Python 2.5 的新功能
¶
-
作者
:
-
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 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.
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 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
条件
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.
另请参阅
-
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, 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
另请参阅
-
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 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.
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
在
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.
另请参阅
-
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
包。
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 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.
另请参阅
-
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 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.
另请参阅
-
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 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
当调用
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
语句。
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
返回
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.
除了
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
协程
, 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
到
None
once the generator has been exhausted.
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, 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__()
方法。
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
表达式
)。
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.
注意
在此情况下，
f
is the same object created by
open()
，因为
__enter__()
返回
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.
新的
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 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:
-
The expression is evaluated and should result in an object called a “context manager”. The context manager must have
__enter__()
and
__exit__()
方法。
-
The context manager’s
__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.
-
若
BLOCK
raises an exception, the
__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.
-
若
BLOCK
didn’t raise an exception, the
__exit__()
method is still called, but
type
,
value
，和
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 contextlib module
¶
新的
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
语句。
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):
...
最后，
closing(object)
函数返回
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)
另请参阅
-
PEP 343
- with 语句
-
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
模块。
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
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.
另请参阅
-
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 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
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 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()
现在返回
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
先于包括
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.
另请参阅
-
PEP 353
- Using ssize_t as the index type
-
PEP written and implemented by Martin von Löwis.
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
，和
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.
另请参阅
-
PEP 357
- Allowing Any Object to be Used for Slicing
-
PEP written and implemented by Travis Oliphant.
其它语言变化
¶
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
类在
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’.
一些范例：
>>> ('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()
返回
True
if any value returned by the iterator is true; otherwise it will return
False
.
all()
返回
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.)
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.)
优化
¶
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.
-
When they were introduced in Python 2.4, the built-in
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.)
-
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 Dörwald and Martin von Löwis.)
-
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 800–1000 digits where the function is 6 times faster. (Contributed by Alan McIntyre and committed at the NeedForSpeed sprint.)
-
It’s now illegal to mix iterating over a file with
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
从
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 Python’s allocator functions instead of the system’s
malloc()
and
free()
. (Contributed by Jack Diederich at the NeedForSpeed sprint.)
-
The code generator’s 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.)
-
Python’s 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 Löwis and Georg Brandl.)
New, Improved, and Removed Modules
¶
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
，和
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)
打印
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
自变量到
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
类在
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()
函数和
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
通用换行符
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.)
-
在
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.
新的
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
，和
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
自变量到
nis.match()
and
nis.maps()
functions. (Contributed by Ben Bell.)
-
The
operator
模块的
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()
方法的
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
，和
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()
函数。
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
。
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
从
__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()
，和
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
类在
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()
，和
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
模块的
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
，和
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
到
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
模块的
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
¶
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
对象。
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.
The ElementTree package
¶
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
，和
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
实例：
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
节点。
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.
|
操作
|
结果
|
|
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)
|
添加
elem2
as a child.
|
|
elem.insert(index, elem2)
|
插入
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.
The hashlib package
¶
新的
hashlib
module, written by Gregory P. Smith, has been added to replace the
md5
and
sha
模块。
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.
另请参阅
The documentation for the
hashlib
模块。
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 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
文件：
conn = sqlite3.connect('/tmp/example')
You can also supply the special name
:memory:
to create a database in RAM.
Once you have a
Connection
，可以创建
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
.
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
。
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()
构建和 C API 变化
¶
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
。
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()
，和
PyMem_Free()
are one family that allocates raw memory, while
PyObject_Malloc()
,
PyObject_Realloc()
，和
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 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 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);
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.
移植到 Python 2.5
¶
This section lists previously described changes that may require changes to your code:
-
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.
-
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
, it’s now possible for
gi_frame
到
None
.
-
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. Previously such comparisons would raise a
UnicodeDecodeError
异常。
-
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
模块的
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.
-
Library: The
pickle
and
cPickle
modules no longer accept a return value of
None
从
__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
而不是
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 family’s
*_Malloc
must be freed with the corresponding family’s
*_Free
函数。
感谢
¶
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.
