What’s new in Python 3.14

Editors:

Adam Turner and Hugo van Kemenade

This article explains the new features in Python 3.14, compared to 3.13. Python 3.14 was released on 7 October 2025. For full details, see the changelog.

See also

PEP 745 – Python 3.14 release schedule

Summary – Release highlights

Python 3.14 is the latest stable release of the Python programming language, with a mix of changes to the language, the implementation, and the standard library. The biggest changes include template string literals, deferred evaluation of annotations, and support for subinterpreters in the standard library.

The library changes include significantly improved capabilities for introspection in asyncio, support for Zstandard via a new compression.zstd module, syntax highlighting in the REPL, as well as the usual deprecations and removals, and improvements in user-friendliness and correctness.

This article doesn’t attempt to provide a complete specification of all new features, but instead gives a convenient overview. For full details refer to the documentation, such as the Library Reference and Language Reference. To understand the complete implementation and design rationale for a change, refer to the PEP for a particular new feature; but note that PEPs usually are not kept up-to-date once a feature has been fully implemented. See Porting to Python 3.14 for guidance on upgrading from earlier versions of Python.

Interpreter improvements:

Significant improvements in the standard library:

C API improvements:

Platform support:

Release changes:

New features

PEP 649 & PEP 749: Deferred evaluation of annotations

The annotations on functions, classes, and modules are no longer evaluated eagerly. Instead, annotations are stored in special-purpose annotate functions and evaluated only when necessary (except if from __future__ import annotations is used).

This change is designed to improve performance and usability of annotations in Python in most circumstances. The runtime cost for defining annotations is minimized, but it remains possible to introspect annotations at runtime. It is no longer necessary to enclose annotations in strings if they contain forward references.

The new annotationlib module provides tools for inspecting deferred annotations. Annotations may be evaluated in the VALUE format (which evaluates annotations to runtime values, similar to the behavior in earlier Python versions), the FORWARDREF format (which replaces undefined names with special markers), and the STRING format (which returns annotations as strings).

This example shows how these formats behave:

>>> from annotationlib import get_annotations, Format
>>> def func(arg: Undefined):
...     pass
>>> get_annotations(func, format=Format.VALUE)
Traceback (most recent call last):
  ...
NameError: name 'Undefined' is not defined
>>> get_annotations(func, format=Format.FORWARDREF)
{'arg': ForwardRef('Undefined', owner=<function func at 0x...>)}
>>> get_annotations(func, format=Format.STRING)
{'arg': 'Undefined'}

The porting section contains guidance on changes that may be needed due to these changes, though in the majority of cases, code will continue working as-is.

(Contributed by Jelle Zijlstra in PEP 749 and gh-119180; PEP 649 was written by Larry Hastings.)

See also

PEP 649

Deferred Evaluation Of Annotations Using Descriptors

PEP 749

Implementing PEP 649

PEP 734: Multiple interpreters in the standard library

The CPython runtime supports running multiple copies of Python in the same process simultaneously and has done so for over 20 years. Each of these separate copies is called an ‘interpreter’. However, the feature had been available only through the C-API.

That limitation is removed in Python 3.14, with the new concurrent.interpreters module.

There are at least two notable reasons why using multiple interpreters has significant benefits:

  • they support a new (to Python), human-friendly concurrency model

  • true multi-core parallelism

For some use cases, concurrency in software improves efficiency and can simplify design, at a high level. At the same time, implementing and maintaining all but the simplest concurrency is often a struggle for the human brain. That especially applies to plain threads (for example, threading), where all memory is shared between all threads.

With multiple isolated interpreters, you can take advantage of a class of concurrency models, like Communicating Sequential Processes (CSP) or the actor model, that have found success in other programming languages, like Smalltalk, Erlang, Haskell, and Go. Think of multiple interpreters as threads but with opt-in sharing.

Regarding multi-core parallelism: as of Python 3.12, interpreters are now sufficiently isolated from one another to be used in parallel (see PEP 684). This unlocks a variety of CPU-intensive use cases for Python that were limited by the GIL.

Using multiple interpreters is similar in many ways to multiprocessing, in that they both provide isolated logical “processes” that can run in parallel, with no sharing by default. However, when using multiple interpreters, an application will use fewer system resources and will operate more efficiently (since it stays within the same process). Think of multiple interpreters as having the isolation of processes with the efficiency of threads.

While the feature has been around for decades, multiple interpreters have not been used widely, due to low awareness and the lack of a standard library module. Consequently, they currently have several notable limitations, which are expected to improve significantly now that the feature is going mainstream.

Current limitations:

  • starting each interpreter has not been optimized yet

  • each interpreter uses more memory than necessary (work continues on extensive internal sharing between interpreters)

  • there aren’t many options yet for truly sharing objects or other data between interpreters (other than memoryview)

  • many third-party extension modules on PyPI are not yet compatible with multiple interpreters (all standard library extension modules are compatible)

  • the approach to writing applications that use multiple isolated interpreters is mostly unfamiliar to Python users, for now

The impact of these limitations will depend on future CPython improvements, how interpreters are used, and what the community solves through PyPI packages. Depending on the use case, the limitations may not have much impact, so try it out!

Furthermore, future CPython releases will reduce or eliminate overhead and provide utilities that are less appropriate on PyPI. In the meantime, most of the limitations can also be addressed through extension modules, meaning PyPI packages can fill any gap for 3.14, and even back to 3.12 where interpreters were finally properly isolated and stopped sharing the GIL. Likewise, libraries on PyPI are expected to emerge for high-level abstractions on top of interpreters.

Regarding extension modules, work is in progress to update some PyPI projects, as well as tools like Cython, pybind11, nanobind, and PyO3. The steps for isolating an extension module are found at Isolating Extension Modules. Isolating a module has a lot of overlap with what is required to support free-threading, so the ongoing work in the community in that area will help accelerate support for multiple interpreters.

Also added in 3.14: concurrent.futures.InterpreterPoolExecutor.

(Contributed by Eric Snow in gh-134939.)

See also

PEP 734

PEP 750: Template string literals

Template strings are a new mechanism for custom string processing. They share the familiar syntax of f-strings but, unlike f-strings, return an object representing the static and interpolated parts of the string, instead of a simple str.

To write a t-string, use a 't' prefix instead of an 'f':

>>> variety = 'Stilton'
>>> template = t'Try some {variety} cheese!'
>>> type(template)
<class 'string.templatelib.Template'>

Template objects provide access to the static and interpolated (in curly braces) parts of a string before they are combined. Iterate over Template instances to access their parts in order:

>>> list(template)
['Try some ', Interpolation('Stilton', 'variety', None, ''), ' cheese!']

It’s easy to write (or call) code to process Template instances. For example, here’s a function that renders static parts lowercase and Interpolation instances uppercase:

from string.templatelib import Interpolation

def lower_upper(template):
    """Render static parts lowercase and interpolations uppercase."""
    parts = []
    for part in template:
        if isinstance(part, Interpolation):
            parts.append(str(part.value).upper())
        else:
            parts.append(part.lower())
    return ''.join(parts)

name = 'Wenslydale'
template = t'Mister {name}'
assert lower_upper(template) == 'mister WENSLYDALE'

Because Template instances distinguish between static strings and interpolations at runtime, they can be useful for sanitising user input. Writing a html() function that escapes user input in HTML is an exercise left to the reader! Template processing code can provide improved flexibility. For instance, a more advanced html() function could accept a dict of HTML attributes directly in the template:

attributes = {'src': 'limburger.jpg', 'alt': 'lovely cheese'}
template = t'<img {attributes}>'
assert html(template) == '<img src="limburger.jpg" alt="lovely cheese" />'

Of course, template processing code does not need to return a string-like result. An even more advanced html() could return a custom type representing a DOM-like structure.

With t-strings in place, developers can write systems that sanitise SQL, make safe shell operations, improve logging, tackle modern ideas in web development (HTML, CSS, and so on), and implement lightweight custom business DSLs.

(Contributed by Jim Baker, Guido van Rossum, Paul Everitt, Koudai Aono, Lysandros Nikolaou, Dave Peck, Adam Turner, Jelle Zijlstra, Bénédikt Tran, and Pablo Galindo Salgado in gh-132661.)

See also

PEP 750.

PEP 768: Safe external debugger interface

Python 3.14 introduces a zero-overhead debugging interface that allows debuggers and profilers to safely attach to running Python processes without stopping or restarting them. This is a significant enhancement to Python’s debugging capabilities, meaning that unsafe alternatives are no longer required.

The new interface provides safe execution points for attaching debugger code without modifying the interpreter’s normal execution path or adding any overhead at runtime. Due to this, tools can now inspect and interact with Python applications in real-time, which is a crucial capability for high-availability systems and production environments.

For convenience, this interface is implemented in the sys.remote_exec() function. For example:

import sys
from tempfile import NamedTemporaryFile

with NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f:
    script_path = f.name
    f.write(f'import my_debugger; my_debugger.connect({os.getpid()})')

# Execute in process with PID 1234
print('Behold! An offering:')
sys.remote_exec(1234, script_path)

This function allows sending Python code to be executed in a target process at the next safe execution point. However, tool authors can also implement the protocol directly as described in the PEP, which details the underlying mechanisms used to safely attach to running processes.

The debugging interface has been carefully designed with security in mind and includes several mechanisms to control access:

(Contributed by Pablo Galindo Salgado, Matt Wozniski, and Ivona Stojanovic in gh-131591.)

See also

PEP 768.

A new type of interpreter

A new type of interpreter has been added to CPython. It uses tail calls between small C functions that implement individual Python opcodes, rather than one large C case statement. For certain newer compilers, this interpreter provides significantly better performance. Preliminary benchmarks suggest a geometric mean of 3-5% faster on the standard pyperformance benchmark suite, depending on platform and architecture. The baseline is Python 3.14 built with Clang 19, without this new interpreter.

This interpreter currently only works with Clang 19 and newer on x86-64 and AArch64 architectures. However, a future release of GCC is expected to support this as well.

This feature is opt-in for now. Enabling profile-guided optimization is highly recommendeded when using the new interpreter as it is the only configuration that has been tested and validated for improved performance. For further information, see --with-tail-call-interp.

Note

This is not to be confused with tail call optimization of Python functions, which is currently not implemented in CPython.

This new interpreter type is an internal implementation detail of the CPython interpreter. It doesn’t change the visible behavior of Python programs at all. It can improve their performance, but doesn’t change anything else.

(Contributed by Ken Jin in gh-128563, with ideas on how to implement this in CPython by Mark Shannon, Garrett Gu, Haoran Xu, and Josh Haberman.)

Free-threaded mode improvements

CPython’s free-threaded mode (PEP 703), initially added in 3.13, has been significantly improved in Python 3.14. The implementation described in PEP 703 has been finished, including C API changes, and temporary workarounds in the interpreter were replaced with more permanent solutions. The specializing adaptive interpreter (PEP 659) is now enabled in free-threaded mode, which along with many other optimizations greatly improves its performance. The performance penalty on single-threaded code in free-threaded mode is now roughly 5-10%, depending on the platform and C compiler used.

From Python 3.14, when compiling extension modules for the free-threaded build of CPython on Windows, the preprocessor variable Py_GIL_DISABLED now needs to be specified by the build backend, as it will no longer be determined automatically by the C compiler. For a running interpreter, the setting that was used at compile time can be found using sysconfig.get_config_var().

The new -X context_aware_warnings flag controls if concurrent safe warnings control is enabled. The flag defaults to true for the free-threaded build and false for the GIL-enabled build.

A new thread_inherit_context flag has been added, which if enabled means that threads created with threading.Thread start with a copy of the Context() of the caller of start(). Most significantly, this makes the warning filtering context established by catch_warnings be “inherited” by threads (or asyncio tasks) started within that context. It also affects other modules that use context variables, such as the decimal context manager. This flag defaults to true for the free-threaded build and false for the GIL-enabled build.

(Contributed by Sam Gross, Matt Page, Neil Schemenauer, Thomas Wouters, Donghee Na, Kirill Podoprigora, Ken Jin, Itamar Oren, Brett Simmers, Dino Viehland, Nathan Goldbaum, Ralf Gommers, Lysandros Nikolaou, Kumar Aditya, Edgar Margffoy, and many others. Some of these contributors are employed by Meta, which has continued to provide significant engineering resources to support this project.)

Improved error messages

  • The interpreter now provides helpful suggestions when it detects typos in Python keywords. When a word that closely resembles a Python keyword is encountered, the interpreter will suggest the correct keyword in the error message. This feature helps programmers quickly identify and fix common typing mistakes. For example:

    >>> whille True:
...     pass
Traceback (most recent call last):
  File "<stdin>", line 1
    whille True:
    ^^^^^^
SyntaxError: invalid syntax. Did you mean 'while'?

    While the feature focuses on the most common cases, some variations of misspellings may still result in regular syntax errors. (Contributed by Pablo Galindo in gh-132449.)

  • elif statements that follow an else block now have a specific error message. (Contributed by Steele Farnsworth in gh-129902.)

    >>> if who == "me":
...     print("It's me!")
... else:
...     print("It's not me!")
... elif who is None:
...     print("Who is it?")
File "<stdin>", line 5
  elif who is None:
  ^^^^
SyntaxError: 'elif' block follows an 'else' block

  • If a statement is passed to the Conditional expressions after else, or one of pass, break, or continue is passed before if, then the error message highlights where the expression is required. (Contributed by Sergey Miryanov in gh-129515.)

    >>> x = 1 if True else pass
Traceback (most recent call last):
  File "<string>", line 1
    x = 1 if True else pass
                       ^^^^
SyntaxError: expected expression after 'else', but statement is given

>>> x = continue if True else break
Traceback (most recent call last):
  File "<string>", line 1
    x = continue if True else break
        ^^^^^^^^
SyntaxError: expected expression before 'if', but statement is given

  • When incorrectly closed strings are detected, the error message suggests that the string may be intended to be part of the string. (Contributed by Pablo Galindo in gh-88535.)

    >>> "The interesting object "The important object" is very important"
Traceback (most recent call last):
SyntaxError: invalid syntax. Is this intended to be part of the string?

  • When strings have incompatible prefixes, the error now shows which prefixes are incompatible. (Contributed by Nikita Sobolev in gh-133197.)

    >>> ub'abc'
  File "<python-input-0>", line 1
    ub'abc'
    ^^
SyntaxError: 'u' and 'b' prefixes are incompatible

  • Improved error messages when using as with incompatible targets in:

    • Imports: import ... as ...

    • From imports: from ... import ... as ...

    • Except handlers: except ... as ...

    • Pattern-match cases: case ... as ...

    (Contributed by Nikita Sobolev in gh-123539, gh-123562, and gh-123440.)

  • Improved error message when trying to add an instance of an unhashable type to a dict or set. (Contributed by CF Bolz-Tereick and Victor Stinner in gh-132828.)

    >>> s = set()
>>> s.add({'pages': 12, 'grade': 'A'})
Traceback (most recent call last):
  File "<python-input-1>", line 1, in <module>
    s.add({'pages': 12, 'grade': 'A'})
    ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot use 'dict' as a set element (unhashable type: 'dict')
>>> d = {}
>>> l = [1, 2, 3]
>>> d[l] = 12
Traceback (most recent call last):
  File "<python-input-4>", line 1, in <module>
    d[l] = 12
    ~^^^
TypeError: cannot use 'list' as a dict key (unhashable type: 'list')

  • Improved error message when an object supporting the synchronous context manager protocol is entered using async with instead of with, and vice versa for the asynchronous context manager protocol. (Contributed by Bénédikt Tran in gh-128398.)

PEP 784: Zstandard support in the standard library

The new compression package contains modules compression.lzma, compression.bz2, compression.gzip and compression.zlib which re-export the lzma, bz2, gzip and zlib modules respectively. The new import names under compression are the preferred names for importing these compression modules from Python 3.14. However, the existing modules names have not been deprecated. Any deprecation or removal of the existing compression modules will occur no sooner than five years after the release of 3.14.

The new compression.zstd module provides compression and decompression APIs for the Zstandard format via bindings to Meta’s zstd library. Zstandard is a widely adopted, highly efficient, and fast compression format. In addition to the APIs introduced in compression.zstd, support for reading and writing Zstandard compressed archives has been added to the tarfile, zipfile, and shutil modules.

Here’s an example of using the new module to compress some data:

from compression import zstd
import math

data = str(math.pi).encode() * 20
compressed = zstd.compress(data)
ratio = len(compressed) / len(data)
print(f"Achieved compression ratio of {ratio}")

As can be seen, the API is similar to the APIs of the lzma and bz2 modules.

(Contributed by Emma Harper Smith, Adam Turner, Gregory P. Smith, Tomas Roun, Victor Stinner, and Rogdham in gh-132983.)

See also

PEP 784.

Asyncio introspection capabilities

Added a new command-line interface to inspect running Python processes using asynchronous tasks, available via python -m asyncio ps PID or python -m asyncio pstree PID.

The ps subcommand inspects the given process ID (PID) and displays information about currently running asyncio tasks. It outputs a task table: a flat listing of all tasks, their names, their coroutine stacks, and which tasks are awaiting them.

The pstree subcommand fetches the same information, but instead renders a visual async call tree, showing coroutine relationships in a hierarchical format. This command is particularly useful for debugging long-running or stuck asynchronous programs. It can help developers quickly identify where a program is blocked, what tasks are pending, and how coroutines are chained together.

For example given this code:

import asyncio

async def play_track(track):
    await asyncio.sleep(5)
    print(f'🎵 Finished: {track}')

async def play_album(name, tracks):
    async with asyncio.TaskGroup() as tg:
        for track in tracks:
            tg.create_task(play_track(track), name=track)

async def main():
    async with asyncio.TaskGroup() as tg:
        tg.create_task(
          play_album('Sundowning', ['TNDNBTG', 'Levitate']),
          name='Sundowning')
        tg.create_task(
          play_album('TMBTE', ['DYWTYLM', 'Aqua Regia']),
          name='TMBTE')

if __name__ == '__main__':
    asyncio.run(main())

Executing the new tool on the running process will yield a table like this:

python -m asyncio ps 12345

tid        task id              task name            coroutine stack                                    awaiter chain                                      awaiter name    awaiter id
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1935500    0x7fc930c18050       Task-1               TaskGroup._aexit -> TaskGroup.__aexit__ -> main                                                                       0x0
1935500    0x7fc930c18230       Sundowning           TaskGroup._aexit -> TaskGroup.__aexit__ -> album   TaskGroup._aexit -> TaskGroup.__aexit__ -> main    Task-1          0x7fc930c18050
1935500    0x7fc93173fa50       TMBTE                TaskGroup._aexit -> TaskGroup.__aexit__ -> album   TaskGroup._aexit -> TaskGroup.__aexit__ -> main    Task-1          0x7fc930c18050
1935500    0x7fc93173fdf0       TNDNBTG              sleep -> play                                      TaskGroup._aexit -> TaskGroup.__aexit__ -> album   Sundowning      0x7fc930c18230
1935500    0x7fc930d32510       Levitate             sleep -> play                                      TaskGroup._aexit -> TaskGroup.__aexit__ -> album   Sundowning      0x7fc930c18230
1935500    0x7fc930d32890       DYWTYLM              sleep -> play                                      TaskGroup._aexit -> TaskGroup.__aexit__ -> album   TMBTE           0x7fc93173fa50
1935500    0x7fc93161ec30       Aqua Regia           sleep -> play                                      TaskGroup._aexit -> TaskGroup.__aexit__ -> album   TMBTE           0x7fc93173fa50

or a tree like this:

python -m asyncio pstree 12345

└── (T) Task-1
    └──  main example.py:13
        └──  TaskGroup.__aexit__ Lib/asyncio/taskgroups.py:72
            └──  TaskGroup._aexit Lib/asyncio/taskgroups.py:121
                ├── (T) Sundowning
                │   └──  album example.py:8
                │       └──  TaskGroup.__aexit__ Lib/asyncio/taskgroups.py:72
                │           └──  TaskGroup._aexit Lib/asyncio/taskgroups.py:121
                │               ├── (T) TNDNBTG
                │               │   └──  play example.py:4
                │               │       └──  sleep Lib/asyncio/tasks.py:702
                │               └── (T) Levitate
                │                   └──  play example.py:4
                │                       └──  sleep Lib/asyncio/tasks.py:702
                └── (T) TMBTE
                    └──  album example.py:8
                        └──  TaskGroup.__aexit__ Lib/asyncio/taskgroups.py:72
                            └──  TaskGroup._aexit Lib/asyncio/taskgroups.py:121
                                ├── (T) DYWTYLM
                                │   └──  play example.py:4
                                │       └──  sleep Lib/asyncio/tasks.py:702
                                └── (T) Aqua Regia
                                    └──  play example.py:4
                                        └──  sleep Lib/asyncio/tasks.py:702

If a cycle is detected in the async await graph (which could indicate a programming issue), the tool raises an error and lists the cycle paths that prevent tree construction:

python -m asyncio pstree 12345

ERROR: await-graph contains cycles - cannot print a tree!

cycle: Task-2 → Task-3 → Task-2

(Contributed by Pablo Galindo, Łukasz Langa, Yury Selivanov, and Marta Gomez Macias in gh-91048.)

Concurrent safe warnings control

The warnings.catch_warnings context manager will now optionally use a context variable for warning filters. This is enabled by setting the context_aware_warnings flag, either with the -X command-line option or an environment variable. This gives predictable warnings control when using catch_warnings combined with multiple threads or asynchronous tasks. The flag defaults to true for the free-threaded build and false for the GIL-enabled build.

(Contributed by Neil Schemenauer and Kumar Aditya in gh-130010.)

Other language changes

  • All Windows code pages are now supported as ‘cpXXX’ codecs on Windows. (Contributed by Serhiy Storchaka in gh-123803.)

  • Implement mixed-mode arithmetic rules combining real and complex numbers as specified by the C standard since C99. (Contributed by Sergey B Kirpichev in gh-69639.)

  • More syntax errors are now detected regardless of optimisation and the -O command-line option. This includes writes to __debug__, incorrect use of await, and asynchronous comprehensions outside asynchronous functions. For example, python -O -c 'assert (__debug__ := 1)' or python -O -c 'assert await 1' now produce SyntaxErrors. (Contributed by Irit Katriel and Jelle Zijlstra in gh-122245 & gh-121637.)

  • When subclassing a pure C type, the C slots for the new type are no longer replaced with a wrapped version on class creation if they are not explicitly overridden in the subclass. (Contributed by Tomasz Pytel in gh-132284.)

Built-ins

Command line and environment

  • The import time flag can now track modules that are already loaded (‘cached’), via the new -X importtime=2. When such a module is imported, the self and cumulative times are replaced by the string cached.

    Values above 2 for -X importtime are now reserved for future use.

    (Contributed by Noah Kim and Adam Turner in gh-118655.)

  • The command-line option -c now automatically dedents its code argument before execution. The auto-dedentation behavior mirrors textwrap.dedent(). (Contributed by Jon Crall and Steven Sun in gh-103998.)

  • -J is no longer a reserved flag for Jython, and now has no special meaning. (Contributed by Adam Turner in gh-133336.)

PEP 758: Allow except and except* expressions without brackets

The except and except* expressions now allow brackets to be omitted when there are multiple exception types and the as clause is not used. For example:

try:
    connect_to_server()
except TimeoutError, ConnectionRefusedError:
    print('The network has ceased to be!')

(Contributed by Pablo Galindo and Brett Cannon in PEP 758 and gh-131831.)

PEP 765: Control flow in finally blocks

The compiler now emits a SyntaxWarning when a return, break, or continue statement have the effect of leaving a finally block. This change is specified in PEP 765.

In situations where this change is inconvenient (such as those where the warnings are redundant due to code linting), the warning filter can be used to turn off all syntax warnings by adding ignore::SyntaxWarning as a filter. This can be specified in combination with a filter that converts other warnings to errors (for example, passing -Werror -Wignore::SyntaxWarning as CLI options, or setting PYTHONWARNINGS=error,ignore::SyntaxWarning).

Note that applying such a filter at runtime using the warnings module will only suppress the warning in code that is compiled after the filter is adjusted. Code that is compiled prior to the filter adjustment (for example, when a module is imported) will still emit the syntax warning.

(Contributed by Irit Katriel in gh-130080.)

Incremental garbage collection

The cycle garbage collector is now incremental. This means that maximum pause times are reduced by an order of magnitude or more for larger heaps.

There are now only two generations: young and old. When gc.collect() is not called directly, the GC is invoked a little less frequently. When invoked, it collects the young generation and an increment of the old generation, instead of collecting one or more generations.

The behavior of gc.collect() changes slightly:

  • gc.collect(1): Performs an increment of garbage collection, rather than collecting generation 1.

  • Other calls to gc.collect() are unchanged.

(Contributed by Mark Shannon in gh-108362.)

Default interactive shell

  • The default interactive shell now highlights Python syntax. The feature is enabled by default, save if PYTHON_BASIC_REPL or any other environment variable that disables colour is set. See Controlling color for details.

    The default color theme for syntax highlighting strives for good contrast and exclusively uses the 4-bit VGA standard ANSI color codes for maximum compatibility. The theme can be customized using an experimental API _colorize.set_theme(). This can be called interactively or in the PYTHONSTARTUP script. Note that this function has no stability guarantees, and may change or be removed.

    (Contributed by Łukasz Langa in gh-131507.)

  • The default interactive shell now supports import auto-completion. This means that typing import co and pressing <Tab> will suggest modules starting with co. Similarly, typing from concurrent import i will suggest submodules of concurrent starting with i. Note that autocompletion of module attributes is not currently supported. (Contributed by Tomas Roun in gh-69605.)

New modules

  • annotationlib: For introspecting annotations. See PEP 749 for more details. (Contributed by Jelle Zijlstra in gh-119180.)

  • compression (including compression.zstd): A package for compression-related modules, including a new module to support the Zstandard compression format. See PEP 784 for more details. (Contributed by Emma Harper Smith, Adam Turner, Gregory P. Smith, Tomas Roun, Victor Stinner, and Rogdham in gh-132983.)

  • concurrent.interpreters: Support for multiple interpreters in the standard library. See PEP 734 for more details. (Contributed by Eric Snow in gh-134939.)

  • string.templatelib: Support for template string literals (t-strings). See PEP 750 for more details. (Contributed by Jim Baker, Guido van Rossum, Paul Everitt, Koudai Aono, Lysandros Nikolaou, Dave Peck, Adam Turner, Jelle Zijlstra, Bénédikt Tran, and Pablo Galindo Salgado in gh-132661.)

Improved modules

argparse

  • The default value of the program name for argparse.ArgumentParser now reflects the way the Python interpreter was instructed to find the __main__ module code. (Contributed by Serhiy Storchaka and Alyssa Coghlan in gh-66436.)

  • Introduced the optional suggest_on_error parameter to argparse.ArgumentParser, enabling suggestions for argument choices and subparser names if mistyped by the user. (Contributed by Savannah Ostrowski in gh-124456.)

  • Enable color for help text, which can be disabled with the optional color parameter to argparse.ArgumentParser. This can also be controlled by environment variables. (Contributed by Hugo van Kemenade in gh-130645.)

ast

  • Add compare(), a function for comparing two ASTs. (Contributed by Batuhan Taskaya and Jeremy Hylton in gh-60191.)

  • Add support for copy.replace() for AST nodes. (Contributed by Bénédikt Tran in gh-121141.)

  • Docstrings are now removed from an optimized AST in optimization level 2. (Contributed by Irit Katriel in gh-123958.)

  • The repr() output for AST nodes now includes more information. (Contributed by Tomas Roun in gh-116022.)

  • When called with an AST as input, the parse() function now always verifies that the root node type is appropriate. (Contributed by Irit Katriel in gh-130139.)

  • Add new options to the command-line interface: --feature-version, --optimize, and --show-empty. (Contributed by Semyon Moroz in gh-133367.)

asyncio

calendar

concurrent.futures

  • Add a new executor class, InterpreterPoolExecutor, which exposes multiple Python interpreters in the same process (‘subinterpreters’) to Python code. This uses a pool of independent Python interpreters to execute calls asynchronously.

    This is separate from the new interpreters module introduced by PEP 734. (Contributed by Eric Snow in gh-124548.)

  • On Unix platforms other than macOS, ‘forkserver’ is now the default start method for ProcessPoolExecutor (replacing ‘fork’). This change does not affect Windows or macOS, where ‘spawn’ remains the default start method.

    If the threading incompatible fork method is required, you must explicitly request it by supplying a multiprocessing context mp_context to ProcessPoolExecutor.

    See forkserver restrictions for information and differences with the fork method and how this change may affect existing code with mutable global shared variables and/or shared objects that can not be automatically pickled.

    (Contributed by Gregory P. Smith in gh-84559.)

  • Add two new methods to ProcessPoolExecutor, terminate_workers() and kill_workers(), as ways to terminate or kill all living worker processes in the given pool. (Contributed by Charles Machalow in gh-130849.)

  • Add the optional buffersize parameter to Executor.map to limit the number of submitted tasks whose results have not yet been yielded. If the buffer is full, iteration over the iterables pauses until a result is yielded from the buffer. (Contributed by Enzo Bonnal and Josh Rosenberg in gh-74028.)

configparser

  • configparser will no longer write config files it cannot read, to improve security. Attempting to write() keys containing delimiters or beginning with the section header pattern will raise an InvalidWriteError. (Contributed by Jacob Lincoln in gh-129270.)

contextvars

ctypes

  • The layout of bit fields in Structure and Union objects is now a closer match to platform defaults (GCC/Clang or MSVC). In particular, fields no longer overlap. (Contributed by Matthias Görgens in gh-97702.)

  • The Structure._layout_ class attribute can now be set to help match a non-default ABI. (Contributed by Petr Viktorin in gh-97702.)

  • The class of Structure/Union field descriptors is now available as CField, and has new attributes to aid debugging and introspection. (Contributed by Petr Viktorin in gh-128715.)

  • On Windows, the COMError exception is now public. (Contributed by Jun Komoda in gh-126686.)

  • On Windows, the CopyComPointer() function is now public. (Contributed by Jun Komoda in gh-127275.)

  • Add memoryview_at(), a function to create a memoryview object that refers to the supplied pointer and length. This works like ctypes.string_at() except it avoids a buffer copy, and is typically useful when implementing pure Python callback functions that are passed dynamically-sized buffers. (Contributed by Rian Hunter in gh-112018.)

  • Complex types, c_float_complex, c_double_complex, and c_longdouble_complex, are now available if both the compiler and the libffi library support complex C types. (Contributed by Sergey B Kirpichev in gh-61103.)

  • Add ctypes.util.dllist() for listing the shared libraries loaded by the current process. (Contributed by Brian Ward in gh-119349.)

  • Move ctypes.POINTER() types cache from a global internal cache (_pointer_type_cache) to the _CData.__pointer_type__ attribute of the corresponding ctypes types. This will stop the cache from growing without limits in some situations. (Contributed by Sergey Miryanov in gh-100926.)

  • The py_object type now supports subscription, making it a generic type. (Contributed by Brian Schubert in gh-132168.)

  • ctypes now supports free-threading builds. (Contributed by Kumar Aditya and Peter Bierma in gh-127945.)

curses

datetime

decimal

difflib

  • Comparison pages with highlighted changes generated by the HtmlDiff class now support ‘dark mode’. (Contributed by Jiahao Li in gh-129939.)

dis

errno

faulthandler

fnmatch

  • Add filterfalse(), a function to reject names matching a given pattern. (Contributed by Bénédikt Tran in gh-74598.)

fractions

functools

getopt

  • Add support for options with optional arguments. (Contributed by Serhiy Storchaka in gh-126374.)

  • Add support for returning intermixed options and non-option arguments in order. (Contributed by Serhiy Storchaka in gh-126390.)

getpass

  • Support keyboard feedback in the getpass() function via the keyword-only optional argument echo_char. Placeholder characters are rendered whenever a character is entered, and removed when a character is deleted. (Contributed by Semyon Moroz in gh-77065.)

graphlib

heapq

hmac

  • Add a built-in implementation for HMAC (RFC 2104) using formally verified code from the HACL* project. This implementation is used as a fallback when the OpenSSL implementation of HMAC is not available. (Contributed by Bénédikt Tran in gh-99108.)

http

  • Directory lists and error pages generated by the http.server module allow the browser to apply its default dark mode. (Contributed by Yorik Hansen in gh-123430.)

  • The http.server module now supports serving over HTTPS using the http.server.HTTPSServer class. This functionality is exposed by the command-line interface (python -m http.server) through the following options:

    (Contributed by Semyon Moroz in gh-85162.)

imaplib

inspect

io

json

linecache

  • getline() can now retrieve source code for frozen modules. (Contributed by Tian Gao in gh-131638.)

logging.handlers

math

  • Added more detailed error messages for domain errors in the module. (Contributed by Charlie Zhao and Sergey B Kirpichev in gh-101410.)

mimetypes

  • Add a public command-line for the module, invoked via python -m mimetypes. (Contributed by Oleg Iarygin and Hugo van Kemenade in gh-93096.)

  • Add several new MIME types based on RFCs and common usage:

    Microsoft and RFC 8081 MIME types for fonts

    • Embedded OpenType: application/vnd.ms-fontobject

    • OpenType Layout (OTF) font/otf

    • TrueType: font/ttf

    • WOFF 1.0 font/woff

    • WOFF 2.0 font/woff2

    RFC 9559 MIME types for Matroska audiovisual data container structures

    • audio with no video: audio/matroska (.mka)

    • video: video/matroska (.mkv)

    • stereoscopic video: video/matroska-3d (.mk3d)

    Images with RFCs

    • RFC 1494: CCITT Group 3 (.g3)

    • RFC 3362: Real-time Facsimile, T.38 (.t38)

    • RFC 3745: JPEG 2000 (.jp2), extension (.jpx) and compound (.jpm)

    • RFC 3950: Tag Image File Format Fax eXtended, TIFF-FX (.tfx)

    • RFC 4047: Flexible Image Transport System (.fits)

    • RFC 7903: Enhanced Metafile (.emf) and Windows Metafile (.wmf)

    Other MIME type additions and changes

    • RFC 2361: Change type for .avi to video/vnd.avi and for .wav to audio/vnd.wave

    • RFC 4337: Add MPEG-4 audio/mp4 (.m4a)

    • RFC 5334: Add Ogg media (.oga, .ogg and .ogx)

    • RFC 6713: Add gzip application/gzip (.gz)

    • RFC 9639: Add FLAC audio/flac (.flac)

    • RFC 9512 application/yaml MIME type for YAML files (.yaml and .yml)

    • Add 7z application/x-7z-compressed (.7z)

    • Add Android Package application/vnd.android.package-archive (.apk) when not strict

    • Add deb application/x-debian-package (.deb)

    • Add glTF binary model/gltf-binary (.glb)

    • Add glTF JSON/ASCII model/gltf+json (.gltf)

    • Add M4V video/x-m4v (.m4v)

    • Add PHP application/x-httpd-php (.php)

    • Add RAR application/vnd.rar (.rar)

    • Add RPM application/x-rpm (.rpm)

    • Add STL model/stl (.stl)

    • Add Windows Media Video video/x-ms-wmv (.wmv)

    • De facto: Add WebM audio/webm (.weba)

    • ECMA-376: Add .docx, .pptx and .xlsx types

    • OASIS: Add OpenDocument .odg, .odp, .ods and .odt types

    • W3C: Add EPUB application/epub+zip (.epub)

    (Contributed by Sahil Prajapati and Hugo van Kemenade in gh-84852, by Sasha “Nelie” Chernykh and Hugo van Kemenade in gh-132056, and by Hugo van Kemenade in gh-89416, gh-85957, and gh-129965.)

multiprocessing

  • On Unix platforms other than macOS, ‘forkserver’ is now the default start method (replacing ‘fork’). This change does not affect Windows or macOS, where ‘spawn’ remains the default start method.

    If the threading incompatible fork method is required, you must explicitly request it via a context from get_context() (preferred) or change the default via set_start_method().

    See forkserver restrictions for information and differences with the fork method and how this change may affect existing code with mutable global shared variables and/or shared objects that can not be automatically pickled.

    (Contributed by Gregory P. Smith in gh-84559.)

  • multiprocessing’s 'forkserver' start method now authenticates its control socket to avoid solely relying on filesystem permissions to restrict what other processes could cause the forkserver to spawn workers and run code. (Contributed by Gregory P. Smith for gh-97514.)

  • The multiprocessing proxy objects for list and dict types gain previously overlooked missing methods:

    • clear() and copy() for proxies of list

    • fromkeys(), reversed(d), d | {}, {} | d, d |= {'b': 2} for proxies of dict

    (Contributed by Roy Hyunjin Han for gh-103134.)

  • Add support for shared set objects via SyncManager.set(). The set() in Manager() method is now available. (Contributed by Mingyu Park in gh-129949.)

  • Add the interrupt() to multiprocessing.Process objects, which terminates the child process by sending SIGINT. This enables finally clauses to print a stack trace for the terminated process. (Contributed by Artem Pulkin in gh-131913.)

operator

  • Add is_none() and is_not_none() as a pair of functions, such that operator.is_none(obj) is equivalent to obj is None and operator.is_not_none(obj) is equivalent to obj is not None. (Contributed by Raymond Hettinger and Nico Mexis in gh-115808.)

os