Whats New in Python 2.5

Author:

A.M. Kuchling

This article explains the new features in Python 2.5. The final release of Python 2.5 is scheduled for August 2006; PEP 356 describes the planned release schedule. Python 2.5 was released on September 19, 2006.

The changes in Python 2.5 are an interesting mix of language and library improvements. The library enhancements will be more important to Pythons user community, I think, because several widely useful packages were added. New modules include ElementTree for XML processing (xml.etree.ElementTree), the SQLite database module (sqlite3), and the ctypes module for calling C functions.

The language changes are of middling significance. Some pleasant new features were added, but most of them arent features that youll use every day. Conditional expressions were finally added to the language using a novel syntax; see section PEP 308: Conditional Expressions. The new with statement will make writing cleanup code easier (section PEP 343: The with statement). Values can now be passed into generators (section PEP 342: New Generator Features). Imports are now visible as either absolute or relative (section PEP 328: Absolute and Relative Imports). Some corner cases of exception handling are handled better (section PEP 341: Unified try/except/finally). All these improvements are worthwhile, but theyre improvements to one specific language feature or another; none of them are broad modifications to Pythons semantics.

As well as the language and library additions, other improvements and bugfixes were made throughout the source tree. A search through the SVN change logs finds there were 353 patches applied and 458 bugs fixed between Python 2.4 and 2.5. (Both figures are likely to be underestimates.)

This article doesnt try to be a complete specification of the new features; instead changes are briefly introduced using helpful examples. For full details, you should always refer to the documentation for Python 2.5 at https://docs.python.org. If you want to understand the complete implementation and design rationale, refer to the PEP for a particular new feature.

Comments, suggestions, and error reports for this document are welcome; please e-mail them to the author or open a bug in the Python bug tracker.

PEP 308: Conditional Expressions

For a long time, people have been requesting a way to write conditional expressions, which are expressions that return value A or value B depending on whether a Boolean value is true or false. A conditional expression lets you write a single assignment statement that has the same effect as the following:

if condition:
    x = true_value
else:
    x = false_value

There have been endless tedious discussions of syntax on both python-dev and comp.lang.python. A vote was even held that found the majority of voters wanted conditional expressions in some form, but there was no syntax that was preferred by a clear majority. Candidates included Cs cond ? true_v : false_v, if cond then true_v else false_v, and 16 other variations.

Guido van Rossum eventually chose a surprising syntax:

x = true_value if condition else false_value

Evaluation is still lazy as in existing Boolean expressions, so the order of evaluation jumps around a bit. The condition expression in the middle is evaluated first, and the true_value expression is evaluated only if the condition was true. Similarly, the false_value expression is only evaluated when the condition is false.

This syntax may seem strange and backwards; why does the condition go in the middle of the expression, and not in the front as in Cs c ? x : y? The decision was checked by applying the new syntax to the modules in the standard library and seeing how the resulting code read. In many cases where a conditional expression is used, one value seems to be the common case and one value is an exceptional case, used only on rarer occasions when the condition isnt met. The conditional syntax makes this pattern a bit more obvious:

contents = ((doc + '\n') if doc else '')

I read the above statement as meaning here contents is usually assigned a value of doc+'\n'; sometimes doc is empty, in which special case an empty string is returned. I doubt I will use conditional expressions very often where there isnt a clear common and uncommon case.

There was some discussion of whether the language should require surrounding conditional expressions with parentheses. The decision was made to not require parentheses in the Python languages grammar, but as a matter of style I think you should always use them. Consider these two statements:

# First version -- no parens
level = 1 if logging else 0

# Second version -- with parens
level = (1 if logging else 0)

In the first version, I think a readers eye might group the statement into level = 1, if logging, else 0, and think that the condition decides whether the assignment to level is performed. The second version reads better, in my opinion, because it makes it clear that the assignment is always performed and the choice is being made between two values.

Another reason for including the brackets: a few odd combinations of list comprehensions and lambdas could look like incorrect conditional expressions. See PEP 308 for some examples. If you put parentheses around your conditional expressions, you wont run into this case.

See also

PEP 308 - Conditional Expressions

PEP written by Guido van Rossum and Raymond D. Hettinger; implemented by Thomas Wouters.

PEP 309: Partial Function Application

The functools module is intended to contain tools for functional-style programming.

One useful tool in this module is the partial() function. For programs written in a functional style, youll sometimes want to construct variants of existing functions that have some of the parameters filled in. Consider a Python function f(a, b, c); you could create a new function g(b, c) that was equivalent to f(1, b, c). This is called partial function application.

partial() takes the arguments (function, arg1, arg2, ... kwarg1=value1, kwarg2=value2). The resulting object is callable, so you can just call it to invoke function with the filled-in arguments.

Heres a small but realistic example:

import functools

def log (message, subsystem):
    "Write the contents of 'message' to the specified subsystem."
    print '%s: %s' % (subsystem, message)
    ...

server_log = functools.partial(log, subsystem='server')
server_log('Unable to open socket')

Heres another example, from a program that uses PyGTK. Here a context-sensitive pop-up menu is being constructed dynamically. The callback provided for the menu option is a partially applied version of the open_item() method, where the first argument has been provided.

...
class Application:
    def open_item(self, path):
       ...
    def init (self):
        open_func = functools.partial(self.open_item, item_path)
        popup_menu.append( ("Open", open_func, 1) )

Another function in the functools module is the update_wrapper(wrapper, wrapped) function that helps you write well-behaved decorators. update_wrapper() copies the name, module, and docstring attribute to a wrapper function so that tracebacks inside the wrapped function are easier to understand. For example, you might write:

def my_decorator(f):
    def wrapper(*args, **kwds):
        print 'Calling decorated function'
        return f(*args, **kwds)
    functools.update_wrapper(wrapper, f)
    return wrapper

wraps() is a decorator that can be used inside your own decorators to copy the wrapped functions information. An alternate version of the previous example would be:

def my_decorator(f):
    @functools.wraps(f)
    def wrapper(*args, **kwds):
        print 'Calling decorated function'
        return f(*args, **kwds)
    return wrapper

See also

PEP 309 - Partial Function Application

PEP proposed and written by Peter Harris; implemented by Hye-Shik Chang and Nick Coghlan, with adaptations by Raymond Hettinger.

PEP 314: Metadata for Python Software Packages v1.1

Some simple dependency support was added to Distutils. The setup() function now has requires, provides, and obsoletes keyword parameters. When you build a source distribution using the sdist command, the dependency information will be recorded in the PKG-INFO file.

Another new keyword parameter is download_url, which should be set to a URL for the packages source code. This means its now possible to look up an entry in the package index, determine the dependencies for a package, and download the required packages.

VERSION = '1.0'
setup(name='PyPackage',
      version=VERSION,
      requires=['numarray', 'zlib (>=1.1.4)'],
      obsoletes=['OldPackage']
      download_url=('http://www.example.com/pypackage/dist/pkg-%s.tar.gz'
                    % VERSION),
     )

Another new enhancement to the Python package index at https://pypi.org is storing source and binary archives for a package. The new upload Distutils command will upload a package to the repository.

Before a package can be uploaded, you must be able to build a distribution using the sdist Distutils command. Once that works, you can run python setup.py upload to add your package to the PyPI archive. Optionally you can GPG-sign the package by supplying the --sign and --identity options.

Package uploading was implemented by Martin von Lwis and Richard Jones.

See also

PEP 314 - Metadata for Python Software Packages v1.1

PEP proposed and written by A.M. Kuchling, Richard Jones, and Fred Drake; implemented by Richard Jones and Fred Drake.

PEP 328: Absolute and Relative Imports

The simpler part of PEP 328 was implemented in Python 2.4: parentheses could now be used to enclose the names imported from a module using the from ... import ... statement, making it easier to import many different names.

The more complicated part has been implemented in Python 2.5: importing a module can be specified to use absolute or package-relative imports. The plan is to move toward making absolute imports the default in future versions of Python.

Lets say you have a package directory like this:

pkg/
pkg/__init__.py
pkg/main.py
pkg/string.py

This defines a package named pkg containing the pkg.main and pkg.string submodules.

Consider the code in the main.py module. What happens if it executes the statement import string? In Python 2.4 and earlier, it will first look in the packages directory to perform a relative import, finds pkg/string.py, imports the contents of that file as the pkg.string module, and that module is bound to the name string in the pkg.main modules namespace.

Thats fine if pkg.string was what you wanted. But what if you wanted Pythons standard string module? Theres no clean way to ignore pkg.string and look for the standard module; generally you had to look at the contents of sys.modules, which is slightly unclean. Holger Krekels py.std package provides a tidier way to perform imports from the standard library, import py; py.std.string.join(), but that package isnt available on all Python installations.

Reading code which relies on relative imports is also less clear, because a reader may be confused about which module, string or pkg.string, is intended to be used. Python users soon learned not to duplicate the names of standard library modules in the names of their packages submodules, but you cant protect against having your submodules name being used for a new module added in a future version of Python.

In Python 2.5, you can switch imports behaviour to absolute imports using a from __future__ import absolute_import directive. This absolute-import behaviour will become the default in a future version (probably Python 2.7). Once absolute imports are the default, import string will always find the standard librarys version. Its suggested that users should begin using absolute imports as much as possible, so its preferable to begin writing from pkg import string in your code.

Relative imports are still possible by adding a leading period to the module name when using the from ... import form:

# Import names from pkg.string
from .string import name1, name2
# Import pkg.string
from . import string

This imports the string module relative to the current package, so in pkg.main this will import name1 and name2 from pkg.string. Additional leading periods perform the relative import starting from the parent of the current package. For example, code in the A.B.C module can do:

from . import D                 # Imports A.B.D
from .. import E                # Imports A.E
from ..F import G               # Imports A.F.G

Leading periods cannot be used with the import modname form of the import statement, only the from ... import form.

See also

PEP 328 - Imports: Multi-Line and Absolute/Relative

PEP written by Aahz; implemented by Thomas Wouters.

https://pylib.readthedocs.io/

The py library by Holger Krekel, which contains the py.std package.

PEP 338: Executing Modules as Scripts

The -m switch added in Python 2.4 to execute a module as a script gained a few more abilities. Instead of being implemented in C code inside the Python interpreter, the switch now uses an implementation in a new module, runpy.

The runpy module implements a more sophisticated import mechanism so that its now possible to run modules in a package such as pychecker.checker. The module also supports alternative import mechanisms such as the zipimport module. This means you can add a .zip archives path to sys.path and then use the -m switch to execute code from the archive.

See also

PEP 338 - Executing modules as scripts

PEP written and implemented by Nick Coghlan.

PEP 341: Unified try/except/finally

Until Python 2.5, the try statement came in two flavours. You could use a finally block to ensure that code is always executed, or one or more except blocks to catch specific exceptions. You couldnt combine both except blocks and a finally block, because generating the right bytecode for the combined version was complicated and it wasnt clear what the semantics of the combined statement should be.

Guido van Rossum spent some time working with Java, which does support the equivalent of combining except blocks and a finally block, and this clarified what the statement should mean. In Python 2.5, you can now write:

try:
    block-1 ...
except Exception1:
    handler-1 ...
except Exception2:
    handler-2 ...
else:
    else-block
finally:
    final-block

The code in block-1 is executed. If the code raises an exception, the various except blocks are tested: if the exception is of class Exception1, handler-1 is executed; otherwise if its of class Exception2, handler-2 is executed, and so forth. If no exception is raised, the else-block is executed.

No matter what happened previously, the final-block is executed once the code block is complete and any raised exceptions handled. Even if theres an error in an exception handler or the else-block and a new exception is raised, the code in the final-block is still run.

See also

PEP 341 - Unifying try-except and try-finally

PEP written by Georg Brandl; implementation by Thomas Lee.

PEP 342: New Generator Features

Python 2.5 adds a simple way to pass values into a generator. As introduced in Python 2.3, generators only produce output; once a generators code was invoked to create an iterator, there was no way to pass any new information into the function when its execution is resumed. Sometimes the ability to pass in some information would be useful. Hackish solutions to this include making the generators code look at a global variable and then changing the global variables value, or passing in some mutable object that callers then modify.

To refresh your memory of basic generators, heres a simple example:

def counter (maximum):
    i = 0
    while i < maximum:
        yield i
        i += 1

When you call counter(10), the result is an iterator that returns the values from 0 up to 9. On encountering the yield statement, the iterator returns the provided value and suspends the functions execution, preserving the local variables. Execution resumes on the following call to the iterators next() method, picking up after the yield statement.

In Python 2.3, yield was a statement; it didnt return any value. In 2.5, yield is now an expression, returning a value that can be assigned to a variable or otherwise operated on:

val = (yield i)

I recommend that you always put parentheses around a yield expression when youre doing something with the returned value, as in the above example. The parentheses arent always necessary, but its easier to always add them instead of having to remember when theyre needed.

(PEP 342 explains the exact rules, which are that a yield-expression must always be parenthesized except when it occurs at the top-level expression on the right-hand side of an assignment. This means you can write val = yield i but have to use parentheses when theres an operation, as in val = (yield i) + 12.)

Values are sent into a generator by calling its send(value) method. The generators code is then resumed and the yield expression returns the specified value. If the regular next() method is called, the yield returns None.

Heres the previous example, modified to allow changing the value of the internal counter.

def counter (maximum):
    i = 0
    while i < maximum:
        val = (yield i)
        # If value provided, change counter
        if val is not None:
            i = val
        else:
            i += 1

And heres an example of changing the counter:

>>> it = counter(10)
>>> print it.next()
0
>>> print it.next()
1
>>> print it.send(8)
8
>>> print it.next()
9
>>> print it.next()
Traceback (most recent call last):
  File "t.py", line 15, in ?
    print it.next()
StopIteration

yield will usually return None, so you should always check for this case. Dont just use its value in expressions unless youre sure that the send() method will be the only method used to resume your generator function.

In addition to send(), there are two other new methods on generators:

• throw(type, value=None, traceback=None) is used to raise an exception inside the generator; the exception is raised by the yield expression where the generators execution is paused.

• close() raises a new GeneratorExit exception inside the generator to terminate the iteration. On receiving this exception, the generators code must either raise GeneratorExit or StopIteration. Catching the GeneratorExit exception and returning a value is illegal and will trigger a RuntimeError; if the function raises some other exception, that exception is propagated to the caller. close() will also be called by Pythons garbage collector when the generator is garbage-collected.

  If you need to run cleanup code when a GeneratorExit occurs, I suggest using a try: ... finally: suite instead of catching GeneratorExit.

The cumulative effect of these changes is to turn generators from one-way producers of information into both producers and consumers.

Generators also become coroutines, a more generalized form of subroutines. Subroutines are entered at one point and exited at another point (the top of the function, and a return statement), but coroutines can be entered, exited, and resumed at many different points (the yield statements). Well have to figure out patterns for using coroutines effectively in Python.

The addition of the close() method has one side effect that isnt obvious. close() is called when a generator is garbage-collected, so this means the generators code gets one last chance to run before the generator is destroyed. This last chance means that try...finally statements in generators can now be guaranteed to work; the finally clause will now always get a chance to run. The syntactic restriction that you couldnt mix yield statements with a try...finally suite has therefore been removed. This seems like a minor bit of language trivia, but using generators and try...finally is actually necessary in order to implement the with statement described by PEP 343. Ill look at this new statement in the following section.

Another even more esoteric effect of this change: previously, the gi_frame attribute of a generator was always a frame object. Its now possible for gi_frame to be None once the generator has been exhausted.

See also

PEP 342 - Coroutines via Enhanced Generators

PEP written by Guido van Rossum and Phillip J. Eby; implemented by Phillip J. Eby. Includes examples of some fancier uses of generators as coroutines.

Earlier versions of these features were proposed in PEP 288 by Raymond Hettinger and PEP 325 by Samuele Pedroni.

https://en.wikipedia.org/wiki/Coroutine

The Wikipedia entry for coroutines.

https://web.archive.org/web/20160321211320/http://www.sidhe.org/~dan/blog/archives/000178.html

An explanation of coroutines from a Perl point of view, written by Dan Sugalski.

PEP 343: The with statement

The with statement clarifies code that previously would use try...finally blocks to ensure that clean-up code is executed. In this section, Ill discuss the statement as it will commonly be used. In the next section, Ill examine the implementation details and show how to write objects for use with this statement.

The with statement is a new control-flow structure whose basic structure is:

with expression [as variable]:
    with-block

The expression is evaluated, and it should result in an object that supports the context management protocol (that is, has __enter__() and __exit__() methods.

The objects __enter__() is called before with-block is executed and therefore can run set-up code. It also may return a value that is bound to the name variable, if given. (Note carefully that variable is not assigned the result of expression.)

After execution of the with-block is finished, the objects __exit__() method is called, even if the block raised an exception, and can therefore run clean-up code.

To enable the statement in Python 2.5, you need to add the following directive to your module:

from __future__ import with_statement

The statement will always be enabled in Python 2.6.

Some standard Python objects now support the context management protocol and can be used with the with statement. File objects are one example:

with open('/etc/passwd', 'r') as f:
    for line in f:
        print line
        ... more processing code ...

After this statement has executed, the file object in f will have been automatically closed, even if the for loop raised an exception part-way through the block.

Note

In this case, f is the same object created by open(), because __enter__() returns self.

The threading modules locks and condition variables also support the with statement:

lock = threading.Lock()
with lock:
    # Critical section of code
    ...

The lock is acquired before the block is executed and always released once the block is complete.

The new localcontext() function in the decimal module makes it easy to save and restore the current decimal context, which encapsulates the desired precision and rounding characteristics for computations:

from decimal import Decimal, Context, localcontext

# Displays with default precision of 28 digits
v = Decimal('578')
print v.sqrt()

with localcontext(Context(prec=16)):
    # All code in this block uses a precision of 16 digits.
    # The original context is restored on exiting the block.
    print v.sqrt()

Writing Context Managers

Under the hood, the with statement is fairly complicated. Most people will only use with in company with existing objects and dont need to know these details, so you can skip the rest of this section if you like. Authors of new objects will need to understand the details of the underlying implementation and should keep reading.

A high-level explanation of the context management protocol is:

• The expression is evaluated and should result in an object called a context manager. The context manager must have __enter__() and __exit__() methods.

• The context managers __enter__() method is called. The value returned is assigned to VAR. If no 'as VAR' clause is present, the value is simply discarded.

• The code in BLOCK is executed.

• If BLOCK raises an exception, the __exit__(type, value, traceback) is called with the exception details, the same values returned by sys.exc_info(). The methods return value controls whether the exception is re-raised: any false value re-raises the exception, and True will result in suppressing it. Youll only rarely want to suppress the exception, because if you do the author of the code containing the with statement will never realize anything went wrong.

• If BLOCK didnt raise an exception, the __exit__() method is still called, but type, value, and traceback are all None.

Lets think through an example. I wont present detailed code but will only sketch the methods necessary for a database that supports transactions.

(For people unfamiliar with database terminology: a set of changes to the database are grouped into a transaction. Transactions can be either committed, meaning that all the changes are written into the database, or rolled back, meaning that the changes are all discarded and the database is unchanged. See any database textbook for more information.)

Lets assume theres an object representing a database connection. Our goal will be to let the user write code like this:

db_connection = DatabaseConnection()
with db_connection as cursor:
    cursor.execute('insert into ...')
    cursor.execute('delete from ...')
    # ... more operations ...

The transaction should be committed if the code in the block runs flawlessly or rolled back if theres an exception. Heres the basic interface for DatabaseConnection that Ill assume:

class DatabaseConnection:
    # Database interface
    def cursor (self):
        "Returns a cursor object and starts a new transaction"
    def commit (self):
        "Commits current transaction"
    def rollback (self):
        "Rolls back current transaction"

The __enter__() method is pretty easy, having only to start a new transaction. For this application the resulting cursor object would be a useful result, so the method will return it. The user can then add as cursor to their with statement to bind the cursor to a variable name.

class DatabaseConnection:
    ...
    def __enter__ (self):
        # Code to start a new transaction
        cursor = self.cursor()
        return cursor

The __exit__() method is the most complicated because its where most of the work has to be done. The method has to check if an exception occurred. If there was no exception, the transaction is committed. The transaction is rolled back if there was an exception.

In the code below, execution will just fall off the end of the function, returning the default value of None. None is false, so the exception will be re-raised automatically. If you wished, you could be more explicit and add a return statement at the marked location.

class DatabaseConnection:
    ...
    def __exit__ (self, type, value, tb):
        if tb is None:
            # No exception, so commit
            self.commit()
        else:
            # Exception occurred, so rollback.
            self.rollback()
            # return False

The contextlib module

The new contextlib module provides some functions and a decorator that are useful for writing objects for use with the with statement.

The decorator is called @~contextlib.contextmanager, and lets you write a single generator function instead of defining a new class. The generator should yield exactly one value. The code up to the yield will be executed as the __enter__() method, and the value yielded will be the methods return value that will get bound to the variable in the with statements as clause, if any. The code after the yield will be executed in the __exit__() method. Any exception raised in the block will be raised by the yield statement.

Our database example from the previous section could be written using this decorator as:

from contextlib import contextmanager

@contextmanager
def db_transaction (connection):
    cursor = connection.cursor()
    try:
        yield cursor
    except:
        connection.rollback()
        raise
    else:
        connection.commit()

db = DatabaseConnection()
with db_transaction(db) as cursor:
    ...

The contextlib module also has a nested(mgr1, mgr2, ...) function that combines a number of context managers so you dont need to write nested with statements. In this example, the single with statement both starts a database transaction and acquires a thread lock:

lock = threading.Lock()
with nested (db_transaction(db), lock) as (cursor, locked):
    ...

Finally, the closing(object) function returns object so that it can be bound to a variable, and calls object.close at the end of the block.

import urllib, sys
from contextlib import closing

with closing(urllib.urlopen('http://www.yahoo.com')) as f:
    for line in f:
        sys.stdout.write(line)

See also

PEP 343 - The with statement

PEP written by Guido van Rossum and Nick Coghlan; implemented by Mike Bland, Guido van Rossum, and Neal Norwitz. The PEP shows the code generated for a with statement, which can be helpful in learning how the statement works.

The documentation for the contextlib module.

PEP 352: Exceptions as New-Style Classes

Exception classes can now be new-style classes, not just classic classes, and the built-in Exception class and all the standard built-in exceptions (NameError, ValueError, etc.) are now new-style classes.

The inheritance hierarchy for exceptions has been rearranged a bit. In 2.5, the inheritance relationships are:

BaseException       # New in Python 2.5
|- KeyboardInterrupt
|- SystemExit
|- Exception
   |- (all other current built-in exceptions)

This rearrangement was done because people often want to catch all exceptions that indicate program errors. KeyboardInterrupt and SystemExit arent errors, though, and usually represent an explicit action such as the user hitting Control-C or code calling sys.exit(). A bare except: will catch all exceptions, so you commonly need to list KeyboardInterrupt and SystemExit in order to re-raise them. The usual pattern is:

try:
    ...
except (KeyboardInterrupt, SystemExit):
    raise
except:
    # Log error...
    # Continue running program...

In Python 2.5, you can now write except Exception to achieve the same result, catching all the exceptions that usually indicate errors but leaving KeyboardInterrupt and SystemExit alone. As in previous versions, a bare except: still catches all exceptions.

The goal for Python 3.0 is to require any class raised as an exception to derive from BaseException or some descendant of BaseException, and future releases in the Python 2.x series may begin to enforce this constraint. Therefore, I suggest you begin making all your exception classes derive from Exception now. Its been suggested that the bare except: form should be removed in Python 3.0, but Guido van Rossum hasnt decided whether to do this or not.

Raising of strings as exceptions, as in the statement raise "Error occurred", is deprecated in Python 2.5 and will trigger a warning. The aim is to be able to remove the string-exception feature in a few releases.

See also

PEP 352 - Required Superclass for Exceptions

PEP written by Brett Cannon and Guido van Rossum; implemented by Brett Cannon.

PEP 353: Using ssize_t as the index type

A wide-ranging change to Pythons C API, using a new Py_ssize_t type definition instead of int, will permit the interpreter to handle more data on 64-bit platforms. This change doesnt affect Pythons capacity on 32-bit platforms.

Various pieces of the Python interpreter used Cs int type to store sizes or counts; for example, the number of items in a list or tuple were stored in an int. The C compilers for most 64-bit platforms still define int as a 32-bit type, so that meant that lists could only hold up to 2**31 - 1 = 2147483647 items. (There are actually a few different programming models that 64-bit C compilers can use see https://unix.org/version2/whatsnew/lp64_wp.html for a discussion but the most commonly available model leaves int as 32 bits.)

A limit of 2147483647 items doesnt really matter on a 32-bit platform because youll run out of memory before hitting the length limit. Each list item requires space for a pointer, which is 4 bytes, plus space for a PyObject representing the item. 2147483647*4 is already more bytes than a 32-bit address space can contain.

Its possible to address that much memory on a 64-bit platform, however. The pointers for a list that size would only require 16 GiB of space, so its not unreasonable that Python programmers might construct lists that large. Therefore, the Python interpreter had to be changed to use some type other than int, and this will be a 64-bit type on 64-bit platforms. The change will cause incompatibilities on 64-bit machines, so it was deemed worth making the transition now, while the number of 64-bit users is still relatively small. (In 5 or 10 years, we may all be on 64-bit machines, and the transition would be more painful then.)

This change most strongly affects authors of C extension modules. Python strings and container types such as lists and tuples now use Py_ssize_t to store their size. Functions such as PyList_Size() now return Py_ssize_t. Code in extension modules may therefore need to have some variables changed to Py_ssize_t.

The PyArg_ParseTuple() and Py_BuildValue() functions have a new conversion code, n, for Py_ssize_t. PyArg_ParseTuple()s s# and t# still output int by default, but you can define the macro PY_SSIZE_T_CLEAN before including Python.h to make them return Py_ssize_t.

PEP 353 has a section on conversion guidelines that extension authors should read to learn about supporting 64-bit platforms.

See also

PEP 353 - Using ssize_t as the index type

PEP written and implemented by Martin von Lwis.

PEP 357: The __index__ method

The NumPy developers had a problem that could only be solved by adding a new special method, __index__(). When using slice notation, as in [start:stop:step], the values of the start, stop, and step indexes must all be either integers or long integers. NumPy defines a variety of specialized integer types corresponding to unsigned and signed integers of 8, 16, 32, and 64 bits, but there was no way to signal that these types could be used as slice indexes.

Slicing cant just use the existing __int__() method because that method is also used to implement coercion to integers. If slicing used __int__(), floating-point numbers would also become legal slice indexes and thats clearly an undesirable behaviour.

Instead, a new special method called __index__() was added. It takes no arguments and returns an integer giving the slice index to use. For example:

class C:
    def __index__ (self):
        return self.value

The return value must be either a Python integer or long integer. The interpreter will check that the type returned is correct, and raises a TypeError if this requirement isnt met.

A corresponding nb_index slot was added to the C-level PyNumberMethods structure to let C extensions implement this protocol. PyNumber_Index(obj) can be used in extension code to call the __index__() function and retrieve its result.

See also

PEP 357 - Allowing Any Object to be Used for Slicing

PEP written and implemented by Travis Oliphant.

Other Language Changes

Here are all of the changes that Python 2.5 makes to the core Python language.

• The dict type has a new hook for letting subclasses provide a default value when a key isnt contained in the dictionary. When a key isnt found, the dictionarys __missing__(key) method will be called. This hook is used to implement the new defaultdict class in the collections module. The following example defines a dictionary that returns zero for any missing key:

  class zerodict (dict):
      def __missing__ (self, key):
          return 0
  
  d = zerodict({1:1, 2:2})
  print d[1], d[2]   # Prints 1, 2
  print d[3], d[4]   # Prints 0, 0
  

• Both 8-bit and Unicode strings have new partition(sep) and rpartition(sep) methods that simplify a common use case.

  The find(S) method is often used to get an index which is then used to slice the string and obtain the pieces that are before and after the separator. partition(sep) condenses this pattern into a single method call that returns a 3-tuple containing the substring before the separator, the separator itself, and the substring after the separator. If the separator isnt found, the first element of the tuple is the entire string and the other two elements are empty. rpartition(sep) also returns a 3-tuple but starts searching from the end of the string; the r stands for reverse.

  Some examples:

  >>> ('http://www.python.org').partition('://')
  ('http', '://', 'www.python.org')
  >>> ('file:/usr/share/doc/index.html').partition('://')
  ('file:/usr/share/doc/index.html', '', '')
  >>> (u'Subject: a quick question').partition(':')
  (u'Subject', u':', u' a quick question')
  >>> 'www.python.org'.rpartition('.')
  ('www.python', '.', 'org')
  >>> 'www.python.org'.rpartition(':')
  ('', '', 'www.python.org')
  

  (Implemented by Fredrik Lundh following a suggestion by Raymond Hettinger.)

• The startswith() and endswith() methods of string types now accept tuples of strings to check for.

  def is_image_file (filename):
      return filename.endswith(('.gif', '.jpg', '.tiff'))
  

  (Implemented by Georg Brandl following a suggestion by Tom Lynn.)

• The min() and max() built-in functions gained a key keyword parameter analogous to the key argument for sort(). This parameter supplies a function that takes a single argument and is called for every value in the list; min()/max() will return the element with the smallest/largest return value from this function. For example, to find the longest string in a list, you can do:

  L = ['medium', 'longest', 'short']
  # Prints 'longest'
  print max(L, key=len)
  # Prints 'short', because lexicographically 'short' has the largest value
  print max(L)
  

  (Contributed by Steven Bethard and Raymond Hettinger.)

• Two new built-in functions, any() and all(), evaluate whether an iterator contains any true or false values. any() returns True if any value returned by the iterator is true; otherwise it will return False. all() returns True only if all of the values returned by the iterator evaluate as true. (Suggested by Guido van Rossum, and implemented by Raymond Hettinger.)

• The result of a classs __hash__() method can now be either a long integer or a regular integer. If a long integer is returned, the hash of that value is taken. In earlier versions the hash value was required to be a regular integer, but in 2.5 the id() built-in was changed to always return non-negative numbers, and users often seem to use id(self) in __hash__() methods (though this is discouraged).

• ASCII is now the default encoding for modules. Its now a syntax error if a module contains string literals with 8-bit characters but doesnt have an encoding declaration. In Python 2.4 this triggered a warning, not a syntax error. See PEP 263 for how to declare a modules encoding; for example, you might add a line like this near the top of the source file:

  # -*- coding: latin1 -*-
  

• A new warning, UnicodeWarning, is triggered when you attempt to compare a Unicode string and an 8-bit string that cant be converted to Unicode using the default ASCII encoding. The result of the comparison is false:

  >>> chr(128) == unichr(128)   # Can't convert chr(128) to Unicode
  __main__:1: UnicodeWarning: Unicode equal comparison failed
    to convert both arguments to Unicode - interpreting them
    as being unequal
  False
  >>> chr(127) == unichr(127)   # chr(127) can be converted
  True
  

  Previously this would raise a UnicodeDecodeError exception, but in 2.5 this could result in puzzling problems when accessing a dictionary. If you looked up unichr(128) and chr(128) was being used as a key, youd get a UnicodeDecodeError exception. Other changes in 2.5 resulted in this exception being raised instead of suppressed by the code in dictobject.c that implements dictionaries.

  Raising an exception for such a comparison is strictly correct, but the change might have broken code, so instead UnicodeWarning was introduced.

  (Implemented by Marc-Andr Lemburg.)

• One error that Python programmers sometimes make is forgetting to include an __init__.py module in a package directory. Debugging this mistake can be confusing, and usually requires running Python with the -v switch to log all the paths searched. In Python 2.5, a new ImportWarning warning is triggered when an import would have picked up a directory as a package but no __init__.py was found. This warning is silently ignored by default; provide the -Wd option when running the Python executable to display the warning message. (Implemented by Thomas Wouters.)

• The list of base classes in a class definition can now be empty. As an example, this is now legal:

  class C():
      pass
  

  (Implemented by Brett Cannon.)

Interactive Interpreter Changes

In the interactive interpreter, quit and exit have long been strings so that new users get a somewhat helpful message when they try to quit:

>>> quit
'Use Ctrl-D (i.e. EOF) to exit.'

In Python 2.5, quit and exit are now objects that still produce string representations of themselves, but are also callable. Newbies who try quit() or exit() will now exit the interpreter as they expect. (Implemented by Georg Brandl.)

The Python executable now accepts the standard long options --help and --version; on Windows, it also accepts the /? option for displaying a help message. (Implemented by Georg Brandl.)

Optimizations

Several of the optimizations were developed at the NeedForSpeed sprint, an event held in Reykjavik, Iceland, from May 2128 2006. The sprint focused on speed enhancements to the CPython implementation and was funded by EWT LLC with local support from CCP Games. Those optimizations added at this sprint are specially marked in the following list.

• When they were introduced in Python 2.4, the built-in set and frozenset types were built on top of Pythons dictionary type. In 2.5 the internal data structure has been customized for implementing sets, and as a result sets will use a third less memory and are somewhat faster. (Implemented by Raymond Hettinger.)

• The speed of some Unicode operations, such as finding substrings, string splitting, and character map encoding and decoding, has been improved. (Substring search and splitting improvements were added by Fredrik Lundh and Andrew Dalke at the NeedForSpeed sprint. Character maps were improved by Walter Drwald and Martin von Lwis.)

• The long(str, base) function is now faster on long digit strings because fewer intermediate results are calculated. The peak is for strings of around 8001000 digits where the function is 6 times faster. (Contributed by Alan McIntyre and committed at the NeedForSpeed sprint.)

• Its now illegal to mix iterating over a file with for line in file and calling the file objects read()/readline()/readlines() methods. Iteration uses an internal buffer and the read*() methods dont use that buffer. Instead they would return the data following the buffer, causing the data to appear out of order. Mixing iteration and these methods will now trigger a ValueError from the read*() method. (Implemented by Thomas Wouters.)

• The struct module now compiles structure format strings into an internal representation and caches this representation, yielding a 20% speedup. (Contributed by Bob Ippolito at the NeedForSpeed sprint.)

• The re module got a 1 or 2% speedup by switching to Pythons allocator functions instead of the systems malloc() and free(). (Contributed by Jack Diederich at the NeedForSpeed sprint.)

• The code generators peephole optimizer now performs simple constant folding in expressions. If you write something like a = 2+3, the code generator will do the arithmetic and produce code corresponding to a = 5. (Proposed and implemented by Raymond Hettinger.)

• Function calls are now faster because code objects now keep the most recently finished frame (a zombie frame) in an internal field of the code object, reusing it the next time the code object is invoked. (Original patch by Michael Hudson, modified by Armin Rigo and Richard Jones; committed at the NeedForSpeed sprint.) Frame objects are also slightly smaller, which may improve cache locality and reduce memory usage a bit. (Contributed by Neal Norwitz.)

• Pythons built-in exceptions are now new-style classes, a change that speeds up instantiation considerably. Exception handling in Python 2.5 is therefore about 30% faster than in 2.4. (Contributed by Richard Jones, Georg Brandl and Sean Reifschneider at the NeedForSpeed sprint.)

• Importing now caches the paths tried, recording whether they exist or not so that the interpreter makes fewer open() and stat() calls on startup. (Contributed by Martin von Lwis and Georg Brandl.)

New, Improved, and Removed Modules

The standard library received many enhancements and bug fixes in Python 2.5. Heres a partial list of the most notable changes, sorted alphabetically by module name. Consult the Misc/NEWS file in the source tree for a more complete list of changes, or look through the SVN logs for all the details.

• The audioop module now supports the a-LAW encoding, and the code for u-LAW encoding has been improved. (Contributed by Lars Immisch.)

• The codecs module gained support for incremental codecs. The codecs.lookup() function now returns a CodecInfo instance instead of a tuple. CodecInfo instances behave like a 4-tuple to preserve backward compatibility but also have the attributes encode, decode, incrementalencoder, incrementaldecoder, streamwriter, and streamreader. Incremental codecs can receive input and produce output in multiple chunks; the output is the same as if the entire input was fed to the non-incremental codec. See the codecs module documentation for details. (Designed and implemented by Walter Drwald.)

• The collections module gained a new type, defaultdict, that subclasses the standard dict type. The new type mostly behaves like a dictionary but constructs a default value when a key isnt present, automatically adding it to the dictionary for the requested key value.

  The first argument to defaultdicts constructor is a factory function that gets called whenever a key is requested but not found. This factory function receives no arguments, so you can use built-in type constructors such as list() or int(). For example, you can make an index of words based on their initial letter like this:

  words = """Nel mezzo del cammin di nostra vita
  mi ritrovai per una selva oscura
  che la diritta via era smarrita""".lower().split()
  
  index = defaultdict(list)
  
  for w in words:
      init_letter = w[0]
      index[init_letter].append(w)
  

  Printing index results in the following output:

  defaultdict(<type 'list'>, {'c': ['cammin', 'che'], 'e': ['era'],
          'd': ['del', 'di', 'diritta'], 'm': ['mezzo', 'mi'],
          'l': ['la'], 'o': ['oscura'], 'n': ['nel', 'nostra'],
          'p': ['per'], 's': ['selva', 'smarrita'],
          'r': ['ritrovai'], 'u': ['una'], 'v': ['vita', 'via']}
  

  (Contributed by Guido van Rossum.)

• The deque double-ended queue type supplied by the collections module now has a remove(value) method that removes the first occurrence of value in the queue, raising ValueError if the value isnt found. (Contributed by Raymond Hettinger.)

• New module: The contextlib module contains helper functions for use with the new with statement. See section The contextlib module for more about this module.

• New module: The cProfile module is a C implementation of the existing profile module that has much lower overhead. The modules interface is the same as profile: you run cProfile.run('main()') to profile a function, can save profile data to a file, etc. Its not yet known if the Hotshot profiler, which is also written in C but doesnt match the profile modules interface, will continue to be maintained in future versions of Python. (Contributed by Armin Rigo.)

  Also, the pstats module for analyzing the data measured by the profiler now supports directing the output to any file object by supplying a stream argument to the Stats constructor. (Contributed by Skip Montanaro.)

• The csv module, which parses files in comma-separated value format, received several enhancements and a number of bugfixes. You can now set the maximum size in bytes of a field by calling the csv.field_size_limit(new_limit) function; omitting the new_limit argument will return the currently set limit. The reader class now has a line_num attribute that counts the number of physical lines read from the source; records can span multiple physical lines, so line_num is not the same as the number of records read.

  The CSV parser is now stricter about multi-line quoted fields. Previously, if a line ended within a quoted field without a terminating newline character, a newline would be inserted into the returned field. This behavior caused problems when reading files that contained carriage return characters within fields, so the code was changed to return the field without inserting newlines. As a consequence, if newlines embedded within fields are important, the input should be split into lines in a manner that preserves the newline characters.

  (Contributed by Skip Montanaro and Andrew McNamara.)

• The datetime class in the datetime module now has a strptime(string, format) method for parsing date strings, contributed by Josh Spoerri. It uses the same format characters as time.strptime() and time.strftime():

  import datetime as dt
  
  ts = dt.datetime.strptime('10:13:15 2006-03-07',
                            '%H:%M:%S %Y-%m-%d')
  

• The difflib.SequenceMatcher.get_matching_blocks() method in the difflib module now guarantees to return a minimal list of blocks describing matching subsequences. Previously, the algorithm would occasionally break a block of matching elements into two list entries. (Enhancement by Tim Peters.)

• The doctest module gained a SKIP option that keeps an example from being executed at all. This is intended for code snippets that are usage examples intended for the reader and arent actually test cases.

  An encoding parameter was added to the testfile() function and the DocFileSuite class to specify the files encoding. This makes it easier to use non-ASCII characters in tests contained within a docstring. (Contributed by Bjorn Tillenius.)

• The email package has been updated to version 4.0. (Contributed by Barry Warsaw.)

• The fileinput module was made more flexible. Unicode filenames are now supported, and a mode parameter that defaults to "r" was added to the input() function to allow opening files in binary or universal newlines mode. Another new parameter, openhook, lets you use a function other than open() to open the input files. Once youre iterating over the set of files, the FileInput objects new fileno() returns the file descriptor for the currently opened file. (Contributed by Georg Brandl.)

• In the gc module, the new get_count() function returns a 3-tuple containing the current collection counts for the three GC generations. This is accounting information for the garbage collector; when these counts reach a specified threshold, a garbage collection sweep will be made. The existing gc.collect() function now takes an optional generation argument of 0, 1, or 2 to specify which generation to collect. (Contributed by Barry Warsaw.)

• The nsmallest() and nlargest() functions in the heapq module now support a key keyword parameter similar to the one provided by the min()/max() functions and the sort() methods. For example:

  >>> import heapq
  >>> L = ["short", 'medium', 'longest', 'longer still']
  >>> heapq.nsmallest(2, L)  # Return two lowest elements, lexicographically
  ['longer still', 'longest']
  >>> heapq.nsmallest(2, L, key=len)   # Return two shortest elements
  ['short', 'medium']
  

  (Contributed by Raymond Hettinger.)

• The itertools.islice() function now accepts None for the start and step arguments. This makes it more compatible with the attributes of slice objects, so that you can now write the following:

  s = slice(5)     # Create slice object
  itertools.islice(iterable, s.start, s.stop, s.step)
  

  (Contributed by Raymond Hettinger.)

• The format() function in the locale module has been modified and two new functions were added, format_string() and currency().

  The format() functions val parameter could previously be a string as long as no more than one %char specifier appeared; now the parameter must be exactly one %char specifier with no surrounding text. An optional monetary parameter was also added which, if True, will use the locales rules for formatting currency in placing a separator between groups of three digits.

  To format strings with multiple %char specifiers, use the new format_string() function that works like format() but also supports mixing %char specifiers with arbitrary text.

  A new currency() function was also added that formats a number according to the current locales settings.

  (Contributed by Georg Brandl.)

• The mailbox module underwent a massive rewrite to add the capability to modify mailboxes in addition to reading them. A new set of classes that include mbox, MH, and Maildir are used to read mailboxes, and have an add(message) method to add messages, remove(key) to remove messages, and lock()/unlock() to lock/unlock the mailbox. The following example converts a maildir-format mailbox into an mbox-format one:

  import mailbox
  
  # 'factory=None' uses email.Message.Message as the class representing
  # individual messages.
  src = mailbox.Maildir('maildir', factory=None)
  dest = mailbox.mbox('/tmp/mbox')
  
  for msg in src:
      dest.add(msg)
  

  (Contributed by Gregory K. Johnson. Funding was provided by Googles 2005 Summer of Code.)

• New module: the msilib module allows creating Microsoft Installer .msi files and CAB files. Some support for reading the .msi database is also included. (Contributed by Martin von Lwis.)

• The nis module now supports accessing domains other than the system default domain by supplying a domain argument to the nis.match() and nis.maps() functions. (Contributed by Ben Bell.)

• The operator modules itemgetter() and attrgetter() functions now support multiple fields. A call such as operator.attrgetter('a', 'b') will return a function that retrieves the a and b attributes. Combining this new feature with the sort() methods key parameter lets you easily sort lists using multiple fields. (Contributed by Raymond Hettinger.)

• The optparse module was updated to version 1.5.1 of the Optik library. The OptionParser class gained an epilog attribute, a string that will be printed after the help message, and a destroy() method to break reference cycles created by the object. (Contributed by Greg Ward.)

• The os module underwent several changes. The stat_float_times variable now defaults to true, meaning that os.stat() will now return time values as floats. (This doesnt necessarily mean that os.stat() will return times that are precise to fractions of a second; not all systems support such precision.)

  Constants named os.SEEK_SET, os.SEEK_CUR, and os.SEEK_END have been added; these are the parameters to the os.lseek() function. Two new constants for locking are os.O_SHLOCK and os.O_EXLOCK.

  Two new functions, wait3() and wait4(), were added. Theyre similar the waitpid() function which waits for a child process to exit and returns a tuple of the process ID and its exit status, but wait3() and wait4() return additional information. wait3() doesnt take a process ID as input, so it waits for any child process to exit and returns a 3-tuple of process-id, exit-status, resource-usage as returned from the resource.getrusage() function. wait4(pid) does take a process ID. (Contributed by Chad J. Schroeder.)

  On FreeBSD, the os.stat() function now returns times with nanosecond resolution, and the returned object now has st_gen and st_birthtime. The st_flags attribute is also available, if the platform supports it. (Contributed by Antti Louko and Diego Petten.)

• The Python debugger provided by the pdb module can now store lists of commands to execute when a breakpoint is reached and execution stops. Once breakpoint #1 has been created, enter commands 1 and enter a series of commands to be executed, finishing the list with end. The command list can include commands that resume execution, such as continue or next. (Contributed by Grgoire Dooms.)

• The pickle and cPickle modules no longer accept a return value of None from the __reduce__() method; the method must return a tuple of arguments instead. The ability to return None was deprecated in Python 2.4, so this completes the removal of the feature.

• The pkgutil module, containing various utility functions for finding packages, was enhanced to support PEP 302s import hooks and now also works for packages stored in ZIP-format archives. (Contributed by Phillip J. Eby.)

• The pybench benchmark suite by Marc-Andr Lemburg is now included in the Tools/pybench directory. The pybench suite is an improvement on the commonly used pystone.py program because pybench provides a more detailed measurement of the interpreters speed. It times particular operations such as function calls, tuple slicing, method lookups, and numeric operations, instead of performing many different operations and reducing the result to a single number as pystone.py does.

• The pyexpat module now uses version 2.0 of the Expat parser. (Contributed by Trent Mick.)

• The Queue class provided by the queue module gained two new methods. join() blocks until all items in the queue have been retrieved and all processing work on the items have been completed. Worker threads call the other new method, task_done(), to signal that processing for an item has been completed. (Contributed by Raymond Hettinger.)

• The old regex and regsub modules, which have been deprecated ever since Python 2.0, have finally been deleted. Other deleted modules: statcache, tzparse, whrandom.

• Also deleted: the lib-old directory, which includes ancient modules such as dircmp and ni, was removed. lib-old wasnt on the default sys.path, so unless your programs explicitly added the directory to sys.path, this removal shouldnt affect your code.

• The rlcompleter module is no longer dependent on importing the readline module and therefore now works on non-Unix platforms. (Patch from Robert Kiendl.)

• The SimpleXMLRPCServer and DocXMLRPCServer classes now have a rpc_paths attribute that constrains XML-RPC operations to a limited set of URL paths; the default is to allow only '/' and '/RPC2'. Setting rpc_paths to None or an empty tuple disables this path checking.

• The socket module now supports AF_NETLINK sockets on Linux, thanks to a patch from Philippe Biondi. Netlink sockets are a Linux-specific mechanism for communications between a user-space process and kernel code; an introductory article about them is at https://www.linuxjournal.com/article/7356. In Python code, netlink addresses are represented as a tuple of 2 integers, (pid, group_mask).

  Two new methods on socket objects, recv_into(buffer) and recvfrom_into(buffer), store the received data in an object that supports the buffer protocol instead of returning the data as a string. This means you can put the data directly into an array or a memory-mapped file.

  Socket objects also gained getfamily(), gettype(), and getproto() accessor methods to retrieve the family, type, and protocol values for the socket.

• New module: the spwd module provides functions for accessing the shadow password database on systems that support shadow passwords.

• The struct is now faster because it compiles format strings into Struct objects with pack() and unpack() methods. This is similar to how the re module lets you create compiled regular expression objects. You can still use the module-level pack() and unpack() functions; theyll create Struct objects and cache them. Or you can use Struct instances directly:

  s = struct.Struct('ih3s')
  
  data = s.pack(1972, 187, 'abc')
  year, number, name = s.unpack(data)
  

  You can also pack and unpack data to and from buffer objects directly using the pack_into(buffer, offset, v1, v2, ...) and unpack_from(buffer, offset) methods. This lets you store data directly into an array or a memory-mapped file.

  (Struct objects were implemented by Bob Ippolito at the NeedForSpeed sprint. Support for buffer objects was added by Martin Blais, also at the NeedForSpeed sprint.)

• The Python developers switched from CVS to Subversion during the 2.5 development process. Information about the exact build version is available as the sys.subversion variable, a 3-tuple of (interpreter-name, branch-name, revision-range). For example, at the time of writing my copy of 2.5 was reporting ('CPython', 'trunk', '45313:45315').

  This information is also available to C extensions via the Py_GetBuildInfo() function that returns a string of build information like this: "trunk:45355:45356M, Apr 13 2006, 07:42:19". (Contributed by Barry Warsaw.)

• Another new function, sys._current_frames(), returns the current stack frames for all running threads as a dictionary mapping thread identifiers to the topmost stack frame currently active in that thread at the time the function is called. (Contributed by Tim Peters.)

• The TarFile class in the tarfile module now has an extractall() method that extracts all members from the archive into the current working directory. Its also possible to set a different directory as the extraction target, and to unpack only a subset of the archives members.

  The compression used for a tarfile opened in stream mode can now be autodetected using the mode 'r|*'. (Contributed by Lars Gustbel.)

• The threading module now lets you set the stack size used when new threads are created. The stack_size([*size*]) function returns the currently configured stack size, and supplying the optional size parameter sets a new value. Not all platforms support changing the stack size, but Windows, POSIX threading, and OS/2 all do. (Contributed by Andrew MacIntyre.)

• The unicodedata module has been updated to use version 4.1.0 of the Unicode character database. Version 3.2.0 is required by some specifications, so its still available as unicodedata.ucd_3_2_0.

• New module: the uuid module generates universally unique identifiers (UUIDs) according to RFC 4122. The RFC defines several different UUID versions that are generated from a starting string, from system properties, or purely randomly. This module contains a UUID class and functions named uuid1(), uuid3(), uuid4(), and uuid5() to generate different versions of UUID. (Version 2 UUIDs are not specified in RFC 4122 and are not supported by this module.)

  >>> import uuid
  >>> # make a UUID based on the host ID and current time
  >>> uuid.uuid1()
  UUID('a8098c1a-f86e-11da-bd1a-00112444be1e')
  
  >>> # make a UUID using an MD5 hash of a namespace UUID and a name
  >>> uuid.uuid3(uuid.NAMESPACE_DNS, 'python.org')
  UUID('6fa459ea-ee8a-3ca4-894e-db77e160355e')
  
  >>> # make a random UUID
  >>> uuid.uuid4()
  UUID('16fd2706-8baf-433b-82eb-8c7fada847da')
  
  >>> # make a UUID using a SHA-1 hash of a namespace UUID and a name
  >>> uuid.uuid5(uuid.NAMESPACE_DNS, 'python.org')
  UUID('886313e1-3b8a-5372-9b90-0c9aee199e5d')
  

  (Contributed by Ka-Ping Yee.)

• The weakref modules WeakKeyDictionary and WeakValueDictionary types gained new methods for iterating over the weak references contained in the dictionary. iterkeyrefs() and keyrefs() methods were added to WeakKeyDictionary, and itervaluerefs() and valuerefs() were added to WeakValueDictionary. (Contributed by Fred L. Drake, Jr.)

• The webbrowser module received a number of enhancements. Its now usable as a script with python -m webbrowser, taking a URL as the argument; there are a number of switches to control the behaviour (-n for a new browser window, -t for a new tab). New module-level functions, open_new() and open_new_tab(), were added to support this. The modules open() function supports an additional feature, an autoraise parameter that signals whether to raise the open window when possible. A number of additional browsers were added to the supported list such as Firefox, Opera, Konqueror, and elinks. (Contributed by Oleg Broytmann and Georg Brandl.)

• The xmlrpclib module now supports returning datetime objects for the XML-RPC date type. Supply use_datetime=True to the loads() function or the Unmarshaller class to enable this feature. (Contributed by Skip Montanaro.)

• The zipfile module now supports the ZIP64 version of the format, meaning that a .zip archive can now be larger than 4 GiB and can contain individual files larger than 4 GiB. (Contributed by Ronald Oussoren.)

• The zlib modules Compress and Decompress objects now support a copy() method that makes a copy of the objects internal state and returns a new Compress or Decompress object. (Contributed by Chris AtLee.)

The ctypes package

The ctypes package, written by Thomas Heller, has been added to the standard library. ctypes lets you call arbitrary functions in shared libraries or DLLs. Long-time users may remember the dl module, which provides functions for loading shared libraries and calling functions in them. The ctypes package is much fancier.

To load a shared library or DLL, you must create an instance of the CDLL class and provide the name or path of the shared library or DLL. Once thats done, you can call arbitrary functions by accessing them as attributes of the CDLL object.

import ctypes

libc = ctypes.CDLL('libc.so.6')
result = libc.printf("Line of output\n")

Type constructors for the various C types are provided: c_int(), c_float(), c_double(), c_char_p() (equivalent to char*), and so forth. Unlike Pythons types, the C versions are all mutable; you can assign to their value attribute to change the wrapped value. Python integers and strings will be automatically converted to the corresponding C types, but for other types you must call the correct type constructor. (And I mean must; getting it wrong will often result in the interpreter crashing with a segmentation fault.)

You shouldnt use c_char_p() with a Python string when the C function will be modifying the memory area, because Python strings are supposed to be immutable; breaking this rule will cause puzzling bugs. When you need a modifiable memory area, use create_string_buffer():

s = "this is a string"
buf = ctypes.create_string_buffer(s)
libc.strfry(buf)

C functions are assumed to return integers, but you can set the restype attribute of the function object to change this:

>>> libc.atof('2.71828')
-1783957616
>>> libc.atof.restype = ctypes.c_double
>>> libc.atof('2.71828')
2.71828

ctypes also provides a wrapper for Pythons C API as the ctypes.pythonapi object. This object does not release the global interpreter lock before calling a function, because the lock must be held when calling into the interpreters code. Theres a py_object type constructor that will create a PyObject* pointer. A simple usage:

import ctypes

d = {}
ctypes.pythonapi.PyObject_SetItem(ctypes.py_object(d),
          ctypes.py_object("abc"),  ctypes.py_object(1))
# d is now {'abc', 1}.

Dont forget to use py_object(); if its omitted you end up with a segmentation fault.

ctypes has been around for a while, but people still write and distribution hand-coded extension modules because you cant rely on ctypes being present. Perhaps developers will begin to write Python wrappers atop a library accessed through ctypes instead of extension modules, now that ctypes is included with core Python.

See also

https://web.archive.org/web/20180410025338/http://starship.python.net/crew/theller/ctypes/

The pre-stdlib ctypes web page, with a tutorial, reference, and FAQ.

The documentation for the ctypes module.

The ElementTree package

A subset of Fredrik Lundhs ElementTree library for processing XML has been added to the standard library as xml.etree. The available modules are ElementTree, ElementPath, and ElementInclude from ElementTree 1.2.6. The cElementTree accelerator module is also included.

The rest of this section will provide a brief overview of using ElementTree. Full documentation for ElementTree is available at https://web.archive.org/web/20201124024954/http://effbot.org/zone/element-index.htm.

ElementTree represents an XML document as a tree of element nodes. The text content of the document is stored as the text and tail attributes of (This is one of the major differences between ElementTree and the Document Object Model; in the DOM there are many different types of node, including TextNode.)

The most commonly used parsing function is parse(), that takes either a string (assumed to contain a filename) or a file-like object and returns an ElementTree instance:

from xml.etree import ElementTree as ET

tree = ET.parse('ex-1.xml')

feed = urllib.urlopen(
          'http://planet.python.org/rss10.xml')
tree = ET.parse(feed)

Once you have an ElementTree instance, you can call its getroot() method to get the root Element node.

Theres also an XML() function that takes a string literal and returns an Element node (not an ElementTree). This function provides a tidy way to incorporate XML fragments, approaching the convenience of an XML literal:

svg = ET.XML("""<svg width="10px" version="1.0">
             </svg>""")
svg.set('height', '320px')
svg.append(elem1)

Each XML element supports some dictionary-like and some list-like access methods. Dictionary-like operations are used to access attribute values, and list-like operations are used to access child nodes.

Operation	Result
elem[n]	Returns nth child element.
elem[m:n]	Returns list of mth through nth child elements.
len(elem)	Returns number of child elements.
list(elem)	Returns list of child elements.
elem.append(elem2)	Adds elem2 as a child.
elem.insert(index, elem2)	Inserts elem2 at the specified location.
del elem[n]	Deletes nth child element.
elem.keys()	Returns list of attribute names.
elem.get(name)	Returns value of attribute name.
elem.set(name, value)	Sets new value for attribute name.
elem.attrib	Retrieves the dictionary containing attributes.
del elem.attrib[name]	Deletes attribute name.

Comments and processing instructions are also represented as Element nodes. To check if a node is a comment or processing instructions:

if elem.tag is ET.Comment:
    ...
elif elem.tag is ET.ProcessingInstruction:
    ...

To generate XML output, you should call the xml.etree.ElementTree.ElementTree.write() method. Like parse(), it can take either a string or a file-like object:

# Encoding is US-ASCII
tree.write('output.xml')

# Encoding is UTF-8
f = open('output.xml', 'w')
tree.write(f, encoding='utf-8')

(Caution: the default encoding used for output is ASCII. For general XML work, where an elements name may contain arbitrary Unicode characters, ASCII isnt a very useful encoding because it will raise an exception if an elements name contains any characters with values greater than 127. Therefore, its best to specify a different encoding such as UTF-8 that can handle any Unicode character.)

This section is only a partial description of the ElementTree interfaces. Please read the packages official documentation for more details.

See also

https://web.archive.org/web/20201124024954/http://effbot.org/zone/element-index.htm

Official documentation for ElementTree.

The hashlib package

A new hashlib module, written by Gregory P. Smith, has been added to replace the md5 and sha modules. hashlib adds support for additional secure hashes (SHA-224, SHA-256, SHA-384, and SHA-512). When available, the module uses OpenSSL for fast platform optimized implementations of algorithms.

The old md5 and sha modules still exist as wrappers around hashlib to preserve backwards compatibility. The new modules interface is very close to that of the old modules, but not identical. The most significant difference is that the constructor functions for creating new hashing objects are named differently.

# Old versions
h = md5.md5()
h = md5.new()

# New version
h = hashlib.md5()

# Old versions
h = sha.sha()
h = sha.new()

# New version
h = hashlib.sha1()

# Hash that weren't previously available
h = hashlib.sha224()
h = hashlib.sha256()
h = hashlib.sha384()
h = hashlib.sha512()

# Alternative form
h = hashlib.new('md5')          # Provide algorithm as a string

Once a hash object has been created, its methods are the same as before: update(string) hashes the specified string into the current digest state, digest() and hexdigest() return the digest value as a binary string or a string of hex digits, and copy() returns a new hashing object with the same digest state.

See also

The documentation for the hashlib module.

The sqlite3 package

The pysqlite module (https://www.pysqlite.org), a wrapper for the SQLite embedded database, has been added to the standard library under the package name sqlite3.

SQLite is a C library that provides a lightweight disk-based database that doesnt require a separate server process and allows accessing the database using a nonstandard variant of the SQL query language. Some applications can use SQLite for internal data storage. Its also possible to prototype an application using SQLite and then port the code to a larger database such as PostgreSQL or Oracle.

pysqlite was written by Gerhard Hring and provides a SQL interface compliant with the DB-API 2.0 specification described by PEP 249.

If youre compiling the Python source yourself, note that the source tree doesnt include the SQLite code, only the wrapper module. Youll need to have the SQLite libraries and headers installed before compiling Python, and the build process will compile the module when the necessary headers are available.

To use the module, you must first create a Connection object that represents the database. Here the data will be stored in the /tmp/example file:

conn = sqlite3.connect('/tmp/example')

You can also supply the special name :memory: to create a database in RAM.

Once you have a Connection, you can create a Cursor object and call its execute() method to perform SQL commands:

c = conn.cursor()

# Create table
c.execute('''create table stocks
(date text, trans text, symbol text,
 qty real, price real)''')

# Insert a row of data
c.execute("""insert into stocks
          values ('2006-01-05','BUY','RHAT',100,35.14)""")

Usually your SQL operations will need to use values from Python variables. You shouldnt assemble your query using Pythons string operations because doing so is insecure; it makes your program vulnerable to an SQL injection attack.

Instead, use the DB-APIs parameter substitution. Put ? as a placeholder wherever you want to use a value, and then provide a tuple of values as the second argument to the cursors execute() method. (Other database modules may use a different placeholder, such as %s or :1.) For example:

# Never do this -- insecure!
symbol = 'IBM'
c.execute("... where symbol = '%s'" % symbol)

# Do this instead
t = (symbol,)
c.execute('select * from stocks where symbol=?', t)

# Larger example
for t in (('2006-03-28', 'BUY', 'IBM', 1000, 45.00),
          ('2006-04-05', 'BUY', 'MSOFT', 1000, 72.00),
          ('2006-04-06', 'SELL', 'IBM', 500, 53.00),
         ):
    c.execute('insert into stocks values (?,?,?,?,?)', t)

To retrieve data after executing a SELECT statement, you can either treat the cursor as an iterator, call the cursors fetchone() method to retrieve a single matching row, or call fetchall() to get a list of the matching rows.

This example uses the iterator form:

>>> c = conn.cursor()
>>> c.execute('select * from stocks order by price')
>>> for row in c:
...    print row
...
(u'2006-01-05', u'BUY', u'RHAT', 100, 35.140000000000001)
(u'2006-03-28', u'BUY', u'IBM', 1000, 45.0)
(u'2006-04-06', u'SELL', u'IBM', 500, 53.0)
(u'2006-04-05', u'BUY', u'MSOFT', 1000, 72.0)
>>>

For more information about the SQL dialect supported by SQLite, see https://www.sqlite.org.

See also

https://www.pysqlite.org

The pysqlite web page.

https://www.sqlite.org

The SQLite web page; the documentation describes the syntax and the available data types for the supported SQL dialect.

The documentation for the sqlite3 module.

PEP 249 - Database API Specification 2.0

PEP written by Marc-Andr Lemburg.

The wsgiref package

The Web Server Gateway Interface (WSGI) v1.0 defines a standard interface between web servers and Python web applications and is described in PEP 333. The wsgiref package is a reference implementation of the WSGI specification.

The package includes a basic HTTP server that will run a WSGI application; this server is useful for debugging but isnt intended for production use. Setting up a server takes only a few lines of code:

from wsgiref import simple_server

wsgi_app = ...

host = ''
port = 8000
httpd = simple_server.make_server(host, port, wsgi_app)
httpd.serve_forever()

See also

https://web.archive.org/web/20160331090247/http://wsgi.readthedocs.org/en/latest/

A central web site for WSGI-related resources.

PEP 333 - Python Web Server Gateway Interface v1.0

PEP written by Phillip J. Eby.

Build and C API Changes

Changes to Pythons build process and to the C API include:

• The Python source tree was converted from CVS to Subversion, in a complex migration procedure that was supervised and flawlessly carried out by Martin von Lwis. The procedure was developed as PEP 347.

• Coverity, a company that markets a source code analysis tool called Prevent, provided the results of their examination of the Python source code. The analysis found about 60 bugs that were quickly fixed. Many of the bugs were refcounting problems, often occurring in error-handling code. See https://scan.coverity.com for the statistics.

• The largest change to the C API came from PEP 353, which modifies the interpreter to use a Py_ssize_t type definition instead of int. See the earlier section PEP 353: Using ssize_t as the index type for a discussion of this change.

• The design of the bytecode compiler has changed a great deal, no longer generating bytecode by traversing the parse tree. Instead the parse tree is converted to an abstract syntax tree (or AST), and it is the abstract syntax tree thats traversed to produce the bytecode.

  Its possible for Python code to obtain AST objects by using the compile() built-in and specifying _ast.PyCF_ONLY_AST as the value of the flags parameter:

  from _ast import PyCF_ONLY_AST
  ast = compile("""a=0
  for i in range(10):
      a += i
  """, "<string>", 'exec', PyCF_ONLY_AST)
  
  assignment = ast.body[0]
  for_loop = ast.body[1]
  

  No official documentation has been written for the AST code yet, but PEP 339 discusses the design. To start learning about the code, read the definition of the various AST nodes in Parser/Python.asdl. A Python script reads this file and generates a set of C structure definitions in Include/Python-ast.h. The PyParser_ASTFromString() and PyParser_ASTFromFile(), defined in Include/pythonrun.h, take Python source as input and return the root of an AST representing the contents. This AST can then be turned into a code object by PyAST_Compile(). For more information, read the source code, and then ask questions on python-dev.

  The AST code was developed under Jeremy Hyltons management, and implemented by (in alphabetical order) Brett Cannon, Nick Coghlan, Grant Edwards, John Ehresman, Kurt Kaiser, Neal Norwitz, Tim Peters, Armin Rigo, and Neil Schemenauer, plus the participants in a number of AST sprints at conferences such as PyCon.

• Evan Joness patch to obmalloc, first described in a talk at PyCon DC 2005, was applied. Python 2.4 allocated small objects in 256K-sized arenas, but never freed arenas. With this patch, Python will free arenas when theyre empty. The net effect is that on some platforms, when you allocate many objects, Pythons memory usage may actually drop when you delete them and the memory may be returned to the operating system. (Implemented by Evan Jones, and reworked by Tim Peters.)

  Note that this change means extension modules must be more careful when allocating memory. Pythons API has many different functions for allocating memory that are grouped into families. For example, PyMem_Malloc(), PyMem_Realloc(), and PyMem_Free() are one family that allocates raw memory, while PyObject_Malloc(), PyObject_Realloc(), and PyObject_Free() are another family thats supposed to be used for creating Python objects.

  Previously these different families all reduced to the platforms malloc() and free() functions. This meant it didnt matter if you got things wrong and allocated memory with the PyMem function but freed it with the PyObject function. With 2.5s changes to obmalloc, these families now do different things and mismatches will probably result in a segfault. You should carefully test your C extension modules with Python 2.5.

• The built-in set types now have an official C API. Call PySet_New() and PyFrozenSet_New() to create a new set, PySet_Add() and PySet_Discard() to add and remove elements, and PySet_Contains() and PySet_Size() to examine the sets state. (Contributed by Raymond Hettinger.)

• C code can now obtain information about the exact revision of the Python interpreter by calling the Py_GetBuildInfo() function that returns a string of build information like this: "trunk:45355:45356M, Apr 13 2006, 07:42:19". (Contributed by Barry Warsaw.)

• Two new macros can be used to indicate C functions that are local to the current file so that a faster calling convention can be used. Py_LOCAL declares the function as returning a value of the specified type and uses a fast-calling qualifier. Py_LOCAL_INLINE does the same thing and also requests the function be inlined. If macro PY_LOCAL_AGGRESSIVE is defined before python.h is included, a set of more aggressive optimizations are enabled for the module; you should benchmark the results to find out if these optimizations actually make the code faster. (Contributed by Fredrik Lundh at the NeedForSpeed sprint.)

• PyErr_NewException(name, base, dict) can now accept a tuple of base classes as its base argument. (Contributed by Georg Brandl.)

• The PyErr_Warn() function for issuing warnings is now deprecated in favour of PyErr_WarnEx(category, message, stacklevel) which lets you specify the number of stack frames separating this function and the caller. A stacklevel of 1 is the function calling PyErr_WarnEx(), 2 is the function above that, and so forth. (Added by Neal Norwitz.)

• The CPython interpreter is still written in C, but the code can now be compiled with a C++ compiler without errors. (Implemented by Anthony Baxter, Martin von Lwis, Skip Montanaro.)

• The PyRange_New() function was removed. It was never documented, never used in the core code, and had dangerously lax error checking. In the unlikely case that your extensions were using it, you can replace it by something like the following:

  range = PyObject_CallFunction((PyObject*) &PyRange_Type, "lll",
                                start, stop, step);
  

Port-Specific Changes

• MacOS X (10.3 and higher): dynamic loading of modules now uses the dlopen() function instead of MacOS-specific functions.

• MacOS X: an --enable-universalsdk switch was added to the configure script that compiles the interpreter as a universal binary able to run on both PowerPC and Intel processors. (Contributed by Ronald Oussoren; bpo-2573.)

• Windows: .dll is no longer supported as a filename extension for extension modules. .pyd is now the only filename extension that will be searched for.

Porting to Python 2.5

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

• ASCII is now the default encoding for modules. Its now a syntax error if a module contains string literals with 8-bit characters but doesnt have an encoding declaration. In Python 2.4 this triggered a warning, not a syntax error.

• Previously, the gi_frame attribute of a generator was always a frame object. Because of the PEP 342 changes described in section PEP 342: New Generator Features, its now possible for gi_frame to be None.

• A new warning, UnicodeWarning, is triggered when you attempt to compare a Unicode string and an 8-bit string that cant be converted to Unicode using the default ASCII encoding. Previously such comparisons would raise a UnicodeDecodeError exception.

• Library: the csv module is now stricter about multi-line quoted fields. If your files contain newlines embedded within fields, the input should be split into lines in a manner which preserves the newline characters.

• Library: the locale modules format() functions would previously accept any string as long as no more than one %char specifier appeared. In Python 2.5, the argument must be exactly one %char specifier with no surrounding text.

• Library: The pickle and cPickle modules no longer accept a return value of None from the __reduce__() method; the method must return a tuple of arguments instead. The modules also no longer accept the deprecated bin keyword parameter.

• Library: The SimpleXMLRPCServer and DocXMLRPCServer classes now have a rpc_paths attribute that constrains XML-RPC operations to a limited set of URL paths; the default is to allow only '/' and '/RPC2'. Setting rpc_paths to None or an empty tuple disables this path checking.

• C API: Many functions now use Py_ssize_t instead of int to allow processing more data on 64-bit machines. Extension code may need to make the same change to avoid warnings and to support 64-bit machines. See the earlier section PEP 353: Using ssize_t as the index type for a discussion of this change.

• C API: The obmalloc changes mean that you must be careful to not mix usage of the PyMem_* and PyObject_* families of functions. Memory allocated with one familys *_Malloc must be freed with the corresponding familys *_Free function.

Acknowledgements

The author would like to thank the following people for offering suggestions, corrections and assistance with various drafts of this article: Georg Brandl, Nick Coghlan, Phillip J. Eby, Lars Gustbel, Raymond Hettinger, Ralf W. Grosse-Kunstleve, Kent Johnson, Iain Lowe, Martin von Lwis, Fredrik Lundh, Andrew McNamara, Skip Montanaro, Gustavo Niemeyer, Paul Prescod, James Pryor, Mike Rovner, Scott Weikart, Barry Warsaw, Thomas Wouters.