What’s New in Python 2.0
************************

Author:
   A.M. Kuchling and Moshe Zadka


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

A new release of Python, version 2.0, was released on October 16,
2000. This article covers the exciting new features in 2.0, highlights
some other useful changes, and points out a few incompatible changes
that may require rewriting code.

Python’s development never completely stops between releases, and a
steady flow of bug fixes and improvements are always being submitted.
A host of minor fixes, a few optimizations, additional docstrings, and
better error messages went into 2.0; to list them all would be
impossible, but they’re certainly significant. Consult the publicly
available CVS logs if you want to see the full list.  This progress is
due to the five developers working for  PythonLabs are now getting
paid to spend their days fixing bugs, and also due to the improved
communication resulting  from moving to SourceForge.


What About Python 1.6?
======================

Python 1.6 can be thought of as the Contractual Obligations Python
release. After the core development team left CNRI in May 2000, CNRI
requested that a 1.6 release be created, containing all the work on
Python that had been performed at CNRI.  Python 1.6 therefore
represents the state of the CVS tree as of May 2000, with the most
significant new feature being Unicode support.  Development continued
after May, of course, so the 1.6 tree received a few fixes to ensure
that it’s forward-compatible with Python 2.0.  1.6 is therefore part
of Python’s evolution, and not a side branch.

So, should you take much interest in Python 1.6?  Probably not.  The
1.6final and 2.0beta1 releases were made on the same day (September 5,
2000), the plan being to finalize Python 2.0 within a month or so.  If
you have applications to maintain, there seems little point in
breaking things by moving to 1.6, fixing them, and then having another
round of breakage within a month by moving to 2.0; you’re better off
just going straight to 2.0.  Most of the really interesting features
described in this document are only in 2.0, because a lot of work was
done between May and September.


New Development Process
=======================

The most important change in Python 2.0 may not be to the code at all,
but to how Python is developed: in May 2000 the Python developers
began using the tools made available by SourceForge for storing
source code, tracking bug reports, and managing the queue of patch
submissions.  To report bugs or submit patches for Python 2.0, use the
bug tracking and patch manager tools available from Python’s project
page, located at https://sourceforge.net/projects/python/.

The most important of the services now hosted at SourceForge is the
Python CVS tree, the version-controlled repository containing the
source code for Python. Previously, there were roughly 7 or so people
who had write access to the CVS tree, and all patches had to be
inspected and checked in by one of the people on this short list.
Obviously, this wasn’t very scalable.  By moving the CVS tree to
SourceForge, it became possible to grant write access to more people;
as of September 2000 there were 27 people able to check in changes, a
fourfold increase.  This makes possible large-scale changes that
wouldn’t be attempted if they’d have to be filtered through the small
group of core developers.  For example, one day Peter Schneider-Kamp
took it into his head to drop K&R C compatibility and convert the C
source for Python to ANSI C. After getting approval on the python-dev
mailing list, he launched into a flurry of checkins that lasted about
a week, other developers joined in to help, and the job was done.  If
there were only 5 people with write access, probably that task would
have been viewed as “nice, but not worth the time and effort needed”
and it would never have gotten done.

The shift to using SourceForge’s services has resulted in a remarkable
increase in the speed of development.  Patches now get submitted,
commented on, revised by people other than the original submitter, and
bounced back and forth between people until the patch is deemed worth
checking in.  Bugs are tracked in one central location and can be
assigned to a specific person for fixing, and we can count the number
of open bugs to measure progress.  This didn’t come without a cost:
developers now have more e-mail to deal with, more mailing lists to
follow, and special tools had to be written for the new environment.
For example, SourceForge sends default patch and bug notification
e-mail messages that are completely unhelpful, so Ka-Ping Yee wrote an
HTML screen-scraper that sends more useful messages.

The ease of adding code caused a few initial growing pains, such as
code was checked in before it was ready or without getting clear
agreement from the developer group.  The approval process that has
emerged is somewhat similar to that used by the Apache group.
Developers can vote +1, +0, -0, or -1 on a patch; +1 and -1 denote
acceptance or rejection, while +0 and -0 mean the developer is mostly
indifferent to the change, though with a slight positive or negative
slant.  The most significant change from the Apache model is that the
voting is essentially advisory, letting Guido van Rossum, who has
Benevolent Dictator For Life status, know what the general opinion is.
He can still ignore the result of a vote, and approve or reject a
change even if the community disagrees with him.

Producing an actual patch is the last step in adding a new feature,
and is usually easy compared to the earlier task of coming up with a
good design. Discussions of new features can often explode into
lengthy mailing list threads, making the discussion hard to follow,
and no one can read every posting to python-dev.  Therefore, a
relatively formal process has been set up to write Python Enhancement
Proposals (PEPs), modelled on the internet RFC process.  PEPs are
draft documents that describe a proposed new feature, and are
continually revised until the community reaches a consensus, either
accepting or rejecting the proposal.  Quoting from the introduction to
**PEP 1**, “PEP Purpose and Guidelines”:

   PEP stands for Python Enhancement Proposal.  A PEP is a design
   document providing information to the Python community, or
   describing a new feature for Python.  The PEP should provide a
   concise technical specification of the feature and a rationale for
   the feature.

   We intend PEPs to be the primary mechanisms for proposing new
   features, for collecting community input on an issue, and for
   documenting the design decisions that have gone into Python.  The
   PEP author is responsible for building consensus within the
   community and documenting dissenting opinions.

Read the rest of **PEP 1** for the details of the PEP editorial
process, style, and format.  PEPs are kept in the Python CVS tree on
SourceForge, though they’re not part of the Python 2.0 distribution,
and are also available in HTML form from
https://www.python.org/dev/peps/.  As of September 2000, there are 25
PEPS, ranging from **PEP 201**, “Lockstep Iteration”, to PEP 225,
“Elementwise/Objectwise Operators”.


Unicode
=======

The largest new feature in Python 2.0 is a new fundamental data type:
Unicode strings.  Unicode uses 16-bit numbers to represent characters
instead of the 8-bit number used by ASCII, meaning that 65,536
distinct characters can be supported.

The final interface for Unicode support was arrived at through
countless often-stormy discussions on the python-dev mailing list, and
mostly implemented by Marc-André Lemburg, based on a Unicode string
type implementation by Fredrik Lundh.  A detailed explanation of the
interface was written up as **PEP 100**, “Python Unicode Integration”.
This article will simply cover the most significant points about the
Unicode interfaces.

In Python source code, Unicode strings are written as "u"string"".
Arbitrary Unicode characters can be written using a new escape
sequence, "\uHHHH", where *HHHH* is a 4-digit hexadecimal number from
0000 to FFFF.  The existing "\xHHHH" escape sequence can also be used,
and octal escapes can be used for characters up to U+01FF, which is
represented by "\777".

Unicode strings, just like regular strings, are an immutable sequence
type. They can be indexed and sliced, but not modified in place.
Unicode strings have an "encode( [encoding] )" method that returns an
8-bit string in the desired encoding.  Encodings are named by strings,
such as "'ascii'", "'utf-8'", "'iso-8859-1'", or whatever.  A codec
API is defined for implementing and registering new encodings that are
then available throughout a Python program. If an encoding isn’t
specified, the default encoding is usually 7-bit ASCII, though it can
be changed for your Python installation by calling the
"sys.setdefaultencoding(encoding)" function in a customized version of
"site.py".

Combining 8-bit and Unicode strings always coerces to Unicode, using
the default ASCII encoding; the result of "'a' + u'bc'" is "u'abc'".

New built-in functions have been added, and existing built-ins
modified to support Unicode:

* "unichr(ch)" returns a Unicode string 1 character long, containing
  the character *ch*.

* "ord(u)", where *u* is a 1-character regular or Unicode string,
  returns the number of the character as an integer.

* "unicode(string [, encoding]  [, errors] )" creates a Unicode string
  from an 8-bit string.  "encoding" is a string naming the encoding to
  use. The "errors" parameter specifies the treatment of characters
  that are invalid for the current encoding; passing "'strict'" as the
  value causes an exception to be raised on any encoding error, while
  "'ignore'" causes errors to be silently ignored and "'replace'" uses
  U+FFFD, the official replacement character, in case of any problems.

* The "exec" statement, and various built-ins such as "eval()",
  "getattr()", and "setattr()" will also accept Unicode strings as
  well as regular strings.  (It’s possible that the process of fixing
  this missed some built-ins; if you find a built-in function that
  accepts strings but doesn’t accept Unicode strings at all, please
  report it as a bug.)

A new module, "unicodedata", provides an interface to Unicode
character properties.  For example, "unicodedata.category(u'A')"
returns the 2-character string ‘Lu’, the ‘L’ denoting it’s a letter,
and ‘u’ meaning that it’s uppercase.
"unicodedata.bidirectional(u'\u0660')" returns ‘AN’, meaning that
U+0660 is an Arabic number.

The "codecs" module contains functions to look up existing encodings
and register new ones.  Unless you want to implement a new encoding,
you’ll most often use the "codecs.lookup(encoding)" function, which
returns a 4-element tuple: "(encode_func, decode_func, stream_reader,
stream_writer)".

* *encode_func* is a function that takes a Unicode string, and returns
  a 2-tuple "(string, length)".  *string* is an 8-bit string
  containing a portion (perhaps all) of the Unicode string converted
  into the given encoding, and *length* tells you how much of the
  Unicode string was converted.

* *decode_func* is the opposite of *encode_func*, taking an 8-bit
  string and returning a 2-tuple "(ustring, length)", consisting of
  the resulting Unicode string *ustring* and the integer *length*
  telling how much of the 8-bit string was consumed.

* *stream_reader* is a class that supports decoding input from a
  stream. *stream_reader(file_obj)* returns an object that supports
  the "read()", "readline()", and "readlines()" methods.  These
  methods will all translate from the given encoding and return
  Unicode strings.

* *stream_writer*, similarly, is a class that supports encoding output
  to a stream.  *stream_writer(file_obj)* returns an object that
  supports the "write()" and "writelines()" methods.  These methods
  expect Unicode strings, translating them to the given encoding on
  output.

For example, the following code writes a Unicode string into a file,
encoding it as UTF-8:

   import codecs

   unistr = u'\u0660\u2000ab ...'

   (UTF8_encode, UTF8_decode,
    UTF8_streamreader, UTF8_streamwriter) = codecs.lookup('UTF-8')

   output = UTF8_streamwriter( open( '/tmp/output', 'wb') )
   output.write( unistr )
   output.close()

The following code would then read UTF-8 input from the file:

   input = UTF8_streamreader( open( '/tmp/output', 'rb') )
   print repr(input.read())
   input.close()

Unicode-aware regular expressions are available through the "re"
module, which has a new underlying implementation called SRE written
by Fredrik Lundh of Secret Labs AB.

A "-U" command line option was added which causes the Python compiler
to interpret all string literals as Unicode string literals. This is
intended to be used in testing and future-proofing your Python code,
since some future version of Python may drop support for 8-bit strings
and provide only Unicode strings.


List Comprehensions
===================

Lists are a workhorse data type in Python, and many programs
manipulate a list at some point.  Two common operations on lists are
to loop over them, and either pick out the elements that meet a
certain criterion, or apply some function to each element.  For
example, given a list of strings, you might want to pull out all the
strings containing a given substring, or strip off trailing whitespace
from each line.

The existing "map()" and "filter()" functions can be used for this
purpose, but they require a function as one of their arguments.  This
is fine if there’s an existing built-in function that can be passed
directly, but if there isn’t, you have to create a little function to
do the required work, and Python’s scoping rules make the result ugly
if the little function needs additional information.  Take the first
example in the previous paragraph, finding all the strings in the list
containing a given substring.  You could write the following to do it:

   # Given the list L, make a list of all strings
   # containing the substring S.
   sublist = filter( lambda s, substring=S:
                        string.find(s, substring) != -1,
                     L)

Because of Python’s scoping rules, a default argument is used so that
the anonymous function created by the "lambda" expression knows what
substring is being searched for.  List comprehensions make this
cleaner:

   sublist = [ s for s in L if string.find(s, S) != -1 ]

List comprehensions have the form:

   [ expression for expr in sequence1
                for expr2 in sequence2 ...
                for exprN in sequenceN
                if condition ]

The "for"…"in" clauses contain the sequences to be iterated over.  The
sequences do not have to be the same length, because they are *not*
iterated over in parallel, but from left to right; this is explained
more clearly in the following paragraphs.  The elements of the
generated list will be the successive values of *expression*.  The
final "if" clause is optional; if present, *expression* is only
evaluated and added to the result if *condition* is true.

To make the semantics very clear, a list comprehension is equivalent
to the following Python code:

   for expr1 in sequence1:
       for expr2 in sequence2:
       ...
           for exprN in sequenceN:
                if (condition):
                     # Append the value of
                     # the expression to the
                     # resulting list.

This means that when there are multiple "for"…"in" clauses, the
resulting list will be equal to the product of the lengths of all the
sequences.  If you have two lists of length 3, the output list is 9
elements long:

   seq1 = 'abc'
   seq2 = (1,2,3)
   >>> [ (x,y) for x in seq1 for y in seq2]
   [('a', 1), ('a', 2), ('a', 3), ('b', 1), ('b', 2), ('b', 3), ('c', 1),
   ('c', 2), ('c', 3)]

To avoid introducing an ambiguity into Python’s grammar, if
*expression* is creating a tuple, it must be surrounded with
parentheses.  The first list comprehension below is a syntax error,
while the second one is correct:

   # Syntax error
   [ x,y for x in seq1 for y in seq2]
   # Correct
   [ (x,y) for x in seq1 for y in seq2]

The idea of list comprehensions originally comes from the functional
programming language Haskell (https://www.haskell.org).  Greg Ewing
argued most effectively for adding them to Python and wrote the
initial list comprehension patch, which was then discussed for a
seemingly endless time on the python-dev mailing list and kept up-to-
date by Skip Montanaro.


Augmented Assignment
====================

Augmented assignment operators, another long-requested feature, have
been added to Python 2.0.  Augmented assignment operators include
"+=", "-=", "*=", and so forth.  For example, the statement "a += 2"
increments the value of the variable  "a" by 2, equivalent to the
slightly lengthier "a = a + 2".

The full list of supported assignment operators is "+=", "-=", "*=",
"/=", "%=", "**=", "&=", "|=", "^=", ">>=", and "<<=".  Python classes
can override the augmented assignment operators by defining methods
named "__iadd__()", "__isub__()", etc.  For example, the following
"Number" class stores a number and supports using += to create a new
instance with an incremented value.

   class Number:
       def __init__(self, value):
           self.value = value
       def __iadd__(self, increment):
           return Number( self.value + increment)

   n = Number(5)
   n += 3
   print n.value

The "__iadd__()" special method is called with the value of the
increment, and should return a new instance with an appropriately
modified value; this return value is bound as the new value of the
variable on the left-hand side.

Augmented assignment operators were first introduced in the C
programming language, and most C-derived languages, such as **awk**,
C++, Java, Perl, and PHP also support them.  The augmented assignment
patch was implemented by Thomas Wouters.


String Methods
==============

Until now string-manipulation functionality was in the "string"
module, which was usually a front-end for the "strop" module written
in C.  The addition of Unicode posed a difficulty for the "strop"
module, because the functions would all need to be rewritten in order
to accept either 8-bit or Unicode strings.  For functions such as
"string.replace()", which takes 3 string arguments, that means eight
possible permutations, and correspondingly complicated code.

Instead, Python 2.0 pushes the problem onto the string type, making
string manipulation functionality available through methods on both
8-bit strings and Unicode strings.

   >>> 'andrew'.capitalize()
   'Andrew'
   >>> 'hostname'.replace('os', 'linux')
   'hlinuxtname'
   >>> 'moshe'.find('sh')
   2

One thing that hasn’t changed, a noteworthy April Fools’ joke
notwithstanding, is that Python strings are immutable. Thus, the
string methods return new strings, and do not modify the string on
which they operate.

The old "string" module is still around for backwards compatibility,
but it mostly acts as a front-end to the new string methods.

Two methods which have no parallel in pre-2.0 versions, although they
did exist in JPython for quite some time, are "startswith()" and
"endswith()". "s.startswith(t)" is equivalent to "s[:len(t)] == t",
while "s.endswith(t)" is equivalent to "s[-len(t):] == t".

One other method which deserves special mention is "join()".  The
"join()" method of a string receives one parameter, a sequence of
strings, and is equivalent to the "string.join()" function from the
old "string" module, with the arguments reversed. In other words,
"s.join(seq)" is equivalent to the old "string.join(seq, s)".


Garbage Collection of Cycles
============================

The C implementation of Python uses reference counting to implement
garbage collection.  Every Python object maintains a count of the
number of references pointing to itself, and adjusts the count as
references are created or destroyed.  Once the reference count reaches
zero, the object is no longer accessible, since you need to have a
reference to an object to access it, and if the count is zero, no
references exist any longer.

Reference counting has some pleasant properties: it’s easy to
understand and implement, and the resulting implementation is
portable, fairly fast, and reacts well with other libraries that
implement their own memory handling schemes.  The major problem with
reference counting is that it sometimes doesn’t realise that objects
are no longer accessible, resulting in a memory leak.  This happens
when there are cycles of references.

Consider the simplest possible cycle,  a class instance which has a
reference to itself:

   instance = SomeClass()
   instance.myself = instance

After the above two lines of code have been executed, the reference
count of "instance" is 2; one reference is from the variable named
"'instance'", and the other is from the "myself" attribute of the
instance.

If the next line of code is "del instance", what happens?  The
reference count of "instance" is decreased by 1, so it has a reference
count of 1; the reference in the "myself" attribute still exists.  Yet
the instance is no longer accessible through Python code, and it could
be deleted.  Several objects can participate in a cycle if they have
references to each other, causing all of the objects to be leaked.

Python 2.0 fixes this problem by periodically executing a cycle
detection algorithm which looks for inaccessible cycles and deletes
the objects involved. A new "gc" module provides functions to perform
a garbage collection, obtain debugging statistics, and tuning the
collector’s parameters.

Running the cycle detection algorithm takes some time, and therefore
will result in some additional overhead.  It is hoped that after we’ve
gotten experience with the cycle collection from using 2.0, Python 2.1
will be able to minimize the overhead with careful tuning.  It’s not
yet obvious how much performance is lost, because benchmarking this is
tricky and depends crucially on how often the program creates and
destroys objects.  The detection of cycles can be disabled when Python
is compiled, if you can’t afford even a tiny speed penalty or suspect
that the cycle collection is buggy, by specifying the "--without-
cycle-gc" switch when running the **configure** script.

Several people tackled this problem and contributed to a solution.  An
early implementation of the cycle detection approach was written by
Toby Kelsey.  The current algorithm was suggested by Eric Tiedemann
during a visit to CNRI, and Guido van Rossum and Neil Schemenauer
wrote two different implementations, which were later integrated by
Neil.  Lots of other people offered suggestions along the way; the
March 2000 archives of the python-dev mailing list contain most of the
relevant discussion, especially in the threads titled “Reference cycle
collection for Python” and “Finalization again”.


Other Core Changes
==================

Various minor changes have been made to Python’s syntax and built-in
functions. None of the changes are very far-reaching, but they’re
handy conveniences.


Minor Language Changes
----------------------

A new syntax makes it more convenient to call a given function with a
tuple of arguments and/or a dictionary of keyword arguments. In Python
1.5 and earlier, you’d use the "apply()" built-in function: "apply(f,
args, kw)" calls the function "f()" with the argument tuple *args* and
the keyword arguments in the dictionary *kw*.  "apply()"  is the same
in 2.0, but thanks to a patch from Greg Ewing, "f(*args, **kw)" is a
shorter and clearer way to achieve the same effect.  This syntax is
symmetrical with the syntax for defining functions:

   def f(*args, **kw):
       # args is a tuple of positional args,
       # kw is a dictionary of keyword args
       ...

The "print" statement can now have its output directed to a file-like
object by following the "print" with  ">> file", similar to the
redirection operator in Unix shells. Previously you’d either have to
use the "write()" method of the file-like object, which lacks the
convenience and simplicity of "print", or you could assign a new value
to "sys.stdout" and then restore the old value.  For sending output to
standard error, it’s much easier to write this:

   print >> sys.stderr, "Warning: action field not supplied"

Modules can now be renamed on importing them, using the syntax "import
module as name" or "from module import name as othername".  The patch
was submitted by Thomas Wouters.

A new format style is available when using the "%" operator; ‘%r’ will
insert the "repr()" of its argument.  This was also added from
symmetry considerations, this time for symmetry with the existing ‘%s’
format style, which inserts the "str()" of its argument.  For example,
"'%r %s' % ('abc', 'abc')" returns a string containing "'abc' abc".

Previously there was no way to implement a class that overrode
Python’s built-in "in" operator and implemented a custom version.
"obj in seq" returns true if *obj* is present in the sequence *seq*;
Python computes this by simply trying every index of the sequence
until either *obj* is found or an "IndexError" is encountered.  Moshe
Zadka contributed a patch which adds a "__contains__()" magic method
for providing a custom implementation for "in". Additionally, new
built-in objects written in C can define what "in" means for them via
a new slot in the sequence protocol.

Earlier versions of Python used a recursive algorithm for deleting
objects. Deeply nested data structures could cause the interpreter to
fill up the C stack and crash; Christian Tismer rewrote the deletion
logic to fix this problem.  On a related note, comparing recursive
objects recursed infinitely and crashed; Jeremy Hylton rewrote the
code to no longer crash, producing a useful result instead.  For
example, after this code:

   a = []
   b = []
   a.append(a)
   b.append(b)

The comparison "a==b" returns true, because the two recursive data
structures are isomorphic. See the thread “trashcan and PR#7” in the
April 2000 archives of the python-dev mailing list for the discussion
leading up to this implementation, and some useful relevant links.
Note that comparisons can now also raise exceptions. In earlier
versions of Python, a comparison operation such as "cmp(a,b)" would
always produce an answer, even if a user-defined "__cmp__()" method
encountered an error, since the resulting exception would simply be
silently swallowed.

Work has been done on porting Python to 64-bit Windows on the Itanium
processor, mostly by Trent Mick of ActiveState.  (Confusingly,
"sys.platform" is still "'win32'" on Win64 because it seems that for
ease of porting, MS Visual C++ treats code as 32 bit on Itanium.)
PythonWin also supports Windows CE; see the Python CE page at
https://pythonce.sourceforge.net/ for more information.

Another new platform is Darwin/MacOS X; initial support for it is in
Python 2.0. Dynamic loading works, if you specify “configure –with-
dyld –with-suffix=.x”. Consult the README in the Python source
distribution for more instructions.

An attempt has been made to alleviate one of Python’s warts, the
often-confusing "NameError" exception when code refers to a local
variable before the variable has been assigned a value.  For example,
the following code raises an exception on the "print" statement in
both 1.5.2 and 2.0; in 1.5.2 a "NameError" exception is raised, while
2.0 raises a new "UnboundLocalError" exception. "UnboundLocalError" is
a subclass of "NameError", so any existing code that expects
"NameError" to be raised should still work.

   def f():
       print "i=",i
       i = i + 1
   f()

Two new exceptions, "TabError" and "IndentationError", have been
introduced.  They’re both subclasses of "SyntaxError", and are raised
when Python code is found to be improperly indented.


Changes to Built-in Functions
-----------------------------

A new built-in, "zip(seq1, seq2, ...)", has been added.  "zip()"
returns a list of tuples where each tuple contains the i-th element
from each of the argument sequences.  The difference between "zip()"
and "map(None, seq1, seq2)" is that "map()" pads the sequences with
"None" if the sequences aren’t all of the same length, while "zip()"
truncates the returned list to the length of the shortest argument
sequence.

The "int()" and "long()" functions now accept an optional “base”
parameter when the first argument is a string. "int('123', 10)"
returns 123, while "int('123', 16)" returns 291.  "int(123, 16)"
raises a "TypeError" exception with the message “can’t convert non-
string with explicit base”.

A new variable holding more detailed version information has been
added to the "sys" module.  "sys.version_info" is a tuple "(major,
minor, micro, level, serial)" For example, in a hypothetical
2.0.1beta1, "sys.version_info" would be "(2, 0, 1, 'beta', 1)".
*level* is a string such as ""alpha"", ""beta"", or ""final"" for a
final release.

Dictionaries have an odd new method, "setdefault(key, default)", which
behaves similarly to the existing "get()" method.  However, if the key
is missing, "setdefault()" both returns the value of *default* as
"get()" would do, and also inserts it into the dictionary as the value
for *key*.  Thus, the following lines of code:

   if dict.has_key( key ): return dict[key]
   else:
       dict[key] = []
       return dict[key]

can be reduced to a single "return dict.setdefault(key, [])"
statement.

The interpreter sets a maximum recursion depth in order to catch
runaway recursion before filling the C stack and causing a core dump
or GPF.. Previously this limit was fixed when you compiled Python, but
in 2.0 the maximum recursion depth can be read and modified using
"sys.getrecursionlimit()" and "sys.setrecursionlimit()". The default
value is 1000, and a rough maximum value for a given platform can be
found by running a new script, "Misc/find_recursionlimit.py".


Porting to 2.0
==============

New Python releases try hard to be compatible with previous releases,
and the record has been pretty good.  However, some changes are
considered useful enough, usually because they fix initial design
decisions that turned out to be actively mistaken, that breaking
backward compatibility can’t always be avoided. This section lists the
changes in Python 2.0 that may cause old Python code to break.

The change which will probably break the most code is tightening up
the arguments accepted by some methods.  Some methods would take
multiple arguments and treat them as a tuple, particularly various
list methods such as "append()" and "insert()". In earlier versions of
Python, if "L" is a list, "L.append( 1,2 )" appends the tuple "(1,2)"
to the list.  In Python 2.0 this causes a "TypeError" exception to be
raised, with the message: ‘append requires exactly 1 argument; 2
given’.  The fix is to simply add an extra set of parentheses to pass
both values as a tuple:  "L.append( (1,2) )".

The earlier versions of these methods were more forgiving because they
used an old function in Python’s C interface to parse their arguments;
2.0 modernizes them to use "PyArg_ParseTuple()", the current argument
parsing function, which provides more helpful error messages and
treats multi-argument calls as errors.  If you absolutely must use 2.0
but can’t fix your code, you can edit "Objects/listobject.c" and
define the preprocessor symbol "NO_STRICT_LIST_APPEND" to preserve the
old behaviour; this isn’t recommended.

Some of the functions in the "socket" module are still forgiving in
this way.  For example, "socket.connect( ('hostname', 25) )()" is the
correct form, passing a tuple representing an IP address, but
"socket.connect( 'hostname', 25 )()" also works. "socket.connect_ex()"
and "socket.bind()" are similarly easy-going.  2.0alpha1 tightened
these functions up, but because the documentation actually used the
erroneous multiple argument form, many people wrote code which would
break with the stricter checking.  GvR backed out the changes in the
face of public reaction, so for the "socket" module, the documentation
was fixed and the multiple argument form is simply marked as
deprecated; it *will* be tightened up again in a future Python
version.

The "\x" escape in string literals now takes exactly 2 hex digits.
Previously it would consume all the hex digits following the ‘x’ and
take the lowest 8 bits of the result, so "\x123456" was equivalent to
"\x56".

The "AttributeError" and "NameError" exceptions have a more friendly
error message, whose text will be something like "'Spam' instance has
no attribute 'eggs'" or "name 'eggs' is not defined".  Previously the
error message was just the missing attribute name "eggs", and code
written to take advantage of this fact will break in 2.0.

Some work has been done to make integers and long integers a bit more
interchangeable.  In 1.5.2, large-file support was added for Solaris,
to allow reading files larger than 2 GiB; this made the "tell()"
method of file objects return a long integer instead of a regular
integer.  Some code would subtract two file offsets and attempt to use
the result to multiply a sequence or slice a string, but this raised a
"TypeError".  In 2.0, long integers can be used to multiply or slice a
sequence, and it’ll behave as you’d intuitively expect it to; "3L *
'abc'" produces ‘abcabcabc’, and "(0,1,2,3)[2L:4L]" produces (2,3).
Long integers can also be used in various contexts where previously
only integers were accepted, such as in the "seek()" method of file
objects, and in the formats supported by the "%" operator ("%d", "%i",
"%x", etc.).  For example, ""%d" % 2L**64" will produce the string
"18446744073709551616".

The subtlest long integer change of all is that the "str()" of a long
integer no longer has a trailing ‘L’ character, though "repr()" still
includes it.  The ‘L’ annoyed many people who wanted to print long
integers that looked just like regular integers, since they had to go
out of their way to chop off the character.  This is no longer a
problem in 2.0, but code which does "str(longval)[:-1]" and assumes
the ‘L’ is there, will now lose the final digit.

Taking the "repr()" of a float now uses a different formatting
precision than "str()".  "repr()" uses "%.17g" format string for C’s
"sprintf()", while "str()" uses "%.12g" as before.  The effect is that
"repr()" may occasionally show more decimal places than  "str()", for
certain numbers.  For example, the number 8.1 can’t be represented
exactly in binary, so "repr(8.1)" is "'8.0999999999999996'", while
str(8.1) is "'8.1'".

The "-X" command-line option, which turned all standard exceptions
into strings instead of classes, has been removed; the standard
exceptions will now always be classes.  The "exceptions" module
containing the standard exceptions was translated from Python to a
built-in C module, written by Barry Warsaw and Fredrik Lundh.


Extending/Embedding Changes
===========================

Some of the changes are under the covers, and will only be apparent to
people writing C extension modules or embedding a Python interpreter
in a larger application.  If you aren’t dealing with Python’s C API,
you can safely skip this section.

The version number of the Python C API was incremented, so C
extensions compiled for 1.5.2 must be recompiled in order to work with
2.0.  On Windows, it’s not possible for Python 2.0 to import a third
party extension built for Python 1.5.x due to how Windows DLLs work,
so Python will raise an exception and the import will fail.

Users of Jim Fulton’s ExtensionClass module will be pleased to find
out that hooks have been added so that ExtensionClasses are now
supported by "isinstance()" and "issubclass()". This means you no
longer have to remember to write code such as "if type(obj) ==
myExtensionClass", but can use the more natural "if isinstance(obj,
myExtensionClass)".

The "Python/importdl.c" file, which was a mass of #ifdefs to support
dynamic loading on many different platforms, was cleaned up and
reorganised by Greg Stein.  "importdl.c" is now quite small, and
platform-specific code has been moved into a bunch of
"Python/dynload_*.c" files.  Another cleanup: there were also a number
of "my*.h" files in the Include/ directory that held various
portability hacks; they’ve been merged into a single file,
"Include/pyport.h".

Vladimir Marangozov’s long-awaited malloc restructuring was completed,
to make it easy to have the Python interpreter use a custom allocator
instead of C’s standard "malloc()".  For documentation, read the
comments in "Include/pymem.h" and "Include/objimpl.h".  For the
lengthy discussions during which the interface was hammered out, see
the web archives of the ‘patches’ and ‘python-dev’ lists at
python.org.

Recent versions of the GUSI development environment for MacOS support
POSIX threads.  Therefore, Python’s POSIX threading support now works
on the Macintosh.  Threading support using the user-space GNU "pth"
library was also contributed.

Threading support on Windows was enhanced, too.  Windows supports
thread locks that use kernel objects only in case of contention; in
the common case when there’s no contention, they use simpler functions
which are an order of magnitude faster.  A threaded version of Python
1.5.2 on NT is twice as slow as an unthreaded version; with the 2.0
changes, the difference is only 10%.  These improvements were
contributed by Yakov Markovitch.

Python 2.0’s source now uses only ANSI C prototypes, so compiling
Python now requires an ANSI C compiler, and can no longer be done
using a compiler that only supports K&R C.

Previously the Python virtual machine used 16-bit numbers in its
bytecode, limiting the size of source files.  In particular, this
affected the maximum size of literal lists and dictionaries in Python
source; occasionally people who are generating Python code would run
into this limit.  A patch by Charles G. Waldman raises the limit from
"2**16" to "2**32".

Three new convenience functions intended for adding constants to a
module’s dictionary at module initialization time were added:
"PyModule_AddObject()", "PyModule_AddIntConstant()", and
"PyModule_AddStringConstant()".  Each of these functions takes a
module object, a null-terminated C string containing the name to be
added, and a third argument for the value to be assigned to the name.
This third argument is, respectively, a Python object, a C long, or a
C string.

A wrapper API was added for Unix-style signal handlers.
"PyOS_getsig()" gets a signal handler and "PyOS_setsig()" will set a
new handler.


Distutils: Making Modules Easy to Install
=========================================

Before Python 2.0, installing modules was a tedious affair – there was
no way to figure out automatically where Python is installed, or what
compiler options to use for extension modules.  Software authors had
to go through an arduous ritual of editing Makefiles and configuration
files, which only really work on Unix and leave Windows and MacOS
unsupported.  Python users faced wildly differing installation
instructions which varied between different extension packages, which
made administering a Python installation something of  a chore.

The SIG for distribution utilities, shepherded by Greg Ward, has
created the Distutils, a system to make package installation much
easier.  They form the "distutils" package, a new part of Python’s
standard library. In the best case, installing a Python module from
source will require the same steps: first you simply mean unpack the
tarball or zip archive, and the run “"python setup.py install"”.  The
platform will be automatically detected, the compiler will be
recognized, C extension modules will be compiled, and the distribution
installed into the proper directory.  Optional command-line arguments
provide more control over the installation process, the distutils
package offers many places to override defaults – separating the build
from the install, building or installing in non-default directories,
and more.

In order to use the Distutils, you need to write a "setup.py" script.
For the simple case, when the software contains only .py files, a
minimal "setup.py" can be just a few lines long:

   from distutils.core import setup
   setup (name = "foo", version = "1.0",
          py_modules = ["module1", "module2"])

The "setup.py" file isn’t much more complicated if the software
consists of a few packages:

   from distutils.core import setup
   setup (name = "foo", version = "1.0",
          packages = ["package", "package.subpackage"])

A C extension can be the most complicated case; here’s an example
taken from the PyXML package:

   from distutils.core import setup, Extension

   expat_extension = Extension('xml.parsers.pyexpat',
        define_macros = [('XML_NS', None)],
        include_dirs = [ 'extensions/expat/xmltok',
                         'extensions/expat/xmlparse' ],
        sources = [ 'extensions/pyexpat.c',
                    'extensions/expat/xmltok/xmltok.c',
                    'extensions/expat/xmltok/xmlrole.c', ]
          )
   setup (name = "PyXML", version = "0.5.4",
          ext_modules =[ expat_extension ] )

The Distutils can also take care of creating source and binary
distributions. The “sdist” command, run by “"python setup.py sdist"’,
builds a source distribution such as "foo-1.0.tar.gz". Adding new
commands isn’t difficult, “bdist_rpm” and “bdist_wininst” commands
have already been contributed to create an RPM distribution and a
Windows installer for the software, respectively.  Commands to create
other distribution formats such as Debian packages and Solaris ".pkg"
files are in various stages of development.

All this is documented in a new manual, *Distributing Python Modules*,
that joins the basic set of Python documentation.


XML Modules
===========

Python 1.5.2 included a simple XML parser in the form of the "xmllib"
module, contributed by Sjoerd Mullender.  Since 1.5.2’s release, two
different interfaces for processing XML have become common: SAX2
(version 2 of the Simple API for XML) provides an event-driven
interface with some similarities to "xmllib", and the DOM (Document
Object Model) provides a tree-based interface, transforming an XML
document into a tree of nodes that can be traversed and modified.
Python 2.0 includes a SAX2 interface and a stripped-down DOM interface
as part of the "xml" package. Here we will give a brief overview of
these new interfaces; consult the Python documentation or the source
code for complete details. The Python XML SIG is also working on
improved documentation.


SAX2 Support
------------

SAX defines an event-driven interface for parsing XML.  To use SAX,
you must write a SAX handler class.  Handler classes inherit from
various classes provided by SAX, and override various methods that
will then be called by the XML parser.  For example, the
"startElement()" and "endElement()" methods are called for every
starting and end tag encountered by the parser, the "characters()"
method is called for every chunk of character data, and so forth.

The advantage of the event-driven approach is that the whole document
doesn’t have to be resident in memory at any one time, which matters
if you are processing really huge documents.  However, writing the SAX
handler class can get very complicated if you’re trying to modify the
document structure in some elaborate way.

For example, this little example program defines a handler that prints
a message for every starting and ending tag, and then parses the file
"hamlet.xml" using it:

   from xml import sax

   class SimpleHandler(sax.ContentHandler):
       def startElement(self, name, attrs):
           print 'Start of element:', name, attrs.keys()

       def endElement(self, name):
           print 'End of element:', name

   # Create a parser object
   parser = sax.make_parser()

   # Tell it what handler to use
   handler = SimpleHandler()
   parser.setContentHandler( handler )

   # Parse a file!
   parser.parse( 'hamlet.xml' )

For more information, consult the Python documentation, or the XML
HOWTO at http://pyxml.sourceforge.net/topics/howto/xml-howto.html.


DOM Support
-----------

The Document Object Model is a tree-based representation for an XML
document.  A top-level "Document" instance is the root of the tree,
and has a single child which is the top-level "Element" instance. This
"Element" has children nodes representing character data and any sub-
elements, which may have further children of their own, and so forth.
Using the DOM you can traverse the resulting tree any way you like,
access element and attribute values, insert and delete nodes, and
convert the tree back into XML.

The DOM is useful for modifying XML documents, because you can create
a DOM tree, modify it by adding new nodes or rearranging subtrees, and
then produce a new XML document as output.  You can also construct a
DOM tree manually and convert it to XML, which can be a more flexible
way of producing XML output than simply writing "<tag1>"…"</tag1>" to
a file.

The DOM implementation included with Python lives in the
"xml.dom.minidom" module.  It’s a lightweight implementation of the
Level 1 DOM with support for XML namespaces.  The  "parse()" and
"parseString()" convenience functions are provided for generating a
DOM tree:

   from xml.dom import minidom
   doc = minidom.parse('hamlet.xml')

"doc" is a "Document" instance.  "Document", like all the other DOM
classes such as "Element" and "Text", is a subclass of the "Node" base
class.  All the nodes in a DOM tree therefore support certain common
methods, such as "toxml()" which returns a string containing the XML
representation of the node and its children.  Each class also has
special methods of its own; for example, "Element" and "Document"
instances have a method to find all child elements with a given tag
name. Continuing from the previous 2-line example:

   perslist = doc.getElementsByTagName( 'PERSONA' )
   print perslist[0].toxml()
   print perslist[1].toxml()

For the *Hamlet* XML file, the above few lines output:

   <PERSONA>CLAUDIUS, king of Denmark. </PERSONA>
   <PERSONA>HAMLET, son to the late, and nephew to the present king.</PERSONA>

The root element of the document is available as
"doc.documentElement", and its children can be easily modified by
deleting, adding, or removing nodes:

   root = doc.documentElement

   # Remove the first child
   root.removeChild( root.childNodes[0] )

   # Move the new first child to the end
   root.appendChild( root.childNodes[0] )

   # Insert the new first child (originally,
   # the third child) before the 20th child.
   root.insertBefore( root.childNodes[0], root.childNodes[20] )

Again, I will refer you to the Python documentation for a complete
listing of the different "Node" classes and their various methods.


Relationship to PyXML
---------------------

The XML Special Interest Group has been working on XML-related Python
code for a while.  Its code distribution, called PyXML, is available
from the SIG’s web pages at
https://www.python.org/community/sigs/current/xml-sig. The PyXML
distribution also used the package name "xml".  If you’ve written
programs that used PyXML, you’re probably wondering about its
compatibility with the 2.0 "xml" package.

The answer is that Python 2.0’s "xml" package isn’t compatible with
PyXML, but can be made compatible by installing a recent version
PyXML.  Many applications can get by with the XML support that is
included with Python 2.0, but more complicated applications will
require that the full PyXML package will be installed.  When
installed, PyXML versions 0.6.0 or greater will replace the "xml"
package shipped with Python, and will be a strict superset of the
standard package, adding a bunch of additional features.  Some of the
additional features in PyXML include:

* 4DOM, a full DOM implementation from FourThought, Inc.

* The xmlproc validating parser, written by Lars Marius Garshol.

* The "sgmlop" parser accelerator module, written by Fredrik Lundh.


Module changes
==============

Lots of improvements and bugfixes were made to Python’s extensive
standard library; some of the affected modules include "readline",
"ConfigParser", "cgi", "calendar", "posix", "readline", "xmllib",
"aifc", "chunk, wave", "random", "shelve", and "nntplib".  Consult the
CVS logs for the exact patch-by-patch details.

Brian Gallew contributed OpenSSL support for the "socket" module.
OpenSSL is an implementation of the Secure Socket Layer, which
encrypts the data being sent over a socket.  When compiling Python,
you can edit "Modules/Setup" to include SSL support, which adds an
additional function to the "socket" module: "socket.ssl(socket,
keyfile, certfile)", which takes a socket object and returns an SSL
socket.  The "httplib" and "urllib" modules were also changed to
support "https://" URLs, though no one has implemented FTP or SMTP
over SSL.

The "httplib" module has been rewritten by Greg Stein to support
HTTP/1.1. Backward compatibility with the 1.5 version of "httplib" is
provided, though using HTTP/1.1 features such as pipelining will
require rewriting code to use a different set of interfaces.

The "Tkinter" module now supports Tcl/Tk version 8.1, 8.2, or 8.3, and
support for the older 7.x versions has been dropped.  The Tkinter
module now supports displaying Unicode strings in Tk widgets. Also,
Fredrik Lundh contributed an optimization which makes operations like
"create_line" and "create_polygon" much faster, especially when using
lots of coordinates.

The "curses" module has been greatly extended, starting from Oliver
Andrich’s enhanced version, to provide many additional functions from
ncurses and SYSV curses, such as colour, alternative character set
support, pads, and mouse support.  This means the module is no longer
compatible with operating systems that only have BSD curses, but there
don’t seem to be any currently maintained OSes that fall into this
category.

As mentioned in the earlier discussion of 2.0’s Unicode support, the
underlying implementation of the regular expressions provided by the
"re" module has been changed.  SRE, a new regular expression engine
written by Fredrik Lundh and partially funded by Hewlett Packard,
supports matching against both 8-bit strings and Unicode strings.


New modules
===========

A number of new modules were added.  We’ll simply list them with brief
descriptions; consult the 2.0 documentation for the details of a
particular module.

* "atexit":  For registering functions to be called before the Python
  interpreter exits. Code that currently sets "sys.exitfunc" directly
  should be changed to  use the "atexit" module instead, importing
  "atexit" and calling "atexit.register()" with  the function to be
  called on exit. (Contributed by Skip Montanaro.)

* "codecs", "encodings", "unicodedata":  Added as part of the new
  Unicode support.

* "filecmp": Supersedes the old "cmp", "cmpcache" and "dircmp"
  modules, which have now become deprecated. (Contributed by Gordon
  MacMillan and Moshe Zadka.)

* "gettext": This module provides internationalization (I18N) and
  localization (L10N) support for Python programs by providing an
  interface to the GNU gettext message catalog library. (Integrated by
  Barry Warsaw, from separate contributions by Martin  von Löwis,
  Peter Funk, and James Henstridge.)

* "linuxaudiodev": Support for the "/dev/audio" device on Linux, a
  twin to the existing "sunaudiodev" module. (Contributed by Peter
  Bosch, with fixes by Jeremy Hylton.)

* "mmap": An interface to memory-mapped files on both Windows and
  Unix.  A file’s contents can be mapped directly into memory, at
  which point it behaves like a mutable string, so its contents can be
  read and modified.  They can even be passed to functions that expect
  ordinary strings, such as the "re" module. (Contributed by Sam
  Rushing, with some extensions by A.M. Kuchling.)

* "pyexpat": An interface to the Expat XML parser. (Contributed by
  Paul Prescod.)

* "robotparser": Parse a "robots.txt" file, which is used for writing
  web spiders that politely avoid certain areas of a web site.  The
  parser accepts the contents of a "robots.txt" file, builds a set of
  rules from it, and can then answer questions about the fetchability
  of a given URL.  (Contributed by Skip Montanaro.)

* "tabnanny": A module/script to  check Python source code for
  ambiguous indentation. (Contributed by Tim Peters.)

* "UserString": A base class useful for deriving objects that behave
  like strings.

* "webbrowser": A module that provides a platform independent way to
  launch a web browser on a specific URL. For each platform, various
  browsers are tried in a specific order. The user can alter which
  browser is launched by setting the *BROWSER* environment variable.
  (Originally inspired by Eric S. Raymond’s patch to "urllib" which
  added similar functionality, but the final module comes from code
  originally  implemented by Fred Drake as
  "Tools/idle/BrowserControl.py", and adapted for the standard library
  by Fred.)

* "_winreg": An interface to the Windows registry.  "_winreg" is an
  adaptation of functions that have been part of PythonWin since 1995,
  but has now been added to the core  distribution, and enhanced to
  support Unicode. "_winreg" was written by Bill Tutt and Mark
  Hammond.

* "zipfile": A module for reading and writing ZIP-format archives.
  These are archives produced by **PKZIP** on DOS/Windows or **zip**
  on Unix, not to be confused with **gzip**-format files (which are
  supported by the "gzip" module) (Contributed by James C. Ahlstrom.)

* "imputil": A module that provides a simpler way for writing
  customized import hooks, in comparison to the existing "ihooks"
  module.  (Implemented by Greg Stein, with much discussion on python-
  dev along the way.)


IDLE Improvements
=================

IDLE is the official Python cross-platform IDE, written using Tkinter.
Python 2.0 includes IDLE 0.6, which adds a number of new features and
improvements.  A partial list:

* UI improvements and optimizations, especially in the area of syntax
  highlighting and auto-indentation.

* The class browser now shows more information, such as the top level
  functions in a module.

* Tab width is now a user settable option. When opening an existing
  Python file, IDLE automatically detects the indentation conventions,
  and adapts.

* There is now support for calling browsers on various platforms, used
  to open the Python documentation in a browser.

* IDLE now has a command line, which is largely similar to  the
  vanilla Python interpreter.

* Call tips were added in many places.

* IDLE can now be installed as a package.

* In the editor window, there is now a line/column bar at the bottom.

* Three new keystroke commands: Check module ("Alt-F5"), Import module
  ("F5") and Run script ("Ctrl-F5").


Deleted and Deprecated Modules
==============================

A few modules have been dropped because they’re obsolete, or because
there are now better ways to do the same thing.  The "stdwin" module
is gone; it was for a platform-independent windowing toolkit that’s no
longer developed.

A number of modules have been moved to the "lib-old" subdirectory:
"cmp", "cmpcache", "dircmp", "dump",  "find", "grep", "packmail",
"poly", "util", "whatsound", "zmod".  If you have code which relies on
a module  that’s been moved to "lib-old", you can simply add that
directory to "sys.path"   to get them back, but you’re encouraged to
update any code that uses these modules.


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

The authors would like to thank the following people for offering
suggestions on various drafts of this article: David Bolen, Mark
Hammond, Gregg Hauser, Jeremy Hylton, Fredrik Lundh, Detlef Lannert,
Aahz Maruch, Skip Montanaro, Vladimir Marangozov, Tobias Polzin, Guido
van Rossum, Neil Schemenauer, and Russ Schmidt.
