Nouveautés de Python 2.2
************************

Author:
   A.M. Kuchling


Introduction
============

This article explains the new features in Python 2.2.2, released on
October 14, 2002.  Python 2.2.2 is a bugfix release of Python 2.2,
originally released on December 21, 2001.

Python 2.2 can be thought of as the « cleanup release ».  There are
some features such as generators and iterators that are completely
new, but most of the changes, significant and far-reaching though they
may be, are aimed at cleaning up irregularities and dark corners of
the language design.

This article doesn’t attempt to provide a complete specification of
the new features, but instead provides a convenient overview.  For
full details, you should refer to the documentation for Python 2.2,
such as the Python Library Reference and the Python Reference Manual.
If you want to understand the complete implementation and design
rationale for a change, refer to the PEP for a particular new feature.


PEP 252 et 253 : Changements concernant les types et classes
============================================================

The largest and most far-reaching changes in Python 2.2 are to
Python’s model of objects and classes.  The changes should be backward
compatible, so it’s likely that your code will continue to run
unchanged, but the changes provide some amazing new capabilities.
Before beginning this, the longest and most complicated section of
this article, I’ll provide an overview of the changes and offer some
comments.

A long time ago I wrote a Web page listing flaws in Python’s design.
One of the most significant flaws was that it’s impossible to subclass
Python types implemented in C.  In particular, it’s not possible to
subclass built-in types, so you can’t just subclass, say, lists in
order to add a single useful method to them. The "UserList" module
provides a class that supports all of the methods of lists and that
can be subclassed further, but there’s lots of C code that expects a
regular Python list and won’t accept a "UserList" instance.

Python 2.2 fixes this, and in the process adds some exciting new
capabilities. A brief summary:

* You can subclass built-in types such as lists and even integers,
  and your subclasses should work in every place that requires the
  original type.

* It’s now possible to define static and class methods, in addition
  to the instance methods available in previous versions of Python.

* It’s also possible to automatically call methods on accessing or
  setting an instance attribute by using a new mechanism called
  *properties*.  Many uses of "__getattr__()" can be rewritten to use
  properties instead, making the resulting code simpler and faster. As
  a small side benefit, attributes can now have docstrings, too.

* The list of legal attributes for an instance can be limited to a
  particular set using *slots*, making it possible to safeguard
  against typos and perhaps make more optimizations possible in future
  versions of Python.

Some users have voiced concern about all these changes.  Sure, they
say, the new features are neat and lend themselves to all sorts of
tricks that weren’t possible in previous versions of Python, but they
also make the language more complicated.  Some people have said that
they’ve always recommended Python for its simplicity, and feel that
its simplicity is being lost.

Personally, I think there’s no need to worry.  Many of the new
features are quite esoteric, and you can write a lot of Python code
without ever needed to be aware of them.  Writing a simple class is no
more difficult than it ever was, so you don’t need to bother learning
or teaching them unless they’re actually needed.  Some very
complicated tasks that were previously only possible from C will now
be possible in pure Python, and to my mind that’s all for the better.

I’m not going to attempt to cover every single corner case and small
change that were required to make the new features work.  Instead this
section will paint only the broad strokes.  See section Related Links,
« Related Links », for further sources of information about Python
2.2’s new object model.


Anciennes et nouvelles classes
------------------------------

First, you should know that Python 2.2 really has two kinds of
classes: classic or old-style classes, and new-style classes.  The
old-style class model is exactly the same as the class model in
earlier versions of Python.  All the new features described in this
section apply only to new-style classes. This divergence isn’t
intended to last forever; eventually old-style classes will be
dropped, possibly in Python 3.0.

So how do you define a new-style class?  You do it by subclassing an
existing new-style class.  Most of Python’s built-in types, such as
integers, lists, dictionaries, and even files, are new-style classes
now.  A new-style class named "object", the base class for all built-
in types, has also been added so if no built-in type is suitable, you
can just subclass "object":

   class C(object):
       def __init__ (self):
           ...
       ...

This means that "class" statements that don’t have any base classes
are always classic classes in Python 2.2.  (Actually you can also
change this by setting a module-level variable named "__metaclass__" —
see **PEP 253** for the details — but it’s easier to just subclass
"object".)

The type objects for the built-in types are available as built-ins,
named using a clever trick.  Python has always had built-in functions
named "int()", "float()", and "str()".  In 2.2, they aren’t functions
any more, but type objects that behave as factories when called.

   >>> int
   <type 'int'>
   >>> int('123')
   123

To make the set of types complete, new type objects such as "dict()"
and "file()" have been added.  Here’s a more interesting example,
adding a "lock()" method to file objects:

   class LockableFile(file):
       def lock (self, operation, length=0, start=0, whence=0):
           import fcntl
           return fcntl.lockf(self.fileno(), operation,
                              length, start, whence)

The now-obsolete "posixfile" module contained a class that emulated
all of a file object’s methods and also added a "lock()" method, but
this class couldn’t be passed to internal functions that expected a
built-in file, something which is possible with our new
"LockableFile".


Descripteurs
------------

In previous versions of Python, there was no consistent way to
discover what attributes and methods were supported by an object.
There were some informal conventions, such as defining "__members__"
and "__methods__" attributes that were lists of names, but often the
author of an extension type or a class wouldn’t bother to define them.
You could fall back on inspecting the "__dict__" of an object, but
when class inheritance or an arbitrary "__getattr__()" hook were in
use this could still be inaccurate.

The one big idea underlying the new class model is that an API for
describing the attributes of an object using *descriptors* has been
formalized. Descriptors specify the value of an attribute, stating
whether it’s a method or a field.  With the descriptor API, static
methods and class methods become possible, as well as more exotic
constructs.

Attribute descriptors are objects that live inside class objects, and
have a few attributes of their own:

* "__name__" is the attribute’s name.

* "__doc__" is the attribute’s docstring.

* "__get__(object)" is a method that retrieves the attribute value
  from *object*.

* "__set__(object, value)" sets the attribute on *object* to
  *value*.

* "__delete__(object, value)" deletes the *value*  attribute of
  *object*.

For example, when you write "obj.x", the steps that Python actually
performs are:

   descriptor = obj.__class__.x
   descriptor.__get__(obj)

For methods, "descriptor.__get__()" returns a temporary object that’s
callable, and wraps up the instance and the method to be called on it.
This is also why static methods and class methods are now possible;
they have descriptors that wrap up just the method, or the method and
the class.  As a brief explanation of these new kinds of methods,
static methods aren’t passed the instance, and therefore resemble
regular functions.  Class methods are passed the class of the object,
but not the object itself.  Static and class methods are defined like
this:

   class C(object):
       def f(arg1, arg2):
           ...
       f = staticmethod(f)

       def g(cls, arg1, arg2):
           ...
       g = classmethod(g)

The "staticmethod()" function takes the function "f()", and returns it
wrapped up in a descriptor so it can be stored in the class object.
You might expect there to be special syntax for creating such methods
("def static f", "defstatic f()", or something like that) but no such
syntax has been defined yet; that’s been left for future versions of
Python.

More new features, such as slots and properties, are also implemented
as new kinds of descriptors, and it’s not difficult to write a
descriptor class that does something novel.  For example, it would be
possible to write a descriptor class that made it possible to write
Eiffel-style preconditions and postconditions for a method.  A class
that used this feature might be defined like this:

   from eiffel import eiffelmethod

   class C(object):
       def f(self, arg1, arg2):
           # The actual function
           ...
       def pre_f(self):
           # Check preconditions
           ...
       def post_f(self):
           # Check postconditions
           ...

       f = eiffelmethod(f, pre_f, post_f)

Note that a person using the new "eiffelmethod()" doesn’t have to
understand anything about descriptors.  This is why I think the new
features don’t increase the basic complexity of the language. There
will be a few wizards who need to know about it in order to write
"eiffelmethod()" or the ZODB or whatever, but most users will just
write code on top of the resulting libraries and ignore the
implementation details.


Héritage multiple : la règle du diamant
---------------------------------------

Multiple inheritance has also been made more useful through changing
the rules under which names are resolved.  Consider this set of
classes (diagram taken from **PEP 253** by Guido van Rossum):

         class A:
           ^ ^  def save(self): ...
          /   \
         /     \
        /       \
       /         \
   class B     class C:
       ^         ^  def save(self): ...
        \       /
         \     /
          \   /
           \ /
         class D

The lookup rule for classic classes is simple but not very smart; the
base classes are searched depth-first, going from left to right.  A
reference to "D.save()" will search the classes "D", "B", and then
"A", where "save()" would be found and returned.  "C.save()" would
never be found at all.  This is bad, because if "C"’s "save()" method
is saving some internal state specific to "C", not calling it will
result in that state never getting saved.

New-style classes follow a different algorithm that’s a bit more
complicated to explain, but does the right thing in this situation.
(Note that Python 2.3 changes this algorithm to one that produces the
same results in most cases, but produces more useful results for
really complicated inheritance graphs.)

1. List all the base classes, following the classic lookup rule and
   include a class multiple times if it’s visited repeatedly.  In the
   above example, the list of visited classes is ["D", "B", "A", "C",
   "A"].

2. Scan the list for duplicated classes.  If any are found, remove
   all but one occurrence, leaving the *last* one in the list.  In the
   above example, the list becomes ["D", "B", "C", "A"] after dropping
   duplicates.

Following this rule, referring to "D.save()" will return "C.save()",
which is the behaviour we’re after.  This lookup rule is the same as
the one followed by Common Lisp.  A new built-in function, "super()",
provides a way to get at a class’s superclasses without having to
reimplement Python’s algorithm. The most commonly used form will be
"super(class, obj)", which returns  a bound superclass object (not the
actual class object).  This form will be used in methods to call a
method in the superclass; for example, "D"’s "save()" method would
look like this:

   class D (B,C):
       def save (self):
           # Call superclass .save()
           super(D, self).save()
           # Save D's private information here
           ...

"super()" can also return unbound superclass objects when called as
"super(class)" or "super(class1, class2)", but this probably won’t
often be useful.


Attribute Access
----------------

A fair number of sophisticated Python classes define hooks for
attribute access using "__getattr__()"; most commonly this is done for
convenience, to make code more readable by automatically mapping an
attribute access such as "obj.parent" into a method call such as
"obj.get_parent".  Python 2.2 adds some new ways of controlling
attribute access.

First, "__getattr__(attr_name)" is still supported by new-style
classes, and nothing about it has changed.  As before, it will be
called when an attempt is made to access "obj.foo" and no attribute
named "foo" is found in the instance’s dictionary.

New-style classes also support a new method,
"__getattribute__(attr_name)".  The difference between the two methods
is that "__getattribute__()" is *always* called whenever any attribute
is accessed, while the old "__getattr__()" is only called if "foo"
isn’t found in the instance’s dictionary.

However, Python 2.2’s support for *properties* will often be a simpler
way to trap attribute references.  Writing a "__getattr__()" method is
complicated because to avoid recursion you can’t use regular attribute
accesses inside them, and instead have to mess around with the
contents of "__dict__". "__getattr__()" methods also end up being
called by Python when it checks for other methods such as "__repr__()"
or "__coerce__()", and so have to be written with this in mind.
Finally, calling a function on every attribute access results in a
sizable performance loss.

"property" is a new built-in type that packages up three functions
that get, set, or delete an attribute, and a docstring.  For example,
if you want to define a "size" attribute that’s computed, but also
settable, you could write:

   class C(object):
       def get_size (self):
           result = ... computation ...
           return result
       def set_size (self, size):
           ... compute something based on the size
           and set internal state appropriately ...

       # Define a property.  The 'delete this attribute'
       # method is defined as None, so the attribute
       # can't be deleted.
       size = property(get_size, set_size,
                       None,
                       "Storage size of this instance")

That is certainly clearer and easier to write than a pair of
"__getattr__()"/"__setattr__()" methods that check for the "size"
attribute and handle it specially while retrieving all other
attributes from the instance’s "__dict__".  Accesses to "size" are
also the only ones which have to perform the work of calling a
function, so references to other attributes run at their usual speed.

Finally, it’s possible to constrain the list of attributes that can be
referenced on an object using the new "__slots__" class attribute.
Python objects are usually very dynamic; at any time it’s possible to
define a new attribute on an instance by just doing "obj.new_attr=1".
A new-style class can define a class attribute named "__slots__" to
limit the legal attributes  to a particular set of names.  An example
will make this clear:

   >>> class C(object):
   ...     __slots__ = ('template', 'name')
   ...
   >>> obj = C()
   >>> print obj.template
   None
   >>> obj.template = 'Test'
   >>> print obj.template
   Test
   >>> obj.newattr = None
   Traceback (most recent call last):
     File "<stdin>", line 1, in ?
   AttributeError: 'C' object has no attribute 'newattr'

Note how you get an "AttributeError" on the attempt to assign to an
attribute not listed in "__slots__".


Related Links
-------------

This section has just been a quick overview of the new features,
giving enough of an explanation to start you programming, but many
details have been simplified or ignored.  Where should you go to get a
more complete picture?

https://docs.python.org/dev/howto/descriptor.html is a lengthy
tutorial introduction to the descriptor features, written by Guido van
Rossum. If my description has whetted your appetite, go read this
tutorial next, because it goes into much more detail about the new
features while still remaining quite easy to read.

Next, there are two relevant PEPs, **PEP 252** and **PEP 253**.  **PEP
252** is titled « Making Types Look More Like Classes », and covers
the descriptor API. **PEP 253** is titled « Subtyping Built-in Types
», and describes the changes to type objects that make it possible to
subtype built-in objects.  **PEP 253** is the more complicated PEP of
the two, and at a few points the necessary explanations of types and
meta-types may cause your head to explode.  Both PEPs were written and
implemented by Guido van Rossum, with substantial assistance from the
rest of the Zope Corp. team.

Finally, there’s the ultimate authority: the source code.  Most of the
machinery for the type handling is in "Objects/typeobject.c", but you
should only resort to it after all other avenues have been exhausted,
including posting a question to python-list or python-dev.


PEP 234 : Itérateurs
====================

Another significant addition to 2.2 is an iteration interface at both
the C and Python levels.  Objects can define how they can be looped
over by callers.

In Python versions up to 2.1, the usual way to make "for item in obj"
work is to define a "__getitem__()" method that looks something like
this:

   def __getitem__(self, index):
       return <next item>

"__getitem__()" is more properly used to define an indexing operation
on an object so that you can write "obj[5]" to retrieve the sixth
element.  It’s a bit misleading when you’re using this only to support
"for" loops. Consider some file-like object that wants to be looped
over; the *index* parameter is essentially meaningless, as the class
probably assumes that a series of "__getitem__()" calls will be made
with *index* incrementing by one each time.  In other words, the
presence of the "__getitem__()" method doesn’t mean that using
"file[5]"  to randomly access the sixth element will work, though it
really should.

In Python 2.2, iteration can be implemented separately, and
"__getitem__()" methods can be limited to classes that really do
support random access.  The basic idea of iterators is  simple.  A new
built-in function, "iter(obj)" or "iter(C, sentinel)", is used to get
an iterator. "iter(obj)" returns an iterator for the object *obj*,
while "iter(C, sentinel)" returns an iterator that will invoke the
callable object *C* until it returns *sentinel* to signal that the
iterator is done.

Python classes can define an "__iter__()" method, which should create
and return a new iterator for the object; if the object is its own
iterator, this method can just return "self".  In particular,
iterators will usually be their own iterators.  Extension types
implemented in C can implement a "tp_iter" function in order to return
an iterator, and extension types that want to behave as iterators can
define a "tp_iternext" function.

So, after all this, what do iterators actually do?  They have one
required method, "next()", which takes no arguments and returns the
next value.  When there are no more values to be returned, calling
"next()" should raise the "StopIteration" exception.

   >>> L = [1,2,3]
   >>> i = iter(L)
   >>> print i
   <iterator object at 0x8116870>
   >>> i.next()
   1
   >>> i.next()
   2
   >>> i.next()
   3
   >>> i.next()
   Traceback (most recent call last):
     File "<stdin>", line 1, in ?
   StopIteration
   >>>

In 2.2, Python’s "for" statement no longer expects a sequence; it
expects something for which "iter()" will return an iterator. For
backward compatibility and convenience, an iterator is automatically
constructed for sequences that don’t implement "__iter__()" or a
"tp_iter" slot, so "for i in [1,2,3]" will still work.  Wherever the
Python interpreter loops over a sequence, it’s been changed to use the
iterator protocol.  This means you can do things like this:

   >>> L = [1,2,3]
   >>> i = iter(L)
   >>> a,b,c = i
   >>> a,b,c
   (1, 2, 3)

Iterator support has been added to some of Python’s basic types.
Calling "iter()" on a dictionary will return an iterator which loops
over its keys:

   >>> m = {'Jan': 1, 'Feb': 2, 'Mar': 3, 'Apr': 4, 'May': 5, 'Jun': 6,
   ...      'Jul': 7, 'Aug': 8, 'Sep': 9, 'Oct': 10, 'Nov': 11, 'Dec': 12}
   >>> for key in m: print key, m[key]
   ...
   Mar 3
   Feb 2
   Aug 8
   Sep 9
   May 5
   Jun 6
   Jul 7
   Jan 1
   Apr 4
   Nov 11
   Dec 12
   Oct 10

That’s just the default behaviour.  If you want to iterate over keys,
values, or key/value pairs, you can explicitly call the "iterkeys()",
"itervalues()", or "iteritems()" methods to get an appropriate
iterator. In a minor related change, the "in" operator now works on
dictionaries, so "key in dict" is now equivalent to
"dict.has_key(key)".

Files also provide an iterator, which calls the "readline()" method
until there are no more lines in the file.  This means you can now
read each line of a file using code like this:

   for line in file:
       # do something for each line
       ...

Note that you can only go forward in an iterator; there’s no way to
get the previous element, reset the iterator, or make a copy of it. An
iterator object could provide such additional capabilities, but the
iterator protocol only requires a "next()" method.

Voir aussi:

  **PEP 234** – Itérateurs
     Written by Ka-Ping Yee and GvR; implemented  by the Python Labs
     crew, mostly by GvR and Tim Peters.


PEP 255 : Générateurs simples
=============================

Generators are another new feature, one that interacts with the
introduction of iterators.

You’re doubtless familiar with how function calls work in Python or C.
When you call a function, it gets a private namespace where its local
variables are created.  When the function reaches a "return"
statement, the local variables are destroyed and the resulting value
is returned to the caller.  A later call to the same function will get
a fresh new set of local variables. But, what if the local variables
weren’t thrown away on exiting a function? What if you could later
resume the function where it left off?  This is what generators
provide; they can be thought of as resumable functions.

Here’s the simplest example of a generator function:

   def generate_ints(N):
       for i in range(N):
           yield i

A new keyword, "yield", was introduced for generators.  Any function
containing a "yield" statement is a generator function; this is
detected by Python’s bytecode compiler which compiles the function
specially as a result.  Because a new keyword was introduced,
generators must be explicitly enabled in a module by including a "from
__future__ import generators" statement near the top of the module’s
source code.  In Python 2.3 this statement will become unnecessary.

When you call a generator function, it doesn’t return a single value;
instead it returns a generator object that supports the iterator
protocol.  On executing the "yield" statement, the generator outputs
the value of "i", similar to a "return" statement.  The big difference
between "yield" and a "return" statement is that on reaching a "yield"
the generator’s state of execution is suspended and local variables
are preserved.  On the next call to the generator’s "next()" method,
the function will resume executing immediately after the "yield"
statement.  (For complicated reasons, the "yield" statement isn’t
allowed inside the "try" block of a "try"…"finally" statement; read
**PEP 255** for a full explanation of the interaction between "yield"
and exceptions.)

Here’s a sample usage of the "generate_ints()" generator:

   >>> gen = generate_ints(3)
   >>> gen
   <generator object at 0x8117f90>
   >>> gen.next()
   0
   >>> gen.next()
   1
   >>> gen.next()
   2
   >>> gen.next()
   Traceback (most recent call last):
     File "<stdin>", line 1, in ?
     File "<stdin>", line 2, in generate_ints
   StopIteration

You could equally write "for i in generate_ints(5)", or "a,b,c =
generate_ints(3)".

Inside a generator function, the "return" statement can only be used
without a value, and signals the end of the procession of values;
afterwards the generator cannot return any further values. "return"
with a value, such as "return 5", is a syntax error inside a generator
function.  The end of the generator’s results can also be indicated by
raising "StopIteration" manually, or by just letting the flow of
execution fall off the bottom of the function.

You could achieve the effect of generators manually by writing your
own class and storing all the local variables of the generator as
instance variables.  For example, returning a list of integers could
be done by setting "self.count" to 0, and having the "next()" method
increment "self.count" and return it. However, for a moderately
complicated generator, writing a corresponding class would be much
messier. "Lib/test/test_generators.py" contains a number of more
interesting examples.  The simplest one implements an in-order
traversal of a tree using generators recursively.

   # A recursive generator that generates Tree leaves in in-order.
   def inorder(t):
       if t:
           for x in inorder(t.left):
               yield x
           yield t.label
           for x in inorder(t.right):
               yield x

Two other examples in "Lib/test/test_generators.py" produce solutions
for the N-Queens problem (placing $N$ queens on an $NxN$ chess board
so that no queen threatens another) and the Knight’s Tour (a route
that takes a knight to every square of an $NxN$ chessboard without
visiting any square twice).

The idea of generators comes from other programming languages,
especially Icon (https://www.cs.arizona.edu/icon/), where the idea of
generators is central.  In Icon, every expression and function call
behaves like a generator.  One example from « An Overview of the Icon
Programming Language » at
https://www.cs.arizona.edu/icon/docs/ipd266.htm gives an idea of what
this looks like:

   sentence := "Store it in the neighboring harbor"
   if (i := find("or", sentence)) > 5 then write(i)

In Icon the "find()" function returns the indexes at which the
substring « or » is found: 3, 23, 33.  In the "if" statement, "i" is
first assigned a value of 3, but 3 is less than 5, so the comparison
fails, and Icon retries it with the second value of 23.  23 is greater
than 5, so the comparison now succeeds, and the code prints the value
23 to the screen.

Python doesn’t go nearly as far as Icon in adopting generators as a
central concept.  Generators are considered a new part of the core
Python language, but learning or using them isn’t compulsory; if they
don’t solve any problems that you have, feel free to ignore them. One
novel feature of Python’s interface as compared to Icon’s is that a
generator’s state is represented as a concrete object (the iterator)
that can be passed around to other functions or stored in a data
structure.

Voir aussi:

  **PEP 255**: Générateurs simples
     Written by Neil Schemenauer, Tim Peters, Magnus Lie Hetland.
     Implemented mostly by Neil Schemenauer and Tim Peters, with other
     fixes from the Python Labs crew.


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

In recent versions, the distinction between regular integers, which
are 32-bit values on most machines, and long integers, which can be of
arbitrary size, was becoming an annoyance.  For example, on platforms
that support files larger than "2**32" bytes, the "tell()" method of
file objects has to return a long integer. However, there were various
bits of Python that expected plain integers and would raise an error
if a long integer was provided instead.  For example, in Python 1.5,
only regular integers could be used as a slice index, and "'abc'[1L:]"
would raise a "TypeError" exception with the message “slice index must
be int”.

Python 2.2 will shift values from short to long integers as required.
The “L” suffix is no longer needed to indicate a long integer literal,
as now the compiler will choose the appropriate type.  (Using the “L”
suffix will be discouraged in future 2.x versions of Python,
triggering a warning in Python 2.4, and probably dropped in Python
3.0.)  Many operations that used to raise an "OverflowError" will now
return a long integer as their result.  For example:

   >>> 1234567890123
   1234567890123L
   >>> 2 ** 64
   18446744073709551616L

In most cases, integers and long integers will now be treated
identically.  You can still distinguish them with the "type()" built-
in function, but that’s rarely needed.

Voir aussi:

  **PEP 237** - Unifying Long Integers and Integers
     Written by Moshe Zadka and Guido van Rossum.  Implemented mostly
     by Guido van Rossum.


PEP 238: Changing the Division Operator
=======================================

The most controversial change in Python 2.2 heralds the start of an
effort to fix an old design flaw that’s been in Python from the
beginning. Currently Python’s division operator, "/", behaves like C’s
division operator when presented with two integer arguments: it
returns an integer result that’s truncated down when there would be a
fractional part.  For example, "3/2" is 1, not 1.5, and "(-1)/2" is
-1, not -0.5.  This means that the results of division can vary
unexpectedly depending on the type of the two operands and because
Python is dynamically typed, it can be difficult to determine the
possible types of the operands.

(The controversy is over whether this is *really* a design flaw, and
whether it’s worth breaking existing code to fix this.  It’s caused
endless discussions on python-dev, and in July 2001 erupted into a
storm of acidly sarcastic postings on *comp.lang.python*. I won’t
argue for either side here and will stick to describing what’s
implemented in 2.2.  Read **PEP 238** for a summary of arguments and
counter-arguments.)

Because this change might break code, it’s being introduced very
gradually. Python 2.2 begins the transition, but the switch won’t be
complete until Python 3.0.

First, I’ll borrow some terminology from **PEP 238**.  « True division
» is the division that most non-programmers are familiar with: 3/2 is
1.5, 1/4 is 0.25, and so forth.  « Floor division » is what Python’s
"/" operator currently does when given integer operands; the result is
the floor of the value returned by true division.  « Classic division
» is the current mixed behaviour of "/"; it returns the result of
floor division when the operands are integers, and returns the result
of true division when one of the operands is a floating-point number.

Here are the changes 2.2 introduces:

* A new operator, "//", is the floor division operator. (Yes, we
  know it looks like C++”s comment symbol.)  "//" *always* performs
  floor division no matter what the types of its operands are, so "1
  // 2" is 0 and "1.0 // 2.0" is also 0.0.

  "//" is always available in Python 2.2; you don’t need to enable it
  using a "__future__" statement.

* By including a "from __future__ import division" in a module, the
  "/" operator will be changed to return the result of true division,
  so "1/2" is 0.5.  Without the "__future__" statement, "/" still
  means classic division. The default meaning of "/" will not change
  until Python 3.0.

* Classes can define methods called "__truediv__()" and
  "__floordiv__()" to overload the two division operators.  At the C
  level, there are also slots in the "PyNumberMethods" structure so
  extension types can define the two operators.

* Python 2.2 supports some command-line arguments for testing
  whether code will work with the changed division semantics.  Running
  python with "-Q warn" will cause a warning to be issued whenever
  division is applied to two integers.  You can use this to find code
  that’s affected by the change and fix it.  By default, Python 2.2
  will simply perform classic division without a warning; the warning
  will be turned on by default in Python 2.3.

Voir aussi:

  **PEP 238** - Changing the Division Operator
     Written by Moshe Zadka and  Guido van Rossum.  Implemented by
     Guido van Rossum..


Unicode Changes
===============

Python’s Unicode support has been enhanced a bit in 2.2.  Unicode
strings are usually stored as UCS-2, as 16-bit unsigned integers.
Python 2.2 can also be compiled to use UCS-4, 32-bit unsigned
integers, as its internal encoding by supplying "--enable-
unicode=ucs4" to the configure script.   (It’s also possible to
specify "--disable-unicode" to completely disable Unicode support.)

When built to use UCS-4 (a « wide Python »), the interpreter can
natively handle Unicode characters from U+000000 to U+110000, so the
range of legal values for the "unichr()" function is expanded
accordingly.  Using an interpreter compiled to use UCS-2 (a « narrow
Python »), values greater than 65535 will still cause "unichr()" to
raise a "ValueError" exception. This is all described in **PEP 261**,
« Support for “wide” Unicode characters »; consult it for further
details.

Another change is simpler to explain. Since their introduction,
Unicode strings have supported an "encode()" method to convert the
string to a selected encoding such as UTF-8 or Latin-1.  A symmetric
"decode([*encoding*])" method has been added to 8-bit strings (though
not to Unicode strings) in 2.2. "decode()" assumes that the string is
in the specified encoding and decodes it, returning whatever is
returned by the codec.

Using this new feature, codecs have been added for tasks not directly
related to Unicode.  For example, codecs have been added for uu-
encoding, MIME’s base64 encoding, and compression with the "zlib"
module:

   >>> s = """Here is a lengthy piece of redundant, overly verbose,
   ... and repetitive text.
   ... """
   >>> data = s.encode('zlib')
   >>> data
   'x\x9c\r\xc9\xc1\r\x80 \x10\x04\xc0?Ul...'
   >>> data.decode('zlib')
   'Here is a lengthy piece of redundant, overly verbose,\nand repetitive text.\n'
   >>> print s.encode('uu')
   begin 666 <data>
   M2&5R92!I<R!A(&QE;F=T:'D@<&EE8V4@;V8@<F5D=6YD86YT+"!O=F5R;'D@
   >=F5R8F]S92P*86YD(')E<&5T:71I=F4@=&5X="X*

   end
   >>> "sheesh".encode('rot-13')
   'furrfu'

To convert a class instance to Unicode, a "__unicode__()" method can
be defined by a class, analogous to "__str__()".

"encode()", "decode()", and "__unicode__()" were implemented by Marc-
André Lemburg.  The changes to support using UCS-4 internally were
implemented by Fredrik Lundh and Martin von Löwis.

Voir aussi:

  **PEP 261** - Support for “wide” Unicode characters
     Written by Paul Prescod.


PEP 227: Nested Scopes
======================

In Python 2.1, statically nested scopes were added as an optional
feature, to be enabled by a "from __future__ import nested_scopes"
directive.  In 2.2 nested scopes no longer need to be specially
enabled, and are now always present.  The rest of this section is a
copy of the description of nested scopes from my « What’s New in
Python 2.1 » document; if you read it when 2.1 came out, you can skip
the rest of this section.

The largest change introduced in Python 2.1, and made complete in 2.2,
is to Python’s scoping rules.  In Python 2.0, at any given time there
are at most three namespaces used to look up variable names: local,
module-level, and the built-in namespace.  This often surprised people
because it didn’t match their intuitive expectations.  For example, a
nested recursive function definition doesn’t work:

   def f():
       ...
       def g(value):
           ...
           return g(value-1) + 1
       ...

The function "g()" will always raise a "NameError" exception, because
the binding of the name "g" isn’t in either its local namespace or in
the module-level namespace.  This isn’t much of a problem in practice
(how often do you recursively define interior functions like this?),
but this also made using the "lambda" statement clumsier, and this was
a problem in practice. In code which uses "lambda" you can often find
local variables being copied by passing them as the default values of
arguments.

   def find(self, name):
       "Return list of any entries equal to 'name'"
       L = filter(lambda x, name=name: x == name,
                  self.list_attribute)
       return L

The readability of Python code written in a strongly functional style
suffers greatly as a result.

The most significant change to Python 2.2 is that static scoping has
been added to the language to fix this problem.  As a first effect,
the "name=name" default argument is now unnecessary in the above
example.  Put simply, when a given variable name is not assigned a
value within a function (by an assignment, or the "def", "class", or
"import" statements), references to the variable will be looked up in
the local namespace of the enclosing scope.  A more detailed
explanation of the rules, and a dissection of the implementation, can
be found in the PEP.

This change may cause some compatibility problems for code where the
same variable name is used both at the module level and as a local
variable within a function that contains further function definitions.
This seems rather unlikely though, since such code would have been
pretty confusing to read in the first place.

One side effect of the change is that the "from module import *" and
"exec" statements have been made illegal inside a function scope under
certain conditions.  The Python reference manual has said all along
that "from module import *" is only legal at the top level of a
module, but the CPython interpreter has never enforced this before.
As part of the implementation of nested scopes, the compiler which
turns Python source into bytecodes has to generate different code to
access variables in a containing scope.  "from module import *" and
"exec" make it impossible for the compiler to figure this out, because
they add names to the local namespace that are unknowable at compile
time. Therefore, if a function contains function definitions or
"lambda" expressions with free variables, the compiler will flag this
by raising a "SyntaxError" exception.

To make the preceding explanation a bit clearer, here’s an example:

   x = 1
   def f():
       # The next line is a syntax error
       exec 'x=2'
       def g():
           return x

Line 4 containing the "exec" statement is a syntax error, since "exec"
would define a new local variable named "x" whose value should be
accessed by "g()".

This shouldn’t be much of a limitation, since "exec" is rarely used in
most Python code (and when it is used, it’s often a sign of a poor
design anyway).

Voir aussi:

  **PEP 227** - Statically Nested Scopes
     Écrit et implémenté par Jeremy Hylton.


New and Improved Modules
========================

* The "xmlrpclib" module was contributed to the standard library by
  Fredrik Lundh, providing support for writing XML-RPC clients.  XML-
  RPC is a simple remote procedure call protocol built on top of HTTP
  and XML. For example, the following snippet retrieves a list of RSS
  channels from the O’Reilly Network, and then  lists the recent
  headlines for one channel:

     import xmlrpclib
     s = xmlrpclib.Server(
           'http://www.oreillynet.com/meerkat/xml-rpc/server.php')
     channels = s.meerkat.getChannels()
     # channels is a list of dictionaries, like this:
     # [{'id': 4, 'title': 'Freshmeat Daily News'}
     #  {'id': 190, 'title': '32Bits Online'},
     #  {'id': 4549, 'title': '3DGamers'}, ... ]

     # Get the items for one channel
     items = s.meerkat.getItems( {'channel': 4} )

     # 'items' is another list of dictionaries, like this:
     # [{'link': 'http://freshmeat.net/releases/52719/',
     #   'description': 'A utility which converts HTML to XSL FO.',
     #   'title': 'html2fo 0.3 (Default)'}, ... ]

  The "SimpleXMLRPCServer" module makes it easy to create
  straightforward XML-RPC servers.  See http://www.xmlrpc.com/ for
  more information about XML-RPC.

* The new "hmac" module implements the HMAC algorithm described by
  **RFC 2104**. (Contributed by Gerhard Häring.)

* Several functions that originally returned lengthy tuples now
  return pseudo-sequences that still behave like tuples but also have
  mnemonic attributes such as memberst_mtime or "tm_year". The
  enhanced functions include "stat()", "fstat()", "statvfs()", and
  "fstatvfs()" in the "os" module, and "localtime()", "gmtime()", and
  "strptime()" in the "time" module.

  For example, to obtain a file’s size using the old tuples, you’d end
  up writing something like "file_size =
  os.stat(filename)[stat.ST_SIZE]", but now this can be written more
  clearly as "file_size = os.stat(filename).st_size".

  The original patch for this feature was contributed by Nick
  Mathewson.

* The Python profiler has been extensively reworked and various
  errors in its output have been corrected.  (Contributed by Fred L.
  Drake, Jr. and Tim Peters.)

* The "socket" module can be compiled to support IPv6; specify the
  "-- enable-ipv6" option to Python’s configure script.  (Contributed
  by Jun-ichiro « itojun » Hagino.)

* Two new format characters were added to the "struct" module for
  64-bit integers on platforms that support the C "long long" type.
  "q" is for a signed 64-bit integer, and "Q" is for an unsigned one.
  The value is returned in Python’s long integer type.  (Contributed
  by Tim Peters.)

* In the interpreter’s interactive mode, there’s a new built-in
  function "help()" that uses the "pydoc" module introduced in Python
  2.1 to provide interactive help. "help(object)" displays any
  available help text about *object*.  "help()" with no argument puts
  you in an online help utility, where you can enter the names of
  functions, classes, or modules to read their help text. (Contributed
  by Guido van Rossum, using Ka-Ping Yee’s "pydoc" module.)

* Various bugfixes and performance improvements have been made to
  the SRE engine underlying the "re" module.  For example, the
  "re.sub()" and "re.split()" functions have been rewritten in C.
  Another contributed patch speeds up certain Unicode character ranges
  by a factor of two, and a new "finditer()"  method that returns an
  iterator over all the non-overlapping matches in  a given string.
  (SRE is maintained by Fredrik Lundh.  The BIGCHARSET patch was
  contributed by Martin von Löwis.)

* The "smtplib" module now supports **RFC 2487**, « Secure SMTP over
  TLS », so it’s now possible to encrypt the SMTP traffic between a
  Python program and the mail transport agent being handed a message.
  "smtplib" also supports SMTP authentication.  (Contributed by
  Gerhard Häring.)

* The "imaplib" module, maintained by Piers Lauder, has support for
  several new extensions: the NAMESPACE extension defined in **RFC
  2342**, SORT, GETACL and SETACL.  (Contributed by Anthony Baxter and
  Michel Pelletier.)

* The "rfc822" module’s parsing of email addresses is now compliant
  with **RFC 2822**, an update to **RFC 822**.  (The module’s name is
  *not* going to be changed to "rfc2822".)  A new package, "email",
  has also been added for parsing and generating e-mail messages.
  (Contributed by Barry Warsaw, and arising out of his work on
  Mailman.)

* The "difflib" module now contains a new "Differ" class for
  producing human-readable lists of changes (a « delta ») between two
  sequences of lines of text.  There are also two generator functions,
  "ndiff()" and "restore()", which respectively return a delta from
  two sequences, or one of the original sequences from a delta. (Grunt
  work contributed by David Goodger, from ndiff.py code by Tim Peters
  who then did the generatorization.)

* New constants "ascii_letters", "ascii_lowercase", and
  "ascii_uppercase" were added to the "string" module.  There were
  several modules in the standard library that used "string.letters"
  to mean the ranges A-Za-z, but that assumption is incorrect when
  locales are in use, because "string.letters" varies depending on the
  set of legal characters defined by the current locale.  The buggy
  modules have all been fixed to use "ascii_letters" instead.
  (Reported by an unknown person; fixed by Fred L. Drake, Jr.)

* The "mimetypes" module now makes it easier to use alternative
  MIME- type databases by the addition of a "MimeTypes" class, which
  takes a list of filenames to be parsed.  (Contributed by Fred L.
  Drake, Jr.)

* A "Timer" class was added to the "threading" module that allows
  scheduling an activity to happen at some future time.  (Contributed
  by Itamar Shtull-Trauring.)


Interpreter Changes and Fixes
=============================

Some of the changes only affect people who deal with the Python
interpreter at the C level because they’re writing Python extension
modules, embedding the interpreter, or just hacking on the interpreter
itself. If you only write Python code, none of the changes described
here will affect you very much.

* Profiling and tracing functions can now be implemented in C, which
  can operate at much higher speeds than Python-based functions and
  should reduce the overhead of profiling and tracing.  This  will be
  of interest to authors of development environments for Python.  Two
  new C functions were added to Python’s API, "PyEval_SetProfile()"
  and "PyEval_SetTrace()". The existing "sys.setprofile()" and
  "sys.settrace()" functions still exist, and have simply been changed
  to use the new C-level interface.  (Contributed by Fred L. Drake,
  Jr.)

* Another low-level API, primarily of interest to implementors of
  Python debuggers and development tools, was added.
  "PyInterpreterState_Head()" and "PyInterpreterState_Next()" let a
  caller walk through all the existing interpreter objects;
  "PyInterpreterState_ThreadHead()" and "PyThreadState_Next()" allow
  looping over all the thread states for a given interpreter.
  (Contributed by David Beazley.)

* The C-level interface to the garbage collector has been changed to
  make it easier to write extension types that support garbage
  collection and to debug misuses of the functions. Various functions
  have slightly different semantics, so a bunch of functions had to be
  renamed.  Extensions that use the old API will still compile but
  will *not* participate in garbage collection, so updating them for
  2.2 should be considered fairly high priority.

  To upgrade an extension module to the new API, perform the following
  steps:

* Rename "Py_TPFLAGS_GC()" to "PyTPFLAGS_HAVE_GC()".

* Use "PyObject_GC_New()" or "PyObject_GC_NewVar()" to allocate

     objects, and "PyObject_GC_Del()" to deallocate them.

* Rename "PyObject_GC_Init()" to "PyObject_GC_Track()" and

     "PyObject_GC_Fini()" to "PyObject_GC_UnTrack()".

* Remove "PyGC_HEAD_SIZE()" from object size calculations.

* Remove calls to "PyObject_AS_GC()" and "PyObject_FROM_GC()".

* A new "et" format sequence was added to "PyArg_ParseTuple()"; "et"
  takes both a parameter and an encoding name, and converts the
  parameter to the given encoding if the parameter turns out to be a
  Unicode string, or leaves it alone if it’s an 8-bit string, assuming
  it to already be in the desired encoding.  This differs from the
  "es" format character, which assumes that 8-bit strings are in
  Python’s default ASCII encoding and converts them to the specified
  new encoding. (Contributed by M.-A. Lemburg, and used for the MBCS
  support on Windows described in the following section.)

* A different argument parsing function, "PyArg_UnpackTuple()", has
  been added that’s simpler and presumably faster.  Instead of
  specifying a format string, the caller simply gives the minimum and
  maximum number of arguments expected, and a set of pointers to
  "PyObject*" variables that will be filled in with argument values.

* Two new flags "METH_NOARGS" and "METH_O" are available in method
  definition tables to simplify implementation of methods with no
  arguments or a single untyped argument. Calling such methods is more
  efficient than calling a corresponding method that uses
  "METH_VARARGS".  Also, the old "METH_OLDARGS" style of writing C
  methods is  now officially deprecated.

* Two new wrapper functions, "PyOS_snprintf()" and
  "PyOS_vsnprintf()" were added to provide  cross-platform
  implementations for the relatively new "snprintf()" and
  "vsnprintf()" C lib APIs. In contrast to the standard "sprintf()"
  and "vsprintf()" functions, the Python versions check the bounds of
  the buffer used to protect against buffer overruns. (Contributed by
  M.-A. Lemburg.)

* The "_PyTuple_Resize()" function has lost an unused parameter, so
  now it takes 2 parameters instead of 3.  The third argument was
  never used, and can simply be discarded when porting code from
  earlier versions to Python 2.2.


Autres changements et corrections
=================================

As usual there were a bunch of other improvements and bugfixes
scattered throughout the source tree.  A search through the CVS change
logs finds there were 527 patches applied and 683 bugs fixed between
Python 2.1 and 2.2; 2.2.1 applied 139 patches and fixed 143 bugs;
2.2.2 applied 106 patches and fixed 82 bugs.  These figures are likely
to be underestimates.

Some of the more notable changes are:

* The code for the MacOS port for Python, maintained by Jack Jansen,
  is now kept in the main Python CVS tree, and many changes have been
  made to support MacOS X.

  The most significant change is the ability to build Python as a
  framework, enabled by supplying the "--enable-framework" option to
  the configure script when compiling Python.  According to Jack
  Jansen, « This installs a self-contained Python installation plus
  the OS X framework « glue » into
  "/Library/Frameworks/Python.framework" (or another location of
  choice). For now there is little immediate added benefit to this
  (actually, there is the disadvantage that you have to change your
  PATH to be able to find Python), but it is the basis for creating a
  full-blown Python application, porting the MacPython IDE, possibly
  using Python as a standard OSA scripting language and much more. »

  Most of the MacPython toolbox modules, which interface to MacOS APIs
  such as windowing, QuickTime, scripting, etc. have been ported to OS
  X, but they’ve been left commented out in "setup.py".  People who
  want to experiment with these modules can uncomment them manually.

* Keyword arguments passed to built-in functions that don’t take
  them now cause a "TypeError" exception to be raised, with the
  message « *function* takes no keyword arguments ».

* Weak references, added in Python 2.1 as an extension module, are
  now part of the core because they’re used in the implementation of
  new- style classes.  The "ReferenceError" exception has therefore
  moved from the "weakref" module to become a built-in exception.

* A new script, "Tools/scripts/cleanfuture.py" by Tim Peters,
  automatically removes obsolete "__future__" statements from Python
  source code.

* An additional *flags* argument has been added to the built-in
  function "compile()", so the behaviour of "__future__" statements
  can now be correctly observed in simulated shells, such as those
  presented by IDLE and other development environments.  This is
  described in **PEP 264**. (Contributed by Michael Hudson.)

* The new license introduced with Python 1.6 wasn’t GPL-compatible.
  This is fixed by some minor textual changes to the 2.2 license, so
  it’s now legal to embed Python inside a GPLed program again.  Note
  that Python itself is not GPLed, but instead is under a license
  that’s essentially equivalent to the BSD license, same as it always
  was.  The license changes were also applied to the Python 2.0.1 and
  2.1.1 releases.

* When presented with a Unicode filename on Windows, Python will now
  convert it to an MBCS encoded string, as used by the Microsoft file
  APIs.  As MBCS is explicitly used by the file APIs, Python’s choice
  of ASCII as the default encoding turns out to be an annoyance.  On
  Unix, the locale’s character set is used if
  "locale.nl_langinfo(CODESET)" is available.  (Windows support was
  contributed by Mark Hammond with assistance from Marc-André Lemburg.
  Unix support was added by Martin von Löwis.)

* Large file support is now enabled on Windows.  (Contributed by Tim
  Peters.)

* The "Tools/scripts/ftpmirror.py" script now parses a ".netrc"
  file, if you have one. (Contributed by Mike Romberg.)

* Some features of the object returned by the "xrange()" function
  are now deprecated, and trigger warnings when they’re accessed;
  they’ll disappear in Python 2.3. "xrange" objects tried to pretend
  they were full sequence types by supporting slicing, sequence
  multiplication, and the "in" operator, but these features were
  rarely used and therefore buggy.  The "tolist()" method and the
  "start", "stop", and "step" attributes are also being deprecated.
  At the C level, the fourth argument to the "PyRange_New()" function,
  "repeat", has also been deprecated.

* There were a bunch of patches to the dictionary implementation,
  mostly to fix potential core dumps if a dictionary contains objects
  that sneakily changed their hash value, or mutated the dictionary
  they were contained in. For a while python-dev fell into a gentle
  rhythm of Michael Hudson finding a case that dumped core, Tim Peters
  fixing the bug, Michael finding another case, and round and round it
  went.

* On Windows, Python can now be compiled with Borland C thanks to a
  number of patches contributed by Stephen Hansen, though the result
  isn’t fully functional yet.  (But this *is* progress…)

* Another Windows enhancement: Wise Solutions generously offered
  PythonLabs use of their InstallerMaster 8.1 system.  Earlier
  PythonLabs Windows installers used Wise 5.0a, which was beginning to
  show its age.  (Packaged up by Tim Peters.)

* Files ending in ".pyw" can now be imported on Windows. ".pyw" is a
  Windows-only thing, used to indicate that a script needs to be run
  using PYTHONW.EXE instead of PYTHON.EXE in order to prevent a DOS
  console from popping up to display the output.  This patch makes it
  possible to import such scripts, in case they’re also usable as
  modules.  (Implemented by David Bolen.)

* On platforms where Python uses the C "dlopen()" function  to load
  extension modules, it’s now possible to set the flags used  by
  "dlopen()" using the "sys.getdlopenflags()" and
  "sys.setdlopenflags()" functions. (Contributed by Bram Stolk.)

* The "pow()" built-in function no longer supports 3 arguments when
  floating-point numbers are supplied. "pow(x, y, z)" returns "(x**y)
  % z", but this is never useful for floating point numbers, and the
  final result varies unpredictably depending on the platform.  A call
  such as "pow(2.0, 8.0, 7.0)" will now raise a "TypeError" exception.


Remerciements
=============

The author would like to thank the following people for offering
suggestions, corrections and assistance with various drafts of this
article: Fred Bremmer, Keith Briggs, Andrew Dalke, Fred L. Drake, Jr.,
Carel Fellinger, David Goodger, Mark Hammond, Stephen Hansen, Michael
Hudson, Jack Jansen, Marc-André Lemburg, Martin von Löwis, Fredrik
Lundh, Michael McLay, Nick Mathewson, Paul Moore, Gustavo Niemeyer,
Don O’Donnell, Joonas Paalasma, Tim Peters, Jens Quade, Tom Reinhardt,
Neil Schemenauer, Guido van Rossum, Greg Ward, Edward Welbourne.
