Python-Dev November 2017

python-dev@python.org

110 participants
73 discussions

congrats on 3.5! Alas, windows 7 users are having problems installing it

by Laura Creighton

webmaster has already heard from 4 people who cannot install it. I sent them to the bug tracker or to python-list but they seem not to have gone either place. Is there some guide I should be sending them to, 'how to debug installation problems'? Laura

11 months

Python startup time

by Victor Stinner

Hi, On Twitter, Raymond Hettinger wrote: "The decision making process on Python-dev is an anti-pattern, governed by anecdotal data and ambiguity over what problem is solved." https://twitter.com/raymondh/status/887069454693158912 About "anecdotal data", I would like to discuss the Python startup time. == Python 3.7 compared to 2.7 == First of all, on speed.python.org, we have: * Python 2.7: 6.4 ms with site, 3.0 ms without site (-S) * master (3.7): 14.5 ms with site, 8.4 ms without site (-S) Python 3.7 startup time is 2.3x slower with site (default mode), or 2.8x slower without site (-S command line option). (I will skip Python 3.4, 3.5 and 3.6 which are much worse than Python 3.7...) So if an user complained about Python 2.7 startup time: be prepared for a 2x - 3x more angry user when "forced" to upgrade to Python 3! == Mercurial vs Git, Python vs C, startup time == Startup time matters a lot for Mercurial since Mercurial is compared to Git. Git and Mercurial have similar features, but Git is written in C whereas Mercurial is written in Python. Quick benchmark on the speed.python.org server: * hg version: 44.6 ms +- 0.2 ms * git --version: 974 us +- 7 us Mercurial startup time is already 45.8x slower than Git whereas tested Mercurial runs on Python 2.7.12. Now try to sell Python 3 to Mercurial developers, with a startup time 2x - 3x slower... I tested Mecurial 3.7.3 and Git 2.7.4 on Ubuntu 16.04.1 using "python3 -m perf command -- ...". == CPython core developers don't care? no, they do care == Christian Heimes, Naoki INADA, Serhiy Storchaka, Yury Selivanov, me (Victor Stinner) and other core developers made multiple changes last years to reduce the number of imports at startup, optimize impotlib, etc. IHMO all these core developers are well aware of the competition of programming languages, and honesty Python startup time isn't "good". So let's compare it to other programming languages similar to Python. == PHP, Ruby, Perl == I measured the startup time of other programming languages which are similar to Python, still on the speed.python.org server using "python3 -m perf command -- ...": * perl -e ' ': 1.18 ms +- 0.01 ms * php -r ' ': 8.57 ms +- 0.05 ms * ruby -e ' ': 32.8 ms +- 0.1 ms Wow, Perl is quite good! PHP seems as good as Python 2 (but Python 3 is worse). Ruby startup time seems less optimized than other languages. Tested versions: * perl 5, version 22, subversion 1 (v5.22.1) * PHP 7.0.18-0ubuntu0.16.04.1 (cli) ( NTS ) * ruby 2.3.1p112 (2016-04-26) [x86_64-linux-gnu] == Quick Google search == I also searched for "python startup time" and "python slow startup time" on Google and found many articles. Some examples: "Reducing the Python startup time" http://www.draketo.de/book/export/html/498 => "The python startup time always nagged me (17-30ms) and I just searched again for a way to reduce it, when I found this: The Python-Launcher caches GTK imports and forks new processes to reduce the startup time of python GUI programs." https://nelsonslog.wordpress.com/2013/04/08/python-startup-time/ => "Wow, Python startup time is worse than I thought." "How to speed up python starting up and/or reduce file search while loading libraries?" https://stackoverflow.com/questions/15474160/how-to-speed-up-python-startin… => "The first time I log to the system and start one command it takes 6 seconds just to show a few line of help. If I immediately issue the same command again it takes 0.1s. After a couple of minutes it gets back to 6s. (proof of short-lived cache)" "How does one optimise the startup of a Python script/program?" https://www.quora.com/How-does-one-optimise-the-startup-of-a-Python-script-… => "I wrote a Python program that would be used very often (imagine 'cd' or 'ls') for very short runtimes, how would I make it start up as fast as possible?" "Python Interpreter Startup time" https://bytes.com/topic/python/answers/34469-pyhton-interpreter-startup-time "Python is very slow to start on Windows 7" https://stackoverflow.com/questions/29997274/python-is-very-slow-to-start-o… => "Python takes 17 times longer to load on my Windows 7 machine than Ubuntu 14.04 running on a VM" => "returns in 0.614s on Windows and 0.036s on Linux" "How to make a fast command line tool in Python" (old article Python 2.5.2) https://files.bemusement.org/talks/OSDC2008-FastPython/ => "(...) some techniques Bazaar uses to start quickly, such as lazy imports." -- So please continue efforts for make Python startup even faster to beat all other programming languages, and finally convince Mercurial to upgrade ;-) Victor

1 year, 10 months

Clarifying Cygwin support in CPython

by Erik Bray

Hi folks, As some people here know I've been working off and on for a while to improve CPython's support of Cygwin. I'm motivated in part by a need to have software working on Python 3.x on Cygwin for the foreseeable future, preferably with minimal graft. (As an incidental side-effect Python's test suite--especially of system-level functionality--serves as an interesting test suite for Cygwin itself too.) This is partly what motivated PEP 539 [1], although that PEP had the advantage of benefiting other POSIX-compatible platforms as well (and in fact was fixing an aspect of CPython that made it unfriendly to supporting other platforms). As far as I can tell, the first commit to Python to add any kind of support for Cygwin was made by Guido (committing a contributed patch) back in 1999 [2]. Since then, bits and pieces have been added for Cygwin's benefit over time, with varying degrees of impact in terms of #ifdefs and the like (for the most part Cygwin does not require *much* in the way of special support, but it does have some differences from a "normal" POSIX-compliant platform, such as the possibility for case-insensitive filesystems and executables that end in .exe). I don't know whether it's ever been "officially supported" but someone with a longer memory of the project can comment on that. I'm not sure if it was discussed at all or not in the context of PEP 11. I have personally put in a fair amount of effort already in either fixing issues on Cygwin (many of these issues also impact MinGW), or more often than not fixing issues in the CPython test suite on Cygwin--these are mostly tests that are broken due to invalid assumptions about the platform (for example, that there is always a "root" user with uid=0; this is not the case on Cygwin). In other cases some tests need to be skipped or worked around due to platform-specific bugs, and Cygwin is hardly the only case of this in the test suite. I also have an experimental AppVeyor configuration for running the tests on Cygwin [3], as well as an experimental buildbot (not available on the internet, but working). These currently rely on a custom branch that includes fixes needed for the test suite to run to completion without crashing or hanging (e.g. https://bugs.python.org/issue31885). It would be nice to add this as an official buildbot, but I'm not sure if it makes sense to do that until it's "green", or at least not crashing. I have several other patches to the tests toward this goal, and am currently down to ~22 tests failing. Before I do any more work on this, however, it would be best to once and for all clarify the support for Cygwin in CPython, as it has never been "officially supported" nor unsupported--this way we can avoid having this discussion every time a patch related to Cygwin comes up. I could provide some arguments for why I believe Cygwin should supported, but before this gets too long I'd just like to float the idea of having the discussion in the first place. It's also not exactly clear to me how to meet the standards in PEP 11 for supporting a platform--in particular it's not clear when a buildbot is considered "stable", or how to achieve that without getting necessary fixes merged into the main branch in the first place. Thanks, Erik [1] https://www.python.org/dev/peps/pep-0539/ [2] https://github.com/python/cpython/commit/717d1fdf2acbef5e6b47d9b4dcf48ef182… [3] https://ci.appveyor.com/project/embray/cpython

2 years

Guarantee ordered dict literals in v3.7?

by Stefan Krah

Hello, would it be possible to guarantee that dict literals are ordered in v3.7? The issue is well-known and the workarounds are tedious, example: https://mail.python.org/pipermail/python-ideas/2015-December/037423.html If the feature is guaranteed now, people can rely on it around v3.9. Stefan Krah

2 years, 7 months

PEP 565: Show DeprecationWarning in __main__

by Nick Coghlan

I've written a short(ish) PEP for the proposal to change the default warnings filters to show DeprecationWarning in __main__: https://www.python.org/dev/peps/pep-0565/ The core proposal itself is just the idea in https://bugs.python.org/issue31975 (i.e. adding "default::DeprecationWarning:__main__" to the default filter set), but the PEP fills in some details on the motivation for the original change to the defaults, and why the current proposal is to add a new filter for __main__, rather than dropping the default DeprecationWarning filter entirely. The PEP also proposes repurposing the existing FutureWarning category to explicitly mean "backwards compatibility warnings that should be shown to users of Python applications" since: - we don't tend to use FutureWarning for its original nominal purpose (changes that will continue to run but will do something different) - FutureWarning was added in 2.3, so it's available in all still supported versions of Python, and is shown by default in all of them - it's at least arguably a less-jargony spelling of DeprecationWarning, and hence more appropriate for displaying to end users that may not have encountered the specific notion of "API deprecation" Cheers, Nick. ============== PEP: 565 Title: Show DeprecationWarning in __main__ Author: Nick Coghlan <ncoghlan(a)gmail.com> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 12-Nov-2017 Python-Version: 3.7 Post-History: 12-Nov-2017 Abstract ======== In Python 2.7 and Python 3.2, the default warning filters were updated to hide DeprecationWarning by default, such that deprecation warnings in development tools that were themselves written in Python (e.g. linters, static analysers, test runners, code generators) wouldn't be visible to their users unless they explicitly opted in to seeing them. However, this change has had the unfortunate side effect of making DeprecationWarning markedly less effective at its primary intended purpose: providing advance notice of breaking changes in APIs (whether in CPython, the standard library, or in third party libraries) to users of those APIs. To improve this situation, this PEP proposes a single adjustment to the default warnings filter: displaying deprecation warnings attributed to the main module by default. This change will mean that code entered at the interactive prompt and code in single file scripts will revert to reporting these warnings by default, while they will continue to be silenced by default for packaged code distributed as part of an importable module. The PEP also proposes a number of small adjustments to the reference interpreter and standard library documentation to help make the warnings subsystem more approachable for new Python developers. Specification ============= The current set of default warnings filters consists of:: ignore::DeprecationWarning ignore::PendingDeprecationWarning ignore::ImportWarning ignore::BytesWarning ignore::ResourceWarning The default ``unittest`` test runner then uses ``warnings.catch_warnings()`` ``warnings.simplefilter('default')`` to override the default filters while running test cases. The change proposed in this PEP is to update the default warning filter list to be:: default::DeprecationWarning:__main__ ignore::DeprecationWarning ignore::PendingDeprecationWarning ignore::ImportWarning ignore::BytesWarning ignore::ResourceWarning This means that in cases where the nominal location of the warning (as determined by the ``stacklevel`` parameter to ``warnings.warn``) is in the ``__main__`` module, the first occurrence of each DeprecationWarning will once again be reported. This change will lead to DeprecationWarning being displayed by default for: * code executed directly at the interactive prompt * code executed directly as part of a single-file script While continuing to be hidden by default for: * code imported from another module in a ``zipapp`` archive's ``__main__.py`` file * code imported from another module in an executable package's ``__main__`` submodule * code imported from an executable script wrapper generated at installation time based on a ``console_scripts`` or ``gui_scripts`` entry point definition As a result, API deprecation warnings encountered by development tools written in Python should continue to be hidden by default for users of those tools While not its originally intended purpose, the standard library documentation will also be updated to explicitly recommend the use of ``FutureWarning`` (rather than ``DeprecationWarning``) for backwards compatibility warnings that are intended to be seen by *users* of an application. This will give the following three distinct categories of backwards compatibility warning, with three different intended audiences: * ``PendingDeprecationWarning``: reported by default only in test runners that override the default set of warning filters. The intended audience is Python developers that take an active interest in ensuring the future compatibility of their software (e.g. professional Python application developers with specific support obligations). * ``DeprecationWarning``: reported by default for code that runs directly in the ``__main__`` module (as such code is considered relatively unlikely to have a dedicated test suite), but relies on test suite based reporting for code in other modules. The intended audience is Python developers that are at risk of upgrades to their dependencies (including upgrades to Python itself) breaking their software (e.g. developers using Python to script environments where someone else is in control of the timing of dependency upgrades). * ``FutureWarning``: always reported by default. The intended audience is users of applications written in Python, rather than other Python developers (e.g. warning about use of a deprecated setting in a configuration file format). Given its presence in the standard library since Python 2.3, ``FutureWarning`` would then also have a secondary use case for libraries and frameworks that support multiple Python versions: as a more reliably visible alternative to ``DeprecationWarning`` in Python 2.7 and versions of Python 3.x prior to 3.7. Motivation ========== As discussed in [1_] and mentioned in [2_], Python 2.7 and Python 3.2 changed the default handling of ``DeprecationWarning`` such that: * the warning was hidden by default during normal code execution * the `unittest`` test runner was updated to re-enable it when running tests The intent was to avoid cases of tooling output like the following:: $ devtool mycode/ /usr/lib/python3.6/site-packages/devtool/cli.py:1: DeprecationWarning: 'async' and 'await' will become reserved keywords in Python 3.7 async = True ... actual tool output ... Even when `devtool` is a tool specifically for Python programmers, this is not a particularly useful warning, as it will be shown on every invocation, even though the main helpful step an end user can take is to report a bug to the developers of ``devtool``. The warning is even less helpful for general purpose developer tools that are used across more languages than just Python. However, this change proved to have unintended consequences for the following audiences: * anyone using a test runner other than the default one built into ``unittest`` (since the request for third party test runners to change their default warnings filters was never made explicitly) * anyone using the default ``unittest`` test runner to test their Python code in a subprocess (since even ``unittest`` only adjusts the warnings settings in the current process) * anyone writing Python code at the interactive prompt or as part of a directly executed script that didn't have a Python level test suite at all In these cases, ``DeprecationWarning`` ended up become almost entirely equivalent to ``PendingDeprecationWarning``: it was simply never seen at all. Limitations on PEP Scope ======================== This PEP exists specifically to explain both the proposed addition to the default warnings filter for 3.7, *and* to more clearly articulate the rationale for the original change to the handling of DeprecationWarning back in Python 2.7 and 3.2. This PEP does not solve all known problems with the current approach to handling deprecation warnings. Most notably: * the default ``unittest`` test runner does not currently report deprecation warnings emitted at module import time, as the warnings filter override is only put in place during test execution, not during test discovery and loading. * the default ``unittest`` test runner does not currently report deprecation warnings in subprocesses, as the warnings filter override is applied directly to the loaded ``warnings`` module, not to the ``PYTHONWARNINGS`` environment variable. * the standard library doesn't provide a straightforward way to opt-in to seeing all warnings emitted *by* a particular dependency prior to upgrading it (the third-party ``warn`` module [3_] does provide this, but enabling it involves monkeypatching the standard library's ``warnings`` module). * re-enabling deprecation warnings by default in __main__ doesn't help in handling cases where software has been factored out into support modules, but those modules still have little or no automated test coverage. Near term, the best currently available answer is to run such applications with ``PYTHONWARNINGS=default::DeprecationWarning`` or ``python -W default::DeprecationWarning`` and pay attention to their ``stderr`` output. Longer term, this is really a question for researchers working on static analysis of Python code: how to reliably find usage of deprecated APIs, and how to infer that an API or parameter is deprecated based on ``warnings.warn`` calls, without actually running either the code providing the API or the code accessing it While these are real problems with the status quo, they're excluded from consideration in this PEP because they're going to require more complex solutions than a single additional entry in the default warnings filter, and resolving them at least potentially won't require going through the PEP process. For anyone interested in pursuing them further, the first two would be ``unittest`` module enhancement requests, the third would be a ``warnings`` module enhancement request, while the last would only require a PEP if inferring API deprecations from their contents was deemed to be an intractable code analysis problem, and an explicit function and parameter marker syntax in annotations was proposed instead. References ========== .. [1] stdlib-sig thread proposing the original default filter change (https://mail.python.org/pipermail/stdlib-sig/2009-November/000789.html) .. [2] Python 2.7 notification of the default warnings filter change (https://docs.python.org/3/whatsnew/2.7.html#changes-to-the-handling-of-depr…) .. [3] Emitting warnings based on the location of the warning itself (https://pypi.org/project/warn/) Copyright ========= This document has been placed in the public domain. -- Nick Coghlan | ncoghlan(a)gmail.com | Brisbane, Australia

2 years, 8 months

What's the status of PEP 505: None-aware operators?

by Lukasz Langa

Hi Mark, it looks like the PEP is dormant for over two years now. I had multiple people ask me over the past few days about it though, so I wanted to ask if this is moving forward. I also cc python-dev to see if anybody here is strongly in favor or against this inclusion. If the idea itself is uncontroversial, I could likely find somebody interested in implementing it. If not for 3.7 then at least for 3.8. - Ł

2 years, 8 months

iso8601 parsing

by Mike Miller

Hi, Could anyone put this five year-old bug about parsing iso8601 format date-times on the front burner? http://bugs.python.org/issue15873 In the comments there's a lot of hand-wringing about different variations that bogged it down, but right now I only need it to handle the output of datetime.isoformat(): >>> dt.isoformat() '2017-10-20T08:20:08.986166+00:00' Perhaps if we could get that minimum first step in, it could be iterated on and made more lenient in the future. Thank you, -Mike

2 years, 8 months

PEP 563: Postponed Evaluation of Annotations (Draft 3)

by Lukasz Langa

Based on the feedback I gather in early November, I'm publishing the third draft for consideration on python-dev. I hope you like it! A nicely formatted rendering is available here: https://www.python.org/dev/peps/pep-0563/ The full list of changes between this version and the previous draft can be found here: https://github.com/ambv/static-annotations/compare/python-dev1...python-dev2 - Ł PEP: 563 Title: Postponed Evaluation of Annotations Version: $Revision$ Last-Modified: $Date$ Author: Łukasz Langa <lukasz(a)langa.pl> Discussions-To: Python-Dev <python-dev(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 8-Sep-2017 Python-Version: 3.7 Post-History: 1-Nov-2017, 21-Nov-2017 Resolution: Abstract ======== PEP 3107 introduced syntax for function annotations, but the semantics were deliberately left undefined. PEP 484 introduced a standard meaning to annotations: type hints. PEP 526 defined variable annotations, explicitly tying them with the type hinting use case. This PEP proposes changing function annotations and variable annotations so that they are no longer evaluated at function definition time. Instead, they are preserved in ``__annotations__`` in string form. This change is going to be introduced gradually, starting with a new ``__future__`` import in Python 3.7. Rationale and Goals =================== PEP 3107 added support for arbitrary annotations on parts of a function definition. Just like default values, annotations are evaluated at function definition time. This creates a number of issues for the type hinting use case: * forward references: when a type hint contains names that have not been defined yet, that definition needs to be expressed as a string literal; * type hints are executed at module import time, which is not computationally free. Postponing the evaluation of annotations solves both problems. Non-goals --------- Just like in PEP 484 and PEP 526, it should be emphasized that **Python will remain a dynamically typed language, and the authors have no desire to ever make type hints mandatory, even by convention.** This PEP is meant to solve the problem of forward references in type annotations. There are still cases outside of annotations where forward references will require usage of string literals. Those are listed in a later section of this document. Annotations without forced evaluation enable opportunities to improve the syntax of type hints. This idea will require its own separate PEP and is not discussed further in this document. Non-typing usage of annotations ------------------------------- While annotations are still available for arbitrary use besides type checking, it is worth mentioning that the design of this PEP, as well as its precursors (PEP 484 and PEP 526), is predominantly motivated by the type hinting use case. In Python 3.8 PEP 484 will graduate from provisional status. Other enhancements to the Python programming language like PEP 544, PEP 557, or PEP 560, are already being built on this basis as they depend on type annotations and the ``typing`` module as defined by PEP 484. In fact, the reason PEP 484 is staying provisional in Python 3.7 is to enable rapid evolution for another release cycle that some of the aforementioned enhancements require. With this in mind, uses for annotations incompatible with the aforementioned PEPs should be considered deprecated. Implementation ============== In Python 4.0, function and variable annotations will no longer be evaluated at definition time. Instead, a string form will be preserved in the respective ``__annotations__`` dictionary. Static type checkers will see no difference in behavior, whereas tools using annotations at runtime will have to perform postponed evaluation. The string form is obtained from the AST during the compilation step, which means that the string form might not preserve the exact formatting of the source. Note: if an annotation was a string literal already, it will still be wrapped in a string. Annotations need to be syntactically valid Python expressions, also when passed as literal strings (i.e. ``compile(literal, '', 'eval')``). Annotations can only use names present in the module scope as postponed evaluation using local names is not reliable (with the sole exception of class-level names resolved by ``typing.get_type_hints()``). Note that as per PEP 526, local variable annotations are not evaluated at all since they are not accessible outside of the function's closure. Enabling the future behavior in Python 3.7 ------------------------------------------ The functionality described above can be enabled starting from Python 3.7 using the following special import:: from __future__ import annotations A reference implementation of this functionality is available `on GitHub <https://github.com/ambv/cpython/tree/string_annotations>`_. Resolving Type Hints at Runtime =============================== To resolve an annotation at runtime from its string form to the result of the enclosed expression, user code needs to evaluate the string. For code that uses type hints, the ``typing.get_type_hints(obj, globalns=None, localns=None)`` function correctly evaluates expressions back from its string form. Note that all valid code currently using ``__annotations__`` should already be doing that since a type annotation can be expressed as a string literal. For code which uses annotations for other purposes, a regular ``eval(ann, globals, locals)`` call is enough to resolve the annotation. In both cases it's important to consider how globals and locals affect the postponed evaluation. An annotation is no longer evaluated at the time of definition and, more importantly, *in the same scope* where it was defined. Consequently, using local state in annotations is no longer possible in general. As for globals, the module where the annotation was defined is the correct context for postponed evaluation. The ``get_type_hints()`` function automatically resolves the correct value of ``globalns`` for functions and classes. It also automatically provides the correct ``localns`` for classes. When running ``eval()``, the value of globals can be gathered in the following way: * function objects hold a reference to their respective globals in an attribute called ``__globals__``; * classes hold the name of the module they were defined in, this can be used to retrieve the respective globals:: cls_globals = vars(sys.modules[SomeClass.__module__]) Note that this needs to be repeated for base classes to evaluate all ``__annotations__``. * modules should use their own ``__dict__``. The value of ``localns`` cannot be reliably retrieved for functions because in all likelihood the stack frame at the time of the call no longer exists. For classes, ``localns`` can be composed by chaining vars of the given class and its base classes (in the method resolution order). Since slots can only be filled after the class was defined, we don't need to consult them for this purpose. Runtime annotation resolution and class decorators -------------------------------------------------- Metaclasses and class decorators that need to resolve annotations for the current class will fail for annotations that use the name of the current class. Example:: def class_decorator(cls): annotations = get_type_hints(cls) # raises NameError on 'C' print(f'Annotations for {cls}: {annotations}') return cls @class_decorator class C: singleton: 'C' = None This was already true before this PEP. The class decorator acts on the class before it's assigned a name in the current definition scope. Runtime annotation resolution and ``TYPE_CHECKING`` --------------------------------------------------- Sometimes there's code that must be seen by a type checker but should not be executed. For such situations the ``typing`` module defines a constant, ``TYPE_CHECKING``, that is considered ``True`` during type checking but ``False`` at runtime. Example:: import typing if typing.TYPE_CHECKING: import expensive_mod def a_func(arg: expensive_mod.SomeClass) -> None: a_var: expensive_mod.SomeClass = arg ... This approach is also useful when handling import cycles. Trying to resolve annotations of ``a_func`` at runtime using ``typing.get_type_hints()`` will fail since the name ``expensive_mod`` is not defined (``TYPE_CHECKING`` variable being ``False`` at runtime). This was already true before this PEP. Backwards Compatibility ======================= This is a backwards incompatible change. Applications depending on arbitrary objects to be directly present in annotations will break if they are not using ``typing.get_type_hints()`` or ``eval()``. Annotations that depend on locals at the time of the function definition will not be resolvable later. Example:: def generate(): A = Optional[int] class C: field: A = 1 def method(self, arg: A) -> None: ... return C X = generate() Trying to resolve annotations of ``X`` later by using ``get_type_hints(X)`` will fail because ``A`` and its enclosing scope no longer exists. Python will make no attempt to disallow such annotations since they can often still be successfully statically analyzed, which is the predominant use case for annotations. Annotations using nested classes and their respective state are still valid. They can use local names or the fully qualified name. Example:: class C: field = 'c_field' def method(self) -> C.field: # this is OK ... def method(self) -> field: # this is OK ... def method(self) -> C.D: # this is OK ... def method(self) -> D: # this is OK ... class D: field2 = 'd_field' def method(self) -> C.D.field2: # this is OK ... def method(self) -> D.field2: # this is OK ... def method(self) -> field2: # this is OK ... def method(self) -> field: # this FAILS, class D doesn't ... # see C's attributes, This was # already true before this PEP. In the presence of an annotation that isn't a syntactically valid expression, SyntaxError is raised at compile time. However, since names aren't resolved at that time, no attempt is made to validate whether used names are correct or not. Deprecation policy ------------------ Starting with Python 3.7, a ``__future__`` import is required to use the described functionality. No warnings are raised. In Python 3.8 a ``PendingDeprecationWarning`` is raised by the compiler in the presence of type annotations in modules without the ``__future__`` import. Starting with Python 3.9 the warning becomes a ``DeprecationWarning``. In Python 4.0 this will become the default behavior. Use of annotations incompatible with this PEP is no longer supported. Forward References ================== Deliberately using a name before it was defined in the module is called a forward reference. For the purpose of this section, we'll call any name imported or defined within a ``if TYPE_CHECKING:`` block a forward reference, too. This PEP addresses the issue of forward references in *type annotations*. The use of string literals will no longer be required in this case. However, there are APIs in the ``typing`` module that use other syntactic constructs of the language, and those will still require working around forward references with string literals. The list includes: * type definitions:: T = TypeVar('T', bound='<type>') UserId = NewType('UserId', '<type>') Employee = NamedTuple('Employee', [('name', '<type>', ('id', '<type>')]) * aliases:: Alias = Optional['<type>'] AnotherAlias = Union['<type>', '<type>'] YetAnotherAlias = '<type>' * casting:: cast('<type>', value) * base classes:: class C(Tuple['<type>', '<type>']): ... Depending on the specific case, some of the cases listed above might be worked around by placing the usage in a ``if TYPE_CHECKING:`` block. This will not work for any code that needs to be available at runtime, notably for base classes and casting. For named tuples, using the new class definition syntax introduced in Python 3.6 solves the issue. In general, fixing the issue for *all* forward references requires changing how module instantiation is performed in Python, from the current single-pass top-down model. This would be a major change in the language and is out of scope for this PEP. Rejected Ideas ============== Keeping the ability to use function local state when defining annotations ------------------------------------------------------------------------- With postponed evaluation, this would require keeping a reference to the frame in which an annotation got created. This could be achieved for example by storing all annotations as lambdas instead of strings. This would be prohibitively expensive for highly annotated code as the frames would keep all their objects alive. That includes predominantly objects that won't ever be accessed again. To be able to address class-level scope, the lambda approach would require a new kind of cell in the interpreter. This would proliferate the number of types that can appear in ``__annotations__``, as well as wouldn't be as introspectable as strings. Note that in the case of nested classes, the functionality to get the effective "globals" and "locals" at definition time is provided by ``typing.get_type_hints()``. If a function generates a class or a function with annotations that have to use local variables, it can populate the given generated object's ``__annotations__`` dictionary directly, without relying on the compiler. Disallowing local state usage for classes, too ---------------------------------------------- This PEP originally proposed limiting names within annotations to only allow names from the model-level scope, including for classes. The author argued this makes name resolution unambiguous, including in cases of conflicts between local names and module-level names. This idea was ultimately rejected in case of classes. Instead, ``typing.get_type_hints()`` got modified to populate the local namespace correctly if class-level annotations are needed. The reasons for rejecting the idea were that it goes against the intuition of how scoping works in Python, and would break enough existing type annotations to make the transition cumbersome. Finally, local scope access is required for class decorators to be able to evaluate type annotations. This is because class decorators are applied before the class receives its name in the outer scope. Introducing a new dictionary for the string literal form instead ---------------------------------------------------------------- Yury Selivanov shared the following idea: 1. Add a new special attribute to functions: ``__annotations_text__``. 2. Make ``__annotations__`` a lazy dynamic mapping, evaluating expressions from the corresponding key in ``__annotations_text__`` just-in-time. This idea is supposed to solve the backwards compatibility issue, removing the need for a new ``__future__`` import. Sadly, this is not enough. Postponed evaluation changes which state the annotation has access to. While postponed evaluation fixes the forward reference problem, it also makes it impossible to access function-level locals anymore. This alone is a source of backwards incompatibility which justifies a deprecation period. A ``__future__`` import is an obvious and explicit indicator of opting in for the new functionality. It also makes it trivial for external tools to recognize the difference between a Python files using the old or the new approach. In the former case, that tool would recognize that local state access is allowed, whereas in the latter case it would recognize that forward references are allowed. Finally, just-in-time evaluation in ``__annotations__`` is an unnecessary step if ``get_type_hints()`` is used later. Dropping annotations with -O ---------------------------- There are two reasons this is not satisfying for the purpose of this PEP. First, this only addresses runtime cost, not forward references, those still cannot be safely used in source code. A library maintainer would never be able to use forward references since that would force the library users to use this new hypothetical -O switch. Second, this throws the baby out with the bath water. Now *no* runtime annotation use can be performed. PEP 557 is one example of a recent development where evaluating type annotations at runtime is useful. All that being said, a granular -O option to drop annotations is a possibility in the future, as it's conceptually compatible with existing -O behavior (dropping docstrings and assert statements). This PEP does not invalidate the idea. Pass string literals in annotations verbatim to ``__annotations__`` ------------------------------------------------------------------- This PEP originally suggested directly storing the contents of a string literal under its respective key in ``__annotations__``. This was meant to simplify support for runtime type checkers. Mark Shannon pointed out this idea was flawed since it wasn't handling situations where strings are only part of a type annotation. The inconsistency of it was always apparent but given that it doesn't fully prevent cases of double-wrapping strings anyway, it is not worth it. Make the name of the future import more verbose ----------------------------------------------- Instead of requiring the following import:: from __future__ import annotations the PEP could call the feature more explicitly, for example ``string_annotations``, ``stringify_annotations``, ``annotation_strings``, ``annotations_as_strings``, ``lazy_anotations``, ``static_annotations``, etc. The problem with those names is that they are very verbose. Each of them besides ``lazy_annotations`` would constitute the longest future feature name in Python. They are long to type and harder to remember than the single-word form. There is precedence of a future import name that sounds overly generic but in practice was obvious to users as to what it does:: from __future__ import division Prior discussion ================ In PEP 484 ---------- The forward reference problem was discussed when PEP 484 was originally drafted, leading to the following statement in the document: A compromise is possible where a ``__future__`` import could enable turning *all* annotations in a given module into string literals, as follows:: from __future__ import annotations class ImSet: def add(self, a: ImSet) -> List[ImSet]: ... assert ImSet.add.__annotations__ == { 'a': 'ImSet', 'return': 'List[ImSet]' } Such a ``__future__`` import statement may be proposed in a separate PEP. python/typing#400 ----------------- The problem was discussed at length on the typing module's GitHub project, under `Issue 400 <https://github.com/python/typing/issues/400>`_. The problem statement there includes critique of generic types requiring imports from ``typing``. This tends to be confusing to beginners: Why this:: from typing import List, Set def dir(o: object = ...) -> List[str]: ... def add_friends(friends: Set[Friend]) -> None: ... But not this:: def dir(o: object = ...) -> list[str]: ... def add_friends(friends: set[Friend]) -> None ... Why this:: up_to_ten = list(range(10)) friends = set() But not this:: from typing import List, Set up_to_ten = List[int](range(10)) friends = Set[Friend]() While typing usability is an interesting problem, it is out of scope of this PEP. Specifically, any extensions of the typing syntax standardized in PEP 484 will require their own respective PEPs and approval. Issue 400 ultimately suggests postponing evaluation of annotations and keeping them as strings in ``__annotations__``, just like this PEP specifies. This idea was received well. Ivan Levkivskyi supported using the ``__future__`` import and suggested unparsing the AST in ``compile.c``. Jukka Lehtosalo pointed out that there are some cases of forward references where types are used outside of annotations and postponed evaluation will not help those. For those cases using the string literal notation would still be required. Those cases are discussed briefly in the "Forward References" section of this PEP. The biggest controversy on the issue was Guido van Rossum's concern that untokenizing annotation expressions back to their string form has no precedent in the Python programming language and feels like a hacky workaround. He said: One thing that comes to mind is that it's a very random change to the language. It might be useful to have a more compact way to indicate deferred execution of expressions (using less syntax than ``lambda:``). But why would the use case of type annotations be so all-important to change the language to do it there first (rather than proposing a more general solution), given that there's already a solution for this particular use case that requires very minimal syntax? Eventually, Ethan Smith and schollii voiced that feedback gathered during PyCon US suggests that the state of forward references needs fixing. Guido van Rossum suggested coming back to the ``__future__`` idea, pointing out that to prevent abuse, it's important for the annotations to be kept both syntactically valid and evaluating correctly at runtime. First draft discussion on python-ideas -------------------------------------- Discussion happened largely in two threads, `the original announcement <https://mail.python.org/pipermail/python-ideas/2017-September/thread.html#4…>`_ and a follow-up called `PEP 563 and expensive backwards compatibility <https://mail.python.org/pipermail/python-ideas/2017-September/thread.html#4…>`_. The PEP received rather warm feedback (4 strongly in favor, 2 in favor with concerns, 2 against). The biggest voice of concern on the former thread being Steven D'Aprano's review stating that the problem definition of the PEP doesn't justify breaking backwards compatibility. In this response Steven seemed mostly concerned about Python no longer supporting evaluation of annotations that depended on local function/class state. A few people voiced concerns that there are libraries using annotations for non-typing purposes. However, none of the named libraries would be invalidated by this PEP. They do require adapting to the new requirement to call ``eval()`` on the annotation with the correct ``globals`` and ``locals`` set. This detail about ``globals`` and ``locals`` having to be correct was picked up by a number of commenters. Nick Coghlan benchmarked turning annotations into lambdas instead of strings, sadly this proved to be much slower at runtime than the current situation. The latter thread was started by Jim J. Jewett who stressed that the ability to properly evaluate annotations is an important requirement and backwards compatibility in that regard is valuable. After some discussion he admitted that side effects in annotations are a code smell and modal support to either perform or not perform evaluation is a messy solution. His biggest concern remained loss of functionality stemming from the evaluation restrictions on global and local scope. Nick Coghlan pointed out that some of those evaluation restrictions from the PEP could be lifted by a clever implementation of an evaluation helper, which could solve self-referencing classes even in the form of a class decorator. He suggested the PEP should provide this helper function in the standard library. Second draft discussion on python-dev ------------------------------------- Discussion happened mainly in the `announcement thread <https://mail.python.org/pipermail/python-dev/2017-November/150062.html>`_, followed by a brief discussion under `Mark Shannon's post <https://mail.python.org/pipermail/python-dev/2017-November/150637.html>`_. Steven D'Aprano was concerned whether it's acceptable for typos to be allowed in annotations after the change proposed by the PEP. Brett Cannon responded that type checkers and other static analyzers (like linters or programming text editors) will catch this type of error. Jukka Lehtosalo added that this situation is analogous to how names in function bodies are not resolved until the function is called. A major topic of discussion was Nick Coghlan's suggestion to store annotations in "thunk form", in other words as a specialized lambda which would be able to access class-level scope (and allow for scope customization at call time). He presented a possible design for it (`indirect attribute cells <https://mail.python.org/pipermail/python-dev/2017-November/150141.html>`_). This was later seen as equivalent to "special forms" in Lisp. Guido van Rossum expressed worry that this sort of feature cannot be safely implemented in twelve weeks (i.e. in time before the Python 3.7 beta freeze). After a while it became clear that the point of division between supporters of the string form vs. supporters of the thunk form is actually about whether annotations should be perceived as a general syntactic element vs. something tied to the type checking use case. Finally, Guido van Rossum declared he's rejecting the thunk idea based on the fact that it would require a new building block in the interpreter. This block would be exposed in annotations, multiplying possible types of values stored in ``__annotations__`` (arbitrary objects, strings, and now thunks). Moreover, thunks aren't as introspectable as strings. Most importantly, Guido van Rossum explicitly stated interest in gradually restricting the use of annotations to static typing (with an optional runtime component). Nick Coghlan got convinced to PEP 563, too, promptly beginning the mandatory bike shedding session on the name of the ``__future__`` import. Many debaters agreed that ``annotations`` seems like an overly broad name for the feature name. Guido van Rossum briefly decided to call it ``string_annotations`` but then changed his mind, arguing that ``division`` is a precedent of a broad name with a clear meaning. The final improvement to the PEP suggested in the discussion by Mark Shannon was the rejection of the temptation to pass string literals through to ``__annotations__`` verbatim. A side-thread of discussion started around the runtime penalty of static typing, with topic like the import time of the ``typing`` module (which is comparable to ``re`` without dependencies, and three times as heavy as ``re`` when counting dependencies). Acknowledgements ================ This document could not be completed without valuable input, encouragement and advice from Guido van Rossum, Jukka Lehtosalo, and Ivan Levkivskyi. Copyright ========= This document has been placed in the public domain. .. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 coding: utf-8 End:

2 years, 8 months

PEPs: ``.. code:: python`` or ``::`` (syntax highlighting)

by Wes Turner

In ReStructuredText, this gets syntax highlighted because of the code directive [1][2][3]: .. code:: python import this def func(*args, **kwargs): pass This also gets syntax highlighted as python[3]: .. code:: python import this def func(*args, **kwargs): pass This does not:: import this def func(*args, **kwargs): pass Syntax highlighting in Docutils 0.9+ is powered by Pygments. If Pygments is not installed, or there is a syntax error, syntax highlighting is absent. GitHub does show Pygments syntax highlighting in .. code:: blocks for .rst and .restructuredtext documents [4] 1. Does the python.org PEP view support .. code:: blocks? [5] 2. Syntax highlighting is an advantage for writers, editors, and readers. 3. Should PEPs use .. code:: blocks to provide this advantage? [1] http://docutils.sourceforge.net/docs/ref/rst/directives.html#code [2] http://www.sphinx-doc.org/en/stable/markup/code.html [3] http://www.sphinx-doc.org/en/stable/config.html#confval-highlight_language [4] https://github.com/python/peps/blob/master/pep-0557.rst [5] https://www.python.org/dev/peps/pep-0557/ https://www.python.org/dev/peps/pep-0458/

2 years, 8 months

Third and hopefully final post: PEP 557, Data Classes

by Eric V. Smith

I've posted a new version of PEP 557, it should soon be available at https://www.python.org/dev/peps/pep-0557/. The only significant changes since the last version are: - changing the "compare" parameter to be "order", since that more accurately reflects what it does. - Having the combination of "eq=False" and "order=True" raise an exception instead of silently changing eq to True. There were no other issues raised with the previous version of the PEP. So with that, I think it's ready for a pronouncement. Eric.

2 years, 8 months

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

Python-Dev November 2017