Mailman 3 February 2020 - Python-Dev

Mixed Python/C debugging
by Skip Montanaro April 26, 2023

April 26, 2023

Having tried comp.lang.python with no response, I turn here... After at least ten years away from Python's run-time interpreter & byte code compiler, I'm getting set to familiarize myself with that again. This will, I think, entail debugging a mixed Python/C environment. I'm an Emacs user and am aware that GDB since 7.0 has support for debugging at the Python code level. Is Emacs+GDB my best bet? Are there any Python IDEs which support C-level breakpoints and debugging? Thanks, Skip

5 6

PEP 448 review
by Guido van Rossum March 29, 2023

March 29, 2023

I'm back, I've re-read the PEP, and I've re-read the long thread with "(no subject)". I think Georg Brandl nailed it: """ *I like the "sequence and dict flattening" part of the PEP, mostly because itis consistent and should be easy to understand, but the comprehension syntaxenhancements seem to be bad for readability and "comprehending" what the codedoes.The call syntax part is a mixed bag on the one hand it is nice to be consistent with the extended possibilities in literals (flattening), but on the other hand there would be small but annoying inconsistencies anyways (e.g. the duplicate kwarg case above).* """ Greg Ewing followed up explaining that the inconsistency between dict flattening and call syntax is inherent in the pre-existing different rules for dicts vs. keyword args: {'a':1, 'a':2} results in {'a':2}, while f(a=1, a=2) is an error. (This form is a SyntaxError; the dynamic case f(a=1, **{'a': 1}) is a TypeError.) For me, allowing f(*a, *b) and f(**d, **e) and all the other combinations for function calls proposed by the PEP is an easy +1 -- it's a straightforward extension of the existing pattern, and anybody who knows what f(x, *a) does will understand f(x, *a, y, *b). Guessing what f(**d, **e) means shouldn't be hard either. Understanding the edge case for duplicate keys with f(**d, **e) is a little harder, but the error messages are pretty clear, and it is not a new edge case. The sequence and dict flattening syntax proposals are also clean and logical -- we already have *-unpacking on the receiving side, so allowing *x in tuple expressions reads pretty naturally (and the similarity with *a in argument lists certainly helps). From here, having [a, *x, b, *y] is also natural, and then the extension to other displays is natural: {a, *x, b, *y} and {a:1, **d, b:2, **e}. This, too, gets a +1 from me. So that leaves comprehensions. IIRC, during the development of the patch we realized that f(*x for x in xs) is sufficiently ambiguous that we decided to disallow it -- note that f(x for x in xs) is already somewhat of a special case because an argument can only be a "bare" generator expression if it is the only argument. The same reasoning doesn't apply (in that form) to list, set and dict comprehensions -- while f(x for x in xs) is identical in meaning to f((x for x in xs)), [x for x in xs] is NOT the same as [(x for x in xs)] (that's a list of one element, and the element is a generator expression). The basic premise of this part of the proposal is that if you have a few iterables, the new proposal (without comprehensions) lets you create a list or generator expression that iterates over all of them, essentially flattening them: >>> xs = [1, 2, 3] >>> ys = ['abc', 'def'] >>> zs = [99] >>> [*xs, *ys, *zs] [1, 2, 3, 'abc', 'def', 99] >>> But now suppose you have a list of iterables: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xss[0], *xss[1], *xss[2]] [1, 2, 3, 'abc', 'def', 99] >>> Wouldn't it be nice if you could write the latter using a comprehension? >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs for xs in xss] [1, 2, 3, 'abc', 'def', 99] >>> This is somewhat seductive, and the following is even nicer: the *xs position may be an expression, e.g.: >>> xss = [[1, 2, 3], ['abc', 'def'], [99]] >>> [*xs[:2] for xs in xss] [1, 2, 'abc', 'def', 99] >>> On the other hand, I had to explore the possibilities here by experimenting in the interpreter, and I discovered some odd edge cases (e.g. you can parenthesize the starred expression, but that seems a syntactic accident). All in all I am personally +0 on the comprehension part of the PEP, and I like that it provides a way to "flatten" a sequence of sequences, but I think very few people in the thread have supported this part. Therefore I would like to ask Neil to update the PEP and the patch to take out the comprehension part, so that the two "easy wins" can make it into Python 3.5 (basically, I am accepting two-thirds of the PEP :-). There is some time yet until alpha 2. I would also like code reviewers (Benjamin?) to start reviewing the patch <http://bugs.python.org/issue2292>, taking into account that the comprehension part needs to be removed. -- --Guido van Rossum (python.org/~guido)

7 17

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com May 18, 2021

May 18, 2021

It has been a while since I posted a copy of PEP 1 to the mailing lists and newsgroups. I've recently done some updating of a few sections, so in the interest of gaining wider community participation in the Python development process, I'm posting the latest revision of PEP 1 here. A version of the PEP is always available on-line at http://www.python.org/peps/pep-0001.html Enjoy, -Barry -------------------- snip snip -------------------- PEP: 1 Title: PEP Purpose and Guidelines Version: $Revision: 1.36 $ Last-Modified: $Date: 2002/07/29 18:34:59 $ Author: Barry A. Warsaw, Jeremy Hylton Status: Active Type: Informational Created: 13-Jun-2000 Post-History: 21-Mar-2001, 29-Jul-2002 What is a PEP? PEP stands for Python Enhancement Proposal. A PEP is a design document providing information to the Python community, or describing a new feature for Python. The PEP should provide a concise technical specification of the feature and a rationale for the feature. We intend PEPs to be the primary mechanisms for proposing new features, for collecting community input on an issue, and for documenting the design decisions that have gone into Python. The PEP author is responsible for building consensus within the community and documenting dissenting opinions. Because the PEPs are maintained as plain text files under CVS control, their revision history is the historical record of the feature proposal[1]. Kinds of PEPs There are two kinds of PEPs. A standards track PEP describes a new feature or implementation for Python. An informational PEP describes a Python design issue, or provides general guidelines or information to the Python community, but does not propose a new feature. Informational PEPs do not necessarily represent a Python community consensus or recommendation, so users and implementors are free to ignore informational PEPs or follow their advice. PEP Work Flow The PEP editor, Barry Warsaw <peps(a)python.org>, assigns numbers for each PEP and changes its status. The PEP process begins with a new idea for Python. It is highly recommended that a single PEP contain a single key proposal or new idea. The more focussed the PEP, the more successfully it tends to be. The PEP editor reserves the right to reject PEP proposals if they appear too unfocussed or too broad. If in doubt, split your PEP into several well-focussed ones. Each PEP must have a champion -- someone who writes the PEP using the style and format described below, shepherds the discussions in the appropriate forums, and attempts to build community consensus around the idea. The PEP champion (a.k.a. Author) should first attempt to ascertain whether the idea is PEP-able. Small enhancements or patches often don't need a PEP and can be injected into the Python development work flow with a patch submission to the SourceForge patch manager[2] or feature request tracker[3]. The PEP champion then emails the PEP editor <peps(a)python.org> with a proposed title and a rough, but fleshed out, draft of the PEP. This draft must be written in PEP style as described below. If the PEP editor approves, he will assign the PEP a number, label it as standards track or informational, give it status 'draft', and create and check-in the initial draft of the PEP. The PEP editor will not unreasonably deny a PEP. Reasons for denying PEP status include duplication of effort, being technically unsound, not providing proper motivation or addressing backwards compatibility, or not in keeping with the Python philosophy. The BDFL (Benevolent Dictator for Life, Guido van Rossum) can be consulted during the approval phase, and is the final arbitrator of the draft's PEP-ability. If a pre-PEP is rejected, the author may elect to take the pre-PEP to the comp.lang.python newsgroup (a.k.a. python-list(a)python.org mailing list) to help flesh it out, gain feedback and consensus from the community at large, and improve the PEP for re-submission. The author of the PEP is then responsible for posting the PEP to the community forums, and marshaling community support for it. As updates are necessary, the PEP author can check in new versions if they have CVS commit permissions, or can email new PEP versions to the PEP editor for committing. Standards track PEPs consists of two parts, a design document and a reference implementation. The PEP should be reviewed and accepted before a reference implementation is begun, unless a reference implementation will aid people in studying the PEP. Standards Track PEPs must include an implementation - in the form of code, patch, or URL to same - before it can be considered Final. PEP authors are responsible for collecting community feedback on a PEP before submitting it for review. A PEP that has not been discussed on python-list(a)python.org and/or python-dev(a)python.org will not be accepted. However, wherever possible, long open-ended discussions on public mailing lists should be avoided. Strategies to keep the discussions efficient include, setting up a separate SIG mailing list for the topic, having the PEP author accept private comments in the early design phases, etc. PEP authors should use their discretion here. Once the authors have completed a PEP, they must inform the PEP editor that it is ready for review. PEPs are reviewed by the BDFL and his chosen consultants, who may accept or reject a PEP or send it back to the author(s) for revision. Once a PEP has been accepted, the reference implementation must be completed. When the reference implementation is complete and accepted by the BDFL, the status will be changed to `Final.' A PEP can also be assigned status `Deferred.' The PEP author or editor can assign the PEP this status when no progress is being made on the PEP. Once a PEP is deferred, the PEP editor can re-assign it to draft status. A PEP can also be `Rejected'. Perhaps after all is said and done it was not a good idea. It is still important to have a record of this fact. PEPs can also be replaced by a different PEP, rendering the original obsolete. This is intended for Informational PEPs, where version 2 of an API can replace version 1. PEP work flow is as follows: Draft -> Accepted -> Final -> Replaced ^ +----> Rejected v Deferred Some informational PEPs may also have a status of `Active' if they are never meant to be completed. E.g. PEP 1. What belongs in a successful PEP? Each PEP should have the following parts: 1. Preamble -- RFC822 style headers containing meta-data about the PEP, including the PEP number, a short descriptive title (limited to a maximum of 44 characters), the names, and optionally the contact info for each author, etc. 2. Abstract -- a short (~200 word) description of the technical issue being addressed. 3. Copyright/public domain -- Each PEP must either be explicitly labelled as placed in the public domain (see this PEP as an example) or licensed under the Open Publication License[4]. 4. Specification -- The technical specification should describe the syntax and semantics of any new language feature. The specification should be detailed enough to allow competing, interoperable implementations for any of the current Python platforms (CPython, JPython, Python .NET). 5. Motivation -- The motivation is critical for PEPs that want to change the Python language. It should clearly explain why the existing language specification is inadequate to address the problem that the PEP solves. PEP submissions without sufficient motivation may be rejected outright. 6. Rationale -- The rationale fleshes out the specification by describing what motivated the design and why particular design decisions were made. It should describe alternate designs that were considered and related work, e.g. how the feature is supported in other languages. The rationale should provide evidence of consensus within the community and discuss important objections or concerns raised during discussion. 7. Backwards Compatibility -- All PEPs that introduce backwards incompatibilities must include a section describing these incompatibilities and their severity. The PEP must explain how the author proposes to deal with these incompatibilities. PEP submissions without a sufficient backwards compatibility treatise may be rejected outright. 8. Reference Implementation -- The reference implementation must be completed before any PEP is given status 'Final,' but it need not be completed before the PEP is accepted. It is better to finish the specification and rationale first and reach consensus on it before writing code. The final implementation must include test code and documentation appropriate for either the Python language reference or the standard library reference. PEP Template PEPs are written in plain ASCII text, and should adhere to a rigid style. There is a Python script that parses this style and converts the plain text PEP to HTML for viewing on the web[5]. PEP 9 contains a boilerplate[7] template you can use to get started writing your PEP. Each PEP must begin with an RFC822 style header preamble. The headers must appear in the following order. Headers marked with `*' are optional and are described below. All other headers are required. PEP: <pep number> Title: <pep title> Version: <cvs version string> Last-Modified: <cvs date string> Author: <list of authors' real names and optionally, email addrs> * Discussions-To: <email address> Status: <Draft | Active | Accepted | Deferred | Final | Replaced> Type: <Informational | Standards Track> * Requires: <pep numbers> Created: <date created on, in dd-mmm-yyyy format> * Python-Version: <version number> Post-History: <dates of postings to python-list and python-dev> * Replaces: <pep number> * Replaced-By: <pep number> The Author: header lists the names and optionally, the email addresses of all the authors/owners of the PEP. The format of the author entry should be address(a)dom.ain (Random J. User) if the email address is included, and just Random J. User if the address is not given. If there are multiple authors, each should be on a separate line following RFC 822 continuation line conventions. Note that personal email addresses in PEPs will be obscured as a defense against spam harvesters. Standards track PEPs must have a Python-Version: header which indicates the version of Python that the feature will be released with. Informational PEPs do not need a Python-Version: header. While a PEP is in private discussions (usually during the initial Draft phase), a Discussions-To: header will indicate the mailing list or URL where the PEP is being discussed. No Discussions-To: header is necessary if the PEP is being discussed privately with the author, or on the python-list or python-dev email mailing lists. Note that email addresses in the Discussions-To: header will not be obscured. Created: records the date that the PEP was assigned a number, while Post-History: is used to record the dates of when new versions of the PEP are posted to python-list and/or python-dev. Both headers should be in dd-mmm-yyyy format, e.g. 14-Aug-2001. PEPs may have a Requires: header, indicating the PEP numbers that this PEP depends on. PEPs may also have a Replaced-By: header indicating that a PEP has been rendered obsolete by a later document; the value is the number of the PEP that replaces the current document. The newer PEP must have a Replaces: header containing the number of the PEP that it rendered obsolete. PEP Formatting Requirements PEP headings must begin in column zero and the initial letter of each word must be capitalized as in book titles. Acronyms should be in all capitals. The body of each section must be indented 4 spaces. Code samples inside body sections should be indented a further 4 spaces, and other indentation can be used as required to make the text readable. You must use two blank lines between the last line of a section's body and the next section heading. You must adhere to the Emacs convention of adding two spaces at the end of every sentence. You should fill your paragraphs to column 70, but under no circumstances should your lines extend past column 79. If your code samples spill over column 79, you should rewrite them. Tab characters must never appear in the document at all. A PEP should include the standard Emacs stanza included by example at the bottom of this PEP. A PEP must contain a Copyright section, and it is strongly recommended to put the PEP in the public domain. When referencing an external web page in the body of a PEP, you should include the title of the page in the text, with a footnote reference to the URL. Do not include the URL in the body text of the PEP. E.g. Refer to the Python Language web site [1] for more details. ... [1] http://www.python.org When referring to another PEP, include the PEP number in the body text, such as "PEP 1". The title may optionally appear. Add a footnote reference that includes the PEP's title and author. It may optionally include the explicit URL on a separate line, but only in the References section. Note that the pep2html.py script will calculate URLs automatically, e.g.: ... Refer to PEP 1 [7] for more information about PEP style ... References [7] PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton http://www.python.org/peps/pep-0001.html If you decide to provide an explicit URL for a PEP, please use this as the URL template: http://www.python.org/peps/pep-xxxx.html PEP numbers in URLs must be padded with zeros from the left, so as to be exactly 4 characters wide, however PEP numbers in text are never padded. Reporting PEP Bugs, or Submitting PEP Updates How you report a bug, or submit a PEP update depends on several factors, such as the maturity of the PEP, the preferences of the PEP author, and the nature of your comments. For the early draft stages of the PEP, it's probably best to send your comments and changes directly to the PEP author. For more mature, or finished PEPs you may want to submit corrections to the SourceForge bug manager[6] or better yet, the SourceForge patch manager[2] so that your changes don't get lost. If the PEP author is a SF developer, assign the bug/patch to him, otherwise assign it to the PEP editor. When in doubt about where to send your changes, please check first with the PEP author and/or PEP editor. PEP authors who are also SF committers, can update the PEPs themselves by using "cvs commit" to commit their changes. Remember to also push the formatted PEP text out to the web by doing the following: % python pep2html.py -i NUM where NUM is the number of the PEP you want to push out. See % python pep2html.py --help for details. Transferring PEP Ownership It occasionally becomes necessary to transfer ownership of PEPs to a new champion. In general, we'd like to retain the original author as a co-author of the transferred PEP, but that's really up to the original author. A good reason to transfer ownership is because the original author no longer has the time or interest in updating it or following through with the PEP process, or has fallen off the face of the 'net (i.e. is unreachable or not responding to email). A bad reason to transfer ownership is because you don't agree with the direction of the PEP. We try to build consensus around a PEP, but if that's not possible, you can always submit a competing PEP. If you are interested assuming ownership of a PEP, send a message asking to take over, addressed to both the original author and the PEP editor <peps(a)python.org>. If the original author doesn't respond to email in a timely manner, the PEP editor will make a unilateral decision (it's not like such decisions can be reversed. :). References and Footnotes [1] This historical record is available by the normal CVS commands for retrieving older revisions. For those without direct access to the CVS tree, you can browse the current and past PEP revisions via the SourceForge web site at http://cvs.sourceforge.net/cgi-bin/cvsweb.cgi/python/nondist/peps/?cvsroot=… [2] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [3] http://sourceforge.net/tracker/?atid=355470&group_id=5470&func=browse [4] http://www.opencontent.org/openpub/ [5] The script referred to here is pep2html.py, which lives in the same directory in the CVS tree as the PEPs themselves. Try "pep2html.py --help" for details. The URL for viewing PEPs on the web is http://www.python.org/peps/ [6] http://sourceforge.net/tracker/?group_id=5470&atid=305470 [7] PEP 9, Sample PEP Template http://www.python.org/peps/pep-0009.html 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 End:

8 14

Boundaries between numbers and identifiers
by Serhiy Storchaka April 15, 2021

April 15, 2021

In Python 2.5 `0or[]` was accepted by the Python parser. It became an error in 2.6 because "0o" became recognizing as an incomplete octal number. `1or[]` still is accepted. On other hand, `1if 2else 3` is accepted despites the fact that "2e" can be recognized as an incomplete floating point number. In this case the tokenizer pushes "e" back and returns "2". Shouldn't it do the same with "0o"? It is possible to make `0or[]` be parseable again. Python implementation is able to tokenize this example: $ echo '0or[]' | ./python -m tokenize 1,0-1,1: NUMBER '0' 1,1-1,3: NAME 'or' 1,3-1,4: OP '[' 1,4-1,5: OP ']' 1,5-1,6: NEWLINE '\n' 2,0-2,0: ENDMARKER '' On other hand, all these examples look weird. There is an assymmetry: `1or 2` is a valid syntax, but `1 or2` is not. It is hard to recognize visually the boundary between a number and the following identifier or keyword, especially if numbers can contain letters ("b", "e", "j", "o", "x") and underscores, and identifiers can contain digits. On both sides of the boundary can be letters, digits, and underscores. I propose to change the Python syntax by adding a requirement that there should be a whitespace or delimiter between a numeric literal and the following keyword.

8 10

The Python 2 death march
by Benjamin Peterson April 16, 2020

April 16, 2020

Hi all, It's finally time to schedule the last releases in Python 2's life. There will be two more releases of Python 2.7: Python 2.7.17 and Python 2.7.18. Python 2.7.17 release candidate 1 will happen on October 5th followed by the final release on October 19th. I'm going to time Python 2.7.18 to coincide with PyCon 2020 in April, so attendees can enjoy some collective catharsis. We'll still say January 1st is the official EOL date. Thanks to Sumana Harihareswara, there's now a FAQ about the Python 2 sunset on the website: https://www.python.org/doc/sunset-python-2/ Regards, Benjamin

24 38

PEP 585: Type Hinting Generics In Standard Collections
by Łukasz Langa March 16, 2020

March 16, 2020

Read in the browser: https://www.python.org/dev/peps/pep-0585/ <https://www.python.org/dev/peps/pep-0585/> Read the source: https://raw.githubusercontent.com/python/peps/master/pep-0585.rst <https://raw.githubusercontent.com/python/peps/master/pep-0585.rst> The following PEP has been discussed on typing-sig already and a prototype implementation exists for it. I'm extending it now for wider feedback on python-dev, with the intent to present the final version for the Steering Council's consideration by mid-March. PEP: 585 Title: Type Hinting Generics In Standard Collections Author: Łukasz Langa <lukasz(a)python.org> Discussions-To: Typing-Sig <typing-sig(a)python.org> Status: Draft Type: Standards Track Content-Type: text/x-rst Created: 03-Mar-2019 Python-Version: 3.9 Abstract Static typing as defined by PEPs 484, 526, 544, 560, and 563 was built incrementally on top of the existing Python runtime and constrained by existing syntax and runtime behavior. This led to the existence of a duplicated collection hierarchy in the typing module due to generics (for example typing.List and the built-in list). This PEP proposes to enable support for the generics syntax in all standard collections currently available in the typing module. Rationale and Goals This change removes the necessity for a parallel type hierarchy in the typing module, making it easier for users to annotate their programs and easier for teachers to teach Python. Terminology Generic (n.) -- a type that can be parametrized, typically a container. Also known as a parametric type or a generic type. For example: dict. Parametrized generic -- a specific instance of a generic with the expected types for container elements provided. Also known as a parametrized type. For example: dict[str, int]. Backwards compatibility Tooling, including type checkers and linters, will have to be adapted to recognize standard collections as generics. On the source level, the newly described functionality requires Python 3.9. For use cases restricted to type annotations, Python files with the "annotations" future-import (available since Python 3.7) can parametrize standard collections, including builtins. To reiterate, that depends on the external tools understanding that this is valid. Implementation Starting with Python 3.7, when from __future__ import annotations is used, function and variable annotations can parametrize standard collections directly. Example: from __future__ import annotations def find(haystack: dict[str, list[int]]) -> int: ... Usefulness of this syntax before PEP 585 <https://www.python.org/dev/peps/pep-0585> is limited as external tooling like Mypy does not recognize standard collections as generic. Moreover, certain features of typing like type aliases or casting require putting types outside of annotations, in runtime context. While these are relatively less common than type annotations, it's important to allow using the same type syntax in all contexts. This is why starting with Python 3.9, the following collections become generic using __class_getitem__() to parametrize contained types: tuple # typing.Tuple list # typing.List dict # typing.Dict set # typing.Set frozenset # typing.FrozenSet type # typing.Type collections.deque collections.defaultdict collections.OrderedDict collections.Counter collections.ChainMap collections.abc.Awaitable collections.abc.Coroutine collections.abc.AsyncIterable collections.abc.AsyncIterator collections.abc.AsyncGenerator collections.abc.Iterable collections.abc.Iterator collections.abc.Generator collections.abc.Reversible collections.abc.Container collections.abc.Collection collections.abc.Callable collections.abc.Set # typing.AbstractSet collections.abc.MutableSet collections.abc.Mapping collections.abc.MutableMapping collections.abc.Sequence collections.abc.MutableSequence collections.abc.ByteString collections.abc.MappingView collections.abc.KeysView collections.abc.ItemsView collections.abc.ValuesView contextlib.AbstractContextManager # typing.ContextManager contextlib.AbstractAsyncContextManager # typing.AsyncContextManager re.Pattern # typing.Pattern, typing.re.Pattern re.Match # typing.Match, typing.re.Match Importing those from typing is deprecated. Due to PEP 563 <https://www.python.org/dev/peps/pep-0563> and the intention to minimize the runtime impact of typing, this deprecation will not generate DeprecationWarnings. Instead, type checkers may warn about such deprecated usage when the target version of the checked program is signalled to be Python 3.9 or newer. It's recommended to allow for those warnings to be silenced on a project-wide basis. The deprecated functionality will be removed from the typing module in the first Python version released 5 years after the release of Python 3.9.0. Parameters to generics are available at runtime Preserving the generic type at runtime enables introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild. Just like with the typing module today, the parametrized generic types listed in the previous section all preserve their type parameters at runtime: >>> list[str] list[str] >>> tuple[int, ...] tuple[int, ...] >>> ChainMap[str, list[str]] collections.ChainMap[str, list[str]] This is implemented using a thin proxy type that forwards all method calls and attribute accesses to the bare origin type with the following exceptions: the __repr__ shows the parametrized type; the __origin__ attribute points at the non-parametrized generic class; the __args__ attribute is a tuple (possibly of length 1) of generic types passed to the original __class_getitem__; the __parameters__ attribute is a lazily computed tuple (possibly empty) of unique type variables found in __args__; the __getitem__ raises an exception to disallow mistakes like dict[str][str]. However it allows e.g. dict[str, T][int] and in that case returns dict[str, int]. This design means that it is possible to create instances of parametrized collections, like: >>> l = list[str]() [] >>> list is list[str] False >>> list == list[str] False >>> list[str] == list[str] True >>> list[str] == list[int] False >>> isinstance([1, 2, 3], list[str]) TypeError: isinstance() arg 2 cannot be a parametrized generic >>> issubclass(list, list[str]) TypeError: issubclass() arg 2 cannot be a parametrized generic >>> isinstance(list[str], types.GenericAlias) True Objects created with bare types and parametrized types are exactly the same. The generic parameters are not preserved in instances created with parametrized types, in other words generic types erase type parameters during object creation. One important consequence of this is that the interpreter does not attempt to type check operations on the collection created with a parametrized type. This provides symmetry between: l: list[str] = [] and: l = list[str]() For accessing the proxy type from Python code, it will be exported from the types module as GenericAlias. Pickling or (shallow- or deep-) copying a GenericAlias instance will preserve the type, origin, attributes and parameters. Forward compatibility Future standard collections must implement the same behavior. Reference implementation A proof-of-concept or prototype implementation <https://bugs.python.org/issue39481> exists. Rejected alternatives Do nothing Keeping the status quo forces Python programmers to perform book-keeping of imports from the typing module for standard collections, making all but the simplest annotations cumbersome to maintain. The existence of parallel types is confusing to newcomers (why is there both list and List?). The above problems also don't exist in user-built generic classes which share runtime functionality and the ability to use them as generic type annotations. Making standard collections harder to use in type hinting from user classes hindered typing adoption and usability. Generics erasure It would be easier to implement __class_getitem__ on the listed standard collections in a way that doesn't preserve the generic type, in other words: >>> list[str] <class 'list'> >>> tuple[int, ...] <class 'tuple'> >>> collections.ChainMap[str, list[str]] <class 'collections.ChainMap'> This is problematic as it breaks backwards compatibility: current equivalents of those types in the typing module do preserve the generic type: >>> from typing import List, Tuple, ChainMap >>> List[str] typing.List[str] >>> Tuple[int, ...] typing.Tuple[int, ...] >>> ChainMap[str, List[str]] typing.ChainMap[str, typing.List[str]] As mentioned in the "Implementation" section, preserving the generic type at runtime enables runtime introspection of the type which can be used for API generation or runtime type checking. Such usage is already present in the wild. Additionally, implementing subscripts as identity functions would make Python less friendly to beginners. Say, if a user is mistakenly passing a list type instead of a list object to a function, and that function is indexing the received object, the code would no longer raise an error. Today: >>> l = list >>> l[-1] TypeError: 'type' object is not subscriptable With __class_getitem__ as an identity function: >>> l = list >>> l[-1] list The indexing being successful here would likely end up raising an exception at a distance, confusing the user. Disallowing instantiation of parametrized types Given that the proxy type which preserves __origin__ and __args__ is mostly useful for runtime introspection purposes, we might have disallowed instantiation of parametrized types. In fact, forbidding instantiation of parametrized types is what the typing module does today for types which parallel builtin collections (instantiation of other parametrized types is allowed). The original reason for this decision was to discourage spurious parametrization which made object creation up to two orders of magnitude slower compared to the special syntax available for those builtin collections. This rationale is not strong enough to allow the exceptional treatment of builtins. All other parametrized types can be instantiated, including parallels of collections in the standard library. Moreover, Python allows for instantiation of lists using list() and some builtin collections don't provide special syntax for instantiation. Making isinstance(obj, list[str]) perform a check ignoring generics An earlier version of this PEP suggested treating parametrized generics like list[str] as equivalent to their non-parametrized variants like list for purposes of isinstance() and issubclass(). This would be symmetrical to how list[str]() creates a regular list. This design was rejected because isinstance() and issubclass() checks with parametrized generics would read like element-by-element runtime type checks. The result of those checks would be surprising, for example: >>> isinstance([1, 2, 3], list[str]) True Note the object doesn't match the provided generic type but isinstance() still returns True because it only checks whether the object is a list. If a library is faced with a parametrized generic and would like to perform an isinstance() check using the base type, that type can be retrieved using the __origin__ attribute on the parametrized generic. Making isinstance(obj, list[str]) perform a runtime type check This functionality requires iterating over the collection which is a destructive operation in some of them. This functionality would have been useful, however implementing the type checker within Python that would deal with complex types, nested type checking, type variables, string forward references, and so on is out of scope for this PEP. Note on the initial draft An early version of this PEP discussed matters beyond generics in standard collections. Those unrelated topics were removed for clarity. Copyright This document is placed in the public domain or under the CC0-1.0-Universal license, whichever is more permissive.

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Pull Request bpo-1812
by Peter Donis March 13, 2020

March 13, 2020

Can someone please review the subject pull request? Pull request link here: https://github.com/python/cpython/pull/17385 Issue tracker link here: https://bugs.python.org/issue1812 Thank you very much! Peter Donis pdonis(a)peterdonis.net

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Last call for comments on PEP 573 (Module State Access from C Extension Methods)
by Stefan Behnel March 11, 2020

March 11, 2020

Hi all, I think that PEP 573 is ready to be accepted, to greatly improve the state of extension modules in CPython 3.9. https://www.python.org/dev/peps/pep-0573/ It has come a long way since the original proposal and went through several iterations and discussions by various interested people, effectively reducing its scope quite a bit. So this is the last call for comments on the latest version of the PEP, before I will pronounce on it. Please keep the discussion in this thread. Stefan

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Stalemate on bringing back PEP 523 support into Python 3.8
by Brett Cannon March 10, 2020

March 10, 2020

An unfortunate side-effect of making PyInterpreterState in Python 3.8 opaque is it removed [PEP 523](https://www.python.org/dev/peps/pep-0523/) support. https://www.python.org/dev/peps/pep-0523/ was opened to try and fix this, but there seems to be a stalemate in the issue. A key question is at what API level should setting the frame evaluation function live at? No one is suggesting the stable ABI, but there isn't agreement between CPython or the internal API (there's also seems to be a suggestion to potentially remove PEP 523 support entirely). And regardless of either, there also seems to be a disagreement about providing getters and setters to continue to try and hide PyInterpreterState regardless of which API level support is provided at (if any). If you have an opinion please weight in on the issue.

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Request to postpone some Python 3.9 incompatible changes to Python 3.10
by Victor Stinner March 7, 2020

March 7, 2020

Hi, Python 3.9 introduces many small incompatible changes which broke tons of Python projects, including popular projects, some of them being unmaintained but still widely used (like nose, last release in 2015). Miro and me consider that Python 3.9 is pushing too much pressure on projects maintainers to either abandon Python 2.7 right now (need to update the CI, the documentation, warn users, etc.), or to introduce a *new* compatibility layer to support Python 3.9: layer which would be dropped as soon as Python 2.7 support will be dropped (soon-ish). Python 3.9 is too early to accumulate so many incompatible changes on purpose, some incompatible changes like the removal of collections aliases to ABC should be reverted, and reapplied on Python 3.10. Python 3.9 would be the last release which still contain compatibility layers for Python 2.7. Said differently, we request to maintain the small compatibility layer in Python for one more cycle, instead of requesting every single project maintainer to maintain it on their side. We consider that the general maintenance burden is lower if it's kept in Python for now. == Fedora COPR notify packages broken by Python 3.9 == In Python 3.9, Victor introduced tons of incompatible changes at the beginning of the devcycle. His plan was to push as many as possible, and later decide what to do... This time has come :-) He wrote PEP 606 "Python Compatibility Version" and we wrote PEP 608 "Coordinated Python release", but both have been rejected. At least, it seems like most people agreed that having a CI to get notified of broken projects should help. We are updating the future Fedora 33 to replace Python 3.8 with Python 3.9. We are using a tool called "COPR" which is like a sandbox and can be seen as the CI discussed previously. It rebuilds Fedora using Python 3.9 as /usr/bin/python3 (and /usr/bin/python !). We now have a good overview of broken packages and which incompatible changes broke most packages. https://fedoraproject.org/wiki/Changes/Python3.9 - Describes the Fedora change. https://copr.fedorainfracloud.org/coprs/g/python/python3.9/monitor/ - Has package failures. Some packages fail because of broken dependencies. https://bugzilla.redhat.com/showdependencytree.cgi?id=PYTHON39 - Has open Python 3.9 bug reports for Fedora packages. Some problems have been fixed upstream already before reaching Fedora, most are only fixed when the Fedora maintainers report the problems back to upstream maintainers. Right now, there are 150+ packages broken by Python 3.9 incompatible changes. == Maintenance burden == Many Python projects have not yet removed Python 2 support and Python 2 CI. It's not that they would be in the "we will not drop Python 2 support ever" camp, it's just that they have not done it yet. Removing Python 2 compatibility code from the codebase and removing it from the documentation and metadata and CI is a boring task, doesn't bring anything to users, and it might take a new major release of the library. At this point, we are very early in 2020 to expect most projects to have already done this. At the same time, we would like them to support Python 3.9 as soon in the release cycle as possible. By removing Python 2 compatibility layers from the 3.9 standard library, we are forcing the projects maintainers to re-invent their own compatibility layers and copy-paste stuff like this around. Example: try: from collections.abc import Sequence except ImprotError: # Python 2.7 doesn't have collections.abc from collections import Sequence While if we remove collections.Sequence in 3.10, they will face this decision early in 2021 and they will simply fix their code by adding ".abc" at the proper places, not needing any more compatibility layers. Of course, there will be projects that will still have declared Python 2 support in 2021, but it will arguably not be that many. While it's certainly tempting to have "more pure" code in the standard library, maintaining the compatibility shims for one more release isn't really that big of a maintenance burden, especially when comparing with dozens (hundreds?) of third party libraries essentially maintaining their own. An good example of a broken package is the nose project which is no longer maintained (according to their website): the last release was in 2015. It remains a very popular test runner. According to libraries.io, it has with 3 million downloads per month, 41.7K dependent repositories and 325 dependent packages. We patched nose in Fedora to fix Python 3.5, 3.6, 3.8 and now 3.9 compatibility issues. People installing nose from PyPI with "pip install" get the unmaintained flavor which is currently completely broken on Python 3.9. Someone should take over the nose project and maintain it again, or every single project using nose should pick another tool (unittest, nose2, pytest, whatever else). Both options will take a lot of time. == Request to revert some incompatible changes == See https://docs.python.org/dev/whatsnew/3.9.html#removed Incompatible changes which require "if <python3>: (...) else: (...)" or "try: <python3 code> except (...): <python2 code>": * Removed tostring/fromstring methods in array.array and base64 modules * Removed collections aliases to ABC classes * Removed fractions.gcd() function (which is similar to math.gcd()) * Remove "U" mode of open(): having to use io.open() just for Python 2 makes the code uglier * Removed old plistlib API: 2.7 doesn't have the new API == Kept incompatible changes == Ok-ish incompatible changes (mostly only affects compatiblity libraries like six): * _dummy_thread and dummy_threading modules removed: broke six, nine and future projects. six and nine are already fixed for 3.9. OK incompatible changes (can be replaced by the same code on 2.7 and 3.9): * isAlive() method of threading.Thread has been removed: Thread.is_alive() method is available in 2.7 and 3.9 * xml.etree.ElementTree.getchildren() and xml.etree.ElementTree.getiterator() methods are removed from 3.9, but list()/iter() works in 2.7 and 3.9 == Call to check for DeprecationWarning in your own projects == You must pay attention to DeprecationWarning in Python 3.9: it will be the last "compatibility layer" release, incompatible changes will be reapplied to Python 3.10. For example, you can use the development mode to see DeprecationWarning and ResourceWarning: use the "-X dev" command line option or set the PYTHONDEVMODE=1 environment variable. Or you can use the PYTHONWARNINGS=default environment variable to see DeprecationWarning. You might even want to treat all warnings as errors to ensure that you don't miss any when you run your test suite in your CI. You can use PYTHONWARNINGS=error, and combine it with PYTHONDEVMODE=1. Warnings filters can be used to ignore warnings in third party code, see the documentation: https://docs.python.org/dev/library/warnings.html#the-warnings-filter -- Victor Stinner and Miro Hrončok for Fedora

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