What’s New in Python 2.3
************************

Author:
   A.M. Kuchling

This article explains the new features in Python 2.3.  Python 2.3 was
released on July 29, 2003.

The main themes for Python 2.3 are polishing some of the features
added in 2.2, adding various small but useful enhancements to the core
language, and expanding the standard library.  The new object model
introduced in the previous version has benefited from 18 months of
bugfixes and from optimization efforts that have improved the
performance of new-style classes.  A few new built-in functions have
been added such as "sum()" and "enumerate()".  The "in" operator can
now be used for substring searches (e.g. ""ab" in "abc"" returns
"True").

Some of the many new library features include Boolean, set, heap, and
date/time data types, the ability to import modules from ZIP-format
archives, metadata support for the long-awaited Python catalog, an
updated version of IDLE, and modules for logging messages, wrapping
text, parsing CSV files, processing command-line options, using
BerkeleyDB databases…  the list of new and enhanced modules is
lengthy.

This article doesn’t attempt to provide a complete specification of
the new features, but instead provides a convenient overview.  For
full details, you should refer to the documentation for Python 2.3,
such as the Python Library Reference and the Python Reference Manual.
If you want to understand the complete implementation and design
rationale, refer to the PEP for a particular new feature.


PEP 218: A Standard Set Datatype
================================

The new "sets" module contains an implementation of a set datatype.
The "Set" class is for mutable sets, sets that can have members added
and removed.  The "ImmutableSet" class is for sets that can’t be
modified, and instances of "ImmutableSet" can therefore be used as
dictionary keys. Sets are built on top of dictionaries, so the
elements within a set must be hashable.

Here’s a simple example:

   >>> import sets
   >>> S = sets.Set([1,2,3])
   >>> S
   Set([1, 2, 3])
   >>> 1 in S
   True
   >>> 0 in S
   False
   >>> S.add(5)
   >>> S.remove(3)
   >>> S
   Set([1, 2, 5])
   >>>

The union and intersection of sets can be computed with the "union()"
and "intersection()" methods; an alternative notation uses the bitwise
operators "&" and "|". Mutable sets also have in-place versions of
these methods, "union_update()" and "intersection_update()".

   >>> S1 = sets.Set([1,2,3])
   >>> S2 = sets.Set([4,5,6])
   >>> S1.union(S2)
   Set([1, 2, 3, 4, 5, 6])
   >>> S1 | S2                  # Alternative notation
   Set([1, 2, 3, 4, 5, 6])
   >>> S1.intersection(S2)
   Set([])
   >>> S1 & S2                  # Alternative notation
   Set([])
   >>> S1.union_update(S2)
   >>> S1
   Set([1, 2, 3, 4, 5, 6])
   >>>

It’s also possible to take the symmetric difference of two sets.  This
is the set of all elements in the union that aren’t in the
intersection.  Another way of putting it is that the symmetric
difference contains all elements that are in exactly one set.  Again,
there’s an alternative notation ("^"), and an in-place version with
the ungainly name "symmetric_difference_update()".

   >>> S1 = sets.Set([1,2,3,4])
   >>> S2 = sets.Set([3,4,5,6])
   >>> S1.symmetric_difference(S2)
   Set([1, 2, 5, 6])
   >>> S1 ^ S2
   Set([1, 2, 5, 6])
   >>>

There are also "issubset()" and "issuperset()" methods for checking
whether one set is a subset or superset of another:

   >>> S1 = sets.Set([1,2,3])
   >>> S2 = sets.Set([2,3])
   >>> S2.issubset(S1)
   True
   >>> S1.issubset(S2)
   False
   >>> S1.issuperset(S2)
   True
   >>>

See also:

  **PEP 218** - Adding a Built-In Set Object Type
     PEP written by Greg V. Wilson. Implemented by Greg V. Wilson,
     Alex Martelli, and GvR.


PEP 255: Simple Generators
==========================

In Python 2.2, generators were added as an optional feature, to be
enabled by a "from __future__ import generators" directive.  In 2.3
generators no longer need to be specially enabled, and are now always
present; this means that "yield" is now always a keyword.  The rest of
this section is a copy of the description of generators from the
“What’s New in Python 2.2” document; if you read it back when Python
2.2 came out, you can skip the rest of this section.

You’re doubtless familiar with how function calls work in Python or C.
When you call a function, it gets a private namespace where its local
variables are created.  When the function reaches a "return"
statement, the local variables are destroyed and the resulting value
is returned to the caller.  A later call to the same function will get
a fresh new set of local variables. But, what if the local variables
weren’t thrown away on exiting a function? What if you could later
resume the function where it left off?  This is what generators
provide; they can be thought of as resumable functions.

Here’s the simplest example of a generator function:

   def generate_ints(N):
       for i in range(N):
           yield i

A new keyword, "yield", was introduced for generators.  Any function
containing a "yield" statement is a generator function; this is
detected by Python’s bytecode compiler which compiles the function
specially as a result.

When you call a generator function, it doesn’t return a single value;
instead it returns a generator object that supports the iterator
protocol.  On executing the "yield" statement, the generator outputs
the value of "i", similar to a "return" statement.  The big difference
between "yield" and a "return" statement is that on reaching a "yield"
the generator’s state of execution is suspended and local variables
are preserved.  On the next call to the generator’s ".next()" method,
the function will resume executing immediately after the "yield"
statement.  (For complicated reasons, the "yield" statement isn’t
allowed inside the "try" block of a "try"…"finally" statement; read
**PEP 255** for a full explanation of the interaction between "yield"
and exceptions.)

Here’s a sample usage of the "generate_ints()" generator:

   >>> gen = generate_ints(3)
   >>> gen
   <generator object at 0x8117f90>
   >>> gen.next()
   0
   >>> gen.next()
   1
   >>> gen.next()
   2
   >>> gen.next()
   Traceback (most recent call last):
     File "stdin", line 1, in ?
     File "stdin", line 2, in generate_ints
   StopIteration

You could equally write "for i in generate_ints(5)", or "a,b,c =
generate_ints(3)".

Inside a generator function, the "return" statement can only be used
without a value, and signals the end of the procession of values;
afterwards the generator cannot return any further values. "return"
with a value, such as "return 5", is a syntax error inside a generator
function.  The end of the generator’s results can also be indicated by
raising "StopIteration" manually, or by just letting the flow of
execution fall off the bottom of the function.

You could achieve the effect of generators manually by writing your
own class and storing all the local variables of the generator as
instance variables.  For example, returning a list of integers could
be done by setting "self.count" to 0, and having the "next()" method
increment "self.count" and return it. However, for a moderately
complicated generator, writing a corresponding class would be much
messier. "Lib/test/test_generators.py" contains a number of more
interesting examples.  The simplest one implements an in-order
traversal of a tree using generators recursively.

   # A recursive generator that generates Tree leaves in in-order.
   def inorder(t):
       if t:
           for x in inorder(t.left):
               yield x
           yield t.label
           for x in inorder(t.right):
               yield x

Two other examples in "Lib/test/test_generators.py" produce solutions
for the N-Queens problem (placing $N$ queens on an $NxN$ chess board
so that no queen threatens another) and the Knight’s Tour (a route
that takes a knight to every square of an $NxN$ chessboard without
visiting any square twice).

The idea of generators comes from other programming languages,
especially Icon (https://www2.cs.arizona.edu/icon/), where the idea of
generators is central.  In Icon, every expression and function call
behaves like a generator.  One example from “An Overview of the Icon
Programming Language” at
https://www2.cs.arizona.edu/icon/docs/ipd266.htm gives an idea of what
this looks like:

   sentence := "Store it in the neighboring harbor"
   if (i := find("or", sentence)) > 5 then write(i)

In Icon the "find()" function returns the indexes at which the
substring “or” is found: 3, 23, 33.  In the "if" statement, "i" is
first assigned a value of 3, but 3 is less than 5, so the comparison
fails, and Icon retries it with the second value of 23.  23 is greater
than 5, so the comparison now succeeds, and the code prints the value
23 to the screen.

Python doesn’t go nearly as far as Icon in adopting generators as a
central concept.  Generators are considered part of the core Python
language, but learning or using them isn’t compulsory; if they don’t
solve any problems that you have, feel free to ignore them. One novel
feature of Python’s interface as compared to Icon’s is that a
generator’s state is represented as a concrete object (the iterator)
that can be passed around to other functions or stored in a data
structure.

See also:

  **PEP 255** - Simple Generators
     Written by Neil Schemenauer, Tim Peters, Magnus Lie Hetland.
     Implemented mostly by Neil Schemenauer and Tim Peters, with other
     fixes from the Python Labs crew.


PEP 263: Source Code Encodings
==============================

Python source files can now be declared as being in different
character set encodings.  Encodings are declared by including a
specially formatted comment in the first or second line of the source
file.  For example, a UTF-8 file can be declared with:

   #!/usr/bin/env python
   # -*- coding: UTF-8 -*-

Without such an encoding declaration, the default encoding used is
7-bit ASCII. Executing or importing modules that contain string
literals with 8-bit characters and have no encoding declaration will
result in a "DeprecationWarning" being signalled by Python 2.3; in 2.4
this will be a syntax error.

The encoding declaration only affects Unicode string literals, which
will be converted to Unicode using the specified encoding.  Note that
Python identifiers are still restricted to ASCII characters, so you
can’t have variable names that use characters outside of the usual
alphanumerics.

See also:

  **PEP 263** - Defining Python Source Code Encodings
     Written by Marc-André Lemburg and Martin von Löwis; implemented
     by Suzuki Hisao and Martin von Löwis.


PEP 273: Importing Modules from ZIP Archives
============================================

The new "zipimport" module adds support for importing modules from a
ZIP-format archive.  You don’t need to import the module explicitly;
it will be automatically imported if a ZIP archive’s filename is added
to "sys.path". For example:

   amk@nyman:~/src/python$ unzip -l /tmp/example.zip
   Archive:  /tmp/example.zip
     Length     Date   Time    Name
    --------    ----   ----    ----
        8467  11-26-02 22:30   jwzthreading.py
    --------                   -------
        8467                   1 file
   amk@nyman:~/src/python$ ./python
   Python 2.3 (#1, Aug 1 2003, 19:54:32)
   >>> import sys
   >>> sys.path.insert(0, '/tmp/example.zip')  # Add .zip file to front of path
   >>> import jwzthreading
   >>> jwzthreading.__file__
   '/tmp/example.zip/jwzthreading.py'
   >>>

An entry in "sys.path" can now be the filename of a ZIP archive. The
ZIP archive can contain any kind of files, but only files named
"*.py", "*.pyc", or "*.pyo" can be imported.  If an archive only
contains "*.py" files, Python will not attempt to modify the archive
by adding the corresponding "*.pyc" file, meaning that if a ZIP
archive doesn’t contain "*.pyc" files, importing may be rather slow.

A path within the archive can also be specified to only import from a
subdirectory; for example, the path "/tmp/example.zip/lib/" would only
import from the "lib/" subdirectory within the archive.

See also:

  **PEP 273** - Import Modules from Zip Archives
     Written by James C. Ahlstrom,  who also provided an
     implementation. Python 2.3 follows the specification in **PEP
     273**,  but uses an implementation written by Just van Rossum
     that uses the import hooks described in **PEP 302**. See section
     PEP 302: New Import Hooks for a description of the new import
     hooks.


PEP 277: Unicode file name support for Windows NT
=================================================

On Windows NT, 2000, and XP, the system stores file names as Unicode
strings. Traditionally, Python has represented file names as byte
strings, which is inadequate because it renders some file names
inaccessible.

Python now allows using arbitrary Unicode strings (within the
limitations of the file system) for all functions that expect file
names, most notably the "open()" built-in function. If a Unicode
string is passed to "os.listdir()", Python now returns a list of
Unicode strings.  A new function, "os.getcwdu()", returns the current
directory as a Unicode string.

Byte strings still work as file names, and on Windows Python will
transparently convert them to Unicode using the "mbcs" encoding.

Other systems also allow Unicode strings as file names but convert
them to byte strings before passing them to the system, which can
cause a "UnicodeError" to be raised. Applications can test whether
arbitrary Unicode strings are supported as file names by checking
"os.path.supports_unicode_filenames", a Boolean value.

Under MacOS, "os.listdir()" may now return Unicode filenames.

See also:

  **PEP 277** - Unicode file name support for Windows NT
     Written by Neil Hodgson; implemented by Neil Hodgson, Martin von
     Löwis, and Mark Hammond.


PEP 278: Universal Newline Support
==================================

The three major operating systems used today are Microsoft Windows,
Apple’s Macintosh OS, and the various Unix derivatives.  A minor
irritation of cross-platform work  is that these three platforms all
use different characters to mark the ends of lines in text files.
Unix uses the linefeed (ASCII character 10), MacOS uses the carriage
return (ASCII character 13), and Windows uses a two-character sequence
of a carriage return plus a newline.

Python’s file objects can now support end of line conventions other
than the one followed by the platform on which Python is running.
Opening a file with the mode "'U'" or "'rU'" will open a file for
reading in *universal newlines* mode.  All three line ending
conventions will be translated to a "'\n'" in the strings returned by
the various file methods such as "read()" and "readline()".

Universal newline support is also used when importing modules and when
executing a file with the "execfile()" function.  This means that
Python modules can be shared between all three operating systems
without needing to convert the line-endings.

This feature can be disabled when compiling Python by specifying the "
--without-universal-newlines" switch when running Python’s
**configure** script.

See also:

  **PEP 278** - Universal Newline Support
     Written and implemented by Jack Jansen.


PEP 279: enumerate()
====================

A new built-in function, "enumerate()", will make certain loops a bit
clearer.  "enumerate(thing)", where *thing* is either an iterator or a
sequence, returns an iterator that will return "(0, thing[0])", "(1,
thing[1])", "(2, thing[2])", and so forth.

A common idiom to change every element of a list looks like this:

   for i in range(len(L)):
       item = L[i]
       # ... compute some result based on item ...
       L[i] = result

This can be rewritten using "enumerate()" as:

   for i, item in enumerate(L):
       # ... compute some result based on item ...
       L[i] = result

See also:

  **PEP 279** - The enumerate() built-in function
     Written and implemented by Raymond D. Hettinger.


PEP 282: The logging Package
============================

A standard package for writing logs, "logging", has been added to
Python 2.3.  It provides a powerful and flexible mechanism for
generating logging output which can then be filtered and processed in
various ways.  A configuration file written in a standard format can
be used to control the logging behavior of a program.  Python includes
handlers that will write log records to standard error or to a file or
socket, send them to the system log, or even e-mail them to a
particular address; of course, it’s also possible to write your own
handler classes.

The "Logger" class is the primary class. Most application code will
deal with one or more "Logger" objects, each one used by a particular
subsystem of the application. Each "Logger" is identified by a name,
and names are organized into a hierarchy using "."  as the component
separator. For example, you might have "Logger" instances named
"server", "server.auth" and "server.network".  The latter two
instances are below "server" in the hierarchy.  This means that if you
turn up the verbosity for "server" or direct "server" messages to a
different handler, the changes will also apply to records logged to
"server.auth" and "server.network". There’s also a root "Logger"
that’s the parent of all other loggers.

For simple uses, the "logging" package contains some convenience
functions that always use the root log:

   import logging

   logging.debug('Debugging information')
   logging.info('Informational message')
   logging.warning('Warning:config file %s not found', 'server.conf')
   logging.error('Error occurred')
   logging.critical('Critical error -- shutting down')

This produces the following output:

   WARNING:root:Warning:config file server.conf not found
   ERROR:root:Error occurred
   CRITICAL:root:Critical error -- shutting down

In the default configuration, informational and debugging messages are
suppressed and the output is sent to standard error.  You can enable
the display of informational and debugging messages by calling the
"setLevel()" method on the root logger.

Notice the "warning()" call’s use of string formatting operators; all
of the functions for logging messages take the arguments "(msg, arg1,
arg2, ...)" and log the string resulting from "msg % (arg1, arg2,
...)".

There’s also an "exception()" function that records the most recent
traceback.  Any of the other functions will also record the traceback
if you specify a true value for the keyword argument *exc_info*.

   def f():
       try:    1/0
       except: logging.exception('Problem recorded')

   f()

This produces the following output:

   ERROR:root:Problem recorded
   Traceback (most recent call last):
     File "t.py", line 6, in f
       1/0
   ZeroDivisionError: integer division or modulo by zero

Slightly more advanced programs will use a logger other than the root
logger. The "getLogger(name)" function is used to get a particular
log, creating it if it doesn’t exist yet. "getLogger(None)" returns
the root logger.

   log = logging.getLogger('server')
    ...
   log.info('Listening on port %i', port)
    ...
   log.critical('Disk full')
    ...

Log records are usually propagated up the hierarchy, so a message
logged to "server.auth" is also seen by "server" and "root", but a
"Logger" can prevent this by setting its "propagate" attribute to
"False".

There are more classes provided by the "logging" package that can be
customized.  When a "Logger" instance is told to log a message, it
creates a "LogRecord" instance that is sent to any number of different
"Handler" instances.  Loggers and handlers can also have an attached
list of filters, and each filter can cause the "LogRecord" to be
ignored or can modify the record before passing it along.  When
they’re finally output, "LogRecord" instances are converted to text by
a "Formatter" class.  All of these classes can be replaced by your own
specially written classes.

With all of these features the "logging" package should provide enough
flexibility for even the most complicated applications.  This is only
an incomplete overview of its features, so please see the package’s
reference documentation for all of the details.  Reading **PEP 282**
will also be helpful.

See also:

  **PEP 282** - A Logging System
     Written by Vinay Sajip and Trent Mick; implemented by Vinay
     Sajip.


PEP 285: A Boolean Type
=======================

A Boolean type was added to Python 2.3.  Two new constants were added
to the "__builtin__" module, "True" and "False".  ("True" and "False"
constants were added to the built-ins in Python 2.2.1, but the 2.2.1
versions are simply set to integer values of 1 and 0 and aren’t a
different type.)

The type object for this new type is named "bool"; the constructor for
it takes any Python value and converts it to "True" or "False".

   >>> bool(1)
   True
   >>> bool(0)
   False
   >>> bool([])
   False
   >>> bool( (1,) )
   True

Most of the standard library modules and built-in functions have been
changed to return Booleans.

   >>> obj = []
   >>> hasattr(obj, 'append')
   True
   >>> isinstance(obj, list)
   True
   >>> isinstance(obj, tuple)
   False

Python’s Booleans were added with the primary goal of making code
clearer.  For example, if you’re reading a function and encounter the
statement "return 1", you might wonder whether the "1" represents a
Boolean truth value, an index, or a coefficient that multiplies some
other quantity.  If the statement is "return True", however, the
meaning of the return value is quite clear.

Python’s Booleans were *not* added for the sake of strict type-
checking.  A very strict language such as Pascal would also prevent
you performing arithmetic with Booleans, and would require that the
expression in an "if" statement always evaluate to a Boolean result.
Python is not this strict and never will be, as **PEP 285** explicitly
says.  This means you can still use any expression in an "if"
statement, even ones that evaluate to a list or tuple or some random
object.  The Boolean type is a subclass of the "int" class so that
arithmetic using a Boolean still works.

   >>> True + 1
   2
   >>> False + 1
   1
   >>> False * 75
   0
   >>> True * 75
   75

To sum up "True" and "False" in a sentence: they’re alternative ways
to spell the integer values 1 and 0, with the single difference that
"str()" and "repr()" return the strings "'True'" and "'False'" instead
of "'1'" and "'0'".

See also:

  **PEP 285** - Adding a bool type
     Written and implemented by GvR.


PEP 293: Codec Error Handling Callbacks
=======================================

When encoding a Unicode string into a byte string, unencodable
characters may be encountered.  So far, Python has allowed specifying
the error processing as either “strict” (raising "UnicodeError"),
“ignore” (skipping the character), or “replace” (using a question mark
in the output string), with “strict” being the default behavior. It
may be desirable to specify alternative processing of such errors,
such as inserting an XML character reference or HTML entity reference
into the converted string.

Python now has a flexible framework to add different processing
strategies.  New error handlers can be added with
"codecs.register_error()", and codecs then can access the error
handler with "codecs.lookup_error()". An equivalent C API has been
added for codecs written in C. The error handler gets the necessary
state information such as the string being converted, the position in
the string where the error was detected, and the target encoding.  The
handler can then either raise an exception or return a replacement
string.

Two additional error handlers have been implemented using this
framework: “backslashreplace” uses Python backslash quoting to
represent unencodable characters and “xmlcharrefreplace” emits XML
character references.

See also:

  **PEP 293** - Codec Error Handling Callbacks
     Written and implemented by Walter Dörwald.


PEP 301: Package Index and Metadata for Distutils
=================================================

Support for the long-requested Python catalog makes its first
appearance in 2.3.

The heart of the catalog is the new Distutils **register** command.
Running "python setup.py register" will collect the metadata
describing a package, such as its name, version, maintainer,
description, &c., and send it to a central catalog server.  The
resulting catalog is available from https://pypi.org.

To make the catalog a bit more useful, a new optional *classifiers*
keyword argument has been added to the Distutils "setup()" function.
A list of Trove-style strings can be supplied to help classify the
software.

Here’s an example "setup.py" with classifiers, written to be
compatible with older versions of the Distutils:

   from distutils import core
   kw = {'name': "Quixote",
         'version': "0.5.1",
         'description': "A highly Pythonic Web application framework",
         # ...
         }

   if (hasattr(core, 'setup_keywords') and
       'classifiers' in core.setup_keywords):
       kw['classifiers'] = \
           ['Topic :: Internet :: WWW/HTTP :: Dynamic Content',
            'Environment :: No Input/Output (Daemon)',
            'Intended Audience :: Developers'],

   core.setup(**kw)

The full list of classifiers can be obtained by running  "python
setup.py register --list-classifiers".

See also:

  **PEP 301** - Package Index and Metadata for Distutils
     Written and implemented by Richard Jones.


PEP 302: New Import Hooks
=========================

While it’s been possible to write custom import hooks ever since the
"ihooks" module was introduced in Python 1.3, no one has ever been
really happy with it because writing new import hooks is difficult and
messy.  There have been various proposed alternatives such as the
"imputil" and "iu" modules, but none of them has ever gained much
acceptance, and none of them were easily usable from C code.

**PEP 302** borrows ideas from its predecessors, especially from
Gordon McMillan’s "iu" module.  Three new items  are added to the
"sys" module:

* "sys.path_hooks" is a list of callable objects; most  often they’ll
  be classes.  Each callable takes a string containing a path and
  either returns an importer object that will handle imports from this
  path or raises an "ImportError" exception if it can’t handle this
  path.

* "sys.path_importer_cache" caches importer objects for each path, so
  "sys.path_hooks" will only need to be traversed once for each path.

* "sys.meta_path" is a list of importer objects that will be traversed
  before "sys.path" is checked.  This list is initially empty, but
  user code can add objects to it.  Additional built-in and frozen
  modules can be imported by an object added to this list.

Importer objects must have a single method, "find_module(fullname,
path=None)".  *fullname* will be a module or package name, e.g.
"string" or "distutils.core".  "find_module()" must return a loader
object that has a single method, "load_module(fullname)", that creates
and returns the corresponding module object.

Pseudo-code for Python’s new import logic, therefore, looks something
like this (simplified a bit; see **PEP 302** for the full details):

   for mp in sys.meta_path:
       loader = mp(fullname)
       if loader is not None:
           <module> = loader.load_module(fullname)

   for path in sys.path:
       for hook in sys.path_hooks:
           try:
               importer = hook(path)
           except ImportError:
               # ImportError, so try the other path hooks
               pass
           else:
               loader = importer.find_module(fullname)
               <module> = loader.load_module(fullname)

   # Not found!
   raise ImportError

See also:

  **PEP 302** - New Import Hooks
     Written by Just van Rossum and Paul Moore. Implemented by Just
     van Rossum.


PEP 305: Comma-separated Files
==============================

Comma-separated files are a format frequently used for exporting data
from databases and spreadsheets.  Python 2.3 adds a parser for comma-
separated files.

Comma-separated format is deceptively simple at first glance:

   Costs,150,200,3.95

Read a line and call "line.split(',')": what could be simpler? But
toss in string data that can contain commas, and things get more
complicated:

   "Costs",150,200,3.95,"Includes taxes, shipping, and sundry items"

A big ugly regular expression can parse this, but using the new  "csv"
package is much simpler:

   import csv

   input = open('datafile', 'rb')
   reader = csv.reader(input)
   for line in reader:
       print line

The "reader()" function takes a number of different options. The field
separator isn’t limited to the comma and can be changed to any
character, and so can the quoting and line-ending characters.

Different dialects of comma-separated files can be defined and
registered; currently there are two dialects, both used by Microsoft
Excel. A separate "csv.writer" class will generate comma-separated
files from a succession of tuples or lists, quoting strings that
contain the delimiter.

See also:

  **PEP 305** - CSV File API
     Written and implemented  by Kevin Altis, Dave Cole, Andrew
     McNamara, Skip Montanaro, Cliff Wells.


PEP 307: Pickle Enhancements
============================

The "pickle" and "cPickle" modules received some attention during the
2.3 development cycle.  In 2.2, new-style classes could be pickled
without difficulty, but they weren’t pickled very compactly; **PEP
307** quotes a trivial example where a new-style class results in a
pickled string three times longer than that for a classic class.

The solution was to invent a new pickle protocol.  The
"pickle.dumps()" function has supported a text-or-binary flag  for a
long time.  In 2.3, this flag is redefined from a Boolean to an
integer: 0 is the old text-mode pickle format, 1 is the old binary
format, and now 2 is a new 2.3-specific format.  A new constant,
"pickle.HIGHEST_PROTOCOL", can be used to select the fanciest protocol
available.

Unpickling is no longer considered a safe operation.  2.2’s "pickle"
provided hooks for trying to prevent unsafe classes from being
unpickled (specifically, a "__safe_for_unpickling__" attribute), but
none of this code was ever audited and therefore it’s all been ripped
out in 2.3.  You should not unpickle untrusted data in any version of
Python.

To reduce the pickling overhead for new-style classes, a new interface
for customizing pickling was added using three special methods:
"__getstate__()", "__setstate__()", and "__getnewargs__()".  Consult
**PEP 307** for the full semantics  of these methods.

As a way to compress pickles yet further, it’s now possible to use
integer codes instead of long strings to identify pickled classes. The
Python Software Foundation will maintain a list of standardized codes;
there’s also a range of codes for private use.  Currently no codes
have been specified.

See also:

  **PEP 307** - Extensions to the pickle protocol
     Written and implemented  by Guido van Rossum and Tim Peters.


Extended Slices
===============

Ever since Python 1.4, the slicing syntax has supported an optional
third “step” or “stride” argument.  For example, these are all legal
Python syntax: "L[1:10:2]", "L[:-1:1]", "L[::-1]".  This was added to
Python at the request of the developers of Numerical Python, which
uses the third argument extensively.  However, Python’s built-in list,
tuple, and string sequence types have never supported this feature,
raising a "TypeError" if you tried it. Michael Hudson contributed a
patch to fix this shortcoming.

For example, you can now easily extract the elements of a list that
have even indexes:

   >>> L = range(10)
   >>> L[::2]
   [0, 2, 4, 6, 8]

Negative values also work to make a copy of the same list in reverse
order:

   >>> L[::-1]
   [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]

This also works for tuples, arrays, and strings:

   >>> s='abcd'
   >>> s[::2]
   'ac'
   >>> s[::-1]
   'dcba'

If you have a mutable sequence such as a list or an array you can
assign to or delete an extended slice, but there are some differences
between assignment to extended and regular slices.  Assignment to a
regular slice can be used to change the length of the sequence:

   >>> a = range(3)
   >>> a
   [0, 1, 2]
   >>> a[1:3] = [4, 5, 6]
   >>> a
   [0, 4, 5, 6]

Extended slices aren’t this flexible.  When assigning to an extended
slice, the list on the right hand side of the statement must contain
the same number of items as the slice it is replacing:

   >>> a = range(4)
   >>> a
   [0, 1, 2, 3]
   >>> a[::2]
   [0, 2]
   >>> a[::2] = [0, -1]
   >>> a
   [0, 1, -1, 3]
   >>> a[::2] = [0,1,2]
   Traceback (most recent call last):
     File "<stdin>", line 1, in ?
   ValueError: attempt to assign sequence of size 3 to extended slice of size 2

Deletion is more straightforward:

   >>> a = range(4)
   >>> a
   [0, 1, 2, 3]
   >>> a[::2]
   [0, 2]
   >>> del a[::2]
   >>> a
   [1, 3]

One can also now pass slice objects to the "__getitem__()" methods of
the built-in sequences:

   >>> range(10).__getitem__(slice(0, 5, 2))
   [0, 2, 4]

Or use slice objects directly in subscripts:

   >>> range(10)[slice(0, 5, 2)]
   [0, 2, 4]

To simplify implementing sequences that support extended slicing,
slice objects now have a method "indices(length)" which, given the
length of a sequence, returns a "(start, stop, step)" tuple that can
be passed directly to "range()". "indices()" handles omitted and out-
of-bounds indices in a manner consistent with regular slices (and this
innocuous phrase hides a welter of confusing details!).  The method is
intended to be used like this:

   class FakeSeq:
       ...
       def calc_item(self, i):
           ...
       def __getitem__(self, item):
           if isinstance(item, slice):
               indices = item.indices(len(self))
               return FakeSeq([self.calc_item(i) for i in range(*indices)])
           else:
               return self.calc_item(i)

From this example you can also see that the built-in "slice" object is
now the type object for the slice type, and is no longer a function.
This is consistent with Python 2.2, where "int", "str", etc.,
underwent the same change.


Other Language Changes
======================

Here are all of the changes that Python 2.3 makes to the core Python
language.

* The "yield" statement is now always a keyword, as described in
  section PEP 255: Simple Generators of this document.

* A new built-in function "enumerate()" was added, as described in
  section PEP 279: enumerate() of this document.

* Two new constants, "True" and "False" were added along with the
  built-in "bool" type, as described in section PEP 285: A Boolean
  Type of this document.

* The "int()" type constructor will now return a long integer instead
  of raising an "OverflowError" when a string or floating-point number
  is too large to fit into an integer.  This can lead to the
  paradoxical result that "isinstance(int(expression), int)" is false,
  but that seems unlikely to cause problems in practice.

* Built-in types now support the extended slicing syntax, as described
  in section Extended Slices of this document.

* A new built-in function, "sum(iterable, start=0)",  adds up the
  numeric items in the iterable object and returns their sum.  "sum()"
  only accepts numbers, meaning that you can’t use it to concatenate a
  bunch of strings. (Contributed by Alex Martelli.)

* "list.insert(pos, value)" used to  insert *value* at the front of
  the list when *pos* was negative.  The behaviour has now been
  changed to be consistent with slice indexing, so when *pos* is -1
  the value will be inserted before the last element, and so forth.

* "list.index(value)", which searches for *value*  within the list and
  returns its index, now takes optional  *start* and *stop* arguments
  to limit the search to  only part of the list.

* Dictionaries have a new method, "pop(key[, *default*])", that
  returns the value corresponding to *key* and removes that key/value
  pair from the dictionary.  If the requested key isn’t present in the
  dictionary, *default* is returned if it’s specified and "KeyError"
  raised if it isn’t.

     >>> d = {1:2}
     >>> d
     {1: 2}
     >>> d.pop(4)
     Traceback (most recent call last):
       File "stdin", line 1, in ?
     KeyError: 4
     >>> d.pop(1)
     2
     >>> d.pop(1)
     Traceback (most recent call last):
       File "stdin", line 1, in ?
     KeyError: 'pop(): dictionary is empty'
     >>> d
     {}
     >>>

  There’s also a new class method,  "dict.fromkeys(iterable, value)",
  that creates a dictionary with keys taken from the supplied iterator
  *iterable* and all values set to *value*, defaulting to "None".

  (Patches contributed by Raymond Hettinger.)

  Also, the "dict()" constructor now accepts keyword arguments to
  simplify creating small dictionaries:

     >>> dict(red=1, blue=2, green=3, black=4)
     {'blue': 2, 'black': 4, 'green': 3, 'red': 1}

  (Contributed by Just van Rossum.)

* The "assert" statement no longer checks the "__debug__" flag, so you
  can no longer disable assertions by assigning to "__debug__".
  Running Python with the "-O" switch will still generate code that
  doesn’t execute any assertions.

* Most type objects are now callable, so you can use them to create
  new objects such as functions, classes, and modules.  (This means
  that the "new" module can be deprecated in a future Python version,
  because you can now use the type objects available in the "types"
  module.) For example, you can create a new module object with the
  following code:

     >>> import types
     >>> m = types.ModuleType('abc','docstring')
     >>> m
     <module 'abc' (built-in)>
     >>> m.__doc__
     'docstring'

* A new warning, "PendingDeprecationWarning" was added to indicate
  features which are in the process of being deprecated.  The warning
  will *not* be printed by default.  To check for use of features that
  will be deprecated in the future, supply
  "-Walways::PendingDeprecationWarning::" on the command line or use
  "warnings.filterwarnings()".

* The process of deprecating string-based exceptions, as in "raise
  "Error occurred"", has begun.  Raising a string will now trigger
  "PendingDeprecationWarning".

* Using "None" as a variable name will now result in a "SyntaxWarning"
  warning.  In a future version of Python, "None" may finally become a
  keyword.

* The "xreadlines()" method of file objects, introduced in Python 2.1,
  is no longer necessary because files now behave as their own
  iterator. "xreadlines()" was originally introduced as a faster way
  to loop over all the lines in a file, but now you can simply write
  "for line in file_obj". File objects also have a new read-only
  "encoding" attribute that gives the encoding used by the file;
  Unicode strings written to the file will be automatically  converted
  to bytes using the given encoding.

* The method resolution order used by new-style classes has changed,
  though you’ll only notice the difference if you have a really
  complicated inheritance hierarchy.  Classic classes are unaffected
  by this change.  Python 2.2 originally used a topological sort of a
  class’s ancestors, but 2.3 now uses the C3 algorithm as described in
  the paper “A Monotonic Superclass Linearization for Dylan”. To
  understand the motivation for this change,  read Michele Simionato’s
  article The Python 2.3 Method Resolution Order, or read the thread
  on python-dev starting with the message at
  https://mail.python.org/pipermail/python-
  dev/2002-October/029035.html. Samuele Pedroni first pointed out the
  problem and also implemented the fix by coding the C3 algorithm.

* Python runs multithreaded programs by switching between threads
  after executing N bytecodes.  The default value for N has been
  increased from 10 to 100 bytecodes, speeding up single-threaded
  applications by reducing the switching overhead.  Some multithreaded
  applications may suffer slower response time, but that’s easily
  fixed by setting the limit back to a lower number using
  "sys.setcheckinterval(N)". The limit can be retrieved with the new
  "sys.getcheckinterval()" function.

* One minor but far-reaching change is that the names of extension
  types defined by the modules included with Python now contain the
  module and a "'.'" in front of the type name.  For example, in
  Python 2.2, if you created a socket and printed its "__class__",
  you’d get this output:

     >>> s = socket.socket()
     >>> s.__class__
     <type 'socket'>

  In 2.3, you get this:

     >>> s.__class__
     <type '_socket.socket'>

* One of the noted incompatibilities between old- and new-style
  classes has been removed: you can now assign to the "__name__" and
  "__bases__" attributes of new-style classes.  There are some
  restrictions on what can be assigned to "__bases__" along the lines
  of those relating to assigning to an instance’s "__class__"
  attribute.


String Changes
--------------

* The "in" operator now works differently for strings. Previously,
  when evaluating "X in Y" where *X* and *Y* are strings, *X* could
  only be a single character. That’s now changed; *X* can be a string
  of any length, and "X in Y" will return "True" if *X* is a substring
  of *Y*.  If *X* is the empty string, the result is always "True".

     >>> 'ab' in 'abcd'
     True
     >>> 'ad' in 'abcd'
     False
     >>> '' in 'abcd'
     True

  Note that this doesn’t tell you where the substring starts; if you
  need that information, use the "find()" string method.

* The "strip()", "lstrip()", and "rstrip()" string methods now have an
  optional argument for specifying the characters to strip.  The
  default is still to remove all whitespace characters:

     >>> '   abc '.strip()
     'abc'
     >>> '><><abc<><><>'.strip('<>')
     'abc'
     >>> '><><abc<><><>\n'.strip('<>')
     'abc<><><>\n'
     >>> u'\u4000\u4001abc\u4000'.strip(u'\u4000')
     u'\u4001abc'
     >>>

  (Suggested by Simon Brunning and implemented by Walter Dörwald.)

* The "startswith()" and "endswith()" string methods now accept
  negative numbers for the *start* and *end* parameters.

* Another new string method is "zfill()", originally a function in the
  "string" module.  "zfill()" pads a numeric string with zeros on the
  left until it’s the specified width. Note that the "%" operator is
  still more flexible and powerful than "zfill()".

     >>> '45'.zfill(4)
     '0045'
     >>> '12345'.zfill(4)
     '12345'
     >>> 'goofy'.zfill(6)
     '0goofy'

  (Contributed by Walter Dörwald.)

* A new type object, "basestring", has been added. Both 8-bit strings
  and Unicode strings inherit from this type, so "isinstance(obj,
  basestring)" will return "True" for either kind of string.  It’s a
  completely abstract type, so you can’t create "basestring"
  instances.

* Interned strings are no longer immortal and will now be garbage-
  collected in the usual way when the only reference to them is from
  the internal dictionary of interned strings.  (Implemented by Oren
  Tirosh.)


Optimizations
-------------

* The creation of new-style class instances has been made much faster;
  they’re now faster than classic classes!

* The "sort()" method of list objects has been extensively rewritten
  by Tim Peters, and the implementation is significantly faster.

* Multiplication of large long integers is now much faster thanks to
  an implementation of Karatsuba multiplication, an algorithm that
  scales better than the *O*(*n*^2) required for the grade-school
  multiplication algorithm.  (Original patch by Christopher A. Craig,
  and significantly reworked by Tim Peters.)

* The "SET_LINENO" opcode is now gone.  This may provide a small speed
  increase, depending on your compiler’s idiosyncrasies. See section
  Other Changes and Fixes for a longer explanation. (Removed by
  Michael Hudson.)

* "xrange()" objects now have their own iterator, making "for i in
  xrange(n)" slightly faster than "for i in range(n)".  (Patch by
  Raymond Hettinger.)

* A number of small rearrangements have been made in various hotspots
  to improve performance, such as inlining a function or removing some
  code.  (Implemented mostly by GvR, but lots of people have
  contributed single changes.)

The net result of the 2.3 optimizations is that Python 2.3 runs the
pystone benchmark around 25% faster than Python 2.2.


New, Improved, and Deprecated Modules
=====================================

As usual, Python’s standard library received a number of enhancements
and bug fixes.  Here’s a partial list of the most notable changes,
sorted alphabetically by module name. Consult the "Misc/NEWS" file in
the source tree for a more complete list of changes, or look through
the CVS logs for all the details.

* The "array" module now supports arrays of Unicode characters using
  the "'u'" format character.  Arrays also now support using the "+="
  assignment operator to add another array’s contents, and the "*="
  assignment operator to repeat an array. (Contributed by Jason
  Orendorff.)

* The "bsddb" module has been replaced by version 4.1.6 of the PyBSDDB
  package, providing a more complete interface to the transactional
  features of the BerkeleyDB library.

  The old version of the module has been renamed to  "bsddb185" and is
  no longer built automatically; you’ll  have to edit "Modules/Setup"
  to enable it.  Note that the new "bsddb" package is intended to be
  compatible with the  old module, so be sure to file bugs if you
  discover any incompatibilities. When upgrading to Python 2.3, if the
  new interpreter is compiled with a new version of  the underlying
  BerkeleyDB library, you will almost certainly have to convert your
  database files to the new version.  You can do this fairly easily
  with the new scripts "db2pickle.py" and "pickle2db.py" which you
  will find in the distribution’s "Tools/scripts" directory.  If
  you’ve already been using the PyBSDDB package and importing it as
  "bsddb3", you will have to change your "import" statements to import
  it as "bsddb".

* The new "bz2" module is an interface to the bz2 data compression
  library. bz2-compressed data is usually smaller than  corresponding
  "zlib"-compressed data. (Contributed by Gustavo Niemeyer.)

* A set of standard date/time types has been added in the new
  "datetime" module.  See the following section for more details.

* The Distutils "Extension" class now supports an extra constructor
  argument named *depends* for listing additional source files that an
  extension depends on.  This lets Distutils recompile the module if
  any of the dependency files are modified.  For example, if
  "sampmodule.c" includes the header file "sample.h", you would create
  the "Extension" object like this:

     ext = Extension("samp",
                     sources=["sampmodule.c"],
                     depends=["sample.h"])

  Modifying "sample.h" would then cause the module to be recompiled.
  (Contributed by Jeremy Hylton.)

* Other minor changes to Distutils: it now checks for the "CC",
  "CFLAGS", "CPP", "LDFLAGS", and "CPPFLAGS" environment variables,
  using them to override the settings in Python’s configuration
  (contributed by Robert Weber).

* Previously the "doctest" module would only search the docstrings of
  public methods and functions for test cases, but it now also
  examines private ones as well.  The "DocTestSuite()" function
  creates a "unittest.TestSuite" object from a set of "doctest" tests.

* The new "gc.get_referents(object)" function returns a list of all
  the objects referenced by *object*.

* The "getopt" module gained a new function, "gnu_getopt()", that
  supports the same arguments as the existing "getopt()" function but
  uses GNU-style scanning mode. The existing "getopt()" stops
  processing options as soon as a non-option argument is encountered,
  but in GNU-style mode processing continues, meaning that options and
  arguments can be mixed.  For example:

     >>> getopt.getopt(['-f', 'filename', 'output', '-v'], 'f:v')
     ([('-f', 'filename')], ['output', '-v'])
     >>> getopt.gnu_getopt(['-f', 'filename', 'output', '-v'], 'f:v')
     ([('-f', 'filename'), ('-v', '')], ['output'])

  (Contributed by Peter Åstrand.)

* The "grp", "pwd", and "resource" modules now return enhanced tuples:

     >>> import grp
     >>> g = grp.getgrnam('amk')
     >>> g.gr_name, g.gr_gid
     ('amk', 500)

* The "gzip" module can now handle files exceeding 2 GiB.

* The new "heapq" module contains an implementation of a heap queue
  algorithm.  A heap is an array-like data structure that keeps items
  in a partially sorted order such that, for every index *k*, "heap[k]
  <= heap[2*k+1]" and "heap[k] <= heap[2*k+2]".  This makes it quick
  to remove the smallest item, and inserting a new item while
  maintaining the heap property is *O*(log *n*).  (See
  https://xlinux.nist.gov/dads//HTML/priorityque.html for more
  information about the priority queue data structure.)

  The "heapq" module provides "heappush()" and "heappop()" functions
  for adding and removing items while maintaining the heap property on
  top of some other mutable Python sequence type.  Here’s an example
  that uses a Python list:

     >>> import heapq
     >>> heap = []
     >>> for item in [3, 7, 5, 11, 1]:
     ...    heapq.heappush(heap, item)
     ...
     >>> heap
     [1, 3, 5, 11, 7]
     >>> heapq.heappop(heap)
     1
     >>> heapq.heappop(heap)
     3
     >>> heap
     [5, 7, 11]

  (Contributed by Kevin O’Connor.)

* The IDLE integrated development environment has been updated using
  the code from the IDLEfork project
  (https://idlefork.sourceforge.net).  The most notable feature is
  that the code being developed is now executed in a subprocess,
  meaning that there’s no longer any need for manual "reload()"
  operations. IDLE’s core code has been incorporated into the standard
  library as the "idlelib" package.

* The "imaplib" module now supports IMAP over SSL. (Contributed by
  Piers Lauder and Tino Lange.)

* The "itertools" contains a number of useful functions for use with
  iterators, inspired by various functions provided by the ML and
  Haskell languages.  For example, "itertools.ifilter(predicate,
  iterator)" returns all elements in the iterator for which the
  function "predicate()" returns "True", and "itertools.repeat(obj,
  N)" returns "obj" *N* times. There are a number of other functions
  in the module; see the package’s reference documentation for
  details. (Contributed by Raymond Hettinger.)

* Two new functions in the "math" module, "degrees(rads)" and
  "radians(degs)", convert between radians and degrees.  Other
  functions in the "math" module such as "math.sin()" and "math.cos()"
  have always required input values measured in radians.  Also, an
  optional *base* argument was added to "math.log()" to make it easier
  to compute logarithms for bases other than "e" and "10".
  (Contributed by Raymond Hettinger.)

* Several new POSIX functions ("getpgid()", "killpg()", "lchown()",
  "loadavg()", "major()", "makedev()", "minor()", and "mknod()") were
  added to the "posix" module that underlies the "os" module.
  (Contributed by Gustavo Niemeyer, Geert Jansen, and Denis S.
  Otkidach.)

* In the "os" module, the "*stat()" family of functions can now report
  fractions of a second in a timestamp.  Such time stamps are
  represented as floats, similar to the value returned by
  "time.time()".

  During testing, it was found that some applications will break if
  time stamps are floats.  For compatibility, when using the tuple
  interface of the "stat_result" time stamps will be represented as
  integers. When using named fields (a feature first introduced in
  Python 2.2), time stamps are still represented as integers, unless
  "os.stat_float_times()" is invoked to enable float return values:

     >>> os.stat("/tmp").st_mtime
     1034791200
     >>> os.stat_float_times(True)
     >>> os.stat("/tmp").st_mtime
     1034791200.6335014

  In Python 2.4, the default will change to always returning floats.

  Application developers should enable this feature only if all their
  libraries work properly when confronted with floating-point time
  stamps, or if they use the tuple API. If used, the feature should be
  activated on an application level instead of trying to enable it on
  a per-use basis.

* The "optparse" module contains a new parser for command-line
  arguments that can convert option values to a particular Python type
  and will automatically generate a usage message.  See the following
  section for  more details.

* The old and never-documented "linuxaudiodev" module has been
  deprecated, and a new version named "ossaudiodev" has been added.
  The module was renamed because the OSS sound drivers can be used on
  platforms other than Linux, and the interface has also been tidied
  and brought up to date in various ways. (Contributed by Greg Ward
  and Nicholas FitzRoy-Dale.)

* The new "platform" module contains a number of functions that try to
  determine various properties of the platform you’re running on.
  There are functions for getting the architecture, CPU type, the
  Windows OS version, and even the Linux distribution version.
  (Contributed by Marc-André Lemburg.)

* The parser objects provided by the "pyexpat" module can now
  optionally buffer character data, resulting in fewer calls to your
  character data handler and therefore faster performance.  Setting
  the parser object’s "buffer_text" attribute to "True" will enable
  buffering.

* The "sample(population, k)" function was added to the "random"
  module.  *population* is a sequence or "xrange" object containing
  the elements of a population, and "sample()" chooses *k* elements
  from the population without replacing chosen elements.  *k* can be
  any value up to "len(population)". For example:

     >>> days = ['Mo', 'Tu', 'We', 'Th', 'Fr', 'St', 'Sn']
     >>> random.sample(days, 3)      # Choose 3 elements
     ['St', 'Sn', 'Th']
     >>> random.sample(days, 7)      # Choose 7 elements
     ['Tu', 'Th', 'Mo', 'We', 'St', 'Fr', 'Sn']
     >>> random.sample(days, 7)      # Choose 7 again
     ['We', 'Mo', 'Sn', 'Fr', 'Tu', 'St', 'Th']
     >>> random.sample(days, 8)      # Can't choose eight
     Traceback (most recent call last):
       File "<stdin>", line 1, in ?
       File "random.py", line 414, in sample
           raise ValueError, "sample larger than population"
     ValueError: sample larger than population
     >>> random.sample(xrange(1,10000,2), 10)   # Choose ten odd nos. under 10000
     [3407, 3805, 1505, 7023, 2401, 2267, 9733, 3151, 8083, 9195]

  The "random" module now uses a new algorithm, the Mersenne Twister,
  implemented in C.  It’s faster and more extensively studied than the
  previous algorithm.

  (All changes contributed by Raymond Hettinger.)

* The "readline" module also gained a number of new functions:
  "get_history_item()", "get_current_history_length()", and
  "redisplay()".

* The "rexec" and "Bastion" modules have been declared dead, and
  attempts to import them will fail with a "RuntimeError".  New-style
  classes provide new ways to break out of the restricted execution
  environment provided by "rexec", and no one has interest in fixing
  them or time to do so.  If you have applications using "rexec",
  rewrite them to use something else.

  (Sticking with Python 2.2 or 2.1 will not make your applications any
  safer because there are known bugs in the "rexec" module in those
  versions.  To repeat: if you’re using "rexec", stop using it
  immediately.)

* The "rotor" module has been deprecated because the  algorithm it
  uses for encryption is not believed to be secure.  If you need
  encryption, use one of the several AES Python modules that are
  available separately.

* The "shutil" module gained a "move(src, dest)" function that
  recursively moves a file or directory to a new location.

* Support for more advanced POSIX signal handling was added to the
  "signal" but then removed again as it proved impossible to make it
  work reliably across platforms.

* The "socket" module now supports timeouts.  You can call the
  "settimeout(t)" method on a socket object to set a timeout of *t*
  seconds. Subsequent socket operations that take longer than *t*
  seconds to complete will abort and raise a "socket.timeout"
  exception.

  The original timeout implementation was by Tim O’Malley.  Michael
  Gilfix integrated it into the Python "socket" module and shepherded
  it through a lengthy review.  After the code was checked in, Guido
  van Rossum rewrote parts of it.  (This is a good example of a
  collaborative development process in action.)

* On Windows, the "socket" module now ships with Secure  Sockets Layer
  (SSL) support.

* The value of the C "PYTHON_API_VERSION" macro is now exposed at the
  Python level as "sys.api_version".  The current exception can be
  cleared by calling the new "sys.exc_clear()" function.

* The new "tarfile" module  allows reading from and writing to
  **tar**-format archive files. (Contributed by Lars Gustäbel.)

* The new "textwrap" module contains functions for wrapping strings
  containing paragraphs of text.  The "wrap(text, width)" function
  takes a string and returns a list containing the text split into
  lines of no more than the chosen width.  The "fill(text, width)"
  function returns a single string, reformatted to fit into lines no
  longer than the chosen width. (As you can guess, "fill()" is built
  on top of "wrap()".  For example:

     >>> import textwrap
     >>> paragraph = "Not a whit, we defy augury: ... more text ..."
     >>> textwrap.wrap(paragraph, 60)
     ["Not a whit, we defy augury: there's a special providence in",
      "the fall of a sparrow. If it be now, 'tis not to come; if it",
      ...]
     >>> print textwrap.fill(paragraph, 35)
     Not a whit, we defy augury: there's
     a special providence in the fall of
     a sparrow. If it be now, 'tis not
     to come; if it be not to come, it
     will be now; if it be not now, yet
     it will come: the readiness is all.
     >>>

  The module also contains a "TextWrapper" class that actually
  implements the text wrapping strategy.   Both the "TextWrapper"
  class and the "wrap()" and "fill()" functions support a number of
  additional keyword arguments for fine-tuning the formatting; consult
  the module’s documentation for details. (Contributed by Greg Ward.)

* The "thread" and "threading" modules now have companion modules,
  "dummy_thread" and "dummy_threading", that provide a do-nothing
  implementation of the "thread" module’s interface for platforms
  where threads are not supported.  The intention is to simplify
  thread-aware modules (ones that *don’t* rely on threads to run) by
  putting the following code at the top:

     try:
         import threading as _threading
     except ImportError:
         import dummy_threading as _threading

  In this example, "_threading" is used as the module name to make it
  clear that the module being used is not necessarily the actual
  "threading" module. Code can call functions and use classes in
  "_threading" whether or not threads are supported, avoiding an "if"
  statement and making the code slightly clearer.  This module will
  not magically make multithreaded code run without threads; code that
  waits for another thread to return or to do something will simply
  hang forever.

* The "time" module’s "strptime()" function has long been an annoyance
  because it uses the platform C library’s "strptime()"
  implementation, and different platforms sometimes have odd bugs.
  Brett Cannon contributed a portable implementation that’s written in
  pure Python and should behave identically on all platforms.

* The new "timeit" module helps measure how long snippets of Python
  code take to execute.  The "timeit.py" file can be run directly from
  the command line, or the module’s "Timer" class can be imported and
  used directly.  Here’s a short example that figures out whether it’s
  faster to convert an 8-bit string to Unicode by appending an empty
  Unicode string to it or by using the "unicode()" function:

     import timeit

     timer1 = timeit.Timer('unicode("abc")')
     timer2 = timeit.Timer('"abc" + u""')

     # Run three trials
     print timer1.repeat(repeat=3, number=100000)
     print timer2.repeat(repeat=3, number=100000)

     # On my laptop this outputs:
     # [0.36831796169281006, 0.37441694736480713, 0.35304892063140869]
     # [0.17574405670166016, 0.18193507194519043, 0.17565798759460449]

* The "Tix" module has received various bug fixes and updates for the
  current version of the Tix package.

* The "Tkinter" module now works with a thread-enabled  version of
  Tcl. Tcl’s threading model requires that widgets only be accessed
  from the thread in which they’re created; accesses from another
  thread can cause Tcl to panic.  For certain Tcl interfaces,
  "Tkinter" will now automatically avoid this  when a widget is
  accessed from a different thread by marshalling a command, passing
  it to the correct thread, and waiting for the results.  Other
  interfaces can’t be handled automatically but "Tkinter" will now
  raise an exception on such an access so that you can at least find
  out about the problem.  See https://mail.python.org/pipermail
  /python-dev/2002-December/031107.html for a more detailed
  explanation of this change.  (Implemented by Martin von Löwis.)

* Calling Tcl methods through "_tkinter" no longer  returns only
  strings. Instead, if Tcl returns other objects those objects are
  converted to their Python equivalent, if one exists, or wrapped with
  a "_tkinter.Tcl_Obj" object if no Python equivalent exists. This
  behavior can be controlled through the "wantobjects()" method of
  "tkapp" objects.

  When using "_tkinter" through the "Tkinter" module (as most Tkinter
  applications will), this feature is always activated. It should not
  cause compatibility problems, since Tkinter would always convert
  string results to Python types where possible.

  If any incompatibilities are found, the old behavior can be restored
  by setting the "wantobjects" variable in the "Tkinter" module to
  false before creating the first "tkapp" object.

     import Tkinter
     Tkinter.wantobjects = 0

  Any breakage caused by this change should be reported as a bug.

* The "UserDict" module has a new "DictMixin" class which defines all
  dictionary methods for classes that already have a minimum mapping
  interface.  This greatly simplifies writing classes that need to be
  substitutable for dictionaries, such as the classes in  the "shelve"
  module.

  Adding the mix-in as a superclass provides the full dictionary
  interface whenever the class defines "__getitem__()",
  "__setitem__()", "__delitem__()", and "keys()". For example:

     >>> import UserDict
     >>> class SeqDict(UserDict.DictMixin):
     ...     """Dictionary lookalike implemented with lists."""
     ...     def __init__(self):
     ...         self.keylist = []
     ...         self.valuelist = []
     ...     def __getitem__(self, key):
     ...         try:
     ...             i = self.keylist.index(key)
     ...         except ValueError:
     ...             raise KeyError
     ...         return self.valuelist[i]
     ...     def __setitem__(self, key, value):
     ...         try:
     ...             i = self.keylist.index(key)
     ...             self.valuelist[i] = value
     ...         except ValueError:
     ...             self.keylist.append(key)
     ...             self.valuelist.append(value)
     ...     def __delitem__(self, key):
     ...         try:
     ...             i = self.keylist.index(key)
     ...         except ValueError:
     ...             raise KeyError
     ...         self.keylist.pop(i)
     ...         self.valuelist.pop(i)
     ...     def keys(self):
     ...         return list(self.keylist)
     ...
     >>> s = SeqDict()
     >>> dir(s)      # See that other dictionary methods are implemented
     ['__cmp__', '__contains__', '__delitem__', '__doc__', '__getitem__',
      '__init__', '__iter__', '__len__', '__module__', '__repr__',
      '__setitem__', 'clear', 'get', 'has_key', 'items', 'iteritems',
      'iterkeys', 'itervalues', 'keylist', 'keys', 'pop', 'popitem',
      'setdefault', 'update', 'valuelist', 'values']

  (Contributed by Raymond Hettinger.)

* The DOM implementation in "xml.dom.minidom" can now generate XML
  output in a particular encoding by providing an optional encoding
  argument to the "toxml()" and "toprettyxml()" methods of DOM nodes.

* The "xmlrpclib" module now supports an XML-RPC extension for
  handling nil data values such as Python’s "None".  Nil values are
  always supported on unmarshalling an XML-RPC response.  To generate
  requests containing "None", you must supply a true value for the
  *allow_none* parameter when creating a "Marshaller" instance.

* The new "DocXMLRPCServer" module allows writing self-documenting
  XML-RPC servers. Run it in demo mode (as a program) to see it in
  action.   Pointing the web browser to the RPC server produces pydoc-
  style documentation; pointing xmlrpclib to the server allows
  invoking the actual methods. (Contributed by Brian Quinlan.)

* Support for internationalized domain names (RFCs 3454, 3490, 3491,
  and 3492) has been added. The “idna” encoding can be used to convert
  between a Unicode domain name and the ASCII-compatible encoding
  (ACE) of that name.

     >{}>{}> u"www.Alliancefrançaise.nu".encode("idna")
     'www.xn--alliancefranaise-npb.nu'

  The "socket" module has also been extended to transparently convert
  Unicode hostnames to the ACE version before passing them to the C
  library. Modules that deal with hostnames such as "httplib" and
  "ftplib") also support Unicode host names; "httplib" also sends HTTP
  "Host" headers using the ACE version of the domain name.  "urllib"
  supports Unicode URLs with non-ASCII host names as long as the
  "path" part of the URL is ASCII only.

  To implement this change, the "stringprep" module, the
  "mkstringprep" tool and the "punycode" encoding have been added.


Date/Time Type
--------------

Date and time types suitable for expressing timestamps were added as
the "datetime" module.  The types don’t support different calendars or
many fancy features, and just stick to the basics of representing
time.

The three primary types are: "date", representing a day, month, and
year; "time", consisting of hour, minute, and second; and "datetime",
which contains all the attributes of both "date" and "time". There’s
also a "timedelta" class representing differences between two points
in time, and time zone logic is implemented by classes inheriting from
the abstract "tzinfo" class.

You can create instances of "date" and "time" by either supplying
keyword arguments to the appropriate constructor, e.g.
"datetime.date(year=1972, month=10, day=15)", or by using one of a
number of class methods.  For example, the "today()" class method
returns the current local date.

Once created, instances of the date/time classes are all immutable.
There are a number of methods for producing formatted strings from
objects:

   >>> import datetime as dt
   >>> now = dt.datetime.now()
   >>> now.isoformat()
   '2002-12-30T21:27:03.994956'
   >>> now.ctime()  # Only available on date, datetime
   'Mon Dec 30 21:27:03 2002'
   >>> now.strftime('%Y %d %b')
   '2002 30 Dec'

The "replace()" method allows modifying one or more fields  of a
"date" or "datetime" instance, returning a new instance:

   >>> d = dt.datetime.now()
   >>> d
   datetime.datetime(2002, 12, 30, 22, 15, 38, 827738)
   >>> d.replace(year=2001, hour=12)
   datetime.datetime(2001, 12, 30, 12, 15, 38, 827738)
   >>>

Instances can be compared, hashed, and converted to strings (the
result is the same as that of "isoformat()").  "date" and "datetime"
instances can be subtracted from each other, and added to "timedelta"
instances.  The largest missing feature is that there’s no standard
library support for parsing strings and getting back a "date" or
"datetime".

For more information, refer to the module’s reference documentation.
(Contributed by Tim Peters.)


The optparse Module
-------------------

The "getopt" module provides simple parsing of command-line arguments.
The new "optparse" module (originally named Optik) provides more
elaborate command-line parsing that follows the Unix conventions,
automatically creates the output for "--help", and can perform
different actions for different options.

You start by creating an instance of "OptionParser" and telling it
what your program’s options are.

   import sys
   from optparse import OptionParser

   op = OptionParser()
   op.add_option('-i', '--input',
                 action='store', type='string', dest='input',
                 help='set input filename')
   op.add_option('-l', '--length',
                 action='store', type='int', dest='length',
                 help='set maximum length of output')

Parsing a command line is then done by calling the "parse_args()"
method.

   options, args = op.parse_args(sys.argv[1:])
   print options
   print args

This returns an object containing all of the option values, and a list
of strings containing the remaining arguments.

Invoking the script with the various arguments now works as you’d
expect it to. Note that the length argument is automatically converted
to an integer.

   $ ./python opt.py -i data arg1
   <Values at 0x400cad4c: {'input': 'data', 'length': None}>
   ['arg1']
   $ ./python opt.py --input=data --length=4
   <Values at 0x400cad2c: {'input': 'data', 'length': 4}>
   []
   $

The help message is automatically generated for you:

   $ ./python opt.py --help
   usage: opt.py [options]

   options:
     -h, --help            show this help message and exit
     -iINPUT, --input=INPUT
                           set input filename
     -lLENGTH, --length=LENGTH
                           set maximum length of output
   $

See the module’s documentation for more details.

Optik was written by Greg Ward, with suggestions from the readers of
the Getopt SIG.


Pymalloc: A Specialized Object Allocator
========================================

Pymalloc, a specialized object allocator written by Vladimir
Marangozov, was a feature added to Python 2.1.  Pymalloc is intended
to be faster than the system "malloc()" and to have less memory
overhead for allocation patterns typical of Python programs. The
allocator uses C’s "malloc()" function to get large pools of memory
and then fulfills smaller memory requests from these pools.

In 2.1 and 2.2, pymalloc was an experimental feature and wasn’t
enabled by default; you had to explicitly enable it when compiling
Python by providing the "--with-pymalloc" option to the **configure**
script.  In 2.3, pymalloc has had further enhancements and is now
enabled by default; you’ll have to supply "--without-pymalloc" to
disable it.

This change is transparent to code written in Python; however,
pymalloc may expose bugs in C extensions.  Authors of C extension
modules should test their code with pymalloc enabled, because some
incorrect code may cause core dumps at runtime.

There’s one particularly common error that causes problems.  There are
a number of memory allocation functions in Python’s C API that have
previously just been aliases for the C library’s "malloc()" and
"free()", meaning that if you accidentally called mismatched functions
the error wouldn’t be noticeable. When the object allocator is
enabled, these functions aren’t aliases of "malloc()" and "free()" any
more, and calling the wrong function to free memory may get you a core
dump.  For example, if memory was allocated using "PyObject_Malloc()",
it has to be freed using "PyObject_Free()", not "free()".  A few
modules included with Python fell afoul of this and had to be fixed;
doubtless there are more third-party modules that will have the same
problem.

As part of this change, the confusing multiple interfaces for
allocating memory have been consolidated down into two API families.
Memory allocated with one family must not be manipulated with
functions from the other family.  There is one family for allocating
chunks of memory and another family of functions specifically for
allocating Python objects.

* To allocate and free an undistinguished chunk of memory use the “raw
  memory” family: "PyMem_Malloc()", "PyMem_Realloc()", and
  "PyMem_Free()".

* The “object memory” family is the interface to the pymalloc facility
  described above and is biased towards a large number of “small”
  allocations: "PyObject_Malloc()", "PyObject_Realloc()", and
  "PyObject_Free()".

* To allocate and free Python objects, use the “object” family
  "PyObject_New", "PyObject_NewVar", and "PyObject_Del()".

Thanks to lots of work by Tim Peters, pymalloc in 2.3 also provides
debugging features to catch memory overwrites and doubled frees in
both extension modules and in the interpreter itself.  To enable this
support, compile a debugging version of the Python interpreter by
running **configure** with "--with-pydebug".

To aid extension writers, a header file "Misc/pymemcompat.h" is
distributed with the source to Python 2.3 that allows Python
extensions to use the 2.3 interfaces to memory allocation while
compiling against any version of Python since 1.5.2.  You would copy
the file from Python’s source distribution and bundle it with the
source of your extension.

See also:

  https://hg.python.org/cpython/file/default/Objects/obmalloc.c
     For the full details of the pymalloc implementation, see the
     comments at the top of the file "Objects/obmalloc.c" in the
     Python source code. The above link points to the file within the
     python.org SVN browser.


Build and C API Changes
=======================

Changes to Python’s build process and to the C API include:

* The cycle detection implementation used by the garbage collection
  has proven to be stable, so it’s now been made mandatory.  You can
  no longer compile Python without it, and the "--with-cycle-gc"
  switch to **configure** has been removed.

* Python can now optionally be built as a shared library
  ("libpython2.3.so") by supplying "--enable-shared" when running
  Python’s **configure** script.  (Contributed by Ondrej Palkovsky.)

* The "DL_EXPORT" and "DL_IMPORT" macros are now deprecated.
  Initialization functions for Python extension modules should now be
  declared using the new macro "PyMODINIT_FUNC", while the Python core
  will generally use the "PyAPI_FUNC" and "PyAPI_DATA" macros.

* The interpreter can be compiled without any docstrings for the
  built-in functions and modules by supplying "--without-doc-strings"
  to the **configure** script. This makes the Python executable about
  10% smaller, but will also mean that you can’t get help for Python’s
  built-ins.  (Contributed by Gustavo Niemeyer.)

* The "PyArg_NoArgs()" macro is now deprecated, and code that uses it
  should be changed.  For Python 2.2 and later, the method definition
  table can specify the "METH_NOARGS" flag, signalling that there are
  no arguments, and the argument checking can then be removed.  If
  compatibility with pre-2.2 versions of Python is important, the code
  could use "PyArg_ParseTuple(args, "")" instead, but this will be
  slower than using "METH_NOARGS".

* "PyArg_ParseTuple()" accepts new format characters for various sizes
  of unsigned integers: "B" for unsigned char, "H" for unsigned short
  int,  "I" for unsigned int,  and "K" for unsigned long long.

* A new function, "PyObject_DelItemString(mapping, char *key)" was
  added as shorthand for "PyObject_DelItem(mapping,
  PyString_New(key))".

* File objects now manage their internal string buffer differently,
  increasing it exponentially when needed.  This results in the
  benchmark tests in "Lib/test/test_bufio.py" speeding up considerably
  (from 57 seconds to 1.7 seconds, according to one measurement).

* It’s now possible to define class and static methods for a C
  extension type by setting either the "METH_CLASS" or "METH_STATIC"
  flags in a method’s "PyMethodDef" structure.

* Python now includes a copy of the Expat XML parser’s source code,
  removing any dependence on a system version or local installation of
  Expat.

* If you dynamically allocate type objects in your extension, you
  should be aware of a change in the rules relating to the
  "__module__" and "__name__" attributes.  In summary, you will want
  to ensure the type’s dictionary contains a "'__module__'" key;
  making the module name the part of the type name leading up to the
  final period will no longer have the desired effect.  For more
  detail, read the API reference documentation or the  source.


Port-Specific Changes
---------------------

Support for a port to IBM’s OS/2 using the EMX runtime environment was
merged into the main Python source tree.  EMX is a POSIX emulation
layer over the OS/2 system APIs.  The Python port for EMX tries to
support all the POSIX-like capability exposed by the EMX runtime, and
mostly succeeds; "fork()" and "fcntl()" are restricted by the
limitations of the underlying emulation layer.  The standard OS/2
port, which uses IBM’s Visual Age compiler, also gained support for
case-sensitive import semantics as part of the integration of the EMX
port into CVS.  (Contributed by Andrew MacIntyre.)

On MacOS, most toolbox modules have been weaklinked to improve
backward compatibility.  This means that modules will no longer fail
to load if a single routine is missing on the current OS version.
Instead calling the missing routine will raise an exception.
(Contributed by Jack Jansen.)

The RPM spec files, found in the "Misc/RPM/" directory in the Python
source distribution, were updated for 2.3.  (Contributed by Sean
Reifschneider.)

Other new platforms now supported by Python include AtheOS
(http://www.atheos.cx/), GNU/Hurd, and OpenVMS.


Other Changes and Fixes
=======================

As usual, there were a bunch of other improvements and bugfixes
scattered throughout the source tree.  A search through the CVS change
logs finds there were 523 patches applied and 514 bugs fixed between
Python 2.2 and 2.3.  Both figures are likely to be underestimates.

Some of the more notable changes are:

* If the "PYTHONINSPECT" environment variable is set, the Python
  interpreter will enter the interactive prompt after running a Python
  program, as if Python had been invoked with the "-i" option. The
  environment variable can be set before running the Python
  interpreter, or it can be set by the Python program as part of its
  execution.

* The "regrtest.py" script now provides a way to allow “all resources
  except *foo*.”  A resource name passed to the "-u" option can now be
  prefixed with a hyphen ("'-'") to mean “remove this resource.”  For
  example, the option ‘"-uall,-bsddb"’ could be used to enable the use
  of all resources except "bsddb".

* The tools used to build the documentation now work under Cygwin as
  well as Unix.

* The "SET_LINENO" opcode has been removed.  Back in the mists of
  time, this opcode was needed to produce line numbers in tracebacks
  and support trace functions (for, e.g., "pdb"). Since Python 1.5,
  the line numbers in tracebacks have been computed using a different
  mechanism that works with “python -O”.  For Python 2.3 Michael
  Hudson implemented a similar scheme to determine when to call the
  trace function, removing the need for "SET_LINENO" entirely.

  It would be difficult to detect any resulting difference from Python
  code, apart from a slight speed up when Python is run without "-O".

  C extensions that access the "f_lineno" field of frame objects
  should instead call "PyCode_Addr2Line(f->f_code, f->f_lasti)". This
  will have the added effect of making the code work as desired under
  “python -O” in earlier versions of Python.

  A nifty new feature is that trace functions can now assign to the
  "f_lineno" attribute of frame objects, changing the line that will
  be executed next.  A "jump" command has been added to the "pdb"
  debugger taking advantage of this new feature. (Implemented by
  Richie Hindle.)


Porting to Python 2.3
=====================

This section lists previously described changes that may require
changes to your code:

* "yield" is now always a keyword; if it’s used as a variable name in
  your code, a different name must be chosen.

* For strings *X* and *Y*, "X in Y" now works if *X* is more than one
  character long.

* The "int()" type constructor will now return a long integer instead
  of raising an "OverflowError" when a string or floating-point number
  is too large to fit into an integer.

* If you have Unicode strings that contain 8-bit characters, you must
  declare the file’s encoding (UTF-8, Latin-1, or whatever) by adding
  a comment to the top of the file.  See section PEP 263: Source Code
  Encodings for more information.

* Calling Tcl methods through "_tkinter" no longer  returns only
  strings. Instead, if Tcl returns other objects those objects are
  converted to their Python equivalent, if one exists, or wrapped with
  a "_tkinter.Tcl_Obj" object if no Python equivalent exists.

* Large octal and hex literals such as "0xffffffff" now trigger a
  "FutureWarning". Currently they’re stored as 32-bit numbers and
  result in a negative value, but in Python 2.4 they’ll become
  positive long integers.

  There are a few ways to fix this warning.  If you really need a
  positive number, just add an "L" to the end of the literal.  If
  you’re trying to get a 32-bit integer with low bits set and have
  previously used an expression such as "~(1 << 31)", it’s probably
  clearest to start with all bits set and clear the desired upper
  bits. For example, to clear just the top bit (bit 31), you could
  write "0xffffffffL &~(1L<<31)".

* You can no longer disable assertions by assigning to "__debug__".

* The Distutils "setup()" function has gained various new keyword
  arguments such as *depends*.  Old versions of the Distutils will
  abort if passed unknown keywords.  A solution is to check for the
  presence of the new "get_distutil_options()" function in your
  "setup.py" and only uses the new keywords with a version of the
  Distutils that supports them:

     from distutils import core

     kw = {'sources': 'foo.c', ...}
     if hasattr(core, 'get_distutil_options'):
         kw['depends'] = ['foo.h']
     ext = Extension(**kw)

* Using "None" as a variable name will now result in a "SyntaxWarning"
  warning.

* Names of extension types defined by the modules included with Python
  now contain the module and a "'.'" in front of the type name.


Acknowledgements
================

The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Jeff Bauer, Simon Brunning, Brett Cannon, Michael Chermside,
Andrew Dalke, Scott David Daniels, Fred L. Drake, Jr., David Fraser,
Kelly Gerber, Raymond Hettinger, Michael Hudson, Chris Lambert, Detlef
Lannert, Martin von Löwis, Andrew MacIntyre, Lalo Martins, Chad
Netzer, Gustavo Niemeyer, Neal Norwitz, Hans Nowak, Chris Reedy,
Francesco Ricciardi, Vinay Sajip, Neil Schemenauer, Roman Suzi, Jason
Tishler, Just van Rossum.
