Python2.5有什么新变化
- 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.
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: 条件表达式. The new 'with
' statement will make
writing cleanup code easier (section PEP 343: "with" 语句). Values can now be passed
into generators (section PEP 342: 生成器的新特性). Imports are now visible as either
absolute or relative (section PEP 328: 绝对导入和相对导入). Some corner cases of exception
handling are handled better (section PEP 341: 统一 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: 条件表达式¶
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:
ifcondition: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
condthentrue_velsefalse_v, and 16 other variations.
Guido van Rossum 最终选择了一种令人惊讶的语法:
x=true_valueifconditionelsefalse_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')ifdocelse'')
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 parenslevel=1ifloggingelse0
# Second version -- with parens
level=(1ifloggingelse0)
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.
参见
- PEP 308 - 条件表达式
PEP 由 Guido van Rossum 和 Raymond D 撰写,由 Thomas Wouters 实现。
PEP 309: 部分功能应用¶
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.
这里有一个很小但很现实的例子:
importfunctoolsdeflog(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.
...classApplication:
defopen_item(self,path):
...
definit(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:
defmy_decorator(f):defwrapper(*args,**kwds):
print'Calling decorated function'
returnf(*args,**kwds)
functools.update_wrapper(wrapper,f)
returnwrapper
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:
defmy_decorator(f):@functools.wraps(f)
defwrapper(*args,**kwds):
print'Calling decorated function'
returnf(*args,**kwds)
returnwrapper
参见
- PEP 309 - Partial Function Application
PEP由 Peter Harris 提出并撰写;由 Hye-Shik Chang 和 Nick Coghlan 实现,并由 Raymond Hettinger 适配。
PEP 314: Python软件包的元数据 v1.1¶
Some simple dependency support was added to Distutils. The setup()
function now has requires
, provides
, and obsoletes
keyword
parameters. When you build a source distribution using the sdist
command,
the dependency information will be recorded in the PKG-INFO
file.
Another new keyword parameter is download_url
, which should be set to a URL
for the 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.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.pyupload 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.
参见
- PEP 314 - Python软件包的元数据 v1.1
PEP 由 A.M. Kuchling, Richard Jones 和 Fred Drake 提出并撰写,由 Richard Jones 和 Fred Drake 实现
PEP 328: 绝对导入和相对导入¶
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 importstring
? 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, importpy;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__importabsolute_import
directive. This absolute-import
behaviour will become the default in a future version (probably Python
2.7). Once absolute imports are the default, importstring
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
frompkgimportstring
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.stringfrom.stringimportname1,name2
# Import pkg.string
from.importstring
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.importD# Imports A.B.Dfrom..importE# Imports A.E
from..FimportG# Imports A.F.G
Leading periods cannot be used with the importmodname
form of the import
statement, only the from...import
form.
参见
- PEP 328 - 导入:多行和绝对/相对导入
PEP 由 Aahz 撰写,由 Thomas Wouters 实现。
- https://pylib.readthedocs.io/
The py library by Holger Krekel, which contains the
py.std
package.
PEP 338: 将模块作为脚本执行¶
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.
参见
- PEP 338 - 将模块作为脚本执行
PEP 由 Nick Coghlan 撰写并实现。
PEP 341: 统一 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...
exceptException1:
handler-1...
exceptException2:
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.
参见
- PEP 341 - 统一 try-except 和 try-finally
PEP 由 Georg Brandl 撰写,由 Thomas Lee 实现。
PEP 342: 生成器的新特性¶
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:
defcounter(maximum):i=0
whilei<maximum:
yieldi
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=(yieldi)
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=yieldi
but have to use parentheses when there's an operation, as in
val=(yieldi)+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.
defcounter(maximum):i=0
whilei<maximum:
val=(yieldi)
# If value provided, change counter
ifvalisnotNone:
i=val
else:
i+=1
And here's an example of changing the counter:
>>> it=counter(10)>>> printit.next()
0
>>> printit.next()
1
>>> printit.send(8)
8
>>> printit.next()
9
>>> printit.next()
Traceback (most recent call last):
File "t.py", line 15, in ?
printit.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 exceptioninside the generator; the exception is raised by the
yield
expressionwhere the generator's execution is paused.
close()
raises a newGeneratorExit
exception inside the generatorto terminate the iteration. On receiving this exception, the generator's code
must either raise
GeneratorExit
orStopIteration
. Catching theGeneratorExit
exception and returning a value is illegal and will triggera
RuntimeError
; if the function raises some other exception, thatexception is propagated to the caller.
close()
will also be called byPython's garbage collector when the generator is garbage-collected.
If you need to run cleanup code when a
GeneratorExit
occurs, I suggestusing a
try:...finally:
suite instead of catchingGeneratorExit
.
这些改变的累积效应是,让生成器从单向的信息生产者变成了既是生产者,又是消费者。
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.
参见
- PEP 342 - 通过增强型生成器实现协程
PEP 由 Guido van Rossum 和 Phillip J. Eby 撰写,由 Phillip J. Eby 实现。包括一些更高级的使用生成器作为协程的示例。
这些功能的早期版本在 PEP 288 (由 Raymond Hettinger 撰写) 和 PEP 325 (由 Samuele Pedroni 撰写)中提出。
- https://en.wikipedia.org/wiki/Coroutine
协程的Wikipedia条目。
- http://www.sidhe.org/~dan/blog/archives/000178.html
An explanation of coroutines from a Perl point of view, written by Dan Sugalski.
PEP 343: "with" 语句¶
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:
withexpression[asvariable]: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__importwith_statement
该语句在Python 2.6 中始终启用。
Some standard Python objects now support the context management protocol and can
be used with the 'with
' statement. File objects are one example:
withopen('/etc/passwd','r')asf:forlineinf:
printline
...moreprocessingcode...
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.
注解
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()withlock:
# 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:
fromdecimalimportDecimal,Context,localcontext# Displays with default precision of 28 digits
v=Decimal('578')
printv.sqrt()
withlocalcontext(Context(prec=16)):
# All code in this block uses a precision of 16 digits.
# The original context is restored on exiting the block.
printv.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__()
methods.
The context manager's
__enter__()
method is called. The value returnedis assigned to VAR. If no
'asVAR'
clause is present, the value is simplydiscarded.
The code in BLOCK is executed.
If BLOCK raises an exception, the
__exit__(type,value,traceback)
is called with the exception details, the same values returned by
sys.exc_info()
. The method's return value controls whether the exceptionis re-raised: any false value re-raises the exception, and
True
will resultin suppressing it. You'll only rarely want to suppress the exception, because
if you do the author of the code containing the '
with
' statement willnever realize anything went wrong.
If BLOCK didn't raise an exception, the
__exit__()
method is stillcalled, 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()withdb_connectionascursor:
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:
classDatabaseConnection:# Database interface
defcursor(self):
"Returns a cursor object and starts a new transaction"
defcommit(self):
"Commits current transaction"
defrollback(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 ascursor
to
their 'with
' statement to bind the cursor to a variable name.
classDatabaseConnection:...
def__enter__(self):
# Code to start a new transaction
cursor=self.cursor()
returncursor
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.
classDatabaseConnection:...
def__exit__(self,type,value,tb):
iftbisNone:
# No exception, so commit
self.commit()
else:
# Exception occurred, so rollback.
self.rollback()
# return False
contextlib 模块¶
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:
fromcontextlibimportcontextmanager@contextmanager
defdb_transaction(connection):
cursor=connection.cursor()
try:
yieldcursor
except:
connection.rollback()
raise
else:
connection.commit()
db=DatabaseConnection()
withdb_transaction(db)ascursor:
...
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()withnested(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.
importurllib,sysfromcontextlibimportclosing
withclosing(urllib.urlopen('http://www.yahoo.com'))asf:
forlineinf:
sys.stdout.write(line)
参见
- 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 statementworks.
contextlib
模块的文档。
PEP 352: 异常作为新型的类¶
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
|-(allothercurrentbuilt-inexceptions)
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 exceptException
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.
参见
- PEP 352 - 异常所需的超类
PEP 由 Brett Cannon 和 Guido van Rossum 撰写,由 Brett Cannon 实现
PEP 353: 使用ssize_t作为索引类型¶
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 spanly 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.
参见
- PEP 353 - 使用ssize_t作为索引类型
PEP 由 Martin von Löwis 撰写并实现。
PEP 357: '__index__' 方法¶
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:
classC:def__index__(self):
returnself.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.
参见
- PEP 357 - 允许将任何对象用于切片
PEP 由 Travis Oliphant 撰写并实现。
其他语言特性修改¶
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 defaultvalue 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 usedto implement the new
defaultdict
class in thecollections
module. The following example defines a dictionary that returns zero for any
missing key:
classzerodict(dict):
def__missing__(self,key):
return0
d=zerodict({1:1,2:2})
printd[1],d[2]# Prints 1, 2
printd[3],d[4]# Prints 0, 0
Both 8-bit and Unicode strings have new
partition(sep)
andrpartition(sep)
methods that simplify a common use case.The
find(S)
method is often used to get an index which is then used toslice the string and obtain the pieces that are before and after the separator.
partition(sep)
condenses this pattern into a single method call thatreturns 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 searchingfrom the end of the string; the
r
stands for 'reverse'.几个例子:
>>> ('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()
andendswith()
methods of string types now accepttuples of strings to check for.
defis_image_file(filename):
returnfilename.endswith(('.gif','.jpg','.tiff'))
(Implemented by Georg Brandl following a suggestion by Tom Lynn.)
The
min()
andmax()
built-in functions gained akey
keywordparameter analogous to the
key
argument forsort()
. This parametersupplies a function that takes a single argument and is called for every value
in the list;
min()
/max()
will return the element with thesmallest/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'
printmax(L,key=len)
# Prints 'short', because lexicographically 'short' has the largest value
printmax(L)
(由 Steven Bethard 和 Raymond Hettinger 贡献。)
Two new built-in functions,
any()
andall()
, evaluate whether aniterator contains any true or false values.
any()
returnsTrue
if any value returned by the iterator is true; otherwise it will return
False
.all()
returnsTrue
only if all of the valuesreturned 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 longinteger 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 alwaysreturn 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 tocompare 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 2.5this could result in puzzling problems when accessing a dictionary. If you
looked up
unichr(128)
andchr(128)
was being used as a key, you'd get aUnicodeDecodeError
exception. Other changes in 2.5 resulted in thisexception 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.(由 Marc-André Lemburg 实现。)
One error that Python programmers sometimes make is forgetting to include an
__init__.py
module in a package directory. Debugging this mistake can beconfusing, and usually requires running Python with the
-v
switch tolog all the paths searched. In Python 2.5, a new
ImportWarning
warning istriggered 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 displaythe 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:
classC():
pass
(由 Brett Cannon 实现。)
交互解释器变更¶
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.)
性能优化¶
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
andfrozenset
types were built on top of Python's dictionary type. In 2.5the 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 stringsbecause 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
forlineinfile
andcalling the file object's
read()
/readline()
/readlines()
methods. Iteration uses an internal buffer and the
read*()
methodsdon'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 theread*()
method.(Implemented by Thomas Wouters.)
The
struct
module now compiles structure format strings into aninternal 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 allocatorfunctions instead of the system's
malloc()
andfree()
.(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 generatorwill do the arithmetic and produce code corresponding to
a=5
. (Proposedand 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()
andstat()
calls onstartup. (Contributed by Martin von Löwis and Georg Brandl.)
新增,改进和删除的模块¶
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 foru-LAW encoding has been improved. (Contributed by Lars Immisch.)
The
codecs
module gained support for incremental codecs. Thecodec.lookup()
function now returns aCodecInfo
instance insteadof a tuple.
CodecInfo
instances behave like a 4-tuple to preservebackward compatibility but also have the attributes
encode
,decode
,incrementalencoder
,incrementaldecoder
,streamwriter
, andstreamreader
. Incremental codecs can receiveinput 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
moduledocumentation for details. (Designed and implemented by Walter Dörwald.)
The
collections
module gained a new type,defaultdict
, thatsubclasses the standard
dict
type. The new type mostly behaves like adictionary 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 functionthat 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()
orint()
. For example, you can make an index of wordsbased 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)
forwinwords:
init_letter=w[0]
index[init_letter].append(w)
打印
index
导致以下输出: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']}
(由 Guido van Rossum 贡献。)
The
deque
double-ended queue type supplied by thecollections
module now has a
remove(value)
method that removes the first occurrenceof 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 usewith the new '
with
' statement. See section contextlib 模块for more about this module.
New module: The
cProfile
module is a C implementation of the existingprofile
module that has much lower overhead. The module's interface isthe same as
profile
: you runcProfile.run('main()')
to profile afunction, 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 futureversions of Python. (Contributed by Armin Rigo.)
Also, the
pstats
module for analyzing the data measured by the profilernow 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_limitargument will return the currently-set limit. The
reader
class now hasa
line_num
attribute that counts the number of physical lines read fromthe source; records can span multiple physical lines, so
line_num
is notthe 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.
(由Skip Montanaro 和 Andrew McNamara 贡献。)
The
datetime
class in thedatetime
module now has astrptime(string,format)
method for parsing date strings, contributedby Josh Spoerri. It uses the same format characters as
time.strptime()
andtime.strftime()
:fromdatetimeimportdatetime
ts=datetime.strptime('10:13:15 2006-03-07',
'%H:%M:%S %Y-%m-%d')
The
SequenceMatcher.get_matching_blocks()
method in thedifflib
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 aSKIP
option that keeps an example frombeing 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 theDocFileSuite
class to specify the file's encoding. This makes iteasier to use non-ASCII characters in tests contained within a docstring.
(Contributed by Bjorn Tillenius.)
email
包已经升级到 4.0 (由 Barry Warsaw 贡献)The
fileinput
module was made more flexible. Unicode filenames are nowsupported, and a mode parameter that defaults to
"r"
was added to theinput()
function to allow opening files in binary or universalnewlines mode. Another new parameter, openhook, lets you use a function
other than
open()
to open the input files. Once you're iterating overthe set of files, the
FileInput
object's newfileno()
returnsthe file descriptor for the currently opened file. (Contributed by Georg
Brandl.)
In the
gc
module, the newget_count()
function returns a 3-tuplecontaining 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()
andnlargest()
functions in theheapq
module now support a
key
keyword parameter similar to the one provided bythe
min()
/max()
functions and thesort()
methods. Forexample:
>>> importheapq
>>> 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']
(由 Raymond Hettinger 贡献。)
The
itertools.islice()
function now acceptsNone
for the start andstep 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)
(由 Raymond Hettinger 贡献。)
The
format()
function in thelocale
module has been modified andtwo new functions were added,
format_string()
andcurrency()
.The
format()
function's val parameter could previously be a string aslong 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 forformatting 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 likeformat()
but also supportsmixing %char specifiers with arbitrary text.
A new
currency()
function was also added that formats a number accordingto the current locale's settings.
(由Georg Brandl 贡献。)
The
mailbox
module underwent a massive rewrite to add the capability tomodify mailboxes in addition to reading them. A new set of classes that include
mbox
,MH
, andMaildir
are used to read mailboxes, andhave an
add(message)
method to add messages,remove(key)
toremove messages, and
lock()
/unlock()
to lock/unlock the mailbox.The following example converts a maildir-format mailbox into an mbox-format
one:
importmailbox
# 'factory=None' uses email.Message.Message as the class representing
# individual messages.
src=mailbox.Maildir('maildir',factory=None)
dest=mailbox.mbox('/tmp/mbox')
formsginsrc:
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 systemdefault domain by supplying a domain argument to the
nis.match()
andnis.maps()
functions. (Contributed by Ben Bell.)The
operator
module'sitemgetter()
andattrgetter()
functions now support multiple fields. A call such as
operator.attrgetter('a','b')
will return a function that retrieves thea
andb
attributes. Combining this new feature with thesort()
method'skey
parameter lets you easily sort lists usingmultiple fields. (Contributed by Raymond Hettinger.)
The
optparse
module was updated to version 1.5.1 of the Optik library.The
OptionParser
class gained anepilog
attribute, a stringthat will be printed after the help message, and a
destroy()
method tobreak reference cycles created by the object. (Contributed by Greg Ward.)
The
os
module underwent several changes. Thestat_float_times
variable now defaults to true, meaning that
os.stat()
will now return timevalues as floats. (This doesn't necessarily mean that
os.stat()
willreturn times that are precise to fractions of a second; not all systems support
such precision.)
Constants named
os.SEEK_SET
,os.SEEK_CUR
, andos.SEEK_END
have been added; these are the parameters to theos.lseek()
function. Two new constants for locking areos.O_SHLOCK
andos.O_EXLOCK
.Two new functions,
wait3()
andwait4()
, were added. They're similarthe
waitpid()
function which waits for a child process to exit and returnsa tuple of the process ID and its exit status, but
wait3()
andwait4()
return additional information.wait3()
doesn't take aprocess 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 nanosecondresolution, and the returned object now has
st_gen
andst_birthtime
. Thest_flags
attribute is also available, if theplatform supports it. (Contributed by Antti Louko and Diego Pettenò.)
The Python debugger provided by the
pdb
module can now store lists ofcommands to execute when a breakpoint is reached and execution stops. Once
breakpoint #1 has been created, enter
commands1
and enter a series ofcommands to be executed, finishing the list with
end
. The command list caninclude commands that resume execution, such as
continue
ornext
.(Contributed by Grégoire Dooms.)
The
pickle
andcPickle
modules no longer accept a return valueof
None
from the__reduce__()
method; the method must return a tupleof arguments instead. The ability to return
None
was deprecated in Python2.4, so this completes the removal of the feature.
The
pkgutil
module, containing various utility functions for findingpackages, 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 thecommonly used
pystone.py
program because pybench provides a moredetailed 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 theQueue
module gained two newmethods.
join()
blocks until all items in the queue have been retrievedand all processing work on the items have been completed. Worker threads call
the other new method,
task_done()
, to signal that processing for an itemhas been completed. (Contributed by Raymond Hettinger.)
The old
regex
andregsub
modules, which have been deprecatedever since Python 2.0, have finally been deleted. Other deleted modules:
statcache
,tzparse
,whrandom
.Also deleted: the
lib-old
directory, which includes ancient modulessuch as
dircmp
andni
, was removed.lib-old
wasn't on thedefault
sys.path
, so unless your programs explicitly added the directory tosys.path
, this removal shouldn't affect your code.The
rlcompleter
module is no longer dependent on importing thereadline
module and therefore now works on non-Unix platforms. (Patchfrom Robert Kiendl.)
The
SimpleXMLRPCServer
andDocXMLRPCServer
classes now have arpc_paths
attribute that constrains XML-RPC operations to a limited setof URL paths; the default is to allow only
'/'
and'/RPC2'
. Settingrpc_paths
toNone
or an empty tuple disables this path checking.The
socket
module now supportsAF_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)
andrecvfrom_into(buffer)
, store the received data in an object thatsupports 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()
, andgetproto()
accessor methods to retrieve the family, type, and protocolvalues for the socket.
New module: the
spwd
module provides functions for accessing the shadowpassword database on systems that support shadow passwords.
The
struct
is now faster because it compiles format strings intoStruct
objects withpack()
andunpack()
methods. This issimilar to how the
re
module lets you create compiled regular expressionobjects. You can still use the module-level
pack()
andunpack()
functions; they'll create
Struct
objects and cache them. Or you canuse
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,...)
andunpack_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 NeedForSpeedsprint. 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 informationlike this:
"trunk:45355:45356M,Apr132006,07:42:19"
. (Contributed byBarry Warsaw.)
Another new function,
sys._current_frames()
, returns the current stackframes 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 thetarfile
module now has anextractall()
method that extracts all members from the archive into thecurrent 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 newthreads are created. The
stack_size([*size*])
function returns thecurrently 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 theUnicode 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 functionsnamed
uuid1()
,uuid3()
,uuid4()
, anduuid5()
togenerate different versions of UUID. (Version 2 UUIDs are not specified in
RFC 4122 and are not supported by this module.)
>>> importuuid
>>> # 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')
(由 Ka-Ping Yee 贡献。)
The
weakref
module'sWeakKeyDictionary
andWeakValueDictionary
types gained new methods for iterating over theweak references contained in the dictionary.
iterkeyrefs()
andkeyrefs()
methods were added toWeakKeyDictionary
, anditervaluerefs()
andvaluerefs()
were added toWeakValueDictionary
. (Contributed by Fred L. Drake, Jr.)The
webbrowser
module received a number of enhancements. It's nowusable as a script with
python-mwebbrowser
, taking a URL as the argument;there are a number of switches to control the behaviour (
-n
for a newbrowser window,
-t
for a new tab). New module-level functions,open_new()
andopen_new_tab()
, were added to support this. Themodule's
open()
function supports an additional feature, an autoraiseparameter 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 returningdatetime
objectsfor the XML-RPC date type. Supply
use_datetime=True
to theloads()
function or the
Unmarshaller
class to enable this feature. (Contributedby 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'sCompress
andDecompress
objects nowsupport a
copy()
method that makes a copy of the object's internal stateand returns a new
Compress
orDecompress
object.(Contributed by Chris AtLee.)
ctypes 包¶
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.
importctypeslibc=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:
importctypesd={}
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.
参见
- http://starship.python.net/crew/theller/ctypes/
ctypes的主页,含教程, 参考和常见问题解答。
ctypes
模块的文档。
ElementTree 包¶
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:
fromxml.etreeimportElementTreeasETtree=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.
运算 | 结果: |
---|---|
| 返回第n个子元素 |
| 返回第m至第n个子元素的列表。 |
| 返回子元素的个数 |
| 返回子元素的列表 |
| 将 elem2 添加为子级。 |
| 在指定位置插入 elem2 。 |
| 删除第n个子元素 |
| 返回属性名称的列表。 |
| 返回 name 属性的值。 |
| 为 name 属性设置新值 |
| 检索包含属性的字典。 |
| Deletes 属性 name |
Comments and processing instructions are also represented as Element
nodes. To check if a node is a comment or processing instructions:
ifelem.tagisET.Comment:...
elifelem.tagisET.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-ASCIItree.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.
参见
- http://effbot.org/zone/element-index.htm
ElementTree 的官方文档
hashlib 包¶
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 versionsh=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.
参见
hashlib
模块的文档。
sqlite3 包¶
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 是一个C语言库,它可以提供一种轻量级的基于磁盘的数据库,这种数据库不需要独立的服务器进程,也允许需要使用一种非标准的 SQL 查询语言来访问它。一些应用程序可以使用 SQLite 作为内部数据存储。可以用它来创建一个应用程序原型,然后再迁移到更大的数据库,比如 PostgreSQL 或 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')
你也可以使用 :memory:
来创建一个内存中的数据库
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
fortin(('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.
下面是一个使用迭代器形式的例子:
>>> c=conn.cursor()>>> c.execute('select * from stocks order by price')
>>> forrowinc:
... printrow
...
(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.
参见
- http://www.pysqlite.org
pysqlite 的主页
- https://www.sqlite.org
SQLite的主页;它的文档详细描述了它所支持的 SQL 方言的语法和可用的数据类型。
sqlite3
模块的文档。
- PEP 249 - DB-API 2.0 规范
Marc-André Lemburg 写的 PEP。
wsgiref 包¶
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:
fromwsgirefimportsimple_serverwsgi_app=...
host=''
port=8000
httpd=simple_server.make_server(host,port,wsgi_app)
httpd.serve_forever()
参见
- http://www.wsgi.org
WSGI相关资源的核心网站。
- PEP 333 - Python Web服务器网关接口 v1.0
PEP 由 Phillip J. Eby 撰写
构建和 C API 的改变¶
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 ofint
. See the earlier section PEP 353: 使用ssize_t作为索引类型 for a discussion of thischange.
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 ofthe flags parameter:
from_astimportPyCF_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 thisfile and generates a set of C structure definitions in
Include/Python-ast.h
. ThePyParser_ASTFromString()
andPyParser_ASTFromFile()
, defined inInclude/pythonrun.h
, takePython 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()
. Formore 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()
, andPyMem_Free()
are one family that allocatesraw memory, while
PyObject_Malloc()
,PyObject_Realloc()
, andPyObject_Free()
are another family that's supposed to be used forcreating Python objects.
Previously these different families all reduced to the platform's
malloc()
andfree()
functions. This meant it didn't matter ifyou got things wrong and allocated memory with the
PyMem()
function butfreed 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()
andPySet_Discard()
to add and remove elements, andPySet_Contains()
and
PySet_Size()
to examine the set's state. (Contributed by RaymondHettinger.)
C code can now obtain information about the exact revision of the Python
interpreter by calling the
Py_GetBuildInfo()
function that returns astring of build information like this:
"trunk:45355:45356M,Apr132006,
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 thespecified type and uses a fast-calling qualifier.
Py_LOCAL_INLINE(type)
does the same thing and also requests thefunction be inlined. If
PY_LOCAL_AGGRESSIVE()
is defined beforepython.h
is included, a set of more aggressive optimizations are enabledfor 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 baseclasses as its base argument. (Contributed by Georg Brandl.)
The
PyErr_Warn()
function for issuing warnings is now deprecated infavour of
PyErr_WarnEx(category,message,stacklevel)
which lets youspecify the number of stack frames separating this function and the caller. A
stacklevel of 1 is the function calling
PyErr_WarnEx()
, 2 is thefunction 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, neverused 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 theconfigure 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 forextension modules.
.pyd
is now the only filename extension that will besearched 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 frameobject. Because of the PEP 342 changes described in section PEP 342: 生成器的新特性,
it's now possible for
gi_frame
to beNone
.A new warning,
UnicodeWarning
, is triggered when you attempt tocompare 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.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
module'sformat()
function's wouldpreviously 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
andcPickle
modules no longer accept areturn value of
None
from the__reduce__()
method; the method mustreturn a tuple of arguments instead. The modules also no longer accept the
deprecated bin keyword parameter.
Library: The
SimpleXMLRPCServer
andDocXMLRPCServer
classes nowhave a
rpc_paths
attribute that constrains XML-RPC operations to alimited set of URL paths; the default is to allow only
'/'
and'/RPC2'
.Setting
rpc_paths
toNone
or an empty tuple disables this pathchecking.
C API: Many functions now use
Py_ssize_t
instead ofint
toallow 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: 使用ssize_t作为索引类型 for a discussion of this change.
C API: The obmalloc changes mean that you must be careful to not mix usage
of the
PyMem_*()
andPyObject_*()
families of functions. Memoryallocated with one family's
*_Malloc()
must be freed with thecorresponding family's
*_Free()
function.
致谢¶
作者感谢以下人员对本文各种草稿给予的建议,更正和协助: 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.
以上是 Python2.5有什么新变化 的全部内容, 来源链接: utcz.com/z/508495.html