What's New in Python 2.3

目录

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 ~frozenset.union and ~frozenset.intersection methods; an alternative notation uses the bitwise operators & and |. Mutable sets also have in-place versions of these methods, !union_update and ~set.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 ~set.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
>>>

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 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.

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.

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.

273 - Import Modules from Zip Archives
Written by James C. Ahlstrom, who also provided an implementation. Python 2.3 follows the specification in 273, but uses an implementation written by Just van Rossum that uses the import hooks described in 302. See section section-pep302 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.

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

single: universal newlines; What's new

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.

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

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 ~logging.Logger class is the primary class. Most application code will deal with one or more ~logging.Logger objects, each one used by a particular subsystem of the application. Each ~logging.Logger is identified by a name, and names are organized into a hierarchy using . as the component separator. For example, you might have ~logging.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 ~logging.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 ~logging.Logger.setLevel method on the root logger.

Notice the ~logging.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 ~logging.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 ~logging.Logger can prevent this by setting its ~logging.Logger.propagate attribute to False.

There are more classes provided by the logging package that can be customized. When a ~logging.Logger instance is told to log a message, it creates a ~logging.LogRecord instance that is sent to any number of different ~logging.Handler instances. Loggers and handlers can also have an attached list of filters, and each filter can cause the ~logging.LogRecord to be ignored or can modify the record before passing it along. When they're finally output, ~logging.LogRecord instances are converted to text by a ~logging.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 282 will also be helpful.

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 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'.

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.

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.

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.

302 borrows ideas from its predecessors, especially from Gordon McMillan's !iu module. Three new items are added to the sys module:

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 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

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 ~csv.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.

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; 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: ~object.__getstate__, ~object.__setstate__, and ~object.__getnewargs__. Consult 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.

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 ~object.__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.

String Changes

Optimizations

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.

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

You can create instances of ~datetime.date and ~datetime.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 ~datetime.date.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 ~datetime.datetime.replace method allows modifying one or more fields of a ~datetime.date or ~datetime.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 ~datetime.datetime.isoformat). ~datetime.date and ~datetime.datetime instances can be subtracted from each other, and added to ~datetime.timedelta instances. The largest missing feature is that there's no standard library support for parsing strings and getting back a ~datetime.date or ~datetime.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 ~optparse.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 ~optparse.OptionParser.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.

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.

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:

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.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:

Porting to Python 2.3

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

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.

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由 壳壳虫 创建于 2026-10-31 17:28:11 UTC
由 壳壳虫 更新于 2026-10-31 17:28:14 UTC