Python-Dev January 2014

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117 participants
92 discussions

PEP 1, PEP Purpose and Guidelines
by barry＠zope.com 18 May '21

18 May '21

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:

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More compact dictionaries with faster iteration
by Raymond Hettinger 03 Jan '15

03 Jan '15

The current memory layout for dictionaries is unnecessarily inefficient. It has a sparse table of 24-byte entries containing the hash value, key pointer, and value pointer. Instead, the 24-byte entries should be stored in a dense table referenced by a sparse table of indices. For example, the dictionary: d = {'timmy': 'red', 'barry': 'green', 'guido': 'blue'} is currently stored as: entries = [['--', '--', '--'], [-8522787127447073495, 'barry', 'green'], ['--', '--', '--'], ['--', '--', '--'], ['--', '--', '--'], [-9092791511155847987, 'timmy', 'red'], ['--', '--', '--'], [-6480567542315338377, 'guido', 'blue']] Instead, the data should be organized as follows: indices = [None, 1, None, None, None, 0, None, 2] entries = [[-9092791511155847987, 'timmy', 'red'], [-8522787127447073495, 'barry', 'green'], [-6480567542315338377, 'guido', 'blue']] Only the data layout needs to change. The hash table algorithms would stay the same. All of the current optimizations would be kept, including key-sharing dicts and custom lookup functions for string-only dicts. There is no change to the hash functions, the table search order, or collision statistics. The memory savings are significant (from 30% to 95% compression depending on the how full the table is). Small dicts (size 0, 1, or 2) get the most benefit. For a sparse table of size t with n entries, the sizes are: curr_size = 24 * t new_size = 24 * n + sizeof(index) * t In the above timmy/barry/guido example, the current size is 192 bytes (eight 24-byte entries) and the new size is 80 bytes (three 24-byte entries plus eight 1-byte indices). That gives 58% compression. Note, the sizeof(index) can be as small as a single byte for small dicts, two bytes for bigger dicts and up to sizeof(Py_ssize_t) for huge dict. In addition to space savings, the new memory layout makes iteration faster. Currently, keys(), values, and items() loop over the sparse table, skipping-over free slots in the hash table. Now, keys/values/items can loop directly over the dense table, using fewer memory accesses. Another benefit is that resizing is faster and touches fewer pieces of memory. Currently, every hash/key/value entry is moved or copied during a resize. In the new layout, only the indices are updated. For the most part, the hash/key/value entries never move (except for an occasional swap to fill a hole left by a deletion). With the reduced memory footprint, we can also expect better cache utilization. For those wanting to experiment with the design, there is a pure Python proof-of-concept here: http://code.activestate.com/recipes/578375 YMMV: Keep in mind that the above size statics assume a build with 64-bit Py_ssize_t and 64-bit pointers. The space savings percentages are a bit different on other builds. Also, note that in many applications, the size of the data dominates the size of the container (i.e. the weight of a bucket of water is mostly the water, not the bucket). Raymond

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Semi-official read-only Github mirror of the CPython Mercurial repository
by Eli Bendersky 15 Oct '14

15 Oct '14

Hi all, https://github.com/python/cpython is now live as a semi-official, *read only* Github mirror of the CPython Mercurial repository. Let me know if you have any problems/concerns. I still haven't decided how often to update it (considering either just N times a day, or maybe use a Hg hook for batching). Suggestions are welcome. The methodology I used to create it is via hg-fast-export. I also tried to pack and gc the git repo as much as possible before the initial Github push - it went down from almost ~2GB to ~200MB (so this is the size of a fresh clone right now). Eli P.S. thanks Jesse for the keys to https://github.com/python

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Reviving restricted mode?
by Guido van Rossum 14 Aug '14

14 Aug '14

I've received some enthusiastic emails from someone who wants to revive restricted mode. He started out with a bunch of patches to the CPython runtime using ctypes, which he attached to an App Engine bug: http://code.google.com/p/googleappengine/issues/detail?id=671 Based on his code (the file secure.py is all you need, included in secure.tar.gz) it seems he believes the only security leaks are __subclasses__, gi_frame and gi_code. (I have since convinced him that if we add "restricted" guards to these attributes, he doesn't need the functions added to sys.) I don't recall the exploits that Samuele once posted that caused the death of rexec.py -- does anyone recall, or have a pointer to the threads? -- --Guido van Rossum (home page: http://www.python.org/~guido/)

19 35

Add Py_REPLACE and Py_XREPLACE macros
by Serhiy Storchaka 25 Feb '14

25 Feb '14

The Py_CLEAR macros is used as safe alternative for following unsafe idiomatic code: Py_XDECREF(ptr); ptr = NULL; But other unsafe idiomatic code is widely used in the sources: Py_XDECREF(ptr); ptr = new_value; Every occurrence of such code is potential bug for same reasons as for Py_CLEAR. It was offered [1] to introduce new macros Py_REPLACE and Py_XREPLACE for safe replace with Py_DECREF and Py_XDECREF respectively. Automatically generated patch contains about 50 replaces [2]. [1] http://bugs.python.org/issue16447 [2] http://bugs.python.org/issue20440

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CLA link from bugs.python.org
by Tim Delaney 16 Feb '14

16 Feb '14

It appears there's no obvious link from bugs.python.org to the contributor agreement - you need to go via the unintuitive link Foundation -> Contribution Forms (and from what I've read, you're prompted when you add a patch to the tracker). I'd suggest that if the "Contributor Form Received" field is "No" in user details, there be a link to http://www.python.org/psf/contrib/. Tim Delaney

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The desired behaviour for resolve() when the path doesn't exist
by Vajrasky Kok 16 Feb '14

16 Feb '14

Dear friends, This is related with ticket 19717: "resolve() fails when the path doesn't exist". Assuming /home/cutecat exists but not /home/cutecat/aa, what is the desired output of Path('/home/cutecat/aa/bb/cc').resolve(strict=False)? Should it be: "/home/cutecat" (the existed path only), "/home/cutecat/aa" (the first non-existed path; my current strategy), or "/home/cutecat/aa/bb/cc" (the default behaviour of os.path.realpath)? Vajrasky

3 5

News from asyncio
by Victor Stinner 02 Feb '14

02 Feb '14

Hi, I'm working for eNovance on the asyncio module, the goal is to use it in the huge OpenStack project (2.5 millions line of code) which currently uses eventlet. I'm trying to fix remaining issues in the asyncio module before Python 3.4 final. The asyncio project is very active but discussions are splitted between its own dedicated mailing list (python-tulip Google group), Tulip bug tracker and Python bug tracker. Please join Tulip mailing list if you are interested to contribute. http://code.google.com/p/tulip/ I would like to share with you the status of the module. Many bugs have been fixed recently. I suppose that new bugs are found because new developers started to play with asyncio since Python 3.4 beta 1. asyncio issues fixed in Python 3.4 beta 3, in a random order: - I wrote most of the asyncio documentation, please help me to improve it! I tried to add many short examples, each time explaining one feature or concept (ex: callbacks, signals, futures, etc.): http://docs.python.org/dev/library/asyncio.html - Characters devices (TTY/PTY) are now supported, useful to control real terminal (not pipes) for subprocesses. On Mac OS X older than Maverick (10.9), the SelectSelector should be used instead of KqueueSelector (kqueue didnd't support character devices) - Tulip #111: StreamReader.readexactly() now raises an IncompleteReadError if the end of stream is reached before we received enough bytes, instead of returning less bytes than requested. - Python #20311: asyncio had a performance issue related to the resolution of selectors and clocks. For example, selectors.EpollSelector has a resolution of 1 millisecond (10^-3), whereas asyncio uses arbitrary timestamps. The issue was fixed by adding a resolution attribute to selectors and a private granularity attribute to asyncio.BaseEventLoop, and use the granularity in asyncio event loop to round times. - New Task.current_task() class method - Guido wrote a web crawler, see examples/crawl.py in Tulip - More symbols are exported in the main asyncio module (ex: Queue, iscouroutine(), etc.) - Charles-François improved the signal handlers: SA_RESTART flag is now set to limit EINTR errors in syscalls - Some optimizations (ex: don't call logger.log() when it's not needed) - Many bugfixes - (sorry if I forgot other changes, see also Tulip history and history of the asyncio module in Python) I also would like to change asyncio to support a "stream-like" API for subprocesses, see Tulip issue #115 (and Python issue #20400): http://code.google.com/p/tulip/issues/detail?id=115 I ported ayncio on Python 2.6 and 2.7, because today OpenStack only uses these Python versions. I created a new project called "Trollius" (instead of "Tulip") because the syntax is a little bit different. "yield from" becomes "yield", and "return x" becomes "raise Return(x)": https://bitbucket.org/enovance/trollius https://pypi.python.org/pypi/trollius If you are interested by the OpenStack part, see my blueprint (something similar to PEPs but for smaller changes) for Oslo Messaing: https://wiki.openstack.org/wiki/Oslo/blueprints/asyncio There is an ongoing effort to port OpenStack to Python 3, eNovance is also working on the portage: https://wiki.openstack.org/wiki/Python3 Victor

6 9

Negative times behaviour in itertools.repeat for Python maintenance releases (2.7, 3.3 and maybe 3.4)
by Vajrasky Kok 01 Feb '14

01 Feb '14

Dear comrades, I would like to bring to your attention my disagreement with Larry Hastings in this ticket: http://bugs.python.org/issue19145 (Inconsistent behaviour in itertools.repeat when using negative times). Let me give you the context: >>> from itertools import repeat >>> repeat('a') repeat('a') >>> repeat('a', times=-1) repeat('a') >>> repeat('a', -1) repeat('a', 0) >>> repeat('a', times=-4) repeat('a', -4) >>> repeat('a', -4) repeat('a', 0) Right now, the only way you can tell repeat to do endless repetitions is to omit the `times` argument or by setting `times` argument to -1 via keyword. Larry intends to fix this in Python 3.5 by making None value to `times` argument as a representative of unlimited repetitions and negative `times` argument (either via keyword or positional) ALWAYS means 0 repetitions. This will ensure repeat has the appropriate signature. I have no qualms about it. All is well. My disagreement is related to Larry's decision not to fix this bug in Python 2.7, 3.3, and 3.4. Both of us agree that we should not let Python 2.7, 3.3, and 3.4 happily accepts None value because that is more than bug fix. What we don't agree is whether we should make negative `times` argument via keyword behaviour needs to be changed or not. He prefer let it be. I prefer we change the behaviour so that negative `times` argument in Python 2.7, 3.3, and 3.4 ALWAYS means 0 repetitions. My argument is that, on all circumstances, argument sent to function via positional or keyword must mean the same thing. Let's consider this hypothetical code: # 0 means draw, positive int means win, negative int means lose result = goals_result_of_the_match() # For every goal of the winning match, we donate money to charity. # Every donation consists of 10 $. import itertools itertools.repeat(donate_money_to_charity(), result) Later programmer B refactor this code: # 0 means draw, positive int means win, negative int means lose result = goals_result_of_the_match() # For every goal of the winning match, we donate money to charity. # Every donation consists of 10 $ from itertools import repeat repeat(object=donate_money_to_charity(), times=result) They use Python 2.7 (remember Python 2.7 is here to stay for a long long time). And imagine the match is lost 0-1 or 1-2 or 2-3 (so the goal difference is negative one / -1). It means they donate money to charity endlessly!!! They can go bankrupt. So I hope my argument is convincing enough. We need to fix this bug in Python 2.7, 3.3, and 3.4, by making `times` argument sent via positional or keyword in itertools.repeat ALWAYS means the same thing, which is 0 repetitions. If this is not possible, at the very least, we need to warn this behaviour in the doc. Whatever decision that comes up from this discussion, I will make peace with it. Vajrasky

12 43

Add PyType_GetSlot
by "Martin v. Löwis" 01 Feb '14

01 Feb '14

I'd like to resolve a long-standing issue of the stable ABI in 3.4: http://bugs.python.org/issue17162 The issue is that, since PyTypeObject is opaque, module authors cannot get at tp_free, which they may need to in order to implement tp_dealloc properly. Rather than providing the proposed specific wrapper for tp_dealloc, I propose to add a generic PyType_GetSlot function. From a stability point of view, exposing slot values is uncritical - it's just that the layout of the type object is hidden. Any objection to adding this before RC1? Regards, Martin

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Python-Dev January 2014