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What’s New in Python 2.6
- Author
-
A.M. Kuchling (amk at amk.ca)
This article explains the new features in Python 2.6, released on October 1 2008. The release schedule is described in PEP 361 .
The major theme of Python 2.6 is preparing the migration path to Python 3.0, a major redesign of the language. Whenever possible, Python 2.6 incorporates new features and syntax from 3.0 while remaining compatible with existing code by not removing older features or syntax. When it’s not possible to do that, Python 2.6 tries to do what it can, adding compatibility functions in a future_builtins
module and a -3
switch to warn about usages that will become unsupported in 3.0.
Some significant new packages have been added to the standard library, such as the multiprocessing
and json
modules, but there aren’t many new features that aren’t related to Python 3.0 in some way.
Python 2.6 also sees a number of improvements and bugfixes throughout the source. A search through the change logs finds there were 259 patches applied and 612 bugs fixed between Python 2.5 and 2.6. Both figures are likely to be underestimates.
This article doesn’t attempt to provide a complete specification of the new features, but instead provides a convenient overview. For full details, you should refer to the documentation for Python 2.6. If you want to understand the rationale for the design and implementation, refer to the PEP for a particular new feature. Whenever possible, “What’s New in Python” links to the bug/patch item for each change.
Python 3.0
The development cycle for Python versions 2.6 and 3.0 was synchronized, with the alpha and beta releases for both versions being made on the same days. The development of 3.0 has influenced many features in 2.6.
Python 3.0 is a far-ranging redesign of Python that breaks compatibility with the 2.x series. This means that existing Python code will need some conversion in order to run on Python 3.0. However, not all the changes in 3.0 necessarily break compatibility. In cases where new features won’t cause existing code to break, they’ve been backported to 2.6 and are described in this document in the appropriate place. Some of the 3.0-derived features are:
A
__complex__()
method for converting objects to a complex number.Alternate syntax for catching exceptions:
except TypeError as exc
.The addition of
functools.reduce()
as a synonym for the built-inreduce()
function.
Python 3.0 adds several new built-in functions and changes the semantics of some existing builtins. Functions that are new in 3.0 such as bin()
have simply been added to Python 2.6, but existing builtins haven’t been changed; instead, the future_builtins
module has versions with the new 3.0 semantics. Code written to be compatible with 3.0 can do from future_builtins import hex, map
as necessary.
A new command-line switch, -3
, enables warnings about features that will be removed in Python 3.0. You can run code with this switch to see how much work will be necessary to port code to 3.0. The value of this switch is available to Python code as the boolean variable sys.py3kwarning
, and to C extension code as Py_Py3kWarningFlag
.
Changes to the Development Process
While 2.6 was being developed, the Python development process underwent two significant changes: we switched from SourceForge’s issue tracker to a customized Roundup installation, and the documentation was converted from LaTeX to reStructuredText.
New Issue Tracker: Roundup
For a long time, the Python developers had been growing increasingly annoyed by SourceForge’s bug tracker. SourceForge’s hosted solution doesn’t permit much customization; for example, it wasn’t possible to customize the life cycle of issues.
The infrastructure committee of the Python Software Foundation therefore posted a call for issue trackers, asking volunteers to set up different products and import some of the bugs and patches from SourceForge. Four different trackers were examined: Jira , Launchpad , Roundup , and Trac . The committee eventually settled on Jira and Roundup as the two candidates. Jira is a commercial product that offers no-cost hosted instances to free-software projects; Roundup is an open-source project that requires volunteers to administer it and a server to host it.
After posting a call for volunteers, a new Roundup installation was set up at https://bugs.python.org . One installation of Roundup can host multiple trackers, and this server now also hosts issue trackers for Jython and for the Python web site. It will surely find other uses in the future. Where possible, this edition of “What’s New in Python” links to the bug/patch item for each change.
Hosting of the Python bug tracker is kindly provided by Upfront Systems of Stellenbosch, South Africa. Martin von Löwis put a lot of effort into importing existing bugs and patches from SourceForge; his scripts for this import operation are at http://svn.python.org/view/tracker/importer/ and may be useful to other projects wishing to move from SourceForge to Roundup.
See also
- https://bugs.python.org
-
The Python bug tracker.
- http://bugs.jython.org :
-
The Jython bug tracker.
- http://roundup.sourceforge.net/
-
Roundup downloads and documentation.
- http://svn.python.org/view/tracker/importer/
-
Martin von Löwis’s conversion scripts.
New Documentation Format: reStructuredText Using Sphinx
The Python documentation was written using LaTeX since the project started around 1989. In the 1980s and early 1990s, most documentation was printed out for later study, not viewed online. LaTeX was widely used because it provided attractive printed output while remaining straightforward to write once the basic rules of the markup were learned.
Today LaTeX is still used for writing publications destined for printing, but the landscape for programming tools has shifted. We no longer print out reams of documentation; instead, we browse through it online and HTML has become the most important format to support. Unfortunately, converting LaTeX to HTML is fairly complicated and Fred L. Drake Jr., the long-time Python documentation editor, spent a lot of time maintaining the conversion process. Occasionally people would suggest converting the documentation into SGML and later XML, but performing a good conversion is a major task and no one ever committed the time required to finish the job.
During the 2.6 development cycle, Georg Brandl put a lot of effort into building a new toolchain for processing the documentation. The resulting package is called Sphinx, and is available from http://sphinx-doc.org/ .
Sphinx concentrates on HTML output, producing attractively styled and modern HTML; printed output is still supported through conversion to LaTeX. The input format is reStructuredText, a markup syntax supporting custom extensions and directives that is commonly used in the Python community.
Sphinx is a standalone package that can be used for writing, and almost two dozen other projects (listed on the Sphinx web site ) have adopted Sphinx as their documentation tool.
See also
- Documenting Python
-
Describes how to write for Python’s documentation.
- Sphinx
-
Documentation and code for the Sphinx toolchain.
- Docutils
-
The underlying reStructuredText parser and toolset.
PEP 343: The ‘with’ statement
The previous version, Python 2.5, added the ‘with
’ statement as an optional feature, to be enabled by a from __future__ import with_statement
directive. In 2.6 the statement no longer needs to be specially enabled; this means that with
is now always a keyword. The rest of this section is a copy of the corresponding section from the “What’s New in Python 2.5” document; if you’re familiar with the ‘with
’ statement from Python 2.5, you can skip this section.
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 control-flow structure whose basic structure is:
with expression [as variable]:
with-block
The expression is evaluated, and it should result in an object that supports the context management protocol (that is, has __enter__()
and __exit__()
methods).
The object’s __enter__()
is called before with-block is executed and therefore can run set-up code. It also may return a value that is bound to the name variable, if given. (Note carefully that variable is not assigned the result of expression.)
After execution of the with-block is finished, the object’s __exit__()
method is called, even if the block raised an exception, and can therefore run clean-up code.
Some standard Python objects now support the context management protocol and can be used with the ‘with
’ statement. File objects are one example:
with open('/etc/passwd', 'r') as f:
for line in f:
print line
... more processing code ...
After this statement has executed, the file object in f will have been automatically closed, even if the for
loop raised an exception part-way through the block.
Note
In this case, f is the same object created by open()
, because file.__enter__()
returns self.
The threading
module’s locks and condition variables also support the ‘with
’ statement:
lock = threading.Lock()
with lock:
# Critical section of code
...
The lock is acquired before the block is executed and always released once the block is complete.
The 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__()
methods.The context manager’s
__enter__()
method is called. The value returned is assigned to VAR. If noas VAR
clause is present, the value is simply discarded.The code in BLOCK is executed.
If BLOCK raises an exception, the context manager’s
__exit__()
method is called with three arguments, the exception details (type, value, traceback
, the same values returned bysys.exc_info()
, which can also beNone
if no exception occurred). The method’s return value controls whether an exception is re-raised: any false value re-raises the exception, andTrue
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.If BLOCK didn’t raise an exception, the
__exit__()
method is still called, but type, value, and traceback are allNone
.
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
The contextlib
module provides some functions and a decorator that are useful when 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.
Using this decorator, our database example from the previous section could be written as:
from contextlib import contextmanager
@contextmanager
def db_transaction(connection):
cursor = connection.cursor()
try:
yield cursor
except:
connection.rollback()
raise
else:
connection.commit()
db = DatabaseConnection()
with db_transaction(db) as cursor:
...
The contextlib
module also has a nested(mgr1, mgr2, ...)
function that combines a number of context managers so you don’t need to write nested ‘with
’ statements. In this example, the single ‘with
’ statement both starts a database transaction and acquires a thread lock:
lock = threading.Lock()
with nested (db_transaction(db), lock) as (cursor, locked):
...
Finally, the closing()
function returns its argument so that it can be bound to a variable, and calls the argument’s .close()
method at the end of the block.
import urllib, sys
from contextlib import closing
with closing(urllib.urlopen('http://www.yahoo.com')) as f:
for line in f:
sys.stdout.write(line)
See also
- PEP 343 - The “with” statement
-
PEP written by Guido van Rossum and Nick Coghlan; implemented by Mike Bland, Guido van Rossum, and Neal Norwitz. The PEP shows the code generated for a ‘
with
’ statement, which can be helpful in learning how the statement works.
The documentation for the contextlib
module.
PEP 366: Explicit Relative Imports From a Main Module
Python’s -m
switch allows running a module as a script. When you ran a module that was located inside a package, relative imports didn’t work correctly.
The fix for Python 2.6 adds a __package__
attribute to modules. When this attribute is present, relative imports will be relative to the value of this attribute instead of the __name__
attribute.
PEP 302-style importers can then set __package__
as necessary. The runpy
module that implements the -m
switch now does this, so relative imports will now work correctly in scripts running from inside a package.
PEP 370: Per-user site-packages
Directory
When you run Python, the module search path sys.path
usually includes a directory whose path ends in "site-packages"
. This directory is intended to hold locally-installed packages available to all users using a machine or a particular site installation.
Python 2.6 introduces a convention for user-specific site directories. The directory varies depending on the platform:
Unix and Mac OS X:
~/.local/
Windows:
%APPDATA%/Python
Within this directory, there will be version-specific subdirectories, such as lib/python2.6/site-packages
on Unix/Mac OS and Python26/site-packages
on Windows.
If you don’t like the default directory, it can be overridden by an environment variable. PYTHONUSERBASE
sets the root directory used for all Python versions supporting this feature. On Windows, the directory for application-specific data can be changed by setting the APPDATA
environment variable. You can also modify the site.py
file for your Python installation.
The feature can be disabled entirely by running Python with the -s
option or setting the PYTHONNOUSERSITE
environment variable.
See also
- PEP 370 - Per-user
site-packages
Directory -
PEP written and implemented by Christian Heimes.
PEP 371: The multiprocessing
Package
The new multiprocessing
package lets Python programs create new processes that will perform a computation and return a result to the parent. The parent and child processes can communicate using queues and pipes, synchronize their operations using locks and semaphores, and can share simple arrays of data.
The multiprocessing
module started out as an exact emulation of the threading
module using processes instead of threads. That goal was discarded along the path to Python 2.6, but the general approach of the module is still similar. The fundamental class is the Process
, which is passed a callable object and a collection of arguments. The start()
method sets the callable running in a subprocess, after which you can call the is_alive()
method to check whether the subprocess is still running and the join()
method to wait for the process to exit.
Here’s a simple example where the subprocess will calculate a factorial. The function doing the calculation is written strangely so that it takes significantly longer when the input argument is a multiple of 4.
import time
from multiprocessing import Process, Queue
def factorial(queue, N):
"Compute a factorial."
# If N is a multiple of 4, this function will take much longer.
if (N % 4) == 0:
time.sleep(.05 * N/4)
# Calculate the result
fact = 1L
for i in range(1, N+1):
fact = fact * i
# Put the result on the queue
queue.put(fact)
if __name__ == '__main__':
queue = Queue()
N = 5
p = Process(target=factorial, args=(queue, N))
p.start()
p.join()
result = queue.get()
print 'Factorial', N, '=', result
A Queue
is used to communicate the result of the factorial. The Queue
object is stored in a global variable. The child process will use the value of the variable when the child was created; because it’s a Queue
, parent and child can use the object to communicate. (If the parent were to change the value of the global variable, the child’s value would be unaffected, and vice versa.)
Two other classes, Pool
and Manager
, provide higher-level interfaces. Pool
will create a fixed number of worker processes, and requests can then be distributed to the workers by calling apply()
or apply_async()
to add a single request, and map()
or map_async()
to add a number of requests. The following code uses a Pool
to spread requests across 5 worker processes and retrieve a list of results:
from multiprocessing import Pool
def factorial(N, dictionary):
"Compute a factorial."
...
p = Pool(5)
result = p.map(factorial, range(1, 1000, 10))
for v in result:
print v
This produces the following output:
1
39916800
51090942171709440000
8222838654177922817725562880000000
33452526613163807108170062053440751665152000000000
...
The other high-level interface, the Manager
class, creates a separate server process that can hold master copies of Python data structures. Other processes can then access and modify these data structures using proxy objects. The following example creates a shared dictionary by calling the dict()
method; the worker processes then insert values into the dictionary. (Locking is not done for you automatically, which doesn’t matter in this example. Manager
’s methods also include Lock()
, RLock()
, and Semaphore()
to create shared locks.)
import time
from multiprocessing import Pool, Manager
def factorial(N, dictionary):
"Compute a factorial."
# Calculate the result
fact = 1L
for i in range(1, N+1):
fact = fact * i
# Store result in dictionary
dictionary[N] = fact
if __name__ == '__main__':
p = Pool(5)
mgr = Manager()
d = mgr.dict() # Create shared dictionary
# Run tasks using the pool
for N in range(1, 1000, 10):
p.apply_async(factorial, (N, d))
# Mark pool as closed -- no more tasks can be added.
p.close()
# Wait for tasks to exit
p.join()
# Output results
for k, v in sorted(d.items()):
print k, v
This will produce the output:
1 1
11 39916800
21 51090942171709440000
31 8222838654177922817725562880000000
41 33452526613163807108170062053440751665152000000000
51 15511187532873822802242430164693032110632597200169861120000...
See also
The documentation for the multiprocessing
module.
- PEP 371 - Addition of the multiprocessing package
-
PEP written by Jesse Noller and Richard Oudkerk; implemented by Richard Oudkerk and Jesse Noller.
PEP 3101: Advanced String Formatting
In Python 3.0, the % operator is supplemented by a more powerful string formatting method, format()
. Support for the str.format()
method has been backported to Python 2.6.
In 2.6, both 8-bit and Unicode strings have a .format() method that treats the string as a template and takes the arguments to be formatted. The formatting template uses curly brackets ({, }) as special characters:
>>> # Substitute positional argument 0 into the string.
>>> "User ID: {0}".format("root")
'User ID: root'
>>> # Use the named keyword arguments
>>> "User ID: {uid} Last seen: {last_login}".format(
... uid="root",
... last_login = "5 Mar 2008 07:20")
'User ID: root Last seen: 5 Mar 2008 07:20'
Curly brackets can be escaped by doubling them:
>>> "Empty dict: {{}}".format()
"Empty dict: {}"
Field names can be integers indicating positional arguments, such as {0}
, {1}
, etc. or names of keyword arguments. You can also supply compound field names that read attributes or access dictionary keys:
>>> import sys
>>> print 'Platform: {0.platform}\nPython version: {0.version}'.format(sys)
Platform: darwin
Python version: 2.6a1+ (trunk:61261M, Mar 5 2008, 20:29:41)
[GCC 4.0.1 (Apple Computer, Inc. build 5367)]'
>>> import mimetypes
>>> 'Content-type: {0[.mp4]}'.format(mimetypes.types_map)
'Content-type: video/mp4'
Note that when using dictionary-style notation such as [.mp4]
, you don’t need to put any quotation marks around the string; it will look up the value using .mp4
as the key. Strings beginning with a number will be converted to an integer. You can’t write more complicated expressions inside a format string.
So far we’ve shown how to specify which field to substitute into the resulting string. The precise formatting used is also controllable by adding a colon followed by a format specifier. For example:
>>> # Field 0: left justify, pad to 15 characters
>>> # Field 1: right justify, pad to 6 characters
>>> fmt = '{0:15} ${1:>6}'
>>> fmt.format('Registration', 35)
'Registration $ 35'
>>> fmt.format('Tutorial', 50)
'Tutorial $ 50'
>>> fmt.format('Banquet', 125)
'Banquet $ 125'
Format specifiers can reference other fields through nesting:
>>> fmt = '{0:{1}}'
>>> width = 15
>>> fmt.format('Invoice #1234', width)
'Invoice #1234 '
>>> width = 35
>>> fmt.format('Invoice #1234', width)
'Invoice #1234 '
The alignment of a field within the desired width can be specified:
Character |
Effect |
---|---|
< (default) |
Left-align |
> |
Right-align |
^ |
Center |
= |
(For numeric types only) Pad after the sign. |
Format specifiers can also include a presentation type, which controls how the value is formatted. For example, floating-point numbers can be formatted as a general number or in exponential notation:
>>> '{0:g}'.format(3.75)
'3.75'
>>> '{0:e}'.format(3.75)
'3.750000e+00'
A variety of presentation types are available. Consult the 2.6 documentation for a complete list; here’s a sample:
|
Binary. Outputs the number in base 2. |
|
Character. Converts the integer to the corresponding Unicode character before printing. |
|
Decimal Integer. Outputs the number in base 10. |
|
Octal format. Outputs the number in base 8. |
|
Hex format. Outputs the number in base 16, using lower-case letters for the digits above 9. |
|
Exponent notation. Prints the number in scientific notation using the letter ‘e’ to indicate the exponent. |
|
General format. This prints the number as a fixed-point number, unless the number is too large, in which case it switches to ‘e’ exponent notation. |
|
Number. This is the same as ‘g’ (for floats) or ‘d’ (for integers), except that it uses the current locale setting to insert the appropriate number separator characters. |
|
Percentage. Multiplies the number by 100 and displays in fixed (‘f’) format, followed by a percent sign. |
Classes and types can define a __format__()
method to control how they’re formatted. It receives a single argument, the format specifier:
def __format__(self, format_spec):
if isinstance(format_spec, unicode):
return unicode(str(self))
else:
return str(self)
There’s also a format()
builtin that will format a single value. It calls the type’s __format__()
method with the provided specifier:
>>> format(75.6564, '.2f')
'75.66'
See also
- Format String Syntax
-
The reference documentation for format fields.
- PEP 3101 - Advanced String Formatting
-
PEP written by Talin. Implemented by Eric Smith.
PEP 3105: print
As a Function
The print
statement becomes the print()
function in Python 3.0. Making print()
a function makes it possible to replace the function by doing def print(...)
or importing a new function from somewhere else.
Python 2.6 has a __future__
import that removes print
as language syntax, letting you use the functional form instead. For example:
>>> from __future__ import print_function
>>> print('# of entries', len(dictionary), file=sys.stderr)
The signature of the new function is:
def print(*args, sep=' ', end='\n', file=None)
The parameters are:
args: positional arguments whose values will be printed out.
sep: the separator, which will be printed between arguments.
end: the ending text, which will be printed after all of the arguments have been output.
file: the file object to which the output will be sent.
See also
- PEP 3105 - Make print a function
-
PEP written by Georg Brandl.
PEP 3110: Exception-Handling Changes
One error that Python programmers occasionally make is writing the following code:
try:
...
except TypeError, ValueError: # Wrong!
...
The author is probably trying to catch both TypeError
and ValueError
exceptions, but this code actually does something different: it will catch TypeError
and bind the resulting exception object to the local name "ValueError"
. The ValueError
exception will not be caught at all. The correct code specifies a tuple of exceptions:
try:
...
except (TypeError, ValueError):
...
This error happens because the use of the comma here is ambiguous: does it indicate two different nodes in the parse tree, or a single node that’s a tuple?
Python 3.0 makes this unambiguous by replacing the comma with the word “as”. To catch an exception and store the exception object in the variable exc
, you must write:
try:
...
except TypeError as exc:
...
Python 3.0 will only support the use of “as”, and therefore interprets the first example as catching two different exceptions. Python 2.6 supports both the comma and “as”, so existing code will continue to work. We therefore suggest using “as” when writing new Python code that will only be executed with 2.6.
See also
- PEP 3110 - Catching Exceptions in Python 3000
-
PEP written and implemented by Collin Winter.
PEP 3112: Byte Literals
Python 3.0 adopts Unicode as the language’s fundamental string type and denotes 8-bit literals differently, either as b'string'
or using a bytes
constructor. For future compatibility, Python 2.6 adds bytes
as a synonym for the str
type, and it also supports the b''
notation.
The 2.6 str
differs from 3.0’s bytes
type in various ways; most notably, the constructor is completely different. In 3.0, bytes([65, 66, 67])
is 3 elements long, containing the bytes representing ABC
; in 2.6, bytes([65, 66, 67])
returns the 12-byte string representing the str()
of the list.
The primary use of bytes
in 2.6 will be to write tests of object type such as isinstance(x, bytes)
. This will help the 2to3 converter, which can’t tell whether 2.x code intends strings to contain either characters or 8-bit bytes; you can now use either bytes
or str
to represent your intention exactly, and the resulting code will also be correct in Python 3.0.
There’s also a __future__
import that causes all string literals to become Unicode strings. This means that \u
escape sequences can be used to include Unicode characters:
from __future__ import unicode_literals
s = ('\u751f\u3080\u304e\u3000\u751f\u3054'
'\u3081\u3000\u751f\u305f\u307e\u3054')
print len(s) # 12 Unicode characters
At the C level, Python 3.0 will rename the existing 8-bit string type, called PyStringObject
in Python 2.x, to PyBytesObject
. Python 2.6 uses #define
to support using the names PyBytesObject()
, PyBytes_Check()
, PyBytes_FromStringAndSize()
, and all the other functions and macros used with strings.
Instances of the bytes
type are immutable just as strings are. A new bytearray
type stores a mutable sequence of bytes:
>>> bytearray([65, 66, 67])
bytearray(b'ABC')
>>> b = bytearray(u'\u21ef\u3244', 'utf-8')
>>> b
bytearray(b'\xe2\x87\xaf\xe3\x89\x84')
>>> b[0] = '\xe3'
>>> b
bytearray(b'\xe3\x87\xaf\xe3\x89\x84')
>>> unicode(str(b), 'utf-8')
u'\u31ef \u3244'
Byte arrays support most of the methods of string types, such as startswith()
/endswith()
, find()
/rfind()
, and some of the methods of lists, such as append()
, pop()
, and reverse()
.
>>> b = bytearray('ABC')
>>> b.append('d')
>>> b.append(ord('e'))
>>> b
bytearray(b'ABCde')
There’s also a corresponding C API, with PyByteArray_FromObject()
, PyByteArray_FromStringAndSize()
, and various other functions.
See also
- PEP 3112 - Bytes literals in Python 3000
-
PEP written by Jason Orendorff; backported to 2.6 by Christian Heimes.
PEP 3116: New I/O Library
Python’s built-in file objects support a number of methods, but file-like objects don’t necessarily support all of them. Objects that imitate files usually support read()
and write()
, but they may not support readline()
, for example. Python 3.0 introduces a layered I/O library in the io
module that separates buffering and text-handling features from the fundamental read and write operations.
There are three levels of abstract base classes provided by the io
module:
RawIOBase
defines raw I/O operations:read()
,readinto()
,write()
,seek()
,tell()
,truncate()
, andclose()
. Most of the methods of this class will often map to a single system call. There are alsoreadable()
,writable()
, andseekable()
methods for determining what operations a given object will allow.Python 3.0 has concrete implementations of this class for files and sockets, but Python 2.6 hasn’t restructured its file and socket objects in this way.
BufferedIOBase
is an abstract base class that buffers data in memory to reduce the number of system calls used, making I/O processing more efficient. It supports all of the methods ofRawIOBase
, and adds araw
attribute holding the underlying raw object.There are five concrete classes implementing this ABC.
BufferedWriter
andBufferedReader
are for objects that support write-only or read-only usage that have aseek()
method for random access.BufferedRandom
objects support read and write access upon the same underlying stream, andBufferedRWPair
is for objects such as TTYs that have both read and write operations acting upon unconnected streams of data. TheBytesIO
class supports reading, writing, and seeking over an in-memory buffer.TextIOBase
: Provides functions for reading and writing strings (remember, strings will be Unicode in Python 3.0), and supporting universal newlines.TextIOBase
defines thereadline()
method and supports iteration upon objects.There are two concrete implementations.
TextIOWrapper
wraps a buffered I/O object, supporting all of the methods for text I/O and adding abuffer
attribute for access to the underlying object.StringIO
simply buffers everything in memory without ever writing anything to disk.(In Python 2.6,
io.StringIO
is implemented in pure Python, so it’s pretty slow. You should therefore stick with the existingStringIO
module orcStringIO
for now. At some point Python 3.0’sio
module will be rewritten into C for speed, and perhaps the C implementation will be backported to the 2.x releases.)
In Python 2.6, the underlying implementations haven’t been restructured to build on top of the io
module’s classes. The module is being provided to make it easier to write code that’s forward-compatible with 3.0, and to save developers the effort of writing their own implementations of buffering and text I/O.
See also
- PEP 3116 - New I/O
-
PEP written by Daniel Stutzbach, Mike Verdone, and Guido van Rossum. Code by Guido van Rossum, Georg Brandl, Walter Doerwald, Jeremy Hylton, Martin von Löwis, Tony Lownds, and others.
PEP 3118: Revised Buffer Protocol
The buffer protocol is a C-level API that lets Python types exchange pointers into their internal representations. A memory-mapped file can be viewed as a buffer of characters, for example, and this lets another module such as re
treat memory-mapped files as a string of characters to be searched.
The primary users of the buffer protocol are numeric-processing packages such as NumPy, which expose the internal representation of arrays so that callers can write data directly into an array instead of going through a slower API. This PEP updates the buffer protocol in light of experience from NumPy development, adding a number of new features such as indicating the shape of an array or locking a memory region.
The most important new C API function is PyObject_GetBuffer(PyObject *obj, Py_buffer *view, int flags)
, which takes an object and a set of flags, and fills in the Py_buffer
structure with information about the object’s memory representation. Objects can use this operation to lock memory in place while an external caller could be modifying the contents, so there’s a corresponding PyBuffer_Release(Py_buffer *view)
to indicate that the external caller is done.
The flags argument to PyObject_GetBuffer()
specifies constraints upon the memory returned. Some examples are:
PyBUF_WRITABLE
indicates that the memory must be writable.
PyBUF_LOCK
requests a read-only or exclusive lock on the memory.
PyBUF_C_CONTIGUOUS
andPyBUF_F_CONTIGUOUS
requests a C-contiguous (last dimension varies the fastest) or Fortran-contiguous (first dimension varies the fastest) array layout.
Two new argument codes for PyArg_ParseTuple()
, s*
and z*
, return locked buffer objects for a parameter.
See also
- PEP 3118 - Revising the buffer protocol
-
PEP written by Travis Oliphant and Carl Banks; implemented by Travis Oliphant.
PEP 3119: Abstract Base Classes
Some object-oriented languages such as Java support interfaces, declaring that a class has a given set of methods or supports a given access protocol. Abstract Base Classes (or ABCs) are an equivalent feature for Python. The ABC support consists of an abc
module containing a metaclass called ABCMeta
, special handling of this metaclass by the isinstance()
and issubclass()
builtins, and a collection of basic ABCs that the Python developers think will be widely useful. Future versions of Python will probably add more ABCs.
Let’s say you have a particular class and wish to know whether it supports dictionary-style access. The phrase “dictionary-style” is vague, however. It probably means that accessing items with obj[1]
works. Does it imply that setting items with obj[2] = value
works? Or that the object will have keys()
, values()
, and items()
methods? What about the iterative variants such as iterkeys()
? copy()
and update()
? Iterating over the object with iter()
?
The Python 2.6 collections
module includes a number of different ABCs that represent these distinctions. Iterable
indicates that a class defines __iter__()
, and Container
means the class defines a __contains__()
method and therefore supports x in y
expressions. The basic dictionary interface of getting items, setting items, and keys()
, values()
, and items()
, is defined by the MutableMapping
ABC.
You can derive your own classes from a particular ABC to indicate they support that ABC’s interface:
import collections
class Storage(collections.MutableMapping):
...
Alternatively, you could write the class without deriving from the desired ABC and instead register the class by calling the ABC’s register()
method:
import collections
class Storage:
...
collections.MutableMapping.register(Storage)
For classes that you write, deriving from the ABC is probably clearer. The register()
method is useful when you’ve written a new ABC that can describe an existing type or class, or if you want to declare that some third-party class implements an ABC. For example, if you defined a PrintableType
ABC, it’s legal to do:
# Register Python's types
PrintableType.register(int)
PrintableType.register(float)
PrintableType.register(str)
Classes should obey the semantics specified by an ABC, but Python can’t check this; it’s up to the class author to understand the ABC’s requirements and to implement the code accordingly.
To check whether an object supports a particular interface, you can now write:
def func(d):
if not isinstance(d, collections.MutableMapping):
raise ValueError("Mapping object expected, not %r" % d)
Don’t feel that you must now begin writing lots of checks as in the above example. Python has a strong tradition of duck-typing, where explicit type-checking is never done and code simply calls methods on an object, trusting that those methods will be there and raising an exception if they aren’t. Be judicious in checking for ABCs and only do it where it’s absolutely necessary.
You can write your own ABCs by using abc.ABCMeta
as the metaclass in a class definition:
from abc import ABCMeta, abstractmethod
class Drawable():
__metaclass__ = ABCMeta
@abstractmethod
def draw(self, x, y, scale=1.0):
pass
def draw_doubled(self, x, y):
self.draw(x, y, scale=2.0)
class Square(Drawable):
def draw(self, x, y, scale):
...
In the Drawable
ABC above, the draw_doubled()
method renders the object at twice its size and can be implemented in terms of other methods described in Drawable
. Classes implementing this ABC therefore don’t need to provide their own implementation of draw_doubled()
, though they can do so. An implementation of draw()
is necessary, though; the ABC can’t provide a useful generic implementation.
You can apply the @abstractmethod
decorator to methods such as draw()
that must be implemented; Python will then raise an exception for classes that don’t define the method. Note that the exception is only raised when you actually try to create an instance of a subclass lacking the method:
>>> class Circle(Drawable):
... pass
...
>>> c = Circle()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class Circle with abstract methods draw
>>>
Abstract data attributes can be declared using the @abstractproperty
decorator:
from abc import abstractproperty
...
@abstractproperty
def readonly(self):
return self._x
Subclasses must then define a readonly()
property.
See also
- PEP 3119 - Introducing Abstract Base Classes
-
PEP written by Guido van Rossum and Talin. Implemented by Guido van Rossum. Backported to 2.6 by Benjamin Aranguren, with Alex Martelli.
PEP 3127: Integer Literal Support and Syntax
Python 3.0 changes the syntax for octal (base-8) integer literals, prefixing them with “0o” or “0O” instead of a leading zero, and adds support for binary (base-2) integer literals, signalled by a “0b” or “0B” prefix.
Python 2.6 doesn’t drop support for a leading 0 signalling an octal number, but it does add support for “0o” and “0b”:
>>> 0o21, 2*8 + 1
(17, 17)
>>> 0b101111
47
The oct()
builtin still returns numbers prefixed with a leading zero, and a new bin()
builtin returns the binary representation for a number:
>>> oct(42)
'052'
>>> future_builtins.oct(42)
'0o52'
>>> bin(173)
'0b10101101'
The int()
and long()
builtins will now accept the “0o” and “0b” prefixes when base-8 or base-2 are requested, or when the base argument is zero (signalling that the base used should be determined from the string):
>>> int ('0o52', 0)
42
>>> int('1101', 2)
13
>>> int('0b1101', 2)
13
>>> int('0b1101', 0)
13
See also
- PEP 3127 - Integer Literal Support and Syntax
-
PEP written by Patrick Maupin; backported to 2.6 by Eric Smith.
PEP 3129: Class Decorators
Decorators have been extended from functions to classes. It’s now legal to write:
@foo
@bar
class A:
pass
This is equivalent to:
class A:
pass
A = foo(bar(A))
See also
- PEP 3129 - Class Decorators
-
PEP written by Collin Winter.
PEP 3141: A Type Hierarchy for Numbers
Python 3.0 adds several abstract base classes for numeric types inspired by Scheme’s numeric tower. These classes were backported to 2.6 as the numbers
module.
The most general ABC is Number
. It defines no operations at all, and only exists to allow checking if an object is a number by doing isinstance(obj, Number)
.
Complex
is a subclass of Number
. Complex numbers can undergo the basic operations of addition, subtraction, multiplication, division, and exponentiation, and you can retrieve the real and imaginary parts and obtain a number’s conjugate. Python’s built-in complex type is an implementation of Complex
.
Real
further derives from Complex
, and adds operations that only work on real numbers: floor()
, trunc()
, rounding, taking the remainder mod N, floor division, and comparisons.
Rational
numbers derive from Real
, have numerator
and denominator
properties, and can be converted to floats. Python 2.6 adds a simple rational-number class, Fraction
, in the fractions
module. (It’s called Fraction
instead of Rational
to avoid a name clash with numbers.Rational
.)
Integral
numbers derive from Rational
, and can be shifted left and right with <<
and >>
, combined using bitwise operations such as &
and |
, and can be used as array indexes and slice boundaries.
In Python 3.0, the PEP slightly redefines the existing builtins round()
, math.floor()
, math.ceil()
, and adds a new one, math.trunc()
, that’s been backported to Python 2.6. math.trunc()
rounds toward zero, returning the closest Integral
that’s between the function’s argument and zero.
See also
- PEP 3141 - A Type Hierarchy for Numbers
-
PEP written by Jeffrey Yasskin.
Scheme’s numerical tower , from the Guile manual.
Scheme’s number datatypes from the R5RS Scheme specification.
The fractions
Module
To fill out the hierarchy of numeric types, the fractions
module provides a rational-number class. Rational numbers store their values as a numerator and denominator forming a fraction, and can exactly represent numbers such as 2/3
that floating-point numbers can only approximate.
The Fraction
constructor takes two Integral
values that will be the numerator and denominator of the resulting fraction.
>>> from fractions import Fraction
>>> a = Fraction(2, 3)
>>> b = Fraction(2, 5)
>>> float(a), float(b)
(0.66666666666666663, 0.40000000000000002)
>>> a+b
Fraction(16, 15)
>>> a/b
Fraction(5, 3)
For converting floating-point numbers to rationals, the float type now has an as_integer_ratio()
method that returns the numerator and denominator for a fraction that evaluates to the same floating-point value:
>>> (2.5) .as_integer_ratio()
(5, 2)
>>> (3.1415) .as_integer_ratio()
(7074029114692207L, 2251799813685248L)
>>> (1./3) .as_integer_ratio()
(6004799503160661L, 18014398509481984L)
Note that values that can only be approximated by floating-point numbers, such as 1./3, are not simplified to the number being approximated; the fraction attempts to match the floating-point value exactly.
The fractions
module is based upon an implementation by Sjoerd Mullender that was in Python’s Demo/classes/
directory for a long time. This implementation was significantly updated by Jeffrey Yasskin.
Other Language Changes
Some smaller changes made to the core Python language are:
Directories and zip archives containing a
__main__.py
file can now be executed directly by passing their name to the interpreter. The directory or zip archive is automatically inserted as the first entry in sys.path. (Suggestion and initial patch by Andy Chu, subsequently revised by Phillip J. Eby and Nick Coghlan; bpo-1739468 .)The
hasattr()
function was catching and ignoring all errors, under the assumption that they meant a__getattr__()
method was failing somehow and the return value ofhasattr()
would therefore beFalse
. This logic shouldn’t be applied toKeyboardInterrupt
andSystemExit
, however; Python 2.6 will no longer discard such exceptions whenhasattr()
encounters them. (Fixed by Benjamin Peterson; bpo-2196 .)When calling a function using the
**
syntax to provide keyword arguments, you are no longer required to use a Python dictionary; any mapping will now work:>>> def f(**kw): ... print sorted(kw) ... >>> ud=UserDict.UserDict() >>> ud['a'] = 1 >>> ud['b'] = 'string' >>> f(**ud) ['a', 'b']
(Contributed by Alexander Belopolsky; bpo-1686487 .)
It’s also become legal to provide keyword arguments after a
*args
argument to a function call.>>> def f(*args, **kw): ... print args, kw ... >>> f(1,2,3, *(4,5,6), keyword=13) (1, 2, 3, 4, 5, 6) {'keyword': 13}
Previously this would have been a syntax error. (Contributed by Amaury Forgeot d’Arc; bpo-3473 .)
A new builtin,
next(iterator, [default])
returns the next item from the specified iterator. If the default argument is supplied, it will be returned if iterator has been exhausted; otherwise, theStopIteration
exception will be raised. (Backported in bpo-2719 .)Tuples now have
index()
andcount()
methods matching the list type’sindex()
andcount()
methods:>>> t = (0,1,2,3,4,0,1,2) >>> t.index(3) 3 >>> t.count(0) 2
(Contributed by Raymond Hettinger)
The built-in types now have improved support for extended slicing syntax, accepting various combinations of
(start, stop, step)
. Previously, the support was partial and certain corner cases wouldn’t work. (Implemented by Thomas Wouters.)Properties now have three attributes,
getter
,setter
anddeleter
, that are decorators providing useful shortcuts for adding a getter, setter or deleter function to an existing property. You would use them like this:class C(object): @property def x(self): return self._x @x.setter def x(self, value): self._x = value @x.deleter def x(self): del self._x class D(C): @C.x.getter def x(self): return self._x * 2 @x.setter def x(self, value): self._x = value / 2
Several methods of the built-in set types now accept multiple iterables:
intersection()
,intersection_update()
,union()
,update()
,difference()
anddifference_update()
.>>> s=set('1234567890') >>> s.intersection('abc123', 'cdf246') # Intersection between all inputs set(['2']) >>> s.difference('246', '789') set(['1', '0', '3', '5'])
(Contributed by Raymond Hettinger.)
Many floating-point features were added. The
float()
function will now turn the stringnan
into an IEEE 754 Not A Number value, and+inf
and-inf
into positive or negative infinity. This works on any platform with IEEE 754 semantics. (Contributed by Christian Heimes; bpo-1635 .)Other functions in the
math
module,isinf()
andisnan()
, return true if their floating-point argument is infinite or Not A Number. (bpo-1640 )Conversion functions were added to convert floating-point numbers into hexadecimal strings (bpo-3008 ). These functions convert floats to and from a string representation without introducing rounding errors from the conversion between decimal and binary. Floats have a
hex()
method that returns a string representation, and thefloat.fromhex()
method converts a string back into a number:>>> a = 3.75 >>> a.hex() '0x1.e000000000000p+1' >>> float.fromhex('0x1.e000000000000p+1') 3.75 >>> b=1./3 >>> b.hex() '0x1.5555555555555p-2'
A numerical nicety: when creating a complex number from two floats on systems that support signed zeros (-0 and +0), the
complex()
constructor will now preserve the sign of the zero. (Fixed by Mark T. Dickinson; bpo-1507 .)Classes that inherit a
__hash__()
method from a parent class can set__hash__ = None
to indicate that the class isn’t hashable. This will makehash(obj)
raise aTypeError
and the class will not be indicated as implementing theHashable
ABC.You should do this when you’ve defined a
__cmp__()
or__eq__()
method that compares objects by their value rather than by identity. All objects have a default hash method that usesid(obj)
as the hash value. There’s no tidy way to remove the__hash__()
method inherited from a parent class, so assigningNone
was implemented as an override. At the C level, extensions can settp_hash
toPyObject_HashNotImplemented()
. (Fixed by Nick Coghlan and Amaury Forgeot d’Arc; bpo-2235 .)The
GeneratorExit
exception now subclassesBaseException
instead ofException
. This means that an exception handler that doesexcept Exception:
will not inadvertently catchGeneratorExit
. (Contributed by Chad Austin; bpo-1537 .)Generator objects now have a
gi_code
attribute that refers to the original code object backing the generator. (Contributed by Collin Winter; bpo-1473257 .)The
compile()
built-in function now accepts keyword arguments as well as positional parameters. (Contributed by Thomas Wouters; bpo-1444529 .)The
complex()
constructor now accepts strings containing parenthesized complex numbers, meaning thatcomplex(repr(cplx))
will now round-trip values. For example,complex('(3+4j)')
now returns the value (3+4j). (bpo-1491866 )The string
translate()
method now acceptsNone
as the translation table parameter, which is treated as the identity transformation. This makes it easier to carry out operations that only delete characters. (Contributed by Bengt Richter and implemented by Raymond Hettinger; bpo-1193128 .)The built-in
dir()
function now checks for a__dir__()
method on the objects it receives. This method must return a list of strings containing the names of valid attributes for the object, and lets the object control the value thatdir()
produces. Objects that have__getattr__()
or__getattribute__()
methods can use this to advertise pseudo-attributes they will honor. (bpo-1591665 )Instance method objects have new attributes for the object and function comprising the method; the new synonym for
im_self
is__self__
, andim_func
is also available as__func__
. The old names are still supported in Python 2.6, but are gone in 3.0.An obscure change: when you use the
locals()
function inside aclass
statement, the resulting dictionary no longer returns free variables. (Free variables, in this case, are variables referenced in theclass
statement that aren’t attributes of the class.)
Optimizations
The
warnings
module has been rewritten in C. This makes it possible to invoke warnings from the parser, and may also make the interpreter’s startup faster. (Contributed by Neal Norwitz and Brett Cannon; bpo-1631171 .)Type objects now have a cache of methods that can reduce the work required to find the correct method implementation for a particular class; once cached, the interpreter doesn’t need to traverse base classes to figure out the right method to call. The cache is cleared if a base class or the class itself is modified, so the cache should remain correct even in the face of Python’s dynamic nature. (Original optimization implemented by Armin Rigo, updated for Python 2.6 by Kevin Jacobs; bpo-1700288 .)
By default, this change is only applied to types that are included with the Python core. Extension modules may not necessarily be compatible with this cache, so they must explicitly add
Py_TPFLAGS_HAVE_VERSION_TAG
to the module’stp_flags
field to enable the method cache. (To be compatible with the method cache, the extension module’s code must not directly access and modify thetp_dict
member of any of the types it implements. Most modules don’t do this, but it’s impossible for the Python interpreter to determine that. See bpo-1878 for some discussion.)Function calls that use keyword arguments are significantly faster by doing a quick pointer comparison, usually saving the time of a full string comparison. (Contributed by Raymond Hettinger, after an initial implementation by Antoine Pitrou; bpo-1819 .)
All of the functions in the
struct
module have been rewritten in C, thanks to work at the Need For Speed sprint. (Contributed by Raymond Hettinger.)Some of the standard built-in types now set a bit in their type objects. This speeds up checking whether an object is a subclass of one of these types. (Contributed by Neal Norwitz.)
Unicode strings now use faster code for detecting whitespace and line breaks; this speeds up the
split()
method by about 25% andsplitlines()
by 35%. (Contributed by Antoine Pitrou.) Memory usage is reduced by using pymalloc for the Unicode string’s data.The
with
statement now stores the__exit__()
method on the stack, producing a small speedup. (Implemented by Jeffrey Yasskin.)To reduce memory usage, the garbage collector will now clear internal free lists when garbage-collecting the highest generation of objects. This may return memory to the operating system sooner.
Interpreter Changes
Two command-line options have been reserved for use by other Python implementations. The -J
switch has been reserved for use by Jython for Jython-specific options, such as switches that are passed to the underlying JVM. -X
has been reserved for options specific to a particular implementation of Python such as CPython, Jython, or IronPython. If either option is used with Python 2.6, the interpreter will report that the option isn’t currently used.
Python can now be prevented from writing .pyc
or .pyo
files by supplying the -B
switch to the Python interpreter, or by setting the PYTHONDONTWRITEBYTECODE
environment variable before running the interpreter. This setting is available to Python programs as the sys.dont_write_bytecode
variable, and Python code can change the value to modify the interpreter’s behaviour. (Contributed by Neal Norwitz and Georg Brandl.)
The encoding used for standard input, output, and standard error can be specified by setting the PYTHONIOENCODING
environment variable before running the interpreter. The value should be a string in the form <encoding>
or <encoding>:<errorhandler>
. The encoding part specifies the encoding’s name, e.g. utf-8
or latin-1
; the optional errorhandler part specifies what to do with characters that can’t be handled by the encoding, and should be one of “error”, “ignore”, or “replace”. (Contributed by Martin von Löwis.)
New and Improved Modules
As in every release, Python’s standard library received a number of enhancements and bug fixes. 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 Subversion logs for all the details.
The
asyncore
andasynchat
modules are being actively maintained again, and a number of patches and bugfixes were applied. (Maintained by Josiah Carlson; see bpo-1736190 for one patch.)The
bsddb
module also has a new maintainer, Jesús Cea Avión, and the package is now available as a standalone package. The web page for the package is www.jcea.es/programacion/pybsddb.htm . The plan is to remove the package from the standard library in Python 3.0, because its pace of releases is much more frequent than Python’s.The
bsddb.dbshelve
module now uses the highest pickling protocol available, instead of restricting itself to protocol 1. (Contributed by W. Barnes.)The
cgi
module will now read variables from the query string of an HTTP POST request. This makes it possible to use form actions with URLs that include query strings such as “/cgi-bin/add.py?category=1”. (Contributed by Alexandre Fiori and Nubis; bpo-1817 .)The
parse_qs()
andparse_qsl()
functions have been relocated from thecgi
module to theurlparse
module. The versions still available in thecgi
module will triggerPendingDeprecationWarning
messages in 2.6 (bpo-600362 ).The
cmath
module underwent extensive revision, contributed by Mark Dickinson and Christian Heimes. Five new functions were added:polar()
converts a complex number to polar form, returning the modulus and argument of the complex number.rect()
does the opposite, turning a modulus, argument pair back into the corresponding complex number.phase()
returns the argument (also called the angle) of a complex number.isnan()
returns True if either the real or imaginary part of its argument is a NaN.isinf()
returns True if either the real or imaginary part of its argument is infinite.
The revisions also improved the numerical soundness of the
cmath
module. For all functions, the real and imaginary parts of the results are accurate to within a few units of least precision (ulps) whenever possible. See bpo-1381 for the details. The branch cuts forasinh()
,atanh()
: andatan()
have also been corrected.The tests for the module have been greatly expanded; nearly 2000 new test cases exercise the algebraic functions.
On IEEE 754 platforms, the
cmath
module now handles IEEE 754 special values and floating-point exceptions in a manner consistent with Annex ‘G’ of the C99 standard.A new data type in the
collections
module:namedtuple(typename, fieldnames)
is a factory function that creates subclasses of the standard tuple whose fields are accessible by name as well as index. For example:>>> var_type = collections.namedtuple('variable', ... 'id name type size') >>> # Names are separated by spaces or commas. >>> # 'id, name, type, size' would also work. >>> var_type._fields ('id', 'name', 'type', 'size') >>> var = var_type(1, 'frequency', 'int', 4) >>> print var[0], var.id # Equivalent 1 1 >>> print var[2], var.type # Equivalent int int >>> var._asdict() {'size': 4, 'type': 'int', 'id': 1, 'name': 'frequency'} >>> v2 = var._replace(name='amplitude') >>> v2 variable(id=1, name='amplitude', type='int', size=4)
Several places in the standard library that returned tuples have been modified to return
namedtuple
instances. For example, theDecimal.as_tuple()
method now returns a named tuple withsign
,digits
, andexponent
fields.(Contributed by Raymond Hettinger.)
Another change to the
collections
module is that thedeque
type now supports an optional maxlen parameter; if supplied, the deque’s size will be restricted to no more than maxlen items. Adding more items to a full deque causes old items to be discarded.>>> from collections import deque >>> dq=deque(maxlen=3) >>> dq deque([], maxlen=3) >>> dq.append(1); dq.append(2); dq.append(3) >>> dq deque([1, 2, 3], maxlen=3) >>> dq.append(4) >>> dq deque([2, 3, 4], maxlen=3)
(Contributed by Raymond Hettinger.)
The
Cookie
module’sMorsel
objects now support anhttponly
attribute. In some browsers. cookies with this attribute set cannot be accessed or manipulated by JavaScript code. (Contributed by Arvin Schnell; bpo-1638033 .)A new window method in the
curses
module,chgat()
, changes the display attributes for a certain number of characters on a single line. (Contributed by Fabian Kreutz.)# Boldface text starting at y=0,x=21 # and affecting the rest of the line. stdscr.chgat(0, 21, curses.A_BOLD)
The
Textbox
class in thecurses.textpad
module now supports editing in insert mode as well as overwrite mode. Insert mode is enabled by supplying a true value for the insert_mode parameter when creating theTextbox
instance.The
datetime
module’sstrftime()
methods now support a%f
format code that expands to the number of microseconds in the object, zero-padded on the left to six places. (Contributed by Skip Montanaro; bpo-1158 .)The
decimal
module was updated to version 1.66 of the General Decimal Specification . New features include some methods for some basic mathematical functions such asexp()
andlog10()
:>>> Decimal(1).exp() Decimal("2.718281828459045235360287471") >>> Decimal("2.7182818").ln() Decimal("0.9999999895305022877376682436") >>> Decimal(1000).log10() Decimal("3")
The
as_tuple()
method ofDecimal
objects now returns a named tuple withsign
,digits
, andexponent
fields.(Implemented by Facundo Batista and Mark Dickinson. Named tuple support added by Raymond Hettinger.)
The
difflib
module’sSequenceMatcher
class now returns named tuples representing matches, witha
,b
, andsize
attributes. (Contributed by Raymond Hettinger.)An optional
timeout
parameter, specifying a timeout measured in seconds, was added to theftplib.FTP
class constructor as well as theconnect()
method. (Added by Facundo Batista.) Also, theFTP
class’sstorbinary()
andstorlines()
now take an optional callback parameter that will be called with each block of data after the data has been sent. (Contributed by Phil Schwartz; bpo-1221598 .)The
reduce()
built-in function is also available in thefunctools
module. In Python 3.0, the builtin has been dropped andreduce()
is only available fromfunctools
; currently there are no plans to drop the builtin in the 2.x series. (Patched by Christian Heimes; bpo-1739906 .)When possible, the
getpass
module will now use/dev/tty
to print a prompt message and read the password, falling back to standard error and standard input. If the password may be echoed to the terminal, a warning is printed before the prompt is displayed. (Contributed by Gregory P. Smith.)The
glob.glob()
function can now return Unicode filenames if a Unicode path was used and Unicode filenames are matched within the directory. (bpo-1001604 )A new function in the
heapq
module,merge(iter1, iter2, ...)
, takes any number of iterables returning data in sorted order, and returns a new generator that returns the contents of all the iterators, also in sorted order. For example:>>> list(heapq.merge([1, 3, 5, 9], [2, 8, 16])) [1, 2, 3, 5, 8, 9, 16]
Another new function,
heappushpop(heap, item)
, pushes item onto heap, then pops off and returns the smallest item. This is more efficient than making a call toheappush()
and thenheappop()
.heapq
is now implemented to only use less-than comparison, instead of the less-than-or-equal comparison it previously used. This makesheapq
’s usage of a type match thelist.sort()
method. (Contributed by Raymond Hettinger.)An optional
timeout
parameter, specifying a timeout measured in seconds, was added to thehttplib.HTTPConnection
andHTTPSConnection
class constructors. (Added by Facundo Batista.)Most of the
inspect
module’s functions, such as