Python 2.7 有什么新变化

Python 2.7 有什么新变化

作者:

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

本文介绍了Python 2.7 的新功能。 Python 2.7 于2010年7月3日发布。

数字处理在许多方面得到了改进，包括浮点数和 Decimal 类。标准库中有一些有用的补充，例如大大增强的 unittest 模块，用于解析命令行选项的 argparse 模块，在 collections 模块中方便的 OrderedDict 和 Counter 类，以及许多其他改进。

Python 2.7计划成为2.x版本的最后一个发布版本，因此我们努力使其成为长期支持的好版本。为了帮助迁移到Python 3，我们在2.7中包含了几个来自Python 3.x系列的新特性。

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 2.7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或在https://bugs.python.org上讨论更改的问题。在可能的情况下，“What's New in Python”链接到每个更改的错误修正/补丁项。

Python 2.x的未来

Python 2.7 是 2.x 系列中的最后一个主版本，因为Python 维护人员已将新功能开发工作的重点转移到了 Python 3.x 系列中。这意味着，尽管 Python 2 会继续修复bug并更新，以便在新的硬件和支持操作系统版本上正确构建，但不会有新的功能发布。

然而，尽管在 Python 2.7 和 Python 3 之间有一个很大的公共子集，并且迁移到该公共子集或直接迁移到 Python 3 所涉及的许多更改可以安全地自动化完成。但是一些其他更改（特别是那些与Unicode处理相关的更改）可能需要仔细考虑，并且最好用自动化回归测试套件进行健壮性测试，以便有效地迁移。

这意味着 Python2.7 将长期保留，为尚未移植到 Python 3 的生产系统提供一个稳定且受支持的基础平台。Python 2.7系列的预期完整生命周期在 PEP 373 中有详细介绍。

长期保留 2.7 版的的一些关键后果：

• 如上所述，与早期的2.x版本相比，2.7版本的维护时间更长。目前，预计核心开发团队将继续支持Python 2.7（接收安全更新和其他错误修复），直到至少2020年（首次发布后10年，相比之下，通常的支持期为18—24个月）。

• 随着 Python 2.7 标准库的老化，有效地利用 Python 包索引（直接或通过重新分发者）对 Python 2 用户来说变得更加重要。除了各种任务的第三方包之外，可用的包还包括与 Python 2 兼容的 Python 3 标准库中的新模块和功能的后端移植，以及各种工具和库，这些工具和库可以让用户更容易迁移到 Python 3。 Python 包用户指南 提供了从 Python 包索引的下载和安装软件的指导。

• 虽然现在增强 Python 2 的首选方法是在Python包索引上发布新包，但这种方法不一定适用于所有情况，尤其是与网络安全相关的情况。在一些特殊情况下，如果在PyPI上发布新的或更新的包无法得到充分的处理，则可以使用Python增强建议过程来提出直接在Python 2标准库中添加新功能。任何此类添加及其添加的维护版本将在下面的 New Features Added to Python 2.7 Maintenance Releases 部分中注明。

对于希望从 Python2 迁移到 Python3 的项目，或者对于希望同时支持 Python2 和 Python3 用户的库和框架开发人员，可以使用各种工具和指南来帮助决定合适的方法并管理所涉及的一些技术细节。建议从 如何将 Python 2 代码移植到 Python 3 操作指南开始。

对于弃用警告处理方式的改变

对于 Python 2.7，一个策略决定是默认情况下禁止只对开发人员有兴趣的警告。 现在，除非另有要求，否则将忽略 DeprecationWarning 及其子类，以防止用户看到应用程序触发的警告。 这个更改也在成为Python 3.2 的分歧点上进行了。 （在 stdlib-sig 上进行了讨论，并在 bpo-7319 中执行。）

在以前的版本中，默认情况下启用了 DeprecationWarning 消息，为 Python 开发人员提供了一个明确的指示，说明他们的代码可能在未来的 Python 主要版本中出现问题。

然而，越来越多基于 Python 的应用程序的用户并不直接参与这些应用程序的开发。 DeprecationWarning 消息与这些用户无关，这让他们担心应用能否真正正常工作，并让应用开发人员承担起回应这些担忧的负担。

显示通过使用 -Wdefault (简写: -Wd) 开关运行 Python，或者在运行 Python 之前将 PYTHONWARNINGS 环境变量设置为 "default" (或 "d")，可以重新启用 DeprecationWarning 消息。 Python 代码也可以通过调用 warnings.simplefilter('default') 重新启用它们。

unittest 模块还会在运行测试时自动重新启用弃用警告。

Python 3.1 特性

就像 Python2.6 集成了 Python3.0 的特性一样，2.7版也集成了 Python3.1 中的一些新特性。2.x 系列继续提供迁移到3.x系列的工具。

3.1 功能的部分列表，这些功能已反向移植到 2.7：

• 用于集合字面值的语法 ({1,2,3} 是一个可变集合)。

• 字典与集合推导式 ({i: i*2 for i in range(3)})。

• 单个 with 语句中使用多个上下文管理器。

• 一个 io 库的新版本，用 C 重写以提升性能。

• PEP 372：将有序字典 添加到收藏集 所描述的有序字典类型。

• PEP 378: 千位分隔符的格式说明符 所描述的新的 "," 格式说明符。

• memoryview 对象。

• importlib 模块的一个较小子集，described below。

• 在很多情况下，浮点数 x 的 repr() 更短：现在它基于最短的十进制字符串 ，保证四舍五入到 x。 与 Python 以前的版本一样，保证 float(repr(x)) 能恢复到 x。

• 浮点数到字符串和字符串到浮点数的转换已正确舍入。 round() 函数现在也能正确舍入。

• PyCapsule 类型，用于为扩展模块提供 C API 。

• PyLong_AsLongAndOverflow() C API 函数 。

其他新的 Python3 模式警告包括：

• operator.isCallable() 和 operator.sequenceIncludes() 在 3.x 中不支持，现在会触发警告。

• -3 开关现在会自动启用:option:!-Qwarn 开关，该开关会在使用经典整除法处理整数和长整数时发出警告。

PEP 372：将有序字典 添加到收藏集

常规 Python 字典以任意顺序遍历键/值对。 多年来，许多作者编写了替代实现，以记住键最初插入的顺序。 基于这些实现的经验，2.7 在 collections 模块中引入了一个新的 OrderedDict 类。

OrderedDict API 提供与普通字典相同的接口 ，但会根据键首次插入的时间，按一定顺序遍历键和值：：

>>> from collections import OrderedDict
>>> d = OrderedDict([('first', 1),
...                  ('second', 2),
...                  ('third', 3)])
>>> d.items()
[('first', 1), ('second', 2), ('third', 3)]

如果新条目覆盖了现有条目，则原插入位置保持不变

>>> d['second'] = 4
>>> d.items()
[('first', 1), ('second', 4), ('third', 3)]

删除条目并重新插入会将其移至末尾

>>> del d['second']
>>> d['second'] = 5
>>> d.items()
[('first', 1), ('third', 3), ('second', 5)]

popitem() 方法有一个可选 last 参数 ，默认为 True 。如果 last 为真 ，则返回并删除最近添加的密钥；如果为 false ，则选择最旧的密钥

>>> od = OrderedDict([(x,0) for x in range(20)])
>>> od.popitem()
(19, 0)
>>> od.popitem()
(18, 0)
>>> od.popitem(last=False)
(0, 0)
>>> od.popitem(last=False)
(1, 0)

比较两个有序字典会同时检查键和值，并要求插入顺序相同

>>> od1 = OrderedDict([('first', 1),
...                    ('second', 2),
...                    ('third', 3)])
>>> od2 = OrderedDict([('third', 3),
...                    ('first', 1),
...                    ('second', 2)])
>>> od1 == od2
False
>>> # Move 'third' key to the end
>>> del od2['third']; od2['third'] = 3
>>> od1 == od2
True

将 OrderedDict 与普通字典进行比较时，会忽略插入顺序，只比较键和值。

OrderedDict 是如何工作的？它维护一个键的双链路列表，在插入新键时将其添加到列表中。二级字典 将键映射到其对应的列表节点 ，因此删除时不必遍历整个链接列表，从而保持 O(1)。

现在，标准库支持在多个模块 中使用有序字典。

• ConfigParser 模块默认使用它们，这意味着现在可以按照原来的顺序读取、修改和写回配置文件。

• The _asdict() method for collections.namedtuple() now returns an ordered dictionary with the values appearing in the same order as the underlying tuple indices.

• json 模块的 JSONDecoder 类构造器扩展了一个 object_pairs_hook 形参 ，允许解码器构建 OrderedDict 实例。此外，还添加了对第三方工具的支持，如 PyYAML 。

参见

PEP 372 - 将有序词典添加到集合中

PEP 由 Armin Ronacher 和 Raymond Hettinger 撰写，由 Raymond Hettinger 实现。

PEP 378: 千位分隔符的格式说明符

为了使程序输出更易读，可以在大数字上添加分隔符，将其显示为 18,446,744,073,709,551,616 而不是 18446744073709551616。

完全通用的解决方案是 locale 模块 ，它可以使用不同的分隔符（北美为”,”，欧洲为”.”）和不同的分组大小，但 locale 使用起来比较复杂，而且不适合多线程应用程序，因为不同的线程会为不同的本地生成输出。

因此，在 str.format() 方法使用的迷你语言中添加了一个简单的逗号分组机制。 在格式化浮点数时，只需在宽度和精度之间加上逗号

>>> '{:20,.2f}'.format(18446744073709551616.0)
'18,446,744,073,709,551,616.00'

格式化整数时，在宽度后面加上逗号：

>>> '{:20,d}'.format(18446744073709551616)
'18,446,744,073,709,551,616'

这种机制完全没有适应性；逗号总是用作分隔符，分组总是以三位数为一组。 逗号格式机制不如 locale 模块通用，但使用起来更方便。

参见

PEP 378 - 千位分隔符的格式说明符

PEP 由 Raymond Hettinger 撰写，由 Eric Smith 实现

PEP 389：用于解析命令行的 argparse 模块

用于解析命令-line参数的 argparse 模块是作为 optparse 模块更强大的替代功能而添加的。

这意味着 Python 现在支持三个不同的用来解析命令行参数的模块: getopt, optparse 和 argparse。 getopt 模块非常接近 C 库的 getopt() 函数，因此它在你编写最终要用 C 来重新编写的 Python 原型代码时很有用处。 optparse 已经变得冗余，但并没有移除它的计划因为许多脚本仍然在使用它，并且也没有自动化更新这些脚本的方式。 （让 argparse API 与 optparse 的接口保持一致的提议曾被讨论但因过于繁琐和困难而被拒绝。）

简而言之，如果你是在编写新脚本并且不需要担心与 Python 较早版本的兼容性，请使用 argparse 而不是 optparse。

以下是为示例代码:

import argparse

parser = argparse.ArgumentParser(description='Command-line example.')

## Add optional switches
parser.add_argument('-v', action='store_true', dest='is_verbose',
                    help='produce verbose output')
parser.add_argument('-o', action='store', dest='output',
                    metavar='FILE',
                    help='direct output to FILE instead of stdout')
parser.add_argument('-C', action='store', type=int, dest='context',
                    metavar='NUM', default=0,
                    help='display NUM lines of added context')

## Allow any number of additional arguments.
parser.add_argument(nargs='*', action='store', dest='inputs',
                    help='input filenames (default is stdin)')

args = parser.parse_args()
print args.__dict__

除非你覆盖它，否则会自动添加 -h 和 --help 开关，并产生格式化良好的输出:

-> ./python.exe argparse-example.py --help
usage: argparse-example.py [-h] [-v] [-o FILE] [-C NUM] [inputs [inputs ...]]

Command-line example.

positional arguments:
  inputs      input filenames (default is stdin)

optional arguments:
  -h, --help  show this help message and exit
  -v          produce verbose output
  -o FILE     direct output to FILE instead of stdout
  -C NUM      display NUM lines of added context

与 optparse 一样，命令行开关和参数将返回为一个具有通过 dest 形参所指定的属性的对象:

-> ./python.exe argparse-example.py -v
{'output': None,
 'is_verbose': True,
 'context': 0,
 'inputs': []}

-> ./python.exe argparse-example.py -v -o /tmp/output -C 4 file1 file2
{'output': '/tmp/output',
 'is_verbose': True,
 'context': 4,
 'inputs': ['file1', 'file2']}

argparse has much fancier validation than optparse; you can specify an exact number of arguments as an integer, 0 or more arguments by passing '*', 1 or more by passing '+', or an optional argument with '?'. A top-level parser can contain sub-parsers to define subcommands that have different sets of switches, as in svn commit, svn checkout, etc. You can specify an argument’s type as FileType, which will automatically open files for you and understands that '-' means standard input or output.

参见

argparse 文档

argparse 模块的文档页面。

Upgrading optparse code

Python 文档的一部分，描述如何转换使用了 optparse 的代码。

PEP 389 - argparse - 新的命令行解析模块

PEP 由 Steven Bethard 撰写并实现。

PEP 391: 基于字典的日志配置

The logging module is very flexible; applications can define a tree of logging subsystems, and each logger in this tree can filter out certain messages, format them differently, and direct messages to a varying number of handlers.

All this flexibility can require a lot of configuration. You can write Python statements to create objects and set their properties, but a complex set-up requires verbose but boring code. logging also supports a fileConfig() function that parses a file, but the file format doesn’t support configuring filters, and it’s messier to generate programmatically.

Python 2.7 adds a dictConfig() function that uses a dictionary to configure logging. There are many ways to produce a dictionary from different sources: construct one with code; parse a file containing JSON; or use a YAML parsing library if one is installed. For more information see 配置函数.

The following example configures two loggers, the root logger and a logger named “network”. Messages sent to the root logger will be sent to the system log using the syslog protocol, and messages to the “network” logger will be written to a network.log file that will be rotated once the log reaches 1MB.

import logging
import logging.config

configdict = {
 'version': 1,    # Configuration schema in use; must be 1 for now
 'formatters': {
     'standard': {
         'format': ('%(asctime)s %(name)-15s '
                    '%(levelname)-8s %(message)s')}},

 'handlers': {'netlog': {'backupCount': 10,
                     'class': 'logging.handlers.RotatingFileHandler',
                     'filename': '/logs/network.log',
                     'formatter': 'standard',
                     'level': 'INFO',
                     'maxBytes': 1000000},
              'syslog': {'class': 'logging.handlers.SysLogHandler',
                         'formatter': 'standard',
                         'level': 'ERROR'}},

 # Specify all the subordinate loggers
 'loggers': {
             'network': {
                         'handlers': ['netlog']
             }
 },
 # Specify properties of the root logger
 'root': {
          'handlers': ['syslog']
 },
}

## Set up configuration
logging.config.dictConfig(configdict)

## As an example, log two error messages
logger = logging.getLogger('/')
logger.error('Database not found')

netlogger = logging.getLogger('network')
netlogger.error('Connection failed')

Three smaller enhancements to the logging module, all implemented by Vinay Sajip, are:

• The SysLogHandler class now supports syslogging over TCP. The constructor has a socktype parameter giving the type of socket to use, either socket.SOCK_DGRAM for UDP or socket.SOCK_STREAM for TCP. The default protocol remains UDP.

• Logger instances gained a getChild() method that retrieves a descendant logger using a relative path. For example, once you retrieve a logger by doing log = getLogger('app'), calling log.getChild('network.listen') is equivalent to getLogger('app.network.listen').

• The LoggerAdapter class gained an isEnabledFor() method that takes a level and returns whether the underlying logger would process a message of that level of importance.

参见

PEP 391 - 基于字典的日志配置

PEP 由 Vinay Sajip 撰写并实现

PEP 3106: 字典视图

字典方法 keys(), values(), and items() 在 Python 3.x 有所不同。 它们将返回名为 view 的对象而不是完整的列表。

在 Python 2.7 中不可能改变 keys(), values() 和 items() 的返回值因为那会破坏大量已有代码。 作为替代 3.x 版本是以新名称 viewkeys(), viewvalues() 和 viewitems() 添加的。

>>> d = dict((i*10, chr(65+i)) for i in range(26))
>>> d
{0: 'A', 130: 'N', 10: 'B', 140: 'O', 20: ..., 250: 'Z'}
>>> d.viewkeys()
dict_keys([0, 130, 10, 140, 20, 150, 30, ..., 250])

视图可以被迭代，但键和条目视图的行为也很像是集合。 & 运算符执行交集运算，| 执行并集运算:

>>> d1 = dict((i*10, chr(65+i)) for i in range(26))
>>> d2 = dict((i**.5, i) for i in range(1000))
>>> d1.viewkeys() & d2.viewkeys()
set([0.0, 10.0, 20.0, 30.0])
>>> d1.viewkeys() | range(0, 30)
set([0, 1, 130, 3, 4, 5, 6, ..., 120, 250])

视图会追踪字典及字典被修改时的内容变化:

>>> vk = d.viewkeys()
>>> vk
dict_keys([0, 130, 10, ..., 250])
>>> d[260] = '&'
>>> vk
dict_keys([0, 130, 260, 10, ..., 250])

但是，请注意在对视图进行迭代时你是不能添加或移除键的:

>>> for k in vk:
...     d[k*2] = k
...
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: dictionary changed size during iteration

You can use the view methods in Python 2.x code, and the 2to3 converter will change them to the standard keys(), values(), and items() methods.

参见

PEP 3106 - 改造 dict.keys(), .values() 和 .items()

PEP 由 Guido van Rossum 撰写。 由 Alexandre Vassalotti 反向移植到 2.7； bpo-1967。

PEP 3137: memoryview 对象

memoryview 对象提供与 bytes 类型的接口相匹配的另一个对象的内存内容的视图。

>>> import string
>>> m = memoryview(string.letters)
>>> m
<memory at 0x37f850>
>>> len(m)           # Returns length of underlying object
52
>>> m[0], m[25], m[26]   # Indexing returns one byte
('a', 'z', 'A')
>>> m2 = m[0:26]         # Slicing returns another memoryview
>>> m2
<memory at 0x37f080>

视图的内容可被转换为一个字节串或整数列表：

>>> m2.tobytes()
'abcdefghijklmnopqrstuvwxyz'
>>> m2.tolist()
[97, 98, 99, 100, 101, 102, 103, ... 121, 122]
>>>

memoryview 对象允许对属于可变对象的下层对象进行修改。

>>> m2[0] = 75
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: cannot modify read-only memory
>>> b = bytearray(string.letters)  # Creating a mutable object
>>> b
bytearray(b'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ')
>>> mb = memoryview(b)
>>> mb[0] = '*'         # Assign to view, changing the bytearray.
>>> b[0:5]              # The bytearray has been changed.
bytearray(b'*bcde')
>>>

参见

PEP 3137 - 不变字节和可变缓冲区

PEP 由 Guido van Rossum 撰写。 由 Travis Oliphant, Antoine Pitrou 等人实现。 由 Antoine Pitrou 向下移植到 2.7; bpo-2396。

其他语言特性修改

对Python 语言核心进行的小改动：

• 已从 Python 3.x 向下移植了集合字面值语法。 使用花括号来标记可变集合的内容；集合与字典的区别在于它不包含冒号及映射的值。 {} 仍然表示空字典；请使用 set() 来表示空集合。

>>> {1, 2, 3, 4, 5}
set([1, 2, 3, 4, 5])
>>> set() # empty set
set([])
>>> {}    # empty dict
{}

由 Alexandre Vassalotti 向下移植; bpo-2335。

• 字典与集合推导式是另一个从 3.x 向下移植的特性，对列表/生成器推导式进行一般化以针对集合与字典使用字面值语法。

>>> {x: x*x for x in range(6)}
{0: 0, 1: 1, 2: 4, 3: 9, 4: 16, 5: 25}
>>> {('a'*x) for x in range(6)}
set(['', 'a', 'aa', 'aaa', 'aaaa', 'aaaaa'])

由 Alexandre Vassalotti 向下移植; bpo-2333。

• 现在 with 语句可以在一个语句中使用多个上下文管理器。 上下文管理器将按从左到右的顺序处理并且每个都会被视为开始一个新的 with 语句。 这意味着:

with A() as a, B() as b:
    ... suite of statements ...

相当于:

with A() as a:
    with B() as b:
        ... suite of statements ...

contextlib.nested() 函数提供了非常类似的功能，因此它不再必要并已被弃用。

(Proposed in <https://codereview.appspot.com/53094>; implemented by Georg Brandl.)

• Conversions between floating-point numbers and strings are now correctly rounded on most platforms. These conversions occur in many different places: str() on floats and complex numbers; the float and complex constructors; numeric formatting; serializing and deserializing floats and complex numbers using the marshal, pickle and json modules; parsing of float and imaginary literals in Python code; and Decimal-to-float conversion.

Related to this, the repr() of a floating-point number x now returns a result based on the shortest decimal string that’s guaranteed to round back to x under correct rounding (with round-half-to-even rounding mode). Previously it gave a string based on rounding x to 17 decimal digits.

The rounding library responsible for this improvement works on Windows and on Unix platforms using the gcc, icc, or suncc compilers. There may be a small number of platforms where correct operation of this code cannot be guaranteed, so the code is not used on such systems. You can find out which code is being used by checking sys.float_repr_style, which will be short if the new code is in use and legacy if it isn’t.

Implemented by Eric Smith and Mark Dickinson, using David Gay’s dtoa.c library; bpo-7117.

• Conversions from long integers and regular integers to floating point now round differently, returning the floating-point number closest to the number. This doesn’t matter for small integers that can be converted exactly, but for large numbers that will unavoidably lose precision, Python 2.7 now approximates more closely. For example, Python 2.6 computed the following:

>>> n = 295147905179352891391
>>> float(n)
2.9514790517935283e+20
>>> n - long(float(n))
65535L

Python 2.7’s floating-point result is larger, but much closer to the true value:

>>> n = 295147905179352891391
>>> float(n)
2.9514790517935289e+20
>>> n - long(float(n))
-1L

(Implemented by Mark Dickinson; bpo-3166.)

Integer division is also more accurate in its rounding behaviours. (Also implemented by Mark Dickinson; bpo-1811.)

• Implicit coercion for complex numbers has been removed; the interpreter will no longer ever attempt to call a __coerce__() method on complex objects. (Removed by Meador Inge and Mark Dickinson; bpo-5211.)

• The str.format() method now supports automatic numbering of the replacement fields. This makes using str.format() more closely resemble using %s formatting:

>>> '{}:{}:{}'.format(2009, 04, 'Sunday')
'2009:4:Sunday'
>>> '{}:{}:{day}'.format(2009, 4, day='Sunday')
'2009:4:Sunday'

The auto-numbering takes the fields from left to right, so the first {...} specifier will use the first argument to str.format(), the next specifier will use the next argument, and so on. You can’t mix auto-numbering and explicit numbering — either number all of your specifier fields or none of them — but you can mix auto-numbering and named fields, as in the second example above. (Contributed by Eric Smith; bpo-5237.)

Complex numbers now correctly support usage with format(), and default to being right-aligned. Specifying a precision or comma-separation applies to both the real and imaginary parts of the number, but a specified field width and alignment is applied to the whole of the resulting 1.5+3j output. (Contributed by Eric Smith; bpo-1588 and bpo-7988.)

The ‘F’ format code now always formats its output using uppercase characters, so it will now produce ‘INF’ and ‘NAN’. (Contributed by Eric Smith; bpo-3382.)

A low-level change: the object.format() method now triggers a PendingDeprecationWarning if it’s passed a format string, because the __format__() method for object converts the object to a string representation and formats that. Previously the method silently applied the format string to the string representation, but that could hide mistakes in Python code. If you’re supplying formatting information such as an alignment or precision, presumably you’re expecting the formatting to be applied in some object-specific way. (Fixed by Eric Smith; bpo-7994.)

• The int() and long() types gained a bit_length method that returns the number of bits necessary to represent its argument in binary:

>>> n = 37
>>> bin(n)
'0b100101'
>>> n.bit_length()
6
>>> n = 2**123-1
>>> n.bit_length()
123
>>> (n+1).bit_length()
124

(Contributed by Fredrik Johansson and Victor Stinner; bpo-3439.)

• The import statement will no longer try an absolute import if a relative import (e.g. from .os import sep) fails. This fixes a bug, but could possibly break certain import statements that were only working by accident. (Fixed by Meador Inge; bpo-7902.)

• It’s now possible for a subclass of the built-in unicode type to override the __unicode__() method. (Implemented by Victor Stinner; bpo-1583863.)

• The bytearray type’s translate() method now accepts None as its first argument. (Fixed by Georg Brandl; bpo-4759.)

• When using @classmethod and @staticmethod to wrap methods as class or static methods, the wrapper object now exposes the wrapped function as their func attribute. (Contributed by Amaury Forgeot d’Arc, after a suggestion by George Sakkis; bpo-5982.)

• When a restricted set of attributes were set using __slots__, deleting an unset attribute would not raise AttributeError as you would expect. Fixed by Benjamin Peterson; bpo-7604.)

• Two new encodings are now supported: “cp720”, used primarily for Arabic text; and “cp858”, a variant of CP 850 that adds the euro symbol. (CP720 contributed by Alexander Belchenko and Amaury Forgeot d’Arc in bpo-1616979; CP858 contributed by Tim Hatch in bpo-8016.)

• The file object will now set the filename attribute on the IOError exception when trying to open a directory on POSIX platforms (noted by Jan Kaliszewski; bpo-4764), and now explicitly checks for and forbids writing to read-only file objects instead of trusting the C library to catch and report the error (fixed by Stefan Krah; bpo-5677).

• The Python tokenizer now translates line endings itself, so the compile() built-in function now accepts code using any line-ending convention. Additionally, it no longer requires that the code end in a newline.

• Extra parentheses in function definitions are illegal in Python 3.x, meaning that you get a syntax error from def f((x)): pass. In Python3-warning mode, Python 2.7 will now warn about this odd usage. (Noted by James Lingard; bpo-7362.)

• It’s now possible to create weak references to old-style class objects. New-style classes were always weak-referenceable. (Fixed by Antoine Pitrou; bpo-8268.)

• When a module object is garbage-collected, the module’s dictionary is now only cleared if no one else is holding a reference to the dictionary (bpo-7140).

解释器改动

A new environment variable, PYTHONWARNINGS, allows controlling warnings. It should be set to a string containing warning settings, equivalent to those used with the -W switch, separated by commas. (Contributed by Brian Curtin; bpo-7301.)

For example, the following setting will print warnings every time they occur, but turn warnings from the Cookie module into an error. (The exact syntax for setting an environment variable varies across operating systems and shells.)

export PYTHONWARNINGS=all,error:::Cookie:0

性能优化

Several performance enhancements have been added:

• A new opcode was added to perform the initial setup for with statements, looking up the enter() and exit() methods. (Contributed by Benjamin Peterson.)

• The garbage collector now performs better for one common usage pattern: when many objects are being allocated without deallocating any of them. This would previously take quadratic time for garbage collection, but now the number of full garbage collections is reduced as the number of objects on the heap grows. The new logic only performs a full garbage collection pass when the middle generation has been collected 10 times and when the number of survivor objects from the middle generation exceeds 10% of the number of objects in the oldest generation. (Suggested by Martin von Löwis and implemented by Antoine Pitrou; bpo-4074.)

• The garbage collector tries to avoid tracking simple containers which can’t be part of a cycle. In Python 2.7, this is now true for tuples and dicts containing atomic types (such as ints, strings, etc.). Transitively, a dict containing tuples of atomic types won’t be tracked either. This helps reduce the cost of each garbage collection by decreasing the number of objects to be considered and traversed by the collector. (Contributed by Antoine Pitrou; bpo-4688.)

• Long integers are now stored internally either in base 2**15 or in base 2**30, the base being determined at build time. Previously, they were always stored in base 2**15. Using base 2**30 gives significant performance improvements on 64-bit machines, but benchmark results on 32-bit machines have been mixed. Therefore, the default is to use base 2**30 on 64-bit machines and base 2**15 on 32-bit machines; on Unix, there’s a new configure option --enable-big-digits that can be used to override this default.

Apart from the performance improvements this change should be invisible to end users, with one exception: for testing and debugging purposes there’s a new structseq sys.long_info that provides information about the internal format, giving the number of bits per digit and the size in bytes of the C type used to store each digit:

>>> import sys
>>> sys.long_info
sys.long_info(bits_per_digit=30, sizeof_digit=4)

（由 Mark Dickinson在 bpo-4258 贡献）

Another set of changes made long objects a few bytes smaller: 2 bytes smaller on 32-bit systems and 6 bytes on 64-bit. (Contributed by Mark Dickinson; bpo-5260.)

• The division algorithm for long integers has been made faster by tightening the inner loop, doing shifts instead of multiplications, and fixing an unnecessary extra iteration. Various benchmarks show speedups of between 50% and 150% for long integer divisions and modulo operations. (Contributed by Mark Dickinson; bpo-5512.) Bitwise operations are also significantly faster (initial patch by Gregory Smith; bpo-1087418).

• The implementation of % checks for the left-side operand being a Python string and special-cases it; this results in a 1—3% performance increase for applications that frequently use % with strings, such as templating libraries. (Implemented by Collin Winter; bpo-5176.)

• List comprehensions with an if condition are compiled into faster bytecode. (Patch by Antoine Pitrou, back-ported to 2.7 by Jeffrey Yasskin; bpo-4715.)

• Converting an integer or long integer to a decimal string was made faster by special-casing base 10 instead of using a generalized conversion function that supports arbitrary bases. (Patch by Gawain Bolton; bpo-6713.)

• The split(), replace(), rindex(), rpartition(), and rsplit() methods of string-like types (strings, Unicode strings, and bytearray objects) now use a fast reverse-search algorithm instead of a character-by-character scan. This is sometimes faster by a factor of 10. (Added by Florent Xicluna; bpo-7462 and bpo-7622.)

• The pickle and cPickle modules now automatically intern the strings used for attribute names, reducing memory usage of the objects resulting from unpickling. (Contributed by Jake McGuire; bpo-5084.)

• The cPickle module now special-cases dictionaries, nearly halving the time required to pickle them. (Contributed by Collin Winter; bpo-5670.)

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