What’s New in Python 2.6
************************

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

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 builtins.  Functions that are new in 3.0
such as "bin()" have simply been added to Python 2.6, but existing
builtins 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 https://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 Löwis put a lot of
effort into importing existing bugs and patches from SourceForge; his
scripts for this import operation are at
http://svn.python.org/view/tracker/importer/ and may be useful to
other projects wishing to move from SourceForge to Roundup.

See also:

  https://bugs.python.org
     The Python bug tracker.

  http://bugs.jython.org:
     The Jython bug tracker.

  http://roundup.sourceforge.net/
     Roundup downloads and documentation.

  http://svn.python.org/view/tracker/importer/
     Martin von Löwis’s conversion scripts.


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

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

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

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

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

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

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

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

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

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

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

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

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


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

   import urllib, sys
   from contextlib import closing

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

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

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

   from multiprocessing import Pool

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

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:

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

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

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

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

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

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

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

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()" builtin 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.

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

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

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

   from __future__ import unicode_literals

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

   print len(s)               # 12 Unicode characters

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

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 Löwis, 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()" builtins, and a
collection of basic ABCs that the Python developers think will be
widely useful.  Future versions of Python will probably add more ABCs.

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

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

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

   import collections

   class Storage(collections.MutableMapping):
       ...

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

   import collections

   class Storage:
       ...

   collections.MutableMapping.register(Storage)

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

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

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

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

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

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

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

   from abc import ABCMeta, abstractmethod

   class Drawable():
       __metaclass__ = ABCMeta

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

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


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

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

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

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

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

   from abc import abstractproperty
   ...

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

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

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

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

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

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

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 builtins
"round()", "math.floor()", "math.ceil()", and adds a new one,
"math.trunc()", that’s been backported to Python 2.6. "math.trunc()"
rounds toward zero, returning the closest "Integral" that’s between
the function’s argument and zero.

See also:

  **PEP 3141** - A Type Hierarchy for Numbers
     PEP written by Jeffrey Yasskin.

  Scheme’s numerical tower, from the Guile manual.

  Scheme’s number datatypes from the R5RS Scheme specification.


The "fractions" Module
----------------------

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:

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

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

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

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

  (Contributed by Alexander Belopolsky; bpo-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; bpo-3473.)

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

* Tuples now have "index()" and "count()" methods matching the list
  type’s "index()" and "count()" methods:

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

  (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; bpo-1635.)

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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; bpo-1631171.)

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

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

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

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

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

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

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

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


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 Löwis.)


New and Improved 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.

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

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

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

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

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

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

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

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

  * "phase()" returns the argument (also called the angle) of a
    complex number.

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

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

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

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

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

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

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

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

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

  (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; bpo-1638033.)

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

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

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

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

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

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

  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;
  bpo-1221598.)

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

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

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

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

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

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

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

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

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

  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:

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

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

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

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

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

  With two iterables, *2N*-tuples are returned.

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

  "combinations(iterable, r)" returns sub-sequences of length *r* from
  the elements of *iterable*.

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

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

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

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

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

  (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; bpo-2138.)

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

  * "acosh()", "asinh()" and "atanh()" compute the inverse
    hyperbolic functions.

  * "log1p()" 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 bpo-711019 and bpo-1640.)

  (Contributed by Christian Heimes and Mark Dickinson.)

* "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;
  bpo-1663329.)

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

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

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

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

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

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

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

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

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

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

  (Contributed by Paul Moore; bpo-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; bpo-1137.)

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

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

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

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

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

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

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

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

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

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

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

  (Contributed by Tarek Ziadé; bpo-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; bpo-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; bpo-2240.)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  (All changes contributed by Lars Gustäbel).

* 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; bpo-1537850.)

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

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

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

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

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

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

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

  (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; bpo-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; bpo-2871.)

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

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

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

  * Better animation of turtle movement and rotation.

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

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

  * Turtles now have an "undo()" method that can roll back actions.

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

  * 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.

  (bpo-1513695)

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

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

  (Added by Facundo Batista.)

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

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

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

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

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

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

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

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

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

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

  (Contributed by Alan McIntyre; bpo-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; bpo-1775025.)

  Finally, "zipfile" now supports using Unicode filenames for archived
  files.  (Contributed by Alexey Borzenkov; bpo-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 cannot accept the
security risk of using an "eval()" call, "literal_eval()" will handle
it safely:

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

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


The "future_builtins" module
----------------------------

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 builtins which return
  lists.

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


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 built-in
Python types. The following example encodes and decodes a dictionary:

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

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

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


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; bpo-1649190.)

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

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

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

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

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

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


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.


Deprecations and Removals
=========================

* String exceptions have been removed.  Attempting to use them
  raises a "TypeError".

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

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

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

* The "gopherlib" module has been removed.

* The "MimeWriter" module and "mimify" module have been deprecated;
  use the "email" package instead.

* The "md5" module has been deprecated; use the "hashlib" module
  instead.

* The "posixfile" module has been deprecated; "fcntl.lockf()"
  provides better locking.

* The "popen2" module has been deprecated; use the "subprocess"
  module.

* The "rgbimg" module has been removed.

* 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.


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.)

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

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

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

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

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

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

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

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

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

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

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

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


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 Löwis.)

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

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

* The "socket" module’s socket objects now have an "ioctl()" method
  that provides a limited interface to the "WSAIoctl()" system
  interface.

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

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

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


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.  (bpo-1490190)

* Many other Mac OS modules have been deprecated and will be 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.

* String exceptions have been removed.  Attempting to use them
  raises a "TypeError".

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

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

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

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

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

* C API: extension data types that shouldn’t be hashable should
  define their "tp_hash" slot to "PyObject_HashNotImplemented()".

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

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

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

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

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


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, Ralph Corderoy, Jim
Jewett, Kent Johnson, Chris Lambacher,  Martin Michlmayr, Antoine
Pitrou, Brian Warner.
