmac.html b/devdocs/python~3.12/library%2Fhmac.html new file mode 100644 index 00000000..b0f44294 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhmac.html @@ -0,0 +1,40 @@ +

hmac — Keyed-Hashing for Message Authentication

Source code: Lib/hmac.py

This module implements the HMAC algorithm as described by RFC 2104.

+hmac.new(key, msg=None, digestmod)
+

Return a new hmac object. key is a bytes or bytearray object giving the secret key. If msg is present, the method call update(msg) is made. digestmod is the digest name, digest constructor or module for the HMAC object to use. It may be any name suitable to hashlib.new(). Despite its argument position, it is required.

Changed in version 3.4: Parameter key can be a bytes or bytearray object. Parameter msg can be of any type supported by hashlib. Parameter digestmod can be the name of a hash algorithm.

Changed in version 3.8: The digestmod argument is now required. Pass it as a keyword argument to avoid awkwardness when you do not have an initial msg.

+
+hmac.digest(key, msg, digest)
+

Return digest of msg for given secret key and digest. The function is equivalent to HMAC(key, msg, digest).digest(), but uses an optimized C or inline implementation, which is faster for messages that fit into memory. The parameters key, msg, and digest have the same meaning as in new().

CPython implementation detail, the optimized C implementation is only used when digest is a string and name of a digest algorithm, which is supported by OpenSSL.

New in version 3.7.

+

An HMAC object has the following methods:

+HMAC.update(msg)
+

Update the hmac object with msg. Repeated calls are equivalent to a single call with the concatenation of all the arguments: m.update(a); m.update(b) is equivalent to m.update(a + b).

Changed in version 3.4: Parameter msg can be of any type supported by hashlib.

+
+HMAC.digest()
+

Return the digest of the bytes passed to the update() method so far. This bytes object will be the same length as the digest_size of the digest given to the constructor. It may contain non-ASCII bytes, including NUL bytes.

Warning

When comparing the output of digest() to an externally supplied digest during a verification routine, it is recommended to use the compare_digest() function instead of the == operator to reduce the vulnerability to timing attacks.

+
+HMAC.hexdigest()
+

Like digest() except the digest is returned as a string twice the length containing only hexadecimal digits. This may be used to exchange the value safely in email or other non-binary environments.

Warning

When comparing the output of hexdigest() to an externally supplied digest during a verification routine, it is recommended to use the compare_digest() function instead of the == operator to reduce the vulnerability to timing attacks.

+
+HMAC.copy()
+

Return a copy (“clone”) of the hmac object. This can be used to efficiently compute the digests of strings that share a common initial substring.

+

A hash object has the following attributes:

+HMAC.digest_size
+

The size of the resulting HMAC digest in bytes.

+
+HMAC.block_size
+

The internal block size of the hash algorithm in bytes.

New in version 3.4.

+
+HMAC.name
+

The canonical name of this HMAC, always lowercase, e.g. hmac-md5.

New in version 3.4.

+

Changed in version 3.10: Removed the undocumented attributes HMAC.digest_cons, HMAC.inner, and HMAC.outer.

This module also provides the following helper function:

+hmac.compare_digest(a, b)
+

Return a == b. This function uses an approach designed to prevent timing analysis by avoiding content-based short circuiting behaviour, making it appropriate for cryptography. a and b must both be of the same type: either str (ASCII only, as e.g. returned by HMAC.hexdigest()), or a bytes-like object.

Note

If a and b are of different lengths, or if an error occurs, a timing attack could theoretically reveal information about the types and lengths of a and b—but not their values.

New in version 3.3.

Changed in version 3.10: The function uses OpenSSL’s CRYPTO_memcmp() internally when available.

+

See also

+Module hashlib +
+
+

The Python module providing secure hash functions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/hmac.html +

+
diff --git a/devdocs/python~3.12/library%2Fhtml.entities.html b/devdocs/python~3.12/library%2Fhtml.entities.html new file mode 100644 index 00000000..23ba000b --- /dev/null +++ b/devdocs/python~3.12/library%2Fhtml.entities.html @@ -0,0 +1,20 @@ +

html.entities — Definitions of HTML general entities

Source code: Lib/html/entities.py

This module defines four dictionaries, html5, name2codepoint, codepoint2name, and entitydefs.

+html.entities.html5
+

A dictionary that maps HTML5 named character references 1 to the equivalent Unicode character(s), e.g. html5['gt;'] == '>'. Note that the trailing semicolon is included in the name (e.g. 'gt;'), however some of the names are accepted by the standard even without the semicolon: in this case the name is present with and without the ';'. See also html.unescape().

New in version 3.3.

+
+html.entities.entitydefs
+

A dictionary mapping XHTML 1.0 entity definitions to their replacement text in ISO Latin-1.

+
+html.entities.name2codepoint
+

A dictionary that maps HTML4 entity names to the Unicode code points.

+
+html.entities.codepoint2name
+

A dictionary that maps Unicode code points to HTML4 entity names.

+

Footnotes

+1
+

See https://html.spec.whatwg.org/multipage/named-characters.html#named-character-references

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/html.entities.html +

+
diff --git a/devdocs/python~3.12/library%2Fhtml.html b/devdocs/python~3.12/library%2Fhtml.html new file mode 100644 index 00000000..e6faf9f9 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhtml.html @@ -0,0 +1,15 @@ +

html — HyperText Markup Language support

Source code: Lib/html/__init__.py

This module defines utilities to manipulate HTML.

+html.escape(s, quote=True)
+

Convert the characters &, < and > in string s to HTML-safe sequences. Use this if you need to display text that might contain such characters in HTML. If the optional flag quote is true, the characters (") and (') are also translated; this helps for inclusion in an HTML attribute value delimited by quotes, as in <a href="...">.

New in version 3.2.

+
+html.unescape(s)
+

Convert all named and numeric character references (e.g. &gt;, &#62;, &#x3e;) in the string s to the corresponding Unicode characters. This function uses the rules defined by the HTML 5 standard for both valid and invalid character references, and the list of +HTML 5 named character references.

New in version 3.4.

+

Submodules in the html package are:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/html.html +

+
diff --git a/devdocs/python~3.12/library%2Fhtml.parser.html b/devdocs/python~3.12/library%2Fhtml.parser.html new file mode 100644 index 00000000..de2bf34a --- /dev/null +++ b/devdocs/python~3.12/library%2Fhtml.parser.html @@ -0,0 +1,162 @@ +

html.parser — Simple HTML and XHTML parser

Source code: Lib/html/parser.py

This module defines a class HTMLParser which serves as the basis for parsing text files formatted in HTML (HyperText Mark-up Language) and XHTML.

+class html.parser.HTMLParser(*, convert_charrefs=True)
+

Create a parser instance able to parse invalid markup.

If convert_charrefs is True (the default), all character references (except the ones in script/style elements) are automatically converted to the corresponding Unicode characters.

An HTMLParser instance is fed HTML data and calls handler methods when start tags, end tags, text, comments, and other markup elements are encountered. The user should subclass HTMLParser and override its methods to implement the desired behavior.

This parser does not check that end tags match start tags or call the end-tag handler for elements which are closed implicitly by closing an outer element.

Changed in version 3.4: convert_charrefs keyword argument added.

Changed in version 3.5: The default value for argument convert_charrefs is now True.

+

Example HTML Parser Application

As a basic example, below is a simple HTML parser that uses the HTMLParser class to print out start tags, end tags, and data as they are encountered:

from html.parser import HTMLParser
+
+class MyHTMLParser(HTMLParser):
+    def handle_starttag(self, tag, attrs):
+        print("Encountered a start tag:", tag)
+
+    def handle_endtag(self, tag):
+        print("Encountered an end tag :", tag)
+
+    def handle_data(self, data):
+        print("Encountered some data  :", data)
+
+parser = MyHTMLParser()
+parser.feed('<html><head><title>Test</title></head>'
+            '<body><h1>Parse me!</h1></body></html>')
+

The output will then be:

Encountered a start tag: html
+Encountered a start tag: head
+Encountered a start tag: title
+Encountered some data  : Test
+Encountered an end tag : title
+Encountered an end tag : head
+Encountered a start tag: body
+Encountered a start tag: h1
+Encountered some data  : Parse me!
+Encountered an end tag : h1
+Encountered an end tag : body
+Encountered an end tag : html
+

HTMLParser Methods

HTMLParser instances have the following methods:

+HTMLParser.feed(data)
+

Feed some text to the parser. It is processed insofar as it consists of complete elements; incomplete data is buffered until more data is fed or close() is called. data must be str.

+
+HTMLParser.close()
+

Force processing of all buffered data as if it were followed by an end-of-file mark. This method may be redefined by a derived class to define additional processing at the end of the input, but the redefined version should always call the HTMLParser base class method close().

+
+HTMLParser.reset()
+

Reset the instance. Loses all unprocessed data. This is called implicitly at instantiation time.

+
+HTMLParser.getpos()
+

Return current line number and offset.

+
+HTMLParser.get_starttag_text()
+

Return the text of the most recently opened start tag. This should not normally be needed for structured processing, but may be useful in dealing with HTML “as deployed” or for re-generating input with minimal changes (whitespace between attributes can be preserved, etc.).

+

The following methods are called when data or markup elements are encountered and they are meant to be overridden in a subclass. The base class implementations do nothing (except for handle_startendtag()):

+HTMLParser.handle_starttag(tag, attrs)
+

This method is called to handle the start tag of an element (e.g. <div id="main">).

The tag argument is the name of the tag converted to lower case. The attrs argument is a list of (name, value) pairs containing the attributes found inside the tag’s <> brackets. The name will be translated to lower case, and quotes in the value have been removed, and character and entity references have been replaced.

For instance, for the tag <A HREF="https://www.cwi.nl/">, this method would be called as handle_starttag('a', [('href', 'https://www.cwi.nl/')]).

All entity references from html.entities are replaced in the attribute values.

+
+HTMLParser.handle_endtag(tag)
+

This method is called to handle the end tag of an element (e.g. </div>).

The tag argument is the name of the tag converted to lower case.

+
+HTMLParser.handle_startendtag(tag, attrs)
+

Similar to handle_starttag(), but called when the parser encounters an XHTML-style empty tag (<img ... />). This method may be overridden by subclasses which require this particular lexical information; the default implementation simply calls handle_starttag() and handle_endtag().

+
+HTMLParser.handle_data(data)
+

This method is called to process arbitrary data (e.g. text nodes and the content of <script>...</script> and <style>...</style>).

+
+HTMLParser.handle_entityref(name)
+

This method is called to process a named character reference of the form &name; (e.g. &gt;), where name is a general entity reference (e.g. 'gt'). This method is never called if convert_charrefs is True.

+
+HTMLParser.handle_charref(name)
+

This method is called to process decimal and hexadecimal numeric character references of the form &#NNN; and &#xNNN;. For example, the decimal equivalent for &gt; is &#62;, whereas the hexadecimal is &#x3E;; in this case the method will receive '62' or 'x3E'. This method is never called if convert_charrefs is True.

+
+HTMLParser.handle_comment(data)
+

This method is called when a comment is encountered (e.g. <!--comment-->).

For example, the comment <!-- comment --> will cause this method to be called with the argument ' comment '.

The content of Internet Explorer conditional comments (condcoms) will also be sent to this method, so, for <!--[if IE 9]>IE9-specific content<![endif]-->, this method will receive '[if IE 9]>IE9-specific content<![endif]'.

+
+HTMLParser.handle_decl(decl)
+

This method is called to handle an HTML doctype declaration (e.g. <!DOCTYPE html>).

The decl parameter will be the entire contents of the declaration inside the <!...> markup (e.g. 'DOCTYPE html').

+
+HTMLParser.handle_pi(data)
+

Method called when a processing instruction is encountered. The data parameter will contain the entire processing instruction. For example, for the processing instruction <?proc color='red'>, this method would be called as handle_pi("proc color='red'"). It is intended to be overridden by a derived class; the base class implementation does nothing.

Note

The HTMLParser class uses the SGML syntactic rules for processing instructions. An XHTML processing instruction using the trailing '?' will cause the '?' to be included in data.

+
+HTMLParser.unknown_decl(data)
+

This method is called when an unrecognized declaration is read by the parser.

The data parameter will be the entire contents of the declaration inside the <![...]> markup. It is sometimes useful to be overridden by a derived class. The base class implementation does nothing.

+

Examples

The following class implements a parser that will be used to illustrate more examples:

from html.parser import HTMLParser
+from html.entities import name2codepoint
+
+class MyHTMLParser(HTMLParser):
+    def handle_starttag(self, tag, attrs):
+        print("Start tag:", tag)
+        for attr in attrs:
+            print("     attr:", attr)
+
+    def handle_endtag(self, tag):
+        print("End tag  :", tag)
+
+    def handle_data(self, data):
+        print("Data     :", data)
+
+    def handle_comment(self, data):
+        print("Comment  :", data)
+
+    def handle_entityref(self, name):
+        c = chr(name2codepoint[name])
+        print("Named ent:", c)
+
+    def handle_charref(self, name):
+        if name.startswith('x'):
+            c = chr(int(name[1:], 16))
+        else:
+            c = chr(int(name))
+        print("Num ent  :", c)
+
+    def handle_decl(self, data):
+        print("Decl     :", data)
+
+parser = MyHTMLParser()
+

Parsing a doctype:

>>> parser.feed('<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" '
+...             '"http://www.w3.org/TR/html4/strict.dtd">')
+Decl     : DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01//EN" "http://www.w3.org/TR/html4/strict.dtd"
+

Parsing an element with a few attributes and a title:

>>> parser.feed('<img src="python-logo.png" alt="The Python logo">')
+Start tag: img
+     attr: ('src', 'python-logo.png')
+     attr: ('alt', 'The Python logo')
+>>>
+>>> parser.feed('<h1>Python</h1>')
+Start tag: h1
+Data     : Python
+End tag  : h1
+

The content of script and style elements is returned as is, without further parsing:

>>> parser.feed('<style type="text/css">#python { color: green }</style>')
+Start tag: style
+     attr: ('type', 'text/css')
+Data     : #python { color: green }
+End tag  : style
+
+>>> parser.feed('<script type="text/javascript">'
+...             'alert("<strong>hello!</strong>");</script>')
+Start tag: script
+     attr: ('type', 'text/javascript')
+Data     : alert("<strong>hello!</strong>");
+End tag  : script
+

Parsing comments:

>>> parser.feed('<!-- a comment -->'
+...             '<!--[if IE 9]>IE-specific content<![endif]-->')
+Comment  :  a comment
+Comment  : [if IE 9]>IE-specific content<![endif]
+

Parsing named and numeric character references and converting them to the correct char (note: these 3 references are all equivalent to '>'):

>>> parser.feed('&gt;&#62;&#x3E;')
+Named ent: >
+Num ent  : >
+Num ent  : >
+

Feeding incomplete chunks to feed() works, but handle_data() might be called more than once (unless convert_charrefs is set to True):

>>> for chunk in ['<sp', 'an>buff', 'ered ', 'text</s', 'pan>']:
+...     parser.feed(chunk)
+...
+Start tag: span
+Data     : buff
+Data     : ered
+Data     : text
+End tag  : span
+

Parsing invalid HTML (e.g. unquoted attributes) also works:

>>> parser.feed('<p><a class=link href=#main>tag soup</p ></a>')
+Start tag: p
+Start tag: a
+     attr: ('class', 'link')
+     attr: ('href', '#main')
+Data     : tag soup
+End tag  : p
+End tag  : a
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/html.parser.html +

+
diff --git a/devdocs/python~3.12/library%2Fhttp.client.html b/devdocs/python~3.12/library%2Fhttp.client.html new file mode 100644 index 00000000..b12fc6e0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhttp.client.html @@ -0,0 +1,225 @@ +

http.client — HTTP protocol client

Source code: Lib/http/client.py

This module defines classes that implement the client side of the HTTP and HTTPS protocols. It is normally not used directly — the module urllib.request uses it to handle URLs that use HTTP and HTTPS.

See also

The Requests package is recommended for a higher-level HTTP client interface.

Note

HTTPS support is only available if Python was compiled with SSL support (through the ssl module).

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The module provides the following classes:

+class http.client.HTTPConnection(host, port=None, [timeout, ]source_address=None, blocksize=8192)
+

An HTTPConnection instance represents one transaction with an HTTP server. It should be instantiated by passing it a host and optional port number. If no port number is passed, the port is extracted from the host string if it has the form host:port, else the default HTTP port (80) is used. If the optional timeout parameter is given, blocking operations (like connection attempts) will timeout after that many seconds (if it is not given, the global default timeout setting is used). The optional source_address parameter may be a tuple of a (host, port) to use as the source address the HTTP connection is made from. The optional blocksize parameter sets the buffer size in bytes for sending a file-like message body.

For example, the following calls all create instances that connect to the server at the same host and port:

>>> h1 = http.client.HTTPConnection('www.python.org')
+>>> h2 = http.client.HTTPConnection('www.python.org:80')
+>>> h3 = http.client.HTTPConnection('www.python.org', 80)
+>>> h4 = http.client.HTTPConnection('www.python.org', 80, timeout=10)
+

Changed in version 3.2: source_address was added.

Changed in version 3.4: The strict parameter was removed. HTTP 0.9-style “Simple Responses” are no longer supported.

Changed in version 3.7: blocksize parameter was added.

+
+class http.client.HTTPSConnection(host, port=None, *, [timeout, ]source_address=None, context=None, blocksize=8192)
+

A subclass of HTTPConnection that uses SSL for communication with secure servers. Default port is 443. If context is specified, it must be a ssl.SSLContext instance describing the various SSL options.

Please read Security considerations for more information on best practices.

Changed in version 3.2: source_address, context and check_hostname were added.

Changed in version 3.2: This class now supports HTTPS virtual hosts if possible (that is, if ssl.HAS_SNI is true).

Changed in version 3.4: The strict parameter was removed. HTTP 0.9-style “Simple Responses” are no longer supported.

Changed in version 3.4.3: This class now performs all the necessary certificate and hostname checks by default. To revert to the previous, unverified, behavior ssl._create_unverified_context() can be passed to the context parameter.

Changed in version 3.8: This class now enables TLS 1.3 ssl.SSLContext.post_handshake_auth for the default context or when cert_file is passed with a custom context.

Changed in version 3.10: This class now sends an ALPN extension with protocol indicator http/1.1 when no context is given. Custom context should set ALPN protocols with set_alpn_protocol().

Changed in version 3.12: The deprecated key_file, cert_file and check_hostname parameters have been removed.

+
+class http.client.HTTPResponse(sock, debuglevel=0, method=None, url=None)
+

Class whose instances are returned upon successful connection. Not instantiated directly by user.

Changed in version 3.4: The strict parameter was removed. HTTP 0.9 style “Simple Responses” are no longer supported.

+

This module provides the following function:

+http.client.parse_headers(fp)
+

Parse the headers from a file pointer fp representing a HTTP request/response. The file has to be a BufferedIOBase reader (i.e. not text) and must provide a valid RFC 2822 style header.

This function returns an instance of http.client.HTTPMessage that holds the header fields, but no payload (the same as HTTPResponse.msg and http.server.BaseHTTPRequestHandler.headers). After returning, the file pointer fp is ready to read the HTTP body.

Note

parse_headers() does not parse the start-line of a HTTP message; it only parses the Name: value lines. The file has to be ready to read these field lines, so the first line should already be consumed before calling the function.

+

The following exceptions are raised as appropriate:

+exception http.client.HTTPException
+

The base class of the other exceptions in this module. It is a subclass of Exception.

+
+exception http.client.NotConnected
+

A subclass of HTTPException.

+
+exception http.client.InvalidURL
+

A subclass of HTTPException, raised if a port is given and is either non-numeric or empty.

+
+exception http.client.UnknownProtocol
+

A subclass of HTTPException.

+
+exception http.client.UnknownTransferEncoding
+

A subclass of HTTPException.

+
+exception http.client.UnimplementedFileMode
+

A subclass of HTTPException.

+
+exception http.client.IncompleteRead
+

A subclass of HTTPException.

+
+exception http.client.ImproperConnectionState
+

A subclass of HTTPException.

+
+exception http.client.CannotSendRequest
+

A subclass of ImproperConnectionState.

+
+exception http.client.CannotSendHeader
+

A subclass of ImproperConnectionState.

+
+exception http.client.ResponseNotReady
+

A subclass of ImproperConnectionState.

+
+exception http.client.BadStatusLine
+

A subclass of HTTPException. Raised if a server responds with a HTTP status code that we don’t understand.

+
+exception http.client.LineTooLong
+

A subclass of HTTPException. Raised if an excessively long line is received in the HTTP protocol from the server.

+
+exception http.client.RemoteDisconnected
+

A subclass of ConnectionResetError and BadStatusLine. Raised by HTTPConnection.getresponse() when the attempt to read the response results in no data read from the connection, indicating that the remote end has closed the connection.

New in version 3.5: Previously, BadStatusLine('') was raised.

+

The constants defined in this module are:

+http.client.HTTP_PORT
+

The default port for the HTTP protocol (always 80).

+
+http.client.HTTPS_PORT
+

The default port for the HTTPS protocol (always 443).

+
+http.client.responses
+

This dictionary maps the HTTP 1.1 status codes to the W3C names.

Example: http.client.responses[http.client.NOT_FOUND] is 'Not Found'.

+

See HTTP status codes for a list of HTTP status codes that are available in this module as constants.

HTTPConnection Objects

HTTPConnection instances have the following methods:

+HTTPConnection.request(method, url, body=None, headers={}, *, encode_chunked=False)
+

This will send a request to the server using the HTTP request method method and the request URI url. The provided url must be an absolute path to conform with RFC 2616 §5.1.2 (unless connecting to an HTTP proxy server or using the OPTIONS or CONNECT methods).

If body is specified, the specified data is sent after the headers are finished. It may be a str, a bytes-like object, an open file object, or an iterable of bytes. If body is a string, it is encoded as ISO-8859-1, the default for HTTP. If it is a bytes-like object, the bytes are sent as is. If it is a file object, the contents of the file is sent; this file object should support at least the read() method. If the file object is an instance of io.TextIOBase, the data returned by the read() method will be encoded as ISO-8859-1, otherwise the data returned by read() is sent as is. If body is an iterable, the elements of the iterable are sent as is until the iterable is exhausted.

The headers argument should be a mapping of extra HTTP headers to send with the request. A Host header must be provided to conform with RFC 2616 §5.1.2 (unless connecting to an HTTP proxy server or using the OPTIONS or CONNECT methods).

If headers contains neither Content-Length nor Transfer-Encoding, but there is a request body, one of those header fields will be added automatically. If body is None, the Content-Length header is set to 0 for methods that expect a body (PUT, POST, and PATCH). If body is a string or a bytes-like object that is not also a file, the Content-Length header is set to its length. Any other type of body (files and iterables in general) will be chunk-encoded, and the Transfer-Encoding header will automatically be set instead of Content-Length.

The encode_chunked argument is only relevant if Transfer-Encoding is specified in headers. If encode_chunked is False, the HTTPConnection object assumes that all encoding is handled by the calling code. If it is True, the body will be chunk-encoded.

For example, to perform a GET request to https://docs.python.org/3/:

>>> import http.client
+>>> host = "docs.python.org"
+>>> conn = http.client.HTTPSConnection(host)
+>>> conn.request("GET", "/3/", headers={"Host": host})
+>>> response = conn.getresponse()
+>>> print(response.status, response.reason)
+200 OK
+

Note

Chunked transfer encoding has been added to the HTTP protocol version 1.1. Unless the HTTP server is known to handle HTTP 1.1, the caller must either specify the Content-Length, or must pass a str or bytes-like object that is not also a file as the body representation.

New in version 3.2: body can now be an iterable.

Changed in version 3.6: If neither Content-Length nor Transfer-Encoding are set in headers, file and iterable body objects are now chunk-encoded. The encode_chunked argument was added. No attempt is made to determine the Content-Length for file objects.

+
+HTTPConnection.getresponse()
+

Should be called after a request is sent to get the response from the server. Returns an HTTPResponse instance.

Note

Note that you must have read the whole response before you can send a new request to the server.

Changed in version 3.5: If a ConnectionError or subclass is raised, the HTTPConnection object will be ready to reconnect when a new request is sent.

+
+HTTPConnection.set_debuglevel(level)
+

Set the debugging level. The default debug level is 0, meaning no debugging output is printed. Any value greater than 0 will cause all currently defined debug output to be printed to stdout. The debuglevel is passed to any new HTTPResponse objects that are created.

New in version 3.1.

+
+HTTPConnection.set_tunnel(host, port=None, headers=None)
+

Set the host and the port for HTTP Connect Tunnelling. This allows running the connection through a proxy server.

The host and port arguments specify the endpoint of the tunneled connection (i.e. the address included in the CONNECT request, not the address of the proxy server).

The headers argument should be a mapping of extra HTTP headers to send with the CONNECT request.

As HTTP/1.1 is used for HTTP CONNECT tunnelling request, as per the RFC, a HTTP Host: header must be provided, matching the authority-form of the request target provided as the destination for the CONNECT request. If a HTTP Host: header is not provided via the headers argument, one is generated and transmitted automatically.

For example, to tunnel through a HTTPS proxy server running locally on port 8080, we would pass the address of the proxy to the HTTPSConnection constructor, and the address of the host that we eventually want to reach to the set_tunnel() method:

>>> import http.client
+>>> conn = http.client.HTTPSConnection("localhost", 8080)
+>>> conn.set_tunnel("www.python.org")
+>>> conn.request("HEAD","/index.html")
+

New in version 3.2.

Changed in version 3.12: HTTP CONNECT tunnelling requests use protocol HTTP/1.1, upgraded from protocol HTTP/1.0. Host: HTTP headers are mandatory for HTTP/1.1, so one will be automatically generated and transmitted if not provided in the headers argument.

+
+HTTPConnection.get_proxy_response_headers()
+

Returns a dictionary with the headers of the response received from the proxy server to the CONNECT request.

If the CONNECT request was not sent, the method returns None.

New in version 3.12.

+
+HTTPConnection.connect()
+

Connect to the server specified when the object was created. By default, this is called automatically when making a request if the client does not already have a connection.

Raises an auditing event http.client.connect with arguments self, host, port.

+
+HTTPConnection.close()
+

Close the connection to the server.

+
+HTTPConnection.blocksize
+

Buffer size in bytes for sending a file-like message body.

New in version 3.7.

+

As an alternative to using the request() method described above, you can also send your request step by step, by using the four functions below.

+HTTPConnection.putrequest(method, url, skip_host=False, skip_accept_encoding=False)
+

This should be the first call after the connection to the server has been made. It sends a line to the server consisting of the method string, the url string, and the HTTP version (HTTP/1.1). To disable automatic sending of Host: or Accept-Encoding: headers (for example to accept additional content encodings), specify skip_host or skip_accept_encoding with non-False values.

+
+HTTPConnection.putheader(header, argument[, ...])
+

Send an RFC 822-style header to the server. It sends a line to the server consisting of the header, a colon and a space, and the first argument. If more arguments are given, continuation lines are sent, each consisting of a tab and an argument.

+
+HTTPConnection.endheaders(message_body=None, *, encode_chunked=False)
+

Send a blank line to the server, signalling the end of the headers. The optional message_body argument can be used to pass a message body associated with the request.

If encode_chunked is True, the result of each iteration of message_body will be chunk-encoded as specified in RFC 7230, Section 3.3.1. How the data is encoded is dependent on the type of message_body. If message_body implements the buffer interface the encoding will result in a single chunk. If message_body is a collections.abc.Iterable, each iteration of message_body will result in a chunk. If message_body is a file object, each call to .read() will result in a chunk. The method automatically signals the end of the chunk-encoded data immediately after message_body.

Note

Due to the chunked encoding specification, empty chunks yielded by an iterator body will be ignored by the chunk-encoder. This is to avoid premature termination of the read of the request by the target server due to malformed encoding.

New in version 3.6: Chunked encoding support. The encode_chunked parameter was added.

+
+HTTPConnection.send(data)
+

Send data to the server. This should be used directly only after the endheaders() method has been called and before getresponse() is called.

Raises an auditing event http.client.send with arguments self, data.

+

HTTPResponse Objects

An HTTPResponse instance wraps the HTTP response from the server. It provides access to the request headers and the entity body. The response is an iterable object and can be used in a with statement.

Changed in version 3.5: The io.BufferedIOBase interface is now implemented and all of its reader operations are supported.

+HTTPResponse.read([amt])
+

Reads and returns the response body, or up to the next amt bytes.

+
+HTTPResponse.readinto(b)
+

Reads up to the next len(b) bytes of the response body into the buffer b. Returns the number of bytes read.

New in version 3.3.

+
+HTTPResponse.getheader(name, default=None)
+

Return the value of the header name, or default if there is no header matching name. If there is more than one header with the name name, return all of the values joined by ‘, ‘. If default is any iterable other than a single string, its elements are similarly returned joined by commas.

+
+HTTPResponse.getheaders()
+

Return a list of (header, value) tuples.

+
+HTTPResponse.fileno()
+

Return the fileno of the underlying socket.

+
+HTTPResponse.msg
+

A http.client.HTTPMessage instance containing the response headers. http.client.HTTPMessage is a subclass of email.message.Message.

+
+HTTPResponse.version
+

HTTP protocol version used by server. 10 for HTTP/1.0, 11 for HTTP/1.1.

+
+HTTPResponse.url
+

URL of the resource retrieved, commonly used to determine if a redirect was followed.

+
+HTTPResponse.headers
+

Headers of the response in the form of an email.message.EmailMessage instance.

+
+HTTPResponse.status
+

Status code returned by server.

+
+HTTPResponse.reason
+

Reason phrase returned by server.

+
+HTTPResponse.debuglevel
+

A debugging hook. If debuglevel is greater than zero, messages will be printed to stdout as the response is read and parsed.

+
+HTTPResponse.closed
+

Is True if the stream is closed.

+
+HTTPResponse.geturl()
+

Deprecated since version 3.9: Deprecated in favor of url.

+
+HTTPResponse.info()
+

Deprecated since version 3.9: Deprecated in favor of headers.

+
+HTTPResponse.getcode()
+

Deprecated since version 3.9: Deprecated in favor of status.

+

Examples

Here is an example session that uses the GET method:

>>> import http.client
+>>> conn = http.client.HTTPSConnection("www.python.org")
+>>> conn.request("GET", "/")
+>>> r1 = conn.getresponse()
+>>> print(r1.status, r1.reason)
+200 OK
+>>> data1 = r1.read()  # This will return entire content.
+>>> # The following example demonstrates reading data in chunks.
+>>> conn.request("GET", "/")
+>>> r1 = conn.getresponse()
+>>> while chunk := r1.read(200):
+...     print(repr(chunk))
+b'<!doctype html>\n<!--[if"...
+...
+>>> # Example of an invalid request
+>>> conn = http.client.HTTPSConnection("docs.python.org")
+>>> conn.request("GET", "/parrot.spam")
+>>> r2 = conn.getresponse()
+>>> print(r2.status, r2.reason)
+404 Not Found
+>>> data2 = r2.read()
+>>> conn.close()
+

Here is an example session that uses the HEAD method. Note that the HEAD method never returns any data.

>>> import http.client
+>>> conn = http.client.HTTPSConnection("www.python.org")
+>>> conn.request("HEAD", "/")
+>>> res = conn.getresponse()
+>>> print(res.status, res.reason)
+200 OK
+>>> data = res.read()
+>>> print(len(data))
+0
+>>> data == b''
+True
+

Here is an example session that uses the POST method:

>>> import http.client, urllib.parse
+>>> params = urllib.parse.urlencode({'@number': 12524, '@type': 'issue', '@action': 'show'})
+>>> headers = {"Content-type": "application/x-www-form-urlencoded",
+...            "Accept": "text/plain"}
+>>> conn = http.client.HTTPConnection("bugs.python.org")
+>>> conn.request("POST", "", params, headers)
+>>> response = conn.getresponse()
+>>> print(response.status, response.reason)
+302 Found
+>>> data = response.read()
+>>> data
+b'Redirecting to <a href="https://bugs.python.org/issue12524">https://bugs.python.org/issue12524</a>'
+>>> conn.close()
+

Client side HTTP PUT requests are very similar to POST requests. The difference lies only on the server side where HTTP servers will allow resources to be created via PUT requests. It should be noted that custom HTTP methods are also handled in urllib.request.Request by setting the appropriate method attribute. Here is an example session that uses the PUT method:

>>> # This creates an HTTP request
+>>> # with the content of BODY as the enclosed representation
+>>> # for the resource http://localhost:8080/file
+...
+>>> import http.client
+>>> BODY = "***filecontents***"
+>>> conn = http.client.HTTPConnection("localhost", 8080)
+>>> conn.request("PUT", "/file", BODY)
+>>> response = conn.getresponse()
+>>> print(response.status, response.reason)
+200, OK
+

HTTPMessage Objects

An http.client.HTTPMessage instance holds the headers from an HTTP response. It is implemented using the email.message.Message class.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/http.client.html +

+
diff --git a/devdocs/python~3.12/library%2Fhttp.cookiejar.html b/devdocs/python~3.12/library%2Fhttp.cookiejar.html new file mode 100644 index 00000000..a5d85980 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhttp.cookiejar.html @@ -0,0 +1,241 @@ +

http.cookiejar — Cookie handling for HTTP clients

Source code: Lib/http/cookiejar.py

The http.cookiejar module defines classes for automatic handling of HTTP cookies. It is useful for accessing web sites that require small pieces of data – cookies – to be set on the client machine by an HTTP response from a web server, and then returned to the server in later HTTP requests.

Both the regular Netscape cookie protocol and the protocol defined by RFC 2965 are handled. RFC 2965 handling is switched off by default. RFC 2109 cookies are parsed as Netscape cookies and subsequently treated either as Netscape or RFC 2965 cookies according to the ‘policy’ in effect. Note that the great majority of cookies on the internet are Netscape cookies. http.cookiejar attempts to follow the de-facto Netscape cookie protocol (which differs substantially from that set out in the original Netscape specification), including taking note of the max-age and port cookie-attributes introduced with RFC 2965.

Note

The various named parameters found in Set-Cookie and Set-Cookie2 headers (eg. domain and expires) are conventionally referred to as attributes. To distinguish them from Python attributes, the documentation for this module uses the term cookie-attribute instead.

The module defines the following exception:

+exception http.cookiejar.LoadError
+

Instances of FileCookieJar raise this exception on failure to load cookies from a file. LoadError is a subclass of OSError.

Changed in version 3.3: LoadError used to be a subtype of IOError, which is now an alias of OSError.

+

The following classes are provided:

+class http.cookiejar.CookieJar(policy=None)
+

policy is an object implementing the CookiePolicy interface.

The CookieJar class stores HTTP cookies. It extracts cookies from HTTP requests, and returns them in HTTP responses. CookieJar instances automatically expire contained cookies when necessary. Subclasses are also responsible for storing and retrieving cookies from a file or database.

+
+class http.cookiejar.FileCookieJar(filename=None, delayload=None, policy=None)
+

policy is an object implementing the CookiePolicy interface. For the other arguments, see the documentation for the corresponding attributes.

A CookieJar which can load cookies from, and perhaps save cookies to, a file on disk. Cookies are NOT loaded from the named file until either the load() or revert() method is called. Subclasses of this class are documented in section FileCookieJar subclasses and co-operation with web browsers.

This should not be initialized directly – use its subclasses below instead.

Changed in version 3.8: The filename parameter supports a path-like object.

+
+class http.cookiejar.CookiePolicy
+

This class is responsible for deciding whether each cookie should be accepted from / returned to the server.

+
+class http.cookiejar.DefaultCookiePolicy(blocked_domains=None, allowed_domains=None, netscape=True, rfc2965=False, rfc2109_as_netscape=None, hide_cookie2=False, strict_domain=False, strict_rfc2965_unverifiable=True, strict_ns_unverifiable=False, strict_ns_domain=DefaultCookiePolicy.DomainLiberal, strict_ns_set_initial_dollar=False, strict_ns_set_path=False, secure_protocols=('https', 'wss'))
+

Constructor arguments should be passed as keyword arguments only. blocked_domains is a sequence of domain names that we never accept cookies from, nor return cookies to. allowed_domains if not None, this is a sequence of the only domains for which we accept and return cookies. secure_protocols is a sequence of protocols for which secure cookies can be added to. By default https and wss (secure websocket) are considered secure protocols. For all other arguments, see the documentation for CookiePolicy and DefaultCookiePolicy objects.

DefaultCookiePolicy implements the standard accept / reject rules for Netscape and RFC 2965 cookies. By default, RFC 2109 cookies (ie. cookies received in a Set-Cookie header with a version cookie-attribute of 1) are treated according to the RFC 2965 rules. However, if RFC 2965 handling is turned off or rfc2109_as_netscape is True, RFC 2109 cookies are ‘downgraded’ by the CookieJar instance to Netscape cookies, by setting the version attribute of the Cookie instance to 0. DefaultCookiePolicy also provides some parameters to allow some fine-tuning of policy.

+
+class http.cookiejar.Cookie
+

This class represents Netscape, RFC 2109 and RFC 2965 cookies. It is not expected that users of http.cookiejar construct their own Cookie instances. Instead, if necessary, call make_cookies() on a CookieJar instance.

+

See also

+Module urllib.request +
+
+

URL opening with automatic cookie handling.

+Module http.cookies +
+
+

HTTP cookie classes, principally useful for server-side code. The http.cookiejar and http.cookies modules do not depend on each other.

https://curl.se/rfc/cookie_spec.html
+
+

The specification of the original Netscape cookie protocol. Though this is still the dominant protocol, the ‘Netscape cookie protocol’ implemented by all the major browsers (and http.cookiejar) only bears a passing resemblance to the one sketched out in cookie_spec.html.

+RFC 2109 - HTTP State Management Mechanism
+
+

Obsoleted by RFC 2965. Uses Set-Cookie with version=1.

+RFC 2965 - HTTP State Management Mechanism
+
+

The Netscape protocol with the bugs fixed. Uses Set-Cookie2 in place of Set-Cookie. Not widely used.

http://kristol.org/cookie/errata.html
+
+

Unfinished errata to RFC 2965.

RFC 2964 - Use of HTTP State Management

CookieJar and FileCookieJar Objects

CookieJar objects support the iterator protocol for iterating over contained Cookie objects.

CookieJar has the following methods:

+

Add correct Cookie header to request.

If policy allows (ie. the rfc2965 and hide_cookie2 attributes of the CookieJar’s CookiePolicy instance are true and false respectively), the Cookie2 header is also added when appropriate.

The request object (usually a urllib.request.Request instance) must support the methods get_full_url(), has_header(), get_header(), header_items(), add_unredirected_header() and the attributes host, type, unverifiable and origin_req_host as documented by urllib.request.

Changed in version 3.3: request object needs origin_req_host attribute. Dependency on a deprecated method get_origin_req_host() has been removed.

+
+CookieJar.extract_cookies(response, request)
+

Extract cookies from HTTP response and store them in the CookieJar, where allowed by policy.

The CookieJar will look for allowable Set-Cookie and Set-Cookie2 headers in the response argument, and store cookies as appropriate (subject to the CookiePolicy.set_ok() method’s approval).

The response object (usually the result of a call to urllib.request.urlopen(), or similar) should support an info() method, which returns an email.message.Message instance.

The request object (usually a urllib.request.Request instance) must support the method get_full_url() and the attributes host, unverifiable and origin_req_host, as documented by urllib.request. The request is used to set default values for cookie-attributes as well as for checking that the cookie is allowed to be set.

Changed in version 3.3: request object needs origin_req_host attribute. Dependency on a deprecated method get_origin_req_host() has been removed.

+
+CookieJar.set_policy(policy)
+

Set the CookiePolicy instance to be used.

+
+CookieJar.make_cookies(response, request)
+

Return sequence of Cookie objects extracted from response object.

See the documentation for extract_cookies() for the interfaces required of the response and request arguments.

+
+

Set a Cookie if policy says it’s OK to do so.

+
+

Set a Cookie, without checking with policy to see whether or not it should be set.

+
+CookieJar.clear([domain[, path[, name]]])
+

Clear some cookies.

If invoked without arguments, clear all cookies. If given a single argument, only cookies belonging to that domain will be removed. If given two arguments, cookies belonging to the specified domain and URL path are removed. If given three arguments, then the cookie with the specified domain, path and name is removed.

Raises KeyError if no matching cookie exists.

+
+CookieJar.clear_session_cookies()
+

Discard all session cookies.

Discards all contained cookies that have a true discard attribute (usually because they had either no max-age or expires cookie-attribute, or an explicit discard cookie-attribute). For interactive browsers, the end of a session usually corresponds to closing the browser window.

Note that the save() method won’t save session cookies anyway, unless you ask otherwise by passing a true ignore_discard argument.

+

FileCookieJar implements the following additional methods:

+FileCookieJar.save(filename=None, ignore_discard=False, ignore_expires=False)
+

Save cookies to a file.

This base class raises NotImplementedError. Subclasses may leave this method unimplemented.

filename is the name of file in which to save cookies. If filename is not specified, self.filename is used (whose default is the value passed to the constructor, if any); if self.filename is None, ValueError is raised.

ignore_discard: save even cookies set to be discarded. ignore_expires: save even cookies that have expired

The file is overwritten if it already exists, thus wiping all the cookies it contains. Saved cookies can be restored later using the load() or revert() methods.

+
+FileCookieJar.load(filename=None, ignore_discard=False, ignore_expires=False)
+

Load cookies from a file.

Old cookies are kept unless overwritten by newly loaded ones.

Arguments are as for save().

The named file must be in the format understood by the class, or LoadError will be raised. Also, OSError may be raised, for example if the file does not exist.

Changed in version 3.3: IOError used to be raised, it is now an alias of OSError.

+
+FileCookieJar.revert(filename=None, ignore_discard=False, ignore_expires=False)
+

Clear all cookies and reload cookies from a saved file.

revert() can raise the same exceptions as load(). If there is a failure, the object’s state will not be altered.

+

FileCookieJar instances have the following public attributes:

+FileCookieJar.filename
+

Filename of default file in which to keep cookies. This attribute may be assigned to.

+
+FileCookieJar.delayload
+

If true, load cookies lazily from disk. This attribute should not be assigned to. This is only a hint, since this only affects performance, not behaviour (unless the cookies on disk are changing). A CookieJar object may ignore it. None of the FileCookieJar classes included in the standard library lazily loads cookies.

+

FileCookieJar subclasses and co-operation with web browsers

The following CookieJar subclasses are provided for reading and writing.

+class http.cookiejar.MozillaCookieJar(filename=None, delayload=None, policy=None)
+

A FileCookieJar that can load from and save cookies to disk in the Mozilla cookies.txt file format (which is also used by curl and the Lynx and Netscape browsers).

Note

This loses information about RFC 2965 cookies, and also about newer or non-standard cookie-attributes such as port.

Warning

Back up your cookies before saving if you have cookies whose loss / corruption would be inconvenient (there are some subtleties which may lead to slight changes in the file over a load / save round-trip).

Also note that cookies saved while Mozilla is running will get clobbered by Mozilla.

+
+class http.cookiejar.LWPCookieJar(filename=None, delayload=None, policy=None)
+

A FileCookieJar that can load from and save cookies to disk in format compatible with the libwww-perl library’s Set-Cookie3 file format. This is convenient if you want to store cookies in a human-readable file.

Changed in version 3.8: The filename parameter supports a path-like object.

+

CookiePolicy Objects

Objects implementing the CookiePolicy interface have the following methods:

+CookiePolicy.set_ok(cookie, request)
+

Return boolean value indicating whether cookie should be accepted from server.

cookie is a Cookie instance. request is an object implementing the interface defined by the documentation for CookieJar.extract_cookies().

+
+CookiePolicy.return_ok(cookie, request)
+

Return boolean value indicating whether cookie should be returned to server.

cookie is a Cookie instance. request is an object implementing the interface defined by the documentation for CookieJar.add_cookie_header().

+
+CookiePolicy.domain_return_ok(domain, request)
+

Return False if cookies should not be returned, given cookie domain.

This method is an optimization. It removes the need for checking every cookie with a particular domain (which might involve reading many files). Returning true from domain_return_ok() and path_return_ok() leaves all the work to return_ok().

If domain_return_ok() returns true for the cookie domain, path_return_ok() is called for the cookie path. Otherwise, path_return_ok() and return_ok() are never called for that cookie domain. If path_return_ok() returns true, return_ok() is called with the Cookie object itself for a full check. Otherwise, return_ok() is never called for that cookie path.

Note that domain_return_ok() is called for every cookie domain, not just for the request domain. For example, the function might be called with both ".example.com" and "www.example.com" if the request domain is "www.example.com". The same goes for path_return_ok().

The request argument is as documented for return_ok().

+
+CookiePolicy.path_return_ok(path, request)
+

Return False if cookies should not be returned, given cookie path.

See the documentation for domain_return_ok().

+

In addition to implementing the methods above, implementations of the CookiePolicy interface must also supply the following attributes, indicating which protocols should be used, and how. All of these attributes may be assigned to.

+CookiePolicy.netscape
+

Implement Netscape protocol.

+
+CookiePolicy.rfc2965
+

Implement RFC 2965 protocol.

+
+CookiePolicy.hide_cookie2
+

Don’t add Cookie2 header to requests (the presence of this header indicates to the server that we understand RFC 2965 cookies).

+

The most useful way to define a CookiePolicy class is by subclassing from DefaultCookiePolicy and overriding some or all of the methods above. CookiePolicy itself may be used as a ‘null policy’ to allow setting and receiving any and all cookies (this is unlikely to be useful).

DefaultCookiePolicy Objects

Implements the standard rules for accepting and returning cookies.

Both RFC 2965 and Netscape cookies are covered. RFC 2965 handling is switched off by default.

The easiest way to provide your own policy is to override this class and call its methods in your overridden implementations before adding your own additional checks:

import http.cookiejar
+class MyCookiePolicy(http.cookiejar.DefaultCookiePolicy):
+    def set_ok(self, cookie, request):
+        if not http.cookiejar.DefaultCookiePolicy.set_ok(self, cookie, request):
+            return False
+        if i_dont_want_to_store_this_cookie(cookie):
+            return False
+        return True
+

In addition to the features required to implement the CookiePolicy interface, this class allows you to block and allow domains from setting and receiving cookies. There are also some strictness switches that allow you to tighten up the rather loose Netscape protocol rules a little bit (at the cost of blocking some benign cookies).

A domain blocklist and allowlist is provided (both off by default). Only domains not in the blocklist and present in the allowlist (if the allowlist is active) participate in cookie setting and returning. Use the blocked_domains constructor argument, and blocked_domains() and set_blocked_domains() methods (and the corresponding argument and methods for allowed_domains). If you set an allowlist, you can turn it off again by setting it to None.

Domains in block or allow lists that do not start with a dot must equal the cookie domain to be matched. For example, "example.com" matches a blocklist entry of "example.com", but "www.example.com" does not. Domains that do start with a dot are matched by more specific domains too. For example, both "www.example.com" and "www.coyote.example.com" match ".example.com" (but "example.com" itself does not). IP addresses are an exception, and must match exactly. For example, if blocked_domains contains "192.168.1.2" and ".168.1.2", 192.168.1.2 is blocked, but 193.168.1.2 is not.

DefaultCookiePolicy implements the following additional methods:

+DefaultCookiePolicy.blocked_domains()
+

Return the sequence of blocked domains (as a tuple).

+
+DefaultCookiePolicy.set_blocked_domains(blocked_domains)
+

Set the sequence of blocked domains.

+
+DefaultCookiePolicy.is_blocked(domain)
+

Return True if domain is on the blocklist for setting or receiving cookies.

+
+DefaultCookiePolicy.allowed_domains()
+

Return None, or the sequence of allowed domains (as a tuple).

+
+DefaultCookiePolicy.set_allowed_domains(allowed_domains)
+

Set the sequence of allowed domains, or None.

+
+DefaultCookiePolicy.is_not_allowed(domain)
+

Return True if domain is not on the allowlist for setting or receiving cookies.

+

DefaultCookiePolicy instances have the following attributes, which are all initialised from the constructor arguments of the same name, and which may all be assigned to.

+DefaultCookiePolicy.rfc2109_as_netscape
+

If true, request that the CookieJar instance downgrade RFC 2109 cookies (ie. cookies received in a Set-Cookie header with a version cookie-attribute of 1) to Netscape cookies by setting the version attribute of the Cookie instance to 0. The default value is None, in which case RFC 2109 cookies are downgraded if and only if RFC 2965 handling is turned off. Therefore, RFC 2109 cookies are downgraded by default.

+

General strictness switches:

+DefaultCookiePolicy.strict_domain
+

Don’t allow sites to set two-component domains with country-code top-level domains like .co.uk, .gov.uk, .co.nz.etc. This is far from perfect and isn’t guaranteed to work!

+

RFC 2965 protocol strictness switches:

+DefaultCookiePolicy.strict_rfc2965_unverifiable
+

Follow RFC 2965 rules on unverifiable transactions (usually, an unverifiable transaction is one resulting from a redirect or a request for an image hosted on another site). If this is false, cookies are never blocked on the basis of verifiability

+

Netscape protocol strictness switches:

+DefaultCookiePolicy.strict_ns_unverifiable
+

Apply RFC 2965 rules on unverifiable transactions even to Netscape cookies.

+
+DefaultCookiePolicy.strict_ns_domain
+

Flags indicating how strict to be with domain-matching rules for Netscape cookies. See below for acceptable values.

+
+DefaultCookiePolicy.strict_ns_set_initial_dollar
+

Ignore cookies in Set-Cookie: headers that have names starting with '$'.

+
+DefaultCookiePolicy.strict_ns_set_path
+

Don’t allow setting cookies whose path doesn’t path-match request URI.

+

strict_ns_domain is a collection of flags. Its value is constructed by or-ing together (for example, DomainStrictNoDots|DomainStrictNonDomain means both flags are set).

+DefaultCookiePolicy.DomainStrictNoDots
+

When setting cookies, the ‘host prefix’ must not contain a dot (eg. www.foo.bar.com can’t set a cookie for .bar.com, because www.foo contains a dot).

+
+DefaultCookiePolicy.DomainStrictNonDomain
+

Cookies that did not explicitly specify a domain cookie-attribute can only be returned to a domain equal to the domain that set the cookie (eg. spam.example.com won’t be returned cookies from example.com that had no domain cookie-attribute).

+
+DefaultCookiePolicy.DomainRFC2965Match
+

When setting cookies, require a full RFC 2965 domain-match.

+

The following attributes are provided for convenience, and are the most useful combinations of the above flags:

+DefaultCookiePolicy.DomainLiberal
+

Equivalent to 0 (ie. all of the above Netscape domain strictness flags switched off).

+
+DefaultCookiePolicy.DomainStrict
+

Equivalent to DomainStrictNoDots|DomainStrictNonDomain.

+

Examples

The first example shows the most common usage of http.cookiejar:

import http.cookiejar, urllib.request
+cj = http.cookiejar.CookieJar()
+opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj))
+r = opener.open("http://example.com/")
+

This example illustrates how to open a URL using your Netscape, Mozilla, or Lynx cookies (assumes Unix/Netscape convention for location of the cookies file):

import os, http.cookiejar, urllib.request
+cj = http.cookiejar.MozillaCookieJar()
+cj.load(os.path.join(os.path.expanduser("~"), ".netscape", "cookies.txt"))
+opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj))
+r = opener.open("http://example.com/")
+

The next example illustrates the use of DefaultCookiePolicy. Turn on RFC 2965 cookies, be more strict about domains when setting and returning Netscape cookies, and block some domains from setting cookies or having them returned:

import urllib.request
+from http.cookiejar import CookieJar, DefaultCookiePolicy
+policy = DefaultCookiePolicy(
+    rfc2965=True, strict_ns_domain=Policy.DomainStrict,
+    blocked_domains=["ads.net", ".ads.net"])
+cj = CookieJar(policy)
+opener = urllib.request.build_opener(urllib.request.HTTPCookieProcessor(cj))
+r = opener.open("http://example.com/")
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/http.cookiejar.html +

+
diff --git a/devdocs/python~3.12/library%2Fhttp.cookies.html b/devdocs/python~3.12/library%2Fhttp.cookies.html new file mode 100644 index 00000000..25e1e7e6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhttp.cookies.html @@ -0,0 +1,130 @@ +

http.cookies — HTTP state management

Source code: Lib/http/cookies.py

The http.cookies module defines classes for abstracting the concept of cookies, an HTTP state management mechanism. It supports both simple string-only cookies, and provides an abstraction for having any serializable data-type as cookie value.

The module formerly strictly applied the parsing rules described in the RFC 2109 and RFC 2068 specifications. It has since been discovered that MSIE 3.0x didn’t follow the character rules outlined in those specs; many current-day browsers and servers have also relaxed parsing rules when it comes to cookie handling. As a result, this module now uses parsing rules that are a bit less strict than they once were.

The character set, string.ascii_letters, string.digits and !#$%&'*+-.^_`|~: denote the set of valid characters allowed by this module in a cookie name (as key).

Changed in version 3.3: Allowed ‘:’ as a valid cookie name character.

Note

On encountering an invalid cookie, CookieError is raised, so if your cookie data comes from a browser you should always prepare for invalid data and catch CookieError on parsing.

+exception http.cookies.CookieError
+

Exception failing because of RFC 2109 invalidity: incorrect attributes, incorrect Set-Cookie header, etc.

+
+class http.cookies.BaseCookie([input])
+

This class is a dictionary-like object whose keys are strings and whose values are Morsel instances. Note that upon setting a key to a value, the value is first converted to a Morsel containing the key and the value.

If input is given, it is passed to the load() method.

+
+class http.cookies.SimpleCookie([input])
+

This class derives from BaseCookie and overrides value_decode() and value_encode(). SimpleCookie supports strings as cookie values. When setting the value, SimpleCookie calls the builtin str() to convert the value to a string. Values received from HTTP are kept as strings.

+

See also

+Module http.cookiejar +
+
+

HTTP cookie handling for web clients. The http.cookiejar and http.cookies modules do not depend on each other.

+RFC 2109 - HTTP State Management Mechanism
+
+

This is the state management specification implemented by this module.

Morsel Objects

+class http.cookies.Morsel
+

Abstract a key/value pair, which has some RFC 2109 attributes.

Morsels are dictionary-like objects, whose set of keys is constant — the valid RFC 2109 attributes, which are:

+expires
+path
+comment
+domain
max-age +
+secure
+version
+httponly
+samesite
+

The attribute httponly specifies that the cookie is only transferred in HTTP requests, and is not accessible through JavaScript. This is intended to mitigate some forms of cross-site scripting.

The attribute samesite specifies that the browser is not allowed to send the cookie along with cross-site requests. This helps to mitigate CSRF attacks. Valid values for this attribute are “Strict” and “Lax”.

The keys are case-insensitive and their default value is ''.

Changed in version 3.5: __eq__() now takes key and value into account.

Changed in version 3.7: Attributes key, value and coded_value are read-only. Use set() for setting them.

Changed in version 3.8: Added support for the samesite attribute.

+
+Morsel.value
+

The value of the cookie.

+
+Morsel.coded_value
+

The encoded value of the cookie — this is what should be sent.

+
+Morsel.key
+

The name of the cookie.

+
+Morsel.set(key, value, coded_value)
+

Set the key, value and coded_value attributes.

+
+Morsel.isReservedKey(K)
+

Whether K is a member of the set of keys of a Morsel.

+
+Morsel.output(attrs=None, header='Set-Cookie:')
+

Return a string representation of the Morsel, suitable to be sent as an HTTP header. By default, all the attributes are included, unless attrs is given, in which case it should be a list of attributes to use. header is by default "Set-Cookie:".

+
+Morsel.js_output(attrs=None)
+

Return an embeddable JavaScript snippet, which, if run on a browser which supports JavaScript, will act the same as if the HTTP header was sent.

The meaning for attrs is the same as in output().

+
+Morsel.OutputString(attrs=None)
+

Return a string representing the Morsel, without any surrounding HTTP or JavaScript.

The meaning for attrs is the same as in output().

+
+Morsel.update(values)
+

Update the values in the Morsel dictionary with the values in the dictionary values. Raise an error if any of the keys in the values dict is not a valid RFC 2109 attribute.

Changed in version 3.5: an error is raised for invalid keys.

+
+Morsel.copy(value)
+

Return a shallow copy of the Morsel object.

Changed in version 3.5: return a Morsel object instead of a dict.

+
+Morsel.setdefault(key, value=None)
+

Raise an error if key is not a valid RFC 2109 attribute, otherwise behave the same as dict.setdefault().

+

Example

The following example demonstrates how to use the http.cookies module.

>>> from http import cookies
+>>> C = cookies.SimpleCookie()
+>>> C["fig"] = "newton"
+>>> C["sugar"] = "wafer"
+>>> print(C) # generate HTTP headers
+Set-Cookie: fig=newton
+Set-Cookie: sugar=wafer
+>>> print(C.output()) # same thing
+Set-Cookie: fig=newton
+Set-Cookie: sugar=wafer
+>>> C = cookies.SimpleCookie()
+>>> C["rocky"] = "road"
+>>> C["rocky"]["path"] = "/cookie"
+>>> print(C.output(header="Cookie:"))
+Cookie: rocky=road; Path=/cookie
+>>> print(C.output(attrs=[], header="Cookie:"))
+Cookie: rocky=road
+>>> C = cookies.SimpleCookie()
+>>> C.load("chips=ahoy; vienna=finger") # load from a string (HTTP header)
+>>> print(C)
+Set-Cookie: chips=ahoy
+Set-Cookie: vienna=finger
+>>> C = cookies.SimpleCookie()
+>>> C.load('keebler="E=everybody; L=\\"Loves\\"; fudge=\\012;";')
+>>> print(C)
+Set-Cookie: keebler="E=everybody; L=\"Loves\"; fudge=\012;"
+>>> C = cookies.SimpleCookie()
+>>> C["oreo"] = "doublestuff"
+>>> C["oreo"]["path"] = "/"
+>>> print(C)
+Set-Cookie: oreo=doublestuff; Path=/
+>>> C = cookies.SimpleCookie()
+>>> C["twix"] = "none for you"
+>>> C["twix"].value
+'none for you'
+>>> C = cookies.SimpleCookie()
+>>> C["number"] = 7 # equivalent to C["number"] = str(7)
+>>> C["string"] = "seven"
+>>> C["number"].value
+'7'
+>>> C["string"].value
+'seven'
+>>> print(C)
+Set-Cookie: number=7
+Set-Cookie: string=seven
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/http.cookies.html +

+
diff --git a/devdocs/python~3.12/library%2Fhttp.html b/devdocs/python~3.12/library%2Fhttp.html new file mode 100644 index 00000000..31107895 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhttp.html @@ -0,0 +1,135 @@ +

http — HTTP modules

Source code: Lib/http/__init__.py

http is a package that collects several modules for working with the HyperText Transfer Protocol:

The http module also defines the following enums that help you work with http related code:

+class http.HTTPStatus
+

New in version 3.5.

A subclass of enum.IntEnum that defines a set of HTTP status codes, reason phrases and long descriptions written in English.

Usage:

>>> from http import HTTPStatus
+>>> HTTPStatus.OK
+HTTPStatus.OK
+>>> HTTPStatus.OK == 200
+True
+>>> HTTPStatus.OK.value
+200
+>>> HTTPStatus.OK.phrase
+'OK'
+>>> HTTPStatus.OK.description
+'Request fulfilled, document follows'
+>>> list(HTTPStatus)
+[HTTPStatus.CONTINUE, HTTPStatus.SWITCHING_PROTOCOLS, ...]
+
+

HTTP status codes

Supported, IANA-registered status codes available in http.HTTPStatus are:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Code

Enum Name

Details

100

CONTINUE

HTTP/1.1 RFC 7231, Section 6.2.1

101

SWITCHING_PROTOCOLS

HTTP/1.1 RFC 7231, Section 6.2.2

102

PROCESSING

WebDAV RFC 2518, Section 10.1

103

EARLY_HINTS

An HTTP Status Code for Indicating Hints RFC 8297

200

OK

HTTP/1.1 RFC 7231, Section 6.3.1

201

CREATED

HTTP/1.1 RFC 7231, Section 6.3.2

202

ACCEPTED

HTTP/1.1 RFC 7231, Section 6.3.3

203

NON_AUTHORITATIVE_INFORMATION

HTTP/1.1 RFC 7231, Section 6.3.4

204

NO_CONTENT

HTTP/1.1 RFC 7231, Section 6.3.5

205

RESET_CONTENT

HTTP/1.1 RFC 7231, Section 6.3.6

206

PARTIAL_CONTENT

HTTP/1.1 RFC 7233, Section 4.1

207

MULTI_STATUS

WebDAV RFC 4918, Section 11.1

208

ALREADY_REPORTED

WebDAV Binding Extensions RFC 5842, Section 7.1 (Experimental)

226

IM_USED

Delta Encoding in HTTP RFC 3229, Section 10.4.1

300

MULTIPLE_CHOICES

HTTP/1.1 RFC 7231, Section 6.4.1

301

MOVED_PERMANENTLY

HTTP/1.1 RFC 7231, Section 6.4.2

302

FOUND

HTTP/1.1 RFC 7231, Section 6.4.3

303

SEE_OTHER

HTTP/1.1 RFC 7231, Section 6.4.4

304

NOT_MODIFIED

HTTP/1.1 RFC 7232, Section 4.1

305

USE_PROXY

HTTP/1.1 RFC 7231, Section 6.4.5

307

TEMPORARY_REDIRECT

HTTP/1.1 RFC 7231, Section 6.4.7

308

PERMANENT_REDIRECT

Permanent Redirect RFC 7238, Section 3 (Experimental)

400

BAD_REQUEST

HTTP/1.1 RFC 7231, Section 6.5.1

401

UNAUTHORIZED

HTTP/1.1 Authentication RFC 7235, Section 3.1

402

PAYMENT_REQUIRED

HTTP/1.1 RFC 7231, Section 6.5.2

403

FORBIDDEN

HTTP/1.1 RFC 7231, Section 6.5.3

404

NOT_FOUND

HTTP/1.1 RFC 7231, Section 6.5.4

405

METHOD_NOT_ALLOWED

HTTP/1.1 RFC 7231, Section 6.5.5

406

NOT_ACCEPTABLE

HTTP/1.1 RFC 7231, Section 6.5.6

407

PROXY_AUTHENTICATION_REQUIRED

HTTP/1.1 Authentication RFC 7235, Section 3.2

408

REQUEST_TIMEOUT

HTTP/1.1 RFC 7231, Section 6.5.7

409

CONFLICT

HTTP/1.1 RFC 7231, Section 6.5.8

410

GONE

HTTP/1.1 RFC 7231, Section 6.5.9

411

LENGTH_REQUIRED

HTTP/1.1 RFC 7231, Section 6.5.10

412

PRECONDITION_FAILED

HTTP/1.1 RFC 7232, Section 4.2

413

REQUEST_ENTITY_TOO_LARGE

HTTP/1.1 RFC 7231, Section 6.5.11

414

REQUEST_URI_TOO_LONG

HTTP/1.1 RFC 7231, Section 6.5.12

415

UNSUPPORTED_MEDIA_TYPE

HTTP/1.1 RFC 7231, Section 6.5.13

416

REQUESTED_RANGE_NOT_SATISFIABLE

HTTP/1.1 Range Requests RFC 7233, Section 4.4

417

EXPECTATION_FAILED

HTTP/1.1 RFC 7231, Section 6.5.14

418

IM_A_TEAPOT

HTCPCP/1.0 RFC 2324, Section 2.3.2

421

MISDIRECTED_REQUEST

HTTP/2 RFC 7540, Section 9.1.2

422

UNPROCESSABLE_ENTITY

WebDAV RFC 4918, Section 11.2

423

LOCKED

WebDAV RFC 4918, Section 11.3

424

FAILED_DEPENDENCY

WebDAV RFC 4918, Section 11.4

425

TOO_EARLY

Using Early Data in HTTP RFC 8470

426

UPGRADE_REQUIRED

HTTP/1.1 RFC 7231, Section 6.5.15

428

PRECONDITION_REQUIRED

Additional HTTP Status Codes RFC 6585

429

TOO_MANY_REQUESTS

Additional HTTP Status Codes RFC 6585

431

REQUEST_HEADER_FIELDS_TOO_LARGE

Additional HTTP Status Codes RFC 6585

451

UNAVAILABLE_FOR_LEGAL_REASONS

An HTTP Status Code to Report Legal Obstacles RFC 7725

500

INTERNAL_SERVER_ERROR

HTTP/1.1 RFC 7231, Section 6.6.1

501

NOT_IMPLEMENTED

HTTP/1.1 RFC 7231, Section 6.6.2

502

BAD_GATEWAY

HTTP/1.1 RFC 7231, Section 6.6.3

503

SERVICE_UNAVAILABLE

HTTP/1.1 RFC 7231, Section 6.6.4

504

GATEWAY_TIMEOUT

HTTP/1.1 RFC 7231, Section 6.6.5

505

HTTP_VERSION_NOT_SUPPORTED

HTTP/1.1 RFC 7231, Section 6.6.6

506

VARIANT_ALSO_NEGOTIATES

Transparent Content Negotiation in HTTP RFC 2295, Section 8.1 (Experimental)

507

INSUFFICIENT_STORAGE

WebDAV RFC 4918, Section 11.5

508

LOOP_DETECTED

WebDAV Binding Extensions RFC 5842, Section 7.2 (Experimental)

510

NOT_EXTENDED

An HTTP Extension Framework RFC 2774, Section 7 (Experimental)

511

NETWORK_AUTHENTICATION_REQUIRED

Additional HTTP Status Codes RFC 6585, Section 6

In order to preserve backwards compatibility, enum values are also present in the http.client module in the form of constants. The enum name is equal to the constant name (i.e. http.HTTPStatus.OK is also available as http.client.OK).

Changed in version 3.7: Added 421 MISDIRECTED_REQUEST status code.

New in version 3.8: Added 451 UNAVAILABLE_FOR_LEGAL_REASONS status code.

New in version 3.9: Added 103 EARLY_HINTS, 418 IM_A_TEAPOT and 425 TOO_EARLY status codes.

HTTP status category

New in version 3.12.

The enum values have several properties to indicate the HTTP status category:

+ + + + + +

Property

Indicates that

Details

is_informational

100 <= status <= 199

HTTP/1.1 RFC 7231, Section 6

is_success

200 <= status <= 299

HTTP/1.1 RFC 7231, Section 6

is_redirection

300 <= status <= 399

HTTP/1.1 RFC 7231, Section 6

is_client_error

400 <= status <= 499

HTTP/1.1 RFC 7231, Section 6

is_server_error

500 <= status <= 599

HTTP/1.1 RFC 7231, Section 6

Usage:

>>> from http import HTTPStatus
+>>> HTTPStatus.OK.is_success
+True
+>>> HTTPStatus.OK.is_client_error
+False
+
+class http.HTTPMethod
+

New in version 3.11.

A subclass of enum.StrEnum that defines a set of HTTP methods and descriptions written in English.

Usage:

>>> from http import HTTPMethod
+>>>
+>>> HTTPMethod.GET
+<HTTPMethod.GET>
+>>> HTTPMethod.GET == 'GET'
+True
+>>> HTTPMethod.GET.value
+'GET'
+>>> HTTPMethod.GET.description
+'Retrieve the target.'
+>>> list(HTTPMethod)
+[<HTTPMethod.CONNECT>,
+ <HTTPMethod.DELETE>,
+ <HTTPMethod.GET>,
+ <HTTPMethod.HEAD>,
+ <HTTPMethod.OPTIONS>,
+ <HTTPMethod.PATCH>,
+ <HTTPMethod.POST>,
+ <HTTPMethod.PUT>,
+ <HTTPMethod.TRACE>]
+
+

HTTP methods

Supported, IANA-registered methods available in http.HTTPMethod are:

+ + + + + + + + + +

Method

Enum Name

Details

GET

GET

HTTP/1.1 RFC 7231, Section 4.3.1

HEAD

HEAD

HTTP/1.1 RFC 7231, Section 4.3.2

POST

POST

HTTP/1.1 RFC 7231, Section 4.3.3

PUT

PUT

HTTP/1.1 RFC 7231, Section 4.3.4

DELETE

DELETE

HTTP/1.1 RFC 7231, Section 4.3.5

CONNECT

CONNECT

HTTP/1.1 RFC 7231, Section 4.3.6

OPTIONS

OPTIONS

HTTP/1.1 RFC 7231, Section 4.3.7

TRACE

TRACE

HTTP/1.1 RFC 7231, Section 4.3.8

PATCH

PATCH

HTTP/1.1 RFC 5789
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/http.html +

+
diff --git a/devdocs/python~3.12/library%2Fhttp.server.html b/devdocs/python~3.12/library%2Fhttp.server.html new file mode 100644 index 00000000..62ff88f0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fhttp.server.html @@ -0,0 +1,162 @@ +

http.server — HTTP servers

Source code: Lib/http/server.py

This module defines classes for implementing HTTP servers.

Warning

http.server is not recommended for production. It only implements basic security checks.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

One class, HTTPServer, is a socketserver.TCPServer subclass. It creates and listens at the HTTP socket, dispatching the requests to a handler. Code to create and run the server looks like this:

def run(server_class=HTTPServer, handler_class=BaseHTTPRequestHandler):
+    server_address = ('', 8000)
+    httpd = server_class(server_address, handler_class)
+    httpd.serve_forever()
+
+class http.server.HTTPServer(server_address, RequestHandlerClass)
+

This class builds on the TCPServer class by storing the server address as instance variables named server_name and server_port. The server is accessible by the handler, typically through the handler’s server instance variable.

+
+class http.server.ThreadingHTTPServer(server_address, RequestHandlerClass)
+

This class is identical to HTTPServer but uses threads to handle requests by using the ThreadingMixIn. This is useful to handle web browsers pre-opening sockets, on which HTTPServer would wait indefinitely.

New in version 3.7.

+

The HTTPServer and ThreadingHTTPServer must be given a RequestHandlerClass on instantiation, of which this module provides three different variants:

+class http.server.BaseHTTPRequestHandler(request, client_address, server)
+

This class is used to handle the HTTP requests that arrive at the server. By itself, it cannot respond to any actual HTTP requests; it must be subclassed to handle each request method (e.g. GET or POST). BaseHTTPRequestHandler provides a number of class and instance variables, and methods for use by subclasses.

The handler will parse the request and the headers, then call a method specific to the request type. The method name is constructed from the request. For example, for the request method SPAM, the do_SPAM() method will be called with no arguments. All of the relevant information is stored in instance variables of the handler. Subclasses should not need to override or extend the __init__() method.

BaseHTTPRequestHandler has the following instance variables:

+client_address
+

Contains a tuple of the form (host, port) referring to the client’s address.

+
+server
+

Contains the server instance.

+
+close_connection
+

Boolean that should be set before handle_one_request() returns, indicating if another request may be expected, or if the connection should be shut down.

+
+requestline
+

Contains the string representation of the HTTP request line. The terminating CRLF is stripped. This attribute should be set by handle_one_request(). If no valid request line was processed, it should be set to the empty string.

+
+command
+

Contains the command (request type). For example, 'GET'.

+
+path
+

Contains the request path. If query component of the URL is present, then path includes the query. Using the terminology of RFC 3986, path here includes hier-part and the query.

+
+request_version
+

Contains the version string from the request. For example, 'HTTP/1.0'.

+
+headers
+

Holds an instance of the class specified by the MessageClass class variable. This instance parses and manages the headers in the HTTP request. The parse_headers() function from http.client is used to parse the headers and it requires that the HTTP request provide a valid RFC 2822 style header.

+
+rfile
+

An io.BufferedIOBase input stream, ready to read from the start of the optional input data.

+
+wfile
+

Contains the output stream for writing a response back to the client. Proper adherence to the HTTP protocol must be used when writing to this stream in order to achieve successful interoperation with HTTP clients.

Changed in version 3.6: This is an io.BufferedIOBase stream.

+

BaseHTTPRequestHandler has the following attributes:

+server_version
+

Specifies the server software version. You may want to override this. The format is multiple whitespace-separated strings, where each string is of the form name[/version]. For example, 'BaseHTTP/0.2'.

+
+sys_version
+

Contains the Python system version, in a form usable by the version_string method and the server_version class variable. For example, 'Python/1.4'.

+
+error_message_format
+

Specifies a format string that should be used by send_error() method for building an error response to the client. The string is filled by default with variables from responses based on the status code that passed to send_error().

+
+error_content_type
+

Specifies the Content-Type HTTP header of error responses sent to the client. The default value is 'text/html'.

+
+protocol_version
+

Specifies the HTTP version to which the server is conformant. It is sent in responses to let the client know the server’s communication capabilities for future requests. If set to 'HTTP/1.1', the server will permit HTTP persistent connections; however, your server must then include an accurate Content-Length header (using send_header()) in all of its responses to clients. For backwards compatibility, the setting defaults to 'HTTP/1.0'.

+
+MessageClass
+

Specifies an email.message.Message-like class to parse HTTP headers. Typically, this is not overridden, and it defaults to http.client.HTTPMessage.

+
+responses
+

This attribute contains a mapping of error code integers to two-element tuples containing a short and long message. For example, {code: (shortmessage, +longmessage)}. The shortmessage is usually used as the message key in an error response, and longmessage as the explain key. It is used by send_response_only() and send_error() methods.

+

A BaseHTTPRequestHandler instance has the following methods:

+handle()
+

Calls handle_one_request() once (or, if persistent connections are enabled, multiple times) to handle incoming HTTP requests. You should never need to override it; instead, implement appropriate do_*() methods.

+
+handle_one_request()
+

This method will parse and dispatch the request to the appropriate do_*() method. You should never need to override it.

+
+handle_expect_100()
+

When an HTTP/1.1 conformant server receives an Expect: 100-continue request header it responds back with a 100 Continue followed by 200 +OK headers. This method can be overridden to raise an error if the server does not want the client to continue. For e.g. server can choose to send 417 +Expectation Failed as a response header and return False.

New in version 3.2.

+
+send_error(code, message=None, explain=None)
+

Sends and logs a complete error reply to the client. The numeric code specifies the HTTP error code, with message as an optional, short, human readable description of the error. The explain argument can be used to provide more detailed information about the error; it will be formatted using the error_message_format attribute and emitted, after a complete set of headers, as the response body. The responses attribute holds the default values for message and explain that will be used if no value is provided; for unknown codes the default value for both is the string ???. The body will be empty if the method is HEAD or the response code is one of the following: 1xx, 204 No Content, 205 Reset Content, 304 Not Modified.

Changed in version 3.4: The error response includes a Content-Length header. Added the explain argument.

+
+send_response(code, message=None)
+

Adds a response header to the headers buffer and logs the accepted request. The HTTP response line is written to the internal buffer, followed by Server and Date headers. The values for these two headers are picked up from the version_string() and date_time_string() methods, respectively. If the server does not intend to send any other headers using the send_header() method, then send_response() should be followed by an end_headers() call.

Changed in version 3.3: Headers are stored to an internal buffer and end_headers() needs to be called explicitly.

+
+send_header(keyword, value)
+

Adds the HTTP header to an internal buffer which will be written to the output stream when either end_headers() or flush_headers() is invoked. keyword should specify the header keyword, with value specifying its value. Note that, after the send_header calls are done, end_headers() MUST BE called in order to complete the operation.

Changed in version 3.2: Headers are stored in an internal buffer.

+
+send_response_only(code, message=None)
+

Sends the response header only, used for the purposes when 100 +Continue response is sent by the server to the client. The headers not buffered and sent directly the output stream.If the message is not specified, the HTTP message corresponding the response code is sent.

New in version 3.2.

+
+end_headers()
+

Adds a blank line (indicating the end of the HTTP headers in the response) to the headers buffer and calls flush_headers().

Changed in version 3.2: The buffered headers are written to the output stream.

+
+flush_headers()
+

Finally send the headers to the output stream and flush the internal headers buffer.

New in version 3.3.

+
+log_request(code='-', size='-')
+

Logs an accepted (successful) request. code should specify the numeric HTTP code associated with the response. If a size of the response is available, then it should be passed as the size parameter.

+
+log_error(...)
+

Logs an error when a request cannot be fulfilled. By default, it passes the message to log_message(), so it takes the same arguments (format and additional values).

+
+log_message(format, ...)
+

Logs an arbitrary message to sys.stderr. This is typically overridden to create custom error logging mechanisms. The format argument is a standard printf-style format string, where the additional arguments to log_message() are applied as inputs to the formatting. The client ip address and current date and time are prefixed to every message logged.

+
+version_string()
+

Returns the server software’s version string. This is a combination of the server_version and sys_version attributes.

+
+date_time_string(timestamp=None)
+

Returns the date and time given by timestamp (which must be None or in the format returned by time.time()), formatted for a message header. If timestamp is omitted, it uses the current date and time.

The result looks like 'Sun, 06 Nov 1994 08:49:37 GMT'.

+
+log_date_time_string()
+

Returns the current date and time, formatted for logging.

+
+address_string()
+

Returns the client address.

Changed in version 3.3: Previously, a name lookup was performed. To avoid name resolution delays, it now always returns the IP address.

+
+
+class http.server.SimpleHTTPRequestHandler(request, client_address, server, directory=None)
+

This class serves files from the directory directory and below, or the current directory if directory is not provided, directly mapping the directory structure to HTTP requests.

New in version 3.7: The directory parameter.

Changed in version 3.9: The directory parameter accepts a path-like object.

A lot of the work, such as parsing the request, is done by the base class BaseHTTPRequestHandler. This class implements the do_GET() and do_HEAD() functions.

The following are defined as class-level attributes of SimpleHTTPRequestHandler:

+server_version
+

This will be "SimpleHTTP/" + __version__, where __version__ is defined at the module level.

+
+extensions_map
+

A dictionary mapping suffixes into MIME types, contains custom overrides for the default system mappings. The mapping is used case-insensitively, and so should contain only lower-cased keys.

Changed in version 3.9: This dictionary is no longer filled with the default system mappings, but only contains overrides.

+

The SimpleHTTPRequestHandler class defines the following methods:

+do_HEAD()
+

This method serves the 'HEAD' request type: it sends the headers it would send for the equivalent GET request. See the do_GET() method for a more complete explanation of the possible headers.

+
+do_GET()
+

The request is mapped to a local file by interpreting the request as a path relative to the current working directory.

If the request was mapped to a directory, the directory is checked for a file named index.html or index.htm (in that order). If found, the file’s contents are returned; otherwise a directory listing is generated by calling the list_directory() method. This method uses os.listdir() to scan the directory, and returns a 404 error response if the listdir() fails.

If the request was mapped to a file, it is opened. Any OSError exception in opening the requested file is mapped to a 404, 'File not found' error. If there was a 'If-Modified-Since' header in the request, and the file was not modified after this time, a 304, 'Not Modified' response is sent. Otherwise, the content type is guessed by calling the guess_type() method, which in turn uses the extensions_map variable, and the file contents are returned.

A 'Content-type:' header with the guessed content type is output, followed by a 'Content-Length:' header with the file’s size and a 'Last-Modified:' header with the file’s modification time.

Then follows a blank line signifying the end of the headers, and then the contents of the file are output. If the file’s MIME type starts with text/ the file is opened in text mode; otherwise binary mode is used.

For example usage, see the implementation of the test function in Lib/http/server.py.

Changed in version 3.7: Support of the 'If-Modified-Since' header.

+
+

The SimpleHTTPRequestHandler class can be used in the following manner in order to create a very basic webserver serving files relative to the current directory:

import http.server
+import socketserver
+
+PORT = 8000
+
+Handler = http.server.SimpleHTTPRequestHandler
+
+with socketserver.TCPServer(("", PORT), Handler) as httpd:
+    print("serving at port", PORT)
+    httpd.serve_forever()
+

SimpleHTTPRequestHandler can also be subclassed to enhance behavior, such as using different index file names by overriding the class attribute index_pages.

http.server can also be invoked directly using the -m switch of the interpreter. Similar to the previous example, this serves files relative to the current directory:

python -m http.server
+

The server listens to port 8000 by default. The default can be overridden by passing the desired port number as an argument:

python -m http.server 9000
+

By default, the server binds itself to all interfaces. The option -b/--bind specifies a specific address to which it should bind. Both IPv4 and IPv6 addresses are supported. For example, the following command causes the server to bind to localhost only:

python -m http.server --bind 127.0.0.1
+

New in version 3.4: --bind argument was introduced.

New in version 3.8: --bind argument enhanced to support IPv6

By default, the server uses the current directory. The option -d/--directory specifies a directory to which it should serve the files. For example, the following command uses a specific directory:

python -m http.server --directory /tmp/
+

New in version 3.7: --directory argument was introduced.

By default, the server is conformant to HTTP/1.0. The option -p/--protocol specifies the HTTP version to which the server is conformant. For example, the following command runs an HTTP/1.1 conformant server:

python -m http.server --protocol HTTP/1.1
+

New in version 3.11: --protocol argument was introduced.

+class http.server.CGIHTTPRequestHandler(request, client_address, server)
+

This class is used to serve either files or output of CGI scripts from the current directory and below. Note that mapping HTTP hierarchic structure to local directory structure is exactly as in SimpleHTTPRequestHandler.

Note

CGI scripts run by the CGIHTTPRequestHandler class cannot execute redirects (HTTP code 302), because code 200 (script output follows) is sent prior to execution of the CGI script. This pre-empts the status code.

The class will however, run the CGI script, instead of serving it as a file, if it guesses it to be a CGI script. Only directory-based CGI are used — the other common server configuration is to treat special extensions as denoting CGI scripts.

The do_GET() and do_HEAD() functions are modified to run CGI scripts and serve the output, instead of serving files, if the request leads to somewhere below the cgi_directories path.

The CGIHTTPRequestHandler defines the following data member:

+cgi_directories
+

This defaults to ['/cgi-bin', '/htbin'] and describes directories to treat as containing CGI scripts.

+

The CGIHTTPRequestHandler defines the following method:

+do_POST()
+

This method serves the 'POST' request type, only allowed for CGI scripts. Error 501, “Can only POST to CGI scripts”, is output when trying to POST to a non-CGI url.

+

Note that CGI scripts will be run with UID of user nobody, for security reasons. Problems with the CGI script will be translated to error 403.

+

CGIHTTPRequestHandler can be enabled in the command line by passing the --cgi option:

python -m http.server --cgi
+

Security Considerations

SimpleHTTPRequestHandler will follow symbolic links when handling requests, this makes it possible for files outside of the specified directory to be served.

Earlier versions of Python did not scrub control characters from the log messages emitted to stderr from python -m http.server or the default BaseHTTPRequestHandler .log_message implementation. This could allow remote clients connecting to your server to send nefarious control codes to your terminal.

New in version 3.12: Control characters are scrubbed in stderr logs.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/http.server.html +

+
diff --git a/devdocs/python~3.12/library%2Fi18n.html b/devdocs/python~3.12/library%2Fi18n.html new file mode 100644 index 00000000..b60b4b35 --- /dev/null +++ b/devdocs/python~3.12/library%2Fi18n.html @@ -0,0 +1,10 @@ +

Internationalization

The modules described in this chapter help you write software that is independent of language and locale by providing mechanisms for selecting a language to be used in program messages or by tailoring output to match local conventions.

The list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/i18n.html +

+
diff --git a/devdocs/python~3.12/library%2Fidle.html b/devdocs/python~3.12/library%2Fidle.html new file mode 100644 index 00000000..2d0c35f1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fidle.html @@ -0,0 +1,177 @@ +

IDLE

Source code: Lib/idlelib/

IDLE is Python’s Integrated Development and Learning Environment.

IDLE has the following features:

Editing and Navigation

Editor windows

IDLE may open editor windows when it starts, depending on settings and how you start IDLE. Thereafter, use the File menu. There can be only one open editor window for a given file.

The title bar contains the name of the file, the full path, and the version of Python and IDLE running the window. The status bar contains the line number (‘Ln’) and column number (‘Col’). Line numbers start with 1; column numbers with 0.

IDLE assumes that files with a known .py* extension contain Python code and that other files do not. Run Python code with the Run menu.

Key bindings

In this section, ‘C’ refers to the Control key on Windows and Unix and the Command key on macOS.

  • +Backspace deletes to the left; Del deletes to the right
  • +C-Backspace delete word left; C-Del delete word to the right
  • Arrow keys and Page Up/Page Down to move around
  • +C-LeftArrow and C-RightArrow moves by words
  • +Home/End go to begin/end of line
  • +C-Home/C-End go to begin/end of file
  • +

    Some useful Emacs bindings are inherited from Tcl/Tk:

    • +C-a beginning of line
    • +C-e end of line
    • +C-k kill line (but doesn’t put it in clipboard)
    • +C-l center window around the insertion point
    • +C-b go backward one character without deleting (usually you can also use the cursor key for this)
    • +C-f go forward one character without deleting (usually you can also use the cursor key for this)
    • +C-p go up one line (usually you can also use the cursor key for this)
    • +C-d delete next character

Standard keybindings (like C-c to copy and C-v to paste) may work. Keybindings are selected in the Configure IDLE dialog.

Automatic indentation

After a block-opening statement, the next line is indented by 4 spaces (in the Python Shell window by one tab). After certain keywords (break, return etc.) the next line is dedented. In leading indentation, Backspace deletes up to 4 spaces if they are there. Tab inserts spaces (in the Python Shell window one tab), number depends on Indent width. Currently, tabs are restricted to four spaces due to Tcl/Tk limitations.

See also the indent/dedent region commands on the Format menu.

Search and Replace

Any selection becomes a search target. However, only selections within a line work because searches are only performed within lines with the terminal newline removed. If [x] Regular expression is checked, the target is interpreted according to the Python re module.

Completions

Completions are supplied, when requested and available, for module names, attributes of classes or functions, or filenames. Each request method displays a completion box with existing names. (See tab completions below for an exception.) For any box, change the name being completed and the item highlighted in the box by typing and deleting characters; by hitting Up, Down, PageUp, PageDown, Home, and End keys; and by a single click within the box. Close the box with Escape, Enter, and double Tab keys or clicks outside the box. A double click within the box selects and closes.

One way to open a box is to type a key character and wait for a predefined interval. This defaults to 2 seconds; customize it in the settings dialog. (To prevent auto popups, set the delay to a large number of milliseconds, such as 100000000.) For imported module names or class or function attributes, type ‘.’. For filenames in the root directory, type os.sep or os.altsep immediately after an opening quote. (On Windows, one can specify a drive first.) Move into subdirectories by typing a directory name and a separator.

Instead of waiting, or after a box is closed, open a completion box immediately with Show Completions on the Edit menu. The default hot key is C-space. If one types a prefix for the desired name before opening the box, the first match or near miss is made visible. The result is the same as if one enters a prefix after the box is displayed. Show Completions after a quote completes filenames in the current directory instead of a root directory.

Hitting Tab after a prefix usually has the same effect as Show Completions. (With no prefix, it indents.) However, if there is only one match to the prefix, that match is immediately added to the editor text without opening a box.

Invoking ‘Show Completions’, or hitting Tab after a prefix, outside of a string and without a preceding ‘.’ opens a box with keywords, builtin names, and available module-level names.

When editing code in an editor (as oppose to Shell), increase the available module-level names by running your code and not restarting the Shell thereafter. This is especially useful after adding imports at the top of a file. This also increases possible attribute completions.

Completion boxes initially exclude names beginning with ‘_’ or, for modules, not included in ‘__all__’. The hidden names can be accessed by typing ‘_’ after ‘.’, either before or after the box is opened.

Calltips

A calltip is shown automatically when one types ( after the name of an accessible function. A function name expression may include dots and subscripts. A calltip remains until it is clicked, the cursor is moved out of the argument area, or ) is typed. Whenever the cursor is in the argument part of a definition, select Edit and “Show Call Tip” on the menu or enter its shortcut to display a calltip.

The calltip consists of the function’s signature and docstring up to the latter’s first blank line or the fifth non-blank line. (Some builtin functions lack an accessible signature.) A ‘/’ or ‘*’ in the signature indicates that the preceding or following arguments are passed by position or name (keyword) only. Details are subject to change.

In Shell, the accessible functions depends on what modules have been imported into the user process, including those imported by Idle itself, and which definitions have been run, all since the last restart.

For example, restart the Shell and enter itertools.count(. A calltip appears because Idle imports itertools into the user process for its own use. (This could change.) Enter turtle.write( and nothing appears. Idle does not itself import turtle. The menu entry and shortcut also do nothing. Enter import turtle. Thereafter, turtle.write( will display a calltip.

In an editor, import statements have no effect until one runs the file. One might want to run a file after writing import statements, after adding function definitions, or after opening an existing file.

Code Context

Within an editor window containing Python code, code context can be toggled in order to show or hide a pane at the top of the window. When shown, this pane freezes the opening lines for block code, such as those beginning with class, def, or if keywords, that would have otherwise scrolled out of view. The size of the pane will be expanded and contracted as needed to show the all current levels of context, up to the maximum number of lines defined in the Configure IDLE dialog (which defaults to 15). If there are no current context lines and the feature is toggled on, a single blank line will display. Clicking on a line in the context pane will move that line to the top of the editor.

The text and background colors for the context pane can be configured under the Highlights tab in the Configure IDLE dialog.

Shell window

In IDLE’s Shell, enter, edit, and recall complete statements. (Most consoles and terminals only work with a single physical line at a time).

Submit a single-line statement for execution by hitting Return with the cursor anywhere on the line. If a line is extended with Backslash (\), the cursor must be on the last physical line. Submit a multi-line compound statement by entering a blank line after the statement.

When one pastes code into Shell, it is not compiled and possibly executed until one hits Return, as specified above. One may edit pasted code first. If one pastes more than one statement into Shell, the result will be a SyntaxError when multiple statements are compiled as if they were one.

Lines containing RESTART mean that the user execution process has been re-started. This occurs when the user execution process has crashed, when one requests a restart on the Shell menu, or when one runs code in an editor window.

The editing features described in previous subsections work when entering code interactively. IDLE’s Shell window also responds to the following keys.

  • +C-c interrupts executing command
  • +C-d sends end-of-file; closes window if typed at a >>> prompt
  • +

    Alt-/ (Expand word) is also useful to reduce typing

    Command history

    • +Alt-p retrieves previous command matching what you have typed. On macOS use C-p.
    • +Alt-n retrieves next. On macOS use C-n.
    • +Return while the cursor is on any previous command retrieves that command

Text colors

Idle defaults to black on white text, but colors text with special meanings. For the shell, these are shell output, shell error, user output, and user error. For Python code, at the shell prompt or in an editor, these are keywords, builtin class and function names, names following class and def, strings, and comments. For any text window, these are the cursor (when present), found text (when possible), and selected text.

IDLE also highlights the soft keywords match, case, and _ in pattern-matching statements. However, this highlighting is not perfect and will be incorrect in some rare cases, including some _-s in case patterns.

Text coloring is done in the background, so uncolorized text is occasionally visible. To change the color scheme, use the Configure IDLE dialog Highlighting tab. The marking of debugger breakpoint lines in the editor and text in popups and dialogs is not user-configurable.

Startup and Code Execution

Upon startup with the -s option, IDLE will execute the file referenced by the environment variables IDLESTARTUP or PYTHONSTARTUP. IDLE first checks for IDLESTARTUP; if IDLESTARTUP is present the file referenced is run. If IDLESTARTUP is not present, IDLE checks for PYTHONSTARTUP. Files referenced by these environment variables are convenient places to store functions that are used frequently from the IDLE shell, or for executing import statements to import common modules.

In addition, Tk also loads a startup file if it is present. Note that the Tk file is loaded unconditionally. This additional file is .Idle.py and is looked for in the user’s home directory. Statements in this file will be executed in the Tk namespace, so this file is not useful for importing functions to be used from IDLE’s Python shell.

Command line usage

idle.py [-c command] [-d] [-e] [-h] [-i] [-r file] [-s] [-t title] [-] [arg] ...
+
+-c command  run command in the shell window
+-d          enable debugger and open shell window
+-e          open editor window
+-h          print help message with legal combinations and exit
+-i          open shell window
+-r file     run file in shell window
+-s          run $IDLESTARTUP or $PYTHONSTARTUP first, in shell window
+-t title    set title of shell window
+-           run stdin in shell (- must be last option before args)
+

If there are arguments:

  • If -, -c, or r is used, all arguments are placed in sys.argv[1:...] and sys.argv[0] is set to '', '-c', or '-r'. No editor window is opened, even if that is the default set in the Options dialog.
  • Otherwise, arguments are files opened for editing and sys.argv reflects the arguments passed to IDLE itself.

Startup failure

IDLE uses a socket to communicate between the IDLE GUI process and the user code execution process. A connection must be established whenever the Shell starts or restarts. (The latter is indicated by a divider line that says ‘RESTART’). If the user process fails to connect to the GUI process, it usually displays a Tk error box with a ‘cannot connect’ message that directs the user here. It then exits.

One specific connection failure on Unix systems results from misconfigured masquerading rules somewhere in a system’s network setup. When IDLE is started from a terminal, one will see a message starting with ** Invalid host:. The valid value is 127.0.0.1 (idlelib.rpc.LOCALHOST). One can diagnose with tcpconnect -irv 127.0.0.1 6543 in one terminal window and tcplisten <same args> in another.

A common cause of failure is a user-written file with the same name as a standard library module, such as random.py and tkinter.py. When such a file is located in the same directory as a file that is about to be run, IDLE cannot import the stdlib file. The current fix is to rename the user file.

Though less common than in the past, an antivirus or firewall program may stop the connection. If the program cannot be taught to allow the connection, then it must be turned off for IDLE to work. It is safe to allow this internal connection because no data is visible on external ports. A similar problem is a network mis-configuration that blocks connections.

Python installation issues occasionally stop IDLE: multiple versions can clash, or a single installation might need admin access. If one undo the clash, or cannot or does not want to run as admin, it might be easiest to completely remove Python and start over.

A zombie pythonw.exe process could be a problem. On Windows, use Task Manager to check for one and stop it if there is. Sometimes a restart initiated by a program crash or Keyboard Interrupt (control-C) may fail to connect. Dismissing the error box or using Restart Shell on the Shell menu may fix a temporary problem.

When IDLE first starts, it attempts to read user configuration files in ~/.idlerc/ (~ is one’s home directory). If there is a problem, an error message should be displayed. Leaving aside random disk glitches, this can be prevented by never editing the files by hand. Instead, use the configuration dialog, under Options. Once there is an error in a user configuration file, the best solution may be to delete it and start over with the settings dialog.

If IDLE quits with no message, and it was not started from a console, try starting it from a console or terminal (python -m idlelib) and see if this results in an error message.

On Unix-based systems with tcl/tk older than 8.6.11 (see About IDLE) certain characters of certain fonts can cause a tk failure with a message to the terminal. This can happen either if one starts IDLE to edit a file with such a character or later when entering such a character. If one cannot upgrade tcl/tk, then re-configure IDLE to use a font that works better.

Running user code

With rare exceptions, the result of executing Python code with IDLE is intended to be the same as executing the same code by the default method, directly with Python in a text-mode system console or terminal window. However, the different interface and operation occasionally affect visible results. For instance, sys.modules starts with more entries, and threading.active_count() returns 2 instead of 1.

By default, IDLE runs user code in a separate OS process rather than in the user interface process that runs the shell and editor. In the execution process, it replaces sys.stdin, sys.stdout, and sys.stderr with objects that get input from and send output to the Shell window. The original values stored in sys.__stdin__, sys.__stdout__, and sys.__stderr__ are not touched, but may be None.

Sending print output from one process to a text widget in another is slower than printing to a system terminal in the same process. This has the most effect when printing multiple arguments, as the string for each argument, each separator, the newline are sent separately. For development, this is usually not a problem, but if one wants to print faster in IDLE, format and join together everything one wants displayed together and then print a single string. Both format strings and str.join() can help combine fields and lines.

IDLE’s standard stream replacements are not inherited by subprocesses created in the execution process, whether directly by user code or by modules such as multiprocessing. If such subprocess use input from sys.stdin or print or write to sys.stdout or sys.stderr, IDLE should be started in a command line window. (On Windows, use python or py rather than pythonw or pyw.) The secondary subprocess will then be attached to that window for input and output.

If sys is reset by user code, such as with importlib.reload(sys), IDLE’s changes are lost and input from the keyboard and output to the screen will not work correctly.

When Shell has the focus, it controls the keyboard and screen. This is normally transparent, but functions that directly access the keyboard and screen will not work. These include system-specific functions that determine whether a key has been pressed and if so, which.

The IDLE code running in the execution process adds frames to the call stack that would not be there otherwise. IDLE wraps sys.getrecursionlimit and sys.setrecursionlimit to reduce the effect of the additional stack frames.

When user code raises SystemExit either directly or by calling sys.exit, IDLE returns to a Shell prompt instead of exiting.

User output in Shell

When a program outputs text, the result is determined by the corresponding output device. When IDLE executes user code, sys.stdout and sys.stderr are connected to the display area of IDLE’s Shell. Some of its features are inherited from the underlying Tk Text widget. Others are programmed additions. Where it matters, Shell is designed for development rather than production runs.

For instance, Shell never throws away output. A program that sends unlimited output to Shell will eventually fill memory, resulting in a memory error. In contrast, some system text windows only keep the last n lines of output. A Windows console, for instance, keeps a user-settable 1 to 9999 lines, with 300 the default.

A Tk Text widget, and hence IDLE’s Shell, displays characters (codepoints) in the BMP (Basic Multilingual Plane) subset of Unicode. Which characters are displayed with a proper glyph and which with a replacement box depends on the operating system and installed fonts. Tab characters cause the following text to begin after the next tab stop. (They occur every 8 ‘characters’). Newline characters cause following text to appear on a new line. Other control characters are ignored or displayed as a space, box, or something else, depending on the operating system and font. (Moving the text cursor through such output with arrow keys may exhibit some surprising spacing behavior.)

>>> s = 'a\tb\a<\x02><\r>\bc\nd'  # Enter 22 chars.
+>>> len(s)
+14
+>>> s  # Display repr(s)
+'a\tb\x07<\x02><\r>\x08c\nd'
+>>> print(s, end='')  # Display s as is.
+# Result varies by OS and font.  Try it.
+

The repr function is used for interactive echo of expression values. It returns an altered version of the input string in which control codes, some BMP codepoints, and all non-BMP codepoints are replaced with escape codes. As demonstrated above, it allows one to identify the characters in a string, regardless of how they are displayed.

Normal and error output are generally kept separate (on separate lines) from code input and each other. They each get different highlight colors.

For SyntaxError tracebacks, the normal ‘^’ marking where the error was detected is replaced by coloring the text with an error highlight. When code run from a file causes other exceptions, one may right click on a traceback line to jump to the corresponding line in an IDLE editor. The file will be opened if necessary.

Shell has a special facility for squeezing output lines down to a ‘Squeezed text’ label. This is done automatically for output over N lines (N = 50 by default). N can be changed in the PyShell section of the General page of the Settings dialog. Output with fewer lines can be squeezed by right clicking on the output. This can be useful lines long enough to slow down scrolling.

Squeezed output is expanded in place by double-clicking the label. It can also be sent to the clipboard or a separate view window by right-clicking the label.

Developing tkinter applications

IDLE is intentionally different from standard Python in order to facilitate development of tkinter programs. Enter import tkinter as tk; +root = tk.Tk() in standard Python and nothing appears. Enter the same in IDLE and a tk window appears. In standard Python, one must also enter root.update() to see the window. IDLE does the equivalent in the background, about 20 times a second, which is about every 50 milliseconds. Next enter b = tk.Button(root, text='button'); b.pack(). Again, nothing visibly changes in standard Python until one enters root.update().

Most tkinter programs run root.mainloop(), which usually does not return until the tk app is destroyed. If the program is run with python -i or from an IDLE editor, a >>> shell prompt does not appear until mainloop() returns, at which time there is nothing left to interact with.

When running a tkinter program from an IDLE editor, one can comment out the mainloop call. One then gets a shell prompt immediately and can interact with the live application. One just has to remember to re-enable the mainloop call when running in standard Python.

Running without a subprocess

By default, IDLE executes user code in a separate subprocess via a socket, which uses the internal loopback interface. This connection is not externally visible and no data is sent to or received from the internet. If firewall software complains anyway, you can ignore it.

If the attempt to make the socket connection fails, Idle will notify you. Such failures are sometimes transient, but if persistent, the problem may be either a firewall blocking the connection or misconfiguration of a particular system. Until the problem is fixed, one can run Idle with the -n command line switch.

If IDLE is started with the -n command line switch it will run in a single process and will not create the subprocess which runs the RPC Python execution server. This can be useful if Python cannot create the subprocess or the RPC socket interface on your platform. However, in this mode user code is not isolated from IDLE itself. Also, the environment is not restarted when Run/Run Module (F5) is selected. If your code has been modified, you must reload() the affected modules and re-import any specific items (e.g. from foo import baz) if the changes are to take effect. For these reasons, it is preferable to run IDLE with the default subprocess if at all possible.

Deprecated since version 3.4.

Help and Preferences

Help sources

Help menu entry “IDLE Help” displays a formatted html version of the IDLE chapter of the Library Reference. The result, in a read-only tkinter text window, is close to what one sees in a web browser. Navigate through the text with a mousewheel, the scrollbar, or up and down arrow keys held down. Or click the TOC (Table of Contents) button and select a section header in the opened box.

Help menu entry “Python Docs” opens the extensive sources of help, including tutorials, available at docs.python.org/x.y, where ‘x.y’ is the currently running Python version. If your system has an off-line copy of the docs (this may be an installation option), that will be opened instead.

Selected URLs can be added or removed from the help menu at any time using the General tab of the Configure IDLE dialog.

Setting preferences

The font preferences, highlighting, keys, and general preferences can be changed via Configure IDLE on the Option menu. Non-default user settings are saved in a .idlerc directory in the user’s home directory. Problems caused by bad user configuration files are solved by editing or deleting one or more of the files in .idlerc.

On the Font tab, see the text sample for the effect of font face and size on multiple characters in multiple languages. Edit the sample to add other characters of personal interest. Use the sample to select monospaced fonts. If particular characters have problems in Shell or an editor, add them to the top of the sample and try changing first size and then font.

On the Highlights and Keys tab, select a built-in or custom color theme and key set. To use a newer built-in color theme or key set with older IDLEs, save it as a new custom theme or key set and it well be accessible to older IDLEs.

IDLE on macOS

Under System Preferences: Dock, one can set “Prefer tabs when opening documents” to “Always”. This setting is not compatible with the tk/tkinter GUI framework used by IDLE, and it breaks a few IDLE features.

Extensions

IDLE contains an extension facility. Preferences for extensions can be changed with the Extensions tab of the preferences dialog. See the beginning of config-extensions.def in the idlelib directory for further information. The only current default extension is zzdummy, an example also used for testing.

idlelib

Source code: Lib/idlelib

The Lib/idlelib package implements the IDLE application. See the rest of this page for how to use IDLE.

The files in idlelib are described in idlelib/README.txt. Access it either in idlelib or click Help => About IDLE on the IDLE menu. This file also maps IDLE menu items to the code that implements the item. Except for files listed under ‘Startup’, the idlelib code is ‘private’ in sense that feature changes can be backported (see PEP 434).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/idle.html +

+
diff --git a/devdocs/python~3.12/library%2Fimaplib.html b/devdocs/python~3.12/library%2Fimaplib.html new file mode 100644 index 00000000..17c4a1d1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fimaplib.html @@ -0,0 +1,220 @@ +

imaplib — IMAP4 protocol client

Source code: Lib/imaplib.py

This module defines three classes, IMAP4, IMAP4_SSL and IMAP4_stream, which encapsulate a connection to an IMAP4 server and implement a large subset of the IMAP4rev1 client protocol as defined in RFC 2060. It is backward compatible with IMAP4 (RFC 1730) servers, but note that the STATUS command is not supported in IMAP4.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Three classes are provided by the imaplib module, IMAP4 is the base class:

+class imaplib.IMAP4(host='', port=IMAP4_PORT, timeout=None)
+

This class implements the actual IMAP4 protocol. The connection is created and protocol version (IMAP4 or IMAP4rev1) is determined when the instance is initialized. If host is not specified, '' (the local host) is used. If port is omitted, the standard IMAP4 port (143) is used. The optional timeout parameter specifies a timeout in seconds for the connection attempt. If timeout is not given or is None, the global default socket timeout is used.

The IMAP4 class supports the with statement. When used like this, the IMAP4 LOGOUT command is issued automatically when the with statement exits. E.g.:

>>> from imaplib import IMAP4
+>>> with IMAP4("domain.org") as M:
+...     M.noop()
+...
+('OK', [b'Nothing Accomplished. d25if65hy903weo.87'])
+

Changed in version 3.5: Support for the with statement was added.

Changed in version 3.9: The optional timeout parameter was added.

+

Three exceptions are defined as attributes of the IMAP4 class:

+exception IMAP4.error
+

Exception raised on any errors. The reason for the exception is passed to the constructor as a string.

+
+exception IMAP4.abort
+

IMAP4 server errors cause this exception to be raised. This is a sub-class of IMAP4.error. Note that closing the instance and instantiating a new one will usually allow recovery from this exception.

+
+exception IMAP4.readonly
+

This exception is raised when a writable mailbox has its status changed by the server. This is a sub-class of IMAP4.error. Some other client now has write permission, and the mailbox will need to be re-opened to re-obtain write permission.

+

There’s also a subclass for secure connections:

+class imaplib.IMAP4_SSL(host='', port=IMAP4_SSL_PORT, *, ssl_context=None, timeout=None)
+

This is a subclass derived from IMAP4 that connects over an SSL encrypted socket (to use this class you need a socket module that was compiled with SSL support). If host is not specified, '' (the local host) is used. If port is omitted, the standard IMAP4-over-SSL port (993) is used. ssl_context is a ssl.SSLContext object which allows bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure. Please read Security considerations for best practices.

The optional timeout parameter specifies a timeout in seconds for the connection attempt. If timeout is not given or is None, the global default socket timeout is used.

Changed in version 3.3: ssl_context parameter was added.

Changed in version 3.4: The class now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

Changed in version 3.9: The optional timeout parameter was added.

Changed in version 3.12: The deprecated keyfile and certfile parameters have been removed.

+

The second subclass allows for connections created by a child process:

+class imaplib.IMAP4_stream(command)
+

This is a subclass derived from IMAP4 that connects to the stdin/stdout file descriptors created by passing command to subprocess.Popen().

+

The following utility functions are defined:

+imaplib.Internaldate2tuple(datestr)
+

Parse an IMAP4 INTERNALDATE string and return corresponding local time. The return value is a time.struct_time tuple or None if the string has wrong format.

+
+imaplib.Int2AP(num)
+

Converts an integer into a bytes representation using characters from the set [A .. P].

+
+imaplib.ParseFlags(flagstr)
+

Converts an IMAP4 FLAGS response to a tuple of individual flags.

+
+imaplib.Time2Internaldate(date_time)
+

Convert date_time to an IMAP4 INTERNALDATE representation. The return value is a string in the form: "DD-Mmm-YYYY HH:MM:SS ++HHMM" (including double-quotes). The date_time argument can be a number (int or float) representing seconds since epoch (as returned by time.time()), a 9-tuple representing local time an instance of time.struct_time (as returned by time.localtime()), an aware instance of datetime.datetime, or a double-quoted string. In the last case, it is assumed to already be in the correct format.

+

Note that IMAP4 message numbers change as the mailbox changes; in particular, after an EXPUNGE command performs deletions the remaining messages are renumbered. So it is highly advisable to use UIDs instead, with the UID command.

At the end of the module, there is a test section that contains a more extensive example of usage.

See also

Documents describing the protocol, sources for servers implementing it, by the University of Washington’s IMAP Information Center can all be found at (Source Code) https://github.com/uw-imap/imap (Not Maintained).

IMAP4 Objects

All IMAP4rev1 commands are represented by methods of the same name, either uppercase or lowercase.

All arguments to commands are converted to strings, except for AUTHENTICATE, and the last argument to APPEND which is passed as an IMAP4 literal. If necessary (the string contains IMAP4 protocol-sensitive characters and isn’t enclosed with either parentheses or double quotes) each string is quoted. However, the password argument to the LOGIN command is always quoted. If you want to avoid having an argument string quoted (eg: the flags argument to STORE) then enclose the string in parentheses (eg: r'(\Deleted)').

Each command returns a tuple: (type, [data, ...]) where type is usually 'OK' or 'NO', and data is either the text from the command response, or mandated results from the command. Each data is either a bytes, or a tuple. If a tuple, then the first part is the header of the response, and the second part contains the data (ie: ‘literal’ value).

The message_set options to commands below is a string specifying one or more messages to be acted upon. It may be a simple message number ('1'), a range of message numbers ('2:4'), or a group of non-contiguous ranges separated by commas ('1:3,6:9'). A range can contain an asterisk to indicate an infinite upper bound ('3:*').

An IMAP4 instance has the following methods:

+IMAP4.append(mailbox, flags, date_time, message)
+

Append message to named mailbox.

+
+IMAP4.authenticate(mechanism, authobject)
+

Authenticate command — requires response processing.

mechanism specifies which authentication mechanism is to be used - it should appear in the instance variable capabilities in the form AUTH=mechanism.

authobject must be a callable object:

data = authobject(response)
+

It will be called to process server continuation responses; the response argument it is passed will be bytes. It should return bytes data that will be base64 encoded and sent to the server. It should return None if the client abort response * should be sent instead.

Changed in version 3.5: string usernames and passwords are now encoded to utf-8 instead of being limited to ASCII.

+
+IMAP4.check()
+

Checkpoint mailbox on server.

+
+IMAP4.close()
+

Close currently selected mailbox. Deleted messages are removed from writable mailbox. This is the recommended command before LOGOUT.

+
+IMAP4.copy(message_set, new_mailbox)
+

Copy message_set messages onto end of new_mailbox.

+
+IMAP4.create(mailbox)
+

Create new mailbox named mailbox.

+
+IMAP4.delete(mailbox)
+

Delete old mailbox named mailbox.

+
+IMAP4.deleteacl(mailbox, who)
+

Delete the ACLs (remove any rights) set for who on mailbox.

+
+IMAP4.enable(capability)
+

Enable capability (see RFC 5161). Most capabilities do not need to be enabled. Currently only the UTF8=ACCEPT capability is supported (see RFC 6855).

New in version 3.5: The enable() method itself, and RFC 6855 support.

+
+IMAP4.expunge()
+

Permanently remove deleted items from selected mailbox. Generates an EXPUNGE response for each deleted message. Returned data contains a list of EXPUNGE message numbers in order received.

+
+IMAP4.fetch(message_set, message_parts)
+

Fetch (parts of) messages. message_parts should be a string of message part names enclosed within parentheses, eg: "(UID BODY[TEXT])". Returned data are tuples of message part envelope and data.

+
+IMAP4.getacl(mailbox)
+

Get the ACLs for mailbox. The method is non-standard, but is supported by the Cyrus server.

+
+IMAP4.getannotation(mailbox, entry, attribute)
+

Retrieve the specified ANNOTATIONs for mailbox. The method is non-standard, but is supported by the Cyrus server.

+
+IMAP4.getquota(root)
+

Get the quota root’s resource usage and limits. This method is part of the IMAP4 QUOTA extension defined in rfc2087.

+
+IMAP4.getquotaroot(mailbox)
+

Get the list of quota roots for the named mailbox. This method is part of the IMAP4 QUOTA extension defined in rfc2087.

+
+IMAP4.list([directory[, pattern]])
+

List mailbox names in directory matching pattern. directory defaults to the top-level mail folder, and pattern defaults to match anything. Returned data contains a list of LIST responses.

+
+IMAP4.login(user, password)
+

Identify the client using a plaintext password. The password will be quoted.

+
+IMAP4.login_cram_md5(user, password)
+

Force use of CRAM-MD5 authentication when identifying the client to protect the password. Will only work if the server CAPABILITY response includes the phrase AUTH=CRAM-MD5.

+
+IMAP4.logout()
+

Shutdown connection to server. Returns server BYE response.

Changed in version 3.8: The method no longer ignores silently arbitrary exceptions.

+
+IMAP4.lsub(directory='""', pattern='*')
+

List subscribed mailbox names in directory matching pattern. directory defaults to the top level directory and pattern defaults to match any mailbox. Returned data are tuples of message part envelope and data.

+
+IMAP4.myrights(mailbox)
+

Show my ACLs for a mailbox (i.e. the rights that I have on mailbox).

+
+IMAP4.namespace()
+

Returns IMAP namespaces as defined in RFC 2342.

+
+IMAP4.noop()
+

Send NOOP to server.

+
+IMAP4.open(host, port, timeout=None)
+

Opens socket to port at host. The optional timeout parameter specifies a timeout in seconds for the connection attempt. If timeout is not given or is None, the global default socket timeout is used. Also note that if the timeout parameter is set to be zero, it will raise a ValueError to reject creating a non-blocking socket. This method is implicitly called by the IMAP4 constructor. The connection objects established by this method will be used in the IMAP4.read(), IMAP4.readline(), IMAP4.send(), and IMAP4.shutdown() methods. You may override this method.

Raises an auditing event imaplib.open with arguments self, host, port.

Changed in version 3.9: The timeout parameter was added.

+
+IMAP4.partial(message_num, message_part, start, length)
+

Fetch truncated part of a message. Returned data is a tuple of message part envelope and data.

+
+IMAP4.proxyauth(user)
+

Assume authentication as user. Allows an authorised administrator to proxy into any user’s mailbox.

+
+IMAP4.read(size)
+

Reads size bytes from the remote server. You may override this method.

+
+IMAP4.readline()
+

Reads one line from the remote server. You may override this method.

+
+IMAP4.recent()
+

Prompt server for an update. Returned data is None if no new messages, else value of RECENT response.

+
+IMAP4.rename(oldmailbox, newmailbox)
+

Rename mailbox named oldmailbox to newmailbox.

+
+IMAP4.response(code)
+

Return data for response code if received, or None. Returns the given code, instead of the usual type.

+
+IMAP4.search(charset, criterion[, ...])
+

Search mailbox for matching messages. charset may be None, in which case no CHARSET will be specified in the request to the server. The IMAP protocol requires that at least one criterion be specified; an exception will be raised when the server returns an error. charset must be None if the UTF8=ACCEPT capability was enabled using the enable() command.

Example:

# M is a connected IMAP4 instance...
+typ, msgnums = M.search(None, 'FROM', '"LDJ"')
+
+# or:
+typ, msgnums = M.search(None, '(FROM "LDJ")')
+
+
+IMAP4.select(mailbox='INBOX', readonly=False)
+

Select a mailbox. Returned data is the count of messages in mailbox (EXISTS response). The default mailbox is 'INBOX'. If the readonly flag is set, modifications to the mailbox are not allowed.

+
+IMAP4.send(data)
+

Sends data to the remote server. You may override this method.

Raises an auditing event imaplib.send with arguments self, data.

+
+IMAP4.setacl(mailbox, who, what)
+

Set an ACL for mailbox. The method is non-standard, but is supported by the Cyrus server.

+
+IMAP4.setannotation(mailbox, entry, attribute[, ...])
+

Set ANNOTATIONs for mailbox. The method is non-standard, but is supported by the Cyrus server.

+
+IMAP4.setquota(root, limits)
+

Set the quota root’s resource limits. This method is part of the IMAP4 QUOTA extension defined in rfc2087.

+
+IMAP4.shutdown()
+

Close connection established in open. This method is implicitly called by IMAP4.logout(). You may override this method.

+
+IMAP4.socket()
+

Returns socket instance used to connect to server.

+
+IMAP4.sort(sort_criteria, charset, search_criterion[, ...])
+

The sort command is a variant of search with sorting semantics for the results. Returned data contains a space separated list of matching message numbers.

Sort has two arguments before the search_criterion argument(s); a parenthesized list of sort_criteria, and the searching charset. Note that unlike search, the searching charset argument is mandatory. There is also a uid sort command which corresponds to sort the way that uid search corresponds to search. The sort command first searches the mailbox for messages that match the given searching criteria using the charset argument for the interpretation of strings in the searching criteria. It then returns the numbers of matching messages.

This is an IMAP4rev1 extension command.

+
+IMAP4.starttls(ssl_context=None)
+

Send a STARTTLS command. The ssl_context argument is optional and should be a ssl.SSLContext object. This will enable encryption on the IMAP connection. Please read Security considerations for best practices.

New in version 3.2.

Changed in version 3.4: The method now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

+
+IMAP4.status(mailbox, names)
+

Request named status conditions for mailbox.

+
+IMAP4.store(message_set, command, flag_list)
+

Alters flag dispositions for messages in mailbox. command is specified by section 6.4.6 of RFC 2060 as being one of “FLAGS”, “+FLAGS”, or “-FLAGS”, optionally with a suffix of “.SILENT”.

For example, to set the delete flag on all messages:

typ, data = M.search(None, 'ALL')
+for num in data[0].split():
+   M.store(num, '+FLAGS', '\\Deleted')
+M.expunge()
+

Note

Creating flags containing ‘]’ (for example: “[test]”) violates RFC 3501 (the IMAP protocol). However, imaplib has historically allowed creation of such tags, and popular IMAP servers, such as Gmail, accept and produce such flags. There are non-Python programs which also create such tags. Although it is an RFC violation and IMAP clients and servers are supposed to be strict, imaplib nonetheless continues to allow such tags to be created for backward compatibility reasons, and as of Python 3.6, handles them if they are sent from the server, since this improves real-world compatibility.

+
+IMAP4.subscribe(mailbox)
+

Subscribe to new mailbox.

+
+IMAP4.thread(threading_algorithm, charset, search_criterion[, ...])
+

The thread command is a variant of search with threading semantics for the results. Returned data contains a space separated list of thread members.

Thread members consist of zero or more messages numbers, delimited by spaces, indicating successive parent and child.

Thread has two arguments before the search_criterion argument(s); a threading_algorithm, and the searching charset. Note that unlike search, the searching charset argument is mandatory. There is also a uid thread command which corresponds to thread the way that uid +search corresponds to search. The thread command first searches the mailbox for messages that match the given searching criteria using the charset argument for the interpretation of strings in the searching criteria. It then returns the matching messages threaded according to the specified threading algorithm.

This is an IMAP4rev1 extension command.

+
+IMAP4.uid(command, arg[, ...])
+

Execute command args with messages identified by UID, rather than message number. Returns response appropriate to command. At least one argument must be supplied; if none are provided, the server will return an error and an exception will be raised.

+
+IMAP4.unsubscribe(mailbox)
+

Unsubscribe from old mailbox.

+
+IMAP4.unselect()
+

imaplib.IMAP4.unselect() frees server’s resources associated with the selected mailbox and returns the server to the authenticated state. This command performs the same actions as imaplib.IMAP4.close(), except that no messages are permanently removed from the currently selected mailbox.

New in version 3.9.

+
+IMAP4.xatom(name[, ...])
+

Allow simple extension commands notified by server in CAPABILITY response.

+

The following attributes are defined on instances of IMAP4:

+IMAP4.PROTOCOL_VERSION
+

The most recent supported protocol in the CAPABILITY response from the server.

+
+IMAP4.debug
+

Integer value to control debugging output. The initialize value is taken from the module variable Debug. Values greater than three trace each command.

+
+IMAP4.utf8_enabled
+

Boolean value that is normally False, but is set to True if an enable() command is successfully issued for the UTF8=ACCEPT capability.

New in version 3.5.

+

IMAP4 Example

Here is a minimal example (without error checking) that opens a mailbox and retrieves and prints all messages:

import getpass, imaplib
+
+M = imaplib.IMAP4()
+M.login(getpass.getuser(), getpass.getpass())
+M.select()
+typ, data = M.search(None, 'ALL')
+for num in data[0].split():
+    typ, data = M.fetch(num, '(RFC822)')
+    print('Message %s\n%s\n' % (num, data[0][1]))
+M.close()
+M.logout()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/imaplib.html +

+
diff --git a/devdocs/python~3.12/library%2Fimportlib.html b/devdocs/python~3.12/library%2Fimportlib.html new file mode 100644 index 00000000..06de603f --- /dev/null +++ b/devdocs/python~3.12/library%2Fimportlib.html @@ -0,0 +1,575 @@ +

importlib — The implementation of import

New in version 3.1.

Source code: Lib/importlib/__init__.py

Introduction

The purpose of the importlib package is three-fold.

One is to provide the implementation of the import statement (and thus, by extension, the __import__() function) in Python source code. This provides an implementation of import which is portable to any Python interpreter. This also provides an implementation which is easier to comprehend than one implemented in a programming language other than Python.

Two, the components to implement import are exposed in this package, making it easier for users to create their own custom objects (known generically as an importer) to participate in the import process.

Three, the package contains modules exposing additional functionality for managing aspects of Python packages:

See also

The import statement
+
+

The language reference for the import statement.

Packages specification
+
+

Original specification of packages. Some semantics have changed since the writing of this document (e.g. redirecting based on None in sys.modules).

+The __import__() function
+
+

The import statement is syntactic sugar for this function.

The initialization of the sys.path module search path
+
+

The initialization of sys.path.

+PEP 235 +
+
+

Import on Case-Insensitive Platforms

+PEP 263 +
+
+

Defining Python Source Code Encodings

+PEP 302 +
+
+

New Import Hooks

+PEP 328 +
+
+

Imports: Multi-Line and Absolute/Relative

+PEP 366 +
+
+

Main module explicit relative imports

+PEP 420 +
+
+

Implicit namespace packages

+PEP 451 +
+
+

A ModuleSpec Type for the Import System

+PEP 488 +
+
+

Elimination of PYO files

+PEP 489 +
+
+

Multi-phase extension module initialization

+PEP 552 +
+
+

Deterministic pycs

+PEP 3120 +
+
+

Using UTF-8 as the Default Source Encoding

+PEP 3147 +
+
+

PYC Repository Directories

Functions

+importlib.__import__(name, globals=None, locals=None, fromlist=(), level=0)
+

An implementation of the built-in __import__() function.

Note

Programmatic importing of modules should use import_module() instead of this function.

+
+importlib.import_module(name, package=None)
+

Import a module. The name argument specifies what module to import in absolute or relative terms (e.g. either pkg.mod or ..mod). If the name is specified in relative terms, then the package argument must be set to the name of the package which is to act as the anchor for resolving the package name (e.g. import_module('..mod', 'pkg.subpkg') will import pkg.mod).

The import_module() function acts as a simplifying wrapper around importlib.__import__(). This means all semantics of the function are derived from importlib.__import__(). The most important difference between these two functions is that import_module() returns the specified package or module (e.g. pkg.mod), while __import__() returns the top-level package or module (e.g. pkg).

If you are dynamically importing a module that was created since the interpreter began execution (e.g., created a Python source file), you may need to call invalidate_caches() in order for the new module to be noticed by the import system.

Changed in version 3.3: Parent packages are automatically imported.

+
+importlib.invalidate_caches()
+

Invalidate the internal caches of finders stored at sys.meta_path. If a finder implements invalidate_caches() then it will be called to perform the invalidation. This function should be called if any modules are created/installed while your program is running to guarantee all finders will notice the new module’s existence.

New in version 3.3.

Changed in version 3.10: Namespace packages created/installed in a different sys.path location after the same namespace was already imported are noticed.

+
+importlib.reload(module)
+

Reload a previously imported module. The argument must be a module object, so it must have been successfully imported before. This is useful if you have edited the module source file using an external editor and want to try out the new version without leaving the Python interpreter. The return value is the module object (which can be different if re-importing causes a different object to be placed in sys.modules).

When reload() is executed:

  • Python module’s code is recompiled and the module-level code re-executed, defining a new set of objects which are bound to names in the module’s dictionary by reusing the loader which originally loaded the module. The init function of extension modules is not called a second time.
  • As with all other objects in Python the old objects are only reclaimed after their reference counts drop to zero.
  • The names in the module namespace are updated to point to any new or changed objects.
  • Other references to the old objects (such as names external to the module) are not rebound to refer to the new objects and must be updated in each namespace where they occur if that is desired.

There are a number of other caveats:

When a module is reloaded, its dictionary (containing the module’s global variables) is retained. Redefinitions of names will override the old definitions, so this is generally not a problem. If the new version of a module does not define a name that was defined by the old version, the old definition remains. This feature can be used to the module’s advantage if it maintains a global table or cache of objects — with a try statement it can test for the table’s presence and skip its initialization if desired:

try:
+    cache
+except NameError:
+    cache = {}
+

It is generally not very useful to reload built-in or dynamically loaded modules. Reloading sys, __main__, builtins and other key modules is not recommended. In many cases extension modules are not designed to be initialized more than once, and may fail in arbitrary ways when reloaded.

If a module imports objects from another module using from … import …, calling reload() for the other module does not redefine the objects imported from it — one way around this is to re-execute the from statement, another is to use import and qualified names (module.name) instead.

If a module instantiates instances of a class, reloading the module that defines the class does not affect the method definitions of the instances — they continue to use the old class definition. The same is true for derived classes.

New in version 3.4.

Changed in version 3.7: ModuleNotFoundError is raised when the module being reloaded lacks a ModuleSpec.

+

importlib.abc – Abstract base classes related to import

Source code: Lib/importlib/abc.py

The importlib.abc module contains all of the core abstract base classes used by import. Some subclasses of the core abstract base classes are also provided to help in implementing the core ABCs.

ABC hierarchy:

object
+ +-- MetaPathFinder
+ +-- PathEntryFinder
+ +-- Loader
+      +-- ResourceLoader --------+
+      +-- InspectLoader          |
+           +-- ExecutionLoader --+
+                                 +-- FileLoader
+                                 +-- SourceLoader
+
+class importlib.abc.MetaPathFinder
+

An abstract base class representing a meta path finder.

New in version 3.3.

Changed in version 3.10: No longer a subclass of Finder.

+find_spec(fullname, path, target=None)
+

An abstract method for finding a spec for the specified module. If this is a top-level import, path will be None. Otherwise, this is a search for a subpackage or module and path will be the value of __path__ from the parent package. If a spec cannot be found, None is returned. When passed in, target is a module object that the finder may use to make a more educated guess about what spec to return. importlib.util.spec_from_loader() may be useful for implementing concrete MetaPathFinders.

New in version 3.4.

+
+invalidate_caches()
+

An optional method which, when called, should invalidate any internal cache used by the finder. Used by importlib.invalidate_caches() when invalidating the caches of all finders on sys.meta_path.

Changed in version 3.4: Returns None when called instead of NotImplemented.

+
+
+class importlib.abc.PathEntryFinder
+

An abstract base class representing a path entry finder. Though it bears some similarities to MetaPathFinder, PathEntryFinder is meant for use only within the path-based import subsystem provided by importlib.machinery.PathFinder.

New in version 3.3.

Changed in version 3.10: No longer a subclass of Finder.

+find_spec(fullname, target=None)
+

An abstract method for finding a spec for the specified module. The finder will search for the module only within the path entry to which it is assigned. If a spec cannot be found, None is returned. When passed in, target is a module object that the finder may use to make a more educated guess about what spec to return. importlib.util.spec_from_loader() may be useful for implementing concrete PathEntryFinders.

New in version 3.4.

+
+invalidate_caches()
+

An optional method which, when called, should invalidate any internal cache used by the finder. Used by importlib.machinery.PathFinder.invalidate_caches() when invalidating the caches of all cached finders.

+
+
+class importlib.abc.Loader
+

An abstract base class for a loader. See PEP 302 for the exact definition for a loader.

Loaders that wish to support resource reading should implement a get_resource_reader() method as specified by importlib.resources.abc.ResourceReader.

Changed in version 3.7: Introduced the optional get_resource_reader() method.

+create_module(spec)
+

A method that returns the module object to use when importing a module. This method may return None, indicating that default module creation semantics should take place.

New in version 3.4.

Changed in version 3.6: This method is no longer optional when exec_module() is defined.

+
+exec_module(module)
+

An abstract method that executes the module in its own namespace when a module is imported or reloaded. The module should already be initialized when exec_module() is called. When this method exists, create_module() must be defined.

New in version 3.4.

Changed in version 3.6: create_module() must also be defined.

+
+load_module(fullname)
+

A legacy method for loading a module. If the module cannot be loaded, ImportError is raised, otherwise the loaded module is returned.

If the requested module already exists in sys.modules, that module should be used and reloaded. Otherwise the loader should create a new module and insert it into sys.modules before any loading begins, to prevent recursion from the import. If the loader inserted a module and the load fails, it must be removed by the loader from sys.modules; modules already in sys.modules before the loader began execution should be left alone.

The loader should set several attributes on the module (note that some of these attributes can change when a module is reloaded):

  • +
    + __name__ +
    +
    +

    The module’s fully qualified name. It is '__main__' for an executed module.

  • +
    + __file__ +
    +
    +

    The location the loader used to load the module. For example, for modules loaded from a .py file this is the filename. It is not set on all modules (e.g. built-in modules).

  • +
    + __cached__ +
    +
    +

    The filename of a compiled version of the module’s code. It is not set on all modules (e.g. built-in modules).

  • +
    + __path__ +
    +
    +

    The list of locations where the package’s submodules will be found. Most of the time this is a single directory. The import system passes this attribute to __import__() and to finders in the same way as sys.path but just for the package. It is not set on non-package modules so it can be used as an indicator that the module is a package.

  • +
    + __package__ +
    +
    +

    The fully qualified name of the package the module is in (or the empty string for a top-level module). If the module is a package then this is the same as __name__.

  • +
    + __loader__ +
    +
    +

    The loader used to load the module.

When exec_module() is available then backwards-compatible functionality is provided.

Changed in version 3.4: Raise ImportError when called instead of NotImplementedError. Functionality provided when exec_module() is available.

Deprecated since version 3.4: The recommended API for loading a module is exec_module() (and create_module()). Loaders should implement it instead of load_module(). The import machinery takes care of all the other responsibilities of load_module() when exec_module() is implemented.

+
+
+class importlib.abc.ResourceLoader
+

An abstract base class for a loader which implements the optional PEP 302 protocol for loading arbitrary resources from the storage back-end.

Deprecated since version 3.7: This ABC is deprecated in favour of supporting resource loading through importlib.resources.abc.ResourceReader.

+abstractmethod get_data(path)
+

An abstract method to return the bytes for the data located at path. Loaders that have a file-like storage back-end that allows storing arbitrary data can implement this abstract method to give direct access to the data stored. OSError is to be raised if the path cannot be found. The path is expected to be constructed using a module’s __file__ attribute or an item from a package’s __path__.

Changed in version 3.4: Raises OSError instead of NotImplementedError.

+
+
+class importlib.abc.InspectLoader
+

An abstract base class for a loader which implements the optional PEP 302 protocol for loaders that inspect modules.

+get_code(fullname)
+

Return the code object for a module, or None if the module does not have a code object (as would be the case, for example, for a built-in module). Raise an ImportError if loader cannot find the requested module.

Note

While the method has a default implementation, it is suggested that it be overridden if possible for performance.

Changed in version 3.4: No longer abstract and a concrete implementation is provided.

+
+abstractmethod get_source(fullname)
+

An abstract method to return the source of a module. It is returned as a text string using universal newlines, translating all recognized line separators into '\n' characters. Returns None if no source is available (e.g. a built-in module). Raises ImportError if the loader cannot find the module specified.

Changed in version 3.4: Raises ImportError instead of NotImplementedError.

+
+is_package(fullname)
+

An optional method to return a true value if the module is a package, a false value otherwise. ImportError is raised if the loader cannot find the module.

Changed in version 3.4: Raises ImportError instead of NotImplementedError.

+
+static source_to_code(data, path='<string>')
+

Create a code object from Python source.

The data argument can be whatever the compile() function supports (i.e. string or bytes). The path argument should be the “path” to where the source code originated from, which can be an abstract concept (e.g. location in a zip file).

With the subsequent code object one can execute it in a module by running exec(code, module.__dict__).

New in version 3.4.

Changed in version 3.5: Made the method static.

+
+exec_module(module)
+

Implementation of Loader.exec_module().

New in version 3.4.

+
+load_module(fullname)
+

Implementation of Loader.load_module().

Deprecated since version 3.4: use exec_module() instead.

+
+
+class importlib.abc.ExecutionLoader
+

An abstract base class which inherits from InspectLoader that, when implemented, helps a module to be executed as a script. The ABC represents an optional PEP 302 protocol.

+abstractmethod get_filename(fullname)
+

An abstract method that is to return the value of __file__ for the specified module. If no path is available, ImportError is raised.

If source code is available, then the method should return the path to the source file, regardless of whether a bytecode was used to load the module.

Changed in version 3.4: Raises ImportError instead of NotImplementedError.

+
+
+class importlib.abc.FileLoader(fullname, path)
+

An abstract base class which inherits from ResourceLoader and ExecutionLoader, providing concrete implementations of ResourceLoader.get_data() and ExecutionLoader.get_filename().

The fullname argument is a fully resolved name of the module the loader is to handle. The path argument is the path to the file for the module.

New in version 3.3.

+name
+

The name of the module the loader can handle.

+
+path
+

Path to the file of the module.

+
+load_module(fullname)
+

Calls super’s load_module().

Deprecated since version 3.4: Use Loader.exec_module() instead.

+
+abstractmethod get_filename(fullname)
+

Returns path.

+
+abstractmethod get_data(path)
+

Reads path as a binary file and returns the bytes from it.

+
+
+class importlib.abc.SourceLoader
+

An abstract base class for implementing source (and optionally bytecode) file loading. The class inherits from both ResourceLoader and ExecutionLoader, requiring the implementation of:

The abstract methods defined by this class are to add optional bytecode file support. Not implementing these optional methods (or causing them to raise NotImplementedError) causes the loader to only work with source code. Implementing the methods allows the loader to work with source and bytecode files; it does not allow for sourceless loading where only bytecode is provided. Bytecode files are an optimization to speed up loading by removing the parsing step of Python’s compiler, and so no bytecode-specific API is exposed.

+path_stats(path)
+

Optional abstract method which returns a dict containing metadata about the specified path. Supported dictionary keys are:

  • +'mtime' (mandatory): an integer or floating-point number representing the modification time of the source code;
  • +'size' (optional): the size in bytes of the source code.

Any other keys in the dictionary are ignored, to allow for future extensions. If the path cannot be handled, OSError is raised.

New in version 3.3.

Changed in version 3.4: Raise OSError instead of NotImplementedError.

+
+path_mtime(path)
+

Optional abstract method which returns the modification time for the specified path.

Deprecated since version 3.3: This method is deprecated in favour of path_stats(). You don’t have to implement it, but it is still available for compatibility purposes. Raise OSError if the path cannot be handled.

Changed in version 3.4: Raise OSError instead of NotImplementedError.

+
+set_data(path, data)
+

Optional abstract method which writes the specified bytes to a file path. Any intermediate directories which do not exist are to be created automatically.

When writing to the path fails because the path is read-only (errno.EACCES/PermissionError), do not propagate the exception.

Changed in version 3.4: No longer raises NotImplementedError when called.

+
+get_code(fullname)
+

Concrete implementation of InspectLoader.get_code().

+
+exec_module(module)
+

Concrete implementation of Loader.exec_module().

New in version 3.4.

+
+load_module(fullname)
+

Concrete implementation of Loader.load_module().

Deprecated since version 3.4: Use exec_module() instead.

+
+get_source(fullname)
+

Concrete implementation of InspectLoader.get_source().

+
+is_package(fullname)
+

Concrete implementation of InspectLoader.is_package(). A module is determined to be a package if its file path (as provided by ExecutionLoader.get_filename()) is a file named __init__ when the file extension is removed and the module name itself does not end in __init__.

+
+
+class importlib.abc.ResourceReader
+

Superseded by TraversableResources

An abstract base class to provide the ability to read resources.

From the perspective of this ABC, a resource is a binary artifact that is shipped within a package. Typically this is something like a data file that lives next to the __init__.py file of the package. The purpose of this class is to help abstract out the accessing of such data files so that it does not matter if the package and its data file(s) are stored in a e.g. zip file versus on the file system.

For any of methods of this class, a resource argument is expected to be a path-like object which represents conceptually just a file name. This means that no subdirectory paths should be included in the resource argument. This is because the location of the package the reader is for, acts as the “directory”. Hence the metaphor for directories and file names is packages and resources, respectively. This is also why instances of this class are expected to directly correlate to a specific package (instead of potentially representing multiple packages or a module).

Loaders that wish to support resource reading are expected to provide a method called get_resource_reader(fullname) which returns an object implementing this ABC’s interface. If the module specified by fullname is not a package, this method should return None. An object compatible with this ABC should only be returned when the specified module is a package.

New in version 3.7.

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.resources.abc.TraversableResources instead.

+abstractmethod open_resource(resource)
+

Returns an opened, file-like object for binary reading of the resource.

If the resource cannot be found, FileNotFoundError is raised.

+
+abstractmethod resource_path(resource)
+

Returns the file system path to the resource.

If the resource does not concretely exist on the file system, raise FileNotFoundError.

+
+abstractmethod is_resource(name)
+

Returns True if the named name is considered a resource. FileNotFoundError is raised if name does not exist.

+
+abstractmethod contents()
+

Returns an iterable of strings over the contents of the package. Do note that it is not required that all names returned by the iterator be actual resources, e.g. it is acceptable to return names for which is_resource() would be false.

Allowing non-resource names to be returned is to allow for situations where how a package and its resources are stored are known a priori and the non-resource names would be useful. For instance, returning subdirectory names is allowed so that when it is known that the package and resources are stored on the file system then those subdirectory names can be used directly.

The abstract method returns an iterable of no items.

+
+
+class importlib.abc.Traversable
+

An object with a subset of pathlib.Path methods suitable for traversing directories and opening files.

For a representation of the object on the file-system, use importlib.resources.as_file().

New in version 3.9.

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.resources.abc.Traversable instead.

+name
+

Abstract. The base name of this object without any parent references.

+
+abstractmethod iterdir()
+

Yield Traversable objects in self.

+
+abstractmethod is_dir()
+

Return True if self is a directory.

+
+abstractmethod is_file()
+

Return True if self is a file.

+
+abstractmethod joinpath(child)
+

Return Traversable child in self.

+
+abstractmethod __truediv__(child)
+

Return Traversable child in self.

+
+abstractmethod open(mode='r', *args, **kwargs)
+

mode may be ‘r’ or ‘rb’ to open as text or binary. Return a handle suitable for reading (same as pathlib.Path.open).

When opening as text, accepts encoding parameters such as those accepted by io.TextIOWrapper.

+
+read_bytes()
+

Read contents of self as bytes.

+
+read_text(encoding=None)
+

Read contents of self as text.

+
+
+class importlib.abc.TraversableResources
+

An abstract base class for resource readers capable of serving the importlib.resources.files() interface. Subclasses importlib.resources.abc.ResourceReader and provides concrete implementations of the importlib.resources.abc.ResourceReader’s abstract methods. Therefore, any loader supplying importlib.abc.TraversableResources also supplies ResourceReader.

Loaders that wish to support resource reading are expected to implement this interface.

New in version 3.9.

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.resources.abc.TraversableResources instead.

+abstractmethod files()
+

Returns a importlib.resources.abc.Traversable object for the loaded package.

+
+

importlib.machinery – Importers and path hooks

Source code: Lib/importlib/machinery.py

This module contains the various objects that help import find and load modules.

+importlib.machinery.SOURCE_SUFFIXES
+

A list of strings representing the recognized file suffixes for source modules.

New in version 3.3.

+
+importlib.machinery.DEBUG_BYTECODE_SUFFIXES
+

A list of strings representing the file suffixes for non-optimized bytecode modules.

New in version 3.3.

Deprecated since version 3.5: Use BYTECODE_SUFFIXES instead.

+
+importlib.machinery.OPTIMIZED_BYTECODE_SUFFIXES
+

A list of strings representing the file suffixes for optimized bytecode modules.

New in version 3.3.

Deprecated since version 3.5: Use BYTECODE_SUFFIXES instead.

+
+importlib.machinery.BYTECODE_SUFFIXES
+

A list of strings representing the recognized file suffixes for bytecode modules (including the leading dot).

New in version 3.3.

Changed in version 3.5: The value is no longer dependent on __debug__.

+
+importlib.machinery.EXTENSION_SUFFIXES
+

A list of strings representing the recognized file suffixes for extension modules.

New in version 3.3.

+
+importlib.machinery.all_suffixes()
+

Returns a combined list of strings representing all file suffixes for modules recognized by the standard import machinery. This is a helper for code which simply needs to know if a filesystem path potentially refers to a module without needing any details on the kind of module (for example, inspect.getmodulename()).

New in version 3.3.

+
+class importlib.machinery.BuiltinImporter
+

An importer for built-in modules. All known built-in modules are listed in sys.builtin_module_names. This class implements the importlib.abc.MetaPathFinder and importlib.abc.InspectLoader ABCs.

Only class methods are defined by this class to alleviate the need for instantiation.

Changed in version 3.5: As part of PEP 489, the builtin importer now implements Loader.create_module() and Loader.exec_module()

+
+class importlib.machinery.FrozenImporter
+

An importer for frozen modules. This class implements the importlib.abc.MetaPathFinder and importlib.abc.InspectLoader ABCs.

Only class methods are defined by this class to alleviate the need for instantiation.

Changed in version 3.4: Gained create_module() and exec_module() methods.

+
+class importlib.machinery.WindowsRegistryFinder
+

Finder for modules declared in the Windows registry. This class implements the importlib.abc.MetaPathFinder ABC.

Only class methods are defined by this class to alleviate the need for instantiation.

New in version 3.3.

Deprecated since version 3.6: Use site configuration instead. Future versions of Python may not enable this finder by default.

+
+class importlib.machinery.PathFinder
+

A Finder for sys.path and package __path__ attributes. This class implements the importlib.abc.MetaPathFinder ABC.

Only class methods are defined by this class to alleviate the need for instantiation.

+classmethod find_spec(fullname, path=None, target=None)
+

Class method that attempts to find a spec for the module specified by fullname on sys.path or, if defined, on path. For each path entry that is searched, sys.path_importer_cache is checked. If a non-false object is found then it is used as the path entry finder to look for the module being searched for. If no entry is found in sys.path_importer_cache, then sys.path_hooks is searched for a finder for the path entry and, if found, is stored in sys.path_importer_cache along with being queried about the module. If no finder is ever found then None is both stored in the cache and returned.

New in version 3.4.

Changed in version 3.5: If the current working directory – represented by an empty string – is no longer valid then None is returned but no value is cached in sys.path_importer_cache.

+
+classmethod invalidate_caches()
+

Calls importlib.abc.PathEntryFinder.invalidate_caches() on all finders stored in sys.path_importer_cache that define the method. Otherwise entries in sys.path_importer_cache set to None are deleted.

Changed in version 3.7: Entries of None in sys.path_importer_cache are deleted.

+

Changed in version 3.4: Calls objects in sys.path_hooks with the current working directory for '' (i.e. the empty string).

+
+class importlib.machinery.FileFinder(path, *loader_details)
+

A concrete implementation of importlib.abc.PathEntryFinder which caches results from the file system.

The path argument is the directory for which the finder is in charge of searching.

The loader_details argument is a variable number of 2-item tuples each containing a loader and a sequence of file suffixes the loader recognizes. The loaders are expected to be callables which accept two arguments of the module’s name and the path to the file found.

The finder will cache the directory contents as necessary, making stat calls for each module search to verify the cache is not outdated. Because cache staleness relies upon the granularity of the operating system’s state information of the file system, there is a potential race condition of searching for a module, creating a new file, and then searching for the module the new file represents. If the operations happen fast enough to fit within the granularity of stat calls, then the module search will fail. To prevent this from happening, when you create a module dynamically, make sure to call importlib.invalidate_caches().

New in version 3.3.

+path
+

The path the finder will search in.

+
+find_spec(fullname, target=None)
+

Attempt to find the spec to handle fullname within path.

New in version 3.4.

+
+invalidate_caches()
+

Clear out the internal cache.

+
+classmethod path_hook(*loader_details)
+

A class method which returns a closure for use on sys.path_hooks. An instance of FileFinder is returned by the closure using the path argument given to the closure directly and loader_details indirectly.

If the argument to the closure is not an existing directory, ImportError is raised.

+
+
+class importlib.machinery.SourceFileLoader(fullname, path)
+

A concrete implementation of importlib.abc.SourceLoader by subclassing importlib.abc.FileLoader and providing some concrete implementations of other methods.

New in version 3.3.

+name
+

The name of the module that this loader will handle.

+
+path
+

The path to the source file.

+
+is_package(fullname)
+

Return True if path appears to be for a package.

+
+path_stats(path)
+

Concrete implementation of importlib.abc.SourceLoader.path_stats().

+
+set_data(path, data)
+

Concrete implementation of importlib.abc.SourceLoader.set_data().

+
+load_module(name=None)
+

Concrete implementation of importlib.abc.Loader.load_module() where specifying the name of the module to load is optional.

Deprecated since version 3.6: Use importlib.abc.Loader.exec_module() instead.

+
+
+class importlib.machinery.SourcelessFileLoader(fullname, path)
+

A concrete implementation of importlib.abc.FileLoader which can import bytecode files (i.e. no source code files exist).

Please note that direct use of bytecode files (and thus not source code files) inhibits your modules from being usable by all Python implementations or new versions of Python which change the bytecode format.

New in version 3.3.

+name
+

The name of the module the loader will handle.

+
+path
+

The path to the bytecode file.

+
+is_package(fullname)
+

Determines if the module is a package based on path.

+
+get_code(fullname)
+

Returns the code object for name created from path.

+
+get_source(fullname)
+

Returns None as bytecode files have no source when this loader is used.

+
+load_module(name=None)
+

Concrete implementation of importlib.abc.Loader.load_module() where specifying the name of the module to load is optional.

Deprecated since version 3.6: Use importlib.abc.Loader.exec_module() instead.

+
+class importlib.machinery.ExtensionFileLoader(fullname, path)
+

A concrete implementation of importlib.abc.ExecutionLoader for extension modules.

The fullname argument specifies the name of the module the loader is to support. The path argument is the path to the extension module’s file.

Note that, by default, importing an extension module will fail in subinterpreters if it doesn’t implement multi-phase init (see PEP 489), even if it would otherwise import successfully.

New in version 3.3.

Changed in version 3.12: Multi-phase init is now required for use in subinterpreters.

+name
+

Name of the module the loader supports.

+
+path
+

Path to the extension module.

+
+create_module(spec)
+

Creates the module object from the given specification in accordance with PEP 489.

New in version 3.5.

+
+exec_module(module)
+

Initializes the given module object in accordance with PEP 489.

New in version 3.5.

+
+is_package(fullname)
+

Returns True if the file path points to a package’s __init__ module based on EXTENSION_SUFFIXES.

+
+get_code(fullname)
+

Returns None as extension modules lack a code object.

+
+get_source(fullname)
+

Returns None as extension modules do not have source code.

+
+get_filename(fullname)
+

Returns path.

New in version 3.4.

+
+
+class importlib.machinery.NamespaceLoader(name, path, path_finder)
+

A concrete implementation of importlib.abc.InspectLoader for namespace packages. This is an alias for a private class and is only made public for introspecting the __loader__ attribute on namespace packages:

>>> from importlib.machinery import NamespaceLoader
+>>> import my_namespace
+>>> isinstance(my_namespace.__loader__, NamespaceLoader)
+True
+>>> import importlib.abc
+>>> isinstance(my_namespace.__loader__, importlib.abc.Loader)
+True
+

New in version 3.11.

+
+class importlib.machinery.ModuleSpec(name, loader, *, origin=None, loader_state=None, is_package=None)
+

A specification for a module’s import-system-related state. This is typically exposed as the module’s __spec__ attribute. In the descriptions below, the names in parentheses give the corresponding attribute available directly on the module object, e.g. module.__spec__.origin == module.__file__. Note, however, that while the values are usually equivalent, they can differ since there is no synchronization between the two objects. For example, it is possible to update the module’s __file__ at runtime and this will not be automatically reflected in the module’s __spec__.origin, and vice versa.

New in version 3.4.

+name
+

(__name__)

The module’s fully qualified name. The finder should always set this attribute to a non-empty string.

+loader
+

(__loader__)

The loader used to load the module. The finder should always set this attribute.

+origin
+

(__file__)

The location the loader should use to load the module. For example, for modules loaded from a .py file this is the filename. The finder should always set this attribute to a meaningful value for the loader to use. In the uncommon case that there is not one (like for namespace packages), it should be set to None.

+submodule_search_locations
+

(__path__)

The list of locations where the package’s submodules will be found. Most of the time this is a single directory. The finder should set this attribute to a list, even an empty one, to indicate to the import system that the module is a package. It should be set to None for non-package modules. It is set automatically later to a special object for namespace packages.

+loader_state
+

The finder may set this attribute to an object containing additional, module-specific data to use when loading the module. Otherwise it should be set to None.

+cached
+

(__cached__)

The filename of a compiled version of the module’s code. The finder should always set this attribute but it may be None for modules that do not need compiled code stored.

+parent
+

(__package__)

(Read-only) The fully qualified name of the package the module is in (or the empty string for a top-level module). If the module is a package then this is the same as name.

+has_location
+
+True if the spec’s origin refers to a loadable location,
+
+

False otherwise. This value impacts how origin is interpreted and how the module’s __file__ is populated.

+

importlib.util – Utility code for importers

Source code: Lib/importlib/util.py

This module contains the various objects that help in the construction of an importer.

+importlib.util.MAGIC_NUMBER
+

The bytes which represent the bytecode version number. If you need help with loading/writing bytecode then consider importlib.abc.SourceLoader.

New in version 3.4.

+
+importlib.util.cache_from_source(path, debug_override=None, *, optimization=None)
+

Return the PEP 3147/PEP 488 path to the byte-compiled file associated with the source path. For example, if path is /foo/bar/baz.py the return value would be /foo/bar/__pycache__/baz.cpython-32.pyc for Python 3.2. The cpython-32 string comes from the current magic tag (see get_tag(); if sys.implementation.cache_tag is not defined then NotImplementedError will be raised).

The optimization parameter is used to specify the optimization level of the bytecode file. An empty string represents no optimization, so /foo/bar/baz.py with an optimization of '' will result in a bytecode path of /foo/bar/__pycache__/baz.cpython-32.pyc. None causes the interpreter’s optimization level to be used. Any other value’s string representation is used, so /foo/bar/baz.py with an optimization of 2 will lead to the bytecode path of /foo/bar/__pycache__/baz.cpython-32.opt-2.pyc. The string representation of optimization can only be alphanumeric, else ValueError is raised.

The debug_override parameter is deprecated and can be used to override the system’s value for __debug__. A True value is the equivalent of setting optimization to the empty string. A False value is the same as setting optimization to 1. If both debug_override an optimization are not None then TypeError is raised.

New in version 3.4.

Changed in version 3.5: The optimization parameter was added and the debug_override parameter was deprecated.

Changed in version 3.6: Accepts a path-like object.

+
+importlib.util.source_from_cache(path)
+

Given the path to a PEP 3147 file name, return the associated source code file path. For example, if path is /foo/bar/__pycache__/baz.cpython-32.pyc the returned path would be /foo/bar/baz.py. path need not exist, however if it does not conform to PEP 3147 or PEP 488 format, a ValueError is raised. If sys.implementation.cache_tag is not defined, NotImplementedError is raised.

New in version 3.4.

Changed in version 3.6: Accepts a path-like object.

+
+importlib.util.decode_source(source_bytes)
+

Decode the given bytes representing source code and return it as a string with universal newlines (as required by importlib.abc.InspectLoader.get_source()).

New in version 3.4.

+
+importlib.util.resolve_name(name, package)
+

Resolve a relative module name to an absolute one.

If name has no leading dots, then name is simply returned. This allows for usage such as importlib.util.resolve_name('sys', __spec__.parent) without doing a check to see if the package argument is needed.

ImportError is raised if name is a relative module name but package is a false value (e.g. None or the empty string). ImportError is also raised if a relative name would escape its containing package (e.g. requesting ..bacon from within the spam package).

New in version 3.3.

Changed in version 3.9: To improve consistency with import statements, raise ImportError instead of ValueError for invalid relative import attempts.

+
+importlib.util.find_spec(name, package=None)
+

Find the spec for a module, optionally relative to the specified package name. If the module is in sys.modules, then sys.modules[name].__spec__ is returned (unless the spec would be None or is not set, in which case ValueError is raised). Otherwise a search using sys.meta_path is done. None is returned if no spec is found.

If name is for a submodule (contains a dot), the parent module is automatically imported.

name and package work the same as for import_module().

New in version 3.4.

Changed in version 3.7: Raises ModuleNotFoundError instead of AttributeError if package is in fact not a package (i.e. lacks a __path__ attribute).

+
+importlib.util.module_from_spec(spec)
+

Create a new module based on spec and spec.loader.create_module.

If spec.loader.create_module does not return None, then any pre-existing attributes will not be reset. Also, no AttributeError will be raised if triggered while accessing spec or setting an attribute on the module.

This function is preferred over using types.ModuleType to create a new module as spec is used to set as many import-controlled attributes on the module as possible.

New in version 3.5.

+
+importlib.util.spec_from_loader(name, loader, *, origin=None, is_package=None)
+

A factory function for creating a ModuleSpec instance based on a loader. The parameters have the same meaning as they do for ModuleSpec. The function uses available loader APIs, such as InspectLoader.is_package(), to fill in any missing information on the spec.

New in version 3.4.

+
+importlib.util.spec_from_file_location(name, location, *, loader=None, submodule_search_locations=None)
+

A factory function for creating a ModuleSpec instance based on the path to a file. Missing information will be filled in on the spec by making use of loader APIs and by the implication that the module will be file-based.

New in version 3.4.

Changed in version 3.6: Accepts a path-like object.

+
+importlib.util.source_hash(source_bytes)
+

Return the hash of source_bytes as bytes. A hash-based .pyc file embeds the source_hash() of the corresponding source file’s contents in its header.

New in version 3.7.

+
+importlib.util._incompatible_extension_module_restrictions(*, disable_check)
+

A context manager that can temporarily skip the compatibility check for extension modules. By default the check is enabled and will fail when a single-phase init module is imported in a subinterpreter. It will also fail for a multi-phase init module that doesn’t explicitly support a per-interpreter GIL, when imported in an interpreter with its own GIL.

Note that this function is meant to accommodate an unusual case; one which is likely to eventually go away. There’s is a pretty good chance this is not what you were looking for.

You can get the same effect as this function by implementing the basic interface of multi-phase init (PEP 489) and lying about support for multiple interpreters (or per-interpreter GIL).

Warning

Using this function to disable the check can lead to unexpected behavior and even crashes. It should only be used during extension module development.

New in version 3.12.

+
+class importlib.util.LazyLoader(loader)
+

A class which postpones the execution of the loader of a module until the module has an attribute accessed.

This class only works with loaders that define exec_module() as control over what module type is used for the module is required. For those same reasons, the loader’s create_module() method must return None or a type for which its __class__ attribute can be mutated along with not using slots. Finally, modules which substitute the object placed into sys.modules will not work as there is no way to properly replace the module references throughout the interpreter safely; ValueError is raised if such a substitution is detected.

Note

For projects where startup time is critical, this class allows for potentially minimizing the cost of loading a module if it is never used. For projects where startup time is not essential then use of this class is heavily discouraged due to error messages created during loading being postponed and thus occurring out of context.

New in version 3.5.

Changed in version 3.6: Began calling create_module(), removing the compatibility warning for importlib.machinery.BuiltinImporter and importlib.machinery.ExtensionFileLoader.

+classmethod factory(loader)
+

A class method which returns a callable that creates a lazy loader. This is meant to be used in situations where the loader is passed by class instead of by instance.

suffixes = importlib.machinery.SOURCE_SUFFIXES
+loader = importlib.machinery.SourceFileLoader
+lazy_loader = importlib.util.LazyLoader.factory(loader)
+finder = importlib.machinery.FileFinder(path, (lazy_loader, suffixes))
+
+
+

Examples

Importing programmatically

To programmatically import a module, use importlib.import_module().

import importlib
+
+itertools = importlib.import_module('itertools')
+

Checking if a module can be imported

If you need to find out if a module can be imported without actually doing the import, then you should use importlib.util.find_spec().

Note that if name is a submodule (contains a dot), importlib.util.find_spec() will import the parent module.

import importlib.util
+import sys
+
+# For illustrative purposes.
+name = 'itertools'
+
+if name in sys.modules:
+    print(f"{name!r} already in sys.modules")
+elif (spec := importlib.util.find_spec(name)) is not None:
+    # If you chose to perform the actual import ...
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[name] = module
+    spec.loader.exec_module(module)
+    print(f"{name!r} has been imported")
+else:
+    print(f"can't find the {name!r} module")
+

Importing a source file directly

To import a Python source file directly, use the following recipe:

import importlib.util
+import sys
+
+# For illustrative purposes.
+import tokenize
+file_path = tokenize.__file__
+module_name = tokenize.__name__
+
+spec = importlib.util.spec_from_file_location(module_name, file_path)
+module = importlib.util.module_from_spec(spec)
+sys.modules[module_name] = module
+spec.loader.exec_module(module)
+

Implementing lazy imports

The example below shows how to implement lazy imports:

>>> import importlib.util
+>>> import sys
+>>> def lazy_import(name):
+...     spec = importlib.util.find_spec(name)
+...     loader = importlib.util.LazyLoader(spec.loader)
+...     spec.loader = loader
+...     module = importlib.util.module_from_spec(spec)
+...     sys.modules[name] = module
+...     loader.exec_module(module)
+...     return module
+...
+>>> lazy_typing = lazy_import("typing")
+>>> #lazy_typing is a real module object,
+>>> #but it is not loaded in memory yet.
+>>> lazy_typing.TYPE_CHECKING
+False
+

Setting up an importer

For deep customizations of import, you typically want to implement an importer. This means managing both the finder and loader side of things. For finders there are two flavours to choose from depending on your needs: a meta path finder or a path entry finder. The former is what you would put on sys.meta_path while the latter is what you create using a path entry hook on sys.path_hooks which works with sys.path entries to potentially create a finder. This example will show you how to register your own importers so that import will use them (for creating an importer for yourself, read the documentation for the appropriate classes defined within this package):

import importlib.machinery
+import sys
+
+# For illustrative purposes only.
+SpamMetaPathFinder = importlib.machinery.PathFinder
+SpamPathEntryFinder = importlib.machinery.FileFinder
+loader_details = (importlib.machinery.SourceFileLoader,
+                  importlib.machinery.SOURCE_SUFFIXES)
+
+# Setting up a meta path finder.
+# Make sure to put the finder in the proper location in the list in terms of
+# priority.
+sys.meta_path.append(SpamMetaPathFinder)
+
+# Setting up a path entry finder.
+# Make sure to put the path hook in the proper location in the list in terms
+# of priority.
+sys.path_hooks.append(SpamPathEntryFinder.path_hook(loader_details))
+

Approximating importlib.import_module() +

Import itself is implemented in Python code, making it possible to expose most of the import machinery through importlib. The following helps illustrate the various APIs that importlib exposes by providing an approximate implementation of importlib.import_module():

import importlib.util
+import sys
+
+def import_module(name, package=None):
+    """An approximate implementation of import."""
+    absolute_name = importlib.util.resolve_name(name, package)
+    try:
+        return sys.modules[absolute_name]
+    except KeyError:
+        pass
+
+    path = None
+    if '.' in absolute_name:
+        parent_name, _, child_name = absolute_name.rpartition('.')
+        parent_module = import_module(parent_name)
+        path = parent_module.__spec__.submodule_search_locations
+    for finder in sys.meta_path:
+        spec = finder.find_spec(absolute_name, path)
+        if spec is not None:
+            break
+    else:
+        msg = f'No module named {absolute_name!r}'
+        raise ModuleNotFoundError(msg, name=absolute_name)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[absolute_name] = module
+    spec.loader.exec_module(module)
+    if path is not None:
+        setattr(parent_module, child_name, module)
+    return module
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/importlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fimportlib.metadata.html b/devdocs/python~3.12/library%2Fimportlib.metadata.html new file mode 100644 index 00000000..53280d81 --- /dev/null +++ b/devdocs/python~3.12/library%2Fimportlib.metadata.html @@ -0,0 +1,83 @@ +

importlib.metadata – Accessing package metadata

New in version 3.8.

Changed in version 3.10: importlib.metadata is no longer provisional.

Source code: Lib/importlib/metadata/__init__.py

importlib.metadata is a library that provides access to the metadata of an installed Distribution Package, such as its entry points or its top-level names (Import Packages, modules, if any). Built in part on Python’s import system, this library intends to replace similar functionality in the entry point API and metadata API of pkg_resources. Along with importlib.resources, this package can eliminate the need to use the older and less efficient pkg_resources package.

importlib.metadata operates on third-party distribution packages installed into Python’s site-packages directory via tools such as pip. Specifically, it works with distributions with discoverable dist-info or egg-info directories, and metadata defined by the Core metadata specifications.

Important

These are not necessarily equivalent to or correspond 1:1 with the top-level import package names that can be imported inside Python code. One distribution package can contain multiple import packages (and single modules), and one top-level import package may map to multiple distribution packages if it is a namespace package. You can use package_distributions() to get a mapping between them.

By default, distribution metadata can live on the file system or in zip archives on sys.path. Through an extension mechanism, the metadata can live almost anywhere.

See also

https://importlib-metadata.readthedocs.io/
+
+

The documentation for importlib_metadata, which supplies a backport of importlib.metadata. This includes an API reference for this module’s classes and functions, as well as a migration guide for existing users of pkg_resources.

Overview

Let’s say you wanted to get the version string for a Distribution Package you’ve installed using pip. We start by creating a virtual environment and installing something into it:

$ python -m venv example
+$ source example/bin/activate
+(example) $ python -m pip install wheel
+

You can get the version string for wheel by running the following:

(example) $ python
+>>> from importlib.metadata import version  
+>>> version('wheel')  
+'0.32.3'
+

You can also get a collection of entry points selectable by properties of the EntryPoint (typically ‘group’ or ‘name’), such as console_scripts, distutils.commands and others. Each group contains a collection of EntryPoint objects.

You can get the metadata for a distribution:

>>> list(metadata('wheel'))  
+['Metadata-Version', 'Name', 'Version', 'Summary', 'Home-page', 'Author', 'Author-email', 'Maintainer', 'Maintainer-email', 'License', 'Project-URL', 'Project-URL', 'Project-URL', 'Keywords', 'Platform', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Classifier', 'Requires-Python', 'Provides-Extra', 'Requires-Dist', 'Requires-Dist']
+

You can also get a distribution’s version number, list its constituent files, and get a list of the distribution’s Distribution requirements.

Functional API

This package provides the following functionality via its public API.

Entry points

The entry_points() function returns a collection of entry points. Entry points are represented by EntryPoint instances; each EntryPoint has a .name, .group, and .value attributes and a .load() method to resolve the value. There are also .module, .attr, and .extras attributes for getting the components of the .value attribute.

Query all entry points:

>>> eps = entry_points()  
+

The entry_points() function returns an EntryPoints object, a collection of all EntryPoint objects with names and groups attributes for convenience:

>>> sorted(eps.groups)  
+['console_scripts', 'distutils.commands', 'distutils.setup_keywords', 'egg_info.writers', 'setuptools.installation']
+

EntryPoints has a select method to select entry points matching specific properties. Select entry points in the console_scripts group:

>>> scripts = eps.select(group='console_scripts')  
+

Equivalently, since entry_points passes keyword arguments through to select:

>>> scripts = entry_points(group='console_scripts')  
+

Pick out a specific script named “wheel” (found in the wheel project):

>>> 'wheel' in scripts.names  
+True
+>>> wheel = scripts['wheel']  
+

Equivalently, query for that entry point during selection:

>>> (wheel,) = entry_points(group='console_scripts', name='wheel')  
+>>> (wheel,) = entry_points().select(group='console_scripts', name='wheel')  
+

Inspect the resolved entry point:

>>> wheel  
+EntryPoint(name='wheel', value='wheel.cli:main', group='console_scripts')
+>>> wheel.module  
+'wheel.cli'
+>>> wheel.attr  
+'main'
+>>> wheel.extras  
+[]
+>>> main = wheel.load()  
+>>> main  
+<function main at 0x103528488>
+

The group and name are arbitrary values defined by the package author and usually a client will wish to resolve all entry points for a particular group. Read the setuptools docs for more information on entry points, their definition, and usage.

Compatibility Note

The “selectable” entry points were introduced in importlib_metadata 3.6 and Python 3.10. Prior to those changes, entry_points accepted no parameters and always returned a dictionary of entry points, keyed by group. With importlib_metadata 5.0 and Python 3.12, entry_points always returns an EntryPoints object. See backports.entry_points_selectable for compatibility options.

Distribution metadata

Every Distribution Package includes some metadata, which you can extract using the metadata() function:

>>> wheel_metadata = metadata('wheel')  
+

The keys of the returned data structure, a PackageMetadata, name the metadata keywords, and the values are returned unparsed from the distribution metadata:

>>> wheel_metadata['Requires-Python']  
+'>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*'
+

PackageMetadata also presents a json attribute that returns all the metadata in a JSON-compatible form per PEP 566:

>>> wheel_metadata.json['requires_python']
+'>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*'
+

Note

The actual type of the object returned by metadata() is an implementation detail and should be accessed only through the interface described by the PackageMetadata protocol.

Changed in version 3.10: The Description is now included in the metadata when presented through the payload. Line continuation characters have been removed.

New in version 3.10: The json attribute was added.

Distribution versions

The version() function is the quickest way to get a Distribution Package’s version number, as a string:

>>> version('wheel')  
+'0.32.3'
+

Distribution files

You can also get the full set of files contained within a distribution. The files() function takes a Distribution Package name and returns all of the files installed by this distribution. Each file object returned is a PackagePath, a pathlib.PurePath derived object with additional dist, size, and hash properties as indicated by the metadata. For example:

>>> util = [p for p in files('wheel') if 'util.py' in str(p)][0]  
+>>> util  
+PackagePath('wheel/util.py')
+>>> util.size  
+859
+>>> util.dist  
+<importlib.metadata._hooks.PathDistribution object at 0x101e0cef0>
+>>> util.hash  
+<FileHash mode: sha256 value: bYkw5oMccfazVCoYQwKkkemoVyMAFoR34mmKBx8R1NI>
+

Once you have the file, you can also read its contents:

>>> print(util.read_text())  
+import base64
+import sys
+...
+def as_bytes(s):
+    if isinstance(s, text_type):
+        return s.encode('utf-8')
+    return s
+

You can also use the locate method to get a the absolute path to the file:

>>> util.locate()  
+PosixPath('/home/gustav/example/lib/site-packages/wheel/util.py')
+

In the case where the metadata file listing files (RECORD or SOURCES.txt) is missing, files() will return None. The caller may wish to wrap calls to files() in always_iterable or otherwise guard against this condition if the target distribution is not known to have the metadata present.

Distribution requirements

To get the full set of requirements for a Distribution Package, use the requires() function:

>>> requires('wheel')  
+["pytest (>=3.0.0) ; extra == 'test'", "pytest-cov ; extra == 'test'"]
+

Mapping import to distribution packages

A convenience method to resolve the Distribution Package name (or names, in the case of a namespace package) that provide each importable top-level Python module or Import Package:

>>> packages_distributions()
+{'importlib_metadata': ['importlib-metadata'], 'yaml': ['PyYAML'], 'jaraco': ['jaraco.classes', 'jaraco.functools'], ...}
+

Some editable installs, do not supply top-level names, and thus this function is not reliable with such installs.

New in version 3.10.

Distributions

While the above API is the most common and convenient usage, you can get all of that information from the Distribution class. A Distribution is an abstract object that represents the metadata for a Python Distribution Package. You can get the Distribution instance:

>>> from importlib.metadata import distribution  
+>>> dist = distribution('wheel')  
+

Thus, an alternative way to get the version number is through the Distribution instance:

>>> dist.version  
+'0.32.3'
+

There are all kinds of additional metadata available on the Distribution instance:

>>> dist.metadata['Requires-Python']  
+'>=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*'
+>>> dist.metadata['License']  
+'MIT'
+

The full set of available metadata is not described here. See the Core metadata specifications for additional details.

Distribution Discovery

By default, this package provides built-in support for discovery of metadata for file system and zip file Distribution Packages. This metadata finder search defaults to sys.path, but varies slightly in how it interprets those values from how other import machinery does. In particular:

  • +importlib.metadata does not honor bytes objects on sys.path.
  • +importlib.metadata will incidentally honor pathlib.Path objects on sys.path even though such values will be ignored for imports.

Extending the search algorithm

Because Distribution Package metadata is not available through sys.path searches, or package loaders directly, the metadata for a distribution is found through import system finders. To find a distribution package’s metadata, importlib.metadata queries the list of meta path finders on sys.meta_path.

By default importlib.metadata installs a finder for distribution packages found on the file system. This finder doesn’t actually find any distributions, but it can find their metadata.

The abstract class importlib.abc.MetaPathFinder defines the interface expected of finders by Python’s import system. importlib.metadata extends this protocol by looking for an optional find_distributions callable on the finders from sys.meta_path and presents this extended interface as the DistributionFinder abstract base class, which defines this abstract method:

@abc.abstractmethod
+def find_distributions(context=DistributionFinder.Context()):
+    """Return an iterable of all Distribution instances capable of
+    loading the metadata for packages for the indicated ``context``.
+    """
+

The DistributionFinder.Context object provides .path and .name properties indicating the path to search and name to match and may supply other relevant context.

What this means in practice is that to support finding distribution package metadata in locations other than the file system, subclass Distribution and implement the abstract methods. Then from a custom finder, return instances of this derived Distribution in the find_distributions() method.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/importlib.metadata.html +

+
diff --git a/devdocs/python~3.12/library%2Fimportlib.resources.abc.html b/devdocs/python~3.12/library%2Fimportlib.resources.abc.html new file mode 100644 index 00000000..f349f95c --- /dev/null +++ b/devdocs/python~3.12/library%2Fimportlib.resources.abc.html @@ -0,0 +1,57 @@ +

importlib.resources.abc – Abstract base classes for resources

Source code: Lib/importlib/resources/abc.py

New in version 3.11.

+class importlib.resources.abc.ResourceReader
+

Superseded by TraversableResources

An abstract base class to provide the ability to read resources.

From the perspective of this ABC, a resource is a binary artifact that is shipped within a package. Typically this is something like a data file that lives next to the __init__.py file of the package. The purpose of this class is to help abstract out the accessing of such data files so that it does not matter if the package and its data file(s) are stored in a e.g. zip file versus on the file system.

For any of methods of this class, a resource argument is expected to be a path-like object which represents conceptually just a file name. This means that no subdirectory paths should be included in the resource argument. This is because the location of the package the reader is for, acts as the “directory”. Hence the metaphor for directories and file names is packages and resources, respectively. This is also why instances of this class are expected to directly correlate to a specific package (instead of potentially representing multiple packages or a module).

Loaders that wish to support resource reading are expected to provide a method called get_resource_reader(fullname) which returns an object implementing this ABC’s interface. If the module specified by fullname is not a package, this method should return None. An object compatible with this ABC should only be returned when the specified module is a package.

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.resources.abc.TraversableResources instead.

+abstractmethod open_resource(resource)
+

Returns an opened, file-like object for binary reading of the resource.

If the resource cannot be found, FileNotFoundError is raised.

+
+abstractmethod resource_path(resource)
+

Returns the file system path to the resource.

If the resource does not concretely exist on the file system, raise FileNotFoundError.

+
+abstractmethod is_resource(name)
+

Returns True if the named name is considered a resource. FileNotFoundError is raised if name does not exist.

+
+abstractmethod contents()
+

Returns an iterable of strings over the contents of the package. Do note that it is not required that all names returned by the iterator be actual resources, e.g. it is acceptable to return names for which is_resource() would be false.

Allowing non-resource names to be returned is to allow for situations where how a package and its resources are stored are known a priori and the non-resource names would be useful. For instance, returning subdirectory names is allowed so that when it is known that the package and resources are stored on the file system then those subdirectory names can be used directly.

The abstract method returns an iterable of no items.

+
+
+class importlib.resources.abc.Traversable
+

An object with a subset of pathlib.Path methods suitable for traversing directories and opening files.

For a representation of the object on the file-system, use importlib.resources.as_file().

+name
+

Abstract. The base name of this object without any parent references.

+
+abstractmethod iterdir()
+

Yield Traversable objects in self.

+
+abstractmethod is_dir()
+

Return True if self is a directory.

+
+abstractmethod is_file()
+

Return True if self is a file.

+
+abstractmethod joinpath(child)
+

Return Traversable child in self.

+
+abstractmethod __truediv__(child)
+

Return Traversable child in self.

+
+abstractmethod open(mode='r', *args, **kwargs)
+

mode may be ‘r’ or ‘rb’ to open as text or binary. Return a handle suitable for reading (same as pathlib.Path.open).

When opening as text, accepts encoding parameters such as those accepted by io.TextIOWrapper.

+
+read_bytes()
+

Read contents of self as bytes.

+
+read_text(encoding=None)
+

Read contents of self as text.

+
+
+class importlib.resources.abc.TraversableResources
+

An abstract base class for resource readers capable of serving the importlib.resources.files() interface. Subclasses ResourceReader and provides concrete implementations of the ResourceReader’s abstract methods. Therefore, any loader supplying TraversableResources also supplies ResourceReader.

Loaders that wish to support resource reading are expected to implement this interface.

+abstractmethod files()
+

Returns a importlib.resources.abc.Traversable object for the loaded package.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/importlib.resources.abc.html +

+
diff --git a/devdocs/python~3.12/library%2Fimportlib.resources.html b/devdocs/python~3.12/library%2Fimportlib.resources.html new file mode 100644 index 00000000..fd734128 --- /dev/null +++ b/devdocs/python~3.12/library%2Fimportlib.resources.html @@ -0,0 +1,48 @@ +

importlib.resources – Package resource reading, opening and access

Source code: Lib/importlib/resources/__init__.py

New in version 3.7.

This module leverages Python’s import system to provide access to resources within packages.

“Resources” are file-like resources associated with a module or package in Python. The resources may be contained directly in a package, within a subdirectory contained in that package, or adjacent to modules outside a package. Resources may be text or binary. As a result, Python module sources (.py) of a package and compilation artifacts (pycache) are technically de-facto resources of that package. In practice, however, resources are primarily those non-Python artifacts exposed specifically by the package author.

Resources can be opened or read in either binary or text mode.

Resources are roughly akin to files inside directories, though it’s important to keep in mind that this is just a metaphor. Resources and packages do not have to exist as physical files and directories on the file system: for example, a package and its resources can be imported from a zip file using zipimport.

Note

This module provides functionality similar to pkg_resources Basic Resource Access without the performance overhead of that package. This makes reading resources included in packages easier, with more stable and consistent semantics.

The standalone backport of this module provides more information on using importlib.resources and migrating from pkg_resources to importlib.resources.

Loaders that wish to support resource reading should implement a get_resource_reader(fullname) method as specified by importlib.resources.abc.ResourceReader.

+class importlib.resources.Anchor
+

Represents an anchor for resources, either a module object or a module name as a string. Defined as Union[str, ModuleType].

+
+importlib.resources.files(anchor: Optional[Anchor] = None)
+

Returns a Traversable object representing the resource container (think directory) and its resources (think files). A Traversable may contain other containers (think subdirectories).

anchor is an optional Anchor. If the anchor is a package, resources are resolved from that package. If a module, resources are resolved adjacent to that module (in the same package or the package root). If the anchor is omitted, the caller’s module is used.

New in version 3.9.

Changed in version 3.12: package parameter was renamed to anchor. anchor can now be a non-package module and if omitted will default to the caller’s module. package is still accepted for compatibility but will raise a DeprecationWarning. Consider passing the anchor positionally or using importlib_resources >= 5.10 for a compatible interface on older Pythons.

+
+importlib.resources.as_file(traversable)
+

Given a Traversable object representing a file or directory, typically from importlib.resources.files(), return a context manager for use in a with statement. The context manager provides a pathlib.Path object.

Exiting the context manager cleans up any temporary file or directory created when the resource was extracted from e.g. a zip file.

Use as_file when the Traversable methods (read_text, etc) are insufficient and an actual file or directory on the file system is required.

New in version 3.9.

Changed in version 3.12: Added support for traversable representing a directory.

+

Deprecated functions

An older, deprecated set of functions is still available, but is scheduled for removal in a future version of Python. The main drawback of these functions is that they do not support directories: they assume all resources are located directly within a package.

+importlib.resources.Package

Whenever a function accepts a Package argument, you can pass in either a module object or a module name as a string. You can only pass module objects whose __spec__.submodule_search_locations is not None.

The Package type is defined as Union[str, ModuleType].

Deprecated since version 3.12.

+
+importlib.resources.Resource
+

For resource arguments of the functions below, you can pass in the name of a resource as a string or a path-like object.

The Resource type is defined as Union[str, os.PathLike].

+
+importlib.resources.open_binary(package, resource)
+

Open for binary reading the resource within package.

package is either a name or a module object which conforms to the Package requirements. resource is the name of the resource to open within package; it may not contain path separators and it may not have sub-resources (i.e. it cannot be a directory). This function returns a typing.BinaryIO instance, a binary I/O stream open for reading.

Deprecated since version 3.11: Calls to this function can be replaced by:

files(package).joinpath(resource).open('rb')
+
+
+importlib.resources.open_text(package, resource, encoding='utf-8', errors='strict')
+

Open for text reading the resource within package. By default, the resource is opened for reading as UTF-8.

package is either a name or a module object which conforms to the Package requirements. resource is the name of the resource to open within package; it may not contain path separators and it may not have sub-resources (i.e. it cannot be a directory). encoding and errors have the same meaning as with built-in open().

This function returns a typing.TextIO instance, a text I/O stream open for reading.

Deprecated since version 3.11: Calls to this function can be replaced by:

files(package).joinpath(resource).open('r', encoding=encoding)
+
+
+importlib.resources.read_binary(package, resource)
+

Read and return the contents of the resource within package as bytes.

package is either a name or a module object which conforms to the Package requirements. resource is the name of the resource to open within package; it may not contain path separators and it may not have sub-resources (i.e. it cannot be a directory). This function returns the contents of the resource as bytes.

Deprecated since version 3.11: Calls to this function can be replaced by:

files(package).joinpath(resource).read_bytes()
+
+
+importlib.resources.read_text(package, resource, encoding='utf-8', errors='strict')
+

Read and return the contents of resource within package as a str. By default, the contents are read as strict UTF-8.

package is either a name or a module object which conforms to the Package requirements. resource is the name of the resource to open within package; it may not contain path separators and it may not have sub-resources (i.e. it cannot be a directory). encoding and errors have the same meaning as with built-in open(). This function returns the contents of the resource as str.

Deprecated since version 3.11: Calls to this function can be replaced by:

files(package).joinpath(resource).read_text(encoding=encoding)
+
+
+importlib.resources.path(package, resource)
+

Return the path to the resource as an actual file system path. This function returns a context manager for use in a with statement. The context manager provides a pathlib.Path object.

Exiting the context manager cleans up any temporary file created when the resource needs to be extracted from e.g. a zip file.

package is either a name or a module object which conforms to the Package requirements. resource is the name of the resource to open within package; it may not contain path separators and it may not have sub-resources (i.e. it cannot be a directory).

Deprecated since version 3.11: Calls to this function can be replaced using as_file():

as_file(files(package).joinpath(resource))
+
+
+importlib.resources.is_resource(package, name)
+

Return True if there is a resource named name in the package, otherwise False. This function does not consider directories to be resources. package is either a name or a module object which conforms to the Package requirements.

Deprecated since version 3.11: Calls to this function can be replaced by:

files(package).joinpath(resource).is_file()
+
+
+importlib.resources.contents(package)
+

Return an iterable over the named items within the package. The iterable returns str resources (e.g. files) and non-resources (e.g. directories). The iterable does not recurse into subdirectories.

package is either a name or a module object which conforms to the Package requirements.

Deprecated since version 3.11: Calls to this function can be replaced by:

(resource.name for resource in files(package).iterdir() if resource.is_file())
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/importlib.resources.html +

+
diff --git a/devdocs/python~3.12/library%2Findex.html b/devdocs/python~3.12/library%2Findex.html new file mode 100644 index 00000000..b90e2e1d --- /dev/null +++ b/devdocs/python~3.12/library%2Findex.html @@ -0,0 +1,40 @@ +

The Python Standard Library

While The Python Language Reference describes the exact syntax and semantics of the Python language, this library reference manual describes the standard library that is distributed with Python. It also describes some of the optional components that are commonly included in Python distributions.

Python’s standard library is very extensive, offering a wide range of facilities as indicated by the long table of contents listed below. The library contains built-in modules (written in C) that provide access to system functionality such as file I/O that would otherwise be inaccessible to Python programmers, as well as modules written in Python that provide standardized solutions for many problems that occur in everyday programming. Some of these modules are explicitly designed to encourage and enhance the portability of Python programs by abstracting away platform-specifics into platform-neutral APIs.

The Python installers for the Windows platform usually include the entire standard library and often also include many additional components. For Unix-like operating systems Python is normally provided as a collection of packages, so it may be necessary to use the packaging tools provided with the operating system to obtain some or all of the optional components.

In addition to the standard library, there is an active collection of hundreds of thousands of components (from individual programs and modules to packages and entire application development frameworks), available from the Python Package Index.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/index.html +

+
diff --git a/devdocs/python~3.12/library%2Finspect.html b/devdocs/python~3.12/library%2Finspect.html new file mode 100644 index 00000000..cd615f03 --- /dev/null +++ b/devdocs/python~3.12/library%2Finspect.html @@ -0,0 +1,553 @@ +

inspect — Inspect live objects

Source code: Lib/inspect.py

The inspect module provides several useful functions to help get information about live objects such as modules, classes, methods, functions, tracebacks, frame objects, and code objects. For example, it can help you examine the contents of a class, retrieve the source code of a method, extract and format the argument list for a function, or get all the information you need to display a detailed traceback.

There are four main kinds of services provided by this module: type checking, getting source code, inspecting classes and functions, and examining the interpreter stack.

Types and members

The getmembers() function retrieves the members of an object such as a class or module. The functions whose names begin with “is” are mainly provided as convenient choices for the second argument to getmembers(). They also help you determine when you can expect to find the following special attributes (see Import-related module attributes for module attributes):

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Type

Attribute

Description

class

__doc__

documentation string

__name__

name with which this class was defined

__qualname__

qualified name

__module__

name of module in which this class was defined

method

__doc__

documentation string

__name__

name with which this method was defined

__qualname__

qualified name

__func__

function object containing implementation of method

__self__

instance to which this method is bound, or None

__module__

name of module in which this method was defined

function

__doc__

documentation string

__name__

name with which this function was defined

__qualname__

qualified name

__code__

code object containing compiled function bytecode

__defaults__

tuple of any default values for positional or keyword parameters

__kwdefaults__

mapping of any default values for keyword-only parameters

__globals__

global namespace in which this function was defined

__builtins__

builtins namespace

__annotations__

mapping of parameters names to annotations; "return" key is reserved for return annotations.

__module__

name of module in which this function was defined

traceback

tb_frame

frame object at this level

tb_lasti

index of last attempted instruction in bytecode

tb_lineno

current line number in Python source code

tb_next

next inner traceback object (called by this level)

frame

f_back

next outer frame object (this frame’s caller)

f_builtins

builtins namespace seen by this frame

f_code

code object being executed in this frame

f_globals

global namespace seen by this frame

f_lasti

index of last attempted instruction in bytecode

f_lineno

current line number in Python source code

f_locals

local namespace seen by this frame

f_trace

tracing function for this frame, or None

code

co_argcount

number of arguments (not including keyword only arguments, * or ** args)

co_code

string of raw compiled bytecode

co_cellvars

tuple of names of cell variables (referenced by containing scopes)

co_consts

tuple of constants used in the bytecode

co_filename

name of file in which this code object was created

co_firstlineno

number of first line in Python source code

co_flags

bitmap of CO_* flags, read more here

co_lnotab

encoded mapping of line numbers to bytecode indices

co_freevars

tuple of names of free variables (referenced via a function’s closure)

co_posonlyargcount

number of positional only arguments

co_kwonlyargcount

number of keyword only arguments (not including ** arg)

co_name

name with which this code object was defined

co_qualname

fully qualified name with which this code object was defined

co_names

tuple of names other than arguments and function locals

co_nlocals

number of local variables

co_stacksize

virtual machine stack space required

co_varnames

tuple of names of arguments and local variables

generator

__name__

name

__qualname__

qualified name

gi_frame

frame

gi_running

is the generator running?

gi_code

code

gi_yieldfrom

object being iterated by yield from, or None

coroutine

__name__

name

__qualname__

qualified name

cr_await

object being awaited on, or None

cr_frame

frame

cr_running

is the coroutine running?

cr_code

code

cr_origin

where coroutine was created, or None. See sys.set_coroutine_origin_tracking_depth()

builtin

__doc__

documentation string

__name__

original name of this function or method

__qualname__

qualified name

__self__

instance to which a method is bound, or None

Changed in version 3.5: Add __qualname__ and gi_yieldfrom attributes to generators.

The __name__ attribute of generators is now set from the function name, instead of the code name, and it can now be modified.

Changed in version 3.7: Add cr_origin attribute to coroutines.

Changed in version 3.10: Add __builtins__ attribute to functions.

+inspect.getmembers(object[, predicate])
+

Return all the members of an object in a list of (name, value) pairs sorted by name. If the optional predicate argument—which will be called with the value object of each member—is supplied, only members for which the predicate returns a true value are included.

Note

getmembers() will only return class attributes defined in the metaclass when the argument is a class and those attributes have been listed in the metaclass’ custom __dir__().

+
+inspect.getmembers_static(object[, predicate])
+

Return all the members of an object in a list of (name, value) pairs sorted by name without triggering dynamic lookup via the descriptor protocol, __getattr__ or __getattribute__. Optionally, only return members that satisfy a given predicate.

Note

getmembers_static() may not be able to retrieve all members that getmembers can fetch (like dynamically created attributes) and may find members that getmembers can’t (like descriptors that raise AttributeError). It can also return descriptor objects instead of instance members in some cases.

New in version 3.11.

+
+inspect.getmodulename(path)
+

Return the name of the module named by the file path, without including the names of enclosing packages. The file extension is checked against all of the entries in importlib.machinery.all_suffixes(). If it matches, the final path component is returned with the extension removed. Otherwise, None is returned.

Note that this function only returns a meaningful name for actual Python modules - paths that potentially refer to Python packages will still return None.

Changed in version 3.3: The function is based directly on importlib.

+
+inspect.ismodule(object)
+

Return True if the object is a module.

+
+inspect.isclass(object)
+

Return True if the object is a class, whether built-in or created in Python code.

+
+inspect.ismethod(object)
+

Return True if the object is a bound method written in Python.

+
+inspect.isfunction(object)
+

Return True if the object is a Python function, which includes functions created by a lambda expression.

+
+inspect.isgeneratorfunction(object)
+

Return True if the object is a Python generator function.

Changed in version 3.8: Functions wrapped in functools.partial() now return True if the wrapped function is a Python generator function.

+
+inspect.isgenerator(object)
+

Return True if the object is a generator.

+
+inspect.iscoroutinefunction(object)
+

Return True if the object is a coroutine function (a function defined with an async def syntax), a functools.partial() wrapping a coroutine function, or a sync function marked with markcoroutinefunction().

New in version 3.5.

Changed in version 3.8: Functions wrapped in functools.partial() now return True if the wrapped function is a coroutine function.

Changed in version 3.12: Sync functions marked with markcoroutinefunction() now return True.

+
+inspect.markcoroutinefunction(func)
+

Decorator to mark a callable as a coroutine function if it would not otherwise be detected by iscoroutinefunction().

This may be of use for sync functions that return a coroutine, if the function is passed to an API that requires iscoroutinefunction().

When possible, using an async def function is preferred. Also acceptable is calling the function and testing the return with iscoroutine().

New in version 3.12.

+
+inspect.iscoroutine(object)
+

Return True if the object is a coroutine created by an async def function.

New in version 3.5.

+
+inspect.isawaitable(object)
+

Return True if the object can be used in await expression.

Can also be used to distinguish generator-based coroutines from regular generators:

import types
+
+def gen():
+    yield
+@types.coroutine
+def gen_coro():
+    yield
+
+assert not isawaitable(gen())
+assert isawaitable(gen_coro())
+

New in version 3.5.

+
+inspect.isasyncgenfunction(object)
+

Return True if the object is an asynchronous generator function, for example:

>>> async def agen():
+...     yield 1
+...
+>>> inspect.isasyncgenfunction(agen)
+True
+

New in version 3.6.

Changed in version 3.8: Functions wrapped in functools.partial() now return True if the wrapped function is a asynchronous generator function.

+
+inspect.isasyncgen(object)
+

Return True if the object is an asynchronous generator iterator created by an asynchronous generator function.

New in version 3.6.

+
+inspect.istraceback(object)
+

Return True if the object is a traceback.

+
+inspect.isframe(object)
+

Return True if the object is a frame.

+
+inspect.iscode(object)
+

Return True if the object is a code.

+
+inspect.isbuiltin(object)
+

Return True if the object is a built-in function or a bound built-in method.

+
+inspect.ismethodwrapper(object)
+

Return True if the type of object is a MethodWrapperType.

These are instances of MethodWrapperType, such as __str__(), __eq__() and __repr__().

New in version 3.11.

+
+inspect.isroutine(object)
+

Return True if the object is a user-defined or built-in function or method.

+
+inspect.isabstract(object)
+

Return True if the object is an abstract base class.

+
+inspect.ismethoddescriptor(object)
+

Return True if the object is a method descriptor, but not if ismethod(), isclass(), isfunction() or isbuiltin() are true.

This, for example, is true of int.__add__. An object passing this test has a __get__() method but not a __set__() method, but beyond that the set of attributes varies. A __name__ attribute is usually sensible, and __doc__ often is.

Methods implemented via descriptors that also pass one of the other tests return False from the ismethoddescriptor() test, simply because the other tests promise more – you can, e.g., count on having the __func__ attribute (etc) when an object passes ismethod().

+
+inspect.isdatadescriptor(object)
+

Return True if the object is a data descriptor.

Data descriptors have a __set__ or a __delete__ method. Examples are properties (defined in Python), getsets, and members. The latter two are defined in C and there are more specific tests available for those types, which is robust across Python implementations. Typically, data descriptors will also have __name__ and __doc__ attributes (properties, getsets, and members have both of these attributes), but this is not guaranteed.

+
+inspect.isgetsetdescriptor(object)
+

Return True if the object is a getset descriptor.

CPython implementation detail: getsets are attributes defined in extension modules via PyGetSetDef structures. For Python implementations without such types, this method will always return False.

+
+inspect.ismemberdescriptor(object)
+

Return True if the object is a member descriptor.

CPython implementation detail: Member descriptors are attributes defined in extension modules via PyMemberDef structures. For Python implementations without such types, this method will always return False.

+

Retrieving source code

+inspect.getdoc(object)
+

Get the documentation string for an object, cleaned up with cleandoc(). If the documentation string for an object is not provided and the object is a class, a method, a property or a descriptor, retrieve the documentation string from the inheritance hierarchy. Return None if the documentation string is invalid or missing.

Changed in version 3.5: Documentation strings are now inherited if not overridden.

+
+inspect.getcomments(object)
+

Return in a single string any lines of comments immediately preceding the object’s source code (for a class, function, or method), or at the top of the Python source file (if the object is a module). If the object’s source code is unavailable, return None. This could happen if the object has been defined in C or the interactive shell.

+
+inspect.getfile(object)
+

Return the name of the (text or binary) file in which an object was defined. This will fail with a TypeError if the object is a built-in module, class, or function.

+
+inspect.getmodule(object)
+

Try to guess which module an object was defined in. Return None if the module cannot be determined.

+
+inspect.getsourcefile(object)
+

Return the name of the Python source file in which an object was defined or None if no way can be identified to get the source. This will fail with a TypeError if the object is a built-in module, class, or function.

+
+inspect.getsourcelines(object)
+

Return a list of source lines and starting line number for an object. The argument may be a module, class, method, function, traceback, frame, or code object. The source code is returned as a list of the lines corresponding to the object and the line number indicates where in the original source file the first line of code was found. An OSError is raised if the source code cannot be retrieved. A TypeError is raised if the object is a built-in module, class, or function.

Changed in version 3.3: OSError is raised instead of IOError, now an alias of the former.

+
+inspect.getsource(object)
+

Return the text of the source code for an object. The argument may be a module, class, method, function, traceback, frame, or code object. The source code is returned as a single string. An OSError is raised if the source code cannot be retrieved. A TypeError is raised if the object is a built-in module, class, or function.

Changed in version 3.3: OSError is raised instead of IOError, now an alias of the former.

+
+inspect.cleandoc(doc)
+

Clean up indentation from docstrings that are indented to line up with blocks of code.

All leading whitespace is removed from the first line. Any leading whitespace that can be uniformly removed from the second line onwards is removed. Empty lines at the beginning and end are subsequently removed. Also, all tabs are expanded to spaces.

+

Introspecting callables with the Signature object

New in version 3.3.

The Signature object represents the call signature of a callable object and its return annotation. To retrieve a Signature object, use the signature() function.

+inspect.signature(callable, *, follow_wrapped=True, globals=None, locals=None, eval_str=False)
+

Return a Signature object for the given callable:

>>> from inspect import signature
+>>> def foo(a, *, b:int, **kwargs):
+...     pass
+
+>>> sig = signature(foo)
+
+>>> str(sig)
+'(a, *, b: int, **kwargs)'
+
+>>> str(sig.parameters['b'])
+'b: int'
+
+>>> sig.parameters['b'].annotation
+<class 'int'>
+

Accepts a wide range of Python callables, from plain functions and classes to functools.partial() objects.

If the passed object has a __signature__ attribute, this function returns it without further computations.

For objects defined in modules using stringized annotations (from __future__ import annotations), signature() will attempt to automatically un-stringize the annotations using get_annotations(). The globals, locals, and eval_str parameters are passed into get_annotations() when resolving the annotations; see the documentation for get_annotations() for instructions on how to use these parameters.

Raises ValueError if no signature can be provided, and TypeError if that type of object is not supported. Also, if the annotations are stringized, and eval_str is not false, the eval() call(s) to un-stringize the annotations in get_annotations() could potentially raise any kind of exception.

A slash(/) in the signature of a function denotes that the parameters prior to it are positional-only. For more info, see the FAQ entry on positional-only parameters.

Changed in version 3.5: The follow_wrapped parameter was added. Pass False to get a signature of callable specifically (callable.__wrapped__ will not be used to unwrap decorated callables.)

Changed in version 3.10: The globals, locals, and eval_str parameters were added.

Note

Some callables may not be introspectable in certain implementations of Python. For example, in CPython, some built-in functions defined in C provide no metadata about their arguments.

+
+class inspect.Signature(parameters=None, *, return_annotation=Signature.empty)
+

A Signature object represents the call signature of a function and its return annotation. For each parameter accepted by the function it stores a Parameter object in its parameters collection.

The optional parameters argument is a sequence of Parameter objects, which is validated to check that there are no parameters with duplicate names, and that the parameters are in the right order, i.e. positional-only first, then positional-or-keyword, and that parameters with defaults follow parameters without defaults.

The optional return_annotation argument can be an arbitrary Python object. It represents the “return” annotation of the callable.

Signature objects are immutable. Use Signature.replace() to make a modified copy.

Changed in version 3.5: Signature objects are now picklable and hashable.

+empty
+

A special class-level marker to specify absence of a return annotation.

+
+parameters
+

An ordered mapping of parameters’ names to the corresponding Parameter objects. Parameters appear in strict definition order, including keyword-only parameters.

Changed in version 3.7: Python only explicitly guaranteed that it preserved the declaration order of keyword-only parameters as of version 3.7, although in practice this order had always been preserved in Python 3.

+
+return_annotation
+

The “return” annotation for the callable. If the callable has no “return” annotation, this attribute is set to Signature.empty.

+
+bind(*args, **kwargs)
+

Create a mapping from positional and keyword arguments to parameters. Returns BoundArguments if *args and **kwargs match the signature, or raises a TypeError.

+
+bind_partial(*args, **kwargs)
+

Works the same way as Signature.bind(), but allows the omission of some required arguments (mimics functools.partial() behavior.) Returns BoundArguments, or raises a TypeError if the passed arguments do not match the signature.

+
+replace(*[, parameters][, return_annotation])
+

Create a new Signature instance based on the instance replace() was invoked on. It is possible to pass different parameters and/or return_annotation to override the corresponding properties of the base signature. To remove return_annotation from the copied Signature, pass in Signature.empty.

>>> def test(a, b):
+...     pass
+...
+>>> sig = signature(test)
+>>> new_sig = sig.replace(return_annotation="new return anno")
+>>> str(new_sig)
+"(a, b) -> 'new return anno'"
+
+
+classmethod from_callable(obj, *, follow_wrapped=True, globals=None, locals=None, eval_str=False)
+

Return a Signature (or its subclass) object for a given callable obj.

This method simplifies subclassing of Signature:

class MySignature(Signature):
+    pass
+sig = MySignature.from_callable(sum)
+assert isinstance(sig, MySignature)
+

Its behavior is otherwise identical to that of signature().

New in version 3.5.

Changed in version 3.10: The globals, locals, and eval_str parameters were added.

+
+
+class inspect.Parameter(name, kind, *, default=Parameter.empty, annotation=Parameter.empty)
+

Parameter objects are immutable. Instead of modifying a Parameter object, you can use Parameter.replace() to create a modified copy.

Changed in version 3.5: Parameter objects are now picklable and hashable.

+empty
+

A special class-level marker to specify absence of default values and annotations.

+
+name
+

The name of the parameter as a string. The name must be a valid Python identifier.

CPython implementation detail: CPython generates implicit parameter names of the form .0 on the code objects used to implement comprehensions and generator expressions.

Changed in version 3.6: These parameter names are now exposed by this module as names like implicit0.

+
+default
+

The default value for the parameter. If the parameter has no default value, this attribute is set to Parameter.empty.

+
+annotation
+

The annotation for the parameter. If the parameter has no annotation, this attribute is set to Parameter.empty.

+
+kind
+

Describes how argument values are bound to the parameter. The possible values are accessible via Parameter (like Parameter.KEYWORD_ONLY), and support comparison and ordering, in the following order:

+ + + + + +

Name

Meaning

POSITIONAL_ONLY

Value must be supplied as a positional argument. Positional only parameters are those which appear before a / entry (if present) in a Python function definition.

POSITIONAL_OR_KEYWORD

Value may be supplied as either a keyword or positional argument (this is the standard binding behaviour for functions implemented in Python.)

VAR_POSITIONAL

A tuple of positional arguments that aren’t bound to any other parameter. This corresponds to a *args parameter in a Python function definition.

KEYWORD_ONLY

Value must be supplied as a keyword argument. Keyword only parameters are those which appear after a * or *args entry in a Python function definition.

VAR_KEYWORD

A dict of keyword arguments that aren’t bound to any other parameter. This corresponds to a **kwargs parameter in a Python function definition.

Example: print all keyword-only arguments without default values:

>>> def foo(a, b, *, c, d=10):
+...     pass
+
+>>> sig = signature(foo)
+>>> for param in sig.parameters.values():
+...     if (param.kind == param.KEYWORD_ONLY and
+...                        param.default is param.empty):
+...         print('Parameter:', param)
+Parameter: c
+
+
+kind.description
+

Describes a enum value of Parameter.kind.

New in version 3.8.

Example: print all descriptions of arguments:

>>> def foo(a, b, *, c, d=10):
+...     pass
+
+>>> sig = signature(foo)
+>>> for param in sig.parameters.values():
+...     print(param.kind.description)
+positional or keyword
+positional or keyword
+keyword-only
+keyword-only
+
+
+replace(*[, name][, kind][, default][, annotation])
+

Create a new Parameter instance based on the instance replaced was invoked on. To override a Parameter attribute, pass the corresponding argument. To remove a default value or/and an annotation from a Parameter, pass Parameter.empty.

>>> from inspect import Parameter
+>>> param = Parameter('foo', Parameter.KEYWORD_ONLY, default=42)
+>>> str(param)
+'foo=42'
+
+>>> str(param.replace()) # Will create a shallow copy of 'param'
+'foo=42'
+
+>>> str(param.replace(default=Parameter.empty, annotation='spam'))
+"foo: 'spam'"
+
+

Changed in version 3.4: In Python 3.3 Parameter objects were allowed to have name set to None if their kind was set to POSITIONAL_ONLY. This is no longer permitted.

+
+class inspect.BoundArguments
+

Result of a Signature.bind() or Signature.bind_partial() call. Holds the mapping of arguments to the function’s parameters.

+arguments
+

A mutable mapping of parameters’ names to arguments’ values. Contains only explicitly bound arguments. Changes in arguments will reflect in args and kwargs.

Should be used in conjunction with Signature.parameters for any argument processing purposes.

Note

Arguments for which Signature.bind() or Signature.bind_partial() relied on a default value are skipped. However, if needed, use BoundArguments.apply_defaults() to add them.

Changed in version 3.9: arguments is now of type dict. Formerly, it was of type collections.OrderedDict.

+
+args
+

A tuple of positional arguments values. Dynamically computed from the arguments attribute.

+
+kwargs
+

A dict of keyword arguments values. Dynamically computed from the arguments attribute.

+
+signature
+

A reference to the parent Signature object.

+
+apply_defaults()
+

Set default values for missing arguments.

For variable-positional arguments (*args) the default is an empty tuple.

For variable-keyword arguments (**kwargs) the default is an empty dict.

>>> def foo(a, b='ham', *args): pass
+>>> ba = inspect.signature(foo).bind('spam')
+>>> ba.apply_defaults()
+>>> ba.arguments
+{'a': 'spam', 'b': 'ham', 'args': ()}
+

New in version 3.5.

+

The args and kwargs properties can be used to invoke functions:

def test(a, *, b):
+    ...
+
+sig = signature(test)
+ba = sig.bind(10, b=20)
+test(*ba.args, **ba.kwargs)
+
+

See also

+PEP 362 - Function Signature Object.
+
+

The detailed specification, implementation details and examples.

Classes and functions

+inspect.getclasstree(classes, unique=False)
+

Arrange the given list of classes into a hierarchy of nested lists. Where a nested list appears, it contains classes derived from the class whose entry immediately precedes the list. Each entry is a 2-tuple containing a class and a tuple of its base classes. If the unique argument is true, exactly one entry appears in the returned structure for each class in the given list. Otherwise, classes using multiple inheritance and their descendants will appear multiple times.

+
+inspect.getfullargspec(func)
+

Get the names and default values of a Python function’s parameters. A named tuple is returned:

FullArgSpec(args, varargs, varkw, defaults, kwonlyargs, kwonlydefaults, +annotations)

args is a list of the positional parameter names. varargs is the name of the * parameter or None if arbitrary positional arguments are not accepted. varkw is the name of the ** parameter or None if arbitrary keyword arguments are not accepted. defaults is an n-tuple of default argument values corresponding to the last n positional parameters, or None if there are no such defaults defined. kwonlyargs is a list of keyword-only parameter names in declaration order. kwonlydefaults is a dictionary mapping parameter names from kwonlyargs to the default values used if no argument is supplied. annotations is a dictionary mapping parameter names to annotations. The special key "return" is used to report the function return value annotation (if any).

Note that signature() and Signature Object provide the recommended API for callable introspection, and support additional behaviours (like positional-only arguments) that are sometimes encountered in extension module APIs. This function is retained primarily for use in code that needs to maintain compatibility with the Python 2 inspect module API.

Changed in version 3.4: This function is now based on signature(), but still ignores __wrapped__ attributes and includes the already bound first parameter in the signature output for bound methods.

Changed in version 3.6: This method was previously documented as deprecated in favour of signature() in Python 3.5, but that decision has been reversed in order to restore a clearly supported standard interface for single-source Python 2/3 code migrating away from the legacy getargspec() API.

Changed in version 3.7: Python only explicitly guaranteed that it preserved the declaration order of keyword-only parameters as of version 3.7, although in practice this order had always been preserved in Python 3.

+
+inspect.getargvalues(frame)
+

Get information about arguments passed into a particular frame. A named tuple ArgInfo(args, varargs, keywords, locals) is returned. args is a list of the argument names. varargs and keywords are the names of the * and ** arguments or None. locals is the locals dictionary of the given frame.

Note

This function was inadvertently marked as deprecated in Python 3.5.

+
+inspect.formatargvalues(args[, varargs, varkw, locals, formatarg, formatvarargs, formatvarkw, formatvalue])
+

Format a pretty argument spec from the four values returned by getargvalues(). The format* arguments are the corresponding optional formatting functions that are called to turn names and values into strings.

Note

This function was inadvertently marked as deprecated in Python 3.5.

+
+inspect.getmro(cls)
+

Return a tuple of class cls’s base classes, including cls, in method resolution order. No class appears more than once in this tuple. Note that the method resolution order depends on cls’s type. Unless a very peculiar user-defined metatype is in use, cls will be the first element of the tuple.

+
+inspect.getcallargs(func, /, *args, **kwds)
+

Bind the args and kwds to the argument names of the Python function or method func, as if it was called with them. For bound methods, bind also the first argument (typically named self) to the associated instance. A dict is returned, mapping the argument names (including the names of the * and ** arguments, if any) to their values from args and kwds. In case of invoking func incorrectly, i.e. whenever func(*args, **kwds) would raise an exception because of incompatible signature, an exception of the same type and the same or similar message is raised. For example:

>>> from inspect import getcallargs
+>>> def f(a, b=1, *pos, **named):
+...     pass
+...
+>>> getcallargs(f, 1, 2, 3) == {'a': 1, 'named': {}, 'b': 2, 'pos': (3,)}
+True
+>>> getcallargs(f, a=2, x=4) == {'a': 2, 'named': {'x': 4}, 'b': 1, 'pos': ()}
+True
+>>> getcallargs(f)
+Traceback (most recent call last):
+...
+TypeError: f() missing 1 required positional argument: 'a'
+

New in version 3.2.

Deprecated since version 3.5: Use Signature.bind() and Signature.bind_partial() instead.

+
+inspect.getclosurevars(func)
+

Get the mapping of external name references in a Python function or method func to their current values. A named tuple ClosureVars(nonlocals, globals, builtins, unbound) is returned. nonlocals maps referenced names to lexical closure variables, globals to the function’s module globals and builtins to the builtins visible from the function body. unbound is the set of names referenced in the function that could not be resolved at all given the current module globals and builtins.

TypeError is raised if func is not a Python function or method.

New in version 3.3.

+
+inspect.unwrap(func, *, stop=None)
+

Get the object wrapped by func. It follows the chain of __wrapped__ attributes returning the last object in the chain.

stop is an optional callback accepting an object in the wrapper chain as its sole argument that allows the unwrapping to be terminated early if the callback returns a true value. If the callback never returns a true value, the last object in the chain is returned as usual. For example, signature() uses this to stop unwrapping if any object in the chain has a __signature__ attribute defined.

ValueError is raised if a cycle is encountered.

New in version 3.4.

+
+inspect.get_annotations(obj, *, globals=None, locals=None, eval_str=False)
+

Compute the annotations dict for an object.

obj may be a callable, class, or module. Passing in an object of any other type raises TypeError.

Returns a dict. get_annotations() returns a new dict every time it’s called; calling it twice on the same object will return two different but equivalent dicts.

This function handles several details for you:

  • If eval_str is true, values of type str will be un-stringized using eval(). This is intended for use with stringized annotations (from __future__ import annotations).
  • If obj doesn’t have an annotations dict, returns an empty dict. (Functions and methods always have an annotations dict; classes, modules, and other types of callables may not.)
  • Ignores inherited annotations on classes. If a class doesn’t have its own annotations dict, returns an empty dict.
  • All accesses to object members and dict values are done using getattr() and dict.get() for safety.
  • Always, always, always returns a freshly created dict.

eval_str controls whether or not values of type str are replaced with the result of calling eval() on those values:

  • If eval_str is true, eval() is called on values of type str. (Note that get_annotations doesn’t catch exceptions; if eval() raises an exception, it will unwind the stack past the get_annotations call.)
  • If eval_str is false (the default), values of type str are unchanged.

globals and locals are passed in to eval(); see the documentation for eval() for more information. If globals or locals is None, this function may replace that value with a context-specific default, contingent on type(obj):

  • If obj is a module, globals defaults to obj.__dict__.
  • If obj is a class, globals defaults to sys.modules[obj.__module__].__dict__ and locals defaults to the obj class namespace.
  • If obj is a callable, globals defaults to obj.__globals__, although if obj is a wrapped function (using functools.update_wrapper()) it is first unwrapped.

Calling get_annotations is best practice for accessing the annotations dict of any object. See Annotations Best Practices for more information on annotations best practices.

New in version 3.10.

+

The interpreter stack

Some of the following functions return FrameInfo objects. For backwards compatibility these objects allow tuple-like operations on all attributes except positions. This behavior is considered deprecated and may be removed in the future.

+class inspect.FrameInfo
+
+frame
+

The frame object that the record corresponds to.

+
+filename
+

The file name associated with the code being executed by the frame this record corresponds to.

+
+lineno
+

The line number of the current line associated with the code being executed by the frame this record corresponds to.

+
+function
+

The function name that is being executed by the frame this record corresponds to.

+
+code_context
+

A list of lines of context from the source code that’s being executed by the frame this record corresponds to.

+
+index
+

The index of the current line being executed in the code_context list.

+
+positions
+

A dis.Positions object containing the start line number, end line number, start column offset, and end column offset associated with the instruction being executed by the frame this record corresponds to.

+

Changed in version 3.5: Return a named tuple instead of a tuple.

Changed in version 3.11: FrameInfo is now a class instance (that is backwards compatible with the previous named tuple).

+
+class inspect.Traceback
+
+filename
+

The file name associated with the code being executed by the frame this traceback corresponds to.

+
+lineno
+

The line number of the current line associated with the code being executed by the frame this traceback corresponds to.

+
+function
+

The function name that is being executed by the frame this traceback corresponds to.

+
+code_context
+

A list of lines of context from the source code that’s being executed by the frame this traceback corresponds to.

+
+index
+

The index of the current line being executed in the code_context list.

+
+positions
+

A dis.Positions object containing the start line number, end line number, start column offset, and end column offset associated with the instruction being executed by the frame this traceback corresponds to.

+

Changed in version 3.11: Traceback is now a class instance (that is backwards compatible with the previous named tuple).

+

Note

Keeping references to frame objects, as found in the first element of the frame records these functions return, can cause your program to create reference cycles. Once a reference cycle has been created, the lifespan of all objects which can be accessed from the objects which form the cycle can become much longer even if Python’s optional cycle detector is enabled. If such cycles must be created, it is important to ensure they are explicitly broken to avoid the delayed destruction of objects and increased memory consumption which occurs.

Though the cycle detector will catch these, destruction of the frames (and local variables) can be made deterministic by removing the cycle in a finally clause. This is also important if the cycle detector was disabled when Python was compiled or using gc.disable(). For example:

def handle_stackframe_without_leak():
+    frame = inspect.currentframe()
+    try:
+        # do something with the frame
+    finally:
+        del frame
+

If you want to keep the frame around (for example to print a traceback later), you can also break reference cycles by using the frame.clear() method.

The optional context argument supported by most of these functions specifies the number of lines of context to return, which are centered around the current line.

+inspect.getframeinfo(frame, context=1)
+

Get information about a frame or traceback object. A Traceback object is returned.

Changed in version 3.11: A Traceback object is returned instead of a named tuple.

+
+inspect.getouterframes(frame, context=1)
+

Get a list of FrameInfo objects for a frame and all outer frames. These frames represent the calls that lead to the creation of frame. The first entry in the returned list represents frame; the last entry represents the outermost call on frame’s stack.

Changed in version 3.5: A list of named tuples FrameInfo(frame, filename, lineno, function, code_context, index) is returned.

Changed in version 3.11: A list of FrameInfo objects is returned.

+
+inspect.getinnerframes(traceback, context=1)
+

Get a list of FrameInfo objects for a traceback’s frame and all inner frames. These frames represent calls made as a consequence of frame. The first entry in the list represents traceback; the last entry represents where the exception was raised.

Changed in version 3.5: A list of named tuples FrameInfo(frame, filename, lineno, function, code_context, index) is returned.

Changed in version 3.11: A list of FrameInfo objects is returned.

+
+inspect.currentframe()
+

Return the frame object for the caller’s stack frame.

CPython implementation detail: This function relies on Python stack frame support in the interpreter, which isn’t guaranteed to exist in all implementations of Python. If running in an implementation without Python stack frame support this function returns None.

+
+inspect.stack(context=1)
+

Return a list of FrameInfo objects for the caller’s stack. The first entry in the returned list represents the caller; the last entry represents the outermost call on the stack.

Changed in version 3.5: A list of named tuples FrameInfo(frame, filename, lineno, function, code_context, index) is returned.

Changed in version 3.11: A list of FrameInfo objects is returned.

+
+inspect.trace(context=1)
+

Return a list of FrameInfo objects for the stack between the current frame and the frame in which an exception currently being handled was raised in. The first entry in the list represents the caller; the last entry represents where the exception was raised.

Changed in version 3.5: A list of named tuples FrameInfo(frame, filename, lineno, function, code_context, index) is returned.

Changed in version 3.11: A list of FrameInfo objects is returned.

+

Fetching attributes statically

Both getattr() and hasattr() can trigger code execution when fetching or checking for the existence of attributes. Descriptors, like properties, will be invoked and __getattr__() and __getattribute__() may be called.

For cases where you want passive introspection, like documentation tools, this can be inconvenient. getattr_static() has the same signature as getattr() but avoids executing code when it fetches attributes.

+inspect.getattr_static(obj, attr, default=None)
+

Retrieve attributes without triggering dynamic lookup via the descriptor protocol, __getattr__() or __getattribute__().

Note: this function may not be able to retrieve all attributes that getattr can fetch (like dynamically created attributes) and may find attributes that getattr can’t (like descriptors that raise AttributeError). It can also return descriptors objects instead of instance members.

If the instance __dict__ is shadowed by another member (for example a property) then this function will be unable to find instance members.

New in version 3.2.

+

getattr_static() does not resolve descriptors, for example slot descriptors or getset descriptors on objects implemented in C. The descriptor object is returned instead of the underlying attribute.

You can handle these with code like the following. Note that for arbitrary getset descriptors invoking these may trigger code execution:

# example code for resolving the builtin descriptor types
+class _foo:
+    __slots__ = ['foo']
+
+slot_descriptor = type(_foo.foo)
+getset_descriptor = type(type(open(__file__)).name)
+wrapper_descriptor = type(str.__dict__['__add__'])
+descriptor_types = (slot_descriptor, getset_descriptor, wrapper_descriptor)
+
+result = getattr_static(some_object, 'foo')
+if type(result) in descriptor_types:
+    try:
+        result = result.__get__()
+    except AttributeError:
+        # descriptors can raise AttributeError to
+        # indicate there is no underlying value
+        # in which case the descriptor itself will
+        # have to do
+        pass
+

Current State of Generators, Coroutines, and Asynchronous Generators

When implementing coroutine schedulers and for other advanced uses of generators, it is useful to determine whether a generator is currently executing, is waiting to start or resume or execution, or has already terminated. getgeneratorstate() allows the current state of a generator to be determined easily.

+inspect.getgeneratorstate(generator)
+

Get current state of a generator-iterator.

Possible states are:

  • GEN_CREATED: Waiting to start execution.
  • GEN_RUNNING: Currently being executed by the interpreter.
  • GEN_SUSPENDED: Currently suspended at a yield expression.
  • GEN_CLOSED: Execution has completed.

New in version 3.2.

+
+inspect.getcoroutinestate(coroutine)
+

Get current state of a coroutine object. The function is intended to be used with coroutine objects created by async def functions, but will accept any coroutine-like object that has cr_running and cr_frame attributes.

Possible states are:

  • CORO_CREATED: Waiting to start execution.
  • CORO_RUNNING: Currently being executed by the interpreter.
  • CORO_SUSPENDED: Currently suspended at an await expression.
  • CORO_CLOSED: Execution has completed.

New in version 3.5.

+
+inspect.getasyncgenstate(agen)
+

Get current state of an asynchronous generator object. The function is intended to be used with asynchronous iterator objects created by async def functions which use the yield statement, but will accept any asynchronous generator-like object that has ag_running and ag_frame attributes.

Possible states are:

  • AGEN_CREATED: Waiting to start execution.
  • AGEN_RUNNING: Currently being executed by the interpreter.
  • AGEN_SUSPENDED: Currently suspended at a yield expression.
  • AGEN_CLOSED: Execution has completed.

New in version 3.12.

+

The current internal state of the generator can also be queried. This is mostly useful for testing purposes, to ensure that internal state is being updated as expected:

+inspect.getgeneratorlocals(generator)
+

Get the mapping of live local variables in generator to their current values. A dictionary is returned that maps from variable names to values. This is the equivalent of calling locals() in the body of the generator, and all the same caveats apply.

If generator is a generator with no currently associated frame, then an empty dictionary is returned. TypeError is raised if generator is not a Python generator object.

CPython implementation detail: This function relies on the generator exposing a Python stack frame for introspection, which isn’t guaranteed to be the case in all implementations of Python. In such cases, this function will always return an empty dictionary.

New in version 3.3.

+
+inspect.getcoroutinelocals(coroutine)
+

This function is analogous to getgeneratorlocals(), but works for coroutine objects created by async def functions.

New in version 3.5.

+
+inspect.getasyncgenlocals(agen)
+

This function is analogous to getgeneratorlocals(), but works for asynchronous generator objects created by async def functions which use the yield statement.

New in version 3.12.

+

Code Objects Bit Flags

Python code objects have a co_flags attribute, which is a bitmap of the following flags:

+inspect.CO_OPTIMIZED
+

The code object is optimized, using fast locals.

+
+inspect.CO_NEWLOCALS
+

If set, a new dict will be created for the frame’s f_locals when the code object is executed.

+
+inspect.CO_VARARGS
+

The code object has a variable positional parameter (*args-like).

+
+inspect.CO_VARKEYWORDS
+

The code object has a variable keyword parameter (**kwargs-like).

+
+inspect.CO_NESTED
+

The flag is set when the code object is a nested function.

+
+inspect.CO_GENERATOR
+

The flag is set when the code object is a generator function, i.e. a generator object is returned when the code object is executed.

+
+inspect.CO_COROUTINE
+

The flag is set when the code object is a coroutine function. When the code object is executed it returns a coroutine object. See PEP 492 for more details.

New in version 3.5.

+
+inspect.CO_ITERABLE_COROUTINE
+

The flag is used to transform generators into generator-based coroutines. Generator objects with this flag can be used in await expression, and can yield from coroutine objects. See PEP 492 for more details.

New in version 3.5.

+
+inspect.CO_ASYNC_GENERATOR
+

The flag is set when the code object is an asynchronous generator function. When the code object is executed it returns an asynchronous generator object. See PEP 525 for more details.

New in version 3.6.

+

Note

The flags are specific to CPython, and may not be defined in other Python implementations. Furthermore, the flags are an implementation detail, and can be removed or deprecated in future Python releases. It’s recommended to use public APIs from the inspect module for any introspection needs.

Buffer flags

+class inspect.BufferFlags
+

This is an enum.IntFlag that represents the flags that can be passed to the __buffer__() method of objects implementing the buffer protocol.

The meaning of the flags is explained at Buffer request types.

+SIMPLE
+
+WRITABLE
+
+FORMAT
+
+ND
+
+STRIDES
+
+C_CONTIGUOUS
+
+F_CONTIGUOUS
+
+ANY_CONTIGUOUS
+
+INDIRECT
+
+CONTIG
+
+CONTIG_RO
+
+STRIDED
+
+STRIDED_RO
+
+RECORDS
+
+RECORDS_RO
+
+FULL
+
+FULL_RO
+
+READ
+
+WRITE
+

New in version 3.12.

+

Command Line Interface

The inspect module also provides a basic introspection capability from the command line.

By default, accepts the name of a module and prints the source of that module. A class or function within the module can be printed instead by appended a colon and the qualified name of the target object.

+--details
+

Print information about the specified object rather than the source code

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/inspect.html +

+
diff --git a/devdocs/python~3.12/library%2Finternet.html b/devdocs/python~3.12/library%2Finternet.html new file mode 100644 index 00000000..11baa772 --- /dev/null +++ b/devdocs/python~3.12/library%2Finternet.html @@ -0,0 +1,32 @@ +

Internet Protocols and Support

The modules described in this chapter implement internet protocols and support for related technology. They are all implemented in Python. Most of these modules require the presence of the system-dependent module socket, which is currently supported on most popular platforms. Here is an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/internet.html +

+
diff --git a/devdocs/python~3.12/library%2Fio.html b/devdocs/python~3.12/library%2Fio.html new file mode 100644 index 00000000..bfa8c0a5 --- /dev/null +++ b/devdocs/python~3.12/library%2Fio.html @@ -0,0 +1,281 @@ +

io — Core tools for working with streams

Source code: Lib/io.py

Overview

The io module provides Python’s main facilities for dealing with various types of I/O. There are three main types of I/O: text I/O, binary I/O and raw I/O. These are generic categories, and various backing stores can be used for each of them. A concrete object belonging to any of these categories is called a file object. Other common terms are stream and file-like object.

Independent of its category, each concrete stream object will also have various capabilities: it can be read-only, write-only, or read-write. It can also allow arbitrary random access (seeking forwards or backwards to any location), or only sequential access (for example in the case of a socket or pipe).

All streams are careful about the type of data you give to them. For example giving a str object to the write() method of a binary stream will raise a TypeError. So will giving a bytes object to the write() method of a text stream.

Changed in version 3.3: Operations that used to raise IOError now raise OSError, since IOError is now an alias of OSError.

Text I/O

Text I/O expects and produces str objects. This means that whenever the backing store is natively made of bytes (such as in the case of a file), encoding and decoding of data is made transparently as well as optional translation of platform-specific newline characters.

The easiest way to create a text stream is with open(), optionally specifying an encoding:

f = open("myfile.txt", "r", encoding="utf-8")
+

In-memory text streams are also available as StringIO objects:

f = io.StringIO("some initial text data")
+

The text stream API is described in detail in the documentation of TextIOBase.

Binary I/O

Binary I/O (also called buffered I/O) expects bytes-like objects and produces bytes objects. No encoding, decoding, or newline translation is performed. This category of streams can be used for all kinds of non-text data, and also when manual control over the handling of text data is desired.

The easiest way to create a binary stream is with open() with 'b' in the mode string:

f = open("myfile.jpg", "rb")
+

In-memory binary streams are also available as BytesIO objects:

f = io.BytesIO(b"some initial binary data: \x00\x01")
+

The binary stream API is described in detail in the docs of BufferedIOBase.

Other library modules may provide additional ways to create text or binary streams. See socket.socket.makefile() for example.

Raw I/O

Raw I/O (also called unbuffered I/O) is generally used as a low-level building-block for binary and text streams; it is rarely useful to directly manipulate a raw stream from user code. Nevertheless, you can create a raw stream by opening a file in binary mode with buffering disabled:

f = open("myfile.jpg", "rb", buffering=0)
+

The raw stream API is described in detail in the docs of RawIOBase.

Text Encoding

The default encoding of TextIOWrapper and open() is locale-specific (locale.getencoding()).

However, many developers forget to specify the encoding when opening text files encoded in UTF-8 (e.g. JSON, TOML, Markdown, etc…) since most Unix platforms use UTF-8 locale by default. This causes bugs because the locale encoding is not UTF-8 for most Windows users. For example:

# May not work on Windows when non-ASCII characters in the file.
+with open("README.md") as f:
+    long_description = f.read()
+

Accordingly, it is highly recommended that you specify the encoding explicitly when opening text files. If you want to use UTF-8, pass encoding="utf-8". To use the current locale encoding, encoding="locale" is supported since Python 3.10.

See also

Python UTF-8 Mode
+
+

Python UTF-8 Mode can be used to change the default encoding to UTF-8 from locale-specific encoding.

+PEP 686 +
+
+

Python 3.15 will make Python UTF-8 Mode default.

Opt-in EncodingWarning

New in version 3.10: See PEP 597 for more details.

To find where the default locale encoding is used, you can enable the -X warn_default_encoding command line option or set the PYTHONWARNDEFAULTENCODING environment variable, which will emit an EncodingWarning when the default encoding is used.

If you are providing an API that uses open() or TextIOWrapper and passes encoding=None as a parameter, you can use text_encoding() so that callers of the API will emit an EncodingWarning if they don’t pass an encoding. However, please consider using UTF-8 by default (i.e. encoding="utf-8") for new APIs.

High-level Module Interface

+io.DEFAULT_BUFFER_SIZE
+

An int containing the default buffer size used by the module’s buffered I/O classes. open() uses the file’s blksize (as obtained by os.stat()) if possible.

+
+io.open(file, mode='r', buffering=- 1, encoding=None, errors=None, newline=None, closefd=True, opener=None)
+

This is an alias for the builtin open() function.

+

This function raises an auditing event open with arguments path, mode and flags. The mode and flags arguments may have been modified or inferred from the original call.

+
+io.open_code(path)
+

Opens the provided file with mode 'rb'. This function should be used when the intent is to treat the contents as executable code.

path should be a str and an absolute path.

The behavior of this function may be overridden by an earlier call to the PyFile_SetOpenCodeHook(). However, assuming that path is a str and an absolute path, open_code(path) should always behave the same as open(path, 'rb'). Overriding the behavior is intended for additional validation or preprocessing of the file.

New in version 3.8.

+
+io.text_encoding(encoding, stacklevel=2, /)
+

This is a helper function for callables that use open() or TextIOWrapper and have an encoding=None parameter.

This function returns encoding if it is not None. Otherwise, it returns "locale" or "utf-8" depending on UTF-8 Mode.

This function emits an EncodingWarning if sys.flags.warn_default_encoding is true and encoding is None. stacklevel specifies where the warning is emitted. For example:

def read_text(path, encoding=None):
+    encoding = io.text_encoding(encoding)  # stacklevel=2
+    with open(path, encoding) as f:
+        return f.read()
+

In this example, an EncodingWarning is emitted for the caller of read_text().

See Text Encoding for more information.

New in version 3.10.

Changed in version 3.11: text_encoding() returns “utf-8” when UTF-8 mode is enabled and encoding is None.

+
+exception io.BlockingIOError
+

This is a compatibility alias for the builtin BlockingIOError exception.

+
+exception io.UnsupportedOperation
+

An exception inheriting OSError and ValueError that is raised when an unsupported operation is called on a stream.

+

See also

+ sys +
+
+

contains the standard IO streams: sys.stdin, sys.stdout, and sys.stderr.

Class hierarchy

The implementation of I/O streams is organized as a hierarchy of classes. First abstract base classes (ABCs), which are used to specify the various categories of streams, then concrete classes providing the standard stream implementations.

Note

The abstract base classes also provide default implementations of some methods in order to help implementation of concrete stream classes. For example, BufferedIOBase provides unoptimized implementations of readinto() and readline().

At the top of the I/O hierarchy is the abstract base class IOBase. It defines the basic interface to a stream. Note, however, that there is no separation between reading and writing to streams; implementations are allowed to raise UnsupportedOperation if they do not support a given operation.

The RawIOBase ABC extends IOBase. It deals with the reading and writing of bytes to a stream. FileIO subclasses RawIOBase to provide an interface to files in the machine’s file system.

The BufferedIOBase ABC extends IOBase. It deals with buffering on a raw binary stream (RawIOBase). Its subclasses, BufferedWriter, BufferedReader, and BufferedRWPair buffer raw binary streams that are writable, readable, and both readable and writable, respectively. BufferedRandom provides a buffered interface to seekable streams. Another BufferedIOBase subclass, BytesIO, is a stream of in-memory bytes.

The TextIOBase ABC extends IOBase. It deals with streams whose bytes represent text, and handles encoding and decoding to and from strings. TextIOWrapper, which extends TextIOBase, is a buffered text interface to a buffered raw stream (BufferedIOBase). Finally, StringIO is an in-memory stream for text.

Argument names are not part of the specification, and only the arguments of open() are intended to be used as keyword arguments.

The following table summarizes the ABCs provided by the io module:

+ + + + +

ABC

Inherits

Stub Methods

Mixin Methods and Properties

IOBase

fileno, seek, and truncate

close, closed, __enter__, __exit__, flush, isatty, __iter__, __next__, readable, readline, readlines, seekable, tell, writable, and writelines

RawIOBase

IOBase

readinto and write

Inherited IOBase methods, read, and readall

BufferedIOBase

IOBase

detach, read, read1, and write

Inherited IOBase methods, readinto, and readinto1

TextIOBase

IOBase

detach, read, readline, and write

Inherited IOBase methods, encoding, errors, and newlines

I/O Base Classes

+class io.IOBase
+

The abstract base class for all I/O classes.

This class provides empty abstract implementations for many methods that derived classes can override selectively; the default implementations represent a file that cannot be read, written or seeked.

Even though IOBase does not declare read() or write() because their signatures will vary, implementations and clients should consider those methods part of the interface. Also, implementations may raise a ValueError (or UnsupportedOperation) when operations they do not support are called.

The basic type used for binary data read from or written to a file is bytes. Other bytes-like objects are accepted as method arguments too. Text I/O classes work with str data.

Note that calling any method (even inquiries) on a closed stream is undefined. Implementations may raise ValueError in this case.

IOBase (and its subclasses) supports the iterator protocol, meaning that an IOBase object can be iterated over yielding the lines in a stream. Lines are defined slightly differently depending on whether the stream is a binary stream (yielding bytes), or a text stream (yielding character strings). See readline() below.

IOBase is also a context manager and therefore supports the with statement. In this example, file is closed after the with statement’s suite is finished—even if an exception occurs:

with open('spam.txt', 'w') as file:
+    file.write('Spam and eggs!')
+

IOBase provides these data attributes and methods:

+close()
+

Flush and close this stream. This method has no effect if the file is already closed. Once the file is closed, any operation on the file (e.g. reading or writing) will raise a ValueError.

As a convenience, it is allowed to call this method more than once; only the first call, however, will have an effect.

+
+closed
+

True if the stream is closed.

+
+fileno()
+

Return the underlying file descriptor (an integer) of the stream if it exists. An OSError is raised if the IO object does not use a file descriptor.

+
+flush()
+

Flush the write buffers of the stream if applicable. This does nothing for read-only and non-blocking streams.

+
+isatty()
+

Return True if the stream is interactive (i.e., connected to a terminal/tty device).

+
+readable()
+

Return True if the stream can be read from. If False, read() will raise OSError.

+
+readline(size=- 1, /)
+

Read and return one line from the stream. If size is specified, at most size bytes will be read.

The line terminator is always b'\n' for binary files; for text files, the newline argument to open() can be used to select the line terminator(s) recognized.

+
+readlines(hint=- 1, /)
+

Read and return a list of lines from the stream. hint can be specified to control the number of lines read: no more lines will be read if the total size (in bytes/characters) of all lines so far exceeds hint.

hint values of 0 or less, as well as None, are treated as no hint.

Note that it’s already possible to iterate on file objects using for +line in file: ... without calling file.readlines().

+
+seek(offset, whence=os.SEEK_SET, /)
+

Change the stream position to the given byte offset, interpreted relative to the position indicated by whence, and return the new absolute position. Values for whence are:

  • +os.SEEK_SET or 0 – start of the stream (the default); offset should be zero or positive
  • +os.SEEK_CUR or 1 – current stream position; offset may be negative
  • +os.SEEK_END or 2 – end of the stream; offset is usually negative

New in version 3.1: The SEEK_* constants.

New in version 3.3: Some operating systems could support additional values, like os.SEEK_HOLE or os.SEEK_DATA. The valid values for a file could depend on it being open in text or binary mode.

+
+seekable()
+

Return True if the stream supports random access. If False, seek(), tell() and truncate() will raise OSError.

+
+tell()
+

Return the current stream position.

+
+truncate(size=None, /)
+

Resize the stream to the given size in bytes (or the current position if size is not specified). The current stream position isn’t changed. This resizing can extend or reduce the current file size. In case of extension, the contents of the new file area depend on the platform (on most systems, additional bytes are zero-filled). The new file size is returned.

Changed in version 3.5: Windows will now zero-fill files when extending.

+
+writable()
+

Return True if the stream supports writing. If False, write() and truncate() will raise OSError.

+
+writelines(lines, /)
+

Write a list of lines to the stream. Line separators are not added, so it is usual for each of the lines provided to have a line separator at the end.

+
+__del__()
+

Prepare for object destruction. IOBase provides a default implementation of this method that calls the instance’s close() method.

+
+
+class io.RawIOBase
+

Base class for raw binary streams. It inherits IOBase.

Raw binary streams typically provide low-level access to an underlying OS device or API, and do not try to encapsulate it in high-level primitives (this functionality is done at a higher-level in buffered binary streams and text streams, described later in this page).

RawIOBase provides these methods in addition to those from IOBase:

+read(size=- 1, /)
+

Read up to size bytes from the object and return them. As a convenience, if size is unspecified or -1, all bytes until EOF are returned. Otherwise, only one system call is ever made. Fewer than size bytes may be returned if the operating system call returns fewer than size bytes.

If 0 bytes are returned, and size was not 0, this indicates end of file. If the object is in non-blocking mode and no bytes are available, None is returned.

The default implementation defers to readall() and readinto().

+
+readall()
+

Read and return all the bytes from the stream until EOF, using multiple calls to the stream if necessary.

+
+readinto(b, /)
+

Read bytes into a pre-allocated, writable bytes-like object b, and return the number of bytes read. For example, b might be a bytearray. If the object is in non-blocking mode and no bytes are available, None is returned.

+
+write(b, /)
+

Write the given bytes-like object, b, to the underlying raw stream, and return the number of bytes written. This can be less than the length of b in bytes, depending on specifics of the underlying raw stream, and especially if it is in non-blocking mode. None is returned if the raw stream is set not to block and no single byte could be readily written to it. The caller may release or mutate b after this method returns, so the implementation should only access b during the method call.

+
+
+class io.BufferedIOBase
+

Base class for binary streams that support some kind of buffering. It inherits IOBase.

The main difference with RawIOBase is that methods read(), readinto() and write() will try (respectively) to read as much input as requested or to consume all given output, at the expense of making perhaps more than one system call.

In addition, those methods can raise BlockingIOError if the underlying raw stream is in non-blocking mode and cannot take or give enough data; unlike their RawIOBase counterparts, they will never return None.

Besides, the read() method does not have a default implementation that defers to readinto().

A typical BufferedIOBase implementation should not inherit from a RawIOBase implementation, but wrap one, like BufferedWriter and BufferedReader do.

BufferedIOBase provides or overrides these data attributes and methods in addition to those from IOBase:

+raw
+

The underlying raw stream (a RawIOBase instance) that BufferedIOBase deals with. This is not part of the BufferedIOBase API and may not exist on some implementations.

+
+detach()
+

Separate the underlying raw stream from the buffer and return it.

After the raw stream has been detached, the buffer is in an unusable state.

Some buffers, like BytesIO, do not have the concept of a single raw stream to return from this method. They raise UnsupportedOperation.

New in version 3.1.

+
+read(size=- 1, /)
+

Read and return up to size bytes. If the argument is omitted, None, or negative, data is read and returned until EOF is reached. An empty bytes object is returned if the stream is already at EOF.

If the argument is positive, and the underlying raw stream is not interactive, multiple raw reads may be issued to satisfy the byte count (unless EOF is reached first). But for interactive raw streams, at most one raw read will be issued, and a short result does not imply that EOF is imminent.

A BlockingIOError is raised if the underlying raw stream is in non blocking-mode, and has no data available at the moment.

+
+read1(size=- 1, /)
+

Read and return up to size bytes, with at most one call to the underlying raw stream’s read() (or readinto()) method. This can be useful if you are implementing your own buffering on top of a BufferedIOBase object.

If size is -1 (the default), an arbitrary number of bytes are returned (more than zero unless EOF is reached).

+
+readinto(b, /)
+

Read bytes into a pre-allocated, writable bytes-like object b and return the number of bytes read. For example, b might be a bytearray.

Like read(), multiple reads may be issued to the underlying raw stream, unless the latter is interactive.

A BlockingIOError is raised if the underlying raw stream is in non blocking-mode, and has no data available at the moment.

+
+readinto1(b, /)
+

Read bytes into a pre-allocated, writable bytes-like object b, using at most one call to the underlying raw stream’s read() (or readinto()) method. Return the number of bytes read.

A BlockingIOError is raised if the underlying raw stream is in non blocking-mode, and has no data available at the moment.

New in version 3.5.

+
+write(b, /)
+

Write the given bytes-like object, b, and return the number of bytes written (always equal to the length of b in bytes, since if the write fails an OSError will be raised). Depending on the actual implementation, these bytes may be readily written to the underlying stream, or held in a buffer for performance and latency reasons.

When in non-blocking mode, a BlockingIOError is raised if the data needed to be written to the raw stream but it couldn’t accept all the data without blocking.

The caller may release or mutate b after this method returns, so the implementation should only access b during the method call.

+
+

Raw File I/O

+class io.FileIO(name, mode='r', closefd=True, opener=None)
+

A raw binary stream representing an OS-level file containing bytes data. It inherits RawIOBase.

The name can be one of two things:

  • a character string or bytes object representing the path to the file which will be opened. In this case closefd must be True (the default) otherwise an error will be raised.
  • an integer representing the number of an existing OS-level file descriptor to which the resulting FileIO object will give access. When the FileIO object is closed this fd will be closed as well, unless closefd is set to False.

The mode can be 'r', 'w', 'x' or 'a' for reading (default), writing, exclusive creation or appending. The file will be created if it doesn’t exist when opened for writing or appending; it will be truncated when opened for writing. FileExistsError will be raised if it already exists when opened for creating. Opening a file for creating implies writing, so this mode behaves in a similar way to 'w'. Add a '+' to the mode to allow simultaneous reading and writing.

The read() (when called with a positive argument), readinto() and write() methods on this class will only make one system call.

A custom opener can be used by passing a callable as opener. The underlying file descriptor for the file object is then obtained by calling opener with (name, flags). opener must return an open file descriptor (passing os.open as opener results in functionality similar to passing None).

The newly created file is non-inheritable.

See the open() built-in function for examples on using the opener parameter.

Changed in version 3.3: The opener parameter was added. The 'x' mode was added.

Changed in version 3.4: The file is now non-inheritable.

FileIO provides these data attributes in addition to those from RawIOBase and IOBase:

+mode
+

The mode as given in the constructor.

+
+name
+

The file name. This is the file descriptor of the file when no name is given in the constructor.

+
+

Buffered Streams

Buffered I/O streams provide a higher-level interface to an I/O device than raw I/O does.

+class io.BytesIO(initial_bytes=b'')
+

A binary stream using an in-memory bytes buffer. It inherits BufferedIOBase. The buffer is discarded when the close() method is called.

The optional argument initial_bytes is a bytes-like object that contains initial data.

BytesIO provides or overrides these methods in addition to those from BufferedIOBase and IOBase:

+getbuffer()
+

Return a readable and writable view over the contents of the buffer without copying them. Also, mutating the view will transparently update the contents of the buffer:

>>> b = io.BytesIO(b"abcdef")
+>>> view = b.getbuffer()
+>>> view[2:4] = b"56"
+>>> b.getvalue()
+b'ab56ef'
+

Note

As long as the view exists, the BytesIO object cannot be resized or closed.

New in version 3.2.

+
+getvalue()
+

Return bytes containing the entire contents of the buffer.

+
+read1(size=- 1, /)
+

In BytesIO, this is the same as read().

Changed in version 3.7: The size argument is now optional.

+
+readinto1(b, /)
+

In BytesIO, this is the same as readinto().

New in version 3.5.

+
+
+class io.BufferedReader(raw, buffer_size=DEFAULT_BUFFER_SIZE)
+

A buffered binary stream providing higher-level access to a readable, non seekable RawIOBase raw binary stream. It inherits BufferedIOBase.

When reading data from this object, a larger amount of data may be requested from the underlying raw stream, and kept in an internal buffer. The buffered data can then be returned directly on subsequent reads.

The constructor creates a BufferedReader for the given readable raw stream and buffer_size. If buffer_size is omitted, DEFAULT_BUFFER_SIZE is used.

BufferedReader provides or overrides these methods in addition to those from BufferedIOBase and IOBase:

+peek(size=0, /)
+

Return bytes from the stream without advancing the position. At most one single read on the raw stream is done to satisfy the call. The number of bytes returned may be less or more than requested.

+
+read(size=- 1, /)
+

Read and return size bytes, or if size is not given or negative, until EOF or if the read call would block in non-blocking mode.

+
+read1(size=- 1, /)
+

Read and return up to size bytes with only one call on the raw stream. If at least one byte is buffered, only buffered bytes are returned. Otherwise, one raw stream read call is made.

Changed in version 3.7: The size argument is now optional.

+
+
+class io.BufferedWriter(raw, buffer_size=DEFAULT_BUFFER_SIZE)
+

A buffered binary stream providing higher-level access to a writeable, non seekable RawIOBase raw binary stream. It inherits BufferedIOBase.

When writing to this object, data is normally placed into an internal buffer. The buffer will be written out to the underlying RawIOBase object under various conditions, including:

The constructor creates a BufferedWriter for the given writeable raw stream. If the buffer_size is not given, it defaults to DEFAULT_BUFFER_SIZE.

BufferedWriter provides or overrides these methods in addition to those from BufferedIOBase and IOBase:

+flush()
+

Force bytes held in the buffer into the raw stream. A BlockingIOError should be raised if the raw stream blocks.

+
+write(b, /)
+

Write the bytes-like object, b, and return the number of bytes written. When in non-blocking mode, a BlockingIOError is raised if the buffer needs to be written out but the raw stream blocks.

+
+
+class io.BufferedRandom(raw, buffer_size=DEFAULT_BUFFER_SIZE)
+

A buffered binary stream providing higher-level access to a seekable RawIOBase raw binary stream. It inherits BufferedReader and BufferedWriter.

The constructor creates a reader and writer for a seekable raw stream, given in the first argument. If the buffer_size is omitted it defaults to DEFAULT_BUFFER_SIZE.

BufferedRandom is capable of anything BufferedReader or BufferedWriter can do. In addition, seek() and tell() are guaranteed to be implemented.

+
+class io.BufferedRWPair(reader, writer, buffer_size=DEFAULT_BUFFER_SIZE, /)
+

A buffered binary stream providing higher-level access to two non seekable RawIOBase raw binary streams—one readable, the other writeable. It inherits BufferedIOBase.

reader and writer are RawIOBase objects that are readable and writeable respectively. If the buffer_size is omitted it defaults to DEFAULT_BUFFER_SIZE.

BufferedRWPair implements all of BufferedIOBase's methods except for detach(), which raises UnsupportedOperation.

Warning

BufferedRWPair does not attempt to synchronize accesses to its underlying raw streams. You should not pass it the same object as reader and writer; use BufferedRandom instead.

+

Text I/O

+class io.TextIOBase
+

Base class for text streams. This class provides a character and line based interface to stream I/O. It inherits IOBase.

TextIOBase provides or overrides these data attributes and methods in addition to those from IOBase:

+encoding
+

The name of the encoding used to decode the stream’s bytes into strings, and to encode strings into bytes.

+
+errors
+

The error setting of the decoder or encoder.

+
+newlines
+

A string, a tuple of strings, or None, indicating the newlines translated so far. Depending on the implementation and the initial constructor flags, this may not be available.

+
+buffer
+

The underlying binary buffer (a BufferedIOBase instance) that TextIOBase deals with. This is not part of the TextIOBase API and may not exist in some implementations.

+
+detach()
+

Separate the underlying binary buffer from the TextIOBase and return it.

After the underlying buffer has been detached, the TextIOBase is in an unusable state.

Some TextIOBase implementations, like StringIO, may not have the concept of an underlying buffer and calling this method will raise UnsupportedOperation.

New in version 3.1.

+
+read(size=- 1, /)
+

Read and return at most size characters from the stream as a single str. If size is negative or None, reads until EOF.

+
+readline(size=- 1, /)
+

Read until newline or EOF and return a single str. If the stream is already at EOF, an empty string is returned.

If size is specified, at most size characters will be read.

+
+seek(offset, whence=SEEK_SET, /)
+

Change the stream position to the given offset. Behaviour depends on the whence parameter. The default value for whence is SEEK_SET.

  • +SEEK_SET or 0: seek from the start of the stream (the default); offset must either be a number returned by TextIOBase.tell(), or zero. Any other offset value produces undefined behaviour.
  • +SEEK_CUR or 1: “seek” to the current position; offset must be zero, which is a no-operation (all other values are unsupported).
  • +SEEK_END or 2: seek to the end of the stream; offset must be zero (all other values are unsupported).

Return the new absolute position as an opaque number.

New in version 3.1: The SEEK_* constants.

+
+tell()
+

Return the current stream position as an opaque number. The number does not usually represent a number of bytes in the underlying binary storage.

+
+write(s, /)
+

Write the string s to the stream and return the number of characters written.

+
+
+class io.TextIOWrapper(buffer, encoding=None, errors=None, newline=None, line_buffering=False, write_through=False)
+

A buffered text stream providing higher-level access to a BufferedIOBase buffered binary stream. It inherits TextIOBase.

encoding gives the name of the encoding that the stream will be decoded or encoded with. It defaults to locale.getencoding(). encoding="locale" can be used to specify the current locale’s encoding explicitly. See Text Encoding for more information.

errors is an optional string that specifies how encoding and decoding errors are to be handled. Pass 'strict' to raise a ValueError exception if there is an encoding error (the default of None has the same effect), or pass 'ignore' to ignore errors. (Note that ignoring encoding errors can lead to data loss.) 'replace' causes a replacement marker (such as '?') to be inserted where there is malformed data. 'backslashreplace' causes malformed data to be replaced by a backslashed escape sequence. When writing, 'xmlcharrefreplace' (replace with the appropriate XML character reference) or 'namereplace' (replace with \N{...} escape sequences) can be used. Any other error handling name that has been registered with codecs.register_error() is also valid.

newline controls how line endings are handled. It can be None, '', '\n', '\r', and '\r\n'. It works as follows:

  • When reading input from the stream, if newline is None, universal newlines mode is enabled. Lines in the input can end in '\n', '\r', or '\r\n', and these are translated into '\n' before being returned to the caller. If newline is '', universal newlines mode is enabled, but line endings are returned to the caller untranslated. If newline has any of the other legal values, input lines are only terminated by the given string, and the line ending is returned to the caller untranslated.
  • When writing output to the stream, if newline is None, any '\n' characters written are translated to the system default line separator, os.linesep. If newline is '' or '\n', no translation takes place. If newline is any of the other legal values, any '\n' characters written are translated to the given string.

If line_buffering is True, flush() is implied when a call to write contains a newline character or a carriage return.

If write_through is True, calls to write() are guaranteed not to be buffered: any data written on the TextIOWrapper object is immediately handled to its underlying binary buffer.

Changed in version 3.3: The write_through argument has been added.

Changed in version 3.3: The default encoding is now locale.getpreferredencoding(False) instead of locale.getpreferredencoding(). Don’t change temporary the locale encoding using locale.setlocale(), use the current locale encoding instead of the user preferred encoding.

Changed in version 3.10: The encoding argument now supports the "locale" dummy encoding name.

TextIOWrapper provides these data attributes and methods in addition to those from TextIOBase and IOBase:

+line_buffering
+

Whether line buffering is enabled.

+
+write_through
+

Whether writes are passed immediately to the underlying binary buffer.

New in version 3.7.

+
+reconfigure(*, encoding=None, errors=None, newline=None, line_buffering=None, write_through=None)
+

Reconfigure this text stream using new settings for encoding, errors, newline, line_buffering and write_through.

Parameters not specified keep current settings, except errors='strict' is used when encoding is specified but errors is not specified.

It is not possible to change the encoding or newline if some data has already been read from the stream. On the other hand, changing encoding after write is possible.

This method does an implicit stream flush before setting the new parameters.

New in version 3.7.

Changed in version 3.11: The method supports encoding="locale" option.

+
+seek(cookie, whence=os.SEEK_SET, /)
+

Set the stream position. Return the new stream position as an int.

Four operations are supported, given by the following argument combinations:

  • +seek(0, SEEK_SET): Rewind to the start of the stream.
  • +seek(cookie, SEEK_SET): Restore a previous position; cookie must be a number returned by tell().
  • +seek(0, SEEK_END): Fast-forward to the end of the stream.
  • +seek(0, SEEK_CUR): Leave the current stream position unchanged.

Any other argument combinations are invalid, and may raise exceptions.

See also

os.SEEK_SET, os.SEEK_CUR, and os.SEEK_END.

+
+tell()
+

Return the stream position as an opaque number. The return value of tell() can be given as input to seek(), to restore a previous stream position.

+
+
+class io.StringIO(initial_value='', newline='\n')
+

A text stream using an in-memory text buffer. It inherits TextIOBase.

The text buffer is discarded when the close() method is called.

The initial value of the buffer can be set by providing initial_value. If newline translation is enabled, newlines will be encoded as if by write(). The stream is positioned at the start of the buffer which emulates opening an existing file in a w+ mode, making it ready for an immediate write from the beginning or for a write that would overwrite the initial value. To emulate opening a file in an a+ mode ready for appending, use f.seek(0, io.SEEK_END) to reposition the stream at the end of the buffer.

The newline argument works like that of TextIOWrapper, except that when writing output to the stream, if newline is None, newlines are written as \n on all platforms.

StringIO provides this method in addition to those from TextIOBase and IOBase:

+getvalue()
+

Return a str containing the entire contents of the buffer. Newlines are decoded as if by read(), although the stream position is not changed.

+

Example usage:

import io
+
+output = io.StringIO()
+output.write('First line.\n')
+print('Second line.', file=output)
+
+# Retrieve file contents -- this will be
+# 'First line.\nSecond line.\n'
+contents = output.getvalue()
+
+# Close object and discard memory buffer --
+# .getvalue() will now raise an exception.
+output.close()
+
+
+class io.IncrementalNewlineDecoder
+

A helper codec that decodes newlines for universal newlines mode. It inherits codecs.IncrementalDecoder.

+

Performance

This section discusses the performance of the provided concrete I/O implementations.

Binary I/O

By reading and writing only large chunks of data even when the user asks for a single byte, buffered I/O hides any inefficiency in calling and executing the operating system’s unbuffered I/O routines. The gain depends on the OS and the kind of I/O which is performed. For example, on some modern OSes such as Linux, unbuffered disk I/O can be as fast as buffered I/O. The bottom line, however, is that buffered I/O offers predictable performance regardless of the platform and the backing device. Therefore, it is almost always preferable to use buffered I/O rather than unbuffered I/O for binary data.

Text I/O

Text I/O over a binary storage (such as a file) is significantly slower than binary I/O over the same storage, because it requires conversions between unicode and binary data using a character codec. This can become noticeable handling huge amounts of text data like large log files. Also, tell() and seek() are both quite slow due to the reconstruction algorithm used.

StringIO, however, is a native in-memory unicode container and will exhibit similar speed to BytesIO.

Multi-threading

FileIO objects are thread-safe to the extent that the operating system calls (such as read(2) under Unix) they wrap are thread-safe too.

Binary buffered objects (instances of BufferedReader, BufferedWriter, BufferedRandom and BufferedRWPair) protect their internal structures using a lock; it is therefore safe to call them from multiple threads at once.

TextIOWrapper objects are not thread-safe.

Reentrancy

Binary buffered objects (instances of BufferedReader, BufferedWriter, BufferedRandom and BufferedRWPair) are not reentrant. While reentrant calls will not happen in normal situations, they can arise from doing I/O in a signal handler. If a thread tries to re-enter a buffered object which it is already accessing, a RuntimeError is raised. Note this doesn’t prohibit a different thread from entering the buffered object.

The above implicitly extends to text files, since the open() function will wrap a buffered object inside a TextIOWrapper. This includes standard streams and therefore affects the built-in print() function as well.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/io.html +

+
diff --git a/devdocs/python~3.12/library%2Fipaddress.html b/devdocs/python~3.12/library%2Fipaddress.html new file mode 100644 index 00000000..f27355de --- /dev/null +++ b/devdocs/python~3.12/library%2Fipaddress.html @@ -0,0 +1,460 @@ +

ipaddress — IPv4/IPv6 manipulation library

Source code: Lib/ipaddress.py

ipaddress provides the capabilities to create, manipulate and operate on IPv4 and IPv6 addresses and networks.

The functions and classes in this module make it straightforward to handle various tasks related to IP addresses, including checking whether or not two hosts are on the same subnet, iterating over all hosts in a particular subnet, checking whether or not a string represents a valid IP address or network definition, and so on.

This is the full module API reference—for an overview and introduction, see An introduction to the ipaddress module.

New in version 3.3.

Convenience factory functions

The ipaddress module provides factory functions to conveniently create IP addresses, networks and interfaces:

+ipaddress.ip_address(address)
+

Return an IPv4Address or IPv6Address object depending on the IP address passed as argument. Either IPv4 or IPv6 addresses may be supplied; integers less than 2**32 will be considered to be IPv4 by default. A ValueError is raised if address does not represent a valid IPv4 or IPv6 address.

>>> ipaddress.ip_address('192.168.0.1')
+IPv4Address('192.168.0.1')
+>>> ipaddress.ip_address('2001:db8::')
+IPv6Address('2001:db8::')
+
+
+ipaddress.ip_network(address, strict=True)
+

Return an IPv4Network or IPv6Network object depending on the IP address passed as argument. address is a string or integer representing the IP network. Either IPv4 or IPv6 networks may be supplied; integers less than 2**32 will be considered to be IPv4 by default. strict is passed to IPv4Network or IPv6Network constructor. A ValueError is raised if address does not represent a valid IPv4 or IPv6 address, or if the network has host bits set.

>>> ipaddress.ip_network('192.168.0.0/28')
+IPv4Network('192.168.0.0/28')
+
+
+ipaddress.ip_interface(address)
+

Return an IPv4Interface or IPv6Interface object depending on the IP address passed as argument. address is a string or integer representing the IP address. Either IPv4 or IPv6 addresses may be supplied; integers less than 2**32 will be considered to be IPv4 by default. A ValueError is raised if address does not represent a valid IPv4 or IPv6 address.

+

One downside of these convenience functions is that the need to handle both IPv4 and IPv6 formats means that error messages provide minimal information on the precise error, as the functions don’t know whether the IPv4 or IPv6 format was intended. More detailed error reporting can be obtained by calling the appropriate version specific class constructors directly.

IP Addresses

Address objects

The IPv4Address and IPv6Address objects share a lot of common attributes. Some attributes that are only meaningful for IPv6 addresses are also implemented by IPv4Address objects, in order to make it easier to write code that handles both IP versions correctly. Address objects are hashable, so they can be used as keys in dictionaries.

+class ipaddress.IPv4Address(address)
+

Construct an IPv4 address. An AddressValueError is raised if address is not a valid IPv4 address.

The following constitutes a valid IPv4 address:

  1. A string in decimal-dot notation, consisting of four decimal integers in the inclusive range 0–255, separated by dots (e.g. 192.168.0.1). Each integer represents an octet (byte) in the address. Leading zeroes are not tolerated to prevent confusion with octal notation.
  2. An integer that fits into 32 bits.
  3. An integer packed into a bytes object of length 4 (most significant octet first).
>>> ipaddress.IPv4Address('192.168.0.1')
+IPv4Address('192.168.0.1')
+>>> ipaddress.IPv4Address(3232235521)
+IPv4Address('192.168.0.1')
+>>> ipaddress.IPv4Address(b'\xC0\xA8\x00\x01')
+IPv4Address('192.168.0.1')
+

Changed in version 3.8: Leading zeros are tolerated, even in ambiguous cases that look like octal notation.

Changed in version 3.10: Leading zeros are no longer tolerated and are treated as an error. IPv4 address strings are now parsed as strict as glibc inet_pton().

Changed in version 3.9.5: The above change was also included in Python 3.9 starting with version 3.9.5.

Changed in version 3.8.12: The above change was also included in Python 3.8 starting with version 3.8.12.

+version
+

The appropriate version number: 4 for IPv4, 6 for IPv6.

+
+max_prefixlen
+

The total number of bits in the address representation for this version: 32 for IPv4, 128 for IPv6.

The prefix defines the number of leading bits in an address that are compared to determine whether or not an address is part of a network.

+
+compressed
+
+exploded
+

The string representation in dotted decimal notation. Leading zeroes are never included in the representation.

As IPv4 does not define a shorthand notation for addresses with octets set to zero, these two attributes are always the same as str(addr) for IPv4 addresses. Exposing these attributes makes it easier to write display code that can handle both IPv4 and IPv6 addresses.

+
+packed
+

The binary representation of this address - a bytes object of the appropriate length (most significant octet first). This is 4 bytes for IPv4 and 16 bytes for IPv6.

+
+reverse_pointer
+

The name of the reverse DNS PTR record for the IP address, e.g.:

>>> ipaddress.ip_address("127.0.0.1").reverse_pointer
+'1.0.0.127.in-addr.arpa'
+>>> ipaddress.ip_address("2001:db8::1").reverse_pointer
+'1.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.0.8.b.d.0.1.0.0.2.ip6.arpa'
+

This is the name that could be used for performing a PTR lookup, not the resolved hostname itself.

New in version 3.5.

+
+is_multicast
+

True if the address is reserved for multicast use. See RFC 3171 (for IPv4) or RFC 2373 (for IPv6).

+
+is_private
+

True if the address is allocated for private networks. See iana-ipv4-special-registry (for IPv4) or iana-ipv6-special-registry (for IPv6).

+
+is_global
+

True if the address is allocated for public networks. See iana-ipv4-special-registry (for IPv4) or iana-ipv6-special-registry (for IPv6).

New in version 3.4.

+
+is_unspecified
+

True if the address is unspecified. See RFC 5735 (for IPv4) or RFC 2373 (for IPv6).

+
+is_reserved
+

True if the address is otherwise IETF reserved.

+
+is_loopback
+

True if this is a loopback address. See RFC 3330 (for IPv4) or RFC 2373 (for IPv6).

+
+

True if the address is reserved for link-local usage. See RFC 3927.

+
+
+IPv4Address.__format__(fmt)
+

Returns a string representation of the IP address, controlled by an explicit format string. fmt can be one of the following: 's', the default option, equivalent to str(), 'b' for a zero-padded binary string, 'X' or 'x' for an uppercase or lowercase hexadecimal representation, or 'n', which is equivalent to 'b' for IPv4 addresses and 'x' for IPv6. For binary and hexadecimal representations, the form specifier '#' and the grouping option '_' are available. __format__ is used by format, str.format and f-strings.

>>> format(ipaddress.IPv4Address('192.168.0.1'))
+'192.168.0.1'
+>>> '{:#b}'.format(ipaddress.IPv4Address('192.168.0.1'))
+'0b11000000101010000000000000000001'
+>>> f'{ipaddress.IPv6Address("2001:db8::1000"):s}'
+'2001:db8::1000'
+>>> format(ipaddress.IPv6Address('2001:db8::1000'), '_X')
+'2001_0DB8_0000_0000_0000_0000_0000_1000'
+>>> '{:#_n}'.format(ipaddress.IPv6Address('2001:db8::1000'))
+'0x2001_0db8_0000_0000_0000_0000_0000_1000'
+

New in version 3.9.

+
+class ipaddress.IPv6Address(address)
+

Construct an IPv6 address. An AddressValueError is raised if address is not a valid IPv6 address.

The following constitutes a valid IPv6 address:

  1. +

    A string consisting of eight groups of four hexadecimal digits, each group representing 16 bits. The groups are separated by colons. This describes an exploded (longhand) notation. The string can also be compressed (shorthand notation) by various means. See RFC 4291 for details. For example, "0000:0000:0000:0000:0000:0abc:0007:0def" can be compressed to "::abc:7:def".

    Optionally, the string may also have a scope zone ID, expressed with a suffix %scope_id. If present, the scope ID must be non-empty, and may not contain %. See RFC 4007 for details. For example, fe80::1234%1 might identify address fe80::1234 on the first link of the node.

  2. An integer that fits into 128 bits.
  3. An integer packed into a bytes object of length 16, big-endian.
>>> ipaddress.IPv6Address('2001:db8::1000')
+IPv6Address('2001:db8::1000')
+>>> ipaddress.IPv6Address('ff02::5678%1')
+IPv6Address('ff02::5678%1')
+
+compressed
+

The short form of the address representation, with leading zeroes in groups omitted and the longest sequence of groups consisting entirely of zeroes collapsed to a single empty group.

This is also the value returned by str(addr) for IPv6 addresses.

+exploded
+

The long form of the address representation, with all leading zeroes and groups consisting entirely of zeroes included.

For the following attributes and methods, see the corresponding documentation of the IPv4Address class:

+packed
+
+reverse_pointer
+
+version
+
+max_prefixlen
+
+is_multicast
+
+is_private
+
+is_global
+
+is_unspecified
+
+is_reserved
+
+is_loopback
+
+

New in version 3.4: is_global

+
+is_site_local
+

True if the address is reserved for site-local usage. Note that the site-local address space has been deprecated by RFC 3879. Use is_private to test if this address is in the space of unique local addresses as defined by RFC 4193.

+
+ipv4_mapped
+

For addresses that appear to be IPv4 mapped addresses (starting with ::FFFF/96), this property will report the embedded IPv4 address. For any other address, this property will be None.

+
+scope_id
+

For scoped addresses as defined by RFC 4007, this property identifies the particular zone of the address’s scope that the address belongs to, as a string. When no scope zone is specified, this property will be None.

+
+sixtofour
+

For addresses that appear to be 6to4 addresses (starting with 2002::/16) as defined by RFC 3056, this property will report the embedded IPv4 address. For any other address, this property will be None.

+
+teredo
+

For addresses that appear to be Teredo addresses (starting with 2001::/32) as defined by RFC 4380, this property will report the embedded (server, client) IP address pair. For any other address, this property will be None.

+
+
+IPv6Address.__format__(fmt)
+

Refer to the corresponding method documentation in IPv4Address.

New in version 3.9.

+

Conversion to Strings and Integers

To interoperate with networking interfaces such as the socket module, addresses must be converted to strings or integers. This is handled using the str() and int() builtin functions:

>>> str(ipaddress.IPv4Address('192.168.0.1'))
+'192.168.0.1'
+>>> int(ipaddress.IPv4Address('192.168.0.1'))
+3232235521
+>>> str(ipaddress.IPv6Address('::1'))
+'::1'
+>>> int(ipaddress.IPv6Address('::1'))
+1
+

Note that IPv6 scoped addresses are converted to integers without scope zone ID.

Operators

Address objects support some operators. Unless stated otherwise, operators can only be applied between compatible objects (i.e. IPv4 with IPv4, IPv6 with IPv6).

Comparison operators

Address objects can be compared with the usual set of comparison operators. Same IPv6 addresses with different scope zone IDs are not equal. Some examples:

>>> IPv4Address('127.0.0.2') > IPv4Address('127.0.0.1')
+True
+>>> IPv4Address('127.0.0.2') == IPv4Address('127.0.0.1')
+False
+>>> IPv4Address('127.0.0.2') != IPv4Address('127.0.0.1')
+True
+>>> IPv6Address('fe80::1234') == IPv6Address('fe80::1234%1')
+False
+>>> IPv6Address('fe80::1234%1') != IPv6Address('fe80::1234%2')
+True
+

Arithmetic operators

Integers can be added to or subtracted from address objects. Some examples:

>>> IPv4Address('127.0.0.2') + 3
+IPv4Address('127.0.0.5')
+>>> IPv4Address('127.0.0.2') - 3
+IPv4Address('126.255.255.255')
+>>> IPv4Address('255.255.255.255') + 1
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ipaddress.AddressValueError: 4294967296 (>= 2**32) is not permitted as an IPv4 address
+

IP Network definitions

The IPv4Network and IPv6Network objects provide a mechanism for defining and inspecting IP network definitions. A network definition consists of a mask and a network address, and as such defines a range of IP addresses that equal the network address when masked (binary AND) with the mask. For example, a network definition with the mask 255.255.255.0 and the network address 192.168.1.0 consists of IP addresses in the inclusive range 192.168.1.0 to 192.168.1.255.

Prefix, net mask and host mask

There are several equivalent ways to specify IP network masks. A prefix /<nbits> is a notation that denotes how many high-order bits are set in the network mask. A net mask is an IP address with some number of high-order bits set. Thus the prefix /24 is equivalent to the net mask 255.255.255.0 in IPv4, or ffff:ff00:: in IPv6. In addition, a host mask is the logical inverse of a net mask, and is sometimes used (for example in Cisco access control lists) to denote a network mask. The host mask equivalent to /24 in IPv4 is 0.0.0.255.

Network objects

All attributes implemented by address objects are implemented by network objects as well. In addition, network objects implement additional attributes. All of these are common between IPv4Network and IPv6Network, so to avoid duplication they are only documented for IPv4Network. Network objects are hashable, so they can be used as keys in dictionaries.

+class ipaddress.IPv4Network(address, strict=True)
+

Construct an IPv4 network definition. address can be one of the following:

  1. +

    A string consisting of an IP address and an optional mask, separated by a slash (/). The IP address is the network address, and the mask can be either a single number, which means it’s a prefix, or a string representation of an IPv4 address. If it’s the latter, the mask is interpreted as a net mask if it starts with a non-zero field, or as a host mask if it starts with a zero field, with the single exception of an all-zero mask which is treated as a net mask. If no mask is provided, it’s considered to be /32.

    For example, the following address specifications are equivalent: 192.168.1.0/24, 192.168.1.0/255.255.255.0 and 192.168.1.0/0.0.0.255.

  2. An integer that fits into 32 bits. This is equivalent to a single-address network, with the network address being address and the mask being /32.
  3. An integer packed into a bytes object of length 4, big-endian. The interpretation is similar to an integer address.
  4. A two-tuple of an address description and a netmask, where the address description is either a string, a 32-bits integer, a 4-bytes packed integer, or an existing IPv4Address object; and the netmask is either an integer representing the prefix length (e.g. 24) or a string representing the prefix mask (e.g. 255.255.255.0).

An AddressValueError is raised if address is not a valid IPv4 address. A NetmaskValueError is raised if the mask is not valid for an IPv4 address.

If strict is True and host bits are set in the supplied address, then ValueError is raised. Otherwise, the host bits are masked out to determine the appropriate network address.

Unless stated otherwise, all network methods accepting other network/address objects will raise TypeError if the argument’s IP version is incompatible to self.

Changed in version 3.5: Added the two-tuple form for the address constructor parameter.

+version
+
+max_prefixlen
+

Refer to the corresponding attribute documentation in IPv4Address.

+
+is_multicast
+
+is_private
+
+is_unspecified
+
+is_reserved
+
+is_loopback
+
+

These attributes are true for the network as a whole if they are true for both the network address and the broadcast address.

+
+network_address
+

The network address for the network. The network address and the prefix length together uniquely define a network.

+
+broadcast_address
+

The broadcast address for the network. Packets sent to the broadcast address should be received by every host on the network.

+
+hostmask
+

The host mask, as an IPv4Address object.

+
+netmask
+

The net mask, as an IPv4Address object.

+
+with_prefixlen
+
+compressed
+
+exploded
+

A string representation of the network, with the mask in prefix notation.

with_prefixlen and compressed are always the same as str(network). exploded uses the exploded form the network address.

+
+with_netmask
+

A string representation of the network, with the mask in net mask notation.

+
+with_hostmask
+

A string representation of the network, with the mask in host mask notation.

+
+num_addresses
+

The total number of addresses in the network.

+
+prefixlen
+

Length of the network prefix, in bits.

+
+hosts()
+

Returns an iterator over the usable hosts in the network. The usable hosts are all the IP addresses that belong to the network, except the network address itself and the network broadcast address. For networks with a mask length of 31, the network address and network broadcast address are also included in the result. Networks with a mask of 32 will return a list containing the single host address.

>>> list(ip_network('192.0.2.0/29').hosts())  
+[IPv4Address('192.0.2.1'), IPv4Address('192.0.2.2'),
+ IPv4Address('192.0.2.3'), IPv4Address('192.0.2.4'),
+ IPv4Address('192.0.2.5'), IPv4Address('192.0.2.6')]
+>>> list(ip_network('192.0.2.0/31').hosts())
+[IPv4Address('192.0.2.0'), IPv4Address('192.0.2.1')]
+>>> list(ip_network('192.0.2.1/32').hosts())
+[IPv4Address('192.0.2.1')]
+
+
+overlaps(other)
+

True if this network is partly or wholly contained in other or other is wholly contained in this network.

+
+address_exclude(network)
+

Computes the network definitions resulting from removing the given network from this one. Returns an iterator of network objects. Raises ValueError if network is not completely contained in this network.

>>> n1 = ip_network('192.0.2.0/28')
+>>> n2 = ip_network('192.0.2.1/32')
+>>> list(n1.address_exclude(n2))  
+[IPv4Network('192.0.2.8/29'), IPv4Network('192.0.2.4/30'),
+ IPv4Network('192.0.2.2/31'), IPv4Network('192.0.2.0/32')]
+
+
+subnets(prefixlen_diff=1, new_prefix=None)
+

The subnets that join to make the current network definition, depending on the argument values. prefixlen_diff is the amount our prefix length should be increased by. new_prefix is the desired new prefix of the subnets; it must be larger than our prefix. One and only one of prefixlen_diff and new_prefix must be set. Returns an iterator of network objects.

>>> list(ip_network('192.0.2.0/24').subnets())
+[IPv4Network('192.0.2.0/25'), IPv4Network('192.0.2.128/25')]
+>>> list(ip_network('192.0.2.0/24').subnets(prefixlen_diff=2))  
+[IPv4Network('192.0.2.0/26'), IPv4Network('192.0.2.64/26'),
+ IPv4Network('192.0.2.128/26'), IPv4Network('192.0.2.192/26')]
+>>> list(ip_network('192.0.2.0/24').subnets(new_prefix=26))  
+[IPv4Network('192.0.2.0/26'), IPv4Network('192.0.2.64/26'),
+ IPv4Network('192.0.2.128/26'), IPv4Network('192.0.2.192/26')]
+>>> list(ip_network('192.0.2.0/24').subnets(new_prefix=23))
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+    raise ValueError('new prefix must be longer')
+ValueError: new prefix must be longer
+>>> list(ip_network('192.0.2.0/24').subnets(new_prefix=25))
+[IPv4Network('192.0.2.0/25'), IPv4Network('192.0.2.128/25')]
+
+
+supernet(prefixlen_diff=1, new_prefix=None)
+

The supernet containing this network definition, depending on the argument values. prefixlen_diff is the amount our prefix length should be decreased by. new_prefix is the desired new prefix of the supernet; it must be smaller than our prefix. One and only one of prefixlen_diff and new_prefix must be set. Returns a single network object.

>>> ip_network('192.0.2.0/24').supernet()
+IPv4Network('192.0.2.0/23')
+>>> ip_network('192.0.2.0/24').supernet(prefixlen_diff=2)
+IPv4Network('192.0.0.0/22')
+>>> ip_network('192.0.2.0/24').supernet(new_prefix=20)
+IPv4Network('192.0.0.0/20')
+
+
+subnet_of(other)
+

Return True if this network is a subnet of other.

>>> a = ip_network('192.168.1.0/24')
+>>> b = ip_network('192.168.1.128/30')
+>>> b.subnet_of(a)
+True
+

New in version 3.7.

+
+supernet_of(other)
+

Return True if this network is a supernet of other.

>>> a = ip_network('192.168.1.0/24')
+>>> b = ip_network('192.168.1.128/30')
+>>> a.supernet_of(b)
+True
+

New in version 3.7.

+
+compare_networks(other)
+

Compare this network to other. In this comparison only the network addresses are considered; host bits aren’t. Returns either -1, 0 or 1.

>>> ip_network('192.0.2.1/32').compare_networks(ip_network('192.0.2.2/32'))
+-1
+>>> ip_network('192.0.2.1/32').compare_networks(ip_network('192.0.2.0/32'))
+1
+>>> ip_network('192.0.2.1/32').compare_networks(ip_network('192.0.2.1/32'))
+0
+

Deprecated since version 3.7: It uses the same ordering and comparison algorithm as “<”, “==”, and “>”

+
+
+class ipaddress.IPv6Network(address, strict=True)
+

Construct an IPv6 network definition. address can be one of the following:

  1. +

    A string consisting of an IP address and an optional prefix length, separated by a slash (/). The IP address is the network address, and the prefix length must be a single number, the prefix. If no prefix length is provided, it’s considered to be /128.

    Note that currently expanded netmasks are not supported. That means 2001:db00::0/24 is a valid argument while 2001:db00::0/ffff:ff00:: is not.

  2. An integer that fits into 128 bits. This is equivalent to a single-address network, with the network address being address and the mask being /128.
  3. An integer packed into a bytes object of length 16, big-endian. The interpretation is similar to an integer address.
  4. A two-tuple of an address description and a netmask, where the address description is either a string, a 128-bits integer, a 16-bytes packed integer, or an existing IPv6Address object; and the netmask is an integer representing the prefix length.

An AddressValueError is raised if address is not a valid IPv6 address. A NetmaskValueError is raised if the mask is not valid for an IPv6 address.

If strict is True and host bits are set in the supplied address, then ValueError is raised. Otherwise, the host bits are masked out to determine the appropriate network address.

Changed in version 3.5: Added the two-tuple form for the address constructor parameter.

+version
+
+max_prefixlen
+
+is_multicast
+
+is_private
+
+is_unspecified
+
+is_reserved
+
+is_loopback
+
+
+network_address
+
+broadcast_address
+
+hostmask
+
+netmask
+
+with_prefixlen
+
+compressed
+
+exploded
+
+with_netmask
+
+with_hostmask
+
+num_addresses
+
+prefixlen
+
+hosts()
+

Returns an iterator over the usable hosts in the network. The usable hosts are all the IP addresses that belong to the network, except the Subnet-Router anycast address. For networks with a mask length of 127, the Subnet-Router anycast address is also included in the result. Networks with a mask of 128 will return a list containing the single host address.

+
+overlaps(other)
+
+address_exclude(network)
+
+subnets(prefixlen_diff=1, new_prefix=None)
+
+supernet(prefixlen_diff=1, new_prefix=None)
+
+subnet_of(other)
+
+supernet_of(other)
+
+compare_networks(other)
+

Refer to the corresponding attribute documentation in IPv4Network.

+
+is_site_local
+

These attribute is true for the network as a whole if it is true for both the network address and the broadcast address.

+
+

Operators

Network objects support some operators. Unless stated otherwise, operators can only be applied between compatible objects (i.e. IPv4 with IPv4, IPv6 with IPv6).

Logical operators

Network objects can be compared with the usual set of logical operators. Network objects are ordered first by network address, then by net mask.

Iteration

Network objects can be iterated to list all the addresses belonging to the network. For iteration, all hosts are returned, including unusable hosts (for usable hosts, use the hosts() method). An example:

>>> for addr in IPv4Network('192.0.2.0/28'):
+...     addr
+...
+IPv4Address('192.0.2.0')
+IPv4Address('192.0.2.1')
+IPv4Address('192.0.2.2')
+IPv4Address('192.0.2.3')
+IPv4Address('192.0.2.4')
+IPv4Address('192.0.2.5')
+IPv4Address('192.0.2.6')
+IPv4Address('192.0.2.7')
+IPv4Address('192.0.2.8')
+IPv4Address('192.0.2.9')
+IPv4Address('192.0.2.10')
+IPv4Address('192.0.2.11')
+IPv4Address('192.0.2.12')
+IPv4Address('192.0.2.13')
+IPv4Address('192.0.2.14')
+IPv4Address('192.0.2.15')
+

Networks as containers of addresses

Network objects can act as containers of addresses. Some examples:

>>> IPv4Network('192.0.2.0/28')[0]
+IPv4Address('192.0.2.0')
+>>> IPv4Network('192.0.2.0/28')[15]
+IPv4Address('192.0.2.15')
+>>> IPv4Address('192.0.2.6') in IPv4Network('192.0.2.0/28')
+True
+>>> IPv4Address('192.0.3.6') in IPv4Network('192.0.2.0/28')
+False
+

Interface objects

Interface objects are hashable, so they can be used as keys in dictionaries.

+class ipaddress.IPv4Interface(address)
+

Construct an IPv4 interface. The meaning of address is as in the constructor of IPv4Network, except that arbitrary host addresses are always accepted.

IPv4Interface is a subclass of IPv4Address, so it inherits all the attributes from that class. In addition, the following attributes are available:

+ip
+

The address (IPv4Address) without network information.

>>> interface = IPv4Interface('192.0.2.5/24')
+>>> interface.ip
+IPv4Address('192.0.2.5')
+
+
+network
+

The network (IPv4Network) this interface belongs to.

>>> interface = IPv4Interface('192.0.2.5/24')
+>>> interface.network
+IPv4Network('192.0.2.0/24')
+
+
+with_prefixlen
+

A string representation of the interface with the mask in prefix notation.

>>> interface = IPv4Interface('192.0.2.5/24')
+>>> interface.with_prefixlen
+'192.0.2.5/24'
+
+
+with_netmask
+

A string representation of the interface with the network as a net mask.

>>> interface = IPv4Interface('192.0.2.5/24')
+>>> interface.with_netmask
+'192.0.2.5/255.255.255.0'
+
+
+with_hostmask
+

A string representation of the interface with the network as a host mask.

>>> interface = IPv4Interface('192.0.2.5/24')
+>>> interface.with_hostmask
+'192.0.2.5/0.0.0.255'
+
+
+
+class ipaddress.IPv6Interface(address)
+

Construct an IPv6 interface. The meaning of address is as in the constructor of IPv6Network, except that arbitrary host addresses are always accepted.

IPv6Interface is a subclass of IPv6Address, so it inherits all the attributes from that class. In addition, the following attributes are available:

+ip
+
+network
+
+with_prefixlen
+
+with_netmask
+
+with_hostmask
+

Refer to the corresponding attribute documentation in IPv4Interface.

+
+

Operators

Interface objects support some operators. Unless stated otherwise, operators can only be applied between compatible objects (i.e. IPv4 with IPv4, IPv6 with IPv6).

Logical operators

Interface objects can be compared with the usual set of logical operators.

For equality comparison (== and !=), both the IP address and network must be the same for the objects to be equal. An interface will not compare equal to any address or network object.

For ordering (<, >, etc) the rules are different. Interface and address objects with the same IP version can be compared, and the address objects will always sort before the interface objects. Two interface objects are first compared by their networks and, if those are the same, then by their IP addresses.

Other Module Level Functions

The module also provides the following module level functions:

+ipaddress.v4_int_to_packed(address)
+

Represent an address as 4 packed bytes in network (big-endian) order. address is an integer representation of an IPv4 IP address. A ValueError is raised if the integer is negative or too large to be an IPv4 IP address.

>>> ipaddress.ip_address(3221225985)
+IPv4Address('192.0.2.1')
+>>> ipaddress.v4_int_to_packed(3221225985)
+b'\xc0\x00\x02\x01'
+
+
+ipaddress.v6_int_to_packed(address)
+

Represent an address as 16 packed bytes in network (big-endian) order. address is an integer representation of an IPv6 IP address. A ValueError is raised if the integer is negative or too large to be an IPv6 IP address.

+
+ipaddress.summarize_address_range(first, last)
+

Return an iterator of the summarized network range given the first and last IP addresses. first is the first IPv4Address or IPv6Address in the range and last is the last IPv4Address or IPv6Address in the range. A TypeError is raised if first or last are not IP addresses or are not of the same version. A ValueError is raised if last is not greater than first or if first address version is not 4 or 6.

>>> [ipaddr for ipaddr in ipaddress.summarize_address_range(
+...    ipaddress.IPv4Address('192.0.2.0'),
+...    ipaddress.IPv4Address('192.0.2.130'))]
+[IPv4Network('192.0.2.0/25'), IPv4Network('192.0.2.128/31'), IPv4Network('192.0.2.130/32')]
+
+
+ipaddress.collapse_addresses(addresses)
+

Return an iterator of the collapsed IPv4Network or IPv6Network objects. addresses is an iterator of IPv4Network or IPv6Network objects. A TypeError is raised if addresses contains mixed version objects.

>>> [ipaddr for ipaddr in
+... ipaddress.collapse_addresses([ipaddress.IPv4Network('192.0.2.0/25'),
+... ipaddress.IPv4Network('192.0.2.128/25')])]
+[IPv4Network('192.0.2.0/24')]
+
+
+ipaddress.get_mixed_type_key(obj)
+

Return a key suitable for sorting between networks and addresses. Address and Network objects are not sortable by default; they’re fundamentally different, so the expression:

IPv4Address('192.0.2.0') <= IPv4Network('192.0.2.0/24')
+

doesn’t make sense. There are some times however, where you may wish to have ipaddress sort these anyway. If you need to do this, you can use this function as the key argument to sorted().

obj is either a network or address object.

+

Custom Exceptions

To support more specific error reporting from class constructors, the module defines the following exceptions:

+exception ipaddress.AddressValueError(ValueError)
+

Any value error related to the address.

+
+exception ipaddress.NetmaskValueError(ValueError)
+

Any value error related to the net mask.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ipaddress.html +

+
diff --git a/devdocs/python~3.12/library%2Fipc.html b/devdocs/python~3.12/library%2Fipc.html new file mode 100644 index 00000000..74ed801c --- /dev/null +++ b/devdocs/python~3.12/library%2Fipc.html @@ -0,0 +1,6 @@ +

Networking and Interprocess Communication

The modules described in this chapter provide mechanisms for networking and inter-processes communication.

Some modules only work for two processes that are on the same machine, e.g. signal and mmap. Other modules support networking protocols that two or more processes can use to communicate across machines.

The list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ipc.html +

+
diff --git a/devdocs/python~3.12/library%2Fitertools.html b/devdocs/python~3.12/library%2Fitertools.html new file mode 100644 index 00000000..f0b76804 --- /dev/null +++ b/devdocs/python~3.12/library%2Fitertools.html @@ -0,0 +1,746 @@ +

itertools — Functions creating iterators for efficient looping

This module implements a number of iterator building blocks inspired by constructs from APL, Haskell, and SML. Each has been recast in a form suitable for Python.

The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an “iterator algebra” making it possible to construct specialized tools succinctly and efficiently in pure Python.

For instance, SML provides a tabulation tool: tabulate(f) which produces a sequence f(0), f(1), .... The same effect can be achieved in Python by combining map() and count() to form map(f, count()).

These tools and their built-in counterparts also work well with the high-speed functions in the operator module. For example, the multiplication operator can be mapped across two vectors to form an efficient dot-product: sum(starmap(operator.mul, zip(vec1, vec2, strict=True))).

Infinite iterators:

+ + + +

Iterator

Arguments

Results

Example

count()

[start[, step]]

start, start+step, start+2*step, …

count(10) --> 10 11 12 13 14 ...

cycle()

p

p0, p1, … plast, p0, p1, …

cycle('ABCD') --> A B C D A B C D ...

repeat()

elem [,n]

elem, elem, elem, … endlessly or up to n times

repeat(10, 3) --> 10 10 10

Iterators terminating on the shortest input sequence:

+ + + + + + + + + + + + + + +

Iterator

Arguments

Results

Example

accumulate()

p [,func]

p0, p0+p1, p0+p1+p2, …

accumulate([1,2,3,4,5]) --> 1 3 6 10 15

batched()

p, n

(p0, p1, …, p_n-1), …

batched('ABCDEFG', n=3) --> ABC DEF G

chain()

p, q, …

p0, p1, … plast, q0, q1, …

chain('ABC', 'DEF') --> A B C D E F

chain.from_iterable()

iterable

p0, p1, … plast, q0, q1, …

chain.from_iterable(['ABC', 'DEF']) --> A B C D E F

compress()

data, selectors

(d[0] if s[0]), (d[1] if s[1]), …

compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F

dropwhile()

pred, seq

seq[n], seq[n+1], starting when pred fails

dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1

filterfalse()

pred, seq

elements of seq where pred(elem) is false

filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8

groupby()

iterable[, key]

sub-iterators grouped by value of key(v)

islice()

seq, [start,] stop [, step]

elements from seq[start:stop:step]

islice('ABCDEFG', 2, None) --> C D E F G

pairwise()

iterable

(p[0], p[1]), (p[1], p[2])

pairwise('ABCDEFG') --> AB BC CD DE EF FG

starmap()

func, seq

func(*seq[0]), func(*seq[1]), …

starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000

takewhile()

pred, seq

seq[0], seq[1], until pred fails

takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4

tee()

it, n

it1, it2, … itn splits one iterator into n

zip_longest()

p, q, …

(p[0], q[0]), (p[1], q[1]), …

zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-

Combinatoric iterators:

+ + + + +

Iterator

Arguments

Results

product()

p, q, … [repeat=1]

cartesian product, equivalent to a nested for-loop

permutations()

p[, r]

r-length tuples, all possible orderings, no repeated elements

combinations()

p, r

r-length tuples, in sorted order, no repeated elements

combinations_with_replacement()

p, r

r-length tuples, in sorted order, with repeated elements
+ + + + +

Examples

Results

product('ABCD', repeat=2)

AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD

permutations('ABCD', 2)

AB AC AD BA BC BD CA CB CD DA DB DC

combinations('ABCD', 2)

AB AC AD BC BD CD

combinations_with_replacement('ABCD', 2)

AA AB AC AD BB BC BD CC CD DD

Itertool functions

The following module functions all construct and return iterators. Some provide streams of infinite length, so they should only be accessed by functions or loops that truncate the stream.

+itertools.accumulate(iterable[, func, *, initial=None])
+

Make an iterator that returns accumulated sums, or accumulated results of other binary functions (specified via the optional func argument).

If func is supplied, it should be a function of two arguments. Elements of the input iterable may be any type that can be accepted as arguments to func. (For example, with the default operation of addition, elements may be any addable type including Decimal or Fraction.)

Usually, the number of elements output matches the input iterable. However, if the keyword argument initial is provided, the accumulation leads off with the initial value so that the output has one more element than the input iterable.

Roughly equivalent to:

def accumulate(iterable, func=operator.add, *, initial=None):
+    'Return running totals'
+    # accumulate([1,2,3,4,5]) --> 1 3 6 10 15
+    # accumulate([1,2,3,4,5], initial=100) --> 100 101 103 106 110 115
+    # accumulate([1,2,3,4,5], operator.mul) --> 1 2 6 24 120
+    it = iter(iterable)
+    total = initial
+    if initial is None:
+        try:
+            total = next(it)
+        except StopIteration:
+            return
+    yield total
+    for element in it:
+        total = func(total, element)
+        yield total
+

There are a number of uses for the func argument. It can be set to min() for a running minimum, max() for a running maximum, or operator.mul() for a running product. Amortization tables can be built by accumulating interest and applying payments:

>>> data = [3, 4, 6, 2, 1, 9, 0, 7, 5, 8]
+>>> list(accumulate(data, operator.mul))     # running product
+[3, 12, 72, 144, 144, 1296, 0, 0, 0, 0]
+>>> list(accumulate(data, max))              # running maximum
+[3, 4, 6, 6, 6, 9, 9, 9, 9, 9]
+
+# Amortize a 5% loan of 1000 with 10 annual payments of 90
+>>> account_update = lambda bal, pmt: round(bal * 1.05) + pmt
+>>> list(accumulate(repeat(-90, 10), account_update, initial=1_000))
+[1000, 960, 918, 874, 828, 779, 728, 674, 618, 559, 497]
+

See functools.reduce() for a similar function that returns only the final accumulated value.

New in version 3.2.

Changed in version 3.3: Added the optional func parameter.

Changed in version 3.8: Added the optional initial parameter.

+
+itertools.batched(iterable, n)
+

Batch data from the iterable into tuples of length n. The last batch may be shorter than n.

Loops over the input iterable and accumulates data into tuples up to size n. The input is consumed lazily, just enough to fill a batch. The result is yielded as soon as the batch is full or when the input iterable is exhausted:

>>> flattened_data = ['roses', 'red', 'violets', 'blue', 'sugar', 'sweet']
+>>> unflattened = list(batched(flattened_data, 2))
+>>> unflattened
+[('roses', 'red'), ('violets', 'blue'), ('sugar', 'sweet')]
+
+>>> for batch in batched('ABCDEFG', 3):
+...     print(batch)
+...
+('A', 'B', 'C')
+('D', 'E', 'F')
+('G',)
+

Roughly equivalent to:

def batched(iterable, n):
+    # batched('ABCDEFG', 3) --> ABC DEF G
+    if n < 1:
+        raise ValueError('n must be at least one')
+    it = iter(iterable)
+    while batch := tuple(islice(it, n)):
+        yield batch
+

New in version 3.12.

+
+itertools.chain(*iterables)
+

Make an iterator that returns elements from the first iterable until it is exhausted, then proceeds to the next iterable, until all of the iterables are exhausted. Used for treating consecutive sequences as a single sequence. Roughly equivalent to:

def chain(*iterables):
+    # chain('ABC', 'DEF') --> A B C D E F
+    for it in iterables:
+        for element in it:
+            yield element
+
+
+classmethod chain.from_iterable(iterable)
+

Alternate constructor for chain(). Gets chained inputs from a single iterable argument that is evaluated lazily. Roughly equivalent to:

def from_iterable(iterables):
+    # chain.from_iterable(['ABC', 'DEF']) --> A B C D E F
+    for it in iterables:
+        for element in it:
+            yield element
+
+
+itertools.combinations(iterable, r)
+

Return r length subsequences of elements from the input iterable.

The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the output tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeated values in each combination.

Roughly equivalent to:

def combinations(iterable, r):
+    # combinations('ABCD', 2) --> AB AC AD BC BD CD
+    # combinations(range(4), 3) --> 012 013 023 123
+    pool = tuple(iterable)
+    n = len(pool)
+    if r > n:
+        return
+    indices = list(range(r))
+    yield tuple(pool[i] for i in indices)
+    while True:
+        for i in reversed(range(r)):
+            if indices[i] != i + n - r:
+                break
+        else:
+            return
+        indices[i] += 1
+        for j in range(i+1, r):
+            indices[j] = indices[j-1] + 1
+        yield tuple(pool[i] for i in indices)
+

The code for combinations() can be also expressed as a subsequence of permutations() after filtering entries where the elements are not in sorted order (according to their position in the input pool):

def combinations(iterable, r):
+    pool = tuple(iterable)
+    n = len(pool)
+    for indices in permutations(range(n), r):
+        if sorted(indices) == list(indices):
+            yield tuple(pool[i] for i in indices)
+

The number of items returned is n! / r! / (n-r)! when 0 <= r <= n or zero when r > n.

+
+itertools.combinations_with_replacement(iterable, r)
+

Return r length subsequences of elements from the input iterable allowing individual elements to be repeated more than once.

The combination tuples are emitted in lexicographic ordering according to the order of the input iterable. So, if the input iterable is sorted, the output tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, the generated combinations will also be unique.

Roughly equivalent to:

def combinations_with_replacement(iterable, r):
+    # combinations_with_replacement('ABC', 2) --> AA AB AC BB BC CC
+    pool = tuple(iterable)
+    n = len(pool)
+    if not n and r:
+        return
+    indices = [0] * r
+    yield tuple(pool[i] for i in indices)
+    while True:
+        for i in reversed(range(r)):
+            if indices[i] != n - 1:
+                break
+        else:
+            return
+        indices[i:] = [indices[i] + 1] * (r - i)
+        yield tuple(pool[i] for i in indices)
+

The code for combinations_with_replacement() can be also expressed as a subsequence of product() after filtering entries where the elements are not in sorted order (according to their position in the input pool):

def combinations_with_replacement(iterable, r):
+    pool = tuple(iterable)
+    n = len(pool)
+    for indices in product(range(n), repeat=r):
+        if sorted(indices) == list(indices):
+            yield tuple(pool[i] for i in indices)
+

The number of items returned is (n+r-1)! / r! / (n-1)! when n > 0.

New in version 3.1.

+
+itertools.compress(data, selectors)
+

Make an iterator that filters elements from data returning only those that have a corresponding element in selectors that evaluates to True. Stops when either the data or selectors iterables has been exhausted. Roughly equivalent to:

def compress(data, selectors):
+    # compress('ABCDEF', [1,0,1,0,1,1]) --> A C E F
+    return (d for d, s in zip(data, selectors) if s)
+

New in version 3.1.

+
+itertools.count(start=0, step=1)
+

Make an iterator that returns evenly spaced values starting with number start. Often used as an argument to map() to generate consecutive data points. Also, used with zip() to add sequence numbers. Roughly equivalent to:

def count(start=0, step=1):
+    # count(10) --> 10 11 12 13 14 ...
+    # count(2.5, 0.5) --> 2.5 3.0 3.5 ...
+    n = start
+    while True:
+        yield n
+        n += step
+

When counting with floating point numbers, better accuracy can sometimes be achieved by substituting multiplicative code such as: (start + step * i +for i in count()).

Changed in version 3.1: Added step argument and allowed non-integer arguments.

+
+itertools.cycle(iterable)
+

Make an iterator returning elements from the iterable and saving a copy of each. When the iterable is exhausted, return elements from the saved copy. Repeats indefinitely. Roughly equivalent to:

def cycle(iterable):
+    # cycle('ABCD') --> A B C D A B C D A B C D ...
+    saved = []
+    for element in iterable:
+        yield element
+        saved.append(element)
+    while saved:
+        for element in saved:
+              yield element
+

Note, this member of the toolkit may require significant auxiliary storage (depending on the length of the iterable).

+
+itertools.dropwhile(predicate, iterable)
+

Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element. Note, the iterator does not produce any output until the predicate first becomes false, so it may have a lengthy start-up time. Roughly equivalent to:

def dropwhile(predicate, iterable):
+    # dropwhile(lambda x: x<5, [1,4,6,4,1]) --> 6 4 1
+    iterable = iter(iterable)
+    for x in iterable:
+        if not predicate(x):
+            yield x
+            break
+    for x in iterable:
+        yield x
+
+
+itertools.filterfalse(predicate, iterable)
+

Make an iterator that filters elements from iterable returning only those for which the predicate is false. If predicate is None, return the items that are false. Roughly equivalent to:

def filterfalse(predicate, iterable):
+    # filterfalse(lambda x: x%2, range(10)) --> 0 2 4 6 8
+    if predicate is None:
+        predicate = bool
+    for x in iterable:
+        if not predicate(x):
+            yield x
+
+
+itertools.groupby(iterable, key=None)
+

Make an iterator that returns consecutive keys and groups from the iterable. The key is a function computing a key value for each element. If not specified or is None, key defaults to an identity function and returns the element unchanged. Generally, the iterable needs to already be sorted on the same key function.

The operation of groupby() is similar to the uniq filter in Unix. It generates a break or new group every time the value of the key function changes (which is why it is usually necessary to have sorted the data using the same key function). That behavior differs from SQL’s GROUP BY which aggregates common elements regardless of their input order.

The returned group is itself an iterator that shares the underlying iterable with groupby(). Because the source is shared, when the groupby() object is advanced, the previous group is no longer visible. So, if that data is needed later, it should be stored as a list:

groups = []
+uniquekeys = []
+data = sorted(data, key=keyfunc)
+for k, g in groupby(data, keyfunc):
+    groups.append(list(g))      # Store group iterator as a list
+    uniquekeys.append(k)
+

groupby() is roughly equivalent to:

class groupby:
+    # [k for k, g in groupby('AAAABBBCCDAABBB')] --> A B C D A B
+    # [list(g) for k, g in groupby('AAAABBBCCD')] --> AAAA BBB CC D
+
+    def __init__(self, iterable, key=None):
+        if key is None:
+            key = lambda x: x
+        self.keyfunc = key
+        self.it = iter(iterable)
+        self.tgtkey = self.currkey = self.currvalue = object()
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        self.id = object()
+        while self.currkey == self.tgtkey:
+            self.currvalue = next(self.it)    # Exit on StopIteration
+            self.currkey = self.keyfunc(self.currvalue)
+        self.tgtkey = self.currkey
+        return (self.currkey, self._grouper(self.tgtkey, self.id))
+
+    def _grouper(self, tgtkey, id):
+        while self.id is id and self.currkey == tgtkey:
+            yield self.currvalue
+            try:
+                self.currvalue = next(self.it)
+            except StopIteration:
+                return
+            self.currkey = self.keyfunc(self.currvalue)
+
+
+itertools.islice(iterable, stop)
itertools.islice(iterable, start, stop[, step]) +
+

Make an iterator that returns selected elements from the iterable. If start is non-zero, then elements from the iterable are skipped until start is reached. Afterward, elements are returned consecutively unless step is set higher than one which results in items being skipped. If stop is None, then iteration continues until the iterator is exhausted, if at all; otherwise, it stops at the specified position.

If start is None, then iteration starts at zero. If step is None, then the step defaults to one.

Unlike regular slicing, islice() does not support negative values for start, stop, or step. Can be used to extract related fields from data where the internal structure has been flattened (for example, a multi-line report may list a name field on every third line).

Roughly equivalent to:

def islice(iterable, *args):
+    # islice('ABCDEFG', 2) --> A B
+    # islice('ABCDEFG', 2, 4) --> C D
+    # islice('ABCDEFG', 2, None) --> C D E F G
+    # islice('ABCDEFG', 0, None, 2) --> A C E G
+    s = slice(*args)
+    start, stop, step = s.start or 0, s.stop or sys.maxsize, s.step or 1
+    it = iter(range(start, stop, step))
+    try:
+        nexti = next(it)
+    except StopIteration:
+        # Consume *iterable* up to the *start* position.
+        for i, element in zip(range(start), iterable):
+            pass
+        return
+    try:
+        for i, element in enumerate(iterable):
+            if i == nexti:
+                yield element
+                nexti = next(it)
+    except StopIteration:
+        # Consume to *stop*.
+        for i, element in zip(range(i + 1, stop), iterable):
+            pass
+
+
+itertools.pairwise(iterable)
+

Return successive overlapping pairs taken from the input iterable.

The number of 2-tuples in the output iterator will be one fewer than the number of inputs. It will be empty if the input iterable has fewer than two values.

Roughly equivalent to:

def pairwise(iterable):
+    # pairwise('ABCDEFG') --> AB BC CD DE EF FG
+    a, b = tee(iterable)
+    next(b, None)
+    return zip(a, b)
+

New in version 3.10.

+
+itertools.permutations(iterable, r=None)
+

Return successive r length permutations of elements in the iterable.

If r is not specified or is None, then r defaults to the length of the iterable and all possible full-length permutations are generated.

The permutation tuples are emitted in lexicographic order according to the order of the input iterable. So, if the input iterable is sorted, the output tuples will be produced in sorted order.

Elements are treated as unique based on their position, not on their value. So if the input elements are unique, there will be no repeated values within a permutation.

Roughly equivalent to:

def permutations(iterable, r=None):
+    # permutations('ABCD', 2) --> AB AC AD BA BC BD CA CB CD DA DB DC
+    # permutations(range(3)) --> 012 021 102 120 201 210
+    pool = tuple(iterable)
+    n = len(pool)
+    r = n if r is None else r
+    if r > n:
+        return
+    indices = list(range(n))
+    cycles = list(range(n, n-r, -1))
+    yield tuple(pool[i] for i in indices[:r])
+    while n:
+        for i in reversed(range(r)):
+            cycles[i] -= 1
+            if cycles[i] == 0:
+                indices[i:] = indices[i+1:] + indices[i:i+1]
+                cycles[i] = n - i
+            else:
+                j = cycles[i]
+                indices[i], indices[-j] = indices[-j], indices[i]
+                yield tuple(pool[i] for i in indices[:r])
+                break
+        else:
+            return
+

The code for permutations() can be also expressed as a subsequence of product(), filtered to exclude entries with repeated elements (those from the same position in the input pool):

def permutations(iterable, r=None):
+    pool = tuple(iterable)
+    n = len(pool)
+    r = n if r is None else r
+    for indices in product(range(n), repeat=r):
+        if len(set(indices)) == r:
+            yield tuple(pool[i] for i in indices)
+

The number of items returned is n! / (n-r)! when 0 <= r <= n or zero when r > n.

+
+itertools.product(*iterables, repeat=1)
+

Cartesian product of input iterables.

Roughly equivalent to nested for-loops in a generator expression. For example, product(A, B) returns the same as ((x,y) for x in A for y in B).

The nested loops cycle like an odometer with the rightmost element advancing on every iteration. This pattern creates a lexicographic ordering so that if the input’s iterables are sorted, the product tuples are emitted in sorted order.

To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A).

This function is roughly equivalent to the following code, except that the actual implementation does not build up intermediate results in memory:

def product(*args, repeat=1):
+    # product('ABCD', 'xy') --> Ax Ay Bx By Cx Cy Dx Dy
+    # product(range(2), repeat=3) --> 000 001 010 011 100 101 110 111
+    pools = [tuple(pool) for pool in args] * repeat
+    result = [[]]
+    for pool in pools:
+        result = [x+[y] for x in result for y in pool]
+    for prod in result:
+        yield tuple(prod)
+

Before product() runs, it completely consumes the input iterables, keeping pools of values in memory to generate the products. Accordingly, it is only useful with finite inputs.

+
+itertools.repeat(object[, times])
+

Make an iterator that returns object over and over again. Runs indefinitely unless the times argument is specified.

Roughly equivalent to:

def repeat(object, times=None):
+    # repeat(10, 3) --> 10 10 10
+    if times is None:
+        while True:
+            yield object
+    else:
+        for i in range(times):
+            yield object
+

A common use for repeat is to supply a stream of constant values to map or zip:

>>> list(map(pow, range(10), repeat(2)))
+[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+
+
+itertools.starmap(function, iterable)
+

Make an iterator that computes the function using arguments obtained from the iterable. Used instead of map() when argument parameters are already grouped in tuples from a single iterable (when the data has been “pre-zipped”).

The difference between map() and starmap() parallels the distinction between function(a,b) and function(*c). Roughly equivalent to:

def starmap(function, iterable):
+    # starmap(pow, [(2,5), (3,2), (10,3)]) --> 32 9 1000
+    for args in iterable:
+        yield function(*args)
+
+
+itertools.takewhile(predicate, iterable)
+

Make an iterator that returns elements from the iterable as long as the predicate is true. Roughly equivalent to:

def takewhile(predicate, iterable):
+    # takewhile(lambda x: x<5, [1,4,6,4,1]) --> 1 4
+    for x in iterable:
+        if predicate(x):
+            yield x
+        else:
+            break
+
+
+itertools.tee(iterable, n=2)
+

Return n independent iterators from a single iterable.

The following Python code helps explain what tee does (although the actual implementation is more complex and uses only a single underlying FIFO queue):

def tee(iterable, n=2):
+    it = iter(iterable)
+    deques = [collections.deque() for i in range(n)]
+    def gen(mydeque):
+        while True:
+            if not mydeque:             # when the local deque is empty
+                try:
+                    newval = next(it)   # fetch a new value and
+                except StopIteration:
+                    return
+                for d in deques:        # load it to all the deques
+                    d.append(newval)
+            yield mydeque.popleft()
+    return tuple(gen(d) for d in deques)
+

Once a tee() has been created, the original iterable should not be used anywhere else; otherwise, the iterable could get advanced without the tee objects being informed.

tee iterators are not threadsafe. A RuntimeError may be raised when simultaneously using iterators returned by the same tee() call, even if the original iterable is threadsafe.

This itertool may require significant auxiliary storage (depending on how much temporary data needs to be stored). In general, if one iterator uses most or all of the data before another iterator starts, it is faster to use list() instead of tee().

+
+itertools.zip_longest(*iterables, fillvalue=None)
+

Make an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted. Roughly equivalent to:

def zip_longest(*args, fillvalue=None):
+    # zip_longest('ABCD', 'xy', fillvalue='-') --> Ax By C- D-
+    iterators = [iter(it) for it in args]
+    num_active = len(iterators)
+    if not num_active:
+        return
+    while True:
+        values = []
+        for i, it in enumerate(iterators):
+            try:
+                value = next(it)
+            except StopIteration:
+                num_active -= 1
+                if not num_active:
+                    return
+                iterators[i] = repeat(fillvalue)
+                value = fillvalue
+            values.append(value)
+        yield tuple(values)
+

If one of the iterables is potentially infinite, then the zip_longest() function should be wrapped with something that limits the number of calls (for example islice() or takewhile()). If not specified, fillvalue defaults to None.

+

Itertools Recipes

This section shows recipes for creating an extended toolset using the existing itertools as building blocks.

The primary purpose of the itertools recipes is educational. The recipes show various ways of thinking about individual tools — for example, that chain.from_iterable is related to the concept of flattening. The recipes also give ideas about ways that the tools can be combined — for example, how compress() and range() can work together. The recipes also show patterns for using itertools with the operator and collections modules as well as with the built-in itertools such as map(), filter(), reversed(), and enumerate().

A secondary purpose of the recipes is to serve as an incubator. The accumulate(), compress(), and pairwise() itertools started out as recipes. Currently, the sliding_window() and iter_index() recipes are being tested to see whether they prove their worth.

Substantially all of these recipes and many, many others can be installed from the more-itertools project found on the Python Package Index:

python -m pip install more-itertools
+

Many of the recipes offer the same high performance as the underlying toolset. Superior memory performance is kept by processing elements one at a time rather than bringing the whole iterable into memory all at once. Code volume is kept small by linking the tools together in a functional style which helps eliminate temporary variables. High speed is retained by preferring “vectorized” building blocks over the use of for-loops and generators which incur interpreter overhead.

import collections
+import functools
+import math
+import operator
+import random
+
+def take(n, iterable):
+    "Return first n items of the iterable as a list."
+    return list(islice(iterable, n))
+
+def prepend(value, iterable):
+    "Prepend a single value in front of an iterable."
+    # prepend(1, [2, 3, 4]) --> 1 2 3 4
+    return chain([value], iterable)
+
+def tabulate(function, start=0):
+    "Return function(0), function(1), ..."
+    return map(function, count(start))
+
+def repeatfunc(func, times=None, *args):
+    """Repeat calls to func with specified arguments.
+
+    Example:  repeatfunc(random.random)
+    """
+    if times is None:
+        return starmap(func, repeat(args))
+    return starmap(func, repeat(args, times))
+
+def flatten(list_of_lists):
+    "Flatten one level of nesting."
+    return chain.from_iterable(list_of_lists)
+
+def ncycles(iterable, n):
+    "Returns the sequence elements n times."
+    return chain.from_iterable(repeat(tuple(iterable), n))
+
+def tail(n, iterable):
+    "Return an iterator over the last n items."
+    # tail(3, 'ABCDEFG') --> E F G
+    return iter(collections.deque(iterable, maxlen=n))
+
+def consume(iterator, n=None):
+    "Advance the iterator n-steps ahead. If n is None, consume entirely."
+    # Use functions that consume iterators at C speed.
+    if n is None:
+        # feed the entire iterator into a zero-length deque
+        collections.deque(iterator, maxlen=0)
+    else:
+        # advance to the empty slice starting at position n
+        next(islice(iterator, n, n), None)
+
+def nth(iterable, n, default=None):
+    "Returns the nth item or a default value."
+    return next(islice(iterable, n, None), default)
+
+def quantify(iterable, pred=bool):
+    "Given a predicate that returns True or False, count the True results."
+    return sum(map(pred, iterable))
+
+def all_equal(iterable):
+    "Returns True if all the elements are equal to each other."
+    g = groupby(iterable)
+    return next(g, True) and not next(g, False)
+
+def first_true(iterable, default=False, pred=None):
+    """Returns the first true value in the iterable.
+
+    If no true value is found, returns *default*
+
+    If *pred* is not None, returns the first item
+    for which pred(item) is true.
+
+    """
+    # first_true([a,b,c], x) --> a or b or c or x
+    # first_true([a,b], x, f) --> a if f(a) else b if f(b) else x
+    return next(filter(pred, iterable), default)
+
+def unique_everseen(iterable, key=None):
+    "List unique elements, preserving order. Remember all elements ever seen."
+    # unique_everseen('AAAABBBCCDAABBB') --> A B C D
+    # unique_everseen('ABBcCAD', str.casefold) --> A B c D
+    seen = set()
+    if key is None:
+        for element in filterfalse(seen.__contains__, iterable):
+            seen.add(element)
+            yield element
+    else:
+        for element in iterable:
+            k = key(element)
+            if k not in seen:
+                seen.add(k)
+                yield element
+
+def unique_justseen(iterable, key=None):
+    "List unique elements, preserving order. Remember only the element just seen."
+    # unique_justseen('AAAABBBCCDAABBB') --> A B C D A B
+    # unique_justseen('ABBcCAD', str.casefold) --> A B c A D
+    if key is None:
+        return map(operator.itemgetter(0), groupby(iterable))
+    return map(next, map(operator.itemgetter(1), groupby(iterable, key)))
+
+def iter_index(iterable, value, start=0, stop=None):
+    "Return indices where a value occurs in a sequence or iterable."
+    # iter_index('AABCADEAF', 'A') --> 0 1 4 7
+    seq_index = getattr(iterable, 'index', None)
+    if seq_index is None:
+        # Slow path for general iterables
+        it = islice(iterable, start, stop)
+        for i, element in enumerate(it, start):
+            if element is value or element == value:
+                yield i
+    else:
+        # Fast path for sequences
+        stop = len(iterable) if stop is None else stop
+        i = start - 1
+        try:
+            while True:
+                yield (i := seq_index(value, i+1, stop))
+        except ValueError:
+            pass
+
+def sliding_window(iterable, n):
+    "Collect data into overlapping fixed-length chunks or blocks."
+    # sliding_window('ABCDEFG', 4) --> ABCD BCDE CDEF DEFG
+    it = iter(iterable)
+    window = collections.deque(islice(it, n-1), maxlen=n)
+    for x in it:
+        window.append(x)
+        yield tuple(window)
+
+def grouper(iterable, n, *, incomplete='fill', fillvalue=None):
+    "Collect data into non-overlapping fixed-length chunks or blocks."
+    # grouper('ABCDEFG', 3, fillvalue='x') --> ABC DEF Gxx
+    # grouper('ABCDEFG', 3, incomplete='strict') --> ABC DEF ValueError
+    # grouper('ABCDEFG', 3, incomplete='ignore') --> ABC DEF
+    args = [iter(iterable)] * n
+    match incomplete:
+        case 'fill':
+            return zip_longest(*args, fillvalue=fillvalue)
+        case 'strict':
+            return zip(*args, strict=True)
+        case 'ignore':
+            return zip(*args)
+        case _:
+            raise ValueError('Expected fill, strict, or ignore')
+
+def roundrobin(*iterables):
+    "Visit input iterables in a cycle until each is exhausted."
+    # roundrobin('ABC', 'D', 'EF') --> A D E B F C
+    # Recipe credited to George Sakkis
+    num_active = len(iterables)
+    nexts = cycle(iter(it).__next__ for it in iterables)
+    while num_active:
+        try:
+            for next in nexts:
+                yield next()
+        except StopIteration:
+            # Remove the iterator we just exhausted from the cycle.
+            num_active -= 1
+            nexts = cycle(islice(nexts, num_active))
+
+def partition(pred, iterable):
+    """Partition entries into false entries and true entries.
+
+    If *pred* is slow, consider wrapping it with functools.lru_cache().
+    """
+    # partition(is_odd, range(10)) --> 0 2 4 6 8   and  1 3 5 7 9
+    t1, t2 = tee(iterable)
+    return filterfalse(pred, t1), filter(pred, t2)
+
+def subslices(seq):
+    "Return all contiguous non-empty subslices of a sequence."
+    # subslices('ABCD') --> A AB ABC ABCD B BC BCD C CD D
+    slices = starmap(slice, combinations(range(len(seq) + 1), 2))
+    return map(operator.getitem, repeat(seq), slices)
+
+def iter_except(func, exception, first=None):
+    """ Call a function repeatedly until an exception is raised.
+
+    Converts a call-until-exception interface to an iterator interface.
+    Like builtins.iter(func, sentinel) but uses an exception instead
+    of a sentinel to end the loop.
+
+    Priority queue iterator:
+        iter_except(functools.partial(heappop, h), IndexError)
+
+    Non-blocking dictionary iterator:
+        iter_except(d.popitem, KeyError)
+
+    Non-blocking deque iterator:
+        iter_except(d.popleft, IndexError)
+
+    Non-blocking iterator over a producer Queue:
+        iter_except(q.get_nowait, Queue.Empty)
+
+    Non-blocking set iterator:
+        iter_except(s.pop, KeyError)
+
+    """
+    try:
+        if first is not None:
+            # For database APIs needing an initial call to db.first()
+            yield first()
+        while True:
+            yield func()
+    except exception:
+        pass
+
+def before_and_after(predicate, it):
+    """ Variant of takewhile() that allows complete
+        access to the remainder of the iterator.
+
+        >>> it = iter('ABCdEfGhI')
+        >>> all_upper, remainder = before_and_after(str.isupper, it)
+        >>> ''.join(all_upper)
+        'ABC'
+        >>> ''.join(remainder)     # takewhile() would lose the 'd'
+        'dEfGhI'
+
+        Note that the true iterator must be fully consumed
+        before the remainder iterator can generate valid results.
+    """
+    it = iter(it)
+    transition = []
+
+    def true_iterator():
+        for elem in it:
+            if predicate(elem):
+                yield elem
+            else:
+                transition.append(elem)
+                return
+
+    return true_iterator(), chain(transition, it)
+

The following recipes have a more mathematical flavor:

def powerset(iterable):
+    "powerset([1,2,3]) --> () (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"
+    s = list(iterable)
+    return chain.from_iterable(combinations(s, r) for r in range(len(s)+1))
+
+def sum_of_squares(it):
+    "Add up the squares of the input values."
+    # sum_of_squares([10, 20, 30]) -> 1400
+    return math.sumprod(*tee(it))
+
+def reshape(matrix, cols):
+    "Reshape a 2-D matrix to have a given number of columns."
+    # reshape([(0, 1), (2, 3), (4, 5)], 3) -->  (0, 1, 2), (3, 4, 5)
+    return batched(chain.from_iterable(matrix), cols)
+
+def transpose(matrix):
+    "Swap the rows and columns of a 2-D matrix."
+    # transpose([(1, 2, 3), (11, 22, 33)]) --> (1, 11) (2, 22) (3, 33)
+    return zip(*matrix, strict=True)
+
+def matmul(m1, m2):
+    "Multiply two matrices."
+    # matmul([(7, 5), (3, 5)], [(2, 5), (7, 9)]) --> (49, 80), (41, 60)
+    n = len(m2[0])
+    return batched(starmap(math.sumprod, product(m1, transpose(m2))), n)
+
+def convolve(signal, kernel):
+    """Discrete linear convolution of two iterables.
+
+    The kernel is fully consumed before the calculations begin.
+    The signal is consumed lazily and can be infinite.
+
+    Convolutions are mathematically commutative.
+    If the signal and kernel are swapped,
+    the output will be the same.
+
+    Article:  https://betterexplained.com/articles/intuitive-convolution/
+    Video:    https://www.youtube.com/watch?v=KuXjwB4LzSA
+    """
+    # convolve(data, [0.25, 0.25, 0.25, 0.25]) --> Moving average (blur)
+    # convolve(data, [1/2, 0, -1/2]) --> 1st derivative estimate
+    # convolve(data, [1, -2, 1]) --> 2nd derivative estimate
+    kernel = tuple(kernel)[::-1]
+    n = len(kernel)
+    padded_signal = chain(repeat(0, n-1), signal, repeat(0, n-1))
+    windowed_signal = sliding_window(padded_signal, n)
+    return map(math.sumprod, repeat(kernel), windowed_signal)
+
+def polynomial_from_roots(roots):
+    """Compute a polynomial's coefficients from its roots.
+
+       (x - 5) (x + 4) (x - 3)  expands to:   x³ -4x² -17x + 60
+    """
+    # polynomial_from_roots([5, -4, 3]) --> [1, -4, -17, 60]
+    factors = zip(repeat(1), map(operator.neg, roots))
+    return list(functools.reduce(convolve, factors, [1]))
+
+def polynomial_eval(coefficients, x):
+    """Evaluate a polynomial at a specific value.
+
+    Computes with better numeric stability than Horner's method.
+    """
+    # Evaluate x³ -4x² -17x + 60 at x = 2.5
+    # polynomial_eval([1, -4, -17, 60], x=2.5) --> 8.125
+    n = len(coefficients)
+    if not n:
+        return type(x)(0)
+    powers = map(pow, repeat(x), reversed(range(n)))
+    return math.sumprod(coefficients, powers)
+
+def polynomial_derivative(coefficients):
+    """Compute the first derivative of a polynomial.
+
+       f(x)  =  x³ -4x² -17x + 60
+       f'(x) = 3x² -8x  -17
+    """
+    # polynomial_derivative([1, -4, -17, 60]) -> [3, -8, -17]
+    n = len(coefficients)
+    powers = reversed(range(1, n))
+    return list(map(operator.mul, coefficients, powers))
+
+def sieve(n):
+    "Primes less than n."
+    # sieve(30) --> 2 3 5 7 11 13 17 19 23 29
+    if n > 2:
+        yield 2
+    start = 3
+    data = bytearray((0, 1)) * (n // 2)
+    limit = math.isqrt(n) + 1
+    for p in iter_index(data, 1, start, limit):
+        yield from iter_index(data, 1, start, p*p)
+        data[p*p : n : p+p] = bytes(len(range(p*p, n, p+p)))
+        start = p*p
+    yield from iter_index(data, 1, start)
+
+def factor(n):
+    "Prime factors of n."
+    # factor(99) --> 3 3 11
+    # factor(1_000_000_000_000_007) --> 47 59 360620266859
+    # factor(1_000_000_000_000_403) --> 1000000000000403
+    for prime in sieve(math.isqrt(n) + 1):
+        while not n % prime:
+            yield prime
+            n //= prime
+            if n == 1:
+                return
+    if n > 1:
+        yield n
+
+def totient(n):
+    "Count of natural numbers up to n that are coprime to n."
+    # https://mathworld.wolfram.com/TotientFunction.html
+    # totient(12) --> 4 because len([1, 5, 7, 11]) == 4
+    for p in unique_justseen(factor(n)):
+        n = n // p * (p - 1)
+    return n
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/itertools.html +

+
diff --git a/devdocs/python~3.12/library%2Fjson.html b/devdocs/python~3.12/library%2Fjson.html new file mode 100644 index 00000000..c0d29888 --- /dev/null +++ b/devdocs/python~3.12/library%2Fjson.html @@ -0,0 +1,206 @@ +

json — JSON encoder and decoder

Source code: Lib/json/__init__.py

JSON (JavaScript Object Notation), specified by RFC 7159 (which obsoletes RFC 4627) and by ECMA-404, is a lightweight data interchange format inspired by JavaScript object literal syntax (although it is not a strict subset of JavaScript 1 ).

Warning

Be cautious when parsing JSON data from untrusted sources. A malicious JSON string may cause the decoder to consume considerable CPU and memory resources. Limiting the size of data to be parsed is recommended.

json exposes an API familiar to users of the standard library marshal and pickle modules.

Encoding basic Python object hierarchies:

>>> import json
+>>> json.dumps(['foo', {'bar': ('baz', None, 1.0, 2)}])
+'["foo", {"bar": ["baz", null, 1.0, 2]}]'
+>>> print(json.dumps("\"foo\bar"))
+"\"foo\bar"
+>>> print(json.dumps('\u1234'))
+"\u1234"
+>>> print(json.dumps('\\'))
+"\\"
+>>> print(json.dumps({"c": 0, "b": 0, "a": 0}, sort_keys=True))
+{"a": 0, "b": 0, "c": 0}
+>>> from io import StringIO
+>>> io = StringIO()
+>>> json.dump(['streaming API'], io)
+>>> io.getvalue()
+'["streaming API"]'
+

Compact encoding:

>>> import json
+>>> json.dumps([1, 2, 3, {'4': 5, '6': 7}], separators=(',', ':'))
+'[1,2,3,{"4":5,"6":7}]'
+

Pretty printing:

>>> import json
+>>> print(json.dumps({'4': 5, '6': 7}, sort_keys=True, indent=4))
+{
+    "4": 5,
+    "6": 7
+}
+

Decoding JSON:

>>> import json
+>>> json.loads('["foo", {"bar":["baz", null, 1.0, 2]}]')
+['foo', {'bar': ['baz', None, 1.0, 2]}]
+>>> json.loads('"\\"foo\\bar"')
+'"foo\x08ar'
+>>> from io import StringIO
+>>> io = StringIO('["streaming API"]')
+>>> json.load(io)
+['streaming API']
+

Specializing JSON object decoding:

>>> import json
+>>> def as_complex(dct):
+...     if '__complex__' in dct:
+...         return complex(dct['real'], dct['imag'])
+...     return dct
+...
+>>> json.loads('{"__complex__": true, "real": 1, "imag": 2}',
+...     object_hook=as_complex)
+(1+2j)
+>>> import decimal
+>>> json.loads('1.1', parse_float=decimal.Decimal)
+Decimal('1.1')
+

Extending JSONEncoder:

>>> import json
+>>> class ComplexEncoder(json.JSONEncoder):
+...     def default(self, obj):
+...         if isinstance(obj, complex):
+...             return [obj.real, obj.imag]
+...         # Let the base class default method raise the TypeError
+...         return json.JSONEncoder.default(self, obj)
+...
+>>> json.dumps(2 + 1j, cls=ComplexEncoder)
+'[2.0, 1.0]'
+>>> ComplexEncoder().encode(2 + 1j)
+'[2.0, 1.0]'
+>>> list(ComplexEncoder().iterencode(2 + 1j))
+['[2.0', ', 1.0', ']']
+

Using json.tool from the shell to validate and pretty-print:

$ echo '{"json":"obj"}' | python -m json.tool
+{
+    "json": "obj"
+}
+$ echo '{1.2:3.4}' | python -m json.tool
+Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
+

See Command Line Interface for detailed documentation.

Note

JSON is a subset of YAML 1.2. The JSON produced by this module’s default settings (in particular, the default separators value) is also a subset of YAML 1.0 and 1.1. This module can thus also be used as a YAML serializer.

Note

This module’s encoders and decoders preserve input and output order by default. Order is only lost if the underlying containers are unordered.

Basic Usage

+json.dump(obj, fp, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
+

Serialize obj as a JSON formatted stream to fp (a .write()-supporting file-like object) using this conversion table.

If skipkeys is true (default: False), then dict keys that are not of a basic type (str, int, float, bool, None) will be skipped instead of raising a TypeError.

The json module always produces str objects, not bytes objects. Therefore, fp.write() must support str input.

If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is.

If check_circular is false (default: True), then the circular reference check for container types will be skipped and a circular reference will result in a RecursionError (or worse).

If allow_nan is false (default: True), then it will be a ValueError to serialize out of range float values (nan, inf, -inf) in strict compliance of the JSON specification. If allow_nan is true, their JavaScript equivalents (NaN, Infinity, -Infinity) will be used.

If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level.

Changed in version 3.2: Allow strings for indent in addition to integers.

If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace.

Changed in version 3.4: Use (',', ': ') as default if indent is not None.

If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised.

If sort_keys is true (default: False), then the output of dictionaries will be sorted by key.

To use a custom JSONEncoder subclass (e.g. one that overrides the default() method to serialize additional types), specify it with the cls kwarg; otherwise JSONEncoder is used.

Changed in version 3.6: All optional parameters are now keyword-only.

Note

Unlike pickle and marshal, JSON is not a framed protocol, so trying to serialize multiple objects with repeated calls to dump() using the same fp will result in an invalid JSON file.

+
+json.dumps(obj, *, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, cls=None, indent=None, separators=None, default=None, sort_keys=False, **kw)
+

Serialize obj to a JSON formatted str using this conversion table. The arguments have the same meaning as in dump().

Note

Keys in key/value pairs of JSON are always of the type str. When a dictionary is converted into JSON, all the keys of the dictionary are coerced to strings. As a result of this, if a dictionary is converted into JSON and then back into a dictionary, the dictionary may not equal the original one. That is, loads(dumps(x)) != x if x has non-string keys.

+
+json.load(fp, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
+

Deserialize fp (a .read()-supporting text file or binary file containing a JSON document) to a Python object using this conversion table.

object_hook is an optional function that will be called with the result of any object literal decoded (a dict). The return value of object_hook will be used instead of the dict. This feature can be used to implement custom decoders (e.g. JSON-RPC class hinting).

object_pairs_hook is an optional function that will be called with the result of any object literal decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.

Changed in version 3.1: Added support for object_pairs_hook.

parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal).

parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float).

Changed in version 3.11: The default parse_int of int() now limits the maximum length of the integer string via the interpreter’s integer string conversion length limitation to help avoid denial of service attacks.

parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered.

Changed in version 3.1: parse_constant doesn’t get called on ‘null’, ‘true’, ‘false’ anymore.

To use a custom JSONDecoder subclass, specify it with the cls kwarg; otherwise JSONDecoder is used. Additional keyword arguments will be passed to the constructor of the class.

If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.

Changed in version 3.6: All optional parameters are now keyword-only.

Changed in version 3.6: fp can now be a binary file. The input encoding should be UTF-8, UTF-16 or UTF-32.

+
+json.loads(s, *, cls=None, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, object_pairs_hook=None, **kw)
+

Deserialize s (a str, bytes or bytearray instance containing a JSON document) to a Python object using this conversion table.

The other arguments have the same meaning as in load().

If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.

Changed in version 3.6: s can now be of type bytes or bytearray. The input encoding should be UTF-8, UTF-16 or UTF-32.

Changed in version 3.9: The keyword argument encoding has been removed.

+

Encoders and Decoders

+class json.JSONDecoder(*, object_hook=None, parse_float=None, parse_int=None, parse_constant=None, strict=True, object_pairs_hook=None)
+

Simple JSON decoder.

Performs the following translations in decoding by default:

+ + + + + + + + +

JSON

Python

object

dict

array

list

string

str

number (int)

int

number (real)

float

true

True

false

False

null

None

It also understands NaN, Infinity, and -Infinity as their corresponding float values, which is outside the JSON spec.

object_hook, if specified, will be called with the result of every JSON object decoded and its return value will be used in place of the given dict. This can be used to provide custom deserializations (e.g. to support JSON-RPC class hinting).

object_pairs_hook, if specified will be called with the result of every JSON object decoded with an ordered list of pairs. The return value of object_pairs_hook will be used instead of the dict. This feature can be used to implement custom decoders. If object_hook is also defined, the object_pairs_hook takes priority.

Changed in version 3.1: Added support for object_pairs_hook.

parse_float, if specified, will be called with the string of every JSON float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for JSON floats (e.g. decimal.Decimal).

parse_int, if specified, will be called with the string of every JSON int to be decoded. By default, this is equivalent to int(num_str). This can be used to use another datatype or parser for JSON integers (e.g. float).

parse_constant, if specified, will be called with one of the following strings: '-Infinity', 'Infinity', 'NaN'. This can be used to raise an exception if invalid JSON numbers are encountered.

If strict is false (True is the default), then control characters will be allowed inside strings. Control characters in this context are those with character codes in the 0–31 range, including '\t' (tab), '\n', '\r' and '\0'.

If the data being deserialized is not a valid JSON document, a JSONDecodeError will be raised.

Changed in version 3.6: All parameters are now keyword-only.

+decode(s)
+

Return the Python representation of s (a str instance containing a JSON document).

JSONDecodeError will be raised if the given JSON document is not valid.

+
+raw_decode(s)
+

Decode a JSON document from s (a str beginning with a JSON document) and return a 2-tuple of the Python representation and the index in s where the document ended.

This can be used to decode a JSON document from a string that may have extraneous data at the end.

+
+
+class json.JSONEncoder(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, sort_keys=False, indent=None, separators=None, default=None)
+

Extensible JSON encoder for Python data structures.

Supports the following objects and types by default:

+ + + + + + + +

Python

JSON

dict

object

list, tuple

array

str

string

int, float, int- & float-derived Enums

number

True

true

False

false

None

null

Changed in version 3.4: Added support for int- and float-derived Enum classes.

To extend this to recognize other objects, subclass and implement a default() method with another method that returns a serializable object for o if possible, otherwise it should call the superclass implementation (to raise TypeError).

If skipkeys is false (the default), a TypeError will be raised when trying to encode keys that are not str, int, float or None. If skipkeys is true, such items are simply skipped.

If ensure_ascii is true (the default), the output is guaranteed to have all incoming non-ASCII characters escaped. If ensure_ascii is false, these characters will be output as-is.

If check_circular is true (the default), then lists, dicts, and custom encoded objects will be checked for circular references during encoding to prevent an infinite recursion (which would cause a RecursionError). Otherwise, no such check takes place.

If allow_nan is true (the default), then NaN, Infinity, and -Infinity will be encoded as such. This behavior is not JSON specification compliant, but is consistent with most JavaScript based encoders and decoders. Otherwise, it will be a ValueError to encode such floats.

If sort_keys is true (default: False), then the output of dictionaries will be sorted by key; this is useful for regression tests to ensure that JSON serializations can be compared on a day-to-day basis.

If indent is a non-negative integer or string, then JSON array elements and object members will be pretty-printed with that indent level. An indent level of 0, negative, or "" will only insert newlines. None (the default) selects the most compact representation. Using a positive integer indent indents that many spaces per level. If indent is a string (such as "\t"), that string is used to indent each level.

Changed in version 3.2: Allow strings for indent in addition to integers.

If specified, separators should be an (item_separator, key_separator) tuple. The default is (', ', ': ') if indent is None and (',', ': ') otherwise. To get the most compact JSON representation, you should specify (',', ':') to eliminate whitespace.

Changed in version 3.4: Use (',', ': ') as default if indent is not None.

If specified, default should be a function that gets called for objects that can’t otherwise be serialized. It should return a JSON encodable version of the object or raise a TypeError. If not specified, TypeError is raised.

Changed in version 3.6: All parameters are now keyword-only.

+default(o)
+

Implement this method in a subclass such that it returns a serializable object for o, or calls the base implementation (to raise a TypeError).

For example, to support arbitrary iterators, you could implement default() like this:

def default(self, o):
+   try:
+       iterable = iter(o)
+   except TypeError:
+       pass
+   else:
+       return list(iterable)
+   # Let the base class default method raise the TypeError
+   return json.JSONEncoder.default(self, o)
+
+
+encode(o)
+

Return a JSON string representation of a Python data structure, o. For example:

>>> json.JSONEncoder().encode({"foo": ["bar", "baz"]})
+'{"foo": ["bar", "baz"]}'
+
+
+iterencode(o)
+

Encode the given object, o, and yield each string representation as available. For example:

for chunk in json.JSONEncoder().iterencode(bigobject):
+    mysocket.write(chunk)
+
+
+

Exceptions

+exception json.JSONDecodeError(msg, doc, pos)
+

Subclass of ValueError with the following additional attributes:

+msg
+

The unformatted error message.

+
+doc
+

The JSON document being parsed.

+
+pos
+

The start index of doc where parsing failed.

+
+lineno
+

The line corresponding to pos.

+
+colno
+

The column corresponding to pos.

+

New in version 3.5.

+

Standard Compliance and Interoperability

The JSON format is specified by RFC 7159 and by ECMA-404. This section details this module’s level of compliance with the RFC. For simplicity, JSONEncoder and JSONDecoder subclasses, and parameters other than those explicitly mentioned, are not considered.

This module does not comply with the RFC in a strict fashion, implementing some extensions that are valid JavaScript but not valid JSON. In particular:

  • Infinite and NaN number values are accepted and output;
  • Repeated names within an object are accepted, and only the value of the last name-value pair is used.

Since the RFC permits RFC-compliant parsers to accept input texts that are not RFC-compliant, this module’s deserializer is technically RFC-compliant under default settings.

Character Encodings

The RFC requires that JSON be represented using either UTF-8, UTF-16, or UTF-32, with UTF-8 being the recommended default for maximum interoperability.

As permitted, though not required, by the RFC, this module’s serializer sets ensure_ascii=True by default, thus escaping the output so that the resulting strings only contain ASCII characters.

Other than the ensure_ascii parameter, this module is defined strictly in terms of conversion between Python objects and Unicode strings, and thus does not otherwise directly address the issue of character encodings.

The RFC prohibits adding a byte order mark (BOM) to the start of a JSON text, and this module’s serializer does not add a BOM to its output. The RFC permits, but does not require, JSON deserializers to ignore an initial BOM in their input. This module’s deserializer raises a ValueError when an initial BOM is present.

The RFC does not explicitly forbid JSON strings which contain byte sequences that don’t correspond to valid Unicode characters (e.g. unpaired UTF-16 surrogates), but it does note that they may cause interoperability problems. By default, this module accepts and outputs (when present in the original str) code points for such sequences.

Infinite and NaN Number Values

The RFC does not permit the representation of infinite or NaN number values. Despite that, by default, this module accepts and outputs Infinity, -Infinity, and NaN as if they were valid JSON number literal values:

>>> # Neither of these calls raises an exception, but the results are not valid JSON
+>>> json.dumps(float('-inf'))
+'-Infinity'
+>>> json.dumps(float('nan'))
+'NaN'
+>>> # Same when deserializing
+>>> json.loads('-Infinity')
+-inf
+>>> json.loads('NaN')
+nan
+

In the serializer, the allow_nan parameter can be used to alter this behavior. In the deserializer, the parse_constant parameter can be used to alter this behavior.

Repeated Names Within an Object

The RFC specifies that the names within a JSON object should be unique, but does not mandate how repeated names in JSON objects should be handled. By default, this module does not raise an exception; instead, it ignores all but the last name-value pair for a given name:

>>> weird_json = '{"x": 1, "x": 2, "x": 3}'
+>>> json.loads(weird_json)
+{'x': 3}
+

The object_pairs_hook parameter can be used to alter this behavior.

Top-level Non-Object, Non-Array Values

The old version of JSON specified by the obsolete RFC 4627 required that the top-level value of a JSON text must be either a JSON object or array (Python dict or list), and could not be a JSON null, boolean, number, or string value. RFC 7159 removed that restriction, and this module does not and has never implemented that restriction in either its serializer or its deserializer.

Regardless, for maximum interoperability, you may wish to voluntarily adhere to the restriction yourself.

Implementation Limitations

Some JSON deserializer implementations may set limits on:

  • the size of accepted JSON texts
  • the maximum level of nesting of JSON objects and arrays
  • the range and precision of JSON numbers
  • the content and maximum length of JSON strings

This module does not impose any such limits beyond those of the relevant Python datatypes themselves or the Python interpreter itself.

When serializing to JSON, beware any such limitations in applications that may consume your JSON. In particular, it is common for JSON numbers to be deserialized into IEEE 754 double precision numbers and thus subject to that representation’s range and precision limitations. This is especially relevant when serializing Python int values of extremely large magnitude, or when serializing instances of “exotic” numerical types such as decimal.Decimal.

Command Line Interface

Source code: Lib/json/tool.py

The json.tool module provides a simple command line interface to validate and pretty-print JSON objects.

If the optional infile and outfile arguments are not specified, sys.stdin and sys.stdout will be used respectively:

$ echo '{"json": "obj"}' | python -m json.tool
+{
+    "json": "obj"
+}
+$ echo '{1.2:3.4}' | python -m json.tool
+Expecting property name enclosed in double quotes: line 1 column 2 (char 1)
+

Changed in version 3.5: The output is now in the same order as the input. Use the --sort-keys option to sort the output of dictionaries alphabetically by key.

Command line options

+infile
+

The JSON file to be validated or pretty-printed:

$ python -m json.tool mp_films.json
+[
+    {
+        "title": "And Now for Something Completely Different",
+        "year": 1971
+    },
+    {
+        "title": "Monty Python and the Holy Grail",
+        "year": 1975
+    }
+]
+

If infile is not specified, read from sys.stdin.

+
+outfile
+

Write the output of the infile to the given outfile. Otherwise, write it to sys.stdout.

+
+--sort-keys
+

Sort the output of dictionaries alphabetically by key.

New in version 3.5.

+
+--no-ensure-ascii
+

Disable escaping of non-ascii characters, see json.dumps() for more information.

New in version 3.9.

+
+--json-lines
+

Parse every input line as separate JSON object.

New in version 3.8.

+
+--indent, --tab, --no-indent, --compact
+

Mutually exclusive options for whitespace control.

New in version 3.9.

+
+-h, --help
+

Show the help message.

+

Footnotes

+1
+

As noted in the errata for RFC 7159, JSON permits literal U+2028 (LINE SEPARATOR) and U+2029 (PARAGRAPH SEPARATOR) characters in strings, whereas JavaScript (as of ECMAScript Edition 5.1) does not.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/json.html +

+
diff --git a/devdocs/python~3.12/library%2Fkeyword.html b/devdocs/python~3.12/library%2Fkeyword.html new file mode 100644 index 00000000..81fc8059 --- /dev/null +++ b/devdocs/python~3.12/library%2Fkeyword.html @@ -0,0 +1,18 @@ +

keyword — Testing for Python keywords

Source code: Lib/keyword.py

This module allows a Python program to determine if a string is a keyword or soft keyword.

+keyword.iskeyword(s)
+

Return True if s is a Python keyword.

+
+keyword.kwlist
+

Sequence containing all the keywords defined for the interpreter. If any keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well.

+
+keyword.issoftkeyword(s)
+

Return True if s is a Python soft keyword.

New in version 3.9.

+
+keyword.softkwlist
+

Sequence containing all the soft keywords defined for the interpreter. If any soft keywords are defined to only be active when particular __future__ statements are in effect, these will be included as well.

New in version 3.9.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/keyword.html +

+
diff --git a/devdocs/python~3.12/library%2Flanguage.html b/devdocs/python~3.12/library%2Flanguage.html new file mode 100644 index 00000000..09fea4ab --- /dev/null +++ b/devdocs/python~3.12/library%2Flanguage.html @@ -0,0 +1,18 @@ +

Python Language Services

Python provides a number of modules to assist in working with the Python language. These modules support tokenizing, parsing, syntax analysis, bytecode disassembly, and various other facilities.

These modules include:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/language.html +

+
diff --git a/devdocs/python~3.12/library%2Flinecache.html b/devdocs/python~3.12/library%2Flinecache.html new file mode 100644 index 00000000..ce9cfb6e --- /dev/null +++ b/devdocs/python~3.12/library%2Flinecache.html @@ -0,0 +1,21 @@ +

linecache — Random access to text lines

Source code: Lib/linecache.py

The linecache module allows one to get any line from a Python source file, while attempting to optimize internally, using a cache, the common case where many lines are read from a single file. This is used by the traceback module to retrieve source lines for inclusion in the formatted traceback.

The tokenize.open() function is used to open files. This function uses tokenize.detect_encoding() to get the encoding of the file; in the absence of an encoding token, the file encoding defaults to UTF-8.

The linecache module defines the following functions:

+linecache.getline(filename, lineno, module_globals=None)
+

Get line lineno from file named filename. This function will never raise an exception — it will return '' on errors (the terminating newline character will be included for lines that are found).

If a file named filename is not found, the function first checks for a PEP 302 __loader__ in module_globals. If there is such a loader and it defines a get_source method, then that determines the source lines (if get_source() returns None, then '' is returned). Finally, if filename is a relative filename, it is looked up relative to the entries in the module search path, sys.path.

+
+linecache.clearcache()
+

Clear the cache. Use this function if you no longer need lines from files previously read using getline().

+
+linecache.checkcache(filename=None)
+

Check the cache for validity. Use this function if files in the cache may have changed on disk, and you require the updated version. If filename is omitted, it will check all the entries in the cache.

+
+linecache.lazycache(filename, module_globals)
+

Capture enough detail about a non-file-based module to permit getting its lines later via getline() even if module_globals is None in the later call. This avoids doing I/O until a line is actually needed, without having to carry the module globals around indefinitely.

New in version 3.5.

+

Example:

>>> import linecache
+>>> linecache.getline(linecache.__file__, 8)
+'import sys\n'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/linecache.html +

+
diff --git a/devdocs/python~3.12/library%2Flocale.html b/devdocs/python~3.12/library%2Flocale.html new file mode 100644 index 00000000..23ec19e5 --- /dev/null +++ b/devdocs/python~3.12/library%2Flocale.html @@ -0,0 +1,187 @@ +

locale — Internationalization services

Source code: Lib/locale.py

The locale module opens access to the POSIX locale database and functionality. The POSIX locale mechanism allows programmers to deal with certain cultural issues in an application, without requiring the programmer to know all the specifics of each country where the software is executed.

The locale module is implemented on top of the _locale module, which in turn uses an ANSI C locale implementation if available.

The locale module defines the following exception and functions:

+exception locale.Error
+

Exception raised when the locale passed to setlocale() is not recognized.

+
+locale.setlocale(category, locale=None)
+

If locale is given and not None, setlocale() modifies the locale setting for the category. The available categories are listed in the data description below. locale may be a string, or an iterable of two strings (language code and encoding). If it’s an iterable, it’s converted to a locale name using the locale aliasing engine. An empty string specifies the user’s default settings. If the modification of the locale fails, the exception Error is raised. If successful, the new locale setting is returned.

If locale is omitted or None, the current setting for category is returned.

setlocale() is not thread-safe on most systems. Applications typically start with a call of

import locale
+locale.setlocale(locale.LC_ALL, '')
+

This sets the locale for all categories to the user’s default setting (typically specified in the LANG environment variable). If the locale is not changed thereafter, using multithreading should not cause problems.

+
+locale.localeconv()
+

Returns the database of the local conventions as a dictionary. This dictionary has the following strings as keys:

+ + + + + + + + + + + + + + + +

Category

Key

Meaning

LC_NUMERIC

'decimal_point'

Decimal point character.

'grouping'

Sequence of numbers specifying which relative positions the 'thousands_sep' is expected. If the sequence is terminated with CHAR_MAX, no further grouping is performed. If the sequence terminates with a 0, the last group size is repeatedly used.

'thousands_sep'

Character used between groups.

LC_MONETARY

'int_curr_symbol'

International currency symbol.

'currency_symbol'

Local currency symbol.

'p_cs_precedes/n_cs_precedes'

Whether the currency symbol precedes the value (for positive resp. negative values).

'p_sep_by_space/n_sep_by_space'

Whether the currency symbol is separated from the value by a space (for positive resp. negative values).

'mon_decimal_point'

Decimal point used for monetary values.

'frac_digits'

Number of fractional digits used in local formatting of monetary values.

'int_frac_digits'

Number of fractional digits used in international formatting of monetary values.

'mon_thousands_sep'

Group separator used for monetary values.

'mon_grouping'

Equivalent to 'grouping', used for monetary values.

'positive_sign'

Symbol used to annotate a positive monetary value.

'negative_sign'

Symbol used to annotate a negative monetary value.

'p_sign_posn/n_sign_posn'

The position of the sign (for positive resp. negative values), see below.

All numeric values can be set to CHAR_MAX to indicate that there is no value specified in this locale.

The possible values for 'p_sign_posn' and 'n_sign_posn' are given below.

+ + + + + + +

Value

Explanation

0

Currency and value are surrounded by parentheses.

1

The sign should precede the value and currency symbol.

2

The sign should follow the value and currency symbol.

3

The sign should immediately precede the value.

4

The sign should immediately follow the value.

CHAR_MAX

Nothing is specified in this locale.

The function temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale or the LC_MONETARY locale if locales are different and numeric or monetary strings are non-ASCII. This temporary change affects other threads.

Changed in version 3.7: The function now temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale in some cases.

+
+locale.nl_langinfo(option)
+

Return some locale-specific information as a string. This function is not available on all systems, and the set of possible options might also vary across platforms. The possible argument values are numbers, for which symbolic constants are available in the locale module.

The nl_langinfo() function accepts one of the following keys. Most descriptions are taken from the corresponding description in the GNU C library.

+locale.CODESET
+

Get a string with the name of the character encoding used in the selected locale.

+
+locale.D_T_FMT
+

Get a string that can be used as a format string for time.strftime() to represent date and time in a locale-specific way.

+
+locale.D_FMT
+

Get a string that can be used as a format string for time.strftime() to represent a date in a locale-specific way.

+
+locale.T_FMT
+

Get a string that can be used as a format string for time.strftime() to represent a time in a locale-specific way.

+
+locale.T_FMT_AMPM
+

Get a format string for time.strftime() to represent time in the am/pm format.

+
DAY_1 ... DAY_7 +
+

Get the name of the n-th day of the week.

Note

This follows the US convention of DAY_1 being Sunday, not the international convention (ISO 8601) that Monday is the first day of the week.

+
ABDAY_1 ... ABDAY_7 +
+

Get the abbreviated name of the n-th day of the week.

+
MON_1 ... MON_12 +
+

Get the name of the n-th month.

+
ABMON_1 ... ABMON_12 +
+

Get the abbreviated name of the n-th month.

+
+locale.RADIXCHAR
+

Get the radix character (decimal dot, decimal comma, etc.).

+
+locale.THOUSEP
+

Get the separator character for thousands (groups of three digits).

+
+locale.YESEXPR
+

Get a regular expression that can be used with the regex function to recognize a positive response to a yes/no question.

+
+locale.NOEXPR
+

Get a regular expression that can be used with the regex(3) function to recognize a negative response to a yes/no question.

Note

The regular expressions for YESEXPR and NOEXPR use syntax suitable for the regex() function from the C library, which might differ from the syntax used in re.

+
+locale.CRNCYSTR
+

Get the currency symbol, preceded by “-” if the symbol should appear before the value, “+” if the symbol should appear after the value, or “.” if the symbol should replace the radix character.

+
+locale.ERA
+

Get a string that represents the era used in the current locale.

Most locales do not define this value. An example of a locale which does define this value is the Japanese one. In Japan, the traditional representation of dates includes the name of the era corresponding to the then-emperor’s reign.

Normally it should not be necessary to use this value directly. Specifying the E modifier in their format strings causes the time.strftime() function to use this information. The format of the returned string is not specified, and therefore you should not assume knowledge of it on different systems.

+
+locale.ERA_D_T_FMT
+

Get a format string for time.strftime() to represent date and time in a locale-specific era-based way.

+
+locale.ERA_D_FMT
+

Get a format string for time.strftime() to represent a date in a locale-specific era-based way.

+
+locale.ERA_T_FMT
+

Get a format string for time.strftime() to represent a time in a locale-specific era-based way.

+
+locale.ALT_DIGITS
+

Get a representation of up to 100 values used to represent the values 0 to 99.

+
+
+locale.getdefaultlocale([envvars])
+

Tries to determine the default locale settings and returns them as a tuple of the form (language code, encoding).

According to POSIX, a program which has not called setlocale(LC_ALL, '') runs using the portable 'C' locale. Calling setlocale(LC_ALL, '') lets it use the default locale as defined by the LANG variable. Since we do not want to interfere with the current locale setting we thus emulate the behavior in the way described above.

To maintain compatibility with other platforms, not only the LANG variable is tested, but a list of variables given as envvars parameter. The first found to be defined will be used. envvars defaults to the search path used in GNU gettext; it must always contain the variable name 'LANG'. The GNU gettext search path contains 'LC_ALL', 'LC_CTYPE', 'LANG' and 'LANGUAGE', in that order.

Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined.

Deprecated since version 3.11, will be removed in version 3.15.

+
+locale.getlocale(category=LC_CTYPE)
+

Returns the current setting for the given locale category as sequence containing language code, encoding. category may be one of the LC_* values except LC_ALL. It defaults to LC_CTYPE.

Except for the code 'C', the language code corresponds to RFC 1766. language code and encoding may be None if their values cannot be determined.

+
+locale.getpreferredencoding(do_setlocale=True)
+

Return the locale encoding used for text data, according to user preferences. User preferences are expressed differently on different systems, and might not be available programmatically on some systems, so this function only returns a guess.

On some systems, it is necessary to invoke setlocale() to obtain the user preferences, so this function is not thread-safe. If invoking setlocale is not necessary or desired, do_setlocale should be set to False.

On Android or if the Python UTF-8 Mode is enabled, always return 'utf-8', the locale encoding and the do_setlocale argument are ignored.

The Python preinitialization configures the LC_CTYPE locale. See also the filesystem encoding and error handler.

Changed in version 3.7: The function now always returns "utf-8" on Android or if the Python UTF-8 Mode is enabled.

+
+locale.getencoding()
+

Get the current locale encoding:

  • On Android and VxWorks, return "utf-8".
  • On Unix, return the encoding of the current LC_CTYPE locale. Return "utf-8" if nl_langinfo(CODESET) returns an empty string: for example, if the current LC_CTYPE locale is not supported.
  • On Windows, return the ANSI code page.

The Python preinitialization configures the LC_CTYPE locale. See also the filesystem encoding and error handler.

This function is similar to getpreferredencoding(False) except this function ignores the Python UTF-8 Mode.

New in version 3.11.

+
+locale.normalize(localename)
+

Returns a normalized locale code for the given locale name. The returned locale code is formatted for use with setlocale(). If normalization fails, the original name is returned unchanged.

If the given encoding is not known, the function defaults to the default encoding for the locale code just like setlocale().

+
+locale.resetlocale(category=LC_ALL)
+

Sets the locale for category to the default setting.

The default setting is determined by calling getdefaultlocale(). category defaults to LC_ALL.

Deprecated since version 3.11, will be removed in version 3.13.

+
+locale.strcoll(string1, string2)
+

Compares two strings according to the current LC_COLLATE setting. As any other compare function, returns a negative, or a positive value, or 0, depending on whether string1 collates before or after string2 or is equal to it.

+
+locale.strxfrm(string)
+

Transforms a string to one that can be used in locale-aware comparisons. For example, strxfrm(s1) < strxfrm(s2) is equivalent to strcoll(s1, s2) < 0. This function can be used when the same string is compared repeatedly, e.g. when collating a sequence of strings.

+
+locale.format_string(format, val, grouping=False, monetary=False)
+

Formats a number val according to the current LC_NUMERIC setting. The format follows the conventions of the % operator. For floating point values, the decimal point is modified if appropriate. If grouping is True, also takes the grouping into account.

If monetary is true, the conversion uses monetary thousands separator and grouping strings.

Processes formatting specifiers as in format % val, but takes the current locale settings into account.

Changed in version 3.7: The monetary keyword parameter was added.

+
+locale.currency(val, symbol=True, grouping=False, international=False)
+

Formats a number val according to the current LC_MONETARY settings.

The returned string includes the currency symbol if symbol is true, which is the default. If grouping is True (which is not the default), grouping is done with the value. If international is True (which is not the default), the international currency symbol is used.

Note

This function will not work with the ‘C’ locale, so you have to set a locale via setlocale() first.

+
+locale.str(float)
+

Formats a floating point number using the same format as the built-in function str(float), but takes the decimal point into account.

+
+locale.delocalize(string)
+

Converts a string into a normalized number string, following the LC_NUMERIC settings.

New in version 3.5.

+
+locale.localize(string, grouping=False, monetary=False)
+

Converts a normalized number string into a formatted string following the LC_NUMERIC settings.

New in version 3.10.

+
+locale.atof(string, func=float)
+

Converts a string to a number, following the LC_NUMERIC settings, by calling func on the result of calling delocalize() on string.

+
+locale.atoi(string)
+

Converts a string to an integer, following the LC_NUMERIC conventions.

+
+locale.LC_CTYPE
+

Locale category for the character type functions. Most importantly, this category defines the text encoding, i.e. how bytes are interpreted as Unicode codepoints. See PEP 538 and PEP 540 for how this variable might be automatically coerced to C.UTF-8 to avoid issues created by invalid settings in containers or incompatible settings passed over remote SSH connections.

Python doesn’t internally use locale-dependent character transformation functions from ctype.h. Instead, an internal pyctype.h provides locale-independent equivalents like Py_TOLOWER.

+
+locale.LC_COLLATE
+

Locale category for sorting strings. The functions strcoll() and strxfrm() of the locale module are affected.

+
+locale.LC_TIME
+

Locale category for the formatting of time. The function time.strftime() follows these conventions.

+
+locale.LC_MONETARY
+

Locale category for formatting of monetary values. The available options are available from the localeconv() function.

+
+locale.LC_MESSAGES
+

Locale category for message display. Python currently does not support application specific locale-aware messages. Messages displayed by the operating system, like those returned by os.strerror() might be affected by this category.

This value may not be available on operating systems not conforming to the POSIX standard, most notably Windows.

+
+locale.LC_NUMERIC
+

Locale category for formatting numbers. The functions format_string(), atoi(), atof() and str() of the locale module are affected by that category. All other numeric formatting operations are not affected.

+
+locale.LC_ALL
+

Combination of all locale settings. If this flag is used when the locale is changed, setting the locale for all categories is attempted. If that fails for any category, no category is changed at all. When the locale is retrieved using this flag, a string indicating the setting for all categories is returned. This string can be later used to restore the settings.

+
+locale.CHAR_MAX
+

This is a symbolic constant used for different values returned by localeconv().

+

Example:

>>> import locale
+>>> loc = locale.getlocale()  # get current locale
+# use German locale; name might vary with platform
+>>> locale.setlocale(locale.LC_ALL, 'de_DE')
+>>> locale.strcoll('f\xe4n', 'foo')  # compare a string containing an umlaut
+>>> locale.setlocale(locale.LC_ALL, '')   # use user's preferred locale
+>>> locale.setlocale(locale.LC_ALL, 'C')  # use default (C) locale
+>>> locale.setlocale(locale.LC_ALL, loc)  # restore saved locale
+

Background, details, hints, tips and caveats

The C standard defines the locale as a program-wide property that may be relatively expensive to change. On top of that, some implementations are broken in such a way that frequent locale changes may cause core dumps. This makes the locale somewhat painful to use correctly.

Initially, when a program is started, the locale is the C locale, no matter what the user’s preferred locale is. There is one exception: the LC_CTYPE category is changed at startup to set the current locale encoding to the user’s preferred locale encoding. The program must explicitly say that it wants the user’s preferred locale settings for other categories by calling setlocale(LC_ALL, '').

It is generally a bad idea to call setlocale() in some library routine, since as a side effect it affects the entire program. Saving and restoring it is almost as bad: it is expensive and affects other threads that happen to run before the settings have been restored.

If, when coding a module for general use, you need a locale independent version of an operation that is affected by the locale (such as certain formats used with time.strftime()), you will have to find a way to do it without using the standard library routine. Even better is convincing yourself that using locale settings is okay. Only as a last resort should you document that your module is not compatible with non-C locale settings.

The only way to perform numeric operations according to the locale is to use the special functions defined by this module: atof(), atoi(), format_string(), str().

There is no way to perform case conversions and character classifications according to the locale. For (Unicode) text strings these are done according to the character value only, while for byte strings, the conversions and classifications are done according to the ASCII value of the byte, and bytes whose high bit is set (i.e., non-ASCII bytes) are never converted or considered part of a character class such as letter or whitespace.

For extension writers and programs that embed Python

Extension modules should never call setlocale(), except to find out what the current locale is. But since the return value can only be used portably to restore it, that is not very useful (except perhaps to find out whether or not the locale is C).

When Python code uses the locale module to change the locale, this also affects the embedding application. If the embedding application doesn’t want this to happen, it should remove the _locale extension module (which does all the work) from the table of built-in modules in the config.c file, and make sure that the _locale module is not accessible as a shared library.

Access to message catalogs

+locale.gettext(msg)
+
+locale.dgettext(domain, msg)
+
+locale.dcgettext(domain, msg, category)
+
+locale.textdomain(domain)
+
+locale.bindtextdomain(domain, dir)
+

The locale module exposes the C library’s gettext interface on systems that provide this interface. It consists of the functions gettext(), dgettext(), dcgettext(), textdomain(), bindtextdomain(), and bind_textdomain_codeset(). These are similar to the same functions in the gettext module, but use the C library’s binary format for message catalogs, and the C library’s search algorithms for locating message catalogs.

Python applications should normally find no need to invoke these functions, and should use gettext instead. A known exception to this rule are applications that link with additional C libraries which internally invoke gettext() or dcgettext(). For these applications, it may be necessary to bind the text domain, so that the libraries can properly locate their message catalogs.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/locale.html +

+
diff --git a/devdocs/python~3.12/library%2Flogging.config.html b/devdocs/python~3.12/library%2Flogging.config.html new file mode 100644 index 00000000..57acc612 --- /dev/null +++ b/devdocs/python~3.12/library%2Flogging.config.html @@ -0,0 +1,222 @@ +

logging.config — Logging configuration

Source code: Lib/logging/config.py

This section describes the API for configuring the logging module.

Configuration functions

The following functions configure the logging module. They are located in the logging.config module. Their use is optional — you can configure the logging module using these functions or by making calls to the main API (defined in logging itself) and defining handlers which are declared either in logging or logging.handlers.

+logging.config.dictConfig(config)
+

Takes the logging configuration from a dictionary. The contents of this dictionary are described in Configuration dictionary schema below.

If an error is encountered during configuration, this function will raise a ValueError, TypeError, AttributeError or ImportError with a suitably descriptive message. The following is a (possibly incomplete) list of conditions which will raise an error:

  • A level which is not a string or which is a string not corresponding to an actual logging level.
  • A propagate value which is not a boolean.
  • An id which does not have a corresponding destination.
  • A non-existent handler id found during an incremental call.
  • An invalid logger name.
  • Inability to resolve to an internal or external object.

Parsing is performed by the DictConfigurator class, whose constructor is passed the dictionary used for configuration, and has a configure() method. The logging.config module has a callable attribute dictConfigClass which is initially set to DictConfigurator. You can replace the value of dictConfigClass with a suitable implementation of your own.

dictConfig() calls dictConfigClass passing the specified dictionary, and then calls the configure() method on the returned object to put the configuration into effect:

def dictConfig(config):
+    dictConfigClass(config).configure()
+

For example, a subclass of DictConfigurator could call DictConfigurator.__init__() in its own __init__(), then set up custom prefixes which would be usable in the subsequent configure() call. dictConfigClass would be bound to this new subclass, and then dictConfig() could be called exactly as in the default, uncustomized state.

New in version 3.2.

+
+logging.config.fileConfig(fname, defaults=None, disable_existing_loggers=True, encoding=None)
+

Reads the logging configuration from a configparser-format file. The format of the file should be as described in Configuration file format. This function can be called several times from an application, allowing an end user to select from various pre-canned configurations (if the developer provides a mechanism to present the choices and load the chosen configuration).

It will raise FileNotFoundError if the file doesn’t exist and RuntimeError if the file is invalid or empty.

Parameters
+
  • +fname – A filename, or a file-like object, or an instance derived from RawConfigParser. If a RawConfigParser-derived instance is passed, it is used as is. Otherwise, a ConfigParser is instantiated, and the configuration read by it from the object passed in fname. If that has a readline() method, it is assumed to be a file-like object and read using read_file(); otherwise, it is assumed to be a filename and passed to read().
  • +defaults – Defaults to be passed to the ConfigParser can be specified in this argument.
  • +disable_existing_loggers – If specified as False, loggers which exist when this call is made are left enabled. The default is True because this enables old behaviour in a backward-compatible way. This behaviour is to disable any existing non-root loggers unless they or their ancestors are explicitly named in the logging configuration.
  • +encoding – The encoding used to open file when fname is filename.

Changed in version 3.4: An instance of a subclass of RawConfigParser is now accepted as a value for fname. This facilitates:

  • Use of a configuration file where logging configuration is just part of the overall application configuration.
  • Use of a configuration read from a file, and then modified by the using application (e.g. based on command-line parameters or other aspects of the runtime environment) before being passed to fileConfig.

New in version 3.10: The encoding parameter is added.

Changed in version 3.12: An exception will be thrown if the provided file doesn’t exist or is invalid or empty.

+
+logging.config.listen(port=DEFAULT_LOGGING_CONFIG_PORT, verify=None)
+

Starts up a socket server on the specified port, and listens for new configurations. If no port is specified, the module’s default DEFAULT_LOGGING_CONFIG_PORT is used. Logging configurations will be sent as a file suitable for processing by dictConfig() or fileConfig(). Returns a Thread instance on which you can call start() to start the server, and which you can join() when appropriate. To stop the server, call stopListening().

The verify argument, if specified, should be a callable which should verify whether bytes received across the socket are valid and should be processed. This could be done by encrypting and/or signing what is sent across the socket, such that the verify callable can perform signature verification and/or decryption. The verify callable is called with a single argument - the bytes received across the socket - and should return the bytes to be processed, or None to indicate that the bytes should be discarded. The returned bytes could be the same as the passed in bytes (e.g. when only verification is done), or they could be completely different (perhaps if decryption were performed).

To send a configuration to the socket, read in the configuration file and send it to the socket as a sequence of bytes preceded by a four-byte length string packed in binary using struct.pack('>L', n).

Note

Because portions of the configuration are passed through eval(), use of this function may open its users to a security risk. While the function only binds to a socket on localhost, and so does not accept connections from remote machines, there are scenarios where untrusted code could be run under the account of the process which calls listen(). Specifically, if the process calling listen() runs on a multi-user machine where users cannot trust each other, then a malicious user could arrange to run essentially arbitrary code in a victim user’s process, simply by connecting to the victim’s listen() socket and sending a configuration which runs whatever code the attacker wants to have executed in the victim’s process. This is especially easy to do if the default port is used, but not hard even if a different port is used. To avoid the risk of this happening, use the verify argument to listen() to prevent unrecognised configurations from being applied.

Changed in version 3.4: The verify argument was added.

Note

If you want to send configurations to the listener which don’t disable existing loggers, you will need to use a JSON format for the configuration, which will use dictConfig() for configuration. This method allows you to specify disable_existing_loggers as False in the configuration you send.

+
+logging.config.stopListening()
+

Stops the listening server which was created with a call to listen(). This is typically called before calling join() on the return value from listen().

+

Security considerations

The logging configuration functionality tries to offer convenience, and in part this is done by offering the ability to convert text in configuration files into Python objects used in logging configuration - for example, as described in User-defined objects. However, these same mechanisms (importing callables from user-defined modules and calling them with parameters from the configuration) could be used to invoke any code you like, and for this reason you should treat configuration files from untrusted sources with extreme caution and satisfy yourself that nothing bad can happen if you load them, before actually loading them.

Configuration dictionary schema

Describing a logging configuration requires listing the various objects to create and the connections between them; for example, you may create a handler named ‘console’ and then say that the logger named ‘startup’ will send its messages to the ‘console’ handler. These objects aren’t limited to those provided by the logging module because you might write your own formatter or handler class. The parameters to these classes may also need to include external objects such as sys.stderr. The syntax for describing these objects and connections is defined in Object connections below.

Dictionary Schema Details

The dictionary passed to dictConfig() must contain the following keys:

  • +version - to be set to an integer value representing the schema version. The only valid value at present is 1, but having this key allows the schema to evolve while still preserving backwards compatibility.

All other keys are optional, but if present they will be interpreted as described below. In all cases below where a ‘configuring dict’ is mentioned, it will be checked for the special '()' key to see if a custom instantiation is required. If so, the mechanism described in User-defined objects below is used to create an instance; otherwise, the context is used to determine what to instantiate.

  • +

    formatters - the corresponding value will be a dict in which each key is a formatter id and each value is a dict describing how to configure the corresponding Formatter instance.

    The configuring dict is searched for the following optional keys which correspond to the arguments passed to create a Formatter object:

    • format
    • datefmt
    • style
    • +validate (since version >=3.8)
    • +defaults (since version >=3.12)

    An optional class key indicates the name of the formatter’s class (as a dotted module and class name). The instantiation arguments are as for Formatter, thus this key is most useful for instantiating a customised subclass of Formatter. For example, the alternative class might present exception tracebacks in an expanded or condensed format. If your formatter requires different or extra configuration keys, you should use User-defined objects.

  • +

    filters - the corresponding value will be a dict in which each key is a filter id and each value is a dict describing how to configure the corresponding Filter instance.

    The configuring dict is searched for the key name (defaulting to the empty string) and this is used to construct a logging.Filter instance.

  • +

    handlers - the corresponding value will be a dict in which each key is a handler id and each value is a dict describing how to configure the corresponding Handler instance.

    The configuring dict is searched for the following keys:

    • +class (mandatory). This is the fully qualified name of the handler class.
    • +level (optional). The level of the handler.
    • +formatter (optional). The id of the formatter for this handler.
    • +

      filters (optional). A list of ids of the filters for this handler.

      Changed in version 3.11: filters can take filter instances in addition to ids.

    All other keys are passed through as keyword arguments to the handler’s constructor. For example, given the snippet:

    handlers:
+  console:
+    class : logging.StreamHandler
+    formatter: brief
+    level   : INFO
+    filters: [allow_foo]
+    stream  : ext://sys.stdout
+  file:
+    class : logging.handlers.RotatingFileHandler
+    formatter: precise
+    filename: logconfig.log
+    maxBytes: 1024
+    backupCount: 3
+

    the handler with id console is instantiated as a logging.StreamHandler, using sys.stdout as the underlying stream. The handler with id file is instantiated as a logging.handlers.RotatingFileHandler with the keyword arguments filename='logconfig.log', maxBytes=1024, backupCount=3.

  • +

    loggers - the corresponding value will be a dict in which each key is a logger name and each value is a dict describing how to configure the corresponding Logger instance.

    The configuring dict is searched for the following keys:

    • +level (optional). The level of the logger.
    • +propagate (optional). The propagation setting of the logger.
    • +

      filters (optional). A list of ids of the filters for this logger.

      Changed in version 3.11: filters can take filter instances in addition to ids.

    • +handlers (optional). A list of ids of the handlers for this logger.

    The specified loggers will be configured according to the level, propagation, filters and handlers specified.

  • +root - this will be the configuration for the root logger. Processing of the configuration will be as for any logger, except that the propagate setting will not be applicable.
  • +

    incremental - whether the configuration is to be interpreted as incremental to the existing configuration. This value defaults to False, which means that the specified configuration replaces the existing configuration with the same semantics as used by the existing fileConfig() API.

    If the specified value is True, the configuration is processed as described in the section on Incremental Configuration.

  • +disable_existing_loggers - whether any existing non-root loggers are to be disabled. This setting mirrors the parameter of the same name in fileConfig(). If absent, this parameter defaults to True. This value is ignored if incremental is True.

Incremental Configuration

It is difficult to provide complete flexibility for incremental configuration. For example, because objects such as filters and formatters are anonymous, once a configuration is set up, it is not possible to refer to such anonymous objects when augmenting a configuration.

Furthermore, there is not a compelling case for arbitrarily altering the object graph of loggers, handlers, filters, formatters at run-time, once a configuration is set up; the verbosity of loggers and handlers can be controlled just by setting levels (and, in the case of loggers, propagation flags). Changing the object graph arbitrarily in a safe way is problematic in a multi-threaded environment; while not impossible, the benefits are not worth the complexity it adds to the implementation.

Thus, when the incremental key of a configuration dict is present and is True, the system will completely ignore any formatters and filters entries, and process only the level settings in the handlers entries, and the level and propagate settings in the loggers and root entries.

Using a value in the configuration dict lets configurations to be sent over the wire as pickled dicts to a socket listener. Thus, the logging verbosity of a long-running application can be altered over time with no need to stop and restart the application.

Object connections

The schema describes a set of logging objects - loggers, handlers, formatters, filters - which are connected to each other in an object graph. Thus, the schema needs to represent connections between the objects. For example, say that, once configured, a particular logger has attached to it a particular handler. For the purposes of this discussion, we can say that the logger represents the source, and the handler the destination, of a connection between the two. Of course in the configured objects this is represented by the logger holding a reference to the handler. In the configuration dict, this is done by giving each destination object an id which identifies it unambiguously, and then using the id in the source object’s configuration to indicate that a connection exists between the source and the destination object with that id.

So, for example, consider the following YAML snippet:

formatters:
+  brief:
+    # configuration for formatter with id 'brief' goes here
+  precise:
+    # configuration for formatter with id 'precise' goes here
+handlers:
+  h1: #This is an id
+   # configuration of handler with id 'h1' goes here
+   formatter: brief
+  h2: #This is another id
+   # configuration of handler with id 'h2' goes here
+   formatter: precise
+loggers:
+  foo.bar.baz:
+    # other configuration for logger 'foo.bar.baz'
+    handlers: [h1, h2]
+

(Note: YAML used here because it’s a little more readable than the equivalent Python source form for the dictionary.)

The ids for loggers are the logger names which would be used programmatically to obtain a reference to those loggers, e.g. foo.bar.baz. The ids for Formatters and Filters can be any string value (such as brief, precise above) and they are transient, in that they are only meaningful for processing the configuration dictionary and used to determine connections between objects, and are not persisted anywhere when the configuration call is complete.

The above snippet indicates that logger named foo.bar.baz should have two handlers attached to it, which are described by the handler ids h1 and h2. The formatter for h1 is that described by id brief, and the formatter for h2 is that described by id precise.

User-defined objects

The schema supports user-defined objects for handlers, filters and formatters. (Loggers do not need to have different types for different instances, so there is no support in this configuration schema for user-defined logger classes.)

Objects to be configured are described by dictionaries which detail their configuration. In some places, the logging system will be able to infer from the context how an object is to be instantiated, but when a user-defined object is to be instantiated, the system will not know how to do this. In order to provide complete flexibility for user-defined object instantiation, the user needs to provide a ‘factory’ - a callable which is called with a configuration dictionary and which returns the instantiated object. This is signalled by an absolute import path to the factory being made available under the special key '()'. Here’s a concrete example:

formatters:
+  brief:
+    format: '%(message)s'
+  default:
+    format: '%(asctime)s %(levelname)-8s %(name)-15s %(message)s'
+    datefmt: '%Y-%m-%d %H:%M:%S'
+  custom:
+      (): my.package.customFormatterFactory
+      bar: baz
+      spam: 99.9
+      answer: 42
+

The above YAML snippet defines three formatters. The first, with id brief, is a standard logging.Formatter instance with the specified format string. The second, with id default, has a longer format and also defines the time format explicitly, and will result in a logging.Formatter initialized with those two format strings. Shown in Python source form, the brief and default formatters have configuration sub-dictionaries:

{
+  'format' : '%(message)s'
+}
+

and:

{
+  'format' : '%(asctime)s %(levelname)-8s %(name)-15s %(message)s',
+  'datefmt' : '%Y-%m-%d %H:%M:%S'
+}
+

respectively, and as these dictionaries do not contain the special key '()', the instantiation is inferred from the context: as a result, standard logging.Formatter instances are created. The configuration sub-dictionary for the third formatter, with id custom, is:

{
+  '()' : 'my.package.customFormatterFactory',
+  'bar' : 'baz',
+  'spam' : 99.9,
+  'answer' : 42
+}
+

and this contains the special key '()', which means that user-defined instantiation is wanted. In this case, the specified factory callable will be used. If it is an actual callable it will be used directly - otherwise, if you specify a string (as in the example) the actual callable will be located using normal import mechanisms. The callable will be called with the remaining items in the configuration sub-dictionary as keyword arguments. In the above example, the formatter with id custom will be assumed to be returned by the call:

my.package.customFormatterFactory(bar='baz', spam=99.9, answer=42)
+

Warning

The values for keys such as bar, spam and answer in the above example should not be configuration dictionaries or references such as cfg://foo or ext://bar, because they will not be processed by the configuration machinery, but passed to the callable as-is.

The key '()' has been used as the special key because it is not a valid keyword parameter name, and so will not clash with the names of the keyword arguments used in the call. The '()' also serves as a mnemonic that the corresponding value is a callable.

Changed in version 3.11: The filters member of handlers and loggers can take filter instances in addition to ids.

You can also specify a special key '.' whose value is a dictionary is a mapping of attribute names to values. If found, the specified attributes will be set on the user-defined object before it is returned. Thus, with the following configuration:

{
+  '()' : 'my.package.customFormatterFactory',
+  'bar' : 'baz',
+  'spam' : 99.9,
+  'answer' : 42,
+  '.' {
+    'foo': 'bar',
+    'baz': 'bozz'
+  }
+}
+

the returned formatter will have attribute foo set to 'bar' and attribute baz set to 'bozz'.

Warning

The values for attributes such as foo and baz in the above example should not be configuration dictionaries or references such as cfg://foo or ext://bar, because they will not be processed by the configuration machinery, but set as attribute values as-is.

Handler configuration order

Handlers are configured in alphabetical order of their keys, and a configured handler replaces the configuration dictionary in (a working copy of) the handlers dictionary in the schema. If you use a construct such as cfg://handlers.foo, then initially handlers['foo'] points to the configuration dictionary for the handler named foo, and later (once that handler has been configured) it points to the configured handler instance. Thus, cfg://handlers.foo could resolve to either a dictionary or a handler instance. In general, it is wise to name handlers in a way such that dependent handlers are configured _after_ any handlers they depend on; that allows something like cfg://handlers.foo to be used in configuring a handler that depends on handler foo. If that dependent handler were named bar, problems would result, because the configuration of bar would be attempted before that of foo, and foo would not yet have been configured. However, if the dependent handler were named foobar, it would be configured after foo, with the result that cfg://handlers.foo would resolve to configured handler foo, and not its configuration dictionary.

Access to external objects

There are times where a configuration needs to refer to objects external to the configuration, for example sys.stderr. If the configuration dict is constructed using Python code, this is straightforward, but a problem arises when the configuration is provided via a text file (e.g. JSON, YAML). In a text file, there is no standard way to distinguish sys.stderr from the literal string 'sys.stderr'. To facilitate this distinction, the configuration system looks for certain special prefixes in string values and treat them specially. For example, if the literal string 'ext://sys.stderr' is provided as a value in the configuration, then the ext:// will be stripped off and the remainder of the value processed using normal import mechanisms.

The handling of such prefixes is done in a way analogous to protocol handling: there is a generic mechanism to look for prefixes which match the regular expression ^(?P<prefix>[a-z]+)://(?P<suffix>.*)$ whereby, if the prefix is recognised, the suffix is processed in a prefix-dependent manner and the result of the processing replaces the string value. If the prefix is not recognised, then the string value will be left as-is.

Access to internal objects

As well as external objects, there is sometimes also a need to refer to objects in the configuration. This will be done implicitly by the configuration system for things that it knows about. For example, the string value 'DEBUG' for a level in a logger or handler will automatically be converted to the value logging.DEBUG, and the handlers, filters and formatter entries will take an object id and resolve to the appropriate destination object.

However, a more generic mechanism is needed for user-defined objects which are not known to the logging module. For example, consider logging.handlers.MemoryHandler, which takes a target argument which is another handler to delegate to. Since the system already knows about this class, then in the configuration, the given target just needs to be the object id of the relevant target handler, and the system will resolve to the handler from the id. If, however, a user defines a my.package.MyHandler which has an alternate handler, the configuration system would not know that the alternate referred to a handler. To cater for this, a generic resolution system allows the user to specify:

handlers:
+  file:
+    # configuration of file handler goes here
+
+  custom:
+    (): my.package.MyHandler
+    alternate: cfg://handlers.file
+

The literal string 'cfg://handlers.file' will be resolved in an analogous way to strings with the ext:// prefix, but looking in the configuration itself rather than the import namespace. The mechanism allows access by dot or by index, in a similar way to that provided by str.format. Thus, given the following snippet:

handlers:
+  email:
+    class: logging.handlers.SMTPHandler
+    mailhost: localhost
+    fromaddr: my_app@domain.tld
+    toaddrs:
+      - support_team@domain.tld
+      - dev_team@domain.tld
+    subject: Houston, we have a problem.
+

in the configuration, the string 'cfg://handlers' would resolve to the dict with key handlers, the string 'cfg://handlers.email would resolve to the dict with key email in the handlers dict, and so on. The string 'cfg://handlers.email.toaddrs[1] would resolve to 'dev_team@domain.tld' and the string 'cfg://handlers.email.toaddrs[0]' would resolve to the value 'support_team@domain.tld'. The subject value could be accessed using either 'cfg://handlers.email.subject' or, equivalently, 'cfg://handlers.email[subject]'. The latter form only needs to be used if the key contains spaces or non-alphanumeric characters. If an index value consists only of decimal digits, access will be attempted using the corresponding integer value, falling back to the string value if needed.

Given a string cfg://handlers.myhandler.mykey.123, this will resolve to config_dict['handlers']['myhandler']['mykey']['123']. If the string is specified as cfg://handlers.myhandler.mykey[123], the system will attempt to retrieve the value from config_dict['handlers']['myhandler']['mykey'][123], and fall back to config_dict['handlers']['myhandler']['mykey']['123'] if that fails.

Import resolution and custom importers

Import resolution, by default, uses the builtin __import__() function to do its importing. You may want to replace this with your own importing mechanism: if so, you can replace the importer attribute of the DictConfigurator or its superclass, the BaseConfigurator class. However, you need to be careful because of the way functions are accessed from classes via descriptors. If you are using a Python callable to do your imports, and you want to define it at class level rather than instance level, you need to wrap it with staticmethod(). For example:

from importlib import import_module
+from logging.config import BaseConfigurator
+
+BaseConfigurator.importer = staticmethod(import_module)
+

You don’t need to wrap with staticmethod() if you’re setting the import callable on a configurator instance.

Configuring QueueHandler and QueueListener

If you want to configure a QueueHandler, noting that this is normally used in conjunction with a QueueListener, you can configure both together. After the configuration, the QueueListener instance will be available as the listener attribute of the created handler, and that in turn will be available to you using getHandlerByName() and passing the name you have used for the QueueHandler in your configuration. The dictionary schema for configuring the pair is shown in the example YAML snippet below.

handlers:
+  qhand:
+    class: logging.handlers.QueueHandler
+    queue: my.module.queue_factory
+    listener: my.package.CustomListener
+    handlers:
+      - hand_name_1
+      - hand_name_2
+      ...
+

The queue and listener keys are optional.

If the queue key is present, the corresponding value can be one of the following:

  • An actual instance of queue.Queue or a subclass thereof. This is of course only possible if you are constructing or modifying the configuration dictionary in code.
  • A string that resolves to a callable which, when called with no arguments, returns the queue.Queue instance to use. That callable could be a queue.Queue subclass or a function which returns a suitable queue instance, such as my.module.queue_factory().
  • A dict with a '()' key which is constructed in the usual way as discussed in User-defined objects. The result of this construction should be a queue.Queue instance.

If the queue key is absent, a standard unbounded queue.Queue instance is created and used.

If the listener key is present, the corresponding value can be one of the following:

  • A subclass of logging.handlers.QueueListener. This is of course only possible if you are constructing or modifying the configuration dictionary in code.
  • A string which resolves to a class which is a subclass of QueueListener, such as 'my.package.CustomListener'.
  • A dict with a '()' key which is constructed in the usual way as discussed in User-defined objects. The result of this construction should be a callable with the same signature as the QueueListener initializer.

If the listener key is absent, logging.handlers.QueueListener is used.

The values under the handlers key are the names of other handlers in the configuration (not shown in the above snippet) which will be passed to the queue listener.

Any custom queue handler and listener classes will need to be defined with the same initialization signatures as QueueHandler and QueueListener.

New in version 3.12.

Configuration file format

The configuration file format understood by fileConfig() is based on configparser functionality. The file must contain sections called [loggers], [handlers] and [formatters] which identify by name the entities of each type which are defined in the file. For each such entity, there is a separate section which identifies how that entity is configured. Thus, for a logger named log01 in the [loggers] section, the relevant configuration details are held in a section [logger_log01]. Similarly, a handler called hand01 in the [handlers] section will have its configuration held in a section called [handler_hand01], while a formatter called form01 in the [formatters] section will have its configuration specified in a section called [formatter_form01]. The root logger configuration must be specified in a section called [logger_root].

Note

The fileConfig() API is older than the dictConfig() API and does not provide functionality to cover certain aspects of logging. For example, you cannot configure Filter objects, which provide for filtering of messages beyond simple integer levels, using fileConfig(). If you need to have instances of Filter in your logging configuration, you will need to use dictConfig(). Note that future enhancements to configuration functionality will be added to dictConfig(), so it’s worth considering transitioning to this newer API when it’s convenient to do so.

Examples of these sections in the file are given below.

[loggers]
+keys=root,log02,log03,log04,log05,log06,log07
+
+[handlers]
+keys=hand01,hand02,hand03,hand04,hand05,hand06,hand07,hand08,hand09
+
+[formatters]
+keys=form01,form02,form03,form04,form05,form06,form07,form08,form09
+

The root logger must specify a level and a list of handlers. An example of a root logger section is given below.

[logger_root]
+level=NOTSET
+handlers=hand01
+

The level entry can be one of DEBUG, INFO, WARNING, ERROR, CRITICAL or NOTSET. For the root logger only, NOTSET means that all messages will be logged. Level values are evaluated in the context of the logging package’s namespace.

The handlers entry is a comma-separated list of handler names, which must appear in the [handlers] section. These names must appear in the [handlers] section and have corresponding sections in the configuration file.

For loggers other than the root logger, some additional information is required. This is illustrated by the following example.

[logger_parser]
+level=DEBUG
+handlers=hand01
+propagate=1
+qualname=compiler.parser
+

The level and handlers entries are interpreted as for the root logger, except that if a non-root logger’s level is specified as NOTSET, the system consults loggers higher up the hierarchy to determine the effective level of the logger. The propagate entry is set to 1 to indicate that messages must propagate to handlers higher up the logger hierarchy from this logger, or 0 to indicate that messages are not propagated to handlers up the hierarchy. The qualname entry is the hierarchical channel name of the logger, that is to say the name used by the application to get the logger.

Sections which specify handler configuration are exemplified by the following.

[handler_hand01]
+class=StreamHandler
+level=NOTSET
+formatter=form01
+args=(sys.stdout,)
+

The class entry indicates the handler’s class (as determined by eval() in the logging package’s namespace). The level is interpreted as for loggers, and NOTSET is taken to mean ‘log everything’.

The formatter entry indicates the key name of the formatter for this handler. If blank, a default formatter (logging._defaultFormatter) is used. If a name is specified, it must appear in the [formatters] section and have a corresponding section in the configuration file.

The args entry, when evaluated in the context of the logging package’s namespace, is the list of arguments to the constructor for the handler class. Refer to the constructors for the relevant handlers, or to the examples below, to see how typical entries are constructed. If not provided, it defaults to ().

The optional kwargs entry, when evaluated in the context of the logging package’s namespace, is the keyword argument dict to the constructor for the handler class. If not provided, it defaults to {}.

[handler_hand02]
+class=FileHandler
+level=DEBUG
+formatter=form02
+args=('python.log', 'w')
+
+[handler_hand03]
+class=handlers.SocketHandler
+level=INFO
+formatter=form03
+args=('localhost', handlers.DEFAULT_TCP_LOGGING_PORT)
+
+[handler_hand04]
+class=handlers.DatagramHandler
+level=WARN
+formatter=form04
+args=('localhost', handlers.DEFAULT_UDP_LOGGING_PORT)
+
+[handler_hand05]
+class=handlers.SysLogHandler
+level=ERROR
+formatter=form05
+args=(('localhost', handlers.SYSLOG_UDP_PORT), handlers.SysLogHandler.LOG_USER)
+
+[handler_hand06]
+class=handlers.NTEventLogHandler
+level=CRITICAL
+formatter=form06
+args=('Python Application', '', 'Application')
+
+[handler_hand07]
+class=handlers.SMTPHandler
+level=WARN
+formatter=form07
+args=('localhost', 'from@abc', ['user1@abc', 'user2@xyz'], 'Logger Subject')
+kwargs={'timeout': 10.0}
+
+[handler_hand08]
+class=handlers.MemoryHandler
+level=NOTSET
+formatter=form08
+target=
+args=(10, ERROR)
+
+[handler_hand09]
+class=handlers.HTTPHandler
+level=NOTSET
+formatter=form09
+args=('localhost:9022', '/log', 'GET')
+kwargs={'secure': True}
+

Sections which specify formatter configuration are typified by the following.

[formatter_form01]
+format=F1 %(asctime)s %(levelname)s %(message)s %(customfield)s
+datefmt=
+style=%
+validate=True
+defaults={'customfield': 'defaultvalue'}
+class=logging.Formatter
+

The arguments for the formatter configuration are the same as the keys in the dictionary schema formatters section.

The defaults entry, when evaluated in the context of the logging package’s namespace, is a dictionary of default values for custom formatting fields. If not provided, it defaults to None.

Note

Due to the use of eval() as described above, there are potential security risks which result from using the listen() to send and receive configurations via sockets. The risks are limited to where multiple users with no mutual trust run code on the same machine; see the listen() documentation for more information.

See also

+Module logging +
+
+

API reference for the logging module.

+Module logging.handlers +
+
+

Useful handlers included with the logging module.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/logging.config.html +

+
diff --git a/devdocs/python~3.12/library%2Flogging.handlers.html b/devdocs/python~3.12/library%2Flogging.handlers.html new file mode 100644 index 00000000..6696ebf2 --- /dev/null +++ b/devdocs/python~3.12/library%2Flogging.handlers.html @@ -0,0 +1,297 @@ +

logging.handlers — Logging handlers

Source code: Lib/logging/handlers.py

The following useful handlers are provided in the package. Note that three of the handlers (StreamHandler, FileHandler and NullHandler) are actually defined in the logging module itself, but have been documented here along with the other handlers.

StreamHandler

The StreamHandler class, located in the core logging package, sends logging output to streams such as sys.stdout, sys.stderr or any file-like object (or, more precisely, any object which supports write() and flush() methods).

+class logging.StreamHandler(stream=None)
+

Returns a new instance of the StreamHandler class. If stream is specified, the instance will use it for logging output; otherwise, sys.stderr will be used.

+emit(record)
+

If a formatter is specified, it is used to format the record. The record is then written to the stream followed by terminator. If exception information is present, it is formatted using traceback.print_exception() and appended to the stream.

+
+flush()
+

Flushes the stream by calling its flush() method. Note that the close() method is inherited from Handler and so does no output, so an explicit flush() call may be needed at times.

+
+setStream(stream)
+

Sets the instance’s stream to the specified value, if it is different. The old stream is flushed before the new stream is set.

Parameters
+

stream – The stream that the handler should use.

Returns
+

the old stream, if the stream was changed, or None if it wasn’t.

New in version 3.7.

+
+terminator
+

String used as the terminator when writing a formatted record to a stream. Default value is '\n'.

If you don’t want a newline termination, you can set the handler instance’s terminator attribute to the empty string.

In earlier versions, the terminator was hardcoded as '\n'.

New in version 3.2.

+
+

FileHandler

The FileHandler class, located in the core logging package, sends logging output to a disk file. It inherits the output functionality from StreamHandler.

+class logging.FileHandler(filename, mode='a', encoding=None, delay=False, errors=None)
+

Returns a new instance of the FileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is specified, it’s used to determine how encoding errors are handled.

Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument.

Changed in version 3.9: The errors parameter was added.

+close()
+

Closes the file.

+
+emit(record)
+

Outputs the record to the file.

Note that if the file was closed due to logging shutdown at exit and the file mode is ‘w’, the record will not be emitted (see bpo-42378).

+
+

NullHandler

New in version 3.1.

The NullHandler class, located in the core logging package, does not do any formatting or output. It is essentially a ‘no-op’ handler for use by library developers.

+class logging.NullHandler
+

Returns a new instance of the NullHandler class.

+emit(record)
+

This method does nothing.

+
+handle(record)
+

This method does nothing.

+
+createLock()
+

This method returns None for the lock, since there is no underlying I/O to which access needs to be serialized.

+
+

See Configuring Logging for a Library for more information on how to use NullHandler.

WatchedFileHandler

The WatchedFileHandler class, located in the logging.handlers module, is a FileHandler which watches the file it is logging to. If the file changes, it is closed and reopened using the file name.

A file change can happen because of usage of programs such as newsyslog and logrotate which perform log file rotation. This handler, intended for use under Unix/Linux, watches the file to see if it has changed since the last emit. (A file is deemed to have changed if its device or inode have changed.) If the file has changed, the old file stream is closed, and the file opened to get a new stream.

This handler is not appropriate for use under Windows, because under Windows open log files cannot be moved or renamed - logging opens the files with exclusive locks - and so there is no need for such a handler. Furthermore, ST_INO is not supported under Windows; stat() always returns zero for this value.

+class logging.handlers.WatchedFileHandler(filename, mode='a', encoding=None, delay=False, errors=None)
+

Returns a new instance of the WatchedFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is provided, it determines how encoding errors are handled.

Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument.

Changed in version 3.9: The errors parameter was added.

+reopenIfNeeded()
+

Checks to see if the file has changed. If it has, the existing stream is flushed and closed and the file opened again, typically as a precursor to outputting the record to the file.

New in version 3.6.

+
+emit(record)
+

Outputs the record to the file, but first calls reopenIfNeeded() to reopen the file if it has changed.

+
+

BaseRotatingHandler

The BaseRotatingHandler class, located in the logging.handlers module, is the base class for the rotating file handlers, RotatingFileHandler and TimedRotatingFileHandler. You should not need to instantiate this class, but it has attributes and methods you may need to override.

+class logging.handlers.BaseRotatingHandler(filename, mode, encoding=None, delay=False, errors=None)
+

The parameters are as for FileHandler. The attributes are:

+namer
+

If this attribute is set to a callable, the rotation_filename() method delegates to this callable. The parameters passed to the callable are those passed to rotation_filename().

Note

The namer function is called quite a few times during rollover, so it should be as simple and as fast as possible. It should also return the same output every time for a given input, otherwise the rollover behaviour may not work as expected.

It’s also worth noting that care should be taken when using a namer to preserve certain attributes in the filename which are used during rotation. For example, RotatingFileHandler expects to have a set of log files whose names contain successive integers, so that rotation works as expected, and TimedRotatingFileHandler deletes old log files (based on the backupCount parameter passed to the handler’s initializer) by determining the oldest files to delete. For this to happen, the filenames should be sortable using the date/time portion of the filename, and a namer needs to respect this. (If a namer is wanted that doesn’t respect this scheme, it will need to be used in a subclass of TimedRotatingFileHandler which overrides the getFilesToDelete() method to fit in with the custom naming scheme.)

New in version 3.3.

+
+rotator
+

If this attribute is set to a callable, the rotate() method delegates to this callable. The parameters passed to the callable are those passed to rotate().

New in version 3.3.

+
+rotation_filename(default_name)
+

Modify the filename of a log file when rotating.

This is provided so that a custom filename can be provided.

The default implementation calls the ‘namer’ attribute of the handler, if it’s callable, passing the default name to it. If the attribute isn’t callable (the default is None), the name is returned unchanged.

Parameters
+

default_name – The default name for the log file.

New in version 3.3.

+
+rotate(source, dest)
+

When rotating, rotate the current log.

The default implementation calls the ‘rotator’ attribute of the handler, if it’s callable, passing the source and dest arguments to it. If the attribute isn’t callable (the default is None), the source is simply renamed to the destination.

Parameters
+
  • +source – The source filename. This is normally the base filename, e.g. ‘test.log’.
  • +dest – The destination filename. This is normally what the source is rotated to, e.g. ‘test.log.1’.

New in version 3.3.

+
+

The reason the attributes exist is to save you having to subclass - you can use the same callables for instances of RotatingFileHandler and TimedRotatingFileHandler. If either the namer or rotator callable raises an exception, this will be handled in the same way as any other exception during an emit() call, i.e. via the handleError() method of the handler.

If you need to make more significant changes to rotation processing, you can override the methods.

For an example, see Using a rotator and namer to customize log rotation processing.

RotatingFileHandler

The RotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files.

+class logging.handlers.RotatingFileHandler(filename, mode='a', maxBytes=0, backupCount=0, encoding=None, delay=False, errors=None)
+

Returns a new instance of the RotatingFileHandler class. The specified file is opened and used as the stream for logging. If mode is not specified, 'a' is used. If encoding is not None, it is used to open the file with that encoding. If delay is true, then file opening is deferred until the first call to emit(). By default, the file grows indefinitely. If errors is provided, it determines how encoding errors are handled.

You can use the maxBytes and backupCount values to allow the file to rollover at a predetermined size. When the size is about to be exceeded, the file is closed and a new file is silently opened for output. Rollover occurs whenever the current log file is nearly maxBytes in length; but if either of maxBytes or backupCount is zero, rollover never occurs, so you generally want to set backupCount to at least 1, and have a non-zero maxBytes. When backupCount is non-zero, the system will save old log files by appending the extensions ‘.1’, ‘.2’ etc., to the filename. For example, with a backupCount of 5 and a base file name of app.log, you would get app.log, app.log.1, app.log.2, up to app.log.5. The file being written to is always app.log. When this file is filled, it is closed and renamed to app.log.1, and if files app.log.1, app.log.2, etc. exist, then they are renamed to app.log.2, app.log.3 etc. respectively.

Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument.

Changed in version 3.9: The errors parameter was added.

+doRollover()
+

Does a rollover, as described above.

+
+emit(record)
+

Outputs the record to the file, catering for rollover as described previously.

+
+

TimedRotatingFileHandler

The TimedRotatingFileHandler class, located in the logging.handlers module, supports rotation of disk log files at certain timed intervals.

+class logging.handlers.TimedRotatingFileHandler(filename, when='h', interval=1, backupCount=0, encoding=None, delay=False, utc=False, atTime=None, errors=None)
+

Returns a new instance of the TimedRotatingFileHandler class. The specified file is opened and used as the stream for logging. On rotating it also sets the filename suffix. Rotating happens based on the product of when and interval.

You can use the when to specify the type of interval. The list of possible values is below. Note that they are not case sensitive.

+ + + + + + +

Value

Type of interval

If/how atTime is used

'S'

Seconds

Ignored

'M'

Minutes

Ignored

'H'

Hours

Ignored

'D'

Days

Ignored

'W0'-'W6'

Weekday (0=Monday)

Used to compute initial rollover time

'midnight'

Roll over at midnight, if atTime not specified, else at time atTime

Used to compute initial rollover time

When using weekday-based rotation, specify ‘W0’ for Monday, ‘W1’ for Tuesday, and so on up to ‘W6’ for Sunday. In this case, the value passed for interval isn’t used.

The system will save old log files by appending extensions to the filename. The extensions are date-and-time based, using the strftime format %Y-%m-%d_%H-%M-%S or a leading portion thereof, depending on the rollover interval.

When computing the next rollover time for the first time (when the handler is created), the last modification time of an existing log file, or else the current time, is used to compute when the next rotation will occur.

If the utc argument is true, times in UTC will be used; otherwise local time is used.

If backupCount is nonzero, at most backupCount files will be kept, and if more would be created when rollover occurs, the oldest one is deleted. The deletion logic uses the interval to determine which files to delete, so changing the interval may leave old files lying around.

If delay is true, then file opening is deferred until the first call to emit().

If atTime is not None, it must be a datetime.time instance which specifies the time of day when rollover occurs, for the cases where rollover is set to happen “at midnight” or “on a particular weekday”. Note that in these cases, the atTime value is effectively used to compute the initial rollover, and subsequent rollovers would be calculated via the normal interval calculation.

If errors is specified, it’s used to determine how encoding errors are handled.

Note

Calculation of the initial rollover time is done when the handler is initialised. Calculation of subsequent rollover times is done only when rollover occurs, and rollover occurs only when emitting output. If this is not kept in mind, it might lead to some confusion. For example, if an interval of “every minute” is set, that does not mean you will always see log files with times (in the filename) separated by a minute; if, during application execution, logging output is generated more frequently than once a minute, then you can expect to see log files with times separated by a minute. If, on the other hand, logging messages are only output once every five minutes (say), then there will be gaps in the file times corresponding to the minutes where no output (and hence no rollover) occurred.

Changed in version 3.4: atTime parameter was added.

Changed in version 3.6: As well as string values, Path objects are also accepted for the filename argument.

Changed in version 3.9: The errors parameter was added.

+doRollover()
+

Does a rollover, as described above.

+
+emit(record)
+

Outputs the record to the file, catering for rollover as described above.

+
+getFilesToDelete()
+

Returns a list of filenames which should be deleted as part of rollover. These are the absolute paths of the oldest backup log files written by the handler.

+
+

SocketHandler

The SocketHandler class, located in the logging.handlers module, sends logging output to a network socket. The base class uses a TCP socket.

+class logging.handlers.SocketHandler(host, port)
+

Returns a new instance of the SocketHandler class intended to communicate with a remote machine whose address is given by host and port.

Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a TCP socket is created.

+close()
+

Closes the socket.

+
+emit()
+

Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. If the connection was previously lost, re-establishes the connection. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.

+
+handleError()
+

Handles an error which has occurred during emit(). The most likely cause is a lost connection. Closes the socket so that we can retry on the next event.

+
+makeSocket()
+

This is a factory method which allows subclasses to define the precise type of socket they want. The default implementation creates a TCP socket (socket.SOCK_STREAM).

+
+makePickle(record)
+

Pickles the record’s attribute dictionary in binary format with a length prefix, and returns it ready for transmission across the socket. The details of this operation are equivalent to:

data = pickle.dumps(record_attr_dict, 1)
+datalen = struct.pack('>L', len(data))
+return datalen + data
+

Note that pickles aren’t completely secure. If you are concerned about security, you may want to override this method to implement a more secure mechanism. For example, you can sign pickles using HMAC and then verify them on the receiving end, or alternatively you can disable unpickling of global objects on the receiving end.

+
+send(packet)
+

Send a pickled byte-string packet to the socket. The format of the sent byte-string is as described in the documentation for makePickle().

This function allows for partial sends, which can happen when the network is busy.

+
+createSocket()
+

Tries to create a socket; on failure, uses an exponential back-off algorithm. On initial failure, the handler will drop the message it was trying to send. When subsequent messages are handled by the same instance, it will not try connecting until some time has passed. The default parameters are such that the initial delay is one second, and if after that delay the connection still can’t be made, the handler will double the delay each time up to a maximum of 30 seconds.

This behaviour is controlled by the following handler attributes:

  • +retryStart (initial delay, defaulting to 1.0 seconds).
  • +retryFactor (multiplier, defaulting to 2.0).
  • +retryMax (maximum delay, defaulting to 30.0 seconds).

This means that if the remote listener starts up after the handler has been used, you could lose messages (since the handler won’t even attempt a connection until the delay has elapsed, but just silently drop messages during the delay period).

+
+

DatagramHandler

The DatagramHandler class, located in the logging.handlers module, inherits from SocketHandler to support sending logging messages over UDP sockets.

+class logging.handlers.DatagramHandler(host, port)
+

Returns a new instance of the DatagramHandler class intended to communicate with a remote machine whose address is given by host and port.

Note

As UDP is not a streaming protocol, there is no persistent connection between an instance of this handler and host. For this reason, when using a network socket, a DNS lookup might have to be made each time an event is logged, which can introduce some latency into the system. If this affects you, you can do a lookup yourself and initialize this handler using the looked-up IP address rather than the hostname.

Changed in version 3.4: If port is specified as None, a Unix domain socket is created using the value in host - otherwise, a UDP socket is created.

+emit()
+

Pickles the record’s attribute dictionary and writes it to the socket in binary format. If there is an error with the socket, silently drops the packet. To unpickle the record at the receiving end into a LogRecord, use the makeLogRecord() function.

+
+makeSocket()
+

The factory method of SocketHandler is here overridden to create a UDP socket (socket.SOCK_DGRAM).

+
+send(s)
+

Send a pickled byte-string to a socket. The format of the sent byte-string is as described in the documentation for SocketHandler.makePickle().

+
+

SysLogHandler

The SysLogHandler class, located in the logging.handlers module, supports sending logging messages to a remote or local Unix syslog.

+class logging.handlers.SysLogHandler(address=('localhost', SYSLOG_UDP_PORT), facility=LOG_USER, socktype=socket.SOCK_DGRAM)
+

Returns a new instance of the SysLogHandler class intended to communicate with a remote Unix machine whose address is given by address in the form of a (host, port) tuple. If address is not specified, ('localhost', 514) is used. The address is used to open a socket. An alternative to providing a (host, port) tuple is providing an address as a string, for example ‘/dev/log’. In this case, a Unix domain socket is used to send the message to the syslog. If facility is not specified, LOG_USER is used. The type of socket opened depends on the socktype argument, which defaults to socket.SOCK_DGRAM and thus opens a UDP socket. To open a TCP socket (for use with the newer syslog daemons such as rsyslog), specify a value of socket.SOCK_STREAM.

Note that if your server is not listening on UDP port 514, SysLogHandler may appear not to work. In that case, check what address you should be using for a domain socket - it’s system dependent. For example, on Linux it’s usually ‘/dev/log’ but on OS/X it’s ‘/var/run/syslog’. You’ll need to check your platform and use the appropriate address (you may need to do this check at runtime if your application needs to run on several platforms). On Windows, you pretty much have to use the UDP option.

Note

On macOS 12.x (Monterey), Apple has changed the behaviour of their syslog daemon - it no longer listens on a domain socket. Therefore, you cannot expect SysLogHandler to work on this system.

See gh-91070 for more information.

Changed in version 3.2: socktype was added.

+close()
+

Closes the socket to the remote host.

+
+createSocket()
+

Tries to create a socket and, if it’s not a datagram socket, connect it to the other end. This method is called during handler initialization, but it’s not regarded as an error if the other end isn’t listening at this point - the method will be called again when emitting an event, if there is no socket at that point.

New in version 3.11.

+
+emit(record)
+

The record is formatted, and then sent to the syslog server. If exception information is present, it is not sent to the server.

Changed in version 3.2.1: (See: bpo-12168.) In earlier versions, the message sent to the syslog daemons was always terminated with a NUL byte, because early versions of these daemons expected a NUL terminated message - even though it’s not in the relevant specification (RFC 5424). More recent versions of these daemons don’t expect the NUL byte but strip it off if it’s there, and even more recent daemons (which adhere more closely to RFC 5424) pass the NUL byte on as part of the message.

To enable easier handling of syslog messages in the face of all these differing daemon behaviours, the appending of the NUL byte has been made configurable, through the use of a class-level attribute, append_nul. This defaults to True (preserving the existing behaviour) but can be set to False on a SysLogHandler instance in order for that instance to not append the NUL terminator.

Changed in version 3.3: (See: bpo-12419.) In earlier versions, there was no facility for an “ident” or “tag” prefix to identify the source of the message. This can now be specified using a class-level attribute, defaulting to "" to preserve existing behaviour, but which can be overridden on a SysLogHandler instance in order for that instance to prepend the ident to every message handled. Note that the provided ident must be text, not bytes, and is prepended to the message exactly as is.

+
+encodePriority(facility, priority)
+

Encodes the facility and priority into an integer. You can pass in strings or integers - if strings are passed, internal mapping dictionaries are used to convert them to integers.

The symbolic LOG_ values are defined in SysLogHandler and mirror the values defined in the sys/syslog.h header file.

Priorities

+ + + + + + + + +

Name (string)

Symbolic value

alert

LOG_ALERT

crit or critical

LOG_CRIT

debug

LOG_DEBUG

emerg or panic

LOG_EMERG

err or error

LOG_ERR

info

LOG_INFO

notice

LOG_NOTICE

warn or warning

LOG_WARNING

Facilities

+ + + + + + + + + + + + + + + + + + + + +

Name (string)

Symbolic value

auth

LOG_AUTH

authpriv

LOG_AUTHPRIV

cron

LOG_CRON

daemon

LOG_DAEMON

ftp

LOG_FTP

kern

LOG_KERN

lpr

LOG_LPR

mail

LOG_MAIL

news

LOG_NEWS

syslog

LOG_SYSLOG

user

LOG_USER

uucp

LOG_UUCP

local0

LOG_LOCAL0

local1

LOG_LOCAL1

local2

LOG_LOCAL2

local3

LOG_LOCAL3

local4

LOG_LOCAL4

local5

LOG_LOCAL5

local6

LOG_LOCAL6

local7

LOG_LOCAL7
+
+mapPriority(levelname)
+

Maps a logging level name to a syslog priority name. You may need to override this if you are using custom levels, or if the default algorithm is not suitable for your needs. The default algorithm maps DEBUG, INFO, WARNING, ERROR and CRITICAL to the equivalent syslog names, and all other level names to ‘warning’.

+
+

NTEventLogHandler

The NTEventLogHandler class, located in the logging.handlers module, supports sending logging messages to a local Windows NT, Windows 2000 or Windows XP event log. Before you can use it, you need Mark Hammond’s Win32 extensions for Python installed.

+class logging.handlers.NTEventLogHandler(appname, dllname=None, logtype='Application')
+

Returns a new instance of the NTEventLogHandler class. The appname is used to define the application name as it appears in the event log. An appropriate registry entry is created using this name. The dllname should give the fully qualified pathname of a .dll or .exe which contains message definitions to hold in the log (if not specified, 'win32service.pyd' is used - this is installed with the Win32 extensions and contains some basic placeholder message definitions. Note that use of these placeholders will make your event logs big, as the entire message source is held in the log. If you want slimmer logs, you have to pass in the name of your own .dll or .exe which contains the message definitions you want to use in the event log). The logtype is one of 'Application', 'System' or 'Security', and defaults to 'Application'.

+close()
+

At this point, you can remove the application name from the registry as a source of event log entries. However, if you do this, you will not be able to see the events as you intended in the Event Log Viewer - it needs to be able to access the registry to get the .dll name. The current version does not do this.

+
+emit(record)
+

Determines the message ID, event category and event type, and then logs the message in the NT event log.

+
+getEventCategory(record)
+

Returns the event category for the record. Override this if you want to specify your own categories. This version returns 0.

+
+getEventType(record)
+

Returns the event type for the record. Override this if you want to specify your own types. This version does a mapping using the handler’s typemap attribute, which is set up in __init__() to a dictionary which contains mappings for DEBUG, INFO, WARNING, ERROR and CRITICAL. If you are using your own levels, you will either need to override this method or place a suitable dictionary in the handler’s typemap attribute.

+
+getMessageID(record)
+

Returns the message ID for the record. If you are using your own messages, you could do this by having the msg passed to the logger being an ID rather than a format string. Then, in here, you could use a dictionary lookup to get the message ID. This version returns 1, which is the base message ID in win32service.pyd.

+
+

SMTPHandler

The SMTPHandler class, located in the logging.handlers module, supports sending logging messages to an email address via SMTP.

+class logging.handlers.SMTPHandler(mailhost, fromaddr, toaddrs, subject, credentials=None, secure=None, timeout=1.0)
+

Returns a new instance of the SMTPHandler class. The instance is initialized with the from and to addresses and subject line of the email. The toaddrs should be a list of strings. To specify a non-standard SMTP port, use the (host, port) tuple format for the mailhost argument. If you use a string, the standard SMTP port is used. If your SMTP server requires authentication, you can specify a (username, password) tuple for the credentials argument.

To specify the use of a secure protocol (TLS), pass in a tuple to the secure argument. This will only be used when authentication credentials are supplied. The tuple should be either an empty tuple, or a single-value tuple with the name of a keyfile, or a 2-value tuple with the names of the keyfile and certificate file. (This tuple is passed to the smtplib.SMTP.starttls() method.)

A timeout can be specified for communication with the SMTP server using the timeout argument.

New in version 3.3: The timeout argument was added.

+emit(record)
+

Formats the record and sends it to the specified addressees.

+
+getSubject(record)
+

If you want to specify a subject line which is record-dependent, override this method.

+
+

MemoryHandler

The MemoryHandler class, located in the logging.handlers module, supports buffering of logging records in memory, periodically flushing them to a target handler. Flushing occurs whenever the buffer is full, or when an event of a certain severity or greater is seen.

MemoryHandler is a subclass of the more general BufferingHandler, which is an abstract class. This buffers logging records in memory. Whenever each record is added to the buffer, a check is made by calling shouldFlush() to see if the buffer should be flushed. If it should, then flush() is expected to do the flushing.

+class logging.handlers.BufferingHandler(capacity)
+

Initializes the handler with a buffer of the specified capacity. Here, capacity means the number of logging records buffered.

+emit(record)
+

Append the record to the buffer. If shouldFlush() returns true, call flush() to process the buffer.

+
+flush()
+

For a BufferingHandler instance, flushing means that it sets the buffer to an empty list. This method can be overwritten to implement more useful flushing behavior.

+
+shouldFlush(record)
+

Return True if the buffer is up to capacity. This method can be overridden to implement custom flushing strategies.

+
+
+class logging.handlers.MemoryHandler(capacity, flushLevel=ERROR, target=None, flushOnClose=True)
+

Returns a new instance of the MemoryHandler class. The instance is initialized with a buffer size of capacity (number of records buffered). If flushLevel is not specified, ERROR is used. If no target is specified, the target will need to be set using setTarget() before this handler does anything useful. If flushOnClose is specified as False, then the buffer is not flushed when the handler is closed. If not specified or specified as True, the previous behaviour of flushing the buffer will occur when the handler is closed.

Changed in version 3.6: The flushOnClose parameter was added.

+close()
+

Calls flush(), sets the target to None and clears the buffer.

+
+flush()
+

For a MemoryHandler instance, flushing means just sending the buffered records to the target, if there is one. The buffer is also cleared when buffered records are sent to the target. Override if you want different behavior.

+
+setTarget(target)
+

Sets the target handler for this handler.

+
+shouldFlush(record)
+

Checks for buffer full or a record at the flushLevel or higher.

+
+

HTTPHandler

The HTTPHandler class, located in the logging.handlers module, supports sending logging messages to a web server, using either GET or POST semantics.

+class logging.handlers.HTTPHandler(host, url, method='GET', secure=False, credentials=None, context=None)
+

Returns a new instance of the HTTPHandler class. The host can be of the form host:port, should you need to use a specific port number. If no method is specified, GET is used. If secure is true, a HTTPS connection will be used. The context parameter may be set to a ssl.SSLContext instance to configure the SSL settings used for the HTTPS connection. If credentials is specified, it should be a 2-tuple consisting of userid and password, which will be placed in a HTTP ‘Authorization’ header using Basic authentication. If you specify credentials, you should also specify secure=True so that your userid and password are not passed in cleartext across the wire.

Changed in version 3.5: The context parameter was added.

+mapLogRecord(record)
+

Provides a dictionary, based on record, which is to be URL-encoded and sent to the web server. The default implementation just returns record.__dict__. This method can be overridden if e.g. only a subset of LogRecord is to be sent to the web server, or if more specific customization of what’s sent to the server is required.

+
+emit(record)
+

Sends the record to the web server as a URL-encoded dictionary. The mapLogRecord() method is used to convert the record to the dictionary to be sent.

+

Note

Since preparing a record for sending it to a web server is not the same as a generic formatting operation, using setFormatter() to specify a Formatter for a HTTPHandler has no effect. Instead of calling format(), this handler calls mapLogRecord() and then urllib.parse.urlencode() to encode the dictionary in a form suitable for sending to a web server.

+

QueueHandler

New in version 3.2.

The QueueHandler class, located in the logging.handlers module, supports sending logging messages to a queue, such as those implemented in the queue or multiprocessing modules.

Along with the QueueListener class, QueueHandler can be used to let handlers do their work on a separate thread from the one which does the logging. This is important in web applications and also other service applications where threads servicing clients need to respond as quickly as possible, while any potentially slow operations (such as sending an email via SMTPHandler) are done on a separate thread.

+class logging.handlers.QueueHandler(queue)
+

Returns a new instance of the QueueHandler class. The instance is initialized with the queue to send messages to. The queue can be any queue-like object; it’s used as-is by the enqueue() method, which needs to know how to send messages to it. The queue is not required to have the task tracking API, which means that you can use SimpleQueue instances for queue.

Note

If you are using multiprocessing, you should avoid using SimpleQueue and instead use multiprocessing.Queue.

+emit(record)
+

Enqueues the result of preparing the LogRecord. Should an exception occur (e.g. because a bounded queue has filled up), the handleError() method is called to handle the error. This can result in the record silently being dropped (if logging.raiseExceptions is False) or a message printed to sys.stderr (if logging.raiseExceptions is True).

+
+prepare(record)
+

Prepares a record for queuing. The object returned by this method is enqueued.

The base implementation formats the record to merge the message, arguments, exception and stack information, if present. It also removes unpickleable items from the record in-place. Specifically, it overwrites the record’s msg and message attributes with the merged message (obtained by calling the handler’s format() method), and sets the args, exc_info and exc_text attributes to None.

You might want to override this method if you want to convert the record to a dict or JSON string, or send a modified copy of the record while leaving the original intact.

Note

The base implementation formats the message with arguments, sets the message and msg attributes to the formatted message and sets the args and exc_text attributes to None to allow pickling and to prevent further attempts at formatting. This means that a handler on the QueueListener side won’t have the information to do custom formatting, e.g. of exceptions. You may wish to subclass QueueHandler and override this method to e.g. avoid setting exc_text to None. Note that the message / msg / args changes are related to ensuring the record is pickleable, and you might or might not be able to avoid doing that depending on whether your args are pickleable. (Note that you may have to consider not only your own code but also code in any libraries that you use.)

+
+enqueue(record)
+

Enqueues the record on the queue using put_nowait(); you may want to override this if you want to use blocking behaviour, or a timeout, or a customized queue implementation.

+
+listener
+

When created via configuration using dictConfig(), this attribute will contain a QueueListener instance for use with this handler. Otherwise, it will be None.

New in version 3.12.

+
+

QueueListener

New in version 3.2.

The QueueListener class, located in the logging.handlers module, supports receiving logging messages from a queue, such as those implemented in the queue or multiprocessing modules. The messages are received from a queue in an internal thread and passed, on the same thread, to one or more handlers for processing. While QueueListener is not itself a handler, it is documented here because it works hand-in-hand with QueueHandler.

Along with the QueueHandler class, QueueListener can be used to let handlers do their work on a separate thread from the one which does the logging. This is important in web applications and also other service applications where threads servicing clients need to respond as quickly as possible, while any potentially slow operations (such as sending an email via SMTPHandler) are done on a separate thread.

+class logging.handlers.QueueListener(queue, *handlers, respect_handler_level=False)
+

Returns a new instance of the QueueListener class. The instance is initialized with the queue to send messages to and a list of handlers which will handle entries placed on the queue. The queue can be any queue-like object; it’s passed as-is to the dequeue() method, which needs to know how to get messages from it. The queue is not required to have the task tracking API (though it’s used if available), which means that you can use SimpleQueue instances for queue.

Note

If you are using multiprocessing, you should avoid using SimpleQueue and instead use multiprocessing.Queue.

If respect_handler_level is True, a handler’s level is respected (compared with the level for the message) when deciding whether to pass messages to that handler; otherwise, the behaviour is as in previous Python versions - to always pass each message to each handler.

Changed in version 3.5: The respect_handler_level argument was added.

+dequeue(block)
+

Dequeues a record and return it, optionally blocking.

The base implementation uses get(). You may want to override this method if you want to use timeouts or work with custom queue implementations.

+
+prepare(record)
+

Prepare a record for handling.

This implementation just returns the passed-in record. You may want to override this method if you need to do any custom marshalling or manipulation of the record before passing it to the handlers.

+
+handle(record)
+

Handle a record.

This just loops through the handlers offering them the record to handle. The actual object passed to the handlers is that which is returned from prepare().

+
+start()
+

Starts the listener.

This starts up a background thread to monitor the queue for LogRecords to process.

+
+stop()
+

Stops the listener.

This asks the thread to terminate, and then waits for it to do so. Note that if you don’t call this before your application exits, there may be some records still left on the queue, which won’t be processed.

+
+enqueue_sentinel()
+

Writes a sentinel to the queue to tell the listener to quit. This implementation uses put_nowait(). You may want to override this method if you want to use timeouts or work with custom queue implementations.

New in version 3.3.

+
+

See also

+Module logging +
+
+

API reference for the logging module.

+Module logging.config +
+
+

Configuration API for the logging module.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/logging.handlers.html +

+
diff --git a/devdocs/python~3.12/library%2Flogging.html b/devdocs/python~3.12/library%2Flogging.html new file mode 100644 index 00000000..a2f97d3b --- /dev/null +++ b/devdocs/python~3.12/library%2Flogging.html @@ -0,0 +1,371 @@ +

logging — Logging facility for Python

Source code: Lib/logging/__init__.py

This module defines functions and classes which implement a flexible event logging system for applications and libraries.

The key benefit of having the logging API provided by a standard library module is that all Python modules can participate in logging, so your application log can include your own messages integrated with messages from third-party modules.

The simplest example:

>>> import logging
+>>> logging.warning('Watch out!')
+WARNING:root:Watch out!
+

The module provides a lot of functionality and flexibility. If you are unfamiliar with logging, the best way to get to grips with it is to view the tutorials (see the links above and on the right).

The basic classes defined by the module, together with their functions, are listed below.

Logger Objects

Loggers have the following attributes and methods. Note that Loggers should NEVER be instantiated directly, but always through the module-level function logging.getLogger(name). Multiple calls to getLogger() with the same name will always return a reference to the same Logger object.

The name is potentially a period-separated hierarchical value, like foo.bar.baz (though it could also be just plain foo, for example). Loggers that are further down in the hierarchical list are children of loggers higher up in the list. For example, given a logger with a name of foo, loggers with names of foo.bar, foo.bar.baz, and foo.bam are all descendants of foo. The logger name hierarchy is analogous to the Python package hierarchy, and identical to it if you organise your loggers on a per-module basis using the recommended construction logging.getLogger(__name__). That’s because in a module, __name__ is the module’s name in the Python package namespace.

+class logging.Logger
+
+propagate
+

If this attribute evaluates to true, events logged to this logger will be passed to the handlers of higher level (ancestor) loggers, in addition to any handlers attached to this logger. Messages are passed directly to the ancestor loggers’ handlers - neither the level nor filters of the ancestor loggers in question are considered.

If this evaluates to false, logging messages are not passed to the handlers of ancestor loggers.

Spelling it out with an example: If the propagate attribute of the logger named A.B.C evaluates to true, any event logged to A.B.C via a method call such as logging.getLogger('A.B.C').error(...) will [subject to passing that logger’s level and filter settings] be passed in turn to any handlers attached to loggers named A.B, A and the root logger, after first being passed to any handlers attached to A.B.C. If any logger in the chain A.B.C, A.B, A has its propagate attribute set to false, then that is the last logger whose handlers are offered the event to handle, and propagation stops at that point.

The constructor sets this attribute to True.

Note

If you attach a handler to a logger and one or more of its ancestors, it may emit the same record multiple times. In general, you should not need to attach a handler to more than one logger - if you just attach it to the appropriate logger which is highest in the logger hierarchy, then it will see all events logged by all descendant loggers, provided that their propagate setting is left set to True. A common scenario is to attach handlers only to the root logger, and to let propagation take care of the rest.

+
+setLevel(level)
+

Sets the threshold for this logger to level. Logging messages which are less severe than level will be ignored; logging messages which have severity level or higher will be emitted by whichever handler or handlers service this logger, unless a handler’s level has been set to a higher severity level than level.

When a logger is created, the level is set to NOTSET (which causes all messages to be processed when the logger is the root logger, or delegation to the parent when the logger is a non-root logger). Note that the root logger is created with level WARNING.

The term ‘delegation to the parent’ means that if a logger has a level of NOTSET, its chain of ancestor loggers is traversed until either an ancestor with a level other than NOTSET is found, or the root is reached.

If an ancestor is found with a level other than NOTSET, then that ancestor’s level is treated as the effective level of the logger where the ancestor search began, and is used to determine how a logging event is handled.

If the root is reached, and it has a level of NOTSET, then all messages will be processed. Otherwise, the root’s level will be used as the effective level.

See Logging Levels for a list of levels.

Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO. Note, however, that levels are internally stored as integers, and methods such as e.g. getEffectiveLevel() and isEnabledFor() will return/expect to be passed integers.

+
+isEnabledFor(level)
+

Indicates if a message of severity level would be processed by this logger. This method checks first the module-level level set by logging.disable(level) and then the logger’s effective level as determined by getEffectiveLevel().

+
+getEffectiveLevel()
+

Indicates the effective level for this logger. If a value other than NOTSET has been set using setLevel(), it is returned. Otherwise, the hierarchy is traversed towards the root until a value other than NOTSET is found, and that value is returned. The value returned is an integer, typically one of logging.DEBUG, logging.INFO etc.

+
+getChild(suffix)
+

Returns a logger which is a descendant to this logger, as determined by the suffix. Thus, logging.getLogger('abc').getChild('def.ghi') would return the same logger as would be returned by logging.getLogger('abc.def.ghi'). This is a convenience method, useful when the parent logger is named using e.g. __name__ rather than a literal string.

New in version 3.2.

+
+getChildren()
+

Returns a set of loggers which are immediate children of this logger. So for example logging.getLogger().getChildren() might return a set containing loggers named foo and bar, but a logger named foo.bar wouldn’t be included in the set. Likewise, logging.getLogger('foo').getChildren() might return a set including a logger named foo.bar, but it wouldn’t include one named foo.bar.baz.

New in version 3.12.

+
+debug(msg, *args, **kwargs)
+

Logs a message with level DEBUG on this logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.) No % formatting operation is performed on msg when no args are supplied.

There are four keyword arguments in kwargs which are inspected: exc_info, stack_info, stacklevel and extra.

If exc_info does not evaluate as false, it causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) or an exception instance is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.

The second optional keyword argument is stack_info, which defaults to False. If true, stack information is added to the logging message, including the actual logging call. Note that this is not the same stack information as that displayed through specifying exc_info: The former is stack frames from the bottom of the stack up to the logging call in the current thread, whereas the latter is information about stack frames which have been unwound, following an exception, while searching for exception handlers.

You can specify stack_info independently of exc_info, e.g. to just show how you got to a certain point in your code, even when no exceptions were raised. The stack frames are printed following a header line which says:

Stack (most recent call last):
+

This mimics the Traceback (most recent call last): which is used when displaying exception frames.

The third optional keyword argument is stacklevel, which defaults to 1. If greater than 1, the corresponding number of stack frames are skipped when computing the line number and function name set in the LogRecord created for the logging event. This can be used in logging helpers so that the function name, filename and line number recorded are not the information for the helper function/method, but rather its caller. The name of this parameter mirrors the equivalent one in the warnings module.

The fourth keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:

FORMAT = '%(asctime)s %(clientip)-15s %(user)-8s %(message)s'
+logging.basicConfig(format=FORMAT)
+d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
+logger = logging.getLogger('tcpserver')
+logger.warning('Protocol problem: %s', 'connection reset', extra=d)
+

would print something like

2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset
+

The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the section on LogRecord attributes for more information on which keys are used by the logging system.)

If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.

While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.

If no handler is attached to this logger (or any of its ancestors, taking into account the relevant Logger.propagate attributes), the message will be sent to the handler set on lastResort.

Changed in version 3.2: The stack_info parameter was added.

Changed in version 3.5: The exc_info parameter can now accept exception instances.

Changed in version 3.8: The stacklevel parameter was added.

+
+info(msg, *args, **kwargs)
+

Logs a message with level INFO on this logger. The arguments are interpreted as for debug().

+
+warning(msg, *args, **kwargs)
+

Logs a message with level WARNING on this logger. The arguments are interpreted as for debug().

Note

There is an obsolete method warn which is functionally identical to warning. As warn is deprecated, please do not use it - use warning instead.

+
+error(msg, *args, **kwargs)
+

Logs a message with level ERROR on this logger. The arguments are interpreted as for debug().

+
+critical(msg, *args, **kwargs)
+

Logs a message with level CRITICAL on this logger. The arguments are interpreted as for debug().

+
+log(level, msg, *args, **kwargs)
+

Logs a message with integer level level on this logger. The other arguments are interpreted as for debug().

+
+exception(msg, *args, **kwargs)
+

Logs a message with level ERROR on this logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This method should only be called from an exception handler.

+
+addFilter(filter)
+

Adds the specified filter filter to this logger.

+
+removeFilter(filter)
+

Removes the specified filter filter from this logger.

+
+filter(record)
+

Apply this logger’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be processed (passed to handlers). If one returns a false value, no further processing of the record occurs.

+
+addHandler(hdlr)
+

Adds the specified handler hdlr to this logger.

+
+removeHandler(hdlr)
+

Removes the specified handler hdlr from this logger.

+
+findCaller(stack_info=False, stacklevel=1)
+

Finds the caller’s source filename and line number. Returns the filename, line number, function name and stack information as a 4-element tuple. The stack information is returned as None unless stack_info is True.

The stacklevel parameter is passed from code calling the debug() and other APIs. If greater than 1, the excess is used to skip stack frames before determining the values to be returned. This will generally be useful when calling logging APIs from helper/wrapper code, so that the information in the event log refers not to the helper/wrapper code, but to the code that calls it.

+
+handle(record)
+

Handles a record by passing it to all handlers associated with this logger and its ancestors (until a false value of propagate is found). This method is used for unpickled records received from a socket, as well as those created locally. Logger-level filtering is applied using filter().

+
+makeRecord(name, level, fn, lno, msg, args, exc_info, func=None, extra=None, sinfo=None)
+

This is a factory method which can be overridden in subclasses to create specialized LogRecord instances.

+
+hasHandlers()
+

Checks to see if this logger has any handlers configured. This is done by looking for handlers in this logger and its parents in the logger hierarchy. Returns True if a handler was found, else False. The method stops searching up the hierarchy whenever a logger with the ‘propagate’ attribute set to false is found - that will be the last logger which is checked for the existence of handlers.

New in version 3.2.

+

Changed in version 3.7: Loggers can now be pickled and unpickled.

+

Logging Levels

The numeric values of logging levels are given in the following table. These are primarily of interest if you want to define your own levels, and need them to have specific values relative to the predefined levels. If you define a level with the same numeric value, it overwrites the predefined value; the predefined name is lost.

+ + + + + + +

Level

Numeric value

What it means / When to use it
+
+logging.NOTSET
+

0

When set on a logger, indicates that ancestor loggers are to be consulted to determine the effective level. If that still resolves to NOTSET, then all events are logged. When set on a handler, all events are handled.
+
+logging.DEBUG
+

10

Detailed information, typically only of interest to a developer trying to diagnose a problem.
+
+logging.INFO
+

20

Confirmation that things are working as expected.
+
+logging.WARNING
+

30

An indication that something unexpected happened, or that a problem might occur in the near future (e.g. ‘disk space low’). The software is still working as expected.
+
+logging.ERROR
+

40

Due to a more serious problem, the software has not been able to perform some function.
+
+logging.CRITICAL
+

50

A serious error, indicating that the program itself may be unable to continue running.

Handler Objects

Handlers have the following attributes and methods. Note that Handler is never instantiated directly; this class acts as a base for more useful subclasses. However, the __init__() method in subclasses needs to call Handler.__init__().

+class logging.Handler
+
+__init__(level=NOTSET)
+

Initializes the Handler instance by setting its level, setting the list of filters to the empty list and creating a lock (using createLock()) for serializing access to an I/O mechanism.

+
+createLock()
+

Initializes a thread lock which can be used to serialize access to underlying I/O functionality which may not be threadsafe.

+
+acquire()
+

Acquires the thread lock created with createLock().

+
+release()
+

Releases the thread lock acquired with acquire().

+
+setLevel(level)
+

Sets the threshold for this handler to level. Logging messages which are less severe than level will be ignored. When a handler is created, the level is set to NOTSET (which causes all messages to be processed).

See Logging Levels for a list of levels.

Changed in version 3.2: The level parameter now accepts a string representation of the level such as ‘INFO’ as an alternative to the integer constants such as INFO.

+
+setFormatter(fmt)
+

Sets the Formatter for this handler to fmt.

+
+addFilter(filter)
+

Adds the specified filter filter to this handler.

+
+removeFilter(filter)
+

Removes the specified filter filter from this handler.

+
+filter(record)
+

Apply this handler’s filters to the record and return True if the record is to be processed. The filters are consulted in turn, until one of them returns a false value. If none of them return a false value, the record will be emitted. If one returns a false value, the handler will not emit the record.

+
+flush()
+

Ensure all logging output has been flushed. This version does nothing and is intended to be implemented by subclasses.

+
+close()
+

Tidy up any resources used by the handler. This version does no output but removes the handler from an internal list of handlers which is closed when shutdown() is called. Subclasses should ensure that this gets called from overridden close() methods.

+
+handle(record)
+

Conditionally emits the specified logging record, depending on filters which may have been added to the handler. Wraps the actual emission of the record with acquisition/release of the I/O thread lock.

+
+handleError(record)
+

This method should be called from handlers when an exception is encountered during an emit() call. If the module-level attribute raiseExceptions is False, exceptions get silently ignored. This is what is mostly wanted for a logging system - most users will not care about errors in the logging system, they are more interested in application errors. You could, however, replace this with a custom handler if you wish. The specified record is the one which was being processed when the exception occurred. (The default value of raiseExceptions is True, as that is more useful during development).

+
+format(record)
+

Do formatting for a record - if a formatter is set, use it. Otherwise, use the default formatter for the module.

+
+emit(record)
+

Do whatever it takes to actually log the specified logging record. This version is intended to be implemented by subclasses and so raises a NotImplementedError.

Warning

This method is called after a handler-level lock is acquired, which is released after this method returns. When you override this method, note that you should be careful when calling anything that invokes other parts of the logging API which might do locking, because that might result in a deadlock. Specifically:

  • Logging configuration APIs acquire the module-level lock, and then individual handler-level locks as those handlers are configured.
  • Many logging APIs lock the module-level lock. If such an API is called from this method, it could cause a deadlock if a configuration call is made on another thread, because that thread will try to acquire the module-level lock before the handler-level lock, whereas this thread tries to acquire the module-level lock after the handler-level lock (because in this method, the handler-level lock has already been acquired).
+
+

For a list of handlers included as standard, see logging.handlers.

Formatter Objects

+class logging.Formatter(fmt=None, datefmt=None, style='%', validate=True, *, defaults=None)
+

Responsible for converting a LogRecord to an output string to be interpreted by a human or external system.

Parameters
+
  • +fmt (str) – A format string in the given style for the logged output as a whole. The possible mapping keys are drawn from the LogRecord object’s LogRecord attributes. If not specified, '%(message)s' is used, which is just the logged message.
  • +datefmt (str) – A format string in the given style for the date/time portion of the logged output. If not specified, the default described in formatTime() is used.
  • +style (str) – Can be one of '%', '{' or '$' and determines how the format string will be merged with its data: using one of printf-style String Formatting (%), str.format() ({) or string.Template ($). This only applies to fmt and datefmt (e.g. '%(message)s' versus '{message}'), not to the actual log messages passed to the logging methods. However, there are other ways to use {- and $-formatting for log messages.
  • +validate (bool) – If True (the default), incorrect or mismatched fmt and style will raise a ValueError; for example, logging.Formatter('%(asctime)s - %(message)s', style='{').
  • +defaults (dict[str, Any]) – A dictionary with default values to use in custom fields. For example, logging.Formatter('%(ip)s %(message)s', defaults={"ip": None}) +

New in version 3.2: The style parameter.

New in version 3.8: The validate parameter.

New in version 3.10: The defaults parameter.

+format(record)
+

The record’s attribute dictionary is used as the operand to a string formatting operation. Returns the resulting string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using msg % args. If the formatting string contains '(asctime)', formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message. Note that the formatted exception information is cached in attribute exc_text. This is useful because the exception information can be pickled and sent across the wire, but you should be careful if you have more than one Formatter subclass which customizes the formatting of exception information. In this case, you will have to clear the cached value (by setting the exc_text attribute to None) after a formatter has done its formatting, so that the next formatter to handle the event doesn’t use the cached value, but recalculates it afresh.

If stack information is available, it’s appended after the exception information, using formatStack() to transform it if necessary.

+
+formatTime(record, datefmt=None)
+

This method should be called from format() by a formatter which wants to make use of a formatted time. This method can be overridden in formatters to provide for any specific requirement, but the basic behavior is as follows: if datefmt (a string) is specified, it is used with time.strftime() to format the creation time of the record. Otherwise, the format ‘%Y-%m-%d %H:%M:%S,uuu’ is used, where the uuu part is a millisecond value and the other letters are as per the time.strftime() documentation. An example time in this format is 2003-01-23 00:29:50,411. The resulting string is returned.

This function uses a user-configurable function to convert the creation time to a tuple. By default, time.localtime() is used; to change this for a particular formatter instance, set the converter attribute to a function with the same signature as time.localtime() or time.gmtime(). To change it for all formatters, for example if you want all logging times to be shown in GMT, set the converter attribute in the Formatter class.

Changed in version 3.3: Previously, the default format was hard-coded as in this example: 2010-09-06 22:38:15,292 where the part before the comma is handled by a strptime format string ('%Y-%m-%d %H:%M:%S'), and the part after the comma is a millisecond value. Because strptime does not have a format placeholder for milliseconds, the millisecond value is appended using another format string, '%s,%03d' — and both of these format strings have been hardcoded into this method. With the change, these strings are defined as class-level attributes which can be overridden at the instance level when desired. The names of the attributes are default_time_format (for the strptime format string) and default_msec_format (for appending the millisecond value).

Changed in version 3.9: The default_msec_format can be None.

+
+formatException(exc_info)
+

Formats the specified exception information (a standard exception tuple as returned by sys.exc_info()) as a string. This default implementation just uses traceback.print_exception(). The resulting string is returned.

+
+formatStack(stack_info)
+

Formats the specified stack information (a string as returned by traceback.print_stack(), but with the last newline removed) as a string. This default implementation just returns the input value.

+
+
+class logging.BufferingFormatter(linefmt=None)
+

A base formatter class suitable for subclassing when you want to format a number of records. You can pass a Formatter instance which you want to use to format each line (that corresponds to a single record). If not specified, the default formatter (which just outputs the event message) is used as the line formatter.

+formatHeader(records)
+

Return a header for a list of records. The base implementation just returns the empty string. You will need to override this method if you want specific behaviour, e.g. to show the count of records, a title or a separator line.

+
+formatFooter(records)
+

Return a footer for a list of records. The base implementation just returns the empty string. You will need to override this method if you want specific behaviour, e.g. to show the count of records or a separator line.

+
+format(records)
+

Return formatted text for a list of records. The base implementation just returns the empty string if there are no records; otherwise, it returns the concatenation of the header, each record formatted with the line formatter, and the footer.

+
+

Filter Objects

Filters can be used by Handlers and Loggers for more sophisticated filtering than is provided by levels. The base filter class only allows events which are below a certain point in the logger hierarchy. For example, a filter initialized with ‘A.B’ will allow events logged by loggers ‘A.B’, ‘A.B.C’, ‘A.B.C.D’, ‘A.B.D’ etc. but not ‘A.BB’, ‘B.A.B’ etc. If initialized with the empty string, all events are passed.

+class logging.Filter(name='')
+

Returns an instance of the Filter class. If name is specified, it names a logger which, together with its children, will have its events allowed through the filter. If name is the empty string, allows every event.

+filter(record)
+

Is the specified record to be logged? Returns false for no, true for yes. Filters can either modify log records in-place or return a completely different record instance which will replace the original log record in any future processing of the event.

+
+

Note that filters attached to handlers are consulted before an event is emitted by the handler, whereas filters attached to loggers are consulted whenever an event is logged (using debug(), info(), etc.), before sending an event to handlers. This means that events which have been generated by descendant loggers will not be filtered by a logger’s filter setting, unless the filter has also been applied to those descendant loggers.

You don’t actually need to subclass Filter: you can pass any instance which has a filter method with the same semantics.

Changed in version 3.2: You don’t need to create specialized Filter classes, or use other classes with a filter method: you can use a function (or other callable) as a filter. The filtering logic will check to see if the filter object has a filter attribute: if it does, it’s assumed to be a Filter and its filter() method is called. Otherwise, it’s assumed to be a callable and called with the record as the single parameter. The returned value should conform to that returned by filter().

Changed in version 3.12: You can now return a LogRecord instance from filters to replace the log record rather than modifying it in place. This allows filters attached to a Handler to modify the log record before it is emitted, without having side effects on other handlers.

Although filters are used primarily to filter records based on more sophisticated criteria than levels, they get to see every record which is processed by the handler or logger they’re attached to: this can be useful if you want to do things like counting how many records were processed by a particular logger or handler, or adding, changing or removing attributes in the LogRecord being processed. Obviously changing the LogRecord needs to be done with some care, but it does allow the injection of contextual information into logs (see Using Filters to impart contextual information).

LogRecord Objects

LogRecord instances are created automatically by the Logger every time something is logged, and can be created manually via makeLogRecord() (for example, from a pickled event received over the wire).

+class logging.LogRecord(name, level, pathname, lineno, msg, args, exc_info, func=None, sinfo=None)
+

Contains all the information pertinent to the event being logged.

The primary information is passed in msg and args, which are combined using msg % args to create the message attribute of the record.

Parameters
+
  • +name (str) – The name of the logger used to log the event represented by this LogRecord. Note that the logger name in the LogRecord will always have this value, even though it may be emitted by a handler attached to a different (ancestor) logger.
  • +level (int) – The numeric level of the logging event (such as 10 for DEBUG, 20 for INFO, etc). Note that this is converted to two attributes of the LogRecord: levelno for the numeric value and levelname for the corresponding level name.
  • +pathname (str) – The full string path of the source file where the logging call was made.
  • +lineno (int) – The line number in the source file where the logging call was made.
  • +msg (Any) – The event description message, which can be a %-format string with placeholders for variable data, or an arbitrary object (see Using arbitrary objects as messages).
  • +args (tuple | dict[str, Any]) – Variable data to merge into the msg argument to obtain the event description.
  • +exc_info (tuple[type[BaseException], BaseException, types.TracebackType] | None) – An exception tuple with the current exception information, as returned by sys.exc_info(), or None if no exception information is available.
  • +func (str | None) – The name of the function or method from which the logging call was invoked.
  • +sinfo (str | None) – A text string representing stack information from the base of the stack in the current thread, up to the logging call.
+getMessage()
+

Returns the message for this LogRecord instance after merging any user-supplied arguments with the message. If the user-supplied message argument to the logging call is not a string, str() is called on it to convert it to a string. This allows use of user-defined classes as messages, whose __str__ method can return the actual format string to be used.

+

Changed in version 3.2: The creation of a LogRecord has been made more configurable by providing a factory which is used to create the record. The factory can be set using getLogRecordFactory() and setLogRecordFactory() (see this for the factory’s signature).

This functionality can be used to inject your own values into a LogRecord at creation time. You can use the following pattern:

old_factory = logging.getLogRecordFactory()
+
+def record_factory(*args, **kwargs):
+    record = old_factory(*args, **kwargs)
+    record.custom_attribute = 0xdecafbad
+    return record
+
+logging.setLogRecordFactory(record_factory)
+

With this pattern, multiple factories could be chained, and as long as they don’t overwrite each other’s attributes or unintentionally overwrite the standard attributes listed above, there should be no surprises.

+

LogRecord attributes

The LogRecord has a number of attributes, most of which are derived from the parameters to the constructor. (Note that the names do not always correspond exactly between the LogRecord constructor parameters and the LogRecord attributes.) These attributes can be used to merge data from the record into the format string. The following table lists (in alphabetical order) the attribute names, their meanings and the corresponding placeholder in a %-style format string.

If you are using {}-formatting (str.format()), you can use {attrname} as the placeholder in the format string. If you are using $-formatting (string.Template), use the form ${attrname}. In both cases, of course, replace attrname with the actual attribute name you want to use.

In the case of {}-formatting, you can specify formatting flags by placing them after the attribute name, separated from it with a colon. For example: a placeholder of {msecs:03.0f} would format a millisecond value of 4 as 004. Refer to the str.format() documentation for full details on the options available to you.

+ + + + + + + + + + + + + + + + + + + + + + +

Attribute name

Format

Description

args

You shouldn’t need to format this yourself.

The tuple of arguments merged into msg to produce message, or a dict whose values are used for the merge (when there is only one argument, and it is a dictionary).

asctime

%(asctime)s

Human-readable time when the LogRecord was created. By default this is of the form ‘2003-07-08 16:49:45,896’ (the numbers after the comma are millisecond portion of the time).

created

%(created)f

Time when the LogRecord was created (as returned by time.time()).

exc_info

You shouldn’t need to format this yourself.

Exception tuple (à la sys.exc_info) or, if no exception has occurred, None.

filename

%(filename)s

Filename portion of pathname.

funcName

%(funcName)s

Name of function containing the logging call.

levelname

%(levelname)s

Text logging level for the message ('DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL').

levelno

%(levelno)s

Numeric logging level for the message (DEBUG, INFO, WARNING, ERROR, CRITICAL).

lineno

%(lineno)d

Source line number where the logging call was issued (if available).

message

%(message)s

The logged message, computed as msg % +args. This is set when Formatter.format() is invoked.

module

%(module)s

Module (name portion of filename).

msecs

%(msecs)d

Millisecond portion of the time when the LogRecord was created.

msg

You shouldn’t need to format this yourself.

The format string passed in the original logging call. Merged with args to produce message, or an arbitrary object (see Using arbitrary objects as messages).

name

%(name)s

Name of the logger used to log the call.

pathname

%(pathname)s

Full pathname of the source file where the logging call was issued (if available).

process

%(process)d

Process ID (if available).

processName

%(processName)s

Process name (if available).

relativeCreated

%(relativeCreated)d

Time in milliseconds when the LogRecord was created, relative to the time the logging module was loaded.

stack_info

You shouldn’t need to format this yourself.

Stack frame information (where available) from the bottom of the stack in the current thread, up to and including the stack frame of the logging call which resulted in the creation of this record.

thread

%(thread)d

Thread ID (if available).

threadName

%(threadName)s

Thread name (if available).

taskName

%(taskName)s

asyncio.Task name (if available).

Changed in version 3.1: processName was added.

Changed in version 3.12: taskName was added.

LoggerAdapter Objects

LoggerAdapter instances are used to conveniently pass contextual information into logging calls. For a usage example, see the section on adding contextual information to your logging output.

+class logging.LoggerAdapter(logger, extra)
+

Returns an instance of LoggerAdapter initialized with an underlying Logger instance and a dict-like object.

+process(msg, kwargs)
+

Modifies the message and/or keyword arguments passed to a logging call in order to insert contextual information. This implementation takes the object passed as extra to the constructor and adds it to kwargs using key ‘extra’. The return value is a (msg, kwargs) tuple which has the (possibly modified) versions of the arguments passed in.

+
+manager
+

Delegates to the underlying manager` on logger.

+
+_log
+

Delegates to the underlying _log`() method on logger.

+

In addition to the above, LoggerAdapter supports the following methods of Logger: debug(), info(), warning(), error(), exception(), critical(), log(), isEnabledFor(), getEffectiveLevel(), setLevel() and hasHandlers(). These methods have the same signatures as their counterparts in Logger, so you can use the two types of instances interchangeably.

Changed in version 3.2: The isEnabledFor(), getEffectiveLevel(), setLevel() and hasHandlers() methods were added to LoggerAdapter. These methods delegate to the underlying logger.

Changed in version 3.6: Attribute manager and method _log() were added, which delegate to the underlying logger and allow adapters to be nested.

+

Thread Safety

The logging module is intended to be thread-safe without any special work needing to be done by its clients. It achieves this though using threading locks; there is one lock to serialize access to the module’s shared data, and each handler also creates a lock to serialize access to its underlying I/O.

If you are implementing asynchronous signal handlers using the signal module, you may not be able to use logging from within such handlers. This is because lock implementations in the threading module are not always re-entrant, and so cannot be invoked from such signal handlers.

Module-Level Functions

In addition to the classes described above, there are a number of module-level functions.

+logging.getLogger(name=None)
+

Return a logger with the specified name or, if name is None, return a logger which is the root logger of the hierarchy. If specified, the name is typically a dot-separated hierarchical name like ‘a’, ‘a.b’ or ‘a.b.c.d’. Choice of these names is entirely up to the developer who is using logging.

All calls to this function with a given name return the same logger instance. This means that logger instances never need to be passed between different parts of an application.

+
+logging.getLoggerClass()
+

Return either the standard Logger class, or the last class passed to setLoggerClass(). This function may be called from within a new class definition, to ensure that installing a customized Logger class will not undo customizations already applied by other code. For example:

class MyLogger(logging.getLoggerClass()):
+    # ... override behaviour here
+
+
+logging.getLogRecordFactory()
+

Return a callable which is used to create a LogRecord.

New in version 3.2: This function has been provided, along with setLogRecordFactory(), to allow developers more control over how the LogRecord representing a logging event is constructed.

See setLogRecordFactory() for more information about the how the factory is called.

+
+logging.debug(msg, *args, **kwargs)
+

Logs a message with level DEBUG on the root logger. The msg is the message format string, and the args are the arguments which are merged into msg using the string formatting operator. (Note that this means that you can use keywords in the format string, together with a single dictionary argument.)

There are three keyword arguments in kwargs which are inspected: exc_info which, if it does not evaluate as false, causes exception information to be added to the logging message. If an exception tuple (in the format returned by sys.exc_info()) or an exception instance is provided, it is used; otherwise, sys.exc_info() is called to get the exception information.

The second optional keyword argument is stack_info, which defaults to False. If true, stack information is added to the logging message, including the actual logging call. Note that this is not the same stack information as that displayed through specifying exc_info: The former is stack frames from the bottom of the stack up to the logging call in the current thread, whereas the latter is information about stack frames which have been unwound, following an exception, while searching for exception handlers.

You can specify stack_info independently of exc_info, e.g. to just show how you got to a certain point in your code, even when no exceptions were raised. The stack frames are printed following a header line which says:

Stack (most recent call last):
+

This mimics the Traceback (most recent call last): which is used when displaying exception frames.

The third optional keyword argument is extra which can be used to pass a dictionary which is used to populate the __dict__ of the LogRecord created for the logging event with user-defined attributes. These custom attributes can then be used as you like. For example, they could be incorporated into logged messages. For example:

FORMAT = '%(asctime)s %(clientip)-15s %(user)-8s %(message)s'
+logging.basicConfig(format=FORMAT)
+d = {'clientip': '192.168.0.1', 'user': 'fbloggs'}
+logging.warning('Protocol problem: %s', 'connection reset', extra=d)
+

would print something like:

2006-02-08 22:20:02,165 192.168.0.1 fbloggs  Protocol problem: connection reset
+

The keys in the dictionary passed in extra should not clash with the keys used by the logging system. (See the Formatter documentation for more information on which keys are used by the logging system.)

If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys.

While this might be annoying, this feature is intended for use in specialized circumstances, such as multi-threaded servers where the same code executes in many contexts, and interesting conditions which arise are dependent on this context (such as remote client IP address and authenticated user name, in the above example). In such circumstances, it is likely that specialized Formatters would be used with particular Handlers.

This function (as well as info(), warning(), error() and critical()) will call basicConfig() if the root logger doesn’t have any handler attached.

Changed in version 3.2: The stack_info parameter was added.

+
+logging.info(msg, *args, **kwargs)
+

Logs a message with level INFO on the root logger. The arguments are interpreted as for debug().

+
+logging.warning(msg, *args, **kwargs)
+

Logs a message with level WARNING on the root logger. The arguments are interpreted as for debug().

Note

There is an obsolete function warn which is functionally identical to warning. As warn is deprecated, please do not use it - use warning instead.

+
+logging.error(msg, *args, **kwargs)
+

Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug().

+
+logging.critical(msg, *args, **kwargs)
+

Logs a message with level CRITICAL on the root logger. The arguments are interpreted as for debug().

+
+logging.exception(msg, *args, **kwargs)
+

Logs a message with level ERROR on the root logger. The arguments are interpreted as for debug(). Exception info is added to the logging message. This function should only be called from an exception handler.

+
+logging.log(level, msg, *args, **kwargs)
+

Logs a message with level level on the root logger. The other arguments are interpreted as for debug().

+
+logging.disable(level=CRITICAL)
+

Provides an overriding level level for all loggers which takes precedence over the logger’s own level. When the need arises to temporarily throttle logging output down across the whole application, this function can be useful. Its effect is to disable all logging calls of severity level and below, so that if you call it with a value of INFO, then all INFO and DEBUG events would be discarded, whereas those of severity WARNING and above would be processed according to the logger’s effective level. If logging.disable(logging.NOTSET) is called, it effectively removes this overriding level, so that logging output again depends on the effective levels of individual loggers.

Note that if you have defined any custom logging level higher than CRITICAL (this is not recommended), you won’t be able to rely on the default value for the level parameter, but will have to explicitly supply a suitable value.

Changed in version 3.7: The level parameter was defaulted to level CRITICAL. See bpo-28524 for more information about this change.

+
+logging.addLevelName(level, levelName)
+

Associates level level with text levelName in an internal dictionary, which is used to map numeric levels to a textual representation, for example when a Formatter formats a message. This function can also be used to define your own levels. The only constraints are that all levels used must be registered using this function, levels should be positive integers and they should increase in increasing order of severity.

Note

If you are thinking of defining your own levels, please see the section on Custom Levels.

+
+logging.getLevelNamesMapping()
+

Returns a mapping from level names to their corresponding logging levels. For example, the string “CRITICAL” maps to CRITICAL. The returned mapping is copied from an internal mapping on each call to this function.

New in version 3.11.

+
+logging.getLevelName(level)
+

Returns the textual or numeric representation of logging level level.

If level is one of the predefined levels CRITICAL, ERROR, WARNING, INFO or DEBUG then you get the corresponding string. If you have associated levels with names using addLevelName() then the name you have associated with level is returned. If a numeric value corresponding to one of the defined levels is passed in, the corresponding string representation is returned.

The level parameter also accepts a string representation of the level such as ‘INFO’. In such cases, this functions returns the corresponding numeric value of the level.

If no matching numeric or string value is passed in, the string ‘Level %s’ % level is returned.

Note

Levels are internally integers (as they need to be compared in the logging logic). This function is used to convert between an integer level and the level name displayed in the formatted log output by means of the %(levelname)s format specifier (see LogRecord attributes), and vice versa.

Changed in version 3.4: In Python versions earlier than 3.4, this function could also be passed a text level, and would return the corresponding numeric value of the level. This undocumented behaviour was considered a mistake, and was removed in Python 3.4, but reinstated in 3.4.2 due to retain backward compatibility.

+
+logging.getHandlerByName(name)
+

Returns a handler with the specified name, or None if there is no handler with that name.

New in version 3.12.

+
+logging.getHandlerNames()
+

Returns an immutable set of all known handler names.

New in version 3.12.

+
+logging.makeLogRecord(attrdict)
+

Creates and returns a new LogRecord instance whose attributes are defined by attrdict. This function is useful for taking a pickled LogRecord attribute dictionary, sent over a socket, and reconstituting it as a LogRecord instance at the receiving end.

+
+logging.basicConfig(**kwargs)
+

Does basic configuration for the logging system by creating a StreamHandler with a default Formatter and adding it to the root logger. The functions debug(), info(), warning(), error() and critical() will call basicConfig() automatically if no handlers are defined for the root logger.

This function does nothing if the root logger already has handlers configured, unless the keyword argument force is set to True.

Note

This function should be called from the main thread before other threads are started. In versions of Python prior to 2.7.1 and 3.2, if this function is called from multiple threads, it is possible (in rare circumstances) that a handler will be added to the root logger more than once, leading to unexpected results such as messages being duplicated in the log.

The following keyword arguments are supported.

+ + + + + + + + + + + +

Format

Description

filename

Specifies that a FileHandler be created, using the specified filename, rather than a StreamHandler.

filemode

If filename is specified, open the file in this mode. Defaults to 'a'.

format

Use the specified format string for the handler. Defaults to attributes levelname, name and message separated by colons.

datefmt

Use the specified date/time format, as accepted by time.strftime().

style

If format is specified, use this style for the format string. One of '%', '{' or '$' for printf-style, str.format() or string.Template respectively. Defaults to '%'.

level

Set the root logger level to the specified level.

stream

Use the specified stream to initialize the StreamHandler. Note that this argument is incompatible with filename - if both are present, a ValueError is raised.

handlers

If specified, this should be an iterable of already created handlers to add to the root logger. Any handlers which don’t already have a formatter set will be assigned the default formatter created in this function. Note that this argument is incompatible with filename or stream - if both are present, a ValueError is raised.

force

If this keyword argument is specified as true, any existing handlers attached to the root logger are removed and closed, before carrying out the configuration as specified by the other arguments.

encoding

If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file.

errors

If this keyword argument is specified along with filename, its value is used when the FileHandler is created, and thus used when opening the output file. If not specified, the value ‘backslashreplace’ is used. Note that if None is specified, it will be passed as such to open(), which means that it will be treated the same as passing ‘errors’.

Changed in version 3.2: The style argument was added.

Changed in version 3.3: The handlers argument was added. Additional checks were added to catch situations where incompatible arguments are specified (e.g. handlers together with stream or filename, or stream together with filename).

Changed in version 3.8: The force argument was added.

Changed in version 3.9: The encoding and errors arguments were added.

+
+logging.shutdown()
+

Informs the logging system to perform an orderly shutdown by flushing and closing all handlers. This should be called at application exit and no further use of the logging system should be made after this call.

When the logging module is imported, it registers this function as an exit handler (see atexit), so normally there’s no need to do that manually.

+
+logging.setLoggerClass(klass)
+

Tells the logging system to use the class klass when instantiating a logger. The class should define __init__() such that only a name argument is required, and the __init__() should call Logger.__init__(). This function is typically called before any loggers are instantiated by applications which need to use custom logger behavior. After this call, as at any other time, do not instantiate loggers directly using the subclass: continue to use the logging.getLogger() API to get your loggers.

+
+logging.setLogRecordFactory(factory)
+

Set a callable which is used to create a LogRecord.

Parameters
+

factory – The factory callable to be used to instantiate a log record.

New in version 3.2: This function has been provided, along with getLogRecordFactory(), to allow developers more control over how the LogRecord representing a logging event is constructed.

The factory has the following signature:

factory(name, level, fn, lno, msg, args, exc_info, func=None, sinfo=None, **kwargs)

name
+

The logger name.

level
+

The logging level (numeric).

fn
+

The full pathname of the file where the logging call was made.

lno
+

The line number in the file where the logging call was made.

msg
+

The logging message.

args
+

The arguments for the logging message.

exc_info
+

An exception tuple, or None.

func
+

The name of the function or method which invoked the logging call.

sinfo
+

A stack traceback such as is provided by traceback.print_stack(), showing the call hierarchy.

kwargs
+

Additional keyword arguments.

+

Module-Level Attributes

+logging.lastResort
+

A “handler of last resort” is available through this attribute. This is a StreamHandler writing to sys.stderr with a level of WARNING, and is used to handle logging events in the absence of any logging configuration. The end result is to just print the message to sys.stderr. This replaces the earlier error message saying that “no handlers could be found for logger XYZ”. If you need the earlier behaviour for some reason, lastResort can be set to None.

New in version 3.2.

+

Integration with the warnings module

The captureWarnings() function can be used to integrate logging with the warnings module.

+logging.captureWarnings(capture)
+

This function is used to turn the capture of warnings by logging on and off.

If capture is True, warnings issued by the warnings module will be redirected to the logging system. Specifically, a warning will be formatted using warnings.formatwarning() and the resulting string logged to a logger named 'py.warnings' with a severity of WARNING.

If capture is False, the redirection of warnings to the logging system will stop, and warnings will be redirected to their original destinations (i.e. those in effect before captureWarnings(True) was called).

+

See also

+Module logging.config +
+
+

Configuration API for the logging module.

+Module logging.handlers +
+
+

Useful handlers included with the logging module.

+PEP 282 - A Logging System
+
+

The proposal which described this feature for inclusion in the Python standard library.

Original Python logging package
+
+

This is the original source for the logging package. The version of the package available from this site is suitable for use with Python 1.5.2, 2.1.x and 2.2.x, which do not include the logging package in the standard library.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/logging.html +

+
diff --git a/devdocs/python~3.12/library%2Flzma.html b/devdocs/python~3.12/library%2Flzma.html new file mode 100644 index 00000000..1bc3d1d8 --- /dev/null +++ b/devdocs/python~3.12/library%2Flzma.html @@ -0,0 +1,115 @@ +

lzma — Compression using the LZMA algorithm

New in version 3.3.

Source code: Lib/lzma.py

This module provides classes and convenience functions for compressing and decompressing data using the LZMA compression algorithm. Also included is a file interface supporting the .xz and legacy .lzma file formats used by the xz utility, as well as raw compressed streams.

The interface provided by this module is very similar to that of the bz2 module. Note that LZMAFile and bz2.BZ2File are not thread-safe, so if you need to use a single LZMAFile instance from multiple threads, it is necessary to protect it with a lock.

+exception lzma.LZMAError
+

This exception is raised when an error occurs during compression or decompression, or while initializing the compressor/decompressor state.

+

Reading and writing compressed files

+lzma.open(filename, mode='rb', *, format=None, check=- 1, preset=None, filters=None, encoding=None, errors=None, newline=None)
+

Open an LZMA-compressed file in binary or text mode, returning a file object.

The filename argument can be either an actual file name (given as a str, bytes or path-like object), in which case the named file is opened, or it can be an existing file object to read from or write to.

The mode argument can be any of "r", "rb", "w", "wb", "x", "xb", "a" or "ab" for binary mode, or "rt", "wt", "xt", or "at" for text mode. The default is "rb".

When opening a file for reading, the format and filters arguments have the same meanings as for LZMADecompressor. In this case, the check and preset arguments should not be used.

When opening a file for writing, the format, check, preset and filters arguments have the same meanings as for LZMACompressor.

For binary mode, this function is equivalent to the LZMAFile constructor: LZMAFile(filename, mode, ...). In this case, the encoding, errors and newline arguments must not be provided.

For text mode, a LZMAFile object is created, and wrapped in an io.TextIOWrapper instance with the specified encoding, error handling behavior, and line ending(s).

Changed in version 3.4: Added support for the "x", "xb" and "xt" modes.

Changed in version 3.6: Accepts a path-like object.

+
+class lzma.LZMAFile(filename=None, mode='r', *, format=None, check=- 1, preset=None, filters=None)
+

Open an LZMA-compressed file in binary mode.

An LZMAFile can wrap an already-open file object, or operate directly on a named file. The filename argument specifies either the file object to wrap, or the name of the file to open (as a str, bytes or path-like object). When wrapping an existing file object, the wrapped file will not be closed when the LZMAFile is closed.

The mode argument can be either "r" for reading (default), "w" for overwriting, "x" for exclusive creation, or "a" for appending. These can equivalently be given as "rb", "wb", "xb" and "ab" respectively.

If filename is a file object (rather than an actual file name), a mode of "w" does not truncate the file, and is instead equivalent to "a".

When opening a file for reading, the input file may be the concatenation of multiple separate compressed streams. These are transparently decoded as a single logical stream.

When opening a file for reading, the format and filters arguments have the same meanings as for LZMADecompressor. In this case, the check and preset arguments should not be used.

When opening a file for writing, the format, check, preset and filters arguments have the same meanings as for LZMACompressor.

LZMAFile supports all the members specified by io.BufferedIOBase, except for detach() and truncate(). Iteration and the with statement are supported.

The following method is also provided:

+peek(size=- 1)
+

Return buffered data without advancing the file position. At least one byte of data will be returned, unless EOF has been reached. The exact number of bytes returned is unspecified (the size argument is ignored).

Note

While calling peek() does not change the file position of the LZMAFile, it may change the position of the underlying file object (e.g. if the LZMAFile was constructed by passing a file object for filename).

+

Changed in version 3.4: Added support for the "x" and "xb" modes.

Changed in version 3.5: The read() method now accepts an argument of None.

Changed in version 3.6: Accepts a path-like object.

+

Compressing and decompressing data in memory

+class lzma.LZMACompressor(format=FORMAT_XZ, check=- 1, preset=None, filters=None)
+

Create a compressor object, which can be used to compress data incrementally.

For a more convenient way of compressing a single chunk of data, see compress().

The format argument specifies what container format should be used. Possible values are:

  • +
    +FORMAT_XZ: The .xz container format.
    +
    +

    This is the default format.

  • +
    +FORMAT_ALONE: The legacy .lzma container format.
    +
    +

    This format is more limited than .xz – it does not support integrity checks or multiple filters.

  • +
    +FORMAT_RAW: A raw data stream, not using any container format.
    +
    +

    This format specifier does not support integrity checks, and requires that you always specify a custom filter chain (for both compression and decompression). Additionally, data compressed in this manner cannot be decompressed using FORMAT_AUTO (see LZMADecompressor).

The check argument specifies the type of integrity check to include in the compressed data. This check is used when decompressing, to ensure that the data has not been corrupted. Possible values are:

  • +CHECK_NONE: No integrity check. This is the default (and the only acceptable value) for FORMAT_ALONE and FORMAT_RAW.
  • +CHECK_CRC32: 32-bit Cyclic Redundancy Check.
  • +CHECK_CRC64: 64-bit Cyclic Redundancy Check. This is the default for FORMAT_XZ.
  • +CHECK_SHA256: 256-bit Secure Hash Algorithm.

If the specified check is not supported, an LZMAError is raised.

The compression settings can be specified either as a preset compression level (with the preset argument), or in detail as a custom filter chain (with the filters argument).

The preset argument (if provided) should be an integer between 0 and 9 (inclusive), optionally OR-ed with the constant PRESET_EXTREME. If neither preset nor filters are given, the default behavior is to use PRESET_DEFAULT (preset level 6). Higher presets produce smaller output, but make the compression process slower.

Note

In addition to being more CPU-intensive, compression with higher presets also requires much more memory (and produces output that needs more memory to decompress). With preset 9 for example, the overhead for an LZMACompressor object can be as high as 800 MiB. For this reason, it is generally best to stick with the default preset.

The filters argument (if provided) should be a filter chain specifier. See Specifying custom filter chains for details.

+compress(data)
+

Compress data (a bytes object), returning a bytes object containing compressed data for at least part of the input. Some of data may be buffered internally, for use in later calls to compress() and flush(). The returned data should be concatenated with the output of any previous calls to compress().

+
+flush()
+

Finish the compression process, returning a bytes object containing any data stored in the compressor’s internal buffers.

The compressor cannot be used after this method has been called.

+
+
+class lzma.LZMADecompressor(format=FORMAT_AUTO, memlimit=None, filters=None)
+

Create a decompressor object, which can be used to decompress data incrementally.

For a more convenient way of decompressing an entire compressed stream at once, see decompress().

The format argument specifies the container format that should be used. The default is FORMAT_AUTO, which can decompress both .xz and .lzma files. Other possible values are FORMAT_XZ, FORMAT_ALONE, and FORMAT_RAW.

The memlimit argument specifies a limit (in bytes) on the amount of memory that the decompressor can use. When this argument is used, decompression will fail with an LZMAError if it is not possible to decompress the input within the given memory limit.

The filters argument specifies the filter chain that was used to create the stream being decompressed. This argument is required if format is FORMAT_RAW, but should not be used for other formats. See Specifying custom filter chains for more information about filter chains.

Note

This class does not transparently handle inputs containing multiple compressed streams, unlike decompress() and LZMAFile. To decompress a multi-stream input with LZMADecompressor, you must create a new decompressor for each stream.

+decompress(data, max_length=- 1)
+

Decompress data (a bytes-like object), returning uncompressed data as bytes. Some of data may be buffered internally, for use in later calls to decompress(). The returned data should be concatenated with the output of any previous calls to decompress().

If max_length is nonnegative, returns at most max_length bytes of decompressed data. If this limit is reached and further output can be produced, the needs_input attribute will be set to False. In this case, the next call to decompress() may provide data as b'' to obtain more of the output.

If all of the input data was decompressed and returned (either because this was less than max_length bytes, or because max_length was negative), the needs_input attribute will be set to True.

Attempting to decompress data after the end of stream is reached raises an EOFError. Any data found after the end of the stream is ignored and saved in the unused_data attribute.

Changed in version 3.5: Added the max_length parameter.

+
+check
+

The ID of the integrity check used by the input stream. This may be CHECK_UNKNOWN until enough of the input has been decoded to determine what integrity check it uses.

+
+eof
+

True if the end-of-stream marker has been reached.

+
+unused_data
+

Data found after the end of the compressed stream.

Before the end of the stream is reached, this will be b"".

+
+needs_input
+

False if the decompress() method can provide more decompressed data before requiring new uncompressed input.

New in version 3.5.

+
+
+lzma.compress(data, format=FORMAT_XZ, check=- 1, preset=None, filters=None)
+

Compress data (a bytes object), returning the compressed data as a bytes object.

See LZMACompressor above for a description of the format, check, preset and filters arguments.

+
+lzma.decompress(data, format=FORMAT_AUTO, memlimit=None, filters=None)
+

Decompress data (a bytes object), returning the uncompressed data as a bytes object.

If data is the concatenation of multiple distinct compressed streams, decompress all of these streams, and return the concatenation of the results.

See LZMADecompressor above for a description of the format, memlimit and filters arguments.

+

Miscellaneous

+lzma.is_check_supported(check)
+

Return True if the given integrity check is supported on this system.

CHECK_NONE and CHECK_CRC32 are always supported. CHECK_CRC64 and CHECK_SHA256 may be unavailable if you are using a version of liblzma that was compiled with a limited feature set.

+

Specifying custom filter chains

A filter chain specifier is a sequence of dictionaries, where each dictionary contains the ID and options for a single filter. Each dictionary must contain the key "id", and may contain additional keys to specify filter-dependent options. Valid filter IDs are as follows:

  • +

    Compression filters:

    • +FILTER_LZMA1 (for use with FORMAT_ALONE)
    • +FILTER_LZMA2 (for use with FORMAT_XZ and FORMAT_RAW)
  • +

    Delta filter:

    • FILTER_DELTA
  • +

    Branch-Call-Jump (BCJ) filters:

    • FILTER_X86
    • FILTER_IA64
    • FILTER_ARM
    • FILTER_ARMTHUMB
    • FILTER_POWERPC
    • FILTER_SPARC

A filter chain can consist of up to 4 filters, and cannot be empty. The last filter in the chain must be a compression filter, and any other filters must be delta or BCJ filters.

Compression filters support the following options (specified as additional entries in the dictionary representing the filter):

  • +preset: A compression preset to use as a source of default values for options that are not specified explicitly.
  • +dict_size: Dictionary size in bytes. This should be between 4 KiB and 1.5 GiB (inclusive).
  • +lc: Number of literal context bits.
  • +lp: Number of literal position bits. The sum lc + lp must be at most 4.
  • +pb: Number of position bits; must be at most 4.
  • +mode: MODE_FAST or MODE_NORMAL.
  • +nice_len: What should be considered a “nice length” for a match. This should be 273 or less.
  • +mf: What match finder to use – MF_HC3, MF_HC4, MF_BT2, MF_BT3, or MF_BT4.
  • +depth: Maximum search depth used by match finder. 0 (default) means to select automatically based on other filter options.

The delta filter stores the differences between bytes, producing more repetitive input for the compressor in certain circumstances. It supports one option, dist. This indicates the distance between bytes to be subtracted. The default is 1, i.e. take the differences between adjacent bytes.

The BCJ filters are intended to be applied to machine code. They convert relative branches, calls and jumps in the code to use absolute addressing, with the aim of increasing the redundancy that can be exploited by the compressor. These filters support one option, start_offset. This specifies the address that should be mapped to the beginning of the input data. The default is 0.

Examples

Reading in a compressed file:

import lzma
+with lzma.open("file.xz") as f:
+    file_content = f.read()
+

Creating a compressed file:

import lzma
+data = b"Insert Data Here"
+with lzma.open("file.xz", "w") as f:
+    f.write(data)
+

Compressing data in memory:

import lzma
+data_in = b"Insert Data Here"
+data_out = lzma.compress(data_in)
+

Incremental compression:

import lzma
+lzc = lzma.LZMACompressor()
+out1 = lzc.compress(b"Some data\n")
+out2 = lzc.compress(b"Another piece of data\n")
+out3 = lzc.compress(b"Even more data\n")
+out4 = lzc.flush()
+# Concatenate all the partial results:
+result = b"".join([out1, out2, out3, out4])
+

Writing compressed data to an already-open file:

import lzma
+with open("file.xz", "wb") as f:
+    f.write(b"This data will not be compressed\n")
+    with lzma.open(f, "w") as lzf:
+        lzf.write(b"This *will* be compressed\n")
+    f.write(b"Not compressed\n")
+

Creating a compressed file using a custom filter chain:

import lzma
+my_filters = [
+    {"id": lzma.FILTER_DELTA, "dist": 5},
+    {"id": lzma.FILTER_LZMA2, "preset": 7 | lzma.PRESET_EXTREME},
+]
+with lzma.open("file.xz", "w", filters=my_filters) as f:
+    f.write(b"blah blah blah")
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/lzma.html +

+
diff --git a/devdocs/python~3.12/library%2Fmailbox.html b/devdocs/python~3.12/library%2Fmailbox.html new file mode 100644 index 00000000..63652cd1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmailbox.html @@ -0,0 +1,535 @@ +

mailbox — Manipulate mailboxes in various formats

Source code: Lib/mailbox.py

This module defines two classes, Mailbox and Message, for accessing and manipulating on-disk mailboxes and the messages they contain. Mailbox offers a dictionary-like mapping from keys to messages. Message extends the email.message module’s Message class with format-specific state and behavior. Supported mailbox formats are Maildir, mbox, MH, Babyl, and MMDF.

See also

+Module email +
+
+

Represent and manipulate messages.

+Mailbox objects

+class mailbox.Mailbox
+

A mailbox, which may be inspected and modified.

The Mailbox class defines an interface and is not intended to be instantiated. Instead, format-specific subclasses should inherit from Mailbox and your code should instantiate a particular subclass.

The Mailbox interface is dictionary-like, with small keys corresponding to messages. Keys are issued by the Mailbox instance with which they will be used and are only meaningful to that Mailbox instance. A key continues to identify a message even if the corresponding message is modified, such as by replacing it with another message.

Messages may be added to a Mailbox instance using the set-like method add() and removed using a del statement or the set-like methods remove() and discard().

Mailbox interface semantics differ from dictionary semantics in some noteworthy ways. Each time a message is requested, a new representation (typically a Message instance) is generated based upon the current state of the mailbox. Similarly, when a message is added to a Mailbox instance, the provided message representation’s contents are copied. In neither case is a reference to the message representation kept by the Mailbox instance.

The default Mailbox iterator iterates over message representations, not keys as the default dictionary iterator does. Moreover, modification of a mailbox during iteration is safe and well-defined. Messages added to the mailbox after an iterator is created will not be seen by the iterator. Messages removed from the mailbox before the iterator yields them will be silently skipped, though using a key from an iterator may result in a KeyError exception if the corresponding message is subsequently removed.

Warning

Be very cautious when modifying mailboxes that might be simultaneously changed by some other process. The safest mailbox format to use for such tasks is Maildir; try to avoid using single-file formats such as mbox for concurrent writing. If you’re modifying a mailbox, you must lock it by calling the lock() and unlock() methods before reading any messages in the file or making any changes by adding or deleting a message. Failing to lock the mailbox runs the risk of losing messages or corrupting the entire mailbox.

Mailbox instances have the following methods:

+add(message)
+

Add message to the mailbox and return the key that has been assigned to it.

Parameter message may be a Message instance, an email.message.Message instance, a string, a byte string, or a file-like object (which should be open in binary mode). If message is an instance of the appropriate format-specific Message subclass (e.g., if it’s an mboxMessage instance and this is an mbox instance), its format-specific information is used. Otherwise, reasonable defaults for format-specific information are used.

Changed in version 3.2: Support for binary input was added.

+
+remove(key)
+__delitem__(key)
+discard(key)
+

Delete the message corresponding to key from the mailbox.

If no such message exists, a KeyError exception is raised if the method was called as remove() or __delitem__() but no exception is raised if the method was called as discard(). The behavior of discard() may be preferred if the underlying mailbox format supports concurrent modification by other processes.

+
+__setitem__(key, message)
+

Replace the message corresponding to key with message. Raise a KeyError exception if no message already corresponds to key.

As with add(), parameter message may be a Message instance, an email.message.Message instance, a string, a byte string, or a file-like object (which should be open in binary mode). If message is an instance of the appropriate format-specific Message subclass (e.g., if it’s an mboxMessage instance and this is an mbox instance), its format-specific information is used. Otherwise, the format-specific information of the message that currently corresponds to key is left unchanged.

+
+iterkeys()
+

Return an iterator over all keys

+
+keys()
+

The same as iterkeys(), except that a list is returned rather than an iterator

+
+itervalues()
+__iter__()
+

Return an iterator over representations of all messages. The messages are represented as instances of the appropriate format-specific Message subclass unless a custom message factory was specified when the Mailbox instance was initialized.

Note

The behavior of __iter__() is unlike that of dictionaries, which iterate over keys.

+
+values()
+

The same as itervalues(), except that a list is returned rather than an iterator

+
+iteritems()
+

Return an iterator over (key, message) pairs, where key is a key and message is a message representation. The messages are represented as instances of the appropriate format-specific Message subclass unless a custom message factory was specified when the Mailbox instance was initialized.

+
+items()
+

The same as iteritems(), except that a list of pairs is returned rather than an iterator of pairs.

+
+get(key, default=None)
+__getitem__(key)
+

Return a representation of the message corresponding to key. If no such message exists, default is returned if the method was called as get() and a KeyError exception is raised if the method was called as __getitem__(). The message is represented as an instance of the appropriate format-specific Message subclass unless a custom message factory was specified when the Mailbox instance was initialized.

+
+get_message(key)
+

Return a representation of the message corresponding to key as an instance of the appropriate format-specific Message subclass, or raise a KeyError exception if no such message exists.

+
+get_bytes(key)
+

Return a byte representation of the message corresponding to key, or raise a KeyError exception if no such message exists.

New in version 3.2.

+
+get_string(key)
+

Return a string representation of the message corresponding to key, or raise a KeyError exception if no such message exists. The message is processed through email.message.Message to convert it to a 7bit clean representation.

+
+get_file(key)
+

Return a file-like representation of the message corresponding to key, or raise a KeyError exception if no such message exists. The file-like object behaves as if open in binary mode. This file should be closed once it is no longer needed.

Changed in version 3.2: The file object really is a binary file; previously it was incorrectly returned in text mode. Also, the file-like object now supports the context manager protocol: you can use a with statement to automatically close it.

Note

Unlike other representations of messages, file-like representations are not necessarily independent of the Mailbox instance that created them or of the underlying mailbox. More specific documentation is provided by each subclass.

+
+__contains__(key)
+

Return True if key corresponds to a message, False otherwise.

+
+__len__()
+

Return a count of messages in the mailbox.

+
+clear()
+

Delete all messages from the mailbox.

+
+pop(key, default=None)
+

Return a representation of the message corresponding to key and delete the message. If no such message exists, return default. The message is represented as an instance of the appropriate format-specific Message subclass unless a custom message factory was specified when the Mailbox instance was initialized.

+
+popitem()
+

Return an arbitrary (key, message) pair, where key is a key and message is a message representation, and delete the corresponding message. If the mailbox is empty, raise a KeyError exception. The message is represented as an instance of the appropriate format-specific Message subclass unless a custom message factory was specified when the Mailbox instance was initialized.

+
+update(arg)
+

Parameter arg should be a key-to-message mapping or an iterable of (key, message) pairs. Updates the mailbox so that, for each given key and message, the message corresponding to key is set to message as if by using __setitem__(). As with __setitem__(), each key must already correspond to a message in the mailbox or else a KeyError exception will be raised, so in general it is incorrect for arg to be a Mailbox instance.

Note

Unlike with dictionaries, keyword arguments are not supported.

+
+flush()
+

Write any pending changes to the filesystem. For some Mailbox subclasses, changes are always written immediately and flush() does nothing, but you should still make a habit of calling this method.

+
+lock()
+

Acquire an exclusive advisory lock on the mailbox so that other processes know not to modify it. An ExternalClashError is raised if the lock is not available. The particular locking mechanisms used depend upon the mailbox format. You should always lock the mailbox before making any modifications to its contents.

+
+unlock()
+

Release the lock on the mailbox, if any.

+
+close()
+

Flush the mailbox, unlock it if necessary, and close any open files. For some Mailbox subclasses, this method does nothing.

+
+

+Maildir objects

+class mailbox.Maildir(dirname, factory=None, create=True)
+

A subclass of Mailbox for mailboxes in Maildir format. Parameter factory is a callable object that accepts a file-like message representation (which behaves as if opened in binary mode) and returns a custom representation. If factory is None, MaildirMessage is used as the default message representation. If create is True, the mailbox is created if it does not exist.

If create is True and the dirname path exists, it will be treated as an existing maildir without attempting to verify its directory layout.

It is for historical reasons that dirname is named as such rather than path.

Maildir is a directory-based mailbox format invented for the qmail mail transfer agent and now widely supported by other programs. Messages in a Maildir mailbox are stored in separate files within a common directory structure. This design allows Maildir mailboxes to be accessed and modified by multiple unrelated programs without data corruption, so file locking is unnecessary.

Maildir mailboxes contain three subdirectories, namely: tmp, new, and cur. Messages are created momentarily in the tmp subdirectory and then moved to the new subdirectory to finalize delivery. A mail user agent may subsequently move the message to the cur subdirectory and store information about the state of the message in a special “info” section appended to its file name.

Folders of the style introduced by the Courier mail transfer agent are also supported. Any subdirectory of the main mailbox is considered a folder if '.' is the first character in its name. Folder names are represented by Maildir without the leading '.'. Each folder is itself a Maildir mailbox but should not contain other folders. Instead, a logical nesting is indicated using '.' to delimit levels, e.g., “Archived.2005.07”.

+colon
+

The Maildir specification requires the use of a colon (':') in certain message file names. However, some operating systems do not permit this character in file names, If you wish to use a Maildir-like format on such an operating system, you should specify another character to use instead. The exclamation point ('!') is a popular choice. For example:

import mailbox
+mailbox.Maildir.colon = '!'
+

The colon attribute may also be set on a per-instance basis.

+

Maildir instances have all of the methods of Mailbox in addition to the following:

+list_folders()
+

Return a list of the names of all folders.

+
+get_folder(folder)
+

Return a Maildir instance representing the folder whose name is folder. A NoSuchMailboxError exception is raised if the folder does not exist.

+
+add_folder(folder)
+

Create a folder whose name is folder and return a Maildir instance representing it.

+
+remove_folder(folder)
+

Delete the folder whose name is folder. If the folder contains any messages, a NotEmptyError exception will be raised and the folder will not be deleted.

+
+clean()
+

Delete temporary files from the mailbox that have not been accessed in the last 36 hours. The Maildir specification says that mail-reading programs should do this occasionally.

+

Some Mailbox methods implemented by Maildir deserve special remarks:

+add(message)
+__setitem__(key, message)
+update(arg)
+

Warning

These methods generate unique file names based upon the current process ID. When using multiple threads, undetected name clashes may occur and cause corruption of the mailbox unless threads are coordinated to avoid using these methods to manipulate the same mailbox simultaneously.

+
+flush()
+

All changes to Maildir mailboxes are immediately applied, so this method does nothing.

+
+lock()
+unlock()
+

Maildir mailboxes do not support (or require) locking, so these methods do nothing.

+
+close()
+

Maildir instances do not keep any open files and the underlying mailboxes do not support locking, so this method does nothing.

+
+get_file(key)
+

Depending upon the host platform, it may not be possible to modify or remove the underlying message while the returned file remains open.

+
+

See also

maildir man page from Courier
+
+

A specification of the format. Describes a common extension for supporting folders.

Using maildir format
+
+

Notes on Maildir by its inventor. Includes an updated name-creation scheme and details on “info” semantics.

+mbox objects

+class mailbox.mbox(path, factory=None, create=True)
+

A subclass of Mailbox for mailboxes in mbox format. Parameter factory is a callable object that accepts a file-like message representation (which behaves as if opened in binary mode) and returns a custom representation. If factory is None, mboxMessage is used as the default message representation. If create is True, the mailbox is created if it does not exist.

The mbox format is the classic format for storing mail on Unix systems. All messages in an mbox mailbox are stored in a single file with the beginning of each message indicated by a line whose first five characters are “From “.

Several variations of the mbox format exist to address perceived shortcomings in the original. In the interest of compatibility, mbox implements the original format, which is sometimes referred to as mboxo. This means that the Content-Length header, if present, is ignored and that any occurrences of “From ” at the beginning of a line in a message body are transformed to “>From ” when storing the message, although occurrences of “>From ” are not transformed to “From ” when reading the message.

Some Mailbox methods implemented by mbox deserve special remarks:

+get_file(key)
+

Using the file after calling flush() or close() on the mbox instance may yield unpredictable results or raise an exception.

+
+lock()
+unlock()
+

Three locking mechanisms are used—dot locking and, if available, the flock() and lockf() system calls.

+
+

See also

mbox man page from tin
+
+

A specification of the format, with details on locking.

Configuring Netscape Mail on Unix: Why The Content-Length Format is Bad
+
+

An argument for using the original mbox format rather than a variation.

“mbox” is a family of several mutually incompatible mailbox formats
+
+

A history of mbox variations.

+MH objects

+class mailbox.MH(path, factory=None, create=True)
+

A subclass of Mailbox for mailboxes in MH format. Parameter factory is a callable object that accepts a file-like message representation (which behaves as if opened in binary mode) and returns a custom representation. If factory is None, MHMessage is used as the default message representation. If create is True, the mailbox is created if it does not exist.

MH is a directory-based mailbox format invented for the MH Message Handling System, a mail user agent. Each message in an MH mailbox resides in its own file. An MH mailbox may contain other MH mailboxes (called folders) in addition to messages. Folders may be nested indefinitely. MH mailboxes also support sequences, which are named lists used to logically group messages without moving them to sub-folders. Sequences are defined in a file called .mh_sequences in each folder.

The MH class manipulates MH mailboxes, but it does not attempt to emulate all of mh’s behaviors. In particular, it does not modify and is not affected by the context or .mh_profile files that are used by mh to store its state and configuration.

MH instances have all of the methods of Mailbox in addition to the following:

+list_folders()
+

Return a list of the names of all folders.

+
+get_folder(folder)
+

Return an MH instance representing the folder whose name is folder. A NoSuchMailboxError exception is raised if the folder does not exist.

+
+add_folder(folder)
+

Create a folder whose name is folder and return an MH instance representing it.

+
+remove_folder(folder)
+

Delete the folder whose name is folder. If the folder contains any messages, a NotEmptyError exception will be raised and the folder will not be deleted.

+
+get_sequences()
+

Return a dictionary of sequence names mapped to key lists. If there are no sequences, the empty dictionary is returned.

+
+set_sequences(sequences)
+

Re-define the sequences that exist in the mailbox based upon sequences, a dictionary of names mapped to key lists, like returned by get_sequences().

+
+pack()
+

Rename messages in the mailbox as necessary to eliminate gaps in numbering. Entries in the sequences list are updated correspondingly.

Note

Already-issued keys are invalidated by this operation and should not be subsequently used.

+

Some Mailbox methods implemented by MH deserve special remarks:

+remove(key)
+__delitem__(key)
+discard(key)
+

These methods immediately delete the message. The MH convention of marking a message for deletion by prepending a comma to its name is not used.

+
+lock()
+unlock()
+

Three locking mechanisms are used—dot locking and, if available, the flock() and lockf() system calls. For MH mailboxes, locking the mailbox means locking the .mh_sequences file and, only for the duration of any operations that affect them, locking individual message files.

+
+get_file(key)
+

Depending upon the host platform, it may not be possible to remove the underlying message while the returned file remains open.

+
+flush()
+

All changes to MH mailboxes are immediately applied, so this method does nothing.

+
+close()
+

MH instances do not keep any open files, so this method is equivalent to unlock().

+
+

See also

nmh - Message Handling System
+
+

Home page of nmh, an updated version of the original mh.

MH & nmh: Email for Users & Programmers
+
+

A GPL-licensed book on mh and nmh, with some information on the mailbox format.

+Babyl objects

+class mailbox.Babyl(path, factory=None, create=True)
+

A subclass of Mailbox for mailboxes in Babyl format. Parameter factory is a callable object that accepts a file-like message representation (which behaves as if opened in binary mode) and returns a custom representation. If factory is None, BabylMessage is used as the default message representation. If create is True, the mailbox is created if it does not exist.

Babyl is a single-file mailbox format used by the Rmail mail user agent included with Emacs. The beginning of a message is indicated by a line containing the two characters Control-Underscore ('\037') and Control-L ('\014'). The end of a message is indicated by the start of the next message or, in the case of the last message, a line containing a Control-Underscore ('\037') character.

Messages in a Babyl mailbox have two sets of headers, original headers and so-called visible headers. Visible headers are typically a subset of the original headers that have been reformatted or abridged to be more attractive. Each message in a Babyl mailbox also has an accompanying list of labels, or short strings that record extra information about the message, and a list of all user-defined labels found in the mailbox is kept in the Babyl options section.

Babyl instances have all of the methods of Mailbox in addition to the following:

+get_labels()
+

Return a list of the names of all user-defined labels used in the mailbox.

Note

The actual messages are inspected to determine which labels exist in the mailbox rather than consulting the list of labels in the Babyl options section, but the Babyl section is updated whenever the mailbox is modified.

+

Some Mailbox methods implemented by Babyl deserve special remarks:

+get_file(key)
+

In Babyl mailboxes, the headers of a message are not stored contiguously with the body of the message. To generate a file-like representation, the headers and body are copied together into an io.BytesIO instance, which has an API identical to that of a file. As a result, the file-like object is truly independent of the underlying mailbox but does not save memory compared to a string representation.

+
+lock()
+unlock()
+

Three locking mechanisms are used—dot locking and, if available, the flock() and lockf() system calls.

+
+

See also

Format of Version 5 Babyl Files
+
+

A specification of the Babyl format.

Reading Mail with Rmail
+
+

The Rmail manual, with some information on Babyl semantics.

+MMDF objects

+class mailbox.MMDF(path, factory=None, create=True)
+

A subclass of Mailbox for mailboxes in MMDF format. Parameter factory is a callable object that accepts a file-like message representation (which behaves as if opened in binary mode) and returns a custom representation. If factory is None, MMDFMessage is used as the default message representation. If create is True, the mailbox is created if it does not exist.

MMDF is a single-file mailbox format invented for the Multichannel Memorandum Distribution Facility, a mail transfer agent. Each message is in the same form as an mbox message but is bracketed before and after by lines containing four Control-A ('\001') characters. As with the mbox format, the beginning of each message is indicated by a line whose first five characters are “From “, but additional occurrences of “From ” are not transformed to “>From ” when storing messages because the extra message separator lines prevent mistaking such occurrences for the starts of subsequent messages.

Some Mailbox methods implemented by MMDF deserve special remarks:

+get_file(key)
+

Using the file after calling flush() or close() on the MMDF instance may yield unpredictable results or raise an exception.

+
+lock()
+unlock()
+

Three locking mechanisms are used—dot locking and, if available, the flock() and lockf() system calls.

+
+

See also

mmdf man page from tin
+
+

A specification of MMDF format from the documentation of tin, a newsreader.

MMDF
+
+

A Wikipedia article describing the Multichannel Memorandum Distribution Facility.

+Message objects

+class mailbox.Message(message=None)
+

A subclass of the email.message module’s Message. Subclasses of mailbox.Message add mailbox-format-specific state and behavior.

If message is omitted, the new instance is created in a default, empty state. If message is an email.message.Message instance, its contents are copied; furthermore, any format-specific information is converted insofar as possible if message is a Message instance. If message is a string, a byte string, or a file, it should contain an RFC 2822-compliant message, which is read and parsed. Files should be open in binary mode, but text mode files are accepted for backward compatibility.

The format-specific state and behaviors offered by subclasses vary, but in general it is only the properties that are not specific to a particular mailbox that are supported (although presumably the properties are specific to a particular mailbox format). For example, file offsets for single-file mailbox formats and file names for directory-based mailbox formats are not retained, because they are only applicable to the original mailbox. But state such as whether a message has been read by the user or marked as important is retained, because it applies to the message itself.

There is no requirement that Message instances be used to represent messages retrieved using Mailbox instances. In some situations, the time and memory required to generate Message representations might not be acceptable. For such situations, Mailbox instances also offer string and file-like representations, and a custom message factory may be specified when a Mailbox instance is initialized.

+

+MaildirMessage objects

+class mailbox.MaildirMessage(message=None)
+

A message with Maildir-specific behaviors. Parameter message has the same meaning as with the Message constructor.

Typically, a mail user agent application moves all of the messages in the new subdirectory to the cur subdirectory after the first time the user opens and closes the mailbox, recording that the messages are old whether or not they’ve actually been read. Each message in cur has an “info” section added to its file name to store information about its state. (Some mail readers may also add an “info” section to messages in new.) The “info” section may take one of two forms: it may contain “2,” followed by a list of standardized flags (e.g., “2,FR”) or it may contain “1,” followed by so-called experimental information. Standard flags for Maildir messages are as follows:

+ + + + + + +

Flag

Meaning

Explanation

D

Draft

Under composition

F

Flagged

Marked as important

P

Passed

Forwarded, resent, or bounced

R

Replied

Replied to

S

Seen

Read

T

Trashed

Marked for subsequent deletion

MaildirMessage instances offer the following methods:

+get_subdir()
+

Return either “new” (if the message should be stored in the new subdirectory) or “cur” (if the message should be stored in the cur subdirectory).

Note

A message is typically moved from new to cur after its mailbox has been accessed, whether or not the message is has been read. A message msg has been read if "S" in msg.get_flags() is True.

+
+set_subdir(subdir)
+

Set the subdirectory the message should be stored in. Parameter subdir must be either “new” or “cur”.

+
+get_flags()
+

Return a string specifying the flags that are currently set. If the message complies with the standard Maildir format, the result is the concatenation in alphabetical order of zero or one occurrence of each of 'D', 'F', 'P', 'R', 'S', and 'T'. The empty string is returned if no flags are set or if “info” contains experimental semantics.

+
+set_flags(flags)
+

Set the flags specified by flags and unset all others.

+
+add_flag(flag)
+

Set the flag(s) specified by flag without changing other flags. To add more than one flag at a time, flag may be a string of more than one character. The current “info” is overwritten whether or not it contains experimental information rather than flags.

+
+remove_flag(flag)
+

Unset the flag(s) specified by flag without changing other flags. To remove more than one flag at a time, flag maybe a string of more than one character. If “info” contains experimental information rather than flags, the current “info” is not modified.

+
+get_date()
+

Return the delivery date of the message as a floating-point number representing seconds since the epoch.

+
+set_date(date)
+

Set the delivery date of the message to date, a floating-point number representing seconds since the epoch.

+
+get_info()
+

Return a string containing the “info” for a message. This is useful for accessing and modifying “info” that is experimental (i.e., not a list of flags).

+
+set_info(info)
+

Set “info” to info, which should be a string.

+
+

When a MaildirMessage instance is created based upon an mboxMessage or MMDFMessage instance, the Status and X-Status headers are omitted and the following conversions take place:

+ + + + + +

Resulting state

mboxMessage or MMDFMessage state

“cur” subdirectory

O flag

F flag

F flag

R flag

A flag

S flag

R flag

T flag

D flag

When a MaildirMessage instance is created based upon an MHMessage instance, the following conversions take place:

+ + + + +

Resulting state

MHMessage state

“cur” subdirectory

“unseen” sequence

“cur” subdirectory and S flag

no “unseen” sequence

F flag

“flagged” sequence

R flag

“replied” sequence

When a MaildirMessage instance is created based upon a BabylMessage instance, the following conversions take place:

+ + + + + +

Resulting state

BabylMessage state

“cur” subdirectory

“unseen” label

“cur” subdirectory and S flag

no “unseen” label

P flag

“forwarded” or “resent” label

R flag

“answered” label

T flag

“deleted” label

+mboxMessage objects

+class mailbox.mboxMessage(message=None)
+

A message with mbox-specific behaviors. Parameter message has the same meaning as with the Message constructor.

Messages in an mbox mailbox are stored together in a single file. The sender’s envelope address and the time of delivery are typically stored in a line beginning with “From ” that is used to indicate the start of a message, though there is considerable variation in the exact format of this data among mbox implementations. Flags that indicate the state of the message, such as whether it has been read or marked as important, are typically stored in Status and X-Status headers.

Conventional flags for mbox messages are as follows:

+ + + + + +

Flag

Meaning

Explanation

R

Read

Read

O

Old

Previously detected by MUA

D

Deleted

Marked for subsequent deletion

F

Flagged

Marked as important

A

Answered

Replied to

The “R” and “O” flags are stored in the Status header, and the “D”, “F”, and “A” flags are stored in the X-Status header. The flags and headers typically appear in the order mentioned.

mboxMessage instances offer the following methods:

+get_from()
+

Return a string representing the “From ” line that marks the start of the message in an mbox mailbox. The leading “From ” and the trailing newline are excluded.

+
+set_from(from_, time_=None)
+

Set the “From ” line to from_, which should be specified without a leading “From ” or trailing newline. For convenience, time_ may be specified and will be formatted appropriately and appended to from_. If time_ is specified, it should be a time.struct_time instance, a tuple suitable for passing to time.strftime(), or True (to use time.gmtime()).

+
+get_flags()
+

Return a string specifying the flags that are currently set. If the message complies with the conventional format, the result is the concatenation in the following order of zero or one occurrence of each of 'R', 'O', 'D', 'F', and 'A'.

+
+set_flags(flags)
+

Set the flags specified by flags and unset all others. Parameter flags should be the concatenation in any order of zero or more occurrences of each of 'R', 'O', 'D', 'F', and 'A'.

+
+add_flag(flag)
+

Set the flag(s) specified by flag without changing other flags. To add more than one flag at a time, flag may be a string of more than one character.

+
+remove_flag(flag)
+

Unset the flag(s) specified by flag without changing other flags. To remove more than one flag at a time, flag maybe a string of more than one character.

+
+

When an mboxMessage instance is created based upon a MaildirMessage instance, a “From ” line is generated based upon the MaildirMessage instance’s delivery date, and the following conversions take place:

+ + + + + +

Resulting state

MaildirMessage state

R flag

S flag

O flag

“cur” subdirectory

D flag

T flag

F flag

F flag

A flag

R flag

When an mboxMessage instance is created based upon an MHMessage instance, the following conversions take place:

+ + + + +

Resulting state

MHMessage state

R flag and O flag

no “unseen” sequence

O flag

“unseen” sequence

F flag

“flagged” sequence

A flag

“replied” sequence

When an mboxMessage instance is created based upon a BabylMessage instance, the following conversions take place:

+ + + + +

Resulting state

BabylMessage state

R flag and O flag

no “unseen” label

O flag

“unseen” label

D flag

“deleted” label

A flag

“answered” label

When a mboxMessage instance is created based upon an MMDFMessage instance, the “From ” line is copied and all flags directly correspond:

+ + + + + +

Resulting state

MMDFMessage state

R flag

R flag

O flag

O flag

D flag

D flag

F flag

F flag

A flag

A flag

+MHMessage objects

+class mailbox.MHMessage(message=None)
+

A message with MH-specific behaviors. Parameter message has the same meaning as with the Message constructor.

MH messages do not support marks or flags in the traditional sense, but they do support sequences, which are logical groupings of arbitrary messages. Some mail reading programs (although not the standard mh and nmh) use sequences in much the same way flags are used with other formats, as follows:

+ + + +

Sequence

Explanation

unseen

Not read, but previously detected by MUA

replied

Replied to

flagged

Marked as important

MHMessage instances offer the following methods:

+get_sequences()
+

Return a list of the names of sequences that include this message.

+
+set_sequences(sequences)
+

Set the list of sequences that include this message.

+
+add_sequence(sequence)
+

Add sequence to the list of sequences that include this message.

+
+remove_sequence(sequence)
+

Remove sequence from the list of sequences that include this message.

+
+

When an MHMessage instance is created based upon a MaildirMessage instance, the following conversions take place:

+ + + +

Resulting state

MaildirMessage state

“unseen” sequence

no S flag

“replied” sequence

R flag

“flagged” sequence

F flag

When an MHMessage instance is created based upon an mboxMessage or MMDFMessage instance, the Status and X-Status headers are omitted and the following conversions take place:

+ + + +

Resulting state

mboxMessage or MMDFMessage state

“unseen” sequence

no R flag

“replied” sequence

A flag

“flagged” sequence

F flag

When an MHMessage instance is created based upon a BabylMessage instance, the following conversions take place:

+ + +

Resulting state

BabylMessage state

“unseen” sequence

“unseen” label

“replied” sequence

“answered” label

+BabylMessage objects

+class mailbox.BabylMessage(message=None)
+

A message with Babyl-specific behaviors. Parameter message has the same meaning as with the Message constructor.

Certain message labels, called attributes, are defined by convention to have special meanings. The attributes are as follows:

+ + + + + + + +

Label

Explanation

unseen

Not read, but previously detected by MUA

deleted

Marked for subsequent deletion

filed

Copied to another file or mailbox

answered

Replied to

forwarded

Forwarded

edited

Modified by the user

resent

Resent

By default, Rmail displays only visible headers. The BabylMessage class, though, uses the original headers because they are more complete. Visible headers may be accessed explicitly if desired.

BabylMessage instances offer the following methods:

+get_labels()
+

Return a list of labels on the message.

+
+set_labels(labels)
+

Set the list of labels on the message to labels.

+
+add_label(label)
+

Add label to the list of labels on the message.

+
+remove_label(label)
+

Remove label from the list of labels on the message.

+
+get_visible()
+

Return an Message instance whose headers are the message’s visible headers and whose body is empty.

+
+set_visible(visible)
+

Set the message’s visible headers to be the same as the headers in message. Parameter visible should be a Message instance, an email.message.Message instance, a string, or a file-like object (which should be open in text mode).

+
+update_visible()
+

When a BabylMessage instance’s original headers are modified, the visible headers are not automatically modified to correspond. This method updates the visible headers as follows: each visible header with a corresponding original header is set to the value of the original header, each visible header without a corresponding original header is removed, and any of Date, From, Reply-To, To, CC, and Subject that are present in the original headers but not the visible headers are added to the visible headers.

+
+

When a BabylMessage instance is created based upon a MaildirMessage instance, the following conversions take place:

+ + + + +

Resulting state

MaildirMessage state

“unseen” label

no S flag

“deleted” label

T flag

“answered” label

R flag

“forwarded” label

P flag

When a BabylMessage instance is created based upon an mboxMessage or MMDFMessage instance, the Status and X-Status headers are omitted and the following conversions take place:

+ + + +

Resulting state

mboxMessage or MMDFMessage state

“unseen” label

no R flag

“deleted” label

D flag

“answered” label

A flag

When a BabylMessage instance is created based upon an MHMessage instance, the following conversions take place:

+ + +

Resulting state

MHMessage state

“unseen” label

“unseen” sequence

“answered” label

“replied” sequence

+MMDFMessage objects

+class mailbox.MMDFMessage(message=None)
+

A message with MMDF-specific behaviors. Parameter message has the same meaning as with the Message constructor.

As with message in an mbox mailbox, MMDF messages are stored with the sender’s address and the delivery date in an initial line beginning with “From “. Likewise, flags that indicate the state of the message are typically stored in Status and X-Status headers.

Conventional flags for MMDF messages are identical to those of mbox message and are as follows:

+ + + + + +

Flag

Meaning

Explanation

R

Read

Read

O

Old

Previously detected by MUA

D

Deleted

Marked for subsequent deletion

F

Flagged

Marked as important

A

Answered

Replied to

The “R” and “O” flags are stored in the Status header, and the “D”, “F”, and “A” flags are stored in the X-Status header. The flags and headers typically appear in the order mentioned.

MMDFMessage instances offer the following methods, which are identical to those offered by mboxMessage:

+get_from()
+

Return a string representing the “From ” line that marks the start of the message in an mbox mailbox. The leading “From ” and the trailing newline are excluded.

+
+set_from(from_, time_=None)
+

Set the “From ” line to from_, which should be specified without a leading “From ” or trailing newline. For convenience, time_ may be specified and will be formatted appropriately and appended to from_. If time_ is specified, it should be a time.struct_time instance, a tuple suitable for passing to time.strftime(), or True (to use time.gmtime()).

+
+get_flags()
+

Return a string specifying the flags that are currently set. If the message complies with the conventional format, the result is the concatenation in the following order of zero or one occurrence of each of 'R', 'O', 'D', 'F', and 'A'.

+
+set_flags(flags)
+

Set the flags specified by flags and unset all others. Parameter flags should be the concatenation in any order of zero or more occurrences of each of 'R', 'O', 'D', 'F', and 'A'.

+
+add_flag(flag)
+

Set the flag(s) specified by flag without changing other flags. To add more than one flag at a time, flag may be a string of more than one character.

+
+remove_flag(flag)
+

Unset the flag(s) specified by flag without changing other flags. To remove more than one flag at a time, flag maybe a string of more than one character.

+
+

When an MMDFMessage instance is created based upon a MaildirMessage instance, a “From ” line is generated based upon the MaildirMessage instance’s delivery date, and the following conversions take place:

+ + + + + +

Resulting state

MaildirMessage state

R flag

S flag

O flag

“cur” subdirectory

D flag

T flag

F flag

F flag

A flag

R flag

When an MMDFMessage instance is created based upon an MHMessage instance, the following conversions take place:

+ + + + +

Resulting state

MHMessage state

R flag and O flag

no “unseen” sequence

O flag

“unseen” sequence

F flag

“flagged” sequence

A flag

“replied” sequence

When an MMDFMessage instance is created based upon a BabylMessage instance, the following conversions take place:

+ + + + +

Resulting state

BabylMessage state

R flag and O flag

no “unseen” label

O flag

“unseen” label

D flag

“deleted” label

A flag

“answered” label

When an MMDFMessage instance is created based upon an mboxMessage instance, the “From ” line is copied and all flags directly correspond:

+ + + + + +

Resulting state

mboxMessage state

R flag

R flag

O flag

O flag

D flag

D flag

F flag

F flag

A flag

A flag

Exceptions

The following exception classes are defined in the mailbox module:

+exception mailbox.Error
+

The based class for all other module-specific exceptions.

+
+exception mailbox.NoSuchMailboxError
+

Raised when a mailbox is expected but is not found, such as when instantiating a Mailbox subclass with a path that does not exist (and with the create parameter set to False), or when opening a folder that does not exist.

+
+exception mailbox.NotEmptyError
+

Raised when a mailbox is not empty but is expected to be, such as when deleting a folder that contains messages.

+
+exception mailbox.ExternalClashError
+

Raised when some mailbox-related condition beyond the control of the program causes it to be unable to proceed, such as when failing to acquire a lock that another program already holds a lock, or when a uniquely generated file name already exists.

+
+exception mailbox.FormatError
+

Raised when the data in a file cannot be parsed, such as when an MH instance attempts to read a corrupted .mh_sequences file.

+

Examples

A simple example of printing the subjects of all messages in a mailbox that seem interesting:

import mailbox
+for message in mailbox.mbox('~/mbox'):
+    subject = message['subject']       # Could possibly be None.
+    if subject and 'python' in subject.lower():
+        print(subject)
+

To copy all mail from a Babyl mailbox to an MH mailbox, converting all of the format-specific information that can be converted:

import mailbox
+destination = mailbox.MH('~/Mail')
+destination.lock()
+for message in mailbox.Babyl('~/RMAIL'):
+    destination.add(mailbox.MHMessage(message))
+destination.flush()
+destination.unlock()
+

This example sorts mail from several mailing lists into different mailboxes, being careful to avoid mail corruption due to concurrent modification by other programs, mail loss due to interruption of the program, or premature termination due to malformed messages in the mailbox:

import mailbox
+import email.errors
+
+list_names = ('python-list', 'python-dev', 'python-bugs')
+
+boxes = {name: mailbox.mbox('~/email/%s' % name) for name in list_names}
+inbox = mailbox.Maildir('~/Maildir', factory=None)
+
+for key in inbox.iterkeys():
+    try:
+        message = inbox[key]
+    except email.errors.MessageParseError:
+        continue                # The message is malformed. Just leave it.
+
+    for name in list_names:
+        list_id = message['list-id']
+        if list_id and name in list_id:
+            # Get mailbox to use
+            box = boxes[name]
+
+            # Write copy to disk before removing original.
+            # If there's a crash, you might duplicate a message, but
+            # that's better than losing a message completely.
+            box.lock()
+            box.add(message)
+            box.flush()
+            box.unlock()
+
+            # Remove original message
+            inbox.lock()
+            inbox.discard(key)
+            inbox.flush()
+            inbox.unlock()
+            break               # Found destination, so stop looking.
+
+for box in boxes.itervalues():
+    box.close()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/mailbox.html +

+
diff --git a/devdocs/python~3.12/library%2Fmarkup.html b/devdocs/python~3.12/library%2Fmarkup.html new file mode 100644 index 00000000..049d29cd --- /dev/null +++ b/devdocs/python~3.12/library%2Fmarkup.html @@ -0,0 +1,23 @@ +

Structured Markup Processing Tools

Python supports a variety of modules to work with various forms of structured data markup. This includes modules to work with the Standard Generalized Markup Language (SGML) and the Hypertext Markup Language (HTML), and several interfaces for working with the Extensible Markup Language (XML).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/markup.html +

+
diff --git a/devdocs/python~3.12/library%2Fmarshal.html b/devdocs/python~3.12/library%2Fmarshal.html new file mode 100644 index 00000000..ba2ce477 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmarshal.html @@ -0,0 +1,23 @@ +

marshal — Internal Python object serialization

This module contains functions that can read and write Python values in a binary format. The format is specific to Python, but independent of machine architecture issues (e.g., you can write a Python value to a file on a PC, transport the file to a Sun, and read it back there). Details of the format are undocumented on purpose; it may change between Python versions (although it rarely does). 1

This is not a general “persistence” module. For general persistence and transfer of Python objects through RPC calls, see the modules pickle and shelve. The marshal module exists mainly to support reading and writing the “pseudo-compiled” code for Python modules of .pyc files. Therefore, the Python maintainers reserve the right to modify the marshal format in backward incompatible ways should the need arise. If you’re serializing and de-serializing Python objects, use the pickle module instead – the performance is comparable, version independence is guaranteed, and pickle supports a substantially wider range of objects than marshal.

Warning

The marshal module is not intended to be secure against erroneous or maliciously constructed data. Never unmarshal data received from an untrusted or unauthenticated source.

Not all Python object types are supported; in general, only objects whose value is independent from a particular invocation of Python can be written and read by this module. The following types are supported: booleans, integers, floating point numbers, complex numbers, strings, bytes, bytearrays, tuples, lists, sets, frozensets, dictionaries, and code objects, where it should be understood that tuples, lists, sets, frozensets and dictionaries are only supported as long as the values contained therein are themselves supported. The singletons None, Ellipsis and StopIteration can also be marshalled and unmarshalled. For format version lower than 3, recursive lists, sets and dictionaries cannot be written (see below).

There are functions that read/write files as well as functions operating on bytes-like objects.

The module defines these functions:

+marshal.dump(value, file[, version])
+

Write the value on the open file. The value must be a supported type. The file must be a writeable binary file.

If the value has (or contains an object that has) an unsupported type, a ValueError exception is raised — but garbage data will also be written to the file. The object will not be properly read back by load().

The version argument indicates the data format that dump should use (see below).

Raises an auditing event marshal.dumps with arguments value, version.

+
+marshal.load(file)
+

Read one value from the open file and return it. If no valid value is read (e.g. because the data has a different Python version’s incompatible marshal format), raise EOFError, ValueError or TypeError. The file must be a readable binary file.

Raises an auditing event marshal.load with no arguments.

Note

If an object containing an unsupported type was marshalled with dump(), load() will substitute None for the unmarshallable type.

Changed in version 3.10: This call used to raise a code.__new__ audit event for each code object. Now it raises a single marshal.load event for the entire load operation.

+
+marshal.dumps(value[, version])
+

Return the bytes object that would be written to a file by dump(value, file). The value must be a supported type. Raise a ValueError exception if value has (or contains an object that has) an unsupported type.

The version argument indicates the data format that dumps should use (see below).

Raises an auditing event marshal.dumps with arguments value, version.

+
+marshal.loads(bytes)
+

Convert the bytes-like object to a value. If no valid value is found, raise EOFError, ValueError or TypeError. Extra bytes in the input are ignored.

Raises an auditing event marshal.loads with argument bytes.

Changed in version 3.10: This call used to raise a code.__new__ audit event for each code object. Now it raises a single marshal.loads event for the entire load operation.

+

In addition, the following constants are defined:

+marshal.version
+

Indicates the format that the module uses. Version 0 is the historical format, version 1 shares interned strings and version 2 uses a binary format for floating point numbers. Version 3 adds support for object instancing and recursion. The current version is 4.

+

Footnotes

+1
+

The name of this module stems from a bit of terminology used by the designers of Modula-3 (amongst others), who use the term “marshalling” for shipping of data around in a self-contained form. Strictly speaking, “to marshal” means to convert some data from internal to external form (in an RPC buffer for instance) and “unmarshalling” for the reverse process.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/marshal.html +

+
diff --git a/devdocs/python~3.12/library%2Fmath.html b/devdocs/python~3.12/library%2Fmath.html new file mode 100644 index 00000000..ed500328 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmath.html @@ -0,0 +1,219 @@ +

math — Mathematical functions

This module provides access to the mathematical functions defined by the C standard.

These functions cannot be used with complex numbers; use the functions of the same name from the cmath module if you require support for complex numbers. The distinction between functions which support complex numbers and those which don’t is made since most users do not want to learn quite as much mathematics as required to understand complex numbers. Receiving an exception instead of a complex result allows earlier detection of the unexpected complex number used as a parameter, so that the programmer can determine how and why it was generated in the first place.

The following functions are provided by this module. Except when explicitly noted otherwise, all return values are floats.

Number-theoretic and representation functions

+math.ceil(x)
+

Return the ceiling of x, the smallest integer greater than or equal to x. If x is not a float, delegates to x.__ceil__, which should return an Integral value.

+
+math.comb(n, k)
+

Return the number of ways to choose k items from n items without repetition and without order.

Evaluates to n! / (k! * (n - k)!) when k <= n and evaluates to zero when k > n.

Also called the binomial coefficient because it is equivalent to the coefficient of k-th term in polynomial expansion of (1 + x)ⁿ.

Raises TypeError if either of the arguments are not integers. Raises ValueError if either of the arguments are negative.

New in version 3.8.

+
+math.copysign(x, y)
+

Return a float with the magnitude (absolute value) of x but the sign of y. On platforms that support signed zeros, copysign(1.0, -0.0) returns -1.0.

+
+math.fabs(x)
+

Return the absolute value of x.

+
+math.factorial(n)
+

Return n factorial as an integer. Raises ValueError if n is not integral or is negative.

Deprecated since version 3.9: Accepting floats with integral values (like 5.0) is deprecated.

+
+math.floor(x)
+

Return the floor of x, the largest integer less than or equal to x. If x is not a float, delegates to x.__floor__, which should return an Integral value.

+
+math.fmod(x, y)
+

Return fmod(x, y), as defined by the platform C library. Note that the Python expression x % y may not return the same result. The intent of the C standard is that fmod(x, y) be exactly (mathematically; to infinite precision) equal to x - n*y for some integer n such that the result has the same sign as x and magnitude less than abs(y). Python’s x % y returns a result with the sign of y instead, and may not be exactly computable for float arguments. For example, fmod(-1e-100, 1e100) is -1e-100, but the result of Python’s -1e-100 % 1e100 is 1e100-1e-100, which cannot be represented exactly as a float, and rounds to the surprising 1e100. For this reason, function fmod() is generally preferred when working with floats, while Python’s x % y is preferred when working with integers.

+
+math.frexp(x)
+

Return the mantissa and exponent of x as the pair (m, e). m is a float and e is an integer such that x == m * 2**e exactly. If x is zero, returns (0.0, 0), otherwise 0.5 <= abs(m) < 1. This is used to “pick apart” the internal representation of a float in a portable way.

+
+math.fsum(iterable)
+

Return an accurate floating point sum of values in the iterable. Avoids loss of precision by tracking multiple intermediate partial sums.

The algorithm’s accuracy depends on IEEE-754 arithmetic guarantees and the typical case where the rounding mode is half-even. On some non-Windows builds, the underlying C library uses extended precision addition and may occasionally double-round an intermediate sum causing it to be off in its least significant bit.

For further discussion and two alternative approaches, see the ASPN cookbook recipes for accurate floating point summation.

+
+math.gcd(*integers)
+

Return the greatest common divisor of the specified integer arguments. If any of the arguments is nonzero, then the returned value is the largest positive integer that is a divisor of all arguments. If all arguments are zero, then the returned value is 0. gcd() without arguments returns 0.

New in version 3.5.

Changed in version 3.9: Added support for an arbitrary number of arguments. Formerly, only two arguments were supported.

+
+math.isclose(a, b, *, rel_tol=1e-09, abs_tol=0.0)
+

Return True if the values a and b are close to each other and False otherwise.

Whether or not two values are considered close is determined according to given absolute and relative tolerances.

rel_tol is the relative tolerance – it is the maximum allowed difference between a and b, relative to the larger absolute value of a or b. For example, to set a tolerance of 5%, pass rel_tol=0.05. The default tolerance is 1e-09, which assures that the two values are the same within about 9 decimal digits. rel_tol must be greater than zero.

abs_tol is the minimum absolute tolerance – useful for comparisons near zero. abs_tol must be at least zero.

If no errors occur, the result will be: abs(a-b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol).

The IEEE 754 special values of NaN, inf, and -inf will be handled according to IEEE rules. Specifically, NaN is not considered close to any other value, including NaN. inf and -inf are only considered close to themselves.

New in version 3.5.

See also

PEP 485 – A function for testing approximate equality

+
+math.isfinite(x)
+

Return True if x is neither an infinity nor a NaN, and False otherwise. (Note that 0.0 is considered finite.)

New in version 3.2.

+
+math.isinf(x)
+

Return True if x is a positive or negative infinity, and False otherwise.

+
+math.isnan(x)
+

Return True if x is a NaN (not a number), and False otherwise.

+
+math.isqrt(n)
+

Return the integer square root of the nonnegative integer n. This is the floor of the exact square root of n, or equivalently the greatest integer a such that a² ≤ n.

For some applications, it may be more convenient to have the least integer a such that n ≤ a², or in other words the ceiling of the exact square root of n. For positive n, this can be computed using a = 1 + isqrt(n - 1).

New in version 3.8.

+
+math.lcm(*integers)
+

Return the least common multiple of the specified integer arguments. If all arguments are nonzero, then the returned value is the smallest positive integer that is a multiple of all arguments. If any of the arguments is zero, then the returned value is 0. lcm() without arguments returns 1.

New in version 3.9.

+
+math.ldexp(x, i)
+

Return x * (2**i). This is essentially the inverse of function frexp().

+
+math.modf(x)
+

Return the fractional and integer parts of x. Both results carry the sign of x and are floats.

+
+math.nextafter(x, y, steps=1)
+

Return the floating-point value steps steps after x towards y.

If x is equal to y, return y, unless steps is zero.

Examples:

  • +math.nextafter(x, math.inf) goes up: towards positive infinity.
  • +math.nextafter(x, -math.inf) goes down: towards minus infinity.
  • +math.nextafter(x, 0.0) goes towards zero.
  • +math.nextafter(x, math.copysign(math.inf, x)) goes away from zero.

See also math.ulp().

Changed in version 3.12: Added the steps argument.

New in version 3.9.

+
+math.perm(n, k=None)
+

Return the number of ways to choose k items from n items without repetition and with order.

Evaluates to n! / (n - k)! when k <= n and evaluates to zero when k > n.

If k is not specified or is None, then k defaults to n and the function returns n!.

Raises TypeError if either of the arguments are not integers. Raises ValueError if either of the arguments are negative.

New in version 3.8.

+
+math.prod(iterable, *, start=1)
+

Calculate the product of all the elements in the input iterable. The default start value for the product is 1.

When the iterable is empty, return the start value. This function is intended specifically for use with numeric values and may reject non-numeric types.

New in version 3.8.

+
+math.remainder(x, y)
+

Return the IEEE 754-style remainder of x with respect to y. For finite x and finite nonzero y, this is the difference x - n*y, where n is the closest integer to the exact value of the quotient x / +y. If x / y is exactly halfway between two consecutive integers, the nearest even integer is used for n. The remainder r = remainder(x, +y) thus always satisfies abs(r) <= 0.5 * abs(y).

Special cases follow IEEE 754: in particular, remainder(x, math.inf) is x for any finite x, and remainder(x, 0) and remainder(math.inf, x) raise ValueError for any non-NaN x. If the result of the remainder operation is zero, that zero will have the same sign as x.

On platforms using IEEE 754 binary floating-point, the result of this operation is always exactly representable: no rounding error is introduced.

New in version 3.7.

+
+math.sumprod(p, q)
+

Return the sum of products of values from two iterables p and q.

Raises ValueError if the inputs do not have the same length.

Roughly equivalent to:

sum(itertools.starmap(operator.mul, zip(p, q, strict=True)))
+

For float and mixed int/float inputs, the intermediate products and sums are computed with extended precision.

New in version 3.12.

+
+math.trunc(x)
+

Return x with the fractional part removed, leaving the integer part. This rounds toward 0: trunc() is equivalent to floor() for positive x, and equivalent to ceil() for negative x. If x is not a float, delegates to x.__trunc__, which should return an Integral value.

+
+math.ulp(x)
+

Return the value of the least significant bit of the float x:

  • If x is a NaN (not a number), return x.
  • If x is negative, return ulp(-x).
  • If x is a positive infinity, return x.
  • If x is equal to zero, return the smallest positive denormalized representable float (smaller than the minimum positive normalized float, sys.float_info.min).
  • If x is equal to the largest positive representable float, return the value of the least significant bit of x, such that the first float smaller than x is x - ulp(x).
  • Otherwise (x is a positive finite number), return the value of the least significant bit of x, such that the first float bigger than x is x + ulp(x).

ULP stands for “Unit in the Last Place”.

See also math.nextafter() and sys.float_info.epsilon.

New in version 3.9.

+

Note that frexp() and modf() have a different call/return pattern than their C equivalents: they take a single argument and return a pair of values, rather than returning their second return value through an ‘output parameter’ (there is no such thing in Python).

For the ceil(), floor(), and modf() functions, note that all floating-point numbers of sufficiently large magnitude are exact integers. Python floats typically carry no more than 53 bits of precision (the same as the platform C double type), in which case any float x with abs(x) >= 2**52 necessarily has no fractional bits.

Power and logarithmic functions

+math.cbrt(x)
+

Return the cube root of x.

New in version 3.11.

+
+math.exp(x)
+

Return e raised to the power x, where e = 2.718281… is the base of natural logarithms. This is usually more accurate than math.e ** x or pow(math.e, x).

+
+math.exp2(x)
+

Return 2 raised to the power x.

New in version 3.11.

+
+math.expm1(x)
+

Return e raised to the power x, minus 1. Here e is the base of natural logarithms. For small floats x, the subtraction in exp(x) - 1 can result in a significant loss of precision; the expm1() function provides a way to compute this quantity to full precision:

>>> from math import exp, expm1
+>>> exp(1e-5) - 1  # gives result accurate to 11 places
+1.0000050000069649e-05
+>>> expm1(1e-5)    # result accurate to full precision
+1.0000050000166668e-05
+

New in version 3.2.

+
+math.log(x[, base])
+

With one argument, return the natural logarithm of x (to base e).

With two arguments, return the logarithm of x to the given base, calculated as log(x)/log(base).

+
+math.log1p(x)
+

Return the natural logarithm of 1+x (base e). The result is calculated in a way which is accurate for x near zero.

+
+math.log2(x)
+

Return the base-2 logarithm of x. This is usually more accurate than log(x, 2).

New in version 3.3.

See also

int.bit_length() returns the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros.

+
+math.log10(x)
+

Return the base-10 logarithm of x. This is usually more accurate than log(x, 10).

+
+math.pow(x, y)
+

Return x raised to the power y. Exceptional cases follow the IEEE 754 standard as far as possible. In particular, pow(1.0, x) and pow(x, 0.0) always return 1.0, even when x is a zero or a NaN. If both x and y are finite, x is negative, and y is not an integer then pow(x, y) is undefined, and raises ValueError.

Unlike the built-in ** operator, math.pow() converts both its arguments to type float. Use ** or the built-in pow() function for computing exact integer powers.

Changed in version 3.11: The special cases pow(0.0, -inf) and pow(-0.0, -inf) were changed to return inf instead of raising ValueError, for consistency with IEEE 754.

+
+math.sqrt(x)
+

Return the square root of x.

+

Trigonometric functions

+math.acos(x)
+

Return the arc cosine of x, in radians. The result is between 0 and pi.

+
+math.asin(x)
+

Return the arc sine of x, in radians. The result is between -pi/2 and pi/2.

+
+math.atan(x)
+

Return the arc tangent of x, in radians. The result is between -pi/2 and pi/2.

+
+math.atan2(y, x)
+

Return atan(y / x), in radians. The result is between -pi and pi. The vector in the plane from the origin to point (x, y) makes this angle with the positive X axis. The point of atan2() is that the signs of both inputs are known to it, so it can compute the correct quadrant for the angle. For example, atan(1) and atan2(1, 1) are both pi/4, but atan2(-1, +-1) is -3*pi/4.

+
+math.cos(x)
+

Return the cosine of x radians.

+
+math.dist(p, q)
+

Return the Euclidean distance between two points p and q, each given as a sequence (or iterable) of coordinates. The two points must have the same dimension.

Roughly equivalent to:

sqrt(sum((px - qx) ** 2.0 for px, qx in zip(p, q)))
+

New in version 3.8.

+
+math.hypot(*coordinates)
+

Return the Euclidean norm, sqrt(sum(x**2 for x in coordinates)). This is the length of the vector from the origin to the point given by the coordinates.

For a two dimensional point (x, y), this is equivalent to computing the hypotenuse of a right triangle using the Pythagorean theorem, sqrt(x*x + y*y).

Changed in version 3.8: Added support for n-dimensional points. Formerly, only the two dimensional case was supported.

Changed in version 3.10: Improved the algorithm’s accuracy so that the maximum error is under 1 ulp (unit in the last place). More typically, the result is almost always correctly rounded to within 1/2 ulp.

+
+math.sin(x)
+

Return the sine of x radians.

+
+math.tan(x)
+

Return the tangent of x radians.

+

Angular conversion

+math.degrees(x)
+

Convert angle x from radians to degrees.

+
+math.radians(x)
+

Convert angle x from degrees to radians.

+

Hyperbolic functions

Hyperbolic functions are analogs of trigonometric functions that are based on hyperbolas instead of circles.

+math.acosh(x)
+

Return the inverse hyperbolic cosine of x.

+
+math.asinh(x)
+

Return the inverse hyperbolic sine of x.

+
+math.atanh(x)
+

Return the inverse hyperbolic tangent of x.

+
+math.cosh(x)
+

Return the hyperbolic cosine of x.

+
+math.sinh(x)
+

Return the hyperbolic sine of x.

+
+math.tanh(x)
+

Return the hyperbolic tangent of x.

+

Special functions

+math.erf(x)
+

Return the error function at x.

The erf() function can be used to compute traditional statistical functions such as the cumulative standard normal distribution:

def phi(x):
+    'Cumulative distribution function for the standard normal distribution'
+    return (1.0 + erf(x / sqrt(2.0))) / 2.0
+

New in version 3.2.

+
+math.erfc(x)
+

Return the complementary error function at x. The complementary error function is defined as 1.0 - erf(x). It is used for large values of x where a subtraction from one would cause a loss of significance.

New in version 3.2.

+
+math.gamma(x)
+

Return the Gamma function at x.

New in version 3.2.

+
+math.lgamma(x)
+

Return the natural logarithm of the absolute value of the Gamma function at x.

New in version 3.2.

+

Constants

+math.pi
+

The mathematical constant π = 3.141592…, to available precision.

+
+math.e
+

The mathematical constant e = 2.718281…, to available precision.

+
+math.tau
+

The mathematical constant τ = 6.283185…, to available precision. Tau is a circle constant equal to 2π, the ratio of a circle’s circumference to its radius. To learn more about Tau, check out Vi Hart’s video Pi is (still) Wrong, and start celebrating Tau day by eating twice as much pie!

New in version 3.6.

+
+math.inf
+

A floating-point positive infinity. (For negative infinity, use -math.inf.) Equivalent to the output of float('inf').

New in version 3.5.

+
+math.nan
+

A floating-point “not a number” (NaN) value. Equivalent to the output of float('nan'). Due to the requirements of the IEEE-754 standard, math.nan and float('nan') are not considered to equal to any other numeric value, including themselves. To check whether a number is a NaN, use the isnan() function to test for NaNs instead of is or ==. Example:

>>> import math
+>>> math.nan == math.nan
+False
+>>> float('nan') == float('nan')
+False
+>>> math.isnan(math.nan)
+True
+>>> math.isnan(float('nan'))
+True
+

Changed in version 3.11: It is now always available.

New in version 3.5.

+

CPython implementation detail: The math module consists mostly of thin wrappers around the platform C math library functions. Behavior in exceptional cases follows Annex F of the C99 standard where appropriate. The current implementation will raise ValueError for invalid operations like sqrt(-1.0) or log(0.0) (where C99 Annex F recommends signaling invalid operation or divide-by-zero), and OverflowError for results that overflow (for example, exp(1000.0)). A NaN will not be returned from any of the functions above unless one or more of the input arguments was a NaN; in that case, most functions will return a NaN, but (again following C99 Annex F) there are some exceptions to this rule, for example pow(float('nan'), 0.0) or hypot(float('nan'), float('inf')).

Note that Python makes no effort to distinguish signaling NaNs from quiet NaNs, and behavior for signaling NaNs remains unspecified. Typical behavior is to treat all NaNs as though they were quiet.

See also

+Module cmath +
+
+

Complex number versions of many of these functions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/math.html +

+
diff --git a/devdocs/python~3.12/library%2Fmimetypes.html b/devdocs/python~3.12/library%2Fmimetypes.html new file mode 100644 index 00000000..7aa6d450 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmimetypes.html @@ -0,0 +1,85 @@ +

mimetypes — Map filenames to MIME types

Source code: Lib/mimetypes.py

The mimetypes module converts between a filename or URL and the MIME type associated with the filename extension. Conversions are provided from filename to MIME type and from MIME type to filename extension; encodings are not supported for the latter conversion.

The module provides one class and a number of convenience functions. The functions are the normal interface to this module, but some applications may be interested in the class as well.

The functions described below provide the primary interface for this module. If the module has not been initialized, they will call init() if they rely on the information init() sets up.

+mimetypes.guess_type(url, strict=True)
+

Guess the type of a file based on its filename, path or URL, given by url. URL can be a string or a path-like object.

The return value is a tuple (type, encoding) where type is None if the type can’t be guessed (missing or unknown suffix) or a string of the form 'type/subtype', usable for a MIME content-type header.

encoding is None for no encoding or the name of the program used to encode (e.g. compress or gzip). The encoding is suitable for use as a Content-Encoding header, not as a Content-Transfer-Encoding header. The mappings are table driven. Encoding suffixes are case sensitive; type suffixes are first tried case sensitively, then case insensitively.

The optional strict argument is a flag specifying whether the list of known MIME types is limited to only the official types registered with IANA. When strict is True (the default), only the IANA types are supported; when strict is False, some additional non-standard but commonly used MIME types are also recognized.

Changed in version 3.8: Added support for url being a path-like object.

+
+mimetypes.guess_all_extensions(type, strict=True)
+

Guess the extensions for a file based on its MIME type, given by type. The return value is a list of strings giving all possible filename extensions, including the leading dot ('.'). The extensions are not guaranteed to have been associated with any particular data stream, but would be mapped to the MIME type type by guess_type().

The optional strict argument has the same meaning as with the guess_type() function.

+
+mimetypes.guess_extension(type, strict=True)
+

Guess the extension for a file based on its MIME type, given by type. The return value is a string giving a filename extension, including the leading dot ('.'). The extension is not guaranteed to have been associated with any particular data stream, but would be mapped to the MIME type type by guess_type(). If no extension can be guessed for type, None is returned.

The optional strict argument has the same meaning as with the guess_type() function.

+

Some additional functions and data items are available for controlling the behavior of the module.

+mimetypes.init(files=None)
+

Initialize the internal data structures. If given, files must be a sequence of file names which should be used to augment the default type map. If omitted, the file names to use are taken from knownfiles; on Windows, the current registry settings are loaded. Each file named in files or knownfiles takes precedence over those named before it. Calling init() repeatedly is allowed.

Specifying an empty list for files will prevent the system defaults from being applied: only the well-known values will be present from a built-in list.

If files is None the internal data structure is completely rebuilt to its initial default value. This is a stable operation and will produce the same results when called multiple times.

Changed in version 3.2: Previously, Windows registry settings were ignored.

+
+mimetypes.read_mime_types(filename)
+

Load the type map given in the file filename, if it exists. The type map is returned as a dictionary mapping filename extensions, including the leading dot ('.'), to strings of the form 'type/subtype'. If the file filename does not exist or cannot be read, None is returned.

+
+mimetypes.add_type(type, ext, strict=True)
+

Add a mapping from the MIME type type to the extension ext. When the extension is already known, the new type will replace the old one. When the type is already known the extension will be added to the list of known extensions.

When strict is True (the default), the mapping will be added to the official MIME types, otherwise to the non-standard ones.

+
+mimetypes.inited
+

Flag indicating whether or not the global data structures have been initialized. This is set to True by init().

+
+mimetypes.knownfiles
+

List of type map file names commonly installed. These files are typically named mime.types and are installed in different locations by different packages.

+
+mimetypes.suffix_map
+

Dictionary mapping suffixes to suffixes. This is used to allow recognition of encoded files for which the encoding and the type are indicated by the same extension. For example, the .tgz extension is mapped to .tar.gz to allow the encoding and type to be recognized separately.

+
+mimetypes.encodings_map
+

Dictionary mapping filename extensions to encoding types.

+
+mimetypes.types_map
+

Dictionary mapping filename extensions to MIME types.

+
+mimetypes.common_types
+

Dictionary mapping filename extensions to non-standard, but commonly found MIME types.

+

An example usage of the module:

>>> import mimetypes
+>>> mimetypes.init()
+>>> mimetypes.knownfiles
+['/etc/mime.types', '/etc/httpd/mime.types', ... ]
+>>> mimetypes.suffix_map['.tgz']
+'.tar.gz'
+>>> mimetypes.encodings_map['.gz']
+'gzip'
+>>> mimetypes.types_map['.tgz']
+'application/x-tar-gz'
+

MimeTypes Objects

The MimeTypes class may be useful for applications which may want more than one MIME-type database; it provides an interface similar to the one of the mimetypes module.

+class mimetypes.MimeTypes(filenames=(), strict=True)
+

This class represents a MIME-types database. By default, it provides access to the same database as the rest of this module. The initial database is a copy of that provided by the module, and may be extended by loading additional mime.types-style files into the database using the read() or readfp() methods. The mapping dictionaries may also be cleared before loading additional data if the default data is not desired.

The optional filenames parameter can be used to cause additional files to be loaded “on top” of the default database.

+suffix_map
+

Dictionary mapping suffixes to suffixes. This is used to allow recognition of encoded files for which the encoding and the type are indicated by the same extension. For example, the .tgz extension is mapped to .tar.gz to allow the encoding and type to be recognized separately. This is initially a copy of the global suffix_map defined in the module.

+
+encodings_map
+

Dictionary mapping filename extensions to encoding types. This is initially a copy of the global encodings_map defined in the module.

+
+types_map
+

Tuple containing two dictionaries, mapping filename extensions to MIME types: the first dictionary is for the non-standards types and the second one is for the standard types. They are initialized by common_types and types_map.

+
+types_map_inv
+

Tuple containing two dictionaries, mapping MIME types to a list of filename extensions: the first dictionary is for the non-standards types and the second one is for the standard types. They are initialized by common_types and types_map.

+
+guess_extension(type, strict=True)
+

Similar to the guess_extension() function, using the tables stored as part of the object.

+
+guess_type(url, strict=True)
+

Similar to the guess_type() function, using the tables stored as part of the object.

+
+guess_all_extensions(type, strict=True)
+

Similar to the guess_all_extensions() function, using the tables stored as part of the object.

+
+read(filename, strict=True)
+

Load MIME information from a file named filename. This uses readfp() to parse the file.

If strict is True, information will be added to list of standard types, else to the list of non-standard types.

+
+readfp(fp, strict=True)
+

Load MIME type information from an open file fp. The file must have the format of the standard mime.types files.

If strict is True, information will be added to the list of standard types, else to the list of non-standard types.

+
+read_windows_registry(strict=True)
+

Load MIME type information from the Windows registry.

Availability: Windows.

If strict is True, information will be added to the list of standard types, else to the list of non-standard types.

New in version 3.2.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/mimetypes.html +

+
diff --git a/devdocs/python~3.12/library%2Fmm.html b/devdocs/python~3.12/library%2Fmm.html new file mode 100644 index 00000000..d1944035 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmm.html @@ -0,0 +1,7 @@ +

Multimedia Services

The modules described in this chapter implement various algorithms or interfaces that are mainly useful for multimedia applications. They are available at the discretion of the installation. Here’s an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/mm.html +

+
diff --git a/devdocs/python~3.12/library%2Fmmap.html b/devdocs/python~3.12/library%2Fmmap.html new file mode 100644 index 00000000..b5842ddd --- /dev/null +++ b/devdocs/python~3.12/library%2Fmmap.html @@ -0,0 +1,136 @@ +

mmap — Memory-mapped file support

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Memory-mapped file objects behave like both bytearray and like file objects. You can use mmap objects in most places where bytearray are expected; for example, you can use the re module to search through a memory-mapped file. You can also change a single byte by doing obj[index] = 97, or change a subsequence by assigning to a slice: obj[i1:i2] = b'...'. You can also read and write data starting at the current file position, and seek() through the file to different positions.

A memory-mapped file is created by the mmap constructor, which is different on Unix and on Windows. In either case you must provide a file descriptor for a file opened for update. If you wish to map an existing Python file object, use its fileno() method to obtain the correct value for the fileno parameter. Otherwise, you can open the file using the os.open() function, which returns a file descriptor directly (the file still needs to be closed when done).

Note

If you want to create a memory-mapping for a writable, buffered file, you should flush() the file first. This is necessary to ensure that local modifications to the buffers are actually available to the mapping.

For both the Unix and Windows versions of the constructor, access may be specified as an optional keyword parameter. access accepts one of four values: ACCESS_READ, ACCESS_WRITE, or ACCESS_COPY to specify read-only, write-through or copy-on-write memory respectively, or ACCESS_DEFAULT to defer to prot. access can be used on both Unix and Windows. If access is not specified, Windows mmap returns a write-through mapping. The initial memory values for all three access types are taken from the specified file. Assignment to an ACCESS_READ memory map raises a TypeError exception. Assignment to an ACCESS_WRITE memory map affects both memory and the underlying file. Assignment to an ACCESS_COPY memory map affects memory but does not update the underlying file.

Changed in version 3.7: Added ACCESS_DEFAULT constant.

To map anonymous memory, -1 should be passed as the fileno along with the length.

+class mmap.mmap(fileno, length, tagname=None, access=ACCESS_DEFAULT[, offset])
+

(Windows version) Maps length bytes from the file specified by the file handle fileno, and creates a mmap object. If length is larger than the current size of the file, the file is extended to contain length bytes. If length is 0, the maximum length of the map is the current size of the file, except that if the file is empty Windows raises an exception (you cannot create an empty mapping on Windows).

tagname, if specified and not None, is a string giving a tag name for the mapping. Windows allows you to have many different mappings against the same file. If you specify the name of an existing tag, that tag is opened, otherwise a new tag of this name is created. If this parameter is omitted or None, the mapping is created without a name. Avoiding the use of the tag parameter will assist in keeping your code portable between Unix and Windows.

offset may be specified as a non-negative integer offset. mmap references will be relative to the offset from the beginning of the file. offset defaults to 0. offset must be a multiple of the ALLOCATIONGRANULARITY.

Raises an auditing event mmap.__new__ with arguments fileno, length, access, offset.

+
class mmap.mmap(fileno, length, flags=MAP_SHARED, prot=PROT_WRITE|PROT_READ, access=ACCESS_DEFAULT[, offset]) +
+

(Unix version) Maps length bytes from the file specified by the file descriptor fileno, and returns a mmap object. If length is 0, the maximum length of the map will be the current size of the file when mmap is called.

flags specifies the nature of the mapping. MAP_PRIVATE creates a private copy-on-write mapping, so changes to the contents of the mmap object will be private to this process, and MAP_SHARED creates a mapping that’s shared with all other processes mapping the same areas of the file. The default value is MAP_SHARED. Some systems have additional possible flags with the full list specified in MAP_* constants.

prot, if specified, gives the desired memory protection; the two most useful values are PROT_READ and PROT_WRITE, to specify that the pages may be read or written. prot defaults to PROT_READ | PROT_WRITE.

access may be specified in lieu of flags and prot as an optional keyword parameter. It is an error to specify both flags, prot and access. See the description of access above for information on how to use this parameter.

offset may be specified as a non-negative integer offset. mmap references will be relative to the offset from the beginning of the file. offset defaults to 0. offset must be a multiple of ALLOCATIONGRANULARITY which is equal to PAGESIZE on Unix systems.

To ensure validity of the created memory mapping the file specified by the descriptor fileno is internally automatically synchronized with the physical backing store on macOS.

This example shows a simple way of using mmap:

import mmap
+
+# write a simple example file
+with open("hello.txt", "wb") as f:
+    f.write(b"Hello Python!\n")
+
+with open("hello.txt", "r+b") as f:
+    # memory-map the file, size 0 means whole file
+    mm = mmap.mmap(f.fileno(), 0)
+    # read content via standard file methods
+    print(mm.readline())  # prints b"Hello Python!\n"
+    # read content via slice notation
+    print(mm[:5])  # prints b"Hello"
+    # update content using slice notation;
+    # note that new content must have same size
+    mm[6:] = b" world!\n"
+    # ... and read again using standard file methods
+    mm.seek(0)
+    print(mm.readline())  # prints b"Hello  world!\n"
+    # close the map
+    mm.close()
+

mmap can also be used as a context manager in a with statement:

import mmap
+
+with mmap.mmap(-1, 13) as mm:
+    mm.write(b"Hello world!")
+

New in version 3.2: Context manager support.

The next example demonstrates how to create an anonymous map and exchange data between the parent and child processes:

import mmap
+import os
+
+mm = mmap.mmap(-1, 13)
+mm.write(b"Hello world!")
+
+pid = os.fork()
+
+if pid == 0:  # In a child process
+    mm.seek(0)
+    print(mm.readline())
+
+    mm.close()
+

Raises an auditing event mmap.__new__ with arguments fileno, length, access, offset.

Memory-mapped file objects support the following methods:

+close()
+

Closes the mmap. Subsequent calls to other methods of the object will result in a ValueError exception being raised. This will not close the open file.

+
+closed
+

True if the file is closed.

New in version 3.2.

+
+find(sub[, start[, end]])
+

Returns the lowest index in the object where the subsequence sub is found, such that sub is contained in the range [start, end]. Optional arguments start and end are interpreted as in slice notation. Returns -1 on failure.

Changed in version 3.5: Writable bytes-like object is now accepted.

+
+flush([offset[, size]])
+

Flushes changes made to the in-memory copy of a file back to disk. Without use of this call there is no guarantee that changes are written back before the object is destroyed. If offset and size are specified, only changes to the given range of bytes will be flushed to disk; otherwise, the whole extent of the mapping is flushed. offset must be a multiple of the PAGESIZE or ALLOCATIONGRANULARITY.

None is returned to indicate success. An exception is raised when the call failed.

Changed in version 3.8: Previously, a nonzero value was returned on success; zero was returned on error under Windows. A zero value was returned on success; an exception was raised on error under Unix.

+
+madvise(option[, start[, length]])
+

Send advice option to the kernel about the memory region beginning at start and extending length bytes. option must be one of the MADV_* constants available on the system. If start and length are omitted, the entire mapping is spanned. On some systems (including Linux), start must be a multiple of the PAGESIZE.

Availability: Systems with the madvise() system call.

New in version 3.8.

+
+move(dest, src, count)
+

Copy the count bytes starting at offset src to the destination index dest. If the mmap was created with ACCESS_READ, then calls to move will raise a TypeError exception.

+
+read([n])
+

Return a bytes containing up to n bytes starting from the current file position. If the argument is omitted, None or negative, return all bytes from the current file position to the end of the mapping. The file position is updated to point after the bytes that were returned.

Changed in version 3.3: Argument can be omitted or None.

+
+read_byte()
+

Returns a byte at the current file position as an integer, and advances the file position by 1.

+
+readline()
+

Returns a single line, starting at the current file position and up to the next newline. The file position is updated to point after the bytes that were returned.

+
+resize(newsize)
+

Resizes the map and the underlying file, if any. If the mmap was created with ACCESS_READ or ACCESS_COPY, resizing the map will raise a TypeError exception.

On Windows: Resizing the map will raise an OSError if there are other maps against the same named file. Resizing an anonymous map (ie against the pagefile) will silently create a new map with the original data copied over up to the length of the new size.

Changed in version 3.11: Correctly fails if attempting to resize when another map is held Allows resize against an anonymous map on Windows

+
+rfind(sub[, start[, end]])
+

Returns the highest index in the object where the subsequence sub is found, such that sub is contained in the range [start, end]. Optional arguments start and end are interpreted as in slice notation. Returns -1 on failure.

Changed in version 3.5: Writable bytes-like object is now accepted.

+
+seek(pos[, whence])
+

Set the file’s current position. whence argument is optional and defaults to os.SEEK_SET or 0 (absolute file positioning); other values are os.SEEK_CUR or 1 (seek relative to the current position) and os.SEEK_END or 2 (seek relative to the file’s end).

+
+size()
+

Return the length of the file, which can be larger than the size of the memory-mapped area.

+
+tell()
+

Returns the current position of the file pointer.

+
+write(bytes)
+

Write the bytes in bytes into memory at the current position of the file pointer and return the number of bytes written (never less than len(bytes), since if the write fails, a ValueError will be raised). The file position is updated to point after the bytes that were written. If the mmap was created with ACCESS_READ, then writing to it will raise a TypeError exception.

Changed in version 3.5: Writable bytes-like object is now accepted.

Changed in version 3.6: The number of bytes written is now returned.

+
+write_byte(byte)
+

Write the integer byte into memory at the current position of the file pointer; the file position is advanced by 1. If the mmap was created with ACCESS_READ, then writing to it will raise a TypeError exception.

+
+

MADV_* Constants

+mmap.MADV_NORMAL
+mmap.MADV_RANDOM
+mmap.MADV_SEQUENTIAL
+mmap.MADV_WILLNEED
+mmap.MADV_DONTNEED
+mmap.MADV_REMOVE
+mmap.MADV_DONTFORK
+mmap.MADV_DOFORK
+mmap.MADV_HWPOISON
+mmap.MADV_MERGEABLE
+mmap.MADV_UNMERGEABLE
+mmap.MADV_SOFT_OFFLINE
+mmap.MADV_HUGEPAGE
+mmap.MADV_NOHUGEPAGE
+mmap.MADV_DONTDUMP
+mmap.MADV_DODUMP
+mmap.MADV_FREE
+mmap.MADV_NOSYNC
+mmap.MADV_AUTOSYNC
+mmap.MADV_NOCORE
+mmap.MADV_CORE
+mmap.MADV_PROTECT
+mmap.MADV_FREE_REUSABLE
+mmap.MADV_FREE_REUSE
+

These options can be passed to mmap.madvise(). Not every option will be present on every system.

Availability: Systems with the madvise() system call.

New in version 3.8.

+

MAP_* Constants

+mmap.MAP_SHARED
+mmap.MAP_PRIVATE
+mmap.MAP_DENYWRITE
+mmap.MAP_EXECUTABLE
+mmap.MAP_ANON
+mmap.MAP_ANONYMOUS
+mmap.MAP_POPULATE
+mmap.MAP_STACK
+mmap.MAP_ALIGNED_SUPER
+mmap.MAP_CONCEAL
+

These are the various flags that can be passed to mmap.mmap(). MAP_ALIGNED_SUPER is only available at FreeBSD and MAP_CONCEAL is only available at OpenBSD. Note that some options might not be present on some systems.

Changed in version 3.10: Added MAP_POPULATE constant.

New in version 3.11: Added MAP_STACK constant.

New in version 3.12: Added MAP_ALIGNED_SUPER constant. Added MAP_CONCEAL constant.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/mmap.html +

+
diff --git a/devdocs/python~3.12/library%2Fmodulefinder.html b/devdocs/python~3.12/library%2Fmodulefinder.html new file mode 100644 index 00000000..2bd0f78c --- /dev/null +++ b/devdocs/python~3.12/library%2Fmodulefinder.html @@ -0,0 +1,65 @@ +

modulefinder — Find modules used by a script

Source code: Lib/modulefinder.py

This module provides a ModuleFinder class that can be used to determine the set of modules imported by a script. modulefinder.py can also be run as a script, giving the filename of a Python script as its argument, after which a report of the imported modules will be printed.

+modulefinder.AddPackagePath(pkg_name, path)
+

Record that the package named pkg_name can be found in the specified path.

+
+modulefinder.ReplacePackage(oldname, newname)
+

Allows specifying that the module named oldname is in fact the package named newname.

+
+class modulefinder.ModuleFinder(path=None, debug=0, excludes=[], replace_paths=[])
+

This class provides run_script() and report() methods to determine the set of modules imported by a script. path can be a list of directories to search for modules; if not specified, sys.path is used. debug sets the debugging level; higher values make the class print debugging messages about what it’s doing. excludes is a list of module names to exclude from the analysis. replace_paths is a list of (oldpath, newpath) tuples that will be replaced in module paths.

+report()
+

Print a report to standard output that lists the modules imported by the script and their paths, as well as modules that are missing or seem to be missing.

+
+run_script(pathname)
+

Analyze the contents of the pathname file, which must contain Python code.

+
+modules
+

A dictionary mapping module names to modules. See Example usage of ModuleFinder.

+
+

Example usage of ModuleFinder

The script that is going to get analyzed later on (bacon.py):

import re, itertools
+
+try:
+    import baconhameggs
+except ImportError:
+    pass
+
+try:
+    import guido.python.ham
+except ImportError:
+    pass
+

The script that will output the report of bacon.py:

from modulefinder import ModuleFinder
+
+finder = ModuleFinder()
+finder.run_script('bacon.py')
+
+print('Loaded modules:')
+for name, mod in finder.modules.items():
+    print('%s: ' % name, end='')
+    print(','.join(list(mod.globalnames.keys())[:3]))
+
+print('-'*50)
+print('Modules not imported:')
+print('\n'.join(finder.badmodules.keys()))
+

Sample output (may vary depending on the architecture):

Loaded modules:
+_types:
+copyreg:  _inverted_registry,_slotnames,__all__
+re._compiler:  isstring,_sre,_optimize_unicode
+_sre:
+re._constants:  REPEAT_ONE,makedict,AT_END_LINE
+sys:
+re:  __module__,finditer,_expand
+itertools:
+__main__:  re,itertools,baconhameggs
+re._parser:  _PATTERNENDERS,SRE_FLAG_UNICODE
+array:
+types:  __module__,IntType,TypeType
+---------------------------------------------------
+Modules not imported:
+guido.python.ham
+baconhameggs
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/modulefinder.html +

+
diff --git a/devdocs/python~3.12/library%2Fmodules.html b/devdocs/python~3.12/library%2Fmodules.html new file mode 100644 index 00000000..f0edb99f --- /dev/null +++ b/devdocs/python~3.12/library%2Fmodules.html @@ -0,0 +1,14 @@ +

Importing Modules

The modules described in this chapter provide new ways to import other Python modules and hooks for customizing the import process.

The full list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/modules.html +

+
diff --git a/devdocs/python~3.12/library%2Fmsvcrt.html b/devdocs/python~3.12/library%2Fmsvcrt.html new file mode 100644 index 00000000..5cd9cf7d --- /dev/null +++ b/devdocs/python~3.12/library%2Fmsvcrt.html @@ -0,0 +1,59 @@ +

msvcrt — Useful routines from the MS VC++ runtime

These functions provide access to some useful capabilities on Windows platforms. Some higher-level modules use these functions to build the Windows implementations of their services. For example, the getpass module uses this in the implementation of the getpass() function.

Further documentation on these functions can be found in the Platform API documentation.

The module implements both the normal and wide char variants of the console I/O api. The normal API deals only with ASCII characters and is of limited use for internationalized applications. The wide char API should be used where ever possible.

Changed in version 3.3: Operations in this module now raise OSError where IOError was raised.

File Operations

+msvcrt.locking(fd, mode, nbytes)
+

Lock part of a file based on file descriptor fd from the C runtime. Raises OSError on failure. The locked region of the file extends from the current file position for nbytes bytes, and may continue beyond the end of the file. mode must be one of the LK_* constants listed below. Multiple regions in a file may be locked at the same time, but may not overlap. Adjacent regions are not merged; they must be unlocked individually.

Raises an auditing event msvcrt.locking with arguments fd, mode, nbytes.

+
+msvcrt.LK_LOCK
+msvcrt.LK_RLCK
+

Locks the specified bytes. If the bytes cannot be locked, the program immediately tries again after 1 second. If, after 10 attempts, the bytes cannot be locked, OSError is raised.

+
+msvcrt.LK_NBLCK
+msvcrt.LK_NBRLCK
+

Locks the specified bytes. If the bytes cannot be locked, OSError is raised.

+
+msvcrt.LK_UNLCK
+

Unlocks the specified bytes, which must have been previously locked.

+
+msvcrt.setmode(fd, flags)
+

Set the line-end translation mode for the file descriptor fd. To set it to text mode, flags should be os.O_TEXT; for binary, it should be os.O_BINARY.

+
+msvcrt.open_osfhandle(handle, flags)
+

Create a C runtime file descriptor from the file handle handle. The flags parameter should be a bitwise OR of os.O_APPEND, os.O_RDONLY, and os.O_TEXT. The returned file descriptor may be used as a parameter to os.fdopen() to create a file object.

Raises an auditing event msvcrt.open_osfhandle with arguments handle, flags.

+
+msvcrt.get_osfhandle(fd)
+

Return the file handle for the file descriptor fd. Raises OSError if fd is not recognized.

Raises an auditing event msvcrt.get_osfhandle with argument fd.

+

Console I/O

+msvcrt.kbhit()
+

Return True if a keypress is waiting to be read.

+
+msvcrt.getch()
+

Read a keypress and return the resulting character as a byte string. Nothing is echoed to the console. This call will block if a keypress is not already available, but will not wait for Enter to be pressed. If the pressed key was a special function key, this will return '\000' or '\xe0'; the next call will return the keycode. The Control-C keypress cannot be read with this function.

+
+msvcrt.getwch()
+

Wide char variant of getch(), returning a Unicode value.

+
+msvcrt.getche()
+

Similar to getch(), but the keypress will be echoed if it represents a printable character.

+
+msvcrt.getwche()
+

Wide char variant of getche(), returning a Unicode value.

+
+msvcrt.putch(char)
+

Print the byte string char to the console without buffering.

+
+msvcrt.putwch(unicode_char)
+

Wide char variant of putch(), accepting a Unicode value.

+
+msvcrt.ungetch(char)
+

Cause the byte string char to be “pushed back” into the console buffer; it will be the next character read by getch() or getche().

+
+msvcrt.ungetwch(unicode_char)
+

Wide char variant of ungetch(), accepting a Unicode value.

+

Other Functions

+msvcrt.heapmin()
+

Force the malloc() heap to clean itself up and return unused blocks to the operating system. On failure, this raises OSError.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/msvcrt.html +

+
diff --git a/devdocs/python~3.12/library%2Fmultiprocessing.html b/devdocs/python~3.12/library%2Fmultiprocessing.html new file mode 100644 index 00000000..65f5be85 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmultiprocessing.html @@ -0,0 +1,1295 @@ +

multiprocessing — Process-based parallelism

Source code: Lib/multiprocessing/

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Introduction

multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both POSIX and Windows.

The multiprocessing module also introduces APIs which do not have analogs in the threading module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,

from multiprocessing import Pool
+
+def f(x):
+    return x*x
+
+if __name__ == '__main__':
+    with Pool(5) as p:
+        print(p.map(f, [1, 2, 3]))
+

will print to standard output

[1, 4, 9]
+

See also

concurrent.futures.ProcessPoolExecutor offers a higher level interface to push tasks to a background process without blocking execution of the calling process. Compared to using the Pool interface directly, the concurrent.futures API more readily allows the submission of work to the underlying process pool to be separated from waiting for the results.

The Process class

In multiprocessing, processes are spawned by creating a Process object and then calling its start() method. Process follows the API of threading.Thread. A trivial example of a multiprocess program is

from multiprocessing import Process
+
+def f(name):
+    print('hello', name)
+
+if __name__ == '__main__':
+    p = Process(target=f, args=('bob',))
+    p.start()
+    p.join()
+

To show the individual process IDs involved, here is an expanded example:

from multiprocessing import Process
+import os
+
+def info(title):
+    print(title)
+    print('module name:', __name__)
+    print('parent process:', os.getppid())
+    print('process id:', os.getpid())
+
+def f(name):
+    info('function f')
+    print('hello', name)
+
+if __name__ == '__main__':
+    info('main line')
+    p = Process(target=f, args=('bob',))
+    p.start()
+    p.join()
+

For an explanation of why the if __name__ == '__main__' part is necessary, see Programming guidelines.

Contexts and start methods

Depending on the platform, multiprocessing supports three ways to start a process. These start methods are

spawn
+
+

The parent process starts a fresh Python interpreter process. The child process will only inherit those resources necessary to run the process object’s run() method. In particular, unnecessary file descriptors and handles from the parent process will not be inherited. Starting a process using this method is rather slow compared to using fork or forkserver.

Available on POSIX and Windows platforms. The default on Windows and macOS.

fork
+
+

The parent process uses os.fork() to fork the Python interpreter. The child process, when it begins, is effectively identical to the parent process. All resources of the parent are inherited by the child process. Note that safely forking a multithreaded process is problematic.

Available on POSIX systems. Currently the default on POSIX except macOS.

Note

The default start method will change away from fork in Python 3.14. Code that requires fork should explicitly specify that via get_context() or set_start_method().

Changed in version 3.12: If Python is able to detect that your process has multiple threads, the os.fork() function that this start method calls internally will raise a DeprecationWarning. Use a different start method. See the os.fork() documentation for further explanation.

forkserver
+
+

When the program starts and selects the forkserver start method, a server process is spawned. From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. The fork server process is single threaded unless system libraries or preloaded imports spawn threads as a side-effect so it is generally safe for it to use os.fork(). No unnecessary resources are inherited.

Available on POSIX platforms which support passing file descriptors over Unix pipes such as Linux.

Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess as macOS system libraries may start threads. See bpo-33725.

Changed in version 3.4: spawn added on all POSIX platforms, and forkserver added for some POSIX platforms. Child processes no longer inherit all of the parents inheritable handles on Windows.

On POSIX using the spawn or forkserver start methods will also start a resource tracker process which tracks the unlinked named system resources (such as named semaphores or SharedMemory objects) created by processes of the program. When all processes have exited the resource tracker unlinks any remaining tracked object. Usually there should be none, but if a process was killed by a signal there may be some “leaked” resources. (Neither leaked semaphores nor shared memory segments will be automatically unlinked until the next reboot. This is problematic for both objects because the system allows only a limited number of named semaphores, and shared memory segments occupy some space in the main memory.)

To select a start method you use the set_start_method() in the if __name__ == '__main__' clause of the main module. For example:

import multiprocessing as mp
+
+def foo(q):
+    q.put('hello')
+
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+    q = mp.Queue()
+    p = mp.Process(target=foo, args=(q,))
+    p.start()
+    print(q.get())
+    p.join()
+

set_start_method() should not be used more than once in the program.

Alternatively, you can use get_context() to obtain a context object. Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.

import multiprocessing as mp
+
+def foo(q):
+    q.put('hello')
+
+if __name__ == '__main__':
+    ctx = mp.get_context('spawn')
+    q = ctx.Queue()
+    p = ctx.Process(target=foo, args=(q,))
+    p.start()
+    print(q.get())
+    p.join()
+

Note that objects related to one context may not be compatible with processes for a different context. In particular, locks created using the fork context cannot be passed to processes started using the spawn or forkserver start methods.

A library which wants to use a particular start method should probably use get_context() to avoid interfering with the choice of the library user.

Warning

The 'spawn' and 'forkserver' start methods generally cannot be used with “frozen” executables (i.e., binaries produced by packages like PyInstaller and cx_Freeze) on POSIX systems. The 'fork' start method may work if code does not use threads.

Exchanging objects between processes

multiprocessing supports two types of communication channel between processes:

Queues

The Queue class is a near clone of queue.Queue. For example:

from multiprocessing import Process, Queue
+
+def f(q):
+    q.put([42, None, 'hello'])
+
+if __name__ == '__main__':
+    q = Queue()
+    p = Process(target=f, args=(q,))
+    p.start()
+    print(q.get())    # prints "[42, None, 'hello']"
+    p.join()
+

Queues are thread and process safe.

Pipes

The Pipe() function returns a pair of connection objects connected by a pipe which by default is duplex (two-way). For example:

from multiprocessing import Process, Pipe
+
+def f(conn):
+    conn.send([42, None, 'hello'])
+    conn.close()
+
+if __name__ == '__main__':
+    parent_conn, child_conn = Pipe()
+    p = Process(target=f, args=(child_conn,))
+    p.start()
+    print(parent_conn.recv())   # prints "[42, None, 'hello']"
+    p.join()
+

The two connection objects returned by Pipe() represent the two ends of the pipe. Each connection object has send() and recv() methods (among others). Note that data in a pipe may become corrupted if two processes (or threads) try to read from or write to the same end of the pipe at the same time. Of course there is no risk of corruption from processes using different ends of the pipe at the same time.

Synchronization between processes

multiprocessing contains equivalents of all the synchronization primitives from threading. For instance one can use a lock to ensure that only one process prints to standard output at a time:

from multiprocessing import Process, Lock
+
+def f(l, i):
+    l.acquire()
+    try:
+        print('hello world', i)
+    finally:
+        l.release()
+
+if __name__ == '__main__':
+    lock = Lock()
+
+    for num in range(10):
+        Process(target=f, args=(lock, num)).start()
+

Without using the lock output from the different processes is liable to get all mixed up.

Sharing state between processes

As mentioned above, when doing concurrent programming it is usually best to avoid using shared state as far as possible. This is particularly true when using multiple processes.

However, if you really do need to use some shared data then multiprocessing provides a couple of ways of doing so.

Shared memory

Data can be stored in a shared memory map using Value or Array. For example, the following code

from multiprocessing import Process, Value, Array
+
+def f(n, a):
+    n.value = 3.1415927
+    for i in range(len(a)):
+        a[i] = -a[i]
+
+if __name__ == '__main__':
+    num = Value('d', 0.0)
+    arr = Array('i', range(10))
+
+    p = Process(target=f, args=(num, arr))
+    p.start()
+    p.join()
+
+    print(num.value)
+    print(arr[:])
+

will print

3.1415927
+[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
+

The 'd' and 'i' arguments used when creating num and arr are typecodes of the kind used by the array module: 'd' indicates a double precision float and 'i' indicates a signed integer. These shared objects will be process and thread-safe.

For more flexibility in using shared memory one can use the multiprocessing.sharedctypes module which supports the creation of arbitrary ctypes objects allocated from shared memory.

Server process

A manager object returned by Manager() controls a server process which holds Python objects and allows other processes to manipulate them using proxies.

A manager returned by Manager() will support types list, dict, Namespace, Lock, RLock, Semaphore, BoundedSemaphore, Condition, Event, Barrier, Queue, Value and Array. For example,

from multiprocessing import Process, Manager
+
+def f(d, l):
+    d[1] = '1'
+    d['2'] = 2
+    d[0.25] = None
+    l.reverse()
+
+if __name__ == '__main__':
+    with Manager() as manager:
+        d = manager.dict()
+        l = manager.list(range(10))
+
+        p = Process(target=f, args=(d, l))
+        p.start()
+        p.join()
+
+        print(d)
+        print(l)
+

will print

{0.25: None, 1: '1', '2': 2}
+[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]
+

Server process managers are more flexible than using shared memory objects because they can be made to support arbitrary object types. Also, a single manager can be shared by processes on different computers over a network. They are, however, slower than using shared memory.

Using a pool of workers

The Pool class represents a pool of worker processes. It has methods which allows tasks to be offloaded to the worker processes in a few different ways.

For example:

from multiprocessing import Pool, TimeoutError
+import time
+import os
+
+def f(x):
+    return x*x
+
+if __name__ == '__main__':
+    # start 4 worker processes
+    with Pool(processes=4) as pool:
+
+        # print "[0, 1, 4,..., 81]"
+        print(pool.map(f, range(10)))
+
+        # print same numbers in arbitrary order
+        for i in pool.imap_unordered(f, range(10)):
+            print(i)
+
+        # evaluate "f(20)" asynchronously
+        res = pool.apply_async(f, (20,))      # runs in *only* one process
+        print(res.get(timeout=1))             # prints "400"
+
+        # evaluate "os.getpid()" asynchronously
+        res = pool.apply_async(os.getpid, ()) # runs in *only* one process
+        print(res.get(timeout=1))             # prints the PID of that process
+
+        # launching multiple evaluations asynchronously *may* use more processes
+        multiple_results = [pool.apply_async(os.getpid, ()) for i in range(4)]
+        print([res.get(timeout=1) for res in multiple_results])
+
+        # make a single worker sleep for 10 seconds
+        res = pool.apply_async(time.sleep, (10,))
+        try:
+            print(res.get(timeout=1))
+        except TimeoutError:
+            print("We lacked patience and got a multiprocessing.TimeoutError")
+
+        print("For the moment, the pool remains available for more work")
+
+    # exiting the 'with'-block has stopped the pool
+    print("Now the pool is closed and no longer available")
+

Note that the methods of a pool should only ever be used by the process which created it.

Note

Functionality within this package requires that the __main__ module be importable by the children. This is covered in Programming guidelines however it is worth pointing out here. This means that some examples, such as the multiprocessing.pool.Pool examples will not work in the interactive interpreter. For example:

>>> from multiprocessing import Pool
+>>> p = Pool(5)
+>>> def f(x):
+...     return x*x
+...
+>>> with p:
+...     p.map(f, [1,2,3])
+Process PoolWorker-1:
+Process PoolWorker-2:
+Process PoolWorker-3:
+Traceback (most recent call last):
+Traceback (most recent call last):
+Traceback (most recent call last):
+AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
+AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
+AttributeError: Can't get attribute 'f' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>
+

(If you try this it will actually output three full tracebacks interleaved in a semi-random fashion, and then you may have to stop the parent process somehow.)

Reference

The multiprocessing package mostly replicates the API of the threading module.

+Process and exceptions

+class multiprocessing.Process(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)
+

Process objects represent activity that is run in a separate process. The Process class has equivalents of all the methods of threading.Thread.

The constructor should always be called with keyword arguments. group should always be None; it exists solely for compatibility with threading.Thread. target is the callable object to be invoked by the run() method. It defaults to None, meaning nothing is called. name is the process name (see name for more details). args is the argument tuple for the target invocation. kwargs is a dictionary of keyword arguments for the target invocation. If provided, the keyword-only daemon argument sets the process daemon flag to True or False. If None (the default), this flag will be inherited from the creating process.

By default, no arguments are passed to target. The args argument, which defaults to (), can be used to specify a list or tuple of the arguments to pass to target.

If a subclass overrides the constructor, it must make sure it invokes the base class constructor (Process.__init__()) before doing anything else to the process.

Changed in version 3.3: Added the daemon argument.

+run()
+

Method representing the process’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with sequential and keyword arguments taken from the args and kwargs arguments, respectively.

Using a list or tuple as the args argument passed to Process achieves the same effect.

Example:

>>> from multiprocessing import Process
+>>> p = Process(target=print, args=[1])
+>>> p.run()
+1
+>>> p = Process(target=print, args=(1,))
+>>> p.run()
+1
+
+
+start()
+

Start the process’s activity.

This must be called at most once per process object. It arranges for the object’s run() method to be invoked in a separate process.

+
+join([timeout])
+

If the optional argument timeout is None (the default), the method blocks until the process whose join() method is called terminates. If timeout is a positive number, it blocks at most timeout seconds. Note that the method returns None if its process terminates or if the method times out. Check the process’s exitcode to determine if it terminated.

A process can be joined many times.

A process cannot join itself because this would cause a deadlock. It is an error to attempt to join a process before it has been started.

+
+name
+

The process’s name. The name is a string used for identification purposes only. It has no semantics. Multiple processes may be given the same name.

The initial name is set by the constructor. If no explicit name is provided to the constructor, a name of the form ‘Process-N1:N2:…:Nk’ is constructed, where each Nk is the N-th child of its parent.

+
+is_alive()
+

Return whether the process is alive.

Roughly, a process object is alive from the moment the start() method returns until the child process terminates.

+
+daemon
+

The process’s daemon flag, a Boolean value. This must be set before start() is called.

The initial value is inherited from the creating process.

When a process exits, it attempts to terminate all of its daemonic child processes.

Note that a daemonic process is not allowed to create child processes. Otherwise a daemonic process would leave its children orphaned if it gets terminated when its parent process exits. Additionally, these are not Unix daemons or services, they are normal processes that will be terminated (and not joined) if non-daemonic processes have exited.

+

In addition to the threading.Thread API, Process objects also support the following attributes and methods:

+pid
+

Return the process ID. Before the process is spawned, this will be None.

+
+exitcode
+

The child’s exit code. This will be None if the process has not yet terminated.

If the child’s run() method returned normally, the exit code will be 0. If it terminated via sys.exit() with an integer argument N, the exit code will be N.

If the child terminated due to an exception not caught within run(), the exit code will be 1. If it was terminated by signal N, the exit code will be the negative value -N.

+
+authkey
+

The process’s authentication key (a byte string).

When multiprocessing is initialized the main process is assigned a random string using os.urandom().

When a Process object is created, it will inherit the authentication key of its parent process, although this may be changed by setting authkey to another byte string.

See Authentication keys.

+
+sentinel
+

A numeric handle of a system object which will become “ready” when the process ends.

You can use this value if you want to wait on several events at once using multiprocessing.connection.wait(). Otherwise calling join() is simpler.

On Windows, this is an OS handle usable with the WaitForSingleObject and WaitForMultipleObjects family of API calls. On POSIX, this is a file descriptor usable with primitives from the select module.

New in version 3.3.

+
+terminate()
+

Terminate the process. On POSIX this is done using the SIGTERM signal; on Windows TerminateProcess() is used. Note that exit handlers and finally clauses, etc., will not be executed.

Note that descendant processes of the process will not be terminated – they will simply become orphaned.

Warning

If this method is used when the associated process is using a pipe or queue then the pipe or queue is liable to become corrupted and may become unusable by other process. Similarly, if the process has acquired a lock or semaphore etc. then terminating it is liable to cause other processes to deadlock.

+
+kill()
+

Same as terminate() but using the SIGKILL signal on POSIX.

New in version 3.7.

+
+close()
+

Close the Process object, releasing all resources associated with it. ValueError is raised if the underlying process is still running. Once close() returns successfully, most other methods and attributes of the Process object will raise ValueError.

New in version 3.7.

+

Note that the start(), join(), is_alive(), terminate() and exitcode methods should only be called by the process that created the process object.

Example usage of some of the methods of Process:

>>> import multiprocessing, time, signal
+>>> mp_context = multiprocessing.get_context('spawn')
+>>> p = mp_context.Process(target=time.sleep, args=(1000,))
+>>> print(p, p.is_alive())
+<...Process ... initial> False
+>>> p.start()
+>>> print(p, p.is_alive())
+<...Process ... started> True
+>>> p.terminate()
+>>> time.sleep(0.1)
+>>> print(p, p.is_alive())
+<...Process ... stopped exitcode=-SIGTERM> False
+>>> p.exitcode == -signal.SIGTERM
+True
+
+
+exception multiprocessing.ProcessError
+

The base class of all multiprocessing exceptions.

+
+exception multiprocessing.BufferTooShort
+

Exception raised by Connection.recv_bytes_into() when the supplied buffer object is too small for the message read.

If e is an instance of BufferTooShort then e.args[0] will give the message as a byte string.

+
+exception multiprocessing.AuthenticationError
+

Raised when there is an authentication error.

+
+exception multiprocessing.TimeoutError
+

Raised by methods with a timeout when the timeout expires.

+

Pipes and Queues

When using multiple processes, one generally uses message passing for communication between processes and avoids having to use any synchronization primitives like locks.

For passing messages one can use Pipe() (for a connection between two processes) or a queue (which allows multiple producers and consumers).

The Queue, SimpleQueue and JoinableQueue types are multi-producer, multi-consumer FIFO queues modelled on the queue.Queue class in the standard library. They differ in that Queue lacks the task_done() and join() methods introduced into Python 2.5’s queue.Queue class.

If you use JoinableQueue then you must call JoinableQueue.task_done() for each task removed from the queue or else the semaphore used to count the number of unfinished tasks may eventually overflow, raising an exception.

Note that one can also create a shared queue by using a manager object – see Managers.

Note

multiprocessing uses the usual queue.Empty and queue.Full exceptions to signal a timeout. They are not available in the multiprocessing namespace so you need to import them from queue.

Note

When an object is put on a queue, the object is pickled and a background thread later flushes the pickled data to an underlying pipe. This has some consequences which are a little surprising, but should not cause any practical difficulties – if they really bother you then you can instead use a queue created with a manager.

  1. After putting an object on an empty queue there may be an infinitesimal delay before the queue’s empty() method returns False and get_nowait() can return without raising queue.Empty.
  2. If multiple processes are enqueuing objects, it is possible for the objects to be received at the other end out-of-order. However, objects enqueued by the same process will always be in the expected order with respect to each other.

Warning

If a process is killed using Process.terminate() or os.kill() while it is trying to use a Queue, then the data in the queue is likely to become corrupted. This may cause any other process to get an exception when it tries to use the queue later on.

Warning

As mentioned above, if a child process has put items on a queue (and it has not used JoinableQueue.cancel_join_thread), then that process will not terminate until all buffered items have been flushed to the pipe.

This means that if you try joining that process you may get a deadlock unless you are sure that all items which have been put on the queue have been consumed. Similarly, if the child process is non-daemonic then the parent process may hang on exit when it tries to join all its non-daemonic children.

Note that a queue created using a manager does not have this issue. See Programming guidelines.

For an example of the usage of queues for interprocess communication see Examples.

+multiprocessing.Pipe([duplex])
+

Returns a pair (conn1, conn2) of Connection objects representing the ends of a pipe.

If duplex is True (the default) then the pipe is bidirectional. If duplex is False then the pipe is unidirectional: conn1 can only be used for receiving messages and conn2 can only be used for sending messages.

+
+class multiprocessing.Queue([maxsize])
+

Returns a process shared queue implemented using a pipe and a few locks/semaphores. When a process first puts an item on the queue a feeder thread is started which transfers objects from a buffer into the pipe.

The usual queue.Empty and queue.Full exceptions from the standard library’s queue module are raised to signal timeouts.

Queue implements all the methods of queue.Queue except for task_done() and join().

+qsize()
+

Return the approximate size of the queue. Because of multithreading/multiprocessing semantics, this number is not reliable.

Note that this may raise NotImplementedError on platforms like macOS where sem_getvalue() is not implemented.

+
+empty()
+

Return True if the queue is empty, False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

+
+full()
+

Return True if the queue is full, False otherwise. Because of multithreading/multiprocessing semantics, this is not reliable.

+
+put(obj[, block[, timeout]])
+

Put obj into the queue. If the optional argument block is True (the default) and timeout is None (the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises the queue.Full exception if no free slot was available within that time. Otherwise (block is False), put an item on the queue if a free slot is immediately available, else raise the queue.Full exception (timeout is ignored in that case).

Changed in version 3.8: If the queue is closed, ValueError is raised instead of AssertionError.

+
+put_nowait(obj)
+

Equivalent to put(obj, False).

+
+get([block[, timeout]])
+

Remove and return an item from the queue. If optional args block is True (the default) and timeout is None (the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises the queue.Empty exception if no item was available within that time. Otherwise (block is False), return an item if one is immediately available, else raise the queue.Empty exception (timeout is ignored in that case).

Changed in version 3.8: If the queue is closed, ValueError is raised instead of OSError.

+
+get_nowait()
+

Equivalent to get(False).

+

multiprocessing.Queue has a few additional methods not found in queue.Queue. These methods are usually unnecessary for most code:

+close()
+

Indicate that no more data will be put on this queue by the current process. The background thread will quit once it has flushed all buffered data to the pipe. This is called automatically when the queue is garbage collected.

+
+join_thread()
+

Join the background thread. This can only be used after close() has been called. It blocks until the background thread exits, ensuring that all data in the buffer has been flushed to the pipe.

By default if a process is not the creator of the queue then on exit it will attempt to join the queue’s background thread. The process can call cancel_join_thread() to make join_thread() do nothing.

+
+cancel_join_thread()
+

Prevent join_thread() from blocking. In particular, this prevents the background thread from being joined automatically when the process exits – see join_thread().

A better name for this method might be allow_exit_without_flush(). It is likely to cause enqueued data to be lost, and you almost certainly will not need to use it. It is really only there if you need the current process to exit immediately without waiting to flush enqueued data to the underlying pipe, and you don’t care about lost data.

+

Note

This class’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the functionality in this class will be disabled, and attempts to instantiate a Queue will result in an ImportError. See bpo-3770 for additional information. The same holds true for any of the specialized queue types listed below.

+
+class multiprocessing.SimpleQueue
+

It is a simplified Queue type, very close to a locked Pipe.

+close()
+

Close the queue: release internal resources.

A queue must not be used anymore after it is closed. For example, get(), put() and empty() methods must no longer be called.

New in version 3.9.

+
+empty()
+

Return True if the queue is empty, False otherwise.

+
+get()
+

Remove and return an item from the queue.

+
+put(item)
+

Put item into the queue.

+
+
+class multiprocessing.JoinableQueue([maxsize])
+

JoinableQueue, a Queue subclass, is a queue which additionally has task_done() and join() methods.

+task_done()
+

Indicate that a formerly enqueued task is complete. Used by queue consumers. For each get() used to fetch a task, a subsequent call to task_done() tells the queue that the processing on the task is complete.

If a join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

Raises a ValueError if called more times than there were items placed in the queue.

+
+join()
+

Block until all items in the queue have been gotten and processed.

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.

+
+

Miscellaneous

+multiprocessing.active_children()
+

Return list of all live children of the current process.

Calling this has the side effect of “joining” any processes which have already finished.

+
+multiprocessing.cpu_count()
+

Return the number of CPUs in the system.

This number is not equivalent to the number of CPUs the current process can use. The number of usable CPUs can be obtained with len(os.sched_getaffinity(0))

When the number of CPUs cannot be determined a NotImplementedError is raised.

See also

os.cpu_count()

+
+multiprocessing.current_process()
+

Return the Process object corresponding to the current process.

An analogue of threading.current_thread().

+
+multiprocessing.parent_process()
+

Return the Process object corresponding to the parent process of the current_process(). For the main process, parent_process will be None.

New in version 3.8.

+
+multiprocessing.freeze_support()
+

Add support for when a program which uses multiprocessing has been frozen to produce a Windows executable. (Has been tested with py2exe, PyInstaller and cx_Freeze.)

One needs to call this function straight after the if __name__ == +'__main__' line of the main module. For example:

from multiprocessing import Process, freeze_support
+
+def f():
+    print('hello world!')
+
+if __name__ == '__main__':
+    freeze_support()
+    Process(target=f).start()
+

If the freeze_support() line is omitted then trying to run the frozen executable will raise RuntimeError.

Calling freeze_support() has no effect when invoked on any operating system other than Windows. In addition, if the module is being run normally by the Python interpreter on Windows (the program has not been frozen), then freeze_support() has no effect.

+
+multiprocessing.get_all_start_methods()
+

Returns a list of the supported start methods, the first of which is the default. The possible start methods are 'fork', 'spawn' and 'forkserver'. Not all platforms support all methods. See Contexts and start methods.

New in version 3.4.

+
+multiprocessing.get_context(method=None)
+

Return a context object which has the same attributes as the multiprocessing module.

If method is None then the default context is returned. Otherwise method should be 'fork', 'spawn', 'forkserver'. ValueError is raised if the specified start method is not available. See Contexts and start methods.

New in version 3.4.

+
+multiprocessing.get_start_method(allow_none=False)
+

Return the name of start method used for starting processes.

If the start method has not been fixed and allow_none is false, then the start method is fixed to the default and the name is returned. If the start method has not been fixed and allow_none is true then None is returned.

The return value can be 'fork', 'spawn', 'forkserver' or None. See Contexts and start methods.

+

Changed in version 3.8: On macOS, the spawn start method is now the default. The fork start method should be considered unsafe as it can lead to crashes of the subprocess. See bpo-33725.

New in version 3.4.

+multiprocessing.set_executable(executable)
+

Set the path of the Python interpreter to use when starting a child process. (By default sys.executable is used). Embedders will probably need to do some thing like

set_executable(os.path.join(sys.exec_prefix, 'pythonw.exe'))
+

before they can create child processes.

Changed in version 3.4: Now supported on POSIX when the 'spawn' start method is used.

Changed in version 3.11: Accepts a path-like object.

+
+multiprocessing.set_forkserver_preload(module_names)
+

Set a list of module names for the forkserver main process to attempt to import so that their already imported state is inherited by forked processes. Any ImportError when doing so is silently ignored. This can be used as a performance enhancement to avoid repeated work in every process.

For this to work, it must be called before the forkserver process has been launched (before creating a Pool or starting a Process).

Only meaningful when using the 'forkserver' start method. See Contexts and start methods.

New in version 3.4.

+
+multiprocessing.set_start_method(method, force=False)
+

Set the method which should be used to start child processes. The method argument can be 'fork', 'spawn' or 'forkserver'. Raises RuntimeError if the start method has already been set and force is not True. If method is None and force is True then the start method is set to None. If method is None and force is False then the context is set to the default context.

Note that this should be called at most once, and it should be protected inside the if __name__ == '__main__' clause of the main module.

See Contexts and start methods.

New in version 3.4.

+

Note

multiprocessing contains no analogues of threading.active_count(), threading.enumerate(), threading.settrace(), threading.setprofile(), threading.Timer, or threading.local.

Connection Objects

Connection objects allow the sending and receiving of picklable objects or strings. They can be thought of as message oriented connected sockets.

Connection objects are usually created using Pipe – see also Listeners and Clients.

+class multiprocessing.connection.Connection
+
+send(obj)
+

Send an object to the other end of the connection which should be read using recv().

The object must be picklable. Very large pickles (approximately 32 MiB+, though it depends on the OS) may raise a ValueError exception.

+
+recv()
+

Return an object sent from the other end of the connection using send(). Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

+
+fileno()
+

Return the file descriptor or handle used by the connection.

+
+close()
+

Close the connection.

This is called automatically when the connection is garbage collected.

+
+poll([timeout])
+

Return whether there is any data available to be read.

If timeout is not specified then it will return immediately. If timeout is a number then this specifies the maximum time in seconds to block. If timeout is None then an infinite timeout is used.

Note that multiple connection objects may be polled at once by using multiprocessing.connection.wait().

+
+send_bytes(buffer[, offset[, size]])
+

Send byte data from a bytes-like object as a complete message.

If offset is given then data is read from that position in buffer. If size is given then that many bytes will be read from buffer. Very large buffers (approximately 32 MiB+, though it depends on the OS) may raise a ValueError exception

+
+recv_bytes([maxlength])
+

Return a complete message of byte data sent from the other end of the connection as a string. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end has closed.

If maxlength is specified and the message is longer than maxlength then OSError is raised and the connection will no longer be readable.

Changed in version 3.3: This function used to raise IOError, which is now an alias of OSError.

+
+recv_bytes_into(buffer[, offset])
+

Read into buffer a complete message of byte data sent from the other end of the connection and return the number of bytes in the message. Blocks until there is something to receive. Raises EOFError if there is nothing left to receive and the other end was closed.

buffer must be a writable bytes-like object. If offset is given then the message will be written into the buffer from that position. Offset must be a non-negative integer less than the length of buffer (in bytes).

If the buffer is too short then a BufferTooShort exception is raised and the complete message is available as e.args[0] where e is the exception instance.

+

Changed in version 3.3: Connection objects themselves can now be transferred between processes using Connection.send() and Connection.recv().

New in version 3.3: Connection objects now support the context management protocol – see Context Manager Types. __enter__() returns the connection object, and __exit__() calls close().

+

For example:

>>> from multiprocessing import Pipe
+>>> a, b = Pipe()
+>>> a.send([1, 'hello', None])
+>>> b.recv()
+[1, 'hello', None]
+>>> b.send_bytes(b'thank you')
+>>> a.recv_bytes()
+b'thank you'
+>>> import array
+>>> arr1 = array.array('i', range(5))
+>>> arr2 = array.array('i', [0] * 10)
+>>> a.send_bytes(arr1)
+>>> count = b.recv_bytes_into(arr2)
+>>> assert count == len(arr1) * arr1.itemsize
+>>> arr2
+array('i', [0, 1, 2, 3, 4, 0, 0, 0, 0, 0])
+

Warning

The Connection.recv() method automatically unpickles the data it receives, which can be a security risk unless you can trust the process which sent the message.

Therefore, unless the connection object was produced using Pipe() you should only use the recv() and send() methods after performing some sort of authentication. See Authentication keys.

Warning

If a process is killed while it is trying to read or write to a pipe then the data in the pipe is likely to become corrupted, because it may become impossible to be sure where the message boundaries lie.

Synchronization primitives

Generally synchronization primitives are not as necessary in a multiprocess program as they are in a multithreaded program. See the documentation for threading module.

Note that one can also create synchronization primitives by using a manager object – see Managers.

+class multiprocessing.Barrier(parties[, action[, timeout]])
+

A barrier object: a clone of threading.Barrier.

New in version 3.3.

+
+class multiprocessing.BoundedSemaphore([value])
+

A bounded semaphore object: a close analog of threading.BoundedSemaphore.

A solitary difference from its close analog exists: its acquire method’s first argument is named block, as is consistent with Lock.acquire().

Note

On macOS, this is indistinguishable from Semaphore because sem_getvalue() is not implemented on that platform.

+
+class multiprocessing.Condition([lock])
+

A condition variable: an alias for threading.Condition.

If lock is specified then it should be a Lock or RLock object from multiprocessing.

Changed in version 3.3: The wait_for() method was added.

+
+class multiprocessing.Event
+

A clone of threading.Event.

+
+class multiprocessing.Lock
+

A non-recursive lock object: a close analog of threading.Lock. Once a process or thread has acquired a lock, subsequent attempts to acquire it from any process or thread will block until it is released; any process or thread may release it. The concepts and behaviors of threading.Lock as it applies to threads are replicated here in multiprocessing.Lock as it applies to either processes or threads, except as noted.

Note that Lock is actually a factory function which returns an instance of multiprocessing.synchronize.Lock initialized with a default context.

Lock supports the context manager protocol and thus may be used in with statements.

+acquire(block=True, timeout=None)
+

Acquire a lock, blocking or non-blocking.

With the block argument set to True (the default), the method call will block until the lock is in an unlocked state, then set it to locked and return True. Note that the name of this first argument differs from that in threading.Lock.acquire().

With the block argument set to False, the method call does not block. If the lock is currently in a locked state, return False; otherwise set the lock to a locked state and return True.

When invoked with a positive, floating-point value for timeout, block for at most the number of seconds specified by timeout as long as the lock can not be acquired. Invocations with a negative value for timeout are equivalent to a timeout of zero. Invocations with a timeout value of None (the default) set the timeout period to infinite. Note that the treatment of negative or None values for timeout differs from the implemented behavior in threading.Lock.acquire(). The timeout argument has no practical implications if the block argument is set to False and is thus ignored. Returns True if the lock has been acquired or False if the timeout period has elapsed.

+
+release()
+

Release a lock. This can be called from any process or thread, not only the process or thread which originally acquired the lock.

Behavior is the same as in threading.Lock.release() except that when invoked on an unlocked lock, a ValueError is raised.

+
+
+class multiprocessing.RLock
+

A recursive lock object: a close analog of threading.RLock. A recursive lock must be released by the process or thread that acquired it. Once a process or thread has acquired a recursive lock, the same process or thread may acquire it again without blocking; that process or thread must release it once for each time it has been acquired.

Note that RLock is actually a factory function which returns an instance of multiprocessing.synchronize.RLock initialized with a default context.

RLock supports the context manager protocol and thus may be used in with statements.

+acquire(block=True, timeout=None)
+

Acquire a lock, blocking or non-blocking.

When invoked with the block argument set to True, block until the lock is in an unlocked state (not owned by any process or thread) unless the lock is already owned by the current process or thread. The current process or thread then takes ownership of the lock (if it does not already have ownership) and the recursion level inside the lock increments by one, resulting in a return value of True. Note that there are several differences in this first argument’s behavior compared to the implementation of threading.RLock.acquire(), starting with the name of the argument itself.

When invoked with the block argument set to False, do not block. If the lock has already been acquired (and thus is owned) by another process or thread, the current process or thread does not take ownership and the recursion level within the lock is not changed, resulting in a return value of False. If the lock is in an unlocked state, the current process or thread takes ownership and the recursion level is incremented, resulting in a return value of True.

Use and behaviors of the timeout argument are the same as in Lock.acquire(). Note that some of these behaviors of timeout differ from the implemented behaviors in threading.RLock.acquire().

+
+release()
+

Release a lock, decrementing the recursion level. If after the decrement the recursion level is zero, reset the lock to unlocked (not owned by any process or thread) and if any other processes or threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling process or thread.

Only call this method when the calling process or thread owns the lock. An AssertionError is raised if this method is called by a process or thread other than the owner or if the lock is in an unlocked (unowned) state. Note that the type of exception raised in this situation differs from the implemented behavior in threading.RLock.release().

+
+
+class multiprocessing.Semaphore([value])
+

A semaphore object: a close analog of threading.Semaphore.

A solitary difference from its close analog exists: its acquire method’s first argument is named block, as is consistent with Lock.acquire().

+

Note

On macOS, sem_timedwait is unsupported, so calling acquire() with a timeout will emulate that function’s behavior using a sleeping loop.

Note

If the SIGINT signal generated by Ctrl-C arrives while the main thread is blocked by a call to BoundedSemaphore.acquire(), Lock.acquire(), RLock.acquire(), Semaphore.acquire(), Condition.acquire() or Condition.wait() then the call will be immediately interrupted and KeyboardInterrupt will be raised.

This differs from the behaviour of threading where SIGINT will be ignored while the equivalent blocking calls are in progress.

Note

Some of this package’s functionality requires a functioning shared semaphore implementation on the host operating system. Without one, the multiprocessing.synchronize module will be disabled, and attempts to import it will result in an ImportError. See bpo-3770 for additional information.

Shared ctypes Objects

It is possible to create shared objects using shared memory which can be inherited by child processes.

+multiprocessing.Value(typecode_or_type, *args, lock=True)
+

Return a ctypes object allocated from shared memory. By default the return value is actually a synchronized wrapper for the object. The object itself can be accessed via the value attribute of a Value.

typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array module. *args is passed on to the constructor for the type.

If lock is True (the default) then a new recursive lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Operations like += which involve a read and write are not atomic. So if, for instance, you want to atomically increment a shared value it is insufficient to just do

counter.value += 1
+

Assuming the associated lock is recursive (which it is by default) you can instead do

with counter.get_lock():
+    counter.value += 1
+

Note that lock is a keyword-only argument.

+
+multiprocessing.Array(typecode_or_type, size_or_initializer, *, lock=True)
+

Return a ctypes array allocated from shared memory. By default the return value is actually a synchronized wrapper for the array.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer, then it determines the length of the array, and the array will be initially zeroed. Otherwise, size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

If lock is True (the default) then a new lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Note that lock is a keyword only argument.

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings.

+

The multiprocessing.sharedctypes module

The multiprocessing.sharedctypes module provides functions for allocating ctypes objects from shared memory which can be inherited by child processes.

Note

Although it is possible to store a pointer in shared memory remember that this will refer to a location in the address space of a specific process. However, the pointer is quite likely to be invalid in the context of a second process and trying to dereference the pointer from the second process may cause a crash.

+multiprocessing.sharedctypes.RawArray(typecode_or_type, size_or_initializer)
+

Return a ctypes array allocated from shared memory.

typecode_or_type determines the type of the elements of the returned array: it is either a ctypes type or a one character typecode of the kind used by the array module. If size_or_initializer is an integer then it determines the length of the array, and the array will be initially zeroed. Otherwise size_or_initializer is a sequence which is used to initialize the array and whose length determines the length of the array.

Note that setting and getting an element is potentially non-atomic – use Array() instead to make sure that access is automatically synchronized using a lock.

+
+multiprocessing.sharedctypes.RawValue(typecode_or_type, *args)
+

Return a ctypes object allocated from shared memory.

typecode_or_type determines the type of the returned object: it is either a ctypes type or a one character typecode of the kind used by the array module. *args is passed on to the constructor for the type.

Note that setting and getting the value is potentially non-atomic – use Value() instead to make sure that access is automatically synchronized using a lock.

Note that an array of ctypes.c_char has value and raw attributes which allow one to use it to store and retrieve strings – see documentation for ctypes.

+
+multiprocessing.sharedctypes.Array(typecode_or_type, size_or_initializer, *, lock=True)
+

The same as RawArray() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes array.

If lock is True (the default) then a new lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Note that lock is a keyword-only argument.

+
+multiprocessing.sharedctypes.Value(typecode_or_type, *args, lock=True)
+

The same as RawValue() except that depending on the value of lock a process-safe synchronization wrapper may be returned instead of a raw ctypes object.

If lock is True (the default) then a new lock object is created to synchronize access to the value. If lock is a Lock or RLock object then that will be used to synchronize access to the value. If lock is False then access to the returned object will not be automatically protected by a lock, so it will not necessarily be “process-safe”.

Note that lock is a keyword-only argument.

+
+multiprocessing.sharedctypes.copy(obj)
+

Return a ctypes object allocated from shared memory which is a copy of the ctypes object obj.

+
+multiprocessing.sharedctypes.synchronized(obj[, lock])
+

Return a process-safe wrapper object for a ctypes object which uses lock to synchronize access. If lock is None (the default) then a multiprocessing.RLock object is created automatically.

A synchronized wrapper will have two methods in addition to those of the object it wraps: get_obj() returns the wrapped object and get_lock() returns the lock object used for synchronization.

Note that accessing the ctypes object through the wrapper can be a lot slower than accessing the raw ctypes object.

Changed in version 3.5: Synchronized objects support the context manager protocol.

+

The table below compares the syntax for creating shared ctypes objects from shared memory with the normal ctypes syntax. (In the table MyStruct is some subclass of ctypes.Structure.)

+ + + + +

ctypes

sharedctypes using type

sharedctypes using typecode

c_double(2.4)

RawValue(c_double, 2.4)

RawValue(‘d’, 2.4)

MyStruct(4, 6)

RawValue(MyStruct, 4, 6)

(c_short * 7)()

RawArray(c_short, 7)

RawArray(‘h’, 7)

(c_int * 3)(9, 2, 8)

RawArray(c_int, (9, 2, 8))

RawArray(‘i’, (9, 2, 8))

Below is an example where a number of ctypes objects are modified by a child process:

from multiprocessing import Process, Lock
+from multiprocessing.sharedctypes import Value, Array
+from ctypes import Structure, c_double
+
+class Point(Structure):
+    _fields_ = [('x', c_double), ('y', c_double)]
+
+def modify(n, x, s, A):
+    n.value **= 2
+    x.value **= 2
+    s.value = s.value.upper()
+    for a in A:
+        a.x **= 2
+        a.y **= 2
+
+if __name__ == '__main__':
+    lock = Lock()
+
+    n = Value('i', 7)
+    x = Value(c_double, 1.0/3.0, lock=False)
+    s = Array('c', b'hello world', lock=lock)
+    A = Array(Point, [(1.875,-6.25), (-5.75,2.0), (2.375,9.5)], lock=lock)
+
+    p = Process(target=modify, args=(n, x, s, A))
+    p.start()
+    p.join()
+
+    print(n.value)
+    print(x.value)
+    print(s.value)
+    print([(a.x, a.y) for a in A])
+

The results printed are

49
+0.1111111111111111
+HELLO WORLD
+[(3.515625, 39.0625), (33.0625, 4.0), (5.640625, 90.25)]
+

Managers

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

+multiprocessing.Manager()
+

Returns a started SyncManager object which can be used for sharing objects between processes. The returned manager object corresponds to a spawned child process and has methods which will create shared objects and return corresponding proxies.

+

Manager processes will be shutdown as soon as they are garbage collected or their parent process exits. The manager classes are defined in the multiprocessing.managers module:

+class multiprocessing.managers.BaseManager(address=None, authkey=None, serializer='pickle', ctx=None, *, shutdown_timeout=1.0)
+

Create a BaseManager object.

Once created one should call start() or get_server().serve_forever() to ensure that the manager object refers to a started manager process.

address is the address on which the manager process listens for new connections. If address is None then an arbitrary one is chosen.

authkey is the authentication key which will be used to check the validity of incoming connections to the server process. If authkey is None then current_process().authkey is used. Otherwise authkey is used and it must be a byte string.

serializer must be 'pickle' (use pickle serialization) or 'xmlrpclib' (use xmlrpc.client serialization).

ctx is a context object, or None (use the current context). See the get_context() function.

shutdown_timeout is a timeout in seconds used to wait until the process used by the manager completes in the shutdown() method. If the shutdown times out, the process is terminated. If terminating the process also times out, the process is killed.

Changed in version 3.11: Added the shutdown_timeout parameter.

+start([initializer[, initargs]])
+

Start a subprocess to start the manager. If initializer is not None then the subprocess will call initializer(*initargs) when it starts.

+
+get_server()
+

Returns a Server object which represents the actual server under the control of the Manager. The Server object supports the serve_forever() method:

>>> from multiprocessing.managers import BaseManager
+>>> manager = BaseManager(address=('', 50000), authkey=b'abc')
+>>> server = manager.get_server()
+>>> server.serve_forever()
+

Server additionally has an address attribute.

+
+connect()
+

Connect a local manager object to a remote manager process:

>>> from multiprocessing.managers import BaseManager
+>>> m = BaseManager(address=('127.0.0.1', 50000), authkey=b'abc')
+>>> m.connect()
+
+
+shutdown()
+

Stop the process used by the manager. This is only available if start() has been used to start the server process.

This can be called multiple times.

+
+register(typeid[, callable[, proxytype[, exposed[, method_to_typeid[, create_method]]]]])
+

A classmethod which can be used for registering a type or callable with the manager class.

typeid is a “type identifier” which is used to identify a particular type of shared object. This must be a string.

callable is a callable used for creating objects for this type identifier. If a manager instance will be connected to the server using the connect() method, or if the create_method argument is False then this can be left as None.

proxytype is a subclass of BaseProxy which is used to create proxies for shared objects with this typeid. If None then a proxy class is created automatically.

exposed is used to specify a sequence of method names which proxies for this typeid should be allowed to access using BaseProxy._callmethod(). (If exposed is None then proxytype._exposed_ is used instead if it exists.) In the case where no exposed list is specified, all “public methods” of the shared object will be accessible. (Here a “public method” means any attribute which has a __call__() method and whose name does not begin with '_'.)

method_to_typeid is a mapping used to specify the return type of those exposed methods which should return a proxy. It maps method names to typeid strings. (If method_to_typeid is None then proxytype._method_to_typeid_ is used instead if it exists.) If a method’s name is not a key of this mapping or if the mapping is None then the object returned by the method will be copied by value.

create_method determines whether a method should be created with name typeid which can be used to tell the server process to create a new shared object and return a proxy for it. By default it is True.

+

BaseManager instances also have one read-only property:

+address
+

The address used by the manager.

+

Changed in version 3.3: Manager objects support the context management protocol – see Context Manager Types. __enter__() starts the server process (if it has not already started) and then returns the manager object. __exit__() calls shutdown().

In previous versions __enter__() did not start the manager’s server process if it was not already started.

+
+class multiprocessing.managers.SyncManager
+

A subclass of BaseManager which can be used for the synchronization of processes. Objects of this type are returned by multiprocessing.Manager().

Its methods create and return Proxy Objects for a number of commonly used data types to be synchronized across processes. This notably includes shared lists and dictionaries.

+Barrier(parties[, action[, timeout]])
+

Create a shared threading.Barrier object and return a proxy for it.

New in version 3.3.

+
+BoundedSemaphore([value])
+

Create a shared threading.BoundedSemaphore object and return a proxy for it.

+
+Condition([lock])
+

Create a shared threading.Condition object and return a proxy for it.

If lock is supplied then it should be a proxy for a threading.Lock or threading.RLock object.

Changed in version 3.3: The wait_for() method was added.

+
+Event()
+

Create a shared threading.Event object and return a proxy for it.

+
+Lock()
+

Create a shared threading.Lock object and return a proxy for it.

+
+Namespace()
+

Create a shared Namespace object and return a proxy for it.

+
+Queue([maxsize])
+

Create a shared queue.Queue object and return a proxy for it.

+
+RLock()
+

Create a shared threading.RLock object and return a proxy for it.

+
+Semaphore([value])
+

Create a shared threading.Semaphore object and return a proxy for it.

+
+Array(typecode, sequence)
+

Create an array and return a proxy for it.

+
+Value(typecode, value)
+

Create an object with a writable value attribute and return a proxy for it.

+
+dict()
dict(mapping) +
dict(sequence) +
+

Create a shared dict object and return a proxy for it.

+
+list()
list(sequence) +
+

Create a shared list object and return a proxy for it.

+

Changed in version 3.6: Shared objects are capable of being nested. For example, a shared container object such as a shared list can contain other shared objects which will all be managed and synchronized by the SyncManager.

+
+class multiprocessing.managers.Namespace
+

A type that can register with SyncManager.

A namespace object has no public methods, but does have writable attributes. Its representation shows the values of its attributes.

However, when using a proxy for a namespace object, an attribute beginning with '_' will be an attribute of the proxy and not an attribute of the referent:

>>> mp_context = multiprocessing.get_context('spawn')
+>>> manager = mp_context.Manager()
+>>> Global = manager.Namespace()
+>>> Global.x = 10
+>>> Global.y = 'hello'
+>>> Global._z = 12.3    # this is an attribute of the proxy
+>>> print(Global)
+Namespace(x=10, y='hello')
+
+

Customized managers

To create one’s own manager, one creates a subclass of BaseManager and uses the register() classmethod to register new types or callables with the manager class. For example:

from multiprocessing.managers import BaseManager
+
+class MathsClass:
+    def add(self, x, y):
+        return x + y
+    def mul(self, x, y):
+        return x * y
+
+class MyManager(BaseManager):
+    pass
+
+MyManager.register('Maths', MathsClass)
+
+if __name__ == '__main__':
+    with MyManager() as manager:
+        maths = manager.Maths()
+        print(maths.add(4, 3))         # prints 7
+        print(maths.mul(7, 8))         # prints 56
+

Using a remote manager

It is possible to run a manager server on one machine and have clients use it from other machines (assuming that the firewalls involved allow it).

Running the following commands creates a server for a single shared queue which remote clients can access:

>>> from multiprocessing.managers import BaseManager
+>>> from queue import Queue
+>>> queue = Queue()
+>>> class QueueManager(BaseManager): pass
+>>> QueueManager.register('get_queue', callable=lambda:queue)
+>>> m = QueueManager(address=('', 50000), authkey=b'abracadabra')
+>>> s = m.get_server()
+>>> s.serve_forever()
+

One client can access the server as follows:

>>> from multiprocessing.managers import BaseManager
+>>> class QueueManager(BaseManager): pass
+>>> QueueManager.register('get_queue')
+>>> m = QueueManager(address=('foo.bar.org', 50000), authkey=b'abracadabra')
+>>> m.connect()
+>>> queue = m.get_queue()
+>>> queue.put('hello')
+

Another client can also use it:

>>> from multiprocessing.managers import BaseManager
+>>> class QueueManager(BaseManager): pass
+>>> QueueManager.register('get_queue')
+>>> m = QueueManager(address=('foo.bar.org', 50000), authkey=b'abracadabra')
+>>> m.connect()
+>>> queue = m.get_queue()
+>>> queue.get()
+'hello'
+

Local processes can also access that queue, using the code from above on the client to access it remotely:

>>> from multiprocessing import Process, Queue
+>>> from multiprocessing.managers import BaseManager
+>>> class Worker(Process):
+...     def __init__(self, q):
+...         self.q = q
+...         super().__init__()
+...     def run(self):
+...         self.q.put('local hello')
+...
+>>> queue = Queue()
+>>> w = Worker(queue)
+>>> w.start()
+>>> class QueueManager(BaseManager): pass
+...
+>>> QueueManager.register('get_queue', callable=lambda: queue)
+>>> m = QueueManager(address=('', 50000), authkey=b'abracadabra')
+>>> s = m.get_server()
+>>> s.serve_forever()
+

Proxy Objects

A proxy is an object which refers to a shared object which lives (presumably) in a different process. The shared object is said to be the referent of the proxy. Multiple proxy objects may have the same referent.

A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy). In this way, a proxy can be used just like its referent can:

>>> mp_context = multiprocessing.get_context('spawn')
+>>> manager = mp_context.Manager()
+>>> l = manager.list([i*i for i in range(10)])
+>>> print(l)
+[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+>>> print(repr(l))
+<ListProxy object, typeid 'list' at 0x...>
+>>> l[4]
+16
+>>> l[2:5]
+[4, 9, 16]
+

Notice that applying str() to a proxy will return the representation of the referent, whereas applying repr() will return the representation of the proxy.

An important feature of proxy objects is that they are picklable so they can be passed between processes. As such, a referent can contain Proxy Objects. This permits nesting of these managed lists, dicts, and other Proxy Objects:

>>> a = manager.list()
+>>> b = manager.list()
+>>> a.append(b)         # referent of a now contains referent of b
+>>> print(a, b)
+[<ListProxy object, typeid 'list' at ...>] []
+>>> b.append('hello')
+>>> print(a[0], b)
+['hello'] ['hello']
+

Similarly, dict and list proxies may be nested inside one another:

>>> l_outer = manager.list([ manager.dict() for i in range(2) ])
+>>> d_first_inner = l_outer[0]
+>>> d_first_inner['a'] = 1
+>>> d_first_inner['b'] = 2
+>>> l_outer[1]['c'] = 3
+>>> l_outer[1]['z'] = 26
+>>> print(l_outer[0])
+{'a': 1, 'b': 2}
+>>> print(l_outer[1])
+{'c': 3, 'z': 26}
+

If standard (non-proxy) list or dict objects are contained in a referent, modifications to those mutable values will not be propagated through the manager because the proxy has no way of knowing when the values contained within are modified. However, storing a value in a container proxy (which triggers a __setitem__ on the proxy object) does propagate through the manager and so to effectively modify such an item, one could re-assign the modified value to the container proxy:

# create a list proxy and append a mutable object (a dictionary)
+lproxy = manager.list()
+lproxy.append({})
+# now mutate the dictionary
+d = lproxy[0]
+d['a'] = 1
+d['b'] = 2
+# at this point, the changes to d are not yet synced, but by
+# updating the dictionary, the proxy is notified of the change
+lproxy[0] = d
+

This approach is perhaps less convenient than employing nested Proxy Objects for most use cases but also demonstrates a level of control over the synchronization.

Note

The proxy types in multiprocessing do nothing to support comparisons by value. So, for instance, we have:

>>> manager.list([1,2,3]) == [1,2,3]
+False
+

One should just use a copy of the referent instead when making comparisons.

+class multiprocessing.managers.BaseProxy
+

Proxy objects are instances of subclasses of BaseProxy.

+_callmethod(methodname[, args[, kwds]])
+

Call and return the result of a method of the proxy’s referent.

If proxy is a proxy whose referent is obj then the expression

proxy._callmethod(methodname, args, kwds)
+

will evaluate the expression

getattr(obj, methodname)(*args, **kwds)
+

in the manager’s process.

The returned value will be a copy of the result of the call or a proxy to a new shared object – see documentation for the method_to_typeid argument of BaseManager.register().

If an exception is raised by the call, then is re-raised by _callmethod(). If some other exception is raised in the manager’s process then this is converted into a RemoteError exception and is raised by _callmethod().

Note in particular that an exception will be raised if methodname has not been exposed.

An example of the usage of _callmethod():

>>> l = manager.list(range(10))
+>>> l._callmethod('__len__')
+10
+>>> l._callmethod('__getitem__', (slice(2, 7),)) # equivalent to l[2:7]
+[2, 3, 4, 5, 6]
+>>> l._callmethod('__getitem__', (20,))          # equivalent to l[20]
+Traceback (most recent call last):
+...
+IndexError: list index out of range
+
+
+_getvalue()
+

Return a copy of the referent.

If the referent is unpicklable then this will raise an exception.

+
+__repr__()
+

Return a representation of the proxy object.

+
+__str__()
+

Return the representation of the referent.

+
+

Cleanup

A proxy object uses a weakref callback so that when it gets garbage collected it deregisters itself from the manager which owns its referent.

A shared object gets deleted from the manager process when there are no longer any proxies referring to it.

Process Pools

One can create a pool of processes which will carry out tasks submitted to it with the Pool class.

+class multiprocessing.pool.Pool([processes[, initializer[, initargs[, maxtasksperchild[, context]]]]])
+

A process pool object which controls a pool of worker processes to which jobs can be submitted. It supports asynchronous results with timeouts and callbacks and has a parallel map implementation.

processes is the number of worker processes to use. If processes is None then the number returned by os.cpu_count() is used.

If initializer is not None then each worker process will call initializer(*initargs) when it starts.

maxtasksperchild is the number of tasks a worker process can complete before it will exit and be replaced with a fresh worker process, to enable unused resources to be freed. The default maxtasksperchild is None, which means worker processes will live as long as the pool.

context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function multiprocessing.Pool() or the Pool() method of a context object. In both cases context is set appropriately.

Note that the methods of the pool object should only be called by the process which created the pool.

Warning

multiprocessing.pool objects have internal resources that need to be properly managed (like any other resource) by using the pool as a context manager or by calling close() and terminate() manually. Failure to do this can lead to the process hanging on finalization.

Note that it is not correct to rely on the garbage collector to destroy the pool as CPython does not assure that the finalizer of the pool will be called (see object.__del__() for more information).

New in version 3.2: maxtasksperchild

New in version 3.4: context

Note

Worker processes within a Pool typically live for the complete duration of the Pool’s work queue. A frequent pattern found in other systems (such as Apache, mod_wsgi, etc) to free resources held by workers is to allow a worker within a pool to complete only a set amount of work before being exiting, being cleaned up and a new process spawned to replace the old one. The maxtasksperchild argument to the Pool exposes this ability to the end user.

+apply(func[, args[, kwds]])
+

Call func with arguments args and keyword arguments kwds. It blocks until the result is ready. Given this blocks, apply_async() is better suited for performing work in parallel. Additionally, func is only executed in one of the workers of the pool.

+
+apply_async(func[, args[, kwds[, callback[, error_callback]]]])
+

A variant of the apply() method which returns a AsyncResult object.

If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.

If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.

Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.

+
+map(func, iterable[, chunksize])
+

A parallel equivalent of the map() built-in function (it supports only one iterable argument though, for multiple iterables see starmap()). It blocks until the result is ready.

This method chops the iterable into a number of chunks which it submits to the process pool as separate tasks. The (approximate) size of these chunks can be specified by setting chunksize to a positive integer.

Note that it may cause high memory usage for very long iterables. Consider using imap() or imap_unordered() with explicit chunksize option for better efficiency.

+
+map_async(func, iterable[, chunksize[, callback[, error_callback]]])
+

A variant of the map() method which returns a AsyncResult object.

If callback is specified then it should be a callable which accepts a single argument. When the result becomes ready callback is applied to it, that is unless the call failed, in which case the error_callback is applied instead.

If error_callback is specified then it should be a callable which accepts a single argument. If the target function fails, then the error_callback is called with the exception instance.

Callbacks should complete immediately since otherwise the thread which handles the results will get blocked.

+
+imap(func, iterable[, chunksize])
+

A lazier version of map().

The chunksize argument is the same as the one used by the map() method. For very long iterables using a large value for chunksize can make the job complete much faster than using the default value of 1.

Also if chunksize is 1 then the next() method of the iterator returned by the imap() method has an optional timeout parameter: next(timeout) will raise multiprocessing.TimeoutError if the result cannot be returned within timeout seconds.

+
+imap_unordered(func, iterable[, chunksize])
+

The same as imap() except that the ordering of the results from the returned iterator should be considered arbitrary. (Only when there is only one worker process is the order guaranteed to be “correct”.)

+
+starmap(func, iterable[, chunksize])
+

Like map() except that the elements of the iterable are expected to be iterables that are unpacked as arguments.

Hence an iterable of [(1,2), (3, 4)] results in [func(1,2), +func(3,4)].

New in version 3.3.

+
+starmap_async(func, iterable[, chunksize[, callback[, error_callback]]])
+

A combination of starmap() and map_async() that iterates over iterable of iterables and calls func with the iterables unpacked. Returns a result object.

New in version 3.3.

+
+close()
+

Prevents any more tasks from being submitted to the pool. Once all the tasks have been completed the worker processes will exit.

+
+terminate()
+

Stops the worker processes immediately without completing outstanding work. When the pool object is garbage collected terminate() will be called immediately.

+
+join()
+

Wait for the worker processes to exit. One must call close() or terminate() before using join().

+

New in version 3.3: Pool objects now support the context management protocol – see Context Manager Types. __enter__() returns the pool object, and __exit__() calls terminate().

+
+class multiprocessing.pool.AsyncResult
+

The class of the result returned by Pool.apply_async() and Pool.map_async().

+get([timeout])
+

Return the result when it arrives. If timeout is not None and the result does not arrive within timeout seconds then multiprocessing.TimeoutError is raised. If the remote call raised an exception then that exception will be reraised by get().

+
+wait([timeout])
+

Wait until the result is available or until timeout seconds pass.

+
+ready()
+

Return whether the call has completed.

+
+successful()
+

Return whether the call completed without raising an exception. Will raise ValueError if the result is not ready.

Changed in version 3.7: If the result is not ready, ValueError is raised instead of AssertionError.

+
+

The following example demonstrates the use of a pool:

from multiprocessing import Pool
+import time
+
+def f(x):
+    return x*x
+
+if __name__ == '__main__':
+    with Pool(processes=4) as pool:         # start 4 worker processes
+        result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously in a single process
+        print(result.get(timeout=1))        # prints "100" unless your computer is *very* slow
+
+        print(pool.map(f, range(10)))       # prints "[0, 1, 4,..., 81]"
+
+        it = pool.imap(f, range(10))
+        print(next(it))                     # prints "0"
+        print(next(it))                     # prints "1"
+        print(it.next(timeout=1))           # prints "4" unless your computer is *very* slow
+
+        result = pool.apply_async(time.sleep, (10,))
+        print(result.get(timeout=1))        # raises multiprocessing.TimeoutError
+

Listeners and Clients

Usually message passing between processes is done using queues or by using Connection objects returned by Pipe().

However, the multiprocessing.connection module allows some extra flexibility. It basically gives a high level message oriented API for dealing with sockets or Windows named pipes. It also has support for digest authentication using the hmac module, and for polling multiple connections at the same time.

+multiprocessing.connection.deliver_challenge(connection, authkey)
+

Send a randomly generated message to the other end of the connection and wait for a reply.

If the reply matches the digest of the message using authkey as the key then a welcome message is sent to the other end of the connection. Otherwise AuthenticationError is raised.

+
+multiprocessing.connection.answer_challenge(connection, authkey)
+

Receive a message, calculate the digest of the message using authkey as the key, and then send the digest back.

If a welcome message is not received, then AuthenticationError is raised.

+
+multiprocessing.connection.Client(address[, family[, authkey]])
+

Attempt to set up a connection to the listener which is using address address, returning a Connection.

The type of the connection is determined by family argument, but this can generally be omitted since it can usually be inferred from the format of address. (See Address Formats)

If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None. AuthenticationError is raised if authentication fails. See Authentication keys.

+
+class multiprocessing.connection.Listener([address[, family[, backlog[, authkey]]]])
+

A wrapper for a bound socket or Windows named pipe which is ‘listening’ for connections.

address is the address to be used by the bound socket or named pipe of the listener object.

Note

If an address of ‘0.0.0.0’ is used, the address will not be a connectable end point on Windows. If you require a connectable end-point, you should use ‘127.0.0.1’.

family is the type of socket (or named pipe) to use. This can be one of the strings 'AF_INET' (for a TCP socket), 'AF_UNIX' (for a Unix domain socket) or 'AF_PIPE' (for a Windows named pipe). Of these only the first is guaranteed to be available. If family is None then the family is inferred from the format of address. If address is also None then a default is chosen. This default is the family which is assumed to be the fastest available. See Address Formats. Note that if family is 'AF_UNIX' and address is None then the socket will be created in a private temporary directory created using tempfile.mkstemp().

If the listener object uses a socket then backlog (1 by default) is passed to the listen() method of the socket once it has been bound.

If authkey is given and not None, it should be a byte string and will be used as the secret key for an HMAC-based authentication challenge. No authentication is done if authkey is None. AuthenticationError is raised if authentication fails. See Authentication keys.

+accept()
+

Accept a connection on the bound socket or named pipe of the listener object and return a Connection object. If authentication is attempted and fails, then AuthenticationError is raised.

+
+close()
+

Close the bound socket or named pipe of the listener object. This is called automatically when the listener is garbage collected. However it is advisable to call it explicitly.

+

Listener objects have the following read-only properties:

+address
+

The address which is being used by the Listener object.

+
+last_accepted
+

The address from which the last accepted connection came. If this is unavailable then it is None.

+

New in version 3.3: Listener objects now support the context management protocol – see Context Manager Types. __enter__() returns the listener object, and __exit__() calls close().

+
+multiprocessing.connection.wait(object_list, timeout=None)
+

Wait till an object in object_list is ready. Returns the list of those objects in object_list which are ready. If timeout is a float then the call blocks for at most that many seconds. If timeout is None then it will block for an unlimited period. A negative timeout is equivalent to a zero timeout.

For both POSIX and Windows, an object can appear in object_list if it is

A connection or socket object is ready when there is data available to be read from it, or the other end has been closed.

POSIX: wait(object_list, timeout) almost equivalent select.select(object_list, [], [], timeout). The difference is that, if select.select() is interrupted by a signal, it can raise OSError with an error number of EINTR, whereas wait() will not.

Windows: An item in object_list must either be an integer handle which is waitable (according to the definition used by the documentation of the Win32 function WaitForMultipleObjects()) or it can be an object with a fileno() method which returns a socket handle or pipe handle. (Note that pipe handles and socket handles are not waitable handles.)

New in version 3.3.

+

Examples

The following server code creates a listener which uses 'secret password' as an authentication key. It then waits for a connection and sends some data to the client:

from multiprocessing.connection import Listener
+from array import array
+
+address = ('localhost', 6000)     # family is deduced to be 'AF_INET'
+
+with Listener(address, authkey=b'secret password') as listener:
+    with listener.accept() as conn:
+        print('connection accepted from', listener.last_accepted)
+
+        conn.send([2.25, None, 'junk', float])
+
+        conn.send_bytes(b'hello')
+
+        conn.send_bytes(array('i', [42, 1729]))
+

The following code connects to the server and receives some data from the server:

from multiprocessing.connection import Client
+from array import array
+
+address = ('localhost', 6000)
+
+with Client(address, authkey=b'secret password') as conn:
+    print(conn.recv())                  # => [2.25, None, 'junk', float]
+
+    print(conn.recv_bytes())            # => 'hello'
+
+    arr = array('i', [0, 0, 0, 0, 0])
+    print(conn.recv_bytes_into(arr))    # => 8
+    print(arr)                          # => array('i', [42, 1729, 0, 0, 0])
+

The following code uses wait() to wait for messages from multiple processes at once:

import time, random
+from multiprocessing import Process, Pipe, current_process
+from multiprocessing.connection import wait
+
+def foo(w):
+    for i in range(10):
+        w.send((i, current_process().name))
+    w.close()
+
+if __name__ == '__main__':
+    readers = []
+
+    for i in range(4):
+        r, w = Pipe(duplex=False)
+        readers.append(r)
+        p = Process(target=foo, args=(w,))
+        p.start()
+        # We close the writable end of the pipe now to be sure that
+        # p is the only process which owns a handle for it.  This
+        # ensures that when p closes its handle for the writable end,
+        # wait() will promptly report the readable end as being ready.
+        w.close()
+
+    while readers:
+        for r in wait(readers):
+            try:
+                msg = r.recv()
+            except EOFError:
+                readers.remove(r)
+            else:
+                print(msg)
+

Address Formats

  • An 'AF_INET' address is a tuple of the form (hostname, port) where hostname is a string and port is an integer.
  • An 'AF_UNIX' address is a string representing a filename on the filesystem.
  • An 'AF_PIPE' address is a string of the form r'\\.\pipe\PipeName'. To use Client() to connect to a named pipe on a remote computer called ServerName one should use an address of the form r'\\ServerName\pipe\PipeName' instead.

Note that any string beginning with two backslashes is assumed by default to be an 'AF_PIPE' address rather than an 'AF_UNIX' address.

Authentication keys

When one uses Connection.recv, the data received is automatically unpickled. Unfortunately unpickling data from an untrusted source is a security risk. Therefore Listener and Client() use the hmac module to provide digest authentication.

An authentication key is a byte string which can be thought of as a password: once a connection is established both ends will demand proof that the other knows the authentication key. (Demonstrating that both ends are using the same key does not involve sending the key over the connection.)

If authentication is requested but no authentication key is specified then the return value of current_process().authkey is used (see Process). This value will be automatically inherited by any Process object that the current process creates. This means that (by default) all processes of a multi-process program will share a single authentication key which can be used when setting up connections between themselves.

Suitable authentication keys can also be generated by using os.urandom().

Logging

Some support for logging is available. Note, however, that the logging package does not use process shared locks so it is possible (depending on the handler type) for messages from different processes to get mixed up.

+multiprocessing.get_logger()
+

Returns the logger used by multiprocessing. If necessary, a new one will be created.

When first created the logger has level logging.NOTSET and no default handler. Messages sent to this logger will not by default propagate to the root logger.

Note that on Windows child processes will only inherit the level of the parent process’s logger – any other customization of the logger will not be inherited.

+
+multiprocessing.log_to_stderr(level=None)
+

This function performs a call to get_logger() but in addition to returning the logger created by get_logger, it adds a handler which sends output to sys.stderr using format '[%(levelname)s/%(processName)s] %(message)s'. You can modify levelname of the logger by passing a level argument.

+

Below is an example session with logging turned on:

>>> import multiprocessing, logging
+>>> logger = multiprocessing.log_to_stderr()
+>>> logger.setLevel(logging.INFO)
+>>> logger.warning('doomed')
+[WARNING/MainProcess] doomed
+>>> m = multiprocessing.Manager()
+[INFO/SyncManager-...] child process calling self.run()
+[INFO/SyncManager-...] created temp directory /.../pymp-...
+[INFO/SyncManager-...] manager serving at '/.../listener-...'
+>>> del m
+[INFO/MainProcess] sending shutdown message to manager
+[INFO/SyncManager-...] manager exiting with exitcode 0
+

For a full table of logging levels, see the logging module.

The multiprocessing.dummy module

multiprocessing.dummy replicates the API of multiprocessing but is no more than a wrapper around the threading module.

In particular, the Pool function provided by multiprocessing.dummy returns an instance of ThreadPool, which is a subclass of Pool that supports all the same method calls but uses a pool of worker threads rather than worker processes.

+class multiprocessing.pool.ThreadPool([processes[, initializer[, initargs]]])
+

A thread pool object which controls a pool of worker threads to which jobs can be submitted. ThreadPool instances are fully interface compatible with Pool instances, and their resources must also be properly managed, either by using the pool as a context manager or by calling close() and terminate() manually.

processes is the number of worker threads to use. If processes is None then the number returned by os.cpu_count() is used.

If initializer is not None then each worker process will call initializer(*initargs) when it starts.

Unlike Pool, maxtasksperchild and context cannot be provided.

Note

A ThreadPool shares the same interface as Pool, which is designed around a pool of processes and predates the introduction of the concurrent.futures module. As such, it inherits some operations that don’t make sense for a pool backed by threads, and it has its own type for representing the status of asynchronous jobs, AsyncResult, that is not understood by any other libraries.

Users should generally prefer to use concurrent.futures.ThreadPoolExecutor, which has a simpler interface that was designed around threads from the start, and which returns concurrent.futures.Future instances that are compatible with many other libraries, including asyncio.

+

Programming guidelines

There are certain guidelines and idioms which should be adhered to when using multiprocessing.

All start methods

The following applies to all start methods.

Avoid shared state

As far as possible one should try to avoid shifting large amounts of data between processes.

It is probably best to stick to using queues or pipes for communication between processes rather than using the lower level synchronization primitives.

Picklability

Ensure that the arguments to the methods of proxies are picklable.

Thread safety of proxies

Do not use a proxy object from more than one thread unless you protect it with a lock.

(There is never a problem with different processes using the same proxy.)

Joining zombie processes

On POSIX when a process finishes but has not been joined it becomes a zombie. There should never be very many because each time a new process starts (or active_children() is called) all completed processes which have not yet been joined will be joined. Also calling a finished process’s Process.is_alive will join the process. Even so it is probably good practice to explicitly join all the processes that you start.

Better to inherit than pickle/unpickle

When using the spawn or forkserver start methods many types from multiprocessing need to be picklable so that child processes can use them. However, one should generally avoid sending shared objects to other processes using pipes or queues. Instead you should arrange the program so that a process which needs access to a shared resource created elsewhere can inherit it from an ancestor process.

Avoid terminating processes

Using the Process.terminate method to stop a process is liable to cause any shared resources (such as locks, semaphores, pipes and queues) currently being used by the process to become broken or unavailable to other processes.

Therefore it is probably best to only consider using Process.terminate on processes which never use any shared resources.

Joining processes that use queues

Bear in mind that a process that has put items in a queue will wait before terminating until all the buffered items are fed by the “feeder” thread to the underlying pipe. (The child process can call the Queue.cancel_join_thread method of the queue to avoid this behaviour.)

This means that whenever you use a queue you need to make sure that all items which have been put on the queue will eventually be removed before the process is joined. Otherwise you cannot be sure that processes which have put items on the queue will terminate. Remember also that non-daemonic processes will be joined automatically.

An example which will deadlock is the following:

from multiprocessing import Process, Queue
+
+def f(q):
+    q.put('X' * 1000000)
+
+if __name__ == '__main__':
+    queue = Queue()
+    p = Process(target=f, args=(queue,))
+    p.start()
+    p.join()                    # this deadlocks
+    obj = queue.get()
+

A fix here would be to swap the last two lines (or simply remove the p.join() line).

Explicitly pass resources to child processes

On POSIX using the fork start method, a child process can make use of a shared resource created in a parent process using a global resource. However, it is better to pass the object as an argument to the constructor for the child process.

Apart from making the code (potentially) compatible with Windows and the other start methods this also ensures that as long as the child process is still alive the object will not be garbage collected in the parent process. This might be important if some resource is freed when the object is garbage collected in the parent process.

So for instance

from multiprocessing import Process, Lock
+
+def f():
+    ... do something using "lock" ...
+
+if __name__ == '__main__':
+    lock = Lock()
+    for i in range(10):
+        Process(target=f).start()
+

should be rewritten as

from multiprocessing import Process, Lock
+
+def f(l):
+    ... do something using "l" ...
+
+if __name__ == '__main__':
+    lock = Lock()
+    for i in range(10):
+        Process(target=f, args=(lock,)).start()
+

Beware of replacing sys.stdin with a “file like object”

multiprocessing originally unconditionally called:

os.close(sys.stdin.fileno())
+

in the multiprocessing.Process._bootstrap() method — this resulted in issues with processes-in-processes. This has been changed to:

sys.stdin.close()
+sys.stdin = open(os.open(os.devnull, os.O_RDONLY), closefd=False)
+

Which solves the fundamental issue of processes colliding with each other resulting in a bad file descriptor error, but introduces a potential danger to applications which replace sys.stdin() with a “file-like object” with output buffering. This danger is that if multiple processes call close() on this file-like object, it could result in the same data being flushed to the object multiple times, resulting in corruption.

If you write a file-like object and implement your own caching, you can make it fork-safe by storing the pid whenever you append to the cache, and discarding the cache when the pid changes. For example:

@property
+def cache(self):
+    pid = os.getpid()
+    if pid != self._pid:
+        self._pid = pid
+        self._cache = []
+    return self._cache
+

For more information, see bpo-5155, bpo-5313 and bpo-5331

The spawn and forkserver start methods

There are a few extra restriction which don’t apply to the fork start method.

More picklability

Ensure that all arguments to Process.__init__() are picklable. Also, if you subclass Process then make sure that instances will be picklable when the Process.start method is called.

Global variables

Bear in mind that if code run in a child process tries to access a global variable, then the value it sees (if any) may not be the same as the value in the parent process at the time that Process.start was called.

However, global variables which are just module level constants cause no problems.

Safe importing of main module

Make sure that the main module can be safely imported by a new Python interpreter without causing unintended side effects (such as starting a new process).

For example, using the spawn or forkserver start method running the following module would fail with a RuntimeError:

from multiprocessing import Process
+
+def foo():
+    print('hello')
+
+p = Process(target=foo)
+p.start()
+

Instead one should protect the “entry point” of the program by using if +__name__ == '__main__': as follows:

from multiprocessing import Process, freeze_support, set_start_method
+
+def foo():
+    print('hello')
+
+if __name__ == '__main__':
+    freeze_support()
+    set_start_method('spawn')
+    p = Process(target=foo)
+    p.start()
+

(The freeze_support() line can be omitted if the program will be run normally instead of frozen.)

This allows the newly spawned Python interpreter to safely import the module and then run the module’s foo() function.

Similar restrictions apply if a pool or manager is created in the main module.

Examples

Demonstration of how to create and use customized managers and proxies:

from multiprocessing import freeze_support
+from multiprocessing.managers import BaseManager, BaseProxy
+import operator
+
+##
+
+class Foo:
+    def f(self):
+        print('you called Foo.f()')
+    def g(self):
+        print('you called Foo.g()')
+    def _h(self):
+        print('you called Foo._h()')
+
+# A simple generator function
+def baz():
+    for i in range(10):
+        yield i*i
+
+# Proxy type for generator objects
+class GeneratorProxy(BaseProxy):
+    _exposed_ = ['__next__']
+    def __iter__(self):
+        return self
+    def __next__(self):
+        return self._callmethod('__next__')
+
+# Function to return the operator module
+def get_operator_module():
+    return operator
+
+##
+
+class MyManager(BaseManager):
+    pass
+
+# register the Foo class; make `f()` and `g()` accessible via proxy
+MyManager.register('Foo1', Foo)
+
+# register the Foo class; make `g()` and `_h()` accessible via proxy
+MyManager.register('Foo2', Foo, exposed=('g', '_h'))
+
+# register the generator function baz; use `GeneratorProxy` to make proxies
+MyManager.register('baz', baz, proxytype=GeneratorProxy)
+
+# register get_operator_module(); make public functions accessible via proxy
+MyManager.register('operator', get_operator_module)
+
+##
+
+def test():
+    manager = MyManager()
+    manager.start()
+
+    print('-' * 20)
+
+    f1 = manager.Foo1()
+    f1.f()
+    f1.g()
+    assert not hasattr(f1, '_h')
+    assert sorted(f1._exposed_) == sorted(['f', 'g'])
+
+    print('-' * 20)
+
+    f2 = manager.Foo2()
+    f2.g()
+    f2._h()
+    assert not hasattr(f2, 'f')
+    assert sorted(f2._exposed_) == sorted(['g', '_h'])
+
+    print('-' * 20)
+
+    it = manager.baz()
+    for i in it:
+        print('<%d>' % i, end=' ')
+    print()
+
+    print('-' * 20)
+
+    op = manager.operator()
+    print('op.add(23, 45) =', op.add(23, 45))
+    print('op.pow(2, 94) =', op.pow(2, 94))
+    print('op._exposed_ =', op._exposed_)
+
+##
+
+if __name__ == '__main__':
+    freeze_support()
+    test()
+

Using Pool:

import multiprocessing
+import time
+import random
+import sys
+
+#
+# Functions used by test code
+#
+
+def calculate(func, args):
+    result = func(*args)
+    return '%s says that %s%s = %s' % (
+        multiprocessing.current_process().name,
+        func.__name__, args, result
+        )
+
+def calculatestar(args):
+    return calculate(*args)
+
+def mul(a, b):
+    time.sleep(0.5 * random.random())
+    return a * b
+
+def plus(a, b):
+    time.sleep(0.5 * random.random())
+    return a + b
+
+def f(x):
+    return 1.0 / (x - 5.0)
+
+def pow3(x):
+    return x ** 3
+
+def noop(x):
+    pass
+
+#
+# Test code
+#
+
+def test():
+    PROCESSES = 4
+    print('Creating pool with %d processes\n' % PROCESSES)
+
+    with multiprocessing.Pool(PROCESSES) as pool:
+        #
+        # Tests
+        #
+
+        TASKS = [(mul, (i, 7)) for i in range(10)] + \
+                [(plus, (i, 8)) for i in range(10)]
+
+        results = [pool.apply_async(calculate, t) for t in TASKS]
+        imap_it = pool.imap(calculatestar, TASKS)
+        imap_unordered_it = pool.imap_unordered(calculatestar, TASKS)
+
+        print('Ordered results using pool.apply_async():')
+        for r in results:
+            print('\t', r.get())
+        print()
+
+        print('Ordered results using pool.imap():')
+        for x in imap_it:
+            print('\t', x)
+        print()
+
+        print('Unordered results using pool.imap_unordered():')
+        for x in imap_unordered_it:
+            print('\t', x)
+        print()
+
+        print('Ordered results using pool.map() --- will block till complete:')
+        for x in pool.map(calculatestar, TASKS):
+            print('\t', x)
+        print()
+
+        #
+        # Test error handling
+        #
+
+        print('Testing error handling:')
+
+        try:
+            print(pool.apply(f, (5,)))
+        except ZeroDivisionError:
+            print('\tGot ZeroDivisionError as expected from pool.apply()')
+        else:
+            raise AssertionError('expected ZeroDivisionError')
+
+        try:
+            print(pool.map(f, list(range(10))))
+        except ZeroDivisionError:
+            print('\tGot ZeroDivisionError as expected from pool.map()')
+        else:
+            raise AssertionError('expected ZeroDivisionError')
+
+        try:
+            print(list(pool.imap(f, list(range(10)))))
+        except ZeroDivisionError:
+            print('\tGot ZeroDivisionError as expected from list(pool.imap())')
+        else:
+            raise AssertionError('expected ZeroDivisionError')
+
+        it = pool.imap(f, list(range(10)))
+        for i in range(10):
+            try:
+                x = next(it)
+            except ZeroDivisionError:
+                if i == 5:
+                    pass
+            except StopIteration:
+                break
+            else:
+                if i == 5:
+                    raise AssertionError('expected ZeroDivisionError')
+
+        assert i == 9
+        print('\tGot ZeroDivisionError as expected from IMapIterator.next()')
+        print()
+
+        #
+        # Testing timeouts
+        #
+
+        print('Testing ApplyResult.get() with timeout:', end=' ')
+        res = pool.apply_async(calculate, TASKS[0])
+        while 1:
+            sys.stdout.flush()
+            try:
+                sys.stdout.write('\n\t%s' % res.get(0.02))
+                break
+            except multiprocessing.TimeoutError:
+                sys.stdout.write('.')
+        print()
+        print()
+
+        print('Testing IMapIterator.next() with timeout:', end=' ')
+        it = pool.imap(calculatestar, TASKS)
+        while 1:
+            sys.stdout.flush()
+            try:
+                sys.stdout.write('\n\t%s' % it.next(0.02))
+            except StopIteration:
+                break
+            except multiprocessing.TimeoutError:
+                sys.stdout.write('.')
+        print()
+        print()
+
+
+if __name__ == '__main__':
+    multiprocessing.freeze_support()
+    test()
+

An example showing how to use queues to feed tasks to a collection of worker processes and collect the results:

import time
+import random
+
+from multiprocessing import Process, Queue, current_process, freeze_support
+
+#
+# Function run by worker processes
+#
+
+def worker(input, output):
+    for func, args in iter(input.get, 'STOP'):
+        result = calculate(func, args)
+        output.put(result)
+
+#
+# Function used to calculate result
+#
+
+def calculate(func, args):
+    result = func(*args)
+    return '%s says that %s%s = %s' % \
+        (current_process().name, func.__name__, args, result)
+
+#
+# Functions referenced by tasks
+#
+
+def mul(a, b):
+    time.sleep(0.5*random.random())
+    return a * b
+
+def plus(a, b):
+    time.sleep(0.5*random.random())
+    return a + b
+
+#
+#
+#
+
+def test():
+    NUMBER_OF_PROCESSES = 4
+    TASKS1 = [(mul, (i, 7)) for i in range(20)]
+    TASKS2 = [(plus, (i, 8)) for i in range(10)]
+
+    # Create queues
+    task_queue = Queue()
+    done_queue = Queue()
+
+    # Submit tasks
+    for task in TASKS1:
+        task_queue.put(task)
+
+    # Start worker processes
+    for i in range(NUMBER_OF_PROCESSES):
+        Process(target=worker, args=(task_queue, done_queue)).start()
+
+    # Get and print results
+    print('Unordered results:')
+    for i in range(len(TASKS1)):
+        print('\t', done_queue.get())
+
+    # Add more tasks using `put()`
+    for task in TASKS2:
+        task_queue.put(task)
+
+    # Get and print some more results
+    for i in range(len(TASKS2)):
+        print('\t', done_queue.get())
+
+    # Tell child processes to stop
+    for i in range(NUMBER_OF_PROCESSES):
+        task_queue.put('STOP')
+
+
+if __name__ == '__main__':
+    freeze_support()
+    test()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/multiprocessing.html +

+
diff --git a/devdocs/python~3.12/library%2Fmultiprocessing.shared_memory.html b/devdocs/python~3.12/library%2Fmultiprocessing.shared_memory.html new file mode 100644 index 00000000..b2b87845 --- /dev/null +++ b/devdocs/python~3.12/library%2Fmultiprocessing.shared_memory.html @@ -0,0 +1,199 @@ +

multiprocessing.shared_memory — Shared memory for direct access across processes

Source code: Lib/multiprocessing/shared_memory.py

New in version 3.8.

This module provides a class, SharedMemory, for the allocation and management of shared memory to be accessed by one or more processes on a multicore or symmetric multiprocessor (SMP) machine. To assist with the life-cycle management of shared memory especially across distinct processes, a BaseManager subclass, SharedMemoryManager, is also provided in the multiprocessing.managers module.

In this module, shared memory refers to “System V style” shared memory blocks (though is not necessarily implemented explicitly as such) and does not refer to “distributed shared memory”. This style of shared memory permits distinct processes to potentially read and write to a common (or shared) region of volatile memory. Processes are conventionally limited to only have access to their own process memory space but shared memory permits the sharing of data between processes, avoiding the need to instead send messages between processes containing that data. Sharing data directly via memory can provide significant performance benefits compared to sharing data via disk or socket or other communications requiring the serialization/deserialization and copying of data.

+class multiprocessing.shared_memory.SharedMemory(name=None, create=False, size=0)
+

Creates a new shared memory block or attaches to an existing shared memory block. Each shared memory block is assigned a unique name. In this way, one process can create a shared memory block with a particular name and a different process can attach to that same shared memory block using that same name.

As a resource for sharing data across processes, shared memory blocks may outlive the original process that created them. When one process no longer needs access to a shared memory block that might still be needed by other processes, the close() method should be called. When a shared memory block is no longer needed by any process, the unlink() method should be called to ensure proper cleanup.

name is the unique name for the requested shared memory, specified as a string. When creating a new shared memory block, if None (the default) is supplied for the name, a novel name will be generated.

create controls whether a new shared memory block is created (True) or an existing shared memory block is attached (False).

size specifies the requested number of bytes when creating a new shared memory block. Because some platforms choose to allocate chunks of memory based upon that platform’s memory page size, the exact size of the shared memory block may be larger or equal to the size requested. When attaching to an existing shared memory block, the size parameter is ignored.

+close()
+

Closes access to the shared memory from this instance. In order to ensure proper cleanup of resources, all instances should call close() once the instance is no longer needed. Note that calling close() does not cause the shared memory block itself to be destroyed.

+
+

Requests that the underlying shared memory block be destroyed. In order to ensure proper cleanup of resources, unlink() should be called once (and only once) across all processes which have need for the shared memory block. After requesting its destruction, a shared memory block may or may not be immediately destroyed and this behavior may differ across platforms. Attempts to access data inside the shared memory block after unlink() has been called may result in memory access errors. Note: the last process relinquishing its hold on a shared memory block may call unlink() and close() in either order.

+
+buf
+

A memoryview of contents of the shared memory block.

+
+name
+

Read-only access to the unique name of the shared memory block.

+
+size
+

Read-only access to size in bytes of the shared memory block.

+
+

The following example demonstrates low-level use of SharedMemory instances:

>>> from multiprocessing import shared_memory
+>>> shm_a = shared_memory.SharedMemory(create=True, size=10)
+>>> type(shm_a.buf)
+<class 'memoryview'>
+>>> buffer = shm_a.buf
+>>> len(buffer)
+10
+>>> buffer[:4] = bytearray([22, 33, 44, 55])  # Modify multiple at once
+>>> buffer[4] = 100                           # Modify single byte at a time
+>>> # Attach to an existing shared memory block
+>>> shm_b = shared_memory.SharedMemory(shm_a.name)
+>>> import array
+>>> array.array('b', shm_b.buf[:5])  # Copy the data into a new array.array
+array('b', [22, 33, 44, 55, 100])
+>>> shm_b.buf[:5] = b'howdy'  # Modify via shm_b using bytes
+>>> bytes(shm_a.buf[:5])      # Access via shm_a
+b'howdy'
+>>> shm_b.close()   # Close each SharedMemory instance
+>>> shm_a.close()
+>>> shm_a.unlink()  # Call unlink only once to release the shared memory
+

The following example demonstrates a practical use of the SharedMemory class with NumPy arrays, accessing the same numpy.ndarray from two distinct Python shells:

>>> # In the first Python interactive shell
+>>> import numpy as np
+>>> a = np.array([1, 1, 2, 3, 5, 8])  # Start with an existing NumPy array
+>>> from multiprocessing import shared_memory
+>>> shm = shared_memory.SharedMemory(create=True, size=a.nbytes)
+>>> # Now create a NumPy array backed by shared memory
+>>> b = np.ndarray(a.shape, dtype=a.dtype, buffer=shm.buf)
+>>> b[:] = a[:]  # Copy the original data into shared memory
+>>> b
+array([1, 1, 2, 3, 5, 8])
+>>> type(b)
+<class 'numpy.ndarray'>
+>>> type(a)
+<class 'numpy.ndarray'>
+>>> shm.name  # We did not specify a name so one was chosen for us
+'psm_21467_46075'
+
+>>> # In either the same shell or a new Python shell on the same machine
+>>> import numpy as np
+>>> from multiprocessing import shared_memory
+>>> # Attach to the existing shared memory block
+>>> existing_shm = shared_memory.SharedMemory(name='psm_21467_46075')
+>>> # Note that a.shape is (6,) and a.dtype is np.int64 in this example
+>>> c = np.ndarray((6,), dtype=np.int64, buffer=existing_shm.buf)
+>>> c
+array([1, 1, 2, 3, 5, 8])
+>>> c[-1] = 888
+>>> c
+array([  1,   1,   2,   3,   5, 888])
+
+>>> # Back in the first Python interactive shell, b reflects this change
+>>> b
+array([  1,   1,   2,   3,   5, 888])
+
+>>> # Clean up from within the second Python shell
+>>> del c  # Unnecessary; merely emphasizing the array is no longer used
+>>> existing_shm.close()
+
+>>> # Clean up from within the first Python shell
+>>> del b  # Unnecessary; merely emphasizing the array is no longer used
+>>> shm.close()
+>>> shm.unlink()  # Free and release the shared memory block at the very end
+
+class multiprocessing.managers.SharedMemoryManager([address[, authkey]])
+

A subclass of BaseManager which can be used for the management of shared memory blocks across processes.

A call to start() on a SharedMemoryManager instance causes a new process to be started. This new process’s sole purpose is to manage the life cycle of all shared memory blocks created through it. To trigger the release of all shared memory blocks managed by that process, call shutdown() on the instance. This triggers a SharedMemory.unlink() call on all of the SharedMemory objects managed by that process and then stops the process itself. By creating SharedMemory instances through a SharedMemoryManager, we avoid the need to manually track and trigger the freeing of shared memory resources.

This class provides methods for creating and returning SharedMemory instances and for creating a list-like object (ShareableList) backed by shared memory.

Refer to multiprocessing.managers.BaseManager for a description of the inherited address and authkey optional input arguments and how they may be used to connect to an existing SharedMemoryManager service from other processes.

+SharedMemory(size)
+

Create and return a new SharedMemory object with the specified size in bytes.

+
+ShareableList(sequence)
+

Create and return a new ShareableList object, initialized by the values from the input sequence.

+
+

The following example demonstrates the basic mechanisms of a SharedMemoryManager:

>>> from multiprocessing.managers import SharedMemoryManager
+>>> smm = SharedMemoryManager()
+>>> smm.start()  # Start the process that manages the shared memory blocks
+>>> sl = smm.ShareableList(range(4))
+>>> sl
+ShareableList([0, 1, 2, 3], name='psm_6572_7512')
+>>> raw_shm = smm.SharedMemory(size=128)
+>>> another_sl = smm.ShareableList('alpha')
+>>> another_sl
+ShareableList(['a', 'l', 'p', 'h', 'a'], name='psm_6572_12221')
+>>> smm.shutdown()  # Calls unlink() on sl, raw_shm, and another_sl
+

The following example depicts a potentially more convenient pattern for using SharedMemoryManager objects via the with statement to ensure that all shared memory blocks are released after they are no longer needed:

>>> with SharedMemoryManager() as smm:
+...     sl = smm.ShareableList(range(2000))
+...     # Divide the work among two processes, storing partial results in sl
+...     p1 = Process(target=do_work, args=(sl, 0, 1000))
+...     p2 = Process(target=do_work, args=(sl, 1000, 2000))
+...     p1.start()
+...     p2.start()  # A multiprocessing.Pool might be more efficient
+...     p1.join()
+...     p2.join()   # Wait for all work to complete in both processes
+...     total_result = sum(sl)  # Consolidate the partial results now in sl
+

When using a SharedMemoryManager in a with statement, the shared memory blocks created using that manager are all released when the with statement’s code block finishes execution.

+class multiprocessing.shared_memory.ShareableList(sequence=None, \*, name=None)
+

Provides a mutable list-like object where all values stored within are stored in a shared memory block. This constrains storable values to only the int (signed 64-bit), float, bool, str (less than 10M bytes each when encoded as utf-8), bytes (less than 10M bytes each), and None built-in data types. It also notably differs from the built-in list type in that these lists can not change their overall length (i.e. no append, insert, etc.) and do not support the dynamic creation of new ShareableList instances via slicing.

sequence is used in populating a new ShareableList full of values. Set to None to instead attach to an already existing ShareableList by its unique shared memory name.

name is the unique name for the requested shared memory, as described in the definition for SharedMemory. When attaching to an existing ShareableList, specify its shared memory block’s unique name while leaving sequence set to None.

Note

A known issue exists for bytes and str values. If they end with \x00 nul bytes or characters, those may be silently stripped when fetching them by index from the ShareableList. This .rstrip(b'\x00') behavior is considered a bug and may go away in the future. See gh-106939.

For applications where rstripping of trailing nulls is a problem, work around it by always unconditionally appending an extra non-0 byte to the end of such values when storing and unconditionally removing it when fetching:

>>> from multiprocessing import shared_memory
+>>> nul_bug_demo = shared_memory.ShareableList(['?\x00', b'\x03\x02\x01\x00\x00\x00'])
+>>> nul_bug_demo[0]
+'?'
+>>> nul_bug_demo[1]
+b'\x03\x02\x01'
+>>> nul_bug_demo.shm.unlink()
+>>> padded = shared_memory.ShareableList(['?\x00\x07', b'\x03\x02\x01\x00\x00\x00\x07'])
+>>> padded[0][:-1]
+'?\x00'
+>>> padded[1][:-1]
+b'\x03\x02\x01\x00\x00\x00'
+>>> padded.shm.unlink()
+
+count(value)
+

Returns the number of occurrences of value.

+
+index(value)
+

Returns first index position of value. Raises ValueError if value is not present.

+
+format
+

Read-only attribute containing the struct packing format used by all currently stored values.

+
+shm
+

The SharedMemory instance where the values are stored.

+
+

The following example demonstrates basic use of a ShareableList instance:

>>> from multiprocessing import shared_memory
+>>> a = shared_memory.ShareableList(['howdy', b'HoWdY', -273.154, 100, None, True, 42])
+>>> [ type(entry) for entry in a ]
+[<class 'str'>, <class 'bytes'>, <class 'float'>, <class 'int'>, <class 'NoneType'>, <class 'bool'>, <class 'int'>]
+>>> a[2]
+-273.154
+>>> a[2] = -78.5
+>>> a[2]
+-78.5
+>>> a[2] = 'dry ice'  # Changing data types is supported as well
+>>> a[2]
+'dry ice'
+>>> a[2] = 'larger than previously allocated storage space'
+Traceback (most recent call last):
+  ...
+ValueError: exceeds available storage for existing str
+>>> a[2]
+'dry ice'
+>>> len(a)
+7
+>>> a.index(42)
+6
+>>> a.count(b'howdy')
+0
+>>> a.count(b'HoWdY')
+1
+>>> a.shm.close()
+>>> a.shm.unlink()
+>>> del a  # Use of a ShareableList after call to unlink() is unsupported
+

The following example depicts how one, two, or many processes may access the same ShareableList by supplying the name of the shared memory block behind it:

>>> b = shared_memory.ShareableList(range(5))         # In a first process
+>>> c = shared_memory.ShareableList(name=b.shm.name)  # In a second process
+>>> c
+ShareableList([0, 1, 2, 3, 4], name='...')
+>>> c[-1] = -999
+>>> b[-1]
+-999
+>>> b.shm.close()
+>>> c.shm.close()
+>>> c.shm.unlink()
+

The following examples demonstrates that ShareableList (and underlying SharedMemory) objects can be pickled and unpickled if needed. Note, that it will still be the same shared object. This happens, because the deserialized object has the same unique name and is just attached to an existing object with the same name (if the object is still alive):

>>> import pickle
+>>> from multiprocessing import shared_memory
+>>> sl = shared_memory.ShareableList(range(10))
+>>> list(sl)
+[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+
>>> deserialized_sl = pickle.loads(pickle.dumps(sl))
+>>> list(deserialized_sl)
+[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+
>>> sl[0] = -1
+>>> deserialized_sl[1] = -2
+>>> list(sl)
+[-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]
+>>> list(deserialized_sl)
+[-1, -2, 2, 3, 4, 5, 6, 7, 8, 9]
+
>>> sl.shm.close()
+>>> sl.shm.unlink()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/multiprocessing.shared_memory.html +

+
diff --git a/devdocs/python~3.12/library%2Fnetdata.html b/devdocs/python~3.12/library%2Fnetdata.html new file mode 100644 index 00000000..f655eddf --- /dev/null +++ b/devdocs/python~3.12/library%2Fnetdata.html @@ -0,0 +1,17 @@ +

Internet Data Handling

This chapter describes modules which support handling data formats commonly used on the internet.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/netdata.html +

+
diff --git a/devdocs/python~3.12/library%2Fnetrc.html b/devdocs/python~3.12/library%2Fnetrc.html new file mode 100644 index 00000000..628c7d38 --- /dev/null +++ b/devdocs/python~3.12/library%2Fnetrc.html @@ -0,0 +1,33 @@ +

netrc — netrc file processing

Source code: Lib/netrc.py

The netrc class parses and encapsulates the netrc file format used by the Unix ftp program and other FTP clients.

+class netrc.netrc([file])
+

A netrc instance or subclass instance encapsulates data from a netrc file. The initialization argument, if present, specifies the file to parse. If no argument is given, the file .netrc in the user’s home directory – as determined by os.path.expanduser() – will be read. Otherwise, a FileNotFoundError exception will be raised. Parse errors will raise NetrcParseError with diagnostic information including the file name, line number, and terminating token. If no argument is specified on a POSIX system, the presence of passwords in the .netrc file will raise a NetrcParseError if the file ownership or permissions are insecure (owned by a user other than the user running the process, or accessible for read or write by any other user). This implements security behavior equivalent to that of ftp and other programs that use .netrc.

Changed in version 3.4: Added the POSIX permission check.

Changed in version 3.7: os.path.expanduser() is used to find the location of the .netrc file when file is not passed as argument.

Changed in version 3.10: netrc try UTF-8 encoding before using locale specific encoding. The entry in the netrc file no longer needs to contain all tokens. The missing tokens’ value default to an empty string. All the tokens and their values now can contain arbitrary characters, like whitespace and non-ASCII characters. If the login name is anonymous, it won’t trigger the security check.

+
+exception netrc.NetrcParseError
+

Exception raised by the netrc class when syntactical errors are encountered in source text. Instances of this exception provide three interesting attributes:

+msg
+

Textual explanation of the error.

+
+filename
+

The name of the source file.

+
+lineno
+

The line number on which the error was found.

+
+

netrc Objects

A netrc instance has the following methods:

+netrc.authenticators(host)
+

Return a 3-tuple (login, account, password) of authenticators for host. If the netrc file did not contain an entry for the given host, return the tuple associated with the ‘default’ entry. If neither matching host nor default entry is available, return None.

+
+netrc.__repr__()
+

Dump the class data as a string in the format of a netrc file. (This discards comments and may reorder the entries.)

+

Instances of netrc have public instance variables:

+netrc.hosts
+

Dictionary mapping host names to (login, account, password) tuples. The ‘default’ entry, if any, is represented as a pseudo-host by that name.

+
+netrc.macros
+

Dictionary mapping macro names to string lists.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/netrc.html +

+
diff --git a/devdocs/python~3.12/library%2Fnumbers.html b/devdocs/python~3.12/library%2Fnumbers.html new file mode 100644 index 00000000..6775e63d --- /dev/null +++ b/devdocs/python~3.12/library%2Fnumbers.html @@ -0,0 +1,112 @@ +

numbers — Numeric abstract base classes

Source code: Lib/numbers.py

The numbers module (PEP 3141) defines a hierarchy of numeric abstract base classes which progressively define more operations. None of the types defined in this module are intended to be instantiated.

+class numbers.Number
+

The root of the numeric hierarchy. If you just want to check if an argument x is a number, without caring what kind, use isinstance(x, Number).

+

The numeric tower

+class numbers.Complex
+

Subclasses of this type describe complex numbers and include the operations that work on the built-in complex type. These are: conversions to complex and bool, real, imag, +, -, *, /, **, abs(), conjugate(), ==, and !=. All except - and != are abstract.

+real
+

Abstract. Retrieves the real component of this number.

+
+imag
+

Abstract. Retrieves the imaginary component of this number.

+
+abstractmethod conjugate()
+

Abstract. Returns the complex conjugate. For example, (1+3j).conjugate() +== (1-3j).

+
+
+class numbers.Real
+

To Complex, Real adds the operations that work on real numbers.

In short, those are: a conversion to float, math.trunc(), round(), math.floor(), math.ceil(), divmod(), //, %, <, <=, >, and >=.

Real also provides defaults for complex(), real, imag, and conjugate().

+
+class numbers.Rational
+

Subtypes Real and adds numerator and denominator properties. It also provides a default for float().

The numerator and denominator values should be instances of Integral and should be in lowest terms with denominator positive.

+numerator
+

Abstract.

+
+denominator
+

Abstract.

+
+
+class numbers.Integral
+

Subtypes Rational and adds a conversion to int. Provides defaults for float(), numerator, and denominator. Adds abstract methods for pow() with modulus and bit-string operations: <<, >>, &, ^, |, ~.

+

Notes for type implementors

Implementors should be careful to make equal numbers equal and hash them to the same values. This may be subtle if there are two different extensions of the real numbers. For example, fractions.Fraction implements hash() as follows:

def __hash__(self):
+    if self.denominator == 1:
+        # Get integers right.
+        return hash(self.numerator)
+    # Expensive check, but definitely correct.
+    if self == float(self):
+        return hash(float(self))
+    else:
+        # Use tuple's hash to avoid a high collision rate on
+        # simple fractions.
+        return hash((self.numerator, self.denominator))
+

Adding More Numeric ABCs

There are, of course, more possible ABCs for numbers, and this would be a poor hierarchy if it precluded the possibility of adding those. You can add MyFoo between Complex and Real with:

class MyFoo(Complex): ...
+MyFoo.register(Real)
+

Implementing the arithmetic operations

We want to implement the arithmetic operations so that mixed-mode operations either call an implementation whose author knew about the types of both arguments, or convert both to the nearest built in type and do the operation there. For subtypes of Integral, this means that __add__() and __radd__() should be defined as:

class MyIntegral(Integral):
+
+    def __add__(self, other):
+        if isinstance(other, MyIntegral):
+            return do_my_adding_stuff(self, other)
+        elif isinstance(other, OtherTypeIKnowAbout):
+            return do_my_other_adding_stuff(self, other)
+        else:
+            return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, MyIntegral):
+            return do_my_adding_stuff(other, self)
+        elif isinstance(other, OtherTypeIKnowAbout):
+            return do_my_other_adding_stuff(other, self)
+        elif isinstance(other, Integral):
+            return int(other) + int(self)
+        elif isinstance(other, Real):
+            return float(other) + float(self)
+        elif isinstance(other, Complex):
+            return complex(other) + complex(self)
+        else:
+            return NotImplemented
+

There are 5 different cases for a mixed-type operation on subclasses of Complex. I’ll refer to all of the above code that doesn’t refer to MyIntegral and OtherTypeIKnowAbout as “boilerplate”. a will be an instance of A, which is a subtype of Complex (a : A <: Complex), and b : B <: +Complex. I’ll consider a + b:

  1. If A defines an __add__() which accepts b, all is well.
  2. If A falls back to the boilerplate code, and it were to return a value from __add__(), we’d miss the possibility that B defines a more intelligent __radd__(), so the boilerplate should return NotImplemented from __add__(). (Or A may not implement __add__() at all.)
  3. Then B’s __radd__() gets a chance. If it accepts a, all is well.
  4. If it falls back to the boilerplate, there are no more possible methods to try, so this is where the default implementation should live.
  5. If B <: A, Python tries B.__radd__ before A.__add__. This is ok, because it was implemented with knowledge of A, so it can handle those instances before delegating to Complex.

If A <: Complex and B <: Real without sharing any other knowledge, then the appropriate shared operation is the one involving the built in complex, and both __radd__() s land there, so a+b +== b+a.

Because most of the operations on any given type will be very similar, it can be useful to define a helper function which generates the forward and reverse instances of any given operator. For example, fractions.Fraction uses:

def _operator_fallbacks(monomorphic_operator, fallback_operator):
+    def forward(a, b):
+        if isinstance(b, (int, Fraction)):
+            return monomorphic_operator(a, b)
+        elif isinstance(b, float):
+            return fallback_operator(float(a), b)
+        elif isinstance(b, complex):
+            return fallback_operator(complex(a), b)
+        else:
+            return NotImplemented
+    forward.__name__ = '__' + fallback_operator.__name__ + '__'
+    forward.__doc__ = monomorphic_operator.__doc__
+
+    def reverse(b, a):
+        if isinstance(a, Rational):
+            # Includes ints.
+            return monomorphic_operator(a, b)
+        elif isinstance(a, Real):
+            return fallback_operator(float(a), float(b))
+        elif isinstance(a, Complex):
+            return fallback_operator(complex(a), complex(b))
+        else:
+            return NotImplemented
+    reverse.__name__ = '__r' + fallback_operator.__name__ + '__'
+    reverse.__doc__ = monomorphic_operator.__doc__
+
+    return forward, reverse
+
+def _add(a, b):
+    """a + b"""
+    return Fraction(a.numerator * b.denominator +
+                    b.numerator * a.denominator,
+                    a.denominator * b.denominator)
+
+__add__, __radd__ = _operator_fallbacks(_add, operator.add)
+
+# ...
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/numbers.html +

+
diff --git a/devdocs/python~3.12/library%2Fnumeric.html b/devdocs/python~3.12/library%2Fnumeric.html new file mode 100644 index 00000000..61836301 --- /dev/null +++ b/devdocs/python~3.12/library%2Fnumeric.html @@ -0,0 +1,17 @@ +

Numeric and Mathematical Modules

The modules described in this chapter provide numeric and math-related functions and data types. The numbers module defines an abstract hierarchy of numeric types. The math and cmath modules contain various mathematical functions for floating-point and complex numbers. The decimal module supports exact representations of decimal numbers, using arbitrary precision arithmetic.

The following modules are documented in this chapter:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/numeric.html +

+
diff --git a/devdocs/python~3.12/library%2Foperator.html b/devdocs/python~3.12/library%2Foperator.html new file mode 100644 index 00000000..785a11a1 --- /dev/null +++ b/devdocs/python~3.12/library%2Foperator.html @@ -0,0 +1,299 @@ +

operator — Standard operators as functions

Source code: Lib/operator.py

The operator module exports a set of efficient functions corresponding to the intrinsic operators of Python. For example, operator.add(x, y) is equivalent to the expression x+y. Many function names are those used for special methods, without the double underscores. For backward compatibility, many of these have a variant with the double underscores kept. The variants without the double underscores are preferred for clarity.

The functions fall into categories that perform object comparisons, logical operations, mathematical operations and sequence operations.

The object comparison functions are useful for all objects, and are named after the rich comparison operators they support:

+operator.lt(a, b)
+operator.le(a, b)
+operator.eq(a, b)
+operator.ne(a, b)
+operator.ge(a, b)
+operator.gt(a, b)
+operator.__lt__(a, b)
+operator.__le__(a, b)
+operator.__eq__(a, b)
+operator.__ne__(a, b)
+operator.__ge__(a, b)
+operator.__gt__(a, b)
+

Perform “rich comparisons” between a and b. Specifically, lt(a, b) is equivalent to a < b, le(a, b) is equivalent to a <= b, eq(a, +b) is equivalent to a == b, ne(a, b) is equivalent to a != b, gt(a, b) is equivalent to a > b and ge(a, b) is equivalent to a +>= b. Note that these functions can return any value, which may or may not be interpretable as a Boolean value. See Comparisons for more information about rich comparisons.

+

The logical operations are also generally applicable to all objects, and support truth tests, identity tests, and boolean operations:

+operator.not_(obj)
+operator.__not__(obj)
+

Return the outcome of not obj. (Note that there is no __not__() method for object instances; only the interpreter core defines this operation. The result is affected by the __bool__() and __len__() methods.)

+
+operator.truth(obj)
+

Return True if obj is true, and False otherwise. This is equivalent to using the bool constructor.

+
+operator.is_(a, b)
+

Return a is b. Tests object identity.

+
+operator.is_not(a, b)
+

Return a is not b. Tests object identity.

+

The mathematical and bitwise operations are the most numerous:

+operator.abs(obj)
+operator.__abs__(obj)
+

Return the absolute value of obj.

+
+operator.add(a, b)
+operator.__add__(a, b)
+

Return a + b, for a and b numbers.

+
+operator.and_(a, b)
+operator.__and__(a, b)
+

Return the bitwise and of a and b.

+
+operator.floordiv(a, b)
+operator.__floordiv__(a, b)
+

Return a // b.

+
+operator.index(a)
+operator.__index__(a)
+

Return a converted to an integer. Equivalent to a.__index__().

Changed in version 3.10: The result always has exact type int. Previously, the result could have been an instance of a subclass of int.

+
+operator.inv(obj)
+operator.invert(obj)
+operator.__inv__(obj)
+operator.__invert__(obj)
+

Return the bitwise inverse of the number obj. This is equivalent to ~obj.

+
+operator.lshift(a, b)
+operator.__lshift__(a, b)
+

Return a shifted left by b.

+
+operator.mod(a, b)
+operator.__mod__(a, b)
+

Return a % b.

+
+operator.mul(a, b)
+operator.__mul__(a, b)
+

Return a * b, for a and b numbers.

+
+operator.matmul(a, b)
+operator.__matmul__(a, b)
+

Return a @ b.

New in version 3.5.

+
+operator.neg(obj)
+operator.__neg__(obj)
+

Return obj negated (-obj).

+
+operator.or_(a, b)
+operator.__or__(a, b)
+

Return the bitwise or of a and b.

+
+operator.pos(obj)
+operator.__pos__(obj)
+

Return obj positive (+obj).

+
+operator.pow(a, b)
+operator.__pow__(a, b)
+

Return a ** b, for a and b numbers.

+
+operator.rshift(a, b)
+operator.__rshift__(a, b)
+

Return a shifted right by b.

+
+operator.sub(a, b)
+operator.__sub__(a, b)
+

Return a - b.

+
+operator.truediv(a, b)
+operator.__truediv__(a, b)
+

Return a / b where 2/3 is .66 rather than 0. This is also known as “true” division.

+
+operator.xor(a, b)
+operator.__xor__(a, b)
+

Return the bitwise exclusive or of a and b.

+

Operations which work with sequences (some of them with mappings too) include:

+operator.concat(a, b)
+operator.__concat__(a, b)
+

Return a + b for a and b sequences.

+
+operator.contains(a, b)
+operator.__contains__(a, b)
+

Return the outcome of the test b in a. Note the reversed operands.

+
+operator.countOf(a, b)
+

Return the number of occurrences of b in a.

+
+operator.delitem(a, b)
+operator.__delitem__(a, b)
+

Remove the value of a at index b.

+
+operator.getitem(a, b)
+operator.__getitem__(a, b)
+

Return the value of a at index b.

+
+operator.indexOf(a, b)
+

Return the index of the first of occurrence of b in a.

+
+operator.setitem(a, b, c)
+operator.__setitem__(a, b, c)
+

Set the value of a at index b to c.

+
+operator.length_hint(obj, default=0)
+

Return an estimated length for the object obj. First try to return its actual length, then an estimate using object.__length_hint__(), and finally return the default value.

New in version 3.4.

+

The following operation works with callables:

+operator.call(obj, /, *args, **kwargs)
+operator.__call__(obj, /, *args, **kwargs)
+

Return obj(*args, **kwargs).

New in version 3.11.

+

The operator module also defines tools for generalized attribute and item lookups. These are useful for making fast field extractors as arguments for map(), sorted(), itertools.groupby(), or other functions that expect a function argument.

+operator.attrgetter(attr)
operator.attrgetter(*attrs) +
+

Return a callable object that fetches attr from its operand. If more than one attribute is requested, returns a tuple of attributes. The attribute names can also contain dots. For example:

  • After f = attrgetter('name'), the call f(b) returns b.name.
  • After f = attrgetter('name', 'date'), the call f(b) returns (b.name, b.date).
  • After f = attrgetter('name.first', 'name.last'), the call f(b) returns (b.name.first, b.name.last).

Equivalent to:

def attrgetter(*items):
+    if any(not isinstance(item, str) for item in items):
+        raise TypeError('attribute name must be a string')
+    if len(items) == 1:
+        attr = items[0]
+        def g(obj):
+            return resolve_attr(obj, attr)
+    else:
+        def g(obj):
+            return tuple(resolve_attr(obj, attr) for attr in items)
+    return g
+
+def resolve_attr(obj, attr):
+    for name in attr.split("."):
+        obj = getattr(obj, name)
+    return obj
+
+
+operator.itemgetter(item)
operator.itemgetter(*items) +
+

Return a callable object that fetches item from its operand using the operand’s __getitem__() method. If multiple items are specified, returns a tuple of lookup values. For example:

  • After f = itemgetter(2), the call f(r) returns r[2].
  • After g = itemgetter(2, 5, 3), the call g(r) returns (r[2], r[5], r[3]).

Equivalent to:

def itemgetter(*items):
+    if len(items) == 1:
+        item = items[0]
+        def g(obj):
+            return obj[item]
+    else:
+        def g(obj):
+            return tuple(obj[item] for item in items)
+    return g
+

The items can be any type accepted by the operand’s __getitem__() method. Dictionaries accept any hashable value. Lists, tuples, and strings accept an index or a slice:

>>> itemgetter(1)('ABCDEFG')
+'B'
+>>> itemgetter(1, 3, 5)('ABCDEFG')
+('B', 'D', 'F')
+>>> itemgetter(slice(2, None))('ABCDEFG')
+'CDEFG'
+>>> soldier = dict(rank='captain', name='dotterbart')
+>>> itemgetter('rank')(soldier)
+'captain'
+

Example of using itemgetter() to retrieve specific fields from a tuple record:

>>> inventory = [('apple', 3), ('banana', 2), ('pear', 5), ('orange', 1)]
+>>> getcount = itemgetter(1)
+>>> list(map(getcount, inventory))
+[3, 2, 5, 1]
+>>> sorted(inventory, key=getcount)
+[('orange', 1), ('banana', 2), ('apple', 3), ('pear', 5)]
+
+
+operator.methodcaller(name, /, *args, **kwargs)
+

Return a callable object that calls the method name on its operand. If additional arguments and/or keyword arguments are given, they will be given to the method as well. For example:

  • After f = methodcaller('name'), the call f(b) returns b.name().
  • After f = methodcaller('name', 'foo', bar=1), the call f(b) returns b.name('foo', bar=1).

Equivalent to:

def methodcaller(name, /, *args, **kwargs):
+    def caller(obj):
+        return getattr(obj, name)(*args, **kwargs)
+    return caller
+
+

Mapping Operators to Functions

This table shows how abstract operations correspond to operator symbols in the Python syntax and the functions in the operator module.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

Operation

Syntax

Function

Addition

a + b

add(a, b)

Concatenation

seq1 + seq2

concat(seq1, seq2)

Containment Test

obj in seq

contains(seq, obj)

Division

a / b

truediv(a, b)

Division

a // b

floordiv(a, b)

Bitwise And

a & b

and_(a, b)

Bitwise Exclusive Or

a ^ b

xor(a, b)

Bitwise Inversion

~ a

invert(a)

Bitwise Or

a | b

or_(a, b)

Exponentiation

a ** b

pow(a, b)

Identity

a is b

is_(a, b)

Identity

a is not b

is_not(a, b)

Indexed Assignment

obj[k] = v

setitem(obj, k, v)

Indexed Deletion

del obj[k]

delitem(obj, k)

Indexing

obj[k]

getitem(obj, k)

Left Shift

a << b

lshift(a, b)

Modulo

a % b

mod(a, b)

Multiplication

a * b

mul(a, b)

Matrix Multiplication

a @ b

matmul(a, b)

Negation (Arithmetic)

- a

neg(a)

Negation (Logical)

not a

not_(a)

Positive

+ a

pos(a)

Right Shift

a >> b

rshift(a, b)

Slice Assignment

seq[i:j] = values

setitem(seq, slice(i, j), values)

Slice Deletion

del seq[i:j]

delitem(seq, slice(i, j))

Slicing

seq[i:j]

getitem(seq, slice(i, j))

String Formatting

s % obj

mod(s, obj)

Subtraction

a - b

sub(a, b)

Truth Test

obj

truth(obj)

Ordering

a < b

lt(a, b)

Ordering

a <= b

le(a, b)

Equality

a == b

eq(a, b)

Difference

a != b

ne(a, b)

Ordering

a >= b

ge(a, b)

Ordering

a > b

gt(a, b)

In-place Operators

Many operations have an “in-place” version. Listed below are functions providing a more primitive access to in-place operators than the usual syntax does; for example, the statement x += y is equivalent to x = operator.iadd(x, y). Another way to put it is to say that z = operator.iadd(x, y) is equivalent to the compound statement z = x; z += y.

In those examples, note that when an in-place method is called, the computation and assignment are performed in two separate steps. The in-place functions listed below only do the first step, calling the in-place method. The second step, assignment, is not handled.

For immutable targets such as strings, numbers, and tuples, the updated value is computed, but not assigned back to the input variable:

>>> a = 'hello'
+>>> iadd(a, ' world')
+'hello world'
+>>> a
+'hello'
+

For mutable targets such as lists and dictionaries, the in-place method will perform the update, so no subsequent assignment is necessary:

>>> s = ['h', 'e', 'l', 'l', 'o']
+>>> iadd(s, [' ', 'w', 'o', 'r', 'l', 'd'])
+['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']
+>>> s
+['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']
+
+operator.iadd(a, b)
+operator.__iadd__(a, b)
+

a = iadd(a, b) is equivalent to a += b.

+
+operator.iand(a, b)
+operator.__iand__(a, b)
+

a = iand(a, b) is equivalent to a &= b.

+
+operator.iconcat(a, b)
+operator.__iconcat__(a, b)
+

a = iconcat(a, b) is equivalent to a += b for a and b sequences.

+
+operator.ifloordiv(a, b)
+operator.__ifloordiv__(a, b)
+

a = ifloordiv(a, b) is equivalent to a //= b.

+
+operator.ilshift(a, b)
+operator.__ilshift__(a, b)
+

a = ilshift(a, b) is equivalent to a <<= b.

+
+operator.imod(a, b)
+operator.__imod__(a, b)
+

a = imod(a, b) is equivalent to a %= b.

+
+operator.imul(a, b)
+operator.__imul__(a, b)
+

a = imul(a, b) is equivalent to a *= b.

+
+operator.imatmul(a, b)
+operator.__imatmul__(a, b)
+

a = imatmul(a, b) is equivalent to a @= b.

New in version 3.5.

+
+operator.ior(a, b)
+operator.__ior__(a, b)
+

a = ior(a, b) is equivalent to a |= b.

+
+operator.ipow(a, b)
+operator.__ipow__(a, b)
+

a = ipow(a, b) is equivalent to a **= b.

+
+operator.irshift(a, b)
+operator.__irshift__(a, b)
+

a = irshift(a, b) is equivalent to a >>= b.

+
+operator.isub(a, b)
+operator.__isub__(a, b)
+

a = isub(a, b) is equivalent to a -= b.

+
+operator.itruediv(a, b)
+operator.__itruediv__(a, b)
+

a = itruediv(a, b) is equivalent to a /= b.

+
+operator.ixor(a, b)
+operator.__ixor__(a, b)
+

a = ixor(a, b) is equivalent to a ^= b.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/operator.html +

+
diff --git a/devdocs/python~3.12/library%2Fos.html b/devdocs/python~3.12/library%2Fos.html new file mode 100644 index 00000000..1af4374a --- /dev/null +++ b/devdocs/python~3.12/library%2Fos.html @@ -0,0 +1,1245 @@ +

os — Miscellaneous operating system interfaces

Source code: Lib/os.py

This module provides a portable way of using operating system dependent functionality. If you just want to read or write a file see open(), if you want to manipulate paths, see the os.path module, and if you want to read all the lines in all the files on the command line see the fileinput module. For creating temporary files and directories see the tempfile module, and for high-level file and directory handling see the shutil module.

Notes on the availability of these functions:

Note

All functions in this module raise OSError (or subclasses thereof) in the case of invalid or inaccessible file names and paths, or other arguments that have the correct type, but are not accepted by the operating system.

+exception os.error
+

An alias for the built-in OSError exception.

+
+os.name
+

The name of the operating system dependent module imported. The following names have currently been registered: 'posix', 'nt', 'java'.

See also

sys.platform has a finer granularity. os.uname() gives system-dependent version information.

The platform module provides detailed checks for the system’s identity.

+

File Names, Command Line Arguments, and Environment Variables

In Python, file names, command line arguments, and environment variables are represented using the string type. On some systems, decoding these strings to and from bytes is necessary before passing them to the operating system. Python uses the filesystem encoding and error handler to perform this conversion (see sys.getfilesystemencoding()).

The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig.

Changed in version 3.1: On some systems, conversion using the file system encoding may fail. In this case, Python uses the surrogateescape encoding error handler, which means that undecodable bytes are replaced by a Unicode character U+DCxx on decoding, and these are again translated to the original byte on encoding.

The file system encoding must guarantee to successfully decode all bytes below 128. If the file system encoding fails to provide this guarantee, API functions can raise UnicodeError.

See also the locale encoding.

Python UTF-8 Mode

New in version 3.7: See PEP 540 for more details.

The Python UTF-8 Mode ignores the locale encoding and forces the usage of the UTF-8 encoding:

Note that the standard stream settings in UTF-8 mode can be overridden by PYTHONIOENCODING (just as they can be in the default locale-aware mode).

As a consequence of the changes in those lower level APIs, other higher level APIs also exhibit different default behaviours:

  • Command line arguments, environment variables and filenames are decoded to text using the UTF-8 encoding.
  • +os.fsdecode() and os.fsencode() use the UTF-8 encoding.
  • +open(), io.open(), and codecs.open() use the UTF-8 encoding by default. However, they still use the strict error handler by default so that attempting to open a binary file in text mode is likely to raise an exception rather than producing nonsense data.

The Python UTF-8 Mode is enabled if the LC_CTYPE locale is C or POSIX at Python startup (see the PyConfig_Read() function).

It can be enabled or disabled using the -X utf8 command line option and the PYTHONUTF8 environment variable.

If the PYTHONUTF8 environment variable is not set at all, then the interpreter defaults to using the current locale settings, unless the current locale is identified as a legacy ASCII-based locale (as described for PYTHONCOERCECLOCALE), and locale coercion is either disabled or fails. In such legacy locales, the interpreter will default to enabling UTF-8 mode unless explicitly instructed not to do so.

The Python UTF-8 Mode can only be enabled at the Python startup. Its value can be read from sys.flags.utf8_mode.

See also the UTF-8 mode on Windows and the filesystem encoding and error handler.

See also

+PEP 686 +
+
+

Python 3.15 will make Python UTF-8 Mode default.

Process Parameters

These functions and data items provide information and operate on the current process and user.

+os.ctermid()
+

Return the filename corresponding to the controlling terminal of the process.

Availability: Unix, not Emscripten, not WASI.

+
+os.environ
+

A mapping object where keys and values are strings that represent the process environment. For example, environ['HOME'] is the pathname of your home directory (on some platforms), and is equivalent to getenv("HOME") in C.

This mapping is captured the first time the os module is imported, typically during Python startup as part of processing site.py. Changes to the environment made after this time are not reflected in os.environ, except for changes made by modifying os.environ directly.

This mapping may be used to modify the environment as well as query the environment. putenv() will be called automatically when the mapping is modified.

On Unix, keys and values use sys.getfilesystemencoding() and 'surrogateescape' error handler. Use environb if you would like to use a different encoding.

On Windows, the keys are converted to uppercase. This also applies when getting, setting, or deleting an item. For example, environ['monty'] = 'python' maps the key 'MONTY' to the value 'python'.

Note

Calling putenv() directly does not change os.environ, so it’s better to modify os.environ.

Note

On some platforms, including FreeBSD and macOS, setting environ may cause memory leaks. Refer to the system documentation for putenv().

You can delete items in this mapping to unset environment variables. unsetenv() will be called automatically when an item is deleted from os.environ, and when one of the pop() or clear() methods is called.

Changed in version 3.9: Updated to support PEP 584’s merge (|) and update (|=) operators.

+
+os.environb
+

Bytes version of environ: a mapping object where both keys and values are bytes objects representing the process environment. environ and environb are synchronized (modifying environb updates environ, and vice versa).

environb is only available if supports_bytes_environ is True.

New in version 3.2.

Changed in version 3.9: Updated to support PEP 584’s merge (|) and update (|=) operators.

+
os.chdir(path) +
os.fchdir(fd) +
os.getcwd() +
+

These functions are described in Files and Directories.

+
+os.fsencode(filename)
+

Encode path-like filename to the filesystem encoding and error handler; return bytes unchanged.

fsdecode() is the reverse function.

New in version 3.2.

Changed in version 3.6: Support added to accept objects implementing the os.PathLike interface.

+
+os.fsdecode(filename)
+

Decode the path-like filename from the filesystem encoding and error handler; return str unchanged.

fsencode() is the reverse function.

New in version 3.2.

Changed in version 3.6: Support added to accept objects implementing the os.PathLike interface.

+
+os.fspath(path)
+

Return the file system representation of the path.

If str or bytes is passed in, it is returned unchanged. Otherwise __fspath__() is called and its value is returned as long as it is a str or bytes object. In all other cases, TypeError is raised.

New in version 3.6.

+
+class os.PathLike
+

An abstract base class for objects representing a file system path, e.g. pathlib.PurePath.

New in version 3.6.

+abstractmethod __fspath__()
+

Return the file system path representation of the object.

The method should only return a str or bytes object, with the preference being for str.

+
+
+os.getenv(key, default=None)
+

Return the value of the environment variable key as a string if it exists, or default if it doesn’t. key is a string. Note that since getenv() uses os.environ, the mapping of getenv() is similarly also captured on import, and the function may not reflect future environment changes.

On Unix, keys and values are decoded with sys.getfilesystemencoding() and 'surrogateescape' error handler. Use os.getenvb() if you would like to use a different encoding.

Availability: Unix, Windows.

+
+os.getenvb(key, default=None)
+

Return the value of the environment variable key as bytes if it exists, or default if it doesn’t. key must be bytes. Note that since getenvb() uses os.environb, the mapping of getenvb() is similarly also captured on import, and the function may not reflect future environment changes.

getenvb() is only available if supports_bytes_environ is True.

Availability: Unix.

New in version 3.2.

+
+os.get_exec_path(env=None)
+

Returns the list of directories that will be searched for a named executable, similar to a shell, when launching a process. env, when specified, should be an environment variable dictionary to lookup the PATH in. By default, when env is None, environ is used.

New in version 3.2.

+
+os.getegid()
+

Return the effective group id of the current process. This corresponds to the “set id” bit on the file being executed in the current process.

Availability: Unix, not Emscripten, not WASI.

+
+os.geteuid()
+

Return the current process’s effective user id.

Availability: Unix, not Emscripten, not WASI.

+
+os.getgid()
+

Return the real group id of the current process.

Availability: Unix.

The function is a stub on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.getgrouplist(user, group, /)
+

Return list of group ids that user belongs to. If group is not in the list, it is included; typically, group is specified as the group ID field from the password record for user, because that group ID will otherwise be potentially omitted.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.getgroups()
+

Return list of supplemental group ids associated with the current process.

Availability: Unix, not Emscripten, not WASI.

Note

On macOS, getgroups() behavior differs somewhat from other Unix platforms. If the Python interpreter was built with a deployment target of 10.5 or earlier, getgroups() returns the list of effective group ids associated with the current user process; this list is limited to a system-defined number of entries, typically 16, and may be modified by calls to setgroups() if suitably privileged. If built with a deployment target greater than 10.5, getgroups() returns the current group access list for the user associated with the effective user id of the process; the group access list may change over the lifetime of the process, it is not affected by calls to setgroups(), and its length is not limited to 16. The deployment target value, MACOSX_DEPLOYMENT_TARGET, can be obtained with sysconfig.get_config_var().

+
+os.getlogin()
+

Return the name of the user logged in on the controlling terminal of the process. For most purposes, it is more useful to use getpass.getuser() since the latter checks the environment variables LOGNAME or USERNAME to find out who the user is, and falls back to pwd.getpwuid(os.getuid())[0] to get the login name of the current real user id.

Availability: Unix, Windows, not Emscripten, not WASI.

+
+os.getpgid(pid)
+

Return the process group id of the process with process id pid. If pid is 0, the process group id of the current process is returned.

Availability: Unix, not Emscripten, not WASI.

+
+os.getpgrp()
+

Return the id of the current process group.

Availability: Unix, not Emscripten, not WASI.

+
+os.getpid()
+

Return the current process id.

The function is a stub on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.getppid()
+

Return the parent’s process id. When the parent process has exited, on Unix the id returned is the one of the init process (1), on Windows it is still the same id, which may be already reused by another process.

Availability: Unix, Windows, not Emscripten, not WASI.

Changed in version 3.2: Added support for Windows.

+
+os.getpriority(which, who)
+

Get program scheduling priority. The value which is one of PRIO_PROCESS, PRIO_PGRP, or PRIO_USER, and who is interpreted relative to which (a process identifier for PRIO_PROCESS, process group identifier for PRIO_PGRP, and a user ID for PRIO_USER). A zero value for who denotes (respectively) the calling process, the process group of the calling process, or the real user ID of the calling process.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.PRIO_PROCESS
+os.PRIO_PGRP
+os.PRIO_USER
+

Parameters for the getpriority() and setpriority() functions.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.PRIO_DARWIN_THREAD
+os.PRIO_DARWIN_PROCESS
+os.PRIO_DARWIN_BG
+os.PRIO_DARWIN_NONUI
+

Parameters for the getpriority() and setpriority() functions.

Availability: macOS

New in version 3.12.

+
+os.getresuid()
+

Return a tuple (ruid, euid, suid) denoting the current process’s real, effective, and saved user ids.

Availability: Unix, not Emscripten, not WASI.

New in version 3.2.

+
+os.getresgid()
+

Return a tuple (rgid, egid, sgid) denoting the current process’s real, effective, and saved group ids.

Availability: Unix, not Emscripten, not WASI.

New in version 3.2.

+
+os.getuid()
+

Return the current process’s real user id.

Availability: Unix.

The function is a stub on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.initgroups(username, gid, /)
+

Call the system initgroups() to initialize the group access list with all of the groups of which the specified username is a member, plus the specified group id.

Availability: Unix, not Emscripten, not WASI.

New in version 3.2.

+
+os.putenv(key, value, /)
+

Set the environment variable named key to the string value. Such changes to the environment affect subprocesses started with os.system(), popen() or fork() and execv().

Assignments to items in os.environ are automatically translated into corresponding calls to putenv(); however, calls to putenv() don’t update os.environ, so it is actually preferable to assign to items of os.environ. This also applies to getenv() and getenvb(), which respectively use os.environ and os.environb in their implementations.

Note

On some platforms, including FreeBSD and macOS, setting environ may cause memory leaks. Refer to the system documentation for putenv().

Raises an auditing event os.putenv with arguments key, value.

Changed in version 3.9: The function is now always available.

+
+os.setegid(egid, /)
+

Set the current process’s effective group id.

Availability: Unix, not Emscripten, not WASI.

+
+os.seteuid(euid, /)
+

Set the current process’s effective user id.

Availability: Unix, not Emscripten, not WASI.

+
+os.setgid(gid, /)
+

Set the current process’ group id.

Availability: Unix, not Emscripten, not WASI.

+
+os.setgroups(groups, /)
+

Set the list of supplemental group ids associated with the current process to groups. groups must be a sequence, and each element must be an integer identifying a group. This operation is typically available only to the superuser.

Availability: Unix, not Emscripten, not WASI.

Note

On macOS, the length of groups may not exceed the system-defined maximum number of effective group ids, typically 16. See the documentation for getgroups() for cases where it may not return the same group list set by calling setgroups().

+
+os.setns(fd, nstype=0)
+

Reassociate the current thread with a Linux namespace. See the setns(2) and namespaces(7) man pages for more details.

If fd refers to a /proc/pid/ns/ link, setns() reassociates the calling thread with the namespace associated with that link, and nstype may be set to one of the CLONE_NEW* constants to impose constraints on the operation (0 means no constraints).

Since Linux 5.8, fd may refer to a PID file descriptor obtained from pidfd_open(). In this case, setns() reassociates the calling thread into one or more of the same namespaces as the thread referred to by fd. This is subject to any constraints imposed by nstype, which is a bit mask combining one or more of the CLONE_NEW* constants, e.g. setns(fd, os.CLONE_NEWUTS | os.CLONE_NEWPID). The caller’s memberships in unspecified namespaces are left unchanged.

fd can be any object with a fileno() method, or a raw file descriptor.

This example reassociates the thread with the init process’s network namespace:

fd = os.open("/proc/1/ns/net", os.O_RDONLY)
+os.setns(fd, os.CLONE_NEWNET)
+os.close(fd)
+

Availability: Linux >= 3.0 with glibc >= 2.14.

New in version 3.12.

See also

The unshare() function.

+
+os.setpgrp()
+

Call the system call setpgrp() or setpgrp(0, 0) depending on which version is implemented (if any). See the Unix manual for the semantics.

Availability: Unix, not Emscripten, not WASI.

+
+os.setpgid(pid, pgrp, /)
+

Call the system call setpgid() to set the process group id of the process with id pid to the process group with id pgrp. See the Unix manual for the semantics.

Availability: Unix, not Emscripten, not WASI.

+
+os.setpriority(which, who, priority)
+

Set program scheduling priority. The value which is one of PRIO_PROCESS, PRIO_PGRP, or PRIO_USER, and who is interpreted relative to which (a process identifier for PRIO_PROCESS, process group identifier for PRIO_PGRP, and a user ID for PRIO_USER). A zero value for who denotes (respectively) the calling process, the process group of the calling process, or the real user ID of the calling process. priority is a value in the range -20 to 19. The default priority is 0; lower priorities cause more favorable scheduling.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.setregid(rgid, egid, /)
+

Set the current process’s real and effective group ids.

Availability: Unix, not Emscripten, not WASI.

+
+os.setresgid(rgid, egid, sgid, /)
+

Set the current process’s real, effective, and saved group ids.

Availability: Unix, not Emscripten, not WASI.

New in version 3.2.

+
+os.setresuid(ruid, euid, suid, /)
+

Set the current process’s real, effective, and saved user ids.

Availability: Unix, not Emscripten, not WASI.

New in version 3.2.

+
+os.setreuid(ruid, euid, /)
+

Set the current process’s real and effective user ids.

Availability: Unix, not Emscripten, not WASI.

+
+os.getsid(pid, /)
+

Call the system call getsid(). See the Unix manual for the semantics.

Availability: Unix, not Emscripten, not WASI.

+
+os.setsid()
+

Call the system call setsid(). See the Unix manual for the semantics.

Availability: Unix, not Emscripten, not WASI.

+
+os.setuid(uid, /)
+

Set the current process’s user id.

Availability: Unix, not Emscripten, not WASI.

+
+os.strerror(code, /)
+

Return the error message corresponding to the error code in code. On platforms where strerror() returns NULL when given an unknown error number, ValueError is raised.

+
+os.supports_bytes_environ
+

True if the native OS type of the environment is bytes (eg. False on Windows).

New in version 3.2.

+
+os.umask(mask, /)
+

Set the current numeric umask and return the previous umask.

The function is a stub on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.uname()
+

Returns information identifying the current operating system. The return value is an object with five attributes:

  • +sysname - operating system name
  • +nodename - name of machine on network (implementation-defined)
  • +release - operating system release
  • +version - operating system version
  • +machine - hardware identifier

For backwards compatibility, this object is also iterable, behaving like a five-tuple containing sysname, nodename, release, version, and machine in that order.

Some systems truncate nodename to 8 characters or to the leading component; a better way to get the hostname is socket.gethostname() or even socket.gethostbyaddr(socket.gethostname()).

Availability: Unix.

Changed in version 3.3: Return type changed from a tuple to a tuple-like object with named attributes.

+
+os.unsetenv(key, /)
+

Unset (delete) the environment variable named key. Such changes to the environment affect subprocesses started with os.system(), popen() or fork() and execv().

Deletion of items in os.environ is automatically translated into a corresponding call to unsetenv(); however, calls to unsetenv() don’t update os.environ, so it is actually preferable to delete items of os.environ.

Raises an auditing event os.unsetenv with argument key.

Changed in version 3.9: The function is now always available and is also available on Windows.

+
+os.unshare(flags)
+

Disassociate parts of the process execution context, and move them into a newly created namespace. See the unshare(2) man page for more details. The flags argument is a bit mask, combining zero or more of the CLONE_* constants, that specifies which parts of the execution context should be unshared from their existing associations and moved to a new namespace. If the flags argument is 0, no changes are made to the calling process’s execution context.

Availability: Linux >= 2.6.16.

New in version 3.12.

See also

The setns() function.

+

Flags to the unshare() function, if the implementation supports them. See unshare(2) in the Linux manual for their exact effect and availability.

+os.CLONE_FILES
+os.CLONE_FS
+os.CLONE_NEWCGROUP
+os.CLONE_NEWIPC
+os.CLONE_NEWNET
+os.CLONE_NEWNS
+os.CLONE_NEWPID
+os.CLONE_NEWTIME
+os.CLONE_NEWUSER
+os.CLONE_NEWUTS
+os.CLONE_SIGHAND
+os.CLONE_SYSVSEM
+os.CLONE_THREAD
+os.CLONE_VM
+

File Object Creation

These functions create new file objects. (See also open() for opening file descriptors.)

+os.fdopen(fd, *args, **kwargs)
+

Return an open file object connected to the file descriptor fd. This is an alias of the open() built-in function and accepts the same arguments. The only difference is that the first argument of fdopen() must always be an integer.

+

File Descriptor Operations

These functions operate on I/O streams referenced using file descriptors.

File descriptors are small integers corresponding to a file that has been opened by the current process. For example, standard input is usually file descriptor 0, standard output is 1, and standard error is 2. Further files opened by a process will then be assigned 3, 4, 5, and so forth. The name “file descriptor” is slightly deceptive; on Unix platforms, sockets and pipes are also referenced by file descriptors.

The fileno() method can be used to obtain the file descriptor associated with a file object when required. Note that using the file descriptor directly will bypass the file object methods, ignoring aspects such as internal buffering of data.

+os.close(fd)
+

Close file descriptor fd.

Note

This function is intended for low-level I/O and must be applied to a file descriptor as returned by os.open() or pipe(). To close a “file object” returned by the built-in function open() or by popen() or fdopen(), use its close() method.

+
+os.closerange(fd_low, fd_high, /)
+

Close all file descriptors from fd_low (inclusive) to fd_high (exclusive), ignoring errors. Equivalent to (but much faster than):

for fd in range(fd_low, fd_high):
+    try:
+        os.close(fd)
+    except OSError:
+        pass
+
+
+os.copy_file_range(src, dst, count, offset_src=None, offset_dst=None)
+

Copy count bytes from file descriptor src, starting from offset offset_src, to file descriptor dst, starting from offset offset_dst. If offset_src is None, then src is read from the current position; respectively for offset_dst.

In Linux kernel older than 5.3, the files pointed by src and dst must reside in the same filesystem, otherwise an OSError is raised with errno set to errno.EXDEV.

This copy is done without the additional cost of transferring data from the kernel to user space and then back into the kernel. Additionally, some filesystems could implement extra optimizations, such as the use of reflinks (i.e., two or more inodes that share pointers to the same copy-on-write disk blocks; supported file systems include btrfs and XFS) and server-side copy (in the case of NFS).

The function copies bytes between two file descriptors. Text options, like the encoding and the line ending, are ignored.

The return value is the amount of bytes copied. This could be less than the amount requested.

Note

On Linux, os.copy_file_range() should not be used for copying a range of a pseudo file from a special filesystem like procfs and sysfs. It will always copy no bytes and return 0 as if the file was empty because of a known Linux kernel issue.

Availability: Linux >= 4.5 with glibc >= 2.27.

New in version 3.8.

+
+os.device_encoding(fd)
+

Return a string describing the encoding of the device associated with fd if it is connected to a terminal; else return None.

On Unix, if the Python UTF-8 Mode is enabled, return 'UTF-8' rather than the device encoding.

Changed in version 3.10: On Unix, the function now implements the Python UTF-8 Mode.

+
+os.dup(fd, /)
+

Return a duplicate of file descriptor fd. The new file descriptor is non-inheritable.

On Windows, when duplicating a standard stream (0: stdin, 1: stdout, 2: stderr), the new file descriptor is inheritable.

Availability: not WASI.

Changed in version 3.4: The new file descriptor is now non-inheritable.

+
+os.dup2(fd, fd2, inheritable=True)
+

Duplicate file descriptor fd to fd2, closing the latter first if necessary. Return fd2. The new file descriptor is inheritable by default or non-inheritable if inheritable is False.

Availability: not WASI.

Changed in version 3.4: Add the optional inheritable parameter.

Changed in version 3.7: Return fd2 on success. Previously, None was always returned.

+
+os.fchmod(fd, mode)
+

Change the mode of the file given by fd to the numeric mode. See the docs for chmod() for possible values of mode. As of Python 3.3, this is equivalent to os.chmod(fd, mode).

Raises an auditing event os.chmod with arguments path, mode, dir_fd.

Availability: Unix.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.fchown(fd, uid, gid)
+

Change the owner and group id of the file given by fd to the numeric uid and gid. To leave one of the ids unchanged, set it to -1. See chown(). As of Python 3.3, this is equivalent to os.chown(fd, uid, +gid).

Raises an auditing event os.chown with arguments path, uid, gid, dir_fd.

Availability: Unix.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

+
+os.fdatasync(fd)
+

Force write of file with filedescriptor fd to disk. Does not force update of metadata.

Availability: Unix.

Note

This function is not available on MacOS.

+
+os.fpathconf(fd, name, /)
+

Return system configuration information relevant to an open file. name specifies the configuration value to retrieve; it may be a string which is the name of a defined system value; these names are specified in a number of standards (POSIX.1, Unix 95, Unix 98, and others). Some platforms define additional names as well. The names known to the host operating system are given in the pathconf_names dictionary. For configuration variables not included in that mapping, passing an integer for name is also accepted.

If name is a string and is not known, ValueError is raised. If a specific value for name is not supported by the host system, even if it is included in pathconf_names, an OSError is raised with errno.EINVAL for the error number.

As of Python 3.3, this is equivalent to os.pathconf(fd, name).

Availability: Unix.

+
+os.fstat(fd)
+

Get the status of the file descriptor fd. Return a stat_result object.

As of Python 3.3, this is equivalent to os.stat(fd).

See also

The stat() function.

+
+os.fstatvfs(fd, /)
+

Return information about the filesystem containing the file associated with file descriptor fd, like statvfs(). As of Python 3.3, this is equivalent to os.statvfs(fd).

Availability: Unix.

+
+os.fsync(fd)
+

Force write of file with filedescriptor fd to disk. On Unix, this calls the native fsync() function; on Windows, the MS _commit() function.

If you’re starting with a buffered Python file object f, first do f.flush(), and then do os.fsync(f.fileno()), to ensure that all internal buffers associated with f are written to disk.

Availability: Unix, Windows.

+
+os.ftruncate(fd, length, /)
+

Truncate the file corresponding to file descriptor fd, so that it is at most length bytes in size. As of Python 3.3, this is equivalent to os.truncate(fd, length).

Raises an auditing event os.truncate with arguments fd, length.

Availability: Unix, Windows.

Changed in version 3.5: Added support for Windows

+
+os.get_blocking(fd, /)
+

Get the blocking mode of the file descriptor: False if the O_NONBLOCK flag is set, True if the flag is cleared.

See also set_blocking() and socket.socket.setblocking().

Availability: Unix, Windows.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

On Windows, this function is limited to pipes.

New in version 3.5.

Changed in version 3.12: Added support for pipes on Windows.

+
+os.isatty(fd, /)
+

Return True if the file descriptor fd is open and connected to a tty(-like) device, else False.

+
+os.lockf(fd, cmd, len, /)
+

Apply, test or remove a POSIX lock on an open file descriptor. fd is an open file descriptor. cmd specifies the command to use - one of F_LOCK, F_TLOCK, F_ULOCK or F_TEST. len specifies the section of the file to lock.

Raises an auditing event os.lockf with arguments fd, cmd, len.

Availability: Unix.

New in version 3.3.

+
+os.F_LOCK
+os.F_TLOCK
+os.F_ULOCK
+os.F_TEST
+

Flags that specify what action lockf() will take.

Availability: Unix.

New in version 3.3.

+
+os.login_tty(fd, /)
+

Prepare the tty of which fd is a file descriptor for a new login session. Make the calling process a session leader; make the tty the controlling tty, the stdin, the stdout, and the stderr of the calling process; close fd.

Availability: Unix, not Emscripten, not WASI.

New in version 3.11.

+
+os.lseek(fd, pos, whence, /)
+

Set the current position of file descriptor fd to position pos, modified by whence, and return the new position in bytes relative to the start of the file. Valid values for whence are:

  • +SEEK_SET or 0 – set pos relative to the beginning of the file
  • +SEEK_CUR or 1 – set pos relative to the current file position
  • +SEEK_END or 2 – set pos relative to the end of the file
  • +SEEK_HOLE – set pos to the next data location, relative to pos +
  • +SEEK_DATA – set pos to the next data hole, relative to pos +

Changed in version 3.3: Add support for SEEK_HOLE and SEEK_DATA.

+
+os.SEEK_SET
+os.SEEK_CUR
+os.SEEK_END
+

Parameters to the lseek() function and the seek() method on file-like objects, for whence to adjust the file position indicator.

+ SEEK_SET +
+
+

Adjust the file position relative to the beginning of the file.

+ SEEK_CUR +
+
+

Adjust the file position relative to the current file position.

+ SEEK_END +
+
+

Adjust the file position relative to the end of the file.

Their values are 0, 1, and 2, respectively.

+
+os.SEEK_HOLE
+os.SEEK_DATA
+

Parameters to the lseek() function and the seek() method on file-like objects, for seeking file data and holes on sparsely allocated files.

+SEEK_DATA
+
+

Adjust the file offset to the next location containing data, relative to the seek position.

+SEEK_HOLE
+
+

Adjust the file offset to the next location containing a hole, relative to the seek position. A hole is defined as a sequence of zeros.

Note

These operations only make sense for filesystems that support them.

Availability: Linux >= 3.1, macOS, Unix

New in version 3.3.

+
+os.open(path, flags, mode=0o777, *, dir_fd=None)
+

Open the file path and set various flags according to flags and possibly its mode according to mode. When computing mode, the current umask value is first masked out. Return the file descriptor for the newly opened file. The new file descriptor is non-inheritable.

For a description of the flag and mode values, see the C run-time documentation; flag constants (like O_RDONLY and O_WRONLY) are defined in the os module. In particular, on Windows adding O_BINARY is needed to open files in binary mode.

This function can support paths relative to directory descriptors with the dir_fd parameter.

Raises an auditing event open with arguments path, mode, flags.

Changed in version 3.4: The new file descriptor is now non-inheritable.

Note

This function is intended for low-level I/O. For normal usage, use the built-in function open(), which returns a file object with read() and write() methods (and many more). To wrap a file descriptor in a file object, use fdopen().

New in version 3.3: The dir_fd argument.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

Changed in version 3.6: Accepts a path-like object.

+

The following constants are options for the flags parameter to the open() function. They can be combined using the bitwise OR operator |. Some of them are not available on all platforms. For descriptions of their availability and use, consult the open(2) manual page on Unix or the MSDN on Windows.

+os.O_RDONLY
+os.O_WRONLY
+os.O_RDWR
+os.O_APPEND
+os.O_CREAT
+os.O_EXCL
+os.O_TRUNC
+

The above constants are available on Unix and Windows.

+
+os.O_DSYNC
+os.O_RSYNC
+os.O_SYNC
+os.O_NDELAY
+os.O_NONBLOCK
+os.O_NOCTTY
+os.O_CLOEXEC
+

The above constants are only available on Unix.

Changed in version 3.3: Add O_CLOEXEC constant.

+
+os.O_BINARY
+os.O_NOINHERIT
+os.O_SHORT_LIVED
+os.O_TEMPORARY
+os.O_RANDOM
+os.O_SEQUENTIAL
+os.O_TEXT
+

The above constants are only available on Windows.

+
+os.O_EVTONLY
+os.O_FSYNC
+os.O_NOFOLLOW_ANY
+

The above constants are only available on macOS.

Changed in version 3.10: Add O_EVTONLY, O_FSYNC, O_SYMLINK and O_NOFOLLOW_ANY constants.

+
+os.O_ASYNC
+os.O_DIRECT
+os.O_DIRECTORY
+os.O_NOFOLLOW
+os.O_NOATIME
+os.O_PATH
+os.O_TMPFILE
+os.O_SHLOCK
+os.O_EXLOCK
+

The above constants are extensions and not present if they are not defined by the C library.

Changed in version 3.4: Add O_PATH on systems that support it. Add O_TMPFILE, only available on Linux Kernel 3.11 or newer.

+
+os.openpty()
+

Open a new pseudo-terminal pair. Return a pair of file descriptors (master, slave) for the pty and the tty, respectively. The new file descriptors are non-inheritable. For a (slightly) more portable approach, use the pty module.

Availability: Unix, not Emscripten, not WASI.

Changed in version 3.4: The new file descriptors are now non-inheritable.

+
+os.pipe()
+

Create a pipe. Return a pair of file descriptors (r, w) usable for reading and writing, respectively. The new file descriptor is non-inheritable.

Availability: Unix, Windows.

Changed in version 3.4: The new file descriptors are now non-inheritable.

+
+os.pipe2(flags, /)
+

Create a pipe with flags set atomically. flags can be constructed by ORing together one or more of these values: O_NONBLOCK, O_CLOEXEC. Return a pair of file descriptors (r, w) usable for reading and writing, respectively.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.posix_fallocate(fd, offset, len, /)
+

Ensures that enough disk space is allocated for the file specified by fd starting from offset and continuing for len bytes.

Availability: Unix, not Emscripten.

New in version 3.3.

+
+os.posix_fadvise(fd, offset, len, advice, /)
+

Announces an intention to access data in a specific pattern thus allowing the kernel to make optimizations. The advice applies to the region of the file specified by fd starting at offset and continuing for len bytes. advice is one of POSIX_FADV_NORMAL, POSIX_FADV_SEQUENTIAL, POSIX_FADV_RANDOM, POSIX_FADV_NOREUSE, POSIX_FADV_WILLNEED or POSIX_FADV_DONTNEED.

Availability: Unix.

New in version 3.3.

+
+os.POSIX_FADV_NORMAL
+os.POSIX_FADV_SEQUENTIAL
+os.POSIX_FADV_RANDOM
+os.POSIX_FADV_NOREUSE
+os.POSIX_FADV_WILLNEED
+os.POSIX_FADV_DONTNEED
+

Flags that can be used in advice in posix_fadvise() that specify the access pattern that is likely to be used.

Availability: Unix.

New in version 3.3.

+
+os.pread(fd, n, offset, /)
+

Read at most n bytes from file descriptor fd at a position of offset, leaving the file offset unchanged.

Return a bytestring containing the bytes read. If the end of the file referred to by fd has been reached, an empty bytes object is returned.

Availability: Unix.

New in version 3.3.

+
+os.preadv(fd, buffers, offset, flags=0, /)
+

Read from a file descriptor fd at a position of offset into mutable bytes-like objects buffers, leaving the file offset unchanged. Transfer data into each buffer until it is full and then move on to the next buffer in the sequence to hold the rest of the data.

The flags argument contains a bitwise OR of zero or more of the following flags:

Return the total number of bytes actually read which can be less than the total capacity of all the objects.

The operating system may set a limit (sysconf() value 'SC_IOV_MAX') on the number of buffers that can be used.

Combine the functionality of os.readv() and os.pread().

Availability: Linux >= 2.6.30, FreeBSD >= 6.0, OpenBSD >= 2.7, AIX >= 7.1.

Using flags requires Linux >= 4.6.

New in version 3.7.

+
+os.RWF_NOWAIT
+

Do not wait for data which is not immediately available. If this flag is specified, the system call will return instantly if it would have to read data from the backing storage or wait for a lock.

If some data was successfully read, it will return the number of bytes read. If no bytes were read, it will return -1 and set errno to errno.EAGAIN.

Availability: Linux >= 4.14.

New in version 3.7.

+
+os.RWF_HIPRI
+

High priority read/write. Allows block-based filesystems to use polling of the device, which provides lower latency, but may use additional resources.

Currently, on Linux, this feature is usable only on a file descriptor opened using the O_DIRECT flag.

Availability: Linux >= 4.6.

New in version 3.7.

+
+os.pwrite(fd, str, offset, /)
+

Write the bytestring in str to file descriptor fd at position of offset, leaving the file offset unchanged.

Return the number of bytes actually written.

Availability: Unix.

New in version 3.3.

+
+os.pwritev(fd, buffers, offset, flags=0, /)
+

Write the buffers contents to file descriptor fd at a offset offset, leaving the file offset unchanged. buffers must be a sequence of bytes-like objects. Buffers are processed in array order. Entire contents of the first buffer is written before proceeding to the second, and so on.

The flags argument contains a bitwise OR of zero or more of the following flags:

Return the total number of bytes actually written.

The operating system may set a limit (sysconf() value 'SC_IOV_MAX') on the number of buffers that can be used.

Combine the functionality of os.writev() and os.pwrite().

Availability: Linux >= 2.6.30, FreeBSD >= 6.0, OpenBSD >= 2.7, AIX >= 7.1.

Using flags requires Linux >= 4.6.

New in version 3.7.

+
+os.RWF_DSYNC
+

Provide a per-write equivalent of the O_DSYNC os.open() flag. This flag effect applies only to the data range written by the system call.

Availability: Linux >= 4.7.

New in version 3.7.

+
+os.RWF_SYNC
+

Provide a per-write equivalent of the O_SYNC os.open() flag. This flag effect applies only to the data range written by the system call.

Availability: Linux >= 4.7.

New in version 3.7.

+
+os.RWF_APPEND
+

Provide a per-write equivalent of the O_APPEND os.open() flag. This flag is meaningful only for os.pwritev(), and its effect applies only to the data range written by the system call. The offset argument does not affect the write operation; the data is always appended to the end of the file. However, if the offset argument is -1, the current file offset is updated.

Availability: Linux >= 4.16.

New in version 3.10.

+
+os.read(fd, n, /)
+

Read at most n bytes from file descriptor fd.

Return a bytestring containing the bytes read. If the end of the file referred to by fd has been reached, an empty bytes object is returned.

Note

This function is intended for low-level I/O and must be applied to a file descriptor as returned by os.open() or pipe(). To read a “file object” returned by the built-in function open() or by popen() or fdopen(), or sys.stdin, use its read() or readline() methods.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+os.sendfile(out_fd, in_fd, offset, count)
os.sendfile(out_fd, in_fd, offset, count, headers=(), trailers=(), flags=0) +
+

Copy count bytes from file descriptor in_fd to file descriptor out_fd starting at offset. Return the number of bytes sent. When EOF is reached return 0.

The first function notation is supported by all platforms that define sendfile().

On Linux, if offset is given as None, the bytes are read from the current position of in_fd and the position of in_fd is updated.

The second case may be used on macOS and FreeBSD where headers and trailers are arbitrary sequences of buffers that are written before and after the data from in_fd is written. It returns the same as the first case.

On macOS and FreeBSD, a value of 0 for count specifies to send until the end of in_fd is reached.

All platforms support sockets as out_fd file descriptor, and some platforms allow other types (e.g. regular file, pipe) as well.

Cross-platform applications should not use headers, trailers and flags arguments.

Availability: Unix, not Emscripten, not WASI.

Note

For a higher-level wrapper of sendfile(), see socket.socket.sendfile().

New in version 3.3.

Changed in version 3.9: Parameters out and in was renamed to out_fd and in_fd.

+
+os.SF_NODISKIO
+os.SF_MNOWAIT
+os.SF_SYNC
+

Parameters to the sendfile() function, if the implementation supports them.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.SF_NOCACHE
+

Parameter to the sendfile() function, if the implementation supports it. The data won’t be cached in the virtual memory and will be freed afterwards.

Availability: Unix, not Emscripten, not WASI.

New in version 3.11.

+
+os.set_blocking(fd, blocking, /)
+

Set the blocking mode of the specified file descriptor. Set the O_NONBLOCK flag if blocking is False, clear the flag otherwise.

See also get_blocking() and socket.socket.setblocking().

Availability: Unix, Windows.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

On Windows, this function is limited to pipes.

New in version 3.5.

Changed in version 3.12: Added support for pipes on Windows.

+
+os.splice(src, dst, count, offset_src=None, offset_dst=None)
+

Transfer count bytes from file descriptor src, starting from offset offset_src, to file descriptor dst, starting from offset offset_dst. At least one of the file descriptors must refer to a pipe. If offset_src is None, then src is read from the current position; respectively for offset_dst. The offset associated to the file descriptor that refers to a pipe must be None. The files pointed by src and dst must reside in the same filesystem, otherwise an OSError is raised with errno set to errno.EXDEV.

This copy is done without the additional cost of transferring data from the kernel to user space and then back into the kernel. Additionally, some filesystems could implement extra optimizations. The copy is done as if both files are opened as binary.

Upon successful completion, returns the number of bytes spliced to or from the pipe. A return value of 0 means end of input. If src refers to a pipe, then this means that there was no data to transfer, and it would not make sense to block because there are no writers connected to the write end of the pipe.

Availability: Linux >= 2.6.17 with glibc >= 2.5

New in version 3.10.

+
+os.SPLICE_F_MOVE
+os.SPLICE_F_NONBLOCK
+os.SPLICE_F_MORE
+

New in version 3.10.

+
+os.readv(fd, buffers, /)
+

Read from a file descriptor fd into a number of mutable bytes-like objects buffers. Transfer data into each buffer until it is full and then move on to the next buffer in the sequence to hold the rest of the data.

Return the total number of bytes actually read which can be less than the total capacity of all the objects.

The operating system may set a limit (sysconf() value 'SC_IOV_MAX') on the number of buffers that can be used.

Availability: Unix.

New in version 3.3.

+
+os.tcgetpgrp(fd, /)
+

Return the process group associated with the terminal given by fd (an open file descriptor as returned by os.open()).

Availability: Unix, not WASI.

+
+os.tcsetpgrp(fd, pg, /)
+

Set the process group associated with the terminal given by fd (an open file descriptor as returned by os.open()) to pg.

Availability: Unix, not WASI.

+
+os.ttyname(fd, /)
+

Return a string which specifies the terminal device associated with file descriptor fd. If fd is not associated with a terminal device, an exception is raised.

Availability: Unix.

+
+os.write(fd, str, /)
+

Write the bytestring in str to file descriptor fd.

Return the number of bytes actually written.

Note

This function is intended for low-level I/O and must be applied to a file descriptor as returned by os.open() or pipe(). To write a “file object” returned by the built-in function open() or by popen() or fdopen(), or sys.stdout or sys.stderr, use its write() method.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+os.writev(fd, buffers, /)
+

Write the contents of buffers to file descriptor fd. buffers must be a sequence of bytes-like objects. Buffers are processed in array order. Entire contents of the first buffer is written before proceeding to the second, and so on.

Returns the total number of bytes actually written.

The operating system may set a limit (sysconf() value 'SC_IOV_MAX') on the number of buffers that can be used.

Availability: Unix.

New in version 3.3.

+

Querying the size of a terminal

New in version 3.3.

+os.get_terminal_size(fd=STDOUT_FILENO, /)
+

Return the size of the terminal window as (columns, lines), tuple of type terminal_size.

The optional argument fd (default STDOUT_FILENO, or standard output) specifies which file descriptor should be queried.

If the file descriptor is not connected to a terminal, an OSError is raised.

shutil.get_terminal_size() is the high-level function which should normally be used, os.get_terminal_size is the low-level implementation.

Availability: Unix, Windows.

+
+class os.terminal_size
+

A subclass of tuple, holding (columns, lines) of the terminal window size.

+columns
+

Width of the terminal window in characters.

+
+lines
+

Height of the terminal window in characters.

+
+

Inheritance of File Descriptors

New in version 3.4.

A file descriptor has an “inheritable” flag which indicates if the file descriptor can be inherited by child processes. Since Python 3.4, file descriptors created by Python are non-inheritable by default.

On UNIX, non-inheritable file descriptors are closed in child processes at the execution of a new program, other file descriptors are inherited.

On Windows, non-inheritable handles and file descriptors are closed in child processes, except for standard streams (file descriptors 0, 1 and 2: stdin, stdout and stderr), which are always inherited. Using spawn* functions, all inheritable handles and all inheritable file descriptors are inherited. Using the subprocess module, all file descriptors except standard streams are closed, and inheritable handles are only inherited if the close_fds parameter is False.

On WebAssembly platforms wasm32-emscripten and wasm32-wasi, the file descriptor cannot be modified.

+os.get_inheritable(fd, /)
+

Get the “inheritable” flag of the specified file descriptor (a boolean).

+
+os.set_inheritable(fd, inheritable, /)
+

Set the “inheritable” flag of the specified file descriptor.

+
+os.get_handle_inheritable(handle, /)
+

Get the “inheritable” flag of the specified handle (a boolean).

Availability: Windows.

+
+os.set_handle_inheritable(handle, inheritable, /)
+

Set the “inheritable” flag of the specified handle.

Availability: Windows.

+

Files and Directories

On some Unix platforms, many of these functions support one or more of these features:

  • +

    specifying a file descriptor: Normally the path argument provided to functions in the os module must be a string specifying a file path. However, some functions now alternatively accept an open file descriptor for their path argument. The function will then operate on the file referred to by the descriptor. (For POSIX systems, Python will call the variant of the function prefixed with f (e.g. call fchdir instead of chdir).)

    You can check whether or not path can be specified as a file descriptor for a particular function on your platform using os.supports_fd. If this functionality is unavailable, using it will raise a NotImplementedError.

    If the function also supports dir_fd or follow_symlinks arguments, it’s an error to specify one of those when supplying path as a file descriptor.

  • +

    paths relative to directory descriptors: If dir_fd is not None, it should be a file descriptor referring to a directory, and the path to operate on should be relative; path will then be relative to that directory. If the path is absolute, dir_fd is ignored. (For POSIX systems, Python will call the variant of the function with an at suffix and possibly prefixed with f (e.g. call faccessat instead of access).

    You can check whether or not dir_fd is supported for a particular function on your platform using os.supports_dir_fd. If it’s unavailable, using it will raise a NotImplementedError.

+os.access(path, mode, *, dir_fd=None, effective_ids=False, follow_symlinks=True)
+

Use the real uid/gid to test for access to path. Note that most operations will use the effective uid/gid, therefore this routine can be used in a suid/sgid environment to test if the invoking user has the specified access to path. mode should be F_OK to test the existence of path, or it can be the inclusive OR of one or more of R_OK, W_OK, and X_OK to test permissions. Return True if access is allowed, False if not. See the Unix man page access(2) for more information.

This function can support specifying paths relative to directory descriptors and not following symlinks.

If effective_ids is True, access() will perform its access checks using the effective uid/gid instead of the real uid/gid. effective_ids may not be supported on your platform; you can check whether or not it is available using os.supports_effective_ids. If it is unavailable, using it will raise a NotImplementedError.

Note

Using access() to check if a user is authorized to e.g. open a file before actually doing so using open() creates a security hole, because the user might exploit the short time interval between checking and opening the file to manipulate it. It’s preferable to use EAFP techniques. For example:

if os.access("myfile", os.R_OK):
+    with open("myfile") as fp:
+        return fp.read()
+return "some default data"
+

is better written as:

try:
+    fp = open("myfile")
+except PermissionError:
+    return "some default data"
+else:
+    with fp:
+        return fp.read()
+

Note

I/O operations may fail even when access() indicates that they would succeed, particularly for operations on network filesystems which may have permissions semantics beyond the usual POSIX permission-bit model.

Changed in version 3.3: Added the dir_fd, effective_ids, and follow_symlinks parameters.

Changed in version 3.6: Accepts a path-like object.

+
+os.F_OK
+os.R_OK
+os.W_OK
+os.X_OK
+

Values to pass as the mode parameter of access() to test the existence, readability, writability and executability of path, respectively.

+
+os.chdir(path)
+

Change the current working directory to path.

This function can support specifying a file descriptor. The descriptor must refer to an opened directory, not an open file.

This function can raise OSError and subclasses such as FileNotFoundError, PermissionError, and NotADirectoryError.

Raises an auditing event os.chdir with argument path.

New in version 3.3: Added support for specifying path as a file descriptor on some platforms.

Changed in version 3.6: Accepts a path-like object.

+
+os.chflags(path, flags, *, follow_symlinks=True)
+

Set the flags of path to the numeric flags. flags may take a combination (bitwise OR) of the following values (as defined in the stat module):

This function can support not following symlinks.

Raises an auditing event os.chflags with arguments path, flags.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3: The follow_symlinks argument.

Changed in version 3.6: Accepts a path-like object.

+
+os.chmod(path, mode, *, dir_fd=None, follow_symlinks=True)
+

Change the mode of path to the numeric mode. mode may take one of the following values (as defined in the stat module) or bitwise ORed combinations of them:

This function can support specifying a file descriptor, paths relative to directory descriptors and not following symlinks.

Note

Although Windows supports chmod(), you can only set the file’s read-only flag with it (via the stat.S_IWRITE and stat.S_IREAD constants or a corresponding integer value). All other bits are ignored.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

Raises an auditing event os.chmod with arguments path, mode, dir_fd.

New in version 3.3: Added support for specifying path as an open file descriptor, and the dir_fd and follow_symlinks arguments.

Changed in version 3.6: Accepts a path-like object.

+
+os.chown(path, uid, gid, *, dir_fd=None, follow_symlinks=True)
+

Change the owner and group id of path to the numeric uid and gid. To leave one of the ids unchanged, set it to -1.

This function can support specifying a file descriptor, paths relative to directory descriptors and not following symlinks.

See shutil.chown() for a higher-level function that accepts names in addition to numeric ids.

Raises an auditing event os.chown with arguments path, uid, gid, dir_fd.

Availability: Unix.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

New in version 3.3: Added support for specifying path as an open file descriptor, and the dir_fd and follow_symlinks arguments.

Changed in version 3.6: Supports a path-like object.

+
+os.chroot(path)
+

Change the root directory of the current process to path.

Availability: Unix, not Emscripten, not WASI.

Changed in version 3.6: Accepts a path-like object.

+
+os.fchdir(fd)
+

Change the current working directory to the directory represented by the file descriptor fd. The descriptor must refer to an opened directory, not an open file. As of Python 3.3, this is equivalent to os.chdir(fd).

Raises an auditing event os.chdir with argument path.

Availability: Unix.

+
+os.getcwd()
+

Return a string representing the current working directory.

+
+os.getcwdb()
+

Return a bytestring representing the current working directory.

Changed in version 3.8: The function now uses the UTF-8 encoding on Windows, rather than the ANSI code page: see PEP 529 for the rationale. The function is no longer deprecated on Windows.

+
+os.lchflags(path, flags)
+

Set the flags of path to the numeric flags, like chflags(), but do not follow symbolic links. As of Python 3.3, this is equivalent to os.chflags(path, flags, follow_symlinks=False).

Raises an auditing event os.chflags with arguments path, flags.

Availability: Unix, not Emscripten, not WASI.

Changed in version 3.6: Accepts a path-like object.

+
+os.lchmod(path, mode)
+

Change the mode of path to the numeric mode. If path is a symlink, this affects the symlink rather than the target. See the docs for chmod() for possible values of mode. As of Python 3.3, this is equivalent to os.chmod(path, mode, follow_symlinks=False).

lchmod() is not part of POSIX, but Unix implementations may have it if changing the mode of symbolic links is supported.

Raises an auditing event os.chmod with arguments path, mode, dir_fd.

Availability: Unix, not Linux, FreeBSD >= 1.3, NetBSD >= 1.3, not OpenBSD

Changed in version 3.6: Accepts a path-like object.

+
+os.lchown(path, uid, gid)
+

Change the owner and group id of path to the numeric uid and gid. This function will not follow symbolic links. As of Python 3.3, this is equivalent to os.chown(path, uid, gid, follow_symlinks=False).

Raises an auditing event os.chown with arguments path, uid, gid, dir_fd.

Availability: Unix.

Changed in version 3.6: Accepts a path-like object.

+
+

Create a hard link pointing to src named dst.

This function can support specifying src_dir_fd and/or dst_dir_fd to supply paths relative to directory descriptors, and not following symlinks.

Raises an auditing event os.link with arguments src, dst, src_dir_fd, dst_dir_fd.

Availability: Unix, Windows, not Emscripten.

Changed in version 3.2: Added Windows support.

New in version 3.3: Added the src_dir_fd, dst_dir_fd, and follow_symlinks arguments.

Changed in version 3.6: Accepts a path-like object for src and dst.

+
+os.listdir(path='.')
+

Return a list containing the names of the entries in the directory given by path. The list is in arbitrary order, and does not include the special entries '.' and '..' even if they are present in the directory. If a file is removed from or added to the directory during the call of this function, whether a name for that file be included is unspecified.

path may be a path-like object. If path is of type bytes (directly or indirectly through the PathLike interface), the filenames returned will also be of type bytes; in all other circumstances, they will be of type str.

This function can also support specifying a file descriptor; the file descriptor must refer to a directory.

Raises an auditing event os.listdir with argument path.

Note

To encode str filenames to bytes, use fsencode().

See also

The scandir() function returns directory entries along with file attribute information, giving better performance for many common use cases.

Changed in version 3.2: The path parameter became optional.

New in version 3.3: Added support for specifying path as an open file descriptor.

Changed in version 3.6: Accepts a path-like object.

+
+os.listdrives()
+

Return a list containing the names of drives on a Windows system.

A drive name typically looks like 'C:\\'. Not every drive name will be associated with a volume, and some may be inaccessible for a variety of reasons, including permissions, network connectivity or missing media. This function does not test for access.

May raise OSError if an error occurs collecting the drive names.

Raises an auditing event os.listdrives with no arguments.

Availability: Windows

New in version 3.12.

+
+os.listmounts(volume)
+

Return a list containing the mount points for a volume on a Windows system.

volume must be represented as a GUID path, like those returned by os.listvolumes(). Volumes may be mounted in multiple locations or not at all. In the latter case, the list will be empty. Mount points that are not associated with a volume will not be returned by this function.

The mount points return by this function will be absolute paths, and may be longer than the drive name.

Raises OSError if the volume is not recognized or if an error occurs collecting the paths.

Raises an auditing event os.listmounts with argument volume.

Availability: Windows

New in version 3.12.

+
+os.listvolumes()
+

Return a list containing the volumes in the system.

Volumes are typically represented as a GUID path that looks like \\?\Volume{xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx}\. Files can usually be accessed through a GUID path, permissions allowing. However, users are generally not familiar with them, and so the recommended use of this function is to retrieve mount points using os.listmounts().

May raise OSError if an error occurs collecting the volumes.

Raises an auditing event os.listvolumes with no arguments.

Availability: Windows

New in version 3.12.

+
+os.lstat(path, *, dir_fd=None)
+

Perform the equivalent of an lstat() system call on the given path. Similar to stat(), but does not follow symbolic links. Return a stat_result object.

On platforms that do not support symbolic links, this is an alias for stat().

As of Python 3.3, this is equivalent to os.stat(path, dir_fd=dir_fd, +follow_symlinks=False).

This function can also support paths relative to directory descriptors.

See also

The stat() function.

Changed in version 3.2: Added support for Windows 6.0 (Vista) symbolic links.

Changed in version 3.3: Added the dir_fd parameter.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.8: On Windows, now opens reparse points that represent another path (name surrogates), including symbolic links and directory junctions. Other kinds of reparse points are resolved by the operating system as for stat().

+
+os.mkdir(path, mode=0o777, *, dir_fd=None)
+

Create a directory named path with numeric mode mode.

If the directory already exists, FileExistsError is raised. If a parent directory in the path does not exist, FileNotFoundError is raised.

On some systems, mode is ignored. Where it is used, the current umask value is first masked out. If bits other than the last 9 (i.e. the last 3 digits of the octal representation of the mode) are set, their meaning is platform-dependent. On some platforms, they are ignored and you should call chmod() explicitly to set them.

This function can also support paths relative to directory descriptors.

It is also possible to create temporary directories; see the tempfile module’s tempfile.mkdtemp() function.

Raises an auditing event os.mkdir with arguments path, mode, dir_fd.

New in version 3.3: The dir_fd argument.

Changed in version 3.6: Accepts a path-like object.

+
+os.makedirs(name, mode=0o777, exist_ok=False)
+

Recursive directory creation function. Like mkdir(), but makes all intermediate-level directories needed to contain the leaf directory.

The mode parameter is passed to mkdir() for creating the leaf directory; see the mkdir() description for how it is interpreted. To set the file permission bits of any newly created parent directories you can set the umask before invoking makedirs(). The file permission bits of existing parent directories are not changed.

If exist_ok is False (the default), a FileExistsError is raised if the target directory already exists.

Note

makedirs() will become confused if the path elements to create include pardir (eg. “..” on UNIX systems).

This function handles UNC paths correctly.

Raises an auditing event os.mkdir with arguments path, mode, dir_fd.

New in version 3.2: The exist_ok parameter.

Changed in version 3.4.1: Before Python 3.4.1, if exist_ok was True and the directory existed, makedirs() would still raise an error if mode did not match the mode of the existing directory. Since this behavior was impossible to implement safely, it was removed in Python 3.4.1. See bpo-21082.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.7: The mode argument no longer affects the file permission bits of newly created intermediate-level directories.

+
+os.mkfifo(path, mode=0o666, *, dir_fd=None)
+

Create a FIFO (a named pipe) named path with numeric mode mode. The current umask value is first masked out from the mode.

This function can also support paths relative to directory descriptors.

FIFOs are pipes that can be accessed like regular files. FIFOs exist until they are deleted (for example with os.unlink()). Generally, FIFOs are used as rendezvous between “client” and “server” type processes: the server opens the FIFO for reading, and the client opens it for writing. Note that mkfifo() doesn’t open the FIFO — it just creates the rendezvous point.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3: The dir_fd argument.

Changed in version 3.6: Accepts a path-like object.

+
+os.mknod(path, mode=0o600, device=0, *, dir_fd=None)
+

Create a filesystem node (file, device special file or named pipe) named path. mode specifies both the permissions to use and the type of node to be created, being combined (bitwise OR) with one of stat.S_IFREG, stat.S_IFCHR, stat.S_IFBLK, and stat.S_IFIFO (those constants are available in stat). For stat.S_IFCHR and stat.S_IFBLK, device defines the newly created device special file (probably using os.makedev()), otherwise it is ignored.

This function can also support paths relative to directory descriptors.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3: The dir_fd argument.

Changed in version 3.6: Accepts a path-like object.

+
+os.major(device, /)
+

Extract the device major number from a raw device number (usually the st_dev or st_rdev field from stat).

+
+os.minor(device, /)
+

Extract the device minor number from a raw device number (usually the st_dev or st_rdev field from stat).

+
+os.makedev(major, minor, /)
+

Compose a raw device number from the major and minor device numbers.

+
+os.pathconf(path, name)
+

Return system configuration information relevant to a named file. name specifies the configuration value to retrieve; it may be a string which is the name of a defined system value; these names are specified in a number of standards (POSIX.1, Unix 95, Unix 98, and others). Some platforms define additional names as well. The names known to the host operating system are given in the pathconf_names dictionary. For configuration variables not included in that mapping, passing an integer for name is also accepted.

If name is a string and is not known, ValueError is raised. If a specific value for name is not supported by the host system, even if it is included in pathconf_names, an OSError is raised with errno.EINVAL for the error number.

This function can support specifying a file descriptor.

Availability: Unix.

Changed in version 3.6: Accepts a path-like object.

+
+os.pathconf_names
+

Dictionary mapping names accepted by pathconf() and fpathconf() to the integer values defined for those names by the host operating system. This can be used to determine the set of names known to the system.

Availability: Unix.

+
+

Return a string representing the path to which the symbolic link points. The result may be either an absolute or relative pathname; if it is relative, it may be converted to an absolute pathname using os.path.join(os.path.dirname(path), result).

If the path is a string object (directly or indirectly through a PathLike interface), the result will also be a string object, and the call may raise a UnicodeDecodeError. If the path is a bytes object (direct or indirectly), the result will be a bytes object.

This function can also support paths relative to directory descriptors.

When trying to resolve a path that may contain links, use realpath() to properly handle recursion and platform differences.

Availability: Unix, Windows.

Changed in version 3.2: Added support for Windows 6.0 (Vista) symbolic links.

New in version 3.3: The dir_fd argument.

Changed in version 3.6: Accepts a path-like object on Unix.

Changed in version 3.8: Accepts a path-like object and a bytes object on Windows.

Changed in version 3.8: Added support for directory junctions, and changed to return the substitution path (which typically includes \\?\ prefix) rather than the optional “print name” field that was previously returned.

+
+os.remove(path, *, dir_fd=None)
+

Remove (delete) the file path. If path is a directory, an OSError is raised. Use rmdir() to remove directories. If the file does not exist, a FileNotFoundError is raised.

This function can support paths relative to directory descriptors.

On Windows, attempting to remove a file that is in use causes an exception to be raised; on Unix, the directory entry is removed but the storage allocated to the file is not made available until the original file is no longer in use.

This function is semantically identical to unlink().

Raises an auditing event os.remove with arguments path, dir_fd.

New in version 3.3: The dir_fd argument.

Changed in version 3.6: Accepts a path-like object.

+
+os.removedirs(name)
+

Remove directories recursively. Works like rmdir() except that, if the leaf directory is successfully removed, removedirs() tries to successively remove every parent directory mentioned in path until an error is raised (which is ignored, because it generally means that a parent directory is not empty). For example, os.removedirs('foo/bar/baz') will first remove the directory 'foo/bar/baz', and then remove 'foo/bar' and 'foo' if they are empty. Raises OSError if the leaf directory could not be successfully removed.

Raises an auditing event os.remove with arguments path, dir_fd.

Changed in version 3.6: Accepts a path-like object.

+
+os.rename(src, dst, *, src_dir_fd=None, dst_dir_fd=None)
+

Rename the file or directory src to dst. If dst exists, the operation will fail with an OSError subclass in a number of cases:

On Windows, if dst exists a FileExistsError is always raised. The operation may fail if src and dst are on different filesystems. Use shutil.move() to support moves to a different filesystem.

On Unix, if src is a file and dst is a directory or vice-versa, an IsADirectoryError or a NotADirectoryError will be raised respectively. If both are directories and dst is empty, dst will be silently replaced. If dst is a non-empty directory, an OSError is raised. If both are files, dst will be replaced silently if the user has permission. The operation may fail on some Unix flavors if src and dst are on different filesystems. If successful, the renaming will be an atomic operation (this is a POSIX requirement).

This function can support specifying src_dir_fd and/or dst_dir_fd to supply paths relative to directory descriptors.

If you want cross-platform overwriting of the destination, use replace().

Raises an auditing event os.rename with arguments src, dst, src_dir_fd, dst_dir_fd.

New in version 3.3: The src_dir_fd and dst_dir_fd arguments.

Changed in version 3.6: Accepts a path-like object for src and dst.

+
+os.renames(old, new)
+

Recursive directory or file renaming function. Works like rename(), except creation of any intermediate directories needed to make the new pathname good is attempted first. After the rename, directories corresponding to rightmost path segments of the old name will be pruned away using removedirs().

Note

This function can fail with the new directory structure made if you lack permissions needed to remove the leaf directory or file.

Raises an auditing event os.rename with arguments src, dst, src_dir_fd, dst_dir_fd.

Changed in version 3.6: Accepts a path-like object for old and new.

+
+os.replace(src, dst, *, src_dir_fd=None, dst_dir_fd=None)
+

Rename the file or directory src to dst. If dst is a non-empty directory, OSError will be raised. If dst exists and is a file, it will be replaced silently if the user has permission. The operation may fail if src and dst are on different filesystems. If successful, the renaming will be an atomic operation (this is a POSIX requirement).

This function can support specifying src_dir_fd and/or dst_dir_fd to supply paths relative to directory descriptors.

Raises an auditing event os.rename with arguments src, dst, src_dir_fd, dst_dir_fd.

New in version 3.3.

Changed in version 3.6: Accepts a path-like object for src and dst.

+
+os.rmdir(path, *, dir_fd=None)
+

Remove (delete) the directory path. If the directory does not exist or is not empty, a FileNotFoundError or an OSError is raised respectively. In order to remove whole directory trees, shutil.rmtree() can be used.

This function can support paths relative to directory descriptors.

Raises an auditing event os.rmdir with arguments path, dir_fd.

New in version 3.3: The dir_fd parameter.

Changed in version 3.6: Accepts a path-like object.

+
+os.scandir(path='.')
+

Return an iterator of os.DirEntry objects corresponding to the entries in the directory given by path. The entries are yielded in arbitrary order, and the special entries '.' and '..' are not included. If a file is removed from or added to the directory after creating the iterator, whether an entry for that file be included is unspecified.

Using scandir() instead of listdir() can significantly increase the performance of code that also needs file type or file attribute information, because os.DirEntry objects expose this information if the operating system provides it when scanning a directory. All os.DirEntry methods may perform a system call, but is_dir() and is_file() usually only require a system call for symbolic links; os.DirEntry.stat() always requires a system call on Unix but only requires one for symbolic links on Windows.

path may be a path-like object. If path is of type bytes (directly or indirectly through the PathLike interface), the type of the name and path attributes of each os.DirEntry will be bytes; in all other circumstances, they will be of type str.

This function can also support specifying a file descriptor; the file descriptor must refer to a directory.

Raises an auditing event os.scandir with argument path.

The scandir() iterator supports the context manager protocol and has the following method:

+scandir.close()
+

Close the iterator and free acquired resources.

This is called automatically when the iterator is exhausted or garbage collected, or when an error happens during iterating. However it is advisable to call it explicitly or use the with statement.

New in version 3.6.

+

The following example shows a simple use of scandir() to display all the files (excluding directories) in the given path that don’t start with '.'. The entry.is_file() call will generally not make an additional system call:

with os.scandir(path) as it:
+    for entry in it:
+        if not entry.name.startswith('.') and entry.is_file():
+            print(entry.name)
+

Note

On Unix-based systems, scandir() uses the system’s opendir() and readdir() functions. On Windows, it uses the Win32 FindFirstFileW and FindNextFileW functions.

New in version 3.5.

New in version 3.6: Added support for the context manager protocol and the close() method. If a scandir() iterator is neither exhausted nor explicitly closed a ResourceWarning will be emitted in its destructor.

The function accepts a path-like object.

Changed in version 3.7: Added support for file descriptors on Unix.

+
+class os.DirEntry
+

Object yielded by scandir() to expose the file path and other file attributes of a directory entry.

scandir() will provide as much of this information as possible without making additional system calls. When a stat() or lstat() system call is made, the os.DirEntry object will cache the result.

os.DirEntry instances are not intended to be stored in long-lived data structures; if you know the file metadata has changed or if a long time has elapsed since calling scandir(), call os.stat(entry.path) to fetch up-to-date information.

Because the os.DirEntry methods can make operating system calls, they may also raise OSError. If you need very fine-grained control over errors, you can catch OSError when calling one of the os.DirEntry methods and handle as appropriate.

To be directly usable as a path-like object, os.DirEntry implements the PathLike interface.

Attributes and methods on a os.DirEntry instance are as follows:

+name
+

The entry’s base filename, relative to the scandir() path argument.

The name attribute will be bytes if the scandir() path argument is of type bytes and str otherwise. Use fsdecode() to decode byte filenames.

+
+path
+

The entry’s full path name: equivalent to os.path.join(scandir_path, +entry.name) where scandir_path is the scandir() path argument. The path is only absolute if the scandir() path argument was absolute. If the scandir() path argument was a file descriptor, the path attribute is the same as the name attribute.

The path attribute will be bytes if the scandir() path argument is of type bytes and str otherwise. Use fsdecode() to decode byte filenames.

+
+inode()
+

Return the inode number of the entry.

The result is cached on the os.DirEntry object. Use os.stat(entry.path, follow_symlinks=False).st_ino to fetch up-to-date information.

On the first, uncached call, a system call is required on Windows but not on Unix.

+
+is_dir(*, follow_symlinks=True)
+

Return True if this entry is a directory or a symbolic link pointing to a directory; return False if the entry is or points to any other kind of file, or if it doesn’t exist anymore.

If follow_symlinks is False, return True only if this entry is a directory (without following symlinks); return False if the entry is any other kind of file or if it doesn’t exist anymore.

The result is cached on the os.DirEntry object, with a separate cache for follow_symlinks True and False. Call os.stat() along with stat.S_ISDIR() to fetch up-to-date information.

On the first, uncached call, no system call is required in most cases. Specifically, for non-symlinks, neither Windows or Unix require a system call, except on certain Unix file systems, such as network file systems, that return dirent.d_type == DT_UNKNOWN. If the entry is a symlink, a system call will be required to follow the symlink unless follow_symlinks is False.

This method can raise OSError, such as PermissionError, but FileNotFoundError is caught and not raised.

+
+is_file(*, follow_symlinks=True)
+

Return True if this entry is a file or a symbolic link pointing to a file; return False if the entry is or points to a directory or other non-file entry, or if it doesn’t exist anymore.

If follow_symlinks is False, return True only if this entry is a file (without following symlinks); return False if the entry is a directory or other non-file entry, or if it doesn’t exist anymore.

The result is cached on the os.DirEntry object. Caching, system calls made, and exceptions raised are as per is_dir().

+
+

Return True if this entry is a symbolic link (even if broken); return False if the entry points to a directory or any kind of file, or if it doesn’t exist anymore.

The result is cached on the os.DirEntry object. Call os.path.islink() to fetch up-to-date information.

On the first, uncached call, no system call is required in most cases. Specifically, neither Windows or Unix require a system call, except on certain Unix file systems, such as network file systems, that return dirent.d_type == DT_UNKNOWN.

This method can raise OSError, such as PermissionError, but FileNotFoundError is caught and not raised.

+
+is_junction()
+

Return True if this entry is a junction (even if broken); return False if the entry points to a regular directory, any kind of file, a symlink, or if it doesn’t exist anymore.

The result is cached on the os.DirEntry object. Call os.path.isjunction() to fetch up-to-date information.

New in version 3.12.

+
+stat(*, follow_symlinks=True)
+

Return a stat_result object for this entry. This method follows symbolic links by default; to stat a symbolic link add the follow_symlinks=False argument.

On Unix, this method always requires a system call. On Windows, it only requires a system call if follow_symlinks is True and the entry is a reparse point (for example, a symbolic link or directory junction).

On Windows, the st_ino, st_dev and st_nlink attributes of the stat_result are always set to zero. Call os.stat() to get these attributes.

The result is cached on the os.DirEntry object, with a separate cache for follow_symlinks True and False. Call os.stat() to fetch up-to-date information.

+

Note that there is a nice correspondence between several attributes and methods of os.DirEntry and of pathlib.Path. In particular, the name attribute has the same meaning, as do the is_dir(), is_file(), is_symlink(), is_junction(), and stat() methods.

New in version 3.5.

Changed in version 3.6: Added support for the PathLike interface. Added support for bytes paths on Windows.

Changed in version 3.12: The st_ctime attribute of a stat result is deprecated on Windows. The file creation time is properly available as st_birthtime, and in the future st_ctime may be changed to return zero or the metadata change time, if available.

+
+os.stat(path, *, dir_fd=None, follow_symlinks=True)
+

Get the status of a file or a file descriptor. Perform the equivalent of a stat() system call on the given path. path may be specified as either a string or bytes – directly or indirectly through the PathLike interface – or as an open file descriptor. Return a stat_result object.

This function normally follows symlinks; to stat a symlink add the argument follow_symlinks=False, or use lstat().

This function can support specifying a file descriptor and not following symlinks.

On Windows, passing follow_symlinks=False will disable following all name-surrogate reparse points, which includes symlinks and directory junctions. Other types of reparse points that do not resemble links or that the operating system is unable to follow will be opened directly. When following a chain of multiple links, this may result in the original link being returned instead of the non-link that prevented full traversal. To obtain stat results for the final path in this case, use the os.path.realpath() function to resolve the path name as far as possible and call lstat() on the result. This does not apply to dangling symlinks or junction points, which will raise the usual exceptions.

Example:

>>> import os
+>>> statinfo = os.stat('somefile.txt')
+>>> statinfo
+os.stat_result(st_mode=33188, st_ino=7876932, st_dev=234881026,
+st_nlink=1, st_uid=501, st_gid=501, st_size=264, st_atime=1297230295,
+st_mtime=1297230027, st_ctime=1297230027)
+>>> statinfo.st_size
+264
+

See also

fstat() and lstat() functions.

New in version 3.3: Added the dir_fd and follow_symlinks arguments, specifying a file descriptor instead of a path.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.8: On Windows, all reparse points that can be resolved by the operating system are now followed, and passing follow_symlinks=False disables following all name surrogate reparse points. If the operating system reaches a reparse point that it is not able to follow, stat now returns the information for the original path as if follow_symlinks=False had been specified instead of raising an error.

+
+class os.stat_result
+

Object whose attributes correspond roughly to the members of the stat structure. It is used for the result of os.stat(), os.fstat() and os.lstat().

Attributes:

+st_mode
+

File mode: file type and file mode bits (permissions).

+
+st_ino
+

Platform dependent, but if non-zero, uniquely identifies the file for a given value of st_dev. Typically:

  • the inode number on Unix,
  • the file index on Windows
+
+st_dev
+

Identifier of the device on which this file resides.

+
+

Number of hard links.

+
+st_uid
+

User identifier of the file owner.

+
+st_gid
+

Group identifier of the file owner.

+
+st_size
+

Size of the file in bytes, if it is a regular file or a symbolic link. The size of a symbolic link is the length of the pathname it contains, without a terminating null byte.

+

Timestamps:

+st_atime
+

Time of most recent access expressed in seconds.

+
+st_mtime
+

Time of most recent content modification expressed in seconds.

+
+st_ctime
+

Time of most recent metadata change expressed in seconds.

Changed in version 3.12: st_ctime is deprecated on Windows. Use st_birthtime for the file creation time. In the future, st_ctime will contain the time of the most recent metadata change, as for other platforms.

+
+st_atime_ns
+

Time of most recent access expressed in nanoseconds as an integer.

+
+st_mtime_ns
+

Time of most recent content modification expressed in nanoseconds as an integer.

+
+st_ctime_ns
+

Time of most recent metadata change expressed in nanoseconds as an integer.

Changed in version 3.12: st_ctime_ns is deprecated on Windows. Use st_birthtime_ns for the file creation time. In the future, st_ctime will contain the time of the most recent metadata change, as for other platforms.

+
+st_birthtime
+

Time of file creation expressed in seconds. This attribute is not always available, and may raise AttributeError.

Changed in version 3.12: st_birthtime is now available on Windows.

+
+st_birthtime_ns
+

Time of file creation expressed in nanoseconds as an integer. This attribute is not always available, and may raise AttributeError.

New in version 3.12.

+

Note

The exact meaning and resolution of the st_atime, st_mtime, st_ctime and st_birthtime attributes depend on the operating system and the file system. For example, on Windows systems using the FAT32 file systems, st_mtime has 2-second resolution, and st_atime has only 1-day resolution. See your operating system documentation for details.

Similarly, although st_atime_ns, st_mtime_ns, st_ctime_ns and st_birthtime_ns are always expressed in nanoseconds, many systems do not provide nanosecond precision. On systems that do provide nanosecond precision, the floating-point object used to store st_atime, st_mtime, st_ctime and st_birthtime cannot preserve all of it, and as such will be slightly inexact. If you need the exact timestamps you should always use st_atime_ns, st_mtime_ns, st_ctime_ns and st_birthtime_ns.

On some Unix systems (such as Linux), the following attributes may also be available:

+st_blocks
+

Number of 512-byte blocks allocated for file. This may be smaller than st_size/512 when the file has holes.

+
+st_blksize
+

“Preferred” blocksize for efficient file system I/O. Writing to a file in smaller chunks may cause an inefficient read-modify-rewrite.

+
+st_rdev
+

Type of device if an inode device.

+
+st_flags
+

User defined flags for file.

+

On other Unix systems (such as FreeBSD), the following attributes may be available (but may be only filled out if root tries to use them):

+st_gen
+

File generation number.

+

On Solaris and derivatives, the following attributes may also be available:

+st_fstype
+

String that uniquely identifies the type of the filesystem that contains the file.

+

On macOS systems, the following attributes may also be available:

+st_rsize
+

Real size of the file.

+
+st_creator
+

Creator of the file.

+
+st_type
+

File type.

+

On Windows systems, the following attributes are also available:

+st_file_attributes
+

Windows file attributes: dwFileAttributes member of the BY_HANDLE_FILE_INFORMATION structure returned by GetFileInformationByHandle(). See the FILE_ATTRIBUTE_* <stat.FILE_ATTRIBUTE_ARCHIVE> constants in the stat module.

+
+st_reparse_tag
+

When st_file_attributes has the FILE_ATTRIBUTE_REPARSE_POINT set, this field contains the tag identifying the type of reparse point. See the IO_REPARSE_TAG_* constants in the stat module.

+

The standard module stat defines functions and constants that are useful for extracting information from a stat structure. (On Windows, some items are filled with dummy values.)

For backward compatibility, a stat_result instance is also accessible as a tuple of at least 10 integers giving the most important (and portable) members of the stat structure, in the order st_mode, st_ino, st_dev, st_nlink, st_uid, st_gid, st_size, st_atime, st_mtime, st_ctime. More items may be added at the end by some implementations. For compatibility with older Python versions, accessing stat_result as a tuple always returns integers.

New in version 3.3: Added the st_atime_ns, st_mtime_ns, and st_ctime_ns members.

New in version 3.5: Added the st_file_attributes member on Windows.

Changed in version 3.5: Windows now returns the file index as st_ino when available.

New in version 3.7: Added the st_fstype member to Solaris/derivatives.

New in version 3.8: Added the st_reparse_tag member on Windows.

Changed in version 3.8: On Windows, the st_mode member now identifies special files as S_IFCHR, S_IFIFO or S_IFBLK as appropriate.

Changed in version 3.12: On Windows, st_ctime is deprecated. Eventually, it will contain the last metadata change time, for consistency with other platforms, but for now still contains creation time. Use st_birthtime for the creation time.

Changed in version 3.12: On Windows, st_ino may now be up to 128 bits, depending on the file system. Previously it would not be above 64 bits, and larger file identifiers would be arbitrarily packed.

Changed in version 3.12: On Windows, st_rdev no longer returns a value. Previously it would contain the same as st_dev, which was incorrect.

New in version 3.12: Added the st_birthtime member on Windows.

+
+os.statvfs(path)
+

Perform a statvfs() system call on the given path. The return value is an object whose attributes describe the filesystem on the given path, and correspond to the members of the statvfs structure, namely: f_bsize, f_frsize, f_blocks, f_bfree, f_bavail, f_files, f_ffree, f_favail, f_flag, f_namemax, f_fsid.

Two module-level constants are defined for the f_flag attribute’s bit-flags: if ST_RDONLY is set, the filesystem is mounted read-only, and if ST_NOSUID is set, the semantics of setuid/setgid bits are disabled or not supported.

Additional module-level constants are defined for GNU/glibc based systems. These are ST_NODEV (disallow access to device special files), ST_NOEXEC (disallow program execution), ST_SYNCHRONOUS (writes are synced at once), ST_MANDLOCK (allow mandatory locks on an FS), ST_WRITE (write on file/directory/symlink), ST_APPEND (append-only file), ST_IMMUTABLE (immutable file), ST_NOATIME (do not update access times), ST_NODIRATIME (do not update directory access times), ST_RELATIME (update atime relative to mtime/ctime).

This function can support specifying a file descriptor.

Availability: Unix.

Changed in version 3.2: The ST_RDONLY and ST_NOSUID constants were added.

New in version 3.3: Added support for specifying path as an open file descriptor.

Changed in version 3.4: The ST_NODEV, ST_NOEXEC, ST_SYNCHRONOUS, ST_MANDLOCK, ST_WRITE, ST_APPEND, ST_IMMUTABLE, ST_NOATIME, ST_NODIRATIME, and ST_RELATIME constants were added.

Changed in version 3.6: Accepts a path-like object.

New in version 3.7: Added f_fsid.

+
+os.supports_dir_fd
+

A set object indicating which functions in the os module accept an open file descriptor for their dir_fd parameter. Different platforms provide different features, and the underlying functionality Python uses to implement the dir_fd parameter is not available on all platforms Python supports. For consistency’s sake, functions that may support dir_fd always allow specifying the parameter, but will throw an exception if the functionality is used when it’s not locally available. (Specifying None for dir_fd is always supported on all platforms.)

To check whether a particular function accepts an open file descriptor for its dir_fd parameter, use the in operator on supports_dir_fd. As an example, this expression evaluates to True if os.stat() accepts open file descriptors for dir_fd on the local platform:

os.stat in os.supports_dir_fd
+

Currently dir_fd parameters only work on Unix platforms; none of them work on Windows.

New in version 3.3.

+
+os.supports_effective_ids
+

A set object indicating whether os.access() permits specifying True for its effective_ids parameter on the local platform. (Specifying False for effective_ids is always supported on all platforms.) If the local platform supports it, the collection will contain os.access(); otherwise it will be empty.

This expression evaluates to True if os.access() supports effective_ids=True on the local platform:

os.access in os.supports_effective_ids
+

Currently effective_ids is only supported on Unix platforms; it does not work on Windows.

New in version 3.3.

+
+os.supports_fd
+

A set object indicating which functions in the os module permit specifying their path parameter as an open file descriptor on the local platform. Different platforms provide different features, and the underlying functionality Python uses to accept open file descriptors as path arguments is not available on all platforms Python supports.

To determine whether a particular function permits specifying an open file descriptor for its path parameter, use the in operator on supports_fd. As an example, this expression evaluates to True if os.chdir() accepts open file descriptors for path on your local platform:

os.chdir in os.supports_fd
+

New in version 3.3.

+
+

A set object indicating which functions in the os module accept False for their follow_symlinks parameter on the local platform. Different platforms provide different features, and the underlying functionality Python uses to implement follow_symlinks is not available on all platforms Python supports. For consistency’s sake, functions that may support follow_symlinks always allow specifying the parameter, but will throw an exception if the functionality is used when it’s not locally available. (Specifying True for follow_symlinks is always supported on all platforms.)

To check whether a particular function accepts False for its follow_symlinks parameter, use the in operator on supports_follow_symlinks. As an example, this expression evaluates to True if you may specify follow_symlinks=False when calling os.stat() on the local platform:

os.stat in os.supports_follow_symlinks
+

New in version 3.3.

+
+

Create a symbolic link pointing to src named dst.

On Windows, a symlink represents either a file or a directory, and does not morph to the target dynamically. If the target is present, the type of the symlink will be created to match. Otherwise, the symlink will be created as a directory if target_is_directory is True or a file symlink (the default) otherwise. On non-Windows platforms, target_is_directory is ignored.

This function can support paths relative to directory descriptors.

Note

On newer versions of Windows 10, unprivileged accounts can create symlinks if Developer Mode is enabled. When Developer Mode is not available/enabled, the SeCreateSymbolicLinkPrivilege privilege is required, or the process must be run as an administrator.

OSError is raised when the function is called by an unprivileged user.

Raises an auditing event os.symlink with arguments src, dst, dir_fd.

Availability: Unix, Windows.

The function is limited on Emscripten and WASI, see WebAssembly platforms for more information.

Changed in version 3.2: Added support for Windows 6.0 (Vista) symbolic links.

New in version 3.3: Added the dir_fd argument, and now allow target_is_directory on non-Windows platforms.

Changed in version 3.6: Accepts a path-like object for src and dst.

Changed in version 3.8: Added support for unelevated symlinks on Windows with Developer Mode.

+
+os.sync()
+

Force write of everything to disk.

Availability: Unix.

New in version 3.3.

+
+os.truncate(path, length)
+

Truncate the file corresponding to path, so that it is at most length bytes in size.

This function can support specifying a file descriptor.

Raises an auditing event os.truncate with arguments path, length.

Availability: Unix, Windows.

New in version 3.3.

Changed in version 3.5: Added support for Windows

Changed in version 3.6: Accepts a path-like object.

+
+

Remove (delete) the file path. This function is semantically identical to remove(); the unlink name is its traditional Unix name. Please see the documentation for remove() for further information.

Raises an auditing event os.remove with arguments path, dir_fd.

New in version 3.3: The dir_fd parameter.

Changed in version 3.6: Accepts a path-like object.

+
+os.utime(path, times=None, *, [ns, ]dir_fd=None, follow_symlinks=True)
+

Set the access and modified times of the file specified by path.

utime() takes two optional parameters, times and ns. These specify the times set on path and are used as follows:

  • If ns is specified, it must be a 2-tuple of the form (atime_ns, mtime_ns) where each member is an int expressing nanoseconds.
  • If times is not None, it must be a 2-tuple of the form (atime, mtime) where each member is an int or float expressing seconds.
  • If times is None and ns is unspecified, this is equivalent to specifying ns=(atime_ns, mtime_ns) where both times are the current time.

It is an error to specify tuples for both times and ns.

Note that the exact times you set here may not be returned by a subsequent stat() call, depending on the resolution with which your operating system records access and modification times; see stat(). The best way to preserve exact times is to use the st_atime_ns and st_mtime_ns fields from the os.stat() result object with the ns parameter to utime().

This function can support specifying a file descriptor, paths relative to directory descriptors and not following symlinks.

Raises an auditing event os.utime with arguments path, times, ns, dir_fd.

New in version 3.3: Added support for specifying path as an open file descriptor, and the dir_fd, follow_symlinks, and ns parameters.

Changed in version 3.6: Accepts a path-like object.

+
+os.walk(top, topdown=True, onerror=None, followlinks=False)
+

Generate the file names in a directory tree by walking the tree either top-down or bottom-up. For each directory in the tree rooted at directory top (including top itself), it yields a 3-tuple (dirpath, dirnames, +filenames).

dirpath is a string, the path to the directory. dirnames is a list of the names of the subdirectories in dirpath (including symlinks to directories, and excluding '.' and '..'). filenames is a list of the names of the non-directory files in dirpath. Note that the names in the lists contain no path components. To get a full path (which begins with top) to a file or directory in dirpath, do os.path.join(dirpath, name). Whether or not the lists are sorted depends on the file system. If a file is removed from or added to the dirpath directory during generating the lists, whether a name for that file be included is unspecified.

If optional argument topdown is True or not specified, the triple for a directory is generated before the triples for any of its subdirectories (directories are generated top-down). If topdown is False, the triple for a directory is generated after the triples for all of its subdirectories (directories are generated bottom-up). No matter the value of topdown, the list of subdirectories is retrieved before the tuples for the directory and its subdirectories are generated.

When topdown is True, the caller can modify the dirnames list in-place (perhaps using del or slice assignment), and walk() will only recurse into the subdirectories whose names remain in dirnames; this can be used to prune the search, impose a specific order of visiting, or even to inform walk() about directories the caller creates or renames before it resumes walk() again. Modifying dirnames when topdown is False has no effect on the behavior of the walk, because in bottom-up mode the directories in dirnames are generated before dirpath itself is generated.

By default, errors from the scandir() call are ignored. If optional argument onerror is specified, it should be a function; it will be called with one argument, an OSError instance. It can report the error to continue with the walk, or raise the exception to abort the walk. Note that the filename is available as the filename attribute of the exception object.

By default, walk() will not walk down into symbolic links that resolve to directories. Set followlinks to True to visit directories pointed to by symlinks, on systems that support them.

Note

Be aware that setting followlinks to True can lead to infinite recursion if a link points to a parent directory of itself. walk() does not keep track of the directories it visited already.

Note

If you pass a relative pathname, don’t change the current working directory between resumptions of walk(). walk() never changes the current directory, and assumes that its caller doesn’t either.

This example displays the number of bytes taken by non-directory files in each directory under the starting directory, except that it doesn’t look under any CVS subdirectory:

import os
+from os.path import join, getsize
+for root, dirs, files in os.walk('python/Lib/email'):
+    print(root, "consumes", end=" ")
+    print(sum(getsize(join(root, name)) for name in files), end=" ")
+    print("bytes in", len(files), "non-directory files")
+    if 'CVS' in dirs:
+        dirs.remove('CVS')  # don't visit CVS directories
+

In the next example (simple implementation of shutil.rmtree()), walking the tree bottom-up is essential, rmdir() doesn’t allow deleting a directory before the directory is empty:

# Delete everything reachable from the directory named in "top",
+# assuming there are no symbolic links.
+# CAUTION:  This is dangerous!  For example, if top == '/', it
+# could delete all your disk files.
+import os
+for root, dirs, files in os.walk(top, topdown=False):
+    for name in files:
+        os.remove(os.path.join(root, name))
+    for name in dirs:
+        os.rmdir(os.path.join(root, name))
+

Raises an auditing event os.walk with arguments top, topdown, onerror, followlinks.

Changed in version 3.5: This function now calls os.scandir() instead of os.listdir(), making it faster by reducing the number of calls to os.stat().

Changed in version 3.6: Accepts a path-like object.

+
+os.fwalk(top='.', topdown=True, onerror=None, *, follow_symlinks=False, dir_fd=None)
+

This behaves exactly like walk(), except that it yields a 4-tuple (dirpath, dirnames, filenames, dirfd), and it supports dir_fd.

dirpath, dirnames and filenames are identical to walk() output, and dirfd is a file descriptor referring to the directory dirpath.

This function always supports paths relative to directory descriptors and not following symlinks. Note however that, unlike other functions, the fwalk() default value for follow_symlinks is False.

Note

Since fwalk() yields file descriptors, those are only valid until the next iteration step, so you should duplicate them (e.g. with dup()) if you want to keep them longer.

This example displays the number of bytes taken by non-directory files in each directory under the starting directory, except that it doesn’t look under any CVS subdirectory:

import os
+for root, dirs, files, rootfd in os.fwalk('python/Lib/email'):
+    print(root, "consumes", end="")
+    print(sum([os.stat(name, dir_fd=rootfd).st_size for name in files]),
+          end="")
+    print("bytes in", len(files), "non-directory files")
+    if 'CVS' in dirs:
+        dirs.remove('CVS')  # don't visit CVS directories
+

In the next example, walking the tree bottom-up is essential: rmdir() doesn’t allow deleting a directory before the directory is empty:

# Delete everything reachable from the directory named in "top",
+# assuming there are no symbolic links.
+# CAUTION:  This is dangerous!  For example, if top == '/', it
+# could delete all your disk files.
+import os
+for root, dirs, files, rootfd in os.fwalk(top, topdown=False):
+    for name in files:
+        os.unlink(name, dir_fd=rootfd)
+    for name in dirs:
+        os.rmdir(name, dir_fd=rootfd)
+

Raises an auditing event os.fwalk with arguments top, topdown, onerror, follow_symlinks, dir_fd.

Availability: Unix.

New in version 3.3.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.7: Added support for bytes paths.

+
+os.memfd_create(name[, flags=os.MFD_CLOEXEC])
+

Create an anonymous file and return a file descriptor that refers to it. flags must be one of the os.MFD_* constants available on the system (or a bitwise ORed combination of them). By default, the new file descriptor is non-inheritable.

The name supplied in name is used as a filename and will be displayed as the target of the corresponding symbolic link in the directory /proc/self/fd/. The displayed name is always prefixed with memfd: and serves only for debugging purposes. Names do not affect the behavior of the file descriptor, and as such multiple files can have the same name without any side effects.

Availability: Linux >= 3.17 with glibc >= 2.27.

New in version 3.8.

+
+os.MFD_CLOEXEC
+os.MFD_ALLOW_SEALING
+os.MFD_HUGETLB
+os.MFD_HUGE_SHIFT
+os.MFD_HUGE_MASK
+os.MFD_HUGE_64KB
+os.MFD_HUGE_512KB
+os.MFD_HUGE_1MB
+os.MFD_HUGE_2MB
+os.MFD_HUGE_8MB
+os.MFD_HUGE_16MB
+os.MFD_HUGE_32MB
+os.MFD_HUGE_256MB
+os.MFD_HUGE_512MB
+os.MFD_HUGE_1GB
+os.MFD_HUGE_2GB
+os.MFD_HUGE_16GB
+

These flags can be passed to memfd_create().

Availability: Linux >= 3.17 with glibc >= 2.27

The MFD_HUGE* flags are only available since Linux 4.14.

New in version 3.8.

+
+os.eventfd(initval[, flags=os.EFD_CLOEXEC])
+

Create and return an event file descriptor. The file descriptors supports raw read() and write() with a buffer size of 8, select(), poll() and similar. See man page eventfd(2) for more information. By default, the new file descriptor is non-inheritable.

initval is the initial value of the event counter. The initial value must be an 32 bit unsigned integer. Please note that the initial value is limited to a 32 bit unsigned int although the event counter is an unsigned 64 bit integer with a maximum value of 264-2.

flags can be constructed from EFD_CLOEXEC, EFD_NONBLOCK, and EFD_SEMAPHORE.

If EFD_SEMAPHORE is specified and the event counter is non-zero, eventfd_read() returns 1 and decrements the counter by one.

If EFD_SEMAPHORE is not specified and the event counter is non-zero, eventfd_read() returns the current event counter value and resets the counter to zero.

If the event counter is zero and EFD_NONBLOCK is not specified, eventfd_read() blocks.

eventfd_write() increments the event counter. Write blocks if the write operation would increment the counter to a value larger than 264-2.

Example:

import os
+
+# semaphore with start value '1'
+fd = os.eventfd(1, os.EFD_SEMAPHORE | os.EFC_CLOEXEC)
+try:
+    # acquire semaphore
+    v = os.eventfd_read(fd)
+    try:
+        do_work()
+    finally:
+        # release semaphore
+        os.eventfd_write(fd, v)
+finally:
+    os.close(fd)
+

Availability: Linux >= 2.6.27 with glibc >= 2.8

New in version 3.10.

+
+os.eventfd_read(fd)
+

Read value from an eventfd() file descriptor and return a 64 bit unsigned int. The function does not verify that fd is an eventfd().

Availability: Linux >= 2.6.27

New in version 3.10.

+
+os.eventfd_write(fd, value)
+

Add value to an eventfd() file descriptor. value must be a 64 bit unsigned int. The function does not verify that fd is an eventfd().

Availability: Linux >= 2.6.27

New in version 3.10.

+
+os.EFD_CLOEXEC
+

Set close-on-exec flag for new eventfd() file descriptor.

Availability: Linux >= 2.6.27

New in version 3.10.

+
+os.EFD_NONBLOCK
+

Set O_NONBLOCK status flag for new eventfd() file descriptor.

Availability: Linux >= 2.6.27

New in version 3.10.

+
+os.EFD_SEMAPHORE
+

Provide semaphore-like semantics for reads from a eventfd() file descriptor. On read the internal counter is decremented by one.

Availability: Linux >= 2.6.30

New in version 3.10.

+

Linux extended attributes

New in version 3.3.

These functions are all available on Linux only.

+os.getxattr(path, attribute, *, follow_symlinks=True)
+

Return the value of the extended filesystem attribute attribute for path. attribute can be bytes or str (directly or indirectly through the PathLike interface). If it is str, it is encoded with the filesystem encoding.

This function can support specifying a file descriptor and not following symlinks.

Raises an auditing event os.getxattr with arguments path, attribute.

Changed in version 3.6: Accepts a path-like object for path and attribute.

+
+os.listxattr(path=None, *, follow_symlinks=True)
+

Return a list of the extended filesystem attributes on path. The attributes in the list are represented as strings decoded with the filesystem encoding. If path is None, listxattr() will examine the current directory.

This function can support specifying a file descriptor and not following symlinks.

Raises an auditing event os.listxattr with argument path.

Changed in version 3.6: Accepts a path-like object.

+
+os.removexattr(path, attribute, *, follow_symlinks=True)
+

Removes the extended filesystem attribute attribute from path. attribute should be bytes or str (directly or indirectly through the PathLike interface). If it is a string, it is encoded with the filesystem encoding and error handler.

This function can support specifying a file descriptor and not following symlinks.

Raises an auditing event os.removexattr with arguments path, attribute.

Changed in version 3.6: Accepts a path-like object for path and attribute.

+
+os.setxattr(path, attribute, value, flags=0, *, follow_symlinks=True)
+

Set the extended filesystem attribute attribute on path to value. attribute must be a bytes or str with no embedded NULs (directly or indirectly through the PathLike interface). If it is a str, it is encoded with the filesystem encoding and error handler. flags may be XATTR_REPLACE or XATTR_CREATE. If XATTR_REPLACE is given and the attribute does not exist, ENODATA will be raised. If XATTR_CREATE is given and the attribute already exists, the attribute will not be created and EEXISTS will be raised.

This function can support specifying a file descriptor and not following symlinks.

Note

A bug in Linux kernel versions less than 2.6.39 caused the flags argument to be ignored on some filesystems.

Raises an auditing event os.setxattr with arguments path, attribute, value, flags.

Changed in version 3.6: Accepts a path-like object for path and attribute.

+
+os.XATTR_SIZE_MAX
+

The maximum size the value of an extended attribute can be. Currently, this is 64 KiB on Linux.

+
+os.XATTR_CREATE
+

This is a possible value for the flags argument in setxattr(). It indicates the operation must create an attribute.

+
+os.XATTR_REPLACE
+

This is a possible value for the flags argument in setxattr(). It indicates the operation must replace an existing attribute.

+

Process Management

These functions may be used to create and manage processes.

The various exec* functions take a list of arguments for the new program loaded into the process. In each case, the first of these arguments is passed to the new program as its own name rather than as an argument a user may have typed on a command line. For the C programmer, this is the argv[0] passed to a program’s main(). For example, os.execv('/bin/echo', +['foo', 'bar']) will only print bar on standard output; foo will seem to be ignored.

+os.abort()
+

Generate a SIGABRT signal to the current process. On Unix, the default behavior is to produce a core dump; on Windows, the process immediately returns an exit code of 3. Be aware that calling this function will not call the Python signal handler registered for SIGABRT with signal.signal().

+
+os.add_dll_directory(path)
+

Add a path to the DLL search path.

This search path is used when resolving dependencies for imported extension modules (the module itself is resolved through sys.path), and also by ctypes.

Remove the directory by calling close() on the returned object or using it in a with statement.

See the Microsoft documentation for more information about how DLLs are loaded.

Raises an auditing event os.add_dll_directory with argument path.

Availability: Windows.

New in version 3.8: Previous versions of CPython would resolve DLLs using the default behavior for the current process. This led to inconsistencies, such as only sometimes searching PATH or the current working directory, and OS functions such as AddDllDirectory having no effect.

In 3.8, the two primary ways DLLs are loaded now explicitly override the process-wide behavior to ensure consistency. See the porting notes for information on updating libraries.

+
+os.execl(path, arg0, arg1, ...)
+os.execle(path, arg0, arg1, ..., env)
+os.execlp(file, arg0, arg1, ...)
+os.execlpe(file, arg0, arg1, ..., env)
+os.execv(path, args)
+os.execve(path, args, env)
+os.execvp(file, args)
+os.execvpe(file, args, env)
+

These functions all execute a new program, replacing the current process; they do not return. On Unix, the new executable is loaded into the current process, and will have the same process id as the caller. Errors will be reported as OSError exceptions.

The current process is replaced immediately. Open file objects and descriptors are not flushed, so if there may be data buffered on these open files, you should flush them using sys.stdout.flush() or os.fsync() before calling an exec* function.

The “l” and “v” variants of the exec* functions differ in how command-line arguments are passed. The “l” variants are perhaps the easiest to work with if the number of parameters is fixed when the code is written; the individual parameters simply become additional parameters to the execl*() functions. The “v” variants are good when the number of parameters is variable, with the arguments being passed in a list or tuple as the args parameter. In either case, the arguments to the child process should start with the name of the command being run, but this is not enforced.

The variants which include a “p” near the end (execlp(), execlpe(), execvp(), and execvpe()) will use the PATH environment variable to locate the program file. When the environment is being replaced (using one of the exec*e variants, discussed in the next paragraph), the new environment is used as the source of the PATH variable. The other variants, execl(), execle(), execv(), and execve(), will not use the PATH variable to locate the executable; path must contain an appropriate absolute or relative path. Relative paths must include at least one slash, even on Windows, as plain names will not be resolved.

For execle(), execlpe(), execve(), and execvpe() (note that these all end in “e”), the env parameter must be a mapping which is used to define the environment variables for the new process (these are used instead of the current process’ environment); the functions execl(), execlp(), execv(), and execvp() all cause the new process to inherit the environment of the current process.

For execve() on some platforms, path may also be specified as an open file descriptor. This functionality may not be supported on your platform; you can check whether or not it is available using os.supports_fd. If it is unavailable, using it will raise a NotImplementedError.

Raises an auditing event os.exec with arguments path, args, env.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.3: Added support for specifying path as an open file descriptor for execve().

Changed in version 3.6: Accepts a path-like object.

+
+os._exit(n)
+

Exit the process with status n, without calling cleanup handlers, flushing stdio buffers, etc.

Note

The standard way to exit is sys.exit(n). _exit() should normally only be used in the child process after a fork().

+

The following exit codes are defined and can be used with _exit(), although they are not required. These are typically used for system programs written in Python, such as a mail server’s external command delivery program.

Note

Some of these may not be available on all Unix platforms, since there is some variation. These constants are defined where they are defined by the underlying platform.

+os.EX_OK
+

Exit code that means no error occurred. May be taken from the defined value of EXIT_SUCCESS on some platforms. Generally has a value of zero.

Availability: Unix, Windows.

+
+os.EX_USAGE
+

Exit code that means the command was used incorrectly, such as when the wrong number of arguments are given.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_DATAERR
+

Exit code that means the input data was incorrect.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_NOINPUT
+

Exit code that means an input file did not exist or was not readable.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_NOUSER
+

Exit code that means a specified user did not exist.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_NOHOST
+

Exit code that means a specified host did not exist.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_UNAVAILABLE
+

Exit code that means that a required service is unavailable.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_SOFTWARE
+

Exit code that means an internal software error was detected.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_OSERR
+

Exit code that means an operating system error was detected, such as the inability to fork or create a pipe.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_OSFILE
+

Exit code that means some system file did not exist, could not be opened, or had some other kind of error.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_CANTCREAT
+

Exit code that means a user specified output file could not be created.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_IOERR
+

Exit code that means that an error occurred while doing I/O on some file.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_TEMPFAIL
+

Exit code that means a temporary failure occurred. This indicates something that may not really be an error, such as a network connection that couldn’t be made during a retryable operation.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_PROTOCOL
+

Exit code that means that a protocol exchange was illegal, invalid, or not understood.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_NOPERM
+

Exit code that means that there were insufficient permissions to perform the operation (but not intended for file system problems).

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_CONFIG
+

Exit code that means that some kind of configuration error occurred.

Availability: Unix, not Emscripten, not WASI.

+
+os.EX_NOTFOUND
+

Exit code that means something like “an entry was not found”.

Availability: Unix, not Emscripten, not WASI.

+
+os.fork()
+

Fork a child process. Return 0 in the child and the child’s process id in the parent. If an error occurs OSError is raised.

Note that some platforms including FreeBSD <= 6.3 and Cygwin have known issues when using fork() from a thread.

Raises an auditing event os.fork with no arguments.

Warning

If you use TLS sockets in an application calling fork(), see the warning in the ssl documentation.

Warning

On macOS the use of this function is unsafe when mixed with using higher-level system APIs, and that includes using urllib.request.

Changed in version 3.8: Calling fork() in a subinterpreter is no longer supported (RuntimeError is raised).

Changed in version 3.12: If Python is able to detect that your process has multiple threads, os.fork() now raises a DeprecationWarning.

We chose to surface this as a warning, when detectable, to better inform developers of a design problem that the POSIX platform specifically notes as not supported. Even in code that appears to work, it has never been safe to mix threading with os.fork() on POSIX platforms. The CPython runtime itself has always made API calls that are not safe for use in the child process when threads existed in the parent (such as malloc and free).

Users of macOS or users of libc or malloc implementations other than those typically found in glibc to date are among those already more likely to experience deadlocks running such code.

See this discussion on fork being incompatible with threads for technical details of why we’re surfacing this longstanding platform compatibility problem to developers.

Availability: POSIX, not Emscripten, not WASI.

+
+os.forkpty()
+

Fork a child process, using a new pseudo-terminal as the child’s controlling terminal. Return a pair of (pid, fd), where pid is 0 in the child, the new child’s process id in the parent, and fd is the file descriptor of the master end of the pseudo-terminal. For a more portable approach, use the pty module. If an error occurs OSError is raised.

Raises an auditing event os.forkpty with no arguments.

Warning

On macOS the use of this function is unsafe when mixed with using higher-level system APIs, and that includes using urllib.request.

Changed in version 3.12: If Python is able to detect that your process has multiple threads, this now raises a DeprecationWarning. See the longer explanation on os.fork().

Changed in version 3.8: Calling forkpty() in a subinterpreter is no longer supported (RuntimeError is raised).

Availability: Unix, not Emscripten, not WASI.

+
+os.kill(pid, sig, /)
+

Send signal sig to the process pid. Constants for the specific signals available on the host platform are defined in the signal module.

Windows: The signal.CTRL_C_EVENT and signal.CTRL_BREAK_EVENT signals are special signals which can only be sent to console processes which share a common console window, e.g., some subprocesses. Any other value for sig will cause the process to be unconditionally killed by the TerminateProcess API, and the exit code will be set to sig. The Windows version of kill() additionally takes process handles to be killed.

See also signal.pthread_kill().

Raises an auditing event os.kill with arguments pid, sig.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.2: Windows support.

+
+os.killpg(pgid, sig, /)
+

Send the signal sig to the process group pgid.

Raises an auditing event os.killpg with arguments pgid, sig.

Availability: Unix, not Emscripten, not WASI.

+
+os.nice(increment, /)
+

Add increment to the process’s “niceness”. Return the new niceness.

Availability: Unix, not Emscripten, not WASI.

+
+os.pidfd_open(pid, flags=0)
+

Return a file descriptor referring to the process pid with flags set. This descriptor can be used to perform process management without races and signals.

See the pidfd_open(2) man page for more details.

Availability: Linux >= 5.3

New in version 3.9.

+os.PIDFD_NONBLOCK
+

This flag indicates that the file descriptor will be non-blocking. If the process referred to by the file descriptor has not yet terminated, then an attempt to wait on the file descriptor using waitid(2) will immediately return the error EAGAIN rather than blocking.

+

Availability: Linux >= 5.10

New in version 3.12.

+
+os.plock(op, /)
+

Lock program segments into memory. The value of op (defined in <sys/lock.h>) determines which segments are locked.

Availability: Unix, not Emscripten, not WASI.

+
+os.popen(cmd, mode='r', buffering=- 1)
+

Open a pipe to or from command cmd. The return value is an open file object connected to the pipe, which can be read or written depending on whether mode is 'r' (default) or 'w'. The buffering argument have the same meaning as the corresponding argument to the built-in open() function. The returned file object reads or writes text strings rather than bytes.

The close method returns None if the subprocess exited successfully, or the subprocess’s return code if there was an error. On POSIX systems, if the return code is positive it represents the return value of the process left-shifted by one byte. If the return code is negative, the process was terminated by the signal given by the negated value of the return code. (For example, the return value might be - signal.SIGKILL if the subprocess was killed.) On Windows systems, the return value contains the signed integer return code from the child process.

On Unix, waitstatus_to_exitcode() can be used to convert the close method result (exit status) into an exit code if it is not None. On Windows, the close method result is directly the exit code (or None).

This is implemented using subprocess.Popen; see that class’s documentation for more powerful ways to manage and communicate with subprocesses.

Availability: not Emscripten, not WASI.

Note

The Python UTF-8 Mode affects encodings used for cmd and pipe contents.

popen() is a simple wrapper around subprocess.Popen. Use subprocess.Popen or subprocess.run() to control options like encodings.

+
+os.posix_spawn(path, argv, env, *, file_actions=None, setpgroup=None, resetids=False, setsid=False, setsigmask=(), setsigdef=(), scheduler=None)
+

Wraps the posix_spawn() C library API for use from Python.

Most users should use subprocess.run() instead of posix_spawn().

The positional-only arguments path, args, and env are similar to execve().

The path parameter is the path to the executable file. The path should contain a directory. Use posix_spawnp() to pass an executable file without directory.

The file_actions argument may be a sequence of tuples describing actions to take on specific file descriptors in the child process between the C library implementation’s fork() and exec() steps. The first item in each tuple must be one of the three type indicator listed below describing the remaining tuple elements:

+os.POSIX_SPAWN_OPEN
+

(os.POSIX_SPAWN_OPEN, fd, path, flags, mode)

Performs os.dup2(os.open(path, flags, mode), fd).

+
+os.POSIX_SPAWN_CLOSE
+

(os.POSIX_SPAWN_CLOSE, fd)

Performs os.close(fd).

+
+os.POSIX_SPAWN_DUP2
+

(os.POSIX_SPAWN_DUP2, fd, new_fd)

Performs os.dup2(fd, new_fd).

+

These tuples correspond to the C library posix_spawn_file_actions_addopen(), posix_spawn_file_actions_addclose(), and posix_spawn_file_actions_adddup2() API calls used to prepare for the posix_spawn() call itself.

The setpgroup argument will set the process group of the child to the value specified. If the value specified is 0, the child’s process group ID will be made the same as its process ID. If the value of setpgroup is not set, the child will inherit the parent’s process group ID. This argument corresponds to the C library POSIX_SPAWN_SETPGROUP flag.

If the resetids argument is True it will reset the effective UID and GID of the child to the real UID and GID of the parent process. If the argument is False, then the child retains the effective UID and GID of the parent. In either case, if the set-user-ID and set-group-ID permission bits are enabled on the executable file, their effect will override the setting of the effective UID and GID. This argument corresponds to the C library POSIX_SPAWN_RESETIDS flag.

If the setsid argument is True, it will create a new session ID for posix_spawn. setsid requires POSIX_SPAWN_SETSID or POSIX_SPAWN_SETSID_NP flag. Otherwise, NotImplementedError is raised.

The setsigmask argument will set the signal mask to the signal set specified. If the parameter is not used, then the child inherits the parent’s signal mask. This argument corresponds to the C library POSIX_SPAWN_SETSIGMASK flag.

The sigdef argument will reset the disposition of all signals in the set specified. This argument corresponds to the C library POSIX_SPAWN_SETSIGDEF flag.

The scheduler argument must be a tuple containing the (optional) scheduler policy and an instance of sched_param with the scheduler parameters. A value of None in the place of the scheduler policy indicates that is not being provided. This argument is a combination of the C library POSIX_SPAWN_SETSCHEDPARAM and POSIX_SPAWN_SETSCHEDULER flags.

Raises an auditing event os.posix_spawn with arguments path, argv, env.

New in version 3.8.

Availability: Unix, not Emscripten, not WASI.

+
+os.posix_spawnp(path, argv, env, *, file_actions=None, setpgroup=None, resetids=False, setsid=False, setsigmask=(), setsigdef=(), scheduler=None)
+

Wraps the posix_spawnp() C library API for use from Python.

Similar to posix_spawn() except that the system searches for the executable file in the list of directories specified by the PATH environment variable (in the same way as for execvp(3)).

Raises an auditing event os.posix_spawn with arguments path, argv, env.

New in version 3.8.

Availability: POSIX, not Emscripten, not WASI.

See posix_spawn() documentation.

+
+os.register_at_fork(*, before=None, after_in_parent=None, after_in_child=None)
+

Register callables to be executed when a new child process is forked using os.fork() or similar process cloning APIs. The parameters are optional and keyword-only. Each specifies a different call point.

  • +before is a function called before forking a child process.
  • +after_in_parent is a function called from the parent process after forking a child process.
  • +after_in_child is a function called from the child process.

These calls are only made if control is expected to return to the Python interpreter. A typical subprocess launch will not trigger them as the child is not going to re-enter the interpreter.

Functions registered for execution before forking are called in reverse registration order. Functions registered for execution after forking (either in the parent or in the child) are called in registration order.

Note that fork() calls made by third-party C code may not call those functions, unless it explicitly calls PyOS_BeforeFork(), PyOS_AfterFork_Parent() and PyOS_AfterFork_Child().

There is no way to unregister a function.

Availability: Unix, not Emscripten, not WASI.

New in version 3.7.

+
+os.spawnl(mode, path, ...)
+os.spawnle(mode, path, ..., env)
+os.spawnlp(mode, file, ...)
+os.spawnlpe(mode, file, ..., env)
+os.spawnv(mode, path, args)
+os.spawnve(mode, path, args, env)
+os.spawnvp(mode, file, args)
+os.spawnvpe(mode, file, args, env)
+

Execute the program path in a new process.

(Note that the subprocess module provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using these functions. Check especially the Replacing Older Functions with the subprocess Module section.)

If mode is P_NOWAIT, this function returns the process id of the new process; if mode is P_WAIT, returns the process’s exit code if it exits normally, or -signal, where signal is the signal that killed the process. On Windows, the process id will actually be the process handle, so can be used with the waitpid() function.

Note on VxWorks, this function doesn’t return -signal when the new process is killed. Instead it raises OSError exception.

The “l” and “v” variants of the spawn* functions differ in how command-line arguments are passed. The “l” variants are perhaps the easiest to work with if the number of parameters is fixed when the code is written; the individual parameters simply become additional parameters to the spawnl*() functions. The “v” variants are good when the number of parameters is variable, with the arguments being passed in a list or tuple as the args parameter. In either case, the arguments to the child process must start with the name of the command being run.

The variants which include a second “p” near the end (spawnlp(), spawnlpe(), spawnvp(), and spawnvpe()) will use the PATH environment variable to locate the program file. When the environment is being replaced (using one of the spawn*e variants, discussed in the next paragraph), the new environment is used as the source of the PATH variable. The other variants, spawnl(), spawnle(), spawnv(), and spawnve(), will not use the PATH variable to locate the executable; path must contain an appropriate absolute or relative path.

For spawnle(), spawnlpe(), spawnve(), and spawnvpe() (note that these all end in “e”), the env parameter must be a mapping which is used to define the environment variables for the new process (they are used instead of the current process’ environment); the functions spawnl(), spawnlp(), spawnv(), and spawnvp() all cause the new process to inherit the environment of the current process. Note that keys and values in the env dictionary must be strings; invalid keys or values will cause the function to fail, with a return value of 127.

As an example, the following calls to spawnlp() and spawnvpe() are equivalent:

import os
+os.spawnlp(os.P_WAIT, 'cp', 'cp', 'index.html', '/dev/null')
+
+L = ['cp', 'index.html', '/dev/null']
+os.spawnvpe(os.P_WAIT, 'cp', L, os.environ)
+

Raises an auditing event os.spawn with arguments mode, path, args, env.

Availability: Unix, Windows, not Emscripten, not WASI.

spawnlp(), spawnlpe(), spawnvp() and spawnvpe() are not available on Windows. spawnle() and spawnve() are not thread-safe on Windows; we advise you to use the subprocess module instead.

Changed in version 3.6: Accepts a path-like object.

+
+os.P_NOWAIT
+os.P_NOWAITO
+

Possible values for the mode parameter to the spawn* family of functions. If either of these values is given, the spawn* functions will return as soon as the new process has been created, with the process id as the return value.

Availability: Unix, Windows.

+
+os.P_WAIT
+

Possible value for the mode parameter to the spawn* family of functions. If this is given as mode, the spawn* functions will not return until the new process has run to completion and will return the exit code of the process the run is successful, or -signal if a signal kills the process.

Availability: Unix, Windows.

+
+os.P_DETACH
+os.P_OVERLAY
+

Possible values for the mode parameter to the spawn* family of functions. These are less portable than those listed above. P_DETACH is similar to P_NOWAIT, but the new process is detached from the console of the calling process. If P_OVERLAY is used, the current process will be replaced; the spawn* function will not return.

Availability: Windows.

+
+os.startfile(path[, operation][, arguments][, cwd][, show_cmd])
+

Start a file with its associated application.

When operation is not specified, this acts like double-clicking the file in Windows Explorer, or giving the file name as an argument to the start command from the interactive command shell: the file is opened with whatever application (if any) its extension is associated.

When another operation is given, it must be a “command verb” that specifies what should be done with the file. Common verbs documented by Microsoft are 'open', 'print' and 'edit' (to be used on files) as well as 'explore' and 'find' (to be used on directories).

When launching an application, specify arguments to be passed as a single string. This argument may have no effect when using this function to launch a document.

The default working directory is inherited, but may be overridden by the cwd argument. This should be an absolute path. A relative path will be resolved against this argument.

Use show_cmd to override the default window style. Whether this has any effect will depend on the application being launched. Values are integers as supported by the Win32 ShellExecute() function.

startfile() returns as soon as the associated application is launched. There is no option to wait for the application to close, and no way to retrieve the application’s exit status. The path parameter is relative to the current directory or cwd. If you want to use an absolute path, make sure the first character is not a slash ('/') Use pathlib or the os.path.normpath() function to ensure that paths are properly encoded for Win32.

To reduce interpreter startup overhead, the Win32 ShellExecute() function is not resolved until this function is first called. If the function cannot be resolved, NotImplementedError will be raised.

Raises an auditing event os.startfile with arguments path, operation.

Raises an auditing event os.startfile/2 with arguments path, operation, arguments, cwd, show_cmd.

Availability: Windows.

Changed in version 3.10: Added the arguments, cwd and show_cmd arguments, and the os.startfile/2 audit event.

+
+os.system(command)
+

Execute the command (a string) in a subshell. This is implemented by calling the Standard C function system(), and has the same limitations. Changes to sys.stdin, etc. are not reflected in the environment of the executed command. If command generates any output, it will be sent to the interpreter standard output stream. The C standard does not specify the meaning of the return value of the C function, so the return value of the Python function is system-dependent.

On Unix, the return value is the exit status of the process encoded in the format specified for wait().

On Windows, the return value is that returned by the system shell after running command. The shell is given by the Windows environment variable COMSPEC: it is usually cmd.exe, which returns the exit status of the command run; on systems using a non-native shell, consult your shell documentation.

The subprocess module provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using this function. See the Replacing Older Functions with the subprocess Module section in the subprocess documentation for some helpful recipes.

On Unix, waitstatus_to_exitcode() can be used to convert the result (exit status) into an exit code. On Windows, the result is directly the exit code.

Raises an auditing event os.system with argument command.

Availability: Unix, Windows, not Emscripten, not WASI.

+
+os.times()
+

Returns the current global process times. The return value is an object with five attributes:

  • +user - user time
  • +system - system time
  • +children_user - user time of all child processes
  • +children_system - system time of all child processes
  • +elapsed - elapsed real time since a fixed point in the past

For backwards compatibility, this object also behaves like a five-tuple containing user, system, children_user, children_system, and elapsed in that order.

See the Unix manual page times(2) and times(3) manual page on Unix or the GetProcessTimes MSDN on Windows. On Windows, only user and system are known; the other attributes are zero.

Availability: Unix, Windows.

Changed in version 3.3: Return type changed from a tuple to a tuple-like object with named attributes.

+
+os.wait()
+

Wait for completion of a child process, and return a tuple containing its pid and exit status indication: a 16-bit number, whose low byte is the signal number that killed the process, and whose high byte is the exit status (if the signal number is zero); the high bit of the low byte is set if a core file was produced.

If there are no children that could be waited for, ChildProcessError is raised.

waitstatus_to_exitcode() can be used to convert the exit status into an exit code.

Availability: Unix, not Emscripten, not WASI.

See also

The other wait*() functions documented below can be used to wait for the completion of a specific child process and have more options. waitpid() is the only one also available on Windows.

+
+os.waitid(idtype, id, options, /)
+

Wait for the completion of a child process.

idtype can be P_PID, P_PGID, P_ALL, or (on Linux) P_PIDFD. The interpretation of id depends on it; see their individual descriptions.

options is an OR combination of flags. At least one of WEXITED, WSTOPPED or WCONTINUED is required; WNOHANG and WNOWAIT are additional optional flags.

The return value is an object representing the data contained in the siginfo_t structure with the following attributes:

  • +si_pid (process ID)
  • +si_uid (real user ID of the child)
  • +si_signo (always SIGCHLD)
  • +si_status (the exit status or signal number, depending on si_code)
  • +si_code (see CLD_EXITED for possible values)

If WNOHANG is specified and there are no matching children in the requested state, None is returned. Otherwise, if there are no matching children that could be waited for, ChildProcessError is raised.

Availability: Unix, not Emscripten, not WASI.

Note

This function is not available on macOS.

New in version 3.3.

+
+os.waitpid(pid, options, /)
+

The details of this function differ on Unix and Windows.

On Unix: Wait for completion of a child process given by process id pid, and return a tuple containing its process id and exit status indication (encoded as for wait()). The semantics of the call are affected by the value of the integer options, which should be 0 for normal operation.

If pid is greater than 0, waitpid() requests status information for that specific process. If pid is 0, the request is for the status of any child in the process group of the current process. If pid is -1, the request pertains to any child of the current process. If pid is less than -1, status is requested for any process in the process group -pid (the absolute value of pid).

options is an OR combination of flags. If it contains WNOHANG and there are no matching children in the requested state, (0, 0) is returned. Otherwise, if there are no matching children that could be waited for, ChildProcessError is raised. Other options that can be used are WUNTRACED and WCONTINUED.

On Windows: Wait for completion of a process given by process handle pid, and return a tuple containing pid, and its exit status shifted left by 8 bits (shifting makes cross-platform use of the function easier). A pid less than or equal to 0 has no special meaning on Windows, and raises an exception. The value of integer options has no effect. pid can refer to any process whose id is known, not necessarily a child process. The spawn* functions called with P_NOWAIT return suitable process handles.

waitstatus_to_exitcode() can be used to convert the exit status into an exit code.

Availability: Unix, Windows, not Emscripten, not WASI.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the function now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+os.wait3(options)
+

Similar to waitpid(), except no process id argument is given and a 3-element tuple containing the child’s process id, exit status indication, and resource usage information is returned. Refer to resource.getrusage() for details on resource usage information. The options argument is the same as that provided to waitpid() and wait4().

waitstatus_to_exitcode() can be used to convert the exit status into an exitcode.

Availability: Unix, not Emscripten, not WASI.

+
+os.wait4(pid, options)
+

Similar to waitpid(), except a 3-element tuple, containing the child’s process id, exit status indication, and resource usage information is returned. Refer to resource.getrusage() for details on resource usage information. The arguments to wait4() are the same as those provided to waitpid().

waitstatus_to_exitcode() can be used to convert the exit status into an exitcode.

Availability: Unix, not Emscripten, not WASI.

+
+os.P_PID
+os.P_PGID
+os.P_ALL
+os.P_PIDFD
+

These are the possible values for idtype in waitid(). They affect how id is interpreted:

  • +P_PID - wait for the child whose PID is id.
  • +P_PGID - wait for any child whose progress group ID is id.
  • +P_ALL - wait for any child; id is ignored.
  • +P_PIDFD - wait for the child identified by the file descriptor id (a process file descriptor created with pidfd_open()).

Availability: Unix, not Emscripten, not WASI.

Note

P_PIDFD is only available on Linux >= 5.4.

New in version 3.3.

New in version 3.9: The P_PIDFD constant.

+
+os.WCONTINUED
+

This options flag for waitpid(), wait3(), wait4(), and waitid() causes child processes to be reported if they have been continued from a job control stop since they were last reported.

Availability: Unix, not Emscripten, not WASI.

+
+os.WEXITED
+

This options flag for waitid() causes child processes that have terminated to be reported.

The other wait* functions always report children that have terminated, so this option is not available for them.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.WSTOPPED
+

This options flag for waitid() causes child processes that have been stopped by the delivery of a signal to be reported.

This option is not available for the other wait* functions.

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

+
+os.WUNTRACED
+

This options flag for waitpid(), wait3(), and wait4() causes child processes to also be reported if they have been stopped but their current state has not been reported since they were stopped.

This option is not available for waitid().

Availability: Unix, not Emscripten, not WASI.

+
+os.WNOHANG
+

This options flag causes waitpid(), wait3(), wait4(), and waitid() to return right away if no child process status is available immediately.

Availability: Unix, not Emscripten, not WASI.

+
+os.WNOWAIT
+

This options flag causes waitid() to leave the child in a waitable state, so that a later wait*() call can be used to retrieve the child status information again.

This option is not available for the other wait* functions.

Availability: Unix, not Emscripten, not WASI.

+
+os.CLD_EXITED
+os.CLD_KILLED
+os.CLD_DUMPED
+os.CLD_TRAPPED
+os.CLD_STOPPED
+os.CLD_CONTINUED
+

These are the possible values for si_code in the result returned by waitid().

Availability: Unix, not Emscripten, not WASI.

New in version 3.3.

Changed in version 3.9: Added CLD_KILLED and CLD_STOPPED values.

+
+os.waitstatus_to_exitcode(status)
+

Convert a wait status to an exit code.

On Unix:

  • If the process exited normally (if WIFEXITED(status) is true), return the process exit status (return WEXITSTATUS(status)): result greater than or equal to 0.
  • If the process was terminated by a signal (if WIFSIGNALED(status) is true), return -signum where signum is the number of the signal that caused the process to terminate (return -WTERMSIG(status)): result less than 0.
  • Otherwise, raise a ValueError.

On Windows, return status shifted right by 8 bits.

On Unix, if the process is being traced or if waitpid() was called with WUNTRACED option, the caller must first check if WIFSTOPPED(status) is true. This function must not be called if WIFSTOPPED(status) is true.

See also

WIFEXITED(), WEXITSTATUS(), WIFSIGNALED(), WTERMSIG(), WIFSTOPPED(), WSTOPSIG() functions.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.9.

+

The following functions take a process status code as returned by system(), wait(), or waitpid() as a parameter. They may be used to determine the disposition of a process.

+os.WCOREDUMP(status, /)
+

Return True if a core dump was generated for the process, otherwise return False.

This function should be employed only if WIFSIGNALED() is true.

Availability: Unix, not Emscripten, not WASI.

+
+os.WIFCONTINUED(status)
+

Return True if a stopped child has been resumed by delivery of SIGCONT (if the process has been continued from a job control stop), otherwise return False.

See WCONTINUED option.

Availability: Unix, not Emscripten, not WASI.

+
+os.WIFSTOPPED(status)
+

Return True if the process was stopped by delivery of a signal, otherwise return False.

WIFSTOPPED() only returns True if the waitpid() call was done using WUNTRACED option or when the process is being traced (see ptrace(2)).

Availability: Unix, not Emscripten, not WASI.

+
+os.WIFSIGNALED(status)
+

Return True if the process was terminated by a signal, otherwise return False.

Availability: Unix, not Emscripten, not WASI.

+
+os.WIFEXITED(status)
+

Return True if the process exited terminated normally, that is, by calling exit() or _exit(), or by returning from main(); otherwise return False.

Availability: Unix, not Emscripten, not WASI.

+
+os.WEXITSTATUS(status)
+

Return the process exit status.

This function should be employed only if WIFEXITED() is true.

Availability: Unix, not Emscripten, not WASI.

+
+os.WSTOPSIG(status)
+

Return the signal which caused the process to stop.

This function should be employed only if WIFSTOPPED() is true.

Availability: Unix, not Emscripten, not WASI.

+
+os.WTERMSIG(status)
+

Return the number of the signal that caused the process to terminate.

This function should be employed only if WIFSIGNALED() is true.

Availability: Unix, not Emscripten, not WASI.

+

Interface to the scheduler

These functions control how a process is allocated CPU time by the operating system. They are only available on some Unix platforms. For more detailed information, consult your Unix manpages.

New in version 3.3.

The following scheduling policies are exposed if they are supported by the operating system.

+os.SCHED_OTHER
+

The default scheduling policy.

+
+os.SCHED_BATCH
+

Scheduling policy for CPU-intensive processes that tries to preserve interactivity on the rest of the computer.

+
+os.SCHED_IDLE
+

Scheduling policy for extremely low priority background tasks.

+
+os.SCHED_SPORADIC
+

Scheduling policy for sporadic server programs.

+
+os.SCHED_FIFO
+

A First In First Out scheduling policy.

+
+os.SCHED_RR
+

A round-robin scheduling policy.

+
+os.SCHED_RESET_ON_FORK
+

This flag can be OR’ed with any other scheduling policy. When a process with this flag set forks, its child’s scheduling policy and priority are reset to the default.

+
+class os.sched_param(sched_priority)
+

This class represents tunable scheduling parameters used in sched_setparam(), sched_setscheduler(), and sched_getparam(). It is immutable.

At the moment, there is only one possible parameter:

+sched_priority
+

The scheduling priority for a scheduling policy.

+
+
+os.sched_get_priority_min(policy)
+

Get the minimum priority value for policy. policy is one of the scheduling policy constants above.

+
+os.sched_get_priority_max(policy)
+

Get the maximum priority value for policy. policy is one of the scheduling policy constants above.

+
+os.sched_setscheduler(pid, policy, param, /)
+

Set the scheduling policy for the process with PID pid. A pid of 0 means the calling process. policy is one of the scheduling policy constants above. param is a sched_param instance.

+
+os.sched_getscheduler(pid, /)
+

Return the scheduling policy for the process with PID pid. A pid of 0 means the calling process. The result is one of the scheduling policy constants above.

+
+os.sched_setparam(pid, param, /)
+

Set the scheduling parameters for the process with PID pid. A pid of 0 means the calling process. param is a sched_param instance.

+
+os.sched_getparam(pid, /)
+

Return the scheduling parameters as a sched_param instance for the process with PID pid. A pid of 0 means the calling process.

+
+os.sched_rr_get_interval(pid, /)
+

Return the round-robin quantum in seconds for the process with PID pid. A pid of 0 means the calling process.

+
+os.sched_yield()
+

Voluntarily relinquish the CPU.

+
+os.sched_setaffinity(pid, mask, /)
+

Restrict the process with PID pid (or the current process if zero) to a set of CPUs. mask is an iterable of integers representing the set of CPUs to which the process should be restricted.

+
+os.sched_getaffinity(pid, /)
+

Return the set of CPUs the process with PID pid is restricted to.

If pid is zero, return the set of CPUs the calling thread of the current process is restricted to.

+

Miscellaneous System Information

+os.confstr(name, /)
+

Return string-valued system configuration values. name specifies the configuration value to retrieve; it may be a string which is the name of a defined system value; these names are specified in a number of standards (POSIX, Unix 95, Unix 98, and others). Some platforms define additional names as well. The names known to the host operating system are given as the keys of the confstr_names dictionary. For configuration variables not included in that mapping, passing an integer for name is also accepted.

If the configuration value specified by name isn’t defined, None is returned.

If name is a string and is not known, ValueError is raised. If a specific value for name is not supported by the host system, even if it is included in confstr_names, an OSError is raised with errno.EINVAL for the error number.

Availability: Unix.

+
+os.confstr_names
+

Dictionary mapping names accepted by confstr() to the integer values defined for those names by the host operating system. This can be used to determine the set of names known to the system.

Availability: Unix.

+
+os.cpu_count()
+

Return the number of logical CPUs in the system. Returns None if undetermined.

This number is not equivalent to the number of logical CPUs the current process can use. len(os.sched_getaffinity(0)) gets the number of logical CPUs the calling thread of the current process is restricted to

New in version 3.4.

+
+os.getloadavg()
+

Return the number of processes in the system run queue averaged over the last 1, 5, and 15 minutes or raises OSError if the load average was unobtainable.

Availability: Unix.

+
+os.sysconf(name, /)
+

Return integer-valued system configuration values. If the configuration value specified by name isn’t defined, -1 is returned. The comments regarding the name parameter for confstr() apply here as well; the dictionary that provides information on the known names is given by sysconf_names.

Availability: Unix.

+
+os.sysconf_names
+

Dictionary mapping names accepted by sysconf() to the integer values defined for those names by the host operating system. This can be used to determine the set of names known to the system.

Availability: Unix.

Changed in version 3.11: Add 'SC_MINSIGSTKSZ' name.

+

The following data values are used to support path manipulation operations. These are defined for all platforms.

Higher-level operations on pathnames are defined in the os.path module.

+os.curdir
+

The constant string used by the operating system to refer to the current directory. This is '.' for Windows and POSIX. Also available via os.path.

+
+os.pardir
+

The constant string used by the operating system to refer to the parent directory. This is '..' for Windows and POSIX. Also available via os.path.

+
+os.sep
+

The character used by the operating system to separate pathname components. This is '/' for POSIX and '\\' for Windows. Note that knowing this is not sufficient to be able to parse or concatenate pathnames — use os.path.split() and os.path.join() — but it is occasionally useful. Also available via os.path.

+
+os.altsep
+

An alternative character used by the operating system to separate pathname components, or None if only one separator character exists. This is set to '/' on Windows systems where sep is a backslash. Also available via os.path.

+
+os.extsep
+

The character which separates the base filename from the extension; for example, the '.' in os.py. Also available via os.path.

+
+os.pathsep
+

The character conventionally used by the operating system to separate search path components (as in PATH), such as ':' for POSIX or ';' for Windows. Also available via os.path.

+
+os.defpath
+

The default search path used by exec*p* and spawn*p* if the environment doesn’t have a 'PATH' key. Also available via os.path.

+
+os.linesep
+

The string used to separate (or, rather, terminate) lines on the current platform. This may be a single character, such as '\n' for POSIX, or multiple characters, for example, '\r\n' for Windows. Do not use os.linesep as a line terminator when writing files opened in text mode (the default); use a single '\n' instead, on all platforms.

+
+os.devnull
+

The file path of the null device. For example: '/dev/null' for POSIX, 'nul' for Windows. Also available via os.path.

+
+os.RTLD_LAZY
+os.RTLD_NOW
+os.RTLD_GLOBAL
+os.RTLD_LOCAL
+os.RTLD_NODELETE
+os.RTLD_NOLOAD
+os.RTLD_DEEPBIND
+

Flags for use with the setdlopenflags() and getdlopenflags() functions. See the Unix manual page dlopen(3) for what the different flags mean.

New in version 3.3.

+

Random numbers

+os.getrandom(size, flags=0)
+

Get up to size random bytes. The function can return less bytes than requested.

These bytes can be used to seed user-space random number generators or for cryptographic purposes.

getrandom() relies on entropy gathered from device drivers and other sources of environmental noise. Unnecessarily reading large quantities of data will have a negative impact on other users of the /dev/random and /dev/urandom devices.

The flags argument is a bit mask that can contain zero or more of the following values ORed together: os.GRND_RANDOM and GRND_NONBLOCK.

See also the Linux getrandom() manual page.

Availability: Linux >= 3.17.

New in version 3.6.

+
+os.urandom(size, /)
+

Return a bytestring of size random bytes suitable for cryptographic use.

This function returns random bytes from an OS-specific randomness source. The returned data should be unpredictable enough for cryptographic applications, though its exact quality depends on the OS implementation.

On Linux, if the getrandom() syscall is available, it is used in blocking mode: block until the system urandom entropy pool is initialized (128 bits of entropy are collected by the kernel). See the PEP 524 for the rationale. On Linux, the getrandom() function can be used to get random bytes in non-blocking mode (using the GRND_NONBLOCK flag) or to poll until the system urandom entropy pool is initialized.

On a Unix-like system, random bytes are read from the /dev/urandom device. If the /dev/urandom device is not available or not readable, the NotImplementedError exception is raised.

On Windows, it will use BCryptGenRandom().

See also

The secrets module provides higher level functions. For an easy-to-use interface to the random number generator provided by your platform, please see random.SystemRandom.

Changed in version 3.6.0: On Linux, getrandom() is now used in blocking mode to increase the security.

Changed in version 3.5.2: On Linux, if the getrandom() syscall blocks (the urandom entropy pool is not initialized yet), fall back on reading /dev/urandom.

Changed in version 3.5: On Linux 3.17 and newer, the getrandom() syscall is now used when available. On OpenBSD 5.6 and newer, the C getentropy() function is now used. These functions avoid the usage of an internal file descriptor.

Changed in version 3.11: On Windows, BCryptGenRandom() is used instead of CryptGenRandom() which is deprecated.

+
+os.GRND_NONBLOCK
+

By default, when reading from /dev/random, getrandom() blocks if no random bytes are available, and when reading from /dev/urandom, it blocks if the entropy pool has not yet been initialized.

If the GRND_NONBLOCK flag is set, then getrandom() does not block in these cases, but instead immediately raises BlockingIOError.

New in version 3.6.

+
+os.GRND_RANDOM
+

If this bit is set, then random bytes are drawn from the /dev/random pool instead of the /dev/urandom pool.

New in version 3.6.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/os.html +

+
diff --git a/devdocs/python~3.12/library%2Fos.path.html b/devdocs/python~3.12/library%2Fos.path.html new file mode 100644 index 00000000..a1733aba --- /dev/null +++ b/devdocs/python~3.12/library%2Fos.path.html @@ -0,0 +1,137 @@ +

os.path — Common pathname manipulations

Source code: Lib/posixpath.py (for POSIX) and Lib/ntpath.py (for Windows).

This module implements some useful functions on pathnames. To read or write files see open(), and for accessing the filesystem see the os module. The path parameters can be passed as strings, or bytes, or any object implementing the os.PathLike protocol.

Unlike a Unix shell, Python does not do any automatic path expansions. Functions such as expanduser() and expandvars() can be invoked explicitly when an application desires shell-like path expansion. (See also the glob module.)

See also

The pathlib module offers high-level path objects.

Note

All of these functions accept either only bytes or only string objects as their parameters. The result is an object of the same type, if a path or file name is returned.

Note

Since different operating systems have different path name conventions, there are several versions of this module in the standard library. The os.path module is always the path module suitable for the operating system Python is running on, and therefore usable for local paths. However, you can also import and use the individual modules if you want to manipulate a path that is always in one of the different formats. They all have the same interface:

  • +posixpath for UNIX-style paths
  • +ntpath for Windows paths

Changed in version 3.8: exists(), lexists(), isdir(), isfile(), islink(), and ismount() now return False instead of raising an exception for paths that contain characters or bytes unrepresentable at the OS level.

+os.path.abspath(path)
+

Return a normalized absolutized version of the pathname path. On most platforms, this is equivalent to calling the function normpath() as follows: normpath(join(os.getcwd(), path)).

Changed in version 3.6: Accepts a path-like object.

+
+os.path.basename(path)
+

Return the base name of pathname path. This is the second element of the pair returned by passing path to the function split(). Note that the result of this function is different from the Unix basename program; where basename for '/foo/bar/' returns 'bar', the basename() function returns an empty string ('').

Changed in version 3.6: Accepts a path-like object.

+
+os.path.commonpath(paths)
+

Return the longest common sub-path of each pathname in the sequence paths. Raise ValueError if paths contain both absolute and relative pathnames, the paths are on the different drives or if paths is empty. Unlike commonprefix(), this returns a valid path.

Availability: Unix, Windows.

New in version 3.5.

Changed in version 3.6: Accepts a sequence of path-like objects.

+
+os.path.commonprefix(list)
+

Return the longest path prefix (taken character-by-character) that is a prefix of all paths in list. If list is empty, return the empty string ('').

Note

This function may return invalid paths because it works a character at a time. To obtain a valid path, see commonpath().

>>> os.path.commonprefix(['/usr/lib', '/usr/local/lib'])
+'/usr/l'
+
+>>> os.path.commonpath(['/usr/lib', '/usr/local/lib'])
+'/usr'
+

Changed in version 3.6: Accepts a path-like object.

+
+os.path.dirname(path)
+

Return the directory name of pathname path. This is the first element of the pair returned by passing path to the function split().

Changed in version 3.6: Accepts a path-like object.

+
+os.path.exists(path)
+

Return True if path refers to an existing path or an open file descriptor. Returns False for broken symbolic links. On some platforms, this function may return False if permission is not granted to execute os.stat() on the requested file, even if the path physically exists.

Changed in version 3.3: path can now be an integer: True is returned if it is an open file descriptor, False otherwise.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.lexists(path)
+

Return True if path refers to an existing path. Returns True for broken symbolic links. Equivalent to exists() on platforms lacking os.lstat().

Changed in version 3.6: Accepts a path-like object.

+
+os.path.expanduser(path)
+

On Unix and Windows, return the argument with an initial component of ~ or ~user replaced by that user’s home directory.

On Unix, an initial ~ is replaced by the environment variable HOME if it is set; otherwise the current user’s home directory is looked up in the password directory through the built-in module pwd. An initial ~user is looked up directly in the password directory.

On Windows, USERPROFILE will be used if set, otherwise a combination of HOMEPATH and HOMEDRIVE will be used. An initial ~user is handled by checking that the last directory component of the current user’s home directory matches USERNAME, and replacing it if so.

If the expansion fails or if the path does not begin with a tilde, the path is returned unchanged.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.8: No longer uses HOME on Windows.

+
+os.path.expandvars(path)
+

Return the argument with environment variables expanded. Substrings of the form $name or ${name} are replaced by the value of environment variable name. Malformed variable names and references to non-existing variables are left unchanged.

On Windows, %name% expansions are supported in addition to $name and ${name}.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.getatime(path)
+

Return the time of last access of path. The return value is a floating point number giving the number of seconds since the epoch (see the time module). Raise OSError if the file does not exist or is inaccessible.

+
+os.path.getmtime(path)
+

Return the time of last modification of path. The return value is a floating point number giving the number of seconds since the epoch (see the time module). Raise OSError if the file does not exist or is inaccessible.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.getctime(path)
+

Return the system’s ctime which, on some systems (like Unix) is the time of the last metadata change, and, on others (like Windows), is the creation time for path. The return value is a number giving the number of seconds since the epoch (see the time module). Raise OSError if the file does not exist or is inaccessible.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.getsize(path)
+

Return the size, in bytes, of path. Raise OSError if the file does not exist or is inaccessible.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.isabs(path)
+

Return True if path is an absolute pathname. On Unix, that means it begins with a slash, on Windows that it begins with a (back)slash after chopping off a potential drive letter.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.isfile(path)
+

Return True if path is an existing regular file. This follows symbolic links, so both islink() and isfile() can be true for the same path.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.isdir(path)
+

Return True if path is an existing directory. This follows symbolic links, so both islink() and isdir() can be true for the same path.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.isjunction(path)
+

Return True if path refers to an existing directory entry that is a junction. Always return False if junctions are not supported on the current platform.

New in version 3.12.

+
+

Return True if path refers to an existing directory entry that is a symbolic link. Always False if symbolic links are not supported by the Python runtime.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.ismount(path)
+

Return True if pathname path is a mount point: a point in a file system where a different file system has been mounted. On POSIX, the function checks whether path’s parent, path/.., is on a different device than path, or whether path/.. and path point to the same i-node on the same device — this should detect mount points for all Unix and POSIX variants. It is not able to reliably detect bind mounts on the same filesystem. On Windows, a drive letter root and a share UNC are always mount points, and for any other path GetVolumePathName is called to see if it is different from the input path.

New in version 3.4: Support for detecting non-root mount points on Windows.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.isdevdrive(path)
+

Return True if pathname path is located on a Windows Dev Drive. A Dev Drive is optimized for developer scenarios, and offers faster performance for reading and writing files. It is recommended for use for source code, temporary build directories, package caches, and other IO-intensive operations.

May raise an error for an invalid path, for example, one without a recognizable drive, but returns False on platforms that do not support Dev Drives. See the Windows documentation for information on enabling and creating Dev Drives.

Availability: Windows.

New in version 3.12.

+
+os.path.join(path, *paths)
+

Join one or more path segments intelligently. The return value is the concatenation of path and all members of *paths, with exactly one directory separator following each non-empty part, except the last. That is, the result will only end in a separator if the last part is either empty or ends in a separator. If a segment is an absolute path (which on Windows requires both a drive and a root), then all previous segments are ignored and joining continues from the absolute path segment.

On Windows, the drive is not reset when a rooted path segment (e.g., r'\foo') is encountered. If a segment is on a different drive or is an absolute path, all previous segments are ignored and the drive is reset. Note that since there is a current directory for each drive, os.path.join("c:", "foo") represents a path relative to the current directory on drive C: (c:foo), not c:\foo.

Changed in version 3.6: Accepts a path-like object for path and paths.

+
+os.path.normcase(path)
+

Normalize the case of a pathname. On Windows, convert all characters in the pathname to lowercase, and also convert forward slashes to backward slashes. On other operating systems, return the path unchanged.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.normpath(path)

Normalize a pathname by collapsing redundant separators and up-level references so that A//B, A/B/, A/./B and A/foo/../B all become A/B. This string manipulation may change the meaning of a path that contains symbolic links. On Windows, it converts forward slashes to backward slashes. To normalize case, use normcase().

Note

On POSIX systems, in accordance with IEEE Std 1003.1 2013 Edition; 4.13 Pathname Resolution, if a pathname begins with exactly two slashes, the first component following the leading characters may be interpreted in an implementation-defined manner, although more than two leading characters shall be treated as a single character.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.realpath(path, *, strict=False)
+

Return the canonical path of the specified filename, eliminating any symbolic links encountered in the path (if they are supported by the operating system).

If a path doesn’t exist or a symlink loop is encountered, and strict is True, OSError is raised. If strict is False, the path is resolved as far as possible and any remainder is appended without checking whether it exists.

Note

This function emulates the operating system’s procedure for making a path canonical, which differs slightly between Windows and UNIX with respect to how links and subsequent path components interact.

Operating system APIs make paths canonical as needed, so it’s not normally necessary to call this function.

Changed in version 3.6: Accepts a path-like object.

Changed in version 3.8: Symbolic links and junctions are now resolved on Windows.

Changed in version 3.10: The strict parameter was added.

+
+os.path.relpath(path, start=os.curdir)
+

Return a relative filepath to path either from the current directory or from an optional start directory. This is a path computation: the filesystem is not accessed to confirm the existence or nature of path or start. On Windows, ValueError is raised when path and start are on different drives.

start defaults to os.curdir.

Availability: Unix, Windows.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.samefile(path1, path2)
+

Return True if both pathname arguments refer to the same file or directory. This is determined by the device number and i-node number and raises an exception if an os.stat() call on either pathname fails.

Availability: Unix, Windows.

Changed in version 3.2: Added Windows support.

Changed in version 3.4: Windows now uses the same implementation as all other platforms.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.sameopenfile(fp1, fp2)
+

Return True if the file descriptors fp1 and fp2 refer to the same file.

Availability: Unix, Windows.

Changed in version 3.2: Added Windows support.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.samestat(stat1, stat2)
+

Return True if the stat tuples stat1 and stat2 refer to the same file. These structures may have been returned by os.fstat(), os.lstat(), or os.stat(). This function implements the underlying comparison used by samefile() and sameopenfile().

Availability: Unix, Windows.

Changed in version 3.4: Added Windows support.

Changed in version 3.6: Accepts a path-like object.

+
+os.path.split(path)
+

Split the pathname path into a pair, (head, tail) where tail is the last pathname component and head is everything leading up to that. The tail part will never contain a slash; if path ends in a slash, tail will be empty. If there is no slash in path, head will be empty. If path is empty, both head and tail are empty. Trailing slashes are stripped from head unless it is the root (one or more slashes only). In all cases, join(head, tail) returns a path to the same location as path (but the strings may differ). Also see the functions dirname() and basename().

Changed in version 3.6: Accepts a path-like object.

+
+os.path.splitdrive(path)
+

Split the pathname path into a pair (drive, tail) where drive is either a mount point or the empty string. On systems which do not use drive specifications, drive will always be the empty string. In all cases, drive ++ tail will be the same as path.

On Windows, splits a pathname into drive/UNC sharepoint and relative path.

If the path contains a drive letter, drive will contain everything up to and including the colon:

>>> splitdrive("c:/dir")
+("c:", "/dir")
+

If the path contains a UNC path, drive will contain the host name and share:

>>> splitdrive("//host/computer/dir")
+("//host/computer", "/dir")
+

Changed in version 3.6: Accepts a path-like object.

+
+os.path.splitroot(path)
+

Split the pathname path into a 3-item tuple (drive, root, tail) where drive is a device name or mount point, root is a string of separators after the drive, and tail is everything after the root. Any of these items may be the empty string. In all cases, drive + root + tail will be the same as path.

On POSIX systems, drive is always empty. The root may be empty (if path is relative), a single forward slash (if path is absolute), or two forward slashes (implementation-defined per IEEE Std 1003.1-2017; 4.13 Pathname Resolution.) For example:

>>> splitroot('/home/sam')
+('', '/', 'home/sam')
+>>> splitroot('//home/sam')
+('', '//', 'home/sam')
+>>> splitroot('///home/sam')
+('', '/', '//home/sam')
+

On Windows, drive may be empty, a drive-letter name, a UNC share, or a device name. The root may be empty, a forward slash, or a backward slash. For example:

>>> splitroot('C:/Users/Sam')
+('C:', '/', 'Users/Sam')
+>>> splitroot('//Server/Share/Users/Sam')
+('//Server/Share', '/', 'Users/Sam')
+

New in version 3.12.

+
+os.path.splitext(path)
+

Split the pathname path into a pair (root, ext) such that root + ext == +path, and the extension, ext, is empty or begins with a period and contains at most one period.

If the path contains no extension, ext will be '':

>>> splitext('bar')
+('bar', '')
+

If the path contains an extension, then ext will be set to this extension, including the leading period. Note that previous periods will be ignored:

>>> splitext('foo.bar.exe')
+('foo.bar', '.exe')
+>>> splitext('/foo/bar.exe')
+('/foo/bar', '.exe')
+

Leading periods of the last component of the path are considered to be part of the root:

>>> splitext('.cshrc')
+('.cshrc', '')
+>>> splitext('/foo/....jpg')
+('/foo/....jpg', '')
+

Changed in version 3.6: Accepts a path-like object.

+
+os.path.supports_unicode_filenames
+

True if arbitrary Unicode strings can be used as file names (within limitations imposed by the file system).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/os.path.html +

+
diff --git a/devdocs/python~3.12/library%2Fpathlib.html b/devdocs/python~3.12/library%2Fpathlib.html new file mode 100644 index 00000000..7ec3802a --- /dev/null +++ b/devdocs/python~3.12/library%2Fpathlib.html @@ -0,0 +1,690 @@ +

pathlib — Object-oriented filesystem paths

New in version 3.4.

Source code: Lib/pathlib.py

This module offers classes representing filesystem paths with semantics appropriate for different operating systems. Path classes are divided between pure paths, which provide purely computational operations without I/O, and concrete paths, which inherit from pure paths but also provide I/O operations.

../_images/pathlib-inheritance.png

If you’ve never used this module before or just aren’t sure which class is right for your task, Path is most likely what you need. It instantiates a concrete path for the platform the code is running on.

Pure paths are useful in some special cases; for example:

  1. If you want to manipulate Windows paths on a Unix machine (or vice versa). You cannot instantiate a WindowsPath when running on Unix, but you can instantiate PureWindowsPath.
  2. You want to make sure that your code only manipulates paths without actually accessing the OS. In this case, instantiating one of the pure classes may be useful since those simply don’t have any OS-accessing operations.

See also

PEP 428: The pathlib module – object-oriented filesystem paths.

See also

For low-level path manipulation on strings, you can also use the os.path module.

Basic use

Importing the main class:

>>> from pathlib import Path
+

Listing subdirectories:

>>> p = Path('.')
+>>> [x for x in p.iterdir() if x.is_dir()]
+[PosixPath('.hg'), PosixPath('docs'), PosixPath('dist'),
+ PosixPath('__pycache__'), PosixPath('build')]
+

Listing Python source files in this directory tree:

>>> list(p.glob('**/*.py'))
+[PosixPath('test_pathlib.py'), PosixPath('setup.py'),
+ PosixPath('pathlib.py'), PosixPath('docs/conf.py'),
+ PosixPath('build/lib/pathlib.py')]
+

Navigating inside a directory tree:

>>> p = Path('/etc')
+>>> q = p / 'init.d' / 'reboot'
+>>> q
+PosixPath('/etc/init.d/reboot')
+>>> q.resolve()
+PosixPath('/etc/rc.d/init.d/halt')
+

Querying path properties:

>>> q.exists()
+True
+>>> q.is_dir()
+False
+

Opening a file:

>>> with q.open() as f: f.readline()
+...
+'#!/bin/bash\n'
+

Pure paths

Pure path objects provide path-handling operations which don’t actually access a filesystem. There are three ways to access these classes, which we also call flavours:

+class pathlib.PurePath(*pathsegments)
+

A generic class that represents the system’s path flavour (instantiating it creates either a PurePosixPath or a PureWindowsPath):

>>> PurePath('setup.py')      # Running on a Unix machine
+PurePosixPath('setup.py')
+

Each element of pathsegments can be either a string representing a path segment, or an object implementing the os.PathLike interface where the __fspath__() method returns a string, such as another path object:

>>> PurePath('foo', 'some/path', 'bar')
+PurePosixPath('foo/some/path/bar')
+>>> PurePath(Path('foo'), Path('bar'))
+PurePosixPath('foo/bar')
+

When pathsegments is empty, the current directory is assumed:

>>> PurePath()
+PurePosixPath('.')
+

If a segment is an absolute path, all previous segments are ignored (like os.path.join()):

>>> PurePath('/etc', '/usr', 'lib64')
+PurePosixPath('/usr/lib64')
+>>> PureWindowsPath('c:/Windows', 'd:bar')
+PureWindowsPath('d:bar')
+

On Windows, the drive is not reset when a rooted relative path segment (e.g., r'\foo') is encountered:

>>> PureWindowsPath('c:/Windows', '/Program Files')
+PureWindowsPath('c:/Program Files')
+

Spurious slashes and single dots are collapsed, but double dots ('..') and leading double slashes ('//') are not, since this would change the meaning of a path for various reasons (e.g. symbolic links, UNC paths):

>>> PurePath('foo//bar')
+PurePosixPath('foo/bar')
+>>> PurePath('//foo/bar')
+PurePosixPath('//foo/bar')
+>>> PurePath('foo/./bar')
+PurePosixPath('foo/bar')
+>>> PurePath('foo/../bar')
+PurePosixPath('foo/../bar')
+

(a naïve approach would make PurePosixPath('foo/../bar') equivalent to PurePosixPath('bar'), which is wrong if foo is a symbolic link to another directory)

Pure path objects implement the os.PathLike interface, allowing them to be used anywhere the interface is accepted.

Changed in version 3.6: Added support for the os.PathLike interface.

+
+class pathlib.PurePosixPath(*pathsegments)
+

A subclass of PurePath, this path flavour represents non-Windows filesystem paths:

>>> PurePosixPath('/etc')
+PurePosixPath('/etc')
+

pathsegments is specified similarly to PurePath.

+
+class pathlib.PureWindowsPath(*pathsegments)
+

A subclass of PurePath, this path flavour represents Windows filesystem paths, including UNC paths:

>>> PureWindowsPath('c:/Program Files/')
+PureWindowsPath('c:/Program Files')
+>>> PureWindowsPath('//server/share/file')
+PureWindowsPath('//server/share/file')
+

pathsegments is specified similarly to PurePath.

+

Regardless of the system you’re running on, you can instantiate all of these classes, since they don’t provide any operation that does system calls.

General properties

Paths are immutable and hashable. Paths of a same flavour are comparable and orderable. These properties respect the flavour’s case-folding semantics:

>>> PurePosixPath('foo') == PurePosixPath('FOO')
+False
+>>> PureWindowsPath('foo') == PureWindowsPath('FOO')
+True
+>>> PureWindowsPath('FOO') in { PureWindowsPath('foo') }
+True
+>>> PureWindowsPath('C:') < PureWindowsPath('d:')
+True
+

Paths of a different flavour compare unequal and cannot be ordered:

>>> PureWindowsPath('foo') == PurePosixPath('foo')
+False
+>>> PureWindowsPath('foo') < PurePosixPath('foo')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: '<' not supported between instances of 'PureWindowsPath' and 'PurePosixPath'
+

Operators

The slash operator helps create child paths, like os.path.join(). If the argument is an absolute path, the previous path is ignored. On Windows, the drive is not reset when the argument is a rooted relative path (e.g., r'\foo'):

>>> p = PurePath('/etc')
+>>> p
+PurePosixPath('/etc')
+>>> p / 'init.d' / 'apache2'
+PurePosixPath('/etc/init.d/apache2')
+>>> q = PurePath('bin')
+>>> '/usr' / q
+PurePosixPath('/usr/bin')
+>>> p / '/an_absolute_path'
+PurePosixPath('/an_absolute_path')
+>>> PureWindowsPath('c:/Windows', '/Program Files')
+PureWindowsPath('c:/Program Files')
+

A path object can be used anywhere an object implementing os.PathLike is accepted:

>>> import os
+>>> p = PurePath('/etc')
+>>> os.fspath(p)
+'/etc'
+

The string representation of a path is the raw filesystem path itself (in native form, e.g. with backslashes under Windows), which you can pass to any function taking a file path as a string:

>>> p = PurePath('/etc')
+>>> str(p)
+'/etc'
+>>> p = PureWindowsPath('c:/Program Files')
+>>> str(p)
+'c:\\Program Files'
+

Similarly, calling bytes on a path gives the raw filesystem path as a bytes object, as encoded by os.fsencode():

>>> bytes(p)
+b'/etc'
+

Note

Calling bytes is only recommended under Unix. Under Windows, the unicode form is the canonical representation of filesystem paths.

Accessing individual parts

To access the individual “parts” (components) of a path, use the following property:

+PurePath.parts
+

A tuple giving access to the path’s various components:

>>> p = PurePath('/usr/bin/python3')
+>>> p.parts
+('/', 'usr', 'bin', 'python3')
+
+>>> p = PureWindowsPath('c:/Program Files/PSF')
+>>> p.parts
+('c:\\', 'Program Files', 'PSF')
+

(note how the drive and local root are regrouped in a single part)

+

Methods and properties

Pure paths provide the following methods and properties:

+PurePath.drive
+

A string representing the drive letter or name, if any:

>>> PureWindowsPath('c:/Program Files/').drive
+'c:'
+>>> PureWindowsPath('/Program Files/').drive
+''
+>>> PurePosixPath('/etc').drive
+''
+

UNC shares are also considered drives:

>>> PureWindowsPath('//host/share/foo.txt').drive
+'\\\\host\\share'
+
+
+PurePath.root
+

A string representing the (local or global) root, if any:

>>> PureWindowsPath('c:/Program Files/').root
+'\\'
+>>> PureWindowsPath('c:Program Files/').root
+''
+>>> PurePosixPath('/etc').root
+'/'
+

UNC shares always have a root:

>>> PureWindowsPath('//host/share').root
+'\\'
+

If the path starts with more than two successive slashes, PurePosixPath collapses them:

>>> PurePosixPath('//etc').root
+'//'
+>>> PurePosixPath('///etc').root
+'/'
+>>> PurePosixPath('////etc').root
+'/'
+

Note

This behavior conforms to The Open Group Base Specifications Issue 6, paragraph 4.11 Pathname Resolution:

“A pathname that begins with two successive slashes may be interpreted in an implementation-defined manner, although more than two leading slashes shall be treated as a single slash.”

+
+PurePath.anchor
+

The concatenation of the drive and root:

>>> PureWindowsPath('c:/Program Files/').anchor
+'c:\\'
+>>> PureWindowsPath('c:Program Files/').anchor
+'c:'
+>>> PurePosixPath('/etc').anchor
+'/'
+>>> PureWindowsPath('//host/share').anchor
+'\\\\host\\share\\'
+
+
+PurePath.parents
+

An immutable sequence providing access to the logical ancestors of the path:

>>> p = PureWindowsPath('c:/foo/bar/setup.py')
+>>> p.parents[0]
+PureWindowsPath('c:/foo/bar')
+>>> p.parents[1]
+PureWindowsPath('c:/foo')
+>>> p.parents[2]
+PureWindowsPath('c:/')
+

Changed in version 3.10: The parents sequence now supports slices and negative index values.

+
+PurePath.parent
+

The logical parent of the path:

>>> p = PurePosixPath('/a/b/c/d')
+>>> p.parent
+PurePosixPath('/a/b/c')
+

You cannot go past an anchor, or empty path:

>>> p = PurePosixPath('/')
+>>> p.parent
+PurePosixPath('/')
+>>> p = PurePosixPath('.')
+>>> p.parent
+PurePosixPath('.')
+

Note

This is a purely lexical operation, hence the following behaviour:

>>> p = PurePosixPath('foo/..')
+>>> p.parent
+PurePosixPath('foo')
+

If you want to walk an arbitrary filesystem path upwards, it is recommended to first call Path.resolve() so as to resolve symlinks and eliminate ".." components.

+
+PurePath.name
+

A string representing the final path component, excluding the drive and root, if any:

>>> PurePosixPath('my/library/setup.py').name
+'setup.py'
+

UNC drive names are not considered:

>>> PureWindowsPath('//some/share/setup.py').name
+'setup.py'
+>>> PureWindowsPath('//some/share').name
+''
+
+
+PurePath.suffix
+

The file extension of the final component, if any:

>>> PurePosixPath('my/library/setup.py').suffix
+'.py'
+>>> PurePosixPath('my/library.tar.gz').suffix
+'.gz'
+>>> PurePosixPath('my/library').suffix
+''
+
+
+PurePath.suffixes
+

A list of the path’s file extensions:

>>> PurePosixPath('my/library.tar.gar').suffixes
+['.tar', '.gar']
+>>> PurePosixPath('my/library.tar.gz').suffixes
+['.tar', '.gz']
+>>> PurePosixPath('my/library').suffixes
+[]
+
+
+PurePath.stem
+

The final path component, without its suffix:

>>> PurePosixPath('my/library.tar.gz').stem
+'library.tar'
+>>> PurePosixPath('my/library.tar').stem
+'library'
+>>> PurePosixPath('my/library').stem
+'library'
+
+
+PurePath.as_posix()
+

Return a string representation of the path with forward slashes (/):

>>> p = PureWindowsPath('c:\\windows')
+>>> str(p)
+'c:\\windows'
+>>> p.as_posix()
+'c:/windows'
+
+
+PurePath.as_uri()
+

Represent the path as a file URI. ValueError is raised if the path isn’t absolute.

>>> p = PurePosixPath('/etc/passwd')
+>>> p.as_uri()
+'file:///etc/passwd'
+>>> p = PureWindowsPath('c:/Windows')
+>>> p.as_uri()
+'file:///c:/Windows'
+
+
+PurePath.is_absolute()
+

Return whether the path is absolute or not. A path is considered absolute if it has both a root and (if the flavour allows) a drive:

>>> PurePosixPath('/a/b').is_absolute()
+True
+>>> PurePosixPath('a/b').is_absolute()
+False
+
+>>> PureWindowsPath('c:/a/b').is_absolute()
+True
+>>> PureWindowsPath('/a/b').is_absolute()
+False
+>>> PureWindowsPath('c:').is_absolute()
+False
+>>> PureWindowsPath('//some/share').is_absolute()
+True
+
+
+PurePath.is_relative_to(other)
+

Return whether or not this path is relative to the other path.

>>> p = PurePath('/etc/passwd')
+>>> p.is_relative_to('/etc')
+True
+>>> p.is_relative_to('/usr')
+False
+

New in version 3.9.

Deprecated since version 3.12, will be removed in version 3.14: Passing additional arguments is deprecated; if supplied, they are joined with other.

+
+PurePath.is_reserved()
+

With PureWindowsPath, return True if the path is considered reserved under Windows, False otherwise. With PurePosixPath, False is always returned.

>>> PureWindowsPath('nul').is_reserved()
+True
+>>> PurePosixPath('nul').is_reserved()
+False
+

File system calls on reserved paths can fail mysteriously or have unintended effects.

+
+PurePath.joinpath(*pathsegments)
+

Calling this method is equivalent to combining the path with each of the given pathsegments in turn:

>>> PurePosixPath('/etc').joinpath('passwd')
+PurePosixPath('/etc/passwd')
+>>> PurePosixPath('/etc').joinpath(PurePosixPath('passwd'))
+PurePosixPath('/etc/passwd')
+>>> PurePosixPath('/etc').joinpath('init.d', 'apache2')
+PurePosixPath('/etc/init.d/apache2')
+>>> PureWindowsPath('c:').joinpath('/Program Files')
+PureWindowsPath('c:/Program Files')
+
+
+PurePath.match(pattern, *, case_sensitive=None)
+

Match this path against the provided glob-style pattern. Return True if matching is successful, False otherwise.

If pattern is relative, the path can be either relative or absolute, and matching is done from the right:

>>> PurePath('a/b.py').match('*.py')
+True
+>>> PurePath('/a/b/c.py').match('b/*.py')
+True
+>>> PurePath('/a/b/c.py').match('a/*.py')
+False
+

If pattern is absolute, the path must be absolute, and the whole path must match:

>>> PurePath('/a.py').match('/*.py')
+True
+>>> PurePath('a/b.py').match('/*.py')
+False
+

The pattern may be another path object; this speeds up matching the same pattern against multiple files:

>>> pattern = PurePath('*.py')
+>>> PurePath('a/b.py').match(pattern)
+True
+

Changed in version 3.12: Accepts an object implementing the os.PathLike interface.

As with other methods, case-sensitivity follows platform defaults:

>>> PurePosixPath('b.py').match('*.PY')
+False
+>>> PureWindowsPath('b.py').match('*.PY')
+True
+

Set case_sensitive to True or False to override this behaviour.

Changed in version 3.12: The case_sensitive parameter was added.

+
+PurePath.relative_to(other, walk_up=False)
+

Compute a version of this path relative to the path represented by other. If it’s impossible, ValueError is raised:

>>> p = PurePosixPath('/etc/passwd')
+>>> p.relative_to('/')
+PurePosixPath('etc/passwd')
+>>> p.relative_to('/etc')
+PurePosixPath('passwd')
+>>> p.relative_to('/usr')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "pathlib.py", line 941, in relative_to
+    raise ValueError(error_message.format(str(self), str(formatted)))
+ValueError: '/etc/passwd' is not in the subpath of '/usr' OR one path is relative and the other is absolute.
+

When walk_up is False (the default), the path must start with other. When the argument is True, .. entries may be added to form the relative path. In all other cases, such as the paths referencing different drives, ValueError is raised.:

>>> p.relative_to('/usr', walk_up=True)
+PurePosixPath('../etc/passwd')
+>>> p.relative_to('foo', walk_up=True)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "pathlib.py", line 941, in relative_to
+    raise ValueError(error_message.format(str(self), str(formatted)))
+ValueError: '/etc/passwd' is not on the same drive as 'foo' OR one path is relative and the other is absolute.
+

Warning

This function is part of PurePath and works with strings. It does not check or access the underlying file structure. This can impact the walk_up option as it assumes that no symlinks are present in the path; call resolve() first if necessary to resolve symlinks.

Changed in version 3.12: The walk_up parameter was added (old behavior is the same as walk_up=False).

Deprecated since version 3.12, will be removed in version 3.14: Passing additional positional arguments is deprecated; if supplied, they are joined with other.

+
+PurePath.with_name(name)
+

Return a new path with the name changed. If the original path doesn’t have a name, ValueError is raised:

>>> p = PureWindowsPath('c:/Downloads/pathlib.tar.gz')
+>>> p.with_name('setup.py')
+PureWindowsPath('c:/Downloads/setup.py')
+>>> p = PureWindowsPath('c:/')
+>>> p.with_name('setup.py')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "/home/antoine/cpython/default/Lib/pathlib.py", line 751, in with_name
+    raise ValueError("%r has an empty name" % (self,))
+ValueError: PureWindowsPath('c:/') has an empty name
+
+
+PurePath.with_stem(stem)
+

Return a new path with the stem changed. If the original path doesn’t have a name, ValueError is raised:

>>> p = PureWindowsPath('c:/Downloads/draft.txt')
+>>> p.with_stem('final')
+PureWindowsPath('c:/Downloads/final.txt')
+>>> p = PureWindowsPath('c:/Downloads/pathlib.tar.gz')
+>>> p.with_stem('lib')
+PureWindowsPath('c:/Downloads/lib.gz')
+>>> p = PureWindowsPath('c:/')
+>>> p.with_stem('')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "/home/antoine/cpython/default/Lib/pathlib.py", line 861, in with_stem
+    return self.with_name(stem + self.suffix)
+  File "/home/antoine/cpython/default/Lib/pathlib.py", line 851, in with_name
+    raise ValueError("%r has an empty name" % (self,))
+ValueError: PureWindowsPath('c:/') has an empty name
+

New in version 3.9.

+
+PurePath.with_suffix(suffix)
+

Return a new path with the suffix changed. If the original path doesn’t have a suffix, the new suffix is appended instead. If the suffix is an empty string, the original suffix is removed:

>>> p = PureWindowsPath('c:/Downloads/pathlib.tar.gz')
+>>> p.with_suffix('.bz2')
+PureWindowsPath('c:/Downloads/pathlib.tar.bz2')
+>>> p = PureWindowsPath('README')
+>>> p.with_suffix('.txt')
+PureWindowsPath('README.txt')
+>>> p = PureWindowsPath('README.txt')
+>>> p.with_suffix('')
+PureWindowsPath('README')
+
+
+PurePath.with_segments(*pathsegments)
+

Create a new path object of the same type by combining the given pathsegments. This method is called whenever a derivative path is created, such as from parent and relative_to(). Subclasses may override this method to pass information to derivative paths, for example:

from pathlib import PurePosixPath
+
+class MyPath(PurePosixPath):
+    def __init__(self, *pathsegments, session_id):
+        super().__init__(*pathsegments)
+        self.session_id = session_id
+
+    def with_segments(self, *pathsegments):
+        return type(self)(*pathsegments, session_id=self.session_id)
+
+etc = MyPath('/etc', session_id=42)
+hosts = etc / 'hosts'
+print(hosts.session_id)  # 42
+

New in version 3.12.

+

Concrete paths

Concrete paths are subclasses of the pure path classes. In addition to operations provided by the latter, they also provide methods to do system calls on path objects. There are three ways to instantiate concrete paths:

+class pathlib.Path(*pathsegments)
+

A subclass of PurePath, this class represents concrete paths of the system’s path flavour (instantiating it creates either a PosixPath or a WindowsPath):

>>> Path('setup.py')
+PosixPath('setup.py')
+

pathsegments is specified similarly to PurePath.

+
+class pathlib.PosixPath(*pathsegments)
+

A subclass of Path and PurePosixPath, this class represents concrete non-Windows filesystem paths:

>>> PosixPath('/etc')
+PosixPath('/etc')
+

pathsegments is specified similarly to PurePath.

+
+class pathlib.WindowsPath(*pathsegments)
+

A subclass of Path and PureWindowsPath, this class represents concrete Windows filesystem paths:

>>> WindowsPath('c:/Program Files/')
+WindowsPath('c:/Program Files')
+

pathsegments is specified similarly to PurePath.

+

You can only instantiate the class flavour that corresponds to your system (allowing system calls on non-compatible path flavours could lead to bugs or failures in your application):

>>> import os
+>>> os.name
+'posix'
+>>> Path('setup.py')
+PosixPath('setup.py')
+>>> PosixPath('setup.py')
+PosixPath('setup.py')
+>>> WindowsPath('setup.py')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "pathlib.py", line 798, in __new__
+    % (cls.__name__,))
+NotImplementedError: cannot instantiate 'WindowsPath' on your system
+

Methods

Concrete paths provide the following methods in addition to pure paths methods. Many of these methods can raise an OSError if a system call fails (for example because the path doesn’t exist).

Changed in version 3.8: exists(), is_dir(), is_file(), is_mount(), is_symlink(), is_block_device(), is_char_device(), is_fifo(), is_socket() now return False instead of raising an exception for paths that contain characters unrepresentable at the OS level.

+classmethod Path.cwd()
+

Return a new path object representing the current directory (as returned by os.getcwd()):

>>> Path.cwd()
+PosixPath('/home/antoine/pathlib')
+
+
+classmethod Path.home()
+

Return a new path object representing the user’s home directory (as returned by os.path.expanduser() with ~ construct). If the home directory can’t be resolved, RuntimeError is raised.

>>> Path.home()
+PosixPath('/home/antoine')
+

New in version 3.5.

+
+Path.stat(*, follow_symlinks=True)
+

Return a os.stat_result object containing information about this path, like os.stat(). The result is looked up at each call to this method.

This method normally follows symlinks; to stat a symlink add the argument follow_symlinks=False, or use lstat().

>>> p = Path('setup.py')
+>>> p.stat().st_size
+956
+>>> p.stat().st_mtime
+1327883547.852554
+

Changed in version 3.10: The follow_symlinks parameter was added.

+
+Path.chmod(mode, *, follow_symlinks=True)
+

Change the file mode and permissions, like os.chmod().

This method normally follows symlinks. Some Unix flavours support changing permissions on the symlink itself; on these platforms you may add the argument follow_symlinks=False, or use lchmod().

>>> p = Path('setup.py')
+>>> p.stat().st_mode
+33277
+>>> p.chmod(0o444)
+>>> p.stat().st_mode
+33060
+

Changed in version 3.10: The follow_symlinks parameter was added.

+
+Path.exists(*, follow_symlinks=True)
+

Return True if the path points to an existing file or directory.

This method normally follows symlinks; to check if a symlink exists, add the argument follow_symlinks=False.

>>> Path('.').exists()
+True
+>>> Path('setup.py').exists()
+True
+>>> Path('/etc').exists()
+True
+>>> Path('nonexistentfile').exists()
+False
+

Changed in version 3.12: The follow_symlinks parameter was added.

+
+Path.expanduser()
+

Return a new path with expanded ~ and ~user constructs, as returned by os.path.expanduser(). If a home directory can’t be resolved, RuntimeError is raised.

>>> p = PosixPath('~/films/Monty Python')
+>>> p.expanduser()
+PosixPath('/home/eric/films/Monty Python')
+

New in version 3.5.

+
+Path.glob(pattern, *, case_sensitive=None)
+

Glob the given relative pattern in the directory represented by this path, yielding all matching files (of any kind):

>>> sorted(Path('.').glob('*.py'))
+[PosixPath('pathlib.py'), PosixPath('setup.py'), PosixPath('test_pathlib.py')]
+>>> sorted(Path('.').glob('*/*.py'))
+[PosixPath('docs/conf.py')]
+

Patterns are the same as for fnmatch, with the addition of “**” which means “this directory and all subdirectories, recursively”. In other words, it enables recursive globbing:

>>> sorted(Path('.').glob('**/*.py'))
+[PosixPath('build/lib/pathlib.py'),
+ PosixPath('docs/conf.py'),
+ PosixPath('pathlib.py'),
+ PosixPath('setup.py'),
+ PosixPath('test_pathlib.py')]
+

By default, or when the case_sensitive keyword-only argument is set to None, this method matches paths using platform-specific casing rules: typically, case-sensitive on POSIX, and case-insensitive on Windows. Set case_sensitive to True or False to override this behaviour.

Note

Using the “**” pattern in large directory trees may consume an inordinate amount of time.

Raises an auditing event pathlib.Path.glob with arguments self, pattern.

Changed in version 3.11: Return only directories if pattern ends with a pathname components separator (sep or altsep).

Changed in version 3.12: The case_sensitive parameter was added.

+
+Path.group()
+

Return the name of the group owning the file. KeyError is raised if the file’s gid isn’t found in the system database.

+
+Path.is_dir()
+

Return True if the path points to a directory (or a symbolic link pointing to a directory), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.is_file()
+

Return True if the path points to a regular file (or a symbolic link pointing to a regular file), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.is_junction()
+

Return True if the path points to a junction, and False for any other type of file. Currently only Windows supports junctions.

New in version 3.12.

+
+Path.is_mount()
+

Return True if the path is a mount point: a point in a file system where a different file system has been mounted. On POSIX, the function checks whether path’s parent, path/.., is on a different device than path, or whether path/.. and path point to the same i-node on the same device — this should detect mount points for all Unix and POSIX variants. On Windows, a mount point is considered to be a drive letter root (e.g. c:\), a UNC share (e.g. \\server\share), or a mounted filesystem directory.

New in version 3.7.

Changed in version 3.12: Windows support was added.

+
+

Return True if the path points to a symbolic link, False otherwise.

False is also returned if the path doesn’t exist; other errors (such as permission errors) are propagated.

+
+Path.is_socket()
+

Return True if the path points to a Unix socket (or a symbolic link pointing to a Unix socket), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.is_fifo()
+

Return True if the path points to a FIFO (or a symbolic link pointing to a FIFO), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.is_block_device()
+

Return True if the path points to a block device (or a symbolic link pointing to a block device), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.is_char_device()
+

Return True if the path points to a character device (or a symbolic link pointing to a character device), False if it points to another kind of file.

False is also returned if the path doesn’t exist or is a broken symlink; other errors (such as permission errors) are propagated.

+
+Path.iterdir()
+

When the path points to a directory, yield path objects of the directory contents:

>>> p = Path('docs')
+>>> for child in p.iterdir(): child
+...
+PosixPath('docs/conf.py')
+PosixPath('docs/_templates')
+PosixPath('docs/make.bat')
+PosixPath('docs/index.rst')
+PosixPath('docs/_build')
+PosixPath('docs/_static')
+PosixPath('docs/Makefile')
+

The children are yielded in arbitrary order, and the special entries '.' and '..' are not included. If a file is removed from or added to the directory after creating the iterator, whether a path object for that file be included is unspecified.

+
+Path.walk(top_down=True, on_error=None, follow_symlinks=False)
+

Generate the file names in a directory tree by walking the tree either top-down or bottom-up.

For each directory in the directory tree rooted at self (including self but excluding ‘.’ and ‘..’), the method yields a 3-tuple of (dirpath, dirnames, filenames).

dirpath is a Path to the directory currently being walked, dirnames is a list of strings for the names of subdirectories in dirpath (excluding '.' and '..'), and filenames is a list of strings for the names of the non-directory files in dirpath. To get a full path (which begins with self) to a file or directory in dirpath, do dirpath / name. Whether or not the lists are sorted is file system-dependent.

If the optional argument top_down is true (which is the default), the triple for a directory is generated before the triples for any of its subdirectories (directories are walked top-down). If top_down is false, the triple for a directory is generated after the triples for all of its subdirectories (directories are walked bottom-up). No matter the value of top_down, the list of subdirectories is retrieved before the triples for the directory and its subdirectories are walked.

When top_down is true, the caller can modify the dirnames list in-place (for example, using del or slice assignment), and Path.walk() will only recurse into the subdirectories whose names remain in dirnames. This can be used to prune the search, or to impose a specific order of visiting, or even to inform Path.walk() about directories the caller creates or renames before it resumes Path.walk() again. Modifying dirnames when top_down is false has no effect on the behavior of Path.walk() since the directories in dirnames have already been generated by the time dirnames is yielded to the caller.

By default, errors from os.scandir() are ignored. If the optional argument on_error is specified, it should be a callable; it will be called with one argument, an OSError instance. The callable can handle the error to continue the walk or re-raise it to stop the walk. Note that the filename is available as the filename attribute of the exception object.

By default, Path.walk() does not follow symbolic links, and instead adds them to the filenames list. Set follow_symlinks to true to resolve symlinks and place them in dirnames and filenames as appropriate for their targets, and consequently visit directories pointed to by symlinks (where supported).

Note

Be aware that setting follow_symlinks to true can lead to infinite recursion if a link points to a parent directory of itself. Path.walk() does not keep track of the directories it has already visited.

Note

Path.walk() assumes the directories it walks are not modified during execution. For example, if a directory from dirnames has been replaced with a symlink and follow_symlinks is false, Path.walk() will still try to descend into it. To prevent such behavior, remove directories from dirnames as appropriate.

Note

Unlike os.walk(), Path.walk() lists symlinks to directories in filenames if follow_symlinks is false.

This example displays the number of bytes used by all files in each directory, while ignoring __pycache__ directories:

from pathlib import Path
+for root, dirs, files in Path("cpython/Lib/concurrent").walk(on_error=print):
+  print(
+      root,
+      "consumes",
+      sum((root / file).stat().st_size for file in files),
+      "bytes in",
+      len(files),
+      "non-directory files"
+  )
+  if '__pycache__' in dirs:
+        dirs.remove('__pycache__')
+

This next example is a simple implementation of shutil.rmtree(). Walking the tree bottom-up is essential as rmdir() doesn’t allow deleting a directory before it is empty:

# Delete everything reachable from the directory "top".
+# CAUTION:  This is dangerous! For example, if top == Path('/'),
+# it could delete all of your files.
+for root, dirs, files in top.walk(top_down=False):
+    for name in files:
+        (root / name).unlink()
+    for name in dirs:
+        (root / name).rmdir()
+

New in version 3.12.

+
+Path.lchmod(mode)
+

Like Path.chmod() but, if the path points to a symbolic link, the symbolic link’s mode is changed rather than its target’s.

+
+Path.lstat()
+

Like Path.stat() but, if the path points to a symbolic link, return the symbolic link’s information rather than its target’s.

+
+Path.mkdir(mode=0o777, parents=False, exist_ok=False)
+

Create a new directory at this given path. If mode is given, it is combined with the process’ umask value to determine the file mode and access flags. If the path already exists, FileExistsError is raised.

If parents is true, any missing parents of this path are created as needed; they are created with the default permissions without taking mode into account (mimicking the POSIX mkdir -p command).

If parents is false (the default), a missing parent raises FileNotFoundError.

If exist_ok is false (the default), FileExistsError is raised if the target directory already exists.

If exist_ok is true, FileExistsError exceptions will be ignored (same behavior as the POSIX mkdir -p command), but only if the last path component is not an existing non-directory file.

Changed in version 3.5: The exist_ok parameter was added.

+
+Path.open(mode='r', buffering=- 1, encoding=None, errors=None, newline=None)
+

Open the file pointed to by the path, like the built-in open() function does:

>>> p = Path('setup.py')
+>>> with p.open() as f:
+...     f.readline()
+...
+'#!/usr/bin/env python3\n'
+
+
+Path.owner()
+

Return the name of the user owning the file. KeyError is raised if the file’s uid isn’t found in the system database.

+
+Path.read_bytes()
+

Return the binary contents of the pointed-to file as a bytes object:

>>> p = Path('my_binary_file')
+>>> p.write_bytes(b'Binary file contents')
+20
+>>> p.read_bytes()
+b'Binary file contents'
+

New in version 3.5.

+
+Path.read_text(encoding=None, errors=None)
+

Return the decoded contents of the pointed-to file as a string:

>>> p = Path('my_text_file')
+>>> p.write_text('Text file contents')
+18
+>>> p.read_text()
+'Text file contents'
+

The file is opened and then closed. The optional parameters have the same meaning as in open().

New in version 3.5.

+
+

Return the path to which the symbolic link points (as returned by os.readlink()):

>>> p = Path('mylink')
+>>> p.symlink_to('setup.py')
+>>> p.readlink()
+PosixPath('setup.py')
+

New in version 3.9.

+
+Path.rename(target)
+

Rename this file or directory to the given target, and return a new Path instance pointing to target. On Unix, if target exists and is a file, it will be replaced silently if the user has permission. On Windows, if target exists, FileExistsError will be raised. target can be either a string or another path object:

>>> p = Path('foo')
+>>> p.open('w').write('some text')
+9
+>>> target = Path('bar')
+>>> p.rename(target)
+PosixPath('bar')
+>>> target.open().read()
+'some text'
+

The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.

It is implemented in terms of os.rename() and gives the same guarantees.

Changed in version 3.8: Added return value, return the new Path instance.

+
+Path.replace(target)
+

Rename this file or directory to the given target, and return a new Path instance pointing to target. If target points to an existing file or empty directory, it will be unconditionally replaced.

The target path may be absolute or relative. Relative paths are interpreted relative to the current working directory, not the directory of the Path object.

Changed in version 3.8: Added return value, return the new Path instance.

+
+Path.absolute()
+

Make the path absolute, without normalization or resolving symlinks. Returns a new path object:

>>> p = Path('tests')
+>>> p
+PosixPath('tests')
+>>> p.absolute()
+PosixPath('/home/antoine/pathlib/tests')
+
+
+Path.resolve(strict=False)
+

Make the path absolute, resolving any symlinks. A new path object is returned:

>>> p = Path()
+>>> p
+PosixPath('.')
+>>> p.resolve()
+PosixPath('/home/antoine/pathlib')
+

“..” components are also eliminated (this is the only method to do so):

>>> p = Path('docs/../setup.py')
+>>> p.resolve()
+PosixPath('/home/antoine/pathlib/setup.py')
+

If the path doesn’t exist and strict is True, FileNotFoundError is raised. If strict is False, the path is resolved as far as possible and any remainder is appended without checking whether it exists. If an infinite loop is encountered along the resolution path, RuntimeError is raised.

Changed in version 3.6: The strict parameter was added (pre-3.6 behavior is strict).

+
+Path.rglob(pattern, *, case_sensitive=None)
+

Glob the given relative pattern recursively. This is like calling Path.glob() with “**/” added in front of the pattern, where patterns are the same as for fnmatch:

>>> sorted(Path().rglob("*.py"))
+[PosixPath('build/lib/pathlib.py'),
+ PosixPath('docs/conf.py'),
+ PosixPath('pathlib.py'),
+ PosixPath('setup.py'),
+ PosixPath('test_pathlib.py')]
+

By default, or when the case_sensitive keyword-only argument is set to None, this method matches paths using platform-specific casing rules: typically, case-sensitive on POSIX, and case-insensitive on Windows. Set case_sensitive to True or False to override this behaviour.

Raises an auditing event pathlib.Path.rglob with arguments self, pattern.

Changed in version 3.11: Return only directories if pattern ends with a pathname components separator (sep or altsep).

Changed in version 3.12: The case_sensitive parameter was added.

+
+Path.rmdir()
+

Remove this directory. The directory must be empty.

+
+Path.samefile(other_path)
+

Return whether this path points to the same file as other_path, which can be either a Path object, or a string. The semantics are similar to os.path.samefile() and os.path.samestat().

An OSError can be raised if either file cannot be accessed for some reason.

>>> p = Path('spam')
+>>> q = Path('eggs')
+>>> p.samefile(q)
+False
+>>> p.samefile('spam')
+True
+

New in version 3.5.

+
+

Make this path a symbolic link to target. Under Windows, target_is_directory must be true (default False) if the link’s target is a directory. Under POSIX, target_is_directory’s value is ignored.

>>> p = Path('mylink')
+>>> p.symlink_to('setup.py')
+>>> p.resolve()
+PosixPath('/home/antoine/pathlib/setup.py')
+>>> p.stat().st_size
+956
+>>> p.lstat().st_size
+8
+

Note

The order of arguments (link, target) is the reverse of os.symlink()’s.

+
+

Make this path a hard link to the same file as target.

Note

The order of arguments (link, target) is the reverse of os.link()’s.

New in version 3.10.

+
+Path.touch(mode=0o666, exist_ok=True)
+

Create a file at this given path. If mode is given, it is combined with the process’ umask value to determine the file mode and access flags. If the file already exists, the function succeeds if exist_ok is true (and its modification time is updated to the current time), otherwise FileExistsError is raised.

+
+

Remove this file or symbolic link. If the path points to a directory, use Path.rmdir() instead.

If missing_ok is false (the default), FileNotFoundError is raised if the path does not exist.

If missing_ok is true, FileNotFoundError exceptions will be ignored (same behavior as the POSIX rm -f command).

Changed in version 3.8: The missing_ok parameter was added.

+
+Path.write_bytes(data)
+

Open the file pointed to in bytes mode, write data to it, and close the file:

>>> p = Path('my_binary_file')
+>>> p.write_bytes(b'Binary file contents')
+20
+>>> p.read_bytes()
+b'Binary file contents'
+

An existing file of the same name is overwritten.

New in version 3.5.

+
+Path.write_text(data, encoding=None, errors=None, newline=None)
+

Open the file pointed to in text mode, write data to it, and close the file:

>>> p = Path('my_text_file')
+>>> p.write_text('Text file contents')
+18
+>>> p.read_text()
+'Text file contents'
+

An existing file of the same name is overwritten. The optional parameters have the same meaning as in open().

New in version 3.5.

Changed in version 3.10: The newline parameter was added.

+

Correspondence to tools in the os module

Below is a table mapping various os functions to their corresponding PurePath/Path equivalent.

Note

Not all pairs of functions/methods below are equivalent. Some of them, despite having some overlapping use-cases, have different semantics. They include os.path.abspath() and Path.absolute(), os.path.relpath() and PurePath.relative_to().

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + +

os and os.path

pathlib

os.path.abspath()

Path.absolute() 1

os.path.realpath()

Path.resolve()

os.chmod()

Path.chmod()

os.mkdir()

Path.mkdir()

os.makedirs()

Path.mkdir()

os.rename()

Path.rename()

os.replace()

Path.replace()

os.rmdir()

Path.rmdir()

os.remove(), os.unlink()

Path.unlink()

os.getcwd()

Path.cwd()

os.path.exists()

Path.exists()

os.path.expanduser()

Path.expanduser() and Path.home()

os.listdir()

Path.iterdir()

os.walk()

Path.walk()

os.path.isdir()

Path.is_dir()

os.path.isfile()

Path.is_file()

os.path.islink()

Path.is_symlink()

os.link()

Path.hardlink_to()

os.symlink()

Path.symlink_to()

os.readlink()

Path.readlink()

os.path.relpath()

PurePath.relative_to() 2

os.stat()

Path.stat(), Path.owner(), Path.group()

os.path.isabs()

PurePath.is_absolute()

os.path.join()

PurePath.joinpath()

os.path.basename()

PurePath.name

os.path.dirname()

PurePath.parent

os.path.samefile()

Path.samefile()

os.path.splitext()

PurePath.stem and PurePath.suffix

Footnotes

+1
+

os.path.abspath() normalizes the resulting path, which may change its meaning in the presence of symlinks, while Path.absolute() does not.

+2
+

PurePath.relative_to() requires self to be the subpath of the argument, but os.path.relpath() does not.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pathlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fpdb.html b/devdocs/python~3.12/library%2Fpdb.html new file mode 100644 index 00000000..2ba6467c --- /dev/null +++ b/devdocs/python~3.12/library%2Fpdb.html @@ -0,0 +1,229 @@ +

pdb — The Python Debugger

Source code: Lib/pdb.py

The module pdb defines an interactive source code debugger for Python programs. It supports setting (conditional) breakpoints and single stepping at the source line level, inspection of stack frames, source code listing, and evaluation of arbitrary Python code in the context of any stack frame. It also supports post-mortem debugging and can be called under program control.

The debugger is extensible – it is actually defined as the class Pdb. This is currently undocumented but easily understood by reading the source. The extension interface uses the modules bdb and cmd.

See also

+Module faulthandler +
+
+

Used to dump Python tracebacks explicitly, on a fault, after a timeout, or on a user signal.

+Module traceback +
+
+

Standard interface to extract, format and print stack traces of Python programs.

The typical usage to break into the debugger is to insert:

import pdb; pdb.set_trace()
+

Or:

breakpoint()
+

at the location you want to break into the debugger, and then run the program. You can then step through the code following this statement, and continue running without the debugger using the continue command.

New in version 3.7: The built-in breakpoint(), when called with defaults, can be used instead of import pdb; pdb.set_trace().

def double(x):
+   breakpoint()
+   return x * 2
+val = 3
+print(f"{val} * 2 is {double(val)}")
+

The debugger’s prompt is (Pdb), which is the indicator that you are in debug mode:

> ...(3)double()
+-> return x * 2
+(Pdb) p x
+3
+(Pdb) continue
+3 * 2 is 6
+

Changed in version 3.3: Tab-completion via the readline module is available for commands and command arguments, e.g. the current global and local names are offered as arguments of the p command.

You can also invoke pdb from the command line to debug other scripts. For example:

python -m pdb myscript.py
+

When invoked as a module, pdb will automatically enter post-mortem debugging if the program being debugged exits abnormally. After post-mortem debugging (or after normal exit of the program), pdb will restart the program. Automatic restarting preserves pdb’s state (such as breakpoints) and in most cases is more useful than quitting the debugger upon program’s exit.

New in version 3.2: -c option is introduced to execute commands as if given in a .pdbrc file, see Debugger Commands.

New in version 3.7: -m option is introduced to execute modules similar to the way python -m does. As with a script, the debugger will pause execution just before the first line of the module.

Typical usage to execute a statement under control of the debugger is:

>>> import pdb
+>>> def f(x):
+...     print(1 / x)
+>>> pdb.run("f(2)")
+> <string>(1)<module>()
+(Pdb) continue
+0.5
+>>>
+

The typical usage to inspect a crashed program is:

>>> import pdb
+>>> def f(x):
+...     print(1 / x)
+...
+>>> f(0)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "<stdin>", line 2, in f
+ZeroDivisionError: division by zero
+>>> pdb.pm()
+> <stdin>(2)f()
+(Pdb) p x
+0
+(Pdb)
+

The module defines the following functions; each enters the debugger in a slightly different way:

+pdb.run(statement, globals=None, locals=None)
+

Execute the statement (given as a string or a code object) under debugger control. The debugger prompt appears before any code is executed; you can set breakpoints and type continue, or you can step through the statement using step or next (all these commands are explained below). The optional globals and locals arguments specify the environment in which the code is executed; by default the dictionary of the module __main__ is used. (See the explanation of the built-in exec() or eval() functions.)

+
+pdb.runeval(expression, globals=None, locals=None)
+

Evaluate the expression (given as a string or a code object) under debugger control. When runeval() returns, it returns the value of the expression. Otherwise this function is similar to run().

+
+pdb.runcall(function, *args, **kwds)
+

Call the function (a function or method object, not a string) with the given arguments. When runcall() returns, it returns whatever the function call returned. The debugger prompt appears as soon as the function is entered.

+
+pdb.set_trace(*, header=None)
+

Enter the debugger at the calling stack frame. This is useful to hard-code a breakpoint at a given point in a program, even if the code is not otherwise being debugged (e.g. when an assertion fails). If given, header is printed to the console just before debugging begins.

Changed in version 3.7: The keyword-only argument header.

+
+pdb.post_mortem(traceback=None)
+

Enter post-mortem debugging of the given traceback object. If no traceback is given, it uses the one of the exception that is currently being handled (an exception must be being handled if the default is to be used).

+
+pdb.pm()
+

Enter post-mortem debugging of the traceback found in sys.last_traceback.

+

The run* functions and set_trace() are aliases for instantiating the Pdb class and calling the method of the same name. If you want to access further features, you have to do this yourself:

+class pdb.Pdb(completekey='tab', stdin=None, stdout=None, skip=None, nosigint=False, readrc=True)
+

Pdb is the debugger class.

The completekey, stdin and stdout arguments are passed to the underlying cmd.Cmd class; see the description there.

The skip argument, if given, must be an iterable of glob-style module name patterns. The debugger will not step into frames that originate in a module that matches one of these patterns. 1

By default, Pdb sets a handler for the SIGINT signal (which is sent when the user presses Ctrl-C on the console) when you give a continue command. This allows you to break into the debugger again by pressing Ctrl-C. If you want Pdb not to touch the SIGINT handler, set nosigint to true.

The readrc argument defaults to true and controls whether Pdb will load .pdbrc files from the filesystem.

Example call to enable tracing with skip:

import pdb; pdb.Pdb(skip=['django.*']).set_trace()
+

Raises an auditing event pdb.Pdb with no arguments.

New in version 3.1: The skip argument.

New in version 3.2: The nosigint argument. Previously, a SIGINT handler was never set by Pdb.

Changed in version 3.6: The readrc argument.

+run(statement, globals=None, locals=None)
+runeval(expression, globals=None, locals=None)
+runcall(function, *args, **kwds)
+set_trace()
+

See the documentation for the functions explained above.

+
+

Debugger Commands

The commands recognized by the debugger are listed below. Most commands can be abbreviated to one or two letters as indicated; e.g. h(elp) means that either h or help can be used to enter the help command (but not he or hel, nor H or Help or HELP). Arguments to commands must be separated by whitespace (spaces or tabs). Optional arguments are enclosed in square brackets ([]) in the command syntax; the square brackets must not be typed. Alternatives in the command syntax are separated by a vertical bar (|).

Entering a blank line repeats the last command entered. Exception: if the last command was a list command, the next 11 lines are listed.

Commands that the debugger doesn’t recognize are assumed to be Python statements and are executed in the context of the program being debugged. Python statements can also be prefixed with an exclamation point (!). This is a powerful way to inspect the program being debugged; it is even possible to change a variable or call a function. When an exception occurs in such a statement, the exception name is printed but the debugger’s state is not changed.

The debugger supports aliases. Aliases can have parameters which allows one a certain level of adaptability to the context under examination.

Multiple commands may be entered on a single line, separated by ;;. (A single ; is not used as it is the separator for multiple commands in a line that is passed to the Python parser.) No intelligence is applied to separating the commands; the input is split at the first ;; pair, even if it is in the middle of a quoted string. A workaround for strings with double semicolons is to use implicit string concatenation ';'';' or ";"";".

To set a temporary global variable, use a convenience variable. A convenience variable is a variable whose name starts with $. For example, $foo = 1 sets a global variable $foo which you can use in the debugger session. The convenience variables are cleared when the program resumes execution so it’s less likely to interfere with your program compared to using normal variables like foo = 1.

There are three preset convenience variables:

  • +$_frame: the current frame you are debugging
  • +$_retval: the return value if the frame is returning
  • +$_exception: the exception if the frame is raising an exception

New in version 3.12.

If a file .pdbrc exists in the user’s home directory or in the current directory, it is read with 'utf-8' encoding and executed as if it had been typed at the debugger prompt. This is particularly useful for aliases. If both files exist, the one in the home directory is read first and aliases defined there can be overridden by the local file.

Changed in version 3.11: .pdbrc is now read with 'utf-8' encoding. Previously, it was read with the system locale encoding.

Changed in version 3.2: .pdbrc can now contain commands that continue debugging, such as continue or next. Previously, these commands had no effect.

+h(elp) [command]
+

Without argument, print the list of available commands. With a command as argument, print help about that command. help pdb displays the full documentation (the docstring of the pdb module). Since the command argument must be an identifier, help exec must be entered to get help on the ! command.

+
+w(here)
+

Print a stack trace, with the most recent frame at the bottom. An arrow (>) indicates the current frame, which determines the context of most commands.

+
+d(own) [count]
+

Move the current frame count (default one) levels down in the stack trace (to a newer frame).

+
+u(p) [count]
+

Move the current frame count (default one) levels up in the stack trace (to an older frame).

+
+b(reak) [([filename:]lineno | function) [, condition]]
+

With a lineno argument, set a break there in the current file. With a function argument, set a break at the first executable statement within that function. The line number may be prefixed with a filename and a colon, to specify a breakpoint in another file (probably one that hasn’t been loaded yet). The file is searched on sys.path. Note that each breakpoint is assigned a number to which all the other breakpoint commands refer.

If a second argument is present, it is an expression which must evaluate to true before the breakpoint is honored.

Without argument, list all breaks, including for each breakpoint, the number of times that breakpoint has been hit, the current ignore count, and the associated condition if any.

+
+tbreak [([filename:]lineno | function) [, condition]]
+

Temporary breakpoint, which is removed automatically when it is first hit. The arguments are the same as for break.

+
+cl(ear) [filename:lineno | bpnumber ...]
+

With a filename:lineno argument, clear all the breakpoints at this line. With a space separated list of breakpoint numbers, clear those breakpoints. Without argument, clear all breaks (but first ask confirmation).

+
+disable bpnumber [bpnumber ...]
+

Disable the breakpoints given as a space separated list of breakpoint numbers. Disabling a breakpoint means it cannot cause the program to stop execution, but unlike clearing a breakpoint, it remains in the list of breakpoints and can be (re-)enabled.

+
+enable bpnumber [bpnumber ...]
+

Enable the breakpoints specified.

+
+ignore bpnumber [count]
+

Set the ignore count for the given breakpoint number. If count is omitted, the ignore count is set to 0. A breakpoint becomes active when the ignore count is zero. When non-zero, the count is decremented each time the breakpoint is reached and the breakpoint is not disabled and any associated condition evaluates to true.

+
+condition bpnumber [condition]
+

Set a new condition for the breakpoint, an expression which must evaluate to true before the breakpoint is honored. If condition is absent, any existing condition is removed; i.e., the breakpoint is made unconditional.

+
+commands [bpnumber]
+

Specify a list of commands for breakpoint number bpnumber. The commands themselves appear on the following lines. Type a line containing just end to terminate the commands. An example:

(Pdb) commands 1
+(com) p some_variable
+(com) end
+(Pdb)
+

To remove all commands from a breakpoint, type commands and follow it immediately with end; that is, give no commands.

With no bpnumber argument, commands refers to the last breakpoint set.

You can use breakpoint commands to start your program up again. Simply use the continue command, or step, or any other command that resumes execution.

Specifying any command resuming execution (currently continue, step, next, return, jump, quit and their abbreviations) terminates the command list (as if that command was immediately followed by end). This is because any time you resume execution (even with a simple next or step), you may encounter another breakpoint—which could have its own command list, leading to ambiguities about which list to execute.

If you use the silent command in the command list, the usual message about stopping at a breakpoint is not printed. This may be desirable for breakpoints that are to print a specific message and then continue. If none of the other commands print anything, you see no sign that the breakpoint was reached.

+
+s(tep)
+

Execute the current line, stop at the first possible occasion (either in a function that is called or on the next line in the current function).

+
+n(ext)
+

Continue execution until the next line in the current function is reached or it returns. (The difference between next and step is that step stops inside a called function, while next executes called functions at (nearly) full speed, only stopping at the next line in the current function.)

+
+unt(il) [lineno]
+

Without argument, continue execution until the line with a number greater than the current one is reached.

With lineno, continue execution until a line with a number greater or equal to lineno is reached. In both cases, also stop when the current frame returns.

Changed in version 3.2: Allow giving an explicit line number.

+
+r(eturn)
+

Continue execution until the current function returns.

+
+c(ont(inue))
+

Continue execution, only stop when a breakpoint is encountered.

+
+j(ump) lineno
+

Set the next line that will be executed. Only available in the bottom-most frame. This lets you jump back and execute code again, or jump forward to skip code that you don’t want to run.

It should be noted that not all jumps are allowed – for instance it is not possible to jump into the middle of a for loop or out of a finally clause.

+
+l(ist) [first[, last]]
+

List source code for the current file. Without arguments, list 11 lines around the current line or continue the previous listing. With . as argument, list 11 lines around the current line. With one argument, list 11 lines around at that line. With two arguments, list the given range; if the second argument is less than the first, it is interpreted as a count.

The current line in the current frame is indicated by ->. If an exception is being debugged, the line where the exception was originally raised or propagated is indicated by >>, if it differs from the current line.

New in version 3.2: The >> marker.

+
+ll | longlist
+

List all source code for the current function or frame. Interesting lines are marked as for list.

New in version 3.2.

+
+a(rgs)
+

Print the arguments of the current function and their current values.

+
+p expression
+

Evaluate expression in the current context and print its value.

Note

print() can also be used, but is not a debugger command — this executes the Python print() function.

+
+pp expression
+

Like the p command, except the value of expression is pretty-printed using the pprint module.

+
+whatis expression
+

Print the type of expression.

+
+source expression
+

Try to get source code of expression and display it.

New in version 3.2.

+
+display [expression]
+

Display the value of expression if it changed, each time execution stops in the current frame.

Without expression, list all display expressions for the current frame.

Note

Display evaluates expression and compares to the result of the previous evaluation of expression, so when the result is mutable, display may not be able to pick up the changes.

Example:

lst = []
+breakpoint()
+pass
+lst.append(1)
+print(lst)
+

Display won’t realize lst has been changed because the result of evaluation is modified in place by lst.append(1) before being compared:

> example.py(3)<module>()
+-> pass
+(Pdb) display lst
+display lst: []
+(Pdb) n
+> example.py(4)<module>()
+-> lst.append(1)
+(Pdb) n
+> example.py(5)<module>()
+-> print(lst)
+(Pdb)
+

You can do some tricks with copy mechanism to make it work:

> example.py(3)<module>()
+-> pass
+(Pdb) display lst[:]
+display lst[:]: []
+(Pdb) n
+> example.py(4)<module>()
+-> lst.append(1)
+(Pdb) n
+> example.py(5)<module>()
+-> print(lst)
+display lst[:]: [1]  [old: []]
+(Pdb)
+

New in version 3.2.

+
+undisplay [expression]
+

Do not display expression anymore in the current frame. Without expression, clear all display expressions for the current frame.

New in version 3.2.

+
+interact
+

Start an interactive interpreter (using the code module) whose global namespace contains all the (global and local) names found in the current scope.

New in version 3.2.

+
+alias [name [command]]
+

Create an alias called name that executes command. The command must not be enclosed in quotes. Replaceable parameters can be indicated by %1, %2, and so on, while %* is replaced by all the parameters. If command is omitted, the current alias for name is shown. If no arguments are given, all aliases are listed.

Aliases may be nested and can contain anything that can be legally typed at the pdb prompt. Note that internal pdb commands can be overridden by aliases. Such a command is then hidden until the alias is removed. Aliasing is recursively applied to the first word of the command line; all other words in the line are left alone.

As an example, here are two useful aliases (especially when placed in the .pdbrc file):

# Print instance variables (usage "pi classInst")
+alias pi for k in %1.__dict__.keys(): print(f"%1.{k} = {%1.__dict__[k]}")
+# Print instance variables in self
+alias ps pi self
+
+
+unalias name
+

Delete the specified alias name.

+
+! statement
+

Execute the (one-line) statement in the context of the current stack frame. The exclamation point can be omitted unless the first word of the statement resembles a debugger command, e.g.:

(Pdb) ! n=42
+(Pdb)
+

To set a global variable, you can prefix the assignment command with a global statement on the same line, e.g.:

(Pdb) global list_options; list_options = ['-l']
+(Pdb)
+
+
+run [args ...]
+restart [args ...]
+

Restart the debugged Python program. If args is supplied, it is split with shlex and the result is used as the new sys.argv. History, breakpoints, actions and debugger options are preserved. restart is an alias for run.

+
+q(uit)
+

Quit from the debugger. The program being executed is aborted.

+
+debug code
+

Enter a recursive debugger that steps through code (which is an arbitrary expression or statement to be executed in the current environment).

+
+retval
+

Print the return value for the last return of the current function.

+

Footnotes

+1
+

Whether a frame is considered to originate in a certain module is determined by the __name__ in the frame globals.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pdb.html +

+
diff --git a/devdocs/python~3.12/library%2Fpersistence.html b/devdocs/python~3.12/library%2Fpersistence.html new file mode 100644 index 00000000..567c4c14 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpersistence.html @@ -0,0 +1,19 @@ +

Data Persistence

The modules described in this chapter support storing Python data in a persistent form on disk. The pickle and marshal modules can turn many Python data types into a stream of bytes and then recreate the objects from the bytes. The various DBM-related modules support a family of hash-based file formats that store a mapping of strings to other strings.

The list of modules described in this chapter is:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/persistence.html +

+
diff --git a/devdocs/python~3.12/library%2Fpickle.html b/devdocs/python~3.12/library%2Fpickle.html new file mode 100644 index 00000000..009047f4 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpickle.html @@ -0,0 +1,397 @@ +

pickle — Python object serialization

Source code: Lib/pickle.py

The pickle module implements binary protocols for serializing and de-serializing a Python object structure. “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,” 1 or “flattening”; however, to avoid confusion, the terms used here are “pickling” and “unpickling”.

Warning

The pickle module is not secure. Only unpickle data you trust.

It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never unpickle data that could have come from an untrusted source, or that could have been tampered with.

Consider signing data with hmac if you need to ensure that it has not been tampered with.

Safer serialization formats such as json may be more appropriate if you are processing untrusted data. See Comparison with json.

Relationship to other Python modules

Comparison with marshal +

Python has a more primitive serialization module called marshal, but in general pickle should always be the preferred way to serialize Python objects. marshal exists primarily to support Python’s .pyc files.

The pickle module differs from marshal in several significant ways:

  • +

    The pickle module keeps track of the objects it has already serialized, so that later references to the same object won’t be serialized again. marshal doesn’t do this.

    This has implications both for recursive objects and object sharing. Recursive objects are objects that contain references to themselves. These are not handled by marshal, and in fact, attempting to marshal recursive objects will crash your Python interpreter. Object sharing happens when there are multiple references to the same object in different places in the object hierarchy being serialized. pickle stores such objects only once, and ensures that all other references point to the master copy. Shared objects remain shared, which can be very important for mutable objects.

  • +marshal cannot be used to serialize user-defined classes and their instances. pickle can save and restore class instances transparently, however the class definition must be importable and live in the same module as when the object was stored.
  • The marshal serialization format is not guaranteed to be portable across Python versions. Because its primary job in life is to support .pyc files, the Python implementers reserve the right to change the serialization format in non-backwards compatible ways should the need arise. The pickle serialization format is guaranteed to be backwards compatible across Python releases provided a compatible pickle protocol is chosen and pickling and unpickling code deals with Python 2 to Python 3 type differences if your data is crossing that unique breaking change language boundary.

Comparison with json +

There are fundamental differences between the pickle protocols and JSON (JavaScript Object Notation):

  • JSON is a text serialization format (it outputs unicode text, although most of the time it is then encoded to utf-8), while pickle is a binary serialization format;
  • JSON is human-readable, while pickle is not;
  • JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific;
  • JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python’s introspection facilities; complex cases can be tackled by implementing specific object APIs);
  • Unlike pickle, deserializing untrusted JSON does not in itself create an arbitrary code execution vulnerability.

See also

The json module: a standard library module allowing JSON serialization and deserialization.

Data stream format

The data format used by pickle is Python-specific. This has the advantage that there are no restrictions imposed by external standards such as JSON or XDR (which can’t represent pointer sharing); however it means that non-Python programs may not be able to reconstruct pickled Python objects.

By default, the pickle data format uses a relatively compact binary representation. If you need optimal size characteristics, you can efficiently compress pickled data.

The module pickletools contains tools for analyzing data streams generated by pickle. pickletools source code has extensive comments about opcodes used by pickle protocols.

There are currently 6 different protocols which can be used for pickling. The higher the protocol used, the more recent the version of Python needed to read the pickle produced.

  • Protocol version 0 is the original “human-readable” protocol and is backwards compatible with earlier versions of Python.
  • Protocol version 1 is an old binary format which is also compatible with earlier versions of Python.
  • Protocol version 2 was introduced in Python 2.3. It provides much more efficient pickling of new-style classes. Refer to PEP 307 for information about improvements brought by protocol 2.
  • Protocol version 3 was added in Python 3.0. It has explicit support for bytes objects and cannot be unpickled by Python 2.x. This was the default protocol in Python 3.0–3.7.
  • Protocol version 4 was added in Python 3.4. It adds support for very large objects, pickling more kinds of objects, and some data format optimizations. It is the default protocol starting with Python 3.8. Refer to PEP 3154 for information about improvements brought by protocol 4.
  • Protocol version 5 was added in Python 3.8. It adds support for out-of-band data and speedup for in-band data. Refer to PEP 574 for information about improvements brought by protocol 5.

Note

Serialization is a more primitive notion than persistence; although pickle reads and writes file objects, it does not handle the issue of naming persistent objects, nor the (even more complicated) issue of concurrent access to persistent objects. The pickle module can transform a complex object into a byte stream and it can transform the byte stream into an object with the same internal structure. Perhaps the most obvious thing to do with these byte streams is to write them onto a file, but it is also conceivable to send them across a network or store them in a database. The shelve module provides a simple interface to pickle and unpickle objects on DBM-style database files.

Module Interface

To serialize an object hierarchy, you simply call the dumps() function. Similarly, to de-serialize a data stream, you call the loads() function. However, if you want more control over serialization and de-serialization, you can create a Pickler or an Unpickler object, respectively.

The pickle module provides the following constants:

+pickle.HIGHEST_PROTOCOL
+

An integer, the highest protocol version available. This value can be passed as a protocol value to functions dump() and dumps() as well as the Pickler constructor.

+
+pickle.DEFAULT_PROTOCOL
+

An integer, the default protocol version used for pickling. May be less than HIGHEST_PROTOCOL. Currently the default protocol is 4, first introduced in Python 3.4 and incompatible with previous versions.

Changed in version 3.0: The default protocol is 3.

Changed in version 3.8: The default protocol is 4.

+

The pickle module provides the following functions to make the pickling process more convenient:

+pickle.dump(obj, file, protocol=None, *, fix_imports=True, buffer_callback=None)
+

Write the pickled representation of the object obj to the open file object file. This is equivalent to Pickler(file, protocol).dump(obj).

Arguments file, protocol, fix_imports and buffer_callback have the same meaning as in the Pickler constructor.

Changed in version 3.8: The buffer_callback argument was added.

+
+pickle.dumps(obj, protocol=None, *, fix_imports=True, buffer_callback=None)
+

Return the pickled representation of the object obj as a bytes object, instead of writing it to a file.

Arguments protocol, fix_imports and buffer_callback have the same meaning as in the Pickler constructor.

Changed in version 3.8: The buffer_callback argument was added.

+
+pickle.load(file, *, fix_imports=True, encoding='ASCII', errors='strict', buffers=None)
+

Read the pickled representation of an object from the open file object file and return the reconstituted object hierarchy specified therein. This is equivalent to Unpickler(file).load().

The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled representation of the object are ignored.

Arguments file, fix_imports, encoding, errors, strict and buffers have the same meaning as in the Unpickler constructor.

Changed in version 3.8: The buffers argument was added.

+
+pickle.loads(data, /, *, fix_imports=True, encoding='ASCII', errors='strict', buffers=None)
+

Return the reconstituted object hierarchy of the pickled representation data of an object. data must be a bytes-like object.

The protocol version of the pickle is detected automatically, so no protocol argument is needed. Bytes past the pickled representation of the object are ignored.

Arguments fix_imports, encoding, errors, strict and buffers have the same meaning as in the Unpickler constructor.

Changed in version 3.8: The buffers argument was added.

+

The pickle module defines three exceptions:

+exception pickle.PickleError
+

Common base class for the other pickling exceptions. It inherits Exception.

+
+exception pickle.PicklingError
+

Error raised when an unpicklable object is encountered by Pickler. It inherits PickleError.

Refer to What can be pickled and unpickled? to learn what kinds of objects can be pickled.

+
+exception pickle.UnpicklingError
+

Error raised when there is a problem unpickling an object, such as a data corruption or a security violation. It inherits PickleError.

Note that other exceptions may also be raised during unpickling, including (but not necessarily limited to) AttributeError, EOFError, ImportError, and IndexError.

+

The pickle module exports three classes, Pickler, Unpickler and PickleBuffer:

+class pickle.Pickler(file, protocol=None, *, fix_imports=True, buffer_callback=None)
+

This takes a binary file for writing a pickle data stream.

The optional protocol argument, an integer, tells the pickler to use the given protocol; supported protocols are 0 to HIGHEST_PROTOCOL. If not specified, the default is DEFAULT_PROTOCOL. If a negative number is specified, HIGHEST_PROTOCOL is selected.

The file argument must have a write() method that accepts a single bytes argument. It can thus be an on-disk file opened for binary writing, an io.BytesIO instance, or any other custom object that meets this interface.

If fix_imports is true and protocol is less than 3, pickle will try to map the new Python 3 names to the old module names used in Python 2, so that the pickle data stream is readable with Python 2.

If buffer_callback is None (the default), buffer views are serialized into file as part of the pickle stream.

If buffer_callback is not None, then it can be called any number of times with a buffer view. If the callback returns a false value (such as None), the given buffer is out-of-band; otherwise the buffer is serialized in-band, i.e. inside the pickle stream.

It is an error if buffer_callback is not None and protocol is None or smaller than 5.

Changed in version 3.8: The buffer_callback argument was added.

+dump(obj)
+

Write the pickled representation of obj to the open file object given in the constructor.

+
+persistent_id(obj)
+

Do nothing by default. This exists so a subclass can override it.

If persistent_id() returns None, obj is pickled as usual. Any other value causes Pickler to emit the returned value as a persistent ID for obj. The meaning of this persistent ID should be defined by Unpickler.persistent_load(). Note that the value returned by persistent_id() cannot itself have a persistent ID.

See Persistence of External Objects for details and examples of uses.

+
+dispatch_table
+

A pickler object’s dispatch table is a registry of reduction functions of the kind which can be declared using copyreg.pickle(). It is a mapping whose keys are classes and whose values are reduction functions. A reduction function takes a single argument of the associated class and should conform to the same interface as a __reduce__() method.

By default, a pickler object will not have a dispatch_table attribute, and it will instead use the global dispatch table managed by the copyreg module. However, to customize the pickling for a specific pickler object one can set the dispatch_table attribute to a dict-like object. Alternatively, if a subclass of Pickler has a dispatch_table attribute then this will be used as the default dispatch table for instances of that class.

See Dispatch Tables for usage examples.

New in version 3.3.

+
+reducer_override(obj)
+

Special reducer that can be defined in Pickler subclasses. This method has priority over any reducer in the dispatch_table. It should conform to the same interface as a __reduce__() method, and can optionally return NotImplemented to fallback on dispatch_table-registered reducers to pickle obj.

For a detailed example, see Custom Reduction for Types, Functions, and Other Objects.

New in version 3.8.

+
+fast
+

Deprecated. Enable fast mode if set to a true value. The fast mode disables the usage of memo, therefore speeding the pickling process by not generating superfluous PUT opcodes. It should not be used with self-referential objects, doing otherwise will cause Pickler to recurse infinitely.

Use pickletools.optimize() if you need more compact pickles.

+
+
+class pickle.Unpickler(file, *, fix_imports=True, encoding='ASCII', errors='strict', buffers=None)
+

This takes a binary file for reading a pickle data stream.

The protocol version of the pickle is detected automatically, so no protocol argument is needed.

The argument file must have three methods, a read() method that takes an integer argument, a readinto() method that takes a buffer argument and a readline() method that requires no arguments, as in the io.BufferedIOBase interface. Thus file can be an on-disk file opened for binary reading, an io.BytesIO object, or any other custom object that meets this interface.

The optional arguments fix_imports, encoding and errors are used to control compatibility support for pickle stream generated by Python 2. If fix_imports is true, pickle will try to map the old Python 2 names to the new names used in Python 3. The encoding and errors tell pickle how to decode 8-bit string instances pickled by Python 2; these default to ‘ASCII’ and ‘strict’, respectively. The encoding can be ‘bytes’ to read these 8-bit string instances as bytes objects. Using encoding='latin1' is required for unpickling NumPy arrays and instances of datetime, date and time pickled by Python 2.

If buffers is None (the default), then all data necessary for deserialization must be contained in the pickle stream. This means that the buffer_callback argument was None when a Pickler was instantiated (or when dump() or dumps() was called).

If buffers is not None, it should be an iterable of buffer-enabled objects that is consumed each time the pickle stream references an out-of-band buffer view. Such buffers have been given in order to the buffer_callback of a Pickler object.

Changed in version 3.8: The buffers argument was added.

+load()
+

Read the pickled representation of an object from the open file object given in the constructor, and return the reconstituted object hierarchy specified therein. Bytes past the pickled representation of the object are ignored.

+
+persistent_load(pid)
+

Raise an UnpicklingError by default.

If defined, persistent_load() should return the object specified by the persistent ID pid. If an invalid persistent ID is encountered, an UnpicklingError should be raised.

See Persistence of External Objects for details and examples of uses.

+
+find_class(module, name)
+

Import module if necessary and return the object called name from it, where the module and name arguments are str objects. Note, unlike its name suggests, find_class() is also used for finding functions.

Subclasses may override this to gain control over what type of objects and how they can be loaded, potentially reducing security risks. Refer to Restricting Globals for details.

Raises an auditing event pickle.find_class with arguments module, name.

+
+
+class pickle.PickleBuffer(buffer)
+

A wrapper for a buffer representing picklable data. buffer must be a buffer-providing object, such as a bytes-like object or a N-dimensional array.

PickleBuffer is itself a buffer provider, therefore it is possible to pass it to other APIs expecting a buffer-providing object, such as memoryview.

PickleBuffer objects can only be serialized using pickle protocol 5 or higher. They are eligible for out-of-band serialization.

New in version 3.8.

+raw()
+

Return a memoryview of the memory area underlying this buffer. The returned object is a one-dimensional, C-contiguous memoryview with format B (unsigned bytes). BufferError is raised if the buffer is neither C- nor Fortran-contiguous.

+
+release()
+

Release the underlying buffer exposed by the PickleBuffer object.

+
+

What can be pickled and unpickled?

The following types can be pickled:

  • built-in constants (None, True, False, Ellipsis, and NotImplemented);
  • integers, floating-point numbers, complex numbers;
  • strings, bytes, bytearrays;
  • tuples, lists, sets, and dictionaries containing only picklable objects;
  • functions (built-in and user-defined) accessible from the top level of a module (using def, not lambda);
  • classes accessible from the top level of a module;
  • instances of such classes whose the result of calling __getstate__() is picklable (see section Pickling Class Instances for details).

Attempts to pickle unpicklable objects will raise the PicklingError exception; when this happens, an unspecified number of bytes may have already been written to the underlying file. Trying to pickle a highly recursive data structure may exceed the maximum recursion depth, a RecursionError will be raised in this case. You can carefully raise this limit with sys.setrecursionlimit().

Note that functions (built-in and user-defined) are pickled by fully qualified name, not by value. 2 This means that only the function name is pickled, along with the name of the containing module and classes. Neither the function’s code, nor any of its function attributes are pickled. Thus the defining module must be importable in the unpickling environment, and the module must contain the named object, otherwise an exception will be raised. 3

Similarly, classes are pickled by fully qualified name, so the same restrictions in the unpickling environment apply. Note that none of the class’s code or data is pickled, so in the following example the class attribute attr is not restored in the unpickling environment:

class Foo:
+    attr = 'A class attribute'
+
+picklestring = pickle.dumps(Foo)
+

These restrictions are why picklable functions and classes must be defined at the top level of a module.

Similarly, when class instances are pickled, their class’s code and data are not pickled along with them. Only the instance data are pickled. This is done on purpose, so you can fix bugs in a class or add methods to the class and still load objects that were created with an earlier version of the class. If you plan to have long-lived objects that will see many versions of a class, it may be worthwhile to put a version number in the objects so that suitable conversions can be made by the class’s __setstate__() method.

Pickling Class Instances

In this section, we describe the general mechanisms available to you to define, customize, and control how class instances are pickled and unpickled.

In most cases, no additional code is needed to make instances picklable. By default, pickle will retrieve the class and the attributes of an instance via introspection. When a class instance is unpickled, its __init__() method is usually not invoked. The default behaviour first creates an uninitialized instance and then restores the saved attributes. The following code shows an implementation of this behaviour:

def save(obj):
+    return (obj.__class__, obj.__dict__)
+
+def restore(cls, attributes):
+    obj = cls.__new__(cls)
+    obj.__dict__.update(attributes)
+    return obj
+

Classes can alter the default behaviour by providing one or several special methods:

+object.__getnewargs_ex__()
+

In protocols 2 and newer, classes that implements the __getnewargs_ex__() method can dictate the values passed to the __new__() method upon unpickling. The method must return a pair (args, kwargs) where args is a tuple of positional arguments and kwargs a dictionary of named arguments for constructing the object. Those will be passed to the __new__() method upon unpickling.

You should implement this method if the __new__() method of your class requires keyword-only arguments. Otherwise, it is recommended for compatibility to implement __getnewargs__().

Changed in version 3.6: __getnewargs_ex__() is now used in protocols 2 and 3.

+
+object.__getnewargs__()
+

This method serves a similar purpose as __getnewargs_ex__(), but supports only positional arguments. It must return a tuple of arguments args which will be passed to the __new__() method upon unpickling.

__getnewargs__() will not be called if __getnewargs_ex__() is defined.

Changed in version 3.6: Before Python 3.6, __getnewargs__() was called instead of __getnewargs_ex__() in protocols 2 and 3.

+
+object.__getstate__()
+

Classes can further influence how their instances are pickled by overriding the method __getstate__(). It is called and the returned object is pickled as the contents for the instance, instead of a default state. There are several cases:

  • For a class that has no instance __dict__ and no __slots__, the default state is None.
  • For a class that has an instance __dict__ and no __slots__, the default state is self.__dict__.
  • For a class that has an instance __dict__ and __slots__, the default state is a tuple consisting of two dictionaries: self.__dict__, and a dictionary mapping slot names to slot values. Only slots that have a value are included in the latter.
  • For a class that has __slots__ and no instance __dict__, the default state is a tuple whose first item is None and whose second item is a dictionary mapping slot names to slot values described in the previous bullet.

Changed in version 3.11: Added the default implementation of the __getstate__() method in the object class.

+
+object.__setstate__(state)
+

Upon unpickling, if the class defines __setstate__(), it is called with the unpickled state. In that case, there is no requirement for the state object to be a dictionary. Otherwise, the pickled state must be a dictionary and its items are assigned to the new instance’s dictionary.

Note

If __getstate__() returns a false value, the __setstate__() method will not be called upon unpickling.

+

Refer to the section Handling Stateful Objects for more information about how to use the methods __getstate__() and __setstate__().

Note

At unpickling time, some methods like __getattr__(), __getattribute__(), or __setattr__() may be called upon the instance. In case those methods rely on some internal invariant being true, the type should implement __new__() to establish such an invariant, as __init__() is not called when unpickling an instance.

As we shall see, pickle does not use directly the methods described above. In fact, these methods are part of the copy protocol which implements the __reduce__() special method. The copy protocol provides a unified interface for retrieving the data necessary for pickling and copying objects. 4

Although powerful, implementing __reduce__() directly in your classes is error prone. For this reason, class designers should use the high-level interface (i.e., __getnewargs_ex__(), __getstate__() and __setstate__()) whenever possible. We will show, however, cases where using __reduce__() is the only option or leads to more efficient pickling or both.

+object.__reduce__()
+

The interface is currently defined as follows. The __reduce__() method takes no argument and shall return either a string or preferably a tuple (the returned object is often referred to as the “reduce value”).

If a string is returned, the string should be interpreted as the name of a global variable. It should be the object’s local name relative to its module; the pickle module searches the module namespace to determine the object’s module. This behaviour is typically useful for singletons.

When a tuple is returned, it must be between two and six items long. Optional items can either be omitted, or None can be provided as their value. The semantics of each item are in order:

  • A callable object that will be called to create the initial version of the object.
  • A tuple of arguments for the callable object. An empty tuple must be given if the callable does not accept any argument.
  • Optionally, the object’s state, which will be passed to the object’s __setstate__() method as previously described. If the object has no such method then, the value must be a dictionary and it will be added to the object’s __dict__ attribute.
  • Optionally, an iterator (and not a sequence) yielding successive items. These items will be appended to the object either using obj.append(item) or, in batch, using obj.extend(list_of_items). This is primarily used for list subclasses, but may be used by other classes as long as they have append() and extend() methods with the appropriate signature. (Whether append() or extend() is used depends on which pickle protocol version is used as well as the number of items to append, so both must be supported.)
  • Optionally, an iterator (not a sequence) yielding successive key-value pairs. These items will be stored to the object using obj[key] = +value. This is primarily used for dictionary subclasses, but may be used by other classes as long as they implement __setitem__().
  • +

    Optionally, a callable with a (obj, state) signature. This callable allows the user to programmatically control the state-updating behavior of a specific object, instead of using obj’s static __setstate__() method. If not None, this callable will have priority over obj’s __setstate__().

    New in version 3.8: The optional sixth tuple item, (obj, state), was added.

+
+object.__reduce_ex__(protocol)
+

Alternatively, a __reduce_ex__() method may be defined. The only difference is this method should take a single integer argument, the protocol version. When defined, pickle will prefer it over the __reduce__() method. In addition, __reduce__() automatically becomes a synonym for the extended version. The main use for this method is to provide backwards-compatible reduce values for older Python releases.

+

Persistence of External Objects

For the benefit of object persistence, the pickle module supports the notion of a reference to an object outside the pickled data stream. Such objects are referenced by a persistent ID, which should be either a string of alphanumeric characters (for protocol 0) 5 or just an arbitrary object (for any newer protocol).

The resolution of such persistent IDs is not defined by the pickle module; it will delegate this resolution to the user-defined methods on the pickler and unpickler, persistent_id() and persistent_load() respectively.

To pickle objects that have an external persistent ID, the pickler must have a custom persistent_id() method that takes an object as an argument and returns either None or the persistent ID for that object. When None is returned, the pickler simply pickles the object as normal. When a persistent ID string is returned, the pickler will pickle that object, along with a marker so that the unpickler will recognize it as a persistent ID.

To unpickle external objects, the unpickler must have a custom persistent_load() method that takes a persistent ID object and returns the referenced object.

Here is a comprehensive example presenting how persistent ID can be used to pickle external objects by reference.

# Simple example presenting how persistent ID can be used to pickle
+# external objects by reference.
+
+import pickle
+import sqlite3
+from collections import namedtuple
+
+# Simple class representing a record in our database.
+MemoRecord = namedtuple("MemoRecord", "key, task")
+
+class DBPickler(pickle.Pickler):
+
+    def persistent_id(self, obj):
+        # Instead of pickling MemoRecord as a regular class instance, we emit a
+        # persistent ID.
+        if isinstance(obj, MemoRecord):
+            # Here, our persistent ID is simply a tuple, containing a tag and a
+            # key, which refers to a specific record in the database.
+            return ("MemoRecord", obj.key)
+        else:
+            # If obj does not have a persistent ID, return None. This means obj
+            # needs to be pickled as usual.
+            return None
+
+
+class DBUnpickler(pickle.Unpickler):
+
+    def __init__(self, file, connection):
+        super().__init__(file)
+        self.connection = connection
+
+    def persistent_load(self, pid):
+        # This method is invoked whenever a persistent ID is encountered.
+        # Here, pid is the tuple returned by DBPickler.
+        cursor = self.connection.cursor()
+        type_tag, key_id = pid
+        if type_tag == "MemoRecord":
+            # Fetch the referenced record from the database and return it.
+            cursor.execute("SELECT * FROM memos WHERE key=?", (str(key_id),))
+            key, task = cursor.fetchone()
+            return MemoRecord(key, task)
+        else:
+            # Always raises an error if you cannot return the correct object.
+            # Otherwise, the unpickler will think None is the object referenced
+            # by the persistent ID.
+            raise pickle.UnpicklingError("unsupported persistent object")
+
+
+def main():
+    import io
+    import pprint
+
+    # Initialize and populate our database.
+    conn = sqlite3.connect(":memory:")
+    cursor = conn.cursor()
+    cursor.execute("CREATE TABLE memos(key INTEGER PRIMARY KEY, task TEXT)")
+    tasks = (
+        'give food to fish',
+        'prepare group meeting',
+        'fight with a zebra',
+        )
+    for task in tasks:
+        cursor.execute("INSERT INTO memos VALUES(NULL, ?)", (task,))
+
+    # Fetch the records to be pickled.
+    cursor.execute("SELECT * FROM memos")
+    memos = [MemoRecord(key, task) for key, task in cursor]
+    # Save the records using our custom DBPickler.
+    file = io.BytesIO()
+    DBPickler(file).dump(memos)
+
+    print("Pickled records:")
+    pprint.pprint(memos)
+
+    # Update a record, just for good measure.
+    cursor.execute("UPDATE memos SET task='learn italian' WHERE key=1")
+
+    # Load the records from the pickle data stream.
+    file.seek(0)
+    memos = DBUnpickler(file, conn).load()
+
+    print("Unpickled records:")
+    pprint.pprint(memos)
+
+
+if __name__ == '__main__':
+    main()
+

Dispatch Tables

If one wants to customize pickling of some classes without disturbing any other code which depends on pickling, then one can create a pickler with a private dispatch table.

The global dispatch table managed by the copyreg module is available as copyreg.dispatch_table. Therefore, one may choose to use a modified copy of copyreg.dispatch_table as a private dispatch table.

For example

f = io.BytesIO()
+p = pickle.Pickler(f)
+p.dispatch_table = copyreg.dispatch_table.copy()
+p.dispatch_table[SomeClass] = reduce_SomeClass
+

creates an instance of pickle.Pickler with a private dispatch table which handles the SomeClass class specially. Alternatively, the code

class MyPickler(pickle.Pickler):
+    dispatch_table = copyreg.dispatch_table.copy()
+    dispatch_table[SomeClass] = reduce_SomeClass
+f = io.BytesIO()
+p = MyPickler(f)
+

does the same but all instances of MyPickler will by default share the private dispatch table. On the other hand, the code

copyreg.pickle(SomeClass, reduce_SomeClass)
+f = io.BytesIO()
+p = pickle.Pickler(f)
+

modifies the global dispatch table shared by all users of the copyreg module.

Handling Stateful Objects

Here’s an example that shows how to modify pickling behavior for a class. The TextReader class opens a text file, and returns the line number and line contents each time its readline() method is called. If a TextReader instance is pickled, all attributes except the file object member are saved. When the instance is unpickled, the file is reopened, and reading resumes from the last location. The __setstate__() and __getstate__() methods are used to implement this behavior.

class TextReader:
+    """Print and number lines in a text file."""
+
+    def __init__(self, filename):
+        self.filename = filename
+        self.file = open(filename)
+        self.lineno = 0
+
+    def readline(self):
+        self.lineno += 1
+        line = self.file.readline()
+        if not line:
+            return None
+        if line.endswith('\n'):
+            line = line[:-1]
+        return "%i: %s" % (self.lineno, line)
+
+    def __getstate__(self):
+        # Copy the object's state from self.__dict__ which contains
+        # all our instance attributes. Always use the dict.copy()
+        # method to avoid modifying the original state.
+        state = self.__dict__.copy()
+        # Remove the unpicklable entries.
+        del state['file']
+        return state
+
+    def __setstate__(self, state):
+        # Restore instance attributes (i.e., filename and lineno).
+        self.__dict__.update(state)
+        # Restore the previously opened file's state. To do so, we need to
+        # reopen it and read from it until the line count is restored.
+        file = open(self.filename)
+        for _ in range(self.lineno):
+            file.readline()
+        # Finally, save the file.
+        self.file = file
+

A sample usage might be something like this:

>>> reader = TextReader("hello.txt")
+>>> reader.readline()
+'1: Hello world!'
+>>> reader.readline()
+'2: I am line number two.'
+>>> new_reader = pickle.loads(pickle.dumps(reader))
+>>> new_reader.readline()
+'3: Goodbye!'
+

Custom Reduction for Types, Functions, and Other Objects

New in version 3.8.

Sometimes, dispatch_table may not be flexible enough. In particular we may want to customize pickling based on another criterion than the object’s type, or we may want to customize the pickling of functions and classes.

For those cases, it is possible to subclass from the Pickler class and implement a reducer_override() method. This method can return an arbitrary reduction tuple (see __reduce__()). It can alternatively return NotImplemented to fallback to the traditional behavior.

If both the dispatch_table and reducer_override() are defined, then reducer_override() method takes priority.

Note

For performance reasons, reducer_override() may not be called for the following objects: None, True, False, and exact instances of int, float, bytes, str, dict, set, frozenset, list and tuple.

Here is a simple example where we allow pickling and reconstructing a given class:

import io
+import pickle
+
+class MyClass:
+    my_attribute = 1
+
+class MyPickler(pickle.Pickler):
+    def reducer_override(self, obj):
+        """Custom reducer for MyClass."""
+        if getattr(obj, "__name__", None) == "MyClass":
+            return type, (obj.__name__, obj.__bases__,
+                          {'my_attribute': obj.my_attribute})
+        else:
+            # For any other object, fallback to usual reduction
+            return NotImplemented
+
+f = io.BytesIO()
+p = MyPickler(f)
+p.dump(MyClass)
+
+del MyClass
+
+unpickled_class = pickle.loads(f.getvalue())
+
+assert isinstance(unpickled_class, type)
+assert unpickled_class.__name__ == "MyClass"
+assert unpickled_class.my_attribute == 1
+

Out-of-band Buffers

New in version 3.8.

In some contexts, the pickle module is used to transfer massive amounts of data. Therefore, it can be important to minimize the number of memory copies, to preserve performance and resource consumption. However, normal operation of the pickle module, as it transforms a graph-like structure of objects into a sequential stream of bytes, intrinsically involves copying data to and from the pickle stream.

This constraint can be eschewed if both the provider (the implementation of the object types to be transferred) and the consumer (the implementation of the communications system) support the out-of-band transfer facilities provided by pickle protocol 5 and higher.

Provider API

The large data objects to be pickled must implement a __reduce_ex__() method specialized for protocol 5 and higher, which returns a PickleBuffer instance (instead of e.g. a bytes object) for any large data.

A PickleBuffer object signals that the underlying buffer is eligible for out-of-band data transfer. Those objects remain compatible with normal usage of the pickle module. However, consumers can also opt-in to tell pickle that they will handle those buffers by themselves.

Consumer API

A communications system can enable custom handling of the PickleBuffer objects generated when serializing an object graph.

On the sending side, it needs to pass a buffer_callback argument to Pickler (or to the dump() or dumps() function), which will be called with each PickleBuffer generated while pickling the object graph. Buffers accumulated by the buffer_callback will not see their data copied into the pickle stream, only a cheap marker will be inserted.

On the receiving side, it needs to pass a buffers argument to Unpickler (or to the load() or loads() function), which is an iterable of the buffers which were passed to buffer_callback. That iterable should produce buffers in the same order as they were passed to buffer_callback. Those buffers will provide the data expected by the reconstructors of the objects whose pickling produced the original PickleBuffer objects.

Between the sending side and the receiving side, the communications system is free to implement its own transfer mechanism for out-of-band buffers. Potential optimizations include the use of shared memory or datatype-dependent compression.

Example

Here is a trivial example where we implement a bytearray subclass able to participate in out-of-band buffer pickling:

class ZeroCopyByteArray(bytearray):
+
+    def __reduce_ex__(self, protocol):
+        if protocol >= 5:
+            return type(self)._reconstruct, (PickleBuffer(self),), None
+        else:
+            # PickleBuffer is forbidden with pickle protocols <= 4.
+            return type(self)._reconstruct, (bytearray(self),)
+
+    @classmethod
+    def _reconstruct(cls, obj):
+        with memoryview(obj) as m:
+            # Get a handle over the original buffer object
+            obj = m.obj
+            if type(obj) is cls:
+                # Original buffer object is a ZeroCopyByteArray, return it
+                # as-is.
+                return obj
+            else:
+                return cls(obj)
+

The reconstructor (the _reconstruct class method) returns the buffer’s providing object if it has the right type. This is an easy way to simulate zero-copy behaviour on this toy example.

On the consumer side, we can pickle those objects the usual way, which when unserialized will give us a copy of the original object:

b = ZeroCopyByteArray(b"abc")
+data = pickle.dumps(b, protocol=5)
+new_b = pickle.loads(data)
+print(b == new_b)  # True
+print(b is new_b)  # False: a copy was made
+

But if we pass a buffer_callback and then give back the accumulated buffers when unserializing, we are able to get back the original object:

b = ZeroCopyByteArray(b"abc")
+buffers = []
+data = pickle.dumps(b, protocol=5, buffer_callback=buffers.append)
+new_b = pickle.loads(data, buffers=buffers)
+print(b == new_b)  # True
+print(b is new_b)  # True: no copy was made
+

This example is limited by the fact that bytearray allocates its own memory: you cannot create a bytearray instance that is backed by another object’s memory. However, third-party datatypes such as NumPy arrays do not have this limitation, and allow use of zero-copy pickling (or making as few copies as possible) when transferring between distinct processes or systems.

See also

PEP 574 – Pickle protocol 5 with out-of-band data

Restricting Globals

By default, unpickling will import any class or function that it finds in the pickle data. For many applications, this behaviour is unacceptable as it permits the unpickler to import and invoke arbitrary code. Just consider what this hand-crafted pickle data stream does when loaded:

>>> import pickle
+>>> pickle.loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
+hello world
+0
+

In this example, the unpickler imports the os.system() function and then apply the string argument “echo hello world”. Although this example is inoffensive, it is not difficult to imagine one that could damage your system.

For this reason, you may want to control what gets unpickled by customizing Unpickler.find_class(). Unlike its name suggests, Unpickler.find_class() is called whenever a global (i.e., a class or a function) is requested. Thus it is possible to either completely forbid globals or restrict them to a safe subset.

Here is an example of an unpickler allowing only few safe classes from the builtins module to be loaded:

import builtins
+import io
+import pickle
+
+safe_builtins = {
+    'range',
+    'complex',
+    'set',
+    'frozenset',
+    'slice',
+}
+
+class RestrictedUnpickler(pickle.Unpickler):
+
+    def find_class(self, module, name):
+        # Only allow safe classes from builtins.
+        if module == "builtins" and name in safe_builtins:
+            return getattr(builtins, name)
+        # Forbid everything else.
+        raise pickle.UnpicklingError("global '%s.%s' is forbidden" %
+                                     (module, name))
+
+def restricted_loads(s):
+    """Helper function analogous to pickle.loads()."""
+    return RestrictedUnpickler(io.BytesIO(s)).load()
+

A sample usage of our unpickler working as intended:

>>> restricted_loads(pickle.dumps([1, 2, range(15)]))
+[1, 2, range(0, 15)]
+>>> restricted_loads(b"cos\nsystem\n(S'echo hello world'\ntR.")
+Traceback (most recent call last):
+  ...
+pickle.UnpicklingError: global 'os.system' is forbidden
+>>> restricted_loads(b'cbuiltins\neval\n'
+...                  b'(S\'getattr(__import__("os"), "system")'
+...                  b'("echo hello world")\'\ntR.')
+Traceback (most recent call last):
+  ...
+pickle.UnpicklingError: global 'builtins.eval' is forbidden
+

As our examples shows, you have to be careful with what you allow to be unpickled. Therefore if security is a concern, you may want to consider alternatives such as the marshalling API in xmlrpc.client or third-party solutions.

Performance

Recent versions of the pickle protocol (from protocol 2 and upwards) feature efficient binary encodings for several common features and built-in types. Also, the pickle module has a transparent optimizer written in C.

Examples

For the simplest code, use the dump() and load() functions.

import pickle
+
+# An arbitrary collection of objects supported by pickle.
+data = {
+    'a': [1, 2.0, 3+4j],
+    'b': ("character string", b"byte string"),
+    'c': {None, True, False}
+}
+
+with open('data.pickle', 'wb') as f:
+    # Pickle the 'data' dictionary using the highest protocol available.
+    pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+

The following example reads the resulting pickled data.

import pickle
+
+with open('data.pickle', 'rb') as f:
+    # The protocol version used is detected automatically, so we do not
+    # have to specify it.
+    data = pickle.load(f)
+

See also

+Module copyreg +
+
+

Pickle interface constructor registration for extension types.

+Module pickletools +
+
+

Tools for working with and analyzing pickled data.

+Module shelve +
+
+

Indexed databases of objects; uses pickle.

+Module copy +
+
+

Shallow and deep object copying.

+Module marshal +
+
+

High-performance serialization of built-in types.

Footnotes

+1
+

Don’t confuse this with the marshal module

+2
+

This is why lambda functions cannot be pickled: all lambda functions share the same name: <lambda>.

+3
+

The exception raised will likely be an ImportError or an AttributeError but it could be something else.

+4
+

The copy module uses this protocol for shallow and deep copying operations.

+5
+

The limitation on alphanumeric characters is due to the fact that persistent IDs in protocol 0 are delimited by the newline character. Therefore if any kind of newline characters occurs in persistent IDs, the resulting pickled data will become unreadable.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pickle.html +

+
diff --git a/devdocs/python~3.12/library%2Fpickletools.html b/devdocs/python~3.12/library%2Fpickletools.html new file mode 100644 index 00000000..78f5ed1c --- /dev/null +++ b/devdocs/python~3.12/library%2Fpickletools.html @@ -0,0 +1,41 @@ +

pickletools — Tools for pickle developers

Source code: Lib/pickletools.py

This module contains various constants relating to the intimate details of the pickle module, some lengthy comments about the implementation, and a few useful functions for analyzing pickled data. The contents of this module are useful for Python core developers who are working on the pickle; ordinary users of the pickle module probably won’t find the pickletools module relevant.

Command line usage

New in version 3.2.

When invoked from the command line, python -m pickletools will disassemble the contents of one or more pickle files. Note that if you want to see the Python object stored in the pickle rather than the details of pickle format, you may want to use -m pickle instead. However, when the pickle file that you want to examine comes from an untrusted source, -m pickletools is a safer option because it does not execute pickle bytecode.

For example, with a tuple (1, 2) pickled in file x.pickle:

$ python -m pickle x.pickle
+(1, 2)
+
+$ python -m pickletools x.pickle
+    0: \x80 PROTO      3
+    2: K    BININT1    1
+    4: K    BININT1    2
+    6: \x86 TUPLE2
+    7: q    BINPUT     0
+    9: .    STOP
+highest protocol among opcodes = 2
+

Command line options

+-a, --annotate
+

Annotate each line with a short opcode description.

+
+-o, --output=<file>
+

Name of a file where the output should be written.

+
+-l, --indentlevel=<num>
+

The number of blanks by which to indent a new MARK level.

+
+-m, --memo
+

When multiple objects are disassembled, preserve memo between disassemblies.

+
+-p, --preamble=<preamble>
+

When more than one pickle file are specified, print given preamble before each disassembly.

+

Programmatic Interface

+pickletools.dis(pickle, out=None, memo=None, indentlevel=4, annotate=0)
+

Outputs a symbolic disassembly of the pickle to the file-like object out, defaulting to sys.stdout. pickle can be a string or a file-like object. memo can be a Python dictionary that will be used as the pickle’s memo; it can be used to perform disassemblies across multiple pickles created by the same pickler. Successive levels, indicated by MARK opcodes in the stream, are indented by indentlevel spaces. If a nonzero value is given to annotate, each opcode in the output is annotated with a short description. The value of annotate is used as a hint for the column where annotation should start.

New in version 3.2: The annotate argument.

+
+pickletools.genops(pickle)
+

Provides an iterator over all of the opcodes in a pickle, returning a sequence of (opcode, arg, pos) triples. opcode is an instance of an OpcodeInfo class; arg is the decoded value, as a Python object, of the opcode’s argument; pos is the position at which this opcode is located. pickle can be a string or a file-like object.

+
+pickletools.optimize(picklestring)
+

Returns a new equivalent pickle string after eliminating unused PUT opcodes. The optimized pickle is shorter, takes less transmission time, requires less storage space, and unpickles more efficiently.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pickletools.html +

+
diff --git a/devdocs/python~3.12/library%2Fpkgutil.html b/devdocs/python~3.12/library%2Fpkgutil.html new file mode 100644 index 00000000..b2926f23 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpkgutil.html @@ -0,0 +1,45 @@ +

pkgutil — Package extension utility

Source code: Lib/pkgutil.py

This module provides utilities for the import system, in particular package support.

+class pkgutil.ModuleInfo(module_finder, name, ispkg)
+

A namedtuple that holds a brief summary of a module’s info.

New in version 3.6.

+
+pkgutil.extend_path(path, name)
+

Extend the search path for the modules which comprise a package. Intended use is to place the following code in a package’s __init__.py:

from pkgutil import extend_path
+__path__ = extend_path(__path__, __name__)
+

For each directory on sys.path that has a subdirectory that matches the package name, add the subdirectory to the package’s __path__. This is useful if one wants to distribute different parts of a single logical package as multiple directories.

It also looks for *.pkg files beginning where * matches the name argument. This feature is similar to *.pth files (see the site module for more information), except that it doesn’t special-case lines starting with import. A *.pkg file is trusted at face value: apart from checking for duplicates, all entries found in a *.pkg file are added to the path, regardless of whether they exist on the filesystem. (This is a feature.)

If the input path is not a list (as is the case for frozen packages) it is returned unchanged. The input path is not modified; an extended copy is returned. Items are only appended to the copy at the end.

It is assumed that sys.path is a sequence. Items of sys.path that are not strings referring to existing directories are ignored. Unicode items on sys.path that cause errors when used as filenames may cause this function to raise an exception (in line with os.path.isdir() behavior).

+
+pkgutil.find_loader(fullname)
+

Retrieve a module loader for the given fullname.

This is a backwards compatibility wrapper around importlib.util.find_spec() that converts most failures to ImportError and only returns the loader rather than the full importlib.machinery.ModuleSpec.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

Changed in version 3.4: Updated to be based on PEP 451

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.util.find_spec() instead.

+
+pkgutil.get_importer(path_item)
+

Retrieve a finder for the given path_item.

The returned finder is cached in sys.path_importer_cache if it was newly created by a path hook.

The cache (or part of it) can be cleared manually if a rescan of sys.path_hooks is necessary.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

+
+pkgutil.get_loader(module_or_name)
+

Get a loader object for module_or_name.

If the module or package is accessible via the normal import mechanism, a wrapper around the relevant part of that machinery is returned. Returns None if the module cannot be found or imported. If the named module is not already imported, its containing package (if any) is imported, in order to establish the package __path__.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

Changed in version 3.4: Updated to be based on PEP 451

Deprecated since version 3.12, will be removed in version 3.14: Use importlib.util.find_spec() instead.

+
+pkgutil.iter_importers(fullname='')
+

Yield finder objects for the given module name.

If fullname contains a '.', the finders will be for the package containing fullname, otherwise they will be all registered top level finders (i.e. those on both sys.meta_path and sys.path_hooks).

If the named module is in a package, that package is imported as a side effect of invoking this function.

If no module name is specified, all top level finders are produced.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

+
+pkgutil.iter_modules(path=None, prefix='')
+

Yields ModuleInfo for all submodules on path, or, if path is None, all top-level modules on sys.path.

path should be either None or a list of paths to look for modules in.

prefix is a string to output on the front of every module name on output.

Note

Only works for a finder which defines an iter_modules() method. This interface is non-standard, so the module also provides implementations for importlib.machinery.FileFinder and zipimport.zipimporter.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

+
+pkgutil.walk_packages(path=None, prefix='', onerror=None)
+

Yields ModuleInfo for all modules recursively on path, or, if path is None, all accessible modules.

path should be either None or a list of paths to look for modules in.

prefix is a string to output on the front of every module name on output.

Note that this function must import all packages (not all modules!) on the given path, in order to access the __path__ attribute to find submodules.

onerror is a function which gets called with one argument (the name of the package which was being imported) if any exception occurs while trying to import a package. If no onerror function is supplied, ImportErrors are caught and ignored, while all other exceptions are propagated, terminating the search.

Examples:

# list all modules python can access
+walk_packages()
+
+# list all submodules of ctypes
+walk_packages(ctypes.__path__, ctypes.__name__ + '.')
+

Note

Only works for a finder which defines an iter_modules() method. This interface is non-standard, so the module also provides implementations for importlib.machinery.FileFinder and zipimport.zipimporter.

Changed in version 3.3: Updated to be based directly on importlib rather than relying on the package internal PEP 302 import emulation.

+
+pkgutil.get_data(package, resource)
+

Get a resource from a package.

This is a wrapper for the loader get_data API. The package argument should be the name of a package, in standard module format (foo.bar). The resource argument should be in the form of a relative filename, using / as the path separator. The parent directory name .. is not allowed, and nor is a rooted name (starting with a /).

The function returns a binary string that is the contents of the specified resource.

For packages located in the filesystem, which have already been imported, this is the rough equivalent of:

d = os.path.dirname(sys.modules[package].__file__)
+data = open(os.path.join(d, resource), 'rb').read()
+

If the package cannot be located or loaded, or it uses a loader which does not support get_data, then None is returned. In particular, the loader for namespace packages does not support get_data.

+
+pkgutil.resolve_name(name)
+

Resolve a name to an object.

This functionality is used in numerous places in the standard library (see bpo-12915) - and equivalent functionality is also in widely used third-party packages such as setuptools, Django and Pyramid.

It is expected that name will be a string in one of the following formats, where W is shorthand for a valid Python identifier and dot stands for a literal period in these pseudo-regexes:

  • W(.W)*
  • W(.W)*:(W(.W)*)?

The first form is intended for backward compatibility only. It assumes that some part of the dotted name is a package, and the rest is an object somewhere within that package, possibly nested inside other objects. Because the place where the package stops and the object hierarchy starts can’t be inferred by inspection, repeated attempts to import must be done with this form.

In the second form, the caller makes the division point clear through the provision of a single colon: the dotted name to the left of the colon is a package to be imported, and the dotted name to the right is the object hierarchy within that package. Only one import is needed in this form. If it ends with the colon, then a module object is returned.

The function will return an object (which might be a module), or raise one of the following exceptions:

ValueError – if name isn’t in a recognised format.

ImportError – if an import failed when it shouldn’t have.

AttributeError – If a failure occurred when traversing the object hierarchy within the imported package to get to the desired object.

New in version 3.9.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pkgutil.html +

+
diff --git a/devdocs/python~3.12/library%2Fplatform.html b/devdocs/python~3.12/library%2Fplatform.html new file mode 100644 index 00000000..dfe0be6d --- /dev/null +++ b/devdocs/python~3.12/library%2Fplatform.html @@ -0,0 +1,89 @@ +

platform — Access to underlying platform’s identifying data

Source code: Lib/platform.py

Note

Specific platforms listed alphabetically, with Linux included in the Unix section.

Cross Platform

+platform.architecture(executable=sys.executable, bits='', linkage='')
+

Queries the given executable (defaults to the Python interpreter binary) for various architecture information.

Returns a tuple (bits, linkage) which contain information about the bit architecture and the linkage format used for the executable. Both values are returned as strings.

Values that cannot be determined are returned as given by the parameter presets. If bits is given as '', the sizeof(pointer) (or sizeof(long) on Python version < 1.5.2) is used as indicator for the supported pointer size.

The function relies on the system’s file command to do the actual work. This is available on most if not all Unix platforms and some non-Unix platforms and then only if the executable points to the Python interpreter. Reasonable defaults are used when the above needs are not met.

Note

On macOS (and perhaps other platforms), executable files may be universal files containing multiple architectures.

To get at the “64-bitness” of the current interpreter, it is more reliable to query the sys.maxsize attribute:

is_64bits = sys.maxsize > 2**32
+
+
+platform.machine()
+

Returns the machine type, e.g. 'AMD64'. An empty string is returned if the value cannot be determined.

+
+platform.node()
+

Returns the computer’s network name (may not be fully qualified!). An empty string is returned if the value cannot be determined.

+
+platform.platform(aliased=False, terse=False)
+

Returns a single string identifying the underlying platform with as much useful information as possible.

The output is intended to be human readable rather than machine parseable. It may look different on different platforms and this is intended.

If aliased is true, the function will use aliases for various platforms that report system names which differ from their common names, for example SunOS will be reported as Solaris. The system_alias() function is used to implement this.

Setting terse to true causes the function to return only the absolute minimum information needed to identify the platform.

Changed in version 3.8: On macOS, the function now uses mac_ver(), if it returns a non-empty release string, to get the macOS version rather than the darwin version.

+
+platform.processor()
+

Returns the (real) processor name, e.g. 'amdk6'.

An empty string is returned if the value cannot be determined. Note that many platforms do not provide this information or simply return the same value as for machine(). NetBSD does this.

+
+platform.python_build()
+

Returns a tuple (buildno, builddate) stating the Python build number and date as strings.

+
+platform.python_compiler()
+

Returns a string identifying the compiler used for compiling Python.

+
+platform.python_branch()
+

Returns a string identifying the Python implementation SCM branch.

+
+platform.python_implementation()
+

Returns a string identifying the Python implementation. Possible return values are: ‘CPython’, ‘IronPython’, ‘Jython’, ‘PyPy’.

+
+platform.python_revision()
+

Returns a string identifying the Python implementation SCM revision.

+
+platform.python_version()
+

Returns the Python version as string 'major.minor.patchlevel'.

Note that unlike the Python sys.version, the returned value will always include the patchlevel (it defaults to 0).

+
+platform.python_version_tuple()
+

Returns the Python version as tuple (major, minor, patchlevel) of strings.

Note that unlike the Python sys.version, the returned value will always include the patchlevel (it defaults to '0').

+
+platform.release()
+

Returns the system’s release, e.g. '2.2.0' or 'NT'. An empty string is returned if the value cannot be determined.

+
+platform.system()
+

Returns the system/OS name, such as 'Linux', 'Darwin', 'Java', 'Windows'. An empty string is returned if the value cannot be determined.

+
+platform.system_alias(system, release, version)
+

Returns (system, release, version) aliased to common marketing names used for some systems. It also does some reordering of the information in some cases where it would otherwise cause confusion.

+
+platform.version()
+

Returns the system’s release version, e.g. '#3 on degas'. An empty string is returned if the value cannot be determined.

+
+platform.uname()
+

Fairly portable uname interface. Returns a namedtuple() containing six attributes: system, node, release, version, machine, and processor.

processor is resolved late, on demand.

Note: the first two attribute names differ from the names presented by os.uname(), where they are named sysname and nodename.

Entries which cannot be determined are set to ''.

Changed in version 3.3: Result changed from a tuple to a namedtuple().

Changed in version 3.9: processor is resolved late instead of immediately.

+

Java Platform

+platform.java_ver(release='', vendor='', vminfo=('', '', ''), osinfo=('', '', ''))
+

Version interface for Jython.

Returns a tuple (release, vendor, vminfo, osinfo) with vminfo being a tuple (vm_name, vm_release, vm_vendor) and osinfo being a tuple (os_name, os_version, os_arch). Values which cannot be determined are set to the defaults given as parameters (which all default to '').

+

Windows Platform

+platform.win32_ver(release='', version='', csd='', ptype='')
+

Get additional version information from the Windows Registry and return a tuple (release, version, csd, ptype) referring to OS release, version number, CSD level (service pack) and OS type (multi/single processor). Values which cannot be determined are set to the defaults given as parameters (which all default to an empty string).

As a hint: ptype is 'Uniprocessor Free' on single processor NT machines and 'Multiprocessor Free' on multi processor machines. The ‘Free’ refers to the OS version being free of debugging code. It could also state ‘Checked’ which means the OS version uses debugging code, i.e. code that checks arguments, ranges, etc.

+
+platform.win32_edition()
+

Returns a string representing the current Windows edition, or None if the value cannot be determined. Possible values include but are not limited to 'Enterprise', 'IoTUAP', 'ServerStandard', and 'nanoserver'.

New in version 3.8.

+
+platform.win32_is_iot()
+

Return True if the Windows edition returned by win32_edition() is recognized as an IoT edition.

New in version 3.8.

+

macOS Platform

+platform.mac_ver(release='', versioninfo=('', '', ''), machine='')
+

Get macOS version information and return it as tuple (release, versioninfo, +machine) with versioninfo being a tuple (version, dev_stage, +non_release_version).

Entries which cannot be determined are set to ''. All tuple entries are strings.

+

Unix Platforms

+platform.libc_ver(executable=sys.executable, lib='', version='', chunksize=16384)
+

Tries to determine the libc version against which the file executable (defaults to the Python interpreter) is linked. Returns a tuple of strings (lib, +version) which default to the given parameters in case the lookup fails.

Note that this function has intimate knowledge of how different libc versions add symbols to the executable is probably only usable for executables compiled using gcc.

The file is read and scanned in chunks of chunksize bytes.

+

Linux Platforms

+platform.freedesktop_os_release()
+

Get operating system identification from os-release file and return it as a dict. The os-release file is a freedesktop.org standard and is available in most Linux distributions. A noticeable exception is Android and Android-based distributions.

Raises OSError or subclass when neither /etc/os-release nor /usr/lib/os-release can be read.

On success, the function returns a dictionary where keys and values are strings. Values have their special characters like " and $ unquoted. The fields NAME, ID, and PRETTY_NAME are always defined according to the standard. All other fields are optional. Vendors may include additional fields.

Note that fields like NAME, VERSION, and VARIANT are strings suitable for presentation to users. Programs should use fields like ID, ID_LIKE, VERSION_ID, or VARIANT_ID to identify Linux distributions.

Example:

def get_like_distro():
+    info = platform.freedesktop_os_release()
+    ids = [info["ID"]]
+    if "ID_LIKE" in info:
+        # ids are space separated and ordered by precedence
+        ids.extend(info["ID_LIKE"].split())
+    return ids
+

New in version 3.10.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/platform.html +

+
diff --git a/devdocs/python~3.12/library%2Fplistlib.html b/devdocs/python~3.12/library%2Fplistlib.html new file mode 100644 index 00000000..5e047537 --- /dev/null +++ b/devdocs/python~3.12/library%2Fplistlib.html @@ -0,0 +1,63 @@ +

plistlib — Generate and parse Apple .plist files

Source code: Lib/plistlib.py

This module provides an interface for reading and writing the “property list” files used by Apple, primarily on macOS and iOS. This module supports both binary and XML plist files.

The property list (.plist) file format is a simple serialization supporting basic object types, like dictionaries, lists, numbers and strings. Usually the top level object is a dictionary.

To write out and to parse a plist file, use the dump() and load() functions.

To work with plist data in bytes objects, use dumps() and loads().

Values can be strings, integers, floats, booleans, tuples, lists, dictionaries (but only with string keys), bytes, bytearray or datetime.datetime objects.

Changed in version 3.4: New API, old API deprecated. Support for binary format plists added.

Changed in version 3.8: Support added for reading and writing UID tokens in binary plists as used by NSKeyedArchiver and NSKeyedUnarchiver.

Changed in version 3.9: Old API removed.

See also

PList manual page
+
+

Apple’s documentation of the file format.

This module defines the following functions:

+plistlib.load(fp, *, fmt=None, dict_type=dict)
+

Read a plist file. fp should be a readable and binary file object. Return the unpacked root object (which usually is a dictionary).

The fmt is the format of the file and the following values are valid:

The dict_type is the type used for dictionaries that are read from the plist file.

XML data for the FMT_XML format is parsed using the Expat parser from xml.parsers.expat – see its documentation for possible exceptions on ill-formed XML. Unknown elements will simply be ignored by the plist parser.

The parser for the binary format raises InvalidFileException when the file cannot be parsed.

New in version 3.4.

+
+plistlib.loads(data, *, fmt=None, dict_type=dict)
+

Load a plist from a bytes object. See load() for an explanation of the keyword arguments.

New in version 3.4.

+
+plistlib.dump(value, fp, *, fmt=FMT_XML, sort_keys=True, skipkeys=False)
+

Write value to a plist file. Fp should be a writable, binary file object.

The fmt argument specifies the format of the plist file and can be one of the following values:

When sort_keys is true (the default) the keys for dictionaries will be written to the plist in sorted order, otherwise they will be written in the iteration order of the dictionary.

When skipkeys is false (the default) the function raises TypeError when a key of a dictionary is not a string, otherwise such keys are skipped.

A TypeError will be raised if the object is of an unsupported type or a container that contains objects of unsupported types.

An OverflowError will be raised for integer values that cannot be represented in (binary) plist files.

New in version 3.4.

+
+plistlib.dumps(value, *, fmt=FMT_XML, sort_keys=True, skipkeys=False)
+

Return value as a plist-formatted bytes object. See the documentation for dump() for an explanation of the keyword arguments of this function.

New in version 3.4.

+

The following classes are available:

+class plistlib.UID(data)
+

Wraps an int. This is used when reading or writing NSKeyedArchiver encoded data, which contains UID (see PList manual).

It has one attribute, data, which can be used to retrieve the int value of the UID. data must be in the range 0 <= data < 2**64.

New in version 3.8.

+

The following constants are available:

+plistlib.FMT_XML
+

The XML format for plist files.

New in version 3.4.

+
+plistlib.FMT_BINARY
+

The binary format for plist files

New in version 3.4.

+

Examples

Generating a plist:

import datetime
+import plistlib
+
+pl = dict(
+    aString = "Doodah",
+    aList = ["A", "B", 12, 32.1, [1, 2, 3]],
+    aFloat = 0.1,
+    anInt = 728,
+    aDict = dict(
+        anotherString = "<hello & hi there!>",
+        aThirdString = "M\xe4ssig, Ma\xdf",
+        aTrueValue = True,
+        aFalseValue = False,
+    ),
+    someData = b"<binary gunk>",
+    someMoreData = b"<lots of binary gunk>" * 10,
+    aDate = datetime.datetime.now()
+)
+print(plistlib.dumps(pl).decode())
+

Parsing a plist:

import plistlib
+
+plist = b"""<plist version="1.0">
+<dict>
+    <key>foo</key>
+    <string>bar</string>
+</dict>
+</plist>"""
+pl = plistlib.loads(plist)
+print(pl["foo"])
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/plistlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fpoplib.html b/devdocs/python~3.12/library%2Fpoplib.html new file mode 100644 index 00000000..a5d39cdb --- /dev/null +++ b/devdocs/python~3.12/library%2Fpoplib.html @@ -0,0 +1,89 @@ +

poplib — POP3 protocol client

Source code: Lib/poplib.py

This module defines a class, POP3, which encapsulates a connection to a POP3 server and implements the protocol as defined in RFC 1939. The POP3 class supports both the minimal and optional command sets from RFC 1939. The POP3 class also supports the STLS command introduced in RFC 2595 to enable encrypted communication on an already established connection.

Additionally, this module provides a class POP3_SSL, which provides support for connecting to POP3 servers that use SSL as an underlying protocol layer.

Note that POP3, though widely supported, is obsolescent. The implementation quality of POP3 servers varies widely, and too many are quite poor. If your mailserver supports IMAP, you would be better off using the imaplib.IMAP4 class, as IMAP servers tend to be better implemented.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The poplib module provides two classes:

+class poplib.POP3(host, port=POP3_PORT[, timeout])
+

This class implements the actual POP3 protocol. The connection is created when the instance is initialized. If port is omitted, the standard POP3 port (110) is used. The optional timeout parameter specifies a timeout in seconds for the connection attempt (if not specified, the global default timeout setting will be used).

Raises an auditing event poplib.connect with arguments self, host, port.

+

All commands will raise an auditing event poplib.putline with arguments self and line, where line is the bytes about to be sent to the remote host.

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket.

+
+class poplib.POP3_SSL(host, port=POP3_SSL_PORT, *, timeout=None, context=None)
+

This is a subclass of POP3 that connects to the server over an SSL encrypted socket. If port is not specified, 995, the standard POP3-over-SSL port is used. timeout works as in the POP3 constructor. context is an optional ssl.SSLContext object which allows bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure. Please read Security considerations for best practices.

Raises an auditing event poplib.connect with arguments self, host, port.

+

All commands will raise an auditing event poplib.putline with arguments self and line, where line is the bytes about to be sent to the remote host.

Changed in version 3.2: context parameter added.

Changed in version 3.4: The class now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket.

Changed in version 3.12: The deprecated keyfile and certfile parameters have been removed.

+

One exception is defined as an attribute of the poplib module:

+exception poplib.error_proto
+

Exception raised on any errors from this module (errors from socket module are not caught). The reason for the exception is passed to the constructor as a string.

+

See also

+Module imaplib +
+
+

The standard Python IMAP module.

Frequently Asked Questions About Fetchmail
+
+

The FAQ for the fetchmail POP/IMAP client collects information on POP3 server variations and RFC noncompliance that may be useful if you need to write an application based on the POP protocol.

POP3 Objects

All POP3 commands are represented by methods of the same name, in lowercase; most return the response text sent by the server.

A POP3 instance has the following methods:

+POP3.set_debuglevel(level)
+

Set the instance’s debugging level. This controls the amount of debugging output printed. The default, 0, produces no debugging output. A value of 1 produces a moderate amount of debugging output, generally a single line per request. A value of 2 or higher produces the maximum amount of debugging output, logging each line sent and received on the control connection.

+
+POP3.getwelcome()
+

Returns the greeting string sent by the POP3 server.

+
+POP3.capa()
+

Query the server’s capabilities as specified in RFC 2449. Returns a dictionary in the form {'name': ['param'...]}.

New in version 3.4.

+
+POP3.user(username)
+

Send user command, response should indicate that a password is required.

+
+POP3.pass_(password)
+

Send password, response includes message count and mailbox size. Note: the mailbox on the server is locked until quit() is called.

+
+POP3.apop(user, secret)
+

Use the more secure APOP authentication to log into the POP3 server.

+
+POP3.rpop(user)
+

Use RPOP authentication (similar to UNIX r-commands) to log into POP3 server.

+
+POP3.stat()
+

Get mailbox status. The result is a tuple of 2 integers: (message count, +mailbox size).

+
+POP3.list([which])
+

Request message list, result is in the form (response, ['mesg_num octets', +...], octets). If which is set, it is the message to list.

+
+POP3.retr(which)
+

Retrieve whole message number which, and set its seen flag. Result is in form (response, ['line', ...], octets).

+
+POP3.dele(which)
+

Flag message number which for deletion. On most servers deletions are not actually performed until QUIT (the major exception is Eudora QPOP, which deliberately violates the RFCs by doing pending deletes on any disconnect).

+
+POP3.rset()
+

Remove any deletion marks for the mailbox.

+
+POP3.noop()
+

Do nothing. Might be used as a keep-alive.

+
+POP3.quit()
+

Signoff: commit changes, unlock mailbox, drop connection.

+
+POP3.top(which, howmuch)
+

Retrieves the message header plus howmuch lines of the message after the header of message number which. Result is in form (response, ['line', ...], +octets).

The POP3 TOP command this method uses, unlike the RETR command, doesn’t set the message’s seen flag; unfortunately, TOP is poorly specified in the RFCs and is frequently broken in off-brand servers. Test this method by hand against the POP3 servers you will use before trusting it.

+
+POP3.uidl(which=None)
+

Return message digest (unique id) list. If which is specified, result contains the unique id for that message in the form 'response mesgnum uid, otherwise result is list (response, ['mesgnum uid', ...], octets).

+
+POP3.utf8()
+

Try to switch to UTF-8 mode. Returns the server response if successful, raises error_proto if not. Specified in RFC 6856.

New in version 3.5.

+
+POP3.stls(context=None)
+

Start a TLS session on the active connection as specified in RFC 2595. This is only allowed before user authentication

context parameter is a ssl.SSLContext object which allows bundling SSL configuration options, certificates and private keys into a single (potentially long-lived) structure. Please read Security considerations for best practices.

This method supports hostname checking via ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

New in version 3.4.

+

Instances of POP3_SSL have no additional methods. The interface of this subclass is identical to its parent.

POP3 Example

Here is a minimal example (without error checking) that opens a mailbox and retrieves and prints all messages:

import getpass, poplib
+
+M = poplib.POP3('localhost')
+M.user(getpass.getuser())
+M.pass_(getpass.getpass())
+numMessages = len(M.list()[1])
+for i in range(numMessages):
+    for j in M.retr(i+1)[1]:
+        print(j)
+

At the end of the module, there is a test section that contains a more extensive example of usage.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/poplib.html +

+
diff --git a/devdocs/python~3.12/library%2Fposix.html b/devdocs/python~3.12/library%2Fposix.html new file mode 100644 index 00000000..08fb6769 --- /dev/null +++ b/devdocs/python~3.12/library%2Fposix.html @@ -0,0 +1,13 @@ +

posix — The most common POSIX system calls

This module provides access to operating system functionality that is standardized by the C Standard and the POSIX standard (a thinly disguised Unix interface).

Availability: Unix.

Do not import this module directly. Instead, import the module os, which provides a portable version of this interface. On Unix, the os module provides a superset of the posix interface. On non-Unix operating systems the posix module is not available, but a subset is always available through the os interface. Once os is imported, there is no performance penalty in using it instead of posix. In addition, os provides some additional functionality, such as automatically calling putenv() when an entry in os.environ is changed.

Errors are reported as exceptions; the usual exceptions are given for type errors, while errors reported by the system calls raise OSError.

Large File Support

Several operating systems (including AIX and Solaris) provide support for files that are larger than 2 GiB from a C programming model where int and long are 32-bit values. This is typically accomplished by defining the relevant size and offset types as 64-bit values. Such files are sometimes referred to as large files.

Large file support is enabled in Python when the size of an off_t is larger than a long and the long long is at least as large as an off_t. It may be necessary to configure and compile Python with certain compiler flags to enable this mode. For example, with Solaris 2.6 and 2.7 you need to do something like:

CFLAGS="`getconf LFS_CFLAGS`" OPT="-g -O2 $CFLAGS" \
+        ./configure
+

On large-file-capable Linux systems, this might work:

CFLAGS='-D_LARGEFILE64_SOURCE -D_FILE_OFFSET_BITS=64' OPT="-g -O2 $CFLAGS" \
+        ./configure
+

Notable Module Contents

In addition to many functions described in the os module documentation, posix defines the following data item:

+posix.environ
+

A dictionary representing the string environment at the time the interpreter was started. Keys and values are bytes on Unix and str on Windows. For example, environ[b'HOME'] (environ['HOME'] on Windows) is the pathname of your home directory, equivalent to getenv("HOME") in C.

Modifying this dictionary does not affect the string environment passed on by execv(), popen() or system(); if you need to change the environment, pass environ to execve() or add variable assignments and export statements to the command string for system() or popen().

Changed in version 3.2: On Unix, keys and values are bytes.

Note

The os module provides an alternate implementation of environ which updates the environment on modification. Note also that updating os.environ will render this dictionary obsolete. Use of the os module version of this is recommended over direct access to the posix module.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/posix.html +

+
diff --git a/devdocs/python~3.12/library%2Fpprint.html b/devdocs/python~3.12/library%2Fpprint.html new file mode 100644 index 00000000..5d90f5d4 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpprint.html @@ -0,0 +1,223 @@ +

pprint — Data pretty printer

Source code: Lib/pprint.py

The pprint module provides a capability to “pretty-print” arbitrary Python data structures in a form which can be used as input to the interpreter. If the formatted structures include objects which are not fundamental Python types, the representation may not be loadable. This may be the case if objects such as files, sockets or classes are included, as well as many other objects which are not representable as Python literals.

The formatted representation keeps objects on a single line if it can, and breaks them onto multiple lines if they don’t fit within the allowed width. Construct PrettyPrinter objects explicitly if you need to adjust the width constraint.

Dictionaries are sorted by key before the display is computed.

Changed in version 3.9: Added support for pretty-printing types.SimpleNamespace.

Changed in version 3.10: Added support for pretty-printing dataclasses.dataclass.

The pprint module defines one class:

+class pprint.PrettyPrinter(indent=1, width=80, depth=None, stream=None, *, compact=False, sort_dicts=True, underscore_numbers=False)
+

Construct a PrettyPrinter instance. This constructor understands several keyword parameters.

stream (default sys.stdout) is a file-like object to which the output will be written by calling its write() method. If both stream and sys.stdout are None, then pprint() silently returns.

Other values configure the manner in which nesting of complex data structures is displayed.

indent (default 1) specifies the amount of indentation added for each nesting level.

depth controls the number of nesting levels which may be printed; if the data structure being printed is too deep, the next contained level is replaced by .... By default, there is no constraint on the depth of the objects being formatted.

width (default 80) specifies the desired maximum number of characters per line in the output. If a structure cannot be formatted within the width constraint, a best effort will be made.

compact impacts the way that long sequences (lists, tuples, sets, etc) are formatted. If compact is false (the default) then each item of a sequence will be formatted on a separate line. If compact is true, as many items as will fit within the width will be formatted on each output line.

If sort_dicts is true (the default), dictionaries will be formatted with their keys sorted, otherwise they will display in insertion order.

If underscore_numbers is true, integers will be formatted with the _ character for a thousands separator, otherwise underscores are not displayed (the default).

Changed in version 3.4: Added the compact parameter.

Changed in version 3.8: Added the sort_dicts parameter.

Changed in version 3.10: Added the underscore_numbers parameter.

Changed in version 3.11: No longer attempts to write to sys.stdout if it is None.

>>> import pprint
+>>> stuff = ['spam', 'eggs', 'lumberjack', 'knights', 'ni']
+>>> stuff.insert(0, stuff[:])
+>>> pp = pprint.PrettyPrinter(indent=4)
+>>> pp.pprint(stuff)
+[   ['spam', 'eggs', 'lumberjack', 'knights', 'ni'],
+    'spam',
+    'eggs',
+    'lumberjack',
+    'knights',
+    'ni']
+>>> pp = pprint.PrettyPrinter(width=41, compact=True)
+>>> pp.pprint(stuff)
+[['spam', 'eggs', 'lumberjack',
+  'knights', 'ni'],
+ 'spam', 'eggs', 'lumberjack', 'knights',
+ 'ni']
+>>> tup = ('spam', ('eggs', ('lumberjack', ('knights', ('ni', ('dead',
+... ('parrot', ('fresh fruit',))))))))
+>>> pp = pprint.PrettyPrinter(depth=6)
+>>> pp.pprint(tup)
+('spam', ('eggs', ('lumberjack', ('knights', ('ni', ('dead', (...)))))))
+
+
+pprint.pformat(object, indent=1, width=80, depth=None, *, compact=False, sort_dicts=True, underscore_numbers=False)
+

Return the formatted representation of object as a string. indent, width, depth, compact, sort_dicts and underscore_numbers are passed to the PrettyPrinter constructor as formatting parameters and their meanings are as described in its documentation above.

+
+pprint.pp(object, *args, sort_dicts=False, **kwargs)
+

Prints the formatted representation of object followed by a newline. If sort_dicts is false (the default), dictionaries will be displayed with their keys in insertion order, otherwise the dict keys will be sorted. args and kwargs will be passed to pprint() as formatting parameters.

New in version 3.8.

+
+pprint.pprint(object, stream=None, indent=1, width=80, depth=None, *, compact=False, sort_dicts=True, underscore_numbers=False)
+

Prints the formatted representation of object on stream, followed by a newline. If stream is None, sys.stdout is used. This may be used in the interactive interpreter instead of the print() function for inspecting values (you can even reassign print = pprint.pprint for use within a scope).

The configuration parameters stream, indent, width, depth, compact, sort_dicts and underscore_numbers are passed to the PrettyPrinter constructor and their meanings are as described in its documentation above.

>>> import pprint
+>>> stuff = ['spam', 'eggs', 'lumberjack', 'knights', 'ni']
+>>> stuff.insert(0, stuff)
+>>> pprint.pprint(stuff)
+[<Recursion on list with id=...>,
+ 'spam',
+ 'eggs',
+ 'lumberjack',
+ 'knights',
+ 'ni']
+
+
+pprint.isreadable(object)
+

Determine if the formatted representation of object is “readable”, or can be used to reconstruct the value using eval(). This always returns False for recursive objects.

>>> pprint.isreadable(stuff)
+False
+
+
+pprint.isrecursive(object)
+

Determine if object requires a recursive representation. This function is subject to the same limitations as noted in saferepr() below and may raise an RecursionError if it fails to detect a recursive object.

+

One more support function is also defined:

+pprint.saferepr(object)
+

Return a string representation of object, protected against recursion in some common data structures, namely instances of dict, list and tuple or subclasses whose __repr__ has not been overridden. If the representation of object exposes a recursive entry, the recursive reference will be represented as <Recursion on typename with id=number>. The representation is not otherwise formatted.

>>> pprint.saferepr(stuff)
+"[<Recursion on list with id=...>, 'spam', 'eggs', 'lumberjack', 'knights', 'ni']"
+
+

PrettyPrinter Objects

PrettyPrinter instances have the following methods:

+PrettyPrinter.pformat(object)
+

Return the formatted representation of object. This takes into account the options passed to the PrettyPrinter constructor.

+
+PrettyPrinter.pprint(object)
+

Print the formatted representation of object on the configured stream, followed by a newline.

+

The following methods provide the implementations for the corresponding functions of the same names. Using these methods on an instance is slightly more efficient since new PrettyPrinter objects don’t need to be created.

+PrettyPrinter.isreadable(object)
+

Determine if the formatted representation of the object is “readable,” or can be used to reconstruct the value using eval(). Note that this returns False for recursive objects. If the depth parameter of the PrettyPrinter is set and the object is deeper than allowed, this returns False.

+
+PrettyPrinter.isrecursive(object)
+

Determine if the object requires a recursive representation.

+

This method is provided as a hook to allow subclasses to modify the way objects are converted to strings. The default implementation uses the internals of the saferepr() implementation.

+PrettyPrinter.format(object, context, maxlevels, level)
+

Returns three values: the formatted version of object as a string, a flag indicating whether the result is readable, and a flag indicating whether recursion was detected. The first argument is the object to be presented. The second is a dictionary which contains the id() of objects that are part of the current presentation context (direct and indirect containers for object that are affecting the presentation) as the keys; if an object needs to be presented which is already represented in context, the third return value should be True. Recursive calls to the format() method should add additional entries for containers to this dictionary. The third argument, maxlevels, gives the requested limit to recursion; this will be 0 if there is no requested limit. This argument should be passed unmodified to recursive calls. The fourth argument, level, gives the current level; recursive calls should be passed a value less than that of the current call.

+

Example

To demonstrate several uses of the pprint() function and its parameters, let’s fetch information about a project from PyPI:

>>> import json
+>>> import pprint
+>>> from urllib.request import urlopen
+>>> with urlopen('https://pypi.org/pypi/sampleproject/json') as resp:
+...     project_info = json.load(resp)['info']
+

In its basic form, pprint() shows the whole object:

>>> pprint.pprint(project_info)
+{'author': 'The Python Packaging Authority',
+ 'author_email': 'pypa-dev@googlegroups.com',
+ 'bugtrack_url': None,
+ 'classifiers': ['Development Status :: 3 - Alpha',
+                 'Intended Audience :: Developers',
+                 'License :: OSI Approved :: MIT License',
+                 'Programming Language :: Python :: 2',
+                 'Programming Language :: Python :: 2.6',
+                 'Programming Language :: Python :: 2.7',
+                 'Programming Language :: Python :: 3',
+                 'Programming Language :: Python :: 3.2',
+                 'Programming Language :: Python :: 3.3',
+                 'Programming Language :: Python :: 3.4',
+                 'Topic :: Software Development :: Build Tools'],
+ 'description': 'A sample Python project\n'
+                '=======================\n'
+                '\n'
+                'This is the description file for the project.\n'
+                '\n'
+                'The file should use UTF-8 encoding and be written using '
+                'ReStructured Text. It\n'
+                'will be used to generate the project webpage on PyPI, and '
+                'should be written for\n'
+                'that purpose.\n'
+                '\n'
+                'Typical contents for this file would include an overview of '
+                'the project, basic\n'
+                'usage examples, etc. Generally, including the project '
+                'changelog in here is not\n'
+                'a good idea, although a simple "What\'s New" section for the '
+                'most recent version\n'
+                'may be appropriate.',
+ 'description_content_type': None,
+ 'docs_url': None,
+ 'download_url': 'UNKNOWN',
+ 'downloads': {'last_day': -1, 'last_month': -1, 'last_week': -1},
+ 'home_page': 'https://github.com/pypa/sampleproject',
+ 'keywords': 'sample setuptools development',
+ 'license': 'MIT',
+ 'maintainer': None,
+ 'maintainer_email': None,
+ 'name': 'sampleproject',
+ 'package_url': 'https://pypi.org/project/sampleproject/',
+ 'platform': 'UNKNOWN',
+ 'project_url': 'https://pypi.org/project/sampleproject/',
+ 'project_urls': {'Download': 'UNKNOWN',
+                  'Homepage': 'https://github.com/pypa/sampleproject'},
+ 'release_url': 'https://pypi.org/project/sampleproject/1.2.0/',
+ 'requires_dist': None,
+ 'requires_python': None,
+ 'summary': 'A sample Python project',
+ 'version': '1.2.0'}
+

The result can be limited to a certain depth (ellipsis is used for deeper contents):

>>> pprint.pprint(project_info, depth=1)
+{'author': 'The Python Packaging Authority',
+ 'author_email': 'pypa-dev@googlegroups.com',
+ 'bugtrack_url': None,
+ 'classifiers': [...],
+ 'description': 'A sample Python project\n'
+                '=======================\n'
+                '\n'
+                'This is the description file for the project.\n'
+                '\n'
+                'The file should use UTF-8 encoding and be written using '
+                'ReStructured Text. It\n'
+                'will be used to generate the project webpage on PyPI, and '
+                'should be written for\n'
+                'that purpose.\n'
+                '\n'
+                'Typical contents for this file would include an overview of '
+                'the project, basic\n'
+                'usage examples, etc. Generally, including the project '
+                'changelog in here is not\n'
+                'a good idea, although a simple "What\'s New" section for the '
+                'most recent version\n'
+                'may be appropriate.',
+ 'description_content_type': None,
+ 'docs_url': None,
+ 'download_url': 'UNKNOWN',
+ 'downloads': {...},
+ 'home_page': 'https://github.com/pypa/sampleproject',
+ 'keywords': 'sample setuptools development',
+ 'license': 'MIT',
+ 'maintainer': None,
+ 'maintainer_email': None,
+ 'name': 'sampleproject',
+ 'package_url': 'https://pypi.org/project/sampleproject/',
+ 'platform': 'UNKNOWN',
+ 'project_url': 'https://pypi.org/project/sampleproject/',
+ 'project_urls': {...},
+ 'release_url': 'https://pypi.org/project/sampleproject/1.2.0/',
+ 'requires_dist': None,
+ 'requires_python': None,
+ 'summary': 'A sample Python project',
+ 'version': '1.2.0'}
+

Additionally, maximum character width can be suggested. If a long object cannot be split, the specified width will be exceeded:

>>> pprint.pprint(project_info, depth=1, width=60)
+{'author': 'The Python Packaging Authority',
+ 'author_email': 'pypa-dev@googlegroups.com',
+ 'bugtrack_url': None,
+ 'classifiers': [...],
+ 'description': 'A sample Python project\n'
+                '=======================\n'
+                '\n'
+                'This is the description file for the '
+                'project.\n'
+                '\n'
+                'The file should use UTF-8 encoding and be '
+                'written using ReStructured Text. It\n'
+                'will be used to generate the project '
+                'webpage on PyPI, and should be written '
+                'for\n'
+                'that purpose.\n'
+                '\n'
+                'Typical contents for this file would '
+                'include an overview of the project, '
+                'basic\n'
+                'usage examples, etc. Generally, including '
+                'the project changelog in here is not\n'
+                'a good idea, although a simple "What\'s '
+                'New" section for the most recent version\n'
+                'may be appropriate.',
+ 'description_content_type': None,
+ 'docs_url': None,
+ 'download_url': 'UNKNOWN',
+ 'downloads': {...},
+ 'home_page': 'https://github.com/pypa/sampleproject',
+ 'keywords': 'sample setuptools development',
+ 'license': 'MIT',
+ 'maintainer': None,
+ 'maintainer_email': None,
+ 'name': 'sampleproject',
+ 'package_url': 'https://pypi.org/project/sampleproject/',
+ 'platform': 'UNKNOWN',
+ 'project_url': 'https://pypi.org/project/sampleproject/',
+ 'project_urls': {...},
+ 'release_url': 'https://pypi.org/project/sampleproject/1.2.0/',
+ 'requires_dist': None,
+ 'requires_python': None,
+ 'summary': 'A sample Python project',
+ 'version': '1.2.0'}
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pprint.html +

+
diff --git a/devdocs/python~3.12/library%2Fprofile.html b/devdocs/python~3.12/library%2Fprofile.html new file mode 100644 index 00000000..32709e4a --- /dev/null +++ b/devdocs/python~3.12/library%2Fprofile.html @@ -0,0 +1,176 @@ +

The Python Profilers

Source code: Lib/profile.py and Lib/pstats.py

Introduction to the profilers

cProfile and profile provide deterministic profiling of Python programs. A profile is a set of statistics that describes how often and for how long various parts of the program executed. These statistics can be formatted into reports via the pstats module.

The Python standard library provides two different implementations of the same profiling interface:

  1. +cProfile is recommended for most users; it’s a C extension with reasonable overhead that makes it suitable for profiling long-running programs. Based on lsprof, contributed by Brett Rosen and Ted Czotter.
  2. +profile, a pure Python module whose interface is imitated by cProfile, but which adds significant overhead to profiled programs. If you’re trying to extend the profiler in some way, the task might be easier with this module. Originally designed and written by Jim Roskind.

Note

The profiler modules are designed to provide an execution profile for a given program, not for benchmarking purposes (for that, there is timeit for reasonably accurate results). This particularly applies to benchmarking Python code against C code: the profilers introduce overhead for Python code, but not for C-level functions, and so the C code would seem faster than any Python one.

Instant User’s Manual

This section is provided for users that “don’t want to read the manual.” It provides a very brief overview, and allows a user to rapidly perform profiling on an existing application.

To profile a function that takes a single argument, you can do:

import cProfile
+import re
+cProfile.run('re.compile("foo|bar")')
+

(Use profile instead of cProfile if the latter is not available on your system.)

The above action would run re.compile() and print profile results like the following:

      214 function calls (207 primitive calls) in 0.002 seconds
+
+Ordered by: cumulative time
+
+ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+     1    0.000    0.000    0.002    0.002 {built-in method builtins.exec}
+     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
+     1    0.000    0.000    0.001    0.001 __init__.py:250(compile)
+     1    0.000    0.000    0.001    0.001 __init__.py:289(_compile)
+     1    0.000    0.000    0.000    0.000 _compiler.py:759(compile)
+     1    0.000    0.000    0.000    0.000 _parser.py:937(parse)
+     1    0.000    0.000    0.000    0.000 _compiler.py:598(_code)
+     1    0.000    0.000    0.000    0.000 _parser.py:435(_parse_sub)
+

The first line indicates that 214 calls were monitored. Of those calls, 207 were primitive, meaning that the call was not induced via recursion. The next line: Ordered by: cumulative time indicates the output is sorted by the cumtime values. The column headings include:

ncalls
+
+

for the number of calls.

tottime
+
+

for the total time spent in the given function (and excluding time made in calls to sub-functions)

percall
+
+

is the quotient of tottime divided by ncalls

cumtime
+
+

is the cumulative time spent in this and all subfunctions (from invocation till exit). This figure is accurate even for recursive functions.

percall
+
+

is the quotient of cumtime divided by primitive calls

filename:lineno(function)
+
+

provides the respective data of each function

When there are two numbers in the first column (for example 3/1), it means that the function recursed. The second value is the number of primitive calls and the former is the total number of calls. Note that when the function does not recurse, these two values are the same, and only the single figure is printed.

Instead of printing the output at the end of the profile run, you can save the results to a file by specifying a filename to the run() function:

import cProfile
+import re
+cProfile.run('re.compile("foo|bar")', 'restats')
+

The pstats.Stats class reads profile results from a file and formats them in various ways.

The files cProfile and profile can also be invoked as a script to profile another script. For example:

python -m cProfile [-o output_file] [-s sort_order] (-m module | myscript.py)
+

-o writes the profile results to a file instead of to stdout

-s specifies one of the sort_stats() sort values to sort the output by. This only applies when -o is not supplied.

-m specifies that a module is being profiled instead of a script.

New in version 3.7: Added the -m option to cProfile.

New in version 3.8: Added the -m option to profile.

The pstats module’s Stats class has a variety of methods for manipulating and printing the data saved into a profile results file:

import pstats
+from pstats import SortKey
+p = pstats.Stats('restats')
+p.strip_dirs().sort_stats(-1).print_stats()
+

The strip_dirs() method removed the extraneous path from all the module names. The sort_stats() method sorted all the entries according to the standard module/line/name string that is printed. The print_stats() method printed out all the statistics. You might try the following sort calls:

p.sort_stats(SortKey.NAME)
+p.print_stats()
+

The first call will actually sort the list by function name, and the second call will print out the statistics. The following are some interesting calls to experiment with:

p.sort_stats(SortKey.CUMULATIVE).print_stats(10)
+

This sorts the profile by cumulative time in a function, and then only prints the ten most significant lines. If you want to understand what algorithms are taking time, the above line is what you would use.

If you were looking to see what functions were looping a lot, and taking a lot of time, you would do:

p.sort_stats(SortKey.TIME).print_stats(10)
+

to sort according to time spent within each function, and then print the statistics for the top ten functions.

You might also try:

p.sort_stats(SortKey.FILENAME).print_stats('__init__')
+

This will sort all the statistics by file name, and then print out statistics for only the class init methods (since they are spelled with __init__ in them). As one final example, you could try:

p.sort_stats(SortKey.TIME, SortKey.CUMULATIVE).print_stats(.5, 'init')
+

This line sorts statistics with a primary key of time, and a secondary key of cumulative time, and then prints out some of the statistics. To be specific, the list is first culled down to 50% (re: .5) of its original size, then only lines containing init are maintained, and that sub-sub-list is printed.

If you wondered what functions called the above functions, you could now (p is still sorted according to the last criteria) do:

p.print_callers(.5, 'init')
+

and you would get a list of callers for each of the listed functions.

If you want more functionality, you’re going to have to read the manual, or guess what the following functions do:

p.print_callees()
+p.add('restats')
+

Invoked as a script, the pstats module is a statistics browser for reading and examining profile dumps. It has a simple line-oriented interface (implemented using cmd) and interactive help.

profile and cProfile Module Reference

Both the profile and cProfile modules provide the following functions:

+profile.run(command, filename=None, sort=- 1)
+

This function takes a single argument that can be passed to the exec() function, and an optional file name. In all cases this routine executes:

exec(command, __main__.__dict__, __main__.__dict__)
+

and gathers profiling statistics from the execution. If no file name is present, then this function automatically creates a Stats instance and prints a simple profiling report. If the sort value is specified, it is passed to this Stats instance to control how the results are sorted.

+
+profile.runctx(command, globals, locals, filename=None, sort=- 1)
+

This function is similar to run(), with added arguments to supply the globals and locals dictionaries for the command string. This routine executes:

exec(command, globals, locals)
+

and gathers profiling statistics as in the run() function above.

+
+class profile.Profile(timer=None, timeunit=0.0, subcalls=True, builtins=True)
+

This class is normally only used if more precise control over profiling is needed than what the cProfile.run() function provides.

A custom timer can be supplied for measuring how long code takes to run via the timer argument. This must be a function that returns a single number representing the current time. If the number is an integer, the timeunit specifies a multiplier that specifies the duration of each unit of time. For example, if the timer returns times measured in thousands of seconds, the time unit would be .001.

Directly using the Profile class allows formatting profile results without writing the profile data to a file:

import cProfile, pstats, io
+from pstats import SortKey
+pr = cProfile.Profile()
+pr.enable()
+# ... do something ...
+pr.disable()
+s = io.StringIO()
+sortby = SortKey.CUMULATIVE
+ps = pstats.Stats(pr, stream=s).sort_stats(sortby)
+ps.print_stats()
+print(s.getvalue())
+

The Profile class can also be used as a context manager (supported only in cProfile module. see Context Manager Types):

import cProfile
+
+with cProfile.Profile() as pr:
+    # ... do something ...
+
+    pr.print_stats()
+

Changed in version 3.8: Added context manager support.

+enable()
+

Start collecting profiling data. Only in cProfile.

+
+disable()
+

Stop collecting profiling data. Only in cProfile.

+
+create_stats()
+

Stop collecting profiling data and record the results internally as the current profile.

+
+print_stats(sort=- 1)
+

Create a Stats object based on the current profile and print the results to stdout.

+
+dump_stats(filename)
+

Write the results of the current profile to filename.

+
+run(cmd)
+

Profile the cmd via exec().

+
+runctx(cmd, globals, locals)
+

Profile the cmd via exec() with the specified global and local environment.

+
+runcall(func, /, *args, **kwargs)
+

Profile func(*args, **kwargs)

+
+

Note that profiling will only work if the called command/function actually returns. If the interpreter is terminated (e.g. via a sys.exit() call during the called command/function execution) no profiling results will be printed.

The Stats Class

Analysis of the profiler data is done using the Stats class.

+class pstats.Stats(*filenames or profile, stream=sys.stdout)
+

This class constructor creates an instance of a “statistics object” from a filename (or list of filenames) or from a Profile instance. Output will be printed to the stream specified by stream.

The file selected by the above constructor must have been created by the corresponding version of profile or cProfile. To be specific, there is no file compatibility guaranteed with future versions of this profiler, and there is no compatibility with files produced by other profilers, or the same profiler run on a different operating system. If several files are provided, all the statistics for identical functions will be coalesced, so that an overall view of several processes can be considered in a single report. If additional files need to be combined with data in an existing Stats object, the add() method can be used.

Instead of reading the profile data from a file, a cProfile.Profile or profile.Profile object can be used as the profile data source.

Stats objects have the following methods:

+strip_dirs()
+

This method for the Stats class removes all leading path information from file names. It is very useful in reducing the size of the printout to fit within (close to) 80 columns. This method modifies the object, and the stripped information is lost. After performing a strip operation, the object is considered to have its entries in a “random” order, as it was just after object initialization and loading. If strip_dirs() causes two function names to be indistinguishable (they are on the same line of the same filename, and have the same function name), then the statistics for these two entries are accumulated into a single entry.

+
+add(*filenames)
+

This method of the Stats class accumulates additional profiling information into the current profiling object. Its arguments should refer to filenames created by the corresponding version of profile.run() or cProfile.run(). Statistics for identically named (re: file, line, name) functions are automatically accumulated into single function statistics.

+
+dump_stats(filename)
+

Save the data loaded into the Stats object to a file named filename. The file is created if it does not exist, and is overwritten if it already exists. This is equivalent to the method of the same name on the profile.Profile and cProfile.Profile classes.

+
+sort_stats(*keys)
+

This method modifies the Stats object by sorting it according to the supplied criteria. The argument can be either a string or a SortKey enum identifying the basis of a sort (example: 'time', 'name', SortKey.TIME or SortKey.NAME). The SortKey enums argument have advantage over the string argument in that it is more robust and less error prone.

When more than one key is provided, then additional keys are used as secondary criteria when there is equality in all keys selected before them. For example, sort_stats(SortKey.NAME, SortKey.FILE) will sort all the entries according to their function name, and resolve all ties (identical function names) by sorting by file name.

For the string argument, abbreviations can be used for any key names, as long as the abbreviation is unambiguous.

The following are the valid string and SortKey:

+ + + + + + + + + + + + + + +

Valid String Arg

Valid enum Arg

Meaning

'calls'

SortKey.CALLS

call count

'cumulative'

SortKey.CUMULATIVE

cumulative time

'cumtime'

N/A

cumulative time

'file'

N/A

file name

'filename'

SortKey.FILENAME

file name

'module'

N/A

file name

'ncalls'

N/A

call count

'pcalls'

SortKey.PCALLS

primitive call count

'line'

SortKey.LINE

line number

'name'

SortKey.NAME

function name

'nfl'

SortKey.NFL

name/file/line

'stdname'

SortKey.STDNAME

standard name

'time'

SortKey.TIME

internal time

'tottime'

N/A

internal time

Note that all sorts on statistics are in descending order (placing most time consuming items first), where as name, file, and line number searches are in ascending order (alphabetical). The subtle distinction between SortKey.NFL and SortKey.STDNAME is that the standard name is a sort of the name as printed, which means that the embedded line numbers get compared in an odd way. For example, lines 3, 20, and 40 would (if the file names were the same) appear in the string order 20, 3 and 40. In contrast, SortKey.NFL does a numeric compare of the line numbers. In fact, sort_stats(SortKey.NFL) is the same as sort_stats(SortKey.NAME, SortKey.FILENAME, SortKey.LINE).

For backward-compatibility reasons, the numeric arguments -1, 0, 1, and 2 are permitted. They are interpreted as 'stdname', 'calls', 'time', and 'cumulative' respectively. If this old style format (numeric) is used, only one sort key (the numeric key) will be used, and additional arguments will be silently ignored.

New in version 3.7: Added the SortKey enum.

+
+reverse_order()
+

This method for the Stats class reverses the ordering of the basic list within the object. Note that by default ascending vs descending order is properly selected based on the sort key of choice.

+
+print_stats(*restrictions)
+

This method for the Stats class prints out a report as described in the profile.run() definition.

The order of the printing is based on the last sort_stats() operation done on the object (subject to caveats in add() and strip_dirs()).

The arguments provided (if any) can be used to limit the list down to the significant entries. Initially, the list is taken to be the complete set of profiled functions. Each restriction is either an integer (to select a count of lines), or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines), or a string that will interpreted as a regular expression (to pattern match the standard name that is printed). If several restrictions are provided, then they are applied sequentially. For example:

print_stats(.1, 'foo:')
+

would first limit the printing to first 10% of list, and then only print functions that were part of filename .*foo:. In contrast, the command:

print_stats('foo:', .1)
+

would limit the list to all functions having file names .*foo:, and then proceed to only print the first 10% of them.

+
+print_callers(*restrictions)
+

This method for the Stats class prints a list of all functions that called each function in the profiled database. The ordering is identical to that provided by print_stats(), and the definition of the restricting argument is also identical. Each caller is reported on its own line. The format differs slightly depending on the profiler that produced the stats:

  • With profile, a number is shown in parentheses after each caller to show how many times this specific call was made. For convenience, a second non-parenthesized number repeats the cumulative time spent in the function at the right.
  • With cProfile, each caller is preceded by three numbers: the number of times this specific call was made, and the total and cumulative times spent in the current function while it was invoked by this specific caller.
+
+print_callees(*restrictions)
+

This method for the Stats class prints a list of all function that were called by the indicated function. Aside from this reversal of direction of calls (re: called vs was called by), the arguments and ordering are identical to the print_callers() method.

+
+get_stats_profile()
+

This method returns an instance of StatsProfile, which contains a mapping of function names to instances of FunctionProfile. Each FunctionProfile instance holds information related to the function’s profile such as how long the function took to run, how many times it was called, etc…

New in version 3.9: Added the following dataclasses: StatsProfile, FunctionProfile. Added the following function: get_stats_profile.

+
+

What Is Deterministic Profiling?

Deterministic profiling is meant to reflect the fact that all function call, function return, and exception events are monitored, and precise timings are made for the intervals between these events (during which time the user’s code is executing). In contrast, statistical profiling (which is not done by this module) randomly samples the effective instruction pointer, and deduces where time is being spent. The latter technique traditionally involves less overhead (as the code does not need to be instrumented), but provides only relative indications of where time is being spent.

In Python, since there is an interpreter active during execution, the presence of instrumented code is not required in order to do deterministic profiling. Python automatically provides a hook (optional callback) for each event. In addition, the interpreted nature of Python tends to add so much overhead to execution, that deterministic profiling tends to only add small processing overhead in typical applications. The result is that deterministic profiling is not that expensive, yet provides extensive run time statistics about the execution of a Python program.

Call count statistics can be used to identify bugs in code (surprising counts), and to identify possible inline-expansion points (high call counts). Internal time statistics can be used to identify “hot loops” that should be carefully optimized. Cumulative time statistics should be used to identify high level errors in the selection of algorithms. Note that the unusual handling of cumulative times in this profiler allows statistics for recursive implementations of algorithms to be directly compared to iterative implementations.

Limitations

One limitation has to do with accuracy of timing information. There is a fundamental problem with deterministic profilers involving accuracy. The most obvious restriction is that the underlying “clock” is only ticking at a rate (typically) of about .001 seconds. Hence no measurements will be more accurate than the underlying clock. If enough measurements are taken, then the “error” will tend to average out. Unfortunately, removing this first error induces a second source of error.

The second problem is that it “takes a while” from when an event is dispatched until the profiler’s call to get the time actually gets the state of the clock. Similarly, there is a certain lag when exiting the profiler event handler from the time that the clock’s value was obtained (and then squirreled away), until the user’s code is once again executing. As a result, functions that are called many times, or call many functions, will typically accumulate this error. The error that accumulates in this fashion is typically less than the accuracy of the clock (less than one clock tick), but it can accumulate and become very significant.

The problem is more important with profile than with the lower-overhead cProfile. For this reason, profile provides a means of calibrating itself for a given platform so that this error can be probabilistically (on the average) removed. After the profiler is calibrated, it will be more accurate (in a least square sense), but it will sometimes produce negative numbers (when call counts are exceptionally low, and the gods of probability work against you :-). ) Do not be alarmed by negative numbers in the profile. They should only appear if you have calibrated your profiler, and the results are actually better than without calibration.

Calibration

The profiler of the profile module subtracts a constant from each event handling time to compensate for the overhead of calling the time function, and socking away the results. By default, the constant is 0. The following procedure can be used to obtain a better constant for a given platform (see Limitations).

import profile
+pr = profile.Profile()
+for i in range(5):
+    print(pr.calibrate(10000))
+

The method executes the number of Python calls given by the argument, directly and again under the profiler, measuring the time for both. It then computes the hidden overhead per profiler event, and returns that as a float. For example, on a 1.8Ghz Intel Core i5 running macOS, and using Python’s time.process_time() as the timer, the magical number is about 4.04e-6.

The object of this exercise is to get a fairly consistent result. If your computer is very fast, or your timer function has poor resolution, you might have to pass 100000, or even 1000000, to get consistent results.

When you have a consistent answer, there are three ways you can use it:

import profile
+
+# 1. Apply computed bias to all Profile instances created hereafter.
+profile.Profile.bias = your_computed_bias
+
+# 2. Apply computed bias to a specific Profile instance.
+pr = profile.Profile()
+pr.bias = your_computed_bias
+
+# 3. Specify computed bias in instance constructor.
+pr = profile.Profile(bias=your_computed_bias)
+

If you have a choice, you are better off choosing a smaller constant, and then your results will “less often” show up as negative in profile statistics.

Using a custom timer

If you want to change how current time is determined (for example, to force use of wall-clock time or elapsed process time), pass the timing function you want to the Profile class constructor:

pr = profile.Profile(your_time_func)
+

The resulting profiler will then call your_time_func. Depending on whether you are using profile.Profile or cProfile.Profile, your_time_func’s return value will be interpreted differently:

+ profile.Profile +
+
+

your_time_func should return a single number, or a list of numbers whose sum is the current time (like what os.times() returns). If the function returns a single time number, or the list of returned numbers has length 2, then you will get an especially fast version of the dispatch routine.

Be warned that you should calibrate the profiler class for the timer function that you choose (see Calibration). For most machines, a timer that returns a lone integer value will provide the best results in terms of low overhead during profiling. (os.times() is pretty bad, as it returns a tuple of floating point values). If you want to substitute a better timer in the cleanest fashion, derive a class and hardwire a replacement dispatch method that best handles your timer call, along with the appropriate calibration constant.

+cProfile.Profile
+
+

your_time_func should return a single number. If it returns integers, you can also invoke the class constructor with a second argument specifying the real duration of one unit of time. For example, if your_integer_time_func returns times measured in thousands of seconds, you would construct the Profile instance as follows:

pr = cProfile.Profile(your_integer_time_func, 0.001)
+

As the cProfile.Profile class cannot be calibrated, custom timer functions should be used with care and should be as fast as possible. For the best results with a custom timer, it might be necessary to hard-code it in the C source of the internal _lsprof module.

Python 3.3 adds several new functions in time that can be used to make precise measurements of process or wall-clock time. For example, see time.perf_counter().

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/profile.html +

+
diff --git a/devdocs/python~3.12/library%2Fpty.html b/devdocs/python~3.12/library%2Fpty.html new file mode 100644 index 00000000..c964e97c --- /dev/null +++ b/devdocs/python~3.12/library%2Fpty.html @@ -0,0 +1,44 @@ +

pty — Pseudo-terminal utilities

Source code: Lib/pty.py

The pty module defines operations for handling the pseudo-terminal concept: starting another process and being able to write to and read from its controlling terminal programmatically.

Availability: Unix.

Pseudo-terminal handling is highly platform dependent. This code is mainly tested on Linux, FreeBSD, and macOS (it is supposed to work on other POSIX platforms but it’s not been thoroughly tested).

The pty module defines the following functions:

+pty.fork()
+

Fork. Connect the child’s controlling terminal to a pseudo-terminal. Return value is (pid, fd). Note that the child gets pid 0, and the fd is invalid. The parent’s return value is the pid of the child, and fd is a file descriptor connected to the child’s controlling terminal (and also to the child’s standard input and output).

Warning

On macOS the use of this function is unsafe when mixed with using higher-level system APIs, and that includes using urllib.request.

+
+pty.openpty()
+

Open a new pseudo-terminal pair, using os.openpty() if possible, or emulation code for generic Unix systems. Return a pair of file descriptors (master, slave), for the master and the slave end, respectively.

+
+pty.spawn(argv[, master_read[, stdin_read]])
+

Spawn a process, and connect its controlling terminal with the current process’s standard io. This is often used to baffle programs which insist on reading from the controlling terminal. It is expected that the process spawned behind the pty will eventually terminate, and when it does spawn will return.

A loop copies STDIN of the current process to the child and data received from the child to STDOUT of the current process. It is not signaled to the child if STDIN of the current process closes down.

The functions master_read and stdin_read are passed a file descriptor which they should read from, and they should always return a byte string. In order to force spawn to return before the child process exits an empty byte array should be returned to signal end of file.

The default implementation for both functions will read and return up to 1024 bytes each time the function is called. The master_read callback is passed the pseudoterminal’s master file descriptor to read output from the child process, and stdin_read is passed file descriptor 0, to read from the parent process’s standard input.

Returning an empty byte string from either callback is interpreted as an end-of-file (EOF) condition, and that callback will not be called after that. If stdin_read signals EOF the controlling terminal can no longer communicate with the parent process OR the child process. Unless the child process will quit without any input, spawn will then loop forever. If master_read signals EOF the same behavior results (on linux at least).

Return the exit status value from os.waitpid() on the child process.

os.waitstatus_to_exitcode() can be used to convert the exit status into an exit code.

Raises an auditing event pty.spawn with argument argv.

Changed in version 3.4: spawn() now returns the status value from os.waitpid() on the child process.

+

Example

The following program acts like the Unix command script(1), using a pseudo-terminal to record all input and output of a terminal session in a “typescript”.

import argparse
+import os
+import pty
+import sys
+import time
+
+parser = argparse.ArgumentParser()
+parser.add_argument('-a', dest='append', action='store_true')
+parser.add_argument('-p', dest='use_python', action='store_true')
+parser.add_argument('filename', nargs='?', default='typescript')
+options = parser.parse_args()
+
+shell = sys.executable if options.use_python else os.environ.get('SHELL', 'sh')
+filename = options.filename
+mode = 'ab' if options.append else 'wb'
+
+with open(filename, mode) as script:
+    def read(fd):
+        data = os.read(fd, 1024)
+        script.write(data)
+        return data
+
+    print('Script started, file is', filename)
+    script.write(('Script started on %s\n' % time.asctime()).encode())
+
+    pty.spawn(shell, read)
+
+    script.write(('Script done on %s\n' % time.asctime()).encode())
+    print('Script done, file is', filename)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pty.html +

+
diff --git a/devdocs/python~3.12/library%2Fpwd.html b/devdocs/python~3.12/library%2Fpwd.html new file mode 100644 index 00000000..578fb27b --- /dev/null +++ b/devdocs/python~3.12/library%2Fpwd.html @@ -0,0 +1,31 @@ +

pwd — The password database

This module provides access to the Unix user account and password database. It is available on all Unix versions.

Availability: Unix, not Emscripten, not WASI.

Password database entries are reported as a tuple-like object, whose attributes correspond to the members of the passwd structure (Attribute field below, see <pwd.h>):

+ + + + + + + +

Index

Attribute

Meaning

0

pw_name

Login name

1

pw_passwd

Optional encrypted password

2

pw_uid

Numerical user ID

3

pw_gid

Numerical group ID

4

pw_gecos

User name or comment field

5

pw_dir

User home directory

6

pw_shell

User command interpreter

The uid and gid items are integers, all others are strings. KeyError is raised if the entry asked for cannot be found.

Note

In traditional Unix the field pw_passwd usually contains a password encrypted with a DES derived algorithm (see module crypt). However most modern unices use a so-called shadow password system. On those unices the pw_passwd field only contains an asterisk ('*') or the letter 'x' where the encrypted password is stored in a file /etc/shadow which is not world readable. Whether the pw_passwd field contains anything useful is system-dependent. If available, the spwd module should be used where access to the encrypted password is required.

It defines the following items:

+pwd.getpwuid(uid)
+

Return the password database entry for the given numeric user ID.

+
+pwd.getpwnam(name)
+

Return the password database entry for the given user name.

+
+pwd.getpwall()
+

Return a list of all available password database entries, in arbitrary order.

+

See also

+Module grp +
+
+

An interface to the group database, similar to this.

+Module spwd +
+
+

An interface to the shadow password database, similar to this.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pwd.html +

+
diff --git a/devdocs/python~3.12/library%2Fpy_compile.html b/devdocs/python~3.12/library%2Fpy_compile.html new file mode 100644 index 00000000..47eea7dc --- /dev/null +++ b/devdocs/python~3.12/library%2Fpy_compile.html @@ -0,0 +1,35 @@ +

py_compile — Compile Python source files

Source code: Lib/py_compile.py

The py_compile module provides a function to generate a byte-code file from a source file, and another function used when the module source file is invoked as a script.

Though not often needed, this function can be useful when installing modules for shared use, especially if some of the users may not have permission to write the byte-code cache files in the directory containing the source code.

+exception py_compile.PyCompileError
+

Exception raised when an error occurs while attempting to compile the file.

+
+py_compile.compile(file, cfile=None, dfile=None, doraise=False, optimize=- 1, invalidation_mode=PycInvalidationMode.TIMESTAMP, quiet=0)
+

Compile a source file to byte-code and write out the byte-code cache file. The source code is loaded from the file named file. The byte-code is written to cfile, which defaults to the PEP 3147/PEP 488 path, ending in .pyc. For example, if file is /foo/bar/baz.py cfile will default to /foo/bar/__pycache__/baz.cpython-32.pyc for Python 3.2. If dfile is specified, it is used instead of file as the name of the source file from which source lines are obtained for display in exception tracebacks. If doraise is true, a PyCompileError is raised when an error is encountered while compiling file. If doraise is false (the default), an error string is written to sys.stderr, but no exception is raised. This function returns the path to byte-compiled file, i.e. whatever cfile value was used.

The doraise and quiet arguments determine how errors are handled while compiling file. If quiet is 0 or 1, and doraise is false, the default behaviour is enabled: an error string is written to sys.stderr, and the function returns None instead of a path. If doraise is true, a PyCompileError is raised instead. However if quiet is 2, no message is written, and doraise has no effect.

If the path that cfile becomes (either explicitly specified or computed) is a symlink or non-regular file, FileExistsError will be raised. This is to act as a warning that import will turn those paths into regular files if it is allowed to write byte-compiled files to those paths. This is a side-effect of import using file renaming to place the final byte-compiled file into place to prevent concurrent file writing issues.

optimize controls the optimization level and is passed to the built-in compile() function. The default of -1 selects the optimization level of the current interpreter.

invalidation_mode should be a member of the PycInvalidationMode enum and controls how the generated bytecode cache is invalidated at runtime. The default is PycInvalidationMode.CHECKED_HASH if the SOURCE_DATE_EPOCH environment variable is set, otherwise the default is PycInvalidationMode.TIMESTAMP.

Changed in version 3.2: Changed default value of cfile to be PEP 3147-compliant. Previous default was file + 'c' ('o' if optimization was enabled). Also added the optimize parameter.

Changed in version 3.4: Changed code to use importlib for the byte-code cache file writing. This means file creation/writing semantics now match what importlib does, e.g. permissions, write-and-move semantics, etc. Also added the caveat that FileExistsError is raised if cfile is a symlink or non-regular file.

Changed in version 3.7: The invalidation_mode parameter was added as specified in PEP 552. If the SOURCE_DATE_EPOCH environment variable is set, invalidation_mode will be forced to PycInvalidationMode.CHECKED_HASH.

Changed in version 3.7.2: The SOURCE_DATE_EPOCH environment variable no longer overrides the value of the invalidation_mode argument, and determines its default value instead.

Changed in version 3.8: The quiet parameter was added.

+
+class py_compile.PycInvalidationMode
+

A enumeration of possible methods the interpreter can use to determine whether a bytecode file is up to date with a source file. The .pyc file indicates the desired invalidation mode in its header. See Cached bytecode invalidation for more information on how Python invalidates .pyc files at runtime.

New in version 3.7.

+TIMESTAMP
+

The .pyc file includes the timestamp and size of the source file, which Python will compare against the metadata of the source file at runtime to determine if the .pyc file needs to be regenerated.

+
+CHECKED_HASH
+

The .pyc file includes a hash of the source file content, which Python will compare against the source at runtime to determine if the .pyc file needs to be regenerated.

+
+UNCHECKED_HASH
+

Like CHECKED_HASH, the .pyc file includes a hash of the source file content. However, Python will at runtime assume the .pyc file is up to date and not validate the .pyc against the source file at all.

This option is useful when the .pycs are kept up to date by some system external to Python like a build system.

+
+

Command-Line Interface

This module can be invoked as a script to compile several source files. The files named in filenames are compiled and the resulting bytecode is cached in the normal manner. This program does not search a directory structure to locate source files; it only compiles files named explicitly. The exit status is nonzero if one of the files could not be compiled.

+<file> ... <fileN>
+-
+

Positional arguments are files to compile. If - is the only parameter, the list of files is taken from standard input.

+
+-q, --quiet
+

Suppress errors output.

+

Changed in version 3.2: Added support for -.

Changed in version 3.10: Added support for -q.

See also

+Module compileall +
+
+

Utilities to compile all Python source files in a directory tree.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/py_compile.html +

+
diff --git a/devdocs/python~3.12/library%2Fpyclbr.html b/devdocs/python~3.12/library%2Fpyclbr.html new file mode 100644 index 00000000..3741e140 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpyclbr.html @@ -0,0 +1,57 @@ +

pyclbr — Python module browser support

Source code: Lib/pyclbr.py

The pyclbr module provides limited information about the functions, classes, and methods defined in a Python-coded module. The information is sufficient to implement a module browser. The information is extracted from the Python source code rather than by importing the module, so this module is safe to use with untrusted code. This restriction makes it impossible to use this module with modules not implemented in Python, including all standard and optional extension modules.

+pyclbr.readmodule(module, path=None)
+

Return a dictionary mapping module-level class names to class descriptors. If possible, descriptors for imported base classes are included. Parameter module is a string with the name of the module to read; it may be the name of a module within a package. If given, path is a sequence of directory paths prepended to sys.path, which is used to locate the module source code.

This function is the original interface and is only kept for back compatibility. It returns a filtered version of the following.

+
+pyclbr.readmodule_ex(module, path=None)
+

Return a dictionary-based tree containing a function or class descriptors for each function and class defined in the module with a def or class statement. The returned dictionary maps module-level function and class names to their descriptors. Nested objects are entered into the children dictionary of their parent. As with readmodule, module names the module to be read and path is prepended to sys.path. If the module being read is a package, the returned dictionary has a key '__path__' whose value is a list containing the package search path.

+

New in version 3.7: Descriptors for nested definitions. They are accessed through the new children attribute. Each has a new parent attribute.

The descriptors returned by these functions are instances of Function and Class classes. Users are not expected to create instances of these classes.

Function Objects

Class Function instances describe functions defined by def statements. They have the following attributes:

+Function.file
+

Name of the file in which the function is defined.

+
+Function.module
+

The name of the module defining the function described.

+
+Function.name
+

The name of the function.

+
+Function.lineno
+

The line number in the file where the definition starts.

+
+Function.parent
+

For top-level functions, None. For nested functions, the parent.

New in version 3.7.

+
+Function.children
+

A dictionary mapping names to descriptors for nested functions and classes.

New in version 3.7.

+
+Function.is_async
+

True for functions that are defined with the async prefix, False otherwise.

New in version 3.10.

+

Class Objects

Class Class instances describe classes defined by class statements. They have the same attributes as Functions and two more.

+Class.file
+

Name of the file in which the class is defined.

+
+Class.module
+

The name of the module defining the class described.

+
+Class.name
+

The name of the class.

+
+Class.lineno
+

The line number in the file where the definition starts.

+
+Class.parent
+

For top-level classes, None. For nested classes, the parent.

New in version 3.7.

+
+Class.children
+

A dictionary mapping names to descriptors for nested functions and classes.

New in version 3.7.

+
+Class.super
+

A list of Class objects which describe the immediate base classes of the class being described. Classes which are named as superclasses but which are not discoverable by readmodule_ex() are listed as a string with the class name instead of as Class objects.

+
+Class.methods
+

A dictionary mapping method names to line numbers. This can be derived from the newer children dictionary, but remains for back-compatibility.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pyclbr.html +

+
diff --git a/devdocs/python~3.12/library%2Fpydoc.html b/devdocs/python~3.12/library%2Fpydoc.html new file mode 100644 index 00000000..1de6e11c --- /dev/null +++ b/devdocs/python~3.12/library%2Fpydoc.html @@ -0,0 +1,7 @@ +

pydoc — Documentation generator and online help system

Source code: Lib/pydoc.py

The pydoc module automatically generates documentation from Python modules. The documentation can be presented as pages of text on the console, served to a web browser, or saved to HTML files.

For modules, classes, functions and methods, the displayed documentation is derived from the docstring (i.e. the __doc__ attribute) of the object, and recursively of its documentable members. If there is no docstring, pydoc tries to obtain a description from the block of comment lines just above the definition of the class, function or method in the source file, or at the top of the module (see inspect.getcomments()).

The built-in function help() invokes the online help system in the interactive interpreter, which uses pydoc to generate its documentation as text on the console. The same text documentation can also be viewed from outside the Python interpreter by running pydoc as a script at the operating system’s command prompt. For example, running

python -m pydoc sys
+

at a shell prompt will display documentation on the sys module, in a style similar to the manual pages shown by the Unix man command. The argument to pydoc can be the name of a function, module, or package, or a dotted reference to a class, method, or function within a module or module in a package. If the argument to pydoc looks like a path (that is, it contains the path separator for your operating system, such as a slash in Unix), and refers to an existing Python source file, then documentation is produced for that file.

Note

In order to find objects and their documentation, pydoc imports the module(s) to be documented. Therefore, any code on module level will be executed on that occasion. Use an if __name__ == '__main__': guard to only execute code when a file is invoked as a script and not just imported.

When printing output to the console, pydoc attempts to paginate the output for easier reading. If the PAGER environment variable is set, pydoc will use its value as a pagination program.

Specifying a -w flag before the argument will cause HTML documentation to be written out to a file in the current directory, instead of displaying text on the console.

Specifying a -k flag before the argument will search the synopsis lines of all available modules for the keyword given as the argument, again in a manner similar to the Unix man command. The synopsis line of a module is the first line of its documentation string.

You can also use pydoc to start an HTTP server on the local machine that will serve documentation to visiting web browsers. python -m pydoc -p 1234 will start a HTTP server on port 1234, allowing you to browse the documentation at http://localhost:1234/ in your preferred web browser. Specifying 0 as the port number will select an arbitrary unused port.

python -m pydoc -n <hostname> will start the server listening at the given hostname. By default the hostname is ‘localhost’ but if you want the server to be reached from other machines, you may want to change the host name that the server responds to. During development this is especially useful if you want to run pydoc from within a container.

python -m pydoc -b will start the server and additionally open a web browser to a module index page. Each served page has a navigation bar at the top where you can Get help on an individual item, Search all modules with a keyword in their synopsis line, and go to the Module index, Topics and Keywords pages.

When pydoc generates documentation, it uses the current environment and path to locate modules. Thus, invoking pydoc spam documents precisely the version of the module you would get if you started the Python interpreter and typed import spam.

Module docs for core modules are assumed to reside in https://docs.python.org/X.Y/library/ where X and Y are the major and minor version numbers of the Python interpreter. This can be overridden by setting the PYTHONDOCS environment variable to a different URL or to a local directory containing the Library Reference Manual pages.

Changed in version 3.2: Added the -b option.

Changed in version 3.3: The -g command line option was removed.

Changed in version 3.4: pydoc now uses inspect.signature() rather than inspect.getfullargspec() to extract signature information from callables.

Changed in version 3.7: Added the -n option.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pydoc.html +

+
diff --git a/devdocs/python~3.12/library%2Fpyexpat.html b/devdocs/python~3.12/library%2Fpyexpat.html new file mode 100644 index 00000000..62b77b35 --- /dev/null +++ b/devdocs/python~3.12/library%2Fpyexpat.html @@ -0,0 +1,361 @@ +

xml.parsers.expat — Fast XML parsing using Expat

Warning

The pyexpat module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

The xml.parsers.expat module is a Python interface to the Expat non-validating XML parser. The module provides a single extension type, xmlparser, that represents the current state of an XML parser. After an xmlparser object has been created, various attributes of the object can be set to handler functions. When an XML document is then fed to the parser, the handler functions are called for the character data and markup in the XML document.

This module uses the pyexpat module to provide access to the Expat parser. Direct use of the pyexpat module is deprecated.

This module provides one exception and one type object:

+exception xml.parsers.expat.ExpatError
+

The exception raised when Expat reports an error. See section ExpatError Exceptions for more information on interpreting Expat errors.

+
+exception xml.parsers.expat.error
+

Alias for ExpatError.

+
+xml.parsers.expat.XMLParserType
+

The type of the return values from the ParserCreate() function.

+

The xml.parsers.expat module contains two functions:

+xml.parsers.expat.ErrorString(errno)
+

Returns an explanatory string for a given error number errno.

+
+xml.parsers.expat.ParserCreate(encoding=None, namespace_separator=None)
+

Creates and returns a new xmlparser object. encoding, if specified, must be a string naming the encoding used by the XML data. Expat doesn’t support as many encodings as Python does, and its repertoire of encodings can’t be extended; it supports UTF-8, UTF-16, ISO-8859-1 (Latin1), and ASCII. If encoding 1 is given it will override the implicit or explicit encoding of the document.

Expat can optionally do XML namespace processing for you, enabled by providing a value for namespace_separator. The value must be a one-character string; a ValueError will be raised if the string has an illegal length (None is considered the same as omission). When namespace processing is enabled, element type names and attribute names that belong to a namespace will be expanded. The element name passed to the element handlers StartElementHandler and EndElementHandler will be the concatenation of the namespace URI, the namespace separator character, and the local part of the name. If the namespace separator is a zero byte (chr(0)) then the namespace URI and the local part will be concatenated without any separator.

For example, if namespace_separator is set to a space character (' ') and the following document is parsed:

<?xml version="1.0"?>
+<root xmlns    = "http://default-namespace.org/"
+      xmlns:py = "http://www.python.org/ns/">
+  <py:elem1 />
+  <elem2 xmlns="" />
+</root>
+

StartElementHandler will receive the following strings for each element:

http://default-namespace.org/ root
+http://www.python.org/ns/ elem1
+elem2
+

Due to limitations in the Expat library used by pyexpat, the xmlparser instance returned can only be used to parse a single XML document. Call ParserCreate for each document to provide unique parser instances.

+

See also

The Expat XML Parser
+
+

Home page of the Expat project.

XMLParser Objects

xmlparser objects have the following methods:

+xmlparser.Parse(data[, isfinal])
+

Parses the contents of the string data, calling the appropriate handler functions to process the parsed data. isfinal must be true on the final call to this method; it allows the parsing of a single file in fragments, not the submission of multiple files. data can be the empty string at any time.

+
+xmlparser.ParseFile(file)
+

Parse XML data reading from the object file. file only needs to provide the read(nbytes) method, returning the empty string when there’s no more data.

+
+xmlparser.SetBase(base)
+

Sets the base to be used for resolving relative URIs in system identifiers in declarations. Resolving relative identifiers is left to the application: this value will be passed through as the base argument to the ExternalEntityRefHandler(), NotationDeclHandler(), and UnparsedEntityDeclHandler() functions.

+
+xmlparser.GetBase()
+

Returns a string containing the base set by a previous call to SetBase(), or None if SetBase() hasn’t been called.

+
+xmlparser.GetInputContext()
+

Returns the input data that generated the current event as a string. The data is in the encoding of the entity which contains the text. When called while an event handler is not active, the return value is None.

+
+xmlparser.ExternalEntityParserCreate(context[, encoding])
+

Create a “child” parser which can be used to parse an external parsed entity referred to by content parsed by the parent parser. The context parameter should be the string passed to the ExternalEntityRefHandler() handler function, described below. The child parser is created with the ordered_attributes and specified_attributes set to the values of this parser.

+
+xmlparser.SetParamEntityParsing(flag)
+

Control parsing of parameter entities (including the external DTD subset). Possible flag values are XML_PARAM_ENTITY_PARSING_NEVER, XML_PARAM_ENTITY_PARSING_UNLESS_STANDALONE and XML_PARAM_ENTITY_PARSING_ALWAYS. Return true if setting the flag was successful.

+
+xmlparser.UseForeignDTD([flag])
+

Calling this with a true value for flag (the default) will cause Expat to call the ExternalEntityRefHandler with None for all arguments to allow an alternate DTD to be loaded. If the document does not contain a document type declaration, the ExternalEntityRefHandler will still be called, but the StartDoctypeDeclHandler and EndDoctypeDeclHandler will not be called.

Passing a false value for flag will cancel a previous call that passed a true value, but otherwise has no effect.

This method can only be called before the Parse() or ParseFile() methods are called; calling it after either of those have been called causes ExpatError to be raised with the code attribute set to errors.codes[errors.XML_ERROR_CANT_CHANGE_FEATURE_ONCE_PARSING].

+

xmlparser objects have the following attributes:

+xmlparser.buffer_size
+

The size of the buffer used when buffer_text is true. A new buffer size can be set by assigning a new integer value to this attribute. When the size is changed, the buffer will be flushed.

+
+xmlparser.buffer_text
+

Setting this to true causes the xmlparser object to buffer textual content returned by Expat to avoid multiple calls to the CharacterDataHandler() callback whenever possible. This can improve performance substantially since Expat normally breaks character data into chunks at every line ending. This attribute is false by default, and may be changed at any time.

+
+xmlparser.buffer_used
+

If buffer_text is enabled, the number of bytes stored in the buffer. These bytes represent UTF-8 encoded text. This attribute has no meaningful interpretation when buffer_text is false.

+
+xmlparser.ordered_attributes
+

Setting this attribute to a non-zero integer causes the attributes to be reported as a list rather than a dictionary. The attributes are presented in the order found in the document text. For each attribute, two list entries are presented: the attribute name and the attribute value. (Older versions of this module also used this format.) By default, this attribute is false; it may be changed at any time.

+
+xmlparser.specified_attributes
+

If set to a non-zero integer, the parser will report only those attributes which were specified in the document instance and not those which were derived from attribute declarations. Applications which set this need to be especially careful to use what additional information is available from the declarations as needed to comply with the standards for the behavior of XML processors. By default, this attribute is false; it may be changed at any time.

+

The following attributes contain values relating to the most recent error encountered by an xmlparser object, and will only have correct values once a call to Parse() or ParseFile() has raised an xml.parsers.expat.ExpatError exception.

+xmlparser.ErrorByteIndex
+

Byte index at which an error occurred.

+
+xmlparser.ErrorCode
+

Numeric code specifying the problem. This value can be passed to the ErrorString() function, or compared to one of the constants defined in the errors object.

+
+xmlparser.ErrorColumnNumber
+

Column number at which an error occurred.

+
+xmlparser.ErrorLineNumber
+

Line number at which an error occurred.

+

The following attributes contain values relating to the current parse location in an xmlparser object. During a callback reporting a parse event they indicate the location of the first of the sequence of characters that generated the event. When called outside of a callback, the position indicated will be just past the last parse event (regardless of whether there was an associated callback).

+xmlparser.CurrentByteIndex
+

Current byte index in the parser input.

+
+xmlparser.CurrentColumnNumber
+

Current column number in the parser input.

+
+xmlparser.CurrentLineNumber
+

Current line number in the parser input.

+

Here is the list of handlers that can be set. To set a handler on an xmlparser object o, use o.handlername = func. handlername must be taken from the following list, and func must be a callable object accepting the correct number of arguments. The arguments are all strings, unless otherwise stated.

+xmlparser.XmlDeclHandler(version, encoding, standalone)
+

Called when the XML declaration is parsed. The XML declaration is the (optional) declaration of the applicable version of the XML recommendation, the encoding of the document text, and an optional “standalone” declaration. version and encoding will be strings, and standalone will be 1 if the document is declared standalone, 0 if it is declared not to be standalone, or -1 if the standalone clause was omitted. This is only available with Expat version 1.95.0 or newer.

+
+xmlparser.StartDoctypeDeclHandler(doctypeName, systemId, publicId, has_internal_subset)
+

Called when Expat begins parsing the document type declaration (<!DOCTYPE +...). The doctypeName is provided exactly as presented. The systemId and publicId parameters give the system and public identifiers if specified, or None if omitted. has_internal_subset will be true if the document contains and internal document declaration subset. This requires Expat version 1.2 or newer.

+
+xmlparser.EndDoctypeDeclHandler()
+

Called when Expat is done parsing the document type declaration. This requires Expat version 1.2 or newer.

+
+xmlparser.ElementDeclHandler(name, model)
+

Called once for each element type declaration. name is the name of the element type, and model is a representation of the content model.

+
+xmlparser.AttlistDeclHandler(elname, attname, type, default, required)
+

Called for each declared attribute for an element type. If an attribute list declaration declares three attributes, this handler is called three times, once for each attribute. elname is the name of the element to which the declaration applies and attname is the name of the attribute declared. The attribute type is a string passed as type; the possible values are 'CDATA', 'ID', 'IDREF', … default gives the default value for the attribute used when the attribute is not specified by the document instance, or None if there is no default value (#IMPLIED values). If the attribute is required to be given in the document instance, required will be true. This requires Expat version 1.95.0 or newer.

+
+xmlparser.StartElementHandler(name, attributes)
+

Called for the start of every element. name is a string containing the element name, and attributes is the element attributes. If ordered_attributes is true, this is a list (see ordered_attributes for a full description). Otherwise it’s a dictionary mapping names to values.

+
+xmlparser.EndElementHandler(name)
+

Called for the end of every element.

+
+xmlparser.ProcessingInstructionHandler(target, data)
+

Called for every processing instruction.

+
+xmlparser.CharacterDataHandler(data)
+

Called for character data. This will be called for normal character data, CDATA marked content, and ignorable whitespace. Applications which must distinguish these cases can use the StartCdataSectionHandler, EndCdataSectionHandler, and ElementDeclHandler callbacks to collect the required information.

+
+xmlparser.UnparsedEntityDeclHandler(entityName, base, systemId, publicId, notationName)
+

Called for unparsed (NDATA) entity declarations. This is only present for version 1.2 of the Expat library; for more recent versions, use EntityDeclHandler instead. (The underlying function in the Expat library has been declared obsolete.)

+
+xmlparser.EntityDeclHandler(entityName, is_parameter_entity, value, base, systemId, publicId, notationName)
+

Called for all entity declarations. For parameter and internal entities, value will be a string giving the declared contents of the entity; this will be None for external entities. The notationName parameter will be None for parsed entities, and the name of the notation for unparsed entities. is_parameter_entity will be true if the entity is a parameter entity or false for general entities (most applications only need to be concerned with general entities). This is only available starting with version 1.95.0 of the Expat library.

+
+xmlparser.NotationDeclHandler(notationName, base, systemId, publicId)
+

Called for notation declarations. notationName, base, and systemId, and publicId are strings if given. If the public identifier is omitted, publicId will be None.

+
+xmlparser.StartNamespaceDeclHandler(prefix, uri)
+

Called when an element contains a namespace declaration. Namespace declarations are processed before the StartElementHandler is called for the element on which declarations are placed.

+
+xmlparser.EndNamespaceDeclHandler(prefix)
+

Called when the closing tag is reached for an element that contained a namespace declaration. This is called once for each namespace declaration on the element in the reverse of the order for which the StartNamespaceDeclHandler was called to indicate the start of each namespace declaration’s scope. Calls to this handler are made after the corresponding EndElementHandler for the end of the element.

+
+xmlparser.CommentHandler(data)
+

Called for comments. data is the text of the comment, excluding the leading '<!--' and trailing '-->'.

+
+xmlparser.StartCdataSectionHandler()
+

Called at the start of a CDATA section. This and EndCdataSectionHandler are needed to be able to identify the syntactical start and end for CDATA sections.

+
+xmlparser.EndCdataSectionHandler()
+

Called at the end of a CDATA section.

+
+xmlparser.DefaultHandler(data)
+

Called for any characters in the XML document for which no applicable handler has been specified. This means characters that are part of a construct which could be reported, but for which no handler has been supplied.

+
+xmlparser.DefaultHandlerExpand(data)
+

This is the same as the DefaultHandler(), but doesn’t inhibit expansion of internal entities. The entity reference will not be passed to the default handler.

+
+xmlparser.NotStandaloneHandler()
+

Called if the XML document hasn’t been declared as being a standalone document. This happens when there is an external subset or a reference to a parameter entity, but the XML declaration does not set standalone to yes in an XML declaration. If this handler returns 0, then the parser will raise an XML_ERROR_NOT_STANDALONE error. If this handler is not set, no exception is raised by the parser for this condition.

+
+xmlparser.ExternalEntityRefHandler(context, base, systemId, publicId)
+

Called for references to external entities. base is the current base, as set by a previous call to SetBase(). The public and system identifiers, systemId and publicId, are strings if given; if the public identifier is not given, publicId will be None. The context value is opaque and should only be used as described below.

For external entities to be parsed, this handler must be implemented. It is responsible for creating the sub-parser using ExternalEntityParserCreate(context), initializing it with the appropriate callbacks, and parsing the entity. This handler should return an integer; if it returns 0, the parser will raise an XML_ERROR_EXTERNAL_ENTITY_HANDLING error, otherwise parsing will continue.

If this handler is not provided, external entities are reported by the DefaultHandler callback, if provided.

+

ExpatError Exceptions

ExpatError exceptions have a number of interesting attributes:

+ExpatError.code
+

Expat’s internal error number for the specific error. The errors.messages dictionary maps these error numbers to Expat’s error messages. For example:

from xml.parsers.expat import ParserCreate, ExpatError, errors
+
+p = ParserCreate()
+try:
+    p.Parse(some_xml_document)
+except ExpatError as err:
+    print("Error:", errors.messages[err.code])
+

The errors module also provides error message constants and a dictionary codes mapping these messages back to the error codes, see below.

+
+ExpatError.lineno
+

Line number on which the error was detected. The first line is numbered 1.

+
+ExpatError.offset
+

Character offset into the line where the error occurred. The first column is numbered 0.

+

Example

The following program defines three handlers that just print out their arguments.

import xml.parsers.expat
+
+# 3 handler functions
+def start_element(name, attrs):
+    print('Start element:', name, attrs)
+def end_element(name):
+    print('End element:', name)
+def char_data(data):
+    print('Character data:', repr(data))
+
+p = xml.parsers.expat.ParserCreate()
+
+p.StartElementHandler = start_element
+p.EndElementHandler = end_element
+p.CharacterDataHandler = char_data
+
+p.Parse("""<?xml version="1.0"?>
+<parent id="top"><child1 name="paul">Text goes here</child1>
+<child2 name="fred">More text</child2>
+</parent>""", 1)
+

The output from this program is:

Start element: parent {'id': 'top'}
+Start element: child1 {'name': 'paul'}
+Character data: 'Text goes here'
+End element: child1
+Character data: '\n'
+Start element: child2 {'name': 'fred'}
+Character data: 'More text'
+End element: child2
+Character data: '\n'
+End element: parent
+

Content Model Descriptions

Content models are described using nested tuples. Each tuple contains four values: the type, the quantifier, the name, and a tuple of children. Children are simply additional content model descriptions.

The values of the first two fields are constants defined in the xml.parsers.expat.model module. These constants can be collected in two groups: the model type group and the quantifier group.

The constants in the model type group are:

xml.parsers.expat.model.XML_CTYPE_ANY +
+

The element named by the model name was declared to have a content model of ANY.

+
xml.parsers.expat.model.XML_CTYPE_CHOICE +
+

The named element allows a choice from a number of options; this is used for content models such as (A | B | C).

+
xml.parsers.expat.model.XML_CTYPE_EMPTY +
+

Elements which are declared to be EMPTY have this model type.

+
xml.parsers.expat.model.XML_CTYPE_MIXED +
+
xml.parsers.expat.model.XML_CTYPE_NAME +
+
xml.parsers.expat.model.XML_CTYPE_SEQ +
+

Models which represent a series of models which follow one after the other are indicated with this model type. This is used for models such as (A, B, C).

+

The constants in the quantifier group are:

xml.parsers.expat.model.XML_CQUANT_NONE +
+

No modifier is given, so it can appear exactly once, as for A.

+
xml.parsers.expat.model.XML_CQUANT_OPT +
+

The model is optional: it can appear once or not at all, as for A?.

+
xml.parsers.expat.model.XML_CQUANT_PLUS +
+

The model must occur one or more times (like A+).

+
xml.parsers.expat.model.XML_CQUANT_REP +
+

The model must occur zero or more times, as for A*.

+

Expat error constants

The following constants are provided in the xml.parsers.expat.errors module. These constants are useful in interpreting some of the attributes of the ExpatError exception objects raised when an error has occurred. Since for backwards compatibility reasons, the constants’ value is the error message and not the numeric error code, you do this by comparing its code attribute with errors.codes[errors.XML_ERROR_CONSTANT_NAME].

The errors module has the following attributes:

+xml.parsers.expat.errors.codes
+

A dictionary mapping string descriptions to their error codes.

New in version 3.2.

+
+xml.parsers.expat.errors.messages
+

A dictionary mapping numeric error codes to their string descriptions.

New in version 3.2.

+
+xml.parsers.expat.errors.XML_ERROR_ASYNC_ENTITY
+
+xml.parsers.expat.errors.XML_ERROR_ATTRIBUTE_EXTERNAL_ENTITY_REF
+

An entity reference in an attribute value referred to an external entity instead of an internal entity.

+
+xml.parsers.expat.errors.XML_ERROR_BAD_CHAR_REF
+

A character reference referred to a character which is illegal in XML (for example, character 0, or ‘&#0;’).

+
+xml.parsers.expat.errors.XML_ERROR_BINARY_ENTITY_REF
+

An entity reference referred to an entity which was declared with a notation, so cannot be parsed.

+
+xml.parsers.expat.errors.XML_ERROR_DUPLICATE_ATTRIBUTE
+

An attribute was used more than once in a start tag.

+
+xml.parsers.expat.errors.XML_ERROR_INCORRECT_ENCODING
+
+xml.parsers.expat.errors.XML_ERROR_INVALID_TOKEN
+

Raised when an input byte could not properly be assigned to a character; for example, a NUL byte (value 0) in a UTF-8 input stream.

+
+xml.parsers.expat.errors.XML_ERROR_JUNK_AFTER_DOC_ELEMENT
+

Something other than whitespace occurred after the document element.

+
+xml.parsers.expat.errors.XML_ERROR_MISPLACED_XML_PI
+

An XML declaration was found somewhere other than the start of the input data.

+
+xml.parsers.expat.errors.XML_ERROR_NO_ELEMENTS
+

The document contains no elements (XML requires all documents to contain exactly one top-level element)..

+
+xml.parsers.expat.errors.XML_ERROR_NO_MEMORY
+

Expat was not able to allocate memory internally.

+
+xml.parsers.expat.errors.XML_ERROR_PARAM_ENTITY_REF
+

A parameter entity reference was found where it was not allowed.

+
+xml.parsers.expat.errors.XML_ERROR_PARTIAL_CHAR
+

An incomplete character was found in the input.

+
+xml.parsers.expat.errors.XML_ERROR_RECURSIVE_ENTITY_REF
+

An entity reference contained another reference to the same entity; possibly via a different name, and possibly indirectly.

+
+xml.parsers.expat.errors.XML_ERROR_SYNTAX
+

Some unspecified syntax error was encountered.

+
+xml.parsers.expat.errors.XML_ERROR_TAG_MISMATCH
+

An end tag did not match the innermost open start tag.

+
+xml.parsers.expat.errors.XML_ERROR_UNCLOSED_TOKEN
+

Some token (such as a start tag) was not closed before the end of the stream or the next token was encountered.

+
+xml.parsers.expat.errors.XML_ERROR_UNDEFINED_ENTITY
+

A reference was made to an entity which was not defined.

+
+xml.parsers.expat.errors.XML_ERROR_UNKNOWN_ENCODING
+

The document encoding is not supported by Expat.

+
+xml.parsers.expat.errors.XML_ERROR_UNCLOSED_CDATA_SECTION
+

A CDATA marked section was not closed.

+
+xml.parsers.expat.errors.XML_ERROR_EXTERNAL_ENTITY_HANDLING
+
+xml.parsers.expat.errors.XML_ERROR_NOT_STANDALONE
+

The parser determined that the document was not “standalone” though it declared itself to be in the XML declaration, and the NotStandaloneHandler was set and returned 0.

+
+xml.parsers.expat.errors.XML_ERROR_UNEXPECTED_STATE
+
+xml.parsers.expat.errors.XML_ERROR_ENTITY_DECLARED_IN_PE
+
+xml.parsers.expat.errors.XML_ERROR_FEATURE_REQUIRES_XML_DTD
+

An operation was requested that requires DTD support to be compiled in, but Expat was configured without DTD support. This should never be reported by a standard build of the xml.parsers.expat module.

+
+xml.parsers.expat.errors.XML_ERROR_CANT_CHANGE_FEATURE_ONCE_PARSING
+

A behavioral change was requested after parsing started that can only be changed before parsing has started. This is (currently) only raised by UseForeignDTD().

+
+xml.parsers.expat.errors.XML_ERROR_UNBOUND_PREFIX
+

An undeclared prefix was found when namespace processing was enabled.

+
+xml.parsers.expat.errors.XML_ERROR_UNDECLARING_PREFIX
+

The document attempted to remove the namespace declaration associated with a prefix.

+
+xml.parsers.expat.errors.XML_ERROR_INCOMPLETE_PE
+

A parameter entity contained incomplete markup.

+
+xml.parsers.expat.errors.XML_ERROR_XML_DECL
+

The document contained no document element at all.

+
+xml.parsers.expat.errors.XML_ERROR_TEXT_DECL
+

There was an error parsing a text declaration in an external entity.

+
+xml.parsers.expat.errors.XML_ERROR_PUBLICID
+

Characters were found in the public id that are not allowed.

+
+xml.parsers.expat.errors.XML_ERROR_SUSPENDED
+

The requested operation was made on a suspended parser, but isn’t allowed. This includes attempts to provide additional input or to stop the parser.

+
+xml.parsers.expat.errors.XML_ERROR_NOT_SUSPENDED
+

An attempt to resume the parser was made when the parser had not been suspended.

+
+xml.parsers.expat.errors.XML_ERROR_ABORTED
+

This should not be reported to Python applications.

+
+xml.parsers.expat.errors.XML_ERROR_FINISHED
+

The requested operation was made on a parser which was finished parsing input, but isn’t allowed. This includes attempts to provide additional input or to stop the parser.

+
+xml.parsers.expat.errors.XML_ERROR_SUSPEND_PE
+
+xml.parsers.expat.errors.XML_ERROR_RESERVED_PREFIX_XML
+

An attempt was made to undeclare reserved namespace prefix xml or to bind it to another namespace URI.

+
+xml.parsers.expat.errors.XML_ERROR_RESERVED_PREFIX_XMLNS
+

An attempt was made to declare or undeclare reserved namespace prefix xmlns.

+
+xml.parsers.expat.errors.XML_ERROR_RESERVED_NAMESPACE_URI
+

An attempt was made to bind the URI of one the reserved namespace prefixes xml and xmlns to another namespace prefix.

+
+xml.parsers.expat.errors.XML_ERROR_INVALID_ARGUMENT
+

This should not be reported to Python applications.

+
+xml.parsers.expat.errors.XML_ERROR_NO_BUFFER
+

This should not be reported to Python applications.

+
+xml.parsers.expat.errors.XML_ERROR_AMPLIFICATION_LIMIT_BREACH
+

The limit on input amplification factor (from DTD and entities) has been breached.

+

Footnotes

+1
+

The encoding string included in XML output should conform to the appropriate standards. For example, “UTF-8” is valid, but “UTF8” is not. See https://www.w3.org/TR/2006/REC-xml11-20060816/#NT-EncodingDecl and https://www.iana.org/assignments/character-sets/character-sets.xhtml.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/pyexpat.html +

+
diff --git a/devdocs/python~3.12/library%2Fpython.html b/devdocs/python~3.12/library%2Fpython.html new file mode 100644 index 00000000..08fdf90a --- /dev/null +++ b/devdocs/python~3.12/library%2Fpython.html @@ -0,0 +1,28 @@ +

Python Runtime Services

The modules described in this chapter provide a wide range of services related to the Python interpreter and its interaction with its environment. Here’s an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/python.html +

+
diff --git a/devdocs/python~3.12/library%2Fqueue.html b/devdocs/python~3.12/library%2Fqueue.html new file mode 100644 index 00000000..8b6bff15 --- /dev/null +++ b/devdocs/python~3.12/library%2Fqueue.html @@ -0,0 +1,102 @@ +

queue — A synchronized queue class

Source code: Lib/queue.py

The queue module implements multi-producer, multi-consumer queues. It is especially useful in threaded programming when information must be exchanged safely between multiple threads. The Queue class in this module implements all the required locking semantics.

The module implements three types of queue, which differ only in the order in which the entries are retrieved. In a FIFO queue, the first tasks added are the first retrieved. In a LIFO queue, the most recently added entry is the first retrieved (operating like a stack). With a priority queue, the entries are kept sorted (using the heapq module) and the lowest valued entry is retrieved first.

Internally, those three types of queues use locks to temporarily block competing threads; however, they are not designed to handle reentrancy within a thread.

In addition, the module implements a “simple” FIFO queue type, SimpleQueue, whose specific implementation provides additional guarantees in exchange for the smaller functionality.

The queue module defines the following classes and exceptions:

+class queue.Queue(maxsize=0)
+

Constructor for a FIFO queue. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed. If maxsize is less than or equal to zero, the queue size is infinite.

+
+class queue.LifoQueue(maxsize=0)
+

Constructor for a LIFO queue. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed. If maxsize is less than or equal to zero, the queue size is infinite.

+
+class queue.PriorityQueue(maxsize=0)
+

Constructor for a priority queue. maxsize is an integer that sets the upperbound limit on the number of items that can be placed in the queue. Insertion will block once this size has been reached, until queue items are consumed. If maxsize is less than or equal to zero, the queue size is infinite.

The lowest valued entries are retrieved first (the lowest valued entry is the one that would be returned by min(entries)). A typical pattern for entries is a tuple in the form: (priority_number, data).

If the data elements are not comparable, the data can be wrapped in a class that ignores the data item and only compares the priority number:

from dataclasses import dataclass, field
+from typing import Any
+
+@dataclass(order=True)
+class PrioritizedItem:
+    priority: int
+    item: Any=field(compare=False)
+
+
+class queue.SimpleQueue
+

Constructor for an unbounded FIFO queue. Simple queues lack advanced functionality such as task tracking.

New in version 3.7.

+
+exception queue.Empty
+

Exception raised when non-blocking get() (or get_nowait()) is called on a Queue object which is empty.

+
+exception queue.Full
+

Exception raised when non-blocking put() (or put_nowait()) is called on a Queue object which is full.

+

Queue Objects

Queue objects (Queue, LifoQueue, or PriorityQueue) provide the public methods described below.

+Queue.qsize()
+

Return the approximate size of the queue. Note, qsize() > 0 doesn’t guarantee that a subsequent get() will not block, nor will qsize() < maxsize guarantee that put() will not block.

+
+Queue.empty()
+

Return True if the queue is empty, False otherwise. If empty() returns True it doesn’t guarantee that a subsequent call to put() will not block. Similarly, if empty() returns False it doesn’t guarantee that a subsequent call to get() will not block.

+
+Queue.full()
+

Return True if the queue is full, False otherwise. If full() returns True it doesn’t guarantee that a subsequent call to get() will not block. Similarly, if full() returns False it doesn’t guarantee that a subsequent call to put() will not block.

+
+Queue.put(item, block=True, timeout=None)
+

Put item into the queue. If optional args block is true and timeout is None (the default), block if necessary until a free slot is available. If timeout is a positive number, it blocks at most timeout seconds and raises the Full exception if no free slot was available within that time. Otherwise (block is false), put an item on the queue if a free slot is immediately available, else raise the Full exception (timeout is ignored in that case).

+
+Queue.put_nowait(item)
+

Equivalent to put(item, block=False).

+
+Queue.get(block=True, timeout=None)
+

Remove and return an item from the queue. If optional args block is true and timeout is None (the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises the Empty exception if no item was available within that time. Otherwise (block is false), return an item if one is immediately available, else raise the Empty exception (timeout is ignored in that case).

Prior to 3.0 on POSIX systems, and for all versions on Windows, if block is true and timeout is None, this operation goes into an uninterruptible wait on an underlying lock. This means that no exceptions can occur, and in particular a SIGINT will not trigger a KeyboardInterrupt.

+
+Queue.get_nowait()
+

Equivalent to get(False).

+

Two methods are offered to support tracking whether enqueued tasks have been fully processed by daemon consumer threads.

+Queue.task_done()
+

Indicate that a formerly enqueued task is complete. Used by queue consumer threads. For each get() used to fetch a task, a subsequent call to task_done() tells the queue that the processing on the task is complete.

If a join() is currently blocking, it will resume when all items have been processed (meaning that a task_done() call was received for every item that had been put() into the queue).

Raises a ValueError if called more times than there were items placed in the queue.

+
+Queue.join()
+

Blocks until all items in the queue have been gotten and processed.

The count of unfinished tasks goes up whenever an item is added to the queue. The count goes down whenever a consumer thread calls task_done() to indicate that the item was retrieved and all work on it is complete. When the count of unfinished tasks drops to zero, join() unblocks.

+

Example of how to wait for enqueued tasks to be completed:

import threading
+import queue
+
+q = queue.Queue()
+
+def worker():
+    while True:
+        item = q.get()
+        print(f'Working on {item}')
+        print(f'Finished {item}')
+        q.task_done()
+
+# Turn-on the worker thread.
+threading.Thread(target=worker, daemon=True).start()
+
+# Send thirty task requests to the worker.
+for item in range(30):
+    q.put(item)
+
+# Block until all tasks are done.
+q.join()
+print('All work completed')
+

SimpleQueue Objects

SimpleQueue objects provide the public methods described below.

+SimpleQueue.qsize()
+

Return the approximate size of the queue. Note, qsize() > 0 doesn’t guarantee that a subsequent get() will not block.

+
+SimpleQueue.empty()
+

Return True if the queue is empty, False otherwise. If empty() returns False it doesn’t guarantee that a subsequent call to get() will not block.

+
+SimpleQueue.put(item, block=True, timeout=None)
+

Put item into the queue. The method never blocks and always succeeds (except for potential low-level errors such as failure to allocate memory). The optional args block and timeout are ignored and only provided for compatibility with Queue.put().

CPython implementation detail: This method has a C implementation which is reentrant. That is, a put() or get() call can be interrupted by another put() call in the same thread without deadlocking or corrupting internal state inside the queue. This makes it appropriate for use in destructors such as __del__ methods or weakref callbacks.

+
+SimpleQueue.put_nowait(item)
+

Equivalent to put(item, block=False), provided for compatibility with Queue.put_nowait().

+
+SimpleQueue.get(block=True, timeout=None)
+

Remove and return an item from the queue. If optional args block is true and timeout is None (the default), block if necessary until an item is available. If timeout is a positive number, it blocks at most timeout seconds and raises the Empty exception if no item was available within that time. Otherwise (block is false), return an item if one is immediately available, else raise the Empty exception (timeout is ignored in that case).

+
+SimpleQueue.get_nowait()
+

Equivalent to get(False).

+

See also

+Class multiprocessing.Queue +
+
+

A queue class for use in a multi-processing (rather than multi-threading) context.

collections.deque is an alternative implementation of unbounded queues with fast atomic append() and popleft() operations that do not require locking and also support indexing.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/queue.html +

+
diff --git a/devdocs/python~3.12/library%2Fquopri.html b/devdocs/python~3.12/library%2Fquopri.html new file mode 100644 index 00000000..71f52854 --- /dev/null +++ b/devdocs/python~3.12/library%2Fquopri.html @@ -0,0 +1,22 @@ +

quopri — Encode and decode MIME quoted-printable data

Source code: Lib/quopri.py

This module performs quoted-printable transport encoding and decoding, as defined in RFC 1521: “MIME (Multipurpose Internet Mail Extensions) Part One: Mechanisms for Specifying and Describing the Format of Internet Message Bodies”. The quoted-printable encoding is designed for data where there are relatively few nonprintable characters; the base64 encoding scheme available via the base64 module is more compact if there are many such characters, as when sending a graphics file.

+quopri.decode(input, output, header=False)
+

Decode the contents of the input file and write the resulting decoded binary data to the output file. input and output must be binary file objects. If the optional argument header is present and true, underscore will be decoded as space. This is used to decode “Q”-encoded headers as described in RFC 1522: “MIME (Multipurpose Internet Mail Extensions) Part Two: Message Header Extensions for Non-ASCII Text”.

+
+quopri.encode(input, output, quotetabs, header=False)
+

Encode the contents of the input file and write the resulting quoted-printable data to the output file. input and output must be binary file objects. quotetabs, a non-optional flag which controls whether to encode embedded spaces and tabs; when true it encodes such embedded whitespace, and when false it leaves them unencoded. Note that spaces and tabs appearing at the end of lines are always encoded, as per RFC 1521. header is a flag which controls if spaces are encoded as underscores as per RFC 1522.

+
+quopri.decodestring(s, header=False)
+

Like decode(), except that it accepts a source bytes and returns the corresponding decoded bytes.

+
+quopri.encodestring(s, quotetabs=False, header=False)
+

Like encode(), except that it accepts a source bytes and returns the corresponding encoded bytes. By default, it sends a False value to quotetabs parameter of the encode() function.

+

See also

+Module base64 +
+
+

Encode and decode MIME base64 data

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/quopri.html +

+
diff --git a/devdocs/python~3.12/library%2Frandom.html b/devdocs/python~3.12/library%2Frandom.html new file mode 100644 index 00000000..d2c479eb --- /dev/null +++ b/devdocs/python~3.12/library%2Frandom.html @@ -0,0 +1,248 @@ +

random — Generate pseudo-random numbers

Source code: Lib/random.py

This module implements pseudo-random number generators for various distributions.

For integers, there is uniform selection from a range. For sequences, there is uniform selection of a random element, a function to generate a random permutation of a list in-place, and a function for random sampling without replacement.

On the real line, there are functions to compute uniform, normal (Gaussian), lognormal, negative exponential, gamma, and beta distributions. For generating distributions of angles, the von Mises distribution is available.

Almost all module functions depend on the basic function random(), which generates a random float uniformly in the half-open range 0.0 <= X < 1.0. Python uses the Mersenne Twister as the core generator. It produces 53-bit precision floats and has a period of 2**19937-1. The underlying implementation in C is both fast and threadsafe. The Mersenne Twister is one of the most extensively tested random number generators in existence. However, being completely deterministic, it is not suitable for all purposes, and is completely unsuitable for cryptographic purposes.

The functions supplied by this module are actually bound methods of a hidden instance of the random.Random class. You can instantiate your own instances of Random to get generators that don’t share state.

Class Random can also be subclassed if you want to use a different basic generator of your own devising: see the documentation on that class for more details.

The random module also provides the SystemRandom class which uses the system function os.urandom() to generate random numbers from sources provided by the operating system.

Warning

The pseudo-random generators of this module should not be used for security purposes. For security or cryptographic uses, see the secrets module.

See also

M. Matsumoto and T. Nishimura, “Mersenne Twister: A 623-dimensionally equidistributed uniform pseudorandom number generator”, ACM Transactions on Modeling and Computer Simulation Vol. 8, No. 1, January pp.3–30 1998.

Complementary-Multiply-with-Carry recipe for a compatible alternative random number generator with a long period and comparatively simple update operations.

Bookkeeping functions

+random.seed(a=None, version=2)
+

Initialize the random number generator.

If a is omitted or None, the current system time is used. If randomness sources are provided by the operating system, they are used instead of the system time (see the os.urandom() function for details on availability).

If a is an int, it is used directly.

With version 2 (the default), a str, bytes, or bytearray object gets converted to an int and all of its bits are used.

With version 1 (provided for reproducing random sequences from older versions of Python), the algorithm for str and bytes generates a narrower range of seeds.

Changed in version 3.2: Moved to the version 2 scheme which uses all of the bits in a string seed.

Changed in version 3.11: The seed must be one of the following types: None, int, float, str, bytes, or bytearray.

+
+random.getstate()
+

Return an object capturing the current internal state of the generator. This object can be passed to setstate() to restore the state.

+
+random.setstate(state)
+

state should have been obtained from a previous call to getstate(), and setstate() restores the internal state of the generator to what it was at the time getstate() was called.

+

Functions for bytes

+random.randbytes(n)
+

Generate n random bytes.

This method should not be used for generating security tokens. Use secrets.token_bytes() instead.

New in version 3.9.

+

Functions for integers

+random.randrange(stop)
random.randrange(start, stop[, step]) +
+

Return a randomly selected element from range(start, stop, step).

This is roughly equivalent to choice(range(start, stop, step)) but supports arbitrarily large ranges and is optimized for common cases.

The positional argument pattern matches the range() function.

Keyword arguments should not be used because they can be interpreted in unexpected ways. For example randrange(start=100) is interpreted as randrange(0, 100, 1).

Changed in version 3.2: randrange() is more sophisticated about producing equally distributed values. Formerly it used a style like int(random()*n) which could produce slightly uneven distributions.

Changed in version 3.12: Automatic conversion of non-integer types is no longer supported. Calls such as randrange(10.0) and randrange(Fraction(10, 1)) now raise a TypeError.

+
+random.randint(a, b)
+

Return a random integer N such that a <= N <= b. Alias for randrange(a, b+1).

+
+random.getrandbits(k)
+

Returns a non-negative Python integer with k random bits. This method is supplied with the Mersenne Twister generator and some other generators may also provide it as an optional part of the API. When available, getrandbits() enables randrange() to handle arbitrarily large ranges.

Changed in version 3.9: This method now accepts zero for k.

+

Functions for sequences

+random.choice(seq)
+

Return a random element from the non-empty sequence seq. If seq is empty, raises IndexError.

+
+random.choices(population, weights=None, *, cum_weights=None, k=1)
+

Return a k sized list of elements chosen from the population with replacement. If the population is empty, raises IndexError.

If a weights sequence is specified, selections are made according to the relative weights. Alternatively, if a cum_weights sequence is given, the selections are made according to the cumulative weights (perhaps computed using itertools.accumulate()). For example, the relative weights [10, 5, 30, 5] are equivalent to the cumulative weights [10, 15, 45, 50]. Internally, the relative weights are converted to cumulative weights before making selections, so supplying the cumulative weights saves work.

If neither weights nor cum_weights are specified, selections are made with equal probability. If a weights sequence is supplied, it must be the same length as the population sequence. It is a TypeError to specify both weights and cum_weights.

The weights or cum_weights can use any numeric type that interoperates with the float values returned by random() (that includes integers, floats, and fractions but excludes decimals). Weights are assumed to be non-negative and finite. A ValueError is raised if all weights are zero.

For a given seed, the choices() function with equal weighting typically produces a different sequence than repeated calls to choice(). The algorithm used by choices() uses floating point arithmetic for internal consistency and speed. The algorithm used by choice() defaults to integer arithmetic with repeated selections to avoid small biases from round-off error.

New in version 3.6.

Changed in version 3.9: Raises a ValueError if all weights are zero.

+
+random.shuffle(x)
+

Shuffle the sequence x in place.

To shuffle an immutable sequence and return a new shuffled list, use sample(x, k=len(x)) instead.

Note that even for small len(x), the total number of permutations of x can quickly grow larger than the period of most random number generators. This implies that most permutations of a long sequence can never be generated. For example, a sequence of length 2080 is the largest that can fit within the period of the Mersenne Twister random number generator.

Changed in version 3.11: Removed the optional parameter random.

+
+random.sample(population, k, *, counts=None)
+

Return a k length list of unique elements chosen from the population sequence. Used for random sampling without replacement.

Returns a new list containing elements from the population while leaving the original population unchanged. The resulting list is in selection order so that all sub-slices will also be valid random samples. This allows raffle winners (the sample) to be partitioned into grand prize and second place winners (the subslices).

Members of the population need not be hashable or unique. If the population contains repeats, then each occurrence is a possible selection in the sample.

Repeated elements can be specified one at a time or with the optional keyword-only counts parameter. For example, sample(['red', 'blue'], +counts=[4, 2], k=5) is equivalent to sample(['red', 'red', 'red', 'red', +'blue', 'blue'], k=5).

To choose a sample from a range of integers, use a range() object as an argument. This is especially fast and space efficient for sampling from a large population: sample(range(10000000), k=60).

If the sample size is larger than the population size, a ValueError is raised.

Changed in version 3.9: Added the counts parameter.

Changed in version 3.11: The population must be a sequence. Automatic conversion of sets to lists is no longer supported.

+

Discrete distributions

The following function generates a discrete distribution.

+random.binomialvariate(n=1, p=0.5)
+

Binomial distribution. Return the number of successes for n independent trials with the probability of success in each trial being p:

Mathematically equivalent to:

sum(random() < p for i in range(n))
+

The number of trials n should be a non-negative integer. The probability of success p should be between 0.0 <= p <= 1.0. The result is an integer in the range 0 <= X <= n.

New in version 3.12.

+

Real-valued distributions

The following functions generate specific real-valued distributions. Function parameters are named after the corresponding variables in the distribution’s equation, as used in common mathematical practice; most of these equations can be found in any statistics text.

+random.random()
+

Return the next random floating point number in the range 0.0 <= X < 1.0

+
+random.uniform(a, b)
+

Return a random floating point number N such that a <= N <= b for a <= b and b <= N <= a for b < a.

The end-point value b may or may not be included in the range depending on floating-point rounding in the equation a + (b-a) * random().

+
+random.triangular(low, high, mode)
+

Return a random floating point number N such that low <= N <= high and with the specified mode between those bounds. The low and high bounds default to zero and one. The mode argument defaults to the midpoint between the bounds, giving a symmetric distribution.

+
+random.betavariate(alpha, beta)
+

Beta distribution. Conditions on the parameters are alpha > 0 and beta > 0. Returned values range between 0 and 1.

+
+random.expovariate(lambd=1.0)
+

Exponential distribution. lambd is 1.0 divided by the desired mean. It should be nonzero. (The parameter would be called “lambda”, but that is a reserved word in Python.) Returned values range from 0 to positive infinity if lambd is positive, and from negative infinity to 0 if lambd is negative.

Changed in version 3.12: Added the default value for lambd.

+
+random.gammavariate(alpha, beta)
+

Gamma distribution. (Not the gamma function!) The shape and scale parameters, alpha and beta, must have positive values. (Calling conventions vary and some sources define ‘beta’ as the inverse of the scale).

The probability distribution function is:

          x ** (alpha - 1) * math.exp(-x / beta)
+pdf(x) =  --------------------------------------
+            math.gamma(alpha) * beta ** alpha
+
+
+random.gauss(mu=0.0, sigma=1.0)
+

Normal distribution, also called the Gaussian distribution. mu is the mean, and sigma is the standard deviation. This is slightly faster than the normalvariate() function defined below.

Multithreading note: When two threads call this function simultaneously, it is possible that they will receive the same return value. This can be avoided in three ways. 1) Have each thread use a different instance of the random number generator. 2) Put locks around all calls. 3) Use the slower, but thread-safe normalvariate() function instead.

Changed in version 3.11: mu and sigma now have default arguments.

+
+random.lognormvariate(mu, sigma)
+

Log normal distribution. If you take the natural logarithm of this distribution, you’ll get a normal distribution with mean mu and standard deviation sigma. mu can have any value, and sigma must be greater than zero.

+
+random.normalvariate(mu=0.0, sigma=1.0)
+

Normal distribution. mu is the mean, and sigma is the standard deviation.

Changed in version 3.11: mu and sigma now have default arguments.

+
+random.vonmisesvariate(mu, kappa)
+

mu is the mean angle, expressed in radians between 0 and 2*pi, and kappa is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to 2*pi.

+
+random.paretovariate(alpha)
+

Pareto distribution. alpha is the shape parameter.

+
+random.weibullvariate(alpha, beta)
+

Weibull distribution. alpha is the scale parameter and beta is the shape parameter.

+

Alternative Generator

+class random.Random([seed])
+

Class that implements the default pseudo-random number generator used by the random module.

Changed in version 3.11: Formerly the seed could be any hashable object. Now it is limited to: None, int, float, str, bytes, or bytearray.

Subclasses of Random should override the following methods if they wish to make use of a different basic generator:

+seed(a=None, version=2)
+

Override this method in subclasses to customise the seed() behaviour of Random instances.

+
+getstate()
+

Override this method in subclasses to customise the getstate() behaviour of Random instances.

+
+setstate(state)
+

Override this method in subclasses to customise the setstate() behaviour of Random instances.

+
+random()
+

Override this method in subclasses to customise the random() behaviour of Random instances.

+

Optionally, a custom generator subclass can also supply the following method:

+getrandbits(k)
+

Override this method in subclasses to customise the getrandbits() behaviour of Random instances.

+
+
+class random.SystemRandom([seed])
+

Class that uses the os.urandom() function for generating random numbers from sources provided by the operating system. Not available on all systems. Does not rely on software state, and sequences are not reproducible. Accordingly, the seed() method has no effect and is ignored. The getstate() and setstate() methods raise NotImplementedError if called.

+

Notes on Reproducibility

Sometimes it is useful to be able to reproduce the sequences given by a pseudo-random number generator. By reusing a seed value, the same sequence should be reproducible from run to run as long as multiple threads are not running.

Most of the random module’s algorithms and seeding functions are subject to change across Python versions, but two aspects are guaranteed not to change:

  • If a new seeding method is added, then a backward compatible seeder will be offered.
  • The generator’s random() method will continue to produce the same sequence when the compatible seeder is given the same seed.

Examples

Basic examples:

>>> random()                          # Random float:  0.0 <= x < 1.0
+0.37444887175646646
+
+>>> uniform(2.5, 10.0)                # Random float:  2.5 <= x <= 10.0
+3.1800146073117523
+
+>>> expovariate(1 / 5)                # Interval between arrivals averaging 5 seconds
+5.148957571865031
+
+>>> randrange(10)                     # Integer from 0 to 9 inclusive
+7
+
+>>> randrange(0, 101, 2)              # Even integer from 0 to 100 inclusive
+26
+
+>>> choice(['win', 'lose', 'draw'])   # Single random element from a sequence
+'draw'
+
+>>> deck = 'ace two three four'.split()
+>>> shuffle(deck)                     # Shuffle a list
+>>> deck
+['four', 'two', 'ace', 'three']
+
+>>> sample([10, 20, 30, 40, 50], k=4) # Four samples without replacement
+[40, 10, 50, 30]
+

Simulations:

>>> # Six roulette wheel spins (weighted sampling with replacement)
+>>> choices(['red', 'black', 'green'], [18, 18, 2], k=6)
+['red', 'green', 'black', 'black', 'red', 'black']
+
+>>> # Deal 20 cards without replacement from a deck
+>>> # of 52 playing cards, and determine the proportion of cards
+>>> # with a ten-value:  ten, jack, queen, or king.
+>>> deal = sample(['tens', 'low cards'], counts=[16, 36], k=20)
+>>> deal.count('tens') / 20
+0.15
+
+>>> # Estimate the probability of getting 5 or more heads from 7 spins
+>>> # of a biased coin that settles on heads 60% of the time.
+>>> sum(binomialvariate(n=7, p=0.6) >= 5 for i in range(10_000)) / 10_000
+0.4169
+
+>>> # Probability of the median of 5 samples being in middle two quartiles
+>>> def trial():
+...     return 2_500 <= sorted(choices(range(10_000), k=5))[2] < 7_500
+...
+>>> sum(trial() for i in range(10_000)) / 10_000
+0.7958
+

Example of statistical bootstrapping using resampling with replacement to estimate a confidence interval for the mean of a sample:

# https://www.thoughtco.com/example-of-bootstrapping-3126155
+from statistics import fmean as mean
+from random import choices
+
+data = [41, 50, 29, 37, 81, 30, 73, 63, 20, 35, 68, 22, 60, 31, 95]
+means = sorted(mean(choices(data, k=len(data))) for i in range(100))
+print(f'The sample mean of {mean(data):.1f} has a 90% confidence '
+      f'interval from {means[5]:.1f} to {means[94]:.1f}')
+

Example of a resampling permutation test to determine the statistical significance or p-value of an observed difference between the effects of a drug versus a placebo:

# Example from "Statistics is Easy" by Dennis Shasha and Manda Wilson
+from statistics import fmean as mean
+from random import shuffle
+
+drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
+placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
+observed_diff = mean(drug) - mean(placebo)
+
+n = 10_000
+count = 0
+combined = drug + placebo
+for i in range(n):
+    shuffle(combined)
+    new_diff = mean(combined[:len(drug)]) - mean(combined[len(drug):])
+    count += (new_diff >= observed_diff)
+
+print(f'{n} label reshufflings produced only {count} instances with a difference')
+print(f'at least as extreme as the observed difference of {observed_diff:.1f}.')
+print(f'The one-sided p-value of {count / n:.4f} leads us to reject the null')
+print(f'hypothesis that there is no difference between the drug and the placebo.')
+

Simulation of arrival times and service deliveries for a multiserver queue:

from heapq import heapify, heapreplace
+from random import expovariate, gauss
+from statistics import mean, quantiles
+
+average_arrival_interval = 5.6
+average_service_time = 15.0
+stdev_service_time = 3.5
+num_servers = 3
+
+waits = []
+arrival_time = 0.0
+servers = [0.0] * num_servers  # time when each server becomes available
+heapify(servers)
+for i in range(1_000_000):
+    arrival_time += expovariate(1.0 / average_arrival_interval)
+    next_server_available = servers[0]
+    wait = max(0.0, next_server_available - arrival_time)
+    waits.append(wait)
+    service_duration = max(0.0, gauss(average_service_time, stdev_service_time))
+    service_completed = arrival_time + wait + service_duration
+    heapreplace(servers, service_completed)
+
+print(f'Mean wait: {mean(waits):.1f}   Max wait: {max(waits):.1f}')
+print('Quartiles:', [round(q, 1) for q in quantiles(waits)])
+

See also

Statistics for Hackers a video tutorial by Jake Vanderplas on statistical analysis using just a few fundamental concepts including simulation, sampling, shuffling, and cross-validation.

Economics Simulation a simulation of a marketplace by Peter Norvig that shows effective use of many of the tools and distributions provided by this module (gauss, uniform, sample, betavariate, choice, triangular, and randrange).

A Concrete Introduction to Probability (using Python) a tutorial by Peter Norvig covering the basics of probability theory, how to write simulations, and how to perform data analysis using Python.

Recipes

These recipes show how to efficiently make random selections from the combinatoric iterators in the itertools module:

def random_product(*args, repeat=1):
+    "Random selection from itertools.product(*args, **kwds)"
+    pools = [tuple(pool) for pool in args] * repeat
+    return tuple(map(random.choice, pools))
+
+def random_permutation(iterable, r=None):
+    "Random selection from itertools.permutations(iterable, r)"
+    pool = tuple(iterable)
+    r = len(pool) if r is None else r
+    return tuple(random.sample(pool, r))
+
+def random_combination(iterable, r):
+    "Random selection from itertools.combinations(iterable, r)"
+    pool = tuple(iterable)
+    n = len(pool)
+    indices = sorted(random.sample(range(n), r))
+    return tuple(pool[i] for i in indices)
+
+def random_combination_with_replacement(iterable, r):
+    "Choose r elements with replacement.  Order the result to match the iterable."
+    # Result will be in set(itertools.combinations_with_replacement(iterable, r)).
+    pool = tuple(iterable)
+    n = len(pool)
+    indices = sorted(random.choices(range(n), k=r))
+    return tuple(pool[i] for i in indices)
+

The default random() returns multiples of 2⁻⁵³ in the range 0.0 ≤ x < 1.0. All such numbers are evenly spaced and are exactly representable as Python floats. However, many other representable floats in that interval are not possible selections. For example, 0.05954861408025609 isn’t an integer multiple of 2⁻⁵³.

The following recipe takes a different approach. All floats in the interval are possible selections. The mantissa comes from a uniform distribution of integers in the range 2⁵² ≤ mantissa < 2⁵³. The exponent comes from a geometric distribution where exponents smaller than -53 occur half as often as the next larger exponent.

from random import Random
+from math import ldexp
+
+class FullRandom(Random):
+
+    def random(self):
+        mantissa = 0x10_0000_0000_0000 | self.getrandbits(52)
+        exponent = -53
+        x = 0
+        while not x:
+            x = self.getrandbits(32)
+            exponent += x.bit_length() - 32
+        return ldexp(mantissa, exponent)
+

All real valued distributions in the class will use the new method:

>>> fr = FullRandom()
+>>> fr.random()
+0.05954861408025609
+>>> fr.expovariate(0.25)
+8.87925541791544
+

The recipe is conceptually equivalent to an algorithm that chooses from all the multiples of 2⁻¹⁰⁷⁴ in the range 0.0 ≤ x < 1.0. All such numbers are evenly spaced, but most have to be rounded down to the nearest representable Python float. (The value 2⁻¹⁰⁷⁴ is the smallest positive unnormalized float and is equal to math.ulp(0.0).)

See also

Generating Pseudo-random Floating-Point Values a paper by Allen B. Downey describing ways to generate more fine-grained floats than normally generated by random().

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/random.html +

+
diff --git a/devdocs/python~3.12/library%2Fre.html b/devdocs/python~3.12/library%2Fre.html new file mode 100644 index 00000000..d6fa9f3e --- /dev/null +++ b/devdocs/python~3.12/library%2Fre.html @@ -0,0 +1,628 @@ +

re — Regular expression operations

Source code: Lib/re/

This module provides regular expression matching operations similar to those found in Perl.

Both patterns and strings to be searched can be Unicode strings (str) as well as 8-bit strings (bytes). However, Unicode strings and 8-bit strings cannot be mixed: that is, you cannot match a Unicode string with a byte pattern or vice-versa; similarly, when asking for a substitution, the replacement string must be of the same type as both the pattern and the search string.

Regular expressions use the backslash character ('\') to indicate special forms or to allow special characters to be used without invoking their special meaning. This collides with Python’s usage of the same character for the same purpose in string literals; for example, to match a literal backslash, one might have to write '\\\\' as the pattern string, because the regular expression must be \\, and each backslash must be expressed as \\ inside a regular Python string literal. Also, please note that any invalid escape sequences in Python’s usage of the backslash in string literals now generate a SyntaxWarning and in the future this will become a SyntaxError. This behaviour will happen even if it is a valid escape sequence for a regular expression.

The solution is to use Python’s raw string notation for regular expression patterns; backslashes are not handled in any special way in a string literal prefixed with 'r'. So r"\n" is a two-character string containing '\' and 'n', while "\n" is a one-character string containing a newline. Usually patterns will be expressed in Python code using this raw string notation.

It is important to note that most regular expression operations are available as module-level functions and methods on compiled regular expressions. The functions are shortcuts that don’t require you to compile a regex object first, but miss some fine-tuning parameters.

See also

The third-party regex module, which has an API compatible with the standard library re module, but offers additional functionality and a more thorough Unicode support.

Regular Expression Syntax

A regular expression (or RE) specifies a set of strings that matches it; the functions in this module let you check if a particular string matches a given regular expression (or if a given regular expression matches a particular string, which comes down to the same thing).

Regular expressions can be concatenated to form new regular expressions; if A and B are both regular expressions, then AB is also a regular expression. In general, if a string p matches A and another string q matches B, the string pq will match AB. This holds unless A or B contain low precedence operations; boundary conditions between A and B; or have numbered group references. Thus, complex expressions can easily be constructed from simpler primitive expressions like the ones described here. For details of the theory and implementation of regular expressions, consult the Friedl book [Frie09], or almost any textbook about compiler construction.

A brief explanation of the format of regular expressions follows. For further information and a gentler presentation, consult the Regular Expression HOWTO.

Regular expressions can contain both special and ordinary characters. Most ordinary characters, like 'A', 'a', or '0', are the simplest regular expressions; they simply match themselves. You can concatenate ordinary characters, so last matches the string 'last'. (In the rest of this section, we’ll write RE’s in this special style, usually without quotes, and strings to be matched 'in single quotes'.)

Some characters, like '|' or '(', are special. Special characters either stand for classes of ordinary characters, or affect how the regular expressions around them are interpreted.

Repetition operators or quantifiers (*, +, ?, {m,n}, etc) cannot be directly nested. This avoids ambiguity with the non-greedy modifier suffix ?, and with other modifiers in other implementations. To apply a second repetition to an inner repetition, parentheses may be used. For example, the expression (?:a{6})* matches any multiple of six 'a' characters.

The special characters are:

+.
+
+

(Dot.) In the default mode, this matches any character except a newline. If the DOTALL flag has been specified, this matches any character including a newline.

+^
+
+

(Caret.) Matches the start of the string, and in MULTILINE mode also matches immediately after each newline.

+$
+
+

Matches the end of the string or just before the newline at the end of the string, and in MULTILINE mode also matches before a newline. foo matches both ‘foo’ and ‘foobar’, while the regular expression foo$ matches only ‘foo’. More interestingly, searching for foo.$ in 'foo1\nfoo2\n' matches ‘foo2’ normally, but ‘foo1’ in MULTILINE mode; searching for a single $ in 'foo\n' will find two (empty) matches: one just before the newline, and one at the end of the string.

+*
+
+

Causes the resulting RE to match 0 or more repetitions of the preceding RE, as many repetitions as are possible. ab* will match ‘a’, ‘ab’, or ‘a’ followed by any number of ‘b’s.

++
+
+

Causes the resulting RE to match 1 or more repetitions of the preceding RE. ab+ will match ‘a’ followed by any non-zero number of ‘b’s; it will not match just ‘a’.

+?
+
+

Causes the resulting RE to match 0 or 1 repetitions of the preceding RE. ab? will match either ‘a’ or ‘ab’.

+*?, +?, ??
+
+

The '*', '+', and '?' quantifiers are all greedy; they match as much text as possible. Sometimes this behaviour isn’t desired; if the RE <.*> is matched against '<a> b <c>', it will match the entire string, and not just '<a>'. Adding ? after the quantifier makes it perform the match in non-greedy or minimal fashion; as few characters as possible will be matched. Using the RE <.*?> will match only '<a>'.

+*+, ++, ?+
+
+

Like the '*', '+', and '?' quantifiers, those where '+' is appended also match as many times as possible. However, unlike the true greedy quantifiers, these do not allow back-tracking when the expression following it fails to match. These are known as possessive quantifiers. For example, a*a will match 'aaaa' because the a* will match all 4 'a's, but, when the final 'a' is encountered, the expression is backtracked so that in the end the a* ends up matching 3 'a's total, and the fourth 'a' is matched by the final 'a'. However, when a*+a is used to match 'aaaa', the a*+ will match all 4 'a', but when the final 'a' fails to find any more characters to match, the expression cannot be backtracked and will thus fail to match. x*+, x++ and x?+ are equivalent to (?>x*), (?>x+) and (?>x?) correspondingly.

New in version 3.11.

+{m}
+
+

Specifies that exactly m copies of the previous RE should be matched; fewer matches cause the entire RE not to match. For example, a{6} will match exactly six 'a' characters, but not five.

+{m,n}
+
+

Causes the resulting RE to match from m to n repetitions of the preceding RE, attempting to match as many repetitions as possible. For example, a{3,5} will match from 3 to 5 'a' characters. Omitting m specifies a lower bound of zero, and omitting n specifies an infinite upper bound. As an example, a{4,}b will match 'aaaab' or a thousand 'a' characters followed by a 'b', but not 'aaab'. The comma may not be omitted or the modifier would be confused with the previously described form.

+{m,n}?
+
+

Causes the resulting RE to match from m to n repetitions of the preceding RE, attempting to match as few repetitions as possible. This is the non-greedy version of the previous quantifier. For example, on the 6-character string 'aaaaaa', a{3,5} will match 5 'a' characters, while a{3,5}? will only match 3 characters.

+{m,n}+
+
+

Causes the resulting RE to match from m to n repetitions of the preceding RE, attempting to match as many repetitions as possible without establishing any backtracking points. This is the possessive version of the quantifier above. For example, on the 6-character string 'aaaaaa', a{3,5}+aa attempt to match 5 'a' characters, then, requiring 2 more 'a's, will need more characters than available and thus fail, while a{3,5}aa will match with a{3,5} capturing 5, then 4 'a's by backtracking and then the final 2 'a's are matched by the final aa in the pattern. x{m,n}+ is equivalent to (?>x{m,n}).

New in version 3.11.

+\
+
+

Either escapes special characters (permitting you to match characters like '*', '?', and so forth), or signals a special sequence; special sequences are discussed below.

If you’re not using a raw string to express the pattern, remember that Python also uses the backslash as an escape sequence in string literals; if the escape sequence isn’t recognized by Python’s parser, the backslash and subsequent character are included in the resulting string. However, if Python would recognize the resulting sequence, the backslash should be repeated twice. This is complicated and hard to understand, so it’s highly recommended that you use raw strings for all but the simplest expressions.

+[]
+
+

Used to indicate a set of characters. In a set:

  • Characters can be listed individually, e.g. [amk] will match 'a', 'm', or 'k'.
  • Ranges of characters can be indicated by giving two characters and separating them by a '-', for example [a-z] will match any lowercase ASCII letter, [0-5][0-9] will match all the two-digits numbers from 00 to 59, and [0-9A-Fa-f] will match any hexadecimal digit. If - is escaped (e.g. [a\-z]) or if it’s placed as the first or last character (e.g. [-a] or [a-]), it will match a literal '-'.
  • Special characters lose their special meaning inside sets. For example, [(+*)] will match any of the literal characters '(', '+', '*', or ')'.
  • Character classes such as \w or \S (defined below) are also accepted inside a set, although the characters they match depends on whether ASCII or LOCALE mode is in force.
  • Characters that are not within a range can be matched by complementing the set. If the first character of the set is '^', all the characters that are not in the set will be matched. For example, [^5] will match any character except '5', and [^^] will match any character except '^'. ^ has no special meaning if it’s not the first character in the set.
  • To match a literal ']' inside a set, precede it with a backslash, or place it at the beginning of the set. For example, both [()[\]{}] and []()[{}] will match a right bracket, as well as left bracket, braces, and parentheses.
  • Support of nested sets and set operations as in Unicode Technical Standard #18 might be added in the future. This would change the syntax, so to facilitate this change a FutureWarning will be raised in ambiguous cases for the time being. That includes sets starting with a literal '[' or containing literal character sequences '--', '&&', '~~', and '||'. To avoid a warning escape them with a backslash.

Changed in version 3.7: FutureWarning is raised if a character set contains constructs that will change semantically in the future.

+|
+
+

A|B, where A and B can be arbitrary REs, creates a regular expression that will match either A or B. An arbitrary number of REs can be separated by the '|' in this way. This can be used inside groups (see below) as well. As the target string is scanned, REs separated by '|' are tried from left to right. When one pattern completely matches, that branch is accepted. This means that once A matches, B will not be tested further, even if it would produce a longer overall match. In other words, the '|' operator is never greedy. To match a literal '|', use \|, or enclose it inside a character class, as in [|].

+(...)
+
+

Matches whatever regular expression is inside the parentheses, and indicates the start and end of a group; the contents of a group can be retrieved after a match has been performed, and can be matched later in the string with the \number special sequence, described below. To match the literals '(' or ')', use \( or \), or enclose them inside a character class: [(], [)].

+(?...)
+
+

This is an extension notation (a '?' following a '(' is not meaningful otherwise). The first character after the '?' determines what the meaning and further syntax of the construct is. Extensions usually do not create a new group; (?P<name>...) is the only exception to this rule. Following are the currently supported extensions.

+(?aiLmsux)
+
+

(One or more letters from the set 'a', 'i', 'L', 'm', 's', 'u', 'x'.) The group matches the empty string; the letters set the corresponding flags: re.A (ASCII-only matching), re.I (ignore case), re.L (locale dependent), re.M (multi-line), re.S (dot matches all), re.U (Unicode matching), and re.X (verbose), for the entire regular expression. (The flags are described in Module Contents.) This is useful if you wish to include the flags as part of the regular expression, instead of passing a flag argument to the re.compile() function. Flags should be used first in the expression string.

Changed in version 3.11: This construction can only be used at the start of the expression.

+(?:...)
+
+

A non-capturing version of regular parentheses. Matches whatever regular expression is inside the parentheses, but the substring matched by the group cannot be retrieved after performing a match or referenced later in the pattern.

+(?aiLmsux-imsx:...)
+
+

(Zero or more letters from the set 'a', 'i', 'L', 'm', 's', 'u', 'x', optionally followed by '-' followed by one or more letters from the 'i', 'm', 's', 'x'.) The letters set or remove the corresponding flags: re.A (ASCII-only matching), re.I (ignore case), re.L (locale dependent), re.M (multi-line), re.S (dot matches all), re.U (Unicode matching), and re.X (verbose), for the part of the expression. (The flags are described in Module Contents.)

The letters 'a', 'L' and 'u' are mutually exclusive when used as inline flags, so they can’t be combined or follow '-'. Instead, when one of them appears in an inline group, it overrides the matching mode in the enclosing group. In Unicode patterns (?a:...) switches to ASCII-only matching, and (?u:...) switches to Unicode matching (default). In byte pattern (?L:...) switches to locale depending matching, and (?a:...) switches to ASCII-only matching (default). This override is only in effect for the narrow inline group, and the original matching mode is restored outside of the group.

New in version 3.6.

Changed in version 3.7: The letters 'a', 'L' and 'u' also can be used in a group.

+(?>...)
+
+

Attempts to match ... as if it was a separate regular expression, and if successful, continues to match the rest of the pattern following it. If the subsequent pattern fails to match, the stack can only be unwound to a point before the (?>...) because once exited, the expression, known as an atomic group, has thrown away all stack points within itself. Thus, (?>.*). would never match anything because first the .* would match all characters possible, then, having nothing left to match, the final . would fail to match. Since there are no stack points saved in the Atomic Group, and there is no stack point before it, the entire expression would thus fail to match.

New in version 3.11.

+(?P<name>...)
+
+

Similar to regular parentheses, but the substring matched by the group is accessible via the symbolic group name name. Group names must be valid Python identifiers, and in bytes patterns they can only contain bytes in the ASCII range. Each group name must be defined only once within a regular expression. A symbolic group is also a numbered group, just as if the group were not named.

Named groups can be referenced in three contexts. If the pattern is (?P<quote>['"]).*?(?P=quote) (i.e. matching a string quoted with either single or double quotes):

+ + + +

Context of reference to group “quote”

Ways to reference it

in the same pattern itself

 +
  • +(?P=quote) (as shown)
  • \1

when processing match object m

 +
  • m.group('quote')
  • +m.end('quote') (etc.)

in a string passed to the repl argument of re.sub()

 +
  • \g<quote>
  • \g<1>
  • \1

Changed in version 3.12: In bytes patterns, group name can only contain bytes in the ASCII range (b'\x00'-b'\x7f').

+(?P=name)
+
+

A backreference to a named group; it matches whatever text was matched by the earlier group named name.

+(?#...)
+
+

A comment; the contents of the parentheses are simply ignored.

+(?=...)
+
+

Matches if ... matches next, but doesn’t consume any of the string. This is called a lookahead assertion. For example, Isaac (?=Asimov) will match 'Isaac ' only if it’s followed by 'Asimov'.

+(?!...)
+
+

Matches if ... doesn’t match next. This is a negative lookahead assertion. For example, Isaac (?!Asimov) will match 'Isaac ' only if it’s not followed by 'Asimov'.

+(?<=...)
+
+

Matches if the current position in the string is preceded by a match for ... that ends at the current position. This is called a positive lookbehind assertion. (?<=abc)def will find a match in 'abcdef', since the lookbehind will back up 3 characters and check if the contained pattern matches. The contained pattern must only match strings of some fixed length, meaning that abc or a|b are allowed, but a* and a{3,4} are not. Note that patterns which start with positive lookbehind assertions will not match at the beginning of the string being searched; you will most likely want to use the search() function rather than the match() function:

>>> import re
+>>> m = re.search('(?<=abc)def', 'abcdef')
+>>> m.group(0)
+'def'
+

This example looks for a word following a hyphen:

>>> m = re.search(r'(?<=-)\w+', 'spam-egg')
+>>> m.group(0)
+'egg'
+

Changed in version 3.5: Added support for group references of fixed length.

+(?<!...)
+
+

Matches if the current position in the string is not preceded by a match for .... This is called a negative lookbehind assertion. Similar to positive lookbehind assertions, the contained pattern must only match strings of some fixed length. Patterns which start with negative lookbehind assertions may match at the beginning of the string being searched.

+(?(id/name)yes-pattern|no-pattern)
+
+

Will try to match with yes-pattern if the group with given id or name exists, and with no-pattern if it doesn’t. no-pattern is optional and can be omitted. For example, (<)?(\w+@\w+(?:\.\w+)+)(?(1)>|$) is a poor email matching pattern, which will match with '<user@host.com>' as well as 'user@host.com', but not with '<user@host.com' nor 'user@host.com>'.

Changed in version 3.12: Group id can only contain ASCII digits. In bytes patterns, group name can only contain bytes in the ASCII range (b'\x00'-b'\x7f').

The special sequences consist of '\' and a character from the list below. If the ordinary character is not an ASCII digit or an ASCII letter, then the resulting RE will match the second character. For example, \$ matches the character '$'.

+\number
+
+

Matches the contents of the group of the same number. Groups are numbered starting from 1. For example, (.+) \1 matches 'the the' or '55 55', but not 'thethe' (note the space after the group). This special sequence can only be used to match one of the first 99 groups. If the first digit of number is 0, or number is 3 octal digits long, it will not be interpreted as a group match, but as the character with octal value number. Inside the '[' and ']' of a character class, all numeric escapes are treated as characters.

+\A
+
+

Matches only at the start of the string.

+\b
+
+

Matches the empty string, but only at the beginning or end of a word. A word is defined as a sequence of word characters. Note that formally, \b is defined as the boundary between a \w and a \W character (or vice versa), or between \w and the beginning/end of the string. This means that r'\bfoo\b' matches 'foo', 'foo.', '(foo)', 'bar foo baz' but not 'foobar' or 'foo3'.

By default Unicode alphanumerics are the ones used in Unicode patterns, but this can be changed by using the ASCII flag. Word boundaries are determined by the current locale if the LOCALE flag is used. Inside a character range, \b represents the backspace character, for compatibility with Python’s string literals.

+\B
+
+

Matches the empty string, but only when it is not at the beginning or end of a word. This means that r'py\B' matches 'python', 'py3', 'py2', but not 'py', 'py.', or 'py!'. \B is just the opposite of \b, so word characters in Unicode patterns are Unicode alphanumerics or the underscore, although this can be changed by using the ASCII flag. Word boundaries are determined by the current locale if the LOCALE flag is used.

+\d
+
+
For Unicode (str) patterns:
+
+

Matches any Unicode decimal digit (that is, any character in Unicode character category [Nd]). This includes [0-9], and also many other digit characters. If the ASCII flag is used only [0-9] is matched.

For 8-bit (bytes) patterns:
+
+

Matches any decimal digit; this is equivalent to [0-9].

+\D
+
+

Matches any character which is not a decimal digit. This is the opposite of \d. If the ASCII flag is used this becomes the equivalent of [^0-9].

+\s
+
+
For Unicode (str) patterns:
+
+

Matches Unicode whitespace characters (which includes [ \t\n\r\f\v], and also many other characters, for example the non-breaking spaces mandated by typography rules in many languages). If the ASCII flag is used, only [ \t\n\r\f\v] is matched.

For 8-bit (bytes) patterns:
+
+

Matches characters considered whitespace in the ASCII character set; this is equivalent to [ \t\n\r\f\v].

+\S
+
+

Matches any character which is not a whitespace character. This is the opposite of \s. If the ASCII flag is used this becomes the equivalent of [^ \t\n\r\f\v].

+\w
+
+
For Unicode (str) patterns:
+
+

Matches Unicode word characters; this includes alphanumeric characters (as defined by str.isalnum()) as well as the underscore (_). If the ASCII flag is used, only [a-zA-Z0-9_] is matched.

For 8-bit (bytes) patterns:
+
+

Matches characters considered alphanumeric in the ASCII character set; this is equivalent to [a-zA-Z0-9_]. If the LOCALE flag is used, matches characters considered alphanumeric in the current locale and the underscore.

+\W
+
+

Matches any character which is not a word character. This is the opposite of \w. If the ASCII flag is used this becomes the equivalent of [^a-zA-Z0-9_]. If the LOCALE flag is used, matches characters which are neither alphanumeric in the current locale nor the underscore.

+\Z
+
+

Matches only at the end of the string.

Most of the escape sequences supported by Python string literals are also accepted by the regular expression parser:

\a      \b      \f      \n
+\N      \r      \t      \u
+\U      \v      \x      \\
+

(Note that \b is used to represent word boundaries, and means “backspace” only inside character classes.)

'\u', '\U', and '\N' escape sequences are only recognized in Unicode patterns. In bytes patterns they are errors. Unknown escapes of ASCII letters are reserved for future use and treated as errors.

Octal escapes are included in a limited form. If the first digit is a 0, or if there are three octal digits, it is considered an octal escape. Otherwise, it is a group reference. As for string literals, octal escapes are always at most three digits in length.

Changed in version 3.3: The '\u' and '\U' escape sequences have been added.

Changed in version 3.6: Unknown escapes consisting of '\' and an ASCII letter now are errors.

Changed in version 3.8: The '\N{name}' escape sequence has been added. As in string literals, it expands to the named Unicode character (e.g. '\N{EM DASH}').

Module Contents

The module defines several functions, constants, and an exception. Some of the functions are simplified versions of the full featured methods for compiled regular expressions. Most non-trivial applications always use the compiled form.

Flags

Changed in version 3.6: Flag constants are now instances of RegexFlag, which is a subclass of enum.IntFlag.

+class re.RegexFlag
+

An enum.IntFlag class containing the regex options listed below.

New in version 3.11: - added to __all__

+
+re.A
+re.ASCII
+

Make \w, \W, \b, \B, \d, \D, \s and \S perform ASCII-only matching instead of full Unicode matching. This is only meaningful for Unicode patterns, and is ignored for byte patterns. Corresponds to the inline flag (?a).

Note that for backward compatibility, the re.U flag still exists (as well as its synonym re.UNICODE and its embedded counterpart (?u)), but these are redundant in Python 3 since matches are Unicode by default for strings (and Unicode matching isn’t allowed for bytes).

+
+re.DEBUG
+

Display debug information about compiled expression. No corresponding inline flag.

+
+re.I
+re.IGNORECASE
+

Perform case-insensitive matching; expressions like [A-Z] will also match lowercase letters. Full Unicode matching (such as Ü matching ü) also works unless the re.ASCII flag is used to disable non-ASCII matches. The current locale does not change the effect of this flag unless the re.LOCALE flag is also used. Corresponds to the inline flag (?i).

Note that when the Unicode patterns [a-z] or [A-Z] are used in combination with the IGNORECASE flag, they will match the 52 ASCII letters and 4 additional non-ASCII letters: ‘İ’ (U+0130, Latin capital letter I with dot above), ‘ı’ (U+0131, Latin small letter dotless i), ‘ſ’ (U+017F, Latin small letter long s) and ‘K’ (U+212A, Kelvin sign). If the ASCII flag is used, only letters ‘a’ to ‘z’ and ‘A’ to ‘Z’ are matched.

+
+re.L
+re.LOCALE
+

Make \w, \W, \b, \B and case-insensitive matching dependent on the current locale. This flag can be used only with bytes patterns. The use of this flag is discouraged as the locale mechanism is very unreliable, it only handles one “culture” at a time, and it only works with 8-bit locales. Unicode matching is already enabled by default in Python 3 for Unicode (str) patterns, and it is able to handle different locales/languages. Corresponds to the inline flag (?L).

Changed in version 3.6: re.LOCALE can be used only with bytes patterns and is not compatible with re.ASCII.

Changed in version 3.7: Compiled regular expression objects with the re.LOCALE flag no longer depend on the locale at compile time. Only the locale at matching time affects the result of matching.

+
+re.M
+re.MULTILINE
+

When specified, the pattern character '^' matches at the beginning of the string and at the beginning of each line (immediately following each newline); and the pattern character '$' matches at the end of the string and at the end of each line (immediately preceding each newline). By default, '^' matches only at the beginning of the string, and '$' only at the end of the string and immediately before the newline (if any) at the end of the string. Corresponds to the inline flag (?m).

+
+re.NOFLAG
+

Indicates no flag being applied, the value is 0. This flag may be used as a default value for a function keyword argument or as a base value that will be conditionally ORed with other flags. Example of use as a default value:

def myfunc(text, flag=re.NOFLAG):
+    return re.match(text, flag)
+

New in version 3.11.

+
+re.S
+re.DOTALL
+

Make the '.' special character match any character at all, including a newline; without this flag, '.' will match anything except a newline. Corresponds to the inline flag (?s).

+
+re.U
+re.UNICODE
+

In Python 2, this flag made special sequences include Unicode characters in matches. Since Python 3, Unicode characters are matched by default.

See A for restricting matching on ASCII characters instead.

This flag is only kept for backward compatibility.

+
+re.X
+re.VERBOSE
+

This flag allows you to write regular expressions that look nicer and are more readable by allowing you to visually separate logical sections of the pattern and add comments. Whitespace within the pattern is ignored, except when in a character class, or when preceded by an unescaped backslash, or within tokens like *?, (?: or (?P<...>. For example, (? : and * ? are not allowed. When a line contains a # that is not in a character class and is not preceded by an unescaped backslash, all characters from the leftmost such # through the end of the line are ignored.

This means that the two following regular expression objects that match a decimal number are functionally equal:

a = re.compile(r"""\d +  # the integral part
+                   \.    # the decimal point
+                   \d *  # some fractional digits""", re.X)
+b = re.compile(r"\d+\.\d*")
+

Corresponds to the inline flag (?x).

+

Functions

+re.compile(pattern, flags=0)
+

Compile a regular expression pattern into a regular expression object, which can be used for matching using its match(), search() and other methods, described below.

The expression’s behaviour can be modified by specifying a flags value. Values can be any of the following variables, combined using bitwise OR (the | operator).

The sequence

prog = re.compile(pattern)
+result = prog.match(string)
+

is equivalent to

result = re.match(pattern, string)
+

but using re.compile() and saving the resulting regular expression object for reuse is more efficient when the expression will be used several times in a single program.

Note

The compiled versions of the most recent patterns passed to re.compile() and the module-level matching functions are cached, so programs that use only a few regular expressions at a time needn’t worry about compiling regular expressions.

+
+re.search(pattern, string, flags=0)
+

Scan through string looking for the first location where the regular expression pattern produces a match, and return a corresponding Match. Return None if no position in the string matches the pattern; note that this is different from finding a zero-length match at some point in the string.

+
+re.match(pattern, string, flags=0)
+

If zero or more characters at the beginning of string match the regular expression pattern, return a corresponding Match. Return None if the string does not match the pattern; note that this is different from a zero-length match.

Note that even in MULTILINE mode, re.match() will only match at the beginning of the string and not at the beginning of each line.

If you want to locate a match anywhere in string, use search() instead (see also search() vs. match()).

+
+re.fullmatch(pattern, string, flags=0)
+

If the whole string matches the regular expression pattern, return a corresponding Match. Return None if the string does not match the pattern; note that this is different from a zero-length match.

New in version 3.4.

+
+re.split(pattern, string, maxsplit=0, flags=0)
+

Split string by the occurrences of pattern. If capturing parentheses are used in pattern, then the text of all groups in the pattern are also returned as part of the resulting list. If maxsplit is nonzero, at most maxsplit splits occur, and the remainder of the string is returned as the final element of the list.

>>> re.split(r'\W+', 'Words, words, words.')
+['Words', 'words', 'words', '']
+>>> re.split(r'(\W+)', 'Words, words, words.')
+['Words', ', ', 'words', ', ', 'words', '.', '']
+>>> re.split(r'\W+', 'Words, words, words.', 1)
+['Words', 'words, words.']
+>>> re.split('[a-f]+', '0a3B9', flags=re.IGNORECASE)
+['0', '3', '9']
+

If there are capturing groups in the separator and it matches at the start of the string, the result will start with an empty string. The same holds for the end of the string:

>>> re.split(r'(\W+)', '...words, words...')
+['', '...', 'words', ', ', 'words', '...', '']
+

That way, separator components are always found at the same relative indices within the result list.

Empty matches for the pattern split the string only when not adjacent to a previous empty match.

>>> re.split(r'\b', 'Words, words, words.')
+['', 'Words', ', ', 'words', ', ', 'words', '.']
+>>> re.split(r'\W*', '...words...')
+['', '', 'w', 'o', 'r', 'd', 's', '', '']
+>>> re.split(r'(\W*)', '...words...')
+['', '...', '', '', 'w', '', 'o', '', 'r', '', 'd', '', 's', '...', '', '', '']
+

Changed in version 3.1: Added the optional flags argument.

Changed in version 3.7: Added support of splitting on a pattern that could match an empty string.

+
+re.findall(pattern, string, flags=0)
+

Return all non-overlapping matches of pattern in string, as a list of strings or tuples. The string is scanned left-to-right, and matches are returned in the order found. Empty matches are included in the result.

The result depends on the number of capturing groups in the pattern. If there are no groups, return a list of strings matching the whole pattern. If there is exactly one group, return a list of strings matching that group. If multiple groups are present, return a list of tuples of strings matching the groups. Non-capturing groups do not affect the form of the result.

>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
+['foot', 'fell', 'fastest']
+>>> re.findall(r'(\w+)=(\d+)', 'set width=20 and height=10')
+[('width', '20'), ('height', '10')]
+

Changed in version 3.7: Non-empty matches can now start just after a previous empty match.

+
+re.finditer(pattern, string, flags=0)
+

Return an iterator yielding Match objects over all non-overlapping matches for the RE pattern in string. The string is scanned left-to-right, and matches are returned in the order found. Empty matches are included in the result.

Changed in version 3.7: Non-empty matches can now start just after a previous empty match.

+
+re.sub(pattern, repl, string, count=0, flags=0)
+

Return the string obtained by replacing the leftmost non-overlapping occurrences of pattern in string by the replacement repl. If the pattern isn’t found, string is returned unchanged. repl can be a string or a function; if it is a string, any backslash escapes in it are processed. That is, \n is converted to a single newline character, \r is converted to a carriage return, and so forth. Unknown escapes of ASCII letters are reserved for future use and treated as errors. Other unknown escapes such as \& are left alone. Backreferences, such as \6, are replaced with the substring matched by group 6 in the pattern. For example:

>>> re.sub(r'def\s+([a-zA-Z_][a-zA-Z_0-9]*)\s*\(\s*\):',
+...        r'static PyObject*\npy_\1(void)\n{',
+...        'def myfunc():')
+'static PyObject*\npy_myfunc(void)\n{'
+

If repl is a function, it is called for every non-overlapping occurrence of pattern. The function takes a single Match argument, and returns the replacement string. For example:

>>> def dashrepl(matchobj):
+...     if matchobj.group(0) == '-': return ' '
+...     else: return '-'
+...
+>>> re.sub('-{1,2}', dashrepl, 'pro----gram-files')
+'pro--gram files'
+>>> re.sub(r'\sAND\s', ' & ', 'Baked Beans And Spam', flags=re.IGNORECASE)
+'Baked Beans & Spam'
+

The pattern may be a string or a Pattern.

The optional argument count is the maximum number of pattern occurrences to be replaced; count must be a non-negative integer. If omitted or zero, all occurrences will be replaced. Empty matches for the pattern are replaced only when not adjacent to a previous empty match, so sub('x*', '-', 'abxd') returns '-a-b--d-'.

In string-type repl arguments, in addition to the character escapes and backreferences described above, \g<name> will use the substring matched by the group named name, as defined by the (?P<name>...) syntax. \g<number> uses the corresponding group number; \g<2> is therefore equivalent to \2, but isn’t ambiguous in a replacement such as \g<2>0. \20 would be interpreted as a reference to group 20, not a reference to group 2 followed by the literal character '0'. The backreference \g<0> substitutes in the entire substring matched by the RE.

Changed in version 3.1: Added the optional flags argument.

Changed in version 3.5: Unmatched groups are replaced with an empty string.

Changed in version 3.6: Unknown escapes in pattern consisting of '\' and an ASCII letter now are errors.

Changed in version 3.7: Unknown escapes in repl consisting of '\' and an ASCII letter now are errors.

Changed in version 3.7: Empty matches for the pattern are replaced when adjacent to a previous non-empty match.

Changed in version 3.12: Group id can only contain ASCII digits. In bytes replacement strings, group name can only contain bytes in the ASCII range (b'\x00'-b'\x7f').

+
+re.subn(pattern, repl, string, count=0, flags=0)
+

Perform the same operation as sub(), but return a tuple (new_string, +number_of_subs_made).

Changed in version 3.1: Added the optional flags argument.

Changed in version 3.5: Unmatched groups are replaced with an empty string.

+
+re.escape(pattern)
+

Escape special characters in pattern. This is useful if you want to match an arbitrary literal string that may have regular expression metacharacters in it. For example:

>>> print(re.escape('https://www.python.org'))
+https://www\.python\.org
+
+>>> legal_chars = string.ascii_lowercase + string.digits + "!#$%&'*+-.^_`|~:"
+>>> print('[%s]+' % re.escape(legal_chars))
+[abcdefghijklmnopqrstuvwxyz0123456789!\#\$%\&'\*\+\-\.\^_`\|\~:]+
+
+>>> operators = ['+', '-', '*', '/', '**']
+>>> print('|'.join(map(re.escape, sorted(operators, reverse=True))))
+/|\-|\+|\*\*|\*
+

This function must not be used for the replacement string in sub() and subn(), only backslashes should be escaped. For example:

>>> digits_re = r'\d+'
+>>> sample = '/usr/sbin/sendmail - 0 errors, 12 warnings'
+>>> print(re.sub(digits_re, digits_re.replace('\\', r'\\'), sample))
+/usr/sbin/sendmail - \d+ errors, \d+ warnings
+

Changed in version 3.3: The '_' character is no longer escaped.

Changed in version 3.7: Only characters that can have special meaning in a regular expression are escaped. As a result, '!', '"', '%', "'", ',', '/', ':', ';', '<', '=', '>', '@', and "`" are no longer escaped.

+
+re.purge()
+

Clear the regular expression cache.

+

Exceptions

+exception re.error(msg, pattern=None, pos=None)
+

Exception raised when a string passed to one of the functions here is not a valid regular expression (for example, it might contain unmatched parentheses) or when some other error occurs during compilation or matching. It is never an error if a string contains no match for a pattern. The error instance has the following additional attributes:

+msg
+

The unformatted error message.

+
+pattern
+

The regular expression pattern.

+
+pos
+

The index in pattern where compilation failed (may be None).

+
+lineno
+

The line corresponding to pos (may be None).

+
+colno
+

The column corresponding to pos (may be None).

+

Changed in version 3.5: Added additional attributes.

+

Regular Expression Objects

+class re.Pattern
+

Compiled regular expression object returned by re.compile().

Changed in version 3.9: re.Pattern supports [] to indicate a Unicode (str) or bytes pattern. See Generic Alias Type.

+
+Pattern.search(string[, pos[, endpos]])
+

Scan through string looking for the first location where this regular expression produces a match, and return a corresponding Match. Return None if no position in the string matches the pattern; note that this is different from finding a zero-length match at some point in the string.

The optional second parameter pos gives an index in the string where the search is to start; it defaults to 0. This is not completely equivalent to slicing the string; the '^' pattern character matches at the real beginning of the string and at positions just after a newline, but not necessarily at the index where the search is to start.

The optional parameter endpos limits how far the string will be searched; it will be as if the string is endpos characters long, so only the characters from pos to endpos - 1 will be searched for a match. If endpos is less than pos, no match will be found; otherwise, if rx is a compiled regular expression object, rx.search(string, 0, 50) is equivalent to rx.search(string[:50], 0).

>>> pattern = re.compile("d")
+>>> pattern.search("dog")     # Match at index 0
+<re.Match object; span=(0, 1), match='d'>
+>>> pattern.search("dog", 1)  # No match; search doesn't include the "d"
+
+
+Pattern.match(string[, pos[, endpos]])
+

If zero or more characters at the beginning of string match this regular expression, return a corresponding Match. Return None if the string does not match the pattern; note that this is different from a zero-length match.

The optional pos and endpos parameters have the same meaning as for the search() method.

>>> pattern = re.compile("o")
+>>> pattern.match("dog")      # No match as "o" is not at the start of "dog".
+>>> pattern.match("dog", 1)   # Match as "o" is the 2nd character of "dog".
+<re.Match object; span=(1, 2), match='o'>
+

If you want to locate a match anywhere in string, use search() instead (see also search() vs. match()).

+
+Pattern.fullmatch(string[, pos[, endpos]])
+

If the whole string matches this regular expression, return a corresponding Match. Return None if the string does not match the pattern; note that this is different from a zero-length match.

The optional pos and endpos parameters have the same meaning as for the search() method.

>>> pattern = re.compile("o[gh]")
+>>> pattern.fullmatch("dog")      # No match as "o" is not at the start of "dog".
+>>> pattern.fullmatch("ogre")     # No match as not the full string matches.
+>>> pattern.fullmatch("doggie", 1, 3)   # Matches within given limits.
+<re.Match object; span=(1, 3), match='og'>
+

New in version 3.4.

+
+Pattern.split(string, maxsplit=0)
+

Identical to the split() function, using the compiled pattern.

+
+Pattern.findall(string[, pos[, endpos]])
+

Similar to the findall() function, using the compiled pattern, but also accepts optional pos and endpos parameters that limit the search region like for search().

+
+Pattern.finditer(string[, pos[, endpos]])
+

Similar to the finditer() function, using the compiled pattern, but also accepts optional pos and endpos parameters that limit the search region like for search().

+
+Pattern.sub(repl, string, count=0)
+

Identical to the sub() function, using the compiled pattern.

+
+Pattern.subn(repl, string, count=0)
+

Identical to the subn() function, using the compiled pattern.

+
+Pattern.flags
+

The regex matching flags. This is a combination of the flags given to compile(), any (?...) inline flags in the pattern, and implicit flags such as UNICODE if the pattern is a Unicode string.

+
+Pattern.groups
+

The number of capturing groups in the pattern.

+
+Pattern.groupindex
+

A dictionary mapping any symbolic group names defined by (?P<id>) to group numbers. The dictionary is empty if no symbolic groups were used in the pattern.

+
+Pattern.pattern
+

The pattern string from which the pattern object was compiled.

+

Changed in version 3.7: Added support of copy.copy() and copy.deepcopy(). Compiled regular expression objects are considered atomic.

Match Objects

Match objects always have a boolean value of True. Since match() and search() return None when there is no match, you can test whether there was a match with a simple if statement:

match = re.search(pattern, string)
+if match:
+    process(match)
+
+class re.Match
+

Match object returned by successful matches and searches.

Changed in version 3.9: re.Match supports [] to indicate a Unicode (str) or bytes match. See Generic Alias Type.

+
+Match.expand(template)
+

Return the string obtained by doing backslash substitution on the template string template, as done by the sub() method. Escapes such as \n are converted to the appropriate characters, and numeric backreferences (\1, \2) and named backreferences (\g<1>, \g<name>) are replaced by the contents of the corresponding group.

Changed in version 3.5: Unmatched groups are replaced with an empty string.

+
+Match.group([group1, ...])
+

Returns one or more subgroups of the match. If there is a single argument, the result is a single string; if there are multiple arguments, the result is a tuple with one item per argument. Without arguments, group1 defaults to zero (the whole match is returned). If a groupN argument is zero, the corresponding return value is the entire matching string; if it is in the inclusive range [1..99], it is the string matching the corresponding parenthesized group. If a group number is negative or larger than the number of groups defined in the pattern, an IndexError exception is raised. If a group is contained in a part of the pattern that did not match, the corresponding result is None. If a group is contained in a part of the pattern that matched multiple times, the last match is returned.

>>> m = re.match(r"(\w+) (\w+)", "Isaac Newton, physicist")
+>>> m.group(0)       # The entire match
+'Isaac Newton'
+>>> m.group(1)       # The first parenthesized subgroup.
+'Isaac'
+>>> m.group(2)       # The second parenthesized subgroup.
+'Newton'
+>>> m.group(1, 2)    # Multiple arguments give us a tuple.
+('Isaac', 'Newton')
+

If the regular expression uses the (?P<name>...) syntax, the groupN arguments may also be strings identifying groups by their group name. If a string argument is not used as a group name in the pattern, an IndexError exception is raised.

A moderately complicated example:

>>> m = re.match(r"(?P<first_name>\w+) (?P<last_name>\w+)", "Malcolm Reynolds")
+>>> m.group('first_name')
+'Malcolm'
+>>> m.group('last_name')
+'Reynolds'
+

Named groups can also be referred to by their index:

>>> m.group(1)
+'Malcolm'
+>>> m.group(2)
+'Reynolds'
+

If a group matches multiple times, only the last match is accessible:

>>> m = re.match(r"(..)+", "a1b2c3")  # Matches 3 times.
+>>> m.group(1)                        # Returns only the last match.
+'c3'
+
+
+Match.__getitem__(g)
+

This is identical to m.group(g). This allows easier access to an individual group from a match:

>>> m = re.match(r"(\w+) (\w+)", "Isaac Newton, physicist")
+>>> m[0]       # The entire match
+'Isaac Newton'
+>>> m[1]       # The first parenthesized subgroup.
+'Isaac'
+>>> m[2]       # The second parenthesized subgroup.
+'Newton'
+

Named groups are supported as well:

>>> m = re.match(r"(?P<first_name>\w+) (?P<last_name>\w+)", "Isaac Newton")
+>>> m['first_name']
+'Isaac'
+>>> m['last_name']
+'Newton'
+

New in version 3.6.

+
+Match.groups(default=None)
+

Return a tuple containing all the subgroups of the match, from 1 up to however many groups are in the pattern. The default argument is used for groups that did not participate in the match; it defaults to None.

For example:

>>> m = re.match(r"(\d+)\.(\d+)", "24.1632")
+>>> m.groups()
+('24', '1632')
+

If we make the decimal place and everything after it optional, not all groups might participate in the match. These groups will default to None unless the default argument is given:

>>> m = re.match(r"(\d+)\.?(\d+)?", "24")
+>>> m.groups()      # Second group defaults to None.
+('24', None)
+>>> m.groups('0')   # Now, the second group defaults to '0'.
+('24', '0')
+
+
+Match.groupdict(default=None)
+

Return a dictionary containing all the named subgroups of the match, keyed by the subgroup name. The default argument is used for groups that did not participate in the match; it defaults to None. For example:

>>> m = re.match(r"(?P<first_name>\w+) (?P<last_name>\w+)", "Malcolm Reynolds")
+>>> m.groupdict()
+{'first_name': 'Malcolm', 'last_name': 'Reynolds'}
+
+
+Match.start([group])
+Match.end([group])
+

Return the indices of the start and end of the substring matched by group; group defaults to zero (meaning the whole matched substring). Return -1 if group exists but did not contribute to the match. For a match object m, and a group g that did contribute to the match, the substring matched by group g (equivalent to m.group(g)) is

m.string[m.start(g):m.end(g)]
+

Note that m.start(group) will equal m.end(group) if group matched a null string. For example, after m = re.search('b(c?)', 'cba'), m.start(0) is 1, m.end(0) is 2, m.start(1) and m.end(1) are both 2, and m.start(2) raises an IndexError exception.

An example that will remove remove_this from email addresses:

>>> email = "tony@tiremove_thisger.net"
+>>> m = re.search("remove_this", email)
+>>> email[:m.start()] + email[m.end():]
+'tony@tiger.net'
+
+
+Match.span([group])
+

For a match m, return the 2-tuple (m.start(group), m.end(group)). Note that if group did not contribute to the match, this is (-1, -1). group defaults to zero, the entire match.

+
+Match.pos
+

The value of pos which was passed to the search() or match() method of a regex object. This is the index into the string at which the RE engine started looking for a match.

+
+Match.endpos
+

The value of endpos which was passed to the search() or match() method of a regex object. This is the index into the string beyond which the RE engine will not go.

+
+Match.lastindex
+

The integer index of the last matched capturing group, or None if no group was matched at all. For example, the expressions (a)b, ((a)(b)), and ((ab)) will have lastindex == 1 if applied to the string 'ab', while the expression (a)(b) will have lastindex == 2, if applied to the same string.

+
+Match.lastgroup
+

The name of the last matched capturing group, or None if the group didn’t have a name, or if no group was matched at all.

+
+Match.re
+

The regular expression object whose match() or search() method produced this match instance.

+
+Match.string
+

The string passed to match() or search().

+

Changed in version 3.7: Added support of copy.copy() and copy.deepcopy(). Match objects are considered atomic.

Regular Expression Examples

Checking for a Pair

In this example, we’ll use the following helper function to display match objects a little more gracefully:

def displaymatch(match):
+    if match is None:
+        return None
+    return '<Match: %r, groups=%r>' % (match.group(), match.groups())
+

Suppose you are writing a poker program where a player’s hand is represented as a 5-character string with each character representing a card, “a” for ace, “k” for king, “q” for queen, “j” for jack, “t” for 10, and “2” through “9” representing the card with that value.

To see if a given string is a valid hand, one could do the following:

>>> valid = re.compile(r"^[a2-9tjqk]{5}$")
+>>> displaymatch(valid.match("akt5q"))  # Valid.
+"<Match: 'akt5q', groups=()>"
+>>> displaymatch(valid.match("akt5e"))  # Invalid.
+>>> displaymatch(valid.match("akt"))    # Invalid.
+>>> displaymatch(valid.match("727ak"))  # Valid.
+"<Match: '727ak', groups=()>"
+

That last hand, "727ak", contained a pair, or two of the same valued cards. To match this with a regular expression, one could use backreferences as such:

>>> pair = re.compile(r".*(.).*\1")
+>>> displaymatch(pair.match("717ak"))     # Pair of 7s.
+"<Match: '717', groups=('7',)>"
+>>> displaymatch(pair.match("718ak"))     # No pairs.
+>>> displaymatch(pair.match("354aa"))     # Pair of aces.
+"<Match: '354aa', groups=('a',)>"
+

To find out what card the pair consists of, one could use the group() method of the match object in the following manner:

>>> pair = re.compile(r".*(.).*\1")
+>>> pair.match("717ak").group(1)
+'7'
+
+# Error because re.match() returns None, which doesn't have a group() method:
+>>> pair.match("718ak").group(1)
+Traceback (most recent call last):
+  File "<pyshell#23>", line 1, in <module>
+    re.match(r".*(.).*\1", "718ak").group(1)
+AttributeError: 'NoneType' object has no attribute 'group'
+
+>>> pair.match("354aa").group(1)
+'a'
+

Simulating scanf()

Python does not currently have an equivalent to scanf(). Regular expressions are generally more powerful, though also more verbose, than scanf() format strings. The table below offers some more-or-less equivalent mappings between scanf() format tokens and regular expressions.

+ + + + + + + + + +

scanf() Token

Regular Expression

%c

.

%5c

.{5}

%d

[-+]?\d+

%e, %E, %f, %g

[-+]?(\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?

%i

[-+]?(0[xX][\dA-Fa-f]+|0[0-7]*|\d+)

%o

[-+]?[0-7]+

%s

\S+

%u

\d+

%x, %X

[-+]?(0[xX])?[\dA-Fa-f]+

To extract the filename and numbers from a string like

/usr/sbin/sendmail - 0 errors, 4 warnings
+

you would use a scanf() format like

%s - %d errors, %d warnings
+

The equivalent regular expression would be

(\S+) - (\d+) errors, (\d+) warnings
+

search() vs. match()

Python offers different primitive operations based on regular expressions:

  • +re.match() checks for a match only at the beginning of the string
  • +re.search() checks for a match anywhere in the string (this is what Perl does by default)
  • +re.fullmatch() checks for entire string to be a match

For example:

>>> re.match("c", "abcdef")    # No match
+>>> re.search("c", "abcdef")   # Match
+<re.Match object; span=(2, 3), match='c'>
+>>> re.fullmatch("p.*n", "python") # Match
+<re.Match object; span=(0, 6), match='python'>
+>>> re.fullmatch("r.*n", "python") # No match
+

Regular expressions beginning with '^' can be used with search() to restrict the match at the beginning of the string:

>>> re.match("c", "abcdef")    # No match
+>>> re.search("^c", "abcdef")  # No match
+>>> re.search("^a", "abcdef")  # Match
+<re.Match object; span=(0, 1), match='a'>
+

Note however that in MULTILINE mode match() only matches at the beginning of the string, whereas using search() with a regular expression beginning with '^' will match at the beginning of each line.

>>> re.match("X", "A\nB\nX", re.MULTILINE)  # No match
+>>> re.search("^X", "A\nB\nX", re.MULTILINE)  # Match
+<re.Match object; span=(4, 5), match='X'>
+

Making a Phonebook

split() splits a string into a list delimited by the passed pattern. The method is invaluable for converting textual data into data structures that can be easily read and modified by Python as demonstrated in the following example that creates a phonebook.

First, here is the input. Normally it may come from a file, here we are using triple-quoted string syntax

>>> text = """Ross McFluff: 834.345.1254 155 Elm Street
+...
+... Ronald Heathmore: 892.345.3428 436 Finley Avenue
+... Frank Burger: 925.541.7625 662 South Dogwood Way
+...
+...
+... Heather Albrecht: 548.326.4584 919 Park Place"""
+

The entries are separated by one or more newlines. Now we convert the string into a list with each nonempty line having its own entry:

>>> entries = re.split("\n+", text)
+>>> entries
+['Ross McFluff: 834.345.1254 155 Elm Street',
+'Ronald Heathmore: 892.345.3428 436 Finley Avenue',
+'Frank Burger: 925.541.7625 662 South Dogwood Way',
+'Heather Albrecht: 548.326.4584 919 Park Place']
+

Finally, split each entry into a list with first name, last name, telephone number, and address. We use the maxsplit parameter of split() because the address has spaces, our splitting pattern, in it:

>>> [re.split(":? ", entry, 3) for entry in entries]
+[['Ross', 'McFluff', '834.345.1254', '155 Elm Street'],
+['Ronald', 'Heathmore', '892.345.3428', '436 Finley Avenue'],
+['Frank', 'Burger', '925.541.7625', '662 South Dogwood Way'],
+['Heather', 'Albrecht', '548.326.4584', '919 Park Place']]
+

The :? pattern matches the colon after the last name, so that it does not occur in the result list. With a maxsplit of 4, we could separate the house number from the street name:

>>> [re.split(":? ", entry, 4) for entry in entries]
+[['Ross', 'McFluff', '834.345.1254', '155', 'Elm Street'],
+['Ronald', 'Heathmore', '892.345.3428', '436', 'Finley Avenue'],
+['Frank', 'Burger', '925.541.7625', '662', 'South Dogwood Way'],
+['Heather', 'Albrecht', '548.326.4584', '919', 'Park Place']]
+

Text Munging

sub() replaces every occurrence of a pattern with a string or the result of a function. This example demonstrates using sub() with a function to “munge” text, or randomize the order of all the characters in each word of a sentence except for the first and last characters:

>>> def repl(m):
+...     inner_word = list(m.group(2))
+...     random.shuffle(inner_word)
+...     return m.group(1) + "".join(inner_word) + m.group(3)
+...
+>>> text = "Professor Abdolmalek, please report your absences promptly."
+>>> re.sub(r"(\w)(\w+)(\w)", repl, text)
+'Poefsrosr Aealmlobdk, pslaee reorpt your abnseces plmrptoy.'
+>>> re.sub(r"(\w)(\w+)(\w)", repl, text)
+'Pofsroser Aodlambelk, plasee reoprt yuor asnebces potlmrpy.'
+

Finding all Adverbs

findall() matches all occurrences of a pattern, not just the first one as search() does. For example, if a writer wanted to find all of the adverbs in some text, they might use findall() in the following manner:

>>> text = "He was carefully disguised but captured quickly by police."
+>>> re.findall(r"\w+ly\b", text)
+['carefully', 'quickly']
+

Finding all Adverbs and their Positions

If one wants more information about all matches of a pattern than the matched text, finditer() is useful as it provides Match objects instead of strings. Continuing with the previous example, if a writer wanted to find all of the adverbs and their positions in some text, they would use finditer() in the following manner:

>>> text = "He was carefully disguised but captured quickly by police."
+>>> for m in re.finditer(r"\w+ly\b", text):
+...     print('%02d-%02d: %s' % (m.start(), m.end(), m.group(0)))
+07-16: carefully
+40-47: quickly
+

Raw String Notation

Raw string notation (r"text") keeps regular expressions sane. Without it, every backslash ('\') in a regular expression would have to be prefixed with another one to escape it. For example, the two following lines of code are functionally identical:

>>> re.match(r"\W(.)\1\W", " ff ")
+<re.Match object; span=(0, 4), match=' ff '>
+>>> re.match("\\W(.)\\1\\W", " ff ")
+<re.Match object; span=(0, 4), match=' ff '>
+

When one wants to match a literal backslash, it must be escaped in the regular expression. With raw string notation, this means r"\\". Without raw string notation, one must use "\\\\", making the following lines of code functionally identical:

>>> re.match(r"\\", r"\\")
+<re.Match object; span=(0, 1), match='\\'>
+>>> re.match("\\\\", r"\\")
+<re.Match object; span=(0, 1), match='\\'>
+

Writing a Tokenizer

A tokenizer or scanner analyzes a string to categorize groups of characters. This is a useful first step in writing a compiler or interpreter.

The text categories are specified with regular expressions. The technique is to combine those into a single master regular expression and to loop over successive matches:

from typing import NamedTuple
+import re
+
+class Token(NamedTuple):
+    type: str
+    value: str
+    line: int
+    column: int
+
+def tokenize(code):
+    keywords = {'IF', 'THEN', 'ENDIF', 'FOR', 'NEXT', 'GOSUB', 'RETURN'}
+    token_specification = [
+        ('NUMBER',   r'\d+(\.\d*)?'),  # Integer or decimal number
+        ('ASSIGN',   r':='),           # Assignment operator
+        ('END',      r';'),            # Statement terminator
+        ('ID',       r'[A-Za-z]+'),    # Identifiers
+        ('OP',       r'[+\-*/]'),      # Arithmetic operators
+        ('NEWLINE',  r'\n'),           # Line endings
+        ('SKIP',     r'[ \t]+'),       # Skip over spaces and tabs
+        ('MISMATCH', r'.'),            # Any other character
+    ]
+    tok_regex = '|'.join('(?P<%s>%s)' % pair for pair in token_specification)
+    line_num = 1
+    line_start = 0
+    for mo in re.finditer(tok_regex, code):
+        kind = mo.lastgroup
+        value = mo.group()
+        column = mo.start() - line_start
+        if kind == 'NUMBER':
+            value = float(value) if '.' in value else int(value)
+        elif kind == 'ID' and value in keywords:
+            kind = value
+        elif kind == 'NEWLINE':
+            line_start = mo.end()
+            line_num += 1
+            continue
+        elif kind == 'SKIP':
+            continue
+        elif kind == 'MISMATCH':
+            raise RuntimeError(f'{value!r} unexpected on line {line_num}')
+        yield Token(kind, value, line_num, column)
+
+statements = '''
+    IF quantity THEN
+        total := total + price * quantity;
+        tax := price * 0.05;
+    ENDIF;
+'''
+
+for token in tokenize(statements):
+    print(token)
+

The tokenizer produces the following output:

Token(type='IF', value='IF', line=2, column=4)
+Token(type='ID', value='quantity', line=2, column=7)
+Token(type='THEN', value='THEN', line=2, column=16)
+Token(type='ID', value='total', line=3, column=8)
+Token(type='ASSIGN', value=':=', line=3, column=14)
+Token(type='ID', value='total', line=3, column=17)
+Token(type='OP', value='+', line=3, column=23)
+Token(type='ID', value='price', line=3, column=25)
+Token(type='OP', value='*', line=3, column=31)
+Token(type='ID', value='quantity', line=3, column=33)
+Token(type='END', value=';', line=3, column=41)
+Token(type='ID', value='tax', line=4, column=8)
+Token(type='ASSIGN', value=':=', line=4, column=12)
+Token(type='ID', value='price', line=4, column=15)
+Token(type='OP', value='*', line=4, column=21)
+Token(type='NUMBER', value=0.05, line=4, column=23)
+Token(type='END', value=';', line=4, column=27)
+Token(type='ENDIF', value='ENDIF', line=5, column=4)
+Token(type='END', value=';', line=5, column=9)
+
+Frie09
+

Friedl, Jeffrey. Mastering Regular Expressions. 3rd ed., O’Reilly Media, 2009. The third edition of the book no longer covers Python at all, but the first edition covered writing good regular expression patterns in great detail.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/re.html +

+
diff --git a/devdocs/python~3.12/library%2Freadline.html b/devdocs/python~3.12/library%2Freadline.html new file mode 100644 index 00000000..149d3d7c --- /dev/null +++ b/devdocs/python~3.12/library%2Freadline.html @@ -0,0 +1,136 @@ +

readline — GNU readline interface

The readline module defines a number of functions to facilitate completion and reading/writing of history files from the Python interpreter. This module can be used directly, or via the rlcompleter module, which supports completion of Python identifiers at the interactive prompt. Settings made using this module affect the behaviour of both the interpreter’s interactive prompt and the prompts offered by the built-in input() function.

Readline keybindings may be configured via an initialization file, typically .inputrc in your home directory. See Readline Init File in the GNU Readline manual for information about the format and allowable constructs of that file, and the capabilities of the Readline library in general.

Note

The underlying Readline library API may be implemented by the libedit library instead of GNU readline. On macOS the readline module detects which library is being used at run time.

The configuration file for libedit is different from that of GNU readline. If you programmatically load configuration strings you can check for the text “libedit” in readline.__doc__ to differentiate between GNU readline and libedit.

If you use editline/libedit readline emulation on macOS, the initialization file located in your home directory is named .editrc. For example, the following content in ~/.editrc will turn ON vi keybindings and TAB completion:

python:bind -v
+python:bind ^I rl_complete
+

Init file

The following functions relate to the init file and user configuration:

+readline.parse_and_bind(string)
+

Execute the init line provided in the string argument. This calls rl_parse_and_bind() in the underlying library.

+
+readline.read_init_file([filename])
+

Execute a readline initialization file. The default filename is the last filename used. This calls rl_read_init_file() in the underlying library.

+

Line buffer

The following functions operate on the line buffer:

+readline.get_line_buffer()
+

Return the current contents of the line buffer (rl_line_buffer in the underlying library).

+
+readline.insert_text(string)
+

Insert text into the line buffer at the cursor position. This calls rl_insert_text() in the underlying library, but ignores the return value.

+
+readline.redisplay()
+

Change what’s displayed on the screen to reflect the current contents of the line buffer. This calls rl_redisplay() in the underlying library.

+

History file

The following functions operate on a history file:

+readline.read_history_file([filename])
+

Load a readline history file, and append it to the history list. The default filename is ~/.history. This calls read_history() in the underlying library.

+
+readline.write_history_file([filename])
+

Save the history list to a readline history file, overwriting any existing file. The default filename is ~/.history. This calls write_history() in the underlying library.

+
+readline.append_history_file(nelements[, filename])
+

Append the last nelements items of history to a file. The default filename is ~/.history. The file must already exist. This calls append_history() in the underlying library. This function only exists if Python was compiled for a version of the library that supports it.

New in version 3.5.

+
+readline.get_history_length()
+readline.set_history_length(length)
+

Set or return the desired number of lines to save in the history file. The write_history_file() function uses this value to truncate the history file, by calling history_truncate_file() in the underlying library. Negative values imply unlimited history file size.

+

History list

The following functions operate on a global history list:

+readline.clear_history()
+

Clear the current history. This calls clear_history() in the underlying library. The Python function only exists if Python was compiled for a version of the library that supports it.

+
+readline.get_current_history_length()
+

Return the number of items currently in the history. (This is different from get_history_length(), which returns the maximum number of lines that will be written to a history file.)

+
+readline.get_history_item(index)
+

Return the current contents of history item at index. The item index is one-based. This calls history_get() in the underlying library.

+
+readline.remove_history_item(pos)
+

Remove history item specified by its position from the history. The position is zero-based. This calls remove_history() in the underlying library.

+
+readline.replace_history_item(pos, line)
+

Replace history item specified by its position with line. The position is zero-based. This calls replace_history_entry() in the underlying library.

+
+readline.add_history(line)
+

Append line to the history buffer, as if it was the last line typed. This calls add_history() in the underlying library.

+
+readline.set_auto_history(enabled)
+

Enable or disable automatic calls to add_history() when reading input via readline. The enabled argument should be a Boolean value that when true, enables auto history, and that when false, disables auto history.

New in version 3.6.

CPython implementation detail: Auto history is enabled by default, and changes to this do not persist across multiple sessions.

+

Startup hooks

+readline.set_startup_hook([function])
+

Set or remove the function invoked by the rl_startup_hook callback of the underlying library. If function is specified, it will be used as the new hook function; if omitted or None, any function already installed is removed. The hook is called with no arguments just before readline prints the first prompt.

+
+readline.set_pre_input_hook([function])
+

Set or remove the function invoked by the rl_pre_input_hook callback of the underlying library. If function is specified, it will be used as the new hook function; if omitted or None, any function already installed is removed. The hook is called with no arguments after the first prompt has been printed and just before readline starts reading input characters. This function only exists if Python was compiled for a version of the library that supports it.

+

Completion

The following functions relate to implementing a custom word completion function. This is typically operated by the Tab key, and can suggest and automatically complete a word being typed. By default, Readline is set up to be used by rlcompleter to complete Python identifiers for the interactive interpreter. If the readline module is to be used with a custom completer, a different set of word delimiters should be set.

+readline.set_completer([function])
+

Set or remove the completer function. If function is specified, it will be used as the new completer function; if omitted or None, any completer function already installed is removed. The completer function is called as function(text, state), for state in 0, 1, 2, …, until it returns a non-string value. It should return the next possible completion starting with text.

The installed completer function is invoked by the entry_func callback passed to rl_completion_matches() in the underlying library. The text string comes from the first parameter to the rl_attempted_completion_function callback of the underlying library.

+
+readline.get_completer()
+

Get the completer function, or None if no completer function has been set.

+
+readline.get_completion_type()
+

Get the type of completion being attempted. This returns the rl_completion_type variable in the underlying library as an integer.

+
+readline.get_begidx()
+readline.get_endidx()
+

Get the beginning or ending index of the completion scope. These indexes are the start and end arguments passed to the rl_attempted_completion_function callback of the underlying library. The values may be different in the same input editing scenario based on the underlying C readline implementation. Ex: libedit is known to behave differently than libreadline.

+
+readline.set_completer_delims(string)
+readline.get_completer_delims()
+

Set or get the word delimiters for completion. These determine the start of the word to be considered for completion (the completion scope). These functions access the rl_completer_word_break_characters variable in the underlying library.

+
+readline.set_completion_display_matches_hook([function])
+

Set or remove the completion display function. If function is specified, it will be used as the new completion display function; if omitted or None, any completion display function already installed is removed. This sets or clears the rl_completion_display_matches_hook callback in the underlying library. The completion display function is called as function(substitution, [matches], longest_match_length) once each time matches need to be displayed.

+

Example

The following example demonstrates how to use the readline module’s history reading and writing functions to automatically load and save a history file named .python_history from the user’s home directory. The code below would normally be executed automatically during interactive sessions from the user’s PYTHONSTARTUP file.

import atexit
+import os
+import readline
+
+histfile = os.path.join(os.path.expanduser("~"), ".python_history")
+try:
+    readline.read_history_file(histfile)
+    # default history len is -1 (infinite), which may grow unruly
+    readline.set_history_length(1000)
+except FileNotFoundError:
+    pass
+
+atexit.register(readline.write_history_file, histfile)
+

This code is actually automatically run when Python is run in interactive mode (see Readline configuration).

The following example achieves the same goal but supports concurrent interactive sessions, by only appending the new history.

import atexit
+import os
+import readline
+histfile = os.path.join(os.path.expanduser("~"), ".python_history")
+
+try:
+    readline.read_history_file(histfile)
+    h_len = readline.get_current_history_length()
+except FileNotFoundError:
+    open(histfile, 'wb').close()
+    h_len = 0
+
+def save(prev_h_len, histfile):
+    new_h_len = readline.get_current_history_length()
+    readline.set_history_length(1000)
+    readline.append_history_file(new_h_len - prev_h_len, histfile)
+atexit.register(save, h_len, histfile)
+

The following example extends the code.InteractiveConsole class to support history save/restore.

import atexit
+import code
+import os
+import readline
+
+class HistoryConsole(code.InteractiveConsole):
+    def __init__(self, locals=None, filename="<console>",
+                 histfile=os.path.expanduser("~/.console-history")):
+        code.InteractiveConsole.__init__(self, locals, filename)
+        self.init_history(histfile)
+
+    def init_history(self, histfile):
+        readline.parse_and_bind("tab: complete")
+        if hasattr(readline, "read_history_file"):
+            try:
+                readline.read_history_file(histfile)
+            except FileNotFoundError:
+                pass
+            atexit.register(self.save_history, histfile)
+
+    def save_history(self, histfile):
+        readline.set_history_length(1000)
+        readline.write_history_file(histfile)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/readline.html +

+
diff --git a/devdocs/python~3.12/library%2Freprlib.html b/devdocs/python~3.12/library%2Freprlib.html new file mode 100644 index 00000000..d18293a7 --- /dev/null +++ b/devdocs/python~3.12/library%2Freprlib.html @@ -0,0 +1,117 @@ +

reprlib — Alternate repr() implementation

Source code: Lib/reprlib.py

The reprlib module provides a means for producing object representations with limits on the size of the resulting strings. This is used in the Python debugger and may be useful in other contexts as well.

This module provides a class, an instance, and a function:

+class reprlib.Repr(*, maxlevel=6, maxtuple=6, maxlist=6, maxarray=5, maxdict=4, maxset=6, maxfrozenset=6, maxdeque=6, maxstring=30, maxlong=40, maxother=30, fillvalue='...', indent=None)
+

Class which provides formatting services useful in implementing functions similar to the built-in repr(); size limits for different object types are added to avoid the generation of representations which are excessively long.

The keyword arguments of the constructor can be used as a shortcut to set the attributes of the Repr instance. Which means that the following initialization:

aRepr = reprlib.Repr(maxlevel=3)
+

Is equivalent to:

aRepr = reprlib.Repr()
+aRepr.maxlevel = 3
+

See section Repr Objects for more information about Repr attributes.

Changed in version 3.12: Allow attributes to be set via keyword arguments.

+
+reprlib.aRepr
+

This is an instance of Repr which is used to provide the repr() function described below. Changing the attributes of this object will affect the size limits used by repr() and the Python debugger.

+
+reprlib.repr(obj)
+

This is the repr() method of aRepr. It returns a string similar to that returned by the built-in function of the same name, but with limits on most sizes.

+

In addition to size-limiting tools, the module also provides a decorator for detecting recursive calls to __repr__() and substituting a placeholder string instead.

+@reprlib.recursive_repr(fillvalue='...')
+

Decorator for __repr__() methods to detect recursive calls within the same thread. If a recursive call is made, the fillvalue is returned, otherwise, the usual __repr__() call is made. For example:

>>> from reprlib import recursive_repr
+>>> class MyList(list):
+...     @recursive_repr()
+...     def __repr__(self):
+...         return '<' + '|'.join(map(repr, self)) + '>'
+...
+>>> m = MyList('abc')
+>>> m.append(m)
+>>> m.append('x')
+>>> print(m)
+<'a'|'b'|'c'|...|'x'>
+

New in version 3.2.

+

Repr Objects

Repr instances provide several attributes which can be used to provide size limits for the representations of different object types, and methods which format specific object types.

+Repr.fillvalue
+

This string is displayed for recursive references. It defaults to ....

New in version 3.11.

+
+Repr.maxlevel
+

Depth limit on the creation of recursive representations. The default is 6.

+
+Repr.maxdict
+Repr.maxlist
+Repr.maxtuple
+Repr.maxset
+Repr.maxfrozenset
+Repr.maxdeque
+Repr.maxarray
+

Limits on the number of entries represented for the named object type. The default is 4 for maxdict, 5 for maxarray, and 6 for the others.

+
+Repr.maxlong
+

Maximum number of characters in the representation for an integer. Digits are dropped from the middle. The default is 40.

+
+Repr.maxstring
+

Limit on the number of characters in the representation of the string. Note that the “normal” representation of the string is used as the character source: if escape sequences are needed in the representation, these may be mangled when the representation is shortened. The default is 30.

+
+Repr.maxother
+

This limit is used to control the size of object types for which no specific formatting method is available on the Repr object. It is applied in a similar manner as maxstring. The default is 20.

+
+Repr.indent
+

If this attribute is set to None (the default), the output is formatted with no line breaks or indentation, like the standard repr(). For example:

>>> example = [
+...     1, 'spam', {'a': 2, 'b': 'spam eggs', 'c': {3: 4.5, 6: []}}, 'ham']
+>>> import reprlib
+>>> aRepr = reprlib.Repr()
+>>> print(aRepr.repr(example))
+[1, 'spam', {'a': 2, 'b': 'spam eggs', 'c': {3: 4.5, 6: []}}, 'ham']
+

If indent is set to a string, each recursion level is placed on its own line, indented by that string:

>>> aRepr.indent = '-->'
+>>> print(aRepr.repr(example))
+[
+-->1,
+-->'spam',
+-->{
+-->-->'a': 2,
+-->-->'b': 'spam eggs',
+-->-->'c': {
+-->-->-->3: 4.5,
+-->-->-->6: [],
+-->-->},
+-->},
+-->'ham',
+]
+

Setting indent to a positive integer value behaves as if it was set to a string with that number of spaces:

>>> aRepr.indent = 4
+>>> print(aRepr.repr(example))
+[
+    1,
+    'spam',
+    {
+        'a': 2,
+        'b': 'spam eggs',
+        'c': {
+            3: 4.5,
+            6: [],
+        },
+    },
+    'ham',
+]
+

New in version 3.12.

+
+Repr.repr(obj)
+

The equivalent to the built-in repr() that uses the formatting imposed by the instance.

+
+Repr.repr1(obj, level)
+

Recursive implementation used by repr(). This uses the type of obj to determine which formatting method to call, passing it obj and level. The type-specific methods should call repr1() to perform recursive formatting, with level - 1 for the value of level in the recursive call.

+
Repr.repr_TYPE(obj, level) +
+

Formatting methods for specific types are implemented as methods with a name based on the type name. In the method name, TYPE is replaced by '_'.join(type(obj).__name__.split()). Dispatch to these methods is handled by repr1(). Type-specific methods which need to recursively format a value should call self.repr1(subobj, level - 1).

+

Subclassing Repr Objects

The use of dynamic dispatching by Repr.repr1() allows subclasses of Repr to add support for additional built-in object types or to modify the handling of types already supported. This example shows how special support for file objects could be added:

import reprlib
+import sys
+
+class MyRepr(reprlib.Repr):
+
+    def repr_TextIOWrapper(self, obj, level):
+        if obj.name in {'<stdin>', '<stdout>', '<stderr>'}:
+            return obj.name
+        return repr(obj)
+
+aRepr = MyRepr()
+print(aRepr.repr(sys.stdin))         # prints '<stdin>'
+
<stdin>
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/reprlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fresource.html b/devdocs/python~3.12/library%2Fresource.html new file mode 100644 index 00000000..9cc59a72 --- /dev/null +++ b/devdocs/python~3.12/library%2Fresource.html @@ -0,0 +1,130 @@ +

resource — Resource usage information

This module provides basic mechanisms for measuring and controlling system resources utilized by a program.

Availability: Unix, not Emscripten, not WASI.

Symbolic constants are used to specify particular system resources and to request usage information about either the current process or its children.

An OSError is raised on syscall failure.

+exception resource.error
+

A deprecated alias of OSError.

Changed in version 3.3: Following PEP 3151, this class was made an alias of OSError.

+

Resource Limits

Resources usage can be limited using the setrlimit() function described below. Each resource is controlled by a pair of limits: a soft limit and a hard limit. The soft limit is the current limit, and may be lowered or raised by a process over time. The soft limit can never exceed the hard limit. The hard limit can be lowered to any value greater than the soft limit, but not raised. (Only processes with the effective UID of the super-user can raise a hard limit.)

The specific resources that can be limited are system dependent. They are described in the getrlimit(2) man page. The resources listed below are supported when the underlying operating system supports them; resources which cannot be checked or controlled by the operating system are not defined in this module for those platforms.

+resource.RLIM_INFINITY
+

Constant used to represent the limit for an unlimited resource.

+
+resource.getrlimit(resource)
+

Returns a tuple (soft, hard) with the current soft and hard limits of resource. Raises ValueError if an invalid resource is specified, or error if the underlying system call fails unexpectedly.

+
+resource.setrlimit(resource, limits)
+

Sets new limits of consumption of resource. The limits argument must be a tuple (soft, hard) of two integers describing the new limits. A value of RLIM_INFINITY can be used to request a limit that is unlimited.

Raises ValueError if an invalid resource is specified, if the new soft limit exceeds the hard limit, or if a process tries to raise its hard limit. Specifying a limit of RLIM_INFINITY when the hard or system limit for that resource is not unlimited will result in a ValueError. A process with the effective UID of super-user can request any valid limit value, including unlimited, but ValueError will still be raised if the requested limit exceeds the system imposed limit.

setrlimit may also raise error if the underlying system call fails.

VxWorks only supports setting RLIMIT_NOFILE.

Raises an auditing event resource.setrlimit with arguments resource, limits.

+
+resource.prlimit(pid, resource[, limits])
+

Combines setrlimit() and getrlimit() in one function and supports to get and set the resources limits of an arbitrary process. If pid is 0, then the call applies to the current process. resource and limits have the same meaning as in setrlimit(), except that limits is optional.

When limits is not given the function returns the resource limit of the process pid. When limits is given the resource limit of the process is set and the former resource limit is returned.

Raises ProcessLookupError when pid can’t be found and PermissionError when the user doesn’t have CAP_SYS_RESOURCE for the process.

Raises an auditing event resource.prlimit with arguments pid, resource, limits.

Availability: Linux >= 2.6.36 with glibc >= 2.13.

New in version 3.4.

+

These symbols define resources whose consumption can be controlled using the setrlimit() and getrlimit() functions described below. The values of these symbols are exactly the constants used by C programs.

The Unix man page for getrlimit(2) lists the available resources. Note that not all systems use the same symbol or same value to denote the same resource. This module does not attempt to mask platform differences — symbols not defined for a platform will not be available from this module on that platform.

+resource.RLIMIT_CORE
+

The maximum size (in bytes) of a core file that the current process can create. This may result in the creation of a partial core file if a larger core would be required to contain the entire process image.

+
+resource.RLIMIT_CPU
+

The maximum amount of processor time (in seconds) that a process can use. If this limit is exceeded, a SIGXCPU signal is sent to the process. (See the signal module documentation for information about how to catch this signal and do something useful, e.g. flush open files to disk.)

+
+resource.RLIMIT_FSIZE
+

The maximum size of a file which the process may create.

+
+resource.RLIMIT_DATA
+

The maximum size (in bytes) of the process’s heap.

+
+resource.RLIMIT_STACK
+

The maximum size (in bytes) of the call stack for the current process. This only affects the stack of the main thread in a multi-threaded process.

+
+resource.RLIMIT_RSS
+

The maximum resident set size that should be made available to the process.

+
+resource.RLIMIT_NPROC
+

The maximum number of processes the current process may create.

+
+resource.RLIMIT_NOFILE
+

The maximum number of open file descriptors for the current process.

+
+resource.RLIMIT_OFILE
+

The BSD name for RLIMIT_NOFILE.

+
+resource.RLIMIT_MEMLOCK
+

The maximum address space which may be locked in memory.

+
+resource.RLIMIT_VMEM
+

The largest area of mapped memory which the process may occupy.

+
+resource.RLIMIT_AS
+

The maximum area (in bytes) of address space which may be taken by the process.

+
+resource.RLIMIT_MSGQUEUE
+

The number of bytes that can be allocated for POSIX message queues.

Availability: Linux >= 2.6.8.

New in version 3.4.

+
+resource.RLIMIT_NICE
+

The ceiling for the process’s nice level (calculated as 20 - rlim_cur).

Availability: Linux >= 2.6.12.

New in version 3.4.

+
+resource.RLIMIT_RTPRIO
+

The ceiling of the real-time priority.

Availability: Linux >= 2.6.12.

New in version 3.4.

+
+resource.RLIMIT_RTTIME
+

The time limit (in microseconds) on CPU time that a process can spend under real-time scheduling without making a blocking syscall.

Availability: Linux >= 2.6.25.

New in version 3.4.

+
+resource.RLIMIT_SIGPENDING
+

The number of signals which the process may queue.

Availability: Linux >= 2.6.8.

New in version 3.4.

+
+resource.RLIMIT_SBSIZE
+

The maximum size (in bytes) of socket buffer usage for this user. This limits the amount of network memory, and hence the amount of mbufs, that this user may hold at any time.

Availability: FreeBSD.

New in version 3.4.

+
+resource.RLIMIT_SWAP
+

The maximum size (in bytes) of the swap space that may be reserved or used by all of this user id’s processes. This limit is enforced only if bit 1 of the vm.overcommit sysctl is set. Please see tuning(7) for a complete description of this sysctl.

Availability: FreeBSD.

New in version 3.4.

+
+resource.RLIMIT_NPTS
+

The maximum number of pseudo-terminals created by this user id.

Availability: FreeBSD.

New in version 3.4.

+
+resource.RLIMIT_KQUEUES
+

The maximum number of kqueues this user id is allowed to create.

Availability: FreeBSD >= 11.

New in version 3.10.

+

Resource Usage

These functions are used to retrieve resource usage information:

+resource.getrusage(who)
+

This function returns an object that describes the resources consumed by either the current process or its children, as specified by the who parameter. The who parameter should be specified using one of the RUSAGE_* constants described below.

A simple example:

from resource import *
+import time
+
+# a non CPU-bound task
+time.sleep(3)
+print(getrusage(RUSAGE_SELF))
+
+# a CPU-bound task
+for i in range(10 ** 8):
+   _ = 1 + 1
+print(getrusage(RUSAGE_SELF))
+

The fields of the return value each describe how a particular system resource has been used, e.g. amount of time spent running is user mode or number of times the process was swapped out of main memory. Some values are dependent on the clock tick internal, e.g. the amount of memory the process is using.

For backward compatibility, the return value is also accessible as a tuple of 16 elements.

The fields ru_utime and ru_stime of the return value are floating point values representing the amount of time spent executing in user mode and the amount of time spent executing in system mode, respectively. The remaining values are integers. Consult the getrusage(2) man page for detailed information about these values. A brief summary is presented here:

+ + + + + + + + + + + + + + + + +

Index

Field

Resource

0

ru_utime

time in user mode (float seconds)

1

ru_stime

time in system mode (float seconds)

2

ru_maxrss

maximum resident set size

3

ru_ixrss

shared memory size

4

ru_idrss

unshared memory size

5

ru_isrss

unshared stack size

6

ru_minflt

page faults not requiring I/O

7

ru_majflt

page faults requiring I/O

8

ru_nswap

number of swap outs

9

ru_inblock

block input operations

10

ru_oublock

block output operations

11

ru_msgsnd

messages sent

12

ru_msgrcv

messages received

13

ru_nsignals

signals received

14

ru_nvcsw

voluntary context switches

15

ru_nivcsw

involuntary context switches

This function will raise a ValueError if an invalid who parameter is specified. It may also raise error exception in unusual circumstances.

+
+resource.getpagesize()
+

Returns the number of bytes in a system page. (This need not be the same as the hardware page size.)

+

The following RUSAGE_* symbols are passed to the getrusage() function to specify which processes information should be provided for.

+resource.RUSAGE_SELF
+

Pass to getrusage() to request resources consumed by the calling process, which is the sum of resources used by all threads in the process.

+
+resource.RUSAGE_CHILDREN
+

Pass to getrusage() to request resources consumed by child processes of the calling process which have been terminated and waited for.

+
+resource.RUSAGE_BOTH
+

Pass to getrusage() to request resources consumed by both the current process and child processes. May not be available on all systems.

+
+resource.RUSAGE_THREAD
+

Pass to getrusage() to request resources consumed by the current thread. May not be available on all systems.

New in version 3.2.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/resource.html +

+
diff --git a/devdocs/python~3.12/library%2Frlcompleter.html b/devdocs/python~3.12/library%2Frlcompleter.html new file mode 100644 index 00000000..0bc005cb --- /dev/null +++ b/devdocs/python~3.12/library%2Frlcompleter.html @@ -0,0 +1,20 @@ +

rlcompleter — Completion function for GNU readline

Source code: Lib/rlcompleter.py

The rlcompleter module defines a completion function suitable to be passed to set_completer() in the readline module.

When this module is imported on a Unix platform with the readline module available, an instance of the Completer class is automatically created and its complete() method is set as the readline completer. The method provides completion of valid Python identifiers and keywords.

Example:

>>> import rlcompleter
+>>> import readline
+>>> readline.parse_and_bind("tab: complete")
+>>> readline. <TAB PRESSED>
+readline.__doc__          readline.get_line_buffer(  readline.read_init_file(
+readline.__file__         readline.insert_text(      readline.set_completer(
+readline.__name__         readline.parse_and_bind(
+>>> readline.
+

The rlcompleter module is designed for use with Python’s interactive mode. Unless Python is run with the -S option, the module is automatically imported and configured (see Readline configuration).

On platforms without readline, the Completer class defined by this module can still be used for custom purposes.

+class rlcompleter.Completer
+

Completer objects have the following method:

+complete(text, state)
+

Return the next possible completion for text.

When called by the readline module, this method is called successively with state == 0, 1, 2, ... until the method returns None.

If called for text that doesn’t include a period character ('.'), it will complete from names currently defined in __main__, builtins and keywords (as defined by the keyword module).

If called for a dotted name, it will try to evaluate anything without obvious side-effects (functions will not be evaluated, but it can generate calls to __getattr__()) up to the last part, and find matches for the rest via the dir() function. Any exception raised during the evaluation of the expression is caught, silenced and None is returned.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/rlcompleter.html +

+
diff --git a/devdocs/python~3.12/library%2Frunpy.html b/devdocs/python~3.12/library%2Frunpy.html new file mode 100644 index 00000000..c5abfda7 --- /dev/null +++ b/devdocs/python~3.12/library%2Frunpy.html @@ -0,0 +1,21 @@ +

runpy — Locating and executing Python modules

Source code: Lib/runpy.py

The runpy module is used to locate and run Python modules without importing them first. Its main use is to implement the -m command line switch that allows scripts to be located using the Python module namespace rather than the filesystem.

Note that this is not a sandbox module - all code is executed in the current process, and any side effects (such as cached imports of other modules) will remain in place after the functions have returned.

Furthermore, any functions and classes defined by the executed code are not guaranteed to work correctly after a runpy function has returned. If that limitation is not acceptable for a given use case, importlib is likely to be a more suitable choice than this module.

The runpy module provides two functions:

+runpy.run_module(mod_name, init_globals=None, run_name=None, alter_sys=False)
+

Execute the code of the specified module and return the resulting module globals dictionary. The module’s code is first located using the standard import mechanism (refer to PEP 302 for details) and then executed in a fresh module namespace.

The mod_name argument should be an absolute module name. If the module name refers to a package rather than a normal module, then that package is imported and the __main__ submodule within that package is then executed and the resulting module globals dictionary returned.

The optional dictionary argument init_globals may be used to pre-populate the module’s globals dictionary before the code is executed. The supplied dictionary will not be modified. If any of the special global variables below are defined in the supplied dictionary, those definitions are overridden by run_module().

The special global variables __name__, __spec__, __file__, __cached__, __loader__ and __package__ are set in the globals dictionary before the module code is executed (Note that this is a minimal set of variables - other variables may be set implicitly as an interpreter implementation detail).

__name__ is set to run_name if this optional argument is not None, to mod_name + '.__main__' if the named module is a package and to the mod_name argument otherwise.

__spec__ will be set appropriately for the actually imported module (that is, __spec__.name will always be mod_name or mod_name + '.__main__, never run_name).

__file__, __cached__, __loader__ and __package__ are set as normal based on the module spec.

If the argument alter_sys is supplied and evaluates to True, then sys.argv[0] is updated with the value of __file__ and sys.modules[__name__] is updated with a temporary module object for the module being executed. Both sys.argv[0] and sys.modules[__name__] are restored to their original values before the function returns.

Note that this manipulation of sys is not thread-safe. Other threads may see the partially initialised module, as well as the altered list of arguments. It is recommended that the sys module be left alone when invoking this function from threaded code.

See also

The -m option offering equivalent functionality from the command line.

Changed in version 3.1: Added ability to execute packages by looking for a __main__ submodule.

Changed in version 3.2: Added __cached__ global variable (see PEP 3147).

Changed in version 3.4: Updated to take advantage of the module spec feature added by PEP 451. This allows __cached__ to be set correctly for modules run this way, as well as ensuring the real module name is always accessible as __spec__.name.

Changed in version 3.12: The setting of __cached__, __loader__, and __package__ are deprecated. See ModuleSpec for alternatives.

+
+runpy.run_path(path_name, init_globals=None, run_name=None)
+

Execute the code at the named filesystem location and return the resulting module globals dictionary. As with a script name supplied to the CPython command line, the supplied path may refer to a Python source file, a compiled bytecode file or a valid sys.path entry containing a __main__ module (e.g. a zipfile containing a top-level __main__.py file).

For a simple script, the specified code is simply executed in a fresh module namespace. For a valid sys.path entry (typically a zipfile or directory), the entry is first added to the beginning of sys.path. The function then looks for and executes a __main__ module using the updated path. Note that there is no special protection against invoking an existing __main__ entry located elsewhere on sys.path if there is no such module at the specified location.

The optional dictionary argument init_globals may be used to pre-populate the module’s globals dictionary before the code is executed. The supplied dictionary will not be modified. If any of the special global variables below are defined in the supplied dictionary, those definitions are overridden by run_path().

The special global variables __name__, __spec__, __file__, __cached__, __loader__ and __package__ are set in the globals dictionary before the module code is executed (Note that this is a minimal set of variables - other variables may be set implicitly as an interpreter implementation detail).

__name__ is set to run_name if this optional argument is not None and to '<run_path>' otherwise.

If the supplied path directly references a script file (whether as source or as precompiled byte code), then __file__ will be set to the supplied path, and __spec__, __cached__, __loader__ and __package__ will all be set to None.

If the supplied path is a reference to a valid sys.path entry, then __spec__ will be set appropriately for the imported __main__ module (that is, __spec__.name will always be __main__). __file__, __cached__, __loader__ and __package__ will be set as normal based on the module spec.

A number of alterations are also made to the sys module. Firstly, sys.path may be altered as described above. sys.argv[0] is updated with the value of path_name and sys.modules[__name__] is updated with a temporary module object for the module being executed. All modifications to items in sys are reverted before the function returns.

Note that, unlike run_module(), the alterations made to sys are not optional in this function as these adjustments are essential to allowing the execution of sys.path entries. As the thread-safety limitations still apply, use of this function in threaded code should be either serialised with the import lock or delegated to a separate process.

See also

Interface options for equivalent functionality on the command line (python path/to/script).

New in version 3.2.

Changed in version 3.4: Updated to take advantage of the module spec feature added by PEP 451. This allows __cached__ to be set correctly in the case where __main__ is imported from a valid sys.path entry rather than being executed directly.

Changed in version 3.12: The setting of __cached__, __loader__, and __package__ are deprecated.

+

See also

+PEP 338 – Executing modules as scripts
+
+

PEP written and implemented by Nick Coghlan.

+PEP 366 – Main module explicit relative imports
+
+

PEP written and implemented by Nick Coghlan.

+PEP 451 – A ModuleSpec Type for the Import System
+
+

PEP written and implemented by Eric Snow

Command line and environment - CPython command line details

The importlib.import_module() function

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/runpy.html +

+
diff --git a/devdocs/python~3.12/library%2Fsched.html b/devdocs/python~3.12/library%2Fsched.html new file mode 100644 index 00000000..5f1ae2a6 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsched.html @@ -0,0 +1,51 @@ +

sched — Event scheduler

Source code: Lib/sched.py

The sched module defines a class which implements a general purpose event scheduler:

+class sched.scheduler(timefunc=time.monotonic, delayfunc=time.sleep)
+

The scheduler class defines a generic interface to scheduling events. It needs two functions to actually deal with the “outside world” — timefunc should be callable without arguments, and return a number (the “time”, in any units whatsoever). The delayfunc function should be callable with one argument, compatible with the output of timefunc, and should delay that many time units. delayfunc will also be called with the argument 0 after each event is run to allow other threads an opportunity to run in multi-threaded applications.

Changed in version 3.3: timefunc and delayfunc parameters are optional.

Changed in version 3.3: scheduler class can be safely used in multi-threaded environments.

+

Example:

>>> import sched, time
+>>> s = sched.scheduler(time.monotonic, time.sleep)
+>>> def print_time(a='default'):
+...     print("From print_time", time.time(), a)
+...
+>>> def print_some_times():
+...     print(time.time())
+...     s.enter(10, 1, print_time)
+...     s.enter(5, 2, print_time, argument=('positional',))
+...     # despite having higher priority, 'keyword' runs after 'positional' as enter() is relative
+...     s.enter(5, 1, print_time, kwargs={'a': 'keyword'})
+...     s.enterabs(1_650_000_000, 10, print_time, argument=("first enterabs",))
+...     s.enterabs(1_650_000_000, 5, print_time, argument=("second enterabs",))
+...     s.run()
+...     print(time.time())
+...
+>>> print_some_times()
+1652342830.3640375
+From print_time 1652342830.3642538 second enterabs
+From print_time 1652342830.3643398 first enterabs
+From print_time 1652342835.3694863 positional
+From print_time 1652342835.3696074 keyword
+From print_time 1652342840.369612 default
+1652342840.3697174
+

Scheduler Objects

scheduler instances have the following methods and attributes:

+scheduler.enterabs(time, priority, action, argument=(), kwargs={})
+

Schedule a new event. The time argument should be a numeric type compatible with the return value of the timefunc function passed to the constructor. Events scheduled for the same time will be executed in the order of their priority. A lower number represents a higher priority.

Executing the event means executing action(*argument, **kwargs). argument is a sequence holding the positional arguments for action. kwargs is a dictionary holding the keyword arguments for action.

Return value is an event which may be used for later cancellation of the event (see cancel()).

Changed in version 3.3: argument parameter is optional.

Changed in version 3.3: kwargs parameter was added.

+
+scheduler.enter(delay, priority, action, argument=(), kwargs={})
+

Schedule an event for delay more time units. Other than the relative time, the other arguments, the effect and the return value are the same as those for enterabs().

Changed in version 3.3: argument parameter is optional.

Changed in version 3.3: kwargs parameter was added.

+
+scheduler.cancel(event)
+

Remove the event from the queue. If event is not an event currently in the queue, this method will raise a ValueError.

+
+scheduler.empty()
+

Return True if the event queue is empty.

+
+scheduler.run(blocking=True)
+

Run all scheduled events. This method will wait (using the delayfunc function passed to the constructor) for the next event, then execute it and so on until there are no more scheduled events.

If blocking is false executes the scheduled events due to expire soonest (if any) and then return the deadline of the next scheduled call in the scheduler (if any).

Either action or delayfunc can raise an exception. In either case, the scheduler will maintain a consistent state and propagate the exception. If an exception is raised by action, the event will not be attempted in future calls to run().

If a sequence of events takes longer to run than the time available before the next event, the scheduler will simply fall behind. No events will be dropped; the calling code is responsible for canceling events which are no longer pertinent.

Changed in version 3.3: blocking parameter was added.

+
+scheduler.queue
+

Read-only attribute returning a list of upcoming events in the order they will be run. Each event is shown as a named tuple with the following fields: time, priority, action, argument, kwargs.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sched.html +

+
diff --git a/devdocs/python~3.12/library%2Fsecrets.html b/devdocs/python~3.12/library%2Fsecrets.html new file mode 100644 index 00000000..6983094f --- /dev/null +++ b/devdocs/python~3.12/library%2Fsecrets.html @@ -0,0 +1,57 @@ +

secrets — Generate secure random numbers for managing secrets

New in version 3.6.

Source code: Lib/secrets.py

The secrets module is used for generating cryptographically strong random numbers suitable for managing data such as passwords, account authentication, security tokens, and related secrets.

In particular, secrets should be used in preference to the default pseudo-random number generator in the random module, which is designed for modelling and simulation, not security or cryptography.

See also

PEP 506

Random numbers

The secrets module provides access to the most secure source of randomness that your operating system provides.

+class secrets.SystemRandom
+

A class for generating random numbers using the highest-quality sources provided by the operating system. See random.SystemRandom for additional details.

+
+secrets.choice(sequence)
+

Return a randomly chosen element from a non-empty sequence.

+
+secrets.randbelow(n)
+

Return a random int in the range [0, n).

+
+secrets.randbits(k)
+

Return an int with k random bits.

+

Generating tokens

The secrets module provides functions for generating secure tokens, suitable for applications such as password resets, hard-to-guess URLs, and similar.

+secrets.token_bytes([nbytes=None])
+

Return a random byte string containing nbytes number of bytes. If nbytes is None or not supplied, a reasonable default is used.

>>> token_bytes(16)  
+b'\xebr\x17D*t\xae\xd4\xe3S\xb6\xe2\xebP1\x8b'
+
+
+secrets.token_hex([nbytes=None])
+

Return a random text string, in hexadecimal. The string has nbytes random bytes, each byte converted to two hex digits. If nbytes is None or not supplied, a reasonable default is used.

>>> token_hex(16)  
+'f9bf78b9a18ce6d46a0cd2b0b86df9da'
+
+
+secrets.token_urlsafe([nbytes=None])
+

Return a random URL-safe text string, containing nbytes random bytes. The text is Base64 encoded, so on average each byte results in approximately 1.3 characters. If nbytes is None or not supplied, a reasonable default is used.

>>> token_urlsafe(16)  
+'Drmhze6EPcv0fN_81Bj-nA'
+
+

How many bytes should tokens use?

To be secure against brute-force attacks, tokens need to have sufficient randomness. Unfortunately, what is considered sufficient will necessarily increase as computers get more powerful and able to make more guesses in a shorter period. As of 2015, it is believed that 32 bytes (256 bits) of randomness is sufficient for the typical use-case expected for the secrets module.

For those who want to manage their own token length, you can explicitly specify how much randomness is used for tokens by giving an int argument to the various token_* functions. That argument is taken as the number of bytes of randomness to use.

Otherwise, if no argument is provided, or if the argument is None, the token_* functions will use a reasonable default instead.

Note

That default is subject to change at any time, including during maintenance releases.

Other functions

+secrets.compare_digest(a, b)
+

Return True if strings or bytes-like objects a and b are equal, otherwise False, using a “constant-time compare” to reduce the risk of timing attacks. See hmac.compare_digest() for additional details.

+

Recipes and best practices

This section shows recipes and best practices for using secrets to manage a basic level of security.

Generate an eight-character alphanumeric password:

import string
+import secrets
+alphabet = string.ascii_letters + string.digits
+password = ''.join(secrets.choice(alphabet) for i in range(8))
+

Note

Applications should not store passwords in a recoverable format, whether plain text or encrypted. They should be salted and hashed using a cryptographically strong one-way (irreversible) hash function.

Generate a ten-character alphanumeric password with at least one lowercase character, at least one uppercase character, and at least three digits:

import string
+import secrets
+alphabet = string.ascii_letters + string.digits
+while True:
+    password = ''.join(secrets.choice(alphabet) for i in range(10))
+    if (any(c.islower() for c in password)
+            and any(c.isupper() for c in password)
+            and sum(c.isdigit() for c in password) >= 3):
+        break
+

Generate an XKCD-style passphrase:

import secrets
+# On standard Linux systems, use a convenient dictionary file.
+# Other platforms may need to provide their own word-list.
+with open('/usr/share/dict/words') as f:
+    words = [word.strip() for word in f]
+    password = ' '.join(secrets.choice(words) for i in range(4))
+

Generate a hard-to-guess temporary URL containing a security token suitable for password recovery applications:

import secrets
+url = 'https://example.com/reset=' + secrets.token_urlsafe()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/secrets.html +

+
diff --git a/devdocs/python~3.12/library%2Fsecurity_warnings.html b/devdocs/python~3.12/library%2Fsecurity_warnings.html new file mode 100644 index 00000000..7d5c0b93 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsecurity_warnings.html @@ -0,0 +1,32 @@ +

Security Considerations

The following modules have specific security considerations:

The -I command line option can be used to run Python in isolated mode. When it cannot be used, the -P option or the PYTHONSAFEPATH environment variable can be used to not prepend a potentially unsafe path to sys.path such as the current directory, the script’s directory or an empty string.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/security_warnings.html +

+
diff --git a/devdocs/python~3.12/library%2Fselect.html b/devdocs/python~3.12/library%2Fselect.html new file mode 100644 index 00000000..5817bc58 --- /dev/null +++ b/devdocs/python~3.12/library%2Fselect.html @@ -0,0 +1,191 @@ +

select — Waiting for I/O completion

This module provides access to the select() and poll() functions available in most operating systems, devpoll() available on Solaris and derivatives, epoll() available on Linux 2.5+ and kqueue() available on most BSD. Note that on Windows, it only works for sockets; on other operating systems, it also works for other file types (in particular, on Unix, it works on pipes). It cannot be used on regular files to determine whether a file has grown since it was last read.

Note

The selectors module allows high-level and efficient I/O multiplexing, built upon the select module primitives. Users are encouraged to use the selectors module instead, unless they want precise control over the OS-level primitives used.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The module defines the following:

+exception select.error
+

A deprecated alias of OSError.

Changed in version 3.3: Following PEP 3151, this class was made an alias of OSError.

+
+select.devpoll()
+

(Only supported on Solaris and derivatives.) Returns a /dev/poll polling object; see section /dev/poll Polling Objects below for the methods supported by devpoll objects.

devpoll() objects are linked to the number of file descriptors allowed at the time of instantiation. If your program reduces this value, devpoll() will fail. If your program increases this value, devpoll() may return an incomplete list of active file descriptors.

The new file descriptor is non-inheritable.

New in version 3.3.

Changed in version 3.4: The new file descriptor is now non-inheritable.

+
+select.epoll(sizehint=- 1, flags=0)
+

(Only supported on Linux 2.5.44 and newer.) Return an edge polling object, which can be used as Edge or Level Triggered interface for I/O events.

sizehint informs epoll about the expected number of events to be registered. It must be positive, or -1 to use the default. It is only used on older systems where epoll_create1() is not available; otherwise it has no effect (though its value is still checked).

flags is deprecated and completely ignored. However, when supplied, its value must be 0 or select.EPOLL_CLOEXEC, otherwise OSError is raised.

See the Edge and Level Trigger Polling (epoll) Objects section below for the methods supported by epolling objects.

epoll objects support the context management protocol: when used in a with statement, the new file descriptor is automatically closed at the end of the block.

The new file descriptor is non-inheritable.

Changed in version 3.3: Added the flags parameter.

Changed in version 3.4: Support for the with statement was added. The new file descriptor is now non-inheritable.

Deprecated since version 3.4: The flags parameter. select.EPOLL_CLOEXEC is used by default now. Use os.set_inheritable() to make the file descriptor inheritable.

+
+select.poll()
+

(Not supported by all operating systems.) Returns a polling object, which supports registering and unregistering file descriptors, and then polling them for I/O events; see section Polling Objects below for the methods supported by polling objects.

+
+select.kqueue()
+

(Only supported on BSD.) Returns a kernel queue object; see section Kqueue Objects below for the methods supported by kqueue objects.

The new file descriptor is non-inheritable.

Changed in version 3.4: The new file descriptor is now non-inheritable.

+
+select.kevent(ident, filter=KQ_FILTER_READ, flags=KQ_EV_ADD, fflags=0, data=0, udata=0)
+

(Only supported on BSD.) Returns a kernel event object; see section Kevent Objects below for the methods supported by kevent objects.

+
+select.select(rlist, wlist, xlist[, timeout])
+

This is a straightforward interface to the Unix select() system call. The first three arguments are iterables of ‘waitable objects’: either integers representing file descriptors or objects with a parameterless method named fileno() returning such an integer:

  • +rlist: wait until ready for reading
  • +wlist: wait until ready for writing
  • +xlist: wait for an “exceptional condition” (see the manual page for what your system considers such a condition)

Empty iterables are allowed, but acceptance of three empty iterables is platform-dependent. (It is known to work on Unix but not on Windows.) The optional timeout argument specifies a time-out as a floating point number in seconds. When the timeout argument is omitted the function blocks until at least one file descriptor is ready. A time-out value of zero specifies a poll and never blocks.

The return value is a triple of lists of objects that are ready: subsets of the first three arguments. When the time-out is reached without a file descriptor becoming ready, three empty lists are returned.

Among the acceptable object types in the iterables are Python file objects (e.g. sys.stdin, or objects returned by open() or os.popen()), socket objects returned by socket.socket(). You may also define a wrapper class yourself, as long as it has an appropriate fileno() method (that really returns a file descriptor, not just a random integer).

Note

File objects on Windows are not acceptable, but sockets are. On Windows, the underlying select() function is provided by the WinSock library, and does not handle file descriptors that don’t originate from WinSock.

Changed in version 3.5: The function is now retried with a recomputed timeout when interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+
+select.PIPE_BUF
+

The minimum number of bytes which can be written without blocking to a pipe when the pipe has been reported as ready for writing by select(), poll() or another interface in this module. This doesn’t apply to other kind of file-like objects such as sockets.

This value is guaranteed by POSIX to be at least 512.

Availability: Unix

New in version 3.2.

+

+/dev/poll Polling Objects

Solaris and derivatives have /dev/poll. While select() is O(highest file descriptor) and poll() is O(number of file descriptors), /dev/poll is O(active file descriptors).

/dev/poll behaviour is very close to the standard poll() object.

+devpoll.close()
+

Close the file descriptor of the polling object.

New in version 3.4.

+
+devpoll.closed
+

True if the polling object is closed.

New in version 3.4.

+
+devpoll.fileno()
+

Return the file descriptor number of the polling object.

New in version 3.4.

+
+devpoll.register(fd[, eventmask])
+

Register a file descriptor with the polling object. Future calls to the poll() method will then check whether the file descriptor has any pending I/O events. fd can be either an integer, or an object with a fileno() method that returns an integer. File objects implement fileno(), so they can also be used as the argument.

eventmask is an optional bitmask describing the type of events you want to check for. The constants are the same that with poll() object. The default value is a combination of the constants POLLIN, POLLPRI, and POLLOUT.

Warning

Registering a file descriptor that’s already registered is not an error, but the result is undefined. The appropriate action is to unregister or modify it first. This is an important difference compared with poll().

+
+devpoll.modify(fd[, eventmask])
+

This method does an unregister() followed by a register(). It is (a bit) more efficient that doing the same explicitly.

+
+devpoll.unregister(fd)
+

Remove a file descriptor being tracked by a polling object. Just like the register() method, fd can be an integer or an object with a fileno() method that returns an integer.

Attempting to remove a file descriptor that was never registered is safely ignored.

+
+devpoll.poll([timeout])
+

Polls the set of registered file descriptors, and returns a possibly empty list containing (fd, event) 2-tuples for the descriptors that have events or errors to report. fd is the file descriptor, and event is a bitmask with bits set for the reported events for that descriptor — POLLIN for waiting input, POLLOUT to indicate that the descriptor can be written to, and so forth. An empty list indicates that the call timed out and no file descriptors had any events to report. If timeout is given, it specifies the length of time in milliseconds which the system will wait for events before returning. If timeout is omitted, -1, or None, the call will block until there is an event for this poll object.

Changed in version 3.5: The function is now retried with a recomputed timeout when interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+

Edge and Level Trigger Polling (epoll) Objects

https://linux.die.net/man/4/epoll

eventmask

+ + + + + + + + + + + + + + +

Constant

Meaning

EPOLLIN

Available for read

EPOLLOUT

Available for write

EPOLLPRI

Urgent data for read

EPOLLERR

Error condition happened on the assoc. fd

EPOLLHUP

Hang up happened on the assoc. fd

EPOLLET

Set Edge Trigger behavior, the default is Level Trigger behavior

EPOLLONESHOT

Set one-shot behavior. After one event is pulled out, the fd is internally disabled

EPOLLEXCLUSIVE

Wake only one epoll object when the associated fd has an event. The default (if this flag is not set) is to wake all epoll objects polling on a fd.

EPOLLRDHUP

Stream socket peer closed connection or shut down writing half of connection.

EPOLLRDNORM

Equivalent to EPOLLIN

EPOLLRDBAND

Priority data band can be read.

EPOLLWRNORM

Equivalent to EPOLLOUT

EPOLLWRBAND

Priority data may be written.

EPOLLMSG

Ignored.

New in version 3.6: EPOLLEXCLUSIVE was added. It’s only supported by Linux Kernel 4.5 or later.

+epoll.close()
+

Close the control file descriptor of the epoll object.

+
+epoll.closed
+

True if the epoll object is closed.

+
+epoll.fileno()
+

Return the file descriptor number of the control fd.

+
+epoll.fromfd(fd)
+

Create an epoll object from a given file descriptor.

+
+epoll.register(fd[, eventmask])
+

Register a fd descriptor with the epoll object.

+
+epoll.modify(fd, eventmask)
+

Modify a registered file descriptor.

+
+epoll.unregister(fd)
+

Remove a registered file descriptor from the epoll object.

Changed in version 3.9: The method no longer ignores the EBADF error.

+
+epoll.poll(timeout=None, maxevents=- 1)
+

Wait for events. timeout in seconds (float)

Changed in version 3.5: The function is now retried with a recomputed timeout when interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+

Polling Objects

The poll() system call, supported on most Unix systems, provides better scalability for network servers that service many, many clients at the same time. poll() scales better because the system call only requires listing the file descriptors of interest, while select() builds a bitmap, turns on bits for the fds of interest, and then afterward the whole bitmap has to be linearly scanned again. select() is O(highest file descriptor), while poll() is O(number of file descriptors).

+poll.register(fd[, eventmask])
+

Register a file descriptor with the polling object. Future calls to the poll() method will then check whether the file descriptor has any pending I/O events. fd can be either an integer, or an object with a fileno() method that returns an integer. File objects implement fileno(), so they can also be used as the argument.

eventmask is an optional bitmask describing the type of events you want to check for, and can be a combination of the constants POLLIN, POLLPRI, and POLLOUT, described in the table below. If not specified, the default value used will check for all 3 types of events.

+ + + + + + + +

Constant

Meaning

POLLIN

There is data to read

POLLPRI

There is urgent data to read

POLLOUT

Ready for output: writing will not block

POLLERR

Error condition of some sort

POLLHUP

Hung up

POLLRDHUP

Stream socket peer closed connection, or shut down writing half of connection

POLLNVAL

Invalid request: descriptor not open

Registering a file descriptor that’s already registered is not an error, and has the same effect as registering the descriptor exactly once.

+
+poll.modify(fd, eventmask)
+

Modifies an already registered fd. This has the same effect as register(fd, eventmask). Attempting to modify a file descriptor that was never registered causes an OSError exception with errno ENOENT to be raised.

+
+poll.unregister(fd)
+

Remove a file descriptor being tracked by a polling object. Just like the register() method, fd can be an integer or an object with a fileno() method that returns an integer.

Attempting to remove a file descriptor that was never registered causes a KeyError exception to be raised.

+
+poll.poll([timeout])
+

Polls the set of registered file descriptors, and returns a possibly empty list containing (fd, event) 2-tuples for the descriptors that have events or errors to report. fd is the file descriptor, and event is a bitmask with bits set for the reported events for that descriptor — POLLIN for waiting input, POLLOUT to indicate that the descriptor can be written to, and so forth. An empty list indicates that the call timed out and no file descriptors had any events to report. If timeout is given, it specifies the length of time in milliseconds which the system will wait for events before returning. If timeout is omitted, negative, or None, the call will block until there is an event for this poll object.

Changed in version 3.5: The function is now retried with a recomputed timeout when interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+

Kqueue Objects

+kqueue.close()
+

Close the control file descriptor of the kqueue object.

+
+kqueue.closed
+

True if the kqueue object is closed.

+
+kqueue.fileno()
+

Return the file descriptor number of the control fd.

+
+kqueue.fromfd(fd)
+

Create a kqueue object from a given file descriptor.

+
+kqueue.control(changelist, max_events[, timeout]) → eventlist
+

Low level interface to kevent

  • changelist must be an iterable of kevent objects or None +
  • max_events must be 0 or a positive integer
  • timeout in seconds (floats possible); the default is None, to wait forever

Changed in version 3.5: The function is now retried with a recomputed timeout when interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale), instead of raising InterruptedError.

+

Kevent Objects

https://man.freebsd.org/cgi/man.cgi?query=kqueue&sektion=2

+kevent.ident
+

Value used to identify the event. The interpretation depends on the filter but it’s usually the file descriptor. In the constructor ident can either be an int or an object with a fileno() method. kevent stores the integer internally.

+
+kevent.filter
+

Name of the kernel filter.

+ + + + + + + + +

Constant

Meaning

KQ_FILTER_READ

Takes a descriptor and returns whenever there is data available to read

KQ_FILTER_WRITE

Takes a descriptor and returns whenever there is data available to write

KQ_FILTER_AIO

AIO requests

KQ_FILTER_VNODE

Returns when one or more of the requested events watched in fflag occurs

KQ_FILTER_PROC

Watch for events on a process id

KQ_FILTER_NETDEV

Watch for events on a network device [not available on macOS]

KQ_FILTER_SIGNAL

Returns whenever the watched signal is delivered to the process

KQ_FILTER_TIMER

Establishes an arbitrary timer
+
+kevent.flags
+

Filter action.

+ + + + + + + + + + +

Constant

Meaning

KQ_EV_ADD

Adds or modifies an event

KQ_EV_DELETE

Removes an event from the queue

KQ_EV_ENABLE

Permitscontrol() to returns the event

KQ_EV_DISABLE

Disablesevent

KQ_EV_ONESHOT

Removes event after first occurrence

KQ_EV_CLEAR

Reset the state after an event is retrieved

KQ_EV_SYSFLAGS

internal event

KQ_EV_FLAG1

internal event

KQ_EV_EOF

Filter specific EOF condition

KQ_EV_ERROR

See return values
+
+kevent.fflags
+

Filter specific flags.

KQ_FILTER_READ and KQ_FILTER_WRITE filter flags:

+ +

Constant

Meaning

KQ_NOTE_LOWAT

low water mark of a socket buffer

KQ_FILTER_VNODE filter flags:

+ + + + + + + +

Constant

Meaning

KQ_NOTE_DELETE

unlink() was called

KQ_NOTE_WRITE

a write occurred

KQ_NOTE_EXTEND

the file was extended

KQ_NOTE_ATTRIB

an attribute was changed

KQ_NOTE_LINK

the link count has changed

KQ_NOTE_RENAME

the file was renamed

KQ_NOTE_REVOKE

access to the file was revoked

KQ_FILTER_PROC filter flags:

+ + + + + + + + +

Constant

Meaning

KQ_NOTE_EXIT

the process has exited

KQ_NOTE_FORK

the process has called fork()

KQ_NOTE_EXEC

the process has executed a new process

KQ_NOTE_PCTRLMASK

internal filter flag

KQ_NOTE_PDATAMASK

internal filter flag

KQ_NOTE_TRACK

follow a process across fork()

KQ_NOTE_CHILD

returned on the child process for NOTE_TRACK

KQ_NOTE_TRACKERR

unable to attach to a child

KQ_FILTER_NETDEV filter flags (not available on macOS):

+ + + +

Constant

Meaning

KQ_NOTE_LINKUP

link is up

KQ_NOTE_LINKDOWN

link is down

KQ_NOTE_LINKINV

link state is invalid
+
+kevent.data
+

Filter specific data.

+
+kevent.udata
+

User defined value.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/select.html +

+
diff --git a/devdocs/python~3.12/library%2Fselectors.html b/devdocs/python~3.12/library%2Fselectors.html new file mode 100644 index 00000000..33c12f96 --- /dev/null +++ b/devdocs/python~3.12/library%2Fselectors.html @@ -0,0 +1,123 @@ +

selectors — High-level I/O multiplexing

New in version 3.4.

Source code: Lib/selectors.py

Introduction

This module allows high-level and efficient I/O multiplexing, built upon the select module primitives. Users are encouraged to use this module instead, unless they want precise control over the OS-level primitives used.

It defines a BaseSelector abstract base class, along with several concrete implementations (KqueueSelector, EpollSelector…), that can be used to wait for I/O readiness notification on multiple file objects. In the following, “file object” refers to any object with a fileno() method, or a raw file descriptor. See file object.

DefaultSelector is an alias to the most efficient implementation available on the current platform: this should be the default choice for most users.

Note

The type of file objects supported depends on the platform: on Windows, sockets are supported, but not pipes, whereas on Unix, both are supported (some other types may be supported as well, such as fifos or special file devices).

See also

+ select +
+
+

Low-level I/O multiplexing module.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Classes

Classes hierarchy:

BaseSelector
++-- SelectSelector
++-- PollSelector
++-- EpollSelector
++-- DevpollSelector
++-- KqueueSelector
+

In the following, events is a bitwise mask indicating which I/O events should be waited for on a given file object. It can be a combination of the modules constants below:

+ + +

Constant

Meaning
+
+selectors.EVENT_READ
+

Available for read
+
+selectors.EVENT_WRITE
+

Available for write
+class selectors.SelectorKey
+

A SelectorKey is a namedtuple used to associate a file object to its underlying file descriptor, selected event mask and attached data. It is returned by several BaseSelector methods.

+fileobj
+

File object registered.

+
+fd
+

Underlying file descriptor.

+
+events
+

Events that must be waited for on this file object.

+
+data
+

Optional opaque data associated to this file object: for example, this could be used to store a per-client session ID.

+
+
+class selectors.BaseSelector
+

A BaseSelector is used to wait for I/O event readiness on multiple file objects. It supports file stream registration, unregistration, and a method to wait for I/O events on those streams, with an optional timeout. It’s an abstract base class, so cannot be instantiated. Use DefaultSelector instead, or one of SelectSelector, KqueueSelector etc. if you want to specifically use an implementation, and your platform supports it. BaseSelector and its concrete implementations support the context manager protocol.

+abstractmethod register(fileobj, events, data=None)
+

Register a file object for selection, monitoring it for I/O events.

fileobj is the file object to monitor. It may either be an integer file descriptor or an object with a fileno() method. events is a bitwise mask of events to monitor. data is an opaque object.

This returns a new SelectorKey instance, or raises a ValueError in case of invalid event mask or file descriptor, or KeyError if the file object is already registered.

+
+abstractmethod unregister(fileobj)
+

Unregister a file object from selection, removing it from monitoring. A file object shall be unregistered prior to being closed.

fileobj must be a file object previously registered.

This returns the associated SelectorKey instance, or raises a KeyError if fileobj is not registered. It will raise ValueError if fileobj is invalid (e.g. it has no fileno() method or its fileno() method has an invalid return value).

+
+modify(fileobj, events, data=None)
+

Change a registered file object’s monitored events or attached data.

This is equivalent to BaseSelector.unregister(fileobj) followed by BaseSelector.register(fileobj, events, data), except that it can be implemented more efficiently.

This returns a new SelectorKey instance, or raises a ValueError in case of invalid event mask or file descriptor, or KeyError if the file object is not registered.

+
+abstractmethod select(timeout=None)
+

Wait until some registered file objects become ready, or the timeout expires.

If timeout > 0, this specifies the maximum wait time, in seconds. If timeout <= 0, the call won’t block, and will report the currently ready file objects. If timeout is None, the call will block until a monitored file object becomes ready.

This returns a list of (key, events) tuples, one for each ready file object.

key is the SelectorKey instance corresponding to a ready file object. events is a bitmask of events ready on this file object.

Note

This method can return before any file object becomes ready or the timeout has elapsed if the current process receives a signal: in this case, an empty list will be returned.

Changed in version 3.5: The selector is now retried with a recomputed timeout when interrupted by a signal if the signal handler did not raise an exception (see PEP 475 for the rationale), instead of returning an empty list of events before the timeout.

+
+close()
+

Close the selector.

This must be called to make sure that any underlying resource is freed. The selector shall not be used once it has been closed.

+
+get_key(fileobj)
+

Return the key associated with a registered file object.

This returns the SelectorKey instance associated to this file object, or raises KeyError if the file object is not registered.

+
+abstractmethod get_map()
+

Return a mapping of file objects to selector keys.

This returns a Mapping instance mapping registered file objects to their associated SelectorKey instance.

+
+
+class selectors.DefaultSelector
+

The default selector class, using the most efficient implementation available on the current platform. This should be the default choice for most users.

+
+class selectors.SelectSelector
+

select.select()-based selector.

+
+class selectors.PollSelector
+

select.poll()-based selector.

+
+class selectors.EpollSelector
+

select.epoll()-based selector.

+fileno()
+

This returns the file descriptor used by the underlying select.epoll() object.

+
+
+class selectors.DevpollSelector
+

select.devpoll()-based selector.

+fileno()
+

This returns the file descriptor used by the underlying select.devpoll() object.

+

New in version 3.5.

+
+class selectors.KqueueSelector
+

select.kqueue()-based selector.

+fileno()
+

This returns the file descriptor used by the underlying select.kqueue() object.

+
+

Examples

Here is a simple echo server implementation:

import selectors
+import socket
+
+sel = selectors.DefaultSelector()
+
+def accept(sock, mask):
+    conn, addr = sock.accept()  # Should be ready
+    print('accepted', conn, 'from', addr)
+    conn.setblocking(False)
+    sel.register(conn, selectors.EVENT_READ, read)
+
+def read(conn, mask):
+    data = conn.recv(1000)  # Should be ready
+    if data:
+        print('echoing', repr(data), 'to', conn)
+        conn.send(data)  # Hope it won't block
+    else:
+        print('closing', conn)
+        sel.unregister(conn)
+        conn.close()
+
+sock = socket.socket()
+sock.bind(('localhost', 1234))
+sock.listen(100)
+sock.setblocking(False)
+sel.register(sock, selectors.EVENT_READ, accept)
+
+while True:
+    events = sel.select()
+    for key, mask in events:
+        callback = key.data
+        callback(key.fileobj, mask)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/selectors.html +

+
diff --git a/devdocs/python~3.12/library%2Fshelve.html b/devdocs/python~3.12/library%2Fshelve.html new file mode 100644 index 00000000..89e223c1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fshelve.html @@ -0,0 +1,63 @@ +

shelve — Python object persistence

Source code: Lib/shelve.py

A “shelf” is a persistent, dictionary-like object. The difference with “dbm” databases is that the values (not the keys!) in a shelf can be essentially arbitrary Python objects — anything that the pickle module can handle. This includes most class instances, recursive data types, and objects containing lots of shared sub-objects. The keys are ordinary strings.

+shelve.open(filename, flag='c', protocol=None, writeback=False)
+

Open a persistent dictionary. The filename specified is the base filename for the underlying database. As a side-effect, an extension may be added to the filename and more than one file may be created. By default, the underlying database file is opened for reading and writing. The optional flag parameter has the same interpretation as the flag parameter of dbm.open().

By default, pickles created with pickle.DEFAULT_PROTOCOL are used to serialize values. The version of the pickle protocol can be specified with the protocol parameter.

Because of Python semantics, a shelf cannot know when a mutable persistent-dictionary entry is modified. By default modified objects are written only when assigned to the shelf (see Example). If the optional writeback parameter is set to True, all entries accessed are also cached in memory, and written back on sync() and close(); this can make it handier to mutate mutable entries in the persistent dictionary, but, if many entries are accessed, it can consume vast amounts of memory for the cache, and it can make the close operation very slow since all accessed entries are written back (there is no way to determine which accessed entries are mutable, nor which ones were actually mutated).

Changed in version 3.10: pickle.DEFAULT_PROTOCOL is now used as the default pickle protocol.

Changed in version 3.11: Accepts path-like object for filename.

Note

Do not rely on the shelf being closed automatically; always call close() explicitly when you don’t need it any more, or use shelve.open() as a context manager:

with shelve.open('spam') as db:
+    db['eggs'] = 'eggs'
+
+

Warning

Because the shelve module is backed by pickle, it is insecure to load a shelf from an untrusted source. Like with pickle, loading a shelf can execute arbitrary code.

Shelf objects support most of methods and operations supported by dictionaries (except copying, constructors and operators | and |=). This eases the transition from dictionary based scripts to those requiring persistent storage.

Two additional methods are supported:

+Shelf.sync()
+

Write back all entries in the cache if the shelf was opened with writeback set to True. Also empty the cache and synchronize the persistent dictionary on disk, if feasible. This is called automatically when the shelf is closed with close().

+
+Shelf.close()
+

Synchronize and close the persistent dict object. Operations on a closed shelf will fail with a ValueError.

+

See also

Persistent dictionary recipe with widely supported storage formats and having the speed of native dictionaries.

Restrictions

  • The choice of which database package will be used (such as dbm.ndbm or dbm.gnu) depends on which interface is available. Therefore it is not safe to open the database directly using dbm. The database is also (unfortunately) subject to the limitations of dbm, if it is used — this means that (the pickled representation of) the objects stored in the database should be fairly small, and in rare cases key collisions may cause the database to refuse updates.
  • The shelve module does not support concurrent read/write access to shelved objects. (Multiple simultaneous read accesses are safe.) When a program has a shelf open for writing, no other program should have it open for reading or writing. Unix file locking can be used to solve this, but this differs across Unix versions and requires knowledge about the database implementation used.
  • On macOS dbm.ndbm can silently corrupt the database file on updates, which can cause hard crashes when trying to read from the database.
+class shelve.Shelf(dict, protocol=None, writeback=False, keyencoding='utf-8')
+

A subclass of collections.abc.MutableMapping which stores pickled values in the dict object.

By default, pickles created with pickle.DEFAULT_PROTOCOL are used to serialize values. The version of the pickle protocol can be specified with the protocol parameter. See the pickle documentation for a discussion of the pickle protocols.

If the writeback parameter is True, the object will hold a cache of all entries accessed and write them back to the dict at sync and close times. This allows natural operations on mutable entries, but can consume much more memory and make sync and close take a long time.

The keyencoding parameter is the encoding used to encode keys before they are used with the underlying dict.

A Shelf object can also be used as a context manager, in which case it will be automatically closed when the with block ends.

Changed in version 3.2: Added the keyencoding parameter; previously, keys were always encoded in UTF-8.

Changed in version 3.4: Added context manager support.

Changed in version 3.10: pickle.DEFAULT_PROTOCOL is now used as the default pickle protocol.

+
+class shelve.BsdDbShelf(dict, protocol=None, writeback=False, keyencoding='utf-8')
+

A subclass of Shelf which exposes first(), next(), previous(), last() and set_location() methods. These are available in the third-party bsddb module from pybsddb but not in other database modules. The dict object passed to the constructor must support those methods. This is generally accomplished by calling one of bsddb.hashopen(), bsddb.btopen() or bsddb.rnopen(). The optional protocol, writeback, and keyencoding parameters have the same interpretation as for the Shelf class.

+
+class shelve.DbfilenameShelf(filename, flag='c', protocol=None, writeback=False)
+

A subclass of Shelf which accepts a filename instead of a dict-like object. The underlying file will be opened using dbm.open(). By default, the file will be created and opened for both read and write. The optional flag parameter has the same interpretation as for the open() function. The optional protocol and writeback parameters have the same interpretation as for the Shelf class.

+

Example

To summarize the interface (key is a string, data is an arbitrary object):

import shelve
+
+d = shelve.open(filename)  # open -- file may get suffix added by low-level
+                           # library
+
+d[key] = data              # store data at key (overwrites old data if
+                           # using an existing key)
+data = d[key]              # retrieve a COPY of data at key (raise KeyError
+                           # if no such key)
+del d[key]                 # delete data stored at key (raises KeyError
+                           # if no such key)
+
+flag = key in d            # true if the key exists
+klist = list(d.keys())     # a list of all existing keys (slow!)
+
+# as d was opened WITHOUT writeback=True, beware:
+d['xx'] = [0, 1, 2]        # this works as expected, but...
+d['xx'].append(3)          # *this doesn't!* -- d['xx'] is STILL [0, 1, 2]!
+
+# having opened d without writeback=True, you need to code carefully:
+temp = d['xx']             # extracts the copy
+temp.append(5)             # mutates the copy
+d['xx'] = temp             # stores the copy right back, to persist it
+
+# or, d=shelve.open(filename,writeback=True) would let you just code
+# d['xx'].append(5) and have it work as expected, BUT it would also
+# consume more memory and make the d.close() operation slower.
+
+d.close()                  # close it
+

See also

+Module dbm +
+
+

Generic interface to dbm-style databases.

+Module pickle +
+
+

Object serialization used by shelve.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/shelve.html +

+
diff --git a/devdocs/python~3.12/library%2Fshlex.html b/devdocs/python~3.12/library%2Fshlex.html new file mode 100644 index 00000000..3bbb9113 --- /dev/null +++ b/devdocs/python~3.12/library%2Fshlex.html @@ -0,0 +1,130 @@ +

shlex — Simple lexical analysis

Source code: Lib/shlex.py

The shlex class makes it easy to write lexical analyzers for simple syntaxes resembling that of the Unix shell. This will often be useful for writing minilanguages, (for example, in run control files for Python applications) or for parsing quoted strings.

The shlex module defines the following functions:

+shlex.split(s, comments=False, posix=True)
+

Split the string s using shell-like syntax. If comments is False (the default), the parsing of comments in the given string will be disabled (setting the commenters attribute of the shlex instance to the empty string). This function operates in POSIX mode by default, but uses non-POSIX mode if the posix argument is false.

Changed in version 3.12: Passing None for s argument now raises an exception, rather than reading sys.stdin.

+
+shlex.join(split_command)
+

Concatenate the tokens of the list split_command and return a string. This function is the inverse of split().

>>> from shlex import join
+>>> print(join(['echo', '-n', 'Multiple words']))
+echo -n 'Multiple words'
+

The returned value is shell-escaped to protect against injection vulnerabilities (see quote()).

New in version 3.8.

+
+shlex.quote(s)
+

Return a shell-escaped version of the string s. The returned value is a string that can safely be used as one token in a shell command line, for cases where you cannot use a list.

Warning

The shlex module is only designed for Unix shells.

The quote() function is not guaranteed to be correct on non-POSIX compliant shells or shells from other operating systems such as Windows. Executing commands quoted by this module on such shells can open up the possibility of a command injection vulnerability.

Consider using functions that pass command arguments with lists such as subprocess.run() with shell=False.

This idiom would be unsafe:

>>> filename = 'somefile; rm -rf ~'
+>>> command = 'ls -l {}'.format(filename)
+>>> print(command)  # executed by a shell: boom!
+ls -l somefile; rm -rf ~
+

quote() lets you plug the security hole:

>>> from shlex import quote
+>>> command = 'ls -l {}'.format(quote(filename))
+>>> print(command)
+ls -l 'somefile; rm -rf ~'
+>>> remote_command = 'ssh home {}'.format(quote(command))
+>>> print(remote_command)
+ssh home 'ls -l '"'"'somefile; rm -rf ~'"'"''
+

The quoting is compatible with UNIX shells and with split():

>>> from shlex import split
+>>> remote_command = split(remote_command)
+>>> remote_command
+['ssh', 'home', "ls -l 'somefile; rm -rf ~'"]
+>>> command = split(remote_command[-1])
+>>> command
+['ls', '-l', 'somefile; rm -rf ~']
+

New in version 3.3.

+

The shlex module defines the following class:

+class shlex.shlex(instream=None, infile=None, posix=False, punctuation_chars=False)
+

A shlex instance or subclass instance is a lexical analyzer object. The initialization argument, if present, specifies where to read characters from. It must be a file-/stream-like object with read() and readline() methods, or a string. If no argument is given, input will be taken from sys.stdin. The second optional argument is a filename string, which sets the initial value of the infile attribute. If the instream argument is omitted or equal to sys.stdin, this second argument defaults to “stdin”. The posix argument defines the operational mode: when posix is not true (default), the shlex instance will operate in compatibility mode. When operating in POSIX mode, shlex will try to be as close as possible to the POSIX shell parsing rules. The punctuation_chars argument provides a way to make the behaviour even closer to how real shells parse. This can take a number of values: the default value, False, preserves the behaviour seen under Python 3.5 and earlier. If set to True, then parsing of the characters ();<>|& is changed: any run of these characters (considered punctuation characters) is returned as a single token. If set to a non-empty string of characters, those characters will be used as the punctuation characters. Any characters in the wordchars attribute that appear in punctuation_chars will be removed from wordchars. See Improved Compatibility with Shells for more information. punctuation_chars can be set only upon shlex instance creation and can’t be modified later.

Changed in version 3.6: The punctuation_chars parameter was added.

+

See also

+Module configparser +
+
+

Parser for configuration files similar to the Windows .ini files.

shlex Objects

A shlex instance has the following methods:

+shlex.get_token()
+

Return a token. If tokens have been stacked using push_token(), pop a token off the stack. Otherwise, read one from the input stream. If reading encounters an immediate end-of-file, eof is returned (the empty string ('') in non-POSIX mode, and None in POSIX mode).

+
+shlex.push_token(str)
+

Push the argument onto the token stack.

+
+shlex.read_token()
+

Read a raw token. Ignore the pushback stack, and do not interpret source requests. (This is not ordinarily a useful entry point, and is documented here only for the sake of completeness.)

+
+shlex.sourcehook(filename)
+

When shlex detects a source request (see source below) this method is given the following token as argument, and expected to return a tuple consisting of a filename and an open file-like object.

Normally, this method first strips any quotes off the argument. If the result is an absolute pathname, or there was no previous source request in effect, or the previous source was a stream (such as sys.stdin), the result is left alone. Otherwise, if the result is a relative pathname, the directory part of the name of the file immediately before it on the source inclusion stack is prepended (this behavior is like the way the C preprocessor handles #include +"file.h").

The result of the manipulations is treated as a filename, and returned as the first component of the tuple, with open() called on it to yield the second component. (Note: this is the reverse of the order of arguments in instance initialization!)

This hook is exposed so that you can use it to implement directory search paths, addition of file extensions, and other namespace hacks. There is no corresponding ‘close’ hook, but a shlex instance will call the close() method of the sourced input stream when it returns EOF.

For more explicit control of source stacking, use the push_source() and pop_source() methods.

+
+shlex.push_source(newstream, newfile=None)
+

Push an input source stream onto the input stack. If the filename argument is specified it will later be available for use in error messages. This is the same method used internally by the sourcehook() method.

+
+shlex.pop_source()
+

Pop the last-pushed input source from the input stack. This is the same method used internally when the lexer reaches EOF on a stacked input stream.

+
+shlex.error_leader(infile=None, lineno=None)
+

This method generates an error message leader in the format of a Unix C compiler error label; the format is '"%s", line %d: ', where the %s is replaced with the name of the current source file and the %d with the current input line number (the optional arguments can be used to override these).

This convenience is provided to encourage shlex users to generate error messages in the standard, parseable format understood by Emacs and other Unix tools.

+

Instances of shlex subclasses have some public instance variables which either control lexical analysis or can be used for debugging:

+shlex.commenters
+

The string of characters that are recognized as comment beginners. All characters from the comment beginner to end of line are ignored. Includes just '#' by default.

+
+shlex.wordchars
+

The string of characters that will accumulate into multi-character tokens. By default, includes all ASCII alphanumerics and underscore. In POSIX mode, the accented characters in the Latin-1 set are also included. If punctuation_chars is not empty, the characters ~-./*?=, which can appear in filename specifications and command line parameters, will also be included in this attribute, and any characters which appear in punctuation_chars will be removed from wordchars if they are present there. If whitespace_split is set to True, this will have no effect.

+
+shlex.whitespace
+

Characters that will be considered whitespace and skipped. Whitespace bounds tokens. By default, includes space, tab, linefeed and carriage-return.

+
+shlex.escape
+

Characters that will be considered as escape. This will be only used in POSIX mode, and includes just '\' by default.

+
+shlex.quotes
+

Characters that will be considered string quotes. The token accumulates until the same quote is encountered again (thus, different quote types protect each other as in the shell.) By default, includes ASCII single and double quotes.

+
+shlex.escapedquotes
+

Characters in quotes that will interpret escape characters defined in escape. This is only used in POSIX mode, and includes just '"' by default.

+
+shlex.whitespace_split
+

If True, tokens will only be split in whitespaces. This is useful, for example, for parsing command lines with shlex, getting tokens in a similar way to shell arguments. When used in combination with punctuation_chars, tokens will be split on whitespace in addition to those characters.

Changed in version 3.8: The punctuation_chars attribute was made compatible with the whitespace_split attribute.

+
+shlex.infile
+

The name of the current input file, as initially set at class instantiation time or stacked by later source requests. It may be useful to examine this when constructing error messages.

+
+shlex.instream
+

The input stream from which this shlex instance is reading characters.

+
+shlex.source
+

This attribute is None by default. If you assign a string to it, that string will be recognized as a lexical-level inclusion request similar to the source keyword in various shells. That is, the immediately following token will be opened as a filename and input will be taken from that stream until EOF, at which point the close() method of that stream will be called and the input source will again become the original input stream. Source requests may be stacked any number of levels deep.

+
+shlex.debug
+

If this attribute is numeric and 1 or more, a shlex instance will print verbose progress output on its behavior. If you need to use this, you can read the module source code to learn the details.

+
+shlex.lineno
+

Source line number (count of newlines seen so far plus one).

+
+shlex.token
+

The token buffer. It may be useful to examine this when catching exceptions.

+
+shlex.eof
+

Token used to determine end of file. This will be set to the empty string (''), in non-POSIX mode, and to None in POSIX mode.

+
+shlex.punctuation_chars
+

A read-only property. Characters that will be considered punctuation. Runs of punctuation characters will be returned as a single token. However, note that no semantic validity checking will be performed: for example, ‘>>>’ could be returned as a token, even though it may not be recognised as such by shells.

New in version 3.6.

+

Parsing Rules

When operating in non-POSIX mode, shlex will try to obey to the following rules.

  • Quote characters are not recognized within words (Do"Not"Separate is parsed as the single word Do"Not"Separate);
  • Escape characters are not recognized;
  • Enclosing characters in quotes preserve the literal value of all characters within the quotes;
  • Closing quotes separate words ("Do"Separate is parsed as "Do" and Separate);
  • If whitespace_split is False, any character not declared to be a word character, whitespace, or a quote will be returned as a single-character token. If it is True, shlex will only split words in whitespaces;
  • EOF is signaled with an empty string ('');
  • It’s not possible to parse empty strings, even if quoted.

When operating in POSIX mode, shlex will try to obey to the following parsing rules.

  • Quotes are stripped out, and do not separate words ("Do"Not"Separate" is parsed as the single word DoNotSeparate);
  • Non-quoted escape characters (e.g. '\') preserve the literal value of the next character that follows;
  • Enclosing characters in quotes which are not part of escapedquotes (e.g. "'") preserve the literal value of all characters within the quotes;
  • Enclosing characters in quotes which are part of escapedquotes (e.g. '"') preserves the literal value of all characters within the quotes, with the exception of the characters mentioned in escape. The escape characters retain its special meaning only when followed by the quote in use, or the escape character itself. Otherwise the escape character will be considered a normal character.
  • EOF is signaled with a None value;
  • Quoted empty strings ('') are allowed.

Improved Compatibility with Shells

New in version 3.6.

The shlex class provides compatibility with the parsing performed by common Unix shells like bash, dash, and sh. To take advantage of this compatibility, specify the punctuation_chars argument in the constructor. This defaults to False, which preserves pre-3.6 behaviour. However, if it is set to True, then parsing of the characters ();<>|& is changed: any run of these characters is returned as a single token. While this is short of a full parser for shells (which would be out of scope for the standard library, given the multiplicity of shells out there), it does allow you to perform processing of command lines more easily than you could otherwise. To illustrate, you can see the difference in the following snippet:

 >>> import shlex
+ >>> text = "a && b; c && d || e; f >'abc'; (def \"ghi\")"
+ >>> s = shlex.shlex(text, posix=True)
+ >>> s.whitespace_split = True
+ >>> list(s)
+ ['a', '&&', 'b;', 'c', '&&', 'd', '||', 'e;', 'f', '>abc;', '(def', 'ghi)']
+ >>> s = shlex.shlex(text, posix=True, punctuation_chars=True)
+ >>> s.whitespace_split = True
+ >>> list(s)
+ ['a', '&&', 'b', ';', 'c', '&&', 'd', '||', 'e', ';', 'f', '>', 'abc', ';',
+ '(', 'def', 'ghi', ')']
+

Of course, tokens will be returned which are not valid for shells, and you’ll need to implement your own error checks on the returned tokens.

Instead of passing True as the value for the punctuation_chars parameter, you can pass a string with specific characters, which will be used to determine which characters constitute punctuation. For example:

>>> import shlex
+>>> s = shlex.shlex("a && b || c", punctuation_chars="|")
+>>> list(s)
+['a', '&', '&', 'b', '||', 'c']
+

Note

When punctuation_chars is specified, the wordchars attribute is augmented with the characters ~-./*?=. That is because these characters can appear in file names (including wildcards) and command-line arguments (e.g. --color=auto). Hence:

>>> import shlex
+>>> s = shlex.shlex('~/a && b-c --color=auto || d *.py?',
+...                 punctuation_chars=True)
+>>> list(s)
+['~/a', '&&', 'b-c', '--color=auto', '||', 'd', '*.py?']
+

However, to match the shell as closely as possible, it is recommended to always use posix and whitespace_split when using punctuation_chars, which will negate wordchars entirely.

For best effect, punctuation_chars should be set in conjunction with posix=True. (Note that posix=False is the default for shlex.)

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/shlex.html +

+
diff --git a/devdocs/python~3.12/library%2Fshutil.html b/devdocs/python~3.12/library%2Fshutil.html new file mode 100644 index 00000000..5951f625 --- /dev/null +++ b/devdocs/python~3.12/library%2Fshutil.html @@ -0,0 +1,151 @@ +

shutil — High-level file operations

Source code: Lib/shutil.py

The shutil module offers a number of high-level operations on files and collections of files. In particular, functions are provided which support file copying and removal. For operations on individual files, see also the os module.

Warning

Even the higher-level file copying functions (shutil.copy(), shutil.copy2()) cannot copy all file metadata.

On POSIX platforms, this means that file owner and group are lost as well as ACLs. On Mac OS, the resource fork and other metadata are not used. This means that resources will be lost and file type and creator codes will not be correct. On Windows, file owners, ACLs and alternate data streams are not copied.

Directory and files operations

+shutil.copyfileobj(fsrc, fdst[, length])
+

Copy the contents of the file-like object fsrc to the file-like object fdst. The integer length, if given, is the buffer size. In particular, a negative length value means to copy the data without looping over the source data in chunks; by default the data is read in chunks to avoid uncontrolled memory consumption. Note that if the current file position of the fsrc object is not 0, only the contents from the current file position to the end of the file will be copied.

+
+shutil.copyfile(src, dst, *, follow_symlinks=True)
+

Copy the contents (no metadata) of the file named src to a file named dst and return dst in the most efficient way possible. src and dst are path-like objects or path names given as strings.

dst must be the complete target file name; look at copy() for a copy that accepts a target directory path. If src and dst specify the same file, SameFileError is raised.

The destination location must be writable; otherwise, an OSError exception will be raised. If dst already exists, it will be replaced. Special files such as character or block devices and pipes cannot be copied with this function.

If follow_symlinks is false and src is a symbolic link, a new symbolic link will be created instead of copying the file src points to.

Raises an auditing event shutil.copyfile with arguments src, dst.

Changed in version 3.3: IOError used to be raised instead of OSError. Added follow_symlinks argument. Now returns dst.

Changed in version 3.4: Raise SameFileError instead of Error. Since the former is a subclass of the latter, this change is backward compatible.

Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section.

+
+exception shutil.SameFileError
+

This exception is raised if source and destination in copyfile() are the same file.

New in version 3.4.

+
+shutil.copymode(src, dst, *, follow_symlinks=True)
+

Copy the permission bits from src to dst. The file contents, owner, and group are unaffected. src and dst are path-like objects or path names given as strings. If follow_symlinks is false, and both src and dst are symbolic links, copymode() will attempt to modify the mode of dst itself (rather than the file it points to). This functionality is not available on every platform; please see copystat() for more information. If copymode() cannot modify symbolic links on the local platform, and it is asked to do so, it will do nothing and return.

Raises an auditing event shutil.copymode with arguments src, dst.

Changed in version 3.3: Added follow_symlinks argument.

+
+shutil.copystat(src, dst, *, follow_symlinks=True)
+

Copy the permission bits, last access time, last modification time, and flags from src to dst. On Linux, copystat() also copies the “extended attributes” where possible. The file contents, owner, and group are unaffected. src and dst are path-like objects or path names given as strings.

If follow_symlinks is false, and src and dst both refer to symbolic links, copystat() will operate on the symbolic links themselves rather than the files the symbolic links refer to—reading the information from the src symbolic link, and writing the information to the dst symbolic link.

Note

Not all platforms provide the ability to examine and modify symbolic links. Python itself can tell you what functionality is locally available.

  • If os.chmod in os.supports_follow_symlinks is True, copystat() can modify the permission bits of a symbolic link.
  • If os.utime in os.supports_follow_symlinks is True, copystat() can modify the last access and modification times of a symbolic link.
  • If os.chflags in os.supports_follow_symlinks is True, copystat() can modify the flags of a symbolic link. (os.chflags is not available on all platforms.)

On platforms where some or all of this functionality is unavailable, when asked to modify a symbolic link, copystat() will copy everything it can. copystat() never returns failure.

Please see os.supports_follow_symlinks for more information.

Raises an auditing event shutil.copystat with arguments src, dst.

Changed in version 3.3: Added follow_symlinks argument and support for Linux extended attributes.

+
+shutil.copy(src, dst, *, follow_symlinks=True)
+

Copies the file src to the file or directory dst. src and dst should be path-like objects or strings. If dst specifies a directory, the file will be copied into dst using the base filename from src. If dst specifies a file that already exists, it will be replaced. Returns the path to the newly created file.

If follow_symlinks is false, and src is a symbolic link, dst will be created as a symbolic link. If follow_symlinks is true and src is a symbolic link, dst will be a copy of the file src refers to.

copy() copies the file data and the file’s permission mode (see os.chmod()). Other metadata, like the file’s creation and modification times, is not preserved. To preserve all file metadata from the original, use copy2() instead.

Raises an auditing event shutil.copyfile with arguments src, dst.

Raises an auditing event shutil.copymode with arguments src, dst.

Changed in version 3.3: Added follow_symlinks argument. Now returns path to the newly created file.

Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section.

+
+shutil.copy2(src, dst, *, follow_symlinks=True)
+

Identical to copy() except that copy2() also attempts to preserve file metadata.

When follow_symlinks is false, and src is a symbolic link, copy2() attempts to copy all metadata from the src symbolic link to the newly created dst symbolic link. However, this functionality is not available on all platforms. On platforms where some or all of this functionality is unavailable, copy2() will preserve all the metadata it can; copy2() never raises an exception because it cannot preserve file metadata.

copy2() uses copystat() to copy the file metadata. Please see copystat() for more information about platform support for modifying symbolic link metadata.

Raises an auditing event shutil.copyfile with arguments src, dst.

Raises an auditing event shutil.copystat with arguments src, dst.

Changed in version 3.3: Added follow_symlinks argument, try to copy extended file system attributes too (currently Linux only). Now returns path to the newly created file.

Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section.

+
+shutil.ignore_patterns(*patterns)
+

This factory function creates a function that can be used as a callable for copytree()'s ignore argument, ignoring files and directories that match one of the glob-style patterns provided. See the example below.

+
+shutil.copytree(src, dst, symlinks=False, ignore=None, copy_function=copy2, ignore_dangling_symlinks=False, dirs_exist_ok=False)
+

Recursively copy an entire directory tree rooted at src to a directory named dst and return the destination directory. All intermediate directories needed to contain dst will also be created by default.

Permissions and times of directories are copied with copystat(), individual files are copied using copy2().

If symlinks is true, symbolic links in the source tree are represented as symbolic links in the new tree and the metadata of the original links will be copied as far as the platform allows; if false or omitted, the contents and metadata of the linked files are copied to the new tree.

When symlinks is false, if the file pointed by the symlink doesn’t exist, an exception will be added in the list of errors raised in an Error exception at the end of the copy process. You can set the optional ignore_dangling_symlinks flag to true if you want to silence this exception. Notice that this option has no effect on platforms that don’t support os.symlink().

If ignore is given, it must be a callable that will receive as its arguments the directory being visited by copytree(), and a list of its contents, as returned by os.listdir(). Since copytree() is called recursively, the ignore callable will be called once for each directory that is copied. The callable must return a sequence of directory and file names relative to the current directory (i.e. a subset of the items in its second argument); these names will then be ignored in the copy process. ignore_patterns() can be used to create such a callable that ignores names based on glob-style patterns.

If exception(s) occur, an Error is raised with a list of reasons.

If copy_function is given, it must be a callable that will be used to copy each file. It will be called with the source path and the destination path as arguments. By default, copy2() is used, but any function that supports the same signature (like copy()) can be used.

If dirs_exist_ok is false (the default) and dst already exists, a FileExistsError is raised. If dirs_exist_ok is true, the copying operation will continue if it encounters existing directories, and files within the dst tree will be overwritten by corresponding files from the src tree.

Raises an auditing event shutil.copytree with arguments src, dst.

Changed in version 3.3: Copy metadata when symlinks is false. Now returns dst.

Changed in version 3.2: Added the copy_function argument to be able to provide a custom copy function. Added the ignore_dangling_symlinks argument to silence dangling symlinks errors when symlinks is false.

Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section.

New in version 3.8: The dirs_exist_ok parameter.

+
+shutil.rmtree(path, ignore_errors=False, onerror=None, *, onexc=None, dir_fd=None)
+

Delete an entire directory tree; path must point to a directory (but not a symbolic link to a directory). If ignore_errors is true, errors resulting from failed removals will be ignored; if false or omitted, such errors are handled by calling a handler specified by onexc or onerror or, if both are omitted, exceptions are propagated to the caller.

This function can support paths relative to directory descriptors.

Note

On platforms that support the necessary fd-based functions a symlink attack resistant version of rmtree() is used by default. On other platforms, the rmtree() implementation is susceptible to a symlink attack: given proper timing and circumstances, attackers can manipulate symlinks on the filesystem to delete files they wouldn’t be able to access otherwise. Applications can use the rmtree.avoids_symlink_attacks function attribute to determine which case applies.

If onexc is provided, it must be a callable that accepts three parameters: function, path, and excinfo.

The first parameter, function, is the function which raised the exception; it depends on the platform and implementation. The second parameter, path, will be the path name passed to function. The third parameter, excinfo, is the exception that was raised. Exceptions raised by onexc will not be caught.

The deprecated onerror is similar to onexc, except that the third parameter it receives is the tuple returned from sys.exc_info().

Raises an auditing event shutil.rmtree with arguments path, dir_fd.

Changed in version 3.3: Added a symlink attack resistant version that is used automatically if platform supports fd-based functions.

Changed in version 3.8: On Windows, will no longer delete the contents of a directory junction before removing the junction.

Changed in version 3.11: The dir_fd parameter.

Changed in version 3.12: Added the onexc parameter, deprecated onerror.

+

Indicates whether the current platform and implementation provides a symlink attack resistant version of rmtree(). Currently this is only true for platforms supporting fd-based directory access functions.

New in version 3.3.

+
+
+shutil.move(src, dst, copy_function=copy2)
+

Recursively move a file or directory (src) to another location (dst) and return the destination.

If the destination is an existing directory, then src is moved inside that directory. If the destination already exists but is not a directory, it may be overwritten depending on os.rename() semantics.

If the destination is on the current filesystem, then os.rename() is used. Otherwise, src is copied to dst using copy_function and then removed. In case of symlinks, a new symlink pointing to the target of src will be created in or as dst and src will be removed.

If copy_function is given, it must be a callable that takes two arguments src and dst, and will be used to copy src to dst if os.rename() cannot be used. If the source is a directory, copytree() is called, passing it the copy_function. The default copy_function is copy2(). Using copy() as the copy_function allows the move to succeed when it is not possible to also copy the metadata, at the expense of not copying any of the metadata.

Raises an auditing event shutil.move with arguments src, dst.

Changed in version 3.3: Added explicit symlink handling for foreign filesystems, thus adapting it to the behavior of GNU’s mv. Now returns dst.

Changed in version 3.5: Added the copy_function keyword argument.

Changed in version 3.8: Platform-specific fast-copy syscalls may be used internally in order to copy the file more efficiently. See Platform-dependent efficient copy operations section.

Changed in version 3.9: Accepts a path-like object for both src and dst.

+
+shutil.disk_usage(path)
+

Return disk usage statistics about the given path as a named tuple with the attributes total, used and free, which are the amount of total, used and free space, in bytes. path may be a file or a directory.

Note

On Unix filesystems, path must point to a path within a mounted filesystem partition. On those platforms, CPython doesn’t attempt to retrieve disk usage information from non-mounted filesystems.

New in version 3.3.

Changed in version 3.8: On Windows, path can now be a file or directory.

Availability: Unix, Windows.

+
+shutil.chown(path, user=None, group=None)
+

Change owner user and/or group of the given path.

user can be a system user name or a uid; the same applies to group. At least one argument is required.

See also os.chown(), the underlying function.

Raises an auditing event shutil.chown with arguments path, user, group.

Availability: Unix.

New in version 3.3.

+
+shutil.which(cmd, mode=os.F_OK | os.X_OK, path=None)
+

Return the path to an executable which would be run if the given cmd was called. If no cmd would be called, return None.

mode is a permission mask passed to os.access(), by default determining if the file exists and executable.

When no path is specified, the results of os.environ() are used, returning either the “PATH” value or a fallback of os.defpath.

On Windows, the current directory is prepended to the path if mode does not include os.X_OK. When the mode does include os.X_OK, the Windows API NeedCurrentDirectoryForExePathW will be consulted to determine if the current directory should be prepended to path. To avoid consulting the current working directory for executables: set the environment variable NoDefaultCurrentDirectoryInExePath.

Also on Windows, the PATHEXT variable is used to resolve commands that may not already include an extension. For example, if you call shutil.which("python"), which() will search PATHEXT to know that it should look for python.exe within the path directories. For example, on Windows:

>>> shutil.which("python")
+'C:\\Python33\\python.EXE'
+

This is also applied when cmd is a path that contains a directory component:

>> shutil.which("C:\\Python33\\python")
+'C:\\Python33\\python.EXE'
+

New in version 3.3.

Changed in version 3.8: The bytes type is now accepted. If cmd type is bytes, the result type is also bytes.

Changed in version 3.12: On Windows, the current directory is no longer prepended to the search path if mode includes os.X_OK and WinAPI NeedCurrentDirectoryForExePathW(cmd) is false, else the current directory is prepended even if it is already in the search path; PATHEXT is used now even when cmd includes a directory component or ends with an extension that is in PATHEXT; and filenames that have no extension can now be found.

Changed in version 3.12.1: On Windows, if mode includes os.X_OK, executables with an extension in PATHEXT will be preferred over executables without a matching extension. This brings behavior closer to that of Python 3.11.

+
+exception shutil.Error
+

This exception collects exceptions that are raised during a multi-file operation. For copytree(), the exception argument is a list of 3-tuples (srcname, dstname, exception).

+

Platform-dependent efficient copy operations

Starting from Python 3.8, all functions involving a file copy (copyfile(), copy(), copy2(), copytree(), and move()) may use platform-specific “fast-copy” syscalls in order to copy the file more efficiently (see bpo-33671). “fast-copy” means that the copying operation occurs within the kernel, avoiding the use of userspace buffers in Python as in “outfd.write(infd.read())”.

On macOS fcopyfile is used to copy the file content (not metadata).

On Linux os.sendfile() is used.

On Windows shutil.copyfile() uses a bigger default buffer size (1 MiB instead of 64 KiB) and a memoryview()-based variant of shutil.copyfileobj() is used.

If the fast-copy operation fails and no data was written in the destination file then shutil will silently fallback on using less efficient copyfileobj() function internally.

Changed in version 3.8.

copytree example

An example that uses the ignore_patterns() helper:

from shutil import copytree, ignore_patterns
+
+copytree(source, destination, ignore=ignore_patterns('*.pyc', 'tmp*'))
+

This will copy everything except .pyc files and files or directories whose name starts with tmp.

Another example that uses the ignore argument to add a logging call:

from shutil import copytree
+import logging
+
+def _logpath(path, names):
+    logging.info('Working in %s', path)
+    return []   # nothing will be ignored
+
+copytree(source, destination, ignore=_logpath)
+

rmtree example

This example shows how to remove a directory tree on Windows where some of the files have their read-only bit set. It uses the onexc callback to clear the readonly bit and reattempt the remove. Any subsequent failure will propagate.

import os, stat
+import shutil
+
+def remove_readonly(func, path, _):
+    "Clear the readonly bit and reattempt the removal"
+    os.chmod(path, stat.S_IWRITE)
+    func(path)
+
+shutil.rmtree(directory, onexc=remove_readonly)
+

Archiving operations

New in version 3.2.

Changed in version 3.5: Added support for the xztar format.

High-level utilities to create and read compressed and archived files are also provided. They rely on the zipfile and tarfile modules.

+shutil.make_archive(base_name, format[, root_dir[, base_dir[, verbose[, dry_run[, owner[, group[, logger]]]]]]])
+

Create an archive file (such as zip or tar) and return its name.

base_name is the name of the file to create, including the path, minus any format-specific extension. format is the archive format: one of “zip” (if the zlib module is available), “tar”, “gztar” (if the zlib module is available), “bztar” (if the bz2 module is available), or “xztar” (if the lzma module is available).

root_dir is a directory that will be the root directory of the archive, all paths in the archive will be relative to it; for example, we typically chdir into root_dir before creating the archive.

base_dir is the directory where we start archiving from; i.e. base_dir will be the common prefix of all files and directories in the archive. base_dir must be given relative to root_dir. See Archiving example with base_dir for how to use base_dir and root_dir together.

root_dir and base_dir both default to the current directory.

If dry_run is true, no archive is created, but the operations that would be executed are logged to logger.

owner and group are used when creating a tar archive. By default, uses the current owner and group.

logger must be an object compatible with PEP 282, usually an instance of logging.Logger.

The verbose argument is unused and deprecated.

Raises an auditing event shutil.make_archive with arguments base_name, format, root_dir, base_dir.

Note

This function is not thread-safe when custom archivers registered with register_archive_format() do not support the root_dir argument. In this case it temporarily changes the current working directory of the process to root_dir to perform archiving.

Changed in version 3.8: The modern pax (POSIX.1-2001) format is now used instead of the legacy GNU format for archives created with format="tar".

Changed in version 3.10.6: This function is now made thread-safe during creation of standard .zip and tar archives.

+
+shutil.get_archive_formats()
+

Return a list of supported formats for archiving. Each element of the returned sequence is a tuple (name, description).

By default shutil provides these formats:

  • +zip: ZIP file (if the zlib module is available).
  • +tar: Uncompressed tar file. Uses POSIX.1-2001 pax format for new archives.
  • +gztar: gzip’ed tar-file (if the zlib module is available).
  • +bztar: bzip2’ed tar-file (if the bz2 module is available).
  • +xztar: xz’ed tar-file (if the lzma module is available).

You can register new formats or provide your own archiver for any existing formats, by using register_archive_format().

+
+shutil.register_archive_format(name, function[, extra_args[, description]])
+

Register an archiver for the format name.

function is the callable that will be used to unpack archives. The callable will receive the base_name of the file to create, followed by the base_dir (which defaults to os.curdir) to start archiving from. Further arguments are passed as keyword arguments: owner, group, dry_run and logger (as passed in make_archive()).

If function has the custom attribute function.supports_root_dir set to True, the root_dir argument is passed as a keyword argument. Otherwise the current working directory of the process is temporarily changed to root_dir before calling function. In this case make_archive() is not thread-safe.

If given, extra_args is a sequence of (name, value) pairs that will be used as extra keywords arguments when the archiver callable is used.

description is used by get_archive_formats() which returns the list of archivers. Defaults to an empty string.

Changed in version 3.12: Added support for functions supporting the root_dir argument.

+
+shutil.unregister_archive_format(name)
+

Remove the archive format name from the list of supported formats.

+
+shutil.unpack_archive(filename[, extract_dir[, format[, filter]]])
+

Unpack an archive. filename is the full path of the archive.

extract_dir is the name of the target directory where the archive is unpacked. If not provided, the current working directory is used.

format is the archive format: one of “zip”, “tar”, “gztar”, “bztar”, or “xztar”. Or any other format registered with register_unpack_format(). If not provided, unpack_archive() will use the archive file name extension and see if an unpacker was registered for that extension. In case none is found, a ValueError is raised.

The keyword-only filter argument is passed to the underlying unpacking function. For zip files, filter is not accepted. For tar files, it is recommended to set it to 'data', unless using features specific to tar and UNIX-like filesystems. (See Extraction filters for details.) The 'data' filter will become the default for tar files in Python 3.14.

Raises an auditing event shutil.unpack_archive with arguments filename, extract_dir, format.

Warning

Never extract archives from untrusted sources without prior inspection. It is possible that files are created outside of the path specified in the extract_dir argument, e.g. members that have absolute filenames starting with “/” or filenames with two dots “..”.

Changed in version 3.7: Accepts a path-like object for filename and extract_dir.

Changed in version 3.12: Added the filter argument.

+
+shutil.register_unpack_format(name, extensions, function[, extra_args[, description]])
+

Registers an unpack format. name is the name of the format and extensions is a list of extensions corresponding to the format, like .zip for Zip files.

function is the callable that will be used to unpack archives. The callable will receive:

  • the path of the archive, as a positional argument;
  • the directory the archive must be extracted to, as a positional argument;
  • possibly a filter keyword argument, if it was given to unpack_archive();
  • additional keyword arguments, specified by extra_args as a sequence of (name, value) tuples.

description can be provided to describe the format, and will be returned by the get_unpack_formats() function.

+
+shutil.unregister_unpack_format(name)
+

Unregister an unpack format. name is the name of the format.

+
+shutil.get_unpack_formats()
+

Return a list of all registered formats for unpacking. Each element of the returned sequence is a tuple (name, extensions, description).

By default shutil provides these formats:

  • +zip: ZIP file (unpacking compressed files works only if the corresponding module is available).
  • +tar: uncompressed tar file.
  • +gztar: gzip’ed tar-file (if the zlib module is available).
  • +bztar: bzip2’ed tar-file (if the bz2 module is available).
  • +xztar: xz’ed tar-file (if the lzma module is available).

You can register new formats or provide your own unpacker for any existing formats, by using register_unpack_format().

+

Archiving example

In this example, we create a gzip’ed tar-file archive containing all files found in the .ssh directory of the user:

>>> from shutil import make_archive
+>>> import os
+>>> archive_name = os.path.expanduser(os.path.join('~', 'myarchive'))
+>>> root_dir = os.path.expanduser(os.path.join('~', '.ssh'))
+>>> make_archive(archive_name, 'gztar', root_dir)
+'/Users/tarek/myarchive.tar.gz'
+

The resulting archive contains:

$ tar -tzvf /Users/tarek/myarchive.tar.gz
+drwx------ tarek/staff       0 2010-02-01 16:23:40 ./
+-rw-r--r-- tarek/staff     609 2008-06-09 13:26:54 ./authorized_keys
+-rwxr-xr-x tarek/staff      65 2008-06-09 13:26:54 ./config
+-rwx------ tarek/staff     668 2008-06-09 13:26:54 ./id_dsa
+-rwxr-xr-x tarek/staff     609 2008-06-09 13:26:54 ./id_dsa.pub
+-rw------- tarek/staff    1675 2008-06-09 13:26:54 ./id_rsa
+-rw-r--r-- tarek/staff     397 2008-06-09 13:26:54 ./id_rsa.pub
+-rw-r--r-- tarek/staff   37192 2010-02-06 18:23:10 ./known_hosts
+

Archiving example with base_dir +

In this example, similar to the one above, we show how to use make_archive(), but this time with the usage of base_dir. We now have the following directory structure:

$ tree tmp
+tmp
+└── root
+    └── structure
+        ├── content
+            └── please_add.txt
+        └── do_not_add.txt
+

In the final archive, please_add.txt should be included, but do_not_add.txt should not. Therefore we use the following:

>>> from shutil import make_archive
+>>> import os
+>>> archive_name = os.path.expanduser(os.path.join('~', 'myarchive'))
+>>> make_archive(
+...     archive_name,
+...     'tar',
+...     root_dir='tmp/root',
+...     base_dir='structure/content',
+... )
+'/Users/tarek/my_archive.tar'
+

Listing the files in the resulting archive gives us:

$ python -m tarfile -l /Users/tarek/myarchive.tar
+structure/content/
+structure/content/please_add.txt
+

Querying the size of the output terminal

+shutil.get_terminal_size(fallback=(columns, lines))
+

Get the size of the terminal window.

For each of the two dimensions, the environment variable, COLUMNS and LINES respectively, is checked. If the variable is defined and the value is a positive integer, it is used.

When COLUMNS or LINES is not defined, which is the common case, the terminal connected to sys.__stdout__ is queried by invoking os.get_terminal_size().

If the terminal size cannot be successfully queried, either because the system doesn’t support querying, or because we are not connected to a terminal, the value given in fallback parameter is used. fallback defaults to (80, 24) which is the default size used by many terminal emulators.

The value returned is a named tuple of type os.terminal_size.

See also: The Single UNIX Specification, Version 2, Other Environment Variables.

New in version 3.3.

Changed in version 3.11: The fallback values are also used if os.get_terminal_size() returns zeroes.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/shutil.html +

+
diff --git a/devdocs/python~3.12/library%2Fsignal.html b/devdocs/python~3.12/library%2Fsignal.html new file mode 100644 index 00000000..f01eb718 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsignal.html @@ -0,0 +1,245 @@ +

signal — Set handlers for asynchronous events

Source code: Lib/signal.py

This module provides mechanisms to use signal handlers in Python.

General rules

The signal.signal() function allows defining custom handlers to be executed when a signal is received. A small number of default handlers are installed: SIGPIPE is ignored (so write errors on pipes and sockets can be reported as ordinary Python exceptions) and SIGINT is translated into a KeyboardInterrupt exception if the parent process has not changed it.

A handler for a particular signal, once set, remains installed until it is explicitly reset (Python emulates the BSD style interface regardless of the underlying implementation), with the exception of the handler for SIGCHLD, which follows the underlying implementation.

On WebAssembly platforms wasm32-emscripten and wasm32-wasi, signals are emulated and therefore behave differently. Several functions and signals are not available on these platforms.

Execution of Python signal handlers

A Python signal handler does not get executed inside the low-level (C) signal handler. Instead, the low-level signal handler sets a flag which tells the virtual machine to execute the corresponding Python signal handler at a later point(for example at the next bytecode instruction). This has consequences:

  • It makes little sense to catch synchronous errors like SIGFPE or SIGSEGV that are caused by an invalid operation in C code. Python will return from the signal handler to the C code, which is likely to raise the same signal again, causing Python to apparently hang. From Python 3.3 onwards, you can use the faulthandler module to report on synchronous errors.
  • A long-running calculation implemented purely in C (such as regular expression matching on a large body of text) may run uninterrupted for an arbitrary amount of time, regardless of any signals received. The Python signal handlers will be called when the calculation finishes.
  • If the handler raises an exception, it will be raised “out of thin air” in the main thread. See the note below for a discussion.

Signals and threads

Python signal handlers are always executed in the main Python thread of the main interpreter, even if the signal was received in another thread. This means that signals can’t be used as a means of inter-thread communication. You can use the synchronization primitives from the threading module instead.

Besides, only the main thread of the main interpreter is allowed to set a new signal handler.

Module contents

Changed in version 3.5: signal (SIG*), handler (SIG_DFL, SIG_IGN) and sigmask (SIG_BLOCK, SIG_UNBLOCK, SIG_SETMASK) related constants listed below were turned into enums (Signals, Handlers and Sigmasks respectively). getsignal(), pthread_sigmask(), sigpending() and sigwait() functions return human-readable enums as Signals objects.

The signal module defines three enums:

+class signal.Signals
+

enum.IntEnum collection of SIG* constants and the CTRL_* constants.

New in version 3.5.

+
+class signal.Handlers
+

enum.IntEnum collection the constants SIG_DFL and SIG_IGN.

New in version 3.5.

+
+class signal.Sigmasks
+

enum.IntEnum collection the constants SIG_BLOCK, SIG_UNBLOCK and SIG_SETMASK.

Availability: Unix.

See the man page sigprocmask(2) and pthread_sigmask(3) for further information.

New in version 3.5.

+

The variables defined in the signal module are:

+signal.SIG_DFL
+

This is one of two standard signal handling options; it will simply perform the default function for the signal. For example, on most systems the default action for SIGQUIT is to dump core and exit, while the default action for SIGCHLD is to simply ignore it.

+
+signal.SIG_IGN
+

This is another standard signal handler, which will simply ignore the given signal.

+
+signal.SIGABRT
+

Abort signal from abort(3).

+
+signal.SIGALRM
+

Timer signal from alarm(2).

Availability: Unix.

+
+signal.SIGBREAK
+

Interrupt from keyboard (CTRL + BREAK).

Availability: Windows.

+
+signal.SIGBUS
+

Bus error (bad memory access).

Availability: Unix.

+
+signal.SIGCHLD
+

Child process stopped or terminated.

Availability: Unix.

+
+signal.SIGCLD
+

Alias to SIGCHLD.

Availability: not macOS.

+
+signal.SIGCONT
+

Continue the process if it is currently stopped

Availability: Unix.

+
+signal.SIGFPE
+

Floating-point exception. For example, division by zero.

See also

ZeroDivisionError is raised when the second argument of a division or modulo operation is zero.

+
+signal.SIGHUP
+

Hangup detected on controlling terminal or death of controlling process.

Availability: Unix.

+
+signal.SIGILL
+

Illegal instruction.

+
+signal.SIGINT
+

Interrupt from keyboard (CTRL + C).

Default action is to raise KeyboardInterrupt.

+
+signal.SIGKILL
+

Kill signal.

It cannot be caught, blocked, or ignored.

Availability: Unix.

+
+signal.SIGPIPE
+

Broken pipe: write to pipe with no readers.

Default action is to ignore the signal.

Availability: Unix.

+
+signal.SIGSEGV
+

Segmentation fault: invalid memory reference.

+
+signal.SIGSTKFLT

Stack fault on coprocessor. The Linux kernel does not raise this signal: it can only be raised in user space.

Availability: Linux.

On architectures where the signal is available. See the man page signal(7) for further information.

New in version 3.11.

+
+signal.SIGTERM
+

Termination signal.

+
+signal.SIGUSR1
+

User-defined signal 1.

Availability: Unix.

+
+signal.SIGUSR2
+

User-defined signal 2.

Availability: Unix.

+
+signal.SIGWINCH
+

Window resize signal.

Availability: Unix.

+
SIG* +
+

All the signal numbers are defined symbolically. For example, the hangup signal is defined as signal.SIGHUP; the variable names are identical to the names used in C programs, as found in <signal.h>. The Unix man page for ‘signal()’ lists the existing signals (on some systems this is signal(2), on others the list is in signal(7)). Note that not all systems define the same set of signal names; only those names defined by the system are defined by this module.

+
+signal.CTRL_C_EVENT
+

The signal corresponding to the Ctrl+C keystroke event. This signal can only be used with os.kill().

Availability: Windows.

New in version 3.2.

+
+signal.CTRL_BREAK_EVENT
+

The signal corresponding to the Ctrl+Break keystroke event. This signal can only be used with os.kill().

Availability: Windows.

New in version 3.2.

+
+signal.NSIG
+

One more than the number of the highest signal number. Use valid_signals() to get valid signal numbers.

+
+signal.ITIMER_REAL
+

Decrements interval timer in real time, and delivers SIGALRM upon expiration.

+
+signal.ITIMER_VIRTUAL
+

Decrements interval timer only when the process is executing, and delivers SIGVTALRM upon expiration.

+
+signal.ITIMER_PROF
+

Decrements interval timer both when the process executes and when the system is executing on behalf of the process. Coupled with ITIMER_VIRTUAL, this timer is usually used to profile the time spent by the application in user and kernel space. SIGPROF is delivered upon expiration.

+
+signal.SIG_BLOCK
+

A possible value for the how parameter to pthread_sigmask() indicating that signals are to be blocked.

New in version 3.3.

+
+signal.SIG_UNBLOCK
+

A possible value for the how parameter to pthread_sigmask() indicating that signals are to be unblocked.

New in version 3.3.

+
+signal.SIG_SETMASK
+

A possible value for the how parameter to pthread_sigmask() indicating that the signal mask is to be replaced.

New in version 3.3.

+

The signal module defines one exception:

+exception signal.ItimerError
+

Raised to signal an error from the underlying setitimer() or getitimer() implementation. Expect this error if an invalid interval timer or a negative time is passed to setitimer(). This error is a subtype of OSError.

New in version 3.3: This error used to be a subtype of IOError, which is now an alias of OSError.

+

The signal module defines the following functions:

+signal.alarm(time)
+

If time is non-zero, this function requests that a SIGALRM signal be sent to the process in time seconds. Any previously scheduled alarm is canceled (only one alarm can be scheduled at any time). The returned value is then the number of seconds before any previously set alarm was to have been delivered. If time is zero, no alarm is scheduled, and any scheduled alarm is canceled. If the return value is zero, no alarm is currently scheduled.

Availability: Unix.

See the man page alarm(2) for further information.

+
+signal.getsignal(signalnum)
+

Return the current signal handler for the signal signalnum. The returned value may be a callable Python object, or one of the special values signal.SIG_IGN, signal.SIG_DFL or None. Here, signal.SIG_IGN means that the signal was previously ignored, signal.SIG_DFL means that the default way of handling the signal was previously in use, and None means that the previous signal handler was not installed from Python.

+
+signal.strsignal(signalnum)
+

Returns the description of signal signalnum, such as “Interrupt” for SIGINT. Returns None if signalnum has no description. Raises ValueError if signalnum is invalid.

New in version 3.8.

+
+signal.valid_signals()
+

Return the set of valid signal numbers on this platform. This can be less than range(1, NSIG) if some signals are reserved by the system for internal use.

New in version 3.8.

+
+signal.pause()
+

Cause the process to sleep until a signal is received; the appropriate handler will then be called. Returns nothing.

Availability: Unix.

See the man page signal(2) for further information.

See also sigwait(), sigwaitinfo(), sigtimedwait() and sigpending().

+
+signal.raise_signal(signum)
+

Sends a signal to the calling process. Returns nothing.

New in version 3.8.

+
+signal.pidfd_send_signal(pidfd, sig, siginfo=None, flags=0)
+

Send signal sig to the process referred to by file descriptor pidfd. Python does not currently support the siginfo parameter; it must be None. The flags argument is provided for future extensions; no flag values are currently defined.

See the pidfd_send_signal(2) man page for more information.

Availability: Linux >= 5.1

New in version 3.9.

+
+signal.pthread_kill(thread_id, signalnum)
+

Send the signal signalnum to the thread thread_id, another thread in the same process as the caller. The target thread can be executing any code (Python or not). However, if the target thread is executing the Python interpreter, the Python signal handlers will be executed by the main thread of the main interpreter. Therefore, the only point of sending a signal to a particular Python thread would be to force a running system call to fail with InterruptedError.

Use threading.get_ident() or the ident attribute of threading.Thread objects to get a suitable value for thread_id.

If signalnum is 0, then no signal is sent, but error checking is still performed; this can be used to check if the target thread is still running.

Raises an auditing event signal.pthread_kill with arguments thread_id, signalnum.

Availability: Unix.

See the man page pthread_kill(3) for further information.

See also os.kill().

New in version 3.3.

+
+signal.pthread_sigmask(how, mask)
+

Fetch and/or change the signal mask of the calling thread. The signal mask is the set of signals whose delivery is currently blocked for the caller. Return the old signal mask as a set of signals.

The behavior of the call is dependent on the value of how, as follows.

  • +SIG_BLOCK: The set of blocked signals is the union of the current set and the mask argument.
  • +SIG_UNBLOCK: The signals in mask are removed from the current set of blocked signals. It is permissible to attempt to unblock a signal which is not blocked.
  • +SIG_SETMASK: The set of blocked signals is set to the mask argument.

mask is a set of signal numbers (e.g. {signal.SIGINT, signal.SIGTERM}). Use valid_signals() for a full mask including all signals.

For example, signal.pthread_sigmask(signal.SIG_BLOCK, []) reads the signal mask of the calling thread.

SIGKILL and SIGSTOP cannot be blocked.

Availability: Unix.

See the man page sigprocmask(2) and pthread_sigmask(3) for further information.

See also pause(), sigpending() and sigwait().

New in version 3.3.

+
+signal.setitimer(which, seconds, interval=0.0)
+

Sets given interval timer (one of signal.ITIMER_REAL, signal.ITIMER_VIRTUAL or signal.ITIMER_PROF) specified by which to fire after seconds (float is accepted, different from alarm()) and after that every interval seconds (if interval is non-zero). The interval timer specified by which can be cleared by setting seconds to zero.

When an interval timer fires, a signal is sent to the process. The signal sent is dependent on the timer being used; signal.ITIMER_REAL will deliver SIGALRM, signal.ITIMER_VIRTUAL sends SIGVTALRM, and signal.ITIMER_PROF will deliver SIGPROF.

The old values are returned as a tuple: (delay, interval).

Attempting to pass an invalid interval timer will cause an ItimerError.

Availability: Unix.

+
+signal.getitimer(which)
+

Returns current value of a given interval timer specified by which.

Availability: Unix.

+
+signal.set_wakeup_fd(fd, *, warn_on_full_buffer=True)
+

Set the wakeup file descriptor to fd. When a signal is received, the signal number is written as a single byte into the fd. This can be used by a library to wakeup a poll or select call, allowing the signal to be fully processed.

The old wakeup fd is returned (or -1 if file descriptor wakeup was not enabled). If fd is -1, file descriptor wakeup is disabled. If not -1, fd must be non-blocking. It is up to the library to remove any bytes from fd before calling poll or select again.

When threads are enabled, this function can only be called from the main thread of the main interpreter; attempting to call it from other threads will cause a ValueError exception to be raised.

There are two common ways to use this function. In both approaches, you use the fd to wake up when a signal arrives, but then they differ in how they determine which signal or signals have arrived.

In the first approach, we read the data out of the fd’s buffer, and the byte values give you the signal numbers. This is simple, but in rare cases it can run into a problem: generally the fd will have a limited amount of buffer space, and if too many signals arrive too quickly, then the buffer may become full, and some signals may be lost. If you use this approach, then you should set warn_on_full_buffer=True, which will at least cause a warning to be printed to stderr when signals are lost.

In the second approach, we use the wakeup fd only for wakeups, and ignore the actual byte values. In this case, all we care about is whether the fd’s buffer is empty or non-empty; a full buffer doesn’t indicate a problem at all. If you use this approach, then you should set warn_on_full_buffer=False, so that your users are not confused by spurious warning messages.

Changed in version 3.5: On Windows, the function now also supports socket handles.

Changed in version 3.7: Added warn_on_full_buffer parameter.

+
+signal.siginterrupt(signalnum, flag)
+

Change system call restart behaviour: if flag is False, system calls will be restarted when interrupted by signal signalnum, otherwise system calls will be interrupted. Returns nothing.

Availability: Unix.

See the man page siginterrupt(3) for further information.

Note that installing a signal handler with signal() will reset the restart behaviour to interruptible by implicitly calling siginterrupt() with a true flag value for the given signal.

+
+signal.signal(signalnum, handler)
+

Set the handler for signal signalnum to the function handler. handler can be a callable Python object taking two arguments (see below), or one of the special values signal.SIG_IGN or signal.SIG_DFL. The previous signal handler will be returned (see the description of getsignal() above). (See the Unix man page signal(2) for further information.)

When threads are enabled, this function can only be called from the main thread of the main interpreter; attempting to call it from other threads will cause a ValueError exception to be raised.

The handler is called with two arguments: the signal number and the current stack frame (None or a frame object; for a description of frame objects, see the description in the type hierarchy or see the attribute descriptions in the inspect module).

On Windows, signal() can only be called with SIGABRT, SIGFPE, SIGILL, SIGINT, SIGSEGV, SIGTERM, or SIGBREAK. A ValueError will be raised in any other case. Note that not all systems define the same set of signal names; an AttributeError will be raised if a signal name is not defined as SIG* module level constant.

+
+signal.sigpending()
+

Examine the set of signals that are pending for delivery to the calling thread (i.e., the signals which have been raised while blocked). Return the set of the pending signals.

Availability: Unix.

See the man page sigpending(2) for further information.

See also pause(), pthread_sigmask() and sigwait().

New in version 3.3.

+
+signal.sigwait(sigset)
+

Suspend execution of the calling thread until the delivery of one of the signals specified in the signal set sigset. The function accepts the signal (removes it from the pending list of signals), and returns the signal number.

Availability: Unix.

See the man page sigwait(3) for further information.

See also pause(), pthread_sigmask(), sigpending(), sigwaitinfo() and sigtimedwait().

New in version 3.3.

+
+signal.sigwaitinfo(sigset)
+

Suspend execution of the calling thread until the delivery of one of the signals specified in the signal set sigset. The function accepts the signal and removes it from the pending list of signals. If one of the signals in sigset is already pending for the calling thread, the function will return immediately with information about that signal. The signal handler is not called for the delivered signal. The function raises an InterruptedError if it is interrupted by a signal that is not in sigset.

The return value is an object representing the data contained in the siginfo_t structure, namely: si_signo, si_code, si_errno, si_pid, si_uid, si_status, si_band.

Availability: Unix.

See the man page sigwaitinfo(2) for further information.

See also pause(), sigwait() and sigtimedwait().

New in version 3.3.

Changed in version 3.5: The function is now retried if interrupted by a signal not in sigset and the signal handler does not raise an exception (see PEP 475 for the rationale).

+
+signal.sigtimedwait(sigset, timeout)
+

Like sigwaitinfo(), but takes an additional timeout argument specifying a timeout. If timeout is specified as 0, a poll is performed. Returns None if a timeout occurs.

Availability: Unix.

See the man page sigtimedwait(2) for further information.

See also pause(), sigwait() and sigwaitinfo().

New in version 3.3.

Changed in version 3.5: The function is now retried with the recomputed timeout if interrupted by a signal not in sigset and the signal handler does not raise an exception (see PEP 475 for the rationale).

+

Examples

Here is a minimal example program. It uses the alarm() function to limit the time spent waiting to open a file; this is useful if the file is for a serial device that may not be turned on, which would normally cause the os.open() to hang indefinitely. The solution is to set a 5-second alarm before opening the file; if the operation takes too long, the alarm signal will be sent, and the handler raises an exception.

import signal, os
+
+def handler(signum, frame):
+    signame = signal.Signals(signum).name
+    print(f'Signal handler called with signal {signame} ({signum})')
+    raise OSError("Couldn't open device!")
+
+# Set the signal handler and a 5-second alarm
+signal.signal(signal.SIGALRM, handler)
+signal.alarm(5)
+
+# This open() may hang indefinitely
+fd = os.open('/dev/ttyS0', os.O_RDWR)
+
+signal.alarm(0)          # Disable the alarm
+

Note on SIGPIPE

Piping output of your program to tools like head(1) will cause a SIGPIPE signal to be sent to your process when the receiver of its standard output closes early. This results in an exception like BrokenPipeError: [Errno 32] Broken pipe. To handle this case, wrap your entry point to catch this exception as follows:

import os
+import sys
+
+def main():
+    try:
+        # simulate large output (your code replaces this loop)
+        for x in range(10000):
+            print("y")
+        # flush output here to force SIGPIPE to be triggered
+        # while inside this try block.
+        sys.stdout.flush()
+    except BrokenPipeError:
+        # Python flushes standard streams on exit; redirect remaining output
+        # to devnull to avoid another BrokenPipeError at shutdown
+        devnull = os.open(os.devnull, os.O_WRONLY)
+        os.dup2(devnull, sys.stdout.fileno())
+        sys.exit(1)  # Python exits with error code 1 on EPIPE
+
+if __name__ == '__main__':
+    main()
+

Do not set SIGPIPE’s disposition to SIG_DFL in order to avoid BrokenPipeError. Doing that would cause your program to exit unexpectedly whenever any socket connection is interrupted while your program is still writing to it.

Note on Signal Handlers and Exceptions

If a signal handler raises an exception, the exception will be propagated to the main thread and may be raised after any bytecode instruction. Most notably, a KeyboardInterrupt may appear at any point during execution. Most Python code, including the standard library, cannot be made robust against this, and so a KeyboardInterrupt (or any other exception resulting from a signal handler) may on rare occasions put the program in an unexpected state.

To illustrate this issue, consider the following code:

class SpamContext:
+    def __init__(self):
+        self.lock = threading.Lock()
+
+    def __enter__(self):
+        # If KeyboardInterrupt occurs here, everything is fine
+        self.lock.acquire()
+        # If KeyboardInterrupt occurs here, __exit__ will not be called
+        ...
+        # KeyboardInterrupt could occur just before the function returns
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        ...
+        self.lock.release()
+

For many programs, especially those that merely want to exit on KeyboardInterrupt, this is not a problem, but applications that are complex or require high reliability should avoid raising exceptions from signal handlers. They should also avoid catching KeyboardInterrupt as a means of gracefully shutting down. Instead, they should install their own SIGINT handler. Below is an example of an HTTP server that avoids KeyboardInterrupt:

import signal
+import socket
+from selectors import DefaultSelector, EVENT_READ
+from http.server import HTTPServer, SimpleHTTPRequestHandler
+
+interrupt_read, interrupt_write = socket.socketpair()
+
+def handler(signum, frame):
+    print('Signal handler called with signal', signum)
+    interrupt_write.send(b'\0')
+signal.signal(signal.SIGINT, handler)
+
+def serve_forever(httpd):
+    sel = DefaultSelector()
+    sel.register(interrupt_read, EVENT_READ)
+    sel.register(httpd, EVENT_READ)
+
+    while True:
+        for key, _ in sel.select():
+            if key.fileobj == interrupt_read:
+                interrupt_read.recv(1)
+                return
+            if key.fileobj == httpd:
+                httpd.handle_request()
+
+print("Serving on port 8000")
+httpd = HTTPServer(('', 8000), SimpleHTTPRequestHandler)
+serve_forever(httpd)
+print("Shutdown...")
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/signal.html +

+
diff --git a/devdocs/python~3.12/library%2Fsite.html b/devdocs/python~3.12/library%2Fsite.html new file mode 100644 index 00000000..37d2d645 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsite.html @@ -0,0 +1,53 @@ +

site — Site-specific configuration hook

Source code: Lib/site.py

This module is automatically imported during initialization. The automatic import can be suppressed using the interpreter’s -S option.

Importing this module will append site-specific paths to the module search path and add a few builtins, unless -S was used. In that case, this module can be safely imported with no automatic modifications to the module search path or additions to the builtins. To explicitly trigger the usual site-specific additions, call the main() function.

Changed in version 3.3: Importing the module used to trigger paths manipulation even when using -S.

It starts by constructing up to four directories from a head and a tail part. For the head part, it uses sys.prefix and sys.exec_prefix; empty heads are skipped. For the tail part, it uses the empty string and then lib/site-packages (on Windows) or lib/pythonX.Y/site-packages (on Unix and macOS). For each of the distinct head-tail combinations, it sees if it refers to an existing directory, and if so, adds it to sys.path and also inspects the newly added path for configuration files.

Changed in version 3.5: Support for the “site-python” directory has been removed.

If a file named “pyvenv.cfg” exists one directory above sys.executable, sys.prefix and sys.exec_prefix are set to that directory and it is also checked for site-packages (sys.base_prefix and sys.base_exec_prefix will always be the “real” prefixes of the Python installation). If “pyvenv.cfg” (a bootstrap configuration file) contains the key “include-system-site-packages” set to anything other than “true” (case-insensitive), the system-level prefixes will not be searched for site-packages; otherwise they will.

A path configuration file is a file whose name has the form name.pth and exists in one of the four directories mentioned above; its contents are additional items (one per line) to be added to sys.path. Non-existing items are never added to sys.path, and no check is made that the item refers to a directory rather than a file. No item is added to sys.path more than once. Blank lines and lines beginning with # are skipped. Lines starting with import (followed by space or tab) are executed.

Note

An executable line in a .pth file is run at every Python startup, regardless of whether a particular module is actually going to be used. Its impact should thus be kept to a minimum. The primary intended purpose of executable lines is to make the corresponding module(s) importable (load 3rd-party import hooks, adjust PATH etc). Any other initialization is supposed to be done upon a module’s actual import, if and when it happens. Limiting a code chunk to a single line is a deliberate measure to discourage putting anything more complex here.

For example, suppose sys.prefix and sys.exec_prefix are set to /usr/local. The Python X.Y library is then installed in /usr/local/lib/pythonX.Y. Suppose this has a subdirectory /usr/local/lib/pythonX.Y/site-packages with three subsubdirectories, foo, bar and spam, and two path configuration files, foo.pth and bar.pth. Assume foo.pth contains the following:

# foo package configuration
+
+foo
+bar
+bletch
+

and bar.pth contains:

# bar package configuration
+
+bar
+

Then the following version-specific directories are added to sys.path, in this order:

/usr/local/lib/pythonX.Y/site-packages/bar
+/usr/local/lib/pythonX.Y/site-packages/foo
+

Note that bletch is omitted because it doesn’t exist; the bar directory precedes the foo directory because bar.pth comes alphabetically before foo.pth; and spam is omitted because it is not mentioned in either path configuration file.

sitecustomize

After these path manipulations, an attempt is made to import a module named sitecustomize, which can perform arbitrary site-specific customizations. It is typically created by a system administrator in the site-packages directory. If this import fails with an ImportError or its subclass exception, and the exception’s name attribute equals to 'sitecustomize', it is silently ignored. If Python is started without output streams available, as with pythonw.exe on Windows (which is used by default to start IDLE), attempted output from sitecustomize is ignored. Any other exception causes a silent and perhaps mysterious failure of the process.

usercustomize

After this, an attempt is made to import a module named usercustomize, which can perform arbitrary user-specific customizations, if ENABLE_USER_SITE is true. This file is intended to be created in the user site-packages directory (see below), which is part of sys.path unless disabled by -s. If this import fails with an ImportError or its subclass exception, and the exception’s name attribute equals to 'usercustomize', it is silently ignored.

Note that for some non-Unix systems, sys.prefix and sys.exec_prefix are empty, and the path manipulations are skipped; however the import of sitecustomize and usercustomize is still attempted.

Readline configuration

On systems that support readline, this module will also import and configure the rlcompleter module, if Python is started in interactive mode and without the -S option. The default behavior is enable tab-completion and to use ~/.python_history as the history save file. To disable it, delete (or override) the sys.__interactivehook__ attribute in your sitecustomize or usercustomize module or your PYTHONSTARTUP file.

Changed in version 3.4: Activation of rlcompleter and history was made automatic.

Module contents

+site.PREFIXES
+

A list of prefixes for site-packages directories.

+
+site.ENABLE_USER_SITE
+

Flag showing the status of the user site-packages directory. True means that it is enabled and was added to sys.path. False means that it was disabled by user request (with -s or PYTHONNOUSERSITE). None means it was disabled for security reasons (mismatch between user or group id and effective id) or by an administrator.

+
+site.USER_SITE
+

Path to the user site-packages for the running Python. Can be None if getusersitepackages() hasn’t been called yet. Default value is ~/.local/lib/pythonX.Y/site-packages for UNIX and non-framework macOS builds, ~/Library/Python/X.Y/lib/python/site-packages for macOS framework builds, and %APPDATA%\Python\PythonXY\site-packages on Windows. This directory is a site directory, which means that .pth files in it will be processed.

+
+site.USER_BASE
+

Path to the base directory for the user site-packages. Can be None if getuserbase() hasn’t been called yet. Default value is ~/.local for UNIX and macOS non-framework builds, ~/Library/Python/X.Y for macOS framework builds, and %APPDATA%\Python for Windows. This value is used to compute the installation directories for scripts, data files, Python modules, etc. for the user installation scheme. See also PYTHONUSERBASE.

+
+site.main()
+

Adds all the standard site-specific directories to the module search path. This function is called automatically when this module is imported, unless the Python interpreter was started with the -S flag.

Changed in version 3.3: This function used to be called unconditionally.

+
+site.addsitedir(sitedir, known_paths=None)
+

Add a directory to sys.path and process its .pth files. Typically used in sitecustomize or usercustomize (see above).

+
+site.getsitepackages()
+

Return a list containing all global site-packages directories.

New in version 3.2.

+
+site.getuserbase()
+

Return the path of the user base directory, USER_BASE. If it is not initialized yet, this function will also set it, respecting PYTHONUSERBASE.

New in version 3.2.

+
+site.getusersitepackages()
+

Return the path of the user-specific site-packages directory, USER_SITE. If it is not initialized yet, this function will also set it, respecting USER_BASE. To determine if the user-specific site-packages was added to sys.path ENABLE_USER_SITE should be used.

New in version 3.2.

+

Command Line Interface

The site module also provides a way to get the user directories from the command line:

$ python -m site --user-site
+/home/user/.local/lib/python3.11/site-packages
+

If it is called without arguments, it will print the contents of sys.path on the standard output, followed by the value of USER_BASE and whether the directory exists, then the same thing for USER_SITE, and finally the value of ENABLE_USER_SITE.

+--user-base
+

Print the path to the user base directory.

+
+--user-site
+

Print the path to the user site-packages directory.

+

If both options are given, user base and user site will be printed (always in this order), separated by os.pathsep.

If any option is given, the script will exit with one of these values: 0 if the user site-packages directory is enabled, 1 if it was disabled by the user, 2 if it is disabled for security reasons or by an administrator, and a value greater than 2 if there is an error.

See also

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/site.html +

+
diff --git a/devdocs/python~3.12/library%2Fsmtplib.html b/devdocs/python~3.12/library%2Fsmtplib.html new file mode 100644 index 00000000..aa068858 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsmtplib.html @@ -0,0 +1,180 @@ +

smtplib — SMTP protocol client

Source code: Lib/smtplib.py

The smtplib module defines an SMTP client session object that can be used to send mail to any internet machine with an SMTP or ESMTP listener daemon. For details of SMTP and ESMTP operation, consult RFC 821 (Simple Mail Transfer Protocol) and RFC 1869 (SMTP Service Extensions).

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

+class smtplib.SMTP(host='', port=0, local_hostname=None, [timeout, ]source_address=None)
+

An SMTP instance encapsulates an SMTP connection. It has methods that support a full repertoire of SMTP and ESMTP operations. If the optional host and port parameters are given, the SMTP connect() method is called with those parameters during initialization. If specified, local_hostname is used as the FQDN of the local host in the HELO/EHLO command. Otherwise, the local hostname is found using socket.getfqdn(). If the connect() call returns anything other than a success code, an SMTPConnectError is raised. The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if not specified, the global default timeout setting will be used). If the timeout expires, TimeoutError is raised. The optional source_address parameter allows binding to some specific source address in a machine with multiple network interfaces, and/or to some specific source TCP port. It takes a 2-tuple (host, port), for the socket to bind to as its source address before connecting. If omitted (or if host or port are '' and/or 0 respectively) the OS default behavior will be used.

For normal use, you should only require the initialization/connect, sendmail(), and SMTP.quit() methods. An example is included below.

The SMTP class supports the with statement. When used like this, the SMTP QUIT command is issued automatically when the with statement exits. E.g.:

>>> from smtplib import SMTP
+>>> with SMTP("domain.org") as smtp:
+...     smtp.noop()
+...
+(250, b'Ok')
+>>>
+

+

All commands will raise an auditing event smtplib.SMTP.send with arguments self and data, where data is the bytes about to be sent to the remote host.

Changed in version 3.3: Support for the with statement was added.

Changed in version 3.3: source_address argument was added.

New in version 3.5: The SMTPUTF8 extension (RFC 6531) is now supported.

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket.

+
+class smtplib.SMTP_SSL(host='', port=0, local_hostname=None, *, [timeout, ]context=None, source_address=None)
+

An SMTP_SSL instance behaves exactly the same as instances of SMTP. SMTP_SSL should be used for situations where SSL is required from the beginning of the connection and using starttls() is not appropriate. If host is not specified, the local host is used. If port is zero, the standard SMTP-over-SSL port (465) is used. The optional arguments local_hostname, timeout and source_address have the same meaning as they do in the SMTP class. context, also optional, can contain a SSLContext and allows configuring various aspects of the secure connection. Please read Security considerations for best practices.

Changed in version 3.3: context was added.

Changed in version 3.3: The source_address argument was added.

Changed in version 3.4: The class now supports hostname check with ssl.SSLContext.check_hostname and Server Name Indication (see ssl.HAS_SNI).

Changed in version 3.9: If the timeout parameter is set to be zero, it will raise a ValueError to prevent the creation of a non-blocking socket

Changed in version 3.12: The deprecated keyfile and certfile parameters have been removed.

+
+class smtplib.LMTP(host='', port=LMTP_PORT, local_hostname=None, source_address=None[, timeout])
+

The LMTP protocol, which is very similar to ESMTP, is heavily based on the standard SMTP client. It’s common to use Unix sockets for LMTP, so our connect() method must support that as well as a regular host:port server. The optional arguments local_hostname and source_address have the same meaning as they do in the SMTP class. To specify a Unix socket, you must use an absolute path for host, starting with a ‘/’.

Authentication is supported, using the regular SMTP mechanism. When using a Unix socket, LMTP generally don’t support or require any authentication, but your mileage might vary.

Changed in version 3.9: The optional timeout parameter was added.

+

A nice selection of exceptions is defined as well:

+exception smtplib.SMTPException
+

Subclass of OSError that is the base exception class for all the other exceptions provided by this module.

Changed in version 3.4: SMTPException became subclass of OSError

+
+exception smtplib.SMTPServerDisconnected
+

This exception is raised when the server unexpectedly disconnects, or when an attempt is made to use the SMTP instance before connecting it to a server.

+
+exception smtplib.SMTPResponseException
+

Base class for all exceptions that include an SMTP error code. These exceptions are generated in some instances when the SMTP server returns an error code. The error code is stored in the smtp_code attribute of the error, and the smtp_error attribute is set to the error message.

+
+exception smtplib.SMTPSenderRefused
+

Sender address refused. In addition to the attributes set by on all SMTPResponseException exceptions, this sets ‘sender’ to the string that the SMTP server refused.

+
+exception smtplib.SMTPRecipientsRefused
+

All recipient addresses refused. The errors for each recipient are accessible through the attribute recipients, which is a dictionary of exactly the same sort as SMTP.sendmail() returns.

+
+exception smtplib.SMTPDataError
+

The SMTP server refused to accept the message data.

+
+exception smtplib.SMTPConnectError
+

Error occurred during establishment of a connection with the server.

+
+exception smtplib.SMTPHeloError
+

The server refused our HELO message.

+
+exception smtplib.SMTPNotSupportedError
+

The command or option attempted is not supported by the server.

New in version 3.5.

+
+exception smtplib.SMTPAuthenticationError
+

SMTP authentication went wrong. Most probably the server didn’t accept the username/password combination provided.

+

See also

+RFC 821 - Simple Mail Transfer Protocol
+
+

Protocol definition for SMTP. This document covers the model, operating procedure, and protocol details for SMTP.

+RFC 1869 - SMTP Service Extensions
+
+

Definition of the ESMTP extensions for SMTP. This describes a framework for extending SMTP with new commands, supporting dynamic discovery of the commands provided by the server, and defines a few additional commands.

SMTP Objects

An SMTP instance has the following methods:

+SMTP.set_debuglevel(level)
+

Set the debug output level. A value of 1 or True for level results in debug messages for connection and for all messages sent to and received from the server. A value of 2 for level results in these messages being timestamped.

Changed in version 3.5: Added debuglevel 2.

+
+SMTP.docmd(cmd, args='')
+

Send a command cmd to the server. The optional argument args is simply concatenated to the command, separated by a space.

This returns a 2-tuple composed of a numeric response code and the actual response line (multiline responses are joined into one long line.)

In normal operation it should not be necessary to call this method explicitly. It is used to implement other methods and may be useful for testing private extensions.

If the connection to the server is lost while waiting for the reply, SMTPServerDisconnected will be raised.

+
+SMTP.connect(host='localhost', port=0)
+

Connect to a host on a given port. The defaults are to connect to the local host at the standard SMTP port (25). If the hostname ends with a colon (':') followed by a number, that suffix will be stripped off and the number interpreted as the port number to use. This method is automatically invoked by the constructor if a host is specified during instantiation. Returns a 2-tuple of the response code and message sent by the server in its connection response.

Raises an auditing event smtplib.connect with arguments self, host, port.

+
+SMTP.helo(name='')
+

Identify yourself to the SMTP server using HELO. The hostname argument defaults to the fully qualified domain name of the local host. The message returned by the server is stored as the helo_resp attribute of the object.

In normal operation it should not be necessary to call this method explicitly. It will be implicitly called by the sendmail() when necessary.

+
+SMTP.ehlo(name='')
+

Identify yourself to an ESMTP server using EHLO. The hostname argument defaults to the fully qualified domain name of the local host. Examine the response for ESMTP option and store them for use by has_extn(). Also sets several informational attributes: the message returned by the server is stored as the ehlo_resp attribute, does_esmtp is set to True or False depending on whether the server supports ESMTP, and esmtp_features will be a dictionary containing the names of the SMTP service extensions this server supports, and their parameters (if any).

Unless you wish to use has_extn() before sending mail, it should not be necessary to call this method explicitly. It will be implicitly called by sendmail() when necessary.

+
+SMTP.ehlo_or_helo_if_needed()
+

This method calls ehlo() and/or helo() if there has been no previous EHLO or HELO command this session. It tries ESMTP EHLO first.

+ SMTPHeloError +
+
+

The server didn’t reply properly to the HELO greeting.

+
+SMTP.has_extn(name)
+

Return True if name is in the set of SMTP service extensions returned by the server, False otherwise. Case is ignored.

+
+SMTP.verify(address)
+

Check the validity of an address on this server using SMTP VRFY. Returns a tuple consisting of code 250 and a full RFC 822 address (including human name) if the user address is valid. Otherwise returns an SMTP error code of 400 or greater and an error string.

Note

Many sites disable SMTP VRFY in order to foil spammers.

+
+SMTP.login(user, password, *, initial_response_ok=True)
+

Log in on an SMTP server that requires authentication. The arguments are the username and the password to authenticate with. If there has been no previous EHLO or HELO command this session, this method tries ESMTP EHLO first. This method will return normally if the authentication was successful, or may raise the following exceptions:

+ SMTPHeloError +
+
+

The server didn’t reply properly to the HELO greeting.

+ SMTPAuthenticationError +
+
+

The server didn’t accept the username/password combination.

+ SMTPNotSupportedError +
+
+

The AUTH command is not supported by the server.

+ SMTPException +
+
+

No suitable authentication method was found.

Each of the authentication methods supported by smtplib are tried in turn if they are advertised as supported by the server. See auth() for a list of supported authentication methods. initial_response_ok is passed through to auth().

Optional keyword argument initial_response_ok specifies whether, for authentication methods that support it, an “initial response” as specified in RFC 4954 can be sent along with the AUTH command, rather than requiring a challenge/response.

Changed in version 3.5: SMTPNotSupportedError may be raised, and the initial_response_ok parameter was added.

+
+SMTP.auth(mechanism, authobject, *, initial_response_ok=True)
+

Issue an SMTP AUTH command for the specified authentication mechanism, and handle the challenge response via authobject.

mechanism specifies which authentication mechanism is to be used as argument to the AUTH command; the valid values are those listed in the auth element of esmtp_features.

authobject must be a callable object taking an optional single argument:

data = authobject(challenge=None)
+

If optional keyword argument initial_response_ok is true, authobject() will be called first with no argument. It can return the RFC 4954 “initial response” ASCII str which will be encoded and sent with the AUTH command as below. If the authobject() does not support an initial response (e.g. because it requires a challenge), it should return None when called with challenge=None. If initial_response_ok is false, then authobject() will not be called first with None.

If the initial response check returns None, or if initial_response_ok is false, authobject() will be called to process the server’s challenge response; the challenge argument it is passed will be a bytes. It should return ASCII str data that will be base64 encoded and sent to the server.

The SMTP class provides authobjects for the CRAM-MD5, PLAIN, and LOGIN mechanisms; they are named SMTP.auth_cram_md5, SMTP.auth_plain, and SMTP.auth_login respectively. They all require that the user and password properties of the SMTP instance are set to appropriate values.

User code does not normally need to call auth directly, but can instead call the login() method, which will try each of the above mechanisms in turn, in the order listed. auth is exposed to facilitate the implementation of authentication methods not (or not yet) supported directly by smtplib.

New in version 3.5.

+
+SMTP.starttls(*, context=None)
+

Put the SMTP connection in TLS (Transport Layer Security) mode. All SMTP commands that follow will be encrypted. You should then call ehlo() again.

If keyfile and certfile are provided, they are used to create an ssl.SSLContext.

Optional context parameter is an ssl.SSLContext object; This is an alternative to using a keyfile and a certfile and if specified both keyfile and certfile should be None.

If there has been no previous EHLO or HELO command this session, this method tries ESMTP EHLO first.

Changed in version 3.12: The deprecated keyfile and certfile parameters have been removed.

+ SMTPHeloError +
+
+

The server didn’t reply properly to the HELO greeting.

+ SMTPNotSupportedError +
+
+

The server does not support the STARTTLS extension.

+ RuntimeError +
+
+

SSL/TLS support is not available to your Python interpreter.

Changed in version 3.3: context was added.

Changed in version 3.4: The method now supports hostname check with SSLContext.check_hostname and Server Name Indicator (see HAS_SNI).

Changed in version 3.5: The error raised for lack of STARTTLS support is now the SMTPNotSupportedError subclass instead of the base SMTPException.

+
+SMTP.sendmail(from_addr, to_addrs, msg, mail_options=(), rcpt_options=())
+

Send mail. The required arguments are an RFC 822 from-address string, a list of RFC 822 to-address strings (a bare string will be treated as a list with 1 address), and a message string. The caller may pass a list of ESMTP options (such as 8bitmime) to be used in MAIL FROM commands as mail_options. ESMTP options (such as DSN commands) that should be used with all RCPT commands can be passed as rcpt_options. (If you need to use different ESMTP options to different recipients you have to use the low-level methods such as mail(), rcpt() and data() to send the message.)

Note

The from_addr and to_addrs parameters are used to construct the message envelope used by the transport agents. sendmail does not modify the message headers in any way.

msg may be a string containing characters in the ASCII range, or a byte string. A string is encoded to bytes using the ascii codec, and lone \r and \n characters are converted to \r\n characters. A byte string is not modified.

If there has been no previous EHLO or HELO command this session, this method tries ESMTP EHLO first. If the server does ESMTP, message size and each of the specified options will be passed to it (if the option is in the feature set the server advertises). If EHLO fails, HELO will be tried and ESMTP options suppressed.

This method will return normally if the mail is accepted for at least one recipient. Otherwise it will raise an exception. That is, if this method does not raise an exception, then someone should get your mail. If this method does not raise an exception, it returns a dictionary, with one entry for each recipient that was refused. Each entry contains a tuple of the SMTP error code and the accompanying error message sent by the server.

If SMTPUTF8 is included in mail_options, and the server supports it, from_addr and to_addrs may contain non-ASCII characters.

This method may raise the following exceptions:

+ SMTPRecipientsRefused +
+
+

All recipients were refused. Nobody got the mail. The recipients attribute of the exception object is a dictionary with information about the refused recipients (like the one returned when at least one recipient was accepted).

+ SMTPHeloError +
+
+

The server didn’t reply properly to the HELO greeting.

+ SMTPSenderRefused +
+
+

The server didn’t accept the from_addr.

+ SMTPDataError +
+
+

The server replied with an unexpected error code (other than a refusal of a recipient).

+ SMTPNotSupportedError +
+
+

SMTPUTF8 was given in the mail_options but is not supported by the server.

Unless otherwise noted, the connection will be open even after an exception is raised.

Changed in version 3.2: msg may be a byte string.

Changed in version 3.5: SMTPUTF8 support added, and SMTPNotSupportedError may be raised if SMTPUTF8 is specified but the server does not support it.

+
+SMTP.send_message(msg, from_addr=None, to_addrs=None, mail_options=(), rcpt_options=())
+

This is a convenience method for calling sendmail() with the message represented by an email.message.Message object. The arguments have the same meaning as for sendmail(), except that msg is a Message object.

If from_addr is None or to_addrs is None, send_message fills those arguments with addresses extracted from the headers of msg as specified in RFC 5322: from_addr is set to the Sender field if it is present, and otherwise to the From field. to_addrs combines the values (if any) of the To, Cc, and Bcc fields from msg. If exactly one set of Resent-* headers appear in the message, the regular headers are ignored and the Resent-* headers are used instead. If the message contains more than one set of Resent-* headers, a ValueError is raised, since there is no way to unambiguously detect the most recent set of Resent- headers.

send_message serializes msg using BytesGenerator with \r\n as the linesep, and calls sendmail() to transmit the resulting message. Regardless of the values of from_addr and to_addrs, send_message does not transmit any Bcc or Resent-Bcc headers that may appear in msg. If any of the addresses in from_addr and to_addrs contain non-ASCII characters and the server does not advertise SMTPUTF8 support, an SMTPNotSupported error is raised. Otherwise the Message is serialized with a clone of its policy with the utf8 attribute set to True, and SMTPUTF8 and BODY=8BITMIME are added to mail_options.

New in version 3.2.

New in version 3.5: Support for internationalized addresses (SMTPUTF8).

+
+SMTP.quit()
+

Terminate the SMTP session and close the connection. Return the result of the SMTP QUIT command.

+

Low-level methods corresponding to the standard SMTP/ESMTP commands HELP, RSET, NOOP, MAIL, RCPT, and DATA are also supported. Normally these do not need to be called directly, so they are not documented here. For details, consult the module code.

SMTP Example

This example prompts the user for addresses needed in the message envelope (‘To’ and ‘From’ addresses), and the message to be delivered. Note that the headers to be included with the message must be included in the message as entered; this example doesn’t do any processing of the RFC 822 headers. In particular, the ‘To’ and ‘From’ addresses must be included in the message headers explicitly.

import smtplib
+
+def prompt(prompt):
+    return input(prompt).strip()
+
+fromaddr = prompt("From: ")
+toaddrs  = prompt("To: ").split()
+print("Enter message, end with ^D (Unix) or ^Z (Windows):")
+
+# Add the From: and To: headers at the start!
+msg = ("From: %s\r\nTo: %s\r\n\r\n"
+       % (fromaddr, ", ".join(toaddrs)))
+while True:
+    try:
+        line = input()
+    except EOFError:
+        break
+    if not line:
+        break
+    msg = msg + line
+
+print("Message length is", len(msg))
+
+server = smtplib.SMTP('localhost')
+server.set_debuglevel(1)
+server.sendmail(fromaddr, toaddrs, msg)
+server.quit()
+

Note

In general, you will want to use the email package’s features to construct an email message, which you can then send via send_message(); see email: Examples.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/smtplib.html +

+
diff --git a/devdocs/python~3.12/library%2Fsocket.html b/devdocs/python~3.12/library%2Fsocket.html new file mode 100644 index 00000000..59e6bfce --- /dev/null +++ b/devdocs/python~3.12/library%2Fsocket.html @@ -0,0 +1,639 @@ +

socket — Low-level networking interface

Source code: Lib/socket.py

This module provides access to the BSD socket interface. It is available on all modern Unix systems, Windows, MacOS, and probably additional platforms.

Note

Some behavior may be platform dependent, since calls are made to the operating system socket APIs.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The Python interface is a straightforward transliteration of the Unix system call and library interface for sockets to Python’s object-oriented style: the socket() function returns a socket object whose methods implement the various socket system calls. Parameter types are somewhat higher-level than in the C interface: as with read() and write() operations on Python files, buffer allocation on receive operations is automatic, and buffer length is implicit on send operations.

See also

+Module socketserver +
+
+

Classes that simplify writing network servers.

+Module ssl +
+
+

A TLS/SSL wrapper for socket objects.

Socket families

Depending on the system and the build options, various socket families are supported by this module.

The address format required by a particular socket object is automatically selected based on the address family specified when the socket object was created. Socket addresses are represented as follows:

  • +

    The address of an AF_UNIX socket bound to a file system node is represented as a string, using the file system encoding and the 'surrogateescape' error handler (see PEP 383). An address in Linux’s abstract namespace is returned as a bytes-like object with an initial null byte; note that sockets in this namespace can communicate with normal file system sockets, so programs intended to run on Linux may need to deal with both types of address. A string or bytes-like object can be used for either type of address when passing it as an argument.

    Changed in version 3.3: Previously, AF_UNIX socket paths were assumed to use UTF-8 encoding.

    Changed in version 3.5: Writable bytes-like object is now accepted.

  • +

    A pair (host, port) is used for the AF_INET address family, where host is a string representing either a hostname in internet domain notation like 'daring.cwi.nl' or an IPv4 address like '100.50.200.5', and port is an integer.

    • For IPv4 addresses, two special forms are accepted instead of a host address: '' represents INADDR_ANY, which is used to bind to all interfaces, and the string '<broadcast>' represents INADDR_BROADCAST. This behavior is not compatible with IPv6, therefore, you may want to avoid these if you intend to support IPv6 with your Python programs.
  • +

    For AF_INET6 address family, a four-tuple (host, port, flowinfo, +scope_id) is used, where flowinfo and scope_id represent the sin6_flowinfo and sin6_scope_id members in struct sockaddr_in6 in C. For socket module methods, flowinfo and scope_id can be omitted just for backward compatibility. Note, however, omission of scope_id can cause problems in manipulating scoped IPv6 addresses.

    Changed in version 3.7: For multicast addresses (with scope_id meaningful) address may not contain %scope_id (or zone id) part. This information is superfluous and may be safely omitted (recommended).

  • +AF_NETLINK sockets are represented as pairs (pid, groups).
  • +

    Linux-only support for TIPC is available using the AF_TIPC address family. TIPC is an open, non-IP based networked protocol designed for use in clustered computer environments. Addresses are represented by a tuple, and the fields depend on the address type. The general tuple form is (addr_type, v1, v2, v3 [, scope]), where:

    • +addr_type is one of TIPC_ADDR_NAMESEQ, TIPC_ADDR_NAME, or TIPC_ADDR_ID.
    • +scope is one of TIPC_ZONE_SCOPE, TIPC_CLUSTER_SCOPE, and TIPC_NODE_SCOPE.
    • +

      If addr_type is TIPC_ADDR_NAME, then v1 is the server type, v2 is the port identifier, and v3 should be 0.

      If addr_type is TIPC_ADDR_NAMESEQ, then v1 is the server type, v2 is the lower port number, and v3 is the upper port number.

      If addr_type is TIPC_ADDR_ID, then v1 is the node, v2 is the reference, and v3 should be set to 0.

  • +

    A tuple (interface, ) is used for the AF_CAN address family, where interface is a string representing a network interface name like 'can0'. The network interface name '' can be used to receive packets from all network interfaces of this family.

    • +CAN_ISOTP protocol require a tuple (interface, rx_addr, tx_addr) where both additional parameters are unsigned long integer that represent a CAN identifier (standard or extended).
    • +CAN_J1939 protocol require a tuple (interface, name, pgn, addr) where additional parameters are 64-bit unsigned integer representing the ECU name, a 32-bit unsigned integer representing the Parameter Group Number (PGN), and an 8-bit integer representing the address.
  • +

    A string or a tuple (id, unit) is used for the SYSPROTO_CONTROL protocol of the PF_SYSTEM family. The string is the name of a kernel control using a dynamically assigned ID. The tuple can be used if ID and unit number of the kernel control are known or if a registered ID is used.

    New in version 3.3.

  • +

    AF_BLUETOOTH supports the following protocols and address formats:

    • +BTPROTO_L2CAP accepts (bdaddr, psm) where bdaddr is the Bluetooth address as a string and psm is an integer.
    • +BTPROTO_RFCOMM accepts (bdaddr, channel) where bdaddr is the Bluetooth address as a string and channel is an integer.
    • +

      BTPROTO_HCI accepts (device_id,) where device_id is either an integer or a string with the Bluetooth address of the interface. (This depends on your OS; NetBSD and DragonFlyBSD expect a Bluetooth address while everything else expects an integer.)

      Changed in version 3.2: NetBSD and DragonFlyBSD support added.

    • +BTPROTO_SCO accepts bdaddr where bdaddr is a bytes object containing the Bluetooth address in a string format. (ex. b'12:23:34:45:56:67') This protocol is not supported under FreeBSD.
  • +

    AF_ALG is a Linux-only socket based interface to Kernel cryptography. An algorithm socket is configured with a tuple of two to four elements (type, name [, feat [, mask]]), where:

    • +type is the algorithm type as string, e.g. aead, hash, skcipher or rng.
    • +name is the algorithm name and operation mode as string, e.g. sha256, hmac(sha256), cbc(aes) or drbg_nopr_ctr_aes256.
    • +feat and mask are unsigned 32bit integers.

    Availability: Linux >= 2.6.38.

    Some algorithm types require more recent Kernels.

    New in version 3.6.

  • +

    AF_VSOCK allows communication between virtual machines and their hosts. The sockets are represented as a (CID, port) tuple where the context ID or CID and port are integers.

    Availability: Linux >= 3.9

    See vsock(7)

    New in version 3.7.

  • +

    AF_PACKET is a low-level interface directly to network devices. The addresses are represented by the tuple (ifname, proto[, pkttype[, hatype[, addr]]]) where:

    • +ifname - String specifying the device name.
    • +proto - The Ethernet protocol number. May be ETH_P_ALL to capture all protocols, one of the ETHERTYPE_* constants or any other Ethernet protocol number.
    • +

      pkttype - Optional integer specifying the packet type:

      • +PACKET_HOST (the default) - Packet addressed to the local host.
      • +PACKET_BROADCAST - Physical-layer broadcast packet.
      • +PACKET_MULTICAST - Packet sent to a physical-layer multicast address.
      • +PACKET_OTHERHOST - Packet to some other host that has been caught by a device driver in promiscuous mode.
      • +PACKET_OUTGOING - Packet originating from the local host that is looped back to a packet socket.
    • +hatype - Optional integer specifying the ARP hardware address type.
    • +addr - Optional bytes-like object specifying the hardware physical address, whose interpretation depends on the device.

    Availability: Linux >= 2.2.

  • +

    AF_QIPCRTR is a Linux-only socket based interface for communicating with services running on co-processors in Qualcomm platforms. The address family is represented as a (node, port) tuple where the node and port are non-negative integers.

    Availability: Linux >= 4.7.

    New in version 3.8.

  • +

    IPPROTO_UDPLITE is a variant of UDP which allows you to specify what portion of a packet is covered with the checksum. It adds two socket options that you can change. self.setsockopt(IPPROTO_UDPLITE, UDPLITE_SEND_CSCOV, length) will change what portion of outgoing packets are covered by the checksum and self.setsockopt(IPPROTO_UDPLITE, UDPLITE_RECV_CSCOV, length) will filter out packets which cover too little of their data. In both cases length should be in range(8, 2**16, 8).

    Such a socket should be constructed with socket(AF_INET, SOCK_DGRAM, IPPROTO_UDPLITE) for IPv4 or socket(AF_INET6, SOCK_DGRAM, IPPROTO_UDPLITE) for IPv6.

    Availability: Linux >= 2.6.20, FreeBSD >= 10.1

    New in version 3.9.

  • +

    AF_HYPERV is a Windows-only socket based interface for communicating with Hyper-V hosts and guests. The address family is represented as a (vm_id, service_id) tuple where the vm_id and service_id are UUID strings.

    The vm_id is the virtual machine identifier or a set of known VMID values if the target is not a specific virtual machine. Known VMID constants defined on socket are:

    • HV_GUID_ZERO
    • HV_GUID_BROADCAST
    • +HV_GUID_WILDCARD - Used to bind on itself and accept connections from all partitions.
    • +HV_GUID_CHILDREN - Used to bind on itself and accept connection from child partitions.
    • +HV_GUID_LOOPBACK - Used as a target to itself.
    • +HV_GUID_PARENT - When used as a bind accepts connection from the parent partition. When used as an address target it will connect to the parent partition.

    The service_id is the service identifier of the registered service.

    New in version 3.12.

If you use a hostname in the host portion of IPv4/v6 socket address, the program may show a nondeterministic behavior, as Python uses the first address returned from the DNS resolution. The socket address will be resolved differently into an actual IPv4/v6 address, depending on the results from DNS resolution and/or the host configuration. For deterministic behavior use a numeric address in host portion.

All errors raise exceptions. The normal exceptions for invalid argument types and out-of-memory conditions can be raised. Errors related to socket or address semantics raise OSError or one of its subclasses.

Non-blocking mode is supported through setblocking(). A generalization of this based on timeouts is supported through settimeout().

Module contents

The module socket exports the following elements.

Exceptions

+exception socket.error
+

A deprecated alias of OSError.

Changed in version 3.3: Following PEP 3151, this class was made an alias of OSError.

+
+exception socket.herror
+

A subclass of OSError, this exception is raised for address-related errors, i.e. for functions that use h_errno in the POSIX C API, including gethostbyname_ex() and gethostbyaddr(). The accompanying value is a pair (h_errno, string) representing an error returned by a library call. h_errno is a numeric value, while string represents the description of h_errno, as returned by the hstrerror() C function.

Changed in version 3.3: This class was made a subclass of OSError.

+
+exception socket.gaierror
+

A subclass of OSError, this exception is raised for address-related errors by getaddrinfo() and getnameinfo(). The accompanying value is a pair (error, string) representing an error returned by a library call. string represents the description of error, as returned by the gai_strerror() C function. The numeric error value will match one of the EAI_* constants defined in this module.

Changed in version 3.3: This class was made a subclass of OSError.

+
+exception socket.timeout
+

A deprecated alias of TimeoutError.

A subclass of OSError, this exception is raised when a timeout occurs on a socket which has had timeouts enabled via a prior call to settimeout() (or implicitly through setdefaulttimeout()). The accompanying value is a string whose value is currently always “timed out”.

Changed in version 3.3: This class was made a subclass of OSError.

Changed in version 3.10: This class was made an alias of TimeoutError.

+

Constants

The AF_* and SOCK_* constants are now AddressFamily and SocketKind IntEnum collections.

New in version 3.4.

+socket.AF_UNIX
+socket.AF_INET
+socket.AF_INET6
+

These constants represent the address (and protocol) families, used for the first argument to socket(). If the AF_UNIX constant is not defined then this protocol is unsupported. More constants may be available depending on the system.

+
+socket.AF_UNSPEC
+

AF_UNSPEC means that getaddrinfo() should return socket addresses for any address family (either IPv4, IPv6, or any other) that can be used.

+
+socket.SOCK_STREAM
+socket.SOCK_DGRAM
+socket.SOCK_RAW
+socket.SOCK_RDM
+socket.SOCK_SEQPACKET
+

These constants represent the socket types, used for the second argument to socket(). More constants may be available depending on the system. (Only SOCK_STREAM and SOCK_DGRAM appear to be generally useful.)

+
+socket.SOCK_CLOEXEC
+socket.SOCK_NONBLOCK
+

These two constants, if defined, can be combined with the socket types and allow you to set some flags atomically (thus avoiding possible race conditions and the need for separate calls).

See also

Secure File Descriptor Handling for a more thorough explanation.

Availability: Linux >= 2.6.27.

New in version 3.2.

+
SO_* +
+socket.SOMAXCONN
MSG_* +
SOL_* +
SCM_* +
IPPROTO_* +
IPPORT_* +
INADDR_* +
IP_* +
IPV6_* +
EAI_* +
AI_* +
NI_* +
TCP_* +
+

Many constants of these forms, documented in the Unix documentation on sockets and/or the IP protocol, are also defined in the socket module. They are generally used in arguments to the setsockopt() and getsockopt() methods of socket objects. In most cases, only those symbols that are defined in the Unix header files are defined; for a few symbols, default values are provided.

Changed in version 3.6: SO_DOMAIN, SO_PROTOCOL, SO_PEERSEC, SO_PASSSEC, TCP_USER_TIMEOUT, TCP_CONGESTION were added.

Changed in version 3.6.5: On Windows, TCP_FASTOPEN, TCP_KEEPCNT appear if run-time Windows supports.

Changed in version 3.7: TCP_NOTSENT_LOWAT was added.

On Windows, TCP_KEEPIDLE, TCP_KEEPINTVL appear if run-time Windows supports.

Changed in version 3.10: IP_RECVTOS was added. Added TCP_KEEPALIVE. On MacOS this constant can be used in the same way that TCP_KEEPIDLE is used on Linux.

Changed in version 3.11: Added TCP_CONNECTION_INFO. On MacOS this constant can be used in the same way that TCP_INFO is used on Linux and BSD.

Changed in version 3.12: Added SO_RTABLE and SO_USER_COOKIE. On OpenBSD and FreeBSD respectively those constants can be used in the same way that SO_MARK is used on Linux. Also added missing TCP socket options from Linux: TCP_MD5SIG, TCP_THIN_LINEAR_TIMEOUTS, TCP_THIN_DUPACK, TCP_REPAIR, TCP_REPAIR_QUEUE, TCP_QUEUE_SEQ, TCP_REPAIR_OPTIONS, TCP_TIMESTAMP, TCP_CC_INFO, TCP_SAVE_SYN, TCP_SAVED_SYN, TCP_REPAIR_WINDOW, TCP_FASTOPEN_CONNECT, TCP_ULP, TCP_MD5SIG_EXT, TCP_FASTOPEN_KEY, TCP_FASTOPEN_NO_COOKIE, TCP_ZEROCOPY_RECEIVE, TCP_INQ, TCP_TX_DELAY. Added IP_PKTINFO, IP_UNBLOCK_SOURCE, IP_BLOCK_SOURCE, IP_ADD_SOURCE_MEMBERSHIP, IP_DROP_SOURCE_MEMBERSHIP.

+
+socket.AF_CAN
+socket.PF_CAN
SOL_CAN_* +
CAN_* +
+

Many constants of these forms, documented in the Linux documentation, are also defined in the socket module.

Availability: Linux >= 2.6.25, NetBSD >= 8.

New in version 3.3.

Changed in version 3.11: NetBSD support was added.

+
+socket.CAN_BCM
CAN_BCM_* +
+

CAN_BCM, in the CAN protocol family, is the broadcast manager (BCM) protocol. Broadcast manager constants, documented in the Linux documentation, are also defined in the socket module.

Availability: Linux >= 2.6.25.

Note

The CAN_BCM_CAN_FD_FRAME flag is only available on Linux >= 4.8.

New in version 3.4.

+
+socket.CAN_RAW_FD_FRAMES
+

Enables CAN FD support in a CAN_RAW socket. This is disabled by default. This allows your application to send both CAN and CAN FD frames; however, you must accept both CAN and CAN FD frames when reading from the socket.

This constant is documented in the Linux documentation.

Availability: Linux >= 3.6.

New in version 3.5.

+
+socket.CAN_RAW_JOIN_FILTERS
+

Joins the applied CAN filters such that only CAN frames that match all given CAN filters are passed to user space.

This constant is documented in the Linux documentation.

Availability: Linux >= 4.1.

New in version 3.9.

+
+socket.CAN_ISOTP
+

CAN_ISOTP, in the CAN protocol family, is the ISO-TP (ISO 15765-2) protocol. ISO-TP constants, documented in the Linux documentation.

Availability: Linux >= 2.6.25.

New in version 3.7.

+
+socket.CAN_J1939
+

CAN_J1939, in the CAN protocol family, is the SAE J1939 protocol. J1939 constants, documented in the Linux documentation.

Availability: Linux >= 5.4.

New in version 3.9.

+
+socket.AF_DIVERT
+socket.PF_DIVERT
+

These two constants, documented in the FreeBSD divert(4) manual page, are also defined in the socket module.

Availability: FreeBSD >= 14.0.

New in version 3.12.

+
+socket.AF_PACKET
+socket.PF_PACKET
PACKET_* +
+

Many constants of these forms, documented in the Linux documentation, are also defined in the socket module.

Availability: Linux >= 2.2.

+
+socket.ETH_P_ALL
+

ETH_P_ALL can be used in the socket constructor as proto for the AF_PACKET family in order to capture every packet, regardless of protocol.

For more information, see the packet(7) manpage.

Availability: Linux.

New in version 3.12.

+
+socket.AF_RDS
+socket.PF_RDS
+socket.SOL_RDS
RDS_* +
+

Many constants of these forms, documented in the Linux documentation, are also defined in the socket module.

Availability: Linux >= 2.6.30.

New in version 3.3.

+
+socket.SIO_RCVALL
+socket.SIO_KEEPALIVE_VALS
+socket.SIO_LOOPBACK_FAST_PATH
RCVALL_* +
+

Constants for Windows’ WSAIoctl(). The constants are used as arguments to the ioctl() method of socket objects.

Changed in version 3.6: SIO_LOOPBACK_FAST_PATH was added.

+
TIPC_* +
+

TIPC related constants, matching the ones exported by the C socket API. See the TIPC documentation for more information.

+
+socket.AF_ALG
+socket.SOL_ALG
ALG_* +
+

Constants for Linux Kernel cryptography.

Availability: Linux >= 2.6.38.

New in version 3.6.

+
+socket.AF_VSOCK
+socket.IOCTL_VM_SOCKETS_GET_LOCAL_CID
VMADDR* +
SO_VM* +
+

Constants for Linux host/guest communication.

Availability: Linux >= 4.8.

New in version 3.7.

+
+

Availability: BSD, macOS.

New in version 3.4.

+
+socket.has_ipv6
+

This constant contains a boolean value which indicates if IPv6 is supported on this platform.

+
+socket.BDADDR_ANY
+socket.BDADDR_LOCAL
+

These are string constants containing Bluetooth addresses with special meanings. For example, BDADDR_ANY can be used to indicate any address when specifying the binding socket with BTPROTO_RFCOMM.

+
+socket.HCI_FILTER
+socket.HCI_TIME_STAMP
+socket.HCI_DATA_DIR
+

For use with BTPROTO_HCI. HCI_FILTER is not available for NetBSD or DragonFlyBSD. HCI_TIME_STAMP and HCI_DATA_DIR are not available for FreeBSD, NetBSD, or DragonFlyBSD.

+
+socket.AF_QIPCRTR
+

Constant for Qualcomm’s IPC router protocol, used to communicate with service providing remote processors.

Availability: Linux >= 4.7.

+
+socket.SCM_CREDS2
+socket.LOCAL_CREDS
+socket.LOCAL_CREDS_PERSISTENT
+

LOCAL_CREDS and LOCAL_CREDS_PERSISTENT can be used with SOCK_DGRAM, SOCK_STREAM sockets, equivalent to Linux/DragonFlyBSD SO_PASSCRED, while LOCAL_CREDS sends the credentials at first read, LOCAL_CREDS_PERSISTENT sends for each read, SCM_CREDS2 must be then used for the latter for the message type.

New in version 3.11.

Availability: FreeBSD.

+
+socket.SO_INCOMING_CPU

Constant to optimize CPU locality, to be used in conjunction with SO_REUSEPORT.

New in version 3.11.

Availability: Linux >= 3.9

+
+socket.AF_HYPERV
+socket.HV_PROTOCOL_RAW
+socket.HVSOCKET_CONNECT_TIMEOUT
+socket.HVSOCKET_CONNECT_TIMEOUT_MAX
+socket.HVSOCKET_CONNECTED_SUSPEND
+socket.HVSOCKET_ADDRESS_FLAG_PASSTHRU
+socket.HV_GUID_ZERO
+socket.HV_GUID_WILDCARD
+socket.HV_GUID_BROADCAST
+socket.HV_GUID_CHILDREN
+socket.HV_GUID_LOOPBACK
+socket.HV_GUID_PARENT
+

Constants for Windows Hyper-V sockets for host/guest communications.

Availability: Windows.

New in version 3.12.

+
+socket.ETHERTYPE_ARP
+socket.ETHERTYPE_IP
+socket.ETHERTYPE_IPV6
+socket.ETHERTYPE_VLAN
+

IEEE 802.3 protocol number. constants.

Availability: Linux, FreeBSD, macOS.

New in version 3.12.

+

Functions

Creating sockets

The following functions all create socket objects.

+class socket.socket(family=AF_INET, type=SOCK_STREAM, proto=0, fileno=None)
+

Create a new socket using the given address family, socket type and protocol number. The address family should be AF_INET (the default), AF_INET6, AF_UNIX, AF_CAN, AF_PACKET, or AF_RDS. The socket type should be SOCK_STREAM (the default), SOCK_DGRAM, SOCK_RAW or perhaps one of the other SOCK_ constants. The protocol number is usually zero and may be omitted or in the case where the address family is AF_CAN the protocol should be one of CAN_RAW, CAN_BCM, CAN_ISOTP or CAN_J1939.

If fileno is specified, the values for family, type, and proto are auto-detected from the specified file descriptor. Auto-detection can be overruled by calling the function with explicit family, type, or proto arguments. This only affects how Python represents e.g. the return value of socket.getpeername() but not the actual OS resource. Unlike socket.fromfd(), fileno will return the same socket and not a duplicate. This may help close a detached socket using socket.close().

The newly created socket is non-inheritable.

Raises an auditing event socket.__new__ with arguments self, family, type, protocol.

Changed in version 3.3: The AF_CAN family was added. The AF_RDS family was added.

Changed in version 3.4: The CAN_BCM protocol was added.

Changed in version 3.4: The returned socket is now non-inheritable.

Changed in version 3.7: The CAN_ISOTP protocol was added.

Changed in version 3.7: When SOCK_NONBLOCK or SOCK_CLOEXEC bit flags are applied to type they are cleared, and socket.type will not reflect them. They are still passed to the underlying system socket() call. Therefore,

sock = socket.socket(
+    socket.AF_INET,
+    socket.SOCK_STREAM | socket.SOCK_NONBLOCK)
+

will still create a non-blocking socket on OSes that support SOCK_NONBLOCK, but sock.type will be set to socket.SOCK_STREAM.

Changed in version 3.9: The CAN_J1939 protocol was added.

Changed in version 3.10: The IPPROTO_MPTCP protocol was added.

+
+socket.socketpair([family[, type[, proto]]])
+

Build a pair of connected socket objects using the given address family, socket type, and protocol number. Address family, socket type, and protocol number are as for the socket() function above. The default family is AF_UNIX if defined on the platform; otherwise, the default is AF_INET.

The newly created sockets are non-inheritable.

Changed in version 3.2: The returned socket objects now support the whole socket API, rather than a subset.

Changed in version 3.4: The returned sockets are now non-inheritable.

Changed in version 3.5: Windows support added.

+
+socket.create_connection(address, timeout=GLOBAL_DEFAULT, source_address=None, *, all_errors=False)
+

Connect to a TCP service listening on the internet address (a 2-tuple (host, port)), and return the socket object. This is a higher-level function than socket.connect(): if host is a non-numeric hostname, it will try to resolve it for both AF_INET and AF_INET6, and then try to connect to all possible addresses in turn until a connection succeeds. This makes it easy to write clients that are compatible to both IPv4 and IPv6.

Passing the optional timeout parameter will set the timeout on the socket instance before attempting to connect. If no timeout is supplied, the global default timeout setting returned by getdefaulttimeout() is used.

If supplied, source_address must be a 2-tuple (host, port) for the socket to bind to as its source address before connecting. If host or port are ‘’ or 0 respectively the OS default behavior will be used.

When a connection cannot be created, an exception is raised. By default, it is the exception from the last address in the list. If all_errors is True, it is an ExceptionGroup containing the errors of all attempts.

Changed in version 3.2: source_address was added.

Changed in version 3.11: all_errors was added.

+
+socket.create_server(address, *, family=AF_INET, backlog=None, reuse_port=False, dualstack_ipv6=False)
+

Convenience function which creates a TCP socket bound to address (a 2-tuple (host, port)) and returns the socket object.

family should be either AF_INET or AF_INET6. backlog is the queue size passed to socket.listen(); if not specified , a default reasonable value is chosen. reuse_port dictates whether to set the SO_REUSEPORT socket option.

If dualstack_ipv6 is true and the platform supports it the socket will be able to accept both IPv4 and IPv6 connections, else it will raise ValueError. Most POSIX platforms and Windows are supposed to support this functionality. When this functionality is enabled the address returned by socket.getpeername() when an IPv4 connection occurs will be an IPv6 address represented as an IPv4-mapped IPv6 address. If dualstack_ipv6 is false it will explicitly disable this functionality on platforms that enable it by default (e.g. Linux). This parameter can be used in conjunction with has_dualstack_ipv6():

import socket
+
+addr = ("", 8080)  # all interfaces, port 8080
+if socket.has_dualstack_ipv6():
+    s = socket.create_server(addr, family=socket.AF_INET6, dualstack_ipv6=True)
+else:
+    s = socket.create_server(addr)
+

Note

On POSIX platforms the SO_REUSEADDR socket option is set in order to immediately reuse previous sockets which were bound on the same address and remained in TIME_WAIT state.

New in version 3.8.

+
+socket.has_dualstack_ipv6()
+

Return True if the platform supports creating a TCP socket which can handle both IPv4 and IPv6 connections.

New in version 3.8.

+
+socket.fromfd(fd, family, type, proto=0)
+

Duplicate the file descriptor fd (an integer as returned by a file object’s fileno() method) and build a socket object from the result. Address family, socket type and protocol number are as for the socket() function above. The file descriptor should refer to a socket, but this is not checked — subsequent operations on the object may fail if the file descriptor is invalid. This function is rarely needed, but can be used to get or set socket options on a socket passed to a program as standard input or output (such as a server started by the Unix inet daemon). The socket is assumed to be in blocking mode.

The newly created socket is non-inheritable.

Changed in version 3.4: The returned socket is now non-inheritable.

+
+socket.fromshare(data)
+

Instantiate a socket from data obtained from the socket.share() method. The socket is assumed to be in blocking mode.

Availability: Windows.

New in version 3.3.

+
+socket.SocketType
+

This is a Python type object that represents the socket object type. It is the same as type(socket(...)).

+

Other functions

The socket module also offers various network-related services:

+socket.close(fd)
+

Close a socket file descriptor. This is like os.close(), but for sockets. On some platforms (most noticeable Windows) os.close() does not work for socket file descriptors.

New in version 3.7.

+
+socket.getaddrinfo(host, port, family=0, type=0, proto=0, flags=0)
+

Translate the host/port argument into a sequence of 5-tuples that contain all the necessary arguments for creating a socket connected to that service. host is a domain name, a string representation of an IPv4/v6 address or None. port is a string service name such as 'http', a numeric port number or None. By passing None as the value of host and port, you can pass NULL to the underlying C API.

The family, type and proto arguments can be optionally specified in order to narrow the list of addresses returned. Passing zero as a value for each of these arguments selects the full range of results. The flags argument can be one or several of the AI_* constants, and will influence how results are computed and returned. For example, AI_NUMERICHOST will disable domain name resolution and will raise an error if host is a domain name.

The function returns a list of 5-tuples with the following structure:

(family, type, proto, canonname, sockaddr)

In these tuples, family, type, proto are all integers and are meant to be passed to the socket() function. canonname will be a string representing the canonical name of the host if AI_CANONNAME is part of the flags argument; else canonname will be empty. sockaddr is a tuple describing a socket address, whose format depends on the returned family (a (address, port) 2-tuple for AF_INET, a (address, port, flowinfo, scope_id) 4-tuple for AF_INET6), and is meant to be passed to the socket.connect() method.

Raises an auditing event socket.getaddrinfo with arguments host, port, family, type, protocol.

The following example fetches address information for a hypothetical TCP connection to example.org on port 80 (results may differ on your system if IPv6 isn’t enabled):

>>> socket.getaddrinfo("example.org", 80, proto=socket.IPPROTO_TCP)
+[(socket.AF_INET6, socket.SOCK_STREAM,
+ 6, '', ('2606:2800:220:1:248:1893:25c8:1946', 80, 0, 0)),
+ (socket.AF_INET, socket.SOCK_STREAM,
+ 6, '', ('93.184.216.34', 80))]
+

Changed in version 3.2: parameters can now be passed using keyword arguments.

Changed in version 3.7: for IPv6 multicast addresses, string representing an address will not contain %scope_id part.

+
+socket.getfqdn([name])
+

Return a fully qualified domain name for name. If name is omitted or empty, it is interpreted as the local host. To find the fully qualified name, the hostname returned by gethostbyaddr() is checked, followed by aliases for the host, if available. The first name which includes a period is selected. In case no fully qualified domain name is available and name was provided, it is returned unchanged. If name was empty or equal to '0.0.0.0', the hostname from gethostname() is returned.

+
+socket.gethostbyname(hostname)
+

Translate a host name to IPv4 address format. The IPv4 address is returned as a string, such as '100.50.200.5'. If the host name is an IPv4 address itself it is returned unchanged. See gethostbyname_ex() for a more complete interface. gethostbyname() does not support IPv6 name resolution, and getaddrinfo() should be used instead for IPv4/v6 dual stack support.

Raises an auditing event socket.gethostbyname with argument hostname.

Availability: not WASI.

+
+socket.gethostbyname_ex(hostname)
+

Translate a host name to IPv4 address format, extended interface. Return a 3-tuple (hostname, aliaslist, ipaddrlist) where hostname is the host’s primary host name, aliaslist is a (possibly empty) list of alternative host names for the same address, and ipaddrlist is a list of IPv4 addresses for the same interface on the same host (often but not always a single address). gethostbyname_ex() does not support IPv6 name resolution, and getaddrinfo() should be used instead for IPv4/v6 dual stack support.

Raises an auditing event socket.gethostbyname with argument hostname.

Availability: not WASI.

+
+socket.gethostname()
+

Return a string containing the hostname of the machine where the Python interpreter is currently executing.

Raises an auditing event socket.gethostname with no arguments.

Note: gethostname() doesn’t always return the fully qualified domain name; use getfqdn() for that.

Availability: not WASI.

+
+socket.gethostbyaddr(ip_address)
+

Return a 3-tuple (hostname, aliaslist, ipaddrlist) where hostname is the primary host name responding to the given ip_address, aliaslist is a (possibly empty) list of alternative host names for the same address, and ipaddrlist is a list of IPv4/v6 addresses for the same interface on the same host (most likely containing only a single address). To find the fully qualified domain name, use the function getfqdn(). gethostbyaddr() supports both IPv4 and IPv6.

Raises an auditing event socket.gethostbyaddr with argument ip_address.

Availability: not WASI.

+
+socket.getnameinfo(sockaddr, flags)
+

Translate a socket address sockaddr into a 2-tuple (host, port). Depending on the settings of flags, the result can contain a fully qualified domain name or numeric address representation in host. Similarly, port can contain a string port name or a numeric port number.

For IPv6 addresses, %scope_id is appended to the host part if sockaddr contains meaningful scope_id. Usually this happens for multicast addresses.

For more information about flags you can consult getnameinfo(3).

Raises an auditing event socket.getnameinfo with argument sockaddr.

Availability: not WASI.

+
+socket.getprotobyname(protocolname)
+

Translate an internet protocol name (for example, 'icmp') to a constant suitable for passing as the (optional) third argument to the socket() function. This is usually only needed for sockets opened in “raw” mode (SOCK_RAW); for the normal socket modes, the correct protocol is chosen automatically if the protocol is omitted or zero.

Availability: not WASI.

+
+socket.getservbyname(servicename[, protocolname])
+

Translate an internet service name and protocol name to a port number for that service. The optional protocol name, if given, should be 'tcp' or 'udp', otherwise any protocol will match.

Raises an auditing event socket.getservbyname with arguments servicename, protocolname.

Availability: not WASI.

+
+socket.getservbyport(port[, protocolname])
+

Translate an internet port number and protocol name to a service name for that service. The optional protocol name, if given, should be 'tcp' or 'udp', otherwise any protocol will match.

Raises an auditing event socket.getservbyport with arguments port, protocolname.

Availability: not WASI.

+
+socket.ntohl(x)
+

Convert 32-bit positive integers from network to host byte order. On machines where the host byte order is the same as network byte order, this is a no-op; otherwise, it performs a 4-byte swap operation.

+
+socket.ntohs(x)
+

Convert 16-bit positive integers from network to host byte order. On machines where the host byte order is the same as network byte order, this is a no-op; otherwise, it performs a 2-byte swap operation.

Changed in version 3.10: Raises OverflowError if x does not fit in a 16-bit unsigned integer.

+
+socket.htonl(x)
+

Convert 32-bit positive integers from host to network byte order. On machines where the host byte order is the same as network byte order, this is a no-op; otherwise, it performs a 4-byte swap operation.

+
+socket.htons(x)
+

Convert 16-bit positive integers from host to network byte order. On machines where the host byte order is the same as network byte order, this is a no-op; otherwise, it performs a 2-byte swap operation.

Changed in version 3.10: Raises OverflowError if x does not fit in a 16-bit unsigned integer.

+
+socket.inet_aton(ip_string)
+

Convert an IPv4 address from dotted-quad string format (for example, ‘123.45.67.89’) to 32-bit packed binary format, as a bytes object four characters in length. This is useful when conversing with a program that uses the standard C library and needs objects of type in_addr, which is the C type for the 32-bit packed binary this function returns.

inet_aton() also accepts strings with less than three dots; see the Unix manual page inet(3) for details.

If the IPv4 address string passed to this function is invalid, OSError will be raised. Note that exactly what is valid depends on the underlying C implementation of inet_aton().

inet_aton() does not support IPv6, and inet_pton() should be used instead for IPv4/v6 dual stack support.

+
+socket.inet_ntoa(packed_ip)
+

Convert a 32-bit packed IPv4 address (a bytes-like object four bytes in length) to its standard dotted-quad string representation (for example, ‘123.45.67.89’). This is useful when conversing with a program that uses the standard C library and needs objects of type in_addr, which is the C type for the 32-bit packed binary data this function takes as an argument.

If the byte sequence passed to this function is not exactly 4 bytes in length, OSError will be raised. inet_ntoa() does not support IPv6, and inet_ntop() should be used instead for IPv4/v6 dual stack support.

Changed in version 3.5: Writable bytes-like object is now accepted.

+
+socket.inet_pton(address_family, ip_string)
+

Convert an IP address from its family-specific string format to a packed, binary format. inet_pton() is useful when a library or network protocol calls for an object of type in_addr (similar to inet_aton()) or in6_addr.

Supported values for address_family are currently AF_INET and AF_INET6. If the IP address string ip_string is invalid, OSError will be raised. Note that exactly what is valid depends on both the value of address_family and the underlying implementation of inet_pton().

Availability: Unix, Windows.

Changed in version 3.4: Windows support added

+
+socket.inet_ntop(address_family, packed_ip)
+

Convert a packed IP address (a bytes-like object of some number of bytes) to its standard, family-specific string representation (for example, '7.10.0.5' or '5aef:2b::8'). inet_ntop() is useful when a library or network protocol returns an object of type in_addr (similar to inet_ntoa()) or in6_addr.

Supported values for address_family are currently AF_INET and AF_INET6. If the bytes object packed_ip is not the correct length for the specified address family, ValueError will be raised. OSError is raised for errors from the call to inet_ntop().

Availability: Unix, Windows.

Changed in version 3.4: Windows support added

Changed in version 3.5: Writable bytes-like object is now accepted.

+
+socket.CMSG_LEN(length)
+

Return the total length, without trailing padding, of an ancillary data item with associated data of the given length. This value can often be used as the buffer size for recvmsg() to receive a single item of ancillary data, but RFC 3542 requires portable applications to use CMSG_SPACE() and thus include space for padding, even when the item will be the last in the buffer. Raises OverflowError if length is outside the permissible range of values.

Availability: Unix, not Emscripten, not WASI.

Most Unix platforms.

New in version 3.3.

+
+socket.CMSG_SPACE(length)
+

Return the buffer size needed for recvmsg() to receive an ancillary data item with associated data of the given length, along with any trailing padding. The buffer space needed to receive multiple items is the sum of the CMSG_SPACE() values for their associated data lengths. Raises OverflowError if length is outside the permissible range of values.

Note that some systems might support ancillary data without providing this function. Also note that setting the buffer size using the results of this function may not precisely limit the amount of ancillary data that can be received, since additional data may be able to fit into the padding area.

Availability: Unix, not Emscripten, not WASI.

most Unix platforms.

New in version 3.3.

+
+socket.getdefaulttimeout()
+

Return the default timeout in seconds (float) for new socket objects. A value of None indicates that new socket objects have no timeout. When the socket module is first imported, the default is None.

+
+socket.setdefaulttimeout(timeout)
+

Set the default timeout in seconds (float) for new socket objects. When the socket module is first imported, the default is None. See settimeout() for possible values and their respective meanings.

+
+socket.sethostname(name)
+

Set the machine’s hostname to name. This will raise an OSError if you don’t have enough rights.

Raises an auditing event socket.sethostname with argument name.

Availability: Unix.

New in version 3.3.

+
+socket.if_nameindex()
+

Return a list of network interface information (index int, name string) tuples. OSError if the system call fails.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.3.

Changed in version 3.8: Windows support was added.

Note

On Windows network interfaces have different names in different contexts (all names are examples):

  • UUID: {FB605B73-AAC2-49A6-9A2F-25416AEA0573} +
  • name: ethernet_32770 +
  • friendly name: vEthernet (nat) +
  • description: Hyper-V Virtual Ethernet Adapter +

This function returns names of the second form from the list, ethernet_32770 in this example case.

+
+socket.if_nametoindex(if_name)
+

Return a network interface index number corresponding to an interface name. OSError if no interface with the given name exists.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.3.

Changed in version 3.8: Windows support was added.

See also

“Interface name” is a name as documented in if_nameindex().

+
+socket.if_indextoname(if_index)
+

Return a network interface name corresponding to an interface index number. OSError if no interface with the given index exists.

Availability: Unix, Windows, not Emscripten, not WASI.

New in version 3.3.

Changed in version 3.8: Windows support was added.

See also

“Interface name” is a name as documented in if_nameindex().

+
+socket.send_fds(sock, buffers, fds[, flags[, address]])
+

Send the list of file descriptors fds over an AF_UNIX socket sock. The fds parameter is a sequence of file descriptors. Consult sendmsg() for the documentation of these parameters.

Availability: Unix, Windows, not Emscripten, not WASI.

Unix platforms supporting sendmsg() and SCM_RIGHTS mechanism.

New in version 3.9.

+
+socket.recv_fds(sock, bufsize, maxfds[, flags])
+

Receive up to maxfds file descriptors from an AF_UNIX socket sock. Return (msg, list(fds), flags, addr). Consult recvmsg() for the documentation of these parameters.

Availability: Unix, Windows, not Emscripten, not WASI.

Unix platforms supporting sendmsg() and SCM_RIGHTS mechanism.

New in version 3.9.

Note

Any truncated integers at the end of the list of file descriptors.

+

Socket Objects

Socket objects have the following methods. Except for makefile(), these correspond to Unix system calls applicable to sockets.

Changed in version 3.2: Support for the context manager protocol was added. Exiting the context manager is equivalent to calling close().

+socket.accept()
+

Accept a connection. The socket must be bound to an address and listening for connections. The return value is a pair (conn, address) where conn is a new socket object usable to send and receive data on the connection, and address is the address bound to the socket on the other end of the connection.

The newly created socket is non-inheritable.

Changed in version 3.4: The socket is now non-inheritable.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.bind(address)
+

Bind the socket to address. The socket must not already be bound. (The format of address depends on the address family — see above.)

Raises an auditing event socket.bind with arguments self, address.

Availability: not WASI.

+
+socket.close()
+

Mark the socket closed. The underlying system resource (e.g. a file descriptor) is also closed when all file objects from makefile() are closed. Once that happens, all future operations on the socket object will fail. The remote end will receive no more data (after queued data is flushed).

Sockets are automatically closed when they are garbage-collected, but it is recommended to close() them explicitly, or to use a with statement around them.

Changed in version 3.6: OSError is now raised if an error occurs when the underlying close() call is made.

Note

close() releases the resource associated with a connection but does not necessarily close the connection immediately. If you want to close the connection in a timely fashion, call shutdown() before close().

+
+socket.connect(address)
+

Connect to a remote socket at address. (The format of address depends on the address family — see above.)

If the connection is interrupted by a signal, the method waits until the connection completes, or raise a TimeoutError on timeout, if the signal handler doesn’t raise an exception and the socket is blocking or has a timeout. For non-blocking sockets, the method raises an InterruptedError exception if the connection is interrupted by a signal (or the exception raised by the signal handler).

Raises an auditing event socket.connect with arguments self, address.

Changed in version 3.5: The method now waits until the connection completes instead of raising an InterruptedError exception if the connection is interrupted by a signal, the signal handler doesn’t raise an exception and the socket is blocking or has a timeout (see the PEP 475 for the rationale).

Availability: not WASI.

+
+socket.connect_ex(address)
+

Like connect(address), but return an error indicator instead of raising an exception for errors returned by the C-level connect() call (other problems, such as “host not found,” can still raise exceptions). The error indicator is 0 if the operation succeeded, otherwise the value of the errno variable. This is useful to support, for example, asynchronous connects.

Raises an auditing event socket.connect with arguments self, address.

Availability: not WASI.

+
+socket.detach()
+

Put the socket object into closed state without actually closing the underlying file descriptor. The file descriptor is returned, and can be reused for other purposes.

New in version 3.2.

+
+socket.dup()
+

Duplicate the socket.

The newly created socket is non-inheritable.

Changed in version 3.4: The socket is now non-inheritable.

Availability: not WASI.

+
+socket.fileno()
+

Return the socket’s file descriptor (a small integer), or -1 on failure. This is useful with select.select().

Under Windows the small integer returned by this method cannot be used where a file descriptor can be used (such as os.fdopen()). Unix does not have this limitation.

+
+socket.get_inheritable()
+

Get the inheritable flag of the socket’s file descriptor or socket’s handle: True if the socket can be inherited in child processes, False if it cannot.

New in version 3.4.

+
+socket.getpeername()
+

Return the remote address to which the socket is connected. This is useful to find out the port number of a remote IPv4/v6 socket, for instance. (The format of the address returned depends on the address family — see above.) On some systems this function is not supported.

+
+socket.getsockname()
+

Return the socket’s own address. This is useful to find out the port number of an IPv4/v6 socket, for instance. (The format of the address returned depends on the address family — see above.)

+
+socket.getsockopt(level, optname[, buflen])
+

Return the value of the given socket option (see the Unix man page getsockopt(2)). The needed symbolic constants (SO_* etc.) are defined in this module. If buflen is absent, an integer option is assumed and its integer value is returned by the function. If buflen is present, it specifies the maximum length of the buffer used to receive the option in, and this buffer is returned as a bytes object. It is up to the caller to decode the contents of the buffer (see the optional built-in module struct for a way to decode C structures encoded as byte strings).

Availability: not WASI.

+
+socket.getblocking()
+

Return True if socket is in blocking mode, False if in non-blocking.

This is equivalent to checking socket.gettimeout() != 0.

New in version 3.7.

+
+socket.gettimeout()
+

Return the timeout in seconds (float) associated with socket operations, or None if no timeout is set. This reflects the last call to setblocking() or settimeout().

+
+socket.ioctl(control, option)
+
Platform
+

Windows

The ioctl() method is a limited interface to the WSAIoctl system interface. Please refer to the Win32 documentation for more information.

On other platforms, the generic fcntl.fcntl() and fcntl.ioctl() functions may be used; they accept a socket object as their first argument.

Currently only the following control codes are supported: SIO_RCVALL, SIO_KEEPALIVE_VALS, and SIO_LOOPBACK_FAST_PATH.

Changed in version 3.6: SIO_LOOPBACK_FAST_PATH was added.

+
+socket.listen([backlog])
+

Enable a server to accept connections. If backlog is specified, it must be at least 0 (if it is lower, it is set to 0); it specifies the number of unaccepted connections that the system will allow before refusing new connections. If not specified, a default reasonable value is chosen.

Availability: not WASI.

Changed in version 3.5: The backlog parameter is now optional.

+
+socket.makefile(mode='r', buffering=None, *, encoding=None, errors=None, newline=None)
+

Return a file object associated with the socket. The exact returned type depends on the arguments given to makefile(). These arguments are interpreted the same way as by the built-in open() function, except the only supported mode values are 'r' (default), 'w' and 'b'.

The socket must be in blocking mode; it can have a timeout, but the file object’s internal buffer may end up in an inconsistent state if a timeout occurs.

Closing the file object returned by makefile() won’t close the original socket unless all other file objects have been closed and socket.close() has been called on the socket object.

Note

On Windows, the file-like object created by makefile() cannot be used where a file object with a file descriptor is expected, such as the stream arguments of subprocess.Popen().

+
+socket.recv(bufsize[, flags])
+

Receive data from the socket. The return value is a bytes object representing the data received. The maximum amount of data to be received at once is specified by bufsize. See the Unix manual page recv(2) for the meaning of the optional argument flags; it defaults to zero.

Note

For best match with hardware and network realities, the value of bufsize should be a relatively small power of 2, for example, 4096.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.recvfrom(bufsize[, flags])
+

Receive data from the socket. The return value is a pair (bytes, address) where bytes is a bytes object representing the data received and address is the address of the socket sending the data. See the Unix manual page recv(2) for the meaning of the optional argument flags; it defaults to zero. (The format of address depends on the address family — see above.)

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

Changed in version 3.7: For multicast IPv6 address, first item of address does not contain %scope_id part anymore. In order to get full IPv6 address use getnameinfo().

+
+socket.recvmsg(bufsize[, ancbufsize[, flags]])
+

Receive normal data (up to bufsize bytes) and ancillary data from the socket. The ancbufsize argument sets the size in bytes of the internal buffer used to receive the ancillary data; it defaults to 0, meaning that no ancillary data will be received. Appropriate buffer sizes for ancillary data can be calculated using CMSG_SPACE() or CMSG_LEN(), and items which do not fit into the buffer might be truncated or discarded. The flags argument defaults to 0 and has the same meaning as for recv().

The return value is a 4-tuple: (data, ancdata, msg_flags, +address). The data item is a bytes object holding the non-ancillary data received. The ancdata item is a list of zero or more tuples (cmsg_level, cmsg_type, cmsg_data) representing the ancillary data (control messages) received: cmsg_level and cmsg_type are integers specifying the protocol level and protocol-specific type respectively, and cmsg_data is a bytes object holding the associated data. The msg_flags item is the bitwise OR of various flags indicating conditions on the received message; see your system documentation for details. If the receiving socket is unconnected, address is the address of the sending socket, if available; otherwise, its value is unspecified.

On some systems, sendmsg() and recvmsg() can be used to pass file descriptors between processes over an AF_UNIX socket. When this facility is used (it is often restricted to SOCK_STREAM sockets), recvmsg() will return, in its ancillary data, items of the form (socket.SOL_SOCKET, +socket.SCM_RIGHTS, fds), where fds is a bytes object representing the new file descriptors as a binary array of the native C int type. If recvmsg() raises an exception after the system call returns, it will first attempt to close any file descriptors received via this mechanism.

Some systems do not indicate the truncated length of ancillary data items which have been only partially received. If an item appears to extend beyond the end of the buffer, recvmsg() will issue a RuntimeWarning, and will return the part of it which is inside the buffer provided it has not been truncated before the start of its associated data.

On systems which support the SCM_RIGHTS mechanism, the following function will receive up to maxfds file descriptors, returning the message data and a list containing the descriptors (while ignoring unexpected conditions such as unrelated control messages being received). See also sendmsg().

import socket, array
+
+def recv_fds(sock, msglen, maxfds):
+    fds = array.array("i")   # Array of ints
+    msg, ancdata, flags, addr = sock.recvmsg(msglen, socket.CMSG_LEN(maxfds * fds.itemsize))
+    for cmsg_level, cmsg_type, cmsg_data in ancdata:
+        if cmsg_level == socket.SOL_SOCKET and cmsg_type == socket.SCM_RIGHTS:
+            # Append data, ignoring any truncated integers at the end.
+            fds.frombytes(cmsg_data[:len(cmsg_data) - (len(cmsg_data) % fds.itemsize)])
+    return msg, list(fds)
+

Availability: Unix.

Most Unix platforms.

New in version 3.3.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.recvmsg_into(buffers[, ancbufsize[, flags]])
+

Receive normal data and ancillary data from the socket, behaving as recvmsg() would, but scatter the non-ancillary data into a series of buffers instead of returning a new bytes object. The buffers argument must be an iterable of objects that export writable buffers (e.g. bytearray objects); these will be filled with successive chunks of the non-ancillary data until it has all been written or there are no more buffers. The operating system may set a limit (sysconf() value SC_IOV_MAX) on the number of buffers that can be used. The ancbufsize and flags arguments have the same meaning as for recvmsg().

The return value is a 4-tuple: (nbytes, ancdata, msg_flags, +address), where nbytes is the total number of bytes of non-ancillary data written into the buffers, and ancdata, msg_flags and address are the same as for recvmsg().

Example:

>>> import socket
+>>> s1, s2 = socket.socketpair()
+>>> b1 = bytearray(b'----')
+>>> b2 = bytearray(b'0123456789')
+>>> b3 = bytearray(b'--------------')
+>>> s1.send(b'Mary had a little lamb')
+22
+>>> s2.recvmsg_into([b1, memoryview(b2)[2:9], b3])
+(22, [], 0, None)
+>>> [b1, b2, b3]
+[bytearray(b'Mary'), bytearray(b'01 had a 9'), bytearray(b'little lamb---')]
+

Availability: Unix.

Most Unix platforms.

New in version 3.3.

+
+socket.recvfrom_into(buffer[, nbytes[, flags]])
+

Receive data from the socket, writing it into buffer instead of creating a new bytestring. The return value is a pair (nbytes, address) where nbytes is the number of bytes received and address is the address of the socket sending the data. See the Unix manual page recv(2) for the meaning of the optional argument flags; it defaults to zero. (The format of address depends on the address family — see above.)

+
+socket.recv_into(buffer[, nbytes[, flags]])
+

Receive up to nbytes bytes from the socket, storing the data into a buffer rather than creating a new bytestring. If nbytes is not specified (or 0), receive up to the size available in the given buffer. Returns the number of bytes received. See the Unix manual page recv(2) for the meaning of the optional argument flags; it defaults to zero.

+
+socket.send(bytes[, flags])
+

Send data to the socket. The socket must be connected to a remote socket. The optional flags argument has the same meaning as for recv() above. Returns the number of bytes sent. Applications are responsible for checking that all data has been sent; if only some of the data was transmitted, the application needs to attempt delivery of the remaining data. For further information on this topic, consult the Socket Programming HOWTO.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.sendall(bytes[, flags])
+

Send data to the socket. The socket must be connected to a remote socket. The optional flags argument has the same meaning as for recv() above. Unlike send(), this method continues to send data from bytes until either all data has been sent or an error occurs. None is returned on success. On error, an exception is raised, and there is no way to determine how much data, if any, was successfully sent.

Changed in version 3.5: The socket timeout is no longer reset each time data is sent successfully. The socket timeout is now the maximum total duration to send all data.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.sendto(bytes, address)
socket.sendto(bytes, flags, address) +
+

Send data to the socket. The socket should not be connected to a remote socket, since the destination socket is specified by address. The optional flags argument has the same meaning as for recv() above. Return the number of bytes sent. (The format of address depends on the address family — see above.)

Raises an auditing event socket.sendto with arguments self, address.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.sendmsg(buffers[, ancdata[, flags[, address]]])
+

Send normal and ancillary data to the socket, gathering the non-ancillary data from a series of buffers and concatenating it into a single message. The buffers argument specifies the non-ancillary data as an iterable of bytes-like objects (e.g. bytes objects); the operating system may set a limit (sysconf() value SC_IOV_MAX) on the number of buffers that can be used. The ancdata argument specifies the ancillary data (control messages) as an iterable of zero or more tuples (cmsg_level, cmsg_type, cmsg_data), where cmsg_level and cmsg_type are integers specifying the protocol level and protocol-specific type respectively, and cmsg_data is a bytes-like object holding the associated data. Note that some systems (in particular, systems without CMSG_SPACE()) might support sending only one control message per call. The flags argument defaults to 0 and has the same meaning as for send(). If address is supplied and not None, it sets a destination address for the message. The return value is the number of bytes of non-ancillary data sent.

The following function sends the list of file descriptors fds over an AF_UNIX socket, on systems which support the SCM_RIGHTS mechanism. See also recvmsg().

import socket, array
+
+def send_fds(sock, msg, fds):
+    return sock.sendmsg([msg], [(socket.SOL_SOCKET, socket.SCM_RIGHTS, array.array("i", fds))])
+

Availability: Unix, not WASI.

Most Unix platforms.

Raises an auditing event socket.sendmsg with arguments self, address.

New in version 3.3.

Changed in version 3.5: If the system call is interrupted and the signal handler does not raise an exception, the method now retries the system call instead of raising an InterruptedError exception (see PEP 475 for the rationale).

+
+socket.sendmsg_afalg([msg, ]*, op[, iv[, assoclen[, flags]]])
+

Specialized version of sendmsg() for AF_ALG socket. Set mode, IV, AEAD associated data length and flags for AF_ALG socket.

Availability: Linux >= 2.6.38.

New in version 3.6.

+
+socket.sendfile(file, offset=0, count=None)
+

Send a file until EOF is reached by using high-performance os.sendfile and return the total number of bytes which were sent. file must be a regular file object opened in binary mode. If os.sendfile is not available (e.g. Windows) or file is not a regular file send() will be used instead. offset tells from where to start reading the file. If specified, count is the total number of bytes to transmit as opposed to sending the file until EOF is reached. File position is updated on return or also in case of error in which case file.tell() can be used to figure out the number of bytes which were sent. The socket must be of SOCK_STREAM type. Non-blocking sockets are not supported.

New in version 3.5.

+
+socket.set_inheritable(inheritable)
+

Set the inheritable flag of the socket’s file descriptor or socket’s handle.

New in version 3.4.

+
+socket.setblocking(flag)
+

Set blocking or non-blocking mode of the socket: if flag is false, the socket is set to non-blocking, else to blocking mode.

This method is a shorthand for certain settimeout() calls:

  • +sock.setblocking(True) is equivalent to sock.settimeout(None) +
  • +sock.setblocking(False) is equivalent to sock.settimeout(0.0) +

Changed in version 3.7: The method no longer applies SOCK_NONBLOCK flag on socket.type.

+
+socket.settimeout(value)
+

Set a timeout on blocking socket operations. The value argument can be a nonnegative floating point number expressing seconds, or None. If a non-zero value is given, subsequent socket operations will raise a timeout exception if the timeout period value has elapsed before the operation has completed. If zero is given, the socket is put in non-blocking mode. If None is given, the socket is put in blocking mode.

For further information, please consult the notes on socket timeouts.

Changed in version 3.7: The method no longer toggles SOCK_NONBLOCK flag on socket.type.

+
+socket.setsockopt(level, optname, value: int)
+
socket.setsockopt(level, optname, value: buffer) +
+
socket.setsockopt(level, optname, None, optlen: int) +
+

Set the value of the given socket option (see the Unix manual page setsockopt(2)). The needed symbolic constants are defined in this module (SO_* etc. <socket-unix-constants>). The value can be an integer, None or a bytes-like object representing a buffer. In the later case it is up to the caller to ensure that the bytestring contains the proper bits (see the optional built-in module struct for a way to encode C structures as bytestrings). When value is set to None, optlen argument is required. It’s equivalent to call setsockopt() C function with optval=NULL and optlen=optlen.

Changed in version 3.5: Writable bytes-like object is now accepted.

Changed in version 3.6: setsockopt(level, optname, None, optlen: int) form added.

Availability: not WASI.

+
+socket.shutdown(how)
+

Shut down one or both halves of the connection. If how is SHUT_RD, further receives are disallowed. If how is SHUT_WR, further sends are disallowed. If how is SHUT_RDWR, further sends and receives are disallowed.

Availability: not WASI.

+
+socket.share(process_id)
+

Duplicate a socket and prepare it for sharing with a target process. The target process must be provided with process_id. The resulting bytes object can then be passed to the target process using some form of interprocess communication and the socket can be recreated there using fromshare(). Once this method has been called, it is safe to close the socket since the operating system has already duplicated it for the target process.

Availability: Windows.

New in version 3.3.

+

Note that there are no methods read() or write(); use recv() and send() without flags argument instead.

Socket objects also have these (read-only) attributes that correspond to the values given to the socket constructor.

+socket.family
+

The socket family.

+
+socket.type
+

The socket type.

+
+socket.proto
+

The socket protocol.

+

Notes on socket timeouts

A socket object can be in one of three modes: blocking, non-blocking, or timeout. Sockets are by default always created in blocking mode, but this can be changed by calling setdefaulttimeout().

  • In blocking mode, operations block until complete or the system returns an error (such as connection timed out).
  • In non-blocking mode, operations fail (with an error that is unfortunately system-dependent) if they cannot be completed immediately: functions from the select module can be used to know when and whether a socket is available for reading or writing.
  • In timeout mode, operations fail if they cannot be completed within the timeout specified for the socket (they raise a timeout exception) or if the system returns an error.

Note

At the operating system level, sockets in timeout mode are internally set in non-blocking mode. Also, the blocking and timeout modes are shared between file descriptors and socket objects that refer to the same network endpoint. This implementation detail can have visible consequences if e.g. you decide to use the fileno() of a socket.

Timeouts and the connect method

The connect() operation is also subject to the timeout setting, and in general it is recommended to call settimeout() before calling connect() or pass a timeout parameter to create_connection(). However, the system network stack may also return a connection timeout error of its own regardless of any Python socket timeout setting.

Timeouts and the accept method

If getdefaulttimeout() is not None, sockets returned by the accept() method inherit that timeout. Otherwise, the behaviour depends on settings of the listening socket:

  • if the listening socket is in blocking mode or in timeout mode, the socket returned by accept() is in blocking mode;
  • if the listening socket is in non-blocking mode, whether the socket returned by accept() is in blocking or non-blocking mode is operating system-dependent. If you want to ensure cross-platform behaviour, it is recommended you manually override this setting.

Example

Here are four minimal example programs using the TCP/IP protocol: a server that echoes all data that it receives back (servicing only one client), and a client using it. Note that a server must perform the sequence socket(), bind(), listen(), accept() (possibly repeating the accept() to service more than one client), while a client only needs the sequence socket(), connect(). Also note that the server does not sendall()/recv() on the socket it is listening on but on the new socket returned by accept().

The first two examples support IPv4 only.

# Echo server program
+import socket
+
+HOST = ''                 # Symbolic name meaning all available interfaces
+PORT = 50007              # Arbitrary non-privileged port
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+    s.bind((HOST, PORT))
+    s.listen(1)
+    conn, addr = s.accept()
+    with conn:
+        print('Connected by', addr)
+        while True:
+            data = conn.recv(1024)
+            if not data: break
+            conn.sendall(data)
+
# Echo client program
+import socket
+
+HOST = 'daring.cwi.nl'    # The remote host
+PORT = 50007              # The same port as used by the server
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+    s.connect((HOST, PORT))
+    s.sendall(b'Hello, world')
+    data = s.recv(1024)
+print('Received', repr(data))
+

The next two examples are identical to the above two, but support both IPv4 and IPv6. The server side will listen to the first address family available (it should listen to both instead). On most of IPv6-ready systems, IPv6 will take precedence and the server may not accept IPv4 traffic. The client side will try to connect to all the addresses returned as a result of the name resolution, and sends traffic to the first one connected successfully.

# Echo server program
+import socket
+import sys
+
+HOST = None               # Symbolic name meaning all available interfaces
+PORT = 50007              # Arbitrary non-privileged port
+s = None
+for res in socket.getaddrinfo(HOST, PORT, socket.AF_UNSPEC,
+                              socket.SOCK_STREAM, 0, socket.AI_PASSIVE):
+    af, socktype, proto, canonname, sa = res
+    try:
+        s = socket.socket(af, socktype, proto)
+    except OSError as msg:
+        s = None
+        continue
+    try:
+        s.bind(sa)
+        s.listen(1)
+    except OSError as msg:
+        s.close()
+        s = None
+        continue
+    break
+if s is None:
+    print('could not open socket')
+    sys.exit(1)
+conn, addr = s.accept()
+with conn:
+    print('Connected by', addr)
+    while True:
+        data = conn.recv(1024)
+        if not data: break
+        conn.send(data)
+
# Echo client program
+import socket
+import sys
+
+HOST = 'daring.cwi.nl'    # The remote host
+PORT = 50007              # The same port as used by the server
+s = None
+for res in socket.getaddrinfo(HOST, PORT, socket.AF_UNSPEC, socket.SOCK_STREAM):
+    af, socktype, proto, canonname, sa = res
+    try:
+        s = socket.socket(af, socktype, proto)
+    except OSError as msg:
+        s = None
+        continue
+    try:
+        s.connect(sa)
+    except OSError as msg:
+        s.close()
+        s = None
+        continue
+    break
+if s is None:
+    print('could not open socket')
+    sys.exit(1)
+with s:
+    s.sendall(b'Hello, world')
+    data = s.recv(1024)
+print('Received', repr(data))
+

The next example shows how to write a very simple network sniffer with raw sockets on Windows. The example requires administrator privileges to modify the interface:

import socket
+
+# the public network interface
+HOST = socket.gethostbyname(socket.gethostname())
+
+# create a raw socket and bind it to the public interface
+s = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_IP)
+s.bind((HOST, 0))
+
+# Include IP headers
+s.setsockopt(socket.IPPROTO_IP, socket.IP_HDRINCL, 1)
+
+# receive all packets
+s.ioctl(socket.SIO_RCVALL, socket.RCVALL_ON)
+
+# receive a packet
+print(s.recvfrom(65565))
+
+# disabled promiscuous mode
+s.ioctl(socket.SIO_RCVALL, socket.RCVALL_OFF)
+

The next example shows how to use the socket interface to communicate to a CAN network using the raw socket protocol. To use CAN with the broadcast manager protocol instead, open a socket with:

socket.socket(socket.AF_CAN, socket.SOCK_DGRAM, socket.CAN_BCM)
+

After binding (CAN_RAW) or connecting (CAN_BCM) the socket, you can use the socket.send() and socket.recv() operations (and their counterparts) on the socket object as usual.

This last example might require special privileges:

import socket
+import struct
+
+
+# CAN frame packing/unpacking (see 'struct can_frame' in <linux/can.h>)
+
+can_frame_fmt = "=IB3x8s"
+can_frame_size = struct.calcsize(can_frame_fmt)
+
+def build_can_frame(can_id, data):
+    can_dlc = len(data)
+    data = data.ljust(8, b'\x00')
+    return struct.pack(can_frame_fmt, can_id, can_dlc, data)
+
+def dissect_can_frame(frame):
+    can_id, can_dlc, data = struct.unpack(can_frame_fmt, frame)
+    return (can_id, can_dlc, data[:can_dlc])
+
+
+# create a raw socket and bind it to the 'vcan0' interface
+s = socket.socket(socket.AF_CAN, socket.SOCK_RAW, socket.CAN_RAW)
+s.bind(('vcan0',))
+
+while True:
+    cf, addr = s.recvfrom(can_frame_size)
+
+    print('Received: can_id=%x, can_dlc=%x, data=%s' % dissect_can_frame(cf))
+
+    try:
+        s.send(cf)
+    except OSError:
+        print('Error sending CAN frame')
+
+    try:
+        s.send(build_can_frame(0x01, b'\x01\x02\x03'))
+    except OSError:
+        print('Error sending CAN frame')
+

Running an example several times with too small delay between executions, could lead to this error:

OSError: [Errno 98] Address already in use
+

This is because the previous execution has left the socket in a TIME_WAIT state, and can’t be immediately reused.

There is a socket flag to set, in order to prevent this, socket.SO_REUSEADDR:

s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+s.bind((HOST, PORT))
+

the SO_REUSEADDR flag tells the kernel to reuse a local socket in TIME_WAIT state, without waiting for its natural timeout to expire.

See also

For an introduction to socket programming (in C), see the following papers:

  • +An Introductory 4.3BSD Interprocess Communication Tutorial, by Stuart Sechrest
  • +An Advanced 4.3BSD Interprocess Communication Tutorial, by Samuel J. Leffler et al,

both in the UNIX Programmer’s Manual, Supplementary Documents 1 (sections PS1:7 and PS1:8). The platform-specific reference material for the various socket-related system calls are also a valuable source of information on the details of socket semantics. For Unix, refer to the manual pages; for Windows, see the WinSock (or Winsock 2) specification. For IPv6-ready APIs, readers may want to refer to RFC 3493 titled Basic Socket Interface Extensions for IPv6.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/socket.html +

+
diff --git a/devdocs/python~3.12/library%2Fsocketserver.html b/devdocs/python~3.12/library%2Fsocketserver.html new file mode 100644 index 00000000..f7ca4c12 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsocketserver.html @@ -0,0 +1,304 @@ +

socketserver — A framework for network servers

Source code: Lib/socketserver.py

The socketserver module simplifies the task of writing network servers.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

There are four basic concrete server classes:

+class socketserver.TCPServer(server_address, RequestHandlerClass, bind_and_activate=True)
+

This uses the internet TCP protocol, which provides for continuous streams of data between the client and server. If bind_and_activate is true, the constructor automatically attempts to invoke server_bind() and server_activate(). The other parameters are passed to the BaseServer base class.

+
+class socketserver.UDPServer(server_address, RequestHandlerClass, bind_and_activate=True)
+

This uses datagrams, which are discrete packets of information that may arrive out of order or be lost while in transit. The parameters are the same as for TCPServer.

+
+class socketserver.UnixStreamServer(server_address, RequestHandlerClass, bind_and_activate=True)
+class socketserver.UnixDatagramServer(server_address, RequestHandlerClass, bind_and_activate=True)
+

These more infrequently used classes are similar to the TCP and UDP classes, but use Unix domain sockets; they’re not available on non-Unix platforms. The parameters are the same as for TCPServer.

+

These four classes process requests synchronously; each request must be completed before the next request can be started. This isn’t suitable if each request takes a long time to complete, because it requires a lot of computation, or because it returns a lot of data which the client is slow to process. The solution is to create a separate process or thread to handle each request; the ForkingMixIn and ThreadingMixIn mix-in classes can be used to support asynchronous behaviour.

Creating a server requires several steps. First, you must create a request handler class by subclassing the BaseRequestHandler class and overriding its handle() method; this method will process incoming requests. Second, you must instantiate one of the server classes, passing it the server’s address and the request handler class. It is recommended to use the server in a with statement. Then call the handle_request() or serve_forever() method of the server object to process one or many requests. Finally, call server_close() to close the socket (unless you used a with statement).

When inheriting from ThreadingMixIn for threaded connection behavior, you should explicitly declare how you want your threads to behave on an abrupt shutdown. The ThreadingMixIn class defines an attribute daemon_threads, which indicates whether or not the server should wait for thread termination. You should set the flag explicitly if you would like threads to behave autonomously; the default is False, meaning that Python will not exit until all threads created by ThreadingMixIn have exited.

Server classes have the same external methods and attributes, no matter what network protocol they use.

Server Creation Notes

There are five classes in an inheritance diagram, four of which represent synchronous servers of four types:

+------------+
+| BaseServer |
++------------+
+      |
+      v
++-----------+        +------------------+
+| TCPServer |------->| UnixStreamServer |
++-----------+        +------------------+
+      |
+      v
++-----------+        +--------------------+
+| UDPServer |------->| UnixDatagramServer |
++-----------+        +--------------------+
+

Note that UnixDatagramServer derives from UDPServer, not from UnixStreamServer — the only difference between an IP and a Unix server is the address family.

+class socketserver.ForkingMixIn
+class socketserver.ThreadingMixIn
+

Forking and threading versions of each type of server can be created using these mix-in classes. For instance, ThreadingUDPServer is created as follows:

class ThreadingUDPServer(ThreadingMixIn, UDPServer):
+    pass
+

The mix-in class comes first, since it overrides a method defined in UDPServer. Setting the various attributes also changes the behavior of the underlying server mechanism.

ForkingMixIn and the Forking classes mentioned below are only available on POSIX platforms that support fork().

+block_on_close
+

ForkingMixIn.server_close waits until all child processes complete, except if block_on_close attribute is False.

ThreadingMixIn.server_close waits until all non-daemon threads complete, except if block_on_close attribute is False.

+
+daemon_threads
+

For ThreadingMixIn use daemonic threads by setting ThreadingMixIn.daemon_threads to True to not wait until threads complete.

+

Changed in version 3.7: ForkingMixIn.server_close and ThreadingMixIn.server_close now waits until all child processes and non-daemonic threads complete. Add a new ForkingMixIn.block_on_close class attribute to opt-in for the pre-3.7 behaviour.

+
+class socketserver.ForkingTCPServer
+class socketserver.ForkingUDPServer
+class socketserver.ThreadingTCPServer
+class socketserver.ThreadingUDPServer
+class socketserver.ForkingUnixStreamServer
+class socketserver.ForkingUnixDatagramServer
+class socketserver.ThreadingUnixStreamServer
+class socketserver.ThreadingUnixDatagramServer
+

These classes are pre-defined using the mix-in classes.

+

New in version 3.12: The ForkingUnixStreamServer and ForkingUnixDatagramServer classes were added.

To implement a service, you must derive a class from BaseRequestHandler and redefine its handle() method. You can then run various versions of the service by combining one of the server classes with your request handler class. The request handler class must be different for datagram or stream services. This can be hidden by using the handler subclasses StreamRequestHandler or DatagramRequestHandler.

Of course, you still have to use your head! For instance, it makes no sense to use a forking server if the service contains state in memory that can be modified by different requests, since the modifications in the child process would never reach the initial state kept in the parent process and passed to each child. In this case, you can use a threading server, but you will probably have to use locks to protect the integrity of the shared data.

On the other hand, if you are building an HTTP server where all data is stored externally (for instance, in the file system), a synchronous class will essentially render the service “deaf” while one request is being handled – which may be for a very long time if a client is slow to receive all the data it has requested. Here a threading or forking server is appropriate.

In some cases, it may be appropriate to process part of a request synchronously, but to finish processing in a forked child depending on the request data. This can be implemented by using a synchronous server and doing an explicit fork in the request handler class handle() method.

Another approach to handling multiple simultaneous requests in an environment that supports neither threads nor fork() (or where these are too expensive or inappropriate for the service) is to maintain an explicit table of partially finished requests and to use selectors to decide which request to work on next (or whether to handle a new incoming request). This is particularly important for stream services where each client can potentially be connected for a long time (if threads or subprocesses cannot be used).

Server Objects

+class socketserver.BaseServer(server_address, RequestHandlerClass)
+

This is the superclass of all Server objects in the module. It defines the interface, given below, but does not implement most of the methods, which is done in subclasses. The two parameters are stored in the respective server_address and RequestHandlerClass attributes.

+fileno()
+

Return an integer file descriptor for the socket on which the server is listening. This function is most commonly passed to selectors, to allow monitoring multiple servers in the same process.

+
+handle_request()
+

Process a single request. This function calls the following methods in order: get_request(), verify_request(), and process_request(). If the user-provided handle() method of the handler class raises an exception, the server’s handle_error() method will be called. If no request is received within timeout seconds, handle_timeout() will be called and handle_request() will return.

+
+serve_forever(poll_interval=0.5)
+

Handle requests until an explicit shutdown() request. Poll for shutdown every poll_interval seconds. Ignores the timeout attribute. It also calls service_actions(), which may be used by a subclass or mixin to provide actions specific to a given service. For example, the ForkingMixIn class uses service_actions() to clean up zombie child processes.

Changed in version 3.3: Added service_actions call to the serve_forever method.

+
+service_actions()
+

This is called in the serve_forever() loop. This method can be overridden by subclasses or mixin classes to perform actions specific to a given service, such as cleanup actions.

New in version 3.3.

+
+shutdown()
+

Tell the serve_forever() loop to stop and wait until it does. shutdown() must be called while serve_forever() is running in a different thread otherwise it will deadlock.

+
+server_close()
+

Clean up the server. May be overridden.

+
+address_family
+

The family of protocols to which the server’s socket belongs. Common examples are socket.AF_INET and socket.AF_UNIX.

+
+RequestHandlerClass
+

The user-provided request handler class; an instance of this class is created for each request.

+
+server_address
+

The address on which the server is listening. The format of addresses varies depending on the protocol family; see the documentation for the socket module for details. For internet protocols, this is a tuple containing a string giving the address, and an integer port number: ('127.0.0.1', 80), for example.

+
+socket
+

The socket object on which the server will listen for incoming requests.

+

The server classes support the following class variables:

+allow_reuse_address
+

Whether the server will allow the reuse of an address. This defaults to False, and can be set in subclasses to change the policy.

+
+request_queue_size
+

The size of the request queue. If it takes a long time to process a single request, any requests that arrive while the server is busy are placed into a queue, up to request_queue_size requests. Once the queue is full, further requests from clients will get a “Connection denied” error. The default value is usually 5, but this can be overridden by subclasses.

+
+socket_type
+

The type of socket used by the server; socket.SOCK_STREAM and socket.SOCK_DGRAM are two common values.

+
+timeout
+

Timeout duration, measured in seconds, or None if no timeout is desired. If handle_request() receives no incoming requests within the timeout period, the handle_timeout() method is called.

+

There are various server methods that can be overridden by subclasses of base server classes like TCPServer; these methods aren’t useful to external users of the server object.

+finish_request(request, client_address)
+

Actually processes the request by instantiating RequestHandlerClass and calling its handle() method.

+
+get_request()
+

Must accept a request from the socket, and return a 2-tuple containing the new socket object to be used to communicate with the client, and the client’s address.

+
+handle_error(request, client_address)
+

This function is called if the handle() method of a RequestHandlerClass instance raises an exception. The default action is to print the traceback to standard error and continue handling further requests.

Changed in version 3.6: Now only called for exceptions derived from the Exception class.

+
+handle_timeout()
+

This function is called when the timeout attribute has been set to a value other than None and the timeout period has passed with no requests being received. The default action for forking servers is to collect the status of any child processes that have exited, while in threading servers this method does nothing.

+
+process_request(request, client_address)
+

Calls finish_request() to create an instance of the RequestHandlerClass. If desired, this function can create a new process or thread to handle the request; the ForkingMixIn and ThreadingMixIn classes do this.

+
+server_activate()
+

Called by the server’s constructor to activate the server. The default behavior for a TCP server just invokes listen() on the server’s socket. May be overridden.

+
+server_bind()
+

Called by the server’s constructor to bind the socket to the desired address. May be overridden.

+
+verify_request(request, client_address)
+

Must return a Boolean value; if the value is True, the request will be processed, and if it’s False, the request will be denied. This function can be overridden to implement access controls for a server. The default implementation always returns True.

+

Changed in version 3.6: Support for the context manager protocol was added. Exiting the context manager is equivalent to calling server_close().

+

Request Handler Objects

+class socketserver.BaseRequestHandler
+

This is the superclass of all request handler objects. It defines the interface, given below. A concrete request handler subclass must define a new handle() method, and can override any of the other methods. A new instance of the subclass is created for each request.

+setup()
+

Called before the handle() method to perform any initialization actions required. The default implementation does nothing.

+
+handle()
+

This function must do all the work required to service a request. The default implementation does nothing. Several instance attributes are available to it; the request is available as request; the client address as client_address; and the server instance as server, in case it needs access to per-server information.

The type of request is different for datagram or stream services. For stream services, request is a socket object; for datagram services, request is a pair of string and socket.

+
+finish()
+

Called after the handle() method to perform any clean-up actions required. The default implementation does nothing. If setup() raises an exception, this function will not be called.

+
+request
+

The new socket.socket object to be used to communicate with the client.

+
+client_address
+

Client address returned by BaseServer.get_request().

+
+server
+

BaseServer object used for handling the request.

+
+
+class socketserver.StreamRequestHandler
+class socketserver.DatagramRequestHandler
+

These BaseRequestHandler subclasses override the setup() and finish() methods, and provide rfile and wfile attributes.

+rfile
+

A file object from which receives the request is read. Support the io.BufferedIOBase readable interface.

+
+wfile
+

A file object to which the reply is written. Support the io.BufferedIOBase writable interface

+

Changed in version 3.6: wfile also supports the io.BufferedIOBase writable interface.

+

Examples

+socketserver.TCPServer Example

This is the server side:

import socketserver
+
+class MyTCPHandler(socketserver.BaseRequestHandler):
+    """
+    The request handler class for our server.
+
+    It is instantiated once per connection to the server, and must
+    override the handle() method to implement communication to the
+    client.
+    """
+
+    def handle(self):
+        # self.request is the TCP socket connected to the client
+        self.data = self.request.recv(1024).strip()
+        print("{} wrote:".format(self.client_address[0]))
+        print(self.data)
+        # just send back the same data, but upper-cased
+        self.request.sendall(self.data.upper())
+
+if __name__ == "__main__":
+    HOST, PORT = "localhost", 9999
+
+    # Create the server, binding to localhost on port 9999
+    with socketserver.TCPServer((HOST, PORT), MyTCPHandler) as server:
+        # Activate the server; this will keep running until you
+        # interrupt the program with Ctrl-C
+        server.serve_forever()
+

An alternative request handler class that makes use of streams (file-like objects that simplify communication by providing the standard file interface):

class MyTCPHandler(socketserver.StreamRequestHandler):
+
+    def handle(self):
+        # self.rfile is a file-like object created by the handler;
+        # we can now use e.g. readline() instead of raw recv() calls
+        self.data = self.rfile.readline().strip()
+        print("{} wrote:".format(self.client_address[0]))
+        print(self.data)
+        # Likewise, self.wfile is a file-like object used to write back
+        # to the client
+        self.wfile.write(self.data.upper())
+

The difference is that the readline() call in the second handler will call recv() multiple times until it encounters a newline character, while the single recv() call in the first handler will just return what has been sent from the client in one sendall() call.

This is the client side:

import socket
+import sys
+
+HOST, PORT = "localhost", 9999
+data = " ".join(sys.argv[1:])
+
+# Create a socket (SOCK_STREAM means a TCP socket)
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+    # Connect to server and send data
+    sock.connect((HOST, PORT))
+    sock.sendall(bytes(data + "\n", "utf-8"))
+
+    # Receive data from the server and shut down
+    received = str(sock.recv(1024), "utf-8")
+
+print("Sent:     {}".format(data))
+print("Received: {}".format(received))
+

The output of the example should look something like this:

Server:

$ python TCPServer.py
+127.0.0.1 wrote:
+b'hello world with TCP'
+127.0.0.1 wrote:
+b'python is nice'
+

Client:

$ python TCPClient.py hello world with TCP
+Sent:     hello world with TCP
+Received: HELLO WORLD WITH TCP
+$ python TCPClient.py python is nice
+Sent:     python is nice
+Received: PYTHON IS NICE
+

+socketserver.UDPServer Example

This is the server side:

import socketserver
+
+class MyUDPHandler(socketserver.BaseRequestHandler):
+    """
+    This class works similar to the TCP handler class, except that
+    self.request consists of a pair of data and client socket, and since
+    there is no connection the client address must be given explicitly
+    when sending data back via sendto().
+    """
+
+    def handle(self):
+        data = self.request[0].strip()
+        socket = self.request[1]
+        print("{} wrote:".format(self.client_address[0]))
+        print(data)
+        socket.sendto(data.upper(), self.client_address)
+
+if __name__ == "__main__":
+    HOST, PORT = "localhost", 9999
+    with socketserver.UDPServer((HOST, PORT), MyUDPHandler) as server:
+        server.serve_forever()
+

This is the client side:

import socket
+import sys
+
+HOST, PORT = "localhost", 9999
+data = " ".join(sys.argv[1:])
+
+# SOCK_DGRAM is the socket type to use for UDP sockets
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+
+# As you can see, there is no connect() call; UDP has no connections.
+# Instead, data is directly sent to the recipient via sendto().
+sock.sendto(bytes(data + "\n", "utf-8"), (HOST, PORT))
+received = str(sock.recv(1024), "utf-8")
+
+print("Sent:     {}".format(data))
+print("Received: {}".format(received))
+

The output of the example should look exactly like for the TCP server example.

Asynchronous Mixins

To build asynchronous handlers, use the ThreadingMixIn and ForkingMixIn classes.

An example for the ThreadingMixIn class:

import socket
+import threading
+import socketserver
+
+class ThreadedTCPRequestHandler(socketserver.BaseRequestHandler):
+
+    def handle(self):
+        data = str(self.request.recv(1024), 'ascii')
+        cur_thread = threading.current_thread()
+        response = bytes("{}: {}".format(cur_thread.name, data), 'ascii')
+        self.request.sendall(response)
+
+class ThreadedTCPServer(socketserver.ThreadingMixIn, socketserver.TCPServer):
+    pass
+
+def client(ip, port, message):
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.connect((ip, port))
+        sock.sendall(bytes(message, 'ascii'))
+        response = str(sock.recv(1024), 'ascii')
+        print("Received: {}".format(response))
+
+if __name__ == "__main__":
+    # Port 0 means to select an arbitrary unused port
+    HOST, PORT = "localhost", 0
+
+    server = ThreadedTCPServer((HOST, PORT), ThreadedTCPRequestHandler)
+    with server:
+        ip, port = server.server_address
+
+        # Start a thread with the server -- that thread will then start one
+        # more thread for each request
+        server_thread = threading.Thread(target=server.serve_forever)
+        # Exit the server thread when the main thread terminates
+        server_thread.daemon = True
+        server_thread.start()
+        print("Server loop running in thread:", server_thread.name)
+
+        client(ip, port, "Hello World 1")
+        client(ip, port, "Hello World 2")
+        client(ip, port, "Hello World 3")
+
+        server.shutdown()
+

The output of the example should look something like this:

$ python ThreadedTCPServer.py
+Server loop running in thread: Thread-1
+Received: Thread-2: Hello World 1
+Received: Thread-3: Hello World 2
+Received: Thread-4: Hello World 3
+

The ForkingMixIn class is used in the same way, except that the server will spawn a new process for each request. Available only on POSIX platforms that support fork().

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/socketserver.html +

+
diff --git a/devdocs/python~3.12/library%2Fsqlite3.html b/devdocs/python~3.12/library%2Fsqlite3.html new file mode 100644 index 00000000..206757c4 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsqlite3.html @@ -0,0 +1,862 @@ +

sqlite3 — DB-API 2.0 interface for SQLite databases

Source code: Lib/sqlite3/

SQLite is a C library that provides a lightweight disk-based database that doesn’t require a separate server process and allows accessing the database using a nonstandard variant of the SQL query language. Some applications can use SQLite for internal data storage. It’s also possible to prototype an application using SQLite and then port the code to a larger database such as PostgreSQL or Oracle.

The sqlite3 module was written by Gerhard Häring. It provides an SQL interface compliant with the DB-API 2.0 specification described by PEP 249, and requires SQLite 3.7.15 or newer.

This document includes four main sections:

See also

https://www.sqlite.org
+
+

The SQLite web page; the documentation describes the syntax and the available data types for the supported SQL dialect.

https://www.w3schools.com/sql/
+
+

Tutorial, reference and examples for learning SQL syntax.

+PEP 249 - Database API Specification 2.0
+
+

PEP written by Marc-André Lemburg.

Tutorial

In this tutorial, you will create a database of Monty Python movies using basic sqlite3 functionality. It assumes a fundamental understanding of database concepts, including cursors and transactions.

First, we need to create a new database and open a database connection to allow sqlite3 to work with it. Call sqlite3.connect() to create a connection to the database tutorial.db in the current working directory, implicitly creating it if it does not exist:

import sqlite3
+con = sqlite3.connect("tutorial.db")
+

The returned Connection object con represents the connection to the on-disk database.

In order to execute SQL statements and fetch results from SQL queries, we will need to use a database cursor. Call con.cursor() to create the Cursor:

cur = con.cursor()
+

Now that we’ve got a database connection and a cursor, we can create a database table movie with columns for title, release year, and review score. For simplicity, we can just use column names in the table declaration – thanks to the flexible typing feature of SQLite, specifying the data types is optional. Execute the CREATE TABLE statement by calling cur.execute(...):

cur.execute("CREATE TABLE movie(title, year, score)")
+

We can verify that the new table has been created by querying the sqlite_master table built-in to SQLite, which should now contain an entry for the movie table definition (see The Schema Table for details). Execute that query by calling cur.execute(...), assign the result to res, and call res.fetchone() to fetch the resulting row:

>>> res = cur.execute("SELECT name FROM sqlite_master")
+>>> res.fetchone()
+('movie',)
+

We can see that the table has been created, as the query returns a tuple containing the table’s name. If we query sqlite_master for a non-existent table spam, res.fetchone() will return None:

>>> res = cur.execute("SELECT name FROM sqlite_master WHERE name='spam'")
+>>> res.fetchone() is None
+True
+

Now, add two rows of data supplied as SQL literals by executing an INSERT statement, once again by calling cur.execute(...):

cur.execute("""
+    INSERT INTO movie VALUES
+        ('Monty Python and the Holy Grail', 1975, 8.2),
+        ('And Now for Something Completely Different', 1971, 7.5)
+""")
+

The INSERT statement implicitly opens a transaction, which needs to be committed before changes are saved in the database (see Transaction control for details). Call con.commit() on the connection object to commit the transaction:

con.commit()
+

We can verify that the data was inserted correctly by executing a SELECT query. Use the now-familiar cur.execute(...) to assign the result to res, and call res.fetchall() to return all resulting rows:

>>> res = cur.execute("SELECT score FROM movie")
+>>> res.fetchall()
+[(8.2,), (7.5,)]
+

The result is a list of two tuples, one per row, each containing that row’s score value.

Now, insert three more rows by calling cur.executemany(...):

data = [
+    ("Monty Python Live at the Hollywood Bowl", 1982, 7.9),
+    ("Monty Python's The Meaning of Life", 1983, 7.5),
+    ("Monty Python's Life of Brian", 1979, 8.0),
+]
+cur.executemany("INSERT INTO movie VALUES(?, ?, ?)", data)
+con.commit()  # Remember to commit the transaction after executing INSERT.
+

Notice that ? placeholders are used to bind data to the query. Always use placeholders instead of string formatting to bind Python values to SQL statements, to avoid SQL injection attacks (see How to use placeholders to bind values in SQL queries for more details).

We can verify that the new rows were inserted by executing a SELECT query, this time iterating over the results of the query:

>>> for row in cur.execute("SELECT year, title FROM movie ORDER BY year"):
+...     print(row)
+(1971, 'And Now for Something Completely Different')
+(1975, 'Monty Python and the Holy Grail')
+(1979, "Monty Python's Life of Brian")
+(1982, 'Monty Python Live at the Hollywood Bowl')
+(1983, "Monty Python's The Meaning of Life")
+

Each row is a two-item tuple of (year, title), matching the columns selected in the query.

Finally, verify that the database has been written to disk by calling con.close() to close the existing connection, opening a new one, creating a new cursor, then querying the database:

>>> con.close()
+>>> new_con = sqlite3.connect("tutorial.db")
+>>> new_cur = new_con.cursor()
+>>> res = new_cur.execute("SELECT title, year FROM movie ORDER BY score DESC")
+>>> title, year = res.fetchone()
+>>> print(f'The highest scoring Monty Python movie is {title!r}, released in {year}')
+The highest scoring Monty Python movie is 'Monty Python and the Holy Grail', released in 1975
+

You’ve now created an SQLite database using the sqlite3 module, inserted data and retrieved values from it in multiple ways.

See also

Reference

Module functions

+sqlite3.connect(database, timeout=5.0, detect_types=0, isolation_level='DEFERRED', check_same_thread=True, factory=sqlite3.Connection, cached_statements=128, uri=False, *, autocommit=sqlite3.LEGACY_TRANSACTION_CONTROL)
+

Open a connection to an SQLite database.

Parameters
+
  • +database (path-like object) – The path to the database file to be opened. You can pass ":memory:" to create an SQLite database existing only in memory, and open a connection to it.
  • +timeout (float) – How many seconds the connection should wait before raising an OperationalError when a table is locked. If another connection opens a transaction to modify a table, that table will be locked until the transaction is committed. Default five seconds.
  • +detect_types (int) – Control whether and how data types not natively supported by SQLite are looked up to be converted to Python types, using the converters registered with register_converter(). Set it to any combination (using |, bitwise or) of PARSE_DECLTYPES and PARSE_COLNAMES to enable this. Column names takes precedence over declared types if both flags are set. Types cannot be detected for generated fields (for example max(data)), even when the detect_types parameter is set; str will be returned instead. By default (0), type detection is disabled.
  • +isolation_level (str | None) – Control legacy transaction handling behaviour. See Connection.isolation_level and Transaction control via the isolation_level attribute for more information. Can be "DEFERRED" (default), "EXCLUSIVE" or "IMMEDIATE"; or None to disable opening transactions implicitly. Has no effect unless Connection.autocommit is set to LEGACY_TRANSACTION_CONTROL (the default).
  • +check_same_thread (bool) – If True (default), ProgrammingError will be raised if the database connection is used by a thread other than the one that created it. If False, the connection may be accessed in multiple threads; write operations may need to be serialized by the user to avoid data corruption. See threadsafety for more information.
  • +factory (Connection) – A custom subclass of Connection to create the connection with, if not the default Connection class.
  • +cached_statements (int) – The number of statements that sqlite3 should internally cache for this connection, to avoid parsing overhead. By default, 128 statements.
  • +uri (bool) – If set to True, database is interpreted as a URI with a file path and an optional query string. The scheme part must be "file:", and the path can be relative or absolute. The query string allows passing parameters to SQLite, enabling various How to work with SQLite URIs.
  • +autocommit (bool) – Control PEP 249 transaction handling behaviour. See Connection.autocommit and Transaction control via the autocommit attribute for more information. autocommit currently defaults to LEGACY_TRANSACTION_CONTROL. The default will change to False in a future Python release.
Return type
+

Connection

Raises an auditing event sqlite3.connect with argument database.

Raises an auditing event sqlite3.connect/handle with argument connection_handle.

New in version 3.4: The uri parameter.

Changed in version 3.7: database can now also be a path-like object, not only a string.

New in version 3.10: The sqlite3.connect/handle auditing event.

New in version 3.12: The autocommit parameter.

+
+sqlite3.complete_statement(statement)
+

Return True if the string statement appears to contain one or more complete SQL statements. No syntactic verification or parsing of any kind is performed, other than checking that there are no unclosed string literals and the statement is terminated by a semicolon.

For example:

>>> sqlite3.complete_statement("SELECT foo FROM bar;")
+True
+>>> sqlite3.complete_statement("SELECT foo")
+False
+

This function may be useful during command-line input to determine if the entered text seems to form a complete SQL statement, or if additional input is needed before calling execute().

See runsource() in Lib/sqlite3/__main__.py for real-world use.

+
+sqlite3.enable_callback_tracebacks(flag, /)
+

Enable or disable callback tracebacks. By default you will not get any tracebacks in user-defined functions, aggregates, converters, authorizer callbacks etc. If you want to debug them, you can call this function with flag set to True. Afterwards, you will get tracebacks from callbacks on sys.stderr. Use False to disable the feature again.

Register an unraisable hook handler for an improved debug experience:

>>> sqlite3.enable_callback_tracebacks(True)
+>>> con = sqlite3.connect(":memory:")
+>>> def evil_trace(stmt):
+...     5/0
+...
+>>> con.set_trace_callback(evil_trace)
+>>> def debug(unraisable):
+...     print(f"{unraisable.exc_value!r} in callback {unraisable.object.__name__}")
+...     print(f"Error message: {unraisable.err_msg}")
+>>> import sys
+>>> sys.unraisablehook = debug
+>>> cur = con.execute("SELECT 1")
+ZeroDivisionError('division by zero') in callback evil_trace
+Error message: None
+
+
+sqlite3.register_adapter(type, adapter, /)
+

Register an adapter callable to adapt the Python type type into an SQLite type. The adapter is called with a Python object of type type as its sole argument, and must return a value of a type that SQLite natively understands.

+
+sqlite3.register_converter(typename, converter, /)
+

Register the converter callable to convert SQLite objects of type typename into a Python object of a specific type. The converter is invoked for all SQLite values of type typename; it is passed a bytes object and should return an object of the desired Python type. Consult the parameter detect_types of connect() for information regarding how type detection works.

Note: typename and the name of the type in your query are matched case-insensitively.

+

Module constants

+sqlite3.LEGACY_TRANSACTION_CONTROL
+

Set autocommit to this constant to select old style (pre-Python 3.12) transaction control behaviour. See Transaction control via the isolation_level attribute for more information.

+
+sqlite3.PARSE_COLNAMES
+

Pass this flag value to the detect_types parameter of connect() to look up a converter function by using the type name, parsed from the query column name, as the converter dictionary key. The type name must be wrapped in square brackets ([]).

SELECT p as "p [point]" FROM test;  ! will look up converter "point"
+

This flag may be combined with PARSE_DECLTYPES using the | (bitwise or) operator.

+
+sqlite3.PARSE_DECLTYPES
+

Pass this flag value to the detect_types parameter of connect() to look up a converter function using the declared types for each column. The types are declared when the database table is created. sqlite3 will look up a converter function using the first word of the declared type as the converter dictionary key. For example:

CREATE TABLE test(
+   i integer primary key,  ! will look up a converter named "integer"
+   p point,                ! will look up a converter named "point"
+   n number(10)            ! will look up a converter named "number"
+ )
+

This flag may be combined with PARSE_COLNAMES using the | (bitwise or) operator.

+
+sqlite3.SQLITE_OK
+sqlite3.SQLITE_DENY
+sqlite3.SQLITE_IGNORE
+

Flags that should be returned by the authorizer_callback callable passed to Connection.set_authorizer(), to indicate whether:

  • Access is allowed (SQLITE_OK),
  • The SQL statement should be aborted with an error (SQLITE_DENY)
  • The column should be treated as a NULL value (SQLITE_IGNORE)
+
+sqlite3.apilevel
+

String constant stating the supported DB-API level. Required by the DB-API. Hard-coded to "2.0".

+
+sqlite3.paramstyle
+

String constant stating the type of parameter marker formatting expected by the sqlite3 module. Required by the DB-API. Hard-coded to "qmark".

Note

The named DB-API parameter style is also supported.

+
+sqlite3.sqlite_version
+

Version number of the runtime SQLite library as a string.

+
+sqlite3.sqlite_version_info
+

Version number of the runtime SQLite library as a tuple of integers.

+
+sqlite3.threadsafety
+

Integer constant required by the DB-API 2.0, stating the level of thread safety the sqlite3 module supports. This attribute is set based on the default threading mode the underlying SQLite library is compiled with. The SQLite threading modes are:

  1. +Single-thread: In this mode, all mutexes are disabled and SQLite is unsafe to use in more than a single thread at once.
  2. +Multi-thread: In this mode, SQLite can be safely used by multiple threads provided that no single database connection is used simultaneously in two or more threads.
  3. +Serialized: In serialized mode, SQLite can be safely used by multiple threads with no restriction.

The mappings from SQLite threading modes to DB-API 2.0 threadsafety levels are as follows:

+ + + +

SQLite threading mode

threadsafety

SQLITE_THREADSAFE

DB-API 2.0 meaning

single-thread

0

0

Threads may not share the module

multi-thread

1

2

Threads may share the module, but not connections

serialized

3

1

Threads may share the module, connections and cursors

Changed in version 3.11: Set threadsafety dynamically instead of hard-coding it to 1.

+
+sqlite3.version
+

Version number of this module as a string. This is not the version of the SQLite library.

Deprecated since version 3.12, will be removed in version 3.14: This constant used to reflect the version number of the pysqlite package, a third-party library which used to upstream changes to sqlite3. Today, it carries no meaning or practical value.

+
+sqlite3.version_info
+

Version number of this module as a tuple of integers. This is not the version of the SQLite library.

Deprecated since version 3.12, will be removed in version 3.14: This constant used to reflect the version number of the pysqlite package, a third-party library which used to upstream changes to sqlite3. Today, it carries no meaning or practical value.

+
+sqlite3.SQLITE_DBCONFIG_DEFENSIVE
+sqlite3.SQLITE_DBCONFIG_DQS_DDL
+sqlite3.SQLITE_DBCONFIG_DQS_DML
+sqlite3.SQLITE_DBCONFIG_ENABLE_FKEY
+sqlite3.SQLITE_DBCONFIG_ENABLE_FTS3_TOKENIZER
+sqlite3.SQLITE_DBCONFIG_ENABLE_LOAD_EXTENSION
+sqlite3.SQLITE_DBCONFIG_ENABLE_QPSG
+sqlite3.SQLITE_DBCONFIG_ENABLE_TRIGGER
+sqlite3.SQLITE_DBCONFIG_ENABLE_VIEW
+sqlite3.SQLITE_DBCONFIG_LEGACY_ALTER_TABLE
+sqlite3.SQLITE_DBCONFIG_LEGACY_FILE_FORMAT
+sqlite3.SQLITE_DBCONFIG_NO_CKPT_ON_CLOSE
+sqlite3.SQLITE_DBCONFIG_RESET_DATABASE
+sqlite3.SQLITE_DBCONFIG_TRIGGER_EQP
+sqlite3.SQLITE_DBCONFIG_TRUSTED_SCHEMA
+sqlite3.SQLITE_DBCONFIG_WRITABLE_SCHEMA
+

These constants are used for the Connection.setconfig() and getconfig() methods.

The availability of these constants varies depending on the version of SQLite Python was compiled with.

New in version 3.12.

See also

https://www.sqlite.org/c3ref/c_dbconfig_defensive.html
+
+

SQLite docs: Database Connection Configuration Options

+

Connection objects

+class sqlite3.Connection
+

Each open SQLite database is represented by a Connection object, which is created using sqlite3.connect(). Their main purpose is creating Cursor objects, and Transaction control.

See also

An SQLite database connection has the following attributes and methods:

+cursor(factory=Cursor)
+

Create and return a Cursor object. The cursor method accepts a single optional parameter factory. If supplied, this must be a callable returning an instance of Cursor or its subclasses.

+
+blobopen(table, column, row, /, *, readonly=False, name='main')
+

Open a Blob handle to an existing BLOB.

Parameters
+
  • +table (str) – The name of the table where the blob is located.
  • +column (str) – The name of the column where the blob is located.
  • +row (str) – The name of the row where the blob is located.
  • +readonly (bool) – Set to True if the blob should be opened without write permissions. Defaults to False.
  • +name (str) – The name of the database where the blob is located. Defaults to "main".
Raises
+

OperationalError – When trying to open a blob in a WITHOUT ROWID table.

Return type
+

Blob

Note

The blob size cannot be changed using the Blob class. Use the SQL function zeroblob to create a blob with a fixed size.

New in version 3.11.

+
+commit()
+

Commit any pending transaction to the database. If autocommit is True, or there is no open transaction, this method does nothing. If autocommit is False, a new transaction is implicitly opened if a pending transaction was committed by this method.

+
+rollback()
+

Roll back to the start of any pending transaction. If autocommit is True, or there is no open transaction, this method does nothing. If autocommit is False, a new transaction is implicitly opened if a pending transaction was rolled back by this method.

+
+close()
+

Close the database connection. If autocommit is False, any pending transaction is implicitly rolled back. If autocommit is True or LEGACY_TRANSACTION_CONTROL, no implicit transaction control is executed. Make sure to commit() before closing to avoid losing pending changes.

+
+execute(sql, parameters=(), /)
+

Create a new Cursor object and call execute() on it with the given sql and parameters. Return the new cursor object.

+
+executemany(sql, parameters, /)
+

Create a new Cursor object and call executemany() on it with the given sql and parameters. Return the new cursor object.

+
+executescript(sql_script, /)
+

Create a new Cursor object and call executescript() on it with the given sql_script. Return the new cursor object.

+
+create_function(name, narg, func, *, deterministic=False)
+

Create or remove a user-defined SQL function.

Parameters
+
  • +name (str) – The name of the SQL function.
  • +narg (int) – The number of arguments the SQL function can accept. If -1, it may take any number of arguments.
  • +func (callback | None) – A callable that is called when the SQL function is invoked. The callable must return a type natively supported by SQLite. Set to None to remove an existing SQL function.
  • +deterministic (bool) – If True, the created SQL function is marked as deterministic, which allows SQLite to perform additional optimizations.
Raises
+

NotSupportedError – If deterministic is used with SQLite versions older than 3.8.3.

New in version 3.8: The deterministic parameter.

Example:

>>> import hashlib
+>>> def md5sum(t):
+...     return hashlib.md5(t).hexdigest()
+>>> con = sqlite3.connect(":memory:")
+>>> con.create_function("md5", 1, md5sum)
+>>> for row in con.execute("SELECT md5(?)", (b"foo",)):
+...     print(row)
+('acbd18db4cc2f85cedef654fccc4a4d8',)
+
+
+create_aggregate(name, n_arg, aggregate_class)
+

Create or remove a user-defined SQL aggregate function.

Parameters
+
  • +name (str) – The name of the SQL aggregate function.
  • +n_arg (int) – The number of arguments the SQL aggregate function can accept. If -1, it may take any number of arguments.
  • +

    aggregate_class (class | None) –

    +

    A class must implement the following methods:

    The number of arguments that the step() method must accept is controlled by n_arg.

    Set to None to remove an existing SQL aggregate function.

Example:

class MySum:
+    def __init__(self):
+        self.count = 0
+
+    def step(self, value):
+        self.count += value
+
+    def finalize(self):
+        return self.count
+
+con = sqlite3.connect(":memory:")
+con.create_aggregate("mysum", 1, MySum)
+cur = con.execute("CREATE TABLE test(i)")
+cur.execute("INSERT INTO test(i) VALUES(1)")
+cur.execute("INSERT INTO test(i) VALUES(2)")
+cur.execute("SELECT mysum(i) FROM test")
+print(cur.fetchone()[0])
+
+con.close()
+
+
+create_window_function(name, num_params, aggregate_class, /)
+

Create or remove a user-defined aggregate window function.

Parameters
+
  • +name (str) – The name of the SQL aggregate window function to create or remove.
  • +num_params (int) – The number of arguments the SQL aggregate window function can accept. If -1, it may take any number of arguments.
  • +

    aggregate_class (class | None) –

    +

    A class that must implement the following methods:

    • +step(): Add a row to the current window.
    • +value(): Return the current value of the aggregate.
    • +inverse(): Remove a row from the current window.
    • +finalize(): Return the final result of the aggregate as a type natively supported by SQLite.

    The number of arguments that the step() and value() methods must accept is controlled by num_params.

    Set to None to remove an existing SQL aggregate window function.

Raises
+

NotSupportedError – If used with a version of SQLite older than 3.25.0, which does not support aggregate window functions.

New in version 3.11.

Example:

# Example taken from https://www.sqlite.org/windowfunctions.html#udfwinfunc
+class WindowSumInt:
+    def __init__(self):
+        self.count = 0
+
+    def step(self, value):
+        """Add a row to the current window."""
+        self.count += value
+
+    def value(self):
+        """Return the current value of the aggregate."""
+        return self.count
+
+    def inverse(self, value):
+        """Remove a row from the current window."""
+        self.count -= value
+
+    def finalize(self):
+        """Return the final value of the aggregate.
+
+        Any clean-up actions should be placed here.
+        """
+        return self.count
+
+
+con = sqlite3.connect(":memory:")
+cur = con.execute("CREATE TABLE test(x, y)")
+values = [
+    ("a", 4),
+    ("b", 5),
+    ("c", 3),
+    ("d", 8),
+    ("e", 1),
+]
+cur.executemany("INSERT INTO test VALUES(?, ?)", values)
+con.create_window_function("sumint", 1, WindowSumInt)
+cur.execute("""
+    SELECT x, sumint(y) OVER (
+        ORDER BY x ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING
+    ) AS sum_y
+    FROM test ORDER BY x
+""")
+print(cur.fetchall())
+
+
+create_collation(name, callable, /)
+

Create a collation named name using the collating function callable. callable is passed two string arguments, and it should return an integer:

  • +1 if the first is ordered higher than the second
  • +-1 if the first is ordered lower than the second
  • +0 if they are ordered equal

The following example shows a reverse sorting collation:

def collate_reverse(string1, string2):
+    if string1 == string2:
+        return 0
+    elif string1 < string2:
+        return 1
+    else:
+        return -1
+
+con = sqlite3.connect(":memory:")
+con.create_collation("reverse", collate_reverse)
+
+cur = con.execute("CREATE TABLE test(x)")
+cur.executemany("INSERT INTO test(x) VALUES(?)", [("a",), ("b",)])
+cur.execute("SELECT x FROM test ORDER BY x COLLATE reverse")
+for row in cur:
+    print(row)
+con.close()
+

Remove a collation function by setting callable to None.

Changed in version 3.11: The collation name can contain any Unicode character. Earlier, only ASCII characters were allowed.

+
+interrupt()
+

Call this method from a different thread to abort any queries that might be executing on the connection. Aborted queries will raise an OperationalError.

+
+set_authorizer(authorizer_callback)
+

Register callable authorizer_callback to be invoked for each attempt to access a column of a table in the database. The callback should return one of SQLITE_OK, SQLITE_DENY, or SQLITE_IGNORE to signal how access to the column should be handled by the underlying SQLite library.

The first argument to the callback signifies what kind of operation is to be authorized. The second and third argument will be arguments or None depending on the first argument. The 4th argument is the name of the database (“main”, “temp”, etc.) if applicable. The 5th argument is the name of the inner-most trigger or view that is responsible for the access attempt or None if this access attempt is directly from input SQL code.

Please consult the SQLite documentation about the possible values for the first argument and the meaning of the second and third argument depending on the first one. All necessary constants are available in the sqlite3 module.

Passing None as authorizer_callback will disable the authorizer.

Changed in version 3.11: Added support for disabling the authorizer using None.

+
+set_progress_handler(progress_handler, n)
+

Register callable progress_handler to be invoked for every n instructions of the SQLite virtual machine. This is useful if you want to get called from SQLite during long-running operations, for example to update a GUI.

If you want to clear any previously installed progress handler, call the method with None for progress_handler.

Returning a non-zero value from the handler function will terminate the currently executing query and cause it to raise a DatabaseError exception.

+
+set_trace_callback(trace_callback)
+

Register callable trace_callback to be invoked for each SQL statement that is actually executed by the SQLite backend.

The only argument passed to the callback is the statement (as str) that is being executed. The return value of the callback is ignored. Note that the backend does not only run statements passed to the Cursor.execute() methods. Other sources include the transaction management of the sqlite3 module and the execution of triggers defined in the current database.

Passing None as trace_callback will disable the trace callback.

Note

Exceptions raised in the trace callback are not propagated. As a development and debugging aid, use enable_callback_tracebacks() to enable printing tracebacks from exceptions raised in the trace callback.

New in version 3.3.

+
+enable_load_extension(enabled, /)
+

Enable the SQLite engine to load SQLite extensions from shared libraries if enabled is True; else, disallow loading SQLite extensions. SQLite extensions can define new functions, aggregates or whole new virtual table implementations. One well-known extension is the fulltext-search extension distributed with SQLite.

Note

The sqlite3 module is not built with loadable extension support by default, because some platforms (notably macOS) have SQLite libraries which are compiled without this feature. To get loadable extension support, you must pass the --enable-loadable-sqlite-extensions option to configure.

Raises an auditing event sqlite3.enable_load_extension with arguments connection, enabled.

New in version 3.2.

Changed in version 3.10: Added the sqlite3.enable_load_extension auditing event.

con.enable_load_extension(True)
+
+# Load the fulltext search extension
+con.execute("select load_extension('./fts3.so')")
+
+# alternatively you can load the extension using an API call:
+# con.load_extension("./fts3.so")
+
+# disable extension loading again
+con.enable_load_extension(False)
+
+# example from SQLite wiki
+con.execute("CREATE VIRTUAL TABLE recipe USING fts3(name, ingredients)")
+con.executescript("""
+    INSERT INTO recipe (name, ingredients) VALUES('broccoli stew', 'broccoli peppers cheese tomatoes');
+    INSERT INTO recipe (name, ingredients) VALUES('pumpkin stew', 'pumpkin onions garlic celery');
+    INSERT INTO recipe (name, ingredients) VALUES('broccoli pie', 'broccoli cheese onions flour');
+    INSERT INTO recipe (name, ingredients) VALUES('pumpkin pie', 'pumpkin sugar flour butter');
+    """)
+for row in con.execute("SELECT rowid, name, ingredients FROM recipe WHERE name MATCH 'pie'"):
+    print(row)
+
+con.close()
+
+
+load_extension(path, /, *, entrypoint=None)
+

Load an SQLite extension from a shared library. Enable extension loading with enable_load_extension() before calling this method.

Parameters
+
  • +path (str) – The path to the SQLite extension.
  • +entrypoint (str | None) – Entry point name. If None (the default), SQLite will come up with an entry point name of its own; see the SQLite docs Loading an Extension for details.

Raises an auditing event sqlite3.load_extension with arguments connection, path.

New in version 3.2.

Changed in version 3.10: Added the sqlite3.load_extension auditing event.

New in version 3.12: The entrypoint parameter.

+
+iterdump()
+

Return an iterator to dump the database as SQL source code. Useful when saving an in-memory database for later restoration. Similar to the .dump command in the sqlite3 shell.

Example:

# Convert file example.db to SQL dump file dump.sql
+con = sqlite3.connect('example.db')
+with open('dump.sql', 'w') as f:
+    for line in con.iterdump():
+        f.write('%s\n' % line)
+con.close()
+

See also

How to handle non-UTF-8 text encodings

+
+backup(target, *, pages=- 1, progress=None, name='main', sleep=0.250)
+

Create a backup of an SQLite database.

Works even if the database is being accessed by other clients or concurrently by the same connection.

Parameters
+
  • +target (Connection) – The database connection to save the backup to.
  • +pages (int) – The number of pages to copy at a time. If equal to or less than 0, the entire database is copied in a single step. Defaults to -1.
  • +progress (callback | None) – If set to a callable, it is invoked with three integer arguments for every backup iteration: the status of the last iteration, the remaining number of pages still to be copied, and the total number of pages. Defaults to None.
  • +name (str) – The name of the database to back up. Either "main" (the default) for the main database, "temp" for the temporary database, or the name of a custom database as attached using the ATTACH DATABASE SQL statement.
  • +sleep (float) – The number of seconds to sleep between successive attempts to back up remaining pages.

Example 1, copy an existing database into another:

def progress(status, remaining, total):
+    print(f'Copied {total-remaining} of {total} pages...')
+
+src = sqlite3.connect('example.db')
+dst = sqlite3.connect('backup.db')
+with dst:
+    src.backup(dst, pages=1, progress=progress)
+dst.close()
+src.close()
+

Example 2, copy an existing database into a transient copy:

src = sqlite3.connect('example.db')
+dst = sqlite3.connect(':memory:')
+src.backup(dst)
+

New in version 3.7.

See also

How to handle non-UTF-8 text encodings

+
+getlimit(category, /)
+

Get a connection runtime limit.

Parameters
+

category (int) – The SQLite limit category to be queried.

Return type
+

int

Raises
+

ProgrammingError – If category is not recognised by the underlying SQLite library.

Example, query the maximum length of an SQL statement for Connection con (the default is 1000000000):

>>> con.getlimit(sqlite3.SQLITE_LIMIT_SQL_LENGTH)
+1000000000
+

New in version 3.11.

+
+setlimit(category, limit, /)
+

Set a connection runtime limit. Attempts to increase a limit above its hard upper bound are silently truncated to the hard upper bound. Regardless of whether or not the limit was changed, the prior value of the limit is returned.

Parameters
+
  • +category (int) – The SQLite limit category to be set.
  • +limit (int) – The value of the new limit. If negative, the current limit is unchanged.
Return type
+

int

Raises
+

ProgrammingError – If category is not recognised by the underlying SQLite library.

Example, limit the number of attached databases to 1 for Connection con (the default limit is 10):

>>> con.setlimit(sqlite3.SQLITE_LIMIT_ATTACHED, 1)
+10
+>>> con.getlimit(sqlite3.SQLITE_LIMIT_ATTACHED)
+1
+

New in version 3.11.

+
+getconfig(op, /)
+

Query a boolean connection configuration option.

Parameters
+

op (int) – A SQLITE_DBCONFIG code.

Return type
+

bool

New in version 3.12.

+
+setconfig(op, enable=True, /)
+

Set a boolean connection configuration option.

Parameters
+
  • +op (int) – A SQLITE_DBCONFIG code.
  • +enable (bool) – True if the configuration option should be enabled (default); False if it should be disabled.

New in version 3.12.

+
+serialize(*, name='main')
+

Serialize a database into a bytes object. For an ordinary on-disk database file, the serialization is just a copy of the disk file. For an in-memory database or a “temp” database, the serialization is the same sequence of bytes which would be written to disk if that database were backed up to disk.

Parameters
+

name (str) – The database name to be serialized. Defaults to "main".

Return type
+

bytes

Note

This method is only available if the underlying SQLite library has the serialize API.

New in version 3.11.

+
+deserialize(data, /, *, name='main')
+

Deserialize a serialized database into a Connection. This method causes the database connection to disconnect from database name, and reopen name as an in-memory database based on the serialization contained in data.

Parameters
+
  • +data (bytes) – A serialized database.
  • +name (str) – The database name to deserialize into. Defaults to "main".
Raises
+

Note

This method is only available if the underlying SQLite library has the deserialize API.

New in version 3.11.

+
+autocommit
+

This attribute controls PEP 249-compliant transaction behaviour. autocommit has three allowed values:

Changing autocommit to False will open a new transaction, and changing it to True will commit any pending transaction.

See Transaction control via the autocommit attribute for more details.

Note

The isolation_level attribute has no effect unless autocommit is LEGACY_TRANSACTION_CONTROL.

New in version 3.12.

+
+in_transaction
+

This read-only attribute corresponds to the low-level SQLite autocommit mode.

True if a transaction is active (there are uncommitted changes), False otherwise.

New in version 3.2.

+
+isolation_level
+

Controls the legacy transaction handling mode of sqlite3. If set to None, transactions are never implicitly opened. If set to one of "DEFERRED", "IMMEDIATE", or "EXCLUSIVE", corresponding to the underlying SQLite transaction behaviour, implicit transaction management is performed.

If not overridden by the isolation_level parameter of connect(), the default is "", which is an alias for "DEFERRED".

Note

Using autocommit to control transaction handling is recommended over using isolation_level. isolation_level has no effect unless autocommit is set to LEGACY_TRANSACTION_CONTROL (the default).

+
+row_factory
+

The initial row_factory for Cursor objects created from this connection. Assigning to this attribute does not affect the row_factory of existing cursors belonging to this connection, only new ones. Is None by default, meaning each row is returned as a tuple.

See How to create and use row factories for more details.

+
+text_factory
+

A callable that accepts a bytes parameter and returns a text representation of it. The callable is invoked for SQLite values with the TEXT data type. By default, this attribute is set to str.

See How to handle non-UTF-8 text encodings for more details.

+
+total_changes
+

Return the total number of database rows that have been modified, inserted, or deleted since the database connection was opened.

+
+

Cursor objects

A Cursor object represents a database cursor which is used to execute SQL statements, and manage the context of a fetch operation. Cursors are created using Connection.cursor(), or by using any of the connection shortcut methods.

Cursor objects are iterators, meaning that if you execute() a SELECT query, you can simply iterate over the cursor to fetch the resulting rows:

for row in cur.execute("SELECT t FROM data"):
+    print(row)
+
+class sqlite3.Cursor
+

A Cursor instance has the following attributes and methods.

+execute(sql, parameters=(), /)
+

Execute a single SQL statement, optionally binding Python values using placeholders.

Parameters
+
Raises
+

ProgrammingError – If sql contains more than one SQL statement.

If autocommit is LEGACY_TRANSACTION_CONTROL, isolation_level is not None, sql is an INSERT, UPDATE, DELETE, or REPLACE statement, and there is no open transaction, a transaction is implicitly opened before executing sql.

Deprecated since version 3.12, will be removed in version 3.14: DeprecationWarning is emitted if named placeholders are used and parameters is a sequence instead of a dict. Starting with Python 3.14, ProgrammingError will be raised instead.

Use executescript() to execute multiple SQL statements.

+
+executemany(sql, parameters, /)
+

For every item in parameters, repeatedly execute the parameterized DML SQL statement sql.

Uses the same implicit transaction handling as execute().

Parameters
+
Raises
+

ProgrammingError – If sql contains more than one SQL statement, or is not a DML statement.

Example:

rows = [
+    ("row1",),
+    ("row2",),
+]
+# cur is an sqlite3.Cursor object
+cur.executemany("INSERT INTO data VALUES(?)", rows)
+

Note

Any resulting rows are discarded, including DML statements with RETURNING clauses.

Deprecated since version 3.12, will be removed in version 3.14: DeprecationWarning is emitted if named placeholders are used and the items in parameters are sequences instead of dicts. Starting with Python 3.14, ProgrammingError will be raised instead.

+
+executescript(sql_script, /)
+

Execute the SQL statements in sql_script. If the autocommit is LEGACY_TRANSACTION_CONTROL and there is a pending transaction, an implicit COMMIT statement is executed first. No other implicit transaction control is performed; any transaction control must be added to sql_script.

sql_script must be a string.

Example:

# cur is an sqlite3.Cursor object
+cur.executescript("""
+    BEGIN;
+    CREATE TABLE person(firstname, lastname, age);
+    CREATE TABLE book(title, author, published);
+    CREATE TABLE publisher(name, address);
+    COMMIT;
+""")
+
+
+fetchone()
+

If row_factory is None, return the next row query result set as a tuple. Else, pass it to the row factory and return its result. Return None if no more data is available.

+
+fetchmany(size=cursor.arraysize)
+

Return the next set of rows of a query result as a list. Return an empty list if no more rows are available.

The number of rows to fetch per call is specified by the size parameter. If size is not given, arraysize determines the number of rows to be fetched. If fewer than size rows are available, as many rows as are available are returned.

Note there are performance considerations involved with the size parameter. For optimal performance, it is usually best to use the arraysize attribute. If the size parameter is used, then it is best for it to retain the same value from one fetchmany() call to the next.

+
+fetchall()
+

Return all (remaining) rows of a query result as a list. Return an empty list if no rows are available. Note that the arraysize attribute can affect the performance of this operation.

+
+close()
+

Close the cursor now (rather than whenever __del__ is called).

The cursor will be unusable from this point forward; a ProgrammingError exception will be raised if any operation is attempted with the cursor.

+
+setinputsizes(sizes, /)
+

Required by the DB-API. Does nothing in sqlite3.

+
+setoutputsize(size, column=None, /)
+

Required by the DB-API. Does nothing in sqlite3.

+
+arraysize
+

Read/write attribute that controls the number of rows returned by fetchmany(). The default value is 1 which means a single row would be fetched per call.

+
+connection
+

Read-only attribute that provides the SQLite database Connection belonging to the cursor. A Cursor object created by calling con.cursor() will have a connection attribute that refers to con:

>>> con = sqlite3.connect(":memory:")
+>>> cur = con.cursor()
+>>> cur.connection == con
+True
+
+
+description
+

Read-only attribute that provides the column names of the last query. To remain compatible with the Python DB API, it returns a 7-tuple for each column where the last six items of each tuple are None.

It is set for SELECT statements without any matching rows as well.

+
+lastrowid
+

Read-only attribute that provides the row id of the last inserted row. It is only updated after successful INSERT or REPLACE statements using the execute() method. For other statements, after executemany() or executescript(), or if the insertion failed, the value of lastrowid is left unchanged. The initial value of lastrowid is None.

Note

Inserts into WITHOUT ROWID tables are not recorded.

Changed in version 3.6: Added support for the REPLACE statement.

+
+rowcount
+

Read-only attribute that provides the number of modified rows for INSERT, UPDATE, DELETE, and REPLACE statements; is -1 for other statements, including CTE queries. It is only updated by the execute() and executemany() methods, after the statement has run to completion. This means that any resulting rows must be fetched in order for rowcount to be updated.

+
+row_factory
+

Control how a row fetched from this Cursor is represented. If None, a row is represented as a tuple. Can be set to the included sqlite3.Row; or a callable that accepts two arguments, a Cursor object and the tuple of row values, and returns a custom object representing an SQLite row.

Defaults to what Connection.row_factory was set to when the Cursor was created. Assigning to this attribute does not affect Connection.row_factory of the parent connection.

See How to create and use row factories for more details.

+
+

Row objects

+class sqlite3.Row
+

A Row instance serves as a highly optimized row_factory for Connection objects. It supports iteration, equality testing, len(), and mapping access by column name and index.

Two Row objects compare equal if they have identical column names and values.

See How to create and use row factories for more details.

+keys()
+

Return a list of column names as strings. Immediately after a query, it is the first member of each tuple in Cursor.description.

+

Changed in version 3.5: Added support of slicing.

+

Blob objects

New in version 3.11.

+class sqlite3.Blob
+

A Blob instance is a file-like object that can read and write data in an SQLite BLOB. Call len(blob) to get the size (number of bytes) of the blob. Use indices and slices for direct access to the blob data.

Use the Blob as a context manager to ensure that the blob handle is closed after use.

con = sqlite3.connect(":memory:")
+con.execute("CREATE TABLE test(blob_col blob)")
+con.execute("INSERT INTO test(blob_col) VALUES(zeroblob(13))")
+
+# Write to our blob, using two write operations:
+with con.blobopen("test", "blob_col", 1) as blob:
+    blob.write(b"hello, ")
+    blob.write(b"world.")
+    # Modify the first and last bytes of our blob
+    blob[0] = ord("H")
+    blob[-1] = ord("!")
+
+# Read the contents of our blob
+with con.blobopen("test", "blob_col", 1) as blob:
+    greeting = blob.read()
+
+print(greeting)  # outputs "b'Hello, world!'"
+
+close()
+

Close the blob.

The blob will be unusable from this point onward. An Error (or subclass) exception will be raised if any further operation is attempted with the blob.

+
+read(length=- 1, /)
+

Read length bytes of data from the blob at the current offset position. If the end of the blob is reached, the data up to EOF will be returned. When length is not specified, or is negative, read() will read until the end of the blob.

+
+write(data, /)
+

Write data to the blob at the current offset. This function cannot change the blob length. Writing beyond the end of the blob will raise ValueError.

+
+tell()
+

Return the current access position of the blob.

+
+seek(offset, origin=os.SEEK_SET, /)
+

Set the current access position of the blob to offset. The origin argument defaults to os.SEEK_SET (absolute blob positioning). Other values for origin are os.SEEK_CUR (seek relative to the current position) and os.SEEK_END (seek relative to the blob’s end).

+
+

PrepareProtocol objects

+class sqlite3.PrepareProtocol
+

The PrepareProtocol type’s single purpose is to act as a PEP 246 style adaption protocol for objects that can adapt themselves to native SQLite types.

+

Exceptions

The exception hierarchy is defined by the DB-API 2.0 (PEP 249).

+exception sqlite3.Warning
+

This exception is not currently raised by the sqlite3 module, but may be raised by applications using sqlite3, for example if a user-defined function truncates data while inserting. Warning is a subclass of Exception.

+
+exception sqlite3.Error
+

The base class of the other exceptions in this module. Use this to catch all errors with one single except statement. Error is a subclass of Exception.

If the exception originated from within the SQLite library, the following two attributes are added to the exception:

+sqlite_errorcode
+

The numeric error code from the SQLite API

New in version 3.11.

+
+sqlite_errorname
+

The symbolic name of the numeric error code from the SQLite API

New in version 3.11.

+
+
+exception sqlite3.InterfaceError
+

Exception raised for misuse of the low-level SQLite C API. In other words, if this exception is raised, it probably indicates a bug in the sqlite3 module. InterfaceError is a subclass of Error.

+
+exception sqlite3.DatabaseError
+

Exception raised for errors that are related to the database. This serves as the base exception for several types of database errors. It is only raised implicitly through the specialised subclasses. DatabaseError is a subclass of Error.

+
+exception sqlite3.DataError
+

Exception raised for errors caused by problems with the processed data, like numeric values out of range, and strings which are too long. DataError is a subclass of DatabaseError.

+
+exception sqlite3.OperationalError
+

Exception raised for errors that are related to the database’s operation, and not necessarily under the control of the programmer. For example, the database path is not found, or a transaction could not be processed. OperationalError is a subclass of DatabaseError.

+
+exception sqlite3.IntegrityError
+

Exception raised when the relational integrity of the database is affected, e.g. a foreign key check fails. It is a subclass of DatabaseError.

+
+exception sqlite3.InternalError
+

Exception raised when SQLite encounters an internal error. If this is raised, it may indicate that there is a problem with the runtime SQLite library. InternalError is a subclass of DatabaseError.

+
+exception sqlite3.ProgrammingError
+

Exception raised for sqlite3 API programming errors, for example supplying the wrong number of bindings to a query, or trying to operate on a closed Connection. ProgrammingError is a subclass of DatabaseError.

+
+exception sqlite3.NotSupportedError
+

Exception raised in case a method or database API is not supported by the underlying SQLite library. For example, setting deterministic to True in create_function(), if the underlying SQLite library does not support deterministic functions. NotSupportedError is a subclass of DatabaseError.

+

SQLite and Python types

SQLite natively supports the following types: NULL, INTEGER, REAL, TEXT, BLOB.

The following Python types can thus be sent to SQLite without any problem:

+ + + + + +

Python type

SQLite type

None

NULL

int

INTEGER

float

REAL

str

TEXT

bytes

BLOB

This is how SQLite types are converted to Python types by default:

+ + + + + +

SQLite type

Python type

NULL

None

INTEGER

int

REAL

float

TEXT

depends on text_factory, str by default

BLOB

bytes

The type system of the sqlite3 module is extensible in two ways: you can store additional Python types in an SQLite database via object adapters, and you can let the sqlite3 module convert SQLite types to Python types via converters.

Default adapters and converters (deprecated)

Note

The default adapters and converters are deprecated as of Python 3.12. Instead, use the Adapter and converter recipes and tailor them to your needs.

The deprecated default adapters and converters consist of:

Note

The default “timestamp” converter ignores UTC offsets in the database and always returns a naive datetime.datetime object. To preserve UTC offsets in timestamps, either leave converters disabled, or register an offset-aware converter with register_converter().

Deprecated since version 3.12.

Command-line interface

The sqlite3 module can be invoked as a script, using the interpreter’s -m switch, in order to provide a simple SQLite shell. The argument signature is as follows:

python -m sqlite3 [-h] [-v] [filename] [sql]
+

Type .quit or CTRL-D to exit the shell.

+-h, --help
+

Print CLI help.

+
+-v, --version
+

Print underlying SQLite library version.

+

New in version 3.12.

How-to guides

How to use placeholders to bind values in SQL queries

SQL operations usually need to use values from Python variables. However, beware of using Python’s string operations to assemble queries, as they are vulnerable to SQL injection attacks. For example, an attacker can simply close the single quote and inject OR TRUE to select all rows:

>>> # Never do this -- insecure!
+>>> symbol = input()
+' OR TRUE; --
+>>> sql = "SELECT * FROM stocks WHERE symbol = '%s'" % symbol
+>>> print(sql)
+SELECT * FROM stocks WHERE symbol = '' OR TRUE; --'
+>>> cur.execute(sql)
+

Instead, use the DB-API’s parameter substitution. To insert a variable into a query string, use a placeholder in the string, and substitute the actual values into the query by providing them as a tuple of values to the second argument of the cursor’s execute() method.

An SQL statement may use one of two kinds of placeholders: question marks (qmark style) or named placeholders (named style). For the qmark style, parameters must be a sequence whose length must match the number of placeholders, or a ProgrammingError is raised. For the named style, parameters must be an instance of a dict (or a subclass), which must contain keys for all named parameters; any extra items are ignored. Here’s an example of both styles:

con = sqlite3.connect(":memory:")
+cur = con.execute("CREATE TABLE lang(name, first_appeared)")
+
+# This is the named style used with executemany():
+data = (
+    {"name": "C", "year": 1972},
+    {"name": "Fortran", "year": 1957},
+    {"name": "Python", "year": 1991},
+    {"name": "Go", "year": 2009},
+)
+cur.executemany("INSERT INTO lang VALUES(:name, :year)", data)
+
+# This is the qmark style used in a SELECT query:
+params = (1972,)
+cur.execute("SELECT * FROM lang WHERE first_appeared = ?", params)
+print(cur.fetchall())
+

Note

PEP 249 numeric placeholders are not supported. If used, they will be interpreted as named placeholders.

How to adapt custom Python types to SQLite values

SQLite supports only a limited set of data types natively. To store custom Python types in SQLite databases, adapt them to one of the Python types SQLite natively understands.

There are two ways to adapt Python objects to SQLite types: letting your object adapt itself, or using an adapter callable. The latter will take precedence above the former. For a library that exports a custom type, it may make sense to enable that type to adapt itself. As an application developer, it may make more sense to take direct control by registering custom adapter functions.

How to write adaptable objects

Suppose we have a Point class that represents a pair of coordinates, x and y, in a Cartesian coordinate system. The coordinate pair will be stored as a text string in the database, using a semicolon to separate the coordinates. This can be implemented by adding a __conform__(self, protocol) method which returns the adapted value. The object passed to protocol will be of type PrepareProtocol.

class Point:
+    def __init__(self, x, y):
+        self.x, self.y = x, y
+
+    def __conform__(self, protocol):
+        if protocol is sqlite3.PrepareProtocol:
+            return f"{self.x};{self.y}"
+
+con = sqlite3.connect(":memory:")
+cur = con.cursor()
+
+cur.execute("SELECT ?", (Point(4.0, -3.2),))
+print(cur.fetchone()[0])
+

How to register adapter callables

The other possibility is to create a function that converts the Python object to an SQLite-compatible type. This function can then be registered using register_adapter().

class Point:
+    def __init__(self, x, y):
+        self.x, self.y = x, y
+
+def adapt_point(point):
+    return f"{point.x};{point.y}"
+
+sqlite3.register_adapter(Point, adapt_point)
+
+con = sqlite3.connect(":memory:")
+cur = con.cursor()
+
+cur.execute("SELECT ?", (Point(1.0, 2.5),))
+print(cur.fetchone()[0])
+

How to convert SQLite values to custom Python types

Writing an adapter lets you convert from custom Python types to SQLite values. To be able to convert from SQLite values to custom Python types, we use converters.

Let’s go back to the Point class. We stored the x and y coordinates separated via semicolons as strings in SQLite.

First, we’ll define a converter function that accepts the string as a parameter and constructs a Point object from it.

Note

Converter functions are always passed a bytes object, no matter the underlying SQLite data type.

def convert_point(s):
+    x, y = map(float, s.split(b";"))
+    return Point(x, y)
+

We now need to tell sqlite3 when it should convert a given SQLite value. This is done when connecting to a database, using the detect_types parameter of connect(). There are three options:

  • Implicit: set detect_types to PARSE_DECLTYPES +
  • Explicit: set detect_types to PARSE_COLNAMES +
  • Both: set detect_types to sqlite3.PARSE_DECLTYPES | sqlite3.PARSE_COLNAMES. Column names take precedence over declared types.

The following example illustrates the implicit and explicit approaches:

class Point:
+    def __init__(self, x, y):
+        self.x, self.y = x, y
+
+    def __repr__(self):
+        return f"Point({self.x}, {self.y})"
+
+def adapt_point(point):
+    return f"{point.x};{point.y}"
+
+def convert_point(s):
+    x, y = list(map(float, s.split(b";")))
+    return Point(x, y)
+
+# Register the adapter and converter
+sqlite3.register_adapter(Point, adapt_point)
+sqlite3.register_converter("point", convert_point)
+
+# 1) Parse using declared types
+p = Point(4.0, -3.2)
+con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES)
+cur = con.execute("CREATE TABLE test(p point)")
+
+cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
+cur.execute("SELECT p FROM test")
+print("with declared types:", cur.fetchone()[0])
+cur.close()
+con.close()
+
+# 2) Parse using column names
+con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_COLNAMES)
+cur = con.execute("CREATE TABLE test(p)")
+
+cur.execute("INSERT INTO test(p) VALUES(?)", (p,))
+cur.execute('SELECT p AS "p [point]" FROM test')
+print("with column names:", cur.fetchone()[0])
+

Adapter and converter recipes

This section shows recipes for common adapters and converters.

import datetime
+import sqlite3
+
+def adapt_date_iso(val):
+    """Adapt datetime.date to ISO 8601 date."""
+    return val.isoformat()
+
+def adapt_datetime_iso(val):
+    """Adapt datetime.datetime to timezone-naive ISO 8601 date."""
+    return val.isoformat()
+
+def adapt_datetime_epoch(val):
+    """Adapt datetime.datetime to Unix timestamp."""
+    return int(val.timestamp())
+
+sqlite3.register_adapter(datetime.date, adapt_date_iso)
+sqlite3.register_adapter(datetime.datetime, adapt_datetime_iso)
+sqlite3.register_adapter(datetime.datetime, adapt_datetime_epoch)
+
+def convert_date(val):
+    """Convert ISO 8601 date to datetime.date object."""
+    return datetime.date.fromisoformat(val.decode())
+
+def convert_datetime(val):
+    """Convert ISO 8601 datetime to datetime.datetime object."""
+    return datetime.datetime.fromisoformat(val.decode())
+
+def convert_timestamp(val):
+    """Convert Unix epoch timestamp to datetime.datetime object."""
+    return datetime.datetime.fromtimestamp(int(val))
+
+sqlite3.register_converter("date", convert_date)
+sqlite3.register_converter("datetime", convert_datetime)
+sqlite3.register_converter("timestamp", convert_timestamp)
+

How to use connection shortcut methods

Using the execute(), executemany(), and executescript() methods of the Connection class, your code can be written more concisely because you don’t have to create the (often superfluous) Cursor objects explicitly. Instead, the Cursor objects are created implicitly and these shortcut methods return the cursor objects. This way, you can execute a SELECT statement and iterate over it directly using only a single call on the Connection object.

# Create and fill the table.
+con = sqlite3.connect(":memory:")
+con.execute("CREATE TABLE lang(name, first_appeared)")
+data = [
+    ("C++", 1985),
+    ("Objective-C", 1984),
+]
+con.executemany("INSERT INTO lang(name, first_appeared) VALUES(?, ?)", data)
+
+# Print the table contents
+for row in con.execute("SELECT name, first_appeared FROM lang"):
+    print(row)
+
+print("I just deleted", con.execute("DELETE FROM lang").rowcount, "rows")
+
+# close() is not a shortcut method and it's not called automatically;
+# the connection object should be closed manually
+con.close()
+

How to use the connection context manager

A Connection object can be used as a context manager that automatically commits or rolls back open transactions when leaving the body of the context manager. If the body of the with statement finishes without exceptions, the transaction is committed. If this commit fails, or if the body of the with statement raises an uncaught exception, the transaction is rolled back. If autocommit is False, a new transaction is implicitly opened after committing or rolling back.

If there is no open transaction upon leaving the body of the with statement, or if autocommit is True, the context manager does nothing.

Note

The context manager neither implicitly opens a new transaction nor closes the connection. If you need a closing context manager, consider using contextlib.closing().

con = sqlite3.connect(":memory:")
+con.execute("CREATE TABLE lang(id INTEGER PRIMARY KEY, name VARCHAR UNIQUE)")
+
+# Successful, con.commit() is called automatically afterwards
+with con:
+    con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))
+
+# con.rollback() is called after the with block finishes with an exception,
+# the exception is still raised and must be caught
+try:
+    with con:
+        con.execute("INSERT INTO lang(name) VALUES(?)", ("Python",))
+except sqlite3.IntegrityError:
+    print("couldn't add Python twice")
+
+# Connection object used as context manager only commits or rollbacks transactions,
+# so the connection object should be closed manually
+con.close()
+

How to work with SQLite URIs

Some useful URI tricks include:

  • Open a database in read-only mode:
>>> con = sqlite3.connect("file:tutorial.db?mode=ro", uri=True)
+>>> con.execute("CREATE TABLE readonly(data)")
+Traceback (most recent call last):
+OperationalError: attempt to write a readonly database
+
  • Do not implicitly create a new database file if it does not already exist; will raise OperationalError if unable to create a new file:
>>> con = sqlite3.connect("file:nosuchdb.db?mode=rw", uri=True)
+Traceback (most recent call last):
+OperationalError: unable to open database file
+
  • Create a shared named in-memory database:
db = "file:mem1?mode=memory&cache=shared"
+con1 = sqlite3.connect(db, uri=True)
+con2 = sqlite3.connect(db, uri=True)
+with con1:
+    con1.execute("CREATE TABLE shared(data)")
+    con1.execute("INSERT INTO shared VALUES(28)")
+res = con2.execute("SELECT data FROM shared")
+assert res.fetchone() == (28,)
+

More information about this feature, including a list of parameters, can be found in the SQLite URI documentation.

How to create and use row factories

By default, sqlite3 represents each row as a tuple. If a tuple does not suit your needs, you can use the sqlite3.Row class or a custom row_factory.

While row_factory exists as an attribute both on the Cursor and the Connection, it is recommended to set Connection.row_factory, so all cursors created from the connection will use the same row factory.

Row provides indexed and case-insensitive named access to columns, with minimal memory overhead and performance impact over a tuple. To use Row as a row factory, assign it to the row_factory attribute:

>>> con = sqlite3.connect(":memory:")
+>>> con.row_factory = sqlite3.Row
+

Queries now return Row objects:

>>> res = con.execute("SELECT 'Earth' AS name, 6378 AS radius")
+>>> row = res.fetchone()
+>>> row.keys()
+['name', 'radius']
+>>> row[0]         # Access by index.
+'Earth'
+>>> row["name"]    # Access by name.
+'Earth'
+>>> row["RADIUS"]  # Column names are case-insensitive.
+6378
+

Note

The FROM clause can be omitted in the SELECT statement, as in the above example. In such cases, SQLite returns a single row with columns defined by expressions, e.g. literals, with the given aliases expr AS alias.

You can create a custom row_factory that returns each row as a dict, with column names mapped to values:

def dict_factory(cursor, row):
+    fields = [column[0] for column in cursor.description]
+    return {key: value for key, value in zip(fields, row)}
+

Using it, queries now return a dict instead of a tuple:

>>> con = sqlite3.connect(":memory:")
+>>> con.row_factory = dict_factory
+>>> for row in con.execute("SELECT 1 AS a, 2 AS b"):
+...     print(row)
+{'a': 1, 'b': 2}
+

The following row factory returns a named tuple:

from collections import namedtuple
+
+def namedtuple_factory(cursor, row):
+    fields = [column[0] for column in cursor.description]
+    cls = namedtuple("Row", fields)
+    return cls._make(row)
+

namedtuple_factory() can be used as follows:

>>> con = sqlite3.connect(":memory:")
+>>> con.row_factory = namedtuple_factory
+>>> cur = con.execute("SELECT 1 AS a, 2 AS b")
+>>> row = cur.fetchone()
+>>> row
+Row(a=1, b=2)
+>>> row[0]  # Indexed access.
+1
+>>> row.b   # Attribute access.
+2
+

With some adjustments, the above recipe can be adapted to use a dataclass, or any other custom class, instead of a namedtuple.

How to handle non-UTF-8 text encodings

By default, sqlite3 uses str to adapt SQLite values with the TEXT data type. This works well for UTF-8 encoded text, but it might fail for other encodings and invalid UTF-8. You can use a custom text_factory to handle such cases.

Because of SQLite’s flexible typing, it is not uncommon to encounter table columns with the TEXT data type containing non-UTF-8 encodings, or even arbitrary data. To demonstrate, let’s assume we have a database with ISO-8859-2 (Latin-2) encoded text, for example a table of Czech-English dictionary entries. Assuming we now have a Connection instance con connected to this database, we can decode the Latin-2 encoded text using this text_factory:

con.text_factory = lambda data: str(data, encoding="latin2")
+

For invalid UTF-8 or arbitrary data in stored in TEXT table columns, you can use the following technique, borrowed from the Unicode HOWTO:

con.text_factory = lambda data: str(data, errors="surrogateescape")
+

Note

The sqlite3 module API does not support strings containing surrogates.

See also

Unicode HOWTO

Explanation

Transaction control

sqlite3 offers multiple methods of controlling whether, when and how database transactions are opened and closed. Transaction control via the autocommit attribute is recommended, while Transaction control via the isolation_level attribute retains the pre-Python 3.12 behaviour.

Transaction control via the autocommit attribute

The recommended way of controlling transaction behaviour is through the Connection.autocommit attribute, which should preferably be set using the autocommit parameter of connect().

It is suggested to set autocommit to False, which implies PEP 249-compliant transaction control. This means:

  • +sqlite3 ensures that a transaction is always open, so connect(), Connection.commit(), and Connection.rollback() will implicitly open a new transaction (immediately after closing the pending one, for the latter two). sqlite3 uses BEGIN DEFERRED statements when opening transactions.
  • Transactions should be committed explicitly using commit().
  • Transactions should be rolled back explicitly using rollback().
  • An implicit rollback is performed if the database is close()-ed with pending changes.

Set autocommit to True to enable SQLite’s autocommit mode. In this mode, Connection.commit() and Connection.rollback() have no effect. Note that SQLite’s autocommit mode is distinct from the PEP 249-compliant Connection.autocommit attribute; use Connection.in_transaction to query the low-level SQLite autocommit mode.

Set autocommit to LEGACY_TRANSACTION_CONTROL to leave transaction control behaviour to the Connection.isolation_level attribute. See Transaction control via the isolation_level attribute for more information.

Transaction control via the isolation_level attribute

Note

The recommended way of controlling transactions is via the autocommit attribute. See Transaction control via the autocommit attribute.

If Connection.autocommit is set to LEGACY_TRANSACTION_CONTROL (the default), transaction behaviour is controlled using the Connection.isolation_level attribute. Otherwise, isolation_level has no effect.

If the connection attribute isolation_level is not None, new transactions are implicitly opened before execute() and executemany() executes INSERT, UPDATE, DELETE, or REPLACE statements; for other statements, no implicit transaction handling is performed. Use the commit() and rollback() methods to respectively commit and roll back pending transactions. You can choose the underlying SQLite transaction behaviour — that is, whether and what type of BEGIN statements sqlite3 implicitly executes – via the isolation_level attribute.

If isolation_level is set to None, no transactions are implicitly opened at all. This leaves the underlying SQLite library in autocommit mode, but also allows the user to perform their own transaction handling using explicit SQL statements. The underlying SQLite library autocommit mode can be queried using the in_transaction attribute.

The executescript() method implicitly commits any pending transaction before execution of the given SQL script, regardless of the value of isolation_level.

Changed in version 3.6: sqlite3 used to implicitly commit an open transaction before DDL statements. This is no longer the case.

Changed in version 3.12: The recommended way of controlling transactions is now via the autocommit attribute.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sqlite3.html +

+
diff --git a/devdocs/python~3.12/library%2Fssl.html b/devdocs/python~3.12/library%2Fssl.html new file mode 100644 index 00000000..0630730f --- /dev/null +++ b/devdocs/python~3.12/library%2Fssl.html @@ -0,0 +1,792 @@ +

ssl — TLS/SSL wrapper for socket objects

Source code: Lib/ssl.py

This module provides access to Transport Layer Security (often known as “Secure Sockets Layer”) encryption and peer authentication facilities for network sockets, both client-side and server-side. This module uses the OpenSSL library. It is available on all modern Unix systems, Windows, macOS, and probably additional platforms, as long as OpenSSL is installed on that platform.

Note

Some behavior may be platform dependent, since calls are made to the operating system socket APIs. The installed version of OpenSSL may also cause variations in behavior. For example, TLSv1.3 comes with OpenSSL version 1.1.1.

Warning

Don’t use this module without reading the Security considerations. Doing so may lead to a false sense of security, as the default settings of the ssl module are not necessarily appropriate for your application.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

This section documents the objects and functions in the ssl module; for more general information about TLS, SSL, and certificates, the reader is referred to the documents in the “See Also” section at the bottom.

This module provides a class, ssl.SSLSocket, which is derived from the socket.socket type, and provides a socket-like wrapper that also encrypts and decrypts the data going over the socket with SSL. It supports additional methods such as getpeercert(), which retrieves the certificate of the other side of the connection, and cipher(), which retrieves the cipher being used for the secure connection.

For more sophisticated applications, the ssl.SSLContext class helps manage settings and certificates, which can then be inherited by SSL sockets created through the SSLContext.wrap_socket() method.

Changed in version 3.5.3: Updated to support linking with OpenSSL 1.1.0

Changed in version 3.6: OpenSSL 0.9.8, 1.0.0 and 1.0.1 are deprecated and no longer supported. In the future the ssl module will require at least OpenSSL 1.0.2 or 1.1.0.

Changed in version 3.10: PEP 644 has been implemented. The ssl module requires OpenSSL 1.1.1 or newer.

Use of deprecated constants and functions result in deprecation warnings.

Functions, Constants, and Exceptions

Socket creation

Instances of SSLSocket must be created using the SSLContext.wrap_socket() method. The helper function create_default_context() returns a new context with secure default settings.

Client socket example with default context and IPv4/IPv6 dual stack:

import socket
+import ssl
+
+hostname = 'www.python.org'
+context = ssl.create_default_context()
+
+with socket.create_connection((hostname, 443)) as sock:
+    with context.wrap_socket(sock, server_hostname=hostname) as ssock:
+        print(ssock.version())
+

Client socket example with custom context and IPv4:

hostname = 'www.python.org'
+# PROTOCOL_TLS_CLIENT requires valid cert chain and hostname
+context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
+context.load_verify_locations('path/to/cabundle.pem')
+
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0) as sock:
+    with context.wrap_socket(sock, server_hostname=hostname) as ssock:
+        print(ssock.version())
+

Server socket example listening on localhost IPv4:

context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)
+context.load_cert_chain('/path/to/certchain.pem', '/path/to/private.key')
+
+with socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0) as sock:
+    sock.bind(('127.0.0.1', 8443))
+    sock.listen(5)
+    with context.wrap_socket(sock, server_side=True) as ssock:
+        conn, addr = ssock.accept()
+        ...
+

Context creation

A convenience function helps create SSLContext objects for common purposes.

+ssl.create_default_context(purpose=Purpose.SERVER_AUTH, cafile=None, capath=None, cadata=None)
+

Return a new SSLContext object with default settings for the given purpose. The settings are chosen by the ssl module, and usually represent a higher security level than when calling the SSLContext constructor directly.

cafile, capath, cadata represent optional CA certificates to trust for certificate verification, as in SSLContext.load_verify_locations(). If all three are None, this function can choose to trust the system’s default CA certificates instead.

The settings are: PROTOCOL_TLS_CLIENT or PROTOCOL_TLS_SERVER, OP_NO_SSLv2, and OP_NO_SSLv3 with high encryption cipher suites without RC4 and without unauthenticated cipher suites. Passing SERVER_AUTH as purpose sets verify_mode to CERT_REQUIRED and either loads CA certificates (when at least one of cafile, capath or cadata is given) or uses SSLContext.load_default_certs() to load default CA certificates.

When keylog_filename is supported and the environment variable SSLKEYLOGFILE is set, create_default_context() enables key logging.

Note

The protocol, options, cipher and other settings may change to more restrictive values anytime without prior deprecation. The values represent a fair balance between compatibility and security.

If your application needs specific settings, you should create a SSLContext and apply the settings yourself.

Note

If you find that when certain older clients or servers attempt to connect with a SSLContext created by this function that they get an error stating “Protocol or cipher suite mismatch”, it may be that they only support SSL3.0 which this function excludes using the OP_NO_SSLv3. SSL3.0 is widely considered to be completely broken. If you still wish to continue to use this function but still allow SSL 3.0 connections you can re-enable them using:

ctx = ssl.create_default_context(Purpose.CLIENT_AUTH)
+ctx.options &= ~ssl.OP_NO_SSLv3
+

New in version 3.4.

Changed in version 3.4.4: RC4 was dropped from the default cipher string.

Changed in version 3.6: ChaCha20/Poly1305 was added to the default cipher string.

3DES was dropped from the default cipher string.

Changed in version 3.8: Support for key logging to SSLKEYLOGFILE was added.

Changed in version 3.10: The context now uses PROTOCOL_TLS_CLIENT or PROTOCOL_TLS_SERVER protocol instead of generic PROTOCOL_TLS.

+

Exceptions

+exception ssl.SSLError
+

Raised to signal an error from the underlying SSL implementation (currently provided by the OpenSSL library). This signifies some problem in the higher-level encryption and authentication layer that’s superimposed on the underlying network connection. This error is a subtype of OSError. The error code and message of SSLError instances are provided by the OpenSSL library.

Changed in version 3.3: SSLError used to be a subtype of socket.error.

+library
+

A string mnemonic designating the OpenSSL submodule in which the error occurred, such as SSL, PEM or X509. The range of possible values depends on the OpenSSL version.

New in version 3.3.

+
+reason
+

A string mnemonic designating the reason this error occurred, for example CERTIFICATE_VERIFY_FAILED. The range of possible values depends on the OpenSSL version.

New in version 3.3.

+
+
+exception ssl.SSLZeroReturnError
+

A subclass of SSLError raised when trying to read or write and the SSL connection has been closed cleanly. Note that this doesn’t mean that the underlying transport (read TCP) has been closed.

New in version 3.3.

+
+exception ssl.SSLWantReadError
+

A subclass of SSLError raised by a non-blocking SSL socket when trying to read or write data, but more data needs to be received on the underlying TCP transport before the request can be fulfilled.

New in version 3.3.

+
+exception ssl.SSLWantWriteError
+

A subclass of SSLError raised by a non-blocking SSL socket when trying to read or write data, but more data needs to be sent on the underlying TCP transport before the request can be fulfilled.

New in version 3.3.

+
+exception ssl.SSLSyscallError
+

A subclass of SSLError raised when a system error was encountered while trying to fulfill an operation on a SSL socket. Unfortunately, there is no easy way to inspect the original errno number.

New in version 3.3.

+
+exception ssl.SSLEOFError
+

A subclass of SSLError raised when the SSL connection has been terminated abruptly. Generally, you shouldn’t try to reuse the underlying transport when this error is encountered.

New in version 3.3.

+
+exception ssl.SSLCertVerificationError
+

A subclass of SSLError raised when certificate validation has failed.

New in version 3.7.

+verify_code
+

A numeric error number that denotes the verification error.

+
+verify_message
+

A human readable string of the verification error.

+
+
+exception ssl.CertificateError
+

An alias for SSLCertVerificationError.

Changed in version 3.7: The exception is now an alias for SSLCertVerificationError.

+

Random generation

+ssl.RAND_bytes(num)
+

Return num cryptographically strong pseudo-random bytes. Raises an SSLError if the PRNG has not been seeded with enough data or if the operation is not supported by the current RAND method. RAND_status() can be used to check the status of the PRNG and RAND_add() can be used to seed the PRNG.

For almost all applications os.urandom() is preferable.

Read the Wikipedia article, Cryptographically secure pseudorandom number generator (CSPRNG), to get the requirements of a cryptographically strong generator.

New in version 3.3.

+
+ssl.RAND_status()
+

Return True if the SSL pseudo-random number generator has been seeded with ‘enough’ randomness, and False otherwise. You can use ssl.RAND_egd() and ssl.RAND_add() to increase the randomness of the pseudo-random number generator.

+
+ssl.RAND_add(bytes, entropy)
+

Mix the given bytes into the SSL pseudo-random number generator. The parameter entropy (a float) is a lower bound on the entropy contained in string (so you can always use 0.0). See RFC 1750 for more information on sources of entropy.

Changed in version 3.5: Writable bytes-like object is now accepted.

+

Certificate handling

+ssl.cert_time_to_seconds(cert_time)
+

Return the time in seconds since the Epoch, given the cert_time string representing the “notBefore” or “notAfter” date from a certificate in "%b %d %H:%M:%S %Y %Z" strptime format (C locale).

Here’s an example:

>>> import ssl
+>>> timestamp = ssl.cert_time_to_seconds("Jan  5 09:34:43 2018 GMT")
+>>> timestamp  
+1515144883
+>>> from datetime import datetime
+>>> print(datetime.utcfromtimestamp(timestamp))  
+2018-01-05 09:34:43
+

“notBefore” or “notAfter” dates must use GMT (RFC 5280).

Changed in version 3.5: Interpret the input time as a time in UTC as specified by ‘GMT’ timezone in the input string. Local timezone was used previously. Return an integer (no fractions of a second in the input format)

+
+ssl.get_server_certificate(addr, ssl_version=PROTOCOL_TLS_CLIENT, ca_certs=None[, timeout])
+

Given the address addr of an SSL-protected server, as a (hostname, port-number) pair, fetches the server’s certificate, and returns it as a PEM-encoded string. If ssl_version is specified, uses that version of the SSL protocol to attempt to connect to the server. If ca_certs is specified, it should be a file containing a list of root certificates, the same format as used for the cafile parameter in SSLContext.load_verify_locations(). The call will attempt to validate the server certificate against that set of root certificates, and will fail if the validation attempt fails. A timeout can be specified with the timeout parameter.

Changed in version 3.3: This function is now IPv6-compatible.

Changed in version 3.5: The default ssl_version is changed from PROTOCOL_SSLv3 to PROTOCOL_TLS for maximum compatibility with modern servers.

Changed in version 3.10: The timeout parameter was added.

+
+ssl.DER_cert_to_PEM_cert(DER_cert_bytes)
+

Given a certificate as a DER-encoded blob of bytes, returns a PEM-encoded string version of the same certificate.

+
+ssl.PEM_cert_to_DER_cert(PEM_cert_string)
+

Given a certificate as an ASCII PEM string, returns a DER-encoded sequence of bytes for that same certificate.

+
+ssl.get_default_verify_paths()
+

Returns a named tuple with paths to OpenSSL’s default cafile and capath. The paths are the same as used by SSLContext.set_default_verify_paths(). The return value is a named tuple DefaultVerifyPaths:

  • +cafile - resolved path to cafile or None if the file doesn’t exist,
  • +capath - resolved path to capath or None if the directory doesn’t exist,
  • +openssl_cafile_env - OpenSSL’s environment key that points to a cafile,
  • +openssl_cafile - hard coded path to a cafile,
  • +openssl_capath_env - OpenSSL’s environment key that points to a capath,
  • +openssl_capath - hard coded path to a capath directory

New in version 3.4.

+
+ssl.enum_certificates(store_name)
+

Retrieve certificates from Windows’ system cert store. store_name may be one of CA, ROOT or MY. Windows may provide additional cert stores, too.

The function returns a list of (cert_bytes, encoding_type, trust) tuples. The encoding_type specifies the encoding of cert_bytes. It is either x509_asn for X.509 ASN.1 data or pkcs_7_asn for PKCS#7 ASN.1 data. Trust specifies the purpose of the certificate as a set of OIDS or exactly True if the certificate is trustworthy for all purposes.

Example:

>>> ssl.enum_certificates("CA")
+[(b'data...', 'x509_asn', {'1.3.6.1.5.5.7.3.1', '1.3.6.1.5.5.7.3.2'}),
+ (b'data...', 'x509_asn', True)]
+

Availability: Windows.

New in version 3.4.

+
+ssl.enum_crls(store_name)
+

Retrieve CRLs from Windows’ system cert store. store_name may be one of CA, ROOT or MY. Windows may provide additional cert stores, too.

The function returns a list of (cert_bytes, encoding_type, trust) tuples. The encoding_type specifies the encoding of cert_bytes. It is either x509_asn for X.509 ASN.1 data or pkcs_7_asn for PKCS#7 ASN.1 data.

Availability: Windows.

New in version 3.4.

+

Constants

All constants are now enum.IntEnum or enum.IntFlag collections.

New in version 3.6.

+ssl.CERT_NONE
+

Possible value for SSLContext.verify_mode. Except for PROTOCOL_TLS_CLIENT, it is the default mode. With client-side sockets, just about any cert is accepted. Validation errors, such as untrusted or expired cert, are ignored and do not abort the TLS/SSL handshake.

In server mode, no certificate is requested from the client, so the client does not send any for client cert authentication.

See the discussion of Security considerations below.

+
+ssl.CERT_OPTIONAL
+

Possible value for SSLContext.verify_mode. In client mode, CERT_OPTIONAL has the same meaning as CERT_REQUIRED. It is recommended to use CERT_REQUIRED for client-side sockets instead.

In server mode, a client certificate request is sent to the client. The client may either ignore the request or send a certificate in order perform TLS client cert authentication. If the client chooses to send a certificate, it is verified. Any verification error immediately aborts the TLS handshake.

Use of this setting requires a valid set of CA certificates to be passed to SSLContext.load_verify_locations().

+
+ssl.CERT_REQUIRED
+

Possible value for SSLContext.verify_mode. In this mode, certificates are required from the other side of the socket connection; an SSLError will be raised if no certificate is provided, or if its validation fails. This mode is not sufficient to verify a certificate in client mode as it does not match hostnames. check_hostname must be enabled as well to verify the authenticity of a cert. PROTOCOL_TLS_CLIENT uses CERT_REQUIRED and enables check_hostname by default.

With server socket, this mode provides mandatory TLS client cert authentication. A client certificate request is sent to the client and the client must provide a valid and trusted certificate.

Use of this setting requires a valid set of CA certificates to be passed to SSLContext.load_verify_locations().

+
+class ssl.VerifyMode
+

enum.IntEnum collection of CERT_* constants.

New in version 3.6.

+
+ssl.VERIFY_DEFAULT
+

Possible value for SSLContext.verify_flags. In this mode, certificate revocation lists (CRLs) are not checked. By default OpenSSL does neither require nor verify CRLs.

New in version 3.4.

+
+ssl.VERIFY_CRL_CHECK_LEAF
+

Possible value for SSLContext.verify_flags. In this mode, only the peer cert is checked but none of the intermediate CA certificates. The mode requires a valid CRL that is signed by the peer cert’s issuer (its direct ancestor CA). If no proper CRL has been loaded with SSLContext.load_verify_locations, validation will fail.

New in version 3.4.

+
+ssl.VERIFY_CRL_CHECK_CHAIN
+

Possible value for SSLContext.verify_flags. In this mode, CRLs of all certificates in the peer cert chain are checked.

New in version 3.4.

+
+ssl.VERIFY_X509_STRICT
+

Possible value for SSLContext.verify_flags to disable workarounds for broken X.509 certificates.

New in version 3.4.

+
+ssl.VERIFY_ALLOW_PROXY_CERTS
+

Possible value for SSLContext.verify_flags to enables proxy certificate verification.

New in version 3.10.

+
+ssl.VERIFY_X509_TRUSTED_FIRST
+

Possible value for SSLContext.verify_flags. It instructs OpenSSL to prefer trusted certificates when building the trust chain to validate a certificate. This flag is enabled by default.

New in version 3.4.4.

+
+ssl.VERIFY_X509_PARTIAL_CHAIN
+

Possible value for SSLContext.verify_flags. It instructs OpenSSL to accept intermediate CAs in the trust store to be treated as trust-anchors, in the same way as the self-signed root CA certificates. This makes it possible to trust certificates issued by an intermediate CA without having to trust its ancestor root CA.

New in version 3.10.

+
+class ssl.VerifyFlags
+

enum.IntFlag collection of VERIFY_* constants.

New in version 3.6.

+
+ssl.PROTOCOL_TLS
+

Selects the highest protocol version that both the client and server support. Despite the name, this option can select both “SSL” and “TLS” protocols.

New in version 3.6.

Deprecated since version 3.10: TLS clients and servers require different default settings for secure communication. The generic TLS protocol constant is deprecated in favor of PROTOCOL_TLS_CLIENT and PROTOCOL_TLS_SERVER.

+
+ssl.PROTOCOL_TLS_CLIENT
+

Auto-negotiate the highest protocol version that both the client and server support, and configure the context client-side connections. The protocol enables CERT_REQUIRED and check_hostname by default.

New in version 3.6.

+
+ssl.PROTOCOL_TLS_SERVER
+

Auto-negotiate the highest protocol version that both the client and server support, and configure the context server-side connections.

New in version 3.6.

+
+ssl.PROTOCOL_SSLv23
+

Alias for PROTOCOL_TLS.

Deprecated since version 3.6: Use PROTOCOL_TLS instead.

+
+ssl.PROTOCOL_SSLv3
+

Selects SSL version 3 as the channel encryption protocol.

This protocol is not available if OpenSSL is compiled with the no-ssl3 option.

Warning

SSL version 3 is insecure. Its use is highly discouraged.

Deprecated since version 3.6: OpenSSL has deprecated all version specific protocols. Use the default protocol PROTOCOL_TLS_SERVER or PROTOCOL_TLS_CLIENT with SSLContext.minimum_version and SSLContext.maximum_version instead.

+
+ssl.PROTOCOL_TLSv1
+

Selects TLS version 1.0 as the channel encryption protocol.

Deprecated since version 3.6: OpenSSL has deprecated all version specific protocols.

+
+ssl.PROTOCOL_TLSv1_1
+

Selects TLS version 1.1 as the channel encryption protocol. Available only with openssl version 1.0.1+.

New in version 3.4.

Deprecated since version 3.6: OpenSSL has deprecated all version specific protocols.

+
+ssl.PROTOCOL_TLSv1_2
+

Selects TLS version 1.2 as the channel encryption protocol. Available only with openssl version 1.0.1+.

New in version 3.4.

Deprecated since version 3.6: OpenSSL has deprecated all version specific protocols.

+
+ssl.OP_ALL
+

Enables workarounds for various bugs present in other SSL implementations. This option is set by default. It does not necessarily set the same flags as OpenSSL’s SSL_OP_ALL constant.

New in version 3.2.

+
+ssl.OP_NO_SSLv2
+

Prevents an SSLv2 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing SSLv2 as the protocol version.

New in version 3.2.

Deprecated since version 3.6: SSLv2 is deprecated

+
+ssl.OP_NO_SSLv3
+

Prevents an SSLv3 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing SSLv3 as the protocol version.

New in version 3.2.

Deprecated since version 3.6: SSLv3 is deprecated

+
+ssl.OP_NO_TLSv1
+

Prevents a TLSv1 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing TLSv1 as the protocol version.

New in version 3.2.

Deprecated since version 3.7: The option is deprecated since OpenSSL 1.1.0, use the new SSLContext.minimum_version and SSLContext.maximum_version instead.

+
+ssl.OP_NO_TLSv1_1
+

Prevents a TLSv1.1 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing TLSv1.1 as the protocol version. Available only with openssl version 1.0.1+.

New in version 3.4.

Deprecated since version 3.7: The option is deprecated since OpenSSL 1.1.0.

+
+ssl.OP_NO_TLSv1_2
+

Prevents a TLSv1.2 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing TLSv1.2 as the protocol version. Available only with openssl version 1.0.1+.

New in version 3.4.

Deprecated since version 3.7: The option is deprecated since OpenSSL 1.1.0.

+
+ssl.OP_NO_TLSv1_3
+

Prevents a TLSv1.3 connection. This option is only applicable in conjunction with PROTOCOL_TLS. It prevents the peers from choosing TLSv1.3 as the protocol version. TLS 1.3 is available with OpenSSL 1.1.1 or later. When Python has been compiled against an older version of OpenSSL, the flag defaults to 0.

New in version 3.7.

Deprecated since version 3.7: The option is deprecated since OpenSSL 1.1.0. It was added to 2.7.15, 3.6.3 and 3.7.0 for backwards compatibility with OpenSSL 1.0.2.

+
+ssl.OP_NO_RENEGOTIATION
+

Disable all renegotiation in TLSv1.2 and earlier. Do not send HelloRequest messages, and ignore renegotiation requests via ClientHello.

This option is only available with OpenSSL 1.1.0h and later.

New in version 3.7.

+
+ssl.OP_CIPHER_SERVER_PREFERENCE
+

Use the server’s cipher ordering preference, rather than the client’s. This option has no effect on client sockets and SSLv2 server sockets.

New in version 3.3.

+
+ssl.OP_SINGLE_DH_USE
+

Prevents re-use of the same DH key for distinct SSL sessions. This improves forward secrecy but requires more computational resources. This option only applies to server sockets.

New in version 3.3.

+
+ssl.OP_SINGLE_ECDH_USE
+

Prevents re-use of the same ECDH key for distinct SSL sessions. This improves forward secrecy but requires more computational resources. This option only applies to server sockets.

New in version 3.3.

+
+ssl.OP_ENABLE_MIDDLEBOX_COMPAT
+

Send dummy Change Cipher Spec (CCS) messages in TLS 1.3 handshake to make a TLS 1.3 connection look more like a TLS 1.2 connection.

This option is only available with OpenSSL 1.1.1 and later.

New in version 3.8.

+
+ssl.OP_NO_COMPRESSION
+

Disable compression on the SSL channel. This is useful if the application protocol supports its own compression scheme.

New in version 3.3.

+
+class ssl.Options
+

enum.IntFlag collection of OP_* constants.

+
+ssl.OP_NO_TICKET
+

Prevent client side from requesting a session ticket.

New in version 3.6.

+
+ssl.OP_IGNORE_UNEXPECTED_EOF
+

Ignore unexpected shutdown of TLS connections.

This option is only available with OpenSSL 3.0.0 and later.

New in version 3.10.

+
+ssl.OP_ENABLE_KTLS
+

Enable the use of the kernel TLS. To benefit from the feature, OpenSSL must have been compiled with support for it, and the negotiated cipher suites and extensions must be supported by it (a list of supported ones may vary by platform and kernel version).

Note that with enabled kernel TLS some cryptographic operations are performed by the kernel directly and not via any available OpenSSL Providers. This might be undesirable if, for example, the application requires all cryptographic operations to be performed by the FIPS provider.

This option is only available with OpenSSL 3.0.0 and later.

New in version 3.12.

+
+ssl.OP_LEGACY_SERVER_CONNECT
+

Allow legacy insecure renegotiation between OpenSSL and unpatched servers only.

New in version 3.12.

+
+ssl.HAS_ALPN
+

Whether the OpenSSL library has built-in support for the Application-Layer Protocol Negotiation TLS extension as described in RFC 7301.

New in version 3.5.

+
+ssl.HAS_NEVER_CHECK_COMMON_NAME
+

Whether the OpenSSL library has built-in support not checking subject common name and SSLContext.hostname_checks_common_name is writeable.

New in version 3.7.

+
+ssl.HAS_ECDH
+

Whether the OpenSSL library has built-in support for the Elliptic Curve-based Diffie-Hellman key exchange. This should be true unless the feature was explicitly disabled by the distributor.

New in version 3.3.

+
+ssl.HAS_SNI
+

Whether the OpenSSL library has built-in support for the Server Name Indication extension (as defined in RFC 6066).

New in version 3.2.

+
+ssl.HAS_NPN
+

Whether the OpenSSL library has built-in support for the Next Protocol Negotiation as described in the Application Layer Protocol Negotiation. When true, you can use the SSLContext.set_npn_protocols() method to advertise which protocols you want to support.

New in version 3.3.

+
+ssl.HAS_SSLv2
+

Whether the OpenSSL library has built-in support for the SSL 2.0 protocol.

New in version 3.7.

+
+ssl.HAS_SSLv3
+

Whether the OpenSSL library has built-in support for the SSL 3.0 protocol.

New in version 3.7.

+
+ssl.HAS_TLSv1
+

Whether the OpenSSL library has built-in support for the TLS 1.0 protocol.

New in version 3.7.

+
+ssl.HAS_TLSv1_1
+

Whether the OpenSSL library has built-in support for the TLS 1.1 protocol.

New in version 3.7.

+
+ssl.HAS_TLSv1_2
+

Whether the OpenSSL library has built-in support for the TLS 1.2 protocol.

New in version 3.7.

+
+ssl.HAS_TLSv1_3
+

Whether the OpenSSL library has built-in support for the TLS 1.3 protocol.

New in version 3.7.

+
+ssl.CHANNEL_BINDING_TYPES
+

List of supported TLS channel binding types. Strings in this list can be used as arguments to SSLSocket.get_channel_binding().

New in version 3.3.

+
+ssl.OPENSSL_VERSION
+

The version string of the OpenSSL library loaded by the interpreter:

>>> ssl.OPENSSL_VERSION
+'OpenSSL 1.0.2k  26 Jan 2017'
+

New in version 3.2.

+
+ssl.OPENSSL_VERSION_INFO
+

A tuple of five integers representing version information about the OpenSSL library:

>>> ssl.OPENSSL_VERSION_INFO
+(1, 0, 2, 11, 15)
+

New in version 3.2.

+
+ssl.OPENSSL_VERSION_NUMBER
+

The raw version number of the OpenSSL library, as a single integer:

>>> ssl.OPENSSL_VERSION_NUMBER
+268443839
+>>> hex(ssl.OPENSSL_VERSION_NUMBER)
+'0x100020bf'
+

New in version 3.2.

+
+ssl.ALERT_DESCRIPTION_HANDSHAKE_FAILURE
+ssl.ALERT_DESCRIPTION_INTERNAL_ERROR
ALERT_DESCRIPTION_* +
+

Alert Descriptions from RFC 5246 and others. The IANA TLS Alert Registry contains this list and references to the RFCs where their meaning is defined.

Used as the return value of the callback function in SSLContext.set_servername_callback().

New in version 3.4.

+
+class ssl.AlertDescription
+

enum.IntEnum collection of ALERT_DESCRIPTION_* constants.

New in version 3.6.

+
+Purpose.SERVER_AUTH
+

Option for create_default_context() and SSLContext.load_default_certs(). This value indicates that the context may be used to authenticate web servers (therefore, it will be used to create client-side sockets).

New in version 3.4.

+
+Purpose.CLIENT_AUTH
+

Option for create_default_context() and SSLContext.load_default_certs(). This value indicates that the context may be used to authenticate web clients (therefore, it will be used to create server-side sockets).

New in version 3.4.

+
+class ssl.SSLErrorNumber
+

enum.IntEnum collection of SSL_ERROR_* constants.

New in version 3.6.

+
+class ssl.TLSVersion
+

enum.IntEnum collection of SSL and TLS versions for SSLContext.maximum_version and SSLContext.minimum_version.

New in version 3.7.

+
+TLSVersion.MINIMUM_SUPPORTED
+
+TLSVersion.MAXIMUM_SUPPORTED
+

The minimum or maximum supported SSL or TLS version. These are magic constants. Their values don’t reflect the lowest and highest available TLS/SSL versions.

+
+TLSVersion.SSLv3
+
+TLSVersion.TLSv1
+
+TLSVersion.TLSv1_1
+
+TLSVersion.TLSv1_2
+
+TLSVersion.TLSv1_3
+

SSL 3.0 to TLS 1.3.

Deprecated since version 3.10: All TLSVersion members except TLSVersion.TLSv1_2 and TLSVersion.TLSv1_3 are deprecated.

+

SSL Sockets

+class ssl.SSLSocket(socket.socket)
+

SSL sockets provide the following methods of Socket Objects:

However, since the SSL (and TLS) protocol has its own framing atop of TCP, the SSL sockets abstraction can, in certain respects, diverge from the specification of normal, OS-level sockets. See especially the notes on non-blocking sockets.

Instances of SSLSocket must be created using the SSLContext.wrap_socket() method.

Changed in version 3.5: The sendfile() method was added.

Changed in version 3.5: The shutdown() does not reset the socket timeout each time bytes are received or sent. The socket timeout is now the maximum total duration of the shutdown.

Deprecated since version 3.6: It is deprecated to create a SSLSocket instance directly, use SSLContext.wrap_socket() to wrap a socket.

Changed in version 3.7: SSLSocket instances must to created with wrap_socket(). In earlier versions, it was possible to create instances directly. This was never documented or officially supported.

Changed in version 3.10: Python now uses SSL_read_ex and SSL_write_ex internally. The functions support reading and writing of data larger than 2 GB. Writing zero-length data no longer fails with a protocol violation error.

+

SSL sockets also have the following additional methods and attributes:

+SSLSocket.read(len=1024, buffer=None)
+

Read up to len bytes of data from the SSL socket and return the result as a bytes instance. If buffer is specified, then read into the buffer instead, and return the number of bytes read.

Raise SSLWantReadError or SSLWantWriteError if the socket is non-blocking and the read would block.

As at any time a re-negotiation is possible, a call to read() can also cause write operations.

Changed in version 3.5: The socket timeout is no longer reset each time bytes are received or sent. The socket timeout is now the maximum total duration to read up to len bytes.

Deprecated since version 3.6: Use recv() instead of read().

+
+SSLSocket.write(buf)
+

Write buf to the SSL socket and return the number of bytes written. The buf argument must be an object supporting the buffer interface.

Raise SSLWantReadError or SSLWantWriteError if the socket is non-blocking and the write would block.

As at any time a re-negotiation is possible, a call to write() can also cause read operations.

Changed in version 3.5: The socket timeout is no longer reset each time bytes are received or sent. The socket timeout is now the maximum total duration to write buf.

Deprecated since version 3.6: Use send() instead of write().

+

Note

The read() and write() methods are the low-level methods that read and write unencrypted, application-level data and decrypt/encrypt it to encrypted, wire-level data. These methods require an active SSL connection, i.e. the handshake was completed and SSLSocket.unwrap() was not called.

Normally you should use the socket API methods like recv() and send() instead of these methods.

+SSLSocket.do_handshake()
+

Perform the SSL setup handshake.

Changed in version 3.4: The handshake method also performs match_hostname() when the check_hostname attribute of the socket’s context is true.

Changed in version 3.5: The socket timeout is no longer reset each time bytes are received or sent. The socket timeout is now the maximum total duration of the handshake.

Changed in version 3.7: Hostname or IP address is matched by OpenSSL during handshake. The function match_hostname() is no longer used. In case OpenSSL refuses a hostname or IP address, the handshake is aborted early and a TLS alert message is sent to the peer.

+
+SSLSocket.getpeercert(binary_form=False)
+

If there is no certificate for the peer on the other end of the connection, return None. If the SSL handshake hasn’t been done yet, raise ValueError.

If the binary_form parameter is False, and a certificate was received from the peer, this method returns a dict instance. If the certificate was not validated, the dict is empty. If the certificate was validated, it returns a dict with several keys, amongst them subject (the principal for which the certificate was issued) and issuer (the principal issuing the certificate). If a certificate contains an instance of the Subject Alternative Name extension (see RFC 3280), there will also be a subjectAltName key in the dictionary.

The subject and issuer fields are tuples containing the sequence of relative distinguished names (RDNs) given in the certificate’s data structure for the respective fields, and each RDN is a sequence of name-value pairs. Here is a real-world example:

{'issuer': ((('countryName', 'IL'),),
+            (('organizationName', 'StartCom Ltd.'),),
+            (('organizationalUnitName',
+              'Secure Digital Certificate Signing'),),
+            (('commonName',
+              'StartCom Class 2 Primary Intermediate Server CA'),)),
+ 'notAfter': 'Nov 22 08:15:19 2013 GMT',
+ 'notBefore': 'Nov 21 03:09:52 2011 GMT',
+ 'serialNumber': '95F0',
+ 'subject': ((('description', '571208-SLe257oHY9fVQ07Z'),),
+             (('countryName', 'US'),),
+             (('stateOrProvinceName', 'California'),),
+             (('localityName', 'San Francisco'),),
+             (('organizationName', 'Electronic Frontier Foundation, Inc.'),),
+             (('commonName', '*.eff.org'),),
+             (('emailAddress', 'hostmaster@eff.org'),)),
+ 'subjectAltName': (('DNS', '*.eff.org'), ('DNS', 'eff.org')),
+ 'version': 3}
+

If the binary_form parameter is True, and a certificate was provided, this method returns the DER-encoded form of the entire certificate as a sequence of bytes, or None if the peer did not provide a certificate. Whether the peer provides a certificate depends on the SSL socket’s role:

  • for a client SSL socket, the server will always provide a certificate, regardless of whether validation was required;
  • for a server SSL socket, the client will only provide a certificate when requested by the server; therefore getpeercert() will return None if you used CERT_NONE (rather than CERT_OPTIONAL or CERT_REQUIRED).

See also SSLContext.check_hostname.

Changed in version 3.2: The returned dictionary includes additional items such as issuer and notBefore.

Changed in version 3.4: ValueError is raised when the handshake isn’t done. The returned dictionary includes additional X509v3 extension items such as crlDistributionPoints, caIssuers and OCSP URIs.

Changed in version 3.9: IPv6 address strings no longer have a trailing new line.

+
+SSLSocket.cipher()
+

Returns a three-value tuple containing the name of the cipher being used, the version of the SSL protocol that defines its use, and the number of secret bits being used. If no connection has been established, returns None.

+
+SSLSocket.shared_ciphers()
+

Return the list of ciphers available in both the client and server. Each entry of the returned list is a three-value tuple containing the name of the cipher, the version of the SSL protocol that defines its use, and the number of secret bits the cipher uses. shared_ciphers() returns None if no connection has been established or the socket is a client socket.

New in version 3.5.

+
+SSLSocket.compression()
+

Return the compression algorithm being used as a string, or None if the connection isn’t compressed.

If the higher-level protocol supports its own compression mechanism, you can use OP_NO_COMPRESSION to disable SSL-level compression.

New in version 3.3.

+
+SSLSocket.get_channel_binding(cb_type='tls-unique')
+

Get channel binding data for current connection, as a bytes object. Returns None if not connected or the handshake has not been completed.

The cb_type parameter allow selection of the desired channel binding type. Valid channel binding types are listed in the CHANNEL_BINDING_TYPES list. Currently only the ‘tls-unique’ channel binding, defined by RFC 5929, is supported. ValueError will be raised if an unsupported channel binding type is requested.

New in version 3.3.

+
+SSLSocket.selected_alpn_protocol()
+

Return the protocol that was selected during the TLS handshake. If SSLContext.set_alpn_protocols() was not called, if the other party does not support ALPN, if this socket does not support any of the client’s proposed protocols, or if the handshake has not happened yet, None is returned.

New in version 3.5.

+
+SSLSocket.selected_npn_protocol()
+

Return the higher-level protocol that was selected during the TLS/SSL handshake. If SSLContext.set_npn_protocols() was not called, or if the other party does not support NPN, or if the handshake has not yet happened, this will return None.

New in version 3.3.

Deprecated since version 3.10: NPN has been superseded by ALPN

+
+SSLSocket.unwrap()
+

Performs the SSL shutdown handshake, which removes the TLS layer from the underlying socket, and returns the underlying socket object. This can be used to go from encrypted operation over a connection to unencrypted. The returned socket should always be used for further communication with the other side of the connection, rather than the original socket.

+
+SSLSocket.verify_client_post_handshake()
+

Requests post-handshake authentication (PHA) from a TLS 1.3 client. PHA can only be initiated for a TLS 1.3 connection from a server-side socket, after the initial TLS handshake and with PHA enabled on both sides, see SSLContext.post_handshake_auth.

The method does not perform a cert exchange immediately. The server-side sends a CertificateRequest during the next write event and expects the client to respond with a certificate on the next read event.

If any precondition isn’t met (e.g. not TLS 1.3, PHA not enabled), an SSLError is raised.

Note

Only available with OpenSSL 1.1.1 and TLS 1.3 enabled. Without TLS 1.3 support, the method raises NotImplementedError.

New in version 3.8.

+
+SSLSocket.version()
+

Return the actual SSL protocol version negotiated by the connection as a string, or None if no secure connection is established. As of this writing, possible return values include "SSLv2", "SSLv3", "TLSv1", "TLSv1.1" and "TLSv1.2". Recent OpenSSL versions may define more return values.

New in version 3.5.

+
+SSLSocket.pending()
+

Returns the number of already decrypted bytes available for read, pending on the connection.

+
+SSLSocket.context
+

The SSLContext object this SSL socket is tied to.

New in version 3.2.

+
+SSLSocket.server_side
+

A boolean which is True for server-side sockets and False for client-side sockets.

New in version 3.2.

+
+SSLSocket.server_hostname
+

Hostname of the server: str type, or None for server-side socket or if the hostname was not specified in the constructor.

New in version 3.2.

Changed in version 3.7: The attribute is now always ASCII text. When server_hostname is an internationalized domain name (IDN), this attribute now stores the A-label form ("xn--pythn-mua.org"), rather than the U-label form ("pythön.org").

+
+SSLSocket.session
+

The SSLSession for this SSL connection. The session is available for client and server side sockets after the TLS handshake has been performed. For client sockets the session can be set before do_handshake() has been called to reuse a session.

New in version 3.6.

+
+SSLSocket.session_reused
+

New in version 3.6.

+

SSL Contexts

New in version 3.2.

An SSL context holds various data longer-lived than single SSL connections, such as SSL configuration options, certificate(s) and private key(s). It also manages a cache of SSL sessions for server-side sockets, in order to speed up repeated connections from the same clients.

+class ssl.SSLContext(protocol=None)
+

Create a new SSL context. You may pass protocol which must be one of the PROTOCOL_* constants defined in this module. The parameter specifies which version of the SSL protocol to use. Typically, the server chooses a particular protocol version, and the client must adapt to the server’s choice. Most of the versions are not interoperable with the other versions. If not specified, the default is PROTOCOL_TLS; it provides the most compatibility with other versions.

Here’s a table showing which versions in a client (down the side) can connect to which versions in a server (along the top):

+ + + + + + +

client / server

SSLv2

SSLv3

TLS 3

TLSv1

TLSv1.1

TLSv1.2

SSLv2

yes

no

no 1

no

no

no

SSLv3

no

yes

no 2

no

no

no

TLS (SSLv23) 3

no 1

no 2

yes

yes

yes

yes

TLSv1

no

no

yes

yes

no

no

TLSv1.1

no

no

yes

no

yes

no

TLSv1.2

no

no

yes

no

no

yes

Footnotes

+1(1,2)
+

SSLContext disables SSLv2 with OP_NO_SSLv2 by default.

+2(1,2)
+

SSLContext disables SSLv3 with OP_NO_SSLv3 by default.

+3(1,2)
+

TLS 1.3 protocol will be available with PROTOCOL_TLS in OpenSSL >= 1.1.1. There is no dedicated PROTOCOL constant for just TLS 1.3.

See also

create_default_context() lets the ssl module choose security settings for a given purpose.

Changed in version 3.6: The context is created with secure default values. The options OP_NO_COMPRESSION, OP_CIPHER_SERVER_PREFERENCE, OP_SINGLE_DH_USE, OP_SINGLE_ECDH_USE, OP_NO_SSLv2, and OP_NO_SSLv3 (except for PROTOCOL_SSLv3) are set by default. The initial cipher suite list contains only HIGH ciphers, no NULL ciphers and no MD5 ciphers.

Deprecated since version 3.10: SSLContext without protocol argument is deprecated. The context class will either require PROTOCOL_TLS_CLIENT or PROTOCOL_TLS_SERVER protocol in the future.

Changed in version 3.10: The default cipher suites now include only secure AES and ChaCha20 ciphers with forward secrecy and security level 2. RSA and DH keys with less than 2048 bits and ECC keys with less than 224 bits are prohibited. PROTOCOL_TLS, PROTOCOL_TLS_CLIENT, and PROTOCOL_TLS_SERVER use TLS 1.2 as minimum TLS version.

+

SSLContext objects have the following methods and attributes:

+SSLContext.cert_store_stats()
+

Get statistics about quantities of loaded X.509 certificates, count of X.509 certificates flagged as CA certificates and certificate revocation lists as dictionary.

Example for a context with one CA cert and one other cert:

>>> context.cert_store_stats()
+{'crl': 0, 'x509_ca': 1, 'x509': 2}
+

New in version 3.4.

+
+SSLContext.load_cert_chain(certfile, keyfile=None, password=None)
+

Load a private key and the corresponding certificate. The certfile string must be the path to a single file in PEM format containing the certificate as well as any number of CA certificates needed to establish the certificate’s authenticity. The keyfile string, if present, must point to a file containing the private key. Otherwise the private key will be taken from certfile as well. See the discussion of Certificates for more information on how the certificate is stored in the certfile.

The password argument may be a function to call to get the password for decrypting the private key. It will only be called if the private key is encrypted and a password is necessary. It will be called with no arguments, and it should return a string, bytes, or bytearray. If the return value is a string it will be encoded as UTF-8 before using it to decrypt the key. Alternatively a string, bytes, or bytearray value may be supplied directly as the password argument. It will be ignored if the private key is not encrypted and no password is needed.

If the password argument is not specified and a password is required, OpenSSL’s built-in password prompting mechanism will be used to interactively prompt the user for a password.

An SSLError is raised if the private key doesn’t match with the certificate.

Changed in version 3.3: New optional argument password.

+
+SSLContext.load_default_certs(purpose=Purpose.SERVER_AUTH)
+

Load a set of default “certification authority” (CA) certificates from default locations. On Windows it loads CA certs from the CA and ROOT system stores. On all systems it calls SSLContext.set_default_verify_paths(). In the future the method may load CA certificates from other locations, too.

The purpose flag specifies what kind of CA certificates are loaded. The default settings Purpose.SERVER_AUTH loads certificates, that are flagged and trusted for TLS web server authentication (client side sockets). Purpose.CLIENT_AUTH loads CA certificates for client certificate verification on the server side.

New in version 3.4.

+
+SSLContext.load_verify_locations(cafile=None, capath=None, cadata=None)
+

Load a set of “certification authority” (CA) certificates used to validate other peers’ certificates when verify_mode is other than CERT_NONE. At least one of cafile or capath must be specified.

This method can also load certification revocation lists (CRLs) in PEM or DER format. In order to make use of CRLs, SSLContext.verify_flags must be configured properly.

The cafile string, if present, is the path to a file of concatenated CA certificates in PEM format. See the discussion of Certificates for more information about how to arrange the certificates in this file.

The capath string, if present, is the path to a directory containing several CA certificates in PEM format, following an OpenSSL specific layout.

The cadata object, if present, is either an ASCII string of one or more PEM-encoded certificates or a bytes-like object of DER-encoded certificates. Like with capath extra lines around PEM-encoded certificates are ignored but at least one certificate must be present.

Changed in version 3.4: New optional argument cadata

+
+SSLContext.get_ca_certs(binary_form=False)
+

Get a list of loaded “certification authority” (CA) certificates. If the binary_form parameter is False each list entry is a dict like the output of SSLSocket.getpeercert(). Otherwise the method returns a list of DER-encoded certificates. The returned list does not contain certificates from capath unless a certificate was requested and loaded by a SSL connection.

Note

Certificates in a capath directory aren’t loaded unless they have been used at least once.

New in version 3.4.

+
+SSLContext.get_ciphers()
+

Get a list of enabled ciphers. The list is in order of cipher priority. See SSLContext.set_ciphers().

Example:

>>> ctx = ssl.SSLContext(ssl.PROTOCOL_SSLv23)
+>>> ctx.set_ciphers('ECDHE+AESGCM:!ECDSA')
+>>> ctx.get_ciphers()
+[{'aead': True,
+  'alg_bits': 256,
+  'auth': 'auth-rsa',
+  'description': 'ECDHE-RSA-AES256-GCM-SHA384 TLSv1.2 Kx=ECDH     Au=RSA  '
+                 'Enc=AESGCM(256) Mac=AEAD',
+  'digest': None,
+  'id': 50380848,
+  'kea': 'kx-ecdhe',
+  'name': 'ECDHE-RSA-AES256-GCM-SHA384',
+  'protocol': 'TLSv1.2',
+  'strength_bits': 256,
+  'symmetric': 'aes-256-gcm'},
+ {'aead': True,
+  'alg_bits': 128,
+  'auth': 'auth-rsa',
+  'description': 'ECDHE-RSA-AES128-GCM-SHA256 TLSv1.2 Kx=ECDH     Au=RSA  '
+                 'Enc=AESGCM(128) Mac=AEAD',
+  'digest': None,
+  'id': 50380847,
+  'kea': 'kx-ecdhe',
+  'name': 'ECDHE-RSA-AES128-GCM-SHA256',
+  'protocol': 'TLSv1.2',
+  'strength_bits': 128,
+  'symmetric': 'aes-128-gcm'}]
+

New in version 3.6.

+
+SSLContext.set_default_verify_paths()
+

Load a set of default “certification authority” (CA) certificates from a filesystem path defined when building the OpenSSL library. Unfortunately, there’s no easy way to know whether this method succeeds: no error is returned if no certificates are to be found. When the OpenSSL library is provided as part of the operating system, though, it is likely to be configured properly.

+
+SSLContext.set_ciphers(ciphers)
+

Set the available ciphers for sockets created with this context. It should be a string in the OpenSSL cipher list format. If no cipher can be selected (because compile-time options or other configuration forbids use of all the specified ciphers), an SSLError will be raised.

Note

when connected, the SSLSocket.cipher() method of SSL sockets will give the currently selected cipher.

TLS 1.3 cipher suites cannot be disabled with set_ciphers().

+
+SSLContext.set_alpn_protocols(protocols)
+

Specify which protocols the socket should advertise during the SSL/TLS handshake. It should be a list of ASCII strings, like ['http/1.1', +'spdy/2'], ordered by preference. The selection of a protocol will happen during the handshake, and will play out according to RFC 7301. After a successful handshake, the SSLSocket.selected_alpn_protocol() method will return the agreed-upon protocol.

This method will raise NotImplementedError if HAS_ALPN is False.

New in version 3.5.

+
+SSLContext.set_npn_protocols(protocols)
+

Specify which protocols the socket should advertise during the SSL/TLS handshake. It should be a list of strings, like ['http/1.1', 'spdy/2'], ordered by preference. The selection of a protocol will happen during the handshake, and will play out according to the Application Layer Protocol Negotiation. After a successful handshake, the SSLSocket.selected_npn_protocol() method will return the agreed-upon protocol.

This method will raise NotImplementedError if HAS_NPN is False.

New in version 3.3.

Deprecated since version 3.10: NPN has been superseded by ALPN

+
+SSLContext.sni_callback
+

Register a callback function that will be called after the TLS Client Hello handshake message has been received by the SSL/TLS server when the TLS client specifies a server name indication. The server name indication mechanism is specified in RFC 6066 section 3 - Server Name Indication.

Only one callback can be set per SSLContext. If sni_callback is set to None then the callback is disabled. Calling this function a subsequent time will disable the previously registered callback.

The callback function will be called with three arguments; the first being the ssl.SSLSocket, the second is a string that represents the server name that the client is intending to communicate (or None if the TLS Client Hello does not contain a server name) and the third argument is the original SSLContext. The server name argument is text. For internationalized domain name, the server name is an IDN A-label ("xn--pythn-mua.org").

A typical use of this callback is to change the ssl.SSLSocket’s SSLSocket.context attribute to a new object of type SSLContext representing a certificate chain that matches the server name.

Due to the early negotiation phase of the TLS connection, only limited methods and attributes are usable like SSLSocket.selected_alpn_protocol() and SSLSocket.context. The SSLSocket.getpeercert(), SSLSocket.cipher() and SSLSocket.compression() methods require that the TLS connection has progressed beyond the TLS Client Hello and therefore will not return meaningful values nor can they be called safely.

The sni_callback function must return None to allow the TLS negotiation to continue. If a TLS failure is required, a constant ALERT_DESCRIPTION_* can be returned. Other return values will result in a TLS fatal error with ALERT_DESCRIPTION_INTERNAL_ERROR.

If an exception is raised from the sni_callback function the TLS connection will terminate with a fatal TLS alert message ALERT_DESCRIPTION_HANDSHAKE_FAILURE.

This method will raise NotImplementedError if the OpenSSL library had OPENSSL_NO_TLSEXT defined when it was built.

New in version 3.7.

+
+SSLContext.set_servername_callback(server_name_callback)
+

This is a legacy API retained for backwards compatibility. When possible, you should use sni_callback instead. The given server_name_callback is similar to sni_callback, except that when the server hostname is an IDN-encoded internationalized domain name, the server_name_callback receives a decoded U-label ("pythön.org").

If there is an decoding error on the server name, the TLS connection will terminate with an ALERT_DESCRIPTION_INTERNAL_ERROR fatal TLS alert message to the client.

New in version 3.4.

+
+SSLContext.load_dh_params(dhfile)
+

Load the key generation parameters for Diffie-Hellman (DH) key exchange. Using DH key exchange improves forward secrecy at the expense of computational resources (both on the server and on the client). The dhfile parameter should be the path to a file containing DH parameters in PEM format.

This setting doesn’t apply to client sockets. You can also use the OP_SINGLE_DH_USE option to further improve security.

New in version 3.3.

+
+SSLContext.set_ecdh_curve(curve_name)
+

Set the curve name for Elliptic Curve-based Diffie-Hellman (ECDH) key exchange. ECDH is significantly faster than regular DH while arguably as secure. The curve_name parameter should be a string describing a well-known elliptic curve, for example prime256v1 for a widely supported curve.

This setting doesn’t apply to client sockets. You can also use the OP_SINGLE_ECDH_USE option to further improve security.

This method is not available if HAS_ECDH is False.

New in version 3.3.

See also

SSL/TLS & Perfect Forward Secrecy
+
+

Vincent Bernat.

+
+SSLContext.wrap_socket(sock, server_side=False, do_handshake_on_connect=True, suppress_ragged_eofs=True, server_hostname=None, session=None)
+

Wrap an existing Python socket sock and return an instance of SSLContext.sslsocket_class (default SSLSocket). The returned SSL socket is tied to the context, its settings and certificates. sock must be a SOCK_STREAM socket; other socket types are unsupported.

The parameter server_side is a boolean which identifies whether server-side or client-side behavior is desired from this socket.

For client-side sockets, the context construction is lazy; if the underlying socket isn’t connected yet, the context construction will be performed after connect() is called on the socket. For server-side sockets, if the socket has no remote peer, it is assumed to be a listening socket, and the server-side SSL wrapping is automatically performed on client connections accepted via the accept() method. The method may raise SSLError.

On client connections, the optional parameter server_hostname specifies the hostname of the service which we are connecting to. This allows a single server to host multiple SSL-based services with distinct certificates, quite similarly to HTTP virtual hosts. Specifying server_hostname will raise a ValueError if server_side is true.

The parameter do_handshake_on_connect specifies whether to do the SSL handshake automatically after doing a socket.connect(), or whether the application program will call it explicitly, by invoking the SSLSocket.do_handshake() method. Calling SSLSocket.do_handshake() explicitly gives the program control over the blocking behavior of the socket I/O involved in the handshake.

The parameter suppress_ragged_eofs specifies how the SSLSocket.recv() method should signal unexpected EOF from the other end of the connection. If specified as True (the default), it returns a normal EOF (an empty bytes object) in response to unexpected EOF errors raised from the underlying socket; if False, it will raise the exceptions back to the caller.

session, see session.

Changed in version 3.5: Always allow a server_hostname to be passed, even if OpenSSL does not have SNI.

Changed in version 3.6: session argument was added.

Changed in version 3.7: The method returns an instance of SSLContext.sslsocket_class instead of hard-coded SSLSocket.

+
+SSLContext.sslsocket_class
+

The return type of SSLContext.wrap_socket(), defaults to SSLSocket. The attribute can be overridden on instance of class in order to return a custom subclass of SSLSocket.

New in version 3.7.

+
+SSLContext.wrap_bio(incoming, outgoing, server_side=False, server_hostname=None, session=None)
+

Wrap the BIO objects incoming and outgoing and return an instance of SSLContext.sslobject_class (default SSLObject). The SSL routines will read input data from the incoming BIO and write data to the outgoing BIO.

The server_side, server_hostname and session parameters have the same meaning as in SSLContext.wrap_socket().

Changed in version 3.6: session argument was added.

Changed in version 3.7: The method returns an instance of SSLContext.sslobject_class instead of hard-coded SSLObject.

+
+SSLContext.sslobject_class
+

The return type of SSLContext.wrap_bio(), defaults to SSLObject. The attribute can be overridden on instance of class in order to return a custom subclass of SSLObject.

New in version 3.7.

+
+SSLContext.session_stats()
+

Get statistics about the SSL sessions created or managed by this context. A dictionary is returned which maps the names of each piece of information to their numeric values. For example, here is the total number of hits and misses in the session cache since the context was created:

>>> stats = context.session_stats()
+>>> stats['hits'], stats['misses']
+(0, 0)
+
+
+SSLContext.check_hostname
+

Whether to match the peer cert’s hostname in SSLSocket.do_handshake(). The context’s verify_mode must be set to CERT_OPTIONAL or CERT_REQUIRED, and you must pass server_hostname to wrap_socket() in order to match the hostname. Enabling hostname checking automatically sets verify_mode from CERT_NONE to CERT_REQUIRED. It cannot be set back to CERT_NONE as long as hostname checking is enabled. The PROTOCOL_TLS_CLIENT protocol enables hostname checking by default. With other protocols, hostname checking must be enabled explicitly.

Example:

import socket, ssl
+
+context = ssl.SSLContext(ssl.PROTOCOL_TLSv1_2)
+context.verify_mode = ssl.CERT_REQUIRED
+context.check_hostname = True
+context.load_default_certs()
+
+s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ssl_sock = context.wrap_socket(s, server_hostname='www.verisign.com')
+ssl_sock.connect(('www.verisign.com', 443))
+

New in version 3.4.

Changed in version 3.7: verify_mode is now automatically changed to CERT_REQUIRED when hostname checking is enabled and verify_mode is CERT_NONE. Previously the same operation would have failed with a ValueError.

+
+SSLContext.keylog_filename
+

Write TLS keys to a keylog file, whenever key material is generated or received. The keylog file is designed for debugging purposes only. The file format is specified by NSS and used by many traffic analyzers such as Wireshark. The log file is opened in append-only mode. Writes are synchronized between threads, but not between processes.

New in version 3.8.

+
+SSLContext.maximum_version
+

A TLSVersion enum member representing the highest supported TLS version. The value defaults to TLSVersion.MAXIMUM_SUPPORTED. The attribute is read-only for protocols other than PROTOCOL_TLS, PROTOCOL_TLS_CLIENT, and PROTOCOL_TLS_SERVER.

The attributes maximum_version, minimum_version and SSLContext.options all affect the supported SSL and TLS versions of the context. The implementation does not prevent invalid combination. For example a context with OP_NO_TLSv1_2 in options and maximum_version set to TLSVersion.TLSv1_2 will not be able to establish a TLS 1.2 connection.

New in version 3.7.

+
+SSLContext.minimum_version
+

Like SSLContext.maximum_version except it is the lowest supported version or TLSVersion.MINIMUM_SUPPORTED.

New in version 3.7.

+
+SSLContext.num_tickets
+

Control the number of TLS 1.3 session tickets of a PROTOCOL_TLS_SERVER context. The setting has no impact on TLS 1.0 to 1.2 connections.

New in version 3.8.

+
+SSLContext.options
+

An integer representing the set of SSL options enabled on this context. The default value is OP_ALL, but you can specify other options such as OP_NO_SSLv2 by ORing them together.

Changed in version 3.6: SSLContext.options returns Options flags:

>>> ssl.create_default_context().options  
+<Options.OP_ALL|OP_NO_SSLv3|OP_NO_SSLv2|OP_NO_COMPRESSION: 2197947391>
+

Deprecated since version 3.7: All OP_NO_SSL* and OP_NO_TLS* options have been deprecated since Python 3.7. Use SSLContext.minimum_version and SSLContext.maximum_version instead.

+
+SSLContext.post_handshake_auth
+

Enable TLS 1.3 post-handshake client authentication. Post-handshake auth is disabled by default and a server can only request a TLS client certificate during the initial handshake. When enabled, a server may request a TLS client certificate at any time after the handshake.

When enabled on client-side sockets, the client signals the server that it supports post-handshake authentication.

When enabled on server-side sockets, SSLContext.verify_mode must be set to CERT_OPTIONAL or CERT_REQUIRED, too. The actual client cert exchange is delayed until SSLSocket.verify_client_post_handshake() is called and some I/O is performed.

New in version 3.8.

+
+SSLContext.protocol
+

The protocol version chosen when constructing the context. This attribute is read-only.

+
+SSLContext.hostname_checks_common_name
+

Whether check_hostname falls back to verify the cert’s subject common name in the absence of a subject alternative name extension (default: true).

New in version 3.7.

Changed in version 3.10: The flag had no effect with OpenSSL before version 1.1.1k. Python 3.8.9, 3.9.3, and 3.10 include workarounds for previous versions.

+
+SSLContext.security_level
+

An integer representing the security level for the context. This attribute is read-only.

New in version 3.10.

+
+SSLContext.verify_flags
+

The flags for certificate verification operations. You can set flags like VERIFY_CRL_CHECK_LEAF by ORing them together. By default OpenSSL does neither require nor verify certificate revocation lists (CRLs).

New in version 3.4.

Changed in version 3.6: SSLContext.verify_flags returns VerifyFlags flags:

>>> ssl.create_default_context().verify_flags  
+<VerifyFlags.VERIFY_X509_TRUSTED_FIRST: 32768>
+
+
+SSLContext.verify_mode
+

Whether to try to verify other peers’ certificates and how to behave if verification fails. This attribute must be one of CERT_NONE, CERT_OPTIONAL or CERT_REQUIRED.

Changed in version 3.6: SSLContext.verify_mode returns VerifyMode enum:

>>> ssl.create_default_context().verify_mode  
+<VerifyMode.CERT_REQUIRED: 2>
+
+

Certificates

Certificates in general are part of a public-key / private-key system. In this system, each principal, (which may be a machine, or a person, or an organization) is assigned a unique two-part encryption key. One part of the key is public, and is called the public key; the other part is kept secret, and is called the private key. The two parts are related, in that if you encrypt a message with one of the parts, you can decrypt it with the other part, and only with the other part.

A certificate contains information about two principals. It contains the name of a subject, and the subject’s public key. It also contains a statement by a second principal, the issuer, that the subject is who they claim to be, and that this is indeed the subject’s public key. The issuer’s statement is signed with the issuer’s private key, which only the issuer knows. However, anyone can verify the issuer’s statement by finding the issuer’s public key, decrypting the statement with it, and comparing it to the other information in the certificate. The certificate also contains information about the time period over which it is valid. This is expressed as two fields, called “notBefore” and “notAfter”.

In the Python use of certificates, a client or server can use a certificate to prove who they are. The other side of a network connection can also be required to produce a certificate, and that certificate can be validated to the satisfaction of the client or server that requires such validation. The connection attempt can be set to raise an exception if the validation fails. Validation is done automatically, by the underlying OpenSSL framework; the application need not concern itself with its mechanics. But the application does usually need to provide sets of certificates to allow this process to take place.

Python uses files to contain certificates. They should be formatted as “PEM” (see RFC 1422), which is a base-64 encoded form wrapped with a header line and a footer line:

-----BEGIN CERTIFICATE-----
+... (certificate in base64 PEM encoding) ...
+-----END CERTIFICATE-----
+

Certificate chains

The Python files which contain certificates can contain a sequence of certificates, sometimes called a certificate chain. This chain should start with the specific certificate for the principal who “is” the client or server, and then the certificate for the issuer of that certificate, and then the certificate for the issuer of that certificate, and so on up the chain till you get to a certificate which is self-signed, that is, a certificate which has the same subject and issuer, sometimes called a root certificate. The certificates should just be concatenated together in the certificate file. For example, suppose we had a three certificate chain, from our server certificate to the certificate of the certification authority that signed our server certificate, to the root certificate of the agency which issued the certification authority’s certificate:

-----BEGIN CERTIFICATE-----
+... (certificate for your server)...
+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----
+... (the certificate for the CA)...
+-----END CERTIFICATE-----
+-----BEGIN CERTIFICATE-----
+... (the root certificate for the CA's issuer)...
+-----END CERTIFICATE-----
+

CA certificates

If you are going to require validation of the other side of the connection’s certificate, you need to provide a “CA certs” file, filled with the certificate chains for each issuer you are willing to trust. Again, this file just contains these chains concatenated together. For validation, Python will use the first chain it finds in the file which matches. The platform’s certificates file can be used by calling SSLContext.load_default_certs(), this is done automatically with create_default_context().

Combined key and certificate

Often the private key is stored in the same file as the certificate; in this case, only the certfile parameter to SSLContext.load_cert_chain() needs to be passed. If the private key is stored with the certificate, it should come before the first certificate in the certificate chain:

-----BEGIN RSA PRIVATE KEY-----
+... (private key in base64 encoding) ...
+-----END RSA PRIVATE KEY-----
+-----BEGIN CERTIFICATE-----
+... (certificate in base64 PEM encoding) ...
+-----END CERTIFICATE-----
+

Self-signed certificates

If you are going to create a server that provides SSL-encrypted connection services, you will need to acquire a certificate for that service. There are many ways of acquiring appropriate certificates, such as buying one from a certification authority. Another common practice is to generate a self-signed certificate. The simplest way to do this is with the OpenSSL package, using something like the following:

% openssl req -new -x509 -days 365 -nodes -out cert.pem -keyout cert.pem
+Generating a 1024 bit RSA private key
+.......++++++
+.............................++++++
+writing new private key to 'cert.pem'
+-----
+You are about to be asked to enter information that will be incorporated
+into your certificate request.
+What you are about to enter is what is called a Distinguished Name or a DN.
+There are quite a few fields but you can leave some blank
+For some fields there will be a default value,
+If you enter '.', the field will be left blank.
+-----
+Country Name (2 letter code) [AU]:US
+State or Province Name (full name) [Some-State]:MyState
+Locality Name (eg, city) []:Some City
+Organization Name (eg, company) [Internet Widgits Pty Ltd]:My Organization, Inc.
+Organizational Unit Name (eg, section) []:My Group
+Common Name (eg, YOUR name) []:myserver.mygroup.myorganization.com
+Email Address []:ops@myserver.mygroup.myorganization.com
+%
+

The disadvantage of a self-signed certificate is that it is its own root certificate, and no one else will have it in their cache of known (and trusted) root certificates.

Examples

Testing for SSL support

To test for the presence of SSL support in a Python installation, user code should use the following idiom:

try:
+    import ssl
+except ImportError:
+    pass
+else:
+    ...  # do something that requires SSL support
+

Client-side operation

This example creates a SSL context with the recommended security settings for client sockets, including automatic certificate verification:

>>> context = ssl.create_default_context()
+

If you prefer to tune security settings yourself, you might create a context from scratch (but beware that you might not get the settings right):

>>> context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
+>>> context.load_verify_locations("/etc/ssl/certs/ca-bundle.crt")
+

(this snippet assumes your operating system places a bundle of all CA certificates in /etc/ssl/certs/ca-bundle.crt; if not, you’ll get an error and have to adjust the location)

The PROTOCOL_TLS_CLIENT protocol configures the context for cert validation and hostname verification. verify_mode is set to CERT_REQUIRED and check_hostname is set to True. All other protocols create SSL contexts with insecure defaults.

When you use the context to connect to a server, CERT_REQUIRED and check_hostname validate the server certificate: it ensures that the server certificate was signed with one of the CA certificates, checks the signature for correctness, and verifies other properties like validity and identity of the hostname:

>>> conn = context.wrap_socket(socket.socket(socket.AF_INET),
+...                            server_hostname="www.python.org")
+>>> conn.connect(("www.python.org", 443))
+

You may then fetch the certificate:

>>> cert = conn.getpeercert()
+

Visual inspection shows that the certificate does identify the desired service (that is, the HTTPS host www.python.org):

>>> pprint.pprint(cert)
+{'OCSP': ('http://ocsp.digicert.com',),
+ 'caIssuers': ('http://cacerts.digicert.com/DigiCertSHA2ExtendedValidationServerCA.crt',),
+ 'crlDistributionPoints': ('http://crl3.digicert.com/sha2-ev-server-g1.crl',
+                           'http://crl4.digicert.com/sha2-ev-server-g1.crl'),
+ 'issuer': ((('countryName', 'US'),),
+            (('organizationName', 'DigiCert Inc'),),
+            (('organizationalUnitName', 'www.digicert.com'),),
+            (('commonName', 'DigiCert SHA2 Extended Validation Server CA'),)),
+ 'notAfter': 'Sep  9 12:00:00 2016 GMT',
+ 'notBefore': 'Sep  5 00:00:00 2014 GMT',
+ 'serialNumber': '01BB6F00122B177F36CAB49CEA8B6B26',
+ 'subject': ((('businessCategory', 'Private Organization'),),
+             (('1.3.6.1.4.1.311.60.2.1.3', 'US'),),
+             (('1.3.6.1.4.1.311.60.2.1.2', 'Delaware'),),
+             (('serialNumber', '3359300'),),
+             (('streetAddress', '16 Allen Rd'),),
+             (('postalCode', '03894-4801'),),
+             (('countryName', 'US'),),
+             (('stateOrProvinceName', 'NH'),),
+             (('localityName', 'Wolfeboro'),),
+             (('organizationName', 'Python Software Foundation'),),
+             (('commonName', 'www.python.org'),)),
+ 'subjectAltName': (('DNS', 'www.python.org'),
+                    ('DNS', 'python.org'),
+                    ('DNS', 'pypi.org'),
+                    ('DNS', 'docs.python.org'),
+                    ('DNS', 'testpypi.org'),
+                    ('DNS', 'bugs.python.org'),
+                    ('DNS', 'wiki.python.org'),
+                    ('DNS', 'hg.python.org'),
+                    ('DNS', 'mail.python.org'),
+                    ('DNS', 'packaging.python.org'),
+                    ('DNS', 'pythonhosted.org'),
+                    ('DNS', 'www.pythonhosted.org'),
+                    ('DNS', 'test.pythonhosted.org'),
+                    ('DNS', 'us.pycon.org'),
+                    ('DNS', 'id.python.org')),
+ 'version': 3}
+

Now the SSL channel is established and the certificate verified, you can proceed to talk with the server:

>>> conn.sendall(b"HEAD / HTTP/1.0\r\nHost: linuxfr.org\r\n\r\n")
+>>> pprint.pprint(conn.recv(1024).split(b"\r\n"))
+[b'HTTP/1.1 200 OK',
+ b'Date: Sat, 18 Oct 2014 18:27:20 GMT',
+ b'Server: nginx',
+ b'Content-Type: text/html; charset=utf-8',
+ b'X-Frame-Options: SAMEORIGIN',
+ b'Content-Length: 45679',
+ b'Accept-Ranges: bytes',
+ b'Via: 1.1 varnish',
+ b'Age: 2188',
+ b'X-Served-By: cache-lcy1134-LCY',
+ b'X-Cache: HIT',
+ b'X-Cache-Hits: 11',
+ b'Vary: Cookie',
+ b'Strict-Transport-Security: max-age=63072000; includeSubDomains',
+ b'Connection: close',
+ b'',
+ b'']
+

See the discussion of Security considerations below.

Server-side operation

For server operation, typically you’ll need to have a server certificate, and private key, each in a file. You’ll first create a context holding the key and the certificate, so that clients can check your authenticity. Then you’ll open a socket, bind it to a port, call listen() on it, and start waiting for clients to connect:

import socket, ssl
+
+context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)
+context.load_cert_chain(certfile="mycertfile", keyfile="mykeyfile")
+
+bindsocket = socket.socket()
+bindsocket.bind(('myaddr.example.com', 10023))
+bindsocket.listen(5)
+

When a client connects, you’ll call accept() on the socket to get the new socket from the other end, and use the context’s SSLContext.wrap_socket() method to create a server-side SSL socket for the connection:

while True:
+    newsocket, fromaddr = bindsocket.accept()
+    connstream = context.wrap_socket(newsocket, server_side=True)
+    try:
+        deal_with_client(connstream)
+    finally:
+        connstream.shutdown(socket.SHUT_RDWR)
+        connstream.close()
+

Then you’ll read data from the connstream and do something with it till you are finished with the client (or the client is finished with you):

def deal_with_client(connstream):
+    data = connstream.recv(1024)
+    # empty data means the client is finished with us
+    while data:
+        if not do_something(connstream, data):
+            # we'll assume do_something returns False
+            # when we're finished with client
+            break
+        data = connstream.recv(1024)
+    # finished with client
+

And go back to listening for new client connections (of course, a real server would probably handle each client connection in a separate thread, or put the sockets in non-blocking mode and use an event loop).

Notes on non-blocking sockets

SSL sockets behave slightly different than regular sockets in non-blocking mode. When working with non-blocking sockets, there are thus several things you need to be aware of:

  • +

    Most SSLSocket methods will raise either SSLWantWriteError or SSLWantReadError instead of BlockingIOError if an I/O operation would block. SSLWantReadError will be raised if a read operation on the underlying socket is necessary, and SSLWantWriteError for a write operation on the underlying socket. Note that attempts to write to an SSL socket may require reading from the underlying socket first, and attempts to read from the SSL socket may require a prior write to the underlying socket.

    Changed in version 3.5: In earlier Python versions, the SSLSocket.send() method returned zero instead of raising SSLWantWriteError or SSLWantReadError.

  • Calling select() tells you that the OS-level socket can be read from (or written to), but it does not imply that there is sufficient data at the upper SSL layer. For example, only part of an SSL frame might have arrived. Therefore, you must be ready to handle SSLSocket.recv() and SSLSocket.send() failures, and retry after another call to select().
  • +

    Conversely, since the SSL layer has its own framing, a SSL socket may still have data available for reading without select() being aware of it. Therefore, you should first call SSLSocket.recv() to drain any potentially available data, and then only block on a select() call if still necessary.

    (of course, similar provisions apply when using other primitives such as poll(), or those in the selectors module)

  • +

    The SSL handshake itself will be non-blocking: the SSLSocket.do_handshake() method has to be retried until it returns successfully. Here is a synopsis using select() to wait for the socket’s readiness:

    while True:
+    try:
+        sock.do_handshake()
+        break
+    except ssl.SSLWantReadError:
+        select.select([sock], [], [])
+    except ssl.SSLWantWriteError:
+        select.select([], [sock], [])
+

See also

The asyncio module supports non-blocking SSL sockets and provides a higher level API. It polls for events using the selectors module and handles SSLWantWriteError, SSLWantReadError and BlockingIOError exceptions. It runs the SSL handshake asynchronously as well.

Memory BIO Support

New in version 3.5.

Ever since the SSL module was introduced in Python 2.6, the SSLSocket class has provided two related but distinct areas of functionality:

  • SSL protocol handling
  • Network IO

The network IO API is identical to that provided by socket.socket, from which SSLSocket also inherits. This allows an SSL socket to be used as a drop-in replacement for a regular socket, making it very easy to add SSL support to an existing application.

Combining SSL protocol handling and network IO usually works well, but there are some cases where it doesn’t. An example is async IO frameworks that want to use a different IO multiplexing model than the “select/poll on a file descriptor” (readiness based) model that is assumed by socket.socket and by the internal OpenSSL socket IO routines. This is mostly relevant for platforms like Windows where this model is not efficient. For this purpose, a reduced scope variant of SSLSocket called SSLObject is provided.

+class ssl.SSLObject
+

A reduced-scope variant of SSLSocket representing an SSL protocol instance that does not contain any network IO methods. This class is typically used by framework authors that want to implement asynchronous IO for SSL through memory buffers.

This class implements an interface on top of a low-level SSL object as implemented by OpenSSL. This object captures the state of an SSL connection but does not provide any network IO itself. IO needs to be performed through separate “BIO” objects which are OpenSSL’s IO abstraction layer.

This class has no public constructor. An SSLObject instance must be created using the wrap_bio() method. This method will create the SSLObject instance and bind it to a pair of BIOs. The incoming BIO is used to pass data from Python to the SSL protocol instance, while the outgoing BIO is used to pass data the other way around.

The following methods are available:

When compared to SSLSocket, this object lacks the following features:

  • Any form of network IO; recv() and send() read and write only to the underlying MemoryBIO buffers.
  • There is no do_handshake_on_connect machinery. You must always manually call do_handshake() to start the handshake.
  • There is no handling of suppress_ragged_eofs. All end-of-file conditions that are in violation of the protocol are reported via the SSLEOFError exception.
  • The method unwrap() call does not return anything, unlike for an SSL socket where it returns the underlying socket.
  • The server_name_callback callback passed to SSLContext.set_servername_callback() will get an SSLObject instance instead of a SSLSocket instance as its first parameter.

Some notes related to the use of SSLObject:

Changed in version 3.7: SSLObject instances must to created with wrap_bio(). In earlier versions, it was possible to create instances directly. This was never documented or officially supported.

+

An SSLObject communicates with the outside world using memory buffers. The class MemoryBIO provides a memory buffer that can be used for this purpose. It wraps an OpenSSL memory BIO (Basic IO) object:

+class ssl.MemoryBIO
+

A memory buffer that can be used to pass data between Python and an SSL protocol instance.

+pending
+

Return the number of bytes currently in the memory buffer.

+
+eof
+

A boolean indicating whether the memory BIO is current at the end-of-file position.

+
+read(n=- 1)
+

Read up to n bytes from the memory buffer. If n is not specified or negative, all bytes are returned.

+
+write(buf)
+

Write the bytes from buf to the memory BIO. The buf argument must be an object supporting the buffer protocol.

The return value is the number of bytes written, which is always equal to the length of buf.

+
+write_eof()
+

Write an EOF marker to the memory BIO. After this method has been called, it is illegal to call write(). The attribute eof will become true after all data currently in the buffer has been read.

+
+

SSL session

New in version 3.6.

+class ssl.SSLSession
+

Session object used by session.

+id
+
+time
+
+timeout
+
+ticket_lifetime_hint
+
+has_ticket
+
+

Security considerations

Best defaults

For client use, if you don’t have any special requirements for your security policy, it is highly recommended that you use the create_default_context() function to create your SSL context. It will load the system’s trusted CA certificates, enable certificate validation and hostname checking, and try to choose reasonably secure protocol and cipher settings.

For example, here is how you would use the smtplib.SMTP class to create a trusted, secure connection to a SMTP server:

>>> import ssl, smtplib
+>>> smtp = smtplib.SMTP("mail.python.org", port=587)
+>>> context = ssl.create_default_context()
+>>> smtp.starttls(context=context)
+(220, b'2.0.0 Ready to start TLS')
+

If a client certificate is needed for the connection, it can be added with SSLContext.load_cert_chain().

By contrast, if you create the SSL context by calling the SSLContext constructor yourself, it will not have certificate validation nor hostname checking enabled by default. If you do so, please read the paragraphs below to achieve a good security level.

Manual settings

Verifying certificates

When calling the SSLContext constructor directly, CERT_NONE is the default. Since it does not authenticate the other peer, it can be insecure, especially in client mode where most of time you would like to ensure the authenticity of the server you’re talking to. Therefore, when in client mode, it is highly recommended to use CERT_REQUIRED. However, it is in itself not sufficient; you also have to check that the server certificate, which can be obtained by calling SSLSocket.getpeercert(), matches the desired service. For many protocols and applications, the service can be identified by the hostname. This common check is automatically performed when SSLContext.check_hostname is enabled.

Changed in version 3.7: Hostname matchings is now performed by OpenSSL. Python no longer uses match_hostname().

In server mode, if you want to authenticate your clients using the SSL layer (rather than using a higher-level authentication mechanism), you’ll also have to specify CERT_REQUIRED and similarly check the client certificate.

Protocol versions

SSL versions 2 and 3 are considered insecure and are therefore dangerous to use. If you want maximum compatibility between clients and servers, it is recommended to use PROTOCOL_TLS_CLIENT or PROTOCOL_TLS_SERVER as the protocol version. SSLv2 and SSLv3 are disabled by default.

>>> client_context = ssl.SSLContext(ssl.PROTOCOL_TLS_CLIENT)
+>>> client_context.minimum_version = ssl.TLSVersion.TLSv1_3
+>>> client_context.maximum_version = ssl.TLSVersion.TLSv1_3
+

The SSL context created above will only allow TLSv1.3 and later (if supported by your system) connections to a server. PROTOCOL_TLS_CLIENT implies certificate validation and hostname checks by default. You have to load certificates into the context.

Cipher selection

If you have advanced security requirements, fine-tuning of the ciphers enabled when negotiating a SSL session is possible through the SSLContext.set_ciphers() method. Starting from Python 3.2.3, the ssl module disables certain weak ciphers by default, but you may want to further restrict the cipher choice. Be sure to read OpenSSL’s documentation about the cipher list format. If you want to check which ciphers are enabled by a given cipher list, use SSLContext.get_ciphers() or the openssl ciphers command on your system.

Multi-processing

If using this module as part of a multi-processed application (using, for example the multiprocessing or concurrent.futures modules), be aware that OpenSSL’s internal random number generator does not properly handle forked processes. Applications must change the PRNG state of the parent process if they use any SSL feature with os.fork(). Any successful call of RAND_add() or RAND_bytes() is sufficient.

TLS 1.3

New in version 3.7.

The TLS 1.3 protocol behaves slightly differently than previous version of TLS/SSL. Some new TLS 1.3 features are not yet available.

  • TLS 1.3 uses a disjunct set of cipher suites. All AES-GCM and ChaCha20 cipher suites are enabled by default. The method SSLContext.set_ciphers() cannot enable or disable any TLS 1.3 ciphers yet, but SSLContext.get_ciphers() returns them.
  • Session tickets are no longer sent as part of the initial handshake and are handled differently. SSLSocket.session and SSLSession are not compatible with TLS 1.3.
  • Client-side certificates are also no longer verified during the initial handshake. A server can request a certificate at any time. Clients process certificate requests while they send or receive application data from the server.
  • TLS 1.3 features like early data, deferred TLS client cert request, signature algorithm configuration, and rekeying are not supported yet.

See also

+Class socket.socket +
+
+

Documentation of underlying socket class

SSL/TLS Strong Encryption: An Introduction
+
+

Intro from the Apache HTTP Server documentation

+RFC 1422: Privacy Enhancement for Internet Electronic Mail: Part II: Certificate-Based Key Management +
+
+

Steve Kent

+RFC 4086: Randomness Requirements for Security +
+
+

Donald E., Jeffrey I. Schiller

+RFC 5280: Internet X.509 Public Key Infrastructure Certificate and Certificate Revocation List (CRL) Profile +
+
+

D. Cooper

+RFC 5246: The Transport Layer Security (TLS) Protocol Version 1.2 +
+
+

T. Dierks et. al.

+RFC 6066: Transport Layer Security (TLS) Extensions +
+
+

D. Eastlake

IANA TLS: Transport Layer Security (TLS) Parameters
+
+

IANA

+RFC 7525: Recommendations for Secure Use of Transport Layer Security (TLS) and Datagram Transport Layer Security (DTLS) +
+
+

IETF

Mozilla’s Server Side TLS recommendations
+
+

Mozilla

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/ssl.html +

+
diff --git a/devdocs/python~3.12/library%2Fstat.html b/devdocs/python~3.12/library%2Fstat.html new file mode 100644 index 00000000..648334a1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fstat.html @@ -0,0 +1,247 @@ +

stat — Interpreting stat() results

Source code: Lib/stat.py

The stat module defines constants and functions for interpreting the results of os.stat(), os.fstat() and os.lstat() (if they exist). For complete details about the stat(), fstat() and lstat() calls, consult the documentation for your system.

Changed in version 3.4: The stat module is backed by a C implementation.

The stat module defines the following functions to test for specific file types:

+stat.S_ISDIR(mode)
+

Return non-zero if the mode is from a directory.

+
+stat.S_ISCHR(mode)
+

Return non-zero if the mode is from a character special device file.

+
+stat.S_ISBLK(mode)
+

Return non-zero if the mode is from a block special device file.

+
+stat.S_ISREG(mode)
+

Return non-zero if the mode is from a regular file.

+
+stat.S_ISFIFO(mode)
+

Return non-zero if the mode is from a FIFO (named pipe).

+
+stat.S_ISLNK(mode)
+

Return non-zero if the mode is from a symbolic link.

+
+stat.S_ISSOCK(mode)
+

Return non-zero if the mode is from a socket.

+
+stat.S_ISDOOR(mode)
+

Return non-zero if the mode is from a door.

New in version 3.4.

+
+stat.S_ISPORT(mode)
+

Return non-zero if the mode is from an event port.

New in version 3.4.

+
+stat.S_ISWHT(mode)
+

Return non-zero if the mode is from a whiteout.

New in version 3.4.

+

Two additional functions are defined for more general manipulation of the file’s mode:

+stat.S_IMODE(mode)
+

Return the portion of the file’s mode that can be set by os.chmod()—that is, the file’s permission bits, plus the sticky bit, set-group-id, and set-user-id bits (on systems that support them).

+
+stat.S_IFMT(mode)
+

Return the portion of the file’s mode that describes the file type (used by the S_IS*() functions above).

+

Normally, you would use the os.path.is*() functions for testing the type of a file; the functions here are useful when you are doing multiple tests of the same file and wish to avoid the overhead of the stat() system call for each test. These are also useful when checking for information about a file that isn’t handled by os.path, like the tests for block and character devices.

Example:

import os, sys
+from stat import *
+
+def walktree(top, callback):
+    '''recursively descend the directory tree rooted at top,
+       calling the callback function for each regular file'''
+
+    for f in os.listdir(top):
+        pathname = os.path.join(top, f)
+        mode = os.lstat(pathname).st_mode
+        if S_ISDIR(mode):
+            # It's a directory, recurse into it
+            walktree(pathname, callback)
+        elif S_ISREG(mode):
+            # It's a file, call the callback function
+            callback(pathname)
+        else:
+            # Unknown file type, print a message
+            print('Skipping %s' % pathname)
+
+def visitfile(file):
+    print('visiting', file)
+
+if __name__ == '__main__':
+    walktree(sys.argv[1], visitfile)
+

An additional utility function is provided to convert a file’s mode in a human readable string:

+stat.filemode(mode)
+

Convert a file’s mode to a string of the form ‘-rwxrwxrwx’.

New in version 3.3.

Changed in version 3.4: The function supports S_IFDOOR, S_IFPORT and S_IFWHT.

+

All the variables below are simply symbolic indexes into the 10-tuple returned by os.stat(), os.fstat() or os.lstat().

+stat.ST_MODE
+

Inode protection mode.

+
+stat.ST_INO
+

Inode number.

+
+stat.ST_DEV
+

Device inode resides on.

+
+

Number of links to the inode.

+
+stat.ST_UID
+

User id of the owner.

+
+stat.ST_GID
+

Group id of the owner.

+
+stat.ST_SIZE
+

Size in bytes of a plain file; amount of data waiting on some special files.

+
+stat.ST_ATIME
+

Time of last access.

+
+stat.ST_MTIME
+

Time of last modification.

+
+stat.ST_CTIME
+

The “ctime” as reported by the operating system. On some systems (like Unix) is the time of the last metadata change, and, on others (like Windows), is the creation time (see platform documentation for details).

+

The interpretation of “file size” changes according to the file type. For plain files this is the size of the file in bytes. For FIFOs and sockets under most flavors of Unix (including Linux in particular), the “size” is the number of bytes waiting to be read at the time of the call to os.stat(), os.fstat(), or os.lstat(); this can sometimes be useful, especially for polling one of these special files after a non-blocking open. The meaning of the size field for other character and block devices varies more, depending on the implementation of the underlying system call.

The variables below define the flags used in the ST_MODE field.

Use of the functions above is more portable than use of the first set of flags:

+stat.S_IFSOCK
+

Socket.

+
+stat.S_IFLNK
+

Symbolic link.

+
+stat.S_IFREG
+

Regular file.

+
+stat.S_IFBLK
+

Block device.

+
+stat.S_IFDIR
+

Directory.

+
+stat.S_IFCHR
+

Character device.

+
+stat.S_IFIFO
+

FIFO.

+
+stat.S_IFDOOR
+

Door.

New in version 3.4.

+
+stat.S_IFPORT
+

Event port.

New in version 3.4.

+
+stat.S_IFWHT
+

Whiteout.

New in version 3.4.

+

Note

S_IFDOOR, S_IFPORT or S_IFWHT are defined as 0 when the platform does not have support for the file types.

The following flags can also be used in the mode argument of os.chmod():

+stat.S_ISUID
+

Set UID bit.

+
+stat.S_ISGID
+

Set-group-ID bit. This bit has several special uses. For a directory it indicates that BSD semantics is to be used for that directory: files created there inherit their group ID from the directory, not from the effective group ID of the creating process, and directories created there will also get the S_ISGID bit set. For a file that does not have the group execution bit (S_IXGRP) set, the set-group-ID bit indicates mandatory file/record locking (see also S_ENFMT).

+
+stat.S_ISVTX
+

Sticky bit. When this bit is set on a directory it means that a file in that directory can be renamed or deleted only by the owner of the file, by the owner of the directory, or by a privileged process.

+
+stat.S_IRWXU
+

Mask for file owner permissions.

+
+stat.S_IRUSR
+

Owner has read permission.

+
+stat.S_IWUSR
+

Owner has write permission.

+
+stat.S_IXUSR
+

Owner has execute permission.

+
+stat.S_IRWXG
+

Mask for group permissions.

+
+stat.S_IRGRP
+

Group has read permission.

+
+stat.S_IWGRP
+

Group has write permission.

+
+stat.S_IXGRP
+

Group has execute permission.

+
+stat.S_IRWXO
+

Mask for permissions for others (not in group).

+
+stat.S_IROTH
+

Others have read permission.

+
+stat.S_IWOTH
+

Others have write permission.

+
+stat.S_IXOTH
+

Others have execute permission.

+
+stat.S_ENFMT
+

System V file locking enforcement. This flag is shared with S_ISGID: file/record locking is enforced on files that do not have the group execution bit (S_IXGRP) set.

+
+stat.S_IREAD
+

Unix V7 synonym for S_IRUSR.

+
+stat.S_IWRITE
+

Unix V7 synonym for S_IWUSR.

+
+stat.S_IEXEC
+

Unix V7 synonym for S_IXUSR.

+

The following flags can be used in the flags argument of os.chflags():

+stat.UF_NODUMP
+

Do not dump the file.

+
+stat.UF_IMMUTABLE
+

The file may not be changed.

+
+stat.UF_APPEND
+

The file may only be appended to.

+
+stat.UF_OPAQUE
+

The directory is opaque when viewed through a union stack.

+
+

The file may not be renamed or deleted.

+
+stat.UF_COMPRESSED
+

The file is stored compressed (macOS 10.6+).

+
+stat.UF_HIDDEN
+

The file should not be displayed in a GUI (macOS 10.5+).

+
+stat.SF_ARCHIVED
+

The file may be archived.

+
+stat.SF_IMMUTABLE
+

The file may not be changed.

+
+stat.SF_APPEND
+

The file may only be appended to.

+
+

The file may not be renamed or deleted.

+
+stat.SF_SNAPSHOT
+

The file is a snapshot file.

+

See the *BSD or macOS systems man page chflags(2) for more information.

On Windows, the following file attribute constants are available for use when testing bits in the st_file_attributes member returned by os.stat(). See the Windows API documentation for more detail on the meaning of these constants.

+stat.FILE_ATTRIBUTE_ARCHIVE
+stat.FILE_ATTRIBUTE_COMPRESSED
+stat.FILE_ATTRIBUTE_DEVICE
+stat.FILE_ATTRIBUTE_DIRECTORY
+stat.FILE_ATTRIBUTE_ENCRYPTED
+stat.FILE_ATTRIBUTE_HIDDEN
+stat.FILE_ATTRIBUTE_INTEGRITY_STREAM
+stat.FILE_ATTRIBUTE_NORMAL
+stat.FILE_ATTRIBUTE_NOT_CONTENT_INDEXED
+stat.FILE_ATTRIBUTE_NO_SCRUB_DATA
+stat.FILE_ATTRIBUTE_OFFLINE
+stat.FILE_ATTRIBUTE_READONLY
+stat.FILE_ATTRIBUTE_REPARSE_POINT
+stat.FILE_ATTRIBUTE_SPARSE_FILE
+stat.FILE_ATTRIBUTE_SYSTEM
+stat.FILE_ATTRIBUTE_TEMPORARY
+stat.FILE_ATTRIBUTE_VIRTUAL
+

New in version 3.5.

+

On Windows, the following constants are available for comparing against the st_reparse_tag member returned by os.lstat(). These are well-known constants, but are not an exhaustive list.

+stat.IO_REPARSE_TAG_MOUNT_POINT
+

New in version 3.8.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/stat.html +

+
diff --git a/devdocs/python~3.12/library%2Fstatistics.html b/devdocs/python~3.12/library%2Fstatistics.html new file mode 100644 index 00000000..53e9c9a5 --- /dev/null +++ b/devdocs/python~3.12/library%2Fstatistics.html @@ -0,0 +1,360 @@ +

statistics — Mathematical statistics functions

New in version 3.4.

Source code: Lib/statistics.py

This module provides functions for calculating mathematical statistics of numeric (Real-valued) data.

The module is not intended to be a competitor to third-party libraries such as NumPy, SciPy, or proprietary full-featured statistics packages aimed at professional statisticians such as Minitab, SAS and Matlab. It is aimed at the level of graphing and scientific calculators.

Unless explicitly noted, these functions support int, float, Decimal and Fraction. Behaviour with other types (whether in the numeric tower or not) is currently unsupported. Collections with a mix of types are also undefined and implementation-dependent. If your input data consists of mixed types, you may be able to use map() to ensure a consistent result, for example: map(float, input_data).

Some datasets use NaN (not a number) values to represent missing data. Since NaNs have unusual comparison semantics, they cause surprising or undefined behaviors in the statistics functions that sort data or that count occurrences. The functions affected are median(), median_low(), median_high(), median_grouped(), mode(), multimode(), and quantiles(). The NaN values should be stripped before calling these functions:

>>> from statistics import median
+>>> from math import isnan
+>>> from itertools import filterfalse
+
+>>> data = [20.7, float('NaN'),19.2, 18.3, float('NaN'), 14.4]
+>>> sorted(data)  # This has surprising behavior
+[20.7, nan, 14.4, 18.3, 19.2, nan]
+>>> median(data)  # This result is unexpected
+16.35
+
+>>> sum(map(isnan, data))    # Number of missing values
+2
+>>> clean = list(filterfalse(isnan, data))  # Strip NaN values
+>>> clean
+[20.7, 19.2, 18.3, 14.4]
+>>> sorted(clean)  # Sorting now works as expected
+[14.4, 18.3, 19.2, 20.7]
+>>> median(clean)       # This result is now well defined
+18.75
+

Averages and measures of central location

These functions calculate an average or typical value from a population or sample.

+ + + + + + + + + + +

mean()

Arithmetic mean (“average”) of data.

fmean()

Fast, floating point arithmetic mean, with optional weighting.

geometric_mean()

Geometric mean of data.

harmonic_mean()

Harmonic mean of data.

median()

Median (middle value) of data.

median_low()

Low median of data.

median_high()

High median of data.

median_grouped()

Median, or 50th percentile, of grouped data.

mode()

Single mode (most common value) of discrete or nominal data.

multimode()

List of modes (most common values) of discrete or nominal data.

quantiles()

Divide data into intervals with equal probability.

Measures of spread

These functions calculate a measure of how much the population or sample tends to deviate from the typical or average values.

+ + + +

pstdev()

Population standard deviation of data.

pvariance()

Population variance of data.

stdev()

Sample standard deviation of data.

variance()

Sample variance of data.

Statistics for relations between two inputs

These functions calculate statistics regarding relations between two inputs.

+ + +

covariance()

Sample covariance for two variables.

correlation()

Pearson and Spearman’s correlation coefficients.

linear_regression()

Slope and intercept for simple linear regression.

Function details

Note: The functions do not require the data given to them to be sorted. However, for reading convenience, most of the examples show sorted sequences.

+statistics.mean(data)
+

Return the sample arithmetic mean of data which can be a sequence or iterable.

The arithmetic mean is the sum of the data divided by the number of data points. It is commonly called “the average”, although it is only one of many different mathematical averages. It is a measure of the central location of the data.

If data is empty, StatisticsError will be raised.

Some examples of use:

>>> mean([1, 2, 3, 4, 4])
+2.8
+>>> mean([-1.0, 2.5, 3.25, 5.75])
+2.625
+
+>>> from fractions import Fraction as F
+>>> mean([F(3, 7), F(1, 21), F(5, 3), F(1, 3)])
+Fraction(13, 21)
+
+>>> from decimal import Decimal as D
+>>> mean([D("0.5"), D("0.75"), D("0.625"), D("0.375")])
+Decimal('0.5625')
+

Note

The mean is strongly affected by outliers and is not necessarily a typical example of the data points. For a more robust, although less efficient, measure of central tendency, see median().

The sample mean gives an unbiased estimate of the true population mean, so that when taken on average over all the possible samples, mean(sample) converges on the true mean of the entire population. If data represents the entire population rather than a sample, then mean(data) is equivalent to calculating the true population mean μ.

+
+statistics.fmean(data, weights=None)
+

Convert data to floats and compute the arithmetic mean.

This runs faster than the mean() function and it always returns a float. The data may be a sequence or iterable. If the input dataset is empty, raises a StatisticsError.

>>> fmean([3.5, 4.0, 5.25])
+4.25
+

Optional weighting is supported. For example, a professor assigns a grade for a course by weighting quizzes at 20%, homework at 20%, a midterm exam at 30%, and a final exam at 30%:

>>> grades = [85, 92, 83, 91]
+>>> weights = [0.20, 0.20, 0.30, 0.30]
+>>> fmean(grades, weights)
+87.6
+

If weights is supplied, it must be the same length as the data or a ValueError will be raised.

New in version 3.8.

Changed in version 3.11: Added support for weights.

+
+statistics.geometric_mean(data)
+

Convert data to floats and compute the geometric mean.

The geometric mean indicates the central tendency or typical value of the data using the product of the values (as opposed to the arithmetic mean which uses their sum).

Raises a StatisticsError if the input dataset is empty, if it contains a zero, or if it contains a negative value. The data may be a sequence or iterable.

No special efforts are made to achieve exact results. (However, this may change in the future.)

>>> round(geometric_mean([54, 24, 36]), 1)
+36.0
+

New in version 3.8.

+
+statistics.harmonic_mean(data, weights=None)
+

Return the harmonic mean of data, a sequence or iterable of real-valued numbers. If weights is omitted or None, then equal weighting is assumed.

The harmonic mean is the reciprocal of the arithmetic mean() of the reciprocals of the data. For example, the harmonic mean of three values a, b and c will be equivalent to 3/(1/a + 1/b + 1/c). If one of the values is zero, the result will be zero.

The harmonic mean is a type of average, a measure of the central location of the data. It is often appropriate when averaging ratios or rates, for example speeds.

Suppose a car travels 10 km at 40 km/hr, then another 10 km at 60 km/hr. What is the average speed?

>>> harmonic_mean([40, 60])
+48.0
+

Suppose a car travels 40 km/hr for 5 km, and when traffic clears, speeds-up to 60 km/hr for the remaining 30 km of the journey. What is the average speed?

>>> harmonic_mean([40, 60], weights=[5, 30])
+56.0
+

StatisticsError is raised if data is empty, any element is less than zero, or if the weighted sum isn’t positive.

The current algorithm has an early-out when it encounters a zero in the input. This means that the subsequent inputs are not tested for validity. (This behavior may change in the future.)

New in version 3.6.

Changed in version 3.10: Added support for weights.

+
+statistics.median(data)
+

Return the median (middle value) of numeric data, using the common “mean of middle two” method. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

The median is a robust measure of central location and is less affected by the presence of outliers. When the number of data points is odd, the middle data point is returned:

>>> median([1, 3, 5])
+3
+

When the number of data points is even, the median is interpolated by taking the average of the two middle values:

>>> median([1, 3, 5, 7])
+4.0
+

This is suited for when your data is discrete, and you don’t mind that the median may not be an actual data point.

If the data is ordinal (supports order operations) but not numeric (doesn’t support addition), consider using median_low() or median_high() instead.

+
+statistics.median_low(data)
+

Return the low median of numeric data. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

The low median is always a member of the data set. When the number of data points is odd, the middle value is returned. When it is even, the smaller of the two middle values is returned.

>>> median_low([1, 3, 5])
+3
+>>> median_low([1, 3, 5, 7])
+3
+

Use the low median when your data are discrete and you prefer the median to be an actual data point rather than interpolated.

+
+statistics.median_high(data)
+

Return the high median of data. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

The high median is always a member of the data set. When the number of data points is odd, the middle value is returned. When it is even, the larger of the two middle values is returned.

>>> median_high([1, 3, 5])
+3
+>>> median_high([1, 3, 5, 7])
+5
+

Use the high median when your data are discrete and you prefer the median to be an actual data point rather than interpolated.

+
+statistics.median_grouped(data, interval=1)
+

Return the median of grouped continuous data, calculated as the 50th percentile, using interpolation. If data is empty, StatisticsError is raised. data can be a sequence or iterable.

>>> median_grouped([52, 52, 53, 54])
+52.5
+

In the following example, the data are rounded, so that each value represents the midpoint of data classes, e.g. 1 is the midpoint of the class 0.5–1.5, 2 is the midpoint of 1.5–2.5, 3 is the midpoint of 2.5–3.5, etc. With the data given, the middle value falls somewhere in the class 3.5–4.5, and interpolation is used to estimate it:

>>> median_grouped([1, 2, 2, 3, 4, 4, 4, 4, 4, 5])
+3.7
+

Optional argument interval represents the class interval, and defaults to 1. Changing the class interval naturally will change the interpolation:

>>> median_grouped([1, 3, 3, 5, 7], interval=1)
+3.25
+>>> median_grouped([1, 3, 3, 5, 7], interval=2)
+3.5
+

This function does not check whether the data points are at least interval apart.

CPython implementation detail: Under some circumstances, median_grouped() may coerce data points to floats. This behaviour is likely to change in the future.

See also

  • “Statistics for the Behavioral Sciences”, Frederick J Gravetter and Larry B Wallnau (8th Edition).
  • The SSMEDIAN function in the Gnome Gnumeric spreadsheet, including this discussion.
+
+statistics.mode(data)
+

Return the single most common data point from discrete or nominal data. The mode (when it exists) is the most typical value and serves as a measure of central location.

If there are multiple modes with the same frequency, returns the first one encountered in the data. If the smallest or largest of those is desired instead, use min(multimode(data)) or max(multimode(data)). If the input data is empty, StatisticsError is raised.

mode assumes discrete data and returns a single value. This is the standard treatment of the mode as commonly taught in schools:

>>> mode([1, 1, 2, 3, 3, 3, 3, 4])
+3
+

The mode is unique in that it is the only statistic in this package that also applies to nominal (non-numeric) data:

>>> mode(["red", "blue", "blue", "red", "green", "red", "red"])
+'red'
+

Changed in version 3.8: Now handles multimodal datasets by returning the first mode encountered. Formerly, it raised StatisticsError when more than one mode was found.

+
+statistics.multimode(data)
+

Return a list of the most frequently occurring values in the order they were first encountered in the data. Will return more than one result if there are multiple modes or an empty list if the data is empty:

>>> multimode('aabbbbccddddeeffffgg')
+['b', 'd', 'f']
+>>> multimode('')
+[]
+

New in version 3.8.

+
+statistics.pstdev(data, mu=None)
+

Return the population standard deviation (the square root of the population variance). See pvariance() for arguments and other details.

>>> pstdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
+0.986893273527251
+
+
+statistics.pvariance(data, mu=None)
+

Return the population variance of data, a non-empty sequence or iterable of real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean.

If the optional second argument mu is given, it is typically the mean of the data. It can also be used to compute the second moment around a point that is not the mean. If it is missing or None (the default), the arithmetic mean is automatically calculated.

Use this function to calculate the variance from the entire population. To estimate the variance from a sample, the variance() function is usually a better choice.

Raises StatisticsError if data is empty.

Examples:

>>> data = [0.0, 0.25, 0.25, 1.25, 1.5, 1.75, 2.75, 3.25]
+>>> pvariance(data)
+1.25
+

If you have already calculated the mean of your data, you can pass it as the optional second argument mu to avoid recalculation:

>>> mu = mean(data)
+>>> pvariance(data, mu)
+1.25
+

Decimals and Fractions are supported:

>>> from decimal import Decimal as D
+>>> pvariance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
+Decimal('24.815')
+
+>>> from fractions import Fraction as F
+>>> pvariance([F(1, 4), F(5, 4), F(1, 2)])
+Fraction(13, 72)
+

Note

When called with the entire population, this gives the population variance σ². When called on a sample instead, this is the biased sample variance s², also known as variance with N degrees of freedom.

If you somehow know the true population mean μ, you may use this function to calculate the variance of a sample, giving the known population mean as the second argument. Provided the data points are a random sample of the population, the result will be an unbiased estimate of the population variance.

+
+statistics.stdev(data, xbar=None)
+

Return the sample standard deviation (the square root of the sample variance). See variance() for arguments and other details.

>>> stdev([1.5, 2.5, 2.5, 2.75, 3.25, 4.75])
+1.0810874155219827
+
+
+statistics.variance(data, xbar=None)
+

Return the sample variance of data, an iterable of at least two real-valued numbers. Variance, or second moment about the mean, is a measure of the variability (spread or dispersion) of data. A large variance indicates that the data is spread out; a small variance indicates it is clustered closely around the mean.

If the optional second argument xbar is given, it should be the mean of data. If it is missing or None (the default), the mean is automatically calculated.

Use this function when your data is a sample from a population. To calculate the variance from the entire population, see pvariance().

Raises StatisticsError if data has fewer than two values.

Examples:

>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
+>>> variance(data)
+1.3720238095238095
+

If you have already calculated the mean of your data, you can pass it as the optional second argument xbar to avoid recalculation:

>>> m = mean(data)
+>>> variance(data, m)
+1.3720238095238095
+

This function does not attempt to verify that you have passed the actual mean as xbar. Using arbitrary values for xbar can lead to invalid or impossible results.

Decimal and Fraction values are supported:

>>> from decimal import Decimal as D
+>>> variance([D("27.5"), D("30.25"), D("30.25"), D("34.5"), D("41.75")])
+Decimal('31.01875')
+
+>>> from fractions import Fraction as F
+>>> variance([F(1, 6), F(1, 2), F(5, 3)])
+Fraction(67, 108)
+

Note

This is the sample variance s² with Bessel’s correction, also known as variance with N-1 degrees of freedom. Provided that the data points are representative (e.g. independent and identically distributed), the result should be an unbiased estimate of the true population variance.

If you somehow know the actual population mean μ you should pass it to the pvariance() function as the mu parameter to get the variance of a sample.

+
+statistics.quantiles(data, *, n=4, method='exclusive')
+

Divide data into n continuous intervals with equal probability. Returns a list of n - 1 cut points separating the intervals.

Set n to 4 for quartiles (the default). Set n to 10 for deciles. Set n to 100 for percentiles which gives the 99 cuts points that separate data into 100 equal sized groups. Raises StatisticsError if n is not least 1.

The data can be any iterable containing sample data. For meaningful results, the number of data points in data should be larger than n. Raises StatisticsError if there are not at least two data points.

The cut points are linearly interpolated from the two nearest data points. For example, if a cut point falls one-third of the distance between two sample values, 100 and 112, the cut-point will evaluate to 104.

The method for computing quantiles can be varied depending on whether the data includes or excludes the lowest and highest possible values from the population.

The default method is “exclusive” and is used for data sampled from a population that can have more extreme values than found in the samples. The portion of the population falling below the i-th of m sorted data points is computed as i / (m + 1). Given nine sample values, the method sorts them and assigns the following percentiles: 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%.

Setting the method to “inclusive” is used for describing population data or for samples that are known to include the most extreme values from the population. The minimum value in data is treated as the 0th percentile and the maximum value is treated as the 100th percentile. The portion of the population falling below the i-th of m sorted data points is computed as (i - 1) / (m - 1). Given 11 sample values, the method sorts them and assigns the following percentiles: 0%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%.

# Decile cut points for empirically sampled data
+>>> data = [105, 129, 87, 86, 111, 111, 89, 81, 108, 92, 110,
+...         100, 75, 105, 103, 109, 76, 119, 99, 91, 103, 129,
+...         106, 101, 84, 111, 74, 87, 86, 103, 103, 106, 86,
+...         111, 75, 87, 102, 121, 111, 88, 89, 101, 106, 95,
+...         103, 107, 101, 81, 109, 104]
+>>> [round(q, 1) for q in quantiles(data, n=10)]
+[81.0, 86.2, 89.0, 99.4, 102.5, 103.6, 106.0, 109.8, 111.0]
+

New in version 3.8.

+
+statistics.covariance(x, y, /)
+

Return the sample covariance of two inputs x and y. Covariance is a measure of the joint variability of two inputs.

Both inputs must be of the same length (no less than two), otherwise StatisticsError is raised.

Examples:

>>> x = [1, 2, 3, 4, 5, 6, 7, 8, 9]
+>>> y = [1, 2, 3, 1, 2, 3, 1, 2, 3]
+>>> covariance(x, y)
+0.75
+>>> z = [9, 8, 7, 6, 5, 4, 3, 2, 1]
+>>> covariance(x, z)
+-7.5
+>>> covariance(z, x)
+-7.5
+

New in version 3.10.

+
+statistics.correlation(x, y, /, *, method='linear')
+

Return the Pearson’s correlation coefficient for two inputs. Pearson’s correlation coefficient r takes values between -1 and +1. It measures the strength and direction of a linear relationship.

If method is “ranked”, computes Spearman’s rank correlation coefficient for two inputs. The data is replaced by ranks. Ties are averaged so that equal values receive the same rank. The resulting coefficient measures the strength of a monotonic relationship.

Spearman’s correlation coefficient is appropriate for ordinal data or for continuous data that doesn’t meet the linear proportion requirement for Pearson’s correlation coefficient.

Both inputs must be of the same length (no less than two), and need not to be constant, otherwise StatisticsError is raised.

Example with Kepler’s laws of planetary motion:

>>> # Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, and  Neptune
+>>> orbital_period = [88, 225, 365, 687, 4331, 10_756, 30_687, 60_190]    # days
+>>> dist_from_sun = [58, 108, 150, 228, 778, 1_400, 2_900, 4_500] # million km
+
+>>> # Show that a perfect monotonic relationship exists
+>>> correlation(orbital_period, dist_from_sun, method='ranked')
+1.0
+
+>>> # Observe that a linear relationship is imperfect
+>>> round(correlation(orbital_period, dist_from_sun), 4)
+0.9882
+
+>>> # Demonstrate Kepler's third law: There is a linear correlation
+>>> # between the square of the orbital period and the cube of the
+>>> # distance from the sun.
+>>> period_squared = [p * p for p in orbital_period]
+>>> dist_cubed = [d * d * d for d in dist_from_sun]
+>>> round(correlation(period_squared, dist_cubed), 4)
+1.0
+

New in version 3.10.

Changed in version 3.12: Added support for Spearman’s rank correlation coefficient.

+
+statistics.linear_regression(x, y, /, *, proportional=False)
+

Return the slope and intercept of simple linear regression parameters estimated using ordinary least squares. Simple linear regression describes the relationship between an independent variable x and a dependent variable y in terms of this linear function:

y = slope * x + intercept + noise

where slope and intercept are the regression parameters that are estimated, and noise represents the variability of the data that was not explained by the linear regression (it is equal to the difference between predicted and actual values of the dependent variable).

Both inputs must be of the same length (no less than two), and the independent variable x cannot be constant; otherwise a StatisticsError is raised.

For example, we can use the release dates of the Monty Python films to predict the cumulative number of Monty Python films that would have been produced by 2019 assuming that they had kept the pace.

>>> year = [1971, 1975, 1979, 1982, 1983]
+>>> films_total = [1, 2, 3, 4, 5]
+>>> slope, intercept = linear_regression(year, films_total)
+>>> round(slope * 2019 + intercept)
+16
+

If proportional is true, the independent variable x and the dependent variable y are assumed to be directly proportional. The data is fit to a line passing through the origin. Since the intercept will always be 0.0, the underlying linear function simplifies to:

y = slope * x + noise

Continuing the example from correlation(), we look to see how well a model based on major planets can predict the orbital distances for dwarf planets:

>>> model = linear_regression(period_squared, dist_cubed, proportional=True)
+>>> slope = model.slope
+
+>>> # Dwarf planets:   Pluto,  Eris,    Makemake, Haumea, Ceres
+>>> orbital_periods = [90_560, 204_199, 111_845, 103_410, 1_680]  # days
+>>> predicted_dist = [math.cbrt(slope * (p * p)) for p in orbital_periods]
+>>> list(map(round, predicted_dist))
+[5912, 10166, 6806, 6459, 414]
+
+>>> [5_906, 10_152, 6_796, 6_450, 414]  # actual distance in million km
+[5906, 10152, 6796, 6450, 414]
+

New in version 3.10.

Changed in version 3.11: Added support for proportional.

+

Exceptions

A single exception is defined:

+exception statistics.StatisticsError
+

Subclass of ValueError for statistics-related exceptions.

+

NormalDist objects

NormalDist is a tool for creating and manipulating normal distributions of a random variable. It is a class that treats the mean and standard deviation of data measurements as a single entity.

Normal distributions arise from the Central Limit Theorem and have a wide range of applications in statistics.

+class statistics.NormalDist(mu=0.0, sigma=1.0)
+

Returns a new NormalDist object where mu represents the arithmetic mean and sigma represents the standard deviation.

If sigma is negative, raises StatisticsError.

+mean
+

A read-only property for the arithmetic mean of a normal distribution.

+
+median
+

A read-only property for the median of a normal distribution.

+
+mode
+

A read-only property for the mode of a normal distribution.

+
+stdev
+

A read-only property for the standard deviation of a normal distribution.

+
+variance
+

A read-only property for the variance of a normal distribution. Equal to the square of the standard deviation.

+
+classmethod from_samples(data)
+

Makes a normal distribution instance with mu and sigma parameters estimated from the data using fmean() and stdev().

The data can be any iterable and should consist of values that can be converted to type float. If data does not contain at least two elements, raises StatisticsError because it takes at least one point to estimate a central value and at least two points to estimate dispersion.

+
+samples(n, *, seed=None)
+

Generates n random samples for a given mean and standard deviation. Returns a list of float values.

If seed is given, creates a new instance of the underlying random number generator. This is useful for creating reproducible results, even in a multi-threading context.

+
+pdf(x)
+

Using a probability density function (pdf), compute the relative likelihood that a random variable X will be near the given value x. Mathematically, it is the limit of the ratio P(x <= +X < x+dx) / dx as dx approaches zero.

The relative likelihood is computed as the probability of a sample occurring in a narrow range divided by the width of the range (hence the word “density”). Since the likelihood is relative to other points, its value can be greater than 1.0.

+
+cdf(x)
+

Using a cumulative distribution function (cdf), compute the probability that a random variable X will be less than or equal to x. Mathematically, it is written P(X <= x).

+
+inv_cdf(p)
+

Compute the inverse cumulative distribution function, also known as the quantile function or the percent-point function. Mathematically, it is written x : P(X <= x) = p.

Finds the value x of the random variable X such that the probability of the variable being less than or equal to that value equals the given probability p.

+
+overlap(other)
+

Measures the agreement between two normal probability distributions. Returns a value between 0.0 and 1.0 giving the overlapping area for the two probability density functions.

+
+quantiles(n=4)
+

Divide the normal distribution into n continuous intervals with equal probability. Returns a list of (n - 1) cut points separating the intervals.

Set n to 4 for quartiles (the default). Set n to 10 for deciles. Set n to 100 for percentiles which gives the 99 cuts points that separate the normal distribution into 100 equal sized groups.

+
+zscore(x)
+

Compute the Standard Score describing x in terms of the number of standard deviations above or below the mean of the normal distribution: (x - mean) / stdev.

New in version 3.9.

+

Instances of NormalDist support addition, subtraction, multiplication and division by a constant. These operations are used for translation and scaling. For example:

>>> temperature_february = NormalDist(5, 2.5)             # Celsius
+>>> temperature_february * (9/5) + 32                     # Fahrenheit
+NormalDist(mu=41.0, sigma=4.5)
+

Dividing a constant by an instance of NormalDist is not supported because the result wouldn’t be normally distributed.

Since normal distributions arise from additive effects of independent variables, it is possible to add and subtract two independent normally distributed random variables represented as instances of NormalDist. For example:

>>> birth_weights = NormalDist.from_samples([2.5, 3.1, 2.1, 2.4, 2.7, 3.5])
+>>> drug_effects = NormalDist(0.4, 0.15)
+>>> combined = birth_weights + drug_effects
+>>> round(combined.mean, 1)
+3.1
+>>> round(combined.stdev, 1)
+0.5
+

New in version 3.8.

+

+NormalDist Examples and Recipes

Classic probability problems

NormalDist readily solves classic probability problems.

For example, given historical data for SAT exams showing that scores are normally distributed with a mean of 1060 and a standard deviation of 195, determine the percentage of students with test scores between 1100 and 1200, after rounding to the nearest whole number:

>>> sat = NormalDist(1060, 195)
+>>> fraction = sat.cdf(1200 + 0.5) - sat.cdf(1100 - 0.5)
+>>> round(fraction * 100.0, 1)
+18.4
+

Find the quartiles and deciles for the SAT scores:

>>> list(map(round, sat.quantiles()))
+[928, 1060, 1192]
+>>> list(map(round, sat.quantiles(n=10)))
+[810, 896, 958, 1011, 1060, 1109, 1162, 1224, 1310]
+

Monte Carlo inputs for simulations

To estimate the distribution for a model than isn’t easy to solve analytically, NormalDist can generate input samples for a Monte Carlo simulation:

>>> def model(x, y, z):
+...     return (3*x + 7*x*y - 5*y) / (11 * z)
+...
+>>> n = 100_000
+>>> X = NormalDist(10, 2.5).samples(n, seed=3652260728)
+>>> Y = NormalDist(15, 1.75).samples(n, seed=4582495471)
+>>> Z = NormalDist(50, 1.25).samples(n, seed=6582483453)
+>>> quantiles(map(model, X, Y, Z))       
+[1.4591308524824727, 1.8035946855390597, 2.175091447274739]
+

Approximating binomial distributions

Normal distributions can be used to approximate Binomial distributions when the sample size is large and when the probability of a successful trial is near 50%.

For example, an open source conference has 750 attendees and two rooms with a 500 person capacity. There is a talk about Python and another about Ruby. In previous conferences, 65% of the attendees preferred to listen to Python talks. Assuming the population preferences haven’t changed, what is the probability that the Python room will stay within its capacity limits?

>>> n = 750             # Sample size
+>>> p = 0.65            # Preference for Python
+>>> q = 1.0 - p         # Preference for Ruby
+>>> k = 500             # Room capacity
+
+>>> # Approximation using the cumulative normal distribution
+>>> from math import sqrt
+>>> round(NormalDist(mu=n*p, sigma=sqrt(n*p*q)).cdf(k + 0.5), 4)
+0.8402
+
+>>> # Solution using the cumulative binomial distribution
+>>> from math import comb, fsum
+>>> round(fsum(comb(n, r) * p**r * q**(n-r) for r in range(k+1)), 4)
+0.8402
+
+>>> # Approximation using a simulation
+>>> from random import seed, choices
+>>> seed(8675309)
+>>> def trial():
+...     return choices(('Python', 'Ruby'), (p, q), k=n).count('Python')
+...
+>>> mean(trial() <= k for i in range(10_000))
+0.8398
+

Naive bayesian classifier

Normal distributions commonly arise in machine learning problems.

Wikipedia has a nice example of a Naive Bayesian Classifier. The challenge is to predict a person’s gender from measurements of normally distributed features including height, weight, and foot size.

We’re given a training dataset with measurements for eight people. The measurements are assumed to be normally distributed, so we summarize the data with NormalDist:

>>> height_male = NormalDist.from_samples([6, 5.92, 5.58, 5.92])
+>>> height_female = NormalDist.from_samples([5, 5.5, 5.42, 5.75])
+>>> weight_male = NormalDist.from_samples([180, 190, 170, 165])
+>>> weight_female = NormalDist.from_samples([100, 150, 130, 150])
+>>> foot_size_male = NormalDist.from_samples([12, 11, 12, 10])
+>>> foot_size_female = NormalDist.from_samples([6, 8, 7, 9])
+

Next, we encounter a new person whose feature measurements are known but whose gender is unknown:

>>> ht = 6.0        # height
+>>> wt = 130        # weight
+>>> fs = 8          # foot size
+

Starting with a 50% prior probability of being male or female, we compute the posterior as the prior times the product of likelihoods for the feature measurements given the gender:

>>> prior_male = 0.5
+>>> prior_female = 0.5
+>>> posterior_male = (prior_male * height_male.pdf(ht) *
+...                   weight_male.pdf(wt) * foot_size_male.pdf(fs))
+
+>>> posterior_female = (prior_female * height_female.pdf(ht) *
+...                     weight_female.pdf(wt) * foot_size_female.pdf(fs))
+

The final prediction goes to the largest posterior. This is known as the maximum a posteriori or MAP:

>>> 'male' if posterior_male > posterior_female else 'female'
+'female'
+

Kernel density estimation

It is possible to estimate a continuous probability density function from a fixed number of discrete samples.

The basic idea is to smooth the data using a kernel function such as a normal distribution, triangular distribution, or uniform distribution. The degree of smoothing is controlled by a single parameter, h, representing the variance of the kernel function.

import math
+
+def kde_normal(sample, h):
+    "Create a continuous probability density function from a sample."
+    # Smooth the sample with a normal distribution of variance h.
+    kernel_h = NormalDist(0.0, math.sqrt(h)).pdf
+    n = len(sample)
+    def pdf(x):
+        return sum(kernel_h(x - x_i) for x_i in sample) / n
+    return pdf
+

Wikipedia has an example where we can use the kde_normal() recipe to generate and plot a probability density function estimated from a small sample:

>>> sample = [-2.1, -1.3, -0.4, 1.9, 5.1, 6.2]
+>>> f_hat = kde_normal(sample, h=2.25)
+>>> xarr = [i/100 for i in range(-750, 1100)]
+>>> yarr = [f_hat(x) for x in xarr]
+

The points in xarr and yarr can be used to make a PDF plot:

Scatter plot of the estimated probability density function.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/statistics.html +

+
diff --git a/devdocs/python~3.12/library%2Fstdtypes.html b/devdocs/python~3.12/library%2Fstdtypes.html new file mode 100644 index 00000000..8d782bcd --- /dev/null +++ b/devdocs/python~3.12/library%2Fstdtypes.html @@ -0,0 +1,1706 @@ +

Built-in Types

The following sections describe the standard types that are built into the interpreter.

The principal built-in types are numerics, sequences, mappings, classes, instances and exceptions.

Some collection classes are mutable. The methods that add, subtract, or rearrange their members in place, and don’t return a specific item, never return the collection instance itself but None.

Some operations are supported by several object types; in particular, practically all objects can be compared for equality, tested for truth value, and converted to a string (with the repr() function or the slightly different str() function). The latter function is implicitly used when an object is written by the print() function.

Truth Value Testing

Any object can be tested for truth value, for use in an if or while condition or as operand of the Boolean operations below.

By default, an object is considered true unless its class defines either a __bool__() method that returns False or a __len__() method that returns zero, when called with the object. 1 Here are most of the built-in objects considered false:

  • constants defined to be false: None and False +
  • zero of any numeric type: 0, 0.0, 0j, Decimal(0), Fraction(0, 1) +
  • empty sequences and collections: '', (), [], {}, set(), range(0) +

Operations and built-in functions that have a Boolean result always return 0 or False for false and 1 or True for true, unless otherwise stated. (Important exception: the Boolean operations or and and always return one of their operands.)

Boolean Operations — and, or, not +

These are the Boolean operations, ordered by ascending priority:

+ + + +

Operation

Result

Notes

x or y

if x is true, then x, else y

(1)

x and y

if x is false, then x, else y

(2)

not x

if x is false, then True, else False

(3)

Notes:

  1. This is a short-circuit operator, so it only evaluates the second argument if the first one is false.
  2. This is a short-circuit operator, so it only evaluates the second argument if the first one is true.
  3. +not has a lower priority than non-Boolean operators, so not a == b is interpreted as not (a == b), and a == not b is a syntax error.

Comparisons

There are eight comparison operations in Python. They all have the same priority (which is higher than that of the Boolean operations). Comparisons can be chained arbitrarily; for example, x < y <= z is equivalent to x < y and +y <= z, except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

This table summarizes the comparison operations:

+ + + + + + + + +

Operation

Meaning

<

strictly less than

<=

less than or equal

>

strictly greater than

>=

greater than or equal

==

equal

!=

not equal

is

object identity

is not

negated object identity

Objects of different types, except different numeric types, never compare equal. The == operator is always defined but for some object types (for example, class objects) is equivalent to is. The <, <=, > and >= operators are only defined where they make sense; for example, they raise a TypeError exception when one of the arguments is a complex number.

Non-identical instances of a class normally compare as non-equal unless the class defines the __eq__() method.

Instances of a class cannot be ordered with respect to other instances of the same class, or other types of object, unless the class defines enough of the methods __lt__(), __le__(), __gt__(), and __ge__() (in general, __lt__() and __eq__() are sufficient, if you want the conventional meanings of the comparison operators).

The behavior of the is and is not operators cannot be customized; also they can be applied to any two objects and never raise an exception.

Two more operations with the same syntactic priority, in and not in, are supported by types that are iterable or implement the __contains__() method.

Numeric Types — int, float, complex

There are three distinct numeric types: integers, floating point numbers, and complex numbers. In addition, Booleans are a subtype of integers. Integers have unlimited precision. Floating point numbers are usually implemented using double in C; information about the precision and internal representation of floating point numbers for the machine on which your program is running is available in sys.float_info. Complex numbers have a real and imaginary part, which are each a floating point number. To extract these parts from a complex number z, use z.real and z.imag. (The standard library includes the additional numeric types fractions.Fraction, for rationals, and decimal.Decimal, for floating-point numbers with user-definable precision.)

Numbers are created by numeric literals or as the result of built-in functions and operators. Unadorned integer literals (including hex, octal and binary numbers) yield integers. Numeric literals containing a decimal point or an exponent sign yield floating point numbers. Appending 'j' or 'J' to a numeric literal yields an imaginary number (a complex number with a zero real part) which you can add to an integer or float to get a complex number with real and imaginary parts.

Python fully supports mixed arithmetic: when a binary arithmetic operator has operands of different numeric types, the operand with the “narrower” type is widened to that of the other, where integer is narrower than floating point, which is narrower than complex. A comparison between numbers of different types behaves as though the exact values of those numbers were being compared. 2

The constructors int(), float(), and complex() can be used to produce numbers of a specific type.

All numeric types (except complex) support the following operations (for priorities of the operations, see Operator precedence):

+ + + + + + + + + + + + + + + + +

Operation

Result

Notes

Full documentation

x + y

sum of x and y

x - y

difference of x and y

x * y

product of x and y

x / y

quotient of x and y

x // y

floored quotient of x and y

(1)(2)

x % y

remainder of x / y

(2)

-x

x negated

+x

x unchanged

abs(x)

absolute value or magnitude of x

abs()

int(x)

x converted to integer

(3)(6)

int()

float(x)

x converted to floating point

(4)(6)

float()

complex(re, im)

a complex number with real part re, imaginary part im. im defaults to zero.

(6)

complex()

c.conjugate()

conjugate of the complex number c

divmod(x, y)

the pair (x // y, x % y)

(2)

divmod()

pow(x, y)

x to the power y

(5)

pow()

x ** y

x to the power y

(5)

Notes:

  1. Also referred to as integer division. For operands of type int, the result has type int. For operands of type float, the result has type float. In general, the result is a whole integer, though the result’s type is not necessarily int. The result is always rounded towards minus infinity: 1//2 is 0, (-1)//2 is -1, 1//(-2) is -1, and (-1)//(-2) is 0.
  2. Not for complex numbers. Instead convert to floats using abs() if appropriate.
  3. Conversion from float to int truncates, discarding the fractional part. See functions math.floor() and math.ceil() for alternative conversions.
  4. float also accepts the strings “nan” and “inf” with an optional prefix “+” or “-” for Not a Number (NaN) and positive or negative infinity.
  5. Python defines pow(0, 0) and 0 ** 0 to be 1, as is common for programming languages.
  6. +

    The numeric literals accepted include the digits 0 to 9 or any Unicode equivalent (code points with the Nd property).

    See the Unicode Standard for a complete list of code points with the Nd property.

All numbers.Real types (int and float) also include the following operations:

+ + + + +

Operation

Result

math.trunc(x)

x truncated to Integral

round(x[, +n])

x rounded to n digits, rounding half to even. If n is omitted, it defaults to 0.

math.floor(x)

the greatest Integral <= x

math.ceil(x)

the least Integral >= x

For additional numeric operations see the math and cmath modules.

Bitwise Operations on Integer Types

Bitwise operations only make sense for integers. The result of bitwise operations is calculated as though carried out in two’s complement with an infinite number of sign bits.

The priorities of the binary bitwise operations are all lower than the numeric operations and higher than the comparisons; the unary operation ~ has the same priority as the other unary numeric operations (+ and -).

This table lists the bitwise operations sorted in ascending priority:

+ + + + + + +

Operation

Result

Notes

x | y

bitwise or of x and y

(4)

x ^ y

bitwise exclusive or of x and y

(4)

x & y

bitwise and of x and y

(4)

x << n

x shifted left by n bits

(1)(2)

x >> n

x shifted right by n bits

(1)(3)

~x

the bits of x inverted

Notes:

  1. Negative shift counts are illegal and cause a ValueError to be raised.
  2. A left shift by n bits is equivalent to multiplication by pow(2, n).
  3. A right shift by n bits is equivalent to floor division by pow(2, n).
  4. Performing these calculations with at least one extra sign extension bit in a finite two’s complement representation (a working bit-width of 1 + max(x.bit_length(), y.bit_length()) or more) is sufficient to get the same result as if there were an infinite number of sign bits.

Additional Methods on Integer Types

The int type implements the numbers.Integral abstract base class. In addition, it provides a few more methods:

+int.bit_length()
+

Return the number of bits necessary to represent an integer in binary, excluding the sign and leading zeros:

>>> n = -37
+>>> bin(n)
+'-0b100101'
+>>> n.bit_length()
+6
+

More precisely, if x is nonzero, then x.bit_length() is the unique positive integer k such that 2**(k-1) <= abs(x) < 2**k. Equivalently, when abs(x) is small enough to have a correctly rounded logarithm, then k = 1 + int(log(abs(x), 2)). If x is zero, then x.bit_length() returns 0.

Equivalent to:

def bit_length(self):
+    s = bin(self)       # binary representation:  bin(-37) --> '-0b100101'
+    s = s.lstrip('-0b') # remove leading zeros and minus sign
+    return len(s)       # len('100101') --> 6
+

New in version 3.1.

+
+int.bit_count()
+

Return the number of ones in the binary representation of the absolute value of the integer. This is also known as the population count. Example:

>>> n = 19
+>>> bin(n)
+'0b10011'
+>>> n.bit_count()
+3
+>>> (-n).bit_count()
+3
+

Equivalent to:

def bit_count(self):
+    return bin(self).count("1")
+

New in version 3.10.

+
+int.to_bytes(length=1, byteorder='big', *, signed=False)
+

Return an array of bytes representing an integer.

>>> (1024).to_bytes(2, byteorder='big')
+b'\x04\x00'
+>>> (1024).to_bytes(10, byteorder='big')
+b'\x00\x00\x00\x00\x00\x00\x00\x00\x04\x00'
+>>> (-1024).to_bytes(10, byteorder='big', signed=True)
+b'\xff\xff\xff\xff\xff\xff\xff\xff\xfc\x00'
+>>> x = 1000
+>>> x.to_bytes((x.bit_length() + 7) // 8, byteorder='little')
+b'\xe8\x03'
+

The integer is represented using length bytes, and defaults to 1. An OverflowError is raised if the integer is not representable with the given number of bytes.

The byteorder argument determines the byte order used to represent the integer, and defaults to "big". If byteorder is "big", the most significant byte is at the beginning of the byte array. If byteorder is "little", the most significant byte is at the end of the byte array.

The signed argument determines whether two’s complement is used to represent the integer. If signed is False and a negative integer is given, an OverflowError is raised. The default value for signed is False.

The default values can be used to conveniently turn an integer into a single byte object:

>>> (65).to_bytes()
+b'A'
+

However, when using the default arguments, don’t try to convert a value greater than 255 or you’ll get an OverflowError.

Equivalent to:

def to_bytes(n, length=1, byteorder='big', signed=False):
+    if byteorder == 'little':
+        order = range(length)
+    elif byteorder == 'big':
+        order = reversed(range(length))
+    else:
+        raise ValueError("byteorder must be either 'little' or 'big'")
+
+    return bytes((n >> i*8) & 0xff for i in order)
+

New in version 3.2.

Changed in version 3.11: Added default argument values for length and byteorder.

+
+classmethod int.from_bytes(bytes, byteorder='big', *, signed=False)
+

Return the integer represented by the given array of bytes.

>>> int.from_bytes(b'\x00\x10', byteorder='big')
+16
+>>> int.from_bytes(b'\x00\x10', byteorder='little')
+4096
+>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=True)
+-1024
+>>> int.from_bytes(b'\xfc\x00', byteorder='big', signed=False)
+64512
+>>> int.from_bytes([255, 0, 0], byteorder='big')
+16711680
+

The argument bytes must either be a bytes-like object or an iterable producing bytes.

The byteorder argument determines the byte order used to represent the integer, and defaults to "big". If byteorder is "big", the most significant byte is at the beginning of the byte array. If byteorder is "little", the most significant byte is at the end of the byte array. To request the native byte order of the host system, use sys.byteorder as the byte order value.

The signed argument indicates whether two’s complement is used to represent the integer.

Equivalent to:

def from_bytes(bytes, byteorder='big', signed=False):
+    if byteorder == 'little':
+        little_ordered = list(bytes)
+    elif byteorder == 'big':
+        little_ordered = list(reversed(bytes))
+    else:
+        raise ValueError("byteorder must be either 'little' or 'big'")
+
+    n = sum(b << i*8 for i, b in enumerate(little_ordered))
+    if signed and little_ordered and (little_ordered[-1] & 0x80):
+        n -= 1 << 8*len(little_ordered)
+
+    return n
+

New in version 3.2.

Changed in version 3.11: Added default argument value for byteorder.

+
+int.as_integer_ratio()
+

Return a pair of integers whose ratio is equal to the original integer and has a positive denominator. The integer ratio of integers (whole numbers) is always the integer as the numerator and 1 as the denominator.

New in version 3.8.

+
+int.is_integer()
+

Returns True. Exists for duck type compatibility with float.is_integer().

New in version 3.12.

+

Additional Methods on Float

The float type implements the numbers.Real abstract base class. float also has the following additional methods.

+float.as_integer_ratio()
+

Return a pair of integers whose ratio is exactly equal to the original float. The ratio is in lowest terms and has a positive denominator. Raises OverflowError on infinities and a ValueError on NaNs.

+
+float.is_integer()
+

Return True if the float instance is finite with integral value, and False otherwise:

>>> (-2.0).is_integer()
+True
+>>> (3.2).is_integer()
+False
+
+

Two methods support conversion to and from hexadecimal strings. Since Python’s floats are stored internally as binary numbers, converting a float to or from a decimal string usually involves a small rounding error. In contrast, hexadecimal strings allow exact representation and specification of floating-point numbers. This can be useful when debugging, and in numerical work.

+float.hex()
+

Return a representation of a floating-point number as a hexadecimal string. For finite floating-point numbers, this representation will always include a leading 0x and a trailing p and exponent.

+
+classmethod float.fromhex(s)
+

Class method to return the float represented by a hexadecimal string s. The string s may have leading and trailing whitespace.

+

Note that float.hex() is an instance method, while float.fromhex() is a class method.

A hexadecimal string takes the form:

[sign] ['0x'] integer ['.' fraction] ['p' exponent]
+

where the optional sign may by either + or -, integer and fraction are strings of hexadecimal digits, and exponent is a decimal integer with an optional leading sign. Case is not significant, and there must be at least one hexadecimal digit in either the integer or the fraction. This syntax is similar to the syntax specified in section 6.4.4.2 of the C99 standard, and also to the syntax used in Java 1.5 onwards. In particular, the output of float.hex() is usable as a hexadecimal floating-point literal in C or Java code, and hexadecimal strings produced by C’s %a format character or Java’s Double.toHexString are accepted by float.fromhex().

Note that the exponent is written in decimal rather than hexadecimal, and that it gives the power of 2 by which to multiply the coefficient. For example, the hexadecimal string 0x3.a7p10 represents the floating-point number (3 + 10./16 + 7./16**2) * 2.0**10, or 3740.0:

>>> float.fromhex('0x3.a7p10')
+3740.0
+

Applying the reverse conversion to 3740.0 gives a different hexadecimal string representing the same number:

>>> float.hex(3740.0)
+'0x1.d380000000000p+11'
+

Hashing of numeric types

For numbers x and y, possibly of different types, it’s a requirement that hash(x) == hash(y) whenever x == y (see the __hash__() method documentation for more details). For ease of implementation and efficiency across a variety of numeric types (including int, float, decimal.Decimal and fractions.Fraction) Python’s hash for numeric types is based on a single mathematical function that’s defined for any rational number, and hence applies to all instances of int and fractions.Fraction, and all finite instances of float and decimal.Decimal. Essentially, this function is given by reduction modulo P for a fixed prime P. The value of P is made available to Python as the modulus attribute of sys.hash_info.

CPython implementation detail: Currently, the prime used is P = 2**31 - 1 on machines with 32-bit C longs and P = 2**61 - 1 on machines with 64-bit C longs.

Here are the rules in detail:

  • If x = m / n is a nonnegative rational number and n is not divisible by P, define hash(x) as m * invmod(n, P) % P, where invmod(n, +P) gives the inverse of n modulo P.
  • If x = m / n is a nonnegative rational number and n is divisible by P (but m is not) then n has no inverse modulo P and the rule above doesn’t apply; in this case define hash(x) to be the constant value sys.hash_info.inf.
  • If x = m / n is a negative rational number define hash(x) as -hash(-x). If the resulting hash is -1, replace it with -2.
  • The particular values sys.hash_info.inf and -sys.hash_info.inf are used as hash values for positive infinity or negative infinity (respectively).
  • For a complex number z, the hash values of the real and imaginary parts are combined by computing hash(z.real) + +sys.hash_info.imag * hash(z.imag), reduced modulo 2**sys.hash_info.width so that it lies in range(-2**(sys.hash_info.width - 1), 2**(sys.hash_info.width - +1)). Again, if the result is -1, it’s replaced with -2.

To clarify the above rules, here’s some example Python code, equivalent to the built-in hash, for computing the hash of a rational number, float, or complex:

import sys, math
+
+def hash_fraction(m, n):
+    """Compute the hash of a rational number m / n.
+
+    Assumes m and n are integers, with n positive.
+    Equivalent to hash(fractions.Fraction(m, n)).
+
+    """
+    P = sys.hash_info.modulus
+    # Remove common factors of P.  (Unnecessary if m and n already coprime.)
+    while m % P == n % P == 0:
+        m, n = m // P, n // P
+
+    if n % P == 0:
+        hash_value = sys.hash_info.inf
+    else:
+        # Fermat's Little Theorem: pow(n, P-1, P) is 1, so
+        # pow(n, P-2, P) gives the inverse of n modulo P.
+        hash_value = (abs(m) % P) * pow(n, P - 2, P) % P
+    if m < 0:
+        hash_value = -hash_value
+    if hash_value == -1:
+        hash_value = -2
+    return hash_value
+
+def hash_float(x):
+    """Compute the hash of a float x."""
+
+    if math.isnan(x):
+        return object.__hash__(x)
+    elif math.isinf(x):
+        return sys.hash_info.inf if x > 0 else -sys.hash_info.inf
+    else:
+        return hash_fraction(*x.as_integer_ratio())
+
+def hash_complex(z):
+    """Compute the hash of a complex number z."""
+
+    hash_value = hash_float(z.real) + sys.hash_info.imag * hash_float(z.imag)
+    # do a signed reduction modulo 2**sys.hash_info.width
+    M = 2**(sys.hash_info.width - 1)
+    hash_value = (hash_value & (M - 1)) - (hash_value & M)
+    if hash_value == -1:
+        hash_value = -2
+    return hash_value
+

Boolean Type - bool

Booleans represent truth values. The bool type has exactly two constant instances: True and False.

The built-in function bool() converts any value to a boolean, if the value can be interpreted as a truth value (see section Truth Value Testing above).

For logical operations, use the boolean operators and, or and not. When applying the bitwise operators &, |, ^ to two booleans, they return a bool equivalent to the logical operations “and”, “or”, “xor”. However, the logical operators and, or and != should be preferred over &, | and ^.

Deprecated since version 3.12: The use of the bitwise inversion operator ~ is deprecated and will raise an error in Python 3.14.

bool is a subclass of int (see Numeric Types — int, float, complex). In many numeric contexts, False and True behave like the integers 0 and 1, respectively. However, relying on this is discouraged; explicitly convert using int() instead.

Iterator Types

Python supports a concept of iteration over containers. This is implemented using two distinct methods; these are used to allow user-defined classes to support iteration. Sequences, described below in more detail, always support the iteration methods.

One method needs to be defined for container objects to provide iterable support:

+container.__iter__()
+

Return an iterator object. The object is required to support the iterator protocol described below. If a container supports different types of iteration, additional methods can be provided to specifically request iterators for those iteration types. (An example of an object supporting multiple forms of iteration would be a tree structure which supports both breadth-first and depth-first traversal.) This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.

+

The iterator objects themselves are required to support the following two methods, which together form the iterator protocol:

+iterator.__iter__()
+

Return the iterator object itself. This is required to allow both containers and iterators to be used with the for and in statements. This method corresponds to the tp_iter slot of the type structure for Python objects in the Python/C API.

+
+iterator.__next__()
+

Return the next item from the iterator. If there are no further items, raise the StopIteration exception. This method corresponds to the tp_iternext slot of the type structure for Python objects in the Python/C API.

+

Python defines several iterator objects to support iteration over general and specific sequence types, dictionaries, and other more specialized forms. The specific types are not important beyond their implementation of the iterator protocol.

Once an iterator’s __next__() method raises StopIteration, it must continue to do so on subsequent calls. Implementations that do not obey this property are deemed broken.

Generator Types

Python’s generators provide a convenient way to implement the iterator protocol. If a container object’s __iter__() method is implemented as a generator, it will automatically return an iterator object (technically, a generator object) supplying the __iter__() and __next__() methods. More information about generators can be found in the documentation for the yield expression.

Sequence Types — list, tuple, range

There are three basic sequence types: lists, tuples, and range objects. Additional sequence types tailored for processing of binary data and text strings are described in dedicated sections.

Common Sequence Operations

The operations in the following table are supported by most sequence types, both mutable and immutable. The collections.abc.Sequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

This table lists the sequence operations sorted in ascending priority. In the table, s and t are sequences of the same type, n, i, j and k are integers and x is an arbitrary object that meets any type and value restrictions imposed by s.

The in and not in operations have the same priorities as the comparison operations. The + (concatenation) and * (repetition) operations have the same priority as the corresponding numeric operations. 3

+ + + + + + + + + + + + +

Operation

Result

Notes

x in s

True if an item of s is equal to x, else False

(1)

x not in s

False if an item of s is equal to x, else True

(1)

s + t

the concatenation of s and t

(6)(7)

s * n or n * s

equivalent to adding s to itself n times

(2)(7)

s[i]

ith item of s, origin 0

(3)

s[i:j]

slice of s from i to j

(3)(4)

s[i:j:k]

slice of s from i to j with step k

(3)(5)

len(s)

length of s

min(s)

smallest item of s

max(s)

largest item of s

s.index(x[, i[, j]])

index of the first occurrence of x in s (at or after index i and before index j)

(8)

s.count(x)

total number of occurrences of x in s

Sequences of the same type also support comparisons. In particular, tuples and lists are compared lexicographically by comparing corresponding elements. This means that to compare equal, every element must compare equal and the two sequences must be of the same type and have the same length. (For full details see Comparisons in the language reference.)

Forward and reversed iterators over mutable sequences access values using an index. That index will continue to march forward (or backward) even if the underlying sequence is mutated. The iterator terminates only when an IndexError or a StopIteration is encountered (or when the index drops below zero).

Notes:

  1. +

    While the in and not in operations are used only for simple containment testing in the general case, some specialised sequences (such as str, bytes and bytearray) also use them for subsequence testing:

    >>> "gg" in "eggs"
+True
+
  2. +

    Values of n less than 0 are treated as 0 (which yields an empty sequence of the same type as s). Note that items in the sequence s are not copied; they are referenced multiple times. This often haunts new Python programmers; consider:

    >>> lists = [[]] * 3
+>>> lists
+[[], [], []]
+>>> lists[0].append(3)
+>>> lists
+[[3], [3], [3]]
+

    What has happened is that [[]] is a one-element list containing an empty list, so all three elements of [[]] * 3 are references to this single empty list. Modifying any of the elements of lists modifies this single list. You can create a list of different lists this way:

    >>> lists = [[] for i in range(3)]
+>>> lists[0].append(3)
+>>> lists[1].append(5)
+>>> lists[2].append(7)
+>>> lists
+[[3], [5], [7]]
+

    Further explanation is available in the FAQ entry How do I create a multidimensional list?.

  3. If i or j is negative, the index is relative to the end of sequence s: len(s) + i or len(s) + j is substituted. But note that -0 is still 0.
  4. The slice of s from i to j is defined as the sequence of items with index k such that i <= k < j. If i or j is greater than len(s), use len(s). If i is omitted or None, use 0. If j is omitted or None, use len(s). If i is greater than or equal to j, the slice is empty.
  5. The slice of s from i to j with step k is defined as the sequence of items with index x = i + n*k such that 0 <= n < (j-i)/k. In other words, the indices are i, i+k, i+2*k, i+3*k and so on, stopping when j is reached (but never including j). When k is positive, i and j are reduced to len(s) if they are greater. When k is negative, i and j are reduced to len(s) - 1 if they are greater. If i or j are omitted or None, they become “end” values (which end depends on the sign of k). Note, k cannot be zero. If k is None, it is treated like 1.
  6. +

    Concatenating immutable sequences always results in a new object. This means that building up a sequence by repeated concatenation will have a quadratic runtime cost in the total sequence length. To get a linear runtime cost, you must switch to one of the alternatives below:

    • if concatenating str objects, you can build a list and use str.join() at the end or else write to an io.StringIO instance and retrieve its value when complete
    • if concatenating bytes objects, you can similarly use bytes.join() or io.BytesIO, or you can do in-place concatenation with a bytearray object. bytearray objects are mutable and have an efficient overallocation mechanism
    • if concatenating tuple objects, extend a list instead
    • for other types, investigate the relevant class documentation
  7. Some sequence types (such as range) only support item sequences that follow specific patterns, and hence don’t support sequence concatenation or repetition.
  8. +index raises ValueError when x is not found in s. Not all implementations support passing the additional arguments i and j. These arguments allow efficient searching of subsections of the sequence. Passing the extra arguments is roughly equivalent to using s[i:j].index(x), only without copying any data and with the returned index being relative to the start of the sequence rather than the start of the slice.

Immutable Sequence Types

The only operation that immutable sequence types generally implement that is not also implemented by mutable sequence types is support for the hash() built-in.

This support allows immutable sequences, such as tuple instances, to be used as dict keys and stored in set and frozenset instances.

Attempting to hash an immutable sequence that contains unhashable values will result in TypeError.

Mutable Sequence Types

The operations in the following table are defined on mutable sequence types. The collections.abc.MutableSequence ABC is provided to make it easier to correctly implement these operations on custom sequence types.

In the table s is an instance of a mutable sequence type, t is any iterable object and x is an arbitrary object that meets any type and value restrictions imposed by s (for example, bytearray only accepts integers that meet the value restriction 0 <= x <= 255).

+ + + + + + + + + + + + + + +

Operation

Result

Notes

s[i] = x

item i of s is replaced by x

s[i:j] = t

slice of s from i to j is replaced by the contents of the iterable t

del s[i:j]

same as s[i:j] = []

s[i:j:k] = t

the elements of s[i:j:k] are replaced by those of t

(1)

del s[i:j:k]

removes the elements of s[i:j:k] from the list

s.append(x)

appends x to the end of the sequence (same as s[len(s):len(s)] = [x])

s.clear()

removes all items from s (same as del s[:])

(5)

s.copy()

creates a shallow copy of s (same as s[:])

(5)

s.extend(t) or s += t

extends s with the contents of t (for the most part the same as s[len(s):len(s)] = t)

s *= n

updates s with its contents repeated n times

(6)

s.insert(i, x)

inserts x into s at the index given by i (same as s[i:i] = [x])

s.pop() or s.pop(i)

retrieves the item at i and also removes it from s

(2)

s.remove(x)

remove the first item from s where s[i] is equal to x

(3)

s.reverse()

reverses the items of s in place

(4)

Notes:

  1. +t must have the same length as the slice it is replacing.
  2. The optional argument i defaults to -1, so that by default the last item is removed and returned.
  3. +remove() raises ValueError when x is not found in s.
  4. The reverse() method modifies the sequence in place for economy of space when reversing a large sequence. To remind users that it operates by side effect, it does not return the reversed sequence.
  5. +

    clear() and copy() are included for consistency with the interfaces of mutable containers that don’t support slicing operations (such as dict and set). copy() is not part of the collections.abc.MutableSequence ABC, but most concrete mutable sequence classes provide it.

    New in version 3.3: clear() and copy() methods.

  6. The value n is an integer, or an object implementing __index__(). Zero and negative values of n clear the sequence. Items in the sequence are not copied; they are referenced multiple times, as explained for s * n under Common Sequence Operations.

Lists

Lists are mutable sequences, typically used to store collections of homogeneous items (where the precise degree of similarity will vary by application).

+class list([iterable])
+

Lists may be constructed in several ways:

  • Using a pair of square brackets to denote the empty list: [] +
  • Using square brackets, separating items with commas: [a], [a, b, c] +
  • Using a list comprehension: [x for x in iterable] +
  • Using the type constructor: list() or list(iterable) +

The constructor builds a list whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a list, a copy is made and returned, similar to iterable[:]. For example, list('abc') returns ['a', 'b', 'c'] and list( (1, 2, 3) ) returns [1, 2, 3]. If no argument is given, the constructor creates a new empty list, [].

Many other operations also produce lists, including the sorted() built-in.

Lists implement all of the common and mutable sequence operations. Lists also provide the following additional method:

+sort(*, key=None, reverse=False)
+

This method sorts the list in place, using only < comparisons between items. Exceptions are not suppressed - if any comparison operations fail, the entire sort operation will fail (and the list will likely be left in a partially modified state).

sort() accepts two arguments that can only be passed by keyword (keyword-only arguments):

key specifies a function of one argument that is used to extract a comparison key from each list element (for example, key=str.lower). The key corresponding to each item in the list is calculated once and then used for the entire sorting process. The default value of None means that list items are sorted directly without calculating a separate key value.

The functools.cmp_to_key() utility is available to convert a 2.x style cmp function to a key function.

reverse is a boolean value. If set to True, then the list elements are sorted as if each comparison were reversed.

This method modifies the sequence in place for economy of space when sorting a large sequence. To remind users that it operates by side effect, it does not return the sorted sequence (use sorted() to explicitly request a new sorted list instance).

The sort() method is guaranteed to be stable. A sort is stable if it guarantees not to change the relative order of elements that compare equal — this is helpful for sorting in multiple passes (for example, sort by department, then by salary grade).

For sorting examples and a brief sorting tutorial, see Sorting HOW TO.

CPython implementation detail: While a list is being sorted, the effect of attempting to mutate, or even inspect, the list is undefined. The C implementation of Python makes the list appear empty for the duration, and raises ValueError if it can detect that the list has been mutated during a sort.

+
+

Tuples

Tuples are immutable sequences, typically used to store collections of heterogeneous data (such as the 2-tuples produced by the enumerate() built-in). Tuples are also used for cases where an immutable sequence of homogeneous data is needed (such as allowing storage in a set or dict instance).

+class tuple([iterable])
+

Tuples may be constructed in a number of ways:

  • Using a pair of parentheses to denote the empty tuple: () +
  • Using a trailing comma for a singleton tuple: a, or (a,) +
  • Separating items with commas: a, b, c or (a, b, c) +
  • Using the tuple() built-in: tuple() or tuple(iterable) +

The constructor builds a tuple whose items are the same and in the same order as iterable’s items. iterable may be either a sequence, a container that supports iteration, or an iterator object. If iterable is already a tuple, it is returned unchanged. For example, tuple('abc') returns ('a', 'b', 'c') and tuple( [1, 2, 3] ) returns (1, 2, 3). If no argument is given, the constructor creates a new empty tuple, ().

Note that it is actually the comma which makes a tuple, not the parentheses. The parentheses are optional, except in the empty tuple case, or when they are needed to avoid syntactic ambiguity. For example, f(a, b, c) is a function call with three arguments, while f((a, b, c)) is a function call with a 3-tuple as the sole argument.

Tuples implement all of the common sequence operations.

+

For heterogeneous collections of data where access by name is clearer than access by index, collections.namedtuple() may be a more appropriate choice than a simple tuple object.

Ranges

The range type represents an immutable sequence of numbers and is commonly used for looping a specific number of times in for loops.

+class range(stop)
class range(start, stop[, step]) +
+

The arguments to the range constructor must be integers (either built-in int or any object that implements the __index__() special method). If the step argument is omitted, it defaults to 1. If the start argument is omitted, it defaults to 0. If step is zero, ValueError is raised.

For a positive step, the contents of a range r are determined by the formula r[i] = start + step*i where i >= 0 and r[i] < stop.

For a negative step, the contents of the range are still determined by the formula r[i] = start + step*i, but the constraints are i >= 0 and r[i] > stop.

A range object will be empty if r[0] does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices.

Ranges containing absolute values larger than sys.maxsize are permitted but some features (such as len()) may raise OverflowError.

Range examples:

>>> list(range(10))
+[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+>>> list(range(1, 11))
+[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+>>> list(range(0, 30, 5))
+[0, 5, 10, 15, 20, 25]
+>>> list(range(0, 10, 3))
+[0, 3, 6, 9]
+>>> list(range(0, -10, -1))
+[0, -1, -2, -3, -4, -5, -6, -7, -8, -9]
+>>> list(range(0))
+[]
+>>> list(range(1, 0))
+[]
+

Ranges implement all of the common sequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern).

+start
+

The value of the start parameter (or 0 if the parameter was not supplied)

+
+stop
+

The value of the stop parameter

+
+step
+

The value of the step parameter (or 1 if the parameter was not supplied)

+
+

The advantage of the range type over a regular list or tuple is that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores the start, stop and step values, calculating individual items and subranges as needed).

Range objects implement the collections.abc.Sequence ABC, and provide features such as containment tests, element index lookup, slicing and support for negative indices (see Sequence Types — list, tuple, range):

>>> r = range(0, 20, 2)
+>>> r
+range(0, 20, 2)
+>>> 11 in r
+False
+>>> 10 in r
+True
+>>> r.index(10)
+5
+>>> r[5]
+10
+>>> r[:5]
+range(0, 10, 2)
+>>> r[-1]
+18
+

Testing range objects for equality with == and != compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have different start, stop and step attributes, for example range(0) == range(2, 1, 3) or range(0, 3, 2) == range(0, 4, 2).)

Changed in version 3.2: Implement the Sequence ABC. Support slicing and negative indices. Test int objects for membership in constant time instead of iterating through all items.

Changed in version 3.3: Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity).

New in version 3.3: The start, stop and step attributes.

See also

  • The linspace recipe shows how to implement a lazy version of range suitable for floating point applications.

Text Sequence Type — str

Textual data in Python is handled with str objects, or strings. Strings are immutable sequences of Unicode code points. String literals are written in a variety of ways:

  • Single quotes: 'allows embedded "double" quotes' +
  • Double quotes: "allows embedded 'single' quotes" +
  • Triple quoted: '''Three single quotes''', """Three double quotes""" +

Triple quoted strings may span multiple lines - all associated whitespace will be included in the string literal.

String literals that are part of a single expression and have only whitespace between them will be implicitly converted to a single string literal. That is, ("spam " "eggs") == "spam eggs".

See String and Bytes literals for more about the various forms of string literal, including supported escape sequences, and the r (“raw”) prefix that disables most escape sequence processing.

Strings may also be created from other objects using the str constructor.

Since there is no separate “character” type, indexing a string produces strings of length 1. That is, for a non-empty string s, s[0] == s[0:1].

There is also no mutable string type, but str.join() or io.StringIO can be used to efficiently construct strings from multiple fragments.

Changed in version 3.3: For backwards compatibility with the Python 2 series, the u prefix is once again permitted on string literals. It has no effect on the meaning of string literals and cannot be combined with the r prefix.

+class str(object='')
class str(object=b'', encoding='utf-8', errors='strict') +
+

Return a string version of object. If object is not provided, returns the empty string. Otherwise, the behavior of str() depends on whether encoding or errors is given, as follows.

If neither encoding nor errors is given, str(object) returns type(object).__str__(object), which is the “informal” or nicely printable string representation of object. For string objects, this is the string itself. If object does not have a __str__() method, then str() falls back to returning repr(object).

If at least one of encoding or errors is given, object should be a bytes-like object (e.g. bytes or bytearray). In this case, if object is a bytes (or bytearray) object, then str(bytes, encoding, errors) is equivalent to bytes.decode(encoding, errors). Otherwise, the bytes object underlying the buffer object is obtained before calling bytes.decode(). See Binary Sequence Types — bytes, bytearray, memoryview and Buffer Protocol for information on buffer objects.

Passing a bytes object to str() without the encoding or errors arguments falls under the first case of returning the informal string representation (see also the -b command-line option to Python). For example:

>>> str(b'Zoot!')
+"b'Zoot!'"
+

For more information on the str class and its methods, see Text Sequence Type — str and the String Methods section below. To output formatted strings, see the f-strings and Format String Syntax sections. In addition, see the Text Processing Services section.

+

String Methods

Strings implement all of the common sequence operations, along with the additional methods described below.

Strings also support two styles of string formatting, one providing a large degree of flexibility and customization (see str.format(), Format String Syntax and Custom String Formatting) and the other based on C printf style formatting that handles a narrower range of types and is slightly harder to use correctly, but is often faster for the cases it can handle (printf-style String Formatting).

The Text Processing Services section of the standard library covers a number of other modules that provide various text related utilities (including regular expression support in the re module).

+str.capitalize()
+

Return a copy of the string with its first character capitalized and the rest lowercased.

Changed in version 3.8: The first character is now put into titlecase rather than uppercase. This means that characters like digraphs will only have their first letter capitalized, instead of the full character.

+
+str.casefold()
+

Return a casefolded copy of the string. Casefolded strings may be used for caseless matching.

Casefolding is similar to lowercasing but more aggressive because it is intended to remove all case distinctions in a string. For example, the German lowercase letter 'ß' is equivalent to "ss". Since it is already lowercase, lower() would do nothing to 'ß'; casefold() converts it to "ss".

The casefolding algorithm is described in section 3.13 ‘Default Case Folding’ of the Unicode Standard.

New in version 3.3.

+
+str.center(width[, fillchar])
+

Return centered in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

+
+str.count(sub[, start[, end]])
+

Return the number of non-overlapping occurrences of substring sub in the range [start, end]. Optional arguments start and end are interpreted as in slice notation.

If sub is empty, returns the number of empty strings between characters which is the length of the string plus one.

+
+str.encode(encoding='utf-8', errors='strict')
+

Return the string encoded to bytes.

encoding defaults to 'utf-8'; see Standard Encodings for possible values.

errors controls how encoding errors are handled. If 'strict' (the default), a UnicodeError exception is raised. Other possible values are 'ignore', 'replace', 'xmlcharrefreplace', 'backslashreplace' and any other name registered via codecs.register_error(). See Error Handlers for details.

For performance reasons, the value of errors is not checked for validity unless an encoding error actually occurs, Python Development Mode is enabled or a debug build is used.

Changed in version 3.1: Added support for keyword arguments.

Changed in version 3.9: The value of the errors argument is now checked in Python Development Mode and in debug mode.

+
+str.endswith(suffix[, start[, end]])
+

Return True if the string ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

+
+str.expandtabs(tabsize=8)
+

Return a copy of the string where all tab characters are replaced by one or more spaces, depending on the current column and the given tab size. Tab positions occur every tabsize characters (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the string, the current column is set to zero and the string is examined character by character. If the character is a tab (\t), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the character is a newline (\n) or return (\r), it is copied and the current column is reset to zero. Any other character is copied unchanged and the current column is incremented by one regardless of how the character is represented when printed.

>>> '01\t012\t0123\t01234'.expandtabs()
+'01      012     0123    01234'
+>>> '01\t012\t0123\t01234'.expandtabs(4)
+'01  012 0123    01234'
+
+
+str.find(sub[, start[, end]])
+

Return the lowest index in the string where substring sub is found within the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:

>>> 'Py' in 'Python'
+True
+
+
+str.format(*args, **kwargs)
+

Perform a string formatting operation. The string on which this method is called can contain literal text or replacement fields delimited by braces {}. Each replacement field contains either the numeric index of a positional argument, or the name of a keyword argument. Returns a copy of the string where each replacement field is replaced with the string value of the corresponding argument.

>>> "The sum of 1 + 2 is {0}".format(1+2)
+'The sum of 1 + 2 is 3'
+

See Format String Syntax for a description of the various formatting options that can be specified in format strings.

Note

When formatting a number (int, float, complex, decimal.Decimal and subclasses) with the n type (ex: '{:n}'.format(1234)), the function temporarily sets the LC_CTYPE locale to the LC_NUMERIC locale to decode decimal_point and thousands_sep fields of localeconv() if they are non-ASCII or longer than 1 byte, and the LC_NUMERIC locale is different than the LC_CTYPE locale. This temporary change affects other threads.

Changed in version 3.7: When formatting a number with the n type, the function sets temporarily the LC_CTYPE locale to the LC_NUMERIC locale in some cases.

+
+str.format_map(mapping)
+

Similar to str.format(**mapping), except that mapping is used directly and not copied to a dict. This is useful if for example mapping is a dict subclass:

>>> class Default(dict):
+...     def __missing__(self, key):
+...         return key
+...
+>>> '{name} was born in {country}'.format_map(Default(name='Guido'))
+'Guido was born in country'
+

New in version 3.2.

+
+str.index(sub[, start[, end]])
+

Like find(), but raise ValueError when the substring is not found.

+
+str.isalnum()
+

Return True if all characters in the string are alphanumeric and there is at least one character, False otherwise. A character c is alphanumeric if one of the following returns True: c.isalpha(), c.isdecimal(), c.isdigit(), or c.isnumeric().

+
+str.isalpha()
+

Return True if all characters in the string are alphabetic and there is at least one character, False otherwise. Alphabetic characters are those characters defined in the Unicode character database as “Letter”, i.e., those with general category property being one of “Lm”, “Lt”, “Lu”, “Ll”, or “Lo”. Note that this is different from the Alphabetic property defined in the section 4.10 ‘Letters, Alphabetic, and Ideographic’ of the Unicode Standard.

+
+str.isascii()
+

Return True if the string is empty or all characters in the string are ASCII, False otherwise. ASCII characters have code points in the range U+0000-U+007F.

New in version 3.7.

+
+str.isdecimal()
+

Return True if all characters in the string are decimal characters and there is at least one character, False otherwise. Decimal characters are those that can be used to form numbers in base 10, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Formally a decimal character is a character in the Unicode General Category “Nd”.

+
+str.isdigit()
+

Return True if all characters in the string are digits and there is at least one character, False otherwise. Digits include decimal characters and digits that need special handling, such as the compatibility superscript digits. This covers digits which cannot be used to form numbers in base 10, like the Kharosthi numbers. Formally, a digit is a character that has the property value Numeric_Type=Digit or Numeric_Type=Decimal.

+
+str.isidentifier()
+

Return True if the string is a valid identifier according to the language definition, section Identifiers and keywords.

keyword.iskeyword() can be used to test whether string s is a reserved identifier, such as def and class.

Example:

>>> from keyword import iskeyword
+
+>>> 'hello'.isidentifier(), iskeyword('hello')
+(True, False)
+>>> 'def'.isidentifier(), iskeyword('def')
+(True, True)
+
+
+str.islower()
+

Return True if all cased characters 4 in the string are lowercase and there is at least one cased character, False otherwise.

+
+str.isnumeric()
+

Return True if all characters in the string are numeric characters, and there is at least one character, False otherwise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. U+2155, VULGAR FRACTION ONE FIFTH. Formally, numeric characters are those with the property value Numeric_Type=Digit, Numeric_Type=Decimal or Numeric_Type=Numeric.

+
+str.isprintable()
+

Return True if all characters in the string are printable or the string is empty, False otherwise. Nonprintable characters are those characters defined in the Unicode character database as “Other” or “Separator”, excepting the ASCII space (0x20) which is considered printable. (Note that printable characters in this context are those which should not be escaped when repr() is invoked on a string. It has no bearing on the handling of strings written to sys.stdout or sys.stderr.)

+
+str.isspace()
+

Return True if there are only whitespace characters in the string and there is at least one character, False otherwise.

A character is whitespace if in the Unicode character database (see unicodedata), either its general category is Zs (“Separator, space”), or its bidirectional class is one of WS, B, or S.

+
+str.istitle()
+

Return True if the string is a titlecased string and there is at least one character, for example uppercase characters may only follow uncased characters and lowercase characters only cased ones. Return False otherwise.

+
+str.isupper()
+

Return True if all cased characters 4 in the string are uppercase and there is at least one cased character, False otherwise.

>>> 'BANANA'.isupper()
+True
+>>> 'banana'.isupper()
+False
+>>> 'baNana'.isupper()
+False
+>>> ' '.isupper()
+False
+
+
+str.join(iterable)
+

Return a string which is the concatenation of the strings in iterable. A TypeError will be raised if there are any non-string values in iterable, including bytes objects. The separator between elements is the string providing this method.

+
+str.ljust(width[, fillchar])
+

Return the string left justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

+
+str.lower()
+

Return a copy of the string with all the cased characters 4 converted to lowercase.

The lowercasing algorithm used is described in section 3.13 ‘Default Case Folding’ of the Unicode Standard.

+
+str.lstrip([chars])
+

Return a copy of the string with leading characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:

>>> '   spacious   '.lstrip()
+'spacious   '
+>>> 'www.example.com'.lstrip('cmowz.')
+'example.com'
+

See str.removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:

>>> 'Arthur: three!'.lstrip('Arthur: ')
+'ee!'
+>>> 'Arthur: three!'.removeprefix('Arthur: ')
+'three!'
+
+
+static str.maketrans(x[, y[, z]])
+

This static method returns a translation table usable for str.translate().

If there is only one argument, it must be a dictionary mapping Unicode ordinals (integers) or characters (strings of length 1) to Unicode ordinals, strings (of arbitrary lengths) or None. Character keys will then be converted to ordinals.

If there are two arguments, they must be strings of equal length, and in the resulting dictionary, each character in x will be mapped to the character at the same position in y. If there is a third argument, it must be a string, whose characters will be mapped to None in the result.

+
+str.partition(sep)
+

Split the string at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing the string itself, followed by two empty strings.

+
+str.removeprefix(prefix, /)
+

If the string starts with the prefix string, return string[len(prefix):]. Otherwise, return a copy of the original string:

>>> 'TestHook'.removeprefix('Test')
+'Hook'
+>>> 'BaseTestCase'.removeprefix('Test')
+'BaseTestCase'
+

New in version 3.9.

+
+str.removesuffix(suffix, /)
+

If the string ends with the suffix string and that suffix is not empty, return string[:-len(suffix)]. Otherwise, return a copy of the original string:

>>> 'MiscTests'.removesuffix('Tests')
+'Misc'
+>>> 'TmpDirMixin'.removesuffix('Tests')
+'TmpDirMixin'
+

New in version 3.9.

+
+str.replace(old, new[, count])
+

Return a copy of the string with all occurrences of substring old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

+
+str.rfind(sub[, start[, end]])
+

Return the highest index in the string where substring sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.

+
+str.rindex(sub[, start[, end]])
+

Like rfind() but raises ValueError when the substring sub is not found.

+
+str.rjust(width[, fillchar])
+

Return the string right justified in a string of length width. Padding is done using the specified fillchar (default is an ASCII space). The original string is returned if width is less than or equal to len(s).

+
+str.rpartition(sep)
+

Split the string at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty strings, followed by the string itself.

+
+str.rsplit(sep=None, maxsplit=- 1)
+

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any whitespace string is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.

+
+str.rstrip([chars])
+

Return a copy of the string with trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:

>>> '   spacious   '.rstrip()
+'   spacious'
+>>> 'mississippi'.rstrip('ipz')
+'mississ'
+

See str.removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:

>>> 'Monty Python'.rstrip(' Python')
+'M'
+>>> 'Monty Python'.removesuffix(' Python')
+'Monty'
+
+
+str.split(sep=None, maxsplit=- 1)
+

Return a list of the words in the string, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example, '1,,2'.split(',') returns ['1', '', '2']). The sep argument may consist of multiple characters (for example, '1<>2<>3'.split('<>') returns ['1', '2', '3']). Splitting an empty string with a specified separator returns [''].

For example:

>>> '1,2,3'.split(',')
+['1', '2', '3']
+>>> '1,2,3'.split(',', maxsplit=1)
+['1', '2,3']
+>>> '1,2,,3,'.split(',')
+['1', '2', '', '3', '']
+

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace. Consequently, splitting an empty string or a string consisting of just whitespace with a None separator returns [].

For example:

>>> '1 2 3'.split()
+['1', '2', '3']
+>>> '1 2 3'.split(maxsplit=1)
+['1', '2 3']
+>>> '   1   2   3   '.split()
+['1', '2', '3']
+
+
+str.splitlines(keepends=False)
+

Return a list of the lines in the string, breaking at line boundaries. Line breaks are not included in the resulting list unless keepends is given and true.

This method splits on the following line boundaries. In particular, the boundaries are a superset of universal newlines.

+ + + + + + + + + + + +

Representation

Description

\n

Line Feed

\r

Carriage Return

\r\n

Carriage Return + Line Feed

\v or \x0b

Line Tabulation

\f or \x0c

Form Feed

\x1c

File Separator

\x1d

Group Separator

\x1e

Record Separator

\x85

Next Line (C1 Control Code)

\u2028

Line Separator

\u2029

Paragraph Separator

Changed in version 3.2: \v and \f added to list of line boundaries.

For example:

>>> 'ab c\n\nde fg\rkl\r\n'.splitlines()
+['ab c', '', 'de fg', 'kl']
+>>> 'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
+['ab c\n', '\n', 'de fg\r', 'kl\r\n']
+

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:

>>> "".splitlines()
+[]
+>>> "One line\n".splitlines()
+['One line']
+

For comparison, split('\n') gives:

>>> ''.split('\n')
+['']
+>>> 'Two lines\n'.split('\n')
+['Two lines', '']
+
+
+str.startswith(prefix[, start[, end]])
+

Return True if string starts with the prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test string beginning at that position. With optional end, stop comparing string at that position.

+
+str.strip([chars])
+

Return a copy of the string with the leading and trailing characters removed. The chars argument is a string specifying the set of characters to be removed. If omitted or None, the chars argument defaults to removing whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:

>>> '   spacious   '.strip()
+'spacious'
+>>> 'www.example.com'.strip('cmowz.')
+'example'
+

The outermost leading and trailing chars argument values are stripped from the string. Characters are removed from the leading end until reaching a string character that is not contained in the set of characters in chars. A similar action takes place on the trailing end. For example:

>>> comment_string = '#....... Section 3.2.1 Issue #32 .......'
+>>> comment_string.strip('.#! ')
+'Section 3.2.1 Issue #32'
+
+
+str.swapcase()
+

Return a copy of the string with uppercase characters converted to lowercase and vice versa. Note that it is not necessarily true that s.swapcase().swapcase() == s.

+
+str.title()
+

Return a titlecased version of the string where words start with an uppercase character and the remaining characters are lowercase.

For example:

>>> 'Hello world'.title()
+'Hello World'
+

The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result:

>>> "they're bill's friends from the UK".title()
+"They'Re Bill'S Friends From The Uk"
+

The string.capwords() function does not have this problem, as it splits words on spaces only.

Alternatively, a workaround for apostrophes can be constructed using regular expressions:

>>> import re
+>>> def titlecase(s):
+...     return re.sub(r"[A-Za-z]+('[A-Za-z]+)?",
+...                   lambda mo: mo.group(0).capitalize(),
+...                   s)
+...
+>>> titlecase("they're bill's friends.")
+"They're Bill's Friends."
+
+
+str.translate(table)
+

Return a copy of the string in which each character has been mapped through the given translation table. The table must be an object that implements indexing via __getitem__(), typically a mapping or sequence. When indexed by a Unicode ordinal (an integer), the table object can do any of the following: return a Unicode ordinal or a string, to map the character to one or more other characters; return None, to delete the character from the return string; or raise a LookupError exception, to map the character to itself.

You can use str.maketrans() to create a translation map from character-to-character mappings in different formats.

See also the codecs module for a more flexible approach to custom character mappings.

+
+str.upper()
+

Return a copy of the string with all the cased characters 4 converted to uppercase. Note that s.upper().isupper() might be False if s contains uncased characters or if the Unicode category of the resulting character(s) is not “Lu” (Letter, uppercase), but e.g. “Lt” (Letter, titlecase).

The uppercasing algorithm used is described in section 3.13 ‘Default Case Folding’ of the Unicode Standard.

+
+str.zfill(width)
+

Return a copy of the string left filled with ASCII '0' digits to make a string of length width. A leading sign prefix ('+'/'-') is handled by inserting the padding after the sign character rather than before. The original string is returned if width is less than or equal to len(s).

For example:

>>> "42".zfill(5)
+'00042'
+>>> "-42".zfill(5)
+'-0042'
+
+

+printf-style String Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals, the str.format() interface, or template strings may help avoid these errors. Each of these alternatives provides their own trade-offs and benefits of simplicity, flexibility, and/or extensibility.

String objects have one unique built-in operation: the % operator (modulo). This is also known as the string formatting or interpolation operator. Given format % values (where format is a string), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format string, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

  1. The '%' character, which marks the start of the specifier.
  2. Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).
  3. Conversion flags (optional), which affect the result of some conversion types.
  4. Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.
  5. Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.
  6. Length modifier (optional).
  7. Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the string must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:

>>> print('%(language)s has %(number)03d quote types.' %
+...       {'language': "Python", "number": 2})
+Python has 002 quote types.
+

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

+ + + + + +

Flag

Meaning

'#'

The value conversion will use the “alternate form” (where defined below).

'0'

The conversion will be zero padded for numeric values.

'-'

The converted value is left adjusted (overrides the '0' conversion if both are given).

' '

(a space) A blank should be left before a positive number (or empty string) produced by a signed conversion.

'+'

A sign character ('+' or '-') will precede the conversion (overrides a “space” flag).

A length modifier (h, l, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

+ + + + + + + + + + + + + + + + + +

Conversion

Meaning

Notes

'd'

Signed integer decimal.

'i'

Signed integer decimal.

'o'

Signed octal value.

(1)

'u'

Obsolete type – it is identical to 'd'.

(6)

'x'

Signed hexadecimal (lowercase).

(2)

'X'

Signed hexadecimal (uppercase).

(2)

'e'

Floating point exponential format (lowercase).

(3)

'E'

Floating point exponential format (uppercase).

(3)

'f'

Floating point decimal format.

(3)

'F'

Floating point decimal format.

(3)

'g'

Floating point format. Uses lowercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'G'

Floating point format. Uses uppercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'c'

Single character (accepts integer or single character string).

'r'

String (converts any Python object using repr()).

(5)

's'

String (converts any Python object using str()).

(5)

'a'

String (converts any Python object using ascii()).

(5)

'%'

No argument is converted, results in a '%' character in the result.

Notes:

  1. The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.
  2. The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.
  3. +

    The alternate form causes the result to always contain a decimal point, even if no digits follow it.

    The precision determines the number of digits after the decimal point and defaults to 6.

  4. +

    The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.

    The precision determines the number of significant digits before and after the decimal point and defaults to 6.

  5. If precision is N, the output is truncated to N characters.
  6. See PEP 237.

Since Python strings have an explicit length, %s conversions do not assume that '\0' is the end of the string.

Changed in version 3.1: %f conversions for numbers whose absolute value is over 1e50 are no longer replaced by %g conversions.

Binary Sequence Types — bytes, bytearray, memoryview

The core built-in types for manipulating binary data are bytes and bytearray. They are supported by memoryview which uses the buffer protocol to access the memory of other binary objects without needing to make a copy.

The array module supports efficient storage of basic data types like 32-bit integers and IEEE754 double-precision floating values.

Bytes Objects

Bytes objects are immutable sequences of single bytes. Since many major binary protocols are based on the ASCII text encoding, bytes objects offer several methods that are only valid when working with ASCII compatible data and are closely related to string objects in a variety of other ways.

+class bytes([source[, encoding[, errors]]])
+

Firstly, the syntax for bytes literals is largely the same as that for string literals, except that a b prefix is added:

  • Single quotes: b'still allows embedded "double" quotes' +
  • Double quotes: b"still allows embedded 'single' quotes" +
  • Triple quoted: b'''3 single quotes''', b"""3 double quotes""" +

Only ASCII characters are permitted in bytes literals (regardless of the declared source code encoding). Any binary values over 127 must be entered into bytes literals using the appropriate escape sequence.

As with string literals, bytes literals may also use a r prefix to disable processing of escape sequences. See String and Bytes literals for more about the various forms of bytes literal, including supported escape sequences.

While bytes literals and representations are based on ASCII text, bytes objects actually behave like immutable sequences of integers, with each value in the sequence restricted such that 0 <= x < 256 (attempts to violate this restriction will trigger ValueError). This is done deliberately to emphasise that while many binary formats include ASCII based elements and can be usefully manipulated with some text-oriented algorithms, this is not generally the case for arbitrary binary data (blindly applying text processing algorithms to binary data formats that are not ASCII compatible will usually lead to data corruption).

In addition to the literal forms, bytes objects can be created in a number of other ways:

  • A zero-filled bytes object of a specified length: bytes(10) +
  • From an iterable of integers: bytes(range(20)) +
  • Copying existing binary data via the buffer protocol: bytes(obj) +

Also see the bytes built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytes type has an additional class method to read data in that format:

+classmethod fromhex(string)
+

This bytes class method returns a bytes object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.

>>> bytes.fromhex('2Ef0 F1f2  ')
+b'.\xf0\xf1\xf2'
+

Changed in version 3.7: bytes.fromhex() now skips all ASCII whitespace in the string, not just spaces.

+

A reverse conversion function exists to transform a bytes object into its hexadecimal representation.

+hex([sep[, bytes_per_sep]])
+

Return a string object containing two hexadecimal digits for each byte in the instance.

>>> b'\xf0\xf1\xf2'.hex()
+'f0f1f2'
+

If you want to make the hex string easier to read, you can specify a single character separator sep parameter to include in the output. By default, this separator will be included between each byte. A second optional bytes_per_sep parameter controls the spacing. Positive values calculate the separator position from the right, negative values from the left.

>>> value = b'\xf0\xf1\xf2'
+>>> value.hex('-')
+'f0-f1-f2'
+>>> value.hex('_', 2)
+'f0_f1f2'
+>>> b'UUDDLRLRAB'.hex(' ', -4)
+'55554444 4c524c52 4142'
+

New in version 3.5.

Changed in version 3.8: bytes.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

+
+

Since bytes objects are sequences of integers (akin to a tuple), for a bytes object b, b[0] will be an integer, while b[0:1] will be a bytes object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytes objects uses the literal format (b'...') since it is often more useful than e.g. bytes([46, 46, 46]). You can always convert a bytes object into a list of integers using list(b).

Bytearray Objects

bytearray objects are a mutable counterpart to bytes objects.

+class bytearray([source[, encoding[, errors]]])
+

There is no dedicated literal syntax for bytearray objects, instead they are always created by calling the constructor:

  • Creating an empty instance: bytearray() +
  • Creating a zero-filled instance with a given length: bytearray(10) +
  • From an iterable of integers: bytearray(range(20)) +
  • Copying existing binary data via the buffer protocol: bytearray(b'Hi!') +

As bytearray objects are mutable, they support the mutable sequence operations in addition to the common bytes and bytearray operations described in Bytes and Bytearray Operations.

Also see the bytearray built-in.

Since 2 hexadecimal digits correspond precisely to a single byte, hexadecimal numbers are a commonly used format for describing binary data. Accordingly, the bytearray type has an additional class method to read data in that format:

+classmethod fromhex(string)
+

This bytearray class method returns bytearray object, decoding the given string object. The string must contain two hexadecimal digits per byte, with ASCII whitespace being ignored.

>>> bytearray.fromhex('2Ef0 F1f2  ')
+bytearray(b'.\xf0\xf1\xf2')
+

Changed in version 3.7: bytearray.fromhex() now skips all ASCII whitespace in the string, not just spaces.

+

A reverse conversion function exists to transform a bytearray object into its hexadecimal representation.

+hex([sep[, bytes_per_sep]])
+

Return a string object containing two hexadecimal digits for each byte in the instance.

>>> bytearray(b'\xf0\xf1\xf2').hex()
+'f0f1f2'
+

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex(), bytearray.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

+
+

Since bytearray objects are sequences of integers (akin to a list), for a bytearray object b, b[0] will be an integer, while b[0:1] will be a bytearray object of length 1. (This contrasts with text strings, where both indexing and slicing will produce a string of length 1)

The representation of bytearray objects uses the bytes literal format (bytearray(b'...')) since it is often more useful than e.g. bytearray([46, 46, 46]). You can always convert a bytearray object into a list of integers using list(b).

Bytes and Bytearray Operations

Both bytes and bytearray objects support the common sequence operations. They interoperate not just with operands of the same type, but with any bytes-like object. Due to this flexibility, they can be freely mixed in operations without causing errors. However, the return type of the result may depend on the order of operands.

Note

The methods on bytes and bytearray objects don’t accept strings as their arguments, just as the methods on strings don’t accept bytes as their arguments. For example, you have to write:

a = "abc"
+b = a.replace("a", "f")
+

and:

a = b"abc"
+b = a.replace(b"a", b"f")
+

Some bytes and bytearray operations assume the use of ASCII compatible binary formats, and hence should be avoided when working with arbitrary binary data. These restrictions are covered below.

Note

Using these ASCII based operations to manipulate binary data that is not stored in an ASCII based format may lead to data corruption.

The following methods on bytes and bytearray objects can be used with arbitrary binary data.

+bytes.count(sub[, start[, end]])
+bytearray.count(sub[, start[, end]])
+

Return the number of non-overlapping occurrences of subsequence sub in the range [start, end]. Optional arguments start and end are interpreted as in slice notation.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

If sub is empty, returns the number of empty slices between characters which is the length of the bytes object plus one.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

+
+bytes.removeprefix(prefix, /)
+bytearray.removeprefix(prefix, /)
+

If the binary data starts with the prefix string, return bytes[len(prefix):]. Otherwise, return a copy of the original binary data:

>>> b'TestHook'.removeprefix(b'Test')
+b'Hook'
+>>> b'BaseTestCase'.removeprefix(b'Test')
+b'BaseTestCase'
+

The prefix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

New in version 3.9.

+
+bytes.removesuffix(suffix, /)
+bytearray.removesuffix(suffix, /)
+

If the binary data ends with the suffix string and that suffix is not empty, return bytes[:-len(suffix)]. Otherwise, return a copy of the original binary data:

>>> b'MiscTests'.removesuffix(b'Tests')
+b'Misc'
+>>> b'TmpDirMixin'.removesuffix(b'Tests')
+b'TmpDirMixin'
+

The suffix may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

New in version 3.9.

+
+bytes.decode(encoding='utf-8', errors='strict')
+bytearray.decode(encoding='utf-8', errors='strict')
+

Return the bytes decoded to a str.

encoding defaults to 'utf-8'; see Standard Encodings for possible values.

errors controls how decoding errors are handled. If 'strict' (the default), a UnicodeError exception is raised. Other possible values are 'ignore', 'replace', and any other name registered via codecs.register_error(). See Error Handlers for details.

For performance reasons, the value of errors is not checked for validity unless a decoding error actually occurs, Python Development Mode is enabled or a debug build is used.

Note

Passing the encoding argument to str allows decoding any bytes-like object directly, without needing to make a temporary bytes or bytearray object.

Changed in version 3.1: Added support for keyword arguments.

Changed in version 3.9: The value of the errors argument is now checked in Python Development Mode and in debug mode.

+
+bytes.endswith(suffix[, start[, end]])
+bytearray.endswith(suffix[, start[, end]])
+

Return True if the binary data ends with the specified suffix, otherwise return False. suffix can also be a tuple of suffixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The suffix(es) to search for may be any bytes-like object.

+
+bytes.find(sub[, start[, end]])
+bytearray.find(sub[, start[, end]])
+

Return the lowest index in the data where the subsequence sub is found, such that sub is contained in the slice s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 if sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Note

The find() method should be used only if you need to know the position of sub. To check if sub is a substring or not, use the in operator:

>>> b'Py' in b'Python'
+True
+

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

+
+bytes.index(sub[, start[, end]])
+bytearray.index(sub[, start[, end]])
+

Like find(), but raise ValueError when the subsequence is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

+
+bytes.join(iterable)
+bytearray.join(iterable)
+

Return a bytes or bytearray object which is the concatenation of the binary data sequences in iterable. A TypeError will be raised if there are any values in iterable that are not bytes-like objects, including str objects. The separator between elements is the contents of the bytes or bytearray object providing this method.

+
+static bytes.maketrans(from, to)
+static bytearray.maketrans(from, to)
+

This static method returns a translation table usable for bytes.translate() that will map each character in from into the character at the same position in to; from and to must both be bytes-like objects and have the same length.

New in version 3.1.

+
+bytes.partition(sep)
+bytearray.partition(sep)
+

Split the sequence at the first occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing a copy of the original sequence, followed by two empty bytes or bytearray objects.

The separator to search for may be any bytes-like object.

+
+bytes.replace(old, new[, count])
+bytearray.replace(old, new[, count])
+

Return a copy of the sequence with all occurrences of subsequence old replaced by new. If the optional argument count is given, only the first count occurrences are replaced.

The subsequence to search for and its replacement may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.rfind(sub[, start[, end]])
+bytearray.rfind(sub[, start[, end]])
+

Return the highest index in the sequence where the subsequence sub is found, such that sub is contained within s[start:end]. Optional arguments start and end are interpreted as in slice notation. Return -1 on failure.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

+
+bytes.rindex(sub[, start[, end]])
+bytearray.rindex(sub[, start[, end]])
+

Like rfind() but raises ValueError when the subsequence sub is not found.

The subsequence to search for may be any bytes-like object or an integer in the range 0 to 255.

Changed in version 3.3: Also accept an integer in the range 0 to 255 as the subsequence.

+
+bytes.rpartition(sep)
+bytearray.rpartition(sep)
+

Split the sequence at the last occurrence of sep, and return a 3-tuple containing the part before the separator, the separator itself or its bytearray copy, and the part after the separator. If the separator is not found, return a 3-tuple containing two empty bytes or bytearray objects, followed by a copy of the original sequence.

The separator to search for may be any bytes-like object.

+
+bytes.startswith(prefix[, start[, end]])
+bytearray.startswith(prefix[, start[, end]])
+

Return True if the binary data starts with the specified prefix, otherwise return False. prefix can also be a tuple of prefixes to look for. With optional start, test beginning at that position. With optional end, stop comparing at that position.

The prefix(es) to search for may be any bytes-like object.

+
+bytes.translate(table, /, delete=b'')
+bytearray.translate(table, /, delete=b'')
+

Return a copy of the bytes or bytearray object where all bytes occurring in the optional argument delete are removed, and the remaining bytes have been mapped through the given translation table, which must be a bytes object of length 256.

You can use the bytes.maketrans() method to create a translation table.

Set the table argument to None for translations that only delete characters:

>>> b'read this short text'.translate(None, b'aeiou')
+b'rd ths shrt txt'
+

Changed in version 3.6: delete is now supported as a keyword argument.

+

The following methods on bytes and bytearray objects have default behaviours that assume the use of ASCII compatible binary formats, but can still be used with arbitrary binary data by passing appropriate arguments. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.

+bytes.center(width[, fillbyte])
+bytearray.center(width[, fillbyte])
+

Return a copy of the object centered in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.ljust(width[, fillbyte])
+bytearray.ljust(width[, fillbyte])
+

Return a copy of the object left justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.lstrip([chars])
+bytearray.lstrip([chars])
+

Return a copy of the sequence with specified leading bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix; rather, all combinations of its values are stripped:

>>> b'   spacious   '.lstrip()
+b'spacious   '
+>>> b'www.example.com'.lstrip(b'cmowz.')
+b'example.com'
+

The binary sequence of byte values to remove may be any bytes-like object. See removeprefix() for a method that will remove a single prefix string rather than all of a set of characters. For example:

>>> b'Arthur: three!'.lstrip(b'Arthur: ')
+b'ee!'
+>>> b'Arthur: three!'.removeprefix(b'Arthur: ')
+b'three!'
+

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.rjust(width[, fillbyte])
+bytearray.rjust(width[, fillbyte])
+

Return a copy of the object right justified in a sequence of length width. Padding is done using the specified fillbyte (default is an ASCII space). For bytes objects, the original sequence is returned if width is less than or equal to len(s).

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.rsplit(sep=None, maxsplit=- 1)
+bytearray.rsplit(sep=None, maxsplit=- 1)
+

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given, at most maxsplit splits are done, the rightmost ones. If sep is not specified or None, any subsequence consisting solely of ASCII whitespace is a separator. Except for splitting from the right, rsplit() behaves like split() which is described in detail below.

+
+bytes.rstrip([chars])
+bytearray.rstrip([chars])
+

Return a copy of the sequence with specified trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a suffix; rather, all combinations of its values are stripped:

>>> b'   spacious   '.rstrip()
+b'   spacious'
+>>> b'mississippi'.rstrip(b'ipz')
+b'mississ'
+

The binary sequence of byte values to remove may be any bytes-like object. See removesuffix() for a method that will remove a single suffix string rather than all of a set of characters. For example:

>>> b'Monty Python'.rstrip(b' Python')
+b'M'
+>>> b'Monty Python'.removesuffix(b' Python')
+b'Monty'
+

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.split(sep=None, maxsplit=- 1)
+bytearray.split(sep=None, maxsplit=- 1)
+

Split the binary sequence into subsequences of the same type, using sep as the delimiter string. If maxsplit is given and non-negative, at most maxsplit splits are done (thus, the list will have at most maxsplit+1 elements). If maxsplit is not specified or is -1, then there is no limit on the number of splits (all possible splits are made).

If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty subsequences (for example, b'1,,2'.split(b',') returns [b'1', b'', b'2']). The sep argument may consist of a multibyte sequence (for example, b'1<>2<>3'.split(b'<>') returns [b'1', b'2', b'3']). Splitting an empty sequence with a specified separator returns [b''] or [bytearray(b'')] depending on the type of object being split. The sep argument may be any bytes-like object.

For example:

>>> b'1,2,3'.split(b',')
+[b'1', b'2', b'3']
+>>> b'1,2,3'.split(b',', maxsplit=1)
+[b'1', b'2,3']
+>>> b'1,2,,3,'.split(b',')
+[b'1', b'2', b'', b'3', b'']
+

If sep is not specified or is None, a different splitting algorithm is applied: runs of consecutive ASCII whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the sequence has leading or trailing whitespace. Consequently, splitting an empty sequence or a sequence consisting solely of ASCII whitespace without a specified separator returns [].

For example:

>>> b'1 2 3'.split()
+[b'1', b'2', b'3']
+>>> b'1 2 3'.split(maxsplit=1)
+[b'1', b'2 3']
+>>> b'   1   2   3   '.split()
+[b'1', b'2', b'3']
+
+
+bytes.strip([chars])
+bytearray.strip([chars])
+

Return a copy of the sequence with specified leading and trailing bytes removed. The chars argument is a binary sequence specifying the set of byte values to be removed - the name refers to the fact this method is usually used with ASCII characters. If omitted or None, the chars argument defaults to removing ASCII whitespace. The chars argument is not a prefix or suffix; rather, all combinations of its values are stripped:

>>> b'   spacious   '.strip()
+b'spacious'
+>>> b'www.example.com'.strip(b'cmowz.')
+b'example'
+

The binary sequence of byte values to remove may be any bytes-like object.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+

The following methods on bytes and bytearray objects assume the use of ASCII compatible binary formats and should not be applied to arbitrary binary data. Note that all of the bytearray methods in this section do not operate in place, and instead produce new objects.

+bytes.capitalize()
+bytearray.capitalize()
+

Return a copy of the sequence with each byte interpreted as an ASCII character, and the first byte capitalized and the rest lowercased. Non-ASCII byte values are passed through unchanged.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.expandtabs(tabsize=8)
+bytearray.expandtabs(tabsize=8)
+

Return a copy of the sequence where all ASCII tab characters are replaced by one or more ASCII spaces, depending on the current column and the given tab size. Tab positions occur every tabsize bytes (default is 8, giving tab positions at columns 0, 8, 16 and so on). To expand the sequence, the current column is set to zero and the sequence is examined byte by byte. If the byte is an ASCII tab character (b'\t'), one or more space characters are inserted in the result until the current column is equal to the next tab position. (The tab character itself is not copied.) If the current byte is an ASCII newline (b'\n') or carriage return (b'\r'), it is copied and the current column is reset to zero. Any other byte value is copied unchanged and the current column is incremented by one regardless of how the byte value is represented when printed:

>>> b'01\t012\t0123\t01234'.expandtabs()
+b'01      012     0123    01234'
+>>> b'01\t012\t0123\t01234'.expandtabs(4)
+b'01  012 0123    01234'
+

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.isalnum()
+bytearray.isalnum()
+

Return True if all bytes in the sequence are alphabetical ASCII characters or ASCII decimal digits and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:

>>> b'ABCabc1'.isalnum()
+True
+>>> b'ABC abc1'.isalnum()
+False
+
+
+bytes.isalpha()
+bytearray.isalpha()
+

Return True if all bytes in the sequence are alphabetic ASCII characters and the sequence is not empty, False otherwise. Alphabetic ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'.

For example:

>>> b'ABCabc'.isalpha()
+True
+>>> b'ABCabc1'.isalpha()
+False
+
+
+bytes.isascii()
+bytearray.isascii()
+

Return True if the sequence is empty or all bytes in the sequence are ASCII, False otherwise. ASCII bytes are in the range 0-0x7F.

New in version 3.7.

+
+bytes.isdigit()
+bytearray.isdigit()
+

Return True if all bytes in the sequence are ASCII decimal digits and the sequence is not empty, False otherwise. ASCII decimal digits are those byte values in the sequence b'0123456789'.

For example:

>>> b'1234'.isdigit()
+True
+>>> b'1.23'.isdigit()
+False
+
+
+bytes.islower()
+bytearray.islower()
+

Return True if there is at least one lowercase ASCII character in the sequence and no uppercase ASCII characters, False otherwise.

For example:

>>> b'hello world'.islower()
+True
+>>> b'Hello world'.islower()
+False
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

+
+bytes.isspace()
+bytearray.isspace()
+

Return True if all bytes in the sequence are ASCII whitespace and the sequence is not empty, False otherwise. ASCII whitespace characters are those byte values in the sequence b' \t\n\r\x0b\f' (space, tab, newline, carriage return, vertical tab, form feed).

+
+bytes.istitle()
+bytearray.istitle()
+

Return True if the sequence is ASCII titlecase and the sequence is not empty, False otherwise. See bytes.title() for more details on the definition of “titlecase”.

For example:

>>> b'Hello World'.istitle()
+True
+>>> b'Hello world'.istitle()
+False
+
+
+bytes.isupper()
+bytearray.isupper()
+

Return True if there is at least one uppercase alphabetic ASCII character in the sequence and no lowercase ASCII characters, False otherwise.

For example:

>>> b'HELLO WORLD'.isupper()
+True
+>>> b'Hello world'.isupper()
+False
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

+
+bytes.lower()
+bytearray.lower()
+

Return a copy of the sequence with all the uppercase ASCII characters converted to their corresponding lowercase counterpart.

For example:

>>> b'Hello World'.lower()
+b'hello world'
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.splitlines(keepends=False)
+bytearray.splitlines(keepends=False)
+

Return a list of the lines in the binary sequence, breaking at ASCII line boundaries. This method uses the universal newlines approach to splitting lines. Line breaks are not included in the resulting list unless keepends is given and true.

For example:

>>> b'ab c\n\nde fg\rkl\r\n'.splitlines()
+[b'ab c', b'', b'de fg', b'kl']
+>>> b'ab c\n\nde fg\rkl\r\n'.splitlines(keepends=True)
+[b'ab c\n', b'\n', b'de fg\r', b'kl\r\n']
+

Unlike split() when a delimiter string sep is given, this method returns an empty list for the empty string, and a terminal line break does not result in an extra line:

>>> b"".split(b'\n'), b"Two lines\n".split(b'\n')
+([b''], [b'Two lines', b''])
+>>> b"".splitlines(), b"One line\n".splitlines()
+([], [b'One line'])
+
+
+bytes.swapcase()
+bytearray.swapcase()
+

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart and vice-versa.

For example:

>>> b'Hello World'.swapcase()
+b'hELLO wORLD'
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Unlike str.swapcase(), it is always the case that bin.swapcase().swapcase() == bin for the binary versions. Case conversions are symmetrical in ASCII, even though that is not generally true for arbitrary Unicode code points.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.title()
+bytearray.title()
+

Return a titlecased version of the binary sequence where words start with an uppercase ASCII character and the remaining characters are lowercase. Uncased byte values are left unmodified.

For example:

>>> b'Hello world'.title()
+b'Hello World'
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'. All other byte values are uncased.

The algorithm uses a simple language-independent definition of a word as groups of consecutive letters. The definition works in many contexts but it means that apostrophes in contractions and possessives form word boundaries, which may not be the desired result:

>>> b"they're bill's friends from the UK".title()
+b"They'Re Bill'S Friends From The Uk"
+

A workaround for apostrophes can be constructed using regular expressions:

>>> import re
+>>> def titlecase(s):
+...     return re.sub(rb"[A-Za-z]+('[A-Za-z]+)?",
+...                   lambda mo: mo.group(0)[0:1].upper() +
+...                              mo.group(0)[1:].lower(),
+...                   s)
+...
+>>> titlecase(b"they're bill's friends.")
+b"They're Bill's Friends."
+

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.upper()
+bytearray.upper()
+

Return a copy of the sequence with all the lowercase ASCII characters converted to their corresponding uppercase counterpart.

For example:

>>> b'Hello World'.upper()
+b'HELLO WORLD'
+

Lowercase ASCII characters are those byte values in the sequence b'abcdefghijklmnopqrstuvwxyz'. Uppercase ASCII characters are those byte values in the sequence b'ABCDEFGHIJKLMNOPQRSTUVWXYZ'.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+
+bytes.zfill(width)
+bytearray.zfill(width)
+

Return a copy of the sequence left filled with ASCII b'0' digits to make a sequence of length width. A leading sign prefix (b'+'/ b'-') is handled by inserting the padding after the sign character rather than before. For bytes objects, the original sequence is returned if width is less than or equal to len(seq).

For example:

>>> b"42".zfill(5)
+b'00042'
+>>> b"-42".zfill(5)
+b'-0042'
+

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

+

+printf-style Bytes Formatting

Note

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). If the value being printed may be a tuple or dictionary, wrap it in a tuple.

Bytes objects (bytes/bytearray) have one unique built-in operation: the % operator (modulo). This is also known as the bytes formatting or interpolation operator. Given format % values (where format is a bytes object), % conversion specifications in format are replaced with zero or more elements of values. The effect is similar to using the sprintf() in the C language.

If format requires a single argument, values may be a single non-tuple object. 5 Otherwise, values must be a tuple with exactly the number of items specified by the format bytes object, or a single mapping object (for example, a dictionary).

A conversion specifier contains two or more characters and has the following components, which must occur in this order:

  1. The '%' character, which marks the start of the specifier.
  2. Mapping key (optional), consisting of a parenthesised sequence of characters (for example, (somename)).
  3. Conversion flags (optional), which affect the result of some conversion types.
  4. Minimum field width (optional). If specified as an '*' (asterisk), the actual width is read from the next element of the tuple in values, and the object to convert comes after the minimum field width and optional precision.
  5. Precision (optional), given as a '.' (dot) followed by the precision. If specified as '*' (an asterisk), the actual precision is read from the next element of the tuple in values, and the value to convert comes after the precision.
  6. Length modifier (optional).
  7. Conversion type.

When the right argument is a dictionary (or other mapping type), then the formats in the bytes object must include a parenthesised mapping key into that dictionary inserted immediately after the '%' character. The mapping key selects the value to be formatted from the mapping. For example:

>>> print(b'%(language)s has %(number)03d quote types.' %
+...       {b'language': b"Python", b"number": 2})
+b'Python has 002 quote types.'
+

In this case no * specifiers may occur in a format (since they require a sequential parameter list).

The conversion flag characters are:

+ + + + + +

Flag

Meaning

'#'

The value conversion will use the “alternate form” (where defined below).

'0'

The conversion will be zero padded for numeric values.

'-'

The converted value is left adjusted (overrides the '0' conversion if both are given).

' '

(a space) A blank should be left before a positive number (or empty string) produced by a signed conversion.

'+'

A sign character ('+' or '-') will precede the conversion (overrides a “space” flag).

A length modifier (h, l, or L) may be present, but is ignored as it is not necessary for Python – so e.g. %ld is identical to %d.

The conversion types are:

+ + + + + + + + + + + + + + + + + + +

Conversion

Meaning

Notes

'd'

Signed integer decimal.

'i'

Signed integer decimal.

'o'

Signed octal value.

(1)

'u'

Obsolete type – it is identical to 'd'.

(8)

'x'

Signed hexadecimal (lowercase).

(2)

'X'

Signed hexadecimal (uppercase).

(2)

'e'

Floating point exponential format (lowercase).

(3)

'E'

Floating point exponential format (uppercase).

(3)

'f'

Floating point decimal format.

(3)

'F'

Floating point decimal format.

(3)

'g'

Floating point format. Uses lowercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'G'

Floating point format. Uses uppercase exponential format if exponent is less than -4 or not less than precision, decimal format otherwise.

(4)

'c'

Single byte (accepts integer or single byte objects).

'b'

Bytes (any object that follows the buffer protocol or has __bytes__()).

(5)

's'

's' is an alias for 'b' and should only be used for Python2/3 code bases.

(6)

'a'

Bytes (converts any Python object using repr(obj).encode('ascii', 'backslashreplace')).

(5)

'r'

'r' is an alias for 'a' and should only be used for Python2/3 code bases.

(7)

'%'

No argument is converted, results in a '%' character in the result.

Notes:

  1. The alternate form causes a leading octal specifier ('0o') to be inserted before the first digit.
  2. The alternate form causes a leading '0x' or '0X' (depending on whether the 'x' or 'X' format was used) to be inserted before the first digit.
  3. +

    The alternate form causes the result to always contain a decimal point, even if no digits follow it.

    The precision determines the number of digits after the decimal point and defaults to 6.

  4. +

    The alternate form causes the result to always contain a decimal point, and trailing zeroes are not removed as they would otherwise be.

    The precision determines the number of significant digits before and after the decimal point and defaults to 6.

  5. If precision is N, the output is truncated to N characters.
  6. +b'%s' is deprecated, but will not be removed during the 3.x series.
  7. +b'%r' is deprecated, but will not be removed during the 3.x series.
  8. See PEP 237.

Note

The bytearray version of this method does not operate in place - it always produces a new object, even if no changes were made.

See also

PEP 461 - Adding % formatting to bytes and bytearray

New in version 3.5.

Memory Views

memoryview objects allow Python code to access the internal data of an object that supports the buffer protocol without copying.

+class memoryview(object)
+

Create a memoryview that references object. object must support the buffer protocol. Built-in objects that support the buffer protocol include bytes and bytearray.

A memoryview has the notion of an element, which is the atomic memory unit handled by the originating object. For many simple types such as bytes and bytearray, an element is a single byte, but other types such as array.array may have bigger elements.

len(view) is equal to the length of tolist, which is the nested list representation of the view. If view.ndim = 1, this is equal to the number of elements in the view.

Changed in version 3.12: If view.ndim == 0, len(view) now raises TypeError instead of returning 1.

The itemsize attribute will give you the number of bytes in a single element.

A memoryview supports slicing and indexing to expose its data. One-dimensional slicing will result in a subview:

>>> v = memoryview(b'abcefg')
+>>> v[1]
+98
+>>> v[-1]
+103
+>>> v[1:4]
+<memory at 0x7f3ddc9f4350>
+>>> bytes(v[1:4])
+b'bce'
+

If format is one of the native format specifiers from the struct module, indexing with an integer or a tuple of integers is also supported and returns a single element with the correct type. One-dimensional memoryviews can be indexed with an integer or a one-integer tuple. Multi-dimensional memoryviews can be indexed with tuples of exactly ndim integers where ndim is the number of dimensions. Zero-dimensional memoryviews can be indexed with the empty tuple.

Here is an example with a non-byte format:

>>> import array
+>>> a = array.array('l', [-11111111, 22222222, -33333333, 44444444])
+>>> m = memoryview(a)
+>>> m[0]
+-11111111
+>>> m[-1]
+44444444
+>>> m[::2].tolist()
+[-11111111, -33333333]
+

If the underlying object is writable, the memoryview supports one-dimensional slice assignment. Resizing is not allowed:

>>> data = bytearray(b'abcefg')
+>>> v = memoryview(data)
+>>> v.readonly
+False
+>>> v[0] = ord(b'z')
+>>> data
+bytearray(b'zbcefg')
+>>> v[1:4] = b'123'
+>>> data
+bytearray(b'z123fg')
+>>> v[2:3] = b'spam'
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: memoryview assignment: lvalue and rvalue have different structures
+>>> v[2:6] = b'spam'
+>>> data
+bytearray(b'z1spam')
+

One-dimensional memoryviews of hashable (read-only) types with formats ‘B’, ‘b’ or ‘c’ are also hashable. The hash is defined as hash(m) == hash(m.tobytes()):

>>> v = memoryview(b'abcefg')
+>>> hash(v) == hash(b'abcefg')
+True
+>>> hash(v[2:4]) == hash(b'ce')
+True
+>>> hash(v[::-2]) == hash(b'abcefg'[::-2])
+True
+

Changed in version 3.3: One-dimensional memoryviews can now be sliced. One-dimensional memoryviews with formats ‘B’, ‘b’ or ‘c’ are now hashable.

Changed in version 3.4: memoryview is now registered automatically with collections.abc.Sequence

Changed in version 3.5: memoryviews can now be indexed with tuple of integers.

memoryview has several methods:

+__eq__(exporter)
+

A memoryview and a PEP 3118 exporter are equal if their shapes are equivalent and if all corresponding values are equal when the operands’ respective format codes are interpreted using struct syntax.

For the subset of struct format strings currently supported by tolist(), v and w are equal if v.tolist() == w.tolist():

>>> import array
+>>> a = array.array('I', [1, 2, 3, 4, 5])
+>>> b = array.array('d', [1.0, 2.0, 3.0, 4.0, 5.0])
+>>> c = array.array('b', [5, 3, 1])
+>>> x = memoryview(a)
+>>> y = memoryview(b)
+>>> x == a == y == b
+True
+>>> x.tolist() == a.tolist() == y.tolist() == b.tolist()
+True
+>>> z = y[::-2]
+>>> z == c
+True
+>>> z.tolist() == c.tolist()
+True
+

If either format string is not supported by the struct module, then the objects will always compare as unequal (even if the format strings and buffer contents are identical):

>>> from ctypes import BigEndianStructure, c_long
+>>> class BEPoint(BigEndianStructure):
+...     _fields_ = [("x", c_long), ("y", c_long)]
+...
+>>> point = BEPoint(100, 200)
+>>> a = memoryview(point)
+>>> b = memoryview(point)
+>>> a == point
+False
+>>> a == b
+False
+

Note that, as with floating point numbers, v is w does not imply v == w for memoryview objects.

Changed in version 3.3: Previous versions compared the raw memory disregarding the item format and the logical array structure.

+
+tobytes(order='C')
+

Return the data in the buffer as a bytestring. This is equivalent to calling the bytes constructor on the memoryview.

>>> m = memoryview(b"abc")
+>>> m.tobytes()
+b'abc'
+>>> bytes(m)
+b'abc'
+

For non-contiguous arrays the result is equal to the flattened list representation with all elements converted to bytes. tobytes() supports all format strings, including those that are not in struct module syntax.

New in version 3.8: order can be {‘C’, ‘F’, ‘A’}. When order is ‘C’ or ‘F’, the data of the original array is converted to C or Fortran order. For contiguous views, ‘A’ returns an exact copy of the physical memory. In particular, in-memory Fortran order is preserved. For non-contiguous views, the data is converted to C first. order=None is the same as order=’C’.

+
+hex([sep[, bytes_per_sep]])
+

Return a string object containing two hexadecimal digits for each byte in the buffer.

>>> m = memoryview(b"abc")
+>>> m.hex()
+'616263'
+

New in version 3.5.

Changed in version 3.8: Similar to bytes.hex(), memoryview.hex() now supports optional sep and bytes_per_sep parameters to insert separators between bytes in the hex output.

+
+tolist()
+

Return the data in the buffer as a list of elements.

>>> memoryview(b'abc').tolist()
+[97, 98, 99]
+>>> import array
+>>> a = array.array('d', [1.1, 2.2, 3.3])
+>>> m = memoryview(a)
+>>> m.tolist()
+[1.1, 2.2, 3.3]
+

Changed in version 3.3: tolist() now supports all single character native formats in struct module syntax as well as multi-dimensional representations.

+
+toreadonly()
+

Return a readonly version of the memoryview object. The original memoryview object is unchanged.

>>> m = memoryview(bytearray(b'abc'))
+>>> mm = m.toreadonly()
+>>> mm.tolist()
+[97, 98, 99]
+>>> mm[0] = 42
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: cannot modify read-only memory
+>>> m[0] = 43
+>>> mm.tolist()
+[43, 98, 99]
+

New in version 3.8.

+
+release()
+

Release the underlying buffer exposed by the memoryview object. Many objects take special actions when a view is held on them (for example, a bytearray would temporarily forbid resizing); therefore, calling release() is handy to remove these restrictions (and free any dangling resources) as soon as possible.

After this method has been called, any further operation on the view raises a ValueError (except release() itself which can be called multiple times):

>>> m = memoryview(b'abc')
+>>> m.release()
+>>> m[0]
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: operation forbidden on released memoryview object
+

The context management protocol can be used for a similar effect, using the with statement:

>>> with memoryview(b'abc') as m:
+...     m[0]
+...
+97
+>>> m[0]
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: operation forbidden on released memoryview object
+

New in version 3.2.

+
+cast(format[, shape])
+

Cast a memoryview to a new format or shape. shape defaults to [byte_length//new_itemsize], which means that the result view will be one-dimensional. The return value is a new memoryview, but the buffer itself is not copied. Supported casts are 1D -> C-contiguous and C-contiguous -> 1D.

The destination format is restricted to a single element native format in struct syntax. One of the formats must be a byte format (‘B’, ‘b’ or ‘c’). The byte length of the result must be the same as the original length. Note that all byte lengths may depend on the operating system.

Cast 1D/long to 1D/unsigned bytes:

>>> import array
+>>> a = array.array('l', [1,2,3])
+>>> x = memoryview(a)
+>>> x.format
+'l'
+>>> x.itemsize
+8
+>>> len(x)
+3
+>>> x.nbytes
+24
+>>> y = x.cast('B')
+>>> y.format
+'B'
+>>> y.itemsize
+1
+>>> len(y)
+24
+>>> y.nbytes
+24
+

Cast 1D/unsigned bytes to 1D/char:

>>> b = bytearray(b'zyz')
+>>> x = memoryview(b)
+>>> x[0] = b'a'
+Traceback (most recent call last):
+  ...
+TypeError: memoryview: invalid type for format 'B'
+>>> y = x.cast('c')
+>>> y[0] = b'a'
+>>> b
+bytearray(b'ayz')
+

Cast 1D/bytes to 3D/ints to 1D/signed char:

>>> import struct
+>>> buf = struct.pack("i"*12, *list(range(12)))
+>>> x = memoryview(buf)
+>>> y = x.cast('i', shape=[2,2,3])
+>>> y.tolist()
+[[[0, 1, 2], [3, 4, 5]], [[6, 7, 8], [9, 10, 11]]]
+>>> y.format
+'i'
+>>> y.itemsize
+4
+>>> len(y)
+2
+>>> y.nbytes
+48
+>>> z = y.cast('b')
+>>> z.format
+'b'
+>>> z.itemsize
+1
+>>> len(z)
+48
+>>> z.nbytes
+48
+

Cast 1D/unsigned long to 2D/unsigned long:

>>> buf = struct.pack("L"*6, *list(range(6)))
+>>> x = memoryview(buf)
+>>> y = x.cast('L', shape=[2,3])
+>>> len(y)
+2
+>>> y.nbytes
+48
+>>> y.tolist()
+[[0, 1, 2], [3, 4, 5]]
+

New in version 3.3.

Changed in version 3.5: The source format is no longer restricted when casting to a byte view.

+

There are also several readonly attributes available:

+obj
+

The underlying object of the memoryview:

>>> b  = bytearray(b'xyz')
+>>> m = memoryview(b)
+>>> m.obj is b
+True
+

New in version 3.3.

+
+nbytes
+

nbytes == product(shape) * itemsize == len(m.tobytes()). This is the amount of space in bytes that the array would use in a contiguous representation. It is not necessarily equal to len(m):

>>> import array
+>>> a = array.array('i', [1,2,3,4,5])
+>>> m = memoryview(a)
+>>> len(m)
+5
+>>> m.nbytes
+20
+>>> y = m[::2]
+>>> len(y)
+3
+>>> y.nbytes
+12
+>>> len(y.tobytes())
+12
+

Multi-dimensional arrays:

>>> import struct
+>>> buf = struct.pack("d"*12, *[1.5*x for x in range(12)])
+>>> x = memoryview(buf)
+>>> y = x.cast('d', shape=[3,4])
+>>> y.tolist()
+[[0.0, 1.5, 3.0, 4.5], [6.0, 7.5, 9.0, 10.5], [12.0, 13.5, 15.0, 16.5]]
+>>> len(y)
+3
+>>> y.nbytes
+96
+

New in version 3.3.

+
+readonly
+

A bool indicating whether the memory is read only.

+
+format
+

A string containing the format (in struct module style) for each element in the view. A memoryview can be created from exporters with arbitrary format strings, but some methods (e.g. tolist()) are restricted to native single element formats.

Changed in version 3.3: format 'B' is now handled according to the struct module syntax. This means that memoryview(b'abc')[0] == b'abc'[0] == 97.

+
+itemsize
+

The size in bytes of each element of the memoryview:

>>> import array, struct
+>>> m = memoryview(array.array('H', [32000, 32001, 32002]))
+>>> m.itemsize
+2
+>>> m[0]
+32000
+>>> struct.calcsize('H') == m.itemsize
+True
+
+
+ndim
+

An integer indicating how many dimensions of a multi-dimensional array the memory represents.

+
+shape
+

A tuple of integers the length of ndim giving the shape of the memory as an N-dimensional array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.

+
+strides
+

A tuple of integers the length of ndim giving the size in bytes to access each element for each dimension of the array.

Changed in version 3.3: An empty tuple instead of None when ndim = 0.

+
+suboffsets
+

Used internally for PIL-style arrays. The value is informational only.

+
+c_contiguous
+

A bool indicating whether the memory is C-contiguous.

New in version 3.3.

+
+f_contiguous
+

A bool indicating whether the memory is Fortran contiguous.

New in version 3.3.

+
+contiguous
+

A bool indicating whether the memory is contiguous.

New in version 3.3.

+
+

Set Types — set, frozenset

A set object is an unordered collection of distinct hashable objects. Common uses include membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference. (For other containers see the built-in dict, list, and tuple classes, and the collections module.)

Like other collections, sets support x in set, len(set), and for x in +set. Being an unordered collection, sets do not record element position or order of insertion. Accordingly, sets do not support indexing, slicing, or other sequence-like behavior.

There are currently two built-in set types, set and frozenset. The set type is mutable — the contents can be changed using methods like add() and remove(). Since it is mutable, it has no hash value and cannot be used as either a dictionary key or as an element of another set. The frozenset type is immutable and hashable — its contents cannot be altered after it is created; it can therefore be used as a dictionary key or as an element of another set.

Non-empty sets (not frozensets) can be created by placing a comma-separated list of elements within braces, for example: {'jack', 'sjoerd'}, in addition to the set constructor.

The constructors for both classes work the same:

+class set([iterable])
+class frozenset([iterable])
+

Return a new set or frozenset object whose elements are taken from iterable. The elements of a set must be hashable. To represent sets of sets, the inner sets must be frozenset objects. If iterable is not specified, a new empty set is returned.

Sets can be created by several means:

  • Use a comma-separated list of elements within braces: {'jack', 'sjoerd'} +
  • Use a set comprehension: {c for c in 'abracadabra' if c not in 'abc'} +
  • Use the type constructor: set(), set('foobar'), set(['a', 'b', 'foo']) +

Instances of set and frozenset provide the following operations:

len(s) +
+

Return the number of elements in set s (cardinality of s).

+
x in s +
+

Test x for membership in s.

+
x not in s +
+

Test x for non-membership in s.

+
+isdisjoint(other)
+

Return True if the set has no elements in common with other. Sets are disjoint if and only if their intersection is the empty set.

+
+issubset(other)
set <= other +
+

Test whether every element in the set is in other.

+
set < other +
+

Test whether the set is a proper subset of other, that is, set <= other and set != other.

+
+issuperset(other)
set >= other +
+

Test whether every element in other is in the set.

+
set > other +
+

Test whether the set is a proper superset of other, that is, set >= +other and set != other.

+
+union(*others)
set | other | ... +
+

Return a new set with elements from the set and all others.

+
+intersection(*others)
set & other & ... +
+

Return a new set with elements common to the set and all others.

+
+difference(*others)
set - other - ... +
+

Return a new set with elements in the set that are not in the others.

+
+symmetric_difference(other)
set ^ other +
+

Return a new set with elements in either the set or other but not both.

+
+copy()
+

Return a shallow copy of the set.

+

Note, the non-operator versions of union(), intersection(), difference(), symmetric_difference(), issubset(), and issuperset() methods will accept any iterable as an argument. In contrast, their operator based counterparts require their arguments to be sets. This precludes error-prone constructions like set('abc') & 'cbs' in favor of the more readable set('abc').intersection('cbs').

Both set and frozenset support set to set comparisons. Two sets are equal if and only if every element of each set is contained in the other (each is a subset of the other). A set is less than another set if and only if the first set is a proper subset of the second set (is a subset, but is not equal). A set is greater than another set if and only if the first set is a proper superset of the second set (is a superset, but is not equal).

Instances of set are compared to instances of frozenset based on their members. For example, set('abc') == frozenset('abc') returns True and so does set('abc') in set([frozenset('abc')]).

The subset and equality comparisons do not generalize to a total ordering function. For example, any two nonempty disjoint sets are not equal and are not subsets of each other, so all of the following return False: a<b, a==b, or a>b.

Since sets only define partial ordering (subset relationships), the output of the list.sort() method is undefined for lists of sets.

Set elements, like dictionary keys, must be hashable.

Binary operations that mix set instances with frozenset return the type of the first operand. For example: frozenset('ab') | +set('bc') returns an instance of frozenset.

The following table lists operations available for set that do not apply to immutable instances of frozenset:

+update(*others)
set |= other | ... +
+

Update the set, adding elements from all others.

+
+intersection_update(*others)
set &= other & ... +
+

Update the set, keeping only elements found in it and all others.

+
+difference_update(*others)
set -= other | ... +
+

Update the set, removing elements found in others.

+
+symmetric_difference_update(other)
set ^= other +
+

Update the set, keeping only elements found in either set, but not in both.

+
+add(elem)
+

Add element elem to the set.

+
+remove(elem)
+

Remove element elem from the set. Raises KeyError if elem is not contained in the set.

+
+discard(elem)
+

Remove element elem from the set if it is present.

+
+pop()
+

Remove and return an arbitrary element from the set. Raises KeyError if the set is empty.

+
+clear()
+

Remove all elements from the set.

+

Note, the non-operator versions of the update(), intersection_update(), difference_update(), and symmetric_difference_update() methods will accept any iterable as an argument.

Note, the elem argument to the __contains__(), remove(), and discard() methods may be a set. To support searching for an equivalent frozenset, a temporary one is created from elem.

+

Mapping Types — dict

A mapping object maps hashable values to arbitrary objects. Mappings are mutable objects. There is currently only one standard mapping type, the dictionary. (For other containers see the built-in list, set, and tuple classes, and the collections module.)

A dictionary’s keys are almost arbitrary values. Values that are not hashable, that is, values containing lists, dictionaries or other mutable types (that are compared by value rather than by object identity) may not be used as keys. Values that compare equal (such as 1, 1.0, and True) can be used interchangeably to index the same dictionary entry.

+class dict(**kwargs)
class dict(mapping, **kwargs) +
class dict(iterable, **kwargs) +
+

Return a new dictionary initialized from an optional positional argument and a possibly empty set of keyword arguments.

Dictionaries can be created by several means:

  • Use a comma-separated list of key: value pairs within braces: {'jack': 4098, 'sjoerd': 4127} or {4098: 'jack', 4127: 'sjoerd'} +
  • Use a dict comprehension: {}, {x: x ** 2 for x in range(10)} +
  • Use the type constructor: dict(), dict([('foo', 100), ('bar', 200)]), dict(foo=100, bar=200) +

If no positional argument is given, an empty dictionary is created. If a positional argument is given and it is a mapping object, a dictionary is created with the same key-value pairs as the mapping object. Otherwise, the positional argument must be an iterable object. Each item in the iterable must itself be an iterable with exactly two objects. The first object of each item becomes a key in the new dictionary, and the second object the corresponding value. If a key occurs more than once, the last value for that key becomes the corresponding value in the new dictionary.

If keyword arguments are given, the keyword arguments and their values are added to the dictionary created from the positional argument. If a key being added is already present, the value from the keyword argument replaces the value from the positional argument.

To illustrate, the following examples all return a dictionary equal to {"one": 1, "two": 2, "three": 3}:

>>> a = dict(one=1, two=2, three=3)
+>>> b = {'one': 1, 'two': 2, 'three': 3}
+>>> c = dict(zip(['one', 'two', 'three'], [1, 2, 3]))
+>>> d = dict([('two', 2), ('one', 1), ('three', 3)])
+>>> e = dict({'three': 3, 'one': 1, 'two': 2})
+>>> f = dict({'one': 1, 'three': 3}, two=2)
+>>> a == b == c == d == e == f
+True
+

Providing keyword arguments as in the first example only works for keys that are valid Python identifiers. Otherwise, any valid keys can be used.

These are the operations that dictionaries support (and therefore, custom mapping types should support too):

list(d) +
+

Return a list of all the keys used in the dictionary d.

+
len(d) +
+

Return the number of items in the dictionary d.

+
d[key] +
+

Return the item of d with key key. Raises a KeyError if key is not in the map.

If a subclass of dict defines a method __missing__() and key is not present, the d[key] operation calls that method with the key key as argument. The d[key] operation then returns or raises whatever is returned or raised by the __missing__(key) call. No other operations or methods invoke __missing__(). If __missing__() is not defined, KeyError is raised. __missing__() must be a method; it cannot be an instance variable:

>>> class Counter(dict):
+...     def __missing__(self, key):
+...         return 0
+...
+>>> c = Counter()
+>>> c['red']
+0
+>>> c['red'] += 1
+>>> c['red']
+1
+

The example above shows part of the implementation of collections.Counter. A different __missing__ method is used by collections.defaultdict.

+
d[key] = value +
+

Set d[key] to value.

+
del d[key] +
+

Remove d[key] from d. Raises a KeyError if key is not in the map.

+
key in d +
+

Return True if d has a key key, else False.

+
key not in d +
+

Equivalent to not key in d.

+
iter(d) +
+

Return an iterator over the keys of the dictionary. This is a shortcut for iter(d.keys()).

+
+clear()
+

Remove all items from the dictionary.

+
+copy()
+

Return a shallow copy of the dictionary.

+
+classmethod fromkeys(iterable[, value])
+

Create a new dictionary with keys from iterable and values set to value.

fromkeys() is a class method that returns a new dictionary. value defaults to None. All of the values refer to just a single instance, so it generally doesn’t make sense for value to be a mutable object such as an empty list. To get distinct values, use a dict comprehension instead.

+
+get(key[, default])
+

Return the value for key if key is in the dictionary, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.

+
+items()
+

Return a new view of the dictionary’s items ((key, value) pairs). See the documentation of view objects.

+
+keys()
+

Return a new view of the dictionary’s keys. See the documentation of view objects.

+
+pop(key[, default])
+

If key is in the dictionary, remove it and return its value, else return default. If default is not given and key is not in the dictionary, a KeyError is raised.

+
+popitem()
+

Remove and return a (key, value) pair from the dictionary. Pairs are returned in LIFO order.

popitem() is useful to destructively iterate over a dictionary, as often used in set algorithms. If the dictionary is empty, calling popitem() raises a KeyError.

Changed in version 3.7: LIFO order is now guaranteed. In prior versions, popitem() would return an arbitrary key/value pair.

+
reversed(d) +
+

Return a reverse iterator over the keys of the dictionary. This is a shortcut for reversed(d.keys()).

New in version 3.8.

+
+setdefault(key[, default])
+

If key is in the dictionary, return its value. If not, insert key with a value of default and return default. default defaults to None.

+
+update([other])
+

Update the dictionary with the key/value pairs from other, overwriting existing keys. Return None.

update() accepts either another dictionary object or an iterable of key/value pairs (as tuples or other iterables of length two). If keyword arguments are specified, the dictionary is then updated with those key/value pairs: d.update(red=1, blue=2).

+
+values()
+

Return a new view of the dictionary’s values. See the documentation of view objects.

An equality comparison between one dict.values() view and another will always return False. This also applies when comparing dict.values() to itself:

>>> d = {'a': 1}
+>>> d.values() == d.values()
+False
+
+
d | other +
+

Create a new dictionary with the merged keys and values of d and other, which must both be dictionaries. The values of other take priority when d and other share keys.

New in version 3.9.

+
d |= other +
+

Update the dictionary d with keys and values from other, which may be either a mapping or an iterable of key/value pairs. The values of other take priority when d and other share keys.

New in version 3.9.

+

Dictionaries compare equal if and only if they have the same (key, +value) pairs (regardless of ordering). Order comparisons (‘<’, ‘<=’, ‘>=’, ‘>’) raise TypeError.

Dictionaries preserve insertion order. Note that updating a key does not affect the order. Keys added after deletion are inserted at the end.

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
+>>> d
+{'one': 1, 'two': 2, 'three': 3, 'four': 4}
+>>> list(d)
+['one', 'two', 'three', 'four']
+>>> list(d.values())
+[1, 2, 3, 4]
+>>> d["one"] = 42
+>>> d
+{'one': 42, 'two': 2, 'three': 3, 'four': 4}
+>>> del d["two"]
+>>> d["two"] = None
+>>> d
+{'one': 42, 'three': 3, 'four': 4, 'two': None}
+

Changed in version 3.7: Dictionary order is guaranteed to be insertion order. This behavior was an implementation detail of CPython from 3.6.

Dictionaries and dictionary views are reversible.

>>> d = {"one": 1, "two": 2, "three": 3, "four": 4}
+>>> d
+{'one': 1, 'two': 2, 'three': 3, 'four': 4}
+>>> list(reversed(d))
+['four', 'three', 'two', 'one']
+>>> list(reversed(d.values()))
+[4, 3, 2, 1]
+>>> list(reversed(d.items()))
+[('four', 4), ('three', 3), ('two', 2), ('one', 1)]
+

Changed in version 3.8: Dictionaries are now reversible.

+

See also

types.MappingProxyType can be used to create a read-only view of a dict.

Dictionary view objects

The objects returned by dict.keys(), dict.values() and dict.items() are view objects. They provide a dynamic view on the dictionary’s entries, which means that when the dictionary changes, the view reflects these changes.

Dictionary views can be iterated over to yield their respective data, and support membership tests:

len(dictview) +
+

Return the number of entries in the dictionary.

+
iter(dictview) +
+

Return an iterator over the keys, values or items (represented as tuples of (key, value)) in the dictionary.

Keys and values are iterated over in insertion order. This allows the creation of (value, key) pairs using zip(): pairs = zip(d.values(), d.keys()). Another way to create the same list is pairs = [(v, k) for (k, v) in d.items()].

Iterating views while adding or deleting entries in the dictionary may raise a RuntimeError or fail to iterate over all entries.

Changed in version 3.7: Dictionary order is guaranteed to be insertion order.

+
x in dictview +
+

Return True if x is in the underlying dictionary’s keys, values or items (in the latter case, x should be a (key, value) tuple).

+
reversed(dictview) +
+

Return a reverse iterator over the keys, values or items of the dictionary. The view will be iterated in reverse order of the insertion.

Changed in version 3.8: Dictionary views are now reversible.

+
dictview.mapping +
+

Return a types.MappingProxyType that wraps the original dictionary to which the view refers.

New in version 3.10.

+

Keys views are set-like since their entries are unique and hashable. Items views also have set-like operations since the (key, value) pairs are unique and the keys are hashable. If all values in an items view are hashable as well, then the items view can interoperate with other sets. (Values views are not treated as set-like since the entries are generally not unique.) For set-like views, all of the operations defined for the abstract base class collections.abc.Set are available (for example, ==, <, or ^). While using set operators, set-like views accept any iterable as the other operand, unlike sets which only accept sets as the input.

An example of dictionary view usage:

>>> dishes = {'eggs': 2, 'sausage': 1, 'bacon': 1, 'spam': 500}
+>>> keys = dishes.keys()
+>>> values = dishes.values()
+
+>>> # iteration
+>>> n = 0
+>>> for val in values:
+...     n += val
+...
+>>> print(n)
+504
+
+>>> # keys and values are iterated over in the same order (insertion order)
+>>> list(keys)
+['eggs', 'sausage', 'bacon', 'spam']
+>>> list(values)
+[2, 1, 1, 500]
+
+>>> # view objects are dynamic and reflect dict changes
+>>> del dishes['eggs']
+>>> del dishes['sausage']
+>>> list(keys)
+['bacon', 'spam']
+
+>>> # set operations
+>>> keys & {'eggs', 'bacon', 'salad'}
+{'bacon'}
+>>> keys ^ {'sausage', 'juice'} == {'juice', 'sausage', 'bacon', 'spam'}
+True
+>>> keys | ['juice', 'juice', 'juice'] == {'bacon', 'spam', 'juice'}
+True
+
+>>> # get back a read-only proxy for the original dictionary
+>>> values.mapping
+mappingproxy({'bacon': 1, 'spam': 500})
+>>> values.mapping['spam']
+500
+

Context Manager Types

Python’s with statement supports the concept of a runtime context defined by a context manager. This is implemented using a pair of methods that allow user-defined classes to define a runtime context that is entered before the statement body is executed and exited when the statement ends:

+contextmanager.__enter__()
+

Enter the runtime context and return either this object or another object related to the runtime context. The value returned by this method is bound to the identifier in the as clause of with statements using this context manager.

An example of a context manager that returns itself is a file object. File objects return themselves from __enter__() to allow open() to be used as the context expression in a with statement.

An example of a context manager that returns a related object is the one returned by decimal.localcontext(). These managers set the active decimal context to a copy of the original decimal context and then return the copy. This allows changes to be made to the current decimal context in the body of the with statement without affecting code outside the with statement.

+
+contextmanager.__exit__(exc_type, exc_val, exc_tb)
+

Exit the runtime context and return a Boolean flag indicating if any exception that occurred should be suppressed. If an exception occurred while executing the body of the with statement, the arguments contain the exception type, value and traceback information. Otherwise, all three arguments are None.

Returning a true value from this method will cause the with statement to suppress the exception and continue execution with the statement immediately following the with statement. Otherwise the exception continues propagating after this method has finished executing. Exceptions that occur during execution of this method will replace any exception that occurred in the body of the with statement.

The exception passed in should never be reraised explicitly - instead, this method should return a false value to indicate that the method completed successfully and does not want to suppress the raised exception. This allows context management code to easily detect whether or not an __exit__() method has actually failed.

+

Python defines several context managers to support easy thread synchronisation, prompt closure of files or other objects, and simpler manipulation of the active decimal arithmetic context. The specific types are not treated specially beyond their implementation of the context management protocol. See the contextlib module for some examples.

Python’s generators and the contextlib.contextmanager decorator provide a convenient way to implement these protocols. If a generator function is decorated with the contextlib.contextmanager decorator, it will return a context manager implementing the necessary __enter__() and __exit__() methods, rather than the iterator produced by an undecorated generator function.

Note that there is no specific slot for any of these methods in the type structure for Python objects in the Python/C API. Extension types wanting to define these methods must provide them as a normal Python accessible method. Compared to the overhead of setting up the runtime context, the overhead of a single class dictionary lookup is negligible.

Type Annotation Types — Generic Alias, Union +

The core built-in types for type annotations are Generic Alias and Union.

Generic Alias Type

GenericAlias objects are generally created by subscripting a class. They are most often used with container classes, such as list or dict. For example, list[int] is a GenericAlias object created by subscripting the list class with the argument int. GenericAlias objects are intended primarily for use with type annotations.

Note

It is generally only possible to subscript a class if the class implements the special method __class_getitem__().

A GenericAlias object acts as a proxy for a generic type, implementing parameterized generics.

For a container class, the argument(s) supplied to a subscription of the class may indicate the type(s) of the elements an object contains. For example, set[bytes] can be used in type annotations to signify a set in which all the elements are of type bytes.

For a class which defines __class_getitem__() but is not a container, the argument(s) supplied to a subscription of the class will often indicate the return type(s) of one or more methods defined on an object. For example, regular expressions can be used on both the str data type and the bytes data type:

  • If x = re.search('foo', 'foo'), x will be a re.Match object where the return values of x.group(0) and x[0] will both be of type str. We can represent this kind of object in type annotations with the GenericAlias re.Match[str].
  • If y = re.search(b'bar', b'bar'), (note the b for bytes), y will also be an instance of re.Match, but the return values of y.group(0) and y[0] will both be of type bytes. In type annotations, we would represent this variety of re.Match objects with re.Match[bytes].

GenericAlias objects are instances of the class types.GenericAlias, which can also be used to create GenericAlias objects directly.

T[X, Y, ...] +
+

Creates a GenericAlias representing a type T parameterized by types X, Y, and more depending on the T used. For example, a function expecting a list containing float elements:

def average(values: list[float]) -> float:
+    return sum(values) / len(values)
+

Another example for mapping objects, using a dict, which is a generic type expecting two type parameters representing the key type and the value type. In this example, the function expects a dict with keys of type str and values of type int:

def send_post_request(url: str, body: dict[str, int]) -> None:
+    ...
+
+

The builtin functions isinstance() and issubclass() do not accept GenericAlias types for their second argument:

>>> isinstance([1, 2], list[str])
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: isinstance() argument 2 cannot be a parameterized generic
+

The Python runtime does not enforce type annotations. This extends to generic types and their type parameters. When creating a container object from a GenericAlias, the elements in the container are not checked against their type. For example, the following code is discouraged, but will run without errors:

>>> t = list[str]
+>>> t([1, 2, 3])
+[1, 2, 3]
+

Furthermore, parameterized generics erase type parameters during object creation:

>>> t = list[str]
+>>> type(t)
+<class 'types.GenericAlias'>
+
+>>> l = t()
+>>> type(l)
+<class 'list'>
+

Calling repr() or str() on a generic shows the parameterized type:

>>> repr(list[int])
+'list[int]'
+
+>>> str(list[int])
+'list[int]'
+

The __getitem__() method of generic containers will raise an exception to disallow mistakes like dict[str][str]:

>>> dict[str][str]
+Traceback (most recent call last):
+  ...
+TypeError: dict[str] is not a generic class
+

However, such expressions are valid when type variables are used. The index must have as many elements as there are type variable items in the GenericAlias object’s __args__.

>>> from typing import TypeVar
+>>> Y = TypeVar('Y')
+>>> dict[str, Y][int]
+dict[str, int]
+

Standard Generic Classes

The following standard library classes support parameterized generics. This list is non-exhaustive.

Special Attributes of GenericAlias objects

All parameterized generics implement special read-only attributes.

+genericalias.__origin__
+

This attribute points at the non-parameterized generic class:

>>> list[int].__origin__
+<class 'list'>
+
+
+genericalias.__args__
+

This attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__() of the generic class:

>>> dict[str, list[int]].__args__
+(<class 'str'>, list[int])
+
+
+genericalias.__parameters__
+

This attribute is a lazily computed tuple (possibly empty) of unique type variables found in __args__:

>>> from typing import TypeVar
+
+>>> T = TypeVar('T')
+>>> list[T].__parameters__
+(~T,)
+

Note

A GenericAlias object with typing.ParamSpec parameters may not have correct __parameters__ after substitution because typing.ParamSpec is intended primarily for static type checking.

+
+genericalias.__unpacked__
+

A boolean that is true if the alias has been unpacked using the * operator (see TypeVarTuple).

New in version 3.11.

+

See also

+PEP 484 - Type Hints
+
+

Introducing Python’s framework for type annotations.

+PEP 585 - Type Hinting Generics In Standard Collections
+
+

Introducing the ability to natively parameterize standard-library classes, provided they implement the special class method __class_getitem__().

+Generics, user-defined generics and typing.Generic +
+
+

Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.

New in version 3.9.

Union Type

A union object holds the value of the | (bitwise or) operation on multiple type objects. These types are intended primarily for type annotations. The union type expression enables cleaner type hinting syntax compared to typing.Union.

X | Y | ... +
+

Defines a union object which holds types X, Y, and so forth. X | Y means either X or Y. It is equivalent to typing.Union[X, Y]. For example, the following function expects an argument of type int or float:

def square(number: int | float) -> int | float:
+    return number ** 2
+

Note

The | operand cannot be used at runtime to define unions where one or more members is a forward reference. For example, int | "Foo", where "Foo" is a reference to a class not yet defined, will fail at runtime. For unions which include forward references, present the whole expression as a string, e.g. "int | Foo".

+
union_object == other +
+

Union objects can be tested for equality with other union objects. Details:

  • +

    Unions of unions are flattened:

    (int | str) | float == int | str | float
+
  • +

    Redundant types are removed:

    int | str | int == int | str
+
  • +

    When comparing unions, the order is ignored:

    int | str == str | int
+
  • +

    It is compatible with typing.Union:

    int | str == typing.Union[int, str]
+
  • +

    Optional types can be spelled as a union with None:

    str | None == typing.Optional[str]
+
+
isinstance(obj, union_object) +
+
issubclass(obj, union_object) +
+

Calls to isinstance() and issubclass() are also supported with a union object:

>>> isinstance("", int | str)
+True
+

However, parameterized generics in union objects cannot be checked:

>>> isinstance(1, int | list[int])  # short-circuit evaluation
+True
+>>> isinstance([1], int | list[int])
+Traceback (most recent call last):
+  ...
+TypeError: isinstance() argument 2 cannot be a parameterized generic
+
+

The user-exposed type for the union object can be accessed from types.UnionType and used for isinstance() checks. An object cannot be instantiated from the type:

>>> import types
+>>> isinstance(int | str, types.UnionType)
+True
+>>> types.UnionType()
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: cannot create 'types.UnionType' instances
+

Note

The __or__() method for type objects was added to support the syntax X | Y. If a metaclass implements __or__(), the Union may override it:

>>> class M(type):
+...     def __or__(self, other):
+...         return "Hello"
+...
+>>> class C(metaclass=M):
+...     pass
+...
+>>> C | int
+'Hello'
+>>> int | C
+int | C
+

See also

PEP 604 – PEP proposing the X | Y syntax and the Union type.

New in version 3.10.

Other Built-in Types

The interpreter supports several other kinds of objects. Most of these support only one or two operations.

Modules

The only special operation on a module is attribute access: m.name, where m is a module and name accesses a name defined in m’s symbol table. Module attributes can be assigned to. (Note that the import statement is not, strictly speaking, an operation on a module object; import +foo does not require a module object named foo to exist, rather it requires an (external) definition for a module named foo somewhere.)

A special attribute of every module is __dict__. This is the dictionary containing the module’s symbol table. Modifying this dictionary will actually change the module’s symbol table, but direct assignment to the __dict__ attribute is not possible (you can write m.__dict__['a'] = 1, which defines m.a to be 1, but you can’t write m.__dict__ = {}). Modifying __dict__ directly is not recommended.

Modules built into the interpreter are written like this: <module 'sys' +(built-in)>. If loaded from a file, they are written as <module 'os' from +'/usr/local/lib/pythonX.Y/os.pyc'>.

Classes and Class Instances

See Objects, values and types and Class definitions for these.

Functions

Function objects are created by function definitions. The only operation on a function object is to call it: func(argument-list).

There are really two flavors of function objects: built-in functions and user-defined functions. Both support the same operation (to call the function), but the implementation is different, hence the different object types.

See Function definitions for more information.

Methods

Methods are functions that are called using the attribute notation. There are two flavors: built-in methods (such as append() on lists) and class instance method. Built-in methods are described with the types that support them.

If you access a method (a function defined in a class namespace) through an instance, you get a special object: a bound method (also called instance method) object. When called, it will add the self argument to the argument list. Bound methods have two special read-only attributes: m.__self__ is the object on which the method operates, and m.__func__ is the function implementing the method. Calling m(arg-1, arg-2, ..., arg-n) is completely equivalent to calling m.__func__(m.__self__, arg-1, arg-2, ..., +arg-n).

Like function objects, bound method objects support getting arbitrary attributes. However, since method attributes are actually stored on the underlying function object (method.__func__), setting method attributes on bound methods is disallowed. Attempting to set an attribute on a method results in an AttributeError being raised. In order to set a method attribute, you need to explicitly set it on the underlying function object:

>>> class C:
+...     def method(self):
+...         pass
+...
+>>> c = C()
+>>> c.method.whoami = 'my name is method'  # can't set on the method
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+AttributeError: 'method' object has no attribute 'whoami'
+>>> c.method.__func__.whoami = 'my name is method'
+>>> c.method.whoami
+'my name is method'
+

See Instance methods for more information.

Code Objects

Code objects are used by the implementation to represent “pseudo-compiled” executable Python code such as a function body. They differ from function objects because they don’t contain a reference to their global execution environment. Code objects are returned by the built-in compile() function and can be extracted from function objects through their __code__ attribute. See also the code module.

Accessing __code__ raises an auditing event object.__getattr__ with arguments obj and "__code__".

A code object can be executed or evaluated by passing it (instead of a source string) to the exec() or eval() built-in functions.

See The standard type hierarchy for more information.

Type Objects

Type objects represent the various object types. An object’s type is accessed by the built-in function type(). There are no special operations on types. The standard module types defines names for all standard built-in types.

Types are written like this: <class 'int'>.

The Null Object

This object is returned by functions that don’t explicitly return a value. It supports no special operations. There is exactly one null object, named None (a built-in name). type(None)() produces the same singleton.

It is written as None.

The Ellipsis Object

This object is commonly used by slicing (see Slicings). It supports no special operations. There is exactly one ellipsis object, named Ellipsis (a built-in name). type(Ellipsis)() produces the Ellipsis singleton.

It is written as Ellipsis or ....

The NotImplemented Object

This object is returned from comparisons and binary operations when they are asked to operate on types they don’t support. See Comparisons for more information. There is exactly one NotImplemented object. type(NotImplemented)() produces the singleton instance.

It is written as NotImplemented.

Internal Objects

See The standard type hierarchy for this information. It describes stack frame objects, traceback objects, and slice objects.

Special Attributes

The implementation adds a few special read-only attributes to several object types, where they are relevant. Some of these are not reported by the dir() built-in function.

+object.__dict__
+

A dictionary or other mapping object used to store an object’s (writable) attributes.

+
+instance.__class__
+

The class to which a class instance belongs.

+
+class.__bases__
+

The tuple of base classes of a class object.

+
+definition.__name__
+

The name of the class, function, method, descriptor, or generator instance.

+
+definition.__qualname__
+

The qualified name of the class, function, method, descriptor, or generator instance.

New in version 3.3.

+
+definition.__type_params__
+

The type parameters of generic classes, functions, and type aliases.

New in version 3.12.

+
+class.__mro__
+

This attribute is a tuple of classes that are considered when looking for base classes during method resolution.

+
+class.mro()
+

This method can be overridden by a metaclass to customize the method resolution order for its instances. It is called at class instantiation, and its result is stored in __mro__.

+
+class.__subclasses__()
+

Each class keeps a list of weak references to its immediate subclasses. This method returns a list of all those references still alive. The list is in definition order. Example:

>>> int.__subclasses__()
+[<class 'bool'>, <enum 'IntEnum'>, <flag 'IntFlag'>, <class 're._constants._NamedIntConstant'>]
+
+

Integer string conversion length limitation

CPython has a global limit for converting between int and str to mitigate denial of service attacks. This limit only applies to decimal or other non-power-of-two number bases. Hexadecimal, octal, and binary conversions are unlimited. The limit can be configured.

The int type in CPython is an arbitrary length number stored in binary form (commonly known as a “bignum”). There exists no algorithm that can convert a string to a binary integer or a binary integer to a string in linear time, unless the base is a power of 2. Even the best known algorithms for base 10 have sub-quadratic complexity. Converting a large value such as int('1' * +500_000) can take over a second on a fast CPU.

Limiting conversion size offers a practical way to avoid CVE-2020-10735.

The limit is applied to the number of digit characters in the input or output string when a non-linear conversion algorithm would be involved. Underscores and the sign are not counted towards the limit.

When an operation would exceed the limit, a ValueError is raised:

>>> import sys
+>>> sys.set_int_max_str_digits(4300)  # Illustrative, this is the default.
+>>> _ = int('2' * 5432)
+Traceback (most recent call last):
+...
+ValueError: Exceeds the limit (4300 digits) for integer string conversion: value has 5432 digits; use sys.set_int_max_str_digits() to increase the limit
+>>> i = int('2' * 4300)
+>>> len(str(i))
+4300
+>>> i_squared = i*i
+>>> len(str(i_squared))
+Traceback (most recent call last):
+...
+ValueError: Exceeds the limit (4300 digits) for integer string conversion; use sys.set_int_max_str_digits() to increase the limit
+>>> len(hex(i_squared))
+7144
+>>> assert int(hex(i_squared), base=16) == i*i  # Hexadecimal is unlimited.
+

The default limit is 4300 digits as provided in sys.int_info.default_max_str_digits. The lowest limit that can be configured is 640 digits as provided in sys.int_info.str_digits_check_threshold.

Verification:

>>> import sys
+>>> assert sys.int_info.default_max_str_digits == 4300, sys.int_info
+>>> assert sys.int_info.str_digits_check_threshold == 640, sys.int_info
+>>> msg = int('578966293710682886880994035146873798396722250538762761564'
+...           '9252925514383915483333812743580549779436104706260696366600'
+...           '571186405732').to_bytes(53, 'big')
+...
+

New in version 3.11.

Affected APIs

The limitation only applies to potentially slow conversions between int and str or bytes:

  • +int(string) with default base 10.
  • +int(string, base) for all bases that are not a power of 2.
  • +str(integer).
  • +repr(integer).
  • any other string conversion to base 10, for example f"{integer}", "{}".format(integer), or b"%d" % integer.

The limitations do not apply to functions with a linear algorithm:

Configuring the limit

Before Python starts up you can use an environment variable or an interpreter command line flag to configure the limit:

From code, you can inspect the current limit and set a new one using these sys APIs:

Information about the default and minimum can be found in sys.int_info:

New in version 3.11.

Caution

Setting a low limit can lead to problems. While rare, code exists that contains integer constants in decimal in their source that exceed the minimum threshold. A consequence of setting the limit is that Python source code containing decimal integer literals longer than the limit will encounter an error during parsing, usually at startup time or import time or even at installation time - anytime an up to date .pyc does not already exist for the code. A workaround for source that contains such large constants is to convert them to 0x hexadecimal form as it has no limit.

Test your application thoroughly if you use a low limit. Ensure your tests run with the limit set early via the environment or flag so that it applies during startup and even during any installation step that may invoke Python to precompile .py sources to .pyc files.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/stdtypes.html +

+
diff --git a/devdocs/python~3.12/library%2Fstring.html b/devdocs/python~3.12/library%2Fstring.html new file mode 100644 index 00000000..e6194b6c --- /dev/null +++ b/devdocs/python~3.12/library%2Fstring.html @@ -0,0 +1,255 @@ +

string — Common string operations

Source code: Lib/string.py

See also

Text Sequence Type — str

String Methods

String constants

The constants defined in this module are:

+string.ascii_letters
+

The concatenation of the ascii_lowercase and ascii_uppercase constants described below. This value is not locale-dependent.

+
+string.ascii_lowercase
+

The lowercase letters 'abcdefghijklmnopqrstuvwxyz'. This value is not locale-dependent and will not change.

+
+string.ascii_uppercase
+

The uppercase letters 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'. This value is not locale-dependent and will not change.

+
+string.digits
+

The string '0123456789'.

+
+string.hexdigits
+

The string '0123456789abcdefABCDEF'.

+
+string.octdigits
+

The string '01234567'.

+
+string.punctuation
+

String of ASCII characters which are considered punctuation characters in the C locale: !"#$%&'()*+,-./:;<=>?@[\]^_`{|}~.

+
+string.printable
+

String of ASCII characters which are considered printable. This is a combination of digits, ascii_letters, punctuation, and whitespace.

+
+string.whitespace
+

A string containing all ASCII characters that are considered whitespace. This includes the characters space, tab, linefeed, return, formfeed, and vertical tab.

+

Custom String Formatting

The built-in string class provides the ability to do complex variable substitutions and value formatting via the format() method described in PEP 3101. The Formatter class in the string module allows you to create and customize your own string formatting behaviors using the same implementation as the built-in format() method.

+class string.Formatter
+

The Formatter class has the following public methods:

+format(format_string, /, *args, **kwargs)
+

The primary API method. It takes a format string and an arbitrary set of positional and keyword arguments. It is just a wrapper that calls vformat().

Changed in version 3.7: A format string argument is now positional-only.

+
+vformat(format_string, args, kwargs)
+

This function does the actual work of formatting. It is exposed as a separate function for cases where you want to pass in a predefined dictionary of arguments, rather than unpacking and repacking the dictionary as individual arguments using the *args and **kwargs syntax. vformat() does the work of breaking up the format string into character data and replacement fields. It calls the various methods described below.

+

In addition, the Formatter defines a number of methods that are intended to be replaced by subclasses:

+parse(format_string)
+

Loop over the format_string and return an iterable of tuples (literal_text, field_name, format_spec, conversion). This is used by vformat() to break the string into either literal text, or replacement fields.

The values in the tuple conceptually represent a span of literal text followed by a single replacement field. If there is no literal text (which can happen if two replacement fields occur consecutively), then literal_text will be a zero-length string. If there is no replacement field, then the values of field_name, format_spec and conversion will be None.

+
+get_field(field_name, args, kwargs)
+

Given field_name as returned by parse() (see above), convert it to an object to be formatted. Returns a tuple (obj, used_key). The default version takes strings of the form defined in PEP 3101, such as “0[name]” or “label.title”. args and kwargs are as passed in to vformat(). The return value used_key has the same meaning as the key parameter to get_value().

+
+get_value(key, args, kwargs)
+

Retrieve a given field value. The key argument will be either an integer or a string. If it is an integer, it represents the index of the positional argument in args; if it is a string, then it represents a named argument in kwargs.

The args parameter is set to the list of positional arguments to vformat(), and the kwargs parameter is set to the dictionary of keyword arguments.

For compound field names, these functions are only called for the first component of the field name; subsequent components are handled through normal attribute and indexing operations.

So for example, the field expression ‘0.name’ would cause get_value() to be called with a key argument of 0. The name attribute will be looked up after get_value() returns by calling the built-in getattr() function.

If the index or keyword refers to an item that does not exist, then an IndexError or KeyError should be raised.

+
+check_unused_args(used_args, args, kwargs)
+

Implement checking for unused arguments if desired. The arguments to this function is the set of all argument keys that were actually referred to in the format string (integers for positional arguments, and strings for named arguments), and a reference to the args and kwargs that was passed to vformat. The set of unused args can be calculated from these parameters. check_unused_args() is assumed to raise an exception if the check fails.

+
+format_field(value, format_spec)
+

format_field() simply calls the global format() built-in. The method is provided so that subclasses can override it.

+
+convert_field(value, conversion)
+

Converts the value (returned by get_field()) given a conversion type (as in the tuple returned by the parse() method). The default version understands ‘s’ (str), ‘r’ (repr) and ‘a’ (ascii) conversion types.

+
+

Format String Syntax

The str.format() method and the Formatter class share the same syntax for format strings (although in the case of Formatter, subclasses can define their own format string syntax). The syntax is related to that of formatted string literals, but it is less sophisticated and, in particular, does not support arbitrary expressions.

Format strings contain “replacement fields” surrounded by curly braces {}. Anything that is not contained in braces is considered literal text, which is copied unchanged to the output. If you need to include a brace character in the literal text, it can be escaped by doubling: {{ and }}.

The grammar for a replacement field is as follows:

+replacement_field ::=  "{" [field_name] ["!" conversion] [":" format_spec] "}"
+field_name        ::=  arg_name ("." attribute_name | "[" element_index "]")*
+arg_name          ::=  [identifier | digit+]
+attribute_name    ::=  identifier
+element_index     ::=  digit+ | index_string
+index_string      ::=  <any source character except "]"> +
+conversion        ::=  "r" | "s" | "a"
+format_spec       ::=  <described in the next section>
+

In less formal terms, the replacement field can start with a field_name that specifies the object whose value is to be formatted and inserted into the output instead of the replacement field. The field_name is optionally followed by a conversion field, which is preceded by an exclamation point '!', and a format_spec, which is preceded by a colon ':'. These specify a non-default format for the replacement value.

See also the Format Specification Mini-Language section.

The field_name itself begins with an arg_name that is either a number or a keyword. If it’s a number, it refers to a positional argument, and if it’s a keyword, it refers to a named keyword argument. An arg_name is treated as a number if a call to str.isdecimal() on the string would return true. If the numerical arg_names in a format string are 0, 1, 2, … in sequence, they can all be omitted (not just some) and the numbers 0, 1, 2, … will be automatically inserted in that order. Because arg_name is not quote-delimited, it is not possible to specify arbitrary dictionary keys (e.g., the strings '10' or ':-]') within a format string. The arg_name can be followed by any number of index or attribute expressions. An expression of the form '.name' selects the named attribute using getattr(), while an expression of the form '[index]' does an index lookup using __getitem__().

Changed in version 3.1: The positional argument specifiers can be omitted for str.format(), so '{} {}'.format(a, b) is equivalent to '{0} {1}'.format(a, b).

Changed in version 3.4: The positional argument specifiers can be omitted for Formatter.

Some simple format string examples:

"First, thou shalt count to {0}"  # References first positional argument
+"Bring me a {}"                   # Implicitly references the first positional argument
+"From {} to {}"                   # Same as "From {0} to {1}"
+"My quest is {name}"              # References keyword argument 'name'
+"Weight in tons {0.weight}"       # 'weight' attribute of first positional arg
+"Units destroyed: {players[0]}"   # First element of keyword argument 'players'.
+

The conversion field causes a type coercion before formatting. Normally, the job of formatting a value is done by the __format__() method of the value itself. However, in some cases it is desirable to force a type to be formatted as a string, overriding its own definition of formatting. By converting the value to a string before calling __format__(), the normal formatting logic is bypassed.

Three conversion flags are currently supported: '!s' which calls str() on the value, '!r' which calls repr() and '!a' which calls ascii().

Some examples:

"Harold's a clever {0!s}"        # Calls str() on the argument first
+"Bring out the holy {name!r}"    # Calls repr() on the argument first
+"More {!a}"                      # Calls ascii() on the argument first
+

The format_spec field contains a specification of how the value should be presented, including such details as field width, alignment, padding, decimal precision and so on. Each value type can define its own “formatting mini-language” or interpretation of the format_spec.

Most built-in types support a common formatting mini-language, which is described in the next section.

A format_spec field can also include nested replacement fields within it. These nested replacement fields may contain a field name, conversion flag and format specification, but deeper nesting is not allowed. The replacement fields within the format_spec are substituted before the format_spec string is interpreted. This allows the formatting of a value to be dynamically specified.

See the Format examples section for some examples.

Format Specification Mini-Language

“Format specifications” are used within replacement fields contained within a format string to define how individual values are presented (see Format String Syntax and f-strings). They can also be passed directly to the built-in format() function. Each formattable type may define how the format specification is to be interpreted.

Most built-in types implement the following options for format specifications, although some of the formatting options are only supported by the numeric types.

A general convention is that an empty format specification produces the same result as if you had called str() on the value. A non-empty format specification typically modifies the result.

The general form of a standard format specifier is:

+format_spec     ::=  [[fill]align][sign]["z"]["#"]["0"][width][grouping_option]["." precision][type]
+fill            ::=  <any character>
+align           ::=  "<" | ">" | "=" | "^"
+sign            ::=  "+" | "-" | " "
+width           ::=  digit+
+grouping_option ::=  "_" | ","
+precision       ::=  digit+
+type            ::=  "b" | "c" | "d" | "e" | "E" | "f" | "F" | "g" | "G" | "n" | "o" | "s" | "x" | "X" | "%"
+

If a valid align value is specified, it can be preceded by a fill character that can be any character and defaults to a space if omitted. It is not possible to use a literal curly brace (”{” or “}”) as the fill character in a formatted string literal or when using the str.format() method. However, it is possible to insert a curly brace with a nested replacement field. This limitation doesn’t affect the format() function.

The meaning of the various alignment options is as follows:

+ + + + +

Option

Meaning

'<'

Forces the field to be left-aligned within the available space (this is the default for most objects).

'>'

Forces the field to be right-aligned within the available space (this is the default for numbers).

'='

Forces the padding to be placed after the sign (if any) but before the digits. This is used for printing fields in the form ‘+000000120’. This alignment option is only valid for numeric types. It becomes the default for numbers when ‘0’ immediately precedes the field width.

'^'

Forces the field to be centered within the available space.

Note that unless a minimum field width is defined, the field width will always be the same size as the data to fill it, so that the alignment option has no meaning in this case.

The sign option is only valid for number types, and can be one of the following:

+ + + +

Option

Meaning

'+'

indicates that a sign should be used for both positive as well as negative numbers.

'-'

indicates that a sign should be used only for negative numbers (this is the default behavior).

space

indicates that a leading space should be used on positive numbers, and a minus sign on negative numbers.

The 'z' option coerces negative zero floating-point values to positive zero after rounding to the format precision. This option is only valid for floating-point presentation types.

Changed in version 3.11: Added the 'z' option (see also PEP 682).

The '#' option causes the “alternate form” to be used for the conversion. The alternate form is defined differently for different types. This option is only valid for integer, float and complex types. For integers, when binary, octal, or hexadecimal output is used, this option adds the respective prefix '0b', '0o', '0x', or '0X' to the output value. For float and complex the alternate form causes the result of the conversion to always contain a decimal-point character, even if no digits follow it. Normally, a decimal-point character appears in the result of these conversions only if a digit follows it. In addition, for 'g' and 'G' conversions, trailing zeros are not removed from the result.

The ',' option signals the use of a comma for a thousands separator. For a locale aware separator, use the 'n' integer presentation type instead.

Changed in version 3.1: Added the ',' option (see also PEP 378).

The '_' option signals the use of an underscore for a thousands separator for floating point presentation types and for integer presentation type 'd'. For integer presentation types 'b', 'o', 'x', and 'X', underscores will be inserted every 4 digits. For other presentation types, specifying this option is an error.

Changed in version 3.6: Added the '_' option (see also PEP 515).

width is a decimal integer defining the minimum total field width, including any prefixes, separators, and other formatting characters. If not specified, then the field width will be determined by the content.

When no explicit alignment is given, preceding the width field by a zero ('0') character enables sign-aware zero-padding for numeric types. This is equivalent to a fill character of '0' with an alignment type of '='.

Changed in version 3.10: Preceding the width field by '0' no longer affects the default alignment for strings.

The precision is a decimal integer indicating how many digits should be displayed after the decimal point for presentation types 'f' and 'F', or before and after the decimal point for presentation types 'g' or 'G'. For string presentation types the field indicates the maximum field size - in other words, how many characters will be used from the field content. The precision is not allowed for integer presentation types.

Finally, the type determines how the data should be presented.

The available string presentation types are:

+ + +

Type

Meaning

's'

String format. This is the default type for strings and may be omitted.

None

The same as 's'.

The available integer presentation types are:

+ + + + + + + + +

Type

Meaning

'b'

Binary format. Outputs the number in base 2.

'c'

Character. Converts the integer to the corresponding unicode character before printing.

'd'

Decimal Integer. Outputs the number in base 10.

'o'

Octal format. Outputs the number in base 8.

'x'

Hex format. Outputs the number in base 16, using lower-case letters for the digits above 9.

'X'

Hex format. Outputs the number in base 16, using upper-case letters for the digits above 9. In case '#' is specified, the prefix '0x' will be upper-cased to '0X' as well.

'n'

Number. This is the same as 'd', except that it uses the current locale setting to insert the appropriate number separator characters.

None

The same as 'd'.

In addition to the above presentation types, integers can be formatted with the floating point presentation types listed below (except 'n' and None). When doing so, float() is used to convert the integer to a floating point number before formatting.

The available presentation types for float and Decimal values are:

+ + + + + + + + + +

Type

Meaning

'e'

Scientific notation. For a given precision p, formats the number in scientific notation with the letter ‘e’ separating the coefficient from the exponent. The coefficient has one digit before and p digits after the decimal point, for a total of p + 1 significant digits. With no precision given, uses a precision of 6 digits after the decimal point for float, and shows all coefficient digits for Decimal. If no digits follow the decimal point, the decimal point is also removed unless the # option is used.

'E'

Scientific notation. Same as 'e' except it uses an upper case ‘E’ as the separator character.

'f'

Fixed-point notation. For a given precision p, formats the number as a decimal number with exactly p digits following the decimal point. With no precision given, uses a precision of 6 digits after the decimal point for float, and uses a precision large enough to show all coefficient digits for Decimal. If no digits follow the decimal point, the decimal point is also removed unless the # option is used.

'F'

Fixed-point notation. Same as 'f', but converts nan to NAN and inf to INF.

'g'

 +

General format. For a given precision p >= 1, this rounds the number to p significant digits and then formats the result in either fixed-point format or in scientific notation, depending on its magnitude. A precision of 0 is treated as equivalent to a precision of 1.

The precise rules are as follows: suppose that the result formatted with presentation type 'e' and precision p-1 would have exponent exp. Then, if m <= exp < p, where m is -4 for floats and -6 for Decimals, the number is formatted with presentation type 'f' and precision p-1-exp. Otherwise, the number is formatted with presentation type 'e' and precision p-1. In both cases insignificant trailing zeros are removed from the significand, and the decimal point is also removed if there are no remaining digits following it, unless the '#' option is used.

With no precision given, uses a precision of 6 significant digits for float. For Decimal, the coefficient of the result is formed from the coefficient digits of the value; scientific notation is used for values smaller than 1e-6 in absolute value and values where the place value of the least significant digit is larger than 1, and fixed-point notation is used otherwise.

Positive and negative infinity, positive and negative zero, and nans, are formatted as inf, -inf, 0, -0 and nan respectively, regardless of the precision.

'G'

General format. Same as 'g' except switches to 'E' if the number gets too large. The representations of infinity and NaN are uppercased, too.

'n'

Number. This is the same as 'g', except that it uses the current locale setting to insert the appropriate number separator characters.

'%'

Percentage. Multiplies the number by 100 and displays in fixed ('f') format, followed by a percent sign.

None

 +

For float this is the same as 'g', except that when fixed-point notation is used to format the result, it always includes at least one digit past the decimal point. The precision used is as large as needed to represent the given value faithfully.

For Decimal, this is the same as either 'g' or 'G' depending on the value of context.capitals for the current decimal context.

The overall effect is to match the output of str() as altered by the other format modifiers.

Format examples

This section contains examples of the str.format() syntax and comparison with the old %-formatting.

In most of the cases the syntax is similar to the old %-formatting, with the addition of the {} and with : used instead of %. For example, '%03.2f' can be translated to '{:03.2f}'.

The new format syntax also supports new and different options, shown in the following examples.

Accessing arguments by position:

>>> '{0}, {1}, {2}'.format('a', 'b', 'c')
+'a, b, c'
+>>> '{}, {}, {}'.format('a', 'b', 'c')  # 3.1+ only
+'a, b, c'
+>>> '{2}, {1}, {0}'.format('a', 'b', 'c')
+'c, b, a'
+>>> '{2}, {1}, {0}'.format(*'abc')      # unpacking argument sequence
+'c, b, a'
+>>> '{0}{1}{0}'.format('abra', 'cad')   # arguments' indices can be repeated
+'abracadabra'
+

Accessing arguments by name:

>>> 'Coordinates: {latitude}, {longitude}'.format(latitude='37.24N', longitude='-115.81W')
+'Coordinates: 37.24N, -115.81W'
+>>> coord = {'latitude': '37.24N', 'longitude': '-115.81W'}
+>>> 'Coordinates: {latitude}, {longitude}'.format(**coord)
+'Coordinates: 37.24N, -115.81W'
+

Accessing arguments’ attributes:

>>> c = 3-5j
+>>> ('The complex number {0} is formed from the real part {0.real} '
+...  'and the imaginary part {0.imag}.').format(c)
+'The complex number (3-5j) is formed from the real part 3.0 and the imaginary part -5.0.'
+>>> class Point:
+...     def __init__(self, x, y):
+...         self.x, self.y = x, y
+...     def __str__(self):
+...         return 'Point({self.x}, {self.y})'.format(self=self)
+...
+>>> str(Point(4, 2))
+'Point(4, 2)'
+

Accessing arguments’ items:

>>> coord = (3, 5)
+>>> 'X: {0[0]};  Y: {0[1]}'.format(coord)
+'X: 3;  Y: 5'
+

Replacing %s and %r:

>>> "repr() shows quotes: {!r}; str() doesn't: {!s}".format('test1', 'test2')
+"repr() shows quotes: 'test1'; str() doesn't: test2"
+

Aligning the text and specifying a width:

>>> '{:<30}'.format('left aligned')
+'left aligned                  '
+>>> '{:>30}'.format('right aligned')
+'                 right aligned'
+>>> '{:^30}'.format('centered')
+'           centered           '
+>>> '{:*^30}'.format('centered')  # use '*' as a fill char
+'***********centered***********'
+

Replacing %+f, %-f, and % f and specifying a sign:

>>> '{:+f}; {:+f}'.format(3.14, -3.14)  # show it always
+'+3.140000; -3.140000'
+>>> '{: f}; {: f}'.format(3.14, -3.14)  # show a space for positive numbers
+' 3.140000; -3.140000'
+>>> '{:-f}; {:-f}'.format(3.14, -3.14)  # show only the minus -- same as '{:f}; {:f}'
+'3.140000; -3.140000'
+

Replacing %x and %o and converting the value to different bases:

>>> # format also supports binary numbers
+>>> "int: {0:d};  hex: {0:x};  oct: {0:o};  bin: {0:b}".format(42)
+'int: 42;  hex: 2a;  oct: 52;  bin: 101010'
+>>> # with 0x, 0o, or 0b as prefix:
+>>> "int: {0:d};  hex: {0:#x};  oct: {0:#o};  bin: {0:#b}".format(42)
+'int: 42;  hex: 0x2a;  oct: 0o52;  bin: 0b101010'
+

Using the comma as a thousands separator:

>>> '{:,}'.format(1234567890)
+'1,234,567,890'
+

Expressing a percentage:

>>> points = 19
+>>> total = 22
+>>> 'Correct answers: {:.2%}'.format(points/total)
+'Correct answers: 86.36%'
+

Using type-specific formatting:

>>> import datetime
+>>> d = datetime.datetime(2010, 7, 4, 12, 15, 58)
+>>> '{:%Y-%m-%d %H:%M:%S}'.format(d)
+'2010-07-04 12:15:58'
+

Nesting arguments and more complex examples:

>>> for align, text in zip('<^>', ['left', 'center', 'right']):
+...     '{0:{fill}{align}16}'.format(text, fill=align, align=align)
+...
+'left<<<<<<<<<<<<'
+'^^^^^center^^^^^'
+'>>>>>>>>>>>right'
+>>>
+>>> octets = [192, 168, 0, 1]
+>>> '{:02X}{:02X}{:02X}{:02X}'.format(*octets)
+'C0A80001'
+>>> int(_, 16)
+3232235521
+>>>
+>>> width = 5
+>>> for num in range(5,12): 
+...     for base in 'dXob':
+...         print('{0:{width}{base}}'.format(num, base=base, width=width), end=' ')
+...     print()
+...
+    5     5     5   101
+    6     6     6   110
+    7     7     7   111
+    8     8    10  1000
+    9     9    11  1001
+   10     A    12  1010
+   11     B    13  1011
+

Template strings

Template strings provide simpler string substitutions as described in PEP 292. A primary use case for template strings is for internationalization (i18n) since in that context, the simpler syntax and functionality makes it easier to translate than other built-in string formatting facilities in Python. As an example of a library built on template strings for i18n, see the flufl.i18n package.

Template strings support $-based substitutions, using the following rules:

  • +$$ is an escape; it is replaced with a single $.
  • +$identifier names a substitution placeholder matching a mapping key of "identifier". By default, "identifier" is restricted to any case-insensitive ASCII alphanumeric string (including underscores) that starts with an underscore or ASCII letter. The first non-identifier character after the $ character terminates this placeholder specification.
  • +${identifier} is equivalent to $identifier. It is required when valid identifier characters follow the placeholder but are not part of the placeholder, such as "${noun}ification".

Any other appearance of $ in the string will result in a ValueError being raised.

The string module provides a Template class that implements these rules. The methods of Template are:

+class string.Template(template)
+

The constructor takes a single argument which is the template string.

+substitute(mapping={}, /, **kwds)
+

Performs the template substitution, returning a new string. mapping is any dictionary-like object with keys that match the placeholders in the template. Alternatively, you can provide keyword arguments, where the keywords are the placeholders. When both mapping and kwds are given and there are duplicates, the placeholders from kwds take precedence.

+
+safe_substitute(mapping={}, /, **kwds)
+

Like substitute(), except that if placeholders are missing from mapping and kwds, instead of raising a KeyError exception, the original placeholder will appear in the resulting string intact. Also, unlike with substitute(), any other appearances of the $ will simply return $ instead of raising ValueError.

While other exceptions may still occur, this method is called “safe” because it always tries to return a usable string instead of raising an exception. In another sense, safe_substitute() may be anything other than safe, since it will silently ignore malformed templates containing dangling delimiters, unmatched braces, or placeholders that are not valid Python identifiers.

+
+is_valid()
+

Returns false if the template has invalid placeholders that will cause substitute() to raise ValueError.

New in version 3.11.

+
+get_identifiers()
+

Returns a list of the valid identifiers in the template, in the order they first appear, ignoring any invalid identifiers.

New in version 3.11.

+

Template instances also provide one public data attribute:

+template
+

This is the object passed to the constructor’s template argument. In general, you shouldn’t change it, but read-only access is not enforced.

+
+

Here is an example of how to use a Template:

>>> from string import Template
+>>> s = Template('$who likes $what')
+>>> s.substitute(who='tim', what='kung pao')
+'tim likes kung pao'
+>>> d = dict(who='tim')
+>>> Template('Give $who $100').substitute(d)
+Traceback (most recent call last):
+...
+ValueError: Invalid placeholder in string: line 1, col 11
+>>> Template('$who likes $what').substitute(d)
+Traceback (most recent call last):
+...
+KeyError: 'what'
+>>> Template('$who likes $what').safe_substitute(d)
+'tim likes $what'
+

Advanced usage: you can derive subclasses of Template to customize the placeholder syntax, delimiter character, or the entire regular expression used to parse template strings. To do this, you can override these class attributes:

  • +delimiter – This is the literal string describing a placeholder introducing delimiter. The default value is $. Note that this should not be a regular expression, as the implementation will call re.escape() on this string as needed. Note further that you cannot change the delimiter after class creation (i.e. a different delimiter must be set in the subclass’s class namespace).
  • +

    idpattern – This is the regular expression describing the pattern for non-braced placeholders. The default value is the regular expression (?a:[_a-z][_a-z0-9]*). If this is given and braceidpattern is None this pattern will also apply to braced placeholders.

    Note

    Since default flags is re.IGNORECASE, pattern [a-z] can match with some non-ASCII characters. That’s why we use the local a flag here.

    Changed in version 3.7: braceidpattern can be used to define separate patterns used inside and outside the braces.

  • +

    braceidpattern – This is like idpattern but describes the pattern for braced placeholders. Defaults to None which means to fall back to idpattern (i.e. the same pattern is used both inside and outside braces). If given, this allows you to define different patterns for braced and unbraced placeholders.

    New in version 3.7.

  • +

    flags – The regular expression flags that will be applied when compiling the regular expression used for recognizing substitutions. The default value is re.IGNORECASE. Note that re.VERBOSE will always be added to the flags, so custom idpatterns must follow conventions for verbose regular expressions.

    New in version 3.2.

Alternatively, you can provide the entire regular expression pattern by overriding the class attribute pattern. If you do this, the value must be a regular expression object with four named capturing groups. The capturing groups correspond to the rules given above, along with the invalid placeholder rule:

  • +escaped – This group matches the escape sequence, e.g. $$, in the default pattern.
  • +named – This group matches the unbraced placeholder name; it should not include the delimiter in capturing group.
  • +braced – This group matches the brace enclosed placeholder name; it should not include either the delimiter or braces in the capturing group.
  • +invalid – This group matches any other delimiter pattern (usually a single delimiter), and it should appear last in the regular expression.

The methods on this class will raise ValueError if the pattern matches the template without one of these named groups matching.

Helper functions

+string.capwords(s, sep=None)
+

Split the argument into words using str.split(), capitalize each word using str.capitalize(), and join the capitalized words using str.join(). If the optional second argument sep is absent or None, runs of whitespace characters are replaced by a single space and leading and trailing whitespace are removed, otherwise sep is used to split and join the words.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/string.html +

+
diff --git a/devdocs/python~3.12/library%2Fstringprep.html b/devdocs/python~3.12/library%2Fstringprep.html new file mode 100644 index 00000000..9f300696 --- /dev/null +++ b/devdocs/python~3.12/library%2Fstringprep.html @@ -0,0 +1,63 @@ +

stringprep — Internet String Preparation

Source code: Lib/stringprep.py

When identifying things (such as host names) in the internet, it is often necessary to compare such identifications for “equality”. Exactly how this comparison is executed may depend on the application domain, e.g. whether it should be case-insensitive or not. It may be also necessary to restrict the possible identifications, to allow only identifications consisting of “printable” characters.

RFC 3454 defines a procedure for “preparing” Unicode strings in internet protocols. Before passing strings onto the wire, they are processed with the preparation procedure, after which they have a certain normalized form. The RFC defines a set of tables, which can be combined into profiles. Each profile must define which tables it uses, and what other optional parts of the stringprep procedure are part of the profile. One example of a stringprep profile is nameprep, which is used for internationalized domain names.

The module stringprep only exposes the tables from RFC 3454. As these tables would be very large to represent as dictionaries or lists, the module uses the Unicode character database internally. The module source code itself was generated using the mkstringprep.py utility.

As a result, these tables are exposed as functions, not as data structures. There are two kinds of tables in the RFC: sets and mappings. For a set, stringprep provides the “characteristic function”, i.e. a function that returns True if the parameter is part of the set. For mappings, it provides the mapping function: given the key, it returns the associated value. Below is a list of all functions available in the module.

+stringprep.in_table_a1(code)
+

Determine whether code is in tableA.1 (Unassigned code points in Unicode 3.2).

+
+stringprep.in_table_b1(code)
+

Determine whether code is in tableB.1 (Commonly mapped to nothing).

+
+stringprep.map_table_b2(code)
+

Return the mapped value for code according to tableB.2 (Mapping for case-folding used with NFKC).

+
+stringprep.map_table_b3(code)
+

Return the mapped value for code according to tableB.3 (Mapping for case-folding used with no normalization).

+
+stringprep.in_table_c11(code)
+

Determine whether code is in tableC.1.1 (ASCII space characters).

+
+stringprep.in_table_c12(code)
+

Determine whether code is in tableC.1.2 (Non-ASCII space characters).

+
+stringprep.in_table_c11_c12(code)
+

Determine whether code is in tableC.1 (Space characters, union of C.1.1 and C.1.2).

+
+stringprep.in_table_c21(code)
+

Determine whether code is in tableC.2.1 (ASCII control characters).

+
+stringprep.in_table_c22(code)
+

Determine whether code is in tableC.2.2 (Non-ASCII control characters).

+
+stringprep.in_table_c21_c22(code)
+

Determine whether code is in tableC.2 (Control characters, union of C.2.1 and C.2.2).

+
+stringprep.in_table_c3(code)
+

Determine whether code is in tableC.3 (Private use).

+
+stringprep.in_table_c4(code)
+

Determine whether code is in tableC.4 (Non-character code points).

+
+stringprep.in_table_c5(code)
+

Determine whether code is in tableC.5 (Surrogate codes).

+
+stringprep.in_table_c6(code)
+

Determine whether code is in tableC.6 (Inappropriate for plain text).

+
+stringprep.in_table_c7(code)
+

Determine whether code is in tableC.7 (Inappropriate for canonical representation).

+
+stringprep.in_table_c8(code)
+

Determine whether code is in tableC.8 (Change display properties or are deprecated).

+
+stringprep.in_table_c9(code)
+

Determine whether code is in tableC.9 (Tagging characters).

+
+stringprep.in_table_d1(code)
+

Determine whether code is in tableD.1 (Characters with bidirectional property “R” or “AL”).

+
+stringprep.in_table_d2(code)
+

Determine whether code is in tableD.2 (Characters with bidirectional property “L”).

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/stringprep.html +

+
diff --git a/devdocs/python~3.12/library%2Fstruct.html b/devdocs/python~3.12/library%2Fstruct.html new file mode 100644 index 00000000..0b7c3639 --- /dev/null +++ b/devdocs/python~3.12/library%2Fstruct.html @@ -0,0 +1,150 @@ +

struct — Interpret bytes as packed binary data

Source code: Lib/struct.py

This module converts between Python values and C structs represented as Python bytes objects. Compact format strings describe the intended conversions to/from Python values. The module’s functions and objects can be used for two largely distinct applications, data exchange with external sources (files or network connections), or data transfer between the Python application and the C layer.

Note

When no prefix character is given, native mode is the default. It packs or unpacks data based on the platform and compiler on which the Python interpreter was built. The result of packing a given C struct includes pad bytes which maintain proper alignment for the C types involved; similarly, alignment is taken into account when unpacking. In contrast, when communicating data between external sources, the programmer is responsible for defining byte ordering and padding between elements. See Byte Order, Size, and Alignment for details.

Several struct functions (and methods of Struct) take a buffer argument. This refers to objects that implement the Buffer Protocol and provide either a readable or read-writable buffer. The most common types used for that purpose are bytes and bytearray, but many other types that can be viewed as an array of bytes implement the buffer protocol, so that they can be read/filled without additional copying from a bytes object.

Functions and Exceptions

The module defines the following exception and functions:

+exception struct.error
+

Exception raised on various occasions; argument is a string describing what is wrong.

+
+struct.pack(format, v1, v2, ...)
+

Return a bytes object containing the values v1, v2, … packed according to the format string format. The arguments must match the values required by the format exactly.

+
+struct.pack_into(format, buffer, offset, v1, v2, ...)
+

Pack the values v1, v2, … according to the format string format and write the packed bytes into the writable buffer buffer starting at position offset. Note that offset is a required argument.

+
+struct.unpack(format, buffer)
+

Unpack from the buffer buffer (presumably packed by pack(format, ...)) according to the format string format. The result is a tuple even if it contains exactly one item. The buffer’s size in bytes must match the size required by the format, as reflected by calcsize().

+
+struct.unpack_from(format, /, buffer, offset=0)
+

Unpack from buffer starting at position offset, according to the format string format. The result is a tuple even if it contains exactly one item. The buffer’s size in bytes, starting at position offset, must be at least the size required by the format, as reflected by calcsize().

+
+struct.iter_unpack(format, buffer)
+

Iteratively unpack from the buffer buffer according to the format string format. This function returns an iterator which will read equally sized chunks from the buffer until all its contents have been consumed. The buffer’s size in bytes must be a multiple of the size required by the format, as reflected by calcsize().

Each iteration yields a tuple as specified by the format string.

New in version 3.4.

+
+struct.calcsize(format)
+

Return the size of the struct (and hence of the bytes object produced by pack(format, ...)) corresponding to the format string format.

+

Format Strings

Format strings describe the data layout when packing and unpacking data. They are built up from format characters, which specify the type of data being packed/unpacked. In addition, special characters control the byte order, size and alignment. Each format string consists of an optional prefix character which describes the overall properties of the data and one or more format characters which describe the actual data values and padding.

Byte Order, Size, and Alignment

By default, C types are represented in the machine’s native format and byte order, and properly aligned by skipping pad bytes if necessary (according to the rules used by the C compiler). This behavior is chosen so that the bytes of a packed struct correspond exactly to the memory layout of the corresponding C struct. Whether to use native byte ordering and padding or standard formats depends on the application.

Alternatively, the first character of the format string can be used to indicate the byte order, size and alignment of the packed data, according to the following table:

+ + + + + +

Character

Byte order

Size

Alignment

@

native

native

native

=

native

standard

none

<

little-endian

standard

none

>

big-endian

standard

none

!

network (= big-endian)

standard

none

If the first character is not one of these, '@' is assumed.

Native byte order is big-endian or little-endian, depending on the host system. For example, Intel x86, AMD64 (x86-64), and Apple M1 are little-endian; IBM z and many legacy architectures are big-endian. Use sys.byteorder to check the endianness of your system.

Native size and alignment are determined using the C compiler’s sizeof expression. This is always combined with native byte order.

Standard size depends only on the format character; see the table in the Format Characters section.

Note the difference between '@' and '=': both use native byte order, but the size and alignment of the latter is standardized.

The form '!' represents the network byte order which is always big-endian as defined in IETF RFC 1700.

There is no way to indicate non-native byte order (force byte-swapping); use the appropriate choice of '<' or '>'.

Notes:

  1. Padding is only automatically added between successive structure members. No padding is added at the beginning or the end of the encoded struct.
  2. No padding is added when using non-native size and alignment, e.g. with ‘<’, ‘>’, ‘=’, and ‘!’.
  3. To align the end of a structure to the alignment requirement of a particular type, end the format with the code for that type with a repeat count of zero. See Examples.

Format Characters

Format characters have the following meaning; the conversion between C and Python values should be obvious given their types. The ‘Standard size’ column refers to the size of the packed value in bytes when using standard size; that is, when the format string starts with one of '<', '>', '!' or '='. When using native size, the size of the packed value is platform-dependent.

+ + + + + + + + + + + + + + + + + + + + + +

Format

C Type

Python type

Standard size

Notes

x

pad byte

no value

(7)

c

char

bytes of length 1

1

b

signed char

integer

1

(1), (2)

B

unsigned char

integer

1

(2)

?

_Bool

bool

1

(1)

h

short

integer

2

(2)

H

unsigned short

integer

2

(2)

i

int

integer

4

(2)

I

unsigned int

integer

4

(2)

l

long

integer

4

(2)

L

unsigned long

integer

4

(2)

q

long long

integer

8

(2)

Q

unsigned long long

integer

8

(2)

n

ssize_t

integer

(3)

N

size_t

integer

(3)

e

(6)

float

2

(4)

f

float

float

4

(4)

d

double

float

8

(4)

s

char[]

bytes

(9)

p

char[]

bytes

(8)

P

void*

integer

(5)

Changed in version 3.3: Added support for the 'n' and 'N' formats.

Changed in version 3.6: Added support for the 'e' format.

Notes:

  1. The '?' conversion code corresponds to the _Bool type defined by C99. If this type is not available, it is simulated using a char. In standard mode, it is always represented by one byte.
  2. +

    When attempting to pack a non-integer using any of the integer conversion codes, if the non-integer has a __index__() method then that method is called to convert the argument to an integer before packing.

    Changed in version 3.2: Added use of the __index__() method for non-integers.

  3. The 'n' and 'N' conversion codes are only available for the native size (selected as the default or with the '@' byte order character). For the standard size, you can use whichever of the other integer formats fits your application.
  4. For the 'f', 'd' and 'e' conversion codes, the packed representation uses the IEEE 754 binary32, binary64 or binary16 format (for 'f', 'd' or 'e' respectively), regardless of the floating-point format used by the platform.
  5. The 'P' format character is only available for the native byte ordering (selected as the default or with the '@' byte order character). The byte order character '=' chooses to use little- or big-endian ordering based on the host system. The struct module does not interpret this as native ordering, so the 'P' format is not available.
  6. The IEEE 754 binary16 “half precision” type was introduced in the 2008 revision of the IEEE 754 standard. It has a sign bit, a 5-bit exponent and 11-bit precision (with 10 bits explicitly stored), and can represent numbers between approximately 6.1e-05 and 6.5e+04 at full precision. This type is not widely supported by C compilers: on a typical machine, an unsigned short can be used for storage, but not for math operations. See the Wikipedia page on the half-precision floating-point format for more information.
  7. When packing, 'x' inserts one NUL byte.
  8. The 'p' format character encodes a “Pascal string”, meaning a short variable-length string stored in a fixed number of bytes, given by the count. The first byte stored is the length of the string, or 255, whichever is smaller. The bytes of the string follow. If the string passed in to pack() is too long (longer than the count minus 1), only the leading count-1 bytes of the string are stored. If the string is shorter than count-1, it is padded with null bytes so that exactly count bytes in all are used. Note that for unpack(), the 'p' format character consumes count bytes, but that the string returned can never contain more than 255 bytes.
  9. For the 's' format character, the count is interpreted as the length of the bytes, not a repeat count like for the other format characters; for example, '10s' means a single 10-byte string mapping to or from a single Python byte string, while '10c' means 10 separate one byte character elements (e.g., cccccccccc) mapping to or from ten different Python byte objects. (See Examples for a concrete demonstration of the difference.) If a count is not given, it defaults to 1. For packing, the string is truncated or padded with null bytes as appropriate to make it fit. For unpacking, the resulting bytes object always has exactly the specified number of bytes. As a special case, '0s' means a single, empty string (while '0c' means 0 characters).

A format character may be preceded by an integral repeat count. For example, the format string '4h' means exactly the same as 'hhhh'.

Whitespace characters between formats are ignored; a count and its format must not contain whitespace though.

When packing a value x using one of the integer formats ('b', 'B', 'h', 'H', 'i', 'I', 'l', 'L', 'q', 'Q'), if x is outside the valid range for that format then struct.error is raised.

Changed in version 3.1: Previously, some of the integer formats wrapped out-of-range values and raised DeprecationWarning instead of struct.error.

For the '?' format character, the return value is either True or False. When packing, the truth value of the argument object is used. Either 0 or 1 in the native or standard bool representation will be packed, and any non-zero value will be True when unpacking.

Examples

Note

Native byte order examples (designated by the '@' format prefix or lack of any prefix character) may not match what the reader’s machine produces as that depends on the platform and compiler.

Pack and unpack integers of three different sizes, using big endian ordering:

>>> from struct import *
+>>> pack(">bhl", 1, 2, 3)
+b'\x01\x00\x02\x00\x00\x00\x03'
+>>> unpack('>bhl', b'\x01\x00\x02\x00\x00\x00\x03')
+(1, 2, 3)
+>>> calcsize('>bhl')
+7
+

Attempt to pack an integer which is too large for the defined field:

>>> pack(">h", 99999)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+struct.error: 'h' format requires -32768 <= number <= 32767
+

Demonstrate the difference between 's' and 'c' format characters:

>>> pack("@ccc", b'1', b'2', b'3')
+b'123'
+>>> pack("@3s", b'123')
+b'123'
+

Unpacked fields can be named by assigning them to variables or by wrapping the result in a named tuple:

>>> record = b'raymond   \x32\x12\x08\x01\x08'
+>>> name, serialnum, school, gradelevel = unpack('<10sHHb', record)
+
+>>> from collections import namedtuple
+>>> Student = namedtuple('Student', 'name serialnum school gradelevel')
+>>> Student._make(unpack('<10sHHb', record))
+Student(name=b'raymond   ', serialnum=4658, school=264, gradelevel=8)
+

The ordering of format characters may have an impact on size in native mode since padding is implicit. In standard mode, the user is responsible for inserting any desired padding. Note in the first pack call below that three NUL bytes were added after the packed '#' to align the following integer on a four-byte boundary. In this example, the output was produced on a little endian machine:

>>> pack('@ci', b'#', 0x12131415)
+b'#\x00\x00\x00\x15\x14\x13\x12'
+>>> pack('@ic', 0x12131415, b'#')
+b'\x15\x14\x13\x12#'
+>>> calcsize('@ci')
+8
+>>> calcsize('@ic')
+5
+

The following format 'llh0l' results in two pad bytes being added at the end, assuming the platform’s longs are aligned on 4-byte boundaries:

>>> pack('@llh0l', 1, 2, 3)
+b'\x00\x00\x00\x01\x00\x00\x00\x02\x00\x03\x00\x00'
+

See also

+Module array +
+
+

Packed binary storage of homogeneous data.

+Module json +
+
+

JSON encoder and decoder.

+Module pickle +
+
+

Python object serialization.

Applications

Two main applications for the struct module exist, data interchange between Python and C code within an application or another application compiled using the same compiler (native formats), and data interchange between applications using agreed upon data layout (standard formats). Generally speaking, the format strings constructed for these two domains are distinct.

Native Formats

When constructing format strings which mimic native layouts, the compiler and machine architecture determine byte ordering and padding. In such cases, the @ format character should be used to specify native byte ordering and data sizes. Internal pad bytes are normally inserted automatically. It is possible that a zero-repeat format code will be needed at the end of a format string to round up to the correct byte boundary for proper alignment of consecutive chunks of data.

Consider these two simple examples (on a 64-bit, little-endian machine):

>>> calcsize('@lhl')
+24
+>>> calcsize('@llh')
+18
+

Data is not padded to an 8-byte boundary at the end of the second format string without the use of extra padding. A zero-repeat format code solves that problem:

>>> calcsize('@llh0l')
+24
+

The 'x' format code can be used to specify the repeat, but for native formats it is better to use a zero-repeat format like '0l'.

By default, native byte ordering and alignment is used, but it is better to be explicit and use the '@' prefix character.

Standard Formats

When exchanging data beyond your process such as networking or storage, be precise. Specify the exact byte order, size, and alignment. Do not assume they match the native order of a particular machine. For example, network byte order is big-endian, while many popular CPUs are little-endian. By defining this explicitly, the user need not care about the specifics of the platform their code is running on. The first character should typically be < or > (or !). Padding is the responsibility of the programmer. The zero-repeat format character won’t work. Instead, the user must explicitly add 'x' pad bytes where needed. Revisiting the examples from the previous section, we have:

>>> calcsize('<qh6xq')
+24
+>>> pack('<qh6xq', 1, 2, 3) == pack('@lhl', 1, 2, 3)
+True
+>>> calcsize('@llh')
+18
+>>> pack('@llh', 1, 2, 3) == pack('<qqh', 1, 2, 3)
+True
+>>> calcsize('<qqh6x')
+24
+>>> calcsize('@llh0l')
+24
+>>> pack('@llh0l', 1, 2, 3) == pack('<qqh6x', 1, 2, 3)
+True
+

The above results (executed on a 64-bit machine) aren’t guaranteed to match when executed on different machines. For example, the examples below were executed on a 32-bit machine:

>>> calcsize('<qqh6x')
+24
+>>> calcsize('@llh0l')
+12
+>>> pack('@llh0l', 1, 2, 3) == pack('<qqh6x', 1, 2, 3)
+False
+

Classes

The struct module also defines the following type:

+class struct.Struct(format)
+

Return a new Struct object which writes and reads binary data according to the format string format. Creating a Struct object once and calling its methods is more efficient than calling module-level functions with the same format since the format string is only compiled once.

Note

The compiled versions of the most recent format strings passed to the module-level functions are cached, so programs that use only a few format strings needn’t worry about reusing a single Struct instance.

Compiled Struct objects support the following methods and attributes:

+pack(v1, v2, ...)
+

Identical to the pack() function, using the compiled format. (len(result) will equal size.)

+
+pack_into(buffer, offset, v1, v2, ...)
+

Identical to the pack_into() function, using the compiled format.

+
+unpack(buffer)
+

Identical to the unpack() function, using the compiled format. The buffer’s size in bytes must equal size.

+
+unpack_from(buffer, offset=0)
+

Identical to the unpack_from() function, using the compiled format. The buffer’s size in bytes, starting at position offset, must be at least size.

+
+iter_unpack(buffer)
+

Identical to the iter_unpack() function, using the compiled format. The buffer’s size in bytes must be a multiple of size.

New in version 3.4.

+
+format
+

The format string used to construct this Struct object.

Changed in version 3.7: The format string type is now str instead of bytes.

+
+size
+

The calculated size of the struct (and hence of the bytes object produced by the pack() method) corresponding to format.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/struct.html +

+
diff --git a/devdocs/python~3.12/library%2Fsubprocess.html b/devdocs/python~3.12/library%2Fsubprocess.html new file mode 100644 index 00000000..64e7977c --- /dev/null +++ b/devdocs/python~3.12/library%2Fsubprocess.html @@ -0,0 +1,342 @@ +

subprocess — Subprocess management

Source code: Lib/subprocess.py

The subprocess module allows you to spawn new processes, connect to their input/output/error pipes, and obtain their return codes. This module intends to replace several older modules and functions:

os.system
+os.spawn*
+

Information about how the subprocess module can be used to replace these modules and functions can be found in the following sections.

See also

PEP 324 – PEP proposing the subprocess module

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Using the subprocess Module

The recommended approach to invoking subprocesses is to use the run() function for all use cases it can handle. For more advanced use cases, the underlying Popen interface can be used directly.

+subprocess.run(args, *, stdin=None, input=None, stdout=None, stderr=None, capture_output=False, shell=False, cwd=None, timeout=None, check=False, encoding=None, errors=None, text=None, env=None, universal_newlines=None, **other_popen_kwargs)
+

Run the command described by args. Wait for command to complete, then return a CompletedProcess instance.

The arguments shown above are merely the most common ones, described below in Frequently Used Arguments (hence the use of keyword-only notation in the abbreviated signature). The full function signature is largely the same as that of the Popen constructor - most of the arguments to this function are passed through to that interface. (timeout, input, check, and capture_output are not.)

If capture_output is true, stdout and stderr will be captured. When used, the internal Popen object is automatically created with stdout=PIPE and stderr=PIPE. The stdout and stderr arguments may not be supplied at the same time as capture_output. If you wish to capture and combine both streams into one, use stdout=PIPE and stderr=STDOUT instead of capture_output.

A timeout may be specified in seconds, it is internally passed on to Popen.communicate(). If the timeout expires, the child process will be killed and waited for. The TimeoutExpired exception will be re-raised after the child process has terminated. The initial process creation itself cannot be interrupted on many platform APIs so you are not guaranteed to see a timeout exception until at least after however long process creation takes.

The input argument is passed to Popen.communicate() and thus to the subprocess’s stdin. If used it must be a byte sequence, or a string if encoding or errors is specified or text is true. When used, the internal Popen object is automatically created with stdin=PIPE, and the stdin argument may not be used as well.

If check is true, and the process exits with a non-zero exit code, a CalledProcessError exception will be raised. Attributes of that exception hold the arguments, the exit code, and stdout and stderr if they were captured.

If encoding or errors are specified, or text is true, file objects for stdin, stdout and stderr are opened in text mode using the specified encoding and errors or the io.TextIOWrapper default. The universal_newlines argument is equivalent to text and is provided for backwards compatibility. By default, file objects are opened in binary mode.

If env is not None, it must be a mapping that defines the environment variables for the new process; these are used instead of the default behavior of inheriting the current process’ environment. It is passed directly to Popen. This mapping can be str to str on any platform or bytes to bytes on POSIX platforms much like os.environ or os.environb.

Examples:

>>> subprocess.run(["ls", "-l"])  # doesn't capture output
+CompletedProcess(args=['ls', '-l'], returncode=0)
+
+>>> subprocess.run("exit 1", shell=True, check=True)
+Traceback (most recent call last):
+  ...
+subprocess.CalledProcessError: Command 'exit 1' returned non-zero exit status 1
+
+>>> subprocess.run(["ls", "-l", "/dev/null"], capture_output=True)
+CompletedProcess(args=['ls', '-l', '/dev/null'], returncode=0,
+stdout=b'crw-rw-rw- 1 root root 1, 3 Jan 23 16:23 /dev/null\n', stderr=b'')
+

New in version 3.5.

Changed in version 3.6: Added encoding and errors parameters

Changed in version 3.7: Added the text parameter, as a more understandable alias of universal_newlines. Added the capture_output parameter.

Changed in version 3.12: Changed Windows shell search order for shell=True. The current directory and %PATH% are replaced with %COMSPEC% and %SystemRoot%\System32\cmd.exe. As a result, dropping a malicious program named cmd.exe into a current directory no longer works.

+
+class subprocess.CompletedProcess
+

The return value from run(), representing a process that has finished.

+args
+

The arguments used to launch the process. This may be a list or a string.

+
+returncode
+

Exit status of the child process. Typically, an exit status of 0 indicates that it ran successfully.

A negative value -N indicates that the child was terminated by signal N (POSIX only).

+
+stdout
+

Captured stdout from the child process. A bytes sequence, or a string if run() was called with an encoding, errors, or text=True. None if stdout was not captured.

If you ran the process with stderr=subprocess.STDOUT, stdout and stderr will be combined in this attribute, and stderr will be None.

+
+stderr
+

Captured stderr from the child process. A bytes sequence, or a string if run() was called with an encoding, errors, or text=True. None if stderr was not captured.

+
+check_returncode()
+

If returncode is non-zero, raise a CalledProcessError.

+

New in version 3.5.

+
+subprocess.DEVNULL
+

Special value that can be used as the stdin, stdout or stderr argument to Popen and indicates that the special file os.devnull will be used.

New in version 3.3.

+
+subprocess.PIPE
+

Special value that can be used as the stdin, stdout or stderr argument to Popen and indicates that a pipe to the standard stream should be opened. Most useful with Popen.communicate().

+
+subprocess.STDOUT
+

Special value that can be used as the stderr argument to Popen and indicates that standard error should go into the same handle as standard output.

+
+exception subprocess.SubprocessError
+

Base class for all other exceptions from this module.

New in version 3.3.

+
+exception subprocess.TimeoutExpired
+

Subclass of SubprocessError, raised when a timeout expires while waiting for a child process.

+cmd
+

Command that was used to spawn the child process.

+
+timeout
+

Timeout in seconds.

+
+output
+

Output of the child process if it was captured by run() or check_output(). Otherwise, None. This is always bytes when any output was captured regardless of the text=True setting. It may remain None instead of b'' when no output was observed.

+
+stdout
+

Alias for output, for symmetry with stderr.

+
+stderr
+

Stderr output of the child process if it was captured by run(). Otherwise, None. This is always bytes when stderr output was captured regardless of the text=True setting. It may remain None instead of b'' when no stderr output was observed.

+

New in version 3.3.

Changed in version 3.5: stdout and stderr attributes added

+
+exception subprocess.CalledProcessError
+

Subclass of SubprocessError, raised when a process run by check_call(), check_output(), or run() (with check=True) returns a non-zero exit status.

+returncode
+

Exit status of the child process. If the process exited due to a signal, this will be the negative signal number.

+
+cmd
+

Command that was used to spawn the child process.

+
+output
+

Output of the child process if it was captured by run() or check_output(). Otherwise, None.

+
+stdout
+

Alias for output, for symmetry with stderr.

+
+stderr
+

Stderr output of the child process if it was captured by run(). Otherwise, None.

+

Changed in version 3.5: stdout and stderr attributes added

+

Frequently Used Arguments

To support a wide variety of use cases, the Popen constructor (and the convenience functions) accept a large number of optional arguments. For most typical use cases, many of these arguments can be safely left at their default values. The arguments that are most commonly needed are:

args is required for all calls and should be a string, or a sequence of program arguments. Providing a sequence of arguments is generally preferred, as it allows the module to take care of any required escaping and quoting of arguments (e.g. to permit spaces in file names). If passing a single string, either shell must be True (see below) or else the string must simply name the program to be executed without specifying any arguments.

stdin, stdout and stderr specify the executed program’s standard input, standard output and standard error file handles, respectively. Valid values are None, PIPE, DEVNULL, an existing file descriptor (a positive integer), and an existing file object with a valid file descriptor. With the default settings of None, no redirection will occur. PIPE indicates that a new pipe to the child should be created. DEVNULL indicates that the special file os.devnull will be used. Additionally, stderr can be STDOUT, which indicates that the stderr data from the child process should be captured into the same file handle as for stdout.

If encoding or errors are specified, or text (also known as universal_newlines) is true, the file objects stdin, stdout and stderr will be opened in text mode using the encoding and errors specified in the call or the defaults for io.TextIOWrapper.

For stdin, line ending characters '\n' in the input will be converted to the default line separator os.linesep. For stdout and stderr, all line endings in the output will be converted to '\n'. For more information see the documentation of the io.TextIOWrapper class when the newline argument to its constructor is None.

If text mode is not used, stdin, stdout and stderr will be opened as binary streams. No encoding or line ending conversion is performed.

New in version 3.6: Added encoding and errors parameters.

New in version 3.7: Added the text parameter as an alias for universal_newlines.

Note

The newlines attribute of the file objects Popen.stdin, Popen.stdout and Popen.stderr are not updated by the Popen.communicate() method.

If shell is True, the specified command will be executed through the shell. This can be useful if you are using Python primarily for the enhanced control flow it offers over most system shells and still want convenient access to other shell features such as shell pipes, filename wildcards, environment variable expansion, and expansion of ~ to a user’s home directory. However, note that Python itself offers implementations of many shell-like features (in particular, glob, fnmatch, os.walk(), os.path.expandvars(), os.path.expanduser(), and shutil).

Changed in version 3.3: When universal_newlines is True, the class uses the encoding locale.getpreferredencoding(False) instead of locale.getpreferredencoding(). See the io.TextIOWrapper class for more information on this change.

Note

Read the Security Considerations section before using shell=True.

These options, along with all of the other options, are described in more detail in the Popen constructor documentation.

Popen Constructor

The underlying process creation and management in this module is handled by the Popen class. It offers a lot of flexibility so that developers are able to handle the less common cases not covered by the convenience functions.

+class subprocess.Popen(args, bufsize=- 1, executable=None, stdin=None, stdout=None, stderr=None, preexec_fn=None, close_fds=True, shell=False, cwd=None, env=None, universal_newlines=None, startupinfo=None, creationflags=0, restore_signals=True, start_new_session=False, pass_fds=(), *, group=None, extra_groups=None, user=None, umask=- 1, encoding=None, errors=None, text=None, pipesize=- 1, process_group=None)
+

Execute a child program in a new process. On POSIX, the class uses os.execvpe()-like behavior to execute the child program. On Windows, the class uses the Windows CreateProcess() function. The arguments to Popen are as follows.

args should be a sequence of program arguments or else a single string or path-like object. By default, the program to execute is the first item in args if args is a sequence. If args is a string, the interpretation is platform-dependent and described below. See the shell and executable arguments for additional differences from the default behavior. Unless otherwise stated, it is recommended to pass args as a sequence.

Warning

For maximum reliability, use a fully qualified path for the executable. To search for an unqualified name on PATH, use shutil.which(). On all platforms, passing sys.executable is the recommended way to launch the current Python interpreter again, and use the -m command-line format to launch an installed module.

Resolving the path of executable (or the first item of args) is platform dependent. For POSIX, see os.execvpe(), and note that when resolving or searching for the executable path, cwd overrides the current working directory and env can override the PATH environment variable. For Windows, see the documentation of the lpApplicationName and lpCommandLine parameters of WinAPI CreateProcess, and note that when resolving or searching for the executable path with shell=False, cwd does not override the current working directory and env cannot override the PATH environment variable. Using a full path avoids all of these variations.

An example of passing some arguments to an external program as a sequence is:

Popen(["/usr/bin/git", "commit", "-m", "Fixes a bug."])
+

On POSIX, if args is a string, the string is interpreted as the name or path of the program to execute. However, this can only be done if not passing arguments to the program.

Note

It may not be obvious how to break a shell command into a sequence of arguments, especially in complex cases. shlex.split() can illustrate how to determine the correct tokenization for args:

>>> import shlex, subprocess
+>>> command_line = input()
+/bin/vikings -input eggs.txt -output "spam spam.txt" -cmd "echo '$MONEY'"
+>>> args = shlex.split(command_line)
+>>> print(args)
+['/bin/vikings', '-input', 'eggs.txt', '-output', 'spam spam.txt', '-cmd', "echo '$MONEY'"]
+>>> p = subprocess.Popen(args) # Success!
+

Note in particular that options (such as -input) and arguments (such as eggs.txt) that are separated by whitespace in the shell go in separate list elements, while arguments that need quoting or backslash escaping when used in the shell (such as filenames containing spaces or the echo command shown above) are single list elements.

On Windows, if args is a sequence, it will be converted to a string in a manner described in Converting an argument sequence to a string on Windows. This is because the underlying CreateProcess() operates on strings.

Changed in version 3.6: args parameter accepts a path-like object if shell is False and a sequence containing path-like objects on POSIX.

Changed in version 3.8: args parameter accepts a path-like object if shell is False and a sequence containing bytes and path-like objects on Windows.

The shell argument (which defaults to False) specifies whether to use the shell as the program to execute. If shell is True, it is recommended to pass args as a string rather than as a sequence.

On POSIX with shell=True, the shell defaults to /bin/sh. If args is a string, the string specifies the command to execute through the shell. This means that the string must be formatted exactly as it would be when typed at the shell prompt. This includes, for example, quoting or backslash escaping filenames with spaces in them. If args is a sequence, the first item specifies the command string, and any additional items will be treated as additional arguments to the shell itself. That is to say, Popen does the equivalent of:

Popen(['/bin/sh', '-c', args[0], args[1], ...])
+

On Windows with shell=True, the COMSPEC environment variable specifies the default shell. The only time you need to specify shell=True on Windows is when the command you wish to execute is built into the shell (e.g. dir or copy). You do not need shell=True to run a batch file or console-based executable.

Note

Read the Security Considerations section before using shell=True.

bufsize will be supplied as the corresponding argument to the open() function when creating the stdin/stdout/stderr pipe file objects:

  • +0 means unbuffered (read and write are one system call and can return short)
  • +1 means line buffered (only usable if text=True or universal_newlines=True)
  • any other positive value means use a buffer of approximately that size
  • negative bufsize (the default) means the system default of io.DEFAULT_BUFFER_SIZE will be used.

Changed in version 3.3.1: bufsize now defaults to -1 to enable buffering by default to match the behavior that most code expects. In versions prior to Python 3.2.4 and 3.3.1 it incorrectly defaulted to 0 which was unbuffered and allowed short reads. This was unintentional and did not match the behavior of Python 2 as most code expected.

The executable argument specifies a replacement program to execute. It is very seldom needed. When shell=False, executable replaces the program to execute specified by args. However, the original args is still passed to the program. Most programs treat the program specified by args as the command name, which can then be different from the program actually executed. On POSIX, the args name becomes the display name for the executable in utilities such as ps. If shell=True, on POSIX the executable argument specifies a replacement shell for the default /bin/sh.

Changed in version 3.6: executable parameter accepts a path-like object on POSIX.

Changed in version 3.8: executable parameter accepts a bytes and path-like object on Windows.

Changed in version 3.12: Changed Windows shell search order for shell=True. The current directory and %PATH% are replaced with %COMSPEC% and %SystemRoot%\System32\cmd.exe. As a result, dropping a malicious program named cmd.exe into a current directory no longer works.

stdin, stdout and stderr specify the executed program’s standard input, standard output and standard error file handles, respectively. Valid values are None, PIPE, DEVNULL, an existing file descriptor (a positive integer), and an existing file object with a valid file descriptor. With the default settings of None, no redirection will occur. PIPE indicates that a new pipe to the child should be created. DEVNULL indicates that the special file os.devnull will be used. Additionally, stderr can be STDOUT, which indicates that the stderr data from the applications should be captured into the same file handle as for stdout.

If preexec_fn is set to a callable object, this object will be called in the child process just before the child is executed. (POSIX only)

Warning

The preexec_fn parameter is NOT SAFE to use in the presence of threads in your application. The child process could deadlock before exec is called.

Note

If you need to modify the environment for the child use the env parameter rather than doing it in a preexec_fn. The start_new_session and process_group parameters should take the place of code using preexec_fn to call os.setsid() or os.setpgid() in the child.

Changed in version 3.8: The preexec_fn parameter is no longer supported in subinterpreters. The use of the parameter in a subinterpreter raises RuntimeError. The new restriction may affect applications that are deployed in mod_wsgi, uWSGI, and other embedded environments.

If close_fds is true, all file descriptors except 0, 1 and 2 will be closed before the child process is executed. Otherwise when close_fds is false, file descriptors obey their inheritable flag as described in Inheritance of File Descriptors.

On Windows, if close_fds is true then no handles will be inherited by the child process unless explicitly passed in the handle_list element of STARTUPINFO.lpAttributeList, or by standard handle redirection.

Changed in version 3.2: The default for close_fds was changed from False to what is described above.

Changed in version 3.7: On Windows the default for close_fds was changed from False to True when redirecting the standard handles. It’s now possible to set close_fds to True when redirecting the standard handles.

pass_fds is an optional sequence of file descriptors to keep open between the parent and child. Providing any pass_fds forces close_fds to be True. (POSIX only)

Changed in version 3.2: The pass_fds parameter was added.

If cwd is not None, the function changes the working directory to cwd before executing the child. cwd can be a string, bytes or path-like object. On POSIX, the function looks for executable (or for the first item in args) relative to cwd if the executable path is a relative path.

Changed in version 3.6: cwd parameter accepts a path-like object on POSIX.

Changed in version 3.7: cwd parameter accepts a path-like object on Windows.

Changed in version 3.8: cwd parameter accepts a bytes object on Windows.

If restore_signals is true (the default) all signals that Python has set to SIG_IGN are restored to SIG_DFL in the child process before the exec. Currently this includes the SIGPIPE, SIGXFZ and SIGXFSZ signals. (POSIX only)

Changed in version 3.2: restore_signals was added.

If start_new_session is true the setsid() system call will be made in the child process prior to the execution of the subprocess.

Availability: POSIX

Changed in version 3.2: start_new_session was added.

If process_group is a non-negative integer, the setpgid(0, value) system call will be made in the child process prior to the execution of the subprocess.

Availability: POSIX

Changed in version 3.11: process_group was added.

If group is not None, the setregid() system call will be made in the child process prior to the execution of the subprocess. If the provided value is a string, it will be looked up via grp.getgrnam() and the value in gr_gid will be used. If the value is an integer, it will be passed verbatim. (POSIX only)

Availability: POSIX

New in version 3.9.

If extra_groups is not None, the setgroups() system call will be made in the child process prior to the execution of the subprocess. Strings provided in extra_groups will be looked up via grp.getgrnam() and the values in gr_gid will be used. Integer values will be passed verbatim. (POSIX only)

Availability: POSIX

New in version 3.9.

If user is not None, the setreuid() system call will be made in the child process prior to the execution of the subprocess. If the provided value is a string, it will be looked up via pwd.getpwnam() and the value in pw_uid will be used. If the value is an integer, it will be passed verbatim. (POSIX only)

Availability: POSIX

New in version 3.9.

If umask is not negative, the umask() system call will be made in the child process prior to the execution of the subprocess.

Availability: POSIX

New in version 3.9.

If env is not None, it must be a mapping that defines the environment variables for the new process; these are used instead of the default behavior of inheriting the current process’ environment. This mapping can be str to str on any platform or bytes to bytes on POSIX platforms much like os.environ or os.environb.

Note

If specified, env must provide any variables required for the program to execute. On Windows, in order to run a side-by-side assembly the specified env must include a valid SystemRoot.

If encoding or errors are specified, or text is true, the file objects stdin, stdout and stderr are opened in text mode with the specified encoding and errors, as described above in Frequently Used Arguments. The universal_newlines argument is equivalent to text and is provided for backwards compatibility. By default, file objects are opened in binary mode.

New in version 3.6: encoding and errors were added.

New in version 3.7: text was added as a more readable alias for universal_newlines.

If given, startupinfo will be a STARTUPINFO object, which is passed to the underlying CreateProcess function. creationflags, if given, can be one or more of the following flags:

pipesize can be used to change the size of the pipe when PIPE is used for stdin, stdout or stderr. The size of the pipe is only changed on platforms that support this (only Linux at this time of writing). Other platforms will ignore this parameter.

New in version 3.10: The pipesize parameter was added.

Popen objects are supported as context managers via the with statement: on exit, standard file descriptors are closed, and the process is waited for.

with Popen(["ifconfig"], stdout=PIPE) as proc:
+    log.write(proc.stdout.read())
+

+

Popen and the other functions in this module that use it raise an auditing event subprocess.Popen with arguments executable, args, cwd, and env. The value for args may be a single string or a list of strings, depending on platform.

Changed in version 3.2: Added context manager support.

Changed in version 3.6: Popen destructor now emits a ResourceWarning warning if the child process is still running.

Changed in version 3.8: Popen can use os.posix_spawn() in some cases for better performance. On Windows Subsystem for Linux and QEMU User Emulation, Popen constructor using os.posix_spawn() no longer raise an exception on errors like missing program, but the child process fails with a non-zero returncode.

+

Exceptions

Exceptions raised in the child process, before the new program has started to execute, will be re-raised in the parent.

The most common exception raised is OSError. This occurs, for example, when trying to execute a non-existent file. Applications should prepare for OSError exceptions. Note that, when shell=True, OSError will be raised by the child only if the selected shell itself was not found. To determine if the shell failed to find the requested application, it is necessary to check the return code or output from the subprocess.

A ValueError will be raised if Popen is called with invalid arguments.

check_call() and check_output() will raise CalledProcessError if the called process returns a non-zero return code.

All of the functions and methods that accept a timeout parameter, such as run() and Popen.communicate() will raise TimeoutExpired if the timeout expires before the process exits.

Exceptions defined in this module all inherit from SubprocessError.

New in version 3.3: The SubprocessError base class was added.

Security Considerations

Unlike some other popen functions, this implementation will never implicitly call a system shell. This means that all characters, including shell metacharacters, can safely be passed to child processes. If the shell is invoked explicitly, via shell=True, it is the application’s responsibility to ensure that all whitespace and metacharacters are quoted appropriately to avoid shell injection vulnerabilities. On some platforms, it is possible to use shlex.quote() for this escaping.

Popen Objects

Instances of the Popen class have the following methods:

+Popen.poll()
+

Check if child process has terminated. Set and return returncode attribute. Otherwise, returns None.

+
+Popen.wait(timeout=None)
+

Wait for child process to terminate. Set and return returncode attribute.

If the process does not terminate after timeout seconds, raise a TimeoutExpired exception. It is safe to catch this exception and retry the wait.

Note

This will deadlock when using stdout=PIPE or stderr=PIPE and the child process generates enough output to a pipe such that it blocks waiting for the OS pipe buffer to accept more data. Use Popen.communicate() when using pipes to avoid that.

Note

When the timeout parameter is not None, then (on POSIX) the function is implemented using a busy loop (non-blocking call and short sleeps). Use the asyncio module for an asynchronous wait: see asyncio.create_subprocess_exec.

Changed in version 3.3: timeout was added.

+
+Popen.communicate(input=None, timeout=None)
+

Interact with process: Send data to stdin. Read data from stdout and stderr, until end-of-file is reached. Wait for process to terminate and set the returncode attribute. The optional input argument should be data to be sent to the child process, or None, if no data should be sent to the child. If streams were opened in text mode, input must be a string. Otherwise, it must be bytes.

communicate() returns a tuple (stdout_data, stderr_data). The data will be strings if streams were opened in text mode; otherwise, bytes.

Note that if you want to send data to the process’s stdin, you need to create the Popen object with stdin=PIPE. Similarly, to get anything other than None in the result tuple, you need to give stdout=PIPE and/or stderr=PIPE too.

If the process does not terminate after timeout seconds, a TimeoutExpired exception will be raised. Catching this exception and retrying communication will not lose any output.

The child process is not killed if the timeout expires, so in order to cleanup properly a well-behaved application should kill the child process and finish communication:

proc = subprocess.Popen(...)
+try:
+    outs, errs = proc.communicate(timeout=15)
+except TimeoutExpired:
+    proc.kill()
+    outs, errs = proc.communicate()
+

Note

The data read is buffered in memory, so do not use this method if the data size is large or unlimited.

Changed in version 3.3: timeout was added.

+
+Popen.send_signal(signal)
+

Sends the signal signal to the child.

Do nothing if the process completed.

Note

On Windows, SIGTERM is an alias for terminate(). CTRL_C_EVENT and CTRL_BREAK_EVENT can be sent to processes started with a creationflags parameter which includes CREATE_NEW_PROCESS_GROUP.

+
+Popen.terminate()
+

Stop the child. On POSIX OSs the method sends SIGTERM to the child. On Windows the Win32 API function TerminateProcess() is called to stop the child.

+
+Popen.kill()
+

Kills the child. On POSIX OSs the function sends SIGKILL to the child. On Windows kill() is an alias for terminate().

+

The following attributes are also set by the class for you to access. Reassigning them to new values is unsupported:

+Popen.args
+

The args argument as it was passed to Popen – a sequence of program arguments or else a single string.

New in version 3.3.

+
+Popen.stdin
+

If the stdin argument was PIPE, this attribute is a writeable stream object as returned by open(). If the encoding or errors arguments were specified or the text or universal_newlines argument was True, the stream is a text stream, otherwise it is a byte stream. If the stdin argument was not PIPE, this attribute is None.

+
+Popen.stdout
+

If the stdout argument was PIPE, this attribute is a readable stream object as returned by open(). Reading from the stream provides output from the child process. If the encoding or errors arguments were specified or the text or universal_newlines argument was True, the stream is a text stream, otherwise it is a byte stream. If the stdout argument was not PIPE, this attribute is None.

+
+Popen.stderr
+

If the stderr argument was PIPE, this attribute is a readable stream object as returned by open(). Reading from the stream provides error output from the child process. If the encoding or errors arguments were specified or the text or universal_newlines argument was True, the stream is a text stream, otherwise it is a byte stream. If the stderr argument was not PIPE, this attribute is None.

+

Warning

Use communicate() rather than .stdin.write, .stdout.read or .stderr.read to avoid deadlocks due to any of the other OS pipe buffers filling up and blocking the child process.

+Popen.pid
+

The process ID of the child process.

Note that if you set the shell argument to True, this is the process ID of the spawned shell.

+
+Popen.returncode
+

The child return code. Initially None, returncode is set by a call to the poll(), wait(), or communicate() methods if they detect that the process has terminated.

A None value indicates that the process hadn’t yet terminated at the time of the last method call.

A negative value -N indicates that the child was terminated by signal N (POSIX only).

+

Windows Popen Helpers

The STARTUPINFO class and following constants are only available on Windows.

+class subprocess.STARTUPINFO(*, dwFlags=0, hStdInput=None, hStdOutput=None, hStdError=None, wShowWindow=0, lpAttributeList=None)
+

Partial support of the Windows STARTUPINFO structure is used for Popen creation. The following attributes can be set by passing them as keyword-only arguments.

Changed in version 3.7: Keyword-only argument support was added.

+dwFlags
+

A bit field that determines whether certain STARTUPINFO attributes are used when the process creates a window.

si = subprocess.STARTUPINFO()
+si.dwFlags = subprocess.STARTF_USESTDHANDLES | subprocess.STARTF_USESHOWWINDOW
+
+
+hStdInput
+

If dwFlags specifies STARTF_USESTDHANDLES, this attribute is the standard input handle for the process. If STARTF_USESTDHANDLES is not specified, the default for standard input is the keyboard buffer.

+
+hStdOutput
+

If dwFlags specifies STARTF_USESTDHANDLES, this attribute is the standard output handle for the process. Otherwise, this attribute is ignored and the default for standard output is the console window’s buffer.

+
+hStdError
+

If dwFlags specifies STARTF_USESTDHANDLES, this attribute is the standard error handle for the process. Otherwise, this attribute is ignored and the default for standard error is the console window’s buffer.

+
+wShowWindow
+

If dwFlags specifies STARTF_USESHOWWINDOW, this attribute can be any of the values that can be specified in the nCmdShow parameter for the ShowWindow function, except for SW_SHOWDEFAULT. Otherwise, this attribute is ignored.

SW_HIDE is provided for this attribute. It is used when Popen is called with shell=True.

+
+lpAttributeList
+

A dictionary of additional attributes for process creation as given in STARTUPINFOEX, see UpdateProcThreadAttribute.

Supported attributes:

handle_list
+
+

Sequence of handles that will be inherited. close_fds must be true if non-empty.

The handles must be temporarily made inheritable by os.set_handle_inheritable() when passed to the Popen constructor, else OSError will be raised with Windows error ERROR_INVALID_PARAMETER (87).

Warning

In a multithreaded process, use caution to avoid leaking handles that are marked inheritable when combining this feature with concurrent calls to other process creation functions that inherit all handles such as os.system(). This also applies to standard handle redirection, which temporarily creates inheritable handles.

New in version 3.7.

+
+

Windows Constants

The subprocess module exposes the following constants.

+subprocess.STD_INPUT_HANDLE
+

The standard input device. Initially, this is the console input buffer, CONIN$.

+
+subprocess.STD_OUTPUT_HANDLE
+

The standard output device. Initially, this is the active console screen buffer, CONOUT$.

+
+subprocess.STD_ERROR_HANDLE
+

The standard error device. Initially, this is the active console screen buffer, CONOUT$.

+
+subprocess.SW_HIDE
+

Hides the window. Another window will be activated.

+
+subprocess.STARTF_USESTDHANDLES
+

Specifies that the STARTUPINFO.hStdInput, STARTUPINFO.hStdOutput, and STARTUPINFO.hStdError attributes contain additional information.

+
+subprocess.STARTF_USESHOWWINDOW
+

Specifies that the STARTUPINFO.wShowWindow attribute contains additional information.

+
+subprocess.CREATE_NEW_CONSOLE
+

The new process has a new console, instead of inheriting its parent’s console (the default).

+
+subprocess.CREATE_NEW_PROCESS_GROUP
+

A Popen creationflags parameter to specify that a new process group will be created. This flag is necessary for using os.kill() on the subprocess.

This flag is ignored if CREATE_NEW_CONSOLE is specified.

+
+subprocess.ABOVE_NORMAL_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have an above average priority.

New in version 3.7.

+
+subprocess.BELOW_NORMAL_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have a below average priority.

New in version 3.7.

+
+subprocess.HIGH_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have a high priority.

New in version 3.7.

+
+subprocess.IDLE_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have an idle (lowest) priority.

New in version 3.7.

+
+subprocess.NORMAL_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have an normal priority. (default)

New in version 3.7.

+
+subprocess.REALTIME_PRIORITY_CLASS
+

A Popen creationflags parameter to specify that a new process will have realtime priority. You should almost never use REALTIME_PRIORITY_CLASS, because this interrupts system threads that manage mouse input, keyboard input, and background disk flushing. This class can be appropriate for applications that “talk” directly to hardware or that perform brief tasks that should have limited interruptions.

New in version 3.7.

+
+subprocess.CREATE_NO_WINDOW
+

A Popen creationflags parameter to specify that a new process will not create a window.

New in version 3.7.

+
+subprocess.DETACHED_PROCESS
+

A Popen creationflags parameter to specify that a new process will not inherit its parent’s console. This value cannot be used with CREATE_NEW_CONSOLE.

New in version 3.7.

+
+subprocess.CREATE_DEFAULT_ERROR_MODE
+

A Popen creationflags parameter to specify that a new process does not inherit the error mode of the calling process. Instead, the new process gets the default error mode. This feature is particularly useful for multithreaded shell applications that run with hard errors disabled.

New in version 3.7.

+
+subprocess.CREATE_BREAKAWAY_FROM_JOB
+

A Popen creationflags parameter to specify that a new process is not associated with the job.

New in version 3.7.

+

Older high-level API

Prior to Python 3.5, these three functions comprised the high level API to subprocess. You can now use run() in many cases, but lots of existing code calls these functions.

+subprocess.call(args, *, stdin=None, stdout=None, stderr=None, shell=False, cwd=None, timeout=None, **other_popen_kwargs)
+

Run the command described by args. Wait for command to complete, then return the returncode attribute.

Code needing to capture stdout or stderr should use run() instead:

run(...).returncode
+

To suppress stdout or stderr, supply a value of DEVNULL.

The arguments shown above are merely some common ones. The full function signature is the same as that of the Popen constructor - this function passes all supplied arguments other than timeout directly through to that interface.

Note

Do not use stdout=PIPE or stderr=PIPE with this function. The child process will block if it generates enough output to a pipe to fill up the OS pipe buffer as the pipes are not being read from.

Changed in version 3.3: timeout was added.

Changed in version 3.12: Changed Windows shell search order for shell=True. The current directory and %PATH% are replaced with %COMSPEC% and %SystemRoot%\System32\cmd.exe. As a result, dropping a malicious program named cmd.exe into a current directory no longer works.

+
+subprocess.check_call(args, *, stdin=None, stdout=None, stderr=None, shell=False, cwd=None, timeout=None, **other_popen_kwargs)
+

Run command with arguments. Wait for command to complete. If the return code was zero then return, otherwise raise CalledProcessError. The CalledProcessError object will have the return code in the returncode attribute. If check_call() was unable to start the process it will propagate the exception that was raised.

Code needing to capture stdout or stderr should use run() instead:

run(..., check=True)
+

To suppress stdout or stderr, supply a value of DEVNULL.

The arguments shown above are merely some common ones. The full function signature is the same as that of the Popen constructor - this function passes all supplied arguments other than timeout directly through to that interface.

Note

Do not use stdout=PIPE or stderr=PIPE with this function. The child process will block if it generates enough output to a pipe to fill up the OS pipe buffer as the pipes are not being read from.

Changed in version 3.3: timeout was added.

Changed in version 3.12: Changed Windows shell search order for shell=True. The current directory and %PATH% are replaced with %COMSPEC% and %SystemRoot%\System32\cmd.exe. As a result, dropping a malicious program named cmd.exe into a current directory no longer works.

+
+subprocess.check_output(args, *, stdin=None, stderr=None, shell=False, cwd=None, encoding=None, errors=None, universal_newlines=None, timeout=None, text=None, **other_popen_kwargs)
+

Run command with arguments and return its output.

If the return code was non-zero it raises a CalledProcessError. The CalledProcessError object will have the return code in the returncode attribute and any output in the output attribute.

This is equivalent to:

run(..., check=True, stdout=PIPE).stdout
+

The arguments shown above are merely some common ones. The full function signature is largely the same as that of run() - most arguments are passed directly through to that interface. One API deviation from run() behavior exists: passing input=None will behave the same as input=b'' (or input='', depending on other arguments) rather than using the parent’s standard input file handle.

By default, this function will return the data as encoded bytes. The actual encoding of the output data may depend on the command being invoked, so the decoding to text will often need to be handled at the application level.

This behaviour may be overridden by setting text, encoding, errors, or universal_newlines to True as described in Frequently Used Arguments and run().

To also capture standard error in the result, use stderr=subprocess.STDOUT:

>>> subprocess.check_output(
+...     "ls non_existent_file; exit 0",
+...     stderr=subprocess.STDOUT,
+...     shell=True)
+'ls: non_existent_file: No such file or directory\n'
+

New in version 3.1.

Changed in version 3.3: timeout was added.

Changed in version 3.4: Support for the input keyword argument was added.

Changed in version 3.6: encoding and errors were added. See run() for details.

New in version 3.7: text was added as a more readable alias for universal_newlines.

Changed in version 3.12: Changed Windows shell search order for shell=True. The current directory and %PATH% are replaced with %COMSPEC% and %SystemRoot%\System32\cmd.exe. As a result, dropping a malicious program named cmd.exe into a current directory no longer works.

+

Replacing Older Functions with the subprocess Module

In this section, “a becomes b” means that b can be used as a replacement for a.

Note

All “a” functions in this section fail (more or less) silently if the executed program cannot be found; the “b” replacements raise OSError instead.

In addition, the replacements using check_output() will fail with a CalledProcessError if the requested operation produces a non-zero return code. The output is still available as the output attribute of the raised exception.

In the following examples, we assume that the relevant functions have already been imported from the subprocess module.

Replacing /bin/sh shell command substitution

output=$(mycmd myarg)
+

becomes:

output = check_output(["mycmd", "myarg"])
+

Replacing shell pipeline

output=$(dmesg | grep hda)
+

becomes:

p1 = Popen(["dmesg"], stdout=PIPE)
+p2 = Popen(["grep", "hda"], stdin=p1.stdout, stdout=PIPE)
+p1.stdout.close()  # Allow p1 to receive a SIGPIPE if p2 exits.
+output = p2.communicate()[0]
+

The p1.stdout.close() call after starting the p2 is important in order for p1 to receive a SIGPIPE if p2 exits before p1.

Alternatively, for trusted input, the shell’s own pipeline support may still be used directly:

output=$(dmesg | grep hda)
+

becomes:

output = check_output("dmesg | grep hda", shell=True)
+

Replacing os.system() +

sts = os.system("mycmd" + " myarg")
+# becomes
+retcode = call("mycmd" + " myarg", shell=True)
+

Notes:

  • Calling the program through the shell is usually not required.
  • The call() return value is encoded differently to that of os.system().
  • The os.system() function ignores SIGINT and SIGQUIT signals while the command is running, but the caller must do this separately when using the subprocess module.

A more realistic example would look like this:

try:
+    retcode = call("mycmd" + " myarg", shell=True)
+    if retcode < 0:
+        print("Child was terminated by signal", -retcode, file=sys.stderr)
+    else:
+        print("Child returned", retcode, file=sys.stderr)
+except OSError as e:
+    print("Execution failed:", e, file=sys.stderr)
+

Replacing the os.spawn family

P_NOWAIT example:

pid = os.spawnlp(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg")
+==>
+pid = Popen(["/bin/mycmd", "myarg"]).pid
+

P_WAIT example:

retcode = os.spawnlp(os.P_WAIT, "/bin/mycmd", "mycmd", "myarg")
+==>
+retcode = call(["/bin/mycmd", "myarg"])
+

Vector example:

os.spawnvp(os.P_NOWAIT, path, args)
+==>
+Popen([path] + args[1:])
+

Environment example:

os.spawnlpe(os.P_NOWAIT, "/bin/mycmd", "mycmd", "myarg", env)
+==>
+Popen(["/bin/mycmd", "myarg"], env={"PATH": "/usr/bin"})
+

Replacing os.popen(), os.popen2(), os.popen3() +

(child_stdin, child_stdout) = os.popen2(cmd, mode, bufsize)
+==>
+p = Popen(cmd, shell=True, bufsize=bufsize,
+          stdin=PIPE, stdout=PIPE, close_fds=True)
+(child_stdin, child_stdout) = (p.stdin, p.stdout)
+
(child_stdin,
+ child_stdout,
+ child_stderr) = os.popen3(cmd, mode, bufsize)
+==>
+p = Popen(cmd, shell=True, bufsize=bufsize,
+          stdin=PIPE, stdout=PIPE, stderr=PIPE, close_fds=True)
+(child_stdin,
+ child_stdout,
+ child_stderr) = (p.stdin, p.stdout, p.stderr)
+
(child_stdin, child_stdout_and_stderr) = os.popen4(cmd, mode, bufsize)
+==>
+p = Popen(cmd, shell=True, bufsize=bufsize,
+          stdin=PIPE, stdout=PIPE, stderr=STDOUT, close_fds=True)
+(child_stdin, child_stdout_and_stderr) = (p.stdin, p.stdout)
+

Return code handling translates as follows:

pipe = os.popen(cmd, 'w')
+...
+rc = pipe.close()
+if rc is not None and rc >> 8:
+    print("There were some errors")
+==>
+process = Popen(cmd, stdin=PIPE)
+...
+process.stdin.close()
+if process.wait() != 0:
+    print("There were some errors")
+

Replacing functions from the popen2 module

Note

If the cmd argument to popen2 functions is a string, the command is executed through /bin/sh. If it is a list, the command is directly executed.

(child_stdout, child_stdin) = popen2.popen2("somestring", bufsize, mode)
+==>
+p = Popen("somestring", shell=True, bufsize=bufsize,
+          stdin=PIPE, stdout=PIPE, close_fds=True)
+(child_stdout, child_stdin) = (p.stdout, p.stdin)
+
(child_stdout, child_stdin) = popen2.popen2(["mycmd", "myarg"], bufsize, mode)
+==>
+p = Popen(["mycmd", "myarg"], bufsize=bufsize,
+          stdin=PIPE, stdout=PIPE, close_fds=True)
+(child_stdout, child_stdin) = (p.stdout, p.stdin)
+

popen2.Popen3 and popen2.Popen4 basically work as subprocess.Popen, except that:

  • +Popen raises an exception if the execution fails.
  • The capturestderr argument is replaced with the stderr argument.
  • +stdin=PIPE and stdout=PIPE must be specified.
  • popen2 closes all file descriptors by default, but you have to specify close_fds=True with Popen to guarantee this behavior on all platforms or past Python versions.

Legacy Shell Invocation Functions

This module also provides the following legacy functions from the 2.x commands module. These operations implicitly invoke the system shell and none of the guarantees described above regarding security and exception handling consistency are valid for these functions.

+subprocess.getstatusoutput(cmd, *, encoding=None, errors=None)
+

Return (exitcode, output) of executing cmd in a shell.

Execute the string cmd in a shell with Popen.check_output() and return a 2-tuple (exitcode, output). encoding and errors are used to decode output; see the notes on Frequently Used Arguments for more details.

A trailing newline is stripped from the output. The exit code for the command can be interpreted as the return code of subprocess. Example:

>>> subprocess.getstatusoutput('ls /bin/ls')
+(0, '/bin/ls')
+>>> subprocess.getstatusoutput('cat /bin/junk')
+(1, 'cat: /bin/junk: No such file or directory')
+>>> subprocess.getstatusoutput('/bin/junk')
+(127, 'sh: /bin/junk: not found')
+>>> subprocess.getstatusoutput('/bin/kill $$')
+(-15, '')
+

Availability: Unix, Windows.

Changed in version 3.3.4: Windows support was added.

The function now returns (exitcode, output) instead of (status, output) as it did in Python 3.3.3 and earlier. exitcode has the same value as returncode.

New in version 3.11: Added encoding and errors arguments.

+
+subprocess.getoutput(cmd, *, encoding=None, errors=None)
+

Return output (stdout and stderr) of executing cmd in a shell.

Like getstatusoutput(), except the exit code is ignored and the return value is a string containing the command’s output. Example:

>>> subprocess.getoutput('ls /bin/ls')
+'/bin/ls'
+

Availability: Unix, Windows.

Changed in version 3.3.4: Windows support added

New in version 3.11: Added encoding and errors arguments.

+

Notes

Converting an argument sequence to a string on Windows

On Windows, an args sequence is converted to a string that can be parsed using the following rules (which correspond to the rules used by the MS C runtime):

  1. Arguments are delimited by white space, which is either a space or a tab.
  2. A string surrounded by double quotation marks is interpreted as a single argument, regardless of white space contained within. A quoted string can be embedded in an argument.
  3. A double quotation mark preceded by a backslash is interpreted as a literal double quotation mark.
  4. Backslashes are interpreted literally, unless they immediately precede a double quotation mark.
  5. If backslashes immediately precede a double quotation mark, every pair of backslashes is interpreted as a literal backslash. If the number of backslashes is odd, the last backslash escapes the next double quotation mark as described in rule 3.

See also

+ shlex +
+
+

Module which provides function to parse and escape command lines.

Disabling use of vfork() or posix_spawn() +

On Linux, subprocess defaults to using the vfork() system call internally when it is safe to do so rather than fork(). This greatly improves performance.

If you ever encounter a presumed highly unusual situation where you need to prevent vfork() from being used by Python, you can set the subprocess._USE_VFORK attribute to a false value.

subprocess._USE_VFORK = False  # See CPython issue gh-NNNNNN.
+

Setting this has no impact on use of posix_spawn() which could use vfork() internally within its libc implementation. There is a similar subprocess._USE_POSIX_SPAWN attribute if you need to prevent use of that.

subprocess._USE_POSIX_SPAWN = False  # See CPython issue gh-NNNNNN.
+

It is safe to set these to false on any Python version. They will have no effect on older versions when unsupported. Do not assume the attributes are available to read. Despite their names, a true value does not indicate that the corresponding function will be used, only that it may be.

Please file issues any time you have to use these private knobs with a way to reproduce the issue you were seeing. Link to that issue from a comment in your code.

New in version 3.8: _USE_POSIX_SPAWN

New in version 3.11: _USE_VFORK

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/subprocess.html +

+
diff --git a/devdocs/python~3.12/library%2Fsymtable.html b/devdocs/python~3.12/library%2Fsymtable.html new file mode 100644 index 00000000..cf352716 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsymtable.html @@ -0,0 +1,117 @@ +

symtable — Access to the compiler’s symbol tables

Source code: Lib/symtable.py

Symbol tables are generated by the compiler from AST just before bytecode is generated. The symbol table is responsible for calculating the scope of every identifier in the code. symtable provides an interface to examine these tables.

Generating Symbol Tables

+symtable.symtable(code, filename, compile_type)
+

Return the toplevel SymbolTable for the Python source code. filename is the name of the file containing the code. compile_type is like the mode argument to compile().

+

Examining Symbol Tables

+class symtable.SymbolTable
+

A namespace table for a block. The constructor is not public.

+get_type()
+

Return the type of the symbol table. Possible values are 'class', 'module', 'function', 'annotation', 'TypeVar bound', 'type alias', and 'type parameter'. The latter four refer to different flavors of annotation scopes.

Changed in version 3.12: Added 'annotation', 'TypeVar bound', 'type alias', and 'type parameter' as possible return values.

+
+get_id()
+

Return the table’s identifier.

+
+get_name()
+

Return the table’s name. This is the name of the class if the table is for a class, the name of the function if the table is for a function, or 'top' if the table is global (get_type() returns 'module'). For type parameter scopes (which are used for generic classes, functions, and type aliases), it is the name of the underlying class, function, or type alias. For type alias scopes, it is the name of the type alias. For TypeVar bound scopes, it is the name of the TypeVar.

+
+get_lineno()
+

Return the number of the first line in the block this table represents.

+
+is_optimized()
+

Return True if the locals in this table can be optimized.

+
+is_nested()
+

Return True if the block is a nested class or function.

+
+has_children()
+

Return True if the block has nested namespaces within it. These can be obtained with get_children().

+
+get_identifiers()
+

Return a view object containing the names of symbols in the table. See the documentation of view objects.

+
+lookup(name)
+

Lookup name in the table and return a Symbol instance.

+
+get_symbols()
+

Return a list of Symbol instances for names in the table.

+
+get_children()
+

Return a list of the nested symbol tables.

+
+
+class symtable.Function
+

A namespace for a function or method. This class inherits SymbolTable.

+get_parameters()
+

Return a tuple containing names of parameters to this function.

+
+get_locals()
+

Return a tuple containing names of locals in this function.

+
+get_globals()
+

Return a tuple containing names of globals in this function.

+
+get_nonlocals()
+

Return a tuple containing names of nonlocals in this function.

+
+get_frees()
+

Return a tuple containing names of free variables in this function.

+
+
+class symtable.Class
+

A namespace of a class. This class inherits SymbolTable.

+get_methods()
+

Return a tuple containing the names of methods declared in the class.

+
+
+class symtable.Symbol
+

An entry in a SymbolTable corresponding to an identifier in the source. The constructor is not public.

+get_name()
+

Return the symbol’s name.

+
+is_referenced()
+

Return True if the symbol is used in its block.

+
+is_imported()
+

Return True if the symbol is created from an import statement.

+
+is_parameter()
+

Return True if the symbol is a parameter.

+
+is_global()
+

Return True if the symbol is global.

+
+is_nonlocal()
+

Return True if the symbol is nonlocal.

+
+is_declared_global()
+

Return True if the symbol is declared global with a global statement.

+
+is_local()
+

Return True if the symbol is local to its block.

+
+is_annotated()
+

Return True if the symbol is annotated.

New in version 3.6.

+
+is_free()
+

Return True if the symbol is referenced in its block, but not assigned to.

+
+is_assigned()
+

Return True if the symbol is assigned to in its block.

+
+is_namespace()
+

Return True if name binding introduces new namespace.

If the name is used as the target of a function or class statement, this will be true.

For example:

>>> table = symtable.symtable("def some_func(): pass", "string", "exec")
+>>> table.lookup("some_func").is_namespace()
+True
+

Note that a single name can be bound to multiple objects. If the result is True, the name may also be bound to other objects, like an int or list, that does not introduce a new namespace.

+
+get_namespaces()
+

Return a list of namespaces bound to this name.

+
+get_namespace()
+

Return the namespace bound to this name. If more than one or no namespace is bound to this name, a ValueError is raised.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/symtable.html +

+
diff --git a/devdocs/python~3.12/library%2Fsys.html b/devdocs/python~3.12/library%2Fsys.html new file mode 100644 index 00000000..952b4489 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsys.html @@ -0,0 +1,607 @@ +

sys — System-specific parameters and functions

This module provides access to some variables used or maintained by the interpreter and to functions that interact strongly with the interpreter. It is always available.

+sys.abiflags
+

On POSIX systems where Python was built with the standard configure script, this contains the ABI flags as specified by PEP 3149.

Changed in version 3.8: Default flags became an empty string (m flag for pymalloc has been removed).

New in version 3.2.

+
+sys.addaudithook(hook)
+

Append the callable hook to the list of active auditing hooks for the current (sub)interpreter.

When an auditing event is raised through the sys.audit() function, each hook will be called in the order it was added with the event name and the tuple of arguments. Native hooks added by PySys_AddAuditHook() are called first, followed by hooks added in the current (sub)interpreter. Hooks can then log the event, raise an exception to abort the operation, or terminate the process entirely.

Note that audit hooks are primarily for collecting information about internal or otherwise unobservable actions, whether by Python or libraries written in Python. They are not suitable for implementing a “sandbox”. In particular, malicious code can trivially disable or bypass hooks added using this function. At a minimum, any security-sensitive hooks must be added using the C API PySys_AddAuditHook() before initialising the runtime, and any modules allowing arbitrary memory modification (such as ctypes) should be completely removed or closely monitored.

+

Calling sys.addaudithook() will itself raise an auditing event named sys.addaudithook with no arguments. If any existing hooks raise an exception derived from RuntimeError, the new hook will not be added and the exception suppressed. As a result, callers cannot assume that their hook has been added unless they control all existing hooks.

See the audit events table for all events raised by CPython, and PEP 578 for the original design discussion.

New in version 3.8.

Changed in version 3.8.1: Exceptions derived from Exception but not RuntimeError are no longer suppressed.

CPython implementation detail: When tracing is enabled (see settrace()), Python hooks are only traced if the callable has a __cantrace__ member that is set to a true value. Otherwise, trace functions will skip the hook.

+
+sys.argv
+

The list of command line arguments passed to a Python script. argv[0] is the script name (it is operating system dependent whether this is a full pathname or not). If the command was executed using the -c command line option to the interpreter, argv[0] is set to the string '-c'. If no script name was passed to the Python interpreter, argv[0] is the empty string.

To loop over the standard input, or the list of files given on the command line, see the fileinput module.

See also sys.orig_argv.

Note

On Unix, command line arguments are passed by bytes from OS. Python decodes them with filesystem encoding and “surrogateescape” error handler. When you need original bytes, you can get it by [os.fsencode(arg) for arg in sys.argv].

+
+sys.audit(event, *args)
+

Raise an auditing event and trigger any active auditing hooks. event is a string identifying the event, and args may contain optional arguments with more information about the event. The number and types of arguments for a given event are considered a public and stable API and should not be modified between releases.

For example, one auditing event is named os.chdir. This event has one argument called path that will contain the requested new working directory.

sys.audit() will call the existing auditing hooks, passing the event name and arguments, and will re-raise the first exception from any hook. In general, if an exception is raised, it should not be handled and the process should be terminated as quickly as possible. This allows hook implementations to decide how to respond to particular events: they can merely log the event or abort the operation by raising an exception.

Hooks are added using the sys.addaudithook() or PySys_AddAuditHook() functions.

The native equivalent of this function is PySys_Audit(). Using the native function is preferred when possible.

See the audit events table for all events raised by CPython.

New in version 3.8.

+
+sys.base_exec_prefix
+

Set during Python startup, before site.py is run, to the same value as exec_prefix. If not running in a virtual environment, the values will stay the same; if site.py finds that a virtual environment is in use, the values of prefix and exec_prefix will be changed to point to the virtual environment, whereas base_prefix and base_exec_prefix will remain pointing to the base Python installation (the one which the virtual environment was created from).

New in version 3.3.

+
+sys.base_prefix
+

Set during Python startup, before site.py is run, to the same value as prefix. If not running in a virtual environment, the values will stay the same; if site.py finds that a virtual environment is in use, the values of prefix and exec_prefix will be changed to point to the virtual environment, whereas base_prefix and base_exec_prefix will remain pointing to the base Python installation (the one which the virtual environment was created from).

New in version 3.3.

+
+sys.byteorder
+

An indicator of the native byte order. This will have the value 'big' on big-endian (most-significant byte first) platforms, and 'little' on little-endian (least-significant byte first) platforms.

+
+sys.builtin_module_names
+

A tuple of strings containing the names of all modules that are compiled into this Python interpreter. (This information is not available in any other way — modules.keys() only lists the imported modules.)

See also the sys.stdlib_module_names list.

+
+sys.call_tracing(func, args)
+

Call func(*args), while tracing is enabled. The tracing state is saved, and restored afterwards. This is intended to be called from a debugger from a checkpoint, to recursively debug or profile some other code.

Tracing is suspended while calling a tracing function set by settrace() or setprofile() to avoid infinite recursion. call_tracing() enables explicit recursion of the tracing function.

+
+sys.copyright
+

A string containing the copyright pertaining to the Python interpreter.

+
+sys._clear_type_cache()
+

Clear the internal type cache. The type cache is used to speed up attribute and method lookups. Use the function only to drop unnecessary references during reference leak debugging.

This function should be used for internal and specialized purposes only.

+
+sys._current_frames()
+

Return a dictionary mapping each thread’s identifier to the topmost stack frame currently active in that thread at the time the function is called. Note that functions in the traceback module can build the call stack given such a frame.

This is most useful for debugging deadlock: this function does not require the deadlocked threads’ cooperation, and such threads’ call stacks are frozen for as long as they remain deadlocked. The frame returned for a non-deadlocked thread may bear no relationship to that thread’s current activity by the time calling code examines the frame.

This function should be used for internal and specialized purposes only.

Raises an auditing event sys._current_frames with no arguments.

+
+sys._current_exceptions()
+

Return a dictionary mapping each thread’s identifier to the topmost exception currently active in that thread at the time the function is called. If a thread is not currently handling an exception, it is not included in the result dictionary.

This is most useful for statistical profiling.

This function should be used for internal and specialized purposes only.

Raises an auditing event sys._current_exceptions with no arguments.

Changed in version 3.12: Each value in the dictionary is now a single exception instance, rather than a 3-tuple as returned from sys.exc_info().

+
+sys.breakpointhook()
+

This hook function is called by built-in breakpoint(). By default, it drops you into the pdb debugger, but it can be set to any other function so that you can choose which debugger gets used.

The signature of this function is dependent on what it calls. For example, the default binding (e.g. pdb.set_trace()) expects no arguments, but you might bind it to a function that expects additional arguments (positional and/or keyword). The built-in breakpoint() function passes its *args and **kws straight through. Whatever breakpointhooks() returns is returned from breakpoint().

The default implementation first consults the environment variable PYTHONBREAKPOINT. If that is set to "0" then this function returns immediately; i.e. it is a no-op. If the environment variable is not set, or is set to the empty string, pdb.set_trace() is called. Otherwise this variable should name a function to run, using Python’s dotted-import nomenclature, e.g. package.subpackage.module.function. In this case, package.subpackage.module would be imported and the resulting module must have a callable named function(). This is run, passing in *args and **kws, and whatever function() returns, sys.breakpointhook() returns to the built-in breakpoint() function.

Note that if anything goes wrong while importing the callable named by PYTHONBREAKPOINT, a RuntimeWarning is reported and the breakpoint is ignored.

Also note that if sys.breakpointhook() is overridden programmatically, PYTHONBREAKPOINT is not consulted.

New in version 3.7.

+
+sys._debugmallocstats()
+

Print low-level information to stderr about the state of CPython’s memory allocator.

If Python is built in debug mode (configure +--with-pydebug option), it also performs some expensive internal consistency checks.

New in version 3.3.

CPython implementation detail: This function is specific to CPython. The exact output format is not defined here, and may change.

+
+sys.dllhandle
+

Integer specifying the handle of the Python DLL.

Availability: Windows.

+
+sys.displayhook(value)
+

If value is not None, this function prints repr(value) to sys.stdout, and saves value in builtins._. If repr(value) is not encodable to sys.stdout.encoding with sys.stdout.errors error handler (which is probably 'strict'), encode it to sys.stdout.encoding with 'backslashreplace' error handler.

sys.displayhook is called on the result of evaluating an expression entered in an interactive Python session. The display of these values can be customized by assigning another one-argument function to sys.displayhook.

Pseudo-code:

def displayhook(value):
+    if value is None:
+        return
+    # Set '_' to None to avoid recursion
+    builtins._ = None
+    text = repr(value)
+    try:
+        sys.stdout.write(text)
+    except UnicodeEncodeError:
+        bytes = text.encode(sys.stdout.encoding, 'backslashreplace')
+        if hasattr(sys.stdout, 'buffer'):
+            sys.stdout.buffer.write(bytes)
+        else:
+            text = bytes.decode(sys.stdout.encoding, 'strict')
+            sys.stdout.write(text)
+    sys.stdout.write("\n")
+    builtins._ = value
+

Changed in version 3.2: Use 'backslashreplace' error handler on UnicodeEncodeError.

+
+sys.dont_write_bytecode
+

If this is true, Python won’t try to write .pyc files on the import of source modules. This value is initially set to True or False depending on the -B command line option and the PYTHONDONTWRITEBYTECODE environment variable, but you can set it yourself to control bytecode file generation.

+
+sys._emscripten_info
+

A named tuple holding information about the environment on the wasm32-emscripten platform. The named tuple is provisional and may change in the future.

+_emscripten_info.emscripten_version
+

Emscripten version as tuple of ints (major, minor, micro), e.g. (3, 1, 8).

+
+_emscripten_info.runtime
+

Runtime string, e.g. browser user agent, 'Node.js v14.18.2', or 'UNKNOWN'.

+
+_emscripten_info.pthreads
+

True if Python is compiled with Emscripten pthreads support.

+
+_emscripten_info.shared_memory
+

True if Python is compiled with shared memory support.

+

Availability: Emscripten.

New in version 3.11.

+
+sys.pycache_prefix
+

If this is set (not None), Python will write bytecode-cache .pyc files to (and read them from) a parallel directory tree rooted at this directory, rather than from __pycache__ directories in the source code tree. Any __pycache__ directories in the source code tree will be ignored and new .pyc files written within the pycache prefix. Thus if you use compileall as a pre-build step, you must ensure you run it with the same pycache prefix (if any) that you will use at runtime.

A relative path is interpreted relative to the current working directory.

This value is initially set based on the value of the -X pycache_prefix=PATH command-line option or the PYTHONPYCACHEPREFIX environment variable (command-line takes precedence). If neither are set, it is None.

New in version 3.8.

+
+sys.excepthook(type, value, traceback)
+

This function prints out a given traceback and exception to sys.stderr.

When an exception other than SystemExit is raised and uncaught, the interpreter calls sys.excepthook with three arguments, the exception class, exception instance, and a traceback object. In an interactive session this happens just before control is returned to the prompt; in a Python program this happens just before the program exits. The handling of such top-level exceptions can be customized by assigning another three-argument function to sys.excepthook.

+

Raise an auditing event sys.excepthook with arguments hook, type, value, traceback when an uncaught exception occurs. If no hook has been set, hook may be None. If any hook raises an exception derived from RuntimeError the call to the hook will be suppressed. Otherwise, the audit hook exception will be reported as unraisable and sys.excepthook will be called.

See also

The sys.unraisablehook() function handles unraisable exceptions and the threading.excepthook() function handles exception raised by threading.Thread.run().

+
+sys.__breakpointhook__
+sys.__displayhook__
+sys.__excepthook__
+sys.__unraisablehook__
+

These objects contain the original values of breakpointhook, displayhook, excepthook, and unraisablehook at the start of the program. They are saved so that breakpointhook, displayhook and excepthook, unraisablehook can be restored in case they happen to get replaced with broken or alternative objects.

New in version 3.7: __breakpointhook__

New in version 3.8: __unraisablehook__

+
+sys.exception()
+

This function, when called while an exception handler is executing (such as an except or except* clause), returns the exception instance that was caught by this handler. When exception handlers are nested within one another, only the exception handled by the innermost handler is accessible.

If no exception handler is executing, this function returns None.

New in version 3.11.

+
+sys.exc_info()
+

This function returns the old-style representation of the handled exception. If an exception e is currently handled (so exception() would return e), exc_info() returns the tuple (type(e), e, e.__traceback__). That is, a tuple containing the type of the exception (a subclass of BaseException), the exception itself, and a traceback object which typically encapsulates the call stack at the point where the exception last occurred.

If no exception is being handled anywhere on the stack, this function return a tuple containing three None values.

Changed in version 3.11: The type and traceback fields are now derived from the value (the exception instance), so when an exception is modified while it is being handled, the changes are reflected in the results of subsequent calls to exc_info().

+
+sys.exec_prefix
+

A string giving the site-specific directory prefix where the platform-dependent Python files are installed; by default, this is also '/usr/local'. This can be set at build time with the --exec-prefix argument to the configure script. Specifically, all configuration files (e.g. the pyconfig.h header file) are installed in the directory exec_prefix/lib/pythonX.Y/config, and shared library modules are installed in exec_prefix/lib/pythonX.Y/lib-dynload, where X.Y is the version number of Python, for example 3.2.

Note

If a virtual environment is in effect, this value will be changed in site.py to point to the virtual environment. The value for the Python installation will still be available, via base_exec_prefix.

+
+sys.executable
+

A string giving the absolute path of the executable binary for the Python interpreter, on systems where this makes sense. If Python is unable to retrieve the real path to its executable, sys.executable will be an empty string or None.

+
+sys.exit([arg])
+

Raise a SystemExit exception, signaling an intention to exit the interpreter.

The optional argument arg can be an integer giving the exit status (defaulting to zero), or another type of object. If it is an integer, zero is considered “successful termination” and any nonzero value is considered “abnormal termination” by shells and the like. Most systems require it to be in the range 0–127, and produce undefined results otherwise. Some systems have a convention for assigning specific meanings to specific exit codes, but these are generally underdeveloped; Unix programs generally use 2 for command line syntax errors and 1 for all other kind of errors. If another type of object is passed, None is equivalent to passing zero, and any other object is printed to stderr and results in an exit code of 1. In particular, sys.exit("some error message") is a quick way to exit a program when an error occurs.

Since exit() ultimately “only” raises an exception, it will only exit the process when called from the main thread, and the exception is not intercepted. Cleanup actions specified by finally clauses of try statements are honored, and it is possible to intercept the exit attempt at an outer level.

Changed in version 3.6: If an error occurs in the cleanup after the Python interpreter has caught SystemExit (such as an error flushing buffered data in the standard streams), the exit status is changed to 120.

+
+sys.flags
+

The named tuple flags exposes the status of command line flags. The attributes are read only.

+ + + + + + + + + + + + + + + + + +
+
+flags.debug
+

-d
+
+flags.inspect
+

-i
+
+flags.interactive
+

-i
+
+flags.isolated
+

-I
+
+flags.optimize
+

-O or -OO
+
+flags.dont_write_bytecode
+

-B
+
+flags.no_user_site
+

-s
+
+flags.no_site
+

-S
+
+flags.ignore_environment
+

-E
+
+flags.verbose
+

-v
+
+flags.bytes_warning
+

-b
+
+flags.quiet
+

-q
+
+flags.hash_randomization
+

-R
+
+flags.dev_mode
+

-X dev (Python Development Mode)
+
+flags.utf8_mode
+

-X utf8
+
+flags.safe_path
+

-P
+
+flags.int_max_str_digits
+

-X int_max_str_digits (integer string conversion length limitation)
+
+flags.warn_default_encoding
+

-X warn_default_encoding

Changed in version 3.2: Added quiet attribute for the new -q flag.

New in version 3.2.3: The hash_randomization attribute.

Changed in version 3.3: Removed obsolete division_warning attribute.

Changed in version 3.4: Added isolated attribute for -I isolated flag.

Changed in version 3.7: Added the dev_mode attribute for the new Python Development Mode and the utf8_mode attribute for the new -X utf8 flag.

Changed in version 3.10: Added warn_default_encoding attribute for -X warn_default_encoding flag.

Changed in version 3.11: Added the safe_path attribute for -P option.

Changed in version 3.11: Added the int_max_str_digits attribute.

+
+sys.float_info
+

A named tuple holding information about the float type. It contains low level information about the precision and internal representation. The values correspond to the various floating-point constants defined in the standard header file float.h for the ‘C’ programming language; see section 5.2.4.2.2 of the 1999 ISO/IEC C standard [C99], ‘Characteristics of floating types’, for details.

+ + + + + + + + + + + +
Attributes of the float_info named tuple

attribute

float.h macro

explanation
+
+float_info.epsilon
+

DBL_EPSILON

 +

difference between 1.0 and the least value greater than 1.0 that is representable as a float.

See also math.ulp().
+
+float_info.dig
+

DBL_DIG

The maximum number of decimal digits that can be faithfully represented in a float; see below.
+
+float_info.mant_dig
+

DBL_MANT_DIG

Float precision: the number of base-radix digits in the significand of a float.
+
+float_info.max
+

DBL_MAX

The maximum representable positive finite float.
+
+float_info.max_exp
+

DBL_MAX_EXP

The maximum integer e such that radix**(e-1) is a representable finite float.
+
+float_info.max_10_exp
+

DBL_MAX_10_EXP

The maximum integer e such that 10**e is in the range of representable finite floats.
+
+float_info.min
+

DBL_MIN

 +

The minimum representable positive normalized float.

Use math.ulp(0.0) to get the smallest positive denormalized representable float.
+
+float_info.min_exp
+

DBL_MIN_EXP

The minimum integer e such that radix**(e-1) is a normalized float.
+
+float_info.min_10_exp
+

DBL_MIN_10_EXP

The minimum integer e such that 10**e is a normalized float.
+
+float_info.radix
+

FLT_RADIX

The radix of exponent representation.
+
+float_info.rounds
+

FLT_ROUNDS

 +

An integer representing the rounding mode for floating-point arithmetic. This reflects the value of the system FLT_ROUNDS macro at interpreter startup time:

  • +-1: indeterminable
  • +0: toward zero
  • +1: to nearest
  • +2: toward positive infinity
  • +3: toward negative infinity

All other values for FLT_ROUNDS characterize implementation-defined rounding behavior.

The attribute sys.float_info.dig needs further explanation. If s is any string representing a decimal number with at most sys.float_info.dig significant digits, then converting s to a float and back again will recover a string representing the same decimal value:

>>> import sys
+>>> sys.float_info.dig
+15
+>>> s = '3.14159265358979'    # decimal string with 15 significant digits
+>>> format(float(s), '.15g')  # convert to float and back -> same value
+'3.14159265358979'
+

But for strings with more than sys.float_info.dig significant digits, this isn’t always true:

>>> s = '9876543211234567'    # 16 significant digits is too many!
+>>> format(float(s), '.16g')  # conversion changes value
+'9876543211234568'
+
+
+sys.float_repr_style
+

A string indicating how the repr() function behaves for floats. If the string has value 'short' then for a finite float x, repr(x) aims to produce a short string with the property that float(repr(x)) == x. This is the usual behaviour in Python 3.1 and later. Otherwise, float_repr_style has value 'legacy' and repr(x) behaves in the same way as it did in versions of Python prior to 3.1.

New in version 3.1.

+
+sys.getallocatedblocks()
+

Return the number of memory blocks currently allocated by the interpreter, regardless of their size. This function is mainly useful for tracking and debugging memory leaks. Because of the interpreter’s internal caches, the result can vary from call to call; you may have to call _clear_type_cache() and gc.collect() to get more predictable results.

If a Python build or implementation cannot reasonably compute this information, getallocatedblocks() is allowed to return 0 instead.

New in version 3.4.

+
+sys.getunicodeinternedsize()
+

Return the number of unicode objects that have been interned.

New in version 3.12.

+
+sys.getandroidapilevel()
+

Return the build time API version of Android as an integer.

Availability: Android.

New in version 3.7.

+
+sys.getdefaultencoding()
+

Return the name of the current default string encoding used by the Unicode implementation.

+
+sys.getdlopenflags()
+

Return the current value of the flags that are used for dlopen() calls. Symbolic names for the flag values can be found in the os module (RTLD_xxx constants, e.g. os.RTLD_LAZY).

Availability: Unix.

+
+sys.getfilesystemencoding()
+

Get the filesystem encoding: the encoding used with the filesystem error handler to convert between Unicode filenames and bytes filenames. The filesystem error handler is returned from getfilesystemencodeerrors().

For best compatibility, str should be used for filenames in all cases, although representing filenames as bytes is also supported. Functions accepting or returning filenames should support either str or bytes and internally convert to the system’s preferred representation.

os.fsencode() and os.fsdecode() should be used to ensure that the correct encoding and errors mode are used.

The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig.

Changed in version 3.2: getfilesystemencoding() result cannot be None anymore.

Changed in version 3.6: Windows is no longer guaranteed to return 'mbcs'. See PEP 529 and _enablelegacywindowsfsencoding() for more information.

Changed in version 3.7: Return 'utf-8' if the Python UTF-8 Mode is enabled.

+
+sys.getfilesystemencodeerrors()
+

Get the filesystem error handler: the error handler used with the filesystem encoding to convert between Unicode filenames and bytes filenames. The filesystem encoding is returned from getfilesystemencoding().

os.fsencode() and os.fsdecode() should be used to ensure that the correct encoding and errors mode are used.

The filesystem encoding and error handler are configured at Python startup by the PyConfig_Read() function: see filesystem_encoding and filesystem_errors members of PyConfig.

New in version 3.6.

+
+sys.get_int_max_str_digits()
+

Returns the current value for the integer string conversion length limitation. See also set_int_max_str_digits().

New in version 3.11.

+
+sys.getrefcount(object)
+

Return the reference count of the object. The count returned is generally one higher than you might expect, because it includes the (temporary) reference as an argument to getrefcount().

Note that the returned value may not actually reflect how many references to the object are actually held. For example, some objects are “immortal” and have a very high refcount that does not reflect the actual number of references. Consequently, do not rely on the returned value to be accurate, other than a value of 0 or 1.

Changed in version 3.12: Immortal objects have very large refcounts that do not match the actual number of references to the object.

+
+sys.getrecursionlimit()
+

Return the current value of the recursion limit, the maximum depth of the Python interpreter stack. This limit prevents infinite recursion from causing an overflow of the C stack and crashing Python. It can be set by setrecursionlimit().

+
+sys.getsizeof(object[, default])
+

Return the size of an object in bytes. The object can be any type of object. All built-in objects will return correct results, but this does not have to hold true for third-party extensions as it is implementation specific.

Only the memory consumption directly attributed to the object is accounted for, not the memory consumption of objects it refers to.

If given, default will be returned if the object does not provide means to retrieve the size. Otherwise a TypeError will be raised.

getsizeof() calls the object’s __sizeof__ method and adds an additional garbage collector overhead if the object is managed by the garbage collector.

See recursive sizeof recipe for an example of using getsizeof() recursively to find the size of containers and all their contents.

+
+sys.getswitchinterval()
+

Return the interpreter’s “thread switch interval”; see setswitchinterval().

New in version 3.2.

+
+sys._getframe([depth])
+

Return a frame object from the call stack. If optional integer depth is given, return the frame object that many calls below the top of the stack. If that is deeper than the call stack, ValueError is raised. The default for depth is zero, returning the frame at the top of the call stack.

Raises an auditing event sys._getframe with argument frame.

CPython implementation detail: This function should be used for internal and specialized purposes only. It is not guaranteed to exist in all implementations of Python.

+
+sys._getframemodulename([depth])
+

Return the name of a module from the call stack. If optional integer depth is given, return the module that many calls below the top of the stack. If that is deeper than the call stack, or if the module is unidentifiable, None is returned. The default for depth is zero, returning the module at the top of the call stack.

Raises an auditing event sys._getframemodulename with argument depth.

CPython implementation detail: This function should be used for internal and specialized purposes only. It is not guaranteed to exist in all implementations of Python.

+
+sys.getprofile()
+

Get the profiler function as set by setprofile().

+
+sys.gettrace()
+

Get the trace function as set by settrace().

CPython implementation detail: The gettrace() function is intended only for implementing debuggers, profilers, coverage tools and the like. Its behavior is part of the implementation platform, rather than part of the language definition, and thus may not be available in all Python implementations.

+
+sys.getwindowsversion()
+

Return a named tuple describing the Windows version currently running. The named elements are major, minor, build, platform, service_pack, service_pack_minor, service_pack_major, suite_mask, product_type and platform_version. service_pack contains a string, platform_version a 3-tuple and all other values are integers. The components can also be accessed by name, so sys.getwindowsversion()[0] is equivalent to sys.getwindowsversion().major. For compatibility with prior versions, only the first 5 elements are retrievable by indexing.

platform will be 2 (VER_PLATFORM_WIN32_NT).

product_type may be one of the following values:

+ + + +

Constant

Meaning

1 (VER_NT_WORKSTATION)

The system is a workstation.

2 (VER_NT_DOMAIN_CONTROLLER)

The system is a domain controller.

3 (VER_NT_SERVER)

The system is a server, but not a domain controller.

This function wraps the Win32 GetVersionEx() function; see the Microsoft documentation on OSVERSIONINFOEX() for more information about these fields.

platform_version returns the major version, minor version and build number of the current operating system, rather than the version that is being emulated for the process. It is intended for use in logging rather than for feature detection.

Note

platform_version derives the version from kernel32.dll which can be of a different version than the OS version. Please use platform module for achieving accurate OS version.

Availability: Windows.

Changed in version 3.2: Changed to a named tuple and added service_pack_minor, service_pack_major, suite_mask, and product_type.

Changed in version 3.6: Added platform_version

+
+sys.get_asyncgen_hooks()
+

Returns an asyncgen_hooks object, which is similar to a namedtuple of the form (firstiter, finalizer), where firstiter and finalizer are expected to be either None or functions which take an asynchronous generator iterator as an argument, and are used to schedule finalization of an asynchronous generator by an event loop.

New in version 3.6: See PEP 525 for more details.

Note

This function has been added on a provisional basis (see PEP 411 for details.)

+
+sys.get_coroutine_origin_tracking_depth()
+

Get the current coroutine origin tracking depth, as set by set_coroutine_origin_tracking_depth().

New in version 3.7.

Note

This function has been added on a provisional basis (see PEP 411 for details.) Use it only for debugging purposes.

+
+sys.hash_info
+

A named tuple giving parameters of the numeric hash implementation. For more details about hashing of numeric types, see Hashing of numeric types.

+hash_info.width
+

The width in bits used for hash values

+
+hash_info.modulus
+

The prime modulus P used for numeric hash scheme

+
+hash_info.inf
+

The hash value returned for a positive infinity

+
+hash_info.nan
+

(This attribute is no longer used)

+
+hash_info.imag
+

The multiplier used for the imaginary part of a complex number

+
+hash_info.algorithm
+

The name of the algorithm for hashing of str, bytes, and memoryview

+
+hash_info.hash_bits
+

The internal output size of the hash algorithm

+
+hash_info.seed_bits
+

The size of the seed key of the hash algorithm

+

New in version 3.2.

Changed in version 3.4: Added algorithm, hash_bits and seed_bits

+
+sys.hexversion
+

The version number encoded as a single integer. This is guaranteed to increase with each version, including proper support for non-production releases. For example, to test that the Python interpreter is at least version 1.5.2, use:

if sys.hexversion >= 0x010502F0:
+    # use some advanced feature
+    ...
+else:
+    # use an alternative implementation or warn the user
+    ...
+

This is called hexversion since it only really looks meaningful when viewed as the result of passing it to the built-in hex() function. The named tuple sys.version_info may be used for a more human-friendly encoding of the same information.

More details of hexversion can be found at API and ABI Versioning.

+
+sys.implementation
+

An object containing information about the implementation of the currently running Python interpreter. The following attributes are required to exist in all Python implementations.

name is the implementation’s identifier, e.g. 'cpython'. The actual string is defined by the Python implementation, but it is guaranteed to be lower case.

version is a named tuple, in the same format as sys.version_info. It represents the version of the Python implementation. This has a distinct meaning from the specific version of the Python language to which the currently running interpreter conforms, which sys.version_info represents. For example, for PyPy 1.8 sys.implementation.version might be sys.version_info(1, 8, 0, 'final', 0), whereas sys.version_info would be sys.version_info(2, 7, 2, 'final', 0). For CPython they are the same value, since it is the reference implementation.

hexversion is the implementation version in hexadecimal format, like sys.hexversion.

cache_tag is the tag used by the import machinery in the filenames of cached modules. By convention, it would be a composite of the implementation’s name and version, like 'cpython-33'. However, a Python implementation may use some other value if appropriate. If cache_tag is set to None, it indicates that module caching should be disabled.

sys.implementation may contain additional attributes specific to the Python implementation. These non-standard attributes must start with an underscore, and are not described here. Regardless of its contents, sys.implementation will not change during a run of the interpreter, nor between implementation versions. (It may change between Python language versions, however.) See PEP 421 for more information.

New in version 3.3.

Note

The addition of new required attributes must go through the normal PEP process. See PEP 421 for more information.

+
+sys.int_info
+

A named tuple that holds information about Python’s internal representation of integers. The attributes are read only.

+int_info.bits_per_digit
+

The number of bits held in each digit. Python integers are stored internally in base 2**int_info.bits_per_digit.

+
+int_info.sizeof_digit
+

The size in bytes of the C type used to represent a digit.

+
+int_info.default_max_str_digits
+

The default value for sys.get_int_max_str_digits() when it is not otherwise explicitly configured.

+
+int_info.str_digits_check_threshold
+

The minimum non-zero value for sys.set_int_max_str_digits(), PYTHONINTMAXSTRDIGITS, or -X int_max_str_digits.

+

New in version 3.1.

Changed in version 3.11: Added default_max_str_digits and str_digits_check_threshold.

+
+sys.__interactivehook__
+

When this attribute exists, its value is automatically called (with no arguments) when the interpreter is launched in interactive mode. This is done after the PYTHONSTARTUP file is read, so that you can set this hook there. The site module sets this.

+

Raises an auditing event cpython.run_interactivehook with the hook object as the argument when the hook is called on startup.

New in version 3.4.

+
+sys.intern(string)
+

Enter string in the table of “interned” strings and return the interned string – which is string itself or a copy. Interning strings is useful to gain a little performance on dictionary lookup – if the keys in a dictionary are interned, and the lookup key is interned, the key comparisons (after hashing) can be done by a pointer compare instead of a string compare. Normally, the names used in Python programs are automatically interned, and the dictionaries used to hold module, class or instance attributes have interned keys.

Interned strings are not immortal; you must keep a reference to the return value of intern() around to benefit from it.

+
+sys.is_finalizing()
+

Return True if the Python interpreter is shutting down, False otherwise.

New in version 3.5.

+
+sys.last_exc
+

This variable is not always defined; it is set to the exception instance when an exception is not handled and the interpreter prints an error message and a stack traceback. Its intended use is to allow an interactive user to import a debugger module and engage in post-mortem debugging without having to re-execute the command that caused the error. (Typical use is import pdb; pdb.pm() to enter the post-mortem debugger; see pdb module for more information.)

New in version 3.12.

+
+sys.last_type
+sys.last_value
+sys.last_traceback
+

These three variables are deprecated; use sys.last_exc instead. They hold the legacy representation of sys.last_exc, as returned from exc_info() above.

+
+sys.maxsize
+

An integer giving the maximum value a variable of type Py_ssize_t can take. It’s usually 2**31 - 1 on a 32-bit platform and 2**63 - 1 on a 64-bit platform.

+
+sys.maxunicode
+

An integer giving the value of the largest Unicode code point, i.e. 1114111 (0x10FFFF in hexadecimal).

Changed in version 3.3: Before PEP 393, sys.maxunicode used to be either 0xFFFF or 0x10FFFF, depending on the configuration option that specified whether Unicode characters were stored as UCS-2 or UCS-4.

+
+sys.meta_path
+

A list of meta path finder objects that have their find_spec() methods called to see if one of the objects can find the module to be imported. By default, it holds entries that implement Python’s default import semantics. The find_spec() method is called with at least the absolute name of the module being imported. If the module to be imported is contained in a package, then the parent package’s __path__ attribute is passed in as a second argument. The method returns a module spec, or None if the module cannot be found.

See also

+ importlib.abc.MetaPathFinder +
+
+

The abstract base class defining the interface of finder objects on meta_path.

+ importlib.machinery.ModuleSpec +
+
+

The concrete class which find_spec() should return instances of.

Changed in version 3.4: Module specs were introduced in Python 3.4, by PEP 451. Earlier versions of Python looked for a method called find_module(). This is still called as a fallback if a meta_path entry doesn’t have a find_spec() method.

+
+sys.modules
+

This is a dictionary that maps module names to modules which have already been loaded. This can be manipulated to force reloading of modules and other tricks. However, replacing the dictionary will not necessarily work as expected and deleting essential items from the dictionary may cause Python to fail. If you want to iterate over this global dictionary always use sys.modules.copy() or tuple(sys.modules) to avoid exceptions as its size may change during iteration as a side effect of code or activity in other threads.

+
+sys.orig_argv
+

The list of the original command line arguments passed to the Python executable.

See also sys.argv.

New in version 3.10.

+
+sys.path
+

A list of strings that specifies the search path for modules. Initialized from the environment variable PYTHONPATH, plus an installation-dependent default.

By default, as initialized upon program startup, a potentially unsafe path is prepended to sys.path (before the entries inserted as a result of PYTHONPATH):

  • +python -m module command line: prepend the current working directory.
  • +python script.py command line: prepend the script’s directory. If it’s a symbolic link, resolve symbolic links.
  • +python -c code and python (REPL) command lines: prepend an empty string, which means the current working directory.

To not prepend this potentially unsafe path, use the -P command line option or the PYTHONSAFEPATH environment variable.

A program is free to modify this list for its own purposes. Only strings should be added to sys.path; all other data types are ignored during import.

See also

  • Module site This describes how to use .pth files to extend sys.path.
+
+sys.path_hooks
+

A list of callables that take a path argument to try to create a finder for the path. If a finder can be created, it is to be returned by the callable, else raise ImportError.

Originally specified in PEP 302.

+
+sys.path_importer_cache
+

A dictionary acting as a cache for finder objects. The keys are paths that have been passed to sys.path_hooks and the values are the finders that are found. If a path is a valid file system path but no finder is found on sys.path_hooks then None is stored.

Originally specified in PEP 302.

+
+sys.platform
+

This string contains a platform identifier that can be used to append platform-specific components to sys.path, for instance.

For Unix systems, except on Linux and AIX, this is the lowercased OS name as returned by uname -s with the first part of the version as returned by uname -r appended, e.g. 'sunos5' or 'freebsd8', at the time when Python was built. Unless you want to test for a specific system version, it is therefore recommended to use the following idiom:

if sys.platform.startswith('freebsd'):
+    # FreeBSD-specific code here...
+elif sys.platform.startswith('linux'):
+    # Linux-specific code here...
+elif sys.platform.startswith('aix'):
+    # AIX-specific code here...
+

For other systems, the values are:

+ + + + + + + +

System

platform value

AIX

'aix'

Emscripten

'emscripten'

Linux

'linux'

WASI

'wasi'

Windows

'win32'

Windows/Cygwin

'cygwin'

macOS

'darwin'

Changed in version 3.3: On Linux, sys.platform doesn’t contain the major version anymore. It is always 'linux', instead of 'linux2' or 'linux3'. Since older Python versions include the version number, it is recommended to always use the startswith idiom presented above.

Changed in version 3.8: On AIX, sys.platform doesn’t contain the major version anymore. It is always 'aix', instead of 'aix5' or 'aix7'. Since older Python versions include the version number, it is recommended to always use the startswith idiom presented above.

See also

os.name has a coarser granularity. os.uname() gives system-dependent version information.

The platform module provides detailed checks for the system’s identity.

+
+sys.platlibdir
+

Name of the platform-specific library directory. It is used to build the path of standard library and the paths of installed extension modules.

It is equal to "lib" on most platforms. On Fedora and SuSE, it is equal to "lib64" on 64-bit platforms which gives the following sys.path paths (where X.Y is the Python major.minor version):

  • +/usr/lib64/pythonX.Y/: Standard library (like os.py of the os module)
  • +/usr/lib64/pythonX.Y/lib-dynload/: C extension modules of the standard library (like the errno module, the exact filename is platform specific)
  • +/usr/lib/pythonX.Y/site-packages/ (always use lib, not sys.platlibdir): Third-party modules
  • +/usr/lib64/pythonX.Y/site-packages/: C extension modules of third-party packages

New in version 3.9.

+
+sys.prefix
+

A string giving the site-specific directory prefix where the platform independent Python files are installed; on Unix, the default is /usr/local. This can be set at build time with the --prefix argument to the configure script. See Installation paths for derived paths.

Note

If a virtual environment is in effect, this value will be changed in site.py to point to the virtual environment. The value for the Python installation will still be available, via base_prefix.

+
+sys.ps1
+sys.ps2
+

Strings specifying the primary and secondary prompt of the interpreter. These are only defined if the interpreter is in interactive mode. Their initial values in this case are '>>> ' and '... '. If a non-string object is assigned to either variable, its str() is re-evaluated each time the interpreter prepares to read a new interactive command; this can be used to implement a dynamic prompt.

+
+sys.setdlopenflags(n)
+

Set the flags used by the interpreter for dlopen() calls, such as when the interpreter loads extension modules. Among other things, this will enable a lazy resolving of symbols when importing a module, if called as sys.setdlopenflags(0). To share symbols across extension modules, call as sys.setdlopenflags(os.RTLD_GLOBAL). Symbolic names for the flag values can be found in the os module (RTLD_xxx constants, e.g. os.RTLD_LAZY).

Availability: Unix.

+
+sys.set_int_max_str_digits(maxdigits)
+

Set the integer string conversion length limitation used by this interpreter. See also get_int_max_str_digits().

New in version 3.11.

+
+sys.setprofile(profilefunc)
+

Set the system’s profile function, which allows you to implement a Python source code profiler in Python. See chapter The Python Profilers for more information on the Python profiler. The system’s profile function is called similarly to the system’s trace function (see settrace()), but it is called with different events, for example it isn’t called for each executed line of code (only on call and return, but the return event is reported even when an exception has been set). The function is thread-specific, but there is no way for the profiler to know about context switches between threads, so it does not make sense to use this in the presence of multiple threads. Also, its return value is not used, so it can simply return None. Error in the profile function will cause itself unset.

Note

The same tracing mechanism is used for setprofile() as settrace(). To trace calls with setprofile() inside a tracing function (e.g. in a debugger breakpoint), see call_tracing().

Profile functions should have three arguments: frame, event, and arg. frame is the current stack frame. event is a string: 'call', 'return', 'c_call', 'c_return', or 'c_exception'. arg depends on the event type.

The events have the following meaning:

+'call'
+
+

A function is called (or some other code block entered). The profile function is called; arg is None.

+'return'
+
+

A function (or other code block) is about to return. The profile function is called; arg is the value that will be returned, or None if the event is caused by an exception being raised.

+'c_call'
+
+

A C function is about to be called. This may be an extension function or a built-in. arg is the C function object.

+'c_return'
+
+

A C function has returned. arg is the C function object.

+'c_exception'
+
+

A C function has raised an exception. arg is the C function object.

Raises an auditing event sys.setprofile with no arguments.

+
+sys.setrecursionlimit(limit)
+

Set the maximum depth of the Python interpreter stack to limit. This limit prevents infinite recursion from causing an overflow of the C stack and crashing Python.

The highest possible limit is platform-dependent. A user may need to set the limit higher when they have a program that requires deep recursion and a platform that supports a higher limit. This should be done with care, because a too-high limit can lead to a crash.

If the new limit is too low at the current recursion depth, a RecursionError exception is raised.

Changed in version 3.5.1: A RecursionError exception is now raised if the new limit is too low at the current recursion depth.

+
+sys.setswitchinterval(interval)
+

Set the interpreter’s thread switch interval (in seconds). This floating-point value determines the ideal duration of the “timeslices” allocated to concurrently running Python threads. Please note that the actual value can be higher, especially if long-running internal functions or methods are used. Also, which thread becomes scheduled at the end of the interval is the operating system’s decision. The interpreter doesn’t have its own scheduler.

New in version 3.2.

+
+sys.settrace(tracefunc)
+

Set the system’s trace function, which allows you to implement a Python source code debugger in Python. The function is thread-specific; for a debugger to support multiple threads, it must register a trace function using settrace() for each thread being debugged or use threading.settrace().

Trace functions should have three arguments: frame, event, and arg. frame is the current stack frame. event is a string: 'call', 'line', 'return', 'exception' or 'opcode'. arg depends on the event type.

The trace function is invoked (with event set to 'call') whenever a new local scope is entered; it should return a reference to a local trace function to be used for the new scope, or None if the scope shouldn’t be traced.

The local trace function should return a reference to itself, or to another function which would then be used as the local trace function for the scope.

If there is any error occurred in the trace function, it will be unset, just like settrace(None) is called.

Note

Tracing is disabled while calling the trace function (e.g. a function set by settrace()). For recursive tracing see call_tracing().

The events have the following meaning:

+'call'
+
+

A function is called (or some other code block entered). The global trace function is called; arg is None; the return value specifies the local trace function.

+'line'
+
+

The interpreter is about to execute a new line of code or re-execute the condition of a loop. The local trace function is called; arg is None; the return value specifies the new local trace function. See Objects/lnotab_notes.txt for a detailed explanation of how this works. Per-line events may be disabled for a frame by setting f_trace_lines to False on that frame.

+'return'
+
+

A function (or other code block) is about to return. The local trace function is called; arg is the value that will be returned, or None if the event is caused by an exception being raised. The trace function’s return value is ignored.

+'exception'
+
+

An exception has occurred. The local trace function is called; arg is a tuple (exception, value, traceback); the return value specifies the new local trace function.

+'opcode'
+
+

The interpreter is about to execute a new opcode (see dis for opcode details). The local trace function is called; arg is None; the return value specifies the new local trace function. Per-opcode events are not emitted by default: they must be explicitly requested by setting f_trace_opcodes to True on the frame.

Note that as an exception is propagated down the chain of callers, an 'exception' event is generated at each level.

For more fine-grained usage, it’s possible to set a trace function by assigning frame.f_trace = tracefunc explicitly, rather than relying on it being set indirectly via the return value from an already installed trace function. This is also required for activating the trace function on the current frame, which settrace() doesn’t do. Note that in order for this to work, a global tracing function must have been installed with settrace() in order to enable the runtime tracing machinery, but it doesn’t need to be the same tracing function (e.g. it could be a low overhead tracing function that simply returns None to disable itself immediately on each frame).

For more information on code and frame objects, refer to The standard type hierarchy.

Raises an auditing event sys.settrace with no arguments.

CPython implementation detail: The settrace() function is intended only for implementing debuggers, profilers, coverage tools and the like. Its behavior is part of the implementation platform, rather than part of the language definition, and thus may not be available in all Python implementations.

Changed in version 3.7: 'opcode' event type added; f_trace_lines and f_trace_opcodes attributes added to frames

+
+sys.set_asyncgen_hooks(firstiter, finalizer)
+

Accepts two optional keyword arguments which are callables that accept an asynchronous generator iterator as an argument. The firstiter callable will be called when an asynchronous generator is iterated for the first time. The finalizer will be called when an asynchronous generator is about to be garbage collected.

Raises an auditing event sys.set_asyncgen_hooks_firstiter with no arguments.

Raises an auditing event sys.set_asyncgen_hooks_finalizer with no arguments.

Two auditing events are raised because the underlying API consists of two calls, each of which must raise its own event.

New in version 3.6: See PEP 525 for more details, and for a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py

Note

This function has been added on a provisional basis (see PEP 411 for details.)

+
+sys.set_coroutine_origin_tracking_depth(depth)
+

Allows enabling or disabling coroutine origin tracking. When enabled, the cr_origin attribute on coroutine objects will contain a tuple of (filename, line number, function name) tuples describing the traceback where the coroutine object was created, with the most recent call first. When disabled, cr_origin will be None.

To enable, pass a depth value greater than zero; this sets the number of frames whose information will be captured. To disable, pass set depth to zero.

This setting is thread-specific.

New in version 3.7.

Note

This function has been added on a provisional basis (see PEP 411 for details.) Use it only for debugging purposes.

+
+sys.activate_stack_trampoline(backend, /)
+

Activate the stack profiler trampoline backend. The only supported backend is "perf".

Availability: Linux.

New in version 3.12.

See also

+
+sys.deactivate_stack_trampoline()
+

Deactivate the current stack profiler trampoline backend.

If no stack profiler is activated, this function has no effect.

Availability: Linux.

New in version 3.12.

+
+sys.is_stack_trampoline_active()
+

Return True if a stack profiler trampoline is active.

Availability: Linux.

New in version 3.12.

+
+sys._enablelegacywindowsfsencoding()
+

Changes the filesystem encoding and error handler to ‘mbcs’ and ‘replace’ respectively, for consistency with versions of Python prior to 3.6.

This is equivalent to defining the PYTHONLEGACYWINDOWSFSENCODING environment variable before launching Python.

See also sys.getfilesystemencoding() and sys.getfilesystemencodeerrors().

Availability: Windows.

New in version 3.6: See PEP 529 for more details.

+
+sys.stdin
+sys.stdout
+sys.stderr
+

File objects used by the interpreter for standard input, output and errors:

  • +stdin is used for all interactive input (including calls to input());
  • +stdout is used for the output of print() and expression statements and for the prompts of input();
  • The interpreter’s own prompts and its error messages go to stderr.

These streams are regular text files like those returned by the open() function. Their parameters are chosen as follows:

  • +

    The encoding and error handling are is initialized from PyConfig.stdio_encoding and PyConfig.stdio_errors.

    On Windows, UTF-8 is used for the console device. Non-character devices such as disk files and pipes use the system locale encoding (i.e. the ANSI codepage). Non-console character devices such as NUL (i.e. where isatty() returns True) use the value of the console input and output codepages at startup, respectively for stdin and stdout/stderr. This defaults to the system locale encoding if the process is not initially attached to a console.

    The special behaviour of the console can be overridden by setting the environment variable PYTHONLEGACYWINDOWSSTDIO before starting Python. In that case, the console codepages are used as for any other character device.

    Under all platforms, you can override the character encoding by setting the PYTHONIOENCODING environment variable before starting Python or by using the new -X utf8 command line option and PYTHONUTF8 environment variable. However, for the Windows console, this only applies when PYTHONLEGACYWINDOWSSTDIO is also set.

  • When interactive, the stdout stream is line-buffered. Otherwise, it is block-buffered like regular text files. The stderr stream is line-buffered in both cases. You can make both streams unbuffered by passing the -u command-line option or setting the PYTHONUNBUFFERED environment variable.

Changed in version 3.9: Non-interactive stderr is now line-buffered instead of fully buffered.

Note

To write or read binary data from/to the standard streams, use the underlying binary buffer object. For example, to write bytes to stdout, use sys.stdout.buffer.write(b'abc').

However, if you are writing a library (and do not control in which context its code will be executed), be aware that the standard streams may be replaced with file-like objects like io.StringIO which do not support the buffer attribute.

+
+sys.__stdin__
+sys.__stdout__
+sys.__stderr__
+

These objects contain the original values of stdin, stderr and stdout at the start of the program. They are used during finalization, and could be useful to print to the actual standard stream no matter if the sys.std* object has been redirected.

It can also be used to restore the actual files to known working file objects in case they have been overwritten with a broken object. However, the preferred way to do this is to explicitly save the previous stream before replacing it, and restore the saved object.

Note

Under some conditions stdin, stdout and stderr as well as the original values __stdin__, __stdout__ and __stderr__ can be None. It is usually the case for Windows GUI apps that aren’t connected to a console and Python apps started with pythonw.

+
+sys.stdlib_module_names
+

A frozenset of strings containing the names of standard library modules.

It is the same on all platforms. Modules which are not available on some platforms and modules disabled at Python build are also listed. All module kinds are listed: pure Python, built-in, frozen and extension modules. Test modules are excluded.

For packages, only the main package is listed: sub-packages and sub-modules are not listed. For example, the email package is listed, but the email.mime sub-package and the email.message sub-module are not listed.

See also the sys.builtin_module_names list.

New in version 3.10.

+
+sys.thread_info
+

A named tuple holding information about the thread implementation.

+thread_info.name
+

The name of the thread implementation:

  • +"nt": Windows threads
  • +"pthread": POSIX threads
  • +"pthread-stubs": stub POSIX threads (on WebAssembly platforms without threading support)
  • +"solaris": Solaris threads
+
+thread_info.lock
+

The name of the lock implementation:

  • +"semaphore": a lock uses a semaphore
  • +"mutex+cond": a lock uses a mutex and a condition variable
  • +None if this information is unknown
+
+thread_info.version
+

The name and version of the thread library. It is a string, or None if this information is unknown.

+

New in version 3.3.

+
+sys.tracebacklimit
+

When this variable is set to an integer value, it determines the maximum number of levels of traceback information printed when an unhandled exception occurs. The default is 1000. When set to 0 or less, all traceback information is suppressed and only the exception type and value are printed.

+
+sys.unraisablehook(unraisable, /)
+

Handle an unraisable exception.

Called when an exception has occurred but there is no way for Python to handle it. For example, when a destructor raises an exception or during garbage collection (gc.collect()).

The unraisable argument has the following attributes:

  • +exc_type: Exception type.
  • +exc_value: Exception value, can be None.
  • +exc_traceback: Exception traceback, can be None.
  • +err_msg: Error message, can be None.
  • +object: Object causing the exception, can be None.

The default hook formats err_msg and object as: f'{err_msg}: {object!r}'; use “Exception ignored in” error message if err_msg is None.

sys.unraisablehook() can be overridden to control how unraisable exceptions are handled.

See also

excepthook() which handles uncaught exceptions.

Warning

Storing exc_value using a custom hook can create a reference cycle. It should be cleared explicitly to break the reference cycle when the exception is no longer needed.

Storing object using a custom hook can resurrect it if it is set to an object which is being finalized. Avoid storing object after the custom hook completes to avoid resurrecting objects.

+

Raise an auditing event sys.unraisablehook with arguments hook, unraisable when an exception that cannot be handled occurs. The unraisable object is the same as what will be passed to the hook. If no hook has been set, hook may be None.

New in version 3.8.

+
+sys.version
+

A string containing the version number of the Python interpreter plus additional information on the build number and compiler used. This string is displayed when the interactive interpreter is started. Do not extract version information out of it, rather, use version_info and the functions provided by the platform module.

+
+sys.api_version
+

The C API version for this interpreter. Programmers may find this useful when debugging version conflicts between Python and extension modules.

+
+sys.version_info
+

A tuple containing the five components of the version number: major, minor, micro, releaselevel, and serial. All values except releaselevel are integers; the release level is 'alpha', 'beta', 'candidate', or 'final'. The version_info value corresponding to the Python version 2.0 is (2, 0, 0, 'final', 0). The components can also be accessed by name, so sys.version_info[0] is equivalent to sys.version_info.major and so on.

Changed in version 3.1: Added named component attributes.

+
+sys.warnoptions
+

This is an implementation detail of the warnings framework; do not modify this value. Refer to the warnings module for more information on the warnings framework.

+
+sys.winver
+

The version number used to form registry keys on Windows platforms. This is stored as string resource 1000 in the Python DLL. The value is normally the major and minor versions of the running Python interpreter. It is provided in the sys module for informational purposes; modifying this value has no effect on the registry keys used by Python.

Availability: Windows.

+
sys.monitoring +
+

Namespace containing functions and constants for register callbacks and controlling monitoring events. See sys.monitoring for details.

+
+sys._xoptions
+

A dictionary of the various implementation-specific flags passed through the -X command-line option. Option names are either mapped to their values, if given explicitly, or to True. Example:

$ ./python -Xa=b -Xc
+Python 3.2a3+ (py3k, Oct 16 2010, 20:14:50)
+[GCC 4.4.3] on linux2
+Type "help", "copyright", "credits" or "license" for more information.
+>>> import sys
+>>> sys._xoptions
+{'a': 'b', 'c': True}
+

CPython implementation detail: This is a CPython-specific way of accessing options passed through -X. Other implementations may export them through other means, or not at all.

New in version 3.2.

+

Citations

+C99
+

ISO/IEC 9899:1999. “Programming languages – C.” A public draft of this standard is available at https://www.open-std.org/jtc1/sc22/wg14/www/docs/n1256.pdf.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sys.html +

+
diff --git a/devdocs/python~3.12/library%2Fsys.monitoring.html b/devdocs/python~3.12/library%2Fsys.monitoring.html new file mode 100644 index 00000000..3e1282f1 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsys.monitoring.html @@ -0,0 +1,113 @@ +

sys.monitoring — Execution event monitoring

New in version 3.12.

Note

sys.monitoring is a namespace within the sys module, not an independent module, so there is no need to import sys.monitoring, simply import sys and then use sys.monitoring.

This namespace provides access to the functions and constants necessary to activate and control event monitoring.

As programs execute, events occur that might be of interest to tools that monitor execution. The sys.monitoring namespace provides means to receive callbacks when events of interest occur.

The monitoring API consists of three components:

Tool identifiers

A tool identifier is an integer and the associated name. Tool identifiers are used to discourage tools from interfering with each other and to allow multiple tools to operate at the same time. Currently tools are completely independent and cannot be used to monitor each other. This restriction may be lifted in the future.

Before registering or activating events, a tool should choose an identifier. Identifiers are integers in the range 0 to 5 inclusive.

Registering and using tools

+sys.monitoring.use_tool_id(tool_id: int, name: str, /) → None
+

Must be called before tool_id can be used. tool_id must be in the range 0 to 5 inclusive. Raises a ValueError if tool_id is in use.

+
+sys.monitoring.free_tool_id(tool_id: int, /) → None
+

Should be called once a tool no longer requires tool_id.

+

Note

free_tool_id() will not disable global or local events associated with tool_id, nor will it unregister any callback functions. This function is only intended to be used to notify the VM that the particular tool_id is no longer in use.

+sys.monitoring.get_tool(tool_id: int, /) → str | None
+

Returns the name of the tool if tool_id is in use, otherwise it returns None. tool_id must be in the range 0 to 5 inclusive.

+

All IDs are treated the same by the VM with regard to events, but the following IDs are pre-defined to make co-operation of tools easier:

sys.monitoring.DEBUGGER_ID = 0
+sys.monitoring.COVERAGE_ID = 1
+sys.monitoring.PROFILER_ID = 2
+sys.monitoring.OPTIMIZER_ID = 5
+

There is no obligation to set an ID, nor is there anything preventing a tool from using an ID even it is already in use. However, tools are encouraged to use a unique ID and respect other tools.

Events

The following events are supported:

+sys.monitoring.events.BRANCH
+

A conditional branch is taken (or not).

+
+sys.monitoring.events.CALL
+

A call in Python code (event occurs before the call).

+
+sys.monitoring.events.C_RAISE
+

An exception raised from any callable, except for Python functions (event occurs after the exit).

+
+sys.monitoring.events.C_RETURN
+

Return from any callable, except for Python functions (event occurs after the return).

+
+sys.monitoring.events.EXCEPTION_HANDLED
+

An exception is handled.

+
+sys.monitoring.events.INSTRUCTION
+

A VM instruction is about to be executed.

+
+sys.monitoring.events.JUMP
+

An unconditional jump in the control flow graph is made.

+
+sys.monitoring.events.LINE
+

An instruction is about to be executed that has a different line number from the preceding instruction.

+
+sys.monitoring.events.PY_RESUME
+

Resumption of a Python function (for generator and coroutine functions), except for throw() calls.

+
+sys.monitoring.events.PY_RETURN
+

Return from a Python function (occurs immediately before the return, the callee’s frame will be on the stack).

+
+sys.monitoring.events.PY_START
+

Start of a Python function (occurs immediately after the call, the callee’s frame will be on the stack)

+
+sys.monitoring.events.PY_THROW
+

A Python function is resumed by a throw() call.

+
+sys.monitoring.events.PY_UNWIND
+

Exit from a Python function during exception unwinding.

+
+sys.monitoring.events.PY_YIELD
+

Yield from a Python function (occurs immediately before the yield, the callee’s frame will be on the stack).

+
+sys.monitoring.events.RAISE
+

An exception is raised, except those that cause a STOP_ITERATION event.

+
+sys.monitoring.events.RERAISE
+

An exception is re-raised, for example at the end of a finally block.

+
+sys.monitoring.events.STOP_ITERATION
+

An artificial StopIteration is raised; see the STOP_ITERATION event.

+

More events may be added in the future.

These events are attributes of the sys.monitoring.events namespace. Each event is represented as a power-of-2 integer constant. To define a set of events, simply bitwise or the individual events together. For example, to specify both PY_RETURN and PY_START events, use the expression PY_RETURN | PY_START.

+sys.monitoring.events.NO_EVENTS
+

An alias for 0 so users can do explict comparisions like:

if get_events(DEBUGGER_ID) == NO_EVENTS:
+    ...
+
+

Events are divided into three groups:

Local events

Local events are associated with normal execution of the program and happen at clearly defined locations. All local events can be disabled. The local events are:

Ancillary events

Ancillary events can be monitored like other events, but are controlled by another event:

The C_RETURN and C_RAISE events are controlled by the CALL event. C_RETURN and C_RAISE events will only be seen if the corresponding CALL event is being monitored.

Other events

Other events are not necessarily tied to a specific location in the program and cannot be individually disabled.

The other events that can be monitored are:

The STOP_ITERATION event

PEP 380 specifies that a StopIteration exception is raised when returning a value from a generator or coroutine. However, this is a very inefficient way to return a value, so some Python implementations, notably CPython 3.12+, do not raise an exception unless it would be visible to other code.

To allow tools to monitor for real exceptions without slowing down generators and coroutines, the STOP_ITERATION event is provided. STOP_ITERATION can be locally disabled, unlike RAISE.

Turning events on and off

In order to monitor an event, it must be turned on and a corresponding callback must be registered. Events can be turned on or off by setting the events either globally or for a particular code object.

Setting events globally

Events can be controlled globally by modifying the set of events being monitored.

+sys.monitoring.get_events(tool_id: int, /) → int
+

Returns the int representing all the active events.

+
+sys.monitoring.set_events(tool_id: int, event_set: int, /) → None
+

Activates all events which are set in event_set. Raises a ValueError if tool_id is not in use.

+

No events are active by default.

Per code object events

Events can also be controlled on a per code object basis.

+sys.monitoring.get_local_events(tool_id: int, code: CodeType, /) → int
+

Returns all the local events for code

+
+sys.monitoring.set_local_events(tool_id: int, code: CodeType, event_set: int, /) → None
+

Activates all the local events for code which are set in event_set. Raises a ValueError if tool_id is not in use.

+

Local events add to global events, but do not mask them. In other words, all global events will trigger for a code object, regardless of the local events.

Disabling events

+sys.monitoring.DISABLE
+

A special value that can be returned from a callback function to disable events for the current code location.

+

Local events can be disabled for a specific code location by returning sys.monitoring.DISABLE from a callback function. This does not change which events are set, or any other code locations for the same event.

Disabling events for specific locations is very important for high performance monitoring. For example, a program can be run under a debugger with no overhead if the debugger disables all monitoring except for a few breakpoints.

+sys.monitoring.restart_events() → None
+

Enable all the events that were disabled by sys.monitoring.DISABLE for all tools.

+

Registering callback functions

To register a callable for events call

+sys.monitoring.register_callback(tool_id: int, event: int, func: Callable | None, /) → Callable | None
+

Registers the callable func for the event with the given tool_id

If another callback was registered for the given tool_id and event, it is unregistered and returned. Otherwise register_callback() returns None.

+

Functions can be unregistered by calling sys.monitoring.register_callback(tool_id, event, None).

Callback functions can be registered and unregistered at any time.

Registering or unregistering a callback function will generate a sys.audit() event.

Callback function arguments

+sys.monitoring.MISSING
+

A special value that is passed to a callback function to indicate that there are no arguments to the call.

+

When an active event occurs, the registered callback function is called. Different events will provide the callback function with different arguments, as follows:

  • +

    PY_START and PY_RESUME:

    func(code: CodeType, instruction_offset: int) -> DISABLE | Any
+
  • +

    PY_RETURN and PY_YIELD:

    func(code: CodeType, instruction_offset: int, retval: object) -> DISABLE | Any
+
  • +

    CALL, C_RAISE and C_RETURN:

    func(code: CodeType, instruction_offset: int, callable: object, arg0: object | MISSING) -> DISABLE | Any
+

    If there are no arguments, arg0 is set to sys.monitoring.MISSING.

  • +

    RAISE, RERAISE, EXCEPTION_HANDLED, PY_UNWIND, PY_THROW and STOP_ITERATION:

    func(code: CodeType, instruction_offset: int, exception: BaseException) -> DISABLE | Any
+
  • +

    LINE:

    func(code: CodeType, line_number: int) -> DISABLE | Any
+
  • +

    BRANCH and JUMP:

    func(code: CodeType, instruction_offset: int, destination_offset: int) -> DISABLE | Any
+

    Note that the destination_offset is where the code will next execute. For an untaken branch this will be the offset of the instruction following the branch.

  • +

    INSTRUCTION:

    func(code: CodeType, instruction_offset: int) -> DISABLE | Any
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sys.monitoring.html +

+
diff --git a/devdocs/python~3.12/library%2Fsys_path_init.html b/devdocs/python~3.12/library%2Fsys_path_init.html new file mode 100644 index 00000000..04e77007 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsys_path_init.html @@ -0,0 +1,8 @@ +

The initialization of the sys.path module search path

A module search path is initialized when Python starts. This module search path may be accessed at sys.path.

The first entry in the module search path is the directory that contains the input script, if there is one. Otherwise, the first entry is the current directory, which is the case when executing the interactive shell, a -c command, or -m module.

The PYTHONPATH environment variable is often used to add directories to the search path. If this environment variable is found then the contents are added to the module search path.

Note

PYTHONPATH will affect all installed Python versions/environments. Be wary of setting this in your shell profile or global environment variables. The site module offers more nuanced techniques as mentioned below.

The next items added are the directories containing standard Python modules as well as any extension modules that these modules depend on. Extension modules are .pyd files on Windows and .so files on other platforms. The directory with the platform-independent Python modules is called prefix. The directory with the extension modules is called exec_prefix.

The PYTHONHOME environment variable may be used to set the prefix and exec_prefix locations. Otherwise these directories are found by using the Python executable as a starting point and then looking for various ‘landmark’ files and directories. Note that any symbolic links are followed so the real Python executable location is used as the search starting point. The Python executable location is called home.

Once home is determined, the prefix directory is found by first looking for pythonmajorversionminorversion.zip (python311.zip). On Windows the zip archive is searched for in home and on Unix the archive is expected to be in lib. Note that the expected zip archive location is added to the module search path even if the archive does not exist. If no archive was found, Python on Windows will continue the search for prefix by looking for Lib\os.py. Python on Unix will look for lib/pythonmajorversion.minorversion/os.py (lib/python3.11/os.py). On Windows prefix and exec_prefix are the same, however on other platforms lib/pythonmajorversion.minorversion/lib-dynload (lib/python3.11/lib-dynload) is searched for and used as an anchor for exec_prefix. On some platforms lib may be lib64 or another value, see sys.platlibdir and PYTHONPLATLIBDIR.

Once found, prefix and exec_prefix are available at sys.prefix and sys.exec_prefix respectively.

Finally, the site module is processed and site-packages directories are added to the module search path. A common way to customize the search path is to create sitecustomize or usercustomize modules as described in the site module documentation.

Note

Certain command line options may further affect path calculations. See -E, -I, -s and -S for further details.

Virtual environments

If Python is run in a virtual environment (as described at Virtual Environments and Packages) then prefix and exec_prefix are specific to the virtual environment.

If a pyvenv.cfg file is found alongside the main executable, or in the directory one level above the executable, the following variations apply:

  • If home is an absolute path and PYTHONHOME is not set, this path is used instead of the path to the main executable when deducing prefix and exec_prefix.

_pth files

To completely override sys.path create a ._pth file with the same name as the shared library or executable (python._pth or python311._pth). The shared library path is always known on Windows, however it may not be available on other platforms. In the ._pth file specify one line for each path to add to sys.path. The file based on the shared library name overrides the one based on the executable, which allows paths to be restricted for any program loading the runtime if desired.

When the file exists, all registry and environment variables are ignored, isolated mode is enabled, and site is not imported unless one line in the file specifies import site. Blank paths and lines starting with # are ignored. Each path may be absolute or relative to the location of the file. Import statements other than to site are not permitted, and arbitrary code cannot be specified.

Note that .pth files (without leading underscore) will be processed normally by the site module when import site has been specified.

Embedded Python

If Python is embedded within another application Py_InitializeFromConfig() and the PyConfig structure can be used to initialize Python. The path specific details are described at Python Path Configuration. Alternatively the older Py_SetPath() can be used to bypass the initialization of the module search path.

See also

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sys_path_init.html +

+
diff --git a/devdocs/python~3.12/library%2Fsysconfig.html b/devdocs/python~3.12/library%2Fsysconfig.html new file mode 100644 index 00000000..9b041979 --- /dev/null +++ b/devdocs/python~3.12/library%2Fsysconfig.html @@ -0,0 +1,147 @@ +

sysconfig — Provide access to Python’s configuration information

New in version 3.2.

Source code: Lib/sysconfig.py

The sysconfig module provides access to Python’s configuration information like the list of installation paths and the configuration variables relevant for the current platform.

Configuration variables

A Python distribution contains a Makefile and a pyconfig.h header file that are necessary to build both the Python binary itself and third-party C extensions compiled using setuptools.

sysconfig puts all variables found in these files in a dictionary that can be accessed using get_config_vars() or get_config_var().

Notice that on Windows, it’s a much smaller set.

+sysconfig.get_config_vars(*args)
+

With no arguments, return a dictionary of all configuration variables relevant for the current platform.

With arguments, return a list of values that result from looking up each argument in the configuration variable dictionary.

For each argument, if the value is not found, return None.

+
+sysconfig.get_config_var(name)
+

Return the value of a single variable name. Equivalent to get_config_vars().get(name).

If name is not found, return None.

+

Example of usage:

>>> import sysconfig
+>>> sysconfig.get_config_var('Py_ENABLE_SHARED')
+0
+>>> sysconfig.get_config_var('LIBDIR')
+'/usr/local/lib'
+>>> sysconfig.get_config_vars('AR', 'CXX')
+['ar', 'g++']
+

Installation paths

Python uses an installation scheme that differs depending on the platform and on the installation options. These schemes are stored in sysconfig under unique identifiers based on the value returned by os.name. The schemes are used by package installers to determine where to copy files to.

Python currently supports nine schemes:

  • +posix_prefix: scheme for POSIX platforms like Linux or macOS. This is the default scheme used when Python or a component is installed.
  • +posix_home: scheme for POSIX platforms, when the home option is used. This scheme defines paths located under a specific home prefix.
  • +posix_user: scheme for POSIX platforms, when the user option is used. This scheme defines paths located under the user’s home directory (site.USER_BASE).
  • +posix_venv: scheme for Python virtual environments on POSIX platforms; by default it is the same as posix_prefix.
  • +nt: scheme for Windows. This is the default scheme used when Python or a component is installed.
  • +nt_user: scheme for Windows, when the user option is used.
  • +nt_venv: scheme for Python virtual environments on Windows; by default it is the same as nt.
  • +venv: a scheme with values from either posix_venv or nt_venv depending on the platform Python runs on.
  • +osx_framework_user: scheme for macOS, when the user option is used.

Each scheme is itself composed of a series of paths and each path has a unique identifier. Python currently uses eight paths:

  • +stdlib: directory containing the standard Python library files that are not platform-specific.
  • +platstdlib: directory containing the standard Python library files that are platform-specific.
  • +platlib: directory for site-specific, platform-specific files.
  • +purelib: directory for site-specific, non-platform-specific files (‘pure’ Python).
  • +include: directory for non-platform-specific header files for the Python C-API.
  • +platinclude: directory for platform-specific header files for the Python C-API.
  • +scripts: directory for script files.
  • +data: directory for data files.

User scheme

This scheme is designed to be the most convenient solution for users that don’t have write permission to the global site-packages directory or don’t want to install into it.

Files will be installed into subdirectories of site.USER_BASE (written as userbase hereafter). This scheme installs pure Python modules and extension modules in the same location (also known as site.USER_SITE).

posix_user

+ + + + + + + +

Path

Installation directory

stdlib

userbase/lib/pythonX.Y

platstdlib

userbase/lib/pythonX.Y

platlib

userbase/lib/pythonX.Y/site-packages

purelib

userbase/lib/pythonX.Y/site-packages

include

userbase/include/pythonX.Y

scripts

userbase/bin

data

userbase

nt_user

+ + + + + + + +

Path

Installation directory

stdlib

userbase\PythonXY

platstdlib

userbase\PythonXY

platlib

userbase\PythonXY\site-packages

purelib

userbase\PythonXY\site-packages

include

userbase\PythonXY\Include

scripts

userbase\PythonXY\Scripts

data

userbase

osx_framework_user

+ + + + + + + +

Path

Installation directory

stdlib

userbase/lib/python

platstdlib

userbase/lib/python

platlib

userbase/lib/python/site-packages

purelib

userbase/lib/python/site-packages

include

userbase/include/pythonX.Y

scripts

userbase/bin

data

userbase

Home scheme

The idea behind the “home scheme” is that you build and maintain a personal stash of Python modules. This scheme’s name is derived from the idea of a “home” directory on Unix, since it’s not unusual for a Unix user to make their home directory have a layout similar to /usr/ or /usr/local/. This scheme can be used by anyone, regardless of the operating system they are installing for.

posix_home

+ + + + + + + + +

Path

Installation directory

stdlib

home/lib/python

platstdlib

home/lib/python

platlib

home/lib/python

purelib

home/lib/python

include

home/include/python

platinclude

home/include/python

scripts

home/bin

data

home

Prefix scheme

The “prefix scheme” is useful when you wish to use one Python installation to perform the build/install (i.e., to run the setup script), but install modules into the third-party module directory of a different Python installation (or something that looks like a different Python installation). If this sounds a trifle unusual, it is—that’s why the user and home schemes come before. However, there are at least two known cases where the prefix scheme will be useful.

First, consider that many Linux distributions put Python in /usr, rather than the more traditional /usr/local. This is entirely appropriate, since in those cases Python is part of “the system” rather than a local add-on. However, if you are installing Python modules from source, you probably want them to go in /usr/local/lib/python2.X rather than /usr/lib/python2.X.

Another possibility is a network filesystem where the name used to write to a remote directory is different from the name used to read it: for example, the Python interpreter accessed as /usr/local/bin/python might search for modules in /usr/local/lib/python2.X, but those modules would have to be installed to, say, /mnt/@server/export/lib/python2.X.

posix_prefix

+ + + + + + + + +

Path

Installation directory

stdlib

prefix/lib/pythonX.Y

platstdlib

prefix/lib/pythonX.Y

platlib

prefix/lib/pythonX.Y/site-packages

purelib

prefix/lib/pythonX.Y/site-packages

include

prefix/include/pythonX.Y

platinclude

prefix/include/pythonX.Y

scripts

prefix/bin

data

prefix

nt

+ + + + + + + + +

Path

Installation directory

stdlib

prefix\Lib

platstdlib

prefix\Lib

platlib

prefix\Lib\site-packages

purelib

prefix\Lib\site-packages

include

prefix\Include

platinclude

prefix\Include

scripts

prefix\Scripts

data

prefix

Installation path functions

sysconfig provides some functions to determine these installation paths.

+sysconfig.get_scheme_names()
+

Return a tuple containing all schemes currently supported in sysconfig.

+
+sysconfig.get_default_scheme()
+

Return the default scheme name for the current platform.

New in version 3.10: This function was previously named _get_default_scheme() and considered an implementation detail.

Changed in version 3.11: When Python runs from a virtual environment, the venv scheme is returned.

+
+sysconfig.get_preferred_scheme(key)
+

Return a preferred scheme name for an installation layout specified by key.

key must be either "prefix", "home", or "user".

The return value is a scheme name listed in get_scheme_names(). It can be passed to sysconfig functions that take a scheme argument, such as get_paths().

New in version 3.10.

Changed in version 3.11: When Python runs from a virtual environment and key="prefix", the venv scheme is returned.

+
+sysconfig._get_preferred_schemes()
+

Return a dict containing preferred scheme names on the current platform. Python implementers and redistributors may add their preferred schemes to the _INSTALL_SCHEMES module-level global value, and modify this function to return those scheme names, to e.g. provide different schemes for system and language package managers to use, so packages installed by either do not mix with those by the other.

End users should not use this function, but get_default_scheme() and get_preferred_scheme() instead.

New in version 3.10.

+
+sysconfig.get_path_names()
+

Return a tuple containing all path names currently supported in sysconfig.

+
+sysconfig.get_path(name[, scheme[, vars[, expand]]])
+

Return an installation path corresponding to the path name, from the install scheme named scheme.

name has to be a value from the list returned by get_path_names().

sysconfig stores installation paths corresponding to each path name, for each platform, with variables to be expanded. For instance the stdlib path for the nt scheme is: {base}/Lib.

get_path() will use the variables returned by get_config_vars() to expand the path. All variables have default values for each platform so one may call this function and get the default value.

If scheme is provided, it must be a value from the list returned by get_scheme_names(). Otherwise, the default scheme for the current platform is used.

If vars is provided, it must be a dictionary of variables that will update the dictionary returned by get_config_vars().

If expand is set to False, the path will not be expanded using the variables.

If name is not found, raise a KeyError.

+
+sysconfig.get_paths([scheme[, vars[, expand]]])
+

Return a dictionary containing all installation paths corresponding to an installation scheme. See get_path() for more information.

If scheme is not provided, will use the default scheme for the current platform.

If vars is provided, it must be a dictionary of variables that will update the dictionary used to expand the paths.

If expand is set to false, the paths will not be expanded.

If scheme is not an existing scheme, get_paths() will raise a KeyError.

+

Other functions

+sysconfig.get_python_version()
+

Return the MAJOR.MINOR Python version number as a string. Similar to '%d.%d' % sys.version_info[:2].

+
+sysconfig.get_platform()
+

Return a string that identifies the current platform.

This is used mainly to distinguish platform-specific build directories and platform-specific built distributions. Typically includes the OS name and version and the architecture (as supplied by ‘os.uname()’), although the exact information included depends on the OS; e.g., on Linux, the kernel version isn’t particularly important.

Examples of returned values:

  • linux-i586
  • linux-alpha (?)
  • solaris-2.6-sun4u

Windows will return one of:

  • win-amd64 (64bit Windows on AMD64, aka x86_64, Intel64, and EM64T)
  • win32 (all others - specifically, sys.platform is returned)

macOS can return:

  • macosx-10.6-ppc
  • macosx-10.4-ppc64
  • macosx-10.3-i386
  • macosx-10.4-fat

For other non-POSIX platforms, currently just returns sys.platform.

+
+sysconfig.is_python_build()
+

Return True if the running Python interpreter was built from source and is being run from its built location, and not from a location resulting from e.g. running make install or installing via a binary installer.

+
+sysconfig.parse_config_h(fp[, vars])
+

Parse a config.h-style file.

fp is a file-like object pointing to the config.h-like file.

A dictionary containing name/value pairs is returned. If an optional dictionary is passed in as the second argument, it is used instead of a new dictionary, and updated with the values read in the file.

+
+sysconfig.get_config_h_filename()
+

Return the path of pyconfig.h.

+
+sysconfig.get_makefile_filename()
+

Return the path of Makefile.

+

Using sysconfig as a script

You can use sysconfig as a script with Python’s -m option:

$ python -m sysconfig
+Platform: "macosx-10.4-i386"
+Python version: "3.2"
+Current installation scheme: "posix_prefix"
+
+Paths:
+        data = "/usr/local"
+        include = "/Users/tarek/Dev/svn.python.org/py3k/Include"
+        platinclude = "."
+        platlib = "/usr/local/lib/python3.2/site-packages"
+        platstdlib = "/usr/local/lib/python3.2"
+        purelib = "/usr/local/lib/python3.2/site-packages"
+        scripts = "/usr/local/bin"
+        stdlib = "/usr/local/lib/python3.2"
+
+Variables:
+        AC_APPLE_UNIVERSAL_BUILD = "0"
+        AIX_GENUINE_CPLUSPLUS = "0"
+        AR = "ar"
+        ARFLAGS = "rc"
+        ...
+

This call will print in the standard output the information returned by get_platform(), get_python_version(), get_path() and get_config_vars().

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/sysconfig.html +

+
diff --git a/devdocs/python~3.12/library%2Fsyslog.html b/devdocs/python~3.12/library%2Fsyslog.html new file mode 100644 index 00000000..0a8e8f0a --- /dev/null +++ b/devdocs/python~3.12/library%2Fsyslog.html @@ -0,0 +1,32 @@ +

syslog — Unix syslog library routines

This module provides an interface to the Unix syslog library routines. Refer to the Unix manual pages for a detailed description of the syslog facility.

Availability: Unix, not Emscripten, not WASI.

This module wraps the system syslog family of routines. A pure Python library that can speak to a syslog server is available in the logging.handlers module as SysLogHandler.

The module defines the following functions:

+syslog.syslog(message)
syslog.syslog(priority, message) +
+

Send the string message to the system logger. A trailing newline is added if necessary. Each message is tagged with a priority composed of a facility and a level. The optional priority argument, which defaults to LOG_INFO, determines the message priority. If the facility is not encoded in priority using logical-or (LOG_INFO | LOG_USER), the value given in the openlog() call is used.

If openlog() has not been called prior to the call to syslog(), openlog() will be called with no arguments.

Raises an auditing event syslog.syslog with arguments priority, message.

Changed in version 3.2: In previous versions, openlog() would not be called automatically if it wasn’t called prior to the call to syslog(), deferring to the syslog implementation to call openlog().

Changed in version 3.12: This function is restricted in subinterpreters. (Only code that runs in multiple interpreters is affected and the restriction is not relevant for most users.) openlog() must be called in the main interpreter before syslog() may be used in a subinterpreter. Otherwise it will raise RuntimeError.

+
+syslog.openlog([ident[, logoption[, facility]]])
+

Logging options of subsequent syslog() calls can be set by calling openlog(). syslog() will call openlog() with no arguments if the log is not currently open.

The optional ident keyword argument is a string which is prepended to every message, and defaults to sys.argv[0] with leading path components stripped. The optional logoption keyword argument (default is 0) is a bit field – see below for possible values to combine. The optional facility keyword argument (default is LOG_USER) sets the default facility for messages which do not have a facility explicitly encoded.

Raises an auditing event syslog.openlog with arguments ident, logoption, facility.

Changed in version 3.2: In previous versions, keyword arguments were not allowed, and ident was required.

Changed in version 3.12: This function is restricted in subinterpreters. (Only code that runs in multiple interpreters is affected and the restriction is not relevant for most users.) This may only be called in the main interpreter. It will raise RuntimeError if called in a subinterpreter.

+
+syslog.closelog()
+

Reset the syslog module values and call the system library closelog().

This causes the module to behave as it does when initially imported. For example, openlog() will be called on the first syslog() call (if openlog() hasn’t already been called), and ident and other openlog() parameters are reset to defaults.

Raises an auditing event syslog.closelog with no arguments.

Changed in version 3.12: This function is restricted in subinterpreters. (Only code that runs in multiple interpreters is affected and the restriction is not relevant for most users.) This may only be called in the main interpreter. It will raise RuntimeError if called in a subinterpreter.

+
+syslog.setlogmask(maskpri)
+

Set the priority mask to maskpri and return the previous mask value. Calls to syslog() with a priority level not set in maskpri are ignored. The default is to log all priorities. The function LOG_MASK(pri) calculates the mask for the individual priority pri. The function LOG_UPTO(pri) calculates the mask for all priorities up to and including pri.

Raises an auditing event syslog.setlogmask with argument maskpri.

+

The module defines the following constants:

Priority levels (high to low):
+
+

LOG_EMERG, LOG_ALERT, LOG_CRIT, LOG_ERR, LOG_WARNING, LOG_NOTICE, LOG_INFO, LOG_DEBUG.

Facilities:
+
+

LOG_KERN, LOG_USER, LOG_MAIL, LOG_DAEMON, LOG_AUTH, LOG_LPR, LOG_NEWS, LOG_UUCP, LOG_CRON, LOG_SYSLOG, LOG_LOCAL0 to LOG_LOCAL7, and, if defined in <syslog.h>, LOG_AUTHPRIV.

Log options:
+
+

LOG_PID, LOG_CONS, LOG_NDELAY, and, if defined in <syslog.h>, LOG_ODELAY, LOG_NOWAIT, and LOG_PERROR.

Examples

Simple example

A simple set of examples:

import syslog
+
+syslog.syslog('Processing started')
+if error:
+    syslog.syslog(syslog.LOG_ERR, 'Processing started')
+

An example of setting some log options, these would include the process ID in logged messages, and write the messages to the destination facility used for mail logging:

syslog.openlog(logoption=syslog.LOG_PID, facility=syslog.LOG_MAIL)
+syslog.syslog('E-mail processing initiated...')
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/syslog.html +

+
diff --git a/devdocs/python~3.12/library%2Ftabnanny.html b/devdocs/python~3.12/library%2Ftabnanny.html new file mode 100644 index 00000000..3e2e5e73 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftabnanny.html @@ -0,0 +1,25 @@ +

tabnanny — Detection of ambiguous indentation

Source code: Lib/tabnanny.py

For the time being this module is intended to be called as a script. However it is possible to import it into an IDE and use the function check() described below.

Note

The API provided by this module is likely to change in future releases; such changes may not be backward compatible.

+tabnanny.check(file_or_dir)
+

If file_or_dir is a directory and not a symbolic link, then recursively descend the directory tree named by file_or_dir, checking all .py files along the way. If file_or_dir is an ordinary Python source file, it is checked for whitespace related problems. The diagnostic messages are written to standard output using the print() function.

+
+tabnanny.verbose
+

Flag indicating whether to print verbose messages. This is incremented by the -v option if called as a script.

+
+tabnanny.filename_only
+

Flag indicating whether to print only the filenames of files containing whitespace related problems. This is set to true by the -q option if called as a script.

+
+exception tabnanny.NannyNag
+

Raised by process_tokens() if detecting an ambiguous indent. Captured and handled in check().

+
+tabnanny.process_tokens(tokens)
+

This function is used by check() to process tokens generated by the tokenize module.

+

See also

+Module tokenize +
+
+

Lexical scanner for Python source code.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tabnanny.html +

+
diff --git a/devdocs/python~3.12/library%2Ftarfile.html b/devdocs/python~3.12/library%2Ftarfile.html new file mode 100644 index 00000000..e5f2b0f6 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftarfile.html @@ -0,0 +1,391 @@ +

tarfile — Read and write tar archive files

Source code: Lib/tarfile.py

The tarfile module makes it possible to read and write tar archives, including those using gzip, bz2 and lzma compression. Use the zipfile module to read or write .zip files, or the higher-level functions in shutil.

Some facts and figures:

Changed in version 3.3: Added support for lzma compression.

Changed in version 3.12: Archives are extracted using a filter, which makes it possible to either limit surprising/dangerous features, or to acknowledge that they are expected and the archive is fully trusted. By default, archives are fully trusted, but this default is deprecated and slated to change in Python 3.14.

+tarfile.open(name=None, mode='r', fileobj=None, bufsize=10240, **kwargs)
+

Return a TarFile object for the pathname name. For detailed information on TarFile objects and the keyword arguments that are allowed, see TarFile Objects.

mode has to be a string of the form 'filemode[:compression]', it defaults to 'r'. Here is a full list of mode combinations:

+ + + + + + + + + + + + + + +

mode

action

'r' or 'r:*'

Open for reading with transparent compression (recommended).

'r:'

Open for reading exclusively without compression.

'r:gz'

Open for reading with gzip compression.

'r:bz2'

Open for reading with bzip2 compression.

'r:xz'

Open for reading with lzma compression.

'x' or 'x:'

Create a tarfile exclusively without compression. Raise a FileExistsError exception if it already exists.

'x:gz'

Create a tarfile with gzip compression. Raise a FileExistsError exception if it already exists.

'x:bz2'

Create a tarfile with bzip2 compression. Raise a FileExistsError exception if it already exists.

'x:xz'

Create a tarfile with lzma compression. Raise a FileExistsError exception if it already exists.

'a' or 'a:'

Open for appending with no compression. The file is created if it does not exist.

'w' or 'w:'

Open for uncompressed writing.

'w:gz'

Open for gzip compressed writing.

'w:bz2'

Open for bzip2 compressed writing.

'w:xz'

Open for lzma compressed writing.

Note that 'a:gz', 'a:bz2' or 'a:xz' is not possible. If mode is not suitable to open a certain (compressed) file for reading, ReadError is raised. Use mode 'r' to avoid this. If a compression method is not supported, CompressionError is raised.

If fileobj is specified, it is used as an alternative to a file object opened in binary mode for name. It is supposed to be at position 0.

For modes 'w:gz', 'x:gz', 'w|gz', 'w:bz2', 'x:bz2', 'w|bz2', tarfile.open() accepts the keyword argument compresslevel (default 9) to specify the compression level of the file.

For modes 'w:xz' and 'x:xz', tarfile.open() accepts the keyword argument preset to specify the compression level of the file.

For special purposes, there is a second format for mode: 'filemode|[compression]'. tarfile.open() will return a TarFile object that processes its data as a stream of blocks. No random seeking will be done on the file. If given, fileobj may be any object that has a read() or write() method (depending on the mode) that works with bytes. bufsize specifies the blocksize and defaults to 20 * 512 bytes. Use this variant in combination with e.g. sys.stdin.buffer, a socket file object or a tape device. However, such a TarFile object is limited in that it does not allow random access, see Examples. The currently possible modes:

+ + + + + + + + + +

Mode

Action

'r|*'

Open a stream of tar blocks for reading with transparent compression.

'r|'

Open a stream of uncompressed tar blocks for reading.

'r|gz'

Open a gzip compressed stream for reading.

'r|bz2'

Open a bzip2 compressed stream for reading.

'r|xz'

Open an lzma compressed stream for reading.

'w|'

Open an uncompressed stream for writing.

'w|gz'

Open a gzip compressed stream for writing.

'w|bz2'

Open a bzip2 compressed stream for writing.

'w|xz'

Open an lzma compressed stream for writing.

Changed in version 3.5: The 'x' (exclusive creation) mode was added.

Changed in version 3.6: The name parameter accepts a path-like object.

Changed in version 3.12: The compresslevel keyword argument also works for streams.

+
class tarfile.TarFile +
+

Class for reading and writing tar archives. Do not use this class directly: use tarfile.open() instead. See TarFile Objects.

+
+tarfile.is_tarfile(name)
+

Return True if name is a tar archive file, that the tarfile module can read. name may be a str, file, or file-like object.

Changed in version 3.9: Support for file and file-like objects.

+

The tarfile module defines the following exceptions:

+exception tarfile.TarError
+

Base class for all tarfile exceptions.

+
+exception tarfile.ReadError
+

Is raised when a tar archive is opened, that either cannot be handled by the tarfile module or is somehow invalid.

+
+exception tarfile.CompressionError
+

Is raised when a compression method is not supported or when the data cannot be decoded properly.

+
+exception tarfile.StreamError
+

Is raised for the limitations that are typical for stream-like TarFile objects.

+
+exception tarfile.ExtractError
+

Is raised for non-fatal errors when using TarFile.extract(), but only if TarFile.errorlevel== 2.

+
+exception tarfile.HeaderError
+

Is raised by TarInfo.frombuf() if the buffer it gets is invalid.

+
+exception tarfile.FilterError
+

Base class for members refused by filters.

+tarinfo
+

Information about the member that the filter refused to extract, as TarInfo.

+
+
+exception tarfile.AbsolutePathError
+

Raised to refuse extracting a member with an absolute path.

+
+exception tarfile.OutsideDestinationError
+

Raised to refuse extracting a member outside the destination directory.

+
+exception tarfile.SpecialFileError
+

Raised to refuse extracting a special file (e.g. a device or pipe).

+
+exception tarfile.AbsoluteLinkError
+

Raised to refuse extracting a symbolic link with an absolute path.

+
+exception tarfile.LinkOutsideDestinationError
+

Raised to refuse extracting a symbolic link pointing outside the destination directory.

+

The following constants are available at the module level:

+tarfile.ENCODING
+

The default character encoding: 'utf-8' on Windows, the value returned by sys.getfilesystemencoding() otherwise.

+
+tarfile.REGTYPE
+tarfile.AREGTYPE
+

A regular file type.

+
+tarfile.LNKTYPE
+

A link (inside tarfile) type.

+
+tarfile.SYMTYPE
+

A symbolic link type.

+
+tarfile.CHRTYPE
+

A character special device type.

+
+tarfile.BLKTYPE
+

A block special device type.

+
+tarfile.DIRTYPE
+

A directory type.

+
+tarfile.FIFOTYPE
+

A FIFO special device type.

+
+tarfile.CONTTYPE
+

A contiguous file type.

+
+tarfile.GNUTYPE_LONGNAME
+

A GNU tar longname type.

+
+

A GNU tar longlink type.

+
+tarfile.GNUTYPE_SPARSE
+

A GNU tar sparse file type.

+

Each of the following constants defines a tar archive format that the tarfile module is able to create. See section Supported tar formats for details.

+tarfile.USTAR_FORMAT
+

POSIX.1-1988 (ustar) format.

+
+tarfile.GNU_FORMAT
+

GNU tar format.

+
+tarfile.PAX_FORMAT
+

POSIX.1-2001 (pax) format.

+
+tarfile.DEFAULT_FORMAT
+

The default format for creating archives. This is currently PAX_FORMAT.

Changed in version 3.8: The default format for new archives was changed to PAX_FORMAT from GNU_FORMAT.

+

See also

+Module zipfile +
+
+

Documentation of the zipfile standard module.

Archiving operations
+
+

Documentation of the higher-level archiving facilities provided by the standard shutil module.

GNU tar manual, Basic Tar Format
+
+

Documentation for tar archive files, including GNU tar extensions.

TarFile Objects

The TarFile object provides an interface to a tar archive. A tar archive is a sequence of blocks. An archive member (a stored file) is made up of a header block followed by data blocks. It is possible to store a file in a tar archive several times. Each archive member is represented by a TarInfo object, see TarInfo Objects for details.

A TarFile object can be used as a context manager in a with statement. It will automatically be closed when the block is completed. Please note that in the event of an exception an archive opened for writing will not be finalized; only the internally used file object will be closed. See the Examples section for a use case.

New in version 3.2: Added support for the context management protocol.

+class tarfile.TarFile(name=None, mode='r', fileobj=None, format=DEFAULT_FORMAT, tarinfo=TarInfo, dereference=False, ignore_zeros=False, encoding=ENCODING, errors='surrogateescape', pax_headers=None, debug=0, errorlevel=1)
+

All following arguments are optional and can be accessed as instance attributes as well.

name is the pathname of the archive. name may be a path-like object. It can be omitted if fileobj is given. In this case, the file object’s name attribute is used if it exists.

mode is either 'r' to read from an existing archive, 'a' to append data to an existing file, 'w' to create a new file overwriting an existing one, or 'x' to create a new file only if it does not already exist.

If fileobj is given, it is used for reading or writing data. If it can be determined, mode is overridden by fileobj’s mode. fileobj will be used from position 0.

Note

fileobj is not closed, when TarFile is closed.

format controls the archive format for writing. It must be one of the constants USTAR_FORMAT, GNU_FORMAT or PAX_FORMAT that are defined at module level. When reading, format will be automatically detected, even if different formats are present in a single archive.

The tarinfo argument can be used to replace the default TarInfo class with a different one.

If dereference is False, add symbolic and hard links to the archive. If it is True, add the content of the target files to the archive. This has no effect on systems that do not support symbolic links.

If ignore_zeros is False, treat an empty block as the end of the archive. If it is True, skip empty (and invalid) blocks and try to get as many members as possible. This is only useful for reading concatenated or damaged archives.

debug can be set from 0 (no debug messages) up to 3 (all debug messages). The messages are written to sys.stderr.

errorlevel controls how extraction errors are handled, see the corresponding attribute.

The encoding and errors arguments define the character encoding to be used for reading or writing the archive and how conversion errors are going to be handled. The default settings will work for most users. See section Unicode issues for in-depth information.

The pax_headers argument is an optional dictionary of strings which will be added as a pax global header if format is PAX_FORMAT.

Changed in version 3.2: Use 'surrogateescape' as the default for the errors argument.

Changed in version 3.5: The 'x' (exclusive creation) mode was added.

Changed in version 3.6: The name parameter accepts a path-like object.

+
+classmethod TarFile.open(...)
+

Alternative constructor. The tarfile.open() function is actually a shortcut to this classmethod.

+
+TarFile.getmember(name)
+

Return a TarInfo object for member name. If name can not be found in the archive, KeyError is raised.

Note

If a member occurs more than once in the archive, its last occurrence is assumed to be the most up-to-date version.

+
+TarFile.getmembers()
+

Return the members of the archive as a list of TarInfo objects. The list has the same order as the members in the archive.

+
+TarFile.getnames()
+

Return the members as a list of their names. It has the same order as the list returned by getmembers().

+
+TarFile.list(verbose=True, *, members=None)
+

Print a table of contents to sys.stdout. If verbose is False, only the names of the members are printed. If it is True, output similar to that of ls -l is produced. If optional members is given, it must be a subset of the list returned by getmembers().

Changed in version 3.5: Added the members parameter.

+
+TarFile.next()
+

Return the next member of the archive as a TarInfo object, when TarFile is opened for reading. Return None if there is no more available.

+
+TarFile.extractall(path='.', members=None, *, numeric_owner=False, filter=None)
+

Extract all members from the archive to the current working directory or directory path. If optional members is given, it must be a subset of the list returned by getmembers(). Directory information like owner, modification time and permissions are set after all members have been extracted. This is done to work around two problems: A directory’s modification time is reset each time a file is created in it. And, if a directory’s permissions do not allow writing, extracting files to it will fail.

If numeric_owner is True, the uid and gid numbers from the tarfile are used to set the owner/group for the extracted files. Otherwise, the named values from the tarfile are used.

The filter argument specifies how members are modified or rejected before extraction. See Extraction filters for details. It is recommended to set this explicitly depending on which tar features you need to support.

Warning

Never extract archives from untrusted sources without prior inspection. It is possible that files are created outside of path, e.g. members that have absolute filenames starting with "/" or filenames with two dots "..".

Set filter='data' to prevent the most dangerous security issues, and read the Extraction filters section for details.

Changed in version 3.5: Added the numeric_owner parameter.

Changed in version 3.6: The path parameter accepts a path-like object.

Changed in version 3.12: Added the filter parameter.

+
+TarFile.extract(member, path='', set_attrs=True, *, numeric_owner=False, filter=None)
+

Extract a member from the archive to the current working directory, using its full name. Its file information is extracted as accurately as possible. member may be a filename or a TarInfo object. You can specify a different directory using path. path may be a path-like object. File attributes (owner, mtime, mode) are set unless set_attrs is false.

The numeric_owner and filter arguments are the same as for extractall().

Note

The extract() method does not take care of several extraction issues. In most cases you should consider using the extractall() method.

Warning

See the warning for extractall().

Set filter='data' to prevent the most dangerous security issues, and read the Extraction filters section for details.

Changed in version 3.2: Added the set_attrs parameter.

Changed in version 3.5: Added the numeric_owner parameter.

Changed in version 3.6: The path parameter accepts a path-like object.

Changed in version 3.12: Added the filter parameter.

+
+TarFile.extractfile(member)
+

Extract a member from the archive as a file object. member may be a filename or a TarInfo object. If member is a regular file or a link, an io.BufferedReader object is returned. For all other existing members, None is returned. If member does not appear in the archive, KeyError is raised.

Changed in version 3.3: Return an io.BufferedReader object.

+
+TarFile.errorlevel: int
+

If errorlevel is 0, errors are ignored when using TarFile.extract() and TarFile.extractall(). Nevertheless, they appear as error messages in the debug output when debug is greater than 0. If 1 (the default), all fatal errors are raised as OSError or FilterError exceptions. If 2, all non-fatal errors are raised as TarError exceptions as well.

Some exceptions, e.g. ones caused by wrong argument types or data corruption, are always raised.

Custom extraction filters should raise FilterError for fatal errors and ExtractError for non-fatal ones.

Note that when an exception is raised, the archive may be partially extracted. It is the user’s responsibility to clean up.

+
+TarFile.extraction_filter
+

New in version 3.12.

The extraction filter used as a default for the filter argument of extract() and extractall().

The attribute may be None or a callable. String names are not allowed for this attribute, unlike the filter argument to extract().

If extraction_filter is None (the default), calling an extraction method without a filter argument will raise a DeprecationWarning, and fall back to the fully_trusted filter, whose dangerous behavior matches previous versions of Python.

In Python 3.14+, leaving extraction_filter=None will cause extraction methods to use the data filter by default.

The attribute may be set on instances or overridden in subclasses. It also is possible to set it on the TarFile class itself to set a global default, although, since it affects all uses of tarfile, it is best practice to only do so in top-level applications or site configuration. To set a global default this way, a filter function needs to be wrapped in staticmethod() to prevent injection of a self argument.

+
+TarFile.add(name, arcname=None, recursive=True, *, filter=None)
+

Add the file name to the archive. name may be any type of file (directory, fifo, symbolic link, etc.). If given, arcname specifies an alternative name for the file in the archive. Directories are added recursively by default. This can be avoided by setting recursive to False. Recursion adds entries in sorted order. If filter is given, it should be a function that takes a TarInfo object argument and returns the changed TarInfo object. If it instead returns None the TarInfo object will be excluded from the archive. See Examples for an example.

Changed in version 3.2: Added the filter parameter.

Changed in version 3.7: Recursion adds entries in sorted order.

+
+TarFile.addfile(tarinfo, fileobj=None)
+

Add the TarInfo object tarinfo to the archive. If fileobj is given, it should be a binary file, and tarinfo.size bytes are read from it and added to the archive. You can create TarInfo objects directly, or by using gettarinfo().

+
+TarFile.gettarinfo(name=None, arcname=None, fileobj=None)
+

Create a TarInfo object from the result of os.stat() or equivalent on an existing file. The file is either named by name, or specified as a file object fileobj with a file descriptor. name may be a path-like object. If given, arcname specifies an alternative name for the file in the archive, otherwise, the name is taken from fileobj’s name attribute, or the name argument. The name should be a text string.

You can modify some of the TarInfo’s attributes before you add it using addfile(). If the file object is not an ordinary file object positioned at the beginning of the file, attributes such as size may need modifying. This is the case for objects such as GzipFile. The name may also be modified, in which case arcname could be a dummy string.

Changed in version 3.6: The name parameter accepts a path-like object.

+
+TarFile.close()
+

Close the TarFile. In write mode, two finishing zero blocks are appended to the archive.

+
+TarFile.pax_headers
+

A dictionary containing key-value pairs of pax global headers.

+

TarInfo Objects

A TarInfo object represents one member in a TarFile. Aside from storing all required attributes of a file (like file type, size, time, permissions, owner etc.), it provides some useful methods to determine its type. It does not contain the file’s data itself.

TarInfo objects are returned by TarFile’s methods getmember(), getmembers() and gettarinfo().

Modifying the objects returned by getmember() or getmembers() will affect all subsequent operations on the archive. For cases where this is unwanted, you can use copy.copy() or call the replace() method to create a modified copy in one step.

Several attributes can be set to None to indicate that a piece of metadata is unused or unknown. Different TarInfo methods handle None differently:

+class tarfile.TarInfo(name='')
+

Create a TarInfo object.

+
+classmethod TarInfo.frombuf(buf, encoding, errors)
+

Create and return a TarInfo object from string buffer buf.

Raises HeaderError if the buffer is invalid.

+
+classmethod TarInfo.fromtarfile(tarfile)
+

Read the next member from the TarFile object tarfile and return it as a TarInfo object.

+
+TarInfo.tobuf(format=DEFAULT_FORMAT, encoding=ENCODING, errors='surrogateescape')
+

Create a string buffer from a TarInfo object. For information on the arguments see the constructor of the TarFile class.

Changed in version 3.2: Use 'surrogateescape' as the default for the errors argument.

+

A TarInfo object has the following public data attributes:

+TarInfo.name: str
+

Name of the archive member.

+
+TarInfo.size: int
+

Size in bytes.

+
+TarInfo.mtime: int | float
+

Time of last modification in seconds since the epoch, as in os.stat_result.st_mtime.

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.mode: int
+

Permission bits, as for os.chmod().

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.type
+

File type. type is usually one of these constants: REGTYPE, AREGTYPE, LNKTYPE, SYMTYPE, DIRTYPE, FIFOTYPE, CONTTYPE, CHRTYPE, BLKTYPE, GNUTYPE_SPARSE. To determine the type of a TarInfo object more conveniently, use the is*() methods below.

+
+TarInfo.linkname: str
+

Name of the target file name, which is only present in TarInfo objects of type LNKTYPE and SYMTYPE.

For symbolic links (SYMTYPE), the linkname is relative to the directory that contains the link. For hard links (LNKTYPE), the linkname is relative to the root of the archive.

+
+TarInfo.uid: int
+

User ID of the user who originally stored this member.

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.gid: int
+

Group ID of the user who originally stored this member.

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.uname: str
+

User name.

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.gname: str
+

Group name.

Changed in version 3.12: Can be set to None for extract() and extractall(), causing extraction to skip applying this attribute.

+
+TarInfo.pax_headers: dict
+

A dictionary containing key-value pairs of an associated pax extended header.

+
+TarInfo.replace(name=..., mtime=..., mode=..., linkname=..., uid=..., gid=..., uname=..., gname=..., deep=True)
+

New in version 3.12.

Return a new copy of the TarInfo object with the given attributes changed. For example, to return a TarInfo with the group name set to 'staff', use:

new_tarinfo = old_tarinfo.replace(gname='staff')
+

By default, a deep copy is made. If deep is false, the copy is shallow, i.e. pax_headers and any custom attributes are shared with the original TarInfo object.

+

A TarInfo object also provides some convenient query methods:

+TarInfo.isfile()
+

Return True if the TarInfo object is a regular file.

+
+TarInfo.isreg()
+

Same as isfile().

+
+TarInfo.isdir()
+

Return True if it is a directory.

+
+TarInfo.issym()
+

Return True if it is a symbolic link.

+
+TarInfo.islnk()
+

Return True if it is a hard link.

+
+TarInfo.ischr()
+

Return True if it is a character device.

+
+TarInfo.isblk()
+

Return True if it is a block device.

+
+TarInfo.isfifo()
+

Return True if it is a FIFO.

+
+TarInfo.isdev()
+

Return True if it is one of character device, block device or FIFO.

+

Extraction filters

New in version 3.12.

The tar format is designed to capture all details of a UNIX-like filesystem, which makes it very powerful. Unfortunately, the features make it easy to create tar files that have unintended – and possibly malicious – effects when extracted. For example, extracting a tar file can overwrite arbitrary files in various ways (e.g. by using absolute paths, .. path components, or symlinks that affect later members).

In most cases, the full functionality is not needed. Therefore, tarfile supports extraction filters: a mechanism to limit functionality, and thus mitigate some of the security issues.

See also

+PEP 706 +
+
+

Contains further motivation and rationale behind the design.

The filter argument to TarFile.extract() or extractall() can be:

  • the string 'fully_trusted': Honor all metadata as specified in the archive. Should be used if the user trusts the archive completely, or implements their own complex verification.
  • the string 'tar': Honor most tar-specific features (i.e. features of UNIX-like filesystems), but block features that are very likely to be surprising or malicious. See tar_filter() for details.
  • the string 'data': Ignore or block most features specific to UNIX-like filesystems. Intended for extracting cross-platform data archives. See data_filter() for details.
  • +

    None (default): Use TarFile.extraction_filter.

    If that is also None (the default), raise a DeprecationWarning, and fall back to the 'fully_trusted' filter, whose dangerous behavior matches previous versions of Python.

    In Python 3.14, the 'data' filter will become the default instead. It’s possible to switch earlier; see TarFile.extraction_filter.

  • +

    A callable which will be called for each extracted member with a TarInfo describing the member and the destination path to where the archive is extracted (i.e. the same path is used for all members):

    filter(member: TarInfo, path: str, /) -> TarInfo | None
+

    The callable is called just before each member is extracted, so it can take the current state of the disk into account. It can:

    • return a TarInfo object which will be used instead of the metadata in the archive, or
    • return None, in which case the member will be skipped, or
    • raise an exception to abort the operation or skip the member, depending on errorlevel. Note that when extraction is aborted, extractall() may leave the archive partially extracted. It does not attempt to clean up.

Default named filters

The pre-defined, named filters are available as functions, so they can be reused in custom filters:

+tarfile.fully_trusted_filter(member, path)
+

Return member unchanged.

This implements the 'fully_trusted' filter.

+
+tarfile.tar_filter(member, path)
+

Implements the 'tar' filter.

  • Strip leading slashes (/ and os.sep) from filenames.
  • +Refuse to extract files with absolute paths (in case the name is absolute even after stripping slashes, e.g. C:/foo on Windows). This raises AbsolutePathError.
  • +Refuse to extract files whose absolute path (after following symlinks) would end up outside the destination. This raises OutsideDestinationError.
  • Clear high mode bits (setuid, setgid, sticky) and group/other write bits (S_IWGRP | S_IWOTH).

Return the modified TarInfo member.

+
+tarfile.data_filter(member, path)
+

Implements the 'data' filter. In addition to what tar_filter does:

  • +

    Refuse to extract links (hard or soft) that link to absolute paths, or ones that link outside the destination.

    This raises AbsoluteLinkError or LinkOutsideDestinationError.

    Note that such files are refused even on platforms that do not support symbolic links.

  • +Refuse to extract device files (including pipes). This raises SpecialFileError.
  • +

    For regular files, including hard links:

  • For other files (directories), set mode to None, so that extraction methods skip applying permission bits.
  • Set user and group info (uid, gid, uname, gname) to None, so that extraction methods skip setting it.

Return the modified TarInfo member.

+

Filter errors

When a filter refuses to extract a file, it will raise an appropriate exception, a subclass of FilterError. This will abort the extraction if TarFile.errorlevel is 1 or more. With errorlevel=0 the error will be logged and the member will be skipped, but extraction will continue.

Hints for further verification

Even with filter='data', tarfile is not suited for extracting untrusted files without prior inspection. Among other issues, the pre-defined filters do not prevent denial-of-service attacks. Users should do additional checks.

Here is an incomplete list of things to consider:

  • Extract to a new temporary directory to prevent e.g. exploiting pre-existing links, and to make it easier to clean up after a failed extraction.
  • When working with untrusted data, use external (e.g. OS-level) limits on disk, memory and CPU usage.
  • Check filenames against an allow-list of characters (to filter out control characters, confusables, foreign path separators, etc.).
  • Check that filenames have expected extensions (discouraging files that execute when you “click on them”, or extension-less files like Windows special device names).
  • Limit the number of extracted files, total size of extracted data, filename length (including symlink length), and size of individual files.
  • Check for files that would be shadowed on case-insensitive filesystems.

Also note that:

  • Tar files may contain multiple versions of the same file. Later ones are expected to overwrite any earlier ones. This feature is crucial to allow updating tape archives, but can be abused maliciously.
  • +tarfile does not protect against issues with “live” data, e.g. an attacker tinkering with the destination (or source) directory while extraction (or archiving) is in progress.

Supporting older Python versions

Extraction filters were added to Python 3.12, but may be backported to older versions as security updates. To check whether the feature is available, use e.g. hasattr(tarfile, 'data_filter') rather than checking the Python version.

The following examples show how to support Python versions with and without the feature. Note that setting extraction_filter will affect any subsequent operations.

  • +

    Fully trusted archive:

    my_tarfile.extraction_filter = (lambda member, path: member)
+my_tarfile.extractall()
+
  • +

    Use the 'data' filter if available, but revert to Python 3.11 behavior ('fully_trusted') if this feature is not available:

    my_tarfile.extraction_filter = getattr(tarfile, 'data_filter',
+                                       (lambda member, path: member))
+my_tarfile.extractall()
+
  • +

    Use the 'data' filter; fail if it is not available:

    my_tarfile.extractall(filter=tarfile.data_filter)
+

    or:

    my_tarfile.extraction_filter = tarfile.data_filter
+my_tarfile.extractall()
+
  • +

    Use the 'data' filter; warn if it is not available:

    if hasattr(tarfile, 'data_filter'):
+    my_tarfile.extractall(filter='data')
+else:
+    # remove this when no longer needed
+    warn_the_user('Extracting may be unsafe; consider updating Python')
+    my_tarfile.extractall()
+

Stateful extraction filter example

While tarfile’s extraction methods take a simple filter callable, custom filters may be more complex objects with an internal state. It may be useful to write these as context managers, to be used like this:

with StatefulFilter() as filter_func:
+    tar.extractall(path, filter=filter_func)
+

Such a filter can be written as, for example:

class StatefulFilter:
+    def __init__(self):
+        self.file_count = 0
+
+    def __enter__(self):
+        return self
+
+    def __call__(self, member, path):
+        self.file_count += 1
+        return member
+
+    def __exit__(self, *exc_info):
+        print(f'{self.file_count} files extracted')
+

Command-Line Interface

New in version 3.4.

The tarfile module provides a simple command-line interface to interact with tar archives.

If you want to create a new tar archive, specify its name after the -c option and then list the filename(s) that should be included:

$ python -m tarfile -c monty.tar  spam.txt eggs.txt
+

Passing a directory is also acceptable:

$ python -m tarfile -c monty.tar life-of-brian_1979/
+

If you want to extract a tar archive into the current directory, use the -e option:

$ python -m tarfile -e monty.tar
+

You can also extract a tar archive into a different directory by passing the directory’s name:

$ python -m tarfile -e monty.tar  other-dir/
+

For a list of the files in a tar archive, use the -l option:

$ python -m tarfile -l monty.tar
+

Command-line options

+-l <tarfile>
+--list <tarfile>
+

List files in a tarfile.

+
+-c <tarfile> <source1> ... <sourceN>
+--create <tarfile> <source1> ... <sourceN>
+

Create tarfile from source files.

+
+-e <tarfile> [<output_dir>]
+--extract <tarfile> [<output_dir>]
+

Extract tarfile into the current directory if output_dir is not specified.

+
+-t <tarfile>
+--test <tarfile>
+

Test whether the tarfile is valid or not.

+
+-v, --verbose
+

Verbose output.

+
+--filter <filtername>
+

Specifies the filter for --extract. See Extraction filters for details. Only string names are accepted (that is, fully_trusted, tar, and data).

+

Examples

How to extract an entire tar archive to the current working directory:

import tarfile
+tar = tarfile.open("sample.tar.gz")
+tar.extractall(filter='data')
+tar.close()
+

How to extract a subset of a tar archive with TarFile.extractall() using a generator function instead of a list:

import os
+import tarfile
+
+def py_files(members):
+    for tarinfo in members:
+        if os.path.splitext(tarinfo.name)[1] == ".py":
+            yield tarinfo
+
+tar = tarfile.open("sample.tar.gz")
+tar.extractall(members=py_files(tar))
+tar.close()
+

How to create an uncompressed tar archive from a list of filenames:

import tarfile
+tar = tarfile.open("sample.tar", "w")
+for name in ["foo", "bar", "quux"]:
+    tar.add(name)
+tar.close()
+

The same example using the with statement:

import tarfile
+with tarfile.open("sample.tar", "w") as tar:
+    for name in ["foo", "bar", "quux"]:
+        tar.add(name)
+

How to read a gzip compressed tar archive and display some member information:

import tarfile
+tar = tarfile.open("sample.tar.gz", "r:gz")
+for tarinfo in tar:
+    print(tarinfo.name, "is", tarinfo.size, "bytes in size and is ", end="")
+    if tarinfo.isreg():
+        print("a regular file.")
+    elif tarinfo.isdir():
+        print("a directory.")
+    else:
+        print("something else.")
+tar.close()
+

How to create an archive and reset the user information using the filter parameter in TarFile.add():

import tarfile
+def reset(tarinfo):
+    tarinfo.uid = tarinfo.gid = 0
+    tarinfo.uname = tarinfo.gname = "root"
+    return tarinfo
+tar = tarfile.open("sample.tar.gz", "w:gz")
+tar.add("foo", filter=reset)
+tar.close()
+

Supported tar formats

There are three tar formats that can be created with the tarfile module:

  • The POSIX.1-1988 ustar format (USTAR_FORMAT). It supports filenames up to a length of at best 256 characters and linknames up to 100 characters. The maximum file size is 8 GiB. This is an old and limited but widely supported format.
  • The GNU tar format (GNU_FORMAT). It supports long filenames and linknames, files bigger than 8 GiB and sparse files. It is the de facto standard on GNU/Linux systems. tarfile fully supports the GNU tar extensions for long names, sparse file support is read-only.
  • +

    The POSIX.1-2001 pax format (PAX_FORMAT). It is the most flexible format with virtually no limits. It supports long filenames and linknames, large files and stores pathnames in a portable way. Modern tar implementations, including GNU tar, bsdtar/libarchive and star, fully support extended pax features; some old or unmaintained libraries may not, but should treat pax archives as if they were in the universally supported ustar format. It is the current default format for new archives.

    It extends the existing ustar format with extra headers for information that cannot be stored otherwise. There are two flavours of pax headers: Extended headers only affect the subsequent file header, global headers are valid for the complete archive and affect all following files. All the data in a pax header is encoded in UTF-8 for portability reasons.

There are some more variants of the tar format which can be read, but not created:

  • The ancient V7 format. This is the first tar format from Unix Seventh Edition, storing only regular files and directories. Names must not be longer than 100 characters, there is no user/group name information. Some archives have miscalculated header checksums in case of fields with non-ASCII characters.
  • The SunOS tar extended format. This format is a variant of the POSIX.1-2001 pax format, but is not compatible.

Unicode issues

The tar format was originally conceived to make backups on tape drives with the main focus on preserving file system information. Nowadays tar archives are commonly used for file distribution and exchanging archives over networks. One problem of the original format (which is the basis of all other formats) is that there is no concept of supporting different character encodings. For example, an ordinary tar archive created on a UTF-8 system cannot be read correctly on a Latin-1 system if it contains non-ASCII characters. Textual metadata (like filenames, linknames, user/group names) will appear damaged. Unfortunately, there is no way to autodetect the encoding of an archive. The pax format was designed to solve this problem. It stores non-ASCII metadata using the universal character encoding UTF-8.

The details of character conversion in tarfile are controlled by the encoding and errors keyword arguments of the TarFile class.

encoding defines the character encoding to use for the metadata in the archive. The default value is sys.getfilesystemencoding() or 'ascii' as a fallback. Depending on whether the archive is read or written, the metadata must be either decoded or encoded. If encoding is not set appropriately, this conversion may fail.

The errors argument defines how characters are treated that cannot be converted. Possible values are listed in section Error Handlers. The default scheme is 'surrogateescape' which Python also uses for its file system calls, see File Names, Command Line Arguments, and Environment Variables.

For PAX_FORMAT archives (the default), encoding is generally not needed because all the metadata is stored using UTF-8. encoding is only used in the rare cases when binary pax headers are decoded or when strings with surrogate characters are stored.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tarfile.html +

+
diff --git a/devdocs/python~3.12/library%2Ftempfile.html b/devdocs/python~3.12/library%2Ftempfile.html new file mode 100644 index 00000000..a1fc3c06 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftempfile.html @@ -0,0 +1,103 @@ +

tempfile — Generate temporary files and directories

Source code: Lib/tempfile.py

This module creates temporary files and directories. It works on all supported platforms. TemporaryFile, NamedTemporaryFile, TemporaryDirectory, and SpooledTemporaryFile are high-level interfaces which provide automatic cleanup and can be used as context managers. mkstemp() and mkdtemp() are lower-level functions which require manual cleanup.

All the user-callable functions and constructors take additional arguments which allow direct control over the location and name of temporary files and directories. Files names used by this module include a string of random characters which allows those files to be securely created in shared temporary directories. To maintain backward compatibility, the argument order is somewhat odd; it is recommended to use keyword arguments for clarity.

The module defines the following user-callable items:

+tempfile.TemporaryFile(mode='w+b', buffering=- 1, encoding=None, newline=None, suffix=None, prefix=None, dir=None, *, errors=None)
+

Return a file-like object that can be used as a temporary storage area. The file is created securely, using the same rules as mkstemp(). It will be destroyed as soon as it is closed (including an implicit close when the object is garbage collected). Under Unix, the directory entry for the file is either not created at all or is removed immediately after the file is created. Other platforms do not support this; your code should not rely on a temporary file created using this function having or not having a visible name in the file system.

The resulting object can be used as a context manager (see Examples). On completion of the context or destruction of the file object the temporary file will be removed from the filesystem.

The mode parameter defaults to 'w+b' so that the file created can be read and written without being closed. Binary mode is used so that it behaves consistently on all platforms without regard for the data that is stored. buffering, encoding, errors and newline are interpreted as for open().

The dir, prefix and suffix parameters have the same meaning and defaults as with mkstemp().

The returned object is a true file object on POSIX platforms. On other platforms, it is a file-like object whose file attribute is the underlying true file object.

The os.O_TMPFILE flag is used if it is available and works (Linux-specific, requires Linux kernel 3.11 or later).

On platforms that are neither Posix nor Cygwin, TemporaryFile is an alias for NamedTemporaryFile.

Raises an auditing event tempfile.mkstemp with argument fullpath.

Changed in version 3.5: The os.O_TMPFILE flag is now used if available.

Changed in version 3.8: Added errors parameter.

+
+tempfile.NamedTemporaryFile(mode='w+b', buffering=- 1, encoding=None, newline=None, suffix=None, prefix=None, dir=None, delete=True, *, errors=None, delete_on_close=True)
+

This function operates exactly as TemporaryFile() does, except the following differences:

  • This function returns a file that is guaranteed to have a visible name in the file system.
  • To manage the named file, it extends the parameters of TemporaryFile() with delete and delete_on_close parameters that determine whether and how the named file should be automatically deleted.

The returned object is always a file-like object whose file attribute is the underlying true file object. This file-like object can be used in a with statement, just like a normal file. The name of the temporary file can be retrieved from the name attribute of the returned file-like object. On Unix, unlike with the TemporaryFile(), the directory entry does not get unlinked immediately after the file creation.

If delete is true (the default) and delete_on_close is true (the default), the file is deleted as soon as it is closed. If delete is true and delete_on_close is false, the file is deleted on context manager exit only, or else when the file-like object is finalized. Deletion is not always guaranteed in this case (see object.__del__()). If delete is false, the value of delete_on_close is ignored.

Therefore to use the name of the temporary file to reopen the file after closing it, either make sure not to delete the file upon closure (set the delete parameter to be false) or, in case the temporary file is created in a with statement, set the delete_on_close parameter to be false. The latter approach is recommended as it provides assistance in automatic cleaning of the temporary file upon the context manager exit.

Opening the temporary file again by its name while it is still open works as follows:

  • On POSIX the file can always be opened again.
  • +

    On Windows, make sure that at least one of the following conditions are fulfilled:

    • +delete is false
    • additional open shares delete access (e.g. by calling os.open() with the flag O_TEMPORARY)
    • +delete is true but delete_on_close is false. Note, that in this case the additional opens that do not share delete access (e.g. created via builtin open()) must be closed before exiting the context manager, else the os.unlink() call on context manager exit will fail with a PermissionError.

On Windows, if delete_on_close is false, and the file is created in a directory for which the user lacks delete access, then the os.unlink() call on exit of the context manager will fail with a PermissionError. This cannot happen when delete_on_close is true because delete access is requested by the open, which fails immediately if the requested access is not granted.

On POSIX (only), a process that is terminated abruptly with SIGKILL cannot automatically delete any NamedTemporaryFiles it created.

Raises an auditing event tempfile.mkstemp with argument fullpath.

Changed in version 3.8: Added errors parameter.

Changed in version 3.12: Added delete_on_close parameter.

+
+class tempfile.SpooledTemporaryFile(max_size=0, mode='w+b', buffering=- 1, encoding=None, newline=None, suffix=None, prefix=None, dir=None, *, errors=None)
+

This class operates exactly as TemporaryFile() does, except that data is spooled in memory until the file size exceeds max_size, or until the file’s fileno() method is called, at which point the contents are written to disk and operation proceeds as with TemporaryFile().

+rollover()
+

The resulting file has one additional method, rollover(), which causes the file to roll over to an on-disk file regardless of its size.

+

The returned object is a file-like object whose _file attribute is either an io.BytesIO or io.TextIOWrapper object (depending on whether binary or text mode was specified) or a true file object, depending on whether rollover() has been called. This file-like object can be used in a with statement, just like a normal file.

Changed in version 3.3: the truncate method now accepts a size argument.

Changed in version 3.8: Added errors parameter.

Changed in version 3.11: Fully implements the io.BufferedIOBase and io.TextIOBase abstract base classes (depending on whether binary or text mode was specified).

+
+class tempfile.TemporaryDirectory(suffix=None, prefix=None, dir=None, ignore_cleanup_errors=False, *, delete=True)
+

This class securely creates a temporary directory using the same rules as mkdtemp(). The resulting object can be used as a context manager (see Examples). On completion of the context or destruction of the temporary directory object, the newly created temporary directory and all its contents are removed from the filesystem.

+name
+

The directory name can be retrieved from the name attribute of the returned object. When the returned object is used as a context manager, the name will be assigned to the target of the as clause in the with statement, if there is one.

+
+cleanup()
+

The directory can be explicitly cleaned up by calling the cleanup() method. If ignore_cleanup_errors is true, any unhandled exceptions during explicit or implicit cleanup (such as a PermissionError removing open files on Windows) will be ignored, and the remaining removable items deleted on a “best-effort” basis. Otherwise, errors will be raised in whatever context cleanup occurs (the cleanup() call, exiting the context manager, when the object is garbage-collected or during interpreter shutdown).

+

The delete parameter can be used to disable cleanup of the directory tree upon exiting the context. While it may seem unusual for a context manager to disable the action taken when exiting the context, it can be useful during debugging or when you need your cleanup behavior to be conditional based on other logic.

Raises an auditing event tempfile.mkdtemp with argument fullpath.

New in version 3.2.

Changed in version 3.10: Added ignore_cleanup_errors parameter.

Changed in version 3.12: Added the delete parameter.

+
+tempfile.mkstemp(suffix=None, prefix=None, dir=None, text=False)
+

Creates a temporary file in the most secure manner possible. There are no race conditions in the file’s creation, assuming that the platform properly implements the os.O_EXCL flag for os.open(). The file is readable and writable only by the creating user ID. If the platform uses permission bits to indicate whether a file is executable, the file is executable by no one. The file descriptor is not inherited by child processes.

Unlike TemporaryFile(), the user of mkstemp() is responsible for deleting the temporary file when done with it.

If suffix is not None, the file name will end with that suffix, otherwise there will be no suffix. mkstemp() does not put a dot between the file name and the suffix; if you need one, put it at the beginning of suffix.

If prefix is not None, the file name will begin with that prefix; otherwise, a default prefix is used. The default is the return value of gettempprefix() or gettempprefixb(), as appropriate.

If dir is not None, the file will be created in that directory; otherwise, a default directory is used. The default directory is chosen from a platform-dependent list, but the user of the application can control the directory location by setting the TMPDIR, TEMP or TMP environment variables. There is thus no guarantee that the generated filename will have any nice properties, such as not requiring quoting when passed to external commands via os.popen().

If any of suffix, prefix, and dir are not None, they must be the same type. If they are bytes, the returned name will be bytes instead of str. If you want to force a bytes return value with otherwise default behavior, pass suffix=b''.

If text is specified and true, the file is opened in text mode. Otherwise, (the default) the file is opened in binary mode.

mkstemp() returns a tuple containing an OS-level handle to an open file (as would be returned by os.open()) and the absolute pathname of that file, in that order.

Raises an auditing event tempfile.mkstemp with argument fullpath.

Changed in version 3.5: suffix, prefix, and dir may now be supplied in bytes in order to obtain a bytes return value. Prior to this, only str was allowed. suffix and prefix now accept and default to None to cause an appropriate default value to be used.

Changed in version 3.6: The dir parameter now accepts a path-like object.

+
+tempfile.mkdtemp(suffix=None, prefix=None, dir=None)
+

Creates a temporary directory in the most secure manner possible. There are no race conditions in the directory’s creation. The directory is readable, writable, and searchable only by the creating user ID.

The user of mkdtemp() is responsible for deleting the temporary directory and its contents when done with it.

The prefix, suffix, and dir arguments are the same as for mkstemp().

mkdtemp() returns the absolute pathname of the new directory.

Raises an auditing event tempfile.mkdtemp with argument fullpath.

Changed in version 3.5: suffix, prefix, and dir may now be supplied in bytes in order to obtain a bytes return value. Prior to this, only str was allowed. suffix and prefix now accept and default to None to cause an appropriate default value to be used.

Changed in version 3.6: The dir parameter now accepts a path-like object.

Changed in version 3.12: mkdtemp() now always returns an absolute path, even if dir is relative.

+
+tempfile.gettempdir()
+

Return the name of the directory used for temporary files. This defines the default value for the dir argument to all functions in this module.

Python searches a standard list of directories to find one which the calling user can create files in. The list is:

  1. The directory named by the TMPDIR environment variable.
  2. The directory named by the TEMP environment variable.
  3. The directory named by the TMP environment variable.
  4. +

    A platform-specific location:

    • On Windows, the directories C:\TEMP, C:\TMP, \TEMP, and \TMP, in that order.
    • On all other platforms, the directories /tmp, /var/tmp, and /usr/tmp, in that order.
  5. As a last resort, the current working directory.

The result of this search is cached, see the description of tempdir below.

Changed in version 3.10: Always returns a str. Previously it would return any tempdir value regardless of type so long as it was not None.

+
+tempfile.gettempdirb()
+

Same as gettempdir() but the return value is in bytes.

New in version 3.5.

+
+tempfile.gettempprefix()
+

Return the filename prefix used to create temporary files. This does not contain the directory component.

+
+tempfile.gettempprefixb()
+

Same as gettempprefix() but the return value is in bytes.

New in version 3.5.

+

The module uses a global variable to store the name of the directory used for temporary files returned by gettempdir(). It can be set directly to override the selection process, but this is discouraged. All functions in this module take a dir argument which can be used to specify the directory. This is the recommended approach that does not surprise other unsuspecting code by changing global API behavior.

+tempfile.tempdir
+

When set to a value other than None, this variable defines the default value for the dir argument to the functions defined in this module, including its type, bytes or str. It cannot be a path-like object.

If tempdir is None (the default) at any call to any of the above functions except gettempprefix() it is initialized following the algorithm described in gettempdir().

Note

Beware that if you set tempdir to a bytes value, there is a nasty side effect: The global default return type of mkstemp() and mkdtemp() changes to bytes when no explicit prefix, suffix, or dir arguments of type str are supplied. Please do not write code expecting or depending on this. This awkward behavior is maintained for compatibility with the historical implementation.

+

Examples

Here are some examples of typical usage of the tempfile module:

>>> import tempfile
+
+# create a temporary file and write some data to it
+>>> fp = tempfile.TemporaryFile()
+>>> fp.write(b'Hello world!')
+# read data from file
+>>> fp.seek(0)
+>>> fp.read()
+b'Hello world!'
+# close the file, it will be removed
+>>> fp.close()
+
+# create a temporary file using a context manager
+>>> with tempfile.TemporaryFile() as fp:
+...     fp.write(b'Hello world!')
+...     fp.seek(0)
+...     fp.read()
+b'Hello world!'
+>>>
+# file is now closed and removed
+
+# create a temporary file using a context manager
+# close the file, use the name to open the file again
+>>> with tempfile.NamedTemporaryFile(delete_on_close=False) as fp:
+...     fp.write(b'Hello world!')
+...     fp.close()
+... # the file is closed, but not removed
+... # open the file again by using its name
+...     with open(fp.name, mode='rb') as f:
+...         f.read()
+b'Hello world!'
+>>>
+# file is now removed
+
+# create a temporary directory using the context manager
+>>> with tempfile.TemporaryDirectory() as tmpdirname:
+...     print('created temporary directory', tmpdirname)
+>>>
+# directory and contents have been removed
+

Deprecated functions and variables

A historical way to create temporary files was to first generate a file name with the mktemp() function and then create a file using this name. Unfortunately this is not secure, because a different process may create a file with this name in the time between the call to mktemp() and the subsequent attempt to create the file by the first process. The solution is to combine the two steps and create the file immediately. This approach is used by mkstemp() and the other functions described above.

+tempfile.mktemp(suffix='', prefix='tmp', dir=None)
+

Deprecated since version 2.3: Use mkstemp() instead.

Return an absolute pathname of a file that did not exist at the time the call is made. The prefix, suffix, and dir arguments are similar to those of mkstemp(), except that bytes file names, suffix=None and prefix=None are not supported.

Warning

Use of this function may introduce a security hole in your program. By the time you get around to doing anything with the file name it returns, someone else may have beaten you to the punch. mktemp() usage can be replaced easily with NamedTemporaryFile(), passing it the delete=False parameter:

>>> f = NamedTemporaryFile(delete=False)
+>>> f.name
+'/tmp/tmptjujjt'
+>>> f.write(b"Hello World!\n")
+13
+>>> f.close()
+>>> os.unlink(f.name)
+>>> os.path.exists(f.name)
+False
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tempfile.html +

+
diff --git a/devdocs/python~3.12/library%2Ftermios.html b/devdocs/python~3.12/library%2Ftermios.html new file mode 100644 index 00000000..8d4c0fdf --- /dev/null +++ b/devdocs/python~3.12/library%2Ftermios.html @@ -0,0 +1,55 @@ +

termios — POSIX style tty control

This module provides an interface to the POSIX calls for tty I/O control. For a complete description of these calls, see termios(3) Unix manual page. It is only available for those Unix versions that support POSIX termios style tty I/O control configured during installation.

Availability: Unix.

All functions in this module take a file descriptor fd as their first argument. This can be an integer file descriptor, such as returned by sys.stdin.fileno(), or a file object, such as sys.stdin itself.

This module also defines all the constants needed to work with the functions provided here; these have the same name as their counterparts in C. Please refer to your system documentation for more information on using these terminal control interfaces.

The module defines the following functions:

+termios.tcgetattr(fd)
+

Return a list containing the tty attributes for file descriptor fd, as follows: [iflag, oflag, cflag, lflag, ispeed, ospeed, cc] where cc is a list of the tty special characters (each a string of length 1, except the items with indices VMIN and VTIME, which are integers when these fields are defined). The interpretation of the flags and the speeds as well as the indexing in the cc array must be done using the symbolic constants defined in the termios module.

+
+termios.tcsetattr(fd, when, attributes)
+

Set the tty attributes for file descriptor fd from the attributes, which is a list like the one returned by tcgetattr(). The when argument determines when the attributes are changed:

+termios.TCSANOW
+

Change attributes immediately.

+
+termios.TCSADRAIN
+

Change attributes after transmitting all queued output.

+
+termios.TCSAFLUSH
+

Change attributes after transmitting all queued output and discarding all queued input.

+
+
+termios.tcsendbreak(fd, duration)
+

Send a break on file descriptor fd. A zero duration sends a break for 0.25–0.5 seconds; a nonzero duration has a system dependent meaning.

+
+termios.tcdrain(fd)
+

Wait until all output written to file descriptor fd has been transmitted.

+
+termios.tcflush(fd, queue)
+

Discard queued data on file descriptor fd. The queue selector specifies which queue: TCIFLUSH for the input queue, TCOFLUSH for the output queue, or TCIOFLUSH for both queues.

+
+termios.tcflow(fd, action)
+

Suspend or resume input or output on file descriptor fd. The action argument can be TCOOFF to suspend output, TCOON to restart output, TCIOFF to suspend input, or TCION to restart input.

+
+termios.tcgetwinsize(fd)
+

Return a tuple (ws_row, ws_col) containing the tty window size for file descriptor fd. Requires termios.TIOCGWINSZ or termios.TIOCGSIZE.

New in version 3.11.

+
+termios.tcsetwinsize(fd, winsize)
+

Set the tty window size for file descriptor fd from winsize, which is a two-item tuple (ws_row, ws_col) like the one returned by tcgetwinsize(). Requires at least one of the pairs (termios.TIOCGWINSZ, termios.TIOCSWINSZ); (termios.TIOCGSIZE, termios.TIOCSSIZE) to be defined.

New in version 3.11.

+

See also

+Module tty +
+
+

Convenience functions for common terminal control operations.

Example

Here’s a function that prompts for a password with echoing turned off. Note the technique using a separate tcgetattr() call and a try … finally statement to ensure that the old tty attributes are restored exactly no matter what happens:

def getpass(prompt="Password: "):
+    import termios, sys
+    fd = sys.stdin.fileno()
+    old = termios.tcgetattr(fd)
+    new = termios.tcgetattr(fd)
+    new[3] = new[3] & ~termios.ECHO          # lflags
+    try:
+        termios.tcsetattr(fd, termios.TCSADRAIN, new)
+        passwd = input(prompt)
+    finally:
+        termios.tcsetattr(fd, termios.TCSADRAIN, old)
+    return passwd
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/termios.html +

+
diff --git a/devdocs/python~3.12/library%2Ftest.html b/devdocs/python~3.12/library%2Ftest.html new file mode 100644 index 00000000..985b9476 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftest.html @@ -0,0 +1,62 @@ +

test — Regression tests package for Python

Note

The test package is meant for internal use by Python only. It is documented for the benefit of the core developers of Python. Any use of this package outside of Python’s standard library is discouraged as code mentioned here can change or be removed without notice between releases of Python.

The test package contains all regression tests for Python as well as the modules test.support and test.regrtest. test.support is used to enhance your tests while test.regrtest drives the testing suite.

Each module in the test package whose name starts with test_ is a testing suite for a specific module or feature. All new tests should be written using the unittest or doctest module. Some older tests are written using a “traditional” testing style that compares output printed to sys.stdout; this style of test is considered deprecated.

See also

+Module unittest +
+
+

Writing PyUnit regression tests.

+Module doctest +
+
+

Tests embedded in documentation strings.

Writing Unit Tests for the test package

It is preferred that tests that use the unittest module follow a few guidelines. One is to name the test module by starting it with test_ and end it with the name of the module being tested. The test methods in the test module should start with test_ and end with a description of what the method is testing. This is needed so that the methods are recognized by the test driver as test methods. Also, no documentation string for the method should be included. A comment (such as # Tests function returns only True or False) should be used to provide documentation for test methods. This is done because documentation strings get printed out if they exist and thus what test is being run is not stated.

A basic boilerplate is often used:

import unittest
+from test import support
+
+class MyTestCase1(unittest.TestCase):
+
+    # Only use setUp() and tearDown() if necessary
+
+    def setUp(self):
+        ... code to execute in preparation for tests ...
+
+    def tearDown(self):
+        ... code to execute to clean up after tests ...
+
+    def test_feature_one(self):
+        # Test feature one.
+        ... testing code ...
+
+    def test_feature_two(self):
+        # Test feature two.
+        ... testing code ...
+
+    ... more test methods ...
+
+class MyTestCase2(unittest.TestCase):
+    ... same structure as MyTestCase1 ...
+
+... more test classes ...
+
+if __name__ == '__main__':
+    unittest.main()
+

This code pattern allows the testing suite to be run by test.regrtest, on its own as a script that supports the unittest CLI, or via the python -m unittest CLI.

The goal for regression testing is to try to break code. This leads to a few guidelines to be followed:

  • The testing suite should exercise all classes, functions, and constants. This includes not just the external API that is to be presented to the outside world but also “private” code.
  • Whitebox testing (examining the code being tested when the tests are being written) is preferred. Blackbox testing (testing only the published user interface) is not complete enough to make sure all boundary and edge cases are tested.
  • Make sure all possible values are tested including invalid ones. This makes sure that not only all valid values are acceptable but also that improper values are handled correctly.
  • Exhaust as many code paths as possible. Test where branching occurs and thus tailor input to make sure as many different paths through the code are taken.
  • Add an explicit test for any bugs discovered for the tested code. This will make sure that the error does not crop up again if the code is changed in the future.
  • Make sure to clean up after your tests (such as close and remove all temporary files).
  • If a test is dependent on a specific condition of the operating system then verify the condition already exists before attempting the test.
  • Import as few modules as possible and do it as soon as possible. This minimizes external dependencies of tests and also minimizes possible anomalous behavior from side-effects of importing a module.
  • +

    Try to maximize code reuse. On occasion, tests will vary by something as small as what type of input is used. Minimize code duplication by subclassing a basic test class with a class that specifies the input:

    class TestFuncAcceptsSequencesMixin:
+
+    func = mySuperWhammyFunction
+
+    def test_func(self):
+        self.func(self.arg)
+
+class AcceptLists(TestFuncAcceptsSequencesMixin, unittest.TestCase):
+    arg = [1, 2, 3]
+
+class AcceptStrings(TestFuncAcceptsSequencesMixin, unittest.TestCase):
+    arg = 'abc'
+
+class AcceptTuples(TestFuncAcceptsSequencesMixin, unittest.TestCase):
+    arg = (1, 2, 3)
+

    When using this pattern, remember that all classes that inherit from unittest.TestCase are run as tests. The Mixin class in the example above does not have any data and so can’t be run by itself, thus it does not inherit from unittest.TestCase.

See also

Test Driven Development
+
+

A book by Kent Beck on writing tests before code.

Running tests using the command-line interface

The test package can be run as a script to drive Python’s regression test suite, thanks to the -m option: python -m test. Under the hood, it uses test.regrtest; the call python -m test.regrtest used in previous Python versions still works. Running the script by itself automatically starts running all regression tests in the test package. It does this by finding all modules in the package whose name starts with test_, importing them, and executing the function test_main() if present or loading the tests via unittest.TestLoader.loadTestsFromModule if test_main does not exist. The names of tests to execute may also be passed to the script. Specifying a single regression test (python -m test test_spam) will minimize output and only print whether the test passed or failed.

Running test directly allows what resources are available for tests to use to be set. You do this by using the -u command-line option. Specifying all as the value for the -u option enables all possible resources: python -m test -uall. If all but one resource is desired (a more common case), a comma-separated list of resources that are not desired may be listed after all. The command python -m test -uall,-audio,-largefile will run test with all resources except the audio and largefile resources. For a list of all resources and more command-line options, run python -m test -h.

Some other ways to execute the regression tests depend on what platform the tests are being executed on. On Unix, you can run make test at the top-level directory where Python was built. On Windows, executing rt.bat from your PCbuild directory will run all regression tests.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/test.html +

+
diff --git a/devdocs/python~3.12/library%2Ftext.html b/devdocs/python~3.12/library%2Ftext.html new file mode 100644 index 00000000..f36fea38 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftext.html @@ -0,0 +1,13 @@ +

Text Processing Services

The modules described in this chapter provide a wide range of string manipulation operations and other text processing services.

The codecs module described under Binary Data Services is also highly relevant to text processing. In addition, see the documentation for Python’s built-in string type in Text Sequence Type — str.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/text.html +

+
diff --git a/devdocs/python~3.12/library%2Ftextwrap.html b/devdocs/python~3.12/library%2Ftextwrap.html new file mode 100644 index 00000000..cec65934 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftextwrap.html @@ -0,0 +1,94 @@ +

textwrap — Text wrapping and filling

Source code: Lib/textwrap.py

The textwrap module provides some convenience functions, as well as TextWrapper, the class that does all the work. If you’re just wrapping or filling one or two text strings, the convenience functions should be good enough; otherwise, you should use an instance of TextWrapper for efficiency.

+textwrap.wrap(text, width=70, *, initial_indent='', subsequent_indent='', expand_tabs=True, replace_whitespace=True, fix_sentence_endings=False, break_long_words=True, drop_whitespace=True, break_on_hyphens=True, tabsize=8, max_lines=None, placeholder=' [...]')
+

Wraps the single paragraph in text (a string) so every line is at most width characters long. Returns a list of output lines, without final newlines.

Optional keyword arguments correspond to the instance attributes of TextWrapper, documented below.

See the TextWrapper.wrap() method for additional details on how wrap() behaves.

+
+textwrap.fill(text, width=70, *, initial_indent='', subsequent_indent='', expand_tabs=True, replace_whitespace=True, fix_sentence_endings=False, break_long_words=True, drop_whitespace=True, break_on_hyphens=True, tabsize=8, max_lines=None, placeholder=' [...]')
+

Wraps the single paragraph in text, and returns a single string containing the wrapped paragraph. fill() is shorthand for

"\n".join(wrap(text, ...))
+

In particular, fill() accepts exactly the same keyword arguments as wrap().

+
+textwrap.shorten(text, width, *, fix_sentence_endings=False, break_long_words=True, break_on_hyphens=True, placeholder=' [...]')
+

Collapse and truncate the given text to fit in the given width.

First the whitespace in text is collapsed (all whitespace is replaced by single spaces). If the result fits in the width, it is returned. Otherwise, enough words are dropped from the end so that the remaining words plus the placeholder fit within width:

>>> textwrap.shorten("Hello  world!", width=12)
+'Hello world!'
+>>> textwrap.shorten("Hello  world!", width=11)
+'Hello [...]'
+>>> textwrap.shorten("Hello world", width=10, placeholder="...")
+'Hello...'
+

Optional keyword arguments correspond to the instance attributes of TextWrapper, documented below. Note that the whitespace is collapsed before the text is passed to the TextWrapper fill() function, so changing the value of tabsize, expand_tabs, drop_whitespace, and replace_whitespace will have no effect.

New in version 3.4.

+
+textwrap.dedent(text)
+

Remove any common leading whitespace from every line in text.

This can be used to make triple-quoted strings line up with the left edge of the display, while still presenting them in the source code in indented form.

Note that tabs and spaces are both treated as whitespace, but they are not equal: the lines "  hello" and "\thello" are considered to have no common leading whitespace.

Lines containing only whitespace are ignored in the input and normalized to a single newline character in the output.

For example:

def test():
+    # end first line with \ to avoid the empty line!
+    s = '''\
+    hello
+      world
+    '''
+    print(repr(s))          # prints '    hello\n      world\n    '
+    print(repr(dedent(s)))  # prints 'hello\n  world\n'
+
+
+textwrap.indent(text, prefix, predicate=None)
+

Add prefix to the beginning of selected lines in text.

Lines are separated by calling text.splitlines(True).

By default, prefix is added to all lines that do not consist solely of whitespace (including any line endings).

For example:

>>> s = 'hello\n\n \nworld'
+>>> indent(s, '  ')
+'  hello\n\n \n  world'
+

The optional predicate argument can be used to control which lines are indented. For example, it is easy to add prefix to even empty and whitespace-only lines:

>>> print(indent(s, '+ ', lambda line: True))
++ hello
++
++
++ world
+

New in version 3.3.

+

wrap(), fill() and shorten() work by creating a TextWrapper instance and calling a single method on it. That instance is not reused, so for applications that process many text strings using wrap() and/or fill(), it may be more efficient to create your own TextWrapper object.

Text is preferably wrapped on whitespaces and right after the hyphens in hyphenated words; only then will long words be broken if necessary, unless TextWrapper.break_long_words is set to false.

+class textwrap.TextWrapper(**kwargs)
+

The TextWrapper constructor accepts a number of optional keyword arguments. Each keyword argument corresponds to an instance attribute, so for example

wrapper = TextWrapper(initial_indent="* ")
+

is the same as

wrapper = TextWrapper()
+wrapper.initial_indent = "* "
+

You can re-use the same TextWrapper object many times, and you can change any of its options through direct assignment to instance attributes between uses.

The TextWrapper instance attributes (and keyword arguments to the constructor) are as follows:

+width
+

(default: 70) The maximum length of wrapped lines. As long as there are no individual words in the input text longer than width, TextWrapper guarantees that no output line will be longer than width characters.

+
+expand_tabs
+

(default: True) If true, then all tab characters in text will be expanded to spaces using the expandtabs() method of text.

+
+tabsize
+

(default: 8) If expand_tabs is true, then all tab characters in text will be expanded to zero or more spaces, depending on the current column and the given tab size.

New in version 3.3.

+
+replace_whitespace
+

(default: True) If true, after tab expansion but before wrapping, the wrap() method will replace each whitespace character with a single space. The whitespace characters replaced are as follows: tab, newline, vertical tab, formfeed, and carriage return ('\t\n\v\f\r').

Note

If expand_tabs is false and replace_whitespace is true, each tab character will be replaced by a single space, which is not the same as tab expansion.

Note

If replace_whitespace is false, newlines may appear in the middle of a line and cause strange output. For this reason, text should be split into paragraphs (using str.splitlines() or similar) which are wrapped separately.

+
+drop_whitespace
+

(default: True) If true, whitespace at the beginning and ending of every line (after wrapping but before indenting) is dropped. Whitespace at the beginning of the paragraph, however, is not dropped if non-whitespace follows it. If whitespace being dropped takes up an entire line, the whole line is dropped.

+
+initial_indent
+

(default: '') String that will be prepended to the first line of wrapped output. Counts towards the length of the first line. The empty string is not indented.

+
+subsequent_indent
+

(default: '') String that will be prepended to all lines of wrapped output except the first. Counts towards the length of each line except the first.

+
+fix_sentence_endings
+

(default: False) If true, TextWrapper attempts to detect sentence endings and ensure that sentences are always separated by exactly two spaces. This is generally desired for text in a monospaced font. However, the sentence detection algorithm is imperfect: it assumes that a sentence ending consists of a lowercase letter followed by one of '.', '!', or '?', possibly followed by one of '"' or "'", followed by a space. One problem with this algorithm is that it is unable to detect the difference between “Dr.” in

[...] Dr. Frankenstein's monster [...]
+

and “Spot.” in

[...] See Spot. See Spot run [...]
+

fix_sentence_endings is false by default.

Since the sentence detection algorithm relies on string.lowercase for the definition of “lowercase letter”, and a convention of using two spaces after a period to separate sentences on the same line, it is specific to English-language texts.

+
+break_long_words
+

(default: True) If true, then words longer than width will be broken in order to ensure that no lines are longer than width. If it is false, long words will not be broken, and some lines may be longer than width. (Long words will be put on a line by themselves, in order to minimize the amount by which width is exceeded.)

+
+break_on_hyphens
+

(default: True) If true, wrapping will occur preferably on whitespaces and right after hyphens in compound words, as it is customary in English. If false, only whitespaces will be considered as potentially good places for line breaks, but you need to set break_long_words to false if you want truly insecable words. Default behaviour in previous versions was to always allow breaking hyphenated words.

+
+max_lines
+

(default: None) If not None, then the output will contain at most max_lines lines, with placeholder appearing at the end of the output.

New in version 3.4.

+
+placeholder
+

(default: ' [...]') String that will appear at the end of the output text if it has been truncated.

New in version 3.4.

+

TextWrapper also provides some public methods, analogous to the module-level convenience functions:

+wrap(text)
+

Wraps the single paragraph in text (a string) so every line is at most width characters long. All wrapping options are taken from instance attributes of the TextWrapper instance. Returns a list of output lines, without final newlines. If the wrapped output has no content, the returned list is empty.

+
+fill(text)
+

Wraps the single paragraph in text, and returns a single string containing the wrapped paragraph.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/textwrap.html +

+
diff --git a/devdocs/python~3.12/library%2Fthreading.html b/devdocs/python~3.12/library%2Fthreading.html new file mode 100644 index 00000000..b841ce7d --- /dev/null +++ b/devdocs/python~3.12/library%2Fthreading.html @@ -0,0 +1,260 @@ +

threading — Thread-based parallelism

Source code: Lib/threading.py

This module constructs higher-level threading interfaces on top of the lower level _thread module.

Changed in version 3.7: This module used to be optional, it is now always available.

See also

concurrent.futures.ThreadPoolExecutor offers a higher level interface to push tasks to a background thread without blocking execution of the calling thread, while still being able to retrieve their results when needed.

queue provides a thread-safe interface for exchanging data between running threads.

asyncio offers an alternative approach to achieving task level concurrency without requiring the use of multiple operating system threads.

Note

In the Python 2.x series, this module contained camelCase names for some methods and functions. These are deprecated as of Python 3.10, but they are still supported for compatibility with Python 2.5 and lower.

CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing or concurrent.futures.ProcessPoolExecutor. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

This module defines the following functions:

+threading.active_count()
+

Return the number of Thread objects currently alive. The returned count is equal to the length of the list returned by enumerate().

The function activeCount is a deprecated alias for this function.

+
+threading.current_thread()
+

Return the current Thread object, corresponding to the caller’s thread of control. If the caller’s thread of control was not created through the threading module, a dummy thread object with limited functionality is returned.

The function currentThread is a deprecated alias for this function.

+
+threading.excepthook(args, /)
+

Handle uncaught exception raised by Thread.run().

The args argument has the following attributes:

  • +exc_type: Exception type.
  • +exc_value: Exception value, can be None.
  • +exc_traceback: Exception traceback, can be None.
  • +thread: Thread which raised the exception, can be None.

If exc_type is SystemExit, the exception is silently ignored. Otherwise, the exception is printed out on sys.stderr.

If this function raises an exception, sys.excepthook() is called to handle it.

threading.excepthook() can be overridden to control how uncaught exceptions raised by Thread.run() are handled.

Storing exc_value using a custom hook can create a reference cycle. It should be cleared explicitly to break the reference cycle when the exception is no longer needed.

Storing thread using a custom hook can resurrect it if it is set to an object which is being finalized. Avoid storing thread after the custom hook completes to avoid resurrecting objects.

See also

sys.excepthook() handles uncaught exceptions.

New in version 3.8.

+
+threading.__excepthook__
+

Holds the original value of threading.excepthook(). It is saved so that the original value can be restored in case they happen to get replaced with broken or alternative objects.

New in version 3.10.

+
+threading.get_ident()
+

Return the ‘thread identifier’ of the current thread. This is a nonzero integer. Its value has no direct meaning; it is intended as a magic cookie to be used e.g. to index a dictionary of thread-specific data. Thread identifiers may be recycled when a thread exits and another thread is created.

New in version 3.3.

+
+threading.get_native_id()
+

Return the native integral Thread ID of the current thread assigned by the kernel. This is a non-negative integer. Its value may be used to uniquely identify this particular thread system-wide (until the thread terminates, after which the value may be recycled by the OS).

Availability: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD.

New in version 3.8.

+
+threading.enumerate()
+

Return a list of all Thread objects currently active. The list includes daemonic threads and dummy thread objects created by current_thread(). It excludes terminated threads and threads that have not yet been started. However, the main thread is always part of the result, even when terminated.

+
+threading.main_thread()
+

Return the main Thread object. In normal conditions, the main thread is the thread from which the Python interpreter was started.

New in version 3.4.

+
+threading.settrace(func)
+

Set a trace function for all threads started from the threading module. The func will be passed to sys.settrace() for each thread, before its run() method is called.

+
+threading.settrace_all_threads(func)
+

Set a trace function for all threads started from the threading module and all Python threads that are currently executing.

The func will be passed to sys.settrace() for each thread, before its run() method is called.

New in version 3.12.

+
+threading.gettrace()
+

Get the trace function as set by settrace().

New in version 3.10.

+
+threading.setprofile(func)
+

Set a profile function for all threads started from the threading module. The func will be passed to sys.setprofile() for each thread, before its run() method is called.

+
+threading.setprofile_all_threads(func)
+

Set a profile function for all threads started from the threading module and all Python threads that are currently executing.

The func will be passed to sys.setprofile() for each thread, before its run() method is called.

New in version 3.12.

+
+threading.getprofile()
+

Get the profiler function as set by setprofile().

New in version 3.10.

+
+threading.stack_size([size])
+

Return the thread stack size used when creating new threads. The optional size argument specifies the stack size to be used for subsequently created threads, and must be 0 (use platform or configured default) or a positive integer value of at least 32,768 (32 KiB). If size is not specified, 0 is used. If changing the thread stack size is unsupported, a RuntimeError is raised. If the specified stack size is invalid, a ValueError is raised and the stack size is unmodified. 32 KiB is currently the minimum supported stack size value to guarantee sufficient stack space for the interpreter itself. Note that some platforms may have particular restrictions on values for the stack size, such as requiring a minimum stack size > 32 KiB or requiring allocation in multiples of the system memory page size - platform documentation should be referred to for more information (4 KiB pages are common; using multiples of 4096 for the stack size is the suggested approach in the absence of more specific information).

Availability: Windows, pthreads.

Unix platforms with POSIX threads support.

+

This module also defines the following constant:

+threading.TIMEOUT_MAX
+

The maximum value allowed for the timeout parameter of blocking functions (Lock.acquire(), RLock.acquire(), Condition.wait(), etc.). Specifying a timeout greater than this value will raise an OverflowError.

New in version 3.2.

+

This module defines a number of classes, which are detailed in the sections below.

The design of this module is loosely based on Java’s threading model. However, where Java makes locks and condition variables basic behavior of every object, they are separate objects in Python. Python’s Thread class supports a subset of the behavior of Java’s Thread class; currently, there are no priorities, no thread groups, and threads cannot be destroyed, stopped, suspended, resumed, or interrupted. The static methods of Java’s Thread class, when implemented, are mapped to module-level functions.

All of the methods described below are executed atomically.

Thread-Local Data

Thread-local data is data whose values are thread specific. To manage thread-local data, just create an instance of local (or a subclass) and store attributes on it:

mydata = threading.local()
+mydata.x = 1
+

The instance’s values will be different for separate threads.

+class threading.local
+

A class that represents thread-local data.

For more details and extensive examples, see the documentation string of the _threading_local module: Lib/_threading_local.py.

+

Thread Objects

The Thread class represents an activity that is run in a separate thread of control. There are two ways to specify the activity: by passing a callable object to the constructor, or by overriding the run() method in a subclass. No other methods (except for the constructor) should be overridden in a subclass. In other words, only override the __init__() and run() methods of this class.

Once a thread object is created, its activity must be started by calling the thread’s start() method. This invokes the run() method in a separate thread of control.

Once the thread’s activity is started, the thread is considered ‘alive’. It stops being alive when its run() method terminates – either normally, or by raising an unhandled exception. The is_alive() method tests whether the thread is alive.

Other threads can call a thread’s join() method. This blocks the calling thread until the thread whose join() method is called is terminated.

A thread has a name. The name can be passed to the constructor, and read or changed through the name attribute.

If the run() method raises an exception, threading.excepthook() is called to handle it. By default, threading.excepthook() ignores silently SystemExit.

A thread can be flagged as a “daemon thread”. The significance of this flag is that the entire Python program exits when only daemon threads are left. The initial value is inherited from the creating thread. The flag can be set through the daemon property or the daemon constructor argument.

Note

Daemon threads are abruptly stopped at shutdown. Their resources (such as open files, database transactions, etc.) may not be released properly. If you want your threads to stop gracefully, make them non-daemonic and use a suitable signalling mechanism such as an Event.

There is a “main thread” object; this corresponds to the initial thread of control in the Python program. It is not a daemon thread.

There is the possibility that “dummy thread objects” are created. These are thread objects corresponding to “alien threads”, which are threads of control started outside the threading module, such as directly from C code. Dummy thread objects have limited functionality; they are always considered alive and daemonic, and cannot be joined. They are never deleted, since it is impossible to detect the termination of alien threads.

+class threading.Thread(group=None, target=None, name=None, args=(), kwargs={}, *, daemon=None)
+

This constructor should always be called with keyword arguments. Arguments are:

group should be None; reserved for future extension when a ThreadGroup class is implemented.

target is the callable object to be invoked by the run() method. Defaults to None, meaning nothing is called.

name is the thread name. By default, a unique name is constructed of the form “Thread-N” where N is a small decimal number, or “Thread-N (target)” where “target” is target.__name__ if the target argument is specified.

args is a list or tuple of arguments for the target invocation. Defaults to ().

kwargs is a dictionary of keyword arguments for the target invocation. Defaults to {}.

If not None, daemon explicitly sets whether the thread is daemonic. If None (the default), the daemonic property is inherited from the current thread.

If the subclass overrides the constructor, it must make sure to invoke the base class constructor (Thread.__init__()) before doing anything else to the thread.

Changed in version 3.10: Use the target name if name argument is omitted.

Changed in version 3.3: Added the daemon argument.

+start()
+

Start the thread’s activity.

It must be called at most once per thread object. It arranges for the object’s run() method to be invoked in a separate thread of control.

This method will raise a RuntimeError if called more than once on the same thread object.

+
+run()
+

Method representing the thread’s activity.

You may override this method in a subclass. The standard run() method invokes the callable object passed to the object’s constructor as the target argument, if any, with positional and keyword arguments taken from the args and kwargs arguments, respectively.

Using list or tuple as the args argument which passed to the Thread could achieve the same effect.

Example:

>>> from threading import Thread
+>>> t = Thread(target=print, args=[1])
+>>> t.run()
+1
+>>> t = Thread(target=print, args=(1,))
+>>> t.run()
+1
+
+
+join(timeout=None)
+

Wait until the thread terminates. This blocks the calling thread until the thread whose join() method is called terminates – either normally or through an unhandled exception – or until the optional timeout occurs.

When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof). As join() always returns None, you must call is_alive() after join() to decide whether a timeout happened – if the thread is still alive, the join() call timed out.

When the timeout argument is not present or None, the operation will block until the thread terminates.

A thread can be joined many times.

join() raises a RuntimeError if an attempt is made to join the current thread as that would cause a deadlock. It is also an error to join() a thread before it has been started and attempts to do so raise the same exception.

+
+name
+

A string used for identification purposes only. It has no semantics. Multiple threads may be given the same name. The initial name is set by the constructor.

+
+getName()
+setName()
+

Deprecated getter/setter API for name; use it directly as a property instead.

Deprecated since version 3.10.

+
+ident
+

The ‘thread identifier’ of this thread or None if the thread has not been started. This is a nonzero integer. See the get_ident() function. Thread identifiers may be recycled when a thread exits and another thread is created. The identifier is available even after the thread has exited.

+
+native_id
+

The Thread ID (TID) of this thread, as assigned by the OS (kernel). This is a non-negative integer, or None if the thread has not been started. See the get_native_id() function. This value may be used to uniquely identify this particular thread system-wide (until the thread terminates, after which the value may be recycled by the OS).

Note

Similar to Process IDs, Thread IDs are only valid (guaranteed unique system-wide) from the time the thread is created until the thread has been terminated.

Availability: Windows, FreeBSD, Linux, macOS, OpenBSD, NetBSD, AIX, DragonFlyBSD.

New in version 3.8.

+
+is_alive()
+

Return whether the thread is alive.

This method returns True just before the run() method starts until just after the run() method terminates. The module function enumerate() returns a list of all alive threads.

+
+daemon
+

A boolean value indicating whether this thread is a daemon thread (True) or not (False). This must be set before start() is called, otherwise RuntimeError is raised. Its initial value is inherited from the creating thread; the main thread is not a daemon thread and therefore all threads created in the main thread default to daemon = False.

The entire Python program exits when no alive non-daemon threads are left.

+
+isDaemon()
+setDaemon()
+

Deprecated getter/setter API for daemon; use it directly as a property instead.

Deprecated since version 3.10.

+
+

Lock Objects

A primitive lock is a synchronization primitive that is not owned by a particular thread when locked. In Python, it is currently the lowest level synchronization primitive available, implemented directly by the _thread extension module.

A primitive lock is in one of two states, “locked” or “unlocked”. It is created in the unlocked state. It has two basic methods, acquire() and release(). When the state is unlocked, acquire() changes the state to locked and returns immediately. When the state is locked, acquire() blocks until a call to release() in another thread changes it to unlocked, then the acquire() call resets it to locked and returns. The release() method should only be called in the locked state; it changes the state to unlocked and returns immediately. If an attempt is made to release an unlocked lock, a RuntimeError will be raised.

Locks also support the context management protocol.

When more than one thread is blocked in acquire() waiting for the state to turn to unlocked, only one thread proceeds when a release() call resets the state to unlocked; which one of the waiting threads proceeds is not defined, and may vary across implementations.

All methods are executed atomically.

+class threading.Lock
+

The class implementing primitive lock objects. Once a thread has acquired a lock, subsequent attempts to acquire it block, until it is released; any thread may release it.

Note that Lock is actually a factory function which returns an instance of the most efficient version of the concrete Lock class that is supported by the platform.

+acquire(blocking=True, timeout=- 1)
+

Acquire a lock, blocking or non-blocking.

When invoked with the blocking argument set to True (the default), block until the lock is unlocked, then set it to locked and return True.

When invoked with the blocking argument set to False, do not block. If a call with blocking set to True would block, return False immediately; otherwise, set the lock to locked and return True.

When invoked with the floating-point timeout argument set to a positive value, block for at most the number of seconds specified by timeout and as long as the lock cannot be acquired. A timeout argument of -1 specifies an unbounded wait. It is forbidden to specify a timeout when blocking is False.

The return value is True if the lock is acquired successfully, False if not (for example if the timeout expired).

Changed in version 3.2: The timeout parameter is new.

Changed in version 3.2: Lock acquisition can now be interrupted by signals on POSIX if the underlying threading implementation supports it.

+
+release()
+

Release a lock. This can be called from any thread, not only the thread which has acquired the lock.

When the lock is locked, reset it to unlocked, and return. If any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed.

When invoked on an unlocked lock, a RuntimeError is raised.

There is no return value.

+
+locked()
+

Return True if the lock is acquired.

+
+

RLock Objects

A reentrant lock is a synchronization primitive that may be acquired multiple times by the same thread. Internally, it uses the concepts of “owning thread” and “recursion level” in addition to the locked/unlocked state used by primitive locks. In the locked state, some thread owns the lock; in the unlocked state, no thread owns it.

To lock the lock, a thread calls its acquire() method; this returns once the thread owns the lock. To unlock the lock, a thread calls its release() method. acquire()/release() call pairs may be nested; only the final release() (the release() of the outermost pair) resets the lock to unlocked and allows another thread blocked in acquire() to proceed.

Reentrant locks also support the context management protocol.

+class threading.RLock
+

This class implements reentrant lock objects. A reentrant lock must be released by the thread that acquired it. Once a thread has acquired a reentrant lock, the same thread may acquire it again without blocking; the thread must release it once for each time it has acquired it.

Note that RLock is actually a factory function which returns an instance of the most efficient version of the concrete RLock class that is supported by the platform.

+acquire(blocking=True, timeout=- 1)
+

Acquire a lock, blocking or non-blocking.

When invoked without arguments: if this thread already owns the lock, increment the recursion level by one, and return immediately. Otherwise, if another thread owns the lock, block until the lock is unlocked. Once the lock is unlocked (not owned by any thread), then grab ownership, set the recursion level to one, and return. If more than one thread is blocked waiting until the lock is unlocked, only one at a time will be able to grab ownership of the lock. There is no return value in this case.

When invoked with the blocking argument set to True, do the same thing as when called without arguments, and return True.

When invoked with the blocking argument set to False, do not block. If a call without an argument would block, return False immediately; otherwise, do the same thing as when called without arguments, and return True.

When invoked with the floating-point timeout argument set to a positive value, block for at most the number of seconds specified by timeout and as long as the lock cannot be acquired. Return True if the lock has been acquired, False if the timeout has elapsed.

Changed in version 3.2: The timeout parameter is new.

+
+release()
+

Release a lock, decrementing the recursion level. If after the decrement it is zero, reset the lock to unlocked (not owned by any thread), and if any other threads are blocked waiting for the lock to become unlocked, allow exactly one of them to proceed. If after the decrement the recursion level is still nonzero, the lock remains locked and owned by the calling thread.

Only call this method when the calling thread owns the lock. A RuntimeError is raised if this method is called when the lock is unlocked.

There is no return value.

+
+

Condition Objects

A condition variable is always associated with some kind of lock; this can be passed in or one will be created by default. Passing one in is useful when several condition variables must share the same lock. The lock is part of the condition object: you don’t have to track it separately.

A condition variable obeys the context management protocol: using the with statement acquires the associated lock for the duration of the enclosed block. The acquire() and release() methods also call the corresponding methods of the associated lock.

Other methods must be called with the associated lock held. The wait() method releases the lock, and then blocks until another thread awakens it by calling notify() or notify_all(). Once awakened, wait() re-acquires the lock and returns. It is also possible to specify a timeout.

The notify() method wakes up one of the threads waiting for the condition variable, if any are waiting. The notify_all() method wakes up all threads waiting for the condition variable.

Note: the notify() and notify_all() methods don’t release the lock; this means that the thread or threads awakened will not return from their wait() call immediately, but only when the thread that called notify() or notify_all() finally relinquishes ownership of the lock.

The typical programming style using condition variables uses the lock to synchronize access to some shared state; threads that are interested in a particular change of state call wait() repeatedly until they see the desired state, while threads that modify the state call notify() or notify_all() when they change the state in such a way that it could possibly be a desired state for one of the waiters. For example, the following code is a generic producer-consumer situation with unlimited buffer capacity:

# Consume one item
+with cv:
+    while not an_item_is_available():
+        cv.wait()
+    get_an_available_item()
+
+# Produce one item
+with cv:
+    make_an_item_available()
+    cv.notify()
+

The while loop checking for the application’s condition is necessary because wait() can return after an arbitrary long time, and the condition which prompted the notify() call may no longer hold true. This is inherent to multi-threaded programming. The wait_for() method can be used to automate the condition checking, and eases the computation of timeouts:

# Consume an item
+with cv:
+    cv.wait_for(an_item_is_available)
+    get_an_available_item()
+

To choose between notify() and notify_all(), consider whether one state change can be interesting for only one or several waiting threads. E.g. in a typical producer-consumer situation, adding one item to the buffer only needs to wake up one consumer thread.

+class threading.Condition(lock=None)
+

This class implements condition variable objects. A condition variable allows one or more threads to wait until they are notified by another thread.

If the lock argument is given and not None, it must be a Lock or RLock object, and it is used as the underlying lock. Otherwise, a new RLock object is created and used as the underlying lock.

Changed in version 3.3: changed from a factory function to a class.

+acquire(*args)
+

Acquire the underlying lock. This method calls the corresponding method on the underlying lock; the return value is whatever that method returns.

+
+release()
+

Release the underlying lock. This method calls the corresponding method on the underlying lock; there is no return value.

+
+wait(timeout=None)
+

Wait until notified or until a timeout occurs. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.

This method releases the underlying lock, and then blocks until it is awakened by a notify() or notify_all() call for the same condition variable in another thread, or until the optional timeout occurs. Once awakened or timed out, it re-acquires the lock and returns.

When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof).

When the underlying lock is an RLock, it is not released using its release() method, since this may not actually unlock the lock when it was acquired multiple times recursively. Instead, an internal interface of the RLock class is used, which really unlocks it even when it has been recursively acquired several times. Another internal interface is then used to restore the recursion level when the lock is reacquired.

The return value is True unless a given timeout expired, in which case it is False.

Changed in version 3.2: Previously, the method always returned None.

+
+wait_for(predicate, timeout=None)
+

Wait until a condition evaluates to true. predicate should be a callable which result will be interpreted as a boolean value. A timeout may be provided giving the maximum time to wait.

This utility method may call wait() repeatedly until the predicate is satisfied, or until a timeout occurs. The return value is the last return value of the predicate and will evaluate to False if the method timed out.

Ignoring the timeout feature, calling this method is roughly equivalent to writing:

while not predicate():
+    cv.wait()
+

Therefore, the same rules apply as with wait(): The lock must be held when called and is re-acquired on return. The predicate is evaluated with the lock held.

New in version 3.2.

+
+notify(n=1)
+

By default, wake up one thread waiting on this condition, if any. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.

This method wakes up at most n of the threads waiting for the condition variable; it is a no-op if no threads are waiting.

The current implementation wakes up exactly n threads, if at least n threads are waiting. However, it’s not safe to rely on this behavior. A future, optimized implementation may occasionally wake up more than n threads.

Note: an awakened thread does not actually return from its wait() call until it can reacquire the lock. Since notify() does not release the lock, its caller should.

+
+notify_all()
+

Wake up all threads waiting on this condition. This method acts like notify(), but wakes up all waiting threads instead of one. If the calling thread has not acquired the lock when this method is called, a RuntimeError is raised.

The method notifyAll is a deprecated alias for this method.

+
+

Semaphore Objects

This is one of the oldest synchronization primitives in the history of computer science, invented by the early Dutch computer scientist Edsger W. Dijkstra (he used the names P() and V() instead of acquire() and release()).

A semaphore manages an internal counter which is decremented by each acquire() call and incremented by each release() call. The counter can never go below zero; when acquire() finds that it is zero, it blocks, waiting until some other thread calls release().

Semaphores also support the context management protocol.

+class threading.Semaphore(value=1)
+

This class implements semaphore objects. A semaphore manages an atomic counter representing the number of release() calls minus the number of acquire() calls, plus an initial value. The acquire() method blocks if necessary until it can return without making the counter negative. If not given, value defaults to 1.

The optional argument gives the initial value for the internal counter; it defaults to 1. If the value given is less than 0, ValueError is raised.

Changed in version 3.3: changed from a factory function to a class.

+acquire(blocking=True, timeout=None)
+

Acquire a semaphore.

When invoked without arguments:

  • If the internal counter is larger than zero on entry, decrement it by one and return True immediately.
  • If the internal counter is zero on entry, block until awoken by a call to release(). Once awoken (and the counter is greater than 0), decrement the counter by 1 and return True. Exactly one thread will be awoken by each call to release(). The order in which threads are awoken should not be relied on.

When invoked with blocking set to False, do not block. If a call without an argument would block, return False immediately; otherwise, do the same thing as when called without arguments, and return True.

When invoked with a timeout other than None, it will block for at most timeout seconds. If acquire does not complete successfully in that interval, return False. Return True otherwise.

Changed in version 3.2: The timeout parameter is new.

+
+release(n=1)
+

Release a semaphore, incrementing the internal counter by n. When it was zero on entry and other threads are waiting for it to become larger than zero again, wake up n of those threads.

Changed in version 3.9: Added the n parameter to release multiple waiting threads at once.

+
+
+class threading.BoundedSemaphore(value=1)
+

Class implementing bounded semaphore objects. A bounded semaphore checks to make sure its current value doesn’t exceed its initial value. If it does, ValueError is raised. In most situations semaphores are used to guard resources with limited capacity. If the semaphore is released too many times it’s a sign of a bug. If not given, value defaults to 1.

Changed in version 3.3: changed from a factory function to a class.

+

+Semaphore Example

Semaphores are often used to guard resources with limited capacity, for example, a database server. In any situation where the size of the resource is fixed, you should use a bounded semaphore. Before spawning any worker threads, your main thread would initialize the semaphore:

maxconnections = 5
+# ...
+pool_sema = BoundedSemaphore(value=maxconnections)
+

Once spawned, worker threads call the semaphore’s acquire and release methods when they need to connect to the server:

with pool_sema:
+    conn = connectdb()
+    try:
+        # ... use connection ...
+    finally:
+        conn.close()
+

The use of a bounded semaphore reduces the chance that a programming error which causes the semaphore to be released more than it’s acquired will go undetected.

Event Objects

This is one of the simplest mechanisms for communication between threads: one thread signals an event and other threads wait for it.

An event object manages an internal flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is true.

+class threading.Event
+

Class implementing event objects. An event manages a flag that can be set to true with the set() method and reset to false with the clear() method. The wait() method blocks until the flag is true. The flag is initially false.

Changed in version 3.3: changed from a factory function to a class.

+is_set()
+

Return True if and only if the internal flag is true.

The method isSet is a deprecated alias for this method.

+
+set()
+

Set the internal flag to true. All threads waiting for it to become true are awakened. Threads that call wait() once the flag is true will not block at all.

+
+clear()
+

Reset the internal flag to false. Subsequently, threads calling wait() will block until set() is called to set the internal flag to true again.

+
+wait(timeout=None)
+

Block until the internal flag is true. If the internal flag is true on entry, return immediately. Otherwise, block until another thread calls set() to set the flag to true, or until the optional timeout occurs.

When the timeout argument is present and not None, it should be a floating point number specifying a timeout for the operation in seconds (or fractions thereof).

This method returns True if and only if the internal flag has been set to true, either before the wait call or after the wait starts, so it will always return True except if a timeout is given and the operation times out.

Changed in version 3.1: Previously, the method always returned None.

+
+

Timer Objects

This class represents an action that should be run only after a certain amount of time has passed — a timer. Timer is a subclass of Thread and as such also functions as an example of creating custom threads.

Timers are started, as with threads, by calling their Timer.start method. The timer can be stopped (before its action has begun) by calling the cancel() method. The interval the timer will wait before executing its action may not be exactly the same as the interval specified by the user.

For example:

def hello():
+    print("hello, world")
+
+t = Timer(30.0, hello)
+t.start()  # after 30 seconds, "hello, world" will be printed
+
+class threading.Timer(interval, function, args=None, kwargs=None)
+

Create a timer that will run function with arguments args and keyword arguments kwargs, after interval seconds have passed. If args is None (the default) then an empty list will be used. If kwargs is None (the default) then an empty dict will be used.

Changed in version 3.3: changed from a factory function to a class.

+cancel()
+

Stop the timer, and cancel the execution of the timer’s action. This will only work if the timer is still in its waiting stage.

+
+

Barrier Objects

New in version 3.2.

This class provides a simple synchronization primitive for use by a fixed number of threads that need to wait for each other. Each of the threads tries to pass the barrier by calling the wait() method and will block until all of the threads have made their wait() calls. At this point, the threads are released simultaneously.

The barrier can be reused any number of times for the same number of threads.

As an example, here is a simple way to synchronize a client and server thread:

b = Barrier(2, timeout=5)
+
+def server():
+    start_server()
+    b.wait()
+    while True:
+        connection = accept_connection()
+        process_server_connection(connection)
+
+def client():
+    b.wait()
+    while True:
+        connection = make_connection()
+        process_client_connection(connection)
+
+class threading.Barrier(parties, action=None, timeout=None)
+

Create a barrier object for parties number of threads. An action, when provided, is a callable to be called by one of the threads when they are released. timeout is the default timeout value if none is specified for the wait() method.

+wait(timeout=None)
+

Pass the barrier. When all the threads party to the barrier have called this function, they are all released simultaneously. If a timeout is provided, it is used in preference to any that was supplied to the class constructor.

The return value is an integer in the range 0 to parties – 1, different for each thread. This can be used to select a thread to do some special housekeeping, e.g.:

i = barrier.wait()
+if i == 0:
+    # Only one thread needs to print this
+    print("passed the barrier")
+

If an action was provided to the constructor, one of the threads will have called it prior to being released. Should this call raise an error, the barrier is put into the broken state.

If the call times out, the barrier is put into the broken state.

This method may raise a BrokenBarrierError exception if the barrier is broken or reset while a thread is waiting.

+
+reset()
+

Return the barrier to the default, empty state. Any threads waiting on it will receive the BrokenBarrierError exception.

Note that using this function may require some external synchronization if there are other threads whose state is unknown. If a barrier is broken it may be better to just leave it and create a new one.

+
+abort()
+

Put the barrier into a broken state. This causes any active or future calls to wait() to fail with the BrokenBarrierError. Use this for example if one of the threads needs to abort, to avoid deadlocking the application.

It may be preferable to simply create the barrier with a sensible timeout value to automatically guard against one of the threads going awry.

+
+parties
+

The number of threads required to pass the barrier.

+
+n_waiting
+

The number of threads currently waiting in the barrier.

+
+broken
+

A boolean that is True if the barrier is in the broken state.

+
+
+exception threading.BrokenBarrierError
+

This exception, a subclass of RuntimeError, is raised when the Barrier object is reset or broken.

+

Using locks, conditions, and semaphores in the with statement

All of the objects provided by this module that have acquire and release methods can be used as context managers for a with statement. The acquire method will be called when the block is entered, and release will be called when the block is exited. Hence, the following snippet:

with some_lock:
+    # do something...
+

is equivalent to:

some_lock.acquire()
+try:
+    # do something...
+finally:
+    some_lock.release()
+

Currently, Lock, RLock, Condition, Semaphore, and BoundedSemaphore objects may be used as with statement context managers.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/threading.html +

+
diff --git a/devdocs/python~3.12/library%2Ftime.html b/devdocs/python~3.12/library%2Ftime.html new file mode 100644 index 00000000..ca3cc0c3 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftime.html @@ -0,0 +1,278 @@ +

time — Time access and conversions

This module provides various time-related functions. For related functionality, see also the datetime and calendar modules.

Although this module is always available, not all functions are available on all platforms. Most of the functions defined in this module call platform C library functions with the same name. It may sometimes be helpful to consult the platform documentation, because the semantics of these functions varies among platforms.

An explanation of some terminology and conventions is in order.

Functions

+time.asctime([t])
+

Convert a tuple or struct_time representing a time as returned by gmtime() or localtime() to a string of the following form: 'Sun Jun 20 23:21:05 1993'. The day field is two characters long and is space padded if the day is a single digit, e.g.: 'Wed Jun  9 04:26:40 1993'.

If t is not provided, the current time as returned by localtime() is used. Locale information is not used by asctime().

Note

Unlike the C function of the same name, asctime() does not add a trailing newline.

+
+time.pthread_getcpuclockid(thread_id)
+

Return the clk_id of the thread-specific CPU-time clock for the specified thread_id.

Use threading.get_ident() or the ident attribute of threading.Thread objects to get a suitable value for thread_id.

Warning

Passing an invalid or expired thread_id may result in undefined behavior, such as segmentation fault.

Availability: Unix

See the man page for pthread_getcpuclockid(3) for further information.

New in version 3.7.

+
+time.clock_getres(clk_id)
+

Return the resolution (precision) of the specified clock clk_id. Refer to Clock ID Constants for a list of accepted values for clk_id.

Availability: Unix.

New in version 3.3.

+
+time.clock_gettime(clk_id) → float
+

Return the time of the specified clock clk_id. Refer to Clock ID Constants for a list of accepted values for clk_id.

Use clock_gettime_ns() to avoid the precision loss caused by the float type.

Availability: Unix.

New in version 3.3.

+
+time.clock_gettime_ns(clk_id) → int
+

Similar to clock_gettime() but return time as nanoseconds.

Availability: Unix.

New in version 3.7.

+
+time.clock_settime(clk_id, time: float)
+

Set the time of the specified clock clk_id. Currently, CLOCK_REALTIME is the only accepted value for clk_id.

Use clock_settime_ns() to avoid the precision loss caused by the float type.

Availability: Unix.

New in version 3.3.

+
+time.clock_settime_ns(clk_id, time: int)
+

Similar to clock_settime() but set time with nanoseconds.

Availability: Unix.

New in version 3.7.

+
+time.ctime([secs])
+

Convert a time expressed in seconds since the epoch to a string of a form: 'Sun Jun 20 23:21:05 1993' representing local time. The day field is two characters long and is space padded if the day is a single digit, e.g.: 'Wed Jun  9 04:26:40 1993'.

If secs is not provided or None, the current time as returned by time() is used. ctime(secs) is equivalent to asctime(localtime(secs)). Locale information is not used by ctime().

+
+time.get_clock_info(name)
+

Get information on the specified clock as a namespace object. Supported clock names and the corresponding functions to read their value are:

The result has the following attributes:

  • +adjustable: True if the clock can be changed automatically (e.g. by a NTP daemon) or manually by the system administrator, False otherwise
  • +implementation: The name of the underlying C function used to get the clock value. Refer to Clock ID Constants for possible values.
  • +monotonic: True if the clock cannot go backward, False otherwise
  • +resolution: The resolution of the clock in seconds (float)

New in version 3.3.

+
+time.gmtime([secs])
+

Convert a time expressed in seconds since the epoch to a struct_time in UTC in which the dst flag is always zero. If secs is not provided or None, the current time as returned by time() is used. Fractions of a second are ignored. See above for a description of the struct_time object. See calendar.timegm() for the inverse of this function.

+
+time.localtime([secs])
+

Like gmtime() but converts to local time. If secs is not provided or None, the current time as returned by time() is used. The dst flag is set to 1 when DST applies to the given time.

localtime() may raise OverflowError, if the timestamp is outside the range of values supported by the platform C localtime() or gmtime() functions, and OSError on localtime() or gmtime() failure. It’s common for this to be restricted to years between 1970 and 2038.

+
+time.mktime(t)
+

This is the inverse function of localtime(). Its argument is the struct_time or full 9-tuple (since the dst flag is needed; use -1 as the dst flag if it is unknown) which expresses the time in local time, not UTC. It returns a floating point number, for compatibility with time(). If the input value cannot be represented as a valid time, either OverflowError or ValueError will be raised (which depends on whether the invalid value is caught by Python or the underlying C libraries). The earliest date for which it can generate a time is platform-dependent.

+
+time.monotonic() → float
+

Return the value (in fractional seconds) of a monotonic clock, i.e. a clock that cannot go backwards. The clock is not affected by system clock updates. The reference point of the returned value is undefined, so that only the difference between the results of two calls is valid.

Use monotonic_ns() to avoid the precision loss caused by the float type.

New in version 3.3.

Changed in version 3.5: The function is now always available and always system-wide.

Changed in version 3.10: On macOS, the function is now system-wide.

+
+time.monotonic_ns() → int
+

Similar to monotonic(), but return time as nanoseconds.

New in version 3.7.

+
+time.perf_counter() → float
+

Return the value (in fractional seconds) of a performance counter, i.e. a clock with the highest available resolution to measure a short duration. It does include time elapsed during sleep and is system-wide. The reference point of the returned value is undefined, so that only the difference between the results of two calls is valid.

Use perf_counter_ns() to avoid the precision loss caused by the float type.

New in version 3.3.

Changed in version 3.10: On Windows, the function is now system-wide.

+
+time.perf_counter_ns() → int
+

Similar to perf_counter(), but return time as nanoseconds.

New in version 3.7.

+
+time.process_time() → float
+

Return the value (in fractional seconds) of the sum of the system and user CPU time of the current process. It does not include time elapsed during sleep. It is process-wide by definition. The reference point of the returned value is undefined, so that only the difference between the results of two calls is valid.

Use process_time_ns() to avoid the precision loss caused by the float type.

New in version 3.3.

+
+time.process_time_ns() → int
+

Similar to process_time() but return time as nanoseconds.

New in version 3.7.

+
+time.sleep(secs)
+

Suspend execution of the calling thread for the given number of seconds. The argument may be a floating point number to indicate a more precise sleep time.

If the sleep is interrupted by a signal and no exception is raised by the signal handler, the sleep is restarted with a recomputed timeout.

The suspension time may be longer than requested by an arbitrary amount, because of the scheduling of other activity in the system.

On Windows, if secs is zero, the thread relinquishes the remainder of its time slice to any other thread that is ready to run. If there are no other threads ready to run, the function returns immediately, and the thread continues execution. On Windows 8.1 and newer the implementation uses a high-resolution timer which provides resolution of 100 nanoseconds. If secs is zero, Sleep(0) is used.

Unix implementation:

  • Use clock_nanosleep() if available (resolution: 1 nanosecond);
  • Or use nanosleep() if available (resolution: 1 nanosecond);
  • Or use select() (resolution: 1 microsecond).

Changed in version 3.11: On Unix, the clock_nanosleep() and nanosleep() functions are now used if available. On Windows, a waitable timer is now used.

Changed in version 3.5: The function now sleeps at least secs even if the sleep is interrupted by a signal, except if the signal handler raises an exception (see PEP 475 for the rationale).

+
+time.strftime(format[, t])
+

Convert a tuple or struct_time representing a time as returned by gmtime() or localtime() to a string as specified by the format argument. If t is not provided, the current time as returned by localtime() is used. format must be a string. ValueError is raised if any field in t is outside of the allowed range.

0 is a legal argument for any position in the time tuple; if it is normally illegal the value is forced to a correct one.

The following directives can be embedded in the format string. They are shown without the optional field width and precision specification, and are replaced by the indicated characters in the strftime() result:

+ + + + + + + + + + + + + + + + + + + + + + + +

Directive

Meaning

Notes

%a

Locale’s abbreviated weekday name.

%A

Locale’s full weekday name.

%b

Locale’s abbreviated month name.

%B

Locale’s full month name.

%c

Locale’s appropriate date and time representation.

%d

Day of the month as a decimal number [01,31].

%H

Hour (24-hour clock) as a decimal number [00,23].

%I

Hour (12-hour clock) as a decimal number [01,12].

%j

Day of the year as a decimal number [001,366].

%m

Month as a decimal number [01,12].

%M

Minute as a decimal number [00,59].

%p

Locale’s equivalent of either AM or PM.

(1)

%S

Second as a decimal number [00,61].

(2)

%U

Week number of the year (Sunday as the first day of the week) as a decimal number [00,53]. All days in a new year preceding the first Sunday are considered to be in week 0.

(3)

%w

Weekday as a decimal number [0(Sunday),6].

%W

Week number of the year (Monday as the first day of the week) as a decimal number [00,53]. All days in a new year preceding the first Monday are considered to be in week 0.

(3)

%x

Locale’s appropriate date representation.

%X

Locale’s appropriate time representation.

%y

Year without century as a decimal number [00,99].

%Y

Year with century as a decimal number.

%z

Time zone offset indicating a positive or negative time difference from UTC/GMT of the form +HHMM or -HHMM, where H represents decimal hour digits and M represents decimal minute digits [-23:59, +23:59]. 1

%Z

Time zone name (no characters if no time zone exists). Deprecated. 1

%%

A literal '%' character.

Notes:

  1. When used with the strptime() function, the %p directive only affects the output hour field if the %I directive is used to parse the hour.
  1. The range really is 0 to 61; value 60 is valid in timestamps representing leap seconds and value 61 is supported for historical reasons.
  2. When used with the strptime() function, %U and %W are only used in calculations when the day of the week and the year are specified.

Here is an example, a format for dates compatible with that specified in the RFC 2822 Internet email standard. 1

>>> from time import gmtime, strftime
+>>> strftime("%a, %d %b %Y %H:%M:%S +0000", gmtime())
+'Thu, 28 Jun 2001 14:17:15 +0000'
+

Additional directives may be supported on certain platforms, but only the ones listed here have a meaning standardized by ANSI C. To see the full set of format codes supported on your platform, consult the strftime(3) documentation.

On some platforms, an optional field width and precision specification can immediately follow the initial '%' of a directive in the following order; this is also not portable. The field width is normally 2 except for %j where it is 3.

+
+time.strptime(string[, format])
+

Parse a string representing a time according to a format. The return value is a struct_time as returned by gmtime() or localtime().

The format parameter uses the same directives as those used by strftime(); it defaults to "%a %b %d %H:%M:%S %Y" which matches the formatting returned by ctime(). If string cannot be parsed according to format, or if it has excess data after parsing, ValueError is raised. The default values used to fill in any missing data when more accurate values cannot be inferred are (1900, 1, 1, 0, 0, 0, 0, 1, -1). Both string and format must be strings.

For example:

>>> import time
+>>> time.strptime("30 Nov 00", "%d %b %y")   
+time.struct_time(tm_year=2000, tm_mon=11, tm_mday=30, tm_hour=0, tm_min=0,
+                 tm_sec=0, tm_wday=3, tm_yday=335, tm_isdst=-1)
+

Support for the %Z directive is based on the values contained in tzname and whether daylight is true. Because of this, it is platform-specific except for recognizing UTC and GMT which are always known (and are considered to be non-daylight savings timezones).

Only the directives specified in the documentation are supported. Because strftime() is implemented per platform it can sometimes offer more directives than those listed. But strptime() is independent of any platform and thus does not necessarily support all directives available that are not documented as supported.

+
+class time.struct_time
+

The type of the time value sequence returned by gmtime(), localtime(), and strptime(). It is an object with a named tuple interface: values can be accessed by index and by attribute name. The following values are present:

+ + + + + + + + + + + +

Index

Attribute

Values

0

 +
+tm_year
+

(for example, 1993)

1

 +
+tm_mon
+

range [1, 12]

2

 +
+tm_day
+

range [1, 31]

3

 +
+tm_hour
+

range [0, 23]

4

 +
+tm_min
+

range [0, 59]

5

 +
+tm_sec
+

range [0, 61]; see Note (2) in strftime()

6

 +
+tm_wday
+

range [0, 6]; Monday is 0

7

 +
+tm_yday
+

range [1, 366]

8

 +
+tm_isdst
+

0, 1 or -1; see below

N/A

 +
+tm_zone
+

abbreviation of timezone name

N/A

 +
+tm_gmtoff
+

offset east of UTC in seconds

Note that unlike the C structure, the month value is a range of [1, 12], not [0, 11].

In calls to mktime(), tm_isdst may be set to 1 when daylight savings time is in effect, and 0 when it is not. A value of -1 indicates that this is not known, and will usually result in the correct state being filled in.

When a tuple with an incorrect length is passed to a function expecting a struct_time, or having elements of the wrong type, a TypeError is raised.

+
+time.time() → float
+

Return the time in seconds since the epoch as a floating point number. The handling of leap seconds is platform dependent. On Windows and most Unix systems, the leap seconds are not counted towards the time in seconds since the epoch. This is commonly referred to as Unix time.

Note that even though the time is always returned as a floating point number, not all systems provide time with a better precision than 1 second. While this function normally returns non-decreasing values, it can return a lower value than a previous call if the system clock has been set back between the two calls.

The number returned by time() may be converted into a more common time format (i.e. year, month, day, hour, etc…) in UTC by passing it to gmtime() function or in local time by passing it to the localtime() function. In both cases a struct_time object is returned, from which the components of the calendar date may be accessed as attributes.

Use time_ns() to avoid the precision loss caused by the float type.

+
+time.time_ns() → int
+

Similar to time() but returns time as an integer number of nanoseconds since the epoch.

New in version 3.7.

+
+time.thread_time() → float
+

Return the value (in fractional seconds) of the sum of the system and user CPU time of the current thread. It does not include time elapsed during sleep. It is thread-specific by definition. The reference point of the returned value is undefined, so that only the difference between the results of two calls in the same thread is valid.

Use thread_time_ns() to avoid the precision loss caused by the float type.

Availability: Linux, Unix, Windows.

Unix systems supporting CLOCK_THREAD_CPUTIME_ID.

New in version 3.7.

+
+time.thread_time_ns() → int
+

Similar to thread_time() but return time as nanoseconds.

New in version 3.7.

+
+time.tzset()
+

Reset the time conversion rules used by the library routines. The environment variable TZ specifies how this is done. It will also set the variables tzname (from the TZ environment variable), timezone (non-DST seconds West of UTC), altzone (DST seconds west of UTC) and daylight (to 0 if this timezone does not have any daylight saving time rules, or to nonzero if there is a time, past, present or future when daylight saving time applies).

Availability: Unix.

Note

Although in many cases, changing the TZ environment variable may affect the output of functions like localtime() without calling tzset(), this behavior should not be relied on.

The TZ environment variable should contain no whitespace.

The standard format of the TZ environment variable is (whitespace added for clarity):

std offset [dst [offset [,start[/time], end[/time]]]]
+

Where the components are:

+std and dst
+
+

Three or more alphanumerics giving the timezone abbreviations. These will be propagated into time.tzname

+offset
+
+

The offset has the form: ± hh[:mm[:ss]]. This indicates the value added the local time to arrive at UTC. If preceded by a ‘-’, the timezone is east of the Prime Meridian; otherwise, it is west. If no offset follows dst, summer time is assumed to be one hour ahead of standard time.

+start[/time], end[/time]
+
+

Indicates when to change to and back from DST. The format of the start and end dates are one of the following:

+Jn
+
+

The Julian day n (1 <= n <= 365). Leap days are not counted, so in all years February 28 is day 59 and March 1 is day 60.

+n
+
+

The zero-based Julian day (0 <= n <= 365). Leap days are counted, and it is possible to refer to February 29.

+Mm.n.d
+
+

The d’th day (0 <= d <= 6) of week n of month m of the year (1 <= n <= 5, 1 <= m <= 12, where week 5 means “the last d day in month m” which may occur in either the fourth or the fifth week). Week 1 is the first week in which the d’th day occurs. Day zero is a Sunday.

time has the same format as offset except that no leading sign (‘-’ or ‘+’) is allowed. The default, if time is not given, is 02:00:00.

>>> os.environ['TZ'] = 'EST+05EDT,M4.1.0,M10.5.0'
+>>> time.tzset()
+>>> time.strftime('%X %x %Z')
+'02:07:36 05/08/03 EDT'
+>>> os.environ['TZ'] = 'AEST-10AEDT-11,M10.5.0,M3.5.0'
+>>> time.tzset()
+>>> time.strftime('%X %x %Z')
+'16:08:12 05/08/03 AEST'
+

On many Unix systems (including *BSD, Linux, Solaris, and Darwin), it is more convenient to use the system’s zoneinfo (tzfile(5)) database to specify the timezone rules. To do this, set the TZ environment variable to the path of the required timezone datafile, relative to the root of the systems ‘zoneinfo’ timezone database, usually located at /usr/share/zoneinfo. For example, 'US/Eastern', 'Australia/Melbourne', 'Egypt' or 'Europe/Amsterdam'.

>>> os.environ['TZ'] = 'US/Eastern'
+>>> time.tzset()
+>>> time.tzname
+('EST', 'EDT')
+>>> os.environ['TZ'] = 'Egypt'
+>>> time.tzset()
+>>> time.tzname
+('EET', 'EEST')
+
+

Clock ID Constants

These constants are used as parameters for clock_getres() and clock_gettime().

+time.CLOCK_BOOTTIME
+

Identical to CLOCK_MONOTONIC, except it also includes any time that the system is suspended.

This allows applications to get a suspend-aware monotonic clock without having to deal with the complications of CLOCK_REALTIME, which may have discontinuities if the time is changed using settimeofday() or similar.

Availability: Linux >= 2.6.39.

New in version 3.7.

+
+time.CLOCK_HIGHRES
+

The Solaris OS has a CLOCK_HIGHRES timer that attempts to use an optimal hardware source, and may give close to nanosecond resolution. CLOCK_HIGHRES is the nonadjustable, high-resolution clock.

Availability: Solaris.

New in version 3.3.

+
+time.CLOCK_MONOTONIC
+

Clock that cannot be set and represents monotonic time since some unspecified starting point.

Availability: Unix.

New in version 3.3.

+
+time.CLOCK_MONOTONIC_RAW
+

Similar to CLOCK_MONOTONIC, but provides access to a raw hardware-based time that is not subject to NTP adjustments.

Availability: Linux >= 2.6.28, macOS >= 10.12.

New in version 3.3.

+
+time.CLOCK_PROCESS_CPUTIME_ID
+

High-resolution per-process timer from the CPU.

Availability: Unix.

New in version 3.3.

+
+time.CLOCK_PROF
+

High-resolution per-process timer from the CPU.

Availability: FreeBSD, NetBSD >= 7, OpenBSD.

New in version 3.7.

+
+time.CLOCK_TAI
+

International Atomic Time

The system must have a current leap second table in order for this to give the correct answer. PTP or NTP software can maintain a leap second table.

Availability: Linux.

New in version 3.9.

+
+time.CLOCK_THREAD_CPUTIME_ID
+

Thread-specific CPU-time clock.

Availability: Unix.

New in version 3.3.

+
+time.CLOCK_UPTIME
+

Time whose absolute value is the time the system has been running and not suspended, providing accurate uptime measurement, both absolute and interval.

Availability: FreeBSD, OpenBSD >= 5.5.

New in version 3.7.

+
+time.CLOCK_UPTIME_RAW
+

Clock that increments monotonically, tracking the time since an arbitrary point, unaffected by frequency or time adjustments and not incremented while the system is asleep.

Availability: macOS >= 10.12.

New in version 3.8.

+

The following constant is the only parameter that can be sent to clock_settime().

+time.CLOCK_REALTIME
+

System-wide real-time clock. Setting this clock requires appropriate privileges.

Availability: Unix.

New in version 3.3.

+

Timezone Constants

+time.altzone
+

The offset of the local DST timezone, in seconds west of UTC, if one is defined. This is negative if the local DST timezone is east of UTC (as in Western Europe, including the UK). Only use this if daylight is nonzero. See note below.

+
+time.daylight
+

Nonzero if a DST timezone is defined. See note below.

+
+time.timezone
+

The offset of the local (non-DST) timezone, in seconds west of UTC (negative in most of Western Europe, positive in the US, zero in the UK). See note below.

+
+time.tzname
+

A tuple of two strings: the first is the name of the local non-DST timezone, the second is the name of the local DST timezone. If no DST timezone is defined, the second string should not be used. See note below.

+

Note

For the above Timezone constants (altzone, daylight, timezone, and tzname), the value is determined by the timezone rules in effect at module load time or the last time tzset() is called and may be incorrect for times in the past. It is recommended to use the tm_gmtoff and tm_zone results from localtime() to obtain timezone information.

See also

+Module datetime +
+
+

More object-oriented interface to dates and times.

+Module locale +
+
+

Internationalization services. The locale setting affects the interpretation of many format specifiers in strftime() and strptime().

+Module calendar +
+
+

General calendar-related functions. timegm() is the inverse of gmtime() from this module.

Footnotes

+1(1,2,3)
+

The use of %Z is now deprecated, but the %z escape that expands to the preferred hour/minute offset is not supported by all ANSI C libraries. Also, a strict reading of the original 1982 RFC 822 standard calls for a two-digit year (%y rather than %Y), but practice moved to 4-digit years long before the year 2000. After that, RFC 822 became obsolete and the 4-digit year has been first recommended by RFC 1123 and then mandated by RFC 2822.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/time.html +

+
diff --git a/devdocs/python~3.12/library%2Ftimeit.html b/devdocs/python~3.12/library%2Ftimeit.html new file mode 100644 index 00000000..14c48434 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftimeit.html @@ -0,0 +1,139 @@ +

timeit — Measure execution time of small code snippets

Source code: Lib/timeit.py

This module provides a simple way to time small bits of Python code. It has both a Command-Line Interface as well as a callable one. It avoids a number of common traps for measuring execution times. See also Tim Peters’ introduction to the “Algorithms” chapter in the second edition of Python Cookbook, published by O’Reilly.

Basic Examples

The following example shows how the Command-Line Interface can be used to compare three different expressions:

$ python -m timeit "'-'.join(str(n) for n in range(100))"
+10000 loops, best of 5: 30.2 usec per loop
+$ python -m timeit "'-'.join([str(n) for n in range(100)])"
+10000 loops, best of 5: 27.5 usec per loop
+$ python -m timeit "'-'.join(map(str, range(100)))"
+10000 loops, best of 5: 23.2 usec per loop
+

This can be achieved from the Python Interface with:

>>> import timeit
+>>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)
+0.3018611848820001
+>>> timeit.timeit('"-".join([str(n) for n in range(100)])', number=10000)
+0.2727368790656328
+>>> timeit.timeit('"-".join(map(str, range(100)))', number=10000)
+0.23702679807320237
+

A callable can also be passed from the Python Interface:

>>> timeit.timeit(lambda: "-".join(map(str, range(100))), number=10000)
+0.19665591977536678
+

Note however that timeit() will automatically determine the number of repetitions only when the command-line interface is used. In the Examples section you can find more advanced examples.

Python Interface

The module defines three convenience functions and a public class:

+timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000, globals=None)
+

Create a Timer instance with the given statement, setup code and timer function and run its timeit() method with number executions. The optional globals argument specifies a namespace in which to execute the code.

Changed in version 3.5: The optional globals parameter was added.

+
+timeit.repeat(stmt='pass', setup='pass', timer=<default timer>, repeat=5, number=1000000, globals=None)
+

Create a Timer instance with the given statement, setup code and timer function and run its repeat() method with the given repeat count and number executions. The optional globals argument specifies a namespace in which to execute the code.

Changed in version 3.5: The optional globals parameter was added.

Changed in version 3.7: Default value of repeat changed from 3 to 5.

+
+timeit.default_timer()
+

The default timer, which is always time.perf_counter(), returns float seconds. An alternative, time.perf_counter_ns, returns integer nanoseconds.

Changed in version 3.3: time.perf_counter() is now the default timer.

+
+class timeit.Timer(stmt='pass', setup='pass', timer=<timer function>, globals=None)
+

Class for timing execution speed of small code snippets.

The constructor takes a statement to be timed, an additional statement used for setup, and a timer function. Both statements default to 'pass'; the timer function is platform-dependent (see the module doc string). stmt and setup may also contain multiple statements separated by ; or newlines, as long as they don’t contain multi-line string literals. The statement will by default be executed within timeit’s namespace; this behavior can be controlled by passing a namespace to globals.

To measure the execution time of the first statement, use the timeit() method. The repeat() and autorange() methods are convenience methods to call timeit() multiple times.

The execution time of setup is excluded from the overall timed execution run.

The stmt and setup parameters can also take objects that are callable without arguments. This will embed calls to them in a timer function that will then be executed by timeit(). Note that the timing overhead is a little larger in this case because of the extra function calls.

Changed in version 3.5: The optional globals parameter was added.

+timeit(number=1000000)
+

Time number executions of the main statement. This executes the setup statement once, and then returns the time it takes to execute the main statement a number of times. The default timer returns seconds as a float. The argument is the number of times through the loop, defaulting to one million. The main statement, the setup statement and the timer function to be used are passed to the constructor.

Note

By default, timeit() temporarily turns off garbage collection during the timing. The advantage of this approach is that it makes independent timings more comparable. The disadvantage is that GC may be an important component of the performance of the function being measured. If so, GC can be re-enabled as the first statement in the setup string. For example:

timeit.Timer('for i in range(10): oct(i)', 'gc.enable()').timeit()
+
+
+autorange(callback=None)
+

Automatically determine how many times to call timeit().

This is a convenience function that calls timeit() repeatedly so that the total time >= 0.2 second, returning the eventual (number of loops, time taken for that number of loops). It calls timeit() with increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the time taken is at least 0.2 seconds.

If callback is given and is not None, it will be called after each trial with two arguments: callback(number, time_taken).

New in version 3.6.

+
+repeat(repeat=5, number=1000000)
+

Call timeit() a few times.

This is a convenience function that calls the timeit() repeatedly, returning a list of results. The first argument specifies how many times to call timeit(). The second argument specifies the number argument for timeit().

Note

It’s tempting to calculate mean and standard deviation from the result vector and report these. However, this is not very useful. In a typical case, the lowest value gives a lower bound for how fast your machine can run the given code snippet; higher values in the result vector are typically not caused by variability in Python’s speed, but by other processes interfering with your timing accuracy. So the min() of the result is probably the only number you should be interested in. After that, you should look at the entire vector and apply common sense rather than statistics.

Changed in version 3.7: Default value of repeat changed from 3 to 5.

+
+print_exc(file=None)
+

Helper to print a traceback from the timed code.

Typical use:

t = Timer(...)       # outside the try/except
+try:
+    t.timeit(...)    # or t.repeat(...)
+except Exception:
+    t.print_exc()
+

The advantage over the standard traceback is that source lines in the compiled template will be displayed. The optional file argument directs where the traceback is sent; it defaults to sys.stderr.

+
+

Command-Line Interface

When called as a program from the command line, the following form is used:

python -m timeit [-n N] [-r N] [-u U] [-s S] [-p] [-v] [-h] [statement ...]
+

Where the following options are understood:

+-n N, --number=N
+

how many times to execute ‘statement’

+
+-r N, --repeat=N
+

how many times to repeat the timer (default 5)

+
+-s S, --setup=S
+

statement to be executed once initially (default pass)

+
+-p, --process
+

measure process time, not wallclock time, using time.process_time() instead of time.perf_counter(), which is the default

New in version 3.3.

+
+-u, --unit=U
+

specify a time unit for timer output; can select nsec, usec, msec, or sec

New in version 3.5.

+
+-v, --verbose
+

print raw timing results; repeat for more digits precision

+
+-h, --help
+

print a short usage message and exit

+

A multi-line statement may be given by specifying each line as a separate statement argument; indented lines are possible by enclosing an argument in quotes and using leading spaces. Multiple -s options are treated similarly.

If -n is not given, a suitable number of loops is calculated by trying increasing numbers from the sequence 1, 2, 5, 10, 20, 50, … until the total time is at least 0.2 seconds.

default_timer() measurements can be affected by other programs running on the same machine, so the best thing to do when accurate timing is necessary is to repeat the timing a few times and use the best time. The -r option is good for this; the default of 5 repetitions is probably enough in most cases. You can use time.process_time() to measure CPU time.

Note

There is a certain baseline overhead associated with executing a pass statement. The code here doesn’t try to hide it, but you should be aware of it. The baseline overhead can be measured by invoking the program without arguments, and it might differ between Python versions.

Examples

It is possible to provide a setup statement that is executed only once at the beginning:

$ python -m timeit -s "text = 'sample string'; char = 'g'" "char in text"
+5000000 loops, best of 5: 0.0877 usec per loop
+$ python -m timeit -s "text = 'sample string'; char = 'g'" "text.find(char)"
+1000000 loops, best of 5: 0.342 usec per loop
+

In the output, there are three fields. The loop count, which tells you how many times the statement body was run per timing loop repetition. The repetition count (‘best of 5’) which tells you how many times the timing loop was repeated, and finally the time the statement body took on average within the best repetition of the timing loop. That is, the time the fastest repetition took divided by the loop count.

>>> import timeit
+>>> timeit.timeit('char in text', setup='text = "sample string"; char = "g"')
+0.41440500499993504
+>>> timeit.timeit('text.find(char)', setup='text = "sample string"; char = "g"')
+1.7246671520006203
+

The same can be done using the Timer class and its methods:

>>> import timeit
+>>> t = timeit.Timer('char in text', setup='text = "sample string"; char = "g"')
+>>> t.timeit()
+0.3955516149999312
+>>> t.repeat()
+[0.40183617287970225, 0.37027556854118704, 0.38344867356679524, 0.3712595970846668, 0.37866875250654886]
+

The following examples show how to time expressions that contain multiple lines. Here we compare the cost of using hasattr() vs. try/except to test for missing and present object attributes:

$ python -m timeit "try:" "  str.__bool__" "except AttributeError:" "  pass"
+20000 loops, best of 5: 15.7 usec per loop
+$ python -m timeit "if hasattr(str, '__bool__'): pass"
+50000 loops, best of 5: 4.26 usec per loop
+
+$ python -m timeit "try:" "  int.__bool__" "except AttributeError:" "  pass"
+200000 loops, best of 5: 1.43 usec per loop
+$ python -m timeit "if hasattr(int, '__bool__'): pass"
+100000 loops, best of 5: 2.23 usec per loop
+
>>> import timeit
+>>> # attribute is missing
+>>> s = """\
+... try:
+...     str.__bool__
+... except AttributeError:
+...     pass
+... """
+>>> timeit.timeit(stmt=s, number=100000)
+0.9138244460009446
+>>> s = "if hasattr(str, '__bool__'): pass"
+>>> timeit.timeit(stmt=s, number=100000)
+0.5829014980008651
+>>>
+>>> # attribute is present
+>>> s = """\
+... try:
+...     int.__bool__
+... except AttributeError:
+...     pass
+... """
+>>> timeit.timeit(stmt=s, number=100000)
+0.04215312199994514
+>>> s = "if hasattr(int, '__bool__'): pass"
+>>> timeit.timeit(stmt=s, number=100000)
+0.08588060699912603
+

To give the timeit module access to functions you define, you can pass a setup parameter which contains an import statement:

def test():
+    """Stupid test function"""
+    L = [i for i in range(100)]
+
+if __name__ == '__main__':
+    import timeit
+    print(timeit.timeit("test()", setup="from __main__ import test"))
+

Another option is to pass globals() to the globals parameter, which will cause the code to be executed within your current global namespace. This can be more convenient than individually specifying imports:

def f(x):
+    return x**2
+def g(x):
+    return x**4
+def h(x):
+    return x**8
+
+import timeit
+print(timeit.timeit('[func(42) for func in (f,g,h)]', globals=globals()))
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/timeit.html +

+
diff --git a/devdocs/python~3.12/library%2Ftk.html b/devdocs/python~3.12/library%2Ftk.html new file mode 100644 index 00000000..d13b61ab --- /dev/null +++ b/devdocs/python~3.12/library%2Ftk.html @@ -0,0 +1,28 @@ +

Graphical User Interfaces with Tk

Tk/Tcl has long been an integral part of Python. It provides a robust and platform independent windowing toolkit, that is available to Python programmers using the tkinter package, and its extension, the tkinter.tix and the tkinter.ttk modules.

The tkinter package is a thin object-oriented layer on top of Tcl/Tk. To use tkinter, you don’t need to write Tcl code, but you will need to consult the Tk documentation, and occasionally the Tcl documentation. tkinter is a set of wrappers that implement the Tk widgets as Python classes.

tkinter’s chief virtues are that it is fast, and that it usually comes bundled with Python. Although its standard documentation is weak, good material is available, which includes: references, tutorials, a book and others. tkinter is also famous for having an outdated look and feel, which has been vastly improved in Tk 8.5. Nevertheless, there are many other GUI libraries that you could be interested in. The Python wiki lists several alternative GUI frameworks and tools.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tk.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.colorchooser.html b/devdocs/python~3.12/library%2Ftkinter.colorchooser.html new file mode 100644 index 00000000..472231f3 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.colorchooser.html @@ -0,0 +1,15 @@ +

tkinter.colorchooser — Color choosing dialog

Source code: Lib/tkinter/colorchooser.py

The tkinter.colorchooser module provides the Chooser class as an interface to the native color picker dialog. Chooser implements a modal color choosing dialog window. The Chooser class inherits from the Dialog class.

+class tkinter.colorchooser.Chooser(master=None, **options)
+
+tkinter.colorchooser.askcolor(color=None, **options)
+

Create a color choosing dialog. A call to this method will show the window, wait for the user to make a selection, and return the selected color (or None) to the caller.

+

See also

+Module tkinter.commondialog +
+
+

Tkinter standard dialog module

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.colorchooser.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.dnd.html b/devdocs/python~3.12/library%2Ftkinter.dnd.html new file mode 100644 index 00000000..db26161a --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.dnd.html @@ -0,0 +1,26 @@ +

tkinter.dnd — Drag and drop support

Source code: Lib/tkinter/dnd.py

Note

This is experimental and due to be deprecated when it is replaced with the Tk DND.

The tkinter.dnd module provides drag-and-drop support for objects within a single application, within the same window or between windows. To enable an object to be dragged, you must create an event binding for it that starts the drag-and-drop process. Typically, you bind a ButtonPress event to a callback function that you write (see Bindings and Events). The function should call dnd_start(), where ‘source’ is the object to be dragged, and ‘event’ is the event that invoked the call (the argument to your callback function).

Selection of a target object occurs as follows:

  1. Top-down search of area under mouse for target widget
  1. Call to <old_target>.dnd_leave(source, event) +
  2. Call to <new_target>.dnd_enter(source, event) +
  3. Call to <target>.dnd_commit(source, event) to notify of drop
  4. Call to <source>.dnd_end(target, event) to signal end of drag-and-drop
+class tkinter.dnd.DndHandler(source, event)
+

The DndHandler class handles drag-and-drop events tracking Motion and ButtonRelease events on the root of the event widget.

+cancel(event=None)
+

Cancel the drag-and-drop process.

+
+finish(event, commit=0)
+

Execute end of drag-and-drop functions.

+
+on_motion(event)
+

Inspect area below mouse for target objects while drag is performed.

+
+on_release(event)
+

Signal end of drag when the release pattern is triggered.

+
+
+tkinter.dnd.dnd_start(source, event)
+

Factory function for drag-and-drop process.

+

See also

Bindings and Events

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.dnd.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.font.html b/devdocs/python~3.12/library%2Ftkinter.font.html new file mode 100644 index 00000000..6b0f35ac --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.font.html @@ -0,0 +1,50 @@ +

tkinter.font — Tkinter font wrapper

Source code: Lib/tkinter/font.py

The tkinter.font module provides the Font class for creating and using named fonts.

The different font weights and slants are:

+tkinter.font.NORMAL
+tkinter.font.BOLD
+tkinter.font.ITALIC
+tkinter.font.ROMAN
+
+class tkinter.font.Font(root=None, font=None, name=None, exists=False, **options)
+

The Font class represents a named font. Font instances are given unique names and can be specified by their family, size, and style configuration. Named fonts are Tk’s method of creating and identifying fonts as a single object, rather than specifying a font by its attributes with each occurrence.

arguments:

additional keyword options (ignored if font is specified):

+actual(option=None, displayof=None)
+

Return the attributes of the font.

+
+cget(option)
+

Retrieve an attribute of the font.

+
+config(**options)
+

Modify attributes of the font.

+
+copy()
+

Return new instance of the current font.

+
+measure(text, displayof=None)
+

Return amount of space the text would occupy on the specified display when formatted in the current font. If no display is specified then the main application window is assumed.

+
+metrics(*options, **kw)
+

Return font-specific data. Options include:

+ascent - distance between baseline and highest point that a
+
+

character of the font can occupy

+descent - distance between baseline and lowest point that a
+
+

character of the font can occupy

+linespace - minimum vertical separation necessary between any two
+
+

characters of the font that ensures no vertical overlap between lines.

fixed - 1 if font is fixed-width else 0

+
+
+tkinter.font.families(root=None, displayof=None)
+

Return the different font families.

+
+tkinter.font.names(root=None)
+

Return the names of defined fonts.

+
+tkinter.font.nametofont(name, root=None)
+

Return a Font representation of a tk named font.

Changed in version 3.10: The root parameter was added.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.font.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.html b/devdocs/python~3.12/library%2Ftkinter.html new file mode 100644 index 00000000..21f78c6f --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.html @@ -0,0 +1,312 @@ +

tkinter — Python interface to Tcl/Tk

Source code: Lib/tkinter/__init__.py

The tkinter package (“Tk interface”) is the standard Python interface to the Tcl/Tk GUI toolkit. Both Tk and tkinter are available on most Unix platforms, including macOS, as well as on Windows systems.

Running python -m tkinter from the command line should open a window demonstrating a simple Tk interface, letting you know that tkinter is properly installed on your system, and also showing what version of Tcl/Tk is installed, so you can read the Tcl/Tk documentation specific to that version.

Tkinter supports a range of Tcl/Tk versions, built either with or without thread support. The official Python binary release bundles Tcl/Tk 8.6 threaded. See the source code for the _tkinter module for more information about supported versions.

Tkinter is not a thin wrapper, but adds a fair amount of its own logic to make the experience more pythonic. This documentation will concentrate on these additions and changes, and refer to the official Tcl/Tk documentation for details that are unchanged.

Note

Tcl/Tk 8.5 (2007) introduced a modern set of themed user interface components along with a new API to use them. Both old and new APIs are still available. Most documentation you will find online still uses the old API and can be woefully outdated.

See also

  • +
    TkDocs
    +
    +

    Extensive tutorial on creating user interfaces with Tkinter. Explains key concepts, and illustrates recommended approaches using the modern API.

  • +
    Tkinter 8.5 reference: a GUI for Python
    +
    +

    Reference documentation for Tkinter 8.5 detailing available classes, methods, and options.

Tcl/Tk Resources:

  • +
    Tk commands
    +
    +

    Comprehensive reference to each of the underlying Tcl/Tk commands used by Tkinter.

  • +
    Tcl/Tk Home Page
    +
    +

    Additional documentation, and links to Tcl/Tk core development.

Books:

Architecture

Tcl/Tk is not a single library but rather consists of a few distinct modules, each with separate functionality and its own official documentation. Python’s binary releases also ship an add-on module together with it.

Tcl
+
+

Tcl is a dynamic interpreted programming language, just like Python. Though it can be used on its own as a general-purpose programming language, it is most commonly embedded into C applications as a scripting engine or an interface to the Tk toolkit. The Tcl library has a C interface to create and manage one or more instances of a Tcl interpreter, run Tcl commands and scripts in those instances, and add custom commands implemented in either Tcl or C. Each interpreter has an event queue, and there are facilities to send events to it and process them. Unlike Python, Tcl’s execution model is designed around cooperative multitasking, and Tkinter bridges this difference (see Threading model for details).

Tk
+
+

Tk is a Tcl package implemented in C that adds custom commands to create and manipulate GUI widgets. Each Tk object embeds its own Tcl interpreter instance with Tk loaded into it. Tk’s widgets are very customizable, though at the cost of a dated appearance. Tk uses Tcl’s event queue to generate and process GUI events.

Ttk
+
+

Themed Tk (Ttk) is a newer family of Tk widgets that provide a much better appearance on different platforms than many of the classic Tk widgets. Ttk is distributed as part of Tk, starting with Tk version 8.5. Python bindings are provided in a separate module, tkinter.ttk.

Internally, Tk and Ttk use facilities of the underlying operating system, i.e., Xlib on Unix/X11, Cocoa on macOS, GDI on Windows.

When your Python application uses a class in Tkinter, e.g., to create a widget, the tkinter module first assembles a Tcl/Tk command string. It passes that Tcl command string to an internal _tkinter binary module, which then calls the Tcl interpreter to evaluate it. The Tcl interpreter will then call into the Tk and/or Ttk packages, which will in turn make calls to Xlib, Cocoa, or GDI.

Tkinter Modules

Support for Tkinter is spread across several modules. Most applications will need the main tkinter module, as well as the tkinter.ttk module, which provides the modern themed widget set and API:

from tkinter import *
+from tkinter import ttk
+
+class tkinter.Tk(screenName=None, baseName=None, className='Tk', useTk=True, sync=False, use=None)
+

Construct a toplevel Tk widget, which is usually the main window of an application, and initialize a Tcl interpreter for this widget. Each instance has its own associated Tcl interpreter.

The Tk class is typically instantiated using all default values. However, the following keyword arguments are currently recognized:

screenName
+
+

When given (as a string), sets the DISPLAY environment variable. (X11 only)

baseName
+
+

Name of the profile file. By default, baseName is derived from the program name (sys.argv[0]).

className
+
+

Name of the widget class. Used as a profile file and also as the name with which Tcl is invoked (argv0 in interp).

useTk
+
+

If True, initialize the Tk subsystem. The tkinter.Tcl() function sets this to False.

sync
+
+

If True, execute all X server commands synchronously, so that errors are reported immediately. Can be used for debugging. (X11 only)

use
+
+

Specifies the id of the window in which to embed the application, instead of it being created as an independent toplevel window. id must be specified in the same way as the value for the -use option for toplevel widgets (that is, it has a form like that returned by winfo_id()).

Note that on some platforms this will only work correctly if id refers to a Tk frame or toplevel that has its -container option enabled.

Tk reads and interprets profile files, named .className.tcl and .baseName.tcl, into the Tcl interpreter and calls exec() on the contents of .className.py and .baseName.py. The path for the profile files is the HOME environment variable or, if that isn’t defined, then os.curdir.

+tk
+

The Tk application object created by instantiating Tk. This provides access to the Tcl interpreter. Each widget that is attached the same instance of Tk has the same value for its tk attribute.

+
+master
+

The widget object that contains this widget. For Tk, the master is None because it is the main window. The terms master and parent are similar and sometimes used interchangeably as argument names; however, calling winfo_parent() returns a string of the widget name whereas master returns the object. parent/child reflects the tree-like relationship while master/slave reflects the container structure.

+
+children
+

The immediate descendants of this widget as a dict with the child widget names as the keys and the child instance objects as the values.

+
+
+tkinter.Tcl(screenName=None, baseName=None, className='Tk', useTk=False)
+

The Tcl() function is a factory function which creates an object much like that created by the Tk class, except that it does not initialize the Tk subsystem. This is most often useful when driving the Tcl interpreter in an environment where one doesn’t want to create extraneous toplevel windows, or where one cannot (such as Unix/Linux systems without an X server). An object created by the Tcl() object can have a Toplevel window created (and the Tk subsystem initialized) by calling its loadtk() method.

+

The modules that provide Tk support include:

+ tkinter +
+
+

Main Tkinter module.

+ tkinter.colorchooser +
+
+

Dialog to let the user choose a color.

+ tkinter.commondialog +
+
+

Base class for the dialogs defined in the other modules listed here.

+ tkinter.filedialog +
+
+

Common dialogs to allow the user to specify a file to open or save.

+ tkinter.font +
+
+

Utilities to help work with fonts.

+ tkinter.messagebox +
+
+

Access to standard Tk dialog boxes.

+ tkinter.scrolledtext +
+
+

Text widget with a vertical scroll bar built in.

+ tkinter.simpledialog +
+
+

Basic dialogs and convenience functions.

+ tkinter.ttk +
+
+

Themed widget set introduced in Tk 8.5, providing modern alternatives for many of the classic widgets in the main tkinter module.

Additional modules:

+ _tkinter +
+
+

A binary module that contains the low-level interface to Tcl/Tk. It is automatically imported by the main tkinter module, and should never be used directly by application programmers. It is usually a shared library (or DLL), but might in some cases be statically linked with the Python interpreter.

+ idlelib +
+
+

Python’s Integrated Development and Learning Environment (IDLE). Based on tkinter.

+tkinter.constants
+
+

Symbolic constants that can be used in place of strings when passing various parameters to Tkinter calls. Automatically imported by the main tkinter module.

+ tkinter.dnd +
+
+

(experimental) Drag-and-drop support for tkinter. This will become deprecated when it is replaced with the Tk DND.

+ tkinter.tix +
+
+

(deprecated) An older third-party Tcl/Tk package that adds several new widgets. Better alternatives for most can be found in tkinter.ttk.

+ turtle +
+
+

Turtle graphics in a Tk window.

Tkinter Life Preserver

This section is not designed to be an exhaustive tutorial on either Tk or Tkinter. For that, refer to one of the external resources noted earlier. Instead, this section provides a very quick orientation to what a Tkinter application looks like, identifies foundational Tk concepts, and explains how the Tkinter wrapper is structured.

The remainder of this section will help you to identify the classes, methods, and options you’ll need in your Tkinter application, and where to find more detailed documentation on them, including in the official Tcl/Tk reference manual.

A Hello World Program

We’ll start by walking through a “Hello World” application in Tkinter. This isn’t the smallest one we could write, but has enough to illustrate some key concepts you’ll need to know.

from tkinter import *
+from tkinter import ttk
+root = Tk()
+frm = ttk.Frame(root, padding=10)
+frm.grid()
+ttk.Label(frm, text="Hello World!").grid(column=0, row=0)
+ttk.Button(frm, text="Quit", command=root.destroy).grid(column=1, row=0)
+root.mainloop()
+

After the imports, the next line creates an instance of the Tk class, which initializes Tk and creates its associated Tcl interpreter. It also creates a toplevel window, known as the root window, which serves as the main window of the application.

The following line creates a frame widget, which in this case will contain a label and a button we’ll create next. The frame is fit inside the root window.

The next line creates a label widget holding a static text string. The grid() method is used to specify the relative layout (position) of the label within its containing frame widget, similar to how tables in HTML work.

A button widget is then created, and placed to the right of the label. When pressed, it will call the destroy() method of the root window.

Finally, the mainloop() method puts everything on the display, and responds to user input until the program terminates.

Important Tk Concepts

Even this simple program illustrates the following key Tk concepts:

widgets
+
+

A Tkinter user interface is made up of individual widgets. Each widget is represented as a Python object, instantiated from classes like ttk.Frame, ttk.Label, and ttk.Button.

widget hierarchy
+
+

Widgets are arranged in a hierarchy. The label and button were contained within a frame, which in turn was contained within the root window. When creating each child widget, its parent widget is passed as the first argument to the widget constructor.

configuration options
+
+

Widgets have configuration options, which modify their appearance and behavior, such as the text to display in a label or button. Different classes of widgets will have different sets of options.

geometry management
+
+

Widgets aren’t automatically added to the user interface when they are created. A geometry manager like grid controls where in the user interface they are placed.

event loop
+
+

Tkinter reacts to user input, changes from your program, and even refreshes the display only when actively running an event loop. If your program isn’t running the event loop, your user interface won’t update.

Understanding How Tkinter Wraps Tcl/Tk

When your application uses Tkinter’s classes and methods, internally Tkinter is assembling strings representing Tcl/Tk commands, and executing those commands in the Tcl interpreter attached to your application’s Tk instance.

Whether it’s trying to navigate reference documentation, trying to find the right method or option, adapting some existing code, or debugging your Tkinter application, there are times that it will be useful to understand what those underlying Tcl/Tk commands look like.

To illustrate, here is the Tcl/Tk equivalent of the main part of the Tkinter script above.

ttk::frame .frm -padding 10
+grid .frm
+grid [ttk::label .frm.lbl -text "Hello World!"] -column 0 -row 0
+grid [ttk::button .frm.btn -text "Quit" -command "destroy ."] -column 1 -row 0
+

Tcl’s syntax is similar to many shell languages, where the first word is the command to be executed, with arguments to that command following it, separated by spaces. Without getting into too many details, notice the following:

  • The commands used to create widgets (like ttk::frame) correspond to widget classes in Tkinter.
  • Tcl widget options (like -text) correspond to keyword arguments in Tkinter.
  • Widgets are referred to by a pathname in Tcl (like .frm.btn), whereas Tkinter doesn’t use names but object references.
  • A widget’s place in the widget hierarchy is encoded in its (hierarchical) pathname, which uses a . (dot) as a path separator. The pathname for the root window is just . (dot). In Tkinter, the hierarchy is defined not by pathname but by specifying the parent widget when creating each child widget.
  • Operations which are implemented as separate commands in Tcl (like grid or destroy) are represented as methods on Tkinter widget objects. As you’ll see shortly, at other times Tcl uses what appear to be method calls on widget objects, which more closely mirror what would is used in Tkinter.

How do I…? What option does…?

If you’re not sure how to do something in Tkinter, and you can’t immediately find it in the tutorial or reference documentation you’re using, there are a few strategies that can be helpful.

First, remember that the details of how individual widgets work may vary across different versions of both Tkinter and Tcl/Tk. If you’re searching documentation, make sure it corresponds to the Python and Tcl/Tk versions installed on your system.

When searching for how to use an API, it helps to know the exact name of the class, option, or method that you’re using. Introspection, either in an interactive Python shell or with print(), can help you identify what you need.

To find out what configuration options are available on any widget, call its configure() method, which returns a dictionary containing a variety of information about each object, including its default and current values. Use keys() to get just the names of each option.

btn = ttk.Button(frm, ...)
+print(btn.configure().keys())
+

As most widgets have many configuration options in common, it can be useful to find out which are specific to a particular widget class. Comparing the list of options to that of a simpler widget, like a frame, is one way to do that.

print(set(btn.configure().keys()) - set(frm.configure().keys()))
+

Similarly, you can find the available methods for a widget object using the standard dir() function. If you try it, you’ll see there are over 200 common widget methods, so again identifying those specific to a widget class is helpful.

print(dir(btn))
+print(set(dir(btn)) - set(dir(frm)))
+

Threading model

Python and Tcl/Tk have very different threading models, which tkinter tries to bridge. If you use threads, you may need to be aware of this.

A Python interpreter may have many threads associated with it. In Tcl, multiple threads can be created, but each thread has a separate Tcl interpreter instance associated with it. Threads can also create more than one interpreter instance, though each interpreter instance can be used only by the one thread that created it.

Each Tk object created by tkinter contains a Tcl interpreter. It also keeps track of which thread created that interpreter. Calls to tkinter can be made from any Python thread. Internally, if a call comes from a thread other than the one that created the Tk object, an event is posted to the interpreter’s event queue, and when executed, the result is returned to the calling Python thread.

Tcl/Tk applications are normally event-driven, meaning that after initialization, the interpreter runs an event loop (i.e. Tk.mainloop()) and responds to events. Because it is single-threaded, event handlers must respond quickly, otherwise they will block other events from being processed. To avoid this, any long-running computations should not run in an event handler, but are either broken into smaller pieces using timers, or run in another thread. This is different from many GUI toolkits where the GUI runs in a completely separate thread from all application code including event handlers.

If the Tcl interpreter is not running the event loop and processing events, any tkinter calls made from threads other than the one running the Tcl interpreter will fail.

A number of special cases exist:

  • Tcl/Tk libraries can be built so they are not thread-aware. In this case, tkinter calls the library from the originating Python thread, even if this is different than the thread that created the Tcl interpreter. A global lock ensures only one call occurs at a time.
  • While tkinter allows you to create more than one instance of a Tk object (with its own interpreter), all interpreters that are part of the same thread share a common event queue, which gets ugly fast. In practice, don’t create more than one instance of Tk at a time. Otherwise, it’s best to create them in separate threads and ensure you’re running a thread-aware Tcl/Tk build.
  • Blocking event handlers are not the only way to prevent the Tcl interpreter from reentering the event loop. It is even possible to run multiple nested event loops or abandon the event loop entirely. If you’re doing anything tricky when it comes to events or threads, be aware of these possibilities.
  • There are a few select tkinter functions that presently work only when called from the thread that created the Tcl interpreter.

Handy Reference

Setting Options

Options control things like the color and border width of a widget. Options can be set in three ways:

At object creation time, using keyword arguments
+
+
fred = Button(self, fg="red", bg="blue")
+
After object creation, treating the option name like a dictionary index
+
+
fred["fg"] = "red"
+fred["bg"] = "blue"
+
Use the config() method to update multiple attrs subsequent to object creation
+
+
fred.config(fg="red", bg="blue")
+

For a complete explanation of a given option and its behavior, see the Tk man pages for the widget in question.

Note that the man pages list “STANDARD OPTIONS” and “WIDGET SPECIFIC OPTIONS” for each widget. The former is a list of options that are common to many widgets, the latter are the options that are idiosyncratic to that particular widget. The Standard Options are documented on the options(3) man page.

No distinction between standard and widget-specific options is made in this document. Some options don’t apply to some kinds of widgets. Whether a given widget responds to a particular option depends on the class of the widget; buttons have a command option, labels do not.

The options supported by a given widget are listed in that widget’s man page, or can be queried at runtime by calling the config() method without arguments, or by calling the keys() method on that widget. The return value of these calls is a dictionary whose key is the name of the option as a string (for example, 'relief') and whose values are 5-tuples.

Some options, like bg are synonyms for common options with long names (bg is shorthand for “background”). Passing the config() method the name of a shorthand option will return a 2-tuple, not 5-tuple. The 2-tuple passed back will contain the name of the synonym and the “real” option (such as ('bg', 'background')).

+ + + + + +

Index

Meaning

Example

0

option name

'relief'

1

option name for database lookup

'relief'

2

option class for database lookup

'Relief'

3

default value

'raised'

4

current value

'groove'

Example:

>>> print(fred.config())
+{'relief': ('relief', 'relief', 'Relief', 'raised', 'groove')}
+

Of course, the dictionary printed will include all the options available and their values. This is meant only as an example.

The Packer

The packer is one of Tk’s geometry-management mechanisms. Geometry managers are used to specify the relative positioning of widgets within their container - their mutual master. In contrast to the more cumbersome placer (which is used less commonly, and we do not cover here), the packer takes qualitative relationship specification - above, to the left of, filling, etc - and works everything out to determine the exact placement coordinates for you.

The size of any master widget is determined by the size of the “slave widgets” inside. The packer is used to control where slave widgets appear inside the master into which they are packed. You can pack widgets into frames, and frames into other frames, in order to achieve the kind of layout you desire. Additionally, the arrangement is dynamically adjusted to accommodate incremental changes to the configuration, once it is packed.

Note that widgets do not appear until they have had their geometry specified with a geometry manager. It’s a common early mistake to leave out the geometry specification, and then be surprised when the widget is created but nothing appears. A widget will appear only after it has had, for example, the packer’s pack() method applied to it.

The pack() method can be called with keyword-option/value pairs that control where the widget is to appear within its container, and how it is to behave when the main application window is resized. Here are some examples:

fred.pack()                     # defaults to side = "top"
+fred.pack(side="left")
+fred.pack(expand=1)
+

Packer Options

For more extensive information on the packer and the options that it can take, see the man pages and page 183 of John Ousterhout’s book.

anchor
+
+

Anchor type. Denotes where the packer is to place each slave in its parcel.

expand
+
+

Boolean, 0 or 1.

fill
+
+

Legal values: 'x', 'y', 'both', 'none'.

ipadx and ipady
+
+

A distance - designating internal padding on each side of the slave widget.

padx and pady
+
+

A distance - designating external padding on each side of the slave widget.

side
+
+

Legal values are: 'left', 'right', 'top', 'bottom'.

Coupling Widget Variables

The current-value setting of some widgets (like text entry widgets) can be connected directly to application variables by using special options. These options are variable, textvariable, onvalue, offvalue, and value. This connection works both ways: if the variable changes for any reason, the widget it’s connected to will be updated to reflect the new value.

Unfortunately, in the current implementation of tkinter it is not possible to hand over an arbitrary Python variable to a widget through a variable or textvariable option. The only kinds of variables for which this works are variables that are subclassed from a class called Variable, defined in tkinter.

There are many useful subclasses of Variable already defined: StringVar, IntVar, DoubleVar, and BooleanVar. To read the current value of such a variable, call the get() method on it, and to change its value you call the set() method. If you follow this protocol, the widget will always track the value of the variable, with no further intervention on your part.

For example:

import tkinter as tk
+
+class App(tk.Frame):
+    def __init__(self, master):
+        super().__init__(master)
+        self.pack()
+
+        self.entrythingy = tk.Entry()
+        self.entrythingy.pack()
+
+        # Create the application variable.
+        self.contents = tk.StringVar()
+        # Set it to some value.
+        self.contents.set("this is a variable")
+        # Tell the entry widget to watch this variable.
+        self.entrythingy["textvariable"] = self.contents
+
+        # Define a callback for when the user hits return.
+        # It prints the current value of the variable.
+        self.entrythingy.bind('<Key-Return>',
+                             self.print_contents)
+
+    def print_contents(self, event):
+        print("Hi. The current entry content is:",
+              self.contents.get())
+
+root = tk.Tk()
+myapp = App(root)
+myapp.mainloop()
+

The Window Manager

In Tk, there is a utility command, wm, for interacting with the window manager. Options to the wm command allow you to control things like titles, placement, icon bitmaps, and the like. In tkinter, these commands have been implemented as methods on the Wm class. Toplevel widgets are subclassed from the Wm class, and so can call the Wm methods directly.

To get at the toplevel window that contains a given widget, you can often just refer to the widget’s master. Of course if the widget has been packed inside of a frame, the master won’t represent a toplevel window. To get at the toplevel window that contains an arbitrary widget, you can call the _root() method. This method begins with an underscore to denote the fact that this function is part of the implementation, and not an interface to Tk functionality.

Here are some examples of typical usage:

import tkinter as tk
+
+class App(tk.Frame):
+    def __init__(self, master=None):
+        super().__init__(master)
+        self.pack()
+
+# create the application
+myapp = App()
+
+#
+# here are method calls to the window manager class
+#
+myapp.master.title("My Do-Nothing Application")
+myapp.master.maxsize(1000, 400)
+
+# start the program
+myapp.mainloop()
+

Tk Option Data Types

anchor
+
+

Legal values are points of the compass: "n", "ne", "e", "se", "s", "sw", "w", "nw", and also "center".

bitmap
+
+

There are eight built-in, named bitmaps: 'error', 'gray25', 'gray50', 'hourglass', 'info', 'questhead', 'question', 'warning'. To specify an X bitmap filename, give the full path to the file, preceded with an @, as in "@/usr/contrib/bitmap/gumby.bit".

boolean
+
+

You can pass integers 0 or 1 or the strings "yes" or "no".

callback
+
+

This is any Python function that takes no arguments. For example:

def print_it():
+    print("hi there")
+fred["command"] = print_it
+
color
+
+

Colors can be given as the names of X colors in the rgb.txt file, or as strings representing RGB values in 4 bit: "#RGB", 8 bit: "#RRGGBB", 12 bit: "#RRRGGGBBB", or 16 bit: "#RRRRGGGGBBBB" ranges, where R,G,B here represent any legal hex digit. See page 160 of Ousterhout’s book for details.

cursor
+
+

The standard X cursor names from cursorfont.h can be used, without the XC_ prefix. For example to get a hand cursor (XC_hand2), use the string "hand2". You can also specify a bitmap and mask file of your own. See page 179 of Ousterhout’s book.

distance
+
+

Screen distances can be specified in either pixels or absolute distances. Pixels are given as numbers and absolute distances as strings, with the trailing character denoting units: c for centimetres, i for inches, m for millimetres, p for printer’s points. For example, 3.5 inches is expressed as "3.5i".

font
+
+

Tk uses a list font name format, such as {courier 10 bold}. Font sizes with positive numbers are measured in points; sizes with negative numbers are measured in pixels.

geometry
+
+

This is a string of the form widthxheight, where width and height are measured in pixels for most widgets (in characters for widgets displaying text). For example: fred["geometry"] = "200x100".

justify
+
+

Legal values are the strings: "left", "center", "right", and "fill".

region
+
+

This is a string with four space-delimited elements, each of which is a legal distance (see above). For example: "2 3 4 5" and "3i 2i 4.5i 2i" and "3c 2c 4c 10.43c" are all legal regions.

relief
+
+

Determines what the border style of a widget will be. Legal values are: "raised", "sunken", "flat", "groove", and "ridge".

scrollcommand
+
+

This is almost always the set() method of some scrollbar widget, but can be any widget method that takes a single argument.

wrap
+
+

Must be one of: "none", "char", or "word".

Bindings and Events

The bind method from the widget command allows you to watch for certain events and to have a callback function trigger when that event type occurs. The form of the bind method is:

def bind(self, sequence, func, add=''):
+

where:

sequence
+
+

is a string that denotes the target kind of event. (See the bind(3tk) man page, and page 201 of John Ousterhout’s book, Tcl and the Tk Toolkit (2nd edition), for details).

func
+
+

is a Python function, taking one argument, to be invoked when the event occurs. An Event instance will be passed as the argument. (Functions deployed this way are commonly known as callbacks.)

add
+
+

is optional, either '' or '+'. Passing an empty string denotes that this binding is to replace any other bindings that this event is associated with. Passing a '+' means that this function is to be added to the list of functions bound to this event type.

For example:

def turn_red(self, event):
+    event.widget["activeforeground"] = "red"
+
+self.button.bind("<Enter>", self.turn_red)
+

Notice how the widget field of the event is being accessed in the turn_red() callback. This field contains the widget that caught the X event. The following table lists the other event fields you can access, and how they are denoted in Tk, which can be useful when referring to the Tk man pages.

+ + + + + + + + +

Tk

Tkinter Event Field

Tk

Tkinter Event Field

%f

focus

%A

char

%h

height

%E

send_event

%k

keycode

%K

keysym

%s

state

%N

keysym_num

%t

time

%T

type

%w

width

%W

widget

%x

x

%X

x_root

%y

y

%Y

y_root

The index Parameter

A number of widgets require “index” parameters to be passed. These are used to point at a specific place in a Text widget, or to particular characters in an Entry widget, or to particular menu items in a Menu widget.

Entry widget indexes (index, view index, etc.)
+
+

Entry widgets have options that refer to character positions in the text being displayed. You can use these tkinter functions to access these special points in text widgets:

Text widget indexes
+
+

The index notation for Text widgets is very rich and is best described in the Tk man pages.

Menu indexes (menu.invoke(), menu.entryconfig(), etc.)
+
+

Some options and methods for menus manipulate specific menu entries. Anytime a menu index is needed for an option or a parameter, you may pass in:

  • an integer which refers to the numeric position of the entry in the widget, counted from the top, starting with 0;
  • the string "active", which refers to the menu position that is currently under the cursor;
  • the string "last" which refers to the last menu item;
  • An integer preceded by @, as in @6, where the integer is interpreted as a y pixel coordinate in the menu’s coordinate system;
  • the string "none", which indicates no menu entry at all, most often used with menu.activate() to deactivate all entries, and finally,
  • a text string that is pattern matched against the label of the menu entry, as scanned from the top of the menu to the bottom. Note that this index type is considered after all the others, which means that matches for menu items labelled last, active, or none may be interpreted as the above literals, instead.

Images

Images of different formats can be created through the corresponding subclass of tkinter.Image:

  • +BitmapImage for images in XBM format.
  • +PhotoImage for images in PGM, PPM, GIF and PNG formats. The latter is supported starting with Tk 8.6.

Either type of image is created through either the file or the data option (other options are available as well).

The image object can then be used wherever an image option is supported by some widget (e.g. labels, buttons, menus). In these cases, Tk will not keep a reference to the image. When the last Python reference to the image object is deleted, the image data is deleted as well, and Tk will display an empty box wherever the image was used.

See also

The Pillow package adds support for formats such as BMP, JPEG, TIFF, and WebP, among others.

File Handlers

Tk allows you to register and unregister a callback function which will be called from the Tk mainloop when I/O is possible on a file descriptor. Only one handler may be registered per file descriptor. Example code:

import tkinter
+widget = tkinter.Tk()
+mask = tkinter.READABLE | tkinter.WRITABLE
+widget.tk.createfilehandler(file, mask, callback)
+...
+widget.tk.deletefilehandler(file)
+

This feature is not available on Windows.

Since you don’t know how many bytes are available for reading, you may not want to use the BufferedIOBase or TextIOBase read() or readline() methods, since these will insist on reading a predefined number of bytes. For sockets, the recv() or recvfrom() methods will work fine; for other files, use raw reads or os.read(file.fileno(), maxbytecount).

+Widget.tk.createfilehandler(file, mask, func)
+

Registers the file handler callback function func. The file argument may either be an object with a fileno() method (such as a file or socket object), or an integer file descriptor. The mask argument is an ORed combination of any of the three constants below. The callback is called as follows:

callback(file, mask)
+
+
+Widget.tk.deletefilehandler(file)
+

Unregisters a file handler.

+
+_tkinter.READABLE
+_tkinter.WRITABLE
+_tkinter.EXCEPTION
+

Constants used in the mask arguments.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.messagebox.html b/devdocs/python~3.12/library%2Ftkinter.messagebox.html new file mode 100644 index 00000000..8f855bb9 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.messagebox.html @@ -0,0 +1,92 @@ +

tkinter.messagebox — Tkinter message prompts

Source code: Lib/tkinter/messagebox.py

The tkinter.messagebox module provides a template base class as well as a variety of convenience methods for commonly used configurations. The message boxes are modal and will return a subset of (True, False, None, OK, CANCEL, YES, NO) based on the user’s selection. Common message box styles and layouts include but are not limited to:

../_images/tk_msg.png
+class tkinter.messagebox.Message(master=None, **options)
+

Create a message window with an application-specified message, an icon and a set of buttons. Each of the buttons in the message window is identified by a unique symbolic name (see the type options).

The following options are supported:

command
+
+

Specifies the function to invoke when the user closes the dialog. The name of the button clicked by the user to close the dialog is passed as argument. This is only available on macOS.

default
+
+

Gives the symbolic name of the default button for this message window (OK, CANCEL, and so on). If this option is not specified, the first button in the dialog will be made the default.

detail
+
+

Specifies an auxiliary message to the main message given by the message option. The message detail will be presented beneath the main message and, where supported by the OS, in a less emphasized font than the main message.

icon
+
+

Specifies an icon to display. If this option is not specified, then the INFO icon will be displayed.

message
+
+

Specifies the message to display in this message box. The default value is an empty string.

parent
+
+

Makes the specified window the logical parent of the message box. The message box is displayed on top of its parent window.

title
+
+

Specifies a string to display as the title of the message box. This option is ignored on macOS, where platform guidelines forbid the use of a title on this kind of dialog.

type
+
+

Arranges for a predefined set of buttons to be displayed.

+show(**options)
+

Display a message window and wait for the user to select one of the buttons. Then return the symbolic name of the selected button. Keyword arguments can override options specified in the constructor.

+
+

Information message box

+tkinter.messagebox.showinfo(title=None, message=None, **options)
+

Creates and displays an information message box with the specified title and message.

+

Warning message boxes

+tkinter.messagebox.showwarning(title=None, message=None, **options)
+

Creates and displays a warning message box with the specified title and message.

+
+tkinter.messagebox.showerror(title=None, message=None, **options)
+

Creates and displays an error message box with the specified title and message.

+

Question message boxes

+tkinter.messagebox.askquestion(title=None, message=None, *, type=YESNO, **options)
+

Ask a question. By default shows buttons YES and NO. Returns the symbolic name of the selected button.

+
+tkinter.messagebox.askokcancel(title=None, message=None, **options)
+

Ask if operation should proceed. Shows buttons OK and CANCEL. Returns True if the answer is ok and False otherwise.

+
+tkinter.messagebox.askretrycancel(title=None, message=None, **options)
+

Ask if operation should be retried. Shows buttons RETRY and CANCEL. Return True if the answer is yes and False otherwise.

+
+tkinter.messagebox.askyesno(title=None, message=None, **options)
+

Ask a question. Shows buttons YES and NO. Returns True if the answer is yes and False otherwise.

+
+tkinter.messagebox.askyesnocancel(title=None, message=None, **options)
+

Ask a question. Shows buttons YES, NO and CANCEL. Return True if the answer is yes, None if cancelled, and False otherwise.

+

Symbolic names of buttons:

+tkinter.messagebox.ABORT = 'abort'
+
+tkinter.messagebox.RETRY = 'retry'
+
+tkinter.messagebox.IGNORE = 'ignore'
+
+tkinter.messagebox.OK = 'ok'
+
+tkinter.messagebox.CANCEL = 'cancel'
+
+tkinter.messagebox.YES = 'yes'
+
+tkinter.messagebox.NO = 'no'
+

Predefined sets of buttons:

+tkinter.messagebox.ABORTRETRYIGNORE = 'abortretryignore'
+

Displays three buttons whose symbolic names are ABORT, RETRY and IGNORE.

+
tkinter.messagebox.OK = 'ok' +
+

Displays one button whose symbolic name is OK.

+
+tkinter.messagebox.OKCANCEL = 'okcancel'
+

Displays two buttons whose symbolic names are OK and CANCEL.

+
+tkinter.messagebox.RETRYCANCEL = 'retrycancel'
+

Displays two buttons whose symbolic names are RETRY and CANCEL.

+
+tkinter.messagebox.YESNO = 'yesno'
+

Displays two buttons whose symbolic names are YES and NO.

+
+tkinter.messagebox.YESNOCANCEL = 'yesnocancel'
+

Displays three buttons whose symbolic names are YES, NO and CANCEL.

+

Icon images:

+tkinter.messagebox.ERROR = 'error'
+
+tkinter.messagebox.INFO = 'info'
+
+tkinter.messagebox.QUESTION = 'question'
+
+tkinter.messagebox.WARNING = 'warning'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.messagebox.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.scrolledtext.html b/devdocs/python~3.12/library%2Ftkinter.scrolledtext.html new file mode 100644 index 00000000..365d22b7 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.scrolledtext.html @@ -0,0 +1,15 @@ +

tkinter.scrolledtext — Scrolled Text Widget

Source code: Lib/tkinter/scrolledtext.py

The tkinter.scrolledtext module provides a class of the same name which implements a basic text widget which has a vertical scroll bar configured to do the “right thing.” Using the ScrolledText class is a lot easier than setting up a text widget and scroll bar directly.

The text widget and scrollbar are packed together in a Frame, and the methods of the Grid and Pack geometry managers are acquired from the Frame object. This allows the ScrolledText widget to be used directly to achieve most normal geometry management behavior.

Should more specific control be necessary, the following attributes are available:

+class tkinter.scrolledtext.ScrolledText(master=None, **kw)
+
+frame
+

The frame which surrounds the text and scroll bar widgets.

+
+vbar
+

The scroll bar widget.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.scrolledtext.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.tix.html b/devdocs/python~3.12/library%2Ftkinter.tix.html new file mode 100644 index 00000000..a47d3c5e --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.tix.html @@ -0,0 +1,136 @@ +

tkinter.tix — Extension widgets for Tk

Source code: Lib/tkinter/tix.py

Deprecated since version 3.6: This Tk extension is unmaintained and should not be used in new code. Use tkinter.ttk instead.

The tkinter.tix (Tk Interface Extension) module provides an additional rich set of widgets. Although the standard Tk library has many useful widgets, they are far from complete. The tkinter.tix library provides most of the commonly needed widgets that are missing from standard Tk: HList, ComboBox, Control (a.k.a. SpinBox) and an assortment of scrollable widgets. tkinter.tix also includes many more widgets that are generally useful in a wide range of applications: NoteBook, FileEntry, PanedWindow, etc; there are more than 40 of them.

With all these new widgets, you can introduce new interaction techniques into applications, creating more useful and more intuitive user interfaces. You can design your application by choosing the most appropriate widgets to match the special needs of your application and users.

See also

Tix Homepage
+
+

The home page for Tix. This includes links to additional documentation and downloads.

Tix Man Pages
+
+

On-line version of the man pages and reference material.

Tix Programming Guide
+
+

On-line version of the programmer’s reference material.

Tix Development Applications
+
+

Tix applications for development of Tix and Tkinter programs. Tide applications work under Tk or Tkinter, and include TixInspect, an inspector to remotely modify and debug Tix/Tk/Tkinter applications.

Using Tix

+class tkinter.tix.Tk(screenName=None, baseName=None, className='Tix')
+

Toplevel widget of Tix which represents mostly the main window of an application. It has an associated Tcl interpreter.

Classes in the tkinter.tix module subclasses the classes in the tkinter. The former imports the latter, so to use tkinter.tix with Tkinter, all you need to do is to import one module. In general, you can just import tkinter.tix, and replace the toplevel call to tkinter.Tk with tix.Tk:

from tkinter import tix
+from tkinter.constants import *
+root = tix.Tk()
+
+

To use tkinter.tix, you must have the Tix widgets installed, usually alongside your installation of the Tk widgets. To test your installation, try the following:

from tkinter import tix
+root = tix.Tk()
+root.tk.eval('package require Tix')
+

Tix Widgets

Tix introduces over 40 widget classes to the tkinter repertoire.

Basic Widgets

+class tkinter.tix.Balloon
+

A Balloon that pops up over a widget to provide help. When the user moves the cursor inside a widget to which a Balloon widget has been bound, a small pop-up window with a descriptive message will be shown on the screen.

+
+class tkinter.tix.ButtonBox
+

The ButtonBox widget creates a box of buttons, such as is commonly used for Ok Cancel.

+
+class tkinter.tix.ComboBox
+

The ComboBox widget is similar to the combo box control in MS Windows. The user can select a choice by either typing in the entry subwidget or selecting from the listbox subwidget.

+
+class tkinter.tix.Control
+

The Control widget is also known as the SpinBox widget. The user can adjust the value by pressing the two arrow buttons or by entering the value directly into the entry. The new value will be checked against the user-defined upper and lower limits.

+
+class tkinter.tix.LabelEntry
+

The LabelEntry widget packages an entry widget and a label into one mega widget. It can be used to simplify the creation of “entry-form” type of interface.

+
+class tkinter.tix.LabelFrame
+

The LabelFrame widget packages a frame widget and a label into one mega widget. To create widgets inside a LabelFrame widget, one creates the new widgets relative to the frame subwidget and manage them inside the frame subwidget.

+
+class tkinter.tix.Meter
+

The Meter widget can be used to show the progress of a background job which may take a long time to execute.

+
+class tkinter.tix.OptionMenu
+

The OptionMenu creates a menu button of options.

+
+class tkinter.tix.PopupMenu
+

The PopupMenu widget can be used as a replacement of the tk_popup command. The advantage of the Tix PopupMenu widget is it requires less application code to manipulate.

+
+class tkinter.tix.Select
+

The Select widget is a container of button subwidgets. It can be used to provide radio-box or check-box style of selection options for the user.

+
+class tkinter.tix.StdButtonBox
+

The StdButtonBox widget is a group of standard buttons for Motif-like dialog boxes.

+

File Selectors

+class tkinter.tix.DirList
+

The DirList widget displays a list view of a directory, its previous directories and its sub-directories. The user can choose one of the directories displayed in the list or change to another directory.

+
+class tkinter.tix.DirTree
+

The DirTree widget displays a tree view of a directory, its previous directories and its sub-directories. The user can choose one of the directories displayed in the list or change to another directory.

+
+class tkinter.tix.DirSelectDialog
+

The DirSelectDialog widget presents the directories in the file system in a dialog window. The user can use this dialog window to navigate through the file system to select the desired directory.

+
+class tkinter.tix.DirSelectBox
+

The DirSelectBox is similar to the standard Motif(TM) directory-selection box. It is generally used for the user to choose a directory. DirSelectBox stores the directories mostly recently selected into a ComboBox widget so that they can be quickly selected again.

+
+class tkinter.tix.ExFileSelectBox
+

The ExFileSelectBox widget is usually embedded in a tixExFileSelectDialog widget. It provides a convenient method for the user to select files. The style of the ExFileSelectBox widget is very similar to the standard file dialog on MS Windows 3.1.

+
+class tkinter.tix.FileSelectBox
+

The FileSelectBox is similar to the standard Motif(TM) file-selection box. It is generally used for the user to choose a file. FileSelectBox stores the files mostly recently selected into a ComboBox widget so that they can be quickly selected again.

+
+class tkinter.tix.FileEntry
+

The FileEntry widget can be used to input a filename. The user can type in the filename manually. Alternatively, the user can press the button widget that sits next to the entry, which will bring up a file selection dialog.

+

Hierarchical ListBox

+class tkinter.tix.HList
+

The HList widget can be used to display any data that have a hierarchical structure, for example, file system directory trees. The list entries are indented and connected by branch lines according to their places in the hierarchy.

+
+class tkinter.tix.CheckList
+

The CheckList widget displays a list of items to be selected by the user. CheckList acts similarly to the Tk checkbutton or radiobutton widgets, except it is capable of handling many more items than checkbuttons or radiobuttons.

+
+class tkinter.tix.Tree
+

The Tree widget can be used to display hierarchical data in a tree form. The user can adjust the view of the tree by opening or closing parts of the tree.

+

Tabular ListBox

+class tkinter.tix.TList
+

The TList widget can be used to display data in a tabular format. The list entries of a TList widget are similar to the entries in the Tk listbox widget. The main differences are (1) the TList widget can display the list entries in a two dimensional format and (2) you can use graphical images as well as multiple colors and fonts for the list entries.

+

Manager Widgets

+class tkinter.tix.PanedWindow
+

The PanedWindow widget allows the user to interactively manipulate the sizes of several panes. The panes can be arranged either vertically or horizontally. The user changes the sizes of the panes by dragging the resize handle between two panes.

+
+class tkinter.tix.ListNoteBook
+

The ListNoteBook widget is very similar to the TixNoteBook widget: it can be used to display many windows in a limited space using a notebook metaphor. The notebook is divided into a stack of pages (windows). At one time only one of these pages can be shown. The user can navigate through these pages by choosing the name of the desired page in the hlist subwidget.

+
+class tkinter.tix.NoteBook
+

The NoteBook widget can be used to display many windows in a limited space using a notebook metaphor. The notebook is divided into a stack of pages. At one time only one of these pages can be shown. The user can navigate through these pages by choosing the visual “tabs” at the top of the NoteBook widget.

+

Image Types

The tkinter.tix module adds:

  • +pixmap capabilities to all tkinter.tix and tkinter widgets to create color images from XPM files.
  • +Compound image types can be used to create images that consists of multiple horizontal lines; each line is composed of a series of items (texts, bitmaps, images or spaces) arranged from left to right. For example, a compound image can be used to display a bitmap and a text string simultaneously in a Tk Button widget.

Miscellaneous Widgets

+class tkinter.tix.InputOnly
+

The InputOnly widgets are to accept inputs from the user, which can be done with the bind command (Unix only).

+

Form Geometry Manager

In addition, tkinter.tix augments tkinter by providing:

+class tkinter.tix.Form
+

The Form geometry manager based on attachment rules for all Tk widgets.

+

Tix Commands

+class tkinter.tix.tixCommand
+

The tix commands provide access to miscellaneous elements of Tix’s internal state and the Tix application context. Most of the information manipulated by these methods pertains to the application as a whole, or to a screen or display, rather than to a particular window.

To view the current settings, the common usage is:

from tkinter import tix
+root = tix.Tk()
+print(root.tix_configure())
+
+
+tixCommand.tix_configure(cnf=None, **kw)
+

Query or modify the configuration options of the Tix application context. If no option is specified, returns a dictionary all of the available options. If option is specified with no value, then the method returns a list describing the one named option (this list will be identical to the corresponding sublist of the value returned if no option is specified). If one or more option-value pairs are specified, then the method modifies the given option(s) to have the given value(s); in this case the method returns an empty string. Option may be any of the configuration options.

+
+tixCommand.tix_cget(option)
+

Returns the current value of the configuration option given by option. Option may be any of the configuration options.

+
+tixCommand.tix_getbitmap(name)
+

Locates a bitmap file of the name name.xpm or name in one of the bitmap directories (see the tix_addbitmapdir() method). By using tix_getbitmap(), you can avoid hard coding the pathnames of the bitmap files in your application. When successful, it returns the complete pathname of the bitmap file, prefixed with the character @. The returned value can be used to configure the bitmap option of the Tk and Tix widgets.

+
+tixCommand.tix_addbitmapdir(directory)
+

Tix maintains a list of directories under which the tix_getimage() and tix_getbitmap() methods will search for image files. The standard bitmap directory is $TIX_LIBRARY/bitmaps. The tix_addbitmapdir() method adds directory into this list. By using this method, the image files of an applications can also be located using the tix_getimage() or tix_getbitmap() method.

+
+tixCommand.tix_filedialog([dlgclass])
+

Returns the file selection dialog that may be shared among different calls from this application. This method will create a file selection dialog widget when it is called the first time. This dialog will be returned by all subsequent calls to tix_filedialog(). An optional dlgclass parameter can be passed as a string to specified what type of file selection dialog widget is desired. Possible options are tix, FileSelectDialog or tixExFileSelectDialog.

+
+tixCommand.tix_getimage(self, name)
+

Locates an image file of the name name.xpm, name.xbm or name.ppm in one of the bitmap directories (see the tix_addbitmapdir() method above). If more than one file with the same name (but different extensions) exist, then the image type is chosen according to the depth of the X display: xbm images are chosen on monochrome displays and color images are chosen on color displays. By using tix_getimage(), you can avoid hard coding the pathnames of the image files in your application. When successful, this method returns the name of the newly created image, which can be used to configure the image option of the Tk and Tix widgets.

+
+tixCommand.tix_option_get(name)
+

Gets the options maintained by the Tix scheme mechanism.

+
+tixCommand.tix_resetoptions(newScheme, newFontSet[, newScmPrio])
+

Resets the scheme and fontset of the Tix application to newScheme and newFontSet, respectively. This affects only those widgets created after this call. Therefore, it is best to call the resetoptions method before the creation of any widgets in a Tix application.

The optional parameter newScmPrio can be given to reset the priority level of the Tk options set by the Tix schemes.

Because of the way Tk handles the X option database, after Tix has been has imported and inited, it is not possible to reset the color schemes and font sets using the tix_config() method. Instead, the tix_resetoptions() method must be used.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.tix.html +

+
diff --git a/devdocs/python~3.12/library%2Ftkinter.ttk.html b/devdocs/python~3.12/library%2Ftkinter.ttk.html new file mode 100644 index 00000000..dd32b691 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftkinter.ttk.html @@ -0,0 +1,471 @@ +

tkinter.ttk — Tk themed widgets

Source code: Lib/tkinter/ttk.py

The tkinter.ttk module provides access to the Tk themed widget set, introduced in Tk 8.5. It provides additional benefits including anti-aliased font rendering under X11 and window transparency (requiring a composition window manager on X11).

The basic idea for tkinter.ttk is to separate, to the extent possible, the code implementing a widget’s behavior from the code implementing its appearance.

See also

Tk Widget Styling Support
+
+

A document introducing theming support for Tk

Using Ttk

To start using Ttk, import its module:

from tkinter import ttk
+

To override the basic Tk widgets, the import should follow the Tk import:

from tkinter import *
+from tkinter.ttk import *
+

That code causes several tkinter.ttk widgets (Button, Checkbutton, Entry, Frame, Label, LabelFrame, Menubutton, PanedWindow, Radiobutton, Scale and Scrollbar) to automatically replace the Tk widgets.

This has the direct benefit of using the new widgets which gives a better look and feel across platforms; however, the replacement widgets are not completely compatible. The main difference is that widget options such as “fg”, “bg” and others related to widget styling are no longer present in Ttk widgets. Instead, use the ttk.Style class for improved styling effects.

See also

Converting existing applications to use Tile widgets
+
+

A monograph (using Tcl terminology) about differences typically encountered when moving applications to use the new widgets.

Ttk Widgets

Ttk comes with 18 widgets, twelve of which already existed in tkinter: Button, Checkbutton, Entry, Frame, Label, LabelFrame, Menubutton, PanedWindow, Radiobutton, Scale, Scrollbar, and Spinbox. The other six are new: Combobox, Notebook, Progressbar, Separator, Sizegrip and Treeview. And all them are subclasses of Widget.

Using the Ttk widgets gives the application an improved look and feel. As discussed above, there are differences in how the styling is coded.

Tk code:

l1 = tkinter.Label(text="Test", fg="black", bg="white")
+l2 = tkinter.Label(text="Test", fg="black", bg="white")
+

Ttk code:

style = ttk.Style()
+style.configure("BW.TLabel", foreground="black", background="white")
+
+l1 = ttk.Label(text="Test", style="BW.TLabel")
+l2 = ttk.Label(text="Test", style="BW.TLabel")
+

For more information about TtkStyling, see the Style class documentation.

Widget

ttk.Widget defines standard options and methods supported by Tk themed widgets and is not supposed to be directly instantiated.

Standard Options

All the ttk Widgets accept the following options:

+ + + + +

Option

Description

class

Specifies the window class. The class is used when querying the option database for the window’s other options, to determine the default bindtags for the window, and to select the widget’s default layout and style. This option is read-only, and may only be specified when the window is created.

cursor

Specifies the mouse cursor to be used for the widget. If set to the empty string (the default), the cursor is inherited for the parent widget.

takefocus

Determines whether the window accepts the focus during keyboard traversal. 0, 1 or an empty string is returned. If 0 is returned, it means that the window should be skipped entirely during keyboard traversal. If 1, it means that the window should receive the input focus as long as it is viewable. And an empty string means that the traversal scripts make the decision about whether or not to focus on the window.

style

May be used to specify a custom widget style.

Scrollable Widget Options

The following options are supported by widgets that are controlled by a scrollbar.

+ + +

Option

Description

xscrollcommand

 +

Used to communicate with horizontal scrollbars.

When the view in the widget’s window change, the widget will generate a Tcl command based on the scrollcommand.

Usually this option consists of the method Scrollbar.set() of some scrollbar. This will cause the scrollbar to be updated whenever the view in the window changes.

yscrollcommand

Used to communicate with vertical scrollbars. For some more information, see above.

Label Options

The following options are supported by labels, buttons and other button-like widgets.

+ + + + + + +

Option

Description

text

Specifies a text string to be displayed inside the widget.

textvariable

Specifies a name whose value will be used in place of the text option resource.

underline

If set, specifies the index (0-based) of a character to underline in the text string. The underline character is used for mnemonic activation.

image

Specifies an image to display. This is a list of 1 or more elements. The first element is the default image name. The rest of the list if a sequence of statespec/value pairs as defined by Style.map(), specifying different images to use when the widget is in a particular state or a combination of states. All images in the list should have the same size.

compound

 +

Specifies how to display the image relative to the text, in the case both text and images options are present. Valid values are:

  • text: display text only
  • image: display image only
  • top, bottom, left, right: display image above, below, left of, or right of the text, respectively.
  • none: the default. display the image if present, otherwise the text.

width

If greater than zero, specifies how much space, in character widths, to allocate for the text label, if less than zero, specifies a minimum width. If zero or unspecified, the natural width of the text label is used.

Compatibility Options

+ +

Option

Description

state

May be set to “normal” or “disabled” to control the “disabled” state bit. This is a write-only option: setting it changes the widget state, but the Widget.state() method does not affect this option.

Widget States

The widget state is a bitmap of independent state flags.

+ + + + + + + + + +

Flag

Description

active

The mouse cursor is over the widget and pressing a mouse button will cause some action to occur

disabled

Widget is disabled under program control

focus

Widget has keyboard focus

pressed

Widget is being pressed

selected

“On”, “true”, or “current” for things like Checkbuttons and radiobuttons

background

Windows and Mac have a notion of an “active” or foreground window. The background state is set for widgets in a background window, and cleared for those in the foreground window

readonly

Widget should not allow user modification

alternate

A widget-specific alternate display format

invalid

The widget’s value is invalid

A state specification is a sequence of state names, optionally prefixed with an exclamation point indicating that the bit is off.

ttk.Widget

Besides the methods described below, the ttk.Widget supports the methods tkinter.Widget.cget() and tkinter.Widget.configure().

+class tkinter.ttk.Widget
+
+identify(x, y)
+

Returns the name of the element at position x y, or the empty string if the point does not lie within any element.

x and y are pixel coordinates relative to the widget.

+
+instate(statespec, callback=None, *args, **kw)
+

Test the widget’s state. If a callback is not specified, returns True if the widget state matches statespec and False otherwise. If callback is specified then it is called with args if widget state matches statespec.

+
+state(statespec=None)
+

Modify or inquire widget state. If statespec is specified, sets the widget state according to it and return a new statespec indicating which flags were changed. If statespec is not specified, returns the currently enabled state flags.

+

statespec will usually be a list or a tuple.

+

Combobox

The ttk.Combobox widget combines a text field with a pop-down list of values. This widget is a subclass of Entry.

Besides the methods inherited from Widget: Widget.cget(), Widget.configure(), Widget.identify(), Widget.instate() and Widget.state(), and the following inherited from Entry: Entry.bbox(), Entry.delete(), Entry.icursor(), Entry.index(), Entry.insert(), Entry.selection(), Entry.xview(), it has some other methods, described at ttk.Combobox.

Options

This widget accepts the following specific options:

+ + + + + + + + +

Option

Description

exportselection

Boolean value. If set, the widget selection is linked to the Window Manager selection (which can be returned by invoking Misc.selection_get, for example).

justify

Specifies how the text is aligned within the widget. One of “left”, “center”, or “right”.

height

Specifies the height of the pop-down listbox, in rows.

postcommand

A script (possibly registered with Misc.register) that is called immediately before displaying the values. It may specify which values to display.

state

One of “normal”, “readonly”, or “disabled”. In the “readonly” state, the value may not be edited directly, and the user can only selection of the values from the dropdown list. In the “normal” state, the text field is directly editable. In the “disabled” state, no interaction is possible.

textvariable

Specifies a name whose value is linked to the widget value. Whenever the value associated with that name changes, the widget value is updated, and vice versa. See tkinter.StringVar.

values

Specifies the list of values to display in the drop-down listbox.

width

Specifies an integer value indicating the desired width of the entry window, in average-size characters of the widget’s font.

Virtual events

The combobox widgets generates a <<ComboboxSelected>> virtual event when the user selects an element from the list of values.

ttk.Combobox

+class tkinter.ttk.Combobox
+
+current(newindex=None)
+

If newindex is specified, sets the combobox value to the element position newindex. Otherwise, returns the index of the current value or -1 if the current value is not in the values list.

+
+get()
+

Returns the current value of the combobox.

+
+set(value)
+

Sets the value of the combobox to value.

+
+

Spinbox

The ttk.Spinbox widget is a ttk.Entry enhanced with increment and decrement arrows. It can be used for numbers or lists of string values. This widget is a subclass of Entry.

Besides the methods inherited from Widget: Widget.cget(), Widget.configure(), Widget.identify(), Widget.instate() and Widget.state(), and the following inherited from Entry: Entry.bbox(), Entry.delete(), Entry.icursor(), Entry.index(), Entry.insert(), Entry.xview(), it has some other methods, described at ttk.Spinbox.

Options

This widget accepts the following specific options:

+ + + + + + + +

Option

Description

from

Float value. If set, this is the minimum value to which the decrement button will decrement. Must be spelled as from_ when used as an argument, since from is a Python keyword.

to

Float value. If set, this is the maximum value to which the increment button will increment.

increment

Float value. Specifies the amount which the increment/decrement buttons change the value. Defaults to 1.0.

values

Sequence of string or float values. If specified, the increment/decrement buttons will cycle through the items in this sequence rather than incrementing or decrementing numbers.

wrap

Boolean value. If True, increment and decrement buttons will cycle from the to value to the from value or the from value to the to value, respectively.

format

String value. This specifies the format of numbers set by the increment/decrement buttons. It must be in the form “%W.Pf”, where W is the padded width of the value, P is the precision, and ‘%’ and ‘f’ are literal.

command

Python callable. Will be called with no arguments whenever either of the increment or decrement buttons are pressed.

Virtual events

The spinbox widget generates an <<Increment>> virtual event when the user presses <Up>, and a <<Decrement>> virtual event when the user presses <Down>.

ttk.Spinbox

+class tkinter.ttk.Spinbox
+
+get()
+

Returns the current value of the spinbox.

+
+set(value)
+

Sets the value of the spinbox to value.

+
+

Notebook

Ttk Notebook widget manages a collection of windows and displays a single one at a time. Each child window is associated with a tab, which the user may select to change the currently displayed window.

Options

This widget accepts the following specific options:

+ + + +

Option

Description

height

If present and greater than zero, specifies the desired height of the pane area (not including internal padding or tabs). Otherwise, the maximum height of all panes is used.

padding

Specifies the amount of extra space to add around the outside of the notebook. The padding is a list up to four length specifications left top right bottom. If fewer than four elements are specified, bottom defaults to top, right defaults to left, and top defaults to left.

width

If present and greater than zero, specified the desired width of the pane area (not including internal padding). Otherwise, the maximum width of all panes is used.

Tab Options

There are also specific options for tabs:

+ + + + + + + +

Option

Description

state

Either “normal”, “disabled” or “hidden”. If “disabled”, then the tab is not selectable. If “hidden”, then the tab is not shown.

sticky

Specifies how the child window is positioned within the pane area. Value is a string containing zero or more of the characters “n”, “s”, “e” or “w”. Each letter refers to a side (north, south, east or west) that the child window will stick to, as per the grid() geometry manager.

padding

Specifies the amount of extra space to add between the notebook and this pane. Syntax is the same as for the option padding used by this widget.

text

Specifies a text to be displayed in the tab.

image

Specifies an image to display in the tab. See the option image described in Widget.

compound

Specifies how to display the image relative to the text, in the case both options text and image are present. See Label Options for legal values.

underline

Specifies the index (0-based) of a character to underline in the text string. The underlined character is used for mnemonic activation if Notebook.enable_traversal() is called.

Tab Identifiers

The tab_id present in several methods of ttk.Notebook may take any of the following forms:

  • An integer between zero and the number of tabs
  • The name of a child window
  • A positional specification of the form “@x,y”, which identifies the tab
  • The literal string “current”, which identifies the currently selected tab
  • The literal string “end”, which returns the number of tabs (only valid for Notebook.index())

Virtual Events

This widget generates a <<NotebookTabChanged>> virtual event after a new tab is selected.

ttk.Notebook

+class tkinter.ttk.Notebook
+
+add(child, **kw)
+

Adds a new tab to the notebook.

If window is currently managed by the notebook but hidden, it is restored to its previous position.

See Tab Options for the list of available options.

+
+forget(tab_id)
+

Removes the tab specified by tab_id, unmaps and unmanages the associated window.

+
+hide(tab_id)
+

Hides the tab specified by tab_id.

The tab will not be displayed, but the associated window remains managed by the notebook and its configuration remembered. Hidden tabs may be restored with the add() command.

+
+identify(x, y)
+

Returns the name of the tab element at position x, y, or the empty string if none.

+
+index(tab_id)
+

Returns the numeric index of the tab specified by tab_id, or the total number of tabs if tab_id is the string “end”.

+
+insert(pos, child, **kw)
+

Inserts a pane at the specified position.

pos is either the string “end”, an integer index, or the name of a managed child. If child is already managed by the notebook, moves it to the specified position.

See Tab Options for the list of available options.

+
+select(tab_id=None)
+

Selects the specified tab_id.

The associated child window will be displayed, and the previously selected window (if different) is unmapped. If tab_id is omitted, returns the widget name of the currently selected pane.

+
+tab(tab_id, option=None, **kw)
+

Query or modify the options of the specific tab_id.

If kw is not given, returns a dictionary of the tab option values. If option is specified, returns the value of that option. Otherwise, sets the options to the corresponding values.

+
+tabs()
+

Returns a list of windows managed by the notebook.

+
+enable_traversal()
+

Enable keyboard traversal for a toplevel window containing this notebook.

This will extend the bindings for the toplevel window containing the notebook as follows:

  • +Control-Tab: selects the tab following the currently selected one.
  • +Shift-Control-Tab: selects the tab preceding the currently selected one.
  • +Alt-K: where K is the mnemonic (underlined) character of any tab, will select that tab.

Multiple notebooks in a single toplevel may be enabled for traversal, including nested notebooks. However, notebook traversal only works properly if all panes have the notebook they are in as master.

+
+

Progressbar

The ttk.Progressbar widget shows the status of a long-running operation. It can operate in two modes: 1) the determinate mode which shows the amount completed relative to the total amount of work to be done and 2) the indeterminate mode which provides an animated display to let the user know that work is progressing.

Options

This widget accepts the following specific options:

+ + + + + + + +

Option

Description

orient

One of “horizontal” or “vertical”. Specifies the orientation of the progress bar.

length

Specifies the length of the long axis of the progress bar (width if horizontal, height if vertical).

mode

One of “determinate” or “indeterminate”.

maximum

A number specifying the maximum value. Defaults to 100.

value

The current value of the progress bar. In “determinate” mode, this represents the amount of work completed. In “indeterminate” mode, it is interpreted as modulo maximum; that is, the progress bar completes one “cycle” when its value increases by maximum.

variable

A name which is linked to the option value. If specified, the value of the progress bar is automatically set to the value of this name whenever the latter is modified.

phase

Read-only option. The widget periodically increments the value of this option whenever its value is greater than 0 and, in determinate mode, less than maximum. This option may be used by the current theme to provide additional animation effects.

ttk.Progressbar

+class tkinter.ttk.Progressbar
+
+start(interval=None)
+

Begin autoincrement mode: schedules a recurring timer event that calls Progressbar.step() every interval milliseconds. If omitted, interval defaults to 50 milliseconds.

+
+step(amount=None)
+

Increments the progress bar’s value by amount.

amount defaults to 1.0 if omitted.

+
+stop()
+

Stop autoincrement mode: cancels any recurring timer event initiated by Progressbar.start() for this progress bar.

+
+

Separator

The ttk.Separator widget displays a horizontal or vertical separator bar.

It has no other methods besides the ones inherited from ttk.Widget.

Options

This widget accepts the following specific option:

+ +

Option

Description

orient

One of “horizontal” or “vertical”. Specifies the orientation of the separator.

Sizegrip

The ttk.Sizegrip widget (also known as a grow box) allows the user to resize the containing toplevel window by pressing and dragging the grip.

This widget has neither specific options nor specific methods, besides the ones inherited from ttk.Widget.

Platform-specific notes

  • On macOS, toplevel windows automatically include a built-in size grip by default. Adding a Sizegrip is harmless, since the built-in grip will just mask the widget.

Bugs

  • If the containing toplevel’s position was specified relative to the right or bottom of the screen (e.g. ….), the Sizegrip widget will not resize the window.
  • This widget supports only “southeast” resizing.

Treeview

The ttk.Treeview widget displays a hierarchical collection of items. Each item has a textual label, an optional image, and an optional list of data values. The data values are displayed in successive columns after the tree label.

The order in which data values are displayed may be controlled by setting the widget option displaycolumns. The tree widget can also display column headings. Columns may be accessed by number or symbolic names listed in the widget option columns. See Column Identifiers.

Each item is identified by a unique name. The widget will generate item IDs if they are not supplied by the caller. There is a distinguished root item, named {}. The root item itself is not displayed; its children appear at the top level of the hierarchy.

Each item also has a list of tags, which can be used to associate event bindings with individual items and control the appearance of the item.

The Treeview widget supports horizontal and vertical scrolling, according to the options described in Scrollable Widget Options and the methods Treeview.xview() and Treeview.yview().

Options

This widget accepts the following specific options:

+ + + + + + +

Option

Description

columns

A list of column identifiers, specifying the number of columns and their names.

displaycolumns

A list of column identifiers (either symbolic or integer indices) specifying which data columns are displayed and the order in which they appear, or the string “#all”.

height

Specifies the number of rows which should be visible. Note: the requested width is determined from the sum of the column widths.

padding

Specifies the internal padding for the widget. The padding is a list of up to four length specifications.

selectmode

 +

Controls how the built-in class bindings manage the selection. One of “extended”, “browse” or “none”. If set to “extended” (the default), multiple items may be selected. If “browse”, only a single item will be selected at a time. If “none”, the selection will not be changed.

Note that the application code and tag bindings can set the selection however they wish, regardless of the value of this option.

show

 +

A list containing zero or more of the following values, specifying which elements of the tree to display.

  • tree: display tree labels in column #0.
  • headings: display the heading row.

The default is “tree headings”, i.e., show all elements.

Note: Column #0 always refers to the tree column, even if show=”tree” is not specified.

Item Options

The following item options may be specified for items in the insert and item widget commands.

+ + + + + +

Option

Description

text

The textual label to display for the item.

image

A Tk Image, displayed to the left of the label.

values

 +

The list of values associated with the item.

Each item should have the same number of values as the widget option columns. If there are fewer values than columns, the remaining values are assumed empty. If there are more values than columns, the extra values are ignored.

open

True/False value indicating whether the item’s children should be displayed or hidden.

tags

A list of tags associated with this item.

Tag Options

The following options may be specified on tags:

+ + + + +

Option

Description

foreground

Specifies the text foreground color.

background

Specifies the cell or item background color.

font

Specifies the font to use when drawing text.

image

Specifies the item image, in case the item’s image option is empty.

Column Identifiers

Column identifiers take any of the following forms:

  • A symbolic name from the list of columns option.
  • An integer n, specifying the nth data column.
  • A string of the form #n, where n is an integer, specifying the nth display column.

Notes:

  • Item’s option values may be displayed in a different order than the order in which they are stored.
  • Column #0 always refers to the tree column, even if show=”tree” is not specified.

A data column number is an index into an item’s option values list; a display column number is the column number in the tree where the values are displayed. Tree labels are displayed in column #0. If option displaycolumns is not set, then data column n is displayed in column #n+1. Again, column #0 always refers to the tree column.

Virtual Events

The Treeview widget generates the following virtual events.

+ + + +

Event

Description

<<TreeviewSelect>>

Generated whenever the selection changes.

<<TreeviewOpen>>

Generated just before settings the focus item to open=True.

<<TreeviewClose>>

Generated just after setting the focus item to open=False.

The Treeview.focus() and Treeview.selection() methods can be used to determine the affected item or items.

ttk.Treeview

+class tkinter.ttk.Treeview
+
+bbox(item, column=None)
+

Returns the bounding box (relative to the treeview widget’s window) of the specified item in the form (x, y, width, height).

If column is specified, returns the bounding box of that cell. If the item is not visible (i.e., if it is a descendant of a closed item or is scrolled offscreen), returns an empty string.

+
+get_children(item=None)
+

Returns the list of children belonging to item.

If item is not specified, returns root children.

+
+set_children(item, *newchildren)
+

Replaces item’s child with newchildren.

Children present in item that are not present in newchildren are detached from the tree. No items in newchildren may be an ancestor of item. Note that not specifying newchildren results in detaching item’s children.

+
+column(column, option=None, **kw)
+

Query or modify the options for the specified column.

If kw is not given, returns a dict of the column option values. If option is specified then the value for that option is returned. Otherwise, sets the options to the corresponding values.

The valid options/values are:

id
+
+

Returns the column name. This is a read-only option.

+anchor: One of the standard Tk anchor values.
+
+

Specifies how the text in this column should be aligned with respect to the cell.

+minwidth: width
+
+

The minimum width of the column in pixels. The treeview widget will not make the column any smaller than specified by this option when the widget is resized or the user drags a column.

+stretch: True/False
+
+

Specifies whether the column’s width should be adjusted when the widget is resized.

+width: width
+
+

The width of the column in pixels.

To configure the tree column, call this with column = “#0”

+
+delete(*items)
+

Delete all specified items and all their descendants.

The root item may not be deleted.

+
+detach(*items)
+

Unlinks all of the specified items from the tree.

The items and all of their descendants are still present, and may be reinserted at another point in the tree, but will not be displayed.

The root item may not be detached.

+
+exists(item)
+

Returns True if the specified item is present in the tree.

+
+focus(item=None)
+

If item is specified, sets the focus item to item. Otherwise, returns the current focus item, or ‘’ if there is none.

+
+heading(column, option=None, **kw)
+

Query or modify the heading options for the specified column.

If kw is not given, returns a dict of the heading option values. If option is specified then the value for that option is returned. Otherwise, sets the options to the corresponding values.

The valid options/values are:

+text: text
+
+

The text to display in the column heading.

+image: imageName
+
+

Specifies an image to display to the right of the column heading.

+anchor: anchor
+
+

Specifies how the heading text should be aligned. One of the standard Tk anchor values.

+command: callback
+
+

A callback to be invoked when the heading label is pressed.

To configure the tree column heading, call this with column = “#0”.

+
+identify(component, x, y)
+

Returns a description of the specified component under the point given by x and y, or the empty string if no such component is present at that position.

+
+identify_row(y)
+

Returns the item ID of the item at position y.

+
+identify_column(x)
+

Returns the data column identifier of the cell at position x.

The tree column has ID #0.

+
+identify_region(x, y)
+

Returns one of:

+ + + + +

region

meaning

heading

Tree heading area.

separator

Space between two columns headings.

tree

The tree area.

cell

A data cell.

Availability: Tk 8.6.

+
+identify_element(x, y)
+

Returns the element at position x, y.

Availability: Tk 8.6.

+
+index(item)
+

Returns the integer index of item within its parent’s list of children.

+
+insert(parent, index, iid=None, **kw)
+

Creates a new item and returns the item identifier of the newly created item.

parent is the item ID of the parent item, or the empty string to create a new top-level item. index is an integer, or the value “end”, specifying where in the list of parent’s children to insert the new item. If index is less than or equal to zero, the new node is inserted at the beginning; if index is greater than or equal to the current number of children, it is inserted at the end. If iid is specified, it is used as the item identifier; iid must not already exist in the tree. Otherwise, a new unique identifier is generated.

See Item Options for the list of available points.

+
+item(item, option=None, **kw)
+

Query or modify the options for the specified item.

If no options are given, a dict with options/values for the item is returned. If option is specified then the value for that option is returned. Otherwise, sets the options to the corresponding values as given by kw.

+
+move(item, parent, index)
+

Moves item to position index in parent’s list of children.

It is illegal to move an item under one of its descendants. If index is less than or equal to zero, item is moved to the beginning; if greater than or equal to the number of children, it is moved to the end. If item was detached it is reattached.

+
+next(item)
+

Returns the identifier of item’s next sibling, or ‘’ if item is the last child of its parent.

+
+parent(item)
+

Returns the ID of the parent of item, or ‘’ if item is at the top level of the hierarchy.

+
+prev(item)
+

Returns the identifier of item’s previous sibling, or ‘’ if item is the first child of its parent.

+
+reattach(item, parent, index)
+

An alias for Treeview.move().

+
+see(item)
+

Ensure that item is visible.

Sets all of item’s ancestors open option to True, and scrolls the widget if necessary so that item is within the visible portion of the tree.

+
+selection()
+

Returns a tuple of selected items.

Changed in version 3.8: selection() no longer takes arguments. For changing the selection state use the following selection methods.

+
+selection_set(*items)
+

items becomes the new selection.

Changed in version 3.6: items can be passed as separate arguments, not just as a single tuple.

+
+selection_add(*items)
+

Add items to the selection.

Changed in version 3.6: items can be passed as separate arguments, not just as a single tuple.

+
+selection_remove(*items)
+

Remove items from the selection.

Changed in version 3.6: items can be passed as separate arguments, not just as a single tuple.

+
+selection_toggle(*items)
+

Toggle the selection state of each item in items.

Changed in version 3.6: items can be passed as separate arguments, not just as a single tuple.

+
+set(item, column=None, value=None)
+

With one argument, returns a dictionary of column/value pairs for the specified item. With two arguments, returns the current value of the specified column. With three arguments, sets the value of given column in given item to the specified value.

+
+tag_bind(tagname, sequence=None, callback=None)
+

Bind a callback for the given event sequence to the tag tagname. When an event is delivered to an item, the callbacks for each of the item’s tags option are called.

+
+tag_configure(tagname, option=None, **kw)
+

Query or modify the options for the specified tagname.

If kw is not given, returns a dict of the option settings for tagname. If option is specified, returns the value for that option for the specified tagname. Otherwise, sets the options to the corresponding values for the given tagname.

+
+tag_has(tagname, item=None)
+

If item is specified, returns 1 or 0 depending on whether the specified item has the given tagname. Otherwise, returns a list of all items that have the specified tag.

Availability: Tk 8.6

+
+xview(*args)
+

Query or modify horizontal position of the treeview.

+
+yview(*args)
+

Query or modify vertical position of the treeview.

+
+

Ttk Styling

Each widget in ttk is assigned a style, which specifies the set of elements making up the widget and how they are arranged, along with dynamic and default settings for element options. By default the style name is the same as the widget’s class name, but it may be overridden by the widget’s style option. If you don’t know the class name of a widget, use the method Misc.winfo_class() (somewidget.winfo_class()).

See also

Tcl’2004 conference presentation
+
+

This document explains how the theme engine works

+class tkinter.ttk.Style
+

This class is used to manipulate the style database.

+configure(style, query_opt=None, **kw)
+

Query or set the default value of the specified option(s) in style.

Each key in kw is an option and each value is a string identifying the value for that option.

For example, to change every default button to be a flat button with some padding and a different background color:

from tkinter import ttk
+import tkinter
+
+root = tkinter.Tk()
+
+ttk.Style().configure("TButton", padding=6, relief="flat",
+   background="#ccc")
+
+btn = ttk.Button(text="Sample")
+btn.pack()
+
+root.mainloop()
+
+
+map(style, query_opt=None, **kw)
+

Query or sets dynamic values of the specified option(s) in style.

Each key in kw is an option and each value should be a list or a tuple (usually) containing statespecs grouped in tuples, lists, or some other preference. A statespec is a compound of one or more states and then a value.

An example may make it more understandable:

import tkinter
+from tkinter import ttk
+
+root = tkinter.Tk()
+
+style = ttk.Style()
+style.map("C.TButton",
+    foreground=[('pressed', 'red'), ('active', 'blue')],
+    background=[('pressed', '!disabled', 'black'), ('active', 'white')]
+    )
+
+colored_btn = ttk.Button(text="Test", style="C.TButton").pack()
+
+root.mainloop()
+

Note that the order of the (states, value) sequences for an option does matter, if the order is changed to [('active', 'blue'), ('pressed', +'red')] in the foreground option, for example, the result would be a blue foreground when the widget were in active or pressed states.

+
+lookup(style, option, state=None, default=None)
+

Returns the value specified for option in style.

If state is specified, it is expected to be a sequence of one or more states. If the default argument is set, it is used as a fallback value in case no specification for option is found.

To check what font a Button uses by default:

from tkinter import ttk
+
+print(ttk.Style().lookup("TButton", "font"))
+
+
+layout(style, layoutspec=None)
+

Define the widget layout for given style. If layoutspec is omitted, return the layout specification for given style.

layoutspec, if specified, is expected to be a list or some other sequence type (excluding strings), where each item should be a tuple and the first item is the layout name and the second item should have the format described in Layouts.

To understand the format, see the following example (it is not intended to do anything useful):

from tkinter import ttk
+import tkinter
+
+root = tkinter.Tk()
+
+style = ttk.Style()
+style.layout("TMenubutton", [
+   ("Menubutton.background", None),
+   ("Menubutton.button", {"children":
+       [("Menubutton.focus", {"children":
+           [("Menubutton.padding", {"children":
+               [("Menubutton.label", {"side": "left", "expand": 1})]
+           })]
+       })]
+   }),
+])
+
+mbtn = ttk.Menubutton(text='Text')
+mbtn.pack()
+root.mainloop()
+
+
+element_create(elementname, etype, *args, **kw)
+

Create a new element in the current theme, of the given etype which is expected to be either “image” or “from”.

If “image” is used, args should contain the default image name followed by statespec/value pairs (this is the imagespec), and kw may have the following options:

border=padding
+
+

padding is a list of up to four integers, specifying the left, top, right, and bottom borders, respectively.

height=height
+
+

Specifies a minimum height for the element. If less than zero, the base image’s height is used as a default.

padding=padding
+
+

Specifies the element’s interior padding. Defaults to border’s value if not specified.

sticky=spec
+
+

Specifies how the image is placed within the final parcel. spec contains zero or more characters “n”, “s”, “w”, or “e”.

width=width
+
+

Specifies a minimum width for the element. If less than zero, the base image’s width is used as a default.

Example:

img1 = tkinter.PhotoImage(master=root, file='button.png')
+img1 = tkinter.PhotoImage(master=root, file='button-pressed.png')
+img1 = tkinter.PhotoImage(master=root, file='button-active.png')
+style = ttk.Style(root)
+style.element_create('Button.button', 'image',
+                     img1, ('pressed', img2), ('active', img3),
+                     border=(2, 4), sticky='we')
+

If “from” is used as the value of etype, element_create() will clone an existing element. args is expected to contain a themename, from which the element will be cloned, and optionally an element to clone from. If this element to clone from is not specified, an empty element will be used. kw is discarded.

Example:

style = ttk.Style(root)
+style.element_create('plain.background', 'from', 'default')
+
+
+element_names()
+

Returns the list of elements defined in the current theme.

+
+element_options(elementname)
+

Returns the list of elementname’s options.

+
+theme_create(themename, parent=None, settings=None)
+

Create a new theme.

It is an error if themename already exists. If parent is specified, the new theme will inherit styles, elements and layouts from the parent theme. If settings are present they are expected to have the same syntax used for theme_settings().

+
+theme_settings(themename, settings)
+

Temporarily sets the current theme to themename, apply specified settings and then restore the previous theme.

Each key in settings is a style and each value may contain the keys ‘configure’, ‘map’, ‘layout’ and ‘element create’ and they are expected to have the same format as specified by the methods Style.configure(), Style.map(), Style.layout() and Style.element_create() respectively.

As an example, let’s change the Combobox for the default theme a bit:

from tkinter import ttk
+import tkinter
+
+root = tkinter.Tk()
+
+style = ttk.Style()
+style.theme_settings("default", {
+   "TCombobox": {
+       "configure": {"padding": 5},
+       "map": {
+           "background": [("active", "green2"),
+                          ("!disabled", "green4")],
+           "fieldbackground": [("!disabled", "green3")],
+           "foreground": [("focus", "OliveDrab1"),
+                          ("!disabled", "OliveDrab2")]
+       }
+   }
+})
+
+combo = ttk.Combobox().pack()
+
+root.mainloop()
+
+
+theme_names()
+

Returns a list of all known themes.

+
+theme_use(themename=None)
+

If themename is not given, returns the theme in use. Otherwise, sets the current theme to themename, refreshes all widgets and emits a <<ThemeChanged>> event.

+
+

Layouts

A layout can be just None, if it takes no options, or a dict of options specifying how to arrange the element. The layout mechanism uses a simplified version of the pack geometry manager: given an initial cavity, each element is allocated a parcel.

The valid options/values are:

+side: whichside
+
+

Specifies which side of the cavity to place the element; one of top, right, bottom or left. If omitted, the element occupies the entire cavity.

+sticky: nswe
+
+

Specifies where the element is placed inside its allocated parcel.

+unit: 0 or 1
+
+

If set to 1, causes the element and all of its descendants to be treated as a single element for the purposes of Widget.identify() et al. It’s used for things like scrollbar thumbs with grips.

+children: [sublayout… ]
+
+

Specifies a list of elements to place inside the element. Each element is a tuple (or other sequence type) where the first item is the layout name, and the other is a Layout.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tkinter.ttk.html +

+
diff --git a/devdocs/python~3.12/library%2Ftoken.html b/devdocs/python~3.12/library%2Ftoken.html new file mode 100644 index 00000000..798b567b --- /dev/null +++ b/devdocs/python~3.12/library%2Ftoken.html @@ -0,0 +1,216 @@ +

token — Constants used with Python parse trees

Source code: Lib/token.py

This module provides constants which represent the numeric values of leaf nodes of the parse tree (terminal tokens). Refer to the file Grammar/Tokens in the Python distribution for the definitions of the names in the context of the language grammar. The specific numeric values which the names map to may change between Python versions.

The module also provides a mapping from numeric codes to names and some functions. The functions mirror definitions in the Python C header files.

+token.tok_name
+

Dictionary mapping the numeric values of the constants defined in this module back to name strings, allowing more human-readable representation of parse trees to be generated.

+
+token.ISTERMINAL(x)
+

Return True for terminal token values.

+
+token.ISNONTERMINAL(x)
+

Return True for non-terminal token values.

+
+token.ISEOF(x)
+

Return True if x is the marker indicating the end of input.

+

The token constants are:

+token.ENDMARKER
+
+token.NAME
+
+token.NUMBER
+
+token.STRING
+
+token.NEWLINE
+
+token.INDENT
+
+token.DEDENT
+
+token.LPAR
+

Token value for "(".

+
+token.RPAR
+

Token value for ")".

+
+token.LSQB
+

Token value for "[".

+
+token.RSQB
+

Token value for "]".

+
+token.COLON
+

Token value for ":".

+
+token.COMMA
+

Token value for ",".

+
+token.SEMI
+

Token value for ";".

+
+token.PLUS
+

Token value for "+".

+
+token.MINUS
+

Token value for "-".

+
+token.STAR
+

Token value for "*".

+
+token.SLASH
+

Token value for "/".

+
+token.VBAR
+

Token value for "|".

+
+token.AMPER
+

Token value for "&".

+
+token.LESS
+

Token value for "<".

+
+token.GREATER
+

Token value for ">".

+
+token.EQUAL
+

Token value for "=".

+
+token.DOT
+

Token value for ".".

+
+token.PERCENT
+

Token value for "%".

+
+token.LBRACE
+

Token value for "{".

+
+token.RBRACE
+

Token value for "}".

+
+token.EQEQUAL
+

Token value for "==".

+
+token.NOTEQUAL
+

Token value for "!=".

+
+token.LESSEQUAL
+

Token value for "<=".

+
+token.GREATEREQUAL
+

Token value for ">=".

+
+token.TILDE
+

Token value for "~".

+
+token.CIRCUMFLEX
+

Token value for "^".

+
+token.LEFTSHIFT
+

Token value for "<<".

+
+token.RIGHTSHIFT
+

Token value for ">>".

+
+token.DOUBLESTAR
+

Token value for "**".

+
+token.PLUSEQUAL
+

Token value for "+=".

+
+token.MINEQUAL
+

Token value for "-=".

+
+token.STAREQUAL
+

Token value for "*=".

+
+token.SLASHEQUAL
+

Token value for "/=".

+
+token.PERCENTEQUAL
+

Token value for "%=".

+
+token.AMPEREQUAL
+

Token value for "&=".

+
+token.VBAREQUAL
+

Token value for "|=".

+
+token.CIRCUMFLEXEQUAL
+

Token value for "^=".

+
+token.LEFTSHIFTEQUAL
+

Token value for "<<=".

+
+token.RIGHTSHIFTEQUAL
+

Token value for ">>=".

+
+token.DOUBLESTAREQUAL
+

Token value for "**=".

+
+token.DOUBLESLASH
+

Token value for "//".

+
+token.DOUBLESLASHEQUAL
+

Token value for "//=".

+
+token.AT
+

Token value for "@".

+
+token.ATEQUAL
+

Token value for "@=".

+
+token.RARROW
+

Token value for "->".

+
+token.ELLIPSIS
+

Token value for "...".

+
+token.COLONEQUAL
+

Token value for ":=".

+
+token.EXCLAMATION
+

Token value for "!".

+
+token.OP
+
+token.AWAIT
+
+token.ASYNC
+
+token.TYPE_IGNORE
+
+token.TYPE_COMMENT
+
+token.SOFT_KEYWORD
+
+token.FSTRING_START
+
+token.FSTRING_MIDDLE
+
+token.FSTRING_END
+
+token.COMMENT
+
+token.NL
+
+token.ERRORTOKEN
+
+token.N_TOKENS
+
+token.NT_OFFSET
+

The following token type values aren’t used by the C tokenizer but are needed for the tokenize module.

token.COMMENT +
+

Token value used to indicate a comment.

+
token.NL +
+

Token value used to indicate a non-terminating newline. The NEWLINE token indicates the end of a logical line of Python code; NL tokens are generated when a logical line of code is continued over multiple physical lines.

+
+token.ENCODING
+

Token value that indicates the encoding used to decode the source bytes into text. The first token returned by tokenize.tokenize() will always be an ENCODING token.

+
token.TYPE_COMMENT +
+

Token value indicating that a type comment was recognized. Such tokens are only produced when ast.parse() is invoked with type_comments=True.

+

Changed in version 3.5: Added AWAIT and ASYNC tokens.

Changed in version 3.7: Added COMMENT, NL and ENCODING tokens.

Changed in version 3.7: Removed AWAIT and ASYNC tokens. “async” and “await” are now tokenized as NAME tokens.

Changed in version 3.8: Added TYPE_COMMENT, TYPE_IGNORE, COLONEQUAL. Added AWAIT and ASYNC tokens back (they’re needed to support parsing older Python versions for ast.parse() with feature_version set to 6 or lower).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/token.html +

+
diff --git a/devdocs/python~3.12/library%2Ftokenize.html b/devdocs/python~3.12/library%2Ftokenize.html new file mode 100644 index 00000000..49d8fb10 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftokenize.html @@ -0,0 +1,132 @@ +

tokenize — Tokenizer for Python source

Source code: Lib/tokenize.py

The tokenize module provides a lexical scanner for Python source code, implemented in Python. The scanner in this module returns comments as tokens as well, making it useful for implementing “pretty-printers”, including colorizers for on-screen displays.

To simplify token stream handling, all operator and delimiter tokens and Ellipsis are returned using the generic OP token type. The exact type can be determined by checking the exact_type property on the named tuple returned from tokenize.tokenize().

Warning

Note that the functions in this module are only designed to parse syntactically valid Python code (code that does not raise when parsed using ast.parse()). The behavior of the functions in this module is undefined when providing invalid Python code and it can change at any point.

Tokenizing Input

The primary entry point is a generator:

+tokenize.tokenize(readline)
+

The tokenize() generator requires one argument, readline, which must be a callable object which provides the same interface as the io.IOBase.readline() method of file objects. Each call to the function should return one line of input as bytes.

The generator produces 5-tuples with these members: the token type; the token string; a 2-tuple (srow, scol) of ints specifying the row and column where the token begins in the source; a 2-tuple (erow, ecol) of ints specifying the row and column where the token ends in the source; and the line on which the token was found. The line passed (the last tuple item) is the physical line. The 5 tuple is returned as a named tuple with the field names: type string start end line.

The returned named tuple has an additional property named exact_type that contains the exact operator type for OP tokens. For all other token types exact_type equals the named tuple type field.

Changed in version 3.1: Added support for named tuples.

Changed in version 3.3: Added support for exact_type.

tokenize() determines the source encoding of the file by looking for a UTF-8 BOM or encoding cookie, according to PEP 263.

+
+tokenize.generate_tokens(readline)
+

Tokenize a source reading unicode strings instead of bytes.

Like tokenize(), the readline argument is a callable returning a single line of input. However, generate_tokens() expects readline to return a str object rather than bytes.

The result is an iterator yielding named tuples, exactly like tokenize(). It does not yield an ENCODING token.

+

All constants from the token module are also exported from tokenize.

Another function is provided to reverse the tokenization process. This is useful for creating tools that tokenize a script, modify the token stream, and write back the modified script.

+tokenize.untokenize(iterable)
+

Converts tokens back into Python source code. The iterable must return sequences with at least two elements, the token type and the token string. Any additional sequence elements are ignored.

The reconstructed script is returned as a single string. The result is guaranteed to tokenize back to match the input so that the conversion is lossless and round-trips are assured. The guarantee applies only to the token type and token string as the spacing between tokens (column positions) may change.

It returns bytes, encoded using the ENCODING token, which is the first token sequence output by tokenize(). If there is no encoding token in the input, it returns a str instead.

+

tokenize() needs to detect the encoding of source files it tokenizes. The function it uses to do this is available:

+tokenize.detect_encoding(readline)
+

The detect_encoding() function is used to detect the encoding that should be used to decode a Python source file. It requires one argument, readline, in the same way as the tokenize() generator.

It will call readline a maximum of twice, and return the encoding used (as a string) and a list of any lines (not decoded from bytes) it has read in.

It detects the encoding from the presence of a UTF-8 BOM or an encoding cookie as specified in PEP 263. If both a BOM and a cookie are present, but disagree, a SyntaxError will be raised. Note that if the BOM is found, 'utf-8-sig' will be returned as an encoding.

If no encoding is specified, then the default of 'utf-8' will be returned.

Use open() to open Python source files: it uses detect_encoding() to detect the file encoding.

+
+tokenize.open(filename)
+

Open a file in read only mode using the encoding detected by detect_encoding().

New in version 3.2.

+
+exception tokenize.TokenError
+

Raised when either a docstring or expression that may be split over several lines is not completed anywhere in the file, for example:

"""Beginning of
+docstring
+

or:

[1,
+ 2,
+ 3
+
+

Command-Line Usage

New in version 3.3.

The tokenize module can be executed as a script from the command line. It is as simple as:

python -m tokenize [-e] [filename.py]
+

The following options are accepted:

+-h, --help
+

show this help message and exit

+
+-e, --exact
+

display token names using the exact type

+

If filename.py is specified its contents are tokenized to stdout. Otherwise, tokenization is performed on stdin.

Examples

Example of a script rewriter that transforms float literals into Decimal objects:

from tokenize import tokenize, untokenize, NUMBER, STRING, NAME, OP
+from io import BytesIO
+
+def decistmt(s):
+    """Substitute Decimals for floats in a string of statements.
+
+    >>> from decimal import Decimal
+    >>> s = 'print(+21.3e-5*-.1234/81.7)'
+    >>> decistmt(s)
+    "print (+Decimal ('21.3e-5')*-Decimal ('.1234')/Decimal ('81.7'))"
+
+    The format of the exponent is inherited from the platform C library.
+    Known cases are "e-007" (Windows) and "e-07" (not Windows).  Since
+    we're only showing 12 digits, and the 13th isn't close to 5, the
+    rest of the output should be platform-independent.
+
+    >>> exec(s)  #doctest: +ELLIPSIS
+    -3.21716034272e-0...7
+
+    Output from calculations with Decimal should be identical across all
+    platforms.
+
+    >>> exec(decistmt(s))
+    -3.217160342717258261933904529E-7
+    """
+    result = []
+    g = tokenize(BytesIO(s.encode('utf-8')).readline)  # tokenize the string
+    for toknum, tokval, _, _, _ in g:
+        if toknum == NUMBER and '.' in tokval:  # replace NUMBER tokens
+            result.extend([
+                (NAME, 'Decimal'),
+                (OP, '('),
+                (STRING, repr(tokval)),
+                (OP, ')')
+            ])
+        else:
+            result.append((toknum, tokval))
+    return untokenize(result).decode('utf-8')
+

Example of tokenizing from the command line. The script:

def say_hello():
+    print("Hello, World!")
+
+say_hello()
+

will be tokenized to the following output where the first column is the range of the line/column coordinates where the token is found, the second column is the name of the token, and the final column is the value of the token (if any)

$ python -m tokenize hello.py
+0,0-0,0:            ENCODING       'utf-8'
+1,0-1,3:            NAME           'def'
+1,4-1,13:           NAME           'say_hello'
+1,13-1,14:          OP             '('
+1,14-1,15:          OP             ')'
+1,15-1,16:          OP             ':'
+1,16-1,17:          NEWLINE        '\n'
+2,0-2,4:            INDENT         '    '
+2,4-2,9:            NAME           'print'
+2,9-2,10:           OP             '('
+2,10-2,25:          STRING         '"Hello, World!"'
+2,25-2,26:          OP             ')'
+2,26-2,27:          NEWLINE        '\n'
+3,0-3,1:            NL             '\n'
+4,0-4,0:            DEDENT         ''
+4,0-4,9:            NAME           'say_hello'
+4,9-4,10:           OP             '('
+4,10-4,11:          OP             ')'
+4,11-4,12:          NEWLINE        '\n'
+5,0-5,0:            ENDMARKER      ''
+

The exact token type names can be displayed using the -e option:

$ python -m tokenize -e hello.py
+0,0-0,0:            ENCODING       'utf-8'
+1,0-1,3:            NAME           'def'
+1,4-1,13:           NAME           'say_hello'
+1,13-1,14:          LPAR           '('
+1,14-1,15:          RPAR           ')'
+1,15-1,16:          COLON          ':'
+1,16-1,17:          NEWLINE        '\n'
+2,0-2,4:            INDENT         '    '
+2,4-2,9:            NAME           'print'
+2,9-2,10:           LPAR           '('
+2,10-2,25:          STRING         '"Hello, World!"'
+2,25-2,26:          RPAR           ')'
+2,26-2,27:          NEWLINE        '\n'
+3,0-3,1:            NL             '\n'
+4,0-4,0:            DEDENT         ''
+4,0-4,9:            NAME           'say_hello'
+4,9-4,10:           LPAR           '('
+4,10-4,11:          RPAR           ')'
+4,11-4,12:          NEWLINE        '\n'
+5,0-5,0:            ENDMARKER      ''
+

Example of tokenizing a file programmatically, reading unicode strings instead of bytes with generate_tokens():

import tokenize
+
+with tokenize.open('hello.py') as f:
+    tokens = tokenize.generate_tokens(f.readline)
+    for token in tokens:
+        print(token)
+

Or reading bytes directly with tokenize():

import tokenize
+
+with open('hello.py', 'rb') as f:
+    tokens = tokenize.tokenize(f.readline)
+    for token in tokens:
+        print(token)
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tokenize.html +

+
diff --git a/devdocs/python~3.12/library%2Ftomllib.html b/devdocs/python~3.12/library%2Ftomllib.html new file mode 100644 index 00000000..ef1e1f68 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftomllib.html @@ -0,0 +1,41 @@ +

tomllib — Parse TOML files

New in version 3.11.

Source code: Lib/tomllib

This module provides an interface for parsing TOML (Tom’s Obvious Minimal Language, https://toml.io). This module does not support writing TOML.

See also

The Tomli-W package is a TOML writer that can be used in conjunction with this module, providing a write API familiar to users of the standard library marshal and pickle modules.

See also

The TOML Kit package is a style-preserving TOML library with both read and write capability. It is a recommended replacement for this module for editing already existing TOML files.

This module defines the following functions:

+tomllib.load(fp, /, *, parse_float=float)
+

Read a TOML file. The first argument should be a readable and binary file object. Return a dict. Convert TOML types to Python using this conversion table.

parse_float will be called with the string of every TOML float to be decoded. By default, this is equivalent to float(num_str). This can be used to use another datatype or parser for TOML floats (e.g. decimal.Decimal). The callable must not return a dict or a list, else a ValueError is raised.

A TOMLDecodeError will be raised on an invalid TOML document.

+
+tomllib.loads(s, /, *, parse_float=float)
+

Load TOML from a str object. Return a dict. Convert TOML types to Python using this conversion table. The parse_float argument has the same meaning as in load().

A TOMLDecodeError will be raised on an invalid TOML document.

+

The following exceptions are available:

+exception tomllib.TOMLDecodeError
+

Subclass of ValueError.

+

Examples

Parsing a TOML file:

import tomllib
+
+with open("pyproject.toml", "rb") as f:
+    data = tomllib.load(f)
+

Parsing a TOML string:

import tomllib
+
+toml_str = """
+python-version = "3.11.0"
+python-implementation = "CPython"
+"""
+
+data = tomllib.loads(toml_str)
+

Conversion Table

+ + + + + + + + + + + + + +

TOML

Python

TOML document

dict

string

str

integer

int

float

float (configurable with parse_float)

boolean

bool

offset date-time

datetime.datetime (tzinfo attribute set to an instance of datetime.timezone)

local date-time

datetime.datetime (tzinfo attribute set to None)

local date

datetime.date

local time

datetime.time

array

list

table

dict

inline table

dict

array of tables

list of dicts
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tomllib.html +

+
diff --git a/devdocs/python~3.12/library%2Ftrace.html b/devdocs/python~3.12/library%2Ftrace.html new file mode 100644 index 00000000..4633bb5d --- /dev/null +++ b/devdocs/python~3.12/library%2Ftrace.html @@ -0,0 +1,94 @@ +

trace — Trace or track Python statement execution

Source code: Lib/trace.py

The trace module allows you to trace program execution, generate annotated statement coverage listings, print caller/callee relationships and list functions executed during a program run. It can be used in another program or from the command line.

See also

Coverage.py
+
+

A popular third-party coverage tool that provides HTML output along with advanced features such as branch coverage.

Command-Line Usage

The trace module can be invoked from the command line. It can be as simple as

python -m trace --count -C . somefile.py ...
+

The above will execute somefile.py and generate annotated listings of all Python modules imported during the execution into the current directory.

+--help
+

Display usage and exit.

+
+--version
+

Display the version of the module and exit.

+

New in version 3.8: Added --module option that allows to run an executable module.

Main options

At least one of the following options must be specified when invoking trace. The --listfuncs option is mutually exclusive with the --trace and --count options. When --listfuncs is provided, neither --count nor --trace are accepted, and vice versa.

+-c, --count
+

Produce a set of annotated listing files upon program completion that shows how many times each statement was executed. See also --coverdir, --file and --no-report below.

+
+-t, --trace
+

Display lines as they are executed.

+
+-l, --listfuncs
+

Display the functions executed by running the program.

+
+-r, --report
+

Produce an annotated list from an earlier program run that used the --count and --file option. This does not execute any code.

+
+-T, --trackcalls
+

Display the calling relationships exposed by running the program.

+

Modifiers

+-f, --file=<file>
+

Name of a file to accumulate counts over several tracing runs. Should be used with the --count option.

+
+-C, --coverdir=<dir>
+

Directory where the report files go. The coverage report for package.module is written to file dir/package/module.cover.

+
+-m, --missing
+

When generating annotated listings, mark lines which were not executed with >>>>>>.

+
+-s, --summary
+

When using --count or --report, write a brief summary to stdout for each file processed.

+
+-R, --no-report
+

Do not generate annotated listings. This is useful if you intend to make several runs with --count, and then produce a single set of annotated listings at the end.

+
+-g, --timing
+

Prefix each line with the time since the program started. Only used while tracing.

+

Filters

These options may be repeated multiple times.

+--ignore-module=<mod>
+

Ignore each of the given module names and its submodules (if it is a package). The argument can be a list of names separated by a comma.

+
+--ignore-dir=<dir>
+

Ignore all modules and packages in the named directory and subdirectories. The argument can be a list of directories separated by os.pathsep.

+

Programmatic Interface

+class trace.Trace(count=1, trace=1, countfuncs=0, countcallers=0, ignoremods=(), ignoredirs=(), infile=None, outfile=None, timing=False)
+

Create an object to trace execution of a single statement or expression. All parameters are optional. count enables counting of line numbers. trace enables line execution tracing. countfuncs enables listing of the functions called during the run. countcallers enables call relationship tracking. ignoremods is a list of modules or packages to ignore. ignoredirs is a list of directories whose modules or packages should be ignored. infile is the name of the file from which to read stored count information. outfile is the name of the file in which to write updated count information. timing enables a timestamp relative to when tracing was started to be displayed.

+run(cmd)
+

Execute the command and gather statistics from the execution with the current tracing parameters. cmd must be a string or code object, suitable for passing into exec().

+
+runctx(cmd, globals=None, locals=None)
+

Execute the command and gather statistics from the execution with the current tracing parameters, in the defined global and local environments. If not defined, globals and locals default to empty dictionaries.

+
+runfunc(func, /, *args, **kwds)
+

Call func with the given arguments under control of the Trace object with the current tracing parameters.

+
+results()
+

Return a CoverageResults object that contains the cumulative results of all previous calls to run, runctx and runfunc for the given Trace instance. Does not reset the accumulated trace results.

+
+
+class trace.CoverageResults
+

A container for coverage results, created by Trace.results(). Should not be created directly by the user.

+update(other)
+

Merge in data from another CoverageResults object.

+
+write_results(show_missing=True, summary=False, coverdir=None)
+

Write coverage results. Set show_missing to show lines that had no hits. Set summary to include in the output the coverage summary per module. coverdir specifies the directory into which the coverage result files will be output. If None, the results for each source file are placed in its directory.

+
+

A simple example demonstrating the use of the programmatic interface:

import sys
+import trace
+
+# create a Trace object, telling it what to ignore, and whether to
+# do tracing or line-counting or both.
+tracer = trace.Trace(
+    ignoredirs=[sys.prefix, sys.exec_prefix],
+    trace=0,
+    count=1)
+
+# run the new command using the given tracer
+tracer.run('main()')
+
+# make a report, placing output in the current directory
+r = tracer.results()
+r.write_results(show_missing=True, coverdir=".")
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/trace.html +

+
diff --git a/devdocs/python~3.12/library%2Ftraceback.html b/devdocs/python~3.12/library%2Ftraceback.html new file mode 100644 index 00000000..f23623ac --- /dev/null +++ b/devdocs/python~3.12/library%2Ftraceback.html @@ -0,0 +1,265 @@ +

traceback — Print or retrieve a stack traceback

Source code: Lib/traceback.py

This module provides a standard interface to extract, format and print stack traces of Python programs. It exactly mimics the behavior of the Python interpreter when it prints a stack trace. This is useful when you want to print stack traces under program control, such as in a “wrapper” around the interpreter.

The module uses traceback objects — these are objects of type types.TracebackType, which are assigned to the __traceback__ field of BaseException instances.

See also

+Module faulthandler +
+
+

Used to dump Python tracebacks explicitly, on a fault, after a timeout, or on a user signal.

+Module pdb +
+
+

Interactive source code debugger for Python programs.

The module defines the following functions:

+traceback.print_tb(tb, limit=None, file=None)
+

Print up to limit stack trace entries from traceback object tb (starting from the caller’s frame) if limit is positive. Otherwise, print the last abs(limit) entries. If limit is omitted or None, all entries are printed. If file is omitted or None, the output goes to sys.stderr; otherwise it should be an open file or file-like object to receive the output.

Changed in version 3.5: Added negative limit support.

+
+traceback.print_exception(exc, /, [value, tb, ]limit=None, file=None, chain=True)
+

Print exception information and stack trace entries from traceback object tb to file. This differs from print_tb() in the following ways:

  • if tb is not None, it prints a header Traceback (most recent +call last): +
  • it prints the exception type and value after the stack trace
  • if type(value) is SyntaxError and value has the appropriate format, it prints the line where the syntax error occurred with a caret indicating the approximate position of the error.

Since Python 3.10, instead of passing value and tb, an exception object can be passed as the first argument. If value and tb are provided, the first argument is ignored in order to provide backwards compatibility.

The optional limit argument has the same meaning as for print_tb(). If chain is true (the default), then chained exceptions (the __cause__ or __context__ attributes of the exception) will be printed as well, like the interpreter itself does when printing an unhandled exception.

Changed in version 3.5: The etype argument is ignored and inferred from the type of value.

Changed in version 3.10: The etype parameter has been renamed to exc and is now positional-only.

+
+traceback.print_exc(limit=None, file=None, chain=True)
+

This is a shorthand for print_exception(sys.exception(), limit, file, +chain).

+
+traceback.print_last(limit=None, file=None, chain=True)
+

This is a shorthand for print_exception(sys.last_exc, limit, file, +chain). In general it will work only after an exception has reached an interactive prompt (see sys.last_exc).

+
+traceback.print_stack(f=None, limit=None, file=None)
+

Print up to limit stack trace entries (starting from the invocation point) if limit is positive. Otherwise, print the last abs(limit) entries. If limit is omitted or None, all entries are printed. The optional f argument can be used to specify an alternate stack frame to start. The optional file argument has the same meaning as for print_tb().

Changed in version 3.5: Added negative limit support.

+
+traceback.extract_tb(tb, limit=None)
+

Return a StackSummary object representing a list of “pre-processed” stack trace entries extracted from the traceback object tb. It is useful for alternate formatting of stack traces. The optional limit argument has the same meaning as for print_tb(). A “pre-processed” stack trace entry is a FrameSummary object containing attributes filename, lineno, name, and line representing the information that is usually printed for a stack trace.

+
+traceback.extract_stack(f=None, limit=None)
+

Extract the raw traceback from the current stack frame. The return value has the same format as for extract_tb(). The optional f and limit arguments have the same meaning as for print_stack().

+
+traceback.format_list(extracted_list)
+

Given a list of tuples or FrameSummary objects as returned by extract_tb() or extract_stack(), return a list of strings ready for printing. Each string in the resulting list corresponds to the item with the same index in the argument list. Each string ends in a newline; the strings may contain internal newlines as well, for those items whose source text line is not None.

+
+traceback.format_exception_only(exc, /[, value])
+

Format the exception part of a traceback using an exception value such as given by sys.last_value. The return value is a list of strings, each ending in a newline. The list contains the exception’s message, which is normally a single string; however, for SyntaxError exceptions, it contains several lines that (when printed) display detailed information about where the syntax error occurred. Following the message, the list contains the exception’s notes.

Since Python 3.10, instead of passing value, an exception object can be passed as the first argument. If value is provided, the first argument is ignored in order to provide backwards compatibility.

Changed in version 3.10: The etype parameter has been renamed to exc and is now positional-only.

Changed in version 3.11: The returned list now includes any notes attached to the exception.

+
+traceback.format_exception(exc, /, [value, tb, ]limit=None, chain=True)
+

Format a stack trace and the exception information. The arguments have the same meaning as the corresponding arguments to print_exception(). The return value is a list of strings, each ending in a newline and some containing internal newlines. When these lines are concatenated and printed, exactly the same text is printed as does print_exception().

Changed in version 3.5: The etype argument is ignored and inferred from the type of value.

Changed in version 3.10: This function’s behavior and signature were modified to match print_exception().

+
+traceback.format_exc(limit=None, chain=True)
+

This is like print_exc(limit) but returns a string instead of printing to a file.

+
+traceback.format_tb(tb, limit=None)
+

A shorthand for format_list(extract_tb(tb, limit)).

+
+traceback.format_stack(f=None, limit=None)
+

A shorthand for format_list(extract_stack(f, limit)).

+
+traceback.clear_frames(tb)
+

Clears the local variables of all the stack frames in a traceback tb by calling the clear() method of each frame object.

New in version 3.4.

+
+traceback.walk_stack(f)
+

Walk a stack following f.f_back from the given frame, yielding the frame and line number for each frame. If f is None, the current stack is used. This helper is used with StackSummary.extract().

New in version 3.5.

+
+traceback.walk_tb(tb)
+

Walk a traceback following tb_next yielding the frame and line number for each frame. This helper is used with StackSummary.extract().

New in version 3.5.

+

The module also defines the following classes:

+TracebackException Objects

New in version 3.5.

TracebackException objects are created from actual exceptions to capture data for later printing in a lightweight fashion.

+class traceback.TracebackException(exc_type, exc_value, exc_traceback, *, limit=None, lookup_lines=True, capture_locals=False, compact=False, max_group_width=15, max_group_depth=10)
+

Capture an exception for later rendering. limit, lookup_lines and capture_locals are as for the StackSummary class.

If compact is true, only data that is required by TracebackException’s format() method is saved in the class attributes. In particular, the __context__ field is calculated only if __cause__ is None and __suppress_context__ is false.

Note that when locals are captured, they are also shown in the traceback.

max_group_width and max_group_depth control the formatting of exception groups (see BaseExceptionGroup). The depth refers to the nesting level of the group, and the width refers to the size of a single exception group’s exceptions array. The formatted output is truncated when either limit is exceeded.

Changed in version 3.10: Added the compact parameter.

Changed in version 3.11: Added the max_group_width and max_group_depth parameters.

+__cause__
+

A TracebackException of the original __cause__.

+
+__context__
+

A TracebackException of the original __context__.

+
+exceptions
+

If self represents an ExceptionGroup, this field holds a list of TracebackException instances representing the nested exceptions. Otherwise it is None.

New in version 3.11.

+
+__suppress_context__
+

The __suppress_context__ value from the original exception.

+
+__notes__
+

The __notes__ value from the original exception, or None if the exception does not have any notes. If it is not None is it formatted in the traceback after the exception string.

New in version 3.11.

+
+stack
+

A StackSummary representing the traceback.

+
+exc_type
+

The class of the original traceback.

+
+filename
+

For syntax errors - the file name where the error occurred.

+
+lineno
+

For syntax errors - the line number where the error occurred.

+
+end_lineno
+

For syntax errors - the end line number where the error occurred. Can be None if not present.

New in version 3.10.

+
+text
+

For syntax errors - the text where the error occurred.

+
+offset
+

For syntax errors - the offset into the text where the error occurred.

+
+end_offset
+

For syntax errors - the end offset into the text where the error occurred. Can be None if not present.

New in version 3.10.

+
+msg
+

For syntax errors - the compiler error message.

+
+classmethod from_exception(exc, *, limit=None, lookup_lines=True, capture_locals=False)
+

Capture an exception for later rendering. limit, lookup_lines and capture_locals are as for the StackSummary class.

Note that when locals are captured, they are also shown in the traceback.

+
+print(*, file=None, chain=True)
+

Print to file (default sys.stderr) the exception information returned by format().

New in version 3.11.

+
+format(*, chain=True)
+

Format the exception.

If chain is not True, __cause__ and __context__ will not be formatted.

The return value is a generator of strings, each ending in a newline and some containing internal newlines. print_exception() is a wrapper around this method which just prints the lines to a file.

+
+format_exception_only()
+

Format the exception part of the traceback.

The return value is a generator of strings, each ending in a newline.

The generator emits the exception’s message followed by its notes (if it has any). The exception message is normally a single string; however, for SyntaxError exceptions, it consists of several lines that (when printed) display detailed information about where the syntax error occurred.

Changed in version 3.11: The exception’s notes are now included in the output.

+
+

+StackSummary Objects

New in version 3.5.

StackSummary objects represent a call stack ready for formatting.

+class traceback.StackSummary
+
+classmethod extract(frame_gen, *, limit=None, lookup_lines=True, capture_locals=False)
+

Construct a StackSummary object from a frame generator (such as is returned by walk_stack() or walk_tb()).

If limit is supplied, only this many frames are taken from frame_gen. If lookup_lines is False, the returned FrameSummary objects will not have read their lines in yet, making the cost of creating the StackSummary cheaper (which may be valuable if it may not actually get formatted). If capture_locals is True the local variables in each FrameSummary are captured as object representations.

Changed in version 3.12: Exceptions raised from repr() on a local variable (when capture_locals is True) are no longer propagated to the caller.

+
+classmethod from_list(a_list)
+

Construct a StackSummary object from a supplied list of FrameSummary objects or old-style list of tuples. Each tuple should be a 4-tuple with filename, lineno, name, line as the elements.

+
+format()
+

Returns a list of strings ready for printing. Each string in the resulting list corresponds to a single frame from the stack. Each string ends in a newline; the strings may contain internal newlines as well, for those items with source text lines.

For long sequences of the same frame and line, the first few repetitions are shown, followed by a summary line stating the exact number of further repetitions.

Changed in version 3.6: Long sequences of repeated frames are now abbreviated.

+
+format_frame_summary(frame_summary)
+

Returns a string for printing one of the frames involved in the stack. This method is called for each FrameSummary object to be printed by StackSummary.format(). If it returns None, the frame is omitted from the output.

New in version 3.11.

+
+

+FrameSummary Objects

New in version 3.5.

A FrameSummary object represents a single frame in a traceback.

+class traceback.FrameSummary(filename, lineno, name, lookup_line=True, locals=None, line=None)
+

Represents a single frame in the traceback or stack that is being formatted or printed. It may optionally have a stringified version of the frame’s locals included in it. If lookup_line is False, the source code is not looked up until the FrameSummary has the line attribute accessed (which also happens when casting it to a tuple). line may be directly provided, and will prevent line lookups happening at all. locals is an optional local variable dictionary, and if supplied the variable representations are stored in the summary for later display.

FrameSummary instances have the following attributes:

+filename
+

The filename of the source code for this frame. Equivalent to accessing f.f_code.co_filename on a frame object f.

+
+lineno
+

The line number of the source code for this frame.

+
+name
+

Equivalent to accessing f.f_code.co_name on a frame object f.

+
+line
+

A string representing the source code for this frame, with leading and trailing whitespace stripped. If the source is not available, it is None.

+
+

Traceback Examples

This simple example implements a basic read-eval-print loop, similar to (but less useful than) the standard Python interactive interpreter loop. For a more complete implementation of the interpreter loop, refer to the code module.

import sys, traceback
+
+def run_user_code(envdir):
+    source = input(">>> ")
+    try:
+        exec(source, envdir)
+    except Exception:
+        print("Exception in user code:")
+        print("-"*60)
+        traceback.print_exc(file=sys.stdout)
+        print("-"*60)
+
+envdir = {}
+while True:
+    run_user_code(envdir)
+

The following example demonstrates the different ways to print and format the exception and traceback:

import sys, traceback
+
+def lumberjack():
+    bright_side_of_life()
+
+def bright_side_of_life():
+    return tuple()[0]
+
+try:
+    lumberjack()
+except IndexError:
+    exc = sys.exception()
+    print("*** print_tb:")
+    traceback.print_tb(exc.__traceback__, limit=1, file=sys.stdout)
+    print("*** print_exception:")
+    traceback.print_exception(exc, limit=2, file=sys.stdout)
+    print("*** print_exc:")
+    traceback.print_exc(limit=2, file=sys.stdout)
+    print("*** format_exc, first and last line:")
+    formatted_lines = traceback.format_exc().splitlines()
+    print(formatted_lines[0])
+    print(formatted_lines[-1])
+    print("*** format_exception:")
+    print(repr(traceback.format_exception(exc)))
+    print("*** extract_tb:")
+    print(repr(traceback.extract_tb(exc.__traceback__)))
+    print("*** format_tb:")
+    print(repr(traceback.format_tb(exc.__traceback__)))
+    print("*** tb_lineno:", exc.__traceback__.tb_lineno)
+

The output for the example would look similar to this:

*** print_tb:
+  File "<doctest...>", line 10, in <module>
+    lumberjack()
+*** print_exception:
+Traceback (most recent call last):
+  File "<doctest...>", line 10, in <module>
+    lumberjack()
+  File "<doctest...>", line 4, in lumberjack
+    bright_side_of_life()
+IndexError: tuple index out of range
+*** print_exc:
+Traceback (most recent call last):
+  File "<doctest...>", line 10, in <module>
+    lumberjack()
+  File "<doctest...>", line 4, in lumberjack
+    bright_side_of_life()
+IndexError: tuple index out of range
+*** format_exc, first and last line:
+Traceback (most recent call last):
+IndexError: tuple index out of range
+*** format_exception:
+['Traceback (most recent call last):\n',
+ '  File "<doctest default[0]>", line 10, in <module>\n    lumberjack()\n',
+ '  File "<doctest default[0]>", line 4, in lumberjack\n    bright_side_of_life()\n',
+ '  File "<doctest default[0]>", line 7, in bright_side_of_life\n    return tuple()[0]\n           ~~~~~~~^^^\n',
+ 'IndexError: tuple index out of range\n']
+*** extract_tb:
+[<FrameSummary file <doctest...>, line 10 in <module>>,
+ <FrameSummary file <doctest...>, line 4 in lumberjack>,
+ <FrameSummary file <doctest...>, line 7 in bright_side_of_life>]
+*** format_tb:
+['  File "<doctest default[0]>", line 10, in <module>\n    lumberjack()\n',
+ '  File "<doctest default[0]>", line 4, in lumberjack\n    bright_side_of_life()\n',
+ '  File "<doctest default[0]>", line 7, in bright_side_of_life\n    return tuple()[0]\n           ~~~~~~~^^^\n']
+*** tb_lineno: 10
+

The following example shows the different ways to print and format the stack:

>>> import traceback
+>>> def another_function():
+...     lumberstack()
+...
+>>> def lumberstack():
+...     traceback.print_stack()
+...     print(repr(traceback.extract_stack()))
+...     print(repr(traceback.format_stack()))
+...
+>>> another_function()
+  File "<doctest>", line 10, in <module>
+    another_function()
+  File "<doctest>", line 3, in another_function
+    lumberstack()
+  File "<doctest>", line 6, in lumberstack
+    traceback.print_stack()
+[('<doctest>', 10, '<module>', 'another_function()'),
+ ('<doctest>', 3, 'another_function', 'lumberstack()'),
+ ('<doctest>', 7, 'lumberstack', 'print(repr(traceback.extract_stack()))')]
+['  File "<doctest>", line 10, in <module>\n    another_function()\n',
+ '  File "<doctest>", line 3, in another_function\n    lumberstack()\n',
+ '  File "<doctest>", line 8, in lumberstack\n    print(repr(traceback.format_stack()))\n']
+

This last example demonstrates the final few formatting functions:

>>> import traceback
+>>> traceback.format_list([('spam.py', 3, '<module>', 'spam.eggs()'),
+...                        ('eggs.py', 42, 'eggs', 'return "bacon"')])
+['  File "spam.py", line 3, in <module>\n    spam.eggs()\n',
+ '  File "eggs.py", line 42, in eggs\n    return "bacon"\n']
+>>> an_error = IndexError('tuple index out of range')
+>>> traceback.format_exception_only(type(an_error), an_error)
+['IndexError: tuple index out of range\n']
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/traceback.html +

+
diff --git a/devdocs/python~3.12/library%2Ftracemalloc.html b/devdocs/python~3.12/library%2Ftracemalloc.html new file mode 100644 index 00000000..fb2b61b9 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftracemalloc.html @@ -0,0 +1,333 @@ +

tracemalloc — Trace memory allocations

New in version 3.4.

Source code: Lib/tracemalloc.py

The tracemalloc module is a debug tool to trace memory blocks allocated by Python. It provides the following information:

To trace most memory blocks allocated by Python, the module should be started as early as possible by setting the PYTHONTRACEMALLOC environment variable to 1, or by using -X tracemalloc command line option. The tracemalloc.start() function can be called at runtime to start tracing Python memory allocations.

By default, a trace of an allocated memory block only stores the most recent frame (1 frame). To store 25 frames at startup: set the PYTHONTRACEMALLOC environment variable to 25, or use the -X tracemalloc=25 command line option.

Examples

Display the top 10

Display the 10 files allocating the most memory:

import tracemalloc
+
+tracemalloc.start()
+
+# ... run your application ...
+
+snapshot = tracemalloc.take_snapshot()
+top_stats = snapshot.statistics('lineno')
+
+print("[ Top 10 ]")
+for stat in top_stats[:10]:
+    print(stat)
+

Example of output of the Python test suite:

[ Top 10 ]
+<frozen importlib._bootstrap>:716: size=4855 KiB, count=39328, average=126 B
+<frozen importlib._bootstrap>:284: size=521 KiB, count=3199, average=167 B
+/usr/lib/python3.4/collections/__init__.py:368: size=244 KiB, count=2315, average=108 B
+/usr/lib/python3.4/unittest/case.py:381: size=185 KiB, count=779, average=243 B
+/usr/lib/python3.4/unittest/case.py:402: size=154 KiB, count=378, average=416 B
+/usr/lib/python3.4/abc.py:133: size=88.7 KiB, count=347, average=262 B
+<frozen importlib._bootstrap>:1446: size=70.4 KiB, count=911, average=79 B
+<frozen importlib._bootstrap>:1454: size=52.0 KiB, count=25, average=2131 B
+<string>:5: size=49.7 KiB, count=148, average=344 B
+/usr/lib/python3.4/sysconfig.py:411: size=48.0 KiB, count=1, average=48.0 KiB
+

We can see that Python loaded 4855 KiB data (bytecode and constants) from modules and that the collections module allocated 244 KiB to build namedtuple types.

See Snapshot.statistics() for more options.

Compute differences

Take two snapshots and display the differences:

import tracemalloc
+tracemalloc.start()
+# ... start your application ...
+
+snapshot1 = tracemalloc.take_snapshot()
+# ... call the function leaking memory ...
+snapshot2 = tracemalloc.take_snapshot()
+
+top_stats = snapshot2.compare_to(snapshot1, 'lineno')
+
+print("[ Top 10 differences ]")
+for stat in top_stats[:10]:
+    print(stat)
+

Example of output before/after running some tests of the Python test suite:

[ Top 10 differences ]
+<frozen importlib._bootstrap>:716: size=8173 KiB (+4428 KiB), count=71332 (+39369), average=117 B
+/usr/lib/python3.4/linecache.py:127: size=940 KiB (+940 KiB), count=8106 (+8106), average=119 B
+/usr/lib/python3.4/unittest/case.py:571: size=298 KiB (+298 KiB), count=589 (+589), average=519 B
+<frozen importlib._bootstrap>:284: size=1005 KiB (+166 KiB), count=7423 (+1526), average=139 B
+/usr/lib/python3.4/mimetypes.py:217: size=112 KiB (+112 KiB), count=1334 (+1334), average=86 B
+/usr/lib/python3.4/http/server.py:848: size=96.0 KiB (+96.0 KiB), count=1 (+1), average=96.0 KiB
+/usr/lib/python3.4/inspect.py:1465: size=83.5 KiB (+83.5 KiB), count=109 (+109), average=784 B
+/usr/lib/python3.4/unittest/mock.py:491: size=77.7 KiB (+77.7 KiB), count=143 (+143), average=557 B
+/usr/lib/python3.4/urllib/parse.py:476: size=71.8 KiB (+71.8 KiB), count=969 (+969), average=76 B
+/usr/lib/python3.4/contextlib.py:38: size=67.2 KiB (+67.2 KiB), count=126 (+126), average=546 B
+

We can see that Python has loaded 8173 KiB of module data (bytecode and constants), and that this is 4428 KiB more than had been loaded before the tests, when the previous snapshot was taken. Similarly, the linecache module has cached 940 KiB of Python source code to format tracebacks, all of it since the previous snapshot.

If the system has little free memory, snapshots can be written on disk using the Snapshot.dump() method to analyze the snapshot offline. Then use the Snapshot.load() method reload the snapshot.

Get the traceback of a memory block

Code to display the traceback of the biggest memory block:

import tracemalloc
+
+# Store 25 frames
+tracemalloc.start(25)
+
+# ... run your application ...
+
+snapshot = tracemalloc.take_snapshot()
+top_stats = snapshot.statistics('traceback')
+
+# pick the biggest memory block
+stat = top_stats[0]
+print("%s memory blocks: %.1f KiB" % (stat.count, stat.size / 1024))
+for line in stat.traceback.format():
+    print(line)
+

Example of output of the Python test suite (traceback limited to 25 frames):

903 memory blocks: 870.1 KiB
+  File "<frozen importlib._bootstrap>", line 716
+  File "<frozen importlib._bootstrap>", line 1036
+  File "<frozen importlib._bootstrap>", line 934
+  File "<frozen importlib._bootstrap>", line 1068
+  File "<frozen importlib._bootstrap>", line 619
+  File "<frozen importlib._bootstrap>", line 1581
+  File "<frozen importlib._bootstrap>", line 1614
+  File "/usr/lib/python3.4/doctest.py", line 101
+    import pdb
+  File "<frozen importlib._bootstrap>", line 284
+  File "<frozen importlib._bootstrap>", line 938
+  File "<frozen importlib._bootstrap>", line 1068
+  File "<frozen importlib._bootstrap>", line 619
+  File "<frozen importlib._bootstrap>", line 1581
+  File "<frozen importlib._bootstrap>", line 1614
+  File "/usr/lib/python3.4/test/support/__init__.py", line 1728
+    import doctest
+  File "/usr/lib/python3.4/test/test_pickletools.py", line 21
+    support.run_doctest(pickletools)
+  File "/usr/lib/python3.4/test/regrtest.py", line 1276
+    test_runner()
+  File "/usr/lib/python3.4/test/regrtest.py", line 976
+    display_failure=not verbose)
+  File "/usr/lib/python3.4/test/regrtest.py", line 761
+    match_tests=ns.match_tests)
+  File "/usr/lib/python3.4/test/regrtest.py", line 1563
+    main()
+  File "/usr/lib/python3.4/test/__main__.py", line 3
+    regrtest.main_in_temp_cwd()
+  File "/usr/lib/python3.4/runpy.py", line 73
+    exec(code, run_globals)
+  File "/usr/lib/python3.4/runpy.py", line 160
+    "__main__", fname, loader, pkg_name)
+

We can see that the most memory was allocated in the importlib module to load data (bytecode and constants) from modules: 870.1 KiB. The traceback is where the importlib loaded data most recently: on the import pdb line of the doctest module. The traceback may change if a new module is loaded.

Pretty top

Code to display the 10 lines allocating the most memory with a pretty output, ignoring <frozen importlib._bootstrap> and <unknown> files:

import linecache
+import os
+import tracemalloc
+
+def display_top(snapshot, key_type='lineno', limit=10):
+    snapshot = snapshot.filter_traces((
+        tracemalloc.Filter(False, "<frozen importlib._bootstrap>"),
+        tracemalloc.Filter(False, "<unknown>"),
+    ))
+    top_stats = snapshot.statistics(key_type)
+
+    print("Top %s lines" % limit)
+    for index, stat in enumerate(top_stats[:limit], 1):
+        frame = stat.traceback[0]
+        print("#%s: %s:%s: %.1f KiB"
+              % (index, frame.filename, frame.lineno, stat.size / 1024))
+        line = linecache.getline(frame.filename, frame.lineno).strip()
+        if line:
+            print('    %s' % line)
+
+    other = top_stats[limit:]
+    if other:
+        size = sum(stat.size for stat in other)
+        print("%s other: %.1f KiB" % (len(other), size / 1024))
+    total = sum(stat.size for stat in top_stats)
+    print("Total allocated size: %.1f KiB" % (total / 1024))
+
+tracemalloc.start()
+
+# ... run your application ...
+
+snapshot = tracemalloc.take_snapshot()
+display_top(snapshot)
+

Example of output of the Python test suite:

Top 10 lines
+#1: Lib/base64.py:414: 419.8 KiB
+    _b85chars2 = [(a + b) for a in _b85chars for b in _b85chars]
+#2: Lib/base64.py:306: 419.8 KiB
+    _a85chars2 = [(a + b) for a in _a85chars for b in _a85chars]
+#3: collections/__init__.py:368: 293.6 KiB
+    exec(class_definition, namespace)
+#4: Lib/abc.py:133: 115.2 KiB
+    cls = super().__new__(mcls, name, bases, namespace)
+#5: unittest/case.py:574: 103.1 KiB
+    testMethod()
+#6: Lib/linecache.py:127: 95.4 KiB
+    lines = fp.readlines()
+#7: urllib/parse.py:476: 71.8 KiB
+    for a in _hexdig for b in _hexdig}
+#8: <string>:5: 62.0 KiB
+#9: Lib/_weakrefset.py:37: 60.0 KiB
+    self.data = set()
+#10: Lib/base64.py:142: 59.8 KiB
+    _b32tab2 = [a + b for a in _b32tab for b in _b32tab]
+6220 other: 3602.8 KiB
+Total allocated size: 5303.1 KiB
+

See Snapshot.statistics() for more options.

Record the current and peak size of all traced memory blocks

The following code computes two sums like 0 + 1 + 2 + ... inefficiently, by creating a list of those numbers. This list consumes a lot of memory temporarily. We can use get_traced_memory() and reset_peak() to observe the small memory usage after the sum is computed as well as the peak memory usage during the computations:

import tracemalloc
+
+tracemalloc.start()
+
+# Example code: compute a sum with a large temporary list
+large_sum = sum(list(range(100000)))
+
+first_size, first_peak = tracemalloc.get_traced_memory()
+
+tracemalloc.reset_peak()
+
+# Example code: compute a sum with a small temporary list
+small_sum = sum(list(range(1000)))
+
+second_size, second_peak = tracemalloc.get_traced_memory()
+
+print(f"{first_size=}, {first_peak=}")
+print(f"{second_size=}, {second_peak=}")
+

Output:

first_size=664, first_peak=3592984
+second_size=804, second_peak=29704
+

Using reset_peak() ensured we could accurately record the peak during the computation of small_sum, even though it is much smaller than the overall peak size of memory blocks since the start() call. Without the call to reset_peak(), second_peak would still be the peak from the computation large_sum (that is, equal to first_peak). In this case, both peaks are much higher than the final memory usage, and which suggests we could optimise (by removing the unnecessary call to list, and writing sum(range(...))).

API

Functions

+tracemalloc.clear_traces()
+

Clear traces of memory blocks allocated by Python.

See also stop().

+
+tracemalloc.get_object_traceback(obj)
+

Get the traceback where the Python object obj was allocated. Return a Traceback instance, or None if the tracemalloc module is not tracing memory allocations or did not trace the allocation of the object.

See also gc.get_referrers() and sys.getsizeof() functions.

+
+tracemalloc.get_traceback_limit()
+

Get the maximum number of frames stored in the traceback of a trace.

The tracemalloc module must be tracing memory allocations to get the limit, otherwise an exception is raised.

The limit is set by the start() function.

+
+tracemalloc.get_traced_memory()
+

Get the current size and peak size of memory blocks traced by the tracemalloc module as a tuple: (current: int, peak: int).

+
+tracemalloc.reset_peak()
+

Set the peak size of memory blocks traced by the tracemalloc module to the current size.

Do nothing if the tracemalloc module is not tracing memory allocations.

This function only modifies the recorded peak size, and does not modify or clear any traces, unlike clear_traces(). Snapshots taken with take_snapshot() before a call to reset_peak() can be meaningfully compared to snapshots taken after the call.

See also get_traced_memory().

New in version 3.9.

+
+tracemalloc.get_tracemalloc_memory()
+

Get the memory usage in bytes of the tracemalloc module used to store traces of memory blocks. Return an int.

+
+tracemalloc.is_tracing()
+

True if the tracemalloc module is tracing Python memory allocations, False otherwise.

See also start() and stop() functions.

+
+tracemalloc.start(nframe: int = 1)
+

Start tracing Python memory allocations: install hooks on Python memory allocators. Collected tracebacks of traces will be limited to nframe frames. By default, a trace of a memory block only stores the most recent frame: the limit is 1. nframe must be greater or equal to 1.

You can still read the original number of total frames that composed the traceback by looking at the Traceback.total_nframe attribute.

Storing more than 1 frame is only useful to compute statistics grouped by 'traceback' or to compute cumulative statistics: see the Snapshot.compare_to() and Snapshot.statistics() methods.

Storing more frames increases the memory and CPU overhead of the tracemalloc module. Use the get_tracemalloc_memory() function to measure how much memory is used by the tracemalloc module.

The PYTHONTRACEMALLOC environment variable (PYTHONTRACEMALLOC=NFRAME) and the -X tracemalloc=NFRAME command line option can be used to start tracing at startup.

See also stop(), is_tracing() and get_traceback_limit() functions.

+
+tracemalloc.stop()
+

Stop tracing Python memory allocations: uninstall hooks on Python memory allocators. Also clears all previously collected traces of memory blocks allocated by Python.

Call take_snapshot() function to take a snapshot of traces before clearing them.

See also start(), is_tracing() and clear_traces() functions.

+
+tracemalloc.take_snapshot()
+

Take a snapshot of traces of memory blocks allocated by Python. Return a new Snapshot instance.

The snapshot does not include memory blocks allocated before the tracemalloc module started to trace memory allocations.

Tracebacks of traces are limited to get_traceback_limit() frames. Use the nframe parameter of the start() function to store more frames.

The tracemalloc module must be tracing memory allocations to take a snapshot, see the start() function.

See also the get_object_traceback() function.

+

DomainFilter

+class tracemalloc.DomainFilter(inclusive: bool, domain: int)
+

Filter traces of memory blocks by their address space (domain).

New in version 3.6.

+inclusive
+

If inclusive is True (include), match memory blocks allocated in the address space domain.

If inclusive is False (exclude), match memory blocks not allocated in the address space domain.

+
+domain
+

Address space of a memory block (int). Read-only property.

+
+

Filter

+class tracemalloc.Filter(inclusive: bool, filename_pattern: str, lineno: int = None, all_frames: bool = False, domain: int = None)
+

Filter on traces of memory blocks.

See the fnmatch.fnmatch() function for the syntax of filename_pattern. The '.pyc' file extension is replaced with '.py'.

Examples:

  • +Filter(True, subprocess.__file__) only includes traces of the subprocess module
  • +Filter(False, tracemalloc.__file__) excludes traces of the tracemalloc module
  • +Filter(False, "<unknown>") excludes empty tracebacks

Changed in version 3.5: The '.pyo' file extension is no longer replaced with '.py'.

Changed in version 3.6: Added the domain attribute.

+domain
+

Address space of a memory block (int or None).

tracemalloc uses the domain 0 to trace memory allocations made by Python. C extensions can use other domains to trace other resources.

+
+inclusive
+

If inclusive is True (include), only match memory blocks allocated in a file with a name matching filename_pattern at line number lineno.

If inclusive is False (exclude), ignore memory blocks allocated in a file with a name matching filename_pattern at line number lineno.

+
+lineno
+

Line number (int) of the filter. If lineno is None, the filter matches any line number.

+
+filename_pattern
+

Filename pattern of the filter (str). Read-only property.

+
+all_frames
+

If all_frames is True, all frames of the traceback are checked. If all_frames is False, only the most recent frame is checked.

This attribute has no effect if the traceback limit is 1. See the get_traceback_limit() function and Snapshot.traceback_limit attribute.

+
+

Frame

+class tracemalloc.Frame
+

Frame of a traceback.

The Traceback class is a sequence of Frame instances.

+filename
+

Filename (str).

+
+lineno
+

Line number (int).

+
+

Snapshot

+class tracemalloc.Snapshot
+

Snapshot of traces of memory blocks allocated by Python.

The take_snapshot() function creates a snapshot instance.

+compare_to(old_snapshot: Snapshot, key_type: str, cumulative: bool = False)
+

Compute the differences with an old snapshot. Get statistics as a sorted list of StatisticDiff instances grouped by key_type.

See the Snapshot.statistics() method for key_type and cumulative parameters.

The result is sorted from the biggest to the smallest by: absolute value of StatisticDiff.size_diff, StatisticDiff.size, absolute value of StatisticDiff.count_diff, Statistic.count and then by StatisticDiff.traceback.

+
+dump(filename)
+

Write the snapshot into a file.

Use load() to reload the snapshot.

+
+filter_traces(filters)
+

Create a new Snapshot instance with a filtered traces sequence, filters is a list of DomainFilter and Filter instances. If filters is an empty list, return a new Snapshot instance with a copy of the traces.

All inclusive filters are applied at once, a trace is ignored if no inclusive filters match it. A trace is ignored if at least one exclusive filter matches it.

Changed in version 3.6: DomainFilter instances are now also accepted in filters.

+
+classmethod load(filename)
+

Load a snapshot from a file.

See also dump().

+
+statistics(key_type: str, cumulative: bool = False)
+

Get statistics as a sorted list of Statistic instances grouped by key_type:

+ + + +

key_type

description

'filename'

filename

'lineno'

filename and line number

'traceback'

traceback

If cumulative is True, cumulate size and count of memory blocks of all frames of the traceback of a trace, not only the most recent frame. The cumulative mode can only be used with key_type equals to 'filename' and 'lineno'.

The result is sorted from the biggest to the smallest by: Statistic.size, Statistic.count and then by Statistic.traceback.

+
+traceback_limit
+

Maximum number of frames stored in the traceback of traces: result of the get_traceback_limit() when the snapshot was taken.

+
+traces
+

Traces of all memory blocks allocated by Python: sequence of Trace instances.

The sequence has an undefined order. Use the Snapshot.statistics() method to get a sorted list of statistics.

+
+

Statistic

+class tracemalloc.Statistic
+

Statistic on memory allocations.

Snapshot.statistics() returns a list of Statistic instances.

See also the StatisticDiff class.

+count
+

Number of memory blocks (int).

+
+size
+

Total size of memory blocks in bytes (int).

+
+traceback
+

Traceback where the memory block was allocated, Traceback instance.

+
+

StatisticDiff

+class tracemalloc.StatisticDiff
+

Statistic difference on memory allocations between an old and a new Snapshot instance.

Snapshot.compare_to() returns a list of StatisticDiff instances. See also the Statistic class.

+count
+

Number of memory blocks in the new snapshot (int): 0 if the memory blocks have been released in the new snapshot.

+
+count_diff
+

Difference of number of memory blocks between the old and the new snapshots (int): 0 if the memory blocks have been allocated in the new snapshot.

+
+size
+

Total size of memory blocks in bytes in the new snapshot (int): 0 if the memory blocks have been released in the new snapshot.

+
+size_diff
+

Difference of total size of memory blocks in bytes between the old and the new snapshots (int): 0 if the memory blocks have been allocated in the new snapshot.

+
+traceback
+

Traceback where the memory blocks were allocated, Traceback instance.

+
+

Trace

+class tracemalloc.Trace
+

Trace of a memory block.

The Snapshot.traces attribute is a sequence of Trace instances.

Changed in version 3.6: Added the domain attribute.

+domain
+

Address space of a memory block (int). Read-only property.

tracemalloc uses the domain 0 to trace memory allocations made by Python. C extensions can use other domains to trace other resources.

+
+size
+

Size of the memory block in bytes (int).

+
+traceback
+

Traceback where the memory block was allocated, Traceback instance.

+
+

Traceback

+class tracemalloc.Traceback
+

Sequence of Frame instances sorted from the oldest frame to the most recent frame.

A traceback contains at least 1 frame. If the tracemalloc module failed to get a frame, the filename "<unknown>" at line number 0 is used.

When a snapshot is taken, tracebacks of traces are limited to get_traceback_limit() frames. See the take_snapshot() function. The original number of frames of the traceback is stored in the Traceback.total_nframe attribute. That allows to know if a traceback has been truncated by the traceback limit.

The Trace.traceback attribute is an instance of Traceback instance.

Changed in version 3.7: Frames are now sorted from the oldest to the most recent, instead of most recent to oldest.

+total_nframe
+

Total number of frames that composed the traceback before truncation. This attribute can be set to None if the information is not available.

+

Changed in version 3.9: The Traceback.total_nframe attribute was added.

+format(limit=None, most_recent_first=False)
+

Format the traceback as a list of lines. Use the linecache module to retrieve lines from the source code. If limit is set, format the limit most recent frames if limit is positive. Otherwise, format the abs(limit) oldest frames. If most_recent_first is True, the order of the formatted frames is reversed, returning the most recent frame first instead of last.

Similar to the traceback.format_tb() function, except that format() does not include newlines.

Example:

print("Traceback (most recent call first):")
+for line in traceback:
+    print(line)
+

Output:

Traceback (most recent call first):
+  File "test.py", line 9
+    obj = Object()
+  File "test.py", line 12
+    tb = tracemalloc.get_object_traceback(f())
+
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tracemalloc.html +

+
diff --git a/devdocs/python~3.12/library%2Ftty.html b/devdocs/python~3.12/library%2Ftty.html new file mode 100644 index 00000000..a000714b --- /dev/null +++ b/devdocs/python~3.12/library%2Ftty.html @@ -0,0 +1,22 @@ +

tty — Terminal control functions

Source code: Lib/tty.py

The tty module defines functions for putting the tty into cbreak and raw modes.

Availability: Unix.

Because it requires the termios module, it will work only on Unix.

The tty module defines the following functions:

+tty.cfmakeraw(mode)
+

Convert the tty attribute list mode, which is a list like the one returned by termios.tcgetattr(), to that of a tty in raw mode.

New in version 3.12.

+
+tty.cfmakecbreak(mode)
+

Convert the tty attribute list mode, which is a list like the one returned by termios.tcgetattr(), to that of a tty in cbreak mode.

New in version 3.12.

+
+tty.setraw(fd, when=termios.TCSAFLUSH)
+

Change the mode of the file descriptor fd to raw. If when is omitted, it defaults to termios.TCSAFLUSH, and is passed to termios.tcsetattr(). The return value of termios.tcgetattr() is saved before setting fd to raw mode; this value is returned.

Changed in version 3.12: The return value is now the original tty attributes, instead of None.

+
+tty.setcbreak(fd, when=termios.TCSAFLUSH)
+

Change the mode of file descriptor fd to cbreak. If when is omitted, it defaults to termios.TCSAFLUSH, and is passed to termios.tcsetattr(). The return value of termios.tcgetattr() is saved before setting fd to cbreak mode; this value is returned.

Changed in version 3.12: The return value is now the original tty attributes, instead of None.

+

See also

+Module termios +
+
+

Low-level terminal control interface.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/tty.html +

+
diff --git a/devdocs/python~3.12/library%2Fturtle.html b/devdocs/python~3.12/library%2Fturtle.html new file mode 100644 index 00000000..568433bb --- /dev/null +++ b/devdocs/python~3.12/library%2Fturtle.html @@ -0,0 +1,1150 @@ +

turtle — Turtle graphics

Source code: Lib/turtle.py

Introduction

Turtle graphics is an implementation of the popular geometric drawing tools introduced in Logo, developed by Wally Feurzeig, Seymour Papert and Cynthia Solomon in 1967.

In Python, turtle graphics provides a representation of a physical “turtle” (a little robot with a pen) that draws on a sheet of paper on the floor.

It’s an effective and well-proven way for learners to encounter programming concepts and interaction with software, as it provides instant, visible feedback. It also provides convenient access to graphical output in general.

Turtle drawing was originally created as an educational tool, to be used by teachers in the classroom. For the programmer who needs to produce some graphical output it can be a way to do that without the overhead of introducing more complex or external libraries into their work.

Tutorial

New users should start here. In this tutorial we’ll explore some of the basics of turtle drawing.

Starting a turtle environment

In a Python shell, import all the objects of the turtle module:

from turtle import *
+

If you run into a No module named '_tkinter' error, you’ll have to install the Tk interface package on your system.

Basic drawing

Send the turtle forward 100 steps:

forward(100)
+

You should see (most likely, in a new window on your display) a line drawn by the turtle, heading East. Change the direction of the turtle, so that it turns 120 degrees left (anti-clockwise):

left(120)
+

Let’s continue by drawing a triangle:

forward(100)
+left(120)
+forward(100)
+

Notice how the turtle, represented by an arrow, points in different directions as you steer it.

Experiment with those commands, and also with backward() and right().

Pen control

Try changing the color - for example, color('blue') - and width of the line - for example, width(3) - and then drawing again.

You can also move the turtle around without drawing, by lifting up the pen: up() before moving. To start drawing again, use down().

The turtle’s position

Send your turtle back to its starting-point (useful if it has disappeared off-screen):

home()
+

The home position is at the center of the turtle’s screen. If you ever need to know them, get the turtle’s x-y co-ordinates with:

pos()
+

Home is at (0, 0).

And after a while, it will probably help to clear the window so we can start anew:

clearscreen()
+

Making algorithmic patterns

Using loops, it’s possible to build up geometric patterns:

for steps in range(100):
+    for c in ('blue', 'red', 'green'):
+        color(c)
+        forward(steps)
+        right(30)
+

- which of course, are limited only by the imagination!

Let’s draw the star shape at the top of this page. We want red lines, filled in with yellow:

color('red')
+fillcolor('yellow')
+

Just as up() and down() determine whether lines will be drawn, filling can be turned on and off:

begin_fill()
+

Next we’ll create a loop:

while True:
+    forward(200)
+    left(170)
+    if abs(pos()) < 1:
+        break
+

abs(pos()) < 1 is a good way to know when the turtle is back at its home position.

Finally, complete the filling:

end_fill()
+

(Note that filling only actually takes place when you give the end_fill() command.)

How to…

This section covers some typical turtle use-cases and approaches.

Get started as quickly as possible

One of the joys of turtle graphics is the immediate, visual feedback that’s available from simple commands - it’s an excellent way to introduce children to programming ideas, with a minimum of overhead (not just children, of course).

The turtle module makes this possible by exposing all its basic functionality as functions, available with from turtle import *. The turtle graphics tutorial covers this approach.

It’s worth noting that many of the turtle commands also have even more terse equivalents, such as fd() for forward(). These are especially useful when working with learners for whom typing is not a skill.

You’ll need to have the Tk interface package installed on your system for turtle graphics to work. Be warned that this is not always straightforward, so check this in advance if you’re planning to use turtle graphics with a learner.

Use the turtle module namespace

Using from turtle import * is convenient - but be warned that it imports a rather large collection of objects, and if you’re doing anything but turtle graphics you run the risk of a name conflict (this becomes even more an issue if you’re using turtle graphics in a script where other modules might be imported).

The solution is to use import turtle - fd() becomes turtle.fd(), width() becomes turtle.width() and so on. (If typing “turtle” over and over again becomes tedious, use for example import turtle +as t instead.)

Use turtle graphics in a script

It’s recommended to use the turtle module namespace as described immediately above, for example:

import turtle as t
+from random import random
+
+for i in range(100):
+    steps = int(random() * 100)
+    angle = int(random() * 360)
+    t.right(angle)
+    t.fd(steps)
+

Another step is also required though - as soon as the script ends, Python will also close the turtle’s window. Add:

t.mainloop()
+

to the end of the script. The script will now wait to be dismissed and will not exit until it is terminated, for example by closing the turtle graphics window.

Use object-oriented turtle graphics

See also

Explanation of the object-oriented interface

Other than for very basic introductory purposes, or for trying things out as quickly as possible, it’s more usual and much more powerful to use the object-oriented approach to turtle graphics. For example, this allows multiple turtles on screen at once.

In this approach, the various turtle commands are methods of objects (mostly of Turtle objects). You can use the object-oriented approach in the shell, but it would be more typical in a Python script.

The example above then becomes:

from turtle import Turtle
+from random import random
+
+t = Turtle()
+for i in range(100):
+    steps = int(random() * 100)
+    angle = int(random() * 360)
+    t.right(angle)
+    t.fd(steps)
+
+t.screen.mainloop()
+

Note the last line. t.screen is an instance of the Screen that a Turtle instance exists on; it’s created automatically along with the turtle.

The turtle’s screen can be customised, for example:

t.screen.title('Object-oriented turtle demo')
+t.screen.bgcolor("orange")
+

Turtle graphics reference

Note

In the following documentation the argument list for functions is given. Methods, of course, have the additional first argument self which is omitted here.

Turtle methods

Turtle motion
+
+
Move and draw
+
Tell Turtle’s state
+
Setting and measurement
+
Pen control
+
+
Drawing state
+
Color control
+
Filling
+
More drawing control
+
Turtle state
+
+
Visibility
+
Appearance
+
Using events
+
Special Turtle methods
+

Methods of TurtleScreen/Screen

Window control
+
Animation control
+
Using screen events
+
Settings and special methods
+
Input methods
+
Methods specific to Screen
+

Methods of RawTurtle/Turtle and corresponding functions

Most of the examples in this section refer to a Turtle instance called turtle.

Turtle motion

+turtle.forward(distance)
+turtle.fd(distance)
+
Parameters
+

distance – a number (integer or float)

Move the turtle forward by the specified distance, in the direction the turtle is headed.

>>> turtle.position()
+(0.00,0.00)
+>>> turtle.forward(25)
+>>> turtle.position()
+(25.00,0.00)
+>>> turtle.forward(-75)
+>>> turtle.position()
+(-50.00,0.00)
+
+
+turtle.back(distance)
+turtle.bk(distance)
+turtle.backward(distance)
+
Parameters
+

distance – a number

Move the turtle backward by distance, opposite to the direction the turtle is headed. Do not change the turtle’s heading.

>>> turtle.position()
+(0.00,0.00)
+>>> turtle.backward(30)
+>>> turtle.position()
+(-30.00,0.00)
+
+
+turtle.right(angle)
+turtle.rt(angle)
+
Parameters
+

angle – a number (integer or float)

Turn turtle right by angle units. (Units are by default degrees, but can be set via the degrees() and radians() functions.) Angle orientation depends on the turtle mode, see mode().

>>> turtle.heading()
+22.0
+>>> turtle.right(45)
+>>> turtle.heading()
+337.0
+
+
+turtle.left(angle)
+turtle.lt(angle)
+
Parameters
+

angle – a number (integer or float)

Turn turtle left by angle units. (Units are by default degrees, but can be set via the degrees() and radians() functions.) Angle orientation depends on the turtle mode, see mode().

>>> turtle.heading()
+22.0
+>>> turtle.left(45)
+>>> turtle.heading()
+67.0
+
+
+turtle.goto(x, y=None)
+turtle.setpos(x, y=None)
+turtle.setposition(x, y=None)
+
Parameters
+
  • +x – a number or a pair/vector of numbers
  • +y – a number or None +

If y is None, x must be a pair of coordinates or a Vec2D (e.g. as returned by pos()).

Move turtle to an absolute position. If the pen is down, draw line. Do not change the turtle’s orientation.

>>> tp = turtle.pos()
+>>> tp
+(0.00,0.00)
+>>> turtle.setpos(60,30)
+>>> turtle.pos()
+(60.00,30.00)
+>>> turtle.setpos((20,80))
+>>> turtle.pos()
+(20.00,80.00)
+>>> turtle.setpos(tp)
+>>> turtle.pos()
+(0.00,0.00)
+
+
+turtle.teleport(x, y=None, *, fill_gap=False)
+
Parameters
+
  • +x – a number or None +
  • +y – a number or None +
  • +fill_gap – a boolean

Move turtle to an absolute position. Unlike goto(x, y), a line will not be drawn. The turtle’s orientation does not change. If currently filling, the polygon(s) teleported from will be filled after leaving, and filling will begin again after teleporting. This can be disabled with fill_gap=True, which makes the imaginary line traveled during teleporting act as a fill barrier like in goto(x, y).

>>> tp = turtle.pos()
+>>> tp
+(0.00,0.00)
+>>> turtle.teleport(60)
+>>> turtle.pos()
+(60.00,0.00)
+>>> turtle.teleport(y=10)
+>>> turtle.pos()
+(60.00,10.00)
+>>> turtle.teleport(20, 30)
+>>> turtle.pos()
+(20.00,30.00)
+
+
+turtle.setx(x)
+
Parameters
+

x – a number (integer or float)

Set the turtle’s first coordinate to x, leave second coordinate unchanged.

>>> turtle.position()
+(0.00,240.00)
+>>> turtle.setx(10)
+>>> turtle.position()
+(10.00,240.00)
+
+
+turtle.sety(y)
+
Parameters
+

y – a number (integer or float)

Set the turtle’s second coordinate to y, leave first coordinate unchanged.

>>> turtle.position()
+(0.00,40.00)
+>>> turtle.sety(-10)
+>>> turtle.position()
+(0.00,-10.00)
+
+
+turtle.setheading(to_angle)
+turtle.seth(to_angle)
+
Parameters
+

to_angle – a number (integer or float)

Set the orientation of the turtle to to_angle. Here are some common directions in degrees:

+ + + + +

standard mode

logo mode

0 - east

0 - north

90 - north

90 - east

180 - west

180 - south

270 - south

270 - west

 
>>> turtle.setheading(90)
+>>> turtle.heading()
+90.0
+
+
+turtle.home()
+

Move turtle to the origin – coordinates (0,0) – and set its heading to its start-orientation (which depends on the mode, see mode()).

>>> turtle.heading()
+90.0
+>>> turtle.position()
+(0.00,-10.00)
+>>> turtle.home()
+>>> turtle.position()
+(0.00,0.00)
+>>> turtle.heading()
+0.0
+
+
+turtle.circle(radius, extent=None, steps=None)
+
Parameters
+
  • +radius – a number
  • +extent – a number (or None)
  • +steps – an integer (or None)

Draw a circle with given radius. The center is radius units left of the turtle; extent – an angle – determines which part of the circle is drawn. If extent is not given, draw the entire circle. If extent is not a full circle, one endpoint of the arc is the current pen position. Draw the arc in counterclockwise direction if radius is positive, otherwise in clockwise direction. Finally the direction of the turtle is changed by the amount of extent.

As the circle is approximated by an inscribed regular polygon, steps determines the number of steps to use. If not given, it will be calculated automatically. May be used to draw regular polygons.

>>> turtle.home()
+>>> turtle.position()
+(0.00,0.00)
+>>> turtle.heading()
+0.0
+>>> turtle.circle(50)
+>>> turtle.position()
+(-0.00,0.00)
+>>> turtle.heading()
+0.0
+>>> turtle.circle(120, 180)  # draw a semicircle
+>>> turtle.position()
+(0.00,240.00)
+>>> turtle.heading()
+180.0
+
+
+turtle.dot(size=None, *color)
+
Parameters
+
  • +size – an integer >= 1 (if given)
  • +color – a colorstring or a numeric color tuple

Draw a circular dot with diameter size, using color. If size is not given, the maximum of pensize+4 and 2*pensize is used.

>>> turtle.home()
+>>> turtle.dot()
+>>> turtle.fd(50); turtle.dot(20, "blue"); turtle.fd(50)
+>>> turtle.position()
+(100.00,-0.00)
+>>> turtle.heading()
+0.0
+
+
+turtle.stamp()
+

Stamp a copy of the turtle shape onto the canvas at the current turtle position. Return a stamp_id for that stamp, which can be used to delete it by calling clearstamp(stamp_id).

>>> turtle.color("blue")
+>>> stamp_id = turtle.stamp()
+>>> turtle.fd(50)
+
+
+turtle.clearstamp(stampid)
+
Parameters
+

stampid – an integer, must be return value of previous stamp() call

Delete stamp with given stampid.

>>> turtle.position()
+(150.00,-0.00)
+>>> turtle.color("blue")
+>>> astamp = turtle.stamp()
+>>> turtle.fd(50)
+>>> turtle.position()
+(200.00,-0.00)
+>>> turtle.clearstamp(astamp)
+>>> turtle.position()
+(200.00,-0.00)
+
+
+turtle.clearstamps(n=None)
+
Parameters
+

n – an integer (or None)

Delete all or first/last n of turtle’s stamps. If n is None, delete all stamps, if n > 0 delete first n stamps, else if n < 0 delete last n stamps.

>>> for i in range(8):
+...     unused_stamp_id = turtle.stamp()
+...     turtle.fd(30)
+>>> turtle.clearstamps(2)
+>>> turtle.clearstamps(-2)
+>>> turtle.clearstamps()
+
+
+turtle.undo()
+

Undo (repeatedly) the last turtle action(s). Number of available undo actions is determined by the size of the undobuffer.

>>> for i in range(4):
+...     turtle.fd(50); turtle.lt(80)
+...
+>>> for i in range(8):
+...     turtle.undo()
+
+
+turtle.speed(speed=None)
+
Parameters
+

speed – an integer in the range 0..10 or a speedstring (see below)

Set the turtle’s speed to an integer value in the range 0..10. If no argument is given, return current speed.

If input is a number greater than 10 or smaller than 0.5, speed is set to 0. Speedstrings are mapped to speedvalues as follows:

  • “fastest”: 0
  • “fast”: 10
  • “normal”: 6
  • “slow”: 3
  • “slowest”: 1

Speeds from 1 to 10 enforce increasingly faster animation of line drawing and turtle turning.

Attention: speed = 0 means that no animation takes place. forward/back makes turtle jump and likewise left/right make the turtle turn instantly.

>>> turtle.speed()
+3
+>>> turtle.speed('normal')
+>>> turtle.speed()
+6
+>>> turtle.speed(9)
+>>> turtle.speed()
+9
+
+

Tell Turtle’s state

+turtle.position()
+turtle.pos()
+

Return the turtle’s current location (x,y) (as a Vec2D vector).

>>> turtle.pos()
+(440.00,-0.00)
+
+
+turtle.towards(x, y=None)
+
Parameters
+
  • +x – a number or a pair/vector of numbers or a turtle instance
  • +y – a number if x is a number, else None +

Return the angle between the line from turtle position to position specified by (x,y), the vector or the other turtle. This depends on the turtle’s start orientation which depends on the mode - “standard”/”world” or “logo”.

>>> turtle.goto(10, 10)
+>>> turtle.towards(0,0)
+225.0
+
+
+turtle.xcor()
+

Return the turtle’s x coordinate.

>>> turtle.home()
+>>> turtle.left(50)
+>>> turtle.forward(100)
+>>> turtle.pos()
+(64.28,76.60)
+>>> print(round(turtle.xcor(), 5))
+64.27876
+
+
+turtle.ycor()
+

Return the turtle’s y coordinate.

>>> turtle.home()
+>>> turtle.left(60)
+>>> turtle.forward(100)
+>>> print(turtle.pos())
+(50.00,86.60)
+>>> print(round(turtle.ycor(), 5))
+86.60254
+
+
+turtle.heading()
+

Return the turtle’s current heading (value depends on the turtle mode, see mode()).

>>> turtle.home()
+>>> turtle.left(67)
+>>> turtle.heading()
+67.0
+
+
+turtle.distance(x, y=None)
+
Parameters
+
  • +x – a number or a pair/vector of numbers or a turtle instance
  • +y – a number if x is a number, else None +

Return the distance from the turtle to (x,y), the given vector, or the given other turtle, in turtle step units.

>>> turtle.home()
+>>> turtle.distance(30,40)
+50.0
+>>> turtle.distance((30,40))
+50.0
+>>> joe = Turtle()
+>>> joe.forward(77)
+>>> turtle.distance(joe)
+77.0
+
+

Settings for measurement

+turtle.degrees(fullcircle=360.0)
+
Parameters
+

fullcircle – a number

Set angle measurement units, i.e. set number of “degrees” for a full circle. Default value is 360 degrees.

>>> turtle.home()
+>>> turtle.left(90)
+>>> turtle.heading()
+90.0
+
+Change angle measurement unit to grad (also known as gon,
+grade, or gradian and equals 1/100-th of the right angle.)
+>>> turtle.degrees(400.0)
+>>> turtle.heading()
+100.0
+>>> turtle.degrees(360)
+>>> turtle.heading()
+90.0
+
+
+turtle.radians()
+

Set the angle measurement units to radians. Equivalent to degrees(2*math.pi).

>>> turtle.home()
+>>> turtle.left(90)
+>>> turtle.heading()
+90.0
+>>> turtle.radians()
+>>> turtle.heading()
+1.5707963267948966
+
+

Pen control

Drawing state

+turtle.pendown()
+turtle.pd()
+turtle.down()
+

Pull the pen down – drawing when moving.

+
+turtle.penup()
+turtle.pu()
+turtle.up()
+

Pull the pen up – no drawing when moving.

+
+turtle.pensize(width=None)
+turtle.width(width=None)
+
Parameters
+

width – a positive number

Set the line thickness to width or return it. If resizemode is set to “auto” and turtleshape is a polygon, that polygon is drawn with the same line thickness. If no argument is given, the current pensize is returned.

>>> turtle.pensize()
+1
+>>> turtle.pensize(10)   # from here on lines of width 10 are drawn
+
+
+turtle.pen(pen=None, **pendict)
+
Parameters
+
  • +pen – a dictionary with some or all of the below listed keys
  • +pendict – one or more keyword-arguments with the below listed keys as keywords

Return or set the pen’s attributes in a “pen-dictionary” with the following key/value pairs:

  • “shown”: True/False
  • “pendown”: True/False
  • “pencolor”: color-string or color-tuple
  • “fillcolor”: color-string or color-tuple
  • “pensize”: positive number
  • “speed”: number in range 0..10
  • “resizemode”: “auto” or “user” or “noresize”
  • “stretchfactor”: (positive number, positive number)
  • “outline”: positive number
  • “tilt”: number

This dictionary can be used as argument for a subsequent call to pen() to restore the former pen-state. Moreover one or more of these attributes can be provided as keyword-arguments. This can be used to set several pen attributes in one statement.

>>> turtle.pen(fillcolor="black", pencolor="red", pensize=10)
+>>> sorted(turtle.pen().items())
+[('fillcolor', 'black'), ('outline', 1), ('pencolor', 'red'),
+ ('pendown', True), ('pensize', 10), ('resizemode', 'noresize'),
+ ('shearfactor', 0.0), ('shown', True), ('speed', 9),
+ ('stretchfactor', (1.0, 1.0)), ('tilt', 0.0)]
+>>> penstate=turtle.pen()
+>>> turtle.color("yellow", "")
+>>> turtle.penup()
+>>> sorted(turtle.pen().items())[:3]
+[('fillcolor', ''), ('outline', 1), ('pencolor', 'yellow')]
+>>> turtle.pen(penstate, fillcolor="green")
+>>> sorted(turtle.pen().items())[:3]
+[('fillcolor', 'green'), ('outline', 1), ('pencolor', 'red')]
+
+
+turtle.isdown()
+

Return True if pen is down, False if it’s up.

>>> turtle.penup()
+>>> turtle.isdown()
+False
+>>> turtle.pendown()
+>>> turtle.isdown()
+True
+
+

Color control

+turtle.pencolor(*args)
+

Return or set the pencolor.

Four input formats are allowed:

+pencolor()
+
+

Return the current pencolor as color specification string or as a tuple (see example). May be used as input to another color/pencolor/fillcolor call.

+pencolor(colorstring)
+
+

Set pencolor to colorstring, which is a Tk color specification string, such as "red", "yellow", or "#33cc8c".

+pencolor((r, g, b))
+
+

Set pencolor to the RGB color represented by the tuple of r, g, and b. Each of r, g, and b must be in the range 0..colormode, where colormode is either 1.0 or 255 (see colormode()).

+pencolor(r, g, b)
+
+

Set pencolor to the RGB color represented by r, g, and b. Each of r, g, and b must be in the range 0..colormode.

If turtleshape is a polygon, the outline of that polygon is drawn with the newly set pencolor.

>>> colormode()
+1.0
+>>> turtle.pencolor()
+'red'
+>>> turtle.pencolor("brown")
+>>> turtle.pencolor()
+'brown'
+>>> tup = (0.2, 0.8, 0.55)
+>>> turtle.pencolor(tup)
+>>> turtle.pencolor()
+(0.2, 0.8, 0.5490196078431373)
+>>> colormode(255)
+>>> turtle.pencolor()
+(51.0, 204.0, 140.0)
+>>> turtle.pencolor('#32c18f')
+>>> turtle.pencolor()
+(50.0, 193.0, 143.0)
+
+
+turtle.fillcolor(*args)
+

Return or set the fillcolor.

Four input formats are allowed:

+fillcolor()
+
+

Return the current fillcolor as color specification string, possibly in tuple format (see example). May be used as input to another color/pencolor/fillcolor call.

+fillcolor(colorstring)
+
+

Set fillcolor to colorstring, which is a Tk color specification string, such as "red", "yellow", or "#33cc8c".

+fillcolor((r, g, b))
+
+

Set fillcolor to the RGB color represented by the tuple of r, g, and b. Each of r, g, and b must be in the range 0..colormode, where colormode is either 1.0 or 255 (see colormode()).

+fillcolor(r, g, b)
+
+

Set fillcolor to the RGB color represented by r, g, and b. Each of r, g, and b must be in the range 0..colormode.

If turtleshape is a polygon, the interior of that polygon is drawn with the newly set fillcolor.

>>> turtle.fillcolor("violet")
+>>> turtle.fillcolor()
+'violet'
+>>> turtle.pencolor()
+(50.0, 193.0, 143.0)
+>>> turtle.fillcolor((50, 193, 143))  # Integers, not floats
+>>> turtle.fillcolor()
+(50.0, 193.0, 143.0)
+>>> turtle.fillcolor('#ffffff')
+>>> turtle.fillcolor()
+(255.0, 255.0, 255.0)
+
+
+turtle.color(*args)
+

Return or set pencolor and fillcolor.

Several input formats are allowed. They use 0 to 3 arguments as follows:

+color()
+
+

Return the current pencolor and the current fillcolor as a pair of color specification strings or tuples as returned by pencolor() and fillcolor().

+color(colorstring), color((r,g,b)), color(r,g,b)
+
+

Inputs as in pencolor(), set both, fillcolor and pencolor, to the given value.

+color(colorstring1, colorstring2), color((r1,g1,b1), (r2,g2,b2))
+
+

Equivalent to pencolor(colorstring1) and fillcolor(colorstring2) and analogously if the other input format is used.

If turtleshape is a polygon, outline and interior of that polygon is drawn with the newly set colors.

>>> turtle.color("red", "green")
+>>> turtle.color()
+('red', 'green')
+>>> color("#285078", "#a0c8f0")
+>>> color()
+((40.0, 80.0, 120.0), (160.0, 200.0, 240.0))
+
+

See also: Screen method colormode().

Filling

+turtle.filling()
+

Return fillstate (True if filling, False else).

>>> turtle.begin_fill()
+>>> if turtle.filling():
+...    turtle.pensize(5)
+... else:
+...    turtle.pensize(3)
+
+
+turtle.begin_fill()
+

To be called just before drawing a shape to be filled.

+
+turtle.end_fill()
+

Fill the shape drawn after the last call to begin_fill().

Whether or not overlap regions for self-intersecting polygons or multiple shapes are filled depends on the operating system graphics, type of overlap, and number of overlaps. For example, the Turtle star above may be either all yellow or have some white regions.

>>> turtle.color("black", "red")
+>>> turtle.begin_fill()
+>>> turtle.circle(80)
+>>> turtle.end_fill()
+
+

More drawing control

+turtle.reset()
+

Delete the turtle’s drawings from the screen, re-center the turtle and set variables to the default values.

>>> turtle.goto(0,-22)
+>>> turtle.left(100)
+>>> turtle.position()
+(0.00,-22.00)
+>>> turtle.heading()
+100.0
+>>> turtle.reset()
+>>> turtle.position()
+(0.00,0.00)
+>>> turtle.heading()
+0.0
+
+
+turtle.clear()
+

Delete the turtle’s drawings from the screen. Do not move turtle. State and position of the turtle as well as drawings of other turtles are not affected.

+
+turtle.write(arg, move=False, align='left', font=('Arial', 8, 'normal'))
+
Parameters
+
  • +arg – object to be written to the TurtleScreen
  • +move – True/False
  • +align – one of the strings “left”, “center” or right”
  • +font – a triple (fontname, fontsize, fonttype)

Write text - the string representation of arg - at the current turtle position according to align (“left”, “center” or “right”) and with the given font. If move is true, the pen is moved to the bottom-right corner of the text. By default, move is False.

>>> turtle.write("Home = ", True, align="center")
+>>> turtle.write((0,0), True)
+
+

Turtle state

Visibility

+turtle.hideturtle()
+turtle.ht()
+

Make the turtle invisible. It’s a good idea to do this while you’re in the middle of doing some complex drawing, because hiding the turtle speeds up the drawing observably.

>>> turtle.hideturtle()
+
+
+turtle.showturtle()
+turtle.st()
+

Make the turtle visible.

>>> turtle.showturtle()
+
+
+turtle.isvisible()
+

Return True if the Turtle is shown, False if it’s hidden.

>>> turtle.hideturtle()
+>>> turtle.isvisible()
+False
+>>> turtle.showturtle()
+>>> turtle.isvisible()
+True
+
+

Appearance

+turtle.shape(name=None)
+
Parameters
+

name – a string which is a valid shapename

Set turtle shape to shape with given name or, if name is not given, return name of current shape. Shape with name must exist in the TurtleScreen’s shape dictionary. Initially there are the following polygon shapes: “arrow”, “turtle”, “circle”, “square”, “triangle”, “classic”. To learn about how to deal with shapes see Screen method register_shape().

>>> turtle.shape()
+'classic'
+>>> turtle.shape("turtle")
+>>> turtle.shape()
+'turtle'
+
+
+turtle.resizemode(rmode=None)
+
Parameters
+

rmode – one of the strings “auto”, “user”, “noresize”

Set resizemode to one of the values: “auto”, “user”, “noresize”. If rmode is not given, return current resizemode. Different resizemodes have the following effects:

  • “auto”: adapts the appearance of the turtle corresponding to the value of pensize.
  • “user”: adapts the appearance of the turtle according to the values of stretchfactor and outlinewidth (outline), which are set by shapesize().
  • “noresize”: no adaption of the turtle’s appearance takes place.

resizemode("user") is called by shapesize() when used with arguments.

>>> turtle.resizemode()
+'noresize'
+>>> turtle.resizemode("auto")
+>>> turtle.resizemode()
+'auto'
+
+
+turtle.shapesize(stretch_wid=None, stretch_len=None, outline=None)
+turtle.turtlesize(stretch_wid=None, stretch_len=None, outline=None)
+
Parameters
+
  • +stretch_wid – positive number
  • +stretch_len – positive number
  • +outline – positive number

Return or set the pen’s attributes x/y-stretchfactors and/or outline. Set resizemode to “user”. If and only if resizemode is set to “user”, the turtle will be displayed stretched according to its stretchfactors: stretch_wid is stretchfactor perpendicular to its orientation, stretch_len is stretchfactor in direction of its orientation, outline determines the width of the shape’s outline.

>>> turtle.shapesize()
+(1.0, 1.0, 1)
+>>> turtle.resizemode("user")
+>>> turtle.shapesize(5, 5, 12)
+>>> turtle.shapesize()
+(5, 5, 12)
+>>> turtle.shapesize(outline=8)
+>>> turtle.shapesize()
+(5, 5, 8)
+
+
+turtle.shearfactor(shear=None)
+
Parameters
+

shear – number (optional)

Set or return the current shearfactor. Shear the turtleshape according to the given shearfactor shear, which is the tangent of the shear angle. Do not change the turtle’s heading (direction of movement). If shear is not given: return the current shearfactor, i. e. the tangent of the shear angle, by which lines parallel to the heading of the turtle are sheared.

>>> turtle.shape("circle")
+>>> turtle.shapesize(5,2)
+>>> turtle.shearfactor(0.5)
+>>> turtle.shearfactor()
+0.5
+
+
+turtle.tilt(angle)
+
Parameters
+

angle – a number

Rotate the turtleshape by angle from its current tilt-angle, but do not change the turtle’s heading (direction of movement).

>>> turtle.reset()
+>>> turtle.shape("circle")
+>>> turtle.shapesize(5,2)
+>>> turtle.tilt(30)
+>>> turtle.fd(50)
+>>> turtle.tilt(30)
+>>> turtle.fd(50)
+
+
+turtle.settiltangle(angle)
+
Parameters
+

angle – a number

Rotate the turtleshape to point in the direction specified by angle, regardless of its current tilt-angle. Do not change the turtle’s heading (direction of movement).

>>> turtle.reset()
+>>> turtle.shape("circle")
+>>> turtle.shapesize(5,2)
+>>> turtle.settiltangle(45)
+>>> turtle.fd(50)
+>>> turtle.settiltangle(-45)
+>>> turtle.fd(50)
+

Deprecated since version 3.1.

+
+turtle.tiltangle(angle=None)
+
Parameters
+

angle – a number (optional)

Set or return the current tilt-angle. If angle is given, rotate the turtleshape to point in the direction specified by angle, regardless of its current tilt-angle. Do not change the turtle’s heading (direction of movement). If angle is not given: return the current tilt-angle, i. e. the angle between the orientation of the turtleshape and the heading of the turtle (its direction of movement).

>>> turtle.reset()
+>>> turtle.shape("circle")
+>>> turtle.shapesize(5,2)
+>>> turtle.tilt(45)
+>>> turtle.tiltangle()
+45.0
+
+
+turtle.shapetransform(t11=None, t12=None, t21=None, t22=None)
+
Parameters
+
  • +t11 – a number (optional)
  • +t12 – a number (optional)
  • +t21 – a number (optional)
  • +t12 – a number (optional)

Set or return the current transformation matrix of the turtle shape.

If none of the matrix elements are given, return the transformation matrix as a tuple of 4 elements. Otherwise set the given elements and transform the turtleshape according to the matrix consisting of first row t11, t12 and second row t21, t22. The determinant t11 * t22 - t12 * t21 must not be zero, otherwise an error is raised. Modify stretchfactor, shearfactor and tiltangle according to the given matrix.

>>> turtle = Turtle()
+>>> turtle.shape("square")
+>>> turtle.shapesize(4,2)
+>>> turtle.shearfactor(-0.5)
+>>> turtle.shapetransform()
+(4.0, -1.0, -0.0, 2.0)
+
+
+turtle.get_shapepoly()
+

Return the current shape polygon as tuple of coordinate pairs. This can be used to define a new shape or components of a compound shape.

>>> turtle.shape("square")
+>>> turtle.shapetransform(4, -1, 0, 2)
+>>> turtle.get_shapepoly()
+((50, -20), (30, 20), (-50, 20), (-30, -20))
+
+

Using events

turtle.onclick(fun, btn=1, add=None) +
+
Parameters
+
  • +fun – a function with two arguments which will be called with the coordinates of the clicked point on the canvas
  • +btn – number of the mouse-button, defaults to 1 (left mouse button)
  • +add – True or False – if True, a new binding will be added, otherwise it will replace a former binding

Bind fun to mouse-click events on this turtle. If fun is None, existing bindings are removed. Example for the anonymous turtle, i.e. the procedural way:

>>> def turn(x, y):
+...     left(180)
+...
+>>> onclick(turn)  # Now clicking into the turtle will turn it.
+>>> onclick(None)  # event-binding will be removed
+
+
+turtle.onrelease(fun, btn=1, add=None)
+
Parameters
+
  • +fun – a function with two arguments which will be called with the coordinates of the clicked point on the canvas
  • +btn – number of the mouse-button, defaults to 1 (left mouse button)
  • +add – True or False – if True, a new binding will be added, otherwise it will replace a former binding

Bind fun to mouse-button-release events on this turtle. If fun is None, existing bindings are removed.

>>> class MyTurtle(Turtle):
+...     def glow(self,x,y):
+...         self.fillcolor("red")
+...     def unglow(self,x,y):
+...         self.fillcolor("")
+...
+>>> turtle = MyTurtle()
+>>> turtle.onclick(turtle.glow)     # clicking on turtle turns fillcolor red,
+>>> turtle.onrelease(turtle.unglow) # releasing turns it to transparent.
+
+
+turtle.ondrag(fun, btn=1, add=None)
+
Parameters
+
  • +fun – a function with two arguments which will be called with the coordinates of the clicked point on the canvas
  • +btn – number of the mouse-button, defaults to 1 (left mouse button)
  • +add – True or False – if True, a new binding will be added, otherwise it will replace a former binding

Bind fun to mouse-move events on this turtle. If fun is None, existing bindings are removed.

Remark: Every sequence of mouse-move-events on a turtle is preceded by a mouse-click event on that turtle.

>>> turtle.ondrag(turtle.goto)
+

Subsequently, clicking and dragging the Turtle will move it across the screen thereby producing handdrawings (if pen is down).

+

Special Turtle methods

+turtle.begin_poly()
+

Start recording the vertices of a polygon. Current turtle position is first vertex of polygon.

+
+turtle.end_poly()
+

Stop recording the vertices of a polygon. Current turtle position is last vertex of polygon. This will be connected with the first vertex.

+
+turtle.get_poly()
+

Return the last recorded polygon.

>>> turtle.home()
+>>> turtle.begin_poly()
+>>> turtle.fd(100)
+>>> turtle.left(20)
+>>> turtle.fd(30)
+>>> turtle.left(60)
+>>> turtle.fd(50)
+>>> turtle.end_poly()
+>>> p = turtle.get_poly()
+>>> register_shape("myFavouriteShape", p)
+
+
+turtle.clone()
+

Create and return a clone of the turtle with same position, heading and turtle properties.

>>> mick = Turtle()
+>>> joe = mick.clone()
+
+
+turtle.getturtle()
+turtle.getpen()
+

Return the Turtle object itself. Only reasonable use: as a function to return the “anonymous turtle”:

>>> pet = getturtle()
+>>> pet.fd(50)
+>>> pet
+<turtle.Turtle object at 0x...>
+
+
+turtle.getscreen()
+

Return the TurtleScreen object the turtle is drawing on. TurtleScreen methods can then be called for that object.

>>> ts = turtle.getscreen()
+>>> ts
+<turtle._Screen object at 0x...>
+>>> ts.bgcolor("pink")
+
+
+turtle.setundobuffer(size)
+
Parameters
+

size – an integer or None

Set or disable undobuffer. If size is an integer, an empty undobuffer of given size is installed. size gives the maximum number of turtle actions that can be undone by the undo() method/function. If size is None, the undobuffer is disabled.

>>> turtle.setundobuffer(42)
+
+
+turtle.undobufferentries()
+

Return number of entries in the undobuffer.

>>> while undobufferentries():
+...     undo()
+
+

Compound shapes

To use compound turtle shapes, which consist of several polygons of different color, you must use the helper class Shape explicitly as described below:

  1. Create an empty Shape object of type “compound”.
  2. +

    Add as many components to this object as desired, using the addcomponent() method.

    For example:

    >>> s = Shape("compound")
+>>> poly1 = ((0,0),(10,-5),(0,10),(-10,-5))
+>>> s.addcomponent(poly1, "red", "blue")
+>>> poly2 = ((0,0),(10,-5),(-10,-5))
+>>> s.addcomponent(poly2, "blue", "red")
+
  3. +

    Now add the Shape to the Screen’s shapelist and use it:

    >>> register_shape("myshape", s)
+>>> shape("myshape")
+

Note

The Shape class is used internally by the register_shape() method in different ways. The application programmer has to deal with the Shape class only when using compound shapes like shown above!

Methods of TurtleScreen/Screen and corresponding functions

Most of the examples in this section refer to a TurtleScreen instance called screen.

Window control

+turtle.bgcolor(*args)
+
Parameters
+

args – a color string or three numbers in the range 0..colormode or a 3-tuple of such numbers

Set or return background color of the TurtleScreen.

>>> screen.bgcolor("orange")
+>>> screen.bgcolor()
+'orange'
+>>> screen.bgcolor("#800080")
+>>> screen.bgcolor()
+(128.0, 0.0, 128.0)
+
+
+turtle.bgpic(picname=None)
+
Parameters
+

picname – a string, name of a gif-file or "nopic", or None

Set background image or return name of current backgroundimage. If picname is a filename, set the corresponding image as background. If picname is "nopic", delete background image, if present. If picname is None, return the filename of the current backgroundimage.

>>> screen.bgpic()
+'nopic'
+>>> screen.bgpic("landscape.gif")
+>>> screen.bgpic()
+"landscape.gif"
+
+
turtle.clear() +
+

Note

This TurtleScreen method is available as a global function only under the name clearscreen. The global function clear is a different one derived from the Turtle method clear.

+
+turtle.clearscreen()
+

Delete all drawings and all turtles from the TurtleScreen. Reset the now empty TurtleScreen to its initial state: white background, no background image, no event bindings and tracing on.

+
turtle.reset() +
+

Note

This TurtleScreen method is available as a global function only under the name resetscreen. The global function reset is another one derived from the Turtle method reset.

+
+turtle.resetscreen()
+

Reset all Turtles on the Screen to their initial state.

+
+turtle.screensize(canvwidth=None, canvheight=None, bg=None)
+
Parameters
+
  • +canvwidth – positive integer, new width of canvas in pixels
  • +canvheight – positive integer, new height of canvas in pixels
  • +bg – colorstring or color-tuple, new background color

If no arguments are given, return current (canvaswidth, canvasheight). Else resize the canvas the turtles are drawing on. Do not alter the drawing window. To observe hidden parts of the canvas, use the scrollbars. With this method, one can make visible those parts of a drawing which were outside the canvas before.

>>> screen.screensize()
+(400, 300)
+>>> screen.screensize(2000,1500)
+>>> screen.screensize()
+(2000, 1500)
+

e.g. to search for an erroneously escaped turtle ;-)

+
+turtle.setworldcoordinates(llx, lly, urx, ury)
+
Parameters
+
  • +llx – a number, x-coordinate of lower left corner of canvas
  • +lly – a number, y-coordinate of lower left corner of canvas
  • +urx – a number, x-coordinate of upper right corner of canvas
  • +ury – a number, y-coordinate of upper right corner of canvas

Set up user-defined coordinate system and switch to mode “world” if necessary. This performs a screen.reset(). If mode “world” is already active, all drawings are redrawn according to the new coordinates.

ATTENTION: in user-defined coordinate systems angles may appear distorted.

>>> screen.reset()
+>>> screen.setworldcoordinates(-50,-7.5,50,7.5)
+>>> for _ in range(72):
+...     left(10)
+...
+>>> for _ in range(8):
+...     left(45); fd(2)   # a regular octagon
+
+

Animation control

+turtle.delay(delay=None)
+
Parameters
+

delay – positive integer

Set or return the drawing delay in milliseconds. (This is approximately the time interval between two consecutive canvas updates.) The longer the drawing delay, the slower the animation.

Optional argument:

>>> screen.delay()
+10
+>>> screen.delay(5)
+>>> screen.delay()
+5
+
+
+turtle.tracer(n=None, delay=None)
+
Parameters
+
  • +n – nonnegative integer
  • +delay – nonnegative integer

Turn turtle animation on/off and set delay for update drawings. If n is given, only each n-th regular screen update is really performed. (Can be used to accelerate the drawing of complex graphics.) When called without arguments, returns the currently stored value of n. Second argument sets delay value (see delay()).

>>> screen.tracer(8, 25)
+>>> dist = 2
+>>> for i in range(200):
+...     fd(dist)
+...     rt(90)
+...     dist += 2
+
+
+turtle.update()
+

Perform a TurtleScreen update. To be used when tracer is turned off.

+

See also the RawTurtle/Turtle method speed().

Using screen events

+turtle.listen(xdummy=None, ydummy=None)
+

Set focus on TurtleScreen (in order to collect key-events). Dummy arguments are provided in order to be able to pass listen() to the onclick method.

+
+turtle.onkey(fun, key)
+turtle.onkeyrelease(fun, key)
+
Parameters
+
  • +fun – a function with no arguments or None +
  • +key – a string: key (e.g. “a”) or key-symbol (e.g. “space”)

Bind fun to key-release event of key. If fun is None, event bindings are removed. Remark: in order to be able to register key-events, TurtleScreen must have the focus. (See method listen().)

>>> def f():
+...     fd(50)
+...     lt(60)
+...
+>>> screen.onkey(f, "Up")
+>>> screen.listen()
+
+
+turtle.onkeypress(fun, key=None)
+
Parameters
+
  • +fun – a function with no arguments or None +
  • +key – a string: key (e.g. “a”) or key-symbol (e.g. “space”)

Bind fun to key-press event of key if key is given, or to any key-press-event if no key is given. Remark: in order to be able to register key-events, TurtleScreen must have focus. (See method listen().)

>>> def f():
+...     fd(50)
+...
+>>> screen.onkey(f, "Up")
+>>> screen.listen()
+
+
+turtle.onclick(fun, btn=1, add=None)
+turtle.onscreenclick(fun, btn=1, add=None)
+
Parameters
+
  • +fun – a function with two arguments which will be called with the coordinates of the clicked point on the canvas
  • +btn – number of the mouse-button, defaults to 1 (left mouse button)
  • +add – True or False – if True, a new binding will be added, otherwise it will replace a former binding

Bind fun to mouse-click events on this screen. If fun is None, existing bindings are removed.

Example for a TurtleScreen instance named screen and a Turtle instance named turtle:

>>> screen.onclick(turtle.goto) # Subsequently clicking into the TurtleScreen will
+>>>                             # make the turtle move to the clicked point.
+>>> screen.onclick(None)        # remove event binding again
+

Note

This TurtleScreen method is available as a global function only under the name onscreenclick. The global function onclick is another one derived from the Turtle method onclick.

+
+turtle.ontimer(fun, t=0)
+
Parameters
+
  • +fun – a function with no arguments
  • +t – a number >= 0

Install a timer that calls fun after t milliseconds.

>>> running = True
+>>> def f():
+...     if running:
+...         fd(50)
+...         lt(60)
+...         screen.ontimer(f, 250)
+>>> f()   ### makes the turtle march around
+>>> running = False
+
+
+turtle.mainloop()
+turtle.done()
+

Starts event loop - calling Tkinter’s mainloop function. Must be the last statement in a turtle graphics program. Must not be used if a script is run from within IDLE in -n mode (No subprocess) - for interactive use of turtle graphics.

>>> screen.mainloop()
+
+

Input methods

+turtle.textinput(title, prompt)
+
Parameters
+
  • +title – string
  • +prompt – string

Pop up a dialog window for input of a string. Parameter title is the title of the dialog window, prompt is a text mostly describing what information to input. Return the string input. If the dialog is canceled, return None.

>>> screen.textinput("NIM", "Name of first player:")
+
+
+turtle.numinput(title, prompt, default=None, minval=None, maxval=None)
+
Parameters
+
  • +title – string
  • +prompt – string
  • +default – number (optional)
  • +minval – number (optional)
  • +maxval – number (optional)

Pop up a dialog window for input of a number. title is the title of the dialog window, prompt is a text mostly describing what numerical information to input. default: default value, minval: minimum value for input, maxval: maximum value for input. The number input must be in the range minval .. maxval if these are given. If not, a hint is issued and the dialog remains open for correction. Return the number input. If the dialog is canceled, return None.

>>> screen.numinput("Poker", "Your stakes:", 1000, minval=10, maxval=10000)
+
+

Settings and special methods

+turtle.mode(mode=None)
+
Parameters
+

mode – one of the strings “standard”, “logo” or “world”

Set turtle mode (“standard”, “logo” or “world”) and perform reset. If mode is not given, current mode is returned.

Mode “standard” is compatible with old turtle. Mode “logo” is compatible with most Logo turtle graphics. Mode “world” uses user-defined “world coordinates”. Attention: in this mode angles appear distorted if x/y unit-ratio doesn’t equal 1.

+ + +

Mode

Initial turtle heading

positive angles

“standard”

to the right (east)

counterclockwise

“logo”

upward (north)

clockwise

 
>>> mode("logo")   # resets turtle heading to north
+>>> mode()
+'logo'
+
+
+turtle.colormode(cmode=None)
+
Parameters
+

cmode – one of the values 1.0 or 255

Return the colormode or set it to 1.0 or 255. Subsequently r, g, b values of color triples have to be in the range 0..*cmode*.

>>> screen.colormode(1)
+>>> turtle.pencolor(240, 160, 80)
+Traceback (most recent call last):
+     ...
+TurtleGraphicsError: bad color sequence: (240, 160, 80)
+>>> screen.colormode()
+1.0
+>>> screen.colormode(255)
+>>> screen.colormode()
+255
+>>> turtle.pencolor(240,160,80)
+
+
+turtle.getcanvas()
+

Return the Canvas of this TurtleScreen. Useful for insiders who know what to do with a Tkinter Canvas.

>>> cv = screen.getcanvas()
+>>> cv
+<turtle.ScrolledCanvas object ...>
+
+
+turtle.getshapes()
+

Return a list of names of all currently available turtle shapes.

>>> screen.getshapes()
+['arrow', 'blank', 'circle', ..., 'turtle']
+
+
+turtle.register_shape(name, shape=None)
+turtle.addshape(name, shape=None)
+

There are three different ways to call this function:

  1. +

    name is the name of a gif-file and shape is None: Install the corresponding image shape.

    >>> screen.register_shape("turtle.gif")
+

    Note

    Image shapes do not rotate when turning the turtle, so they do not display the heading of the turtle!

  2. +

    name is an arbitrary string and shape is a tuple of pairs of coordinates: Install the corresponding polygon shape.

    >>> screen.register_shape("triangle", ((5,-3), (0,5), (-5,-3)))
+
  3. +name is an arbitrary string and shape is a (compound) Shape object: Install the corresponding compound shape.

Add a turtle shape to TurtleScreen’s shapelist. Only thusly registered shapes can be used by issuing the command shape(shapename).

+
+turtle.turtles()
+

Return the list of turtles on the screen.

>>> for turtle in screen.turtles():
+...     turtle.color("red")
+
+
+turtle.window_height()
+

Return the height of the turtle window.

>>> screen.window_height()
+480
+
+
+turtle.window_width()
+

Return the width of the turtle window.

>>> screen.window_width()
+640
+
+

Methods specific to Screen, not inherited from TurtleScreen

+turtle.bye()
+

Shut the turtlegraphics window.

+
+turtle.exitonclick()
+

Bind bye() method to mouse clicks on the Screen.

If the value “using_IDLE” in the configuration dictionary is False (default value), also enter mainloop. Remark: If IDLE with the -n switch (no subprocess) is used, this value should be set to True in turtle.cfg. In this case IDLE’s own mainloop is active also for the client script.

+
+turtle.setup(width=_CFG['width'], height=_CFG['height'], startx=_CFG['leftright'], starty=_CFG['topbottom'])
+

Set the size and position of the main window. Default values of arguments are stored in the configuration dictionary and can be changed via a turtle.cfg file.

Parameters
+
  • +width – if an integer, a size in pixels, if a float, a fraction of the screen; default is 50% of screen
  • +height – if an integer, the height in pixels, if a float, a fraction of the screen; default is 75% of screen
  • +startx – if positive, starting position in pixels from the left edge of the screen, if negative from the right edge, if None, center window horizontally
  • +starty – if positive, starting position in pixels from the top edge of the screen, if negative from the bottom edge, if None, center window vertically
>>> screen.setup (width=200, height=200, startx=0, starty=0)
+>>>              # sets window to 200x200 pixels, in upper left of screen
+>>> screen.setup(width=.75, height=0.5, startx=None, starty=None)
+>>>              # sets window to 75% of screen by 50% of screen and centers
+
+
+turtle.title(titlestring)
+
Parameters
+

titlestring – a string that is shown in the titlebar of the turtle graphics window

Set title of turtle window to titlestring.

>>> screen.title("Welcome to the turtle zoo!")
+
+

Public classes

+class turtle.RawTurtle(canvas)
+class turtle.RawPen(canvas)
+
Parameters
+

canvas – a tkinter.Canvas, a ScrolledCanvas or a TurtleScreen

Create a turtle. The turtle has all methods described above as “methods of Turtle/RawTurtle”.

+
+class turtle.Turtle
+

Subclass of RawTurtle, has the same interface but draws on a default Screen object created automatically when needed for the first time.

+
+class turtle.TurtleScreen(cv)
+
Parameters
+

cv – a tkinter.Canvas

Provides screen oriented methods like bgcolor() etc. that are described above.

+
+class turtle.Screen
+

Subclass of TurtleScreen, with four methods added.

+
+class turtle.ScrolledCanvas(master)
+
Parameters
+

master – some Tkinter widget to contain the ScrolledCanvas, i.e. a Tkinter-canvas with scrollbars added

Used by class Screen, which thus automatically provides a ScrolledCanvas as playground for the turtles.

+
+class turtle.Shape(type_, data)
+
Parameters
+

type_ – one of the strings “polygon”, “image”, “compound”

Data structure modeling shapes. The pair (type_, data) must follow this specification:

+ + + +

type_

data

“polygon”

a polygon-tuple, i.e. a tuple of pairs of coordinates

“image”

an image (in this form only used internally!)

“compound”

None (a compound shape has to be constructed using the addcomponent() method)
+addcomponent(poly, fill, outline=None)
+
Parameters
+
  • +poly – a polygon, i.e. a tuple of pairs of numbers
  • +fill – a color the poly will be filled with
  • +outline – a color for the poly’s outline (if given)

Example:

>>> poly = ((0,0),(10,-5),(0,10),(-10,-5))
+>>> s = Shape("compound")
+>>> s.addcomponent(poly, "red", "blue")
+>>> # ... add more components and then use register_shape()
+

See Compound shapes.

+
+
+class turtle.Vec2D(x, y)
+

A two-dimensional vector class, used as a helper class for implementing turtle graphics. May be useful for turtle graphics programs too. Derived from tuple, so a vector is a tuple!

Provides (for a, b vectors, k number):

  • +a + b vector addition
  • +a - b vector subtraction
  • +a * b inner product
  • +k * a and a * k multiplication with scalar
  • +abs(a) absolute value of a
  • +a.rotate(angle) rotation
+

Explanation

A turtle object draws on a screen object, and there a number of key classes in the turtle object-oriented interface that can be used to create them and relate them to each other.

A Turtle instance will automatically create a Screen instance if one is not already present.

Turtle is a subclass of RawTurtle, which doesn’t automatically create a drawing surface - a canvas will need to be provided or created for it. The canvas can be a tkinter.Canvas, ScrolledCanvas or TurtleScreen.

TurtleScreen is the basic drawing surface for a turtle. Screen is a subclass of TurtleScreen, and includes some additional methods for managing its appearance (including size and title) and behaviour. TurtleScreen’s constructor needs a tkinter.Canvas or a ScrolledCanvas as an argument.

The functional interface for turtle graphics uses the various methods of Turtle and TurtleScreen/Screen. Behind the scenes, a screen object is automatically created whenever a function derived from a Screen method is called. Similarly, a turtle object is automatically created whenever any of the functions derived from a Turtle method is called.

To use multiple turtles on a screen, the object-oriented interface must be used.

Help and configuration

How to use help

The public methods of the Screen and Turtle classes are documented extensively via docstrings. So these can be used as online-help via the Python help facilities:

  • When using IDLE, tooltips show the signatures and first lines of the docstrings of typed in function-/method calls.
  • +

    Calling help() on methods or functions displays the docstrings:

    >>> help(Screen.bgcolor)
+Help on method bgcolor in module turtle:
+
+bgcolor(self, *args) unbound turtle.Screen method
+    Set or return backgroundcolor of the TurtleScreen.
+
+    Arguments (if given): a color string or three numbers
+    in the range 0..colormode or a 3-tuple of such numbers.
+
+
+    >>> screen.bgcolor("orange")
+    >>> screen.bgcolor()
+    "orange"
+    >>> screen.bgcolor(0.5,0,0.5)
+    >>> screen.bgcolor()
+    "#800080"
+
+>>> help(Turtle.penup)
+Help on method penup in module turtle:
+
+penup(self) unbound turtle.Turtle method
+    Pull the pen up -- no drawing when moving.
+
+    Aliases: penup | pu | up
+
+    No argument
+
+    >>> turtle.penup()
+
  • +

    The docstrings of the functions which are derived from methods have a modified form:

    >>> help(bgcolor)
+Help on function bgcolor in module turtle:
+
+bgcolor(*args)
+    Set or return backgroundcolor of the TurtleScreen.
+
+    Arguments (if given): a color string or three numbers
+    in the range 0..colormode or a 3-tuple of such numbers.
+
+    Example::
+
+      >>> bgcolor("orange")
+      >>> bgcolor()
+      "orange"
+      >>> bgcolor(0.5,0,0.5)
+      >>> bgcolor()
+      "#800080"
+
+>>> help(penup)
+Help on function penup in module turtle:
+
+penup()
+    Pull the pen up -- no drawing when moving.
+
+    Aliases: penup | pu | up
+
+    No argument
+
+    Example:
+    >>> penup()
+

These modified docstrings are created automatically together with the function definitions that are derived from the methods at import time.

Translation of docstrings into different languages

There is a utility to create a dictionary the keys of which are the method names and the values of which are the docstrings of the public methods of the classes Screen and Turtle.

+turtle.write_docstringdict(filename='turtle_docstringdict')
+
Parameters
+

filename – a string, used as filename

Create and write docstring-dictionary to a Python script with the given filename. This function has to be called explicitly (it is not used by the turtle graphics classes). The docstring dictionary will be written to the Python script filename.py. It is intended to serve as a template for translation of the docstrings into different languages.

+

If you (or your students) want to use turtle with online help in your native language, you have to translate the docstrings and save the resulting file as e.g. turtle_docstringdict_german.py.

If you have an appropriate entry in your turtle.cfg file this dictionary will be read in at import time and will replace the original English docstrings.

At the time of this writing there are docstring dictionaries in German and in Italian. (Requests please to glingl@aon.at.)

How to configure Screen and Turtles

The built-in default configuration mimics the appearance and behaviour of the old turtle module in order to retain best possible compatibility with it.

If you want to use a different configuration which better reflects the features of this module or which better fits to your needs, e.g. for use in a classroom, you can prepare a configuration file turtle.cfg which will be read at import time and modify the configuration according to its settings.

The built in configuration would correspond to the following turtle.cfg:

width = 0.5
+height = 0.75
+leftright = None
+topbottom = None
+canvwidth = 400
+canvheight = 300
+mode = standard
+colormode = 1.0
+delay = 10
+undobuffersize = 1000
+shape = classic
+pencolor = black
+fillcolor = black
+resizemode = noresize
+visible = True
+language = english
+exampleturtle = turtle
+examplescreen = screen
+title = Python Turtle Graphics
+using_IDLE = False
+

Short explanation of selected entries:

  • The first four lines correspond to the arguments of the Screen.setup method.
  • Line 5 and 6 correspond to the arguments of the method Screen.screensize.
  • +shape can be any of the built-in shapes, e.g: arrow, turtle, etc. For more info try help(shape).
  • If you want to use no fill color (i.e. make the turtle transparent), you have to write fillcolor = "" (but all nonempty strings must not have quotes in the cfg file).
  • If you want to reflect the turtle its state, you have to use resizemode = +auto.
  • If you set e.g. language = italian the docstringdict turtle_docstringdict_italian.py will be loaded at import time (if present on the import path, e.g. in the same directory as turtle).
  • The entries exampleturtle and examplescreen define the names of these objects as they occur in the docstrings. The transformation of method-docstrings to function-docstrings will delete these names from the docstrings.
  • +using_IDLE: Set this to True if you regularly work with IDLE and its -n switch (“no subprocess”). This will prevent exitonclick() to enter the mainloop.

There can be a turtle.cfg file in the directory where turtle is stored and an additional one in the current working directory. The latter will override the settings of the first one.

The Lib/turtledemo directory contains a turtle.cfg file. You can study it as an example and see its effects when running the demos (preferably not from within the demo-viewer).

turtledemo — Demo scripts

The turtledemo package includes a set of demo scripts. These scripts can be run and viewed using the supplied demo viewer as follows:

python -m turtledemo
+

Alternatively, you can run the demo scripts individually. For example,

python -m turtledemo.bytedesign
+

The turtledemo package directory contains:

  • A demo viewer __main__.py which can be used to view the sourcecode of the scripts and run them at the same time.
  • Multiple scripts demonstrating different features of the turtle module. Examples can be accessed via the Examples menu. They can also be run standalone.
  • A turtle.cfg file which serves as an example of how to write and use such files.

The demo scripts are:

+ + + + + + + + + + + + + + + + + + + +

Name

Description

Features

bytedesign

complex classical turtle graphics pattern

tracer(), delay, update()

chaos

graphs Verhulst dynamics, shows that computer’s computations can generate results sometimes against the common sense expectations

world coordinates

clock

analog clock showing time of your computer

turtles as clock’s hands, ontimer

colormixer

experiment with r, g, b

ondrag()

forest

3 breadth-first trees

randomization

fractalcurves

Hilbert & Koch curves

recursion

lindenmayer

ethnomathematics (indian kolams)

L-System

minimal_hanoi

Towers of Hanoi

Rectangular Turtles as Hanoi discs (shape, shapesize)

nim

play the classical nim game with three heaps of sticks against the computer.

turtles as nimsticks, event driven (mouse, keyboard)

paint

super minimalistic drawing program

onclick()

peace

elementary

turtle: appearance and animation

penrose

aperiodic tiling with kites and darts

stamp()

planet_and_moon

simulation of gravitational system

compound shapes, Vec2D

rosette

a pattern from the wikipedia article on turtle graphics

clone(), undo()

round_dance

dancing turtles rotating pairwise in opposite direction

compound shapes, clone shapesize, tilt, get_shapepoly, update

sorting_animate

visual demonstration of different sorting methods

simple alignment, randomization

tree

a (graphical) breadth first tree (using generators)

clone()

two_canvases

simple design

turtles on two canvases

yinyang

another elementary example

circle()

Have fun!

Changes since Python 2.6

  • The methods Turtle.tracer, Turtle.window_width and Turtle.window_height have been eliminated. Methods with these names and functionality are now available only as methods of Screen. The functions derived from these remain available. (In fact already in Python 2.6 these methods were merely duplications of the corresponding TurtleScreen/Screen methods.)
  • The method Turtle.fill() has been eliminated. The behaviour of begin_fill() and end_fill() have changed slightly: now every filling process must be completed with an end_fill() call.
  • A method Turtle.filling has been added. It returns a boolean value: True if a filling process is under way, False otherwise. This behaviour corresponds to a fill() call without arguments in Python 2.6.

Changes since Python 3.0

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/turtle.html +

+
diff --git a/devdocs/python~3.12/library%2Ftypes.html b/devdocs/python~3.12/library%2Ftypes.html new file mode 100644 index 00000000..1e51ac44 --- /dev/null +++ b/devdocs/python~3.12/library%2Ftypes.html @@ -0,0 +1,200 @@ +

types — Dynamic type creation and names for built-in types

Source code: Lib/types.py

This module defines utility functions to assist in dynamic creation of new types.

It also defines names for some object types that are used by the standard Python interpreter, but not exposed as builtins like int or str are.

Finally, it provides some additional type-related utility classes and functions that are not fundamental enough to be builtins.

Dynamic Type Creation

+types.new_class(name, bases=(), kwds=None, exec_body=None)
+

Creates a class object dynamically using the appropriate metaclass.

The first three arguments are the components that make up a class definition header: the class name, the base classes (in order), the keyword arguments (such as metaclass).

The exec_body argument is a callback that is used to populate the freshly created class namespace. It should accept the class namespace as its sole argument and update the namespace directly with the class contents. If no callback is provided, it has the same effect as passing in lambda ns: None.

New in version 3.3.

+
+types.prepare_class(name, bases=(), kwds=None)
+

Calculates the appropriate metaclass and creates the class namespace.

The arguments are the components that make up a class definition header: the class name, the base classes (in order) and the keyword arguments (such as metaclass).

The return value is a 3-tuple: metaclass, namespace, kwds

metaclass is the appropriate metaclass, namespace is the prepared class namespace and kwds is an updated copy of the passed in kwds argument with any 'metaclass' entry removed. If no kwds argument is passed in, this will be an empty dict.

New in version 3.3.

Changed in version 3.6: The default value for the namespace element of the returned tuple has changed. Now an insertion-order-preserving mapping is used when the metaclass does not have a __prepare__ method.

+

See also

Metaclasses
+
+

Full details of the class creation process supported by these functions

+PEP 3115 - Metaclasses in Python 3000
+
+

Introduced the __prepare__ namespace hook

+types.resolve_bases(bases)
+

Resolve MRO entries dynamically as specified by PEP 560.

This function looks for items in bases that are not instances of type, and returns a tuple where each such object that has an __mro_entries__() method is replaced with an unpacked result of calling this method. If a bases item is an instance of type, or it doesn’t have an __mro_entries__() method, then it is included in the return tuple unchanged.

New in version 3.7.

+
+types.get_original_bases(cls, /)
+

Return the tuple of objects originally given as the bases of cls before the __mro_entries__() method has been called on any bases (following the mechanisms laid out in PEP 560). This is useful for introspecting Generics.

For classes that have an __orig_bases__ attribute, this function returns the value of cls.__orig_bases__. For classes without the __orig_bases__ attribute, cls.__bases__ is returned.

Examples:

from typing import TypeVar, Generic, NamedTuple, TypedDict
+
+T = TypeVar("T")
+class Foo(Generic[T]): ...
+class Bar(Foo[int], float): ...
+class Baz(list[str]): ...
+Eggs = NamedTuple("Eggs", [("a", int), ("b", str)])
+Spam = TypedDict("Spam", {"a": int, "b": str})
+
+assert Bar.__bases__ == (Foo, float)
+assert get_original_bases(Bar) == (Foo[int], float)
+
+assert Baz.__bases__ == (list,)
+assert get_original_bases(Baz) == (list[str],)
+
+assert Eggs.__bases__ == (tuple,)
+assert get_original_bases(Eggs) == (NamedTuple,)
+
+assert Spam.__bases__ == (dict,)
+assert get_original_bases(Spam) == (TypedDict,)
+
+assert int.__bases__ == (object,)
+assert get_original_bases(int) == (object,)
+

New in version 3.12.

+

See also

PEP 560 - Core support for typing module and generic types

Standard Interpreter Types

This module provides names for many of the types that are required to implement a Python interpreter. It deliberately avoids including some of the types that arise only incidentally during processing such as the listiterator type.

Typical use of these names is for isinstance() or issubclass() checks.

If you instantiate any of these types, note that signatures may vary between Python versions.

Standard names are defined for the following types:

+types.NoneType
+

The type of None.

New in version 3.10.

+
+types.FunctionType
+types.LambdaType
+

The type of user-defined functions and functions created by lambda expressions.

Raises an auditing event function.__new__ with argument code.

The audit event only occurs for direct instantiation of function objects, and is not raised for normal compilation.

+
+types.GeneratorType
+

The type of generator-iterator objects, created by generator functions.

+
+types.CoroutineType
+

The type of coroutine objects, created by async def functions.

New in version 3.5.

+
+types.AsyncGeneratorType
+

The type of asynchronous generator-iterator objects, created by asynchronous generator functions.

New in version 3.6.

+
+class types.CodeType(**kwargs)
+

The type for code objects such as returned by compile().

Raises an auditing event code.__new__ with arguments code, filename, name, argcount, posonlyargcount, kwonlyargcount, nlocals, stacksize, flags.

Note that the audited arguments may not match the names or positions required by the initializer. The audit event only occurs for direct instantiation of code objects, and is not raised for normal compilation.

+replace(**kwargs)
+

Return a copy of the code object with new values for the specified fields.

New in version 3.8.

+
+
+types.CellType
+

The type for cell objects: such objects are used as containers for a function’s free variables.

New in version 3.8.

+
+types.MethodType
+

The type of methods of user-defined class instances.

+
+types.BuiltinFunctionType
+types.BuiltinMethodType
+

The type of built-in functions like len() or sys.exit(), and methods of built-in classes. (Here, the term “built-in” means “written in C”.)

+
+types.WrapperDescriptorType
+

The type of methods of some built-in data types and base classes such as object.__init__() or object.__lt__().

New in version 3.7.

+
+types.MethodWrapperType
+

The type of bound methods of some built-in data types and base classes. For example it is the type of object().__str__.

New in version 3.7.

+
+types.NotImplementedType
+

The type of NotImplemented.

New in version 3.10.

+
+types.MethodDescriptorType
+

The type of methods of some built-in data types such as str.join().

New in version 3.7.

+
+types.ClassMethodDescriptorType
+

The type of unbound class methods of some built-in data types such as dict.__dict__['fromkeys'].

New in version 3.7.

+
+class types.ModuleType(name, doc=None)
+

The type of modules. The constructor takes the name of the module to be created and optionally its docstring.

Note

Use importlib.util.module_from_spec() to create a new module if you wish to set the various import-controlled attributes.

+__doc__
+

The docstring of the module. Defaults to None.

+
+__loader__
+

The loader which loaded the module. Defaults to None.

This attribute is to match importlib.machinery.ModuleSpec.loader as stored in the __spec__ object.

Note

A future version of Python may stop setting this attribute by default. To guard against this potential change, preferably read from the __spec__ attribute instead or use getattr(module, "__loader__", None) if you explicitly need to use this attribute.

Changed in version 3.4: Defaults to None. Previously the attribute was optional.

+
+__name__
+

The name of the module. Expected to match importlib.machinery.ModuleSpec.name.

+
+__package__
+

Which package a module belongs to. If the module is top-level (i.e. not a part of any specific package) then the attribute should be set to '', else it should be set to the name of the package (which can be __name__ if the module is a package itself). Defaults to None.

This attribute is to match importlib.machinery.ModuleSpec.parent as stored in the __spec__ object.

Note

A future version of Python may stop setting this attribute by default. To guard against this potential change, preferably read from the __spec__ attribute instead or use getattr(module, "__package__", None) if you explicitly need to use this attribute.

Changed in version 3.4: Defaults to None. Previously the attribute was optional.

+
+__spec__
+

A record of the module’s import-system-related state. Expected to be an instance of importlib.machinery.ModuleSpec.

New in version 3.4.

+
+
+types.EllipsisType
+

The type of Ellipsis.

New in version 3.10.

+
+class types.GenericAlias(t_origin, t_args)
+

The type of parameterized generics such as list[int].

t_origin should be a non-parameterized generic class, such as list, tuple or dict. t_args should be a tuple (possibly of length 1) of types which parameterize t_origin:

>>> from types import GenericAlias
+
+>>> list[int] == GenericAlias(list, (int,))
+True
+>>> dict[str, int] == GenericAlias(dict, (str, int))
+True
+

New in version 3.9.

Changed in version 3.9.2: This type can now be subclassed.

See also

Generic Alias Types
+
+

In-depth documentation on instances of types.GenericAlias

+PEP 585 - Type Hinting Generics In Standard Collections
+
+

Introducing the types.GenericAlias class

+
+class types.UnionType
+

The type of union type expressions.

New in version 3.10.

+
+class types.TracebackType(tb_next, tb_frame, tb_lasti, tb_lineno)
+

The type of traceback objects such as found in sys.exception().__traceback__.

See the language reference for details of the available attributes and operations, and guidance on creating tracebacks dynamically.

+
+types.FrameType
+

The type of frame objects such as found in tb.tb_frame if tb is a traceback object.

+
+types.GetSetDescriptorType
+

The type of objects defined in extension modules with PyGetSetDef, such as FrameType.f_locals or array.array.typecode. This type is used as descriptor for object attributes; it has the same purpose as the property type, but for classes defined in extension modules.

+
+types.MemberDescriptorType
+

The type of objects defined in extension modules with PyMemberDef, such as datetime.timedelta.days. This type is used as descriptor for simple C data members which use standard conversion functions; it has the same purpose as the property type, but for classes defined in extension modules.

CPython implementation detail: In other implementations of Python, this type may be identical to GetSetDescriptorType.

+
+class types.MappingProxyType(mapping)
+

Read-only proxy of a mapping. It provides a dynamic view on the mapping’s entries, which means that when the mapping changes, the view reflects these changes.

New in version 3.3.

Changed in version 3.9: Updated to support the new union (|) operator from PEP 584, which simply delegates to the underlying mapping.

key in proxy +
+

Return True if the underlying mapping has a key key, else False.

+
proxy[key] +
+

Return the item of the underlying mapping with key key. Raises a KeyError if key is not in the underlying mapping.

+
iter(proxy) +
+

Return an iterator over the keys of the underlying mapping. This is a shortcut for iter(proxy.keys()).

+
len(proxy) +
+

Return the number of items in the underlying mapping.

+
+copy()
+

Return a shallow copy of the underlying mapping.

+
+get(key[, default])
+

Return the value for key if key is in the underlying mapping, else default. If default is not given, it defaults to None, so that this method never raises a KeyError.

+
+items()
+

Return a new view of the underlying mapping’s items ((key, value) pairs).

+
+keys()
+

Return a new view of the underlying mapping’s keys.

+
+values()
+

Return a new view of the underlying mapping’s values.

+
reversed(proxy) +
+

Return a reverse iterator over the keys of the underlying mapping.

New in version 3.9.

+
hash(proxy) +
+

Return a hash of the underlying mapping.

New in version 3.12.

+
+

Additional Utility Classes and Functions

+class types.SimpleNamespace
+

A simple object subclass that provides attribute access to its namespace, as well as a meaningful repr.

Unlike object, with SimpleNamespace you can add and remove attributes. If a SimpleNamespace object is initialized with keyword arguments, those are directly added to the underlying namespace.

The type is roughly equivalent to the following code:

class SimpleNamespace:
+    def __init__(self, /, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def __repr__(self):
+        items = (f"{k}={v!r}" for k, v in self.__dict__.items())
+        return "{}({})".format(type(self).__name__, ", ".join(items))
+
+    def __eq__(self, other):
+        if isinstance(self, SimpleNamespace) and isinstance(other, SimpleNamespace):
+           return self.__dict__ == other.__dict__
+        return NotImplemented
+

SimpleNamespace may be useful as a replacement for class NS: pass. However, for a structured record type use namedtuple() instead.

New in version 3.3.

Changed in version 3.9: Attribute order in the repr changed from alphabetical to insertion (like dict).

+
+types.DynamicClassAttribute(fget=None, fset=None, fdel=None, doc=None)
+

Route attribute access on a class to __getattr__.

This is a descriptor, used to define attributes that act differently when accessed through an instance and through a class. Instance access remains normal, but access to an attribute through a class will be routed to the class’s __getattr__ method; this is done by raising AttributeError.

This allows one to have properties active on an instance, and have virtual attributes on the class with the same name (see enum.Enum for an example).

New in version 3.4.

+

Coroutine Utility Functions

+types.coroutine(gen_func)
+

This function transforms a generator function into a coroutine function which returns a generator-based coroutine. The generator-based coroutine is still a generator iterator, but is also considered to be a coroutine object and is awaitable. However, it may not necessarily implement the __await__() method.

If gen_func is a generator function, it will be modified in-place.

If gen_func is not a generator function, it will be wrapped. If it returns an instance of collections.abc.Generator, the instance will be wrapped in an awaitable proxy object. All other types of objects will be returned as is.

New in version 3.5.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/types.html +

+
diff --git a/devdocs/python~3.12/library%2Ftyping.html b/devdocs/python~3.12/library%2Ftyping.html new file mode 100644 index 00000000..4e46541e --- /dev/null +++ b/devdocs/python~3.12/library%2Ftyping.html @@ -0,0 +1,1504 @@ +

typing — Support for type hints

New in version 3.5.

Source code: Lib/typing.py

Note

The Python runtime does not enforce function and variable type annotations. They can be used by third party tools such as type checkers, IDEs, linters, etc.

This module provides runtime support for type hints. For the original specification of the typing system, see PEP 484. For a simplified introduction to type hints, see PEP 483.

The function below takes and returns a string and is annotated as follows:

def greeting(name: str) -> str:
+    return 'Hello ' + name
+

In the function greeting, the argument name is expected to be of type str and the return type str. Subtypes are accepted as arguments.

New features are frequently added to the typing module. The typing_extensions package provides backports of these new features to older versions of Python.

For a summary of deprecated features and a deprecation timeline, please see Deprecation Timeline of Major Features.

See also

“Typing cheat sheet”
+
+

A quick overview of type hints (hosted at the mypy docs)

“Type System Reference” section of the mypy docs +
+
+

The Python typing system is standardised via PEPs, so this reference should broadly apply to most Python type checkers. (Some parts may still be specific to mypy.)

“Static Typing with Python”
+
+

Type-checker-agnostic documentation written by the community detailing type system features, useful typing related tools and typing best practices.

Relevant PEPs

Since the initial introduction of type hints in PEP 484 and PEP 483, a number of PEPs have modified and enhanced Python’s framework for type annotations:

The full list of PEPs

Type aliases

A type alias is defined using the type statement, which creates an instance of TypeAliasType. In this example, Vector and list[float] will be treated equivalently by static type checkers:

type Vector = list[float]
+
+def scale(scalar: float, vector: Vector) -> Vector:
+    return [scalar * num for num in vector]
+
+# passes type checking; a list of floats qualifies as a Vector.
+new_vector = scale(2.0, [1.0, -4.2, 5.4])
+

Type aliases are useful for simplifying complex type signatures. For example:

from collections.abc import Sequence
+
+type ConnectionOptions = dict[str, str]
+type Address = tuple[str, int]
+type Server = tuple[Address, ConnectionOptions]
+
+def broadcast_message(message: str, servers: Sequence[Server]) -> None:
+    ...
+
+# The static type checker will treat the previous type signature as
+# being exactly equivalent to this one.
+def broadcast_message(
+        message: str,
+        servers: Sequence[tuple[tuple[str, int], dict[str, str]]]) -> None:
+    ...
+

The type statement is new in Python 3.12. For backwards compatibility, type aliases can also be created through simple assignment:

Vector = list[float]
+

Or marked with TypeAlias to make it explicit that this is a type alias, not a normal variable assignment:

from typing import TypeAlias
+
+Vector: TypeAlias = list[float]
+

NewType

Use the NewType helper to create distinct types:

from typing import NewType
+
+UserId = NewType('UserId', int)
+some_id = UserId(524313)
+

The static type checker will treat the new type as if it were a subclass of the original type. This is useful in helping catch logical errors:

def get_user_name(user_id: UserId) -> str:
+    ...
+
+# passes type checking
+user_a = get_user_name(UserId(42351))
+
+# fails type checking; an int is not a UserId
+user_b = get_user_name(-1)
+

You may still perform all int operations on a variable of type UserId, but the result will always be of type int. This lets you pass in a UserId wherever an int might be expected, but will prevent you from accidentally creating a UserId in an invalid way:

# 'output' is of type 'int', not 'UserId'
+output = UserId(23413) + UserId(54341)
+

Note that these checks are enforced only by the static type checker. At runtime, the statement Derived = NewType('Derived', Base) will make Derived a callable that immediately returns whatever parameter you pass it. That means the expression Derived(some_value) does not create a new class or introduce much overhead beyond that of a regular function call.

More precisely, the expression some_value is Derived(some_value) is always true at runtime.

It is invalid to create a subtype of Derived:

from typing import NewType
+
+UserId = NewType('UserId', int)
+
+# Fails at runtime and does not pass type checking
+class AdminUserId(UserId): pass
+

However, it is possible to create a NewType based on a ‘derived’ NewType:

from typing import NewType
+
+UserId = NewType('UserId', int)
+
+ProUserId = NewType('ProUserId', UserId)
+

and typechecking for ProUserId will work as expected.

See PEP 484 for more details.

Note

Recall that the use of a type alias declares two types to be equivalent to one another. Doing type Alias = Original will make the static type checker treat Alias as being exactly equivalent to Original in all cases. This is useful when you want to simplify complex type signatures.

In contrast, NewType declares one type to be a subtype of another. Doing Derived = NewType('Derived', Original) will make the static type checker treat Derived as a subclass of Original, which means a value of type Original cannot be used in places where a value of type Derived is expected. This is useful when you want to prevent logic errors with minimal runtime cost.

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function. As a result, there is some additional runtime cost when calling NewType over a regular function.

Changed in version 3.11: The performance of calling NewType has been restored to its level in Python 3.9.

Annotating callable objects

Functions – or other callable objects – can be annotated using collections.abc.Callable or typing.Callable. Callable[[int], str] signifies a function that takes a single parameter of type int and returns a str.

For example:

from collections.abc import Callable, Awaitable
+
+def feeder(get_next_item: Callable[[], str]) -> None:
+    ...  # Body
+
+def async_query(on_success: Callable[[int], None],
+                on_error: Callable[[int, Exception], None]) -> None:
+    ...  # Body
+
+async def on_update(value: str) -> None:
+    ...  # Body
+
+callback: Callable[[str], Awaitable[None]] = on_update
+

The subscription syntax must always be used with exactly two values: the argument list and the return type. The argument list must be a list of types, a ParamSpec, Concatenate, or an ellipsis. The return type must be a single type.

If a literal ellipsis ... is given as the argument list, it indicates that a callable with any arbitrary parameter list would be acceptable:

def concat(x: str, y: str) -> str:
+    return x + y
+
+x: Callable[..., str]
+x = str     # OK
+x = concat  # Also OK
+

Callable cannot express complex signatures such as functions that take a variadic number of arguments, overloaded functions, or functions that have keyword-only parameters. However, these signatures can be expressed by defining a Protocol class with a __call__() method:

from collections.abc import Iterable
+from typing import Protocol
+
+class Combiner(Protocol):
+    def __call__(self, *vals: bytes, maxlen: int | None = None) -> list[bytes]: ...
+
+def batch_proc(data: Iterable[bytes], cb_results: Combiner) -> bytes:
+    for item in data:
+        ...
+
+def good_cb(*vals: bytes, maxlen: int | None = None) -> list[bytes]:
+    ...
+def bad_cb(*vals: bytes, maxitems: int | None) -> list[bytes]:
+    ...
+
+batch_proc([], good_cb)  # OK
+batch_proc([], bad_cb)   # Error! Argument 2 has incompatible type because of
+                         # different name and kind in the callback
+

Callables which take other callables as arguments may indicate that their parameter types are dependent on each other using ParamSpec. Additionally, if that callable adds or removes arguments from other callables, the Concatenate operator may be used. They take the form Callable[ParamSpecVariable, ReturnType] and Callable[Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable], ReturnType] respectively.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

See also

The documentation for ParamSpec and Concatenate provides examples of usage in Callable.

Generics

Since type information about objects kept in containers cannot be statically inferred in a generic way, many container classes in the standard library support subscription to denote the expected types of container elements.

from collections.abc import Mapping, Sequence
+
+class Employee: ...
+
+# Sequence[Employee] indicates that all elements in the sequence
+# must be instances of "Employee".
+# Mapping[str, str] indicates that all keys and all values in the mapping
+# must be strings.
+def notify_by_email(employees: Sequence[Employee],
+                    overrides: Mapping[str, str]) -> None: ...
+

Generic functions and classes can be parameterized by using type parameter syntax:

from collections.abc import Sequence
+
+def first[T](l: Sequence[T]) -> T:  # Function is generic over the TypeVar "T"
+    return l[0]
+

Or by using the TypeVar factory directly:

from collections.abc import Sequence
+from typing import TypeVar
+
+U = TypeVar('U')                  # Declare type variable "U"
+
+def second(l: Sequence[U]) -> U:  # Function is generic over the TypeVar "U"
+    return l[1]
+

Changed in version 3.12: Syntactic support for generics is new in Python 3.12.

Annotating tuples

For most containers in Python, the typing system assumes that all elements in the container will be of the same type. For example:

from collections.abc import Mapping
+
+# Type checker will infer that all elements in ``x`` are meant to be ints
+x: list[int] = []
+
+# Type checker error: ``list`` only accepts a single type argument:
+y: list[int, str] = [1, 'foo']
+
+# Type checker will infer that all keys in ``z`` are meant to be strings,
+# and that all values in ``z`` are meant to be either strings or ints
+z: Mapping[str, str | int] = {}
+

list only accepts one type argument, so a type checker would emit an error on the y assignment above. Similarly, Mapping only accepts two type arguments: the first indicates the type of the keys, and the second indicates the type of the values.

Unlike most other Python containers, however, it is common in idiomatic Python code for tuples to have elements which are not all of the same type. For this reason, tuples are special-cased in Python’s typing system. tuple accepts any number of type arguments:

# OK: ``x`` is assigned to a tuple of length 1 where the sole element is an int
+x: tuple[int] = (5,)
+
+# OK: ``y`` is assigned to a tuple of length 2;
+# element 1 is an int, element 2 is a str
+y: tuple[int, str] = (5, "foo")
+
+# Error: the type annotation indicates a tuple of length 1,
+# but ``z`` has been assigned to a tuple of length 3
+z: tuple[int] = (1, 2, 3)
+

To denote a tuple which could be of any length, and in which all elements are of the same type T, use tuple[T, ...]. To denote an empty tuple, use tuple[()]. Using plain tuple as an annotation is equivalent to using tuple[Any, ...]:

x: tuple[int, ...] = (1, 2)
+# These reassignments are OK: ``tuple[int, ...]`` indicates x can be of any length
+x = (1, 2, 3)
+x = ()
+# This reassignment is an error: all elements in ``x`` must be ints
+x = ("foo", "bar")
+
+# ``y`` can only ever be assigned to an empty tuple
+y: tuple[()] = ()
+
+z: tuple = ("foo", "bar")
+# These reassignments are OK: plain ``tuple`` is equivalent to ``tuple[Any, ...]``
+z = (1, 2, 3)
+z = ()
+

The type of class objects

A variable annotated with C may accept a value of type C. In contrast, a variable annotated with type[C] (or typing.Type[C]) may accept values that are classes themselves – specifically, it will accept the class object of C. For example:

a = 3         # Has type ``int``
+b = int       # Has type ``type[int]``
+c = type(a)   # Also has type ``type[int]``
+

Note that type[C] is covariant:

class User: ...
+class ProUser(User): ...
+class TeamUser(User): ...
+
+def make_new_user(user_class: type[User]) -> User:
+    # ...
+    return user_class()
+
+make_new_user(User)      # OK
+make_new_user(ProUser)   # Also OK: ``type[ProUser]`` is a subtype of ``type[User]``
+make_new_user(TeamUser)  # Still fine
+make_new_user(User())    # Error: expected ``type[User]`` but got ``User``
+make_new_user(int)       # Error: ``type[int]`` is not a subtype of ``type[User]``
+

The only legal parameters for type are classes, Any, type variables, and unions of any of these types. For example:

def new_non_team_user(user_class: type[BasicUser | ProUser]): ...
+
+new_non_team_user(BasicUser)  # OK
+new_non_team_user(ProUser)    # OK
+new_non_team_user(TeamUser)   # Error: ``type[TeamUser]`` is not a subtype
+                              # of ``type[BasicUser | ProUser]``
+new_non_team_user(User)       # Also an error
+

type[Any] is equivalent to type, which is the root of Python’s metaclass hierarchy.

User-defined generic types

A user-defined class can be defined as a generic class.

from logging import Logger
+
+class LoggedVar[T]:
+    def __init__(self, value: T, name: str, logger: Logger) -> None:
+        self.name = name
+        self.logger = logger
+        self.value = value
+
+    def set(self, new: T) -> None:
+        self.log('Set ' + repr(self.value))
+        self.value = new
+
+    def get(self) -> T:
+        self.log('Get ' + repr(self.value))
+        return self.value
+
+    def log(self, message: str) -> None:
+        self.logger.info('%s: %s', self.name, message)
+

This syntax indicates that the class LoggedVar is parameterised around a single type variable T . This also makes T valid as a type within the class body.

Generic classes implicitly inherit from Generic. For compatibility with Python 3.11 and lower, it is also possible to inherit explicitly from Generic to indicate a generic class:

from typing import TypeVar, Generic
+
+T = TypeVar('T')
+
+class LoggedVar(Generic[T]):
+    ...
+

Generic classes have __class_getitem__() methods, meaning they can be parameterised at runtime (e.g. LoggedVar[int] below):

from collections.abc import Iterable
+
+def zero_all_vars(vars: Iterable[LoggedVar[int]]) -> None:
+    for var in vars:
+        var.set(0)
+

A generic type can have any number of type variables. All varieties of TypeVar are permissible as parameters for a generic type:

from typing import TypeVar, Generic, Sequence
+
+class WeirdTrio[T, B: Sequence[bytes], S: (int, str)]:
+    ...
+
+OldT = TypeVar('OldT', contravariant=True)
+OldB = TypeVar('OldB', bound=Sequence[bytes], covariant=True)
+OldS = TypeVar('OldS', int, str)
+
+class OldWeirdTrio(Generic[OldT, OldB, OldS]):
+    ...
+

Each type variable argument to Generic must be distinct. This is thus invalid:

from typing import TypeVar, Generic
+...
+
+class Pair[M, M]:  # SyntaxError
+    ...
+
+T = TypeVar('T')
+
+class Pair(Generic[T, T]):   # INVALID
+    ...
+

Generic classes can also inherit from other classes:

from collections.abc import Sized
+
+class LinkedList[T](Sized):
+    ...
+

When inheriting from generic classes, some type parameters could be fixed:

from collections.abc import Mapping
+
+class MyDict[T](Mapping[str, T]):
+    ...
+

In this case MyDict has a single parameter, T.

Using a generic class without specifying type parameters assumes Any for each position. In the following example, MyIterable is not generic but implicitly inherits from Iterable[Any]:

from collections.abc import Iterable
+
+class MyIterable(Iterable): # Same as Iterable[Any]
+    ...
+

User-defined generic type aliases are also supported. Examples:

from collections.abc import Iterable
+
+type Response[S] = Iterable[S] | int
+
+# Return type here is same as Iterable[str] | int
+def response(query: str) -> Response[str]:
+    ...
+
+type Vec[T] = Iterable[tuple[T, T]]
+
+def inproduct[T: (int, float, complex)](v: Vec[T]) -> T: # Same as Iterable[tuple[T, T]]
+    return sum(x*y for x, y in v)
+

For backward compatibility, generic type aliases can also be created through a simple assignment:

from collections.abc import Iterable
+from typing import TypeVar
+
+S = TypeVar("S")
+Response = Iterable[S] | int
+

Changed in version 3.7: Generic no longer has a custom metaclass.

Changed in version 3.12: Syntactic support for generics and type aliases is new in version 3.12. Previously, generic classes had to explicitly inherit from Generic or contain a type variable in one of their bases.

User-defined generics for parameter expressions are also supported via parameter specification variables in the form [**P]. The behavior is consistent with type variables’ described above as parameter specification variables are treated by the typing module as a specialized type variable. The one exception to this is that a list of types can be used to substitute a ParamSpec:

>>> class Z[T, **P]: ...  # T is a TypeVar; P is a ParamSpec
+...
+>>> Z[int, [dict, float]]
+__main__.Z[int, [dict, float]]
+

Classes generic over a ParamSpec can also be created using explicit inheritance from Generic. In this case, ** is not used:

from typing import ParamSpec, Generic
+
+P = ParamSpec('P')
+
+class Z(Generic[P]):
+    ...
+

Another difference between TypeVar and ParamSpec is that a generic with only one parameter specification variable will accept parameter lists in the forms X[[Type1, Type2, ...]] and also X[Type1, Type2, ...] for aesthetic reasons. Internally, the latter is converted to the former, so the following are equivalent:

>>> class X[**P]: ...
+...
+>>> X[int, str]
+__main__.X[[int, str]]
+>>> X[[int, str]]
+__main__.X[[int, str]]
+

Note that generics with ParamSpec may not have correct __parameters__ after substitution in some cases because they are intended primarily for static type checking.

Changed in version 3.10: Generic can now be parameterized over parameter expressions. See ParamSpec and PEP 612 for more details.

A user-defined generic class can have ABCs as base classes without a metaclass conflict. Generic metaclasses are not supported. The outcome of parameterizing generics is cached, and most types in the typing module are hashable and comparable for equality.

The Any type

A special kind of type is Any. A static type checker will treat every type as being compatible with Any and Any as being compatible with every type.

This means that it is possible to perform any operation or method call on a value of type Any and assign it to any variable:

from typing import Any
+
+a: Any = None
+a = []          # OK
+a = 2           # OK
+
+s: str = ''
+s = a           # OK
+
+def foo(item: Any) -> int:
+    # Passes type checking; 'item' could be any type,
+    # and that type might have a 'bar' method
+    item.bar()
+    ...
+

Notice that no type checking is performed when assigning a value of type Any to a more precise type. For example, the static type checker did not report an error when assigning a to s even though s was declared to be of type str and receives an int value at runtime!

Furthermore, all functions without a return type or parameter types will implicitly default to using Any:

def legacy_parser(text):
+    ...
+    return data
+
+# A static type checker will treat the above
+# as having the same signature as:
+def legacy_parser(text: Any) -> Any:
+    ...
+    return data
+

This behavior allows Any to be used as an escape hatch when you need to mix dynamically and statically typed code.

Contrast the behavior of Any with the behavior of object. Similar to Any, every type is a subtype of object. However, unlike Any, the reverse is not true: object is not a subtype of every other type.

That means when the type of a value is object, a type checker will reject almost all operations on it, and assigning it to a variable (or using it as a return value) of a more specialized type is a type error. For example:

def hash_a(item: object) -> int:
+    # Fails type checking; an object does not have a 'magic' method.
+    item.magic()
+    ...
+
+def hash_b(item: Any) -> int:
+    # Passes type checking
+    item.magic()
+    ...
+
+# Passes type checking, since ints and strs are subclasses of object
+hash_a(42)
+hash_a("foo")
+
+# Passes type checking, since Any is compatible with all types
+hash_b(42)
+hash_b("foo")
+

Use object to indicate that a value could be any type in a typesafe manner. Use Any to indicate that a value is dynamically typed.

Nominal vs structural subtyping

Initially PEP 484 defined the Python static type system as using nominal subtyping. This means that a class A is allowed where a class B is expected if and only if A is a subclass of B.

This requirement previously also applied to abstract base classes, such as Iterable. The problem with this approach is that a class had to be explicitly marked to support them, which is unpythonic and unlike what one would normally do in idiomatic dynamically typed Python code. For example, this conforms to PEP 484:

from collections.abc import Sized, Iterable, Iterator
+
+class Bucket(Sized, Iterable[int]):
+    ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[int]: ...
+

PEP 544 allows to solve this problem by allowing users to write the above code without explicit base classes in the class definition, allowing Bucket to be implicitly considered a subtype of both Sized and Iterable[int] by static type checkers. This is known as structural subtyping (or static duck-typing):

from collections.abc import Iterator, Iterable
+
+class Bucket:  # Note: no base classes
+    ...
+    def __len__(self) -> int: ...
+    def __iter__(self) -> Iterator[int]: ...
+
+def collect(items: Iterable[int]) -> int: ...
+result = collect(Bucket())  # Passes type check
+

Moreover, by subclassing a special class Protocol, a user can define new custom protocols to fully enjoy structural subtyping (see examples below).

Module contents

The typing module defines the following classes, functions and decorators.

Special typing primitives

Special types

These can be used as types in annotations. They do not support subscription using [].

+typing.Any
+

Special type indicating an unconstrained type.

  • Every type is compatible with Any.
  • +Any is compatible with every type.

Changed in version 3.11: Any can now be used as a base class. This can be useful for avoiding type checker errors with classes that can duck type anywhere or are highly dynamic.

+
+typing.AnyStr
+

A constrained type variable.

Definition:

AnyStr = TypeVar('AnyStr', str, bytes)
+

AnyStr is meant to be used for functions that may accept str or bytes arguments but cannot allow the two to mix.

For example:

def concat(a: AnyStr, b: AnyStr) -> AnyStr:
+    return a + b
+
+concat("foo", "bar")    # OK, output has type 'str'
+concat(b"foo", b"bar")  # OK, output has type 'bytes'
+concat("foo", b"bar")   # Error, cannot mix str and bytes
+

Note that, despite its name, AnyStr has nothing to do with the Any type, nor does it mean “any string”. In particular, AnyStr and str | bytes are different from each other and have different use cases:

# Invalid use of AnyStr:
+# The type variable is used only once in the function signature,
+# so cannot be "solved" by the type checker
+def greet_bad(cond: bool) -> AnyStr:
+    return "hi there!" if cond else b"greetings!"
+
+# The better way of annotating this function:
+def greet_proper(cond: bool) -> str | bytes:
+    return "hi there!" if cond else b"greetings!"
+
+
+typing.LiteralString
+

Special type that includes only literal strings.

Any string literal is compatible with LiteralString, as is another LiteralString. However, an object typed as just str is not. A string created by composing LiteralString-typed objects is also acceptable as a LiteralString.

Example:

def run_query(sql: LiteralString) -> None:
+    ...
+
+def caller(arbitrary_string: str, literal_string: LiteralString) -> None:
+    run_query("SELECT * FROM students")  # OK
+    run_query(literal_string)  # OK
+    run_query("SELECT * FROM " + literal_string)  # OK
+    run_query(arbitrary_string)  # type checker error
+    run_query(  # type checker error
+        f"SELECT * FROM students WHERE name = {arbitrary_string}"
+    )
+

LiteralString is useful for sensitive APIs where arbitrary user-generated strings could generate problems. For example, the two cases above that generate type checker errors could be vulnerable to an SQL injection attack.

See PEP 675 for more details.

New in version 3.11.

+
+typing.Never
+

The bottom type, a type that has no members.

This can be used to define a function that should never be called, or a function that never returns:

from typing import Never
+
+def never_call_me(arg: Never) -> None:
+    pass
+
+def int_or_str(arg: int | str) -> None:
+    never_call_me(arg)  # type checker error
+    match arg:
+        case int():
+            print("It's an int")
+        case str():
+            print("It's a str")
+        case _:
+            never_call_me(arg)  # OK, arg is of type Never
+

New in version 3.11: On older Python versions, NoReturn may be used to express the same concept. Never was added to make the intended meaning more explicit.

+
+typing.NoReturn
+

Special type indicating that a function never returns.

For example:

from typing import NoReturn
+
+def stop() -> NoReturn:
+    raise RuntimeError('no way')
+

NoReturn can also be used as a bottom type, a type that has no values. Starting in Python 3.11, the Never type should be used for this concept instead. Type checkers should treat the two equivalently.

New in version 3.5.4.

New in version 3.6.2.

+
+typing.Self
+

Special type to represent the current enclosed class.

For example:

from typing import Self, reveal_type
+
+class Foo:
+    def return_self(self) -> Self:
+        ...
+        return self
+
+class SubclassOfFoo(Foo): pass
+
+reveal_type(Foo().return_self())  # Revealed type is "Foo"
+reveal_type(SubclassOfFoo().return_self())  # Revealed type is "SubclassOfFoo"
+

This annotation is semantically equivalent to the following, albeit in a more succinct fashion:

from typing import TypeVar
+
+Self = TypeVar("Self", bound="Foo")
+
+class Foo:
+    def return_self(self: Self) -> Self:
+        ...
+        return self
+

In general, if something returns self, as in the above examples, you should use Self as the return annotation. If Foo.return_self was annotated as returning "Foo", then the type checker would infer the object returned from SubclassOfFoo.return_self as being of type Foo rather than SubclassOfFoo.

Other common use cases include:

  • +classmethods that are used as alternative constructors and return instances of the cls parameter.
  • Annotating an __enter__() method which returns self.

You should not use Self as the return annotation if the method is not guaranteed to return an instance of a subclass when the class is subclassed:

class Eggs:
+    # Self would be an incorrect return annotation here,
+    # as the object returned is always an instance of Eggs,
+    # even in subclasses
+    def returns_eggs(self) -> "Eggs":
+        return Eggs()
+

See PEP 673 for more details.

New in version 3.11.

+
+typing.TypeAlias
+

Special annotation for explicitly declaring a type alias.

For example:

from typing import TypeAlias
+
+Factors: TypeAlias = list[int]
+

TypeAlias is particularly useful on older Python versions for annotating aliases that make use of forward references, as it can be hard for type checkers to distinguish these from normal variable assignments:

from typing import Generic, TypeAlias, TypeVar
+
+T = TypeVar("T")
+
+# "Box" does not exist yet,
+# so we have to use quotes for the forward reference on Python <3.12.
+# Using ``TypeAlias`` tells the type checker that this is a type alias declaration,
+# not a variable assignment to a string.
+BoxOfStrings: TypeAlias = "Box[str]"
+
+class Box(Generic[T]):
+    @classmethod
+    def make_box_of_strings(cls) -> BoxOfStrings: ...
+

See PEP 613 for more details.

New in version 3.10.

Deprecated since version 3.12: TypeAlias is deprecated in favor of the type statement, which creates instances of TypeAliasType and which natively supports forward references. Note that while TypeAlias and TypeAliasType serve similar purposes and have similar names, they are distinct and the latter is not the type of the former. Removal of TypeAlias is not currently planned, but users are encouraged to migrate to type statements.

+

Special forms

These can be used as types in annotations. They all support subscription using [], but each has a unique syntax.

+typing.Union
+

Union type; Union[X, Y] is equivalent to X | Y and means either X or Y.

To define a union, use e.g. Union[int, str] or the shorthand int | str. Using that shorthand is recommended. Details:

  • The arguments must be types and there must be at least one.
  • +

    Unions of unions are flattened, e.g.:

    Union[Union[int, str], float] == Union[int, str, float]
+
  • +

    Unions of a single argument vanish, e.g.:

    Union[int] == int  # The constructor actually returns int
+
  • +

    Redundant arguments are skipped, e.g.:

    Union[int, str, int] == Union[int, str] == int | str
+
  • +

    When comparing unions, the argument order is ignored, e.g.:

    Union[int, str] == Union[str, int]
+
  • You cannot subclass or instantiate a Union.
  • You cannot write Union[X][Y].

Changed in version 3.7: Don’t remove explicit subclasses from unions at runtime.

Changed in version 3.10: Unions can now be written as X | Y. See union type expressions.

+
+typing.Optional
+

Optional[X] is equivalent to X | None (or Union[X, None]).

Note that this is not the same concept as an optional argument, which is one that has a default. An optional argument with a default does not require the Optional qualifier on its type annotation just because it is optional. For example:

def foo(arg: int = 0) -> None:
+    ...
+

On the other hand, if an explicit value of None is allowed, the use of Optional is appropriate, whether the argument is optional or not. For example:

def foo(arg: Optional[int] = None) -> None:
+    ...
+

Changed in version 3.10: Optional can now be written as X | None. See union type expressions.

+
+typing.Concatenate
+

Special form for annotating higher-order functions.

Concatenate can be used in conjunction with Callable and ParamSpec to annotate a higher-order callable which adds, removes, or transforms parameters of another callable. Usage is in the form Concatenate[Arg1Type, Arg2Type, ..., ParamSpecVariable]. Concatenate is currently only valid when used as the first argument to a Callable. The last parameter to Concatenate must be a ParamSpec or ellipsis (...).

For example, to annotate a decorator with_lock which provides a threading.Lock to the decorated function, Concatenate can be used to indicate that with_lock expects a callable which takes in a Lock as the first argument, and returns a callable with a different type signature. In this case, the ParamSpec indicates that the returned callable’s parameter types are dependent on the parameter types of the callable being passed in:

from collections.abc import Callable
+from threading import Lock
+from typing import Concatenate
+
+# Use this lock to ensure that only one thread is executing a function
+# at any time.
+my_lock = Lock()
+
+def with_lock[**P, R](f: Callable[Concatenate[Lock, P], R]) -> Callable[P, R]:
+    '''A type-safe decorator which provides a lock.'''
+    def inner(*args: P.args, **kwargs: P.kwargs) -> R:
+        # Provide the lock as the first argument.
+        return f(my_lock, *args, **kwargs)
+    return inner
+
+@with_lock
+def sum_threadsafe(lock: Lock, numbers: list[float]) -> float:
+    '''Add a list of numbers together in a thread-safe manner.'''
+    with lock:
+        return sum(numbers)
+
+# We don't need to pass in the lock ourselves thanks to the decorator.
+sum_threadsafe([1.1, 2.2, 3.3])
+

New in version 3.10.

See also

+
+typing.Literal
+

Special typing form to define “literal types”.

Literal can be used to indicate to type checkers that the annotated object has a value equivalent to one of the provided literals.

For example:

def validate_simple(data: Any) -> Literal[True]:  # always returns True
+    ...
+
+type Mode = Literal['r', 'rb', 'w', 'wb']
+def open_helper(file: str, mode: Mode) -> str:
+    ...
+
+open_helper('/some/path', 'r')      # Passes type check
+open_helper('/other/path', 'typo')  # Error in type checker
+

Literal[...] cannot be subclassed. At runtime, an arbitrary value is allowed as type argument to Literal[...], but type checkers may impose restrictions. See PEP 586 for more details about literal types.

New in version 3.8.

Changed in version 3.9.1: Literal now de-duplicates parameters. Equality comparisons of Literal objects are no longer order dependent. Literal objects will now raise a TypeError exception during equality comparisons if one of their parameters are not hashable.

+
+typing.ClassVar
+

Special type construct to mark class variables.

As introduced in PEP 526, a variable annotation wrapped in ClassVar indicates that a given attribute is intended to be used as a class variable and should not be set on instances of that class. Usage:

class Starship:
+    stats: ClassVar[dict[str, int]] = {} # class variable
+    damage: int = 10                     # instance variable
+

ClassVar accepts only types and cannot be further subscribed.

ClassVar is not a class itself, and should not be used with isinstance() or issubclass(). ClassVar does not change Python runtime behavior, but it can be used by third-party type checkers. For example, a type checker might flag the following code as an error:

enterprise_d = Starship(3000)
+enterprise_d.stats = {} # Error, setting class variable on instance
+Starship.stats = {}     # This is OK
+

New in version 3.5.3.

+
+typing.Final
+

Special typing construct to indicate final names to type checkers.

Final names cannot be reassigned in any scope. Final names declared in class scopes cannot be overridden in subclasses.

For example:

MAX_SIZE: Final = 9000
+MAX_SIZE += 1  # Error reported by type checker
+
+class Connection:
+    TIMEOUT: Final[int] = 10
+
+class FastConnector(Connection):
+    TIMEOUT = 1  # Error reported by type checker
+

There is no runtime checking of these properties. See PEP 591 for more details.

New in version 3.8.

+
+typing.Required
+

Special typing construct to mark a TypedDict key as required.

This is mainly useful for total=False TypedDicts. See TypedDict and PEP 655 for more details.

New in version 3.11.

+
+typing.NotRequired
+

Special typing construct to mark a TypedDict key as potentially missing.

See TypedDict and PEP 655 for more details.

New in version 3.11.

+
+typing.Annotated
+

Special typing form to add context-specific metadata to an annotation.

Add metadata x to a given type T by using the annotation Annotated[T, x]. Metadata added using Annotated can be used by static analysis tools or at runtime. At runtime, the metadata is stored in a __metadata__ attribute.

If a library or tool encounters an annotation Annotated[T, x] and has no special logic for the metadata, it should ignore the metadata and simply treat the annotation as T. As such, Annotated can be useful for code that wants to use annotations for purposes outside Python’s static typing system.

Using Annotated[T, x] as an annotation still allows for static typechecking of T, as type checkers will simply ignore the metadata x. In this way, Annotated differs from the @no_type_check decorator, which can also be used for adding annotations outside the scope of the typing system, but completely disables typechecking for a function or class.

The responsibility of how to interpret the metadata lies with the tool or library encountering an Annotated annotation. A tool or library encountering an Annotated type can scan through the metadata elements to determine if they are of interest (e.g., using isinstance()).

Annotated[<type>, <metadata>] +
+

Here is an example of how you might use Annotated to add metadata to type annotations if you were doing range analysis:

@dataclass
+class ValueRange:
+    lo: int
+    hi: int
+
+T1 = Annotated[int, ValueRange(-10, 5)]
+T2 = Annotated[T1, ValueRange(-20, 3)]
+

Details of the syntax:

  • The first argument to Annotated must be a valid type
  • +

    Multiple metadata elements can be supplied (Annotated supports variadic arguments):

    @dataclass
+class ctype:
+    kind: str
+
+Annotated[int, ValueRange(3, 10), ctype("char")]
+

    It is up to the tool consuming the annotations to decide whether the client is allowed to add multiple metadata elements to one annotation and how to merge those annotations.

  • +Annotated must be subscripted with at least two arguments ( Annotated[int] is not valid)
  • +

    The order of the metadata elements is preserved and matters for equality checks:

    assert Annotated[int, ValueRange(3, 10), ctype("char")] != Annotated[
+    int, ctype("char"), ValueRange(3, 10)
+]
+
  • +

    Nested Annotated types are flattened. The order of the metadata elements starts with the innermost annotation:

    assert Annotated[Annotated[int, ValueRange(3, 10)], ctype("char")] == Annotated[
+    int, ValueRange(3, 10), ctype("char")
+]
+
  • +

    Duplicated metadata elements are not removed:

    assert Annotated[int, ValueRange(3, 10)] != Annotated[
+    int, ValueRange(3, 10), ValueRange(3, 10)
+]
+
  • +

    Annotated can be used with nested and generic aliases:

    @dataclass
+class MaxLen:
+    value: int
+
+type Vec[T] = Annotated[list[tuple[T, T]], MaxLen(10)]
+
+# When used in a type annotation, a type checker will treat "V" the same as
+# ``Annotated[list[tuple[int, int]], MaxLen(10)]``:
+type V = Vec[int]
+
  • +

    Annotated cannot be used with an unpacked TypeVarTuple:

    type Variadic[*Ts] = Annotated[*Ts, Ann1]  # NOT valid
+

    This would be equivalent to:

    Annotated[T1, T2, T3, ..., Ann1]
+

    where T1, T2, etc. are TypeVars. This would be invalid: only one type should be passed to Annotated.

  • +

    By default, get_type_hints() strips the metadata from annotations. Pass include_extras=True to have the metadata preserved:

    >>> from typing import Annotated, get_type_hints
+>>> def func(x: Annotated[int, "metadata"]) -> None: pass
+...
+>>> get_type_hints(func)
+{'x': <class 'int'>, 'return': <class 'NoneType'>}
+>>> get_type_hints(func, include_extras=True)
+{'x': typing.Annotated[int, 'metadata'], 'return': <class 'NoneType'>}
+
  • +

    At runtime, the metadata associated with an Annotated type can be retrieved via the __metadata__ attribute:

    >>> from typing import Annotated
+>>> X = Annotated[int, "very", "important", "metadata"]
+>>> X
+typing.Annotated[int, 'very', 'important', 'metadata']
+>>> X.__metadata__
+('very', 'important', 'metadata')
+

See also

+PEP 593 - Flexible function and variable annotations
+
+

The PEP introducing Annotated to the standard library.

New in version 3.9.

+
+typing.TypeGuard
+

Special typing construct for marking user-defined type guard functions.

TypeGuard can be used to annotate the return type of a user-defined type guard function. TypeGuard only accepts a single type argument. At runtime, functions marked this way should return a boolean.

TypeGuard aims to benefit type narrowing – a technique used by static type checkers to determine a more precise type of an expression within a program’s code flow. Usually type narrowing is done by analyzing conditional code flow and applying the narrowing to a block of code. The conditional expression here is sometimes referred to as a “type guard”:

def is_str(val: str | float):
+    # "isinstance" type guard
+    if isinstance(val, str):
+        # Type of ``val`` is narrowed to ``str``
+        ...
+    else:
+        # Else, type of ``val`` is narrowed to ``float``.
+        ...
+

Sometimes it would be convenient to use a user-defined boolean function as a type guard. Such a function should use TypeGuard[...] as its return type to alert static type checkers to this intention.

Using -> TypeGuard tells the static type checker that for a given function:

  1. The return value is a boolean.
  2. If the return value is True, the type of its argument is the type inside TypeGuard.

For example:

def is_str_list(val: list[object]) -> TypeGuard[list[str]]:
+    '''Determines whether all objects in the list are strings'''
+    return all(isinstance(x, str) for x in val)
+
+def func1(val: list[object]):
+    if is_str_list(val):
+        # Type of ``val`` is narrowed to ``list[str]``.
+        print(" ".join(val))
+    else:
+        # Type of ``val`` remains as ``list[object]``.
+        print("Not a list of strings!")
+

If is_str_list is a class or instance method, then the type in TypeGuard maps to the type of the second parameter after cls or self.

In short, the form def foo(arg: TypeA) -> TypeGuard[TypeB]: ..., means that if foo(arg) returns True, then arg narrows from TypeA to TypeB.

Note

TypeB need not be a narrower form of TypeA – it can even be a wider form. The main reason is to allow for things like narrowing list[object] to list[str] even though the latter is not a subtype of the former, since list is invariant. The responsibility of writing type-safe type guards is left to the user.

TypeGuard also works with type variables. See PEP 647 for more details.

New in version 3.10.

+
+typing.Unpack
+

Typing operator to conceptually mark an object as having been unpacked.

For example, using the unpack operator * on a type variable tuple is equivalent to using Unpack to mark the type variable tuple as having been unpacked:

Ts = TypeVarTuple('Ts')
+tup: tuple[*Ts]
+# Effectively does:
+tup: tuple[Unpack[Ts]]
+

In fact, Unpack can be used interchangeably with * in the context of typing.TypeVarTuple and builtins.tuple types. You might see Unpack being used explicitly in older versions of Python, where * couldn’t be used in certain places:

# In older versions of Python, TypeVarTuple and Unpack
+# are located in the `typing_extensions` backports package.
+from typing_extensions import TypeVarTuple, Unpack
+
+Ts = TypeVarTuple('Ts')
+tup: tuple[*Ts]         # Syntax error on Python <= 3.10!
+tup: tuple[Unpack[Ts]]  # Semantically equivalent, and backwards-compatible
+

Unpack can also be used along with typing.TypedDict for typing **kwargs in a function signature:

from typing import TypedDict, Unpack
+
+class Movie(TypedDict):
+    name: str
+    year: int
+
+# This function expects two keyword arguments - `name` of type `str`
+# and `year` of type `int`.
+def foo(**kwargs: Unpack[Movie]): ...
+

See PEP 692 for more details on using Unpack for **kwargs typing.

New in version 3.11.

+

Building generic types and type aliases

The following classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating generic types and type aliases.

These objects can be created through special syntax (type parameter lists and the type statement). For compatibility with Python 3.11 and earlier, they can also be created without the dedicated syntax, as documented below.

+class typing.Generic
+

Abstract base class for generic types.

A generic type is typically declared by adding a list of type parameters after the class name:

class Mapping[KT, VT]:
+    def __getitem__(self, key: KT) -> VT:
+        ...
+        # Etc.
+

Such a class implicitly inherits from Generic. The runtime semantics of this syntax are discussed in the Language Reference.

This class can then be used as follows:

def lookup_name[X, Y](mapping: Mapping[X, Y], key: X, default: Y) -> Y:
+    try:
+        return mapping[key]
+    except KeyError:
+        return default
+

Here the brackets after the function name indicate a generic function.

For backwards compatibility, generic classes can also be declared by explicitly inheriting from Generic. In this case, the type parameters must be declared separately:

KT = TypeVar('KT')
+VT = TypeVar('VT')
+
+class Mapping(Generic[KT, VT]):
+    def __getitem__(self, key: KT) -> VT:
+        ...
+        # Etc.
+
+
+class typing.TypeVar(name, *constraints, bound=None, covariant=False, contravariant=False, infer_variance=False)
+

Type variable.

The preferred way to construct a type variable is via the dedicated syntax for generic functions, generic classes, and generic type aliases:

class Sequence[T]:  # T is a TypeVar
+    ...
+

This syntax can also be used to create bound and constrained type variables:

class StrSequence[S: str]:  # S is a TypeVar bound to str
+    ...
+
+
+class StrOrBytesSequence[A: (str, bytes)]:  # A is a TypeVar constrained to str or bytes
+    ...
+

However, if desired, reusable type variables can also be constructed manually, like so:

T = TypeVar('T')  # Can be anything
+S = TypeVar('S', bound=str)  # Can be any subtype of str
+A = TypeVar('A', str, bytes)  # Must be exactly str or bytes
+

Type variables exist primarily for the benefit of static type checkers. They serve as the parameters for generic types as well as for generic function and type alias definitions. See Generic for more information on generic types. Generic functions work as follows:

def repeat[T](x: T, n: int) -> Sequence[T]:
+    """Return a list containing n references to x."""
+    return [x]*n
+
+
+def print_capitalized[S: str](x: S) -> S:
+    """Print x capitalized, and return x."""
+    print(x.capitalize())
+    return x
+
+
+def concatenate[A: (str, bytes)](x: A, y: A) -> A:
+    """Add two strings or bytes objects together."""
+    return x + y
+

Note that type variables can be bound, constrained, or neither, but cannot be both bound and constrained.

The variance of type variables is inferred by type checkers when they are created through the type parameter syntax or when infer_variance=True is passed. Manually created type variables may be explicitly marked covariant or contravariant by passing covariant=True or contravariant=True. By default, manually created type variables are invariant. See PEP 484 and PEP 695 for more details.

Bound type variables and constrained type variables have different semantics in several important ways. Using a bound type variable means that the TypeVar will be solved using the most specific type possible:

x = print_capitalized('a string')
+reveal_type(x)  # revealed type is str
+
+class StringSubclass(str):
+    pass
+
+y = print_capitalized(StringSubclass('another string'))
+reveal_type(y)  # revealed type is StringSubclass
+
+z = print_capitalized(45)  # error: int is not a subtype of str
+

Type variables can be bound to concrete types, abstract types (ABCs or protocols), and even unions of types:

# Can be anything with an __abs__ method
+def print_abs[T: SupportsAbs](arg: T) -> None:
+    print("Absolute value:", abs(arg))
+
+U = TypeVar('U', bound=str|bytes)  # Can be any subtype of the union str|bytes
+V = TypeVar('V', bound=SupportsAbs)  # Can be anything with an __abs__ method
+

Using a constrained type variable, however, means that the TypeVar can only ever be solved as being exactly one of the constraints given:

a = concatenate('one', 'two')
+reveal_type(a)  # revealed type is str
+
+b = concatenate(StringSubclass('one'), StringSubclass('two'))
+reveal_type(b)  # revealed type is str, despite StringSubclass being passed in
+
+c = concatenate('one', b'two')  # error: type variable 'A' can be either str or bytes in a function call, but not both
+

At runtime, isinstance(x, T) will raise TypeError.

+__name__
+

The name of the type variable.

+
+__covariant__
+

Whether the type var has been explicitly marked as covariant.

+
+__contravariant__
+

Whether the type var has been explicitly marked as contravariant.

+
+__infer_variance__
+

Whether the type variable’s variance should be inferred by type checkers.

New in version 3.12.

+
+__bound__
+

The bound of the type variable, if any.

Changed in version 3.12: For type variables created through type parameter syntax, the bound is evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

+
+__constraints__
+

A tuple containing the constraints of the type variable, if any.

Changed in version 3.12: For type variables created through type parameter syntax, the constraints are evaluated only when the attribute is accessed, not when the type variable is created (see Lazy evaluation).

+

Changed in version 3.12: Type variables can now be declared using the type parameter syntax introduced by PEP 695. The infer_variance parameter was added.

+
+class typing.TypeVarTuple(name)
+

Type variable tuple. A specialized form of type variable that enables variadic generics.

Type variable tuples can be declared in type parameter lists using a single asterisk (*) before the name:

def move_first_element_to_last[T, *Ts](tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
+    return (*tup[1:], tup[0])
+

Or by explicitly invoking the TypeVarTuple constructor:

T = TypeVar("T")
+Ts = TypeVarTuple("Ts")
+
+def move_first_element_to_last(tup: tuple[T, *Ts]) -> tuple[*Ts, T]:
+    return (*tup[1:], tup[0])
+

A normal type variable enables parameterization with a single type. A type variable tuple, in contrast, allows parameterization with an arbitrary number of types by acting like an arbitrary number of type variables wrapped in a tuple. For example:

# T is bound to int, Ts is bound to ()
+# Return value is (1,), which has type tuple[int]
+move_first_element_to_last(tup=(1,))
+
+# T is bound to int, Ts is bound to (str,)
+# Return value is ('spam', 1), which has type tuple[str, int]
+move_first_element_to_last(tup=(1, 'spam'))
+
+# T is bound to int, Ts is bound to (str, float)
+# Return value is ('spam', 3.0, 1), which has type tuple[str, float, int]
+move_first_element_to_last(tup=(1, 'spam', 3.0))
+
+# This fails to type check (and fails at runtime)
+# because tuple[()] is not compatible with tuple[T, *Ts]
+# (at least one element is required)
+move_first_element_to_last(tup=())
+

Note the use of the unpacking operator * in tuple[T, *Ts]. Conceptually, you can think of Ts as a tuple of type variables (T1, T2, ...). tuple[T, *Ts] would then become tuple[T, *(T1, T2, ...)], which is equivalent to tuple[T, T1, T2, ...]. (Note that in older versions of Python, you might see this written using Unpack instead, as Unpack[Ts].)

Type variable tuples must always be unpacked. This helps distinguish type variable tuples from normal type variables:

x: Ts          # Not valid
+x: tuple[Ts]   # Not valid
+x: tuple[*Ts]  # The correct way to do it
+

Type variable tuples can be used in the same contexts as normal type variables. For example, in class definitions, arguments, and return types:

class Array[*Shape]:
+    def __getitem__(self, key: tuple[*Shape]) -> float: ...
+    def __abs__(self) -> "Array[*Shape]": ...
+    def get_shape(self) -> tuple[*Shape]: ...
+

Type variable tuples can be happily combined with normal type variables:

class Array[DType, *Shape]:  # This is fine
+    pass
+
+class Array2[*Shape, DType]:  # This would also be fine
+    pass
+
+class Height: ...
+class Width: ...
+
+float_array_1d: Array[float, Height] = Array()     # Totally fine
+int_array_2d: Array[int, Height, Width] = Array()  # Yup, fine too
+

However, note that at most one type variable tuple may appear in a single list of type arguments or type parameters:

x: tuple[*Ts, *Ts]            # Not valid
+class Array[*Shape, *Shape]:  # Not valid
+    pass
+

Finally, an unpacked type variable tuple can be used as the type annotation of *args:

def call_soon[*Ts](
+         callback: Callable[[*Ts], None],
+         *args: *Ts
+) -> None:
+    ...
+    callback(*args)
+

In contrast to non-unpacked annotations of *args - e.g. *args: int, which would specify that all arguments are int - *args: *Ts enables reference to the types of the individual arguments in *args. Here, this allows us to ensure the types of the *args passed to call_soon match the types of the (positional) arguments of callback.

See PEP 646 for more details on type variable tuples.

+__name__
+

The name of the type variable tuple.

+

New in version 3.11.

Changed in version 3.12: Type variable tuples can now be declared using the type parameter syntax introduced by PEP 695.

+
+class typing.ParamSpec(name, *, bound=None, covariant=False, contravariant=False)
+

Parameter specification variable. A specialized version of type variables.

In type parameter lists, parameter specifications can be declared with two asterisks (**):

type IntFunc[**P] = Callable[P, int]
+

For compatibility with Python 3.11 and earlier, ParamSpec objects can also be created as follows:

P = ParamSpec('P')
+

Parameter specification variables exist primarily for the benefit of static type checkers. They are used to forward the parameter types of one callable to another callable – a pattern commonly found in higher order functions and decorators. They are only valid when used in Concatenate, or as the first argument to Callable, or as parameters for user-defined Generics. See Generic for more information on generic types.

For example, to add basic logging to a function, one can create a decorator add_logging to log function calls. The parameter specification variable tells the type checker that the callable passed into the decorator and the new callable returned by it have inter-dependent type parameters:

from collections.abc import Callable
+import logging
+
+def add_logging[T, **P](f: Callable[P, T]) -> Callable[P, T]:
+    '''A type-safe decorator to add logging to a function.'''
+    def inner(*args: P.args, **kwargs: P.kwargs) -> T:
+        logging.info(f'{f.__name__} was called')
+        return f(*args, **kwargs)
+    return inner
+
+@add_logging
+def add_two(x: float, y: float) -> float:
+    '''Add two numbers together.'''
+    return x + y
+

Without ParamSpec, the simplest way to annotate this previously was to use a TypeVar with bound Callable[..., Any]. However this causes two problems:

  1. The type checker can’t type check the inner function because *args and **kwargs have to be typed Any.
  2. +cast() may be required in the body of the add_logging decorator when returning the inner function, or the static type checker must be told to ignore the return inner.
+args
+
+kwargs
+

Since ParamSpec captures both positional and keyword parameters, P.args and P.kwargs can be used to split a ParamSpec into its components. P.args represents the tuple of positional parameters in a given call and should only be used to annotate *args. P.kwargs represents the mapping of keyword parameters to their values in a given call, and should be only be used to annotate **kwargs. Both attributes require the annotated parameter to be in scope. At runtime, P.args and P.kwargs are instances respectively of ParamSpecArgs and ParamSpecKwargs.

+
+__name__
+

The name of the parameter specification.

+

Parameter specification variables created with covariant=True or contravariant=True can be used to declare covariant or contravariant generic types. The bound argument is also accepted, similar to TypeVar. However the actual semantics of these keywords are yet to be decided.

New in version 3.10.

Changed in version 3.12: Parameter specifications can now be declared using the type parameter syntax introduced by PEP 695.

Note

Only parameter specification variables defined in global scope can be pickled.

See also

+
+typing.ParamSpecArgs
+
+typing.ParamSpecKwargs
+

Arguments and keyword arguments attributes of a ParamSpec. The P.args attribute of a ParamSpec is an instance of ParamSpecArgs, and P.kwargs is an instance of ParamSpecKwargs. They are intended for runtime introspection and have no special meaning to static type checkers.

Calling get_origin() on either of these objects will return the original ParamSpec:

>>> from typing import ParamSpec, get_origin
+>>> P = ParamSpec("P")
+>>> get_origin(P.args) is P
+True
+>>> get_origin(P.kwargs) is P
+True
+

New in version 3.10.

+
+class typing.TypeAliasType(name, value, *, type_params=())
+

The type of type aliases created through the type statement.

Example:

>>> type Alias = int
+>>> type(Alias)
+<class 'typing.TypeAliasType'>
+

New in version 3.12.

+__name__
+

The name of the type alias:

>>> type Alias = int
+>>> Alias.__name__
+'Alias'
+
+
+__module__
+

The module in which the type alias was defined:

>>> type Alias = int
+>>> Alias.__module__
+'__main__'
+
+
+__type_params__
+

The type parameters of the type alias, or an empty tuple if the alias is not generic:

>>> type ListOrSet[T] = list[T] | set[T]
+>>> ListOrSet.__type_params__
+(T,)
+>>> type NotGeneric = int
+>>> NotGeneric.__type_params__
+()
+
+
+__value__
+

The type alias’s value. This is lazily evaluated, so names used in the definition of the alias are not resolved until the __value__ attribute is accessed:

>>> type Mutually = Recursive
+>>> type Recursive = Mutually
+>>> Mutually
+Mutually
+>>> Recursive
+Recursive
+>>> Mutually.__value__
+Recursive
+>>> Recursive.__value__
+Mutually
+
+
+

Other special directives

These functions and classes should not be used directly as annotations. Their intended purpose is to be building blocks for creating and declaring types.

+class typing.NamedTuple
+

Typed version of collections.namedtuple().

Usage:

class Employee(NamedTuple):
+    name: str
+    id: int
+

This is equivalent to:

Employee = collections.namedtuple('Employee', ['name', 'id'])
+

To give a field a default value, you can assign to it in the class body:

class Employee(NamedTuple):
+    name: str
+    id: int = 3
+
+employee = Employee('Guido')
+assert employee.id == 3
+

Fields with a default value must come after any fields without a default.

The resulting class has an extra attribute __annotations__ giving a dict that maps the field names to the field types. (The field names are in the _fields attribute and the default values are in the _field_defaults attribute, both of which are part of the namedtuple() API.)

NamedTuple subclasses can also have docstrings and methods:

class Employee(NamedTuple):
+    """Represents an employee."""
+    name: str
+    id: int = 3
+
+    def __repr__(self) -> str:
+        return f'<Employee {self.name}, id={self.id}>'
+

NamedTuple subclasses can be generic:

class Group[T](NamedTuple):
+    key: T
+    group: list[T]
+

Backward-compatible usage:

# For creating a generic NamedTuple on Python 3.11 or lower
+class Group(NamedTuple, Generic[T]):
+    key: T
+    group: list[T]
+
+# A functional syntax is also supported
+Employee = NamedTuple('Employee', [('name', str), ('id', int)])
+

Changed in version 3.6: Added support for PEP 526 variable annotation syntax.

Changed in version 3.6.1: Added support for default values, methods, and docstrings.

Changed in version 3.8: The _field_types and __annotations__ attributes are now regular dictionaries instead of instances of OrderedDict.

Changed in version 3.9: Removed the _field_types attribute in favor of the more standard __annotations__ attribute which has the same information.

Changed in version 3.11: Added support for generic namedtuples.

+
+class typing.NewType(name, tp)
+

Helper class to create low-overhead distinct types.

A NewType is considered a distinct type by a typechecker. At runtime, however, calling a NewType returns its argument unchanged.

Usage:

UserId = NewType('UserId', int)  # Declare the NewType "UserId"
+first_user = UserId(1)  # "UserId" returns the argument unchanged at runtime
+
+__module__
+

The module in which the new type is defined.

+
+__name__
+

The name of the new type.

+
+__supertype__
+

The type that the new type is based on.

+

New in version 3.5.2.

Changed in version 3.10: NewType is now a class rather than a function.

+
+class typing.Protocol(Generic)
+

Base class for protocol classes.

Protocol classes are defined like this:

class Proto(Protocol):
+    def meth(self) -> int:
+        ...
+

Such classes are primarily used with static type checkers that recognize structural subtyping (static duck-typing), for example:

class C:
+    def meth(self) -> int:
+        return 0
+
+def func(x: Proto) -> int:
+    return x.meth()
+
+func(C())  # Passes static type check
+

See PEP 544 for more details. Protocol classes decorated with runtime_checkable() (described later) act as simple-minded runtime protocols that check only the presence of given attributes, ignoring their type signatures.

Protocol classes can be generic, for example:

class GenProto[T](Protocol):
+    def meth(self) -> T:
+        ...
+

In code that needs to be compatible with Python 3.11 or older, generic Protocols can be written as follows:

T = TypeVar("T")
+
+class GenProto(Protocol[T]):
+    def meth(self) -> T:
+        ...
+

New in version 3.8.

+
+@typing.runtime_checkable
+

Mark a protocol class as a runtime protocol.

Such a protocol can be used with isinstance() and issubclass(). This raises TypeError when applied to a non-protocol class. This allows a simple-minded structural check, very similar to “one trick ponies” in collections.abc such as Iterable. For example:

@runtime_checkable
+class Closable(Protocol):
+    def close(self): ...
+
+assert isinstance(open('/some/file'), Closable)
+
+@runtime_checkable
+class Named(Protocol):
+    name: str
+
+import threading
+assert isinstance(threading.Thread(name='Bob'), Named)
+

Note

runtime_checkable() will check only the presence of the required methods or attributes, not their type signatures or types. For example, ssl.SSLObject is a class, therefore it passes an issubclass() check against Callable. However, the ssl.SSLObject.__init__ method exists only to raise a TypeError with a more informative message, therefore making it impossible to call (instantiate) ssl.SSLObject.

Note

An isinstance() check against a runtime-checkable protocol can be surprisingly slow compared to an isinstance() check against a non-protocol class. Consider using alternative idioms such as hasattr() calls for structural checks in performance-sensitive code.

New in version 3.8.

Changed in version 3.12: The internal implementation of isinstance() checks against runtime-checkable protocols now uses inspect.getattr_static() to look up attributes (previously, hasattr() was used). As a result, some objects which used to be considered instances of a runtime-checkable protocol may no longer be considered instances of that protocol on Python 3.12+, and vice versa. Most users are unlikely to be affected by this change.

Changed in version 3.12: The members of a runtime-checkable protocol are now considered “frozen” at runtime as soon as the class has been created. Monkey-patching attributes onto a runtime-checkable protocol will still work, but will have no impact on isinstance() checks comparing objects to the protocol. See “What’s new in Python 3.12” for more details.

+
+class typing.TypedDict(dict)
+

Special construct to add type hints to a dictionary. At runtime it is a plain dict.

TypedDict declares a dictionary type that expects all of its instances to have a certain set of keys, where each key is associated with a value of a consistent type. This expectation is not checked at runtime but is only enforced by type checkers. Usage:

class Point2D(TypedDict):
+    x: int
+    y: int
+    label: str
+
+a: Point2D = {'x': 1, 'y': 2, 'label': 'good'}  # OK
+b: Point2D = {'z': 3, 'label': 'bad'}           # Fails type check
+
+assert Point2D(x=1, y=2, label='first') == dict(x=1, y=2, label='first')
+

To allow using this feature with older versions of Python that do not support PEP 526, TypedDict supports two additional equivalent syntactic forms:

  • +

    Using a literal dict as the second argument:

    Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': str})
+
  • +

    Using keyword arguments:

    Point2D = TypedDict('Point2D', x=int, y=int, label=str)
+

Deprecated since version 3.11, will be removed in version 3.13: The keyword-argument syntax is deprecated in 3.11 and will be removed in 3.13. It may also be unsupported by static type checkers.

The functional syntax should also be used when any of the keys are not valid identifiers, for example because they are keywords or contain hyphens. Example:

# raises SyntaxError
+class Point2D(TypedDict):
+    in: int  # 'in' is a keyword
+    x-y: int  # name with hyphens
+
+# OK, functional syntax
+Point2D = TypedDict('Point2D', {'in': int, 'x-y': int})
+

By default, all keys must be present in a TypedDict. It is possible to mark individual keys as non-required using NotRequired:

class Point2D(TypedDict):
+    x: int
+    y: int
+    label: NotRequired[str]
+
+# Alternative syntax
+Point2D = TypedDict('Point2D', {'x': int, 'y': int, 'label': NotRequired[str]})
+

This means that a Point2D TypedDict can have the label key omitted.

It is also possible to mark all keys as non-required by default by specifying a totality of False:

class Point2D(TypedDict, total=False):
+    x: int
+    y: int
+
+# Alternative syntax
+Point2D = TypedDict('Point2D', {'x': int, 'y': int}, total=False)
+

This means that a Point2D TypedDict can have any of the keys omitted. A type checker is only expected to support a literal False or True as the value of the total argument. True is the default, and makes all items defined in the class body required.

Individual keys of a total=False TypedDict can be marked as required using Required:

class Point2D(TypedDict, total=False):
+    x: Required[int]
+    y: Required[int]
+    label: str
+
+# Alternative syntax
+Point2D = TypedDict('Point2D', {
+    'x': Required[int],
+    'y': Required[int],
+    'label': str
+}, total=False)
+

It is possible for a TypedDict type to inherit from one or more other TypedDict types using the class-based syntax. Usage:

class Point3D(Point2D):
+    z: int
+

Point3D has three items: x, y and z. It is equivalent to this definition:

class Point3D(TypedDict):
+    x: int
+    y: int
+    z: int
+

A TypedDict cannot inherit from a non-TypedDict class, except for Generic. For example:

class X(TypedDict):
+    x: int
+
+class Y(TypedDict):
+    y: int
+
+class Z(object): pass  # A non-TypedDict class
+
+class XY(X, Y): pass  # OK
+
+class XZ(X, Z): pass  # raises TypeError
+

A TypedDict can be generic:

class Group[T](TypedDict):
+    key: T
+    group: list[T]
+

To create a generic TypedDict that is compatible with Python 3.11 or lower, inherit from Generic explicitly:

T = TypeVar("T")
+
+class Group(TypedDict, Generic[T]):
+    key: T
+    group: list[T]
+

A TypedDict can be introspected via annotations dicts (see Annotations Best Practices for more information on annotations best practices), __total__, __required_keys__, and __optional_keys__.

+__total__
+

Point2D.__total__ gives the value of the total argument. Example:

>>> from typing import TypedDict
+>>> class Point2D(TypedDict): pass
+>>> Point2D.__total__
+True
+>>> class Point2D(TypedDict, total=False): pass
+>>> Point2D.__total__
+False
+>>> class Point3D(Point2D): pass
+>>> Point3D.__total__
+True
+

This attribute reflects only the value of the total argument to the current TypedDict class, not whether the class is semantically total. For example, a TypedDict with __total__ set to True may have keys marked with NotRequired, or it may inherit from another TypedDict with total=False. Therefore, it is generally better to use __required_keys__ and __optional_keys__ for introspection.

+
+__required_keys__
+

New in version 3.9.

+
+__optional_keys__
+

Point2D.__required_keys__ and Point2D.__optional_keys__ return frozenset objects containing required and non-required keys, respectively.

Keys marked with Required will always appear in __required_keys__ and keys marked with NotRequired will always appear in __optional_keys__.

For backwards compatibility with Python 3.10 and below, it is also possible to use inheritance to declare both required and non-required keys in the same TypedDict . This is done by declaring a TypedDict with one value for the total argument and then inheriting from it in another TypedDict with a different value for total:

>>> class Point2D(TypedDict, total=False):
+...     x: int
+...     y: int
+...
+>>> class Point3D(Point2D):
+...     z: int
+...
+>>> Point3D.__required_keys__ == frozenset({'z'})
+True
+>>> Point3D.__optional_keys__ == frozenset({'x', 'y'})
+True
+

New in version 3.9.

Note

If from __future__ import annotations is used or if annotations are given as strings, annotations are not evaluated when the TypedDict is defined. Therefore, the runtime introspection that __required_keys__ and __optional_keys__ rely on may not work properly, and the values of the attributes may be incorrect.

+

See PEP 589 for more examples and detailed rules of using TypedDict.

New in version 3.8.

Changed in version 3.11: Added support for marking individual keys as Required or NotRequired. See PEP 655.

Changed in version 3.11: Added support for generic TypedDicts.

+

Protocols

The following protocols are provided by the typing module. All are decorated with @runtime_checkable.

+class typing.SupportsAbs
+

An ABC with one abstract method __abs__ that is covariant in its return type.

+
+class typing.SupportsBytes
+

An ABC with one abstract method __bytes__.

+
+class typing.SupportsComplex
+

An ABC with one abstract method __complex__.

+
+class typing.SupportsFloat
+

An ABC with one abstract method __float__.

+
+class typing.SupportsIndex
+

An ABC with one abstract method __index__.

New in version 3.8.

+
+class typing.SupportsInt
+

An ABC with one abstract method __int__.

+
+class typing.SupportsRound
+

An ABC with one abstract method __round__ that is covariant in its return type.

+

ABCs for working with IO

+class typing.IO
+class typing.TextIO
+class typing.BinaryIO
+

Generic type IO[AnyStr] and its subclasses TextIO(IO[str]) and BinaryIO(IO[bytes]) represent the types of I/O streams such as returned by open().

+

Functions and decorators

+typing.cast(typ, val)
+

Cast a value to a type.

This returns the value unchanged. To the type checker this signals that the return value has the designated type, but at runtime we intentionally don’t check anything (we want this to be as fast as possible).

+
+typing.assert_type(val, typ, /)
+

Ask a static type checker to confirm that val has an inferred type of typ.

At runtime this does nothing: it returns the first argument unchanged with no checks or side effects, no matter the actual type of the argument.

When a static type checker encounters a call to assert_type(), it emits an error if the value is not of the specified type:

def greet(name: str) -> None:
+    assert_type(name, str)  # OK, inferred type of `name` is `str`
+    assert_type(name, int)  # type checker error
+

This function is useful for ensuring the type checker’s understanding of a script is in line with the developer’s intentions:

def complex_function(arg: object):
+    # Do some complex type-narrowing logic,
+    # after which we hope the inferred type will be `int`
+    ...
+    # Test whether the type checker correctly understands our function
+    assert_type(arg, int)
+

New in version 3.11.

+
+typing.assert_never(arg, /)
+

Ask a static type checker to confirm that a line of code is unreachable.

Example:

def int_or_str(arg: int | str) -> None:
+    match arg:
+        case int():
+            print("It's an int")
+        case str():
+            print("It's a str")
+        case _ as unreachable:
+            assert_never(unreachable)
+

Here, the annotations allow the type checker to infer that the last case can never execute, because arg is either an int or a str, and both options are covered by earlier cases.

If a type checker finds that a call to assert_never() is reachable, it will emit an error. For example, if the type annotation for arg was instead int | str | float, the type checker would emit an error pointing out that unreachable is of type float. For a call to assert_never to pass type checking, the inferred type of the argument passed in must be the bottom type, Never, and nothing else.

At runtime, this throws an exception when called.

See also

Unreachable Code and Exhaustiveness Checking has more information about exhaustiveness checking with static typing.

New in version 3.11.

+
+typing.reveal_type(obj, /)
+

Ask a static type checker to reveal the inferred type of an expression.

When a static type checker encounters a call to this function, it emits a diagnostic with the inferred type of the argument. For example:

x: int = 1
+reveal_type(x)  # Revealed type is "builtins.int"
+

This can be useful when you want to debug how your type checker handles a particular piece of code.

At runtime, this function prints the runtime type of its argument to sys.stderr and returns the argument unchanged (allowing the call to be used within an expression):

x = reveal_type(1)  # prints "Runtime type is int"
+print(x)  # prints "1"
+

Note that the runtime type may be different from (more or less specific than) the type statically inferred by a type checker.

Most type checkers support reveal_type() anywhere, even if the name is not imported from typing. Importing the name from typing, however, allows your code to run without runtime errors and communicates intent more clearly.

New in version 3.11.

+
+@typing.dataclass_transform(*, eq_default=True, order_default=False, kw_only_default=False, frozen_default=False, field_specifiers=(), **kwargs)
+

Decorator to mark an object as providing dataclass-like behavior.

dataclass_transform may be used to decorate a class, metaclass, or a function that is itself a decorator. The presence of @dataclass_transform() tells a static type checker that the decorated object performs runtime “magic” that transforms a class in a similar way to @dataclasses.dataclass.

Example usage with a decorator function:

@dataclass_transform()
+def create_model[T](cls: type[T]) -> type[T]:
+    ...
+    return cls
+
+@create_model
+class CustomerModel:
+    id: int
+    name: str
+

On a base class:

@dataclass_transform()
+class ModelBase: ...
+
+class CustomerModel(ModelBase):
+    id: int
+    name: str
+

On a metaclass:

@dataclass_transform()
+class ModelMeta(type): ...
+
+class ModelBase(metaclass=ModelMeta): ...
+
+class CustomerModel(ModelBase):
+    id: int
+    name: str
+

The CustomerModel classes defined above will be treated by type checkers similarly to classes created with @dataclasses.dataclass. For example, type checkers will assume these classes have __init__ methods that accept id and name.

The decorated class, metaclass, or function may accept the following bool arguments which type checkers will assume have the same effect as they would have on the @dataclasses.dataclass decorator: init, eq, order, unsafe_hash, frozen, match_args, kw_only, and slots. It must be possible for the value of these arguments (True or False) to be statically evaluated.

The arguments to the dataclass_transform decorator can be used to customize the default behaviors of the decorated class, metaclass, or function:

Parameters
+
  • +eq_default (bool) – Indicates whether the eq parameter is assumed to be True or False if it is omitted by the caller. Defaults to True.
  • +order_default (bool) – Indicates whether the order parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
  • +kw_only_default (bool) – Indicates whether the kw_only parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.
  • +

    frozen_default (bool) –

    +

    Indicates whether the frozen parameter is assumed to be True or False if it is omitted by the caller. Defaults to False.

    New in version 3.12.

  • +field_specifiers (tuple[Callable[..., Any], ...]) – Specifies a static list of supported classes or functions that describe fields, similar to dataclasses.field(). Defaults to ().
  • +**kwargs (Any) – Arbitrary other keyword arguments are accepted in order to allow for possible future extensions.

Type checkers recognize the following optional parameters on field specifiers:

+ + + + + + +
Recognised parameters for field specifiers

Parameter name

Description

init

Indicates whether the field should be included in the synthesized __init__ method. If unspecified, init defaults to True.

default

Provides the default value for the field.

default_factory

Provides a runtime callback that returns the default value for the field. If neither default nor default_factory are specified, the field is assumed to have no default value and must be provided a value when the class is instantiated.

factory

An alias for the default_factory parameter on field specifiers.

kw_only

Indicates whether the field should be marked as keyword-only. If True, the field will be keyword-only. If False, it will not be keyword-only. If unspecified, the value of the kw_only parameter on the object decorated with dataclass_transform will be used, or if that is unspecified, the value of kw_only_default on dataclass_transform will be used.

alias

Provides an alternative name for the field. This alternative name is used in the synthesized __init__ method.

At runtime, this decorator records its arguments in the __dataclass_transform__ attribute on the decorated object. It has no other runtime effect.

See PEP 681 for more details.

New in version 3.11.

+
+@typing.overload
+

Decorator for creating overloaded functions and methods.

The @overload decorator allows describing functions and methods that support multiple different combinations of argument types. A series of @overload-decorated definitions must be followed by exactly one non-@overload-decorated definition (for the same function/method).

@overload-decorated definitions are for the benefit of the type checker only, since they will be overwritten by the non-@overload-decorated definition. The non-@overload-decorated definition, meanwhile, will be used at runtime but should be ignored by a type checker. At runtime, calling an @overload-decorated function directly will raise NotImplementedError.

An example of overload that gives a more precise type than can be expressed using a union or a type variable:

@overload
+def process(response: None) -> None:
+    ...
+@overload
+def process(response: int) -> tuple[int, str]:
+    ...
+@overload
+def process(response: bytes) -> str:
+    ...
+def process(response):
+    ...  # actual implementation goes here
+

See PEP 484 for more details and comparison with other typing semantics.

Changed in version 3.11: Overloaded functions can now be introspected at runtime using get_overloads().

+
+typing.get_overloads(func)
+

Return a sequence of @overload-decorated definitions for func.

func is the function object for the implementation of the overloaded function. For example, given the definition of process in the documentation for @overload, get_overloads(process) will return a sequence of three function objects for the three defined overloads. If called on a function with no overloads, get_overloads() returns an empty sequence.

get_overloads() can be used for introspecting an overloaded function at runtime.

New in version 3.11.

+
+typing.clear_overloads()
+

Clear all registered overloads in the internal registry.

This can be used to reclaim the memory used by the registry.

New in version 3.11.

+
+@typing.final
+

Decorator to indicate final methods and final classes.

Decorating a method with @final indicates to a type checker that the method cannot be overridden in a subclass. Decorating a class with @final indicates that it cannot be subclassed.

For example:

class Base:
+    @final
+    def done(self) -> None:
+        ...
+class Sub(Base):
+    def done(self) -> None:  # Error reported by type checker
+        ...
+
+@final
+class Leaf:
+    ...
+class Other(Leaf):  # Error reported by type checker
+    ...
+

There is no runtime checking of these properties. See PEP 591 for more details.

New in version 3.8.

Changed in version 3.11: The decorator will now attempt to set a __final__ attribute to True on the decorated object. Thus, a check like if getattr(obj, "__final__", False) can be used at runtime to determine whether an object obj has been marked as final. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

+
+@typing.no_type_check
+

Decorator to indicate that annotations are not type hints.

This works as a class or function decorator. With a class, it applies recursively to all methods and classes defined in that class (but not to methods defined in its superclasses or subclasses). Type checkers will ignore all annotations in a function or class with this decorator.

@no_type_check mutates the decorated object in place.

+
+@typing.no_type_check_decorator
+

Decorator to give another decorator the no_type_check() effect.

This wraps the decorator with something that wraps the decorated function in no_type_check().

+
+@typing.override
+

Decorator to indicate that a method in a subclass is intended to override a method or attribute in a superclass.

Type checkers should emit an error if a method decorated with @override does not, in fact, override anything. This helps prevent bugs that may occur when a base class is changed without an equivalent change to a child class.

For example:

class Base:
+    def log_status(self) -> None:
+        ...
+
+class Sub(Base):
+    @override
+    def log_status(self) -> None:  # Okay: overrides Base.log_status
+        ...
+
+    @override
+    def done(self) -> None:  # Error reported by type checker
+        ...
+

There is no runtime checking of this property.

The decorator will attempt to set an __override__ attribute to True on the decorated object. Thus, a check like if getattr(obj, "__override__", False) can be used at runtime to determine whether an object obj has been marked as an override. If the decorated object does not support setting attributes, the decorator returns the object unchanged without raising an exception.

See PEP 698 for more details.

New in version 3.12.

+
+@typing.type_check_only
+

Decorator to mark a class or function as unavailable at runtime.

This decorator is itself not available at runtime. It is mainly intended to mark classes that are defined in type stub files if an implementation returns an instance of a private class:

@type_check_only
+class Response:  # private or not available at runtime
+    code: int
+    def get_header(self, name: str) -> str: ...
+
+def fetch_response() -> Response: ...
+

Note that returning instances of private classes is not recommended. It is usually preferable to make such classes public.

+

Introspection helpers

+typing.get_type_hints(obj, globalns=None, localns=None, include_extras=False)
+

Return a dictionary containing type hints for a function, method, module or class object.

This is often the same as obj.__annotations__. In addition, forward references encoded as string literals are handled by evaluating them in globals and locals namespaces. For a class C, return a dictionary constructed by merging all the __annotations__ along C.__mro__ in reverse order.

The function recursively replaces all Annotated[T, ...] with T, unless include_extras is set to True (see Annotated for more information). For example:

class Student(NamedTuple):
+    name: Annotated[str, 'some marker']
+
+assert get_type_hints(Student) == {'name': str}
+assert get_type_hints(Student, include_extras=False) == {'name': str}
+assert get_type_hints(Student, include_extras=True) == {
+    'name': Annotated[str, 'some marker']
+}
+

Note

get_type_hints() does not work with imported type aliases that include forward references. Enabling postponed evaluation of annotations (PEP 563) may remove the need for most forward references.

Changed in version 3.9: Added include_extras parameter as part of PEP 593. See the documentation on Annotated for more information.

Changed in version 3.11: Previously, Optional[t] was added for function and method annotations if a default value equal to None was set. Now the annotation is returned unchanged.

+
+typing.get_origin(tp)
+

Get the unsubscripted version of a type: for a typing object of the form X[Y, Z, ...] return X.

If X is a typing-module alias for a builtin or collections class, it will be normalized to the original class. If X is an instance of ParamSpecArgs or ParamSpecKwargs, return the underlying ParamSpec. Return None for unsupported objects.

Examples:

assert get_origin(str) is None
+assert get_origin(Dict[str, int]) is dict
+assert get_origin(Union[int, str]) is Union
+P = ParamSpec('P')
+assert get_origin(P.args) is P
+assert get_origin(P.kwargs) is P
+

New in version 3.8.

+
+typing.get_args(tp)
+

Get type arguments with all substitutions performed: for a typing object of the form X[Y, Z, ...] return (Y, Z, ...).

If X is a union or Literal contained in another generic type, the order of (Y, Z, ...) may be different from the order of the original arguments [Y, Z, ...] due to type caching. Return () for unsupported objects.

Examples:

assert get_args(int) == ()
+assert get_args(Dict[int, str]) == (int, str)
+assert get_args(Union[int, str]) == (int, str)
+

New in version 3.8.

+
+typing.is_typeddict(tp)
+

Check if a type is a TypedDict.

For example:

class Film(TypedDict):
+    title: str
+    year: int
+
+assert is_typeddict(Film)
+assert not is_typeddict(list | str)
+
+# TypedDict is a factory for creating typed dicts,
+# not a typed dict itself
+assert not is_typeddict(TypedDict)
+

New in version 3.10.

+
+class typing.ForwardRef
+

Class used for internal typing representation of string forward references.

For example, List["SomeClass"] is implicitly transformed into List[ForwardRef("SomeClass")]. ForwardRef should not be instantiated by a user, but may be used by introspection tools.

Note

PEP 585 generic types such as list["SomeClass"] will not be implicitly transformed into list[ForwardRef("SomeClass")] and thus will not automatically resolve to list[SomeClass].

New in version 3.7.4.

+

Constant

+typing.TYPE_CHECKING
+

A special constant that is assumed to be True by 3rd party static type checkers. It is False at runtime.

Usage:

if TYPE_CHECKING:
+    import expensive_mod
+
+def fun(arg: 'expensive_mod.SomeType') -> None:
+    local_var: expensive_mod.AnotherType = other_fun()
+

The first type annotation must be enclosed in quotes, making it a “forward reference”, to hide the expensive_mod reference from the interpreter runtime. Type annotations for local variables are not evaluated, so the second annotation does not need to be enclosed in quotes.

Note

If from __future__ import annotations is used, annotations are not evaluated at function definition time. Instead, they are stored as strings in __annotations__. This makes it unnecessary to use quotes around the annotation (see PEP 563).

New in version 3.5.2.

+

Deprecated aliases

This module defines several deprecated aliases to pre-existing standard library classes. These were originally included in the typing module in order to support parameterizing these generic classes using []. However, the aliases became redundant in Python 3.9 when the corresponding pre-existing classes were enhanced to support [] (see PEP 585).

The redundant types are deprecated as of Python 3.9. However, while the aliases may be removed at some point, removal of these aliases is not currently planned. As such, no deprecation warnings are currently issued by the interpreter for these aliases.

If at some point it is decided to remove these deprecated aliases, a deprecation warning will be issued by the interpreter for at least two releases prior to removal. The aliases are guaranteed to remain in the typing module without deprecation warnings until at least Python 3.14.

Type checkers are encouraged to flag uses of the deprecated types if the program they are checking targets a minimum Python version of 3.9 or newer.

Aliases to built-in types

+class typing.Dict(dict, MutableMapping[KT, VT])
+

Deprecated alias to dict.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Mapping rather than to use dict or typing.Dict.

This type can be used as follows:

def count_words(text: str) -> Dict[str, int]:
+    ...
+

Deprecated since version 3.9: builtins.dict now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.List(list, MutableSequence[T])
+

Deprecated alias to list.

Note that to annotate arguments, it is preferred to use an abstract collection type such as Sequence or Iterable rather than to use list or typing.List.

This type may be used as follows:

def vec2[T: (int, float)](x: T, y: T) -> List[T]:
+    return [x, y]
+
+def keep_positives[T: (int, float)](vector: Sequence[T]) -> List[T]:
+    return [item for item in vector if item > 0]
+

Deprecated since version 3.9: builtins.list now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Set(set, MutableSet[T])
+

Deprecated alias to builtins.set.

Note that to annotate arguments, it is preferred to use an abstract collection type such as AbstractSet rather than to use set or typing.Set.

Deprecated since version 3.9: builtins.set now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.FrozenSet(frozenset, AbstractSet[T_co])
+

Deprecated alias to builtins.frozenset.

Deprecated since version 3.9: builtins.frozenset now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+typing.Tuple
+

Deprecated alias for tuple.

tuple and Tuple are special-cased in the type system; see Annotating tuples for more details.

Deprecated since version 3.9: builtins.tuple now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Type(Generic[CT_co])
+

Deprecated alias to type.

See The type of class objects for details on using type or typing.Type in type annotations.

New in version 3.5.2.

Deprecated since version 3.9: builtins.type now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+

Aliases to types in collections +

+class typing.DefaultDict(collections.defaultdict, MutableMapping[KT, VT])
+

Deprecated alias to collections.defaultdict.

New in version 3.5.2.

Deprecated since version 3.9: collections.defaultdict now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.OrderedDict(collections.OrderedDict, MutableMapping[KT, VT])
+

Deprecated alias to collections.OrderedDict.

New in version 3.7.2.

Deprecated since version 3.9: collections.OrderedDict now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.ChainMap(collections.ChainMap, MutableMapping[KT, VT])
+

Deprecated alias to collections.ChainMap.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.ChainMap now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Counter(collections.Counter, Dict[T, int])
+

Deprecated alias to collections.Counter.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.Counter now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Deque(deque, MutableSequence[T])
+

Deprecated alias to collections.deque.

New in version 3.5.4.

New in version 3.6.1.

Deprecated since version 3.9: collections.deque now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+

Aliases to other concrete types

Deprecated since version 3.8, will be removed in version 3.13: The typing.io namespace is deprecated and will be removed. These types should be directly imported from typing instead.

+class typing.Pattern
+class typing.Match
+

Deprecated aliases corresponding to the return types from re.compile() and re.match().

These types (and the corresponding functions) are generic over AnyStr. Pattern can be specialised as Pattern[str] or Pattern[bytes]; Match can be specialised as Match[str] or Match[bytes].

Deprecated since version 3.8, will be removed in version 3.13: The typing.re namespace is deprecated and will be removed. These types should be directly imported from typing instead.

Deprecated since version 3.9: Classes Pattern and Match from re now support []. See PEP 585 and Generic Alias Type.

+
+class typing.Text
+

Deprecated alias for str.

Text is provided to supply a forward compatible path for Python 2 code: in Python 2, Text is an alias for unicode.

Use Text to indicate that a value must contain a unicode string in a manner that is compatible with both Python 2 and Python 3:

def add_unicode_checkmark(text: Text) -> Text:
+    return text + u' \u2713'
+

New in version 3.5.2.

Deprecated since version 3.11: Python 2 is no longer supported, and most type checkers also no longer support type checking Python 2 code. Removal of the alias is not currently planned, but users are encouraged to use str instead of Text.

+

Aliases to container ABCs in collections.abc +

+class typing.AbstractSet(Collection[T_co])
+

Deprecated alias to collections.abc.Set.

Deprecated since version 3.9: collections.abc.Set now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.ByteString(Sequence[int])
+

This type represents the types bytes, bytearray, and memoryview of byte sequences.

Deprecated since version 3.9, will be removed in version 3.14: Prefer collections.abc.Buffer, or a union like bytes | bytearray | memoryview.

+
+class typing.Collection(Sized, Iterable[T_co], Container[T_co])
+

Deprecated alias to collections.abc.Collection.

New in version 3.6.0.

Deprecated since version 3.9: collections.abc.Collection now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Container(Generic[T_co])
+

Deprecated alias to collections.abc.Container.

Deprecated since version 3.9: collections.abc.Container now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.ItemsView(MappingView, AbstractSet[tuple[KT_co, VT_co]])
+

Deprecated alias to collections.abc.ItemsView.

Deprecated since version 3.9: collections.abc.ItemsView now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.KeysView(MappingView, AbstractSet[KT_co])
+

Deprecated alias to collections.abc.KeysView.

Deprecated since version 3.9: collections.abc.KeysView now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Mapping(Collection[KT], Generic[KT, VT_co])
+

Deprecated alias to collections.abc.Mapping.

This type can be used as follows:

def get_position_in_index(word_list: Mapping[str, int], word: str) -> int:
+    return word_list[word]
+

Deprecated since version 3.9: collections.abc.Mapping now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.MappingView(Sized)
+

Deprecated alias to collections.abc.MappingView.

Deprecated since version 3.9: collections.abc.MappingView now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.MutableMapping(Mapping[KT, VT])
+

Deprecated alias to collections.abc.MutableMapping.

Deprecated since version 3.9: collections.abc.MutableMapping now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.MutableSequence(Sequence[T])
+

Deprecated alias to collections.abc.MutableSequence.

Deprecated since version 3.9: collections.abc.MutableSequence now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.MutableSet(AbstractSet[T])
+

Deprecated alias to collections.abc.MutableSet.

Deprecated since version 3.9: collections.abc.MutableSet now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Sequence(Reversible[T_co], Collection[T_co])
+

Deprecated alias to collections.abc.Sequence.

Deprecated since version 3.9: collections.abc.Sequence now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.ValuesView(MappingView, Collection[_VT_co])
+

Deprecated alias to collections.abc.ValuesView.

Deprecated since version 3.9: collections.abc.ValuesView now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+

Aliases to asynchronous ABCs in collections.abc +

+class typing.Coroutine(Awaitable[ReturnType], Generic[YieldType, SendType, ReturnType])
+

Deprecated alias to collections.abc.Coroutine.

The variance and order of type variables correspond to those of Generator, for example:

from collections.abc import Coroutine
+c: Coroutine[list[str], str, int]  # Some coroutine defined elsewhere
+x = c.send('hi')                   # Inferred type of 'x' is list[str]
+async def bar() -> None:
+    y = await c                    # Inferred type of 'y' is int
+

New in version 3.5.3.

Deprecated since version 3.9: collections.abc.Coroutine now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.AsyncGenerator(AsyncIterator[YieldType], Generic[YieldType, SendType])
+

Deprecated alias to collections.abc.AsyncGenerator.

An async generator can be annotated by the generic type AsyncGenerator[YieldType, SendType]. For example:

async def echo_round() -> AsyncGenerator[int, float]:
+    sent = yield 0
+    while sent >= 0.0:
+        rounded = await round(sent)
+        sent = yield rounded
+

Unlike normal generators, async generators cannot return a value, so there is no ReturnType type parameter. As with Generator, the SendType behaves contravariantly.

If your generator will only yield values, set the SendType to None:

async def infinite_stream(start: int) -> AsyncGenerator[int, None]:
+    while True:
+        yield start
+        start = await increment(start)
+

Alternatively, annotate your generator as having a return type of either AsyncIterable[YieldType] or AsyncIterator[YieldType]:

async def infinite_stream(start: int) -> AsyncIterator[int]:
+    while True:
+        yield start
+        start = await increment(start)
+

New in version 3.6.1.

Deprecated since version 3.9: collections.abc.AsyncGenerator now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.AsyncIterable(Generic[T_co])
+

Deprecated alias to collections.abc.AsyncIterable.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.AsyncIterable now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.AsyncIterator(AsyncIterable[T_co])
+

Deprecated alias to collections.abc.AsyncIterator.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.AsyncIterator now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Awaitable(Generic[T_co])
+

Deprecated alias to collections.abc.Awaitable.

New in version 3.5.2.

Deprecated since version 3.9: collections.abc.Awaitable now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+

Aliases to other ABCs in collections.abc +

+class typing.Iterable(Generic[T_co])
+

Deprecated alias to collections.abc.Iterable.

Deprecated since version 3.9: collections.abc.Iterable now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Iterator(Iterable[T_co])
+

Deprecated alias to collections.abc.Iterator.

Deprecated since version 3.9: collections.abc.Iterator now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+typing.Callable
+

Deprecated alias to collections.abc.Callable.

See Annotating callable objects for details on how to use collections.abc.Callable and typing.Callable in type annotations.

Deprecated since version 3.9: collections.abc.Callable now supports subscripting ([]). See PEP 585 and Generic Alias Type.

Changed in version 3.10: Callable now supports ParamSpec and Concatenate. See PEP 612 for more details.

+
+class typing.Generator(Iterator[YieldType], Generic[YieldType, SendType, ReturnType])
+

Deprecated alias to collections.abc.Generator.

A generator can be annotated by the generic type Generator[YieldType, SendType, ReturnType]. For example:

def echo_round() -> Generator[int, float, str]:
+    sent = yield 0
+    while sent >= 0:
+        sent = yield round(sent)
+    return 'Done'
+

Note that unlike many other generics in the typing module, the SendType of Generator behaves contravariantly, not covariantly or invariantly.

If your generator will only yield values, set the SendType and ReturnType to None:

def infinite_stream(start: int) -> Generator[int, None, None]:
+    while True:
+        yield start
+        start += 1
+

Alternatively, annotate your generator as having a return type of either Iterable[YieldType] or Iterator[YieldType]:

def infinite_stream(start: int) -> Iterator[int]:
+    while True:
+        yield start
+        start += 1
+

Deprecated since version 3.9: collections.abc.Generator now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Hashable
+

Deprecated alias to collections.abc.Hashable.

Deprecated since version 3.12: Use collections.abc.Hashable directly instead.

+
+class typing.Reversible(Iterable[T_co])
+

Deprecated alias to collections.abc.Reversible.

Deprecated since version 3.9: collections.abc.Reversible now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.Sized
+

Deprecated alias to collections.abc.Sized.

Deprecated since version 3.12: Use collections.abc.Sized directly instead.

+

Aliases to contextlib ABCs

+class typing.ContextManager(Generic[T_co])
+

Deprecated alias to contextlib.AbstractContextManager.

New in version 3.5.4.

New in version 3.6.0.

Deprecated since version 3.9: contextlib.AbstractContextManager now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+
+class typing.AsyncContextManager(Generic[T_co])
+

Deprecated alias to contextlib.AbstractAsyncContextManager.

New in version 3.5.4.

New in version 3.6.2.

Deprecated since version 3.9: contextlib.AbstractAsyncContextManager now supports subscripting ([]). See PEP 585 and Generic Alias Type.

+

Deprecation Timeline of Major Features

Certain features in typing are deprecated and may be removed in a future version of Python. The following table summarizes major deprecations for your convenience. This is subject to change, and not all deprecations are listed.

+ + + + + + +

Feature

Deprecated in

Projected removal

PEP/issue

typing.io and typing.re submodules

3.8

3.13

bpo-38291

typing versions of standard collections

3.9

Undecided (see Deprecated aliases for more information)

PEP 585

typing.ByteString

3.9

3.14

gh-91896

typing.Text

3.11

Undecided

gh-92332

typing.Hashable and typing.Sized

3.12

Undecided

gh-94309

typing.TypeAlias

3.12

Undecided

PEP 695
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/typing.html +

+
diff --git a/devdocs/python~3.12/library%2Funicodedata.html b/devdocs/python~3.12/library%2Funicodedata.html new file mode 100644 index 00000000..777aa615 --- /dev/null +++ b/devdocs/python~3.12/library%2Funicodedata.html @@ -0,0 +1,70 @@ +

unicodedata — Unicode Database

This module provides access to the Unicode Character Database (UCD) which defines character properties for all Unicode characters. The data contained in this database is compiled from the UCD version 15.0.0.

The module uses the same names and symbols as defined by Unicode Standard Annex #44, “Unicode Character Database”. It defines the following functions:

+unicodedata.lookup(name)
+

Look up character by name. If a character with the given name is found, return the corresponding character. If not found, KeyError is raised.

Changed in version 3.3: Support for name aliases 1 and named sequences 2 has been added.

+
+unicodedata.name(chr[, default])
+

Returns the name assigned to the character chr as a string. If no name is defined, default is returned, or, if not given, ValueError is raised.

+
+unicodedata.decimal(chr[, default])
+

Returns the decimal value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.

+
+unicodedata.digit(chr[, default])
+

Returns the digit value assigned to the character chr as integer. If no such value is defined, default is returned, or, if not given, ValueError is raised.

+
+unicodedata.numeric(chr[, default])
+

Returns the numeric value assigned to the character chr as float. If no such value is defined, default is returned, or, if not given, ValueError is raised.

+
+unicodedata.category(chr)
+

Returns the general category assigned to the character chr as string.

+
+unicodedata.bidirectional(chr)
+

Returns the bidirectional class assigned to the character chr as string. If no such value is defined, an empty string is returned.

+
+unicodedata.combining(chr)
+

Returns the canonical combining class assigned to the character chr as integer. Returns 0 if no combining class is defined.

+
+unicodedata.east_asian_width(chr)
+

Returns the east asian width assigned to the character chr as string.

+
+unicodedata.mirrored(chr)
+

Returns the mirrored property assigned to the character chr as integer. Returns 1 if the character has been identified as a “mirrored” character in bidirectional text, 0 otherwise.

+
+unicodedata.decomposition(chr)
+

Returns the character decomposition mapping assigned to the character chr as string. An empty string is returned in case no such mapping is defined.

+
+unicodedata.normalize(form, unistr)
+

Return the normal form form for the Unicode string unistr. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’.

The Unicode standard defines various normalization forms of a Unicode string, based on the definition of canonical equivalence and compatibility equivalence. In Unicode, several characters can be expressed in various way. For example, the character U+00C7 (LATIN CAPITAL LETTER C WITH CEDILLA) can also be expressed as the sequence U+0043 (LATIN CAPITAL LETTER C) U+0327 (COMBINING CEDILLA).

For each character, there are two normal forms: normal form C and normal form D. Normal form D (NFD) is also known as canonical decomposition, and translates each character into its decomposed form. Normal form C (NFC) first applies a canonical decomposition, then composes pre-combined characters again.

In addition to these two forms, there are two additional normal forms based on compatibility equivalence. In Unicode, certain characters are supported which normally would be unified with other characters. For example, U+2160 (ROMAN NUMERAL ONE) is really the same thing as U+0049 (LATIN CAPITAL LETTER I). However, it is supported in Unicode for compatibility with existing character sets (e.g. gb2312).

The normal form KD (NFKD) will apply the compatibility decomposition, i.e. replace all compatibility characters with their equivalents. The normal form KC (NFKC) first applies the compatibility decomposition, followed by the canonical composition.

Even if two unicode strings are normalized and look the same to a human reader, if one has combining characters and the other doesn’t, they may not compare equal.

+
+unicodedata.is_normalized(form, unistr)
+

Return whether the Unicode string unistr is in the normal form form. Valid values for form are ‘NFC’, ‘NFKC’, ‘NFD’, and ‘NFKD’.

New in version 3.8.

+

In addition, the module exposes the following constant:

+unicodedata.unidata_version
+

The version of the Unicode database used in this module.

+
+unicodedata.ucd_3_2_0
+

This is an object that has the same methods as the entire module, but uses the Unicode database version 3.2 instead, for applications that require this specific version of the Unicode database (such as IDNA).

+

Examples:

>>> import unicodedata
+>>> unicodedata.lookup('LEFT CURLY BRACKET')
+'{'
+>>> unicodedata.name('/')
+'SOLIDUS'
+>>> unicodedata.decimal('9')
+9
+>>> unicodedata.decimal('a')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: not a decimal
+>>> unicodedata.category('A')  # 'L'etter, 'u'ppercase
+'Lu'
+>>> unicodedata.bidirectional('\u0660') # 'A'rabic, 'N'umber
+'AN'
+

Footnotes

+1
+

https://www.unicode.org/Public/15.0.0/ucd/NameAliases.txt

+2
+

https://www.unicode.org/Public/15.0.0/ucd/NamedSequences.txt

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/unicodedata.html +

+
diff --git a/devdocs/python~3.12/library%2Funittest.html b/devdocs/python~3.12/library%2Funittest.html new file mode 100644 index 00000000..e21aa765 --- /dev/null +++ b/devdocs/python~3.12/library%2Funittest.html @@ -0,0 +1,761 @@ +

unittest — Unit testing framework

Source code: Lib/unittest/__init__.py

(If you are already familiar with the basic concepts of testing, you might want to skip to the list of assert methods.)

The unittest unit testing framework was originally inspired by JUnit and has a similar flavor as major unit testing frameworks in other languages. It supports test automation, sharing of setup and shutdown code for tests, aggregation of tests into collections, and independence of the tests from the reporting framework.

To achieve this, unittest supports some important concepts in an object-oriented way:

test fixture
+
+

A test fixture represents the preparation needed to perform one or more tests, and any associated cleanup actions. This may involve, for example, creating temporary or proxy databases, directories, or starting a server process.

test case
+
+

A test case is the individual unit of testing. It checks for a specific response to a particular set of inputs. unittest provides a base class, TestCase, which may be used to create new test cases.

test suite
+
+

A test suite is a collection of test cases, test suites, or both. It is used to aggregate tests that should be executed together.

test runner
+
+

A test runner is a component which orchestrates the execution of tests and provides the outcome to the user. The runner may use a graphical interface, a textual interface, or return a special value to indicate the results of executing the tests.

See also

+Module doctest +
+
+

Another test-support module with a very different flavor.

Simple Smalltalk Testing: With Patterns
+
+

Kent Beck’s original paper on testing frameworks using the pattern shared by unittest.

pytest
+
+

Third-party unittest framework with a lighter-weight syntax for writing tests. For example, assert func(10) == 42.

The Python Testing Tools Taxonomy
+
+

An extensive list of Python testing tools including functional testing frameworks and mock object libraries.

Testing in Python Mailing List
+
+

A special-interest-group for discussion of testing, and testing tools, in Python.

The script Tools/unittestgui/unittestgui.py in the Python source distribution is a GUI tool for test discovery and execution. This is intended largely for ease of use for those new to unit testing. For production environments it is recommended that tests be driven by a continuous integration system such as Buildbot, Jenkins, GitHub Actions, or AppVeyor.

Basic example

The unittest module provides a rich set of tools for constructing and running tests. This section demonstrates that a small subset of the tools suffice to meet the needs of most users.

Here is a short script to test three string methods:

import unittest
+
+class TestStringMethods(unittest.TestCase):
+
+    def test_upper(self):
+        self.assertEqual('foo'.upper(), 'FOO')
+
+    def test_isupper(self):
+        self.assertTrue('FOO'.isupper())
+        self.assertFalse('Foo'.isupper())
+
+    def test_split(self):
+        s = 'hello world'
+        self.assertEqual(s.split(), ['hello', 'world'])
+        # check that s.split fails when the separator is not a string
+        with self.assertRaises(TypeError):
+            s.split(2)
+
+if __name__ == '__main__':
+    unittest.main()
+

A testcase is created by subclassing unittest.TestCase. The three individual tests are defined with methods whose names start with the letters test. This naming convention informs the test runner about which methods represent tests.

The crux of each test is a call to assertEqual() to check for an expected result; assertTrue() or assertFalse() to verify a condition; or assertRaises() to verify that a specific exception gets raised. These methods are used instead of the assert statement so the test runner can accumulate all test results and produce a report.

The setUp() and tearDown() methods allow you to define instructions that will be executed before and after each test method. They are covered in more detail in the section Organizing test code.

The final block shows a simple way to run the tests. unittest.main() provides a command-line interface to the test script. When run from the command line, the above script produces an output that looks like this:

...
+----------------------------------------------------------------------
+Ran 3 tests in 0.000s
+
+OK
+

Passing the -v option to your test script will instruct unittest.main() to enable a higher level of verbosity, and produce the following output:

test_isupper (__main__.TestStringMethods.test_isupper) ... ok
+test_split (__main__.TestStringMethods.test_split) ... ok
+test_upper (__main__.TestStringMethods.test_upper) ... ok
+
+----------------------------------------------------------------------
+Ran 3 tests in 0.001s
+
+OK
+

The above examples show the most commonly used unittest features which are sufficient to meet many everyday testing needs. The remainder of the documentation explores the full feature set from first principles.

Changed in version 3.11: The behavior of returning a value from a test method (other than the default None value), is now deprecated.

Command-Line Interface

The unittest module can be used from the command line to run tests from modules, classes or even individual test methods:

python -m unittest test_module1 test_module2
+python -m unittest test_module.TestClass
+python -m unittest test_module.TestClass.test_method
+

You can pass in a list with any combination of module names, and fully qualified class or method names.

Test modules can be specified by file path as well:

python -m unittest tests/test_something.py
+

This allows you to use the shell filename completion to specify the test module. The file specified must still be importable as a module. The path is converted to a module name by removing the ‘.py’ and converting path separators into ‘.’. If you want to execute a test file that isn’t importable as a module you should execute the file directly instead.

You can run tests with more detail (higher verbosity) by passing in the -v flag:

python -m unittest -v test_module
+

When executed without arguments Test Discovery is started:

python -m unittest
+

For a list of all the command-line options:

python -m unittest -h
+

Changed in version 3.2: In earlier versions it was only possible to run individual test methods and not modules or classes.

Command-line options

unittest supports these command-line options:

+-b, --buffer
+

The standard output and standard error streams are buffered during the test run. Output during a passing test is discarded. Output is echoed normally on test fail or error and is added to the failure messages.

+
+-c, --catch
+

Control-C during the test run waits for the current test to end and then reports all the results so far. A second Control-C raises the normal KeyboardInterrupt exception.

See Signal Handling for the functions that provide this functionality.

+
+-f, --failfast
+

Stop the test run on the first error or failure.

+
+-k
+

Only run test methods and classes that match the pattern or substring. This option may be used multiple times, in which case all test cases that match any of the given patterns are included.

Patterns that contain a wildcard character (*) are matched against the test name using fnmatch.fnmatchcase(); otherwise simple case-sensitive substring matching is used.

Patterns are matched against the fully qualified test method name as imported by the test loader.

For example, -k foo matches foo_tests.SomeTest.test_something, bar_tests.SomeTest.test_foo, but not bar_tests.FooTest.test_something.

+
+--locals
+

Show local variables in tracebacks.

+
+--durations N
+

Show the N slowest test cases (N=0 for all).

+

New in version 3.2: The command-line options -b, -c and -f were added.

New in version 3.5: The command-line option --locals.

New in version 3.7: The command-line option -k.

New in version 3.12: The command-line option --durations.

The command line can also be used for test discovery, for running all of the tests in a project or just a subset.

Test Discovery

New in version 3.2.

Unittest supports simple test discovery. In order to be compatible with test discovery, all of the test files must be modules or packages importable from the top-level directory of the project (this means that their filenames must be valid identifiers).

Test discovery is implemented in TestLoader.discover(), but can also be used from the command line. The basic command-line usage is:

cd project_directory
+python -m unittest discover
+

Note

As a shortcut, python -m unittest is the equivalent of python -m unittest discover. If you want to pass arguments to test discovery the discover sub-command must be used explicitly.

The discover sub-command has the following options:

+-v, --verbose
+

Verbose output

+
+-s, --start-directory directory
+

Directory to start discovery (. default)

+
+-p, --pattern pattern
+

Pattern to match test files (test*.py default)

+
+-t, --top-level-directory directory
+

Top level directory of project (defaults to start directory)

+

The -s, -p, and -t options can be passed in as positional arguments in that order. The following two command lines are equivalent:

python -m unittest discover -s project_directory -p "*_test.py"
+python -m unittest discover project_directory "*_test.py"
+

As well as being a path it is possible to pass a package name, for example myproject.subpackage.test, as the start directory. The package name you supply will then be imported and its location on the filesystem will be used as the start directory.

Caution

Test discovery loads tests by importing them. Once test discovery has found all the test files from the start directory you specify it turns the paths into package names to import. For example foo/bar/baz.py will be imported as foo.bar.baz.

If you have a package installed globally and attempt test discovery on a different copy of the package then the import could happen from the wrong place. If this happens test discovery will warn you and exit.

If you supply the start directory as a package name rather than a path to a directory then discover assumes that whichever location it imports from is the location you intended, so you will not get the warning.

Test modules and packages can customize test loading and discovery by through the load_tests protocol.

Changed in version 3.4: Test discovery supports namespace packages for the start directory. Note that you need to specify the top level directory too (e.g. python -m unittest discover -s root/namespace -t root).

Changed in version 3.11: unittest dropped the namespace packages support in Python 3.11. It has been broken since Python 3.7. Start directory and subdirectories containing tests must be regular package that have __init__.py file.

Directories containing start directory still can be a namespace package. In this case, you need to specify start directory as dotted package name, and target directory explicitly. For example:

# proj/  <-- current directory
+#   namespace/
+#     mypkg/
+#       __init__.py
+#       test_mypkg.py
+
+python -m unittest discover -s namespace.mypkg -t .
+

Organizing test code

The basic building blocks of unit testing are test cases — single scenarios that must be set up and checked for correctness. In unittest, test cases are represented by unittest.TestCase instances. To make your own test cases you must write subclasses of TestCase or use FunctionTestCase.

The testing code of a TestCase instance should be entirely self contained, such that it can be run either in isolation or in arbitrary combination with any number of other test cases.

The simplest TestCase subclass will simply implement a test method (i.e. a method whose name starts with test) in order to perform specific testing code:

import unittest
+
+class DefaultWidgetSizeTestCase(unittest.TestCase):
+    def test_default_widget_size(self):
+        widget = Widget('The widget')
+        self.assertEqual(widget.size(), (50, 50))
+

Note that in order to test something, we use one of the assert* methods provided by the TestCase base class. If the test fails, an exception will be raised with an explanatory message, and unittest will identify the test case as a failure. Any other exceptions will be treated as errors.

Tests can be numerous, and their set-up can be repetitive. Luckily, we can factor out set-up code by implementing a method called setUp(), which the testing framework will automatically call for every single test we run:

import unittest
+
+class WidgetTestCase(unittest.TestCase):
+    def setUp(self):
+        self.widget = Widget('The widget')
+
+    def test_default_widget_size(self):
+        self.assertEqual(self.widget.size(), (50,50),
+                         'incorrect default size')
+
+    def test_widget_resize(self):
+        self.widget.resize(100,150)
+        self.assertEqual(self.widget.size(), (100,150),
+                         'wrong size after resize')
+

Note

The order in which the various tests will be run is determined by sorting the test method names with respect to the built-in ordering for strings.

If the setUp() method raises an exception while the test is running, the framework will consider the test to have suffered an error, and the test method will not be executed.

Similarly, we can provide a tearDown() method that tidies up after the test method has been run:

import unittest
+
+class WidgetTestCase(unittest.TestCase):
+    def setUp(self):
+        self.widget = Widget('The widget')
+
+    def tearDown(self):
+        self.widget.dispose()
+

If setUp() succeeded, tearDown() will be run whether the test method succeeded or not.

Such a working environment for the testing code is called a test fixture. A new TestCase instance is created as a unique test fixture used to execute each individual test method. Thus setUp(), tearDown(), and __init__() will be called once per test.

It is recommended that you use TestCase implementations to group tests together according to the features they test. unittest provides a mechanism for this: the test suite, represented by unittest’s TestSuite class. In most cases, calling unittest.main() will do the right thing and collect all the module’s test cases for you and execute them.

However, should you want to customize the building of your test suite, you can do it yourself:

def suite():
+    suite = unittest.TestSuite()
+    suite.addTest(WidgetTestCase('test_default_widget_size'))
+    suite.addTest(WidgetTestCase('test_widget_resize'))
+    return suite
+
+if __name__ == '__main__':
+    runner = unittest.TextTestRunner()
+    runner.run(suite())
+

You can place the definitions of test cases and test suites in the same modules as the code they are to test (such as widget.py), but there are several advantages to placing the test code in a separate module, such as test_widget.py:

  • The test module can be run standalone from the command line.
  • The test code can more easily be separated from shipped code.
  • There is less temptation to change test code to fit the code it tests without a good reason.
  • Test code should be modified much less frequently than the code it tests.
  • Tested code can be refactored more easily.
  • Tests for modules written in C must be in separate modules anyway, so why not be consistent?
  • If the testing strategy changes, there is no need to change the source code.

Re-using old test code

Some users will find that they have existing test code that they would like to run from unittest, without converting every old test function to a TestCase subclass.

For this reason, unittest provides a FunctionTestCase class. This subclass of TestCase can be used to wrap an existing test function. Set-up and tear-down functions can also be provided.

Given the following test function:

def testSomething():
+    something = makeSomething()
+    assert something.name is not None
+    # ...
+

one can create an equivalent test case instance as follows, with optional set-up and tear-down methods:

testcase = unittest.FunctionTestCase(testSomething,
+                                     setUp=makeSomethingDB,
+                                     tearDown=deleteSomethingDB)
+

Note

Even though FunctionTestCase can be used to quickly convert an existing test base over to a unittest-based system, this approach is not recommended. Taking the time to set up proper TestCase subclasses will make future test refactorings infinitely easier.

In some cases, the existing tests may have been written using the doctest module. If so, doctest provides a DocTestSuite class that can automatically build unittest.TestSuite instances from the existing doctest-based tests.

Skipping tests and expected failures

New in version 3.1.

Unittest supports skipping individual test methods and even whole classes of tests. In addition, it supports marking a test as an “expected failure,” a test that is broken and will fail, but shouldn’t be counted as a failure on a TestResult.

Skipping a test is simply a matter of using the skip() decorator or one of its conditional variants, calling TestCase.skipTest() within a setUp() or test method, or raising SkipTest directly.

Basic skipping looks like this:

class MyTestCase(unittest.TestCase):
+
+    @unittest.skip("demonstrating skipping")
+    def test_nothing(self):
+        self.fail("shouldn't happen")
+
+    @unittest.skipIf(mylib.__version__ < (1, 3),
+                     "not supported in this library version")
+    def test_format(self):
+        # Tests that work for only a certain version of the library.
+        pass
+
+    @unittest.skipUnless(sys.platform.startswith("win"), "requires Windows")
+    def test_windows_support(self):
+        # windows specific testing code
+        pass
+
+    def test_maybe_skipped(self):
+        if not external_resource_available():
+            self.skipTest("external resource not available")
+        # test code that depends on the external resource
+        pass
+

This is the output of running the example above in verbose mode:

test_format (__main__.MyTestCase.test_format) ... skipped 'not supported in this library version'
+test_nothing (__main__.MyTestCase.test_nothing) ... skipped 'demonstrating skipping'
+test_maybe_skipped (__main__.MyTestCase.test_maybe_skipped) ... skipped 'external resource not available'
+test_windows_support (__main__.MyTestCase.test_windows_support) ... skipped 'requires Windows'
+
+----------------------------------------------------------------------
+Ran 4 tests in 0.005s
+
+OK (skipped=4)
+

Classes can be skipped just like methods:

@unittest.skip("showing class skipping")
+class MySkippedTestCase(unittest.TestCase):
+    def test_not_run(self):
+        pass
+

TestCase.setUp() can also skip the test. This is useful when a resource that needs to be set up is not available.

Expected failures use the expectedFailure() decorator.

class ExpectedFailureTestCase(unittest.TestCase):
+    @unittest.expectedFailure
+    def test_fail(self):
+        self.assertEqual(1, 0, "broken")
+

It’s easy to roll your own skipping decorators by making a decorator that calls skip() on the test when it wants it to be skipped. This decorator skips the test unless the passed object has a certain attribute:

def skipUnlessHasattr(obj, attr):
+    if hasattr(obj, attr):
+        return lambda func: func
+    return unittest.skip("{!r} doesn't have {!r}".format(obj, attr))
+

The following decorators and exception implement test skipping and expected failures:

+@unittest.skip(reason)
+

Unconditionally skip the decorated test. reason should describe why the test is being skipped.

+
+@unittest.skipIf(condition, reason)
+

Skip the decorated test if condition is true.

+
+@unittest.skipUnless(condition, reason)
+

Skip the decorated test unless condition is true.

+
+@unittest.expectedFailure
+

Mark the test as an expected failure or error. If the test fails or errors in the test function itself (rather than in one of the test fixture methods) then it will be considered a success. If the test passes, it will be considered a failure.

+
+exception unittest.SkipTest(reason)
+

This exception is raised to skip a test.

Usually you can use TestCase.skipTest() or one of the skipping decorators instead of raising this directly.

+

Skipped tests will not have setUp() or tearDown() run around them. Skipped classes will not have setUpClass() or tearDownClass() run. Skipped modules will not have setUpModule() or tearDownModule() run.

Distinguishing test iterations using subtests

New in version 3.4.

When there are very small differences among your tests, for instance some parameters, unittest allows you to distinguish them inside the body of a test method using the subTest() context manager.

For example, the following test:

class NumbersTest(unittest.TestCase):
+
+    def test_even(self):
+        """
+        Test that numbers between 0 and 5 are all even.
+        """
+        for i in range(0, 6):
+            with self.subTest(i=i):
+                self.assertEqual(i % 2, 0)
+

will produce the following output:

======================================================================
+FAIL: test_even (__main__.NumbersTest.test_even) (i=1)
+Test that numbers between 0 and 5 are all even.
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "subtests.py", line 11, in test_even
+    self.assertEqual(i % 2, 0)
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^
+AssertionError: 1 != 0
+
+======================================================================
+FAIL: test_even (__main__.NumbersTest.test_even) (i=3)
+Test that numbers between 0 and 5 are all even.
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "subtests.py", line 11, in test_even
+    self.assertEqual(i % 2, 0)
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^
+AssertionError: 1 != 0
+
+======================================================================
+FAIL: test_even (__main__.NumbersTest.test_even) (i=5)
+Test that numbers between 0 and 5 are all even.
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "subtests.py", line 11, in test_even
+    self.assertEqual(i % 2, 0)
+    ^^^^^^^^^^^^^^^^^^^^^^^^^^
+AssertionError: 1 != 0
+

Without using a subtest, execution would stop after the first failure, and the error would be less easy to diagnose because the value of i wouldn’t be displayed:

======================================================================
+FAIL: test_even (__main__.NumbersTest.test_even)
+----------------------------------------------------------------------
+Traceback (most recent call last):
+  File "subtests.py", line 32, in test_even
+    self.assertEqual(i % 2, 0)
+AssertionError: 1 != 0
+

Classes and functions

This section describes in depth the API of unittest.

Test cases

+class unittest.TestCase(methodName='runTest')
+

Instances of the TestCase class represent the logical test units in the unittest universe. This class is intended to be used as a base class, with specific tests being implemented by concrete subclasses. This class implements the interface needed by the test runner to allow it to drive the tests, and methods that the test code can use to check for and report various kinds of failure.

Each instance of TestCase will run a single base method: the method named methodName. In most uses of TestCase, you will neither change the methodName nor reimplement the default runTest() method.

Changed in version 3.2: TestCase can be instantiated successfully without providing a methodName. This makes it easier to experiment with TestCase from the interactive interpreter.

TestCase instances provide three groups of methods: one group used to run the test, another used by the test implementation to check conditions and report failures, and some inquiry methods allowing information about the test itself to be gathered.

Methods in the first group (running the test) are:

+setUp()
+

Method called to prepare the test fixture. This is called immediately before calling the test method; other than AssertionError or SkipTest, any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing.

+
+tearDown()
+

Method called immediately after the test method has been called and the result recorded. This is called even if the test method raised an exception, so the implementation in subclasses may need to be particularly careful about checking internal state. Any exception, other than AssertionError or SkipTest, raised by this method will be considered an additional error rather than a test failure (thus increasing the total number of reported errors). This method will only be called if the setUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing.

+
+setUpClass()
+

A class method called before tests in an individual class are run. setUpClass is called with the class as the only argument and must be decorated as a classmethod():

@classmethod
+def setUpClass(cls):
+    ...
+

See Class and Module Fixtures for more details.

New in version 3.2.

+
+tearDownClass()
+

A class method called after tests in an individual class have run. tearDownClass is called with the class as the only argument and must be decorated as a classmethod():

@classmethod
+def tearDownClass(cls):
+    ...
+

See Class and Module Fixtures for more details.

New in version 3.2.

+
+run(result=None)
+

Run the test, collecting the result into the TestResult object passed as result. If result is omitted or None, a temporary result object is created (by calling the defaultTestResult() method) and used. The result object is returned to run()’s caller.

The same effect may be had by simply calling the TestCase instance.

Changed in version 3.3: Previous versions of run did not return the result. Neither did calling an instance.

+
+skipTest(reason)
+

Calling this during a test method or setUp() skips the current test. See Skipping tests and expected failures for more information.

New in version 3.1.

+
+subTest(msg=None, **params)
+

Return a context manager which executes the enclosed code block as a subtest. msg and params are optional, arbitrary values which are displayed whenever a subtest fails, allowing you to identify them clearly.

A test case can contain any number of subtest declarations, and they can be arbitrarily nested.

See Distinguishing test iterations using subtests for more information.

New in version 3.4.

+
+debug()
+

Run the test without collecting the result. This allows exceptions raised by the test to be propagated to the caller, and can be used to support running tests under a debugger.

+

The TestCase class provides several assert methods to check for and report failures. The following table lists the most commonly used methods (see the tables below for more assert methods):

+ + + + + + + + + + + + +

Method

Checks that

New in

assertEqual(a, b)

a == b

assertNotEqual(a, b)

a != b

assertTrue(x)

bool(x) is True

assertFalse(x)

bool(x) is False

assertIs(a, b)

a is b

3.1

assertIsNot(a, b)

a is not b

3.1

assertIsNone(x)

x is None

3.1

assertIsNotNone(x)

x is not None

3.1

assertIn(a, b)

a in b

3.1

assertNotIn(a, b)

a not in b

3.1

assertIsInstance(a, b)

isinstance(a, b)

3.2

assertNotIsInstance(a, b)

not isinstance(a, b)

3.2

All the assert methods accept a msg argument that, if specified, is used as the error message on failure (see also longMessage). Note that the msg keyword argument can be passed to assertRaises(), assertRaisesRegex(), assertWarns(), assertWarnsRegex() only when they are used as a context manager.

+assertEqual(first, second, msg=None)
+

Test that first and second are equal. If the values do not compare equal, the test will fail.

In addition, if first and second are the exact same type and one of list, tuple, dict, set, frozenset or str or any type that a subclass registers with addTypeEqualityFunc() the type-specific equality function will be called in order to generate a more useful default error message (see also the list of type-specific methods).

Changed in version 3.1: Added the automatic calling of type-specific equality function.

Changed in version 3.2: assertMultiLineEqual() added as the default type equality function for comparing strings.

+
+assertNotEqual(first, second, msg=None)
+

Test that first and second are not equal. If the values do compare equal, the test will fail.

+
+assertTrue(expr, msg=None)
+assertFalse(expr, msg=None)
+

Test that expr is true (or false).

Note that this is equivalent to bool(expr) is True and not to expr +is True (use assertIs(expr, True) for the latter). This method should also be avoided when more specific methods are available (e.g. assertEqual(a, b) instead of assertTrue(a == b)), because they provide a better error message in case of failure.

+
+assertIs(first, second, msg=None)
+assertIsNot(first, second, msg=None)
+

Test that first and second are (or are not) the same object.

New in version 3.1.

+
+assertIsNone(expr, msg=None)
+assertIsNotNone(expr, msg=None)
+

Test that expr is (or is not) None.

New in version 3.1.

+
+assertIn(member, container, msg=None)
+assertNotIn(member, container, msg=None)
+

Test that member is (or is not) in container.

New in version 3.1.

+
+assertIsInstance(obj, cls, msg=None)
+assertNotIsInstance(obj, cls, msg=None)
+

Test that obj is (or is not) an instance of cls (which can be a class or a tuple of classes, as supported by isinstance()). To check for the exact type, use assertIs(type(obj), cls).

New in version 3.2.

+

It is also possible to check the production of exceptions, warnings, and log messages using the following methods:

+ + + + + + +

Method

Checks that

New in

assertRaises(exc, fun, *args, **kwds)

fun(*args, **kwds) raises exc

assertRaisesRegex(exc, r, fun, *args, **kwds)

fun(*args, **kwds) raises exc and the message matches regex r

3.1

assertWarns(warn, fun, *args, **kwds)

fun(*args, **kwds) raises warn

3.2

assertWarnsRegex(warn, r, fun, *args, **kwds)

fun(*args, **kwds) raises warn and the message matches regex r

3.2

assertLogs(logger, level)

The with block logs on logger with minimum level

3.4

assertNoLogs(logger, level)

 +
+The with block does not log on
+
+

logger with minimum level

3.10
+assertRaises(exception, callable, *args, **kwds)
assertRaises(exception, *, msg=None) +
+

Test that an exception is raised when callable is called with any positional or keyword arguments that are also passed to assertRaises(). The test passes if exception is raised, is an error if another exception is raised, or fails if no exception is raised. To catch any of a group of exceptions, a tuple containing the exception classes may be passed as exception.

If only the exception and possibly the msg arguments are given, return a context manager so that the code under test can be written inline rather than as a function:

with self.assertRaises(SomeException):
+    do_something()
+

When used as a context manager, assertRaises() accepts the additional keyword argument msg.

The context manager will store the caught exception object in its exception attribute. This can be useful if the intention is to perform additional checks on the exception raised:

with self.assertRaises(SomeException) as cm:
+    do_something()
+
+the_exception = cm.exception
+self.assertEqual(the_exception.error_code, 3)
+

Changed in version 3.1: Added the ability to use assertRaises() as a context manager.

Changed in version 3.2: Added the exception attribute.

Changed in version 3.3: Added the msg keyword argument when used as a context manager.

+
+assertRaisesRegex(exception, regex, callable, *args, **kwds)
assertRaisesRegex(exception, regex, *, msg=None) +
+

Like assertRaises() but also tests that regex matches on the string representation of the raised exception. regex may be a regular expression object or a string containing a regular expression suitable for use by re.search(). Examples:

self.assertRaisesRegex(ValueError, "invalid literal for.*XYZ'$",
+                       int, 'XYZ')
+

or:

with self.assertRaisesRegex(ValueError, 'literal'):
+   int('XYZ')
+

New in version 3.1: Added under the name assertRaisesRegexp.

Changed in version 3.2: Renamed to assertRaisesRegex().

Changed in version 3.3: Added the msg keyword argument when used as a context manager.

+
+assertWarns(warning, callable, *args, **kwds)
assertWarns(warning, *, msg=None) +
+

Test that a warning is triggered when callable is called with any positional or keyword arguments that are also passed to assertWarns(). The test passes if warning is triggered and fails if it isn’t. Any exception is an error. To catch any of a group of warnings, a tuple containing the warning classes may be passed as warnings.

If only the warning and possibly the msg arguments are given, return a context manager so that the code under test can be written inline rather than as a function:

with self.assertWarns(SomeWarning):
+    do_something()
+

When used as a context manager, assertWarns() accepts the additional keyword argument msg.

The context manager will store the caught warning object in its warning attribute, and the source line which triggered the warnings in the filename and lineno attributes. This can be useful if the intention is to perform additional checks on the warning caught:

with self.assertWarns(SomeWarning) as cm:
+    do_something()
+
+self.assertIn('myfile.py', cm.filename)
+self.assertEqual(320, cm.lineno)
+

This method works regardless of the warning filters in place when it is called.

New in version 3.2.

Changed in version 3.3: Added the msg keyword argument when used as a context manager.

+
+assertWarnsRegex(warning, regex, callable, *args, **kwds)
assertWarnsRegex(warning, regex, *, msg=None) +
+

Like assertWarns() but also tests that regex matches on the message of the triggered warning. regex may be a regular expression object or a string containing a regular expression suitable for use by re.search(). Example:

self.assertWarnsRegex(DeprecationWarning,
+                      r'legacy_function\(\) is deprecated',
+                      legacy_function, 'XYZ')
+

or:

with self.assertWarnsRegex(RuntimeWarning, 'unsafe frobnicating'):
+    frobnicate('/etc/passwd')
+

New in version 3.2.

Changed in version 3.3: Added the msg keyword argument when used as a context manager.

+
+assertLogs(logger=None, level=None)
+

A context manager to test that at least one message is logged on the logger or one of its children, with at least the given level.

If given, logger should be a logging.Logger object or a str giving the name of a logger. The default is the root logger, which will catch all messages that were not blocked by a non-propagating descendent logger.

If given, level should be either a numeric logging level or its string equivalent (for example either "ERROR" or logging.ERROR). The default is logging.INFO.

The test passes if at least one message emitted inside the with block matches the logger and level conditions, otherwise it fails.

The object returned by the context manager is a recording helper which keeps tracks of the matching log messages. It has two attributes:

+records
+

A list of logging.LogRecord objects of the matching log messages.

+
+output
+

A list of str objects with the formatted output of matching messages.

+

Example:

with self.assertLogs('foo', level='INFO') as cm:
+    logging.getLogger('foo').info('first message')
+    logging.getLogger('foo.bar').error('second message')
+self.assertEqual(cm.output, ['INFO:foo:first message',
+                             'ERROR:foo.bar:second message'])
+

New in version 3.4.

+
+assertNoLogs(logger=None, level=None)
+

A context manager to test that no messages are logged on the logger or one of its children, with at least the given level.

If given, logger should be a logging.Logger object or a str giving the name of a logger. The default is the root logger, which will catch all messages.

If given, level should be either a numeric logging level or its string equivalent (for example either "ERROR" or logging.ERROR). The default is logging.INFO.

Unlike assertLogs(), nothing will be returned by the context manager.

New in version 3.10.

+

There are also other methods used to perform more specific checks, such as:

+ + + + + + + + + +

Method

Checks that

New in

assertAlmostEqual(a, b)

round(a-b, 7) == 0

assertNotAlmostEqual(a, b)

round(a-b, 7) != 0

assertGreater(a, b)

a > b

3.1

assertGreaterEqual(a, b)

a >= b

3.1

assertLess(a, b)

a < b

3.1

assertLessEqual(a, b)

a <= b

3.1

assertRegex(s, r)

r.search(s)

3.1

assertNotRegex(s, r)

not r.search(s)

3.2

assertCountEqual(a, b)

a and b have the same elements in the same number, regardless of their order.

3.2
+assertAlmostEqual(first, second, places=7, msg=None, delta=None)
+assertNotAlmostEqual(first, second, places=7, msg=None, delta=None)
+

Test that first and second are approximately (or not approximately) equal by computing the difference, rounding to the given number of decimal places (default 7), and comparing to zero. Note that these methods round the values to the given number of decimal places (i.e. like the round() function) and not significant digits.

If delta is supplied instead of places then the difference between first and second must be less or equal to (or greater than) delta.

Supplying both delta and places raises a TypeError.

Changed in version 3.2: assertAlmostEqual() automatically considers almost equal objects that compare equal. assertNotAlmostEqual() automatically fails if the objects compare equal. Added the delta keyword argument.

+
+assertGreater(first, second, msg=None)
+assertGreaterEqual(first, second, msg=None)
+assertLess(first, second, msg=None)
+assertLessEqual(first, second, msg=None)
+

Test that first is respectively >, >=, < or <= than second depending on the method name. If not, the test will fail:

>>> self.assertGreaterEqual(3, 4)
+AssertionError: "3" unexpectedly not greater than or equal to "4"
+

New in version 3.1.

+
+assertRegex(text, regex, msg=None)
+assertNotRegex(text, regex, msg=None)
+

Test that a regex search matches (or does not match) text. In case of failure, the error message will include the pattern and the text (or the pattern and the part of text that unexpectedly matched). regex may be a regular expression object or a string containing a regular expression suitable for use by re.search().

New in version 3.1: Added under the name assertRegexpMatches.

Changed in version 3.2: The method assertRegexpMatches() has been renamed to assertRegex().

New in version 3.2: assertNotRegex().

+
+assertCountEqual(first, second, msg=None)
+

Test that sequence first contains the same elements as second, regardless of their order. When they don’t, an error message listing the differences between the sequences will be generated.

Duplicate elements are not ignored when comparing first and second. It verifies whether each element has the same count in both sequences. Equivalent to: assertEqual(Counter(list(first)), Counter(list(second))) but works with sequences of unhashable objects as well.

New in version 3.2.

+

The assertEqual() method dispatches the equality check for objects of the same type to different type-specific methods. These methods are already implemented for most of the built-in types, but it’s also possible to register new methods using addTypeEqualityFunc():

+addTypeEqualityFunc(typeobj, function)
+

Registers a type-specific method called by assertEqual() to check if two objects of exactly the same typeobj (not subclasses) compare equal. function must take two positional arguments and a third msg=None keyword argument just as assertEqual() does. It must raise self.failureException(msg) when inequality between the first two parameters is detected – possibly providing useful information and explaining the inequalities in details in the error message.

New in version 3.1.

+

The list of type-specific methods automatically used by assertEqual() are summarized in the following table. Note that it’s usually not necessary to invoke these methods directly.

+ + + + + + +

Method

Used to compare

New in

assertMultiLineEqual(a, b)

strings

3.1

assertSequenceEqual(a, b)

sequences

3.1

assertListEqual(a, b)

lists

3.1

assertTupleEqual(a, b)

tuples

3.1

assertSetEqual(a, b)

sets or frozensets

3.1

assertDictEqual(a, b)

dicts

3.1
+assertMultiLineEqual(first, second, msg=None)
+

Test that the multiline string first is equal to the string second. When not equal a diff of the two strings highlighting the differences will be included in the error message. This method is used by default when comparing strings with assertEqual().

New in version 3.1.

+
+assertSequenceEqual(first, second, msg=None, seq_type=None)
+

Tests that two sequences are equal. If a seq_type is supplied, both first and second must be instances of seq_type or a failure will be raised. If the sequences are different an error message is constructed that shows the difference between the two.

This method is not called directly by assertEqual(), but it’s used to implement assertListEqual() and assertTupleEqual().

New in version 3.1.

+
+assertListEqual(first, second, msg=None)
+assertTupleEqual(first, second, msg=None)
+

Tests that two lists or tuples are equal. If not, an error message is constructed that shows only the differences between the two. An error is also raised if either of the parameters are of the wrong type. These methods are used by default when comparing lists or tuples with assertEqual().

New in version 3.1.

+
+assertSetEqual(first, second, msg=None)
+

Tests that two sets are equal. If not, an error message is constructed that lists the differences between the sets. This method is used by default when comparing sets or frozensets with assertEqual().

Fails if either of first or second does not have a set.difference() method.

New in version 3.1.

+
+assertDictEqual(first, second, msg=None)
+

Test that two dictionaries are equal. If not, an error message is constructed that shows the differences in the dictionaries. This method will be used by default to compare dictionaries in calls to assertEqual().

New in version 3.1.

+

Finally the TestCase provides the following methods and attributes:

+fail(msg=None)
+

Signals a test failure unconditionally, with msg or None for the error message.

+
+failureException
+

This class attribute gives the exception raised by the test method. If a test framework needs to use a specialized exception, possibly to carry additional information, it must subclass this exception in order to “play fair” with the framework. The initial value of this attribute is AssertionError.

+
+longMessage
+

This class attribute determines what happens when a custom failure message is passed as the msg argument to an assertXYY call that fails. True is the default value. In this case, the custom message is appended to the end of the standard failure message. When set to False, the custom message replaces the standard message.

The class setting can be overridden in individual test methods by assigning an instance attribute, self.longMessage, to True or False before calling the assert methods.

The class setting gets reset before each test call.

New in version 3.1.

+
+maxDiff
+

This attribute controls the maximum length of diffs output by assert methods that report diffs on failure. It defaults to 80*8 characters. Assert methods affected by this attribute are assertSequenceEqual() (including all the sequence comparison methods that delegate to it), assertDictEqual() and assertMultiLineEqual().

Setting maxDiff to None means that there is no maximum length of diffs.

New in version 3.2.

+

Testing frameworks can use the following methods to collect information on the test:

+countTestCases()
+

Return the number of tests represented by this test object. For TestCase instances, this will always be 1.

+
+defaultTestResult()
+

Return an instance of the test result class that should be used for this test case class (if no other result instance is provided to the run() method).

For TestCase instances, this will always be an instance of TestResult; subclasses of TestCase should override this as necessary.

+
+id()
+

Return a string identifying the specific test case. This is usually the full name of the test method, including the module and class name.

+
+shortDescription()
+

Returns a description of the test, or None if no description has been provided. The default implementation of this method returns the first line of the test method’s docstring, if available, or None.

Changed in version 3.1: In 3.1 this was changed to add the test name to the short description even in the presence of a docstring. This caused compatibility issues with unittest extensions and adding the test name was moved to the TextTestResult in Python 3.2.

+
+addCleanup(function, /, *args, **kwargs)
+

Add a function to be called after tearDown() to cleanup resources used during the test. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addCleanup() when they are added.

If setUp() fails, meaning that tearDown() is not called, then any cleanup functions added will still be called.

New in version 3.1.

+
+enterContext(cm)
+

Enter the supplied context manager. If successful, also add its __exit__() method as a cleanup function by addCleanup() and return the result of the __enter__() method.

New in version 3.11.

+
+doCleanups()
+

This method is called unconditionally after tearDown(), or after setUp() if setUp() raises an exception.

It is responsible for calling all the cleanup functions added by addCleanup(). If you need cleanup functions to be called prior to tearDown() then you can call doCleanups() yourself.

doCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.

New in version 3.1.

+
+classmethod addClassCleanup(function, /, *args, **kwargs)
+

Add a function to be called after tearDownClass() to cleanup resources used during the test class. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addClassCleanup() when they are added.

If setUpClass() fails, meaning that tearDownClass() is not called, then any cleanup functions added will still be called.

New in version 3.8.

+
+classmethod enterClassContext(cm)
+

Enter the supplied context manager. If successful, also add its __exit__() method as a cleanup function by addClassCleanup() and return the result of the __enter__() method.

New in version 3.11.

+
+classmethod doClassCleanups()
+

This method is called unconditionally after tearDownClass(), or after setUpClass() if setUpClass() raises an exception.

It is responsible for calling all the cleanup functions added by addClassCleanup(). If you need cleanup functions to be called prior to tearDownClass() then you can call doClassCleanups() yourself.

doClassCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.

New in version 3.8.

+
+
+class unittest.IsolatedAsyncioTestCase(methodName='runTest')
+

This class provides an API similar to TestCase and also accepts coroutines as test functions.

New in version 3.8.

+coroutine asyncSetUp()
+

Method called to prepare the test fixture. This is called after setUp(). This is called immediately before calling the test method; other than AssertionError or SkipTest, any exception raised by this method will be considered an error rather than a test failure. The default implementation does nothing.

+
+coroutine asyncTearDown()
+

Method called immediately after the test method has been called and the result recorded. This is called before tearDown(). This is called even if the test method raised an exception, so the implementation in subclasses may need to be particularly careful about checking internal state. Any exception, other than AssertionError or SkipTest, raised by this method will be considered an additional error rather than a test failure (thus increasing the total number of reported errors). This method will only be called if the asyncSetUp() succeeds, regardless of the outcome of the test method. The default implementation does nothing.

+
+addAsyncCleanup(function, /, *args, **kwargs)
+

This method accepts a coroutine that can be used as a cleanup function.

+
+coroutine enterAsyncContext(cm)
+

Enter the supplied asynchronous context manager. If successful, also add its __aexit__() method as a cleanup function by addAsyncCleanup() and return the result of the __aenter__() method.

New in version 3.11.

+
+run(result=None)
+

Sets up a new event loop to run the test, collecting the result into the TestResult object passed as result. If result is omitted or None, a temporary result object is created (by calling the defaultTestResult() method) and used. The result object is returned to run()’s caller. At the end of the test all the tasks in the event loop are cancelled.

+

An example illustrating the order:

from unittest import IsolatedAsyncioTestCase
+
+events = []
+
+
+class Test(IsolatedAsyncioTestCase):
+
+
+    def setUp(self):
+        events.append("setUp")
+
+    async def asyncSetUp(self):
+        self._async_connection = await AsyncConnection()
+        events.append("asyncSetUp")
+
+    async def test_response(self):
+        events.append("test_response")
+        response = await self._async_connection.get("https://example.com")
+        self.assertEqual(response.status_code, 200)
+        self.addAsyncCleanup(self.on_cleanup)
+
+    def tearDown(self):
+        events.append("tearDown")
+
+    async def asyncTearDown(self):
+        await self._async_connection.close()
+        events.append("asyncTearDown")
+
+    async def on_cleanup(self):
+        events.append("cleanup")
+
+if __name__ == "__main__":
+    unittest.main()
+

After running the test, events would contain ["setUp", "asyncSetUp", "test_response", "asyncTearDown", "tearDown", "cleanup"].

+
+class unittest.FunctionTestCase(testFunc, setUp=None, tearDown=None, description=None)
+

This class implements the portion of the TestCase interface which allows the test runner to drive the test, but does not provide the methods which test code can use to check and report errors. This is used to create test cases using legacy test code, allowing it to be integrated into a unittest-based test framework.

+

Grouping tests

+class unittest.TestSuite(tests=())
+

This class represents an aggregation of individual test cases and test suites. The class presents the interface needed by the test runner to allow it to be run as any other test case. Running a TestSuite instance is the same as iterating over the suite, running each test individually.

If tests is given, it must be an iterable of individual test cases or other test suites that will be used to build the suite initially. Additional methods are provided to add test cases and suites to the collection later on.

TestSuite objects behave much like TestCase objects, except they do not actually implement a test. Instead, they are used to aggregate tests into groups of tests that should be run together. Some additional methods are available to add tests to TestSuite instances:

+addTest(test)
+

Add a TestCase or TestSuite to the suite.

+
+addTests(tests)
+

Add all the tests from an iterable of TestCase and TestSuite instances to this test suite.

This is equivalent to iterating over tests, calling addTest() for each element.

+

TestSuite shares the following methods with TestCase:

+run(result)
+

Run the tests associated with this suite, collecting the result into the test result object passed as result. Note that unlike TestCase.run(), TestSuite.run() requires the result object to be passed in.

+
+debug()
+

Run the tests associated with this suite without collecting the result. This allows exceptions raised by the test to be propagated to the caller and can be used to support running tests under a debugger.

+
+countTestCases()
+

Return the number of tests represented by this test object, including all individual tests and sub-suites.

+
+__iter__()
+

Tests grouped by a TestSuite are always accessed by iteration. Subclasses can lazily provide tests by overriding __iter__(). Note that this method may be called several times on a single suite (for example when counting tests or comparing for equality) so the tests returned by repeated iterations before TestSuite.run() must be the same for each call iteration. After TestSuite.run(), callers should not rely on the tests returned by this method unless the caller uses a subclass that overrides TestSuite._removeTestAtIndex() to preserve test references.

Changed in version 3.2: In earlier versions the TestSuite accessed tests directly rather than through iteration, so overriding __iter__() wasn’t sufficient for providing tests.

Changed in version 3.4: In earlier versions the TestSuite held references to each TestCase after TestSuite.run(). Subclasses can restore that behavior by overriding TestSuite._removeTestAtIndex().

+

In the typical usage of a TestSuite object, the run() method is invoked by a TestRunner rather than by the end-user test harness.

+

Loading and running tests

+class unittest.TestLoader
+

The TestLoader class is used to create test suites from classes and modules. Normally, there is no need to create an instance of this class; the unittest module provides an instance that can be shared as unittest.defaultTestLoader. Using a subclass or instance, however, allows customization of some configurable properties.

TestLoader objects have the following attributes:

+errors
+

A list of the non-fatal errors encountered while loading tests. Not reset by the loader at any point. Fatal errors are signalled by the relevant method raising an exception to the caller. Non-fatal errors are also indicated by a synthetic test that will raise the original error when run.

New in version 3.5.

+

TestLoader objects have the following methods:

+loadTestsFromTestCase(testCaseClass)
+

Return a suite of all test cases contained in the TestCase-derived testCaseClass.

A test case instance is created for each method named by getTestCaseNames(). By default these are the method names beginning with test. If getTestCaseNames() returns no methods, but the runTest() method is implemented, a single test case is created for that method instead.

+
+loadTestsFromModule(module, *, pattern=None)
+

Return a suite of all test cases contained in the given module. This method searches module for classes derived from TestCase and creates an instance of the class for each test method defined for the class.

Note

While using a hierarchy of TestCase-derived classes can be convenient in sharing fixtures and helper functions, defining test methods on base classes that are not intended to be instantiated directly does not play well with this method. Doing so, however, can be useful when the fixtures are different and defined in subclasses.

If a module provides a load_tests function it will be called to load the tests. This allows modules to customize test loading. This is the load_tests protocol. The pattern argument is passed as the third argument to load_tests.

Changed in version 3.2: Support for load_tests added.

Changed in version 3.5: Support for a keyword-only argument pattern has been added.

Changed in version 3.12: The undocumented and unofficial use_load_tests parameter has been removed.

+
+loadTestsFromName(name, module=None)
+

Return a suite of all test cases given a string specifier.

The specifier name is a “dotted name” that may resolve either to a module, a test case class, a test method within a test case class, a TestSuite instance, or a callable object which returns a TestCase or TestSuite instance. These checks are applied in the order listed here; that is, a method on a possible test case class will be picked up as “a test method within a test case class”, rather than “a callable object”.

For example, if you have a module SampleTests containing a TestCase-derived class SampleTestCase with three test methods (test_one(), test_two(), and test_three()), the specifier 'SampleTests.SampleTestCase' would cause this method to return a suite which will run all three test methods. Using the specifier 'SampleTests.SampleTestCase.test_two' would cause it to return a test suite which will run only the test_two() test method. The specifier can refer to modules and packages which have not been imported; they will be imported as a side-effect.

The method optionally resolves name relative to the given module.

Changed in version 3.5: If an ImportError or AttributeError occurs while traversing name then a synthetic test that raises that error when run will be returned. These errors are included in the errors accumulated by self.errors.

+
+loadTestsFromNames(names, module=None)
+

Similar to loadTestsFromName(), but takes a sequence of names rather than a single name. The return value is a test suite which supports all the tests defined for each name.

+
+getTestCaseNames(testCaseClass)
+

Return a sorted sequence of method names found within testCaseClass; this should be a subclass of TestCase.

+
+discover(start_dir, pattern='test*.py', top_level_dir=None)
+

Find all the test modules by recursing into subdirectories from the specified start directory, and return a TestSuite object containing them. Only test files that match pattern will be loaded. (Using shell style pattern matching.) Only module names that are importable (i.e. are valid Python identifiers) will be loaded.

All test modules must be importable from the top level of the project. If the start directory is not the top level directory then the top level directory must be specified separately.

If importing a module fails, for example due to a syntax error, then this will be recorded as a single error and discovery will continue. If the import failure is due to SkipTest being raised, it will be recorded as a skip instead of an error.

If a package (a directory containing a file named __init__.py) is found, the package will be checked for a load_tests function. If this exists then it will be called package.load_tests(loader, tests, pattern). Test discovery takes care to ensure that a package is only checked for tests once during an invocation, even if the load_tests function itself calls loader.discover.

If load_tests exists then discovery does not recurse into the package, load_tests is responsible for loading all tests in the package.

The pattern is deliberately not stored as a loader attribute so that packages can continue discovery themselves. top_level_dir is stored so load_tests does not need to pass this argument in to loader.discover().

start_dir can be a dotted module name as well as a directory.

New in version 3.2.

Changed in version 3.4: Modules that raise SkipTest on import are recorded as skips, not errors.

Changed in version 3.4: start_dir can be a namespace packages.

Changed in version 3.4: Paths are sorted before being imported so that execution order is the same even if the underlying file system’s ordering is not dependent on file name.

Changed in version 3.5: Found packages are now checked for load_tests regardless of whether their path matches pattern, because it is impossible for a package name to match the default pattern.

Changed in version 3.11: start_dir can not be a namespace packages. It has been broken since Python 3.7 and Python 3.11 officially remove it.

+

The following attributes of a TestLoader can be configured either by subclassing or assignment on an instance:

+testMethodPrefix
+

String giving the prefix of method names which will be interpreted as test methods. The default value is 'test'.

This affects getTestCaseNames() and all the loadTestsFrom* methods.

+
+sortTestMethodsUsing
+

Function to be used to compare method names when sorting them in getTestCaseNames() and all the loadTestsFrom* methods.

+
+suiteClass
+

Callable object that constructs a test suite from a list of tests. No methods on the resulting object are needed. The default value is the TestSuite class.

This affects all the loadTestsFrom* methods.

+
+testNamePatterns
+

List of Unix shell-style wildcard test name patterns that test methods have to match to be included in test suites (see -k option).

If this attribute is not None (the default), all test methods to be included in test suites must match one of the patterns in this list. Note that matches are always performed using fnmatch.fnmatchcase(), so unlike patterns passed to the -k option, simple substring patterns will have to be converted using * wildcards.

This affects all the loadTestsFrom* methods.

New in version 3.7.

+
+
+class unittest.TestResult
+

This class is used to compile information about which tests have succeeded and which have failed.

A TestResult object stores the results of a set of tests. The TestCase and TestSuite classes ensure that results are properly recorded; test authors do not need to worry about recording the outcome of tests.

Testing frameworks built on top of unittest may want access to the TestResult object generated by running a set of tests for reporting purposes; a TestResult instance is returned by the TestRunner.run() method for this purpose.

TestResult instances have the following attributes that will be of interest when inspecting the results of running a set of tests:

+errors
+

A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents a test which raised an unexpected exception.

+
+failures
+

A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents a test where a failure was explicitly signalled using the assert* methods.

+
+skipped
+

A list containing 2-tuples of TestCase instances and strings holding the reason for skipping the test.

New in version 3.1.

+
+expectedFailures
+

A list containing 2-tuples of TestCase instances and strings holding formatted tracebacks. Each tuple represents an expected failure or error of the test case.

+
+unexpectedSuccesses
+

A list containing TestCase instances that were marked as expected failures, but succeeded.

+
+collectedDurations
+

A list containing 2-tuples of test case names and floats representing the elapsed time of each test which was run.

New in version 3.12.

+
+shouldStop
+

Set to True when the execution of tests should stop by stop().

+
+testsRun
+

The total number of tests run so far.

+
+buffer
+

If set to true, sys.stdout and sys.stderr will be buffered in between startTest() and stopTest() being called. Collected output will only be echoed onto the real sys.stdout and sys.stderr if the test fails or errors. Any output is also attached to the failure / error message.

New in version 3.2.

+
+failfast
+

If set to true stop() will be called on the first failure or error, halting the test run.

New in version 3.2.

+
+tb_locals
+

If set to true then local variables will be shown in tracebacks.

New in version 3.5.

+
+wasSuccessful()
+

Return True if all tests run so far have passed, otherwise returns False.

Changed in version 3.4: Returns False if there were any unexpectedSuccesses from tests marked with the expectedFailure() decorator.

+
+stop()
+

This method can be called to signal that the set of tests being run should be aborted by setting the shouldStop attribute to True. TestRunner objects should respect this flag and return without running any additional tests.

For example, this feature is used by the TextTestRunner class to stop the test framework when the user signals an interrupt from the keyboard. Interactive tools which provide TestRunner implementations can use this in a similar manner.

+

The following methods of the TestResult class are used to maintain the internal data structures, and may be extended in subclasses to support additional reporting requirements. This is particularly useful in building tools which support interactive reporting while tests are being run.

+startTest(test)
+

Called when the test case test is about to be run.

+
+stopTest(test)
+

Called after the test case test has been executed, regardless of the outcome.

+
+startTestRun()
+

Called once before any tests are executed.

New in version 3.1.

+
+stopTestRun()
+

Called once after all tests are executed.

New in version 3.1.

+
+addError(test, err)
+

Called when the test case test raises an unexpected exception. err is a tuple of the form returned by sys.exc_info(): (type, value, +traceback).

The default implementation appends a tuple (test, formatted_err) to the instance’s errors attribute, where formatted_err is a formatted traceback derived from err.

+
+addFailure(test, err)
+

Called when the test case test signals a failure. err is a tuple of the form returned by sys.exc_info(): (type, value, traceback).

The default implementation appends a tuple (test, formatted_err) to the instance’s failures attribute, where formatted_err is a formatted traceback derived from err.

+
+addSuccess(test)
+

Called when the test case test succeeds.

The default implementation does nothing.

+
+addSkip(test, reason)
+

Called when the test case test is skipped. reason is the reason the test gave for skipping.

The default implementation appends a tuple (test, reason) to the instance’s skipped attribute.

+
+addExpectedFailure(test, err)
+

Called when the test case test fails or errors, but was marked with the expectedFailure() decorator.

The default implementation appends a tuple (test, formatted_err) to the instance’s expectedFailures attribute, where formatted_err is a formatted traceback derived from err.

+
+addUnexpectedSuccess(test)
+

Called when the test case test was marked with the expectedFailure() decorator, but succeeded.

The default implementation appends the test to the instance’s unexpectedSuccesses attribute.

+
+addSubTest(test, subtest, outcome)
+

Called when a subtest finishes. test is the test case corresponding to the test method. subtest is a custom TestCase instance describing the subtest.

If outcome is None, the subtest succeeded. Otherwise, it failed with an exception where outcome is a tuple of the form returned by sys.exc_info(): (type, value, traceback).

The default implementation does nothing when the outcome is a success, and records subtest failures as normal failures.

New in version 3.4.

+
+addDuration(test, elapsed)
+

Called when the test case finishes. elapsed is the time represented in seconds, and it includes the execution of cleanup functions.

New in version 3.12.

+
+
+class unittest.TextTestResult(stream, descriptions, verbosity, *, durations=None)
+

A concrete implementation of TestResult used by the TextTestRunner. Subclasses should accept **kwargs to ensure compatibility as the interface changes.

New in version 3.2.

New in version 3.12: Added durations keyword argument.

+
+unittest.defaultTestLoader
+

Instance of the TestLoader class intended to be shared. If no customization of the TestLoader is needed, this instance can be used instead of repeatedly creating new instances.

+
+class unittest.TextTestRunner(stream=None, descriptions=True, verbosity=1, failfast=False, buffer=False, resultclass=None, warnings=None, *, tb_locals=False, durations=None)
+

A basic test runner implementation that outputs results to a stream. If stream is None, the default, sys.stderr is used as the output stream. This class has a few configurable parameters, but is essentially very simple. Graphical applications which run test suites should provide alternate implementations. Such implementations should accept **kwargs as the interface to construct runners changes when features are added to unittest.

By default this runner shows DeprecationWarning, PendingDeprecationWarning, ResourceWarning and ImportWarning even if they are ignored by default. This behavior can be overridden using Python’s -Wd or -Wa options (see Warning control) and leaving warnings to None.

Changed in version 3.2: Added the warnings parameter.

Changed in version 3.2: The default stream is set to sys.stderr at instantiation time rather than import time.

Changed in version 3.5: Added the tb_locals parameter.

Changed in version 3.12: Added the durations parameter.

+_makeResult()
+

This method returns the instance of TestResult used by run(). It is not intended to be called directly, but can be overridden in subclasses to provide a custom TestResult.

_makeResult() instantiates the class or callable passed in the TextTestRunner constructor as the resultclass argument. It defaults to TextTestResult if no resultclass is provided. The result class is instantiated with the following arguments:

stream, descriptions, verbosity
+
+
+run(test)
+

This method is the main public interface to the TextTestRunner. This method takes a TestSuite or TestCase instance. A TestResult is created by calling _makeResult() and the test(s) are run and the results printed to stdout.

+
+
+unittest.main(module='__main__', defaultTest=None, argv=None, testRunner=None, testLoader=unittest.defaultTestLoader, exit=True, verbosity=1, failfast=None, catchbreak=None, buffer=None, warnings=None)
+

A command-line program that loads a set of tests from module and runs them; this is primarily for making test modules conveniently executable. The simplest use for this function is to include the following line at the end of a test script:

if __name__ == '__main__':
+    unittest.main()
+

You can run tests with more detailed information by passing in the verbosity argument:

if __name__ == '__main__':
+    unittest.main(verbosity=2)
+

The defaultTest argument is either the name of a single test or an iterable of test names to run if no test names are specified via argv. If not specified or None and no test names are provided via argv, all tests found in module are run.

The argv argument can be a list of options passed to the program, with the first element being the program name. If not specified or None, the values of sys.argv are used.

The testRunner argument can either be a test runner class or an already created instance of it. By default main calls sys.exit() with an exit code indicating success (0) or failure (1) of the tests run. An exit code of 5 indicates that no tests were run.

The testLoader argument has to be a TestLoader instance, and defaults to defaultTestLoader.

main supports being used from the interactive interpreter by passing in the argument exit=False. This displays the result on standard output without calling sys.exit():

>>> from unittest import main
+>>> main(module='test_module', exit=False)
+

The failfast, catchbreak and buffer parameters have the same effect as the same-name command-line options.

The warnings argument specifies the warning filter that should be used while running the tests. If it’s not specified, it will remain None if a -W option is passed to python (see Warning control), otherwise it will be set to 'default'.

Calling main actually returns an instance of the TestProgram class. This stores the result of the tests run as the result attribute.

Changed in version 3.1: The exit parameter was added.

Changed in version 3.2: The verbosity, failfast, catchbreak, buffer and warnings parameters were added.

Changed in version 3.4: The defaultTest parameter was changed to also accept an iterable of test names.

+

load_tests Protocol

New in version 3.2.

Modules or packages can customize how tests are loaded from them during normal test runs or test discovery by implementing a function called load_tests.

If a test module defines load_tests it will be called by TestLoader.loadTestsFromModule() with the following arguments:

load_tests(loader, standard_tests, pattern)
+

where pattern is passed straight through from loadTestsFromModule. It defaults to None.

It should return a TestSuite.

loader is the instance of TestLoader doing the loading. standard_tests are the tests that would be loaded by default from the module. It is common for test modules to only want to add or remove tests from the standard set of tests. The third argument is used when loading packages as part of test discovery.

A typical load_tests function that loads tests from a specific set of TestCase classes may look like:

test_cases = (TestCase1, TestCase2, TestCase3)
+
+def load_tests(loader, tests, pattern):
+    suite = TestSuite()
+    for test_class in test_cases:
+        tests = loader.loadTestsFromTestCase(test_class)
+        suite.addTests(tests)
+    return suite
+

If discovery is started in a directory containing a package, either from the command line or by calling TestLoader.discover(), then the package __init__.py will be checked for load_tests. If that function does not exist, discovery will recurse into the package as though it were just another directory. Otherwise, discovery of the package’s tests will be left up to load_tests which is called with the following arguments:

load_tests(loader, standard_tests, pattern)
+

This should return a TestSuite representing all the tests from the package. (standard_tests will only contain tests collected from __init__.py.)

Because the pattern is passed into load_tests the package is free to continue (and potentially modify) test discovery. A ‘do nothing’ load_tests function for a test package would look like:

def load_tests(loader, standard_tests, pattern):
+    # top level directory cached on loader instance
+    this_dir = os.path.dirname(__file__)
+    package_tests = loader.discover(start_dir=this_dir, pattern=pattern)
+    standard_tests.addTests(package_tests)
+    return standard_tests
+

Changed in version 3.5: Discovery no longer checks package names for matching pattern due to the impossibility of package names matching the default pattern.

Class and Module Fixtures

Class and module level fixtures are implemented in TestSuite. When the test suite encounters a test from a new class then tearDownClass() from the previous class (if there is one) is called, followed by setUpClass() from the new class.

Similarly if a test is from a different module from the previous test then tearDownModule from the previous module is run, followed by setUpModule from the new module.

After all the tests have run the final tearDownClass and tearDownModule are run.

Note that shared fixtures do not play well with [potential] features like test parallelization and they break test isolation. They should be used with care.

The default ordering of tests created by the unittest test loaders is to group all tests from the same modules and classes together. This will lead to setUpClass / setUpModule (etc) being called exactly once per class and module. If you randomize the order, so that tests from different modules and classes are adjacent to each other, then these shared fixture functions may be called multiple times in a single test run.

Shared fixtures are not intended to work with suites with non-standard ordering. A BaseTestSuite still exists for frameworks that don’t want to support shared fixtures.

If there are any exceptions raised during one of the shared fixture functions the test is reported as an error. Because there is no corresponding test instance an _ErrorHolder object (that has the same interface as a TestCase) is created to represent the error. If you are just using the standard unittest test runner then this detail doesn’t matter, but if you are a framework author it may be relevant.

setUpClass and tearDownClass

These must be implemented as class methods:

import unittest
+
+class Test(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls._connection = createExpensiveConnectionObject()
+
+    @classmethod
+    def tearDownClass(cls):
+        cls._connection.destroy()
+

If you want the setUpClass and tearDownClass on base classes called then you must call up to them yourself. The implementations in TestCase are empty.

If an exception is raised during a setUpClass then the tests in the class are not run and the tearDownClass is not run. Skipped classes will not have setUpClass or tearDownClass run. If the exception is a SkipTest exception then the class will be reported as having been skipped instead of as an error.

setUpModule and tearDownModule

These should be implemented as functions:

def setUpModule():
+    createConnection()
+
+def tearDownModule():
+    closeConnection()
+

If an exception is raised in a setUpModule then none of the tests in the module will be run and the tearDownModule will not be run. If the exception is a SkipTest exception then the module will be reported as having been skipped instead of as an error.

To add cleanup code that must be run even in the case of an exception, use addModuleCleanup:

+unittest.addModuleCleanup(function, /, *args, **kwargs)
+

Add a function to be called after tearDownModule() to cleanup resources used during the test class. Functions will be called in reverse order to the order they are added (LIFO). They are called with any arguments and keyword arguments passed into addModuleCleanup() when they are added.

If setUpModule() fails, meaning that tearDownModule() is not called, then any cleanup functions added will still be called.

New in version 3.8.

+
+classmethod unittest.enterModuleContext(cm)
+

Enter the supplied context manager. If successful, also add its __exit__() method as a cleanup function by addModuleCleanup() and return the result of the __enter__() method.

New in version 3.11.

+
+unittest.doModuleCleanups()
+

This function is called unconditionally after tearDownModule(), or after setUpModule() if setUpModule() raises an exception.

It is responsible for calling all the cleanup functions added by addModuleCleanup(). If you need cleanup functions to be called prior to tearDownModule() then you can call doModuleCleanups() yourself.

doModuleCleanups() pops methods off the stack of cleanup functions one at a time, so it can be called at any time.

New in version 3.8.

+

Signal Handling

New in version 3.2.

The -c/--catch command-line option to unittest, along with the catchbreak parameter to unittest.main(), provide more friendly handling of control-C during a test run. With catch break behavior enabled control-C will allow the currently running test to complete, and the test run will then end and report all the results so far. A second control-c will raise a KeyboardInterrupt in the usual way.

The control-c handling signal handler attempts to remain compatible with code or tests that install their own signal.SIGINT handler. If the unittest handler is called but isn’t the installed signal.SIGINT handler, i.e. it has been replaced by the system under test and delegated to, then it calls the default handler. This will normally be the expected behavior by code that replaces an installed handler and delegates to it. For individual tests that need unittest control-c handling disabled the removeHandler() decorator can be used.

There are a few utility functions for framework authors to enable control-c handling functionality within test frameworks.

+unittest.installHandler()
+

Install the control-c handler. When a signal.SIGINT is received (usually in response to the user pressing control-c) all registered results have stop() called.

+
+unittest.registerResult(result)
+

Register a TestResult object for control-c handling. Registering a result stores a weak reference to it, so it doesn’t prevent the result from being garbage collected.

Registering a TestResult object has no side-effects if control-c handling is not enabled, so test frameworks can unconditionally register all results they create independently of whether or not handling is enabled.

+
+unittest.removeResult(result)
+

Remove a registered result. Once a result has been removed then stop() will no longer be called on that result object in response to a control-c.

+
+unittest.removeHandler(function=None)
+

When called without arguments this function removes the control-c handler if it has been installed. This function can also be used as a test decorator to temporarily remove the handler while the test is being executed:

@unittest.removeHandler
+def test_signal_handling(self):
+    ...
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/unittest.html +

+
diff --git a/devdocs/python~3.12/library%2Funittest.mock.html b/devdocs/python~3.12/library%2Funittest.mock.html new file mode 100644 index 00000000..109252df --- /dev/null +++ b/devdocs/python~3.12/library%2Funittest.mock.html @@ -0,0 +1,1289 @@ +

unittest.mock — mock object library

New in version 3.3.

Source code: Lib/unittest/mock.py

unittest.mock is a library for testing in Python. It allows you to replace parts of your system under test with mock objects and make assertions about how they have been used.

unittest.mock provides a core Mock class removing the need to create a host of stubs throughout your test suite. After performing an action, you can make assertions about which methods / attributes were used and arguments they were called with. You can also specify return values and set needed attributes in the normal way.

Additionally, mock provides a patch() decorator that handles patching module and class level attributes within the scope of a test, along with sentinel for creating unique objects. See the quick guide for some examples of how to use Mock, MagicMock and patch().

Mock is designed for use with unittest and is based on the ‘action -> assertion’ pattern instead of ‘record -> replay’ used by many mocking frameworks.

There is a backport of unittest.mock for earlier versions of Python, available as mock on PyPI.

Quick Guide

Mock and MagicMock objects create all attributes and methods as you access them and store details of how they have been used. You can configure them, to specify return values or limit what attributes are available, and then make assertions about how they have been used:

>>> from unittest.mock import MagicMock
+>>> thing = ProductionClass()
+>>> thing.method = MagicMock(return_value=3)
+>>> thing.method(3, 4, 5, key='value')
+3
+>>> thing.method.assert_called_with(3, 4, 5, key='value')
+

side_effect allows you to perform side effects, including raising an exception when a mock is called:

>>> from unittest.mock import Mock
+>>> mock = Mock(side_effect=KeyError('foo'))
+>>> mock()
+Traceback (most recent call last):
+ ...
+KeyError: 'foo'
+
>>> values = {'a': 1, 'b': 2, 'c': 3}
+>>> def side_effect(arg):
+...     return values[arg]
+...
+>>> mock.side_effect = side_effect
+>>> mock('a'), mock('b'), mock('c')
+(1, 2, 3)
+>>> mock.side_effect = [5, 4, 3, 2, 1]
+>>> mock(), mock(), mock()
+(5, 4, 3)
+

Mock has many other ways you can configure it and control its behaviour. For example the spec argument configures the mock to take its specification from another object. Attempting to access attributes or methods on the mock that don’t exist on the spec will fail with an AttributeError.

The patch() decorator / context manager makes it easy to mock classes or objects in a module under test. The object you specify will be replaced with a mock (or other object) during the test and restored when the test ends:

>>> from unittest.mock import patch
+>>> @patch('module.ClassName2')
+... @patch('module.ClassName1')
+... def test(MockClass1, MockClass2):
+...     module.ClassName1()
+...     module.ClassName2()
+...     assert MockClass1 is module.ClassName1
+...     assert MockClass2 is module.ClassName2
+...     assert MockClass1.called
+...     assert MockClass2.called
+...
+>>> test()
+

Note

When you nest patch decorators the mocks are passed in to the decorated function in the same order they applied (the normal Python order that decorators are applied). This means from the bottom up, so in the example above the mock for module.ClassName1 is passed in first.

With patch() it matters that you patch objects in the namespace where they are looked up. This is normally straightforward, but for a quick guide read where to patch.

As well as a decorator patch() can be used as a context manager in a with statement:

>>> with patch.object(ProductionClass, 'method', return_value=None) as mock_method:
+...     thing = ProductionClass()
+...     thing.method(1, 2, 3)
+...
+>>> mock_method.assert_called_once_with(1, 2, 3)
+

There is also patch.dict() for setting values in a dictionary just during a scope and restoring the dictionary to its original state when the test ends:

>>> foo = {'key': 'value'}
+>>> original = foo.copy()
+>>> with patch.dict(foo, {'newkey': 'newvalue'}, clear=True):
+...     assert foo == {'newkey': 'newvalue'}
+...
+>>> assert foo == original
+

Mock supports the mocking of Python magic methods. The easiest way of using magic methods is with the MagicMock class. It allows you to do things like:

>>> mock = MagicMock()
+>>> mock.__str__.return_value = 'foobarbaz'
+>>> str(mock)
+'foobarbaz'
+>>> mock.__str__.assert_called_with()
+

Mock allows you to assign functions (or other Mock instances) to magic methods and they will be called appropriately. The MagicMock class is just a Mock variant that has all of the magic methods pre-created for you (well, all the useful ones anyway).

The following is an example of using magic methods with the ordinary Mock class:

>>> mock = Mock()
+>>> mock.__str__ = Mock(return_value='wheeeeee')
+>>> str(mock)
+'wheeeeee'
+

For ensuring that the mock objects in your tests have the same api as the objects they are replacing, you can use auto-speccing. Auto-speccing can be done through the autospec argument to patch, or the create_autospec() function. Auto-speccing creates mock objects that have the same attributes and methods as the objects they are replacing, and any functions and methods (including constructors) have the same call signature as the real object.

This ensures that your mocks will fail in the same way as your production code if they are used incorrectly:

>>> from unittest.mock import create_autospec
+>>> def function(a, b, c):
+...     pass
+...
+>>> mock_function = create_autospec(function, return_value='fishy')
+>>> mock_function(1, 2, 3)
+'fishy'
+>>> mock_function.assert_called_once_with(1, 2, 3)
+>>> mock_function('wrong arguments')
+Traceback (most recent call last):
+ ...
+TypeError: <lambda>() takes exactly 3 arguments (1 given)
+

create_autospec() can also be used on classes, where it copies the signature of the __init__ method, and on callable objects where it copies the signature of the __call__ method.

The Mock Class

Mock is a flexible mock object intended to replace the use of stubs and test doubles throughout your code. Mocks are callable and create attributes as new mocks when you access them 1. Accessing the same attribute will always return the same mock. Mocks record how you use them, allowing you to make assertions about what your code has done to them.

MagicMock is a subclass of Mock with all the magic methods pre-created and ready to use. There are also non-callable variants, useful when you are mocking out objects that aren’t callable: NonCallableMock and NonCallableMagicMock

The patch() decorators makes it easy to temporarily replace classes in a particular module with a Mock object. By default patch() will create a MagicMock for you. You can specify an alternative class of Mock using the new_callable argument to patch().

+class unittest.mock.Mock(spec=None, side_effect=None, return_value=DEFAULT, wraps=None, name=None, spec_set=None, unsafe=False, **kwargs)
+

Create a new Mock object. Mock takes several optional arguments that specify the behaviour of the Mock object:

  • +

    spec: This can be either a list of strings or an existing object (a class or instance) that acts as the specification for the mock object. If you pass in an object then a list of strings is formed by calling dir on the object (excluding unsupported magic attributes and methods). Accessing any attribute not in this list will raise an AttributeError.

    If spec is an object (rather than a list of strings) then __class__ returns the class of the spec object. This allows mocks to pass isinstance() tests.

  • +spec_set: A stricter variant of spec. If used, attempting to set or get an attribute on the mock that isn’t on the object passed as spec_set will raise an AttributeError.
  • +

    side_effect: A function to be called whenever the Mock is called. See the side_effect attribute. Useful for raising exceptions or dynamically changing return values. The function is called with the same arguments as the mock, and unless it returns DEFAULT, the return value of this function is used as the return value.

    Alternatively side_effect can be an exception class or instance. In this case the exception will be raised when the mock is called.

    If side_effect is an iterable then each call to the mock will return the next value from the iterable.

    A side_effect can be cleared by setting it to None.

  • +return_value: The value returned when the mock is called. By default this is a new Mock (created on first access). See the return_value attribute.
  • +

    unsafe: By default, accessing any attribute whose name starts with assert, assret, asert, aseert or assrt will raise an AttributeError. Passing unsafe=True will allow access to these attributes.

    New in version 3.5.

  • +

    wraps: Item for the mock object to wrap. If wraps is not None then calling the Mock will pass the call through to the wrapped object (returning the real result). Attribute access on the mock will return a Mock object that wraps the corresponding attribute of the wrapped object (so attempting to access an attribute that doesn’t exist will raise an AttributeError).

    If the mock has an explicit return_value set then calls are not passed to the wrapped object and the return_value is returned instead.

  • +name: If the mock has a name then it will be used in the repr of the mock. This can be useful for debugging. The name is propagated to child mocks.

Mocks can also be called with arbitrary keyword arguments. These will be used to set attributes on the mock after it is created. See the configure_mock() method for details.

+assert_called()
+

Assert that the mock was called at least once.

>>> mock = Mock()
+>>> mock.method()
+<Mock name='mock.method()' id='...'>
+>>> mock.method.assert_called()
+

New in version 3.6.

+
+assert_called_once()
+

Assert that the mock was called exactly once.

>>> mock = Mock()
+>>> mock.method()
+<Mock name='mock.method()' id='...'>
+>>> mock.method.assert_called_once()
+>>> mock.method()
+<Mock name='mock.method()' id='...'>
+>>> mock.method.assert_called_once()
+Traceback (most recent call last):
+...
+AssertionError: Expected 'method' to have been called once. Called 2 times.
+

New in version 3.6.

+
+assert_called_with(*args, **kwargs)
+

This method is a convenient way of asserting that the last call has been made in a particular way:

>>> mock = Mock()
+>>> mock.method(1, 2, 3, test='wow')
+<Mock name='mock.method()' id='...'>
+>>> mock.method.assert_called_with(1, 2, 3, test='wow')
+
+
+assert_called_once_with(*args, **kwargs)
+

Assert that the mock was called exactly once and that call was with the specified arguments.

>>> mock = Mock(return_value=None)
+>>> mock('foo', bar='baz')
+>>> mock.assert_called_once_with('foo', bar='baz')
+>>> mock('other', bar='values')
+>>> mock.assert_called_once_with('other', bar='values')
+Traceback (most recent call last):
+  ...
+AssertionError: Expected 'mock' to be called once. Called 2 times.
+
+
+assert_any_call(*args, **kwargs)
+

assert the mock has been called with the specified arguments.

The assert passes if the mock has ever been called, unlike assert_called_with() and assert_called_once_with() that only pass if the call is the most recent one, and in the case of assert_called_once_with() it must also be the only call.

>>> mock = Mock(return_value=None)
+>>> mock(1, 2, arg='thing')
+>>> mock('some', 'thing', 'else')
+>>> mock.assert_any_call(1, 2, arg='thing')
+
+
+assert_has_calls(calls, any_order=False)
+

assert the mock has been called with the specified calls. The mock_calls list is checked for the calls.

If any_order is false then the calls must be sequential. There can be extra calls before or after the specified calls.

If any_order is true then the calls can be in any order, but they must all appear in mock_calls.

>>> mock = Mock(return_value=None)
+>>> mock(1)
+>>> mock(2)
+>>> mock(3)
+>>> mock(4)
+>>> calls = [call(2), call(3)]
+>>> mock.assert_has_calls(calls)
+>>> calls = [call(4), call(2), call(3)]
+>>> mock.assert_has_calls(calls, any_order=True)
+
+
+assert_not_called()
+

Assert the mock was never called.

>>> m = Mock()
+>>> m.hello.assert_not_called()
+>>> obj = m.hello()
+>>> m.hello.assert_not_called()
+Traceback (most recent call last):
+  ...
+AssertionError: Expected 'hello' to not have been called. Called 1 times.
+

New in version 3.5.

+
+reset_mock(*, return_value=False, side_effect=False)
+

The reset_mock method resets all the call attributes on a mock object:

>>> mock = Mock(return_value=None)
+>>> mock('hello')
+>>> mock.called
+True
+>>> mock.reset_mock()
+>>> mock.called
+False
+

Changed in version 3.6: Added two keyword-only arguments to the reset_mock function.

This can be useful where you want to make a series of assertions that reuse the same object. Note that reset_mock() doesn’t clear the return value, side_effect or any child attributes you have set using normal assignment by default. In case you want to reset return_value or side_effect, then pass the corresponding parameter as True. Child mocks and the return value mock (if any) are reset as well.

Note

return_value, and side_effect are keyword-only arguments.

+
+mock_add_spec(spec, spec_set=False)
+

Add a spec to a mock. spec can either be an object or a list of strings. Only attributes on the spec can be fetched as attributes from the mock.

If spec_set is true then only attributes on the spec can be set.

+
+attach_mock(mock, attribute)
+

Attach a mock as an attribute of this one, replacing its name and parent. Calls to the attached mock will be recorded in the method_calls and mock_calls attributes of this one.

+
+configure_mock(**kwargs)
+

Set attributes on the mock through keyword arguments.

Attributes plus return values and side effects can be set on child mocks using standard dot notation and unpacking a dictionary in the method call:

>>> mock = Mock()
+>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
+>>> mock.configure_mock(**attrs)
+>>> mock.method()
+3
+>>> mock.other()
+Traceback (most recent call last):
+  ...
+KeyError
+

The same thing can be achieved in the constructor call to mocks:

>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
+>>> mock = Mock(some_attribute='eggs', **attrs)
+>>> mock.some_attribute
+'eggs'
+>>> mock.method()
+3
+>>> mock.other()
+Traceback (most recent call last):
+  ...
+KeyError
+

configure_mock() exists to make it easier to do configuration after the mock has been created.

+
+__dir__()
+

Mock objects limit the results of dir(some_mock) to useful results. For mocks with a spec this includes all the permitted attributes for the mock.

See FILTER_DIR for what this filtering does, and how to switch it off.

+
+_get_child_mock(**kw)
+

Create the child mocks for attributes and return value. By default child mocks will be the same type as the parent. Subclasses of Mock may want to override this to customize the way child mocks are made.

For non-callable mocks the callable variant will be used (rather than any custom subclass).

+
+called
+

A boolean representing whether or not the mock object has been called:

>>> mock = Mock(return_value=None)
+>>> mock.called
+False
+>>> mock()
+>>> mock.called
+True
+
+
+call_count
+

An integer telling you how many times the mock object has been called:

>>> mock = Mock(return_value=None)
+>>> mock.call_count
+0
+>>> mock()
+>>> mock()
+>>> mock.call_count
+2
+
+
+return_value
+

Set this to configure the value returned by calling the mock:

>>> mock = Mock()
+>>> mock.return_value = 'fish'
+>>> mock()
+'fish'
+

The default return value is a mock object and you can configure it in the normal way:

>>> mock = Mock()
+>>> mock.return_value.attribute = sentinel.Attribute
+>>> mock.return_value()
+<Mock name='mock()()' id='...'>
+>>> mock.return_value.assert_called_with()
+

return_value can also be set in the constructor:

>>> mock = Mock(return_value=3)
+>>> mock.return_value
+3
+>>> mock()
+3
+
+
+side_effect
+

This can either be a function to be called when the mock is called, an iterable or an exception (class or instance) to be raised.

If you pass in a function it will be called with same arguments as the mock and unless the function returns the DEFAULT singleton the call to the mock will then return whatever the function returns. If the function returns DEFAULT then the mock will return its normal value (from the return_value).

If you pass in an iterable, it is used to retrieve an iterator which must yield a value on every call. This value can either be an exception instance to be raised, or a value to be returned from the call to the mock (DEFAULT handling is identical to the function case).

An example of a mock that raises an exception (to test exception handling of an API):

>>> mock = Mock()
+>>> mock.side_effect = Exception('Boom!')
+>>> mock()
+Traceback (most recent call last):
+  ...
+Exception: Boom!
+

Using side_effect to return a sequence of values:

>>> mock = Mock()
+>>> mock.side_effect = [3, 2, 1]
+>>> mock(), mock(), mock()
+(3, 2, 1)
+

Using a callable:

>>> mock = Mock(return_value=3)
+>>> def side_effect(*args, **kwargs):
+...     return DEFAULT
+...
+>>> mock.side_effect = side_effect
+>>> mock()
+3
+

side_effect can be set in the constructor. Here’s an example that adds one to the value the mock is called with and returns it:

>>> side_effect = lambda value: value + 1
+>>> mock = Mock(side_effect=side_effect)
+>>> mock(3)
+4
+>>> mock(-8)
+-7
+

Setting side_effect to None clears it:

>>> m = Mock(side_effect=KeyError, return_value=3)
+>>> m()
+Traceback (most recent call last):
+ ...
+KeyError
+>>> m.side_effect = None
+>>> m()
+3
+
+
+call_args
+

This is either None (if the mock hasn’t been called), or the arguments that the mock was last called with. This will be in the form of a tuple: the first member, which can also be accessed through the args property, is any ordered arguments the mock was called with (or an empty tuple) and the second member, which can also be accessed through the kwargs property, is any keyword arguments (or an empty dictionary).

>>> mock = Mock(return_value=None)
+>>> print(mock.call_args)
+None
+>>> mock()
+>>> mock.call_args
+call()
+>>> mock.call_args == ()
+True
+>>> mock(3, 4)
+>>> mock.call_args
+call(3, 4)
+>>> mock.call_args == ((3, 4),)
+True
+>>> mock.call_args.args
+(3, 4)
+>>> mock.call_args.kwargs
+{}
+>>> mock(3, 4, 5, key='fish', next='w00t!')
+>>> mock.call_args
+call(3, 4, 5, key='fish', next='w00t!')
+>>> mock.call_args.args
+(3, 4, 5)
+>>> mock.call_args.kwargs
+{'key': 'fish', 'next': 'w00t!'}
+

call_args, along with members of the lists call_args_list, method_calls and mock_calls are call objects. These are tuples, so they can be unpacked to get at the individual arguments and make more complex assertions. See calls as tuples.

Changed in version 3.8: Added args and kwargs properties.

+
+call_args_list
+

This is a list of all the calls made to the mock object in sequence (so the length of the list is the number of times it has been called). Before any calls have been made it is an empty list. The call object can be used for conveniently constructing lists of calls to compare with call_args_list.

>>> mock = Mock(return_value=None)
+>>> mock()
+>>> mock(3, 4)
+>>> mock(key='fish', next='w00t!')
+>>> mock.call_args_list
+[call(), call(3, 4), call(key='fish', next='w00t!')]
+>>> expected = [(), ((3, 4),), ({'key': 'fish', 'next': 'w00t!'},)]
+>>> mock.call_args_list == expected
+True
+

Members of call_args_list are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

+
+method_calls
+

As well as tracking calls to themselves, mocks also track calls to methods and attributes, and their methods and attributes:

>>> mock = Mock()
+>>> mock.method()
+<Mock name='mock.method()' id='...'>
+>>> mock.property.method.attribute()
+<Mock name='mock.property.method.attribute()' id='...'>
+>>> mock.method_calls
+[call.method(), call.property.method.attribute()]
+

Members of method_calls are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

+
+mock_calls
+

mock_calls records all calls to the mock object, its methods, magic methods and return value mocks.

>>> mock = MagicMock()
+>>> result = mock(1, 2, 3)
+>>> mock.first(a=3)
+<MagicMock name='mock.first()' id='...'>
+>>> mock.second()
+<MagicMock name='mock.second()' id='...'>
+>>> int(mock)
+1
+>>> result(1)
+<MagicMock name='mock()()' id='...'>
+>>> expected = [call(1, 2, 3), call.first(a=3), call.second(),
+... call.__int__(), call()(1)]
+>>> mock.mock_calls == expected
+True
+

Members of mock_calls are call objects. These can be unpacked as tuples to get at the individual arguments. See calls as tuples.

Note

The way mock_calls are recorded means that where nested calls are made, the parameters of ancestor calls are not recorded and so will always compare equal:

>>> mock = MagicMock()
+>>> mock.top(a=3).bottom()
+<MagicMock name='mock.top().bottom()' id='...'>
+>>> mock.mock_calls
+[call.top(a=3), call.top().bottom()]
+>>> mock.mock_calls[-1] == call.top(a=-1).bottom()
+True
+
+
+__class__
+

Normally the __class__ attribute of an object will return its type. For a mock object with a spec, __class__ returns the spec class instead. This allows mock objects to pass isinstance() tests for the object they are replacing / masquerading as:

>>> mock = Mock(spec=3)
+>>> isinstance(mock, int)
+True
+

__class__ is assignable to, this allows a mock to pass an isinstance() check without forcing you to use a spec:

>>> mock = Mock()
+>>> mock.__class__ = dict
+>>> isinstance(mock, dict)
+True
+
+
+
+class unittest.mock.NonCallableMock(spec=None, wraps=None, name=None, spec_set=None, **kwargs)
+

A non-callable version of Mock. The constructor parameters have the same meaning of Mock, with the exception of return_value and side_effect which have no meaning on a non-callable mock.

+

Mock objects that use a class or an instance as a spec or spec_set are able to pass isinstance() tests:

>>> mock = Mock(spec=SomeClass)
+>>> isinstance(mock, SomeClass)
+True
+>>> mock = Mock(spec_set=SomeClass())
+>>> isinstance(mock, SomeClass)
+True
+

The Mock classes have support for mocking magic methods. See magic methods for the full details.

The mock classes and the patch() decorators all take arbitrary keyword arguments for configuration. For the patch() decorators the keywords are passed to the constructor of the mock being created. The keyword arguments are for configuring attributes of the mock:

>>> m = MagicMock(attribute=3, other='fish')
+>>> m.attribute
+3
+>>> m.other
+'fish'
+

The return value and side effect of child mocks can be set in the same way, using dotted notation. As you can’t use dotted names directly in a call you have to create a dictionary and unpack it using **:

>>> attrs = {'method.return_value': 3, 'other.side_effect': KeyError}
+>>> mock = Mock(some_attribute='eggs', **attrs)
+>>> mock.some_attribute
+'eggs'
+>>> mock.method()
+3
+>>> mock.other()
+Traceback (most recent call last):
+  ...
+KeyError
+

A callable mock which was created with a spec (or a spec_set) will introspect the specification object’s signature when matching calls to the mock. Therefore, it can match the actual call’s arguments regardless of whether they were passed positionally or by name:

>>> def f(a, b, c): pass
+...
+>>> mock = Mock(spec=f)
+>>> mock(1, 2, c=3)
+<Mock name='mock()' id='140161580456576'>
+>>> mock.assert_called_with(1, 2, 3)
+>>> mock.assert_called_with(a=1, b=2, c=3)
+

This applies to assert_called_with(), assert_called_once_with(), assert_has_calls() and assert_any_call(). When Autospeccing, it will also apply to method calls on the mock object.

Changed in version 3.4: Added signature introspection on specced and autospecced mock objects.

+class unittest.mock.PropertyMock(*args, **kwargs)
+

A mock intended to be used as a property, or other descriptor, on a class. PropertyMock provides __get__() and __set__() methods so you can specify a return value when it is fetched.

Fetching a PropertyMock instance from an object calls the mock, with no args. Setting it calls the mock with the value being set.

>>> class Foo:
+...     @property
+...     def foo(self):
+...         return 'something'
+...     @foo.setter
+...     def foo(self, value):
+...         pass
+...
+>>> with patch('__main__.Foo.foo', new_callable=PropertyMock) as mock_foo:
+...     mock_foo.return_value = 'mockity-mock'
+...     this_foo = Foo()
+...     print(this_foo.foo)
+...     this_foo.foo = 6
+...
+mockity-mock
+>>> mock_foo.mock_calls
+[call(), call(6)]
+
+

Because of the way mock attributes are stored you can’t directly attach a PropertyMock to a mock object. Instead you can attach it to the mock type object:

>>> m = MagicMock()
+>>> p = PropertyMock(return_value=3)
+>>> type(m).foo = p
+>>> m.foo
+3
+>>> p.assert_called_once_with()
+
+class unittest.mock.AsyncMock(spec=None, side_effect=None, return_value=DEFAULT, wraps=None, name=None, spec_set=None, unsafe=False, **kwargs)
+

An asynchronous version of MagicMock. The AsyncMock object will behave so the object is recognized as an async function, and the result of a call is an awaitable.

>>> mock = AsyncMock()
+>>> asyncio.iscoroutinefunction(mock)
+True
+>>> inspect.isawaitable(mock())  
+True
+

The result of mock() is an async function which will have the outcome of side_effect or return_value after it has been awaited:

  • if side_effect is a function, the async function will return the result of that function,
  • if side_effect is an exception, the async function will raise the exception,
  • if side_effect is an iterable, the async function will return the next value of the iterable, however, if the sequence of result is exhausted, StopAsyncIteration is raised immediately,
  • if side_effect is not defined, the async function will return the value defined by return_value, hence, by default, the async function returns a new AsyncMock object.

Setting the spec of a Mock or MagicMock to an async function will result in a coroutine object being returned after calling.

>>> async def async_func(): pass
+...
+>>> mock = MagicMock(async_func)
+>>> mock
+<MagicMock spec='function' id='...'>
+>>> mock()  
+<coroutine object AsyncMockMixin._mock_call at ...>
+

Setting the spec of a Mock, MagicMock, or AsyncMock to a class with asynchronous and synchronous functions will automatically detect the synchronous functions and set them as MagicMock (if the parent mock is AsyncMock or MagicMock) or Mock (if the parent mock is Mock). All asynchronous functions will be AsyncMock.

>>> class ExampleClass:
+...     def sync_foo():
+...         pass
+...     async def async_foo():
+...         pass
+...
+>>> a_mock = AsyncMock(ExampleClass)
+>>> a_mock.sync_foo
+<MagicMock name='mock.sync_foo' id='...'>
+>>> a_mock.async_foo
+<AsyncMock name='mock.async_foo' id='...'>
+>>> mock = Mock(ExampleClass)
+>>> mock.sync_foo
+<Mock name='mock.sync_foo' id='...'>
+>>> mock.async_foo
+<AsyncMock name='mock.async_foo' id='...'>
+

New in version 3.8.

+assert_awaited()
+

Assert that the mock was awaited at least once. Note that this is separate from the object having been called, the await keyword must be used:

>>> mock = AsyncMock()
+>>> async def main(coroutine_mock):
+...     await coroutine_mock
+...
+>>> coroutine_mock = mock()
+>>> mock.called
+True
+>>> mock.assert_awaited()
+Traceback (most recent call last):
+...
+AssertionError: Expected mock to have been awaited.
+>>> asyncio.run(main(coroutine_mock))
+>>> mock.assert_awaited()
+
+
+assert_awaited_once()
+

Assert that the mock was awaited exactly once.

>>> mock = AsyncMock()
+>>> async def main():
+...     await mock()
+...
+>>> asyncio.run(main())
+>>> mock.assert_awaited_once()
+>>> asyncio.run(main())
+>>> mock.method.assert_awaited_once()
+Traceback (most recent call last):
+...
+AssertionError: Expected mock to have been awaited once. Awaited 2 times.
+
+
+assert_awaited_with(*args, **kwargs)
+

Assert that the last await was with the specified arguments.

>>> mock = AsyncMock()
+>>> async def main(*args, **kwargs):
+...     await mock(*args, **kwargs)
+...
+>>> asyncio.run(main('foo', bar='bar'))
+>>> mock.assert_awaited_with('foo', bar='bar')
+>>> mock.assert_awaited_with('other')
+Traceback (most recent call last):
+...
+AssertionError: expected call not found.
+Expected: mock('other')
+Actual: mock('foo', bar='bar')
+
+
+assert_awaited_once_with(*args, **kwargs)
+

Assert that the mock was awaited exactly once and with the specified arguments.

>>> mock = AsyncMock()
+>>> async def main(*args, **kwargs):
+...     await mock(*args, **kwargs)
+...
+>>> asyncio.run(main('foo', bar='bar'))
+>>> mock.assert_awaited_once_with('foo', bar='bar')
+>>> asyncio.run(main('foo', bar='bar'))
+>>> mock.assert_awaited_once_with('foo', bar='bar')
+Traceback (most recent call last):
+...
+AssertionError: Expected mock to have been awaited once. Awaited 2 times.
+
+
+assert_any_await(*args, **kwargs)
+

Assert the mock has ever been awaited with the specified arguments.

>>> mock = AsyncMock()
+>>> async def main(*args, **kwargs):
+...     await mock(*args, **kwargs)
+...
+>>> asyncio.run(main('foo', bar='bar'))
+>>> asyncio.run(main('hello'))
+>>> mock.assert_any_await('foo', bar='bar')
+>>> mock.assert_any_await('other')
+Traceback (most recent call last):
+...
+AssertionError: mock('other') await not found
+
+
+assert_has_awaits(calls, any_order=False)
+

Assert the mock has been awaited with the specified calls. The await_args_list list is checked for the awaits.

If any_order is false then the awaits must be sequential. There can be extra calls before or after the specified awaits.

If any_order is true then the awaits can be in any order, but they must all appear in await_args_list.

>>> mock = AsyncMock()
+>>> async def main(*args, **kwargs):
+...     await mock(*args, **kwargs)
+...
+>>> calls = [call("foo"), call("bar")]
+>>> mock.assert_has_awaits(calls)
+Traceback (most recent call last):
+...
+AssertionError: Awaits not found.
+Expected: [call('foo'), call('bar')]
+Actual: []
+>>> asyncio.run(main('foo'))
+>>> asyncio.run(main('bar'))
+>>> mock.assert_has_awaits(calls)
+
+
+assert_not_awaited()
+

Assert that the mock was never awaited.

>>> mock = AsyncMock()
+>>> mock.assert_not_awaited()
+
+
+reset_mock(*args, **kwargs)
+

See Mock.reset_mock(). Also sets await_count to 0, await_args to None, and clears the await_args_list.

+
+await_count
+

An integer keeping track of how many times the mock object has been awaited.

>>> mock = AsyncMock()
+>>> async def main():
+...     await mock()
+...
+>>> asyncio.run(main())
+>>> mock.await_count
+1
+>>> asyncio.run(main())
+>>> mock.await_count
+2
+
+
+await_args
+

This is either None (if the mock hasn’t been awaited), or the arguments that the mock was last awaited with. Functions the same as Mock.call_args.

>>> mock = AsyncMock()
+>>> async def main(*args):
+...     await mock(*args)
+...
+>>> mock.await_args
+>>> asyncio.run(main('foo'))
+>>> mock.await_args
+call('foo')
+>>> asyncio.run(main('bar'))
+>>> mock.await_args
+call('bar')
+
+
+await_args_list
+

This is a list of all the awaits made to the mock object in sequence (so the length of the list is the number of times it has been awaited). Before any awaits have been made it is an empty list.

>>> mock = AsyncMock()
+>>> async def main(*args):
+...     await mock(*args)
+...
+>>> mock.await_args_list
+[]
+>>> asyncio.run(main('foo'))
+>>> mock.await_args_list
+[call('foo')]
+>>> asyncio.run(main('bar'))
+>>> mock.await_args_list
+[call('foo'), call('bar')]
+
+
+

Calling

Mock objects are callable. The call will return the value set as the return_value attribute. The default return value is a new Mock object; it is created the first time the return value is accessed (either explicitly or by calling the Mock) - but it is stored and the same one returned each time.

Calls made to the object will be recorded in the attributes like call_args and call_args_list.

If side_effect is set then it will be called after the call has been recorded, so if side_effect raises an exception the call is still recorded.

The simplest way to make a mock raise an exception when called is to make side_effect an exception class or instance:

>>> m = MagicMock(side_effect=IndexError)
+>>> m(1, 2, 3)
+Traceback (most recent call last):
+  ...
+IndexError
+>>> m.mock_calls
+[call(1, 2, 3)]
+>>> m.side_effect = KeyError('Bang!')
+>>> m('two', 'three', 'four')
+Traceback (most recent call last):
+  ...
+KeyError: 'Bang!'
+>>> m.mock_calls
+[call(1, 2, 3), call('two', 'three', 'four')]
+

If side_effect is a function then whatever that function returns is what calls to the mock return. The side_effect function is called with the same arguments as the mock. This allows you to vary the return value of the call dynamically, based on the input:

>>> def side_effect(value):
+...     return value + 1
+...
+>>> m = MagicMock(side_effect=side_effect)
+>>> m(1)
+2
+>>> m(2)
+3
+>>> m.mock_calls
+[call(1), call(2)]
+

If you want the mock to still return the default return value (a new mock), or any set return value, then there are two ways of doing this. Either return mock.return_value from inside side_effect, or return DEFAULT:

>>> m = MagicMock()
+>>> def side_effect(*args, **kwargs):
+...     return m.return_value
+...
+>>> m.side_effect = side_effect
+>>> m.return_value = 3
+>>> m()
+3
+>>> def side_effect(*args, **kwargs):
+...     return DEFAULT
+...
+>>> m.side_effect = side_effect
+>>> m()
+3
+

To remove a side_effect, and return to the default behaviour, set the side_effect to None:

>>> m = MagicMock(return_value=6)
+>>> def side_effect(*args, **kwargs):
+...     return 3
+...
+>>> m.side_effect = side_effect
+>>> m()
+3
+>>> m.side_effect = None
+>>> m()
+6
+

The side_effect can also be any iterable object. Repeated calls to the mock will return values from the iterable (until the iterable is exhausted and a StopIteration is raised):

>>> m = MagicMock(side_effect=[1, 2, 3])
+>>> m()
+1
+>>> m()
+2
+>>> m()
+3
+>>> m()
+Traceback (most recent call last):
+  ...
+StopIteration
+

If any members of the iterable are exceptions they will be raised instead of returned:

>>> iterable = (33, ValueError, 66)
+>>> m = MagicMock(side_effect=iterable)
+>>> m()
+33
+>>> m()
+Traceback (most recent call last):
+ ...
+ValueError
+>>> m()
+66
+

Deleting Attributes

Mock objects create attributes on demand. This allows them to pretend to be objects of any type.

You may want a mock object to return False to a hasattr() call, or raise an AttributeError when an attribute is fetched. You can do this by providing an object as a spec for a mock, but that isn’t always convenient.

You “block” attributes by deleting them. Once deleted, accessing an attribute will raise an AttributeError.

>>> mock = MagicMock()
+>>> hasattr(mock, 'm')
+True
+>>> del mock.m
+>>> hasattr(mock, 'm')
+False
+>>> del mock.f
+>>> mock.f
+Traceback (most recent call last):
+    ...
+AttributeError: f
+

Mock names and the name attribute

Since “name” is an argument to the Mock constructor, if you want your mock object to have a “name” attribute you can’t just pass it in at creation time. There are two alternatives. One option is to use configure_mock():

>>> mock = MagicMock()
+>>> mock.configure_mock(name='my_name')
+>>> mock.name
+'my_name'
+

A simpler option is to simply set the “name” attribute after mock creation:

>>> mock = MagicMock()
+>>> mock.name = "foo"
+

Attaching Mocks as Attributes

When you attach a mock as an attribute of another mock (or as the return value) it becomes a “child” of that mock. Calls to the child are recorded in the method_calls and mock_calls attributes of the parent. This is useful for configuring child mocks and then attaching them to the parent, or for attaching mocks to a parent that records all calls to the children and allows you to make assertions about the order of calls between mocks:

>>> parent = MagicMock()
+>>> child1 = MagicMock(return_value=None)
+>>> child2 = MagicMock(return_value=None)
+>>> parent.child1 = child1
+>>> parent.child2 = child2
+>>> child1(1)
+>>> child2(2)
+>>> parent.mock_calls
+[call.child1(1), call.child2(2)]
+

The exception to this is if the mock has a name. This allows you to prevent the “parenting” if for some reason you don’t want it to happen.

>>> mock = MagicMock()
+>>> not_a_child = MagicMock(name='not-a-child')
+>>> mock.attribute = not_a_child
+>>> mock.attribute()
+<MagicMock name='not-a-child()' id='...'>
+>>> mock.mock_calls
+[]
+

Mocks created for you by patch() are automatically given names. To attach mocks that have names to a parent you use the attach_mock() method:

>>> thing1 = object()
+>>> thing2 = object()
+>>> parent = MagicMock()
+>>> with patch('__main__.thing1', return_value=None) as child1:
+...     with patch('__main__.thing2', return_value=None) as child2:
+...         parent.attach_mock(child1, 'child1')
+...         parent.attach_mock(child2, 'child2')
+...         child1('one')
+...         child2('two')
+...
+>>> parent.mock_calls
+[call.child1('one'), call.child2('two')]
+
+1
+

The only exceptions are magic methods and attributes (those that have leading and trailing double underscores). Mock doesn’t create these but instead raises an AttributeError. This is because the interpreter will often implicitly request these methods, and gets very confused to get a new Mock object when it expects a magic method. If you need magic method support see magic methods.

The patchers

The patch decorators are used for patching objects only within the scope of the function they decorate. They automatically handle the unpatching for you, even if exceptions are raised. All of these functions can also be used in with statements or as class decorators.

patch

Note

The key is to do the patching in the right namespace. See the section where to patch.

+unittest.mock.patch(target, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
+

patch() acts as a function decorator, class decorator or a context manager. Inside the body of the function or with statement, the target is patched with a new object. When the function/with statement exits the patch is undone.

If new is omitted, then the target is replaced with an AsyncMock if the patched object is an async function or a MagicMock otherwise. If patch() is used as a decorator and new is omitted, the created mock is passed in as an extra argument to the decorated function. If patch() is used as a context manager the created mock is returned by the context manager.

target should be a string in the form 'package.module.ClassName'. The target is imported and the specified object replaced with the new object, so the target must be importable from the environment you are calling patch() from. The target is imported when the decorated function is executed, not at decoration time.

The spec and spec_set keyword arguments are passed to the MagicMock if patch is creating one for you.

In addition you can pass spec=True or spec_set=True, which causes patch to pass in the object being mocked as the spec/spec_set object.

new_callable allows you to specify a different class, or callable object, that will be called to create the new object. By default AsyncMock is used for async functions and MagicMock for the rest.

A more powerful form of spec is autospec. If you set autospec=True then the mock will be created with a spec from the object being replaced. All attributes of the mock will also have the spec of the corresponding attribute of the object being replaced. Methods and functions being mocked will have their arguments checked and will raise a TypeError if they are called with the wrong signature. For mocks replacing a class, their return value (the ‘instance’) will have the same spec as the class. See the create_autospec() function and Autospeccing.

Instead of autospec=True you can pass autospec=some_object to use an arbitrary object as the spec instead of the one being replaced.

By default patch() will fail to replace attributes that don’t exist. If you pass in create=True, and the attribute doesn’t exist, patch will create the attribute for you when the patched function is called, and delete it again after the patched function has exited. This is useful for writing tests against attributes that your production code creates at runtime. It is off by default because it can be dangerous. With it switched on you can write passing tests against APIs that don’t actually exist!

Note

Changed in version 3.5: If you are patching builtins in a module then you don’t need to pass create=True, it will be added by default.

Patch can be used as a TestCase class decorator. It works by decorating each test method in the class. This reduces the boilerplate code when your test methods share a common patchings set. patch() finds tests by looking for method names that start with patch.TEST_PREFIX. By default this is 'test', which matches the way unittest finds tests. You can specify an alternative prefix by setting patch.TEST_PREFIX.

Patch can be used as a context manager, with the with statement. Here the patching applies to the indented block after the with statement. If you use “as” then the patched object will be bound to the name after the “as”; very useful if patch() is creating a mock object for you.

patch() takes arbitrary keyword arguments. These will be passed to AsyncMock if the patched object is asynchronous, to MagicMock otherwise or to new_callable if specified.

patch.dict(...), patch.multiple(...) and patch.object(...) are available for alternate use-cases.

+

patch() as function decorator, creating the mock for you and passing it into the decorated function:

>>> @patch('__main__.SomeClass')
+... def function(normal_argument, mock_class):
+...     print(mock_class is SomeClass)
+...
+>>> function(None)
+True
+

Patching a class replaces the class with a MagicMock instance. If the class is instantiated in the code under test then it will be the return_value of the mock that will be used.

If the class is instantiated multiple times you could use side_effect to return a new mock each time. Alternatively you can set the return_value to be anything you want.

To configure return values on methods of instances on the patched class you must do this on the return_value. For example:

>>> class Class:
+...     def method(self):
+...         pass
+...
+>>> with patch('__main__.Class') as MockClass:
+...     instance = MockClass.return_value
+...     instance.method.return_value = 'foo'
+...     assert Class() is instance
+...     assert Class().method() == 'foo'
+...
+

If you use spec or spec_set and patch() is replacing a class, then the return value of the created mock will have the same spec.

>>> Original = Class
+>>> patcher = patch('__main__.Class', spec=True)
+>>> MockClass = patcher.start()
+>>> instance = MockClass()
+>>> assert isinstance(instance, Original)
+>>> patcher.stop()
+

The new_callable argument is useful where you want to use an alternative class to the default MagicMock for the created mock. For example, if you wanted a NonCallableMock to be used:

>>> thing = object()
+>>> with patch('__main__.thing', new_callable=NonCallableMock) as mock_thing:
+...     assert thing is mock_thing
+...     thing()
+...
+Traceback (most recent call last):
+  ...
+TypeError: 'NonCallableMock' object is not callable
+

Another use case might be to replace an object with an io.StringIO instance:

>>> from io import StringIO
+>>> def foo():
+...     print('Something')
+...
+>>> @patch('sys.stdout', new_callable=StringIO)
+... def test(mock_stdout):
+...     foo()
+...     assert mock_stdout.getvalue() == 'Something\n'
+...
+>>> test()
+

When patch() is creating a mock for you, it is common that the first thing you need to do is to configure the mock. Some of that configuration can be done in the call to patch. Any arbitrary keywords you pass into the call will be used to set attributes on the created mock:

>>> patcher = patch('__main__.thing', first='one', second='two')
+>>> mock_thing = patcher.start()
+>>> mock_thing.first
+'one'
+>>> mock_thing.second
+'two'
+

As well as attributes on the created mock attributes, like the return_value and side_effect, of child mocks can also be configured. These aren’t syntactically valid to pass in directly as keyword arguments, but a dictionary with these as keys can still be expanded into a patch() call using **:

>>> config = {'method.return_value': 3, 'other.side_effect': KeyError}
+>>> patcher = patch('__main__.thing', **config)
+>>> mock_thing = patcher.start()
+>>> mock_thing.method()
+3
+>>> mock_thing.other()
+Traceback (most recent call last):
+  ...
+KeyError
+

By default, attempting to patch a function in a module (or a method or an attribute in a class) that does not exist will fail with AttributeError:

>>> @patch('sys.non_existing_attribute', 42)
+... def test():
+...     assert sys.non_existing_attribute == 42
+...
+>>> test()
+Traceback (most recent call last):
+  ...
+AttributeError: <module 'sys' (built-in)> does not have the attribute 'non_existing_attribute'
+

but adding create=True in the call to patch() will make the previous example work as expected:

>>> @patch('sys.non_existing_attribute', 42, create=True)
+... def test(mock_stdout):
+...     assert sys.non_existing_attribute == 42
+...
+>>> test()
+

Changed in version 3.8: patch() now returns an AsyncMock if the target is an async function.

patch.object

+patch.object(target, attribute, new=DEFAULT, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
+

patch the named member (attribute) on an object (target) with a mock object.

patch.object() can be used as a decorator, class decorator or a context manager. Arguments new, spec, create, spec_set, autospec and new_callable have the same meaning as for patch(). Like patch(), patch.object() takes arbitrary keyword arguments for configuring the mock object it creates.

When used as a class decorator patch.object() honours patch.TEST_PREFIX for choosing which methods to wrap.

+

You can either call patch.object() with three arguments or two arguments. The three argument form takes the object to be patched, the attribute name and the object to replace the attribute with.

When calling with the two argument form you omit the replacement object, and a mock is created for you and passed in as an extra argument to the decorated function:

>>> @patch.object(SomeClass, 'class_method')
+... def test(mock_method):
+...     SomeClass.class_method(3)
+...     mock_method.assert_called_with(3)
+...
+>>> test()
+

spec, create and the other arguments to patch.object() have the same meaning as they do for patch().

patch.dict

+patch.dict(in_dict, values=(), clear=False, **kwargs)
+

Patch a dictionary, or dictionary like object, and restore the dictionary to its original state after the test.

in_dict can be a dictionary or a mapping like container. If it is a mapping then it must at least support getting, setting and deleting items plus iterating over keys.

in_dict can also be a string specifying the name of the dictionary, which will then be fetched by importing it.

values can be a dictionary of values to set in the dictionary. values can also be an iterable of (key, value) pairs.

If clear is true then the dictionary will be cleared before the new values are set.

patch.dict() can also be called with arbitrary keyword arguments to set values in the dictionary.

Changed in version 3.8: patch.dict() now returns the patched dictionary when used as a context manager.

+

patch.dict() can be used as a context manager, decorator or class decorator:

>>> foo = {}
+>>> @patch.dict(foo, {'newkey': 'newvalue'})
+... def test():
+...     assert foo == {'newkey': 'newvalue'}
+...
+>>> test()
+>>> assert foo == {}
+

When used as a class decorator patch.dict() honours patch.TEST_PREFIX (default to 'test') for choosing which methods to wrap:

>>> import os
+>>> import unittest
+>>> from unittest.mock import patch
+>>> @patch.dict('os.environ', {'newkey': 'newvalue'})
+... class TestSample(unittest.TestCase):
+...     def test_sample(self):
+...         self.assertEqual(os.environ['newkey'], 'newvalue')
+

If you want to use a different prefix for your test, you can inform the patchers of the different prefix by setting patch.TEST_PREFIX. For more details about how to change the value of see TEST_PREFIX.

patch.dict() can be used to add members to a dictionary, or simply let a test change a dictionary, and ensure the dictionary is restored when the test ends.

>>> foo = {}
+>>> with patch.dict(foo, {'newkey': 'newvalue'}) as patched_foo:
+...     assert foo == {'newkey': 'newvalue'}
+...     assert patched_foo == {'newkey': 'newvalue'}
+...     # You can add, update or delete keys of foo (or patched_foo, it's the same dict)
+...     patched_foo['spam'] = 'eggs'
+...
+>>> assert foo == {}
+>>> assert patched_foo == {}
+
>>> import os
+>>> with patch.dict('os.environ', {'newkey': 'newvalue'}):
+...     print(os.environ['newkey'])
+...
+newvalue
+>>> assert 'newkey' not in os.environ
+

Keywords can be used in the patch.dict() call to set values in the dictionary:

>>> mymodule = MagicMock()
+>>> mymodule.function.return_value = 'fish'
+>>> with patch.dict('sys.modules', mymodule=mymodule):
+...     import mymodule
+...     mymodule.function('some', 'args')
+...
+'fish'
+

patch.dict() can be used with dictionary like objects that aren’t actually dictionaries. At the very minimum they must support item getting, setting, deleting and either iteration or membership test. This corresponds to the magic methods __getitem__(), __setitem__(), __delitem__() and either __iter__() or __contains__().

>>> class Container:
+...     def __init__(self):
+...         self.values = {}
+...     def __getitem__(self, name):
+...         return self.values[name]
+...     def __setitem__(self, name, value):
+...         self.values[name] = value
+...     def __delitem__(self, name):
+...         del self.values[name]
+...     def __iter__(self):
+...         return iter(self.values)
+...
+>>> thing = Container()
+>>> thing['one'] = 1
+>>> with patch.dict(thing, one=2, two=3):
+...     assert thing['one'] == 2
+...     assert thing['two'] == 3
+...
+>>> assert thing['one'] == 1
+>>> assert list(thing) == ['one']
+

patch.multiple

+patch.multiple(target, spec=None, create=False, spec_set=None, autospec=None, new_callable=None, **kwargs)
+

Perform multiple patches in a single call. It takes the object to be patched (either as an object or a string to fetch the object by importing) and keyword arguments for the patches:

with patch.multiple(settings, FIRST_PATCH='one', SECOND_PATCH='two'):
+    ...
+

Use DEFAULT as the value if you want patch.multiple() to create mocks for you. In this case the created mocks are passed into a decorated function by keyword, and a dictionary is returned when patch.multiple() is used as a context manager.

patch.multiple() can be used as a decorator, class decorator or a context manager. The arguments spec, spec_set, create, autospec and new_callable have the same meaning as for patch(). These arguments will be applied to all patches done by patch.multiple().

When used as a class decorator patch.multiple() honours patch.TEST_PREFIX for choosing which methods to wrap.

+

If you want patch.multiple() to create mocks for you, then you can use DEFAULT as the value. If you use patch.multiple() as a decorator then the created mocks are passed into the decorated function by keyword.

>>> thing = object()
+>>> other = object()
+
+>>> @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
+... def test_function(thing, other):
+...     assert isinstance(thing, MagicMock)
+...     assert isinstance(other, MagicMock)
+...
+>>> test_function()
+

patch.multiple() can be nested with other patch decorators, but put arguments passed by keyword after any of the standard arguments created by patch():

>>> @patch('sys.exit')
+... @patch.multiple('__main__', thing=DEFAULT, other=DEFAULT)
+... def test_function(mock_exit, other, thing):
+...     assert 'other' in repr(other)
+...     assert 'thing' in repr(thing)
+...     assert 'exit' in repr(mock_exit)
+...
+>>> test_function()
+

If patch.multiple() is used as a context manager, the value returned by the context manager is a dictionary where created mocks are keyed by name:

>>> with patch.multiple('__main__', thing=DEFAULT, other=DEFAULT) as values:
+...     assert 'other' in repr(values['other'])
+...     assert 'thing' in repr(values['thing'])
+...     assert values['thing'] is thing
+...     assert values['other'] is other
+...
+

patch methods: start and stop

All the patchers have start() and stop() methods. These make it simpler to do patching in setUp methods or where you want to do multiple patches without nesting decorators or with statements.

To use them call patch(), patch.object() or patch.dict() as normal and keep a reference to the returned patcher object. You can then call start() to put the patch in place and stop() to undo it.

If you are using patch() to create a mock for you then it will be returned by the call to patcher.start.

>>> patcher = patch('package.module.ClassName')
+>>> from package import module
+>>> original = module.ClassName
+>>> new_mock = patcher.start()
+>>> assert module.ClassName is not original
+>>> assert module.ClassName is new_mock
+>>> patcher.stop()
+>>> assert module.ClassName is original
+>>> assert module.ClassName is not new_mock
+

A typical use case for this might be for doing multiple patches in the setUp method of a TestCase:

>>> class MyTest(unittest.TestCase):
+...     def setUp(self):
+...         self.patcher1 = patch('package.module.Class1')
+...         self.patcher2 = patch('package.module.Class2')
+...         self.MockClass1 = self.patcher1.start()
+...         self.MockClass2 = self.patcher2.start()
+...
+...     def tearDown(self):
+...         self.patcher1.stop()
+...         self.patcher2.stop()
+...
+...     def test_something(self):
+...         assert package.module.Class1 is self.MockClass1
+...         assert package.module.Class2 is self.MockClass2
+...
+>>> MyTest('test_something').run()
+

Caution

If you use this technique you must ensure that the patching is “undone” by calling stop. This can be fiddlier than you might think, because if an exception is raised in the setUp then tearDown is not called. unittest.TestCase.addCleanup() makes this easier:

>>> class MyTest(unittest.TestCase):
+...     def setUp(self):
+...         patcher = patch('package.module.Class')
+...         self.MockClass = patcher.start()
+...         self.addCleanup(patcher.stop)
+...
+...     def test_something(self):
+...         assert package.module.Class is self.MockClass
+...
+

As an added bonus you no longer need to keep a reference to the patcher object.

It is also possible to stop all patches which have been started by using patch.stopall().

+patch.stopall()
+

Stop all active patches. Only stops patches started with start.

+

patch builtins

You can patch any builtins within a module. The following example patches builtin ord():

>>> @patch('__main__.ord')
+... def test(mock_ord):
+...     mock_ord.return_value = 101
+...     print(ord('c'))
+...
+>>> test()
+101
+

TEST_PREFIX

All of the patchers can be used as class decorators. When used in this way they wrap every test method on the class. The patchers recognise methods that start with 'test' as being test methods. This is the same way that the unittest.TestLoader finds test methods by default.

It is possible that you want to use a different prefix for your tests. You can inform the patchers of the different prefix by setting patch.TEST_PREFIX:

>>> patch.TEST_PREFIX = 'foo'
+>>> value = 3
+>>>
+>>> @patch('__main__.value', 'not three')
+... class Thing:
+...     def foo_one(self):
+...         print(value)
+...     def foo_two(self):
+...         print(value)
+...
+>>>
+>>> Thing().foo_one()
+not three
+>>> Thing().foo_two()
+not three
+>>> value
+3
+

Nesting Patch Decorators

If you want to perform multiple patches then you can simply stack up the decorators.

You can stack up multiple patch decorators using this pattern:

>>> @patch.object(SomeClass, 'class_method')
+... @patch.object(SomeClass, 'static_method')
+... def test(mock1, mock2):
+...     assert SomeClass.static_method is mock1
+...     assert SomeClass.class_method is mock2
+...     SomeClass.static_method('foo')
+...     SomeClass.class_method('bar')
+...     return mock1, mock2
+...
+>>> mock1, mock2 = test()
+>>> mock1.assert_called_once_with('foo')
+>>> mock2.assert_called_once_with('bar')
+

Note that the decorators are applied from the bottom upwards. This is the standard way that Python applies decorators. The order of the created mocks passed into your test function matches this order.

Where to patch

patch() works by (temporarily) changing the object that a name points to with another one. There can be many names pointing to any individual object, so for patching to work you must ensure that you patch the name used by the system under test.

The basic principle is that you patch where an object is looked up, which is not necessarily the same place as where it is defined. A couple of examples will help to clarify this.

Imagine we have a project that we want to test with the following structure:

a.py
+    -> Defines SomeClass
+
+b.py
+    -> from a import SomeClass
+    -> some_function instantiates SomeClass
+

Now we want to test some_function but we want to mock out SomeClass using patch(). The problem is that when we import module b, which we will have to do then it imports SomeClass from module a. If we use patch() to mock out a.SomeClass then it will have no effect on our test; module b already has a reference to the real SomeClass and it looks like our patching had no effect.

The key is to patch out SomeClass where it is used (or where it is looked up). In this case some_function will actually look up SomeClass in module b, where we have imported it. The patching should look like:

@patch('b.SomeClass')
+

However, consider the alternative scenario where instead of from a import +SomeClass module b does import a and some_function uses a.SomeClass. Both of these import forms are common. In this case the class we want to patch is being looked up in the module and so we have to patch a.SomeClass instead:

@patch('a.SomeClass')
+

Patching Descriptors and Proxy Objects

Both patch and patch.object correctly patch and restore descriptors: class methods, static methods and properties. You should patch these on the class rather than an instance. They also work with some objects that proxy attribute access, like the django settings object.

MagicMock and magic method support

Mocking Magic Methods

Mock supports mocking the Python protocol methods, also known as “magic methods”. This allows mock objects to replace containers or other objects that implement Python protocols.

Because magic methods are looked up differently from normal methods 2, this support has been specially implemented. This means that only specific magic methods are supported. The supported list includes almost all of them. If there are any missing that you need please let us know.

You mock magic methods by setting the method you are interested in to a function or a mock instance. If you are using a function then it must take self as the first argument 3.

>>> def __str__(self):
+...     return 'fooble'
+...
+>>> mock = Mock()
+>>> mock.__str__ = __str__
+>>> str(mock)
+'fooble'
+
>>> mock = Mock()
+>>> mock.__str__ = Mock()
+>>> mock.__str__.return_value = 'fooble'
+>>> str(mock)
+'fooble'
+
>>> mock = Mock()
+>>> mock.__iter__ = Mock(return_value=iter([]))
+>>> list(mock)
+[]
+

One use case for this is for mocking objects used as context managers in a with statement:

>>> mock = Mock()
+>>> mock.__enter__ = Mock(return_value='foo')
+>>> mock.__exit__ = Mock(return_value=False)
+>>> with mock as m:
+...     assert m == 'foo'
+...
+>>> mock.__enter__.assert_called_with()
+>>> mock.__exit__.assert_called_with(None, None, None)
+

Calls to magic methods do not appear in method_calls, but they are recorded in mock_calls.

Note

If you use the spec keyword argument to create a mock then attempting to set a magic method that isn’t in the spec will raise an AttributeError.

The full list of supported magic methods is:

  • +__hash__, __sizeof__, __repr__ and __str__ +
  • +__dir__, __format__ and __subclasses__ +
  • +__round__, __floor__, __trunc__ and __ceil__ +
  • Comparisons: __lt__, __gt__, __le__, __ge__, __eq__ and __ne__ +
  • Container methods: __getitem__, __setitem__, __delitem__, __contains__, __len__, __iter__, __reversed__ and __missing__ +
  • Context manager: __enter__, __exit__, __aenter__ and __aexit__ +
  • Unary numeric methods: __neg__, __pos__ and __invert__ +
  • The numeric methods (including right hand and in-place variants): __add__, __sub__, __mul__, __matmul__, __truediv__, __floordiv__, __mod__, __divmod__, __lshift__, __rshift__, __and__, __xor__, __or__, and __pow__ +
  • Numeric conversion methods: __complex__, __int__, __float__ and __index__ +
  • Descriptor methods: __get__, __set__ and __delete__ +
  • Pickling: __reduce__, __reduce_ex__, __getinitargs__, __getnewargs__, __getstate__ and __setstate__ +
  • File system path representation: __fspath__ +
  • Asynchronous iteration methods: __aiter__ and __anext__ +

Changed in version 3.8: Added support for os.PathLike.__fspath__().

Changed in version 3.8: Added support for __aenter__, __aexit__, __aiter__ and __anext__.

The following methods exist but are not supported as they are either in use by mock, can’t be set dynamically, or can cause problems:

  • +__getattr__, __setattr__, __init__ and __new__ +
  • +__prepare__, __instancecheck__, __subclasscheck__, __del__ +

Magic Mock

There are two MagicMock variants: MagicMock and NonCallableMagicMock.

+class unittest.mock.MagicMock(*args, **kw)
+

MagicMock is a subclass of Mock with default implementations of most of the magic methods. You can use MagicMock without having to configure the magic methods yourself.

The constructor parameters have the same meaning as for Mock.

If you use the spec or spec_set arguments then only magic methods that exist in the spec will be created.

+
+class unittest.mock.NonCallableMagicMock(*args, **kw)
+

A non-callable version of MagicMock.

The constructor parameters have the same meaning as for MagicMock, with the exception of return_value and side_effect which have no meaning on a non-callable mock.

+

The magic methods are setup with MagicMock objects, so you can configure them and use them in the usual way:

>>> mock = MagicMock()
+>>> mock[3] = 'fish'
+>>> mock.__setitem__.assert_called_with(3, 'fish')
+>>> mock.__getitem__.return_value = 'result'
+>>> mock[2]
+'result'
+

By default many of the protocol methods are required to return objects of a specific type. These methods are preconfigured with a default return value, so that they can be used without you having to do anything if you aren’t interested in the return value. You can still set the return value manually if you want to change the default.

Methods and their defaults:

  • +__lt__: NotImplemented +
  • +__gt__: NotImplemented +
  • +__le__: NotImplemented +
  • +__ge__: NotImplemented +
  • +__int__: 1 +
  • +__contains__: False +
  • +__len__: 0 +
  • +__iter__: iter([]) +
  • +__exit__: False +
  • +__aexit__: False +
  • +__complex__: 1j +
  • +__float__: 1.0 +
  • +__bool__: True +
  • +__index__: 1 +
  • +__hash__: default hash for the mock
  • +__str__: default str for the mock
  • +__sizeof__: default sizeof for the mock

For example:

>>> mock = MagicMock()
+>>> int(mock)
+1
+>>> len(mock)
+0
+>>> list(mock)
+[]
+>>> object() in mock
+False
+

The two equality methods, __eq__() and __ne__(), are special. They do the default equality comparison on identity, using the side_effect attribute, unless you change their return value to return something else:

>>> MagicMock() == 3
+False
+>>> MagicMock() != 3
+True
+>>> mock = MagicMock()
+>>> mock.__eq__.return_value = True
+>>> mock == 3
+True
+

The return value of MagicMock.__iter__() can be any iterable object and isn’t required to be an iterator:

>>> mock = MagicMock()
+>>> mock.__iter__.return_value = ['a', 'b', 'c']
+>>> list(mock)
+['a', 'b', 'c']
+>>> list(mock)
+['a', 'b', 'c']
+

If the return value is an iterator, then iterating over it once will consume it and subsequent iterations will result in an empty list:

>>> mock.__iter__.return_value = iter(['a', 'b', 'c'])
+>>> list(mock)
+['a', 'b', 'c']
+>>> list(mock)
+[]
+

MagicMock has all of the supported magic methods configured except for some of the obscure and obsolete ones. You can still set these up if you want.

Magic methods that are supported but not setup by default in MagicMock are:

  • __subclasses__
  • __dir__
  • __format__
  • +__get__, __set__ and __delete__ +
  • +__reversed__ and __missing__ +
  • +__reduce__, __reduce_ex__, __getinitargs__, __getnewargs__, __getstate__ and __setstate__ +
  • __getformat__
+2
+

Magic methods should be looked up on the class rather than the instance. Different versions of Python are inconsistent about applying this rule. The supported protocol methods should work with all supported versions of Python.

+3
+

The function is basically hooked up to the class, but each Mock instance is kept isolated from the others.

Helpers

sentinel

+unittest.mock.sentinel
+

The sentinel object provides a convenient way of providing unique objects for your tests.

Attributes are created on demand when you access them by name. Accessing the same attribute will always return the same object. The objects returned have a sensible repr so that test failure messages are readable.

Changed in version 3.7: The sentinel attributes now preserve their identity when they are copied or pickled.

+

Sometimes when testing you need to test that a specific object is passed as an argument to another method, or returned. It can be common to create named sentinel objects to test this. sentinel provides a convenient way of creating and testing the identity of objects like this.

In this example we monkey patch method to return sentinel.some_object:

>>> real = ProductionClass()
+>>> real.method = Mock(name="method")
+>>> real.method.return_value = sentinel.some_object
+>>> result = real.method()
+>>> assert result is sentinel.some_object
+>>> result
+sentinel.some_object
+

DEFAULT

+unittest.mock.DEFAULT
+

The DEFAULT object is a pre-created sentinel (actually sentinel.DEFAULT). It can be used by side_effect functions to indicate that the normal return value should be used.

+

call

+unittest.mock.call(*args, **kwargs)
+

call() is a helper object for making simpler assertions, for comparing with call_args, call_args_list, mock_calls and method_calls. call() can also be used with assert_has_calls().

>>> m = MagicMock(return_value=None)
+>>> m(1, 2, a='foo', b='bar')
+>>> m()
+>>> m.call_args_list == [call(1, 2, a='foo', b='bar'), call()]
+True
+
+
+call.call_list()
+

For a call object that represents multiple calls, call_list() returns a list of all the intermediate calls as well as the final call.

+

call_list is particularly useful for making assertions on “chained calls”. A chained call is multiple calls on a single line of code. This results in multiple entries in mock_calls on a mock. Manually constructing the sequence of calls can be tedious.

call_list() can construct the sequence of calls from the same chained call:

>>> m = MagicMock()
+>>> m(1).method(arg='foo').other('bar')(2.0)
+<MagicMock name='mock().method().other()()' id='...'>
+>>> kall = call(1).method(arg='foo').other('bar')(2.0)
+>>> kall.call_list()
+[call(1),
+ call().method(arg='foo'),
+ call().method().other('bar'),
+ call().method().other()(2.0)]
+>>> m.mock_calls == kall.call_list()
+True
+

A call object is either a tuple of (positional args, keyword args) or (name, positional args, keyword args) depending on how it was constructed. When you construct them yourself this isn’t particularly interesting, but the call objects that are in the Mock.call_args, Mock.call_args_list and Mock.mock_calls attributes can be introspected to get at the individual arguments they contain.

The call objects in Mock.call_args and Mock.call_args_list are two-tuples of (positional args, keyword args) whereas the call objects in Mock.mock_calls, along with ones you construct yourself, are three-tuples of (name, positional args, keyword args).

You can use their “tupleness” to pull out the individual arguments for more complex introspection and assertions. The positional arguments are a tuple (an empty tuple if there are no positional arguments) and the keyword arguments are a dictionary:

>>> m = MagicMock(return_value=None)
+>>> m(1, 2, 3, arg='one', arg2='two')
+>>> kall = m.call_args
+>>> kall.args
+(1, 2, 3)
+>>> kall.kwargs
+{'arg': 'one', 'arg2': 'two'}
+>>> kall.args is kall[0]
+True
+>>> kall.kwargs is kall[1]
+True
+
>>> m = MagicMock()
+>>> m.foo(4, 5, 6, arg='two', arg2='three')
+<MagicMock name='mock.foo()' id='...'>
+>>> kall = m.mock_calls[0]
+>>> name, args, kwargs = kall
+>>> name
+'foo'
+>>> args
+(4, 5, 6)
+>>> kwargs
+{'arg': 'two', 'arg2': 'three'}
+>>> name is m.mock_calls[0][0]
+True
+

create_autospec

+unittest.mock.create_autospec(spec, spec_set=False, instance=False, **kwargs)
+

Create a mock object using another object as a spec. Attributes on the mock will use the corresponding attribute on the spec object as their spec.

Functions or methods being mocked will have their arguments checked to ensure that they are called with the correct signature.

If spec_set is True then attempting to set attributes that don’t exist on the spec object will raise an AttributeError.

If a class is used as a spec then the return value of the mock (the instance of the class) will have the same spec. You can use a class as the spec for an instance object by passing instance=True. The returned mock will only be callable if instances of the mock are callable.

create_autospec() also takes arbitrary keyword arguments that are passed to the constructor of the created mock.

+

See Autospeccing for examples of how to use auto-speccing with create_autospec() and the autospec argument to patch().

Changed in version 3.8: create_autospec() now returns an AsyncMock if the target is an async function.

ANY

+unittest.mock.ANY
+

Sometimes you may need to make assertions about some of the arguments in a call to mock, but either not care about some of the arguments or want to pull them individually out of call_args and make more complex assertions on them.

To ignore certain arguments you can pass in objects that compare equal to everything. Calls to assert_called_with() and assert_called_once_with() will then succeed no matter what was passed in.

>>> mock = Mock(return_value=None)
+>>> mock('foo', bar=object())
+>>> mock.assert_called_once_with('foo', bar=ANY)
+

ANY can also be used in comparisons with call lists like mock_calls:

>>> m = MagicMock(return_value=None)
+>>> m(1)
+>>> m(1, 2)
+>>> m(object())
+>>> m.mock_calls == [call(1), call(1, 2), ANY]
+True
+

FILTER_DIR

+unittest.mock.FILTER_DIR
+

FILTER_DIR is a module level variable that controls the way mock objects respond to dir(). The default is True, which uses the filtering described below, to only show useful members. If you dislike this filtering, or need to switch it off for diagnostic purposes, then set mock.FILTER_DIR = False.

With filtering on, dir(some_mock) shows only useful attributes and will include any dynamically created attributes that wouldn’t normally be shown. If the mock was created with a spec (or autospec of course) then all the attributes from the original are shown, even if they haven’t been accessed yet:

>>> dir(Mock())
+['assert_any_call',
+ 'assert_called',
+ 'assert_called_once',
+ 'assert_called_once_with',
+ 'assert_called_with',
+ 'assert_has_calls',
+ 'assert_not_called',
+ 'attach_mock',
+ ...
+>>> from urllib import request
+>>> dir(Mock(spec=request))
+['AbstractBasicAuthHandler',
+ 'AbstractDigestAuthHandler',
+ 'AbstractHTTPHandler',
+ 'BaseHandler',
+ ...
+

Many of the not-very-useful (private to Mock rather than the thing being mocked) underscore and double underscore prefixed attributes have been filtered from the result of calling dir() on a Mock. If you dislike this behaviour you can switch it off by setting the module level switch FILTER_DIR:

>>> from unittest import mock
+>>> mock.FILTER_DIR = False
+>>> dir(mock.Mock())
+['_NonCallableMock__get_return_value',
+ '_NonCallableMock__get_side_effect',
+ '_NonCallableMock__return_value_doc',
+ '_NonCallableMock__set_return_value',
+ '_NonCallableMock__set_side_effect',
+ '__call__',
+ '__class__',
+ ...
+

Alternatively you can just use vars(my_mock) (instance members) and dir(type(my_mock)) (type members) to bypass the filtering irrespective of mock.FILTER_DIR.

mock_open

+unittest.mock.mock_open(mock=None, read_data=None)
+

A helper function to create a mock to replace the use of open(). It works for open() called directly or used as a context manager.

The mock argument is the mock object to configure. If None (the default) then a MagicMock will be created for you, with the API limited to methods or attributes available on standard file handles.

read_data is a string for the read(), readline(), and readlines() methods of the file handle to return. Calls to those methods will take data from read_data until it is depleted. The mock of these methods is pretty simplistic: every time the mock is called, the read_data is rewound to the start. If you need more control over the data that you are feeding to the tested code you will need to customize this mock for yourself. When that is insufficient, one of the in-memory filesystem packages on PyPI can offer a realistic filesystem for testing.

Changed in version 3.4: Added readline() and readlines() support. The mock of read() changed to consume read_data rather than returning it on each call.

Changed in version 3.5: read_data is now reset on each call to the mock.

Changed in version 3.8: Added __iter__() to implementation so that iteration (such as in for loops) correctly consumes read_data.

+

Using open() as a context manager is a great way to ensure your file handles are closed properly and is becoming common:

with open('/some/path', 'w') as f:
+    f.write('something')
+

The issue is that even if you mock out the call to open() it is the returned object that is used as a context manager (and has __enter__() and __exit__() called).

Mocking context managers with a MagicMock is common enough and fiddly enough that a helper function is useful.

>>> m = mock_open()
+>>> with patch('__main__.open', m):
+...     with open('foo', 'w') as h:
+...         h.write('some stuff')
+...
+>>> m.mock_calls
+[call('foo', 'w'),
+ call().__enter__(),
+ call().write('some stuff'),
+ call().__exit__(None, None, None)]
+>>> m.assert_called_once_with('foo', 'w')
+>>> handle = m()
+>>> handle.write.assert_called_once_with('some stuff')
+

And for reading files:

>>> with patch('__main__.open', mock_open(read_data='bibble')) as m:
+...     with open('foo') as h:
+...         result = h.read()
+...
+>>> m.assert_called_once_with('foo')
+>>> assert result == 'bibble'
+

Autospeccing

Autospeccing is based on the existing spec feature of mock. It limits the api of mocks to the api of an original object (the spec), but it is recursive (implemented lazily) so that attributes of mocks only have the same api as the attributes of the spec. In addition mocked functions / methods have the same call signature as the original so they raise a TypeError if they are called incorrectly.

Before I explain how auto-speccing works, here’s why it is needed.

Mock is a very powerful and flexible object, but it suffers from two flaws when used to mock out objects from a system under test. One of these flaws is specific to the Mock api and the other is a more general problem with using mock objects.

First the problem specific to Mock. Mock has two assert methods that are extremely handy: assert_called_with() and assert_called_once_with().

>>> mock = Mock(name='Thing', return_value=None)
+>>> mock(1, 2, 3)
+>>> mock.assert_called_once_with(1, 2, 3)
+>>> mock(1, 2, 3)
+>>> mock.assert_called_once_with(1, 2, 3)
+Traceback (most recent call last):
+ ...
+AssertionError: Expected 'mock' to be called once. Called 2 times.
+

Because mocks auto-create attributes on demand, and allow you to call them with arbitrary arguments, if you misspell one of these assert methods then your assertion is gone:

>>> mock = Mock(name='Thing', return_value=None)
+>>> mock(1, 2, 3)
+>>> mock.assret_called_once_with(4, 5, 6)  # Intentional typo!
+

Your tests can pass silently and incorrectly because of the typo.

The second issue is more general to mocking. If you refactor some of your code, rename members and so on, any tests for code that is still using the old api but uses mocks instead of the real objects will still pass. This means your tests can all pass even though your code is broken.

Note that this is another reason why you need integration tests as well as unit tests. Testing everything in isolation is all fine and dandy, but if you don’t test how your units are “wired together” there is still lots of room for bugs that tests might have caught.

mock already provides a feature to help with this, called speccing. If you use a class or instance as the spec for a mock then you can only access attributes on the mock that exist on the real class:

>>> from urllib import request
+>>> mock = Mock(spec=request.Request)
+>>> mock.assret_called_with  # Intentional typo!
+Traceback (most recent call last):
+ ...
+AttributeError: Mock object has no attribute 'assret_called_with'
+

The spec only applies to the mock itself, so we still have the same issue with any methods on the mock:

>>> mock.has_data()
+<mock.Mock object at 0x...>
+>>> mock.has_data.assret_called_with()  # Intentional typo!
+

Auto-speccing solves this problem. You can either pass autospec=True to patch() / patch.object() or use the create_autospec() function to create a mock with a spec. If you use the autospec=True argument to patch() then the object that is being replaced will be used as the spec object. Because the speccing is done “lazily” (the spec is created as attributes on the mock are accessed) you can use it with very complex or deeply nested objects (like modules that import modules that import modules) without a big performance hit.

Here’s an example of it in use:

>>> from urllib import request
+>>> patcher = patch('__main__.request', autospec=True)
+>>> mock_request = patcher.start()
+>>> request is mock_request
+True
+>>> mock_request.Request
+<MagicMock name='request.Request' spec='Request' id='...'>
+

You can see that request.Request has a spec. request.Request takes two arguments in the constructor (one of which is self). Here’s what happens if we try to call it incorrectly:

>>> req = request.Request()
+Traceback (most recent call last):
+ ...
+TypeError: <lambda>() takes at least 2 arguments (1 given)
+

The spec also applies to instantiated classes (i.e. the return value of specced mocks):

>>> req = request.Request('foo')
+>>> req
+<NonCallableMagicMock name='request.Request()' spec='Request' id='...'>
+

Request objects are not callable, so the return value of instantiating our mocked out request.Request is a non-callable mock. With the spec in place any typos in our asserts will raise the correct error:

>>> req.add_header('spam', 'eggs')
+<MagicMock name='request.Request().add_header()' id='...'>
+>>> req.add_header.assret_called_with  # Intentional typo!
+Traceback (most recent call last):
+ ...
+AttributeError: Mock object has no attribute 'assret_called_with'
+>>> req.add_header.assert_called_with('spam', 'eggs')
+

In many cases you will just be able to add autospec=True to your existing patch() calls and then be protected against bugs due to typos and api changes.

As well as using autospec through patch() there is a create_autospec() for creating autospecced mocks directly:

>>> from urllib import request
+>>> mock_request = create_autospec(request)
+>>> mock_request.Request('foo', 'bar')
+<NonCallableMagicMock name='mock.Request()' spec='Request' id='...'>
+

This isn’t without caveats and limitations however, which is why it is not the default behaviour. In order to know what attributes are available on the spec object, autospec has to introspect (access attributes) the spec. As you traverse attributes on the mock a corresponding traversal of the original object is happening under the hood. If any of your specced objects have properties or descriptors that can trigger code execution then you may not be able to use autospec. On the other hand it is much better to design your objects so that introspection is safe 4.

A more serious problem is that it is common for instance attributes to be created in the __init__() method and not to exist on the class at all. autospec can’t know about any dynamically created attributes and restricts the api to visible attributes.

>>> class Something:
+...   def __init__(self):
+...     self.a = 33
+...
+>>> with patch('__main__.Something', autospec=True):
+...   thing = Something()
+...   thing.a
+...
+Traceback (most recent call last):
+  ...
+AttributeError: Mock object has no attribute 'a'
+

There are a few different ways of resolving this problem. The easiest, but not necessarily the least annoying, way is to simply set the required attributes on the mock after creation. Just because autospec doesn’t allow you to fetch attributes that don’t exist on the spec it doesn’t prevent you setting them:

>>> with patch('__main__.Something', autospec=True):
+...   thing = Something()
+...   thing.a = 33
+...
+

There is a more aggressive version of both spec and autospec that does prevent you setting non-existent attributes. This is useful if you want to ensure your code only sets valid attributes too, but obviously it prevents this particular scenario:

>>> with patch('__main__.Something', autospec=True, spec_set=True):
+...   thing = Something()
+...   thing.a = 33
+...
+Traceback (most recent call last):
+ ...
+AttributeError: Mock object has no attribute 'a'
+

Probably the best way of solving the problem is to add class attributes as default values for instance members initialised in __init__(). Note that if you are only setting default attributes in __init__() then providing them via class attributes (shared between instances of course) is faster too. e.g.

class Something:
+    a = 33
+

This brings up another issue. It is relatively common to provide a default value of None for members that will later be an object of a different type. None would be useless as a spec because it wouldn’t let you access any attributes or methods on it. As None is never going to be useful as a spec, and probably indicates a member that will normally of some other type, autospec doesn’t use a spec for members that are set to None. These will just be ordinary mocks (well - MagicMocks):

>>> class Something:
+...     member = None
+...
+>>> mock = create_autospec(Something)
+>>> mock.member.foo.bar.baz()
+<MagicMock name='mock.member.foo.bar.baz()' id='...'>
+

If modifying your production classes to add defaults isn’t to your liking then there are more options. One of these is simply to use an instance as the spec rather than the class. The other is to create a subclass of the production class and add the defaults to the subclass without affecting the production class. Both of these require you to use an alternative object as the spec. Thankfully patch() supports this - you can simply pass the alternative object as the autospec argument:

>>> class Something:
+...   def __init__(self):
+...     self.a = 33
+...
+>>> class SomethingForTest(Something):
+...   a = 33
+...
+>>> p = patch('__main__.Something', autospec=SomethingForTest)
+>>> mock = p.start()
+>>> mock.a
+<NonCallableMagicMock name='Something.a' spec='int' id='...'>
+
+4
+

This only applies to classes or already instantiated objects. Calling a mocked class to create a mock instance does not create a real instance. It is only attribute lookups - along with calls to dir() - that are done.

Sealing mocks

+unittest.mock.seal(mock)
+

Seal will disable the automatic creation of mocks when accessing an attribute of the mock being sealed or any of its attributes that are already mocks recursively.

If a mock instance with a name or a spec is assigned to an attribute it won’t be considered in the sealing chain. This allows one to prevent seal from fixing part of the mock object.

>>> mock = Mock()
+>>> mock.submock.attribute1 = 2
+>>> mock.not_submock = mock.Mock(name="sample_name")
+>>> seal(mock)
+>>> mock.new_attribute  # This will raise AttributeError.
+>>> mock.submock.attribute2  # This will raise AttributeError.
+>>> mock.not_submock.attribute2  # This won't raise.
+

New in version 3.7.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/unittest.mock.html +

+
diff --git a/devdocs/python~3.12/library%2Funix.html b/devdocs/python~3.12/library%2Funix.html new file mode 100644 index 00000000..b68eb236 --- /dev/null +++ b/devdocs/python~3.12/library%2Funix.html @@ -0,0 +1,12 @@ +

Unix Specific Services

The modules described in this chapter provide interfaces to features that are unique to the Unix operating system, or in some cases to some or many variants of it. Here’s an overview:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/unix.html +

+
diff --git a/devdocs/python~3.12/library%2Furllib.error.html b/devdocs/python~3.12/library%2Furllib.error.html new file mode 100644 index 00000000..eeff5037 --- /dev/null +++ b/devdocs/python~3.12/library%2Furllib.error.html @@ -0,0 +1,36 @@ +

urllib.error — Exception classes raised by urllib.request

Source code: Lib/urllib/error.py

The urllib.error module defines the exception classes for exceptions raised by urllib.request. The base exception class is URLError.

The following exceptions are raised by urllib.error as appropriate:

+exception urllib.error.URLError
+

The handlers raise this exception (or derived exceptions) when they run into a problem. It is a subclass of OSError.

+reason
+

The reason for this error. It can be a message string or another exception instance.

+

Changed in version 3.3: URLError used to be a subtype of IOError, which is now an alias of OSError.

+
+exception urllib.error.HTTPError(url, code, msg, hdrs, fp)
+

Though being an exception (a subclass of URLError), an HTTPError can also function as a non-exceptional file-like return value (the same thing that urlopen() returns). This is useful when handling exotic HTTP errors, such as requests for authentication.

+url
+

Contains the request URL. An alias for filename attribute.

+
+code
+

An HTTP status code as defined in RFC 2616. This numeric value corresponds to a value found in the dictionary of codes as found in http.server.BaseHTTPRequestHandler.responses.

+
+reason
+

This is usually a string explaining the reason for this error. An alias for msg attribute.

+
+headers
+

The HTTP response headers for the HTTP request that caused the HTTPError. An alias for hdrs attribute.

New in version 3.4.

+
+fp
+

A file-like object where the HTTP error body can be read from.

+
+
+exception urllib.error.ContentTooShortError(msg, content)
+

This exception is raised when the urlretrieve() function detects that the amount of the downloaded data is less than the expected amount (given by the Content-Length header).

+content
+

The downloaded (and supposedly truncated) data.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/urllib.error.html +

+
diff --git a/devdocs/python~3.12/library%2Furllib.html b/devdocs/python~3.12/library%2Furllib.html new file mode 100644 index 00000000..4eb00406 --- /dev/null +++ b/devdocs/python~3.12/library%2Furllib.html @@ -0,0 +1,11 @@ +

urllib — URL handling modules

Source code: Lib/urllib/

urllib is a package that collects several modules for working with URLs:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/urllib.html +

+
diff --git a/devdocs/python~3.12/library%2Furllib.parse.html b/devdocs/python~3.12/library%2Furllib.parse.html new file mode 100644 index 00000000..459e37f2 --- /dev/null +++ b/devdocs/python~3.12/library%2Furllib.parse.html @@ -0,0 +1,173 @@ +

urllib.parse — Parse URLs into components

Source code: Lib/urllib/parse.py

This module defines a standard interface to break Uniform Resource Locator (URL) strings up in components (addressing scheme, network location, path etc.), to combine the components back into a URL string, and to convert a “relative URL” to an absolute URL given a “base URL.”

The module has been designed to match the internet RFC on Relative Uniform Resource Locators. It supports the following URL schemes: file, ftp, gopher, hdl, http, https, imap, mailto, mms, news, nntp, prospero, rsync, rtsp, rtsps, rtspu, sftp, shttp, sip, sips, snews, svn, svn+ssh, telnet, wais, ws, wss.

The urllib.parse module defines functions that fall into two broad categories: URL parsing and URL quoting. These are covered in detail in the following sections.

URL Parsing

The URL parsing functions focus on splitting a URL string into its components, or on combining URL components into a URL string.

+urllib.parse.urlparse(urlstring, scheme='', allow_fragments=True)
+

Parse a URL into six components, returning a 6-item named tuple. This corresponds to the general structure of a URL: scheme://netloc/path;parameters?query#fragment. Each tuple item is a string, possibly empty. The components are not broken up into smaller parts (for example, the network location is a single string), and % escapes are not expanded. The delimiters as shown above are not part of the result, except for a leading slash in the path component, which is retained if present. For example:

>>> from urllib.parse import urlparse
+>>> urlparse("scheme://netloc/path;parameters?query#fragment")
+ParseResult(scheme='scheme', netloc='netloc', path='/path;parameters', params='',
+            query='query', fragment='fragment')
+>>> o = urlparse("http://docs.python.org:80/3/library/urllib.parse.html?"
+...              "highlight=params#url-parsing")
+>>> o
+ParseResult(scheme='http', netloc='docs.python.org:80',
+            path='/3/library/urllib.parse.html', params='',
+            query='highlight=params', fragment='url-parsing')
+>>> o.scheme
+'http'
+>>> o.netloc
+'docs.python.org:80'
+>>> o.hostname
+'docs.python.org'
+>>> o.port
+80
+>>> o._replace(fragment="").geturl()
+'http://docs.python.org:80/3/library/urllib.parse.html?highlight=params'
+

Following the syntax specifications in RFC 1808, urlparse recognizes a netloc only if it is properly introduced by ‘//’. Otherwise the input is presumed to be a relative URL and thus to start with a path component.

>>> from urllib.parse import urlparse
+>>> urlparse('//www.cwi.nl:80/%7Eguido/Python.html')
+ParseResult(scheme='', netloc='www.cwi.nl:80', path='/%7Eguido/Python.html',
+            params='', query='', fragment='')
+>>> urlparse('www.cwi.nl/%7Eguido/Python.html')
+ParseResult(scheme='', netloc='', path='www.cwi.nl/%7Eguido/Python.html',
+            params='', query='', fragment='')
+>>> urlparse('help/Python.html')
+ParseResult(scheme='', netloc='', path='help/Python.html', params='',
+            query='', fragment='')
+

The scheme argument gives the default addressing scheme, to be used only if the URL does not specify one. It should be the same type (text or bytes) as urlstring, except that the default value '' is always allowed, and is automatically converted to b'' if appropriate.

If the allow_fragments argument is false, fragment identifiers are not recognized. Instead, they are parsed as part of the path, parameters or query component, and fragment is set to the empty string in the return value.

The return value is a named tuple, which means that its items can be accessed by index or as named attributes, which are:

+ + + + + + + + + + +

Attribute

Index

Value

Value if not present

scheme

0

URL scheme specifier

scheme parameter

netloc

1

Network location part

empty string

path

2

Hierarchical path

empty string

params

3

Parameters for last path element

empty string

query

4

Query component

empty string

fragment

5

Fragment identifier

empty string

username

User name

None

password

Password

None

hostname

Host name (lower case)

None

port

Port number as integer, if present

None

Reading the port attribute will raise a ValueError if an invalid port is specified in the URL. See section Structured Parse Results for more information on the result object.

Unmatched square brackets in the netloc attribute will raise a ValueError.

Characters in the netloc attribute that decompose under NFKC normalization (as used by the IDNA encoding) into any of /, ?, #, @, or : will raise a ValueError. If the URL is decomposed before parsing, no error will be raised.

As is the case with all named tuples, the subclass has a few additional methods and attributes that are particularly useful. One such method is _replace(). The _replace() method will return a new ParseResult object replacing specified fields with new values.

>>> from urllib.parse import urlparse
+>>> u = urlparse('//www.cwi.nl:80/%7Eguido/Python.html')
+>>> u
+ParseResult(scheme='', netloc='www.cwi.nl:80', path='/%7Eguido/Python.html',
+            params='', query='', fragment='')
+>>> u._replace(scheme='http')
+ParseResult(scheme='http', netloc='www.cwi.nl:80', path='/%7Eguido/Python.html',
+            params='', query='', fragment='')
+

Warning

urlparse() does not perform validation. See URL parsing security for details.

Changed in version 3.2: Added IPv6 URL parsing capabilities.

Changed in version 3.3: The fragment is now parsed for all URL schemes (unless allow_fragment is false), in accordance with RFC 3986. Previously, an allowlist of schemes that support fragments existed.

Changed in version 3.6: Out-of-range port numbers now raise ValueError, instead of returning None.

Changed in version 3.8: Characters that affect netloc parsing under NFKC normalization will now raise ValueError.

+
+urllib.parse.parse_qs(qs, keep_blank_values=False, strict_parsing=False, encoding='utf-8', errors='replace', max_num_fields=None, separator='&')
+

Parse a query string given as a string argument (data of type application/x-www-form-urlencoded). Data are returned as a dictionary. The dictionary keys are the unique query variable names and the values are lists of values for each name.

The optional argument keep_blank_values is a flag indicating whether blank values in percent-encoded queries should be treated as blank strings. A true value indicates that blanks should be retained as blank strings. The default false value indicates that blank values are to be ignored and treated as if they were not included.

The optional argument strict_parsing is a flag indicating what to do with parsing errors. If false (the default), errors are silently ignored. If true, errors raise a ValueError exception.

The optional encoding and errors parameters specify how to decode percent-encoded sequences into Unicode characters, as accepted by the bytes.decode() method.

The optional argument max_num_fields is the maximum number of fields to read. If set, then throws a ValueError if there are more than max_num_fields fields read.

The optional argument separator is the symbol to use for separating the query arguments. It defaults to &.

Use the urllib.parse.urlencode() function (with the doseq parameter set to True) to convert such dictionaries into query strings.

Changed in version 3.2: Add encoding and errors parameters.

Changed in version 3.8: Added max_num_fields parameter.

Changed in version 3.10: Added separator parameter with the default value of &. Python versions earlier than Python 3.10 allowed using both ; and & as query parameter separator. This has been changed to allow only a single separator key, with & as the default separator.

+
+urllib.parse.parse_qsl(qs, keep_blank_values=False, strict_parsing=False, encoding='utf-8', errors='replace', max_num_fields=None, separator='&')
+

Parse a query string given as a string argument (data of type application/x-www-form-urlencoded). Data are returned as a list of name, value pairs.

The optional argument keep_blank_values is a flag indicating whether blank values in percent-encoded queries should be treated as blank strings. A true value indicates that blanks should be retained as blank strings. The default false value indicates that blank values are to be ignored and treated as if they were not included.

The optional argument strict_parsing is a flag indicating what to do with parsing errors. If false (the default), errors are silently ignored. If true, errors raise a ValueError exception.

The optional encoding and errors parameters specify how to decode percent-encoded sequences into Unicode characters, as accepted by the bytes.decode() method.

The optional argument max_num_fields is the maximum number of fields to read. If set, then throws a ValueError if there are more than max_num_fields fields read.

The optional argument separator is the symbol to use for separating the query arguments. It defaults to &.

Use the urllib.parse.urlencode() function to convert such lists of pairs into query strings.

Changed in version 3.2: Add encoding and errors parameters.

Changed in version 3.8: Added max_num_fields parameter.

Changed in version 3.10: Added separator parameter with the default value of &. Python versions earlier than Python 3.10 allowed using both ; and & as query parameter separator. This has been changed to allow only a single separator key, with & as the default separator.

+
+urllib.parse.urlunparse(parts)
+

Construct a URL from a tuple as returned by urlparse(). The parts argument can be any six-item iterable. This may result in a slightly different, but equivalent URL, if the URL that was parsed originally had unnecessary delimiters (for example, a ? with an empty query; the RFC states that these are equivalent).

+
+urllib.parse.urlsplit(urlstring, scheme='', allow_fragments=True)
+

This is similar to urlparse(), but does not split the params from the URL. This should generally be used instead of urlparse() if the more recent URL syntax allowing parameters to be applied to each segment of the path portion of the URL (see RFC 2396) is wanted. A separate function is needed to separate the path segments and parameters. This function returns a 5-item named tuple:

(addressing scheme, network location, path, query, fragment identifier).
+

The return value is a named tuple, its items can be accessed by index or as named attributes:

+ + + + + + + + + +

Attribute

Index

Value

Value if not present

scheme

0

URL scheme specifier

scheme parameter

netloc

1

Network location part

empty string

path

2

Hierarchical path

empty string

query

3

Query component

empty string

fragment

4

Fragment identifier

empty string

username

User name

None

password

Password

None

hostname

Host name (lower case)

None

port

Port number as integer, if present

None

Reading the port attribute will raise a ValueError if an invalid port is specified in the URL. See section Structured Parse Results for more information on the result object.

Unmatched square brackets in the netloc attribute will raise a ValueError.

Characters in the netloc attribute that decompose under NFKC normalization (as used by the IDNA encoding) into any of /, ?, #, @, or : will raise a ValueError. If the URL is decomposed before parsing, no error will be raised.

Following some of the WHATWG spec that updates RFC 3986, leading C0 control and space characters are stripped from the URL. \n, \r and tab \t characters are removed from the URL at any position.

Warning

urlsplit() does not perform validation. See URL parsing security for details.

Changed in version 3.6: Out-of-range port numbers now raise ValueError, instead of returning None.

Changed in version 3.8: Characters that affect netloc parsing under NFKC normalization will now raise ValueError.

Changed in version 3.10: ASCII newline and tab characters are stripped from the URL.

Changed in version 3.12: Leading WHATWG C0 control and space characters are stripped from the URL.

+
+urllib.parse.urlunsplit(parts)
+

Combine the elements of a tuple as returned by urlsplit() into a complete URL as a string. The parts argument can be any five-item iterable. This may result in a slightly different, but equivalent URL, if the URL that was parsed originally had unnecessary delimiters (for example, a ? with an empty query; the RFC states that these are equivalent).

+
+urllib.parse.urljoin(base, url, allow_fragments=True)
+

Construct a full (“absolute”) URL by combining a “base URL” (base) with another URL (url). Informally, this uses components of the base URL, in particular the addressing scheme, the network location and (part of) the path, to provide missing components in the relative URL. For example:

>>> from urllib.parse import urljoin
+>>> urljoin('http://www.cwi.nl/%7Eguido/Python.html', 'FAQ.html')
+'http://www.cwi.nl/%7Eguido/FAQ.html'
+

The allow_fragments argument has the same meaning and default as for urlparse().

Note

If url is an absolute URL (that is, it starts with // or scheme://), the url’s hostname and/or scheme will be present in the result. For example:

>>> urljoin('http://www.cwi.nl/%7Eguido/Python.html',
+...         '//www.python.org/%7Eguido')
+'http://www.python.org/%7Eguido'
+

If you do not want that behavior, preprocess the url with urlsplit() and urlunsplit(), removing possible scheme and netloc parts.

Changed in version 3.5: Behavior updated to match the semantics defined in RFC 3986.

+
+urllib.parse.urldefrag(url)
+

If url contains a fragment identifier, return a modified version of url with no fragment identifier, and the fragment identifier as a separate string. If there is no fragment identifier in url, return url unmodified and an empty string.

The return value is a named tuple, its items can be accessed by index or as named attributes:

+ + +

Attribute

Index

Value

Value if not present

url

0

URL with no fragment

empty string

fragment

1

Fragment identifier

empty string

See section Structured Parse Results for more information on the result object.

Changed in version 3.2: Result is a structured object rather than a simple 2-tuple.

+
+urllib.parse.unwrap(url)
+

Extract the url from a wrapped URL (that is, a string formatted as <URL:scheme://host/path>, <scheme://host/path>, URL:scheme://host/path or scheme://host/path). If url is not a wrapped URL, it is returned without changes.

+

URL parsing security

The urlsplit() and urlparse() APIs do not perform validation of inputs. They may not raise errors on inputs that other applications consider invalid. They may also succeed on some inputs that might not be considered URLs elsewhere. Their purpose is for practical functionality rather than purity.

Instead of raising an exception on unusual input, they may instead return some component parts as empty strings. Or components may contain more than perhaps they should.

We recommend that users of these APIs where the values may be used anywhere with security implications code defensively. Do some verification within your code before trusting a returned component part. Does that scheme make sense? Is that a sensible path? Is there anything strange about that hostname? etc.

What constitutes a URL is not universally well defined. Different applications have different needs and desired constraints. For instance the living WHATWG spec describes what user facing web clients such as a web browser require. While RFC 3986 is more general. These functions incorporate some aspects of both, but cannot be claimed compliant with either. The APIs and existing user code with expectations on specific behaviors predate both standards leading us to be very cautious about making API behavior changes.

Parsing ASCII Encoded Bytes

The URL parsing functions were originally designed to operate on character strings only. In practice, it is useful to be able to manipulate properly quoted and encoded URLs as sequences of ASCII bytes. Accordingly, the URL parsing functions in this module all operate on bytes and bytearray objects in addition to str objects.

If str data is passed in, the result will also contain only str data. If bytes or bytearray data is passed in, the result will contain only bytes data.

Attempting to mix str data with bytes or bytearray in a single function call will result in a TypeError being raised, while attempting to pass in non-ASCII byte values will trigger UnicodeDecodeError.

To support easier conversion of result objects between str and bytes, all return values from URL parsing functions provide either an encode() method (when the result contains str data) or a decode() method (when the result contains bytes data). The signatures of these methods match those of the corresponding str and bytes methods (except that the default encoding is 'ascii' rather than 'utf-8'). Each produces a value of a corresponding type that contains either bytes data (for encode() methods) or str data (for decode() methods).

Applications that need to operate on potentially improperly quoted URLs that may contain non-ASCII data will need to do their own decoding from bytes to characters before invoking the URL parsing methods.

The behaviour described in this section applies only to the URL parsing functions. The URL quoting functions use their own rules when producing or consuming byte sequences as detailed in the documentation of the individual URL quoting functions.

Changed in version 3.2: URL parsing functions now accept ASCII encoded byte sequences

Structured Parse Results

The result objects from the urlparse(), urlsplit() and urldefrag() functions are subclasses of the tuple type. These subclasses add the attributes listed in the documentation for those functions, the encoding and decoding support described in the previous section, as well as an additional method:

+urllib.parse.SplitResult.geturl()
+

Return the re-combined version of the original URL as a string. This may differ from the original URL in that the scheme may be normalized to lower case and empty components may be dropped. Specifically, empty parameters, queries, and fragment identifiers will be removed.

For urldefrag() results, only empty fragment identifiers will be removed. For urlsplit() and urlparse() results, all noted changes will be made to the URL returned by this method.

The result of this method remains unchanged if passed back through the original parsing function:

>>> from urllib.parse import urlsplit
+>>> url = 'HTTP://www.Python.org/doc/#'
+>>> r1 = urlsplit(url)
+>>> r1.geturl()
+'http://www.Python.org/doc/'
+>>> r2 = urlsplit(r1.geturl())
+>>> r2.geturl()
+'http://www.Python.org/doc/'
+
+

The following classes provide the implementations of the structured parse results when operating on str objects:

+class urllib.parse.DefragResult(url, fragment)
+

Concrete class for urldefrag() results containing str data. The encode() method returns a DefragResultBytes instance.

New in version 3.2.

+
+class urllib.parse.ParseResult(scheme, netloc, path, params, query, fragment)
+

Concrete class for urlparse() results containing str data. The encode() method returns a ParseResultBytes instance.

+
+class urllib.parse.SplitResult(scheme, netloc, path, query, fragment)
+

Concrete class for urlsplit() results containing str data. The encode() method returns a SplitResultBytes instance.

+

The following classes provide the implementations of the parse results when operating on bytes or bytearray objects:

+class urllib.parse.DefragResultBytes(url, fragment)
+

Concrete class for urldefrag() results containing bytes data. The decode() method returns a DefragResult instance.

New in version 3.2.

+
+class urllib.parse.ParseResultBytes(scheme, netloc, path, params, query, fragment)
+

Concrete class for urlparse() results containing bytes data. The decode() method returns a ParseResult instance.

New in version 3.2.

+
+class urllib.parse.SplitResultBytes(scheme, netloc, path, query, fragment)
+

Concrete class for urlsplit() results containing bytes data. The decode() method returns a SplitResult instance.

New in version 3.2.

+

URL Quoting

The URL quoting functions focus on taking program data and making it safe for use as URL components by quoting special characters and appropriately encoding non-ASCII text. They also support reversing these operations to recreate the original data from the contents of a URL component if that task isn’t already covered by the URL parsing functions above.

+urllib.parse.quote(string, safe='/', encoding=None, errors=None)
+

Replace special characters in string using the %xx escape. Letters, digits, and the characters '_.-~' are never quoted. By default, this function is intended for quoting the path section of a URL. The optional safe parameter specifies additional ASCII characters that should not be quoted — its default value is '/'.

string may be either a str or a bytes object.

Changed in version 3.7: Moved from RFC 2396 to RFC 3986 for quoting URL strings. “~” is now included in the set of unreserved characters.

The optional encoding and errors parameters specify how to deal with non-ASCII characters, as accepted by the str.encode() method. encoding defaults to 'utf-8'. errors defaults to 'strict', meaning unsupported characters raise a UnicodeEncodeError. encoding and errors must not be supplied if string is a bytes, or a TypeError is raised.

Note that quote(string, safe, encoding, errors) is equivalent to quote_from_bytes(string.encode(encoding, errors), safe).

Example: quote('/El Niño/') yields '/El%20Ni%C3%B1o/'.

+
+urllib.parse.quote_plus(string, safe='', encoding=None, errors=None)
+

Like quote(), but also replace spaces with plus signs, as required for quoting HTML form values when building up a query string to go into a URL. Plus signs in the original string are escaped unless they are included in safe. It also does not have safe default to '/'.

Example: quote_plus('/El Niño/') yields '%2FEl+Ni%C3%B1o%2F'.

+
+urllib.parse.quote_from_bytes(bytes, safe='/')
+

Like quote(), but accepts a bytes object rather than a str, and does not perform string-to-bytes encoding.

Example: quote_from_bytes(b'a&\xef') yields 'a%26%EF'.

+
+urllib.parse.unquote(string, encoding='utf-8', errors='replace')
+

Replace %xx escapes with their single-character equivalent. The optional encoding and errors parameters specify how to decode percent-encoded sequences into Unicode characters, as accepted by the bytes.decode() method.

string may be either a str or a bytes object.

encoding defaults to 'utf-8'. errors defaults to 'replace', meaning invalid sequences are replaced by a placeholder character.

Example: unquote('/El%20Ni%C3%B1o/') yields '/El Niño/'.

Changed in version 3.9: string parameter supports bytes and str objects (previously only str).

+
+urllib.parse.unquote_plus(string, encoding='utf-8', errors='replace')
+

Like unquote(), but also replace plus signs with spaces, as required for unquoting HTML form values.

string must be a str.

Example: unquote_plus('/El+Ni%C3%B1o/') yields '/El Niño/'.

+
+urllib.parse.unquote_to_bytes(string)
+

Replace %xx escapes with their single-octet equivalent, and return a bytes object.

string may be either a str or a bytes object.

If it is a str, unescaped non-ASCII characters in string are encoded into UTF-8 bytes.

Example: unquote_to_bytes('a%26%EF') yields b'a&\xef'.

+
+urllib.parse.urlencode(query, doseq=False, safe='', encoding=None, errors=None, quote_via=quote_plus)
+

Convert a mapping object or a sequence of two-element tuples, which may contain str or bytes objects, to a percent-encoded ASCII text string. If the resultant string is to be used as a data for POST operation with the urlopen() function, then it should be encoded to bytes, otherwise it would result in a TypeError.

The resulting string is a series of key=value pairs separated by '&' characters, where both key and value are quoted using the quote_via function. By default, quote_plus() is used to quote the values, which means spaces are quoted as a '+' character and ‘/’ characters are encoded as %2F, which follows the standard for GET requests (application/x-www-form-urlencoded). An alternate function that can be passed as quote_via is quote(), which will encode spaces as %20 and not encode ‘/’ characters. For maximum control of what is quoted, use quote and specify a value for safe.

When a sequence of two-element tuples is used as the query argument, the first element of each tuple is a key and the second is a value. The value element in itself can be a sequence and in that case, if the optional parameter doseq evaluates to True, individual key=value pairs separated by '&' are generated for each element of the value sequence for the key. The order of parameters in the encoded string will match the order of parameter tuples in the sequence.

The safe, encoding, and errors parameters are passed down to quote_via (the encoding and errors parameters are only passed when a query element is a str).

To reverse this encoding process, parse_qs() and parse_qsl() are provided in this module to parse query strings into Python data structures.

Refer to urllib examples to find out how the urllib.parse.urlencode() method can be used for generating the query string of a URL or data for a POST request.

Changed in version 3.2: query supports bytes and string objects.

New in version 3.5: quote_via parameter.

+

See also

+WHATWG - URL Living standard
+
+

Working Group for the URL Standard that defines URLs, domains, IP addresses, the application/x-www-form-urlencoded format, and their API.

+RFC 3986 - Uniform Resource Identifiers
+
+

This is the current standard (STD66). Any changes to urllib.parse module should conform to this. Certain deviations could be observed, which are mostly for backward compatibility purposes and for certain de-facto parsing requirements as commonly observed in major browsers.

+RFC 2732 - Format for Literal IPv6 Addresses in URL’s.
+
+

This specifies the parsing requirements of IPv6 URLs.

+RFC 2396 - Uniform Resource Identifiers (URI): Generic Syntax
+
+

Document describing the generic syntactic requirements for both Uniform Resource Names (URNs) and Uniform Resource Locators (URLs).

+RFC 2368 - The mailto URL scheme.
+
+

Parsing requirements for mailto URL schemes.

+RFC 1808 - Relative Uniform Resource Locators
+
+

This Request For Comments includes the rules for joining an absolute and a relative URL, including a fair number of “Abnormal Examples” which govern the treatment of border cases.

+RFC 1738 - Uniform Resource Locators (URL)
+
+

This specifies the formal syntax and semantics of absolute URLs.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/urllib.parse.html +

+
diff --git a/devdocs/python~3.12/library%2Furllib.request.html b/devdocs/python~3.12/library%2Furllib.request.html new file mode 100644 index 00000000..7bcda2d5 --- /dev/null +++ b/devdocs/python~3.12/library%2Furllib.request.html @@ -0,0 +1,407 @@ +

urllib.request — Extensible library for opening URLs

Source code: Lib/urllib/request.py

The urllib.request module defines functions and classes which help in opening URLs (mostly HTTP) in a complex world — basic and digest authentication, redirections, cookies and more.

See also

The Requests package is recommended for a higher-level HTTP client interface.

Warning

On macOS it is unsafe to use this module in programs using os.fork() because the getproxies() implementation for macOS uses a higher-level system API. Set the environment variable no_proxy to * to avoid this problem (e.g. os.environ["no_proxy"] = "*").

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The urllib.request module defines the following functions:

+urllib.request.urlopen(url, data=None, [timeout, ]*, cafile=None, capath=None, cadefault=False, context=None)
+

Open url, which can be either a string containing a valid, properly encoded URL, or a Request object.

data must be an object specifying additional data to be sent to the server, or None if no such data is needed. See Request for details.

urllib.request module uses HTTP/1.1 and includes Connection:close header in its HTTP requests.

The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if not specified, the global default timeout setting will be used). This actually only works for HTTP, HTTPS and FTP connections.

If context is specified, it must be a ssl.SSLContext instance describing the various SSL options. See HTTPSConnection for more details.

The optional cafile and capath parameters specify a set of trusted CA certificates for HTTPS requests. cafile should point to a single file containing a bundle of CA certificates, whereas capath should point to a directory of hashed certificate files. More information can be found in ssl.SSLContext.load_verify_locations().

The cadefault parameter is ignored.

This function always returns an object which can work as a context manager and has the properties url, headers, and status. See urllib.response.addinfourl for more detail on these properties.

For HTTP and HTTPS URLs, this function returns a http.client.HTTPResponse object slightly modified. In addition to the three new methods above, the msg attribute contains the same information as the reason attribute — the reason phrase returned by server — instead of the response headers as it is specified in the documentation for HTTPResponse.

For FTP, file, and data URLs and requests explicitly handled by legacy URLopener and FancyURLopener classes, this function returns a urllib.response.addinfourl object.

Raises URLError on protocol errors.

Note that None may be returned if no handler handles the request (though the default installed global OpenerDirector uses UnknownHandler to ensure this never happens).

In addition, if proxy settings are detected (for example, when a *_proxy environment variable like http_proxy is set), ProxyHandler is default installed and makes sure the requests are handled through the proxy.

The legacy urllib.urlopen function from Python 2.6 and earlier has been discontinued; urllib.request.urlopen() corresponds to the old urllib2.urlopen. Proxy handling, which was done by passing a dictionary parameter to urllib.urlopen, can be obtained by using ProxyHandler objects.

+

The default opener raises an auditing event urllib.Request with arguments fullurl, data, headers, method taken from the request object.

Changed in version 3.2: cafile and capath were added.

Changed in version 3.2: HTTPS virtual hosts are now supported if possible (that is, if ssl.HAS_SNI is true).

New in version 3.2: data can be an iterable object.

Changed in version 3.3: cadefault was added.

Changed in version 3.4.3: context was added.

Changed in version 3.10: HTTPS connection now send an ALPN extension with protocol indicator http/1.1 when no context is given. Custom context should set ALPN protocols with set_alpn_protocol().

Deprecated since version 3.6: cafile, capath and cadefault are deprecated in favor of context. Please use ssl.SSLContext.load_cert_chain() instead, or let ssl.create_default_context() select the system’s trusted CA certificates for you.

+
+urllib.request.install_opener(opener)
+

Install an OpenerDirector instance as the default global opener. Installing an opener is only necessary if you want urlopen to use that opener; otherwise, simply call OpenerDirector.open() instead of urlopen(). The code does not check for a real OpenerDirector, and any class with the appropriate interface will work.

+
+urllib.request.build_opener([handler, ...])
+

Return an OpenerDirector instance, which chains the handlers in the order given. handlers can be either instances of BaseHandler, or subclasses of BaseHandler (in which case it must be possible to call the constructor without any parameters). Instances of the following classes will be in front of the handlers, unless the handlers contain them, instances of them or subclasses of them: ProxyHandler (if proxy settings are detected), UnknownHandler, HTTPHandler, HTTPDefaultErrorHandler, HTTPRedirectHandler, FTPHandler, FileHandler, HTTPErrorProcessor.

If the Python installation has SSL support (i.e., if the ssl module can be imported), HTTPSHandler will also be added.

A BaseHandler subclass may also change its handler_order attribute to modify its position in the handlers list.

+
+urllib.request.pathname2url(path)
+

Convert the pathname path from the local syntax for a path to the form used in the path component of a URL. This does not produce a complete URL. The return value will already be quoted using the quote() function.

+
+urllib.request.url2pathname(path)
+

Convert the path component path from a percent-encoded URL to the local syntax for a path. This does not accept a complete URL. This function uses unquote() to decode path.

+
+urllib.request.getproxies()
+

This helper function returns a dictionary of scheme to proxy server URL mappings. It scans the environment for variables named <scheme>_proxy, in a case insensitive approach, for all operating systems first, and when it cannot find it, looks for proxy information from System Configuration for macOS and Windows Systems Registry for Windows. If both lowercase and uppercase environment variables exist (and disagree), lowercase is preferred.

Note

If the environment variable REQUEST_METHOD is set, which usually indicates your script is running in a CGI environment, the environment variable HTTP_PROXY (uppercase _PROXY) will be ignored. This is because that variable can be injected by a client using the “Proxy:” HTTP header. If you need to use an HTTP proxy in a CGI environment, either use ProxyHandler explicitly, or make sure the variable name is in lowercase (or at least the _proxy suffix).

+

The following classes are provided:

+class urllib.request.Request(url, data=None, headers={}, origin_req_host=None, unverifiable=False, method=None)
+

This class is an abstraction of a URL request.

url should be a string containing a valid, properly encoded URL.

data must be an object specifying additional data to send to the server, or None if no such data is needed. Currently HTTP requests are the only ones that use data. The supported object types include bytes, file-like objects, and iterables of bytes-like objects. If no Content-Length nor Transfer-Encoding header field has been provided, HTTPHandler will set these headers according to the type of data. Content-Length will be used to send bytes objects, while Transfer-Encoding: chunked as specified in RFC 7230, Section 3.3.1 will be used to send files and other iterables.

For an HTTP POST request method, data should be a buffer in the standard application/x-www-form-urlencoded format. The urllib.parse.urlencode() function takes a mapping or sequence of 2-tuples and returns an ASCII string in this format. It should be encoded to bytes before being used as the data parameter.

headers should be a dictionary, and will be treated as if add_header() was called with each key and value as arguments. This is often used to “spoof” the User-Agent header value, which is used by a browser to identify itself – some HTTP servers only allow requests coming from common browsers as opposed to scripts. For example, Mozilla Firefox may identify itself as "Mozilla/5.0 +(X11; U; Linux i686) Gecko/20071127 Firefox/2.0.0.11", while urllib’s default user agent string is "Python-urllib/2.6" (on Python 2.6). All header keys are sent in camel case.

An appropriate Content-Type header should be included if the data argument is present. If this header has not been provided and data is not None, Content-Type: application/x-www-form-urlencoded will be added as a default.

The next two arguments are only of interest for correct handling of third-party HTTP cookies:

origin_req_host should be the request-host of the origin transaction, as defined by RFC 2965. It defaults to http.cookiejar.request_host(self). This is the host name or IP address of the original request that was initiated by the user. For example, if the request is for an image in an HTML document, this should be the request-host of the request for the page containing the image.

unverifiable should indicate whether the request is unverifiable, as defined by RFC 2965. It defaults to False. An unverifiable request is one whose URL the user did not have the option to approve. For example, if the request is for an image in an HTML document, and the user had no option to approve the automatic fetching of the image, this should be true.

method should be a string that indicates the HTTP request method that will be used (e.g. 'HEAD'). If provided, its value is stored in the method attribute and is used by get_method(). The default is 'GET' if data is None or 'POST' otherwise. Subclasses may indicate a different default method by setting the method attribute in the class itself.

Note

The request will not work as expected if the data object is unable to deliver its content more than once (e.g. a file or an iterable that can produce the content only once) and the request is retried for HTTP redirects or authentication. The data is sent to the HTTP server right away after the headers. There is no support for a 100-continue expectation in the library.

Changed in version 3.3: Request.method argument is added to the Request class.

Changed in version 3.4: Default Request.method may be indicated at the class level.

Changed in version 3.6: Do not raise an error if the Content-Length has not been provided and data is neither None nor a bytes object. Fall back to use chunked transfer encoding instead.

+
+class urllib.request.OpenerDirector
+

The OpenerDirector class opens URLs via BaseHandlers chained together. It manages the chaining of handlers, and recovery from errors.

+
+class urllib.request.BaseHandler
+

This is the base class for all registered handlers — and handles only the simple mechanics of registration.

+
+class urllib.request.HTTPDefaultErrorHandler
+

A class which defines a default handler for HTTP error responses; all responses are turned into HTTPError exceptions.

+
+class urllib.request.HTTPRedirectHandler
+

A class to handle redirections.

+
+class urllib.request.HTTPCookieProcessor(cookiejar=None)
+

A class to handle HTTP Cookies.

+
+class urllib.request.ProxyHandler(proxies=None)
+

Cause requests to go through a proxy. If proxies is given, it must be a dictionary mapping protocol names to URLs of proxies. The default is to read the list of proxies from the environment variables <protocol>_proxy. If no proxy environment variables are set, then in a Windows environment proxy settings are obtained from the registry’s Internet Settings section, and in a macOS environment proxy information is retrieved from the System Configuration Framework.

To disable autodetected proxy pass an empty dictionary.

The no_proxy environment variable can be used to specify hosts which shouldn’t be reached via proxy; if set, it should be a comma-separated list of hostname suffixes, optionally with :port appended, for example cern.ch,ncsa.uiuc.edu,some.host:8080.

Note

HTTP_PROXY will be ignored if a variable REQUEST_METHOD is set; see the documentation on getproxies().

+
+class urllib.request.HTTPPasswordMgr
+

Keep a database of (realm, uri) -> (user, password) mappings.

+
+class urllib.request.HTTPPasswordMgrWithDefaultRealm
+

Keep a database of (realm, uri) -> (user, password) mappings. A realm of None is considered a catch-all realm, which is searched if no other realm fits.

+
+class urllib.request.HTTPPasswordMgrWithPriorAuth
+

A variant of HTTPPasswordMgrWithDefaultRealm that also has a database of uri -> is_authenticated mappings. Can be used by a BasicAuth handler to determine when to send authentication credentials immediately instead of waiting for a 401 response first.

New in version 3.5.

+
+class urllib.request.AbstractBasicAuthHandler(password_mgr=None)
+

This is a mixin class that helps with HTTP authentication, both to the remote host and to a proxy. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported. If passwd_mgr also provides is_authenticated and update_authenticated methods (see HTTPPasswordMgrWithPriorAuth Objects), then the handler will use the is_authenticated result for a given URI to determine whether or not to send authentication credentials with the request. If is_authenticated returns True for the URI, credentials are sent. If is_authenticated is False, credentials are not sent, and then if a 401 response is received the request is re-sent with the authentication credentials. If authentication succeeds, update_authenticated is called to set is_authenticated True for the URI, so that subsequent requests to the URI or any of its super-URIs will automatically include the authentication credentials.

New in version 3.5: Added is_authenticated support.

+
+class urllib.request.HTTPBasicAuthHandler(password_mgr=None)
+

Handle authentication with the remote host. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported. HTTPBasicAuthHandler will raise a ValueError when presented with a wrong Authentication scheme.

+
+class urllib.request.ProxyBasicAuthHandler(password_mgr=None)
+

Handle authentication with the proxy. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported.

+
+class urllib.request.AbstractDigestAuthHandler(password_mgr=None)
+

This is a mixin class that helps with HTTP authentication, both to the remote host and to a proxy. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported.

+
+class urllib.request.HTTPDigestAuthHandler(password_mgr=None)
+

Handle authentication with the remote host. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported. When both Digest Authentication Handler and Basic Authentication Handler are both added, Digest Authentication is always tried first. If the Digest Authentication returns a 40x response again, it is sent to Basic Authentication handler to Handle. This Handler method will raise a ValueError when presented with an authentication scheme other than Digest or Basic.

Changed in version 3.3: Raise ValueError on unsupported Authentication Scheme.

+
+class urllib.request.ProxyDigestAuthHandler(password_mgr=None)
+

Handle authentication with the proxy. password_mgr, if given, should be something that is compatible with HTTPPasswordMgr; refer to section HTTPPasswordMgr Objects for information on the interface that must be supported.

+
+class urllib.request.HTTPHandler
+

A class to handle opening of HTTP URLs.

+
+class urllib.request.HTTPSHandler(debuglevel=0, context=None, check_hostname=None)
+

A class to handle opening of HTTPS URLs. context and check_hostname have the same meaning as in http.client.HTTPSConnection.

Changed in version 3.2: context and check_hostname were added.

+
+class urllib.request.FileHandler
+

Open local files.

+
+class urllib.request.DataHandler
+

Open data URLs.

New in version 3.4.

+
+class urllib.request.FTPHandler
+

Open FTP URLs.

+
+class urllib.request.CacheFTPHandler
+

Open FTP URLs, keeping a cache of open FTP connections to minimize delays.

+
+class urllib.request.UnknownHandler
+

A catch-all class to handle unknown URLs.

+
+class urllib.request.HTTPErrorProcessor
+

Process HTTP error responses.

+

Request Objects

The following methods describe Request’s public interface, and so all may be overridden in subclasses. It also defines several public attributes that can be used by clients to inspect the parsed request.

+Request.full_url
+

The original URL passed to the constructor.

Changed in version 3.4.

Request.full_url is a property with setter, getter and a deleter. Getting full_url returns the original request URL with the fragment, if it was present.

+
+Request.type
+

The URI scheme.

+
+Request.host
+

The URI authority, typically a host, but may also contain a port separated by a colon.

+
+Request.origin_req_host
+

The original host for the request, without port.

+
+Request.selector
+

The URI path. If the Request uses a proxy, then selector will be the full URL that is passed to the proxy.

+
+Request.data
+

The entity body for the request, or None if not specified.

Changed in version 3.4: Changing value of Request.data now deletes “Content-Length” header if it was previously set or calculated.

+
+Request.unverifiable
+

boolean, indicates whether the request is unverifiable as defined by RFC 2965.

+
+Request.method
+

The HTTP request method to use. By default its value is None, which means that get_method() will do its normal computation of the method to be used. Its value can be set (thus overriding the default computation in get_method()) either by providing a default value by setting it at the class level in a Request subclass, or by passing a value in to the Request constructor via the method argument.

New in version 3.3.

Changed in version 3.4: A default value can now be set in subclasses; previously it could only be set via the constructor argument.

+
+Request.get_method()
+

Return a string indicating the HTTP request method. If Request.method is not None, return its value, otherwise return 'GET' if Request.data is None, or 'POST' if it’s not. This is only meaningful for HTTP requests.

Changed in version 3.3: get_method now looks at the value of Request.method.

+
+Request.add_header(key, val)
+

Add another header to the request. Headers are currently ignored by all handlers except HTTP handlers, where they are added to the list of headers sent to the server. Note that there cannot be more than one header with the same name, and later calls will overwrite previous calls in case the key collides. Currently, this is no loss of HTTP functionality, since all headers which have meaning when used more than once have a (header-specific) way of gaining the same functionality using only one header. Note that headers added using this method are also added to redirected requests.

+
+Request.add_unredirected_header(key, header)
+

Add a header that will not be added to a redirected request.

+
+Request.has_header(header)
+

Return whether the instance has the named header (checks both regular and unredirected).

+
+Request.remove_header(header)
+

Remove named header from the request instance (both from regular and unredirected headers).

New in version 3.4.

+
+Request.get_full_url()
+

Return the URL given in the constructor.

Changed in version 3.4.

Returns Request.full_url

+
+Request.set_proxy(host, type)
+

Prepare the request by connecting to a proxy server. The host and type will replace those of the instance, and the instance’s selector will be the original URL given in the constructor.

+
+Request.get_header(header_name, default=None)
+

Return the value of the given header. If the header is not present, return the default value.

+
+Request.header_items()
+

Return a list of tuples (header_name, header_value) of the Request headers.

+

Changed in version 3.4: The request methods add_data, has_data, get_data, get_type, get_host, get_selector, get_origin_req_host and is_unverifiable that were deprecated since 3.3 have been removed.

OpenerDirector Objects

OpenerDirector instances have the following methods:

+OpenerDirector.add_handler(handler)
+

handler should be an instance of BaseHandler. The following methods are searched, and added to the possible chains (note that HTTP errors are a special case). Note that, in the following, protocol should be replaced with the actual protocol to handle, for example http_response() would be the HTTP protocol response handler. Also type should be replaced with the actual HTTP code, for example http_error_404() would handle HTTP 404 errors.

  • +

    <protocol>_open() — signal that the handler knows how to open protocol URLs.

    See BaseHandler.<protocol>_open() for more information.

  • +

    http_error_<type>() — signal that the handler knows how to handle HTTP errors with HTTP error code type.

    See BaseHandler.http_error_<nnn>() for more information.

  • +<protocol>_error() — signal that the handler knows how to handle errors from (non-http) protocol.
  • +

    <protocol>_request() — signal that the handler knows how to pre-process protocol requests.

    See BaseHandler.<protocol>_request() for more information.

  • +

    <protocol>_response() — signal that the handler knows how to post-process protocol responses.

    See BaseHandler.<protocol>_response() for more information.

+
+OpenerDirector.open(url, data=None[, timeout])
+

Open the given url (which can be a request object or a string), optionally passing the given data. Arguments, return values and exceptions raised are the same as those of urlopen() (which simply calls the open() method on the currently installed global OpenerDirector). The optional timeout parameter specifies a timeout in seconds for blocking operations like the connection attempt (if not specified, the global default timeout setting will be used). The timeout feature actually works only for HTTP, HTTPS and FTP connections.

+
+OpenerDirector.error(proto, *args)
+

Handle an error of the given protocol. This will call the registered error handlers for the given protocol with the given arguments (which are protocol specific). The HTTP protocol is a special case which uses the HTTP response code to determine the specific error handler; refer to the http_error_<type>() methods of the handler classes.

Return values and exceptions raised are the same as those of urlopen().

+

OpenerDirector objects open URLs in three stages:

The order in which these methods are called within each stage is determined by sorting the handler instances.

  1. Every handler with a method named like <protocol>_request() has that method called to pre-process the request.
  2. +

    Handlers with a method named like <protocol>_open() are called to handle the request. This stage ends when a handler either returns a non-None value (ie. a response), or raises an exception (usually URLError). Exceptions are allowed to propagate.

    In fact, the above algorithm is first tried for methods named default_open(). If all such methods return None, the algorithm is repeated for methods named like <protocol>_open(). If all such methods return None, the algorithm is repeated for methods named unknown_open().

    Note that the implementation of these methods may involve calls of the parent OpenerDirector instance’s open() and error() methods.

  3. Every handler with a method named like <protocol>_response() has that method called to post-process the response.

BaseHandler Objects

BaseHandler objects provide a couple of methods that are directly useful, and others that are meant to be used by derived classes. These are intended for direct use:

+BaseHandler.add_parent(director)
+

Add a director as parent.

+
+BaseHandler.close()
+

Remove any parents.

+

The following attribute and methods should only be used by classes derived from BaseHandler.

Note

The convention has been adopted that subclasses defining <protocol>_request() or <protocol>_response() methods are named *Processor; all others are named *Handler.

+BaseHandler.parent
+

A valid OpenerDirector, which can be used to open using a different protocol, or handle errors.

+
+BaseHandler.default_open(req)
+

This method is not defined in BaseHandler, but subclasses should define it if they want to catch all URLs.

This method, if implemented, will be called by the parent OpenerDirector. It should return a file-like object as described in the return value of the open() method of OpenerDirector, or None. It should raise URLError, unless a truly exceptional thing happens (for example, MemoryError should not be mapped to URLError).

This method will be called before any protocol-specific open method.

+
BaseHandler.<protocol>_open(req) +
+

This method is not defined in BaseHandler, but subclasses should define it if they want to handle URLs with the given protocol.

This method, if defined, will be called by the parent OpenerDirector. Return values should be the same as for default_open().

+
+BaseHandler.unknown_open(req)
+

This method is not defined in BaseHandler, but subclasses should define it if they want to catch all URLs with no specific registered handler to open it.

This method, if implemented, will be called by the parent OpenerDirector. Return values should be the same as for default_open().

+
+BaseHandler.http_error_default(req, fp, code, msg, hdrs)
+

This method is not defined in BaseHandler, but subclasses should override it if they intend to provide a catch-all for otherwise unhandled HTTP errors. It will be called automatically by the OpenerDirector getting the error, and should not normally be called in other circumstances.

req will be a Request object, fp will be a file-like object with the HTTP error body, code will be the three-digit code of the error, msg will be the user-visible explanation of the code and hdrs will be a mapping object with the headers of the error.

Return values and exceptions raised should be the same as those of urlopen().

+
BaseHandler.http_error_<nnn>(req, fp, code, msg, hdrs) +
+

nnn should be a three-digit HTTP error code. This method is also not defined in BaseHandler, but will be called, if it exists, on an instance of a subclass, when an HTTP error with code nnn occurs.

Subclasses should override this method to handle specific HTTP errors.

Arguments, return values and exceptions raised should be the same as for http_error_default().

+
BaseHandler.<protocol>_request(req) +
+

This method is not defined in BaseHandler, but subclasses should define it if they want to pre-process requests of the given protocol.

This method, if defined, will be called by the parent OpenerDirector. req will be a Request object. The return value should be a Request object.

+
BaseHandler.<protocol>_response(req, response) +
+

This method is not defined in BaseHandler, but subclasses should define it if they want to post-process responses of the given protocol.

This method, if defined, will be called by the parent OpenerDirector. req will be a Request object. response will be an object implementing the same interface as the return value of urlopen(). The return value should implement the same interface as the return value of urlopen().

+

HTTPRedirectHandler Objects

Note

Some HTTP redirections require action from this module’s client code. If this is the case, HTTPError is raised. See RFC 2616 for details of the precise meanings of the various redirection codes.

An HTTPError exception raised as a security consideration if the HTTPRedirectHandler is presented with a redirected URL which is not an HTTP, HTTPS or FTP URL.

+HTTPRedirectHandler.redirect_request(req, fp, code, msg, hdrs, newurl)
+

Return a Request or None in response to a redirect. This is called by the default implementations of the http_error_30*() methods when a redirection is received from the server. If a redirection should take place, return a new Request to allow http_error_30*() to perform the redirect to newurl. Otherwise, raise HTTPError if no other handler should try to handle this URL, or return None if you can’t but another handler might.

Note

The default implementation of this method does not strictly follow RFC 2616, which says that 301 and 302 responses to POST requests must not be automatically redirected without confirmation by the user. In reality, browsers do allow automatic redirection of these responses, changing the POST to a GET, and the default implementation reproduces this behavior.

+
+HTTPRedirectHandler.http_error_301(req, fp, code, msg, hdrs)
+

Redirect to the Location: or URI: URL. This method is called by the parent OpenerDirector when getting an HTTP ‘moved permanently’ response.

+
+HTTPRedirectHandler.http_error_302(req, fp, code, msg, hdrs)
+

The same as http_error_301(), but called for the ‘found’ response.

+
+HTTPRedirectHandler.http_error_303(req, fp, code, msg, hdrs)
+

The same as http_error_301(), but called for the ‘see other’ response.

+
+HTTPRedirectHandler.http_error_307(req, fp, code, msg, hdrs)
+

The same as http_error_301(), but called for the ‘temporary redirect’ response. It does not allow changing the request method from POST to GET.

+
+HTTPRedirectHandler.http_error_308(req, fp, code, msg, hdrs)
+

The same as http_error_301(), but called for the ‘permanent redirect’ response. It does not allow changing the request method from POST to GET.

New in version 3.11.

+

HTTPCookieProcessor Objects

HTTPCookieProcessor instances have one attribute:

+HTTPCookieProcessor.cookiejar
+

The http.cookiejar.CookieJar in which cookies are stored.

+

ProxyHandler Objects

ProxyHandler.<protocol>_open(request) +
+

The ProxyHandler will have a method <protocol>_open() for every protocol which has a proxy in the proxies dictionary given in the constructor. The method will modify requests to go through the proxy, by calling request.set_proxy(), and call the next handler in the chain to actually execute the protocol.

+

HTTPPasswordMgr Objects

These methods are available on HTTPPasswordMgr and HTTPPasswordMgrWithDefaultRealm objects.

+HTTPPasswordMgr.add_password(realm, uri, user, passwd)
+

uri can be either a single URI, or a sequence of URIs. realm, user and passwd must be strings. This causes (user, passwd) to be used as authentication tokens when authentication for realm and a super-URI of any of the given URIs is given.

+
+HTTPPasswordMgr.find_user_password(realm, authuri)
+

Get user/password for given realm and URI, if any. This method will return (None, None) if there is no matching user/password.

For HTTPPasswordMgrWithDefaultRealm objects, the realm None will be searched if the given realm has no matching user/password.

+

HTTPPasswordMgrWithPriorAuth Objects

This password manager extends HTTPPasswordMgrWithDefaultRealm to support tracking URIs for which authentication credentials should always be sent.

+HTTPPasswordMgrWithPriorAuth.add_password(realm, uri, user, passwd, is_authenticated=False)
+

realm, uri, user, passwd are as for HTTPPasswordMgr.add_password(). is_authenticated sets the initial value of the is_authenticated flag for the given URI or list of URIs. If is_authenticated is specified as True, realm is ignored.

+
+HTTPPasswordMgrWithPriorAuth.find_user_password(realm, authuri)
+

Same as for HTTPPasswordMgrWithDefaultRealm objects

+
+HTTPPasswordMgrWithPriorAuth.update_authenticated(self, uri, is_authenticated=False)
+

Update the is_authenticated flag for the given uri or list of URIs.

+
+HTTPPasswordMgrWithPriorAuth.is_authenticated(self, authuri)
+

Returns the current state of the is_authenticated flag for the given URI.

+

AbstractBasicAuthHandler Objects

+AbstractBasicAuthHandler.http_error_auth_reqed(authreq, host, req, headers)
+

Handle an authentication request by getting a user/password pair, and re-trying the request. authreq should be the name of the header where the information about the realm is included in the request, host specifies the URL and path to authenticate for, req should be the (failed) Request object, and headers should be the error headers.

host is either an authority (e.g. "python.org") or a URL containing an authority component (e.g. "http://python.org/"). In either case, the authority must not contain a userinfo component (so, "python.org" and "python.org:80" are fine, "joe:password@python.org" is not).

+

HTTPBasicAuthHandler Objects

+HTTPBasicAuthHandler.http_error_401(req, fp, code, msg, hdrs)
+

Retry the request with authentication information, if available.

+

ProxyBasicAuthHandler Objects

+ProxyBasicAuthHandler.http_error_407(req, fp, code, msg, hdrs)
+

Retry the request with authentication information, if available.

+

AbstractDigestAuthHandler Objects

+AbstractDigestAuthHandler.http_error_auth_reqed(authreq, host, req, headers)
+

authreq should be the name of the header where the information about the realm is included in the request, host should be the host to authenticate to, req should be the (failed) Request object, and headers should be the error headers.

+

HTTPDigestAuthHandler Objects

+HTTPDigestAuthHandler.http_error_401(req, fp, code, msg, hdrs)
+

Retry the request with authentication information, if available.

+

ProxyDigestAuthHandler Objects

+ProxyDigestAuthHandler.http_error_407(req, fp, code, msg, hdrs)
+

Retry the request with authentication information, if available.

+

HTTPHandler Objects

+HTTPHandler.http_open(req)
+

Send an HTTP request, which can be either GET or POST, depending on req.has_data().

+

HTTPSHandler Objects

+HTTPSHandler.https_open(req)
+

Send an HTTPS request, which can be either GET or POST, depending on req.has_data().

+

FileHandler Objects

+FileHandler.file_open(req)
+

Open the file locally, if there is no host name, or the host name is 'localhost'.

Changed in version 3.2: This method is applicable only for local hostnames. When a remote hostname is given, an URLError is raised.

+

DataHandler Objects

+DataHandler.data_open(req)
+

Read a data URL. This kind of URL contains the content encoded in the URL itself. The data URL syntax is specified in RFC 2397. This implementation ignores white spaces in base64 encoded data URLs so the URL may be wrapped in whatever source file it comes from. But even though some browsers don’t mind about a missing padding at the end of a base64 encoded data URL, this implementation will raise an ValueError in that case.

+

FTPHandler Objects

+FTPHandler.ftp_open(req)
+

Open the FTP file indicated by req. The login is always done with empty username and password.

+

CacheFTPHandler Objects

CacheFTPHandler objects are FTPHandler objects with the following additional methods:

+CacheFTPHandler.setTimeout(t)
+

Set timeout of connections to t seconds.

+
+CacheFTPHandler.setMaxConns(m)
+

Set maximum number of cached connections to m.

+

UnknownHandler Objects

+UnknownHandler.unknown_open()
+

Raise a URLError exception.

+

HTTPErrorProcessor Objects

+HTTPErrorProcessor.http_response(request, response)
+

Process HTTP error responses.

For 200 error codes, the response object is returned immediately.

For non-200 error codes, this simply passes the job on to the http_error_<type>() handler methods, via OpenerDirector.error(). Eventually, HTTPDefaultErrorHandler will raise an HTTPError if no other handler handles the error.

+
+HTTPErrorProcessor.https_response(request, response)
+

Process HTTPS error responses.

The behavior is same as http_response().

+

Examples

In addition to the examples below, more examples are given in HOWTO Fetch Internet Resources Using The urllib Package.

This example gets the python.org main page and displays the first 300 bytes of it.

>>> import urllib.request
+>>> with urllib.request.urlopen('http://www.python.org/') as f:
+...     print(f.read(300))
+...
+b'<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">\n\n\n<html
+xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">\n\n<head>\n
+<meta http-equiv="content-type" content="text/html; charset=utf-8" />\n
+<title>Python Programming '
+

Note that urlopen returns a bytes object. This is because there is no way for urlopen to automatically determine the encoding of the byte stream it receives from the HTTP server. In general, a program will decode the returned bytes object to string once it determines or guesses the appropriate encoding.

The following W3C document, https://www.w3.org/International/O-charset, lists the various ways in which an (X)HTML or an XML document could have specified its encoding information.

As the python.org website uses utf-8 encoding as specified in its meta tag, we will use the same for decoding the bytes object.

>>> with urllib.request.urlopen('http://www.python.org/') as f:
+...     print(f.read(100).decode('utf-8'))
+...
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+"http://www.w3.org/TR/xhtml1/DTD/xhtm
+

It is also possible to achieve the same result without using the context manager approach.

>>> import urllib.request
+>>> f = urllib.request.urlopen('http://www.python.org/')
+>>> print(f.read(100).decode('utf-8'))
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+"http://www.w3.org/TR/xhtml1/DTD/xhtm
+

In the following example, we are sending a data-stream to the stdin of a CGI and reading the data it returns to us. Note that this example will only work when the Python installation supports SSL.

>>> import urllib.request
+>>> req = urllib.request.Request(url='https://localhost/cgi-bin/test.cgi',
+...                       data=b'This data is passed to stdin of the CGI')
+>>> with urllib.request.urlopen(req) as f:
+...     print(f.read().decode('utf-8'))
+...
+Got Data: "This data is passed to stdin of the CGI"
+

The code for the sample CGI used in the above example is:

#!/usr/bin/env python
+import sys
+data = sys.stdin.read()
+print('Content-type: text/plain\n\nGot Data: "%s"' % data)
+

Here is an example of doing a PUT request using Request:

import urllib.request
+DATA = b'some data'
+req = urllib.request.Request(url='http://localhost:8080', data=DATA, method='PUT')
+with urllib.request.urlopen(req) as f:
+    pass
+print(f.status)
+print(f.reason)
+

Use of Basic HTTP Authentication:

import urllib.request
+# Create an OpenerDirector with support for Basic HTTP Authentication...
+auth_handler = urllib.request.HTTPBasicAuthHandler()
+auth_handler.add_password(realm='PDQ Application',
+                          uri='https://mahler:8092/site-updates.py',
+                          user='klem',
+                          passwd='kadidd!ehopper')
+opener = urllib.request.build_opener(auth_handler)
+# ...and install it globally so it can be used with urlopen.
+urllib.request.install_opener(opener)
+urllib.request.urlopen('http://www.example.com/login.html')
+

build_opener() provides many handlers by default, including a ProxyHandler. By default, ProxyHandler uses the environment variables named <scheme>_proxy, where <scheme> is the URL scheme involved. For example, the http_proxy environment variable is read to obtain the HTTP proxy’s URL.

This example replaces the default ProxyHandler with one that uses programmatically supplied proxy URLs, and adds proxy authorization support with ProxyBasicAuthHandler.

proxy_handler = urllib.request.ProxyHandler({'http': 'http://www.example.com:3128/'})
+proxy_auth_handler = urllib.request.ProxyBasicAuthHandler()
+proxy_auth_handler.add_password('realm', 'host', 'username', 'password')
+
+opener = urllib.request.build_opener(proxy_handler, proxy_auth_handler)
+# This time, rather than install the OpenerDirector, we use it directly:
+opener.open('http://www.example.com/login.html')
+

Adding HTTP headers:

Use the headers argument to the Request constructor, or:

import urllib.request
+req = urllib.request.Request('http://www.example.com/')
+req.add_header('Referer', 'http://www.python.org/')
+# Customize the default User-Agent header value:
+req.add_header('User-Agent', 'urllib-example/0.1 (Contact: . . .)')
+r = urllib.request.urlopen(req)
+

OpenerDirector automatically adds a User-Agent header to every Request. To change this:

import urllib.request
+opener = urllib.request.build_opener()
+opener.addheaders = [('User-agent', 'Mozilla/5.0')]
+opener.open('http://www.example.com/')
+

Also, remember that a few standard headers (Content-Length, Content-Type and Host) are added when the Request is passed to urlopen() (or OpenerDirector.open()).

Here is an example session that uses the GET method to retrieve a URL containing parameters:

>>> import urllib.request
+>>> import urllib.parse
+>>> params = urllib.parse.urlencode({'spam': 1, 'eggs': 2, 'bacon': 0})
+>>> url = "http://www.musi-cal.com/cgi-bin/query?%s" % params
+>>> with urllib.request.urlopen(url) as f:
+...     print(f.read().decode('utf-8'))
+...
+

The following example uses the POST method instead. Note that params output from urlencode is encoded to bytes before it is sent to urlopen as data:

>>> import urllib.request
+>>> import urllib.parse
+>>> data = urllib.parse.urlencode({'spam': 1, 'eggs': 2, 'bacon': 0})
+>>> data = data.encode('ascii')
+>>> with urllib.request.urlopen("http://requestb.in/xrbl82xr", data) as f:
+...     print(f.read().decode('utf-8'))
+...
+

The following example uses an explicitly specified HTTP proxy, overriding environment settings:

>>> import urllib.request
+>>> proxies = {'http': 'http://proxy.example.com:8080/'}
+>>> opener = urllib.request.FancyURLopener(proxies)
+>>> with opener.open("http://www.python.org") as f:
+...     f.read().decode('utf-8')
+...
+

The following example uses no proxies at all, overriding environment settings:

>>> import urllib.request
+>>> opener = urllib.request.FancyURLopener({})
+>>> with opener.open("http://www.python.org/") as f:
+...     f.read().decode('utf-8')
+...
+

Legacy interface

The following functions and classes are ported from the Python 2 module urllib (as opposed to urllib2). They might become deprecated at some point in the future.

+urllib.request.urlretrieve(url, filename=None, reporthook=None, data=None)
+

Copy a network object denoted by a URL to a local file. If the URL points to a local file, the object will not be copied unless filename is supplied. Return a tuple (filename, headers) where filename is the local file name under which the object can be found, and headers is whatever the info() method of the object returned by urlopen() returned (for a remote object). Exceptions are the same as for urlopen().

The second argument, if present, specifies the file location to copy to (if absent, the location will be a tempfile with a generated name). The third argument, if present, is a callable that will be called once on establishment of the network connection and once after each block read thereafter. The callable will be passed three arguments; a count of blocks transferred so far, a block size in bytes, and the total size of the file. The third argument may be -1 on older FTP servers which do not return a file size in response to a retrieval request.

The following example illustrates the most common usage scenario:

>>> import urllib.request
+>>> local_filename, headers = urllib.request.urlretrieve('http://python.org/')
+>>> html = open(local_filename)
+>>> html.close()
+

If the url uses the http: scheme identifier, the optional data argument may be given to specify a POST request (normally the request type is GET). The data argument must be a bytes object in standard application/x-www-form-urlencoded format; see the urllib.parse.urlencode() function.

urlretrieve() will raise ContentTooShortError when it detects that the amount of data available was less than the expected amount (which is the size reported by a Content-Length header). This can occur, for example, when the download is interrupted.

The Content-Length is treated as a lower bound: if there’s more data to read, urlretrieve reads more data, but if less data is available, it raises the exception.

You can still retrieve the downloaded data in this case, it is stored in the content attribute of the exception instance.

If no Content-Length header was supplied, urlretrieve can not check the size of the data it has downloaded, and just returns it. In this case you just have to assume that the download was successful.

+
+urllib.request.urlcleanup()
+

Cleans up temporary files that may have been left behind by previous calls to urlretrieve().

+
+class urllib.request.URLopener(proxies=None, **x509)
+

Deprecated since version 3.3.

Base class for opening and reading URLs. Unless you need to support opening objects using schemes other than http:, ftp:, or file:, you probably want to use FancyURLopener.

By default, the URLopener class sends a User-Agent header of urllib/VVV, where VVV is the urllib version number. Applications can define their own User-Agent header by subclassing URLopener or FancyURLopener and setting the class attribute version to an appropriate string value in the subclass definition.

The optional proxies parameter should be a dictionary mapping scheme names to proxy URLs, where an empty dictionary turns proxies off completely. Its default value is None, in which case environmental proxy settings will be used if present, as discussed in the definition of urlopen(), above.

Additional keyword parameters, collected in x509, may be used for authentication of the client when using the https: scheme. The keywords key_file and cert_file are supported to provide an SSL key and certificate; both are needed to support client authentication.

URLopener objects will raise an OSError exception if the server returns an error code.

+open(fullurl, data=None)
+

Open fullurl using the appropriate protocol. This method sets up cache and proxy information, then calls the appropriate open method with its input arguments. If the scheme is not recognized, open_unknown() is called. The data argument has the same meaning as the data argument of urlopen().

This method always quotes fullurl using quote().

+
+open_unknown(fullurl, data=None)
+

Overridable interface to open unknown URL types.

+
+retrieve(url, filename=None, reporthook=None, data=None)
+

Retrieves the contents of url and places it in filename. The return value is a tuple consisting of a local filename and either an email.message.Message object containing the response headers (for remote URLs) or None (for local URLs). The caller must then open and read the contents of filename. If filename is not given and the URL refers to a local file, the input filename is returned. If the URL is non-local and filename is not given, the filename is the output of tempfile.mktemp() with a suffix that matches the suffix of the last path component of the input URL. If reporthook is given, it must be a function accepting three numeric parameters: A chunk number, the maximum size chunks are read in and the total size of the download (-1 if unknown). It will be called once at the start and after each chunk of data is read from the network. reporthook is ignored for local URLs.

If the url uses the http: scheme identifier, the optional data argument may be given to specify a POST request (normally the request type is GET). The data argument must in standard application/x-www-form-urlencoded format; see the urllib.parse.urlencode() function.

+
+version
+

Variable that specifies the user agent of the opener object. To get urllib to tell servers that it is a particular user agent, set this in a subclass as a class variable or in the constructor before calling the base constructor.

+
+
+class urllib.request.FancyURLopener(...)
+

Deprecated since version 3.3.

FancyURLopener subclasses URLopener providing default handling for the following HTTP response codes: 301, 302, 303, 307 and 401. For the 30x response codes listed above, the Location header is used to fetch the actual URL. For 401 response codes (authentication required), basic HTTP authentication is performed. For the 30x response codes, recursion is bounded by the value of the maxtries attribute, which defaults to 10.

For all other response codes, the method http_error_default() is called which you can override in subclasses to handle the error appropriately.

Note

According to the letter of RFC 2616, 301 and 302 responses to POST requests must not be automatically redirected without confirmation by the user. In reality, browsers do allow automatic redirection of these responses, changing the POST to a GET, and urllib reproduces this behaviour.

The parameters to the constructor are the same as those for URLopener.

Note

When performing basic authentication, a FancyURLopener instance calls its prompt_user_passwd() method. The default implementation asks the users for the required information on the controlling terminal. A subclass may override this method to support more appropriate behavior if needed.

The FancyURLopener class offers one additional method that should be overloaded to provide the appropriate behavior:

+prompt_user_passwd(host, realm)
+

Return information needed to authenticate the user at the given host in the specified security realm. The return value should be a tuple, (user, +password), which can be used for basic authentication.

The implementation prompts for this information on the terminal; an application should override this method to use an appropriate interaction model in the local environment.

+
+

urllib.request Restrictions

  • +

    Currently, only the following protocols are supported: HTTP (versions 0.9 and 1.0), FTP, local files, and data URLs.

    Changed in version 3.4: Added support for data URLs.

  • The caching feature of urlretrieve() has been disabled until someone finds the time to hack proper processing of Expiration time headers.
  • There should be a function to query whether a particular URL is in the cache.
  • For backward compatibility, if a URL appears to point to a local file but the file can’t be opened, the URL is re-interpreted using the FTP protocol. This can sometimes cause confusing error messages.
  • The urlopen() and urlretrieve() functions can cause arbitrarily long delays while waiting for a network connection to be set up. This means that it is difficult to build an interactive web client using these functions without using threads.
  • The data returned by urlopen() or urlretrieve() is the raw data returned by the server. This may be binary data (such as an image), plain text or (for example) HTML. The HTTP protocol provides type information in the reply header, which can be inspected by looking at the Content-Type header. If the returned data is HTML, you can use the module html.parser to parse it.
  • The code handling the FTP protocol cannot differentiate between a file and a directory. This can lead to unexpected behavior when attempting to read a URL that points to a file that is not accessible. If the URL ends in a /, it is assumed to refer to a directory and will be handled accordingly. But if an attempt to read a file leads to a 550 error (meaning the URL cannot be found or is not accessible, often for permission reasons), then the path is treated as a directory in order to handle the case when a directory is specified by a URL but the trailing / has been left off. This can cause misleading results when you try to fetch a file whose read permissions make it inaccessible; the FTP code will try to read it, fail with a 550 error, and then perform a directory listing for the unreadable file. If fine-grained control is needed, consider using the ftplib module, subclassing FancyURLopener, or changing _urlopener to meet your needs.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/urllib.request.html +

+
diff --git a/devdocs/python~3.12/library%2Furllib.robotparser.html b/devdocs/python~3.12/library%2Furllib.robotparser.html new file mode 100644 index 00000000..116f14f1 --- /dev/null +++ b/devdocs/python~3.12/library%2Furllib.robotparser.html @@ -0,0 +1,51 @@ +

urllib.robotparser — Parser for robots.txt

Source code: Lib/urllib/robotparser.py

This module provides a single class, RobotFileParser, which answers questions about whether or not a particular user agent can fetch a URL on the web site that published the robots.txt file. For more details on the structure of robots.txt files, see http://www.robotstxt.org/orig.html.

+class urllib.robotparser.RobotFileParser(url='')
+

This class provides methods to read, parse and answer questions about the robots.txt file at url.

+set_url(url)
+

Sets the URL referring to a robots.txt file.

+
+read()
+

Reads the robots.txt URL and feeds it to the parser.

+
+parse(lines)
+

Parses the lines argument.

+
+can_fetch(useragent, url)
+

Returns True if the useragent is allowed to fetch the url according to the rules contained in the parsed robots.txt file.

+
+mtime()
+

Returns the time the robots.txt file was last fetched. This is useful for long-running web spiders that need to check for new robots.txt files periodically.

+
+modified()
+

Sets the time the robots.txt file was last fetched to the current time.

+
+crawl_delay(useragent)
+

Returns the value of the Crawl-delay parameter from robots.txt for the useragent in question. If there is no such parameter or it doesn’t apply to the useragent specified or the robots.txt entry for this parameter has invalid syntax, return None.

New in version 3.6.

+
+request_rate(useragent)
+

Returns the contents of the Request-rate parameter from robots.txt as a named tuple RequestRate(requests, seconds). If there is no such parameter or it doesn’t apply to the useragent specified or the robots.txt entry for this parameter has invalid syntax, return None.

New in version 3.6.

+
+site_maps()
+

Returns the contents of the Sitemap parameter from robots.txt in the form of a list(). If there is no such parameter or the robots.txt entry for this parameter has invalid syntax, return None.

New in version 3.8.

+
+

The following example demonstrates basic use of the RobotFileParser class:

>>> import urllib.robotparser
+>>> rp = urllib.robotparser.RobotFileParser()
+>>> rp.set_url("http://www.musi-cal.com/robots.txt")
+>>> rp.read()
+>>> rrate = rp.request_rate("*")
+>>> rrate.requests
+3
+>>> rrate.seconds
+20
+>>> rp.crawl_delay("*")
+6
+>>> rp.can_fetch("*", "http://www.musi-cal.com/cgi-bin/search?city=San+Francisco")
+False
+>>> rp.can_fetch("*", "http://www.musi-cal.com/")
+True
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/urllib.robotparser.html +

+
diff --git a/devdocs/python~3.12/library%2Fuuid.html b/devdocs/python~3.12/library%2Fuuid.html new file mode 100644 index 00000000..942f76f0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fuuid.html @@ -0,0 +1,187 @@ +

uuid — UUID objects according to RFC 4122

Source code: Lib/uuid.py

This module provides immutable UUID objects (the UUID class) and the functions uuid1(), uuid3(), uuid4(), uuid5() for generating version 1, 3, 4, and 5 UUIDs as specified in RFC 4122.

If all you want is a unique ID, you should probably call uuid1() or uuid4(). Note that uuid1() may compromise privacy since it creates a UUID containing the computer’s network address. uuid4() creates a random UUID.

Depending on support from the underlying platform, uuid1() may or may not return a “safe” UUID. A safe UUID is one which is generated using synchronization methods that ensure no two processes can obtain the same UUID. All instances of UUID have an is_safe attribute which relays any information about the UUID’s safety, using this enumeration:

+class uuid.SafeUUID
+

New in version 3.7.

+safe
+

The UUID was generated by the platform in a multiprocessing-safe way.

+
+unsafe
+

The UUID was not generated in a multiprocessing-safe way.

+
+unknown
+

The platform does not provide information on whether the UUID was generated safely or not.

+
+
+class uuid.UUID(hex=None, bytes=None, bytes_le=None, fields=None, int=None, version=None, *, is_safe=SafeUUID.unknown)
+

Create a UUID from either a string of 32 hexadecimal digits, a string of 16 bytes in big-endian order as the bytes argument, a string of 16 bytes in little-endian order as the bytes_le argument, a tuple of six integers (32-bit time_low, 16-bit time_mid, 16-bit time_hi_version, 8-bit clock_seq_hi_variant, 8-bit clock_seq_low, 48-bit node) as the fields argument, or a single 128-bit integer as the int argument. When a string of hex digits is given, curly braces, hyphens, and a URN prefix are all optional. For example, these expressions all yield the same UUID:

UUID('{12345678-1234-5678-1234-567812345678}')
+UUID('12345678123456781234567812345678')
+UUID('urn:uuid:12345678-1234-5678-1234-567812345678')
+UUID(bytes=b'\x12\x34\x56\x78'*4)
+UUID(bytes_le=b'\x78\x56\x34\x12\x34\x12\x78\x56' +
+              b'\x12\x34\x56\x78\x12\x34\x56\x78')
+UUID(fields=(0x12345678, 0x1234, 0x5678, 0x12, 0x34, 0x567812345678))
+UUID(int=0x12345678123456781234567812345678)
+

Exactly one of hex, bytes, bytes_le, fields, or int must be given. The version argument is optional; if given, the resulting UUID will have its variant and version number set according to RFC 4122, overriding bits in the given hex, bytes, bytes_le, fields, or int.

Comparison of UUID objects are made by way of comparing their UUID.int attributes. Comparison with a non-UUID object raises a TypeError.

str(uuid) returns a string in the form 12345678-1234-5678-1234-567812345678 where the 32 hexadecimal digits represent the UUID.

+

UUID instances have these read-only attributes:

+UUID.bytes
+

The UUID as a 16-byte string (containing the six integer fields in big-endian byte order).

+
+UUID.bytes_le
+

The UUID as a 16-byte string (with time_low, time_mid, and time_hi_version in little-endian byte order).

+
+UUID.fields
+

A tuple of the six integer fields of the UUID, which are also available as six individual attributes and two derived attributes:

+
+ + + + + + + + +

Field

Meaning
+
+UUID.time_low
+

The first 32 bits of the UUID.
+
+UUID.time_mid
+

The next 16 bits of the UUID.
+
+UUID.time_hi_version
+

The next 16 bits of the UUID.
+
+UUID.clock_seq_hi_variant
+

The next 8 bits of the UUID.
+
+UUID.clock_seq_low
+

The next 8 bits of the UUID.
+
+UUID.node
+

The last 48 bits of the UUID.
+
+UUID.time
+

The 60-bit timestamp.
+
+UUID.clock_seq
+

The 14-bit sequence number.
+UUID.hex
+

The UUID as a 32-character lowercase hexadecimal string.

+
+UUID.int
+

The UUID as a 128-bit integer.

+
+UUID.urn
+

The UUID as a URN as specified in RFC 4122.

+
+UUID.variant
+

The UUID variant, which determines the internal layout of the UUID. This will be one of the constants RESERVED_NCS, RFC_4122, RESERVED_MICROSOFT, or RESERVED_FUTURE.

+
+UUID.version
+

The UUID version number (1 through 5, meaningful only when the variant is RFC_4122).

+
+UUID.is_safe
+

An enumeration of SafeUUID which indicates whether the platform generated the UUID in a multiprocessing-safe way.

New in version 3.7.

+

The uuid module defines the following functions:

+uuid.getnode()
+

Get the hardware address as a 48-bit positive integer. The first time this runs, it may launch a separate program, which could be quite slow. If all attempts to obtain the hardware address fail, we choose a random 48-bit number with the multicast bit (least significant bit of the first octet) set to 1 as recommended in RFC 4122. “Hardware address” means the MAC address of a network interface. On a machine with multiple network interfaces, universally administered MAC addresses (i.e. where the second least significant bit of the first octet is unset) will be preferred over locally administered MAC addresses, but with no other ordering guarantees.

Changed in version 3.7: Universally administered MAC addresses are preferred over locally administered MAC addresses, since the former are guaranteed to be globally unique, while the latter are not.

+
+uuid.uuid1(node=None, clock_seq=None)
+

Generate a UUID from a host ID, sequence number, and the current time. If node is not given, getnode() is used to obtain the hardware address. If clock_seq is given, it is used as the sequence number; otherwise a random 14-bit sequence number is chosen.

+
+uuid.uuid3(namespace, name)
+

Generate a UUID based on the MD5 hash of a namespace identifier (which is a UUID) and a name (which is a bytes object or a string that will be encoded using UTF-8).

+
+uuid.uuid4()
+

Generate a random UUID.

+
+uuid.uuid5(namespace, name)
+

Generate a UUID based on the SHA-1 hash of a namespace identifier (which is a UUID) and a name (which is a bytes object or a string that will be encoded using UTF-8).

+

The uuid module defines the following namespace identifiers for use with uuid3() or uuid5().

+uuid.NAMESPACE_DNS
+

When this namespace is specified, the name string is a fully qualified domain name.

+
+uuid.NAMESPACE_URL
+

When this namespace is specified, the name string is a URL.

+
+uuid.NAMESPACE_OID
+

When this namespace is specified, the name string is an ISO OID.

+
+uuid.NAMESPACE_X500
+

When this namespace is specified, the name string is an X.500 DN in DER or a text output format.

+

The uuid module defines the following constants for the possible values of the variant attribute:

+uuid.RESERVED_NCS
+

Reserved for NCS compatibility.

+
+uuid.RFC_4122
+

Specifies the UUID layout given in RFC 4122.

+
+uuid.RESERVED_MICROSOFT
+

Reserved for Microsoft compatibility.

+
+uuid.RESERVED_FUTURE
+

Reserved for future definition.

+

See also

+RFC 4122 - A Universally Unique IDentifier (UUID) URN Namespace
+
+

This specification defines a Uniform Resource Name namespace for UUIDs, the internal format of UUIDs, and methods of generating UUIDs.

Command-Line Usage

New in version 3.12.

The uuid module can be executed as a script from the command line.

python -m uuid [-h] [-u {uuid1,uuid3,uuid4,uuid5}] [-n NAMESPACE] [-N NAME]
+

The following options are accepted:

+-h, --help
+

Show the help message and exit.

+
+-u <uuid>
+--uuid <uuid>
+

Specify the function name to use to generate the uuid. By default uuid4() is used.

+
+-n <namespace>
+--namespace <namespace>
+

The namespace is a UUID, or @ns where ns is a well-known predefined UUID addressed by namespace name. Such as @dns, @url, @oid, and @x500. Only required for uuid3() / uuid5() functions.

+
+-N <name>
+--name <name>
+

The name used as part of generating the uuid. Only required for uuid3() / uuid5() functions.

+

Example

Here are some examples of typical usage of the uuid module:

>>> import uuid
+
+>>> # make a UUID based on the host ID and current time
+>>> uuid.uuid1()
+UUID('a8098c1a-f86e-11da-bd1a-00112444be1e')
+
+>>> # make a UUID using an MD5 hash of a namespace UUID and a name
+>>> uuid.uuid3(uuid.NAMESPACE_DNS, 'python.org')
+UUID('6fa459ea-ee8a-3ca4-894e-db77e160355e')
+
+>>> # make a random UUID
+>>> uuid.uuid4()
+UUID('16fd2706-8baf-433b-82eb-8c7fada847da')
+
+>>> # make a UUID using a SHA-1 hash of a namespace UUID and a name
+>>> uuid.uuid5(uuid.NAMESPACE_DNS, 'python.org')
+UUID('886313e1-3b8a-5372-9b90-0c9aee199e5d')
+
+>>> # make a UUID from a string of hex digits (braces and hyphens ignored)
+>>> x = uuid.UUID('{00010203-0405-0607-0809-0a0b0c0d0e0f}')
+
+>>> # convert a UUID to a string of hex digits in standard form
+>>> str(x)
+'00010203-0405-0607-0809-0a0b0c0d0e0f'
+
+>>> # get the raw 16 bytes of the UUID
+>>> x.bytes
+b'\x00\x01\x02\x03\x04\x05\x06\x07\x08\t\n\x0b\x0c\r\x0e\x0f'
+
+>>> # make a UUID from a 16-byte string
+>>> uuid.UUID(bytes=x.bytes)
+UUID('00010203-0405-0607-0809-0a0b0c0d0e0f')
+

Command-Line Example

Here are some examples of typical usage of the uuid command line interface:

# generate a random uuid - by default uuid4() is used
+$ python -m uuid
+
+# generate a uuid using uuid1()
+$ python -m uuid -u uuid1
+
+# generate a uuid using uuid5
+$ python -m uuid -u uuid5 -n @url -N example.com
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/uuid.html +

+
diff --git a/devdocs/python~3.12/library%2Fvenv.html b/devdocs/python~3.12/library%2Fvenv.html new file mode 100644 index 00000000..3c18a726 --- /dev/null +++ b/devdocs/python~3.12/library%2Fvenv.html @@ -0,0 +1,318 @@ +

venv — Creation of virtual environments

New in version 3.3.

Source code: Lib/venv/

The venv module supports creating lightweight “virtual environments”, each with their own independent set of Python packages installed in their site directories. A virtual environment is created on top of an existing Python installation, known as the virtual environment’s “base” Python, and may optionally be isolated from the packages in the base environment, so only those explicitly installed in the virtual environment are available.

When used from within a virtual environment, common installation tools such as pip will install Python packages into a virtual environment without needing to be told to do so explicitly.

A virtual environment is (amongst other things):

See PEP 405 for more background on Python virtual environments.

See also

Python Packaging User Guide: Creating and using virtual environments

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Creating virtual environments

Creation of virtual environments is done by executing the command venv:

python -m venv /path/to/new/virtual/environment
+

Running this command creates the target directory (creating any parent directories that don’t exist already) and places a pyvenv.cfg file in it with a home key pointing to the Python installation from which the command was run (a common name for the target directory is .venv). It also creates a bin (or Scripts on Windows) subdirectory containing a copy/symlink of the Python binary/binaries (as appropriate for the platform or arguments used at environment creation time). It also creates an (initially empty) lib/pythonX.Y/site-packages subdirectory (on Windows, this is Lib\site-packages). If an existing directory is specified, it will be re-used.

Deprecated since version 3.6: pyvenv was the recommended tool for creating virtual environments for Python 3.3 and 3.4, and is deprecated in Python 3.6.

Changed in version 3.5: The use of venv is now recommended for creating virtual environments.

On Windows, invoke the venv command as follows:

c:\>Python35\python -m venv c:\path\to\myenv
+

Alternatively, if you configured the PATH and PATHEXT variables for your Python installation:

c:\>python -m venv c:\path\to\myenv
+

The command, if run with -h, will show the available options:

usage: venv [-h] [--system-site-packages] [--symlinks | --copies] [--clear]
+            [--upgrade] [--without-pip] [--prompt PROMPT] [--upgrade-deps]
+            ENV_DIR [ENV_DIR ...]
+
+Creates virtual Python environments in one or more target directories.
+
+positional arguments:
+  ENV_DIR               A directory to create the environment in.
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --system-site-packages
+                        Give the virtual environment access to the system
+                        site-packages dir.
+  --symlinks            Try to use symlinks rather than copies, when symlinks
+                        are not the default for the platform.
+  --copies              Try to use copies rather than symlinks, even when
+                        symlinks are the default for the platform.
+  --clear               Delete the contents of the environment directory if it
+                        already exists, before environment creation.
+  --upgrade             Upgrade the environment directory to use this version
+                        of Python, assuming Python has been upgraded in-place.
+  --without-pip         Skips installing or upgrading pip in the virtual
+                        environment (pip is bootstrapped by default)
+  --prompt PROMPT       Provides an alternative prompt prefix for this
+                        environment.
+  --upgrade-deps        Upgrade core dependencies (pip) to the
+                        latest version in PyPI
+
+Once an environment has been created, you may wish to activate it, e.g. by
+sourcing an activate script in its bin directory.
+

Changed in version 3.12: setuptools is no longer a core venv dependency.

Changed in version 3.9: Add --upgrade-deps option to upgrade pip + setuptools to the latest on PyPI

Changed in version 3.4: Installs pip by default, added the --without-pip and --copies options

Changed in version 3.4: In earlier versions, if the target directory already existed, an error was raised, unless the --clear or --upgrade option was provided.

Note

While symlinks are supported on Windows, they are not recommended. Of particular note is that double-clicking python.exe in File Explorer will resolve the symlink eagerly and ignore the virtual environment.

Note

On Microsoft Windows, it may be required to enable the Activate.ps1 script by setting the execution policy for the user. You can do this by issuing the following PowerShell command:

PS C:> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser

See About Execution Policies for more information.

The created pyvenv.cfg file also includes the include-system-site-packages key, set to true if venv is run with the --system-site-packages option, false otherwise.

Unless the --without-pip option is given, ensurepip will be invoked to bootstrap pip into the virtual environment.

Multiple paths can be given to venv, in which case an identical virtual environment will be created, according to the given options, at each provided path.

How venvs work

When a Python interpreter is running from a virtual environment, sys.prefix and sys.exec_prefix point to the directories of the virtual environment, whereas sys.base_prefix and sys.base_exec_prefix point to those of the base Python used to create the environment. It is sufficient to check sys.prefix != sys.base_prefix to determine if the current interpreter is running from a virtual environment.

A virtual environment may be “activated” using a script in its binary directory (bin on POSIX; Scripts on Windows). This will prepend that directory to your PATH, so that running python will invoke the environment’s Python interpreter and you can run installed scripts without having to use their full path. The invocation of the activation script is platform-specific (<venv> must be replaced by the path to the directory containing the virtual environment):

+ + + + + + +

Platform

Shell

Command to activate virtual environment

POSIX

bash/zsh

$ source <venv>/bin/activate

fish

$ source <venv>/bin/activate.fish

csh/tcsh

$ source <venv>/bin/activate.csh

PowerShell

$ <venv>/bin/Activate.ps1

Windows

cmd.exe

C:\> <venv>\Scripts\activate.bat

PowerShell

PS C:\> <venv>\Scripts\Activate.ps1

New in version 3.4: fish and csh activation scripts.

New in version 3.8: PowerShell activation scripts installed under POSIX for PowerShell Core support.

You don’t specifically need to activate a virtual environment, as you can just specify the full path to that environment’s Python interpreter when invoking Python. Furthermore, all scripts installed in the environment should be runnable without activating it.

In order to achieve this, scripts installed into virtual environments have a “shebang” line which points to the environment’s Python interpreter, i.e. #!/<path-to-venv>/bin/python. This means that the script will run with that interpreter regardless of the value of PATH. On Windows, “shebang” line processing is supported if you have the Python Launcher for Windows installed. Thus, double-clicking an installed script in a Windows Explorer window should run it with the correct interpreter without the environment needing to be activated or on the PATH.

When a virtual environment has been activated, the VIRTUAL_ENV environment variable is set to the path of the environment. Since explicitly activating a virtual environment is not required to use it, VIRTUAL_ENV cannot be relied upon to determine whether a virtual environment is being used.

Warning

Because scripts installed in environments should not expect the environment to be activated, their shebang lines contain the absolute paths to their environment’s interpreters. Because of this, environments are inherently non-portable, in the general case. You should always have a simple means of recreating an environment (for example, if you have a requirements file requirements.txt, you can invoke pip install -r +requirements.txt using the environment’s pip to install all of the packages needed by the environment). If for any reason you need to move the environment to a new location, you should recreate it at the desired location and delete the one at the old location. If you move an environment because you moved a parent directory of it, you should recreate the environment in its new location. Otherwise, software installed into the environment may not work as expected.

You can deactivate a virtual environment by typing deactivate in your shell. The exact mechanism is platform-specific and is an internal implementation detail (typically, a script or shell function will be used).

API

The high-level method described above makes use of a simple API which provides mechanisms for third-party virtual environment creators to customize environment creation according to their needs, the EnvBuilder class.

+class venv.EnvBuilder(system_site_packages=False, clear=False, symlinks=False, upgrade=False, with_pip=False, prompt=None, upgrade_deps=False)
+

The EnvBuilder class accepts the following keyword arguments on instantiation:

  • +system_site_packages – a Boolean value indicating that the system Python site-packages should be available to the environment (defaults to False).
  • +clear – a Boolean value which, if true, will delete the contents of any existing target directory, before creating the environment.
  • +symlinks – a Boolean value indicating whether to attempt to symlink the Python binary rather than copying.
  • +upgrade – a Boolean value which, if true, will upgrade an existing environment with the running Python - for use when that Python has been upgraded in-place (defaults to False).
  • +with_pip – a Boolean value which, if true, ensures pip is installed in the virtual environment. This uses ensurepip with the --default-pip option.
  • +prompt – a String to be used after virtual environment is activated (defaults to None which means directory name of the environment would be used). If the special string "." is provided, the basename of the current directory is used as the prompt.
  • +upgrade_deps – Update the base venv modules to the latest on PyPI

Changed in version 3.4: Added the with_pip parameter

New in version 3.6: Added the prompt parameter

New in version 3.9: Added the upgrade_deps parameter

Creators of third-party virtual environment tools will be free to use the provided EnvBuilder class as a base class.

The returned env-builder is an object which has a method, create:

+create(env_dir)
+

Create a virtual environment by specifying the target directory (absolute or relative to the current directory) which is to contain the virtual environment. The create method will either create the environment in the specified directory, or raise an appropriate exception.

The create method of the EnvBuilder class illustrates the hooks available for subclass customization:

def create(self, env_dir):
+    """
+    Create a virtualized Python environment in a directory.
+    env_dir is the target directory to create an environment in.
+    """
+    env_dir = os.path.abspath(env_dir)
+    context = self.ensure_directories(env_dir)
+    self.create_configuration(context)
+    self.setup_python(context)
+    self.setup_scripts(context)
+    self.post_setup(context)
+

Each of the methods ensure_directories(), create_configuration(), setup_python(), setup_scripts() and post_setup() can be overridden.

+
+ensure_directories(env_dir)
+

Creates the environment directory and all necessary subdirectories that don’t already exist, and returns a context object. This context object is just a holder for attributes (such as paths) for use by the other methods. If the EnvBuilder is created with the arg clear=True, contents of the environment directory will be cleared and then all necessary subdirectories will be recreated.

The returned context object is a types.SimpleNamespace with the following attributes:

  • +env_dir - The location of the virtual environment. Used for __VENV_DIR__ in activation scripts (see install_scripts()).
  • +env_name - The name of the virtual environment. Used for __VENV_NAME__ in activation scripts (see install_scripts()).
  • +prompt - The prompt to be used by the activation scripts. Used for __VENV_PROMPT__ in activation scripts (see install_scripts()).
  • +executable - The underlying Python executable used by the virtual environment. This takes into account the case where a virtual environment is created from another virtual environment.
  • +inc_path - The include path for the virtual environment.
  • +lib_path - The purelib path for the virtual environment.
  • +bin_path - The script path for the virtual environment.
  • +bin_name - The name of the script path relative to the virtual environment location. Used for __VENV_BIN_NAME__ in activation scripts (see install_scripts()).
  • +env_exe - The name of the Python interpreter in the virtual environment. Used for __VENV_PYTHON__ in activation scripts (see install_scripts()).
  • +env_exec_cmd - The name of the Python interpreter, taking into account filesystem redirections. This can be used to run Python in the virtual environment.

Changed in version 3.12: The attribute lib_path was added to the context, and the context object was documented.

Changed in version 3.11: The venv sysconfig installation scheme is used to construct the paths of the created directories.

+
+create_configuration(context)
+

Creates the pyvenv.cfg configuration file in the environment.

+
+setup_python(context)
+

Creates a copy or symlink to the Python executable in the environment. On POSIX systems, if a specific executable python3.x was used, symlinks to python and python3 will be created pointing to that executable, unless files with those names already exist.

+
+setup_scripts(context)
+

Installs activation scripts appropriate to the platform into the virtual environment.

+
+upgrade_dependencies(context)
+

Upgrades the core venv dependency packages (currently pip) in the environment. This is done by shelling out to the pip executable in the environment.

New in version 3.9.

Changed in version 3.12: setuptools is no longer a core venv dependency.

+
+post_setup(context)
+

A placeholder method which can be overridden in third party implementations to pre-install packages in the virtual environment or perform other post-creation steps.

+

Changed in version 3.7.2: Windows now uses redirector scripts for python[w].exe instead of copying the actual binaries. In 3.7.2 only setup_python() does nothing unless running from a build in the source tree.

Changed in version 3.7.3: Windows copies the redirector scripts as part of setup_python() instead of setup_scripts(). This was not the case in 3.7.2. When using symlinks, the original executables will be linked.

In addition, EnvBuilder provides this utility method that can be called from setup_scripts() or post_setup() in subclasses to assist in installing custom scripts into the virtual environment.

+install_scripts(context, path)
+

path is the path to a directory that should contain subdirectories “common”, “posix”, “nt”, each containing scripts destined for the bin directory in the environment. The contents of “common” and the directory corresponding to os.name are copied after some text replacement of placeholders:

  • +__VENV_DIR__ is replaced with the absolute path of the environment directory.
  • +__VENV_NAME__ is replaced with the environment name (final path segment of environment directory).
  • +__VENV_PROMPT__ is replaced with the prompt (the environment name surrounded by parentheses and with a following space)
  • +__VENV_BIN_NAME__ is replaced with the name of the bin directory (either bin or Scripts).
  • +__VENV_PYTHON__ is replaced with the absolute path of the environment’s executable.

The directories are allowed to exist (for when an existing environment is being upgraded).

+
+

There is also a module-level convenience function:

+venv.create(env_dir, system_site_packages=False, clear=False, symlinks=False, with_pip=False, prompt=None, upgrade_deps=False)
+

Create an EnvBuilder with the given keyword arguments, and call its create() method with the env_dir argument.

New in version 3.3.

Changed in version 3.4: Added the with_pip parameter

Changed in version 3.6: Added the prompt parameter

Changed in version 3.9: Added the upgrade_deps parameter

+

An example of extending EnvBuilder +

The following script shows how to extend EnvBuilder by implementing a subclass which installs setuptools and pip into a created virtual environment:

import os
+import os.path
+from subprocess import Popen, PIPE
+import sys
+from threading import Thread
+from urllib.parse import urlparse
+from urllib.request import urlretrieve
+import venv
+
+class ExtendedEnvBuilder(venv.EnvBuilder):
+    """
+    This builder installs setuptools and pip so that you can pip or
+    easy_install other packages into the created virtual environment.
+
+    :param nodist: If true, setuptools and pip are not installed into the
+                   created virtual environment.
+    :param nopip: If true, pip is not installed into the created
+                  virtual environment.
+    :param progress: If setuptools or pip are installed, the progress of the
+                     installation can be monitored by passing a progress
+                     callable. If specified, it is called with two
+                     arguments: a string indicating some progress, and a
+                     context indicating where the string is coming from.
+                     The context argument can have one of three values:
+                     'main', indicating that it is called from virtualize()
+                     itself, and 'stdout' and 'stderr', which are obtained
+                     by reading lines from the output streams of a subprocess
+                     which is used to install the app.
+
+                     If a callable is not specified, default progress
+                     information is output to sys.stderr.
+    """
+
+    def __init__(self, *args, **kwargs):
+        self.nodist = kwargs.pop('nodist', False)
+        self.nopip = kwargs.pop('nopip', False)
+        self.progress = kwargs.pop('progress', None)
+        self.verbose = kwargs.pop('verbose', False)
+        super().__init__(*args, **kwargs)
+
+    def post_setup(self, context):
+        """
+        Set up any packages which need to be pre-installed into the
+        virtual environment being created.
+
+        :param context: The information for the virtual environment
+                        creation request being processed.
+        """
+        os.environ['VIRTUAL_ENV'] = context.env_dir
+        if not self.nodist:
+            self.install_setuptools(context)
+        # Can't install pip without setuptools
+        if not self.nopip and not self.nodist:
+            self.install_pip(context)
+
+    def reader(self, stream, context):
+        """
+        Read lines from a subprocess' output stream and either pass to a progress
+        callable (if specified) or write progress information to sys.stderr.
+        """
+        progress = self.progress
+        while True:
+            s = stream.readline()
+            if not s:
+                break
+            if progress is not None:
+                progress(s, context)
+            else:
+                if not self.verbose:
+                    sys.stderr.write('.')
+                else:
+                    sys.stderr.write(s.decode('utf-8'))
+                sys.stderr.flush()
+        stream.close()
+
+    def install_script(self, context, name, url):
+        _, _, path, _, _, _ = urlparse(url)
+        fn = os.path.split(path)[-1]
+        binpath = context.bin_path
+        distpath = os.path.join(binpath, fn)
+        # Download script into the virtual environment's binaries folder
+        urlretrieve(url, distpath)
+        progress = self.progress
+        if self.verbose:
+            term = '\n'
+        else:
+            term = ''
+        if progress is not None:
+            progress('Installing %s ...%s' % (name, term), 'main')
+        else:
+            sys.stderr.write('Installing %s ...%s' % (name, term))
+            sys.stderr.flush()
+        # Install in the virtual environment
+        args = [context.env_exe, fn]
+        p = Popen(args, stdout=PIPE, stderr=PIPE, cwd=binpath)
+        t1 = Thread(target=self.reader, args=(p.stdout, 'stdout'))
+        t1.start()
+        t2 = Thread(target=self.reader, args=(p.stderr, 'stderr'))
+        t2.start()
+        p.wait()
+        t1.join()
+        t2.join()
+        if progress is not None:
+            progress('done.', 'main')
+        else:
+            sys.stderr.write('done.\n')
+        # Clean up - no longer needed
+        os.unlink(distpath)
+
+    def install_setuptools(self, context):
+        """
+        Install setuptools in the virtual environment.
+
+        :param context: The information for the virtual environment
+                        creation request being processed.
+        """
+        url = "https://bootstrap.pypa.io/ez_setup.py"
+        self.install_script(context, 'setuptools', url)
+        # clear up the setuptools archive which gets downloaded
+        pred = lambda o: o.startswith('setuptools-') and o.endswith('.tar.gz')
+        files = filter(pred, os.listdir(context.bin_path))
+        for f in files:
+            f = os.path.join(context.bin_path, f)
+            os.unlink(f)
+
+    def install_pip(self, context):
+        """
+        Install pip in the virtual environment.
+
+        :param context: The information for the virtual environment
+                        creation request being processed.
+        """
+        url = 'https://bootstrap.pypa.io/get-pip.py'
+        self.install_script(context, 'pip', url)
+
+
+def main(args=None):
+    import argparse
+
+    parser = argparse.ArgumentParser(prog=__name__,
+                                     description='Creates virtual Python '
+                                                 'environments in one or '
+                                                 'more target '
+                                                 'directories.')
+    parser.add_argument('dirs', metavar='ENV_DIR', nargs='+',
+                        help='A directory in which to create the '
+                             'virtual environment.')
+    parser.add_argument('--no-setuptools', default=False,
+                        action='store_true', dest='nodist',
+                        help="Don't install setuptools or pip in the "
+                             "virtual environment.")
+    parser.add_argument('--no-pip', default=False,
+                        action='store_true', dest='nopip',
+                        help="Don't install pip in the virtual "
+                             "environment.")
+    parser.add_argument('--system-site-packages', default=False,
+                        action='store_true', dest='system_site',
+                        help='Give the virtual environment access to the '
+                             'system site-packages dir.')
+    if os.name == 'nt':
+        use_symlinks = False
+    else:
+        use_symlinks = True
+    parser.add_argument('--symlinks', default=use_symlinks,
+                        action='store_true', dest='symlinks',
+                        help='Try to use symlinks rather than copies, '
+                             'when symlinks are not the default for '
+                             'the platform.')
+    parser.add_argument('--clear', default=False, action='store_true',
+                        dest='clear', help='Delete the contents of the '
+                                           'virtual environment '
+                                           'directory if it already '
+                                           'exists, before virtual '
+                                           'environment creation.')
+    parser.add_argument('--upgrade', default=False, action='store_true',
+                        dest='upgrade', help='Upgrade the virtual '
+                                             'environment directory to '
+                                             'use this version of '
+                                             'Python, assuming Python '
+                                             'has been upgraded '
+                                             'in-place.')
+    parser.add_argument('--verbose', default=False, action='store_true',
+                        dest='verbose', help='Display the output '
+                                             'from the scripts which '
+                                             'install setuptools and pip.')
+    options = parser.parse_args(args)
+    if options.upgrade and options.clear:
+        raise ValueError('you cannot supply --upgrade and --clear together.')
+    builder = ExtendedEnvBuilder(system_site_packages=options.system_site,
+                                   clear=options.clear,
+                                   symlinks=options.symlinks,
+                                   upgrade=options.upgrade,
+                                   nodist=options.nodist,
+                                   nopip=options.nopip,
+                                   verbose=options.verbose)
+    for d in options.dirs:
+        builder.create(d)
+
+if __name__ == '__main__':
+    rc = 1
+    try:
+        main()
+        rc = 0
+    except Exception as e:
+        print('Error: %s' % e, file=sys.stderr)
+    sys.exit(rc)
+

This script is also available for download online.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/venv.html +

+
diff --git a/devdocs/python~3.12/library%2Fwarnings.html b/devdocs/python~3.12/library%2Fwarnings.html new file mode 100644 index 00000000..89f0f5ee --- /dev/null +++ b/devdocs/python~3.12/library%2Fwarnings.html @@ -0,0 +1,119 @@ +

warnings — Warning control

Source code: Lib/warnings.py

Warning messages are typically issued in situations where it is useful to alert the user of some condition in a program, where that condition (normally) doesn’t warrant raising an exception and terminating the program. For example, one might want to issue a warning when a program uses an obsolete module.

Python programmers issue warnings by calling the warn() function defined in this module. (C programmers use PyErr_WarnEx(); see Exception Handling for details).

Warning messages are normally written to sys.stderr, but their disposition can be changed flexibly, from ignoring all warnings to turning them into exceptions. The disposition of warnings can vary based on the warning category, the text of the warning message, and the source location where it is issued. Repetitions of a particular warning for the same source location are typically suppressed.

There are two stages in warning control: first, each time a warning is issued, a determination is made whether a message should be issued or not; next, if a message is to be issued, it is formatted and printed using a user-settable hook.

The determination whether to issue a warning message is controlled by the warning filter, which is a sequence of matching rules and actions. Rules can be added to the filter by calling filterwarnings() and reset to its default state by calling resetwarnings().

The printing of warning messages is done by calling showwarning(), which may be overridden; the default implementation of this function formats the message by calling formatwarning(), which is also available for use by custom implementations.

See also

logging.captureWarnings() allows you to handle all warnings with the standard logging infrastructure.

Warning Categories

There are a number of built-in exceptions that represent warning categories. This categorization is useful to be able to filter out groups of warnings.

While these are technically built-in exceptions, they are documented here, because conceptually they belong to the warnings mechanism.

User code can define additional warning categories by subclassing one of the standard warning categories. A warning category must always be a subclass of the Warning class.

The following warnings category classes are currently defined:

+ + + + + + + + + + + +

Class

Description

Warning

This is the base class of all warning category classes. It is a subclass of Exception.

UserWarning

The default category for warn().

DeprecationWarning

Base category for warnings about deprecated features when those warnings are intended for other Python developers (ignored by default, unless triggered by code in __main__).

SyntaxWarning

Base category for warnings about dubious syntactic features.

RuntimeWarning

Base category for warnings about dubious runtime features.

FutureWarning

Base category for warnings about deprecated features when those warnings are intended for end users of applications that are written in Python.

PendingDeprecationWarning

Base category for warnings about features that will be deprecated in the future (ignored by default).

ImportWarning

Base category for warnings triggered during the process of importing a module (ignored by default).

UnicodeWarning

Base category for warnings related to Unicode.

BytesWarning

Base category for warnings related to bytes and bytearray.

ResourceWarning

Base category for warnings related to resource usage (ignored by default).

Changed in version 3.7: Previously DeprecationWarning and FutureWarning were distinguished based on whether a feature was being removed entirely or changing its behaviour. They are now distinguished based on their intended audience and the way they’re handled by the default warnings filters.

The Warnings Filter

The warnings filter controls whether warnings are ignored, displayed, or turned into errors (raising an exception).

Conceptually, the warnings filter maintains an ordered list of filter specifications; any specific warning is matched against each filter specification in the list in turn until a match is found; the filter determines the disposition of the match. Each entry is a tuple of the form (action, message, category, module, lineno), where:

  • +

    action is one of the following strings:

    + + + + + + +

    Value

    Disposition

    "default"

    print the first occurrence of matching warnings for each location (module + line number) where the warning is issued

    "error"

    turn matching warnings into exceptions

    "ignore"

    never print matching warnings

    "always"

    always print matching warnings

    "module"

    print the first occurrence of matching warnings for each module where the warning is issued (regardless of line number)

    "once"

    print only the first occurrence of matching warnings, regardless of location
  • +message is a string containing a regular expression that the start of the warning message must match, case-insensitively. In -W and PYTHONWARNINGS, message is a literal string that the start of the warning message must contain (case-insensitively), ignoring any whitespace at the start or end of message.
  • +category is a class (a subclass of Warning) of which the warning category must be a subclass in order to match.
  • +module is a string containing a regular expression that the start of the fully qualified module name must match, case-sensitively. In -W and PYTHONWARNINGS, module is a literal string that the fully qualified module name must be equal to (case-sensitively), ignoring any whitespace at the start or end of module.
  • +lineno is an integer that the line number where the warning occurred must match, or 0 to match all line numbers.

Since the Warning class is derived from the built-in Exception class, to turn a warning into an error we simply raise category(message).

If a warning is reported and doesn’t match any registered filter then the “default” action is applied (hence its name).

Describing Warning Filters

The warnings filter is initialized by -W options passed to the Python interpreter command line and the PYTHONWARNINGS environment variable. The interpreter saves the arguments for all supplied entries without interpretation in sys.warnoptions; the warnings module parses these when it is first imported (invalid options are ignored, after printing a message to sys.stderr).

Individual warnings filters are specified as a sequence of fields separated by colons:

action:message:category:module:line
+

The meaning of each of these fields is as described in The Warnings Filter. When listing multiple filters on a single line (as for PYTHONWARNINGS), the individual filters are separated by commas and the filters listed later take precedence over those listed before them (as they’re applied left-to-right, and the most recently applied filters take precedence over earlier ones).

Commonly used warning filters apply to either all warnings, warnings in a particular category, or warnings raised by particular modules or packages. Some examples:

default                      # Show all warnings (even those ignored by default)
+ignore                       # Ignore all warnings
+error                        # Convert all warnings to errors
+error::ResourceWarning       # Treat ResourceWarning messages as errors
+default::DeprecationWarning  # Show DeprecationWarning messages
+ignore,default:::mymodule    # Only report warnings triggered by "mymodule"
+error:::mymodule             # Convert warnings to errors in "mymodule"
+

Default Warning Filter

By default, Python installs several warning filters, which can be overridden by the -W command-line option, the PYTHONWARNINGS environment variable and calls to filterwarnings().

In regular release builds, the default warning filter has the following entries (in order of precedence):

default::DeprecationWarning:__main__
+ignore::DeprecationWarning
+ignore::PendingDeprecationWarning
+ignore::ImportWarning
+ignore::ResourceWarning
+

In a debug build, the list of default warning filters is empty.

Changed in version 3.2: DeprecationWarning is now ignored by default in addition to PendingDeprecationWarning.

Changed in version 3.7: DeprecationWarning is once again shown by default when triggered directly by code in __main__.

Changed in version 3.7: BytesWarning no longer appears in the default filter list and is instead configured via sys.warnoptions when -b is specified twice.

Overriding the default filter

Developers of applications written in Python may wish to hide all Python level warnings from their users by default, and only display them when running tests or otherwise working on the application. The sys.warnoptions attribute used to pass filter configurations to the interpreter can be used as a marker to indicate whether or not warnings should be disabled:

import sys
+
+if not sys.warnoptions:
+    import warnings
+    warnings.simplefilter("ignore")
+

Developers of test runners for Python code are advised to instead ensure that all warnings are displayed by default for the code under test, using code like:

import sys
+
+if not sys.warnoptions:
+    import os, warnings
+    warnings.simplefilter("default") # Change the filter in this process
+    os.environ["PYTHONWARNINGS"] = "default" # Also affect subprocesses
+

Finally, developers of interactive shells that run user code in a namespace other than __main__ are advised to ensure that DeprecationWarning messages are made visible by default, using code like the following (where user_ns is the module used to execute code entered interactively):

import warnings
+warnings.filterwarnings("default", category=DeprecationWarning,
+                                   module=user_ns.get("__name__"))
+

Temporarily Suppressing Warnings

If you are using code that you know will raise a warning, such as a deprecated function, but do not want to see the warning (even when warnings have been explicitly configured via the command line), then it is possible to suppress the warning using the catch_warnings context manager:

import warnings
+
+def fxn():
+    warnings.warn("deprecated", DeprecationWarning)
+
+with warnings.catch_warnings():
+    warnings.simplefilter("ignore")
+    fxn()
+

While within the context manager all warnings will simply be ignored. This allows you to use known-deprecated code without having to see the warning while not suppressing the warning for other code that might not be aware of its use of deprecated code. Note: this can only be guaranteed in a single-threaded application. If two or more threads use the catch_warnings context manager at the same time, the behavior is undefined.

Testing Warnings

To test warnings raised by code, use the catch_warnings context manager. With it you can temporarily mutate the warnings filter to facilitate your testing. For instance, do the following to capture all raised warnings to check:

import warnings
+
+def fxn():
+    warnings.warn("deprecated", DeprecationWarning)
+
+with warnings.catch_warnings(record=True) as w:
+    # Cause all warnings to always be triggered.
+    warnings.simplefilter("always")
+    # Trigger a warning.
+    fxn()
+    # Verify some things
+    assert len(w) == 1
+    assert issubclass(w[-1].category, DeprecationWarning)
+    assert "deprecated" in str(w[-1].message)
+

One can also cause all warnings to be exceptions by using error instead of always. One thing to be aware of is that if a warning has already been raised because of a once/default rule, then no matter what filters are set the warning will not be seen again unless the warnings registry related to the warning has been cleared.

Once the context manager exits, the warnings filter is restored to its state when the context was entered. This prevents tests from changing the warnings filter in unexpected ways between tests and leading to indeterminate test results. The showwarning() function in the module is also restored to its original value. Note: this can only be guaranteed in a single-threaded application. If two or more threads use the catch_warnings context manager at the same time, the behavior is undefined.

When testing multiple operations that raise the same kind of warning, it is important to test them in a manner that confirms each operation is raising a new warning (e.g. set warnings to be raised as exceptions and check the operations raise exceptions, check that the length of the warning list continues to increase after each operation, or else delete the previous entries from the warnings list before each new operation).

Updating Code For New Versions of Dependencies

Warning categories that are primarily of interest to Python developers (rather than end users of applications written in Python) are ignored by default.

Notably, this “ignored by default” list includes DeprecationWarning (for every module except __main__), which means developers should make sure to test their code with typically ignored warnings made visible in order to receive timely notifications of future breaking API changes (whether in the standard library or third party packages).

In the ideal case, the code will have a suitable test suite, and the test runner will take care of implicitly enabling all warnings when running tests (the test runner provided by the unittest module does this).

In less ideal cases, applications can be checked for use of deprecated interfaces by passing -Wd to the Python interpreter (this is shorthand for -W default) or setting PYTHONWARNINGS=default in the environment. This enables default handling for all warnings, including those that are ignored by default. To change what action is taken for encountered warnings you can change what argument is passed to -W (e.g. -W error). See the -W flag for more details on what is possible.

Available Functions

+warnings.warn(message, category=None, stacklevel=1, source=None, \*, skip_file_prefixes=None)
+

Issue a warning, or maybe ignore it or raise an exception. The category argument, if given, must be a warning category class; it defaults to UserWarning. Alternatively, message can be a Warning instance, in which case category will be ignored and message.__class__ will be used. In this case, the message text will be str(message). This function raises an exception if the particular warning issued is changed into an error by the warnings filter. The stacklevel argument can be used by wrapper functions written in Python, like this:

def deprecated_api(message):
+    warnings.warn(message, DeprecationWarning, stacklevel=2)
+

This makes the warning refer to deprecated_api’s caller, rather than to the source of deprecated_api itself (since the latter would defeat the purpose of the warning message).

The skip_file_prefixes keyword argument can be used to indicate which stack frames are ignored when counting stack levels. This can be useful when you want the warning to always appear at call sites outside of a package when a constant stacklevel does not fit all call paths or is otherwise challenging to maintain. If supplied, it must be a tuple of strings. When prefixes are supplied, stacklevel is implicitly overridden to be max(2, +stacklevel). To cause a warning to be attributed to the caller from outside of the current package you might write:

# example/lower.py
+_warn_skips = (os.path.dirname(__file__),)
+
+def one_way(r_luxury_yacht=None, t_wobbler_mangrove=None):
+    if r_luxury_yacht:
+        warnings.warn("Please migrate to t_wobbler_mangrove=.",
+                      skip_file_prefixes=_warn_skips)
+
+# example/higher.py
+from . import lower
+
+def another_way(**kw):
+    lower.one_way(**kw)
+

This makes the warning refer to both the example.lower.one_way() and package.higher.another_way() call sites only from calling code living outside of example package.

source, if supplied, is the destroyed object which emitted a ResourceWarning.

Changed in version 3.6: Added source parameter.

Changed in version 3.12: Added skip_file_prefixes.

+
+warnings.warn_explicit(message, category, filename, lineno, module=None, registry=None, module_globals=None, source=None)
+

This is a low-level interface to the functionality of warn(), passing in explicitly the message, category, filename and line number, and optionally the module name and the registry (which should be the __warningregistry__ dictionary of the module). The module name defaults to the filename with .py stripped; if no registry is passed, the warning is never suppressed. message must be a string and category a subclass of Warning or message may be a Warning instance, in which case category will be ignored.

module_globals, if supplied, should be the global namespace in use by the code for which the warning is issued. (This argument is used to support displaying source for modules found in zipfiles or other non-filesystem import sources).

source, if supplied, is the destroyed object which emitted a ResourceWarning.

Changed in version 3.6: Add the source parameter.

+
+warnings.showwarning(message, category, filename, lineno, file=None, line=None)
+

Write a warning to a file. The default implementation calls formatwarning(message, category, filename, lineno, line) and writes the resulting string to file, which defaults to sys.stderr. You may replace this function with any callable by assigning to warnings.showwarning. line is a line of source code to be included in the warning message; if line is not supplied, showwarning() will try to read the line specified by filename and lineno.

+
+warnings.formatwarning(message, category, filename, lineno, line=None)
+

Format a warning the standard way. This returns a string which may contain embedded newlines and ends in a newline. line is a line of source code to be included in the warning message; if line is not supplied, formatwarning() will try to read the line specified by filename and lineno.

+
+warnings.filterwarnings(action, message='', category=Warning, module='', lineno=0, append=False)
+

Insert an entry into the list of warnings filter specifications. The entry is inserted at the front by default; if append is true, it is inserted at the end. This checks the types of the arguments, compiles the message and module regular expressions, and inserts them as a tuple in the list of warnings filters. Entries closer to the front of the list override entries later in the list, if both match a particular warning. Omitted arguments default to a value that matches everything.

+
+warnings.simplefilter(action, category=Warning, lineno=0, append=False)
+

Insert a simple entry into the list of warnings filter specifications. The meaning of the function parameters is as for filterwarnings(), but regular expressions are not needed as the filter inserted always matches any message in any module as long as the category and line number match.

+
+warnings.resetwarnings()
+

Reset the warnings filter. This discards the effect of all previous calls to filterwarnings(), including that of the -W command line options and calls to simplefilter().

+

Available Context Managers

+class warnings.catch_warnings(*, record=False, module=None, action=None, category=Warning, lineno=0, append=False)
+

A context manager that copies and, upon exit, restores the warnings filter and the showwarning() function. If the record argument is False (the default) the context manager returns None on entry. If record is True, a list is returned that is progressively populated with objects as seen by a custom showwarning() function (which also suppresses output to sys.stdout). Each object in the list has attributes with the same names as the arguments to showwarning().

The module argument takes a module that will be used instead of the module returned when you import warnings whose filter will be protected. This argument exists primarily for testing the warnings module itself.

If the action argument is not None, the remaining arguments are passed to simplefilter() as if it were called immediately on entering the context.

Note

The catch_warnings manager works by replacing and then later restoring the module’s showwarning() function and internal list of filter specifications. This means the context manager is modifying global state and therefore is not thread-safe.

Changed in version 3.11: Added the action, category, lineno, and append parameters.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/warnings.html +

+
diff --git a/devdocs/python~3.12/library%2Fwave.html b/devdocs/python~3.12/library%2Fwave.html new file mode 100644 index 00000000..c98ae67b --- /dev/null +++ b/devdocs/python~3.12/library%2Fwave.html @@ -0,0 +1,98 @@ +

wave — Read and write WAV files

Source code: Lib/wave.py

The wave module provides a convenient interface to the Waveform Audio “WAVE” (or “WAV”) file format. Only uncompressed PCM encoded wave files are supported.

Changed in version 3.12: Support for WAVE_FORMAT_EXTENSIBLE headers was added, provided that the extended format is KSDATAFORMAT_SUBTYPE_PCM.

The wave module defines the following function and exception:

+wave.open(file, mode=None)
+

If file is a string, open the file by that name, otherwise treat it as a file-like object. mode can be:

+'rb'
+
+

Read only mode.

+'wb'
+
+

Write only mode.

Note that it does not allow read/write WAV files.

A mode of 'rb' returns a Wave_read object, while a mode of 'wb' returns a Wave_write object. If mode is omitted and a file-like object is passed as file, file.mode is used as the default value for mode.

If you pass in a file-like object, the wave object will not close it when its close() method is called; it is the caller’s responsibility to close the file object.

The open() function may be used in a with statement. When the with block completes, the Wave_read.close() or Wave_write.close() method is called.

Changed in version 3.4: Added support for unseekable files.

+
+exception wave.Error
+

An error raised when something is impossible because it violates the WAV specification or hits an implementation deficiency.

+

Wave_read Objects

+class wave.Wave_read
+

Read a WAV file.

Wave_read objects, as returned by open(), have the following methods:

+close()
+

Close the stream if it was opened by wave, and make the instance unusable. This is called automatically on object collection.

+
+getnchannels()
+

Returns number of audio channels (1 for mono, 2 for stereo).

+
+getsampwidth()
+

Returns sample width in bytes.

+
+getframerate()
+

Returns sampling frequency.

+
+getnframes()
+

Returns number of audio frames.

+
+getcomptype()
+

Returns compression type ('NONE' is the only supported type).

+
+getcompname()
+

Human-readable version of getcomptype(). Usually 'not compressed' parallels 'NONE'.

+
+getparams()
+

Returns a namedtuple() (nchannels, sampwidth, +framerate, nframes, comptype, compname), equivalent to output of the get*() methods.

+
+readframes(n)
+

Reads and returns at most n frames of audio, as a bytes object.

+
+rewind()
+

Rewind the file pointer to the beginning of the audio stream.

+

The following two methods are defined for compatibility with the aifc module, and don’t do anything interesting.

+getmarkers()
+

Returns None.

+
+getmark(id)
+

Raise an error.

+

The following two methods define a term “position” which is compatible between them, and is otherwise implementation dependent.

+setpos(pos)
+

Set the file pointer to the specified position.

+
+tell()
+

Return current file pointer position.

+
+

Wave_write Objects

+class wave.Wave_write
+

Write a WAV file.

Wave_write objects, as returned by open().

For seekable output streams, the wave header will automatically be updated to reflect the number of frames actually written. For unseekable streams, the nframes value must be accurate when the first frame data is written. An accurate nframes value can be achieved either by calling setnframes() or setparams() with the number of frames that will be written before close() is called and then using writeframesraw() to write the frame data, or by calling writeframes() with all of the frame data to be written. In the latter case writeframes() will calculate the number of frames in the data and set nframes accordingly before writing the frame data.

Changed in version 3.4: Added support for unseekable files.

Wave_write objects have the following methods:

+close()
+

Make sure nframes is correct, and close the file if it was opened by wave. This method is called upon object collection. It will raise an exception if the output stream is not seekable and nframes does not match the number of frames actually written.

+
+setnchannels(n)
+

Set the number of channels.

+
+setsampwidth(n)
+

Set the sample width to n bytes.

+
+setframerate(n)
+

Set the frame rate to n.

Changed in version 3.2: A non-integral input to this method is rounded to the nearest integer.

+
+setnframes(n)
+

Set the number of frames to n. This will be changed later if the number of frames actually written is different (this update attempt will raise an error if the output stream is not seekable).

+
+setcomptype(type, name)
+

Set the compression type and description. At the moment, only compression type NONE is supported, meaning no compression.

+
+setparams(tuple)
+

The tuple should be (nchannels, sampwidth, framerate, nframes, comptype, +compname), with values valid for the set*() methods. Sets all parameters.

+
+tell()
+

Return current position in the file, with the same disclaimer for the Wave_read.tell() and Wave_read.setpos() methods.

+
+writeframesraw(data)
+

Write audio frames, without correcting nframes.

Changed in version 3.4: Any bytes-like object is now accepted.

+
+writeframes(data)
+

Write audio frames and make sure nframes is correct. It will raise an error if the output stream is not seekable and the total number of frames that have been written after data has been written does not match the previously set value for nframes.

Changed in version 3.4: Any bytes-like object is now accepted.

Note that it is invalid to set any parameters after calling writeframes() or writeframesraw(), and any attempt to do so will raise wave.Error.

+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/wave.html +

+
diff --git a/devdocs/python~3.12/library%2Fweakref.html b/devdocs/python~3.12/library%2Fweakref.html new file mode 100644 index 00000000..0fa6dac3 --- /dev/null +++ b/devdocs/python~3.12/library%2Fweakref.html @@ -0,0 +1,223 @@ +

weakref — Weak references

Source code: Lib/weakref.py

The weakref module allows the Python programmer to create weak references to objects.

In the following, the term referent means the object which is referred to by a weak reference.

A weak reference to an object is not enough to keep the object alive: when the only remaining references to a referent are weak references, garbage collection is free to destroy the referent and reuse its memory for something else. However, until the object is actually destroyed the weak reference may return the object even if there are no strong references to it.

A primary use for weak references is to implement caches or mappings holding large objects, where it’s desired that a large object not be kept alive solely because it appears in a cache or mapping.

For example, if you have a number of large binary image objects, you may wish to associate a name with each. If you used a Python dictionary to map names to images, or images to names, the image objects would remain alive just because they appeared as values or keys in the dictionaries. The WeakKeyDictionary and WeakValueDictionary classes supplied by the weakref module are an alternative, using weak references to construct mappings that don’t keep objects alive solely because they appear in the mapping objects. If, for example, an image object is a value in a WeakValueDictionary, then when the last remaining references to that image object are the weak references held by weak mappings, garbage collection can reclaim the object, and its corresponding entries in weak mappings are simply deleted.

WeakKeyDictionary and WeakValueDictionary use weak references in their implementation, setting up callback functions on the weak references that notify the weak dictionaries when a key or value has been reclaimed by garbage collection. WeakSet implements the set interface, but keeps weak references to its elements, just like a WeakKeyDictionary does.

finalize provides a straight forward way to register a cleanup function to be called when an object is garbage collected. This is simpler to use than setting up a callback function on a raw weak reference, since the module automatically ensures that the finalizer remains alive until the object is collected.

Most programs should find that using one of these weak container types or finalize is all they need – it’s not usually necessary to create your own weak references directly. The low-level machinery is exposed by the weakref module for the benefit of advanced uses.

Not all objects can be weakly referenced. Objects which support weak references include class instances, functions written in Python (but not in C), instance methods, sets, frozensets, some file objects, generators, type objects, sockets, arrays, deques, regular expression pattern objects, and code objects.

Changed in version 3.2: Added support for thread.lock, threading.Lock, and code objects.

Several built-in types such as list and dict do not directly support weak references but can add support through subclassing:

class Dict(dict):
+    pass
+
+obj = Dict(red=1, green=2, blue=3)   # this object is weak referenceable
+

CPython implementation detail: Other built-in types such as tuple and int do not support weak references even when subclassed.

Extension types can easily be made to support weak references; see Weak Reference Support.

When __slots__ are defined for a given type, weak reference support is disabled unless a '__weakref__' string is also present in the sequence of strings in the __slots__ declaration. See __slots__ documentation for details.

+class weakref.ref(object[, callback])
+

Return a weak reference to object. The original object can be retrieved by calling the reference object if the referent is still alive; if the referent is no longer alive, calling the reference object will cause None to be returned. If callback is provided and not None, and the returned weakref object is still alive, the callback will be called when the object is about to be finalized; the weak reference object will be passed as the only parameter to the callback; the referent will no longer be available.

It is allowable for many weak references to be constructed for the same object. Callbacks registered for each weak reference will be called from the most recently registered callback to the oldest registered callback.

Exceptions raised by the callback will be noted on the standard error output, but cannot be propagated; they are handled in exactly the same way as exceptions raised from an object’s __del__() method.

Weak references are hashable if the object is hashable. They will maintain their hash value even after the object was deleted. If hash() is called the first time only after the object was deleted, the call will raise TypeError.

Weak references support tests for equality, but not ordering. If the referents are still alive, two references have the same equality relationship as their referents (regardless of the callback). If either referent has been deleted, the references are equal only if the reference objects are the same object.

This is a subclassable type rather than a factory function.

+__callback__
+

This read-only attribute returns the callback currently associated to the weakref. If there is no callback or if the referent of the weakref is no longer alive then this attribute will have value None.

+

Changed in version 3.4: Added the __callback__ attribute.

+
+weakref.proxy(object[, callback])
+

Return a proxy to object which uses a weak reference. This supports use of the proxy in most contexts instead of requiring the explicit dereferencing used with weak reference objects. The returned object will have a type of either ProxyType or CallableProxyType, depending on whether object is callable. Proxy objects are not hashable regardless of the referent; this avoids a number of problems related to their fundamentally mutable nature, and prevents their use as dictionary keys. callback is the same as the parameter of the same name to the ref() function.

Accessing an attribute of the proxy object after the referent is garbage collected raises ReferenceError.

Changed in version 3.8: Extended the operator support on proxy objects to include the matrix multiplication operators @ and @=.

+
+weakref.getweakrefcount(object)
+

Return the number of weak references and proxies which refer to object.

+
+weakref.getweakrefs(object)
+

Return a list of all weak reference and proxy objects which refer to object.

+
+class weakref.WeakKeyDictionary([dict])
+

Mapping class that references keys weakly. Entries in the dictionary will be discarded when there is no longer a strong reference to the key. This can be used to associate additional data with an object owned by other parts of an application without adding attributes to those objects. This can be especially useful with objects that override attribute accesses.

Note that when a key with equal value to an existing key (but not equal identity) is inserted into the dictionary, it replaces the value but does not replace the existing key. Due to this, when the reference to the original key is deleted, it also deletes the entry in the dictionary:

>>> class T(str): pass
+...
+>>> k1, k2 = T(), T()
+>>> d = weakref.WeakKeyDictionary()
+>>> d[k1] = 1   # d = {k1: 1}
+>>> d[k2] = 2   # d = {k1: 2}
+>>> del k1      # d = {}
+

A workaround would be to remove the key prior to reassignment:

>>> class T(str): pass
+...
+>>> k1, k2 = T(), T()
+>>> d = weakref.WeakKeyDictionary()
+>>> d[k1] = 1   # d = {k1: 1}
+>>> del d[k1]
+>>> d[k2] = 2   # d = {k2: 2}
+>>> del k1      # d = {k2: 2}
+

Changed in version 3.9: Added support for | and |= operators, specified in PEP 584.

+

WeakKeyDictionary objects have an additional method that exposes the internal references directly. The references are not guaranteed to be “live” at the time they are used, so the result of calling the references needs to be checked before being used. This can be used to avoid creating references that will cause the garbage collector to keep the keys around longer than needed.

+WeakKeyDictionary.keyrefs()
+

Return an iterable of the weak references to the keys.

+
+class weakref.WeakValueDictionary([dict])
+

Mapping class that references values weakly. Entries in the dictionary will be discarded when no strong reference to the value exists any more.

Changed in version 3.9: Added support for | and |= operators, as specified in PEP 584.

+

WeakValueDictionary objects have an additional method that has the same issues as the WeakKeyDictionary.keyrefs() method.

+WeakValueDictionary.valuerefs()
+

Return an iterable of the weak references to the values.

+
+class weakref.WeakSet([elements])
+

Set class that keeps weak references to its elements. An element will be discarded when no strong reference to it exists any more.

+
+class weakref.WeakMethod(method[, callback])
+

A custom ref subclass which simulates a weak reference to a bound method (i.e., a method defined on a class and looked up on an instance). Since a bound method is ephemeral, a standard weak reference cannot keep hold of it. WeakMethod has special code to recreate the bound method until either the object or the original function dies:

>>> class C:
+...     def method(self):
+...         print("method called!")
+...
+>>> c = C()
+>>> r = weakref.ref(c.method)
+>>> r()
+>>> r = weakref.WeakMethod(c.method)
+>>> r()
+<bound method C.method of <__main__.C object at 0x7fc859830220>>
+>>> r()()
+method called!
+>>> del c
+>>> gc.collect()
+0
+>>> r()
+>>>
+

callback is the same as the parameter of the same name to the ref() function.

New in version 3.4.

+
+class weakref.finalize(obj, func, /, *args, **kwargs)
+

Return a callable finalizer object which will be called when obj is garbage collected. Unlike an ordinary weak reference, a finalizer will always survive until the reference object is collected, greatly simplifying lifecycle management.

A finalizer is considered alive until it is called (either explicitly or at garbage collection), and after that it is dead. Calling a live finalizer returns the result of evaluating func(*arg, **kwargs), whereas calling a dead finalizer returns None.

Exceptions raised by finalizer callbacks during garbage collection will be shown on the standard error output, but cannot be propagated. They are handled in the same way as exceptions raised from an object’s __del__() method or a weak reference’s callback.

When the program exits, each remaining live finalizer is called unless its atexit attribute has been set to false. They are called in reverse order of creation.

A finalizer will never invoke its callback during the later part of the interpreter shutdown when module globals are liable to have been replaced by None.

+__call__()
+

If self is alive then mark it as dead and return the result of calling func(*args, **kwargs). If self is dead then return None.

+
+detach()
+

If self is alive then mark it as dead and return the tuple (obj, func, args, kwargs). If self is dead then return None.

+
+peek()
+

If self is alive then return the tuple (obj, func, args, +kwargs). If self is dead then return None.

+
+alive
+

Property which is true if the finalizer is alive, false otherwise.

+
+atexit
+

A writable boolean property which by default is true. When the program exits, it calls all remaining live finalizers for which atexit is true. They are called in reverse order of creation.

+

Note

It is important to ensure that func, args and kwargs do not own any references to obj, either directly or indirectly, since otherwise obj will never be garbage collected. In particular, func should not be a bound method of obj.

New in version 3.4.

+
+weakref.ReferenceType
+

The type object for weak references objects.

+
+weakref.ProxyType
+

The type object for proxies of objects which are not callable.

+
+weakref.CallableProxyType
+

The type object for proxies of callable objects.

+
+weakref.ProxyTypes
+

Sequence containing all the type objects for proxies. This can make it simpler to test if an object is a proxy without being dependent on naming both proxy types.

+

See also

+PEP 205 - Weak References
+
+

The proposal and rationale for this feature, including links to earlier implementations and information about similar features in other languages.

Weak Reference Objects

Weak reference objects have no methods and no attributes besides ref.__callback__. A weak reference object allows the referent to be obtained, if it still exists, by calling it:

>>> import weakref
+>>> class Object:
+...     pass
+...
+>>> o = Object()
+>>> r = weakref.ref(o)
+>>> o2 = r()
+>>> o is o2
+True
+

If the referent no longer exists, calling the reference object returns None:

>>> del o, o2
+>>> print(r())
+None
+

Testing that a weak reference object is still live should be done using the expression ref() is not None. Normally, application code that needs to use a reference object should follow this pattern:

# r is a weak reference object
+o = r()
+if o is None:
+    # referent has been garbage collected
+    print("Object has been deallocated; can't frobnicate.")
+else:
+    print("Object is still live!")
+    o.do_something_useful()
+

Using a separate test for “liveness” creates race conditions in threaded applications; another thread can cause a weak reference to become invalidated before the weak reference is called; the idiom shown above is safe in threaded applications as well as single-threaded applications.

Specialized versions of ref objects can be created through subclassing. This is used in the implementation of the WeakValueDictionary to reduce the memory overhead for each entry in the mapping. This may be most useful to associate additional information with a reference, but could also be used to insert additional processing on calls to retrieve the referent.

This example shows how a subclass of ref can be used to store additional information about an object and affect the value that’s returned when the referent is accessed:

import weakref
+
+class ExtendedRef(weakref.ref):
+    def __init__(self, ob, callback=None, /, **annotations):
+        super().__init__(ob, callback)
+        self.__counter = 0
+        for k, v in annotations.items():
+            setattr(self, k, v)
+
+    def __call__(self):
+        """Return a pair containing the referent and the number of
+        times the reference has been called.
+        """
+        ob = super().__call__()
+        if ob is not None:
+            self.__counter += 1
+            ob = (ob, self.__counter)
+        return ob
+

Example

This simple example shows how an application can use object IDs to retrieve objects that it has seen before. The IDs of the objects can then be used in other data structures without forcing the objects to remain alive, but the objects can still be retrieved by ID if they do.

import weakref
+
+_id2obj_dict = weakref.WeakValueDictionary()
+
+def remember(obj):
+    oid = id(obj)
+    _id2obj_dict[oid] = obj
+    return oid
+
+def id2obj(oid):
+    return _id2obj_dict[oid]
+

Finalizer Objects

The main benefit of using finalize is that it makes it simple to register a callback without needing to preserve the returned finalizer object. For instance

>>> import weakref
+>>> class Object:
+...     pass
+...
+>>> kenny = Object()
+>>> weakref.finalize(kenny, print, "You killed Kenny!")  
+<finalize object at ...; for 'Object' at ...>
+>>> del kenny
+You killed Kenny!
+

The finalizer can be called directly as well. However the finalizer will invoke the callback at most once.

>>> def callback(x, y, z):
+...     print("CALLBACK")
+...     return x + y + z
+...
+>>> obj = Object()
+>>> f = weakref.finalize(obj, callback, 1, 2, z=3)
+>>> assert f.alive
+>>> assert f() == 6
+CALLBACK
+>>> assert not f.alive
+>>> f()                     # callback not called because finalizer dead
+>>> del obj                 # callback not called because finalizer dead
+

You can unregister a finalizer using its detach() method. This kills the finalizer and returns the arguments passed to the constructor when it was created.

>>> obj = Object()
+>>> f = weakref.finalize(obj, callback, 1, 2, z=3)
+>>> f.detach()                                           
+(<...Object object ...>, <function callback ...>, (1, 2), {'z': 3})
+>>> newobj, func, args, kwargs = _
+>>> assert not f.alive
+>>> assert newobj is obj
+>>> assert func(*args, **kwargs) == 6
+CALLBACK
+

Unless you set the atexit attribute to False, a finalizer will be called when the program exits if it is still alive. For instance

>>> obj = Object()
+>>> weakref.finalize(obj, print, "obj dead or exiting")
+<finalize object at ...; for 'Object' at ...>
+>>> exit()
+obj dead or exiting
+

Comparing finalizers with __del__() methods

Suppose we want to create a class whose instances represent temporary directories. The directories should be deleted with their contents when the first of the following events occurs:

  • the object is garbage collected,
  • the object’s remove() method is called, or
  • the program exits.

We might try to implement the class using a __del__() method as follows:

class TempDir:
+    def __init__(self):
+        self.name = tempfile.mkdtemp()
+
+    def remove(self):
+        if self.name is not None:
+            shutil.rmtree(self.name)
+            self.name = None
+
+    @property
+    def removed(self):
+        return self.name is None
+
+    def __del__(self):
+        self.remove()
+

Starting with Python 3.4, __del__() methods no longer prevent reference cycles from being garbage collected, and module globals are no longer forced to None during interpreter shutdown. So this code should work without any issues on CPython.

However, handling of __del__() methods is notoriously implementation specific, since it depends on internal details of the interpreter’s garbage collector implementation.

A more robust alternative can be to define a finalizer which only references the specific functions and objects that it needs, rather than having access to the full state of the object:

class TempDir:
+    def __init__(self):
+        self.name = tempfile.mkdtemp()
+        self._finalizer = weakref.finalize(self, shutil.rmtree, self.name)
+
+    def remove(self):
+        self._finalizer()
+
+    @property
+    def removed(self):
+        return not self._finalizer.alive
+

Defined like this, our finalizer only receives a reference to the details it needs to clean up the directory appropriately. If the object never gets garbage collected the finalizer will still be called at exit.

The other advantage of weakref based finalizers is that they can be used to register finalizers for classes where the definition is controlled by a third party, such as running code when a module is unloaded:

import weakref, sys
+def unloading_module():
+    # implicit reference to the module globals from the function body
+weakref.finalize(sys.modules[__name__], unloading_module)
+

Note

If you create a finalizer object in a daemonic thread just as the program exits then there is the possibility that the finalizer does not get called at exit. However, in a daemonic thread atexit.register(), try: ... finally: ... and with: ... do not guarantee that cleanup occurs either.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/weakref.html +

+
diff --git a/devdocs/python~3.12/library%2Fwebbrowser.html b/devdocs/python~3.12/library%2Fwebbrowser.html new file mode 100644 index 00000000..b650ec03 --- /dev/null +++ b/devdocs/python~3.12/library%2Fwebbrowser.html @@ -0,0 +1,65 @@ +

webbrowser — Convenient web-browser controller

Source code: Lib/webbrowser.py

The webbrowser module provides a high-level interface to allow displaying web-based documents to users. Under most circumstances, simply calling the open() function from this module will do the right thing.

Under Unix, graphical browsers are preferred under X11, but text-mode browsers will be used if graphical browsers are not available or an X11 display isn’t available. If text-mode browsers are used, the calling process will block until the user exits the browser.

If the environment variable BROWSER exists, it is interpreted as the os.pathsep-separated list of browsers to try ahead of the platform defaults. When the value of a list part contains the string %s, then it is interpreted as a literal browser command line to be used with the argument URL substituted for %s; if the part does not contain %s, it is simply interpreted as the name of the browser to launch. 1

For non-Unix platforms, or when a remote browser is available on Unix, the controlling process will not wait for the user to finish with the browser, but allow the remote browser to maintain its own windows on the display. If remote browsers are not available on Unix, the controlling process will launch a new browser and wait.

The script webbrowser can be used as a command-line interface for the module. It accepts a URL as the argument. It accepts the following optional parameters: -n opens the URL in a new browser window, if possible; -t opens the URL in a new browser page (“tab”). The options are, naturally, mutually exclusive. Usage example:

python -m webbrowser -t "https://www.python.org"
+

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

The following exception is defined:

+exception webbrowser.Error
+

Exception raised when a browser control error occurs.

+

The following functions are defined:

+webbrowser.open(url, new=0, autoraise=True)
+

Display url using the default browser. If new is 0, the url is opened in the same browser window if possible. If new is 1, a new browser window is opened if possible. If new is 2, a new browser page (“tab”) is opened if possible. If autoraise is True, the window is raised if possible (note that under many window managers this will occur regardless of the setting of this variable).

Note that on some platforms, trying to open a filename using this function, may work and start the operating system’s associated program. However, this is neither supported nor portable.

Raises an auditing event webbrowser.open with argument url.

+
+webbrowser.open_new(url)
+

Open url in a new window of the default browser, if possible, otherwise, open url in the only browser window.

+
+webbrowser.open_new_tab(url)
+

Open url in a new page (“tab”) of the default browser, if possible, otherwise equivalent to open_new().

+
+webbrowser.get(using=None)
+

Return a controller object for the browser type using. If using is None, return a controller for a default browser appropriate to the caller’s environment.

+
+webbrowser.register(name, constructor, instance=None, *, preferred=False)
+

Register the browser type name. Once a browser type is registered, the get() function can return a controller for that browser type. If instance is not provided, or is None, constructor will be called without parameters to create an instance when needed. If instance is provided, constructor will never be called, and may be None.

Setting preferred to True makes this browser a preferred result for a get() call with no argument. Otherwise, this entry point is only useful if you plan to either set the BROWSER variable or call get() with a nonempty argument matching the name of a handler you declare.

Changed in version 3.7: preferred keyword-only parameter was added.

+

A number of browser types are predefined. This table gives the type names that may be passed to the get() function and the corresponding instantiations for the controller classes, all defined in this module.

+ + + + + + + + + + + + + + + + + + +

Type Name

Class Name

Notes

'mozilla'

Mozilla('mozilla')

'firefox'

Mozilla('mozilla')

'epiphany'

Epiphany('epiphany')

'kfmclient'

Konqueror()

(1)

'konqueror'

Konqueror()

(1)

'kfm'

Konqueror()

(1)

'opera'

Opera()

'links'

GenericBrowser('links')

'elinks'

Elinks('elinks')

'lynx'

GenericBrowser('lynx')

'w3m'

GenericBrowser('w3m')

'windows-default'

WindowsDefault

(2)

'macosx'

MacOSXOSAScript('default')

(3)

'safari'

MacOSXOSAScript('safari')

(3)

'google-chrome'

Chrome('google-chrome')

'chrome'

Chrome('chrome')

'chromium'

Chromium('chromium')

'chromium-browser'

Chromium('chromium-browser')

Notes:

  1. “Konqueror” is the file manager for the KDE desktop environment for Unix, and only makes sense to use if KDE is running. Some way of reliably detecting KDE would be nice; the KDEDIR variable is not sufficient. Note also that the name “kfm” is used even when using the konqueror command with KDE 2 — the implementation selects the best strategy for running Konqueror.
  2. Only on Windows platforms.
  3. Only on macOS platform.

New in version 3.3: Support for Chrome/Chromium has been added.

Changed in version 3.12: Support for several obsolete browsers has been removed. Removed browsers include Grail, Mosaic, Netscape, Galeon, Skipstone, Iceape, and Firefox versions 35 and below.

Deprecated since version 3.11, will be removed in version 3.13: MacOSX is deprecated, use MacOSXOSAScript instead.

Here are some simple examples:

url = 'https://docs.python.org/'
+
+# Open URL in a new tab, if a browser window is already open.
+webbrowser.open_new_tab(url)
+
+# Open URL in new window, raising the window if possible.
+webbrowser.open_new(url)
+

Browser Controller Objects

Browser controllers provide these methods which parallel three of the module-level convenience functions:

+webbrowser.name
+

System-dependent name for the browser.

+
+controller.open(url, new=0, autoraise=True)
+

Display url using the browser handled by this controller. If new is 1, a new browser window is opened if possible. If new is 2, a new browser page (“tab”) is opened if possible.

+
+controller.open_new(url)
+

Open url in a new window of the browser handled by this controller, if possible, otherwise, open url in the only browser window. Alias open_new().

+
+controller.open_new_tab(url)
+

Open url in a new page (“tab”) of the browser handled by this controller, if possible, otherwise equivalent to open_new().

+

Footnotes

+1
+

Executables named here without a full path will be searched in the directories given in the PATH environment variable.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/webbrowser.html +

+
diff --git a/devdocs/python~3.12/library%2Fwindows.html b/devdocs/python~3.12/library%2Fwindows.html new file mode 100644 index 00000000..1d8309da --- /dev/null +++ b/devdocs/python~3.12/library%2Fwindows.html @@ -0,0 +1,10 @@ +

MS Windows Specific Services

This chapter describes modules that are only available on MS Windows platforms.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/windows.html +

+
diff --git a/devdocs/python~3.12/library%2Fwinreg.html b/devdocs/python~3.12/library%2Fwinreg.html new file mode 100644 index 00000000..a0629e31 --- /dev/null +++ b/devdocs/python~3.12/library%2Fwinreg.html @@ -0,0 +1,199 @@ +

winreg — Windows registry access

These functions expose the Windows registry API to Python. Instead of using an integer as the registry handle, a handle object is used to ensure that the handles are closed correctly, even if the programmer neglects to explicitly close them.

Changed in version 3.3: Several functions in this module used to raise a WindowsError, which is now an alias of OSError.

Functions

This module offers the following functions:

+winreg.CloseKey(hkey)
+

Closes a previously opened registry key. The hkey argument specifies a previously opened key.

Note

If hkey is not closed using this method (or via hkey.Close()), it is closed when the hkey object is destroyed by Python.

+
+winreg.ConnectRegistry(computer_name, key)
+

Establishes a connection to a predefined registry handle on another computer, and returns a handle object.

computer_name is the name of the remote computer, of the form r"\\computername". If None, the local computer is used.

key is the predefined handle to connect to.

The return value is the handle of the opened key. If the function fails, an OSError exception is raised.

Raises an auditing event winreg.ConnectRegistry with arguments computer_name, key.

Changed in version 3.3: See above.

+
+winreg.CreateKey(key, sub_key)
+

Creates or opens the specified key, returning a handle object.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that names the key this method opens or creates.

If key is one of the predefined keys, sub_key may be None. In that case, the handle returned is the same key handle passed in to the function.

If the key already exists, this function opens the existing key.

The return value is the handle of the opened key. If the function fails, an OSError exception is raised.

Raises an auditing event winreg.CreateKey with arguments key, sub_key, access.

Raises an auditing event winreg.OpenKey/result with argument key.

Changed in version 3.3: See above.

+
+winreg.CreateKeyEx(key, sub_key, reserved=0, access=KEY_WRITE)
+

Creates or opens the specified key, returning a handle object.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that names the key this method opens or creates.

reserved is a reserved integer, and must be zero. The default is zero.

access is an integer that specifies an access mask that describes the desired security access for the key. Default is KEY_WRITE. See Access Rights for other allowed values.

If key is one of the predefined keys, sub_key may be None. In that case, the handle returned is the same key handle passed in to the function.

If the key already exists, this function opens the existing key.

The return value is the handle of the opened key. If the function fails, an OSError exception is raised.

Raises an auditing event winreg.CreateKey with arguments key, sub_key, access.

Raises an auditing event winreg.OpenKey/result with argument key.

New in version 3.2.

Changed in version 3.3: See above.

+
+winreg.DeleteKey(key, sub_key)
+

Deletes the specified key.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that must be a subkey of the key identified by the key parameter. This value must not be None, and the key may not have subkeys.

This method can not delete keys with subkeys.

If the method succeeds, the entire key, including all of its values, is removed. If the method fails, an OSError exception is raised.

Raises an auditing event winreg.DeleteKey with arguments key, sub_key, access.

Changed in version 3.3: See above.

+
+winreg.DeleteKeyEx(key, sub_key, access=KEY_WOW64_64KEY, reserved=0)
+

Deletes the specified key.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that must be a subkey of the key identified by the key parameter. This value must not be None, and the key may not have subkeys.

reserved is a reserved integer, and must be zero. The default is zero.

access is an integer that specifies an access mask that describes the desired security access for the key. Default is KEY_WOW64_64KEY. On 32-bit Windows, the WOW64 constants are ignored. See Access Rights for other allowed values.

This method can not delete keys with subkeys.

If the method succeeds, the entire key, including all of its values, is removed. If the method fails, an OSError exception is raised.

On unsupported Windows versions, NotImplementedError is raised.

Raises an auditing event winreg.DeleteKey with arguments key, sub_key, access.

New in version 3.2.

Changed in version 3.3: See above.

+
+winreg.DeleteValue(key, value)
+

Removes a named value from a registry key.

key is an already open key, or one of the predefined HKEY_* constants.

value is a string that identifies the value to remove.

Raises an auditing event winreg.DeleteValue with arguments key, value.

+
+winreg.EnumKey(key, index)
+

Enumerates subkeys of an open registry key, returning a string.

key is an already open key, or one of the predefined HKEY_* constants.

index is an integer that identifies the index of the key to retrieve.

The function retrieves the name of one subkey each time it is called. It is typically called repeatedly until an OSError exception is raised, indicating, no more values are available.

Raises an auditing event winreg.EnumKey with arguments key, index.

Changed in version 3.3: See above.

+
+winreg.EnumValue(key, index)
+

Enumerates values of an open registry key, returning a tuple.

key is an already open key, or one of the predefined HKEY_* constants.

index is an integer that identifies the index of the value to retrieve.

The function retrieves the name of one subkey each time it is called. It is typically called repeatedly, until an OSError exception is raised, indicating no more values.

The result is a tuple of 3 items:

+ + + +

Index

Meaning

0

A string that identifies the value name

1

An object that holds the value data, and whose type depends on the underlying registry type

2

An integer that identifies the type of the value data (see table in docs for SetValueEx())

Raises an auditing event winreg.EnumValue with arguments key, index.

Changed in version 3.3: See above.

+
+winreg.ExpandEnvironmentStrings(str)
+

Expands environment variable placeholders %NAME% in strings like REG_EXPAND_SZ:

>>> ExpandEnvironmentStrings('%windir%')
+'C:\\Windows'
+

Raises an auditing event winreg.ExpandEnvironmentStrings with argument str.

+
+winreg.FlushKey(key)
+

Writes all the attributes of a key to the registry.

key is an already open key, or one of the predefined HKEY_* constants.

It is not necessary to call FlushKey() to change a key. Registry changes are flushed to disk by the registry using its lazy flusher. Registry changes are also flushed to disk at system shutdown. Unlike CloseKey(), the FlushKey() method returns only when all the data has been written to the registry. An application should only call FlushKey() if it requires absolute certainty that registry changes are on disk.

Note

If you don’t know whether a FlushKey() call is required, it probably isn’t.

+
+winreg.LoadKey(key, sub_key, file_name)
+

Creates a subkey under the specified key and stores registration information from a specified file into that subkey.

key is a handle returned by ConnectRegistry() or one of the constants HKEY_USERS or HKEY_LOCAL_MACHINE.

sub_key is a string that identifies the subkey to load.

file_name is the name of the file to load registry data from. This file must have been created with the SaveKey() function. Under the file allocation table (FAT) file system, the filename may not have an extension.

A call to LoadKey() fails if the calling process does not have the SE_RESTORE_PRIVILEGE privilege. Note that privileges are different from permissions – see the RegLoadKey documentation for more details.

If key is a handle returned by ConnectRegistry(), then the path specified in file_name is relative to the remote computer.

Raises an auditing event winreg.LoadKey with arguments key, sub_key, file_name.

+
+winreg.OpenKey(key, sub_key, reserved=0, access=KEY_READ)
+winreg.OpenKeyEx(key, sub_key, reserved=0, access=KEY_READ)
+

Opens the specified key, returning a handle object.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that identifies the sub_key to open.

reserved is a reserved integer, and must be zero. The default is zero.

access is an integer that specifies an access mask that describes the desired security access for the key. Default is KEY_READ. See Access Rights for other allowed values.

The result is a new handle to the specified key.

If the function fails, OSError is raised.

Raises an auditing event winreg.OpenKey with arguments key, sub_key, access.

Raises an auditing event winreg.OpenKey/result with argument key.

Changed in version 3.2: Allow the use of named arguments.

Changed in version 3.3: See above.

+
+winreg.QueryInfoKey(key)
+

Returns information about a key, as a tuple.

key is an already open key, or one of the predefined HKEY_* constants.

The result is a tuple of 3 items:

+ + + +

Index

Meaning

0

An integer giving the number of sub keys this key has.

1

An integer giving the number of values this key has.

2

An integer giving when the key was last modified (if available) as 100’s of nanoseconds since Jan 1, 1601.

Raises an auditing event winreg.QueryInfoKey with argument key.

+
+winreg.QueryValue(key, sub_key)
+

Retrieves the unnamed value for a key, as a string.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that holds the name of the subkey with which the value is associated. If this parameter is None or empty, the function retrieves the value set by the SetValue() method for the key identified by key.

Values in the registry have name, type, and data components. This method retrieves the data for a key’s first value that has a NULL name. But the underlying API call doesn’t return the type, so always use QueryValueEx() if possible.

Raises an auditing event winreg.QueryValue with arguments key, sub_key, value_name.

+
+winreg.QueryValueEx(key, value_name)
+

Retrieves the type and data for a specified value name associated with an open registry key.

key is an already open key, or one of the predefined HKEY_* constants.

value_name is a string indicating the value to query.

The result is a tuple of 2 items:

+ + +

Index

Meaning

0

The value of the registry item.

1

An integer giving the registry type for this value (see table in docs for SetValueEx())

Raises an auditing event winreg.QueryValue with arguments key, sub_key, value_name.

+
+winreg.SaveKey(key, file_name)
+

Saves the specified key, and all its subkeys to the specified file.

key is an already open key, or one of the predefined HKEY_* constants.

file_name is the name of the file to save registry data to. This file cannot already exist. If this filename includes an extension, it cannot be used on file allocation table (FAT) file systems by the LoadKey() method.

If key represents a key on a remote computer, the path described by file_name is relative to the remote computer. The caller of this method must possess the SeBackupPrivilege security privilege. Note that privileges are different than permissions – see the Conflicts Between User Rights and Permissions documentation for more details.

This function passes NULL for security_attributes to the API.

Raises an auditing event winreg.SaveKey with arguments key, file_name.

+
+winreg.SetValue(key, sub_key, type, value)
+

Associates a value with a specified key.

key is an already open key, or one of the predefined HKEY_* constants.

sub_key is a string that names the subkey with which the value is associated.

type is an integer that specifies the type of the data. Currently this must be REG_SZ, meaning only strings are supported. Use the SetValueEx() function for support for other data types.

value is a string that specifies the new value.

If the key specified by the sub_key parameter does not exist, the SetValue function creates it.

Value lengths are limited by available memory. Long values (more than 2048 bytes) should be stored as files with the filenames stored in the configuration registry. This helps the registry perform efficiently.

The key identified by the key parameter must have been opened with KEY_SET_VALUE access.

Raises an auditing event winreg.SetValue with arguments key, sub_key, type, value.

+
+winreg.SetValueEx(key, value_name, reserved, type, value)
+

Stores data in the value field of an open registry key.

key is an already open key, or one of the predefined HKEY_* constants.

value_name is a string that names the subkey with which the value is associated.

reserved can be anything – zero is always passed to the API.

type is an integer that specifies the type of the data. See Value Types for the available types.

value is a string that specifies the new value.

This method can also set additional value and type information for the specified key. The key identified by the key parameter must have been opened with KEY_SET_VALUE access.

To open the key, use the CreateKey() or OpenKey() methods.

Value lengths are limited by available memory. Long values (more than 2048 bytes) should be stored as files with the filenames stored in the configuration registry. This helps the registry perform efficiently.

Raises an auditing event winreg.SetValue with arguments key, sub_key, type, value.

+
+winreg.DisableReflectionKey(key)
+

Disables registry reflection for 32-bit processes running on a 64-bit operating system.

key is an already open key, or one of the predefined HKEY_* constants.

Will generally raise NotImplementedError if executed on a 32-bit operating system.

If the key is not on the reflection list, the function succeeds but has no effect. Disabling reflection for a key does not affect reflection of any subkeys.

Raises an auditing event winreg.DisableReflectionKey with argument key.

+
+winreg.EnableReflectionKey(key)
+

Restores registry reflection for the specified disabled key.

key is an already open key, or one of the predefined HKEY_* constants.

Will generally raise NotImplementedError if executed on a 32-bit operating system.

Restoring reflection for a key does not affect reflection of any subkeys.

Raises an auditing event winreg.EnableReflectionKey with argument key.

+
+winreg.QueryReflectionKey(key)
+

Determines the reflection state for the specified key.

key is an already open key, or one of the predefined HKEY_* constants.

Returns True if reflection is disabled.

Will generally raise NotImplementedError if executed on a 32-bit operating system.

Raises an auditing event winreg.QueryReflectionKey with argument key.

+

Constants

The following constants are defined for use in many winreg functions.

HKEY_* Constants

+winreg.HKEY_CLASSES_ROOT
+

Registry entries subordinate to this key define types (or classes) of documents and the properties associated with those types. Shell and COM applications use the information stored under this key.

+
+winreg.HKEY_CURRENT_USER
+

Registry entries subordinate to this key define the preferences of the current user. These preferences include the settings of environment variables, data about program groups, colors, printers, network connections, and application preferences.

+
+winreg.HKEY_LOCAL_MACHINE
+

Registry entries subordinate to this key define the physical state of the computer, including data about the bus type, system memory, and installed hardware and software.

+
+winreg.HKEY_USERS
+

Registry entries subordinate to this key define the default user configuration for new users on the local computer and the user configuration for the current user.

+
+winreg.HKEY_PERFORMANCE_DATA
+

Registry entries subordinate to this key allow you to access performance data. The data is not actually stored in the registry; the registry functions cause the system to collect the data from its source.

+
+winreg.HKEY_CURRENT_CONFIG
+

Contains information about the current hardware profile of the local computer system.

+
+winreg.HKEY_DYN_DATA
+

This key is not used in versions of Windows after 98.

+

Access Rights

For more information, see Registry Key Security and Access.

+winreg.KEY_ALL_ACCESS
+

Combines the STANDARD_RIGHTS_REQUIRED, KEY_QUERY_VALUE, KEY_SET_VALUE, KEY_CREATE_SUB_KEY, KEY_ENUMERATE_SUB_KEYS, KEY_NOTIFY, and KEY_CREATE_LINK access rights.

+
+winreg.KEY_WRITE
+

Combines the STANDARD_RIGHTS_WRITE, KEY_SET_VALUE, and KEY_CREATE_SUB_KEY access rights.

+
+winreg.KEY_READ
+

Combines the STANDARD_RIGHTS_READ, KEY_QUERY_VALUE, KEY_ENUMERATE_SUB_KEYS, and KEY_NOTIFY values.

+
+winreg.KEY_EXECUTE
+

Equivalent to KEY_READ.

+
+winreg.KEY_QUERY_VALUE
+

Required to query the values of a registry key.

+
+winreg.KEY_SET_VALUE
+

Required to create, delete, or set a registry value.

+
+winreg.KEY_CREATE_SUB_KEY
+

Required to create a subkey of a registry key.

+
+winreg.KEY_ENUMERATE_SUB_KEYS
+

Required to enumerate the subkeys of a registry key.

+
+winreg.KEY_NOTIFY
+

Required to request change notifications for a registry key or for subkeys of a registry key.

+
+

Reserved for system use.

+

64-bit Specific

For more information, see Accessing an Alternate Registry View.

+winreg.KEY_WOW64_64KEY
+

Indicates that an application on 64-bit Windows should operate on the 64-bit registry view. On 32-bit Windows, this constant is ignored.

+
+winreg.KEY_WOW64_32KEY
+

Indicates that an application on 64-bit Windows should operate on the 32-bit registry view. On 32-bit Windows, this constant is ignored.

+

Value Types

For more information, see Registry Value Types.

+winreg.REG_BINARY
+

Binary data in any form.

+
+winreg.REG_DWORD
+

32-bit number.

+
+winreg.REG_DWORD_LITTLE_ENDIAN
+

A 32-bit number in little-endian format. Equivalent to REG_DWORD.

+
+winreg.REG_DWORD_BIG_ENDIAN
+

A 32-bit number in big-endian format.

+
+winreg.REG_EXPAND_SZ
+

Null-terminated string containing references to environment variables (%PATH%).

+
+

A Unicode symbolic link.

+
+winreg.REG_MULTI_SZ
+

A sequence of null-terminated strings, terminated by two null characters. (Python handles this termination automatically.)

+
+winreg.REG_NONE
+

No defined value type.

+
+winreg.REG_QWORD
+

A 64-bit number.

New in version 3.6.

+
+winreg.REG_QWORD_LITTLE_ENDIAN
+

A 64-bit number in little-endian format. Equivalent to REG_QWORD.

New in version 3.6.

+
+winreg.REG_RESOURCE_LIST
+

A device-driver resource list.

+
+winreg.REG_FULL_RESOURCE_DESCRIPTOR
+

A hardware setting.

+
+winreg.REG_RESOURCE_REQUIREMENTS_LIST
+

A hardware resource list.

+
+winreg.REG_SZ
+

A null-terminated string.

+

Registry Handle Objects

This object wraps a Windows HKEY object, automatically closing it when the object is destroyed. To guarantee cleanup, you can call either the Close() method on the object, or the CloseKey() function.

All registry functions in this module return one of these objects.

All registry functions in this module which accept a handle object also accept an integer, however, use of the handle object is encouraged.

Handle objects provide semantics for __bool__() – thus

if handle:
+    print("Yes")
+

will print Yes if the handle is currently valid (has not been closed or detached).

The object also support comparison semantics, so handle objects will compare true if they both reference the same underlying Windows handle value.

Handle objects can be converted to an integer (e.g., using the built-in int() function), in which case the underlying Windows handle value is returned. You can also use the Detach() method to return the integer handle, and also disconnect the Windows handle from the handle object.

+PyHKEY.Close()
+

Closes the underlying Windows handle.

If the handle is already closed, no error is raised.

+
+PyHKEY.Detach()
+

Detaches the Windows handle from the handle object.

The result is an integer that holds the value of the handle before it is detached. If the handle is already detached or closed, this will return zero.

After calling this function, the handle is effectively invalidated, but the handle is not closed. You would call this function when you need the underlying Win32 handle to exist beyond the lifetime of the handle object.

Raises an auditing event winreg.PyHKEY.Detach with argument key.

+
+PyHKEY.__enter__()
+PyHKEY.__exit__(*exc_info)
+

The HKEY object implements __enter__() and __exit__() and thus supports the context protocol for the with statement:

with OpenKey(HKEY_LOCAL_MACHINE, "foo") as key:
+    ...  # work with key
+

will automatically close key when control leaves the with block.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/winreg.html +

+
diff --git a/devdocs/python~3.12/library%2Fwinsound.html b/devdocs/python~3.12/library%2Fwinsound.html new file mode 100644 index 00000000..5db0826f --- /dev/null +++ b/devdocs/python~3.12/library%2Fwinsound.html @@ -0,0 +1,70 @@ +

winsound — Sound-playing interface for Windows

The winsound module provides access to the basic sound-playing machinery provided by Windows platforms. It includes functions and several constants.

+winsound.Beep(frequency, duration)
+

Beep the PC’s speaker. The frequency parameter specifies frequency, in hertz, of the sound, and must be in the range 37 through 32,767. The duration parameter specifies the number of milliseconds the sound should last. If the system is not able to beep the speaker, RuntimeError is raised.

+
+winsound.PlaySound(sound, flags)
+

Call the underlying PlaySound() function from the Platform API. The sound parameter may be a filename, a system sound alias, audio data as a bytes-like object, or None. Its interpretation depends on the value of flags, which can be a bitwise ORed combination of the constants described below. If the sound parameter is None, any currently playing waveform sound is stopped. If the system indicates an error, RuntimeError is raised.

+
+winsound.MessageBeep(type=MB_OK)
+

Call the underlying MessageBeep() function from the Platform API. This plays a sound as specified in the registry. The type argument specifies which sound to play; possible values are -1, MB_ICONASTERISK, MB_ICONEXCLAMATION, MB_ICONHAND, MB_ICONQUESTION, and MB_OK, all described below. The value -1 produces a “simple beep”; this is the final fallback if a sound cannot be played otherwise. If the system indicates an error, RuntimeError is raised.

+
+winsound.SND_FILENAME
+

The sound parameter is the name of a WAV file. Do not use with SND_ALIAS.

+
+winsound.SND_ALIAS
+

The sound parameter is a sound association name from the registry. If the registry contains no such name, play the system default sound unless SND_NODEFAULT is also specified. If no default sound is registered, raise RuntimeError. Do not use with SND_FILENAME.

All Win32 systems support at least the following; most systems support many more:

+ + + + + +

PlaySound() name

Corresponding Control Panel Sound name

'SystemAsterisk'

Asterisk

'SystemExclamation'

Exclamation

'SystemExit'

Exit Windows

'SystemHand'

Critical Stop

'SystemQuestion'

Question

For example:

import winsound
+# Play Windows exit sound.
+winsound.PlaySound("SystemExit", winsound.SND_ALIAS)
+
+# Probably play Windows default sound, if any is registered (because
+# "*" probably isn't the registered name of any sound).
+winsound.PlaySound("*", winsound.SND_ALIAS)
+
+
+winsound.SND_LOOP
+

Play the sound repeatedly. The SND_ASYNC flag must also be used to avoid blocking. Cannot be used with SND_MEMORY.

+
+winsound.SND_MEMORY
+

The sound parameter to PlaySound() is a memory image of a WAV file, as a bytes-like object.

Note

This module does not support playing from a memory image asynchronously, so a combination of this flag and SND_ASYNC will raise RuntimeError.

+
+winsound.SND_PURGE
+

Stop playing all instances of the specified sound.

Note

This flag is not supported on modern Windows platforms.

+
+winsound.SND_ASYNC
+

Return immediately, allowing sounds to play asynchronously.

+
+winsound.SND_NODEFAULT
+

If the specified sound cannot be found, do not play the system default sound.

+
+winsound.SND_NOSTOP
+

Do not interrupt sounds currently playing.

+
+winsound.SND_NOWAIT
+

Return immediately if the sound driver is busy.

Note

This flag is not supported on modern Windows platforms.

+
+winsound.MB_ICONASTERISK
+

Play the SystemDefault sound.

+
+winsound.MB_ICONEXCLAMATION
+

Play the SystemExclamation sound.

+
+winsound.MB_ICONHAND
+

Play the SystemHand sound.

+
+winsound.MB_ICONQUESTION
+

Play the SystemQuestion sound.

+
+winsound.MB_OK
+

Play the SystemDefault sound.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/winsound.html +

+
diff --git a/devdocs/python~3.12/library%2Fwsgiref.html b/devdocs/python~3.12/library%2Fwsgiref.html new file mode 100644 index 00000000..46d894fb --- /dev/null +++ b/devdocs/python~3.12/library%2Fwsgiref.html @@ -0,0 +1,298 @@ +

wsgiref — WSGI Utilities and Reference Implementation

Source code: Lib/wsgiref

The Web Server Gateway Interface (WSGI) is a standard interface between web server software and web applications written in Python. Having a standard interface makes it easy to use an application that supports WSGI with a number of different web servers.

Only authors of web servers and programming frameworks need to know every detail and corner case of the WSGI design. You don’t need to understand every detail of WSGI just to install a WSGI application or to write a web application using an existing framework.

wsgiref is a reference implementation of the WSGI specification that can be used to add WSGI support to a web server or framework. It provides utilities for manipulating WSGI environment variables and response headers, base classes for implementing WSGI servers, a demo HTTP server that serves WSGI applications, types for static type checking, and a validation tool that checks WSGI servers and applications for conformance to the WSGI specification (PEP 3333).

See wsgi.readthedocs.io for more information about WSGI, and links to tutorials and other resources.

wsgiref.util – WSGI environment utilities

This module provides a variety of utility functions for working with WSGI environments. A WSGI environment is a dictionary containing HTTP request variables as described in PEP 3333. All of the functions taking an environ parameter expect a WSGI-compliant dictionary to be supplied; please see PEP 3333 for a detailed specification and WSGIEnvironment for a type alias that can be used in type annotations.

+wsgiref.util.guess_scheme(environ)
+

Return a guess for whether wsgi.url_scheme should be “http” or “https”, by checking for a HTTPS environment variable in the environ dictionary. The return value is a string.

This function is useful when creating a gateway that wraps CGI or a CGI-like protocol such as FastCGI. Typically, servers providing such protocols will include a HTTPS variable with a value of “1”, “yes”, or “on” when a request is received via SSL. So, this function returns “https” if such a value is found, and “http” otherwise.

+
+wsgiref.util.request_uri(environ, include_query=True)
+

Return the full request URI, optionally including the query string, using the algorithm found in the “URL Reconstruction” section of PEP 3333. If include_query is false, the query string is not included in the resulting URI.

+
+wsgiref.util.application_uri(environ)
+

Similar to request_uri(), except that the PATH_INFO and QUERY_STRING variables are ignored. The result is the base URI of the application object addressed by the request.

+
+wsgiref.util.shift_path_info(environ)
+

Shift a single name from PATH_INFO to SCRIPT_NAME and return the name. The environ dictionary is modified in-place; use a copy if you need to keep the original PATH_INFO or SCRIPT_NAME intact.

If there are no remaining path segments in PATH_INFO, None is returned.

Typically, this routine is used to process each portion of a request URI path, for example to treat the path as a series of dictionary keys. This routine modifies the passed-in environment to make it suitable for invoking another WSGI application that is located at the target URI. For example, if there is a WSGI application at /foo, and the request URI path is /foo/bar/baz, and the WSGI application at /foo calls shift_path_info(), it will receive the string “bar”, and the environment will be updated to be suitable for passing to a WSGI application at /foo/bar. That is, SCRIPT_NAME will change from /foo to /foo/bar, and PATH_INFO will change from /bar/baz to /baz.

When PATH_INFO is just a “/”, this routine returns an empty string and appends a trailing slash to SCRIPT_NAME, even though empty path segments are normally ignored, and SCRIPT_NAME doesn’t normally end in a slash. This is intentional behavior, to ensure that an application can tell the difference between URIs ending in /x from ones ending in /x/ when using this routine to do object traversal.

+
+wsgiref.util.setup_testing_defaults(environ)
+

Update environ with trivial defaults for testing purposes.

This routine adds various parameters required for WSGI, including HTTP_HOST, SERVER_NAME, SERVER_PORT, REQUEST_METHOD, SCRIPT_NAME, PATH_INFO, and all of the PEP 3333-defined wsgi.* variables. It only supplies default values, and does not replace any existing settings for these variables.

This routine is intended to make it easier for unit tests of WSGI servers and applications to set up dummy environments. It should NOT be used by actual WSGI servers or applications, since the data is fake!

Example usage:

from wsgiref.util import setup_testing_defaults
+from wsgiref.simple_server import make_server
+
+# A relatively simple WSGI application. It's going to print out the
+# environment dictionary after being updated by setup_testing_defaults
+def simple_app(environ, start_response):
+    setup_testing_defaults(environ)
+
+    status = '200 OK'
+    headers = [('Content-type', 'text/plain; charset=utf-8')]
+
+    start_response(status, headers)
+
+    ret = [("%s: %s\n" % (key, value)).encode("utf-8")
+           for key, value in environ.items()]
+    return ret
+
+with make_server('', 8000, simple_app) as httpd:
+    print("Serving on port 8000...")
+    httpd.serve_forever()
+
+

In addition to the environment functions above, the wsgiref.util module also provides these miscellaneous utilities:

+wsgiref.util.is_hop_by_hop(header_name)
+

Return True if ‘header_name’ is an HTTP/1.1 “Hop-by-Hop” header, as defined by RFC 2616.

+
+class wsgiref.util.FileWrapper(filelike, blksize=8192)
+

A concrete implementation of the wsgiref.types.FileWrapper protocol used to convert a file-like object to an iterator. The resulting objects are iterables. As the object is iterated over, the optional blksize parameter will be repeatedly passed to the filelike object’s read() method to obtain bytestrings to yield. When read() returns an empty bytestring, iteration is ended and is not resumable.

If filelike has a close() method, the returned object will also have a close() method, and it will invoke the filelike object’s close() method when called.

Example usage:

from io import StringIO
+from wsgiref.util import FileWrapper
+
+# We're using a StringIO-buffer for as the file-like object
+filelike = StringIO("This is an example file-like object"*10)
+wrapper = FileWrapper(filelike, blksize=5)
+
+for chunk in wrapper:
+    print(chunk)
+

Changed in version 3.11: Support for __getitem__() method has been removed.

+

wsgiref.headers – WSGI response header tools

This module provides a single class, Headers, for convenient manipulation of WSGI response headers using a mapping-like interface.

+class wsgiref.headers.Headers([headers])
+

Create a mapping-like object wrapping headers, which must be a list of header name/value tuples as described in PEP 3333. The default value of headers is an empty list.

Headers objects support typical mapping operations including __getitem__(), get(), __setitem__(), setdefault(), __delitem__() and __contains__(). For each of these methods, the key is the header name (treated case-insensitively), and the value is the first value associated with that header name. Setting a header deletes any existing values for that header, then adds a new value at the end of the wrapped header list. Headers’ existing order is generally maintained, with new headers added to the end of the wrapped list.

Unlike a dictionary, Headers objects do not raise an error when you try to get or delete a key that isn’t in the wrapped header list. Getting a nonexistent header just returns None, and deleting a nonexistent header does nothing.

Headers objects also support keys(), values(), and items() methods. The lists returned by keys() and items() can include the same key more than once if there is a multi-valued header. The len() of a Headers object is the same as the length of its items(), which is the same as the length of the wrapped header list. In fact, the items() method just returns a copy of the wrapped header list.

Calling bytes() on a Headers object returns a formatted bytestring suitable for transmission as HTTP response headers. Each header is placed on a line with its value, separated by a colon and a space. Each line is terminated by a carriage return and line feed, and the bytestring is terminated with a blank line.

In addition to their mapping interface and formatting features, Headers objects also have the following methods for querying and adding multi-valued headers, and for adding headers with MIME parameters:

+get_all(name)
+

Return a list of all the values for the named header.

The returned list will be sorted in the order they appeared in the original header list or were added to this instance, and may contain duplicates. Any fields deleted and re-inserted are always appended to the header list. If no fields exist with the given name, returns an empty list.

+
+add_header(name, value, **_params)
+

Add a (possibly multi-valued) header, with optional MIME parameters specified via keyword arguments.

name is the header field to add. Keyword arguments can be used to set MIME parameters for the header field. Each parameter must be a string or None. Underscores in parameter names are converted to dashes, since dashes are illegal in Python identifiers, but many MIME parameter names include dashes. If the parameter value is a string, it is added to the header value parameters in the form name="value". If it is None, only the parameter name is added. (This is used for MIME parameters without a value.) Example usage:

h.add_header('content-disposition', 'attachment', filename='bud.gif')
+

The above will add a header that looks like this:

Content-Disposition: attachment; filename="bud.gif"
+
+

Changed in version 3.5: headers parameter is optional.

+

wsgiref.simple_server – a simple WSGI HTTP server

This module implements a simple HTTP server (based on http.server) that serves WSGI applications. Each server instance serves a single WSGI application on a given host and port. If you want to serve multiple applications on a single host and port, you should create a WSGI application that parses PATH_INFO to select which application to invoke for each request. (E.g., using the shift_path_info() function from wsgiref.util.)

+wsgiref.simple_server.make_server(host, port, app, server_class=WSGIServer, handler_class=WSGIRequestHandler)
+

Create a new WSGI server listening on host and port, accepting connections for app. The return value is an instance of the supplied server_class, and will process requests using the specified handler_class. app must be a WSGI application object, as defined by PEP 3333.

Example usage:

from wsgiref.simple_server import make_server, demo_app
+
+with make_server('', 8000, demo_app) as httpd:
+    print("Serving HTTP on port 8000...")
+
+    # Respond to requests until process is killed
+    httpd.serve_forever()
+
+    # Alternative: serve one request, then exit
+    httpd.handle_request()
+
+
+wsgiref.simple_server.demo_app(environ, start_response)
+

This function is a small but complete WSGI application that returns a text page containing the message “Hello world!” and a list of the key/value pairs provided in the environ parameter. It’s useful for verifying that a WSGI server (such as wsgiref.simple_server) is able to run a simple WSGI application correctly.

+
+class wsgiref.simple_server.WSGIServer(server_address, RequestHandlerClass)
+

Create a WSGIServer instance. server_address should be a (host,port) tuple, and RequestHandlerClass should be the subclass of http.server.BaseHTTPRequestHandler that will be used to process requests.

You do not normally need to call this constructor, as the make_server() function can handle all the details for you.

WSGIServer is a subclass of http.server.HTTPServer, so all of its methods (such as serve_forever() and handle_request()) are available. WSGIServer also provides these WSGI-specific methods:

+set_app(application)
+

Sets the callable application as the WSGI application that will receive requests.

+
+get_app()
+

Returns the currently set application callable.

+

Normally, however, you do not need to use these additional methods, as set_app() is normally called by make_server(), and the get_app() exists mainly for the benefit of request handler instances.

+
+class wsgiref.simple_server.WSGIRequestHandler(request, client_address, server)
+

Create an HTTP handler for the given request (i.e. a socket), client_address (a (host,port) tuple), and server (WSGIServer instance).

You do not need to create instances of this class directly; they are automatically created as needed by WSGIServer objects. You can, however, subclass this class and supply it as a handler_class to the make_server() function. Some possibly relevant methods for overriding in subclasses:

+get_environ()
+

Return a WSGIEnvironment dictionary for a request. The default implementation copies the contents of the WSGIServer object’s base_environ dictionary attribute and then adds various headers derived from the HTTP request. Each call to this method should return a new dictionary containing all of the relevant CGI environment variables as specified in PEP 3333.

+
+get_stderr()
+

Return the object that should be used as the wsgi.errors stream. The default implementation just returns sys.stderr.

+
+handle()
+

Process the HTTP request. The default implementation creates a handler instance using a wsgiref.handlers class to implement the actual WSGI application interface.

+
+

wsgiref.validate — WSGI conformance checker

When creating new WSGI application objects, frameworks, servers, or middleware, it can be useful to validate the new code’s conformance using wsgiref.validate. This module provides a function that creates WSGI application objects that validate communications between a WSGI server or gateway and a WSGI application object, to check both sides for protocol conformance.

Note that this utility does not guarantee complete PEP 3333 compliance; an absence of errors from this module does not necessarily mean that errors do not exist. However, if this module does produce an error, then it is virtually certain that either the server or application is not 100% compliant.

This module is based on the paste.lint module from Ian Bicking’s “Python Paste” library.

+wsgiref.validate.validator(application)
+

Wrap application and return a new WSGI application object. The returned application will forward all requests to the original application, and will check that both the application and the server invoking it are conforming to the WSGI specification and to RFC 2616.

Any detected nonconformance results in an AssertionError being raised; note, however, that how these errors are handled is server-dependent. For example, wsgiref.simple_server and other servers based on wsgiref.handlers (that don’t override the error handling methods to do something else) will simply output a message that an error has occurred, and dump the traceback to sys.stderr or some other error stream.

This wrapper may also generate output using the warnings module to indicate behaviors that are questionable but which may not actually be prohibited by PEP 3333. Unless they are suppressed using Python command-line options or the warnings API, any such warnings will be written to sys.stderr (not wsgi.errors, unless they happen to be the same object).

Example usage:

from wsgiref.validate import validator
+from wsgiref.simple_server import make_server
+
+# Our callable object which is intentionally not compliant to the
+# standard, so the validator is going to break
+def simple_app(environ, start_response):
+    status = '200 OK'  # HTTP Status
+    headers = [('Content-type', 'text/plain')]  # HTTP Headers
+    start_response(status, headers)
+
+    # This is going to break because we need to return a list, and
+    # the validator is going to inform us
+    return b"Hello World"
+
+# This is the application wrapped in a validator
+validator_app = validator(simple_app)
+
+with make_server('', 8000, validator_app) as httpd:
+    print("Listening on port 8000....")
+    httpd.serve_forever()
+
+

wsgiref.handlers – server/gateway base classes

This module provides base handler classes for implementing WSGI servers and gateways. These base classes handle most of the work of communicating with a WSGI application, as long as they are given a CGI-like environment, along with input, output, and error streams.

+class wsgiref.handlers.CGIHandler
+

CGI-based invocation via sys.stdin, sys.stdout, sys.stderr and os.environ. This is useful when you have a WSGI application and want to run it as a CGI script. Simply invoke CGIHandler().run(app), where app is the WSGI application object you wish to invoke.

This class is a subclass of BaseCGIHandler that sets wsgi.run_once to true, wsgi.multithread to false, and wsgi.multiprocess to true, and always uses sys and os to obtain the necessary CGI streams and environment.

+
+class wsgiref.handlers.IISCGIHandler
+

A specialized alternative to CGIHandler, for use when deploying on Microsoft’s IIS web server, without having set the config allowPathInfo option (IIS>=7) or metabase allowPathInfoForScriptMappings (IIS<7).

By default, IIS gives a PATH_INFO that duplicates the SCRIPT_NAME at the front, causing problems for WSGI applications that wish to implement routing. This handler strips any such duplicated path.

IIS can be configured to pass the correct PATH_INFO, but this causes another bug where PATH_TRANSLATED is wrong. Luckily this variable is rarely used and is not guaranteed by WSGI. On IIS<7, though, the setting can only be made on a vhost level, affecting all other script mappings, many of which break when exposed to the PATH_TRANSLATED bug. For this reason IIS<7 is almost never deployed with the fix (Even IIS7 rarely uses it because there is still no UI for it.).

There is no way for CGI code to tell whether the option was set, so a separate handler class is provided. It is used in the same way as CGIHandler, i.e., by calling IISCGIHandler().run(app), where app is the WSGI application object you wish to invoke.

New in version 3.2.

+
+class wsgiref.handlers.BaseCGIHandler(stdin, stdout, stderr, environ, multithread=True, multiprocess=False)
+

Similar to CGIHandler, but instead of using the sys and os modules, the CGI environment and I/O streams are specified explicitly. The multithread and multiprocess values are used to set the wsgi.multithread and wsgi.multiprocess flags for any applications run by the handler instance.

This class is a subclass of SimpleHandler intended for use with software other than HTTP “origin servers”. If you are writing a gateway protocol implementation (such as CGI, FastCGI, SCGI, etc.) that uses a Status: header to send an HTTP status, you probably want to subclass this instead of SimpleHandler.

+
+class wsgiref.handlers.SimpleHandler(stdin, stdout, stderr, environ, multithread=True, multiprocess=False)
+

Similar to BaseCGIHandler, but designed for use with HTTP origin servers. If you are writing an HTTP server implementation, you will probably want to subclass this instead of BaseCGIHandler.

This class is a subclass of BaseHandler. It overrides the __init__(), get_stdin(), get_stderr(), add_cgi_vars(), _write(), and _flush() methods to support explicitly setting the environment and streams via the constructor. The supplied environment and streams are stored in the stdin, stdout, stderr, and environ attributes.

The write() method of stdout should write each chunk in full, like io.BufferedIOBase.

+
+class wsgiref.handlers.BaseHandler
+

This is an abstract base class for running WSGI applications. Each instance will handle a single HTTP request, although in principle you could create a subclass that was reusable for multiple requests.

BaseHandler instances have only one method intended for external use:

+run(app)
+

Run the specified WSGI application, app.

+

All of the other BaseHandler methods are invoked by this method in the process of running the application, and thus exist primarily to allow customizing the process.

The following methods MUST be overridden in a subclass:

+_write(data)
+

Buffer the bytes data for transmission to the client. It’s okay if this method actually transmits the data; BaseHandler just separates write and flush operations for greater efficiency when the underlying system actually has such a distinction.

+
+_flush()
+

Force buffered data to be transmitted to the client. It’s okay if this method is a no-op (i.e., if _write() actually sends the data).

+
+get_stdin()
+

Return an object compatible with InputStream suitable for use as the wsgi.input of the request currently being processed.

+
+get_stderr()
+

Return an object compatible with ErrorStream suitable for use as the wsgi.errors of the request currently being processed.

+
+add_cgi_vars()
+

Insert CGI variables for the current request into the environ attribute.

+

Here are some other methods and attributes you may wish to override. This list is only a summary, however, and does not include every method that can be overridden. You should consult the docstrings and source code for additional information before attempting to create a customized BaseHandler subclass.

Attributes and methods for customizing the WSGI environment:

+wsgi_multithread
+

The value to be used for the wsgi.multithread environment variable. It defaults to true in BaseHandler, but may have a different default (or be set by the constructor) in the other subclasses.

+
+wsgi_multiprocess
+

The value to be used for the wsgi.multiprocess environment variable. It defaults to true in BaseHandler, but may have a different default (or be set by the constructor) in the other subclasses.

+
+wsgi_run_once
+

The value to be used for the wsgi.run_once environment variable. It defaults to false in BaseHandler, but CGIHandler sets it to true by default.

+
+os_environ
+

The default environment variables to be included in every request’s WSGI environment. By default, this is a copy of os.environ at the time that wsgiref.handlers was imported, but subclasses can either create their own at the class or instance level. Note that the dictionary should be considered read-only, since the default value is shared between multiple classes and instances.

+
+server_software
+

If the origin_server attribute is set, this attribute’s value is used to set the default SERVER_SOFTWARE WSGI environment variable, and also to set a default Server: header in HTTP responses. It is ignored for handlers (such as BaseCGIHandler and CGIHandler) that are not HTTP origin servers.

Changed in version 3.3: The term “Python” is replaced with implementation specific term like “CPython”, “Jython” etc.

+
+get_scheme()
+

Return the URL scheme being used for the current request. The default implementation uses the guess_scheme() function from wsgiref.util to guess whether the scheme should be “http” or “https”, based on the current request’s environ variables.

+
+setup_environ()
+

Set the environ attribute to a fully populated WSGI environment. The default implementation uses all of the above methods and attributes, plus the get_stdin(), get_stderr(), and add_cgi_vars() methods and the wsgi_file_wrapper attribute. It also inserts a SERVER_SOFTWARE key if not present, as long as the origin_server attribute is a true value and the server_software attribute is set.

+

Methods and attributes for customizing exception handling:

+log_exception(exc_info)
+

Log the exc_info tuple in the server log. exc_info is a (type, value, +traceback) tuple. The default implementation simply writes the traceback to the request’s wsgi.errors stream and flushes it. Subclasses can override this method to change the format or retarget the output, mail the traceback to an administrator, or whatever other action may be deemed suitable.

+
+traceback_limit
+

The maximum number of frames to include in tracebacks output by the default log_exception() method. If None, all frames are included.

+
+error_output(environ, start_response)
+

This method is a WSGI application to generate an error page for the user. It is only invoked if an error occurs before headers are sent to the client.

This method can access the current error using sys.exception(), and should pass that information to start_response when calling it (as described in the “Error Handling” section of PEP 3333).

The default implementation just uses the error_status, error_headers, and error_body attributes to generate an output page. Subclasses can override this to produce more dynamic error output.

Note, however, that it’s not recommended from a security perspective to spit out diagnostics to any old user; ideally, you should have to do something special to enable diagnostic output, which is why the default implementation doesn’t include any.

+
+error_status
+

The HTTP status used for error responses. This should be a status string as defined in PEP 3333; it defaults to a 500 code and message.

+
+error_headers
+

The HTTP headers used for error responses. This should be a list of WSGI response headers ((name, value) tuples), as described in PEP 3333. The default list just sets the content type to text/plain.

+
+error_body
+

The error response body. This should be an HTTP response body bytestring. It defaults to the plain text, “A server error occurred. Please contact the administrator.”

+

Methods and attributes for PEP 3333’s “Optional Platform-Specific File Handling” feature:

+wsgi_file_wrapper
+

A wsgi.file_wrapper factory, compatible with wsgiref.types.FileWrapper, or None. The default value of this attribute is the wsgiref.util.FileWrapper class.

+
+sendfile()
+

Override to implement platform-specific file transmission. This method is called only if the application’s return value is an instance of the class specified by the wsgi_file_wrapper attribute. It should return a true value if it was able to successfully transmit the file, so that the default transmission code will not be executed. The default implementation of this method just returns a false value.

+

Miscellaneous methods and attributes:

+origin_server
+

This attribute should be set to a true value if the handler’s _write() and _flush() are being used to communicate directly to the client, rather than via a CGI-like gateway protocol that wants the HTTP status in a special Status: header.

This attribute’s default value is true in BaseHandler, but false in BaseCGIHandler and CGIHandler.

+
+http_version
+

If origin_server is true, this string attribute is used to set the HTTP version of the response set to the client. It defaults to "1.0".

+
+
+wsgiref.handlers.read_environ()
+

Transcode CGI variables from os.environ to PEP 3333 “bytes in unicode” strings, returning a new dictionary. This function is used by CGIHandler and IISCGIHandler in place of directly using os.environ, which is not necessarily WSGI-compliant on all platforms and web servers using Python 3 – specifically, ones where the OS’s actual environment is Unicode (i.e. Windows), or ones where the environment is bytes, but the system encoding used by Python to decode it is anything other than ISO-8859-1 (e.g. Unix systems using UTF-8).

If you are implementing a CGI-based handler of your own, you probably want to use this routine instead of just copying values out of os.environ directly.

New in version 3.2.

+

wsgiref.types – WSGI types for static type checking

This module provides various types for static type checking as described in PEP 3333.

New in version 3.11.

+class wsgiref.types.StartResponse
+

A typing.Protocol describing start_response() callables (PEP 3333).

+
+wsgiref.types.WSGIEnvironment
+

A type alias describing a WSGI environment dictionary.

+
+wsgiref.types.WSGIApplication
+

A type alias describing a WSGI application callable.

+
+class wsgiref.types.InputStream
+

A typing.Protocol describing a WSGI Input Stream.

+
+class wsgiref.types.ErrorStream
+

A typing.Protocol describing a WSGI Error Stream.

+
+class wsgiref.types.FileWrapper
+

A typing.Protocol describing a file wrapper. See wsgiref.util.FileWrapper for a concrete implementation of this protocol.

+

Examples

This is a working “Hello World” WSGI application:

"""
+Every WSGI application must have an application object - a callable
+object that accepts two arguments. For that purpose, we're going to
+use a function (note that you're not limited to a function, you can
+use a class for example). The first argument passed to the function
+is a dictionary containing CGI-style environment variables and the
+second variable is the callable object.
+"""
+from wsgiref.simple_server import make_server
+
+
+def hello_world_app(environ, start_response):
+    status = "200 OK"  # HTTP Status
+    headers = [("Content-type", "text/plain; charset=utf-8")]  # HTTP Headers
+    start_response(status, headers)
+
+    # The returned object is going to be printed
+    return [b"Hello World"]
+
+with make_server("", 8000, hello_world_app) as httpd:
+    print("Serving on port 8000...")
+
+    # Serve until process is killed
+    httpd.serve_forever()
+

Example of a WSGI application serving the current directory, accept optional directory and port number (default: 8000) on the command line:

"""
+Small wsgiref based web server. Takes a path to serve from and an
+optional port number (defaults to 8000), then tries to serve files.
+MIME types are guessed from the file names, 404 errors are raised
+if the file is not found.
+"""
+import mimetypes
+import os
+import sys
+from wsgiref import simple_server, util
+
+
+def app(environ, respond):
+    # Get the file name and MIME type
+    fn = os.path.join(path, environ["PATH_INFO"][1:])
+    if "." not in fn.split(os.path.sep)[-1]:
+        fn = os.path.join(fn, "index.html")
+    mime_type = mimetypes.guess_type(fn)[0]
+
+    # Return 200 OK if file exists, otherwise 404 Not Found
+    if os.path.exists(fn):
+        respond("200 OK", [("Content-Type", mime_type)])
+        return util.FileWrapper(open(fn, "rb"))
+    else:
+        respond("404 Not Found", [("Content-Type", "text/plain")])
+        return [b"not found"]
+
+
+if __name__ == "__main__":
+    # Get the path and port from command-line arguments
+    path = sys.argv[1] if len(sys.argv) > 1 else os.getcwd()
+    port = int(sys.argv[2]) if len(sys.argv) > 2 else 8000
+
+    # Make and start the server until control-c
+    httpd = simple_server.make_server("", port, app)
+    print(f"Serving {path} on port {port}, control-C to stop")
+    try:
+        httpd.serve_forever()
+    except KeyboardInterrupt:
+        print("Shutting down.")
+        httpd.server_close()
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/wsgiref.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.dom.html b/devdocs/python~3.12/library%2Fxml.dom.html new file mode 100644 index 00000000..be3564ee --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.dom.html @@ -0,0 +1,322 @@ +

xml.dom — The Document Object Model API

Source code: Lib/xml/dom/__init__.py

The Document Object Model, or “DOM,” is a cross-language API from the World Wide Web Consortium (W3C) for accessing and modifying XML documents. A DOM implementation presents an XML document as a tree structure, or allows client code to build such a structure from scratch. It then gives access to the structure through a set of objects which provided well-known interfaces.

The DOM is extremely useful for random-access applications. SAX only allows you a view of one bit of the document at a time. If you are looking at one SAX element, you have no access to another. If you are looking at a text node, you have no access to a containing element. When you write a SAX application, you need to keep track of your program’s position in the document somewhere in your own code. SAX does not do it for you. Also, if you need to look ahead in the XML document, you are just out of luck.

Some applications are simply impossible in an event driven model with no access to a tree. Of course you could build some sort of tree yourself in SAX events, but the DOM allows you to avoid writing that code. The DOM is a standard tree representation for XML data.

The Document Object Model is being defined by the W3C in stages, or “levels” in their terminology. The Python mapping of the API is substantially based on the DOM Level 2 recommendation.

DOM applications typically start by parsing some XML into a DOM. How this is accomplished is not covered at all by DOM Level 1, and Level 2 provides only limited improvements: There is a DOMImplementation object class which provides access to Document creation methods, but no way to access an XML reader/parser/Document builder in an implementation-independent way. There is also no well-defined way to access these methods without an existing Document object. In Python, each DOM implementation will provide a function getDOMImplementation(). DOM Level 3 adds a Load/Store specification, which defines an interface to the reader, but this is not yet available in the Python standard library.

Once you have a DOM document object, you can access the parts of your XML document through its properties and methods. These properties are defined in the DOM specification; this portion of the reference manual describes the interpretation of the specification in Python.

The specification provided by the W3C defines the DOM API for Java, ECMAScript, and OMG IDL. The Python mapping defined here is based in large part on the IDL version of the specification, but strict compliance is not required (though implementations are free to support the strict mapping from IDL). See section Conformance for a detailed discussion of mapping requirements.

See also

Document Object Model (DOM) Level 2 Specification
+
+

The W3C recommendation upon which the Python DOM API is based.

Document Object Model (DOM) Level 1 Specification
+
+

The W3C recommendation for the DOM supported by xml.dom.minidom.

Python Language Mapping Specification
+
+

This specifies the mapping from OMG IDL to Python.

Module Contents

The xml.dom contains the following functions:

+xml.dom.registerDOMImplementation(name, factory)
+

Register the factory function with the name name. The factory function should return an object which implements the DOMImplementation interface. The factory function can return the same object every time, or a new one for each call, as appropriate for the specific implementation (e.g. if that implementation supports some customization).

+
+xml.dom.getDOMImplementation(name=None, features=())
+

Return a suitable DOM implementation. The name is either well-known, the module name of a DOM implementation, or None. If it is not None, imports the corresponding module and returns a DOMImplementation object if the import succeeds. If no name is given, and if the environment variable PYTHON_DOM is set, this variable is used to find the implementation.

If name is not given, this examines the available implementations to find one with the required feature set. If no implementation can be found, raise an ImportError. The features list must be a sequence of (feature, +version) pairs which are passed to the hasFeature() method on available DOMImplementation objects.

+

Some convenience constants are also provided:

+xml.dom.EMPTY_NAMESPACE
+

The value used to indicate that no namespace is associated with a node in the DOM. This is typically found as the namespaceURI of a node, or used as the namespaceURI parameter to a namespaces-specific method.

+
+xml.dom.XML_NAMESPACE
+

The namespace URI associated with the reserved prefix xml, as defined by Namespaces in XML (section 4).

+
+xml.dom.XMLNS_NAMESPACE
+

The namespace URI for namespace declarations, as defined by Document Object Model (DOM) Level 2 Core Specification (section 1.1.8).

+
+xml.dom.XHTML_NAMESPACE
+

The URI of the XHTML namespace as defined by XHTML 1.0: The Extensible HyperText Markup Language (section 3.1.1).

+

In addition, xml.dom contains a base Node class and the DOM exception classes. The Node class provided by this module does not implement any of the methods or attributes defined by the DOM specification; concrete DOM implementations must provide those. The Node class provided as part of this module does provide the constants used for the nodeType attribute on concrete Node objects; they are located within the class rather than at the module level to conform with the DOM specifications.

Objects in the DOM

The definitive documentation for the DOM is the DOM specification from the W3C.

Note that DOM attributes may also be manipulated as nodes instead of as simple strings. It is fairly rare that you must do this, however, so this usage is not yet documented.

+ + + + + + + + + + +

Interface

Section

Purpose

DOMImplementation

DOMImplementation Objects

Interface to the underlying implementation.

Node

Node Objects

Base interface for most objects in a document.

NodeList

NodeList Objects

Interface for a sequence of nodes.

DocumentType

DocumentType Objects

Information about the declarations needed to process a document.

Document

Document Objects

Object which represents an entire document.

Element

Element Objects

Element nodes in the document hierarchy.

Attr

Attr Objects

Attribute value nodes on element nodes.

Comment

Comment Objects

Representation of comments in the source document.

Text

Text and CDATASection Objects

Nodes containing textual content from the document.

ProcessingInstruction

ProcessingInstruction Objects

Processing instruction representation.

An additional section describes the exceptions defined for working with the DOM in Python.

DOMImplementation Objects

The DOMImplementation interface provides a way for applications to determine the availability of particular features in the DOM they are using. DOM Level 2 added the ability to create new Document and DocumentType objects using the DOMImplementation as well.

+DOMImplementation.hasFeature(feature, version)
+

Return True if the feature identified by the pair of strings feature and version is implemented.

+
+DOMImplementation.createDocument(namespaceUri, qualifiedName, doctype)
+

Return a new Document object (the root of the DOM), with a child Element object having the given namespaceUri and qualifiedName. The doctype must be a DocumentType object created by createDocumentType(), or None. In the Python DOM API, the first two arguments can also be None in order to indicate that no Element child is to be created.

+
+DOMImplementation.createDocumentType(qualifiedName, publicId, systemId)
+

Return a new DocumentType object that encapsulates the given qualifiedName, publicId, and systemId strings, representing the information contained in an XML document type declaration.

+

Node Objects

All of the components of an XML document are subclasses of Node.

+Node.nodeType
+

An integer representing the node type. Symbolic constants for the types are on the Node object: ELEMENT_NODE, ATTRIBUTE_NODE, TEXT_NODE, CDATA_SECTION_NODE, ENTITY_NODE, PROCESSING_INSTRUCTION_NODE, COMMENT_NODE, DOCUMENT_NODE, DOCUMENT_TYPE_NODE, NOTATION_NODE. This is a read-only attribute.

+
+Node.parentNode
+

The parent of the current node, or None for the document node. The value is always a Node object or None. For Element nodes, this will be the parent element, except for the root element, in which case it will be the Document object. For Attr nodes, this is always None. This is a read-only attribute.

+
+Node.attributes
+

A NamedNodeMap of attribute objects. Only elements have actual values for this; others provide None for this attribute. This is a read-only attribute.

+
+Node.previousSibling
+

The node that immediately precedes this one with the same parent. For instance the element with an end-tag that comes just before the self element’s start-tag. Of course, XML documents are made up of more than just elements so the previous sibling could be text, a comment, or something else. If this node is the first child of the parent, this attribute will be None. This is a read-only attribute.

+
+Node.nextSibling
+

The node that immediately follows this one with the same parent. See also previousSibling. If this is the last child of the parent, this attribute will be None. This is a read-only attribute.

+
+Node.childNodes
+

A list of nodes contained within this node. This is a read-only attribute.

+
+Node.firstChild
+

The first child of the node, if there are any, or None. This is a read-only attribute.

+
+Node.lastChild
+

The last child of the node, if there are any, or None. This is a read-only attribute.

+
+Node.localName
+

The part of the tagName following the colon if there is one, else the entire tagName. The value is a string.

+
+Node.prefix
+

The part of the tagName preceding the colon if there is one, else the empty string. The value is a string, or None.

+
+Node.namespaceURI
+

The namespace associated with the element name. This will be a string or None. This is a read-only attribute.

+
+Node.nodeName
+

This has a different meaning for each node type; see the DOM specification for details. You can always get the information you would get here from another property such as the tagName property for elements or the name property for attributes. For all node types, the value of this attribute will be either a string or None. This is a read-only attribute.

+
+Node.nodeValue
+

This has a different meaning for each node type; see the DOM specification for details. The situation is similar to that with nodeName. The value is a string or None.

+
+Node.hasAttributes()
+

Return True if the node has any attributes.

+
+Node.hasChildNodes()
+

Return True if the node has any child nodes.

+
+Node.isSameNode(other)
+

Return True if other refers to the same node as this node. This is especially useful for DOM implementations which use any sort of proxy architecture (because more than one object can refer to the same node).

Note

This is based on a proposed DOM Level 3 API which is still in the “working draft” stage, but this particular interface appears uncontroversial. Changes from the W3C will not necessarily affect this method in the Python DOM interface (though any new W3C API for this would also be supported).

+
+Node.appendChild(newChild)
+

Add a new child node to this node at the end of the list of children, returning newChild. If the node was already in the tree, it is removed first.

+
+Node.insertBefore(newChild, refChild)
+

Insert a new child node before an existing child. It must be the case that refChild is a child of this node; if not, ValueError is raised. newChild is returned. If refChild is None, it inserts newChild at the end of the children’s list.

+
+Node.removeChild(oldChild)
+

Remove a child node. oldChild must be a child of this node; if not, ValueError is raised. oldChild is returned on success. If oldChild will not be used further, its unlink() method should be called.

+
+Node.replaceChild(newChild, oldChild)
+

Replace an existing node with a new node. It must be the case that oldChild is a child of this node; if not, ValueError is raised.

+
+Node.normalize()
+

Join adjacent text nodes so that all stretches of text are stored as single Text instances. This simplifies processing text from a DOM tree for many applications.

+
+Node.cloneNode(deep)
+

Clone this node. Setting deep means to clone all child nodes as well. This returns the clone.

+

NodeList Objects

A NodeList represents a sequence of nodes. These objects are used in two ways in the DOM Core recommendation: an Element object provides one as its list of child nodes, and the getElementsByTagName() and getElementsByTagNameNS() methods of Node return objects with this interface to represent query results.

The DOM Level 2 recommendation defines one method and one attribute for these objects:

+NodeList.item(i)
+

Return the i’th item from the sequence, if there is one, or None. The index i is not allowed to be less than zero or greater than or equal to the length of the sequence.

+
+NodeList.length
+

The number of nodes in the sequence.

+

In addition, the Python DOM interface requires that some additional support is provided to allow NodeList objects to be used as Python sequences. All NodeList implementations must include support for __len__() and __getitem__(); this allows iteration over the NodeList in for statements and proper support for the len() built-in function.

If a DOM implementation supports modification of the document, the NodeList implementation must also support the __setitem__() and __delitem__() methods.

DocumentType Objects

Information about the notations and entities declared by a document (including the external subset if the parser uses it and can provide the information) is available from a DocumentType object. The DocumentType for a document is available from the Document object’s doctype attribute; if there is no DOCTYPE declaration for the document, the document’s doctype attribute will be set to None instead of an instance of this interface.

DocumentType is a specialization of Node, and adds the following attributes:

+DocumentType.publicId
+

The public identifier for the external subset of the document type definition. This will be a string or None.

+
+DocumentType.systemId
+

The system identifier for the external subset of the document type definition. This will be a URI as a string, or None.

+
+DocumentType.internalSubset
+

A string giving the complete internal subset from the document. This does not include the brackets which enclose the subset. If the document has no internal subset, this should be None.

+
+DocumentType.name
+

The name of the root element as given in the DOCTYPE declaration, if present.

+
+DocumentType.entities
+

This is a NamedNodeMap giving the definitions of external entities. For entity names defined more than once, only the first definition is provided (others are ignored as required by the XML recommendation). This may be None if the information is not provided by the parser, or if no entities are defined.

+
+DocumentType.notations
+

This is a NamedNodeMap giving the definitions of notations. For notation names defined more than once, only the first definition is provided (others are ignored as required by the XML recommendation). This may be None if the information is not provided by the parser, or if no notations are defined.

+

Document Objects

A Document represents an entire XML document, including its constituent elements, attributes, processing instructions, comments etc. Remember that it inherits properties from Node.

+Document.documentElement
+

The one and only root element of the document.

+
+Document.createElement(tagName)
+

Create and return a new element node. The element is not inserted into the document when it is created. You need to explicitly insert it with one of the other methods such as insertBefore() or appendChild().

+
+Document.createElementNS(namespaceURI, tagName)
+

Create and return a new element with a namespace. The tagName may have a prefix. The element is not inserted into the document when it is created. You need to explicitly insert it with one of the other methods such as insertBefore() or appendChild().

+
+Document.createTextNode(data)
+

Create and return a text node containing the data passed as a parameter. As with the other creation methods, this one does not insert the node into the tree.

+
+Document.createComment(data)
+

Create and return a comment node containing the data passed as a parameter. As with the other creation methods, this one does not insert the node into the tree.

+
+Document.createProcessingInstruction(target, data)
+

Create and return a processing instruction node containing the target and data passed as parameters. As with the other creation methods, this one does not insert the node into the tree.

+
+Document.createAttribute(name)
+

Create and return an attribute node. This method does not associate the attribute node with any particular element. You must use setAttributeNode() on the appropriate Element object to use the newly created attribute instance.

+
+Document.createAttributeNS(namespaceURI, qualifiedName)
+

Create and return an attribute node with a namespace. The tagName may have a prefix. This method does not associate the attribute node with any particular element. You must use setAttributeNode() on the appropriate Element object to use the newly created attribute instance.

+
+Document.getElementsByTagName(tagName)
+

Search for all descendants (direct children, children’s children, etc.) with a particular element type name.

+
+Document.getElementsByTagNameNS(namespaceURI, localName)
+

Search for all descendants (direct children, children’s children, etc.) with a particular namespace URI and localname. The localname is the part of the namespace after the prefix.

+

Element Objects

Element is a subclass of Node, so inherits all the attributes of that class.

+Element.tagName
+

The element type name. In a namespace-using document it may have colons in it. The value is a string.

+
+Element.getElementsByTagName(tagName)
+

Same as equivalent method in the Document class.

+
+Element.getElementsByTagNameNS(namespaceURI, localName)
+

Same as equivalent method in the Document class.

+
+Element.hasAttribute(name)
+

Return True if the element has an attribute named by name.

+
+Element.hasAttributeNS(namespaceURI, localName)
+

Return True if the element has an attribute named by namespaceURI and localName.

+
+Element.getAttribute(name)
+

Return the value of the attribute named by name as a string. If no such attribute exists, an empty string is returned, as if the attribute had no value.

+
+Element.getAttributeNode(attrname)
+

Return the Attr node for the attribute named by attrname.

+
+Element.getAttributeNS(namespaceURI, localName)
+

Return the value of the attribute named by namespaceURI and localName as a string. If no such attribute exists, an empty string is returned, as if the attribute had no value.

+
+Element.getAttributeNodeNS(namespaceURI, localName)
+

Return an attribute value as a node, given a namespaceURI and localName.

+
+Element.removeAttribute(name)
+

Remove an attribute by name. If there is no matching attribute, a NotFoundErr is raised.

+
+Element.removeAttributeNode(oldAttr)
+

Remove and return oldAttr from the attribute list, if present. If oldAttr is not present, NotFoundErr is raised.

+
+Element.removeAttributeNS(namespaceURI, localName)
+

Remove an attribute by name. Note that it uses a localName, not a qname. No exception is raised if there is no matching attribute.

+
+Element.setAttribute(name, value)
+

Set an attribute value from a string.

+
+Element.setAttributeNode(newAttr)
+

Add a new attribute node to the element, replacing an existing attribute if necessary if the name attribute matches. If a replacement occurs, the old attribute node will be returned. If newAttr is already in use, InuseAttributeErr will be raised.

+
+Element.setAttributeNodeNS(newAttr)
+

Add a new attribute node to the element, replacing an existing attribute if necessary if the namespaceURI and localName attributes match. If a replacement occurs, the old attribute node will be returned. If newAttr is already in use, InuseAttributeErr will be raised.

+
+Element.setAttributeNS(namespaceURI, qname, value)
+

Set an attribute value from a string, given a namespaceURI and a qname. Note that a qname is the whole attribute name. This is different than above.

+

Attr Objects

Attr inherits from Node, so inherits all its attributes.

+Attr.name
+

The attribute name. In a namespace-using document it may include a colon.

+
+Attr.localName
+

The part of the name following the colon if there is one, else the entire name. This is a read-only attribute.

+
+Attr.prefix
+

The part of the name preceding the colon if there is one, else the empty string.

+
+Attr.value
+

The text value of the attribute. This is a synonym for the nodeValue attribute.

+

NamedNodeMap Objects

NamedNodeMap does not inherit from Node.

+NamedNodeMap.length
+

The length of the attribute list.

+
+NamedNodeMap.item(index)
+

Return an attribute with a particular index. The order you get the attributes in is arbitrary but will be consistent for the life of a DOM. Each item is an attribute node. Get its value with the value attribute.

+

There are also experimental methods that give this class more mapping behavior. You can use them or you can use the standardized getAttribute*() family of methods on the Element objects.

Comment Objects

Comment represents a comment in the XML document. It is a subclass of Node, but cannot have child nodes.

+Comment.data
+

The content of the comment as a string. The attribute contains all characters between the leading <!-- and trailing -->, but does not include them.

+

Text and CDATASection Objects

The Text interface represents text in the XML document. If the parser and DOM implementation support the DOM’s XML extension, portions of the text enclosed in CDATA marked sections are stored in CDATASection objects. These two interfaces are identical, but provide different values for the nodeType attribute.

These interfaces extend the Node interface. They cannot have child nodes.

+Text.data
+

The content of the text node as a string.

+

Note

The use of a CDATASection node does not indicate that the node represents a complete CDATA marked section, only that the content of the node was part of a CDATA section. A single CDATA section may be represented by more than one node in the document tree. There is no way to determine whether two adjacent CDATASection nodes represent different CDATA marked sections.

ProcessingInstruction Objects

Represents a processing instruction in the XML document; this inherits from the Node interface and cannot have child nodes.

+ProcessingInstruction.target
+

The content of the processing instruction up to the first whitespace character. This is a read-only attribute.

+
+ProcessingInstruction.data
+

The content of the processing instruction following the first whitespace character.

+

Exceptions

The DOM Level 2 recommendation defines a single exception, DOMException, and a number of constants that allow applications to determine what sort of error occurred. DOMException instances carry a code attribute that provides the appropriate value for the specific exception.

The Python DOM interface provides the constants, but also expands the set of exceptions so that a specific exception exists for each of the exception codes defined by the DOM. The implementations must raise the appropriate specific exception, each of which carries the appropriate value for the code attribute.

+exception xml.dom.DOMException
+

Base exception class used for all specific DOM exceptions. This exception class cannot be directly instantiated.

+
+exception xml.dom.DomstringSizeErr
+

Raised when a specified range of text does not fit into a string. This is not known to be used in the Python DOM implementations, but may be received from DOM implementations not written in Python.

+
+exception xml.dom.HierarchyRequestErr
+

Raised when an attempt is made to insert a node where the node type is not allowed.

+
+exception xml.dom.IndexSizeErr
+

Raised when an index or size parameter to a method is negative or exceeds the allowed values.

+
+exception xml.dom.InuseAttributeErr
+

Raised when an attempt is made to insert an Attr node that is already present elsewhere in the document.

+
+exception xml.dom.InvalidAccessErr
+

Raised if a parameter or an operation is not supported on the underlying object.

+
+exception xml.dom.InvalidCharacterErr
+

This exception is raised when a string parameter contains a character that is not permitted in the context it’s being used in by the XML 1.0 recommendation. For example, attempting to create an Element node with a space in the element type name will cause this error to be raised.

+
+exception xml.dom.InvalidModificationErr
+

Raised when an attempt is made to modify the type of a node.

+
+exception xml.dom.InvalidStateErr
+

Raised when an attempt is made to use an object that is not defined or is no longer usable.

+
+exception xml.dom.NamespaceErr
+

If an attempt is made to change any object in a way that is not permitted with regard to the Namespaces in XML recommendation, this exception is raised.

+
+exception xml.dom.NotFoundErr
+

Exception when a node does not exist in the referenced context. For example, NamedNodeMap.removeNamedItem() will raise this if the node passed in does not exist in the map.

+
+exception xml.dom.NotSupportedErr
+

Raised when the implementation does not support the requested type of object or operation.

+
+exception xml.dom.NoDataAllowedErr
+

This is raised if data is specified for a node which does not support data.

+
+exception xml.dom.NoModificationAllowedErr
+

Raised on attempts to modify an object where modifications are not allowed (such as for read-only nodes).

+
+exception xml.dom.SyntaxErr
+

Raised when an invalid or illegal string is specified.

+
+exception xml.dom.WrongDocumentErr
+

Raised when a node is inserted in a different document than it currently belongs to, and the implementation does not support migrating the node from one document to the other.

+

The exception codes defined in the DOM recommendation map to the exceptions described above according to this table:

+ + + + + + + + + + + + + + + +

Constant

Exception

DOMSTRING_SIZE_ERR

DomstringSizeErr

HIERARCHY_REQUEST_ERR

HierarchyRequestErr

INDEX_SIZE_ERR

IndexSizeErr

INUSE_ATTRIBUTE_ERR

InuseAttributeErr

INVALID_ACCESS_ERR

InvalidAccessErr

INVALID_CHARACTER_ERR

InvalidCharacterErr

INVALID_MODIFICATION_ERR

InvalidModificationErr

INVALID_STATE_ERR

InvalidStateErr

NAMESPACE_ERR

NamespaceErr

NOT_FOUND_ERR

NotFoundErr

NOT_SUPPORTED_ERR

NotSupportedErr

NO_DATA_ALLOWED_ERR

NoDataAllowedErr

NO_MODIFICATION_ALLOWED_ERR

NoModificationAllowedErr

SYNTAX_ERR

SyntaxErr

WRONG_DOCUMENT_ERR

WrongDocumentErr

Conformance

This section describes the conformance requirements and relationships between the Python DOM API, the W3C DOM recommendations, and the OMG IDL mapping for Python.

Type Mapping

The IDL types used in the DOM specification are mapped to Python types according to the following table.

+ + + + + + +

IDL Type

Python Type

boolean

bool or int

int

int

long int

int

unsigned int

int

DOMString

str or bytes

null

None

Accessor Methods

The mapping from OMG IDL to Python defines accessor functions for IDL attribute declarations in much the way the Java mapping does. Mapping the IDL declarations

readonly attribute string someValue;
+         attribute string anotherValue;
+

yields three accessor functions: a “get” method for someValue (_get_someValue()), and “get” and “set” methods for anotherValue (_get_anotherValue() and _set_anotherValue()). The mapping, in particular, does not require that the IDL attributes are accessible as normal Python attributes: object.someValue is not required to work, and may raise an AttributeError.

The Python DOM API, however, does require that normal attribute access work. This means that the typical surrogates generated by Python IDL compilers are not likely to work, and wrapper objects may be needed on the client if the DOM objects are accessed via CORBA. While this does require some additional consideration for CORBA DOM clients, the implementers with experience using DOM over CORBA from Python do not consider this a problem. Attributes that are declared readonly may not restrict write access in all DOM implementations.

In the Python DOM API, accessor functions are not required. If provided, they should take the form defined by the Python IDL mapping, but these methods are considered unnecessary since the attributes are accessible directly from Python. “Set” accessors should never be provided for readonly attributes.

The IDL definitions do not fully embody the requirements of the W3C DOM API, such as the notion of certain objects, such as the return value of getElementsByTagName(), being “live”. The Python DOM API does not require implementations to enforce such requirements.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.dom.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.dom.minidom.html b/devdocs/python~3.12/library%2Fxml.dom.minidom.html new file mode 100644 index 00000000..42143c15 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.dom.minidom.html @@ -0,0 +1,115 @@ +

xml.dom.minidom — Minimal DOM implementation

Source code: Lib/xml/dom/minidom.py

xml.dom.minidom is a minimal implementation of the Document Object Model interface, with an API similar to that in other languages. It is intended to be simpler than the full DOM and also significantly smaller. Users who are not already proficient with the DOM should consider using the xml.etree.ElementTree module for their XML processing instead.

Warning

The xml.dom.minidom module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

DOM applications typically start by parsing some XML into a DOM. With xml.dom.minidom, this is done through the parse functions:

from xml.dom.minidom import parse, parseString
+
+dom1 = parse('c:\\temp\\mydata.xml')  # parse an XML file by name
+
+datasource = open('c:\\temp\\mydata.xml')
+dom2 = parse(datasource)  # parse an open file
+
+dom3 = parseString('<myxml>Some data<empty/> some more data</myxml>')
+

The parse() function can take either a filename or an open file object.

+xml.dom.minidom.parse(filename_or_file, parser=None, bufsize=None)
+

Return a Document from the given input. filename_or_file may be either a file name, or a file-like object. parser, if given, must be a SAX2 parser object. This function will change the document handler of the parser and activate namespace support; other parser configuration (like setting an entity resolver) must have been done in advance.

+

If you have XML in a string, you can use the parseString() function instead:

+xml.dom.minidom.parseString(string, parser=None)
+

Return a Document that represents the string. This method creates an io.StringIO object for the string and passes that on to parse().

+

Both functions return a Document object representing the content of the document.

What the parse() and parseString() functions do is connect an XML parser with a “DOM builder” that can accept parse events from any SAX parser and convert them into a DOM tree. The name of the functions are perhaps misleading, but are easy to grasp when learning the interfaces. The parsing of the document will be completed before these functions return; it’s simply that these functions do not provide a parser implementation themselves.

You can also create a Document by calling a method on a “DOM Implementation” object. You can get this object either by calling the getDOMImplementation() function in the xml.dom package or the xml.dom.minidom module. Once you have a Document, you can add child nodes to it to populate the DOM:

from xml.dom.minidom import getDOMImplementation
+
+impl = getDOMImplementation()
+
+newdoc = impl.createDocument(None, "some_tag", None)
+top_element = newdoc.documentElement
+text = newdoc.createTextNode('Some textual content.')
+top_element.appendChild(text)
+

Once you have a DOM document object, you can access the parts of your XML document through its properties and methods. These properties are defined in the DOM specification. The main property of the document object is the documentElement property. It gives you the main element in the XML document: the one that holds all others. Here is an example program:

dom3 = parseString("<myxml>Some data</myxml>")
+assert dom3.documentElement.tagName == "myxml"
+

When you are finished with a DOM tree, you may optionally call the unlink() method to encourage early cleanup of the now-unneeded objects. unlink() is an xml.dom.minidom-specific extension to the DOM API that renders the node and its descendants essentially useless. Otherwise, Python’s garbage collector will eventually take care of the objects in the tree.

See also

Document Object Model (DOM) Level 1 Specification
+
+

The W3C recommendation for the DOM supported by xml.dom.minidom.

DOM Objects

The definition of the DOM API for Python is given as part of the xml.dom module documentation. This section lists the differences between the API and xml.dom.minidom.

+

Break internal references within the DOM so that it will be garbage collected on versions of Python without cyclic GC. Even when cyclic GC is available, using this can make large amounts of memory available sooner, so calling this on DOM objects as soon as they are no longer needed is good practice. This only needs to be called on the Document object, but may be called on child nodes to discard children of that node.

You can avoid calling this method explicitly by using the with statement. The following code will automatically unlink dom when the with block is exited:

with xml.dom.minidom.parse(datasource) as dom:
+    ... # Work with dom.
+
+
+Node.writexml(writer, indent='', addindent='', newl='', encoding=None, standalone=None)
+

Write XML to the writer object. The writer receives texts but not bytes as input, it should have a write() method which matches that of the file object interface. The indent parameter is the indentation of the current node. The addindent parameter is the incremental indentation to use for subnodes of the current one. The newl parameter specifies the string to use to terminate newlines.

For the Document node, an additional keyword argument encoding can be used to specify the encoding field of the XML header.

Similarly, explicitly stating the standalone argument causes the standalone document declarations to be added to the prologue of the XML document. If the value is set to True, standalone="yes" is added, otherwise it is set to "no". Not stating the argument will omit the declaration from the document.

Changed in version 3.8: The writexml() method now preserves the attribute order specified by the user.

Changed in version 3.9: The standalone parameter was added.

+
+Node.toxml(encoding=None, standalone=None)
+

Return a string or byte string containing the XML represented by the DOM node.

With an explicit encoding 1 argument, the result is a byte string in the specified encoding. With no encoding argument, the result is a Unicode string, and the XML declaration in the resulting string does not specify an encoding. Encoding this string in an encoding other than UTF-8 is likely incorrect, since UTF-8 is the default encoding of XML.

The standalone argument behaves exactly as in writexml().

Changed in version 3.8: The toxml() method now preserves the attribute order specified by the user.

Changed in version 3.9: The standalone parameter was added.

+
+Node.toprettyxml(indent='\t', newl='\n', encoding=None, standalone=None)
+

Return a pretty-printed version of the document. indent specifies the indentation string and defaults to a tabulator; newl specifies the string emitted at the end of each line and defaults to \n.

The encoding argument behaves like the corresponding argument of toxml().

The standalone argument behaves exactly as in writexml().

Changed in version 3.8: The toprettyxml() method now preserves the attribute order specified by the user.

Changed in version 3.9: The standalone parameter was added.

+

DOM Example

This example program is a fairly realistic example of a simple program. In this particular case, we do not take much advantage of the flexibility of the DOM.

import xml.dom.minidom
+
+document = """\
+<slideshow>
+<title>Demo slideshow</title>
+<slide><title>Slide title</title>
+<point>This is a demo</point>
+<point>Of a program for processing slides</point>
+</slide>
+
+<slide><title>Another demo slide</title>
+<point>It is important</point>
+<point>To have more than</point>
+<point>one slide</point>
+</slide>
+</slideshow>
+"""
+
+dom = xml.dom.minidom.parseString(document)
+
+def getText(nodelist):
+    rc = []
+    for node in nodelist:
+        if node.nodeType == node.TEXT_NODE:
+            rc.append(node.data)
+    return ''.join(rc)
+
+def handleSlideshow(slideshow):
+    print("<html>")
+    handleSlideshowTitle(slideshow.getElementsByTagName("title")[0])
+    slides = slideshow.getElementsByTagName("slide")
+    handleToc(slides)
+    handleSlides(slides)
+    print("</html>")
+
+def handleSlides(slides):
+    for slide in slides:
+        handleSlide(slide)
+
+def handleSlide(slide):
+    handleSlideTitle(slide.getElementsByTagName("title")[0])
+    handlePoints(slide.getElementsByTagName("point"))
+
+def handleSlideshowTitle(title):
+    print(f"<title>{getText(title.childNodes)}</title>")
+
+def handleSlideTitle(title):
+    print(f"<h2>{getText(title.childNodes)}</h2>")
+
+def handlePoints(points):
+    print("<ul>")
+    for point in points:
+        handlePoint(point)
+    print("</ul>")
+
+def handlePoint(point):
+    print(f"<li>{getText(point.childNodes)}</li>")
+
+def handleToc(slides):
+    for slide in slides:
+        title = slide.getElementsByTagName("title")[0]
+        print(f"<p>{getText(title.childNodes)}</p>")
+
+handleSlideshow(dom)
+

minidom and the DOM standard

The xml.dom.minidom module is essentially a DOM 1.0-compatible DOM with some DOM 2 features (primarily namespace features).

Usage of the DOM interface in Python is straight-forward. The following mapping rules apply:

  • Interfaces are accessed through instance objects. Applications should not instantiate the classes themselves; they should use the creator functions available on the Document object. Derived interfaces support all operations (and attributes) from the base interfaces, plus any new operations.
  • Operations are used as methods. Since the DOM uses only in parameters, the arguments are passed in normal order (from left to right). There are no optional arguments. void operations return None.
  • IDL attributes map to instance attributes. For compatibility with the OMG IDL language mapping for Python, an attribute foo can also be accessed through accessor methods _get_foo() and _set_foo(). readonly attributes must not be changed; this is not enforced at runtime.
  • The types short int, unsigned int, unsigned long long, and boolean all map to Python integer objects.
  • The type DOMString maps to Python strings. xml.dom.minidom supports either bytes or strings, but will normally produce strings. Values of type DOMString may also be None where allowed to have the IDL null value by the DOM specification from the W3C.
  • +const declarations map to variables in their respective scope (e.g. xml.dom.minidom.Node.PROCESSING_INSTRUCTION_NODE); they must not be changed.
  • +DOMException is currently not supported in xml.dom.minidom. Instead, xml.dom.minidom uses standard Python exceptions such as TypeError and AttributeError.
  • +NodeList objects are implemented using Python’s built-in list type. These objects provide the interface defined in the DOM specification, but with earlier versions of Python they do not support the official API. They are, however, much more “Pythonic” than the interface defined in the W3C recommendations.

The following interfaces have no implementation in xml.dom.minidom:

  • DOMTimeStamp
  • EntityReference

Most of these reflect information in the XML document that is not of general utility to most DOM users.

Footnotes

+1
+

The encoding name included in the XML output should conform to the appropriate standards. For example, “UTF-8” is valid, but “UTF8” is not valid in an XML document’s declaration, even though Python accepts it as an encoding name. See https://www.w3.org/TR/2006/REC-xml11-20060816/#NT-EncodingDecl and https://www.iana.org/assignments/character-sets/character-sets.xhtml.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.dom.minidom.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.dom.pulldom.html b/devdocs/python~3.12/library%2Fxml.dom.pulldom.html new file mode 100644 index 00000000..c8f1b894 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.dom.pulldom.html @@ -0,0 +1,58 @@ +

xml.dom.pulldom — Support for building partial DOM trees

Source code: Lib/xml/dom/pulldom.py

The xml.dom.pulldom module provides a “pull parser” which can also be asked to produce DOM-accessible fragments of the document where necessary. The basic concept involves pulling “events” from a stream of incoming XML and processing them. In contrast to SAX which also employs an event-driven processing model together with callbacks, the user of a pull parser is responsible for explicitly pulling events from the stream, looping over those events until either processing is finished or an error condition occurs.

Warning

The xml.dom.pulldom module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

Changed in version 3.7.1: The SAX parser no longer processes general external entities by default to increase security by default. To enable processing of external entities, pass a custom parser instance in:

from xml.dom.pulldom import parse
+from xml.sax import make_parser
+from xml.sax.handler import feature_external_ges
+
+parser = make_parser()
+parser.setFeature(feature_external_ges, True)
+parse(filename, parser=parser)
+

Example:

from xml.dom import pulldom
+
+doc = pulldom.parse('sales_items.xml')
+for event, node in doc:
+    if event == pulldom.START_ELEMENT and node.tagName == 'item':
+        if int(node.getAttribute('price')) > 50:
+            doc.expandNode(node)
+            print(node.toxml())
+

event is a constant and can be one of:

node is an object of type xml.dom.minidom.Document, xml.dom.minidom.Element or xml.dom.minidom.Text.

Since the document is treated as a “flat” stream of events, the document “tree” is implicitly traversed and the desired elements are found regardless of their depth in the tree. In other words, one does not need to consider hierarchical issues such as recursive searching of the document nodes, although if the context of elements were important, one would either need to maintain some context-related state (i.e. remembering where one is in the document at any given point) or to make use of the DOMEventStream.expandNode() method and switch to DOM-related processing.

+class xml.dom.pulldom.PullDom(documentFactory=None)
+

Subclass of xml.sax.handler.ContentHandler.

+
+class xml.dom.pulldom.SAX2DOM(documentFactory=None)
+

Subclass of xml.sax.handler.ContentHandler.

+
+xml.dom.pulldom.parse(stream_or_string, parser=None, bufsize=None)
+

Return a DOMEventStream from the given input. stream_or_string may be either a file name, or a file-like object. parser, if given, must be an XMLReader object. This function will change the document handler of the parser and activate namespace support; other parser configuration (like setting an entity resolver) must have been done in advance.

+

If you have XML in a string, you can use the parseString() function instead:

+xml.dom.pulldom.parseString(string, parser=None)
+

Return a DOMEventStream that represents the (Unicode) string.

+
+xml.dom.pulldom.default_bufsize
+

Default value for the bufsize parameter to parse().

The value of this variable can be changed before calling parse() and the new value will take effect.

+

DOMEventStream Objects

+class xml.dom.pulldom.DOMEventStream(stream, parser, bufsize)
+

Changed in version 3.11: Support for __getitem__() method has been removed.

+getEvent()
+

Return a tuple containing event and the current node as xml.dom.minidom.Document if event equals START_DOCUMENT, xml.dom.minidom.Element if event equals START_ELEMENT or END_ELEMENT or xml.dom.minidom.Text if event equals CHARACTERS. The current node does not contain information about its children, unless expandNode() is called.

+
+expandNode(node)
+

Expands all children of node into node. Example:

from xml.dom import pulldom
+
+xml = '<html><title>Foo</title> <p>Some text <div>and more</div></p> </html>'
+doc = pulldom.parseString(xml)
+for event, node in doc:
+    if event == pulldom.START_ELEMENT and node.tagName == 'p':
+        # Following statement only prints '<p/>'
+        print(node.toxml())
+        doc.expandNode(node)
+        # Following statement prints node with all its children '<p>Some text <div>and more</div></p>'
+        print(node.toxml())
+
+
+reset()
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.dom.pulldom.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.etree.elementtree.html b/devdocs/python~3.12/library%2Fxml.etree.elementtree.html new file mode 100644 index 00000000..936b73da --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.etree.elementtree.html @@ -0,0 +1,541 @@ +

xml.etree.ElementTree — The ElementTree XML API

Source code: Lib/xml/etree/ElementTree.py

The xml.etree.ElementTree module implements a simple and efficient API for parsing and creating XML data.

Changed in version 3.3: This module will use a fast implementation whenever available.

Deprecated since version 3.3: The xml.etree.cElementTree module is deprecated.

Warning

The xml.etree.ElementTree module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

Tutorial

This is a short tutorial for using xml.etree.ElementTree (ET in short). The goal is to demonstrate some of the building blocks and basic concepts of the module.

XML tree and elements

XML is an inherently hierarchical data format, and the most natural way to represent it is with a tree. ET has two classes for this purpose - ElementTree represents the whole XML document as a tree, and Element represents a single node in this tree. Interactions with the whole document (reading and writing to/from files) are usually done on the ElementTree level. Interactions with a single XML element and its sub-elements are done on the Element level.

Parsing XML

We’ll be using the following XML document as the sample data for this section:

<?xml version="1.0"?>
+<data>
+    <country name="Liechtenstein">
+        <rank>1</rank>
+        <year>2008</year>
+        <gdppc>141100</gdppc>
+        <neighbor name="Austria" direction="E"/>
+        <neighbor name="Switzerland" direction="W"/>
+    </country>
+    <country name="Singapore">
+        <rank>4</rank>
+        <year>2011</year>
+        <gdppc>59900</gdppc>
+        <neighbor name="Malaysia" direction="N"/>
+    </country>
+    <country name="Panama">
+        <rank>68</rank>
+        <year>2011</year>
+        <gdppc>13600</gdppc>
+        <neighbor name="Costa Rica" direction="W"/>
+        <neighbor name="Colombia" direction="E"/>
+    </country>
+</data>
+

We can import this data by reading from a file:

import xml.etree.ElementTree as ET
+tree = ET.parse('country_data.xml')
+root = tree.getroot()
+

Or directly from a string:

root = ET.fromstring(country_data_as_string)
+

fromstring() parses XML from a string directly into an Element, which is the root element of the parsed tree. Other parsing functions may create an ElementTree. Check the documentation to be sure.

As an Element, root has a tag and a dictionary of attributes:

>>> root.tag
+'data'
+>>> root.attrib
+{}
+

It also has children nodes over which we can iterate:

>>> for child in root:
+...     print(child.tag, child.attrib)
+...
+country {'name': 'Liechtenstein'}
+country {'name': 'Singapore'}
+country {'name': 'Panama'}
+

Children are nested, and we can access specific child nodes by index:

>>> root[0][1].text
+'2008'
+

Note

Not all elements of the XML input will end up as elements of the parsed tree. Currently, this module skips over any XML comments, processing instructions, and document type declarations in the input. Nevertheless, trees built using this module’s API rather than parsing from XML text can have comments and processing instructions in them; they will be included when generating XML output. A document type declaration may be accessed by passing a custom TreeBuilder instance to the XMLParser constructor.

Pull API for non-blocking parsing

Most parsing functions provided by this module require the whole document to be read at once before returning any result. It is possible to use an XMLParser and feed data into it incrementally, but it is a push API that calls methods on a callback target, which is too low-level and inconvenient for most needs. Sometimes what the user really wants is to be able to parse XML incrementally, without blocking operations, while enjoying the convenience of fully constructed Element objects.

The most powerful tool for doing this is XMLPullParser. It does not require a blocking read to obtain the XML data, and is instead fed with data incrementally with XMLPullParser.feed() calls. To get the parsed XML elements, call XMLPullParser.read_events(). Here is an example:

>>> parser = ET.XMLPullParser(['start', 'end'])
+>>> parser.feed('<mytag>sometext')
+>>> list(parser.read_events())
+[('start', <Element 'mytag' at 0x7fa66db2be58>)]
+>>> parser.feed(' more text</mytag>')
+>>> for event, elem in parser.read_events():
+...     print(event)
+...     print(elem.tag, 'text=', elem.text)
+...
+end
+mytag text= sometext more text
+

The obvious use case is applications that operate in a non-blocking fashion where the XML data is being received from a socket or read incrementally from some storage device. In such cases, blocking reads are unacceptable.

Because it’s so flexible, XMLPullParser can be inconvenient to use for simpler use-cases. If you don’t mind your application blocking on reading XML data but would still like to have incremental parsing capabilities, take a look at iterparse(). It can be useful when you’re reading a large XML document and don’t want to hold it wholly in memory.

Finding interesting elements

Element has some useful methods that help iterate recursively over all the sub-tree below it (its children, their children, and so on). For example, Element.iter():

>>> for neighbor in root.iter('neighbor'):
+...     print(neighbor.attrib)
+...
+{'name': 'Austria', 'direction': 'E'}
+{'name': 'Switzerland', 'direction': 'W'}
+{'name': 'Malaysia', 'direction': 'N'}
+{'name': 'Costa Rica', 'direction': 'W'}
+{'name': 'Colombia', 'direction': 'E'}
+

Element.findall() finds only elements with a tag which are direct children of the current element. Element.find() finds the first child with a particular tag, and Element.text accesses the element’s text content. Element.get() accesses the element’s attributes:

>>> for country in root.findall('country'):
+...     rank = country.find('rank').text
+...     name = country.get('name')
+...     print(name, rank)
+...
+Liechtenstein 1
+Singapore 4
+Panama 68
+

More sophisticated specification of which elements to look for is possible by using XPath.

Modifying an XML File

ElementTree provides a simple way to build XML documents and write them to files. The ElementTree.write() method serves this purpose.

Once created, an Element object may be manipulated by directly changing its fields (such as Element.text), adding and modifying attributes (Element.set() method), as well as adding new children (for example with Element.append()).

Let’s say we want to add one to each country’s rank, and add an updated attribute to the rank element:

>>> for rank in root.iter('rank'):
+...     new_rank = int(rank.text) + 1
+...     rank.text = str(new_rank)
+...     rank.set('updated', 'yes')
+...
+>>> tree.write('output.xml')
+

Our XML now looks like this:

<?xml version="1.0"?>
+<data>
+    <country name="Liechtenstein">
+        <rank updated="yes">2</rank>
+        <year>2008</year>
+        <gdppc>141100</gdppc>
+        <neighbor name="Austria" direction="E"/>
+        <neighbor name="Switzerland" direction="W"/>
+    </country>
+    <country name="Singapore">
+        <rank updated="yes">5</rank>
+        <year>2011</year>
+        <gdppc>59900</gdppc>
+        <neighbor name="Malaysia" direction="N"/>
+    </country>
+    <country name="Panama">
+        <rank updated="yes">69</rank>
+        <year>2011</year>
+        <gdppc>13600</gdppc>
+        <neighbor name="Costa Rica" direction="W"/>
+        <neighbor name="Colombia" direction="E"/>
+    </country>
+</data>
+

We can remove elements using Element.remove(). Let’s say we want to remove all countries with a rank higher than 50:

>>> for country in root.findall('country'):
+...     # using root.findall() to avoid removal during traversal
+...     rank = int(country.find('rank').text)
+...     if rank > 50:
+...         root.remove(country)
+...
+>>> tree.write('output.xml')
+

Note that concurrent modification while iterating can lead to problems, just like when iterating and modifying Python lists or dicts. Therefore, the example first collects all matching elements with root.findall(), and only then iterates over the list of matches.

Our XML now looks like this:

<?xml version="1.0"?>
+<data>
+    <country name="Liechtenstein">
+        <rank updated="yes">2</rank>
+        <year>2008</year>
+        <gdppc>141100</gdppc>
+        <neighbor name="Austria" direction="E"/>
+        <neighbor name="Switzerland" direction="W"/>
+    </country>
+    <country name="Singapore">
+        <rank updated="yes">5</rank>
+        <year>2011</year>
+        <gdppc>59900</gdppc>
+        <neighbor name="Malaysia" direction="N"/>
+    </country>
+</data>
+

Building XML documents

The SubElement() function also provides a convenient way to create new sub-elements for a given element:

>>> a = ET.Element('a')
+>>> b = ET.SubElement(a, 'b')
+>>> c = ET.SubElement(a, 'c')
+>>> d = ET.SubElement(c, 'd')
+>>> ET.dump(a)
+<a><b /><c><d /></c></a>
+

Parsing XML with Namespaces

If the XML input has namespaces, tags and attributes with prefixes in the form prefix:sometag get expanded to {uri}sometag where the prefix is replaced by the full URI. Also, if there is a default namespace, that full URI gets prepended to all of the non-prefixed tags.

Here is an XML example that incorporates two namespaces, one with the prefix “fictional” and the other serving as the default namespace:

<?xml version="1.0"?>
+<actors xmlns:fictional="http://characters.example.com"
+        xmlns="http://people.example.com">
+    <actor>
+        <name>John Cleese</name>
+        <fictional:character>Lancelot</fictional:character>
+        <fictional:character>Archie Leach</fictional:character>
+    </actor>
+    <actor>
+        <name>Eric Idle</name>
+        <fictional:character>Sir Robin</fictional:character>
+        <fictional:character>Gunther</fictional:character>
+        <fictional:character>Commander Clement</fictional:character>
+    </actor>
+</actors>
+

One way to search and explore this XML example is to manually add the URI to every tag or attribute in the xpath of a find() or findall():

root = fromstring(xml_text)
+for actor in root.findall('{http://people.example.com}actor'):
+    name = actor.find('{http://people.example.com}name')
+    print(name.text)
+    for char in actor.findall('{http://characters.example.com}character'):
+        print(' |-->', char.text)
+

A better way to search the namespaced XML example is to create a dictionary with your own prefixes and use those in the search functions:

ns = {'real_person': 'http://people.example.com',
+      'role': 'http://characters.example.com'}
+
+for actor in root.findall('real_person:actor', ns):
+    name = actor.find('real_person:name', ns)
+    print(name.text)
+    for char in actor.findall('role:character', ns):
+        print(' |-->', char.text)
+

These two approaches both output:

John Cleese
+ |--> Lancelot
+ |--> Archie Leach
+Eric Idle
+ |--> Sir Robin
+ |--> Gunther
+ |--> Commander Clement
+

XPath support

This module provides limited support for XPath expressions for locating elements in a tree. The goal is to support a small subset of the abbreviated syntax; a full XPath engine is outside the scope of the module.

Example

Here’s an example that demonstrates some of the XPath capabilities of the module. We’ll be using the countrydata XML document from the Parsing XML section:

import xml.etree.ElementTree as ET
+
+root = ET.fromstring(countrydata)
+
+# Top-level elements
+root.findall(".")
+
+# All 'neighbor' grand-children of 'country' children of the top-level
+# elements
+root.findall("./country/neighbor")
+
+# Nodes with name='Singapore' that have a 'year' child
+root.findall(".//year/..[@name='Singapore']")
+
+# 'year' nodes that are children of nodes with name='Singapore'
+root.findall(".//*[@name='Singapore']/year")
+
+# All 'neighbor' nodes that are the second child of their parent
+root.findall(".//neighbor[2]")
+

For XML with namespaces, use the usual qualified {namespace}tag notation:

# All dublin-core "title" tags in the document
+root.findall(".//{http://purl.org/dc/elements/1.1/}title")
+

Supported XPath syntax

+ + + + + + + + + + + + + + +

Syntax

Meaning

tag

 +

Selects all child elements with the given tag. For example, spam selects all child elements named spam, and spam/egg selects all grandchildren named egg in all children named spam. {namespace}* selects all tags in the given namespace, {*}spam selects tags named spam in any (or no) namespace, and {}* only selects tags that are not in a namespace.

Changed in version 3.8: Support for star-wildcards was added.

*

Selects all child elements, including comments and processing instructions. For example, */egg selects all grandchildren named egg.

.

Selects the current node. This is mostly useful at the beginning of the path, to indicate that it’s a relative path.

//

Selects all subelements, on all levels beneath the current element. For example, .//egg selects all egg elements in the entire tree.

..

Selects the parent element. Returns None if the path attempts to reach the ancestors of the start element (the element find was called on).

[@attrib]

Selects all elements that have the given attribute.

[@attrib='value']

Selects all elements for which the given attribute has the given value. The value cannot contain quotes.

[@attrib!='value']

 +

Selects all elements for which the given attribute does not have the given value. The value cannot contain quotes.

New in version 3.10.

[tag]

Selects all elements that have a child named tag. Only immediate children are supported.

[.='text']

 +

Selects all elements whose complete text content, including descendants, equals the given text.

New in version 3.7.

[.!='text']

 +

Selects all elements whose complete text content, including descendants, does not equal the given text.

New in version 3.10.

[tag='text']

Selects all elements that have a child named tag whose complete text content, including descendants, equals the given text.

[tag!='text']

 +

Selects all elements that have a child named tag whose complete text content, including descendants, does not equal the given text.

New in version 3.10.

[position]

Selects all elements that are located at the given position. The position can be either an integer (1 is the first position), the expression last() (for the last position), or a position relative to the last position (e.g. last()-1).

Predicates (expressions within square brackets) must be preceded by a tag name, an asterisk, or another predicate. position predicates must be preceded by a tag name.

Reference

Functions

+xml.etree.ElementTree.canonicalize(xml_data=None, *, out=None, from_file=None, **options)
+

C14N 2.0 transformation function.

Canonicalization is a way to normalise XML output in a way that allows byte-by-byte comparisons and digital signatures. It reduced the freedom that XML serializers have and instead generates a more constrained XML representation. The main restrictions regard the placement of namespace declarations, the ordering of attributes, and ignorable whitespace.

This function takes an XML data string (xml_data) or a file path or file-like object (from_file) as input, converts it to the canonical form, and writes it out using the out file(-like) object, if provided, or returns it as a text string if not. The output file receives text, not bytes. It should therefore be opened in text mode with utf-8 encoding.

Typical uses:

xml_data = "<root>...</root>"
+print(canonicalize(xml_data))
+
+with open("c14n_output.xml", mode='w', encoding='utf-8') as out_file:
+    canonicalize(xml_data, out=out_file)
+
+with open("c14n_output.xml", mode='w', encoding='utf-8') as out_file:
+    canonicalize(from_file="inputfile.xml", out=out_file)
+

The configuration options are as follows:

  • +with_comments: set to true to include comments (default: false)
  • +
    +strip_text: set to true to strip whitespace before and after text content
    +
    +

    (default: false)

  • +
    +rewrite_prefixes: set to true to replace namespace prefixes by “n{number}”
    +
    +

    (default: false)

  • +
    +qname_aware_tags: a set of qname aware tag names in which prefixes
    +
    +

    should be replaced in text content (default: empty)

  • +
    +qname_aware_attrs: a set of qname aware attribute names in which prefixes
    +
    +

    should be replaced in text content (default: empty)

  • +exclude_attrs: a set of attribute names that should not be serialised
  • +exclude_tags: a set of tag names that should not be serialised

In the option list above, “a set” refers to any collection or iterable of strings, no ordering is expected.

New in version 3.8.

+
+xml.etree.ElementTree.Comment(text=None)
+

Comment element factory. This factory function creates a special element that will be serialized as an XML comment by the standard serializer. The comment string can be either a bytestring or a Unicode string. text is a string containing the comment string. Returns an element instance representing a comment.

Note that XMLParser skips over comments in the input instead of creating comment objects for them. An ElementTree will only contain comment nodes if they have been inserted into to the tree using one of the Element methods.

+
+xml.etree.ElementTree.dump(elem)
+

Writes an element tree or element structure to sys.stdout. This function should be used for debugging only.

The exact output format is implementation dependent. In this version, it’s written as an ordinary XML file.

elem is an element tree or an individual element.

Changed in version 3.8: The dump() function now preserves the attribute order specified by the user.

+
+xml.etree.ElementTree.fromstring(text, parser=None)
+

Parses an XML section from a string constant. Same as XML(). text is a string containing XML data. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns an Element instance.

+
+xml.etree.ElementTree.fromstringlist(sequence, parser=None)
+

Parses an XML document from a sequence of string fragments. sequence is a list or other sequence containing XML data fragments. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns an Element instance.

New in version 3.2.

+
+xml.etree.ElementTree.indent(tree, space=' ', level=0)
+

Appends whitespace to the subtree to indent the tree visually. This can be used to generate pretty-printed XML output. tree can be an Element or ElementTree. space is the whitespace string that will be inserted for each indentation level, two space characters by default. For indenting partial subtrees inside of an already indented tree, pass the initial indentation level as level.

New in version 3.9.

+
+xml.etree.ElementTree.iselement(element)
+

Check if an object appears to be a valid element object. element is an element instance. Return True if this is an element object.

+
+xml.etree.ElementTree.iterparse(source, events=None, parser=None)
+

Parses an XML section into an element tree incrementally, and reports what’s going on to the user. source is a filename or file object containing XML data. events is a sequence of events to report back. The supported events are the strings "start", "end", "comment", "pi", "start-ns" and "end-ns" (the “ns” events are used to get detailed namespace information). If events is omitted, only "end" events are reported. parser is an optional parser instance. If not given, the standard XMLParser parser is used. parser must be a subclass of XMLParser and can only use the default TreeBuilder as a target. Returns an iterator providing (event, elem) pairs; it has a root attribute that references the root element of the resulting XML tree once source is fully read.

Note that while iterparse() builds the tree incrementally, it issues blocking reads on source (or the file it names). As such, it’s unsuitable for applications where blocking reads can’t be made. For fully non-blocking parsing, see XMLPullParser.

Note

iterparse() only guarantees that it has seen the “>” character of a starting tag when it emits a “start” event, so the attributes are defined, but the contents of the text and tail attributes are undefined at that point. The same applies to the element children; they may or may not be present.

If you need a fully populated element, look for “end” events instead.

Deprecated since version 3.4: The parser argument.

Changed in version 3.8: The comment and pi events were added.

+
+xml.etree.ElementTree.parse(source, parser=None)
+

Parses an XML section into an element tree. source is a filename or file object containing XML data. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns an ElementTree instance.

+
+xml.etree.ElementTree.ProcessingInstruction(target, text=None)
+

PI element factory. This factory function creates a special element that will be serialized as an XML processing instruction. target is a string containing the PI target. text is a string containing the PI contents, if given. Returns an element instance, representing a processing instruction.

Note that XMLParser skips over processing instructions in the input instead of creating comment objects for them. An ElementTree will only contain processing instruction nodes if they have been inserted into to the tree using one of the Element methods.

+
+xml.etree.ElementTree.register_namespace(prefix, uri)
+

Registers a namespace prefix. The registry is global, and any existing mapping for either the given prefix or the namespace URI will be removed. prefix is a namespace prefix. uri is a namespace uri. Tags and attributes in this namespace will be serialized with the given prefix, if at all possible.

New in version 3.2.

+
+xml.etree.ElementTree.SubElement(parent, tag, attrib={}, **extra)
+

Subelement factory. This function creates an element instance, and appends it to an existing element.

The element name, attribute names, and attribute values can be either bytestrings or Unicode strings. parent is the parent element. tag is the subelement name. attrib is an optional dictionary, containing element attributes. extra contains additional attributes, given as keyword arguments. Returns an element instance.

+
+xml.etree.ElementTree.tostring(element, encoding='us-ascii', method='xml', *, xml_declaration=None, default_namespace=None, short_empty_elements=True)
+

Generates a string representation of an XML element, including all subelements. element is an Element instance. encoding 1 is the output encoding (default is US-ASCII). Use encoding="unicode" to generate a Unicode string (otherwise, a bytestring is generated). method is either "xml", "html" or "text" (default is "xml"). xml_declaration, default_namespace and short_empty_elements has the same meaning as in ElementTree.write(). Returns an (optionally) encoded string containing the XML data.

New in version 3.4: The short_empty_elements parameter.

New in version 3.8: The xml_declaration and default_namespace parameters.

Changed in version 3.8: The tostring() function now preserves the attribute order specified by the user.

+
+xml.etree.ElementTree.tostringlist(element, encoding='us-ascii', method='xml', *, xml_declaration=None, default_namespace=None, short_empty_elements=True)
+

Generates a string representation of an XML element, including all subelements. element is an Element instance. encoding 1 is the output encoding (default is US-ASCII). Use encoding="unicode" to generate a Unicode string (otherwise, a bytestring is generated). method is either "xml", "html" or "text" (default is "xml"). xml_declaration, default_namespace and short_empty_elements has the same meaning as in ElementTree.write(). Returns a list of (optionally) encoded strings containing the XML data. It does not guarantee any specific sequence, except that b"".join(tostringlist(element)) == tostring(element).

New in version 3.2.

New in version 3.4: The short_empty_elements parameter.

New in version 3.8: The xml_declaration and default_namespace parameters.

Changed in version 3.8: The tostringlist() function now preserves the attribute order specified by the user.

+
+xml.etree.ElementTree.XML(text, parser=None)
+

Parses an XML section from a string constant. This function can be used to embed “XML literals” in Python code. text is a string containing XML data. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns an Element instance.

+
+xml.etree.ElementTree.XMLID(text, parser=None)
+

Parses an XML section from a string constant, and also returns a dictionary which maps from element id:s to elements. text is a string containing XML data. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns a tuple containing an Element instance and a dictionary.

+

XInclude support

This module provides limited support for XInclude directives, via the xml.etree.ElementInclude helper module. This module can be used to insert subtrees and text strings into element trees, based on information in the tree.

Example

Here’s an example that demonstrates use of the XInclude module. To include an XML document in the current document, use the {http://www.w3.org/2001/XInclude}include element and set the parse attribute to "xml", and use the href attribute to specify the document to include.

<?xml version="1.0"?>
+<document xmlns:xi="http://www.w3.org/2001/XInclude">
+  <xi:include href="source.xml" parse="xml" />
+</document>
+

By default, the href attribute is treated as a file name. You can use custom loaders to override this behaviour. Also note that the standard helper does not support XPointer syntax.

To process this file, load it as usual, and pass the root element to the xml.etree.ElementTree module:

from xml.etree import ElementTree, ElementInclude
+
+tree = ElementTree.parse("document.xml")
+root = tree.getroot()
+
+ElementInclude.include(root)
+

The ElementInclude module replaces the {http://www.w3.org/2001/XInclude}include element with the root element from the source.xml document. The result might look something like this:

<document xmlns:xi="http://www.w3.org/2001/XInclude">
+  <para>This is a paragraph.</para>
+</document>
+

If the parse attribute is omitted, it defaults to “xml”. The href attribute is required.

To include a text document, use the {http://www.w3.org/2001/XInclude}include element, and set the parse attribute to “text”:

<?xml version="1.0"?>
+<document xmlns:xi="http://www.w3.org/2001/XInclude">
+  Copyright (c) <xi:include href="year.txt" parse="text" />.
+</document>
+

The result might look something like:

<document xmlns:xi="http://www.w3.org/2001/XInclude">
+  Copyright (c) 2003.
+</document>
+

Reference

Functions

+xml.etree.ElementInclude.default_loader(href, parse, encoding=None)
+

Default loader. This default loader reads an included resource from disk. href is a URL. parse is for parse mode either “xml” or “text”. encoding is an optional text encoding. If not given, encoding is utf-8. Returns the expanded resource. If the parse mode is "xml", this is an ElementTree instance. If the parse mode is “text”, this is a Unicode string. If the loader fails, it can return None or raise an exception.

+
+xml.etree.ElementInclude.include(elem, loader=None, base_url=None, max_depth=6)
+

This function expands XInclude directives. elem is the root element. loader is an optional resource loader. If omitted, it defaults to default_loader(). If given, it should be a callable that implements the same interface as default_loader(). base_url is base URL of the original file, to resolve relative include file references. max_depth is the maximum number of recursive inclusions. Limited to reduce the risk of malicious content explosion. Pass a negative value to disable the limitation.

Returns the expanded resource. If the parse mode is "xml", this is an ElementTree instance. If the parse mode is “text”, this is a Unicode string. If the loader fails, it can return None or raise an exception.

New in version 3.9: The base_url and max_depth parameters.

+

Element Objects

+class xml.etree.ElementTree.Element(tag, attrib={}, **extra)
+

Element class. This class defines the Element interface, and provides a reference implementation of this interface.

The element name, attribute names, and attribute values can be either bytestrings or Unicode strings. tag is the element name. attrib is an optional dictionary, containing element attributes. extra contains additional attributes, given as keyword arguments.

+tag
+

A string identifying what kind of data this element represents (the element type, in other words).

+
+text
+tail
+

These attributes can be used to hold additional data associated with the element. Their values are usually strings but may be any application-specific object. If the element is created from an XML file, the text attribute holds either the text between the element’s start tag and its first child or end tag, or None, and the tail attribute holds either the text between the element’s end tag and the next tag, or None. For the XML data

<a><b>1<c>2<d/>3</c></b>4</a>
+

the a element has None for both text and tail attributes, the b element has text "1" and tail "4", the c element has text "2" and tail None, and the d element has text None and tail "3".

To collect the inner text of an element, see itertext(), for example "".join(element.itertext()).

Applications may store arbitrary objects in these attributes.

+
+attrib
+

A dictionary containing the element’s attributes. Note that while the attrib value is always a real mutable Python dictionary, an ElementTree implementation may choose to use another internal representation, and create the dictionary only if someone asks for it. To take advantage of such implementations, use the dictionary methods below whenever possible.

+

The following dictionary-like methods work on the element attributes.

+clear()
+

Resets an element. This function removes all subelements, clears all attributes, and sets the text and tail attributes to None.

+
+get(key, default=None)
+

Gets the element attribute named key.

Returns the attribute value, or default if the attribute was not found.

+
+items()
+

Returns the element attributes as a sequence of (name, value) pairs. The attributes are returned in an arbitrary order.

+
+keys()
+

Returns the elements attribute names as a list. The names are returned in an arbitrary order.

+
+set(key, value)
+

Set the attribute key on the element to value.

+

The following methods work on the element’s children (subelements).

+append(subelement)
+

Adds the element subelement to the end of this element’s internal list of subelements. Raises TypeError if subelement is not an Element.

+
+extend(subelements)
+

Appends subelements from a sequence object with zero or more elements. Raises TypeError if a subelement is not an Element.

New in version 3.2.

+
+find(match, namespaces=None)
+

Finds the first subelement matching match. match may be a tag name or a path. Returns an element instance or None. namespaces is an optional mapping from namespace prefix to full name. Pass '' as prefix to move all unprefixed tag names in the expression into the given namespace.

+
+findall(match, namespaces=None)
+

Finds all matching subelements, by tag name or path. Returns a list containing all matching elements in document order. namespaces is an optional mapping from namespace prefix to full name. Pass '' as prefix to move all unprefixed tag names in the expression into the given namespace.

+
+findtext(match, default=None, namespaces=None)
+

Finds text for the first subelement matching match. match may be a tag name or a path. Returns the text content of the first matching element, or default if no element was found. Note that if the matching element has no text content an empty string is returned. namespaces is an optional mapping from namespace prefix to full name. Pass '' as prefix to move all unprefixed tag names in the expression into the given namespace.

+
+insert(index, subelement)
+

Inserts subelement at the given position in this element. Raises TypeError if subelement is not an Element.

+
+iter(tag=None)
+

Creates a tree iterator with the current element as the root. The iterator iterates over this element and all elements below it, in document (depth first) order. If tag is not None or '*', only elements whose tag equals tag are returned from the iterator. If the tree structure is modified during iteration, the result is undefined.

New in version 3.2.

+
+iterfind(match, namespaces=None)
+

Finds all matching subelements, by tag name or path. Returns an iterable yielding all matching elements in document order. namespaces is an optional mapping from namespace prefix to full name.

New in version 3.2.

+
+itertext()
+

Creates a text iterator. The iterator loops over this element and all subelements, in document order, and returns all inner text.

New in version 3.2.

+
+makeelement(tag, attrib)
+

Creates a new element object of the same type as this element. Do not call this method, use the SubElement() factory function instead.

+
+remove(subelement)
+

Removes subelement from the element. Unlike the find* methods this method compares elements based on the instance identity, not on tag value or contents.

+

Element objects also support the following sequence type methods for working with subelements: __delitem__(), __getitem__(), __setitem__(), __len__().

Caution: Elements with no subelements will test as False. Testing the truth value of an Element is deprecated and will raise an exception in Python 3.14. Use specific len(elem) or elem is None test instead.:

element = root.find('foo')
+
+if not element:  # careful!
+    print("element not found, or element has no subelements")
+
+if element is None:
+    print("element not found")
+

Changed in version 3.12: Testing the truth value of an Element emits DeprecationWarning.

Prior to Python 3.8, the serialisation order of the XML attributes of elements was artificially made predictable by sorting the attributes by their name. Based on the now guaranteed ordering of dicts, this arbitrary reordering was removed in Python 3.8 to preserve the order in which attributes were originally parsed or created by user code.

In general, user code should try not to depend on a specific ordering of attributes, given that the XML Information Set explicitly excludes the attribute order from conveying information. Code should be prepared to deal with any ordering on input. In cases where deterministic XML output is required, e.g. for cryptographic signing or test data sets, canonical serialisation is available with the canonicalize() function.

In cases where canonical output is not applicable but a specific attribute order is still desirable on output, code should aim for creating the attributes directly in the desired order, to avoid perceptual mismatches for readers of the code. In cases where this is difficult to achieve, a recipe like the following can be applied prior to serialisation to enforce an order independently from the Element creation:

def reorder_attributes(root):
+    for el in root.iter():
+        attrib = el.attrib
+        if len(attrib) > 1:
+            # adjust attribute order, e.g. by sorting
+            attribs = sorted(attrib.items())
+            attrib.clear()
+            attrib.update(attribs)
+
+

ElementTree Objects

+class xml.etree.ElementTree.ElementTree(element=None, file=None)
+

ElementTree wrapper class. This class represents an entire element hierarchy, and adds some extra support for serialization to and from standard XML.

element is the root element. The tree is initialized with the contents of the XML file if given.

+_setroot(element)
+

Replaces the root element for this tree. This discards the current contents of the tree, and replaces it with the given element. Use with care. element is an element instance.

+
+find(match, namespaces=None)
+

Same as Element.find(), starting at the root of the tree.

+
+findall(match, namespaces=None)
+

Same as Element.findall(), starting at the root of the tree.

+
+findtext(match, default=None, namespaces=None)
+

Same as Element.findtext(), starting at the root of the tree.

+
+getroot()
+

Returns the root element for this tree.

+
+iter(tag=None)
+

Creates and returns a tree iterator for the root element. The iterator loops over all elements in this tree, in section order. tag is the tag to look for (default is to return all elements).

+
+iterfind(match, namespaces=None)
+

Same as Element.iterfind(), starting at the root of the tree.

New in version 3.2.

+
+parse(source, parser=None)
+

Loads an external XML section into this element tree. source is a file name or file object. parser is an optional parser instance. If not given, the standard XMLParser parser is used. Returns the section root element.

+
+write(file, encoding='us-ascii', xml_declaration=None, default_namespace=None, method='xml', *, short_empty_elements=True)
+

Writes the element tree to a file, as XML. file is a file name, or a file object opened for writing. encoding 1 is the output encoding (default is US-ASCII). xml_declaration controls if an XML declaration should be added to the file. Use False for never, True for always, None for only if not US-ASCII or UTF-8 or Unicode (default is None). default_namespace sets the default XML namespace (for “xmlns”). method is either "xml", "html" or "text" (default is "xml"). The keyword-only short_empty_elements parameter controls the formatting of elements that contain no content. If True (the default), they are emitted as a single self-closed tag, otherwise they are emitted as a pair of start/end tags.

The output is either a string (str) or binary (bytes). This is controlled by the encoding argument. If encoding is "unicode", the output is a string; otherwise, it’s binary. Note that this may conflict with the type of file if it’s an open file object; make sure you do not try to write a string to a binary stream and vice versa.

New in version 3.4: The short_empty_elements parameter.

Changed in version 3.8: The write() method now preserves the attribute order specified by the user.

+
+

This is the XML file that is going to be manipulated:

<html>
+    <head>
+        <title>Example page</title>
+    </head>
+    <body>
+        <p>Moved to <a href="http://example.org/">example.org</a>
+        or <a href="http://example.com/">example.com</a>.</p>
+    </body>
+</html>
+

Example of changing the attribute “target” of every link in first paragraph:

>>> from xml.etree.ElementTree import ElementTree
+>>> tree = ElementTree()
+>>> tree.parse("index.xhtml")
+<Element 'html' at 0xb77e6fac>
+>>> p = tree.find("body/p")     # Finds first occurrence of tag p in body
+>>> p
+<Element 'p' at 0xb77ec26c>
+>>> links = list(p.iter("a"))   # Returns list of all links
+>>> links
+[<Element 'a' at 0xb77ec2ac>, <Element 'a' at 0xb77ec1cc>]
+>>> for i in links:             # Iterates through all found links
+...     i.attrib["target"] = "blank"
+...
+>>> tree.write("output.xhtml")
+

QName Objects

+class xml.etree.ElementTree.QName(text_or_uri, tag=None)
+

QName wrapper. This can be used to wrap a QName attribute value, in order to get proper namespace handling on output. text_or_uri is a string containing the QName value, in the form {uri}local, or, if the tag argument is given, the URI part of a QName. If tag is given, the first argument is interpreted as a URI, and this argument is interpreted as a local name. QName instances are opaque.

+

TreeBuilder Objects

+class xml.etree.ElementTree.TreeBuilder(element_factory=None, *, comment_factory=None, pi_factory=None, insert_comments=False, insert_pis=False)
+

Generic element structure builder. This builder converts a sequence of start, data, end, comment and pi method calls to a well-formed element structure. You can use this class to build an element structure using a custom XML parser, or a parser for some other XML-like format.

element_factory, when given, must be a callable accepting two positional arguments: a tag and a dict of attributes. It is expected to return a new element instance.

The comment_factory and pi_factory functions, when given, should behave like the Comment() and ProcessingInstruction() functions to create comments and processing instructions. When not given, the default factories will be used. When insert_comments and/or insert_pis is true, comments/pis will be inserted into the tree if they appear within the root element (but not outside of it).

+close()
+

Flushes the builder buffers, and returns the toplevel document element. Returns an Element instance.

+
+data(data)
+

Adds text to the current element. data is a string. This should be either a bytestring, or a Unicode string.

+
+end(tag)
+

Closes the current element. tag is the element name. Returns the closed element.

+
+start(tag, attrs)
+

Opens a new element. tag is the element name. attrs is a dictionary containing element attributes. Returns the opened element.

+
+comment(text)
+

Creates a comment with the given text. If insert_comments is true, this will also add it to the tree.

New in version 3.8.

+
+pi(target, text)
+

Creates a comment with the given target name and text. If insert_pis is true, this will also add it to the tree.

New in version 3.8.

+

In addition, a custom TreeBuilder object can provide the following methods:

+doctype(name, pubid, system)
+

Handles a doctype declaration. name is the doctype name. pubid is the public identifier. system is the system identifier. This method does not exist on the default TreeBuilder class.

New in version 3.2.

+
+start_ns(prefix, uri)
+

Is called whenever the parser encounters a new namespace declaration, before the start() callback for the opening element that defines it. prefix is '' for the default namespace and the declared namespace prefix name otherwise. uri is the namespace URI.

New in version 3.8.

+
+end_ns(prefix)
+

Is called after the end() callback of an element that declared a namespace prefix mapping, with the name of the prefix that went out of scope.

New in version 3.8.

+
+
+class xml.etree.ElementTree.C14NWriterTarget(write, *, with_comments=False, strip_text=False, rewrite_prefixes=False, qname_aware_tags=None, qname_aware_attrs=None, exclude_attrs=None, exclude_tags=None)
+

A C14N 2.0 writer. Arguments are the same as for the canonicalize() function. This class does not build a tree but translates the callback events directly into a serialised form using the write function.

New in version 3.8.

+

XMLParser Objects

+class xml.etree.ElementTree.XMLParser(*, target=None, encoding=None)
+

This class is the low-level building block of the module. It uses xml.parsers.expat for efficient, event-based parsing of XML. It can be fed XML data incrementally with the feed() method, and parsing events are translated to a push API - by invoking callbacks on the target object. If target is omitted, the standard TreeBuilder is used. If encoding 1 is given, the value overrides the encoding specified in the XML file.

Changed in version 3.8: Parameters are now keyword-only. The html argument no longer supported.

+close()
+

Finishes feeding data to the parser. Returns the result of calling the close() method of the target passed during construction; by default, this is the toplevel document element.

+
+feed(data)
+

Feeds data to the parser. data is encoded data.

+

XMLParser.feed() calls target's start(tag, attrs_dict) method for each opening tag, its end(tag) method for each closing tag, and data is processed by method data(data). For further supported callback methods, see the TreeBuilder class. XMLParser.close() calls target's method close(). XMLParser can be used not only for building a tree structure. This is an example of counting the maximum depth of an XML file:

>>> from xml.etree.ElementTree import XMLParser
+>>> class MaxDepth:                     # The target object of the parser
+...     maxDepth = 0
+...     depth = 0
+...     def start(self, tag, attrib):   # Called for each opening tag.
+...         self.depth += 1
+...         if self.depth > self.maxDepth:
+...             self.maxDepth = self.depth
+...     def end(self, tag):             # Called for each closing tag.
+...         self.depth -= 1
+...     def data(self, data):
+...         pass            # We do not need to do anything with data.
+...     def close(self):    # Called when all data has been parsed.
+...         return self.maxDepth
+...
+>>> target = MaxDepth()
+>>> parser = XMLParser(target=target)
+>>> exampleXml = """
+... <a>
+...   <b>
+...   </b>
+...   <b>
+...     <c>
+...       <d>
+...       </d>
+...     </c>
+...   </b>
+... </a>"""
+>>> parser.feed(exampleXml)
+>>> parser.close()
+4
+
+

XMLPullParser Objects

+class xml.etree.ElementTree.XMLPullParser(events=None)
+

A pull parser suitable for non-blocking applications. Its input-side API is similar to that of XMLParser, but instead of pushing calls to a callback target, XMLPullParser collects an internal list of parsing events and lets the user read from it. events is a sequence of events to report back. The supported events are the strings "start", "end", "comment", "pi", "start-ns" and "end-ns" (the “ns” events are used to get detailed namespace information). If events is omitted, only "end" events are reported.

+feed(data)
+

Feed the given bytes data to the parser.

+
+close()
+

Signal the parser that the data stream is terminated. Unlike XMLParser.close(), this method always returns None. Any events not yet retrieved when the parser is closed can still be read with read_events().

+
+read_events()
+

Return an iterator over the events which have been encountered in the data fed to the parser. The iterator yields (event, elem) pairs, where event is a string representing the type of event (e.g. "end") and elem is the encountered Element object, or other context value as follows.

  • +start, end: the current Element.
  • +comment, pi: the current comment / processing instruction
  • +start-ns: a tuple (prefix, uri) naming the declared namespace mapping.
  • +end-ns: None (this may change in a future version)

Events provided in a previous call to read_events() will not be yielded again. Events are consumed from the internal queue only when they are retrieved from the iterator, so multiple readers iterating in parallel over iterators obtained from read_events() will have unpredictable results.

+

Note

XMLPullParser only guarantees that it has seen the “>” character of a starting tag when it emits a “start” event, so the attributes are defined, but the contents of the text and tail attributes are undefined at that point. The same applies to the element children; they may or may not be present.

If you need a fully populated element, look for “end” events instead.

New in version 3.4.

Changed in version 3.8: The comment and pi events were added.

+

Exceptions

+class xml.etree.ElementTree.ParseError
+

XML parse error, raised by the various parsing methods in this module when parsing fails. The string representation of an instance of this exception will contain a user-friendly error message. In addition, it will have the following attributes available:

+code
+

A numeric error code from the expat parser. See the documentation of xml.parsers.expat for the list of error codes and their meanings.

+
+position
+

A tuple of line, column numbers, specifying where the error occurred.

+
+

Footnotes

+1(1,2,3,4)
+

The encoding string included in XML output should conform to the appropriate standards. For example, “UTF-8” is valid, but “UTF8” is not. See https://www.w3.org/TR/2006/REC-xml11-20060816/#NT-EncodingDecl and https://www.iana.org/assignments/character-sets/character-sets.xhtml.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.etree.elementtree.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.html b/devdocs/python~3.12/library%2Fxml.html new file mode 100644 index 00000000..3db9b4c2 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.html @@ -0,0 +1,32 @@ +

XML Processing Modules

Source code: Lib/xml/

Python’s interfaces for processing XML are grouped in the xml package.

Warning

The XML modules are not secure against erroneous or maliciously constructed data. If you need to parse untrusted or unauthenticated data see the XML vulnerabilities and The defusedxml Package sections.

It is important to note that modules in the xml package require that there be at least one SAX-compliant XML parser available. The Expat parser is included with Python, so the xml.parsers.expat module will always be available.

The documentation for the xml.dom and xml.sax packages are the definition of the Python bindings for the DOM and SAX interfaces.

The XML handling submodules are:

XML vulnerabilities

The XML processing modules are not secure against maliciously constructed data. An attacker can abuse XML features to carry out denial of service attacks, access local files, generate network connections to other machines, or circumvent firewalls.

The following table gives an overview of the known attacks and whether the various modules are vulnerable to them.

+ + + + + +

kind

sax

etree

minidom

pulldom

xmlrpc

billion laughs

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

quadratic blowup

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

Vulnerable (1)

external entity expansion

Safe (5)

Safe (2)

Safe (3)

Safe (5)

Safe (4)

DTD retrieval

Safe (5)

Safe

Safe

Safe (5)

Safe

decompression bomb

Safe

Safe

Safe

Safe

Vulnerable
  1. Expat 2.4.1 and newer is not vulnerable to the “billion laughs” and “quadratic blowup” vulnerabilities. Items still listed as vulnerable due to potential reliance on system-provided libraries. Check pyexpat.EXPAT_VERSION.
  2. +xml.etree.ElementTree doesn’t expand external entities and raises a ParseError when an entity occurs.
  3. +xml.dom.minidom doesn’t expand external entities and simply returns the unexpanded entity verbatim.
  4. +xmlrpc.client doesn’t expand external entities and omits them.
  5. Since Python 3.7.1, external general entities are no longer processed by default.
billion laughs / exponential entity expansion
+
+

The Billion Laughs attack – also known as exponential entity expansion – uses multiple levels of nested entities. Each entity refers to another entity several times, and the final entity definition contains a small string. The exponential expansion results in several gigabytes of text and consumes lots of memory and CPU time.

quadratic blowup entity expansion
+
+

A quadratic blowup attack is similar to a Billion Laughs attack; it abuses entity expansion, too. Instead of nested entities it repeats one large entity with a couple of thousand chars over and over again. The attack isn’t as efficient as the exponential case but it avoids triggering parser countermeasures that forbid deeply nested entities.

external entity expansion
+
+

Entity declarations can contain more than just text for replacement. They can also point to external resources or local files. The XML parser accesses the resource and embeds the content into the XML document.

+DTD retrieval
+
+

Some XML libraries like Python’s xml.dom.pulldom retrieve document type definitions from remote or local locations. The feature has similar implications as the external entity expansion issue.

decompression bomb
+
+

Decompression bombs (aka ZIP bomb) apply to all XML libraries that can parse compressed XML streams such as gzipped HTTP streams or LZMA-compressed files. For an attacker it can reduce the amount of transmitted data by three magnitudes or more.

The documentation for defusedxml on PyPI has further information about all known attack vectors with examples and references.

The defusedxml Package

defusedxml is a pure Python package with modified subclasses of all stdlib XML parsers that prevent any potentially malicious operation. Use of this package is recommended for any server code that parses untrusted XML data. The package also ships with example exploits and extended documentation on more XML exploits such as XPath injection.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.sax.handler.html b/devdocs/python~3.12/library%2Fxml.sax.handler.html new file mode 100644 index 00000000..78ed491d --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.sax.handler.html @@ -0,0 +1,120 @@ +

xml.sax.handler — Base classes for SAX handlers

Source code: Lib/xml/sax/handler.py

The SAX API defines five kinds of handlers: content handlers, DTD handlers, error handlers, entity resolvers and lexical handlers. Applications normally only need to implement those interfaces whose events they are interested in; they can implement the interfaces in a single object or in multiple objects. Handler implementations should inherit from the base classes provided in the module xml.sax.handler, so that all methods get default implementations.

+class xml.sax.handler.ContentHandler
+

This is the main callback interface in SAX, and the one most important to applications. The order of events in this interface mirrors the order of the information in the document.

+
+class xml.sax.handler.DTDHandler
+

Handle DTD events.

This interface specifies only those DTD events required for basic parsing (unparsed entities and attributes).

+
+class xml.sax.handler.EntityResolver
+

Basic interface for resolving entities. If you create an object implementing this interface, then register the object with your Parser, the parser will call the method in your object to resolve all external entities.

+
+class xml.sax.handler.ErrorHandler
+

Interface used by the parser to present error and warning messages to the application. The methods of this object control whether errors are immediately converted to exceptions or are handled in some other way.

+
+class xml.sax.handler.LexicalHandler
+

Interface used by the parser to represent low frequency events which may not be of interest to many applications.

+

In addition to these classes, xml.sax.handler provides symbolic constants for the feature and property names.

+xml.sax.handler.feature_namespaces
+
+xml.sax.handler.feature_namespace_prefixes
+
+xml.sax.handler.feature_string_interning
+
+xml.sax.handler.feature_validation
+
+xml.sax.handler.feature_external_ges
+
+xml.sax.handler.feature_external_pes
+
+xml.sax.handler.all_features
+

List of all features.

+
+xml.sax.handler.property_lexical_handler
+
+xml.sax.handler.property_declaration_handler
+
+xml.sax.handler.property_dom_node
+
+xml.sax.handler.property_xml_string
+
+xml.sax.handler.all_properties
+

List of all known property names.

+

ContentHandler Objects

Users are expected to subclass ContentHandler to support their application. The following methods are called by the parser on the appropriate events in the input document:

+ContentHandler.setDocumentLocator(locator)
+

Called by the parser to give the application a locator for locating the origin of document events.

SAX parsers are strongly encouraged (though not absolutely required) to supply a locator: if it does so, it must supply the locator to the application by invoking this method before invoking any of the other methods in the DocumentHandler interface.

The locator allows the application to determine the end position of any document-related event, even if the parser is not reporting an error. Typically, the application will use this information for reporting its own errors (such as character content that does not match an application’s business rules). The information returned by the locator is probably not sufficient for use with a search engine.

Note that the locator will return correct information only during the invocation of the events in this interface. The application should not attempt to use it at any other time.

+
+ContentHandler.startDocument()
+

Receive notification of the beginning of a document.

The SAX parser will invoke this method only once, before any other methods in this interface or in DTDHandler (except for setDocumentLocator()).

+
+ContentHandler.endDocument()
+

Receive notification of the end of a document.

The SAX parser will invoke this method only once, and it will be the last method invoked during the parse. The parser shall not invoke this method until it has either abandoned parsing (because of an unrecoverable error) or reached the end of input.

+
+ContentHandler.startPrefixMapping(prefix, uri)
+

Begin the scope of a prefix-URI Namespace mapping.

The information from this event is not necessary for normal Namespace processing: the SAX XML reader will automatically replace prefixes for element and attribute names when the feature_namespaces feature is enabled (the default).

There are cases, however, when applications need to use prefixes in character data or in attribute values, where they cannot safely be expanded automatically; the startPrefixMapping() and endPrefixMapping() events supply the information to the application to expand prefixes in those contexts itself, if necessary.

Note that startPrefixMapping() and endPrefixMapping() events are not guaranteed to be properly nested relative to each-other: all startPrefixMapping() events will occur before the corresponding startElement() event, and all endPrefixMapping() events will occur after the corresponding endElement() event, but their order is not guaranteed.

+
+ContentHandler.endPrefixMapping(prefix)
+

End the scope of a prefix-URI mapping.

See startPrefixMapping() for details. This event will always occur after the corresponding endElement() event, but the order of endPrefixMapping() events is not otherwise guaranteed.

+
+ContentHandler.startElement(name, attrs)
+

Signals the start of an element in non-namespace mode.

The name parameter contains the raw XML 1.0 name of the element type as a string and the attrs parameter holds an object of the Attributes interface (see The Attributes Interface) containing the attributes of the element. The object passed as attrs may be re-used by the parser; holding on to a reference to it is not a reliable way to keep a copy of the attributes. To keep a copy of the attributes, use the copy() method of the attrs object.

+
+ContentHandler.endElement(name)
+

Signals the end of an element in non-namespace mode.

The name parameter contains the name of the element type, just as with the startElement() event.

+
+ContentHandler.startElementNS(name, qname, attrs)
+

Signals the start of an element in namespace mode.

The name parameter contains the name of the element type as a (uri, +localname) tuple, the qname parameter contains the raw XML 1.0 name used in the source document, and the attrs parameter holds an instance of the AttributesNS interface (see The AttributesNS Interface) containing the attributes of the element. If no namespace is associated with the element, the uri component of name will be None. The object passed as attrs may be re-used by the parser; holding on to a reference to it is not a reliable way to keep a copy of the attributes. To keep a copy of the attributes, use the copy() method of the attrs object.

Parsers may set the qname parameter to None, unless the feature_namespace_prefixes feature is activated.

+
+ContentHandler.endElementNS(name, qname)
+

Signals the end of an element in namespace mode.

The name parameter contains the name of the element type, just as with the startElementNS() method, likewise the qname parameter.

+
+ContentHandler.characters(content)
+

Receive notification of character data.

The Parser will call this method to report each chunk of character data. SAX parsers may return all contiguous character data in a single chunk, or they may split it into several chunks; however, all of the characters in any single event must come from the same external entity so that the Locator provides useful information.

content may be a string or bytes instance; the expat reader module always produces strings.

Note

The earlier SAX 1 interface provided by the Python XML Special Interest Group used a more Java-like interface for this method. Since most parsers used from Python did not take advantage of the older interface, the simpler signature was chosen to replace it. To convert old code to the new interface, use content instead of slicing content with the old offset and length parameters.

+
+ContentHandler.ignorableWhitespace(whitespace)
+

Receive notification of ignorable whitespace in element content.

Validating Parsers must use this method to report each chunk of ignorable whitespace (see the W3C XML 1.0 recommendation, section 2.10): non-validating parsers may also use this method if they are capable of parsing and using content models.

SAX parsers may return all contiguous whitespace in a single chunk, or they may split it into several chunks; however, all of the characters in any single event must come from the same external entity, so that the Locator provides useful information.

+
+ContentHandler.processingInstruction(target, data)
+

Receive notification of a processing instruction.

The Parser will invoke this method once for each processing instruction found: note that processing instructions may occur before or after the main document element.

A SAX parser should never report an XML declaration (XML 1.0, section 2.8) or a text declaration (XML 1.0, section 4.3.1) using this method.

+
+ContentHandler.skippedEntity(name)
+

Receive notification of a skipped entity.

The Parser will invoke this method once for each entity skipped. Non-validating processors may skip entities if they have not seen the declarations (because, for example, the entity was declared in an external DTD subset). All processors may skip external entities, depending on the values of the feature_external_ges and the feature_external_pes properties.

+

DTDHandler Objects

DTDHandler instances provide the following methods:

+DTDHandler.notationDecl(name, publicId, systemId)
+

Handle a notation declaration event.

+
+DTDHandler.unparsedEntityDecl(name, publicId, systemId, ndata)
+

Handle an unparsed entity declaration event.

+

EntityResolver Objects

+EntityResolver.resolveEntity(publicId, systemId)
+

Resolve the system identifier of an entity and return either the system identifier to read from as a string, or an InputSource to read from. The default implementation returns systemId.

+

ErrorHandler Objects

Objects with this interface are used to receive error and warning information from the XMLReader. If you create an object that implements this interface, then register the object with your XMLReader, the parser will call the methods in your object to report all warnings and errors. There are three levels of errors available: warnings, (possibly) recoverable errors, and unrecoverable errors. All methods take a SAXParseException as the only parameter. Errors and warnings may be converted to an exception by raising the passed-in exception object.

+ErrorHandler.error(exception)
+

Called when the parser encounters a recoverable error. If this method does not raise an exception, parsing may continue, but further document information should not be expected by the application. Allowing the parser to continue may allow additional errors to be discovered in the input document.

+
+ErrorHandler.fatalError(exception)
+

Called when the parser encounters an error it cannot recover from; parsing is expected to terminate when this method returns.

+
+ErrorHandler.warning(exception)
+

Called when the parser presents minor warning information to the application. Parsing is expected to continue when this method returns, and document information will continue to be passed to the application. Raising an exception in this method will cause parsing to end.

+

LexicalHandler Objects

Optional SAX2 handler for lexical events.

This handler is used to obtain lexical information about an XML document. Lexical information includes information describing the document encoding used and XML comments embedded in the document, as well as section boundaries for the DTD and for any CDATA sections. The lexical handlers are used in the same manner as content handlers.

Set the LexicalHandler of an XMLReader by using the setProperty method with the property identifier 'http://xml.org/sax/properties/lexical-handler'.

+LexicalHandler.comment(content)
+

Reports a comment anywhere in the document (including the DTD and outside the document element).

+
+LexicalHandler.startDTD(name, public_id, system_id)
+

Reports the start of the DTD declarations if the document has an associated DTD.

+
+LexicalHandler.endDTD()
+

Reports the end of DTD declaration.

+
+LexicalHandler.startCDATA()
+

Reports the start of a CDATA marked section.

The contents of the CDATA marked section will be reported through the characters handler.

+
+LexicalHandler.endCDATA()
+

Reports the end of a CDATA marked section.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.sax.handler.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.sax.html b/devdocs/python~3.12/library%2Fxml.sax.html new file mode 100644 index 00000000..efefeecf --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.sax.html @@ -0,0 +1,47 @@ +

xml.sax — Support for SAX2 parsers

Source code: Lib/xml/sax/__init__.py

The xml.sax package provides a number of modules which implement the Simple API for XML (SAX) interface for Python. The package itself provides the SAX exceptions and the convenience functions which will be most used by users of the SAX API.

Warning

The xml.sax module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

Changed in version 3.7.1: The SAX parser no longer processes general external entities by default to increase security. Before, the parser created network connections to fetch remote files or loaded local files from the file system for DTD and entities. The feature can be enabled again with method setFeature() on the parser object and argument feature_external_ges.

The convenience functions are:

+xml.sax.make_parser(parser_list=[])
+

Create and return a SAX XMLReader object. The first parser found will be used. If parser_list is provided, it must be an iterable of strings which name modules that have a function named create_parser(). Modules listed in parser_list will be used before modules in the default list of parsers.

Changed in version 3.8: The parser_list argument can be any iterable, not just a list.

+
+xml.sax.parse(filename_or_stream, handler, error_handler=handler.ErrorHandler())
+

Create a SAX parser and use it to parse a document. The document, passed in as filename_or_stream, can be a filename or a file object. The handler parameter needs to be a SAX ContentHandler instance. If error_handler is given, it must be a SAX ErrorHandler instance; if omitted, SAXParseException will be raised on all errors. There is no return value; all work must be done by the handler passed in.

+
+xml.sax.parseString(string, handler, error_handler=handler.ErrorHandler())
+

Similar to parse(), but parses from a buffer string received as a parameter. string must be a str instance or a bytes-like object.

Changed in version 3.5: Added support of str instances.

+

A typical SAX application uses three kinds of objects: readers, handlers and input sources. “Reader” in this context is another term for parser, i.e. some piece of code that reads the bytes or characters from the input source, and produces a sequence of events. The events then get distributed to the handler objects, i.e. the reader invokes a method on the handler. A SAX application must therefore obtain a reader object, create or open the input sources, create the handlers, and connect these objects all together. As the final step of preparation, the reader is called to parse the input. During parsing, methods on the handler objects are called based on structural and syntactic events from the input data.

For these objects, only the interfaces are relevant; they are normally not instantiated by the application itself. Since Python does not have an explicit notion of interface, they are formally introduced as classes, but applications may use implementations which do not inherit from the provided classes. The InputSource, Locator, Attributes, AttributesNS, and XMLReader interfaces are defined in the module xml.sax.xmlreader. The handler interfaces are defined in xml.sax.handler. For convenience, InputSource (which is often instantiated directly) and the handler classes are also available from xml.sax. These interfaces are described below.

In addition to these classes, xml.sax provides the following exception classes.

+exception xml.sax.SAXException(msg, exception=None)
+

Encapsulate an XML error or warning. This class can contain basic error or warning information from either the XML parser or the application: it can be subclassed to provide additional functionality or to add localization. Note that although the handlers defined in the ErrorHandler interface receive instances of this exception, it is not required to actually raise the exception — it is also useful as a container for information.

When instantiated, msg should be a human-readable description of the error. The optional exception parameter, if given, should be None or an exception that was caught by the parsing code and is being passed along as information.

This is the base class for the other SAX exception classes.

+
+exception xml.sax.SAXParseException(msg, exception, locator)
+

Subclass of SAXException raised on parse errors. Instances of this class are passed to the methods of the SAX ErrorHandler interface to provide information about the parse error. This class supports the SAX Locator interface as well as the SAXException interface.

+
+exception xml.sax.SAXNotRecognizedException(msg, exception=None)
+

Subclass of SAXException raised when a SAX XMLReader is confronted with an unrecognized feature or property. SAX applications and extensions may use this class for similar purposes.

+
+exception xml.sax.SAXNotSupportedException(msg, exception=None)
+

Subclass of SAXException raised when a SAX XMLReader is asked to enable a feature that is not supported, or to set a property to a value that the implementation does not support. SAX applications and extensions may use this class for similar purposes.

+

See also

SAX: The Simple API for XML
+
+

This site is the focal point for the definition of the SAX API. It provides a Java implementation and online documentation. Links to implementations and historical information are also available.

+Module xml.sax.handler +
+
+

Definitions of the interfaces for application-provided objects.

+Module xml.sax.saxutils +
+
+

Convenience functions for use in SAX applications.

+Module xml.sax.xmlreader +
+
+

Definitions of the interfaces for parser-provided objects.

SAXException Objects

The SAXException exception class supports the following methods:

+SAXException.getMessage()
+

Return a human-readable message describing the error condition.

+
+SAXException.getException()
+

Return an encapsulated exception object, or None.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.sax.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.sax.reader.html b/devdocs/python~3.12/library%2Fxml.sax.reader.html new file mode 100644 index 00000000..b5d0ec57 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.sax.reader.html @@ -0,0 +1,141 @@ +

xml.sax.xmlreader — Interface for XML parsers

Source code: Lib/xml/sax/xmlreader.py

SAX parsers implement the XMLReader interface. They are implemented in a Python module, which must provide a function create_parser(). This function is invoked by xml.sax.make_parser() with no arguments to create a new parser object.

+class xml.sax.xmlreader.XMLReader
+

Base class which can be inherited by SAX parsers.

+
+class xml.sax.xmlreader.IncrementalParser
+

In some cases, it is desirable not to parse an input source at once, but to feed chunks of the document as they get available. Note that the reader will normally not read the entire file, but read it in chunks as well; still parse() won’t return until the entire document is processed. So these interfaces should be used if the blocking behaviour of parse() is not desirable.

When the parser is instantiated it is ready to begin accepting data from the feed method immediately. After parsing has been finished with a call to close the reset method must be called to make the parser ready to accept new data, either from feed or using the parse method.

Note that these methods must not be called during parsing, that is, after parse has been called and before it returns.

By default, the class also implements the parse method of the XMLReader interface using the feed, close and reset methods of the IncrementalParser interface as a convenience to SAX 2.0 driver writers.

+
+class xml.sax.xmlreader.Locator
+

Interface for associating a SAX event with a document location. A locator object will return valid results only during calls to DocumentHandler methods; at any other time, the results are unpredictable. If information is not available, methods may return None.

+
+class xml.sax.xmlreader.InputSource(system_id=None)
+

Encapsulation of the information needed by the XMLReader to read entities.

This class may include information about the public identifier, system identifier, byte stream (possibly with character encoding information) and/or the character stream of an entity.

Applications will create objects of this class for use in the XMLReader.parse() method and for returning from EntityResolver.resolveEntity.

An InputSource belongs to the application, the XMLReader is not allowed to modify InputSource objects passed to it from the application, although it may make copies and modify those.

+
+class xml.sax.xmlreader.AttributesImpl(attrs)
+

This is an implementation of the Attributes interface (see section The Attributes Interface). This is a dictionary-like object which represents the element attributes in a startElement() call. In addition to the most useful dictionary operations, it supports a number of other methods as described by the interface. Objects of this class should be instantiated by readers; attrs must be a dictionary-like object containing a mapping from attribute names to attribute values.

+
+class xml.sax.xmlreader.AttributesNSImpl(attrs, qnames)
+

Namespace-aware variant of AttributesImpl, which will be passed to startElementNS(). It is derived from AttributesImpl, but understands attribute names as two-tuples of namespaceURI and localname. In addition, it provides a number of methods expecting qualified names as they appear in the original document. This class implements the AttributesNS interface (see section The AttributesNS Interface).

+

XMLReader Objects

The XMLReader interface supports the following methods:

+XMLReader.parse(source)
+

Process an input source, producing SAX events. The source object can be a system identifier (a string identifying the input source – typically a file name or a URL), a pathlib.Path or path-like object, or an InputSource object. When parse() returns, the input is completely processed, and the parser object can be discarded or reset.

Changed in version 3.5: Added support of character streams.

Changed in version 3.8: Added support of path-like objects.

+
+XMLReader.getContentHandler()
+

Return the current ContentHandler.

+
+XMLReader.setContentHandler(handler)
+

Set the current ContentHandler. If no ContentHandler is set, content events will be discarded.

+
+XMLReader.getDTDHandler()
+

Return the current DTDHandler.

+
+XMLReader.setDTDHandler(handler)
+

Set the current DTDHandler. If no DTDHandler is set, DTD events will be discarded.

+
+XMLReader.getEntityResolver()
+

Return the current EntityResolver.

+
+XMLReader.setEntityResolver(handler)
+

Set the current EntityResolver. If no EntityResolver is set, attempts to resolve an external entity will result in opening the system identifier for the entity, and fail if it is not available.

+
+XMLReader.getErrorHandler()
+

Return the current ErrorHandler.

+
+XMLReader.setErrorHandler(handler)
+

Set the current error handler. If no ErrorHandler is set, errors will be raised as exceptions, and warnings will be printed.

+
+XMLReader.setLocale(locale)
+

Allow an application to set the locale for errors and warnings.

SAX parsers are not required to provide localization for errors and warnings; if they cannot support the requested locale, however, they must raise a SAX exception. Applications may request a locale change in the middle of a parse.

+
+XMLReader.getFeature(featurename)
+

Return the current setting for feature featurename. If the feature is not recognized, SAXNotRecognizedException is raised. The well-known featurenames are listed in the module xml.sax.handler.

+
+XMLReader.setFeature(featurename, value)
+

Set the featurename to value. If the feature is not recognized, SAXNotRecognizedException is raised. If the feature or its setting is not supported by the parser, SAXNotSupportedException is raised.

+
+XMLReader.getProperty(propertyname)
+

Return the current setting for property propertyname. If the property is not recognized, a SAXNotRecognizedException is raised. The well-known propertynames are listed in the module xml.sax.handler.

+
+XMLReader.setProperty(propertyname, value)
+

Set the propertyname to value. If the property is not recognized, SAXNotRecognizedException is raised. If the property or its setting is not supported by the parser, SAXNotSupportedException is raised.

+

IncrementalParser Objects

Instances of IncrementalParser offer the following additional methods:

+IncrementalParser.feed(data)
+

Process a chunk of data.

+
+IncrementalParser.close()
+

Assume the end of the document. That will check well-formedness conditions that can be checked only at the end, invoke handlers, and may clean up resources allocated during parsing.

+
+IncrementalParser.reset()
+

This method is called after close has been called to reset the parser so that it is ready to parse new documents. The results of calling parse or feed after close without calling reset are undefined.

+

Locator Objects

Instances of Locator provide these methods:

+Locator.getColumnNumber()
+

Return the column number where the current event begins.

+
+Locator.getLineNumber()
+

Return the line number where the current event begins.

+
+Locator.getPublicId()
+

Return the public identifier for the current event.

+
+Locator.getSystemId()
+

Return the system identifier for the current event.

+

InputSource Objects

+InputSource.setPublicId(id)
+

Sets the public identifier of this InputSource.

+
+InputSource.getPublicId()
+

Returns the public identifier of this InputSource.

+
+InputSource.setSystemId(id)
+

Sets the system identifier of this InputSource.

+
+InputSource.getSystemId()
+

Returns the system identifier of this InputSource.

+
+InputSource.setEncoding(encoding)
+

Sets the character encoding of this InputSource.

The encoding must be a string acceptable for an XML encoding declaration (see section 4.3.3 of the XML recommendation).

The encoding attribute of the InputSource is ignored if the InputSource also contains a character stream.

+
+InputSource.getEncoding()
+

Get the character encoding of this InputSource.

+
+InputSource.setByteStream(bytefile)
+

Set the byte stream (a binary file) for this input source.

The SAX parser will ignore this if there is also a character stream specified, but it will use a byte stream in preference to opening a URI connection itself.

If the application knows the character encoding of the byte stream, it should set it with the setEncoding method.

+
+InputSource.getByteStream()
+

Get the byte stream for this input source.

The getEncoding method will return the character encoding for this byte stream, or None if unknown.

+
+InputSource.setCharacterStream(charfile)
+

Set the character stream (a text file) for this input source.

If there is a character stream specified, the SAX parser will ignore any byte stream and will not attempt to open a URI connection to the system identifier.

+
+InputSource.getCharacterStream()
+

Get the character stream for this input source.

+

The Attributes Interface

Attributes objects implement a portion of the mapping protocol, including the methods copy(), get(), __contains__(), items(), keys(), and values(). The following methods are also provided:

+Attributes.getLength()
+

Return the number of attributes.

+
+Attributes.getNames()
+

Return the names of the attributes.

+
+Attributes.getType(name)
+

Returns the type of the attribute name, which is normally 'CDATA'.

+
+Attributes.getValue(name)
+

Return the value of attribute name.

+

The AttributesNS Interface

This interface is a subtype of the Attributes interface (see section The Attributes Interface). All methods supported by that interface are also available on AttributesNS objects.

The following methods are also available:

+AttributesNS.getValueByQName(name)
+

Return the value for a qualified name.

+
+AttributesNS.getNameByQName(name)
+

Return the (namespace, localname) pair for a qualified name.

+
+AttributesNS.getQNameByName(name)
+

Return the qualified name for a (namespace, localname) pair.

+
+AttributesNS.getQNames()
+

Return the qualified names of all attributes.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.sax.reader.html +

+
diff --git a/devdocs/python~3.12/library%2Fxml.sax.utils.html b/devdocs/python~3.12/library%2Fxml.sax.utils.html new file mode 100644 index 00000000..f438507b --- /dev/null +++ b/devdocs/python~3.12/library%2Fxml.sax.utils.html @@ -0,0 +1,26 @@ +

xml.sax.saxutils — SAX Utilities

Source code: Lib/xml/sax/saxutils.py

The module xml.sax.saxutils contains a number of classes and functions that are commonly useful when creating SAX applications, either in direct use, or as base classes.

+xml.sax.saxutils.escape(data, entities={})
+

Escape '&', '<', and '>' in a string of data.

You can escape other strings of data by passing a dictionary as the optional entities parameter. The keys and values must all be strings; each key will be replaced with its corresponding value. The characters '&', '<' and '>' are always escaped, even if entities is provided.

Note

This function should only be used to escape characters that can’t be used directly in XML. Do not use this function as a general string translation function.

+
+xml.sax.saxutils.unescape(data, entities={})
+

Unescape '&amp;', '&lt;', and '&gt;' in a string of data.

You can unescape other strings of data by passing a dictionary as the optional entities parameter. The keys and values must all be strings; each key will be replaced with its corresponding value. '&amp', '&lt;', and '&gt;' are always unescaped, even if entities is provided.

+
+xml.sax.saxutils.quoteattr(data, entities={})
+

Similar to escape(), but also prepares data to be used as an attribute value. The return value is a quoted version of data with any additional required replacements. quoteattr() will select a quote character based on the content of data, attempting to avoid encoding any quote characters in the string. If both single- and double-quote characters are already in data, the double-quote characters will be encoded and data will be wrapped in double-quotes. The resulting string can be used directly as an attribute value:

>>> print("<element attr=%s>" % quoteattr("ab ' cd \" ef"))
+<element attr="ab ' cd &quot; ef">
+

This function is useful when generating attribute values for HTML or any SGML using the reference concrete syntax.

+
+class xml.sax.saxutils.XMLGenerator(out=None, encoding='iso-8859-1', short_empty_elements=False)
+

This class implements the ContentHandler interface by writing SAX events back into an XML document. In other words, using an XMLGenerator as the content handler will reproduce the original document being parsed. out should be a file-like object which will default to sys.stdout. encoding is the encoding of the output stream which defaults to 'iso-8859-1'. short_empty_elements controls the formatting of elements that contain no content: if False (the default) they are emitted as a pair of start/end tags, if set to True they are emitted as a single self-closed tag.

New in version 3.2: The short_empty_elements parameter.

+
+class xml.sax.saxutils.XMLFilterBase(base)
+

This class is designed to sit between an XMLReader and the client application’s event handlers. By default, it does nothing but pass requests up to the reader and events on to the handlers unmodified, but subclasses can override specific methods to modify the event stream or the configuration requests as they pass through.

+
+xml.sax.saxutils.prepare_input_source(source, base='')
+

This function takes an input source and an optional base URL and returns a fully resolved InputSource object ready for reading. The input source can be given as a string, a file-like object, or an InputSource object; parsers will use this function to implement the polymorphic source argument to their parse() method.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xml.sax.utils.html +

+
diff --git a/devdocs/python~3.12/library%2Fxmlrpc.client.html b/devdocs/python~3.12/library%2Fxmlrpc.client.html new file mode 100644 index 00000000..74e7e565 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxmlrpc.client.html @@ -0,0 +1,243 @@ +

xmlrpc.client — XML-RPC client access

Source code: Lib/xmlrpc/client.py

XML-RPC is a Remote Procedure Call method that uses XML passed via HTTP(S) as a transport. With it, a client can call methods with parameters on a remote server (the server is named by a URI) and get back structured data. This module supports writing XML-RPC client code; it handles all the details of translating between conformable Python objects and XML on the wire.

Warning

The xmlrpc.client module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

Changed in version 3.5: For HTTPS URIs, xmlrpc.client now performs all the necessary certificate and hostname checks by default.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

+class xmlrpc.client.ServerProxy(uri, transport=None, encoding=None, verbose=False, allow_none=False, use_datetime=False, use_builtin_types=False, *, headers=(), context=None)
+

A ServerProxy instance is an object that manages communication with a remote XML-RPC server. The required first argument is a URI (Uniform Resource Indicator), and will normally be the URL of the server. The optional second argument is a transport factory instance; by default it is an internal SafeTransport instance for https: URLs and an internal HTTP Transport instance otherwise. The optional third argument is an encoding, by default UTF-8. The optional fourth argument is a debugging flag.

The following parameters govern the use of the returned proxy instance. If allow_none is true, the Python constant None will be translated into XML; the default behaviour is for None to raise a TypeError. This is a commonly used extension to the XML-RPC specification, but isn’t supported by all clients and servers; see http://ontosys.com/xml-rpc/extensions.php for a description. The use_builtin_types flag can be used to cause date/time values to be presented as datetime.datetime objects and binary data to be presented as bytes objects; this flag is false by default. datetime.datetime, bytes and bytearray objects may be passed to calls. The headers parameter is an optional sequence of HTTP headers to send with each request, expressed as a sequence of 2-tuples representing the header name and value. (e.g. [('Header-Name', 'value')]). The obsolete use_datetime flag is similar to use_builtin_types but it applies only to date/time values.

+

Changed in version 3.3: The use_builtin_types flag was added.

Changed in version 3.8: The headers parameter was added.

Both the HTTP and HTTPS transports support the URL syntax extension for HTTP Basic Authentication: http://user:pass@host:port/path. The user:pass portion will be base64-encoded as an HTTP ‘Authorization’ header, and sent to the remote server as part of the connection process when invoking an XML-RPC method. You only need to use this if the remote server requires a Basic Authentication user and password. If an HTTPS URL is provided, context may be ssl.SSLContext and configures the SSL settings of the underlying HTTPS connection.

The returned instance is a proxy object with methods that can be used to invoke corresponding RPC calls on the remote server. If the remote server supports the introspection API, the proxy can also be used to query the remote server for the methods it supports (service discovery) and fetch other server-associated metadata.

Types that are conformable (e.g. that can be marshalled through XML), include the following (and except where noted, they are unmarshalled as the same Python type):

+ + + + + + + + + + +

XML-RPC type

Python type

boolean

bool

int, i1, i2, i4, i8 or biginteger

int in range from -2147483648 to 2147483647. Values get the <int> tag.

double or float

float. Values get the <double> tag.

string

str

array

list or tuple containing conformable elements. Arrays are returned as lists.

struct

dict. Keys must be strings, values may be any conformable type. Objects of user-defined classes can be passed in; only their __dict__ attribute is transmitted.

dateTime.iso8601

DateTime or datetime.datetime. Returned type depends on values of use_builtin_types and use_datetime flags.

base64

Binary, bytes or bytearray. Returned type depends on the value of the use_builtin_types flag.

nil

The None constant. Passing is allowed only if allow_none is true.

bigdecimal

decimal.Decimal. Returned type only.

This is the full set of data types supported by XML-RPC. Method calls may also raise a special Fault instance, used to signal XML-RPC server errors, or ProtocolError used to signal an error in the HTTP/HTTPS transport layer. Both Fault and ProtocolError derive from a base class called Error. Note that the xmlrpc client module currently does not marshal instances of subclasses of built-in types.

When passing strings, characters special to XML such as <, >, and & will be automatically escaped. However, it’s the caller’s responsibility to ensure that the string is free of characters that aren’t allowed in XML, such as the control characters with ASCII values between 0 and 31 (except, of course, tab, newline and carriage return); failing to do this will result in an XML-RPC request that isn’t well-formed XML. If you have to pass arbitrary bytes via XML-RPC, use bytes or bytearray classes or the Binary wrapper class described below.

Server is retained as an alias for ServerProxy for backwards compatibility. New code should use ServerProxy.

Changed in version 3.5: Added the context argument.

Changed in version 3.6: Added support of type tags with prefixes (e.g. ex:nil). Added support of unmarshalling additional types used by Apache XML-RPC implementation for numerics: i1, i2, i8, biginteger, float and bigdecimal. See https://ws.apache.org/xmlrpc/types.html for a description.

See also

XML-RPC HOWTO
+
+

A good description of XML-RPC operation and client software in several languages. Contains pretty much everything an XML-RPC client developer needs to know.

XML-RPC Introspection
+
+

Describes the XML-RPC protocol extension for introspection.

XML-RPC Specification
+
+

The official specification.

ServerProxy Objects

A ServerProxy instance has a method corresponding to each remote procedure call accepted by the XML-RPC server. Calling the method performs an RPC, dispatched by both name and argument signature (e.g. the same method name can be overloaded with multiple argument signatures). The RPC finishes by returning a value, which may be either returned data in a conformant type or a Fault or ProtocolError object indicating an error.

Servers that support the XML introspection API support some common methods grouped under the reserved system attribute:

+ServerProxy.system.listMethods()
+

This method returns a list of strings, one for each (non-system) method supported by the XML-RPC server.

+
+ServerProxy.system.methodSignature(name)
+

This method takes one parameter, the name of a method implemented by the XML-RPC server. It returns an array of possible signatures for this method. A signature is an array of types. The first of these types is the return type of the method, the rest are parameters.

Because multiple signatures (ie. overloading) is permitted, this method returns a list of signatures rather than a singleton.

Signatures themselves are restricted to the top level parameters expected by a method. For instance if a method expects one array of structs as a parameter, and it returns a string, its signature is simply “string, array”. If it expects three integers and returns a string, its signature is “string, int, int, int”.

If no signature is defined for the method, a non-array value is returned. In Python this means that the type of the returned value will be something other than list.

+
+ServerProxy.system.methodHelp(name)
+

This method takes one parameter, the name of a method implemented by the XML-RPC server. It returns a documentation string describing the use of that method. If no such string is available, an empty string is returned. The documentation string may contain HTML markup.

+

Changed in version 3.5: Instances of ServerProxy support the context manager protocol for closing the underlying transport.

A working example follows. The server code:

from xmlrpc.server import SimpleXMLRPCServer
+
+def is_even(n):
+    return n % 2 == 0
+
+server = SimpleXMLRPCServer(("localhost", 8000))
+print("Listening on port 8000...")
+server.register_function(is_even, "is_even")
+server.serve_forever()
+

The client code for the preceding server:

import xmlrpc.client
+
+with xmlrpc.client.ServerProxy("http://localhost:8000/") as proxy:
+    print("3 is even: %s" % str(proxy.is_even(3)))
+    print("100 is even: %s" % str(proxy.is_even(100)))
+

DateTime Objects

+class xmlrpc.client.DateTime
+

This class may be initialized with seconds since the epoch, a time tuple, an ISO 8601 time/date string, or a datetime.datetime instance. It has the following methods, supported mainly for internal use by the marshalling/unmarshalling code:

+decode(string)
+

Accept a string as the instance’s new time value.

+
+encode(out)
+

Write the XML-RPC encoding of this DateTime item to the out stream object.

+

It also supports certain of Python’s built-in operators through rich comparison and __repr__() methods.

+

A working example follows. The server code:

import datetime
+from xmlrpc.server import SimpleXMLRPCServer
+import xmlrpc.client
+
+def today():
+    today = datetime.datetime.today()
+    return xmlrpc.client.DateTime(today)
+
+server = SimpleXMLRPCServer(("localhost", 8000))
+print("Listening on port 8000...")
+server.register_function(today, "today")
+server.serve_forever()
+

The client code for the preceding server:

import xmlrpc.client
+import datetime
+
+proxy = xmlrpc.client.ServerProxy("http://localhost:8000/")
+
+today = proxy.today()
+# convert the ISO8601 string to a datetime object
+converted = datetime.datetime.strptime(today.value, "%Y%m%dT%H:%M:%S")
+print("Today: %s" % converted.strftime("%d.%m.%Y, %H:%M"))
+

Binary Objects

+class xmlrpc.client.Binary
+

This class may be initialized from bytes data (which may include NULs). The primary access to the content of a Binary object is provided by an attribute:

+data
+

The binary data encapsulated by the Binary instance. The data is provided as a bytes object.

+

Binary objects have the following methods, supported mainly for internal use by the marshalling/unmarshalling code:

+decode(bytes)
+

Accept a base64 bytes object and decode it as the instance’s new data.

+
+encode(out)
+

Write the XML-RPC base 64 encoding of this binary item to the out stream object.

The encoded data will have newlines every 76 characters as per RFC 2045 section 6.8, which was the de facto standard base64 specification when the XML-RPC spec was written.

+

It also supports certain of Python’s built-in operators through __eq__() and __ne__() methods.

+

Example usage of the binary objects. We’re going to transfer an image over XMLRPC:

from xmlrpc.server import SimpleXMLRPCServer
+import xmlrpc.client
+
+def python_logo():
+    with open("python_logo.jpg", "rb") as handle:
+        return xmlrpc.client.Binary(handle.read())
+
+server = SimpleXMLRPCServer(("localhost", 8000))
+print("Listening on port 8000...")
+server.register_function(python_logo, 'python_logo')
+
+server.serve_forever()
+

The client gets the image and saves it to a file:

import xmlrpc.client
+
+proxy = xmlrpc.client.ServerProxy("http://localhost:8000/")
+with open("fetched_python_logo.jpg", "wb") as handle:
+    handle.write(proxy.python_logo().data)
+

Fault Objects

+class xmlrpc.client.Fault
+

A Fault object encapsulates the content of an XML-RPC fault tag. Fault objects have the following attributes:

+faultCode
+

An int indicating the fault type.

+
+faultString
+

A string containing a diagnostic message associated with the fault.

+
+

In the following example we’re going to intentionally cause a Fault by returning a complex type object. The server code:

from xmlrpc.server import SimpleXMLRPCServer
+
+# A marshalling error is going to occur because we're returning a
+# complex number
+def add(x, y):
+    return x+y+0j
+
+server = SimpleXMLRPCServer(("localhost", 8000))
+print("Listening on port 8000...")
+server.register_function(add, 'add')
+
+server.serve_forever()
+

The client code for the preceding server:

import xmlrpc.client
+
+proxy = xmlrpc.client.ServerProxy("http://localhost:8000/")
+try:
+    proxy.add(2, 5)
+except xmlrpc.client.Fault as err:
+    print("A fault occurred")
+    print("Fault code: %d" % err.faultCode)
+    print("Fault string: %s" % err.faultString)
+

ProtocolError Objects

+class xmlrpc.client.ProtocolError
+

A ProtocolError object describes a protocol error in the underlying transport layer (such as a 404 ‘not found’ error if the server named by the URI does not exist). It has the following attributes:

+url
+

The URI or URL that triggered the error.

+
+errcode
+

The error code.

+
+errmsg
+

The error message or diagnostic string.

+
+headers
+

A dict containing the headers of the HTTP/HTTPS request that triggered the error.

+
+

In the following example we’re going to intentionally cause a ProtocolError by providing an invalid URI:

import xmlrpc.client
+
+# create a ServerProxy with a URI that doesn't respond to XMLRPC requests
+proxy = xmlrpc.client.ServerProxy("http://google.com/")
+
+try:
+    proxy.some_method()
+except xmlrpc.client.ProtocolError as err:
+    print("A protocol error occurred")
+    print("URL: %s" % err.url)
+    print("HTTP/HTTPS headers: %s" % err.headers)
+    print("Error code: %d" % err.errcode)
+    print("Error message: %s" % err.errmsg)
+

MultiCall Objects

The MultiCall object provides a way to encapsulate multiple calls to a remote server into a single request 1.

+class xmlrpc.client.MultiCall(server)
+

Create an object used to boxcar method calls. server is the eventual target of the call. Calls can be made to the result object, but they will immediately return None, and only store the call name and parameters in the MultiCall object. Calling the object itself causes all stored calls to be transmitted as a single system.multicall request. The result of this call is a generator; iterating over this generator yields the individual results.

+

A usage example of this class follows. The server code:

from xmlrpc.server import SimpleXMLRPCServer
+
+def add(x, y):
+    return x + y
+
+def subtract(x, y):
+    return x - y
+
+def multiply(x, y):
+    return x * y
+
+def divide(x, y):
+    return x // y
+
+# A simple server with simple arithmetic functions
+server = SimpleXMLRPCServer(("localhost", 8000))
+print("Listening on port 8000...")
+server.register_multicall_functions()
+server.register_function(add, 'add')
+server.register_function(subtract, 'subtract')
+server.register_function(multiply, 'multiply')
+server.register_function(divide, 'divide')
+server.serve_forever()
+

The client code for the preceding server:

import xmlrpc.client
+
+proxy = xmlrpc.client.ServerProxy("http://localhost:8000/")
+multicall = xmlrpc.client.MultiCall(proxy)
+multicall.add(7, 3)
+multicall.subtract(7, 3)
+multicall.multiply(7, 3)
+multicall.divide(7, 3)
+result = multicall()
+
+print("7+3=%d, 7-3=%d, 7*3=%d, 7//3=%d" % tuple(result))
+

Convenience Functions

+xmlrpc.client.dumps(params, methodname=None, methodresponse=None, encoding=None, allow_none=False)
+

Convert params into an XML-RPC request. or into a response if methodresponse is true. params can be either a tuple of arguments or an instance of the Fault exception class. If methodresponse is true, only a single value can be returned, meaning that params must be of length 1. encoding, if supplied, is the encoding to use in the generated XML; the default is UTF-8. Python’s None value cannot be used in standard XML-RPC; to allow using it via an extension, provide a true value for allow_none.

+
+xmlrpc.client.loads(data, use_datetime=False, use_builtin_types=False)
+

Convert an XML-RPC request or response into Python objects, a (params, +methodname). params is a tuple of argument; methodname is a string, or None if no method name is present in the packet. If the XML-RPC packet represents a fault condition, this function will raise a Fault exception. The use_builtin_types flag can be used to cause date/time values to be presented as datetime.datetime objects and binary data to be presented as bytes objects; this flag is false by default.

The obsolete use_datetime flag is similar to use_builtin_types but it applies only to date/time values.

Changed in version 3.3: The use_builtin_types flag was added.

+

Example of Client Usage

# simple test program (from the XML-RPC specification)
+from xmlrpc.client import ServerProxy, Error
+
+# server = ServerProxy("http://localhost:8000") # local server
+with ServerProxy("http://betty.userland.com") as proxy:
+
+    print(proxy)
+
+    try:
+        print(proxy.examples.getStateName(41))
+    except Error as v:
+        print("ERROR", v)
+

To access an XML-RPC server through a HTTP proxy, you need to define a custom transport. The following example shows how:

import http.client
+import xmlrpc.client
+
+class ProxiedTransport(xmlrpc.client.Transport):
+
+    def set_proxy(self, host, port=None, headers=None):
+        self.proxy = host, port
+        self.proxy_headers = headers
+
+    def make_connection(self, host):
+        connection = http.client.HTTPConnection(*self.proxy)
+        connection.set_tunnel(host, headers=self.proxy_headers)
+        self._connection = host, connection
+        return connection
+
+transport = ProxiedTransport()
+transport.set_proxy('proxy-server', 8080)
+server = xmlrpc.client.ServerProxy('http://betty.userland.com', transport=transport)
+print(server.examples.getStateName(41))
+

Example of Client and Server Usage

See SimpleXMLRPCServer Example.

Footnotes

+1
+

This approach has been first presented in a discussion on xmlrpc.com.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xmlrpc.client.html +

+
diff --git a/devdocs/python~3.12/library%2Fxmlrpc.html b/devdocs/python~3.12/library%2Fxmlrpc.html new file mode 100644 index 00000000..9adeb09e --- /dev/null +++ b/devdocs/python~3.12/library%2Fxmlrpc.html @@ -0,0 +1,6 @@ +

xmlrpc — XMLRPC server and client modules

XML-RPC is a Remote Procedure Call method that uses XML passed via HTTP as a transport. With it, a client can call methods with parameters on a remote server (the server is named by a URI) and get back structured data.

xmlrpc is a package that collects server and client modules implementing XML-RPC. The modules are:

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xmlrpc.html +

+
diff --git a/devdocs/python~3.12/library%2Fxmlrpc.server.html b/devdocs/python~3.12/library%2Fxmlrpc.server.html new file mode 100644 index 00000000..f1d4a478 --- /dev/null +++ b/devdocs/python~3.12/library%2Fxmlrpc.server.html @@ -0,0 +1,190 @@ +

xmlrpc.server — Basic XML-RPC servers

Source code: Lib/xmlrpc/server.py

The xmlrpc.server module provides a basic server framework for XML-RPC servers written in Python. Servers can either be free standing, using SimpleXMLRPCServer, or embedded in a CGI environment, using CGIXMLRPCRequestHandler.

Warning

The xmlrpc.server module is not secure against maliciously constructed data. If you need to parse untrusted or unauthenticated data see XML vulnerabilities.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

+class xmlrpc.server.SimpleXMLRPCServer(addr, requestHandler=SimpleXMLRPCRequestHandler, logRequests=True, allow_none=False, encoding=None, bind_and_activate=True, use_builtin_types=False)
+

Create a new server instance. This class provides methods for registration of functions that can be called by the XML-RPC protocol. The requestHandler parameter should be a factory for request handler instances; it defaults to SimpleXMLRPCRequestHandler. The addr and requestHandler parameters are passed to the socketserver.TCPServer constructor. If logRequests is true (the default), requests will be logged; setting this parameter to false will turn off logging. The allow_none and encoding parameters are passed on to xmlrpc.client and control the XML-RPC responses that will be returned from the server. The bind_and_activate parameter controls whether server_bind() and server_activate() are called immediately by the constructor; it defaults to true. Setting it to false allows code to manipulate the allow_reuse_address class variable before the address is bound. The use_builtin_types parameter is passed to the loads() function and controls which types are processed when date/times values or binary data are received; it defaults to false.

Changed in version 3.3: The use_builtin_types flag was added.

+
+class xmlrpc.server.CGIXMLRPCRequestHandler(allow_none=False, encoding=None, use_builtin_types=False)
+

Create a new instance to handle XML-RPC requests in a CGI environment. The allow_none and encoding parameters are passed on to xmlrpc.client and control the XML-RPC responses that will be returned from the server. The use_builtin_types parameter is passed to the loads() function and controls which types are processed when date/times values or binary data are received; it defaults to false.

Changed in version 3.3: The use_builtin_types flag was added.

+
+class xmlrpc.server.SimpleXMLRPCRequestHandler
+

Create a new request handler instance. This request handler supports POST requests and modifies logging so that the logRequests parameter to the SimpleXMLRPCServer constructor parameter is honored.

+

SimpleXMLRPCServer Objects

The SimpleXMLRPCServer class is based on socketserver.TCPServer and provides a means of creating simple, stand alone XML-RPC servers.

+SimpleXMLRPCServer.register_function(function=None, name=None)
+

Register a function that can respond to XML-RPC requests. If name is given, it will be the method name associated with function, otherwise function.__name__ will be used. name is a string, and may contain characters not legal in Python identifiers, including the period character.

This method can also be used as a decorator. When used as a decorator, name can only be given as a keyword argument to register function under name. If no name is given, function.__name__ will be used.

Changed in version 3.7: register_function() can be used as a decorator.

+
+SimpleXMLRPCServer.register_instance(instance, allow_dotted_names=False)
+

Register an object which is used to expose method names which have not been registered using register_function(). If instance contains a _dispatch() method, it is called with the requested method name and the parameters from the request. Its API is def _dispatch(self, method, params) (note that params does not represent a variable argument list). If it calls an underlying function to perform its task, that function is called as func(*params), expanding the parameter list. The return value from _dispatch() is returned to the client as the result. If instance does not have a _dispatch() method, it is searched for an attribute matching the name of the requested method.

If the optional allow_dotted_names argument is true and the instance does not have a _dispatch() method, then if the requested method name contains periods, each component of the method name is searched for individually, with the effect that a simple hierarchical search is performed. The value found from this search is then called with the parameters from the request, and the return value is passed back to the client.

Warning

Enabling the allow_dotted_names option allows intruders to access your module’s global variables and may allow intruders to execute arbitrary code on your machine. Only use this option on a secure, closed network.

+
+SimpleXMLRPCServer.register_introspection_functions()
+

Registers the XML-RPC introspection functions system.listMethods, system.methodHelp and system.methodSignature.

+
+SimpleXMLRPCServer.register_multicall_functions()
+

Registers the XML-RPC multicall function system.multicall.

+
+SimpleXMLRPCRequestHandler.rpc_paths
+

An attribute value that must be a tuple listing valid path portions of the URL for receiving XML-RPC requests. Requests posted to other paths will result in a 404 “no such page” HTTP error. If this tuple is empty, all paths will be considered valid. The default value is ('/', '/RPC2').

+

SimpleXMLRPCServer Example

Server code:

from xmlrpc.server import SimpleXMLRPCServer
+from xmlrpc.server import SimpleXMLRPCRequestHandler
+
+# Restrict to a particular path.
+class RequestHandler(SimpleXMLRPCRequestHandler):
+    rpc_paths = ('/RPC2',)
+
+# Create server
+with SimpleXMLRPCServer(('localhost', 8000),
+                        requestHandler=RequestHandler) as server:
+    server.register_introspection_functions()
+
+    # Register pow() function; this will use the value of
+    # pow.__name__ as the name, which is just 'pow'.
+    server.register_function(pow)
+
+    # Register a function under a different name
+    def adder_function(x, y):
+        return x + y
+    server.register_function(adder_function, 'add')
+
+    # Register an instance; all the methods of the instance are
+    # published as XML-RPC methods (in this case, just 'mul').
+    class MyFuncs:
+        def mul(self, x, y):
+            return x * y
+
+    server.register_instance(MyFuncs())
+
+    # Run the server's main loop
+    server.serve_forever()
+

The following client code will call the methods made available by the preceding server:

import xmlrpc.client
+
+s = xmlrpc.client.ServerProxy('http://localhost:8000')
+print(s.pow(2,3))  # Returns 2**3 = 8
+print(s.add(2,3))  # Returns 5
+print(s.mul(5,2))  # Returns 5*2 = 10
+
+# Print list of available methods
+print(s.system.listMethods())
+

register_function() can also be used as a decorator. The previous server example can register functions in a decorator way:

from xmlrpc.server import SimpleXMLRPCServer
+from xmlrpc.server import SimpleXMLRPCRequestHandler
+
+class RequestHandler(SimpleXMLRPCRequestHandler):
+    rpc_paths = ('/RPC2',)
+
+with SimpleXMLRPCServer(('localhost', 8000),
+                        requestHandler=RequestHandler) as server:
+    server.register_introspection_functions()
+
+    # Register pow() function; this will use the value of
+    # pow.__name__ as the name, which is just 'pow'.
+    server.register_function(pow)
+
+    # Register a function under a different name, using
+    # register_function as a decorator. *name* can only be given
+    # as a keyword argument.
+    @server.register_function(name='add')
+    def adder_function(x, y):
+        return x + y
+
+    # Register a function under function.__name__.
+    @server.register_function
+    def mul(x, y):
+        return x * y
+
+    server.serve_forever()
+

The following example included in the Lib/xmlrpc/server.py module shows a server allowing dotted names and registering a multicall function.

Warning

Enabling the allow_dotted_names option allows intruders to access your module’s global variables and may allow intruders to execute arbitrary code on your machine. Only use this example only within a secure, closed network.

import datetime
+
+class ExampleService:
+    def getData(self):
+        return '42'
+
+    class currentTime:
+        @staticmethod
+        def getCurrentTime():
+            return datetime.datetime.now()
+
+with SimpleXMLRPCServer(("localhost", 8000)) as server:
+    server.register_function(pow)
+    server.register_function(lambda x,y: x+y, 'add')
+    server.register_instance(ExampleService(), allow_dotted_names=True)
+    server.register_multicall_functions()
+    print('Serving XML-RPC on localhost port 8000')
+    try:
+        server.serve_forever()
+    except KeyboardInterrupt:
+        print("\nKeyboard interrupt received, exiting.")
+        sys.exit(0)
+

This ExampleService demo can be invoked from the command line:

python -m xmlrpc.server
+

The client that interacts with the above server is included in Lib/xmlrpc/client.py:

server = ServerProxy("http://localhost:8000")
+
+try:
+    print(server.currentTime.getCurrentTime())
+except Error as v:
+    print("ERROR", v)
+
+multi = MultiCall(server)
+multi.getData()
+multi.pow(2,9)
+multi.add(1,2)
+try:
+    for response in multi():
+        print(response)
+except Error as v:
+    print("ERROR", v)
+

This client which interacts with the demo XMLRPC server can be invoked as:

python -m xmlrpc.client
+

CGIXMLRPCRequestHandler

The CGIXMLRPCRequestHandler class can be used to handle XML-RPC requests sent to Python CGI scripts.

+CGIXMLRPCRequestHandler.register_function(function=None, name=None)
+

Register a function that can respond to XML-RPC requests. If name is given, it will be the method name associated with function, otherwise function.__name__ will be used. name is a string, and may contain characters not legal in Python identifiers, including the period character.

This method can also be used as a decorator. When used as a decorator, name can only be given as a keyword argument to register function under name. If no name is given, function.__name__ will be used.

Changed in version 3.7: register_function() can be used as a decorator.

+
+CGIXMLRPCRequestHandler.register_instance(instance)
+

Register an object which is used to expose method names which have not been registered using register_function(). If instance contains a _dispatch() method, it is called with the requested method name and the parameters from the request; the return value is returned to the client as the result. If instance does not have a _dispatch() method, it is searched for an attribute matching the name of the requested method; if the requested method name contains periods, each component of the method name is searched for individually, with the effect that a simple hierarchical search is performed. The value found from this search is then called with the parameters from the request, and the return value is passed back to the client.

+
+CGIXMLRPCRequestHandler.register_introspection_functions()
+

Register the XML-RPC introspection functions system.listMethods, system.methodHelp and system.methodSignature.

+
+CGIXMLRPCRequestHandler.register_multicall_functions()
+

Register the XML-RPC multicall function system.multicall.

+
+CGIXMLRPCRequestHandler.handle_request(request_text=None)
+

Handle an XML-RPC request. If request_text is given, it should be the POST data provided by the HTTP server, otherwise the contents of stdin will be used.

+

Example:

class MyFuncs:
+    def mul(self, x, y):
+        return x * y
+
+
+handler = CGIXMLRPCRequestHandler()
+handler.register_function(pow)
+handler.register_function(lambda x,y: x+y, 'add')
+handler.register_introspection_functions()
+handler.register_instance(MyFuncs())
+handler.handle_request()
+

Documenting XMLRPC server

These classes extend the above classes to serve HTML documentation in response to HTTP GET requests. Servers can either be free standing, using DocXMLRPCServer, or embedded in a CGI environment, using DocCGIXMLRPCRequestHandler.

+class xmlrpc.server.DocXMLRPCServer(addr, requestHandler=DocXMLRPCRequestHandler, logRequests=True, allow_none=False, encoding=None, bind_and_activate=True, use_builtin_types=True)
+

Create a new server instance. All parameters have the same meaning as for SimpleXMLRPCServer; requestHandler defaults to DocXMLRPCRequestHandler.

Changed in version 3.3: The use_builtin_types flag was added.

+
+class xmlrpc.server.DocCGIXMLRPCRequestHandler
+

Create a new instance to handle XML-RPC requests in a CGI environment.

+
+class xmlrpc.server.DocXMLRPCRequestHandler
+

Create a new request handler instance. This request handler supports XML-RPC POST requests, documentation GET requests, and modifies logging so that the logRequests parameter to the DocXMLRPCServer constructor parameter is honored.

+

DocXMLRPCServer Objects

The DocXMLRPCServer class is derived from SimpleXMLRPCServer and provides a means of creating self-documenting, stand alone XML-RPC servers. HTTP POST requests are handled as XML-RPC method calls. HTTP GET requests are handled by generating pydoc-style HTML documentation. This allows a server to provide its own web-based documentation.

+DocXMLRPCServer.set_server_title(server_title)
+

Set the title used in the generated HTML documentation. This title will be used inside the HTML “title” element.

+
+DocXMLRPCServer.set_server_name(server_name)
+

Set the name used in the generated HTML documentation. This name will appear at the top of the generated documentation inside a “h1” element.

+
+DocXMLRPCServer.set_server_documentation(server_documentation)
+

Set the description used in the generated HTML documentation. This description will appear as a paragraph, below the server name, in the documentation.

+

DocCGIXMLRPCRequestHandler

The DocCGIXMLRPCRequestHandler class is derived from CGIXMLRPCRequestHandler and provides a means of creating self-documenting, XML-RPC CGI scripts. HTTP POST requests are handled as XML-RPC method calls. HTTP GET requests are handled by generating pydoc-style HTML documentation. This allows a server to provide its own web-based documentation.

+DocCGIXMLRPCRequestHandler.set_server_title(server_title)
+

Set the title used in the generated HTML documentation. This title will be used inside the HTML “title” element.

+
+DocCGIXMLRPCRequestHandler.set_server_name(server_name)
+

Set the name used in the generated HTML documentation. This name will appear at the top of the generated documentation inside a “h1” element.

+
+DocCGIXMLRPCRequestHandler.set_server_documentation(server_documentation)
+

Set the description used in the generated HTML documentation. This description will appear as a paragraph, below the server name, in the documentation.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/xmlrpc.server.html +

+
diff --git a/devdocs/python~3.12/library%2Fzipapp.html b/devdocs/python~3.12/library%2Fzipapp.html new file mode 100644 index 00000000..6574d8fb --- /dev/null +++ b/devdocs/python~3.12/library%2Fzipapp.html @@ -0,0 +1,54 @@ +

zipapp — Manage executable Python zip archives

New in version 3.5.

Source code: Lib/zipapp.py

This module provides tools to manage the creation of zip files containing Python code, which can be executed directly by the Python interpreter. The module provides both a Command-Line Interface and a Python API.

Basic Example

The following example shows how the Command-Line Interface can be used to create an executable archive from a directory containing Python code. When run, the archive will execute the main function from the module myapp in the archive.

$ python -m zipapp myapp -m "myapp:main"
+$ python myapp.pyz
+<output from myapp>
+

Command-Line Interface

When called as a program from the command line, the following form is used:

$ python -m zipapp source [options]
+

If source is a directory, this will create an archive from the contents of source. If source is a file, it should be an archive, and it will be copied to the target archive (or the contents of its shebang line will be displayed if the –info option is specified).

The following options are understood:

+-o <output>, --output=<output>
+

Write the output to a file named output. If this option is not specified, the output filename will be the same as the input source, with the extension .pyz added. If an explicit filename is given, it is used as is (so a .pyz extension should be included if required).

An output filename must be specified if the source is an archive (and in that case, output must not be the same as source).

+
+-p <interpreter>, --python=<interpreter>
+

Add a #! line to the archive specifying interpreter as the command to run. Also, on POSIX, make the archive executable. The default is to write no #! line, and not make the file executable.

+
+-m <mainfn>, --main=<mainfn>
+

Write a __main__.py file to the archive that executes mainfn. The mainfn argument should have the form “pkg.mod:fn”, where “pkg.mod” is a package/module in the archive, and “fn” is a callable in the given module. The __main__.py file will execute that callable.

--main cannot be specified when copying an archive.

+
+-c, --compress
+

Compress files with the deflate method, reducing the size of the output file. By default, files are stored uncompressed in the archive.

--compress has no effect when copying an archive.

New in version 3.7.

+
+--info
+

Display the interpreter embedded in the archive, for diagnostic purposes. In this case, any other options are ignored and SOURCE must be an archive, not a directory.

+
+-h, --help
+

Print a short usage message and exit.

+

Python API

The module defines two convenience functions:

+zipapp.create_archive(source, target=None, interpreter=None, main=None, filter=None, compressed=False)
+

Create an application archive from source. The source can be any of the following:

  • The name of a directory, or a path-like object referring to a directory, in which case a new application archive will be created from the content of that directory.
  • The name of an existing application archive file, or a path-like object referring to such a file, in which case the file is copied to the target (modifying it to reflect the value given for the interpreter argument). The file name should include the .pyz extension, if required.
  • A file object open for reading in bytes mode. The content of the file should be an application archive, and the file object is assumed to be positioned at the start of the archive.

The target argument determines where the resulting archive will be written:

  • If it is the name of a file, or a path-like object, the archive will be written to that file.
  • If it is an open file object, the archive will be written to that file object, which must be open for writing in bytes mode.
  • If the target is omitted (or None), the source must be a directory and the target will be a file with the same name as the source, with a .pyz extension added.

The interpreter argument specifies the name of the Python interpreter with which the archive will be executed. It is written as a “shebang” line at the start of the archive. On POSIX, this will be interpreted by the OS, and on Windows it will be handled by the Python launcher. Omitting the interpreter results in no shebang line being written. If an interpreter is specified, and the target is a filename, the executable bit of the target file will be set.

The main argument specifies the name of a callable which will be used as the main program for the archive. It can only be specified if the source is a directory, and the source does not already contain a __main__.py file. The main argument should take the form “pkg.module:callable” and the archive will be run by importing “pkg.module” and executing the given callable with no arguments. It is an error to omit main if the source is a directory and does not contain a __main__.py file, as otherwise the resulting archive would not be executable.

The optional filter argument specifies a callback function that is passed a Path object representing the path to the file being added (relative to the source directory). It should return True if the file is to be added.

The optional compressed argument determines whether files are compressed. If set to True, files in the archive are compressed with the deflate method; otherwise, files are stored uncompressed. This argument has no effect when copying an existing archive.

If a file object is specified for source or target, it is the caller’s responsibility to close it after calling create_archive.

When copying an existing archive, file objects supplied only need read and readline, or write methods. When creating an archive from a directory, if the target is a file object it will be passed to the zipfile.ZipFile class, and must supply the methods needed by that class.

New in version 3.7: Added the filter and compressed arguments.

+
+zipapp.get_interpreter(archive)
+

Return the interpreter specified in the #! line at the start of the archive. If there is no #! line, return None. The archive argument can be a filename or a file-like object open for reading in bytes mode. It is assumed to be at the start of the archive.

+

Examples

Pack up a directory into an archive, and run it.

$ python -m zipapp myapp
+$ python myapp.pyz
+<output from myapp>
+

The same can be done using the create_archive() function:

>>> import zipapp
+>>> zipapp.create_archive('myapp', 'myapp.pyz')
+

To make the application directly executable on POSIX, specify an interpreter to use.

$ python -m zipapp myapp -p "/usr/bin/env python"
+$ ./myapp.pyz
+<output from myapp>
+

To replace the shebang line on an existing archive, create a modified archive using the create_archive() function:

>>> import zipapp
+>>> zipapp.create_archive('old_archive.pyz', 'new_archive.pyz', '/usr/bin/python3')
+

To update the file in place, do the replacement in memory using a BytesIO object, and then overwrite the source afterwards. Note that there is a risk when overwriting a file in place that an error will result in the loss of the original file. This code does not protect against such errors, but production code should do so. Also, this method will only work if the archive fits in memory:

>>> import zipapp
+>>> import io
+>>> temp = io.BytesIO()
+>>> zipapp.create_archive('myapp.pyz', temp, '/usr/bin/python2')
+>>> with open('myapp.pyz', 'wb') as f:
+>>>     f.write(temp.getvalue())
+

Specifying the Interpreter

Note that if you specify an interpreter and then distribute your application archive, you need to ensure that the interpreter used is portable. The Python launcher for Windows supports most common forms of POSIX #! line, but there are other issues to consider:

  • If you use “/usr/bin/env python” (or other forms of the “python” command, such as “/usr/bin/python”), you need to consider that your users may have either Python 2 or Python 3 as their default, and write your code to work under both versions.
  • If you use an explicit version, for example “/usr/bin/env python3” your application will not work for users who do not have that version. (This may be what you want if you have not made your code Python 2 compatible).
  • There is no way to say “python X.Y or later”, so be careful of using an exact version like “/usr/bin/env python3.4” as you will need to change your shebang line for users of Python 3.5, for example.

Typically, you should use an “/usr/bin/env python2” or “/usr/bin/env python3”, depending on whether your code is written for Python 2 or 3.

Creating Standalone Applications with zipapp

Using the zipapp module, it is possible to create self-contained Python programs, which can be distributed to end users who only need to have a suitable version of Python installed on their system. The key to doing this is to bundle all of the application’s dependencies into the archive, along with the application code.

The steps to create a standalone archive are as follows:

  1. Create your application in a directory as normal, so you have a myapp directory containing a __main__.py file, and any supporting application code.
  2. +

    Install all of your application’s dependencies into the myapp directory, using pip:

    $ python -m pip install -r requirements.txt --target myapp
+

    (this assumes you have your project requirements in a requirements.txt file - if not, you can just list the dependencies manually on the pip command line).

  3. +

    Package the application using:

    $ python -m zipapp -p "interpreter" myapp
+

This will produce a standalone executable, which can be run on any machine with the appropriate interpreter available. See Specifying the Interpreter for details. It can be shipped to users as a single file.

On Unix, the myapp.pyz file is executable as it stands. You can rename the file to remove the .pyz extension if you prefer a “plain” command name. On Windows, the myapp.pyz[w] file is executable by virtue of the fact that the Python interpreter registers the .pyz and .pyzw file extensions when installed.

Caveats

If your application depends on a package that includes a C extension, that package cannot be run from a zip file (this is an OS limitation, as executable code must be present in the filesystem for the OS loader to load it). In this case, you can exclude that dependency from the zipfile, and either require your users to have it installed, or ship it alongside your zipfile and add code to your __main__.py to include the directory containing the unzipped module in sys.path. In this case, you will need to make sure to ship appropriate binaries for your target architecture(s) (and potentially pick the correct version to add to sys.path at runtime, based on the user’s machine).

The Python Zip Application Archive Format

Python has been able to execute zip files which contain a __main__.py file since version 2.6. In order to be executed by Python, an application archive simply has to be a standard zip file containing a __main__.py file which will be run as the entry point for the application. As usual for any Python script, the parent of the script (in this case the zip file) will be placed on sys.path and thus further modules can be imported from the zip file.

The zip file format allows arbitrary data to be prepended to a zip file. The zip application format uses this ability to prepend a standard POSIX “shebang” line to the file (#!/path/to/interpreter).

Formally, the Python zip application format is therefore:

  1. An optional shebang line, containing the characters b'#!' followed by an interpreter name, and then a newline (b'\n') character. The interpreter name can be anything acceptable to the OS “shebang” processing, or the Python launcher on Windows. The interpreter should be encoded in UTF-8 on Windows, and in sys.getfilesystemencoding() on POSIX.
  2. Standard zipfile data, as generated by the zipfile module. The zipfile content must include a file called __main__.py (which must be in the “root” of the zipfile - i.e., it cannot be in a subdirectory). The zipfile data can be compressed or uncompressed.

If an application archive has a shebang line, it may have the executable bit set on POSIX systems, to allow it to be executed directly.

There is no requirement that the tools in this module are used to create application archives - the module is a convenience, but archives in the above format created by any means are acceptable to Python.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/zipapp.html +

+
diff --git a/devdocs/python~3.12/library%2Fzipfile.html b/devdocs/python~3.12/library%2Fzipfile.html new file mode 100644 index 00000000..1a36e187 --- /dev/null +++ b/devdocs/python~3.12/library%2Fzipfile.html @@ -0,0 +1,251 @@ +

zipfile — Work with ZIP archives

Source code: Lib/zipfile/

The ZIP file format is a common archive and compression standard. This module provides tools to create, read, write, append, and list a ZIP file. Any advanced use of this module will require an understanding of the format, as defined in PKZIP Application Note.

This module does not currently handle multi-disk ZIP files. It can handle ZIP files that use the ZIP64 extensions (that is ZIP files that are more than 4 GiB in size). It supports decryption of encrypted files in ZIP archives, but it currently cannot create an encrypted file. Decryption is extremely slow as it is implemented in native Python rather than C.

The module defines the following items:

+exception zipfile.BadZipFile
+

The error raised for bad ZIP files.

New in version 3.2.

+
+exception zipfile.BadZipfile
+

Alias of BadZipFile, for compatibility with older Python versions.

Deprecated since version 3.2.

+
+exception zipfile.LargeZipFile
+

The error raised when a ZIP file would require ZIP64 functionality but that has not been enabled.

+
class zipfile.ZipFile +
+

The class for reading and writing ZIP files. See section ZipFile Objects for constructor details.

+
class zipfile.Path +
+

Class that implements a subset of the interface provided by pathlib.Path, including the full importlib.resources.abc.Traversable interface.

New in version 3.8.

+
class zipfile.PyZipFile +
+

Class for creating ZIP archives containing Python libraries.

+
+class zipfile.ZipInfo(filename='NoName', date_time=(1980, 1, 1, 0, 0, 0))
+

Class used to represent information about a member of an archive. Instances of this class are returned by the getinfo() and infolist() methods of ZipFile objects. Most users of the zipfile module will not need to create these, but only use those created by this module. filename should be the full name of the archive member, and date_time should be a tuple containing six fields which describe the time of the last modification to the file; the fields are described in section ZipInfo Objects.

+
+zipfile.is_zipfile(filename)
+

Returns True if filename is a valid ZIP file based on its magic number, otherwise returns False. filename may be a file or file-like object too.

Changed in version 3.1: Support for file and file-like objects.

+
+zipfile.ZIP_STORED
+

The numeric constant for an uncompressed archive member.

+
+zipfile.ZIP_DEFLATED
+

The numeric constant for the usual ZIP compression method. This requires the zlib module.

+
+zipfile.ZIP_BZIP2
+

The numeric constant for the BZIP2 compression method. This requires the bz2 module.

New in version 3.3.

+
+zipfile.ZIP_LZMA
+

The numeric constant for the LZMA compression method. This requires the lzma module.

New in version 3.3.

Note

The ZIP file format specification has included support for bzip2 compression since 2001, and for LZMA compression since 2006. However, some tools (including older Python releases) do not support these compression methods, and may either refuse to process the ZIP file altogether, or fail to extract individual files.

+

See also

PKZIP Application Note
+
+

Documentation on the ZIP file format by Phil Katz, the creator of the format and algorithms used.

Info-ZIP Home Page
+
+

Information about the Info-ZIP project’s ZIP archive programs and development libraries.

ZipFile Objects

+class zipfile.ZipFile(file, mode='r', compression=ZIP_STORED, allowZip64=True, compresslevel=None, *, strict_timestamps=True, metadata_encoding=None)
+

Open a ZIP file, where file can be a path to a file (a string), a file-like object or a path-like object.

The mode parameter should be 'r' to read an existing file, 'w' to truncate and write a new file, 'a' to append to an existing file, or 'x' to exclusively create and write a new file. If mode is 'x' and file refers to an existing file, a FileExistsError will be raised. If mode is 'a' and file refers to an existing ZIP file, then additional files are added to it. If file does not refer to a ZIP file, then a new ZIP archive is appended to the file. This is meant for adding a ZIP archive to another file (such as python.exe). If mode is 'a' and the file does not exist at all, it is created. If mode is 'r' or 'a', the file should be seekable.

compression is the ZIP compression method to use when writing the archive, and should be ZIP_STORED, ZIP_DEFLATED, ZIP_BZIP2 or ZIP_LZMA; unrecognized values will cause NotImplementedError to be raised. If ZIP_DEFLATED, ZIP_BZIP2 or ZIP_LZMA is specified but the corresponding module (zlib, bz2 or lzma) is not available, RuntimeError is raised. The default is ZIP_STORED.

If allowZip64 is True (the default) zipfile will create ZIP files that use the ZIP64 extensions when the zipfile is larger than 4 GiB. If it is false zipfile will raise an exception when the ZIP file would require ZIP64 extensions.

The compresslevel parameter controls the compression level to use when writing files to the archive. When using ZIP_STORED or ZIP_LZMA it has no effect. When using ZIP_DEFLATED integers 0 through 9 are accepted (see zlib for more information). When using ZIP_BZIP2 integers 1 through 9 are accepted (see bz2 for more information).

The strict_timestamps argument, when set to False, allows to zip files older than 1980-01-01 at the cost of setting the timestamp to 1980-01-01. Similar behavior occurs with files newer than 2107-12-31, the timestamp is also set to the limit.

When mode is 'r', metadata_encoding may be set to the name of a codec, which will be used to decode metadata such as the names of members and ZIP comments.

If the file is created with mode 'w', 'x' or 'a' and then closed without adding any files to the archive, the appropriate ZIP structures for an empty archive will be written to the file.

ZipFile is also a context manager and therefore supports the with statement. In the example, myzip is closed after the with statement’s suite is finished—even if an exception occurs:

with ZipFile('spam.zip', 'w') as myzip:
+    myzip.write('eggs.txt')
+

Note

metadata_encoding is an instance-wide setting for the ZipFile. It is not currently possible to set this on a per-member basis.

This attribute is a workaround for legacy implementations which produce archives with names in the current locale encoding or code page (mostly on Windows). According to the .ZIP standard, the encoding of metadata may be specified to be either IBM code page (default) or UTF-8 by a flag in the archive header. That flag takes precedence over metadata_encoding, which is a Python-specific extension.

New in version 3.2: Added the ability to use ZipFile as a context manager.

Changed in version 3.3: Added support for bzip2 and lzma compression.

Changed in version 3.4: ZIP64 extensions are enabled by default.

Changed in version 3.5: Added support for writing to unseekable streams. Added support for the 'x' mode.

Changed in version 3.6: Previously, a plain RuntimeError was raised for unrecognized compression values.

Changed in version 3.6.2: The file parameter accepts a path-like object.

Changed in version 3.7: Add the compresslevel parameter.

New in version 3.8: The strict_timestamps keyword-only argument

Changed in version 3.11: Added support for specifying member name encoding for reading metadata in the zipfile’s directory and file headers.

+
+ZipFile.close()
+

Close the archive file. You must call close() before exiting your program or essential records will not be written.

+
+ZipFile.getinfo(name)
+

Return a ZipInfo object with information about the archive member name. Calling getinfo() for a name not currently contained in the archive will raise a KeyError.

+
+ZipFile.infolist()
+

Return a list containing a ZipInfo object for each member of the archive. The objects are in the same order as their entries in the actual ZIP file on disk if an existing archive was opened.

+
+ZipFile.namelist()
+

Return a list of archive members by name.

+
+ZipFile.open(name, mode='r', pwd=None, *, force_zip64=False)
+

Access a member of the archive as a binary file-like object. name can be either the name of a file within the archive or a ZipInfo object. The mode parameter, if included, must be 'r' (the default) or 'w'. pwd is the password used to decrypt encrypted ZIP files as a bytes object.

open() is also a context manager and therefore supports the with statement:

with ZipFile('spam.zip') as myzip:
+    with myzip.open('eggs.txt') as myfile:
+        print(myfile.read())
+

With mode 'r' the file-like object (ZipExtFile) is read-only and provides the following methods: read(), readline(), readlines(), seek(), tell(), __iter__(), __next__(). These objects can operate independently of the ZipFile.

With mode='w', a writable file handle is returned, which supports the write() method. While a writable file handle is open, attempting to read or write other files in the ZIP file will raise a ValueError.

When writing a file, if the file size is not known in advance but may exceed 2 GiB, pass force_zip64=True to ensure that the header format is capable of supporting large files. If the file size is known in advance, construct a ZipInfo object with file_size set, and use that as the name parameter.

Note

The open(), read() and extract() methods can take a filename or a ZipInfo object. You will appreciate this when trying to read a ZIP file that contains members with duplicate names.

Changed in version 3.6: Removed support of mode='U'. Use io.TextIOWrapper for reading compressed text files in universal newlines mode.

Changed in version 3.6: ZipFile.open() can now be used to write files into the archive with the mode='w' option.

Changed in version 3.6: Calling open() on a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

+
+ZipFile.extract(member, path=None, pwd=None)
+

Extract a member from the archive to the current working directory; member must be its full name or a ZipInfo object. Its file information is extracted as accurately as possible. path specifies a different directory to extract to. member can be a filename or a ZipInfo object. pwd is the password used for encrypted files as a bytes object.

Returns the normalized path created (a directory or new file).

Note

If a member filename is an absolute path, a drive/UNC sharepoint and leading (back)slashes will be stripped, e.g.: ///foo/bar becomes foo/bar on Unix, and C:\foo\bar becomes foo\bar on Windows. And all ".." components in a member filename will be removed, e.g.: ../../foo../../ba..r becomes foo../ba..r. On Windows illegal characters (:, <, >, |, ", ?, and *) replaced by underscore (_).

Changed in version 3.6: Calling extract() on a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

Changed in version 3.6.2: The path parameter accepts a path-like object.

+
+ZipFile.extractall(path=None, members=None, pwd=None)
+

Extract all members from the archive to the current working directory. path specifies a different directory to extract to. members is optional and must be a subset of the list returned by namelist(). pwd is the password used for encrypted files as a bytes object.

Warning

Never extract archives from untrusted sources without prior inspection. It is possible that files are created outside of path, e.g. members that have absolute filenames starting with "/" or filenames with two dots "..". This module attempts to prevent that. See extract() note.

Changed in version 3.6: Calling extractall() on a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

Changed in version 3.6.2: The path parameter accepts a path-like object.

+
+ZipFile.printdir()
+

Print a table of contents for the archive to sys.stdout.

+
+ZipFile.setpassword(pwd)
+

Set pwd (a bytes object) as default password to extract encrypted files.

+
+ZipFile.read(name, pwd=None)
+

Return the bytes of the file name in the archive. name is the name of the file in the archive, or a ZipInfo object. The archive must be open for read or append. pwd is the password used for encrypted files as a bytes object and, if specified, overrides the default password set with setpassword(). Calling read() on a ZipFile that uses a compression method other than ZIP_STORED, ZIP_DEFLATED, ZIP_BZIP2 or ZIP_LZMA will raise a NotImplementedError. An error will also be raised if the corresponding compression module is not available.

Changed in version 3.6: Calling read() on a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

+
+ZipFile.testzip()
+

Read all the files in the archive and check their CRC’s and file headers. Return the name of the first bad file, or else return None.

Changed in version 3.6: Calling testzip() on a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

+
+ZipFile.write(filename, arcname=None, compress_type=None, compresslevel=None)
+

Write the file named filename to the archive, giving it the archive name arcname (by default, this will be the same as filename, but without a drive letter and with leading path separators removed). If given, compress_type overrides the value given for the compression parameter to the constructor for the new entry. Similarly, compresslevel will override the constructor if given. The archive must be open with mode 'w', 'x' or 'a'.

Note

The ZIP file standard historically did not specify a metadata encoding, but strongly recommended CP437 (the original IBM PC encoding) for interoperability. Recent versions allow use of UTF-8 (only). In this module, UTF-8 will automatically be used to write the member names if they contain any non-ASCII characters. It is not possible to write member names in any encoding other than ASCII or UTF-8.

Note

Archive names should be relative to the archive root, that is, they should not start with a path separator.

Note

If arcname (or filename, if arcname is not given) contains a null byte, the name of the file in the archive will be truncated at the null byte.

Note

A leading slash in the filename may lead to the archive being impossible to open in some zip programs on Windows systems.

Changed in version 3.6: Calling write() on a ZipFile created with mode 'r' or a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

+
+ZipFile.writestr(zinfo_or_arcname, data, compress_type=None, compresslevel=None)
+

Write a file into the archive. The contents is data, which may be either a str or a bytes instance; if it is a str, it is encoded as UTF-8 first. zinfo_or_arcname is either the file name it will be given in the archive, or a ZipInfo instance. If it’s an instance, at least the filename, date, and time must be given. If it’s a name, the date and time is set to the current date and time. The archive must be opened with mode 'w', 'x' or 'a'.

If given, compress_type overrides the value given for the compression parameter to the constructor for the new entry, or in the zinfo_or_arcname (if that is a ZipInfo instance). Similarly, compresslevel will override the constructor if given.

Note

When passing a ZipInfo instance as the zinfo_or_arcname parameter, the compression method used will be that specified in the compress_type member of the given ZipInfo instance. By default, the ZipInfo constructor sets this member to ZIP_STORED.

Changed in version 3.2: The compress_type argument.

Changed in version 3.6: Calling writestr() on a ZipFile created with mode 'r' or a closed ZipFile will raise a ValueError. Previously, a RuntimeError was raised.

+
+ZipFile.mkdir(zinfo_or_directory, mode=511)
+

Create a directory inside the archive. If zinfo_or_directory is a string, a directory is created inside the archive with the mode that is specified in the mode argument. If, however, zinfo_or_directory is a ZipInfo instance then the mode argument is ignored.

The archive must be opened with mode 'w', 'x' or 'a'.

New in version 3.11.

+

The following data attributes are also available:

+ZipFile.filename
+

Name of the ZIP file.

+
+ZipFile.debug
+

The level of debug output to use. This may be set from 0 (the default, no output) to 3 (the most output). Debugging information is written to sys.stdout.

+
+ZipFile.comment
+

The comment associated with the ZIP file as a bytes object. If assigning a comment to a ZipFile instance created with mode 'w', 'x' or 'a', it should be no longer than 65535 bytes. Comments longer than this will be truncated.

+

Path Objects

+class zipfile.Path(root, at='')
+

Construct a Path object from a root zipfile (which may be a ZipFile instance or file suitable for passing to the ZipFile constructor).

at specifies the location of this Path within the zipfile, e.g. ‘dir/file.txt’, ‘dir/’, or ‘’. Defaults to the empty string, indicating the root.

+

Path objects expose the following features of pathlib.Path objects:

Path objects are traversable using the / operator or joinpath.

+Path.name
+

The final path component.

+
+Path.open(mode='r', *, pwd, **)
+

Invoke ZipFile.open() on the current path. Allows opening for read or write, text or binary through supported modes: ‘r’, ‘w’, ‘rb’, ‘wb’. Positional and keyword arguments are passed through to io.TextIOWrapper when opened as text and ignored otherwise. pwd is the pwd parameter to ZipFile.open().

Changed in version 3.9: Added support for text and binary modes for open. Default mode is now text.

Changed in version 3.11.2: The encoding parameter can be supplied as a positional argument without causing a TypeError. As it could in 3.9. Code needing to be compatible with unpatched 3.10 and 3.11 versions must pass all io.TextIOWrapper arguments, encoding included, as keywords.

+
+Path.iterdir()
+

Enumerate the children of the current directory.

+
+Path.is_dir()
+

Return True if the current context references a directory.

+
+Path.is_file()
+

Return True if the current context references a file.

+
+Path.exists()
+

Return True if the current context references a file or directory in the zip file.

+
+Path.suffix
+

The file extension of the final component.

New in version 3.11: Added Path.suffix property.

+
+Path.stem
+

The final path component, without its suffix.

New in version 3.11: Added Path.stem property.

+
+Path.suffixes
+

A list of the path’s file extensions.

New in version 3.11: Added Path.suffixes property.

+
+Path.read_text(*, **)
+

Read the current file as unicode text. Positional and keyword arguments are passed through to io.TextIOWrapper (except buffer, which is implied by the context).

Changed in version 3.11.2: The encoding parameter can be supplied as a positional argument without causing a TypeError. As it could in 3.9. Code needing to be compatible with unpatched 3.10 and 3.11 versions must pass all io.TextIOWrapper arguments, encoding included, as keywords.

+
+Path.read_bytes()
+

Read the current file as bytes.

+
+Path.joinpath(*other)
+

Return a new Path object with each of the other arguments joined. The following are equivalent:

>>> Path(...).joinpath('child').joinpath('grandchild')
+>>> Path(...).joinpath('child', 'grandchild')
+>>> Path(...) / 'child' / 'grandchild'
+

Changed in version 3.10: Prior to 3.10, joinpath was undocumented and accepted exactly one parameter.

+

The zipp project provides backports of the latest path object functionality to older Pythons. Use zipp.Path in place of zipfile.Path for early access to changes.

PyZipFile Objects

The PyZipFile constructor takes the same parameters as the ZipFile constructor, and one additional parameter, optimize.

+class zipfile.PyZipFile(file, mode='r', compression=ZIP_STORED, allowZip64=True, optimize=- 1)
+

New in version 3.2: The optimize parameter.

Changed in version 3.4: ZIP64 extensions are enabled by default.

Instances have one method in addition to those of ZipFile objects:

+writepy(pathname, basename='', filterfunc=None)
+

Search for files *.py and add the corresponding file to the archive.

If the optimize parameter to PyZipFile was not given or -1, the corresponding file is a *.pyc file, compiling if necessary.

If the optimize parameter to PyZipFile was 0, 1 or 2, only files with that optimization level (see compile()) are added to the archive, compiling if necessary.

If pathname is a file, the filename must end with .py, and just the (corresponding *.pyc) file is added at the top level (no path information). If pathname is a file that does not end with .py, a RuntimeError will be raised. If it is a directory, and the directory is not a package directory, then all the files *.pyc are added at the top level. If the directory is a package directory, then all *.pyc are added under the package name as a file path, and if any subdirectories are package directories, all of these are added recursively in sorted order.

basename is intended for internal use only.

filterfunc, if given, must be a function taking a single string argument. It will be passed each path (including each individual full file path) before it is added to the archive. If filterfunc returns a false value, the path will not be added, and if it is a directory its contents will be ignored. For example, if our test files are all either in test directories or start with the string test_, we can use a filterfunc to exclude them:

>>> zf = PyZipFile('myprog.zip')
+>>> def notests(s):
+...     fn = os.path.basename(s)
+...     return (not (fn == 'test' or fn.startswith('test_')))
+...
+>>> zf.writepy('myprog', filterfunc=notests)
+

The writepy() method makes archives with file names like this:

string.pyc                   # Top level name
+test/__init__.pyc            # Package directory
+test/testall.pyc             # Module test.testall
+test/bogus/__init__.pyc      # Subpackage directory
+test/bogus/myfile.pyc        # Submodule test.bogus.myfile
+

New in version 3.4: The filterfunc parameter.

Changed in version 3.6.2: The pathname parameter accepts a path-like object.

Changed in version 3.7: Recursion sorts directory entries.

+
+

ZipInfo Objects

Instances of the ZipInfo class are returned by the getinfo() and infolist() methods of ZipFile objects. Each object stores information about a single member of the ZIP archive.

There is one classmethod to make a ZipInfo instance for a filesystem file:

+classmethod ZipInfo.from_file(filename, arcname=None, *, strict_timestamps=True)
+

Construct a ZipInfo instance for a file on the filesystem, in preparation for adding it to a zip file.

filename should be the path to a file or directory on the filesystem.

If arcname is specified, it is used as the name within the archive. If arcname is not specified, the name will be the same as filename, but with any drive letter and leading path separators removed.

The strict_timestamps argument, when set to False, allows to zip files older than 1980-01-01 at the cost of setting the timestamp to 1980-01-01. Similar behavior occurs with files newer than 2107-12-31, the timestamp is also set to the limit.

New in version 3.6.

Changed in version 3.6.2: The filename parameter accepts a path-like object.

New in version 3.8: The strict_timestamps keyword-only argument

+

Instances have the following methods and attributes:

+ZipInfo.is_dir()
+

Return True if this archive member is a directory.

This uses the entry’s name: directories should always end with /.

New in version 3.6.

+
+ZipInfo.filename
+

Name of the file in the archive.

+
+ZipInfo.date_time
+

The time and date of the last modification to the archive member. This is a tuple of six values:

+ + + + + + +

Index

Value

0

Year (>= 1980)

1

Month (one-based)

2

Day of month (one-based)

3

Hours (zero-based)

4

Minutes (zero-based)

5

Seconds (zero-based)

Note

The ZIP file format does not support timestamps before 1980.

+
+ZipInfo.compress_type
+

Type of compression for the archive member.

+
+ZipInfo.comment
+

Comment for the individual archive member as a bytes object.

+
+ZipInfo.extra
+

Expansion field data. The PKZIP Application Note contains some comments on the internal structure of the data contained in this bytes object.

+
+ZipInfo.create_system
+

System which created ZIP archive.

+
+ZipInfo.create_version
+

PKZIP version which created ZIP archive.

+
+ZipInfo.extract_version
+

PKZIP version needed to extract archive.

+
+ZipInfo.reserved
+

Must be zero.

+
+ZipInfo.flag_bits
+

ZIP flag bits.

+
+ZipInfo.volume
+

Volume number of file header.

+
+ZipInfo.internal_attr
+

Internal attributes.

+
+ZipInfo.external_attr
+

External file attributes.

+
+ZipInfo.header_offset
+

Byte offset to the file header.

+
+ZipInfo.CRC
+

CRC-32 of the uncompressed file.

+
+ZipInfo.compress_size
+

Size of the compressed data.

+
+ZipInfo.file_size
+

Size of the uncompressed file.

+

Command-Line Interface

The zipfile module provides a simple command-line interface to interact with ZIP archives.

If you want to create a new ZIP archive, specify its name after the -c option and then list the filename(s) that should be included:

$ python -m zipfile -c monty.zip spam.txt eggs.txt
+

Passing a directory is also acceptable:

$ python -m zipfile -c monty.zip life-of-brian_1979/
+

If you want to extract a ZIP archive into the specified directory, use the -e option:

$ python -m zipfile -e monty.zip target-dir/
+

For a list of the files in a ZIP archive, use the -l option:

$ python -m zipfile -l monty.zip
+

Command-line options

+-l <zipfile>
+--list <zipfile>
+

List files in a zipfile.

+
+-c <zipfile> <source1> ... <sourceN>
+--create <zipfile> <source1> ... <sourceN>
+

Create zipfile from source files.

+
+-e <zipfile> <output_dir>
+--extract <zipfile> <output_dir>
+

Extract zipfile into target directory.

+
+-t <zipfile>
+--test <zipfile>
+

Test whether the zipfile is valid or not.

+
+--metadata-encoding <encoding>
+

Specify encoding of member names for -l, -e and -t.

New in version 3.11.

+

Decompression pitfalls

The extraction in zipfile module might fail due to some pitfalls listed below.

From file itself

Decompression may fail due to incorrect password / CRC checksum / ZIP format or unsupported compression method / decryption.

File System limitations

Exceeding limitations on different file systems can cause decompression failed. Such as allowable characters in the directory entries, length of the file name, length of the pathname, size of a single file, and number of files, etc.

Resources limitations

The lack of memory or disk volume would lead to decompression failed. For example, decompression bombs (aka ZIP bomb) apply to zipfile library that can cause disk volume exhaustion.

Interruption

Interruption during the decompression, such as pressing control-C or killing the decompression process may result in incomplete decompression of the archive.

Default behaviors of extraction

Not knowing the default extraction behaviors can cause unexpected decompression results. For example, when extracting the same archive twice, it overwrites files without asking.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/zipfile.html +

+
diff --git a/devdocs/python~3.12/library%2Fzipimport.html b/devdocs/python~3.12/library%2Fzipimport.html new file mode 100644 index 00000000..33cb4e7e --- /dev/null +++ b/devdocs/python~3.12/library%2Fzipimport.html @@ -0,0 +1,70 @@ +

zipimport — Import modules from Zip archives

Source code: Lib/zipimport.py

This module adds the ability to import Python modules (*.py, *.pyc) and packages from ZIP-format archives. It is usually not needed to use the zipimport module explicitly; it is automatically used by the built-in import mechanism for sys.path items that are paths to ZIP archives.

Typically, sys.path is a list of directory names as strings. This module also allows an item of sys.path to be a string naming a ZIP file archive. The ZIP archive can contain a subdirectory structure to support package imports, and a path within the archive can be specified to only import from a subdirectory. For example, the path example.zip/lib/ would only import from the lib/ subdirectory within the archive.

Any files may be present in the ZIP archive, but importers are only invoked for .py and .pyc files. ZIP import of dynamic modules (.pyd, .so) is disallowed. Note that if an archive only contains .py files, Python will not attempt to modify the archive by adding the corresponding .pyc file, meaning that if a ZIP archive doesn’t contain .pyc files, importing may be rather slow.

Changed in version 3.8: Previously, ZIP archives with an archive comment were not supported.

See also

PKZIP Application Note
+
+

Documentation on the ZIP file format by Phil Katz, the creator of the format and algorithms used.

+PEP 273 - Import Modules from Zip Archives
+
+

Written by James C. Ahlstrom, who also provided an implementation. Python 2.3 follows the specification in PEP 273, but uses an implementation written by Just van Rossum that uses the import hooks described in PEP 302.

+importlib - The implementation of the import machinery
+
+

Package providing the relevant protocols for all importers to implement.

This module defines an exception:

+exception zipimport.ZipImportError
+

Exception raised by zipimporter objects. It’s a subclass of ImportError, so it can be caught as ImportError, too.

+

zipimporter Objects

zipimporter is the class for importing ZIP files.

+class zipimport.zipimporter(archivepath)
+

Create a new zipimporter instance. archivepath must be a path to a ZIP file, or to a specific path within a ZIP file. For example, an archivepath of foo/bar.zip/lib will look for modules in the lib directory inside the ZIP file foo/bar.zip (provided that it exists).

ZipImportError is raised if archivepath doesn’t point to a valid ZIP archive.

Changed in version 3.12: Methods find_loader() and find_module(), deprecated in 3.10 are now removed. Use find_spec() instead.

+create_module(spec)
+

Implementation of importlib.abc.Loader.create_module() that returns None to explicitly request the default semantics.

New in version 3.10.

+
+exec_module(module)
+

Implementation of importlib.abc.Loader.exec_module().

New in version 3.10.

+
+find_spec(fullname, target=None)
+

An implementation of importlib.abc.PathEntryFinder.find_spec().

New in version 3.10.

+
+get_code(fullname)
+

Return the code object for the specified module. Raise ZipImportError if the module couldn’t be imported.

+
+get_data(pathname)
+

Return the data associated with pathname. Raise OSError if the file wasn’t found.

Changed in version 3.3: IOError used to be raised, it is now an alias of OSError.

+
+get_filename(fullname)
+

Return the value __file__ would be set to if the specified module was imported. Raise ZipImportError if the module couldn’t be imported.

New in version 3.1.

+
+get_source(fullname)
+

Return the source code for the specified module. Raise ZipImportError if the module couldn’t be found, return None if the archive does contain the module, but has no source for it.

+
+is_package(fullname)
+

Return True if the module specified by fullname is a package. Raise ZipImportError if the module couldn’t be found.

+
+load_module(fullname)
+

Load the module specified by fullname. fullname must be the fully qualified (dotted) module name. Returns the imported module on success, raises ZipImportError on failure.

Deprecated since version 3.10: Use exec_module() instead.

+
+invalidate_caches()
+

Clear out the internal cache of information about files found within the ZIP archive.

New in version 3.10.

+
+archive
+

The file name of the importer’s associated ZIP file, without a possible subpath.

+
+prefix
+

The subpath within the ZIP file where modules are searched. This is the empty string for zipimporter objects which point to the root of the ZIP file.

+

The archive and prefix attributes, when combined with a slash, equal the original archivepath argument given to the zipimporter constructor.

+

Examples

Here is an example that imports a module from a ZIP archive - note that the zipimport module is not explicitly used.

$ unzip -l example.zip
+Archive:  example.zip
+  Length     Date   Time    Name
+ --------    ----   ----    ----
+     8467  11-26-02 22:30   jwzthreading.py
+ --------                   -------
+     8467                   1 file
+$ ./python
+Python 2.3 (#1, Aug 1 2003, 19:54:32)
+>>> import sys
+>>> sys.path.insert(0, 'example.zip')  # Add .zip file to front of path
+>>> import jwzthreading
+>>> jwzthreading.__file__
+'example.zip/jwzthreading.py'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/zipimport.html +

+
diff --git a/devdocs/python~3.12/library%2Fzlib.html b/devdocs/python~3.12/library%2Fzlib.html new file mode 100644 index 00000000..5a7e0fc0 --- /dev/null +++ b/devdocs/python~3.12/library%2Fzlib.html @@ -0,0 +1,68 @@ +

zlib — Compression compatible with gzip

For applications that require data compression, the functions in this module allow compression and decompression, using the zlib library. The zlib library has its own home page at https://www.zlib.net. There are known incompatibilities between the Python module and versions of the zlib library earlier than 1.1.3; 1.1.3 has a security vulnerability, so we recommend using 1.1.4 or later.

zlib’s functions have many options and often need to be used in a particular order. This documentation doesn’t attempt to cover all of the permutations; consult the zlib manual at http://www.zlib.net/manual.html for authoritative information.

For reading and writing .gz files see the gzip module.

The available exception and functions in this module are:

+exception zlib.error
+

Exception raised on compression and decompression errors.

+
+zlib.adler32(data[, value])
+

Computes an Adler-32 checksum of data. (An Adler-32 checksum is almost as reliable as a CRC32 but can be computed much more quickly.) The result is an unsigned 32-bit integer. If value is present, it is used as the starting value of the checksum; otherwise, a default value of 1 is used. Passing in value allows computing a running checksum over the concatenation of several inputs. The algorithm is not cryptographically strong, and should not be used for authentication or digital signatures. Since the algorithm is designed for use as a checksum algorithm, it is not suitable for use as a general hash algorithm.

Changed in version 3.0: The result is always unsigned.

+
+zlib.compress(data, /, level=- 1, wbits=MAX_WBITS)
+

Compresses the bytes in data, returning a bytes object containing compressed data. level is an integer from 0 to 9 or -1 controlling the level of compression; 1 (Z_BEST_SPEED) is fastest and produces the least compression, 9 (Z_BEST_COMPRESSION) is slowest and produces the most. 0 (Z_NO_COMPRESSION) is no compression. The default value is -1 (Z_DEFAULT_COMPRESSION). Z_DEFAULT_COMPRESSION represents a default compromise between speed and compression (currently equivalent to level 6).

The wbits argument controls the size of the history buffer (or the “window size”) used when compressing data, and whether a header and trailer is included in the output. It can take several ranges of values, defaulting to 15 (MAX_WBITS):

  • +9 to +15: The base-two logarithm of the window size, which therefore ranges between 512 and 32768. Larger values produce better compression at the expense of greater memory usage. The resulting output will include a zlib-specific header and trailer.
  • −9 to −15: Uses the absolute value of wbits as the window size logarithm, while producing a raw output stream with no header or trailing checksum.
  • +25 to +31 = 16 + (9 to 15): Uses the low 4 bits of the value as the window size logarithm, while including a basic gzip header and trailing checksum in the output.

Raises the error exception if any error occurs.

Changed in version 3.6: level can now be used as a keyword parameter.

Changed in version 3.11: The wbits parameter is now available to set window bits and compression type.

+
+zlib.compressobj(level=-1, method=DEFLATED, wbits=MAX_WBITS, memLevel=DEF_MEM_LEVEL, strategy=Z_DEFAULT_STRATEGY[, zdict])
+

Returns a compression object, to be used for compressing data streams that won’t fit into memory at once.

level is the compression level – an integer from 0 to 9 or -1. A value of 1 (Z_BEST_SPEED) is fastest and produces the least compression, while a value of 9 (Z_BEST_COMPRESSION) is slowest and produces the most. 0 (Z_NO_COMPRESSION) is no compression. The default value is -1 (Z_DEFAULT_COMPRESSION). Z_DEFAULT_COMPRESSION represents a default compromise between speed and compression (currently equivalent to level 6).

method is the compression algorithm. Currently, the only supported value is DEFLATED.

The wbits parameter controls the size of the history buffer (or the “window size”), and what header and trailer format will be used. It has the same meaning as described for compress().

The memLevel argument controls the amount of memory used for the internal compression state. Valid values range from 1 to 9. Higher values use more memory, but are faster and produce smaller output.

strategy is used to tune the compression algorithm. Possible values are Z_DEFAULT_STRATEGY, Z_FILTERED, Z_HUFFMAN_ONLY, Z_RLE (zlib 1.2.0.1) and Z_FIXED (zlib 1.2.2.2).

zdict is a predefined compression dictionary. This is a sequence of bytes (such as a bytes object) containing subsequences that are expected to occur frequently in the data that is to be compressed. Those subsequences that are expected to be most common should come at the end of the dictionary.

Changed in version 3.3: Added the zdict parameter and keyword argument support.

+
+zlib.crc32(data[, value])
+

Computes a CRC (Cyclic Redundancy Check) checksum of data. The result is an unsigned 32-bit integer. If value is present, it is used as the starting value of the checksum; otherwise, a default value of 0 is used. Passing in value allows computing a running checksum over the concatenation of several inputs. The algorithm is not cryptographically strong, and should not be used for authentication or digital signatures. Since the algorithm is designed for use as a checksum algorithm, it is not suitable for use as a general hash algorithm.

Changed in version 3.0: The result is always unsigned.

+
+zlib.decompress(data, /, wbits=MAX_WBITS, bufsize=DEF_BUF_SIZE)
+

Decompresses the bytes in data, returning a bytes object containing the uncompressed data. The wbits parameter depends on the format of data, and is discussed further below. If bufsize is given, it is used as the initial size of the output buffer. Raises the error exception if any error occurs.

The wbits parameter controls the size of the history buffer (or “window size”), and what header and trailer format is expected. It is similar to the parameter for compressobj(), but accepts more ranges of values:

  • +8 to +15: The base-two logarithm of the window size. The input must include a zlib header and trailer.
  • 0: Automatically determine the window size from the zlib header. Only supported since zlib 1.2.3.5.
  • −8 to −15: Uses the absolute value of wbits as the window size logarithm. The input must be a raw stream with no header or trailer.
  • +24 to +31 = 16 + (8 to 15): Uses the low 4 bits of the value as the window size logarithm. The input must include a gzip header and trailer.
  • +40 to +47 = 32 + (8 to 15): Uses the low 4 bits of the value as the window size logarithm, and automatically accepts either the zlib or gzip format.

When decompressing a stream, the window size must not be smaller than the size originally used to compress the stream; using a too-small value may result in an error exception. The default wbits value corresponds to the largest window size and requires a zlib header and trailer to be included.

bufsize is the initial size of the buffer used to hold decompressed data. If more space is required, the buffer size will be increased as needed, so you don’t have to get this value exactly right; tuning it will only save a few calls to malloc().

Changed in version 3.6: wbits and bufsize can be used as keyword arguments.

+
+zlib.decompressobj(wbits=MAX_WBITS[, zdict])
+

Returns a decompression object, to be used for decompressing data streams that won’t fit into memory at once.

The wbits parameter controls the size of the history buffer (or the “window size”), and what header and trailer format is expected. It has the same meaning as described for decompress().

The zdict parameter specifies a predefined compression dictionary. If provided, this must be the same dictionary as was used by the compressor that produced the data that is to be decompressed.

Note

If zdict is a mutable object (such as a bytearray), you must not modify its contents between the call to decompressobj() and the first call to the decompressor’s decompress() method.

Changed in version 3.3: Added the zdict parameter.

+

Compression objects support the following methods:

+Compress.compress(data)
+

Compress data, returning a bytes object containing compressed data for at least part of the data in data. This data should be concatenated to the output produced by any preceding calls to the compress() method. Some input may be kept in internal buffers for later processing.

+
+Compress.flush([mode])
+

All pending input is processed, and a bytes object containing the remaining compressed output is returned. mode can be selected from the constants Z_NO_FLUSH, Z_PARTIAL_FLUSH, Z_SYNC_FLUSH, Z_FULL_FLUSH, Z_BLOCK (zlib 1.2.3.4), or Z_FINISH, defaulting to Z_FINISH. Except Z_FINISH, all constants allow compressing further bytestrings of data, while Z_FINISH finishes the compressed stream and prevents compressing any more data. After calling flush() with mode set to Z_FINISH, the compress() method cannot be called again; the only realistic action is to delete the object.

+
+Compress.copy()
+

Returns a copy of the compression object. This can be used to efficiently compress a set of data that share a common initial prefix.

+

Changed in version 3.8: Added copy.copy() and copy.deepcopy() support to compression objects.

Decompression objects support the following methods and attributes:

+Decompress.unused_data
+

A bytes object which contains any bytes past the end of the compressed data. That is, this remains b"" until the last byte that contains compression data is available. If the whole bytestring turned out to contain compressed data, this is b"", an empty bytes object.

+
+Decompress.unconsumed_tail
+

A bytes object that contains any data that was not consumed by the last decompress() call because it exceeded the limit for the uncompressed data buffer. This data has not yet been seen by the zlib machinery, so you must feed it (possibly with further data concatenated to it) back to a subsequent decompress() method call in order to get correct output.

+
+Decompress.eof
+

A boolean indicating whether the end of the compressed data stream has been reached.

This makes it possible to distinguish between a properly formed compressed stream, and an incomplete or truncated one.

New in version 3.3.

+
+Decompress.decompress(data, max_length=0)
+

Decompress data, returning a bytes object containing the uncompressed data corresponding to at least part of the data in string. This data should be concatenated to the output produced by any preceding calls to the decompress() method. Some of the input data may be preserved in internal buffers for later processing.

If the optional parameter max_length is non-zero then the return value will be no longer than max_length. This may mean that not all of the compressed input can be processed; and unconsumed data will be stored in the attribute unconsumed_tail. This bytestring must be passed to a subsequent call to decompress() if decompression is to continue. If max_length is zero then the whole input is decompressed, and unconsumed_tail is empty.

Changed in version 3.6: max_length can be used as a keyword argument.

+
+Decompress.flush([length])
+

All pending input is processed, and a bytes object containing the remaining uncompressed output is returned. After calling flush(), the decompress() method cannot be called again; the only realistic action is to delete the object.

The optional parameter length sets the initial size of the output buffer.

+
+Decompress.copy()
+

Returns a copy of the decompression object. This can be used to save the state of the decompressor midway through the data stream in order to speed up random seeks into the stream at a future point.

+

Changed in version 3.8: Added copy.copy() and copy.deepcopy() support to decompression objects.

Information about the version of the zlib library in use is available through the following constants:

+zlib.ZLIB_VERSION
+

The version string of the zlib library that was used for building the module. This may be different from the zlib library actually used at runtime, which is available as ZLIB_RUNTIME_VERSION.

+
+zlib.ZLIB_RUNTIME_VERSION
+

The version string of the zlib library actually loaded by the interpreter.

New in version 3.3.

+

See also

+Module gzip +
+
+

Reading and writing gzip-format files.

http://www.zlib.net
+
+

The zlib library home page.

http://www.zlib.net/manual.html
+
+

The zlib manual explains the semantics and usage of the library’s many functions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/zlib.html +

+
diff --git a/devdocs/python~3.12/library%2Fzoneinfo.html b/devdocs/python~3.12/library%2Fzoneinfo.html new file mode 100644 index 00000000..425eb65a --- /dev/null +++ b/devdocs/python~3.12/library%2Fzoneinfo.html @@ -0,0 +1,103 @@ +

zoneinfo — IANA time zone support

New in version 3.9.

Source code: Lib/zoneinfo

The zoneinfo module provides a concrete time zone implementation to support the IANA time zone database as originally specified in PEP 615. By default, zoneinfo uses the system’s time zone data if available; if no system time zone data is available, the library will fall back to using the first-party tzdata package available on PyPI.

See also

+Module: datetime +
+
+

Provides the time and datetime types with which the ZoneInfo class is designed to be used.

Package tzdata +
+
+

First-party package maintained by the CPython core developers to supply time zone data via PyPI.

Availability: not Emscripten, not WASI.

This module does not work or is not available on WebAssembly platforms wasm32-emscripten and wasm32-wasi. See WebAssembly platforms for more information.

Using ZoneInfo +

ZoneInfo is a concrete implementation of the datetime.tzinfo abstract base class, and is intended to be attached to tzinfo, either via the constructor, the datetime.replace method or datetime.astimezone:

>>> from zoneinfo import ZoneInfo
+>>> from datetime import datetime, timedelta
+
+>>> dt = datetime(2020, 10, 31, 12, tzinfo=ZoneInfo("America/Los_Angeles"))
+>>> print(dt)
+2020-10-31 12:00:00-07:00
+
+>>> dt.tzname()
+'PDT'
+

Datetimes constructed in this way are compatible with datetime arithmetic and handle daylight saving time transitions with no further intervention:

>>> dt_add = dt + timedelta(days=1)
+
+>>> print(dt_add)
+2020-11-01 12:00:00-08:00
+
+>>> dt_add.tzname()
+'PST'
+

These time zones also support the fold attribute introduced in PEP 495. During offset transitions which induce ambiguous times (such as a daylight saving time to standard time transition), the offset from before the transition is used when fold=0, and the offset after the transition is used when fold=1, for example:

>>> dt = datetime(2020, 11, 1, 1, tzinfo=ZoneInfo("America/Los_Angeles"))
+>>> print(dt)
+2020-11-01 01:00:00-07:00
+
+>>> print(dt.replace(fold=1))
+2020-11-01 01:00:00-08:00
+

When converting from another time zone, the fold will be set to the correct value:

>>> from datetime import timezone
+>>> LOS_ANGELES = ZoneInfo("America/Los_Angeles")
+>>> dt_utc = datetime(2020, 11, 1, 8, tzinfo=timezone.utc)
+
+>>> # Before the PDT -> PST transition
+>>> print(dt_utc.astimezone(LOS_ANGELES))
+2020-11-01 01:00:00-07:00
+
+>>> # After the PDT -> PST transition
+>>> print((dt_utc + timedelta(hours=1)).astimezone(LOS_ANGELES))
+2020-11-01 01:00:00-08:00
+

Data sources

The zoneinfo module does not directly provide time zone data, and instead pulls time zone information from the system time zone database or the first-party PyPI package tzdata, if available. Some systems, including notably Windows systems, do not have an IANA database available, and so for projects targeting cross-platform compatibility that require time zone data, it is recommended to declare a dependency on tzdata. If neither system data nor tzdata are available, all calls to ZoneInfo will raise ZoneInfoNotFoundError.

Configuring the data sources

When ZoneInfo(key) is called, the constructor first searches the directories specified in TZPATH for a file matching key, and on failure looks for a match in the tzdata package. This behavior can be configured in three ways:

  1. The default TZPATH when not otherwise specified can be configured at compile time.
  2. +TZPATH can be configured using an environment variable.
  3. At runtime, the search path can be manipulated using the reset_tzpath() function.

Compile-time configuration

The default TZPATH includes several common deployment locations for the time zone database (except on Windows, where there are no “well-known” locations for time zone data). On POSIX systems, downstream distributors and those building Python from source who know where their system time zone data is deployed may change the default time zone path by specifying the compile-time option TZPATH (or, more likely, the configure +flag --with-tzpath), which should be a string delimited by os.pathsep.

On all platforms, the configured value is available as the TZPATH key in sysconfig.get_config_var().

Environment configuration

When initializing TZPATH (either at import time or whenever reset_tzpath() is called with no arguments), the zoneinfo module will use the environment variable PYTHONTZPATH, if it exists, to set the search path.

+PYTHONTZPATH
+

This is an os.pathsep-separated string containing the time zone search path to use. It must consist of only absolute rather than relative paths. Relative components specified in PYTHONTZPATH will not be used, but otherwise the behavior when a relative path is specified is implementation-defined; CPython will raise InvalidTZPathWarning, but other implementations are free to silently ignore the erroneous component or raise an exception.

+

To set the system to ignore the system data and use the tzdata package instead, set PYTHONTZPATH="".

Runtime configuration

The TZ search path can also be configured at runtime using the reset_tzpath() function. This is generally not an advisable operation, though it is reasonable to use it in test functions that require the use of a specific time zone path (or require disabling access to the system time zones).

The ZoneInfo class

+class zoneinfo.ZoneInfo(key)
+

A concrete datetime.tzinfo subclass that represents an IANA time zone specified by the string key. Calls to the primary constructor will always return objects that compare identically; put another way, barring cache invalidation via ZoneInfo.clear_cache(), for all values of key, the following assertion will always be true:

a = ZoneInfo(key)
+b = ZoneInfo(key)
+assert a is b
+

key must be in the form of a relative, normalized POSIX path, with no up-level references. The constructor will raise ValueError if a non-conforming key is passed.

If no file matching key is found, the constructor will raise ZoneInfoNotFoundError.

+

The ZoneInfo class has two alternate constructors:

+classmethod ZoneInfo.from_file(fobj, /, key=None)
+

Constructs a ZoneInfo object from a file-like object returning bytes (e.g. a file opened in binary mode or an io.BytesIO object). Unlike the primary constructor, this always constructs a new object.

The key parameter sets the name of the zone for the purposes of __str__() and __repr__().

Objects created via this constructor cannot be pickled (see pickling).

+
+classmethod ZoneInfo.no_cache(key)
+

An alternate constructor that bypasses the constructor’s cache. It is identical to the primary constructor, but returns a new object on each call. This is most likely to be useful for testing or demonstration purposes, but it can also be used to create a system with a different cache invalidation strategy.

Objects created via this constructor will also bypass the cache of a deserializing process when unpickled.

Caution

Using this constructor may change the semantics of your datetimes in surprising ways, only use it if you know that you need to.

+

The following class methods are also available:

+classmethod ZoneInfo.clear_cache(*, only_keys=None)
+

A method for invalidating the cache on the ZoneInfo class. If no arguments are passed, all caches are invalidated and the next call to the primary constructor for each key will return a new instance.

If an iterable of key names is passed to the only_keys parameter, only the specified keys will be removed from the cache. Keys passed to only_keys but not found in the cache are ignored.

Warning

Invoking this function may change the semantics of datetimes using ZoneInfo in surprising ways; this modifies module state and thus may have wide-ranging effects. Only use it if you know that you need to.

+

The class has one attribute:

+ZoneInfo.key
+

This is a read-only attribute that returns the value of key passed to the constructor, which should be a lookup key in the IANA time zone database (e.g. America/New_York, Europe/Paris or Asia/Tokyo).

For zones constructed from file without specifying a key parameter, this will be set to None.

Note

Although it is a somewhat common practice to expose these to end users, these values are designed to be primary keys for representing the relevant zones and not necessarily user-facing elements. Projects like CLDR (the Unicode Common Locale Data Repository) can be used to get more user-friendly strings from these keys.

+

String representations

The string representation returned when calling str on a ZoneInfo object defaults to using the ZoneInfo.key attribute (see the note on usage in the attribute documentation):

>>> zone = ZoneInfo("Pacific/Kwajalein")
+>>> str(zone)
+'Pacific/Kwajalein'
+
+>>> dt = datetime(2020, 4, 1, 3, 15, tzinfo=zone)
+>>> f"{dt.isoformat()} [{dt.tzinfo}]"
+'2020-04-01T03:15:00+12:00 [Pacific/Kwajalein]'
+

For objects constructed from a file without specifying a key parameter, str falls back to calling repr(). ZoneInfo’s repr is implementation-defined and not necessarily stable between versions, but it is guaranteed not to be a valid ZoneInfo key.

Pickle serialization

Rather than serializing all transition data, ZoneInfo objects are serialized by key, and ZoneInfo objects constructed from files (even those with a value for key specified) cannot be pickled.

The behavior of a ZoneInfo file depends on how it was constructed:

  1. +

    ZoneInfo(key): When constructed with the primary constructor, a ZoneInfo object is serialized by key, and when deserialized, the deserializing process uses the primary and thus it is expected that these are expected to be the same object as other references to the same time zone. For example, if europe_berlin_pkl is a string containing a pickle constructed from ZoneInfo("Europe/Berlin"), one would expect the following behavior:

    >>> a = ZoneInfo("Europe/Berlin")
+>>> b = pickle.loads(europe_berlin_pkl)
+>>> a is b
+True
+
  2. +

    ZoneInfo.no_cache(key): When constructed from the cache-bypassing constructor, the ZoneInfo object is also serialized by key, but when deserialized, the deserializing process uses the cache bypassing constructor. If europe_berlin_pkl_nc is a string containing a pickle constructed from ZoneInfo.no_cache("Europe/Berlin"), one would expect the following behavior:

    >>> a = ZoneInfo("Europe/Berlin")
+>>> b = pickle.loads(europe_berlin_pkl_nc)
+>>> a is b
+False
+
  3. +ZoneInfo.from_file(fobj, /, key=None): When constructed from a file, the ZoneInfo object raises an exception on pickling. If an end user wants to pickle a ZoneInfo constructed from a file, it is recommended that they use a wrapper type or a custom serialization function: either serializing by key or storing the contents of the file object and serializing that.

This method of serialization requires that the time zone data for the required key be available on both the serializing and deserializing side, similar to the way that references to classes and functions are expected to exist in both the serializing and deserializing environments. It also means that no guarantees are made about the consistency of results when unpickling a ZoneInfo pickled in an environment with a different version of the time zone data.

Functions

+zoneinfo.available_timezones()
+

Get a set containing all the valid keys for IANA time zones available anywhere on the time zone path. This is recalculated on every call to the function.

This function only includes canonical zone names and does not include “special” zones such as those under the posix/ and right/ directories, or the posixrules zone.

Caution

This function may open a large number of files, as the best way to determine if a file on the time zone path is a valid time zone is to read the “magic string” at the beginning.

Note

These values are not designed to be exposed to end-users; for user facing elements, applications should use something like CLDR (the Unicode Common Locale Data Repository) to get more user-friendly strings. See also the cautionary note on ZoneInfo.key.

+
+zoneinfo.reset_tzpath(to=None)
+

Sets or resets the time zone search path (TZPATH) for the module. When called with no arguments, TZPATH is set to the default value.

Calling reset_tzpath will not invalidate the ZoneInfo cache, and so calls to the primary ZoneInfo constructor will only use the new TZPATH in the case of a cache miss.

The to parameter must be a sequence of strings or os.PathLike and not a string, all of which must be absolute paths. ValueError will be raised if something other than an absolute path is passed.

+

Globals

+zoneinfo.TZPATH
+

A read-only sequence representing the time zone search path – when constructing a ZoneInfo from a key, the key is joined to each entry in the TZPATH, and the first file found is used.

TZPATH may contain only absolute paths, never relative paths, regardless of how it is configured.

The object that zoneinfo.TZPATH points to may change in response to a call to reset_tzpath(), so it is recommended to use zoneinfo.TZPATH rather than importing TZPATH from zoneinfo or assigning a long-lived variable to zoneinfo.TZPATH.

For more information on configuring the time zone search path, see Configuring the data sources.

+

Exceptions and warnings

+exception zoneinfo.ZoneInfoNotFoundError
+

Raised when construction of a ZoneInfo object fails because the specified key could not be found on the system. This is a subclass of KeyError.

+
+exception zoneinfo.InvalidTZPathWarning
+

Raised when PYTHONTZPATH contains an invalid component that will be filtered out, such as a relative path.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/library/zoneinfo.html +

+
diff --git a/devdocs/python~3.12/license.html b/devdocs/python~3.12/license.html new file mode 100644 index 00000000..25c94d61 --- /dev/null +++ b/devdocs/python~3.12/license.html @@ -0,0 +1,791 @@ +

History and License

History of the software

Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum (CWI, see https://www.cwi.nl/) in the Netherlands as a successor of a language called ABC. Guido remains Python’s principal author, although it includes many contributions from others.

In 1995, Guido continued his work on Python at the Corporation for National Research Initiatives (CNRI, see https://www.cnri.reston.va.us/) in Reston, Virginia where he released several versions of the software.

In May 2000, Guido and the Python core development team moved to BeOpen.com to form the BeOpen PythonLabs team. In October of the same year, the PythonLabs team moved to Digital Creations (now Zope Corporation; see https://www.zope.org/). In 2001, the Python Software Foundation (PSF, see https://www.python.org/psf/) was formed, a non-profit organization created specifically to own Python-related Intellectual Property. Zope Corporation is a sponsoring member of the PSF.

All Python releases are Open Source (see https://opensource.org/ for the Open Source Definition). Historically, most, but not all, Python releases have also been GPL-compatible; the table below summarizes the various releases.

+ + + + + + + + + + + +

Release

Derived from

Year

Owner

GPL compatible?

0.9.0 thru 1.2

n/a

1991-1995

CWI

yes

1.3 thru 1.5.2

1.2

1995-1999

CNRI

yes

1.6

1.5.2

2000

CNRI

no

2.0

1.6

2000

BeOpen.com

no

1.6.1

1.6

2001

CNRI

no

2.1

2.0+1.6.1

2001

PSF

no

2.0.1

2.0+1.6.1

2001

PSF

yes

2.1.1

2.1+2.0.1

2001

PSF

yes

2.1.2

2.1.1

2002

PSF

yes

2.1.3

2.1.2

2002

PSF

yes

2.2 and above

2.1.1

2001-now

PSF

yes

Note

GPL-compatible doesn’t mean that we’re distributing Python under the GPL. All Python licenses, unlike the GPL, let you distribute a modified version without making your changes open source. The GPL-compatible licenses make it possible to combine Python with other software that is released under the GPL; the others don’t.

Thanks to the many outside volunteers who have worked under Guido’s direction to make these releases possible.

Terms and conditions for accessing or otherwise using Python

Python software and documentation are licensed under the PSF License Agreement.

Starting with Python 3.8.6, examples, recipes, and other code in the documentation are dual licensed under the PSF License Agreement and the Zero-Clause BSD license.

Some software incorporated into Python is under different licenses. The licenses are listed with code falling under that license. See Licenses and Acknowledgements for Incorporated Software for an incomplete list of these licenses.

PSF LICENSE AGREEMENT FOR PYTHON 3.12.1

1. This LICENSE AGREEMENT is between the Python Software Foundation ("PSF"), and
+   the Individual or Organization ("Licensee") accessing and otherwise using Python
+   3.12.1 software in source or binary form and its associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, PSF hereby
+   grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+   analyze, test, perform and/or display publicly, prepare derivative works,
+   distribute, and otherwise use Python 3.12.1 alone or in any derivative
+   version, provided, however, that PSF's License Agreement and PSF's notice of
+   copyright, i.e., "Copyright © 2001-2023 Python Software Foundation; All Rights
+   Reserved" are retained in Python 3.12.1 alone or in any derivative version
+   prepared by Licensee.
+
+3. In the event Licensee prepares a derivative work that is based on or
+   incorporates Python 3.12.1 or any part thereof, and wants to make the
+   derivative work available to others as provided herein, then Licensee hereby
+   agrees to include in any such work a brief summary of the changes made to Python
+   3.12.1.
+
+4. PSF is making Python 3.12.1 available to Licensee on an "AS IS" basis.
+   PSF MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED.  BY WAY OF
+   EXAMPLE, BUT NOT LIMITATION, PSF MAKES NO AND DISCLAIMS ANY REPRESENTATION OR
+   WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE
+   USE OF PYTHON 3.12.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. PSF SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 3.12.1
+   FOR ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF
+   MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 3.12.1, OR ANY DERIVATIVE
+   THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material breach of
+   its terms and conditions.
+
+7. Nothing in this License Agreement shall be deemed to create any relationship
+   of agency, partnership, or joint venture between PSF and Licensee.  This License
+   Agreement does not grant permission to use PSF trademarks or trade name in a
+   trademark sense to endorse or promote products or services of Licensee, or any
+   third party.
+
+8. By copying, installing or otherwise using Python 3.12.1, Licensee agrees
+   to be bound by the terms and conditions of this License Agreement.

BEOPEN.COM LICENSE AGREEMENT FOR PYTHON 2.0

BEOPEN PYTHON OPEN SOURCE LICENSE AGREEMENT VERSION 1

1. This LICENSE AGREEMENT is between BeOpen.com ("BeOpen"), having an office at
+   160 Saratoga Avenue, Santa Clara, CA 95051, and the Individual or Organization
+   ("Licensee") accessing and otherwise using this software in source or binary
+   form and its associated documentation ("the Software").
+
+2. Subject to the terms and conditions of this BeOpen Python License Agreement,
+   BeOpen hereby grants Licensee a non-exclusive, royalty-free, world-wide license
+   to reproduce, analyze, test, perform and/or display publicly, prepare derivative
+   works, distribute, and otherwise use the Software alone or in any derivative
+   version, provided, however, that the BeOpen Python License is retained in the
+   Software, alone or in any derivative version prepared by Licensee.
+
+3. BeOpen is making the Software available to Licensee on an "AS IS" basis.
+   BEOPEN MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED.  BY WAY OF
+   EXAMPLE, BUT NOT LIMITATION, BEOPEN MAKES NO AND DISCLAIMS ANY REPRESENTATION OR
+   WARRANTY OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE
+   USE OF THE SOFTWARE WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
+
+4. BEOPEN SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF THE SOFTWARE FOR
+   ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF USING,
+   MODIFYING OR DISTRIBUTING THE SOFTWARE, OR ANY DERIVATIVE THEREOF, EVEN IF
+   ADVISED OF THE POSSIBILITY THEREOF.
+
+5. This License Agreement will automatically terminate upon a material breach of
+   its terms and conditions.
+
+6. This License Agreement shall be governed by and interpreted in all respects
+   by the law of the State of California, excluding conflict of law provisions.
+   Nothing in this License Agreement shall be deemed to create any relationship of
+   agency, partnership, or joint venture between BeOpen and Licensee.  This License
+   Agreement does not grant permission to use BeOpen trademarks or trade names in a
+   trademark sense to endorse or promote products or services of Licensee, or any
+   third party.  As an exception, the "BeOpen Python" logos available at
+   http://www.pythonlabs.com/logos.html may be used according to the permissions
+   granted on that web page.
+
+7. By copying, installing or otherwise using the software, Licensee agrees to be
+   bound by the terms and conditions of this License Agreement.
+

CNRI LICENSE AGREEMENT FOR PYTHON 1.6.1

1. This LICENSE AGREEMENT is between the Corporation for National Research
+   Initiatives, having an office at 1895 Preston White Drive, Reston, VA 20191
+   ("CNRI"), and the Individual or Organization ("Licensee") accessing and
+   otherwise using Python 1.6.1 software in source or binary form and its
+   associated documentation.
+
+2. Subject to the terms and conditions of this License Agreement, CNRI hereby
+   grants Licensee a nonexclusive, royalty-free, world-wide license to reproduce,
+   analyze, test, perform and/or display publicly, prepare derivative works,
+   distribute, and otherwise use Python 1.6.1 alone or in any derivative version,
+   provided, however, that CNRI's License Agreement and CNRI's notice of copyright,
+   i.e., "Copyright © 1995-2001 Corporation for National Research Initiatives; All
+   Rights Reserved" are retained in Python 1.6.1 alone or in any derivative version
+   prepared by Licensee.  Alternately, in lieu of CNRI's License Agreement,
+   Licensee may substitute the following text (omitting the quotes): "Python 1.6.1
+   is made available subject to the terms and conditions in CNRI's License
+   Agreement.  This Agreement together with Python 1.6.1 may be located on the
+   internet using the following unique, persistent identifier (known as a handle):
+   1895.22/1013.  This Agreement may also be obtained from a proxy server on the
+   internet using the following URL: http://hdl.handle.net/1895.22/1013."
+
+3. In the event Licensee prepares a derivative work that is based on or
+   incorporates Python 1.6.1 or any part thereof, and wants to make the derivative
+   work available to others as provided herein, then Licensee hereby agrees to
+   include in any such work a brief summary of the changes made to Python 1.6.1.
+
+4. CNRI is making Python 1.6.1 available to Licensee on an "AS IS" basis.  CNRI
+   MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED.  BY WAY OF EXAMPLE,
+   BUT NOT LIMITATION, CNRI MAKES NO AND DISCLAIMS ANY REPRESENTATION OR WARRANTY
+   OF MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF
+   PYTHON 1.6.1 WILL NOT INFRINGE ANY THIRD PARTY RIGHTS.
+
+5. CNRI SHALL NOT BE LIABLE TO LICENSEE OR ANY OTHER USERS OF PYTHON 1.6.1 FOR
+   ANY INCIDENTAL, SPECIAL, OR CONSEQUENTIAL DAMAGES OR LOSS AS A RESULT OF
+   MODIFYING, DISTRIBUTING, OR OTHERWISE USING PYTHON 1.6.1, OR ANY DERIVATIVE
+   THEREOF, EVEN IF ADVISED OF THE POSSIBILITY THEREOF.
+
+6. This License Agreement will automatically terminate upon a material breach of
+   its terms and conditions.
+
+7. This License Agreement shall be governed by the federal intellectual property
+   law of the United States, including without limitation the federal copyright
+   law, and, to the extent such U.S. federal law does not apply, by the law of the
+   Commonwealth of Virginia, excluding Virginia's conflict of law provisions.
+   Notwithstanding the foregoing, with regard to derivative works based on Python
+   1.6.1 that incorporate non-separable material that was previously distributed
+   under the GNU General Public License (GPL), the law of the Commonwealth of
+   Virginia shall govern this License Agreement only as to issues arising under or
+   with respect to Paragraphs 4, 5, and 7 of this License Agreement.  Nothing in
+   this License Agreement shall be deemed to create any relationship of agency,
+   partnership, or joint venture between CNRI and Licensee.  This License Agreement
+   does not grant permission to use CNRI trademarks or trade name in a trademark
+   sense to endorse or promote products or services of Licensee, or any third
+   party.
+
+8. By clicking on the "ACCEPT" button where indicated, or by copying, installing
+   or otherwise using Python 1.6.1, Licensee agrees to be bound by the terms and
+   conditions of this License Agreement.
+

CWI LICENSE AGREEMENT FOR PYTHON 0.9.0 THROUGH 1.2

Copyright © 1991 - 1995, Stichting Mathematisch Centrum Amsterdam, The
+Netherlands.  All rights reserved.
+
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose and without fee is hereby granted, provided that
+the above copyright notice appear in all copies and that both that copyright
+notice and this permission notice appear in supporting documentation, and that
+the name of Stichting Mathematisch Centrum or CWI not be used in advertising or
+publicity pertaining to distribution of the software without specific, written
+prior permission.
+
+STICHTING MATHEMATISCH CENTRUM DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS
+SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN NO
+EVENT SHALL STICHTING MATHEMATISCH CENTRUM BE LIABLE FOR ANY SPECIAL, INDIRECT
+OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE,
+DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS
+SOFTWARE.
+

ZERO-CLAUSE BSD LICENSE FOR CODE IN THE PYTHON 3.12.1 DOCUMENTATION

Permission to use, copy, modify, and/or distribute this software for any
+purpose with or without fee is hereby granted.
+
+THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
+REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
+AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
+INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
+LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
+OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
+PERFORMANCE OF THIS SOFTWARE.
+

Licenses and Acknowledgements for Incorporated Software

This section is an incomplete, but growing list of licenses and acknowledgements for third-party software incorporated in the Python distribution.

Mersenne Twister

The _random C extension underlying the random module includes code based on a download from http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MT2002/emt19937ar.html. The following are the verbatim comments from the original code:

A C-program for MT19937, with initialization improved 2002/1/26.
+Coded by Takuji Nishimura and Makoto Matsumoto.
+
+Before using, initialize the state by using init_genrand(seed)
+or init_by_array(init_key, key_length).
+
+Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+ 1. Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+ 2. Redistributions in binary form must reproduce the above copyright
+    notice, this list of conditions and the following disclaimer in the
+    documentation and/or other materials provided with the distribution.
+
+ 3. The names of its contributors may not be used to endorse or promote
+    products derived from this software without specific prior written
+    permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+Any feedback is very welcome.
+http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
+email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
+

Sockets

The socket module uses the functions, getaddrinfo(), and getnameinfo(), which are coded in separate source files from the WIDE Project, https://www.wide.ad.jp/.

Copyright (C) 1995, 1996, 1997, and 1998 WIDE Project.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+3. Neither the name of the project nor the names of its contributors
+   may be used to endorse or promote products derived from this software
+   without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE PROJECT AND CONTRIBUTORS ``AS IS'' AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE PROJECT OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE.
+

Asynchronous socket services

The test.support.asynchat and test.support.asyncore modules contain the following notice:

Copyright 1996 by Sam Rushing
+
+                        All Rights Reserved
+
+Permission to use, copy, modify, and distribute this software and
+its documentation for any purpose and without fee is hereby
+granted, provided that the above copyright notice appear in all
+copies and that both that copyright notice and this permission
+notice appear in supporting documentation, and that the name of Sam
+Rushing not be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior
+permission.
+
+SAM RUSHING DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE,
+INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS, IN
+NO EVENT SHALL SAM RUSHING BE LIABLE FOR ANY SPECIAL, INDIRECT OR
+CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS
+OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT,
+NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN
+CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+

Execution tracing

The trace module contains the following notice:

portions copyright 2001, Autonomous Zones Industries, Inc., all rights...
+err...  reserved and offered to the public under the terms of the
+Python 2.2 license.
+Author: Zooko O'Whielacronx
+http://zooko.com/
+mailto:zooko@zooko.com
+
+Copyright 2000, Mojam Media, Inc., all rights reserved.
+Author: Skip Montanaro
+
+Copyright 1999, Bioreason, Inc., all rights reserved.
+Author: Andrew Dalke
+
+Copyright 1995-1997, Automatrix, Inc., all rights reserved.
+Author: Skip Montanaro
+
+Copyright 1991-1995, Stichting Mathematisch Centrum, all rights reserved.
+
+
+Permission to use, copy, modify, and distribute this Python software and
+its associated documentation for any purpose without fee is hereby
+granted, provided that the above copyright notice appears in all copies,
+and that both that copyright notice and this permission notice appear in
+supporting documentation, and that the name of neither Automatrix,
+Bioreason or Mojam Media be used in advertising or publicity pertaining to
+distribution of the software without specific, written prior permission.
+

UUencode and UUdecode functions

The uu module contains the following notice:

Copyright 1994 by Lance Ellinghouse
+Cathedral City, California Republic, United States of America.
+                       All Rights Reserved
+Permission to use, copy, modify, and distribute this software and its
+documentation for any purpose and without fee is hereby granted,
+provided that the above copyright notice appear in all copies and that
+both that copyright notice and this permission notice appear in
+supporting documentation, and that the name of Lance Ellinghouse
+not be used in advertising or publicity pertaining to distribution
+of the software without specific, written prior permission.
+LANCE ELLINGHOUSE DISCLAIMS ALL WARRANTIES WITH REGARD TO
+THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND
+FITNESS, IN NO EVENT SHALL LANCE ELLINGHOUSE CENTRUM BE LIABLE
+FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
+WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
+ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT
+OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
+
+Modified by Jack Jansen, CWI, July 1995:
+- Use binascii module to do the actual line-by-line conversion
+  between ascii and binary. This results in a 1000-fold speedup. The C
+  version is still 5 times faster, though.
+- Arguments more compliant with Python standard
+

XML Remote Procedure Calls

The xmlrpc.client module contains the following notice:

    The XML-RPC client interface is
+
+Copyright (c) 1999-2002 by Secret Labs AB
+Copyright (c) 1999-2002 by Fredrik Lundh
+
+By obtaining, using, and/or copying this software and/or its
+associated documentation, you agree that you have read, understood,
+and will comply with the following terms and conditions:
+
+Permission to use, copy, modify, and distribute this software and
+its associated documentation for any purpose and without fee is
+hereby granted, provided that the above copyright notice appears in
+all copies, and that both that copyright notice and this permission
+notice appear in supporting documentation, and that the name of
+Secret Labs AB or the author not be used in advertising or publicity
+pertaining to distribution of the software without specific, written
+prior permission.
+
+SECRET LABS AB AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD
+TO THIS SOFTWARE, INCLUDING ALL IMPLIED WARRANTIES OF MERCHANT-
+ABILITY AND FITNESS.  IN NO EVENT SHALL SECRET LABS AB OR THE AUTHOR
+BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY
+DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS,
+WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS
+ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE
+OF THIS SOFTWARE.
+

test_epoll

The test.test_epoll module contains the following notice:

Copyright (c) 2001-2006 Twisted Matrix Laboratories.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

Select kqueue

The select module contains the following notice for the kqueue interface:

Copyright (c) 2000 Doug White, 2006 James Knight, 2007 Christian Heimes
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS ``AS IS'' AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE.
+

SipHash24

The file Python/pyhash.c contains Marek Majkowski’ implementation of Dan Bernstein’s SipHash24 algorithm. It contains the following note:

<MIT License>
+Copyright (c) 2013  Marek Majkowski <marek@popcount.org>
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+</MIT License>
+
+Original location:
+   https://github.com/majek/csiphash/
+
+Solution inspired by code from:
+   Samuel Neves (supercop/crypto_auth/siphash24/little)
+   djb (supercop/crypto_auth/siphash24/little2)
+   Jean-Philippe Aumasson (https://131002.net/siphash/siphash24.c)
+

strtod and dtoa

The file Python/dtoa.c, which supplies C functions dtoa and strtod for conversion of C doubles to and from strings, is derived from the file of the same name by David M. Gay, currently available from https://web.archive.org/web/20220517033456/http://www.netlib.org/fp/dtoa.c. The original file, as retrieved on March 16, 2009, contains the following copyright and licensing notice:

/****************************************************************
+ *
+ * The author of this software is David M. Gay.
+ *
+ * Copyright (c) 1991, 2000, 2001 by Lucent Technologies.
+ *
+ * Permission to use, copy, modify, and distribute this software for any
+ * purpose without fee is hereby granted, provided that this entire notice
+ * is included in all copies of any software which is or includes a copy
+ * or modification of this software and in all copies of the supporting
+ * documentation for such software.
+ *
+ * THIS SOFTWARE IS BEING PROVIDED "AS IS", WITHOUT ANY EXPRESS OR IMPLIED
+ * WARRANTY.  IN PARTICULAR, NEITHER THE AUTHOR NOR LUCENT MAKES ANY
+ * REPRESENTATION OR WARRANTY OF ANY KIND CONCERNING THE MERCHANTABILITY
+ * OF THIS SOFTWARE OR ITS FITNESS FOR ANY PARTICULAR PURPOSE.
+ *
+ ***************************************************************/
+

OpenSSL

The modules hashlib, posix, ssl, crypt use the OpenSSL library for added performance if made available by the operating system. Additionally, the Windows and macOS installers for Python may include a copy of the OpenSSL libraries, so we include a copy of the OpenSSL license here. For the OpenSSL 3.0 release, and later releases derived from that, the Apache License v2 applies:

                              Apache License
+                        Version 2.0, January 2004
+                     https://www.apache.org/licenses/
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+   "License" shall mean the terms and conditions for use, reproduction,
+   and distribution as defined by Sections 1 through 9 of this document.
+
+   "Licensor" shall mean the copyright owner or entity authorized by
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+
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+   other entities that control, are controlled by, or are under common
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+   direction or management of such entity, whether by contract or
+   otherwise, or (ii) ownership of fifty percent (50%) or more of the
+   outstanding shares, or (iii) beneficial ownership of such entity.
+
+   "You" (or "Your") shall mean an individual or Legal Entity
+   exercising permissions granted by this License.
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+   including but not limited to software source code, documentation
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+
+   "Work" shall mean the work of authorship, whether in Source or
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+   (an example is provided in the Appendix below).
+
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+   editorial revisions, annotations, elaborations, or other modifications
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+
+5. Submission of Contributions. Unless You explicitly state otherwise,
+   any Contribution intentionally submitted for inclusion in the Work
+   by You to the Licensor shall be under the terms and conditions of
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+
+6. Trademarks. This License does not grant permission to use the trade
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+
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+   PARTICULAR PURPOSE. You are solely responsible for determining the
+   appropriateness of using or redistributing the Work and assume any
+   risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory,
+   whether in tort (including negligence), contract, or otherwise,
+   unless required by applicable law (such as deliberate and grossly
+   negligent acts) or agreed to in writing, shall any Contributor be
+   liable to You for damages, including any direct, indirect, special,
+   incidental, or consequential damages of any character arising as a
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+   of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
+

expat

The pyexpat extension is built using an included copy of the expat sources unless the build is configured --with-system-expat:

Copyright (c) 1998, 1999, 2000 Thai Open Source Software Center Ltd
+                               and Clark Cooper
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+

libffi

The _ctypes C extension underlying the ctypes module is built using an included copy of the libffi sources unless the build is configured --with-system-libffi:

Copyright (c) 1996-2008  Red Hat, Inc and others.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+``Software''), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be included
+in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED ``AS IS'', WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT.  IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
+

zlib

The zlib extension is built using an included copy of the zlib sources if the zlib version found on the system is too old to be used for the build:

Copyright (C) 1995-2011 Jean-loup Gailly and Mark Adler
+
+This software is provided 'as-is', without any express or implied
+warranty.  In no event will the authors be held liable for any damages
+arising from the use of this software.
+
+Permission is granted to anyone to use this software for any purpose,
+including commercial applications, and to alter it and redistribute it
+freely, subject to the following restrictions:
+
+1. The origin of this software must not be misrepresented; you must not
+   claim that you wrote the original software. If you use this software
+   in a product, an acknowledgment in the product documentation would be
+   appreciated but is not required.
+
+2. Altered source versions must be plainly marked as such, and must not be
+   misrepresented as being the original software.
+
+3. This notice may not be removed or altered from any source distribution.
+
+Jean-loup Gailly        Mark Adler
+jloup@gzip.org          madler@alumni.caltech.edu
+

cfuhash

The implementation of the hash table used by the tracemalloc is based on the cfuhash project:

Copyright (c) 2005 Don Owens
+All rights reserved.
+
+This code is released under the BSD license:
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+  * Redistributions of source code must retain the above copyright
+    notice, this list of conditions and the following disclaimer.
+
+  * Redistributions in binary form must reproduce the above
+    copyright notice, this list of conditions and the following
+    disclaimer in the documentation and/or other materials provided
+    with the distribution.
+
+  * Neither the name of the author nor the names of its
+    contributors may be used to endorse or promote products derived
+    from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
+FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
+INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED
+OF THE POSSIBILITY OF SUCH DAMAGE.
+

libmpdec

The _decimal C extension underlying the decimal module is built using an included copy of the libmpdec library unless the build is configured --with-system-libmpdec:

Copyright (c) 2008-2020 Stefan Krah. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+1. Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE AUTHOR AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED.  IN NO EVENT SHALL THE AUTHOR OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS
+OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGE.
+

W3C C14N test suite

The C14N 2.0 test suite in the test package (Lib/test/xmltestdata/c14n-20/) was retrieved from the W3C website at https://www.w3.org/TR/xml-c14n2-testcases/ and is distributed under the 3-clause BSD license:

Copyright (c) 2013 W3C(R) (MIT, ERCIM, Keio, Beihang),
+All Rights Reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions
+are met:
+
+* Redistributions of works must retain the original copyright notice,
+  this list of conditions and the following disclaimer.
+* Redistributions in binary form must reproduce the original copyright
+  notice, this list of conditions and the following disclaimer in the
+  documentation and/or other materials provided with the distribution.
+* Neither the name of the W3C nor the names of its contributors may be
+  used to endorse or promote products derived from this work without
+  specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+

Audioop

The audioop module uses the code base in g771.c file of the SoX project:

Programming the AdLib/Sound Blaster
+FM Music Chips
+Version 2.0 (24 Feb 1992)
+Copyright (c) 1991, 1992 by Jeffrey S. Lee
+jlee@smylex.uucp
+Warranty and Copyright Policy
+This document is provided on an "as-is" basis, and its author makes
+no warranty or representation, express or implied, with respect to
+its quality performance or fitness for a particular purpose.  In no
+event will the author of this document be liable for direct, indirect,
+special, incidental, or consequential damages arising out of the use
+or inability to use the information contained within.  Use of this
+document is at your own risk.
+This file may be used and copied freely so long as the applicable
+copyright notices are retained, and no modifications are made to the
+text of the document.  No money shall be charged for its distribution
+beyond reasonable shipping, handling and duplication costs, nor shall
+proprietary changes be made to this document so that it cannot be
+distributed freely.  This document may not be included in published
+material or commercial packages without the written consent of its
+author.
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/license.html +

+
diff --git a/devdocs/python~3.12/metadata b/devdocs/python~3.12/metadata new file mode 100644 index 00000000..aa43b008 --- /dev/null +++ b/devdocs/python~3.12/metadata @@ -0,0 +1,2 @@ +(1 (name . "Python") (slug . "python~3.12") (type . "python") (links (home . "https://www.python.org/") (code . "https://github.com/python/cpython")) (version . "3.12") (release . "3.12.1") (mtime . 1704473584) (db_size . 17684105) (attribution . "© 2001–2023 Python Software Foundation
+ Licensed under the PSF License.")) \ No newline at end of file diff --git a/devdocs/python~3.12/py-modindex.html b/devdocs/python~3.12/py-modindex.html new file mode 100644 index 00000000..0cb039bd --- /dev/null +++ b/devdocs/python~3.12/py-modindex.html @@ -0,0 +1,1452 @@ +

Python Module Index

_ | a | b | c | d | e | f | g | h | i | j | k | l | m | n | o | p | q | r | s | t | u | v | w | x | z
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
_ +
__future__ + Future statement definitions +
__main__ + The environment where top-level code is run. Covers command-line interfaces, import-time behavior, and ``__name__ == '__main__'``. +
_thread + Low-level threading API. +
_tkinter + A binary module that contains the low-level interface to Tcl/Tk. +
a +
abc + Abstract base classes according to :pep:`3119`. +
aifc + +Deprecated: Read and write audio files in AIFF or AIFC format. +
argparse + Command-line option and argument parsing library. +
array + Space efficient arrays of uniformly typed numeric values. +
ast + Abstract Syntax Tree classes and manipulation. +
asyncio + Asynchronous I/O. +
atexit + Register and execute cleanup functions. +
audioop + +Deprecated: Manipulate raw audio data. +
b +
base64 + RFC 4648: Base16, Base32, Base64 Data Encodings; Base85 and Ascii85 +
bdb + Debugger framework. +
binascii + Tools for converting between binary and various ASCII-encoded binary representations. +
bisect + Array bisection algorithms for binary searching. +
builtins + The module that provides the built-in namespace. +
bz2 + Interfaces for bzip2 compression and decompression. +
c +
calendar + Functions for working with calendars, including some emulation of the Unix cal program. +
cgi + +Deprecated: Helpers for running Python scripts via the Common Gateway Interface. +
cgitb + +Deprecated: Configurable traceback handler for CGI scripts. +
chunk + +Deprecated: Module to read IFF chunks. +
cmath + Mathematical functions for complex numbers. +
cmd + Build line-oriented command interpreters. +
code + Facilities to implement read-eval-print loops. +
codecs + Encode and decode data and streams. +
codeop + Compile (possibly incomplete) Python code. +
collections + Container datatypes +
collections.abc + Abstract base classes for containers +
colorsys + Conversion functions between RGB and other color systems. +
compileall + Tools for byte-compiling all Python source files in a directory tree. +
concurrent + +
concurrent.futures + Execute computations concurrently using threads or processes. +
configparser + Configuration file parser. +
contextlib + Utilities for with-statement contexts. +
contextvars + Context Variables +
copy + Shallow and deep copy operations. +
copyreg + Register pickle support functions. +
cProfile + +
crypt (Unix) + +Deprecated: The crypt() function used to check Unix passwords. +
csv + Write and read tabular data to and from delimited files. +
ctypes + A foreign function library for Python. +
curses (Unix) + An interface to the curses library, providing portable terminal handling. +
curses.ascii + Constants and set-membership functions for ASCII characters. +
curses.panel + A panel stack extension that adds depth to curses windows. +
curses.textpad + Emacs-like input editing in a curses window. +
d +
dataclasses + Generate special methods on user-defined classes. +
datetime + Basic date and time types. +
dbm + Interfaces to various Unix "database" formats. +
dbm.dumb + Portable implementation of the simple DBM interface. +
dbm.gnu (Unix) + GNU's reinterpretation of dbm. +
dbm.ndbm (Unix) + The standard "database" interface, based on ndbm. +
decimal + Implementation of the General Decimal Arithmetic Specification. +
difflib + Helpers for computing differences between objects. +
dis + Disassembler for Python bytecode. +
doctest + Test pieces of code within docstrings. +
e +
email + Package supporting the parsing, manipulating, and generating email messages. +
email.charset + Character Sets +
email.contentmanager + Storing and Retrieving Content from MIME Parts +
email.encoders + Encoders for email message payloads. +
email.errors + The exception classes used by the email package. +
email.generator + Generate flat text email messages from a message structure. +
email.header + Representing non-ASCII headers +
email.headerregistry + Automatic Parsing of headers based on the field name +
email.iterators + Iterate over a message object tree. +
email.message + The base class representing email messages. +
email.mime + Build MIME messages. +
email.parser + Parse flat text email messages to produce a message object structure. +
email.policy + Controlling the parsing and generating of messages +
email.utils + Miscellaneous email package utilities. +
encodings + +
encodings.idna + Internationalized Domain Names implementation +
encodings.mbcs + Windows ANSI codepage +
encodings.utf_8_sig + UTF-8 codec with BOM signature +
ensurepip + Bootstrapping the "pip" installer into an existing Python installation or virtual environment. +
enum + Implementation of an enumeration class. +
errno + Standard errno system symbols. +
f +
faulthandler + Dump the Python traceback. +
fcntl (Unix) + The fcntl() and ioctl() system calls. +
filecmp + Compare files efficiently. +
fileinput + Loop over standard input or a list of files. +
fnmatch + Unix shell style filename pattern matching. +
fractions + Rational numbers. +
ftplib + FTP protocol client (requires sockets). +
functools + Higher-order functions and operations on callable objects. +
g +
gc + Interface to the cycle-detecting garbage collector. +
getopt + Portable parser for command line options; support both short and long option names. +
getpass + Portable reading of passwords and retrieval of the userid. +
gettext + Multilingual internationalization services. +
glob + Unix shell style pathname pattern expansion. +
graphlib + Functionality to operate with graph-like structures +
grp (Unix) + The group database (getgrnam() and friends). +
gzip + Interfaces for gzip compression and decompression using file objects. +
h +
hashlib + Secure hash and message digest algorithms. +
heapq + Heap queue algorithm (a.k.a. priority queue). +
hmac + Keyed-Hashing for Message Authentication (HMAC) implementation +
html + Helpers for manipulating HTML. +
html.entities + Definitions of HTML general entities. +
html.parser + A simple parser that can handle HTML and XHTML. +
http + HTTP status codes and messages +
http.client + HTTP and HTTPS protocol client (requires sockets). +
http.cookiejar + Classes for automatic handling of HTTP cookies. +
http.cookies + Support for HTTP state management (cookies). +
http.server + HTTP server and request handlers. +
i +
idlelib + Implementation package for the IDLE shell/editor. +
imaplib + IMAP4 protocol client (requires sockets). +
imghdr + +Deprecated: Determine the type of image contained in a file or byte stream. +
importlib + The implementation of the import machinery. +
importlib.abc + Abstract base classes related to import +
importlib.machinery + Importers and path hooks +
importlib.metadata + Accessing package metadata +
importlib.resources + Package resource reading, opening, and access +
importlib.resources.abc + Abstract base classes for resources +
importlib.util + Utility code for importers +
inspect + Extract information and source code from live objects. +
io + Core tools for working with streams. +
ipaddress + IPv4/IPv6 manipulation library. +
itertools + Functions creating iterators for efficient looping. +
j +
json + Encode and decode the JSON format. +
json.tool + A command line to validate and pretty-print JSON. +
k +
keyword + Test whether a string is a keyword in Python. +
l +
lib2to3 + The 2to3 library +
linecache + Provides random access to individual lines from text files. +
locale + Internationalization services. +
logging + Flexible event logging system for applications. +
logging.config + Configuration of the logging module. +
logging.handlers + Handlers for the logging module. +
lzma + A Python wrapper for the liblzma compression library. +
m +
mailbox + Manipulate mailboxes in various formats +
mailcap + +Deprecated: Mailcap file handling. +
marshal + Convert Python objects to streams of bytes and back (with different constraints). +
math + Mathematical functions (sin() etc.). +
mimetypes + Mapping of filename extensions to MIME types. +
mmap + Interface to memory-mapped files for Unix and Windows. +
modulefinder + Find modules used by a script. +
msilib (Windows) + +Deprecated: Creation of Microsoft Installer files, and CAB files. +
msvcrt (Windows) + Miscellaneous useful routines from the MS VC++ runtime. +
multiprocessing + Process-based parallelism. +
multiprocessing.connection + API for dealing with sockets. +
multiprocessing.dummy + Dumb wrapper around threading. +
multiprocessing.managers + Share data between process with shared objects. +
multiprocessing.pool + Create pools of processes. +
multiprocessing.shared_memory + Provides shared memory for direct access across processes. +
multiprocessing.sharedctypes + Allocate ctypes objects from shared memory. +
n +
netrc + Loading of .netrc files. +
nis (Unix) + +Deprecated: Interface to Sun's NIS (Yellow Pages) library. +
nntplib + +Deprecated: NNTP protocol client (requires sockets). +
numbers + Numeric abstract base classes (Complex, Real, Integral, etc.). +
o +
operator + Functions corresponding to the standard operators. +
optparse + +Deprecated: Command-line option parsing library. +
os + Miscellaneous operating system interfaces. +
os.path + Operations on pathnames. +
ossaudiodev (Linux, FreeBSD) + +Deprecated: Access to OSS-compatible audio devices. +
p +
pathlib + Object-oriented filesystem paths +
pdb + The Python debugger for interactive interpreters. +
pickle + Convert Python objects to streams of bytes and back. +
pickletools + Contains extensive comments about the pickle protocols and pickle-machine opcodes, as well as some useful functions. +
pipes (Unix) + +Deprecated: A Python interface to Unix shell pipelines. +
pkgutil + Utilities for the import system. +
platform + Retrieves as much platform identifying data as possible. +
plistlib + Generate and parse Apple plist files. +
poplib + POP3 protocol client (requires sockets). +
posix (Unix) + The most common POSIX system calls (normally used via module os). +
pprint + Data pretty printer. +
profile + Python source profiler. +
pstats + Statistics object for use with the profiler. +
pty (Unix) + Pseudo-Terminal Handling for Unix. +
pwd (Unix) + The password database (getpwnam() and friends). +
py_compile + Generate byte-code files from Python source files. +
pyclbr + Supports information extraction for a Python module browser. +
pydoc + Documentation generator and online help system. +
q +
queue + A synchronized queue class. +
quopri + Encode and decode files using the MIME quoted-printable encoding. +
r +
random + Generate pseudo-random numbers with various common distributions. +
re + Regular expression operations. +
readline (Unix) + GNU readline support for Python. +
reprlib + Alternate repr() implementation with size limits. +
resource (Unix) + An interface to provide resource usage information on the current process. +
rlcompleter + Python identifier completion, suitable for the GNU readline library. +
runpy + Locate and run Python modules without importing them first. +
s +
sched + General purpose event scheduler. +
secrets + Generate secure random numbers for managing secrets. +
select + Wait for I/O completion on multiple streams. +
selectors + High-level I/O multiplexing. +
shelve + Python object persistence. +
shlex + Simple lexical analysis for Unix shell-like languages. +
shutil + High-level file operations, including copying. +
signal + Set handlers for asynchronous events. +
site + Module responsible for site-specific configuration. +
sitecustomize + +
smtplib + SMTP protocol client (requires sockets). +
sndhdr + +Deprecated: Determine type of a sound file. +
socket + Low-level networking interface. +
socketserver + A framework for network servers. +
spwd (Unix) + +Deprecated: The shadow password database (getspnam() and friends). +
sqlite3 + A DB-API 2.0 implementation using SQLite 3.x. +
ssl + TLS/SSL wrapper for socket objects +
stat + Utilities for interpreting the results of os.stat(), os.lstat() and os.fstat(). +
statistics + Mathematical statistics functions +
string + Common string operations. +
stringprep + String preparation, as per RFC 3453 +
struct + Interpret bytes as packed binary data. +
subprocess + Subprocess management. +
sunau + +Deprecated: Provide an interface to the Sun AU sound format. +
symtable + Interface to the compiler's internal symbol tables. +
sys + Access system-specific parameters and functions. +
sys.monitoring + Access and control event monitoring +
sysconfig + Python's configuration information +
syslog (Unix) + An interface to the Unix syslog library routines. +
t +
tabnanny + Tool for detecting white space related problems in Python source files in a directory tree. +
tarfile + Read and write tar-format archive files. +
telnetlib + +Deprecated: Telnet client class. +
tempfile + Generate temporary files and directories. +
termios (Unix) + POSIX style tty control. +
test + Regression tests package containing the testing suite for Python. +
test.regrtest + Drives the regression test suite. +
test.support + Support for Python's regression test suite. +
test.support.bytecode_helper + Support tools for testing correct bytecode generation. +
test.support.import_helper + Support for import tests. +
test.support.os_helper + Support for os tests. +
test.support.script_helper + Support for Python's script execution tests. +
test.support.socket_helper + Support for socket tests. +
test.support.threading_helper + Support for threading tests. +
test.support.warnings_helper + Support for warnings tests. +
textwrap + Text wrapping and filling +
threading + Thread-based parallelism. +
time + Time access and conversions. +
timeit + Measure the execution time of small code snippets. +
tkinter + Interface to Tcl/Tk for graphical user interfaces +
tkinter.colorchooser (Tk) + Color choosing dialog +
tkinter.commondialog (Tk) + Tkinter base class for dialogs +
tkinter.dnd (Tk) + Tkinter drag-and-drop interface +
tkinter.filedialog (Tk) + Dialog classes for file selection +
tkinter.font (Tk) + Tkinter font-wrapping class +
tkinter.messagebox (Tk) + Various types of alert dialogs +
tkinter.scrolledtext (Tk) + Text widget with a vertical scroll bar. +
tkinter.simpledialog (Tk) + Simple dialog windows +
tkinter.tix + Tk Extension Widgets for Tkinter +
tkinter.ttk + Tk themed widget set +
token + Constants representing terminal nodes of the parse tree. +
tokenize + Lexical scanner for Python source code. +
tomllib + Parse TOML files. +
trace + Trace or track Python statement execution. +
traceback + Print or retrieve a stack traceback. +
tracemalloc + Trace memory allocations. +
tty (Unix) + Utility functions that perform common terminal control operations. +
turtle + An educational framework for simple graphics applications +
turtledemo + A viewer for example turtle scripts +
types + Names for built-in types. +
typing + Support for type hints (see :pep:`484`). +
u +
unicodedata + Access the Unicode Database. +
unittest + Unit testing framework for Python. +
unittest.mock + Mock object library. +
urllib + +
urllib.error + Exception classes raised by urllib.request. +
urllib.parse + Parse URLs into or assemble them from components. +
urllib.request + Extensible library for opening URLs. +
urllib.response + Response classes used by urllib. +
urllib.robotparser + Load a robots.txt file and answer questions about fetchability of other URLs. +
usercustomize + +
uu + +Deprecated: Encode and decode files in uuencode format. +
uuid + UUID objects (universally unique identifiers) according to RFC 4122 +
v +
venv + Creation of virtual environments. +
w +
warnings + Issue warning messages and control their disposition. +
wave + Provide an interface to the WAV sound format. +
weakref + Support for weak references and weak dictionaries. +
webbrowser + Easy-to-use controller for web browsers. +
winreg (Windows) + Routines and objects for manipulating the Windows registry. +
winsound (Windows) + Access to the sound-playing machinery for Windows. +
wsgiref + WSGI Utilities and Reference Implementation. +
wsgiref.handlers + WSGI server/gateway base classes. +
wsgiref.headers + WSGI response header tools. +
wsgiref.simple_server + A simple WSGI HTTP server. +
wsgiref.types + WSGI types for static type checking +
wsgiref.util + WSGI environment utilities. +
wsgiref.validate + WSGI conformance checker. +
x +
xdrlib + +Deprecated: Encoders and decoders for the External Data Representation (XDR). +
xml + Package containing XML processing modules +
xml.dom + Document Object Model API for Python. +
xml.dom.minidom + Minimal Document Object Model (DOM) implementation. +
xml.dom.pulldom + Support for building partial DOM trees from SAX events. +
xml.etree.ElementInclude + +
xml.etree.ElementTree + Implementation of the ElementTree API. +
xml.parsers.expat + An interface to the Expat non-validating XML parser. +
xml.parsers.expat.errors + +
xml.parsers.expat.model + +
xml.sax + Package containing SAX2 base classes and convenience functions. +
xml.sax.handler + Base classes for SAX event handlers. +
xml.sax.saxutils + Convenience functions and classes for use with SAX. +
xml.sax.xmlreader + Interface which SAX-compliant XML parsers must implement. +
xmlrpc + +
xmlrpc.client + XML-RPC client access. +
xmlrpc.server + Basic XML-RPC server implementations. +
z +
zipapp + Manage executable Python zip archives +
zipfile + Read and write ZIP-format archive files. +
zipimport + Support for importing Python modules from ZIP archives. +
zlib + Low-level interface to compression and decompression routines compatible with gzip. +
zoneinfo + IANA time zone support +
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/py-modindex.html +

+
diff --git a/devdocs/python~3.12/reference%2Fcompound_stmts.html b/devdocs/python~3.12/reference%2Fcompound_stmts.html new file mode 100644 index 00000000..f6989d47 --- /dev/null +++ b/devdocs/python~3.12/reference%2Fcompound_stmts.html @@ -0,0 +1,487 @@ +

Compound statements

Compound statements contain (groups of) other statements; they affect or control the execution of those other statements in some way. In general, compound statements span multiple lines, although in simple incarnations a whole compound statement may be contained in one line.

The if, while and for statements implement traditional control flow constructs. try specifies exception handlers and/or cleanup code for a group of statements, while the with statement allows the execution of initialization and finalization code around a block of code. Function and class definitions are also syntactically compound statements.

A compound statement consists of one or more ‘clauses.’ A clause consists of a header and a ‘suite.’ The clause headers of a particular compound statement are all at the same indentation level. Each clause header begins with a uniquely identifying keyword and ends with a colon. A suite is a group of statements controlled by a clause. A suite can be one or more semicolon-separated simple statements on the same line as the header, following the header’s colon, or it can be one or more indented statements on subsequent lines. Only the latter form of a suite can contain nested compound statements; the following is illegal, mostly because it wouldn’t be clear to which if clause a following else clause would belong:

if test1: if test2: print(x)
+

Also note that the semicolon binds tighter than the colon in this context, so that in the following example, either all or none of the print() calls are executed:

if x < y < z: print(x); print(y); print(z)
+

Summarizing:

+compound_stmt ::=  if_stmt
+                   | while_stmt
+                   | for_stmt
+                   | try_stmt
+                   | with_stmt
+                   | match_stmt
+                   | funcdef
+                   | classdef
+                   | async_with_stmt
+                   | async_for_stmt
+                   | async_funcdef
+suite         ::=  stmt_list NEWLINE | NEWLINE INDENT statement+ DEDENT
+statement     ::=  stmt_list NEWLINE | compound_stmt
+stmt_list     ::=  simple_stmt (";" simple_stmt)* [";"]
+

Note that statements always end in a NEWLINE possibly followed by a DEDENT. Also note that optional continuation clauses always begin with a keyword that cannot start a statement, thus there are no ambiguities (the ‘dangling else’ problem is solved in Python by requiring nested if statements to be indented).

The formatting of the grammar rules in the following sections places each clause on a separate line for clarity.

+8.1. The if statement

The if statement is used for conditional execution:

+if_stmt ::=  "if" assignment_expression ":" suite
+             ("elif" assignment_expression ":" suite)*
+             ["else" ":" suite]
+

It selects exactly one of the suites by evaluating the expressions one by one until one is found to be true (see section Boolean operations for the definition of true and false); then that suite is executed (and no other part of the if statement is executed or evaluated). If all expressions are false, the suite of the else clause, if present, is executed.

+8.2. The while statement

The while statement is used for repeated execution as long as an expression is true:

+while_stmt ::=  "while" assignment_expression ":" suite
+                ["else" ":" suite]
+

This repeatedly tests the expression and, if it is true, executes the first suite; if the expression is false (which may be the first time it is tested) the suite of the else clause, if present, is executed and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and goes back to testing the expression.

+8.3. The for statement

The for statement is used to iterate over the elements of a sequence (such as a string, tuple or list) or other iterable object:

+for_stmt ::=  "for" target_list "in" starred_list ":" suite
+              ["else" ":" suite]
+

The starred_list expression is evaluated once; it should yield an iterable object. An iterator is created for that iterable. The first item provided by the iterator is then assigned to the target list using the standard rules for assignments (see Assignment statements), and the suite is executed. This repeats for each item provided by the iterator. When the iterator is exhausted, the suite in the else clause, if present, is executed, and the loop terminates.

A break statement executed in the first suite terminates the loop without executing the else clause’s suite. A continue statement executed in the first suite skips the rest of the suite and continues with the next item, or with the else clause if there is no next item.

The for-loop makes assignments to the variables in the target list. This overwrites all previous assignments to those variables including those made in the suite of the for-loop:

for i in range(10):
+    print(i)
+    i = 5             # this will not affect the for-loop
+                      # because i will be overwritten with the next
+                      # index in the range
+

Names in the target list are not deleted when the loop is finished, but if the sequence is empty, they will not have been assigned to at all by the loop. Hint: the built-in type range() represents immutable arithmetic sequences of integers. For instance, iterating range(3) successively yields 0, 1, and then 2.

Changed in version 3.11: Starred elements are now allowed in the expression list.

+8.4. The try statement

The try statement specifies exception handlers and/or cleanup code for a group of statements:

+try_stmt  ::=  try1_stmt | try2_stmt | try3_stmt
+try1_stmt ::=  "try" ":" suite
+               ("except" [expression ["as" identifier]] ":" suite)+
+               ["else" ":" suite]
+               ["finally" ":" suite]
+try2_stmt ::=  "try" ":" suite
+               ("except" "*" expression ["as" identifier] ":" suite)+
+               ["else" ":" suite]
+               ["finally" ":" suite]
+try3_stmt ::=  "try" ":" suite
+               "finally" ":" suite
+

Additional information on exceptions can be found in section Exceptions, and information on using the raise statement to generate exceptions may be found in section The raise statement.

+8.4.1. except clause

The except clause(s) specify one or more exception handlers. When no exception occurs in the try clause, no exception handler is executed. When an exception occurs in the try suite, a search for an exception handler is started. This search inspects the except clauses in turn until one is found that matches the exception. An expression-less except clause, if present, must be last; it matches any exception. For an except clause with an expression, that expression is evaluated, and the clause matches the exception if the resulting object is “compatible” with the exception. An object is compatible with an exception if the object is the class or a non-virtual base class of the exception object, or a tuple containing an item that is the class or a non-virtual base class of the exception object.

If no except clause matches the exception, the search for an exception handler continues in the surrounding code and on the invocation stack. 1

If the evaluation of an expression in the header of an except clause raises an exception, the original search for a handler is canceled and a search starts for the new exception in the surrounding code and on the call stack (it is treated as if the entire try statement raised the exception).

When a matching except clause is found, the exception is assigned to the target specified after the as keyword in that except clause, if present, and the except clause’s suite is executed. All except clauses must have an executable block. When the end of this block is reached, execution continues normally after the entire try statement. (This means that if two nested handlers exist for the same exception, and the exception occurs in the try clause of the inner handler, the outer handler will not handle the exception.)

When an exception has been assigned using as target, it is cleared at the end of the except clause. This is as if

except E as N:
+    foo
+

was translated to

except E as N:
+    try:
+        foo
+    finally:
+        del N
+

This means the exception must be assigned to a different name to be able to refer to it after the except clause. Exceptions are cleared because with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs.

Before an except clause’s suite is executed, the exception is stored in the sys module, where it can be accessed from within the body of the except clause by calling sys.exception(). When leaving an exception handler, the exception stored in the sys module is reset to its previous value:

>>> print(sys.exception())
+None
+>>> try:
+...     raise TypeError
+... except:
+...     print(repr(sys.exception()))
+...     try:
+...          raise ValueError
+...     except:
+...         print(repr(sys.exception()))
+...     print(repr(sys.exception()))
+...
+TypeError()
+ValueError()
+TypeError()
+>>> print(sys.exception())
+None
+

+8.4.2. except* clause

The except* clause(s) are used for handling ExceptionGroups. The exception type for matching is interpreted as in the case of except, but in the case of exception groups we can have partial matches when the type matches some of the exceptions in the group. This means that multiple except* clauses can execute, each handling part of the exception group. Each clause executes at most once and handles an exception group of all matching exceptions. Each exception in the group is handled by at most one except* clause, the first that matches it.

>>> try:
+...     raise ExceptionGroup("eg",
+...         [ValueError(1), TypeError(2), OSError(3), OSError(4)])
+... except* TypeError as e:
+...     print(f'caught {type(e)} with nested {e.exceptions}')
+... except* OSError as e:
+...     print(f'caught {type(e)} with nested {e.exceptions}')
+...
+caught <class 'ExceptionGroup'> with nested (TypeError(2),)
+caught <class 'ExceptionGroup'> with nested (OSError(3), OSError(4))
+  + Exception Group Traceback (most recent call last):
+  |   File "<stdin>", line 2, in <module>
+  | ExceptionGroup: eg
+  +-+---------------- 1 ----------------
+    | ValueError: 1
+    +------------------------------------
+

Any remaining exceptions that were not handled by any except* clause are re-raised at the end, along with all exceptions that were raised from within the except* clauses. If this list contains more than one exception to reraise, they are combined into an exception group.

If the raised exception is not an exception group and its type matches one of the except* clauses, it is caught and wrapped by an exception group with an empty message string.

>>> try:
+...     raise BlockingIOError
+... except* BlockingIOError as e:
+...     print(repr(e))
+...
+ExceptionGroup('', (BlockingIOError()))
+

An except* clause must have a matching type, and this type cannot be a subclass of BaseExceptionGroup. It is not possible to mix except and except* in the same try. break, continue and return cannot appear in an except* clause.

+8.4.3. else clause

The optional else clause is executed if the control flow leaves the try suite, no exception was raised, and no return, continue, or break statement was executed. Exceptions in the else clause are not handled by the preceding except clauses.

+8.4.4. finally clause

If finally is present, it specifies a ‘cleanup’ handler. The try clause is executed, including any except and else clauses. If an exception occurs in any of the clauses and is not handled, the exception is temporarily saved. The finally clause is executed. If there is a saved exception it is re-raised at the end of the finally clause. If the finally clause raises another exception, the saved exception is set as the context of the new exception. If the finally clause executes a return, break or continue statement, the saved exception is discarded:

>>> def f():
+...     try:
+...         1/0
+...     finally:
+...         return 42
+...
+>>> f()
+42
+

The exception information is not available to the program during execution of the finally clause.

When a return, break or continue statement is executed in the try suite of a try…finally statement, the finally clause is also executed ‘on the way out.’

The return value of a function is determined by the last return statement executed. Since the finally clause always executes, a return statement executed in the finally clause will always be the last one executed:

>>> def foo():
+...     try:
+...         return 'try'
+...     finally:
+...         return 'finally'
+...
+>>> foo()
+'finally'
+

Changed in version 3.8: Prior to Python 3.8, a continue statement was illegal in the finally clause due to a problem with the implementation.

+8.5. The with statement

The with statement is used to wrap the execution of a block with methods defined by a context manager (see section With Statement Context Managers). This allows common try…except…finally usage patterns to be encapsulated for convenient reuse.

+with_stmt          ::=  "with" ( "(" with_stmt_contents ","? ")" | with_stmt_contents ) ":" suite
+with_stmt_contents ::=  with_item ("," with_item)*
+with_item          ::=  expression ["as" target]
+

The execution of the with statement with one “item” proceeds as follows:

  1. The context expression (the expression given in the with_item) is evaluated to obtain a context manager.
  2. The context manager’s __enter__() is loaded for later use.
  3. The context manager’s __exit__() is loaded for later use.
  4. The context manager’s __enter__() method is invoked.
  5. +

    If a target was included in the with statement, the return value from __enter__() is assigned to it.

    Note

    The with statement guarantees that if the __enter__() method returns without an error, then __exit__() will always be called. Thus, if an error occurs during the assignment to the target list, it will be treated the same as an error occurring within the suite would be. See step 7 below.

  6. The suite is executed.
  7. +

    The context manager’s __exit__() method is invoked. If an exception caused the suite to be exited, its type, value, and traceback are passed as arguments to __exit__(). Otherwise, three None arguments are supplied.

    If the suite was exited due to an exception, and the return value from the __exit__() method was false, the exception is reraised. If the return value was true, the exception is suppressed, and execution continues with the statement following the with statement.

    If the suite was exited for any reason other than an exception, the return value from __exit__() is ignored, and execution proceeds at the normal location for the kind of exit that was taken.

The following code:

with EXPRESSION as TARGET:
+    SUITE
+

is semantically equivalent to:

manager = (EXPRESSION)
+enter = type(manager).__enter__
+exit = type(manager).__exit__
+value = enter(manager)
+hit_except = False
+
+try:
+    TARGET = value
+    SUITE
+except:
+    hit_except = True
+    if not exit(manager, *sys.exc_info()):
+        raise
+finally:
+    if not hit_except:
+        exit(manager, None, None, None)
+

With more than one item, the context managers are processed as if multiple with statements were nested:

with A() as a, B() as b:
+    SUITE
+

is semantically equivalent to:

with A() as a:
+    with B() as b:
+        SUITE
+

You can also write multi-item context managers in multiple lines if the items are surrounded by parentheses. For example:

with (
+    A() as a,
+    B() as b,
+):
+    SUITE
+

Changed in version 3.1: Support for multiple context expressions.

Changed in version 3.10: Support for using grouping parentheses to break the statement in multiple lines.

See also

+PEP 343 - The “with” statement
+
+

The specification, background, and examples for the Python with statement.

+8.6. The match statement

New in version 3.10.

The match statement is used for pattern matching. Syntax:

+match_stmt   ::=  'match' subject_expr ":" NEWLINE INDENT case_block+ DEDENT
+subject_expr ::=  star_named_expression "," star_named_expressions?
+                  | named_expression
+case_block   ::=  'case' patterns [guard] ":" block
+

Note

This section uses single quotes to denote soft keywords.

Pattern matching takes a pattern as input (following case) and a subject value (following match). The pattern (which may contain subpatterns) is matched against the subject value. The outcomes are:

  • A match success or failure (also termed a pattern success or failure).
  • Possible binding of matched values to a name. The prerequisites for this are further discussed below.

The match and case keywords are soft keywords.

See also

  • +PEP 634 – Structural Pattern Matching: Specification
  • +PEP 636 – Structural Pattern Matching: Tutorial

+8.6.1. Overview

Here’s an overview of the logical flow of a match statement:

  1. The subject expression subject_expr is evaluated and a resulting subject value obtained. If the subject expression contains a comma, a tuple is constructed using the standard rules.
  2. +

    Each pattern in a case_block is attempted to match with the subject value. The specific rules for success or failure are described below. The match attempt can also bind some or all of the standalone names within the pattern. The precise pattern binding rules vary per pattern type and are specified below. Name bindings made during a successful pattern match outlive the executed block and can be used after the match statement.

    Note

    During failed pattern matches, some subpatterns may succeed. Do not rely on bindings being made for a failed match. Conversely, do not rely on variables remaining unchanged after a failed match. The exact behavior is dependent on implementation and may vary. This is an intentional decision made to allow different implementations to add optimizations.

  3. +

    If the pattern succeeds, the corresponding guard (if present) is evaluated. In this case all name bindings are guaranteed to have happened.

    • If the guard evaluates as true or is missing, the block inside case_block is executed.
    • Otherwise, the next case_block is attempted as described above.
    • If there are no further case blocks, the match statement is completed.

Note

Users should generally never rely on a pattern being evaluated. Depending on implementation, the interpreter may cache values or use other optimizations which skip repeated evaluations.

A sample match statement:

>>> flag = False
+>>> match (100, 200):
+...    case (100, 300):  # Mismatch: 200 != 300
+...        print('Case 1')
+...    case (100, 200) if flag:  # Successful match, but guard fails
+...        print('Case 2')
+...    case (100, y):  # Matches and binds y to 200
+...        print(f'Case 3, y: {y}')
+...    case _:  # Pattern not attempted
+...        print('Case 4, I match anything!')
+...
+Case 3, y: 200
+

In this case, if flag is a guard. Read more about that in the next section.

+8.6.2. Guards

+guard ::=  "if" named_expression
+

A guard (which is part of the case) must succeed for code inside the case block to execute. It takes the form: if followed by an expression.

The logical flow of a case block with a guard follows:

  1. Check that the pattern in the case block succeeded. If the pattern failed, the guard is not evaluated and the next case block is checked.
  2. +

    If the pattern succeeded, evaluate the guard.

    • If the guard condition evaluates as true, the case block is selected.
    • If the guard condition evaluates as false, the case block is not selected.
    • If the guard raises an exception during evaluation, the exception bubbles up.

Guards are allowed to have side effects as they are expressions. Guard evaluation must proceed from the first to the last case block, one at a time, skipping case blocks whose pattern(s) don’t all succeed. (I.e., guard evaluation must happen in order.) Guard evaluation must stop once a case block is selected.

+8.6.3. Irrefutable Case Blocks

An irrefutable case block is a match-all case block. A match statement may have at most one irrefutable case block, and it must be last.

A case block is considered irrefutable if it has no guard and its pattern is irrefutable. A pattern is considered irrefutable if we can prove from its syntax alone that it will always succeed. Only the following patterns are irrefutable:

+8.6.4. Patterns

Note

This section uses grammar notations beyond standard EBNF:

  • the notation SEP.RULE+ is shorthand for RULE (SEP RULE)* +
  • the notation !RULE is shorthand for a negative lookahead assertion

The top-level syntax for patterns is:

+patterns       ::=  open_sequence_pattern | pattern
+pattern        ::=  as_pattern | or_pattern
+closed_pattern ::=  | literal_pattern
+                    | capture_pattern
+                    | wildcard_pattern
+                    | value_pattern
+                    | group_pattern
+                    | sequence_pattern
+                    | mapping_pattern
+                    | class_pattern
+

The descriptions below will include a description “in simple terms” of what a pattern does for illustration purposes (credits to Raymond Hettinger for a document that inspired most of the descriptions). Note that these descriptions are purely for illustration purposes and may not reflect the underlying implementation. Furthermore, they do not cover all valid forms.

+8.6.4.1. OR Patterns

An OR pattern is two or more patterns separated by vertical bars |. Syntax:

+or_pattern ::=  "|".closed_pattern+
+

Only the final subpattern may be irrefutable, and each subpattern must bind the same set of names to avoid ambiguity.

An OR pattern matches each of its subpatterns in turn to the subject value, until one succeeds. The OR pattern is then considered successful. Otherwise, if none of the subpatterns succeed, the OR pattern fails.

In simple terms, P1 | P2 | ... will try to match P1, if it fails it will try to match P2, succeeding immediately if any succeeds, failing otherwise.

+8.6.4.2. AS Patterns

An AS pattern matches an OR pattern on the left of the as keyword against a subject. Syntax:

+as_pattern ::=  or_pattern "as" capture_pattern
+

If the OR pattern fails, the AS pattern fails. Otherwise, the AS pattern binds the subject to the name on the right of the as keyword and succeeds. capture_pattern cannot be a _.

In simple terms P as NAME will match with P, and on success it will set NAME = <subject>.

+8.6.4.3. Literal Patterns

A literal pattern corresponds to most literals in Python. Syntax:

+literal_pattern ::=  signed_number
+                     | signed_number "+" NUMBER
+                     | signed_number "-" NUMBER
+                     | strings
+                     | "None"
+                     | "True"
+                     | "False"
+                     | signed_number: NUMBER | "-" NUMBER
+

The rule strings and the token NUMBER are defined in the standard Python grammar. Triple-quoted strings are supported. Raw strings and byte strings are supported. f-strings are not supported.

The forms signed_number '+' NUMBER and signed_number '-' NUMBER are for expressing complex numbers; they require a real number on the left and an imaginary number on the right. E.g. 3 + 4j.

In simple terms, LITERAL will succeed only if <subject> == LITERAL. For the singletons None, True and False, the is operator is used.

+8.6.4.4. Capture Patterns

A capture pattern binds the subject value to a name. Syntax:

+capture_pattern ::=  !'_' NAME
+

A single underscore _ is not a capture pattern (this is what !'_' expresses). It is instead treated as a wildcard_pattern.

In a given pattern, a given name can only be bound once. E.g. case x, x: ... is invalid while case [x] | x: ... is allowed.

Capture patterns always succeed. The binding follows scoping rules established by the assignment expression operator in PEP 572; the name becomes a local variable in the closest containing function scope unless there’s an applicable global or nonlocal statement.

In simple terms NAME will always succeed and it will set NAME = <subject>.

+8.6.4.5. Wildcard Patterns

A wildcard pattern always succeeds (matches anything) and binds no name. Syntax:

+wildcard_pattern ::=  '_'
+

_ is a soft keyword within any pattern, but only within patterns. It is an identifier, as usual, even within match subject expressions, guards, and case blocks.

In simple terms, _ will always succeed.

+8.6.4.6. Value Patterns

A value pattern represents a named value in Python. Syntax:

+value_pattern ::=  attr
+attr          ::=  name_or_attr "." NAME
+name_or_attr  ::=  attr | NAME
+

The dotted name in the pattern is looked up using standard Python name resolution rules. The pattern succeeds if the value found compares equal to the subject value (using the == equality operator).

In simple terms NAME1.NAME2 will succeed only if <subject> == NAME1.NAME2

Note

If the same value occurs multiple times in the same match statement, the interpreter may cache the first value found and reuse it rather than repeat the same lookup. This cache is strictly tied to a given execution of a given match statement.

+8.6.4.7. Group Patterns

A group pattern allows users to add parentheses around patterns to emphasize the intended grouping. Otherwise, it has no additional syntax. Syntax:

+group_pattern ::=  "(" pattern ")"
+

In simple terms (P) has the same effect as P.

+8.6.4.8. Sequence Patterns

A sequence pattern contains several subpatterns to be matched against sequence elements. The syntax is similar to the unpacking of a list or tuple.

+sequence_pattern       ::=  "[" [maybe_sequence_pattern] "]"
+                            | "(" [open_sequence_pattern] ")"
+open_sequence_pattern  ::=  maybe_star_pattern "," [maybe_sequence_pattern]
+maybe_sequence_pattern ::=  ",".maybe_star_pattern+ ","?
+maybe_star_pattern     ::=  star_pattern | pattern
+star_pattern           ::=  "*" (capture_pattern | wildcard_pattern)
+

There is no difference if parentheses or square brackets are used for sequence patterns (i.e. (...) vs [...] ).

Note

A single pattern enclosed in parentheses without a trailing comma (e.g. (3 | 4)) is a group pattern. While a single pattern enclosed in square brackets (e.g. [3 | 4]) is still a sequence pattern.

At most one star subpattern may be in a sequence pattern. The star subpattern may occur in any position. If no star subpattern is present, the sequence pattern is a fixed-length sequence pattern; otherwise it is a variable-length sequence pattern.

The following is the logical flow for matching a sequence pattern against a subject value:

  1. If the subject value is not a sequence 2, the sequence pattern fails.
  2. If the subject value is an instance of str, bytes or bytearray the sequence pattern fails.
  3. +

    The subsequent steps depend on whether the sequence pattern is fixed or variable-length.

    If the sequence pattern is fixed-length:

    1. If the length of the subject sequence is not equal to the number of subpatterns, the sequence pattern fails
    2. Subpatterns in the sequence pattern are matched to their corresponding items in the subject sequence from left to right. Matching stops as soon as a subpattern fails. If all subpatterns succeed in matching their corresponding item, the sequence pattern succeeds.

    Otherwise, if the sequence pattern is variable-length:

    1. If the length of the subject sequence is less than the number of non-star subpatterns, the sequence pattern fails.
    2. The leading non-star subpatterns are matched to their corresponding items as for fixed-length sequences.
    3. If the previous step succeeds, the star subpattern matches a list formed of the remaining subject items, excluding the remaining items corresponding to non-star subpatterns following the star subpattern.
    4. Remaining non-star subpatterns are matched to their corresponding subject items, as for a fixed-length sequence.

    Note

    The length of the subject sequence is obtained via len() (i.e. via the __len__() protocol). This length may be cached by the interpreter in a similar manner as value patterns.

In simple terms [P1, P2, P3, … , P<N>] matches only if all the following happens:

  • check <subject> is a sequence
  • len(subject) == <N>
  • +P1 matches <subject>[0] (note that this match can also bind names)
  • +P2 matches <subject>[1] (note that this match can also bind names)
  • … and so on for the corresponding pattern/element.

+8.6.4.9. Mapping Patterns

A mapping pattern contains one or more key-value patterns. The syntax is similar to the construction of a dictionary. Syntax:

+mapping_pattern     ::=  "{" [items_pattern] "}"
+items_pattern       ::=  ",".key_value_pattern+ ","?
+key_value_pattern   ::=  (literal_pattern | value_pattern) ":" pattern
+                         | double_star_pattern
+double_star_pattern ::=  "**" capture_pattern
+

At most one double star pattern may be in a mapping pattern. The double star pattern must be the last subpattern in the mapping pattern.

Duplicate keys in mapping patterns are disallowed. Duplicate literal keys will raise a SyntaxError. Two keys that otherwise have the same value will raise a ValueError at runtime.

The following is the logical flow for matching a mapping pattern against a subject value:

  1. If the subject value is not a mapping 3,the mapping pattern fails.
  2. If every key given in the mapping pattern is present in the subject mapping, and the pattern for each key matches the corresponding item of the subject mapping, the mapping pattern succeeds.
  3. If duplicate keys are detected in the mapping pattern, the pattern is considered invalid. A SyntaxError is raised for duplicate literal values; or a ValueError for named keys of the same value.

Note

Key-value pairs are matched using the two-argument form of the mapping subject’s get() method. Matched key-value pairs must already be present in the mapping, and not created on-the-fly via __missing__() or __getitem__().

In simple terms {KEY1: P1, KEY2: P2, ... } matches only if all the following happens:

  • check <subject> is a mapping
  • KEY1 in <subject>
  • +P1 matches <subject>[KEY1] +
  • … and so on for the corresponding KEY/pattern pair.

+8.6.4.10. Class Patterns

A class pattern represents a class and its positional and keyword arguments (if any). Syntax:

+class_pattern       ::=  name_or_attr "(" [pattern_arguments ","?] ")"
+pattern_arguments   ::=  positional_patterns ["," keyword_patterns]
+                         | keyword_patterns
+positional_patterns ::=  ",".pattern+
+keyword_patterns    ::=  ",".keyword_pattern+
+keyword_pattern     ::=  NAME "=" pattern
+

The same keyword should not be repeated in class patterns.

The following is the logical flow for matching a class pattern against a subject value:

  1. If name_or_attr is not an instance of the builtin type , raise TypeError.
  2. If the subject value is not an instance of name_or_attr (tested via isinstance()), the class pattern fails.
  3. +

    If no pattern arguments are present, the pattern succeeds. Otherwise, the subsequent steps depend on whether keyword or positional argument patterns are present.

    For a number of built-in types (specified below), a single positional subpattern is accepted which will match the entire subject; for these types keyword patterns also work as for other types.

    If only keyword patterns are present, they are processed as follows, one by one:

    I. The keyword is looked up as an attribute on the subject.

    • If this raises an exception other than AttributeError, the exception bubbles up.
    • If this raises AttributeError, the class pattern has failed.
    • Else, the subpattern associated with the keyword pattern is matched against the subject’s attribute value. If this fails, the class pattern fails; if this succeeds, the match proceeds to the next keyword.

    II. If all keyword patterns succeed, the class pattern succeeds.

    If any positional patterns are present, they are converted to keyword patterns using the __match_args__ attribute on the class name_or_attr before matching:

    I. The equivalent of getattr(cls, "__match_args__", ()) is called.

    • If this raises an exception, the exception bubbles up.
    • If the returned value is not a tuple, the conversion fails and TypeError is raised.
    • If there are more positional patterns than len(cls.__match_args__), TypeError is raised.
    • Otherwise, positional pattern i is converted to a keyword pattern using __match_args__[i] as the keyword. __match_args__[i] must be a string; if not TypeError is raised.
    • If there are duplicate keywords, TypeError is raised.

    See also

    Customizing positional arguments in class pattern matching

    II. Once all positional patterns have been converted to keyword patterns,
    +
    +

    the match proceeds as if there were only keyword patterns.

    For the following built-in types the handling of positional subpatterns is different:

    These classes accept a single positional argument, and the pattern there is matched against the whole object rather than an attribute. For example int(0|1) matches the value 0, but not the value 0.0.

In simple terms CLS(P1, attr=P2) matches only if the following happens:

  • isinstance(<subject>, CLS)
  • convert P1 to a keyword pattern using CLS.__match_args__ +
  • +

    For each keyword argument attr=P2:

    • hasattr(<subject>, "attr")
    • +P2 matches <subject>.attr +
  • … and so on for the corresponding keyword argument/pattern pair.

See also

  • +PEP 634 – Structural Pattern Matching: Specification
  • +PEP 636 – Structural Pattern Matching: Tutorial

+8.7. Function definitions

A function definition defines a user-defined function object (see section The standard type hierarchy):

+funcdef                   ::=  [decorators] "def" funcname [type_params] "(" [parameter_list] ")"
+                               ["->" expression] ":" suite
+decorators                ::=  decorator+
+decorator                 ::=  "@" assignment_expression NEWLINE
+parameter_list            ::=  defparameter ("," defparameter)* "," "/" ["," [parameter_list_no_posonly]]
+                                 | parameter_list_no_posonly
+parameter_list_no_posonly ::=  defparameter ("," defparameter)* ["," [parameter_list_starargs]]
+                               | parameter_list_starargs
+parameter_list_starargs   ::=  "*" [parameter] ("," defparameter)* ["," ["**" parameter [","]]]
+                               | "**" parameter [","]
+parameter                 ::=  identifier [":" expression]
+defparameter              ::=  parameter ["=" expression]
+funcname                  ::=  identifier
+

A function definition is an executable statement. Its execution binds the function name in the current local namespace to a function object (a wrapper around the executable code for the function). This function object contains a reference to the current global namespace as the global namespace to be used when the function is called.

The function definition does not execute the function body; this gets executed only when the function is called. 4

A function definition may be wrapped by one or more decorator expressions. Decorator expressions are evaluated when the function is defined, in the scope that contains the function definition. The result must be a callable, which is invoked with the function object as the only argument. The returned value is bound to the function name instead of the function object. Multiple decorators are applied in nested fashion. For example, the following code

@f1(arg)
+@f2
+def func(): pass
+

is roughly equivalent to

def func(): pass
+func = f1(arg)(f2(func))
+

except that the original function is not temporarily bound to the name func.

Changed in version 3.9: Functions may be decorated with any valid assignment_expression. Previously, the grammar was much more restrictive; see PEP 614 for details.

A list of type parameters may be given in square brackets between the function’s name and the opening parenthesis for its parameter list. This indicates to static type checkers that the function is generic. At runtime, the type parameters can be retrieved from the function’s __type_params__ attribute. See Generic functions for more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

When one or more parameters have the form parameter = expression, the function is said to have “default parameter values.” For a parameter with a default value, the corresponding argument may be omitted from a call, in which case the parameter’s default value is substituted. If a parameter has a default value, all following parameters up until the “*” must also have a default value — this is a syntactic restriction that is not expressed by the grammar.

Default parameter values are evaluated from left to right when the function definition is executed. This means that the expression is evaluated once, when the function is defined, and that the same “pre-computed” value is used for each call. This is especially important to understand when a default parameter value is a mutable object, such as a list or a dictionary: if the function modifies the object (e.g. by appending an item to a list), the default parameter value is in effect modified. This is generally not what was intended. A way around this is to use None as the default, and explicitly test for it in the body of the function, e.g.:

def whats_on_the_telly(penguin=None):
+    if penguin is None:
+        penguin = []
+    penguin.append("property of the zoo")
+    return penguin
+

Function call semantics are described in more detail in section Calls. A function call always assigns values to all parameters mentioned in the parameter list, either from positional arguments, from keyword arguments, or from default values. If the form “*identifier” is present, it is initialized to a tuple receiving any excess positional parameters, defaulting to the empty tuple. If the form “**identifier” is present, it is initialized to a new ordered mapping receiving any excess keyword arguments, defaulting to a new empty mapping of the same type. Parameters after “*” or “*identifier” are keyword-only parameters and may only be passed by keyword arguments. Parameters before “/” are positional-only parameters and may only be passed by positional arguments.

Changed in version 3.8: The / function parameter syntax may be used to indicate positional-only parameters. See PEP 570 for details.

Parameters may have an annotation of the form “: expression” following the parameter name. Any parameter may have an annotation, even those of the form *identifier or **identifier. Functions may have “return” annotation of the form “-> expression” after the parameter list. These annotations can be any valid Python expression. The presence of annotations does not change the semantics of a function. The annotation values are available as values of a dictionary keyed by the parameters’ names in the __annotations__ attribute of the function object. If the annotations import from __future__ is used, annotations are preserved as strings at runtime which enables postponed evaluation. Otherwise, they are evaluated when the function definition is executed. In this case annotations may be evaluated in a different order than they appear in the source code.

It is also possible to create anonymous functions (functions not bound to a name), for immediate use in expressions. This uses lambda expressions, described in section Lambdas. Note that the lambda expression is merely a shorthand for a simplified function definition; a function defined in a “def” statement can be passed around or assigned to another name just like a function defined by a lambda expression. The “def” form is actually more powerful since it allows the execution of multiple statements and annotations.

Programmer’s note: Functions are first-class objects. A “def” statement executed inside a function definition defines a local function that can be returned or passed around. Free variables used in the nested function can access the local variables of the function containing the def. See section Naming and binding for details.

See also

+PEP 3107 - Function Annotations
+
+

The original specification for function annotations.

+PEP 484 - Type Hints
+
+

Definition of a standard meaning for annotations: type hints.

+PEP 526 - Syntax for Variable Annotations
+
+

Ability to type hint variable declarations, including class variables and instance variables.

+PEP 563 - Postponed Evaluation of Annotations
+
+

Support for forward references within annotations by preserving annotations in a string form at runtime instead of eager evaluation.

+PEP 318 - Decorators for Functions and Methods
+
+

Function and method decorators were introduced. Class decorators were introduced in PEP 3129.

+8.8. Class definitions

A class definition defines a class object (see section The standard type hierarchy):

+classdef    ::=  [decorators] "class" classname [type_params] [inheritance] ":" suite
+inheritance ::=  "(" [argument_list] ")"
+classname   ::=  identifier
+

A class definition is an executable statement. The inheritance list usually gives a list of base classes (see Metaclasses for more advanced uses), so each item in the list should evaluate to a class object which allows subclassing. Classes without an inheritance list inherit, by default, from the base class object; hence,

class Foo:
+    pass
+

is equivalent to

class Foo(object):
+    pass
+

The class’s suite is then executed in a new execution frame (see Naming and binding), using a newly created local namespace and the original global namespace. (Usually, the suite contains mostly function definitions.) When the class’s suite finishes execution, its execution frame is discarded but its local namespace is saved. 5 A class object is then created using the inheritance list for the base classes and the saved local namespace for the attribute dictionary. The class name is bound to this class object in the original local namespace.

The order in which attributes are defined in the class body is preserved in the new class’s __dict__. Note that this is reliable only right after the class is created and only for classes that were defined using the definition syntax.

Class creation can be customized heavily using metaclasses.

Classes can also be decorated: just like when decorating functions,

@f1(arg)
+@f2
+class Foo: pass
+

is roughly equivalent to

class Foo: pass
+Foo = f1(arg)(f2(Foo))
+

The evaluation rules for the decorator expressions are the same as for function decorators. The result is then bound to the class name.

Changed in version 3.9: Classes may be decorated with any valid assignment_expression. Previously, the grammar was much more restrictive; see PEP 614 for details.

A list of type parameters may be given in square brackets immediately after the class’s name. This indicates to static type checkers that the class is generic. At runtime, the type parameters can be retrieved from the class’s __type_params__ attribute. See Generic classes for more.

Changed in version 3.12: Type parameter lists are new in Python 3.12.

Programmer’s note: Variables defined in the class definition are class attributes; they are shared by instances. Instance attributes can be set in a method with self.name = value. Both class and instance attributes are accessible through the notation “self.name”, and an instance attribute hides a class attribute with the same name when accessed in this way. Class attributes can be used as defaults for instance attributes, but using mutable values there can lead to unexpected results. Descriptors can be used to create instance variables with different implementation details.

See also

+PEP 3115 - Metaclasses in Python 3000
+
+

The proposal that changed the declaration of metaclasses to the current syntax, and the semantics for how classes with metaclasses are constructed.

+PEP 3129 - Class Decorators
+
+

The proposal that added class decorators. Function and method decorators were introduced in PEP 318.

+8.9. Coroutines

New in version 3.5.

+8.9.1. Coroutine function definition

+async_funcdef ::=  [decorators] "async" "def" funcname "(" [parameter_list] ")"
+                   ["->" expression] ":" suite
+

Execution of Python coroutines can be suspended and resumed at many points (see coroutine). await expressions, async for and async with can only be used in the body of a coroutine function.

Functions defined with async def syntax are always coroutine functions, even if they do not contain await or async keywords.

It is a SyntaxError to use a yield from expression inside the body of a coroutine function.

An example of a coroutine function:

async def func(param1, param2):
+    do_stuff()
+    await some_coroutine()
+

Changed in version 3.7: await and async are now keywords; previously they were only treated as such inside the body of a coroutine function.

+8.9.2. The async for statement

+async_for_stmt ::=  "async" for_stmt
+

An asynchronous iterable provides an __aiter__ method that directly returns an asynchronous iterator, which can call asynchronous code in its __anext__ method.

The async for statement allows convenient iteration over asynchronous iterables.

The following code:

async for TARGET in ITER:
+    SUITE
+else:
+    SUITE2
+

Is semantically equivalent to:

iter = (ITER)
+iter = type(iter).__aiter__(iter)
+running = True
+
+while running:
+    try:
+        TARGET = await type(iter).__anext__(iter)
+    except StopAsyncIteration:
+        running = False
+    else:
+        SUITE
+else:
+    SUITE2
+

See also __aiter__() and __anext__() for details.

It is a SyntaxError to use an async for statement outside the body of a coroutine function.

+8.9.3. The async with statement

+async_with_stmt ::=  "async" with_stmt
+

An asynchronous context manager is a context manager that is able to suspend execution in its enter and exit methods.

The following code:

async with EXPRESSION as TARGET:
+    SUITE
+

is semantically equivalent to:

manager = (EXPRESSION)
+aenter = type(manager).__aenter__
+aexit = type(manager).__aexit__
+value = await aenter(manager)
+hit_except = False
+
+try:
+    TARGET = value
+    SUITE
+except:
+    hit_except = True
+    if not await aexit(manager, *sys.exc_info()):
+        raise
+finally:
+    if not hit_except:
+        await aexit(manager, None, None, None)
+

See also __aenter__() and __aexit__() for details.

It is a SyntaxError to use an async with statement outside the body of a coroutine function.

See also

+PEP 492 - Coroutines with async and await syntax
+
+

The proposal that made coroutines a proper standalone concept in Python, and added supporting syntax.

+8.10. Type parameter lists

New in version 3.12.

+type_params  ::=  "[" type_param ("," type_param)* "]"
+type_param   ::=  typevar | typevartuple | paramspec
+typevar      ::=  identifier (":" expression)?
+typevartuple ::=  "*" identifier
+paramspec    ::=  "**" identifier
+

Functions (including coroutines), classes and type aliases may contain a type parameter list:

def max[T](args: list[T]) -> T:
+    ...
+
+async def amax[T](args: list[T]) -> T:
+    ...
+
+class Bag[T]:
+    def __iter__(self) -> Iterator[T]:
+        ...
+
+    def add(self, arg: T) -> None:
+        ...
+
+type ListOrSet[T] = list[T] | set[T]
+

Semantically, this indicates that the function, class, or type alias is generic over a type variable. This information is primarily used by static type checkers, and at runtime, generic objects behave much like their non-generic counterparts.

Type parameters are declared in square brackets ([]) immediately after the name of the function, class, or type alias. The type parameters are accessible within the scope of the generic object, but not elsewhere. Thus, after a declaration def func[T](): pass, the name T is not available in the module scope. Below, the semantics of generic objects are described with more precision. The scope of type parameters is modeled with a special function (technically, an annotation scope) that wraps the creation of the generic object.

Generic functions, classes, and type aliases have a __type_params__ attribute listing their type parameters.

Type parameters come in three kinds:

  • +typing.TypeVar, introduced by a plain name (e.g., T). Semantically, this represents a single type to a type checker.
  • +typing.TypeVarTuple, introduced by a name prefixed with a single asterisk (e.g., *Ts). Semantically, this stands for a tuple of any number of types.
  • +typing.ParamSpec, introduced by a name prefixed with two asterisks (e.g., **P). Semantically, this stands for the parameters of a callable.

typing.TypeVar declarations can define bounds and constraints with a colon (:) followed by an expression. A single expression after the colon indicates a bound (e.g. T: int). Semantically, this means that the typing.TypeVar can only represent types that are a subtype of this bound. A parenthesized tuple of expressions after the colon indicates a set of constraints (e.g. T: (str, bytes)). Each member of the tuple should be a type (again, this is not enforced at runtime). Constrained type variables can only take on one of the types in the list of constraints.

For typing.TypeVars declared using the type parameter list syntax, the bound and constraints are not evaluated when the generic object is created, but only when the value is explicitly accessed through the attributes __bound__ and __constraints__. To accomplish this, the bounds or constraints are evaluated in a separate annotation scope.

typing.TypeVarTuples and typing.ParamSpecs cannot have bounds or constraints.

The following example indicates the full set of allowed type parameter declarations:

def overly_generic[
+   SimpleTypeVar,
+   TypeVarWithBound: int,
+   TypeVarWithConstraints: (str, bytes),
+   *SimpleTypeVarTuple,
+   **SimpleParamSpec,
+](
+   a: SimpleTypeVar,
+   b: TypeVarWithBound,
+   c: Callable[SimpleParamSpec, TypeVarWithConstraints],
+   *d: SimpleTypeVarTuple,
+): ...
+

+8.10.1. Generic functions

Generic functions are declared as follows:

def func[T](arg: T): ...
+

This syntax is equivalent to:

annotation-def TYPE_PARAMS_OF_func():
+    T = typing.TypeVar("T")
+    def func(arg: T): ...
+    func.__type_params__ = (T,)
+    return func
+func = TYPE_PARAMS_OF_func()
+

Here annotation-def indicates an annotation scope, which is not actually bound to any name at runtime. (One other liberty is taken in the translation: the syntax does not go through attribute access on the typing module, but creates an instance of typing.TypeVar directly.)

The annotations of generic functions are evaluated within the annotation scope used for declaring the type parameters, but the function’s defaults and decorators are not.

The following example illustrates the scoping rules for these cases, as well as for additional flavors of type parameters:

@decorator
+def func[T: int, *Ts, **P](*args: *Ts, arg: Callable[P, T] = some_default):
+    ...
+

Except for the lazy evaluation of the TypeVar bound, this is equivalent to:

DEFAULT_OF_arg = some_default
+
+annotation-def TYPE_PARAMS_OF_func():
+
+    annotation-def BOUND_OF_T():
+        return int
+    # In reality, BOUND_OF_T() is evaluated only on demand.
+    T = typing.TypeVar("T", bound=BOUND_OF_T())
+
+    Ts = typing.TypeVarTuple("Ts")
+    P = typing.ParamSpec("P")
+
+    def func(*args: *Ts, arg: Callable[P, T] = DEFAULT_OF_arg):
+        ...
+
+    func.__type_params__ = (T, Ts, P)
+    return func
+func = decorator(TYPE_PARAMS_OF_func())
+

The capitalized names like DEFAULT_OF_arg are not actually bound at runtime.

+8.10.2. Generic classes

Generic classes are declared as follows:

class Bag[T]: ...
+

This syntax is equivalent to:

annotation-def TYPE_PARAMS_OF_Bag():
+    T = typing.TypeVar("T")
+    class Bag(typing.Generic[T]):
+        __type_params__ = (T,)
+        ...
+    return Bag
+Bag = TYPE_PARAMS_OF_Bag()
+

Here again annotation-def (not a real keyword) indicates an annotation scope, and the name TYPE_PARAMS_OF_Bag is not actually bound at runtime.

Generic classes implicitly inherit from typing.Generic. The base classes and keyword arguments of generic classes are evaluated within the type scope for the type parameters, and decorators are evaluated outside that scope. This is illustrated by this example:

@decorator
+class Bag(Base[T], arg=T): ...
+

This is equivalent to:

annotation-def TYPE_PARAMS_OF_Bag():
+    T = typing.TypeVar("T")
+    class Bag(Base[T], typing.Generic[T], arg=T):
+        __type_params__ = (T,)
+        ...
+    return Bag
+Bag = decorator(TYPE_PARAMS_OF_Bag())
+

+8.10.3. Generic type aliases

The type statement can also be used to create a generic type alias:

type ListOrSet[T] = list[T] | set[T]
+

Except for the lazy evaluation of the value, this is equivalent to:

annotation-def TYPE_PARAMS_OF_ListOrSet():
+    T = typing.TypeVar("T")
+
+    annotation-def VALUE_OF_ListOrSet():
+        return list[T] | set[T]
+    # In reality, the value is lazily evaluated
+    return typing.TypeAliasType("ListOrSet", VALUE_OF_ListOrSet(), type_params=(T,))
+ListOrSet = TYPE_PARAMS_OF_ListOrSet()
+

Here, annotation-def (not a real keyword) indicates an annotation scope. The capitalized names like TYPE_PARAMS_OF_ListOrSet are not actually bound at runtime.

Footnotes

+1
+

The exception is propagated to the invocation stack unless there is a finally clause which happens to raise another exception. That new exception causes the old one to be lost.

+2
+

In pattern matching, a sequence is defined as one of the following:

The following standard library classes are sequences:

Note

Subject values of type str, bytes, and bytearray do not match sequence patterns.

+3
+

In pattern matching, a mapping is defined as one of the following:

The standard library classes dict and types.MappingProxyType are mappings.

+4
+

A string literal appearing as the first statement in the function body is transformed into the function’s __doc__ attribute and therefore the function’s docstring.

+5
+

A string literal appearing as the first statement in the class body is transformed into the namespace’s __doc__ item and therefore the class’s docstring.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/compound_stmts.html +

+
diff --git a/devdocs/python~3.12/reference%2Fdatamodel.html b/devdocs/python~3.12/reference%2Fdatamodel.html new file mode 100644 index 00000000..85356260 --- /dev/null +++ b/devdocs/python~3.12/reference%2Fdatamodel.html @@ -0,0 +1,810 @@ +

Data model

+3.1. Objects, values and types

Objects are Python’s abstraction for data. All data in a Python program is represented by objects or by relations between objects. (In a sense, and in conformance to Von Neumann’s model of a “stored program computer”, code is also represented by objects.)

Every object has an identity, a type and a value. An object’s identity never changes once it has been created; you may think of it as the object’s address in memory. The ‘is’ operator compares the identity of two objects; the id() function returns an integer representing its identity.

CPython implementation detail: For CPython, id(x) is the memory address where x is stored.

An object’s type determines the operations that the object supports (e.g., “does it have a length?”) and also defines the possible values for objects of that type. The type() function returns an object’s type (which is an object itself). Like its identity, an object’s type is also unchangeable. 1

The value of some objects can change. Objects whose value can change are said to be mutable; objects whose value is unchangeable once they are created are called immutable. (The value of an immutable container object that contains a reference to a mutable object can change when the latter’s value is changed; however the container is still considered immutable, because the collection of objects it contains cannot be changed. So, immutability is not strictly the same as having an unchangeable value, it is more subtle.) An object’s mutability is determined by its type; for instance, numbers, strings and tuples are immutable, while dictionaries and lists are mutable.

Objects are never explicitly destroyed; however, when they become unreachable they may be garbage-collected. An implementation is allowed to postpone garbage collection or omit it altogether — it is a matter of implementation quality how garbage collection is implemented, as long as no objects are collected that are still reachable.

CPython implementation detail: CPython currently uses a reference-counting scheme with (optional) delayed detection of cyclically linked garbage, which collects most objects as soon as they become unreachable, but is not guaranteed to collect garbage containing circular references. See the documentation of the gc module for information on controlling the collection of cyclic garbage. Other implementations act differently and CPython may change. Do not depend on immediate finalization of objects when they become unreachable (so you should always close files explicitly).

Note that the use of the implementation’s tracing or debugging facilities may keep objects alive that would normally be collectable. Also note that catching an exception with a ‘try…except’ statement may keep objects alive.

Some objects contain references to “external” resources such as open files or windows. It is understood that these resources are freed when the object is garbage-collected, but since garbage collection is not guaranteed to happen, such objects also provide an explicit way to release the external resource, usually a close() method. Programs are strongly recommended to explicitly close such objects. The ‘try…finally’ statement and the ‘with’ statement provide convenient ways to do this.

Some objects contain references to other objects; these are called containers. Examples of containers are tuples, lists and dictionaries. The references are part of a container’s value. In most cases, when we talk about the value of a container, we imply the values, not the identities of the contained objects; however, when we talk about the mutability of a container, only the identities of the immediately contained objects are implied. So, if an immutable container (like a tuple) contains a reference to a mutable object, its value changes if that mutable object is changed.

Types affect almost all aspects of object behavior. Even the importance of object identity is affected in some sense: for immutable types, operations that compute new values may actually return a reference to any existing object with the same type and value, while for mutable objects this is not allowed. E.g., after a = 1; b = 1, a and b may or may not refer to the same object with the value one, depending on the implementation, but after c = []; d = +[], c and d are guaranteed to refer to two different, unique, newly created empty lists. (Note that c = d = [] assigns the same object to both c and d.)

+3.2. The standard type hierarchy

Below is a list of the types that are built into Python. Extension modules (written in C, Java, or other languages, depending on the implementation) can define additional types. Future versions of Python may add types to the type hierarchy (e.g., rational numbers, efficiently stored arrays of integers, etc.), although such additions will often be provided via the standard library instead.

Some of the type descriptions below contain a paragraph listing ‘special attributes.’ These are attributes that provide access to the implementation and are not intended for general use. Their definition may change in the future.

+3.2.1. None

This type has a single value. There is a single object with this value. This object is accessed through the built-in name None. It is used to signify the absence of a value in many situations, e.g., it is returned from functions that don’t explicitly return anything. Its truth value is false.

+3.2.2. NotImplemented

This type has a single value. There is a single object with this value. This object is accessed through the built-in name NotImplemented. Numeric methods and rich comparison methods should return this value if they do not implement the operation for the operands provided. (The interpreter will then try the reflected operation, or some other fallback, depending on the operator.) It should not be evaluated in a boolean context.

See Implementing the arithmetic operations for more details.

Changed in version 3.9: Evaluating NotImplemented in a boolean context is deprecated. While it currently evaluates as true, it will emit a DeprecationWarning. It will raise a TypeError in a future version of Python.

+3.2.3. Ellipsis

This type has a single value. There is a single object with this value. This object is accessed through the literal ... or the built-in name Ellipsis. Its truth value is true.

+3.2.4. numbers.Number +

These are created by numeric literals and returned as results by arithmetic operators and arithmetic built-in functions. Numeric objects are immutable; once created their value never changes. Python numbers are of course strongly related to mathematical numbers, but subject to the limitations of numerical representation in computers.

The string representations of the numeric classes, computed by __repr__() and __str__(), have the following properties:

  • They are valid numeric literals which, when passed to their class constructor, produce an object having the value of the original numeric.
  • The representation is in base 10, when possible.
  • Leading zeros, possibly excepting a single zero before a decimal point, are not shown.
  • Trailing zeros, possibly excepting a single zero after a decimal point, are not shown.
  • A sign is shown only when the number is negative.

Python distinguishes between integers, floating point numbers, and complex numbers:

+3.2.4.1. numbers.Integral +

These represent elements from the mathematical set of integers (positive and negative).

Note

The rules for integer representation are intended to give the most meaningful interpretation of shift and mask operations involving negative integers.

There are two types of integers:

+Integers (int)
+
+

These represent numbers in an unlimited range, subject to available (virtual) memory only. For the purpose of shift and mask operations, a binary representation is assumed, and negative numbers are represented in a variant of 2’s complement which gives the illusion of an infinite string of sign bits extending to the left.

+Booleans (bool)
+
+

These represent the truth values False and True. The two objects representing the values False and True are the only Boolean objects. The Boolean type is a subtype of the integer type, and Boolean values behave like the values 0 and 1, respectively, in almost all contexts, the exception being that when converted to a string, the strings "False" or "True" are returned, respectively.

+3.2.4.2. numbers.Real (float)

These represent machine-level double precision floating point numbers. You are at the mercy of the underlying machine architecture (and C or Java implementation) for the accepted range and handling of overflow. Python does not support single-precision floating point numbers; the savings in processor and memory usage that are usually the reason for using these are dwarfed by the overhead of using objects in Python, so there is no reason to complicate the language with two kinds of floating point numbers.

+3.2.4.3. numbers.Complex (complex)

These represent complex numbers as a pair of machine-level double precision floating point numbers. The same caveats apply as for floating point numbers. The real and imaginary parts of a complex number z can be retrieved through the read-only attributes z.real and z.imag.

+3.2.5. Sequences

These represent finite ordered sets indexed by non-negative numbers. The built-in function len() returns the number of items of a sequence. When the length of a sequence is n, the index set contains the numbers 0, 1, …, n-1. Item i of sequence a is selected by a[i].

Sequences also support slicing: a[i:j] selects all items with index k such that i <= k < j. When used as an expression, a slice is a sequence of the same type. This implies that the index set is renumbered so that it starts at 0.

Some sequences also support “extended slicing” with a third “step” parameter: a[i:j:k] selects all items of a with index x where x = i + n*k, n >= 0 and i <= x < j.

Sequences are distinguished according to their mutability:

+3.2.5.1. Immutable sequences

An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be changed; however, the collection of objects directly referenced by an immutable object cannot change.)

The following types are immutable sequences:

Strings
+
+

A string is a sequence of values that represent Unicode code points. All the code points in the range U+0000 - U+10FFFF can be represented in a string. Python doesn’t have a char type; instead, every code point in the string is represented as a string object with length 1. The built-in function ord() converts a code point from its string form to an integer in the range 0 - 10FFFF; chr() converts an integer in the range 0 - 10FFFF to the corresponding length 1 string object. str.encode() can be used to convert a str to bytes using the given text encoding, and bytes.decode() can be used to achieve the opposite.

Tuples
+
+

The items of a tuple are arbitrary Python objects. Tuples of two or more items are formed by comma-separated lists of expressions. A tuple of one item (a ‘singleton’) can be formed by affixing a comma to an expression (an expression by itself does not create a tuple, since parentheses must be usable for grouping of expressions). An empty tuple can be formed by an empty pair of parentheses.

Bytes
+
+

A bytes object is an immutable array. The items are 8-bit bytes, represented by integers in the range 0 <= x < 256. Bytes literals (like b'abc') and the built-in bytes() constructor can be used to create bytes objects. Also, bytes objects can be decoded to strings via the decode() method.

+3.2.5.2. Mutable sequences

Mutable sequences can be changed after they are created. The subscription and slicing notations can be used as the target of assignment and del (delete) statements.

Note

The collections and array module provide additional examples of mutable sequence types.

There are currently two intrinsic mutable sequence types:

Lists
+
+

The items of a list are arbitrary Python objects. Lists are formed by placing a comma-separated list of expressions in square brackets. (Note that there are no special cases needed to form lists of length 0 or 1.)

Byte Arrays
+
+

A bytearray object is a mutable array. They are created by the built-in bytearray() constructor. Aside from being mutable (and hence unhashable), byte arrays otherwise provide the same interface and functionality as immutable bytes objects.

+3.2.6. Set types

These represent unordered, finite sets of unique, immutable objects. As such, they cannot be indexed by any subscript. However, they can be iterated over, and the built-in function len() returns the number of items in a set. Common uses for sets are fast membership testing, removing duplicates from a sequence, and computing mathematical operations such as intersection, union, difference, and symmetric difference.

For set elements, the same immutability rules apply as for dictionary keys. Note that numeric types obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0), only one of them can be contained in a set.

There are currently two intrinsic set types:

Sets
+
+

These represent a mutable set. They are created by the built-in set() constructor and can be modified afterwards by several methods, such as add().

Frozen sets
+
+

These represent an immutable set. They are created by the built-in frozenset() constructor. As a frozenset is immutable and hashable, it can be used again as an element of another set, or as a dictionary key.

+3.2.7. Mappings

These represent finite sets of objects indexed by arbitrary index sets. The subscript notation a[k] selects the item indexed by k from the mapping a; this can be used in expressions and as the target of assignments or del statements. The built-in function len() returns the number of items in a mapping.

There is currently a single intrinsic mapping type:

+3.2.7.1. Dictionaries

These represent finite sets of objects indexed by nearly arbitrary values. The only types of values not acceptable as keys are values containing lists or dictionaries or other mutable types that are compared by value rather than by object identity, the reason being that the efficient implementation of dictionaries requires a key’s hash value to remain constant. Numeric types used for keys obey the normal rules for numeric comparison: if two numbers compare equal (e.g., 1 and 1.0) then they can be used interchangeably to index the same dictionary entry.

Dictionaries preserve insertion order, meaning that keys will be produced in the same order they were added sequentially over the dictionary. Replacing an existing key does not change the order, however removing a key and re-inserting it will add it to the end instead of keeping its old place.

Dictionaries are mutable; they can be created by the {...} notation (see section Dictionary displays).

The extension modules dbm.ndbm and dbm.gnu provide additional examples of mapping types, as does the collections module.

Changed in version 3.7: Dictionaries did not preserve insertion order in versions of Python before 3.6. In CPython 3.6, insertion order was preserved, but it was considered an implementation detail at that time rather than a language guarantee.

+3.2.8. Callable types

These are the types to which the function call operation (see section Calls) can be applied:

+3.2.8.1. User-defined functions

A user-defined function object is created by a function definition (see section Function definitions). It should be called with an argument list containing the same number of items as the function’s formal parameter list.

+3.2.8.1.1. Special read-only attributes
+ + +

Attribute

Meaning
+
+function.__globals__
+

A reference to the dictionary that holds the function’s global variables – the global namespace of the module in which the function was defined.
+
+function.__closure__
+
 +

None or a tuple of cells that contain bindings for the function’s free variables.

A cell object has the attribute cell_contents. This can be used to get the value of the cell, as well as set the value.
+3.2.8.1.2. Special writable attributes

Most of these attributes check the type of the assigned value:

+ + + + + + + + + + +

Attribute

Meaning
+
+function.__doc__
+

The function’s documentation string, or None if unavailable. Not inherited by subclasses.
+
+function.__name__
+

The function’s name. See also: __name__ attributes.
+
+function.__qualname__
+
 +

The function’s qualified name. See also: __qualname__ attributes.

New in version 3.3.
+
+function.__module__
+

The name of the module the function was defined in, or None if unavailable.
+
+function.__defaults__
+

A tuple containing default parameter values for those parameters that have defaults, or None if no parameters have a default value.
+
+function.__code__
+

The code object representing the compiled function body.
+
+function.__dict__
+

The namespace supporting arbitrary function attributes. See also: __dict__ attributes.
+
+function.__annotations__
+

A dictionary containing annotations of parameters. The keys of the dictionary are the parameter names, and 'return' for the return annotation, if provided. See also: Annotations Best Practices.
+
+function.__kwdefaults__
+

A dictionary containing defaults for keyword-only parameters.
+
+function.__type_params__
+
 +

A tuple containing the type parameters of a generic function.

New in version 3.12.

Function objects also support getting and setting arbitrary attributes, which can be used, for example, to attach metadata to functions. Regular attribute dot-notation is used to get and set such attributes.

CPython implementation detail: CPython’s current implementation only supports function attributes on user-defined functions. Function attributes on built-in functions may be supported in the future.

Additional information about a function’s definition can be retrieved from its code object (accessible via the __code__ attribute).

+3.2.8.2. Instance methods

An instance method object combines a class, a class instance and any callable object (normally a user-defined function).

Special read-only attributes:

+ + + + +
+
+method.__self__
+

Refers to the class instance object to which the method is bound
+
+method.__func__
+

Refers to the original function object
+
+method.__doc__
+

The method’s documentation (same as method.__func__.__doc__). A string if the original function had a docstring, else None.
+
+method.__name__
+

The name of the method (same as method.__func__.__name__)
+
+method.__module__
+

The name of the module the method was defined in, or None if unavailable.

Methods also support accessing (but not setting) the arbitrary function attributes on the underlying function object.

User-defined method objects may be created when getting an attribute of a class (perhaps via an instance of that class), if that attribute is a user-defined function object or a classmethod object.

When an instance method object is created by retrieving a user-defined function object from a class via one of its instances, its __self__ attribute is the instance, and the method object is said to be bound. The new method’s __func__ attribute is the original function object.

When an instance method object is created by retrieving a classmethod object from a class or instance, its __self__ attribute is the class itself, and its __func__ attribute is the function object underlying the class method.

When an instance method object is called, the underlying function (__func__) is called, inserting the class instance (__self__) in front of the argument list. For instance, when C is a class which contains a definition for a function f(), and x is an instance of C, calling x.f(1) is equivalent to calling C.f(x, 1).

When an instance method object is derived from a classmethod object, the “class instance” stored in __self__ will actually be the class itself, so that calling either x.f(1) or C.f(1) is equivalent to calling f(C,1) where f is the underlying function.

Note that the transformation from function object to instance method object happens each time the attribute is retrieved from the instance. In some cases, a fruitful optimization is to assign the attribute to a local variable and call that local variable. Also notice that this transformation only happens for user-defined functions; other callable objects (and all non-callable objects) are retrieved without transformation. It is also important to note that user-defined functions which are attributes of a class instance are not converted to bound methods; this only happens when the function is an attribute of the class.

+3.2.8.3. Generator functions

A function or method which uses the yield statement (see section The yield statement) is called a generator function. Such a function, when called, always returns an iterator object which can be used to execute the body of the function: calling the iterator’s iterator.__next__() method will cause the function to execute until it provides a value using the yield statement. When the function executes a return statement or falls off the end, a StopIteration exception is raised and the iterator will have reached the end of the set of values to be returned.

+3.2.8.4. Coroutine functions

A function or method which is defined using async def is called a coroutine function. Such a function, when called, returns a coroutine object. It may contain await expressions, as well as async with and async for statements. See also the Coroutine Objects section.

+3.2.8.5. Asynchronous generator functions

A function or method which is defined using async def and which uses the yield statement is called a asynchronous generator function. Such a function, when called, returns an asynchronous iterator object which can be used in an async for statement to execute the body of the function.

Calling the asynchronous iterator’s aiterator.__anext__ method will return an awaitable which when awaited will execute until it provides a value using the yield expression. When the function executes an empty return statement or falls off the end, a StopAsyncIteration exception is raised and the asynchronous iterator will have reached the end of the set of values to be yielded.

+3.2.8.6. Built-in functions

A built-in function object is a wrapper around a C function. Examples of built-in functions are len() and math.sin() (math is a standard built-in module). The number and type of the arguments are determined by the C function. Special read-only attributes:

  • +__doc__ is the function’s documentation string, or None if unavailable. See function.__doc__.
  • +__name__ is the function’s name. See function.__name__.
  • +__self__ is set to None (but see the next item).
  • +__module__ is the name of the module the function was defined in or None if unavailable. See function.__module__.

+3.2.8.7. Built-in methods

This is really a different disguise of a built-in function, this time containing an object passed to the C function as an implicit extra argument. An example of a built-in method is alist.append(), assuming alist is a list object. In this case, the special read-only attribute __self__ is set to the object denoted by alist. (The attribute has the same semantics as it does with other instance methods.)

+3.2.8.8. Classes

Classes are callable. These objects normally act as factories for new instances of themselves, but variations are possible for class types that override __new__(). The arguments of the call are passed to __new__() and, in the typical case, to __init__() to initialize the new instance.

+3.2.8.9. Class Instances

Instances of arbitrary classes can be made callable by defining a __call__() method in their class.

+3.2.9. Modules

Modules are a basic organizational unit of Python code, and are created by the import system as invoked either by the import statement, or by calling functions such as importlib.import_module() and built-in __import__(). A module object has a namespace implemented by a dictionary object (this is the dictionary referenced by the __globals__ attribute of functions defined in the module). Attribute references are translated to lookups in this dictionary, e.g., m.x is equivalent to m.__dict__["x"]. A module object does not contain the code object used to initialize the module (since it isn’t needed once the initialization is done).

Attribute assignment updates the module’s namespace dictionary, e.g., m.x = 1 is equivalent to m.__dict__["x"] = 1.

Predefined (writable) attributes:

+ __name__ +
+
+

The module’s name.

+__doc__
+
+

The module’s documentation string, or None if unavailable.

+ __file__ +
+
+

The pathname of the file from which the module was loaded, if it was loaded from a file. The __file__ attribute may be missing for certain types of modules, such as C modules that are statically linked into the interpreter. For extension modules loaded dynamically from a shared library, it’s the pathname of the shared library file.

+__annotations__
+
+

A dictionary containing variable annotations collected during module body execution. For best practices on working with __annotations__, please see Annotations Best Practices.

Special read-only attribute: __dict__ is the module’s namespace as a dictionary object.

CPython implementation detail: Because of the way CPython clears module dictionaries, the module dictionary will be cleared when the module falls out of scope even if the dictionary still has live references. To avoid this, copy the dictionary or keep the module around while using its dictionary directly.

+3.2.10. Custom classes

Custom class types are typically created by class definitions (see section Class definitions). A class has a namespace implemented by a dictionary object. Class attribute references are translated to lookups in this dictionary, e.g., C.x is translated to C.__dict__["x"] (although there are a number of hooks which allow for other means of locating attributes). When the attribute name is not found there, the attribute search continues in the base classes. This search of the base classes uses the C3 method resolution order which behaves correctly even in the presence of ‘diamond’ inheritance structures where there are multiple inheritance paths leading back to a common ancestor. Additional details on the C3 MRO used by Python can be found in the documentation accompanying the 2.3 release at https://www.python.org/download/releases/2.3/mro/.

When a class attribute reference (for class C, say) would yield a class method object, it is transformed into an instance method object whose __self__ attribute is C. When it would yield a staticmethod object, it is transformed into the object wrapped by the static method object. See section Implementing Descriptors for another way in which attributes retrieved from a class may differ from those actually contained in its __dict__.

Class attribute assignments update the class’s dictionary, never the dictionary of a base class.

A class object can be called (see above) to yield a class instance (see below).

Special attributes:

+ __name__ +
+
+

The class name.

+__module__
+
+

The name of the module in which the class was defined.

+ __dict__ +
+
+

The dictionary containing the class’s namespace.

+ __bases__ +
+
+

A tuple containing the base classes, in the order of their occurrence in the base class list.

+__doc__
+
+

The class’s documentation string, or None if undefined.

+__annotations__
+
+

A dictionary containing variable annotations collected during class body execution. For best practices on working with __annotations__, please see Annotations Best Practices.

+__type_params__
+
+

A tuple containing the type parameters of a generic class.

+3.2.11. Class instances

A class instance is created by calling a class object (see above). A class instance has a namespace implemented as a dictionary which is the first place in which attribute references are searched. When an attribute is not found there, and the instance’s class has an attribute by that name, the search continues with the class attributes. If a class attribute is found that is a user-defined function object, it is transformed into an instance method object whose __self__ attribute is the instance. Static method and class method objects are also transformed; see above under “Classes”. See section Implementing Descriptors for another way in which attributes of a class retrieved via its instances may differ from the objects actually stored in the class’s __dict__. If no class attribute is found, and the object’s class has a __getattr__() method, that is called to satisfy the lookup.

Attribute assignments and deletions update the instance’s dictionary, never a class’s dictionary. If the class has a __setattr__() or __delattr__() method, this is called instead of updating the instance dictionary directly.

Class instances can pretend to be numbers, sequences, or mappings if they have methods with certain special names. See section Special method names.

Special attributes: __dict__ is the attribute dictionary; __class__ is the instance’s class.

+3.2.12. I/O objects (also known as file objects)

A file object represents an open file. Various shortcuts are available to create file objects: the open() built-in function, and also os.popen(), os.fdopen(), and the makefile() method of socket objects (and perhaps by other functions or methods provided by extension modules).

The objects sys.stdin, sys.stdout and sys.stderr are initialized to file objects corresponding to the interpreter’s standard input, output and error streams; they are all open in text mode and therefore follow the interface defined by the io.TextIOBase abstract class.

+3.2.13. Internal types

A few types used internally by the interpreter are exposed to the user. Their definitions may change with future versions of the interpreter, but they are mentioned here for completeness.

+3.2.13.1. Code objects

Code objects represent byte-compiled executable Python code, or bytecode. The difference between a code object and a function object is that the function object contains an explicit reference to the function’s globals (the module in which it was defined), while a code object contains no context; also the default argument values are stored in the function object, not in the code object (because they represent values calculated at run-time). Unlike function objects, code objects are immutable and contain no references (directly or indirectly) to mutable objects.

+3.2.13.1.1. Special read-only attributes
+ + + + + + + + + + + + + + + + +
+
+codeobject.co_name
+

The function name
+
+codeobject.co_qualname
+

The fully qualified function name
+
+codeobject.co_argcount
+

The total number of positional parameters (including positional-only parameters and parameters with default values) that the function has
+
+codeobject.co_posonlyargcount
+

The number of positional-only parameters (including arguments with default values) that the function has
+
+codeobject.co_kwonlyargcount
+

The number of keyword-only parameters (including arguments with default values) that the function has
+
+codeobject.co_nlocals
+

The number of local variables used by the function (including parameters)
+
+codeobject.co_varnames
+

A tuple containing the names of the local variables in the function (starting with the parameter names)
+
+codeobject.co_cellvars
+

A tuple containing the names of local variables that are referenced by nested functions inside the function
+
+codeobject.co_freevars
+

A tuple containing the names of free variables in the function
+
+codeobject.co_code
+

A string representing the sequence of bytecode instructions in the function
+
+codeobject.co_consts
+

A tuple containing the literals used by the bytecode in the function
+
+codeobject.co_names
+

A tuple containing the names used by the bytecode in the function
+
+codeobject.co_filename
+

The name of the file from which the code was compiled
+
+codeobject.co_firstlineno
+

The line number of the first line of the function
+
+codeobject.co_lnotab
+
 +

A string encoding the mapping from bytecode offsets to line numbers. For details, see the source code of the interpreter.

Deprecated since version 3.12: This attribute of code objects is deprecated, and may be removed in Python 3.14.
+
+codeobject.co_stacksize
+

The required stack size of the code object
+
+codeobject.co_flags
+

An integer encoding a number of flags for the interpreter.

The following flag bits are defined for co_flags: bit 0x04 is set if the function uses the *arguments syntax to accept an arbitrary number of positional arguments; bit 0x08 is set if the function uses the **keywords syntax to accept arbitrary keyword arguments; bit 0x20 is set if the function is a generator. See Code Objects Bit Flags for details on the semantics of each flags that might be present.

Future feature declarations (from __future__ import division) also use bits in co_flags to indicate whether a code object was compiled with a particular feature enabled: bit 0x2000 is set if the function was compiled with future division enabled; bits 0x10 and 0x1000 were used in earlier versions of Python.

Other bits in co_flags are reserved for internal use.

If a code object represents a function, the first item in co_consts is the documentation string of the function, or None if undefined.

+3.2.13.1.2. Methods on code objects
+codeobject.co_positions()
+

Returns an iterable over the source code positions of each bytecode instruction in the code object.

The iterator returns tuples containing the (start_line, end_line, +start_column, end_column). The i-th tuple corresponds to the position of the source code that compiled to the i-th instruction. Column information is 0-indexed utf-8 byte offsets on the given source line.

This positional information can be missing. A non-exhaustive lists of cases where this may happen:

  • Running the interpreter with -X no_debug_ranges.
  • Loading a pyc file compiled while using -X no_debug_ranges.
  • Position tuples corresponding to artificial instructions.
  • Line and column numbers that can’t be represented due to implementation specific limitations.

When this occurs, some or all of the tuple elements can be None.

New in version 3.11.

Note

This feature requires storing column positions in code objects which may result in a small increase of disk usage of compiled Python files or interpreter memory usage. To avoid storing the extra information and/or deactivate printing the extra traceback information, the -X no_debug_ranges command line flag or the PYTHONNODEBUGRANGES environment variable can be used.

+
+codeobject.co_lines()
+

Returns an iterator that yields information about successive ranges of bytecodes. Each item yielded is a (start, end, lineno) tuple:

  • +start (an int) represents the offset (inclusive) of the start of the bytecode range
  • +end (an int) represents the offset (inclusive) of the end of the bytecode range
  • +lineno is an int representing the line number of the bytecode range, or None if the bytecodes in the given range have no line number

The items yielded generated will have the following properties:

  • The first range yielded will have a start of 0.
  • The (start, end) ranges will be non-decreasing and consecutive. That is, for any pair of tuples, the start of the second will be equal to the end of the first.
  • No range will be backwards: end >= start for all triples.
  • The tuple yielded will have end equal to the size of the bytecode.

Zero-width ranges, where start == end, are allowed. Zero-width ranges are used for lines that are present in the source code, but have been eliminated by the bytecode compiler.

New in version 3.10.

See also

+PEP 626 - Precise line numbers for debugging and other tools.
+
+

The PEP that introduced the co_lines() method.

+

+3.2.13.2. Frame objects

Frame objects represent execution frames. They may occur in traceback objects, and are also passed to registered trace functions.

+3.2.13.2.1. Special read-only attributes
+ + + + + +
+
+frame.f_back
+

Points to the previous stack frame (towards the caller), or None if this is the bottom stack frame
+
+frame.f_code
+

The code object being executed in this frame. Accessing this attribute raises an auditing event object.__getattr__ with arguments obj and "f_code".
+
+frame.f_locals
+

The dictionary used by the frame to look up local variables
+
+frame.f_globals
+

The dictionary used by the frame to look up global variables
+
+frame.f_builtins
+

The dictionary used by the frame to look up built-in (intrinsic) names
+
+frame.f_lasti
+

The “precise instruction” of the frame object (this is an index into the bytecode string of the code object)
+3.2.13.2.2. Special writable attributes
+ + + +
+
+frame.f_trace
+

If not None, this is a function called for various events during code execution (this is used by debuggers). Normally an event is triggered for each new source line (see f_trace_lines).
+
+frame.f_trace_lines
+

Set this attribute to False to disable triggering a tracing event for each source line.
+
+frame.f_trace_opcodes
+

Set this attribute to True to allow per-opcode events to be requested. Note that this may lead to undefined interpreter behaviour if exceptions raised by the trace function escape to the function being traced.
+
+frame.f_lineno
+

The current line number of the frame – writing to this from within a trace function jumps to the given line (only for the bottom-most frame). A debugger can implement a Jump command (aka Set Next Statement) by writing to this attribute.
+3.2.13.2.3. Frame object methods

Frame objects support one method:

+frame.clear()
+

This method clears all references to local variables held by the frame. Also, if the frame belonged to a generator, the generator is finalized. This helps break reference cycles involving frame objects (for example when catching an exception and storing its traceback for later use).

RuntimeError is raised if the frame is currently executing.

New in version 3.4.

+

+3.2.13.3. Traceback objects

Traceback objects represent the stack trace of an exception. A traceback object is implicitly created when an exception occurs, and may also be explicitly created by calling types.TracebackType.

Changed in version 3.7: Traceback objects can now be explicitly instantiated from Python code.

For implicitly created tracebacks, when the search for an exception handler unwinds the execution stack, at each unwound level a traceback object is inserted in front of the current traceback. When an exception handler is entered, the stack trace is made available to the program. (See section The try statement.) It is accessible as the third item of the tuple returned by sys.exc_info(), and as the __traceback__ attribute of the caught exception.

When the program contains no suitable handler, the stack trace is written (nicely formatted) to the standard error stream; if the interpreter is interactive, it is also made available to the user as sys.last_traceback.

For explicitly created tracebacks, it is up to the creator of the traceback to determine how the tb_next attributes should be linked to form a full stack trace.

Special read-only attributes:

+ + +
+
+traceback.tb_frame
+
 +

Points to the execution frame of the current level.

Accessing this attribute raises an auditing event object.__getattr__ with arguments obj and "tb_frame".
+
+traceback.tb_lineno
+

Gives the line number where the exception occurred
+
+traceback.tb_lasti
+

Indicates the “precise instruction”.

The line number and last instruction in the traceback may differ from the line number of its frame object if the exception occurred in a try statement with no matching except clause or with a finally clause.

+traceback.tb_next
+

The special writable attribute tb_next is the next level in the stack trace (towards the frame where the exception occurred), or None if there is no next level.

Changed in version 3.7: This attribute is now writable

+

+3.2.13.4. Slice objects

Slice objects are used to represent slices for __getitem__() methods. They are also created by the built-in slice() function.

Special read-only attributes: start is the lower bound; stop is the upper bound; step is the step value; each is None if omitted. These attributes can have any type.

Slice objects support one method:

+slice.indices(self, length)
+

This method takes a single integer argument length and computes information about the slice that the slice object would describe if applied to a sequence of length items. It returns a tuple of three integers; respectively these are the start and stop indices and the step or stride length of the slice. Missing or out-of-bounds indices are handled in a manner consistent with regular slices.

+

+3.2.13.5. Static method objects

Static method objects provide a way of defeating the transformation of function objects to method objects described above. A static method object is a wrapper around any other object, usually a user-defined method object. When a static method object is retrieved from a class or a class instance, the object actually returned is the wrapped object, which is not subject to any further transformation. Static method objects are also callable. Static method objects are created by the built-in staticmethod() constructor.

+3.2.13.6. Class method objects

A class method object, like a static method object, is a wrapper around another object that alters the way in which that object is retrieved from classes and class instances. The behaviour of class method objects upon such retrieval is described above, under “User-defined methods”. Class method objects are created by the built-in classmethod() constructor.

+3.3. Special method names

A class can implement certain operations that are invoked by special syntax (such as arithmetic operations or subscripting and slicing) by defining methods with special names. This is Python’s approach to operator overloading, allowing classes to define their own behavior with respect to language operators. For instance, if a class defines a method named __getitem__(), and x is an instance of this class, then x[i] is roughly equivalent to type(x).__getitem__(x, i). Except where mentioned, attempts to execute an operation raise an exception when no appropriate method is defined (typically AttributeError or TypeError).

Setting a special method to None indicates that the corresponding operation is not available. For example, if a class sets __iter__() to None, the class is not iterable, so calling iter() on its instances will raise a TypeError (without falling back to __getitem__()). 2

When implementing a class that emulates any built-in type, it is important that the emulation only be implemented to the degree that it makes sense for the object being modelled. For example, some sequences may work well with retrieval of individual elements, but extracting a slice may not make sense. (One example of this is the NodeList interface in the W3C’s Document Object Model.)

+3.3.1. Basic customization

+object.__new__(cls[, ...])
+

Called to create a new instance of class cls. __new__() is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument. The remaining arguments are those passed to the object constructor expression (the call to the class). The return value of __new__() should be the new object instance (usually an instance of cls).

Typical implementations create a new instance of the class by invoking the superclass’s __new__() method using super().__new__(cls[, ...]) with appropriate arguments and then modifying the newly created instance as necessary before returning it.

If __new__() is invoked during object construction and it returns an instance of cls, then the new instance’s __init__() method will be invoked like __init__(self[, ...]), where self is the new instance and the remaining arguments are the same as were passed to the object constructor.

If __new__() does not return an instance of cls, then the new instance’s __init__() method will not be invoked.

__new__() is intended mainly to allow subclasses of immutable types (like int, str, or tuple) to customize instance creation. It is also commonly overridden in custom metaclasses in order to customize class creation.

+
+object.__init__(self[, ...])
+

Called after the instance has been created (by __new__()), but before it is returned to the caller. The arguments are those passed to the class constructor expression. If a base class has an __init__() method, the derived class’s __init__() method, if any, must explicitly call it to ensure proper initialization of the base class part of the instance; for example: super().__init__([args...]).

Because __new__() and __init__() work together in constructing objects (__new__() to create it, and __init__() to customize it), no non-None value may be returned by __init__(); doing so will cause a TypeError to be raised at runtime.

+
+object.__del__(self)
+

Called when the instance is about to be destroyed. This is also called a finalizer or (improperly) a destructor. If a base class has a __del__() method, the derived class’s __del__() method, if any, must explicitly call it to ensure proper deletion of the base class part of the instance.

It is possible (though not recommended!) for the __del__() method to postpone destruction of the instance by creating a new reference to it. This is called object resurrection. It is implementation-dependent whether __del__() is called a second time when a resurrected object is about to be destroyed; the current CPython implementation only calls it once.

It is not guaranteed that __del__() methods are called for objects that still exist when the interpreter exits.

Note

del x doesn’t directly call x.__del__() — the former decrements the reference count for x by one, and the latter is only called when x’s reference count reaches zero.

CPython implementation detail: It is possible for a reference cycle to prevent the reference count of an object from going to zero. In this case, the cycle will be later detected and deleted by the cyclic garbage collector. A common cause of reference cycles is when an exception has been caught in a local variable. The frame’s locals then reference the exception, which references its own traceback, which references the locals of all frames caught in the traceback.

See also

Documentation for the gc module.

Warning

Due to the precarious circumstances under which __del__() methods are invoked, exceptions that occur during their execution are ignored, and a warning is printed to sys.stderr instead. In particular:

  • +__del__() can be invoked when arbitrary code is being executed, including from any arbitrary thread. If __del__() needs to take a lock or invoke any other blocking resource, it may deadlock as the resource may already be taken by the code that gets interrupted to execute __del__().
  • +__del__() can be executed during interpreter shutdown. As a consequence, the global variables it needs to access (including other modules) may already have been deleted or set to None. Python guarantees that globals whose name begins with a single underscore are deleted from their module before other globals are deleted; if no other references to such globals exist, this may help in assuring that imported modules are still available at the time when the __del__() method is called.
+
+
+object.__repr__(self)
+

Called by the repr() built-in function to compute the “official” string representation of an object. If at all possible, this should look like a valid Python expression that could be used to recreate an object with the same value (given an appropriate environment). If this is not possible, a string of the form <...some useful description...> should be returned. The return value must be a string object. If a class defines __repr__() but not __str__(), then __repr__() is also used when an “informal” string representation of instances of that class is required.

This is typically used for debugging, so it is important that the representation is information-rich and unambiguous.

+
+
+object.__str__(self)
+

Called by str(object) and the built-in functions format() and print() to compute the “informal” or nicely printable string representation of an object. The return value must be a string object.

This method differs from object.__repr__() in that there is no expectation that __str__() return a valid Python expression: a more convenient or concise representation can be used.

The default implementation defined by the built-in type object calls object.__repr__().

+
+object.__bytes__(self)
+

Called by bytes to compute a byte-string representation of an object. This should return a bytes object.

+
+
+object.__format__(self, format_spec)
+

Called by the format() built-in function, and by extension, evaluation of formatted string literals and the str.format() method, to produce a “formatted” string representation of an object. The format_spec argument is a string that contains a description of the formatting options desired. The interpretation of the format_spec argument is up to the type implementing __format__(), however most classes will either delegate formatting to one of the built-in types, or use a similar formatting option syntax.

See Format Specification Mini-Language for a description of the standard formatting syntax.

The return value must be a string object.

Changed in version 3.4: The __format__ method of object itself raises a TypeError if passed any non-empty string.

Changed in version 3.7: object.__format__(x, '') is now equivalent to str(x) rather than format(str(x), '').

+
+object.__lt__(self, other)
+object.__le__(self, other)
+object.__eq__(self, other)
+object.__ne__(self, other)
+object.__gt__(self, other)
+object.__ge__(self, other)
+

These are the so-called “rich comparison” methods. The correspondence between operator symbols and method names is as follows: x<y calls x.__lt__(y), x<=y calls x.__le__(y), x==y calls x.__eq__(y), x!=y calls x.__ne__(y), x>y calls x.__gt__(y), and x>=y calls x.__ge__(y).

A rich comparison method may return the singleton NotImplemented if it does not implement the operation for a given pair of arguments. By convention, False and True are returned for a successful comparison. However, these methods can return any value, so if the comparison operator is used in a Boolean context (e.g., in the condition of an if statement), Python will call bool() on the value to determine if the result is true or false.

By default, object implements __eq__() by using is, returning NotImplemented in the case of a false comparison: True if x is y else NotImplemented. For __ne__(), by default it delegates to __eq__() and inverts the result unless it is NotImplemented. There are no other implied relationships among the comparison operators or default implementations; for example, the truth of (x<y or x==y) does not imply x<=y. To automatically generate ordering operations from a single root operation, see functools.total_ordering().

See the paragraph on __hash__() for some important notes on creating hashable objects which support custom comparison operations and are usable as dictionary keys.

There are no swapped-argument versions of these methods (to be used when the left argument does not support the operation but the right argument does); rather, __lt__() and __gt__() are each other’s reflection, __le__() and __ge__() are each other’s reflection, and __eq__() and __ne__() are their own reflection. If the operands are of different types, and right operand’s type is a direct or indirect subclass of the left operand’s type, the reflected method of the right operand has priority, otherwise the left operand’s method has priority. Virtual subclassing is not considered.

+
+object.__hash__(self)
+

Called by built-in function hash() and for operations on members of hashed collections including set, frozenset, and dict. The __hash__() method should return an integer. The only required property is that objects which compare equal have the same hash value; it is advised to mix together the hash values of the components of the object that also play a part in comparison of objects by packing them into a tuple and hashing the tuple. Example:

def __hash__(self):
+    return hash((self.name, self.nick, self.color))
+

Note

hash() truncates the value returned from an object’s custom __hash__() method to the size of a Py_ssize_t. This is typically 8 bytes on 64-bit builds and 4 bytes on 32-bit builds. If an object’s __hash__() must interoperate on builds of different bit sizes, be sure to check the width on all supported builds. An easy way to do this is with python -c "import sys; print(sys.hash_info.width)".

If a class does not define an __eq__() method it should not define a __hash__() operation either; if it defines __eq__() but not __hash__(), its instances will not be usable as items in hashable collections. If a class defines mutable objects and implements an __eq__() method, it should not implement __hash__(), since the implementation of hashable collections requires that a key’s hash value is immutable (if the object’s hash value changes, it will be in the wrong hash bucket).

User-defined classes have __eq__() and __hash__() methods by default; with them, all objects compare unequal (except with themselves) and x.__hash__() returns an appropriate value such that x == y implies both that x is y and hash(x) == hash(y).

A class that overrides __eq__() and does not define __hash__() will have its __hash__() implicitly set to None. When the __hash__() method of a class is None, instances of the class will raise an appropriate TypeError when a program attempts to retrieve their hash value, and will also be correctly identified as unhashable when checking isinstance(obj, collections.abc.Hashable).

If a class that overrides __eq__() needs to retain the implementation of __hash__() from a parent class, the interpreter must be told this explicitly by setting __hash__ = <ParentClass>.__hash__.

If a class that does not override __eq__() wishes to suppress hash support, it should include __hash__ = None in the class definition. A class which defines its own __hash__() that explicitly raises a TypeError would be incorrectly identified as hashable by an isinstance(obj, collections.abc.Hashable) call.

Note

By default, the __hash__() values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

This is intended to provide protection against a denial-of-service caused by carefully chosen inputs that exploit the worst case performance of a dict insertion, O(n2) complexity. See http://ocert.org/advisories/ocert-2011-003.html for details.

Changing hash values affects the iteration order of sets. Python has never made guarantees about this ordering (and it typically varies between 32-bit and 64-bit builds).

See also PYTHONHASHSEED.

Changed in version 3.3: Hash randomization is enabled by default.

+
+object.__bool__(self)
+

Called to implement truth value testing and the built-in operation bool(); should return False or True. When this method is not defined, __len__() is called, if it is defined, and the object is considered true if its result is nonzero. If a class defines neither __len__() nor __bool__(), all its instances are considered true.

+

+3.3.2. Customizing attribute access

The following methods can be defined to customize the meaning of attribute access (use of, assignment to, or deletion of x.name) for class instances.

+object.__getattr__(self, name)
+

Called when the default attribute access fails with an AttributeError (either __getattribute__() raises an AttributeError because name is not an instance attribute or an attribute in the class tree for self; or __get__() of a name property raises AttributeError). This method should either return the (computed) attribute value or raise an AttributeError exception.

Note that if the attribute is found through the normal mechanism, __getattr__() is not called. (This is an intentional asymmetry between __getattr__() and __setattr__().) This is done both for efficiency reasons and because otherwise __getattr__() would have no way to access other attributes of the instance. Note that at least for instance variables, you can fake total control by not inserting any values in the instance attribute dictionary (but instead inserting them in another object). See the __getattribute__() method below for a way to actually get total control over attribute access.

+
+object.__getattribute__(self, name)
+

Called unconditionally to implement attribute accesses for instances of the class. If the class also defines __getattr__(), the latter will not be called unless __getattribute__() either calls it explicitly or raises an AttributeError. This method should return the (computed) attribute value or raise an AttributeError exception. In order to avoid infinite recursion in this method, its implementation should always call the base class method with the same name to access any attributes it needs, for example, object.__getattribute__(self, name).

Note

This method may still be bypassed when looking up special methods as the result of implicit invocation via language syntax or built-in functions. See Special method lookup.

+

For certain sensitive attribute accesses, raises an auditing event object.__getattr__ with arguments obj and name.

+
+object.__setattr__(self, name, value)
+

Called when an attribute assignment is attempted. This is called instead of the normal mechanism (i.e. store the value in the instance dictionary). name is the attribute name, value is the value to be assigned to it.

If __setattr__() wants to assign to an instance attribute, it should call the base class method with the same name, for example, object.__setattr__(self, name, value).

+

For certain sensitive attribute assignments, raises an auditing event object.__setattr__ with arguments obj, name, value.

+
+object.__delattr__(self, name)
+

Like __setattr__() but for attribute deletion instead of assignment. This should only be implemented if del obj.name is meaningful for the object.

+

For certain sensitive attribute deletions, raises an auditing event object.__delattr__ with arguments obj and name.

+
+object.__dir__(self)
+

Called when dir() is called on the object. A sequence must be returned. dir() converts the returned sequence to a list and sorts it.

+

+3.3.2.1. Customizing module attribute access

Special names __getattr__ and __dir__ can be also used to customize access to module attributes. The __getattr__ function at the module level should accept one argument which is the name of an attribute and return the computed value or raise an AttributeError. If an attribute is not found on a module object through the normal lookup, i.e. object.__getattribute__(), then __getattr__ is searched in the module __dict__ before raising an AttributeError. If found, it is called with the attribute name and the result is returned.

The __dir__ function should accept no arguments, and return a sequence of strings that represents the names accessible on module. If present, this function overrides the standard dir() search on a module.

For a more fine grained customization of the module behavior (setting attributes, properties, etc.), one can set the __class__ attribute of a module object to a subclass of types.ModuleType. For example:

import sys
+from types import ModuleType
+
+class VerboseModule(ModuleType):
+    def __repr__(self):
+        return f'Verbose {self.__name__}'
+
+    def __setattr__(self, attr, value):
+        print(f'Setting {attr}...')
+        super().__setattr__(attr, value)
+
+sys.modules[__name__].__class__ = VerboseModule
+

Note

Defining module __getattr__ and setting module __class__ only affect lookups made using the attribute access syntax – directly accessing the module globals (whether by code within the module, or via a reference to the module’s globals dictionary) is unaffected.

Changed in version 3.5: __class__ module attribute is now writable.

New in version 3.7: __getattr__ and __dir__ module attributes.

See also

+PEP 562 - Module __getattr__ and __dir__
+
+

Describes the __getattr__ and __dir__ functions on modules.

+3.3.2.2. Implementing Descriptors

The following methods only apply when an instance of the class containing the method (a so-called descriptor class) appears in an owner class (the descriptor must be in either the owner’s class dictionary or in the class dictionary for one of its parents). In the examples below, “the attribute” refers to the attribute whose name is the key of the property in the owner class’ __dict__.

+object.__get__(self, instance, owner=None)
+

Called to get the attribute of the owner class (class attribute access) or of an instance of that class (instance attribute access). The optional owner argument is the owner class, while instance is the instance that the attribute was accessed through, or None when the attribute is accessed through the owner.

This method should return the computed attribute value or raise an AttributeError exception.

PEP 252 specifies that __get__() is callable with one or two arguments. Python’s own built-in descriptors support this specification; however, it is likely that some third-party tools have descriptors that require both arguments. Python’s own __getattribute__() implementation always passes in both arguments whether they are required or not.

+
+object.__set__(self, instance, value)
+

Called to set the attribute on an instance instance of the owner class to a new value, value.

Note, adding __set__() or __delete__() changes the kind of descriptor to a “data descriptor”. See Invoking Descriptors for more details.

+
+object.__delete__(self, instance)
+

Called to delete the attribute on an instance instance of the owner class.

+

Instances of descriptors may also have the __objclass__ attribute present:

+object.__objclass__
+

The attribute __objclass__ is interpreted by the inspect module as specifying the class where this object was defined (setting this appropriately can assist in runtime introspection of dynamic class attributes). For callables, it may indicate that an instance of the given type (or a subclass) is expected or required as the first positional argument (for example, CPython sets this attribute for unbound methods that are implemented in C).

+

+3.3.2.3. Invoking Descriptors

In general, a descriptor is an object attribute with “binding behavior”, one whose attribute access has been overridden by methods in the descriptor protocol: __get__(), __set__(), and __delete__(). If any of those methods are defined for an object, it is said to be a descriptor.

The default behavior for attribute access is to get, set, or delete the attribute from an object’s dictionary. For instance, a.x has a lookup chain starting with a.__dict__['x'], then type(a).__dict__['x'], and continuing through the base classes of type(a) excluding metaclasses.

However, if the looked-up value is an object defining one of the descriptor methods, then Python may override the default behavior and invoke the descriptor method instead. Where this occurs in the precedence chain depends on which descriptor methods were defined and how they were called.

The starting point for descriptor invocation is a binding, a.x. How the arguments are assembled depends on a:

Direct Call
+
+

The simplest and least common call is when user code directly invokes a descriptor method: x.__get__(a).

Instance Binding
+
+

If binding to an object instance, a.x is transformed into the call: type(a).__dict__['x'].__get__(a, type(a)).

Class Binding
+
+

If binding to a class, A.x is transformed into the call: A.__dict__['x'].__get__(None, A).

Super Binding
+
+

A dotted lookup such as super(A, a).x searches a.__class__.__mro__ for a base class B following A and then returns B.__dict__['x'].__get__(a, A). If not a descriptor, x is returned unchanged.

For instance bindings, the precedence of descriptor invocation depends on which descriptor methods are defined. A descriptor can define any combination of __get__(), __set__() and __delete__(). If it does not define __get__(), then accessing the attribute will return the descriptor object itself unless there is a value in the object’s instance dictionary. If the descriptor defines __set__() and/or __delete__(), it is a data descriptor; if it defines neither, it is a non-data descriptor. Normally, data descriptors define both __get__() and __set__(), while non-data descriptors have just the __get__() method. Data descriptors with __get__() and __set__() (and/or __delete__()) defined always override a redefinition in an instance dictionary. In contrast, non-data descriptors can be overridden by instances.

Python methods (including those decorated with @staticmethod and @classmethod) are implemented as non-data descriptors. Accordingly, instances can redefine and override methods. This allows individual instances to acquire behaviors that differ from other instances of the same class.

The property() function is implemented as a data descriptor. Accordingly, instances cannot override the behavior of a property.

+3.3.2.4. __slots__

__slots__ allow us to explicitly declare data members (like properties) and deny the creation of __dict__ and __weakref__ (unless explicitly declared in __slots__ or available in a parent.)

The space saved over using __dict__ can be significant. Attribute lookup speed can be significantly improved as well.

+object.__slots__
+

This class variable can be assigned a string, iterable, or sequence of strings with variable names used by instances. __slots__ reserves space for the declared variables and prevents the automatic creation of __dict__ and __weakref__ for each instance.

+

Notes on using __slots__:

  • When inheriting from a class without __slots__, the __dict__ and __weakref__ attribute of the instances will always be accessible.
  • Without a __dict__ variable, instances cannot be assigned new variables not listed in the __slots__ definition. Attempts to assign to an unlisted variable name raises AttributeError. If dynamic assignment of new variables is desired, then add '__dict__' to the sequence of strings in the __slots__ declaration.
  • Without a __weakref__ variable for each instance, classes defining __slots__ do not support weak references to its instances. If weak reference support is needed, then add '__weakref__' to the sequence of strings in the __slots__ declaration.
  • +__slots__ are implemented at the class level by creating descriptors for each variable name. As a result, class attributes cannot be used to set default values for instance variables defined by __slots__; otherwise, the class attribute would overwrite the descriptor assignment.
  • The action of a __slots__ declaration is not limited to the class where it is defined. __slots__ declared in parents are available in child classes. However, child subclasses will get a __dict__ and __weakref__ unless they also define __slots__ (which should only contain names of any additional slots).
  • If a class defines a slot also defined in a base class, the instance variable defined by the base class slot is inaccessible (except by retrieving its descriptor directly from the base class). This renders the meaning of the program undefined. In the future, a check may be added to prevent this.
  • +TypeError will be raised if nonempty __slots__ are defined for a class derived from a "variable-length" built-in type such as int, bytes, and tuple.
  • Any non-string iterable may be assigned to __slots__.
  • If a dictionary is used to assign __slots__, the dictionary keys will be used as the slot names. The values of the dictionary can be used to provide per-attribute docstrings that will be recognised by inspect.getdoc() and displayed in the output of help().
  • +__class__ assignment works only if both classes have the same __slots__.
  • +Multiple inheritance with multiple slotted parent classes can be used, but only one parent is allowed to have attributes created by slots (the other bases must have empty slot layouts) - violations raise TypeError.
  • If an iterator is used for __slots__ then a descriptor is created for each of the iterator’s values. However, the __slots__ attribute will be an empty iterator.

+3.3.3. Customizing class creation

Whenever a class inherits from another class, __init_subclass__() is called on the parent class. This way, it is possible to write classes which change the behavior of subclasses. This is closely related to class decorators, but where class decorators only affect the specific class they’re applied to, __init_subclass__ solely applies to future subclasses of the class defining the method.

+classmethod object.__init_subclass__(cls)
+

This method is called whenever the containing class is subclassed. cls is then the new subclass. If defined as a normal instance method, this method is implicitly converted to a class method.

Keyword arguments which are given to a new class are passed to the parent’s class __init_subclass__. For compatibility with other classes using __init_subclass__, one should take out the needed keyword arguments and pass the others over to the base class, as in:

class Philosopher:
+    def __init_subclass__(cls, /, default_name, **kwargs):
+        super().__init_subclass__(**kwargs)
+        cls.default_name = default_name
+
+class AustralianPhilosopher(Philosopher, default_name="Bruce"):
+    pass
+

The default implementation object.__init_subclass__ does nothing, but raises an error if it is called with any arguments.

Note

The metaclass hint metaclass is consumed by the rest of the type machinery, and is never passed to __init_subclass__ implementations. The actual metaclass (rather than the explicit hint) can be accessed as type(cls).

New in version 3.6.

+

When a class is created, type.__new__() scans the class variables and makes callbacks to those with a __set_name__() hook.

+object.__set_name__(self, owner, name)
+

Automatically called at the time the owning class owner is created. The object has been assigned to name in that class:

class A:
+    x = C()  # Automatically calls: x.__set_name__(A, 'x')
+

If the class variable is assigned after the class is created, __set_name__() will not be called automatically. If needed, __set_name__() can be called directly:

class A:
+   pass
+
+c = C()
+A.x = c                  # The hook is not called
+c.__set_name__(A, 'x')   # Manually invoke the hook
+

See Creating the class object for more details.

New in version 3.6.

+

+3.3.3.1. Metaclasses

By default, classes are constructed using type(). The class body is executed in a new namespace and the class name is bound locally to the result of type(name, bases, namespace).

The class creation process can be customized by passing the metaclass keyword argument in the class definition line, or by inheriting from an existing class that included such an argument. In the following example, both MyClass and MySubclass are instances of Meta:

class Meta(type):
+    pass
+
+class MyClass(metaclass=Meta):
+    pass
+
+class MySubclass(MyClass):
+    pass
+

Any other keyword arguments that are specified in the class definition are passed through to all metaclass operations described below.

When a class definition is executed, the following steps occur:

  • MRO entries are resolved;
  • the appropriate metaclass is determined;
  • the class namespace is prepared;
  • the class body is executed;
  • the class object is created.

+3.3.3.2. Resolving MRO entries

+object.__mro_entries__(self, bases)
+

If a base that appears in a class definition is not an instance of type, then an __mro_entries__() method is searched on the base. If an __mro_entries__() method is found, the base is substituted with the result of a call to __mro_entries__() when creating the class. The method is called with the original bases tuple passed to the bases parameter, and must return a tuple of classes that will be used instead of the base. The returned tuple may be empty: in these cases, the original base is ignored.

+

See also

+ types.resolve_bases() +
+
+

Dynamically resolve bases that are not instances of type.

+ types.get_original_bases() +
+
+

Retrieve a class’s “original bases” prior to modifications by __mro_entries__().

+PEP 560 +
+
+

Core support for typing module and generic types.

+3.3.3.3. Determining the appropriate metaclass

The appropriate metaclass for a class definition is determined as follows:

  • if no bases and no explicit metaclass are given, then type() is used;
  • if an explicit metaclass is given and it is not an instance of type(), then it is used directly as the metaclass;
  • if an instance of type() is given as the explicit metaclass, or bases are defined, then the most derived metaclass is used.

The most derived metaclass is selected from the explicitly specified metaclass (if any) and the metaclasses (i.e. type(cls)) of all specified base classes. The most derived metaclass is one which is a subtype of all of these candidate metaclasses. If none of the candidate metaclasses meets that criterion, then the class definition will fail with TypeError.

+3.3.3.4. Preparing the class namespace

Once the appropriate metaclass has been identified, then the class namespace is prepared. If the metaclass has a __prepare__ attribute, it is called as namespace = metaclass.__prepare__(name, bases, **kwds) (where the additional keyword arguments, if any, come from the class definition). The __prepare__ method should be implemented as a classmethod. The namespace returned by __prepare__ is passed in to __new__, but when the final class object is created the namespace is copied into a new dict.

If the metaclass has no __prepare__ attribute, then the class namespace is initialised as an empty ordered mapping.

See also

+PEP 3115 - Metaclasses in Python 3000
+
+

Introduced the __prepare__ namespace hook

+3.3.3.5. Executing the class body

The class body is executed (approximately) as exec(body, globals(), namespace). The key difference from a normal call to exec() is that lexical scoping allows the class body (including any methods) to reference names from the current and outer scopes when the class definition occurs inside a function.

However, even when the class definition occurs inside the function, methods defined inside the class still cannot see names defined at the class scope. Class variables must be accessed through the first parameter of instance or class methods, or through the implicit lexically scoped __class__ reference described in the next section.

+3.3.3.6. Creating the class object

Once the class namespace has been populated by executing the class body, the class object is created by calling metaclass(name, bases, namespace, **kwds) (the additional keywords passed here are the same as those passed to __prepare__).

This class object is the one that will be referenced by the zero-argument form of super(). __class__ is an implicit closure reference created by the compiler if any methods in a class body refer to either __class__ or super. This allows the zero argument form of super() to correctly identify the class being defined based on lexical scoping, while the class or instance that was used to make the current call is identified based on the first argument passed to the method.

CPython implementation detail: In CPython 3.6 and later, the __class__ cell is passed to the metaclass as a __classcell__ entry in the class namespace. If present, this must be propagated up to the type.__new__ call in order for the class to be initialised correctly. Failing to do so will result in a RuntimeError in Python 3.8.

When using the default metaclass type, or any metaclass that ultimately calls type.__new__, the following additional customization steps are invoked after creating the class object:

  1. The type.__new__ method collects all of the attributes in the class namespace that define a __set_name__() method;
  2. Those __set_name__ methods are called with the class being defined and the assigned name of that particular attribute;
  3. The __init_subclass__() hook is called on the immediate parent of the new class in its method resolution order.

After the class object is created, it is passed to the class decorators included in the class definition (if any) and the resulting object is bound in the local namespace as the defined class.

When a new class is created by type.__new__, the object provided as the namespace parameter is copied to a new ordered mapping and the original object is discarded. The new copy is wrapped in a read-only proxy, which becomes the __dict__ attribute of the class object.

See also

+PEP 3135 - New super
+
+

Describes the implicit __class__ closure reference

+3.3.3.7. Uses for metaclasses

The potential uses for metaclasses are boundless. Some ideas that have been explored include enum, logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.

+3.3.4. Customizing instance and subclass checks

The following methods are used to override the default behavior of the isinstance() and issubclass() built-in functions.

In particular, the metaclass abc.ABCMeta implements these methods in order to allow the addition of Abstract Base Classes (ABCs) as “virtual base classes” to any class or type (including built-in types), including other ABCs.

+class.__instancecheck__(self, instance)
+

Return true if instance should be considered a (direct or indirect) instance of class. If defined, called to implement isinstance(instance, +class).

+
+class.__subclasscheck__(self, subclass)
+

Return true if subclass should be considered a (direct or indirect) subclass of class. If defined, called to implement issubclass(subclass, +class).

+

Note that these methods are looked up on the type (metaclass) of a class. They cannot be defined as class methods in the actual class. This is consistent with the lookup of special methods that are called on instances, only in this case the instance is itself a class.

See also

+PEP 3119 - Introducing Abstract Base Classes
+
+

Includes the specification for customizing isinstance() and issubclass() behavior through __instancecheck__() and __subclasscheck__(), with motivation for this functionality in the context of adding Abstract Base Classes (see the abc module) to the language.

+3.3.5. Emulating generic types

When using type annotations, it is often useful to parameterize a generic type using Python’s square-brackets notation. For example, the annotation list[int] might be used to signify a list in which all the elements are of type int.

See also

+PEP 484 - Type Hints
+
+

Introducing Python’s framework for type annotations

Generic Alias Types
+
+

Documentation for objects representing parameterized generic classes

+Generics, user-defined generics and typing.Generic +
+
+

Documentation on how to implement generic classes that can be parameterized at runtime and understood by static type-checkers.

A class can generally only be parameterized if it defines the special class method __class_getitem__().

+classmethod object.__class_getitem__(cls, key)
+

Return an object representing the specialization of a generic class by type arguments found in key.

When defined on a class, __class_getitem__() is automatically a class method. As such, there is no need for it to be decorated with @classmethod when it is defined.

+

+3.3.5.1. The purpose of __class_getitem__ +

The purpose of __class_getitem__() is to allow runtime parameterization of standard-library generic classes in order to more easily apply type hints to these classes.

To implement custom generic classes that can be parameterized at runtime and understood by static type-checkers, users should either inherit from a standard library class that already implements __class_getitem__(), or inherit from typing.Generic, which has its own implementation of __class_getitem__().

Custom implementations of __class_getitem__() on classes defined outside of the standard library may not be understood by third-party type-checkers such as mypy. Using __class_getitem__() on any class for purposes other than type hinting is discouraged.

+3.3.5.2. __class_getitem__ versus __getitem__ +

Usually, the subscription of an object using square brackets will call the __getitem__() instance method defined on the object’s class. However, if the object being subscribed is itself a class, the class method __class_getitem__() may be called instead. __class_getitem__() should return a GenericAlias object if it is properly defined.

Presented with the expression obj[x], the Python interpreter follows something like the following process to decide whether __getitem__() or __class_getitem__() should be called:

from inspect import isclass
+
+def subscribe(obj, x):
+    """Return the result of the expression 'obj[x]'"""
+
+    class_of_obj = type(obj)
+
+    # If the class of obj defines __getitem__,
+    # call class_of_obj.__getitem__(obj, x)
+    if hasattr(class_of_obj, '__getitem__'):
+        return class_of_obj.__getitem__(obj, x)
+
+    # Else, if obj is a class and defines __class_getitem__,
+    # call obj.__class_getitem__(x)
+    elif isclass(obj) and hasattr(obj, '__class_getitem__'):
+        return obj.__class_getitem__(x)
+
+    # Else, raise an exception
+    else:
+        raise TypeError(
+            f"'{class_of_obj.__name__}' object is not subscriptable"
+        )
+

In Python, all classes are themselves instances of other classes. The class of a class is known as that class’s metaclass, and most classes have the type class as their metaclass. type does not define __getitem__(), meaning that expressions such as list[int], dict[str, float] and tuple[str, bytes] all result in __class_getitem__() being called:

>>> # list has class "type" as its metaclass, like most classes:
+>>> type(list)
+<class 'type'>
+>>> type(dict) == type(list) == type(tuple) == type(str) == type(bytes)
+True
+>>> # "list[int]" calls "list.__class_getitem__(int)"
+>>> list[int]
+list[int]
+>>> # list.__class_getitem__ returns a GenericAlias object:
+>>> type(list[int])
+<class 'types.GenericAlias'>
+

However, if a class has a custom metaclass that defines __getitem__(), subscribing the class may result in different behaviour. An example of this can be found in the enum module:

>>> from enum import Enum
+>>> class Menu(Enum):
+...     """A breakfast menu"""
+...     SPAM = 'spam'
+...     BACON = 'bacon'
+...
+>>> # Enum classes have a custom metaclass:
+>>> type(Menu)
+<class 'enum.EnumMeta'>
+>>> # EnumMeta defines __getitem__,
+>>> # so __class_getitem__ is not called,
+>>> # and the result is not a GenericAlias object:
+>>> Menu['SPAM']
+<Menu.SPAM: 'spam'>
+>>> type(Menu['SPAM'])
+<enum 'Menu'>
+

See also

+PEP 560 - Core Support for typing module and generic types
+
+

Introducing __class_getitem__(), and outlining when a subscription results in __class_getitem__() being called instead of __getitem__()

+3.3.6. Emulating callable objects

+object.__call__(self[, args...])
+

Called when the instance is “called” as a function; if this method is defined, x(arg1, arg2, ...) roughly translates to type(x).__call__(x, arg1, ...).

+

+3.3.7. Emulating container types

The following methods can be defined to implement container objects. Containers usually are sequences (such as lists or tuples) or mappings (like dictionaries), but can represent other containers as well. The first set of methods is used either to emulate a sequence or to emulate a mapping; the difference is that for a sequence, the allowable keys should be the integers k for which 0 <= k < +N where N is the length of the sequence, or slice objects, which define a range of items. It is also recommended that mappings provide the methods keys(), values(), items(), get(), clear(), setdefault(), pop(), popitem(), copy(), and update() behaving similar to those for Python’s standard dictionary objects. The collections.abc module provides a MutableMapping abstract base class to help create those methods from a base set of __getitem__(), __setitem__(), __delitem__(), and keys(). Mutable sequences should provide methods append(), count(), index(), extend(), insert(), pop(), remove(), reverse() and sort(), like Python standard list objects. Finally, sequence types should implement addition (meaning concatenation) and multiplication (meaning repetition) by defining the methods __add__(), __radd__(), __iadd__(), __mul__(), __rmul__() and __imul__() described below; they should not define other numerical operators. It is recommended that both mappings and sequences implement the __contains__() method to allow efficient use of the in operator; for mappings, in should search the mapping’s keys; for sequences, it should search through the values. It is further recommended that both mappings and sequences implement the __iter__() method to allow efficient iteration through the container; for mappings, __iter__() should iterate through the object’s keys; for sequences, it should iterate through the values.

+object.__len__(self)
+

Called to implement the built-in function len(). Should return the length of the object, an integer >= 0. Also, an object that doesn’t define a __bool__() method and whose __len__() method returns zero is considered to be false in a Boolean context.

CPython implementation detail: In CPython, the length is required to be at most sys.maxsize. If the length is larger than sys.maxsize some features (such as len()) may raise OverflowError. To prevent raising OverflowError by truth value testing, an object must define a __bool__() method.

+
+object.__length_hint__(self)
+

Called to implement operator.length_hint(). Should return an estimated length for the object (which may be greater or less than the actual length). The length must be an integer >= 0. The return value may also be NotImplemented, which is treated the same as if the __length_hint__ method didn’t exist at all. This method is purely an optimization and is never required for correctness.

New in version 3.4.

+

Note

Slicing is done exclusively with the following three methods. A call like

a[1:2] = b
+

is translated to

a[slice(1, 2, None)] = b
+

and so forth. Missing slice items are always filled in with None.

+object.__getitem__(self, key)
+

Called to implement evaluation of self[key]. For sequence types, the accepted keys should be integers. Optionally, they may support slice objects as well. Negative index support is also optional. If key is of an inappropriate type, TypeError may be raised; if key is a value outside the set of indexes for the sequence (after any special interpretation of negative values), IndexError should be raised. For mapping types, if key is missing (not in the container), KeyError should be raised.

Note

for loops expect that an IndexError will be raised for illegal indexes to allow proper detection of the end of the sequence.

Note

When subscripting a class, the special class method __class_getitem__() may be called instead of __getitem__(). See __class_getitem__ versus __getitem__ for more details.

+
+object.__setitem__(self, key, value)
+

Called to implement assignment to self[key]. Same note as for __getitem__(). This should only be implemented for mappings if the objects support changes to the values for keys, or if new keys can be added, or for sequences if elements can be replaced. The same exceptions should be raised for improper key values as for the __getitem__() method.

+
+object.__delitem__(self, key)
+

Called to implement deletion of self[key]. Same note as for __getitem__(). This should only be implemented for mappings if the objects support removal of keys, or for sequences if elements can be removed from the sequence. The same exceptions should be raised for improper key values as for the __getitem__() method.

+
+object.__missing__(self, key)
+

Called by dict.__getitem__() to implement self[key] for dict subclasses when key is not in the dictionary.

+
+object.__iter__(self)
+

This method is called when an iterator is required for a container. This method should return a new iterator object that can iterate over all the objects in the container. For mappings, it should iterate over the keys of the container.

+
+object.__reversed__(self)
+

Called (if present) by the reversed() built-in to implement reverse iteration. It should return a new iterator object that iterates over all the objects in the container in reverse order.

If the __reversed__() method is not provided, the reversed() built-in will fall back to using the sequence protocol (__len__() and __getitem__()). Objects that support the sequence protocol should only provide __reversed__() if they can provide an implementation that is more efficient than the one provided by reversed().

+

The membership test operators (in and not in) are normally implemented as an iteration through a container. However, container objects can supply the following special method with a more efficient implementation, which also does not require the object be iterable.

+object.__contains__(self, item)
+

Called to implement membership test operators. Should return true if item is in self, false otherwise. For mapping objects, this should consider the keys of the mapping rather than the values or the key-item pairs.

For objects that don’t define __contains__(), the membership test first tries iteration via __iter__(), then the old sequence iteration protocol via __getitem__(), see this section in the language reference.

+

+3.3.8. Emulating numeric types

The following methods can be defined to emulate numeric objects. Methods corresponding to operations that are not supported by the particular kind of number implemented (e.g., bitwise operations for non-integral numbers) should be left undefined.

+object.__add__(self, other)
+object.__sub__(self, other)
+object.__mul__(self, other)
+object.__matmul__(self, other)
+object.__truediv__(self, other)
+object.__floordiv__(self, other)
+object.__mod__(self, other)
+object.__divmod__(self, other)
+object.__pow__(self, other[, modulo])
+object.__lshift__(self, other)
+object.__rshift__(self, other)
+object.__and__(self, other)
+object.__xor__(self, other)
+object.__or__(self, other)
+

These methods are called to implement the binary arithmetic operations (+, -, *, @, /, //, %, divmod(), pow(), **, <<, >>, &, ^, |). For instance, to evaluate the expression x + y, where x is an instance of a class that has an __add__() method, type(x).__add__(x, y) is called. The __divmod__() method should be the equivalent to using __floordiv__() and __mod__(); it should not be related to __truediv__(). Note that __pow__() should be defined to accept an optional third argument if the ternary version of the built-in pow() function is to be supported.

If one of those methods does not support the operation with the supplied arguments, it should return NotImplemented.

+
+object.__radd__(self, other)
+object.__rsub__(self, other)
+object.__rmul__(self, other)
+object.__rmatmul__(self, other)
+object.__rtruediv__(self, other)
+object.__rfloordiv__(self, other)
+object.__rmod__(self, other)
+object.__rdivmod__(self, other)
+object.__rpow__(self, other[, modulo])
+object.__rlshift__(self, other)
+object.__rrshift__(self, other)
+object.__rand__(self, other)
+object.__rxor__(self, other)
+object.__ror__(self, other)
+

These methods are called to implement the binary arithmetic operations (+, -, *, @, /, //, %, divmod(), pow(), **, <<, >>, &, ^, |) with reflected (swapped) operands. These functions are only called if the left operand does not support the corresponding operation 3 and the operands are of different types. 4 For instance, to evaluate the expression x - y, where y is an instance of a class that has an __rsub__() method, type(y).__rsub__(y, x) is called if type(x).__sub__(x, y) returns NotImplemented.

Note that ternary pow() will not try calling __rpow__() (the coercion rules would become too complicated).

Note

If the right operand’s type is a subclass of the left operand’s type and that subclass provides a different implementation of the reflected method for the operation, this method will be called before the left operand’s non-reflected method. This behavior allows subclasses to override their ancestors’ operations.

+
+object.__iadd__(self, other)
+object.__isub__(self, other)
+object.__imul__(self, other)
+object.__imatmul__(self, other)
+object.__itruediv__(self, other)
+object.__ifloordiv__(self, other)
+object.__imod__(self, other)
+object.__ipow__(self, other[, modulo])
+object.__ilshift__(self, other)
+object.__irshift__(self, other)
+object.__iand__(self, other)
+object.__ixor__(self, other)
+object.__ior__(self, other)
+

These methods are called to implement the augmented arithmetic assignments (+=, -=, *=, @=, /=, //=, %=, **=, <<=, >>=, &=, ^=, |=). These methods should attempt to do the operation in-place (modifying self) and return the result (which could be, but does not have to be, self). If a specific method is not defined, the augmented assignment falls back to the normal methods. For instance, if x is an instance of a class with an __iadd__() method, x += y is equivalent to x = x.__iadd__(y) . Otherwise, x.__add__(y) and y.__radd__(x) are considered, as with the evaluation of x + y. In certain situations, augmented assignment can result in unexpected errors (see Why does a_tuple[i] += [‘item’] raise an exception when the addition works?), but this behavior is in fact part of the data model.

+
+object.__neg__(self)
+object.__pos__(self)
+object.__abs__(self)
+object.__invert__(self)
+

Called to implement the unary arithmetic operations (-, +, abs() and ~).

+
+object.__complex__(self)
+object.__int__(self)
+object.__float__(self)
+

Called to implement the built-in functions complex(), int() and float(). Should return a value of the appropriate type.

+
+object.__index__(self)
+

Called to implement operator.index(), and whenever Python needs to losslessly convert the numeric object to an integer object (such as in slicing, or in the built-in bin(), hex() and oct() functions). Presence of this method indicates that the numeric object is an integer type. Must return an integer.

If __int__(), __float__() and __complex__() are not defined then corresponding built-in functions int(), float() and complex() fall back to __index__().

+
+object.__round__(self[, ndigits])
+object.__trunc__(self)
+object.__floor__(self)
+object.__ceil__(self)
+

Called to implement the built-in function round() and math functions trunc(), floor() and ceil(). Unless ndigits is passed to __round__() all these methods should return the value of the object truncated to an Integral (typically an int).

The built-in function int() falls back to __trunc__() if neither __int__() nor __index__() is defined.

Changed in version 3.11: The delegation of int() to __trunc__() is deprecated.

+

+3.3.9. With Statement Context Managers

A context manager is an object that defines the runtime context to be established when executing a with statement. The context manager handles the entry into, and the exit from, the desired runtime context for the execution of the block of code. Context managers are normally invoked using the with statement (described in section The with statement), but can also be used by directly invoking their methods.

Typical uses of context managers include saving and restoring various kinds of global state, locking and unlocking resources, closing opened files, etc.

For more information on context managers, see Context Manager Types.

+object.__enter__(self)
+

Enter the runtime context related to this object. The with statement will bind this method’s return value to the target(s) specified in the as clause of the statement, if any.

+
+object.__exit__(self, exc_type, exc_value, traceback)
+

Exit the runtime context related to this object. The parameters describe the exception that caused the context to be exited. If the context was exited without an exception, all three arguments will be None.

If an exception is supplied, and the method wishes to suppress the exception (i.e., prevent it from being propagated), it should return a true value. Otherwise, the exception will be processed normally upon exit from this method.

Note that __exit__() methods should not reraise the passed-in exception; this is the caller’s responsibility.

+

See also

+PEP 343 - The “with” statement
+
+

The specification, background, and examples for the Python with statement.

+3.3.10. Customizing positional arguments in class pattern matching

When using a class name in a pattern, positional arguments in the pattern are not allowed by default, i.e. case MyClass(x, y) is typically invalid without special support in MyClass. To be able to use that kind of pattern, the class needs to define a __match_args__ attribute.

+object.__match_args__
+

This class variable can be assigned a tuple of strings. When this class is used in a class pattern with positional arguments, each positional argument will be converted into a keyword argument, using the corresponding value in __match_args__ as the keyword. The absence of this attribute is equivalent to setting it to ().

+

For example, if MyClass.__match_args__ is ("left", "center", "right") that means that case MyClass(x, y) is equivalent to case MyClass(left=x, center=y). Note that the number of arguments in the pattern must be smaller than or equal to the number of elements in __match_args__; if it is larger, the pattern match attempt will raise a TypeError.

New in version 3.10.

See also

+PEP 634 - Structural Pattern Matching
+
+

The specification for the Python match statement.

+3.3.11. Emulating buffer types

The buffer protocol provides a way for Python objects to expose efficient access to a low-level memory array. This protocol is implemented by builtin types such as bytes and memoryview, and third-party libraries may define additional buffer types.

While buffer types are usually implemented in C, it is also possible to implement the protocol in Python.

+object.__buffer__(self, flags)
+

Called when a buffer is requested from self (for example, by the memoryview constructor). The flags argument is an integer representing the kind of buffer requested, affecting for example whether the returned buffer is read-only or writable. inspect.BufferFlags provides a convenient way to interpret the flags. The method must return a memoryview object.

+
+object.__release_buffer__(self, buffer)
+

Called when a buffer is no longer needed. The buffer argument is a memoryview object that was previously returned by __buffer__(). The method must release any resources associated with the buffer. This method should return None. Buffer objects that do not need to perform any cleanup are not required to implement this method.

+

New in version 3.12.

See also

+PEP 688 - Making the buffer protocol accessible in Python
+
+

Introduces the Python __buffer__ and __release_buffer__ methods.

+ collections.abc.Buffer +
+
+

ABC for buffer types.

+3.3.12. Special method lookup

For custom classes, implicit invocations of special methods are only guaranteed to work correctly if defined on an object’s type, not in the object’s instance dictionary. That behaviour is the reason why the following code raises an exception:

>>> class C:
+...     pass
+...
+>>> c = C()
+>>> c.__len__ = lambda: 5
+>>> len(c)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: object of type 'C' has no len()
+

The rationale behind this behaviour lies with a number of special methods such as __hash__() and __repr__() that are implemented by all objects, including type objects. If the implicit lookup of these methods used the conventional lookup process, they would fail when invoked on the type object itself:

>>> 1 .__hash__() == hash(1)
+True
+>>> int.__hash__() == hash(int)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: descriptor '__hash__' of 'int' object needs an argument
+

Incorrectly attempting to invoke an unbound method of a class in this way is sometimes referred to as ‘metaclass confusion’, and is avoided by bypassing the instance when looking up special methods:

>>> type(1).__hash__(1) == hash(1)
+True
+>>> type(int).__hash__(int) == hash(int)
+True
+

In addition to bypassing any instance attributes in the interest of correctness, implicit special method lookup generally also bypasses the __getattribute__() method even of the object’s metaclass:

>>> class Meta(type):
+...     def __getattribute__(*args):
+...         print("Metaclass getattribute invoked")
+...         return type.__getattribute__(*args)
+...
+>>> class C(object, metaclass=Meta):
+...     def __len__(self):
+...         return 10
+...     def __getattribute__(*args):
+...         print("Class getattribute invoked")
+...         return object.__getattribute__(*args)
+...
+>>> c = C()
+>>> c.__len__()                 # Explicit lookup via instance
+Class getattribute invoked
+10
+>>> type(c).__len__(c)          # Explicit lookup via type
+Metaclass getattribute invoked
+10
+>>> len(c)                      # Implicit lookup
+10
+

Bypassing the __getattribute__() machinery in this fashion provides significant scope for speed optimisations within the interpreter, at the cost of some flexibility in the handling of special methods (the special method must be set on the class object itself in order to be consistently invoked by the interpreter).

+3.4. Coroutines

+3.4.1. Awaitable Objects

An awaitable object generally implements an __await__() method. Coroutine objects returned from async def functions are awaitable.

Note

The generator iterator objects returned from generators decorated with types.coroutine() are also awaitable, but they do not implement __await__().

+object.__await__(self)
+

Must return an iterator. Should be used to implement awaitable objects. For instance, asyncio.Future implements this method to be compatible with the await expression.

Note

The language doesn’t place any restriction on the type or value of the objects yielded by the iterator returned by __await__, as this is specific to the implementation of the asynchronous execution framework (e.g. asyncio) that will be managing the awaitable object.

+

New in version 3.5.

See also

PEP 492 for additional information about awaitable objects.

+3.4.2. Coroutine Objects

Coroutine objects are awaitable objects. A coroutine’s execution can be controlled by calling __await__() and iterating over the result. When the coroutine has finished executing and returns, the iterator raises StopIteration, and the exception’s value attribute holds the return value. If the coroutine raises an exception, it is propagated by the iterator. Coroutines should not directly raise unhandled StopIteration exceptions.

Coroutines also have the methods listed below, which are analogous to those of generators (see Generator-iterator methods). However, unlike generators, coroutines do not directly support iteration.

Changed in version 3.5.2: It is a RuntimeError to await on a coroutine more than once.

+coroutine.send(value)
+

Starts or resumes execution of the coroutine. If value is None, this is equivalent to advancing the iterator returned by __await__(). If value is not None, this method delegates to the send() method of the iterator that caused the coroutine to suspend. The result (return value, StopIteration, or other exception) is the same as when iterating over the __await__() return value, described above.

+
+coroutine.throw(value)
coroutine.throw(type[, value[, traceback]]) +
+

Raises the specified exception in the coroutine. This method delegates to the throw() method of the iterator that caused the coroutine to suspend, if it has such a method. Otherwise, the exception is raised at the suspension point. The result (return value, StopIteration, or other exception) is the same as when iterating over the __await__() return value, described above. If the exception is not caught in the coroutine, it propagates back to the caller.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

+
+coroutine.close()
+

Causes the coroutine to clean itself up and exit. If the coroutine is suspended, this method first delegates to the close() method of the iterator that caused the coroutine to suspend, if it has such a method. Then it raises GeneratorExit at the suspension point, causing the coroutine to immediately clean itself up. Finally, the coroutine is marked as having finished executing, even if it was never started.

Coroutine objects are automatically closed using the above process when they are about to be destroyed.

+

+3.4.3. Asynchronous Iterators

An asynchronous iterator can call asynchronous code in its __anext__ method.

Asynchronous iterators can be used in an async for statement.

+object.__aiter__(self)
+

Must return an asynchronous iterator object.

+
+object.__anext__(self)
+

Must return an awaitable resulting in a next value of the iterator. Should raise a StopAsyncIteration error when the iteration is over.

+

An example of an asynchronous iterable object:

class Reader:
+    async def readline(self):
+        ...
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self):
+        val = await self.readline()
+        if val == b'':
+            raise StopAsyncIteration
+        return val
+

New in version 3.5.

Changed in version 3.7: Prior to Python 3.7, __aiter__() could return an awaitable that would resolve to an asynchronous iterator.

Starting with Python 3.7, __aiter__() must return an asynchronous iterator object. Returning anything else will result in a TypeError error.

+3.4.4. Asynchronous Context Managers

An asynchronous context manager is a context manager that is able to suspend execution in its __aenter__ and __aexit__ methods.

Asynchronous context managers can be used in an async with statement.

+object.__aenter__(self)
+

Semantically similar to __enter__(), the only difference being that it must return an awaitable.

+
+object.__aexit__(self, exc_type, exc_value, traceback)
+

Semantically similar to __exit__(), the only difference being that it must return an awaitable.

+

An example of an asynchronous context manager class:

class AsyncContextManager:
+    async def __aenter__(self):
+        await log('entering context')
+
+    async def __aexit__(self, exc_type, exc, tb):
+        await log('exiting context')
+

New in version 3.5.

Footnotes

+1
+

It is possible in some cases to change an object’s type, under certain controlled conditions. It generally isn’t a good idea though, since it can lead to some very strange behaviour if it is handled incorrectly.

+2
+

The __hash__(), __iter__(), __reversed__(), and __contains__() methods have special handling for this; others will still raise a TypeError, but may do so by relying on the behavior that None is not callable.

+3
+

“Does not support” here means that the class has no such method, or the method returns NotImplemented. Do not set the method to None if you want to force fallback to the right operand’s reflected method—that will instead have the opposite effect of explicitly blocking such fallback.

+4
+

For operands of the same type, it is assumed that if the non-reflected method – such as __add__() – fails then the overall operation is not supported, which is why the reflected method is not called.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/datamodel.html +

+
diff --git a/devdocs/python~3.12/reference%2Fexecutionmodel.html b/devdocs/python~3.12/reference%2Fexecutionmodel.html new file mode 100644 index 00000000..80a17a8e --- /dev/null +++ b/devdocs/python~3.12/reference%2Fexecutionmodel.html @@ -0,0 +1,51 @@ +

Execution model

+4.1. Structure of a program

A Python program is constructed from code blocks. A block is a piece of Python program text that is executed as a unit. The following are blocks: a module, a function body, and a class definition. Each command typed interactively is a block. A script file (a file given as standard input to the interpreter or specified as a command line argument to the interpreter) is a code block. A script command (a command specified on the interpreter command line with the -c option) is a code block. A module run as a top level script (as module __main__) from the command line using a -m argument is also a code block. The string argument passed to the built-in functions eval() and exec() is a code block.

A code block is executed in an execution frame. A frame contains some administrative information (used for debugging) and determines where and how execution continues after the code block’s execution has completed.

+4.2. Naming and binding

+4.2.1. Binding of names

Names refer to objects. Names are introduced by name binding operations.

The following constructs bind names:

  • formal parameters to functions,
  • class definitions,
  • function definitions,
  • assignment expressions,
  • +

    targets that are identifiers if occurring in an assignment:

    • +for loop header,
    • after as in a with statement, except clause, except* clause, or in the as-pattern in structural pattern matching,
    • in a capture pattern in structural pattern matching
  • +import statements.
  • +type statements.
  • +type parameter lists.

The import statement of the form from ... import * binds all names defined in the imported module, except those beginning with an underscore. This form may only be used at the module level.

A target occurring in a del statement is also considered bound for this purpose (though the actual semantics are to unbind the name).

Each assignment or import statement occurs within a block defined by a class or function definition or at the module level (the top-level code block).

If a name is bound in a block, it is a local variable of that block, unless declared as nonlocal or global. If a name is bound at the module level, it is a global variable. (The variables of the module code block are local and global.) If a variable is used in a code block but not defined there, it is a free variable.

Each occurrence of a name in the program text refers to the binding of that name established by the following name resolution rules.

+4.2.2. Resolution of names

A scope defines the visibility of a name within a block. If a local variable is defined in a block, its scope includes that block. If the definition occurs in a function block, the scope extends to any blocks contained within the defining one, unless a contained block introduces a different binding for the name.

When a name is used in a code block, it is resolved using the nearest enclosing scope. The set of all such scopes visible to a code block is called the block’s environment.

When a name is not found at all, a NameError exception is raised. If the current scope is a function scope, and the name refers to a local variable that has not yet been bound to a value at the point where the name is used, an UnboundLocalError exception is raised. UnboundLocalError is a subclass of NameError.

If a name binding operation occurs anywhere within a code block, all uses of the name within the block are treated as references to the current block. This can lead to errors when a name is used within a block before it is bound. This rule is subtle. Python lacks declarations and allows name binding operations to occur anywhere within a code block. The local variables of a code block can be determined by scanning the entire text of the block for name binding operations. See the FAQ entry on UnboundLocalError for examples.

If the global statement occurs within a block, all uses of the names specified in the statement refer to the bindings of those names in the top-level namespace. Names are resolved in the top-level namespace by searching the global namespace, i.e. the namespace of the module containing the code block, and the builtins namespace, the namespace of the module builtins. The global namespace is searched first. If the names are not found there, the builtins namespace is searched. The global statement must precede all uses of the listed names.

The global statement has the same scope as a name binding operation in the same block. If the nearest enclosing scope for a free variable contains a global statement, the free variable is treated as a global.

The nonlocal statement causes corresponding names to refer to previously bound variables in the nearest enclosing function scope. SyntaxError is raised at compile time if the given name does not exist in any enclosing function scope. Type parameters cannot be rebound with the nonlocal statement.

The namespace for a module is automatically created the first time a module is imported. The main module for a script is always called __main__.

Class definition blocks and arguments to exec() and eval() are special in the context of name resolution. A class definition is an executable statement that may use and define names. These references follow the normal rules for name resolution with an exception that unbound local variables are looked up in the global namespace. The namespace of the class definition becomes the attribute dictionary of the class. The scope of names defined in a class block is limited to the class block; it does not extend to the code blocks of methods. This includes comprehensions and generator expressions, but it does not include annotation scopes, which have access to their enclosing class scopes. This means that the following will fail:

class A:
+    a = 42
+    b = list(a + i for i in range(10))
+

However, the following will succeed:

class A:
+    type Alias = Nested
+    class Nested: pass
+
+print(A.Alias.__value__)  # <type 'A.Nested'>
+

+4.2.3. Annotation scopes

Type parameter lists and type statements introduce annotation scopes, which behave mostly like function scopes, but with some exceptions discussed below. Annotations currently do not use annotation scopes, but they are expected to use annotation scopes in Python 3.13 when PEP 649 is implemented.

Annotation scopes are used in the following contexts:

  • Type parameter lists for generic type aliases.
  • Type parameter lists for generic functions. A generic function’s annotations are executed within the annotation scope, but its defaults and decorators are not.
  • Type parameter lists for generic classes. A generic class’s base classes and keyword arguments are executed within the annotation scope, but its decorators are not.
  • The bounds and constraints for type variables (lazily evaluated).
  • The value of type aliases (lazily evaluated).

Annotation scopes differ from function scopes in the following ways:

  • Annotation scopes have access to their enclosing class namespace. If an annotation scope is immediately within a class scope, or within another annotation scope that is immediately within a class scope, the code in the annotation scope can use names defined in the class scope as if it were executed directly within the class body. This contrasts with regular functions defined within classes, which cannot access names defined in the class scope.
  • Expressions in annotation scopes cannot contain yield, yield from, await, or := expressions. (These expressions are allowed in other scopes contained within the annotation scope.)
  • Names defined in annotation scopes cannot be rebound with nonlocal statements in inner scopes. This includes only type parameters, as no other syntactic elements that can appear within annotation scopes can introduce new names.
  • While annotation scopes have an internal name, that name is not reflected in the __qualname__ of objects defined within the scope. Instead, the __qualname__ of such objects is as if the object were defined in the enclosing scope.

New in version 3.12: Annotation scopes were introduced in Python 3.12 as part of PEP 695.

+4.2.4. Lazy evaluation

The values of type aliases created through the type statement are lazily evaluated. The same applies to the bounds and constraints of type variables created through the type parameter syntax. This means that they are not evaluated when the type alias or type variable is created. Instead, they are only evaluated when doing so is necessary to resolve an attribute access.

Example:

>>> type Alias = 1/0
+>>> Alias.__value__
+Traceback (most recent call last):
+  ...
+ZeroDivisionError: division by zero
+>>> def func[T: 1/0](): pass
+>>> T = func.__type_params__[0]
+>>> T.__bound__
+Traceback (most recent call last):
+  ...
+ZeroDivisionError: division by zero
+

Here the exception is raised only when the __value__ attribute of the type alias or the __bound__ attribute of the type variable is accessed.

This behavior is primarily useful for references to types that have not yet been defined when the type alias or type variable is created. For example, lazy evaluation enables creation of mutually recursive type aliases:

from typing import Literal
+
+type SimpleExpr = int | Parenthesized
+type Parenthesized = tuple[Literal["("], Expr, Literal[")"]]
+type Expr = SimpleExpr | tuple[SimpleExpr, Literal["+", "-"], Expr]
+

Lazily evaluated values are evaluated in annotation scope, which means that names that appear inside the lazily evaluated value are looked up as if they were used in the immediately enclosing scope.

New in version 3.12.

+4.2.5. Builtins and restricted execution

CPython implementation detail: Users should not touch __builtins__; it is strictly an implementation detail. Users wanting to override values in the builtins namespace should import the builtins module and modify its attributes appropriately.

The builtins namespace associated with the execution of a code block is actually found by looking up the name __builtins__ in its global namespace; this should be a dictionary or a module (in the latter case the module’s dictionary is used). By default, when in the __main__ module, __builtins__ is the built-in module builtins; when in any other module, __builtins__ is an alias for the dictionary of the builtins module itself.

+4.2.6. Interaction with dynamic features

Name resolution of free variables occurs at runtime, not at compile time. This means that the following code will print 42:

i = 10
+def f():
+    print(i)
+i = 42
+f()
+

The eval() and exec() functions do not have access to the full environment for resolving names. Names may be resolved in the local and global namespaces of the caller. Free variables are not resolved in the nearest enclosing namespace, but in the global namespace. 1 The exec() and eval() functions have optional arguments to override the global and local namespace. If only one namespace is specified, it is used for both.

+4.3. Exceptions

Exceptions are a means of breaking out of the normal flow of control of a code block in order to handle errors or other exceptional conditions. An exception is raised at the point where the error is detected; it may be handled by the surrounding code block or by any code block that directly or indirectly invoked the code block where the error occurred.

The Python interpreter raises an exception when it detects a run-time error (such as division by zero). A Python program can also explicitly raise an exception with the raise statement. Exception handlers are specified with the try … except statement. The finally clause of such a statement can be used to specify cleanup code which does not handle the exception, but is executed whether an exception occurred or not in the preceding code.

Python uses the “termination” model of error handling: an exception handler can find out what happened and continue execution at an outer level, but it cannot repair the cause of the error and retry the failing operation (except by re-entering the offending piece of code from the top).

When an exception is not handled at all, the interpreter terminates execution of the program, or returns to its interactive main loop. In either case, it prints a stack traceback, except when the exception is SystemExit.

Exceptions are identified by class instances. The except clause is selected depending on the class of the instance: it must reference the class of the instance or a non-virtual base class thereof. The instance can be received by the handler and can carry additional information about the exceptional condition.

Note

Exception messages are not part of the Python API. Their contents may change from one version of Python to the next without warning and should not be relied on by code which will run under multiple versions of the interpreter.

See also the description of the try statement in section The try statement and raise statement in section The raise statement.

Footnotes

+1
+

This limitation occurs because the code that is executed by these operations is not available at the time the module is compiled.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/executionmodel.html +

+
diff --git a/devdocs/python~3.12/reference%2Fexpressions.html b/devdocs/python~3.12/reference%2Fexpressions.html new file mode 100644 index 00000000..f0de06af --- /dev/null +++ b/devdocs/python~3.12/reference%2Fexpressions.html @@ -0,0 +1,284 @@ +

Expressions

This chapter explains the meaning of the elements of expressions in Python.

Syntax Notes: In this and the following chapters, extended BNF notation will be used to describe syntax, not lexical analysis. When (one alternative of) a syntax rule has the form

+name ::=  othername
+

and no semantics are given, the semantics of this form of name are the same as for othername.

+6.1. Arithmetic conversions

When a description of an arithmetic operator below uses the phrase “the numeric arguments are converted to a common type”, this means that the operator implementation for built-in types works as follows:

  • If either argument is a complex number, the other is converted to complex;
  • otherwise, if either argument is a floating point number, the other is converted to floating point;
  • otherwise, both must be integers and no conversion is necessary.

Some additional rules apply for certain operators (e.g., a string as a left argument to the ‘%’ operator). Extensions must define their own conversion behavior.

+6.2. Atoms

Atoms are the most basic elements of expressions. The simplest atoms are identifiers or literals. Forms enclosed in parentheses, brackets or braces are also categorized syntactically as atoms. The syntax for atoms is:

+atom      ::=  identifier | literal | enclosure
+enclosure ::=  parenth_form | list_display | dict_display | set_display
+               | generator_expression | yield_atom
+

+6.2.1. Identifiers (Names)

An identifier occurring as an atom is a name. See section Identifiers and keywords for lexical definition and section Naming and binding for documentation of naming and binding.

When the name is bound to an object, evaluation of the atom yields that object. When a name is not bound, an attempt to evaluate it raises a NameError exception.

Private name mangling: When an identifier that textually occurs in a class definition begins with two or more underscore characters and does not end in two or more underscores, it is considered a private name of that class. Private names are transformed to a longer form before code is generated for them. The transformation inserts the class name, with leading underscores removed and a single underscore inserted, in front of the name. For example, the identifier __spam occurring in a class named Ham will be transformed to _Ham__spam. This transformation is independent of the syntactical context in which the identifier is used. If the transformed name is extremely long (longer than 255 characters), implementation defined truncation may happen. If the class name consists only of underscores, no transformation is done.

+6.2.2. Literals

Python supports string and bytes literals and various numeric literals:

+literal ::=  stringliteral | bytesliteral
+             | integer | floatnumber | imagnumber
+

Evaluation of a literal yields an object of the given type (string, bytes, integer, floating point number, complex number) with the given value. The value may be approximated in the case of floating point and imaginary (complex) literals. See section Literals for details.

All literals correspond to immutable data types, and hence the object’s identity is less important than its value. Multiple evaluations of literals with the same value (either the same occurrence in the program text or a different occurrence) may obtain the same object or a different object with the same value.

+6.2.3. Parenthesized forms

A parenthesized form is an optional expression list enclosed in parentheses:

+parenth_form ::=  "(" [starred_expression] ")"
+

A parenthesized expression list yields whatever that expression list yields: if the list contains at least one comma, it yields a tuple; otherwise, it yields the single expression that makes up the expression list.

An empty pair of parentheses yields an empty tuple object. Since tuples are immutable, the same rules as for literals apply (i.e., two occurrences of the empty tuple may or may not yield the same object).

Note that tuples are not formed by the parentheses, but rather by use of the comma. The exception is the empty tuple, for which parentheses are required — allowing unparenthesized “nothing” in expressions would cause ambiguities and allow common typos to pass uncaught.

+6.2.4. Displays for lists, sets and dictionaries

For constructing a list, a set or a dictionary Python provides special syntax called “displays”, each of them in two flavors:

  • either the container contents are listed explicitly, or
  • they are computed via a set of looping and filtering instructions, called a comprehension.

Common syntax elements for comprehensions are:

+comprehension ::=  assignment_expression comp_for
+comp_for      ::=  ["async"] "for" target_list "in" or_test [comp_iter]
+comp_iter     ::=  comp_for | comp_if
+comp_if       ::=  "if" or_test [comp_iter]
+

The comprehension consists of a single expression followed by at least one for clause and zero or more for or if clauses. In this case, the elements of the new container are those that would be produced by considering each of the for or if clauses a block, nesting from left to right, and evaluating the expression to produce an element each time the innermost block is reached.

However, aside from the iterable expression in the leftmost for clause, the comprehension is executed in a separate implicitly nested scope. This ensures that names assigned to in the target list don’t “leak” into the enclosing scope.

The iterable expression in the leftmost for clause is evaluated directly in the enclosing scope and then passed as an argument to the implicitly nested scope. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: [x*y for x in range(10) for y in range(x, x+10)].

To ensure the comprehension always results in a container of the appropriate type, yield and yield from expressions are prohibited in the implicitly nested scope.

Since Python 3.6, in an async def function, an async for clause may be used to iterate over a asynchronous iterator. A comprehension in an async def function may consist of either a for or async for clause following the leading expression, may contain additional for or async for clauses, and may also use await expressions. If a comprehension contains either async for clauses or await expressions or other asynchronous comprehensions it is called an asynchronous comprehension. An asynchronous comprehension may suspend the execution of the coroutine function in which it appears. See also PEP 530.

New in version 3.6: Asynchronous comprehensions were introduced.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

Changed in version 3.11: Asynchronous comprehensions are now allowed inside comprehensions in asynchronous functions. Outer comprehensions implicitly become asynchronous.

+6.2.5. List displays

A list display is a possibly empty series of expressions enclosed in square brackets:

+list_display ::=  "[" [starred_list | comprehension] "]"
+

A list display yields a new list object, the contents being specified by either a list of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and placed into the list object in that order. When a comprehension is supplied, the list is constructed from the elements resulting from the comprehension.

+6.2.6. Set displays

A set display is denoted by curly braces and distinguishable from dictionary displays by the lack of colons separating keys and values:

+set_display ::=  "{" (starred_list | comprehension) "}"
+

A set display yields a new mutable set object, the contents being specified by either a sequence of expressions or a comprehension. When a comma-separated list of expressions is supplied, its elements are evaluated from left to right and added to the set object. When a comprehension is supplied, the set is constructed from the elements resulting from the comprehension.

An empty set cannot be constructed with {}; this literal constructs an empty dictionary.

+6.2.7. Dictionary displays

A dictionary display is a possibly empty series of dict items (key/value pairs) enclosed in curly braces:

+dict_display       ::=  "{" [dict_item_list | dict_comprehension] "}"
+dict_item_list     ::=  dict_item ("," dict_item)* [","]
+dict_item          ::=  expression ":" expression | "**" or_expr
+dict_comprehension ::=  expression ":" expression comp_for
+

A dictionary display yields a new dictionary object.

If a comma-separated sequence of dict items is given, they are evaluated from left to right to define the entries of the dictionary: each key object is used as a key into the dictionary to store the corresponding value. This means that you can specify the same key multiple times in the dict item list, and the final dictionary’s value for that key will be the last one given.

A double asterisk ** denotes dictionary unpacking. Its operand must be a mapping. Each mapping item is added to the new dictionary. Later values replace values already set by earlier dict items and earlier dictionary unpackings.

New in version 3.5: Unpacking into dictionary displays, originally proposed by PEP 448.

A dict comprehension, in contrast to list and set comprehensions, needs two expressions separated with a colon followed by the usual “for” and “if” clauses. When the comprehension is run, the resulting key and value elements are inserted in the new dictionary in the order they are produced.

Restrictions on the types of the key values are listed earlier in section The standard type hierarchy. (To summarize, the key type should be hashable, which excludes all mutable objects.) Clashes between duplicate keys are not detected; the last value (textually rightmost in the display) stored for a given key value prevails.

Changed in version 3.8: Prior to Python 3.8, in dict comprehensions, the evaluation order of key and value was not well-defined. In CPython, the value was evaluated before the key. Starting with 3.8, the key is evaluated before the value, as proposed by PEP 572.

+6.2.8. Generator expressions

A generator expression is a compact generator notation in parentheses:

+generator_expression ::=  "(" expression comp_for ")"
+

A generator expression yields a new generator object. Its syntax is the same as for comprehensions, except that it is enclosed in parentheses instead of brackets or curly braces.

Variables used in the generator expression are evaluated lazily when the __next__() method is called for the generator object (in the same fashion as normal generators). However, the iterable expression in the leftmost for clause is immediately evaluated, so that an error produced by it will be emitted at the point where the generator expression is defined, rather than at the point where the first value is retrieved. Subsequent for clauses and any filter condition in the leftmost for clause cannot be evaluated in the enclosing scope as they may depend on the values obtained from the leftmost iterable. For example: (x*y for x in range(10) for y in range(x, x+10)).

The parentheses can be omitted on calls with only one argument. See section Calls for details.

To avoid interfering with the expected operation of the generator expression itself, yield and yield from expressions are prohibited in the implicitly defined generator.

If a generator expression contains either async for clauses or await expressions it is called an asynchronous generator expression. An asynchronous generator expression returns a new asynchronous generator object, which is an asynchronous iterator (see Asynchronous Iterators).

New in version 3.6: Asynchronous generator expressions were introduced.

Changed in version 3.7: Prior to Python 3.7, asynchronous generator expressions could only appear in async def coroutines. Starting with 3.7, any function can use asynchronous generator expressions.

Changed in version 3.8: yield and yield from prohibited in the implicitly nested scope.

+6.2.9. Yield expressions

+yield_atom       ::=  "(" yield_expression ")"
+yield_expression ::=  "yield" [expression_list | "from" expression]
+

The yield expression is used when defining a generator function or an asynchronous generator function and thus can only be used in the body of a function definition. Using a yield expression in a function’s body causes that function to be a generator function, and using it in an async def function’s body causes that coroutine function to be an asynchronous generator function. For example:

def gen():  # defines a generator function
+    yield 123
+
+async def agen(): # defines an asynchronous generator function
+    yield 123
+

Due to their side effects on the containing scope, yield expressions are not permitted as part of the implicitly defined scopes used to implement comprehensions and generator expressions.

Changed in version 3.8: Yield expressions prohibited in the implicitly nested scopes used to implement comprehensions and generator expressions.

Generator functions are described below, while asynchronous generator functions are described separately in section Asynchronous generator functions.

When a generator function is called, it returns an iterator known as a generator. That generator then controls the execution of the generator function. The execution starts when one of the generator’s methods is called. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the generator’s caller, or None if expression_list is omitted. By suspended, we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by calling one of the generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __next__() is used (typically via either a for or the next() builtin) then the result is None. Otherwise, if send() is used, then the result will be the value passed in to that method.

All of this makes generator functions quite similar to coroutines; they yield multiple times, they have more than one entry point and their execution can be suspended. The only difference is that a generator function cannot control where the execution should continue after it yields; the control is always transferred to the generator’s caller.

Yield expressions are allowed anywhere in a try construct. If the generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), the generator-iterator’s close() method will be called, allowing any pending finally clauses to execute.

When yield from <expr> is used, the supplied expression must be an iterable. The values produced by iterating that iterable are passed directly to the caller of the current generator’s methods. Any values passed in with send() and any exceptions passed in with throw() are passed to the underlying iterator if it has the appropriate methods. If this is not the case, then send() will raise AttributeError or TypeError, while throw() will just raise the passed in exception immediately.

When the underlying iterator is complete, the value attribute of the raised StopIteration instance becomes the value of the yield expression. It can be either set explicitly when raising StopIteration, or automatically when the subiterator is a generator (by returning a value from the subgenerator).

Changed in version 3.3: Added yield from <expr> to delegate control flow to a subiterator.

The parentheses may be omitted when the yield expression is the sole expression on the right hand side of an assignment statement.

See also

+PEP 255 - Simple Generators
+
+

The proposal for adding generators and the yield statement to Python.

+PEP 342 - Coroutines via Enhanced Generators
+
+

The proposal to enhance the API and syntax of generators, making them usable as simple coroutines.

+PEP 380 - Syntax for Delegating to a Subgenerator
+
+

The proposal to introduce the yield_from syntax, making delegation to subgenerators easy.

+PEP 525 - Asynchronous Generators
+
+

The proposal that expanded on PEP 492 by adding generator capabilities to coroutine functions.

+6.2.9.1. Generator-iterator methods

This subsection describes the methods of a generator iterator. They can be used to control the execution of a generator function.

Note that calling any of the generator methods below when the generator is already executing raises a ValueError exception.

+generator.__next__()
+

Starts the execution of a generator function or resumes it at the last executed yield expression. When a generator function is resumed with a __next__() method, the current yield expression always evaluates to None. The execution then continues to the next yield expression, where the generator is suspended again, and the value of the expression_list is returned to __next__()’s caller. If the generator exits without yielding another value, a StopIteration exception is raised.

This method is normally called implicitly, e.g. by a for loop, or by the built-in next() function.

+
+generator.send(value)
+

Resumes the execution and “sends” a value into the generator function. The value argument becomes the result of the current yield expression. The send() method returns the next value yielded by the generator, or raises StopIteration if the generator exits without yielding another value. When send() is called to start the generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

+
+generator.throw(value)
generator.throw(type[, value[, traceback]]) +
+

Raises an exception at the point where the generator was paused, and returns the next value yielded by the generator function. If the generator exits without yielding another value, a StopIteration exception is raised. If the generator function does not catch the passed-in exception, or raises a different exception, then that exception propagates to the caller.

In typical use, this is called with a single exception instance similar to the way the raise keyword is used.

For backwards compatibility, however, the second signature is supported, following a convention from older versions of Python. The type argument should be an exception class, and value should be an exception instance. If the value is not provided, the type constructor is called to get an instance. If traceback is provided, it is set on the exception, otherwise any existing __traceback__ attribute stored in value may be cleared.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

+
+generator.close()
+

Raises a GeneratorExit at the point where the generator function was paused. If the generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), close returns to its caller. If the generator yields a value, a RuntimeError is raised. If the generator raises any other exception, it is propagated to the caller. close() does nothing if the generator has already exited due to an exception or normal exit.

+

+6.2.9.2. Examples

Here is a simple example that demonstrates the behavior of generators and generator functions:

>>> def echo(value=None):
+...     print("Execution starts when 'next()' is called for the first time.")
+...     try:
+...         while True:
+...             try:
+...                 value = (yield value)
+...             except Exception as e:
+...                 value = e
+...     finally:
+...         print("Don't forget to clean up when 'close()' is called.")
+...
+>>> generator = echo(1)
+>>> print(next(generator))
+Execution starts when 'next()' is called for the first time.
+1
+>>> print(next(generator))
+None
+>>> print(generator.send(2))
+2
+>>> generator.throw(TypeError, "spam")
+TypeError('spam',)
+>>> generator.close()
+Don't forget to clean up when 'close()' is called.
+

For examples using yield from, see PEP 380: Syntax for Delegating to a Subgenerator in “What’s New in Python.”

+6.2.9.3. Asynchronous generator functions

The presence of a yield expression in a function or method defined using async def further defines the function as an asynchronous generator function.

When an asynchronous generator function is called, it returns an asynchronous iterator known as an asynchronous generator object. That object then controls the execution of the generator function. An asynchronous generator object is typically used in an async for statement in a coroutine function analogously to how a generator object would be used in a for statement.

Calling one of the asynchronous generator’s methods returns an awaitable object, and the execution starts when this object is awaited on. At that time, the execution proceeds to the first yield expression, where it is suspended again, returning the value of expression_list to the awaiting coroutine. As with a generator, suspension means that all local state is retained, including the current bindings of local variables, the instruction pointer, the internal evaluation stack, and the state of any exception handling. When the execution is resumed by awaiting on the next object returned by the asynchronous generator’s methods, the function can proceed exactly as if the yield expression were just another external call. The value of the yield expression after resuming depends on the method which resumed the execution. If __anext__() is used then the result is None. Otherwise, if asend() is used, then the result will be the value passed in to that method.

If an asynchronous generator happens to exit early by break, the caller task being cancelled, or other exceptions, the generator’s async cleanup code will run and possibly raise exceptions or access context variables in an unexpected context–perhaps after the lifetime of tasks it depends, or during the event loop shutdown when the async-generator garbage collection hook is called. To prevent this, the caller must explicitly close the async generator by calling aclose() method to finalize the generator and ultimately detach it from the event loop.

In an asynchronous generator function, yield expressions are allowed anywhere in a try construct. However, if an asynchronous generator is not resumed before it is finalized (by reaching a zero reference count or by being garbage collected), then a yield expression within a try construct could result in a failure to execute pending finally clauses. In this case, it is the responsibility of the event loop or scheduler running the asynchronous generator to call the asynchronous generator-iterator’s aclose() method and run the resulting coroutine object, thus allowing any pending finally clauses to execute.

To take care of finalization upon event loop termination, an event loop should define a finalizer function which takes an asynchronous generator-iterator and presumably calls aclose() and executes the coroutine. This finalizer may be registered by calling sys.set_asyncgen_hooks(). When first iterated over, an asynchronous generator-iterator will store the registered finalizer to be called upon finalization. For a reference example of a finalizer method see the implementation of asyncio.Loop.shutdown_asyncgens in Lib/asyncio/base_events.py.

The expression yield from <expr> is a syntax error when used in an asynchronous generator function.

+6.2.9.4. Asynchronous generator-iterator methods

This subsection describes the methods of an asynchronous generator iterator, which are used to control the execution of a generator function.

+coroutine agen.__anext__()
+

Returns an awaitable which when run starts to execute the asynchronous generator or resumes it at the last executed yield expression. When an asynchronous generator function is resumed with an __anext__() method, the current yield expression always evaluates to None in the returned awaitable, which when run will continue to the next yield expression. The value of the expression_list of the yield expression is the value of the StopIteration exception raised by the completing coroutine. If the asynchronous generator exits without yielding another value, the awaitable instead raises a StopAsyncIteration exception, signalling that the asynchronous iteration has completed.

This method is normally called implicitly by a async for loop.

+
+coroutine agen.asend(value)
+

Returns an awaitable which when run resumes the execution of the asynchronous generator. As with the send() method for a generator, this “sends” a value into the asynchronous generator function, and the value argument becomes the result of the current yield expression. The awaitable returned by the asend() method will return the next value yielded by the generator as the value of the raised StopIteration, or raises StopAsyncIteration if the asynchronous generator exits without yielding another value. When asend() is called to start the asynchronous generator, it must be called with None as the argument, because there is no yield expression that could receive the value.

+
+coroutine agen.athrow(value)
coroutine agen.athrow(type[, value[, traceback]]) +
+

Returns an awaitable that raises an exception of type type at the point where the asynchronous generator was paused, and returns the next value yielded by the generator function as the value of the raised StopIteration exception. If the asynchronous generator exits without yielding another value, a StopAsyncIteration exception is raised by the awaitable. If the generator function does not catch the passed-in exception, or raises a different exception, then when the awaitable is run that exception propagates to the caller of the awaitable.

Changed in version 3.12: The second signature (type[, value[, traceback]]) is deprecated and may be removed in a future version of Python.

+
+coroutine agen.aclose()
+

Returns an awaitable that when run will throw a GeneratorExit into the asynchronous generator function at the point where it was paused. If the asynchronous generator function then exits gracefully, is already closed, or raises GeneratorExit (by not catching the exception), then the returned awaitable will raise a StopIteration exception. Any further awaitables returned by subsequent calls to the asynchronous generator will raise a StopAsyncIteration exception. If the asynchronous generator yields a value, a RuntimeError is raised by the awaitable. If the asynchronous generator raises any other exception, it is propagated to the caller of the awaitable. If the asynchronous generator has already exited due to an exception or normal exit, then further calls to aclose() will return an awaitable that does nothing.

+

+6.3. Primaries

Primaries represent the most tightly bound operations of the language. Their syntax is:

+primary ::=  atom | attributeref | subscription | slicing | call
+

+6.3.1. Attribute references

An attribute reference is a primary followed by a period and a name:

+attributeref ::=  primary "." identifier
+

The primary must evaluate to an object of a type that supports attribute references, which most objects do. This object is then asked to produce the attribute whose name is the identifier. The type and value produced is determined by the object. Multiple evaluations of the same attribute reference may yield different objects.

This production can be customized by overriding the __getattribute__() method or the __getattr__() method. The __getattribute__() method is called first and either returns a value or raises AttributeError if the attribute is not available.

If an AttributeError is raised and the object has a __getattr__() method, that method is called as a fallback.

+6.3.2. Subscriptions

The subscription of an instance of a container class will generally select an element from the container. The subscription of a generic class will generally return a GenericAlias object.

+subscription ::=  primary "[" expression_list "]"
+

When an object is subscripted, the interpreter will evaluate the primary and the expression list.

The primary must evaluate to an object that supports subscription. An object may support subscription through defining one or both of __getitem__() and __class_getitem__(). When the primary is subscripted, the evaluated result of the expression list will be passed to one of these methods. For more details on when __class_getitem__ is called instead of __getitem__, see __class_getitem__ versus __getitem__.

If the expression list contains at least one comma, it will evaluate to a tuple containing the items of the expression list. Otherwise, the expression list will evaluate to the value of the list’s sole member.

For built-in objects, there are two types of objects that support subscription via __getitem__():

  1. Mappings. If the primary is a mapping, the expression list must evaluate to an object whose value is one of the keys of the mapping, and the subscription selects the value in the mapping that corresponds to that key. An example of a builtin mapping class is the dict class.
  2. Sequences. If the primary is a sequence, the expression list must evaluate to an int or a slice (as discussed in the following section). Examples of builtin sequence classes include the str, list and tuple classes.

The formal syntax makes no special provision for negative indices in sequences. However, built-in sequences all provide a __getitem__() method that interprets negative indices by adding the length of the sequence to the index so that, for example, x[-1] selects the last item of x. The resulting value must be a nonnegative integer less than the number of items in the sequence, and the subscription selects the item whose index is that value (counting from zero). Since the support for negative indices and slicing occurs in the object’s __getitem__() method, subclasses overriding this method will need to explicitly add that support.

A string is a special kind of sequence whose items are characters. A character is not a separate data type but a string of exactly one character.

+6.3.3. Slicings

A slicing selects a range of items in a sequence object (e.g., a string, tuple or list). Slicings may be used as expressions or as targets in assignment or del statements. The syntax for a slicing:

+slicing      ::=  primary "[" slice_list "]"
+slice_list   ::=  slice_item ("," slice_item)* [","]
+slice_item   ::=  expression | proper_slice
+proper_slice ::=  [lower_bound] ":" [upper_bound] [ ":" [stride] ]
+lower_bound  ::=  expression
+upper_bound  ::=  expression
+stride       ::=  expression
+

There is ambiguity in the formal syntax here: anything that looks like an expression list also looks like a slice list, so any subscription can be interpreted as a slicing. Rather than further complicating the syntax, this is disambiguated by defining that in this case the interpretation as a subscription takes priority over the interpretation as a slicing (this is the case if the slice list contains no proper slice).

The semantics for a slicing are as follows. The primary is indexed (using the same __getitem__() method as normal subscription) with a key that is constructed from the slice list, as follows. If the slice list contains at least one comma, the key is a tuple containing the conversion of the slice items; otherwise, the conversion of the lone slice item is the key. The conversion of a slice item that is an expression is that expression. The conversion of a proper slice is a slice object (see section The standard type hierarchy) whose start, stop and step attributes are the values of the expressions given as lower bound, upper bound and stride, respectively, substituting None for missing expressions.

+6.3.4. Calls

A call calls a callable object (e.g., a function) with a possibly empty series of arguments:

+call                 ::=  primary "(" [argument_list [","] | comprehension] ")"
+argument_list        ::=  positional_arguments ["," starred_and_keywords]
+                            ["," keywords_arguments]
+                          | starred_and_keywords ["," keywords_arguments]
+                          | keywords_arguments
+positional_arguments ::=  positional_item ("," positional_item)*
+positional_item      ::=  assignment_expression | "*" expression
+starred_and_keywords ::=  ("*" expression | keyword_item)
+                          ("," "*" expression | "," keyword_item)*
+keywords_arguments   ::=  (keyword_item | "**" expression)
+                          ("," keyword_item | "," "**" expression)*
+keyword_item         ::=  identifier "=" expression
+

An optional trailing comma may be present after the positional and keyword arguments but does not affect the semantics.

The primary must evaluate to a callable object (user-defined functions, built-in functions, methods of built-in objects, class objects, methods of class instances, and all objects having a __call__() method are callable). All argument expressions are evaluated before the call is attempted. Please refer to section Function definitions for the syntax of formal parameter lists.

If keyword arguments are present, they are first converted to positional arguments, as follows. First, a list of unfilled slots is created for the formal parameters. If there are N positional arguments, they are placed in the first N slots. Next, for each keyword argument, the identifier is used to determine the corresponding slot (if the identifier is the same as the first formal parameter name, the first slot is used, and so on). If the slot is already filled, a TypeError exception is raised. Otherwise, the argument is placed in the slot, filling it (even if the expression is None, it fills the slot). When all arguments have been processed, the slots that are still unfilled are filled with the corresponding default value from the function definition. (Default values are calculated, once, when the function is defined; thus, a mutable object such as a list or dictionary used as default value will be shared by all calls that don’t specify an argument value for the corresponding slot; this should usually be avoided.) If there are any unfilled slots for which no default value is specified, a TypeError exception is raised. Otherwise, the list of filled slots is used as the argument list for the call.

CPython implementation detail: An implementation may provide built-in functions whose positional parameters do not have names, even if they are ‘named’ for the purpose of documentation, and which therefore cannot be supplied by keyword. In CPython, this is the case for functions implemented in C that use PyArg_ParseTuple() to parse their arguments.

If there are more positional arguments than there are formal parameter slots, a TypeError exception is raised, unless a formal parameter using the syntax *identifier is present; in this case, that formal parameter receives a tuple containing the excess positional arguments (or an empty tuple if there were no excess positional arguments).

If any keyword argument does not correspond to a formal parameter name, a TypeError exception is raised, unless a formal parameter using the syntax **identifier is present; in this case, that formal parameter receives a dictionary containing the excess keyword arguments (using the keywords as keys and the argument values as corresponding values), or a (new) empty dictionary if there were no excess keyword arguments.

If the syntax *expression appears in the function call, expression must evaluate to an iterable. Elements from these iterables are treated as if they were additional positional arguments. For the call f(x1, x2, *y, x3, x4), if y evaluates to a sequence y1, …, yM, this is equivalent to a call with M+4 positional arguments x1, x2, y1, …, yM, x3, x4.

A consequence of this is that although the *expression syntax may appear after explicit keyword arguments, it is processed before the keyword arguments (and any **expression arguments – see below). So:

>>> def f(a, b):
+...     print(a, b)
+...
+>>> f(b=1, *(2,))
+2 1
+>>> f(a=1, *(2,))
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: f() got multiple values for keyword argument 'a'
+>>> f(1, *(2,))
+1 2
+

It is unusual for both keyword arguments and the *expression syntax to be used in the same call, so in practice this confusion does not often arise.

If the syntax **expression appears in the function call, expression must evaluate to a mapping, the contents of which are treated as additional keyword arguments. If a parameter matching a key has already been given a value (by an explicit keyword argument, or from another unpacking), a TypeError exception is raised.

When **expression is used, each key in this mapping must be a string. Each value from the mapping is assigned to the first formal parameter eligible for keyword assignment whose name is equal to the key. A key need not be a Python identifier (e.g. "max-temp °F" is acceptable, although it will not match any formal parameter that could be declared). If there is no match to a formal parameter the key-value pair is collected by the ** parameter, if there is one, or if there is not, a TypeError exception is raised.

Formal parameters using the syntax *identifier or **identifier cannot be used as positional argument slots or as keyword argument names.

Changed in version 3.5: Function calls accept any number of * and ** unpackings, positional arguments may follow iterable unpackings (*), and keyword arguments may follow dictionary unpackings (**). Originally proposed by PEP 448.

A call always returns some value, possibly None, unless it raises an exception. How this value is computed depends on the type of the callable object.

If it is—

a user-defined function:
+
+

The code block for the function is executed, passing it the argument list. The first thing the code block will do is bind the formal parameters to the arguments; this is described in section Function definitions. When the code block executes a return statement, this specifies the return value of the function call.

a built-in function or method:
+
+

The result is up to the interpreter; see Built-in Functions for the descriptions of built-in functions and methods.

a class object:
+
+

A new instance of that class is returned.

a class instance method:
+
+

The corresponding user-defined function is called, with an argument list that is one longer than the argument list of the call: the instance becomes the first argument.

a class instance:
+
+

The class must define a __call__() method; the effect is then the same as if that method was called.

+6.4. Await expression

Suspend the execution of coroutine on an awaitable object. Can only be used inside a coroutine function.

+await_expr ::=  "await" primary
+

New in version 3.5.

+6.5. The power operator

The power operator binds more tightly than unary operators on its left; it binds less tightly than unary operators on its right. The syntax is:

+power ::=  (await_expr | primary) ["**" u_expr]
+

Thus, in an unparenthesized sequence of power and unary operators, the operators are evaluated from right to left (this does not constrain the evaluation order for the operands): -1**2 results in -1.

The power operator has the same semantics as the built-in pow() function, when called with two arguments: it yields its left argument raised to the power of its right argument. The numeric arguments are first converted to a common type, and the result is of that type.

For int operands, the result has the same type as the operands unless the second argument is negative; in that case, all arguments are converted to float and a float result is delivered. For example, 10**2 returns 100, but 10**-2 returns 0.01.

Raising 0.0 to a negative power results in a ZeroDivisionError. Raising a negative number to a fractional power results in a complex number. (In earlier versions it raised a ValueError.)

This operation can be customized using the special __pow__() method.

+6.6. Unary arithmetic and bitwise operations

All unary arithmetic and bitwise operations have the same priority:

+u_expr ::=  power | "-" u_expr | "+" u_expr | "~" u_expr
+

The unary - (minus) operator yields the negation of its numeric argument; the operation can be overridden with the __neg__() special method.

The unary + (plus) operator yields its numeric argument unchanged; the operation can be overridden with the __pos__() special method.

The unary ~ (invert) operator yields the bitwise inversion of its integer argument. The bitwise inversion of x is defined as -(x+1). It only applies to integral numbers or to custom objects that override the __invert__() special method.

In all three cases, if the argument does not have the proper type, a TypeError exception is raised.

+6.7. Binary arithmetic operations

The binary arithmetic operations have the conventional priority levels. Note that some of these operations also apply to certain non-numeric types. Apart from the power operator, there are only two levels, one for multiplicative operators and one for additive operators:

+m_expr ::=  u_expr | m_expr "*" u_expr | m_expr "@" m_expr |
+            m_expr "//" u_expr | m_expr "/" u_expr |
+            m_expr "%" u_expr
+a_expr ::=  m_expr | a_expr "+" m_expr | a_expr "-" m_expr
+

The * (multiplication) operator yields the product of its arguments. The arguments must either both be numbers, or one argument must be an integer and the other must be a sequence. In the former case, the numbers are converted to a common type and then multiplied together. In the latter case, sequence repetition is performed; a negative repetition factor yields an empty sequence.

This operation can be customized using the special __mul__() and __rmul__() methods.

The @ (at) operator is intended to be used for matrix multiplication. No builtin Python types implement this operator.

New in version 3.5.

The / (division) and // (floor division) operators yield the quotient of their arguments. The numeric arguments are first converted to a common type. Division of integers yields a float, while floor division of integers results in an integer; the result is that of mathematical division with the ‘floor’ function applied to the result. Division by zero raises the ZeroDivisionError exception.

This operation can be customized using the special __truediv__() and __floordiv__() methods.

The % (modulo) operator yields the remainder from the division of the first argument by the second. The numeric arguments are first converted to a common type. A zero right argument raises the ZeroDivisionError exception. The arguments may be floating point numbers, e.g., 3.14%0.7 equals 0.34 (since 3.14 equals 4*0.7 + 0.34.) The modulo operator always yields a result with the same sign as its second operand (or zero); the absolute value of the result is strictly smaller than the absolute value of the second operand 1.

The floor division and modulo operators are connected by the following identity: x == (x//y)*y + (x%y). Floor division and modulo are also connected with the built-in function divmod(): divmod(x, y) == (x//y, +x%y). 2.

In addition to performing the modulo operation on numbers, the % operator is also overloaded by string objects to perform old-style string formatting (also known as interpolation). The syntax for string formatting is described in the Python Library Reference, section printf-style String Formatting.

The modulo operation can be customized using the special __mod__() method.

The floor division operator, the modulo operator, and the divmod() function are not defined for complex numbers. Instead, convert to a floating point number using the abs() function if appropriate.

The + (addition) operator yields the sum of its arguments. The arguments must either both be numbers or both be sequences of the same type. In the former case, the numbers are converted to a common type and then added together. In the latter case, the sequences are concatenated.

This operation can be customized using the special __add__() and __radd__() methods.

The - (subtraction) operator yields the difference of its arguments. The numeric arguments are first converted to a common type.

This operation can be customized using the special __sub__() method.

+6.8. Shifting operations

The shifting operations have lower priority than the arithmetic operations:

+shift_expr ::=  a_expr | shift_expr ("<<" | ">>") a_expr
+

These operators accept integers as arguments. They shift the first argument to the left or right by the number of bits given by the second argument.

This operation can be customized using the special __lshift__() and __rshift__() methods.

A right shift by n bits is defined as floor division by pow(2,n). A left shift by n bits is defined as multiplication with pow(2,n).

+6.9. Binary bitwise operations

Each of the three bitwise operations has a different priority level:

+and_expr ::=  shift_expr | and_expr "&" shift_expr
+xor_expr ::=  and_expr | xor_expr "^" and_expr
+or_expr  ::=  xor_expr | or_expr "|" xor_expr
+

The & operator yields the bitwise AND of its arguments, which must be integers or one of them must be a custom object overriding __and__() or __rand__() special methods.

The ^ operator yields the bitwise XOR (exclusive OR) of its arguments, which must be integers or one of them must be a custom object overriding __xor__() or __rxor__() special methods.

The | operator yields the bitwise (inclusive) OR of its arguments, which must be integers or one of them must be a custom object overriding __or__() or __ror__() special methods.

+6.10. Comparisons

Unlike C, all comparison operations in Python have the same priority, which is lower than that of any arithmetic, shifting or bitwise operation. Also unlike C, expressions like a < b < c have the interpretation that is conventional in mathematics:

+comparison    ::=  or_expr (comp_operator or_expr)*
+comp_operator ::=  "<" | ">" | "==" | ">=" | "<=" | "!="
+                   | "is" ["not"] | ["not"] "in"
+

Comparisons yield boolean values: True or False. Custom rich comparison methods may return non-boolean values. In this case Python will call bool() on such value in boolean contexts.

Comparisons can be chained arbitrarily, e.g., x < y <= z is equivalent to x < y and y <= z, except that y is evaluated only once (but in both cases z is not evaluated at all when x < y is found to be false).

Formally, if a, b, c, …, y, z are expressions and op1, op2, …, opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z, except that each expression is evaluated at most once.

Note that a op1 b op2 c doesn’t imply any kind of comparison between a and c, so that, e.g., x < y > z is perfectly legal (though perhaps not pretty).

+6.10.1. Value comparisons

The operators <, >, ==, >=, <=, and != compare the values of two objects. The objects do not need to have the same type.

Chapter Objects, values and types states that objects have a value (in addition to type and identity). The value of an object is a rather abstract notion in Python: For example, there is no canonical access method for an object’s value. Also, there is no requirement that the value of an object should be constructed in a particular way, e.g. comprised of all its data attributes. Comparison operators implement a particular notion of what the value of an object is. One can think of them as defining the value of an object indirectly, by means of their comparison implementation.

Because all types are (direct or indirect) subtypes of object, they inherit the default comparison behavior from object. Types can customize their comparison behavior by implementing rich comparison methods like __lt__(), described in Basic customization.

The default behavior for equality comparison (== and !=) is based on the identity of the objects. Hence, equality comparison of instances with the same identity results in equality, and equality comparison of instances with different identities results in inequality. A motivation for this default behavior is the desire that all objects should be reflexive (i.e. x is y implies x == y).

A default order comparison (<, >, <=, and >=) is not provided; an attempt raises TypeError. A motivation for this default behavior is the lack of a similar invariant as for equality.

The behavior of the default equality comparison, that instances with different identities are always unequal, may be in contrast to what types will need that have a sensible definition of object value and value-based equality. Such types will need to customize their comparison behavior, and in fact, a number of built-in types have done that.

The following list describes the comparison behavior of the most important built-in types.

  • +

    Numbers of built-in numeric types (Numeric Types — int, float, complex) and of the standard library types fractions.Fraction and decimal.Decimal can be compared within and across their types, with the restriction that complex numbers do not support order comparison. Within the limits of the types involved, they compare mathematically (algorithmically) correct without loss of precision.

    The not-a-number values float('NaN') and decimal.Decimal('NaN') are special. Any ordered comparison of a number to a not-a-number value is false. A counter-intuitive implication is that not-a-number values are not equal to themselves. For example, if x = float('NaN'), 3 < x, x < 3 and x == x are all false, while x != x is true. This behavior is compliant with IEEE 754.

  • +None and NotImplemented are singletons. PEP 8 advises that comparisons for singletons should always be done with is or is not, never the equality operators.
  • Binary sequences (instances of bytes or bytearray) can be compared within and across their types. They compare lexicographically using the numeric values of their elements.
  • +

    Strings (instances of str) compare lexicographically using the numerical Unicode code points (the result of the built-in function ord()) of their characters. 3

    Strings and binary sequences cannot be directly compared.

  • +

    Sequences (instances of tuple, list, or range) can be compared only within each of their types, with the restriction that ranges do not support order comparison. Equality comparison across these types results in inequality, and ordering comparison across these types raises TypeError.

    Sequences compare lexicographically using comparison of corresponding elements. The built-in containers typically assume identical objects are equal to themselves. That lets them bypass equality tests for identical objects to improve performance and to maintain their internal invariants.

    Lexicographical comparison between built-in collections works as follows:

    • For two collections to compare equal, they must be of the same type, have the same length, and each pair of corresponding elements must compare equal (for example, [1,2] == (1,2) is false because the type is not the same).
    • Collections that support order comparison are ordered the same as their first unequal elements (for example, [1,2,x] <= [1,2,y] has the same value as x <= y). If a corresponding element does not exist, the shorter collection is ordered first (for example, [1,2] < [1,2,3] is true).
  • +

    Mappings (instances of dict) compare equal if and only if they have equal (key, value) pairs. Equality comparison of the keys and values enforces reflexivity.

    Order comparisons (<, >, <=, and >=) raise TypeError.

  • +

    Sets (instances of set or frozenset) can be compared within and across their types.

    They define order comparison operators to mean subset and superset tests. Those relations do not define total orderings (for example, the two sets {1,2} and {2,3} are not equal, nor subsets of one another, nor supersets of one another). Accordingly, sets are not appropriate arguments for functions which depend on total ordering (for example, min(), max(), and sorted() produce undefined results given a list of sets as inputs).

    Comparison of sets enforces reflexivity of its elements.

  • Most other built-in types have no comparison methods implemented, so they inherit the default comparison behavior.

User-defined classes that customize their comparison behavior should follow some consistency rules, if possible:

  • +

    Equality comparison should be reflexive. In other words, identical objects should compare equal:

    x is y implies x == y

  • +

    Comparison should be symmetric. In other words, the following expressions should have the same result:

    x == y and y == x

    x != y and y != x

    x < y and y > x

    x <= y and y >= x

  • +

    Comparison should be transitive. The following (non-exhaustive) examples illustrate that:

    x > y and y > z implies x > z

    x < y and y <= z implies x < z

  • +

    Inverse comparison should result in the boolean negation. In other words, the following expressions should have the same result:

    x == y and not x != y

    x < y and not x >= y (for total ordering)

    x > y and not x <= y (for total ordering)

    The last two expressions apply to totally ordered collections (e.g. to sequences, but not to sets or mappings). See also the total_ordering() decorator.

  • The hash() result should be consistent with equality. Objects that are equal should either have the same hash value, or be marked as unhashable.

Python does not enforce these consistency rules. In fact, the not-a-number values are an example for not following these rules.

+6.10.2. Membership test operations

The operators in and not in test for membership. x in +s evaluates to True if x is a member of s, and False otherwise. x not in s returns the negation of x in s. All built-in sequences and set types support this as well as dictionary, for which in tests whether the dictionary has a given key. For container types such as list, tuple, set, frozenset, dict, or collections.deque, the expression x in y is equivalent to any(x is e or x == e for e in y).

For the string and bytes types, x in y is True if and only if x is a substring of y. An equivalent test is y.find(x) != -1. Empty strings are always considered to be a substring of any other string, so "" in "abc" will return True.

For user-defined classes which define the __contains__() method, x in +y returns True if y.__contains__(x) returns a true value, and False otherwise.

For user-defined classes which do not define __contains__() but do define __iter__(), x in y is True if some value z, for which the expression x is z or x == z is true, is produced while iterating over y. If an exception is raised during the iteration, it is as if in raised that exception.

Lastly, the old-style iteration protocol is tried: if a class defines __getitem__(), x in y is True if and only if there is a non-negative integer index i such that x is y[i] or x == y[i], and no lower integer index raises the IndexError exception. (If any other exception is raised, it is as if in raised that exception).

The operator not in is defined to have the inverse truth value of in.

+6.10.3. Identity comparisons

The operators is and is not test for an object’s identity: x +is y is true if and only if x and y are the same object. An Object’s identity is determined using the id() function. x is not y yields the inverse truth value. 4

+6.11. Boolean operations

+or_test  ::=  and_test | or_test "or" and_test
+and_test ::=  not_test | and_test "and" not_test
+not_test ::=  comparison | "not" not_test
+

In the context of Boolean operations, and also when expressions are used by control flow statements, the following values are interpreted as false: False, None, numeric zero of all types, and empty strings and containers (including strings, tuples, lists, dictionaries, sets and frozensets). All other values are interpreted as true. User-defined objects can customize their truth value by providing a __bool__() method.

The operator not yields True if its argument is false, False otherwise.

The expression x and y first evaluates x; if x is false, its value is returned; otherwise, y is evaluated and the resulting value is returned.

The expression x or y first evaluates x; if x is true, its value is returned; otherwise, y is evaluated and the resulting value is returned.

Note that neither and nor or restrict the value and type they return to False and True, but rather return the last evaluated argument. This is sometimes useful, e.g., if s is a string that should be replaced by a default value if it is empty, the expression s or 'foo' yields the desired value. Because not has to create a new value, it returns a boolean value regardless of the type of its argument (for example, not 'foo' produces False rather than ''.)

+6.12. Assignment expressions

+assignment_expression ::=  [identifier ":="] expression
+

An assignment expression (sometimes also called a “named expression” or “walrus”) assigns an expression to an identifier, while also returning the value of the expression.

One common use case is when handling matched regular expressions:

if matching := pattern.search(data):
+    do_something(matching)
+

Or, when processing a file stream in chunks:

while chunk := file.read(9000):
+    process(chunk)
+

Assignment expressions must be surrounded by parentheses when used as expression statements and when used as sub-expressions in slicing, conditional, lambda, keyword-argument, and comprehension-if expressions and in assert, with, and assignment statements. In all other places where they can be used, parentheses are not required, including in if and while statements.

New in version 3.8: See PEP 572 for more details about assignment expressions.

+6.13. Conditional expressions

+conditional_expression ::=  or_test ["if" or_test "else" expression]
+expression             ::=  conditional_expression | lambda_expr
+

Conditional expressions (sometimes called a “ternary operator”) have the lowest priority of all Python operations.

The expression x if C else y first evaluates the condition, C rather than x. If C is true, x is evaluated and its value is returned; otherwise, y is evaluated and its value is returned.

See PEP 308 for more details about conditional expressions.

+6.14. Lambdas

+lambda_expr ::=  "lambda" [parameter_list] ":" expression
+

Lambda expressions (sometimes called lambda forms) are used to create anonymous functions. The expression lambda parameters: expression yields a function object. The unnamed object behaves like a function object defined with:

def <lambda>(parameters):
+    return expression
+

See section Function definitions for the syntax of parameter lists. Note that functions created with lambda expressions cannot contain statements or annotations.

+6.15. Expression lists

+expression_list    ::=  expression ("," expression)* [","]
+starred_list       ::=  starred_item ("," starred_item)* [","]
+starred_expression ::=  expression | (starred_item ",")* [starred_item]
+starred_item       ::=  assignment_expression | "*" or_expr
+

Except when part of a list or set display, an expression list containing at least one comma yields a tuple. The length of the tuple is the number of expressions in the list. The expressions are evaluated from left to right.

An asterisk * denotes iterable unpacking. Its operand must be an iterable. The iterable is expanded into a sequence of items, which are included in the new tuple, list, or set, at the site of the unpacking.

New in version 3.5: Iterable unpacking in expression lists, originally proposed by PEP 448.

The trailing comma is required only to create a single tuple (a.k.a. a singleton); it is optional in all other cases. A single expression without a trailing comma doesn’t create a tuple, but rather yields the value of that expression. (To create an empty tuple, use an empty pair of parentheses: ().)

+6.16. Evaluation order

Python evaluates expressions from left to right. Notice that while evaluating an assignment, the right-hand side is evaluated before the left-hand side.

In the following lines, expressions will be evaluated in the arithmetic order of their suffixes:

expr1, expr2, expr3, expr4
+(expr1, expr2, expr3, expr4)
+{expr1: expr2, expr3: expr4}
+expr1 + expr2 * (expr3 - expr4)
+expr1(expr2, expr3, *expr4, **expr5)
+expr3, expr4 = expr1, expr2
+

+6.17. Operator precedence

The following table summarizes the operator precedence in Python, from highest precedence (most binding) to lowest precedence (least binding). Operators in the same box have the same precedence. Unless the syntax is explicitly given, operators are binary. Operators in the same box group left to right (except for exponentiation and conditional expressions, which group from right to left).

Note that comparisons, membership tests, and identity tests, all have the same precedence and have a left-to-right chaining feature as described in the Comparisons section.

+ + + + + + + + + + + + + + + + + + +

Operator

Description
+

(expressions...),

[expressions...], {key: value...}, {expressions...}

Binding or parenthesized expression, list display, dictionary display, set display

x[index], x[index:index], x(arguments...), x.attribute

Subscription, slicing, call, attribute reference

await x

Await expression

**

Exponentiation 5

+x, -x, ~x

Positive, negative, bitwise NOT

*, @, /, //, %

Multiplication, matrix multiplication, division, floor division, remainder 6

+, -

Addition and subtraction

<<, >>

Shifts

&

Bitwise AND

^

Bitwise XOR

|

Bitwise OR

in, not in, is, is not, <, <=, >, >=, !=, ==

Comparisons, including membership tests and identity tests

not x

Boolean NOT

and

Boolean AND

or

Boolean OR

if – else

Conditional expression

lambda

Lambda expression

:=

Assignment expression

Footnotes

+1
+

While abs(x%y) < abs(y) is true mathematically, for floats it may not be true numerically due to roundoff. For example, and assuming a platform on which a Python float is an IEEE 754 double-precision number, in order that -1e-100 % +1e100 have the same sign as 1e100, the computed result is -1e-100 + +1e100, which is numerically exactly equal to 1e100. The function math.fmod() returns a result whose sign matches the sign of the first argument instead, and so returns -1e-100 in this case. Which approach is more appropriate depends on the application.

+2
+

If x is very close to an exact integer multiple of y, it’s possible for x//y to be one larger than (x-x%y)//y due to rounding. In such cases, Python returns the latter result, in order to preserve that divmod(x,y)[0] * y + x % y be very close to x.

+3
+

The Unicode standard distinguishes between code points (e.g. U+0041) and abstract characters (e.g. “LATIN CAPITAL LETTER A”). While most abstract characters in Unicode are only represented using one code point, there is a number of abstract characters that can in addition be represented using a sequence of more than one code point. For example, the abstract character “LATIN CAPITAL LETTER C WITH CEDILLA” can be represented as a single precomposed character at code position U+00C7, or as a sequence of a base character at code position U+0043 (LATIN CAPITAL LETTER C), followed by a combining character at code position U+0327 (COMBINING CEDILLA).

The comparison operators on strings compare at the level of Unicode code points. This may be counter-intuitive to humans. For example, "\u00C7" == "\u0043\u0327" is False, even though both strings represent the same abstract character “LATIN CAPITAL LETTER C WITH CEDILLA”.

To compare strings at the level of abstract characters (that is, in a way intuitive to humans), use unicodedata.normalize().

+4
+

Due to automatic garbage-collection, free lists, and the dynamic nature of descriptors, you may notice seemingly unusual behaviour in certain uses of the is operator, like those involving comparisons between instance methods, or constants. Check their documentation for more info.

+5
+

The power operator ** binds less tightly than an arithmetic or bitwise unary operator on its right, that is, 2**-1 is 0.5.

+6
+

The % operator is also used for string formatting; the same precedence applies.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/expressions.html +

+
diff --git a/devdocs/python~3.12/reference%2Fgrammar.html b/devdocs/python~3.12/reference%2Fgrammar.html new file mode 100644 index 00000000..bdf7bd76 --- /dev/null +++ b/devdocs/python~3.12/reference%2Fgrammar.html @@ -0,0 +1,907 @@ +

Full Grammar specification

This is the full Python grammar, derived directly from the grammar used to generate the CPython parser (see Grammar/python.gram). The version here omits details related to code generation and error recovery.

The notation is a mixture of EBNF and PEG. In particular, & followed by a symbol, token or parenthesized group indicates a positive lookahead (i.e., is required to match but not consumed), while ! indicates a negative lookahead (i.e., is required not to match). We use the | separator to mean PEG’s “ordered choice” (written as / in traditional PEG grammars). See PEP 617 for more details on the grammar’s syntax.

# PEG grammar for Python
+
+
+
+# ========================= START OF THE GRAMMAR =========================
+
+# General grammatical elements and rules:
+#
+# * Strings with double quotes (") denote SOFT KEYWORDS
+# * Strings with single quotes (') denote KEYWORDS
+# * Upper case names (NAME) denote tokens in the Grammar/Tokens file
+# * Rule names starting with "invalid_" are used for specialized syntax errors
+#     - These rules are NOT used in the first pass of the parser.
+#     - Only if the first pass fails to parse, a second pass including the invalid
+#       rules will be executed.
+#     - If the parser fails in the second phase with a generic syntax error, the
+#       location of the generic failure of the first pass will be used (this avoids
+#       reporting incorrect locations due to the invalid rules).
+#     - The order of the alternatives involving invalid rules matter
+#       (like any rule in PEG).
+#
+# Grammar Syntax (see PEP 617 for more information):
+#
+# rule_name: expression
+#   Optionally, a type can be included right after the rule name, which
+#   specifies the return type of the C or Python function corresponding to the
+#   rule:
+# rule_name[return_type]: expression
+#   If the return type is omitted, then a void * is returned in C and an Any in
+#   Python.
+# e1 e2
+#   Match e1, then match e2.
+# e1 | e2
+#   Match e1 or e2.
+#   The first alternative can also appear on the line after the rule name for
+#   formatting purposes. In that case, a | must be used before the first
+#   alternative, like so:
+#       rule_name[return_type]:
+#            | first_alt
+#            | second_alt
+# ( e )
+#   Match e (allows also to use other operators in the group like '(e)*')
+# [ e ] or e?
+#   Optionally match e.
+# e*
+#   Match zero or more occurrences of e.
+# e+
+#   Match one or more occurrences of e.
+# s.e+
+#   Match one or more occurrences of e, separated by s. The generated parse tree
+#   does not include the separator. This is otherwise identical to (e (s e)*).
+# &e
+#   Succeed if e can be parsed, without consuming any input.
+# !e
+#   Fail if e can be parsed, without consuming any input.
+# ~
+#   Commit to the current alternative, even if it fails to parse.
+#
+
+# STARTING RULES
+# ==============
+
+file: [statements] ENDMARKER 
+interactive: statement_newline 
+eval: expressions NEWLINE* ENDMARKER 
+func_type: '(' [type_expressions] ')' '->' expression NEWLINE* ENDMARKER 
+
+# GENERAL STATEMENTS
+# ==================
+
+statements: statement+ 
+
+statement: compound_stmt  | simple_stmts 
+
+statement_newline:
+    | compound_stmt NEWLINE 
+    | simple_stmts
+    | NEWLINE 
+    | ENDMARKER 
+
+simple_stmts:
+    | simple_stmt !';' NEWLINE  # Not needed, there for speedup
+    | ';'.simple_stmt+ [';'] NEWLINE 
+
+# NOTE: assignment MUST precede expression, else parsing a simple assignment
+# will throw a SyntaxError.
+simple_stmt:
+    | assignment
+    | type_alias
+    | star_expressions 
+    | return_stmt
+    | import_stmt
+    | raise_stmt
+    | 'pass' 
+    | del_stmt
+    | yield_stmt
+    | assert_stmt
+    | 'break' 
+    | 'continue' 
+    | global_stmt
+    | nonlocal_stmt
+
+compound_stmt:
+    | function_def
+    | if_stmt
+    | class_def
+    | with_stmt
+    | for_stmt
+    | try_stmt
+    | while_stmt
+    | match_stmt
+
+# SIMPLE STATEMENTS
+# =================
+
+# NOTE: annotated_rhs may start with 'yield'; yield_expr must start with 'yield'
+assignment:
+    | NAME ':' expression ['=' annotated_rhs ] 
+    | ('(' single_target ')' 
+         | single_subscript_attribute_target) ':' expression ['=' annotated_rhs ] 
+    | (star_targets '=' )+ (yield_expr | star_expressions) !'=' [TYPE_COMMENT] 
+    | single_target augassign ~ (yield_expr | star_expressions) 
+
+annotated_rhs: yield_expr | star_expressions
+
+augassign:
+    | '+=' 
+    | '-=' 
+    | '*=' 
+    | '@=' 
+    | '/=' 
+    | '%=' 
+    | '&=' 
+    | '|=' 
+    | '^=' 
+    | '<<=' 
+    | '>>=' 
+    | '**=' 
+    | '//=' 
+
+return_stmt:
+    | 'return' [star_expressions] 
+
+raise_stmt:
+    | 'raise' expression ['from' expression ] 
+    | 'raise' 
+
+global_stmt: 'global' ','.NAME+ 
+
+nonlocal_stmt: 'nonlocal' ','.NAME+ 
+
+del_stmt:
+    | 'del' del_targets &(';' | NEWLINE) 
+
+yield_stmt: yield_expr 
+
+assert_stmt: 'assert' expression [',' expression ] 
+
+import_stmt:
+    | import_name
+    | import_from
+
+# Import statements
+# -----------------
+
+import_name: 'import' dotted_as_names 
+# note below: the ('.' | '...') is necessary because '...' is tokenized as ELLIPSIS
+import_from:
+    | 'from' ('.' | '...')* dotted_name 'import' import_from_targets 
+    | 'from' ('.' | '...')+ 'import' import_from_targets 
+import_from_targets:
+    | '(' import_from_as_names [','] ')' 
+    | import_from_as_names !','
+    | '*' 
+import_from_as_names:
+    | ','.import_from_as_name+ 
+import_from_as_name:
+    | NAME ['as' NAME ] 
+dotted_as_names:
+    | ','.dotted_as_name+ 
+dotted_as_name:
+    | dotted_name ['as' NAME ] 
+dotted_name:
+    | dotted_name '.' NAME 
+    | NAME
+
+# COMPOUND STATEMENTS
+# ===================
+
+# Common elements
+# ---------------
+
+block:
+    | NEWLINE INDENT statements DEDENT 
+    | simple_stmts
+
+decorators: ('@' named_expression NEWLINE )+ 
+
+# Class definitions
+# -----------------
+
+class_def:
+    | decorators class_def_raw 
+    | class_def_raw
+
+class_def_raw:
+    | 'class' NAME [type_params] ['(' [arguments] ')' ] ':' block 
+
+# Function definitions
+# --------------------
+
+function_def:
+    | decorators function_def_raw 
+    | function_def_raw
+
+function_def_raw:
+    | 'def' NAME [type_params] '(' [params] ')' ['->' expression ] ':' [func_type_comment] block 
+    | ASYNC 'def' NAME [type_params] '(' [params] ')' ['->' expression ] ':' [func_type_comment] block 
+
+# Function parameters
+# -------------------
+
+params:
+    | parameters
+
+parameters:
+    | slash_no_default param_no_default* param_with_default* [star_etc] 
+    | slash_with_default param_with_default* [star_etc] 
+    | param_no_default+ param_with_default* [star_etc] 
+    | param_with_default+ [star_etc] 
+    | star_etc 
+
+# Some duplication here because we can't write (',' | &')'),
+# which is because we don't support empty alternatives (yet).
+
+slash_no_default:
+    | param_no_default+ '/' ',' 
+    | param_no_default+ '/' &')' 
+slash_with_default:
+    | param_no_default* param_with_default+ '/' ',' 
+    | param_no_default* param_with_default+ '/' &')' 
+
+star_etc:
+    | '*' param_no_default param_maybe_default* [kwds] 
+    | '*' param_no_default_star_annotation param_maybe_default* [kwds] 
+    | '*' ',' param_maybe_default+ [kwds] 
+    | kwds 
+
+kwds:
+    | '**' param_no_default 
+
+# One parameter.  This *includes* a following comma and type comment.
+#
+# There are three styles:
+# - No default
+# - With default
+# - Maybe with default
+#
+# There are two alternative forms of each, to deal with type comments:
+# - Ends in a comma followed by an optional type comment
+# - No comma, optional type comment, must be followed by close paren
+# The latter form is for a final parameter without trailing comma.
+#
+
+param_no_default:
+    | param ',' TYPE_COMMENT? 
+    | param TYPE_COMMENT? &')' 
+param_no_default_star_annotation:
+    | param_star_annotation ',' TYPE_COMMENT? 
+    | param_star_annotation TYPE_COMMENT? &')' 
+param_with_default:
+    | param default ',' TYPE_COMMENT? 
+    | param default TYPE_COMMENT? &')' 
+param_maybe_default:
+    | param default? ',' TYPE_COMMENT? 
+    | param default? TYPE_COMMENT? &')' 
+param: NAME annotation? 
+param_star_annotation: NAME star_annotation 
+annotation: ':' expression 
+star_annotation: ':' star_expression 
+default: '=' expression  | invalid_default
+
+# If statement
+# ------------
+
+if_stmt:
+    | 'if' named_expression ':' block elif_stmt 
+    | 'if' named_expression ':' block [else_block] 
+elif_stmt:
+    | 'elif' named_expression ':' block elif_stmt 
+    | 'elif' named_expression ':' block [else_block] 
+else_block:
+    | 'else' ':' block 
+
+# While statement
+# ---------------
+
+while_stmt:
+    | 'while' named_expression ':' block [else_block] 
+
+# For statement
+# -------------
+
+for_stmt:
+    | 'for' star_targets 'in' ~ star_expressions ':' [TYPE_COMMENT] block [else_block] 
+    | ASYNC 'for' star_targets 'in' ~ star_expressions ':' [TYPE_COMMENT] block [else_block] 
+
+# With statement
+# --------------
+
+with_stmt:
+    | 'with' '(' ','.with_item+ ','? ')' ':' block 
+    | 'with' ','.with_item+ ':' [TYPE_COMMENT] block 
+    | ASYNC 'with' '(' ','.with_item+ ','? ')' ':' block 
+    | ASYNC 'with' ','.with_item+ ':' [TYPE_COMMENT] block 
+
+with_item:
+    | expression 'as' star_target &(',' | ')' | ':') 
+    | expression 
+
+# Try statement
+# -------------
+
+try_stmt:
+    | 'try' ':' block finally_block 
+    | 'try' ':' block except_block+ [else_block] [finally_block] 
+    | 'try' ':' block except_star_block+ [else_block] [finally_block] 
+
+
+# Except statement
+# ----------------
+
+except_block:
+    | 'except' expression ['as' NAME ] ':' block 
+    | 'except' ':' block 
+except_star_block:
+    | 'except' '*' expression ['as' NAME ] ':' block 
+finally_block:
+    | 'finally' ':' block 
+
+# Match statement
+# ---------------
+
+match_stmt:
+    | "match" subject_expr ':' NEWLINE INDENT case_block+ DEDENT 
+
+subject_expr:
+    | star_named_expression ',' star_named_expressions? 
+    | named_expression
+
+case_block:
+    | "case" patterns guard? ':' block 
+
+guard: 'if' named_expression 
+
+patterns:
+    | open_sequence_pattern 
+    | pattern
+
+pattern:
+    | as_pattern
+    | or_pattern
+
+as_pattern:
+    | or_pattern 'as' pattern_capture_target 
+
+or_pattern:
+    | '|'.closed_pattern+ 
+
+closed_pattern:
+    | literal_pattern
+    | capture_pattern
+    | wildcard_pattern
+    | value_pattern
+    | group_pattern
+    | sequence_pattern
+    | mapping_pattern
+    | class_pattern
+
+# Literal patterns are used for equality and identity constraints
+literal_pattern:
+    | signed_number !('+' | '-') 
+    | complex_number 
+    | strings 
+    | 'None' 
+    | 'True' 
+    | 'False' 
+
+# Literal expressions are used to restrict permitted mapping pattern keys
+literal_expr:
+    | signed_number !('+' | '-')
+    | complex_number
+    | strings
+    | 'None' 
+    | 'True' 
+    | 'False' 
+
+complex_number:
+    | signed_real_number '+' imaginary_number 
+    | signed_real_number '-' imaginary_number  
+
+signed_number:
+    | NUMBER
+    | '-' NUMBER 
+
+signed_real_number:
+    | real_number
+    | '-' real_number 
+
+real_number:
+    | NUMBER 
+
+imaginary_number:
+    | NUMBER 
+
+capture_pattern:
+    | pattern_capture_target 
+
+pattern_capture_target:
+    | !"_" NAME !('.' | '(' | '=') 
+
+wildcard_pattern:
+    | "_" 
+
+value_pattern:
+    | attr !('.' | '(' | '=') 
+
+attr:
+    | name_or_attr '.' NAME 
+
+name_or_attr:
+    | attr
+    | NAME
+
+group_pattern:
+    | '(' pattern ')' 
+
+sequence_pattern:
+    | '[' maybe_sequence_pattern? ']' 
+    | '(' open_sequence_pattern? ')' 
+
+open_sequence_pattern:
+    | maybe_star_pattern ',' maybe_sequence_pattern? 
+
+maybe_sequence_pattern:
+    | ','.maybe_star_pattern+ ','? 
+
+maybe_star_pattern:
+    | star_pattern
+    | pattern
+
+star_pattern:
+    | '*' pattern_capture_target 
+    | '*' wildcard_pattern 
+
+mapping_pattern:
+    | '{' '}' 
+    | '{' double_star_pattern ','? '}' 
+    | '{' items_pattern ',' double_star_pattern ','? '}' 
+    | '{' items_pattern ','? '}' 
+
+items_pattern:
+    | ','.key_value_pattern+
+
+key_value_pattern:
+    | (literal_expr | attr) ':' pattern 
+
+double_star_pattern:
+    | '**' pattern_capture_target 
+
+class_pattern:
+    | name_or_attr '(' ')' 
+    | name_or_attr '(' positional_patterns ','? ')' 
+    | name_or_attr '(' keyword_patterns ','? ')' 
+    | name_or_attr '(' positional_patterns ',' keyword_patterns ','? ')' 
+
+positional_patterns:
+    | ','.pattern+ 
+
+keyword_patterns:
+    | ','.keyword_pattern+
+
+keyword_pattern:
+    | NAME '=' pattern 
+
+# Type statement
+# ---------------
+
+type_alias:
+    | "type" NAME [type_params] '=' expression 
+
+# Type parameter declaration
+# --------------------------
+
+type_params: '[' type_param_seq  ']' 
+
+type_param_seq: ','.type_param+ [','] 
+
+type_param:
+    | NAME [type_param_bound] 
+    | '*' NAME ':' expression 
+    | '*' NAME 
+    | '**' NAME ':' expression 
+    | '**' NAME 
+
+type_param_bound: ':' expression 
+
+# EXPRESSIONS
+# -----------
+
+expressions:
+    | expression (',' expression )+ [','] 
+    | expression ',' 
+    | expression
+
+expression:
+    | disjunction 'if' disjunction 'else' expression 
+    | disjunction
+    | lambdef
+
+yield_expr:
+    | 'yield' 'from' expression 
+    | 'yield' [star_expressions] 
+
+star_expressions:
+    | star_expression (',' star_expression )+ [','] 
+    | star_expression ',' 
+    | star_expression
+
+star_expression:
+    | '*' bitwise_or 
+    | expression
+
+star_named_expressions: ','.star_named_expression+ [','] 
+
+star_named_expression:
+    | '*' bitwise_or 
+    | named_expression
+
+assignment_expression:
+    | NAME ':=' ~ expression 
+
+named_expression:
+    | assignment_expression
+    | expression !':='
+
+disjunction:
+    | conjunction ('or' conjunction )+ 
+    | conjunction
+
+conjunction:
+    | inversion ('and' inversion )+ 
+    | inversion
+
+inversion:
+    | 'not' inversion 
+    | comparison
+
+# Comparison operators
+# --------------------
+
+comparison:
+    | bitwise_or compare_op_bitwise_or_pair+ 
+    | bitwise_or
+
+compare_op_bitwise_or_pair:
+    | eq_bitwise_or
+    | noteq_bitwise_or
+    | lte_bitwise_or
+    | lt_bitwise_or
+    | gte_bitwise_or
+    | gt_bitwise_or
+    | notin_bitwise_or
+    | in_bitwise_or
+    | isnot_bitwise_or
+    | is_bitwise_or
+
+eq_bitwise_or: '==' bitwise_or 
+noteq_bitwise_or:
+    | ('!=' ) bitwise_or 
+lte_bitwise_or: '<=' bitwise_or 
+lt_bitwise_or: '<' bitwise_or 
+gte_bitwise_or: '>=' bitwise_or 
+gt_bitwise_or: '>' bitwise_or 
+notin_bitwise_or: 'not' 'in' bitwise_or 
+in_bitwise_or: 'in' bitwise_or 
+isnot_bitwise_or: 'is' 'not' bitwise_or 
+is_bitwise_or: 'is' bitwise_or 
+
+# Bitwise operators
+# -----------------
+
+bitwise_or:
+    | bitwise_or '|' bitwise_xor 
+    | bitwise_xor
+
+bitwise_xor:
+    | bitwise_xor '^' bitwise_and 
+    | bitwise_and
+
+bitwise_and:
+    | bitwise_and '&' shift_expr 
+    | shift_expr
+
+shift_expr:
+    | shift_expr '<<' sum 
+    | shift_expr '>>' sum 
+    | sum
+
+# Arithmetic operators
+# --------------------
+
+sum:
+    | sum '+' term 
+    | sum '-' term 
+    | term
+
+term:
+    | term '*' factor 
+    | term '/' factor 
+    | term '//' factor 
+    | term '%' factor 
+    | term '@' factor 
+    | factor
+
+factor:
+    | '+' factor 
+    | '-' factor 
+    | '~' factor 
+    | power
+
+power:
+    | await_primary '**' factor 
+    | await_primary
+
+# Primary elements
+# ----------------
+
+# Primary elements are things like "obj.something.something", "obj[something]", "obj(something)", "obj" ...
+
+await_primary:
+    | AWAIT primary 
+    | primary
+
+primary:
+    | primary '.' NAME 
+    | primary genexp 
+    | primary '(' [arguments] ')' 
+    | primary '[' slices ']' 
+    | atom
+
+slices:
+    | slice !',' 
+    | ','.(slice | starred_expression)+ [','] 
+
+slice:
+    | [expression] ':' [expression] [':' [expression] ] 
+    | named_expression 
+
+atom:
+    | NAME
+    | 'True' 
+    | 'False' 
+    | 'None' 
+    | strings
+    | NUMBER
+    | (tuple | group | genexp)
+    | (list | listcomp)
+    | (dict | set | dictcomp | setcomp)
+    | '...' 
+
+group:
+    | '(' (yield_expr | named_expression) ')' 
+
+# Lambda functions
+# ----------------
+
+lambdef:
+    | 'lambda' [lambda_params] ':' expression 
+
+lambda_params:
+    | lambda_parameters
+
+# lambda_parameters etc. duplicates parameters but without annotations
+# or type comments, and if there's no comma after a parameter, we expect
+# a colon, not a close parenthesis.  (For more, see parameters above.)
+#
+lambda_parameters:
+    | lambda_slash_no_default lambda_param_no_default* lambda_param_with_default* [lambda_star_etc] 
+    | lambda_slash_with_default lambda_param_with_default* [lambda_star_etc] 
+    | lambda_param_no_default+ lambda_param_with_default* [lambda_star_etc] 
+    | lambda_param_with_default+ [lambda_star_etc] 
+    | lambda_star_etc 
+
+lambda_slash_no_default:
+    | lambda_param_no_default+ '/' ',' 
+    | lambda_param_no_default+ '/' &':' 
+
+lambda_slash_with_default:
+    | lambda_param_no_default* lambda_param_with_default+ '/' ',' 
+    | lambda_param_no_default* lambda_param_with_default+ '/' &':' 
+
+lambda_star_etc:
+    | '*' lambda_param_no_default lambda_param_maybe_default* [lambda_kwds] 
+    | '*' ',' lambda_param_maybe_default+ [lambda_kwds] 
+    | lambda_kwds 
+
+lambda_kwds:
+    | '**' lambda_param_no_default 
+
+lambda_param_no_default:
+    | lambda_param ',' 
+    | lambda_param &':' 
+lambda_param_with_default:
+    | lambda_param default ',' 
+    | lambda_param default &':' 
+lambda_param_maybe_default:
+    | lambda_param default? ',' 
+    | lambda_param default? &':' 
+lambda_param: NAME 
+
+# LITERALS
+# ========
+
+fstring_middle:
+    | fstring_replacement_field
+    | FSTRING_MIDDLE 
+fstring_replacement_field:
+    | '{' (yield_expr | star_expressions) '='? [fstring_conversion] [fstring_full_format_spec] '}' 
+fstring_conversion:
+    | "!" NAME 
+fstring_full_format_spec:
+    | ':' fstring_format_spec* 
+fstring_format_spec:
+    | FSTRING_MIDDLE 
+    | fstring_replacement_field
+fstring:
+    | FSTRING_START fstring_middle* FSTRING_END 
+
+string: STRING 
+strings: (fstring|string)+ 
+
+list:
+    | '[' [star_named_expressions] ']' 
+
+tuple:
+    | '(' [star_named_expression ',' [star_named_expressions]  ] ')' 
+
+set: '{' star_named_expressions '}' 
+
+# Dicts
+# -----
+
+dict:
+    | '{' [double_starred_kvpairs] '}' 
+
+double_starred_kvpairs: ','.double_starred_kvpair+ [','] 
+
+double_starred_kvpair:
+    | '**' bitwise_or 
+    | kvpair
+
+kvpair: expression ':' expression 
+
+# Comprehensions & Generators
+# ---------------------------
+
+for_if_clauses:
+    | for_if_clause+ 
+
+for_if_clause:
+    | ASYNC 'for' star_targets 'in' ~ disjunction ('if' disjunction )* 
+    | 'for' star_targets 'in' ~ disjunction ('if' disjunction )* 
+
+listcomp:
+    | '[' named_expression for_if_clauses ']' 
+
+setcomp:
+    | '{' named_expression for_if_clauses '}' 
+
+genexp:
+    | '(' ( assignment_expression | expression !':=') for_if_clauses ')' 
+
+dictcomp:
+    | '{' kvpair for_if_clauses '}' 
+
+# FUNCTION CALL ARGUMENTS
+# =======================
+
+arguments:
+    | args [','] &')' 
+
+args:
+    | ','.(starred_expression | ( assignment_expression | expression !':=') !'=')+ [',' kwargs ] 
+    | kwargs 
+
+kwargs:
+    | ','.kwarg_or_starred+ ',' ','.kwarg_or_double_starred+ 
+    | ','.kwarg_or_starred+
+    | ','.kwarg_or_double_starred+
+
+starred_expression:
+    | '*' expression 
+
+kwarg_or_starred:
+    | NAME '=' expression 
+    | starred_expression 
+
+kwarg_or_double_starred:
+    | NAME '=' expression 
+    | '**' expression 
+
+# ASSIGNMENT TARGETS
+# ==================
+
+# Generic targets
+# ---------------
+
+# NOTE: star_targets may contain *bitwise_or, targets may not.
+star_targets:
+    | star_target !',' 
+    | star_target (',' star_target )* [','] 
+
+star_targets_list_seq: ','.star_target+ [','] 
+
+star_targets_tuple_seq:
+    | star_target (',' star_target )+ [','] 
+    | star_target ',' 
+
+star_target:
+    | '*' (!'*' star_target) 
+    | target_with_star_atom
+
+target_with_star_atom:
+    | t_primary '.' NAME !t_lookahead 
+    | t_primary '[' slices ']' !t_lookahead 
+    | star_atom
+
+star_atom:
+    | NAME 
+    | '(' target_with_star_atom ')' 
+    | '(' [star_targets_tuple_seq] ')' 
+    | '[' [star_targets_list_seq] ']' 
+
+single_target:
+    | single_subscript_attribute_target
+    | NAME 
+    | '(' single_target ')' 
+
+single_subscript_attribute_target:
+    | t_primary '.' NAME !t_lookahead 
+    | t_primary '[' slices ']' !t_lookahead 
+
+t_primary:
+    | t_primary '.' NAME &t_lookahead 
+    | t_primary '[' slices ']' &t_lookahead 
+    | t_primary genexp &t_lookahead 
+    | t_primary '(' [arguments] ')' &t_lookahead 
+    | atom &t_lookahead 
+
+t_lookahead: '(' | '[' | '.'
+
+# Targets for del statements
+# --------------------------
+
+del_targets: ','.del_target+ [','] 
+
+del_target:
+    | t_primary '.' NAME !t_lookahead 
+    | t_primary '[' slices ']' !t_lookahead 
+    | del_t_atom
+
+del_t_atom:
+    | NAME 
+    | '(' del_target ')' 
+    | '(' [del_targets] ')' 
+    | '[' [del_targets] ']' 
+
+# TYPING ELEMENTS
+# ---------------
+
+# type_expressions allow */** but ignore them
+type_expressions:
+    | ','.expression+ ',' '*' expression ',' '**' expression 
+    | ','.expression+ ',' '*' expression 
+    | ','.expression+ ',' '**' expression 
+    | '*' expression ',' '**' expression 
+    | '*' expression 
+    | '**' expression 
+    | ','.expression+ 
+
+func_type_comment:
+    | NEWLINE TYPE_COMMENT &(NEWLINE INDENT)   # Must be followed by indented block
+    | TYPE_COMMENT
+
+# ========================= END OF THE GRAMMAR ===========================
+
+
+
+# ========================= START OF INVALID RULES =======================
+
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/grammar.html +

+
diff --git a/devdocs/python~3.12/reference%2Fimport.html b/devdocs/python~3.12/reference%2Fimport.html new file mode 100644 index 00000000..b2304ca6 --- /dev/null +++ b/devdocs/python~3.12/reference%2Fimport.html @@ -0,0 +1,118 @@ +

The import system

Python code in one module gains access to the code in another module by the process of importing it. The import statement is the most common way of invoking the import machinery, but it is not the only way. Functions such as importlib.import_module() and built-in __import__() can also be used to invoke the import machinery.

The import statement combines two operations; it searches for the named module, then it binds the results of that search to a name in the local scope. The search operation of the import statement is defined as a call to the __import__() function, with the appropriate arguments. The return value of __import__() is used to perform the name binding operation of the import statement. See the import statement for the exact details of that name binding operation.

A direct call to __import__() performs only the module search and, if found, the module creation operation. While certain side-effects may occur, such as the importing of parent packages, and the updating of various caches (including sys.modules), only the import statement performs a name binding operation.

When an import statement is executed, the standard builtin __import__() function is called. Other mechanisms for invoking the import system (such as importlib.import_module()) may choose to bypass __import__() and use their own solutions to implement import semantics.

When a module is first imported, Python searches for the module and if found, it creates a module object 1, initializing it. If the named module cannot be found, a ModuleNotFoundError is raised. Python implements various strategies to search for the named module when the import machinery is invoked. These strategies can be modified and extended by using various hooks described in the sections below.

Changed in version 3.3: The import system has been updated to fully implement the second phase of PEP 302. There is no longer any implicit import machinery - the full import system is exposed through sys.meta_path. In addition, native namespace package support has been implemented (see PEP 420).

+5.1. importlib

The importlib module provides a rich API for interacting with the import system. For example importlib.import_module() provides a recommended, simpler API than built-in __import__() for invoking the import machinery. Refer to the importlib library documentation for additional detail.

+5.2. Packages

Python has only one type of module object, and all modules are of this type, regardless of whether the module is implemented in Python, C, or something else. To help organize modules and provide a naming hierarchy, Python has a concept of packages.

You can think of packages as the directories on a file system and modules as files within directories, but don’t take this analogy too literally since packages and modules need not originate from the file system. For the purposes of this documentation, we’ll use this convenient analogy of directories and files. Like file system directories, packages are organized hierarchically, and packages may themselves contain subpackages, as well as regular modules.

It’s important to keep in mind that all packages are modules, but not all modules are packages. Or put another way, packages are just a special kind of module. Specifically, any module that contains a __path__ attribute is considered a package.

All modules have a name. Subpackage names are separated from their parent package name by a dot, akin to Python’s standard attribute access syntax. Thus you might have a package called email, which in turn has a subpackage called email.mime and a module within that subpackage called email.mime.text.

+5.2.1. Regular packages

Python defines two types of packages, regular packages and namespace packages. Regular packages are traditional packages as they existed in Python 3.2 and earlier. A regular package is typically implemented as a directory containing an __init__.py file. When a regular package is imported, this __init__.py file is implicitly executed, and the objects it defines are bound to names in the package’s namespace. The __init__.py file can contain the same Python code that any other module can contain, and Python will add some additional attributes to the module when it is imported.

For example, the following file system layout defines a top level parent package with three subpackages:

parent/
+    __init__.py
+    one/
+        __init__.py
+    two/
+        __init__.py
+    three/
+        __init__.py
+

Importing parent.one will implicitly execute parent/__init__.py and parent/one/__init__.py. Subsequent imports of parent.two or parent.three will execute parent/two/__init__.py and parent/three/__init__.py respectively.

+5.2.2. Namespace packages

A namespace package is a composite of various portions, where each portion contributes a subpackage to the parent package. Portions may reside in different locations on the file system. Portions may also be found in zip files, on the network, or anywhere else that Python searches during import. Namespace packages may or may not correspond directly to objects on the file system; they may be virtual modules that have no concrete representation.

Namespace packages do not use an ordinary list for their __path__ attribute. They instead use a custom iterable type which will automatically perform a new search for package portions on the next import attempt within that package if the path of their parent package (or sys.path for a top level package) changes.

With namespace packages, there is no parent/__init__.py file. In fact, there may be multiple parent directories found during import search, where each one is provided by a different portion. Thus parent/one may not be physically located next to parent/two. In this case, Python will create a namespace package for the top-level parent package whenever it or one of its subpackages is imported.

See also PEP 420 for the namespace package specification.

+5.3. Searching

To begin the search, Python needs the fully qualified name of the module (or package, but for the purposes of this discussion, the difference is immaterial) being imported. This name may come from various arguments to the import statement, or from the parameters to the importlib.import_module() or __import__() functions.

This name will be used in various phases of the import search, and it may be the dotted path to a submodule, e.g. foo.bar.baz. In this case, Python first tries to import foo, then foo.bar, and finally foo.bar.baz. If any of the intermediate imports fail, a ModuleNotFoundError is raised.

+5.3.1. The module cache

The first place checked during import search is sys.modules. This mapping serves as a cache of all modules that have been previously imported, including the intermediate paths. So if foo.bar.baz was previously imported, sys.modules will contain entries for foo, foo.bar, and foo.bar.baz. Each key will have as its value the corresponding module object.

During import, the module name is looked up in sys.modules and if present, the associated value is the module satisfying the import, and the process completes. However, if the value is None, then a ModuleNotFoundError is raised. If the module name is missing, Python will continue searching for the module.

sys.modules is writable. Deleting a key may not destroy the associated module (as other modules may hold references to it), but it will invalidate the cache entry for the named module, causing Python to search anew for the named module upon its next import. The key can also be assigned to None, forcing the next import of the module to result in a ModuleNotFoundError.

Beware though, as if you keep a reference to the module object, invalidate its cache entry in sys.modules, and then re-import the named module, the two module objects will not be the same. By contrast, importlib.reload() will reuse the same module object, and simply reinitialise the module contents by rerunning the module’s code.

+5.3.2. Finders and loaders

If the named module is not found in sys.modules, then Python’s import protocol is invoked to find and load the module. This protocol consists of two conceptual objects, finders and loaders. A finder’s job is to determine whether it can find the named module using whatever strategy it knows about. Objects that implement both of these interfaces are referred to as importers - they return themselves when they find that they can load the requested module.

Python includes a number of default finders and importers. The first one knows how to locate built-in modules, and the second knows how to locate frozen modules. A third default finder searches an import path for modules. The import path is a list of locations that may name file system paths or zip files. It can also be extended to search for any locatable resource, such as those identified by URLs.

The import machinery is extensible, so new finders can be added to extend the range and scope of module searching.

Finders do not actually load modules. If they can find the named module, they return a module spec, an encapsulation of the module’s import-related information, which the import machinery then uses when loading the module.

The following sections describe the protocol for finders and loaders in more detail, including how you can create and register new ones to extend the import machinery.

Changed in version 3.4: In previous versions of Python, finders returned loaders directly, whereas now they return module specs which contain loaders. Loaders are still used during import but have fewer responsibilities.

+5.3.3. Import hooks

The import machinery is designed to be extensible; the primary mechanism for this are the import hooks. There are two types of import hooks: meta hooks and import path hooks.

Meta hooks are called at the start of import processing, before any other import processing has occurred, other than sys.modules cache look up. This allows meta hooks to override sys.path processing, frozen modules, or even built-in modules. Meta hooks are registered by adding new finder objects to sys.meta_path, as described below.

Import path hooks are called as part of sys.path (or package.__path__) processing, at the point where their associated path item is encountered. Import path hooks are registered by adding new callables to sys.path_hooks as described below.

+5.3.4. The meta path

When the named module is not found in sys.modules, Python next searches sys.meta_path, which contains a list of meta path finder objects. These finders are queried in order to see if they know how to handle the named module. Meta path finders must implement a method called find_spec() which takes three arguments: a name, an import path, and (optionally) a target module. The meta path finder can use any strategy it wants to determine whether it can handle the named module or not.

If the meta path finder knows how to handle the named module, it returns a spec object. If it cannot handle the named module, it returns None. If sys.meta_path processing reaches the end of its list without returning a spec, then a ModuleNotFoundError is raised. Any other exceptions raised are simply propagated up, aborting the import process.

The find_spec() method of meta path finders is called with two or three arguments. The first is the fully qualified name of the module being imported, for example foo.bar.baz. The second argument is the path entries to use for the module search. For top-level modules, the second argument is None, but for submodules or subpackages, the second argument is the value of the parent package’s __path__ attribute. If the appropriate __path__ attribute cannot be accessed, a ModuleNotFoundError is raised. The third argument is an existing module object that will be the target of loading later. The import system passes in a target module only during reload.

The meta path may be traversed multiple times for a single import request. For example, assuming none of the modules involved has already been cached, importing foo.bar.baz will first perform a top level import, calling mpf.find_spec("foo", None, None) on each meta path finder (mpf). After foo has been imported, foo.bar will be imported by traversing the meta path a second time, calling mpf.find_spec("foo.bar", foo.__path__, None). Once foo.bar has been imported, the final traversal will call mpf.find_spec("foo.bar.baz", foo.bar.__path__, None).

Some meta path finders only support top level imports. These importers will always return None when anything other than None is passed as the second argument.

Python’s default sys.meta_path has three meta path finders, one that knows how to import built-in modules, one that knows how to import frozen modules, and one that knows how to import modules from an import path (i.e. the path based finder).

Changed in version 3.4: The find_spec() method of meta path finders replaced find_module(), which is now deprecated. While it will continue to work without change, the import machinery will try it only if the finder does not implement find_spec().

Changed in version 3.10: Use of find_module() by the import system now raises ImportWarning.

Changed in version 3.12: find_module() has been removed. Use find_spec() instead.

+5.4. Loading

If and when a module spec is found, the import machinery will use it (and the loader it contains) when loading the module. Here is an approximation of what happens during the loading portion of import:

module = None
+if spec.loader is not None and hasattr(spec.loader, 'create_module'):
+    # It is assumed 'exec_module' will also be defined on the loader.
+    module = spec.loader.create_module(spec)
+if module is None:
+    module = ModuleType(spec.name)
+# The import-related module attributes get set here:
+_init_module_attrs(spec, module)
+
+if spec.loader is None:
+    # unsupported
+    raise ImportError
+if spec.origin is None and spec.submodule_search_locations is not None:
+    # namespace package
+    sys.modules[spec.name] = module
+elif not hasattr(spec.loader, 'exec_module'):
+    module = spec.loader.load_module(spec.name)
+else:
+    sys.modules[spec.name] = module
+    try:
+        spec.loader.exec_module(module)
+    except BaseException:
+        try:
+            del sys.modules[spec.name]
+        except KeyError:
+            pass
+        raise
+return sys.modules[spec.name]
+

Note the following details:

  • If there is an existing module object with the given name in sys.modules, import will have already returned it.
  • The module will exist in sys.modules before the loader executes the module code. This is crucial because the module code may (directly or indirectly) import itself; adding it to sys.modules beforehand prevents unbounded recursion in the worst case and multiple loading in the best.
  • If loading fails, the failing module – and only the failing module – gets removed from sys.modules. Any module already in the sys.modules cache, and any module that was successfully loaded as a side-effect, must remain in the cache. This contrasts with reloading where even the failing module is left in sys.modules.
  • After the module is created but before execution, the import machinery sets the import-related module attributes (“_init_module_attrs” in the pseudo-code example above), as summarized in a later section.
  • Module execution is the key moment of loading in which the module’s namespace gets populated. Execution is entirely delegated to the loader, which gets to decide what gets populated and how.
  • The module created during loading and passed to exec_module() may not be the one returned at the end of import 2.

Changed in version 3.4: The import system has taken over the boilerplate responsibilities of loaders. These were previously performed by the importlib.abc.Loader.load_module() method.

+5.4.1. Loaders

Module loaders provide the critical function of loading: module execution. The import machinery calls the importlib.abc.Loader.exec_module() method with a single argument, the module object to execute. Any value returned from exec_module() is ignored.

Loaders must satisfy the following requirements:

  • If the module is a Python module (as opposed to a built-in module or a dynamically loaded extension), the loader should execute the module’s code in the module’s global name space (module.__dict__).
  • If the loader cannot execute the module, it should raise an ImportError, although any other exception raised during exec_module() will be propagated.

In many cases, the finder and loader can be the same object; in such cases the find_spec() method would just return a spec with the loader set to self.

Module loaders may opt in to creating the module object during loading by implementing a create_module() method. It takes one argument, the module spec, and returns the new module object to use during loading. create_module() does not need to set any attributes on the module object. If the method returns None, the import machinery will create the new module itself.

New in version 3.4: The create_module() method of loaders.

Changed in version 3.4: The load_module() method was replaced by exec_module() and the import machinery assumed all the boilerplate responsibilities of loading.

For compatibility with existing loaders, the import machinery will use the load_module() method of loaders if it exists and the loader does not also implement exec_module(). However, load_module() has been deprecated and loaders should implement exec_module() instead.

The load_module() method must implement all the boilerplate loading functionality described above in addition to executing the module. All the same constraints apply, with some additional clarification:

  • If there is an existing module object with the given name in sys.modules, the loader must use that existing module. (Otherwise, importlib.reload() will not work correctly.) If the named module does not exist in sys.modules, the loader must create a new module object and add it to sys.modules.
  • The module must exist in sys.modules before the loader executes the module code, to prevent unbounded recursion or multiple loading.
  • If loading fails, the loader must remove any modules it has inserted into sys.modules, but it must remove only the failing module(s), and only if the loader itself has loaded the module(s) explicitly.

Changed in version 3.5: A DeprecationWarning is raised when exec_module() is defined but create_module() is not.

Changed in version 3.6: An ImportError is raised when exec_module() is defined but create_module() is not.

Changed in version 3.10: Use of load_module() will raise ImportWarning.

+5.4.2. Submodules

When a submodule is loaded using any mechanism (e.g. importlib APIs, the import or import-from statements, or built-in __import__()) a binding is placed in the parent module’s namespace to the submodule object. For example, if package spam has a submodule foo, after importing spam.foo, spam will have an attribute foo which is bound to the submodule. Let’s say you have the following directory structure:

spam/
+    __init__.py
+    foo.py
+

and spam/__init__.py has the following line in it:

from .foo import Foo
+

then executing the following puts name bindings for foo and Foo in the spam module:

>>> import spam
+>>> spam.foo
+<module 'spam.foo' from '/tmp/imports/spam/foo.py'>
+>>> spam.Foo
+<class 'spam.foo.Foo'>
+

Given Python’s familiar name binding rules this might seem surprising, but it’s actually a fundamental feature of the import system. The invariant holding is that if you have sys.modules['spam'] and sys.modules['spam.foo'] (as you would after the above import), the latter must appear as the foo attribute of the former.

+5.4.3. Module spec

The import machinery uses a variety of information about each module during import, especially before loading. Most of the information is common to all modules. The purpose of a module’s spec is to encapsulate this import-related information on a per-module basis.

Using a spec during import allows state to be transferred between import system components, e.g. between the finder that creates the module spec and the loader that executes it. Most importantly, it allows the import machinery to perform the boilerplate operations of loading, whereas without a module spec the loader had that responsibility.

The module’s spec is exposed as the __spec__ attribute on a module object. See ModuleSpec for details on the contents of the module spec.

New in version 3.4.

+5.4.5. module.__path__

By definition, if a module has a __path__ attribute, it is a package.

A package’s __path__ attribute is used during imports of its subpackages. Within the import machinery, it functions much the same as sys.path, i.e. providing a list of locations to search for modules during import. However, __path__ is typically much more constrained than sys.path.

__path__ must be an iterable of strings, but it may be empty. The same rules used for sys.path also apply to a package’s __path__, and sys.path_hooks (described below) are consulted when traversing a package’s __path__.

A package’s __init__.py file may set or alter the package’s __path__ attribute, and this was typically the way namespace packages were implemented prior to PEP 420. With the adoption of PEP 420, namespace packages no longer need to supply __init__.py files containing only __path__ manipulation code; the import machinery automatically sets __path__ correctly for the namespace package.

+5.4.6. Module reprs

By default, all modules have a usable repr, however depending on the attributes set above, and in the module’s spec, you can more explicitly control the repr of module objects.

If the module has a spec (__spec__), the import machinery will try to generate a repr from it. If that fails or there is no spec, the import system will craft a default repr using whatever information is available on the module. It will try to use the module.__name__, module.__file__, and module.__loader__ as input into the repr, with defaults for whatever information is missing.

Here are the exact rules used:

  • If the module has a __spec__ attribute, the information in the spec is used to generate the repr. The “name”, “loader”, “origin”, and “has_location” attributes are consulted.
  • If the module has a __file__ attribute, this is used as part of the module’s repr.
  • If the module has no __file__ but does have a __loader__ that is not None, then the loader’s repr is used as part of the module’s repr.
  • Otherwise, just use the module’s __name__ in the repr.

Changed in version 3.12: Use of module_repr(), having been deprecated since Python 3.4, was removed in Python 3.12 and is no longer called during the resolution of a module’s repr.

+5.4.7. Cached bytecode invalidation

Before Python loads cached bytecode from a .pyc file, it checks whether the cache is up-to-date with the source .py file. By default, Python does this by storing the source’s last-modified timestamp and size in the cache file when writing it. At runtime, the import system then validates the cache file by checking the stored metadata in the cache file against the source’s metadata.

Python also supports “hash-based” cache files, which store a hash of the source file’s contents rather than its metadata. There are two variants of hash-based .pyc files: checked and unchecked. For checked hash-based .pyc files, Python validates the cache file by hashing the source file and comparing the resulting hash with the hash in the cache file. If a checked hash-based cache file is found to be invalid, Python regenerates it and writes a new checked hash-based cache file. For unchecked hash-based .pyc files, Python simply assumes the cache file is valid if it exists. Hash-based .pyc files validation behavior may be overridden with the --check-hash-based-pycs flag.

Changed in version 3.7: Added hash-based .pyc files. Previously, Python only supported timestamp-based invalidation of bytecode caches.

+5.5. The Path Based Finder

As mentioned previously, Python comes with several default meta path finders. One of these, called the path based finder (PathFinder), searches an import path, which contains a list of path entries. Each path entry names a location to search for modules.

The path based finder itself doesn’t know how to import anything. Instead, it traverses the individual path entries, associating each of them with a path entry finder that knows how to handle that particular kind of path.

The default set of path entry finders implement all the semantics for finding modules on the file system, handling special file types such as Python source code (.py files), Python byte code (.pyc files) and shared libraries (e.g. .so files). When supported by the zipimport module in the standard library, the default path entry finders also handle loading all of these file types (other than shared libraries) from zipfiles.

Path entries need not be limited to file system locations. They can refer to URLs, database queries, or any other location that can be specified as a string.

The path based finder provides additional hooks and protocols so that you can extend and customize the types of searchable path entries. For example, if you wanted to support path entries as network URLs, you could write a hook that implements HTTP semantics to find modules on the web. This hook (a callable) would return a path entry finder supporting the protocol described below, which was then used to get a loader for the module from the web.

A word of warning: this section and the previous both use the term finder, distinguishing between them by using the terms meta path finder and path entry finder. These two types of finders are very similar, support similar protocols, and function in similar ways during the import process, but it’s important to keep in mind that they are subtly different. In particular, meta path finders operate at the beginning of the import process, as keyed off the sys.meta_path traversal.

By contrast, path entry finders are in a sense an implementation detail of the path based finder, and in fact, if the path based finder were to be removed from sys.meta_path, none of the path entry finder semantics would be invoked.

+5.5.1. Path entry finders

The path based finder is responsible for finding and loading Python modules and packages whose location is specified with a string path entry. Most path entries name locations in the file system, but they need not be limited to this.

As a meta path finder, the path based finder implements the find_spec() protocol previously described, however it exposes additional hooks that can be used to customize how modules are found and loaded from the import path.

Three variables are used by the path based finder, sys.path, sys.path_hooks and sys.path_importer_cache. The __path__ attributes on package objects are also used. These provide additional ways that the import machinery can be customized.

sys.path contains a list of strings providing search locations for modules and packages. It is initialized from the PYTHONPATH environment variable and various other installation- and implementation-specific defaults. Entries in sys.path can name directories on the file system, zip files, and potentially other “locations” (see the site module) that should be searched for modules, such as URLs, or database queries. Only strings should be present on sys.path; all other data types are ignored.

The path based finder is a meta path finder, so the import machinery begins the import path search by calling the path based finder’s find_spec() method as described previously. When the path argument to find_spec() is given, it will be a list of string paths to traverse - typically a package’s __path__ attribute for an import within that package. If the path argument is None, this indicates a top level import and sys.path is used.

The path based finder iterates over every entry in the search path, and for each of these, looks for an appropriate path entry finder (PathEntryFinder) for the path entry. Because this can be an expensive operation (e.g. there may be stat() call overheads for this search), the path based finder maintains a cache mapping path entries to path entry finders. This cache is maintained in sys.path_importer_cache (despite the name, this cache actually stores finder objects rather than being limited to importer objects). In this way, the expensive search for a particular path entry location’s path entry finder need only be done once. User code is free to remove cache entries from sys.path_importer_cache forcing the path based finder to perform the path entry search again.

If the path entry is not present in the cache, the path based finder iterates over every callable in sys.path_hooks. Each of the path entry hooks in this list is called with a single argument, the path entry to be searched. This callable may either return a path entry finder that can handle the path entry, or it may raise ImportError. An ImportError is used by the path based finder to signal that the hook cannot find a path entry finder for that path entry. The exception is ignored and import path iteration continues. The hook should expect either a string or bytes object; the encoding of bytes objects is up to the hook (e.g. it may be a file system encoding, UTF-8, or something else), and if the hook cannot decode the argument, it should raise ImportError.

If sys.path_hooks iteration ends with no path entry finder being returned, then the path based finder’s find_spec() method will store None in sys.path_importer_cache (to indicate that there is no finder for this path entry) and return None, indicating that this meta path finder could not find the module.

If a path entry finder is returned by one of the path entry hook callables on sys.path_hooks, then the following protocol is used to ask the finder for a module spec, which is then used when loading the module.

The current working directory – denoted by an empty string – is handled slightly differently from other entries on sys.path. First, if the current working directory is found to not exist, no value is stored in sys.path_importer_cache. Second, the value for the current working directory is looked up fresh for each module lookup. Third, the path used for sys.path_importer_cache and returned by importlib.machinery.PathFinder.find_spec() will be the actual current working directory and not the empty string.

+5.5.2. Path entry finder protocol

In order to support imports of modules and initialized packages and also to contribute portions to namespace packages, path entry finders must implement the find_spec() method.

find_spec() takes two arguments: the fully qualified name of the module being imported, and the (optional) target module. find_spec() returns a fully populated spec for the module. This spec will always have “loader” set (with one exception).

To indicate to the import machinery that the spec represents a namespace portion, the path entry finder sets submodule_search_locations to a list containing the portion.

Changed in version 3.4: find_spec() replaced find_loader() and find_module(), both of which are now deprecated, but will be used if find_spec() is not defined.

Older path entry finders may implement one of these two deprecated methods instead of find_spec(). The methods are still respected for the sake of backward compatibility. However, if find_spec() is implemented on the path entry finder, the legacy methods are ignored.

find_loader() takes one argument, the fully qualified name of the module being imported. find_loader() returns a 2-tuple where the first item is the loader and the second item is a namespace portion.

For backwards compatibility with other implementations of the import protocol, many path entry finders also support the same, traditional find_module() method that meta path finders support. However path entry finder find_module() methods are never called with a path argument (they are expected to record the appropriate path information from the initial call to the path hook).

The find_module() method on path entry finders is deprecated, as it does not allow the path entry finder to contribute portions to namespace packages. If both find_loader() and find_module() exist on a path entry finder, the import system will always call find_loader() in preference to find_module().

Changed in version 3.10: Calls to find_module() and find_loader() by the import system will raise ImportWarning.

Changed in version 3.12: find_module() and find_loader() have been removed.

+5.6. Replacing the standard import system

The most reliable mechanism for replacing the entire import system is to delete the default contents of sys.meta_path, replacing them entirely with a custom meta path hook.

If it is acceptable to only alter the behaviour of import statements without affecting other APIs that access the import system, then replacing the builtin __import__() function may be sufficient. This technique may also be employed at the module level to only alter the behaviour of import statements within that module.

To selectively prevent the import of some modules from a hook early on the meta path (rather than disabling the standard import system entirely), it is sufficient to raise ModuleNotFoundError directly from find_spec() instead of returning None. The latter indicates that the meta path search should continue, while raising an exception terminates it immediately.

+5.7. Package Relative Imports

Relative imports use leading dots. A single leading dot indicates a relative import, starting with the current package. Two or more leading dots indicate a relative import to the parent(s) of the current package, one level per dot after the first. For example, given the following package layout:

package/
+    __init__.py
+    subpackage1/
+        __init__.py
+        moduleX.py
+        moduleY.py
+    subpackage2/
+        __init__.py
+        moduleZ.py
+    moduleA.py
+

In either subpackage1/moduleX.py or subpackage1/__init__.py, the following are valid relative imports:

from .moduleY import spam
+from .moduleY import spam as ham
+from . import moduleY
+from ..subpackage1 import moduleY
+from ..subpackage2.moduleZ import eggs
+from ..moduleA import foo
+

Absolute imports may use either the import <> or from <> import <> syntax, but relative imports may only use the second form; the reason for this is that:

import XXX.YYY.ZZZ
+

should expose XXX.YYY.ZZZ as a usable expression, but .moduleY is not a valid expression.

+5.8. Special considerations for __main__

The __main__ module is a special case relative to Python’s import system. As noted elsewhere, the __main__ module is directly initialized at interpreter startup, much like sys and builtins. However, unlike those two, it doesn’t strictly qualify as a built-in module. This is because the manner in which __main__ is initialized depends on the flags and other options with which the interpreter is invoked.

+5.8.1. __main__.__spec__

Depending on how __main__ is initialized, __main__.__spec__ gets set appropriately or to None.

When Python is started with the -m option, __spec__ is set to the module spec of the corresponding module or package. __spec__ is also populated when the __main__ module is loaded as part of executing a directory, zipfile or other sys.path entry.

In the remaining cases __main__.__spec__ is set to None, as the code used to populate the __main__ does not correspond directly with an importable module:

  • interactive prompt
  • +-c option
  • running from stdin
  • running directly from a source or bytecode file

Note that __main__.__spec__ is always None in the last case, even if the file could technically be imported directly as a module instead. Use the -m switch if valid module metadata is desired in __main__.

Note also that even when __main__ corresponds with an importable module and __main__.__spec__ is set accordingly, they’re still considered distinct modules. This is due to the fact that blocks guarded by if __name__ == "__main__": checks only execute when the module is used to populate the __main__ namespace, and not during normal import.

+5.9. References

The import machinery has evolved considerably since Python’s early days. The original specification for packages is still available to read, although some details have changed since the writing of that document.

The original specification for sys.meta_path was PEP 302, with subsequent extension in PEP 420.

PEP 420 introduced namespace packages for Python 3.3. PEP 420 also introduced the find_loader() protocol as an alternative to find_module().

PEP 366 describes the addition of the __package__ attribute for explicit relative imports in main modules.

PEP 328 introduced absolute and explicit relative imports and initially proposed __name__ for semantics PEP 366 would eventually specify for __package__.

PEP 338 defines executing modules as scripts.

PEP 451 adds the encapsulation of per-module import state in spec objects. It also off-loads most of the boilerplate responsibilities of loaders back onto the import machinery. These changes allow the deprecation of several APIs in the import system and also addition of new methods to finders and loaders.

Footnotes

+1
+

See types.ModuleType.

+2
+

The importlib implementation avoids using the return value directly. Instead, it gets the module object by looking the module name up in sys.modules. The indirect effect of this is that an imported module may replace itself in sys.modules. This is implementation-specific behavior that is not guaranteed to work in other Python implementations.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/import.html +

+
diff --git a/devdocs/python~3.12/reference%2Findex.html b/devdocs/python~3.12/reference%2Findex.html new file mode 100644 index 00000000..edd51ead --- /dev/null +++ b/devdocs/python~3.12/reference%2Findex.html @@ -0,0 +1,15 @@ +

The Python Language Reference

This reference manual describes the syntax and “core semantics” of the language. It is terse, but attempts to be exact and complete. The semantics of non-essential built-in object types and of the built-in functions and modules are described in The Python Standard Library. For an informal introduction to the language, see The Python Tutorial. For C or C++ programmers, two additional manuals exist: Extending and Embedding the Python Interpreter describes the high-level picture of how to write a Python extension module, and the Python/C API Reference Manual describes the interfaces available to C/C++ programmers in detail.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/index.html +

+
diff --git a/devdocs/python~3.12/reference%2Fintroduction.html b/devdocs/python~3.12/reference%2Fintroduction.html new file mode 100644 index 00000000..8ba5173f --- /dev/null +++ b/devdocs/python~3.12/reference%2Fintroduction.html @@ -0,0 +1,21 @@ +

Introduction

This reference manual describes the Python programming language. It is not intended as a tutorial.

While I am trying to be as precise as possible, I chose to use English rather than formal specifications for everything except syntax and lexical analysis. This should make the document more understandable to the average reader, but will leave room for ambiguities. Consequently, if you were coming from Mars and tried to re-implement Python from this document alone, you might have to guess things and in fact you would probably end up implementing quite a different language. On the other hand, if you are using Python and wonder what the precise rules about a particular area of the language are, you should definitely be able to find them here. If you would like to see a more formal definition of the language, maybe you could volunteer your time — or invent a cloning machine :-).

It is dangerous to add too many implementation details to a language reference document — the implementation may change, and other implementations of the same language may work differently. On the other hand, CPython is the one Python implementation in widespread use (although alternate implementations continue to gain support), and its particular quirks are sometimes worth being mentioned, especially where the implementation imposes additional limitations. Therefore, you’ll find short “implementation notes” sprinkled throughout the text.

Every Python implementation comes with a number of built-in and standard modules. These are documented in The Python Standard Library. A few built-in modules are mentioned when they interact in a significant way with the language definition.

+1.1. Alternate Implementations

Though there is one Python implementation which is by far the most popular, there are some alternate implementations which are of particular interest to different audiences.

Known implementations include:

CPython
+
+

This is the original and most-maintained implementation of Python, written in C. New language features generally appear here first.

Jython
+
+

Python implemented in Java. This implementation can be used as a scripting language for Java applications, or can be used to create applications using the Java class libraries. It is also often used to create tests for Java libraries. More information can be found at the Jython website.

Python for .NET
+
+

This implementation actually uses the CPython implementation, but is a managed .NET application and makes .NET libraries available. It was created by Brian Lloyd. For more information, see the Python for .NET home page.

IronPython
+
+

An alternate Python for .NET. Unlike Python.NET, this is a complete Python implementation that generates IL, and compiles Python code directly to .NET assemblies. It was created by Jim Hugunin, the original creator of Jython. For more information, see the IronPython website.

PyPy
+
+

An implementation of Python written completely in Python. It supports several advanced features not found in other implementations like stackless support and a Just in Time compiler. One of the goals of the project is to encourage experimentation with the language itself by making it easier to modify the interpreter (since it is written in Python). Additional information is available on the PyPy project’s home page.

Each of these implementations varies in some way from the language as documented in this manual, or introduces specific information beyond what’s covered in the standard Python documentation. Please refer to the implementation-specific documentation to determine what else you need to know about the specific implementation you’re using.

+1.2. Notation

The descriptions of lexical analysis and syntax use a modified Backus–Naur form (BNF) grammar notation. This uses the following style of definition:

+name      ::=  lc_letter (lc_letter | "_")*
+lc_letter ::=  "a"..."z"
+

The first line says that a name is an lc_letter followed by a sequence of zero or more lc_letters and underscores. An lc_letter in turn is any of the single characters 'a' through 'z'. (This rule is actually adhered to for the names defined in lexical and grammar rules in this document.)

Each rule begins with a name (which is the name defined by the rule) and ::=. A vertical bar (|) is used to separate alternatives; it is the least binding operator in this notation. A star (*) means zero or more repetitions of the preceding item; likewise, a plus (+) means one or more repetitions, and a phrase enclosed in square brackets ([ ]) means zero or one occurrences (in other words, the enclosed phrase is optional). The * and + operators bind as tightly as possible; parentheses are used for grouping. Literal strings are enclosed in quotes. White space is only meaningful to separate tokens. Rules are normally contained on a single line; rules with many alternatives may be formatted alternatively with each line after the first beginning with a vertical bar.

In lexical definitions (as the example above), two more conventions are used: Two literal characters separated by three dots mean a choice of any single character in the given (inclusive) range of ASCII characters. A phrase between angular brackets (<...>) gives an informal description of the symbol defined; e.g., this could be used to describe the notion of ‘control character’ if needed.

Even though the notation used is almost the same, there is a big difference between the meaning of lexical and syntactic definitions: a lexical definition operates on the individual characters of the input source, while a syntax definition operates on the stream of tokens generated by the lexical analysis. All uses of BNF in the next chapter (“Lexical Analysis”) are lexical definitions; uses in subsequent chapters are syntactic definitions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/introduction.html +

+
diff --git a/devdocs/python~3.12/reference%2Flexical_analysis.html b/devdocs/python~3.12/reference%2Flexical_analysis.html new file mode 100644 index 00000000..c9356e34 --- /dev/null +++ b/devdocs/python~3.12/reference%2Flexical_analysis.html @@ -0,0 +1,235 @@ +

Lexical analysis

A Python program is read by a parser. Input to the parser is a stream of tokens, generated by the lexical analyzer. This chapter describes how the lexical analyzer breaks a file into tokens.

Python reads program text as Unicode code points; the encoding of a source file can be given by an encoding declaration and defaults to UTF-8, see PEP 3120 for details. If the source file cannot be decoded, a SyntaxError is raised.

+2.1. Line structure

A Python program is divided into a number of logical lines.

+2.1.1. Logical lines

The end of a logical line is represented by the token NEWLINE. Statements cannot cross logical line boundaries except where NEWLINE is allowed by the syntax (e.g., between statements in compound statements). A logical line is constructed from one or more physical lines by following the explicit or implicit line joining rules.

+2.1.2. Physical lines

A physical line is a sequence of characters terminated by an end-of-line sequence. In source files and strings, any of the standard platform line termination sequences can be used - the Unix form using ASCII LF (linefeed), the Windows form using the ASCII sequence CR LF (return followed by linefeed), or the old Macintosh form using the ASCII CR (return) character. All of these forms can be used equally, regardless of platform. The end of input also serves as an implicit terminator for the final physical line.

When embedding Python, source code strings should be passed to Python APIs using the standard C conventions for newline characters (the \n character, representing ASCII LF, is the line terminator).

+2.1.3. Comments

A comment starts with a hash character (#) that is not part of a string literal, and ends at the end of the physical line. A comment signifies the end of the logical line unless the implicit line joining rules are invoked. Comments are ignored by the syntax.

+2.1.4. Encoding declarations

If a comment in the first or second line of the Python script matches the regular expression coding[=:]\s*([-\w.]+), this comment is processed as an encoding declaration; the first group of this expression names the encoding of the source code file. The encoding declaration must appear on a line of its own. If it is the second line, the first line must also be a comment-only line. The recommended forms of an encoding expression are

# -*- coding: <encoding-name> -*-
+

which is recognized also by GNU Emacs, and

# vim:fileencoding=<encoding-name>
+

which is recognized by Bram Moolenaar’s VIM.

If no encoding declaration is found, the default encoding is UTF-8. In addition, if the first bytes of the file are the UTF-8 byte-order mark (b'\xef\xbb\xbf'), the declared file encoding is UTF-8 (this is supported, among others, by Microsoft’s notepad).

If an encoding is declared, the encoding name must be recognized by Python (see Standard Encodings). The encoding is used for all lexical analysis, including string literals, comments and identifiers.

+2.1.5. Explicit line joining

Two or more physical lines may be joined into logical lines using backslash characters (\), as follows: when a physical line ends in a backslash that is not part of a string literal or comment, it is joined with the following forming a single logical line, deleting the backslash and the following end-of-line character. For example:

if 1900 < year < 2100 and 1 <= month <= 12 \
+   and 1 <= day <= 31 and 0 <= hour < 24 \
+   and 0 <= minute < 60 and 0 <= second < 60:   # Looks like a valid date
+        return 1
+

A line ending in a backslash cannot carry a comment. A backslash does not continue a comment. A backslash does not continue a token except for string literals (i.e., tokens other than string literals cannot be split across physical lines using a backslash). A backslash is illegal elsewhere on a line outside a string literal.

+2.1.6. Implicit line joining

Expressions in parentheses, square brackets or curly braces can be split over more than one physical line without using backslashes. For example:

month_names = ['Januari', 'Februari', 'Maart',      # These are the
+               'April',   'Mei',      'Juni',       # Dutch names
+               'Juli',    'Augustus', 'September',  # for the months
+               'Oktober', 'November', 'December']   # of the year
+

Implicitly continued lines can carry comments. The indentation of the continuation lines is not important. Blank continuation lines are allowed. There is no NEWLINE token between implicit continuation lines. Implicitly continued lines can also occur within triple-quoted strings (see below); in that case they cannot carry comments.

+2.1.7. Blank lines

A logical line that contains only spaces, tabs, formfeeds and possibly a comment, is ignored (i.e., no NEWLINE token is generated). During interactive input of statements, handling of a blank line may differ depending on the implementation of the read-eval-print loop. In the standard interactive interpreter, an entirely blank logical line (i.e. one containing not even whitespace or a comment) terminates a multi-line statement.

+2.1.8. Indentation

Leading whitespace (spaces and tabs) at the beginning of a logical line is used to compute the indentation level of the line, which in turn is used to determine the grouping of statements.

Tabs are replaced (from left to right) by one to eight spaces such that the total number of characters up to and including the replacement is a multiple of eight (this is intended to be the same rule as used by Unix). The total number of spaces preceding the first non-blank character then determines the line’s indentation. Indentation cannot be split over multiple physical lines using backslashes; the whitespace up to the first backslash determines the indentation.

Indentation is rejected as inconsistent if a source file mixes tabs and spaces in a way that makes the meaning dependent on the worth of a tab in spaces; a TabError is raised in that case.

Cross-platform compatibility note: because of the nature of text editors on non-UNIX platforms, it is unwise to use a mixture of spaces and tabs for the indentation in a single source file. It should also be noted that different platforms may explicitly limit the maximum indentation level.

A formfeed character may be present at the start of the line; it will be ignored for the indentation calculations above. Formfeed characters occurring elsewhere in the leading whitespace have an undefined effect (for instance, they may reset the space count to zero).

The indentation levels of consecutive lines are used to generate INDENT and DEDENT tokens, using a stack, as follows.

Before the first line of the file is read, a single zero is pushed on the stack; this will never be popped off again. The numbers pushed on the stack will always be strictly increasing from bottom to top. At the beginning of each logical line, the line’s indentation level is compared to the top of the stack. If it is equal, nothing happens. If it is larger, it is pushed on the stack, and one INDENT token is generated. If it is smaller, it must be one of the numbers occurring on the stack; all numbers on the stack that are larger are popped off, and for each number popped off a DEDENT token is generated. At the end of the file, a DEDENT token is generated for each number remaining on the stack that is larger than zero.

Here is an example of a correctly (though confusingly) indented piece of Python code:

def perm(l):
+        # Compute the list of all permutations of l
+    if len(l) <= 1:
+                  return [l]
+    r = []
+    for i in range(len(l)):
+             s = l[:i] + l[i+1:]
+             p = perm(s)
+             for x in p:
+              r.append(l[i:i+1] + x)
+    return r
+

The following example shows various indentation errors:

 def perm(l):                       # error: first line indented
+for i in range(len(l)):             # error: not indented
+    s = l[:i] + l[i+1:]
+        p = perm(l[:i] + l[i+1:])   # error: unexpected indent
+        for x in p:
+                r.append(l[i:i+1] + x)
+            return r                # error: inconsistent dedent
+

(Actually, the first three errors are detected by the parser; only the last error is found by the lexical analyzer — the indentation of return r does not match a level popped off the stack.)

+2.1.9. Whitespace between tokens

Except at the beginning of a logical line or in string literals, the whitespace characters space, tab and formfeed can be used interchangeably to separate tokens. Whitespace is needed between two tokens only if their concatenation could otherwise be interpreted as a different token (e.g., ab is one token, but a b is two tokens).

+2.2. Other tokens

Besides NEWLINE, INDENT and DEDENT, the following categories of tokens exist: identifiers, keywords, literals, operators, and delimiters. Whitespace characters (other than line terminators, discussed earlier) are not tokens, but serve to delimit tokens. Where ambiguity exists, a token comprises the longest possible string that forms a legal token, when read from left to right.

+2.3. Identifiers and keywords

Identifiers (also referred to as names) are described by the following lexical definitions.

The syntax of identifiers in Python is based on the Unicode standard annex UAX-31, with elaboration and changes as defined below; see also PEP 3131 for further details.

Within the ASCII range (U+0001..U+007F), the valid characters for identifiers are the same as in Python 2.x: the uppercase and lowercase letters A through Z, the underscore _ and, except for the first character, the digits 0 through 9.

Python 3.0 introduces additional characters from outside the ASCII range (see PEP 3131). For these characters, the classification uses the version of the Unicode Character Database as included in the unicodedata module.

Identifiers are unlimited in length. Case is significant.

+identifier   ::=  xid_start xid_continue*
+id_start     ::=  <all characters in general categories Lu, Ll, Lt, Lm, Lo, Nl, the underscore, and characters with the Other_ID_Start property>
+id_continue  ::=  <all characters in id_start, plus characters in the categories Mn, Mc, Nd, Pc and others with the Other_ID_Continue property>
+xid_start    ::=  <all characters in id_start whose NFKC normalization is in "id_start xid_continue*">
+xid_continue ::=  <all characters in id_continue whose NFKC normalization is in "id_continue*">
+

The Unicode category codes mentioned above stand for:

  • +Lu - uppercase letters
  • +Ll - lowercase letters
  • +Lt - titlecase letters
  • +Lm - modifier letters
  • +Lo - other letters
  • +Nl - letter numbers
  • +Mn - nonspacing marks
  • +Mc - spacing combining marks
  • +Nd - decimal numbers
  • +Pc - connector punctuations
  • +Other_ID_Start - explicit list of characters in PropList.txt to support backwards compatibility
  • +Other_ID_Continue - likewise

All identifiers are converted into the normal form NFKC while parsing; comparison of identifiers is based on NFKC.

A non-normative HTML file listing all valid identifier characters for Unicode 15.0.0 can be found at https://www.unicode.org/Public/15.0.0/ucd/DerivedCoreProperties.txt

+2.3.1. Keywords

The following identifiers are used as reserved words, or keywords of the language, and cannot be used as ordinary identifiers. They must be spelled exactly as written here:

False      await      else       import     pass
+None       break      except     in         raise
+True       class      finally    is         return
+and        continue   for        lambda     try
+as         def        from       nonlocal   while
+assert     del        global     not        with
+async      elif       if         or         yield
+

+2.3.2. Soft Keywords

New in version 3.10.

Some identifiers are only reserved under specific contexts. These are known as soft keywords. The identifiers match, case, type and _ can syntactically act as keywords in certain contexts, but this distinction is done at the parser level, not when tokenizing.

As soft keywords, their use in the grammar is possible while still preserving compatibility with existing code that uses these names as identifier names.

match, case, and _ are used in the match statement. type is used in the type statement.

Changed in version 3.12: type is now a soft keyword.

+2.3.3. Reserved classes of identifiers

Certain classes of identifiers (besides keywords) have special meanings. These classes are identified by the patterns of leading and trailing underscore characters:

+_*
+
+

Not imported by from module import *.

+_
+
+

In a case pattern within a match statement, _ is a soft keyword that denotes a wildcard.

Separately, the interactive interpreter makes the result of the last evaluation available in the variable _. (It is stored in the builtins module, alongside built-in functions like print.)

Elsewhere, _ is a regular identifier. It is often used to name “special” items, but it is not special to Python itself.

Note

The name _ is often used in conjunction with internationalization; refer to the documentation for the gettext module for more information on this convention.

It is also commonly used for unused variables.

+__*__
+
+

System-defined names, informally known as “dunder” names. These names are defined by the interpreter and its implementation (including the standard library). Current system names are discussed in the Special method names section and elsewhere. More will likely be defined in future versions of Python. Any use of __*__ names, in any context, that does not follow explicitly documented use, is subject to breakage without warning.

+__*
+
+

Class-private names. Names in this category, when used within the context of a class definition, are re-written to use a mangled form to help avoid name clashes between “private” attributes of base and derived classes. See section Identifiers (Names).

+2.4. Literals

Literals are notations for constant values of some built-in types.

+2.4.1. String and Bytes literals

String literals are described by the following lexical definitions:

+stringliteral   ::=  [stringprefix](shortstring | longstring)
+stringprefix    ::=  "r" | "u" | "R" | "U" | "f" | "F"
+                     | "fr" | "Fr" | "fR" | "FR" | "rf" | "rF" | "Rf" | "RF"
+shortstring     ::=  "'" shortstringitem* "'" | '"' shortstringitem* '"'
+longstring      ::=  "'''" longstringitem* "'''" | '"""' longstringitem* '"""'
+shortstringitem ::=  shortstringchar | stringescapeseq
+longstringitem  ::=  longstringchar | stringescapeseq
+shortstringchar ::=  <any source character except "\" or newline or the quote>
+longstringchar  ::=  <any source character except "\">
+stringescapeseq ::=  "\" <any source character>
+
+bytesliteral   ::=  bytesprefix(shortbytes | longbytes)
+bytesprefix    ::=  "b" | "B" | "br" | "Br" | "bR" | "BR" | "rb" | "rB" | "Rb" | "RB"
+shortbytes     ::=  "'" shortbytesitem* "'" | '"' shortbytesitem* '"'
+longbytes      ::=  "'''" longbytesitem* "'''" | '"""' longbytesitem* '"""'
+shortbytesitem ::=  shortbyteschar | bytesescapeseq
+longbytesitem  ::=  longbyteschar | bytesescapeseq
+shortbyteschar ::=  <any ASCII character except "\" or newline or the quote>
+longbyteschar  ::=  <any ASCII character except "\">
+bytesescapeseq ::=  "\" <any ASCII character>
+

One syntactic restriction not indicated by these productions is that whitespace is not allowed between the stringprefix or bytesprefix and the rest of the literal. The source character set is defined by the encoding declaration; it is UTF-8 if no encoding declaration is given in the source file; see section Encoding declarations.

In plain English: Both types of literals can be enclosed in matching single quotes (') or double quotes ("). They can also be enclosed in matching groups of three single or double quotes (these are generally referred to as triple-quoted strings). The backslash (\) character is used to give special meaning to otherwise ordinary characters like n, which means ‘newline’ when escaped (\n). It can also be used to escape characters that otherwise have a special meaning, such as newline, backslash itself, or the quote character. See escape sequences below for examples.

Bytes literals are always prefixed with 'b' or 'B'; they produce an instance of the bytes type instead of the str type. They may only contain ASCII characters; bytes with a numeric value of 128 or greater must be expressed with escapes.

Both string and bytes literals may optionally be prefixed with a letter 'r' or 'R'; such strings are called raw strings and treat backslashes as literal characters. As a result, in string literals, '\U' and '\u' escapes in raw strings are not treated specially. Given that Python 2.x’s raw unicode literals behave differently than Python 3.x’s the 'ur' syntax is not supported.

New in version 3.3: The 'rb' prefix of raw bytes literals has been added as a synonym of 'br'.

New in version 3.3: Support for the unicode legacy literal (u'value') was reintroduced to simplify the maintenance of dual Python 2.x and 3.x codebases. See PEP 414 for more information.

A string literal with 'f' or 'F' in its prefix is a formatted string literal; see f-strings. The 'f' may be combined with 'r', but not with 'b' or 'u', therefore raw formatted strings are possible, but formatted bytes literals are not.

In triple-quoted literals, unescaped newlines and quotes are allowed (and are retained), except that three unescaped quotes in a row terminate the literal. (A “quote” is the character used to open the literal, i.e. either ' or ".)

+2.4.1.1. Escape sequences

Unless an 'r' or 'R' prefix is present, escape sequences in string and bytes literals are interpreted according to rules similar to those used by Standard C. The recognized escape sequences are:

+ + + + + + + + + + + + + +

Escape Sequence

Meaning

Notes

\<newline>

Backslash and newline ignored

(1)

\\

Backslash (\)

\'

Single quote (')

\"

Double quote (")

\a

ASCII Bell (BEL)

\b

ASCII Backspace (BS)

\f

ASCII Formfeed (FF)

\n

ASCII Linefeed (LF)

\r

ASCII Carriage Return (CR)

\t

ASCII Horizontal Tab (TAB)

\v

ASCII Vertical Tab (VT)

\ooo

Character with octal value ooo

(2,4)

\xhh

Character with hex value hh

(3,4)

Escape sequences only recognized in string literals are:

+ + + +

Escape Sequence

Meaning

Notes

\N{name}

Character named name in the Unicode database

(5)

\uxxxx

Character with 16-bit hex value xxxx

(6)

\Uxxxxxxxx

Character with 32-bit hex value xxxxxxxx

(7)

Notes:

  1. +

    A backslash can be added at the end of a line to ignore the newline:

    >>> 'This string will not include \
+... backslashes or newline characters.'
+'This string will not include backslashes or newline characters.'
+

    The same result can be achieved using triple-quoted strings, or parentheses and string literal concatenation.

  2. +

    As in Standard C, up to three octal digits are accepted.

    Changed in version 3.11: Octal escapes with value larger than 0o377 produce a DeprecationWarning.

    Changed in version 3.12: Octal escapes with value larger than 0o377 produce a SyntaxWarning. In a future Python version they will be eventually a SyntaxError.

  3. Unlike in Standard C, exactly two hex digits are required.
  4. In a bytes literal, hexadecimal and octal escapes denote the byte with the given value. In a string literal, these escapes denote a Unicode character with the given value.
  5. +

    Changed in version 3.3: Support for name aliases 1 has been added.

  6. Exactly four hex digits are required.
  7. Any Unicode character can be encoded this way. Exactly eight hex digits are required.

Unlike Standard C, all unrecognized escape sequences are left in the string unchanged, i.e., the backslash is left in the result. (This behavior is useful when debugging: if an escape sequence is mistyped, the resulting output is more easily recognized as broken.) It is also important to note that the escape sequences only recognized in string literals fall into the category of unrecognized escapes for bytes literals.

Changed in version 3.6: Unrecognized escape sequences produce a DeprecationWarning.

Changed in version 3.12: Unrecognized escape sequences produce a SyntaxWarning. In a future Python version they will be eventually a SyntaxError.

Even in a raw literal, quotes can be escaped with a backslash, but the backslash remains in the result; for example, r"\"" is a valid string literal consisting of two characters: a backslash and a double quote; r"\" is not a valid string literal (even a raw string cannot end in an odd number of backslashes). Specifically, a raw literal cannot end in a single backslash (since the backslash would escape the following quote character). Note also that a single backslash followed by a newline is interpreted as those two characters as part of the literal, not as a line continuation.

+2.4.2. String literal concatenation

Multiple adjacent string or bytes literals (delimited by whitespace), possibly using different quoting conventions, are allowed, and their meaning is the same as their concatenation. Thus, "hello" 'world' is equivalent to "helloworld". This feature can be used to reduce the number of backslashes needed, to split long strings conveniently across long lines, or even to add comments to parts of strings, for example:

re.compile("[A-Za-z_]"       # letter or underscore
+           "[A-Za-z0-9_]*"   # letter, digit or underscore
+          )
+

Note that this feature is defined at the syntactical level, but implemented at compile time. The ‘+’ operator must be used to concatenate string expressions at run time. Also note that literal concatenation can use different quoting styles for each component (even mixing raw strings and triple quoted strings), and formatted string literals may be concatenated with plain string literals.

+2.4.3. f-strings

New in version 3.6.

A formatted string literal or f-string is a string literal that is prefixed with 'f' or 'F'. These strings may contain replacement fields, which are expressions delimited by curly braces {}. While other string literals always have a constant value, formatted strings are really expressions evaluated at run time.

Escape sequences are decoded like in ordinary string literals (except when a literal is also marked as a raw string). After decoding, the grammar for the contents of the string is:

+f_string          ::=  (literal_char | "{{" | "}}" | replacement_field)*
+replacement_field ::=  "{" f_expression ["="] ["!" conversion] [":" format_spec] "}"
+f_expression      ::=  (conditional_expression | "*" or_expr)
+                         ("," conditional_expression | "," "*" or_expr)* [","]
+                       | yield_expression
+conversion        ::=  "s" | "r" | "a"
+format_spec       ::=  (literal_char | NULL | replacement_field)*
+literal_char      ::=  <any code point except "{", "}" or NULL>
+

The parts of the string outside curly braces are treated literally, except that any doubled curly braces '{{' or '}}' are replaced with the corresponding single curly brace. A single opening curly bracket '{' marks a replacement field, which starts with a Python expression. To display both the expression text and its value after evaluation, (useful in debugging), an equal sign '=' may be added after the expression. A conversion field, introduced by an exclamation point '!' may follow. A format specifier may also be appended, introduced by a colon ':'. A replacement field ends with a closing curly bracket '}'.

Expressions in formatted string literals are treated like regular Python expressions surrounded by parentheses, with a few exceptions. An empty expression is not allowed, and both lambda and assignment expressions := must be surrounded by explicit parentheses. Each expression is evaluated in the context where the formatted string literal appears, in order from left to right. Replacement expressions can contain newlines in both single-quoted and triple-quoted f-strings and they can contain comments. Everything that comes after a # inside a replacement field is a comment (even closing braces and quotes). In that case, replacement fields must be closed in a different line.

>>> f"abc{a # This is a comment }"
+... + 3}"
+'abc5'
+

Changed in version 3.7: Prior to Python 3.7, an await expression and comprehensions containing an async for clause were illegal in the expressions in formatted string literals due to a problem with the implementation.

Changed in version 3.12: Prior to Python 3.12, comments were not allowed inside f-string replacement fields.

When the equal sign '=' is provided, the output will have the expression text, the '=' and the evaluated value. Spaces after the opening brace '{', within the expression and after the '=' are all retained in the output. By default, the '=' causes the repr() of the expression to be provided, unless there is a format specified. When a format is specified it defaults to the str() of the expression unless a conversion '!r' is declared.

New in version 3.8: The equal sign '='.

If a conversion is specified, the result of evaluating the expression is converted before formatting. Conversion '!s' calls str() on the result, '!r' calls repr(), and '!a' calls ascii().

The result is then formatted using the format() protocol. The format specifier is passed to the __format__() method of the expression or conversion result. An empty string is passed when the format specifier is omitted. The formatted result is then included in the final value of the whole string.

Top-level format specifiers may include nested replacement fields. These nested fields may include their own conversion fields and format specifiers, but may not include more deeply nested replacement fields. The format specifier mini-language is the same as that used by the str.format() method.

Formatted string literals may be concatenated, but replacement fields cannot be split across literals.

Some examples of formatted string literals:

>>> name = "Fred"
+>>> f"He said his name is {name!r}."
+"He said his name is 'Fred'."
+>>> f"He said his name is {repr(name)}."  # repr() is equivalent to !r
+"He said his name is 'Fred'."
+>>> width = 10
+>>> precision = 4
+>>> value = decimal.Decimal("12.34567")
+>>> f"result: {value:{width}.{precision}}"  # nested fields
+'result:      12.35'
+>>> today = datetime(year=2017, month=1, day=27)
+>>> f"{today:%B %d, %Y}"  # using date format specifier
+'January 27, 2017'
+>>> f"{today=:%B %d, %Y}" # using date format specifier and debugging
+'today=January 27, 2017'
+>>> number = 1024
+>>> f"{number:#0x}"  # using integer format specifier
+'0x400'
+>>> foo = "bar"
+>>> f"{ foo = }" # preserves whitespace
+" foo = 'bar'"
+>>> line = "The mill's closed"
+>>> f"{line = }"
+'line = "The mill\'s closed"'
+>>> f"{line = :20}"
+"line = The mill's closed   "
+>>> f"{line = !r:20}"
+'line = "The mill\'s closed" '
+

Reusing the outer f-string quoting type inside a replacement field is permitted:

>>> a = dict(x=2)
+>>> f"abc {a["x"]} def"
+'abc 2 def'
+

Changed in version 3.12: Prior to Python 3.12, reuse of the same quoting type of the outer f-string inside a replacement field was not possible.

Backslashes are also allowed in replacement fields and are evaluated the same way as in any other context:

>>> a = ["a", "b", "c"]
+>>> print(f"List a contains:\n{"\n".join(a)}")
+List a contains:
+a
+b
+c
+

Changed in version 3.12: Prior to Python 3.12, backslashes were not permitted inside an f-string replacement field.

Formatted string literals cannot be used as docstrings, even if they do not include expressions.

>>> def foo():
+...     f"Not a docstring"
+...
+>>> foo.__doc__ is None
+True
+

See also PEP 498 for the proposal that added formatted string literals, and str.format(), which uses a related format string mechanism.

+2.4.4. Numeric literals

There are three types of numeric literals: integers, floating point numbers, and imaginary numbers. There are no complex literals (complex numbers can be formed by adding a real number and an imaginary number).

Note that numeric literals do not include a sign; a phrase like -1 is actually an expression composed of the unary operator ‘-’ and the literal 1.

+2.4.5. Integer literals

Integer literals are described by the following lexical definitions:

+integer      ::=  decinteger | bininteger | octinteger | hexinteger
+decinteger   ::=  nonzerodigit (["_"] digit)* | "0"+ (["_"] "0")*
+bininteger   ::=  "0" ("b" | "B") (["_"] bindigit)+
+octinteger   ::=  "0" ("o" | "O") (["_"] octdigit)+
+hexinteger   ::=  "0" ("x" | "X") (["_"] hexdigit)+
+nonzerodigit ::=  "1"..."9"
+digit        ::=  "0"..."9"
+bindigit     ::=  "0" | "1"
+octdigit     ::=  "0"..."7"
+hexdigit     ::=  digit | "a"..."f" | "A"..."F"
+

There is no limit for the length of integer literals apart from what can be stored in available memory.

Underscores are ignored for determining the numeric value of the literal. They can be used to group digits for enhanced readability. One underscore can occur between digits, and after base specifiers like 0x.

Note that leading zeros in a non-zero decimal number are not allowed. This is for disambiguation with C-style octal literals, which Python used before version 3.0.

Some examples of integer literals:

7     2147483647                        0o177    0b100110111
+3     79228162514264337593543950336     0o377    0xdeadbeef
+      100_000_000_000                   0b_1110_0101
+

Changed in version 3.6: Underscores are now allowed for grouping purposes in literals.

+2.4.6. Floating point literals

Floating point literals are described by the following lexical definitions:

+floatnumber   ::=  pointfloat | exponentfloat
+pointfloat    ::=  [digitpart] fraction | digitpart "."
+exponentfloat ::=  (digitpart | pointfloat) exponent
+digitpart     ::=  digit (["_"] digit)*
+fraction      ::=  "." digitpart
+exponent      ::=  ("e" | "E") ["+" | "-"] digitpart
+

Note that the integer and exponent parts are always interpreted using radix 10. For example, 077e010 is legal, and denotes the same number as 77e10. The allowed range of floating point literals is implementation-dependent. As in integer literals, underscores are supported for digit grouping.

Some examples of floating point literals:

3.14    10.    .001    1e100    3.14e-10    0e0    3.14_15_93
+

Changed in version 3.6: Underscores are now allowed for grouping purposes in literals.

+2.4.7. Imaginary literals

Imaginary literals are described by the following lexical definitions:

+imagnumber ::=  (floatnumber | digitpart) ("j" | "J")
+

An imaginary literal yields a complex number with a real part of 0.0. Complex numbers are represented as a pair of floating point numbers and have the same restrictions on their range. To create a complex number with a nonzero real part, add a floating point number to it, e.g., (3+4j). Some examples of imaginary literals:

3.14j   10.j    10j     .001j   1e100j   3.14e-10j   3.14_15_93j
+

+2.5. Operators

The following tokens are operators:

+       -       *       **      /       //      %      @
+<<      >>      &       |       ^       ~       :=
+<       >       <=      >=      ==      !=
+

+2.6. Delimiters

The following tokens serve as delimiters in the grammar:

(       )       [       ]       {       }
+,       :       .       ;       @       =       ->
++=      -=      *=      /=      //=     %=      @=
+&=      |=      ^=      >>=     <<=     **=
+

The period can also occur in floating-point and imaginary literals. A sequence of three periods has a special meaning as an ellipsis literal. The second half of the list, the augmented assignment operators, serve lexically as delimiters, but also perform an operation.

The following printing ASCII characters have special meaning as part of other tokens or are otherwise significant to the lexical analyzer:

'       "       #       \
+

The following printing ASCII characters are not used in Python. Their occurrence outside string literals and comments is an unconditional error:

$       ?       `
+

Footnotes

+1
+

https://www.unicode.org/Public/15.0.0/ucd/NameAliases.txt

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/lexical_analysis.html +

+
diff --git a/devdocs/python~3.12/reference%2Fsimple_stmts.html b/devdocs/python~3.12/reference%2Fsimple_stmts.html new file mode 100644 index 00000000..e77c7ab5 --- /dev/null +++ b/devdocs/python~3.12/reference%2Fsimple_stmts.html @@ -0,0 +1,196 @@ +

Simple statements

A simple statement is comprised within a single logical line. Several simple statements may occur on a single line separated by semicolons. The syntax for simple statements is:

+simple_stmt ::=  expression_stmt
+                 | assert_stmt
+                 | assignment_stmt
+                 | augmented_assignment_stmt
+                 | annotated_assignment_stmt
+                 | pass_stmt
+                 | del_stmt
+                 | return_stmt
+                 | yield_stmt
+                 | raise_stmt
+                 | break_stmt
+                 | continue_stmt
+                 | import_stmt
+                 | future_stmt
+                 | global_stmt
+                 | nonlocal_stmt
+                 | type_stmt
+

+7.1. Expression statements

Expression statements are used (mostly interactively) to compute and write a value, or (usually) to call a procedure (a function that returns no meaningful result; in Python, procedures return the value None). Other uses of expression statements are allowed and occasionally useful. The syntax for an expression statement is:

+expression_stmt ::=  starred_expression
+

An expression statement evaluates the expression list (which may be a single expression).

In interactive mode, if the value is not None, it is converted to a string using the built-in repr() function and the resulting string is written to standard output on a line by itself (except if the result is None, so that procedure calls do not cause any output.)

+7.2. Assignment statements

Assignment statements are used to (re)bind names to values and to modify attributes or items of mutable objects:

+assignment_stmt ::=  (target_list "=")+ (starred_expression | yield_expression)
+target_list     ::=  target ("," target)* [","]
+target          ::=  identifier
+                     | "(" [target_list] ")"
+                     | "[" [target_list] "]"
+                     | attributeref
+                     | subscription
+                     | slicing
+                     | "*" target
+

(See section Primaries for the syntax definitions for attributeref, subscription, and slicing.)

An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right.

Assignment is defined recursively depending on the form of the target (list). When a target is part of a mutable object (an attribute reference, subscription or slicing), the mutable object must ultimately perform the assignment and decide about its validity, and may raise an exception if the assignment is unacceptable. The rules observed by various types and the exceptions raised are given with the definition of the object types (see section The standard type hierarchy).

Assignment of an object to a target list, optionally enclosed in parentheses or square brackets, is recursively defined as follows.

  • If the target list is a single target with no trailing comma, optionally in parentheses, the object is assigned to that target.
  • +

    Else:

    • If the target list contains one target prefixed with an asterisk, called a “starred” target: The object must be an iterable with at least as many items as there are targets in the target list, minus one. The first items of the iterable are assigned, from left to right, to the targets before the starred target. The final items of the iterable are assigned to the targets after the starred target. A list of the remaining items in the iterable is then assigned to the starred target (the list can be empty).
    • Else: The object must be an iterable with the same number of items as there are targets in the target list, and the items are assigned, from left to right, to the corresponding targets.

Assignment of an object to a single target is recursively defined as follows.

  • +

    If the target is an identifier (name):

    • If the name does not occur in a global or nonlocal statement in the current code block: the name is bound to the object in the current local namespace.
    • Otherwise: the name is bound to the object in the global namespace or the outer namespace determined by nonlocal, respectively.

    The name is rebound if it was already bound. This may cause the reference count for the object previously bound to the name to reach zero, causing the object to be deallocated and its destructor (if it has one) to be called.

  • +

    If the target is an attribute reference: The primary expression in the reference is evaluated. It should yield an object with assignable attributes; if this is not the case, TypeError is raised. That object is then asked to assign the assigned object to the given attribute; if it cannot perform the assignment, it raises an exception (usually but not necessarily AttributeError).

    Note: If the object is a class instance and the attribute reference occurs on both sides of the assignment operator, the right-hand side expression, a.x can access either an instance attribute or (if no instance attribute exists) a class attribute. The left-hand side target a.x is always set as an instance attribute, creating it if necessary. Thus, the two occurrences of a.x do not necessarily refer to the same attribute: if the right-hand side expression refers to a class attribute, the left-hand side creates a new instance attribute as the target of the assignment:

    class Cls:
+    x = 3             # class variable
+inst = Cls()
+inst.x = inst.x + 1   # writes inst.x as 4 leaving Cls.x as 3
+

    This description does not necessarily apply to descriptor attributes, such as properties created with property().

  • +

    If the target is a subscription: The primary expression in the reference is evaluated. It should yield either a mutable sequence object (such as a list) or a mapping object (such as a dictionary). Next, the subscript expression is evaluated.

    If the primary is a mutable sequence object (such as a list), the subscript must yield an integer. If it is negative, the sequence’s length is added to it. The resulting value must be a nonnegative integer less than the sequence’s length, and the sequence is asked to assign the assigned object to its item with that index. If the index is out of range, IndexError is raised (assignment to a subscripted sequence cannot add new items to a list).

    If the primary is a mapping object (such as a dictionary), the subscript must have a type compatible with the mapping’s key type, and the mapping is then asked to create a key/value pair which maps the subscript to the assigned object. This can either replace an existing key/value pair with the same key value, or insert a new key/value pair (if no key with the same value existed).

    For user-defined objects, the __setitem__() method is called with appropriate arguments.

  • If the target is a slicing: The primary expression in the reference is evaluated. It should yield a mutable sequence object (such as a list). The assigned object should be a sequence object of the same type. Next, the lower and upper bound expressions are evaluated, insofar they are present; defaults are zero and the sequence’s length. The bounds should evaluate to integers. If either bound is negative, the sequence’s length is added to it. The resulting bounds are clipped to lie between zero and the sequence’s length, inclusive. Finally, the sequence object is asked to replace the slice with the items of the assigned sequence. The length of the slice may be different from the length of the assigned sequence, thus changing the length of the target sequence, if the target sequence allows it.

CPython implementation detail: In the current implementation, the syntax for targets is taken to be the same as for expressions, and invalid syntax is rejected during the code generation phase, causing less detailed error messages.

Although the definition of assignment implies that overlaps between the left-hand side and the right-hand side are ‘simultaneous’ (for example a, b = +b, a swaps two variables), overlaps within the collection of assigned-to variables occur left-to-right, sometimes resulting in confusion. For instance, the following program prints [0, 2]:

x = [0, 1]
+i = 0
+i, x[i] = 1, 2         # i is updated, then x[i] is updated
+print(x)
+

See also

+PEP 3132 - Extended Iterable Unpacking
+
+

The specification for the *target feature.

+7.2.1. Augmented assignment statements

Augmented assignment is the combination, in a single statement, of a binary operation and an assignment statement:

+augmented_assignment_stmt ::=  augtarget augop (expression_list | yield_expression)
+augtarget                 ::=  identifier | attributeref | subscription | slicing
+augop                     ::=  "+=" | "-=" | "*=" | "@=" | "/=" | "//=" | "%=" | "**="
+                               | ">>=" | "<<=" | "&=" | "^=" | "|="
+

(See section Primaries for the syntax definitions of the last three symbols.)

An augmented assignment evaluates the target (which, unlike normal assignment statements, cannot be an unpacking) and the expression list, performs the binary operation specific to the type of assignment on the two operands, and assigns the result to the original target. The target is only evaluated once.

An augmented assignment expression like x += 1 can be rewritten as x = x + +1 to achieve a similar, but not exactly equal effect. In the augmented version, x is only evaluated once. Also, when possible, the actual operation is performed in-place, meaning that rather than creating a new object and assigning that to the target, the old object is modified instead.

Unlike normal assignments, augmented assignments evaluate the left-hand side before evaluating the right-hand side. For example, a[i] += f(x) first looks-up a[i], then it evaluates f(x) and performs the addition, and lastly, it writes the result back to a[i].

With the exception of assigning to tuples and multiple targets in a single statement, the assignment done by augmented assignment statements is handled the same way as normal assignments. Similarly, with the exception of the possible in-place behavior, the binary operation performed by augmented assignment is the same as the normal binary operations.

For targets which are attribute references, the same caveat about class and instance attributes applies as for regular assignments.

+7.2.2. Annotated assignment statements

Annotation assignment is the combination, in a single statement, of a variable or attribute annotation and an optional assignment statement:

+annotated_assignment_stmt ::=  augtarget ":" expression
+                               ["=" (starred_expression | yield_expression)]
+

The difference from normal Assignment statements is that only a single target is allowed.

For simple names as assignment targets, if in class or module scope, the annotations are evaluated and stored in a special class or module attribute __annotations__ that is a dictionary mapping from variable names (mangled if private) to evaluated annotations. This attribute is writable and is automatically created at the start of class or module body execution, if annotations are found statically.

For expressions as assignment targets, the annotations are evaluated if in class or module scope, but not stored.

If a name is annotated in a function scope, then this name is local for that scope. Annotations are never evaluated and stored in function scopes.

If the right hand side is present, an annotated assignment performs the actual assignment before evaluating annotations (where applicable). If the right hand side is not present for an expression target, then the interpreter evaluates the target except for the last __setitem__() or __setattr__() call.

See also

+PEP 526 - Syntax for Variable Annotations
+
+

The proposal that added syntax for annotating the types of variables (including class variables and instance variables), instead of expressing them through comments.

+PEP 484 - Type hints
+
+

The proposal that added the typing module to provide a standard syntax for type annotations that can be used in static analysis tools and IDEs.

Changed in version 3.8: Now annotated assignments allow the same expressions in the right hand side as regular assignments. Previously, some expressions (like un-parenthesized tuple expressions) caused a syntax error.

+7.3. The assert statement

Assert statements are a convenient way to insert debugging assertions into a program:

+assert_stmt ::=  "assert" expression ["," expression]
+

The simple form, assert expression, is equivalent to

if __debug__:
+    if not expression: raise AssertionError
+

The extended form, assert expression1, expression2, is equivalent to

if __debug__:
+    if not expression1: raise AssertionError(expression2)
+

These equivalences assume that __debug__ and AssertionError refer to the built-in variables with those names. In the current implementation, the built-in variable __debug__ is True under normal circumstances, False when optimization is requested (command line option -O). The current code generator emits no code for an assert statement when optimization is requested at compile time. Note that it is unnecessary to include the source code for the expression that failed in the error message; it will be displayed as part of the stack trace.

Assignments to __debug__ are illegal. The value for the built-in variable is determined when the interpreter starts.

+7.4. The pass statement

+pass_stmt ::=  "pass"
+

pass is a null operation — when it is executed, nothing happens. It is useful as a placeholder when a statement is required syntactically, but no code needs to be executed, for example:

def f(arg): pass    # a function that does nothing (yet)
+
+class C: pass       # a class with no methods (yet)
+

+7.5. The del statement

+del_stmt ::=  "del" target_list
+

Deletion is recursively defined very similar to the way assignment is defined. Rather than spelling it out in full details, here are some hints.

Deletion of a target list recursively deletes each target, from left to right.

Deletion of a name removes the binding of that name from the local or global namespace, depending on whether the name occurs in a global statement in the same code block. If the name is unbound, a NameError exception will be raised.

Deletion of attribute references, subscriptions and slicings is passed to the primary object involved; deletion of a slicing is in general equivalent to assignment of an empty slice of the right type (but even this is determined by the sliced object).

Changed in version 3.2: Previously it was illegal to delete a name from the local namespace if it occurs as a free variable in a nested block.

+7.6. The return statement

+return_stmt ::=  "return" [expression_list]
+

return may only occur syntactically nested in a function definition, not within a nested class definition.

If an expression list is present, it is evaluated, else None is substituted.

return leaves the current function call with the expression list (or None) as return value.

When return passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the function.

In a generator function, the return statement indicates that the generator is done and will cause StopIteration to be raised. The returned value (if any) is used as an argument to construct StopIteration and becomes the StopIteration.value attribute.

In an asynchronous generator function, an empty return statement indicates that the asynchronous generator is done and will cause StopAsyncIteration to be raised. A non-empty return statement is a syntax error in an asynchronous generator function.

+7.7. The yield statement

+yield_stmt ::=  yield_expression
+

A yield statement is semantically equivalent to a yield expression. The yield statement can be used to omit the parentheses that would otherwise be required in the equivalent yield expression statement. For example, the yield statements

yield <expr>
+yield from <expr>
+

are equivalent to the yield expression statements

(yield <expr>)
+(yield from <expr>)
+

Yield expressions and statements are only used when defining a generator function, and are only used in the body of the generator function. Using yield in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.

For full details of yield semantics, refer to the Yield expressions section.

+7.8. The raise statement

+raise_stmt ::=  "raise" [expression ["from" expression]]
+

If no expressions are present, raise re-raises the exception that is currently being handled, which is also known as the active exception. If there isn’t currently an active exception, a RuntimeError exception is raised indicating that this is an error.

Otherwise, raise evaluates the first expression as the exception object. It must be either a subclass or an instance of BaseException. If it is a class, the exception instance will be obtained when needed by instantiating the class with no arguments.

The type of the exception is the exception instance’s class, the value is the instance itself.

A traceback object is normally created automatically when an exception is raised and attached to it as the __traceback__ attribute. You can create an exception and set your own traceback in one step using the with_traceback() exception method (which returns the same exception instance, with its traceback set to its argument), like so:

raise Exception("foo occurred").with_traceback(tracebackobj)
+

The from clause is used for exception chaining: if given, the second expression must be another exception class or instance. If the second expression is an exception instance, it will be attached to the raised exception as the __cause__ attribute (which is writable). If the expression is an exception class, the class will be instantiated and the resulting exception instance will be attached to the raised exception as the __cause__ attribute. If the raised exception is not handled, both exceptions will be printed:

>>> try:
+...     print(1 / 0)
+... except Exception as exc:
+...     raise RuntimeError("Something bad happened") from exc
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+    print(1 / 0)
+          ~~^~~
+ZeroDivisionError: division by zero
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+    raise RuntimeError("Something bad happened") from exc
+RuntimeError: Something bad happened
+

A similar mechanism works implicitly if a new exception is raised when an exception is already being handled. An exception may be handled when an except or finally clause, or a with statement, is used. The previous exception is then attached as the new exception’s __context__ attribute:

>>> try:
+...     print(1 / 0)
+... except:
+...     raise RuntimeError("Something bad happened")
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+    print(1 / 0)
+          ~~^~~
+ZeroDivisionError: division by zero
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+    raise RuntimeError("Something bad happened")
+RuntimeError: Something bad happened
+

Exception chaining can be explicitly suppressed by specifying None in the from clause:

>>> try:
+...     print(1 / 0)
+... except:
+...     raise RuntimeError("Something bad happened") from None
+...
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+RuntimeError: Something bad happened
+

Additional information on exceptions can be found in section Exceptions, and information about handling exceptions is in section The try statement.

Changed in version 3.3: None is now permitted as Y in raise X from Y.

New in version 3.3: The __suppress_context__ attribute to suppress automatic display of the exception context.

Changed in version 3.11: If the traceback of the active exception is modified in an except clause, a subsequent raise statement re-raises the exception with the modified traceback. Previously, the exception was re-raised with the traceback it had when it was caught.

+7.9. The break statement

+break_stmt ::=  "break"
+

break may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop.

It terminates the nearest enclosing loop, skipping the optional else clause if the loop has one.

If a for loop is terminated by break, the loop control target keeps its current value.

When break passes control out of a try statement with a finally clause, that finally clause is executed before really leaving the loop.

+7.10. The continue statement

+continue_stmt ::=  "continue"
+

continue may only occur syntactically nested in a for or while loop, but not nested in a function or class definition within that loop. It continues with the next cycle of the nearest enclosing loop.

When continue passes control out of a try statement with a finally clause, that finally clause is executed before really starting the next loop cycle.

+7.11. The import statement

+import_stmt     ::=  "import" module ["as" identifier] ("," module ["as" identifier])*
+                     | "from" relative_module "import" identifier ["as" identifier]
+                     ("," identifier ["as" identifier])*
+                     | "from" relative_module "import" "(" identifier ["as" identifier]
+                     ("," identifier ["as" identifier])* [","] ")"
+                     | "from" relative_module "import" "*"
+module          ::=  (identifier ".")* identifier
+relative_module ::=  "."* module | "."+
+

The basic import statement (no from clause) is executed in two steps:

  1. find a module, loading and initializing it if necessary
  2. define a name or names in the local namespace for the scope where the import statement occurs.

When the statement contains multiple clauses (separated by commas) the two steps are carried out separately for each clause, just as though the clauses had been separated out into individual import statements.

The details of the first step, finding and loading modules, are described in greater detail in the section on the import system, which also describes the various types of packages and modules that can be imported, as well as all the hooks that can be used to customize the import system. Note that failures in this step may indicate either that the module could not be located, or that an error occurred while initializing the module, which includes execution of the module’s code.

If the requested module is retrieved successfully, it will be made available in the local namespace in one of three ways:

  • If the module name is followed by as, then the name following as is bound directly to the imported module.
  • If no other name is specified, and the module being imported is a top level module, the module’s name is bound in the local namespace as a reference to the imported module
  • If the module being imported is not a top level module, then the name of the top level package that contains the module is bound in the local namespace as a reference to the top level package. The imported module must be accessed using its full qualified name rather than directly

The from form uses a slightly more complex process:

  1. find the module specified in the from clause, loading and initializing it if necessary;
  2. +

    for each of the identifiers specified in the import clauses:

    1. check if the imported module has an attribute by that name
    2. if not, attempt to import a submodule with that name and then check the imported module again for that attribute
    3. if the attribute is not found, ImportError is raised.
    4. otherwise, a reference to that value is stored in the local namespace, using the name in the as clause if it is present, otherwise using the attribute name

Examples:

import foo                 # foo imported and bound locally
+import foo.bar.baz         # foo, foo.bar, and foo.bar.baz imported, foo bound locally
+import foo.bar.baz as fbb  # foo, foo.bar, and foo.bar.baz imported, foo.bar.baz bound as fbb
+from foo.bar import baz    # foo, foo.bar, and foo.bar.baz imported, foo.bar.baz bound as baz
+from foo import attr       # foo imported and foo.attr bound as attr
+

If the list of identifiers is replaced by a star ('*'), all public names defined in the module are bound in the local namespace for the scope where the import statement occurs.

The public names defined by a module are determined by checking the module’s namespace for a variable named __all__; if defined, it must be a sequence of strings which are names defined or imported by that module. The names given in __all__ are all considered public and are required to exist. If __all__ is not defined, the set of public names includes all names found in the module’s namespace which do not begin with an underscore character ('_'). __all__ should contain the entire public API. It is intended to avoid accidentally exporting items that are not part of the API (such as library modules which were imported and used within the module).

The wild card form of import — from module import * — is only allowed at the module level. Attempting to use it in class or function definitions will raise a SyntaxError.

When specifying what module to import you do not have to specify the absolute name of the module. When a module or package is contained within another package it is possible to make a relative import within the same top package without having to mention the package name. By using leading dots in the specified module or package after from you can specify how high to traverse up the current package hierarchy without specifying exact names. One leading dot means the current package where the module making the import exists. Two dots means up one package level. Three dots is up two levels, etc. So if you execute from . import mod from a module in the pkg package then you will end up importing pkg.mod. If you execute from ..subpkg2 +import mod from within pkg.subpkg1 you will import pkg.subpkg2.mod. The specification for relative imports is contained in the Package Relative Imports section.

importlib.import_module() is provided to support applications that determine dynamically the modules to be loaded.

Raises an auditing event import with arguments module, filename, sys.path, sys.meta_path, sys.path_hooks.

+7.11.1. Future statements

A future statement is a directive to the compiler that a particular module should be compiled using syntax or semantics that will be available in a specified future release of Python where the feature becomes standard.

The future statement is intended to ease migration to future versions of Python that introduce incompatible changes to the language. It allows use of the new features on a per-module basis before the release in which the feature becomes standard.

+future_stmt ::=  "from" "__future__" "import" feature ["as" identifier]
+                 ("," feature ["as" identifier])*
+                 | "from" "__future__" "import" "(" feature ["as" identifier]
+                 ("," feature ["as" identifier])* [","] ")"
+feature     ::=  identifier
+

A future statement must appear near the top of the module. The only lines that can appear before a future statement are:

  • the module docstring (if any),
  • comments,
  • blank lines, and
  • other future statements.

The only feature that requires using the future statement is annotations (see PEP 563).

All historical features enabled by the future statement are still recognized by Python 3. The list includes absolute_import, division, generators, generator_stop, unicode_literals, print_function, nested_scopes and with_statement. They are all redundant because they are always enabled, and only kept for backwards compatibility.

A future statement is recognized and treated specially at compile time: Changes to the semantics of core constructs are often implemented by generating different code. It may even be the case that a new feature introduces new incompatible syntax (such as a new reserved word), in which case the compiler may need to parse the module differently. Such decisions cannot be pushed off until runtime.

For any given release, the compiler knows which feature names have been defined, and raises a compile-time error if a future statement contains a feature not known to it.

The direct runtime semantics are the same as for any import statement: there is a standard module __future__, described later, and it will be imported in the usual way at the time the future statement is executed.

The interesting runtime semantics depend on the specific feature enabled by the future statement.

Note that there is nothing special about the statement:

import __future__ [as name]
+

That is not a future statement; it’s an ordinary import statement with no special semantics or syntax restrictions.

Code compiled by calls to the built-in functions exec() and compile() that occur in a module M containing a future statement will, by default, use the new syntax or semantics associated with the future statement. This can be controlled by optional arguments to compile() — see the documentation of that function for details.

A future statement typed at an interactive interpreter prompt will take effect for the rest of the interpreter session. If an interpreter is started with the -i option, is passed a script name to execute, and the script includes a future statement, it will be in effect in the interactive session started after the script is executed.

See also

+PEP 236 - Back to the __future__
+
+

The original proposal for the __future__ mechanism.

+7.12. The global statement

+global_stmt ::=  "global" identifier ("," identifier)*
+

The global statement is a declaration which holds for the entire current code block. It means that the listed identifiers are to be interpreted as globals. It would be impossible to assign to a global variable without global, although free variables may refer to globals without being declared global.

Names listed in a global statement must not be used in the same code block textually preceding that global statement.

Names listed in a global statement must not be defined as formal parameters, or as targets in with statements or except clauses, or in a for target list, class definition, function definition, import statement, or variable annotation.

CPython implementation detail: The current implementation does not enforce some of these restrictions, but programs should not abuse this freedom, as future implementations may enforce them or silently change the meaning of the program.

Programmer’s note: global is a directive to the parser. It applies only to code parsed at the same time as the global statement. In particular, a global statement contained in a string or code object supplied to the built-in exec() function does not affect the code block containing the function call, and code contained in such a string is unaffected by global statements in the code containing the function call. The same applies to the eval() and compile() functions.

+7.13. The nonlocal statement

+nonlocal_stmt ::=  "nonlocal" identifier ("," identifier)*
+

The nonlocal statement causes the listed identifiers to refer to previously bound variables in the nearest enclosing scope excluding globals. This is important because the default behavior for binding is to search the local namespace first. The statement allows encapsulated code to rebind variables outside of the local scope besides the global (module) scope.

Names listed in a nonlocal statement, unlike those listed in a global statement, must refer to pre-existing bindings in an enclosing scope (the scope in which a new binding should be created cannot be determined unambiguously).

Names listed in a nonlocal statement must not collide with pre-existing bindings in the local scope.

See also

+PEP 3104 - Access to Names in Outer Scopes
+
+

The specification for the nonlocal statement.

+7.14. The type statement

+type_stmt ::=  'type' identifier [type_params] "=" expression
+

The type statement declares a type alias, which is an instance of typing.TypeAliasType.

For example, the following statement creates a type alias:

type Point = tuple[float, float]
+

This code is roughly equivalent to:

annotation-def VALUE_OF_Point():
+    return tuple[float, float]
+Point = typing.TypeAliasType("Point", VALUE_OF_Point())
+

annotation-def indicates an annotation scope, which behaves mostly like a function, but with several small differences.

The value of the type alias is evaluated in the annotation scope. It is not evaluated when the type alias is created, but only when the value is accessed through the type alias’s __value__ attribute (see Lazy evaluation). This allows the type alias to refer to names that are not yet defined.

Type aliases may be made generic by adding a type parameter list after the name. See Generic type aliases for more.

type is a soft keyword.

New in version 3.12.

See also

+PEP 695 - Type Parameter Syntax
+
+

Introduced the type statement and syntax for generic classes and functions.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/simple_stmts.html +

+
diff --git a/devdocs/python~3.12/reference%2Ftoplevel_components.html b/devdocs/python~3.12/reference%2Ftoplevel_components.html new file mode 100644 index 00000000..158bbb99 --- /dev/null +++ b/devdocs/python~3.12/reference%2Ftoplevel_components.html @@ -0,0 +1,16 @@ +

Top-level components

The Python interpreter can get its input from a number of sources: from a script passed to it as standard input or as program argument, typed in interactively, from a module source file, etc. This chapter gives the syntax used in these cases.

+9.1. Complete Python programs

While a language specification need not prescribe how the language interpreter is invoked, it is useful to have a notion of a complete Python program. A complete Python program is executed in a minimally initialized environment: all built-in and standard modules are available, but none have been initialized, except for sys (various system services), builtins (built-in functions, exceptions and None) and __main__. The latter is used to provide the local and global namespace for execution of the complete program.

The syntax for a complete Python program is that for file input, described in the next section.

The interpreter may also be invoked in interactive mode; in this case, it does not read and execute a complete program but reads and executes one statement (possibly compound) at a time. The initial environment is identical to that of a complete program; each statement is executed in the namespace of __main__.

A complete program can be passed to the interpreter in three forms: with the -c string command line option, as a file passed as the first command line argument, or as standard input. If the file or standard input is a tty device, the interpreter enters interactive mode; otherwise, it executes the file as a complete program.

+9.2. File input

All input read from non-interactive files has the same form:

+file_input ::=  (NEWLINE | statement)*
+

This syntax is used in the following situations:

  • when parsing a complete Python program (from a file or from a string);
  • when parsing a module;
  • when parsing a string passed to the exec() function;

+9.3. Interactive input

Input in interactive mode is parsed using the following grammar:

+interactive_input ::=  [stmt_list] NEWLINE | compound_stmt NEWLINE
+

Note that a (top-level) compound statement must be followed by a blank line in interactive mode; this is needed to help the parser detect the end of the input.

+9.4. Expression input

eval() is used for expression input. It ignores leading whitespace. The string argument to eval() must have the following form:

+eval_input ::=  expression_list NEWLINE*
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/reference/toplevel_components.html +

+
diff --git a/devdocs/python~3.12/search.html b/devdocs/python~3.12/search.html new file mode 100644 index 00000000..29ec5536 --- /dev/null +++ b/devdocs/python~3.12/search.html @@ -0,0 +1,6 @@ +

Search

Searching for multiple words only shows matches that contain all words.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/search.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fappendix.html b/devdocs/python~3.12/tutorial%2Fappendix.html new file mode 100644 index 00000000..dda7e109 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fappendix.html @@ -0,0 +1,25 @@ +

Appendix

+16.1. Interactive Mode

+16.1.1. Error Handling

When an error occurs, the interpreter prints an error message and a stack trace. In interactive mode, it then returns to the primary prompt; when input came from a file, it exits with a nonzero exit status after printing the stack trace. (Exceptions handled by an except clause in a try statement are not errors in this context.) Some errors are unconditionally fatal and cause an exit with a nonzero exit; this applies to internal inconsistencies and some cases of running out of memory. All error messages are written to the standard error stream; normal output from executed commands is written to standard output.

Typing the interrupt character (usually Control-C or Delete) to the primary or secondary prompt cancels the input and returns to the primary prompt. 1 Typing an interrupt while a command is executing raises the KeyboardInterrupt exception, which may be handled by a try statement.

+16.1.2. Executable Python Scripts

On BSD’ish Unix systems, Python scripts can be made directly executable, like shell scripts, by putting the line

#!/usr/bin/env python3.5
+

(assuming that the interpreter is on the user’s PATH) at the beginning of the script and giving the file an executable mode. The #! must be the first two characters of the file. On some platforms, this first line must end with a Unix-style line ending ('\n'), not a Windows ('\r\n') line ending. Note that the hash, or pound, character, '#', is used to start a comment in Python.

The script can be given an executable mode, or permission, using the chmod command.

$ chmod +x myscript.py
+

On Windows systems, there is no notion of an “executable mode”. The Python installer automatically associates .py files with python.exe so that a double-click on a Python file will run it as a script. The extension can also be .pyw, in that case, the console window that normally appears is suppressed.

+16.1.3. The Interactive Startup File

When you use Python interactively, it is frequently handy to have some standard commands executed every time the interpreter is started. You can do this by setting an environment variable named PYTHONSTARTUP to the name of a file containing your start-up commands. This is similar to the .profile feature of the Unix shells.

This file is only read in interactive sessions, not when Python reads commands from a script, and not when /dev/tty is given as the explicit source of commands (which otherwise behaves like an interactive session). It is executed in the same namespace where interactive commands are executed, so that objects that it defines or imports can be used without qualification in the interactive session. You can also change the prompts sys.ps1 and sys.ps2 in this file.

If you want to read an additional start-up file from the current directory, you can program this in the global start-up file using code like if +os.path.isfile('.pythonrc.py'): exec(open('.pythonrc.py').read()). If you want to use the startup file in a script, you must do this explicitly in the script:

import os
+filename = os.environ.get('PYTHONSTARTUP')
+if filename and os.path.isfile(filename):
+    with open(filename) as fobj:
+        startup_file = fobj.read()
+    exec(startup_file)
+

+16.1.4. The Customization Modules

Python provides two hooks to let you customize it: sitecustomize and usercustomize. To see how it works, you need first to find the location of your user site-packages directory. Start Python and run this code:

>>> import site
+>>> site.getusersitepackages()
+'/home/user/.local/lib/python3.5/site-packages'
+

Now you can create a file named usercustomize.py in that directory and put anything you want in it. It will affect every invocation of Python, unless it is started with the -s option to disable the automatic import.

sitecustomize works in the same way, but is typically created by an administrator of the computer in the global site-packages directory, and is imported before usercustomize. See the documentation of the site module for more details.

Footnotes

+1
+

A problem with the GNU Readline package may prevent this.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/appendix.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fappetite.html b/devdocs/python~3.12/tutorial%2Fappetite.html new file mode 100644 index 00000000..95363563 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fappetite.html @@ -0,0 +1,6 @@ +

Whetting Your Appetite

If you do much work on computers, eventually you find that there’s some task you’d like to automate. For example, you may wish to perform a search-and-replace over a large number of text files, or rename and rearrange a bunch of photo files in a complicated way. Perhaps you’d like to write a small custom database, or a specialized GUI application, or a simple game.

If you’re a professional software developer, you may have to work with several C/C++/Java libraries but find the usual write/compile/test/re-compile cycle is too slow. Perhaps you’re writing a test suite for such a library and find writing the testing code a tedious task. Or maybe you’ve written a program that could use an extension language, and you don’t want to design and implement a whole new language for your application.

Python is just the language for you.

You could write a Unix shell script or Windows batch files for some of these tasks, but shell scripts are best at moving around files and changing text data, not well-suited for GUI applications or games. You could write a C/C++/Java program, but it can take a lot of development time to get even a first-draft program. Python is simpler to use, available on Windows, macOS, and Unix operating systems, and will help you get the job done more quickly.

Python is simple to use, but it is a real programming language, offering much more structure and support for large programs than shell scripts or batch files can offer. On the other hand, Python also offers much more error checking than C, and, being a very-high-level language, it has high-level data types built in, such as flexible arrays and dictionaries. Because of its more general data types Python is applicable to a much larger problem domain than Awk or even Perl, yet many things are at least as easy in Python as in those languages.

Python allows you to split your program into modules that can be reused in other Python programs. It comes with a large collection of standard modules that you can use as the basis of your programs — or as examples to start learning to program in Python. Some of these modules provide things like file I/O, system calls, sockets, and even interfaces to graphical user interface toolkits like Tk.

Python is an interpreted language, which can save you considerable time during program development because no compilation and linking is necessary. The interpreter can be used interactively, which makes it easy to experiment with features of the language, to write throw-away programs, or to test functions during bottom-up program development. It is also a handy desk calculator.

Python enables programs to be written compactly and readably. Programs written in Python are typically much shorter than equivalent C, C++, or Java programs, for several reasons:

Python is extensible: if you know how to program in C it is easy to add a new built-in function or module to the interpreter, either to perform critical operations at maximum speed, or to link Python programs to libraries that may only be available in binary form (such as a vendor-specific graphics library). Once you are really hooked, you can link the Python interpreter into an application written in C and use it as an extension or command language for that application.

By the way, the language is named after the BBC show “Monty Python’s Flying Circus” and has nothing to do with reptiles. Making references to Monty Python skits in documentation is not only allowed, it is encouraged!

Now that you are all excited about Python, you’ll want to examine it in some more detail. Since the best way to learn a language is to use it, the tutorial invites you to play with the Python interpreter as you read.

In the next chapter, the mechanics of using the interpreter are explained. This is rather mundane information, but essential for trying out the examples shown later.

The rest of the tutorial introduces various features of the Python language and system through examples, beginning with simple expressions, statements and data types, through functions and modules, and finally touching upon advanced concepts like exceptions and user-defined classes.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/appetite.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fclasses.html b/devdocs/python~3.12/tutorial%2Fclasses.html new file mode 100644 index 00000000..c742b6a8 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fclasses.html @@ -0,0 +1,284 @@ +

Classes

Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.

Compared with other programming languages, Python’s class mechanism adds classes with a minimum of new syntax and semantics. It is a mixture of the class mechanisms found in C++ and Modula-3. Python classes provide all the standard features of Object Oriented Programming: the class inheritance mechanism allows multiple base classes, a derived class can override any methods of its base class or classes, and a method can call the method of a base class with the same name. Objects can contain arbitrary amounts and kinds of data. As is true for modules, classes partake of the dynamic nature of Python: they are created at runtime, and can be modified further after creation.

In C++ terminology, normally class members (including the data members) are public (except see below Private Variables), and all member functions are virtual. As in Modula-3, there are no shorthands for referencing the object’s members from its methods: the method function is declared with an explicit first argument representing the object, which is provided implicitly by the call. As in Smalltalk, classes themselves are objects. This provides semantics for importing and renaming. Unlike C++ and Modula-3, built-in types can be used as base classes for extension by the user. Also, like in C++, most built-in operators with special syntax (arithmetic operators, subscripting etc.) can be redefined for class instances.

(Lacking universally accepted terminology to talk about classes, I will make occasional use of Smalltalk and C++ terms. I would use Modula-3 terms, since its object-oriented semantics are closer to those of Python than C++, but I expect that few readers have heard of it.)

+9.1. A Word About Names and Objects

Objects have individuality, and multiple names (in multiple scopes) can be bound to the same object. This is known as aliasing in other languages. This is usually not appreciated on a first glance at Python, and can be safely ignored when dealing with immutable basic types (numbers, strings, tuples). However, aliasing has a possibly surprising effect on the semantics of Python code involving mutable objects such as lists, dictionaries, and most other types. This is usually used to the benefit of the program, since aliases behave like pointers in some respects. For example, passing an object is cheap since only a pointer is passed by the implementation; and if a function modifies an object passed as an argument, the caller will see the change — this eliminates the need for two different argument passing mechanisms as in Pascal.

+9.2. Python Scopes and Namespaces

Before introducing classes, I first have to tell you something about Python’s scope rules. Class definitions play some neat tricks with namespaces, and you need to know how scopes and namespaces work to fully understand what’s going on. Incidentally, knowledge about this subject is useful for any advanced Python programmer.

Let’s begin with some definitions.

A namespace is a mapping from names to objects. Most namespaces are currently implemented as Python dictionaries, but that’s normally not noticeable in any way (except for performance), and it may change in the future. Examples of namespaces are: the set of built-in names (containing functions such as abs(), and built-in exception names); the global names in a module; and the local names in a function invocation. In a sense the set of attributes of an object also form a namespace. The important thing to know about namespaces is that there is absolutely no relation between names in different namespaces; for instance, two different modules may both define a function maximize without confusion — users of the modules must prefix it with the module name.

By the way, I use the word attribute for any name following a dot — for example, in the expression z.real, real is an attribute of the object z. Strictly speaking, references to names in modules are attribute references: in the expression modname.funcname, modname is a module object and funcname is an attribute of it. In this case there happens to be a straightforward mapping between the module’s attributes and the global names defined in the module: they share the same namespace! 1

Attributes may be read-only or writable. In the latter case, assignment to attributes is possible. Module attributes are writable: you can write modname.the_answer = 42. Writable attributes may also be deleted with the del statement. For example, del modname.the_answer will remove the attribute the_answer from the object named by modname.

Namespaces are created at different moments and have different lifetimes. The namespace containing the built-in names is created when the Python interpreter starts up, and is never deleted. The global namespace for a module is created when the module definition is read in; normally, module namespaces also last until the interpreter quits. The statements executed by the top-level invocation of the interpreter, either read from a script file or interactively, are considered part of a module called __main__, so they have their own global namespace. (The built-in names actually also live in a module; this is called builtins.)

The local namespace for a function is created when the function is called, and deleted when the function returns or raises an exception that is not handled within the function. (Actually, forgetting would be a better way to describe what actually happens.) Of course, recursive invocations each have their own local namespace.

A scope is a textual region of a Python program where a namespace is directly accessible. “Directly accessible” here means that an unqualified reference to a name attempts to find the name in the namespace.

Although scopes are determined statically, they are used dynamically. At any time during execution, there are 3 or 4 nested scopes whose namespaces are directly accessible:

  • the innermost scope, which is searched first, contains the local names
  • the scopes of any enclosing functions, which are searched starting with the nearest enclosing scope, contain non-local, but also non-global names
  • the next-to-last scope contains the current module’s global names
  • the outermost scope (searched last) is the namespace containing built-in names

If a name is declared global, then all references and assignments go directly to the next-to-last scope containing the module’s global names. To rebind variables found outside of the innermost scope, the nonlocal statement can be used; if not declared nonlocal, those variables are read-only (an attempt to write to such a variable will simply create a new local variable in the innermost scope, leaving the identically named outer variable unchanged).

Usually, the local scope references the local names of the (textually) current function. Outside functions, the local scope references the same namespace as the global scope: the module’s namespace. Class definitions place yet another namespace in the local scope.

It is important to realize that scopes are determined textually: the global scope of a function defined in a module is that module’s namespace, no matter from where or by what alias the function is called. On the other hand, the actual search for names is done dynamically, at run time — however, the language definition is evolving towards static name resolution, at “compile” time, so don’t rely on dynamic name resolution! (In fact, local variables are already determined statically.)

A special quirk of Python is that – if no global or nonlocal statement is in effect – assignments to names always go into the innermost scope. Assignments do not copy data — they just bind names to objects. The same is true for deletions: the statement del x removes the binding of x from the namespace referenced by the local scope. In fact, all operations that introduce new names use the local scope: in particular, import statements and function definitions bind the module or function name in the local scope.

The global statement can be used to indicate that particular variables live in the global scope and should be rebound there; the nonlocal statement indicates that particular variables live in an enclosing scope and should be rebound there.

+9.2.1. Scopes and Namespaces Example

This is an example demonstrating how to reference the different scopes and namespaces, and how global and nonlocal affect variable binding:

def scope_test():
+    def do_local():
+        spam = "local spam"
+
+    def do_nonlocal():
+        nonlocal spam
+        spam = "nonlocal spam"
+
+    def do_global():
+        global spam
+        spam = "global spam"
+
+    spam = "test spam"
+    do_local()
+    print("After local assignment:", spam)
+    do_nonlocal()
+    print("After nonlocal assignment:", spam)
+    do_global()
+    print("After global assignment:", spam)
+
+scope_test()
+print("In global scope:", spam)
+

The output of the example code is:

After local assignment: test spam
+After nonlocal assignment: nonlocal spam
+After global assignment: nonlocal spam
+In global scope: global spam
+

Note how the local assignment (which is default) didn’t change scope_test's binding of spam. The nonlocal assignment changed scope_test's binding of spam, and the global assignment changed the module-level binding.

You can also see that there was no previous binding for spam before the global assignment.

+9.3. A First Look at Classes

Classes introduce a little bit of new syntax, three new object types, and some new semantics.

+9.3.1. Class Definition Syntax

The simplest form of class definition looks like this:

class ClassName:
+    <statement-1>
+    .
+    .
+    .
+    <statement-N>
+

Class definitions, like function definitions (def statements) must be executed before they have any effect. (You could conceivably place a class definition in a branch of an if statement, or inside a function.)

In practice, the statements inside a class definition will usually be function definitions, but other statements are allowed, and sometimes useful — we’ll come back to this later. The function definitions inside a class normally have a peculiar form of argument list, dictated by the calling conventions for methods — again, this is explained later.

When a class definition is entered, a new namespace is created, and used as the local scope — thus, all assignments to local variables go into this new namespace. In particular, function definitions bind the name of the new function here.

When a class definition is left normally (via the end), a class object is created. This is basically a wrapper around the contents of the namespace created by the class definition; we’ll learn more about class objects in the next section. The original local scope (the one in effect just before the class definition was entered) is reinstated, and the class object is bound here to the class name given in the class definition header (ClassName in the example).

+9.3.2. Class Objects

Class objects support two kinds of operations: attribute references and instantiation.

Attribute references use the standard syntax used for all attribute references in Python: obj.name. Valid attribute names are all the names that were in the class’s namespace when the class object was created. So, if the class definition looked like this:

class MyClass:
+    """A simple example class"""
+    i = 12345
+
+    def f(self):
+        return 'hello world'
+

then MyClass.i and MyClass.f are valid attribute references, returning an integer and a function object, respectively. Class attributes can also be assigned to, so you can change the value of MyClass.i by assignment. __doc__ is also a valid attribute, returning the docstring belonging to the class: "A simple example class".

Class instantiation uses function notation. Just pretend that the class object is a parameterless function that returns a new instance of the class. For example (assuming the above class):

x = MyClass()
+

creates a new instance of the class and assigns this object to the local variable x.

The instantiation operation (“calling” a class object) creates an empty object. Many classes like to create objects with instances customized to a specific initial state. Therefore a class may define a special method named __init__(), like this:

def __init__(self):
+    self.data = []
+

When a class defines an __init__() method, class instantiation automatically invokes __init__() for the newly created class instance. So in this example, a new, initialized instance can be obtained by:

x = MyClass()
+

Of course, the __init__() method may have arguments for greater flexibility. In that case, arguments given to the class instantiation operator are passed on to __init__(). For example,

>>> class Complex:
+...     def __init__(self, realpart, imagpart):
+...         self.r = realpart
+...         self.i = imagpart
+...
+>>> x = Complex(3.0, -4.5)
+>>> x.r, x.i
+(3.0, -4.5)
+

+9.3.3. Instance Objects

Now what can we do with instance objects? The only operations understood by instance objects are attribute references. There are two kinds of valid attribute names: data attributes and methods.

data attributes correspond to “instance variables” in Smalltalk, and to “data members” in C++. Data attributes need not be declared; like local variables, they spring into existence when they are first assigned to. For example, if x is the instance of MyClass created above, the following piece of code will print the value 16, without leaving a trace:

x.counter = 1
+while x.counter < 10:
+    x.counter = x.counter * 2
+print(x.counter)
+del x.counter
+

The other kind of instance attribute reference is a method. A method is a function that “belongs to” an object. (In Python, the term method is not unique to class instances: other object types can have methods as well. For example, list objects have methods called append, insert, remove, sort, and so on. However, in the following discussion, we’ll use the term method exclusively to mean methods of class instance objects, unless explicitly stated otherwise.)

Valid method names of an instance object depend on its class. By definition, all attributes of a class that are function objects define corresponding methods of its instances. So in our example, x.f is a valid method reference, since MyClass.f is a function, but x.i is not, since MyClass.i is not. But x.f is not the same thing as MyClass.f — it is a method object, not a function object.

+9.3.4. Method Objects

Usually, a method is called right after it is bound:

x.f()
+

In the MyClass example, this will return the string 'hello world'. However, it is not necessary to call a method right away: x.f is a method object, and can be stored away and called at a later time. For example:

xf = x.f
+while True:
+    print(xf())
+

will continue to print hello world until the end of time.

What exactly happens when a method is called? You may have noticed that x.f() was called without an argument above, even though the function definition for f() specified an argument. What happened to the argument? Surely Python raises an exception when a function that requires an argument is called without any — even if the argument isn’t actually used…

Actually, you may have guessed the answer: the special thing about methods is that the instance object is passed as the first argument of the function. In our example, the call x.f() is exactly equivalent to MyClass.f(x). In general, calling a method with a list of n arguments is equivalent to calling the corresponding function with an argument list that is created by inserting the method’s instance object before the first argument.

If you still don’t understand how methods work, a look at the implementation can perhaps clarify matters. When a non-data attribute of an instance is referenced, the instance’s class is searched. If the name denotes a valid class attribute that is a function object, a method object is created by packing (pointers to) the instance object and the function object just found together in an abstract object: this is the method object. When the method object is called with an argument list, a new argument list is constructed from the instance object and the argument list, and the function object is called with this new argument list.

+9.3.5. Class and Instance Variables

Generally speaking, instance variables are for data unique to each instance and class variables are for attributes and methods shared by all instances of the class:

class Dog:
+
+    kind = 'canine'         # class variable shared by all instances
+
+    def __init__(self, name):
+        self.name = name    # instance variable unique to each instance
+
+>>> d = Dog('Fido')
+>>> e = Dog('Buddy')
+>>> d.kind                  # shared by all dogs
+'canine'
+>>> e.kind                  # shared by all dogs
+'canine'
+>>> d.name                  # unique to d
+'Fido'
+>>> e.name                  # unique to e
+'Buddy'
+

As discussed in A Word About Names and Objects, shared data can have possibly surprising effects with involving mutable objects such as lists and dictionaries. For example, the tricks list in the following code should not be used as a class variable because just a single list would be shared by all Dog instances:

class Dog:
+
+    tricks = []             # mistaken use of a class variable
+
+    def __init__(self, name):
+        self.name = name
+
+    def add_trick(self, trick):
+        self.tricks.append(trick)
+
+>>> d = Dog('Fido')
+>>> e = Dog('Buddy')
+>>> d.add_trick('roll over')
+>>> e.add_trick('play dead')
+>>> d.tricks                # unexpectedly shared by all dogs
+['roll over', 'play dead']
+

Correct design of the class should use an instance variable instead:

class Dog:
+
+    def __init__(self, name):
+        self.name = name
+        self.tricks = []    # creates a new empty list for each dog
+
+    def add_trick(self, trick):
+        self.tricks.append(trick)
+
+>>> d = Dog('Fido')
+>>> e = Dog('Buddy')
+>>> d.add_trick('roll over')
+>>> e.add_trick('play dead')
+>>> d.tricks
+['roll over']
+>>> e.tricks
+['play dead']
+

+9.4. Random Remarks

If the same attribute name occurs in both an instance and in a class, then attribute lookup prioritizes the instance:

>>> class Warehouse:
+...    purpose = 'storage'
+...    region = 'west'
+...
+>>> w1 = Warehouse()
+>>> print(w1.purpose, w1.region)
+storage west
+>>> w2 = Warehouse()
+>>> w2.region = 'east'
+>>> print(w2.purpose, w2.region)
+storage east
+

Data attributes may be referenced by methods as well as by ordinary users (“clients”) of an object. In other words, classes are not usable to implement pure abstract data types. In fact, nothing in Python makes it possible to enforce data hiding — it is all based upon convention. (On the other hand, the Python implementation, written in C, can completely hide implementation details and control access to an object if necessary; this can be used by extensions to Python written in C.)

Clients should use data attributes with care — clients may mess up invariants maintained by the methods by stamping on their data attributes. Note that clients may add data attributes of their own to an instance object without affecting the validity of the methods, as long as name conflicts are avoided — again, a naming convention can save a lot of headaches here.

There is no shorthand for referencing data attributes (or other methods!) from within methods. I find that this actually increases the readability of methods: there is no chance of confusing local variables and instance variables when glancing through a method.

Often, the first argument of a method is called self. This is nothing more than a convention: the name self has absolutely no special meaning to Python. Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention.

Any function object that is a class attribute defines a method for instances of that class. It is not necessary that the function definition is textually enclosed in the class definition: assigning a function object to a local variable in the class is also ok. For example:

# Function defined outside the class
+def f1(self, x, y):
+    return min(x, x+y)
+
+class C:
+    f = f1
+
+    def g(self):
+        return 'hello world'
+
+    h = g
+

Now f, g and h are all attributes of class C that refer to function objects, and consequently they are all methods of instances of C — h being exactly equivalent to g. Note that this practice usually only serves to confuse the reader of a program.

Methods may call other methods by using method attributes of the self argument:

class Bag:
+    def __init__(self):
+        self.data = []
+
+    def add(self, x):
+        self.data.append(x)
+
+    def addtwice(self, x):
+        self.add(x)
+        self.add(x)
+

Methods may reference global names in the same way as ordinary functions. The global scope associated with a method is the module containing its definition. (A class is never used as a global scope.) While one rarely encounters a good reason for using global data in a method, there are many legitimate uses of the global scope: for one thing, functions and modules imported into the global scope can be used by methods, as well as functions and classes defined in it. Usually, the class containing the method is itself defined in this global scope, and in the next section we’ll find some good reasons why a method would want to reference its own class.

Each value is an object, and therefore has a class (also called its type). It is stored as object.__class__.

+9.5. Inheritance

Of course, a language feature would not be worthy of the name “class” without supporting inheritance. The syntax for a derived class definition looks like this:

class DerivedClassName(BaseClassName):
+    <statement-1>
+    .
+    .
+    .
+    <statement-N>
+

The name BaseClassName must be defined in a namespace accessible from the scope containing the derived class definition. In place of a base class name, other arbitrary expressions are also allowed. This can be useful, for example, when the base class is defined in another module:

class DerivedClassName(modname.BaseClassName):
+

Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.

There’s nothing special about instantiation of derived classes: DerivedClassName() creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, descending down the chain of base classes if necessary, and the method reference is valid if this yields a function object.

Derived classes may override methods of their base classes. Because methods have no special privileges when calling other methods of the same object, a method of a base class that calls another method defined in the same base class may end up calling a method of a derived class that overrides it. (For C++ programmers: all methods in Python are effectively virtual.)

An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call BaseClassName.methodname(self, +arguments). This is occasionally useful to clients as well. (Note that this only works if the base class is accessible as BaseClassName in the global scope.)

Python has two built-in functions that work with inheritance:

  • Use isinstance() to check an instance’s type: isinstance(obj, int) will be True only if obj.__class__ is int or some class derived from int.
  • Use issubclass() to check class inheritance: issubclass(bool, int) is True since bool is a subclass of int. However, issubclass(float, int) is False since float is not a subclass of int.

+9.5.1. Multiple Inheritance

Python supports a form of multiple inheritance as well. A class definition with multiple base classes looks like this:

class DerivedClassName(Base1, Base2, Base3):
+    <statement-1>
+    .
+    .
+    .
+    <statement-N>
+

For most purposes, in the simplest cases, you can think of the search for attributes inherited from a parent class as depth-first, left-to-right, not searching twice in the same class where there is an overlap in the hierarchy. Thus, if an attribute is not found in DerivedClassName, it is searched for in Base1, then (recursively) in the base classes of Base1, and if it was not found there, it was searched for in Base2, and so on.

In fact, it is slightly more complex than that; the method resolution order changes dynamically to support cooperative calls to super(). This approach is known in some other multiple-inheritance languages as call-next-method and is more powerful than the super call found in single-inheritance languages.

Dynamic ordering is necessary because all cases of multiple inheritance exhibit one or more diamond relationships (where at least one of the parent classes can be accessed through multiple paths from the bottommost class). For example, all classes inherit from object, so any case of multiple inheritance provides more than one path to reach object. To keep the base classes from being accessed more than once, the dynamic algorithm linearizes the search order in a way that preserves the left-to-right ordering specified in each class, that calls each parent only once, and that is monotonic (meaning that a class can be subclassed without affecting the precedence order of its parents). Taken together, these properties make it possible to design reliable and extensible classes with multiple inheritance. For more detail, see https://www.python.org/download/releases/2.3/mro/.

+9.6. Private Variables

“Private” instance variables that cannot be accessed except from inside an object don’t exist in Python. However, there is a convention that is followed by most Python code: a name prefixed with an underscore (e.g. _spam) should be treated as a non-public part of the API (whether it is a function, a method or a data member). It should be considered an implementation detail and subject to change without notice.

Since there is a valid use-case for class-private members (namely to avoid name clashes of names with names defined by subclasses), there is limited support for such a mechanism, called name mangling. Any identifier of the form __spam (at least two leading underscores, at most one trailing underscore) is textually replaced with _classname__spam, where classname is the current class name with leading underscore(s) stripped. This mangling is done without regard to the syntactic position of the identifier, as long as it occurs within the definition of a class.

Name mangling is helpful for letting subclasses override methods without breaking intraclass method calls. For example:

class Mapping:
+    def __init__(self, iterable):
+        self.items_list = []
+        self.__update(iterable)
+
+    def update(self, iterable):
+        for item in iterable:
+            self.items_list.append(item)
+
+    __update = update   # private copy of original update() method
+
+class MappingSubclass(Mapping):
+
+    def update(self, keys, values):
+        # provides new signature for update()
+        # but does not break __init__()
+        for item in zip(keys, values):
+            self.items_list.append(item)
+

The above example would work even if MappingSubclass were to introduce a __update identifier since it is replaced with _Mapping__update in the Mapping class and _MappingSubclass__update in the MappingSubclass class respectively.

Note that the mangling rules are designed mostly to avoid accidents; it still is possible to access or modify a variable that is considered private. This can even be useful in special circumstances, such as in the debugger.

Notice that code passed to exec() or eval() does not consider the classname of the invoking class to be the current class; this is similar to the effect of the global statement, the effect of which is likewise restricted to code that is byte-compiled together. The same restriction applies to getattr(), setattr() and delattr(), as well as when referencing __dict__ directly.

+9.7. Odds and Ends

Sometimes it is useful to have a data type similar to the Pascal “record” or C “struct”, bundling together a few named data items. The idiomatic approach is to use dataclasses for this purpose:

from dataclasses import dataclass
+
+@dataclass
+class Employee:
+    name: str
+    dept: str
+    salary: int
+
>>> john = Employee('john', 'computer lab', 1000)
+>>> john.dept
+'computer lab'
+>>> john.salary
+1000
+

A piece of Python code that expects a particular abstract data type can often be passed a class that emulates the methods of that data type instead. For instance, if you have a function that formats some data from a file object, you can define a class with methods read() and readline() that get the data from a string buffer instead, and pass it as an argument.

Instance method objects have attributes, too: m.__self__ is the instance object with the method m(), and m.__func__ is the function object corresponding to the method.

+9.8. Iterators

By now you have probably noticed that most container objects can be looped over using a for statement:

for element in [1, 2, 3]:
+    print(element)
+for element in (1, 2, 3):
+    print(element)
+for key in {'one':1, 'two':2}:
+    print(key)
+for char in "123":
+    print(char)
+for line in open("myfile.txt"):
+    print(line, end='')
+

This style of access is clear, concise, and convenient. The use of iterators pervades and unifies Python. Behind the scenes, the for statement calls iter() on the container object. The function returns an iterator object that defines the method __next__() which accesses elements in the container one at a time. When there are no more elements, __next__() raises a StopIteration exception which tells the for loop to terminate. You can call the __next__() method using the next() built-in function; this example shows how it all works:

>>> s = 'abc'
+>>> it = iter(s)
+>>> it
+<str_iterator object at 0x10c90e650>
+>>> next(it)
+'a'
+>>> next(it)
+'b'
+>>> next(it)
+'c'
+>>> next(it)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+    next(it)
+StopIteration
+

Having seen the mechanics behind the iterator protocol, it is easy to add iterator behavior to your classes. Define an __iter__() method which returns an object with a __next__() method. If the class defines __next__(), then __iter__() can just return self:

class Reverse:
+    """Iterator for looping over a sequence backwards."""
+    def __init__(self, data):
+        self.data = data
+        self.index = len(data)
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.index == 0:
+            raise StopIteration
+        self.index = self.index - 1
+        return self.data[self.index]
+
>>> rev = Reverse('spam')
+>>> iter(rev)
+<__main__.Reverse object at 0x00A1DB50>
+>>> for char in rev:
+...     print(char)
+...
+m
+a
+p
+s
+

+9.9. Generators

Generators are a simple and powerful tool for creating iterators. They are written like regular functions but use the yield statement whenever they want to return data. Each time next() is called on it, the generator resumes where it left off (it remembers all the data values and which statement was last executed). An example shows that generators can be trivially easy to create:

def reverse(data):
+    for index in range(len(data)-1, -1, -1):
+        yield data[index]
+
>>> for char in reverse('golf'):
+...     print(char)
+...
+f
+l
+o
+g
+

Anything that can be done with generators can also be done with class-based iterators as described in the previous section. What makes generators so compact is that the __iter__() and __next__() methods are created automatically.

Another key feature is that the local variables and execution state are automatically saved between calls. This made the function easier to write and much more clear than an approach using instance variables like self.index and self.data.

In addition to automatic method creation and saving program state, when generators terminate, they automatically raise StopIteration. In combination, these features make it easy to create iterators with no more effort than writing a regular function.

+9.10. Generator Expressions

Some simple generators can be coded succinctly as expressions using a syntax similar to list comprehensions but with parentheses instead of square brackets. These expressions are designed for situations where the generator is used right away by an enclosing function. Generator expressions are more compact but less versatile than full generator definitions and tend to be more memory friendly than equivalent list comprehensions.

Examples:

>>> sum(i*i for i in range(10))                 # sum of squares
+285
+
+>>> xvec = [10, 20, 30]
+>>> yvec = [7, 5, 3]
+>>> sum(x*y for x,y in zip(xvec, yvec))         # dot product
+260
+
+>>> unique_words = set(word for line in page  for word in line.split())
+
+>>> valedictorian = max((student.gpa, student.name) for student in graduates)
+
+>>> data = 'golf'
+>>> list(data[i] for i in range(len(data)-1, -1, -1))
+['f', 'l', 'o', 'g']
+

Footnotes

+1
+

Except for one thing. Module objects have a secret read-only attribute called __dict__ which returns the dictionary used to implement the module’s namespace; the name __dict__ is an attribute but not a global name. Obviously, using this violates the abstraction of namespace implementation, and should be restricted to things like post-mortem debuggers.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/classes.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fcontrolflow.html b/devdocs/python~3.12/tutorial%2Fcontrolflow.html new file mode 100644 index 00000000..4afaed7c --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fcontrolflow.html @@ -0,0 +1,449 @@ +

More Control Flow Tools

As well as the while statement just introduced, Python uses a few more that we will encounter in this chapter.

+4.1. if Statements

Perhaps the most well-known statement type is the if statement. For example:

>>> x = int(input("Please enter an integer: "))
+Please enter an integer: 42
+>>> if x < 0:
+...     x = 0
+...     print('Negative changed to zero')
+... elif x == 0:
+...     print('Zero')
+... elif x == 1:
+...     print('Single')
+... else:
+...     print('More')
+...
+More
+

There can be zero or more elif parts, and the else part is optional. The keyword ‘elif’ is short for ‘else if’, and is useful to avoid excessive indentation. An if … elif … elif … sequence is a substitute for the switch or case statements found in other languages.

If you’re comparing the same value to several constants, or checking for specific types or attributes, you may also find the match statement useful. For more details see match Statements.

+4.2. for Statements

The for statement in Python differs a bit from what you may be used to in C or Pascal. Rather than always iterating over an arithmetic progression of numbers (like in Pascal), or giving the user the ability to define both the iteration step and halting condition (as C), Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. For example (no pun intended):

>>> # Measure some strings:
+... words = ['cat', 'window', 'defenestrate']
+>>> for w in words:
+...     print(w, len(w))
+...
+cat 3
+window 6
+defenestrate 12
+

Code that modifies a collection while iterating over that same collection can be tricky to get right. Instead, it is usually more straight-forward to loop over a copy of the collection or to create a new collection:

# Create a sample collection
+users = {'Hans': 'active', 'Éléonore': 'inactive', '景太郎': 'active'}
+
+# Strategy:  Iterate over a copy
+for user, status in users.copy().items():
+    if status == 'inactive':
+        del users[user]
+
+# Strategy:  Create a new collection
+active_users = {}
+for user, status in users.items():
+    if status == 'active':
+        active_users[user] = status
+

+4.3. The range() Function

If you do need to iterate over a sequence of numbers, the built-in function range() comes in handy. It generates arithmetic progressions:

>>> for i in range(5):
+...     print(i)
+...
+0
+1
+2
+3
+4
+

The given end point is never part of the generated sequence; range(10) generates 10 values, the legal indices for items of a sequence of length 10. It is possible to let the range start at another number, or to specify a different increment (even negative; sometimes this is called the ‘step’):

>>> list(range(5, 10))
+[5, 6, 7, 8, 9]
+
+>>> list(range(0, 10, 3))
+[0, 3, 6, 9]
+
+>>> list(range(-10, -100, -30))
+[-10, -40, -70]
+

To iterate over the indices of a sequence, you can combine range() and len() as follows:

>>> a = ['Mary', 'had', 'a', 'little', 'lamb']
+>>> for i in range(len(a)):
+...     print(i, a[i])
+...
+0 Mary
+1 had
+2 a
+3 little
+4 lamb
+

In most such cases, however, it is convenient to use the enumerate() function, see Looping Techniques.

A strange thing happens if you just print a range:

>>> range(10)
+range(0, 10)
+

In many ways the object returned by range() behaves as if it is a list, but in fact it isn’t. It is an object which returns the successive items of the desired sequence when you iterate over it, but it doesn’t really make the list, thus saving space.

We say such an object is iterable, that is, suitable as a target for functions and constructs that expect something from which they can obtain successive items until the supply is exhausted. We have seen that the for statement is such a construct, while an example of a function that takes an iterable is sum():

>>> sum(range(4))  # 0 + 1 + 2 + 3
+6
+

Later we will see more functions that return iterables and take iterables as arguments. In chapter Data Structures, we will discuss in more detail about list().

+4.4. break and continue Statements, and else Clauses on Loops

The break statement breaks out of the innermost enclosing for or while loop.

A for or while loop can include an else clause.

In a for loop, the else clause is executed after the loop reaches its final iteration.

In a while loop, it’s executed after the loop’s condition becomes false.

In either kind of loop, the else clause is not executed if the loop was terminated by a break.

This is exemplified in the following for loop, which searches for prime numbers:

>>> for n in range(2, 10):
+...     for x in range(2, n):
+...         if n % x == 0:
+...             print(n, 'equals', x, '*', n//x)
+...             break
+...     else:
+...         # loop fell through without finding a factor
+...         print(n, 'is a prime number')
+...
+2 is a prime number
+3 is a prime number
+4 equals 2 * 2
+5 is a prime number
+6 equals 2 * 3
+7 is a prime number
+8 equals 2 * 4
+9 equals 3 * 3
+

(Yes, this is the correct code. Look closely: the else clause belongs to the for loop, not the if statement.)

When used with a loop, the else clause has more in common with the else clause of a try statement than it does with that of if statements: a try statement’s else clause runs when no exception occurs, and a loop’s else clause runs when no break occurs. For more on the try statement and exceptions, see Handling Exceptions.

The continue statement, also borrowed from C, continues with the next iteration of the loop:

>>> for num in range(2, 10):
+...     if num % 2 == 0:
+...         print("Found an even number", num)
+...         continue
+...     print("Found an odd number", num)
+...
+Found an even number 2
+Found an odd number 3
+Found an even number 4
+Found an odd number 5
+Found an even number 6
+Found an odd number 7
+Found an even number 8
+Found an odd number 9
+

+4.5. pass Statements

The pass statement does nothing. It can be used when a statement is required syntactically but the program requires no action. For example:

>>> while True:
+...     pass  # Busy-wait for keyboard interrupt (Ctrl+C)
+...
+

This is commonly used for creating minimal classes:

>>> class MyEmptyClass:
+...     pass
+...
+

Another place pass can be used is as a place-holder for a function or conditional body when you are working on new code, allowing you to keep thinking at a more abstract level. The pass is silently ignored:

>>> def initlog(*args):
+...     pass   # Remember to implement this!
+...
+

+4.6. match Statements

A match statement takes an expression and compares its value to successive patterns given as one or more case blocks. This is superficially similar to a switch statement in C, Java or JavaScript (and many other languages), but it’s more similar to pattern matching in languages like Rust or Haskell. Only the first pattern that matches gets executed and it can also extract components (sequence elements or object attributes) from the value into variables.

The simplest form compares a subject value against one or more literals:

def http_error(status):
+    match status:
+        case 400:
+            return "Bad request"
+        case 404:
+            return "Not found"
+        case 418:
+            return "I'm a teapot"
+        case _:
+            return "Something's wrong with the internet"
+

Note the last block: the “variable name” _ acts as a wildcard and never fails to match. If no case matches, none of the branches is executed.

You can combine several literals in a single pattern using | (“or”):

case 401 | 403 | 404:
+    return "Not allowed"
+

Patterns can look like unpacking assignments, and can be used to bind variables:

# point is an (x, y) tuple
+match point:
+    case (0, 0):
+        print("Origin")
+    case (0, y):
+        print(f"Y={y}")
+    case (x, 0):
+        print(f"X={x}")
+    case (x, y):
+        print(f"X={x}, Y={y}")
+    case _:
+        raise ValueError("Not a point")
+

Study that one carefully! The first pattern has two literals, and can be thought of as an extension of the literal pattern shown above. But the next two patterns combine a literal and a variable, and the variable binds a value from the subject (point). The fourth pattern captures two values, which makes it conceptually similar to the unpacking assignment (x, y) = point.

If you are using classes to structure your data you can use the class name followed by an argument list resembling a constructor, but with the ability to capture attributes into variables:

class Point:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+def where_is(point):
+    match point:
+        case Point(x=0, y=0):
+            print("Origin")
+        case Point(x=0, y=y):
+            print(f"Y={y}")
+        case Point(x=x, y=0):
+            print(f"X={x}")
+        case Point():
+            print("Somewhere else")
+        case _:
+            print("Not a point")
+

You can use positional parameters with some builtin classes that provide an ordering for their attributes (e.g. dataclasses). You can also define a specific position for attributes in patterns by setting the __match_args__ special attribute in your classes. If it’s set to (“x”, “y”), the following patterns are all equivalent (and all bind the y attribute to the var variable):

Point(1, var)
+Point(1, y=var)
+Point(x=1, y=var)
+Point(y=var, x=1)
+

A recommended way to read patterns is to look at them as an extended form of what you would put on the left of an assignment, to understand which variables would be set to what. Only the standalone names (like var above) are assigned to by a match statement. Dotted names (like foo.bar), attribute names (the x= and y= above) or class names (recognized by the “(…)” next to them like Point above) are never assigned to.

Patterns can be arbitrarily nested. For example, if we have a short list of Points, with __match_args__ added, we could match it like this:

class Point:
+    __match_args__ = ('x', 'y')
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+
+match points:
+    case []:
+        print("No points")
+    case [Point(0, 0)]:
+        print("The origin")
+    case [Point(x, y)]:
+        print(f"Single point {x}, {y}")
+    case [Point(0, y1), Point(0, y2)]:
+        print(f"Two on the Y axis at {y1}, {y2}")
+    case _:
+        print("Something else")
+

We can add an if clause to a pattern, known as a “guard”. If the guard is false, match goes on to try the next case block. Note that value capture happens before the guard is evaluated:

match point:
+    case Point(x, y) if x == y:
+        print(f"Y=X at {x}")
+    case Point(x, y):
+        print(f"Not on the diagonal")
+

Several other key features of this statement:

  • Like unpacking assignments, tuple and list patterns have exactly the same meaning and actually match arbitrary sequences. An important exception is that they don’t match iterators or strings.
  • Sequence patterns support extended unpacking: [x, y, *rest] and (x, y, +*rest) work similar to unpacking assignments. The name after * may also be _, so (x, y, *_) matches a sequence of at least two items without binding the remaining items.
  • Mapping patterns: {"bandwidth": b, "latency": l} captures the "bandwidth" and "latency" values from a dictionary. Unlike sequence patterns, extra keys are ignored. An unpacking like **rest is also supported. (But **_ would be redundant, so it is not allowed.)
  • +

    Subpatterns may be captured using the as keyword:

    case (Point(x1, y1), Point(x2, y2) as p2): ...
+

    will capture the second element of the input as p2 (as long as the input is a sequence of two points)

  • Most literals are compared by equality, however the singletons True, False and None are compared by identity.
  • +

    Patterns may use named constants. These must be dotted names to prevent them from being interpreted as capture variable:

    from enum import Enum
+class Color(Enum):
+    RED = 'red'
+    GREEN = 'green'
+    BLUE = 'blue'
+
+color = Color(input("Enter your choice of 'red', 'blue' or 'green': "))
+
+match color:
+    case Color.RED:
+        print("I see red!")
+    case Color.GREEN:
+        print("Grass is green")
+    case Color.BLUE:
+        print("I'm feeling the blues :(")
+

For a more detailed explanation and additional examples, you can look into PEP 636 which is written in a tutorial format.

+4.7. Defining Functions

We can create a function that writes the Fibonacci series to an arbitrary boundary:

>>> def fib(n):    # write Fibonacci series up to n
+...     """Print a Fibonacci series up to n."""
+...     a, b = 0, 1
+...     while a < n:
+...         print(a, end=' ')
+...         a, b = b, a+b
+...     print()
+...
+>>> # Now call the function we just defined:
+... fib(2000)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597
+

The keyword def introduces a function definition. It must be followed by the function name and the parenthesized list of formal parameters. The statements that form the body of the function start at the next line, and must be indented.

The first statement of the function body can optionally be a string literal; this string literal is the function’s documentation string, or docstring. (More about docstrings can be found in the section Documentation Strings.) There are tools which use docstrings to automatically produce online or printed documentation, or to let the user interactively browse through code; it’s good practice to include docstrings in code that you write, so make a habit of it.

The execution of a function introduces a new symbol table used for the local variables of the function. More precisely, all variable assignments in a function store the value in the local symbol table; whereas variable references first look in the local symbol table, then in the local symbol tables of enclosing functions, then in the global symbol table, and finally in the table of built-in names. Thus, global variables and variables of enclosing functions cannot be directly assigned a value within a function (unless, for global variables, named in a global statement, or, for variables of enclosing functions, named in a nonlocal statement), although they may be referenced.

The actual parameters (arguments) to a function call are introduced in the local symbol table of the called function when it is called; thus, arguments are passed using call by value (where the value is always an object reference, not the value of the object). 1 When a function calls another function, or calls itself recursively, a new local symbol table is created for that call.

A function definition associates the function name with the function object in the current symbol table. The interpreter recognizes the object pointed to by that name as a user-defined function. Other names can also point to that same function object and can also be used to access the function:

>>> fib
+<function fib at 10042ed0>
+>>> f = fib
+>>> f(100)
+0 1 1 2 3 5 8 13 21 34 55 89
+

Coming from other languages, you might object that fib is not a function but a procedure since it doesn’t return a value. In fact, even functions without a return statement do return a value, albeit a rather boring one. This value is called None (it’s a built-in name). Writing the value None is normally suppressed by the interpreter if it would be the only value written. You can see it if you really want to using print():

>>> fib(0)
+>>> print(fib(0))
+None
+

It is simple to write a function that returns a list of the numbers of the Fibonacci series, instead of printing it:

>>> def fib2(n):  # return Fibonacci series up to n
+...     """Return a list containing the Fibonacci series up to n."""
+...     result = []
+...     a, b = 0, 1
+...     while a < n:
+...         result.append(a)    # see below
+...         a, b = b, a+b
+...     return result
+...
+>>> f100 = fib2(100)    # call it
+>>> f100                # write the result
+[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
+

This example, as usual, demonstrates some new Python features:

  • The return statement returns with a value from a function. return without an expression argument returns None. Falling off the end of a function also returns None.
  • The statement result.append(a) calls a method of the list object result. A method is a function that ‘belongs’ to an object and is named obj.methodname, where obj is some object (this may be an expression), and methodname is the name of a method that is defined by the object’s type. Different types define different methods. Methods of different types may have the same name without causing ambiguity. (It is possible to define your own object types and methods, using classes, see Classes) The method append() shown in the example is defined for list objects; it adds a new element at the end of the list. In this example it is equivalent to result = result + [a], but more efficient.

+4.8. More on Defining Functions

It is also possible to define functions with a variable number of arguments. There are three forms, which can be combined.

+4.8.1. Default Argument Values

The most useful form is to specify a default value for one or more arguments. This creates a function that can be called with fewer arguments than it is defined to allow. For example:

def ask_ok(prompt, retries=4, reminder='Please try again!'):
+    while True:
+        reply = input(prompt)
+        if reply in {'y', 'ye', 'yes'}:
+            return True
+        if reply in {'n', 'no', 'nop', 'nope'}:
+            return False
+        retries = retries - 1
+        if retries < 0:
+            raise ValueError('invalid user response')
+        print(reminder)
+

This function can be called in several ways:

  • giving only the mandatory argument: ask_ok('Do you really want to quit?') +
  • giving one of the optional arguments: ask_ok('OK to overwrite the file?', 2) +
  • or even giving all arguments: ask_ok('OK to overwrite the file?', 2, 'Come on, only yes or no!') +

This example also introduces the in keyword. This tests whether or not a sequence contains a certain value.

The default values are evaluated at the point of function definition in the defining scope, so that

i = 5
+
+def f(arg=i):
+    print(arg)
+
+i = 6
+f()
+

will print 5.

Important warning: The default value is evaluated only once. This makes a difference when the default is a mutable object such as a list, dictionary, or instances of most classes. For example, the following function accumulates the arguments passed to it on subsequent calls:

def f(a, L=[]):
+    L.append(a)
+    return L
+
+print(f(1))
+print(f(2))
+print(f(3))
+

This will print

[1]
+[1, 2]
+[1, 2, 3]
+

If you don’t want the default to be shared between subsequent calls, you can write the function like this instead:

def f(a, L=None):
+    if L is None:
+        L = []
+    L.append(a)
+    return L
+

+4.8.2. Keyword Arguments

Functions can also be called using keyword arguments of the form kwarg=value. For instance, the following function:

def parrot(voltage, state='a stiff', action='voom', type='Norwegian Blue'):
+    print("-- This parrot wouldn't", action, end=' ')
+    print("if you put", voltage, "volts through it.")
+    print("-- Lovely plumage, the", type)
+    print("-- It's", state, "!")
+

accepts one required argument (voltage) and three optional arguments (state, action, and type). This function can be called in any of the following ways:

parrot(1000)                                          # 1 positional argument
+parrot(voltage=1000)                                  # 1 keyword argument
+parrot(voltage=1000000, action='VOOOOOM')             # 2 keyword arguments
+parrot(action='VOOOOOM', voltage=1000000)             # 2 keyword arguments
+parrot('a million', 'bereft of life', 'jump')         # 3 positional arguments
+parrot('a thousand', state='pushing up the daisies')  # 1 positional, 1 keyword
+

but all the following calls would be invalid:

parrot()                     # required argument missing
+parrot(voltage=5.0, 'dead')  # non-keyword argument after a keyword argument
+parrot(110, voltage=220)     # duplicate value for the same argument
+parrot(actor='John Cleese')  # unknown keyword argument
+

In a function call, keyword arguments must follow positional arguments. All the keyword arguments passed must match one of the arguments accepted by the function (e.g. actor is not a valid argument for the parrot function), and their order is not important. This also includes non-optional arguments (e.g. parrot(voltage=1000) is valid too). No argument may receive a value more than once. Here’s an example that fails due to this restriction:

>>> def function(a):
+...     pass
+...
+>>> function(0, a=0)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: function() got multiple values for argument 'a'
+

When a final formal parameter of the form **name is present, it receives a dictionary (see Mapping Types — dict) containing all keyword arguments except for those corresponding to a formal parameter. This may be combined with a formal parameter of the form *name (described in the next subsection) which receives a tuple containing the positional arguments beyond the formal parameter list. (*name must occur before **name.) For example, if we define a function like this:

def cheeseshop(kind, *arguments, **keywords):
+    print("-- Do you have any", kind, "?")
+    print("-- I'm sorry, we're all out of", kind)
+    for arg in arguments:
+        print(arg)
+    print("-" * 40)
+    for kw in keywords:
+        print(kw, ":", keywords[kw])
+

It could be called like this:

cheeseshop("Limburger", "It's very runny, sir.",
+           "It's really very, VERY runny, sir.",
+           shopkeeper="Michael Palin",
+           client="John Cleese",
+           sketch="Cheese Shop Sketch")
+

and of course it would print:

-- Do you have any Limburger ?
+-- I'm sorry, we're all out of Limburger
+It's very runny, sir.
+It's really very, VERY runny, sir.
+----------------------------------------
+shopkeeper : Michael Palin
+client : John Cleese
+sketch : Cheese Shop Sketch
+

Note that the order in which the keyword arguments are printed is guaranteed to match the order in which they were provided in the function call.

+4.8.3. Special parameters

By default, arguments may be passed to a Python function either by position or explicitly by keyword. For readability and performance, it makes sense to restrict the way arguments can be passed so that a developer need only look at the function definition to determine if items are passed by position, by position or keyword, or by keyword.

A function definition may look like:

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
+      -----------    ----------     ----------
+        |             |                  |
+        |        Positional or keyword   |
+        |                                - Keyword only
+         -- Positional only
+

where / and * are optional. If used, these symbols indicate the kind of parameter by how the arguments may be passed to the function: positional-only, positional-or-keyword, and keyword-only. Keyword parameters are also referred to as named parameters.

+4.8.3.1. Positional-or-Keyword Arguments

If / and * are not present in the function definition, arguments may be passed to a function by position or by keyword.

+4.8.3.2. Positional-Only Parameters

Looking at this in a bit more detail, it is possible to mark certain parameters as positional-only. If positional-only, the parameters’ order matters, and the parameters cannot be passed by keyword. Positional-only parameters are placed before a / (forward-slash). The / is used to logically separate the positional-only parameters from the rest of the parameters. If there is no / in the function definition, there are no positional-only parameters.

Parameters following the / may be positional-or-keyword or keyword-only.

+4.8.3.3. Keyword-Only Arguments

To mark parameters as keyword-only, indicating the parameters must be passed by keyword argument, place an * in the arguments list just before the first keyword-only parameter.

+4.8.3.4. Function Examples

Consider the following example function definitions paying close attention to the markers / and *:

>>> def standard_arg(arg):
+...     print(arg)
+...
+>>> def pos_only_arg(arg, /):
+...     print(arg)
+...
+>>> def kwd_only_arg(*, arg):
+...     print(arg)
+...
+>>> def combined_example(pos_only, /, standard, *, kwd_only):
+...     print(pos_only, standard, kwd_only)
+

The first function definition, standard_arg, the most familiar form, places no restrictions on the calling convention and arguments may be passed by position or keyword:

>>> standard_arg(2)
+2
+
+>>> standard_arg(arg=2)
+2
+

The second function pos_only_arg is restricted to only use positional parameters as there is a / in the function definition:

>>> pos_only_arg(1)
+1
+
+>>> pos_only_arg(arg=1)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: pos_only_arg() got some positional-only arguments passed as keyword arguments: 'arg'
+

The third function kwd_only_args only allows keyword arguments as indicated by a * in the function definition:

>>> kwd_only_arg(3)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: kwd_only_arg() takes 0 positional arguments but 1 was given
+
+>>> kwd_only_arg(arg=3)
+3
+

And the last uses all three calling conventions in the same function definition:

>>> combined_example(1, 2, 3)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: combined_example() takes 2 positional arguments but 3 were given
+
+>>> combined_example(1, 2, kwd_only=3)
+1 2 3
+
+>>> combined_example(1, standard=2, kwd_only=3)
+1 2 3
+
+>>> combined_example(pos_only=1, standard=2, kwd_only=3)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: combined_example() got some positional-only arguments passed as keyword arguments: 'pos_only'
+

Finally, consider this function definition which has a potential collision between the positional argument name and **kwds which has name as a key:

def foo(name, **kwds):
+    return 'name' in kwds
+

There is no possible call that will make it return True as the keyword 'name' will always bind to the first parameter. For example:

>>> foo(1, **{'name': 2})
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: foo() got multiple values for argument 'name'
+>>>
+

But using / (positional only arguments), it is possible since it allows name as a positional argument and 'name' as a key in the keyword arguments:

>>> def foo(name, /, **kwds):
+...     return 'name' in kwds
+...
+>>> foo(1, **{'name': 2})
+True
+

In other words, the names of positional-only parameters can be used in **kwds without ambiguity.

+4.8.3.5. Recap

The use case will determine which parameters to use in the function definition:

def f(pos1, pos2, /, pos_or_kwd, *, kwd1, kwd2):
+

As guidance:

  • Use positional-only if you want the name of the parameters to not be available to the user. This is useful when parameter names have no real meaning, if you want to enforce the order of the arguments when the function is called or if you need to take some positional parameters and arbitrary keywords.
  • Use keyword-only when names have meaning and the function definition is more understandable by being explicit with names or you want to prevent users relying on the position of the argument being passed.
  • For an API, use positional-only to prevent breaking API changes if the parameter’s name is modified in the future.

+4.8.4. Arbitrary Argument Lists

Finally, the least frequently used option is to specify that a function can be called with an arbitrary number of arguments. These arguments will be wrapped up in a tuple (see Tuples and Sequences). Before the variable number of arguments, zero or more normal arguments may occur.

def write_multiple_items(file, separator, *args):
+    file.write(separator.join(args))
+

Normally, these variadic arguments will be last in the list of formal parameters, because they scoop up all remaining input arguments that are passed to the function. Any formal parameters which occur after the *args parameter are ‘keyword-only’ arguments, meaning that they can only be used as keywords rather than positional arguments.

>>> def concat(*args, sep="/"):
+...     return sep.join(args)
+...
+>>> concat("earth", "mars", "venus")
+'earth/mars/venus'
+>>> concat("earth", "mars", "venus", sep=".")
+'earth.mars.venus'
+

+4.8.5. Unpacking Argument Lists

The reverse situation occurs when the arguments are already in a list or tuple but need to be unpacked for a function call requiring separate positional arguments. For instance, the built-in range() function expects separate start and stop arguments. If they are not available separately, write the function call with the *-operator to unpack the arguments out of a list or tuple:

>>> list(range(3, 6))            # normal call with separate arguments
+[3, 4, 5]
+>>> args = [3, 6]
+>>> list(range(*args))            # call with arguments unpacked from a list
+[3, 4, 5]
+

In the same fashion, dictionaries can deliver keyword arguments with the **-operator:

>>> def parrot(voltage, state='a stiff', action='voom'):
+...     print("-- This parrot wouldn't", action, end=' ')
+...     print("if you put", voltage, "volts through it.", end=' ')
+...     print("E's", state, "!")
+...
+>>> d = {"voltage": "four million", "state": "bleedin' demised", "action": "VOOM"}
+>>> parrot(**d)
+-- This parrot wouldn't VOOM if you put four million volts through it. E's bleedin' demised !
+

+4.8.6. Lambda Expressions

Small anonymous functions can be created with the lambda keyword. This function returns the sum of its two arguments: lambda a, b: a+b. Lambda functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Semantically, they are just syntactic sugar for a normal function definition. Like nested function definitions, lambda functions can reference variables from the containing scope:

>>> def make_incrementor(n):
+...     return lambda x: x + n
+...
+>>> f = make_incrementor(42)
+>>> f(0)
+42
+>>> f(1)
+43
+

The above example uses a lambda expression to return a function. Another use is to pass a small function as an argument:

>>> pairs = [(1, 'one'), (2, 'two'), (3, 'three'), (4, 'four')]
+>>> pairs.sort(key=lambda pair: pair[1])
+>>> pairs
+[(4, 'four'), (1, 'one'), (3, 'three'), (2, 'two')]
+

+4.8.7. Documentation Strings

Here are some conventions about the content and formatting of documentation strings.

The first line should always be a short, concise summary of the object’s purpose. For brevity, it should not explicitly state the object’s name or type, since these are available by other means (except if the name happens to be a verb describing a function’s operation). This line should begin with a capital letter and end with a period.

If there are more lines in the documentation string, the second line should be blank, visually separating the summary from the rest of the description. The following lines should be one or more paragraphs describing the object’s calling conventions, its side effects, etc.

The Python parser does not strip indentation from multi-line string literals in Python, so tools that process documentation have to strip indentation if desired. This is done using the following convention. The first non-blank line after the first line of the string determines the amount of indentation for the entire documentation string. (We can’t use the first line since it is generally adjacent to the string’s opening quotes so its indentation is not apparent in the string literal.) Whitespace “equivalent” to this indentation is then stripped from the start of all lines of the string. Lines that are indented less should not occur, but if they occur all their leading whitespace should be stripped. Equivalence of whitespace should be tested after expansion of tabs (to 8 spaces, normally).

Here is an example of a multi-line docstring:

>>> def my_function():
+...     """Do nothing, but document it.
+...
+...     No, really, it doesn't do anything.
+...     """
+...     pass
+...
+>>> print(my_function.__doc__)
+Do nothing, but document it.
+
+    No, really, it doesn't do anything.
+

+4.8.8. Function Annotations

Function annotations are completely optional metadata information about the types used by user-defined functions (see PEP 3107 and PEP 484 for more information).

Annotations are stored in the __annotations__ attribute of the function as a dictionary and have no effect on any other part of the function. Parameter annotations are defined by a colon after the parameter name, followed by an expression evaluating to the value of the annotation. Return annotations are defined by a literal ->, followed by an expression, between the parameter list and the colon denoting the end of the def statement. The following example has a required argument, an optional argument, and the return value annotated:

>>> def f(ham: str, eggs: str = 'eggs') -> str:
+...     print("Annotations:", f.__annotations__)
+...     print("Arguments:", ham, eggs)
+...     return ham + ' and ' + eggs
+...
+>>> f('spam')
+Annotations: {'ham': <class 'str'>, 'return': <class 'str'>, 'eggs': <class 'str'>}
+Arguments: spam eggs
+'spam and eggs'
+

+4.9. Intermezzo: Coding Style

Now that you are about to write longer, more complex pieces of Python, it is a good time to talk about coding style. Most languages can be written (or more concise, formatted) in different styles; some are more readable than others. Making it easy for others to read your code is always a good idea, and adopting a nice coding style helps tremendously for that.

For Python, PEP 8 has emerged as the style guide that most projects adhere to; it promotes a very readable and eye-pleasing coding style. Every Python developer should read it at some point; here are the most important points extracted for you:

  • +

    Use 4-space indentation, and no tabs.

    4 spaces are a good compromise between small indentation (allows greater nesting depth) and large indentation (easier to read). Tabs introduce confusion, and are best left out.

  • +

    Wrap lines so that they don’t exceed 79 characters.

    This helps users with small displays and makes it possible to have several code files side-by-side on larger displays.

  • Use blank lines to separate functions and classes, and larger blocks of code inside functions.
  • When possible, put comments on a line of their own.
  • Use docstrings.
  • Use spaces around operators and after commas, but not directly inside bracketing constructs: a = f(1, 2) + g(3, 4).
  • Name your classes and functions consistently; the convention is to use UpperCamelCase for classes and lowercase_with_underscores for functions and methods. Always use self as the name for the first method argument (see A First Look at Classes for more on classes and methods).
  • Don’t use fancy encodings if your code is meant to be used in international environments. Python’s default, UTF-8, or even plain ASCII work best in any case.
  • Likewise, don’t use non-ASCII characters in identifiers if there is only the slightest chance people speaking a different language will read or maintain the code.

Footnotes

+1
+

Actually, call by object reference would be a better description, since if a mutable object is passed, the caller will see any changes the callee makes to it (items inserted into a list).

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/controlflow.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fdatastructures.html b/devdocs/python~3.12/tutorial%2Fdatastructures.html new file mode 100644 index 00000000..c2d284c6 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fdatastructures.html @@ -0,0 +1,325 @@ +

Data Structures

This chapter describes some things you’ve learned about already in more detail, and adds some new things as well.

+5.1. More on Lists

The list data type has some more methods. Here are all of the methods of list objects:

list.append(x) +
+

Add an item to the end of the list. Equivalent to a[len(a):] = [x].

+
list.extend(iterable) +
+

Extend the list by appending all the items from the iterable. Equivalent to a[len(a):] = iterable.

+
list.insert(i, x) +
+

Insert an item at a given position. The first argument is the index of the element before which to insert, so a.insert(0, x) inserts at the front of the list, and a.insert(len(a), x) is equivalent to a.append(x).

+
list.remove(x) +
+

Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item.

+
list.pop([i]) +
+

Remove the item at the given position in the list, and return it. If no index is specified, a.pop() removes and returns the last item in the list. (The square brackets around the i in the method signature denote that the parameter is optional, not that you should type square brackets at that position. You will see this notation frequently in the Python Library Reference.)

+
list.clear() +
+

Remove all items from the list. Equivalent to del a[:].

+
list.index(x[, start[, end]]) +
+

Return zero-based index in the list of the first item whose value is equal to x. Raises a ValueError if there is no such item.

The optional arguments start and end are interpreted as in the slice notation and are used to limit the search to a particular subsequence of the list. The returned index is computed relative to the beginning of the full sequence rather than the start argument.

+
list.count(x) +
+

Return the number of times x appears in the list.

+
list.sort(*, key=None, reverse=False) +
+

Sort the items of the list in place (the arguments can be used for sort customization, see sorted() for their explanation).

+
list.reverse() +
+

Reverse the elements of the list in place.

+
list.copy() +
+

Return a shallow copy of the list. Equivalent to a[:].

+

An example that uses most of the list methods:

>>> fruits = ['orange', 'apple', 'pear', 'banana', 'kiwi', 'apple', 'banana']
+>>> fruits.count('apple')
+2
+>>> fruits.count('tangerine')
+0
+>>> fruits.index('banana')
+3
+>>> fruits.index('banana', 4)  # Find next banana starting at position 4
+6
+>>> fruits.reverse()
+>>> fruits
+['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange']
+>>> fruits.append('grape')
+>>> fruits
+['banana', 'apple', 'kiwi', 'banana', 'pear', 'apple', 'orange', 'grape']
+>>> fruits.sort()
+>>> fruits
+['apple', 'apple', 'banana', 'banana', 'grape', 'kiwi', 'orange', 'pear']
+>>> fruits.pop()
+'pear'
+

You might have noticed that methods like insert, remove or sort that only modify the list have no return value printed – they return the default None. 1 This is a design principle for all mutable data structures in Python.

Another thing you might notice is that not all data can be sorted or compared. For instance, [None, 'hello', 10] doesn’t sort because integers can’t be compared to strings and None can’t be compared to other types. Also, there are some types that don’t have a defined ordering relation. For example, 3+4j < 5+7j isn’t a valid comparison.

+5.1.1. Using Lists as Stacks

The list methods make it very easy to use a list as a stack, where the last element added is the first element retrieved (“last-in, first-out”). To add an item to the top of the stack, use append(). To retrieve an item from the top of the stack, use pop() without an explicit index. For example:

>>> stack = [3, 4, 5]
+>>> stack.append(6)
+>>> stack.append(7)
+>>> stack
+[3, 4, 5, 6, 7]
+>>> stack.pop()
+7
+>>> stack
+[3, 4, 5, 6]
+>>> stack.pop()
+6
+>>> stack.pop()
+5
+>>> stack
+[3, 4]
+

+5.1.2. Using Lists as Queues

It is also possible to use a list as a queue, where the first element added is the first element retrieved (“first-in, first-out”); however, lists are not efficient for this purpose. While appends and pops from the end of list are fast, doing inserts or pops from the beginning of a list is slow (because all of the other elements have to be shifted by one).

To implement a queue, use collections.deque which was designed to have fast appends and pops from both ends. For example:

>>> from collections import deque
+>>> queue = deque(["Eric", "John", "Michael"])
+>>> queue.append("Terry")           # Terry arrives
+>>> queue.append("Graham")          # Graham arrives
+>>> queue.popleft()                 # The first to arrive now leaves
+'Eric'
+>>> queue.popleft()                 # The second to arrive now leaves
+'John'
+>>> queue                           # Remaining queue in order of arrival
+deque(['Michael', 'Terry', 'Graham'])
+

+5.1.3. List Comprehensions

List comprehensions provide a concise way to create lists. Common applications are to make new lists where each element is the result of some operations applied to each member of another sequence or iterable, or to create a subsequence of those elements that satisfy a certain condition.

For example, assume we want to create a list of squares, like:

>>> squares = []
+>>> for x in range(10):
+...     squares.append(x**2)
+...
+>>> squares
+[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
+

Note that this creates (or overwrites) a variable named x that still exists after the loop completes. We can calculate the list of squares without any side effects using:

squares = list(map(lambda x: x**2, range(10)))
+

or, equivalently:

squares = [x**2 for x in range(10)]
+

which is more concise and readable.

A list comprehension consists of brackets containing an expression followed by a for clause, then zero or more for or if clauses. The result will be a new list resulting from evaluating the expression in the context of the for and if clauses which follow it. For example, this listcomp combines the elements of two lists if they are not equal:

>>> [(x, y) for x in [1,2,3] for y in [3,1,4] if x != y]
+[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
+

and it’s equivalent to:

>>> combs = []
+>>> for x in [1,2,3]:
+...     for y in [3,1,4]:
+...         if x != y:
+...             combs.append((x, y))
+...
+>>> combs
+[(1, 3), (1, 4), (2, 3), (2, 1), (2, 4), (3, 1), (3, 4)]
+

Note how the order of the for and if statements is the same in both these snippets.

If the expression is a tuple (e.g. the (x, y) in the previous example), it must be parenthesized.

>>> vec = [-4, -2, 0, 2, 4]
+>>> # create a new list with the values doubled
+>>> [x*2 for x in vec]
+[-8, -4, 0, 4, 8]
+>>> # filter the list to exclude negative numbers
+>>> [x for x in vec if x >= 0]
+[0, 2, 4]
+>>> # apply a function to all the elements
+>>> [abs(x) for x in vec]
+[4, 2, 0, 2, 4]
+>>> # call a method on each element
+>>> freshfruit = ['  banana', '  loganberry ', 'passion fruit  ']
+>>> [weapon.strip() for weapon in freshfruit]
+['banana', 'loganberry', 'passion fruit']
+>>> # create a list of 2-tuples like (number, square)
+>>> [(x, x**2) for x in range(6)]
+[(0, 0), (1, 1), (2, 4), (3, 9), (4, 16), (5, 25)]
+>>> # the tuple must be parenthesized, otherwise an error is raised
+>>> [x, x**2 for x in range(6)]
+  File "<stdin>", line 1
+    [x, x**2 for x in range(6)]
+     ^^^^^^^
+SyntaxError: did you forget parentheses around the comprehension target?
+>>> # flatten a list using a listcomp with two 'for'
+>>> vec = [[1,2,3], [4,5,6], [7,8,9]]
+>>> [num for elem in vec for num in elem]
+[1, 2, 3, 4, 5, 6, 7, 8, 9]
+

List comprehensions can contain complex expressions and nested functions:

>>> from math import pi
+>>> [str(round(pi, i)) for i in range(1, 6)]
+['3.1', '3.14', '3.142', '3.1416', '3.14159']
+

+5.1.4. Nested List Comprehensions

The initial expression in a list comprehension can be any arbitrary expression, including another list comprehension.

Consider the following example of a 3x4 matrix implemented as a list of 3 lists of length 4:

>>> matrix = [
+...     [1, 2, 3, 4],
+...     [5, 6, 7, 8],
+...     [9, 10, 11, 12],
+... ]
+

The following list comprehension will transpose rows and columns:

>>> [[row[i] for row in matrix] for i in range(4)]
+[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
+

As we saw in the previous section, the inner list comprehension is evaluated in the context of the for that follows it, so this example is equivalent to:

>>> transposed = []
+>>> for i in range(4):
+...     transposed.append([row[i] for row in matrix])
+...
+>>> transposed
+[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
+

which, in turn, is the same as:

>>> transposed = []
+>>> for i in range(4):
+...     # the following 3 lines implement the nested listcomp
+...     transposed_row = []
+...     for row in matrix:
+...         transposed_row.append(row[i])
+...     transposed.append(transposed_row)
+...
+>>> transposed
+[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]
+

In the real world, you should prefer built-in functions to complex flow statements. The zip() function would do a great job for this use case:

>>> list(zip(*matrix))
+[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]
+

See Unpacking Argument Lists for details on the asterisk in this line.

+5.2. The del statement

There is a way to remove an item from a list given its index instead of its value: the del statement. This differs from the pop() method which returns a value. The del statement can also be used to remove slices from a list or clear the entire list (which we did earlier by assignment of an empty list to the slice). For example:

>>> a = [-1, 1, 66.25, 333, 333, 1234.5]
+>>> del a[0]
+>>> a
+[1, 66.25, 333, 333, 1234.5]
+>>> del a[2:4]
+>>> a
+[1, 66.25, 1234.5]
+>>> del a[:]
+>>> a
+[]
+

del can also be used to delete entire variables:

>>> del a
+

Referencing the name a hereafter is an error (at least until another value is assigned to it). We’ll find other uses for del later.

+5.3. Tuples and Sequences

We saw that lists and strings have many common properties, such as indexing and slicing operations. They are two examples of sequence data types (see Sequence Types — list, tuple, range). Since Python is an evolving language, other sequence data types may be added. There is also another standard sequence data type: the tuple.

A tuple consists of a number of values separated by commas, for instance:

>>> t = 12345, 54321, 'hello!'
+>>> t[0]
+12345
+>>> t
+(12345, 54321, 'hello!')
+>>> # Tuples may be nested:
+... u = t, (1, 2, 3, 4, 5)
+>>> u
+((12345, 54321, 'hello!'), (1, 2, 3, 4, 5))
+>>> # Tuples are immutable:
+... t[0] = 88888
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: 'tuple' object does not support item assignment
+>>> # but they can contain mutable objects:
+... v = ([1, 2, 3], [3, 2, 1])
+>>> v
+([1, 2, 3], [3, 2, 1])
+

As you see, on output tuples are always enclosed in parentheses, so that nested tuples are interpreted correctly; they may be input with or without surrounding parentheses, although often parentheses are necessary anyway (if the tuple is part of a larger expression). It is not possible to assign to the individual items of a tuple, however it is possible to create tuples which contain mutable objects, such as lists.

Though tuples may seem similar to lists, they are often used in different situations and for different purposes. Tuples are immutable, and usually contain a heterogeneous sequence of elements that are accessed via unpacking (see later in this section) or indexing (or even by attribute in the case of namedtuples). Lists are mutable, and their elements are usually homogeneous and are accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1 items: the syntax has some extra quirks to accommodate these. Empty tuples are constructed by an empty pair of parentheses; a tuple with one item is constructed by following a value with a comma (it is not sufficient to enclose a single value in parentheses). Ugly, but effective. For example:

>>> empty = ()
+>>> singleton = 'hello',    # <-- note trailing comma
+>>> len(empty)
+0
+>>> len(singleton)
+1
+>>> singleton
+('hello',)
+

The statement t = 12345, 54321, 'hello!' is an example of tuple packing: the values 12345, 54321 and 'hello!' are packed together in a tuple. The reverse operation is also possible:

>>> x, y, z = t
+

This is called, appropriately enough, sequence unpacking and works for any sequence on the right-hand side. Sequence unpacking requires that there are as many variables on the left side of the equals sign as there are elements in the sequence. Note that multiple assignment is really just a combination of tuple packing and sequence unpacking.

+5.4. Sets

Python also includes a data type for sets. A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Curly braces or the set() function can be used to create sets. Note: to create an empty set you have to use set(), not {}; the latter creates an empty dictionary, a data structure that we discuss in the next section.

Here is a brief demonstration:

>>> basket = {'apple', 'orange', 'apple', 'pear', 'orange', 'banana'}
+>>> print(basket)                      # show that duplicates have been removed
+{'orange', 'banana', 'pear', 'apple'}
+>>> 'orange' in basket                 # fast membership testing
+True
+>>> 'crabgrass' in basket
+False
+
+>>> # Demonstrate set operations on unique letters from two words
+...
+>>> a = set('abracadabra')
+>>> b = set('alacazam')
+>>> a                                  # unique letters in a
+{'a', 'r', 'b', 'c', 'd'}
+>>> a - b                              # letters in a but not in b
+{'r', 'd', 'b'}
+>>> a | b                              # letters in a or b or both
+{'a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'}
+>>> a & b                              # letters in both a and b
+{'a', 'c'}
+>>> a ^ b                              # letters in a or b but not both
+{'r', 'd', 'b', 'm', 'z', 'l'}
+

Similarly to list comprehensions, set comprehensions are also supported:

>>> a = {x for x in 'abracadabra' if x not in 'abc'}
+>>> a
+{'r', 'd'}
+

+5.5. Dictionaries

Another useful data type built into Python is the dictionary (see Mapping Types — dict). Dictionaries are sometimes found in other languages as “associative memories” or “associative arrays”. Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys. Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().

It is best to think of a dictionary as a set of key: value pairs, with the requirement that the keys are unique (within one dictionary). A pair of braces creates an empty dictionary: {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.

The main operations on a dictionary are storing a value with some key and extracting the value given the key. It is also possible to delete a key:value pair with del. If you store using a key that is already in use, the old value associated with that key is forgotten. It is an error to extract a value using a non-existent key.

Performing list(d) on a dictionary returns a list of all the keys used in the dictionary, in insertion order (if you want it sorted, just use sorted(d) instead). To check whether a single key is in the dictionary, use the in keyword.

Here is a small example using a dictionary:

>>> tel = {'jack': 4098, 'sape': 4139}
+>>> tel['guido'] = 4127
+>>> tel
+{'jack': 4098, 'sape': 4139, 'guido': 4127}
+>>> tel['jack']
+4098
+>>> del tel['sape']
+>>> tel['irv'] = 4127
+>>> tel
+{'jack': 4098, 'guido': 4127, 'irv': 4127}
+>>> list(tel)
+['jack', 'guido', 'irv']
+>>> sorted(tel)
+['guido', 'irv', 'jack']
+>>> 'guido' in tel
+True
+>>> 'jack' not in tel
+False
+

The dict() constructor builds dictionaries directly from sequences of key-value pairs:

>>> dict([('sape', 4139), ('guido', 4127), ('jack', 4098)])
+{'sape': 4139, 'guido': 4127, 'jack': 4098}
+

In addition, dict comprehensions can be used to create dictionaries from arbitrary key and value expressions:

>>> {x: x**2 for x in (2, 4, 6)}
+{2: 4, 4: 16, 6: 36}
+

When the keys are simple strings, it is sometimes easier to specify pairs using keyword arguments:

>>> dict(sape=4139, guido=4127, jack=4098)
+{'sape': 4139, 'guido': 4127, 'jack': 4098}
+

+5.6. Looping Techniques

When looping through dictionaries, the key and corresponding value can be retrieved at the same time using the items() method.

>>> knights = {'gallahad': 'the pure', 'robin': 'the brave'}
+>>> for k, v in knights.items():
+...     print(k, v)
+...
+gallahad the pure
+robin the brave
+

When looping through a sequence, the position index and corresponding value can be retrieved at the same time using the enumerate() function.

>>> for i, v in enumerate(['tic', 'tac', 'toe']):
+...     print(i, v)
+...
+0 tic
+1 tac
+2 toe
+

To loop over two or more sequences at the same time, the entries can be paired with the zip() function.

>>> questions = ['name', 'quest', 'favorite color']
+>>> answers = ['lancelot', 'the holy grail', 'blue']
+>>> for q, a in zip(questions, answers):
+...     print('What is your {0}?  It is {1}.'.format(q, a))
+...
+What is your name?  It is lancelot.
+What is your quest?  It is the holy grail.
+What is your favorite color?  It is blue.
+

To loop over a sequence in reverse, first specify the sequence in a forward direction and then call the reversed() function.

>>> for i in reversed(range(1, 10, 2)):
+...     print(i)
+...
+9
+7
+5
+3
+1
+

To loop over a sequence in sorted order, use the sorted() function which returns a new sorted list while leaving the source unaltered.

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
+>>> for i in sorted(basket):
+...     print(i)
+...
+apple
+apple
+banana
+orange
+orange
+pear
+

Using set() on a sequence eliminates duplicate elements. The use of sorted() in combination with set() over a sequence is an idiomatic way to loop over unique elements of the sequence in sorted order.

>>> basket = ['apple', 'orange', 'apple', 'pear', 'orange', 'banana']
+>>> for f in sorted(set(basket)):
+...     print(f)
+...
+apple
+banana
+orange
+pear
+

It is sometimes tempting to change a list while you are looping over it; however, it is often simpler and safer to create a new list instead.

>>> import math
+>>> raw_data = [56.2, float('NaN'), 51.7, 55.3, 52.5, float('NaN'), 47.8]
+>>> filtered_data = []
+>>> for value in raw_data:
+...     if not math.isnan(value):
+...         filtered_data.append(value)
+...
+>>> filtered_data
+[56.2, 51.7, 55.3, 52.5, 47.8]
+

+5.7. More on Conditions

The conditions used in while and if statements can contain any operators, not just comparisons.

The comparison operators in and not in are membership tests that determine whether a value is in (or not in) a container. The operators is and is not compare whether two objects are really the same object. All comparison operators have the same priority, which is lower than that of all numerical operators.

Comparisons can be chained. For example, a < b == c tests whether a is less than b and moreover b equals c.

Comparisons may be combined using the Boolean operators and and or, and the outcome of a comparison (or of any other Boolean expression) may be negated with not. These have lower priorities than comparison operators; between them, not has the highest priority and or the lowest, so that A and +not B or C is equivalent to (A and (not B)) or C. As always, parentheses can be used to express the desired composition.

The Boolean operators and and or are so-called short-circuit operators: their arguments are evaluated from left to right, and evaluation stops as soon as the outcome is determined. For example, if A and C are true but B is false, A and B and C does not evaluate the expression C. When used as a general value and not as a Boolean, the return value of a short-circuit operator is the last evaluated argument.

It is possible to assign the result of a comparison or other Boolean expression to a variable. For example,

>>> string1, string2, string3 = '', 'Trondheim', 'Hammer Dance'
+>>> non_null = string1 or string2 or string3
+>>> non_null
+'Trondheim'
+

Note that in Python, unlike C, assignment inside expressions must be done explicitly with the walrus operator :=. This avoids a common class of problems encountered in C programs: typing = in an expression when == was intended.

+5.8. Comparing Sequences and Other Types

Sequence objects typically may be compared to other objects with the same sequence type. The comparison uses lexicographical ordering: first the first two items are compared, and if they differ this determines the outcome of the comparison; if they are equal, the next two items are compared, and so on, until either sequence is exhausted. If two items to be compared are themselves sequences of the same type, the lexicographical comparison is carried out recursively. If all items of two sequences compare equal, the sequences are considered equal. If one sequence is an initial sub-sequence of the other, the shorter sequence is the smaller (lesser) one. Lexicographical ordering for strings uses the Unicode code point number to order individual characters. Some examples of comparisons between sequences of the same type:

(1, 2, 3)              < (1, 2, 4)
+[1, 2, 3]              < [1, 2, 4]
+'ABC' < 'C' < 'Pascal' < 'Python'
+(1, 2, 3, 4)           < (1, 2, 4)
+(1, 2)                 < (1, 2, -1)
+(1, 2, 3)             == (1.0, 2.0, 3.0)
+(1, 2, ('aa', 'ab'))   < (1, 2, ('abc', 'a'), 4)
+

Note that comparing objects of different types with < or > is legal provided that the objects have appropriate comparison methods. For example, mixed numeric types are compared according to their numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing an arbitrary ordering, the interpreter will raise a TypeError exception.

Footnotes

+1
+

Other languages may return the mutated object, which allows method chaining, such as d->insert("a")->remove("b")->sort();.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/datastructures.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Ferrors.html b/devdocs/python~3.12/tutorial%2Ferrors.html new file mode 100644 index 00000000..f7a09d7a --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Ferrors.html @@ -0,0 +1,321 @@ +

Errors and Exceptions

Until now error messages haven’t been more than mentioned, but if you have tried out the examples you have probably seen some. There are (at least) two distinguishable kinds of errors: syntax errors and exceptions.

+8.1. Syntax Errors

Syntax errors, also known as parsing errors, are perhaps the most common kind of complaint you get while you are still learning Python:

>>> while True print('Hello world')
+  File "<stdin>", line 1
+    while True print('Hello world')
+                   ^
+SyntaxError: invalid syntax
+

The parser repeats the offending line and displays a little ‘arrow’ pointing at the earliest point in the line where the error was detected. The error is caused by (or at least detected at) the token preceding the arrow: in the example, the error is detected at the function print(), since a colon (':') is missing before it. File name and line number are printed so you know where to look in case the input came from a script.

+8.2. Exceptions

Even if a statement or expression is syntactically correct, it may cause an error when an attempt is made to execute it. Errors detected during execution are called exceptions and are not unconditionally fatal: you will soon learn how to handle them in Python programs. Most exceptions are not handled by programs, however, and result in error messages as shown here:

>>> 10 * (1/0)
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ZeroDivisionError: division by zero
+>>> 4 + spam*3
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+NameError: name 'spam' is not defined
+>>> '2' + 2
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: can only concatenate str (not "int") to str
+

The last line of the error message indicates what happened. Exceptions come in different types, and the type is printed as part of the message: the types in the example are ZeroDivisionError, NameError and TypeError. The string printed as the exception type is the name of the built-in exception that occurred. This is true for all built-in exceptions, but need not be true for user-defined exceptions (although it is a useful convention). Standard exception names are built-in identifiers (not reserved keywords).

The rest of the line provides detail based on the type of exception and what caused it.

The preceding part of the error message shows the context where the exception occurred, in the form of a stack traceback. In general it contains a stack traceback listing source lines; however, it will not display lines read from standard input.

Built-in Exceptions lists the built-in exceptions and their meanings.

+8.3. Handling Exceptions

It is possible to write programs that handle selected exceptions. Look at the following example, which asks the user for input until a valid integer has been entered, but allows the user to interrupt the program (using Control-C or whatever the operating system supports); note that a user-generated interruption is signalled by raising the KeyboardInterrupt exception.

>>> while True:
+...     try:
+...         x = int(input("Please enter a number: "))
+...         break
+...     except ValueError:
+...         print("Oops!  That was no valid number.  Try again...")
+...
+

The try statement works as follows.

  • First, the try clause (the statement(s) between the try and except keywords) is executed.
  • If no exception occurs, the except clause is skipped and execution of the try statement is finished.
  • If an exception occurs during execution of the try clause, the rest of the clause is skipped. Then, if its type matches the exception named after the except keyword, the except clause is executed, and then execution continues after the try/except block.
  • If an exception occurs which does not match the exception named in the except clause, it is passed on to outer try statements; if no handler is found, it is an unhandled exception and execution stops with an error message.

A try statement may have more than one except clause, to specify handlers for different exceptions. At most one handler will be executed. Handlers only handle exceptions that occur in the corresponding try clause, not in other handlers of the same try statement. An except clause may name multiple exceptions as a parenthesized tuple, for example:

... except (RuntimeError, TypeError, NameError):
+...     pass
+

A class in an except clause is compatible with an exception if it is the same class or a base class thereof (but not the other way around — an except clause listing a derived class is not compatible with a base class). For example, the following code will print B, C, D in that order:

class B(Exception):
+    pass
+
+class C(B):
+    pass
+
+class D(C):
+    pass
+
+for cls in [B, C, D]:
+    try:
+        raise cls()
+    except D:
+        print("D")
+    except C:
+        print("C")
+    except B:
+        print("B")
+

Note that if the except clauses were reversed (with except B first), it would have printed B, B, B — the first matching except clause is triggered.

When an exception occurs, it may have associated values, also known as the exception’s arguments. The presence and types of the arguments depend on the exception type.

The except clause may specify a variable after the exception name. The variable is bound to the exception instance which typically has an args attribute that stores the arguments. For convenience, builtin exception types define __str__() to print all the arguments without explicitly accessing .args.

>>> try:
+...     raise Exception('spam', 'eggs')
+... except Exception as inst:
+...     print(type(inst))    # the exception type
+...     print(inst.args)     # arguments stored in .args
+...     print(inst)          # __str__ allows args to be printed directly,
+...                          # but may be overridden in exception subclasses
+...     x, y = inst.args     # unpack args
+...     print('x =', x)
+...     print('y =', y)
+...
+<class 'Exception'>
+('spam', 'eggs')
+('spam', 'eggs')
+x = spam
+y = eggs
+

The exception’s __str__() output is printed as the last part (‘detail’) of the message for unhandled exceptions.

BaseException is the common base class of all exceptions. One of its subclasses, Exception, is the base class of all the non-fatal exceptions. Exceptions which are not subclasses of Exception are not typically handled, because they are used to indicate that the program should terminate. They include SystemExit which is raised by sys.exit() and KeyboardInterrupt which is raised when a user wishes to interrupt the program.

Exception can be used as a wildcard that catches (almost) everything. However, it is good practice to be as specific as possible with the types of exceptions that we intend to handle, and to allow any unexpected exceptions to propagate on.

The most common pattern for handling Exception is to print or log the exception and then re-raise it (allowing a caller to handle the exception as well):

import sys
+
+try:
+    f = open('myfile.txt')
+    s = f.readline()
+    i = int(s.strip())
+except OSError as err:
+    print("OS error:", err)
+except ValueError:
+    print("Could not convert data to an integer.")
+except Exception as err:
+    print(f"Unexpected {err=}, {type(err)=}")
+    raise
+

The try … except statement has an optional else clause, which, when present, must follow all except clauses. It is useful for code that must be executed if the try clause does not raise an exception. For example:

for arg in sys.argv[1:]:
+    try:
+        f = open(arg, 'r')
+    except OSError:
+        print('cannot open', arg)
+    else:
+        print(arg, 'has', len(f.readlines()), 'lines')
+        f.close()
+

The use of the else clause is better than adding additional code to the try clause because it avoids accidentally catching an exception that wasn’t raised by the code being protected by the try … except statement.

Exception handlers do not handle only exceptions that occur immediately in the try clause, but also those that occur inside functions that are called (even indirectly) in the try clause. For example:

>>> def this_fails():
+...     x = 1/0
+...
+>>> try:
+...     this_fails()
+... except ZeroDivisionError as err:
+...     print('Handling run-time error:', err)
+...
+Handling run-time error: division by zero
+

+8.4. Raising Exceptions

The raise statement allows the programmer to force a specified exception to occur. For example:

>>> raise NameError('HiThere')
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+NameError: HiThere
+

The sole argument to raise indicates the exception to be raised. This must be either an exception instance or an exception class (a class that derives from BaseException, such as Exception or one of its subclasses). If an exception class is passed, it will be implicitly instantiated by calling its constructor with no arguments:

raise ValueError  # shorthand for 'raise ValueError()'
+

If you need to determine whether an exception was raised but don’t intend to handle it, a simpler form of the raise statement allows you to re-raise the exception:

>>> try:
+...     raise NameError('HiThere')
+... except NameError:
+...     print('An exception flew by!')
+...     raise
+...
+An exception flew by!
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+NameError: HiThere
+

+8.5. Exception Chaining

If an unhandled exception occurs inside an except section, it will have the exception being handled attached to it and included in the error message:

>>> try:
+...     open("database.sqlite")
+... except OSError:
+...     raise RuntimeError("unable to handle error")
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+FileNotFoundError: [Errno 2] No such file or directory: 'database.sqlite'
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+RuntimeError: unable to handle error
+

To indicate that an exception is a direct consequence of another, the raise statement allows an optional from clause:

# exc must be exception instance or None.
+raise RuntimeError from exc
+

This can be useful when you are transforming exceptions. For example:

>>> def func():
+...     raise ConnectionError
+...
+>>> try:
+...     func()
+... except ConnectionError as exc:
+...     raise RuntimeError('Failed to open database') from exc
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+  File "<stdin>", line 2, in func
+ConnectionError
+
+The above exception was the direct cause of the following exception:
+
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+RuntimeError: Failed to open database
+

It also allows disabling automatic exception chaining using the from None idiom:

>>> try:
+...     open('database.sqlite')
+... except OSError:
+...     raise RuntimeError from None
+...
+Traceback (most recent call last):
+  File "<stdin>", line 4, in <module>
+RuntimeError
+

For more information about chaining mechanics, see Built-in Exceptions.

+8.6. User-defined Exceptions

Programs may name their own exceptions by creating a new exception class (see Classes for more about Python classes). Exceptions should typically be derived from the Exception class, either directly or indirectly.

Exception classes can be defined which do anything any other class can do, but are usually kept simple, often only offering a number of attributes that allow information about the error to be extracted by handlers for the exception.

Most exceptions are defined with names that end in “Error”, similar to the naming of the standard exceptions.

Many standard modules define their own exceptions to report errors that may occur in functions they define.

+8.7. Defining Clean-up Actions

The try statement has another optional clause which is intended to define clean-up actions that must be executed under all circumstances. For example:

>>> try:
+...     raise KeyboardInterrupt
+... finally:
+...     print('Goodbye, world!')
+...
+Goodbye, world!
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+KeyboardInterrupt
+

If a finally clause is present, the finally clause will execute as the last task before the try statement completes. The finally clause runs whether or not the try statement produces an exception. The following points discuss more complex cases when an exception occurs:

  • If an exception occurs during execution of the try clause, the exception may be handled by an except clause. If the exception is not handled by an except clause, the exception is re-raised after the finally clause has been executed.
  • An exception could occur during execution of an except or else clause. Again, the exception is re-raised after the finally clause has been executed.
  • If the finally clause executes a break, continue or return statement, exceptions are not re-raised.
  • If the try statement reaches a break, continue or return statement, the finally clause will execute just prior to the break, continue or return statement’s execution.
  • If a finally clause includes a return statement, the returned value will be the one from the finally clause’s return statement, not the value from the try clause’s return statement.

For example:

>>> def bool_return():
+...     try:
+...         return True
+...     finally:
+...         return False
+...
+>>> bool_return()
+False
+

A more complicated example:

>>> def divide(x, y):
+...     try:
+...         result = x / y
+...     except ZeroDivisionError:
+...         print("division by zero!")
+...     else:
+...         print("result is", result)
+...     finally:
+...         print("executing finally clause")
+...
+>>> divide(2, 1)
+result is 2.0
+executing finally clause
+>>> divide(2, 0)
+division by zero!
+executing finally clause
+>>> divide("2", "1")
+executing finally clause
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+  File "<stdin>", line 3, in divide
+TypeError: unsupported operand type(s) for /: 'str' and 'str'
+

As you can see, the finally clause is executed in any event. The TypeError raised by dividing two strings is not handled by the except clause and therefore re-raised after the finally clause has been executed.

In real world applications, the finally clause is useful for releasing external resources (such as files or network connections), regardless of whether the use of the resource was successful.

+8.8. Predefined Clean-up Actions

Some objects define standard clean-up actions to be undertaken when the object is no longer needed, regardless of whether or not the operation using the object succeeded or failed. Look at the following example, which tries to open a file and print its contents to the screen.

for line in open("myfile.txt"):
+    print(line, end="")
+

The problem with this code is that it leaves the file open for an indeterminate amount of time after this part of the code has finished executing. This is not an issue in simple scripts, but can be a problem for larger applications. The with statement allows objects like files to be used in a way that ensures they are always cleaned up promptly and correctly.

with open("myfile.txt") as f:
+    for line in f:
+        print(line, end="")
+

After the statement is executed, the file f is always closed, even if a problem was encountered while processing the lines. Objects which, like files, provide predefined clean-up actions will indicate this in their documentation.

+8.9. Raising and Handling Multiple Unrelated Exceptions

There are situations where it is necessary to report several exceptions that have occurred. This is often the case in concurrency frameworks, when several tasks may have failed in parallel, but there are also other use cases where it is desirable to continue execution and collect multiple errors rather than raise the first exception.

The builtin ExceptionGroup wraps a list of exception instances so that they can be raised together. It is an exception itself, so it can be caught like any other exception.

>>> def f():
+...     excs = [OSError('error 1'), SystemError('error 2')]
+...     raise ExceptionGroup('there were problems', excs)
+...
+>>> f()
+  + Exception Group Traceback (most recent call last):
+  |   File "<stdin>", line 1, in <module>
+  |   File "<stdin>", line 3, in f
+  | ExceptionGroup: there were problems
+  +-+---------------- 1 ----------------
+    | OSError: error 1
+    +---------------- 2 ----------------
+    | SystemError: error 2
+    +------------------------------------
+>>> try:
+...     f()
+... except Exception as e:
+...     print(f'caught {type(e)}: e')
+...
+caught <class 'ExceptionGroup'>: e
+>>>
+

By using except* instead of except, we can selectively handle only the exceptions in the group that match a certain type. In the following example, which shows a nested exception group, each except* clause extracts from the group exceptions of a certain type while letting all other exceptions propagate to other clauses and eventually to be reraised.

>>> def f():
+...     raise ExceptionGroup(
+...         "group1",
+...         [
+...             OSError(1),
+...             SystemError(2),
+...             ExceptionGroup(
+...                 "group2",
+...                 [
+...                     OSError(3),
+...                     RecursionError(4)
+...                 ]
+...             )
+...         ]
+...     )
+...
+>>> try:
+...     f()
+... except* OSError as e:
+...     print("There were OSErrors")
+... except* SystemError as e:
+...     print("There were SystemErrors")
+...
+There were OSErrors
+There were SystemErrors
+  + Exception Group Traceback (most recent call last):
+  |   File "<stdin>", line 2, in <module>
+  |   File "<stdin>", line 2, in f
+  | ExceptionGroup: group1
+  +-+---------------- 1 ----------------
+    | ExceptionGroup: group2
+    +-+---------------- 1 ----------------
+      | RecursionError: 4
+      +------------------------------------
+>>>
+

Note that the exceptions nested in an exception group must be instances, not types. This is because in practice the exceptions would typically be ones that have already been raised and caught by the program, along the following pattern:

>>> excs = []
+... for test in tests:
+...     try:
+...         test.run()
+...     except Exception as e:
+...         excs.append(e)
+...
+>>> if excs:
+...    raise ExceptionGroup("Test Failures", excs)
+...
+

+8.10. Enriching Exceptions with Notes

When an exception is created in order to be raised, it is usually initialized with information that describes the error that has occurred. There are cases where it is useful to add information after the exception was caught. For this purpose, exceptions have a method add_note(note) that accepts a string and adds it to the exception’s notes list. The standard traceback rendering includes all notes, in the order they were added, after the exception.

>>> try:
+...     raise TypeError('bad type')
+... except Exception as e:
+...     e.add_note('Add some information')
+...     e.add_note('Add some more information')
+...     raise
+...
+Traceback (most recent call last):
+  File "<stdin>", line 2, in <module>
+TypeError: bad type
+Add some information
+Add some more information
+>>>
+

For example, when collecting exceptions into an exception group, we may want to add context information for the individual errors. In the following each exception in the group has a note indicating when this error has occurred.

>>> def f():
+...     raise OSError('operation failed')
+...
+>>> excs = []
+>>> for i in range(3):
+...     try:
+...         f()
+...     except Exception as e:
+...         e.add_note(f'Happened in Iteration {i+1}')
+...         excs.append(e)
+...
+>>> raise ExceptionGroup('We have some problems', excs)
+  + Exception Group Traceback (most recent call last):
+  |   File "<stdin>", line 1, in <module>
+  | ExceptionGroup: We have some problems (3 sub-exceptions)
+  +-+---------------- 1 ----------------
+    | Traceback (most recent call last):
+    |   File "<stdin>", line 3, in <module>
+    |   File "<stdin>", line 2, in f
+    | OSError: operation failed
+    | Happened in Iteration 1
+    +---------------- 2 ----------------
+    | Traceback (most recent call last):
+    |   File "<stdin>", line 3, in <module>
+    |   File "<stdin>", line 2, in f
+    | OSError: operation failed
+    | Happened in Iteration 2
+    +---------------- 3 ----------------
+    | Traceback (most recent call last):
+    |   File "<stdin>", line 3, in <module>
+    |   File "<stdin>", line 2, in f
+    | OSError: operation failed
+    | Happened in Iteration 3
+    +------------------------------------
+>>>
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/errors.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Ffloatingpoint.html b/devdocs/python~3.12/tutorial%2Ffloatingpoint.html new file mode 100644 index 00000000..5cf143aa --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Ffloatingpoint.html @@ -0,0 +1,89 @@ +

Floating Point Arithmetic: Issues and Limitations

Floating-point numbers are represented in computer hardware as base 2 (binary) fractions. For example, the decimal fraction 0.625 has value 6/10 + 2/100 + 5/1000, and in the same way the binary fraction 0.101 has value 1/2 + 0/4 + 1/8. These two fractions have identical values, the only real difference being that the first is written in base 10 fractional notation, and the second in base 2.

Unfortunately, most decimal fractions cannot be represented exactly as binary fractions. A consequence is that, in general, the decimal floating-point numbers you enter are only approximated by the binary floating-point numbers actually stored in the machine.

The problem is easier to understand at first in base 10. Consider the fraction 1/3. You can approximate that as a base 10 fraction:

0.3
+

or, better,

0.33
+

or, better,

0.333
+

and so on. No matter how many digits you’re willing to write down, the result will never be exactly 1/3, but will be an increasingly better approximation of 1/3.

In the same way, no matter how many base 2 digits you’re willing to use, the decimal value 0.1 cannot be represented exactly as a base 2 fraction. In base 2, 1/10 is the infinitely repeating fraction

0.0001100110011001100110011001100110011001100110011...
+

Stop at any finite number of bits, and you get an approximation. On most machines today, floats are approximated using a binary fraction with the numerator using the first 53 bits starting with the most significant bit and with the denominator as a power of two. In the case of 1/10, the binary fraction is 3602879701896397 / 2 ** 55 which is close to but not exactly equal to the true value of 1/10.

Many users are not aware of the approximation because of the way values are displayed. Python only prints a decimal approximation to the true decimal value of the binary approximation stored by the machine. On most machines, if Python were to print the true decimal value of the binary approximation stored for 0.1, it would have to display:

>>> 0.1
+0.1000000000000000055511151231257827021181583404541015625
+

That is more digits than most people find useful, so Python keeps the number of digits manageable by displaying a rounded value instead:

>>> 1 / 10
+0.1
+

Just remember, even though the printed result looks like the exact value of 1/10, the actual stored value is the nearest representable binary fraction.

Interestingly, there are many different decimal numbers that share the same nearest approximate binary fraction. For example, the numbers 0.1 and 0.10000000000000001 and 0.1000000000000000055511151231257827021181583404541015625 are all approximated by 3602879701896397 / 2 ** 55. Since all of these decimal values share the same approximation, any one of them could be displayed while still preserving the invariant eval(repr(x)) == x.

Historically, the Python prompt and built-in repr() function would choose the one with 17 significant digits, 0.10000000000000001. Starting with Python 3.1, Python (on most systems) is now able to choose the shortest of these and simply display 0.1.

Note that this is in the very nature of binary floating-point: this is not a bug in Python, and it is not a bug in your code either. You’ll see the same kind of thing in all languages that support your hardware’s floating-point arithmetic (although some languages may not display the difference by default, or in all output modes).

For more pleasant output, you may wish to use string formatting to produce a limited number of significant digits:

>>> format(math.pi, '.12g')  # give 12 significant digits
+'3.14159265359'
+
+>>> format(math.pi, '.2f')   # give 2 digits after the point
+'3.14'
+
+>>> repr(math.pi)
+'3.141592653589793'
+

It’s important to realize that this is, in a real sense, an illusion: you’re simply rounding the display of the true machine value.

One illusion may beget another. For example, since 0.1 is not exactly 1/10, summing three values of 0.1 may not yield exactly 0.3, either:

>>> 0.1 + 0.1 + 0.1 == 0.3
+False
+

Also, since the 0.1 cannot get any closer to the exact value of 1/10 and 0.3 cannot get any closer to the exact value of 3/10, then pre-rounding with round() function cannot help:

>>> round(0.1, 1) + round(0.1, 1) + round(0.1, 1) == round(0.3, 1)
+False
+

Though the numbers cannot be made closer to their intended exact values, the math.isclose() function can be useful for comparing inexact values:

>>> math.isclose(0.1 + 0.1 + 0.1, 0.3)
+True
+

Alternatively, the round() function can be used to compare rough approximations:

>>> round(math.pi, ndigits=2) == round(22 / 7, ndigits=2)
+True
+

Binary floating-point arithmetic holds many surprises like this. The problem with “0.1” is explained in precise detail below, in the “Representation Error” section. See Examples of Floating Point Problems for a pleasant summary of how binary floating-point works and the kinds of problems commonly encountered in practice. Also see The Perils of Floating Point for a more complete account of other common surprises.

As that says near the end, “there are no easy answers.” Still, don’t be unduly wary of floating-point! The errors in Python float operations are inherited from the floating-point hardware, and on most machines are on the order of no more than 1 part in 2**53 per operation. That’s more than adequate for most tasks, but you do need to keep in mind that it’s not decimal arithmetic and that every float operation can suffer a new rounding error.

While pathological cases do exist, for most casual use of floating-point arithmetic you’ll see the result you expect in the end if you simply round the display of your final results to the number of decimal digits you expect. str() usually suffices, and for finer control see the str.format() method’s format specifiers in Format String Syntax.

For use cases which require exact decimal representation, try using the decimal module which implements decimal arithmetic suitable for accounting applications and high-precision applications.

Another form of exact arithmetic is supported by the fractions module which implements arithmetic based on rational numbers (so the numbers like 1/3 can be represented exactly).

If you are a heavy user of floating-point operations you should take a look at the NumPy package and many other packages for mathematical and statistical operations supplied by the SciPy project. See <https://scipy.org>.

Python provides tools that may help on those rare occasions when you really do want to know the exact value of a float. The float.as_integer_ratio() method expresses the value of a float as a fraction:

>>> x = 3.14159
+>>> x.as_integer_ratio()
+(3537115888337719, 1125899906842624)
+

Since the ratio is exact, it can be used to losslessly recreate the original value:

>>> x == 3537115888337719 / 1125899906842624
+True
+

The float.hex() method expresses a float in hexadecimal (base 16), again giving the exact value stored by your computer:

>>> x.hex()
+'0x1.921f9f01b866ep+1'
+

This precise hexadecimal representation can be used to reconstruct the float value exactly:

>>> x == float.fromhex('0x1.921f9f01b866ep+1')
+True
+

Since the representation is exact, it is useful for reliably porting values across different versions of Python (platform independence) and exchanging data with other languages that support the same format (such as Java and C99).

Another helpful tool is the sum() function which helps mitigate loss-of-precision during summation. It uses extended precision for intermediate rounding steps as values are added onto a running total. That can make a difference in overall accuracy so that the errors do not accumulate to the point where they affect the final total:

>>> 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 == 1.0
+False
+>>> sum([0.1] * 10) == 1.0
+True
+

The math.fsum() goes further and tracks all of the “lost digits” as values are added onto a running total so that the result has only a single rounding. This is slower than sum() but will be more accurate in uncommon cases where large magnitude inputs mostly cancel each other out leaving a final sum near zero:

>>> arr = [-0.10430216751806065, -266310978.67179024, 143401161448607.16,
+...        -143401161400469.7, 266262841.31058735, -0.003244936839808227]
+>>> float(sum(map(Fraction, arr)))   # Exact summation with single rounding
+8.042173697819788e-13
+>>> math.fsum(arr)                   # Single rounding
+8.042173697819788e-13
+>>> sum(arr)                         # Multiple roundings in extended precision
+8.042178034628478e-13
+>>> total = 0.0
+>>> for x in arr:
+...     total += x                   # Multiple roundings in standard precision
+...
+>>> total                            # Straight addition has no correct digits!
+-0.0051575902860057365
+

+15.1. Representation Error

This section explains the “0.1” example in detail, and shows how you can perform an exact analysis of cases like this yourself. Basic familiarity with binary floating-point representation is assumed.

Representation error refers to the fact that some (most, actually) decimal fractions cannot be represented exactly as binary (base 2) fractions. This is the chief reason why Python (or Perl, C, C++, Java, Fortran, and many others) often won’t display the exact decimal number you expect.

Why is that? 1/10 is not exactly representable as a binary fraction. Since at least 2000, almost all machines use IEEE 754 binary floating-point arithmetic, and almost all platforms map Python floats to IEEE 754 binary64 “double precision” values. IEEE 754 binary64 values contain 53 bits of precision, so on input the computer strives to convert 0.1 to the closest fraction it can of the form J/2**N where J is an integer containing exactly 53 bits. Rewriting

1 / 10 ~= J / (2**N)
+

as

J ~= 2**N / 10
+

and recalling that J has exactly 53 bits (is >= 2**52 but < 2**53), the best value for N is 56:

>>> 2**52 <=  2**56 // 10  < 2**53
+True
+

That is, 56 is the only value for N that leaves J with exactly 53 bits. The best possible value for J is then that quotient rounded:

>>> q, r = divmod(2**56, 10)
+>>> r
+6
+

Since the remainder is more than half of 10, the best approximation is obtained by rounding up:

>>> q+1
+7205759403792794
+

Therefore the best possible approximation to 1/10 in IEEE 754 double precision is:

7205759403792794 / 2 ** 56
+

Dividing both the numerator and denominator by two reduces the fraction to:

3602879701896397 / 2 ** 55
+

Note that since we rounded up, this is actually a little bit larger than 1/10; if we had not rounded up, the quotient would have been a little bit smaller than 1/10. But in no case can it be exactly 1/10!

So the computer never “sees” 1/10: what it sees is the exact fraction given above, the best IEEE 754 double approximation it can get:

>>> 0.1 * 2 ** 55
+3602879701896397.0
+

If we multiply that fraction by 10**55, we can see the value out to 55 decimal digits:

>>> 3602879701896397 * 10 ** 55 // 2 ** 55
+1000000000000000055511151231257827021181583404541015625
+

meaning that the exact number stored in the computer is equal to the decimal value 0.1000000000000000055511151231257827021181583404541015625. Instead of displaying the full decimal value, many languages (including older versions of Python), round the result to 17 significant digits:

>>> format(0.1, '.17f')
+'0.10000000000000001'
+

The fractions and decimal modules make these calculations easy:

>>> from decimal import Decimal
+>>> from fractions import Fraction
+
+>>> Fraction.from_float(0.1)
+Fraction(3602879701896397, 36028797018963968)
+
+>>> (0.1).as_integer_ratio()
+(3602879701896397, 36028797018963968)
+
+>>> Decimal.from_float(0.1)
+Decimal('0.1000000000000000055511151231257827021181583404541015625')
+
+>>> format(Decimal.from_float(0.1), '.17')
+'0.10000000000000001'
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/floatingpoint.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Findex.html b/devdocs/python~3.12/tutorial%2Findex.html new file mode 100644 index 00000000..f9960825 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Findex.html @@ -0,0 +1,34 @@ +

The Python Tutorial

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python’s elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms.

The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python web site, https://www.python.org/, and may be freely distributed. The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

The Python interpreter is easily extended with new functions and data types implemented in C or C++ (or other languages callable from C). Python is also suitable as an extension language for customizable applications.

This tutorial introduces the reader informally to the basic concepts and features of the Python language and system. It helps to have a Python interpreter handy for hands-on experience, but all examples are self-contained, so the tutorial can be read off-line as well.

For a description of standard objects and modules, see The Python Standard Library. The Python Language Reference gives a more formal definition of the language. To write extensions in C or C++, read Extending and Embedding the Python Interpreter and Python/C API Reference Manual. There are also several books covering Python in depth.

This tutorial does not attempt to be comprehensive and cover every single feature, or even every commonly used feature. Instead, it introduces many of Python’s most noteworthy features, and will give you a good idea of the language’s flavor and style. After reading it, you will be able to read and write Python modules and programs, and you will be ready to learn more about the various Python library modules described in The Python Standard Library.

The Glossary is also worth going through.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/index.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Finputoutput.html b/devdocs/python~3.12/tutorial%2Finputoutput.html new file mode 100644 index 00000000..a01dfb13 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Finputoutput.html @@ -0,0 +1,173 @@ +

Input and Output

There are several ways to present the output of a program; data can be printed in a human-readable form, or written to a file for future use. This chapter will discuss some of the possibilities.

+7.1. Fancier Output Formatting

So far we’ve encountered two ways of writing values: expression statements and the print() function. (A third way is using the write() method of file objects; the standard output file can be referenced as sys.stdout. See the Library Reference for more information on this.)

Often you’ll want more control over the formatting of your output than simply printing space-separated values. There are several ways to format output.

  • +

    To use formatted string literals, begin a string with f or F before the opening quotation mark or triple quotation mark. Inside this string, you can write a Python expression between { and } characters that can refer to variables or literal values.

    >>> year = 2016
+>>> event = 'Referendum'
+>>> f'Results of the {year} {event}'
+'Results of the 2016 Referendum'
+
  • +

    The str.format() method of strings requires more manual effort. You’ll still use { and } to mark where a variable will be substituted and can provide detailed formatting directives, but you’ll also need to provide the information to be formatted.

    >>> yes_votes = 42_572_654
+>>> no_votes = 43_132_495
+>>> percentage = yes_votes / (yes_votes + no_votes)
+>>> '{:-9} YES votes  {:2.2%}'.format(yes_votes, percentage)
+' 42572654 YES votes  49.67%'
+
  • Finally, you can do all the string handling yourself by using string slicing and concatenation operations to create any layout you can imagine. The string type has some methods that perform useful operations for padding strings to a given column width.

When you don’t need fancy output but just want a quick display of some variables for debugging purposes, you can convert any value to a string with the repr() or str() functions.

The str() function is meant to return representations of values which are fairly human-readable, while repr() is meant to generate representations which can be read by the interpreter (or will force a SyntaxError if there is no equivalent syntax). For objects which don’t have a particular representation for human consumption, str() will return the same value as repr(). Many values, such as numbers or structures like lists and dictionaries, have the same representation using either function. Strings, in particular, have two distinct representations.

Some examples:

>>> s = 'Hello, world.'
+>>> str(s)
+'Hello, world.'
+>>> repr(s)
+"'Hello, world.'"
+>>> str(1/7)
+'0.14285714285714285'
+>>> x = 10 * 3.25
+>>> y = 200 * 200
+>>> s = 'The value of x is ' + repr(x) + ', and y is ' + repr(y) + '...'
+>>> print(s)
+The value of x is 32.5, and y is 40000...
+>>> # The repr() of a string adds string quotes and backslashes:
+... hello = 'hello, world\n'
+>>> hellos = repr(hello)
+>>> print(hellos)
+'hello, world\n'
+>>> # The argument to repr() may be any Python object:
+... repr((x, y, ('spam', 'eggs')))
+"(32.5, 40000, ('spam', 'eggs'))"
+

The string module contains a Template class that offers yet another way to substitute values into strings, using placeholders like $x and replacing them with values from a dictionary, but offers much less control of the formatting.

+7.1.1. Formatted String Literals

Formatted string literals (also called f-strings for short) let you include the value of Python expressions inside a string by prefixing the string with f or F and writing expressions as {expression}.

An optional format specifier can follow the expression. This allows greater control over how the value is formatted. The following example rounds pi to three places after the decimal:

>>> import math
+>>> print(f'The value of pi is approximately {math.pi:.3f}.')
+The value of pi is approximately 3.142.
+

Passing an integer after the ':' will cause that field to be a minimum number of characters wide. This is useful for making columns line up.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}
+>>> for name, phone in table.items():
+...     print(f'{name:10} ==> {phone:10d}')
+...
+Sjoerd     ==>       4127
+Jack       ==>       4098
+Dcab       ==>       7678
+

Other modifiers can be used to convert the value before it is formatted. '!a' applies ascii(), '!s' applies str(), and '!r' applies repr():

>>> animals = 'eels'
+>>> print(f'My hovercraft is full of {animals}.')
+My hovercraft is full of eels.
+>>> print(f'My hovercraft is full of {animals!r}.')
+My hovercraft is full of 'eels'.
+

The = specifier can be used to expand an expression to the text of the expression, an equal sign, then the representation of the evaluated expression:

>>> bugs = 'roaches'
+>>> count = 13
+>>> area = 'living room'
+>>> print(f'Debugging {bugs=} {count=} {area=}')
+Debugging bugs='roaches' count=13 area='living room'
+

See self-documenting expressions for more information on the = specifier. For a reference on these format specifications, see the reference guide for the Format Specification Mini-Language.

+7.1.2. The String format() Method

Basic usage of the str.format() method looks like this:

>>> print('We are the {} who say "{}!"'.format('knights', 'Ni'))
+We are the knights who say "Ni!"
+

The brackets and characters within them (called format fields) are replaced with the objects passed into the str.format() method. A number in the brackets can be used to refer to the position of the object passed into the str.format() method.

>>> print('{0} and {1}'.format('spam', 'eggs'))
+spam and eggs
+>>> print('{1} and {0}'.format('spam', 'eggs'))
+eggs and spam
+

If keyword arguments are used in the str.format() method, their values are referred to by using the name of the argument.

>>> print('This {food} is {adjective}.'.format(
+...       food='spam', adjective='absolutely horrible'))
+This spam is absolutely horrible.
+

Positional and keyword arguments can be arbitrarily combined:

>>> print('The story of {0}, {1}, and {other}.'.format('Bill', 'Manfred',
+...                                                    other='Georg'))
+The story of Bill, Manfred, and Georg.
+

If you have a really long format string that you don’t want to split up, it would be nice if you could reference the variables to be formatted by name instead of by position. This can be done by simply passing the dict and using square brackets '[]' to access the keys.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
+>>> print('Jack: {0[Jack]:d}; Sjoerd: {0[Sjoerd]:d}; '
+...       'Dcab: {0[Dcab]:d}'.format(table))
+Jack: 4098; Sjoerd: 4127; Dcab: 8637678
+

This could also be done by passing the table dictionary as keyword arguments with the ** notation.

>>> table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 8637678}
+>>> print('Jack: {Jack:d}; Sjoerd: {Sjoerd:d}; Dcab: {Dcab:d}'.format(**table))
+Jack: 4098; Sjoerd: 4127; Dcab: 8637678
+

This is particularly useful in combination with the built-in function vars(), which returns a dictionary containing all local variables.

As an example, the following lines produce a tidily aligned set of columns giving integers and their squares and cubes:

>>> for x in range(1, 11):
+...     print('{0:2d} {1:3d} {2:4d}'.format(x, x*x, x*x*x))
+...
+ 1   1    1
+ 2   4    8
+ 3   9   27
+ 4  16   64
+ 5  25  125
+ 6  36  216
+ 7  49  343
+ 8  64  512
+ 9  81  729
+10 100 1000
+

For a complete overview of string formatting with str.format(), see Format String Syntax.

+7.1.3. Manual String Formatting

Here’s the same table of squares and cubes, formatted manually:

>>> for x in range(1, 11):
+...     print(repr(x).rjust(2), repr(x*x).rjust(3), end=' ')
+...     # Note use of 'end' on previous line
+...     print(repr(x*x*x).rjust(4))
+...
+ 1   1    1
+ 2   4    8
+ 3   9   27
+ 4  16   64
+ 5  25  125
+ 6  36  216
+ 7  49  343
+ 8  64  512
+ 9  81  729
+10 100 1000
+

(Note that the one space between each column was added by the way print() works: it always adds spaces between its arguments.)

The str.rjust() method of string objects right-justifies a string in a field of a given width by padding it with spaces on the left. There are similar methods str.ljust() and str.center(). These methods do not write anything, they just return a new string. If the input string is too long, they don’t truncate it, but return it unchanged; this will mess up your column lay-out but that’s usually better than the alternative, which would be lying about a value. (If you really want truncation you can always add a slice operation, as in x.ljust(n)[:n].)

There is another method, str.zfill(), which pads a numeric string on the left with zeros. It understands about plus and minus signs:

>>> '12'.zfill(5)
+'00012'
+>>> '-3.14'.zfill(7)
+'-003.14'
+>>> '3.14159265359'.zfill(5)
+'3.14159265359'
+

+7.1.4. Old string formatting

The % operator (modulo) can also be used for string formatting. Given 'string' +% values, instances of % in string are replaced with zero or more elements of values. This operation is commonly known as string interpolation. For example:

>>> import math
+>>> print('The value of pi is approximately %5.3f.' % math.pi)
+The value of pi is approximately 3.142.
+

More information can be found in the printf-style String Formatting section.

+7.2. Reading and Writing Files

open() returns a file object, and is most commonly used with two positional arguments and one keyword argument: open(filename, mode, encoding=None)

>>> f = open('workfile', 'w', encoding="utf-8")
+

The first argument is a string containing the filename. The second argument is another string containing a few characters describing the way in which the file will be used. mode can be 'r' when the file will only be read, 'w' for only writing (an existing file with the same name will be erased), and 'a' opens the file for appending; any data written to the file is automatically added to the end. 'r+' opens the file for both reading and writing. The mode argument is optional; 'r' will be assumed if it’s omitted.

Normally, files are opened in text mode, that means, you read and write strings from and to the file, which are encoded in a specific encoding. If encoding is not specified, the default is platform dependent (see open()). Because UTF-8 is the modern de-facto standard, encoding="utf-8" is recommended unless you know that you need to use a different encoding. Appending a 'b' to the mode opens the file in binary mode. Binary mode data is read and written as bytes objects. You can not specify encoding when opening file in binary mode.

In text mode, the default when reading is to convert platform-specific line endings (\n on Unix, \r\n on Windows) to just \n. When writing in text mode, the default is to convert occurrences of \n back to platform-specific line endings. This behind-the-scenes modification to file data is fine for text files, but will corrupt binary data like that in JPEG or EXE files. Be very careful to use binary mode when reading and writing such files.

It is good practice to use the with keyword when dealing with file objects. The advantage is that the file is properly closed after its suite finishes, even if an exception is raised at some point. Using with is also much shorter than writing equivalent try-finally blocks:

>>> with open('workfile', encoding="utf-8") as f:
+...     read_data = f.read()
+
+>>> # We can check that the file has been automatically closed.
+>>> f.closed
+True
+

If you’re not using the with keyword, then you should call f.close() to close the file and immediately free up any system resources used by it.

Warning

Calling f.write() without using the with keyword or calling f.close() might result in the arguments of f.write() not being completely written to the disk, even if the program exits successfully.

After a file object is closed, either by a with statement or by calling f.close(), attempts to use the file object will automatically fail.

>>> f.close()
+>>> f.read()
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+ValueError: I/O operation on closed file.
+

+7.2.1. Methods of File Objects

The rest of the examples in this section will assume that a file object called f has already been created.

To read a file’s contents, call f.read(size), which reads some quantity of data and returns it as a string (in text mode) or bytes object (in binary mode). size is an optional numeric argument. When size is omitted or negative, the entire contents of the file will be read and returned; it’s your problem if the file is twice as large as your machine’s memory. Otherwise, at most size characters (in text mode) or size bytes (in binary mode) are read and returned. If the end of the file has been reached, f.read() will return an empty string ('').

>>> f.read()
+'This is the entire file.\n'
+>>> f.read()
+''
+

f.readline() reads a single line from the file; a newline character (\n) is left at the end of the string, and is only omitted on the last line of the file if the file doesn’t end in a newline. This makes the return value unambiguous; if f.readline() returns an empty string, the end of the file has been reached, while a blank line is represented by '\n', a string containing only a single newline.

>>> f.readline()
+'This is the first line of the file.\n'
+>>> f.readline()
+'Second line of the file\n'
+>>> f.readline()
+''
+

For reading lines from a file, you can loop over the file object. This is memory efficient, fast, and leads to simple code:

>>> for line in f:
+...     print(line, end='')
+...
+This is the first line of the file.
+Second line of the file
+

If you want to read all the lines of a file in a list you can also use list(f) or f.readlines().

f.write(string) writes the contents of string to the file, returning the number of characters written.

>>> f.write('This is a test\n')
+15
+

Other types of objects need to be converted – either to a string (in text mode) or a bytes object (in binary mode) – before writing them:

>>> value = ('the answer', 42)
+>>> s = str(value)  # convert the tuple to string
+>>> f.write(s)
+18
+

f.tell() returns an integer giving the file object’s current position in the file represented as number of bytes from the beginning of the file when in binary mode and an opaque number when in text mode.

To change the file object’s position, use f.seek(offset, whence). The position is computed from adding offset to a reference point; the reference point is selected by the whence argument. A whence value of 0 measures from the beginning of the file, 1 uses the current file position, and 2 uses the end of the file as the reference point. whence can be omitted and defaults to 0, using the beginning of the file as the reference point.

>>> f = open('workfile', 'rb+')
+>>> f.write(b'0123456789abcdef')
+16
+>>> f.seek(5)      # Go to the 6th byte in the file
+5
+>>> f.read(1)
+b'5'
+>>> f.seek(-3, 2)  # Go to the 3rd byte before the end
+13
+>>> f.read(1)
+b'd'
+

In text files (those opened without a b in the mode string), only seeks relative to the beginning of the file are allowed (the exception being seeking to the very file end with seek(0, 2)) and the only valid offset values are those returned from the f.tell(), or zero. Any other offset value produces undefined behaviour.

File objects have some additional methods, such as isatty() and truncate() which are less frequently used; consult the Library Reference for a complete guide to file objects.

+7.2.2. Saving structured data with json +

Strings can easily be written to and read from a file. Numbers take a bit more effort, since the read() method only returns strings, which will have to be passed to a function like int(), which takes a string like '123' and returns its numeric value 123. When you want to save more complex data types like nested lists and dictionaries, parsing and serializing by hand becomes complicated.

Rather than having users constantly writing and debugging code to save complicated data types to files, Python allows you to use the popular data interchange format called JSON (JavaScript Object Notation). The standard module called json can take Python data hierarchies, and convert them to string representations; this process is called serializing. Reconstructing the data from the string representation is called deserializing. Between serializing and deserializing, the string representing the object may have been stored in a file or data, or sent over a network connection to some distant machine.

Note

The JSON format is commonly used by modern applications to allow for data exchange. Many programmers are already familiar with it, which makes it a good choice for interoperability.

If you have an object x, you can view its JSON string representation with a simple line of code:

>>> import json
+>>> x = [1, 'simple', 'list']
+>>> json.dumps(x)
+'[1, "simple", "list"]'
+

Another variant of the dumps() function, called dump(), simply serializes the object to a text file. So if f is a text file object opened for writing, we can do this:

json.dump(x, f)
+

To decode the object again, if f is a binary file or text file object which has been opened for reading:

x = json.load(f)
+

Note

JSON files must be encoded in UTF-8. Use encoding="utf-8" when opening JSON file as a text file for both of reading and writing.

This simple serialization technique can handle lists and dictionaries, but serializing arbitrary class instances in JSON requires a bit of extra effort. The reference for the json module contains an explanation of this.

See also

pickle - the pickle module

Contrary to JSON, pickle is a protocol which allows the serialization of arbitrarily complex Python objects. As such, it is specific to Python and cannot be used to communicate with applications written in other languages. It is also insecure by default: deserializing pickle data coming from an untrusted source can execute arbitrary code, if the data was crafted by a skilled attacker.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/inputoutput.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Finteractive.html b/devdocs/python~3.12/tutorial%2Finteractive.html new file mode 100644 index 00000000..a68d65e7 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Finteractive.html @@ -0,0 +1,8 @@ +

Interactive Input Editing and History Substitution

Some versions of the Python interpreter support editing of the current input line and history substitution, similar to facilities found in the Korn shell and the GNU Bash shell. This is implemented using the GNU Readline library, which supports various styles of editing. This library has its own documentation which we won’t duplicate here.

+14.1. Tab Completion and History Editing

Completion of variable and module names is automatically enabled at interpreter startup so that the Tab key invokes the completion function; it looks at Python statement names, the current local variables, and the available module names. For dotted expressions such as string.a, it will evaluate the expression up to the final '.' and then suggest completions from the attributes of the resulting object. Note that this may execute application-defined code if an object with a __getattr__() method is part of the expression. The default configuration also saves your history into a file named .python_history in your user directory. The history will be available again during the next interactive interpreter session.

+14.2. Alternatives to the Interactive Interpreter

This facility is an enormous step forward compared to earlier versions of the interpreter; however, some wishes are left: It would be nice if the proper indentation were suggested on continuation lines (the parser knows if an indent token is required next). The completion mechanism might use the interpreter’s symbol table. A command to check (or even suggest) matching parentheses, quotes, etc., would also be useful.

One alternative enhanced interactive interpreter that has been around for quite some time is IPython, which features tab completion, object exploration and advanced history management. It can also be thoroughly customized and embedded into other applications. Another similar enhanced interactive environment is bpython.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/interactive.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Finterpreter.html b/devdocs/python~3.12/tutorial%2Finterpreter.html new file mode 100644 index 00000000..c0a9eaf3 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Finterpreter.html @@ -0,0 +1,29 @@ +

Using the Python Interpreter

+2.1. Invoking the Interpreter

The Python interpreter is usually installed as /usr/local/bin/python3.12 on those machines where it is available; putting /usr/local/bin in your Unix shell’s search path makes it possible to start it by typing the command:

python3.12
+

to the shell. 1 Since the choice of the directory where the interpreter lives is an installation option, other places are possible; check with your local Python guru or system administrator. (E.g., /usr/local/python is a popular alternative location.)

On Windows machines where you have installed Python from the Microsoft Store, the python3.12 command will be available. If you have the py.exe launcher installed, you can use the py command. See Excursus: Setting environment variables for other ways to launch Python.

Typing an end-of-file character (Control-D on Unix, Control-Z on Windows) at the primary prompt causes the interpreter to exit with a zero exit status. If that doesn’t work, you can exit the interpreter by typing the following command: quit().

The interpreter’s line-editing features include interactive editing, history substitution and code completion on systems that support the GNU Readline library. Perhaps the quickest check to see whether command line editing is supported is typing Control-P to the first Python prompt you get. If it beeps, you have command line editing; see Appendix Interactive Input Editing and History Substitution for an introduction to the keys. If nothing appears to happen, or if ^P is echoed, command line editing isn’t available; you’ll only be able to use backspace to remove characters from the current line.

The interpreter operates somewhat like the Unix shell: when called with standard input connected to a tty device, it reads and executes commands interactively; when called with a file name argument or with a file as standard input, it reads and executes a script from that file.

A second way of starting the interpreter is python -c command [arg] ..., which executes the statement(s) in command, analogous to the shell’s -c option. Since Python statements often contain spaces or other characters that are special to the shell, it is usually advised to quote command in its entirety.

Some Python modules are also useful as scripts. These can be invoked using python -m module [arg] ..., which executes the source file for module as if you had spelled out its full name on the command line.

When a script file is used, it is sometimes useful to be able to run the script and enter interactive mode afterwards. This can be done by passing -i before the script.

All command line options are described in Command line and environment.

+2.1.1. Argument Passing

When known to the interpreter, the script name and additional arguments thereafter are turned into a list of strings and assigned to the argv variable in the sys module. You can access this list by executing import +sys. The length of the list is at least one; when no script and no arguments are given, sys.argv[0] is an empty string. When the script name is given as '-' (meaning standard input), sys.argv[0] is set to '-'. When -c command is used, sys.argv[0] is set to '-c'. When -m module is used, sys.argv[0] is set to the full name of the located module. Options found after -c command or -m module are not consumed by the Python interpreter’s option processing but left in sys.argv for the command or module to handle.

+2.1.2. Interactive Mode

When commands are read from a tty, the interpreter is said to be in interactive mode. In this mode it prompts for the next command with the primary prompt, usually three greater-than signs (>>>); for continuation lines it prompts with the secondary prompt, by default three dots (...). The interpreter prints a welcome message stating its version number and a copyright notice before printing the first prompt:

$ python3.12
+Python 3.12 (default, April 4 2022, 09:25:04)
+[GCC 10.2.0] on linux
+Type "help", "copyright", "credits" or "license" for more information.
+>>>
+

Continuation lines are needed when entering a multi-line construct. As an example, take a look at this if statement:

>>> the_world_is_flat = True
+>>> if the_world_is_flat:
+...     print("Be careful not to fall off!")
+...
+Be careful not to fall off!
+

For more on interactive mode, see Interactive Mode.

+2.2. The Interpreter and Its Environment

+2.2.1. Source Code Encoding

By default, Python source files are treated as encoded in UTF-8. In that encoding, characters of most languages in the world can be used simultaneously in string literals, identifiers and comments — although the standard library only uses ASCII characters for identifiers, a convention that any portable code should follow. To display all these characters properly, your editor must recognize that the file is UTF-8, and it must use a font that supports all the characters in the file.

To declare an encoding other than the default one, a special comment line should be added as the first line of the file. The syntax is as follows:

# -*- coding: encoding -*-
+

where encoding is one of the valid codecs supported by Python.

For example, to declare that Windows-1252 encoding is to be used, the first line of your source code file should be:

# -*- coding: cp1252 -*-
+

One exception to the first line rule is when the source code starts with a UNIX “shebang” line. In this case, the encoding declaration should be added as the second line of the file. For example:

#!/usr/bin/env python3
+# -*- coding: cp1252 -*-
+

Footnotes

+1
+

On Unix, the Python 3.x interpreter is by default not installed with the executable named python, so that it does not conflict with a simultaneously installed Python 2.x executable.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/interpreter.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fintroduction.html b/devdocs/python~3.12/tutorial%2Fintroduction.html new file mode 100644 index 00000000..ea5fbec0 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fintroduction.html @@ -0,0 +1,252 @@ +

An Informal Introduction to Python

In the following examples, input and output are distinguished by the presence or absence of prompts (>>> and …): to repeat the example, you must type everything after the prompt, when the prompt appears; lines that do not begin with a prompt are output from the interpreter. Note that a secondary prompt on a line by itself in an example means you must type a blank line; this is used to end a multi-line command.

You can toggle the display of prompts and output by clicking on >>> in the upper-right corner of an example box. If you hide the prompts and output for an example, then you can easily copy and paste the input lines into your interpreter.

Many of the examples in this manual, even those entered at the interactive prompt, include comments. Comments in Python start with the hash character, #, and extend to the end of the physical line. A comment may appear at the start of a line or following whitespace or code, but not within a string literal. A hash character within a string literal is just a hash character. Since comments are to clarify code and are not interpreted by Python, they may be omitted when typing in examples.

Some examples:

# this is the first comment
+spam = 1  # and this is the second comment
+          # ... and now a third!
+text = "# This is not a comment because it's inside quotes."
+

+3.1. Using Python as a Calculator

Let’s try some simple Python commands. Start the interpreter and wait for the primary prompt, >>>. (It shouldn’t take long.)

+3.1.1. Numbers

The interpreter acts as a simple calculator: you can type an expression at it and it will write the value. Expression syntax is straightforward: the operators +, -, * and / can be used to perform arithmetic; parentheses (()) can be used for grouping. For example:

>>> 2 + 2
+4
+>>> 50 - 5*6
+20
+>>> (50 - 5*6) / 4
+5.0
+>>> 8 / 5  # division always returns a floating point number
+1.6
+

The integer numbers (e.g. 2, 4, 20) have type int, the ones with a fractional part (e.g. 5.0, 1.6) have type float. We will see more about numeric types later in the tutorial.

Division (/) always returns a float. To do floor division and get an integer result you can use the // operator; to calculate the remainder you can use %:

>>> 17 / 3  # classic division returns a float
+5.666666666666667
+>>>
+>>> 17 // 3  # floor division discards the fractional part
+5
+>>> 17 % 3  # the % operator returns the remainder of the division
+2
+>>> 5 * 3 + 2  # floored quotient * divisor + remainder
+17
+

With Python, it is possible to use the ** operator to calculate powers 1:

>>> 5 ** 2  # 5 squared
+25
+>>> 2 ** 7  # 2 to the power of 7
+128
+

The equal sign (=) is used to assign a value to a variable. Afterwards, no result is displayed before the next interactive prompt:

>>> width = 20
+>>> height = 5 * 9
+>>> width * height
+900
+

If a variable is not “defined” (assigned a value), trying to use it will give you an error:

>>> n  # try to access an undefined variable
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+NameError: name 'n' is not defined
+

There is full support for floating point; operators with mixed type operands convert the integer operand to floating point:

>>> 4 * 3.75 - 1
+14.0
+

In interactive mode, the last printed expression is assigned to the variable _. This means that when you are using Python as a desk calculator, it is somewhat easier to continue calculations, for example:

>>> tax = 12.5 / 100
+>>> price = 100.50
+>>> price * tax
+12.5625
+>>> price + _
+113.0625
+>>> round(_, 2)
+113.06
+

This variable should be treated as read-only by the user. Don’t explicitly assign a value to it — you would create an independent local variable with the same name masking the built-in variable with its magic behavior.

In addition to int and float, Python supports other types of numbers, such as Decimal and Fraction. Python also has built-in support for complex numbers, and uses the j or J suffix to indicate the imaginary part (e.g. 3+5j).

+3.1.2. Text

Python can manipulate text (represented by type str, so-called “strings”) as well as numbers. This includes characters “!”, words “rabbit”, names “Paris”, sentences “Got your back.”, etc. “Yay! :)”. They can be enclosed in single quotes ('...') or double quotes ("...") with the same result 2.

>>> 'spam eggs'  # single quotes
+'spam eggs'
+>>> "Paris rabbit got your back :)! Yay!"  # double quotes
+'Paris rabbit got your back :)! Yay!'
+>>> '1975'  # digits and numerals enclosed in quotes are also strings
+'1975'
+

To quote a quote, we need to “escape” it, by preceding it with \. Alternatively, we can use the other type of quotation marks:

>>> 'doesn\'t'  # use \' to escape the single quote...
+"doesn't"
+>>> "doesn't"  # ...or use double quotes instead
+"doesn't"
+>>> '"Yes," they said.'
+'"Yes," they said.'
+>>> "\"Yes,\" they said."
+'"Yes," they said.'
+>>> '"Isn\'t," they said.'
+'"Isn\'t," they said.'
+

In the Python shell, the string definition and output string can look different. The print() function produces a more readable output, by omitting the enclosing quotes and by printing escaped and special characters:

>>> s = 'First line.\nSecond line.'  # \n means newline
+>>> s  # without print(), special characters are included in the string
+'First line.\nSecond line.'
+>>> print(s)  # with print(), special characters are interpreted, so \n produces new line
+First line.
+Second line.
+

If you don’t want characters prefaced by \ to be interpreted as special characters, you can use raw strings by adding an r before the first quote:

>>> print('C:\some\name')  # here \n means newline!
+C:\some
+ame
+>>> print(r'C:\some\name')  # note the r before the quote
+C:\some\name
+

There is one subtle aspect to raw strings: a raw string may not end in an odd number of \ characters; see the FAQ entry for more information and workarounds.

String literals can span multiple lines. One way is using triple-quotes: """...""" or '''...'''. End of lines are automatically included in the string, but it’s possible to prevent this by adding a \ at the end of the line. The following example:

print("""\
+Usage: thingy [OPTIONS]
+     -h                        Display this usage message
+     -H hostname               Hostname to connect to
+""")
+

produces the following output (note that the initial newline is not included):

Usage: thingy [OPTIONS]
+     -h                        Display this usage message
+     -H hostname               Hostname to connect to
+

Strings can be concatenated (glued together) with the + operator, and repeated with *:

>>> # 3 times 'un', followed by 'ium'
+>>> 3 * 'un' + 'ium'
+'unununium'
+

Two or more string literals (i.e. the ones enclosed between quotes) next to each other are automatically concatenated.

>>> 'Py' 'thon'
+'Python'
+

This feature is particularly useful when you want to break long strings:

>>> text = ('Put several strings within parentheses '
+...         'to have them joined together.')
+>>> text
+'Put several strings within parentheses to have them joined together.'
+

This only works with two literals though, not with variables or expressions:

>>> prefix = 'Py'
+>>> prefix 'thon'  # can't concatenate a variable and a string literal
+  File "<stdin>", line 1
+    prefix 'thon'
+           ^^^^^^
+SyntaxError: invalid syntax
+>>> ('un' * 3) 'ium'
+  File "<stdin>", line 1
+    ('un' * 3) 'ium'
+               ^^^^^
+SyntaxError: invalid syntax
+

If you want to concatenate variables or a variable and a literal, use +:

>>> prefix + 'thon'
+'Python'
+

Strings can be indexed (subscripted), with the first character having index 0. There is no separate character type; a character is simply a string of size one:

>>> word = 'Python'
+>>> word[0]  # character in position 0
+'P'
+>>> word[5]  # character in position 5
+'n'
+

Indices may also be negative numbers, to start counting from the right:

>>> word[-1]  # last character
+'n'
+>>> word[-2]  # second-last character
+'o'
+>>> word[-6]
+'P'
+

Note that since -0 is the same as 0, negative indices start from -1.

In addition to indexing, slicing is also supported. While indexing is used to obtain individual characters, slicing allows you to obtain a substring:

>>> word[0:2]  # characters from position 0 (included) to 2 (excluded)
+'Py'
+>>> word[2:5]  # characters from position 2 (included) to 5 (excluded)
+'tho'
+

Slice indices have useful defaults; an omitted first index defaults to zero, an omitted second index defaults to the size of the string being sliced.

>>> word[:2]   # character from the beginning to position 2 (excluded)
+'Py'
+>>> word[4:]   # characters from position 4 (included) to the end
+'on'
+>>> word[-2:]  # characters from the second-last (included) to the end
+'on'
+

Note how the start is always included, and the end always excluded. This makes sure that s[:i] + s[i:] is always equal to s:

>>> word[:2] + word[2:]
+'Python'
+>>> word[:4] + word[4:]
+'Python'
+

One way to remember how slices work is to think of the indices as pointing between characters, with the left edge of the first character numbered 0. Then the right edge of the last character of a string of n characters has index n, for example:

 +---+---+---+---+---+---+
+ | P | y | t | h | o | n |
+ +---+---+---+---+---+---+
+ 0   1   2   3   4   5   6
+-6  -5  -4  -3  -2  -1
+

The first row of numbers gives the position of the indices 0…6 in the string; the second row gives the corresponding negative indices. The slice from i to j consists of all characters between the edges labeled i and j, respectively.

For non-negative indices, the length of a slice is the difference of the indices, if both are within bounds. For example, the length of word[1:3] is 2.

Attempting to use an index that is too large will result in an error:

>>> word[42]  # the word only has 6 characters
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+IndexError: string index out of range
+

However, out of range slice indexes are handled gracefully when used for slicing:

>>> word[4:42]
+'on'
+>>> word[42:]
+''
+

Python strings cannot be changed — they are immutable. Therefore, assigning to an indexed position in the string results in an error:

>>> word[0] = 'J'
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: 'str' object does not support item assignment
+>>> word[2:] = 'py'
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+TypeError: 'str' object does not support item assignment
+

If you need a different string, you should create a new one:

>>> 'J' + word[1:]
+'Jython'
+>>> word[:2] + 'py'
+'Pypy'
+

The built-in function len() returns the length of a string:

>>> s = 'supercalifragilisticexpialidocious'
+>>> len(s)
+34
+

See also

Text Sequence Type — str
+
+

Strings are examples of sequence types, and support the common operations supported by such types.

String Methods
+
+

Strings support a large number of methods for basic transformations and searching.

f-strings
+
+

String literals that have embedded expressions.

Format String Syntax
+
+

Information about string formatting with str.format().

printf-style String Formatting
+
+

The old formatting operations invoked when strings are the left operand of the % operator are described in more detail here.

+3.1.3. Lists

Python knows a number of compound data types, used to group together other values. The most versatile is the list, which can be written as a list of comma-separated values (items) between square brackets. Lists might contain items of different types, but usually the items all have the same type.

>>> squares = [1, 4, 9, 16, 25]
+>>> squares
+[1, 4, 9, 16, 25]
+

Like strings (and all other built-in sequence types), lists can be indexed and sliced:

>>> squares[0]  # indexing returns the item
+1
+>>> squares[-1]
+25
+>>> squares[-3:]  # slicing returns a new list
+[9, 16, 25]
+

All slice operations return a new list containing the requested elements. This means that the following slice returns a shallow copy of the list:

>>> squares[:]
+[1, 4, 9, 16, 25]
+

Lists also support operations like concatenation:

>>> squares + [36, 49, 64, 81, 100]
+[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
+

Unlike strings, which are immutable, lists are a mutable type, i.e. it is possible to change their content:

>>> cubes = [1, 8, 27, 65, 125]  # something's wrong here
+>>> 4 ** 3  # the cube of 4 is 64, not 65!
+64
+>>> cubes[3] = 64  # replace the wrong value
+>>> cubes
+[1, 8, 27, 64, 125]
+

You can also add new items at the end of the list, by using the list.append() method (we will see more about methods later):

>>> cubes.append(216)  # add the cube of 6
+>>> cubes.append(7 ** 3)  # and the cube of 7
+>>> cubes
+[1, 8, 27, 64, 125, 216, 343]
+

Assignment to slices is also possible, and this can even change the size of the list or clear it entirely:

>>> letters = ['a', 'b', 'c', 'd', 'e', 'f', 'g']
+>>> letters
+['a', 'b', 'c', 'd', 'e', 'f', 'g']
+>>> # replace some values
+>>> letters[2:5] = ['C', 'D', 'E']
+>>> letters
+['a', 'b', 'C', 'D', 'E', 'f', 'g']
+>>> # now remove them
+>>> letters[2:5] = []
+>>> letters
+['a', 'b', 'f', 'g']
+>>> # clear the list by replacing all the elements with an empty list
+>>> letters[:] = []
+>>> letters
+[]
+

The built-in function len() also applies to lists:

>>> letters = ['a', 'b', 'c', 'd']
+>>> len(letters)
+4
+

It is possible to nest lists (create lists containing other lists), for example:

>>> a = ['a', 'b', 'c']
+>>> n = [1, 2, 3]
+>>> x = [a, n]
+>>> x
+[['a', 'b', 'c'], [1, 2, 3]]
+>>> x[0]
+['a', 'b', 'c']
+>>> x[0][1]
+'b'
+

+3.2. First Steps Towards Programming

Of course, we can use Python for more complicated tasks than adding two and two together. For instance, we can write an initial sub-sequence of the Fibonacci series as follows:

>>> # Fibonacci series:
+... # the sum of two elements defines the next
+... a, b = 0, 1
+>>> while a < 10:
+...     print(a)
+...     a, b = b, a+b
+...
+0
+1
+1
+2
+3
+5
+8
+

This example introduces several new features.

  • The first line contains a multiple assignment: the variables a and b simultaneously get the new values 0 and 1. On the last line this is used again, demonstrating that the expressions on the right-hand side are all evaluated first before any of the assignments take place. The right-hand side expressions are evaluated from the left to the right.
  • The while loop executes as long as the condition (here: a < 10) remains true. In Python, like in C, any non-zero integer value is true; zero is false. The condition may also be a string or list value, in fact any sequence; anything with a non-zero length is true, empty sequences are false. The test used in the example is a simple comparison. The standard comparison operators are written the same as in C: < (less than), > (greater than), == (equal to), <= (less than or equal to), >= (greater than or equal to) and != (not equal to).
  • The body of the loop is indented: indentation is Python’s way of grouping statements. At the interactive prompt, you have to type a tab or space(s) for each indented line. In practice you will prepare more complicated input for Python with a text editor; all decent text editors have an auto-indent facility. When a compound statement is entered interactively, it must be followed by a blank line to indicate completion (since the parser cannot guess when you have typed the last line). Note that each line within a basic block must be indented by the same amount.
  • +

    The print() function writes the value of the argument(s) it is given. It differs from just writing the expression you want to write (as we did earlier in the calculator examples) in the way it handles multiple arguments, floating point quantities, and strings. Strings are printed without quotes, and a space is inserted between items, so you can format things nicely, like this:

    >>> i = 256*256
+>>> print('The value of i is', i)
+The value of i is 65536
+

    The keyword argument end can be used to avoid the newline after the output, or end the output with a different string:

    >>> a, b = 0, 1
+>>> while a < 1000:
+...     print(a, end=',')
+...     a, b = b, a+b
+...
+0,1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987,
+

Footnotes

+1
+

Since ** has higher precedence than -, -3**2 will be interpreted as -(3**2) and thus result in -9. To avoid this and get 9, you can use (-3)**2.

+2
+

Unlike other languages, special characters such as \n have the same meaning with both single ('...') and double ("...") quotes. The only difference between the two is that within single quotes you don’t need to escape " (but you have to escape \') and vice versa.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/introduction.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fmodules.html b/devdocs/python~3.12/tutorial%2Fmodules.html new file mode 100644 index 00000000..032e6f42 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fmodules.html @@ -0,0 +1,184 @@ +

Modules

If you quit from the Python interpreter and enter it again, the definitions you have made (functions and variables) are lost. Therefore, if you want to write a somewhat longer program, you are better off using a text editor to prepare the input for the interpreter and running it with that file as input instead. This is known as creating a script. As your program gets longer, you may want to split it into several files for easier maintenance. You may also want to use a handy function that you’ve written in several programs without copying its definition into each program.

To support this, Python has a way to put definitions in a file and use them in a script or in an interactive instance of the interpreter. Such a file is called a module; definitions from a module can be imported into other modules or into the main module (the collection of variables that you have access to in a script executed at the top level and in calculator mode).

A module is a file containing Python definitions and statements. The file name is the module name with the suffix .py appended. Within a module, the module’s name (as a string) is available as the value of the global variable __name__. For instance, use your favorite text editor to create a file called fibo.py in the current directory with the following contents:

# Fibonacci numbers module
+
+def fib(n):    # write Fibonacci series up to n
+    a, b = 0, 1
+    while a < n:
+        print(a, end=' ')
+        a, b = b, a+b
+    print()
+
+def fib2(n):   # return Fibonacci series up to n
+    result = []
+    a, b = 0, 1
+    while a < n:
+        result.append(a)
+        a, b = b, a+b
+    return result
+

Now enter the Python interpreter and import this module with the following command:

>>> import fibo
+

This does not add the names of the functions defined in fibo directly to the current namespace (see Python Scopes and Namespaces for more details); it only adds the module name fibo there. Using the module name you can access the functions:

>>> fibo.fib(1000)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987
+>>> fibo.fib2(100)
+[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]
+>>> fibo.__name__
+'fibo'
+

If you intend to use a function often you can assign it to a local name:

>>> fib = fibo.fib
+>>> fib(500)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+

+6.1. More on Modules

A module can contain executable statements as well as function definitions. These statements are intended to initialize the module. They are executed only the first time the module name is encountered in an import statement. 1 (They are also run if the file is executed as a script.)

Each module has its own private namespace, which is used as the global namespace by all functions defined in the module. Thus, the author of a module can use global variables in the module without worrying about accidental clashes with a user’s global variables. On the other hand, if you know what you are doing you can touch a module’s global variables with the same notation used to refer to its functions, modname.itemname.

Modules can import other modules. It is customary but not required to place all import statements at the beginning of a module (or script, for that matter). The imported module names, if placed at the top level of a module (outside any functions or classes), are added to the module’s global namespace.

There is a variant of the import statement that imports names from a module directly into the importing module’s namespace. For example:

>>> from fibo import fib, fib2
+>>> fib(500)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+

This does not introduce the module name from which the imports are taken in the local namespace (so in the example, fibo is not defined).

There is even a variant to import all names that a module defines:

>>> from fibo import *
+>>> fib(500)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+

This imports all names except those beginning with an underscore (_). In most cases Python programmers do not use this facility since it introduces an unknown set of names into the interpreter, possibly hiding some things you have already defined.

Note that in general the practice of importing * from a module or package is frowned upon, since it often causes poorly readable code. However, it is okay to use it to save typing in interactive sessions.

If the module name is followed by as, then the name following as is bound directly to the imported module.

>>> import fibo as fib
+>>> fib.fib(500)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+

This is effectively importing the module in the same way that import fibo will do, with the only difference of it being available as fib.

It can also be used when utilising from with similar effects:

>>> from fibo import fib as fibonacci
+>>> fibonacci(500)
+0 1 1 2 3 5 8 13 21 34 55 89 144 233 377
+

Note

For efficiency reasons, each module is only imported once per interpreter session. Therefore, if you change your modules, you must restart the interpreter – or, if it’s just one module you want to test interactively, use importlib.reload(), e.g. import importlib; +importlib.reload(modulename).

+6.1.1. Executing modules as scripts

When you run a Python module with

python fibo.py <arguments>
+

the code in the module will be executed, just as if you imported it, but with the __name__ set to "__main__". That means that by adding this code at the end of your module:

if __name__ == "__main__":
+    import sys
+    fib(int(sys.argv[1]))
+

you can make the file usable as a script as well as an importable module, because the code that parses the command line only runs if the module is executed as the “main” file:

$ python fibo.py 50
+0 1 1 2 3 5 8 13 21 34
+

If the module is imported, the code is not run:

>>> import fibo
+>>>
+

This is often used either to provide a convenient user interface to a module, or for testing purposes (running the module as a script executes a test suite).

+6.1.2. The Module Search Path

When a module named spam is imported, the interpreter first searches for a built-in module with that name. These module names are listed in sys.builtin_module_names. If not found, it then searches for a file named spam.py in a list of directories given by the variable sys.path. sys.path is initialized from these locations:

  • The directory containing the input script (or the current directory when no file is specified).
  • +PYTHONPATH (a list of directory names, with the same syntax as the shell variable PATH).
  • The installation-dependent default (by convention including a site-packages directory, handled by the site module).

More details are at The initialization of the sys.path module search path.

Note

On file systems which support symlinks, the directory containing the input script is calculated after the symlink is followed. In other words the directory containing the symlink is not added to the module search path.

After initialization, Python programs can modify sys.path. The directory containing the script being run is placed at the beginning of the search path, ahead of the standard library path. This means that scripts in that directory will be loaded instead of modules of the same name in the library directory. This is an error unless the replacement is intended. See section Standard Modules for more information.

+6.1.3. “Compiled” Python files

To speed up loading modules, Python caches the compiled version of each module in the __pycache__ directory under the name module.version.pyc, where the version encodes the format of the compiled file; it generally contains the Python version number. For example, in CPython release 3.3 the compiled version of spam.py would be cached as __pycache__/spam.cpython-33.pyc. This naming convention allows compiled modules from different releases and different versions of Python to coexist.

Python checks the modification date of the source against the compiled version to see if it’s out of date and needs to be recompiled. This is a completely automatic process. Also, the compiled modules are platform-independent, so the same library can be shared among systems with different architectures.

Python does not check the cache in two circumstances. First, it always recompiles and does not store the result for the module that’s loaded directly from the command line. Second, it does not check the cache if there is no source module. To support a non-source (compiled only) distribution, the compiled module must be in the source directory, and there must not be a source module.

Some tips for experts:

  • You can use the -O or -OO switches on the Python command to reduce the size of a compiled module. The -O switch removes assert statements, the -OO switch removes both assert statements and __doc__ strings. Since some programs may rely on having these available, you should only use this option if you know what you’re doing. “Optimized” modules have an opt- tag and are usually smaller. Future releases may change the effects of optimization.
  • A program doesn’t run any faster when it is read from a .pyc file than when it is read from a .py file; the only thing that’s faster about .pyc files is the speed with which they are loaded.
  • The module compileall can create .pyc files for all modules in a directory.
  • There is more detail on this process, including a flow chart of the decisions, in PEP 3147.

+6.2. Standard Modules

Python comes with a library of standard modules, described in a separate document, the Python Library Reference (“Library Reference” hereafter). Some modules are built into the interpreter; these provide access to operations that are not part of the core of the language but are nevertheless built in, either for efficiency or to provide access to operating system primitives such as system calls. The set of such modules is a configuration option which also depends on the underlying platform. For example, the winreg module is only provided on Windows systems. One particular module deserves some attention: sys, which is built into every Python interpreter. The variables sys.ps1 and sys.ps2 define the strings used as primary and secondary prompts:

>>> import sys
+>>> sys.ps1
+'>>> '
+>>> sys.ps2
+'... '
+>>> sys.ps1 = 'C> '
+C> print('Yuck!')
+Yuck!
+C>
+

These two variables are only defined if the interpreter is in interactive mode.

The variable sys.path is a list of strings that determines the interpreter’s search path for modules. It is initialized to a default path taken from the environment variable PYTHONPATH, or from a built-in default if PYTHONPATH is not set. You can modify it using standard list operations:

>>> import sys
+>>> sys.path.append('/ufs/guido/lib/python')
+

+6.3. The dir() Function

The built-in function dir() is used to find out which names a module defines. It returns a sorted list of strings:

>>> import fibo, sys
+>>> dir(fibo)
+['__name__', 'fib', 'fib2']
+>>> dir(sys)  
+['__breakpointhook__', '__displayhook__', '__doc__', '__excepthook__',
+ '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__',
+ '__stderr__', '__stdin__', '__stdout__', '__unraisablehook__',
+ '_clear_type_cache', '_current_frames', '_debugmallocstats', '_framework',
+ '_getframe', '_git', '_home', '_xoptions', 'abiflags', 'addaudithook',
+ 'api_version', 'argv', 'audit', 'base_exec_prefix', 'base_prefix',
+ 'breakpointhook', 'builtin_module_names', 'byteorder', 'call_tracing',
+ 'callstats', 'copyright', 'displayhook', 'dont_write_bytecode', 'exc_info',
+ 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info',
+ 'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_origin_tracking_depth',
+ 'getallocatedblocks', 'getdefaultencoding', 'getdlopenflags',
+ 'getfilesystemencodeerrors', 'getfilesystemencoding', 'getprofile',
+ 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval',
+ 'gettrace', 'hash_info', 'hexversion', 'implementation', 'int_info',
+ 'intern', 'is_finalizing', 'last_traceback', 'last_type', 'last_value',
+ 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks',
+ 'path_importer_cache', 'platform', 'prefix', 'ps1', 'ps2', 'pycache_prefix',
+ 'set_asyncgen_hooks', 'set_coroutine_origin_tracking_depth', 'setdlopenflags',
+ 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr',
+ 'stdin', 'stdout', 'thread_info', 'unraisablehook', 'version', 'version_info',
+ 'warnoptions']
+

Without arguments, dir() lists the names you have defined currently:

>>> a = [1, 2, 3, 4, 5]
+>>> import fibo
+>>> fib = fibo.fib
+>>> dir()
+['__builtins__', '__name__', 'a', 'fib', 'fibo', 'sys']
+

Note that it lists all types of names: variables, modules, functions, etc.

dir() does not list the names of built-in functions and variables. If you want a list of those, they are defined in the standard module builtins:

>>> import builtins
+>>> dir(builtins)  
+['ArithmeticError', 'AssertionError', 'AttributeError', 'BaseException',
+ 'BlockingIOError', 'BrokenPipeError', 'BufferError', 'BytesWarning',
+ 'ChildProcessError', 'ConnectionAbortedError', 'ConnectionError',
+ 'ConnectionRefusedError', 'ConnectionResetError', 'DeprecationWarning',
+ 'EOFError', 'Ellipsis', 'EnvironmentError', 'Exception', 'False',
+ 'FileExistsError', 'FileNotFoundError', 'FloatingPointError',
+ 'FutureWarning', 'GeneratorExit', 'IOError', 'ImportError',
+ 'ImportWarning', 'IndentationError', 'IndexError', 'InterruptedError',
+ 'IsADirectoryError', 'KeyError', 'KeyboardInterrupt', 'LookupError',
+ 'MemoryError', 'NameError', 'None', 'NotADirectoryError', 'NotImplemented',
+ 'NotImplementedError', 'OSError', 'OverflowError',
+ 'PendingDeprecationWarning', 'PermissionError', 'ProcessLookupError',
+ 'ReferenceError', 'ResourceWarning', 'RuntimeError', 'RuntimeWarning',
+ 'StopIteration', 'SyntaxError', 'SyntaxWarning', 'SystemError',
+ 'SystemExit', 'TabError', 'TimeoutError', 'True', 'TypeError',
+ 'UnboundLocalError', 'UnicodeDecodeError', 'UnicodeEncodeError',
+ 'UnicodeError', 'UnicodeTranslateError', 'UnicodeWarning', 'UserWarning',
+ 'ValueError', 'Warning', 'ZeroDivisionError', '_', '__build_class__',
+ '__debug__', '__doc__', '__import__', '__name__', '__package__', 'abs',
+ 'all', 'any', 'ascii', 'bin', 'bool', 'bytearray', 'bytes', 'callable',
+ 'chr', 'classmethod', 'compile', 'complex', 'copyright', 'credits',
+ 'delattr', 'dict', 'dir', 'divmod', 'enumerate', 'eval', 'exec', 'exit',
+ 'filter', 'float', 'format', 'frozenset', 'getattr', 'globals', 'hasattr',
+ 'hash', 'help', 'hex', 'id', 'input', 'int', 'isinstance', 'issubclass',
+ 'iter', 'len', 'license', 'list', 'locals', 'map', 'max', 'memoryview',
+ 'min', 'next', 'object', 'oct', 'open', 'ord', 'pow', 'print', 'property',
+ 'quit', 'range', 'repr', 'reversed', 'round', 'set', 'setattr', 'slice',
+ 'sorted', 'staticmethod', 'str', 'sum', 'super', 'tuple', 'type', 'vars',
+ 'zip']
+

+6.4. Packages

Packages are a way of structuring Python’s module namespace by using “dotted module names”. For example, the module name A.B designates a submodule named B in a package named A. Just like the use of modules saves the authors of different modules from having to worry about each other’s global variable names, the use of dotted module names saves the authors of multi-module packages like NumPy or Pillow from having to worry about each other’s module names.

Suppose you want to design a collection of modules (a “package”) for the uniform handling of sound files and sound data. There are many different sound file formats (usually recognized by their extension, for example: .wav, .aiff, .au), so you may need to create and maintain a growing collection of modules for the conversion between the various file formats. There are also many different operations you might want to perform on sound data (such as mixing, adding echo, applying an equalizer function, creating an artificial stereo effect), so in addition you will be writing a never-ending stream of modules to perform these operations. Here’s a possible structure for your package (expressed in terms of a hierarchical filesystem):

sound/                          Top-level package
+      __init__.py               Initialize the sound package
+      formats/                  Subpackage for file format conversions
+              __init__.py
+              wavread.py
+              wavwrite.py
+              aiffread.py
+              aiffwrite.py
+              auread.py
+              auwrite.py
+              ...
+      effects/                  Subpackage for sound effects
+              __init__.py
+              echo.py
+              surround.py
+              reverse.py
+              ...
+      filters/                  Subpackage for filters
+              __init__.py
+              equalizer.py
+              vocoder.py
+              karaoke.py
+              ...
+

When importing the package, Python searches through the directories on sys.path looking for the package subdirectory.

The __init__.py files are required to make Python treat directories containing the file as packages (unless using a namespace package, a relatively advanced feature). This prevents directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path. In the simplest case, __init__.py can just be an empty file, but it can also execute initialization code for the package or set the __all__ variable, described later.

Users of the package can import individual modules from the package, for example:

import sound.effects.echo
+

This loads the submodule sound.effects.echo. It must be referenced with its full name.

sound.effects.echo.echofilter(input, output, delay=0.7, atten=4)
+

An alternative way of importing the submodule is:

from sound.effects import echo
+

This also loads the submodule echo, and makes it available without its package prefix, so it can be used as follows:

echo.echofilter(input, output, delay=0.7, atten=4)
+

Yet another variation is to import the desired function or variable directly:

from sound.effects.echo import echofilter
+

Again, this loads the submodule echo, but this makes its function echofilter() directly available:

echofilter(input, output, delay=0.7, atten=4)
+

Note that when using from package import item, the item can be either a submodule (or subpackage) of the package, or some other name defined in the package, like a function, class or variable. The import statement first tests whether the item is defined in the package; if not, it assumes it is a module and attempts to load it. If it fails to find it, an ImportError exception is raised.

Contrarily, when using syntax like import item.subitem.subsubitem, each item except for the last must be a package; the last item can be a module or a package but can’t be a class or function or variable defined in the previous item.

+6.4.1. Importing * From a Package

Now what happens when the user writes from sound.effects import *? Ideally, one would hope that this somehow goes out to the filesystem, finds which submodules are present in the package, and imports them all. This could take a long time and importing sub-modules might have unwanted side-effects that should only happen when the sub-module is explicitly imported.

The only solution is for the package author to provide an explicit index of the package. The import statement uses the following convention: if a package’s __init__.py code defines a list named __all__, it is taken to be the list of module names that should be imported when from package import * is encountered. It is up to the package author to keep this list up-to-date when a new version of the package is released. Package authors may also decide not to support it, if they don’t see a use for importing * from their package. For example, the file sound/effects/__init__.py could contain the following code:

__all__ = ["echo", "surround", "reverse"]
+

This would mean that from sound.effects import * would import the three named submodules of the sound.effects package.

Be aware that submodules might become shadowed by locally defined names. For example, if you added a reverse function to the sound/effects/__init__.py file, the from sound.effects import * would only import the two submodules echo and surround, but not the reverse submodule, because it is shadowed by the locally defined reverse function:

__all__ = [
+    "echo",      # refers to the 'echo.py' file
+    "surround",  # refers to the 'surround.py' file
+    "reverse",   # !!! refers to the 'reverse' function now !!!
+]
+
+def reverse(msg: str):  # <-- this name shadows the 'reverse.py' submodule
+    return msg[::-1]    #     in the case of a 'from sound.effects import *'
+

If __all__ is not defined, the statement from sound.effects import * does not import all submodules from the package sound.effects into the current namespace; it only ensures that the package sound.effects has been imported (possibly running any initialization code in __init__.py) and then imports whatever names are defined in the package. This includes any names defined (and submodules explicitly loaded) by __init__.py. It also includes any submodules of the package that were explicitly loaded by previous import statements. Consider this code:

import sound.effects.echo
+import sound.effects.surround
+from sound.effects import *
+

In this example, the echo and surround modules are imported in the current namespace because they are defined in the sound.effects package when the from...import statement is executed. (This also works when __all__ is defined.)

Although certain modules are designed to export only names that follow certain patterns when you use import *, it is still considered bad practice in production code.

Remember, there is nothing wrong with using from package import +specific_submodule! In fact, this is the recommended notation unless the importing module needs to use submodules with the same name from different packages.

+6.4.2. Intra-package References

When packages are structured into subpackages (as with the sound package in the example), you can use absolute imports to refer to submodules of siblings packages. For example, if the module sound.filters.vocoder needs to use the echo module in the sound.effects package, it can use from +sound.effects import echo.

You can also write relative imports, with the from module import name form of import statement. These imports use leading dots to indicate the current and parent packages involved in the relative import. From the surround module for example, you might use:

from . import echo
+from .. import formats
+from ..filters import equalizer
+

Note that relative imports are based on the name of the current module. Since the name of the main module is always "__main__", modules intended for use as the main module of a Python application must always use absolute imports.

+6.4.3. Packages in Multiple Directories

Packages support one more special attribute, __path__. This is initialized to be a list containing the name of the directory holding the package’s __init__.py before the code in that file is executed. This variable can be modified; doing so affects future searches for modules and subpackages contained in the package.

While this feature is not often needed, it can be used to extend the set of modules found in a package.

Footnotes

+1
+

In fact function definitions are also ‘statements’ that are ‘executed’; the execution of a module-level function definition adds the function name to the module’s global namespace.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/modules.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fstdlib.html b/devdocs/python~3.12/tutorial%2Fstdlib.html new file mode 100644 index 00000000..0ed5b646 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fstdlib.html @@ -0,0 +1,152 @@ +

Brief Tour of the Standard Library

+10.1. Operating System Interface

The os module provides dozens of functions for interacting with the operating system:

>>> import os
+>>> os.getcwd()      # Return the current working directory
+'C:\\Python312'
+>>> os.chdir('/server/accesslogs')   # Change current working directory
+>>> os.system('mkdir today')   # Run the command mkdir in the system shell
+0
+

Be sure to use the import os style instead of from os import *. This will keep os.open() from shadowing the built-in open() function which operates much differently.

The built-in dir() and help() functions are useful as interactive aids for working with large modules like os:

>>> import os
+>>> dir(os)
+<returns a list of all module functions>
+>>> help(os)
+<returns an extensive manual page created from the module's docstrings>
+

For daily file and directory management tasks, the shutil module provides a higher level interface that is easier to use:

>>> import shutil
+>>> shutil.copyfile('data.db', 'archive.db')
+'archive.db'
+>>> shutil.move('/build/executables', 'installdir')
+'installdir'
+

+10.2. File Wildcards

The glob module provides a function for making file lists from directory wildcard searches:

>>> import glob
+>>> glob.glob('*.py')
+['primes.py', 'random.py', 'quote.py']
+

+10.3. Command Line Arguments

Common utility scripts often need to process command line arguments. These arguments are stored in the sys module’s argv attribute as a list. For instance, let’s take the following demo.py file:

# File demo.py
+import sys
+print(sys.argv)
+

Here is the output from running python demo.py one two three at the command line:

['demo.py', 'one', 'two', 'three']
+

The argparse module provides a more sophisticated mechanism to process command line arguments. The following script extracts one or more filenames and an optional number of lines to be displayed:

import argparse
+
+parser = argparse.ArgumentParser(
+    prog='top',
+    description='Show top lines from each file')
+parser.add_argument('filenames', nargs='+')
+parser.add_argument('-l', '--lines', type=int, default=10)
+args = parser.parse_args()
+print(args)
+

When run at the command line with python top.py --lines=5 alpha.txt +beta.txt, the script sets args.lines to 5 and args.filenames to ['alpha.txt', 'beta.txt'].

+10.4. Error Output Redirection and Program Termination

The sys module also has attributes for stdin, stdout, and stderr. The latter is useful for emitting warnings and error messages to make them visible even when stdout has been redirected:

>>> sys.stderr.write('Warning, log file not found starting a new one\n')
+Warning, log file not found starting a new one
+

The most direct way to terminate a script is to use sys.exit().

+10.5. String Pattern Matching

The re module provides regular expression tools for advanced string processing. For complex matching and manipulation, regular expressions offer succinct, optimized solutions:

>>> import re
+>>> re.findall(r'\bf[a-z]*', 'which foot or hand fell fastest')
+['foot', 'fell', 'fastest']
+>>> re.sub(r'(\b[a-z]+) \1', r'\1', 'cat in the the hat')
+'cat in the hat'
+

When only simple capabilities are needed, string methods are preferred because they are easier to read and debug:

>>> 'tea for too'.replace('too', 'two')
+'tea for two'
+

+10.6. Mathematics

The math module gives access to the underlying C library functions for floating point math:

>>> import math
+>>> math.cos(math.pi / 4)
+0.70710678118654757
+>>> math.log(1024, 2)
+10.0
+

The random module provides tools for making random selections:

>>> import random
+>>> random.choice(['apple', 'pear', 'banana'])
+'apple'
+>>> random.sample(range(100), 10)   # sampling without replacement
+[30, 83, 16, 4, 8, 81, 41, 50, 18, 33]
+>>> random.random()    # random float
+0.17970987693706186
+>>> random.randrange(6)    # random integer chosen from range(6)
+4
+

The statistics module calculates basic statistical properties (the mean, median, variance, etc.) of numeric data:

>>> import statistics
+>>> data = [2.75, 1.75, 1.25, 0.25, 0.5, 1.25, 3.5]
+>>> statistics.mean(data)
+1.6071428571428572
+>>> statistics.median(data)
+1.25
+>>> statistics.variance(data)
+1.3720238095238095
+

The SciPy project <https://scipy.org> has many other modules for numerical computations.

+10.7. Internet Access

There are a number of modules for accessing the internet and processing internet protocols. Two of the simplest are urllib.request for retrieving data from URLs and smtplib for sending mail:

>>> from urllib.request import urlopen
+>>> with urlopen('http://worldtimeapi.org/api/timezone/etc/UTC.txt') as response:
+...     for line in response:
+...         line = line.decode()             # Convert bytes to a str
+...         if line.startswith('datetime'):
+...             print(line.rstrip())         # Remove trailing newline
+...
+datetime: 2022-01-01T01:36:47.689215+00:00
+
+>>> import smtplib
+>>> server = smtplib.SMTP('localhost')
+>>> server.sendmail('soothsayer@example.org', 'jcaesar@example.org',
+... """To: jcaesar@example.org
+... From: soothsayer@example.org
+...
+... Beware the Ides of March.
+... """)
+>>> server.quit()
+

(Note that the second example needs a mailserver running on localhost.)

+10.8. Dates and Times

The datetime module supplies classes for manipulating dates and times in both simple and complex ways. While date and time arithmetic is supported, the focus of the implementation is on efficient member extraction for output formatting and manipulation. The module also supports objects that are timezone aware.

>>> # dates are easily constructed and formatted
+>>> from datetime import date
+>>> now = date.today()
+>>> now
+datetime.date(2003, 12, 2)
+>>> now.strftime("%m-%d-%y. %d %b %Y is a %A on the %d day of %B.")
+'12-02-03. 02 Dec 2003 is a Tuesday on the 02 day of December.'
+
+>>> # dates support calendar arithmetic
+>>> birthday = date(1964, 7, 31)
+>>> age = now - birthday
+>>> age.days
+14368
+

+10.9. Data Compression

Common data archiving and compression formats are directly supported by modules including: zlib, gzip, bz2, lzma, zipfile and tarfile.

>>> import zlib
+>>> s = b'witch which has which witches wrist watch'
+>>> len(s)
+41
+>>> t = zlib.compress(s)
+>>> len(t)
+37
+>>> zlib.decompress(t)
+b'witch which has which witches wrist watch'
+>>> zlib.crc32(s)
+226805979
+

+10.10. Performance Measurement

Some Python users develop a deep interest in knowing the relative performance of different approaches to the same problem. Python provides a measurement tool that answers those questions immediately.

For example, it may be tempting to use the tuple packing and unpacking feature instead of the traditional approach to swapping arguments. The timeit module quickly demonstrates a modest performance advantage:

>>> from timeit import Timer
+>>> Timer('t=a; a=b; b=t', 'a=1; b=2').timeit()
+0.57535828626024577
+>>> Timer('a,b = b,a', 'a=1; b=2').timeit()
+0.54962537085770791
+

In contrast to timeit’s fine level of granularity, the profile and pstats modules provide tools for identifying time critical sections in larger blocks of code.

+10.11. Quality Control

One approach for developing high quality software is to write tests for each function as it is developed and to run those tests frequently during the development process.

The doctest module provides a tool for scanning a module and validating tests embedded in a program’s docstrings. Test construction is as simple as cutting-and-pasting a typical call along with its results into the docstring. This improves the documentation by providing the user with an example and it allows the doctest module to make sure the code remains true to the documentation:

def average(values):
+    """Computes the arithmetic mean of a list of numbers.
+
+    >>> print(average([20, 30, 70]))
+    40.0
+    """
+    return sum(values) / len(values)
+
+import doctest
+doctest.testmod()   # automatically validate the embedded tests
+

The unittest module is not as effortless as the doctest module, but it allows a more comprehensive set of tests to be maintained in a separate file:

import unittest
+
+class TestStatisticalFunctions(unittest.TestCase):
+
+    def test_average(self):
+        self.assertEqual(average([20, 30, 70]), 40.0)
+        self.assertEqual(round(average([1, 5, 7]), 1), 4.3)
+        with self.assertRaises(ZeroDivisionError):
+            average([])
+        with self.assertRaises(TypeError):
+            average(20, 30, 70)
+
+unittest.main()  # Calling from the command line invokes all tests
+

+10.12. Batteries Included

Python has a “batteries included” philosophy. This is best seen through the sophisticated and robust capabilities of its larger packages. For example:

  • The xmlrpc.client and xmlrpc.server modules make implementing remote procedure calls into an almost trivial task. Despite the modules’ names, no direct knowledge or handling of XML is needed.
  • The email package is a library for managing email messages, including MIME and other RFC 2822-based message documents. Unlike smtplib and poplib which actually send and receive messages, the email package has a complete toolset for building or decoding complex message structures (including attachments) and for implementing internet encoding and header protocols.
  • The json package provides robust support for parsing this popular data interchange format. The csv module supports direct reading and writing of files in Comma-Separated Value format, commonly supported by databases and spreadsheets. XML processing is supported by the xml.etree.ElementTree, xml.dom and xml.sax packages. Together, these modules and packages greatly simplify data interchange between Python applications and other tools.
  • The sqlite3 module is a wrapper for the SQLite database library, providing a persistent database that can be updated and accessed using slightly nonstandard SQL syntax.
  • Internationalization is supported by a number of modules including gettext, locale, and the codecs package.
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/stdlib.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fstdlib2.html b/devdocs/python~3.12/tutorial%2Fstdlib2.html new file mode 100644 index 00000000..51a82b22 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fstdlib2.html @@ -0,0 +1,192 @@ +

Brief Tour of the Standard Library — Part II

This second tour covers more advanced modules that support professional programming needs. These modules rarely occur in small scripts.

+11.1. Output Formatting

The reprlib module provides a version of repr() customized for abbreviated displays of large or deeply nested containers:

>>> import reprlib
+>>> reprlib.repr(set('supercalifragilisticexpialidocious'))
+"{'a', 'c', 'd', 'e', 'f', 'g', ...}"
+

The pprint module offers more sophisticated control over printing both built-in and user defined objects in a way that is readable by the interpreter. When the result is longer than one line, the “pretty printer” adds line breaks and indentation to more clearly reveal data structure:

>>> import pprint
+>>> t = [[[['black', 'cyan'], 'white', ['green', 'red']], [['magenta',
+...     'yellow'], 'blue']]]
+...
+>>> pprint.pprint(t, width=30)
+[[[['black', 'cyan'],
+   'white',
+   ['green', 'red']],
+  [['magenta', 'yellow'],
+   'blue']]]
+

The textwrap module formats paragraphs of text to fit a given screen width:

>>> import textwrap
+>>> doc = """The wrap() method is just like fill() except that it returns
+... a list of strings instead of one big string with newlines to separate
+... the wrapped lines."""
+...
+>>> print(textwrap.fill(doc, width=40))
+The wrap() method is just like fill()
+except that it returns a list of strings
+instead of one big string with newlines
+to separate the wrapped lines.
+

The locale module accesses a database of culture specific data formats. The grouping attribute of locale’s format function provides a direct way of formatting numbers with group separators:

>>> import locale
+>>> locale.setlocale(locale.LC_ALL, 'English_United States.1252')
+'English_United States.1252'
+>>> conv = locale.localeconv()          # get a mapping of conventions
+>>> x = 1234567.8
+>>> locale.format_string("%d", x, grouping=True)
+'1,234,567'
+>>> locale.format_string("%s%.*f", (conv['currency_symbol'],
+...                      conv['frac_digits'], x), grouping=True)
+'$1,234,567.80'
+

+11.2. Templating

The string module includes a versatile Template class with a simplified syntax suitable for editing by end-users. This allows users to customize their applications without having to alter the application.

The format uses placeholder names formed by $ with valid Python identifiers (alphanumeric characters and underscores). Surrounding the placeholder with braces allows it to be followed by more alphanumeric letters with no intervening spaces. Writing $$ creates a single escaped $:

>>> from string import Template
+>>> t = Template('${village}folk send $$10 to $cause.')
+>>> t.substitute(village='Nottingham', cause='the ditch fund')
+'Nottinghamfolk send $10 to the ditch fund.'
+

The substitute() method raises a KeyError when a placeholder is not supplied in a dictionary or a keyword argument. For mail-merge style applications, user supplied data may be incomplete and the safe_substitute() method may be more appropriate — it will leave placeholders unchanged if data is missing:

>>> t = Template('Return the $item to $owner.')
+>>> d = dict(item='unladen swallow')
+>>> t.substitute(d)
+Traceback (most recent call last):
+  ...
+KeyError: 'owner'
+>>> t.safe_substitute(d)
+'Return the unladen swallow to $owner.'
+

Template subclasses can specify a custom delimiter. For example, a batch renaming utility for a photo browser may elect to use percent signs for placeholders such as the current date, image sequence number, or file format:

>>> import time, os.path
+>>> photofiles = ['img_1074.jpg', 'img_1076.jpg', 'img_1077.jpg']
+>>> class BatchRename(Template):
+...     delimiter = '%'
+...
+>>> fmt = input('Enter rename style (%d-date %n-seqnum %f-format):  ')
+Enter rename style (%d-date %n-seqnum %f-format):  Ashley_%n%f
+
+>>> t = BatchRename(fmt)
+>>> date = time.strftime('%d%b%y')
+>>> for i, filename in enumerate(photofiles):
+...     base, ext = os.path.splitext(filename)
+...     newname = t.substitute(d=date, n=i, f=ext)
+...     print('{0} --> {1}'.format(filename, newname))
+
+img_1074.jpg --> Ashley_0.jpg
+img_1076.jpg --> Ashley_1.jpg
+img_1077.jpg --> Ashley_2.jpg
+

Another application for templating is separating program logic from the details of multiple output formats. This makes it possible to substitute custom templates for XML files, plain text reports, and HTML web reports.

+11.3. Working with Binary Data Record Layouts

The struct module provides pack() and unpack() functions for working with variable length binary record formats. The following example shows how to loop through header information in a ZIP file without using the zipfile module. Pack codes "H" and "I" represent two and four byte unsigned numbers respectively. The "<" indicates that they are standard size and in little-endian byte order:

import struct
+
+with open('myfile.zip', 'rb') as f:
+    data = f.read()
+
+start = 0
+for i in range(3):                      # show the first 3 file headers
+    start += 14
+    fields = struct.unpack('<IIIHH', data[start:start+16])
+    crc32, comp_size, uncomp_size, filenamesize, extra_size = fields
+
+    start += 16
+    filename = data[start:start+filenamesize]
+    start += filenamesize
+    extra = data[start:start+extra_size]
+    print(filename, hex(crc32), comp_size, uncomp_size)
+
+    start += extra_size + comp_size     # skip to the next header
+

+11.4. Multi-threading

Threading is a technique for decoupling tasks which are not sequentially dependent. Threads can be used to improve the responsiveness of applications that accept user input while other tasks run in the background. A related use case is running I/O in parallel with computations in another thread.

The following code shows how the high level threading module can run tasks in background while the main program continues to run:

import threading, zipfile
+
+class AsyncZip(threading.Thread):
+    def __init__(self, infile, outfile):
+        threading.Thread.__init__(self)
+        self.infile = infile
+        self.outfile = outfile
+
+    def run(self):
+        f = zipfile.ZipFile(self.outfile, 'w', zipfile.ZIP_DEFLATED)
+        f.write(self.infile)
+        f.close()
+        print('Finished background zip of:', self.infile)
+
+background = AsyncZip('mydata.txt', 'myarchive.zip')
+background.start()
+print('The main program continues to run in foreground.')
+
+background.join()    # Wait for the background task to finish
+print('Main program waited until background was done.')
+

The principal challenge of multi-threaded applications is coordinating threads that share data or other resources. To that end, the threading module provides a number of synchronization primitives including locks, events, condition variables, and semaphores.

While those tools are powerful, minor design errors can result in problems that are difficult to reproduce. So, the preferred approach to task coordination is to concentrate all access to a resource in a single thread and then use the queue module to feed that thread with requests from other threads. Applications using Queue objects for inter-thread communication and coordination are easier to design, more readable, and more reliable.

+11.5. Logging

The logging module offers a full featured and flexible logging system. At its simplest, log messages are sent to a file or to sys.stderr:

import logging
+logging.debug('Debugging information')
+logging.info('Informational message')
+logging.warning('Warning:config file %s not found', 'server.conf')
+logging.error('Error occurred')
+logging.critical('Critical error -- shutting down')
+

This produces the following output:

WARNING:root:Warning:config file server.conf not found
+ERROR:root:Error occurred
+CRITICAL:root:Critical error -- shutting down
+

By default, informational and debugging messages are suppressed and the output is sent to standard error. Other output options include routing messages through email, datagrams, sockets, or to an HTTP Server. New filters can select different routing based on message priority: DEBUG, INFO, WARNING, ERROR, and CRITICAL.

The logging system can be configured directly from Python or can be loaded from a user editable configuration file for customized logging without altering the application.

+11.6. Weak References

Python does automatic memory management (reference counting for most objects and garbage collection to eliminate cycles). The memory is freed shortly after the last reference to it has been eliminated.

This approach works fine for most applications but occasionally there is a need to track objects only as long as they are being used by something else. Unfortunately, just tracking them creates a reference that makes them permanent. The weakref module provides tools for tracking objects without creating a reference. When the object is no longer needed, it is automatically removed from a weakref table and a callback is triggered for weakref objects. Typical applications include caching objects that are expensive to create:

>>> import weakref, gc
+>>> class A:
+...     def __init__(self, value):
+...         self.value = value
+...     def __repr__(self):
+...         return str(self.value)
+...
+>>> a = A(10)                   # create a reference
+>>> d = weakref.WeakValueDictionary()
+>>> d['primary'] = a            # does not create a reference
+>>> d['primary']                # fetch the object if it is still alive
+10
+>>> del a                       # remove the one reference
+>>> gc.collect()                # run garbage collection right away
+0
+>>> d['primary']                # entry was automatically removed
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+    d['primary']                # entry was automatically removed
+  File "C:/python312/lib/weakref.py", line 46, in __getitem__
+    o = self.data[key]()
+KeyError: 'primary'
+

+11.7. Tools for Working with Lists

Many data structure needs can be met with the built-in list type. However, sometimes there is a need for alternative implementations with different performance trade-offs.

The array module provides an array() object that is like a list that stores only homogeneous data and stores it more compactly. The following example shows an array of numbers stored as two byte unsigned binary numbers (typecode "H") rather than the usual 16 bytes per entry for regular lists of Python int objects:

>>> from array import array
+>>> a = array('H', [4000, 10, 700, 22222])
+>>> sum(a)
+26932
+>>> a[1:3]
+array('H', [10, 700])
+

The collections module provides a deque() object that is like a list with faster appends and pops from the left side but slower lookups in the middle. These objects are well suited for implementing queues and breadth first tree searches:

>>> from collections import deque
+>>> d = deque(["task1", "task2", "task3"])
+>>> d.append("task4")
+>>> print("Handling", d.popleft())
+Handling task1
+
unsearched = deque([starting_node])
+def breadth_first_search(unsearched):
+    node = unsearched.popleft()
+    for m in gen_moves(node):
+        if is_goal(m):
+            return m
+        unsearched.append(m)
+

In addition to alternative list implementations, the library also offers other tools such as the bisect module with functions for manipulating sorted lists:

>>> import bisect
+>>> scores = [(100, 'perl'), (200, 'tcl'), (400, 'lua'), (500, 'python')]
+>>> bisect.insort(scores, (300, 'ruby'))
+>>> scores
+[(100, 'perl'), (200, 'tcl'), (300, 'ruby'), (400, 'lua'), (500, 'python')]
+

The heapq module provides functions for implementing heaps based on regular lists. The lowest valued entry is always kept at position zero. This is useful for applications which repeatedly access the smallest element but do not want to run a full list sort:

>>> from heapq import heapify, heappop, heappush
+>>> data = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
+>>> heapify(data)                      # rearrange the list into heap order
+>>> heappush(data, -5)                 # add a new entry
+>>> [heappop(data) for i in range(3)]  # fetch the three smallest entries
+[-5, 0, 1]
+

+11.8. Decimal Floating Point Arithmetic

The decimal module offers a Decimal datatype for decimal floating point arithmetic. Compared to the built-in float implementation of binary floating point, the class is especially helpful for

  • financial applications and other uses which require exact decimal representation,
  • control over precision,
  • control over rounding to meet legal or regulatory requirements,
  • tracking of significant decimal places, or
  • applications where the user expects the results to match calculations done by hand.

For example, calculating a 5% tax on a 70 cent phone charge gives different results in decimal floating point and binary floating point. The difference becomes significant if the results are rounded to the nearest cent:

>>> from decimal import *
+>>> round(Decimal('0.70') * Decimal('1.05'), 2)
+Decimal('0.74')
+>>> round(.70 * 1.05, 2)
+0.73
+

The Decimal result keeps a trailing zero, automatically inferring four place significance from multiplicands with two place significance. Decimal reproduces mathematics as done by hand and avoids issues that can arise when binary floating point cannot exactly represent decimal quantities.

Exact representation enables the Decimal class to perform modulo calculations and equality tests that are unsuitable for binary floating point:

>>> Decimal('1.00') % Decimal('.10')
+Decimal('0.00')
+>>> 1.00 % 0.10
+0.09999999999999995
+
+>>> sum([Decimal('0.1')]*10) == Decimal('1.0')
+True
+>>> 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 + 0.1 == 1.0
+False
+

The decimal module provides arithmetic with as much precision as needed:

>>> getcontext().prec = 36
+>>> Decimal(1) / Decimal(7)
+Decimal('0.142857142857142857142857142857142857')
+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/stdlib2.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fvenv.html b/devdocs/python~3.12/tutorial%2Fvenv.html new file mode 100644 index 00000000..577209f9 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fvenv.html @@ -0,0 +1,74 @@ +

Virtual Environments and Packages

+12.1. Introduction

Python applications will often use packages and modules that don’t come as part of the standard library. Applications will sometimes need a specific version of a library, because the application may require that a particular bug has been fixed or the application may be written using an obsolete version of the library’s interface.

This means it may not be possible for one Python installation to meet the requirements of every application. If application A needs version 1.0 of a particular module but application B needs version 2.0, then the requirements are in conflict and installing either version 1.0 or 2.0 will leave one application unable to run.

The solution for this problem is to create a virtual environment, a self-contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages.

Different applications can then use different virtual environments. To resolve the earlier example of conflicting requirements, application A can have its own virtual environment with version 1.0 installed while application B has another virtual environment with version 2.0. If application B requires a library be upgraded to version 3.0, this will not affect application A’s environment.

+12.2. Creating Virtual Environments

The module used to create and manage virtual environments is called venv. venv will usually install the most recent version of Python that you have available. If you have multiple versions of Python on your system, you can select a specific Python version by running python3 or whichever version you want.

To create a virtual environment, decide upon a directory where you want to place it, and run the venv module as a script with the directory path:

python -m venv tutorial-env
+

This will create the tutorial-env directory if it doesn’t exist, and also create directories inside it containing a copy of the Python interpreter and various supporting files.

A common directory location for a virtual environment is .venv. This name keeps the directory typically hidden in your shell and thus out of the way while giving it a name that explains why the directory exists. It also prevents clashing with .env environment variable definition files that some tooling supports.

Once you’ve created a virtual environment, you may activate it.

On Windows, run:

tutorial-env\Scripts\activate
+

On Unix or MacOS, run:

source tutorial-env/bin/activate
+

(This script is written for the bash shell. If you use the csh or fish shells, there are alternate activate.csh and activate.fish scripts you should use instead.)

Activating the virtual environment will change your shell’s prompt to show what virtual environment you’re using, and modify the environment so that running python will get you that particular version and installation of Python. For example:

$ source ~/envs/tutorial-env/bin/activate
+(tutorial-env) $ python
+Python 3.5.1 (default, May  6 2016, 10:59:36)
+  ...
+>>> import sys
+>>> sys.path
+['', '/usr/local/lib/python35.zip', ...,
+'~/envs/tutorial-env/lib/python3.5/site-packages']
+>>>
+

To deactivate a virtual environment, type:

deactivate
+

into the terminal.

+12.3. Managing Packages with pip

You can install, upgrade, and remove packages using a program called pip. By default pip will install packages from the Python Package Index. You can browse the Python Package Index by going to it in your web browser.

pip has a number of subcommands: “install”, “uninstall”, “freeze”, etc. (Consult the Installing Python Modules guide for complete documentation for pip.)

You can install the latest version of a package by specifying a package’s name:

(tutorial-env) $ python -m pip install novas
+Collecting novas
+  Downloading novas-3.1.1.3.tar.gz (136kB)
+Installing collected packages: novas
+  Running setup.py install for novas
+Successfully installed novas-3.1.1.3
+

You can also install a specific version of a package by giving the package name followed by == and the version number:

(tutorial-env) $ python -m pip install requests==2.6.0
+Collecting requests==2.6.0
+  Using cached requests-2.6.0-py2.py3-none-any.whl
+Installing collected packages: requests
+Successfully installed requests-2.6.0
+

If you re-run this command, pip will notice that the requested version is already installed and do nothing. You can supply a different version number to get that version, or you can run python +-m pip install --upgrade to upgrade the package to the latest version:

(tutorial-env) $ python -m pip install --upgrade requests
+Collecting requests
+Installing collected packages: requests
+  Found existing installation: requests 2.6.0
+    Uninstalling requests-2.6.0:
+      Successfully uninstalled requests-2.6.0
+Successfully installed requests-2.7.0
+

python -m pip uninstall followed by one or more package names will remove the packages from the virtual environment.

python -m pip show will display information about a particular package:

(tutorial-env) $ python -m pip show requests
+---
+Metadata-Version: 2.0
+Name: requests
+Version: 2.7.0
+Summary: Python HTTP for Humans.
+Home-page: http://python-requests.org
+Author: Kenneth Reitz
+Author-email: me@kennethreitz.com
+License: Apache 2.0
+Location: /Users/akuchling/envs/tutorial-env/lib/python3.4/site-packages
+Requires:
+

python -m pip list will display all of the packages installed in the virtual environment:

(tutorial-env) $ python -m pip list
+novas (3.1.1.3)
+numpy (1.9.2)
+pip (7.0.3)
+requests (2.7.0)
+setuptools (16.0)
+

python -m pip freeze will produce a similar list of the installed packages, but the output uses the format that python -m pip install expects. A common convention is to put this list in a requirements.txt file:

(tutorial-env) $ python -m pip freeze > requirements.txt
+(tutorial-env) $ cat requirements.txt
+novas==3.1.1.3
+numpy==1.9.2
+requests==2.7.0
+

The requirements.txt can then be committed to version control and shipped as part of an application. Users can then install all the necessary packages with install -r:

(tutorial-env) $ python -m pip install -r requirements.txt
+Collecting novas==3.1.1.3 (from -r requirements.txt (line 1))
+  ...
+Collecting numpy==1.9.2 (from -r requirements.txt (line 2))
+  ...
+Collecting requests==2.7.0 (from -r requirements.txt (line 3))
+  ...
+Installing collected packages: novas, numpy, requests
+  Running setup.py install for novas
+Successfully installed novas-3.1.1.3 numpy-1.9.2 requests-2.7.0
+

pip has many more options. Consult the Installing Python Modules guide for complete documentation for pip. When you’ve written a package and want to make it available on the Python Package Index, consult the Python packaging user guide.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/venv.html +

+
diff --git a/devdocs/python~3.12/tutorial%2Fwhatnow.html b/devdocs/python~3.12/tutorial%2Fwhatnow.html new file mode 100644 index 00000000..bd6845f9 --- /dev/null +++ b/devdocs/python~3.12/tutorial%2Fwhatnow.html @@ -0,0 +1,17 @@ +

What Now?

Reading this tutorial has probably reinforced your interest in using Python — you should be eager to apply Python to solving your real-world problems. Where should you go to learn more?

This tutorial is part of Python’s documentation set. Some other documents in the set are:

More Python resources:

For Python-related questions and problem reports, you can post to the newsgroup comp.lang.python, or send them to the mailing list at python-list@python.org. The newsgroup and mailing list are gatewayed, so messages posted to one will automatically be forwarded to the other. There are hundreds of postings a day, asking (and answering) questions, suggesting new features, and announcing new modules. Mailing list archives are available at https://mail.python.org/pipermail/.

Before posting, be sure to check the list of Frequently Asked Questions (also called the FAQ). The FAQ answers many of the questions that come up again and again, and may already contain the solution for your problem.

Footnotes

+1
+

“Cheese Shop” is a Monty Python’s sketch: a customer enters a cheese shop, but whatever cheese he asks for, the clerk says it’s missing.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/tutorial/whatnow.html +

+
diff --git a/devdocs/python~3.12/using%2Fcmdline.html b/devdocs/python~3.12/using%2Fcmdline.html new file mode 100644 index 00000000..b73fef80 --- /dev/null +++ b/devdocs/python~3.12/using%2Fcmdline.html @@ -0,0 +1,265 @@ +

Command line and environment

The CPython interpreter scans the command line and the environment for various settings.

CPython implementation detail: Other implementations’ command line schemes may differ. See Alternate Implementations for further resources.

+1.1. Command line

When invoking Python, you may specify any of these options:

python [-bBdEhiIOqsSuvVWx?] [-c command | -m module-name | script | - ] [args]
+

The most common use case is, of course, a simple invocation of a script:

python myscript.py
+

+1.1.1. Interface options

The interpreter interface resembles that of the UNIX shell, but provides some additional methods of invocation:

  • When called with standard input connected to a tty device, it prompts for commands and executes them until an EOF (an end-of-file character, you can produce that with Ctrl-D on UNIX or Ctrl-Z, Enter on Windows) is read.
  • When called with a file name argument or with a file as standard input, it reads and executes a script from that file.
  • When called with a directory name argument, it reads and executes an appropriately named script from that directory.
  • When called with -c command, it executes the Python statement(s) given as command. Here command may contain multiple statements separated by newlines. Leading whitespace is significant in Python statements!
  • When called with -m module-name, the given module is located on the Python module path and executed as a script.

In non-interactive mode, the entire input is parsed before it is executed.

An interface option terminates the list of options consumed by the interpreter, all consecutive arguments will end up in sys.argv – note that the first element, subscript zero (sys.argv[0]), is a string reflecting the program’s source.

+-c <command>
+

Execute the Python code in command. command can be one or more statements separated by newlines, with significant leading whitespace as in normal module code.

If this option is given, the first element of sys.argv will be "-c" and the current directory will be added to the start of sys.path (allowing modules in that directory to be imported as top level modules).

Raises an auditing event cpython.run_command with argument command.

+
+-m <module-name>
+

Search sys.path for the named module and execute its contents as the __main__ module.

Since the argument is a module name, you must not give a file extension (.py). The module name should be a valid absolute Python module name, but the implementation may not always enforce this (e.g. it may allow you to use a name that includes a hyphen).

Package names (including namespace packages) are also permitted. When a package name is supplied instead of a normal module, the interpreter will execute <pkg>.__main__ as the main module. This behaviour is deliberately similar to the handling of directories and zipfiles that are passed to the interpreter as the script argument.

Note

This option cannot be used with built-in modules and extension modules written in C, since they do not have Python module files. However, it can still be used for precompiled modules, even if the original source file is not available.

If this option is given, the first element of sys.argv will be the full path to the module file (while the module file is being located, the first element will be set to "-m"). As with the -c option, the current directory will be added to the start of sys.path.

-I option can be used to run the script in isolated mode where sys.path contains neither the current directory nor the user’s site-packages directory. All PYTHON* environment variables are ignored, too.

Many standard library modules contain code that is invoked on their execution as a script. An example is the timeit module:

python -m timeit -s "setup here" "benchmarked code here"
+python -m timeit -h # for details
+

Raises an auditing event cpython.run_module with argument module-name.

See also

+ runpy.run_module() +
+
+

Equivalent functionality directly available to Python code

PEP 338 – Executing modules as scripts

Changed in version 3.1: Supply the package name to run a __main__ submodule.

Changed in version 3.4: namespace packages are also supported

+
- +
+

Read commands from standard input (sys.stdin). If standard input is a terminal, -i is implied.

If this option is given, the first element of sys.argv will be "-" and the current directory will be added to the start of sys.path.

Raises an auditing event cpython.run_stdin with no arguments.

+
<script> +
+

Execute the Python code contained in script, which must be a filesystem path (absolute or relative) referring to either a Python file, a directory containing a __main__.py file, or a zipfile containing a __main__.py file.

If this option is given, the first element of sys.argv will be the script name as given on the command line.

If the script name refers directly to a Python file, the directory containing that file is added to the start of sys.path, and the file is executed as the __main__ module.

If the script name refers to a directory or zipfile, the script name is added to the start of sys.path and the __main__.py file in that location is executed as the __main__ module.

-I option can be used to run the script in isolated mode where sys.path contains neither the script’s directory nor the user’s site-packages directory. All PYTHON* environment variables are ignored, too.

Raises an auditing event cpython.run_file with argument filename.

See also

+ runpy.run_path() +
+
+

Equivalent functionality directly available to Python code

+

If no interface option is given, -i is implied, sys.argv[0] is an empty string ("") and the current directory will be added to the start of sys.path. Also, tab-completion and history editing is automatically enabled, if available on your platform (see Readline configuration).

See also

Invoking the Interpreter

Changed in version 3.4: Automatic enabling of tab-completion and history editing.

+1.1.2. Generic options

+-?
+-h
+--help
+

Print a short description of all command line options and corresponding environment variables and exit.

+
+--help-env
+

Print a short description of Python-specific environment variables and exit.

New in version 3.11.

+
+--help-xoptions
+

Print a description of implementation-specific -X options and exit.

New in version 3.11.

+
+--help-all
+

Print complete usage information and exit.

New in version 3.11.

+
+-V
+--version
+

Print the Python version number and exit. Example output could be:

Python 3.8.0b2+
+

When given twice, print more information about the build, like:

Python 3.8.0b2+ (3.8:0c076caaa8, Apr 20 2019, 21:55:00)
+[GCC 6.2.0 20161005]
+

New in version 3.6: The -VV option.

+

+1.1.3. Miscellaneous options

+-b
+

Issue a warning when comparing bytes or bytearray with str or bytes with int. Issue an error when the option is given twice (-bb).

Changed in version 3.5: Affects comparisons of bytes with int.

+
+-B
+

If given, Python won’t try to write .pyc files on the import of source modules. See also PYTHONDONTWRITEBYTECODE.

+
+--check-hash-based-pycs default|always|never
+

Control the validation behavior of hash-based .pyc files. See Cached bytecode invalidation. When set to default, checked and unchecked hash-based bytecode cache files are validated according to their default semantics. When set to always, all hash-based .pyc files, whether checked or unchecked, are validated against their corresponding source file. When set to never, hash-based .pyc files are not validated against their corresponding source files.

The semantics of timestamp-based .pyc files are unaffected by this option.

+
+-d
+

Turn on parser debugging output (for expert only). See also the PYTHONDEBUG environment variable.

This option requires a debug build of Python, otherwise it’s ignored.

+
+-E
+

Ignore all PYTHON* environment variables, e.g. PYTHONPATH and PYTHONHOME, that might be set.

See also the -P and -I (isolated) options.

+
+-i
+

When a script is passed as first argument or the -c option is used, enter interactive mode after executing the script or the command, even when sys.stdin does not appear to be a terminal. The PYTHONSTARTUP file is not read.

This can be useful to inspect global variables or a stack trace when a script raises an exception. See also PYTHONINSPECT.

+
+-I
+

Run Python in isolated mode. This also implies -E, -P and -s options.

In isolated mode sys.path contains neither the script’s directory nor the user’s site-packages directory. All PYTHON* environment variables are ignored, too. Further restrictions may be imposed to prevent the user from injecting malicious code.

New in version 3.4.

+
+-O
+

Remove assert statements and any code conditional on the value of __debug__. Augment the filename for compiled (bytecode) files by adding .opt-1 before the .pyc extension (see PEP 488). See also PYTHONOPTIMIZE.

Changed in version 3.5: Modify .pyc filenames according to PEP 488.

+
+-OO
+

Do -O and also discard docstrings. Augment the filename for compiled (bytecode) files by adding .opt-2 before the .pyc extension (see PEP 488).

Changed in version 3.5: Modify .pyc filenames according to PEP 488.

+
+-P
+

Don’t prepend a potentially unsafe path to sys.path:

  • +python -m module command line: Don’t prepend the current working directory.
  • +python script.py command line: Don’t prepend the script’s directory. If it’s a symbolic link, resolve symbolic links.
  • +python -c code and python (REPL) command lines: Don’t prepend an empty string, which means the current working directory.

See also the PYTHONSAFEPATH environment variable, and -E and -I (isolated) options.

New in version 3.11.

+
+-q
+

Don’t display the copyright and version messages even in interactive mode.

New in version 3.2.

+
+-R
+

Turn on hash randomization. This option only has an effect if the PYTHONHASHSEED environment variable is set to 0, since hash randomization is enabled by default.

On previous versions of Python, this option turns on hash randomization, so that the __hash__() values of str and bytes objects are “salted” with an unpredictable random value. Although they remain constant within an individual Python process, they are not predictable between repeated invocations of Python.

Hash randomization is intended to provide protection against a denial-of-service caused by carefully chosen inputs that exploit the worst case performance of a dict construction, O(n2) complexity. See http://ocert.org/advisories/ocert-2011-003.html for details.

PYTHONHASHSEED allows you to set a fixed value for the hash seed secret.

Changed in version 3.7: The option is no longer ignored.

New in version 3.2.3.

+
+-s
+

Don’t add the user site-packages directory to sys.path.

See also

PEP 370 – Per user site-packages directory

+
+-S
+

Disable the import of the module site and the site-dependent manipulations of sys.path that it entails. Also disable these manipulations if site is explicitly imported later (call site.main() if you want them to be triggered).

+
+-u
+

Force the stdout and stderr streams to be unbuffered. This option has no effect on the stdin stream.

See also PYTHONUNBUFFERED.

Changed in version 3.7: The text layer of the stdout and stderr streams now is unbuffered.

+
+-v
+

Print a message each time a module is initialized, showing the place (filename or built-in module) from which it is loaded. When given twice (-vv), print a message for each file that is checked for when searching for a module. Also provides information on module cleanup at exit.

Changed in version 3.10: The site module reports the site-specific paths and .pth files being processed.

See also PYTHONVERBOSE.

+
+-W arg
+

Warning control. Python’s warning machinery by default prints warning messages to sys.stderr.

The simplest settings apply a particular action unconditionally to all warnings emitted by a process (even those that are otherwise ignored by default):

-Wdefault  # Warn once per call location
+-Werror    # Convert to exceptions
+-Walways   # Warn every time
+-Wmodule   # Warn once per calling module
+-Wonce     # Warn once per Python process
+-Wignore   # Never warn
+

The action names can be abbreviated as desired and the interpreter will resolve them to the appropriate action name. For example, -Wi is the same as -Wignore.

The full form of argument is:

action:message:category:module:lineno
+

Empty fields match all values; trailing empty fields may be omitted. For example -W ignore::DeprecationWarning ignores all DeprecationWarning warnings.

The action field is as explained above but only applies to warnings that match the remaining fields.

The message field must match the whole warning message; this match is case-insensitive.

The category field matches the warning category (ex: DeprecationWarning). This must be a class name; the match test whether the actual warning category of the message is a subclass of the specified warning category.

The module field matches the (fully qualified) module name; this match is case-sensitive.

The lineno field matches the line number, where zero matches all line numbers and is thus equivalent to an omitted line number.

Multiple -W options can be given; when a warning matches more than one option, the action for the last matching option is performed. Invalid -W options are ignored (though, a warning message is printed about invalid options when the first warning is issued).

Warnings can also be controlled using the PYTHONWARNINGS environment variable and from within a Python program using the warnings module. For example, the warnings.filterwarnings() function can be used to use a regular expression on the warning message.

See The Warnings Filter and Describing Warning Filters for more details.

+
+-x
+

Skip the first line of the source, allowing use of non-Unix forms of #!cmd. This is intended for a DOS specific hack only.

+
+-X
+

Reserved for various implementation-specific options. CPython currently defines the following possible values:

  • +-X faulthandler to enable faulthandler. See also PYTHONFAULTHANDLER.
  • +-X showrefcount to output the total reference count and number of used memory blocks when the program finishes or after each statement in the interactive interpreter. This only works on debug builds.
  • +-X tracemalloc to start tracing Python memory allocations using the tracemalloc module. By default, only the most recent frame is stored in a traceback of a trace. Use -X tracemalloc=NFRAME to start tracing with a traceback limit of NFRAME frames. See tracemalloc.start() and PYTHONTRACEMALLOC for more information.
  • +-X int_max_str_digits configures the integer string conversion length limitation. See also PYTHONINTMAXSTRDIGITS.
  • +-X importtime to show how long each import takes. It shows module name, cumulative time (including nested imports) and self time (excluding nested imports). Note that its output may be broken in multi-threaded application. Typical usage is python3 -X importtime -c 'import +asyncio'. See also PYTHONPROFILEIMPORTTIME.
  • +-X dev: enable Python Development Mode, introducing additional runtime checks that are too expensive to be enabled by default.
  • +-X utf8 enables the Python UTF-8 Mode. -X utf8=0 explicitly disables Python UTF-8 Mode (even when it would otherwise activate automatically). See also PYTHONUTF8.
  • +-X pycache_prefix=PATH enables writing .pyc files to a parallel tree rooted at the given directory instead of to the code tree. See also PYTHONPYCACHEPREFIX.
  • +-X warn_default_encoding issues a EncodingWarning when the locale-specific default encoding is used for opening files. See also PYTHONWARNDEFAULTENCODING.
  • +-X no_debug_ranges disables the inclusion of the tables mapping extra location information (end line, start column offset and end column offset) to every instruction in code objects. This is useful when smaller code objects and pyc files are desired as well as suppressing the extra visual location indicators when the interpreter displays tracebacks. See also PYTHONNODEBUGRANGES.
  • +-X frozen_modules determines whether or not frozen modules are ignored by the import machinery. A value of “on” means they get imported and “off” means they are ignored. The default is “on” if this is an installed Python (the normal case). If it’s under development (running from the source tree) then the default is “off”. Note that the “importlib_bootstrap” and “importlib_bootstrap_external” frozen modules are always used, even if this flag is set to “off”.
  • +-X perf enables support for the Linux perf profiler. When this option is provided, the perf profiler will be able to report Python calls. This option is only available on some platforms and will do nothing if is not supported on the current system. The default value is “off”. See also PYTHONPERFSUPPORT and Python support for the Linux perf profiler.

It also allows passing arbitrary values and retrieving them through the sys._xoptions dictionary.

Changed in version 3.2: The -X option was added.

New in version 3.3: The -X faulthandler option.

New in version 3.4: The -X showrefcount and -X tracemalloc options.

New in version 3.6: The -X showalloccount option.

New in version 3.7: The -X importtime, -X dev and -X utf8 options.

New in version 3.8: The -X pycache_prefix option. The -X dev option now logs close() exceptions in io.IOBase destructor.

Changed in version 3.9: Using -X dev option, check encoding and errors arguments on string encoding and decoding operations.

The -X showalloccount option has been removed.

New in version 3.10: The -X warn_default_encoding option. Removed the -X oldparser option.

New in version 3.11: The -X no_debug_ranges option.

New in version 3.11: The -X frozen_modules option.

New in version 3.11: The -X int_max_str_digits option.

New in version 3.12: The -X perf option.

+

+1.1.4. Options you shouldn’t use

+-J
+

Reserved for use by Jython.

+

+1.2. Environment variables

These environment variables influence Python’s behavior, they are processed before the command-line switches other than -E or -I. It is customary that command-line switches override environmental variables where there is a conflict.

+PYTHONHOME
+

Change the location of the standard Python libraries. By default, the libraries are searched in prefix/lib/pythonversion and exec_prefix/lib/pythonversion, where prefix and exec_prefix are installation-dependent directories, both defaulting to /usr/local.

When PYTHONHOME is set to a single directory, its value replaces both prefix and exec_prefix. To specify different values for these, set PYTHONHOME to prefix:exec_prefix.

+
+PYTHONPATH
+

Augment the default search path for module files. The format is the same as the shell’s PATH: one or more directory pathnames separated by os.pathsep (e.g. colons on Unix or semicolons on Windows). Non-existent directories are silently ignored.

In addition to normal directories, individual PYTHONPATH entries may refer to zipfiles containing pure Python modules (in either source or compiled form). Extension modules cannot be imported from zipfiles.

The default search path is installation dependent, but generally begins with prefix/lib/pythonversion (see PYTHONHOME above). It is always appended to PYTHONPATH.

An additional directory will be inserted in the search path in front of PYTHONPATH as described above under Interface options. The search path can be manipulated from within a Python program as the variable sys.path.

+
+PYTHONSAFEPATH
+

If this is set to a non-empty string, don’t prepend a potentially unsafe path to sys.path: see the -P option for details.

New in version 3.11.

+
+PYTHONPLATLIBDIR
+

If this is set to a non-empty string, it overrides the sys.platlibdir value.

New in version 3.9.

+
+PYTHONSTARTUP
+

If this is the name of a readable file, the Python commands in that file are executed before the first prompt is displayed in interactive mode. The file is executed in the same namespace where interactive commands are executed so that objects defined or imported in it can be used without qualification in the interactive session. You can also change the prompts sys.ps1 and sys.ps2 and the hook sys.__interactivehook__ in this file.

+

Raises an auditing event cpython.run_startup with the filename as the argument when called on startup.

+
+PYTHONOPTIMIZE
+

If this is set to a non-empty string it is equivalent to specifying the -O option. If set to an integer, it is equivalent to specifying -O multiple times.

+
+PYTHONBREAKPOINT
+

If this is set, it names a callable using dotted-path notation. The module containing the callable will be imported and then the callable will be run by the default implementation of sys.breakpointhook() which itself is called by built-in breakpoint(). If not set, or set to the empty string, it is equivalent to the value “pdb.set_trace”. Setting this to the string “0” causes the default implementation of sys.breakpointhook() to do nothing but return immediately.

New in version 3.7.

+
+PYTHONDEBUG
+

If this is set to a non-empty string it is equivalent to specifying the -d option. If set to an integer, it is equivalent to specifying -d multiple times.

This environment variable requires a debug build of Python, otherwise it’s ignored.

+
+PYTHONINSPECT
+

If this is set to a non-empty string it is equivalent to specifying the -i option.

This variable can also be modified by Python code using os.environ to force inspect mode on program termination.

+
+PYTHONUNBUFFERED
+

If this is set to a non-empty string it is equivalent to specifying the -u option.

+
+PYTHONVERBOSE
+

If this is set to a non-empty string it is equivalent to specifying the -v option. If set to an integer, it is equivalent to specifying -v multiple times.

+
+PYTHONCASEOK
+

If this is set, Python ignores case in import statements. This only works on Windows and macOS.

+
+PYTHONDONTWRITEBYTECODE
+

If this is set to a non-empty string, Python won’t try to write .pyc files on the import of source modules. This is equivalent to specifying the -B option.

+
+PYTHONPYCACHEPREFIX
+

If this is set, Python will write .pyc files in a mirror directory tree at this path, instead of in __pycache__ directories within the source tree. This is equivalent to specifying the -X pycache_prefix=PATH option.

New in version 3.8.

+
+PYTHONHASHSEED
+

If this variable is not set or set to random, a random value is used to seed the hashes of str and bytes objects.

If PYTHONHASHSEED is set to an integer value, it is used as a fixed seed for generating the hash() of the types covered by the hash randomization.

Its purpose is to allow repeatable hashing, such as for selftests for the interpreter itself, or to allow a cluster of python processes to share hash values.

The integer must be a decimal number in the range [0,4294967295]. Specifying the value 0 will disable hash randomization.

New in version 3.2.3.

+
+PYTHONINTMAXSTRDIGITS
+

If this variable is set to an integer, it is used to configure the interpreter’s global integer string conversion length limitation.

New in version 3.11.

+
+PYTHONIOENCODING
+

If this is set before running the interpreter, it overrides the encoding used for stdin/stdout/stderr, in the syntax encodingname:errorhandler. Both the encodingname and the :errorhandler parts are optional and have the same meaning as in str.encode().

For stderr, the :errorhandler part is ignored; the handler will always be 'backslashreplace'.

Changed in version 3.4: The encodingname part is now optional.

Changed in version 3.6: On Windows, the encoding specified by this variable is ignored for interactive console buffers unless PYTHONLEGACYWINDOWSSTDIO is also specified. Files and pipes redirected through the standard streams are not affected.

+
+PYTHONNOUSERSITE
+

If this is set, Python won’t add the user site-packages directory to sys.path.

See also

PEP 370 – Per user site-packages directory

+
+PYTHONUSERBASE
+

Defines the user base directory, which is used to compute the path of the user site-packages directory and installation paths for python -m pip install --user.

See also

PEP 370 – Per user site-packages directory

+
+PYTHONEXECUTABLE
+

If this environment variable is set, sys.argv[0] will be set to its value instead of the value got through the C runtime. Only works on macOS.

+
+PYTHONWARNINGS
+

This is equivalent to the -W option. If set to a comma separated string, it is equivalent to specifying -W multiple times, with filters later in the list taking precedence over those earlier in the list.

The simplest settings apply a particular action unconditionally to all warnings emitted by a process (even those that are otherwise ignored by default):

PYTHONWARNINGS=default  # Warn once per call location
+PYTHONWARNINGS=error    # Convert to exceptions
+PYTHONWARNINGS=always   # Warn every time
+PYTHONWARNINGS=module   # Warn once per calling module
+PYTHONWARNINGS=once     # Warn once per Python process
+PYTHONWARNINGS=ignore   # Never warn
+

See The Warnings Filter and Describing Warning Filters for more details.

+
+PYTHONFAULTHANDLER
+

If this environment variable is set to a non-empty string, faulthandler.enable() is called at startup: install a handler for SIGSEGV, SIGFPE, SIGABRT, SIGBUS and SIGILL signals to dump the Python traceback. This is equivalent to -X faulthandler option.

New in version 3.3.

+
+PYTHONTRACEMALLOC
+

If this environment variable is set to a non-empty string, start tracing Python memory allocations using the tracemalloc module. The value of the variable is the maximum number of frames stored in a traceback of a trace. For example, PYTHONTRACEMALLOC=1 stores only the most recent frame. See the tracemalloc.start() function for more information. This is equivalent to setting the -X tracemalloc option.

New in version 3.4.

+
+PYTHONPROFILEIMPORTTIME
+

If this environment variable is set to a non-empty string, Python will show how long each import takes. This is equivalent to setting the -X importtime option.

New in version 3.7.

+
+PYTHONASYNCIODEBUG
+

If this environment variable is set to a non-empty string, enable the debug mode of the asyncio module.

New in version 3.4.

+
+PYTHONMALLOC
+

Set the Python memory allocators and/or install debug hooks.

Set the family of memory allocators used by Python:

Install debug hooks:

  • +debug: install debug hooks on top of the default memory allocators.
  • +malloc_debug: same as malloc but also install debug hooks.
  • +pymalloc_debug: same as pymalloc but also install debug hooks.

Changed in version 3.7: Added the "default" allocator.

New in version 3.6.

+
+PYTHONMALLOCSTATS
+

If set to a non-empty string, Python will print statistics of the pymalloc memory allocator every time a new pymalloc object arena is created, and on shutdown.

This variable is ignored if the PYTHONMALLOC environment variable is used to force the malloc() allocator of the C library, or if Python is configured without pymalloc support.

Changed in version 3.6: This variable can now also be used on Python compiled in release mode. It now has no effect if set to an empty string.

+
+PYTHONLEGACYWINDOWSFSENCODING
+

If set to a non-empty string, the default filesystem encoding and error handler mode will revert to their pre-3.6 values of ‘mbcs’ and ‘replace’, respectively. Otherwise, the new defaults ‘utf-8’ and ‘surrogatepass’ are used.

This may also be enabled at runtime with sys._enablelegacywindowsfsencoding().

Availability: Windows.

New in version 3.6: See PEP 529 for more details.

+
+PYTHONLEGACYWINDOWSSTDIO
+

If set to a non-empty string, does not use the new console reader and writer. This means that Unicode characters will be encoded according to the active console code page, rather than using utf-8.

This variable is ignored if the standard streams are redirected (to files or pipes) rather than referring to console buffers.

Availability: Windows.

New in version 3.6.

+
+PYTHONCOERCECLOCALE
+

If set to the value 0, causes the main Python command line application to skip coercing the legacy ASCII-based C and POSIX locales to a more capable UTF-8 based alternative.

If this variable is not set (or is set to a value other than 0), the LC_ALL locale override environment variable is also not set, and the current locale reported for the LC_CTYPE category is either the default C locale, or else the explicitly ASCII-based POSIX locale, then the Python CLI will attempt to configure the following locales for the LC_CTYPE category in the order listed before loading the interpreter runtime:

  • C.UTF-8
  • C.utf8
  • UTF-8

If setting one of these locale categories succeeds, then the LC_CTYPE environment variable will also be set accordingly in the current process environment before the Python runtime is initialized. This ensures that in addition to being seen by both the interpreter itself and other locale-aware components running in the same process (such as the GNU readline library), the updated setting is also seen in subprocesses (regardless of whether or not those processes are running a Python interpreter), as well as in operations that query the environment rather than the current C locale (such as Python’s own locale.getdefaultlocale()).

Configuring one of these locales (either explicitly or via the above implicit locale coercion) automatically enables the surrogateescape error handler for sys.stdin and sys.stdout (sys.stderr continues to use backslashreplace as it does in any other locale). This stream handling behavior can be overridden using PYTHONIOENCODING as usual.

For debugging purposes, setting PYTHONCOERCECLOCALE=warn will cause Python to emit warning messages on stderr if either the locale coercion activates, or else if a locale that would have triggered coercion is still active when the Python runtime is initialized.

Also note that even when locale coercion is disabled, or when it fails to find a suitable target locale, PYTHONUTF8 will still activate by default in legacy ASCII-based locales. Both features must be disabled in order to force the interpreter to use ASCII instead of UTF-8 for system interfaces.

Availability: Unix.

New in version 3.7: See PEP 538 for more details.

+
+PYTHONDEVMODE
+

If this environment variable is set to a non-empty string, enable Python Development Mode, introducing additional runtime checks that are too expensive to be enabled by default. This is equivalent to setting the -X dev option.

New in version 3.7.

+
+PYTHONUTF8
+

If set to 1, enable the Python UTF-8 Mode.

If set to 0, disable the Python UTF-8 Mode.

Setting any other non-empty string causes an error during interpreter initialisation.

New in version 3.7.

+
+PYTHONWARNDEFAULTENCODING
+

If this environment variable is set to a non-empty string, issue a EncodingWarning when the locale-specific default encoding is used.

See Opt-in EncodingWarning for details.

New in version 3.10.

+
+PYTHONNODEBUGRANGES
+

If this variable is set, it disables the inclusion of the tables mapping extra location information (end line, start column offset and end column offset) to every instruction in code objects. This is useful when smaller code objects and pyc files are desired as well as suppressing the extra visual location indicators when the interpreter displays tracebacks.

New in version 3.11.

+
+PYTHONPERFSUPPORT
+

If this variable is set to a nonzero value, it enables support for the Linux perf profiler so Python calls can be detected by it.

If set to 0, disable Linux perf profiler support.

See also the -X perf command-line option and Python support for the Linux perf profiler.

New in version 3.12.

+

+1.2.1. Debug-mode variables

+PYTHONDUMPREFS
+

If set, Python will dump objects and reference counts still alive after shutting down the interpreter.

Need Python configured with the --with-trace-refs build option.

+
+PYTHONDUMPREFSFILE=FILENAME
+

If set, Python will dump objects and reference counts still alive after shutting down the interpreter into a file called FILENAME.

Need Python configured with the --with-trace-refs build option.

New in version 3.11.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/cmdline.html +

+
diff --git a/devdocs/python~3.12/using%2Fconfigure.html b/devdocs/python~3.12/using%2Fconfigure.html new file mode 100644 index 00000000..32f8be70 --- /dev/null +++ b/devdocs/python~3.12/using%2Fconfigure.html @@ -0,0 +1,388 @@ +

Configure Python

+3.1. Build Requirements

Features required to build CPython:

Changed in version 3.11: C11 compiler, IEEE 754 and NaN support are now required. On Windows, Visual Studio 2017 or later is required.

Changed in version 3.10: OpenSSL 1.1.1 is now required.

Changed in version 3.7: Thread support and OpenSSL 1.0.2 are now required.

Changed in version 3.6: Selected C99 features are now required, like <stdint.h> and static +inline functions.

Changed in version 3.5: On Windows, Visual Studio 2015 or later is required.

See also PEP 7 “Style Guide for C Code” and PEP 11 “CPython platform support”.

+3.2. Generated files

To reduce build dependencies, Python source code contains multiple generated files. Commands to regenerate all generated files:

make regen-all
+make regen-stdlib-module-names
+make regen-limited-abi
+make regen-configure
+

The Makefile.pre.in file documents generated files, their inputs, and tools used to regenerate them. Search for regen-* make targets.

+3.2.1. configure script

The make regen-configure command regenerates the aclocal.m4 file and the configure script using the Tools/build/regen-configure.sh shell script which uses an Ubuntu container to get the same tools versions and have a reproducible output.

The container is optional, the following command can be run locally:

autoreconf -ivf -Werror
+

The generated files can change depending on the exact autoconf-archive, aclocal and pkg-config versions.

+3.3. Configure Options

List all ./configure script options using:

./configure --help
+

See also the Misc/SpecialBuilds.txt in the Python source distribution.

+3.3.1. General Options

+--enable-loadable-sqlite-extensions
+

Support loadable extensions in the _sqlite extension module (default is no) of the sqlite3 module.

See the sqlite3.Connection.enable_load_extension() method of the sqlite3 module.

New in version 3.6.

+
+--disable-ipv6
+

Disable IPv6 support (enabled by default if supported), see the socket module.

+
+--enable-big-digits=[15|30]
+

Define the size in bits of Python int digits: 15 or 30 bits.

By default, the digit size is 30.

Define the PYLONG_BITS_IN_DIGIT to 15 or 30.

See sys.int_info.bits_per_digit.

+
+--with-suffix=SUFFIX
+

Set the Python executable suffix to SUFFIX.

The default suffix is .exe on Windows and macOS (python.exe executable), .js on Emscripten node, .html on Emscripten browser, .wasm on WASI, and an empty string on other platforms (python executable).

Changed in version 3.11: The default suffix on WASM platform is one of .js, .html or .wasm.

+
+--with-tzpath=<list of absolute paths separated by pathsep>
+

Select the default time zone search path for zoneinfo.TZPATH. See the Compile-time configuration of the zoneinfo module.

Default: /usr/share/zoneinfo:/usr/lib/zoneinfo:/usr/share/lib/zoneinfo:/etc/zoneinfo.

See os.pathsep path separator.

New in version 3.9.

+
+--without-decimal-contextvar
+

Build the _decimal extension module using a thread-local context rather than a coroutine-local context (default), see the decimal module.

See decimal.HAVE_CONTEXTVAR and the contextvars module.

New in version 3.9.

+
+--with-dbmliborder=<list of backend names>
+

Override order to check db backends for the dbm module

A valid value is a colon (:) separated string with the backend names:

  • +ndbm;
  • +gdbm;
  • +bdb.
+
+--without-c-locale-coercion
+

Disable C locale coercion to a UTF-8 based locale (enabled by default).

Don’t define the PY_COERCE_C_LOCALE macro.

See PYTHONCOERCECLOCALE and the PEP 538.

+
+--without-freelists
+

Disable all freelists except the empty tuple singleton.

New in version 3.11.

+
+--with-platlibdir=DIRNAME
+

Python library directory name (default is lib).

Fedora and SuSE use lib64 on 64-bit platforms.

See sys.platlibdir.

New in version 3.9.

+
+--with-wheel-pkg-dir=PATH
+

Directory of wheel packages used by the ensurepip module (none by default).

Some Linux distribution packaging policies recommend against bundling dependencies. For example, Fedora installs wheel packages in the /usr/share/python-wheels/ directory and don’t install the ensurepip._bundled package.

New in version 3.10.

+
+--with-pkg-config=[check|yes|no]
+

Whether configure should use pkg-config to detect build dependencies.

  • +check (default): pkg-config is optional
  • +yes: pkg-config is mandatory
  • +no: configure does not use pkg-config even when present

New in version 3.11.

+
+--enable-pystats
+

Turn on internal statistics gathering.

The statistics will be dumped to a arbitrary (probably unique) file in /tmp/py_stats/, or C:\temp\py_stats\ on Windows. If that directory does not exist, results will be printed on stdout.

Use Tools/scripts/summarize_stats.py to read the stats.

New in version 3.11.

+

+3.3.2. WebAssembly Options

+--with-emscripten-target=[browser|node]
+

Set build flavor for wasm32-emscripten.

  • +browser (default): preload minimal stdlib, default MEMFS.
  • +node: NODERAWFS and pthread support.

New in version 3.11.

+
+--enable-wasm-dynamic-linking
+

Turn on dynamic linking support for WASM.

Dynamic linking enables dlopen. File size of the executable increases due to limited dead code elimination and additional features.

New in version 3.11.

+
+--enable-wasm-pthreads
+

Turn on pthreads support for WASM.

New in version 3.11.

+

+3.3.3. Install Options

+--prefix=PREFIX
+

Install architecture-independent files in PREFIX. On Unix, it defaults to /usr/local.

This value can be retrieved at runtime using sys.prefix.

As an example, one can use --prefix="$HOME/.local/" to install a Python in its home directory.

+
+--exec-prefix=EPREFIX
+

Install architecture-dependent files in EPREFIX, defaults to --prefix.

This value can be retrieved at runtime using sys.exec_prefix.

+
+--disable-test-modules
+

Don’t build nor install test modules, like the test package or the _testcapi extension module (built and installed by default).

New in version 3.10.

+
+--with-ensurepip=[upgrade|install|no]
+

Select the ensurepip command run on Python installation:

  • +upgrade (default): run python -m ensurepip --altinstall --upgrade command.
  • +install: run python -m ensurepip --altinstall command;
  • +no: don’t run ensurepip;

New in version 3.6.

+

+3.3.4. Performance options

Configuring Python using --enable-optimizations --with-lto (PGO + LTO) is recommended for best performance. The experimental --enable-bolt flag can also be used to improve performance.

+--enable-optimizations
+

Enable Profile Guided Optimization (PGO) using PROFILE_TASK (disabled by default).

The C compiler Clang requires llvm-profdata program for PGO. On macOS, GCC also requires it: GCC is just an alias to Clang on macOS.

Disable also semantic interposition in libpython if --enable-shared and GCC is used: add -fno-semantic-interposition to the compiler and linker flags.

New in version 3.6.

Changed in version 3.10: Use -fno-semantic-interposition on GCC.

+
+PROFILE_TASK
+

Environment variable used in the Makefile: Python command line arguments for the PGO generation task.

Default: -m test --pgo --timeout=$(TESTTIMEOUT).

New in version 3.8.

+
+--with-lto=[full|thin|no|yes]
+

Enable Link Time Optimization (LTO) in any build (disabled by default).

The C compiler Clang requires llvm-ar for LTO (ar on macOS), as well as an LTO-aware linker (ld.gold or lld).

New in version 3.6.

New in version 3.11: To use ThinLTO feature, use --with-lto=thin on Clang.

Changed in version 3.12: Use ThinLTO as the default optimization policy on Clang if the compiler accepts the flag.

+
+--enable-bolt
+

Enable usage of the BOLT post-link binary optimizer (disabled by default).

BOLT is part of the LLVM project but is not always included in their binary distributions. This flag requires that llvm-bolt and merge-fdata are available.

BOLT is still a fairly new project so this flag should be considered experimental for now. Because this tool operates on machine code its success is dependent on a combination of the build environment + the other optimization configure args + the CPU architecture, and not all combinations are supported. BOLT versions before LLVM 16 are known to crash BOLT under some scenarios. Use of LLVM 16 or newer for BOLT optimization is strongly encouraged.

The BOLT_INSTRUMENT_FLAGS and BOLT_APPLY_FLAGS configure variables can be defined to override the default set of arguments for llvm-bolt to instrument and apply BOLT data to binaries, respectively.

New in version 3.12.

+
+--with-computed-gotos
+

Enable computed gotos in evaluation loop (enabled by default on supported compilers).

+
+--without-pymalloc
+

Disable the specialized Python memory allocator pymalloc (enabled by default).

See also PYTHONMALLOC environment variable.

+
+--without-doc-strings
+

Disable static documentation strings to reduce the memory footprint (enabled by default). Documentation strings defined in Python are not affected.

Don’t define the WITH_DOC_STRINGS macro.

See the PyDoc_STRVAR() macro.

+
+--enable-profiling
+

Enable C-level code profiling with gprof (disabled by default).

+
+--with-strict-overflow
+

Add -fstrict-overflow to the C compiler flags (by default we add -fno-strict-overflow instead).

+

+3.3.5. Python Debug Build

A debug build is Python built with the --with-pydebug configure option.

Effects of a debug build:

  • Display all warnings by default: the list of default warning filters is empty in the warnings module.
  • Add d to sys.abiflags.
  • Add sys.gettotalrefcount() function.
  • Add -X showrefcount command line option.
  • Add -d command line option and PYTHONDEBUG environment variable to debug the parser.
  • Add support for the __lltrace__ variable: enable low-level tracing in the bytecode evaluation loop if the variable is defined.
  • Install debug hooks on memory allocators to detect buffer overflow and other memory errors.
  • Define Py_DEBUG and Py_REF_DEBUG macros.
  • +

    Add runtime checks: code surrounded by #ifdef Py_DEBUG and #endif. Enable assert(...) and _PyObject_ASSERT(...) assertions: don’t set the NDEBUG macro (see also the --with-assertions configure option). Main runtime checks:

    • Add sanity checks on the function arguments.
    • Unicode and int objects are created with their memory filled with a pattern to detect usage of uninitialized objects.
    • Ensure that functions which can clear or replace the current exception are not called with an exception raised.
    • Check that deallocator functions don’t change the current exception.
    • The garbage collector (gc.collect() function) runs some basic checks on objects consistency.
    • The Py_SAFE_DOWNCAST() macro checks for integer underflow and overflow when downcasting from wide types to narrow types.

See also the Python Development Mode and the --with-trace-refs configure option.

Changed in version 3.8: Release builds and debug builds are now ABI compatible: defining the Py_DEBUG macro no longer implies the Py_TRACE_REFS macro (see the --with-trace-refs option), which introduces the only ABI incompatibility.

+3.3.6. Debug options

+--with-pydebug
+

Build Python in debug mode: define the Py_DEBUG macro (disabled by default).

+
+--with-trace-refs
+

Enable tracing references for debugging purpose (disabled by default).

Effects:

  • Define the Py_TRACE_REFS macro.
  • Add sys.getobjects() function.
  • Add PYTHONDUMPREFS environment variable.

This build is not ABI compatible with release build (default build) or debug build (Py_DEBUG and Py_REF_DEBUG macros).

New in version 3.8.

+
+--with-assertions
+

Build with C assertions enabled (default is no): assert(...); and _PyObject_ASSERT(...);.

If set, the NDEBUG macro is not defined in the OPT compiler variable.

See also the --with-pydebug option (debug build) which also enables assertions.

New in version 3.6.

+
+--with-valgrind
+

Enable Valgrind support (default is no).

+
+--with-dtrace
+

Enable DTrace support (default is no).

See Instrumenting CPython with DTrace and SystemTap.

New in version 3.6.

+
+--with-address-sanitizer
+

Enable AddressSanitizer memory error detector, asan (default is no).

New in version 3.6.

+
+--with-memory-sanitizer
+

Enable MemorySanitizer allocation error detector, msan (default is no).

New in version 3.6.

+
+--with-undefined-behavior-sanitizer
+

Enable UndefinedBehaviorSanitizer undefined behaviour detector, ubsan (default is no).

New in version 3.6.

+

+3.3.7. Linker options

+--enable-shared
+

Enable building a shared Python library: libpython (default is no).

+
+--without-static-libpython
+

Do not build libpythonMAJOR.MINOR.a and do not install python.o (built and enabled by default).

New in version 3.10.

+

+3.3.8. Libraries options

+--with-libs='lib1 ...'
+

Link against additional libraries (default is no).

+
+--with-system-expat
+

Build the pyexpat module using an installed expat library (default is no).

+
+--with-system-libmpdec
+

Build the _decimal extension module using an installed mpdec library, see the decimal module (default is no).

New in version 3.3.

+
+--with-readline=editline
+

Use editline library for backend of the readline module.

Define the WITH_EDITLINE macro.

New in version 3.10.

+
+--without-readline
+

Don’t build the readline module (built by default).

Don’t define the HAVE_LIBREADLINE macro.

New in version 3.10.

+
+--with-libm=STRING
+

Override libm math library to STRING (default is system-dependent).

+
+--with-libc=STRING
+

Override libc C library to STRING (default is system-dependent).

+
+--with-openssl=DIR
+

Root of the OpenSSL directory.

New in version 3.7.

+
+--with-openssl-rpath=[no|auto|DIR]
+

Set runtime library directory (rpath) for OpenSSL libraries:

  • +no (default): don’t set rpath;
  • +auto: auto-detect rpath from --with-openssl and pkg-config;
  • +DIR: set an explicit rpath.

New in version 3.10.

+

+3.3.9. Security Options

+--with-hash-algorithm=[fnv|siphash13|siphash24]
+

Select hash algorithm for use in Python/pyhash.c:

  • +siphash13 (default);
  • +siphash24;
  • +fnv.

New in version 3.4.

New in version 3.11: siphash13 is added and it is the new default.

+
+--with-builtin-hashlib-hashes=md5,sha1,sha256,sha512,sha3,blake2
+

Built-in hash modules:

  • +md5;
  • +sha1;
  • +sha256;
  • +sha512;
  • +sha3 (with shake);
  • +blake2.

New in version 3.9.

+
+--with-ssl-default-suites=[python|openssl|STRING]
+

Override the OpenSSL default cipher suites string:

  • +python (default): use Python’s preferred selection;
  • +openssl: leave OpenSSL’s defaults untouched;
  • +STRING: use a custom string

See the ssl module.

New in version 3.7.

Changed in version 3.10: The settings python and STRING also set TLS 1.2 as minimum protocol version.

+

+3.3.10. macOS Options

See Mac/README.rst.

+--enable-universalsdk
+
+--enable-universalsdk=SDKDIR
+

Create a universal binary build. SDKDIR specifies which macOS SDK should be used to perform the build (default is no).

+
+--enable-framework
+
+--enable-framework=INSTALLDIR
+

Create a Python.framework rather than a traditional Unix install. Optional INSTALLDIR specifies the installation path (default is no).

+
+--with-universal-archs=ARCH
+

Specify the kind of universal binary that should be created. This option is only valid when --enable-universalsdk is set.

Options:

  • +universal2;
  • +32-bit;
  • +64-bit;
  • +3-way;
  • +intel;
  • +intel-32;
  • +intel-64;
  • +all.
+
+--with-framework-name=FRAMEWORK
+

Specify the name for the python framework on macOS only valid when --enable-framework is set (default: Python).

+

+3.3.11. Cross Compiling Options

Cross compiling, also known as cross building, can be used to build Python for another CPU architecture or platform. Cross compiling requires a Python interpreter for the build platform. The version of the build Python must match the version of the cross compiled host Python.

+--build=BUILD
+

configure for building on BUILD, usually guessed by config.guess.

+
+--host=HOST
+

cross-compile to build programs to run on HOST (target platform)

+
+--with-build-python=path/to/python
+

path to build python binary for cross compiling

New in version 3.11.

+
+CONFIG_SITE=file
+

An environment variable that points to a file with configure overrides.

Example config.site file:

# config.site-aarch64
+ac_cv_buggy_getaddrinfo=no
+ac_cv_file__dev_ptmx=yes
+ac_cv_file__dev_ptc=no
+
+

Cross compiling example:

CONFIG_SITE=config.site-aarch64 ../configure \
+    --build=x86_64-pc-linux-gnu \
+    --host=aarch64-unknown-linux-gnu \
+    --with-build-python=../x86_64/python
+

+3.4. Python Build System

+3.4.1. Main files of the build system

  • +configure.ac => configure;
  • +Makefile.pre.in => Makefile (created by configure);
  • +pyconfig.h (created by configure);
  • +Modules/Setup: C extensions built by the Makefile using Module/makesetup shell script;

+3.4.2. Main build steps

  • C files (.c) are built as object files (.o).
  • A static libpython library (.a) is created from objects files.
  • +python.o and the static libpython library are linked into the final python program.
  • C extensions are built by the Makefile (see Modules/Setup).

+3.4.3. Main Makefile targets

  • +make: Build Python with the standard library.
  • +make platform:: build the python program, but don’t build the standard library extension modules.
  • +make profile-opt: build Python using Profile Guided Optimization (PGO). You can use the configure --enable-optimizations option to make this the default target of the make command (make all or just make).
  • +make buildbottest: Build Python and run the Python test suite, the same way than buildbots test Python. Set TESTTIMEOUT variable (in seconds) to change the test timeout (1200 by default: 20 minutes).
  • +make install: Build and install Python.
  • +make regen-all: Regenerate (almost) all generated files; make regen-stdlib-module-names and autoconf must be run separately for the remaining generated files.
  • +make clean: Remove built files.
  • +make distclean: Same than make clean, but remove also files created by the configure script.

+3.4.4. C extensions

Some C extensions are built as built-in modules, like the sys module. They are built with the Py_BUILD_CORE_BUILTIN macro defined. Built-in modules have no __file__ attribute:

>>> import sys
+>>> sys
+<module 'sys' (built-in)>
+>>> sys.__file__
+Traceback (most recent call last):
+  File "<stdin>", line 1, in <module>
+AttributeError: module 'sys' has no attribute '__file__'
+

Other C extensions are built as dynamic libraries, like the _asyncio module. They are built with the Py_BUILD_CORE_MODULE macro defined. Example on Linux x86-64:

>>> import _asyncio
+>>> _asyncio
+<module '_asyncio' from '/usr/lib64/python3.9/lib-dynload/_asyncio.cpython-39-x86_64-linux-gnu.so'>
+>>> _asyncio.__file__
+'/usr/lib64/python3.9/lib-dynload/_asyncio.cpython-39-x86_64-linux-gnu.so'
+

Modules/Setup is used to generate Makefile targets to build C extensions. At the beginning of the files, C extensions are built as built-in modules. Extensions defined after the *shared* marker are built as dynamic libraries.

The PyAPI_FUNC(), PyAPI_DATA() and PyMODINIT_FUNC macros of Include/exports.h are defined differently depending if the Py_BUILD_CORE_MODULE macro is defined:

  • Use Py_EXPORTED_SYMBOL if the Py_BUILD_CORE_MODULE is defined
  • Use Py_IMPORTED_SYMBOL otherwise.

If the Py_BUILD_CORE_BUILTIN macro is used by mistake on a C extension built as a shared library, its PyInit_xxx() function is not exported, causing an ImportError on import.

+3.5. Compiler and linker flags

Options set by the ./configure script and environment variables and used by Makefile.

+3.5.1. Preprocessor flags

+CONFIGURE_CPPFLAGS
+

Value of CPPFLAGS variable passed to the ./configure script.

New in version 3.6.

+
+CPPFLAGS
+

(Objective) C/C++ preprocessor flags, e.g. -Iinclude_dir if you have headers in a nonstandard directory include_dir.

Both CPPFLAGS and LDFLAGS need to contain the shell’s value to be able to build extension modules using the directories specified in the environment variables.

+
+BASECPPFLAGS
+

New in version 3.4.

+
+PY_CPPFLAGS
+

Extra preprocessor flags added for building the interpreter object files.

Default: $(BASECPPFLAGS) -I. -I$(srcdir)/Include $(CONFIGURE_CPPFLAGS) $(CPPFLAGS).

New in version 3.2.

+

+3.5.2. Compiler flags

+CC
+

C compiler command.

Example: gcc -pthread.

+
+CXX
+

C++ compiler command.

Example: g++ -pthread.

+
+CFLAGS
+

C compiler flags.

+
+CFLAGS_NODIST
+

CFLAGS_NODIST is used for building the interpreter and stdlib C extensions. Use it when a compiler flag should not be part of CFLAGS once Python is installed (gh-65320).

In particular, CFLAGS should not contain:

  • the compiler flag -I (for setting the search path for include files). The -I flags are processed from left to right, and any flags in CFLAGS would take precedence over user- and package-supplied -I flags.
  • hardening flags such as -Werror because distributions cannot control whether packages installed by users conform to such heightened standards.

New in version 3.5.

+
+COMPILEALL_OPTS
+

Options passed to the compileall command line when building PYC files in make install. Default: -j0.

New in version 3.12.

+
+EXTRA_CFLAGS
+

Extra C compiler flags.

+
+CONFIGURE_CFLAGS
+

Value of CFLAGS variable passed to the ./configure script.

New in version 3.2.

+
+CONFIGURE_CFLAGS_NODIST
+

Value of CFLAGS_NODIST variable passed to the ./configure script.

New in version 3.5.

+
+BASECFLAGS
+

Base compiler flags.

+
+OPT
+

Optimization flags.

+
+CFLAGS_ALIASING
+

Strict or non-strict aliasing flags used to compile Python/dtoa.c.

New in version 3.7.

+
+CCSHARED
+

Compiler flags used to build a shared library.

For example, -fPIC is used on Linux and on BSD.

+
+CFLAGSFORSHARED
+

Extra C flags added for building the interpreter object files.

Default: $(CCSHARED) when --enable-shared is used, or an empty string otherwise.

+
+PY_CFLAGS
+

Default: $(BASECFLAGS) $(OPT) $(CONFIGURE_CFLAGS) $(CFLAGS) $(EXTRA_CFLAGS).

+
+PY_CFLAGS_NODIST
+

Default: $(CONFIGURE_CFLAGS_NODIST) $(CFLAGS_NODIST) -I$(srcdir)/Include/internal.

New in version 3.5.

+
+PY_STDMODULE_CFLAGS
+

C flags used for building the interpreter object files.

Default: $(PY_CFLAGS) $(PY_CFLAGS_NODIST) $(PY_CPPFLAGS) $(CFLAGSFORSHARED).

New in version 3.7.

+
+PY_CORE_CFLAGS
+

Default: $(PY_STDMODULE_CFLAGS) -DPy_BUILD_CORE.

New in version 3.2.

+
+PY_BUILTIN_MODULE_CFLAGS
+

Compiler flags to build a standard library extension module as a built-in module, like the posix module.

Default: $(PY_STDMODULE_CFLAGS) -DPy_BUILD_CORE_BUILTIN.

New in version 3.8.

+
+PURIFY
+

Purify command. Purify is a memory debugger program.

Default: empty string (not used).

+

+3.5.3. Linker flags

+LINKCC
+

Linker command used to build programs like python and _testembed.

Default: $(PURIFY) $(CC).

+
+CONFIGURE_LDFLAGS
+

Value of LDFLAGS variable passed to the ./configure script.

Avoid assigning CFLAGS, LDFLAGS, etc. so users can use them on the command line to append to these values without stomping the pre-set values.

New in version 3.2.

+
+LDFLAGS_NODIST
+

LDFLAGS_NODIST is used in the same manner as CFLAGS_NODIST. Use it when a linker flag should not be part of LDFLAGS once Python is installed (gh-65320).

In particular, LDFLAGS should not contain:

  • the compiler flag -L (for setting the search path for libraries). The -L flags are processed from left to right, and any flags in LDFLAGS would take precedence over user- and package-supplied -L flags.
+
+CONFIGURE_LDFLAGS_NODIST
+

Value of LDFLAGS_NODIST variable passed to the ./configure script.

New in version 3.8.

+
+LDFLAGS
+

Linker flags, e.g. -Llib_dir if you have libraries in a nonstandard directory lib_dir.

Both CPPFLAGS and LDFLAGS need to contain the shell’s value to be able to build extension modules using the directories specified in the environment variables.

+
+LIBS
+

Linker flags to pass libraries to the linker when linking the Python executable.

Example: -lrt.

+
+LDSHARED
+

Command to build a shared library.

Default: @LDSHARED@ $(PY_LDFLAGS).

+
+BLDSHARED
+

Command to build libpython shared library.

Default: @BLDSHARED@ $(PY_CORE_LDFLAGS).

+
+PY_LDFLAGS
+

Default: $(CONFIGURE_LDFLAGS) $(LDFLAGS).

+
+PY_LDFLAGS_NODIST
+

Default: $(CONFIGURE_LDFLAGS_NODIST) $(LDFLAGS_NODIST).

New in version 3.8.

+
+PY_CORE_LDFLAGS
+

Linker flags used for building the interpreter object files.

New in version 3.8.

+
+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/configure.html +

+
diff --git a/devdocs/python~3.12/using%2Feditors.html b/devdocs/python~3.12/using%2Feditors.html new file mode 100644 index 00000000..317e71e4 --- /dev/null +++ b/devdocs/python~3.12/using%2Feditors.html @@ -0,0 +1,6 @@ +

Editors and IDEs

There are a number of IDEs that support Python programming language. Many editors and IDEs provide syntax highlighting, debugging tools, and PEP 8 checks.

Please go to Python Editors and Integrated Development Environments for a comprehensive list.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/editors.html +

+
diff --git a/devdocs/python~3.12/using%2Findex.html b/devdocs/python~3.12/using%2Findex.html new file mode 100644 index 00000000..64ebd80a --- /dev/null +++ b/devdocs/python~3.12/using%2Findex.html @@ -0,0 +1,28 @@ +

Python Setup and Usage

This part of the documentation is devoted to general information on the setup of the Python environment on different platforms, the invocation of the interpreter and things that make working with Python easier.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/index.html +

+
diff --git a/devdocs/python~3.12/using%2Fmac.html b/devdocs/python~3.12/using%2Fmac.html new file mode 100644 index 00000000..8a429d4c --- /dev/null +++ b/devdocs/python~3.12/using%2Fmac.html @@ -0,0 +1,17 @@ +

Using Python on a Mac

Author
+

Bob Savage <bobsavage@mac.com>

Python on a Mac running macOS is in principle very similar to Python on any other Unix platform, but there are a number of additional features such as the IDE and the Package Manager that are worth pointing out.

+5.1. Getting and Installing MacPython

macOS used to come with Python 2.7 pre-installed between versions 10.8 and 12.3. You are invited to install the most recent version of Python 3 from the Python website (https://www.python.org). A current “universal binary” build of Python, which runs natively on the Mac’s new Intel and legacy PPC CPU’s, is available there.

What you get after installing is a number of things:

  • A Python 3.12 folder in your Applications folder. In here you find IDLE, the development environment that is a standard part of official Python distributions; and PythonLauncher, which handles double-clicking Python scripts from the Finder.
  • A framework /Library/Frameworks/Python.framework, which includes the Python executable and libraries. The installer adds this location to your shell path. To uninstall MacPython, you can simply remove these three things. A symlink to the Python executable is placed in /usr/local/bin/.

The Apple-provided build of Python is installed in /System/Library/Frameworks/Python.framework and /usr/bin/python, respectively. You should never modify or delete these, as they are Apple-controlled and are used by Apple- or third-party software. Remember that if you choose to install a newer Python version from python.org, you will have two different but functional Python installations on your computer, so it will be important that your paths and usages are consistent with what you want to do.

IDLE includes a help menu that allows you to access Python documentation. If you are completely new to Python you should start reading the tutorial introduction in that document.

If you are familiar with Python on other Unix platforms you should read the section on running Python scripts from the Unix shell.

+5.1.1. How to run a Python script

Your best way to get started with Python on macOS is through the IDLE integrated development environment, see section The IDE and use the Help menu when the IDE is running.

If you want to run Python scripts from the Terminal window command line or from the Finder you first need an editor to create your script. macOS comes with a number of standard Unix command line editors, vim and emacs among them. If you want a more Mac-like editor, BBEdit or TextWrangler from Bare Bones Software (see http://www.barebones.com/products/bbedit/index.html) are good choices, as is TextMate (see https://macromates.com/). Other editors include Gvim (https://macvim.org/macvim/) and Aquamacs (http://aquamacs.org/).

To run your script from the Terminal window you must make sure that /usr/local/bin is in your shell search path.

To run your script from the Finder you have two options:

  • Drag it to PythonLauncher +
  • Select PythonLauncher as the default application to open your script (or any .py script) through the finder Info window and double-click it. PythonLauncher has various preferences to control how your script is launched. Option-dragging allows you to change these for one invocation, or use its Preferences menu to change things globally.

+5.1.2. Running scripts with a GUI

With older versions of Python, there is one macOS quirk that you need to be aware of: programs that talk to the Aqua window manager (in other words, anything that has a GUI) need to be run in a special way. Use pythonw instead of python to start such scripts.

With Python 3.9, you can use either python or pythonw.

+5.1.3. Configuration

Python on macOS honors all standard Unix environment variables such as PYTHONPATH, but setting these variables for programs started from the Finder is non-standard as the Finder does not read your .profile or .cshrc at startup. You need to create a file ~/.MacOSX/environment.plist. See Apple’s Technical Document QA1067 for details.

For more information on installation Python packages in MacPython, see section Installing Additional Python Packages.

+5.2. The IDE

MacPython ships with the standard IDLE development environment. A good introduction to using IDLE can be found at http://www.hashcollision.org/hkn/python/idle_intro/index.html.

+5.3. Installing Additional Python Packages

This section has moved to the Python Packaging User Guide.

+5.4. GUI Programming on the Mac

There are several options for building GUI applications on the Mac with Python.

PyObjC is a Python binding to Apple’s Objective-C/Cocoa framework, which is the foundation of most modern Mac development. Information on PyObjC is available from https://pypi.org/project/pyobjc/.

The standard Python GUI toolkit is tkinter, based on the cross-platform Tk toolkit (https://www.tcl.tk). An Aqua-native version of Tk is bundled with macOS by Apple, and the latest version can be downloaded and installed from https://www.activestate.com; it can also be built from source.

wxPython is another popular cross-platform GUI toolkit that runs natively on macOS. Packages and documentation are available from https://www.wxpython.org.

PyQt is another popular cross-platform GUI toolkit that runs natively on macOS. More information can be found at https://riverbankcomputing.com/software/pyqt/intro.

+5.5. Distributing Python Applications on the Mac

The standard tool for deploying standalone Python applications on the Mac is py2app. More information on installing and using py2app can be found at https://pypi.org/project/py2app/.

+5.6. Other Resources

The MacPython mailing list is an excellent support resource for Python users and developers on the Mac:

https://www.python.org/community/sigs/current/pythonmac-sig/

Another useful resource is the MacPython wiki:

https://wiki.python.org/moin/MacPython

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/mac.html +

+
diff --git a/devdocs/python~3.12/using%2Funix.html b/devdocs/python~3.12/using%2Funix.html new file mode 100644 index 00000000..60f7b2b2 --- /dev/null +++ b/devdocs/python~3.12/using%2Funix.html @@ -0,0 +1,60 @@ +

Using Python on Unix platforms

+2.1. Getting and installing the latest version of Python

+2.1.1. On Linux

Python comes preinstalled on most Linux distributions, and is available as a package on all others. However there are certain features you might want to use that are not available on your distro’s package. You can easily compile the latest version of Python from source.

In the event that Python doesn’t come preinstalled and isn’t in the repositories as well, you can easily make packages for your own distro. Have a look at the following links:

See also

https://www.debian.org/doc/manuals/maint-guide/first.en.html
+
+

for Debian users

https://en.opensuse.org/Portal:Packaging
+
+

for OpenSuse users

https://docs.fedoraproject.org/en-US/package-maintainers/Packaging_Tutorial_GNU_Hello/
+
+

for Fedora users

https://slackbook.org/html/package-management-making-packages.html
+
+

for Slackware users

+2.1.2. On FreeBSD and OpenBSD

  • +

    FreeBSD users, to add the package use:

    pkg install python3
+
  • +

    OpenBSD users, to add the package use:

    pkg_add -r python
+
+pkg_add ftp://ftp.openbsd.org/pub/OpenBSD/4.2/packages/<insert your architecture here>/python-<version>.tgz
+

    For example i386 users get the 2.5.1 version of Python using:

    pkg_add ftp://ftp.openbsd.org/pub/OpenBSD/4.2/packages/i386/python-2.5.1p2.tgz
+

+2.2. Building Python

If you want to compile CPython yourself, first thing you should do is get the source. You can download either the latest release’s source or just grab a fresh clone. (If you want to contribute patches, you will need a clone.)

The build process consists of the usual commands:

./configure
+make
+make install
+

Configuration options and caveats for specific Unix platforms are extensively documented in the README.rst file in the root of the Python source tree.

Warning

make install can overwrite or masquerade the python3 binary. make altinstall is therefore recommended instead of make install since it only installs exec_prefix/bin/pythonversion.

+2.4. Miscellaneous

To easily use Python scripts on Unix, you need to make them executable, e.g. with

$ chmod +x script
+

and put an appropriate Shebang line at the top of the script. A good choice is usually

#!/usr/bin/env python3
+

which searches for the Python interpreter in the whole PATH. However, some Unices may not have the env command, so you may need to hardcode /usr/bin/python3 as the interpreter path.

To use shell commands in your Python scripts, look at the subprocess module.

+2.5. Custom OpenSSL

  1. +

    To use your vendor’s OpenSSL configuration and system trust store, locate the directory with openssl.cnf file or symlink in /etc. On most distribution the file is either in /etc/ssl or /etc/pki/tls. The directory should also contain a cert.pem file and/or a certs directory.

    $ find /etc/ -name openssl.cnf -printf "%h\n"
+/etc/ssl
+
  2. +

    Download, build, and install OpenSSL. Make sure you use install_sw and not install. The install_sw target does not override openssl.cnf.

    $ curl -O https://www.openssl.org/source/openssl-VERSION.tar.gz
+$ tar xzf openssl-VERSION
+$ pushd openssl-VERSION
+$ ./config \
+    --prefix=/usr/local/custom-openssl \
+    --libdir=lib \
+    --openssldir=/etc/ssl
+$ make -j1 depend
+$ make -j8
+$ make install_sw
+$ popd
+
  3. +

    Build Python with custom OpenSSL (see the configure --with-openssl and --with-openssl-rpath options)

    $ pushd python-3.x.x
+$ ./configure -C \
+    --with-openssl=/usr/local/custom-openssl \
+    --with-openssl-rpath=auto \
+    --prefix=/usr/local/python-3.x.x
+$ make -j8
+$ make altinstall
+

Note

Patch releases of OpenSSL have a backwards compatible ABI. You don’t need to recompile Python to update OpenSSL. It’s sufficient to replace the custom OpenSSL installation with a newer version.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/unix.html +

+
diff --git a/devdocs/python~3.12/using%2Fwindows.html b/devdocs/python~3.12/using%2Fwindows.html new file mode 100644 index 00000000..9aa38dea --- /dev/null +++ b/devdocs/python~3.12/using%2Fwindows.html @@ -0,0 +1,165 @@ +

Using Python on Windows

This document aims to give an overview of Windows-specific behaviour you should know about when using Python on Microsoft Windows.

Unlike most Unix systems and services, Windows does not include a system supported installation of Python. To make Python available, the CPython team has compiled Windows installers (MSI packages) with every release for many years. These installers are primarily intended to add a per-user installation of Python, with the core interpreter and library being used by a single user. The installer is also able to install for all users of a single machine, and a separate ZIP file is available for application-local distributions.

As specified in PEP 11, a Python release only supports a Windows platform while Microsoft considers the platform under extended support. This means that Python 3.12 supports Windows 8.1 and newer. If you require Windows 7 support, please install Python 3.8.

There are a number of different installers available for Windows, each with certain benefits and downsides.

The full installer contains all components and is the best option for developers using Python for any kind of project.

The Microsoft Store package is a simple installation of Python that is suitable for running scripts and packages, and using IDLE or other development environments. It requires Windows 10 and above, but can be safely installed without corrupting other programs. It also provides many convenient commands for launching Python and its tools.

The nuget.org packages are lightweight installations intended for continuous integration systems. It can be used to build Python packages or run scripts, but is not updateable and has no user interface tools.

The embeddable package is a minimal package of Python suitable for embedding into a larger application.

+4.1. The full installer

+4.1.1. Installation steps

Four Python 3.12 installers are available for download - two each for the 32-bit and 64-bit versions of the interpreter. The web installer is a small initial download, and it will automatically download the required components as necessary. The offline installer includes the components necessary for a default installation and only requires an internet connection for optional features. See Installing Without Downloading for other ways to avoid downloading during installation.

After starting the installer, one of two options may be selected:

../_images/win_installer.png

If you select “Install Now”:

  • You will not need to be an administrator (unless a system update for the C Runtime Library is required or you install the Python Launcher for Windows for all users)
  • Python will be installed into your user directory
  • The Python Launcher for Windows will be installed according to the option at the bottom of the first page
  • The standard library, test suite, launcher and pip will be installed
  • If selected, the install directory will be added to your PATH +
  • Shortcuts will only be visible for the current user

Selecting “Customize installation” will allow you to select the features to install, the installation location and other options or post-install actions. To install debugging symbols or binaries, you will need to use this option.

To perform an all-users installation, you should select “Customize installation”. In this case:

  • You may be required to provide administrative credentials or approval
  • Python will be installed into the Program Files directory
  • The Python Launcher for Windows will be installed into the Windows directory
  • Optional features may be selected during installation
  • The standard library can be pre-compiled to bytecode
  • If selected, the install directory will be added to the system PATH +
  • Shortcuts are available for all users

+4.1.2. Removing the MAX_PATH Limitation

Windows historically has limited path lengths to 260 characters. This meant that paths longer than this would not resolve and errors would result.

In the latest versions of Windows, this limitation can be expanded to approximately 32,000 characters. Your administrator will need to activate the “Enable Win32 long paths” group policy, or set LongPathsEnabled to 1 in the registry key HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\FileSystem.

This allows the open() function, the os module and most other path functionality to accept and return paths longer than 260 characters.

After changing the above option, no further configuration is required.

Changed in version 3.6: Support for long paths was enabled in Python.

+4.1.3. Installing Without UI

All of the options available in the installer UI can also be specified from the command line, allowing scripted installers to replicate an installation on many machines without user interaction. These options may also be set without suppressing the UI in order to change some of the defaults.

The following options (found by executing the installer with /?) can be passed into the installer:

+ + + + + + +

Name

Description

/passive

to display progress without requiring user interaction

/quiet

to install/uninstall without displaying any UI

/simple

to prevent user customization

/uninstall

to remove Python (without confirmation)

/layout [directory]

to pre-download all components

/log [filename]

to specify log files location

All other options are passed as name=value, where the value is usually 0 to disable a feature, 1 to enable a feature, or a path. The full list of available options is shown below.

+ + + + + + + + + + + + + + + + + + + + + + + + + +

Name

Description

Default

InstallAllUsers

Perform a system-wide installation.

0

TargetDir

The installation directory

Selected based on InstallAllUsers

DefaultAllUsersTargetDir

The default installation directory for all-user installs

%ProgramFiles%\Python X.Y or %ProgramFiles(x86)%\Python X.Y

DefaultJustForMeTargetDir

The default install directory for just-for-me installs

%LocalAppData%\Programs\Python\PythonXY or %LocalAppData%\Programs\Python\PythonXY-32 or %LocalAppData%\Programs\Python\PythonXY-64

DefaultCustomTargetDir

The default custom install directory displayed in the UI

(empty)

AssociateFiles

Create file associations if the launcher is also installed.

1

CompileAll

Compile all .py files to .pyc.

0

PrependPath

Prepend install and Scripts directories to PATH and add .PY to PATHEXT

0

AppendPath

Append install and Scripts directories to PATH and add .PY to PATHEXT

0

Shortcuts

Create shortcuts for the interpreter, documentation and IDLE if installed.

1

Include_doc

Install Python manual

1

Include_debug

Install debug binaries

0

Include_dev

Install developer headers and libraries. Omitting this may lead to an unusable installation.

1

Include_exe

Install python.exe and related files. Omitting this may lead to an unusable installation.

1

Include_launcher

Install Python Launcher for Windows.

1

InstallLauncherAllUsers

Installs the launcher for all users. Also requires Include_launcher to be set to 1

1

Include_lib

Install standard library and extension modules. Omitting this may lead to an unusable installation.

1

Include_pip

Install bundled pip and setuptools

1

Include_symbols

Install debugging symbols (*.pdb)

0

Include_tcltk

Install Tcl/Tk support and IDLE

1

Include_test

Install standard library test suite

1

Include_tools

Install utility scripts

1

LauncherOnly

Only installs the launcher. This will override most other options.

0

SimpleInstall

Disable most install UI

0

SimpleInstallDescription

A custom message to display when the simplified install UI is used.

(empty)

For example, to silently install a default, system-wide Python installation, you could use the following command (from an elevated command prompt):

python-3.9.0.exe /quiet InstallAllUsers=1 PrependPath=1 Include_test=0
+

To allow users to easily install a personal copy of Python without the test suite, you could provide a shortcut with the following command. This will display a simplified initial page and disallow customization:

python-3.9.0.exe InstallAllUsers=0 Include_launcher=0 Include_test=0
+    SimpleInstall=1 SimpleInstallDescription="Just for me, no test suite."
+

(Note that omitting the launcher also omits file associations, and is only recommended for per-user installs when there is also a system-wide installation that included the launcher.)

The options listed above can also be provided in a file named unattend.xml alongside the executable. This file specifies a list of options and values. When a value is provided as an attribute, it will be converted to a number if possible. Values provided as element text are always left as strings. This example file sets the same options as the previous example:

<Options>
+    <Option Name="InstallAllUsers" Value="no" />
+    <Option Name="Include_launcher" Value="0" />
+    <Option Name="Include_test" Value="no" />
+    <Option Name="SimpleInstall" Value="yes" />
+    <Option Name="SimpleInstallDescription">Just for me, no test suite</Option>
+</Options>
+

+4.1.4. Installing Without Downloading

As some features of Python are not included in the initial installer download, selecting those features may require an internet connection. To avoid this need, all possible components may be downloaded on-demand to create a complete layout that will no longer require an internet connection regardless of the selected features. Note that this download may be bigger than required, but where a large number of installations are going to be performed it is very useful to have a locally cached copy.

Execute the following command from Command Prompt to download all possible required files. Remember to substitute python-3.9.0.exe for the actual name of your installer, and to create layouts in their own directories to avoid collisions between files with the same name.

python-3.9.0.exe /layout [optional target directory]
+

You may also specify the /quiet option to hide the progress display.

+4.1.5. Modifying an install

Once Python has been installed, you can add or remove features through the Programs and Features tool that is part of Windows. Select the Python entry and choose “Uninstall/Change” to open the installer in maintenance mode.

“Modify” allows you to add or remove features by modifying the checkboxes - unchanged checkboxes will not install or remove anything. Some options cannot be changed in this mode, such as the install directory; to modify these, you will need to remove and then reinstall Python completely.

“Repair” will verify all the files that should be installed using the current settings and replace any that have been removed or modified.

“Uninstall” will remove Python entirely, with the exception of the Python Launcher for Windows, which has its own entry in Programs and Features.

+4.2. The Microsoft Store package

New in version 3.7.2.

The Microsoft Store package is an easily installable Python interpreter that is intended mainly for interactive use, for example, by students.

To install the package, ensure you have the latest Windows 10 updates and search the Microsoft Store app for “Python 3.12”. Ensure that the app you select is published by the Python Software Foundation, and install it.

Warning

Python will always be available for free on the Microsoft Store. If you are asked to pay for it, you have not selected the correct package.

After installation, Python may be launched by finding it in Start. Alternatively, it will be available from any Command Prompt or PowerShell session by typing python. Further, pip and IDLE may be used by typing pip or idle. IDLE can also be found in Start.

All three commands are also available with version number suffixes, for example, as python3.exe and python3.x.exe as well as python.exe (where 3.x is the specific version you want to launch, such as 3.12). Open “Manage App Execution Aliases” through Start to select which version of Python is associated with each command. It is recommended to make sure that pip and idle are consistent with whichever version of python is selected.

Virtual environments can be created with python -m venv and activated and used as normal.

If you have installed another version of Python and added it to your PATH variable, it will be available as python.exe rather than the one from the Microsoft Store. To access the new installation, use python3.exe or python3.x.exe.

The py.exe launcher will detect this Python installation, but will prefer installations from the traditional installer.

To remove Python, open Settings and use Apps and Features, or else find Python in Start and right-click to select Uninstall. Uninstalling will remove all packages you installed directly into this Python installation, but will not remove any virtual environments

+4.2.1. Known issues

+4.2.1.1. Redirection of local data, registry, and temporary paths

Because of restrictions on Microsoft Store apps, Python scripts may not have full write access to shared locations such as TEMP and the registry. Instead, it will write to a private copy. If your scripts must modify the shared locations, you will need to install the full installer.

At runtime, Python will use a private copy of well-known Windows folders and the registry. For example, if the environment variable %APPDATA% is c:\Users\<user>\AppData\, then when writing to C:\Users\<user>\AppData\Local will write to C:\Users\<user>\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\Local\.

When reading files, Windows will return the file from the private folder, or if that does not exist, the real Windows directory. For example reading C:\Windows\System32 returns the contents of C:\Windows\System32 plus the contents of C:\Program Files\WindowsApps\package_name\VFS\SystemX86.

You can find the real path of any existing file using os.path.realpath():

>>> import os
+>>> test_file = 'C:\\Users\\example\\AppData\\Local\\test.txt'
+>>> os.path.realpath(test_file)
+'C:\\Users\\example\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\Local\\test.txt'
+

When writing to the Windows Registry, the following behaviors exist:

  • Reading from HKLM\\Software is allowed and results are merged with the registry.dat file in the package.
  • Writing to HKLM\\Software is not allowed if the corresponding key/value exists, i.e. modifying existing keys.
  • Writing to HKLM\\Software is allowed as long as a corresponding key/value does not exist in the package and the user has the correct access permissions.

For more detail on the technical basis for these limitations, please consult Microsoft’s documentation on packaged full-trust apps, currently available at docs.microsoft.com/en-us/windows/msix/desktop/desktop-to-uwp-behind-the-scenes

+4.3. The nuget.org packages

New in version 3.5.2.

The nuget.org package is a reduced size Python environment intended for use on continuous integration and build systems that do not have a system-wide install of Python. While nuget is “the package manager for .NET”, it also works perfectly fine for packages containing build-time tools.

Visit nuget.org for the most up-to-date information on using nuget. What follows is a summary that is sufficient for Python developers.

The nuget.exe command line tool may be downloaded directly from https://aka.ms/nugetclidl, for example, using curl or PowerShell. With the tool, the latest version of Python for 64-bit or 32-bit machines is installed using:

nuget.exe install python -ExcludeVersion -OutputDirectory .
+nuget.exe install pythonx86 -ExcludeVersion -OutputDirectory .
+

To select a particular version, add a -Version 3.x.y. The output directory may be changed from ., and the package will be installed into a subdirectory. By default, the subdirectory is named the same as the package, and without the -ExcludeVersion option this name will include the specific version installed. Inside the subdirectory is a tools directory that contains the Python installation:

# Without -ExcludeVersion
+> .\python.3.5.2\tools\python.exe -V
+Python 3.5.2
+
+# With -ExcludeVersion
+> .\python\tools\python.exe -V
+Python 3.5.2
+

In general, nuget packages are not upgradeable, and newer versions should be installed side-by-side and referenced using the full path. Alternatively, delete the package directory manually and install it again. Many CI systems will do this automatically if they do not preserve files between builds.

Alongside the tools directory is a build\native directory. This contains a MSBuild properties file python.props that can be used in a C++ project to reference the Python install. Including the settings will automatically use the headers and import libraries in your build.

The package information pages on nuget.org are www.nuget.org/packages/python for the 64-bit version and www.nuget.org/packages/pythonx86 for the 32-bit version.

+4.4. The embeddable package

New in version 3.5.

The embedded distribution is a ZIP file containing a minimal Python environment. It is intended for acting as part of another application, rather than being directly accessed by end-users.

When extracted, the embedded distribution is (almost) fully isolated from the user’s system, including environment variables, system registry settings, and installed packages. The standard library is included as pre-compiled and optimized .pyc files in a ZIP, and python3.dll, python37.dll, python.exe and pythonw.exe are all provided. Tcl/tk (including all dependents, such as Idle), pip and the Python documentation are not included.

Note

The embedded distribution does not include the Microsoft C Runtime and it is the responsibility of the application installer to provide this. The runtime may have already been installed on a user’s system previously or automatically via Windows Update, and can be detected by finding ucrtbase.dll in the system directory.

Third-party packages should be installed by the application installer alongside the embedded distribution. Using pip to manage dependencies as for a regular Python installation is not supported with this distribution, though with some care it may be possible to include and use pip for automatic updates. In general, third-party packages should be treated as part of the application (“vendoring”) so that the developer can ensure compatibility with newer versions before providing updates to users.

The two recommended use cases for this distribution are described below.

+4.4.1. Python Application

An application written in Python does not necessarily require users to be aware of that fact. The embedded distribution may be used in this case to include a private version of Python in an install package. Depending on how transparent it should be (or conversely, how professional it should appear), there are two options.

Using a specialized executable as a launcher requires some coding, but provides the most transparent experience for users. With a customized launcher, there are no obvious indications that the program is running on Python: icons can be customized, company and version information can be specified, and file associations behave properly. In most cases, a custom launcher should simply be able to call Py_Main with a hard-coded command line.

The simpler approach is to provide a batch file or generated shortcut that directly calls the python.exe or pythonw.exe with the required command-line arguments. In this case, the application will appear to be Python and not its actual name, and users may have trouble distinguishing it from other running Python processes or file associations.

With the latter approach, packages should be installed as directories alongside the Python executable to ensure they are available on the path. With the specialized launcher, packages can be located in other locations as there is an opportunity to specify the search path before launching the application.

+4.4.2. Embedding Python

Applications written in native code often require some form of scripting language, and the embedded Python distribution can be used for this purpose. In general, the majority of the application is in native code, and some part will either invoke python.exe or directly use python3.dll. For either case, extracting the embedded distribution to a subdirectory of the application installation is sufficient to provide a loadable Python interpreter.

As with the application use, packages can be installed to any location as there is an opportunity to specify search paths before initializing the interpreter. Otherwise, there is no fundamental differences between using the embedded distribution and a regular installation.

+4.5. Alternative bundles

Besides the standard CPython distribution, there are modified packages including additional functionality. The following is a list of popular versions and their key features:

ActivePython
+
+

Installer with multi-platform compatibility, documentation, PyWin32

Anaconda
+
+

Popular scientific modules (such as numpy, scipy and pandas) and the conda package manager.

Enthought Deployment Manager
+
+

“The Next Generation Python Environment and Package Manager”.

Previously Enthought provided Canopy, but it reached end of life in 2016.

WinPython
+
+

Windows-specific distribution with prebuilt scientific packages and tools for building packages.

Note that these packages may not include the latest versions of Python or other libraries, and are not maintained or supported by the core Python team.

+4.6. Configuring Python

To run Python conveniently from a command prompt, you might consider changing some default environment variables in Windows. While the installer provides an option to configure the PATH and PATHEXT variables for you, this is only reliable for a single, system-wide installation. If you regularly use multiple versions of Python, consider using the Python Launcher for Windows.

+4.6.1. Excursus: Setting environment variables

Windows allows environment variables to be configured permanently at both the User level and the System level, or temporarily in a command prompt.

To temporarily set environment variables, open Command Prompt and use the set command:

C:\>set PATH=C:\Program Files\Python 3.9;%PATH%
+C:\>set PYTHONPATH=%PYTHONPATH%;C:\My_python_lib
+C:\>python
+

These changes will apply to any further commands executed in that console, and will be inherited by any applications started from the console.

Including the variable name within percent signs will expand to the existing value, allowing you to add your new value at either the start or the end. Modifying PATH by adding the directory containing python.exe to the start is a common way to ensure the correct version of Python is launched.

To permanently modify the default environment variables, click Start and search for ‘edit environment variables’, or open System properties, Advanced system settings and click the Environment Variables button. In this dialog, you can add or modify User and System variables. To change System variables, you need non-restricted access to your machine (i.e. Administrator rights).

Note

Windows will concatenate User variables after System variables, which may cause unexpected results when modifying PATH.

The PYTHONPATH variable is used by all versions of Python, so you should not permanently configure it unless the listed paths only include code that is compatible with all of your installed Python versions.

See also

https://docs.microsoft.com/en-us/windows/win32/procthread/environment-variables
+
+

Overview of environment variables on Windows

https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/set_1
+
+

The set command, for temporarily modifying environment variables

https://docs.microsoft.com/en-us/windows-server/administration/windows-commands/setx
+
+

The setx command, for permanently modifying environment variables

+4.6.2. Finding the Python executable

Changed in version 3.5.

Besides using the automatically created start menu entry for the Python interpreter, you might want to start Python in the command prompt. The installer has an option to set that up for you.

On the first page of the installer, an option labelled “Add Python to PATH” may be selected to have the installer add the install location into the PATH. The location of the Scripts\ folder is also added. This allows you to type python to run the interpreter, and pip for the package installer. Thus, you can also execute your scripts with command line options, see Command line documentation.

If you don’t enable this option at install time, you can always re-run the installer, select Modify, and enable it. Alternatively, you can manually modify the PATH using the directions in Excursus: Setting environment variables. You need to set your PATH environment variable to include the directory of your Python installation, delimited by a semicolon from other entries. An example variable could look like this (assuming the first two entries already existed):

C:\WINDOWS\system32;C:\WINDOWS;C:\Program Files\Python 3.9
+

+4.7. UTF-8 mode

New in version 3.7.

Windows still uses legacy encodings for the system encoding (the ANSI Code Page). Python uses it for the default encoding of text files (e.g. locale.getencoding()).

This may cause issues because UTF-8 is widely used on the internet and most Unix systems, including WSL (Windows Subsystem for Linux).

You can use the Python UTF-8 Mode to change the default text encoding to UTF-8. You can enable the Python UTF-8 Mode via the -X utf8 command line option, or the PYTHONUTF8=1 environment variable. See PYTHONUTF8 for enabling UTF-8 mode, and Excursus: Setting environment variables for how to modify environment variables.

When the Python UTF-8 Mode is enabled, you can still use the system encoding (the ANSI Code Page) via the “mbcs” codec.

Note that adding PYTHONUTF8=1 to the default environment variables will affect all Python 3.7+ applications on your system. If you have any Python 3.7+ applications which rely on the legacy system encoding, it is recommended to set the environment variable temporarily or use the -X utf8 command line option.

Note

Even when UTF-8 mode is disabled, Python uses UTF-8 by default on Windows for:

+4.8. Python Launcher for Windows

New in version 3.3.

The Python launcher for Windows is a utility which aids in locating and executing of different Python versions. It allows scripts (or the command-line) to indicate a preference for a specific Python version, and will locate and execute that version.

Unlike the PATH variable, the launcher will correctly select the most appropriate version of Python. It will prefer per-user installations over system-wide ones, and orders by language version rather than using the most recently installed version.

The launcher was originally specified in PEP 397.

+4.8.1. Getting started

+4.8.1.1. From the command-line

Changed in version 3.6.

System-wide installations of Python 3.3 and later will put the launcher on your PATH. The launcher is compatible with all available versions of Python, so it does not matter which version is installed. To check that the launcher is available, execute the following command in Command Prompt:

py
+

You should find that the latest version of Python you have installed is started - it can be exited as normal, and any additional command-line arguments specified will be sent directly to Python.

If you have multiple versions of Python installed (e.g., 3.7 and 3.12) you will have noticed that Python 3.12 was started - to launch Python 3.7, try the command:

py -3.7
+

If you want the latest version of Python 2 you have installed, try the command:

py -2
+

If you see the following error, you do not have the launcher installed:

'py' is not recognized as an internal or external command,
+operable program or batch file.
+

The command:

py --list
+

displays the currently installed version(s) of Python.

The -x.y argument is the short form of the -V:Company/Tag argument, which allows selecting a specific Python runtime, including those that may have come from somewhere other than python.org. Any runtime registered by following PEP 514 will be discoverable. The --list command lists all available runtimes using the -V: format.

When using the -V: argument, specifying the Company will limit selection to runtimes from that provider, while specifying only the Tag will select from all providers. Note that omitting the slash implies a tag:

# Select any '3.*' tagged runtime
+py -V:3
+
+# Select any 'PythonCore' released runtime
+py -V:PythonCore/
+
+# Select PythonCore's latest Python 3 runtime
+py -V:PythonCore/3
+

The short form of the argument (-3) only ever selects from core Python releases, and not other distributions. However, the longer form (-V:3) will select from any.

The Company is matched on the full string, case-insenitive. The Tag is matched oneither the full string, or a prefix, provided the next character is a dot or a hyphen. This allows -V:3.1 to match 3.1-32, but not 3.10. Tags are sorted using numerical ordering (3.10 is newer than 3.1), but are compared using text (-V:3.01 does not match 3.1).

+4.8.1.2. Virtual environments

New in version 3.5.

If the launcher is run with no explicit Python version specification, and a virtual environment (created with the standard library venv module or the external virtualenv tool) active, the launcher will run the virtual environment’s interpreter rather than the global one. To run the global interpreter, either deactivate the virtual environment, or explicitly specify the global Python version.

+4.8.1.3. From a script

Let’s create a test Python script - create a file called hello.py with the following contents

#! python
+import sys
+sys.stdout.write("hello from Python %s\n" % (sys.version,))
+

From the directory in which hello.py lives, execute the command:

py hello.py
+

You should notice the version number of your latest Python 2.x installation is printed. Now try changing the first line to be:

#! python3
+

Re-executing the command should now print the latest Python 3.x information. As with the above command-line examples, you can specify a more explicit version qualifier. Assuming you have Python 3.7 installed, try changing the first line to #! python3.7 and you should find the 3.7 version information printed.

Note that unlike interactive use, a bare “python” will use the latest version of Python 2.x that you have installed. This is for backward compatibility and for compatibility with Unix, where the command python typically refers to Python 2.

+4.8.1.4. From file associations

The launcher should have been associated with Python files (i.e. .py, .pyw, .pyc files) when it was installed. This means that when you double-click on one of these files from Windows explorer the launcher will be used, and therefore you can use the same facilities described above to have the script specify the version which should be used.

The key benefit of this is that a single launcher can support multiple Python versions at the same time depending on the contents of the first line.

+4.8.2. Shebang Lines

If the first line of a script file starts with #!, it is known as a “shebang” line. Linux and other Unix like operating systems have native support for such lines and they are commonly used on such systems to indicate how a script should be executed. This launcher allows the same facilities to be used with Python scripts on Windows and the examples above demonstrate their use.

To allow shebang lines in Python scripts to be portable between Unix and Windows, this launcher supports a number of ‘virtual’ commands to specify which interpreter to use. The supported virtual commands are:

  • /usr/bin/env
  • /usr/bin/python
  • /usr/local/bin/python
  • python

For example, if the first line of your script starts with

#! /usr/bin/python
+

The default Python will be located and used. As many Python scripts written to work on Unix will already have this line, you should find these scripts can be used by the launcher without modification. If you are writing a new script on Windows which you hope will be useful on Unix, you should use one of the shebang lines starting with /usr.

Any of the above virtual commands can be suffixed with an explicit version (either just the major version, or the major and minor version). Furthermore the 32-bit version can be requested by adding “-32” after the minor version. I.e. /usr/bin/python3.7-32 will request usage of the 32-bit python 3.7.

New in version 3.7: Beginning with python launcher 3.7 it is possible to request 64-bit version by the “-64” suffix. Furthermore it is possible to specify a major and architecture without minor (i.e. /usr/bin/python3-64).

Changed in version 3.11: The “-64” suffix is deprecated, and now implies “any architecture that is not provably i386/32-bit”. To request a specific environment, use the new -V:TAG argument with the complete tag.

The /usr/bin/env form of shebang line has one further special property. Before looking for installed Python interpreters, this form will search the executable PATH for a Python executable matching the name provided as the first argument. This corresponds to the behaviour of the Unix env program, which performs a PATH search. If an executable matching the first argument after the env command cannot be found, but the argument starts with python, it will be handled as described for the other virtual commands. The environment variable PYLAUNCHER_NO_SEARCH_PATH may be set (to any value) to skip this search of PATH.

Shebang lines that do not match any of these patterns are looked up in the [commands] section of the launcher’s .INI file. This may be used to handle certain commands in a way that makes sense for your system. The name of the command must be a single argument (no spaces in the shebang executable), and the value substituted is the full path to the executable (additional arguments specified in the .INI will be quoted as part of the filename).

[commands]
+/bin/xpython=C:\Program Files\XPython\python.exe
+

Any commands not found in the .INI file are treated as Windows executable paths that are absolute or relative to the directory containing the script file. This is a convenience for Windows-only scripts, such as those generated by an installer, since the behavior is not compatible with Unix-style shells. These paths may be quoted, and may include multiple arguments, after which the path to the script and any additional arguments will be appended.

+4.8.3. Arguments in shebang lines

The shebang lines can also specify additional options to be passed to the Python interpreter. For example, if you have a shebang line:

#! /usr/bin/python -v
+

Then Python will be started with the -v option

+4.8.4. Customization

+4.8.4.1. Customization via INI files

Two .ini files will be searched by the launcher - py.ini in the current user’s application data directory (%LOCALAPPDATA% or $env:LocalAppData) and py.ini in the same directory as the launcher. The same .ini files are used for both the ‘console’ version of the launcher (i.e. py.exe) and for the ‘windows’ version (i.e. pyw.exe).

Customization specified in the “application directory” will have precedence over the one next to the executable, so a user, who may not have write access to the .ini file next to the launcher, can override commands in that global .ini file.

+4.8.4.2. Customizing default Python versions

In some cases, a version qualifier can be included in a command to dictate which version of Python will be used by the command. A version qualifier starts with a major version number and can optionally be followed by a period (‘.’) and a minor version specifier. Furthermore it is possible to specify if a 32 or 64 bit implementation shall be requested by adding “-32” or “-64”.

For example, a shebang line of #!python has no version qualifier, while #!python3 has a version qualifier which specifies only a major version.

If no version qualifiers are found in a command, the environment variable PY_PYTHON can be set to specify the default version qualifier. If it is not set, the default is “3”. The variable can specify any value that may be passed on the command line, such as “3”, “3.7”, “3.7-32” or “3.7-64”. (Note that the “-64” option is only available with the launcher included with Python 3.7 or newer.)

If no minor version qualifiers are found, the environment variable PY_PYTHON{major} (where {major} is the current major version qualifier as determined above) can be set to specify the full version. If no such option is found, the launcher will enumerate the installed Python versions and use the latest minor release found for the major version, which is likely, although not guaranteed, to be the most recently installed version in that family.

On 64-bit Windows with both 32-bit and 64-bit implementations of the same (major.minor) Python version installed, the 64-bit version will always be preferred. This will be true for both 32-bit and 64-bit implementations of the launcher - a 32-bit launcher will prefer to execute a 64-bit Python installation of the specified version if available. This is so the behavior of the launcher can be predicted knowing only what versions are installed on the PC and without regard to the order in which they were installed (i.e., without knowing whether a 32 or 64-bit version of Python and corresponding launcher was installed last). As noted above, an optional “-32” or “-64” suffix can be used on a version specifier to change this behaviour.

Examples:

  • If no relevant options are set, the commands python and python2 will use the latest Python 2.x version installed and the command python3 will use the latest Python 3.x installed.
  • The command python3.7 will not consult any options at all as the versions are fully specified.
  • If PY_PYTHON=3, the commands python and python3 will both use the latest installed Python 3 version.
  • If PY_PYTHON=3.7-32, the command python will use the 32-bit implementation of 3.7 whereas the command python3 will use the latest installed Python (PY_PYTHON was not considered at all as a major version was specified.)
  • If PY_PYTHON=3 and PY_PYTHON3=3.7, the commands python and python3 will both use specifically 3.7

In addition to environment variables, the same settings can be configured in the .INI file used by the launcher. The section in the INI file is called [defaults] and the key name will be the same as the environment variables without the leading PY_ prefix (and note that the key names in the INI file are case insensitive.) The contents of an environment variable will override things specified in the INI file.

For example:

  • Setting PY_PYTHON=3.7 is equivalent to the INI file containing:
[defaults]
+python=3.7
+
  • Setting PY_PYTHON=3 and PY_PYTHON3=3.7 is equivalent to the INI file containing:
[defaults]
+python=3
+python3=3.7
+

+4.8.5. Diagnostics

If an environment variable PYLAUNCHER_DEBUG is set (to any value), the launcher will print diagnostic information to stderr (i.e. to the console). While this information manages to be simultaneously verbose and terse, it should allow you to see what versions of Python were located, why a particular version was chosen and the exact command-line used to execute the target Python. It is primarily intended for testing and debugging.

+4.8.6. Dry Run

If an environment variable PYLAUNCHER_DRYRUN is set (to any value), the launcher will output the command it would have run, but will not actually launch Python. This may be useful for tools that want to use the launcher to detect and then launch Python directly. Note that the command written to standard output is always encoded using UTF-8, and may not render correctly in the console.

+4.8.7. Install on demand

If an environment variable PYLAUNCHER_ALLOW_INSTALL is set (to any value), and the requested Python version is not installed but is available on the Microsoft Store, the launcher will attempt to install it. This may require user interaction to complete, and you may need to run the command again.

An additional PYLAUNCHER_ALWAYS_INSTALL variable causes the launcher to always try to install Python, even if it is detected. This is mainly intended for testing (and should be used with PYLAUNCHER_DRYRUN).

+4.8.8. Return codes

The following exit codes may be returned by the Python launcher. Unfortunately, there is no way to distinguish these from the exit code of Python itself.

The names of codes are as used in the sources, and are only for reference. There is no way to access or resolve them apart from reading this page. Entries are listed in alphabetical order of names.

+ + + + + + + +

Name

Value

Description

RC_BAD_VENV_CFG

107

A pyvenv.cfg was found but is corrupt.

RC_CREATE_PROCESS

101

Failed to launch Python.

RC_INSTALLING

111

An install was started, but the command will need to be re-run after it completes.

RC_INTERNAL_ERROR

109

Unexpected error. Please report a bug.

RC_NO_COMMANDLINE

108

Unable to obtain command line from the operating system.

RC_NO_PYTHON

103

Unable to locate the requested version.

RC_NO_VENV_CFG

106

A pyvenv.cfg was required but not found.

+4.9. Finding modules

These notes supplement the description at The initialization of the sys.path module search path with detailed Windows notes.

When no ._pth file is found, this is how sys.path is populated on Windows:

  • An empty entry is added at the start, which corresponds to the current directory.
  • If the environment variable PYTHONPATH exists, as described in Environment variables, its entries are added next. Note that on Windows, paths in this variable must be separated by semicolons, to distinguish them from the colon used in drive identifiers (C:\ etc.).
  • Additional “application paths” can be added in the registry as subkeys of \SOFTWARE\Python\PythonCore{version}\PythonPath under both the HKEY_CURRENT_USER and HKEY_LOCAL_MACHINE hives. Subkeys which have semicolon-delimited path strings as their default value will cause each path to be added to sys.path. (Note that all known installers only use HKLM, so HKCU is typically empty.)
  • If the environment variable PYTHONHOME is set, it is assumed as “Python Home”. Otherwise, the path of the main Python executable is used to locate a “landmark file” (either Lib\os.py or pythonXY.zip) to deduce the “Python Home”. If a Python home is found, the relevant sub-directories added to sys.path (Lib, plat-win, etc) are based on that folder. Otherwise, the core Python path is constructed from the PythonPath stored in the registry.
  • If the Python Home cannot be located, no PYTHONPATH is specified in the environment, and no registry entries can be found, a default path with relative entries is used (e.g. .\Lib;.\plat-win, etc).

If a pyvenv.cfg file is found alongside the main executable or in the directory one level above the executable, the following variations apply:

  • If home is an absolute path and PYTHONHOME is not set, this path is used instead of the path to the main executable when deducing the home location.

The end result of all this is:

  • When running python.exe, or any other .exe in the main Python directory (either an installed version, or directly from the PCbuild directory), the core path is deduced, and the core paths in the registry are ignored. Other “application paths” in the registry are always read.
  • When Python is hosted in another .exe (different directory, embedded via COM, etc), the “Python Home” will not be deduced, so the core path from the registry is used. Other “application paths” in the registry are always read.
  • If Python can’t find its home and there are no registry value (frozen .exe, some very strange installation setup) you get a path with some default, but relative, paths.

For those who want to bundle Python into their application or distribution, the following advice will prevent conflicts with other installations:

  • Include a ._pth file alongside your executable containing the directories to include. This will ignore paths listed in the registry and environment variables, and also ignore site unless import site is listed.
  • If you are loading python3.dll or python37.dll in your own executable, explicitly call Py_SetPath() or (at least) Py_SetProgramName() before Py_Initialize().
  • Clear and/or overwrite PYTHONPATH and set PYTHONHOME before launching python.exe from your application.
  • If you cannot use the previous suggestions (for example, you are a distribution that allows people to run python.exe directly), ensure that the landmark file (Lib\os.py) exists in your install directory. (Note that it will not be detected inside a ZIP file, but a correctly named ZIP file will be detected instead.)

These will ensure that the files in a system-wide installation will not take precedence over the copy of the standard library bundled with your application. Otherwise, your users may experience problems using your application. Note that the first suggestion is the best, as the others may still be susceptible to non-standard paths in the registry and user site-packages.

Changed in version 3.6: Add ._pth file support and removes applocal option from pyvenv.cfg.

Changed in version 3.6: Add pythonXX.zip as a potential landmark when directly adjacent to the executable.

Deprecated since version 3.6: Modules specified in the registry under Modules (not PythonPath) may be imported by importlib.machinery.WindowsRegistryFinder. This finder is enabled on Windows in 3.6.0 and earlier, but may need to be explicitly added to sys.meta_path in the future.

+4.10. Additional modules

Even though Python aims to be portable among all platforms, there are features that are unique to Windows. A couple of modules, both in the standard library and external, and snippets exist to use these features.

The Windows-specific standard modules are documented in MS Windows Specific Services.

+4.10.1. PyWin32

The PyWin32 module by Mark Hammond is a collection of modules for advanced Windows-specific support. This includes utilities for:

PythonWin is a sample MFC application shipped with PyWin32. It is an embeddable IDE with a built-in debugger.

See also

Win32 How Do I…?
+
+

by Tim Golden

Python and COM
+
+

by David and Paul Boddie

+4.10.2. cx_Freeze

cx_Freeze wraps Python scripts into executable Windows programs (*.exe files). When you have done this, you can distribute your application without requiring your users to install Python.

+4.11. Compiling Python on Windows

If you want to compile CPython yourself, first thing you should do is get the source. You can download either the latest release’s source or just grab a fresh checkout.

The source tree contains a build solution and project files for Microsoft Visual Studio, which is the compiler used to build the official Python releases. These files are in the PCbuild directory.

Check PCbuild/readme.txt for general information on the build process.

For extension modules, consult Building C and C++ Extensions on Windows.

+4.12. Other Platforms

With ongoing development of Python, some platforms that used to be supported earlier are no longer supported (due to the lack of users or developers). Check PEP 11 for details on all unsupported platforms.

See Python for Windows for detailed information about platforms with pre-compiled installers.

+

+ © 2001–2023 Python Software Foundation
Licensed under the PSF License.
+ https://docs.python.org/3.12/using/windows.html +

+
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