What’s New in Python 2.6
| Author: | A.M. Kuchling (amk at amk.ca) |
|---|
| Release: | 2.6 |
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| Date: | October 02, 2008 |
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This article explains the new features in Python 2.6, released on October 1
2008. The release schedule is described in PEP 361.
The major theme of Python 2.6 is preparing the migration path to
Python 3.0, a major redesign of the language. Whenever possible,
Python 2.6 incorporates new features and syntax from 3.0 while
remaining compatible with existing code by not removing older features
or syntax. When it’s not possible to do that, Python 2.6 tries to do
what it can, adding compatibility functions in a
future_builtins module and a -3 switch to warn about
usages that will become unsupported in 3.0.
Some significant new packages have been added to the standard library,
such as the multiprocessing and json modules, but
there aren’t many new features that aren’t related to Python 3.0 in
some way.
Python 2.6 also sees a number of improvements and bugfixes throughout
the source. A search through the change logs finds there were 259
patches applied and 612 bugs fixed between Python 2.5 and 2.6. Both
figures are likely to be underestimates.
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 2.6. If
you want to understand the rationale for the design and
implementation, refer to the PEP for a particular new feature.
Whenever possible, “What’s New in Python” links to the bug/patch item
for each change.
Python 3.0
The development cycle for Python versions 2.6 and 3.0 was
synchronized, with the alpha and beta releases for both versions being
made on the same days. The development of 3.0 has influenced many
features in 2.6.
Python 3.0 is a far-ranging redesign of Python that breaks
compatibility with the 2.x series. This means that existing Python
code will need some conversion in order to run on
Python 3.0. However, not all the changes in 3.0 necessarily break
compatibility. In cases where new features won’t cause existing code
to break, they’ve been backported to 2.6 and are described in this
document in the appropriate place. Some of the 3.0-derived features
are:
- A __complex__() method for converting objects to a complex number.
- Alternate syntax for catching exceptions: except TypeError as exc.
- The addition of functools.reduce() as a synonym for the built-in
reduce() function.
Python 3.0 adds several new built-in functions and changes the
semantics of some existing built-ins. Functions that are new in 3.0
such as bin() have simply been added to Python 2.6, but existing
built-ins haven’t been changed; instead, the future_builtins
module has versions with the new 3.0 semantics. Code written to be
compatible with 3.0 can do from future_builtins import hex, map as
necessary.
A new command-line switch, -3, enables warnings
about features that will be removed in Python 3.0. You can run code
with this switch to see how much work will be necessary to port
code to 3.0. The value of this switch is available
to Python code as the boolean variable sys.py3kwarning,
and to C extension code as Py_Py3kWarningFlag.
See also
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.
Changes to the Development Process
While 2.6 was being developed, the Python development process
underwent two significant changes: we switched from SourceForge’s
issue tracker to a customized Roundup installation, and the
documentation was converted from LaTeX to reStructuredText.
New Issue Tracker: Roundup
For a long time, the Python developers had been growing increasingly
annoyed by SourceForge’s bug tracker. SourceForge’s hosted solution
doesn’t permit much customization; for example, it wasn’t possible to
customize the life cycle of issues.
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.
After posting a call for volunteers, a new Roundup installation was
set up at http://bugs.python.org. One installation of Roundup can
host multiple trackers, and this server now also hosts issue trackers
for Jython and for the Python web site. It will surely find
other uses in the future. Where possible,
this edition of “What’s New in Python” links to the bug/patch
item for each change.
Hosting of the Python bug tracker is kindly provided by
Upfront Systems
of Stellenbosch, South Africa. Martin von Loewis 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 wished to move from SourceForge to Roundup.
New Documentation Format: reStructuredText Using Sphinx
The Python documentation was written using LaTeX since the project
started around 1989. In the 1980s and early 1990s, most documentation
was printed out for later study, not viewed online. LaTeX was widely
used because it provided attractive printed output while remaining
straightforward to write once the basic rules of the markup were
learned.
Today LaTeX is still used for writing publications destined for
printing, but the landscape for programming tools has shifted. We no
longer print out reams of documentation; instead, we browse through it
online and HTML has become the most important format to support.
Unfortunately, converting LaTeX to HTML is fairly complicated and Fred
L. Drake Jr., the long-time Python documentation editor, spent a lot
of time maintaining the conversion process. Occasionally people would
suggest converting the documentation into SGML and later XML, but
performing a good conversion is a major task and no one ever committed
the time required to finish the job.
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.pocoo.org/.
Sphinx concentrates on HTML output, producing attractively styled and
modern HTML; printed output is still supported through conversion to
LaTeX. The input format is reStructuredText, a markup syntax
supporting custom extensions and directives that is commonly used in
the Python community.
Sphinx is a standalone package that can be used for writing, and
almost two dozen other projects
(listed on the Sphinx web site)
have adopted Sphinx as their documentation tool.
See also
- Documenting Python
- Describes how to write for Python’s documentation.
- Sphinx
- Documentation and code for the Sphinx toolchain.
- Docutils
- The underlying reStructuredText parser and toolset.
PEP 343: The ‘with’ statement
The previous version, Python 2.5, added the ‘with‘
statement as an optional feature, to be enabled by a from __future__
import with_statement directive. In 2.6 the statement no longer needs to
be specially enabled; this means that with is now always a
keyword. The rest of this section is a copy of the corresponding
section from the “What’s New in Python 2.5” document; if you’re
familiar with the ‘with‘ statement
from Python 2.5, you can skip this section.
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‘ statement is a control-flow structure whose basic
structure is:
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.
Some standard Python objects now support the context management protocol and can
be used with the ‘with‘ statement. File objects are one example:
with open('/etc/passwd', 'r') as f:
for line in f:
print line
... more processing code ...
After this statement has executed, the file object in f will have been
automatically closed, even if the for loop raised an exception part-
way through the block.
Note
In this case, f is the same object created by open(), because
file.__enter__() returns self.
The threading module’s locks and condition variables also support the
‘with‘ statement:
lock = threading.Lock()
with lock:
# Critical section of code
...
The lock is acquired before the block is executed and always released once the
block is complete.
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()
Writing Context Managers
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.
A high-level explanation of the context management protocol is:
- 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.
- The code in BLOCK is executed.
- If BLOCK raises an exception, the __exit__(type, value, traceback)()
is called with the exception details, the same values returned by
sys.exc_info(). The method’s return value controls whether the 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
The contextlib module
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(object)() function returns object so that it can be
bound to a variable, and calls object.close 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)
See also
- PEP 343 - The “with” statement
- 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.
The documentation for the contextlib module.
PEP 366: Explicit Relative Imports From a Main Module
Python’s -m switch allows running a module as a script.
When you ran a module that was located inside a package, relative
imports didn’t work correctly.
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: Per-user site-packages Directory
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 introduces a convention for user-specific site directories.
The directory varies depending on the platform:
- Unix and 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.
See also
- PEP 370 - Per-user site-packages Directory
- PEP written and implemented by Christian Heimes.
PEP 371: The multiprocessing Package
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 input parameter N and
the result. 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
This produces the following output:
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
This will produce the output:
1 1
11 39916800
21 51090942171709440000
31 8222838654177922817725562880000000
41 33452526613163807108170062053440751665152000000000
51 15511187532873822802242430164693032110632597200169861120000...
See also
The documentation for the multiprocessing module.
- PEP 371 - Addition of the multiprocessing package
- PEP written by Jesse Noller and Richard Oudkerk;
implemented by Richard Oudkerk and Jesse Noller.
PEP 3101: Advanced String Formatting
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:
format("Empty dict: {{}}") -> "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
'Platform: {0.platform}\nPython version: {0.version}'.format(sys) ->
'Platform: darwin\n
Python version: 2.6a1+ (trunk:61261M, Mar 5 2008, 20:29:41) \n
[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'
Format specifiers can reference other fields through nesting:
fmt = '{0:{1}}'
width = 15
fmt.format('Invoice #1234', width) ->
'Invoice #1234 '
width = 35
fmt.format('Invoice #1234', width) ->
'Invoice #1234 '
The alignment of a field within the desired width can be specified:
| Character |
Effect |
|---|
| < (default) |
Left-align |
| > |
Right-align |
| ^ |
Center |
| = |
(For numeric types only) Pad after the sign. |
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' - Binary. Outputs the number in base 2.
'c' - Character. Converts the integer to the corresponding
Unicode character before printing.
'd' - Decimal Integer. Outputs the number in base 10.
'o' - Octal format. Outputs the number in base 8.
'x' - Hex format. Outputs the number in base 16, using lower-
case letters for the digits above 9.
'e' - Exponent notation. Prints the number in scientific
notation using the letter 'e' to indicate the exponent.
'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() built-in that will format a single
value. It calls the type’s __format__() method with the
provided specifier:
>>> format(75.6564, '.2f')
'75.66'
See also
- Format String Syntax
- The reference documentation for format fields.
- PEP 3101 - Advanced String Formatting
- PEP written by Talin. Implemented by Eric Smith.
PEP 3105: print As a Function
The print statement becomes the print() function in Python 3.0.
Making print() a function makes it possible to replace the function
by doing def print(...) or importing a new function from somewhere else.
Python 2.6 has a __future__ import that removes print as language
syntax, letting you use the functional form instead. For example:
from __future__ import print_function
print('# of entries', len(dictionary), file=sys.stderr)
The signature of the new function is:
def print(*args, sep=' ', end='\n', file=None)
The parameters are:
- args: positional arguments whose values will be printed out.
- sep: the separator, which will be printed between arguments.
- end: the ending text, which will be printed after all of the
arguments have been output.
- file: the file object to which the output will be sent.
See also
- PEP 3105 - Make print a function
- PEP written by Georg Brandl.
PEP 3110: Exception-Handling Changes
One error that Python programmers occasionally make
is writing the following code:
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.
See also
- PEP 3110 - Catching Exceptions in Python 3000
- PEP written and implemented by Collin Winter.
PEP 3112: Byte Literals
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.
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.
Instances of the bytes type are immutable just
as strings are. A new bytearray type stores a mutable
sequence of bytes:
>>> 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'
Byte arrays support most of the methods of string types, such as
startswith()/endswith(), find()/rfind(),
and some of the methods of lists, such as append(),
pop(), and reverse().
>>> b = bytearray('ABC')
>>> b.append('d')
>>> b.append(ord('e'))
>>> b
bytearray(b'ABCde')
There’s also a corresponding C API, with
PyByteArray_FromObject,
PyByteArray_FromStringAndSize,
and various other functions.
See also
- PEP 3112 - Bytes literals in Python 3000
- PEP written by Jason Orendorff; backported to 2.6 by Christian Heimes.
PEP 3116: New I/O Library
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.
See also
- PEP 3116 - New 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 Loewis, Tony Lownds, and others.
PEP 3118: Revised Buffer Protocol
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 indicates that the memory must be writable.
- 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.
Two new argument codes for PyArg_ParseTuple,
s* and z*, return locked buffer objects for a parameter.
See also
- PEP 3118 - Revising the buffer protocol
- PEP written by Travis Oliphant and Carl Banks; implemented by
Travis Oliphant.
PEP 3119: Abstract Base Classes
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()
built-ins, 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.
See also
- PEP 3119 - Introducing Abstract Base Classes
- 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: Integer Literal Support and Syntax
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() built-in still returns numbers
prefixed with a leading zero, and a new bin()
built-in returns the binary representation for a number:
>>> oct(42)
'052'
>>> future_builtins.oct(42)
'0o52'
>>> bin(173)
'0b10101101'
The int() and long() built-ins 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
See also
- PEP 3127 - Integer Literal Support and Syntax
- PEP written by Patrick Maupin; backported to 2.6 by
Eric Smith.
PEP 3129: Class Decorators
Decorators have been extended from functions to classes. It’s now legal to
write:
@foo
@bar
class A:
pass
This is equivalent to:
class A:
pass
A = foo(bar(A))
See also
- PEP 3129 - Class Decorators
- PEP written by Collin Winter.
PEP 3141: A Type Hierarchy for Numbers
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 built-ins
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.
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.
Other Language Changes
Some smaller changes made to the core Python language are:
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; issue 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']
(Contributed by Alexander Belopolsky; issue 1686487.)
It’s also become legal to provide keyword arguments after a *args argument
to a function call.
>>> def f(*args, **kw):
... print args, kw
...
>>> f(1,2,3, *(4,5,6), keyword=13)
(1, 2, 3, 4, 5, 6) {'keyword': 13}
Previously this would have been a syntax error.
(Contributed by Amaury Forgeot d’Arc; issue 3473.)
A new built-in, 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 issue 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
(Contributed by Raymond Hettinger)
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'])
(Contributed by Raymond Hettinger.)
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; issue 1635.)
Other functions in the math module, isinf() and
isnan(), return true if their floating-point argument is
infinite or Not A Number. (issue 1640)
Conversion functions were added to convert floating-point numbers
into hexadecimal strings (issue 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; issue 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; issue 2235.)
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.
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; issue 1537.)
Generator objects now have a gi_code attribute that refers to
the original code object backing the generator.
(Contributed by Collin Winter; issue 1473257.)
The compile() built-in function now accepts keyword arguments
as well as positional parameters. (Contributed by Thomas Wouters;
issue 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). (issue 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; issue 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.
(issue 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 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.)
Optimizations
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; issue 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; issue 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 issue 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; issue 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.
Interpreter Changes
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 Loewis.)
New, Improved, and Deprecated Modules
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.
(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.
The asyncore and asynchat modules are
being actively maintained again, and a number of patches and bugfixes
were applied. (Maintained by Josiah Carlson; see issue 1736190 for
one patch.)
The bsddb module also has a new maintainer, Jes?s Cea, 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; issue 1551443.)
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; issue 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
(issue 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 issue 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.
(Contributed by Raymond Hettinger.)
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)
(Contributed by Raymond Hettinger.)
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; issue 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; issue 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")
The as_tuple() method of Decimal objects now returns a
named tuple with sign, digits, and exponent fields.
(Implemented by Facundo Batista and Mark Dickinson. Named tuple
support added by Raymond Hettinger.)
The difflib module’s SequenceMatcher class
now returns named tuples representing matches,
with a, b, and size attributes.
(Contributed by Raymond Hettinger.)
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; issue 1221598.)
The reduce() built-in function is also available in the
functools module. In Python 3.0, the built-in has been
dropped and reduce() is only available from functools;
currently there are no plans to drop the built-in in the 2.x series.
(Patched by Christian Heimes; issue 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. (issue 1001604)
The gopherlib module has been removed.
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:
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.)
Some new functions in the module include
isgenerator(), isgeneratorfunction(),
and isabstract().
The itertools module gained several new functions.
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:
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.
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:
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.
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) returns sub-sequences of length r from
the elements of iterable.
itertools.combinations('123', 2) ->
('1', '2'), ('1', '3'), ('2', '3')
itertools.combinations('123', 3) ->
('1', '2', '3')
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.
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.
chain.from_iterable([[1,2,3], [4,5,6]]) ->
1, 2, 3, 4, 5, 6
(All contributed by Raymond Hettinger.)
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.
Several new functions were added to the math module:
- 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; issue 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;
issue 2819.)
- acosh(), asinh()
and atanh() compute the inverse hyperbolic functions.
- log1p() returns the natural logarithm of 1+x
(base e).
- 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 issue 711019 and
issue 1640.)
(Contributed by Christian Heimes and Mark Dickinson.)
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.
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'
(Contributed by Georg Brandl, after a suggestion by Gregory Petrosyan.)
The attrgetter() function now accepts dotted names and performs
the corresponding attribute lookups:
>>> inst_name = operator.attrgetter(
... '__class__.__name__')
>>> inst_name('')
'str'
>>> inst_name(help)
'_Helper'
(Contributed by Georg Brandl, after a suggestion by Barry Warsaw.)
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; issue 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; issue 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. (issue 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').
(issue 115886)
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; issue 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;
issue 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; issue 1393667.)
The posixfile module has been deprecated; fcntl.lockf()
provides better locking.
The 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;
issue 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.)
The popen2 module has been deprecated; use the subprocess
module.
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
>>> pkgutil.get_data('test', 'exception_hierarchy.txt')
'BaseException
+-- SystemExit
+-- KeyboardInterrupt
+-- GeneratorExit
+-- Exception
+-- StopIteration
+-- StandardError
...'
>>>
(Contributed by Paul Moore; issue 2439.)
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; issue 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; issue 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; issue 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; issue 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;
issue 3487.)
The rgbimg module has been removed.
The rlcompleter module’s Completer.complete() method
will now ignore exceptions triggered while evaluating a name.
(Fixed by Lorenz Quack; issue 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; issue 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; issue 1657.)
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.
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'))
(Contributed by Tarek Ziad?; issue 2663.)
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.
(Contributed by Adam Olsen; issue 1583.)
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; issue 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; issue 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;
issue 829951.)
The socket module now supports TIPC (http://tipc.sf.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; issue 1646.)
A new function, create_connection(), takes an address
and connects to it using an optional timeout value, returning
the connected socket object.
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;
issue 742598, issue 1193577.)
The sqlite3 module, maintained by Gerhard Haering,
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; issue 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; issue 2898.)
It’s now possible to determine the current profiler and tracer functions
by calling sys.getprofile() and sys.gettrace().
(Contributed by Georg Brandl; issue 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.
(All changes contributed by Lars Gust?bel).
An optional timeout parameter was added to the
telnetlib.Telnet class constructor, specifying a timeout
measured in seconds. (Added by Facundo Batista.)
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; issue 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; issue 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 (issue 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"
(Contributed by Brett Cannon.)
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.
>>>
(Contributed by Dwayne Bailey; issue 1581073.)
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;
issue 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;
issue 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; issue 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.
- A turtle.cfg file can be used to customize the starting appearance
of the turtle’s screen.
- The module’s docstrings can be replaced by new docstrings that have been
translated into another language.
(issue 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>
>>>
(Added by Facundo Batista.)
The Unicode database provided by the unicodedata module
has been updated to version 5.1.0. (Updated by
Martin von Loewis; issue 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 issue 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 (issue 3781).
The XML-RPC SimpleXMLRPCServer and DocXMLRPCServer
classes can now be prevented from immediately opening and binding to
their socket by passing True 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; issue 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. (issue 1330538) The code can also handle
dates before 1900 (contributed by Ralf Schmitt; issue 2014)
and 64-bit integers represented by using <i8> in XML-RPC responses
(contributed by Riku Lindblad; issue 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()
(Contributed by Alan McIntyre; issue 467924.)
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; issue 1775025.)
Finally, zipfile now supports using Unicode filenames
for archived files. (Contributed by Alexey Borzenkov; issue 1734346.)
The ast module
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)
This outputs a deeply nested tree:
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 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.
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.
The functions in this module currently include:
- 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 built-ins 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.
The json module: 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 builtin 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.
The plistlib module: A Property-List Parser
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.
Using the module is simple:
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;
issue 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; issue 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.
Improved SSL Support
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.
See also
The documentation for the ssl module.
Build and C API Changes
Changes to Python’s build process and to the C API include:
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; issue 1551895.)
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; issue 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; issue 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; issue 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.
(issue 1629)
Distutils now places C extensions it builds in a
different directory when running on a debug version of Python.
(Contributed by Collin Winter; issue 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 check”, 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.)
Port-Specific Changes: Windows
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 Loewis.)
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; issue 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.
(issue 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; issue 2125.)
Port-Specific Changes: Mac OS X
- When compiling a framework build of Python, you can now specify the
framework name to be used by providing the
--with-framework-name= option to the
configure script.
- The macfs module has been removed. This in turn required the
macostools.touched() function to be removed because it depended on the
macfs module. (issue 1490190)
- Many other Mac OS modules have been deprecated and will removed in
Python 3.0:
_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, and
terminalcommand.
Port-Specific Changes: IRIX
A number of old IRIX-specific modules were deprecated and will
be removed in Python 3.0:
al and AL,
cd,
cddb,
cdplayer,
CL and cl,
DEVICE,
ERRNO,
FILE,
FL and fl,
flp,
fm,
GET,
GLWS,
GL and gl,
IN,
IOCTL,
jpeg,
panelparser,
readcd,
SV and sv,
torgb,
videoreader, and
WAIT.
Porting to Python 2.6
This section lists previously described changes and other bugfixes
that may require changes to your code:
Classes that aren’t supposed to be hashable should
set __hash__ = None in their definitions to indicate
the fact.
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 issue 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; issue 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. (issue 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.
Acknowledgements
The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Georg Brandl, Steve Brown, Nick Coghlan, Jim Jewett, Kent
Johnson, Chris Lambacher, Antoine Pitrou.
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