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| Author: | A.M. Kuchling |
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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.
The changes in Python 2.5 are an interesting mix of language and library
improvements. The library enhancements will be more important to Python’s user
community, I think, because several widely-useful packages were added. New
modules include ElementTree for XML processing (xml.etree),
the SQLite database module (sqlite), 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 aren’t features that you’ll 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 they’re improvements to one specific language feature or
another; none of them are broad modifications to Python’s 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 doesn’t 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.
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 C’s 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 C’s 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
isn’t 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 isn’t 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 language’s 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 reader’s 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 won’t run into this case.
See also
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, you’ll 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.
Here’s 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')
Here’s 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 function’s 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
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 package’s source code. This means it’s 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.python.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 Löwis and Richard Jones.
See also
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.
Let’s 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 package’s 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 module’s namespace.
That’s fine if pkg.string was what you wanted. But what if you wanted
Python’s standard string module? There’s 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 Krekel’s
py.std package provides a tidier way to perform imports from the standard
library, import py; py.std.string.join(), but that package isn’t 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
can’t protect against having your submodule’s name being used for a new module
added in a future version of Python.
In Python 2.5, you can switch import‘s 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 library’s version. It’s suggested that users should begin
using absolute imports as much as possible, so it’s 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
py.std package.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
it’s 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 archive’s path to
sys.path and then use the -m switch to execute code from the
archive.
See also
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 couldn’t
combine both except blocks and a finally block, because
generating the right bytecode for the combined version was complicated and it
wasn’t 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 it’s 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 there’s 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
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 generator’s 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 generator’s code look at a global variable and then changing the global variable’s value, or passing in some mutable object that callers then modify.
To refresh your memory of basic generators, here’s 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 function’s execution, preserving the
local variables. Execution resumes on the following call to the iterator’s
next() method, picking up after the yield statement.
In Python 2.3, yield was a statement; it didn’t 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 you’re doing something with the returned value, as in the above example.
The parentheses aren’t always necessary, but it’s easier to always add them
instead of having to remember when they’re 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 there’s an operation, as in
val = (yield i) + 12.)
Values are sent into a generator by calling its send(value) method. The
generator’s code is then resumed and the yield expression returns the
specified value. If the regular next() method is called, the
yield returns None.
Here’s 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 here’s 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. Don’t just use its value in expressions unless you’re 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 generator’s execution is paused.
close() raises a new GeneratorExit exception inside the generator
to terminate the iteration. On receiving this exception, the generator’s 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
Python’s 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).
We’ll have to figure out patterns for using coroutines effectively in Python.
The addition of the close() method has one side effect that isn’t obvious.
close() is called when a generator is garbage-collected, so this means the
generator’s 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 couldn’t 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. I’ll 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. It’s now
possible for gi_frame to be None once the generator has been
exhausted.
See also
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.
The ‘with‘ statement clarifies code that previously would use
try...finally blocks to ensure that clean-up code is executed. In this
section, I’ll discuss the statement as it will commonly be used. In the next
section, I’ll 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 object’s __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 object’s __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
file.__enter__() returns self.
The threading module’s 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()
Under the hood, the ‘with‘ statement is fairly complicated. Most
people will only use ‘with‘ in company with existing objects and
don’t 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:
__enter__() and __exit__()
methods.__enter__() method is called. The value returned
is assigned to VAR. If no 'as VAR' clause is present, the value is simply
discarded.__exit__(type, value, traceback)
is called with the exception details, the same values returned by
sys.exc_info(). The method’s return value controls whether the exception
is re-raised: any false value re-raises the exception, and True will result
in suppressing it. You’ll 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.__exit__() method is still
called, but type, value, and traceback are all None.Let’s think through an example. I won’t 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.)
Let’s assume there’s 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 there’s an exception. Here’s the basic interface for
DatabaseConnection that I’ll 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 it’s 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 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 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 method’s return
value that will get bound to the variable in the ‘with‘ statement’s
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 don’t 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
with‘ statement, which can be helpful in learning how the statement
works.The documentation for the contextlib module.
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
aren’t 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. It’s been suggested that the bare except: form should
be removed in Python 3.0, but Guido van Rossum hasn’t 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
A wide-ranging change to Python’s 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 doesn’t affect Python’s capacity on 32-bit
platforms.
Various pieces of the Python interpreter used C’s 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
http://www.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 doesn’t really matter on a 32-bit platform because
you’ll 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.
It’s 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 it’s 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
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 can’t 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 that’s 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 isn’t 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
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 isn’t contained in the dictionary. When a key isn’t found, the
dictionary’s __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 isn’t 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 class’s __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. It’s now a syntax error if a module contains string literals with 8-bit characters but doesn’t have an encoding declaration. In Python 2.4 this triggered a warning, not a syntax error. See PEP 263 for how to declare a module’s 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 can’t 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, you’d 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-André 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.)
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.)
Several of the optimizations were developed at the NeedForSpeed sprint, an event held in Reykjavik, Iceland, from May 21–28 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.
set and
frozenset types were built on top of Python’s 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.)long(str, base) function is now faster on long digit strings
because fewer intermediate results are calculated. The peak is for strings of
around 800–1000 digits where the function is 6 times faster. (Contributed by
Alan McIntyre and committed at the NeedForSpeed sprint.)for line in file and
calling the file object’s read()/readline()/readlines()
methods. Iteration uses an internal buffer and the read*() methods
don’t 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.)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.)re module got a 1 or 2% speedup by switching to Python’s allocator
functions instead of the system’s malloc() and free().
(Contributed by Jack Diederich at the NeedForSpeed sprint.)a = 2+3, the code generator
will do the arithmetic and produce code corresponding to a = 5. (Proposed
and implemented by Raymond Hettinger.)open() and stat() calls on
startup. (Contributed by Martin von Löwis and Georg Brandl.)The standard library received many enhancements and bug fixes in Python 2.5.
Here’s 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
codec.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 Dörwald.)
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 isn’t present,
automatically adding it to the dictionary for the requested key value.
The first argument to defaultdict‘s 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 isn’t 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 module’s interface is
the same as profile: you run cProfile.run('main()') to profile a
function, can save profile data to a file, etc. It’s not yet known if the
Hotshot profiler, which is also written in C but doesn’t match the
profile module’s 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():
from datetime import datetime
ts = datetime.strptime('10:13:15 2006-03-07',
'%H:%M:%S %Y-%m-%d')
The 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 aren’t actually test cases.
An encoding parameter was added to the testfile() function and the
DocFileSuite class to specify the file’s 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 you’re iterating over
the set of files, the FileInput object’s 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() function’s 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 locale’s 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 locale’s 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 Google’s 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 Löwis.)
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 module’s 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() method’s 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 doesn’t 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. They’re 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() doesn’t 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 Pettenò.)
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 Grégoire 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 302’s import hooks and now also works for
packages stored in ZIP-format archives. (Contributed by Phillip J. Eby.)
The pybench benchmark suite by Marc-André 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 interpreter’s 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 wasn’t on the
default sys.path, so unless your programs explicitly added the directory to
sys.path, this removal shouldn’t 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; they’ll 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. It’s also possible to set a different directory as
the extraction target, and to unpack only a subset of the archive’s members.
The compression used for a tarfile opened in stream mode can now be autodetected
using the mode 'r|*'. (Contributed by Lars Gustäbel.)
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 it’s 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 module’s 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. It’s 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
module’s 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 module’s Compress and Decompress objects now
support a copy() method that makes a copy of the object’s internal state
and returns a new Compress or Decompress object.
(Contributed by Chris AtLee.)
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 that’s 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 Python’s 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 shouldn’t 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 Python’s 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 interpreter’s code. There’s 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}.
Don’t forget to use py_object(); if it’s 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 can’t 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
The documentation for the ctypes module.
A subset of Fredrik Lundh’s 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 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.
There’s 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 n’th child element. |
elem[m:n] |
Returns list of m’th through n’th 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 n’th 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 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 element’s name may contain arbitrary Unicode characters, ASCII isn’t a very useful encoding because it will raise an exception if an element’s name contains any characters with values greater than 127. Therefore, it’s 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 package’s official documentation for more details.
See also
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 module’s 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 pysqlite module (http://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 doesn’t 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. It’s 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 Häring and provides a SQL interface compliant with the DB-API 2.0 specification described by PEP 249.
If you’re compiling the Python source yourself, note that the source tree doesn’t include the SQLite code, only the wrapper module. You’ll 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 shouldn’t assemble your query using Python’s string operations because doing so is insecure; it makes your program vulnerable to an SQL injection attack.
Instead, use the DB-API’s 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 cursor’s 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 cursor’s 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
The documentation for the sqlite3 module.
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 isn’t 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
Changes to Python’s 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 Löwis. 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 that’s traversed to produce the bytecode.
It’s 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 Hylton’s 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 Jones’s 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 they’re empty. The net effect is that on some platforms, when you allocate many objects, Python’s 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. Python’s 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 that’s supposed to be used for
creating Python objects.
Previously these different families all reduced to the platform’s
malloc() and free() functions. This meant it didn’t matter if
you got things wrong and allocated memory with the PyMem() function but
freed it with the PyObject() function. With 2.5’s 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 set’s 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(type) declares the function as returning a value of the
specified type and uses a fast-calling qualifier.
Py_LOCAL_INLINE(type) does the same thing and also requests the
function be inlined. If 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 Löwis, 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);
dlopen() function instead of MacOS-specific functions.--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.).dll is no longer supported as a filename extension for
extension modules. .pyd is now the only filename extension that will be
searched for.This section lists previously described changes that may require changes to your code:
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,
it’s now possible for gi_frame to be None.UnicodeWarning, is triggered when you attempt to
compare a Unicode string and an 8-bit string that can’t be converted to Unicode
using the default ASCII encoding. Previously such comparisons would raise a
UnicodeDecodeError exception.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.locale module’s format() function’s 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.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.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.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.PyMem_*() and PyObject_*() families of functions. Memory
allocated with one family’s *_Malloc() must be freed with the
corresponding family’s *_Free() function.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 Gustäbel, Raymond Hettinger, Ralf W. Grosse-Kunstleve, Kent Johnson, Iain Lowe, Martin von Löwis, Fredrik Lundh, Andrew McNamara, Skip Montanaro, Gustavo Niemeyer, Paul Prescod, James Pryor, Mike Rovner, Scott Weikart, Barry Warsaw, Thomas Wouters.