What's New in Python 2.5
************************

Author:
   A.M. Kuchling

This article explains the new features in Python 2.5.  The final
release of Python 2.5 is scheduled for August 2006; **PEP 356**
describes the planned release schedule.  Python 2.5 was released on
September 19, 2006.

The changes in Python 2.5 are an interesting mix of language and
library improvements. The library enhancements will be more important
to Python's user community, I think, because several widely useful
packages were added.  New modules include ElementTree for XML
processing ("xml.etree"), the SQLite database module ("sqlite"), and
the "ctypes" module for calling C functions.

The language changes are of middling significance.  Some pleasant new
features were added, but most of them aren't features that you'll use
every day. Conditional expressions were finally added to the language
using a novel syntax; see section PEP 308: Conditional Expressions.
The new '"with"' statement will make writing cleanup code easier
(section PEP 343: The 'with' statement).  Values can now be passed
into generators (section PEP 342: New Generator Features).  Imports
are now visible as either absolute or relative (section PEP 328:
Absolute and Relative Imports).  Some corner cases of exception
handling are handled better (section PEP 341: Unified
try/except/finally).  All these improvements are worthwhile, but
they're improvements to one specific language feature or another; none
of them are broad modifications to Python's semantics.

As well as the language and library additions, other improvements and
bugfixes were made throughout the source tree.  A search through the
SVN change logs finds there were 353 patches applied and 458 bugs
fixed between Python 2.4 and 2.5.  (Both figures are likely to be
underestimates.)

This article doesn't try to be a complete specification of the new
features; instead changes are briefly introduced using helpful
examples.  For full details, you should always refer to the
documentation for Python 2.5 at https://docs.python.org. If you want
to understand the complete implementation and design rationale, refer
to the PEP for a particular new feature.

Comments, suggestions, and error reports for this document are
welcome; please e-mail them to the author or open a bug in the Python
bug tracker.


PEP 308: Conditional Expressions
================================

For a long time, people have been requesting a way to write
conditional expressions, which are expressions that return value A or
value B depending on whether a Boolean value is true or false.  A
conditional expression lets you write a single assignment statement
that has the same effect as the following:

   if condition:
       x = true_value
   else:
       x = false_value

There have been endless tedious discussions of syntax on both python-
dev and comp.lang.python.  A vote was even held that found the
majority of voters wanted conditional expressions in some form, but
there was no syntax that was preferred by a clear majority. Candidates
included C's "cond ? true_v : false_v", "if cond then true_v else
false_v", and 16 other variations.

Guido van Rossum eventually chose a surprising syntax:

   x = true_value if condition else false_value

Evaluation is still lazy as in existing Boolean expressions, so the
order of evaluation jumps around a bit.  The *condition* expression in
the middle is evaluated first, and the *true_value* expression is
evaluated only if the condition was true.  Similarly, the
*false_value* expression is only evaluated when the condition is
false.

This syntax may seem strange and backwards; why does the condition go
in the *middle* of the expression, and not in the front as in C's "c ?
x : y"?  The decision was checked by applying the new syntax to the
modules in the standard library and seeing how the resulting code
read.  In many cases where a conditional expression is used, one value
seems to be the 'common case' and one value is an 'exceptional case',
used only on rarer occasions when the condition isn't met.  The
conditional syntax makes this pattern a bit more obvious:

   contents = ((doc + '\n') if doc else '')

I read the above statement as meaning "here *contents* is  usually
assigned a value of "doc+'\n'"; sometimes  *doc* is empty, in which
special case an empty string is returned."   I doubt I will use
conditional expressions very often where there  isn't a clear common
and uncommon case.

There was some discussion of whether the language should require
surrounding conditional expressions with parentheses.  The decision
was made to *not* require parentheses in the Python language's
grammar, but as a matter of style I think you should always use them.
Consider these two statements:

   # First version -- no parens
   level = 1 if logging else 0

   # Second version -- with parens
   level = (1 if logging else 0)

In the first version, I think a reader's eye might group the statement
into 'level = 1', 'if logging', 'else 0', and think that the condition
decides whether the assignment to *level* is performed.  The second
version reads better, in my opinion, because it makes it clear that
the assignment is always performed and the choice is being made
between two values.

Another reason for including the brackets: a few odd combinations of
list comprehensions and lambdas could look like incorrect conditional
expressions. See **PEP 308** for some examples.  If you put
parentheses around your conditional expressions, you won't run into
this case.

Vedi anche:

  **PEP 308** - Conditional Expressions
     PEP written by Guido van Rossum and Raymond D. Hettinger;
     implemented by Thomas Wouters.


PEP 309: Partial Function Application
=====================================

The "functools" module is intended to contain tools for functional-
style programming.

One useful tool in this module is the "partial()" function. For
programs written in a functional style, you'll sometimes want to
construct variants of existing functions that have some of the
parameters filled in.  Consider a Python function "f(a, b, c)"; you
could create a new function "g(b, c)" that was equivalent to "f(1, b,
c)".  This is called "partial function application".

"partial()" takes the arguments "(function, arg1, arg2, ...
kwarg1=value1, kwarg2=value2)".  The resulting object is callable, so
you can just call it to invoke *function* with the filled-in
arguments.

Here's a small but realistic example:

   import functools

   def log (message, subsystem):
       "Write the contents of 'message' to the specified subsystem."
       print '%s: %s' % (subsystem, message)
       ...

   server_log = functools.partial(log, subsystem='server')
   server_log('Unable to open socket')

Here's another example, from a program that uses PyGTK.  Here a
context-sensitive pop-up menu is being constructed dynamically.  The
callback provided for the menu option is a partially applied version
of the "open_item()" method, where the first argument has been
provided.

   ...
   class Application:
       def open_item(self, path):
          ...
       def init (self):
           open_func = functools.partial(self.open_item, item_path)
           popup_menu.append( ("Open", open_func, 1) )

Another function in the "functools" module is the
"update_wrapper(wrapper, wrapped)" function that helps you write well-
behaved decorators.  "update_wrapper()" copies the name, module, and
docstring attribute to a wrapper function so that tracebacks inside
the wrapped function are easier to understand.  For example, you might
write:

   def my_decorator(f):
       def wrapper(*args, **kwds):
           print 'Calling decorated function'
           return f(*args, **kwds)
       functools.update_wrapper(wrapper, f)
       return wrapper

"wraps()" is a decorator that can be used inside your own decorators
to copy the wrapped function's information.  An alternate  version of
the previous example would be:

   def my_decorator(f):
       @functools.wraps(f)
       def wrapper(*args, **kwds):
           print 'Calling decorated function'
           return f(*args, **kwds)
       return wrapper

Vedi anche:

  **PEP 309** - Partial Function Application
     PEP proposed and written by Peter Harris; implemented by Hye-Shik
     Chang and Nick Coghlan, with adaptations by Raymond Hettinger.


PEP 314: Metadata for Python Software Packages v1.1
===================================================

Some simple dependency support was added to Distutils.  The "setup()"
function now has "requires", "provides", and "obsoletes" keyword
parameters.  When you build a source distribution using the "sdist"
command, the dependency information will be recorded in the "PKG-INFO"
file.

Another new keyword parameter is "download_url", which should be set
to a URL for the package's source code.  This means it's now possible
to look up an entry in the package index, determine the dependencies
for a package, and download the required packages.

   VERSION = '1.0'
   setup(name='PyPackage',
         version=VERSION,
         requires=['numarray', 'zlib (>=1.1.4)'],
         obsoletes=['OldPackage']
         download_url=('http://www.example.com/pypackage/dist/pkg-%s.tar.gz'
                       % VERSION),
        )

Another new enhancement to the Python package index at
https://pypi.org is storing source and binary archives for a package.
The new **upload** Distutils command will upload a package to the
repository.

Before a package can be uploaded, you must be able to build a
distribution using the **sdist** Distutils command.  Once that works,
you can run "python setup.py upload" to add your package to the PyPI
archive.  Optionally you can GPG-sign the package by supplying the "--
sign" and "--identity" options.

Package uploading was implemented by Martin von Löwis and Richard
Jones.

Vedi anche:

  **PEP 314** - Metadata for Python Software Packages v1.1
     PEP proposed and written by A.M. Kuchling, Richard Jones, and
     Fred Drake; implemented by Richard Jones and Fred Drake.


PEP 328: Absolute and Relative Imports
======================================

The simpler part of **PEP 328** was implemented in Python 2.4:
parentheses could now be used to enclose the names imported from a
module using the "from ... import ..." statement, making it easier to
import many different names.

The more complicated part has been implemented in Python 2.5:
importing a module can be specified to use absolute or package-
relative imports.  The plan is to move toward making absolute imports
the default in future versions of Python.

Let's say you have a package directory like this:

   pkg/
   pkg/__init__.py
   pkg/main.py
   pkg/string.py

This defines a package named "pkg" containing the "pkg.main" and
"pkg.string" submodules.

Consider the code in the "main.py" module.  What happens if it
executes the statement "import string"?  In Python 2.4 and earlier, it
will first look in the package's directory to perform a relative
import, finds "pkg/string.py", imports the contents of that file as
the "pkg.string" module, and that module is bound to the name "string"
in the "pkg.main" module's namespace.

That's fine if "pkg.string" was what you wanted.  But what if you
wanted Python's standard "string" module?  There's no clean way to
ignore "pkg.string" and look for the standard module; generally you
had to look at the contents of "sys.modules", which is slightly
unclean.    Holger Krekel's "py.std" package provides a tidier way to
perform imports from the standard library, "import py;
py.std.string.join()", but that package isn't available on all Python
installations.

Reading code which relies on relative imports is also less clear,
because a reader may be confused about which module, "string" or
"pkg.string", is intended to be used.  Python users soon learned not
to duplicate the names of standard library modules in the names of
their packages' submodules, but you can't protect against having your
submodule's name being used for a new module added in a future version
of Python.

In Python 2.5, you can switch "import"'s behaviour to  absolute
imports using a "from __future__ import absolute_import" directive.
This absolute-import behaviour will become the default in a future
version (probably Python 2.7).  Once absolute imports  are the
default, "import string" will always find the standard library's
version. It's suggested that users should begin using absolute imports
as much as possible, so it's preferable to begin writing "from pkg
import string" in your code.

Relative imports are still possible by adding a leading period  to the
module name when using the "from ... import" form:

   # Import names from pkg.string
   from .string import name1, name2
   # Import pkg.string
   from . import string

This imports the "string" module relative to the current package, so
in "pkg.main" this will import *name1* and *name2* from "pkg.string".
Additional leading periods perform the relative import starting from
the parent of the current package.  For example, code in the "A.B.C"
module can do:

   from . import D                 # Imports A.B.D
   from .. import E                # Imports A.E
   from ..F import G               # Imports A.F.G

Leading periods cannot be used with the "import modname"  form of the
import statement, only the "from ... import" form.

Vedi anche:

  **PEP 328** - Imports: Multi-Line and Absolute/Relative
     PEP written by Aahz; implemented by Thomas Wouters.

  https://pylib.readthedocs.io/
     The py library by Holger Krekel, which contains the "py.std"
     package.


PEP 338: Executing Modules as Scripts
=====================================

The "-m" switch added in Python 2.4 to execute a module as a script
gained a few more abilities.  Instead of being implemented in C code
inside the Python interpreter, the switch now uses an implementation
in a new module, "runpy".

The "runpy" module implements a more sophisticated import mechanism so
that it's now possible to run modules in a package such as
"pychecker.checker". The module also supports alternative import
mechanisms such as the "zipimport" module.  This means you can add a
.zip archive's path to "sys.path" and then use the "-m" switch to
execute code from the archive.

Vedi anche:

  **PEP 338** - Executing modules as scripts
     PEP written and  implemented by Nick Coghlan.


PEP 341: Unified try/except/finally
===================================

Until Python 2.5, the "try" statement came in two flavours. You could
use a "finally" block to ensure that code is always executed, or one
or more "except" blocks to catch  specific exceptions.  You couldn't
combine both "except" blocks and a "finally" block, because generating
the right bytecode for the combined version was complicated and it
wasn't clear what the semantics of the combined statement should be.

Guido van Rossum spent some time working with Java, which does support
the equivalent of combining "except" blocks and a "finally" block, and
this clarified what the statement should mean.  In Python 2.5, you can
now write:

   try:
       block-1 ...
   except Exception1:
       handler-1 ...
   except Exception2:
       handler-2 ...
   else:
       else-block
   finally:
       final-block

The code in *block-1* is executed.  If the code raises an exception,
the various "except" blocks are tested: if the exception is of class
"Exception1", *handler-1* is executed; otherwise if it's of class
"Exception2", *handler-2* is executed, and so forth.  If no exception
is raised, the *else-block* is executed.

No matter what happened previously, the *final-block* is executed once
the code block is complete and any raised exceptions handled. Even if
there's an error in an exception handler or the *else-block* and a new
exception is raised, the code in the *final-block* is still run.

Vedi anche:

  **PEP 341** - Unifying try-except and try-finally
     PEP written by Georg Brandl;  implementation by Thomas Lee.


PEP 342: New Generator Features
===============================

Python 2.5 adds a simple way to pass values *into* a generator. As
introduced in Python 2.3, generators only produce output; once a
generator's code was invoked to create an iterator, there was no way
to pass any new information into the function when its execution is
resumed.  Sometimes the ability to pass in some information would be
useful.  Hackish solutions to this include making the generator's code
look at a global variable and then changing the global variable's
value, or passing in some mutable object that callers then modify.

To refresh your memory of basic generators, here's a simple example:

   def counter (maximum):
       i = 0
       while i < maximum:
           yield i
           i += 1

When you call "counter(10)", the result is an iterator that returns
the values from 0 up to 9.  On encountering the "yield" statement, the
iterator returns the provided value and suspends the function's
execution, preserving the local variables. Execution resumes on the
following call to the iterator's "next()" method, picking up after the
"yield" statement.

In Python 2.3, "yield" was a statement; it didn't return any value.
In 2.5, "yield" is now an expression, returning a value that can be
assigned to a variable or otherwise operated on:

   val = (yield i)

I recommend that you always put parentheses around a "yield"
expression when you're doing something with the returned value, as in
the above example. The parentheses aren't always necessary, but it's
easier to always add them instead of having to remember when they're
needed.

(**PEP 342** explains the exact rules, which are that a
"yield"-expression must always be parenthesized except when it occurs
at the top-level expression on the right-hand side of an assignment.
This means you can write "val = yield i" but have to use parentheses
when there's an operation, as in "val = (yield i) + 12".)

Values are sent into a generator by calling its "send(value)" method.
The generator's code is then resumed and the "yield" expression
returns the specified *value*.  If the regular "next()" method is
called, the "yield" returns "None".

Here's the previous example, modified to allow changing the value of
the internal counter.

   def counter (maximum):
       i = 0
       while i < maximum:
           val = (yield i)
           # If value provided, change counter
           if val is not None:
               i = val
           else:
               i += 1

And here's an example of changing the counter:

   >>> it = counter(10)
   >>> print it.next()
   0
   >>> print it.next()
   1
   >>> print it.send(8)
   8
   >>> print it.next()
   9
   >>> print it.next()
   Traceback (most recent call last):
     File "t.py", line 15, in ?
       print it.next()
   StopIteration

"yield" will usually return "None", so you should always check for
this case.  Don't just use its value in expressions unless you're sure
that the "send()" method will be the only method used to resume your
generator function.

In addition to "send()", there are two other new methods on
generators:

* "throw(type, value=None, traceback=None)" is used to raise an
  exception inside the generator; the exception is raised by the
  "yield" expression where the generator's execution is paused.

* "close()" raises a new "GeneratorExit" exception inside the
  generator to terminate the iteration.  On receiving this exception,
  the generator's code must either raise "GeneratorExit" or
  "StopIteration".  Catching the "GeneratorExit" exception and
  returning a value is illegal and will trigger a "RuntimeError"; if
  the function raises some other exception, that exception is
  propagated to the caller.  "close()" will also be called by Python's
  garbage collector when the generator is garbage-collected.

  If you need to run cleanup code when a "GeneratorExit" occurs, I
  suggest using a "try: ... finally:" suite instead of  catching
  "GeneratorExit".

The cumulative effect of these changes is to turn generators from one-
way producers of information into both producers and consumers.

Generators also become *coroutines*, a more generalized form of
subroutines. Subroutines are entered at one point and exited at
another point (the top of the function, and a "return" statement), but
coroutines can be entered, exited, and resumed at many different
points (the "yield" statements). We'll have to figure out patterns for
using coroutines effectively in Python.

The addition of the "close()" method has one side effect that isn't
obvious. "close()" is called when a generator is garbage-collected, so
this means the generator's code gets one last chance to run before the
generator is destroyed. This last chance means that "try...finally"
statements in generators can now be guaranteed to work; the "finally"
clause will now always get a chance to run.  The syntactic restriction
that you couldn't mix "yield" statements with a "try...finally" suite
has therefore been removed.  This seems like a minor bit of language
trivia, but using generators and "try...finally" is actually necessary
in order to implement the "with" statement described by **PEP 343**.
I'll look at this new statement in the following  section.

Another even more esoteric effect of this change: previously, the
"gi_frame" attribute of a generator was always a frame object. It's
now possible for "gi_frame" to be "None" once the generator has been
exhausted.

Vedi anche:

  **PEP 342** - Coroutines via Enhanced Generators
     PEP written by  Guido van Rossum and Phillip J. Eby; implemented
     by Phillip J. Eby.  Includes examples of  some fancier uses of
     generators as coroutines.

     Earlier versions of these features were proposed in  **PEP 288**
     by Raymond Hettinger and **PEP 325** by Samuele Pedroni.

  https://en.wikipedia.org/wiki/Coroutine
     The Wikipedia entry for  coroutines.

  https://web.archive.org/web/20160321211320/http://www.sidhe.org/~da
  n/blog/archives/000178.html
     An explanation of coroutines from a Perl point of view, written
     by Dan Sugalski.


PEP 343: The 'with' statement
=============================

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 new 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.

To enable the statement in Python 2.5, you need to add the following
directive to your module:

   from __future__ import with_statement

The statement will always be enabled in Python 2.6.

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.

Nota:

  In this case, *f* is the same object created by "open()", because
  "__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 new "localcontext()" function in the "decimal" module makes it
easy to save and restore the current decimal context, which
encapsulates the desired precision and rounding characteristics for
computations:

   from decimal import Decimal, Context, localcontext

   # Displays with default precision of 28 digits
   v = Decimal('578')
   print v.sqrt()

   with localcontext(Context(prec=16)):
       # All code in this block uses a precision of 16 digits.
       # The original context is restored on exiting the block.
       print v.sqrt()


Writing Context Managers
------------------------

Under the hood, the '"with"' statement is fairly complicated. Most
people will only use '"with"' in company with existing objects and
don't need to know these details, so you can skip the rest of this
section if you like.  Authors of new objects will need to understand
the details of the underlying implementation and should keep reading.

A high-level explanation of the context management protocol is:

* The expression is evaluated and should result in an object called a
  "context manager".  The context manager must have "__enter__()" and
  "__exit__()" methods.

* The context manager's "__enter__()" method is called.  The value
  returned is assigned to *VAR*.  If no "'as VAR'" clause is present,
  the value is simply discarded.

* The code in *BLOCK* is executed.

* If *BLOCK* raises an exception, the "__exit__(type, value,
  traceback)" is called with the exception details, the same values
  returned by "sys.exc_info()".  The method's return value controls
  whether the exception is re-raised: any false value re-raises the
  exception, and "True" will result in suppressing it.  You'll only
  rarely want to suppress the exception, because if you do the author
  of the code containing the '"with"' statement will never realize
  anything went wrong.

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

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

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

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

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

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

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

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

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

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

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

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


The contextlib module
---------------------

The new "contextlib" module provides some functions and a decorator
that are useful for 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.

Our database example from the previous section could be written  using
this decorator as:

   from contextlib import contextmanager

   @contextmanager
   def db_transaction (connection):
       cursor = connection.cursor()
       try:
           yield cursor
       except:
           connection.rollback()
           raise
       else:
           connection.commit()

   db = DatabaseConnection()
   with db_transaction(db) as cursor:
       ...

The "contextlib" module also has a "nested(mgr1, mgr2, ...)" function
that combines a number of context managers so you don't need to write
nested '"with"' statements.  In this example, the single '"with"'
statement both starts a database transaction and acquires a thread
lock:

   lock = threading.Lock()
   with nested (db_transaction(db), lock) as (cursor, locked):
       ...

Finally, the "closing(object)" function returns *object* so that it
can be bound to a variable, and calls "object.close" at the end of the
block.

   import urllib, sys
   from contextlib import closing

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

Vedi anche:

  **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 352: Exceptions as New-Style Classes
========================================

Exception classes can now be new-style classes, not just classic
classes, and the built-in "Exception" class and all the standard
built-in exceptions ("NameError", "ValueError", etc.) are now new-
style classes.

The inheritance hierarchy for exceptions has been rearranged a bit. In
2.5, the inheritance relationships are:

   BaseException       # New in Python 2.5
   |- KeyboardInterrupt
   |- SystemExit
   |- Exception
      |- (all other current built-in exceptions)

This rearrangement was done because people often want to catch all
exceptions that indicate program errors.  "KeyboardInterrupt" and
"SystemExit" aren't errors, though, and usually represent an explicit
action such as the user hitting "Control"-"C" or code calling
"sys.exit()".  A bare "except:" will catch all exceptions, so you
commonly need to list "KeyboardInterrupt" and "SystemExit" in order to
re-raise them.  The usual pattern is:

   try:
       ...
   except (KeyboardInterrupt, SystemExit):
       raise
   except:
       # Log error...
       # Continue running program...

In Python 2.5, you can now write "except Exception" to achieve the
same result, catching all the exceptions that usually indicate errors
but leaving "KeyboardInterrupt" and "SystemExit" alone.  As in
previous versions, a bare "except:" still catches all exceptions.

The goal for Python 3.0 is to require any class raised as an exception
to derive from "BaseException" or some descendant of "BaseException",
and future releases in the Python 2.x series may begin to enforce this
constraint. Therefore, I suggest you begin making all your exception
classes derive from "Exception" now.  It's been suggested that the
bare "except:" form should be removed in Python 3.0, but Guido van
Rossum hasn't decided whether to do this or not.

Raising of strings as exceptions, as in the statement "raise "Error
occurred"", is deprecated in Python 2.5 and will trigger a warning.
The aim is to be able to remove the string-exception feature in a few
releases.

Vedi anche:

  **PEP 352** - Required Superclass for Exceptions
     PEP written by  Brett Cannon and Guido van Rossum; implemented by
     Brett Cannon.


PEP 353: Using ssize_t as the index type
========================================

A wide-ranging change to Python's C API, using a new  "Py_ssize_t"
type definition instead of int,  will permit the interpreter to handle
more data on 64-bit platforms. This change doesn't affect Python's
capacity on 32-bit platforms.

Various pieces of the Python interpreter used C's int type to store
sizes or counts; for example, the number of items in a list or tuple
were stored in an int.  The C compilers for most 64-bit platforms
still define int as a 32-bit type, so that meant that lists could only
hold up to "2**31 - 1" = 2147483647 items. (There are actually a few
different programming models that 64-bit C compilers can use -- see
https://unix.org/version2/whatsnew/lp64_wp.html for a discussion --
but the most commonly available model leaves int as 32 bits.)

A limit of 2147483647 items doesn't really matter on a 32-bit platform
because you'll run out of memory before hitting the length limit. Each
list item requires space for a pointer, which is 4 bytes, plus space
for a "PyObject" representing the item.  2147483647*4 is already more
bytes than a 32-bit address space can contain.

It's possible to address that much memory on a 64-bit platform,
however.  The pointers for a list that size would only require 16 GiB
of space, so it's not unreasonable that Python programmers might
construct lists that large. Therefore, the Python interpreter had to
be changed to use some type other than int, and this will be a 64-bit
type on 64-bit platforms.  The change will cause incompatibilities on
64-bit machines, so it was deemed worth making the transition now,
while the number of 64-bit users is still relatively small. (In 5 or
10 years, we may *all* be on 64-bit machines, and the transition would
be more painful then.)

This change most strongly affects authors of C extension modules.
Python strings and container types such as lists and tuples  now use
"Py_ssize_t" to store their size.   Functions such as "PyList_Size()"
now return "Py_ssize_t".  Code in extension modules may therefore need
to have some variables changed to "Py_ssize_t".

The "PyArg_ParseTuple()" and "Py_BuildValue()" functions have a new
conversion code, "n", for "Py_ssize_t".   "PyArg_ParseTuple()"'s "s#"
and "t#" still output int by default, but you can define the macro
"PY_SSIZE_T_CLEAN" before including "Python.h"  to make them return
"Py_ssize_t".

**PEP 353** has a section on conversion guidelines that  extension
authors should read to learn about supporting 64-bit platforms.

Vedi anche:

  **PEP 353** - Using ssize_t as the index type
     PEP written and implemented by Martin von Löwis.


PEP 357: The '__index__' method
===============================

The NumPy developers had a problem that could only be solved by adding
a new special method, "__index__()".  When using slice notation, as in
"[start:stop:step]", the values of the *start*, *stop*, and *step*
indexes must all be either integers or long integers.  NumPy defines a
variety of specialized integer types corresponding to unsigned and
signed integers of 8, 16, 32, and 64 bits, but there was no way to
signal that these types could be used as slice indexes.

Slicing can't just use the existing "__int__()" method because that
method is also used to implement coercion to integers.  If slicing
used "__int__()", floating-point numbers would also become legal slice
indexes and that's clearly an undesirable behaviour.

Instead, a new special method called "__index__()" was added.  It
takes no arguments and returns an integer giving the slice index to
use.  For example:

   class C:
       def __index__ (self):
           return self.value

The return value must be either a Python integer or long integer. The
interpreter will check that the type returned is correct, and raises a
"TypeError" if this requirement isn't met.

A corresponding "nb_index" slot was added to the C-level
"PyNumberMethods" structure to let C extensions implement this
protocol. "PyNumber_Index(obj)" can be used in extension code to call
the "__index__()" function and retrieve its result.

Vedi anche:

  **PEP 357** - Allowing Any Object to be Used for Slicing
     PEP written  and implemented by Travis Oliphant.


Other Language Changes
======================

Here are all of the changes that Python 2.5 makes to the core Python
language.

* The "dict" type has a new hook for letting subclasses provide a
  default value when a key isn't contained in the dictionary. When a
  key isn't found, the dictionary's "__missing__(key)" method will be
  called.  This hook is used to implement the new "defaultdict" class
  in the "collections" module.  The following example defines a
  dictionary  that returns zero for any missing key:

     class zerodict (dict):
         def __missing__ (self, key):
             return 0

     d = zerodict({1:1, 2:2})
     print d[1], d[2]   # Prints 1, 2
     print d[3], d[4]   # Prints 0, 0

* Both 8-bit and Unicode strings have new "partition(sep)"  and
  "rpartition(sep)" methods that simplify a common use case.

  The "find(S)" method is often used to get an index which is then
  used to slice the string and obtain the pieces that are before and
  after the separator. "partition(sep)" condenses this pattern into a
  single method call that returns a 3-tuple containing the substring
  before the separator, the separator itself, and the substring after
  the separator.  If the separator isn't found, the first element of
  the tuple is the entire string and the other two elements are empty.
  "rpartition(sep)" also returns a 3-tuple but starts searching from
  the end of the string; the "r" stands for 'reverse'.

  Some examples:

     >>> ('http://www.python.org').partition('://')
     ('http', '://', 'www.python.org')
     >>> ('file:/usr/share/doc/index.html').partition('://')
     ('file:/usr/share/doc/index.html', '', '')
     >>> (u'Subject: a quick question').partition(':')
     (u'Subject', u':', u' a quick question')
     >>> 'www.python.org'.rpartition('.')
     ('www.python', '.', 'org')
     >>> 'www.python.org'.rpartition(':')
     ('', '', 'www.python.org')

  (Implemented by Fredrik Lundh following a suggestion by Raymond
  Hettinger.)

* The "startswith()" and "endswith()" methods of string types now
  accept tuples of strings to check for.

     def is_image_file (filename):
         return filename.endswith(('.gif', '.jpg', '.tiff'))

  (Implemented by Georg Brandl following a suggestion by Tom Lynn.)

* The "min()" and "max()" built-in functions gained a "key" keyword
  parameter analogous to the "key" argument for "sort()".  This
  parameter supplies a function that takes a single argument and is
  called for every value in the list; "min()"/"max()" will return the
  element with the smallest/largest return value from this function.
  For example, to find the longest string in a list, you can do:

     L = ['medium', 'longest', 'short']
     # Prints 'longest'
     print max(L, key=len)
     # Prints 'short', because lexicographically 'short' has the largest value
     print max(L)

  (Contributed by Steven Bethard and Raymond Hettinger.)

* Two new built-in functions, "any()" and "all()", evaluate whether an
  iterator contains any true or false values.  "any()" returns "True"
  if any value returned by the iterator is true; otherwise it will
  return "False".  "all()" returns "True" only if all of the values
  returned by the iterator evaluate as true. (Suggested by Guido van
  Rossum, and implemented by Raymond Hettinger.)

* The result of a class's "__hash__()" method can now be either a long
  integer or a regular integer.  If a long integer is returned, the
  hash of that value is taken.  In earlier versions the hash value was
  required to be a regular integer, but in 2.5 the "id()" built-in was
  changed to always return non-negative numbers, and users often seem
  to use "id(self)" in "__hash__()" methods (though this is
  discouraged).

* ASCII is now the default encoding for modules.  It's now  a syntax
  error if a module contains string literals with 8-bit characters but
  doesn't have an encoding declaration.  In Python 2.4 this triggered
  a warning, not a syntax error.  See **PEP 263**  for how to declare
  a module's encoding; for example, you might add  a line like this
  near the top of the source file:

     # -*- coding: latin1 -*-

* A new warning, "UnicodeWarning", is triggered when  you attempt to
  compare a Unicode string and an 8-bit string  that can't be
  converted to Unicode using the default ASCII encoding.   The result
  of the comparison is false:

     >>> chr(128) == unichr(128)   # Can't convert chr(128) to Unicode
     __main__:1: UnicodeWarning: Unicode equal comparison failed
       to convert both arguments to Unicode - interpreting them
       as being unequal
     False
     >>> chr(127) == unichr(127)   # chr(127) can be converted
     True

  Previously this would raise a "UnicodeDecodeError" exception, but in
  2.5 this could result in puzzling problems when accessing a
  dictionary.  If you looked up "unichr(128)" and "chr(128)" was being
  used as a key, you'd get a "UnicodeDecodeError" exception.  Other
  changes in 2.5 resulted in this exception being raised instead of
  suppressed by the code in "dictobject.c" that implements
  dictionaries.

  Raising an exception for such a comparison is strictly correct, but
  the change might have broken code, so instead  "UnicodeWarning" was
  introduced.

  (Implemented by Marc-André Lemburg.)

* One error that Python programmers sometimes make is forgetting to
  include an "__init__.py" module in a package directory. Debugging
  this mistake can be confusing, and usually requires running Python
  with the "-v" switch to log all the paths searched. In Python 2.5, a
  new "ImportWarning" warning is triggered when an import would have
  picked up a directory as a package but no "__init__.py" was found.
  This warning is silently ignored by default; provide the "-Wd"
  option when running the Python executable to display the warning
  message. (Implemented by Thomas Wouters.)

* The list of base classes in a class definition can now be empty.
  As an example, this is now legal:

     class C():
         pass

  (Implemented by Brett Cannon.)


Interactive Interpreter Changes
-------------------------------

In the interactive interpreter, "quit" and "exit"  have long been
strings so that new users get a somewhat helpful message when they try
to quit:

   >>> quit
   'Use Ctrl-D (i.e. EOF) to exit.'

In Python 2.5, "quit" and "exit" are now objects that still produce
string representations of themselves, but are also callable. Newbies
who try "quit()" or "exit()" will now exit the interpreter as they
expect.  (Implemented by Georg Brandl.)

The Python executable now accepts the standard long options  "--help"
and "--version"; on Windows,  it also accepts the "/?" option for
displaying a help message. (Implemented by Georg Brandl.)


Optimizations
-------------

Several of the optimizations were developed at the NeedForSpeed
sprint, an event held in Reykjavik, Iceland, from May 21--28 2006. The
sprint focused on speed enhancements to the CPython implementation and
was funded by EWT LLC with local support from CCP Games.  Those
optimizations added at this sprint are specially marked in the
following list.

* When they were introduced  in Python 2.4, the built-in "set" and
  "frozenset" types were built on top of Python's dictionary type.
  In 2.5 the internal data structure has been customized for
  implementing sets, and as a result sets will use a third less memory
  and are somewhat faster. (Implemented by Raymond Hettinger.)

* The speed of some Unicode operations, such as finding substrings,
  string splitting, and character map encoding and decoding, has been
  improved. (Substring search and splitting improvements were added by
  Fredrik Lundh and Andrew Dalke at the NeedForSpeed sprint. Character
  maps were improved by Walter Dörwald and Martin von Löwis.)

* The "long(str, base)" function is now faster on long digit strings
  because fewer intermediate results are calculated.  The peak is for
  strings of around 800--1000 digits where  the function is 6 times
  faster. (Contributed by Alan McIntyre and committed at the
  NeedForSpeed sprint.)

* It's now illegal to mix iterating over a file  with "for line in
  file" and calling  the file object's
  "read()"/"readline()"/"readlines()" methods.  Iteration uses an
  internal buffer and the  "read*()" methods don't use that buffer.
  Instead they would return the data following the buffer, causing the
  data to appear out of order.  Mixing iteration and these methods
  will now trigger a "ValueError" from the "read*()" method.
  (Implemented by Thomas Wouters.)

* The "struct" module now compiles structure format  strings into an
  internal representation and caches this representation, yielding a
  20% speedup. (Contributed by Bob Ippolito at the NeedForSpeed
  sprint.)

* The "re" module got a 1 or 2% speedup by switching to  Python's
  allocator functions instead of the system's  "malloc()" and
  "free()". (Contributed by Jack Diederich at the NeedForSpeed
  sprint.)

* The code generator's peephole optimizer now performs simple constant
  folding in expressions.  If you write something like "a = 2+3", the
  code generator will do the arithmetic and produce code corresponding
  to "a = 5".  (Proposed and implemented  by Raymond Hettinger.)

* Function calls are now faster because code objects now keep  the
  most recently finished frame (a "zombie frame") in an internal field
  of the code object, reusing it the next time the code object is
  invoked.  (Original patch by Michael Hudson, modified by Armin Rigo
  and Richard Jones; committed at the NeedForSpeed sprint.)  Frame
  objects are also slightly smaller, which may improve cache locality
  and reduce memory usage a bit.  (Contributed by Neal Norwitz.)

* Python's built-in exceptions are now new-style classes, a change
  that speeds up instantiation considerably.  Exception handling in
  Python 2.5 is therefore about 30% faster than in 2.4. (Contributed
  by Richard Jones, Georg Brandl and Sean Reifschneider at the
  NeedForSpeed sprint.)

* Importing now caches the paths tried, recording whether  they exist
  or not so that the interpreter makes fewer  "open()" and "stat()"
  calls on startup. (Contributed by Martin von Löwis and Georg
  Brandl.)


New, Improved, and Removed Modules
==================================

The standard library received many enhancements and bug fixes in
Python 2.5. 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
SVN logs for all the details.

* The "audioop" module now supports the a-LAW encoding, and the code
  for u-LAW encoding has been improved.  (Contributed by Lars
  Immisch.)

* The "codecs" module gained support for incremental codecs.  The
  "codec.lookup()" function now returns a "CodecInfo" instance instead
  of a tuple. "CodecInfo" instances behave like a 4-tuple to preserve
  backward compatibility but also have the attributes "encode",
  "decode", "incrementalencoder", "incrementaldecoder",
  "streamwriter", and "streamreader".  Incremental codecs  can receive
  input and produce output in multiple chunks; the output is the same
  as if the entire input was fed to the non-incremental codec. See the
  "codecs" module documentation for details. (Designed and implemented
  by Walter Dörwald.)

* The "collections" module gained a new type, "defaultdict", that
  subclasses the standard "dict" type.  The new type mostly behaves
  like a dictionary but constructs a default value when a key isn't
  present, automatically adding it to the dictionary for the requested
  key value.

  The first argument to "defaultdict"'s constructor is a factory
  function that gets called whenever a key is requested but not found.
  This factory function receives no arguments, so you can use built-in
  type constructors such as "list()" or "int()".  For example,  you
  can make an index of words based on their initial letter like this:

     words = """Nel mezzo del cammin di nostra vita
     mi ritrovai per una selva oscura
     che la diritta via era smarrita""".lower().split()

     index = defaultdict(list)

     for w in words:
         init_letter = w[0]
         index[init_letter].append(w)

  Printing "index" results in the following output:

     defaultdict(<type 'list'>, {'c': ['cammin', 'che'], 'e': ['era'],
             'd': ['del', 'di', 'diritta'], 'm': ['mezzo', 'mi'],
             'l': ['la'], 'o': ['oscura'], 'n': ['nel', 'nostra'],
             'p': ['per'], 's': ['selva', 'smarrita'],
             'r': ['ritrovai'], 'u': ['una'], 'v': ['vita', 'via']}

  (Contributed by Guido van Rossum.)

* The "deque" double-ended queue type supplied by the "collections"
  module now has a "remove(value)" method that removes the first
  occurrence of *value* in the queue, raising "ValueError" if the
  value isn't found. (Contributed by Raymond Hettinger.)

* New module: The "contextlib" module contains helper functions for
  use with the new '"with"' statement.  See section The contextlib
  module for more about this module.

* New module: The "cProfile" module is a C implementation of  the
  existing "profile" module that has much lower overhead. The module's
  interface is the same as "profile": you run "cProfile.run('main()')"
  to profile a function, can save profile data to a file, etc.  It's
  not yet known if the Hotshot profiler, which is also written in C
  but doesn't match the "profile" module's interface, will continue to
  be maintained in future versions of Python.  (Contributed by Armin
  Rigo.)

  Also, the "pstats" module for analyzing the data measured by the
  profiler now supports directing the output to any file object by
  supplying a *stream* argument to the "Stats" constructor.
  (Contributed by Skip Montanaro.)

* The "csv" module, which parses files in comma-separated value
  format, received several enhancements and a number of bugfixes.  You
  can now set the maximum size in bytes of a field by calling the
  "csv.field_size_limit(new_limit)" function; omitting the *new_limit*
  argument will return the currently set limit.  The "reader" class
  now has a "line_num" attribute that counts the number of physical
  lines read from the source; records can span multiple physical
  lines, so "line_num" is not the same as the number of records read.

  The CSV parser is now stricter about multi-line quoted fields.
  Previously, if a line ended within a quoted field without a
  terminating newline character, a newline would be inserted into the
  returned field. This behavior caused problems when reading files
  that contained carriage return characters within fields, so the code
  was changed to return the field without inserting newlines. As a
  consequence, if newlines embedded within fields are important, the
  input should be split into lines in a manner that preserves the
  newline characters.

  (Contributed by Skip Montanaro and Andrew McNamara.)

* The "datetime" class in the "datetime"  module now has a
  "strptime(string, format)"  method for parsing date strings,
  contributed by Josh Spoerri. It uses the same format characters as
  "time.strptime()" and "time.strftime()":

     from datetime import datetime

     ts = datetime.strptime('10:13:15 2006-03-07',
                            '%H:%M:%S %Y-%m-%d')

* The "SequenceMatcher.get_matching_blocks()" method in the "difflib"
  module now guarantees to return a minimal list of blocks describing
  matching subsequences.  Previously, the algorithm would occasionally
  break a block of matching elements into two list entries.
  (Enhancement by Tim Peters.)

* The "doctest" module gained a "SKIP" option that keeps an example
  from being executed at all.  This is intended for code snippets that
  are usage examples intended for the reader and aren't actually test
  cases.

  An *encoding* parameter was added to the "testfile()" function and
  the "DocFileSuite" class to specify the file's encoding.  This makes
  it easier to use non-ASCII characters in  tests contained within a
  docstring. (Contributed by Bjorn Tillenius.)

* The "email" package has been updated to version 4.0. (Contributed by
  Barry Warsaw.)

* The "fileinput" module was made more flexible. Unicode filenames are
  now supported, and a *mode* parameter that defaults to ""r"" was
  added to the "input()" function to allow opening files in binary or
  *universal newlines* mode.  Another new parameter, *openhook*, lets
  you use a function other than "open()"  to open the input files.
  Once you're iterating over the set of files, the "FileInput"
  object's new "fileno()" returns the file descriptor for the
  currently opened file. (Contributed by Georg Brandl.)

* In the "gc" module, the new "get_count()" function returns a 3-tuple
  containing the current collection counts for the three GC
  generations.  This is accounting information for the garbage
  collector; when these counts reach a specified threshold, a garbage
  collection sweep will be made.  The existing "gc.collect()" function
  now takes an optional *generation* argument of 0, 1, or 2 to specify
  which generation to collect. (Contributed by Barry Warsaw.)

* The "nsmallest()" and  "nlargest()" functions in the "heapq" module
  now support a "key" keyword parameter similar to the one provided by
  the "min()"/"max()" functions and the "sort()" methods.  For
  example:

     >>> import heapq
     >>> L = ["short", 'medium', 'longest', 'longer still']
     >>> heapq.nsmallest(2, L)  # Return two lowest elements, lexicographically
     ['longer still', 'longest']
     >>> heapq.nsmallest(2, L, key=len)   # Return two shortest elements
     ['short', 'medium']

  (Contributed by Raymond Hettinger.)

* The "itertools.islice()" function now accepts "None" for the start
  and step arguments.  This makes it more compatible with the
  attributes of slice objects, so that you can now write the
  following:

     s = slice(5)     # Create slice object
     itertools.islice(iterable, s.start, s.stop, s.step)

  (Contributed by Raymond Hettinger.)

* The "format()" function in the "locale" module has been modified and
  two new functions were added, "format_string()" and "currency()".

  The "format()" function's *val* parameter could previously be a
  string as long as no more than one %char specifier appeared; now the
  parameter must be exactly one %char specifier with no surrounding
  text.  An optional *monetary* parameter was also added which, if
  "True", will use the locale's rules for formatting currency in
  placing a separator between groups of three digits.

  To format strings with multiple %char specifiers, use the new
  "format_string()" function that works like "format()" but also
  supports mixing %char specifiers with arbitrary text.

  A new "currency()" function was also added that formats a number
  according to the current locale's settings.

  (Contributed by Georg Brandl.)

* The "mailbox" module underwent a massive rewrite to add the
  capability to modify mailboxes in addition to reading them.  A new
  set of classes that include "mbox", "MH", and "Maildir" are used to
  read mailboxes, and have an "add(message)" method to add messages,
  "remove(key)" to remove messages, and "lock()"/"unlock()" to
  lock/unlock the mailbox. The following example converts a maildir-
  format mailbox into an mbox-format one:

     import mailbox

     # 'factory=None' uses email.Message.Message as the class representing
     # individual messages.
     src = mailbox.Maildir('maildir', factory=None)
     dest = mailbox.mbox('/tmp/mbox')

     for msg in src:
         dest.add(msg)

  (Contributed by Gregory K. Johnson.  Funding was provided by
  Google's 2005 Summer of Code.)

* New module: the "msilib" module allows creating Microsoft Installer
  ".msi" files and CAB files.  Some support for reading the ".msi"
  database is also included. (Contributed by Martin von Löwis.)

* The "nis" module now supports accessing domains other than the
  system default domain by supplying a *domain* argument to the
  "nis.match()" and "nis.maps()" functions. (Contributed by Ben Bell.)

* The "operator" module's "itemgetter()"  and "attrgetter()" functions
  now support multiple fields.   A call such as
  "operator.attrgetter('a', 'b')" will return a function  that
  retrieves the "a" and "b" attributes.  Combining  this new feature
  with the "sort()" method's "key" parameter  lets you easily sort
  lists using multiple fields. (Contributed by Raymond Hettinger.)

* The "optparse" module was updated to version 1.5.1 of the Optik
  library. The "OptionParser" class gained an "epilog" attribute, a
  string that will be printed after the help message, and a
  "destroy()" method to break reference cycles created by the object.
  (Contributed by Greg Ward.)

* The "os" module underwent several changes.  The "stat_float_times"
  variable now defaults to true, meaning that "os.stat()" will now
  return time values as floats.  (This doesn't necessarily mean that
  "os.stat()" will return times that are precise to fractions of a
  second; not all systems support such precision.)

  Constants named "os.SEEK_SET", "os.SEEK_CUR", and "os.SEEK_END" have
  been added; these are the parameters to the "os.lseek()" function.
  Two new constants for locking are "os.O_SHLOCK" and "os.O_EXLOCK".

  Two new functions, "wait3()" and "wait4()", were added.  They're
  similar the "waitpid()" function which waits for a child process to
  exit and returns a tuple of the process ID and its exit status, but
  "wait3()" and "wait4()" return additional information.  "wait3()"
  doesn't take a process ID as input, so it waits for any child
  process to exit and returns a 3-tuple of *process-id*, *exit-
  status*, *resource-usage* as returned from the
  "resource.getrusage()" function. "wait4(pid)" does take a process
  ID. (Contributed by Chad J. Schroeder.)

  On FreeBSD, the "os.stat()" function now returns  times with
  nanosecond resolution, and the returned object now has "st_gen" and
  "st_birthtime". The "st_flags" attribute is also available, if the
  platform supports it. (Contributed by Antti Louko and  Diego
  Pettenò.)

* The Python debugger provided by the "pdb" module can now store lists
  of commands to execute when a breakpoint is reached and execution
  stops.  Once breakpoint #1 has been created, enter "commands 1" and
  enter a series of commands to be executed, finishing the list with
  "end".  The command list can include commands that resume execution,
  such as "continue" or "next". (Contributed by Grégoire Dooms.)

* The "pickle" and "cPickle" modules no longer accept a return value
  of "None" from the "__reduce__()" method; the method must return a
  tuple of arguments instead.  The ability to return "None" was
  deprecated in Python 2.4, so this completes the removal of the
  feature.

* The "pkgutil" module, containing various utility functions for
  finding packages, was enhanced to support **PEP 302**'s import hooks
  and now also works for packages stored in ZIP-format archives.
  (Contributed by Phillip J. Eby.)

* The pybench benchmark suite by Marc-André Lemburg is now included in
  the "Tools/pybench" directory.  The pybench suite is an improvement
  on the commonly used "pystone.py" program because pybench provides a
  more detailed measurement of the interpreter's speed.  It times
  particular operations such as function calls, tuple slicing, method
  lookups, and numeric operations, instead of performing many
  different operations and reducing the result to a single number as
  "pystone.py" does.

* The "pyexpat" module now uses version 2.0 of the Expat parser.
  (Contributed by Trent Mick.)

* The "Queue" class provided by the "Queue" module gained two new
  methods.  "join()" blocks until all items in the queue have been
  retrieved and all processing work on the items  have been completed.
  Worker threads call the other new method,  "task_done()", to signal
  that processing for an item has been completed.  (Contributed by
  Raymond Hettinger.)

* The old "regex" and "regsub" modules, which have been  deprecated
  ever since Python 2.0, have finally been deleted.   Other deleted
  modules: "statcache", "tzparse", "whrandom".

* Also deleted: the "lib-old" directory, which includes ancient
  modules such as "dircmp" and "ni", was removed.  "lib-old" wasn't on
  the default "sys.path", so unless your programs explicitly added the
  directory to "sys.path", this removal shouldn't affect your code.

* The "rlcompleter" module is no longer  dependent on importing the
  "readline" module and therefore now works on non-Unix platforms.
  (Patch from Robert Kiendl.)

* The "SimpleXMLRPCServer" and "DocXMLRPCServer"  classes now have a
  "rpc_paths" attribute that constrains XML-RPC operations to a
  limited set of URL paths; the default is to allow only "'/'" and
  "'/RPC2'".  Setting "rpc_paths" to "None" or an empty tuple disables
  this path checking.

* The "socket" module now supports "AF_NETLINK" sockets on Linux,
  thanks to a patch from Philippe Biondi.   Netlink sockets are a
  Linux-specific mechanism for communications between a user-space
  process and kernel code; an introductory  article about them is at
  https://www.linuxjournal.com/article/7356. In Python code, netlink
  addresses are represented as a tuple of 2 integers, "(pid,
  group_mask)".

  Two new methods on socket objects, "recv_into(buffer)" and
  "recvfrom_into(buffer)", store the received data in an object  that
  supports the buffer protocol instead of returning the data as a
  string.  This means you can put the data directly into an array or a
  memory-mapped file.

  Socket objects also gained "getfamily()", "gettype()", and
  "getproto()" accessor methods to retrieve the family, type, and
  protocol values for the socket.

* New module: the "spwd" module provides functions for accessing the
  shadow password database on systems that support  shadow passwords.

* The "struct" is now faster because it  compiles format strings into
  "Struct" objects with "pack()" and "unpack()" methods.  This is
  similar to how the "re" module lets you create compiled regular
  expression objects.  You can still use the module-level  "pack()"
  and "unpack()" functions; they'll create  "Struct" objects and cache
  them.  Or you can use  "Struct" instances directly:

     s = struct.Struct('ih3s')

     data = s.pack(1972, 187, 'abc')
     year, number, name = s.unpack(data)

  You can also pack and unpack data to and from buffer objects
  directly using the "pack_into(buffer, offset, v1, v2, ...)" and
  "unpack_from(buffer, offset)" methods.  This lets you store data
  directly into an array or a memory-mapped file.

  ("Struct" objects were implemented by Bob Ippolito at the
  NeedForSpeed sprint.  Support for buffer objects was added by Martin
  Blais, also at the NeedForSpeed sprint.)

* The Python developers switched from CVS to Subversion during the 2.5
  development process.  Information about the exact build version is
  available as the "sys.subversion" variable, a 3-tuple of
  "(interpreter-name, branch-name, revision-range)".  For example, at
  the time of writing my copy of 2.5 was reporting "('CPython',
  'trunk', '45313:45315')".

  This information is also available to C extensions via the
  "Py_GetBuildInfo()" function that returns a  string of build
  information like this: ""trunk:45355:45356M, Apr 13 2006,
  07:42:19"".   (Contributed by Barry Warsaw.)

* Another new function, "sys._current_frames()", returns the current
  stack frames for all running threads as a dictionary mapping thread
  identifiers to the topmost stack frame currently active in that
  thread at the time the function is called.  (Contributed by Tim
  Peters.)

* The "TarFile" class in the "tarfile" module now has an
  "extractall()" method that extracts all members from the archive
  into the current working directory.  It's also possible to set a
  different directory as the extraction target, and to unpack only a
  subset of the archive's members.

  The compression used for a tarfile opened in stream mode can now be
  autodetected using the mode "'r|*'". (Contributed by Lars Gustäbel.)

* The "threading" module now lets you set the stack size used when new
  threads are created. The "stack_size([*size*])" function returns the
  currently configured stack size, and supplying the optional *size*
  parameter sets a new value.  Not all platforms support changing the
  stack size, but Windows, POSIX threading, and OS/2 all do.
  (Contributed by Andrew MacIntyre.)

* The "unicodedata" module has been updated to use version 4.1.0 of
  the Unicode character database.  Version 3.2.0 is required  by some
  specifications, so it's still available as  "unicodedata.ucd_3_2_0".

* New module: the  "uuid" module generates  universally unique
  identifiers (UUIDs) according to **RFC 4122**.  The RFC defines
  several different UUID versions that are generated from a starting
  string, from system properties, or purely randomly.  This module
  contains a "UUID" class and  functions named "uuid1()", "uuid3()",
  "uuid4()",  and  "uuid5()" to generate different versions of UUID.
  (Version 2 UUIDs  are not specified in **RFC 4122** and are not
  supported by this module.)

     >>> import uuid
     >>> # make a UUID based on the host ID and current time
     >>> uuid.uuid1()
     UUID('a8098c1a-f86e-11da-bd1a-00112444be1e')

     >>> # make a UUID using an MD5 hash of a namespace UUID and a name
     >>> uuid.uuid3(uuid.NAMESPACE_DNS, 'python.org')
     UUID('6fa459ea-ee8a-3ca4-894e-db77e160355e')

     >>> # make a random UUID
     >>> uuid.uuid4()
     UUID('16fd2706-8baf-433b-82eb-8c7fada847da')

     >>> # make a UUID using a SHA-1 hash of a namespace UUID and a name
     >>> uuid.uuid5(uuid.NAMESPACE_DNS, 'python.org')
     UUID('886313e1-3b8a-5372-9b90-0c9aee199e5d')

  (Contributed by Ka-Ping Yee.)

* The "weakref" module's "WeakKeyDictionary" and "WeakValueDictionary"
  types gained new methods for iterating over the weak references
  contained in the dictionary.  "iterkeyrefs()" and "keyrefs()"
  methods were added to "WeakKeyDictionary", and "itervaluerefs()" and
  "valuerefs()" were added to "WeakValueDictionary".  (Contributed by
  Fred L. Drake, Jr.)

* The "webbrowser" module received a number of enhancements. It's now
  usable as a script with "python -m webbrowser", taking a URL as the
  argument; there are a number of switches  to control the behaviour
  ("-n" for a new browser window,  "-t" for a new tab).  New module-
  level functions, "open_new()" and "open_new_tab()", were added  to
  support this.  The module's "open()" function supports an additional
  feature, an *autoraise* parameter that signals whether to raise the
  open window when possible. A number of additional browsers were
  added to the supported list such as Firefox, Opera, Konqueror, and
  elinks.  (Contributed by Oleg Broytmann and Georg Brandl.)

* The "xmlrpclib" module now supports returning  "datetime" objects
  for the XML-RPC date type.  Supply  "use_datetime=True" to the
  "loads()" function or the "Unmarshaller" class to enable this
  feature. (Contributed by Skip Montanaro.)

* The "zipfile" module now supports the ZIP64 version of the  format,
  meaning that a .zip archive can now be larger than 4 GiB and can
  contain individual files larger than 4 GiB.  (Contributed by Ronald
  Oussoren.)

* The "zlib" module's "Compress" and "Decompress" objects now support
  a "copy()" method that makes a copy of the  object's internal state
  and returns a new  "Compress" or "Decompress" object. (Contributed
  by Chris AtLee.)


The ctypes package
------------------

The "ctypes" package, written by Thomas Heller, has been added  to the
standard library.  "ctypes" lets you call arbitrary functions  in
shared libraries or DLLs.  Long-time users may remember the "dl"
module, which provides functions for loading shared libraries and
calling functions in them. The "ctypes" package is much fancier.

To load a shared library or DLL, you must create an instance of the
"CDLL" class and provide the name or path of the shared library or
DLL. Once that's done, you can call arbitrary functions by accessing
them as attributes of the "CDLL" object.

   import ctypes

   libc = ctypes.CDLL('libc.so.6')
   result = libc.printf("Line of output\n")

Type constructors for the various C types are provided: "c_int()",
"c_float()", "c_double()", "c_char_p()" (equivalent to char*), and so
forth.  Unlike Python's types, the C versions are all mutable; you can
assign to their "value" attribute to change the wrapped value.  Python
integers and strings will be automatically converted to the
corresponding C types, but for other types you  must call the correct
type constructor.  (And I mean *must*;  getting it wrong will often
result in the interpreter crashing with a segmentation fault.)

You shouldn't use "c_char_p()" with a Python string when the C
function will be modifying the memory area, because Python strings are
supposed to be immutable; breaking this rule will cause puzzling bugs.
When you need a modifiable memory area, use "create_string_buffer()":

   s = "this is a string"
   buf = ctypes.create_string_buffer(s)
   libc.strfry(buf)

C functions are assumed to return integers, but you can set the
"restype" attribute of the function object to  change this:

   >>> libc.atof('2.71828')
   -1783957616
   >>> libc.atof.restype = ctypes.c_double
   >>> libc.atof('2.71828')
   2.71828

"ctypes" also provides a wrapper for Python's C API  as the
"ctypes.pythonapi" object.  This object does *not*  release the global
interpreter lock before calling a function, because the lock must be
held when calling into the interpreter's code.   There's a "py_object"
type constructor that will create a  PyObject* pointer.  A simple
usage:

   import ctypes

   d = {}
   ctypes.pythonapi.PyObject_SetItem(ctypes.py_object(d),
             ctypes.py_object("abc"),  ctypes.py_object(1))
   # d is now {'abc', 1}.

Don't forget to use "py_object()"; if it's omitted you end  up with a
segmentation fault.

"ctypes" has been around for a while, but people still write  and
distribution hand-coded extension modules because you can't rely on
"ctypes" being present. Perhaps developers will begin to write  Python
wrappers atop a library accessed through "ctypes" instead of extension
modules, now that "ctypes" is included with core Python.

Vedi anche:

  https://web.archive.org/web/20180410025338/http://starship.python.n
  et/crew/theller/ctypes/
     The pre-stdlib ctypes web page, with a tutorial, reference, and
     FAQ.

  The documentation  for the "ctypes" module.


The ElementTree package
-----------------------

A subset of Fredrik Lundh's ElementTree library for processing XML has
been added to the standard library as "xml.etree".  The available
modules are "ElementTree", "ElementPath", and "ElementInclude" from
ElementTree 1.2.6.    The "cElementTree" accelerator module is also
included.

The rest of this section will provide a brief overview of using
ElementTree. Full documentation for ElementTree is available at
https://web.archive.org/web/20201124024954/http://effbot.org/zone
/element-index.htm.

ElementTree represents an XML document as a tree of element nodes. The
text content of the document is stored as the "text" and "tail"
attributes of  (This is one of the major differences between
ElementTree and the Document Object Model; in the DOM there are many
different types of node, including "TextNode".)

The most commonly used parsing function is "parse()", that takes
either a string (assumed to contain a filename) or a file-like object
and returns an "ElementTree" instance:

   from xml.etree import ElementTree as ET

   tree = ET.parse('ex-1.xml')

   feed = urllib.urlopen(
             'http://planet.python.org/rss10.xml')
   tree = ET.parse(feed)

Once you have an "ElementTree" instance, you can call its "getroot()"
method to get the root "Element" node.

There's also an "XML()" function that takes a string literal and
returns an "Element" node (not an "ElementTree").   This function
provides a tidy way to incorporate XML fragments, approaching the
convenience of an XML literal:

   svg = ET.XML("""<svg width="10px" version="1.0">
                </svg>""")
   svg.set('height', '320px')
   svg.append(elem1)

Each XML element supports some dictionary-like and some list-like
access methods.  Dictionary-like operations are used to access
attribute values, and list-like operations are used to access child
nodes.

+---------------------------------+----------------------------------------------+
| Operation                       | Result                                       |
|=================================|==============================================|
| "elem[n]"                       | Returns n'th child element.                  |
+---------------------------------+----------------------------------------------+
| "elem[m:n]"                     | Returns list of m'th through n'th child      |
|                                 | elements.                                    |
+---------------------------------+----------------------------------------------+
| "len(elem)"                     | Returns number of child elements.            |
+---------------------------------+----------------------------------------------+
| "list(elem)"                    | Returns list of child elements.              |
+---------------------------------+----------------------------------------------+
| "elem.append(elem2)"            | Adds *elem2* as a child.                     |
+---------------------------------+----------------------------------------------+
| "elem.insert(index, elem2)"     | Inserts *elem2* at the specified location.   |
+---------------------------------+----------------------------------------------+
| "del elem[n]"                   | Deletes n'th child element.                  |
+---------------------------------+----------------------------------------------+
| "elem.keys()"                   | Returns list of attribute names.             |
+---------------------------------+----------------------------------------------+
| "elem.get(name)"                | Returns value of attribute *name*.           |
+---------------------------------+----------------------------------------------+
| "elem.set(name, value)"         | Sets new value for attribute *name*.         |
+---------------------------------+----------------------------------------------+
| "elem.attrib"                   | Retrieves the dictionary containing          |
|                                 | attributes.                                  |
+---------------------------------+----------------------------------------------+
| "del elem.attrib[name]"         | Deletes attribute *name*.                    |
+---------------------------------+----------------------------------------------+

Comments and processing instructions are also represented as "Element"
nodes.  To check if a node is a comment or processing instructions:

   if elem.tag is ET.Comment:
       ...
   elif elem.tag is ET.ProcessingInstruction:
       ...

To generate XML output, you should call the "ElementTree.write()"
method. Like "parse()", it can take either a string or a file-like
object:

   # Encoding is US-ASCII
   tree.write('output.xml')

   # Encoding is UTF-8
   f = open('output.xml', 'w')
   tree.write(f, encoding='utf-8')

(Caution: the default encoding used for output is ASCII.  For general
XML work, where an element's name may contain arbitrary Unicode
characters, ASCII isn't a very useful encoding because it will raise
an exception if an element's name contains any characters with values
greater than 127.  Therefore, it's best to specify a different
encoding such as UTF-8 that can handle any Unicode character.)

This section is only a partial description of the ElementTree
interfaces. Please read the package's official documentation for more
details.

Vedi anche:

  https://web.archive.org/web/20201124024954/http://effbot.org/zone
  /element-index.htm
     Official documentation for ElementTree.


The hashlib package
-------------------

A new "hashlib" module, written by Gregory P. Smith,  has been added
to replace the "md5" and "sha" modules.  "hashlib" adds support for
additional secure hashes (SHA-224, SHA-256, SHA-384, and SHA-512).
When available, the module uses OpenSSL for fast platform optimized
implementations of algorithms.

The old "md5" and "sha" modules still exist as wrappers around hashlib
to preserve backwards compatibility.  The new module's interface is
very close to that of the old modules, but not identical. The most
significant difference is that the constructor functions for creating
new hashing objects are named differently.

   # Old versions
   h = md5.md5()
   h = md5.new()

   # New version
   h = hashlib.md5()

   # Old versions
   h = sha.sha()
   h = sha.new()

   # New version
   h = hashlib.sha1()

   # Hash that weren't previously available
   h = hashlib.sha224()
   h = hashlib.sha256()
   h = hashlib.sha384()
   h = hashlib.sha512()

   # Alternative form
   h = hashlib.new('md5')          # Provide algorithm as a string

Once a hash object has been created, its methods are the same as
before: "update(string)" hashes the specified string into the  current
digest state, "digest()" and "hexdigest()" return the digest value as
a binary string or a string of hex digits, and "copy()" returns a new
hashing object with the same digest state.

Vedi anche: The documentation  for the "hashlib" module.


The sqlite3 package
-------------------

The pysqlite module (https://www.pysqlite.org), a wrapper for the
SQLite embedded database, has been added to the standard library under
the package name "sqlite3".

SQLite is a C library that provides a lightweight disk-based database
that doesn't require a separate server process and allows accessing
the database using a nonstandard variant of the SQL query language.
Some applications can use SQLite for internal data storage.  It's also
possible to prototype an application using SQLite and then port the
code to a larger database such as PostgreSQL or Oracle.

pysqlite was written by Gerhard Häring and provides a SQL interface
compliant with the DB-API 2.0 specification described by **PEP 249**.

If you're compiling the Python source yourself, note that the source
tree doesn't include the SQLite code, only the wrapper module. You'll
need to have the SQLite libraries and headers installed before
compiling Python, and the build process will compile the module when
the necessary headers are available.

To use the module, you must first create a "Connection" object that
represents the database.  Here the data will be stored in the
"/tmp/example" file:

   conn = sqlite3.connect('/tmp/example')

You can also supply the special name ":memory:" to create a database
in RAM.

Once you have a "Connection", you can create a "Cursor"  object and
call its "execute()" method to perform SQL commands:

   c = conn.cursor()

   # Create table
   c.execute('''create table stocks
   (date text, trans text, symbol text,
    qty real, price real)''')

   # Insert a row of data
   c.execute("""insert into stocks
             values ('2006-01-05','BUY','RHAT',100,35.14)""")

Usually your SQL operations will need to use values from Python
variables.  You shouldn't assemble your query using Python's string
operations because doing so is insecure; it makes your program
vulnerable to an SQL injection attack.

Instead, use the DB-API's parameter substitution.  Put "?" as a
placeholder wherever you want to use a value, and then provide a tuple
of values as the second argument to the cursor's "execute()" method.
(Other database modules may use a different placeholder, such as "%s"
or ":1".) For example:

   # Never do this -- insecure!
   symbol = 'IBM'
   c.execute("... where symbol = '%s'" % symbol)

   # Do this instead
   t = (symbol,)
   c.execute('select * from stocks where symbol=?', t)

   # Larger example
   for t in (('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
             ('2006-04-05', 'BUY', 'MSOFT', 1000, 72.00),
             ('2006-04-06', 'SELL', 'IBM', 500, 53.00),
            ):
       c.execute('insert into stocks values (?,?,?,?,?)', t)

To retrieve data after executing a SELECT statement, you can either
treat the cursor as an iterator, call the cursor's "fetchone()" method
to retrieve a single matching row,  or call "fetchall()" to get a list
of the matching rows.

This example uses the iterator form:

   >>> c = conn.cursor()
   >>> c.execute('select * from stocks order by price')
   >>> for row in c:
   ...    print row
   ...
   (u'2006-01-05', u'BUY', u'RHAT', 100, 35.140000000000001)
   (u'2006-03-28', u'BUY', u'IBM', 1000, 45.0)
   (u'2006-04-06', u'SELL', u'IBM', 500, 53.0)
   (u'2006-04-05', u'BUY', u'MSOFT', 1000, 72.0)
   >>>

For more information about the SQL dialect supported by SQLite, see
https://www.sqlite.org.

Vedi anche:

  https://www.pysqlite.org
     The pysqlite web page.

  https://www.sqlite.org
     The SQLite web page; the documentation describes the syntax and
     the available data types for the supported SQL dialect.

  The documentation  for the "sqlite3" module.

  **PEP 249** - Database API Specification 2.0
     PEP written by Marc-André Lemburg.


The wsgiref package
-------------------

The Web Server Gateway Interface (WSGI) v1.0 defines a standard
interface between web servers and Python web applications and is
described in **PEP 333**. The "wsgiref" package is a reference
implementation of the WSGI specification.

The package includes a basic HTTP server that will run a WSGI
application; this server is useful for debugging but isn't intended
for  production use.  Setting up a server takes only a few lines of
code:

   from wsgiref import simple_server

   wsgi_app = ...

   host = ''
   port = 8000
   httpd = simple_server.make_server(host, port, wsgi_app)
   httpd.serve_forever()

Vedi anche:

  https://web.archive.org/web/20160331090247/http://wsgi.readthedocs.
  org/en/latest/
     A central web site for WSGI-related resources.

  **PEP 333** - Python Web Server Gateway Interface v1.0
     PEP written by Phillip J. Eby.


Build and C API Changes
=======================

Changes to Python's build process and to the C API include:

* The Python source tree was converted from CVS to Subversion,  in a
  complex migration procedure that was supervised and flawlessly
  carried out by Martin von Löwis.  The procedure was developed as
  **PEP 347**.

* Coverity, a company that markets a source code analysis tool called
  Prevent, provided the results of their examination of the Python
  source code.  The analysis found about 60 bugs that  were quickly
  fixed.  Many of the bugs were refcounting problems, often occurring
  in error-handling code.  See https://scan.coverity.com for the
  statistics.

* The largest change to the C API came from **PEP 353**, which
  modifies the interpreter to use a "Py_ssize_t" type definition
  instead of int.  See the earlier section PEP 353: Using ssize_t as
  the index type for a discussion of this change.

* The design of the bytecode compiler has changed a great deal,  no
  longer generating bytecode by traversing the parse tree.  Instead
  the parse tree is converted to an abstract syntax tree (or AST), and
  it is  the abstract syntax tree that's traversed to produce the
  bytecode.

  It's possible for Python code to obtain AST objects by using the
  "compile()" built-in and specifying "_ast.PyCF_ONLY_AST" as the
  value of the  *flags* parameter:

     from _ast import PyCF_ONLY_AST
     ast = compile("""a=0
     for i in range(10):
         a += i
     """, "<string>", 'exec', PyCF_ONLY_AST)

     assignment = ast.body[0]
     for_loop = ast.body[1]

  No official documentation has been written for the AST code yet, but
  **PEP 339** discusses the design.  To start learning about the code,
  read the definition of the various AST nodes in
  "Parser/Python.asdl".  A Python script reads this file and generates
  a set of C structure definitions in "Include/Python-ast.h".  The
  "PyParser_ASTFromString()" and "PyParser_ASTFromFile()", defined in
  "Include/pythonrun.h", take Python source as input and return the
  root of an AST representing the contents. This AST can then be
  turned into a code object by "PyAST_Compile()".  For more
  information, read the source code, and then ask questions on python-
  dev.

  The AST code was developed under Jeremy Hylton's management, and
  implemented by (in alphabetical order) Brett Cannon, Nick Coghlan,
  Grant Edwards, John Ehresman, Kurt Kaiser, Neal Norwitz, Tim Peters,
  Armin Rigo, and Neil Schemenauer, plus the participants in a number
  of AST sprints at conferences such as PyCon.

* Evan Jones's patch to obmalloc, first described in a talk at PyCon
  DC 2005, was applied.  Python 2.4 allocated small objects in 256K-
  sized arenas, but never freed arenas.  With this patch, Python will
  free arenas when they're empty.  The net effect is that on some
  platforms, when you allocate many objects, Python's memory usage may
  actually drop when you delete them and the memory may be returned to
  the operating system.  (Implemented by Evan Jones, and reworked by
  Tim Peters.)

  Note that this change means extension modules must be more careful
  when allocating memory.  Python's API has many different functions
  for allocating memory that are grouped into families.  For example,
  "PyMem_Malloc()", "PyMem_Realloc()", and "PyMem_Free()" are one
  family that allocates raw memory, while "PyObject_Malloc()",
  "PyObject_Realloc()", and "PyObject_Free()" are another family
  that's supposed to be used for creating Python objects.

  Previously these different families all reduced to the platform's
  "malloc()" and "free()" functions.  This meant  it didn't matter if
  you got things wrong and allocated memory with the "PyMem" function
  but freed it with the "PyObject" function.  With 2.5's changes to
  obmalloc, these families now do different things and mismatches will
  probably result in a segfault.  You should carefully test your C
  extension modules with Python 2.5.

* The built-in set types now have an official C API.  Call
  "PySet_New()" and "PyFrozenSet_New()" to create a new set,
  "PySet_Add()" and "PySet_Discard()" to add and remove elements, and
  "PySet_Contains()" and "PySet_Size()" to examine the set's state.
  (Contributed by Raymond Hettinger.)

* C code can now obtain information about the exact revision of the
  Python interpreter by calling the  "Py_GetBuildInfo()" function that
  returns a string of build information like this:
  ""trunk:45355:45356M, Apr 13 2006, 07:42:19"".   (Contributed by
  Barry Warsaw.)

* Two new macros can be used to indicate C functions that are local to
  the current file so that a faster calling convention can be used.
  "Py_LOCAL" declares the function as returning a value of the
  specified *type* and uses a fast-calling qualifier.
  "Py_LOCAL_INLINE" does the same thing and also requests the function
  be inlined.  If macro "PY_LOCAL_AGGRESSIVE" is defined before
  "python.h" is included, a set of more aggressive optimizations are
  enabled for the module; you should benchmark the results to find out
  if these optimizations actually make the code faster.  (Contributed
  by Fredrik Lundh at the NeedForSpeed sprint.)

* "PyErr_NewException(name, base, dict)" can now accept a tuple of
  base classes as its *base* argument.  (Contributed by Georg Brandl.)

* The "PyErr_Warn()" function for issuing warnings is now deprecated
  in favour of "PyErr_WarnEx(category, message, stacklevel)" which
  lets you specify the number of stack frames separating this function
  and the caller.  A *stacklevel* of 1 is the function calling
  "PyErr_WarnEx()", 2 is the function above that, and so forth.
  (Added by Neal Norwitz.)

* The CPython interpreter is still written in C, but  the code can now
  be compiled with a C++ compiler without errors.   (Implemented by
  Anthony Baxter, Martin von Löwis, Skip Montanaro.)

* The "PyRange_New()" function was removed.  It was never documented,
  never used in the core code, and had dangerously lax error checking.
  In the unlikely case that your extensions were using it, you can
  replace it by something like the following:

     range = PyObject_CallFunction((PyObject*) &PyRange_Type, "lll",
                                   start, stop, step);


Port-Specific Changes
---------------------

* MacOS X (10.3 and higher): dynamic loading of modules now uses the
  "dlopen()" function instead of MacOS-specific functions.

* MacOS X: an "--enable-universalsdk" switch was added to the
  **configure** script that compiles the interpreter as a universal
  binary able to run on both PowerPC and Intel processors.
  (Contributed by Ronald Oussoren; bpo-2573.)

* Windows: ".dll" is no longer supported as a filename extension for
  extension modules.  ".pyd" is now the only filename extension that
  will be searched for.


Porting to Python 2.5
=====================

This section lists previously described changes that may require
changes to your code:

* ASCII is now the default encoding for modules.  It's now  a syntax
  error if a module contains string literals with 8-bit characters but
  doesn't have an encoding declaration.  In Python 2.4 this triggered
  a warning, not a syntax error.

* Previously, the "gi_frame" attribute of a generator was always a
  frame object.  Because of the **PEP 342** changes described in
  section PEP 342: New Generator Features, it's now possible for
  "gi_frame" to be "None".

* A new warning, "UnicodeWarning", is triggered when  you attempt to
  compare a Unicode string and an 8-bit string that can't be converted
  to Unicode using the default ASCII encoding.  Previously such
  comparisons would raise a "UnicodeDecodeError" exception.

* Library: the "csv" module is now stricter about multi-line quoted
  fields. If your files contain newlines embedded within fields, the
  input should be split into lines in a manner which preserves the
  newline characters.

* Library: the "locale" module's  "format()" function's would
  previously  accept any string as long as no more than one %char
  specifier appeared.  In Python 2.5, the argument must be exactly one
  %char specifier with no surrounding text.

* Library: The "pickle" and "cPickle" modules no longer accept a
  return value of "None" from the "__reduce__()" method; the method
  must return a tuple of arguments instead.  The modules also no
  longer accept the deprecated *bin* keyword parameter.

* Library: The "SimpleXMLRPCServer" and "DocXMLRPCServer"  classes now
  have a "rpc_paths" attribute that constrains XML-RPC operations to a
  limited set of URL paths; the default is to allow only "'/'" and
  "'/RPC2'". Setting  "rpc_paths" to "None" or an empty tuple disables
  this path checking.

* C API: Many functions now use "Py_ssize_t"  instead of int to allow
  processing more data on 64-bit machines.  Extension code may need to
  make the same change to avoid warnings and to support 64-bit
  machines.  See the earlier section PEP 353: Using ssize_t as the
  index type for a discussion of this change.

* C API:  The obmalloc changes mean that  you must be careful to not
  mix usage of the "PyMem_*" and "PyObject_*" families of functions.
  Memory allocated with  one family's "*_Malloc" must be  freed with
  the corresponding family's "*_Free" function.


Acknowledgements
================

The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Georg Brandl, Nick Coghlan, Phillip J. Eby, Lars Gustäbel,
Raymond Hettinger, Ralf W. Grosse-Kunstleve, Kent Johnson, Iain Lowe,
Martin von Löwis, Fredrik Lundh, Andrew McNamara, Skip Montanaro,
Gustavo Niemeyer, Paul Prescod, James Pryor, Mike Rovner, Scott
Weikart, Barry Warsaw, Thomas Wouters.
