Python 2.7 有什麼新功能

作者:

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

This article explains the new features in Python 2.7. Python 2.7 was released on July 3, 2010.

Numeric handling has been improved in many ways, for both floating-point numbers and for the Decimal class. There are some useful additions to the standard library, such as a greatly enhanced unittest module, the argparse module for parsing command-line options, convenient OrderedDict and Counter classes in the collections module, and many other improvements.

Python 2.7 is planned to be the last of the 2.x releases, so we worked on making it a good release for the long term. To help with porting to Python 3, several new features from the Python 3.x series have been included in 2.7.

This article doesn't attempt to provide a complete specification of the new features, but instead provides a convenient overview. For full details, you should refer to the documentation for Python 2.7 at https://docs.python.org. If you want to understand the rationale for the design and implementation, refer to the PEP for a particular new feature or the issue on https://bugs.python.org in which a change was discussed. Whenever possible, "What's New in Python" links to the bug/patch item for each change.

Python 2.x 的未來

Python 2.7 is the last major release in the 2.x series, as the Python maintainers have shifted the focus of their new feature development efforts to the Python 3.x series. This means that while Python 2 continues to receive bug fixes, and to be updated to build correctly on new hardware and versions of supported operated systems, there will be no new full feature releases for the language or standard library.

However, while there is a large common subset between Python 2.7 and Python 3, and many of the changes involved in migrating to that common subset, or directly to Python 3, can be safely automated, some other changes (notably those associated with Unicode handling) may require careful consideration, and preferably robust automated regression test suites, to migrate effectively.

This means that Python 2.7 will remain in place for a long time, providing a stable and supported base platform for production systems that have not yet been ported to Python 3. The full expected lifecycle of the Python 2.7 series is detailed in PEP 373.

Some key consequences of the long-term significance of 2.7 are:

  • As noted above, the 2.7 release has a much longer period of maintenance when compared to earlier 2.x versions. Python 2.7 is currently expected to remain supported by the core development team (receiving security updates and other bug fixes) until at least 2020 (10 years after its initial release, compared to the more typical support period of 18--24 months).

  • As the Python 2.7 standard library ages, making effective use of the Python Package Index (either directly or via a redistributor) becomes more important for Python 2 users. In addition to a wide variety of third party packages for various tasks, the available packages include backports of new modules and features from the Python 3 standard library that are compatible with Python 2, as well as various tools and libraries that can make it easier to migrate to Python 3. The Python Packaging User Guide provides guidance on downloading and installing software from the Python Package Index.

  • While the preferred approach to enhancing Python 2 is now the publication of new packages on the Python Package Index, this approach doesn't necessarily work in all cases, especially those related to network security. In exceptional cases that cannot be handled adequately by publishing new or updated packages on PyPI, the Python Enhancement Proposal process may be used to make the case for adding new features directly to the Python 2 standard library. Any such additions, and the maintenance releases where they were added, will be noted in the New Features Added to Python 2.7 Maintenance Releases section below.

For projects wishing to migrate from Python 2 to Python 3, or for library and framework developers wishing to support users on both Python 2 and Python 3, there are a variety of tools and guides available to help decide on a suitable approach and manage some of the technical details involved. The recommended starting point is the 如何將 Python 2 的程式碼移植到 Python 3 HOWTO guide.

Changes to the Handling of Deprecation Warnings

For Python 2.7, a policy decision was made to silence warnings only of interest to developers by default. DeprecationWarning and its descendants are now ignored unless otherwise requested, preventing users from seeing warnings triggered by an application. This change was also made in the branch that became Python 3.2. (Discussed on stdlib-sig and carried out in bpo-7319.)

In previous releases, DeprecationWarning messages were enabled by default, providing Python developers with a clear indication of where their code may break in a future major version of Python.

However, there are increasingly many users of Python-based applications who are not directly involved in the development of those applications. DeprecationWarning messages are irrelevant to such users, making them worry about an application that's actually working correctly and burdening application developers with responding to these concerns.

You can re-enable display of DeprecationWarning messages by running Python with the -Wdefault (short form: -Wd) switch, or by setting the PYTHONWARNINGS environment variable to "default" (or "d") before running Python. Python code can also re-enable them by calling warnings.simplefilter('default').

The unittest module also automatically reenables deprecation warnings when running tests.

Python 3.1 Features

Much as Python 2.6 incorporated features from Python 3.0, version 2.7 incorporates some of the new features in Python 3.1. The 2.x series continues to provide tools for migrating to the 3.x series.

A partial list of 3.1 features that were backported to 2.7:

  • The syntax for set literals ({1,2,3} is a mutable set).

  • Dictionary and set comprehensions ({i: i*2 for i in range(3)}).

  • Multiple context managers in a single with statement.

  • A new version of the io library, rewritten in C for performance.

  • The ordered-dictionary type described in PEP 372: Adding an Ordered Dictionary to collections.

  • The new "," format specifier described in PEP 378: Format Specifier for Thousands Separator.

  • memoryview 物件。

  • A small subset of the importlib module, described below.

  • The repr() of a float x is shorter in many cases: it's now based on the shortest decimal string that's guaranteed to round back to x. As in previous versions of Python, it's guaranteed that float(repr(x)) recovers x.

  • Float-to-string and string-to-float conversions are correctly rounded. The round() function is also now correctly rounded.

  • The PyCapsule type, used to provide a C API for extension modules.

  • PyLong_AsLongAndOverflow() C API 函式。

Other new Python3-mode warnings include:

  • operator.isCallable() and operator.sequenceIncludes(), which are not supported in 3.x, now trigger warnings.

  • The -3 switch now automatically enables the -Qwarn switch that causes warnings about using classic division with integers and long integers.

PEP 372: Adding an Ordered Dictionary to collections

Regular Python dictionaries iterate over key/value pairs in arbitrary order. Over the years, a number of authors have written alternative implementations that remember the order that the keys were originally inserted. Based on the experiences from those implementations, 2.7 introduces a new OrderedDict class in the collections module.

The OrderedDict API provides the same interface as regular dictionaries but iterates over keys and values in a guaranteed order depending on when a key was first inserted:

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

If a new entry overwrites an existing entry, the original insertion position is left unchanged:

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

Deleting an entry and reinserting it will move it to the end:

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

The popitem() method has an optional last argument that defaults to True. If last is true, the most recently added key is returned and removed; if it's false, the oldest key is selected:

>>> 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)

Comparing two ordered dictionaries checks both the keys and values, and requires that the insertion order was the same:

>>> 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

Comparing an OrderedDict with a regular dictionary ignores the insertion order and just compares the keys and values.

How does the OrderedDict work? It maintains a doubly linked list of keys, appending new keys to the list as they're inserted. A secondary dictionary maps keys to their corresponding list node, so deletion doesn't have to traverse the entire linked list and therefore remains O(1).

The standard library now supports use of ordered dictionaries in several modules.

  • The ConfigParser module uses them by default, meaning that configuration files can now be read, modified, and then written back in their original order.

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

  • The json module's JSONDecoder class constructor was extended with an object_pairs_hook parameter to allow OrderedDict instances to be built by the decoder. Support was also added for third-party tools like PyYAML.

也參考

PEP 372 - 在 collections 中新增有序字典

PEP written by Armin Ronacher and Raymond Hettinger; implemented by Raymond Hettinger.

PEP 378: Format Specifier for Thousands Separator

To make program output more readable, it can be useful to add separators to large numbers, rendering them as 18,446,744,073,709,551,616 instead of 18446744073709551616.

The fully general solution for doing this is the locale module, which can use different separators ("," in North America, "." in Europe) and different grouping sizes, but locale is complicated to use and unsuitable for multi-threaded applications where different threads are producing output for different locales.

Therefore, a simple comma-grouping mechanism has been added to the mini-language used by the str.format() method. When formatting a floating-point number, simply include a comma between the width and the precision:

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

When formatting an integer, include the comma after the width:

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

This mechanism is not adaptable at all; commas are always used as the separator and the grouping is always into three-digit groups. The comma-formatting mechanism isn't as general as the locale module, but it's easier to use.

也參考

PEP 378 - Format Specifier for Thousands Separator

由 Raymond Hettinger 撰寫 PEP；由 Eric Smith 實作。

PEP 389: The argparse Module for Parsing Command Lines

The argparse module for parsing command-line arguments was added as a more powerful replacement for the optparse module.

This means Python now supports three different modules for parsing command-line arguments: getopt, optparse, and argparse. The getopt module closely resembles the C library's getopt() function, so it remains useful if you're writing a Python prototype that will eventually be rewritten in C. optparse becomes redundant, but there are no plans to remove it because there are many scripts still using it, and there's no automated way to update these scripts. (Making the argparse API consistent with optparse's interface was discussed but rejected as too messy and difficult.)

In short, if you're writing a new script and don't need to worry about compatibility with earlier versions of Python, use argparse instead of optparse.

以下是個範例：

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__

Unless you override it, -h and --help switches are automatically added, and produce neatly formatted output:

-> ./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