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
-------------------- snip snip --------------------
Title: PEP Purpose and Guidelines
Version: $Revision: 1.36 $
Last-Modified: $Date: 2002/07/29 18:34:59 $
Author: Barry A. Warsaw, Jeremy Hylton
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
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
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 or feature request tracker.
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
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
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
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
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. 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
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.
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.
PEP 9 contains a boilerplate 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
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  for more details.
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  for more information about PEP style
 PEP 1, PEP Purpose and Guidelines, Warsaw, Hylton
If you decide to provide an explicit URL for a PEP, please use
this as the URL template:
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
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 or better yet, the SourceForge patch manager 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
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
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
References and Footnotes
 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
 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
 PEP 9, Sample PEP Template
This document has been placed in the public domain.
webmaster has already heard from 4 people who cannot install it.
I sent them to the bug tracker or to python-list but they seem
not to have gone either place. Is there some guide I should be
sending them to, 'how to debug installation problems'?
tl;dr The summary is that I have a patch that improves CPython
performance up to 5-10% on macro benchmarks. Benchmarks results on
Macbook Pro/Mac OS X, desktop CPU/Linux, server CPU/Linux are available
at . There are no slowdowns that I could reproduce consistently.
There are twodifferent optimizations that yield this speedup:
LOAD_METHOD/CALL_METHOD opcodes and per-opcode cache in ceval loop.
LOAD_METHOD & CALL_METHOD
We had a lot of conversations with Victor about his PEP 509, and he sent
me a link to his amazing compilation of notes about CPython performance
. One optimization that he pointed out to me was LOAD/CALL_METHOD
opcodes, an idea first originated in PyPy.
There is a patch that implements this optimization, it's tracked here:
. There are some low level details that I explained in the issue,
but I'll go over the high level design in this email as well.
Every time you access a method attribute on an object, a BoundMethod
object is created. It is a fairly expensive operation, despite a
freelist of BoundMethods (so that memory allocation is generally
avoided). The idea is to detect what looks like a method call in the
compiler, and emit a pair of specialized bytecodes for that.
So instead of LOAD_GLOBAL/LOAD_ATTR/CALL_FUNCTION we will have
LOAD_METHOD looks at the object on top of the stack, and checks if the
name resolves to a method or to a regular attribute. If it's a method,
then we push the unbound method object and the object to the stack. If
it's an attribute, we push the resolved attribute and NULL.
When CALL_METHOD looks at the stack it knows how to call the unbound
method properly (pushing the object as a first arg), or how to call a
This idea does make CPython faster around 2-4%. And it surely doesn't
make it slower. I think it's a safe bet to at least implement this
optimization in CPython 3.6.
So far, the patch only optimizes positional-only method calls. It's
possible to optimize all kind of calls, but this will necessitate 3 more
opcodes (explained in the issue). We'll need to do some careful
benchmarking to see if it's really needed.
Per-opcode cache in ceval
While reading PEP 509, I was thinking about how we can use
dict->ma_version in ceval to speed up globals lookups. One of the key
assumptions (and this is what makes JITs possible) is that real-life
programs don't modify globals and rebind builtins (often), and that most
code paths operate on objects of the same type.
In CPython, all pure Python functions have code objects. When you call
a function, ceval executes its code object in a frame. Frames contain
contextual information, including pointers to the globals and builtins
dict. The key observation here is that almost all code objects always
have same pointers to the globals (the module they were defined in) and
to the builtins. And it's not a good programming practice to mutate
globals or rebind builtins.
Let's look at this function:
Here are its opcodes:
2 0 LOAD_GLOBAL 0 (print)
3 LOAD_GLOBAL 1 (ham)
6 CALL_FUNCTION 1 (1 positional, 0 keyword pair)
10 LOAD_CONST 0 (None)
The opcodes we want to optimize are LAOD_GLOBAL, 0 and 3. Let's look at
the first one, that loads the 'print' function from builtins. The
opcode knows the following bits of information:
- its offset (0),
- its argument (0 -> 'print'),
- its type (LOAD_GLOBAL).
And these bits of information will *never* change. So if this opcode
could resolve the 'print' name (from globals or builtins, likely the
latter) and save the pointer to it somewhere, along with
globals->ma_version and builtins->ma_version, it could, on its second
call, just load this cached info back, check that the globals and
builtins dict haven't changed and push the cached ref to the stack.
That would save it from doing two dict lookups.
We can also optimize LOAD_METHOD. There are high chances, that 'obj' in
'obj.method()' will be of the same type every time we execute the code
object. So if we'd have an opcodes cache, LOAD_METHOD could then cache
a pointer to the resolved unbound method, a pointer to obj.__class__,
and tp_version_tag of obj.__class__. Then it would only need to check
if the cached object type is the same (and that it wasn't modified) and
that obj.__dict__ doesn't override 'method'. Long story short, this
caching really speeds up method calls on types implemented in C.
list.append becomes very fast, because list doesn't have a __dict__, so
the check is very cheap (with cache).
A straightforward way to implement such a cache is simple, but consumes
a lot of memory, that would be just wasted, since we only need such a
cache for LOAD_GLOBAL and LOAD_METHOD opcodes. So we have to be creative
about the cache design. Here's what I came up with:
1. We add a few fields to the code object.
2. ceval will count how many times each code object is executed.
3. When the code object is executed over ~900 times, we mark it as
"hot". We also create an 'unsigned char' array "MAPPING", with length
set to match the length of the code object. So we have a 1-to-1 mapping
between opcodes and MAPPING array.
4. Next ~100 calls, while the code object is "hot", LOAD_GLOBAL and
LOAD_METHOD do "MAPPING[opcode_offset()]++".
5. After 1024 calls to the code object, ceval loop will iterate through
the MAPPING, counting all opcodes that were executed more than 50 times.
6. We then create an array of cache structs "CACHE" (here's a link to
the updated code.h file: ). We update MAPPING to be a mapping
between opcode position and position in the CACHE. The code object is
7. When the code object is "optimized", LOAD_METHOD and LOAD_GLOBAL use
the CACHE array for fast path.
8. When there is a cache miss, i.e. the builtins/global/obj.__dict__
were mutated, the opcode marks its entry in 'CACHE' as deoptimized, and
it will never try to use the cache again.
Here's a link to the issue tracker with the first version of the patch:
. I'm working on the patch in a github repo here: .
There are many things about this algorithm that we can improve/tweak.
Perhaps we should profile code objects longer, or account for time they
were executed. Maybe we shouldn't deoptimize opcodes on their first
cache miss. Maybe we can come up with better data structures. We also
need to profile the memory and see how much more this cache will require.
One thing I'm certain about, is that we can get a 5-10% speedup of
CPython with relatively low memory impact. And I think it's worth
If you're interested in these kind of optimizations, please help with
code reviews, ideas, profiling and benchmarks. The latter is especially
important, I'd never imagine how hard it is to come up with a good macro
I also want to thank my company MagicStack (magic.io) for sponsoring
Since we're all talking about making Python faster, I thought I'd drop
some previous ideas I've had here in case (1) someone wants to actually
do them, and (2) they really are new ideas that haven't failed in the
past. Mostly I was thinking about startup time.
Here are the list of modules imported on clean startup on my Windows,
US-English machine (from -v and cleaned up a bit):
Obviously the easiest first thing is to remove or delay unnecessary
imports. But a while ago I used a native profiler to trace through this
and the most impactful modules were the encodings:
While I don't doubt that we need all of these for *some* reason,
aliases, cp437 and cp1252 are relatively expensive modules to import.
Mostly due to having large static dictionaries or data structures
generated on startup.
Given this is static and mostly read-only information, I see no
reason why we couldn't either generate completely static versions of
them, or better yet compile the resulting data structures into the core
(: If being able to write to some of the encoding data is used by
some people, I vote for breaking that for 3.6 and making it read-only.)
This is probably the code snippet that bothered me the most:
### Encoding table
It shows up in many of the encodings modules, and while it is not a bad
function in itself, we are obviously generating a known data structure
on every startup. Storing these in static data is a tradeoff between
disk space and startup performance, and one I think it likely to be
Anyway, just an idea if someone wants to try it and see what
improvements we can get. I'd love to do it myself, but when it actually
comes to finding time I keep coming up short.
P.S. If you just want to discuss optimisation techniques or benchmarking
in general, without specific application to CPython 3.6, there's a whole
internet out there. Please don't make me the cause of a pointless
I am looking for tutorials on basically how to write the python language.
As well I want to know how to write wrappers for things like alsa and
directx and coreaudio and jackd and pulseaudio.
I would be looking to write wrappers for coreaudio in mac and coreaudio for
windows as well as alsa. i think it would benefit you to have this
I am looking to aid in development of computers and languages and tutorials
on how to write the actual python language would be super useful.
but yeah im looking to write applications like guitarix rakarrack blender
I am also going to build my own computers so these tutorials would help a
anyhow thanks for your time
Check out and cd into Python trunk.
% grep -Ri win16 * | wc
10 66 625
% grep -Ri nextstep | wc
23 119 1328
% grep -Ri rhapsody * | wc
47 269 3390
% grep -Ri msdos * | wc
56 381 3895
% grep -Ri ms-dos * | wc
20 180 1425
win16! *laughs* *wipes tear from eye*
It's currently 2016. Perhaps it's time to remove all vestiges of these
unsupported operating systems nobody's cared about since a year that
started with a '1'?
p.s. I suspect some of the uses of "rhapsody" are actually live code
used for OS X. So this isn't necessarily dead code, some of it is
merely long-out-of-date comments.
p.p.s. At least there were no references to "taligent", "copland", or
How to resolve distinguishing between documentation and implementation
if current implementation is incorrect, but third-party code can
implicitly depends on it?
For example see issue26198. Currently buffer overflow of predefined
buffer for "es#" and "et#" format units causes TypeError (with
misleading message, but this is other story). The correct and
*documented* exception is ValueError. User code can depend on current
behavior, because TypeError is what is raised now for this type of
errors, and this is what is raised for other types of errors. Unlikely
authors of such code read the documentation, otherwise this issue would
be reported earlier. On other hand, looks these format units are rarely
used with predefined buffer (never in the stdlib since 3.5).
I think it is obvious that the code in the development branch should be
changed to produce documented and more logical exception. But what about
bugfix releases? Changing the documentation would be misleading,
changing the code can break existing code (unlikely, but).
> An off-topic: have you ever tried hg.python.org/benchmarks
> or compare MicroPython vs CPython? I'm curious if MicroPython
> is faster -- in that case we'll try to copy some optimization
I've tried a small number of those benchmarks, but not in any rigorous
way, and not enough to compare properly with CPython. Maybe one day I
(or someone) will get to it and report results :)
One thing that makes MP fast is the use of pointer tagging and
stuffing of small integers within object pointers. Thus integer
arithmetic below 2**30 (on 32-bit arch) requires no heap.
> Do you use opcode dictionary caching only for LOAD_GLOBAL-like
> opcodes? Do you have an equivalent of LOAD_FAST, or you use
> dicts to store local variables?
The opcodes that have dict caching are:
LOAD_METHOD (not implemented yet in mainline repo)
For local variables we use LOAD_FAST and STORE_FAST (and DELETE_FAST).
Actually, there are 16 dedicated opcodes for loading from positions
0-15, and 16 for storing to these positions. Eg:
Mostly this is done to save RAM, since LOAD_FAST_0 is 1 byte.
> If we change the opcode size, it will probably affect libraries
> that compose or modify code objects. Modules like "dis" will
> also need to be updated. And that's probably just a tip of the
> We can still implement your approach if we add a separate
> private 'unsigned char' array to each code object, so that
> LOAD_GLOBAL can store the key offsets. It should be a bit
> faster than my current patch, since it has one less level
> of indirection. But this way we loose the ability to
> optimize LOAD_METHOD, simply because it requires more memory
> for its cache. In any case, I'll experiment!
Problem with that approach (having a separate array for offset_guess)
is that how do you know where to look into that array for a given
LOAD_GLOBAL opcode? The second LOAD_GLOBAL in your bytecode should
look into the second entry in the array, but how does it know?
I'd love to experiment implementing my original caching idea with
CPython, but no time!