What's New in Python 2.4
************************

Author:
   A.M. Kuchling

This article explains the new features in Python 2.4.1, released on
March 30, 2005.

Python 2.4 is a medium-sized release.  It doesn't introduce as many
changes as the radical Python 2.2, but introduces more features than
the conservative 2.3 release.  The most significant new language
features are function decorators and generator expressions; most other
changes are to the standard library.

According to the CVS change logs, there were 481 patches applied and
502 bugs fixed between Python 2.3 and 2.4.  Both figures are likely to
be underestimates.

This article doesn't attempt to provide a complete specification of
every single new feature, but instead provides a brief introduction to
each feature.  For full details, you should refer to the documentation
for Python 2.4, such as the Python Library Reference and the Python
Reference Manual.  Often you will be referred to the PEP for a
particular new feature for explanations of the implementation and
design rationale.


PEP 218: Built-In Set Objects
=============================

Python 2.3 introduced the "sets" module.  C implementations of set
data types have now been added to the Python core as two new built-in
types, "set(iterable)" and "frozenset(iterable)".  They provide high
speed operations for membership testing, for eliminating duplicates
from sequences, and for mathematical operations like unions,
intersections, differences, and symmetric differences.

   >>> a = set('abracadabra')              # form a set from a string
   >>> 'z' in a                            # fast membership testing
   False
   >>> a                                   # unique letters in a
   set(['a', 'r', 'b', 'c', 'd'])
   >>> ''.join(a)                          # convert back into a string
   'arbcd'

   >>> b = set('alacazam')                 # form a second set
   >>> a - b                               # letters in a but not in b
   set(['r', 'd', 'b'])
   >>> a | b                               # letters in either a or b
   set(['a', 'c', 'r', 'd', 'b', 'm', 'z', 'l'])
   >>> a & b                               # letters in both a and b
   set(['a', 'c'])
   >>> a ^ b                               # letters in a or b but not both
   set(['r', 'd', 'b', 'm', 'z', 'l'])

   >>> a.add('z')                          # add a new element
   >>> a.update('wxy')                     # add multiple new elements
   >>> a
   set(['a', 'c', 'b', 'd', 'r', 'w', 'y', 'x', 'z'])
   >>> a.remove('x')                       # take one element out
   >>> a
   set(['a', 'c', 'b', 'd', 'r', 'w', 'y', 'z'])

The "frozenset()" type is an immutable version of "set()". Since it is
immutable and hashable, it may be used as a dictionary key or as a
member of another set.

The "sets" module remains in the standard library, and may be useful
if you wish to subclass the "Set" or "ImmutableSet" classes.  There
are currently no plans to deprecate the module.

See also:

  **PEP 218** - Adding a Built-In Set Object Type
     Originally proposed by Greg Wilson and ultimately implemented by
     Raymond Hettinger.


PEP 237: Unifying Long Integers and Integers
============================================

The lengthy transition process for this PEP, begun in Python 2.2,
takes another step forward in Python 2.4.  In 2.3, certain integer
operations that would behave differently after int/long unification
triggered "FutureWarning" warnings and returned values limited to 32
or 64 bits (depending on your platform).  In 2.4, these expressions no
longer produce a warning and instead produce a different result that's
usually a long integer.

The problematic expressions are primarily left shifts and lengthy
hexadecimal and octal constants.  For example, "2 << 32" results in a
warning in 2.3, evaluating to 0 on 32-bit platforms.  In Python 2.4,
this expression now returns the correct answer, 8589934592.

See also:

  **PEP 237** - Unifying Long Integers and Integers
     Original PEP written by Moshe Zadka and GvR.  The changes for 2.4
     were implemented by  Kalle Svensson.


PEP 289: Generator Expressions
==============================

The iterator feature introduced in Python 2.2 and the "itertools"
module make it easier to write programs that loop through large data
sets without having the entire data set in memory at one time.  List
comprehensions don't fit into this picture very well because they
produce a Python list object containing all of the items.  This
unavoidably pulls all of the objects into memory, which can be a
problem if your data set is very large.  When trying to write a
functionally styled program, it would be natural to write something
like:

   links = [link for link in get_all_links() if not link.followed]
   for link in links:
       ...

instead of

   for link in get_all_links():
       if link.followed:
           continue
       ...

The first form is more concise and perhaps more readable, but if
you're dealing with a large number of link objects you'd have to write
the second form to avoid having all link objects in memory at the same
time.

Generator expressions work similarly to list comprehensions but don't
materialize the entire list; instead they create a generator that will
return elements one by one.  The above example could be written as:

   links = (link for link in get_all_links() if not link.followed)
   for link in links:
       ...

Generator expressions always have to be written inside parentheses, as
in the above example.  The parentheses signalling a function call also
count, so if you want to create an iterator that will be immediately
passed to a function you could write:

   print sum(obj.count for obj in list_all_objects())

Generator expressions differ from list comprehensions in various small
ways. Most notably, the loop variable (*obj* in the above example) is
not accessible outside of the generator expression.  List
comprehensions leave the variable assigned to its last value; future
versions of Python will change this, making list comprehensions match
generator expressions in this respect.

See also:

  **PEP 289** - Generator Expressions
     Proposed by Raymond Hettinger and implemented by Jiwon Seo with
     early efforts steered by Hye-Shik Chang.


PEP 292: Simpler String Substitutions
=====================================

Some new classes in the standard library provide an alternative
mechanism for substituting variables into strings; this style of
substitution may be better for applications where untrained users need
to edit templates.

The usual way of substituting variables by name is the "%" operator:

   >>> '%(page)i: %(title)s' % {'page':2, 'title': 'The Best of Times'}
   '2: The Best of Times'

When writing the template string, it can be easy to forget the "i" or
"s" after the closing parenthesis.  This isn't a big problem if the
template is in a Python module, because you run the code, get an
"Unsupported format character" "ValueError", and fix the problem.
However, consider an application such as Mailman where template
strings or translations are being edited by users who aren't aware of
the Python language.  The format string's syntax is complicated to
explain to such users, and if they make a mistake, it's difficult to
provide helpful feedback to them.

PEP 292 adds a "Template" class to the "string" module that uses "$"
to indicate a substitution:

   >>> import string
   >>> t = string.Template('$page: $title')
   >>> t.substitute({'page':2, 'title': 'The Best of Times'})
   '2: The Best of Times'

If a key is missing from the dictionary, the "substitute()" method
will raise a "KeyError".  There's also a "safe_substitute()" method
that ignores missing keys:

   >>> t = string.Template('$page: $title')
   >>> t.safe_substitute({'page':3})
   '3: $title'

See also:

  **PEP 292** - Simpler String Substitutions
     Written and implemented  by Barry Warsaw.


PEP 318: Decorators for Functions and Methods
=============================================

Python 2.2 extended Python's object model by adding static methods and
class methods, but it didn't extend Python's syntax to provide any new
way of defining static or class methods.  Instead, you had to write a
"def" statement in the usual way, and pass the resulting method to a
"staticmethod()" or "classmethod()" function that would wrap up the
function as a method of the new type. Your code would look like this:

   class C:
      def meth (cls):
          ...

      meth = classmethod(meth)   # Rebind name to wrapped-up class method

If the method was very long, it would be easy to miss or forget the
"classmethod()" invocation after the function body.

The intention was always to add some syntax to make such definitions
more readable, but at the time of 2.2's release a good syntax was not
obvious.  Today a good syntax *still* isn't obvious but users are
asking for easier access to the feature; a new syntactic feature has
been added to meet this need.

The new feature is called "function decorators".  The name comes from
the idea that "classmethod()", "staticmethod()", and friends are
storing additional information on a function object; they're
*decorating* functions with more details.

The notation borrows from Java and uses the "'@'" character as an
indicator. Using the new syntax, the example above would be written:

   class C:

      @classmethod
      def meth (cls):
          ...

The "@classmethod" is shorthand for the "meth=classmethod(meth)"
assignment. More generally, if you have the following:

   @A
   @B
   @C
   def f ():
       ...

It's equivalent to the following pre-decorator code:

   def f(): ...
   f = A(B(C(f)))

Decorators must come on the line before a function definition, one
decorator per line, and can't be on the same line as the def
statement, meaning that "@A def f(): ..." is illegal.  You can only
decorate function definitions, either at the module level or inside a
class; you can't decorate class definitions.

A decorator is just a function that takes the function to be decorated
as an argument and returns either the same function or some new
object.  The return value of the decorator need not be callable
(though it typically is), unless further decorators will be applied to
the result.  It's easy to write your own decorators.  The following
simple example just sets an attribute on the function object:

   >>> def deco(func):
   ...    func.attr = 'decorated'
   ...    return func
   ...
   >>> @deco
   ... def f(): pass
   ...
   >>> f
   <function f at 0x402ef0d4>
   >>> f.attr
   'decorated'
   >>>

As a slightly more realistic example, the following decorator checks
that the supplied argument is an integer:

   def require_int (func):
       def wrapper (arg):
           assert isinstance(arg, int)
           return func(arg)

       return wrapper

   @require_int
   def p1 (arg):
       print arg

   @require_int
   def p2(arg):
       print arg*2

An example in **PEP 318** contains a fancier version of this idea that
lets you both specify the required type and check the returned type.

Decorator functions can take arguments.  If arguments are supplied,
your decorator function is called with only those arguments and must
return a new decorator function; this function must take a single
function and return a function, as previously described.  In other
words, "@A @B @C(args)" becomes:

   def f(): ...
   _deco = C(args)
   f = A(B(_deco(f)))

Getting this right can be slightly brain-bending, but it's not too
difficult.

A small related change makes the "func_name" attribute of functions
writable.  This attribute is used to display function names in
tracebacks, so decorators should change the name of any new function
that's constructed and returned.

See also:

  **PEP 318** - Decorators for Functions, Methods and Classes
     Written  by Kevin D. Smith, Jim Jewett, and Skip Montanaro.
     Several people wrote patches implementing function decorators,
     but the one that was actually checked in was patch #979728,
     written by Mark Russell.

  https://wiki.python.org/moin/PythonDecoratorLibrary
     This Wiki page contains several examples of decorators.


PEP 322: Reverse Iteration
==========================

A new built-in function, "reversed(seq)", takes a sequence and returns
an iterator that loops over the elements of the sequence  in reverse
order.

   >>> for i in reversed(xrange(1,4)):
   ...    print i
   ...
   3
   2
   1

Compared to extended slicing, such as "range(1,4)[::-1]", "reversed()"
is easier to read, runs faster, and uses substantially less memory.

Note that "reversed()" only accepts sequences, not arbitrary
iterators.  If you want to reverse an iterator, first convert it to  a
list with "list()".

   >>> input = open('/etc/passwd', 'r')
   >>> for line in reversed(list(input)):
   ...   print line
   ...
   root:*:0:0:System Administrator:/var/root:/bin/tcsh
     ...

See also:

  **PEP 322** - Reverse Iteration
     Written and implemented by Raymond Hettinger.


PEP 324: New subprocess Module
==============================

The standard library provides a number of ways to execute a
subprocess, offering different features and different levels of
complexity. "os.system(command)" is easy to use, but slow (it runs a
shell process which executes the command) and dangerous (you have to
be careful about escaping the shell's metacharacters).  The "popen2"
module offers classes that can capture standard output and standard
error from the subprocess, but the naming is confusing.  The
"subprocess" module cleans  this up, providing a unified interface
that offers all the features you might need.

Instead of "popen2"'s collection of classes, "subprocess" contains a
single class called "Popen"  whose constructor supports a number of
different keyword arguments.

   class Popen(args, bufsize=0, executable=None,
               stdin=None, stdout=None, stderr=None,
               preexec_fn=None, close_fds=False, shell=False,
               cwd=None, env=None, universal_newlines=False,
               startupinfo=None, creationflags=0):

*args* is commonly a sequence of strings that will be the arguments to
the program executed as the subprocess.  (If the *shell* argument is
true, *args* can be a string which will then be passed on to the shell
for interpretation, just as "os.system()" does.)

*stdin*, *stdout*, and *stderr* specify what the subprocess's input,
output, and error streams will be.  You can provide a file object or a
file descriptor, or you can use the constant "subprocess.PIPE" to
create a pipe between the subprocess and the parent.

The constructor has a number of handy options:

* *close_fds* requests that all file descriptors be closed before
  running the subprocess.

* *cwd* specifies the working directory in which the subprocess will
  be executed (defaulting to whatever the parent's working directory
  is).

* *env* is a dictionary specifying environment variables.

* *preexec_fn* is a function that gets called before the child is
  started.

* *universal_newlines* opens the child's input and output using
  Python's *universal newlines* feature.

Once you've created the "Popen" instance,  you can call its "wait()"
method to pause until the subprocess has exited, "poll()" to check if
it's exited without pausing,  or "communicate(data)" to send the
string *data* to the subprocess's standard input.
"communicate(data)"  then reads any data that the subprocess has sent
to its standard output  or standard error, returning a tuple
"(stdout_data, stderr_data)".

"call()" is a shortcut that passes its arguments along to the "Popen"
constructor, waits for the command to complete, and returns the status
code of the subprocess.  It can serve as a safer analog to
"os.system()":

   sts = subprocess.call(['dpkg', '-i', '/tmp/new-package.deb'])
   if sts == 0:
       # Success
       ...
   else:
       # dpkg returned an error
       ...

The command is invoked without use of the shell.  If you really do
want to  use the shell, you can add "shell=True" as a keyword argument
and provide a string instead of a sequence:

   sts = subprocess.call('dpkg -i /tmp/new-package.deb', shell=True)

The PEP takes various examples of shell and Python code and shows how
they'd be translated into Python code that uses "subprocess".  Reading
this section of the PEP is highly recommended.

See also:

  **PEP 324** - subprocess - New process module
     Written and implemented by Peter Åstrand, with assistance from
     Fredrik Lundh and others.


PEP 327: Decimal Data Type
==========================

Python has always supported floating-point (FP) numbers, based on the
underlying C double type, as a data type.  However, while most
programming languages provide a floating-point type, many people (even
programmers) are unaware that floating-point numbers don't represent
certain decimal fractions accurately.  The new "Decimal" type can
represent these fractions accurately, up to a user-specified precision
limit.


Why is Decimal needed?
----------------------

The limitations arise from the representation used for floating-point
numbers. FP numbers are made up of three components:

* The sign, which is positive or negative.

* The mantissa, which is a single-digit binary number   followed by a
  fractional part.  For example, "1.01" in base-2 notation is "1 + 0/2
  + 1/4", or 1.25 in decimal notation.

* The exponent, which tells where the decimal point is located in the
  number represented.

For example, the number 1.25 has positive sign, a mantissa value of
1.01 (in binary), and an exponent of 0 (the decimal point doesn't need
to be shifted). The number 5 has the same sign and mantissa, but the
exponent is 2 because the mantissa is multiplied by 4 (2 to the power
of the exponent 2); 1.25 * 4 equals 5.

Modern systems usually provide floating-point support that conforms to
a standard called IEEE 754.  C's double type is usually implemented as
a 64-bit IEEE 754 number, which uses 52 bits of space for the
mantissa.  This means that numbers can only be specified to 52 bits of
precision.  If you're trying to represent numbers whose expansion
repeats endlessly, the expansion is cut off after 52 bits.
Unfortunately, most software needs to produce output in base 10, and
common fractions in base 10 are often repeating decimals in binary.
For example, 1.1 decimal is binary "1.0001100110011 ..."; .1 = 1/16 +
1/32 + 1/256 plus an infinite number of additional terms.  IEEE 754
has to chop off that infinitely repeated decimal after 52 digits, so
the representation is slightly inaccurate.

Sometimes you can see this inaccuracy when the number is printed:

   >>> 1.1
   1.1000000000000001

The inaccuracy isn't always visible when you print the number because
the FP-to-decimal-string conversion is provided by the C library, and
most C libraries try to produce sensible output.  Even if it's not
displayed, however, the inaccuracy is still there and subsequent
operations can magnify the error.

For many applications this doesn't matter.  If I'm plotting points and
displaying them on my monitor, the difference between 1.1 and
1.1000000000000001 is too small to be visible.  Reports often limit
output to a certain number of decimal places, and if you round the
number to two or three or even eight decimal places, the error is
never apparent.  However, for applications where it does matter,  it's
a lot of work to implement your own custom arithmetic routines.

Hence, the "Decimal" type was created.


The "Decimal" type
------------------

A new module, "decimal", was added to Python's standard library.  It
contains two classes, "Decimal" and "Context".  "Decimal" instances
represent numbers, and "Context" instances are used to wrap up various
settings such as the precision and default rounding mode.

"Decimal" instances are immutable, like regular Python integers and FP
numbers; once it's been created, you can't change the value an
instance represents.  "Decimal" instances can be created from integers
or strings:

   >>> import decimal
   >>> decimal.Decimal(1972)
   Decimal("1972")
   >>> decimal.Decimal("1.1")
   Decimal("1.1")

You can also provide tuples containing the sign, the mantissa
represented  as a tuple of decimal digits, and the exponent:

   >>> decimal.Decimal((1, (1, 4, 7, 5), -2))
   Decimal("-14.75")

Cautionary note: the sign bit is a Boolean value, so 0 is positive and
1 is negative.

Converting from floating-point numbers poses a bit of a problem:
should the FP number representing 1.1 turn into the decimal number for
exactly 1.1, or for 1.1 plus whatever inaccuracies are introduced? The
decision was to dodge the issue and leave such a conversion out of the
API.  Instead, you should convert the floating-point number into a
string using the desired precision and pass the string to the
"Decimal" constructor:

   >>> f = 1.1
   >>> decimal.Decimal(str(f))
   Decimal("1.1")
   >>> decimal.Decimal('%.12f' % f)
   Decimal("1.100000000000")

Once you have "Decimal" instances, you can perform the usual
mathematical operations on them.  One limitation: exponentiation
requires an integer exponent:

   >>> a = decimal.Decimal('35.72')
   >>> b = decimal.Decimal('1.73')
   >>> a+b
   Decimal("37.45")
   >>> a-b
   Decimal("33.99")
   >>> a*b
   Decimal("61.7956")
   >>> a/b
   Decimal("20.64739884393063583815028902")
   >>> a ** 2
   Decimal("1275.9184")
   >>> a**b
   Traceback (most recent call last):
     ...
   decimal.InvalidOperation: x ** (non-integer)

You can combine "Decimal" instances with integers, but not with
floating-point numbers:

   >>> a + 4
   Decimal("39.72")
   >>> a + 4.5
   Traceback (most recent call last):
     ...
   TypeError: You can interact Decimal only with int, long or Decimal data types.
   >>>

"Decimal" numbers can be used with the "math" and "cmath" modules, but
note that they'll be immediately converted to  floating-point numbers
before the operation is performed, resulting in a possible loss of
precision and accuracy.  You'll also get back a regular floating-point
number and not a "Decimal".

   >>> import math, cmath
   >>> d = decimal.Decimal('123456789012.345')
   >>> math.sqrt(d)
   351364.18288201344
   >>> cmath.sqrt(-d)
   351364.18288201344j

"Decimal" instances have a "sqrt()" method that returns a "Decimal",
but if you need other things such as trigonometric functions you'll
have to implement them.

   >>> d.sqrt()
   Decimal("351364.1828820134592177245001")


The "Context" type
------------------

Instances of the "Context" class encapsulate several settings for
decimal operations:

* "prec" is the precision, the number of decimal places.

* "rounding" specifies the rounding mode.  The "decimal" module has
  constants for the various possibilities: "ROUND_DOWN",
  "ROUND_CEILING",  "ROUND_HALF_EVEN", and various others.

* "traps" is a dictionary specifying what happens on encountering
  certain error conditions: either  an exception is raised or  a value
  is returned.  Some examples of error conditions are division by
  zero, loss of precision, and overflow.

There's a thread-local default context available by calling
"getcontext()"; you can change the properties of this context to alter
the default precision, rounding, or trap handling.  The following
example shows the effect of changing the precision of the default
context:

   >>> decimal.getcontext().prec
   28
   >>> decimal.Decimal(1) / decimal.Decimal(7)
   Decimal("0.1428571428571428571428571429")
   >>> decimal.getcontext().prec = 9
   >>> decimal.Decimal(1) / decimal.Decimal(7)
   Decimal("0.142857143")

The default action for error conditions is selectable; the module can
either return a special value such as infinity or not-a-number, or
exceptions can be raised:

   >>> decimal.Decimal(1) / decimal.Decimal(0)
   Traceback (most recent call last):
     ...
   decimal.DivisionByZero: x / 0
   >>> decimal.getcontext().traps[decimal.DivisionByZero] = False
   >>> decimal.Decimal(1) / decimal.Decimal(0)
   Decimal("Infinity")
   >>>

The "Context" instance also has various methods for formatting
numbers such as "to_eng_string()" and "to_sci_string()".

For more information, see the documentation for the "decimal" module,
which includes a quick-start tutorial and a reference.

See also:

  **PEP 327** - Decimal Data Type
     Written by Facundo Batista and implemented by Facundo Batista,
     Eric Price, Raymond Hettinger, Aahz, and Tim Peters.

  http://www.lahey.com/float.htm
     The article uses Fortran code to illustrate many of the problems
     that floating-point inaccuracy can cause.

  http://speleotrove.com/decimal/
     A description of a decimal-based representation.  This
     representation is being proposed as a standard, and underlies the
     new Python decimal type.  Much of this material was written by
     Mike Cowlishaw, designer of the Rexx language.


PEP 328: Multi-line Imports
===========================

One language change is a small syntactic tweak aimed at making it
easier to import many names from a module.  In a "from module import
names" statement, *names* is a sequence of names separated by commas.
If the sequence is  very long, you can either write multiple imports
from the same module, or you can use backslashes to escape the line
endings like this:

   from SimpleXMLRPCServer import SimpleXMLRPCServer,\
               SimpleXMLRPCRequestHandler,\
               CGIXMLRPCRequestHandler,\
               resolve_dotted_attribute

The syntactic change in Python 2.4 simply allows putting the names
within parentheses.  Python ignores newlines within a parenthesized
expression, so the backslashes are no longer needed:

   from SimpleXMLRPCServer import (SimpleXMLRPCServer,
                                   SimpleXMLRPCRequestHandler,
                                   CGIXMLRPCRequestHandler,
                                   resolve_dotted_attribute)

The PEP also proposes that all "import" statements be absolute
imports, with a leading "." character to indicate a relative import.
This part of the PEP was not implemented for Python 2.4, but was
completed for Python 2.5.

See also:

  **PEP 328** - Imports: Multi-Line and Absolute/Relative
     Written by Aahz.  Multi-line imports were implemented by Dima
     Dorfman.


PEP 331: Locale-Independent Float/String Conversions
====================================================

The "locale" modules lets Python software select various conversions
and display conventions that are localized to a particular country or
language. However, the module was careful to not change the numeric
locale because various functions in Python's implementation required
that the numeric locale remain set to the "'C'" locale.  Often this
was because the code was using the C library's "atof()" function.

Not setting the numeric locale caused trouble for extensions that used
third-party C libraries, however, because they wouldn't have the
correct locale set. The motivating example was GTK+, whose user
interface widgets weren't displaying numbers in the current locale.

The solution described in the PEP is to add three new functions to the
Python API that perform ASCII-only conversions, ignoring the locale
setting:

* "PyOS_ascii_strtod(str, ptr)"  and "PyOS_ascii_atof(str, ptr)" both
  convert a string to a C double.

* "PyOS_ascii_formatd(buffer, buf_len, format, d)" converts a double
  to an ASCII string.

The code for these functions came from the GLib library (https
://developer-old.gnome.org/glib/2.26/), whose developers kindly
relicensed the relevant functions and donated them to the Python
Software Foundation.  The "locale" module  can now change the numeric
locale, letting extensions such as GTK+  produce the correct results.

See also:

  **PEP 331** - Locale-Independent Float/String Conversions
     Written by Christian R. Reis, and implemented by Gustavo
     Carneiro.


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

Here are all of the changes that Python 2.4 makes to the core Python
language.

* Decorators for functions and methods were added (**PEP 318**).

* Built-in "set()" and "frozenset()" types were  added (**PEP 218**).
  Other new built-ins include the "reversed(seq)" function (**PEP
  322**).

* Generator expressions were added (**PEP 289**).

* Certain numeric expressions no longer return values restricted to 32
  or 64 bits (**PEP 237**).

* You can now put parentheses around the list of names in a "from
  module import names" statement (**PEP 328**).

* The "dict.update()" method now accepts the same argument forms as
  the "dict" constructor.  This includes any mapping, any iterable of
  key/value pairs, and keyword arguments. (Contributed by Raymond
  Hettinger.)

* The string methods "ljust()", "rjust()", and "center()" now take an
  optional argument for specifying a fill character other than a
  space. (Contributed by Raymond Hettinger.)

* Strings also gained an "rsplit()" method that works like the
  "split()" method but splits from the end of the string.
  (Contributed by Sean Reifschneider.)

     >>> 'www.python.org'.split('.', 1)
     ['www', 'python.org']
     'www.python.org'.rsplit('.', 1)
     ['www.python', 'org']

* Three keyword parameters, *cmp*, *key*, and *reverse*, were added to
  the "sort()" method of lists. These parameters make some common
  usages of "sort()" simpler. All of these parameters are optional.

  For the *cmp* parameter, the value should be a comparison function
  that takes two parameters and returns -1, 0, or +1 depending on how
  the parameters compare. This function will then be used to sort the
  list.  Previously this was the only parameter that could be provided
  to "sort()".

  *key* should be a single-parameter function that takes a list
  element and returns a comparison key for the element.  The list is
  then sorted using the comparison keys.  The following example sorts
  a list case-insensitively:

     >>> L = ['A', 'b', 'c', 'D']
     >>> L.sort()                 # Case-sensitive sort
     >>> L
     ['A', 'D', 'b', 'c']
     >>> # Using 'key' parameter to sort list
     >>> L.sort(key=lambda x: x.lower())
     >>> L
     ['A', 'b', 'c', 'D']
     >>> # Old-fashioned way
     >>> L.sort(cmp=lambda x,y: cmp(x.lower(), y.lower()))
     >>> L
     ['A', 'b', 'c', 'D']

  The last example, which uses the *cmp* parameter, is the old way to
  perform a case-insensitive sort.  It works but is slower than using
  a *key* parameter. Using *key* calls "lower()" method once for each
  element in the list while using *cmp* will call it twice for each
  comparison, so using *key* saves on invocations of the "lower()"
  method.

  For simple key functions and comparison functions, it is often
  possible to avoid a "lambda" expression by using an unbound method
  instead.  For example, the above case-insensitive sort is best
  written as:

     >>> L.sort(key=str.lower)
     >>> L
     ['A', 'b', 'c', 'D']

  Finally, the *reverse* parameter takes a Boolean value.  If the
  value is true, the list will be sorted into reverse order. Instead
  of "L.sort(); L.reverse()", you can now write
  "L.sort(reverse=True)".

  The results of sorting are now guaranteed to be stable.  This means
  that two entries with equal keys will be returned in the same order
  as they were input. For example, you can sort a list of people by
  name, and then sort the list by age, resulting in a list sorted by
  age where people with the same age are in name-sorted order.

  (All changes to "sort()" contributed by Raymond Hettinger.)

* There is a new built-in function "sorted(iterable)" that works like
  the in-place "list.sort()" method but can be used in expressions.
  The differences are:

* the input may be any iterable;

* a newly formed copy is sorted, leaving the original intact; and

* the expression returns the new sorted copy

     >>> L = [9,7,8,3,2,4,1,6,5]
     >>> [10+i for i in sorted(L)]       # usable in a list comprehension
     [11, 12, 13, 14, 15, 16, 17, 18, 19]
     >>> L                               # original is left unchanged
     [9,7,8,3,2,4,1,6,5]
     >>> sorted('Monty Python')          # any iterable may be an input
     [' ', 'M', 'P', 'h', 'n', 'n', 'o', 'o', 't', 't', 'y', 'y']

     >>> # List the contents of a dict sorted by key values
     >>> colormap = dict(red=1, blue=2, green=3, black=4, yellow=5)
     >>> for k, v in sorted(colormap.iteritems()):
     ...     print k, v
     ...
     black 4
     blue 2
     green 3
     red 1
     yellow 5

  (Contributed by Raymond Hettinger.)

* Integer operations will no longer trigger an "OverflowWarning". The
  "OverflowWarning" warning will disappear in Python 2.5.

* The interpreter gained a new switch, "-m", that takes a name,
  searches for the corresponding  module on "sys.path", and runs the
  module as a script. For example,  you can now run the Python
  profiler with "python -m profile". (Contributed by Nick Coghlan.)

* The "eval(expr, globals, locals)" and "execfile(filename, globals,
  locals)" functions and the "exec" statement now accept any mapping
  type for the *locals* parameter.  Previously this had to be a
  regular Python dictionary.  (Contributed by Raymond Hettinger.)

* The "zip()" built-in function and "itertools.izip()" now return an
  empty list if called with no arguments. Previously they raised a
  "TypeError" exception.  This makes them more suitable for use with
  variable length argument lists:

     >>> def transpose(array):
     ...    return zip(*array)
     ...
     >>> transpose([(1,2,3), (4,5,6)])
     [(1, 4), (2, 5), (3, 6)]
     >>> transpose([])
     []

  (Contributed by Raymond Hettinger.)

* Encountering a failure while importing a module no longer leaves a
  partially initialized module object in "sys.modules".  The
  incomplete module object left behind would fool further imports of
  the same module into succeeding, leading to confusing errors.
  (Fixed by Tim Peters.)

* "None" is now a constant; code that binds a new value to  the name
  "None" is now a syntax error. (Contributed by Raymond Hettinger.)


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

* The inner loops for list and tuple slicing were optimized and now
  run about one-third faster.  The inner loops for dictionaries were
  also optimized, resulting in performance boosts for "keys()",
  "values()", "items()", "iterkeys()", "itervalues()", and
  "iteritems()". (Contributed by Raymond Hettinger.)

* The machinery for growing and shrinking lists was optimized for
  speed and for space efficiency.  Appending and popping from lists
  now runs faster due to more efficient code paths and less frequent
  use of the underlying system "realloc()".  List comprehensions also
  benefit.   "list.extend()" was also optimized and no longer converts
  its argument into a temporary list before extending the base list.
  (Contributed by Raymond Hettinger.)

* "list()", "tuple()", "map()", "filter()", and "zip()" now run
  several times faster with non-sequence arguments that supply a
  "__len__()" method.  (Contributed by Raymond Hettinger.)

* The methods "list.__getitem__()", "dict.__getitem__()", and
  "dict.__contains__()" are now implemented as "method_descriptor"
  objects rather than "wrapper_descriptor" objects.  This form of
  access doubles their performance and makes them more suitable for
  use as arguments to functionals: "map(mydict.__getitem__, keylist)".
  (Contributed by Raymond Hettinger.)

* Added a new opcode, "LIST_APPEND", that simplifies the generated
  bytecode for list comprehensions and speeds them up by about a
  third.  (Contributed by Raymond Hettinger.)

* The peephole bytecode optimizer has been improved to  produce
  shorter, faster bytecode; remarkably, the resulting bytecode is
  more readable.  (Enhanced by Raymond Hettinger.)

* String concatenations in statements of the form "s = s + "abc"" and
  "s += "abc"" are now performed more efficiently in certain
  circumstances.  This optimization won't be present in other Python
  implementations such as Jython, so you shouldn't rely on it; using
  the "join()" method of strings is still recommended when you want to
  efficiently glue a large number of strings together. (Contributed by
  Armin Rigo.)

The net result of the 2.4 optimizations is that Python 2.4 runs the
pystone benchmark around 5% faster than Python 2.3 and 35% faster than
Python 2.2. (pystone is not a particularly good benchmark, but it's
the most commonly used measurement of Python's performance.  Your own
applications may show greater or smaller benefits from Python 2.4.)


New, Improved, and Deprecated Modules
=====================================

As usual, Python's standard library received a number of enhancements
and bug fixes.  Here's a partial list of the most notable changes,
sorted alphabetically by module name. Consult the "Misc/NEWS" file in
the source tree for a more complete list of changes, or look through
the CVS logs for all the details.

* The "asyncore" module's "loop()" function now has a *count*
  parameter that lets you perform a limited number of passes through
  the polling loop.  The default is still to loop forever.

* The "base64" module now has more complete **RFC 3548** support for
  Base64, Base32, and Base16 encoding and decoding, including optional
  case folding and optional alternative alphabets. (Contributed by
  Barry Warsaw.)

* The "bisect" module now has an underlying C implementation for
  improved performance. (Contributed by Dmitry Vasiliev.)

* The CJKCodecs collections of East Asian codecs, maintained by Hye-
  Shik Chang, was integrated into 2.4.   The new encodings are:

* Chinese (PRC): gb2312, gbk, gb18030, big5hkscs, hz

* Chinese (ROC): big5, cp950

* Japanese: cp932, euc-jis-2004, euc-jp, euc-jisx0213, iso-2022-jp,
     iso-2022-jp-1, iso-2022-jp-2, iso-2022-jp-3, iso-2022-jp-ext,
     iso-2022-jp-2004, shift-jis, shift-jisx0213, shift-jis-2004

* Korean: cp949, euc-kr, johab, iso-2022-kr

* Some other new encodings were added: HP Roman8,  ISO_8859-11,
  ISO_8859-16, PCTP-154, and TIS-620.

* The UTF-8 and UTF-16 codecs now cope better with receiving partial
  input. Previously the "StreamReader" class would try to read more
  data, making it impossible to resume decoding from the stream.  The
  "read()" method will now return as much data as it can and future
  calls will resume decoding where previous ones left off.
  (Implemented by Walter Dörwald.)

* There is a new "collections" module for  various specialized
  collection datatypes.  Currently it contains just one type, "deque",
  a double-ended queue that supports efficiently adding and removing
  elements from either end:

     >>> from collections import deque
     >>> d = deque('ghi')        # make a new deque with three items
     >>> d.append('j')           # add a new entry to the right side
     >>> d.appendleft('f')       # add a new entry to the left side
     >>> d                       # show the representation of the deque
     deque(['f', 'g', 'h', 'i', 'j'])
     >>> d.pop()                 # return and remove the rightmost item
     'j'
     >>> d.popleft()             # return and remove the leftmost item
     'f'
     >>> list(d)                 # list the contents of the deque
     ['g', 'h', 'i']
     >>> 'h' in d                # search the deque
     True

  Several modules, such as the "Queue" and "threading" modules, now
  take advantage of "collections.deque" for improved performance.
  (Contributed by Raymond Hettinger.)

* The "ConfigParser" classes have been enhanced slightly. The "read()"
  method now returns a list of the files that were successfully
  parsed, and the "set()" method raises "TypeError" if passed a
  *value* argument that isn't a string.   (Contributed by John
  Belmonte and David Goodger.)

* The "curses" module now supports the ncurses extension
  "use_default_colors()".  On platforms where the terminal supports
  transparency, this makes it possible to use a transparent
  background. (Contributed by Jörg Lehmann.)

* The "difflib" module now includes an "HtmlDiff" class that creates
  an HTML table showing a side by side comparison of two versions of a
  text. (Contributed by Dan Gass.)

* The "email" package was updated to version 3.0,  which dropped
  various deprecated APIs and removes support for Python versions
  earlier than 2.3.  The 3.0 version of the package uses a new
  incremental parser for MIME messages, available in the
  "email.FeedParser" module.  The new parser doesn't require reading
  the entire message into memory, and doesn't raise exceptions if a
  message is malformed; instead it records any problems in the
  "defect" attribute of the message.  (Developed by Anthony Baxter,
  Barry Warsaw, Thomas Wouters, and others.)

* The "heapq" module has been converted to C.  The resulting tenfold
  improvement in speed makes the module suitable for handling high
  volumes of data.  In addition, the module has two new functions
  "nlargest()" and "nsmallest()" that use heaps to find the N largest
  or smallest values in a dataset without the expense of a full sort.
  (Contributed by Raymond Hettinger.)

* The "httplib" module now contains constants for HTTP status codes
  defined in various HTTP-related RFC documents.  Constants have names
  such as "OK", "CREATED", "CONTINUE", and "MOVED_PERMANENTLY"; use
  pydoc to get a full list.  (Contributed by Andrew Eland.)

* The "imaplib" module now supports IMAP's THREAD command (contributed
  by Yves Dionne) and new "deleteacl()" and "myrights()" methods
  (contributed by Arnaud Mazin).

* The "itertools" module gained a "groupby(iterable[, *func*])"
  function. *iterable* is something that can be iterated over to
  return a stream of elements, and the optional *func* parameter is a
  function that takes an element and returns a key value; if omitted,
  the key is simply the element itself.  "groupby()" then groups the
  elements into subsequences which have matching values of the key,
  and returns a series of 2-tuples containing the key value and an
  iterator over the subsequence.

  Here's an example to make this clearer.  The *key* function simply
  returns whether a number is even or odd, so the result of
  "groupby()" is to return consecutive runs of odd or even numbers.

     >>> import itertools
     >>> L = [2, 4, 6, 7, 8, 9, 11, 12, 14]
     >>> for key_val, it in itertools.groupby(L, lambda x: x % 2):
     ...    print key_val, list(it)
     ...
     0 [2, 4, 6]
     1 [7]
     0 [8]
     1 [9, 11]
     0 [12, 14]
     >>>

  "groupby()" is typically used with sorted input.  The logic for
  "groupby()" is similar to the Unix "uniq" filter which makes it
  handy for eliminating, counting, or identifying duplicate elements:

     >>> word = 'abracadabra'
     >>> letters = sorted(word)   # Turn string into a sorted list of letters
     >>> letters
     ['a', 'a', 'a', 'a', 'a', 'b', 'b', 'c', 'd', 'r', 'r']
     >>> for k, g in itertools.groupby(letters):
     ...    print k, list(g)
     ...
     a ['a', 'a', 'a', 'a', 'a']
     b ['b', 'b']
     c ['c']
     d ['d']
     r ['r', 'r']
     >>> # List unique letters
     >>> [k for k, g in groupby(letters)]
     ['a', 'b', 'c', 'd', 'r']
     >>> # Count letter occurrences
     >>> [(k, len(list(g))) for k, g in groupby(letters)]
     [('a', 5), ('b', 2), ('c', 1), ('d', 1), ('r', 2)]

  (Contributed by Hye-Shik Chang.)

* "itertools" also gained a function named "tee(iterator, N)" that
  returns *N* independent iterators that replicate *iterator*.  If *N*
  is omitted, the default is 2.

     >>> L = [1,2,3]
     >>> i1, i2 = itertools.tee(L)
     >>> i1,i2
     (<itertools.tee object at 0x402c2080>, <itertools.tee object at 0x402c2090>)
     >>> list(i1)               # Run the first iterator to exhaustion
     [1, 2, 3]
     >>> list(i2)               # Run the second iterator to exhaustion
     [1, 2, 3]

  Note that "tee()" has to keep copies of the values returned  by the
  iterator; in the worst case, it may need to keep all of them.   This
  should therefore be used carefully if the leading iterator can run
  far ahead of the trailing iterator in a long stream of inputs. If
  the separation is large, then you might as well use  "list()"
  instead.  When the iterators track closely with one another, "tee()"
  is ideal.  Possible applications include bookmarking, windowing, or
  lookahead iterators. (Contributed by Raymond Hettinger.)

* A number of functions were added to the "locale"  module, such as
  "bind_textdomain_codeset()" to specify a particular encoding and a
  family of "l*gettext()" functions that return messages in the chosen
  encoding. (Contributed by Gustavo Niemeyer.)

* Some keyword arguments were added to the "logging" package's
  "basicConfig()" function to simplify log configuration.  The default
  behavior is to log messages to standard error, but various keyword
  arguments can be specified to log to a particular file, change the
  logging format, or set the logging level. For example:

     import logging
     logging.basicConfig(filename='/var/log/application.log',
         level=0,  # Log all messages
         format='%(levelname):%(process):%(thread):%(message)')

  Other additions to the "logging" package include a "log(level, msg)"
  convenience method, as well as a "TimedRotatingFileHandler" class
  that rotates its log files at a timed interval.  The module already
  had "RotatingFileHandler", which rotated logs once the file exceeded
  a certain size.  Both classes derive from a new
  "BaseRotatingHandler" class that can be used to implement other
  rotating handlers.

  (Changes implemented by Vinay Sajip.)

* The "marshal" module now shares interned strings on unpacking a
  data structure.  This may shrink the size of certain pickle strings,
  but the primary effect is to make ".pyc" files significantly
  smaller. (Contributed by Martin von Löwis.)

* The "nntplib" module's "NNTP" class gained "description()" and
  "descriptions()" methods to retrieve  newsgroup descriptions for a
  single group or for a range of groups. (Contributed by Jürgen A.
  Erhard.)

* Two new functions were added to the "operator" module,
  "attrgetter(attr)" and "itemgetter(index)". Both functions return
  callables that take a single argument and return the corresponding
  attribute or item; these callables make excellent data extractors
  when used with "map()" or "sorted()".  For example:

     >>> L = [('c', 2), ('d', 1), ('a', 4), ('b', 3)]
     >>> map(operator.itemgetter(0), L)
     ['c', 'd', 'a', 'b']
     >>> map(operator.itemgetter(1), L)
     [2, 1, 4, 3]
     >>> sorted(L, key=operator.itemgetter(1)) # Sort list by second tuple item
     [('d', 1), ('c', 2), ('b', 3), ('a', 4)]

  (Contributed by Raymond Hettinger.)

* The "optparse" module was updated in various ways.  The module now
  passes its messages through "gettext.gettext()", making it possible
  to internationalize Optik's help and error messages.  Help messages
  for options can now include the string "'%default'", which will be
  replaced by the option's default value.  (Contributed by Greg Ward.)

* The long-term plan is to deprecate the "rfc822" module in some
  future Python release in favor of the "email" package. To this end,
  the "email.Utils.formatdate()" function has been changed to make it
  usable as a replacement for "rfc822.formatdate()".  You may want to
  write new e-mail processing code with this in mind.  (Change
  implemented by Anthony Baxter.)

* A new "urandom(n)" function was added to the "os" module, returning
  a string containing *n* bytes of random data.  This function
  provides access to platform-specific sources of randomness such as
  "/dev/urandom" on Linux or the Windows CryptoAPI.  (Contributed by
  Trevor Perrin.)

* Another new function: "os.path.lexists(path)"  returns true if the
  file specified by *path* exists, whether or not it's a symbolic
  link.  This differs from the existing "os.path.exists(path)"
  function, which returns false if *path* is a symlink that points to
  a destination that doesn't exist. (Contributed by Beni Cherniavsky.)

* A new "getsid()" function was added to the "posix" module that
  underlies the "os" module. (Contributed by J. Raynor.)

* The "poplib" module now supports POP over SSL.  (Contributed by
  Hector Urtubia.)

* The "profile" module can now profile C extension functions.
  (Contributed by Nick Bastin.)

* The "random" module has a new method called "getrandbits(N)" that
  returns a long integer *N* bits in length.  The existing
  "randrange()" method now uses "getrandbits()" where appropriate,
  making generation of arbitrarily large random numbers more
  efficient.  (Contributed by Raymond Hettinger.)

* The regular expression language accepted by the "re" module was
  extended with simple conditional expressions, written as
  "(?(group)A|B)".  *group* is either a numeric group ID or a group
  name defined with "(?P<group>...)" earlier in the expression.  If
  the specified group matched, the regular expression pattern *A* will
  be tested against the string; if the group didn't match, the pattern
  *B* will be used instead. (Contributed by Gustavo Niemeyer.)

* The "re" module is also no longer recursive, thanks to a massive
  amount of work by Gustavo Niemeyer.  In a recursive regular
  expression engine, certain patterns result in a large amount of C
  stack space being consumed, and it was possible to overflow the
  stack. For example, if you matched a 30000-byte string of "a"
  characters against the expression "(a|b)+", one stack frame was
  consumed per character.  Python 2.3 tried to check for stack
  overflow and raise a "RuntimeError" exception, but certain patterns
  could sidestep the checking and if you were unlucky Python could
  segfault. Python 2.4's regular expression engine can match this
  pattern without problems.

* The "signal" module now performs tighter error-checking on the
  parameters to the "signal.signal()" function.  For example, you
  can't set a handler on the "SIGKILL" signal; previous versions of
  Python would quietly accept this, but 2.4 will raise a
  "RuntimeError" exception.

* Two new functions were added to the "socket" module. "socketpair()"
  returns a pair of connected sockets and "getservbyport(port)" looks
  up the service name for a given port number. (Contributed by Dave
  Cole and Barry Warsaw.)

* The "sys.exitfunc()" function has been deprecated.  Code should be
  using the existing "atexit" module, which correctly handles calling
  multiple exit functions.  Eventually "sys.exitfunc()" will become a
  purely internal interface, accessed only by "atexit".

* The "tarfile" module now generates GNU-format tar files by default.
  (Contributed by Lars Gustäbel.)

* The "threading" module now has an elegantly simple way to support
  thread-local data.  The module contains a "local" class whose
  attribute values are local to different threads.

     import threading

     data = threading.local()
     data.number = 42
     data.url = ('www.python.org', 80)

  Other threads can assign and retrieve their own values for the
  "number" and "url" attributes.  You can subclass "local" to
  initialize attributes or to add methods. (Contributed by Jim
  Fulton.)

* The "timeit" module now automatically disables periodic garbage
  collection during the timing loop.  This change makes consecutive
  timings more comparable.  (Contributed by Raymond Hettinger.)

* The "weakref" module now supports a wider variety of objects
  including Python functions, class instances, sets, frozensets,
  deques, arrays, files, sockets, and regular expression pattern
  objects. (Contributed by Raymond Hettinger.)

* The "xmlrpclib" module now supports a multi-call extension for
  transmitting multiple XML-RPC calls in a single HTTP operation.
  (Contributed by Brian Quinlan.)

* The "mpz", "rotor", and "xreadlines" modules have  been removed.


cookielib
---------

The "cookielib" library supports client-side handling for HTTP
cookies, mirroring the "Cookie" module's server-side cookie support.
Cookies are stored in cookie jars; the library transparently stores
cookies offered by the web server in the cookie jar, and fetches the
cookie from the jar when connecting to the server. As in web browsers,
policy objects control whether cookies are accepted or not.

In order to store cookies across sessions, two implementations of
cookie jars are provided: one that stores cookies in the Netscape
format so applications can use the Mozilla or Lynx cookie files, and
one that stores cookies in the same format as the Perl libwww library.

"urllib2" has been changed to interact with "cookielib":
"HTTPCookieProcessor" manages a cookie jar that is used when accessing
URLs.

This module was contributed by John J. Lee.


doctest
-------

The "doctest" module underwent considerable refactoring thanks to
Edward Loper and Tim Peters.  Testing can still be as simple as
running "doctest.testmod()", but the refactorings allow customizing
the module's operation in various ways

The new "DocTestFinder" class extracts the tests from a given
object's docstrings:

   def f (x, y):
       """>>> f(2,2)
   4
   >>> f(3,2)
   6
       """
       return x*y

   finder = doctest.DocTestFinder()

   # Get list of DocTest instances
   tests = finder.find(f)

The new "DocTestRunner" class then runs individual tests and can
produce a summary of the results:

   runner = doctest.DocTestRunner()
   for t in tests:
       tried, failed = runner.run(t)

   runner.summarize(verbose=1)

The above example produces the following output:

   1 items passed all tests:
      2 tests in f
   2 tests in 1 items.
   2 passed and 0 failed.
   Test passed.

"DocTestRunner" uses an instance of the "OutputChecker" class to
compare the expected output with the actual output.  This class takes
a number of different flags that customize its behaviour; ambitious
users can also write a completely new subclass of "OutputChecker".

The default output checker provides a number of handy features. For
example, with the "doctest.ELLIPSIS" option flag, an ellipsis ("...")
in the expected output matches any substring,  making it easier to
accommodate outputs that vary in minor ways:

   def o (n):
       """>>> o(1)
   <__main__.C instance at 0x...>
   >>>
   """

Another special string, "<BLANKLINE>", matches a blank line:

   def p (n):
       """>>> p(1)
   <BLANKLINE>
   >>>
   """

Another new capability is producing a diff-style display of the output
by specifying the "doctest.REPORT_UDIFF" (unified diffs),
"doctest.REPORT_CDIFF" (context diffs), or "doctest.REPORT_NDIFF"
(delta-style) option flags.  For example:

   def g (n):
       """>>> g(4)
   here
   is
   a
   lengthy
   >>>"""
       L = 'here is a rather lengthy list of words'.split()
       for word in L[:n]:
           print word

Running the above function's tests with "doctest.REPORT_UDIFF"
specified, you get the following output:

   **********************************************************************
   File "t.py", line 15, in g
   Failed example:
       g(4)
   Differences (unified diff with -expected +actual):
       @@ -2,3 +2,3 @@
        is
        a
       -lengthy
       +rather
   **********************************************************************


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

Some of the changes to Python's build process and to the C API are:

* Three new convenience macros were added for common return values
  from extension functions: "Py_RETURN_NONE", "Py_RETURN_TRUE", and
  "Py_RETURN_FALSE". (Contributed by Brett Cannon.)

* Another new macro, "Py_CLEAR",  decreases the reference count of
  *obj* and sets *obj* to the null pointer.  (Contributed by Jim
  Fulton.)

* A new function, "PyTuple_Pack(N, obj1, obj2, ..., objN)", constructs
  tuples from a variable length argument list of Python objects.
  (Contributed by Raymond Hettinger.)

* A new function, "PyDict_Contains(d, k)", implements fast dictionary
  lookups without masking exceptions raised during the look-up
  process. (Contributed by Raymond Hettinger.)

* The Py_IS_NAN(X) macro returns 1 if  its float or double argument
  *X* is a NaN.   (Contributed by Tim Peters.)

* C code can avoid unnecessary locking by using the new
  "PyEval_ThreadsInitialized()" function to tell  if any thread
  operations have been performed.  If this function  returns false, no
  lock operations are needed. (Contributed by Nick Coghlan.)

* A new function, "PyArg_VaParseTupleAndKeywords()", is the same as
  "PyArg_ParseTupleAndKeywords()" but takes a  "va_list" instead of a
  number of arguments. (Contributed by Greg Chapman.)

* A new method flag, "METH_COEXIST", allows a function defined in
  slots to co-exist with a "PyCFunction" having the same name.  This
  can halve the access time for a method such as "set.__contains__()".
  (Contributed by Raymond Hettinger.)

* Python can now be built with additional profiling for the
  interpreter itself, intended as an aid to people developing the
  Python core.  Providing "--enable-profiling" to the **configure**
  script will let you profile the interpreter with **gprof**, and
  providing the "--with-tsc" switch enables profiling using the
  Pentium's Time-Stamp-Counter register.  Note that the "--with-tsc"
  switch is slightly misnamed, because the profiling feature also
  works on the PowerPC platform, though that processor architecture
  doesn't call that register "the TSC register".  (Contributed by
  Jeremy Hylton.)

* The "tracebackobject" type has been renamed to "PyTracebackObject".


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

* The Windows port now builds under MSVC++ 7.1 as well as version 6.
  (Contributed by Martin von Löwis.)


Porting to Python 2.4
=====================

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

* Left shifts and hexadecimal/octal constants that are too  large no
  longer trigger a "FutureWarning" and return  a value limited to 32
  or 64 bits; instead they return a long integer.

* Integer operations will no longer trigger an "OverflowWarning". The
  "OverflowWarning" warning will disappear in Python 2.5.

* The "zip()" built-in function and "itertools.izip()" now return  an
  empty list instead of raising a "TypeError" exception if called with
  no arguments.

* You can no longer compare the "date" and "datetime" instances
  provided by the "datetime" module.  Two  instances of different
  classes will now always be unequal, and  relative comparisons ("<",
  ">") will raise a "TypeError".

* "dircache.listdir()" now passes exceptions to the caller instead of
  returning empty lists.

* "LexicalHandler.startDTD()" used to receive the public and system
  IDs in the wrong order.  This has been corrected; applications
  relying on the wrong order need to be fixed.

* "fcntl.ioctl()" now warns if the *mutate*  argument is omitted and
  relevant.

* The "tarfile" module now generates GNU-format tar files by default.

* Encountering a failure while importing a module no longer leaves a
  partially initialized module object in "sys.modules".

* "None" is now a constant; code that binds a new value to  the name
  "None" is now a syntax error.

* The "signals.signal()" function now raises a "RuntimeError"
  exception for certain illegal values; previously these errors would
  pass silently.  For example, you can no longer set a handler on the
  "SIGKILL" signal.


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

The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Koray Can, Hye-Shik Chang, Michael Dyck, Raymond Hettinger,
Brian Hurt, Hamish Lawson, Fredrik Lundh, Sean Reifschneider,
Sadruddin Rejeb.
