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:
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
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
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/)
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
Hello.
I would like to discuss on the language summit a potential inclusion
of cffi[1] into stdlib. This is a project Armin Rigo has been working
for a while, with some input from other developers. It seems that the
main reason why people would prefer ctypes over cffi these days is
"because it's included in stdlib", which is not generally the reason I
would like to hear. Our calls to not use C extensions and to use an
FFI instead has seen very limited success with ctypes and quite a lot
more since cffi got released. The API is fairly stable right now with
minor changes going in and it'll definitely stablize until Python 3.4
release. Notable projects using it:
* pypycore - gevent main loop ported to cffi
* pgsql2cffi
* sdl-cffi bindings
* tls-cffi bindings
* lxml-cffi port
* pyzmq
* cairo-cffi
* a bunch of others
So relatively a lot given that the project is not even a year old (it
got 0.1 release in June). As per documentation, the advantages over
ctypes:
* The goal is to call C code from Python. You should be able to do so
without learning a 3rd language: every alternative requires you to
learn their own language (Cython, SWIG) or API (ctypes). So we tried
to assume that you know Python and C and minimize the extra bits of
API that you need to learn.
* Keep all the Python-related logic in Python so that you don’t need
to write much C code (unlike CPython native C extensions).
* Work either at the level of the ABI (Application Binary Interface)
or the API (Application Programming Interface). Usually, C libraries
have a specified C API but often not an ABI (e.g. they may document a
“struct” as having at least these fields, but maybe more). (ctypes
works at the ABI level, whereas Cython and native C extensions work at
the API level.)
* We try to be complete. For now some C99 constructs are not
supported, but all C89 should be, including macros (and including
macro “abuses”, which you can manually wrap in saner-looking C
functions).
* We attempt to support both PyPy and CPython, with a reasonable path
for other Python implementations like IronPython and Jython.
* Note that this project is not about embedding executable C code in
Python, unlike Weave. This is about calling existing C libraries from
Python.
so among other things, making a cffi extension gives you the same
level of security as writing C (and unlike ctypes) and brings quite a
bit more flexibility (API vs ABI issue) that let's you wrap arbitrary
libraries, even those full of macros.
Cheers,
fijal
.. [1]: http://cffi.readthedocs.org/en/release-0.5/
Hello,
Following the python-dev discussion, I've written a PEP to recap the
proposal and the various arguments. It's inlined below, and it will
probably appear soon at http://www.python.org/dev/peps/pep-0455/, too.
Regards
Antoine.
PEP: 455
Title: Adding a key-transforming dictionary to collections
Version: $Revision$
Last-Modified: $Date$
Author: Antoine Pitrou <solipsis(a)pitrou.net>
Status: Draft
Type: Standards Track
Content-Type: text/x-rst
Created: 13-Sep-2013
Python-Version: 3.4
Post-History:
Abstract
========
This PEP proposes a new data structure for the ``collections`` module,
called "TransformDict" in this PEP. This structure is a mutable mapping
which transforms the key using a given function when doing a lookup, but
retains the original key when reading.
Rationale
=========
Numerous specialized versions of this pattern exist. The most common
is a case-insensitive case-preserving dict, i.e. a dict-like container
which matches keys in a case-insensitive fashion but retains the
original casing. It is a very common need in network programming, as
many protocols feature some arrays of "key / value" properties in their
messages, where the keys are textual strings whose casing isn't
relevant.
Another common request is an identity dict, where keys are matched
according to their respective id()s instead of normal matching.
Both are instances of a more general pattern, where a given
transformation function is applied to keys when looking them up: that
function being ``str.lower`` in the former example and the built-in
``id`` function in the latter.
(it can be said that the pattern *projects* keys from the user-visible
set onto the internal lookup set, hence this PEP's title)
Semantics
=========
TransformDict is a ``MutableMapping`` implementation: it faithfully
implements the well-known API of mutable mappings, as ``dict`` itself
and other dict-like classes in the standard library. Therefore, this
PEP won't rehash the semantics of most TransformDict methods.
The transformation function needn't be bijective, it can be strictly
surjective as in the case-insensitive example::
>>> d = TransformDict(str.lower)
>>> d['SomeKey'] = 5
>>> d['somekey']
5
>>> d['SOMEKEY']
5
TransformDict retains the first key used when creating an entry::
>>> d = TransformDict(str.lower)
>>> d['SomeKey'] = 1
>>> d['somekey'] = 2
>>> list(d.items())
[('SomeKey', 2)]
The original keys needn't be hashable, as long as the transformation
function returns a hashable one::
>>> d = TransformDict(id)
>>> l = [None]
>>> d[l] = 5
>>> l in d
True
Constructor
-----------
As shown in the example aboves, creating a TransformDict requires
passing the key transformation function as the first argument (much
like creating a ``defaultdict`` requires passing the factory function
as first argument).
The constructor also takes other optional arguments which can be used
to initialize the TransformDict with certain key-value pairs. Those
optional arguments are the same as in the ``dict`` and ``defaultdict``
constructors::
>>> d = TransformDict(str.lower, [('Foo': 1)], Bar=2)
>>> sorted(d.items())
[('Bar', 2), ('Foo', 1)]
Alternative proposals and questions
===================================
Retaining the last original key
-------------------------------
Most python-dev respondents found retaining the first user-supplied key
more intuitive than retaining the last. Also, it matches the dict
object's own behaviour when using different but equal keys::
>>> d = {}
>>> d[1] = 'hello'
>>> d[1.0] = 'world'
>>> d
{1: 'world'}
Furthermore, explicitly retaining the last key in a first-key-retaining
scheme is still possible using the following approach::
d.pop(key, None)
d[key] = value
while the converse (retaining the first key in a last-key-retaining
scheme) doesn't look possible without rewriting part of the container's
code.
Using an encoder / decoder pair
-------------------------------
Using a function pair isn't necessary, since the original key is
retained by the container. Moreover, an encoder / decoder pair would
require the transformation to be bijective, which prevents important
use cases like case-insensitive matching.
Providing a transformation function for values
----------------------------------------------
Dictionary values are not used for lookup, their semantics are totally
irrelevant to the container's operation. Therefore, there is no point
in having both an "original" and a "transformed" value: the transformed
value wouldn't be used for anything.
Providing a specialized container, not generic
----------------------------------------------
It was asked why we would provide the generic TransformDict construct
rather than a specialized case-insensitive dict variant. The answer
is that it's nearly as cheap (code-wise and performance-wise) to provide
the generic construct, and it can fill more use cases.
Implementation
==============
A patch for the collections module is tracked on the bug tracker at
http://bugs.python.org/issue18986.
Existing work
=============
Case-insensitive dicts are a popular request:
*
http://twistedmatrix.com/documents/current/api/twisted.python.util.Insensit…
* https://mail.python.org/pipermail/python-list/2013-May/647243.html
* https://mail.python.org/pipermail/python-list/2005-April/296208.html
* https://mail.python.org/pipermail/python-list/2004-June/241748.html
* http://bugs.python.org/msg197376
* http://stackoverflow.com/a/2082169
* http://stackoverflow.com/a/3296782
* http://code.activestate.com/recipes/66315-case-insensitive-dictionary/
* https://gist.github.com/babakness/3901174
* http://www.wikier.org/blog/key-insensitive-dictionary-in-python
* http://en.sharejs.com/python/14534
* http://www.voidspace.org.uk/python/archive.shtml#caseless
Identity dicts have been requested too:
* https://mail.python.org/pipermail/python-ideas/2010-May/007235.html
* http://www.gossamer-threads.com/lists/python/python/209527
Python's own pickle module uses identity lookups for object
memoization:
http://hg.python.org/cpython/file/0e70bf1f32a3/Lib/pickle.py#l234
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:
hi, everyone:
I want to compile python 3.3 with bz2 support on RedHat 5.5 but fail to do that. Here is how I do it:
1、download bzip2 and compile it(make、make -f Makefile_libbz2_so、make install)
2、chang to python 3.3 source directory : ./configure --with-bz2=/usr/local/include
3、make
4、make install
after installation complete, I test it：
[root@localhost Python-3.3.0]# python3 -c "import bz2"
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/local/lib/python3.3/bz2.py", line 21, in <module>
from _bz2 import BZ2Compressor, BZ2Decompressor
ImportError: No module named '_bz2'
By the way, RedHat 5.5 has a built-in python 2.4.3. Would it be a problem?
In the 'PEP453 ready for pronouncement' thread, Donald said
>> Because reality is that new users are still likely to be using Python 2.7.
>> Python 3 is just now starting to be really usable, however there's a huge
>> corpus of existing tutorials, course work, books etc for Python 2.7. As
>> Python 3 becomes more usable that existing corpus of material will be
>> ported over to Python 3 but in the interim there is still a pretty large
>> hurdle for new users to get over.
And Guido replied
> Based on my day-to-day experience this is still very true. (And yes, I'm
> slowly turning the tide. But it will take a long time and I am committed to
> giving users the choice.)
Widely-used and linked web resources tend to persist for a very
long time, so we shouldn't use the prevalence of Python 2 resources
as a reason for excessive caution. The key question is how much
good material is available based on Python 3 - and this has improved
significantly over the past couple of years. The classic "How to
Think Like a Computer Scientist" has an excellent Python 3 version
available at http://interactivepython.org, for example.
Things are changing with print media, too. Pragmatic Programmers are
about to publish the 2nd edition of "Practical Programming", based
on Python 3. Most of the major academic publishers have released
Python 3 books in the last 12 months. The tide is definitely
turning, perhaps has already turned.
Encouraging the continued use of 2.7 for existing programmers is
entirely justifiable, but for *newcomers* to programming I think it
is now much harder to justify. A stronger case could have been made
a couple of years ago, when many important packages were not yet
available for Python 3, but things have changed. Even big frameworks
like Django are now usable with Python 3. If we aren't yet past the
point where package availability shouldn't be regarded as an adoption
barrier by beginners, we are surely very close.
I've been teaching Python as a first language to university students
for many years now, initially with Python 2 and for the last few
years with Python 3. In my experience, they encounter fewer problems
with Python 3 (just as Guido intended, no doubt :) The one stumbling
block in the past has been package availability for project work,
but I don't expect that to a problem this year. All of the web and
GUI development work that they'll be doing with me, for example,
will be done entirely in Python 3.
Nick
