这个文档所针对的是一个已不再受支持的 Python 旧版本。 你应当升级版本，并阅读 Python 当前稳定版本的文档.

# Python 2.6 有什么新变化

作者

  A.M. Kuchling (amk at amk.ca)

本文介绍了 Python 2.6 的新特性，它发布于 2008 年 10 月 1 日。 发布日程说明见 PEP 361。

Python 2.6 的主题是为迁移到 Python 3.0 做准备，这是 Python 语言的一次重大重新设计。 只要有可能，Python 2.6 就会纳入 3.0 的新特性和语法，同时通过不删除旧特性或语法来保持与现有代码的兼容。 当无法做到这一点时，Python 2.6 会尽力而为，在 `future_builtins` 模块中添加兼容性函数，并通过 `-3` 开关来警告将在 3.0 中变得不支持的用法。

标准库中增加了一些重要的新包，如 `multiprocessing` 和 `json` 模块等，但与 Python 3.0 完全无关联的新特性并不多。

Python 2.6 还对整个源代码进行了大量改进和错误修复。 通过搜索更改日志我们发现在 Python 2.5 和 2.6 之间应用了 259 个补丁并修复了 612 个错误。 这两个数字可能都被低估了。

本文并不试图提供新特性的完整规范说明，而是提供一个方便的概览。 要了解完整的细节，请参阅 Python 2.6 的文档。 如果你想了解有关设计和实现的具体考量，请参阅特定新特性 的 PEP。 在可能的情况下，“Python 有什么新变化”为每个更改的错误修正/补丁项提供链接。

## Python 3.0

Python版本2.6和3.0的开发周期是同步的，两个版本的alpha和beta版本是在同一天发布的。3.0的发展影响了2.6中的许多功能。

Python 3.0 是对 Python 的大范围重新设计，打破了与 2.x 系列的兼容性。 这意味着现有的 Python 代码需要进行一些转换才能在 Python 3.0 上运行。 不过，并非 3.0 中的所有更改都会破坏兼容性。 在新特性不会导致现有代码崩溃的情况下，它们会被回溯到 2.6，并在本文档的适当位置进行描述。 部分 3.0 衍生功能包括:

- 用于将对象转换为复数的 `__complex__()` 方法。

- 用于捕获异常的替代语法: `except TypeError as exc`。

- 增加 `functools.reduce()` 作为内置 `reduce()` 函数的同义词。

Python 3.0 新增了一些内置函数并对部分现有内置函数的语法进行了修改。 在 3.0 中新增的函数如 `bin()` 已直接添加到 Python 2.6 中，但现有内置函数则未修改；替代做法是在 `future_builtins` 模块中包含具有 3.0 新语法的版本。 要与 3.0 兼容的代码可以在必要时执行 `from future_builtins import hex, map`。

一个新的命令行开关 `-3` 可以对 Python 3.0 将移除的特性发出警告。 您可以使用该开关运行代码，以了解将代码移植到 3.0 所需的工作量。 Python 代码可以使用 `sys.py3kwarning` 访问该开关的值，C 扩展代码可以使用 `Py_Py3kWarningFlag` 访问该开关的值。

参见

The 3xxx series of PEPs, which contains proposals for Python 3.0. PEP 3000 describes the development process for Python 3.0. Start with PEP 3100 that describes the general goals for Python 3.0, and then explore the higher-numbered PEPS that propose specific features.

## 开发过程的变化

在开发2.6时，Python开发过程经历了两个重大变化：我们从SourceForge的问题跟踪程序切换到定制的Roundup安装，文档从LaTeX转换为reStructuredText。

### 新问题追踪：简述

很长一段时间以来，Python开发人员对SourceForge的bug跟踪器越来越恼火。SourceForge的托管解决方案不允许进行大量定制；例如，无法定制问题的生命周期。

The infrastructure committee of the Python Software Foundation therefore posted a call for issue trackers, asking volunteers to set up different products and import some of the bugs and patches from SourceForge. Four different trackers were examined: Jira, Launchpad, Roundup, and Trac. The committee eventually settled on Jira and Roundup as the two candidates. Jira is a commercial product that offers no-cost hosted instances to free-software projects; Roundup is an open-source project that requires volunteers to administer it and a server to host it.

在发出志愿者号召后，在https://bugs.python.org的一个Roundup的安装可以托管多个跟踪器，现在该服务器还托管Jython和Python网站的问题跟踪器。它肯定会在未来找到其他用途。在可能的情况下，此版本的“What's New in Python”链接到每个更改的bug/补丁项。

Hosting of the Python bug tracker is kindly provided by Upfront Systems of Stellenbosch, South Africa. Martin von Löwis put a lot of effort into importing existing bugs and patches from SourceForge; his scripts for this import operation are at http://svn.python.org/view/tracker/importer/ and may be useful to other projects wishing to move from SourceForge to Roundup.

参见

https://bugs.python.org

  Python 的错误追踪器

http://bugs.jython.org:

  Jython 的错误追踪器

http://roundup.sourceforge.net/

  Roundup 下载和文档。

http://svn.python.org/view/tracker/importer/

  Martin von Löwis 的转换脚本。

### 新的文档格式：使用 Sphinx 的 reStructuredText

自 1989 年左右项目启动以来，Python 文档一直使用 LaTeX 编写。在 1980 年代和 1990 年代早期，大多数文档都是打印出来供日后学习的，而不是在网上查看。 LaTeX 被广泛使用，因为它既能提供美观的打印输出，又能在掌握了标记的基本规则后直接进行编写。

如今 LaTeX 仍被用于编写印刷出版物，但编程工具的格局已经发生了变化。 我们不再打印成堆的文档，取而代之的是在线浏览，HTML 已成为最重要的支持格式。 不幸的是，将 LaTeX 转换为 HTML 相当复杂，长期担任 Python 文档编辑的 Fred L. Drake Jr. 花了许多时间在维护转换过程上。 偶尔有人会建议将文档转换成 SGML，之后再转换成 XML，但进行良好的转换是一项艰巨的任务，从来没有人投入所需的时间来完成这项工作。

During the 2.6 development cycle, Georg Brandl put a lot of effort into building a new toolchain for processing the documentation. The resulting package is called Sphinx, and is available from http://sphinx-doc.org/.

Sphinx 专注于 HTML 输出，可生成吸引人风格的现代 HTML；通过转换为 LaTeX，仍可支持打印输出。输入格式是 reStructuredText，这是一种支持自定义扩展和指令的标记语法，在 Python 社区很常用。

Sphinx 是一个可用于写文档的独立软件包，将近二十多个其他项目 (列在 Sphinx 网站 上) 已采用 Sphinx 作为其文档工具。

参见

Documenting Python

  描述如何编写Python文档。

Sphinx

  Sphinx工具链的文档和代码。

Docutils

  reStructuredText 的基础解析器和工具集。

## PEP 343: "with" 语句

在 Python 2.5 之前的版本中，"`with`" 语句是一个可选功能，可以通过 `from __future__ import with_statement` 指令启用。 在 2.6 中，该语句不再需要特别启用；这意味着 `with` 现在总是一个关键字。 本节的其余部分是“Python 2.5 新特性”文档中相应部分的复制；如果您熟悉 Python 2.5 中的 '`with`' 语句，可以跳过本节。

The '`with`' statement clarifies code that previously would use `try...finally` blocks to ensure that clean-up code is executed. In this section, I'll discuss the statement as it will commonly be used. In the next section, I'll examine the implementation details and show how to write objects for use with this statement.

The '`with`' 语是一种基本结构如下所示的流程控制结构:

    with expression [as variable]:
        with-block

The expression is evaluated, and it should result in an object that supports the context management protocol (that is, has `__enter__()` and `__exit__()` methods).

The object's `__enter__()` is called before with-block is executed and therefore can run set-up code. It also may return a value that is bound to the name variable, if given. (Note carefully that variable is not assigned the result of expression.)

After execution of the with-block is finished, the object's `__exit__()` method is called, even if the block raised an exception, and can therefore run clean-up code.

一些标准 Python 对象现在已支持上下文管理协议并可被用于 '`with`' 语句。 文件对象就是一个例子:

    with open('/etc/passwd', 'r') as f:
        for line in f:
            print line
            ... more processing code ...

在此语句被执行之后，文件对象 f 将被自动关闭，即使是当 `for` 循环在代码块中间引发了异常的时候也是如此。

注解

In this case, f is the same object created by `open()`, because `file.__enter__()` returns self.

`threading` 模块的加锁和条件变量也支持 '`with`' 语句:

    lock = threading.Lock()
    with lock:
        # Critical section of code
        ...

这个锁会在代码块被执行之前锁定并总是会在代码块完成之后释放。

The `localcontext()` function in the `decimal` module makes it easy to save and restore the current decimal context, which encapsulates the desired precision and rounding characteristics for computations:

    from decimal import Decimal, Context, localcontext

    # Displays with default precision of 28 digits
    v = Decimal('578')
    print v.sqrt()

    with localcontext(Context(prec=16)):
        # All code in this block uses a precision of 16 digits.
        # The original context is restored on exiting the block.
        print v.sqrt()

### 编写上下文管理器

Under the hood, the '`with`' statement is fairly complicated. Most people will only use '`with`' in company with existing objects and don't need to know these details, so you can skip the rest of this section if you like. Authors of new objects will need to understand the details of the underlying implementation and should keep reading.

在更高层级上对于上下文管理器协议的解释:

- The expression is evaluated and should result in an object called a "context manager". The context manager must have `__enter__()` and `__exit__()` methods.

- The context manager's `__enter__()` method is called. The value returned is assigned to VAR. If no `as VAR` clause is present, the value is simply discarded.

- BLOCK 中的代码会被执行。

- If BLOCK raises an exception, the context manager's `__exit__()` method is called with three arguments, the exception details (`type, value, traceback`, the same values returned by `sys.exc_info()`, which can also be `None` if no exception occurred). The method's return value controls whether an exception is re-raised: any false value re-raises the exception, and `True` will result in suppressing it. You'll only rarely want to suppress the exception, because if you do the author of the code containing the '`with`' statement will never realize anything went wrong.

- If BLOCK didn't raise an exception, the `__exit__()` method is still called, but type, value, and traceback are all `None`.

Let's think through an example. I won't present detailed code but will only sketch the methods necessary for a database that supports transactions.

(For people unfamiliar with database terminology: a set of changes to the database are grouped into a transaction. Transactions can be either committed, meaning that all the changes are written into the database, or rolled back, meaning that the changes are all discarded and the database is unchanged. See any database textbook for more information.)

Let's assume there's an object representing a database connection. Our goal will be to let the user write code like this:

    db_connection = DatabaseConnection()
    with db_connection as cursor:
        cursor.execute('insert into ...')
        cursor.execute('delete from ...')
        # ... more operations ...

The transaction should be committed if the code in the block runs flawlessly or rolled back if there's an exception. Here's the basic interface for `DatabaseConnection` that I'll assume:

    class DatabaseConnection:
        # Database interface
        def cursor(self):
            "Returns a cursor object and starts a new transaction"
        def commit(self):
            "Commits current transaction"
        def rollback(self):
            "Rolls back current transaction"

The `__enter__()` method is pretty easy, having only to start a new transaction. For this application the resulting cursor object would be a useful result, so the method will return it. The user can then add `as cursor` to their '`with`' statement to bind the cursor to a variable name.

    class DatabaseConnection:
        ...
        def __enter__(self):
            # Code to start a new transaction
            cursor = self.cursor()
            return cursor

The `__exit__()` method is the most complicated because it's where most of the work has to be done. The method has to check if an exception occurred. If there was no exception, the transaction is committed. The transaction is rolled back if there was an exception.

In the code below, execution will just fall off the end of the function, returning the default value of `None`. `None` is false, so the exception will be re-raised automatically. If you wished, you could be more explicit and add a `return` statement at the marked location.

    class DatabaseConnection:
        ...
        def __exit__(self, type, value, tb):
            if tb is None:
                # No exception, so commit
                self.commit()
            else:
                # Exception occurred, so rollback.
                self.rollback()
                # return False

### contextlib 模块

The `contextlib` module provides some functions and a decorator that are useful when writing objects for use with the '`with`' statement.

The decorator is called `contextmanager()`, and lets you write a single generator function instead of defining a new class. The generator should yield exactly one value. The code up to the `yield` will be executed as the `__enter__()` method, and the value yielded will be the method's return value that will get bound to the variable in the '`with`' statement's `as` clause, if any. The code after the `yield` will be executed in the `__exit__()` method. Any exception raised in the block will be raised by the `yield` statement.

Using this decorator, our database example from the previous section could be written as:

    from contextlib import contextmanager

    @contextmanager
    def db_transaction(connection):
        cursor = connection.cursor()
        try:
            yield cursor
        except:
            connection.rollback()
            raise
        else:
            connection.commit()

    db = DatabaseConnection()
    with db_transaction(db) as cursor:
        ...

The `contextlib` module also has a `nested(mgr1, mgr2, ...)` function that combines a number of context managers so you don't need to write nested '`with`' statements. In this example, the single '`with`' statement both starts a database transaction and acquires a thread lock:

    lock = threading.Lock()
    with nested (db_transaction(db), lock) as (cursor, locked):
        ...

Finally, the `closing()` function returns its argument so that it can be bound to a variable, and calls the argument's `.close()` method at the end of the block.

    import urllib, sys
    from contextlib import closing

    with closing(urllib.urlopen('http://www.yahoo.com')) as f:
        for line in f:
            sys.stdout.write(line)

参见

PEP 343 - "with" 语句

  PEP written by Guido van Rossum and Nick Coghlan; implemented by Mike Bland, Guido van Rossum, and Neal Norwitz. The PEP shows the code generated for a '`with`' statement, which can be helpful in learning how the statement works.

`contextlib` 模块的文档。

## PEP 366: 从主模块显式相对导入

Python 的 `-m` 开关允许将一个模块作为脚本来运行。 当你运行一个位于某个包内的模块时，相对导入将无法正确运作。

The fix for Python 2.6 adds a `__package__` attribute to modules. When this attribute is present, relative imports will be relative to the value of this attribute instead of the `__name__` attribute.

PEP 302-style importers can then set `__package__` as necessary. The `runpy` module that implements the `-m` switch now does this, so relative imports will now work correctly in scripts running from inside a package.

## PEP 370: 分用户的 site-packages 目录

When you run Python, the module search path `sys.path` usually includes a directory whose path ends in `"site-packages"`. This directory is intended to hold locally-installed packages available to all users using a machine or a particular site installation.

Python 2.6 引入了一个用于用户专属站点目录的惯例。 该目录根据具体系统平台各不相同:

- Unix 和 Mac OS X: `~/.local/`

- Windows: `%APPDATA%/Python`

Within this directory, there will be version-specific subdirectories, such as `lib/python2.6/site-packages` on Unix/Mac OS and `Python26/site-packages` on Windows.

If you don't like the default directory, it can be overridden by an environment variable. `PYTHONUSERBASE` sets the root directory used for all Python versions supporting this feature. On Windows, the directory for application-specific data can be changed by setting the `APPDATA` environment variable. You can also modify the `site.py` file for your Python installation.

The feature can be disabled entirely by running Python with the `-s` option or setting the `PYTHONNOUSERSITE` environment variable.

参见

PEP 370 - 分用户的 site-packages 目录

  PEP 由 Christian Heimes 撰写并实现

## PEP 371: 多任务处理包

The new `multiprocessing` package lets Python programs create new processes that will perform a computation and return a result to the parent. The parent and child processes can communicate using queues and pipes, synchronize their operations using locks and semaphores, and can share simple arrays of data.

The `multiprocessing` module started out as an exact emulation of the `threading` module using processes instead of threads. That goal was discarded along the path to Python 2.6, but the general approach of the module is still similar. The fundamental class is the `Process`, which is passed a callable object and a collection of arguments. The `start()` method sets the callable running in a subprocess, after which you can call the `is_alive()` method to check whether the subprocess is still running and the `join()` method to wait for the process to exit.

Here's a simple example where the subprocess will calculate a factorial. The function doing the calculation is written strangely so that it takes significantly longer when the input argument is a multiple of 4.

    import time
    from multiprocessing import Process, Queue

    def factorial(queue, N):
        "Compute a factorial."
        # If N is a multiple of 4, this function will take much longer.
        if (N % 4) == 0:
            time.sleep(.05 * N/4)

        # Calculate the result
        fact = 1L
        for i in range(1, N+1):
            fact = fact * i

        # Put the result on the queue
        queue.put(fact)

    if __name__ == '__main__':
        queue = Queue()

        N = 5

        p = Process(target=factorial, args=(queue, N))
        p.start()
        p.join()

        result = queue.get()
        print 'Factorial', N, '=', result

A `Queue` is used to communicate the result of the factorial. The `Queue` object is stored in a global variable. The child process will use the value of the variable when the child was created; because it's a `Queue`, parent and child can use the object to communicate. (If the parent were to change the value of the global variable, the child's value would be unaffected, and vice versa.)

Two other classes, `Pool` and `Manager`, provide higher-level interfaces. `Pool` will create a fixed number of worker processes, and requests can then be distributed to the workers by calling `apply()` or `apply_async()` to add a single request, and `map()` or `map_async()` to add a number of requests. The following code uses a `Pool` to spread requests across 5 worker processes and retrieve a list of results:

    from multiprocessing import Pool

    def factorial(N, dictionary):
        "Compute a factorial."
        ...
    p = Pool(5)
    result = p.map(factorial, range(1, 1000, 10))
    for v in result:
        print v

这会产生以下输出:

    1
    39916800
    51090942171709440000
    8222838654177922817725562880000000
    33452526613163807108170062053440751665152000000000
    ...

The other high-level interface, the `Manager` class, creates a separate server process that can hold master copies of Python data structures. Other processes can then access and modify these data structures using proxy objects. The following example creates a shared dictionary by calling the `dict()` method; the worker processes then insert values into the dictionary. (Locking is not done for you automatically, which doesn't matter in this example. `Manager`'s methods also include `Lock()`, `RLock()`, and `Semaphore()` to create shared locks.)

    import time
    from multiprocessing import Pool, Manager

    def factorial(N, dictionary):
        "Compute a factorial."
        # Calculate the result
        fact = 1L
        for i in range(1, N+1):
            fact = fact * i

        # Store result in dictionary
        dictionary[N] = fact

    if __name__ == '__main__':
        p = Pool(5)
        mgr = Manager()
        d = mgr.dict()         # Create shared dictionary

        # Run tasks using the pool
        for N in range(1, 1000, 10):
            p.apply_async(factorial, (N, d))

        # Mark pool as closed -- no more tasks can be added.
        p.close()

        # Wait for tasks to exit
        p.join()

        # Output results
        for k, v in sorted(d.items()):
            print k, v

这将产生如下输出:

    1 1
    11 39916800
    21 51090942171709440000
    31 8222838654177922817725562880000000
    41 33452526613163807108170062053440751665152000000000
    51 15511187532873822802242430164693032110632597200169861120000...

参见

`multiprocessing` 模块的文档。

PEP 371 - 添加多任务处理包

  PEP 由 Jesse Noller 和 Richard Oudkerk 撰写，由 Richard Oudkerk 和 Jesse Noller 实现

## PEP 3101: 高级字符串格式

In Python 3.0, the % operator is supplemented by a more powerful string formatting method, `format()`. Support for the `str.format()` method has been backported to Python 2.6.

In 2.6, both 8-bit and Unicode strings have a .format() method that treats the string as a template and takes the arguments to be formatted. The formatting template uses curly brackets ({, }) as special characters:

    >>> # Substitute positional argument 0 into the string.
    >>> "User ID: {0}".format("root")
    'User ID: root'
    >>> # Use the named keyword arguments
    >>> "User ID: {uid}   Last seen: {last_login}".format(
    ...    uid="root",
    ...    last_login = "5 Mar 2008 07:20")
    'User ID: root   Last seen: 5 Mar 2008 07:20'

Curly brackets can be escaped by doubling them:

    >>> "Empty dict: {{}}".format()
    "Empty dict: {}"

Field names can be integers indicating positional arguments, such as `{0}`, `{1}`, etc. or names of keyword arguments. You can also supply compound field names that read attributes or access dictionary keys:

    >>> import sys
    >>> print 'Platform: {0.platform}\nPython version: {0.version}'.format(sys)
    Platform: darwin
    Python version: 2.6a1+ (trunk:61261M, Mar  5 2008, 20:29:41)
    [GCC 4.0.1 (Apple Computer, Inc. build 5367)]'

    >>> import mimetypes
    >>> 'Content-type: {0[.mp4]}'.format(mimetypes.types_map)
    'Content-type: video/mp4'

Note that when using dictionary-style notation such as `[.mp4]`, you don't need to put any quotation marks around the string; it will look up the value using `.mp4` as the key. Strings beginning with a number will be converted to an integer. You can't write more complicated expressions inside a format string.

So far we've shown how to specify which field to substitute into the resulting string. The precise formatting used is also controllable by adding a colon followed by a format specifier. For example:

    >>> # Field 0: left justify, pad to 15 characters
    >>> # Field 1: right justify, pad to 6 characters
    >>> fmt = '{0:15} ${1:>6}'
    >>> fmt.format('Registration', 35)
    'Registration    $    35'
    >>> fmt.format('Tutorial', 50)
    'Tutorial        $    50'
    >>> fmt.format('Banquet', 125)
    'Banquet         $   125'

格式说明符可以通过嵌套来引用其他字段:

    >>> fmt = '{0:{1}}'
    >>> width = 15
    >>> fmt.format('Invoice #1234', width)
    'Invoice #1234  '
    >>> width = 35
    >>> fmt.format('Invoice #1234', width)
    'Invoice #1234                      '

可以指定所需宽度内的字段对齐方式：

字符 | 效果
--- | ---
< (默认) | 左对齐
> | 右对齐
^ | 居中对齐
= | （仅适用于数字类型）在符号后加空格。

Format specifiers can also include a presentation type, which controls how the value is formatted. For example, floating-point numbers can be formatted as a general number or in exponential notation:

    >>> '{0:g}'.format(3.75)
    '3.75'
    >>> '{0:e}'.format(3.75)
    '3.750000e+00'

A variety of presentation types are available. Consult the 2.6 documentation for a complete list; here's a sample:

`b` | 二进制。输出以2为底的数字。
`c` | 字符。在打印之前将整数转换为相应的Unicode字符。
`d` | 十进制整数。 输出以 10 为基数的数字。
`o` | 八进制格式。 输出以 8 为基数的数字。
`x` | 十六进制格式。 输出以 16 为基数的数字，使用小写字母表示 9 以上的数码。
`e` | 指数表示法。用字母 'e' 以科学计数法打印数字以表示指数。
`g` | General format. This prints the number as a fixed-point number, unless the number is too large, in which case it switches to 'e' exponent notation.
`n` | Number. This is the same as 'g' (for floats) or 'd' (for integers), except that it uses the current locale setting to insert the appropriate number separator characters.
`%` | Percentage. Multiplies the number by 100 and displays in fixed ('f') format, followed by a percent sign.

Classes and types can define a `__format__()` method to control how they're formatted. It receives a single argument, the format specifier:

    def __format__(self, format_spec):
        if isinstance(format_spec, unicode):
            return unicode(str(self))
        else:
            return str(self)

There's also a `format()` builtin that will format a single value. It calls the type's `__format__()` method with the provided specifier:

    >>> format(75.6564, '.2f')
    '75.66'

参见

格式字符串语法

  格式字段的参考文档。

PEP 3101 - 高级字符串格式

  PEP 由 Eric V. Smith 撰写并实现

## PEP 3105: `print` 改为函数

在 Python 3.0 中 `print` 语句变成了 `print()` 函数。 将 `print()` 变成函数使得可以通过 `def print(...)` 或从其他地方导入一个新函数来替换该函数。

Python 2.6 提供了 `__future__` 导入语句来移除 `print` 语法，让你可以改用函数形式。 例如:

    >>> from __future__ import print_function
    >>> print('# of entries', len(dictionary), file=sys.stderr)

新函数的签名为:

    def print(*args, sep=' ', end='\n', file=None)

形参包括:

> - args: 相应值将会被打印的位置参数。

> - sep: 分隔符，它将在参数之间被打印。

> - end: 结束文本，它将在所有参数输出完毕之后被打印。

> - file: 将被作为输出发送目标的文件对象。

参见

PEP 3105 - print 改为函数

  PEP 由 Georg Brandl 撰写

## PEP 3110: 异常处理的变更

Python 程序员偶尔会犯的一个错误是编写这样的代码:

    try:
        ...
    except TypeError, ValueError:  # Wrong!
        ...

The author is probably trying to catch both `TypeError` and `ValueError` exceptions, but this code actually does something different: it will catch `TypeError` and bind the resulting exception object to the local name `"ValueError"`. The `ValueError` exception will not be caught at all. The correct code specifies a tuple of exceptions:

    try:
        ...
    except (TypeError, ValueError):
        ...

This error happens because the use of the comma here is ambiguous: does it indicate two different nodes in the parse tree, or a single node that's a tuple?

Python 3.0 makes this unambiguous by replacing the comma with the word "as". To catch an exception and store the exception object in the variable `exc`, you must write:

    try:
        ...
    except TypeError as exc:
        ...

Python 3.0 will only support the use of "as", and therefore interprets the first example as catching two different exceptions. Python 2.6 supports both the comma and "as", so existing code will continue to work. We therefore suggest using "as" when writing new Python code that will only be executed with 2.6.

参见

PEP 3110 - 在 Python 3000 中捕获异常

  PEP 由 Collin Winter 撰写并实现

## PEP 3112: 字节字面值

Python 3.0 adopts Unicode as the language's fundamental string type and denotes 8-bit literals differently, either as `b'string'` or using a `bytes` constructor. For future compatibility, Python 2.6 adds `bytes` as a synonym for the `str` type, and it also supports the `b''` notation.

The 2.6 `str` differs from 3.0's `bytes` type in various ways; most notably, the constructor is completely different. In 3.0, `bytes([65, 66, 67])` is 3 elements long, containing the bytes representing `ABC`; in 2.6, `bytes([65, 66, 67])` returns the 12-byte string representing the `str()` of the list.

The primary use of `bytes` in 2.6 will be to write tests of object type such as `isinstance(x, bytes)`. This will help the 2to3 converter, which can't tell whether 2.x code intends strings to contain either characters or 8-bit bytes; you can now use either `bytes` or `str` to represent your intention exactly, and the resulting code will also be correct in Python 3.0.

There's also a `__future__` import that causes all string literals to become Unicode strings. This means that `\u` escape sequences can be used to include Unicode characters:

    from __future__ import unicode_literals

    s = ('\u751f\u3080\u304e\u3000\u751f\u3054'
         '\u3081\u3000\u751f\u305f\u307e\u3054')

    print len(s)               # 12 Unicode characters

At the C level, Python 3.0 will rename the existing 8-bit string type, called `PyStringObject` in Python 2.x, to `PyBytesObject`. Python 2.6 uses `#define` to support using the names `PyBytesObject()`, `PyBytes_Check()`, `PyBytes_FromStringAndSize()`, and all the other functions and macros used with strings.

`bytes` 类型的实例与字符串一样属于不可变对象。 新增的 `bytearray` 类型则用于存储可变的字节序列:

    >>> bytearray([65, 66, 67])
    bytearray(b'ABC')
    >>> b = bytearray(u'\u21ef\u3244', 'utf-8')
    >>> b
    bytearray(b'\xe2\x87\xaf\xe3\x89\x84')
    >>> b[0] = '\xe3'
    >>> b
    bytearray(b'\xe3\x87\xaf\xe3\x89\x84')
    >>> unicode(str(b), 'utf-8')
    u'\u31ef \u3244'

字节数组支持大部分的字符串类型方法，如 `startswith()`/`endswith()`, `find()`/`rfind()`，以及列表的某些方法，如 `append()`, `pop()` 和 `reverse()`。

    >>> b = bytearray('ABC')
    >>> b.append('d')
    >>> b.append(ord('e'))
    >>> b
    bytearray(b'ABCde')

也有一个相应的 C API，包含 `PyByteArray_FromObject()`, `PyByteArray_FromStringAndSize()` 以及各种其他函数。

参见

PEP 3112 - Python 3000 中的字节字面值

  PEP 由 Jason Orendorff 撰写， 补丁2.6 由 Christian Heimes 撰写。

## PEP 3116: 新 I/O 库

Python's built-in file objects support a number of methods, but file-like objects don't necessarily support all of them. Objects that imitate files usually support `read()` and `write()`, but they may not support `readline()`, for example. Python 3.0 introduces a layered I/O library in the `io` module that separates buffering and text-handling features from the fundamental read and write operations.

There are three levels of abstract base classes provided by the `io` module:

- `RawIOBase` defines raw I/O operations: `read()`, `readinto()`, `write()`, `seek()`, `tell()`, `truncate()`, and `close()`. Most of the methods of this class will often map to a single system call. There are also `readable()`, `writable()`, and `seekable()` methods for determining what operations a given object will allow.

  Python 3.0 has concrete implementations of this class for files and sockets, but Python 2.6 hasn't restructured its file and socket objects in this way.

- `BufferedIOBase` is an abstract base class that buffers data in memory to reduce the number of system calls used, making I/O processing more efficient. It supports all of the methods of `RawIOBase`, and adds a `raw` attribute holding the underlying raw object.

  There are five concrete classes implementing this ABC. `BufferedWriter` and `BufferedReader` are for objects that support write-only or read-only usage that have a `seek()` method for random access. `BufferedRandom` objects support read and write access upon the same underlying stream, and `BufferedRWPair` is for objects such as TTYs that have both read and write operations acting upon unconnected streams of data. The `BytesIO` class supports reading, writing, and seeking over an in-memory buffer.

- `TextIOBase`: Provides functions for reading and writing strings (remember, strings will be Unicode in Python 3.0), and supporting universal newlines. `TextIOBase` defines the `readline()` method and supports iteration upon objects.

  There are two concrete implementations. `TextIOWrapper` wraps a buffered I/O object, supporting all of the methods for text I/O and adding a `buffer` attribute for access to the underlying object. `StringIO` simply buffers everything in memory without ever writing anything to disk.

  (In Python 2.6, `io.StringIO` is implemented in pure Python, so it's pretty slow. You should therefore stick with the existing `StringIO` module or `cStringIO` for now. At some point Python 3.0's `io` module will be rewritten into C for speed, and perhaps the C implementation will be backported to the 2.x releases.)

In Python 2.6, the underlying implementations haven't been restructured to build on top of the `io` module's classes. The module is being provided to make it easier to write code that's forward-compatible with 3.0, and to save developers the effort of writing their own implementations of buffering and text I/O.

参见

PEP 3116 - 新 I/O

  PEP written by Daniel Stutzbach, Mike Verdone, and Guido van Rossum. Code by Guido van Rossum, Georg Brandl, Walter Doerwald, Jeremy Hylton, Martin von Löwis, Tony Lownds, and others.

## PEP 3118: 修改缓冲区协议

The buffer protocol is a C-level API that lets Python types exchange pointers into their internal representations. A memory-mapped file can be viewed as a buffer of characters, for example, and this lets another module such as `re` treat memory-mapped files as a string of characters to be searched.

The primary users of the buffer protocol are numeric-processing packages such as NumPy, which expose the internal representation of arrays so that callers can write data directly into an array instead of going through a slower API. This PEP updates the buffer protocol in light of experience from NumPy development, adding a number of new features such as indicating the shape of an array or locking a memory region.

The most important new C API function is `PyObject_GetBuffer(PyObject *obj, Py_buffer *view, int flags)`, which takes an object and a set of flags, and fills in the `Py_buffer` structure with information about the object's memory representation. Objects can use this operation to lock memory in place while an external caller could be modifying the contents, so there's a corresponding `PyBuffer_Release(Py_buffer *view)` to indicate that the external caller is done.

The flags argument to `PyObject_GetBuffer()` specifies constraints upon the memory returned. Some examples are:

> - `PyBUF_WRITABLE` 指定内存必须为可写。

> - `PyBUF_LOCK` requests a read-only or exclusive lock on the memory.

> - `PyBUF_C_CONTIGUOUS` and `PyBUF_F_CONTIGUOUS` requests a C-contiguous (last dimension varies the fastest) or Fortran-contiguous (first dimension varies the fastest) array layout.

两个用于 `PyArg_ParseTuple()` 的新参数代码 `s*` 和 `z*`，将为形参返回锁定的缓冲区对象。

参见

PEP 3118 - 修改缓冲区协议

  PEP 由 Travis Oliphant 和 Carl Banks 撰写，由 Travis Oliphant 实现。

## PEP 3119: 抽象基类

Some object-oriented languages such as Java support interfaces, declaring that a class has a given set of methods or supports a given access protocol. Abstract Base Classes (or ABCs) are an equivalent feature for Python. The ABC support consists of an `abc` module containing a metaclass called `ABCMeta`, special handling of this metaclass by the `isinstance()` and `issubclass()` builtins, and a collection of basic ABCs that the Python developers think will be widely useful. Future versions of Python will probably add more ABCs.

Let's say you have a particular class and wish to know whether it supports dictionary-style access. The phrase "dictionary-style" is vague, however. It probably means that accessing items with `obj[1]` works. Does it imply that setting items with `obj[2] = value` works? Or that the object will have `keys()`, `values()`, and `items()` methods? What about the iterative variants such as `iterkeys()`? `copy()` and `update()`? Iterating over the object with `iter()`?

The Python 2.6 `collections` module includes a number of different ABCs that represent these distinctions. `Iterable` indicates that a class defines `__iter__()`, and `Container` means the class defines a `__contains__()` method and therefore supports `x in y` expressions. The basic dictionary interface of getting items, setting items, and `keys()`, `values()`, and `items()`, is defined by the `MutableMapping` ABC.

You can derive your own classes from a particular ABC to indicate they support that ABC's interface:

    import collections

    class Storage(collections.MutableMapping):
        ...

Alternatively, you could write the class without deriving from the desired ABC and instead register the class by calling the ABC's `register()` method:

    import collections

    class Storage:
        ...

    collections.MutableMapping.register(Storage)

For classes that you write, deriving from the ABC is probably clearer. The `register()` method is useful when you've written a new ABC that can describe an existing type or class, or if you want to declare that some third-party class implements an ABC. For example, if you defined a `PrintableType` ABC, it's legal to do:

    # Register Python's types
    PrintableType.register(int)
    PrintableType.register(float)
    PrintableType.register(str)

Classes should obey the semantics specified by an ABC, but Python can't check this; it's up to the class author to understand the ABC's requirements and to implement the code accordingly.

To check whether an object supports a particular interface, you can now write:

    def func(d):
        if not isinstance(d, collections.MutableMapping):
            raise ValueError("Mapping object expected, not %r" % d)

Don't feel that you must now begin writing lots of checks as in the above example. Python has a strong tradition of duck-typing, where explicit type-checking is never done and code simply calls methods on an object, trusting that those methods will be there and raising an exception if they aren't. Be judicious in checking for ABCs and only do it where it's absolutely necessary.

You can write your own ABCs by using `abc.ABCMeta` as the metaclass in a class definition:

    from abc import ABCMeta, abstractmethod

    class Drawable():
        __metaclass__ = ABCMeta

        @abstractmethod
        def draw(self, x, y, scale=1.0):
            pass

        def draw_doubled(self, x, y):
            self.draw(x, y, scale=2.0)

    class Square(Drawable):
        def draw(self, x, y, scale):
            ...

In the `Drawable` ABC above, the `draw_doubled()` method renders the object at twice its size and can be implemented in terms of other methods described in `Drawable`. Classes implementing this ABC therefore don't need to provide their own implementation of `draw_doubled()`, though they can do so. An implementation of `draw()` is necessary, though; the ABC can't provide a useful generic implementation.

You can apply the `@abstractmethod` decorator to methods such as `draw()` that must be implemented; Python will then raise an exception for classes that don't define the method. Note that the exception is only raised when you actually try to create an instance of a subclass lacking the method:

    >>> class Circle(Drawable):
    ...     pass
    ...
    >>> c = Circle()
    Traceback (most recent call last):
      File "<stdin>", line 1, in <module>
    TypeError: Can't instantiate abstract class Circle with abstract methods draw
    >>>

Abstract data attributes can be declared using the `@abstractproperty` decorator:

    from abc import abstractproperty
    ...

    @abstractproperty
    def readonly(self):
       return self._x

Subclasses must then define a `readonly()` property.

参见

PEP 3119 - 引入抽象基类

  PEP written by Guido van Rossum and Talin. Implemented by Guido van Rossum. Backported to 2.6 by Benjamin Aranguren, with Alex Martelli.

## PEP 3127: 整型文字支持和语法

Python 3.0 changes the syntax for octal (base-8) integer literals, prefixing them with "0o" or "0O" instead of a leading zero, and adds support for binary (base-2) integer literals, signalled by a "0b" or "0B" prefix.

Python 2.6 doesn't drop support for a leading 0 signalling an octal number, but it does add support for "0o" and "0b":

    >>> 0o21, 2*8 + 1
    (17, 17)
    >>> 0b101111
    47

The `oct()` builtin still returns numbers prefixed with a leading zero, and a new `bin()` builtin returns the binary representation for a number:

    >>> oct(42)
    '052'
    >>> future_builtins.oct(42)
    '0o52'
    >>> bin(173)
    '0b10101101'

The `int()` and `long()` builtins will now accept the "0o" and "0b" prefixes when base-8 or base-2 are requested, or when the base argument is zero (signalling that the base used should be determined from the string):

    >>> int ('0o52', 0)
    42
    >>> int('1101', 2)
    13
    >>> int('0b1101', 2)
    13
    >>> int('0b1101', 0)
    13

参见

PEP 3127 - 整型文字支持和语法

  PEP written by Patrick Maupin; backported to 2.6 by Eric Smith.

## PEP 3129: 类装饰器

装饰器已从函数扩展到类。 现在可以合法地编写:

    @foo
    @bar
    class A:
      pass

这相当于：

    class A:
      pass

    A = foo(bar(A))

参见

PEP 3129 - 类装饰器

  PEP 由 Collin Winter 撰写

## PEP 3141: 数字的类型层级结构

Python 3.0 adds several abstract base classes for numeric types inspired by Scheme's numeric tower. These classes were backported to 2.6 as the `numbers` module.

The most general ABC is `Number`. It defines no operations at all, and only exists to allow checking if an object is a number by doing `isinstance(obj, Number)`.

`Complex` is a subclass of `Number`. Complex numbers can undergo the basic operations of addition, subtraction, multiplication, division, and exponentiation, and you can retrieve the real and imaginary parts and obtain a number's conjugate. Python's built-in complex type is an implementation of `Complex`.

`Real` further derives from `Complex`, and adds operations that only work on real numbers: `floor()`, `trunc()`, rounding, taking the remainder mod N, floor division, and comparisons.

`Rational` numbers derive from `Real`, have `numerator` and `denominator` properties, and can be converted to floats. Python 2.6 adds a simple rational-number class, `Fraction`, in the `fractions` module. (It's called `Fraction` instead of `Rational` to avoid a name clash with `numbers.Rational`.)

`Integral` numbers derive from `Rational`, and can be shifted left and right with `<<` and `>>`, combined using bitwise operations such as `&` and `|`, and can be used as array indexes and slice boundaries.

In Python 3.0, the PEP slightly redefines the existing builtins `round()`, `math.floor()`, `math.ceil()`, and adds a new one, `math.trunc()`, that's been backported to Python 2.6. `math.trunc()` rounds toward zero, returning the closest `Integral` that's between the function's argument and zero.

参见

PEP 3141 - A Type Hierarchy for Numbers

  PEP 由 Jeffrey Yasskin 撰写

Scheme's numerical tower, from the Guile manual.

Scheme's number datatypes from the R5RS Scheme specification.

### `fractions` 模块

To fill out the hierarchy of numeric types, the `fractions` module provides a rational-number class. Rational numbers store their values as a numerator and denominator forming a fraction, and can exactly represent numbers such as `2/3` that floating-point numbers can only approximate.

The `Fraction` constructor takes two `Integral` values that will be the numerator and denominator of the resulting fraction.

    >>> from fractions import Fraction
    >>> a = Fraction(2, 3)
    >>> b = Fraction(2, 5)
    >>> float(a), float(b)
    (0.66666666666666663, 0.40000000000000002)
    >>> a+b
    Fraction(16, 15)
    >>> a/b
    Fraction(5, 3)

For converting floating-point numbers to rationals, the float type now has an `as_integer_ratio()` method that returns the numerator and denominator for a fraction that evaluates to the same floating-point value:

    >>> (2.5) .as_integer_ratio()
    (5, 2)
    >>> (3.1415) .as_integer_ratio()
    (7074029114692207L, 2251799813685248L)
    >>> (1./3) .as_integer_ratio()
    (6004799503160661L, 18014398509481984L)

Note that values that can only be approximated by floating-point numbers, such as 1./3, are not simplified to the number being approximated; the fraction attempts to match the floating-point value exactly.

The `fractions` module is based upon an implementation by Sjoerd Mullender that was in Python's `Demo/classes/` directory for a long time. This implementation was significantly updated by Jeffrey Yasskin.

## 其他语言特性修改

对Python 语言核心进行的小改动：

- Directories and zip archives containing a `__main__.py` file can now be executed directly by passing their name to the interpreter. The directory or zip archive is automatically inserted as the first entry in sys.path. (Suggestion and initial patch by Andy Chu, subsequently revised by Phillip J. Eby and Nick Coghlan; bpo-1739468.)

- The `hasattr()` function was catching and ignoring all errors, under the assumption that they meant a `__getattr__()` method was failing somehow and the return value of `hasattr()` would therefore be `False`. This logic shouldn't be applied to `KeyboardInterrupt` and `SystemExit`, however; Python 2.6 will no longer discard such exceptions when `hasattr()` encounters them. (Fixed by Benjamin Peterson; bpo-2196.)

- When calling a function using the `**` syntax to provide keyword arguments, you are no longer required to use a Python dictionary; any mapping will now work:

      >>> def f(**kw):
      ...    print sorted(kw)
      ...
      >>> ud=UserDict.UserDict()
      >>> ud['a'] = 1
      >>> ud['b'] = 'string'
      >>> f(**ud)
      ['a', 'b']

  （由 Alexander Belopolsky 在 bpo-1686487 中贡献。）

  在函数调用的 `*args` 参数之后提供关键字参数也是合法的。

      >>> def f(*args, **kw):
      ...     print args, kw
      ...
      >>> f(1,2,3, *(4,5,6), keyword=13)
      (1, 2, 3, 4, 5, 6) {'keyword': 13}

  在之前版本中这会导致语法错误。 （由 Amaury Forgeot d'Arc 贡献；bpo-3473。）

- A new builtin, `next(iterator, [default])` returns the next item from the specified iterator. If the default argument is supplied, it will be returned if iterator has been exhausted; otherwise, the `StopIteration` exception will be raised. (Backported in bpo-2719.)

- Tuples now have `index()` and `count()` methods matching the list type's `index()` and `count()` methods:

      >>> t = (0,1,2,3,4,0,1,2)
      >>> t.index(3)
      3
      >>> t.count(0)
      2

  （由 Raymond Hettinger 贡献）

- The built-in types now have improved support for extended slicing syntax, accepting various combinations of `(start, stop, step)`. Previously, the support was partial and certain corner cases wouldn't work. (Implemented by Thomas Wouters.)

- Properties now have three attributes, `getter`, `setter` and `deleter`, that are decorators providing useful shortcuts for adding a getter, setter or deleter function to an existing property. You would use them like this:

      class C(object):
          @property
          def x(self):
              return self._x

          @x.setter
          def x(self, value):
              self._x = value

          @x.deleter
          def x(self):
              del self._x

      class D(C):
          @C.x.getter
          def x(self):
              return self._x * 2

          @x.setter
          def x(self, value):
              self._x = value / 2

- Several methods of the built-in set types now accept multiple iterables: `intersection()`, `intersection_update()`, `union()`, `update()`, `difference()` and `difference_update()`.

      >>> s=set('1234567890')
      >>> s.intersection('abc123', 'cdf246')  # Intersection between all inputs
      set(['2'])
      >>> s.difference('246', '789')
      set(['1', '0', '3', '5'])

  （由 Raymond Hettinger 贡献。）

- Many floating-point features were added. The `float()` function will now turn the string `nan` into an IEEE 754 Not A Number value, and `+inf` and `-inf` into positive or negative infinity. This works on any platform with IEEE 754 semantics. (Contributed by Christian Heimes; bpo-1635.)

  Other functions in the `math` module, `isinf()` and `isnan()`, return true if their floating-point argument is infinite or Not A Number. (bpo-1640)

  Conversion functions were added to convert floating-point numbers into hexadecimal strings (bpo-3008). These functions convert floats to and from a string representation without introducing rounding errors from the conversion between decimal and binary. Floats have a `hex()` method that returns a string representation, and the `float.fromhex()` method converts a string back into a number:

      >>> a = 3.75
      >>> a.hex()
      '0x1.e000000000000p+1'
      >>> float.fromhex('0x1.e000000000000p+1')
      3.75
      >>> b=1./3
      >>> b.hex()
      '0x1.5555555555555p-2'

- A numerical nicety: when creating a complex number from two floats on systems that support signed zeros (-0 and +0), the `complex()` constructor will now preserve the sign of the zero. (Fixed by Mark T. Dickinson; bpo-1507.)

- Classes that inherit a `__hash__()` method from a parent class can set `__hash__ = None` to indicate that the class isn't hashable. This will make `hash(obj)` raise a `TypeError` and the class will not be indicated as implementing the `Hashable` ABC.

  You should do this when you've defined a `__cmp__()` or `__eq__()` method that compares objects by their value rather than by identity. All objects have a default hash method that uses `id(obj)` as the hash value. There's no tidy way to remove the `__hash__()` method inherited from a parent class, so assigning `None` was implemented as an override. At the C level, extensions can set `tp_hash` to `PyObject_HashNotImplemented()`. (Fixed by Nick Coghlan and Amaury Forgeot d'Arc; bpo-2235.)

- The `GeneratorExit` exception now subclasses `BaseException` instead of `Exception`. This means that an exception handler that does `except Exception:` will not inadvertently catch `GeneratorExit`. (Contributed by Chad Austin; bpo-1537.)

- Generator objects now have a `gi_code` attribute that refers to the original code object backing the generator. (Contributed by Collin Winter; bpo-1473257.)

- The `compile()` built-in function now accepts keyword arguments as well as positional parameters. (Contributed by Thomas Wouters; bpo-1444529.)

- The `complex()` constructor now accepts strings containing parenthesized complex numbers, meaning that `complex(repr(cplx))` will now round-trip values. For example, `complex('(3+4j)')` now returns the value (3+4j). (bpo-1491866)

- The string `translate()` method now accepts `None` as the translation table parameter, which is treated as the identity transformation. This makes it easier to carry out operations that only delete characters. (Contributed by Bengt Richter and implemented by Raymond Hettinger; bpo-1193128.)

- The built-in `dir()` function now checks for a `__dir__()` method on the objects it receives. This method must return a list of strings containing the names of valid attributes for the object, and lets the object control the value that `dir()` produces. Objects that have `__getattr__()` or `__getattribute__()` methods can use this to advertise pseudo-attributes they will honor. (bpo-1591665)

- Instance method objects have new attributes for the object and function comprising the method; the new synonym for `im_self` is `__self__`, and `im_func` is also available as `__func__`. The old names are still supported in Python 2.6, but are gone in 3.0.

- An obscure change: when you use the `locals()` function inside a `class` statement, the resulting dictionary no longer returns free variables. (Free variables, in this case, are variables referenced in the `class` statement that aren't attributes of the class.)

### 性能优化

- The `warnings` module has been rewritten in C. This makes it possible to invoke warnings from the parser, and may also make the interpreter's startup faster. (Contributed by Neal Norwitz and Brett Cannon; bpo-1631171.)

- Type objects now have a cache of methods that can reduce the work required to find the correct method implementation for a particular class; once cached, the interpreter doesn't need to traverse base classes to figure out the right method to call. The cache is cleared if a base class or the class itself is modified, so the cache should remain correct even in the face of Python's dynamic nature. (Original optimization implemented by Armin Rigo, updated for Python 2.6 by Kevin Jacobs; bpo-1700288.)

  By default, this change is only applied to types that are included with the Python core. Extension modules may not necessarily be compatible with this cache, so they must explicitly add `Py_TPFLAGS_HAVE_VERSION_TAG` to the module's `tp_flags` field to enable the method cache. (To be compatible with the method cache, the extension module's code must not directly access and modify the `tp_dict` member of any of the types it implements. Most modules don't do this, but it's impossible for the Python interpreter to determine that. See bpo-1878 for some discussion.)

- Function calls that use keyword arguments are significantly faster by doing a quick pointer comparison, usually saving the time of a full string comparison. (Contributed by Raymond Hettinger, after an initial implementation by Antoine Pitrou; bpo-1819.)

- All of the functions in the `struct` module have been rewritten in C, thanks to work at the Need For Speed sprint. (Contributed by Raymond Hettinger.)

- Some of the standard built-in types now set a bit in their type objects. This speeds up checking whether an object is a subclass of one of these types. (Contributed by Neal Norwitz.)

- Unicode strings now use faster code for detecting whitespace and line breaks; this speeds up the `split()` method by about 25% and `splitlines()` by 35%. (Contributed by Antoine Pitrou.) Memory usage is reduced by using pymalloc for the Unicode string's data.

- The `with` statement now stores the `__exit__()` method on the stack, producing a small speedup. (Implemented by Jeffrey Yasskin.)

- To reduce memory usage, the garbage collector will now clear internal free lists when garbage-collecting the highest generation of objects. This may return memory to the operating system sooner.

### 解释器改动

Two command-line options have been reserved for use by other Python implementations. The `-J` switch has been reserved for use by Jython for Jython-specific options, such as switches that are passed to the underlying JVM. `-X` has been reserved for options specific to a particular implementation of Python such as CPython, Jython, or IronPython. If either option is used with Python 2.6, the interpreter will report that the option isn't currently used.

Python can now be prevented from writing `.pyc` or `.pyo` files by supplying the `-B` switch to the Python interpreter, or by setting the `PYTHONDONTWRITEBYTECODE` environment variable before running the interpreter. This setting is available to Python programs as the `sys.dont_write_bytecode` variable, and Python code can change the value to modify the interpreter's behaviour. (Contributed by Neal Norwitz and Georg Brandl.)

The encoding used for standard input, output, and standard error can be specified by setting the `PYTHONIOENCODING` environment variable before running the interpreter. The value should be a string in the form `<encoding>` or `<encoding>:<errorhandler>`. The encoding part specifies the encoding's name, e.g. `utf-8` or `latin-1`; the optional errorhandler part specifies what to do with characters that can't be handled by the encoding, and should be one of "error", "ignore", or "replace". (Contributed by Martin von Löwis.)

## 新增和改进的模块

As in every release, 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 Subversion logs for all the details.

- The `asyncore` and `asynchat` modules are being actively maintained again, and a number of patches and bugfixes were applied. (Maintained by Josiah Carlson; see bpo-1736190 for one patch.)

- The `bsddb` module also has a new maintainer, Jesús Cea Avión, and the package is now available as a standalone package. The web page for the package is www.jcea.es/programacion/pybsddb.htm. The plan is to remove the package from the standard library in Python 3.0, because its pace of releases is much more frequent than Python's.

  The `bsddb.dbshelve` module now uses the highest pickling protocol available, instead of restricting itself to protocol 1. (Contributed by W. Barnes.)

- The `cgi` module will now read variables from the query string of an HTTP POST request. This makes it possible to use form actions with URLs that include query strings such as "/cgi-bin/add.py?category=1". (Contributed by Alexandre Fiori and Nubis; bpo-1817.)

  The `parse_qs()` and `parse_qsl()` functions have been relocated from the `cgi` module to the `urlparse` module. The versions still available in the `cgi` module will trigger `PendingDeprecationWarning` messages in 2.6 (bpo-600362).

- The `cmath` module underwent extensive revision, contributed by Mark Dickinson and Christian Heimes. Five new functions were added:

  - `polar()` converts a complex number to polar form, returning the modulus and argument of the complex number.

  - `rect()` does the opposite, turning a modulus, argument pair back into the corresponding complex number.

  - `phase()` returns the argument (also called the angle) of a complex number.

  - `isnan()` returns True if either the real or imaginary part of its argument is a NaN.

  - `isinf()` returns True if either the real or imaginary part of its argument is infinite.

  The revisions also improved the numerical soundness of the `cmath` module. For all functions, the real and imaginary parts of the results are accurate to within a few units of least precision (ulps) whenever possible. See bpo-1381 for the details. The branch cuts for `asinh()`, `atanh()`: and `atan()` have also been corrected.

  The tests for the module have been greatly expanded; nearly 2000 new test cases exercise the algebraic functions.

  On IEEE 754 platforms, the `cmath` module now handles IEEE 754 special values and floating-point exceptions in a manner consistent with Annex 'G' of the C99 standard.

- A new data type in the `collections` module: `namedtuple(typename, fieldnames)` is a factory function that creates subclasses of the standard tuple whose fields are accessible by name as well as index. For example:

      >>> var_type = collections.namedtuple('variable',
      ...             'id name type size')
      >>> # Names are separated by spaces or commas.
      >>> # 'id, name, type, size' would also work.
      >>> var_type._fields
      ('id', 'name', 'type', 'size')

      >>> var = var_type(1, 'frequency', 'int', 4)
      >>> print var[0], var.id    # Equivalent
      1 1
      >>> print var[2], var.type  # Equivalent
      int int
      >>> var._asdict()
      {'size': 4, 'type': 'int', 'id': 1, 'name': 'frequency'}
      >>> v2 = var._replace(name='amplitude')
      >>> v2
      variable(id=1, name='amplitude', type='int', size=4)

  Several places in the standard library that returned tuples have been modified to return `namedtuple` instances. For example, the `Decimal.as_tuple()` method now returns a named tuple with `sign`, `digits`, and `exponent` fields.

  （由 Raymond Hettinger 贡献。）

- Another change to the `collections` module is that the `deque` type now supports an optional maxlen parameter; if supplied, the deque's size will be restricted to no more than maxlen items. Adding more items to a full deque causes old items to be discarded.

      >>> from collections import deque
      >>> dq=deque(maxlen=3)
      >>> dq
      deque([], maxlen=3)
      >>> dq.append(1); dq.append(2); dq.append(3)
      >>> dq
      deque([1, 2, 3], maxlen=3)
      >>> dq.append(4)
      >>> dq
      deque([2, 3, 4], maxlen=3)

  （由 Raymond Hettinger 贡献。）

- The `Cookie` module's `Morsel` objects now support an `httponly` attribute. In some browsers. cookies with this attribute set cannot be accessed or manipulated by JavaScript code. (Contributed by Arvin Schnell; bpo-1638033.)

- A new window method in the `curses` module, `chgat()`, changes the display attributes for a certain number of characters on a single line. (Contributed by Fabian Kreutz.)

      # Boldface text starting at y=0,x=21
      # and affecting the rest of the line.
      stdscr.chgat(0, 21, curses.A_BOLD)

  The `Textbox` class in the `curses.textpad` module now supports editing in insert mode as well as overwrite mode. Insert mode is enabled by supplying a true value for the insert_mode parameter when creating the `Textbox` instance.

- The `datetime` module's `strftime()` methods now support a `%f` format code that expands to the number of microseconds in the object, zero-padded on the left to six places. (Contributed by Skip Montanaro; bpo-1158.)

- The `decimal` module was updated to version 1.66 of the General Decimal Specification. New features include some methods for some basic mathematical functions such as `exp()` and `log10()`:

      >>> Decimal(1).exp()
      Decimal("2.718281828459045235360287471")
      >>> Decimal("2.7182818").ln()
      Decimal("0.9999999895305022877376682436")
      >>> Decimal(1000).log10()
      Decimal("3")

  现在 `Decimal` 对象的 `as_tuple()` 方法将返回一个由 `sign`, `digits` 和 `exponent` 字段组成的具名元组。

  （由 Facundo Batista 和 Mark Dickinson 实现。 具名元组支持由 Raymond Hettinger 添加。）

- 现在 `difflib` 模块的 `SequenceMatcher` 类将返回代表匹配结果的具名元组，包含 `a`, `b` 和 `size` 等属性。 （由 Raymond Hettinger 贡献。）

- An optional `timeout` parameter, specifying a timeout measured in seconds, was added to the `ftplib.FTP` class constructor as well as the `connect()` method. (Added by Facundo Batista.) Also, the `FTP` class's `storbinary()` and `storlines()` now take an optional callback parameter that will be called with each block of data after the data has been sent. (Contributed by Phil Schwartz; bpo-1221598.)

- The `reduce()` built-in function is also available in the `functools` module. In Python 3.0, the builtin has been dropped and `reduce()` is only available from `functools`; currently there are no plans to drop the builtin in the 2.x series. (Patched by Christian Heimes; bpo-1739906.)

- When possible, the `getpass` module will now use `/dev/tty` to print a prompt message and read the password, falling back to standard error and standard input. If the password may be echoed to the terminal, a warning is printed before the prompt is displayed. (Contributed by Gregory P. Smith.)

- The `glob.glob()` function can now return Unicode filenames if a Unicode path was used and Unicode filenames are matched within the directory. (bpo-1001604)

- A new function in the `heapq` module, `merge(iter1, iter2, ...)`, takes any number of iterables returning data in sorted order, and returns a new generator that returns the contents of all the iterators, also in sorted order. For example:

      >>> list(heapq.merge([1, 3, 5, 9], [2, 8, 16]))
      [1, 2, 3, 5, 8, 9, 16]

  Another new function, `heappushpop(heap, item)`, pushes item onto heap, then pops off and returns the smallest item. This is more efficient than making a call to `heappush()` and then `heappop()`.

  `heapq` is now implemented to only use less-than comparison, instead of the less-than-or-equal comparison it previously used. This makes `heapq`'s usage of a type match the `list.sort()` method. (Contributed by Raymond Hettinger.)

- An optional `timeout` parameter, specifying a timeout measured in seconds, was added to the `httplib.HTTPConnection` and `HTTPSConnection` class constructors. (Added by Facundo Batista.)

- Most of the `inspect` module's functions, such as `getmoduleinfo()` and `getargs()`, now return named tuples. In addition to behaving like tuples, the elements of the return value can also be accessed as attributes. (Contributed by Raymond Hettinger.)

  此模块中的新增函数包括 `isgenerator()`, `isgeneratorfunction()` 和 `isabstract()`。

- `itertools` 模块增加了几个新函数。

  `izip_longest(iter1, iter2, ...[, fillvalue])` makes tuples from each of the elements; if some of the iterables are shorter than others, the missing values are set to fillvalue. For example:

      >>> tuple(itertools.izip_longest([1,2,3], [1,2,3,4,5]))
      ((1, 1), (2, 2), (3, 3), (None, 4), (None, 5))

  `product(iter1, iter2, ..., [repeat=N])` returns the Cartesian product of the supplied iterables, a set of tuples containing every possible combination of the elements returned from each iterable.

      >>> list(itertools.product([1,2,3], [4,5,6]))
      [(1, 4), (1, 5), (1, 6),
       (2, 4), (2, 5), (2, 6),
       (3, 4), (3, 5), (3, 6)]

  The optional repeat keyword argument is used for taking the product of an iterable or a set of iterables with themselves, repeated N times. With a single iterable argument, N-tuples are returned:

      >>> list(itertools.product([1,2], repeat=3))
      [(1, 1, 1), (1, 1, 2), (1, 2, 1), (1, 2, 2),
       (2, 1, 1), (2, 1, 2), (2, 2, 1), (2, 2, 2)]

  With two iterables, 2N-tuples are returned.

      >>> list(itertools.product([1,2], [3,4], repeat=2))
      [(1, 3, 1, 3), (1, 3, 1, 4), (1, 3, 2, 3), (1, 3, 2, 4),
       (1, 4, 1, 3), (1, 4, 1, 4), (1, 4, 2, 3), (1, 4, 2, 4),
       (2, 3, 1, 3), (2, 3, 1, 4), (2, 3, 2, 3), (2, 3, 2, 4),
       (2, 4, 1, 3), (2, 4, 1, 4), (2, 4, 2, 3), (2, 4, 2, 4)]

  `combinations(iterable, r)` 基于 iterable 的元素返回长度为 r 的子序列。

      >>> list(itertools.combinations('123', 2))
      [('1', '2'), ('1', '3'), ('2', '3')]
      >>> list(itertools.combinations('123', 3))
      [('1', '2', '3')]
      >>> list(itertools.combinations('1234', 3))
      [('1', '2', '3'), ('1', '2', '4'),
       ('1', '3', '4'), ('2', '3', '4')]

  `permutations(iter[, r])` returns all the permutations of length r of the iterable's elements. If r is not specified, it will default to the number of elements produced by the iterable.

      >>> list(itertools.permutations([1,2,3,4], 2))
      [(1, 2), (1, 3), (1, 4),
       (2, 1), (2, 3), (2, 4),
       (3, 1), (3, 2), (3, 4),
       (4, 1), (4, 2), (4, 3)]

  `itertools.chain(*iterables)` is an existing function in `itertools` that gained a new constructor in Python 2.6. `itertools.chain.from_iterable(iterable)` takes a single iterable that should return other iterables. `chain()` will then return all the elements of the first iterable, then all the elements of the second, and so on.

      >>> list(itertools.chain.from_iterable([[1,2,3], [4,5,6]]))
      [1, 2, 3, 4, 5, 6]

  （全部由 Raymond Hettinger 贡献。）

- The `logging` module's `FileHandler` class and its subclasses `WatchedFileHandler`, `RotatingFileHandler`, and `TimedRotatingFileHandler` now have an optional delay parameter to their constructors. If delay is true, opening of the log file is deferred until the first `emit()` call is made. (Contributed by Vinay Sajip.)

  `TimedRotatingFileHandler` also has a utc constructor parameter. If the argument is true, UTC time will be used in determining when midnight occurs and in generating filenames; otherwise local time will be used.

- 为 `math` 模块添加了一些新函数:

  - `isinf()` and `isnan()` determine whether a given float is a (positive or negative) infinity or a NaN (Not a Number), respectively.

  - `copysign()` copies the sign bit of an IEEE 754 number, returning the absolute value of x combined with the sign bit of y. For example, `math.copysign(1, -0.0)` returns -1.0. (Contributed by Christian Heimes.)

  - `factorial()` computes the factorial of a number. (Contributed by Raymond Hettinger; bpo-2138.)

  - `fsum()` adds up the stream of numbers from an iterable, and is careful to avoid loss of precision through using partial sums. (Contributed by Jean Brouwers, Raymond Hettinger, and Mark Dickinson; bpo-2819.)

  - `acosh()`, `asinh()` and `atanh()` compute the inverse hyperbolic functions.

  - `log1p()` 返回 1+x (以 e 为底) 的自然对数。

  - `trunc()` rounds a number toward zero, returning the closest `Integral` that's between the function's argument and zero. Added as part of the backport of PEP 3141's type hierarchy for numbers.

- The `math` module has been improved to give more consistent behaviour across platforms, especially with respect to handling of floating-point exceptions and IEEE 754 special values.

  Whenever possible, the module follows the recommendations of the C99 standard about 754's special values. For example, `sqrt(-1.)` should now give a `ValueError` across almost all platforms, while `sqrt(float('NaN'))` should return a NaN on all IEEE 754 platforms. Where Annex 'F' of the C99 standard recommends signaling 'divide-by-zero' or 'invalid', Python will raise `ValueError`. Where Annex 'F' of the C99 standard recommends signaling 'overflow', Python will raise `OverflowError`. (See bpo-711019 and bpo-1640.)

  （由 Christian Heimes 和 Mark Dickinson 贡献。）

- `mmap` objects now have a `rfind()` method that searches for a substring beginning at the end of the string and searching backwards. The `find()` method also gained an end parameter giving an index at which to stop searching. (Contributed by John Lenton.)

- The `operator` module gained a `methodcaller()` function that takes a name and an optional set of arguments, returning a callable that will call the named function on any arguments passed to it. For example:

      >>> # Equivalent to lambda s: s.replace('old', 'new')
      >>> replacer = operator.methodcaller('replace', 'old', 'new')
      >>> replacer('old wine in old bottles')
      'new wine in new bottles'

  （由 Gregory Petrosyan 提供建议，之后由 Georg Brandl 贡献。）

  现在 `attrgetter()` 函数可接受带点号的名称并执行相应的属性查找:

      >>> inst_name = operator.attrgetter(
      ...        '__class__.__name__')
      >>> inst_name('')
      'str'
      >>> inst_name(help)
      '_Helper'

  （由 Barry Warsaw 提供建议，之后由 Georg Brandl 贡献。）

- The `os` module now wraps several new system calls. `fchmod(fd, mode)` and `fchown(fd, uid, gid)` change the mode and ownership of an opened file, and `lchmod(path, mode)` changes the mode of a symlink. (Contributed by Georg Brandl and Christian Heimes.)

  `chflags()` and `lchflags()` are wrappers for the corresponding system calls (where they're available), changing the flags set on a file. Constants for the flag values are defined in the `stat` module; some possible values include `UF_IMMUTABLE` to signal the file may not be changed and `UF_APPEND` to indicate that data can only be appended to the file. (Contributed by M. Levinson.)

  `os.closerange(low, high)` efficiently closes all file descriptors from low to high, ignoring any errors and not including high itself. This function is now used by the `subprocess` module to make starting processes faster. (Contributed by Georg Brandl; bpo-1663329.)

- The `os.environ` object's `clear()` method will now unset the environment variables using `os.unsetenv()` in addition to clearing the object's keys. (Contributed by Martin Horcicka; bpo-1181.)

- The `os.walk()` function now has a `followlinks` parameter. If set to True, it will follow symlinks pointing to directories and visit the directory's contents. For backward compatibility, the parameter's default value is false. Note that the function can fall into an infinite recursion if there's a symlink that points to a parent directory. (bpo-1273829)

- In the `os.path` module, the `splitext()` function has been changed to not split on leading period characters. This produces better results when operating on Unix's dot-files. For example, `os.path.splitext('.ipython')` now returns `('.ipython', '')` instead of `('', '.ipython')`. (bpo-1115886)

  A new function, `os.path.relpath(path, start='.')`, returns a relative path from the `start` path, if it's supplied, or from the current working directory to the destination `path`. (Contributed by Richard Barran; bpo-1339796.)

  On Windows, `os.path.expandvars()` will now expand environment variables given in the form "%var%", and "~user" will be expanded into the user's home directory path. (Contributed by Josiah Carlson; bpo-957650.)

- The Python debugger provided by the `pdb` module gained a new command: "run" restarts the Python program being debugged and can optionally take new command-line arguments for the program. (Contributed by Rocky Bernstein; bpo-1393667.)

- The `pdb.post_mortem()` function, used to begin debugging a traceback, will now use the traceback returned by `sys.exc_info()` if no traceback is supplied. (Contributed by Facundo Batista; bpo-1106316.)

- The `pickletools` module now has an `optimize()` function that takes a string containing a pickle and removes some unused opcodes, returning a shorter pickle that contains the same data structure. (Contributed by Raymond Hettinger.)

- A `get_data()` function was added to the `pkgutil` module that returns the contents of resource files included with an installed Python package. For example:

      >>> import pkgutil
      >>> print pkgutil.get_data('test', 'exception_hierarchy.txt')
      BaseException
       +-- SystemExit
       +-- KeyboardInterrupt
       +-- GeneratorExit
       +-- Exception
            +-- StopIteration
            +-- StandardError
       ...

  （由 Paul Moore 在 bpo-2439 中贡献。）

- The `pyexpat` module's `Parser` objects now allow setting their `buffer_size` attribute to change the size of the buffer used to hold character data. (Contributed by Achim Gaedke; bpo-1137.)

- The `Queue` module now provides queue variants that retrieve entries in different orders. The `PriorityQueue` class stores queued items in a heap and retrieves them in priority order, and `LifoQueue` retrieves the most recently added entries first, meaning that it behaves like a stack. (Contributed by Raymond Hettinger.)

- The `random` module's `Random` objects can now be pickled on a 32-bit system and unpickled on a 64-bit system, and vice versa. Unfortunately, this change also means that Python 2.6's `Random` objects can't be unpickled correctly on earlier versions of Python. (Contributed by Shawn Ligocki; bpo-1727780.)

  The new `triangular(low, high, mode)` function returns random numbers following a triangular distribution. The returned values are between low and high, not including high itself, and with mode as the most frequently occurring value in the distribution. (Contributed by Wladmir van der Laan and Raymond Hettinger; bpo-1681432.)

- Long regular expression searches carried out by the `re` module will check for signals being delivered, so time-consuming searches can now be interrupted. (Contributed by Josh Hoyt and Ralf Schmitt; bpo-846388.)

  The regular expression module is implemented by compiling bytecodes for a tiny regex-specific virtual machine. Untrusted code could create malicious strings of bytecode directly and cause crashes, so Python 2.6 includes a verifier for the regex bytecode. (Contributed by Guido van Rossum from work for Google App Engine; bpo-3487.)

- The `rlcompleter` module's `Completer.complete()` method will now ignore exceptions triggered while evaluating a name. (Fixed by Lorenz Quack; bpo-2250.)

- The `sched` module's `scheduler` instances now have a read-only `queue` attribute that returns the contents of the scheduler's queue, represented as a list of named tuples with the fields `(time, priority, action, argument)`. (Contributed by Raymond Hettinger; bpo-1861.)

- The `select` module now has wrapper functions for the Linux `epoll()` and BSD `kqueue()` system calls. `modify()` method was added to the existing `poll` objects; `pollobj.modify(fd, eventmask)` takes a file descriptor or file object and an event mask, modifying the recorded event mask for that file. (Contributed by Christian Heimes; bpo-1657.)

- The `shutil.copytree()` function now has an optional ignore argument that takes a callable object. This callable will receive each directory path and a list of the directory's contents, and returns a list of names that will be ignored, not copied.

  The `shutil` module also provides an `ignore_patterns()` function for use with this new parameter. `ignore_patterns()` takes an arbitrary number of glob-style patterns and returns a callable that will ignore any files and directories that match any of these patterns. The following example copies a directory tree, but skips both `.svn` directories and Emacs backup files, which have names ending with '~':

      shutil.copytree('Doc/library', '/tmp/library',
                      ignore=shutil.ignore_patterns('*~', '.svn'))

  （由 Tarek Ziadé 在 bpo-2663 中贡献。）

- Integrating signal handling with GUI handling event loops like those used by Tkinter or GTk+ has long been a problem; most software ends up polling, waking up every fraction of a second to check if any GUI events have occurred. The `signal` module can now make this more efficient. Calling `signal.set_wakeup_fd(fd)` sets a file descriptor to be used; when a signal is received, a byte is written to that file descriptor. There's also a C-level function, `PySignal_SetWakeupFd()`, for setting the descriptor.

  Event loops will use this by opening a pipe to create two descriptors, one for reading and one for writing. The writable descriptor will be passed to `set_wakeup_fd()`, and the readable descriptor will be added to the list of descriptors monitored by the event loop via `select()` or `poll()`. On receiving a signal, a byte will be written and the main event loop will be woken up, avoiding the need to poll.

  （由 Adam Olsen 在 bpo-1583 中贡献。）

  The `siginterrupt()` function is now available from Python code, and allows changing whether signals can interrupt system calls or not. (Contributed by Ralf Schmitt.)

  The `setitimer()` and `getitimer()` functions have also been added (where they're available). `setitimer()` allows setting interval timers that will cause a signal to be delivered to the process after a specified time, measured in wall-clock time, consumed process time, or combined process+system time. (Contributed by Guilherme Polo; bpo-2240.)

- The `smtplib` module now supports SMTP over SSL thanks to the addition of the `SMTP_SSL` class. This class supports an interface identical to the existing `SMTP` class. (Contributed by Monty Taylor.) Both class constructors also have an optional `timeout` parameter that specifies a timeout for the initial connection attempt, measured in seconds. (Contributed by Facundo Batista.)

  An implementation of the LMTP protocol (RFC 2033) was also added to the module. LMTP is used in place of SMTP when transferring e-mail between agents that don't manage a mail queue. (LMTP implemented by Leif Hedstrom; bpo-957003.)

  `SMTP.starttls()` now complies with RFC 3207 and forgets any knowledge obtained from the server not obtained from the TLS negotiation itself. (Patch contributed by Bill Fenner; bpo-829951.)

- The `socket` module now supports TIPC (http://tipc.sourceforge.net/), a high-performance non-IP-based protocol designed for use in clustered environments. TIPC addresses are 4- or 5-tuples. (Contributed by Alberto Bertogli; bpo-1646.)

  A new function, `create_connection()`, takes an address and connects to it using an optional timeout value, returning the connected socket object. This function also looks up the address's type and connects to it using IPv4 or IPv6 as appropriate. Changing your code to use `create_connection()` instead of `socket(socket.AF_INET, ...)` may be all that's required to make your code work with IPv6.

- The base classes in the `SocketServer` module now support calling a `handle_timeout()` method after a span of inactivity specified by the server's `timeout` attribute. (Contributed by Michael Pomraning.) The `serve_forever()` method now takes an optional poll interval measured in seconds, controlling how often the server will check for a shutdown request. (Contributed by Pedro Werneck and Jeffrey Yasskin; bpo-742598, bpo-1193577.)

- The `sqlite3` module, maintained by Gerhard Häring, has been updated from version 2.3.2 in Python 2.5 to version 2.4.1.

- The `struct` module now supports the C99 `_Bool` type, using the format character `'?'`. (Contributed by David Remahl.)

- The `Popen` objects provided by the `subprocess` module now have `terminate()`, `kill()`, and `send_signal()` methods. On Windows, `send_signal()` only supports the `SIGTERM` signal, and all these methods are aliases for the Win32 API function `TerminateProcess()`. (Contributed by Christian Heimes.)

- A new variable in the `sys` module, `float_info`, is an object containing information derived from the `float.h` file about the platform's floating-point support. Attributes of this object include `mant_dig` (number of digits in the mantissa), `epsilon` (smallest difference between 1.0 and the next largest value representable), and several others. (Contributed by Christian Heimes; bpo-1534.)

  Another new variable, `dont_write_bytecode`, controls whether Python writes any `.pyc` or `.pyo` files on importing a module. If this variable is true, the compiled files are not written. The variable is initially set on start-up by supplying the `-B` switch to the Python interpreter, or by setting the `PYTHONDONTWRITEBYTECODE` environment variable before running the interpreter. Python code can subsequently change the value of this variable to control whether bytecode files are written or not. (Contributed by Neal Norwitz and Georg Brandl.)

  Information about the command-line arguments supplied to the Python interpreter is available by reading attributes of a named tuple available as `sys.flags`. For example, the `verbose` attribute is true if Python was executed in verbose mode, `debug` is true in debugging mode, etc. These attributes are all read-only. (Contributed by Christian Heimes.)

  A new function, `getsizeof()`, takes a Python object and returns the amount of memory used by the object, measured in bytes. Built-in objects return correct results; third-party extensions may not, but can define a `__sizeof__()` method to return the object's size. (Contributed by Robert Schuppenies; bpo-2898.)

  It's now possible to determine the current profiler and tracer functions by calling `sys.getprofile()` and `sys.gettrace()`. (Contributed by Georg Brandl; bpo-1648.)

- The `tarfile` module now supports POSIX.1-2001 (pax) tarfiles in addition to the POSIX.1-1988 (ustar) and GNU tar formats that were already supported. The default format is GNU tar; specify the `format` parameter to open a file using a different format:

      tar = tarfile.open("output.tar", "w",
                         format=tarfile.PAX_FORMAT)

  The new `encoding` and `errors` parameters specify an encoding and an error handling scheme for character conversions. `'strict'`, `'ignore'`, and `'replace'` are the three standard ways Python can handle errors,; `'utf-8'` is a special value that replaces bad characters with their UTF-8 representation. (Character conversions occur because the PAX format supports Unicode filenames, defaulting to UTF-8 encoding.)

  The `TarFile.add()` method now accepts an `exclude` argument that's a function that can be used to exclude certain filenames from an archive. The function must take a filename and return true if the file should be excluded or false if it should be archived. The function is applied to both the name initially passed to `add()` and to the names of files in recursively-added directories.

  （所有改变均由 Lars Gustäbel 贡献）。

- 为 `telnetlib.Telnet` 类构造器增加了可选的 `timeout` 形参，指定以秒为单位的超时设置。 （由 Facundo Batista 添加。）

- The `tempfile.NamedTemporaryFile` class usually deletes the temporary file it created when the file is closed. This behaviour can now be changed by passing `delete=False` to the constructor. (Contributed by Damien Miller; bpo-1537850.)

  A new class, `SpooledTemporaryFile`, behaves like a temporary file but stores its data in memory until a maximum size is exceeded. On reaching that limit, the contents will be written to an on-disk temporary file. (Contributed by Dustin J. Mitchell.)

  The `NamedTemporaryFile` and `SpooledTemporaryFile` classes both work as context managers, so you can write `with tempfile.NamedTemporaryFile() as tmp: ...`. (Contributed by Alexander Belopolsky; bpo-2021.)

- The `test.test_support` module gained a number of context managers useful for writing tests. `EnvironmentVarGuard()` is a context manager that temporarily changes environment variables and automatically restores them to their old values.

  Another context manager, `TransientResource`, can surround calls to resources that may or may not be available; it will catch and ignore a specified list of exceptions. For example, a network test may ignore certain failures when connecting to an external web site:

      with test_support.TransientResource(IOError,
                                      errno=errno.ETIMEDOUT):
          f = urllib.urlopen('https://sf.net')
          ...

  Finally, `check_warnings()` resets the `warning` module's warning filters and returns an object that will record all warning messages triggered (bpo-3781):

      with test_support.check_warnings() as wrec:
          warnings.simplefilter("always")
          # ... code that triggers a warning ...
          assert str(wrec.message) == "function is outdated"
          assert len(wrec.warnings) == 1, "Multiple warnings raised"

  （由 Brett Cannon 贡献。）

- The `textwrap` module can now preserve existing whitespace at the beginnings and ends of the newly-created lines by specifying `drop_whitespace=False` as an argument:

      >>> S = """This  sentence  has a bunch   of
      ...   extra   whitespace."""
      >>> print textwrap.fill(S, width=15)
      This  sentence
      has a bunch
      of    extra
      whitespace.
      >>> print textwrap.fill(S, drop_whitespace=False, width=15)
      This  sentence
        has a bunch
         of    extra
         whitespace.
      >>>

  （由 Dwayne Bailey 在 bpo-1581073 中贡献。）

- The `threading` module API is being changed to use properties such as `daemon` instead of `setDaemon()` and `isDaemon()` methods, and some methods have been renamed to use underscores instead of camel-case; for example, the `activeCount()` method is renamed to `active_count()`. Both the 2.6 and 3.0 versions of the module support the same properties and renamed methods, but don't remove the old methods. No date has been set for the deprecation of the old APIs in Python 3.x; the old APIs won't be removed in any 2.x version. (Carried out by several people, most notably Benjamin Peterson.)

  The `threading` module's `Thread` objects gained an `ident` property that returns the thread's identifier, a nonzero integer. (Contributed by Gregory P. Smith; bpo-2871.)

- The `timeit` module now accepts callables as well as strings for the statement being timed and for the setup code. Two convenience functions were added for creating `Timer` instances: `repeat(stmt, setup, time, repeat, number)` and `timeit(stmt, setup, time, number)` create an instance and call the corresponding method. (Contributed by Erik Demaine; bpo-1533909.)

- The `Tkinter` module now accepts lists and tuples for options, separating the elements by spaces before passing the resulting value to Tcl/Tk. (Contributed by Guilherme Polo; bpo-2906.)

- The `turtle` module for turtle graphics was greatly enhanced by Gregor Lingl. New features in the module include:

  - Better animation of turtle movement and rotation.

  - Control over turtle movement using the new `delay()`, `tracer()`, and `speed()` methods.

  - The ability to set new shapes for the turtle, and to define a new coordinate system.

  - Turtles now have an `undo()` method that can roll back actions.

  - Simple support for reacting to input events such as mouse and keyboard activity, making it possible to write simple games.

  - `turtle.cfg` 文件可被用来定制海龟绘图屏幕的初始外观。

  - The module's docstrings can be replaced by new docstrings that have been translated into another language.

  (bpo-1513695)

- An optional `timeout` parameter was added to the `urllib.urlopen()` function and the `urllib.ftpwrapper` class constructor, as well as the `urllib2.urlopen()` function. The parameter specifies a timeout measured in seconds. For example:

      >>> u = urllib2.urlopen("http://slow.example.com",
                              timeout=3)
      Traceback (most recent call last):
        ...
      urllib2.URLError: <urlopen error timed out>
      >>>

  （由 Facundo Batista 添加。）

- The Unicode database provided by the `unicodedata` module has been updated to version 5.1.0. (Updated by Martin von Löwis; bpo-3811.)

- The `warnings` module's `formatwarning()` and `showwarning()` gained an optional line argument that can be used to supply the line of source code. (Added as part of bpo-1631171, which re-implemented part of the `warnings` module in C code.)

  A new function, `catch_warnings()`, is a context manager intended for testing purposes that lets you temporarily modify the warning filters and then restore their original values (bpo-3781).

- The XML-RPC `SimpleXMLRPCServer` and `DocXMLRPCServer` classes can now be prevented from immediately opening and binding to their socket by passing `False` as the bind_and_activate constructor parameter. This can be used to modify the instance's `allow_reuse_address` attribute before calling the `server_bind()` and `server_activate()` methods to open the socket and begin listening for connections. (Contributed by Peter Parente; bpo-1599845.)

  `SimpleXMLRPCServer` also has a `_send_traceback_header` attribute; if true, the exception and formatted traceback are returned as HTTP headers "X-Exception" and "X-Traceback". This feature is for debugging purposes only and should not be used on production servers because the tracebacks might reveal passwords or other sensitive information. (Contributed by Alan McIntyre as part of his project for Google's Summer of Code 2007.)

- The `xmlrpclib` module no longer automatically converts `datetime.date` and `datetime.time` to the `xmlrpclib.DateTime` type; the conversion semantics were not necessarily correct for all applications. Code using `xmlrpclib` should convert `date` and `time` instances. (bpo-1330538) The code can also handle dates before 1900 (contributed by Ralf Schmitt; bpo-2014) and 64-bit integers represented by using `<i8>` in XML-RPC responses (contributed by Riku Lindblad; bpo-2985).

- The `zipfile` module's `ZipFile` class now has `extract()` and `extractall()` methods that will unpack a single file or all the files in the archive to the current directory, or to a specified directory:

      z = zipfile.ZipFile('python-251.zip')

      # Unpack a single file, writing it relative
      # to the /tmp directory.
      z.extract('Python/sysmodule.c', '/tmp')

      # Unpack all the files in the archive.
      z.extractall()

  （由 Alan McIntyre 在 bpo-467924 中贡献。）

  The `open()`, `read()` and `extract()` methods can now take either a filename or a `ZipInfo` object. This is useful when an archive accidentally contains a duplicated filename. (Contributed by Graham Horler; bpo-1775025.)

  Finally, `zipfile` now supports using Unicode filenames for archived files. (Contributed by Alexey Borzenkov; bpo-1734346.)

### `ast` 模块

The `ast` module provides an Abstract Syntax Tree representation of Python code, and Armin Ronacher contributed a set of helper functions that perform a variety of common tasks. These will be useful for HTML templating packages, code analyzers, and similar tools that process Python code.

The `parse()` function takes an expression and returns an AST. The `dump()` function outputs a representation of a tree, suitable for debugging:

    import ast

    t = ast.parse("""
    d = {}
    for i in 'abcdefghijklm':
        d[i + i] = ord(i) - ord('a') + 1
    print d
    """)
    print ast.dump(t)

输出是一棵深度嵌套的树:

    Module(body=[
      Assign(targets=[
        Name(id='d', ctx=Store())
       ], value=Dict(keys=[], values=[]))
      For(target=Name(id='i', ctx=Store()),
          iter=Str(s='abcdefghijklm'), body=[
        Assign(targets=[
          Subscript(value=
            Name(id='d', ctx=Load()),
              slice=
              Index(value=
                BinOp(left=Name(id='i', ctx=Load()), op=Add(),
                 right=Name(id='i', ctx=Load()))), ctx=Store())
         ], value=
         BinOp(left=
          BinOp(left=
           Call(func=
            Name(id='ord', ctx=Load()), args=[
              Name(id='i', ctx=Load())
             ], keywords=[], starargs=None, kwargs=None),
           op=Sub(), right=Call(func=
            Name(id='ord', ctx=Load()), args=[
              Str(s='a')
             ], keywords=[], starargs=None, kwargs=None)),
           op=Add(), right=Num(n=1)))
        ], orelse=[])
       Print(dest=None, values=[
         Name(id='d', ctx=Load())
       ], nl=True)
     ])

The `literal_eval()` method takes a string or an AST representing a literal expression, parses and evaluates it, and returns the resulting value. A literal expression is a Python expression containing only strings, numbers, dictionaries, etc. but no statements or function calls. If you need to evaluate an expression but cannot accept the security risk of using an `eval()` call, `literal_eval()` will handle it safely:

    >>> literal = '("a", "b", {2:4, 3:8, 1:2})'
    >>> print ast.literal_eval(literal)
    ('a', 'b', {1: 2, 2: 4, 3: 8})
    >>> print ast.literal_eval('"a" + "b"')
    Traceback (most recent call last):
      ...
    ValueError: malformed string

The module also includes `NodeVisitor` and `NodeTransformer` classes for traversing and modifying an AST, and functions for common transformations such as changing line numbers.

### `future_builtins` 模块

Python 3.0 makes many changes to the repertoire of built-in functions, and most of the changes can't be introduced in the Python 2.x series because they would break compatibility. The `future_builtins` module provides versions of these built-in functions that can be imported when writing 3.0-compatible code.

目前此模块中的函数包括:

- `ascii(obj)`: equivalent to `repr()`. In Python 3.0, `repr()` will return a Unicode string, while `ascii()` will return a pure ASCII bytestring.

- `filter(predicate, iterable)`, `map(func, iterable1, ...)`: the 3.0 versions return iterators, unlike the 2.x builtins which return lists.

- `hex(value)`, `oct(value)`: instead of calling the `__hex__()` or `__oct__()` methods, these versions will call the `__index__()` method and convert the result to hexadecimal or octal. `oct()` will use the new `0o` notation for its result.

### `json` 模块: JavaScript Object Notation

The new `json` module supports the encoding and decoding of Python types in JSON (Javascript Object Notation). JSON is a lightweight interchange format often used in web applications. For more information about JSON, see http://www.json.org.

`json` comes with support for decoding and encoding most built-in Python types. The following example encodes and decodes a dictionary:

    >>> import json
    >>> data = {"spam": "foo", "parrot": 42}
    >>> in_json = json.dumps(data) # Encode the data
    >>> in_json
    '{"parrot": 42, "spam": "foo"}'
    >>> json.loads(in_json) # Decode into a Python object
    {"spam": "foo", "parrot": 42}

It's also possible to write your own decoders and encoders to support more types. Pretty-printing of the JSON strings is also supported.

`json` (originally called simplejson) was written by Bob Ippolito.

### `plistlib` 模块：属性列表解析器

The `.plist` format is commonly used on Mac OS X to store basic data types (numbers, strings, lists, and dictionaries) by serializing them into an XML-based format. It resembles the XML-RPC serialization of data types.

Despite being primarily used on Mac OS X, the format has nothing Mac-specific about it and the Python implementation works on any platform that Python supports, so the `plistlib` module has been promoted to the standard library.

此模块的用法很简单:

    import sys
    import plistlib
    import datetime

    # Create data structure
    data_struct = dict(lastAccessed=datetime.datetime.now(),
                       version=1,
                       categories=('Personal','Shared','Private'))

    # Create string containing XML.
    plist_str = plistlib.writePlistToString(data_struct)
    new_struct = plistlib.readPlistFromString(plist_str)
    print data_struct
    print new_struct

    # Write data structure to a file and read it back.
    plistlib.writePlist(data_struct, '/tmp/customizations.plist')
    new_struct = plistlib.readPlist('/tmp/customizations.plist')

    # read/writePlist accepts file-like objects as well as paths.
    plistlib.writePlist(data_struct, sys.stdout)

### ctypes Enhancements

Thomas Heller continued to maintain and enhance the `ctypes` module.

`ctypes` now supports a `c_bool` datatype that represents the C99 `bool` type. (Contributed by David Remahl; bpo-1649190.)

The `ctypes` string, buffer and array types have improved support for extended slicing syntax, where various combinations of `(start, stop, step)` are supplied. (Implemented by Thomas Wouters.)

All `ctypes` data types now support `from_buffer()` and `from_buffer_copy()` methods that create a ctypes instance based on a provided buffer object. `from_buffer_copy()` copies the contents of the object, while `from_buffer()` will share the same memory area.

A new calling convention tells `ctypes` to clear the `errno` or Win32 LastError variables at the outset of each wrapped call. (Implemented by Thomas Heller; bpo-1798.)

You can now retrieve the Unix `errno` variable after a function call. When creating a wrapped function, you can supply `use_errno=True` as a keyword parameter to the `DLL()` function and then call the module-level methods `set_errno()` and `get_errno()` to set and retrieve the error value.

The Win32 LastError variable is similarly supported by the `DLL()`, `OleDLL()`, and `WinDLL()` functions. You supply `use_last_error=True` as a keyword parameter and then call the module-level methods `set_last_error()` and `get_last_error()`.

The `byref()` function, used to retrieve a pointer to a ctypes instance, now has an optional offset parameter that is a byte count that will be added to the returned pointer.

### 改进的 SSL 支持

Bill Janssen made extensive improvements to Python 2.6's support for the Secure Sockets Layer by adding a new module, `ssl`, that's built atop the OpenSSL library. This new module provides more control over the protocol negotiated, the X.509 certificates used, and has better support for writing SSL servers (as opposed to clients) in Python. The existing SSL support in the `socket` module hasn't been removed and continues to work, though it will be removed in Python 3.0.

To use the new module, you must first create a TCP connection in the usual way and then pass it to the `ssl.wrap_socket()` function. It's possible to specify whether a certificate is required, and to obtain certificate info by calling the `getpeercert()` method.

参见

`ssl` 模块的文档。

## 弃用和移除

- String exceptions have been removed. Attempting to use them raises a `TypeError`.

- Changes to the `Exception` interface as dictated by PEP 352 continue to be made. For 2.6, the `message` attribute is being deprecated in favor of the `args` attribute.

- (3.0-warning mode) Python 3.0 will feature a reorganized standard library that will drop many outdated modules and rename others. Python 2.6 running in 3.0-warning mode will warn about these modules when they are imported.

  The list of deprecated modules is: `audiodev`, `bgenlocations`, `buildtools`, `bundlebuilder`, `Canvas`, `compiler`, `dircache`, `dl`, `fpformat`, `gensuitemodule`, `ihooks`, `imageop`, `imgfile`, `linuxaudiodev`, `mhlib`, `mimetools`, `multifile`, `new`, `pure`, `statvfs`, `sunaudiodev`, `test.testall`, and `toaiff`.

- `gopherlib` 模块已被移除。

- The `MimeWriter` module and `mimify` module have been deprecated; use the `email` package instead.

- The `md5` module has been deprecated; use the `hashlib` module instead.

- The `posixfile` module has been deprecated; `fcntl.lockf()` provides better locking.

- The `popen2` module has been deprecated; use the `subprocess` module.

- `rgbimg` 模块已被移除。

- The `sets` module has been deprecated; it's better to use the built-in `set` and `frozenset` types.

- The `sha` module has been deprecated; use the `hashlib` module instead.

## 构建和 C API 的改变

针对 Python 构建过程和 C API 的改变包括:

- Python now must be compiled with C89 compilers (after 19 years!). This means that the Python source tree has dropped its own implementations of `memmove()` and `strerror()`, which are in the C89 standard library.

- Python 2.6 can be built with Microsoft Visual Studio 2008 (version 9.0), and this is the new default compiler. See the `PCbuild` directory for the build files. (Implemented by Christian Heimes.)

- On Mac OS X, Python 2.6 can be compiled as a 4-way universal build. The configure script can take a `--with-universal-archs=[32-bit|64-bit|all]` switch, controlling whether the binaries are built for 32-bit architectures (x86, PowerPC), 64-bit (x86-64 and PPC-64), or both. (Contributed by Ronald Oussoren.)

- The BerkeleyDB module now has a C API object, available as `bsddb.db.api`. This object can be used by other C extensions that wish to use the `bsddb` module for their own purposes. (Contributed by Duncan Grisby.)

- The new buffer interface, previously described in the PEP 3118 section, adds `PyObject_GetBuffer()` and `PyBuffer_Release()`, as well as a few other functions.

- Python's use of the C stdio library is now thread-safe, or at least as thread-safe as the underlying library is. A long-standing potential bug occurred if one thread closed a file object while another thread was reading from or writing to the object. In 2.6 file objects have a reference count, manipulated by the `PyFile_IncUseCount()` and `PyFile_DecUseCount()` functions. File objects can't be closed unless the reference count is zero. `PyFile_IncUseCount()` should be called while the GIL is still held, before carrying out an I/O operation using the `FILE *` pointer, and `PyFile_DecUseCount()` should be called immediately after the GIL is re-acquired. (Contributed by Antoine Pitrou and Gregory P. Smith.)

- Importing modules simultaneously in two different threads no longer deadlocks; it will now raise an `ImportError`. A new API function, `PyImport_ImportModuleNoBlock()`, will look for a module in `sys.modules` first, then try to import it after acquiring an import lock. If the import lock is held by another thread, an `ImportError` is raised. (Contributed by Christian Heimes.)

- Several functions return information about the platform's floating-point support. `PyFloat_GetMax()` returns the maximum representable floating point value, and `PyFloat_GetMin()` returns the minimum positive value. `PyFloat_GetInfo()` returns an object containing more information from the `float.h` file, such as `"mant_dig"` (number of digits in the mantissa), `"epsilon"` (smallest difference between 1.0 and the next largest value representable), and several others. (Contributed by Christian Heimes; bpo-1534.)

- C functions and methods that use `PyComplex_AsCComplex()` will now accept arguments that have a `__complex__()` method. In particular, the functions in the `cmath` module will now accept objects with this method. This is a backport of a Python 3.0 change. (Contributed by Mark Dickinson; bpo-1675423.)

- Python's C API now includes two functions for case-insensitive string comparisons, `PyOS_stricmp(char*, char*)` and `PyOS_strnicmp(char*, char*, Py_ssize_t)`. (Contributed by Christian Heimes; bpo-1635.)

- Many C extensions define their own little macro for adding integers and strings to the module's dictionary in the `init*` function. Python 2.6 finally defines standard macros for adding values to a module, `PyModule_AddStringMacro` and `PyModule_AddIntMacro()`. (Contributed by Christian Heimes.)

- Some macros were renamed in both 3.0 and 2.6 to make it clearer that they are macros, not functions. `Py_Size()` became `Py_SIZE()`, `Py_Type()` became `Py_TYPE()`, and `Py_Refcnt()` became `Py_REFCNT()`. The mixed-case macros are still available in Python 2.6 for backward compatibility. (bpo-1629)

- Distutils now places C extensions it builds in a different directory when running on a debug version of Python. (Contributed by Collin Winter; bpo-1530959.)

- Several basic data types, such as integers and strings, maintain internal free lists of objects that can be re-used. The data structures for these free lists now follow a naming convention: the variable is always named `free_list`, the counter is always named `numfree`, and a macro `Py<typename>_MAXFREELIST` is always defined.

- A new Makefile target, "make patchcheck", prepares the Python source tree for making a patch: it fixes trailing whitespace in all modified `.py` files, checks whether the documentation has been changed, and reports whether the `Misc/ACKS` and `Misc/NEWS` files have been updated. (Contributed by Brett Cannon.)

  Another new target, "make profile-opt", compiles a Python binary using GCC's profile-guided optimization. It compiles Python with profiling enabled, runs the test suite to obtain a set of profiling results, and then compiles using these results for optimization. (Contributed by Gregory P. Smith.)

### 特定于 Windows 的更改：

- The support for Windows 95, 98, ME and NT4 has been dropped. Python 2.6 requires at least Windows 2000 SP4.

- The new default compiler on Windows is Visual Studio 2008 (version 9.0). The build directories for Visual Studio 2003 (version 7.1) and 2005 (version 8.0) were moved into the PC/ directory. The new `PCbuild` directory supports cross compilation for X64, debug builds and Profile Guided Optimization (PGO). PGO builds are roughly 10% faster than normal builds. (Contributed by Christian Heimes with help from Amaury Forgeot d'Arc and Martin von Löwis.)

- The `msvcrt` module now supports both the normal and wide char variants of the console I/O API. The `getwch()` function reads a keypress and returns a Unicode value, as does the `getwche()` function. The `putwch()` function takes a Unicode character and writes it to the console. (Contributed by Christian Heimes.)

- `os.path.expandvars()` will now expand environment variables in the form "%var%", and "~user" will be expanded into the user's home directory path. (Contributed by Josiah Carlson; bpo-957650.)

- The `socket` module's socket objects now have an `ioctl()` method that provides a limited interface to the `WSAIoctl()` system interface.

- The `_winreg` module now has a function, `ExpandEnvironmentStrings()`, that expands environment variable references such as `%NAME%` in an input string. The handle objects provided by this module now support the context protocol, so they can be used in `with` statements. (Contributed by Christian Heimes.)

  `_winreg` also has better support for x64 systems, exposing the `DisableReflectionKey()`, `EnableReflectionKey()`, and `QueryReflectionKey()` functions, which enable and disable registry reflection for 32-bit processes running on 64-bit systems. (bpo-1753245)

- The `msilib` module's `Record` object gained `GetInteger()` and `GetString()` methods that return field values as an integer or a string. (Contributed by Floris Bruynooghe; bpo-2125.)

### 特定于 Mac OS X 的更改：

- 现在，在编译Python的框架版本时，可以为 configure 脚本添加 `--with-framework-name=` 选项来指定要使用的框架名称。

- `macfs` 模块已被删除。这反过来要求删除 `macostools.touched()` 函数，因为它依赖于 `macfs` 模块。 (bpo-1490190)

- 许多其他 Mac OS 模块已弃用并将在 Python 3.0 中被删除: `_builtinSuites`, `aepack`, `aetools`, `aetypes`, `applesingle`, `appletrawmain`, `appletrunner`, `argvemulator`, `Audio_mac`, `autoGIL`, `Carbon`, `cfmfile`, `CodeWarrior`, `ColorPicker`, `EasyDialogs`, `Explorer`, `Finder`, `FrameWork`, `findertools`, `ic`, `icglue`, `icopen`, `macerrors`, `MacOS`, `macfs`, `macostools`, `macresource`, `MiniAEFrame`, `Nav`, `Netscape`, `OSATerminology`, `pimp`, `PixMapWrapper`, `StdSuites`, `SystemEvents`, `Terminal` 和 `terminalcommand`。

### 特定于 IRIX 的更改：

许多旧的 IRIX 专用模块已被弃用，并将在Python 3.0中删除： `al` 和 `AL`, `cd`, `cddb`, `cdplayer`, `CL` 和 `cl`, `DEVICE`, `ERRNO`, `FILE`, `FL` 和 `fl`, `flp`, `fm`, `GET`, `GLWS`, `GL` 和 `gl`, `IN`, `IOCTL`, `jpeg`, `panelparser`, `readcd`, `SV` 和 `sv`, `torgb`, `videoreader`, 和 `WAIT`.

## 移植到Python 2.6

本节列出了先前描述的改变以及可能需要修改你的代码的其他问题修正:

- 预期为不可哈希的类应当在其定义中设置 `__hash__ = None` 来指明这一点。

- String exceptions have been removed. Attempting to use them raises a `TypeError`.

- The `__init__()` method of `collections.deque` now clears any existing contents of the deque before adding elements from the iterable. This change makes the behavior match `list.__init__()`.

- `object.__init__()` previously accepted arbitrary arguments and keyword arguments, ignoring them. In Python 2.6, this is no longer allowed and will result in a `TypeError`. This will affect `__init__()` methods that end up calling the corresponding method on `object` (perhaps through using `super()`). See bpo-1683368 for discussion.

- The `Decimal` constructor now accepts leading and trailing whitespace when passed a string. Previously it would raise an `InvalidOperation` exception. On the other hand, the `create_decimal()` method of `Context` objects now explicitly disallows extra whitespace, raising a `ConversionSyntax` exception.

- Due to an implementation accident, if you passed a file path to the built-in `__import__()` function, it would actually import the specified file. This was never intended to work, however, and the implementation now explicitly checks for this case and raises an `ImportError`.

- C API: the `PyImport_Import()` and `PyImport_ImportModule()` functions now default to absolute imports, not relative imports. This will affect C extensions that import other modules.

- C API: extension data types that shouldn't be hashable should define their `tp_hash` slot to `PyObject_HashNotImplemented()`.

- The `socket` module exception `socket.error` now inherits from `IOError`. Previously it wasn't a subclass of `StandardError` but now it is, through `IOError`. (Implemented by Gregory P. Smith; bpo-1706815.)

- The `xmlrpclib` module no longer automatically converts `datetime.date` and `datetime.time` to the `xmlrpclib.DateTime` type; the conversion semantics were not necessarily correct for all applications. Code using `xmlrpclib` should convert `date` and `time` instances. (bpo-1330538)

- (3.0-warning mode) The `Exception` class now warns when accessed using slicing or index access; having `Exception` behave like a tuple is being phased out.

- (3.0-warning mode) inequality comparisons between two dictionaries or two objects that don't implement comparison methods are reported as warnings. `dict1 == dict2` still works, but `dict1 < dict2` is being phased out.

  Comparisons between cells, which are an implementation detail of Python's scoping rules, also cause warnings because such comparisons are forbidden entirely in 3.0.

## 致谢

作者感谢以下人员对本文各种草稿给予的建议，更正和协助： Georg Brandl, Steve Brown, Nick Coghlan, Ralph Corderoy, Jim Jewett, Kent Johnson, Chris Lambacher, Martin Michlmayr, Antoine Pitrou, Brian Warner.