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| Author: | A.M. Kuchling and Moshe Zadka |
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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.
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.
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”.
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.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).
(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.(ustring, length), consisting of the resulting Unicode
string ustring and the integer length telling how much of the 8-bit string
was consumed.read(),
readline(), and readlines() methods. These methods will all
translate from the given encoding and return Unicode strings.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.
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 statement 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 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.
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).
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”.
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.
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 http://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.
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.
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.
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.
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.
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.
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.
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.
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:
sgmlop parser accelerator module, written by Fredrik Lundh.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.
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 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:
Alt-F5), Import module (F5) and
Run script (Ctrl-F5).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.
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.