Python 2.3 有什么新变化
***********************

作者:
   A.M. Kuchling

本文介绍了 Python 2.3 的新特性。 Python 2.3 发布于 2003 年 7 月 29 日
。

Python 2.3 的主要主题是完善在 2.2 中添加的一些功能、为核心语言添加各种
小但实用的增强功能，以及扩展标准库。 上一版本引入的新对象模型已经受益
于 18 个月的错误修复和优化努力，这些优化提升了新式类的性能。 新增了几
个内置函数，例如 "sum()" 和 "enumerate()"。 "in" 操作符现在可以用于子
字符串搜索 (例如，""ab" in "abc"" 将返回 "True")。

许多新库功能包括布尔值、集合、堆、日期/时间数据类型，从ZIP格式的归档文
件中导入模块的能力，期待已久的 Python 目录的元数据支持，更新版本的
IDLE，以及用于日志记录、文本包装、解析 CSV 文件、处理命令行选项、使用
BerkeleyDB 数据库的模块…… 新模块和增强模块的列表相当长。

本文并不试图提供对新功能的完整规范，而是提供了一个方便的概览。 有关详
细信息，你应该参考 Python 2.3 的文档，例如 Python 库参考和 Python 参考
手册。 如果你想了解完整的实现和设计原理，请参阅特定新功能的 PEP。


PEP 218: 标准集合数据类型
=========================

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.

这是一个简单的示例：

   >>> 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])
   >>>

另外还有 "issubset()" 和 "issuperset()" 方法用来检查一个集合是否为另一
个集合的子集或超集:

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

也參考:

  **PEP 218** - 添加内置Set对象类型
     PEP 由 Greg V. Wilson 撰写 ; 由 Greg V. Wilson, Alex Martelli 和
     GvR 实现。


PEP 255: 简单的生成器
=====================

在 Python 2.2 中，生成器作为一个可选特性被添加，需要通过 "from
__future__ import generators" 指令来启用。 在 2.3 版本中，生成器不再需
要特别启用，现在总是存在；这意味着 "yield" 现在始终是一个关键字。 本节
的其余部分是从《Python 2.2的新特性》文档中复制的生成器描述；如果你在
Python 2.2 发布时已经阅读过，可以跳过本节的其余部分。

你一定熟悉在 Python 或 C 语言中函数调用的工作方式。 当你调用一个函数时
，它会获得一个私有命名空间，在这个命名空间中创建其局部变量。 当函数执
行到 "return" 语句时，这些局部变量会被销毁，并将结果值返回给调用者。
稍后对同一个函数的调用将获得一套全新的局部变量。 但是，如果局部变量在
函数退出时不被丢弃呢？如果你可以在函数停止的地方稍后恢复执行呢？这就是
生成器所提供的功能；它们可以被视为可恢复的函数。

这里是一个生成器函数的最简示例:

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

一个新的关键字 "yield" 被引入用于生成器。 任何包含 "yield" 语句的函数
都是生成器函数；这由 Python 的字节码编译器检测到，并因此对函数进行特殊
编译。

当您调用生成器函数时，它不会返回一个单独的值；相反，它会返回一个支持迭
代器协议的生成器对象。在执行 "yield" 语句时，生成器会输出 "i" 的值 ，
类似于 "return" 语句。 "yield" 与 "return" 语句之间的最大区别在于，在
到达 "yield" 时，生成器的执行状态会暂停，并保留本地变量。 在下一次调用
生成器 的 ".next()" 方法时，函数将在 "yield" 语句之后立即恢复执行。 （
由于复杂的原因，"yield" 语句不允许在 "try"..."finally" 语句的 "try" 代
码块内出现；有关 "yield" 和异常之间交互的完整解释，请阅读 **PEP 255**
。）

这里是 "generate_ints()" 生成器的用法示例:

   >>> 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

你可以等价地写成 "for i in generate_ints(5)" 或 "a,b,c =
generate_ints(3)"。

在生成器函数内部， "return" 语句只能不带值使用，并表示值的生成过程结束
；之后，生成器不能再返回任何值。在生成器函数内部，带值的 "return"，例
如 "return 5"，是语法错误。生成器结果的结束也可以通过手动引发
"StopIteration" 异常来指示，或者只是让执行流自然地从函数底部流出。

你可以通过编写自己的类并将生成器的所有局部变量存储为实例变量，手动实现
生成器的效果。例如，返回一个整数列表可以通过将 "self.count" 设置为0，
并让 "next()" 方法递增 "self.count" 并返回它。然而，对于一个中等复杂的
生成器，编写一个相应的类将会更加混乱。"Lib/test/test_generators.py" 包
含了一些更有趣的例子。其中最简单的一个使用生成器递归实现了树的中序遍历
：

   # 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

在 "Lib/test/test_generators.py" 中还有另外两个例子，它们分别解决了N皇
后问题（在$NxN$的棋盘上放置$N$个皇后，使得没有任何皇后威胁到其他皇后）
和骑士巡游问题（在$NxN$的棋盘上，骑士访问每一个方格且不重复访问任何方
格的路径）。

The idea of generators comes from other programming languages,
especially Icon (https://www.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://www.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并不像Icon那样将生成器作为核心概念来采用。生成器被视为Python核心
语言的一部分，但学习或使用它们并不是强制的；如果它们不能解决你遇到的问
题，可以完全忽略它们。与Icon相比，Python接口的一个新颖特性是生成器的状
态表示为一个具体的对象（迭代器），可以传递给其他函数或存储在数据结构中
。

也參考:

  **PEP 255** - 简单生成器
     由 Neil Schemenauer, Tim Peters, Magnus Lie Hetland 撰写。 主要由
     Neil Schemenauer 和 Tim Peters 实现，并包含来自 Python Labs 团队
     的修正。


PEP 263: 源代码的字符编码格式
=============================

现在可以声明Python源文件使用不同的字符集编码。通过在源文件的第一行或第
二行包含特定格式的注释来声明编码。例如，一个UTF-8文件可以这样声明：

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

如果没有这样的编码声明，默认使用7位ASCII编码。执行或导入包含8位字符的
字符串字面量且没有编码声明的模块时，在Python 2.3中会触发
"DeprecationWarning" 警告；而在Python 2.4中，这将成为语法错误

编码声明只影响Unicode字符串字面量，这些字面量将使用指定的编码转换为
Unicode。请注意，Python的标识符仍然限制为ASCII字符，因此变量名不能使用
超出常规字母数字字符范围的字符。

也參考:

  **PEP 263** - 定义 Python 源代码的编码格式
     由 Marc-André Lemburg 和 Martin von Löwis 撰写 ; 由 Suzuki Hisao
     和 Martin von Löwis 实现。


PEP 273: 从ZIP压缩包导入模块
============================

通过新的 "zipimport" 模块增加了从 ZIP 格式归档文件导入模块的支持。 你
不需要显式地导入模块；它将在 ZIP 归档文件名被添加到 "sys.path" 的情况
下自动导入。 例如：

   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.

也參考:

  **PEP 273** - 从 ZIP 压缩包导入模块
     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: 新导入钩子 for a description of the new import hooks.


PEP 277: 针对 Windows NT 的 Unicode 文件名支持
==============================================

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.

字节串仍可被用作文件名，并且在 Windows 上 Python 将透明地使用 "mbcs"
编码格式将其转换为 Unicode。

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.

在 MacOS 下，"os.listdir()" 现在可以返回 Unicode 文件名。

也參考:

  **PEP 277** - 针对 Windows NT 的 Unicode 文件名支持
     由 Neil Hodgson 撰写 ; 由 Neil Hodgson, Martin von Löwis 和 Mark
     Hammond 实现。


PEP 278: 通用换行支持
=====================

目前使用的三大操作系统是微软的 Windows、苹果的 Macintosh OS 和各种
Unix 衍生系统。跨平台工作的一个小麻烦是，这三个平台都使用不同的字符来
标记文本文件中的行结束。Unix 使用换行符（ASCII 字符 10），MacOS 使用回
车符（ASCII 字符 13），Windows 使用回车符加换行符的双字符序列。

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.

在编译 Python 时，可以通过在运行 Python 的 **configure** 脚本时指定 "
--without-universal-newlines" 开关禁用该功能。

也參考:

  **PEP 278** - 通用换行支持
     由 Jack Jansen 撰写并实现。


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

新的内置函数 "enumerate()" 将使某些循环更加清晰。 在
"enumerate(thing)" 中，如果 *thing* 是迭代器或序列，则返回一个迭代器，
该迭代器将返回 "(0, thing[0])"，"(1, thing[1])"，"(2, thing[2])"，以此
类推。

改变一个列表中每个元素的常见写法看起来像是这样:

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

可以使用 "enumerate()" 重写为：

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

也參考:

  **PEP 279** - 内置函数 enumerate()
     由 Raymond D. Hettinger 撰写并实现。


PEP 282: logging 包
===================

Python 2.3 中新增了一个用于编写日志的标准软件包 "logging"。 它为生成日
志输出提供了一个强大而灵活的机制，这些输出可以通过各种方式进行过滤和处
理。用标准格式编写的配置文件可以用来控制程序的日志行为。 Python 包含的
处理器可以将日志记录写入标准错误、文件或套接字，发送到系统日志，甚至通
过电子邮件发送到特定地址；当然，您也可以编写自己的处理器类。

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.

为了简化使用，"logging" 包提供了一些始终使用根日志的便捷函数:

   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')

这会产生以下输出:

   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()

这会产生以下输出:

   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.

"logging" 软件包具有所有这些功能，即使是最复杂的应用程序也能灵活运用。
本文仅是对其功能的不完整概述，因此请参阅软件包的参考文档了解所有细节。
阅读 **PEP 282** 也会有所帮助。

也參考:

  **PEP 282** - Logging 系统
     由 Vinay Sajip 和 Trent Mick 撰写 ; 由 Vinay Sajip 实现。


PEP 285: 布尔类型
=================

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.)

这个新类型的类型对象名为 "bool"；它的构造函数接收任何 Python 值，并将
其转换为 "True" 或 "False"。:

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

大多数标准库模块和内置函数都改为返回布尔值：

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

添加 Python 布尔运算的主要目的是使代码更清晰。 例如，如果您在阅读一个
函数时遇到 "return 1" 语句，您可能会想知道 "1" 代表的是布尔真值、索引
还是乘以其他量的系数。 然而，如果语句是 "return True"，返回值的含义就
非常清楚了。

Python 的布尔值 *不是* 为了严格的类型检查而添加的。 像 Pascal 这样非常
严格的语言也会阻止您使用布尔进行算术运算，并要求 "if" 语句中的表达式总
是求布尔结果。 正如 **PEP 285** 所明确指出的，Python 没有这么严格，以
后也不会有。 这意味着您仍然可以在 "if" 语句中使用任何表达式，甚至是求
值为 list、tuple 或一些随机对象的表达式。 布尔类型是 "int" 类的子类，
因此使用布尔值进行算术运算仍然有效：

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

用一句话概括 "True" 和 "False": 它们是拼写整数值 1 和 0 的另一种方式，
唯一不同的是 "str()" 和 "repr()" 返回的字符串是 "'True'" 和 "'False'"
，而不是 "'1'" 和 "'0'"。

也參考:

  **PEP 285** - 添加布尔类型
     由 GvR 撰写并实现。


PEP 293: 编解码器错误处理回调
=============================

将 Unicode 字符串编码为字节字符串时，可能会遇到无法编码的字符。 到目前
为止，Python 允许将错误处理指定为 "strict" (引发 "UnicodeError")、
"ignore" (跳过该字符) 或 "replace" (在输出字符串中使用问号)，其中
"strict" 是默认行为。 可能需要指定对此类错误的其他处理方式，例如在转换
后的字符串中插入 XML 字符引用或 HTML 实体引用。

Python 现在有一个灵活的框架，可以添加不同的处理策略。可以通过
"codecs.register_error()" 添加新的错误处理器，然后编解码器可以通过
"codecs.lookup_error()" 访问错误处理器。 错误处理器会获取必要的状态信
息，如正在转换的字符串、字符串中检测到错误的位置以及目标编码。 然后，
处理器可以引发异常或返回替换字符串。

使用该框架还实现了两个额外的错误处理器: "backslashreplace" 使用 Python
反斜杠引号来表示无法编码的字符，而 "xmlcharrefreplace" 则转换为 XML 字
符引用。

也參考:

  **PEP 293** - 编解码器错误处理回调
     由 Walter Dörwald 撰写并实现。


PEP 301: Distutils的软件包索引和元数据
======================================

广受期待的对 Python 编目的支持在 2.3 版中首次出现。

编目功能的核心是新的 Distutils **register** 命令。 运行 "python
setup.py register" 将会收集描述软件包的元数据，例如其名称、版本、维护
者、描述信息等等，并将其发送给中央编目服务器。 结果编目数据可在
https://pypi.org 获取。

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.

下面是一个带有分类器的 "setup.py" 示例，其编写是为了兼容旧版本的
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)

完整的 classifiers 列表可通过运行 "python setup.py register --list-
classifiers" 来获取。

也參考:

  **PEP 301** - Distutils 的软件包索引和元数据
     由 Richard Jones 撰写并实现。


PEP 302: 新导入钩子
===================

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" 是一个可调用对象列表，通常是类。 每个可调用对象都接
  收一个包含路径的字符串，然后返回一个可处理从该路径导入的导入器对象，
  如果不能处理该路径，则引发 "ImportError" 异常。

* "sys.path_importer_cache" 会缓存每条路径的导入器对象，因此
  "sys.path_hooks" 只需为每条路径遍历一次。

* "sys.meta_path" 是一个导入器对象列表，在检查 "sys.path" 之前将遍历该
  列表。 该列表最初为空，但用户代码可以向其中添加对象。 其他内置模块和
  冻结模块可以通过添加到该列表中的对象导入。

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.

因此，Python 新导入逻辑的伪代码如下 (略有简化；详情请参见 **PEP 302**)
：

   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

也參考:

  **PEP 302** - 新导入钩
     由 Just van Rossum 和 Paul Moore 撰写 ; 由 Just van Rossum 实现。


PEP 305: 逗号分隔文件
=====================

以逗号作为分隔符的文件是一种常用于从数据库和电子表格导出数据的格式。
Python 2.3 增加了一个针对逗号分隔文件的解析器。

逗号分隔文件乍一看非常简单:

   Costs,150,200,3.95

读取一行并调用 "line.split(',')": 再简单不过了吧？ 但是考虑到可能包含
逗号的字符串数据，事件就变得复杂起来:

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

一个大的丑陋的正则表达式可以解析这些内容，但使用新的 "csv" 软件包要简
单得多：

   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.

也參考:

  该实现在“Python 增强提议” - PEP *305* (CSV 文件 API)  中被提出
     由 Kevin Altis, Dave Cole, Andrew McNamara, Skip Montanaro, Cliff
     Wells 撰写并实现。


PEP 307：对 pickle 的改进
=========================

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.

解决办法就是发明一种新的 pickle 协议。 "pickle.dumps()" 函数很早就支持
文本或二进制标志。 在 2.3 中，该标志从布尔值重新定义为整数：0 表示旧的
文本模式 pickle 格式，1 表示旧的二进制格式，现在 2 表示新的 2.3 专用格
式。 一个新常量 "pickle.HIGHEST_PROTOCOL" 可用来选择最先进的协议。

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.

为了进一步压缩 pickle 类，现在可以使用整数代码而不是长字符串来标识
pickle 类。 Python 软件基金会将维护一个标准化代码列表；还有一系列供私
人使用的代码。 目前还没有指定任何代码。

也參考:

  **PEP 307** - pickle 协议的扩展
     PEP 由 Guido van Rossum 和 Tim Peters 撰写和实现。


扩展切片
========

从 Python 1.4 开始，切片语法支持可选的第三个“step”或“stride”参数。例如
，这些都是合法的 Python 语法: "L[1:10:2]"，"L[:-1:1]"，"L[::-1]"。 这
是应 Numerical Python 开发者的要求添加到 Python 中的，因为 Numerical
Python 广泛使用第三个参数。 然而，Python 内置的 list、tuple 和字符串序
列类型从未支持过这一特性，如果您尝试使用，会引发 "TypeError"。 Michael
Hudson 提供了一个补丁来修复这一缺陷。

例如，您现在可以轻松地提取出具有偶数索引的列表元素：

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

也可以用负值以按相反顺序复制相同的列表：

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

这也适用于元组、数组和字符串：

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

如果你有一个可变序列如列表或数组，你可以对扩展切片进行赋值或删除，但对
扩展切片的赋值与对常规切片的赋值有一些区别。对常规片段的赋值可以用来改
变序列的长度：

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

扩展分片则没有这种灵活性。 在为扩展分片赋值时，语句右侧的列表必须包含
与要替换的分片相同数量的项目：

   >>> 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

删除操作更为直观：

   >>> 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]

或者直接在下标中使用切片对象：

   >>> 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)

从这个例子中还可以看到，内置的 "slice" 对象现在是 slice 类型的类型对象
，而不再是函数。 这与 Python 2.2 是一致的，在 Python 2.2 中，"int"，
"str" 等也经历了同样的变化。


其他语言特性修改
================

以下是 Python 2.3 针对核心 Python 语言的所有改变。

* "yield" 语句现在将始终是关键字，如本文档的 PEP 255: 简单的生成器 一
  节所描述的。

* 新增内置函数 "enumerate()"，如本文档的 PEP 279: enumerate() 一节所描
  述的。

* 新增两个常量 "True" 和 "False" 以及内置的 "bool" 类型，如本文档的
  PEP 285: 布尔类型 一节所描述的。

* "int()" 类型构造函数现在会返回一个长整数，而不会在字符串或浮点数太大
  而无法放入整数时引发 "OverflowError"。 这可能会导致
  "isinstance(int(expression), int)" 为假的矛盾结果，但在实践中似乎不
  太可能造成问题。

* 内置类型现在支持扩展的切分语法，详见本文档 扩展切片 一节。

* 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.)

* 以前 "list.insert(pos, value)" 在 *pos* 为负值时会将 *value* 插入到
  列表的前面。 现在，该行为已被修改为与切片索引一致，因此当 *pos* 为
  -1 时，值将被插入最后一个元素之前，以此类推。

* "list.index(value)" 会在列表中搜索 *value*，并返回其索引，现在可以使
  用可选的 *start* 和 *stop* 参数，将搜索范围限制在列表的一部分。

* 字典有一个新方法 "pop(key[, *default*])"，可返回 *key* 对应的值，并
  从字典中删除该键/值对。如果请求的键不在字典中，如果指定了 *default*
  ，则返回 *default*，如果没有指定则会引发 "KeyError"。

     >>> 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
     {}
     >>>

  还有一个新的类方法 "dict.fromkeys(iterable, value)"，用于创建一个字
  典，其键取自所提供的迭代器 *iterable*，所有值设置为 *value*，默认为
  "None"。

  （由 Raymond Hettinger 贡献补丁。）

  此外，现在 "dict()" 构建器可接受关键字参数以简化小型字典的创建:

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

  （由 Just van Rossum 贡献。）

* "assert" 语句将不再检查 "__debug__" 旗标，因此你无法再通过为
  "__debug__" 赋值来禁用断言。 使用 "-O" 开关运行 Python 仍会生成不执
  行任何断言的代码。

* 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'

* 添加了一个新的警告 "PendingDeprecationWarning"，用于指示正在被废弃的
  功能。 默认情况下 *不会* 打印该警告。 要检查是否使用了将来会被废弃的
  功能，可在命令行中提供 "-Walways::PendingDeprecationWarning::" 或使
  用 "warnings.filterwarnings()"。

* 与 "raise "Error occurred"" 一样，基于字符串的异常的废弃过程已经开始
  。 现在，引发字符串异常将触发 "PendingDeprecationWarning"。

* 现在使用 "None" 作为变量名将导致 "SyntaxWarning" 警告。 在未来的
  Python 版本中，"None" 将最终成为一个保留关键字。

* 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 "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'>

  在 2.3 中，您会得到以下信息：

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

* 旧式和新式类之间的不兼容问题之一已被消除：您现在可以为新式类的
  "__name__" 和 "__bases__" 属性赋值。 对 "__bases__" 的赋值有一些限制
  ，与对实例的 "__class__" 属性的赋值类似。


字符串的改变
------------

* "in" 运算符现在对字符串的作用不同了。 以前，当计算 "X in Y" 时，*X*
  和 *Y* 都是字符串，*X* 只能是单字符。 现在情况有所改变；*X* 可以是任
  意长度的字符串，如果 *X* 是 *Y* 的子串，"X in Y" 将返回 "True"。 如
  果 *X* 是空字符串，结果总是 "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'
     >>>

  （由 Simon Brunning 提议并由 Walter Dörwald 实现。）

* 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'

  （由 Walter Dörwald 贡献。）

* 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.)


性能优化
--------

* 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*n) 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
  其他的改变和修正 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.


新增，改进和弃用的模块
======================

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"])

  修改 "sample.h" 将导致模块被重新编译。 （由 Jeremy Hylton 贡献。）

* 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.

* 新的 "gc.get_referents(object)" 函数将返回由 *object* 引用的所有对象
  组成的列表。

* 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'])

  （由 Peter Åstrand 贡献。）

* 现在 "grp", "pwd" 和 "resource" 模块将返回加强版的元组:

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

* 现在 "gzip" 模块能够处理超过 2 GiB 的文件。

* 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(lg 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]

  （由 Kevin O'Connor 贡献。）

* The IDLE integrated development environment has been updated using
  the code from the IDLEfork project
  (http://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

  在 Python 2.4 中，默认将改为总是返回浮点数。

  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]

  现在 "random" 模块使用新的“梅森旋转”算法，并以 C 实现。 它的速度更快
  并且与之前的算法相比研究更充分。

  （所有改变均由 Raymond Hettinger 贡献。）

* 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.)

* 在 Windows，"socket" 模块现在将附带安全套接字层（SSL）支持。

* 现在 C "PYTHON_API_VERSION" 宏的值将在 Python 层级上暴露为
  "sys.api_version"。 当前的异常可通过调用新的 "sys.exc_clear()" 函数
  来清除。

* 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']

  （由 Raymond Hettinger 贡献。）

* 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.

  为实现此项更改，增加了 "stringprep" 模块，"mkstringprep" 工具以及
  "punycode" 编码格式。


Date/Time 类型
--------------

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 "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
   >>> now = datetime.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 = datetime.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.)


optparse 模块
-------------

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
   $

有关更多详细信息，请参见模块的文档。

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.

也參考:

  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.


构建和 C API 的改变
===================

针对 Python 构建过程和 C API 的改变包括:

* 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.


移植专属的改变
--------------

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.


其他的改变和修正
================

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.

一些较为重要的改变:

* 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.)


移植到 Python 2.3
=================

本节列出了先前描述的可能需要修改你的代码的改变：

* 现在 "yield" 始终是一个关键字；如果它在你的代码中被用作变量名，则必
  须选择不同的名称。

* 对于字符串 *X* 和 *Y*，"X in Y" 现在当 *X* 长度超过一个字符时也是有
  效的。

* 现在 "int()" 类型构造器在字符串或浮点数因太大而无法以整数类型来容纳
  时将返回一个长整数而不是引发 "OverflowError"。

* 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: 源代码的字符
  编码格式 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.


致谢
====

作者感谢以下人员为本文的各种草案提供建议，更正和帮助： 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.
