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****************************
What's New in Python 2.7
****************************
:Author: A.M. Kuchling (amk at amk.ca)
.. hyperlink all the methods & functions.
.. T_STRING_INPLACE not described in main docs
.. $Id$
Rules for maintenance:
* Anyone can add text to this document. Do not spend very much time
on the wording of your changes, because your text will probably
get rewritten to some degree.
* The maintainer will go through Misc/NEWS periodically and add
changes; it's therefore more important to add your changes to
Misc/NEWS than to this file.
* This is not a complete list of every single change; completeness
is the purpose of Misc/NEWS. Some changes I consider too small
or esoteric to include. If such a change is added to the text,
I'll just remove it. (This is another reason you shouldn't spend
too much time on writing your addition.)
* If you want to draw your new text to the attention of the
maintainer, add 'XXX' to the beginning of the paragraph or
section.
* It's OK to just add a fragmentary note about a change. For
example: "XXX Describe the transmogrify() function added to the
socket module." The maintainer will research the change and
write the necessary text.
* You can comment out your additions if you like, but it's not
necessary (especially when a final release is some months away).
* Credit the author of a patch or bugfix. Just the name is
sufficient; the e-mail address isn't necessary.
* It's helpful to add the bug/patch number in a parenthetical comment.
XXX Describe the transmogrify() function added to the socket
module.
(Contributed by P.Y. Developer; :issue:`12345`.)
This saves the maintainer some effort going through the SVN logs
when researching a change.
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 :class:`~decimal.Decimal` class.
There are some useful additions to the standard library, such as a
greatly enhanced :mod:`unittest` module, the :mod:`argparse` module
for parsing command-line options, convenient :class:`~collections.OrderedDict`
and :class:`~collections.Counter` classes in the :mod:`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.
.. _whatsnew27-python31:
The Future for Python 2.x
=========================
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
<https://packaging.python.org>`__ 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
:ref:`py27-maintenance-enhancements` 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 :ref:`pyporting-howto` 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. :exc:`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 :issue:`7319`.)
In previous releases, :exc:`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. :exc:`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 :exc:`DeprecationWarning` messages by
running Python with the :option:`-Wdefault <-W>` (short form:
:option:`-Wd <-W>`) switch, or by setting the :envvar:`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 :keyword:`with` statement.
* A new version of the :mod:`io` library, rewritten in C for performance.
* The ordered-dictionary type described in :ref:`pep-0372`.
* The new ``","`` format specifier described in :ref:`pep-0378`.
* The :class:`memoryview` object.
* A small subset of the :mod:`importlib` module,
`described below <#importlib-section>`__.
* The :func:`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 :func:`round` function is also now correctly rounded.
* The :c:type:`PyCapsule` type, used to provide a C API for extension modules.
* The :c:func:`PyLong_AsLongAndOverflow` C API function.
Other new Python3-mode warnings include:
* :func:`!operator.isCallable` and :func:`!operator.sequenceIncludes`,
which are not supported in 3.x, now trigger warnings.
* The :option:`!-3` switch now automatically
enables the :option:`!-Qwarn` switch that causes warnings
about using classic division with integers and long integers.
.. ========================================================================
.. Large, PEP-level features and changes should be described here.
.. ========================================================================
.. _pep-0372:
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
:class:`~collections.OrderedDict` class in the :mod:`collections` module.
The :class:`~collections.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 :meth:`~collections.OrderedDict.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 :class:`~collections.OrderedDict` with a regular dictionary
ignores the insertion order and just compares the keys and values.
How does the :class:`~collections.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 :mod:`ConfigParser <configparser>` module uses them by default, meaning that
configuration files can now be read, modified, and then written back
in their original order.
* The :meth:`~collections.somenamedtuple._asdict` method for
:func:`collections.namedtuple` now returns an ordered dictionary with the
values appearing in the same order as the underlying tuple indices.
* The :mod:`json` module's :class:`~json.JSONDecoder` class
constructor was extended with an *object_pairs_hook* parameter to
allow :class:`OrderedDict` instances to be built by the decoder.
Support was also added for third-party tools like
`PyYAML <https://pyyaml.org/>`_.
.. seealso::
:pep:`372` - Adding an ordered dictionary to collections
PEP written by Armin Ronacher and Raymond Hettinger;
implemented by Raymond Hettinger.
.. _pep-0378:
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 :mod:`locale` module,
which can use different separators ("," in North America, "." in
Europe) and different grouping sizes, but :mod:`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 :meth:`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 :mod:`locale`
module, but it's easier to use.
.. seealso::
:pep:`378` - Format Specifier for Thousands Separator
PEP written by Raymond Hettinger; implemented by Eric Smith.
PEP 389: The argparse Module for Parsing Command Lines
======================================================
The :mod:`argparse` module for parsing command-line arguments was
added as a more powerful replacement for the
:mod:`optparse` module.
This means Python now supports three different modules for parsing
command-line arguments: :mod:`getopt`, :mod:`optparse`, and
:mod:`argparse`. The :mod:`getopt` module closely resembles the C
library's :c:func:`!getopt` function, so it remains useful if you're writing a
Python prototype that will eventually be rewritten in C.
:mod:`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 :mod:`argparse`
API consistent with :mod:`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
:mod:`argparse` instead of :mod:`optparse`.
Here's an example::
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, :option:`!-h` and :option:`!--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 message and exit
-v produce verbose output
-o FILE direct output to FILE instead of stdout
-C NUM display NUM lines of added context
As with :mod:`optparse`, the command-line switches and arguments
are returned as an object with attributes named by the *dest* parameters::
-> ./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']}
:mod:`argparse` has much fancier validation than :mod:`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 :class:`~argparse.FileType`, which will
automatically open files for you and understands that ``'-'`` means
standard input or output.
.. seealso::
:mod:`argparse` documentation
The documentation page of the argparse module.
:ref:`upgrading-optparse-code`
Part of the Python documentation, describing how to convert
code that uses :mod:`optparse`.
:pep:`389` - argparse - New Command Line Parsing Module
PEP written and implemented by Steven Bethard.
PEP 391: Dictionary-Based Configuration For Logging
====================================================
The :mod:`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.
:mod:`logging` also supports a :func:`~logging.config.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 :func:`~logging.config.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 :ref:`logging-config-api`.
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 :file:`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 :mod:`logging` module, all
implemented by Vinay Sajip, are:
.. rev79293
* The :class:`~logging.handlers.SysLogHandler` class now supports
syslogging over TCP. The constructor has a *socktype* parameter
giving the type of socket to use, either :const:`socket.SOCK_DGRAM`
for UDP or :const:`socket.SOCK_STREAM` for TCP. The default
protocol remains UDP.
* :class:`~logging.Logger` instances gained a :meth:`~logging.Logger.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 :class:`~logging.LoggerAdapter` class gained an
:meth:`~logging.Logger.isEnabledFor` method that takes a
*level* and returns whether the underlying logger would
process a message of that level of importance.
.. XXX: Logger objects don't have a class declaration so the link don't work
.. seealso::
:pep:`391` - Dictionary-Based Configuration For Logging
PEP written and implemented by Vinay Sajip.
PEP 3106: Dictionary Views
====================================================
The dictionary methods :meth:`~dict.keys`, :meth:`~dict.values`, and
:meth:`~dict.items` are different in Python 3.x. They return an object
called a :dfn:`view` instead of a fully materialized list.
It's not possible to change the return values of :meth:`~dict.keys`,
:meth:`~dict.values`, and :meth:`~dict.items` in Python 2.7 because
too much code would break. Instead the 3.x versions were added
under the new names :meth:`!viewkeys`, :meth:`!viewvalues`,
and :meth:`!viewitems`.
::
>>> 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])
Views can be iterated over, but the key and item views also behave
like sets. The ``&`` operator performs intersection, and ``|``
performs a union::
>>> 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])
The view keeps track of the dictionary and its contents change as the
dictionary is modified::
>>> vk = d.viewkeys()
>>> vk
dict_keys([0, 130, 10, ..., 250])
>>> d[260] = '&'
>>> vk
dict_keys([0, 130, 260, 10, ..., 250])
However, note that you can't add or remove keys while you're iterating
over the view::
>>> 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 :meth:`~dict.keys`,
:meth:`~dict.values`, and :meth:`~dict.items` methods.
.. seealso::
:pep:`3106` - Revamping dict.keys(), .values() and .items()
PEP written by Guido van Rossum.
Backported to 2.7 by Alexandre Vassalotti; :issue:`1967`.
PEP 3137: The memoryview Object
====================================================
The :class:`memoryview` object provides a view of another object's
memory content that matches the :class:`bytes` type's interface.
.. doctest::
:options: +SKIP
>>> 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>
The content of the view can be converted to a string of bytes or
a list of integers:
.. doctest::
:options: +SKIP
>>> m2.tobytes()
'abcdefghijklmnopqrstuvwxyz'
>>> m2.tolist()
[97, 98, 99, 100, 101, 102, 103, ... 121, 122]
>>>
:class:`memoryview` objects allow modifying the underlying object if
it's a mutable object.
.. doctest::
:options: +SKIP
>>> 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')
>>>
.. seealso::
:pep:`3137` - Immutable Bytes and Mutable Buffer
PEP written by Guido van Rossum.
Implemented by Travis Oliphant, Antoine Pitrou and others.
Backported to 2.7 by Antoine Pitrou; :issue:`2396`.
Other Language Changes
======================
Some smaller changes made to the core Python language are:
* The syntax for set literals has been backported from Python 3.x.
Curly brackets are used to surround the contents of the resulting
mutable set; set literals are
distinguished from dictionaries by not containing colons and values.
``{}`` continues to represent an empty dictionary; use
``set()`` for an empty set.
.. doctest::
:options: +SKIP
>>> {1, 2, 3, 4, 5}
set([1, 2, 3, 4, 5])
>>> set() # empty set
set([])
>>> {} # empty dict
{}
Backported by Alexandre Vassalotti; :issue:`2335`.
* Dictionary and set comprehensions are another feature backported from
3.x, generalizing list/generator comprehensions to use
the literal syntax for sets and dictionaries.
.. doctest::
:options: +SKIP
>>> {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'])
Backported by Alexandre Vassalotti; :issue:`2333`.
* The :keyword:`with` statement can now use multiple context managers
in one statement. Context managers are processed from left to right
and each one is treated as beginning a new :keyword:`!with` statement.
This means that::
with A() as a, B() as b:
... suite of statements ...
is equivalent to::
with A() as a:
with B() as b:
... suite of statements ...
The :func:`!contextlib.nested` function provides a very similar
function, so it's no longer necessary and has been deprecated.
(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: :func:`str` on
floats and complex numbers; the :class:`float` and :class:`complex`
constructors;
numeric formatting; serializing and
deserializing floats and complex numbers using the
:mod:`marshal`, :mod:`pickle`
and :mod:`json` modules;
parsing of float and imaginary literals in Python code;
and :class:`~decimal.Decimal`-to-float conversion.
Related to this, the :func:`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.
.. maybe add an example?
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 :data:`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
:file:`dtoa.c` library; :issue:`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; :issue:`3166`.)
Integer division is also more accurate in its rounding behaviours. (Also
implemented by Mark Dickinson; :issue:`1811`.)
* Implicit coercion for complex numbers has been removed; the interpreter
will no longer ever attempt to call a :meth:`!__coerce__` method on complex
objects. (Removed by Meador Inge and Mark Dickinson; :issue:`5211`.)
* The :meth:`str.format` method now supports automatic numbering of the replacement
fields. This makes using :meth:`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 :meth:`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; :issue:`5237`.)
Complex numbers now correctly support usage with :func:`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; :issue:`1588` and :issue:`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; :issue:`3382`.)
A low-level change: the :meth:`object.__format__` method now triggers
a :exc:`PendingDeprecationWarning` if it's passed a format string,
because the :meth:`!__format__` method for :class:`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; :issue:`7994`.)
* The :func:`int` and :func:`!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; :issue:`3439`.)
* The :keyword:`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 :keyword:`!import`
statements that were only working by accident. (Fixed by Meador Inge;
:issue:`7902`.)
* It's now possible for a subclass of the built-in :class:`!unicode` type
to override the :meth:`!__unicode__` method. (Implemented by
Victor Stinner; :issue:`1583863`.)
* The :class:`bytearray` type's :meth:`~bytearray.translate` method now accepts
``None`` as its first argument. (Fixed by Georg Brandl;
:issue:`4759`.)
.. XXX bytearray doesn't seem to be documented
* When using :deco:`classmethod` and
:deco:`staticmethod` to wrap
methods as class or static methods, the wrapper object now
exposes the wrapped function as their :attr:`~method.__func__`
attribute.
(Contributed by Amaury Forgeot d'Arc, after a suggestion by
George Sakkis; :issue:`5982`.)
* When a restricted set of attributes were set using ``__slots__``,
deleting an unset attribute would not raise :exc:`AttributeError`
as you would expect. Fixed by Benjamin Peterson; :issue:`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 :issue:`1616979`; CP858 contributed by Tim Hatch in
:issue:`8016`.)
* The :class:`!file` object will now set the :attr:`!filename` attribute
on the :exc:`IOError` exception when trying to open a directory
on POSIX platforms (noted by Jan Kaliszewski; :issue:`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; :issue:`5677`).
* The Python tokenizer now translates line endings itself, so the
:func:`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; :issue:`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; :issue:`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 (:issue:`7140`).
.. ======================================================================
.. _new-27-interpreter:
Interpreter Changes
-------------------------------
A new environment variable, :envvar:`PYTHONWARNINGS`,
allows controlling warnings. It should be set to a string
containing warning settings, equivalent to those
used with the :option:`-W` switch, separated by commas.
(Contributed by Brian Curtin; :issue:`7301`.)
For example, the following setting will print warnings every time
they occur, but turn warnings from the :mod:`Cookie <http.cookies>` module into an
error. (The exact syntax for setting an environment variable varies
across operating systems and shells.)
::
export PYTHONWARNINGS=all,error:::Cookie:0
.. ======================================================================
Optimizations
-------------
Several performance enhancements have been added:
* A new opcode was added to perform the initial setup for
:keyword:`with` statements, looking up the :meth:`~object.__enter__` and
:meth:`~object.__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; :issue:`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; :issue:`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
: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 :data:`!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)
(Contributed by Mark Dickinson; :issue:`4258`.)
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; :issue:`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; :issue:`5512`.)
Bitwise operations are also significantly faster (initial patch by
Gregory Smith; :issue:`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; :issue:`5176`.)
* List comprehensions with an ``if`` condition are compiled into
faster bytecode. (Patch by Antoine Pitrou, back-ported to 2.7