What's New in Python 2.1
************************

Author:
   A.M. Kuchling


簡介
====

This article explains the new features in Python 2.1.  While there
aren't as many changes in 2.1 as there were in Python 2.0, there are
still some pleasant surprises in store.  2.1 is the first release to
be steered through the use of Python Enhancement Proposals, or PEPs,
so most of the sizable changes have accompanying PEPs that provide
more complete documentation and a design rationale for the change.
This article doesn't attempt to document the new features completely,
but simply provides an overview of the new features for Python
programmers. Refer to the Python 2.1 documentation, or to the specific
PEP, for more details about any new feature that particularly
interests you.

One recent goal of the Python development team has been to accelerate
the pace of new releases, with a new release coming every 6 to 9
months. 2.1 is the first release to come out at this faster pace, with
the first alpha appearing in January, 3 months after the final version
of 2.0 was released.

The final release of Python 2.1 was made on April 17, 2001.


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

The largest change in Python 2.1 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" expression 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 2.1 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).

Compatibility concerns have led to nested scopes being introduced
gradually; in Python 2.1, they aren't enabled by default, but can be
turned on within a module by using a future statement as described in
**PEP 236**.  (See the following section for further discussion of
**PEP 236**.)  In Python 2.2, nested scopes will become the default
and there will be no way to turn them off, but users will have had all
of 2.1's lifetime to fix any breakage resulting from their
introduction.

也參考:

  **PEP 227** - Statically Nested Scopes
     Written and implemented by Jeremy Hylton.


PEP 236: __future__ Directives
==============================

The reaction to nested scopes was widespread concern about the dangers
of breaking code with the 2.1 release, and it was strong enough to
make the Pythoneers take a more conservative approach.  This approach
consists of introducing a convention for enabling optional
functionality in release N that will become compulsory in release N+1.

The syntax uses a "from...import" statement using the reserved module
name "__future__".  Nested scopes can be enabled by the following
statement:

   from __future__ import nested_scopes

While it looks like a normal "import" statement, it's not; there are
strict rules on where such a future statement can be put. They can
only be at the top of a module, and must precede any Python code or
regular "import" statements.  This is because such statements can
affect how the Python bytecode compiler parses code and generates
bytecode, so they must precede any statement that will result in
bytecodes being produced.

也參考:

  **PEP 236** - Back to the "__future__"
     Written by Tim Peters, and primarily implemented by Jeremy
     Hylton.


PEP 207: Rich Comparisons
=========================

In earlier versions, Python's support for implementing comparisons on
user-defined classes and extension types was quite simple. Classes
could implement a "__cmp__()" method that was given two instances of a
class, and could only return 0 if they were equal or +1 or -1 if they
weren't; the method couldn't raise an exception or return anything
other than a Boolean value.  Users of Numeric Python often found this
model too weak and restrictive, because in the number-crunching
programs that numeric Python is used for, it would be more useful to
be able to perform elementwise comparisons of two matrices, returning
a matrix containing the results of a given comparison for each
element.  If the two matrices are of different sizes, then the compare
has to be able to raise an exception to signal the error.

In Python 2.1, rich comparisons were added in order to support this
need. Python classes can now individually overload each of the "<",
"<=", ">", ">=", "==", and "!=" operations.  The new magic method
names are:

+-------------+------------------+
| Operation   | Method name      |
|=============|==================|
| "<"         | "__lt__()"       |
+-------------+------------------+
| "<="        | "__le__()"       |
+-------------+------------------+
| ">"         | "__gt__()"       |
+-------------+------------------+
| ">="        | "__ge__()"       |
+-------------+------------------+
| "=="        | "__eq__()"       |
+-------------+------------------+
| "!="        | "__ne__()"       |
+-------------+------------------+

(The magic methods are named after the corresponding Fortran operators
".LT.". ".LE.", &c.  Numeric programmers are almost certainly quite
familiar with these names and will find them easy to remember.)

Each of these magic methods is of the form "method(self, other)",
where "self" will be the object on the left-hand side of the operator,
while "other" will be the object on the right-hand side.  For example,
the expression "A < B" will cause "A.__lt__(B)" to be called.

Each of these magic methods can return anything at all: a Boolean, a
matrix, a list, or any other Python object.  Alternatively they can
raise an exception if the comparison is impossible, inconsistent, or
otherwise meaningless.

The built-in "cmp(A,B)" function can use the rich comparison
machinery, and now accepts an optional argument specifying which
comparison operation to use; this is given as one of the strings
""<"", ""<="", "">"", "">="", ""=="", or ""!="".  If called without
the optional third argument, "cmp()" will only return -1, 0, or +1 as
in previous versions of Python; otherwise it will call the appropriate
method and can return any Python object.

There are also corresponding changes of interest to C programmers;
there's a new slot "tp_richcmp" in type objects and an API for
performing a given rich comparison.  I won't cover the C API here, but
will refer you to **PEP 207**, or to 2.1's C API documentation, for
the full list of related functions.

也參考:

  **PEP 207** - Rich Comparisons
     Written by Guido van Rossum, heavily based on earlier work by
     David Ascher, and implemented by Guido van Rossum.


PEP 230: Warning Framework
==========================

Over its 10 years of existence, Python has accumulated a certain
number of obsolete modules and features along the way.  It's difficult
to know when a feature is safe to remove, since there's no way of
knowing how much code uses it --- perhaps no programs depend on the
feature, or perhaps many do.  To enable removing old features in a
more structured way, a warning framework was added. When the Python
developers want to get rid of a feature, it will first trigger a
warning in the next version of Python.  The following Python version
can then drop the feature, and users will have had a full release
cycle to remove uses of the old feature.

Python 2.1 adds the warning framework to be used in this scheme.  It
adds a "warnings" module that provide functions to issue warnings, and
to filter out warnings that you don't want to be displayed. Third-
party modules can also use this framework to deprecate old features
that they no longer wish to support.

For example, in Python 2.1 the "regex" module is deprecated, so
importing it causes a warning to be printed:

   >>> import regex
   __main__:1: DeprecationWarning: the regex module
            is deprecated; please use the re module
   >>>

Warnings can be issued by calling the "warnings.warn()" function:

   warnings.warn("feature X no longer supported")

The first parameter is the warning message; an additional optional
parameters can be used to specify a particular warning category.

Filters can be added to disable certain warnings; a regular expression
pattern can be applied to the message or to the module name in order
to suppress a warning.  For example, you may have a program that uses
the "regex" module and not want to spare the time to convert it to use
the "re" module right now.  The warning can be suppressed by calling

   import warnings
   warnings.filterwarnings(action = 'ignore',
                           message='.*regex module is deprecated',
                           category=DeprecationWarning,
                           module = '__main__')

This adds a filter that will apply only to warnings of the class
"DeprecationWarning" triggered in the "__main__" module, and applies a
regular expression to only match the message about the "regex" module
being deprecated, and will cause such warnings to be ignored.
Warnings can also be printed only once, printed every time the
offending code is executed, or turned into exceptions that will cause
the program to stop (unless the exceptions are caught in the usual
way, of course).

Functions were also added to Python's C API for issuing warnings;
refer to PEP 230 or to Python's API documentation for the details.

也參考:

  **PEP 5** - Guidelines for Language Evolution
     Written by Paul Prescod, to specify procedures to be followed
     when removing old features from Python.  The policy described in
     this PEP hasn't been officially adopted, but the eventual policy
     probably won't be too different from Prescod's proposal.

  **PEP 230** - Warning Framework
     Written and implemented by Guido van Rossum.


PEP 229: New Build System
=========================

When compiling Python, the user had to go in and edit the
"Modules/Setup" file in order to enable various additional modules;
the default set is relatively small and limited to modules that
compile on most Unix platforms. This means that on Unix platforms with
many more features, most notably Linux, Python installations often
don't contain all useful modules they could.

Python 2.0 added the Distutils, a set of modules for distributing and
installing extensions.  In Python 2.1, the Distutils are used to
compile much of the standard library of extension modules,
autodetecting which ones are supported on the current machine.  It's
hoped that this will make Python installations easier and more
featureful.

Instead of having to edit the "Modules/Setup" file in order to enable
modules, a "setup.py" script in the top directory of the Python source
distribution is run at build time, and attempts to discover which
modules can be enabled by examining the modules and header files on
the system.  If a module is configured in "Modules/Setup", the
"setup.py" script won't attempt to compile that module and will defer
to the "Modules/Setup" file's contents.  This provides a way to
specific any strange command-line flags or libraries that are required
for a specific platform.

In another far-reaching change to the build mechanism, Neil
Schemenauer restructured things so Python now uses a single makefile
that isn't recursive, instead of makefiles in the top directory and in
each of the "Python/", "Parser/", "Objects/", and "Modules/"
subdirectories.  This makes building Python faster and also makes
hacking the Makefiles clearer and simpler.

也參考:

  **PEP 229** - Using Distutils to Build Python
     Written and implemented by A.M. Kuchling.


PEP 205: Weak References
========================

Weak references, available through the "weakref" module, are a minor
but useful new data type in the Python programmer's toolbox.

Storing a reference to an object (say, in a dictionary or a list) has
the side effect of keeping that object alive forever.  There are a few
specific cases where this behaviour is undesirable, object caches
being the most common one, and another being circular references in
data structures such as trees.

For example, consider a memoizing function that caches the results of
another function "f(x)" by storing the function's argument and its
result in a dictionary:

   _cache = {}
   def memoize(x):
       if _cache.has_key(x):
           return _cache[x]

       retval = f(x)

       # Cache the returned object
       _cache[x] = retval

       return retval

This version works for simple things such as integers, but it has a
side effect; the "_cache" dictionary holds a reference to the return
values, so they'll never be deallocated until the Python process exits
and cleans up. This isn't very noticeable for integers, but if "f()"
returns an object, or a data structure that takes up a lot of memory,
this can be a problem.

Weak references provide a way to implement a cache that won't keep
objects alive beyond their time.  If an object is only accessible
through weak references, the object will be deallocated and the weak
references will now indicate that the object it referred to no longer
exists.  A weak reference to an object *obj* is created by calling "wr
= weakref.ref(obj)".  The object being referred to is returned by
calling the weak reference as if it were a function: "wr()".  It will
return the referenced object, or "None" if the object no longer
exists.

This makes it possible to write a "memoize()" function whose cache
doesn't keep objects alive, by storing weak references in the cache.

   _cache = {}
   def memoize(x):
       if _cache.has_key(x):
           obj = _cache[x]()
           # If weak reference object still exists,
           # return it
           if obj is not None: return obj

       retval = f(x)

       # Cache a weak reference
       _cache[x] = weakref.ref(retval)

       return retval

The "weakref" module also allows creating proxy objects which behave
like weak references --- an object referenced only by proxy objects is
deallocated -- but instead of requiring an explicit call to retrieve
the object, the proxy transparently forwards all operations to the
object as long as the object still exists.  If the object is
deallocated, attempting to use a proxy will cause a
"weakref.ReferenceError" exception to be raised.

   proxy = weakref.proxy(obj)
   proxy.attr   # Equivalent to obj.attr
   proxy.meth() # Equivalent to obj.meth()
   del obj
   proxy.attr   # raises weakref.ReferenceError

也參考:

  **PEP 205** - Weak References
     Written and implemented by Fred L. Drake, Jr.


PEP 232: Function Attributes
============================

In Python 2.1, functions can now have arbitrary information attached
to them. People were often using docstrings to hold information about
functions and methods, because the "__doc__" attribute was the only
way of attaching any information to a function.  For example, in the
Zope Web application server, functions are marked as safe for public
access by having a docstring, and in John Aycock's SPARK parsing
framework, docstrings hold parts of the BNF grammar to be parsed.
This overloading is unfortunate, since docstrings are really intended
to hold a function's documentation; for example, it means you can't
properly document functions intended for private use in Zope.

Arbitrary attributes can now be set and retrieved on functions using
the regular Python syntax:

   def f(): pass

   f.publish = 1
   f.secure = 1
   f.grammar = "A ::= B (C D)*"

The dictionary containing attributes can be accessed as the function's
"__dict__". Unlike the "__dict__" attribute of class instances, in
functions you can actually assign a new dictionary to "__dict__",
though the new value is restricted to a regular Python dictionary; you
*can't* be tricky and set it to a "UserDict" instance, or any other
random object that behaves like a mapping.

也參考:

  **PEP 232** - Function Attributes
     Written and implemented by Barry Warsaw.


PEP 235: Importing Modules on Case-Insensitive Platforms
========================================================

Some operating systems have filesystems that are case-insensitive,
MacOS and Windows being the primary examples; on these systems, it's
impossible to distinguish the filenames "FILE.PY" and "file.py", even
though they do store the file's name  in its original case (they're
case-preserving, too).

In Python 2.1, the "import" statement will work to simulate case-
sensitivity on case-insensitive platforms.  Python will now search for
the first case-sensitive match by default, raising an "ImportError" if
no such file is found, so "import file" will not import a module named
"FILE.PY". Case-insensitive matching can be requested by setting the
"PYTHONCASEOK" environment variable before starting the Python
interpreter.


PEP 217: Interactive Display Hook
=================================

When using the Python interpreter interactively, the output of
commands is displayed using the built-in "repr()" function. In Python
2.1, the variable "sys.displayhook()" can be set to a callable object
which will be called instead of "repr()". For example, you can set it
to a special pretty-printing function:

   >>> # Create a recursive data structure
   ... L = [1,2,3]
   >>> L.append(L)
   >>> L # Show Python's default output
   [1, 2, 3, [...]]
   >>> # Use pprint.pprint() as the display function
   ... import sys, pprint
   >>> sys.displayhook = pprint.pprint
   >>> L
   [1, 2, 3,  <Recursion on list with id=135143996>]
   >>>

也參考:

  **PEP 217** - Display Hook for Interactive Use
     Written and implemented by Moshe Zadka.


PEP 208: New Coercion Model
===========================

How numeric coercion is done at the C level was significantly
modified.  This will only affect the authors of C extensions to
Python, allowing them more flexibility in writing extension types that
support numeric operations.

Extension types can now set the type flag "Py_TPFLAGS_CHECKTYPES" in
their "PyTypeObject" structure to indicate that they support the new
coercion model. In such extension types, the numeric slot functions
can no longer assume that they'll be passed two arguments of the same
type; instead they may be passed two arguments of differing types, and
can then perform their own internal coercion. If the slot function is
passed a type it can't handle, it can indicate the failure by
returning a reference to the "Py_NotImplemented" singleton value. The
numeric functions of the other type will then be tried, and perhaps
they can handle the operation; if the other type also returns
"Py_NotImplemented", then a "TypeError" will be raised.  Numeric
methods written in Python can also return "Py_NotImplemented", causing
the interpreter to act as if the method did not exist (perhaps raising
a "TypeError", perhaps trying another object's numeric methods).

也參考:

  **PEP 208** - Reworking the Coercion Model
     Written and implemented by Neil Schemenauer, heavily based upon
     earlier work by Marc-André Lemburg.  Read this to understand the
     fine points of how numeric operations will now be processed at
     the C level.


PEP 241: Metadata in Python Packages
====================================

A common complaint from Python users is that there's no single catalog
of all the Python modules in existence.  T. Middleton's Vaults of
Parnassus at http://www.vex.net/parnassus/ are the largest catalog of
Python modules, but registering software at the Vaults is optional,
and many people don't bother.

As a first small step toward fixing the problem, Python software
packaged using the Distutils **sdist** command will include a file
named "PKG-INFO" containing information about the package such as its
name, version, and author (metadata, in cataloguing terminology).
**PEP 241** contains the full list of fields that can be present in
the "PKG-INFO" file.  As people began to package their software using
Python 2.1, more and more packages will include metadata, making it
possible to build automated cataloguing systems and experiment with
them.  With the result experience, perhaps it'll be possible to design
a really good catalog and then build support for it into Python 2.2.
For example, the Distutils **sdist** and **bdist_*** commands could
support an "upload" option that would automatically upload your
package to a catalog server.

You can start creating packages containing "PKG-INFO" even if you're
not using Python 2.1, since a new release of the Distutils will be
made for users of earlier Python versions.  Version 1.0.2 of the
Distutils includes the changes described in **PEP 241**, as well as
various bugfixes and enhancements.  It will be available from the
Distutils SIG at https://www.python.org/community/sigs/current
/distutils-sig/.

也參考:

  **PEP 241** - Metadata for Python Software Packages
     Written and implemented by A.M. Kuchling.

  **PEP 243** - Module Repository Upload Mechanism
     Written by Sean Reifschneider, this draft PEP describes a
     proposed mechanism for uploading  Python packages to a central
     server.


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

* Ka-Ping Yee contributed two new modules: "inspect.py", a module
  for getting information about live Python code, and "pydoc.py", a
  module for interactively converting docstrings to HTML or text.  As
  a bonus, "Tools/scripts/pydoc", which is now automatically
  installed, uses "pydoc.py" to display documentation given a Python
  module, package, or class name.  For example, "pydoc xml.dom"
  displays the following:

     Python Library Documentation: package xml.dom in xml

     NAME
         xml.dom - W3C Document Object Model implementation for Python.

     FILE
         /usr/local/lib/python2.1/xml/dom/__init__.pyc

     DESCRIPTION
         The Python mapping of the Document Object Model is documented in the
         Python Library Reference in the section on the xml.dom package.

         This package contains the following modules:
           ...

  "pydoc" also includes a Tk-based interactive help browser.   "pydoc"
  quickly becomes addictive; try it out!

* Two different modules for unit testing were added to the standard
  library. The "doctest" module, contributed by Tim Peters, provides a
  testing framework based on running embedded examples in docstrings
  and comparing the results against the expected output.  PyUnit,
  contributed by Steve Purcell, is a unit testing framework inspired
  by JUnit, which was in turn an adaptation of Kent Beck's Smalltalk
  testing framework.  See http://pyunit.sourceforge.net/ for more
  information about PyUnit.

* The "difflib" module contains a class, "SequenceMatcher", which
  compares two sequences and computes the changes required to
  transform one sequence into the other.  For example, this module can
  be used to write a tool similar to the Unix **diff** program, and in
  fact the sample program "Tools/scripts/ndiff.py" demonstrates how to
  write such a script.

* "curses.panel", a wrapper for the panel library, part of ncurses
  and of SYSV curses, was contributed by Thomas Gellekum.  The panel
  library provides windows with the additional feature of depth.
  Windows can be moved higher or lower in the depth ordering, and the
  panel library figures out where panels overlap and which sections
  are visible.

* The PyXML package has gone through a few releases since Python
  2.0, and Python 2.1 includes an updated version of the "xml"
  package. Some of the noteworthy changes include support for Expat
  1.2 and later versions, the ability for Expat parsers to handle
  files in any encoding supported by Python, and various bugfixes for
  SAX, DOM, and the "minidom" module.

* Ping also contributed another hook for handling uncaught
  exceptions. "sys.excepthook()" can be set to a callable object.
  When an exception isn't caught by any "try"..."except" blocks, the
  exception will be passed to "sys.excepthook()", which can then do
  whatever it likes.  At the Ninth Python Conference, Ping
  demonstrated an application for this hook: printing an extended
  traceback that not only lists the stack frames, but also lists the
  function arguments and the local variables for each frame.

* Various functions in the "time" module, such as "asctime()" and
  "localtime()", require a floating point argument containing the time
  in seconds since the epoch.  The most common use of these functions
  is to work with the current time, so the floating point argument has
  been made optional; when a value isn't provided, the current time
  will be used.  For example, log file entries usually need a string
  containing the current time; in Python 2.1, "time.asctime()" can be
  used, instead of the lengthier
  "time.asctime(time.localtime(time.time()))" that was previously
  required.

  This change was proposed and implemented by Thomas Wouters.

* The "ftplib" module now defaults to retrieving files in passive
  mode, because passive mode is more likely to work from behind a
  firewall.  This request came from the Debian bug tracking system,
  since other Debian packages use "ftplib" to retrieve files and then
  don't work from behind a firewall. It's deemed unlikely that this
  will cause problems for anyone, because Netscape defaults to passive
  mode and few people complain, but if passive mode is unsuitable for
  your application or network setup, call "set_pasv(0)" on FTP objects
  to disable passive mode.

* Support for raw socket access has been added to the "socket"
  module, contributed by Grant Edwards.

* The "pstats" module now contains a simple interactive statistics
  browser for displaying timing profiles for Python programs, invoked
  when the module is run as a script.  Contributed by  Eric S.
  Raymond.

* A new implementation-dependent function, "sys._getframe([depth])",
  has been added to return a given frame object from the current call
  stack. "sys._getframe()" returns the frame at the top of the call
  stack;  if the optional integer argument *depth* is supplied, the
  function returns the frame that is *depth* calls below the top of
  the stack.  For example, "sys._getframe(1)" returns the caller's
  frame object.

  This function is only present in CPython, not in Jython or the .NET
  implementation.  Use it for debugging, and resist the temptation to
  put it into production code.


Other Changes and Fixes
=======================

There were relatively few smaller changes made in Python 2.1 due to
the shorter release cycle.  A search through the CVS change logs turns
up 117 patches applied, and 136 bugs fixed; both figures are likely to
be underestimates.  Some of the more notable changes are:

* A specialized object allocator is now optionally available, that
  should be faster than the system "malloc()" and have less memory
  overhead.  The allocator uses C's "malloc()" function to get large
  pools of memory, and then fulfills smaller memory requests from
  these pools.  It can be enabled by providing the "--with-pymalloc"
  option to the **configure** script; see "Objects/obmalloc.c" for the
  implementation details.

  Authors of C extension modules should test their code with the
  object allocator enabled, because some incorrect code may break,
  causing core dumps at runtime. There are a bunch of memory
  allocation functions in Python's C API that have previously been
  just aliases for the C library's "malloc()" and "free()", meaning
  that if you accidentally called mismatched functions, the error
  wouldn't be noticeable.  When the object allocator is enabled, these
  functions aren't aliases of "malloc()" and "free()" any more, and
  calling the wrong function to free memory will get you a core dump.
  For example, if memory was allocated using "PyMem_New()", it has to
  be freed using "PyMem_Del()", not "free()".  A few modules included
  with Python fell afoul of this and had to be fixed; doubtless there
  are more third-party modules that will have the same problem.

  The object allocator was contributed by Vladimir Marangozov.

* The speed of line-oriented file I/O has been improved because
  people often complain about its lack of speed, and because it's
  often been used as a naïve benchmark.  The "readline()" method of
  file objects has therefore been rewritten to be much faster.  The
  exact amount of the speedup will vary from platform to platform
  depending on how slow the C library's "getc()" was, but is around
  66%, and potentially much faster on some particular operating
  systems. Tim Peters did much of the benchmarking and coding for this
  change, motivated by a discussion in comp.lang.python.

  A new module and method for file objects was also added, contributed
  by Jeff Epler. The new method, "xreadlines()", is similar to the
  existing "xrange()" built-in.  "xreadlines()" returns an opaque
  sequence object that only supports being iterated over, reading a
  line on every iteration but not reading the entire file into memory
  as the existing "readlines()" method does. You'd use it like this:

     for line in sys.stdin.xreadlines():
         # ... do something for each line ...
         ...

  For a fuller discussion of the line I/O changes, see the python-dev
  summary for January 1--15, 2001 at https://mail.python.org/pipermail
  /python-dev/2001-January/.

* A new method, "popitem()", was added to dictionaries to enable
  destructively iterating through the contents of a dictionary; this
  can be faster for large dictionaries because there's no need to
  construct a list containing all the keys or values. "D.popitem()"
  removes a random "(key, value)" pair from the dictionary "D" and
  returns it as a 2-tuple.  This was implemented mostly by Tim Peters
  and Guido van Rossum, after a suggestion and preliminary patch by
  Moshe Zadka.

* Modules can now control which names are imported when "from module
  import *" is used, by defining an "__all__" attribute containing a
  list of names that will be imported.  One common complaint is that
  if the module imports other modules such as "sys" or "string", "from
  module import *" will add them to the importing module's namespace.
  To fix this, simply list the public names in "__all__":

     # List public names
     __all__ = ['Database', 'open']

  A stricter version of this patch was first suggested and implemented
  by Ben Wolfson, but after some python-dev discussion, a weaker final
  version was checked in.

* Applying "repr()" to strings previously used octal escapes for
  non- printable characters; for example, a newline was "'\012'".
  This was a vestigial trace of Python's C ancestry, but today octal
  is of very little practical use.  Ka-Ping Yee suggested using hex
  escapes instead of octal ones, and using the "\n", "\t", "\r"
  escapes for the appropriate characters, and implemented this new
  formatting.

* Syntax errors detected at compile-time can now raise exceptions
  containing the filename and line number of the error, a pleasant
  side effect of the compiler reorganization done by Jeremy Hylton.

* C extensions which import other modules have been changed to use
  "PyImport_ImportModule()", which means that they will use any import
  hooks that have been installed.  This is also encouraged for third-
  party extensions that need to import some other module from C code.

* The size of the Unicode character database was shrunk by another
  340K thanks to Fredrik Lundh.

* Some new ports were contributed: MacOS X (by Steven Majewski),
  Cygwin (by Jason Tishler); RISCOS (by Dietmar Schwertberger);
  Unixware 7  (by Billy G. Allie).

And there's the usual list of minor bugfixes, minor memory leaks,
docstring edits, and other tweaks, too lengthy to be worth itemizing;
see the CVS logs for the full details if you want them.


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

The author would like to thank the following people for offering
suggestions on various drafts of this article: Graeme Cross, David
Goodger, Jay Graves, Michael Hudson, Marc-André Lemburg, Fredrik
Lundh, Neil Schemenauer, Thomas Wouters.
