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-in reduce() 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 . See also The 3xxx series of PEPs, which contains proposals for Python 3.0. PEP 3000 describes the development process for Python 3.0. Start with PEP 3100 that describes the general goals for Python 3.0, and then explore the higher-numbered PEPS that propose specific features.

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 no as 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 by sys.exc_info() , which can also be None if no exception occurred). The method’s return value controls whether an exception is re-raised: any false value re-raises the exception, and True will result in suppressing it. You’ll only rarely want to suppress the exception, because if you do the author of the code containing the ‘ with ’ statement will never realize anything went wrong.

If BLOCK didn’t raise an exception, the __exit__() method is still called, but type, value, and traceback are all None . Let’s think through an example. I won’t present detailed code but will only sketch the methods necessary for a database that supports transactions. (For people unfamiliar with database terminology: a set of changes to the database are grouped into a transaction. Transactions can be either committed, meaning that all the changes are written into the database, or rolled back, meaning that the changes are all discarded and the database is unchanged. See any database textbook for more information.) Let’s assume there’s an object representing a database connection. Our goal will be to let the user write code like this: db_connection = DatabaseConnection () with db_connection as cursor : cursor . execute ( 'insert into ...' ) cursor . execute ( 'delete from ...' ) # ... more operations ... The transaction should be committed if the code in the block runs flawlessly or rolled back if there’s an exception. Here’s the basic interface for DatabaseConnection that I’ll assume: class DatabaseConnection : # Database interface def cursor ( self ): "Returns a cursor object and starts a new transaction" def commit ( self ): "Commits current transaction" def rollback ( self ): "Rolls back current transaction" The __enter__() method is pretty easy, having only to start a new transaction. For this application the resulting cursor object would be a useful result, so the method will return it. The user can then add as cursor to their ‘ with ’ statement to bind the cursor to a variable name. class DatabaseConnection : ... def __enter__ ( self ): # Code to start a new transaction cursor = self . cursor () return cursor The __exit__() method is the most complicated because it’s where most of the work has to be done. The method has to check if an exception occurred. If there was no exception, the transaction is committed. The transaction is rolled back if there was an exception. In the code below, execution will just fall off the end of the function, returning the default value of None . None is false, so the exception will be re-raised automatically. If you wished, you could be more explicit and add a return statement at the marked location. class DatabaseConnection : ... def __exit__ ( self , type , value , tb ): if tb is None : # No exception, so commit self . commit () else : # Exception occurred, so rollback. self . rollback () # return False The 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 = 1 L 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 = 1 L 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}

Python 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: b Binary. Outputs the number in base 2. c Character. Converts the integer to the corresponding Unicode character before printing. d Decimal Integer. Outputs the number in base 10. o Octal format. Outputs the number in base 8. x Hex format. Outputs the number in base 16, using lower-case letters for the digits above 9. e Exponent notation. Prints the number in scientific notation using the letter ‘e’ to indicate the exponent. g 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. n 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 = '

' , 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() , and close() . Most of the methods of this class will often map to a single system call. There are also readable() , writable() , and seekable() 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 of RawIOBase , and adds a raw attribute holding the underlying raw object. There are five concrete classes implementing this ABC. BufferedWriter and BufferedReader are for objects that support write-only or read-only usage that have a seek() method for random access. BufferedRandom objects support read and write access upon the same underlying stream, and BufferedRWPair is for objects such as TTYs that have both read and write operations acting upon unconnected streams of data. The BytesIO 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 the readline() 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 a buffer 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 existing StringIO module or cStringIO for now. At some point Python 3.0’s io 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 and PyBUF_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 of hasattr() would therefore be False . This logic shouldn’t be applied to KeyboardInterrupt and SystemExit , however; Python 2.6 will no longer discard such exceptions when hasattr() 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, the StopIteration exception will be raised. (Backported in bpo-2719.)

Tuples now have index() and count() methods matching the list type’s index() and count() 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 and deleter , 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() and difference_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 string nan 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() and isnan() , 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 the float.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 make hash(obj) raise a TypeError and the class will not be indicated as implementing the Hashable 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 uses id(obj) as the hash value. There’s no tidy way to remove the __hash__() method inherited from a parent class, so assigning None was implemented as an override. At the C level, extensions can set tp_hash to PyObject_HashNotImplemented() . (Fixed by Nick Coghlan and Amaury Forgeot d’Arc; bpo-2235.)

The GeneratorExit exception now subclasses BaseException instead of Exception . This means that an exception handler that does except Exception: will not inadvertently catch GeneratorExit . (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 that complex(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 accepts None 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 that dir() 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__ , and im_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 a class statement, the resulting dictionary no longer returns free variables. (Free variables, in this case, are variables referenced in the class 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’s tp_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 the tp_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% and splitlines() 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 and asynchat 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() and parse_qsl() functions have been relocated from the cgi module to the urlparse module. The versions still available in the cgi module will trigger PendingDeprecationWarning 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 for asinh() , atanh() : and atan() 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, the Decimal.as_tuple() method now returns a named tuple with sign , digits , and exponent fields. (Contributed by Raymond Hettinger.)

Another change to the collections module is that the deque 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’s Morsel objects now support an httponly 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 the curses.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 the Textbox instance.

The datetime module’s strftime() 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 as exp() and log10() : >>> Decimal ( 1 ) . exp () Decimal("2.718281828459045235360287471") >>> Decimal ( "2.7182818" ) . ln () Decimal("0.9999999895305022877376682436") >>> Decimal ( 1000 ) . log10 () Decimal("3") The as_tuple() method of Decimal objects now returns a named tuple with sign , digits , and exponent fields. (Implemented by Facundo Batista and Mark Dickinson. Named tuple support added by Raymond Hettinger.)

The difflib module’s SequenceMatcher class now returns named tuples representing matches, with a , b , and size attributes. (Contributed by Raymond Hettinger.)

An optional timeout parameter, specifying a timeout measured in seconds, was added to the ftplib.FTP class constructor as well as the connect() method. (Added by Facundo Batista.) Also, the FTP class’s storbinary() and storlines() 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 the functools module. In Python 3.0, the builtin has been dropped and reduce() is only available from functools ; 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 to heappush() and then heappop() . heapq is now implemented to only use less-than comparison, instead of the less-than-or-equal comparison it previously used. This makes heapq ’s usage of a type match the list.sort() method. (Contributed by Raymond Hettinger.)

An optional timeout parameter, specifying a timeout measured in seconds, was added to the httplib.HTTPConnection and HTTPSConnection class constructors. (Added by Facundo Batista.)

Most of the inspect module’s functions, such as getmoduleinfo() and getargs() , now return named tuples. In addition to behaving like tuples, the elements of the return value can also be accessed as attributes. (Contributed by Raymond Hettinger.) Some new functions in the module include isgenerator() , isgeneratorfunction() , and isabstract() .

The itertools module gained several new functions. izip_longest(iter1, iter2, ...[, fillvalue]) makes tuples from each of the elements; if some of the iterables are shorter than others, the missing values are set to fillvalue. For example: >>> tuple ( itertools . izip_longest ([ 1 , 2 , 3 ], [ 1 , 2 , 3 , 4 , 5 ])) ((1, 1), (2, 2), (3, 3), (None, 4), (None, 5)) product(iter1, iter2, ..., [repeat=N]) returns the Cartesian product of the supplied iterables, a set of tuples containing every possible combination of the elements returned from each iterable. >>> list ( itertools . product ([ 1 , 2 , 3 ], [ 4 , 5 , 6 ])) [(1, 4), (1, 5), (1, 6), (2, 4), (2, 5), (2, 6), (3, 4), (3, 5), (3, 6)] The optional repeat keyword argument is used for taking the product of an iterable or a set of iterables with themselves, repeated N times. With a single iterable argument, N-tuples are returned: >>> list ( itertools . product ([ 1 , 2 ], repeat = 3 )) [(1, 1, 1), (1, 1, 2), (1, 2, 1), (1, 2, 2), (2, 1, 1), (2, 1, 2), (2, 2, 1), (2, 2, 2)] With two iterables, 2N-tuples are returned. >>> list ( itertools . product ([ 1 , 2 ], [ 3 , 4 ], repeat = 2 )) [(1, 3, 1, 3), (1, 3, 1, 4), (1, 3, 2, 3), (1, 3, 2, 4), (1, 4, 1, 3), (1, 4, 1, 4), (1, 4, 2, 3), (1, 4, 2, 4), (2, 3, 1, 3), (2, 3, 1, 4), (2, 3, 2, 3), (2, 3, 2, 4), (2, 4, 1, 3), (2, 4, 1, 4), (2, 4, 2, 3), (2, 4, 2, 4)] combinations(iterable, r) returns sub-sequences of length r from the elements of iterable. >>> list ( itertools . combinations ( '123' , 2 )) [('1', '2'), ('1', '3'), ('2', '3')] >>> list ( itertools . combinations ( '123' , 3 )) [('1', '2', '3')] >>> list ( itertools . combinations ( '1234' , 3 )) [('1', '2', '3'), ('1', '2', '4'), ('1', '3', '4'), ('2', '3', '4')] permutations(iter[, r]) returns all the permutations of length r of the iterable’s elements. If r is not specified, it will default to the number of elements produced by the iterable. >>> list ( itertools . permutations ([ 1 , 2 , 3 , 4 ], 2 )) [(1, 2), (1, 3), (1, 4), (2, 1), (2, 3), (2, 4), (3, 1), (3, 2), (3, 4), (4, 1), (4, 2), (4, 3)] itertools.chain(*iterables) is an existing function in itertools that gained a new constructor in Python 2.6. itertools.chain.from_iterable(iterable) takes a single iterable that should return other iterables. chain() will then return all the elements of the first iterable, then all the elements of the second, and so on. >>> list ( itertools . chain . from_iterable ([[ 1 , 2 , 3 ], [ 4 , 5 , 6 ]])) [1, 2, 3, 4, 5, 6] (All contributed by Raymond Hettinger.)

The logging module’s FileHandler class and its subclasses WatchedFileHandler , RotatingFileHandler , and TimedRotatingFileHandler now have an optional delay parameter to their constructors. If delay is true, opening of the log file is deferred until the first emit() call is made. (Contributed by Vinay Sajip.) TimedRotatingFileHandler also has a utc constructor parameter. If the argument is true, UTC time will be used in determining when midnight occurs and in generating filenames; otherwise local time will be used.

Several new functions were added to the math module: isinf() and isnan() determine whether a given float is a (positive or negative) infinity or a NaN (Not a Number), respectively. copysign() copies the sign bit of an IEEE 754 number, returning the absolute value of x combined with the sign bit of y. For example, math.copysign(1, -0.0) returns -1.0. (Contributed by Christian Heimes.) factorial() computes the factorial of a number. (Contributed by Raymond Hettinger; bpo-2138.) fsum() adds up the stream of numbers from an iterable, and is careful to avoid loss of precision through using partial sums. (Contributed by Jean Brouwers, Raymond Hettinger, and Mark Dickinson; bpo-2819.) acosh() , asinh() and atanh() compute the inverse hyperbolic functions. log1p() returns the natural logarithm of 1+x (base e). trunc() rounds a number toward zero, returning the closest Integral that’s between the function’s argument and zero. Added as part of the backport of PEP 3141’s type hierarchy for numbers.

The math module has been improved to give more consistent behaviour across platforms, especially with respect to handling of floating-point exceptions and IEEE 754 special values. Whenever possible, the module follows the recommendations of the C99 standard about 754’s special values. For example, sqrt(-1.) should now give a ValueError across almost all platforms, while sqrt(float('NaN')) should return a NaN on all IEEE 754 platforms. Where Annex ‘F’ of the C99 standard recommends signaling ‘divide-by-zero’ or ‘invalid’, Python will raise ValueError . Where Annex ‘F’ of the C99 standard recommends signaling ‘overflow’, Python will raise OverflowError . (See bpo-711019 and bpo-1640.) (Contributed by Christian Heimes and Mark Dickinson.)

mmap objects now have a rfind() method that searches for a substring beginning at the end of the string and searching backwards. The find() method also gained an end parameter giving an index at which to stop searching. (Contributed by John Lenton.)

The operator module gained a methodcaller() function that takes a name and an optional set of arguments, returning a callable that will call the named function on any arguments passed to it. For example: >>> # Equivalent to lambda s: s.replace('old', 'new') >>> replacer = operator . methodcaller ( 'replace' , 'old' , 'new' ) >>> replacer ( 'old wine in old bottles' ) 'new wine in new bottles' (Contributed by Georg Brandl, after a suggestion by Gregory Petrosyan.) The attrgetter() function now accepts dotted names and performs the corresponding attribute lookups: >>> inst_name = operator . attrgetter ( ... '__class__.__name__' ) >>> inst_name ( '' ) 'str' >>> inst_name ( help ) '_Helper' (Contributed by Georg Brandl, after a suggestion by Barry Warsaw.)

The os module now wraps several new system calls. fchmod(fd, mode) and fchown(fd, uid, gid) change the mode and ownership of an opened file, and lchmod(path, mode) changes the mode of a symlink. (Contributed by Georg Brandl and Christian Heimes.) chflags() and lchflags() are wrappers for the corresponding system calls (where they’re available), changing the flags set on a file. Constants for the flag values are defined in the stat module; some possible values include UF_IMMUTABLE to signal the file may not be changed and UF_APPEND to indicate that data can only be appended to the file. (Contributed by M. Levinson.) os.closerange(low, high) efficiently closes all file descriptors from low to high, ignoring any errors and not including high itself. This function is now used by the subprocess module to make starting processes faster. (Contributed by Georg Brandl; bpo-1663329.)

The os.environ object’s clear() method will now unset the environment variables using os.unsetenv() in addition to clearing the object’s keys. (Contributed by Martin Horcicka; bpo-1181.)

The os.walk() function now has a followlinks parameter. If set to True, it will follow symlinks pointing to directories and visit the directory’s contents. For backward compatibility, the parameter’s default value is false. Note that the function can fall into an infinite recursion if there’s a symlink that points to a parent directory. (bpo-1273829)

In the os.path module, the splitext() function has been changed to not split on leading period characters. This produces better results when operating on Unix’s dot-files. For example, os.path.splitext('.ipython') now returns ('.ipython', '') instead of ('', '.ipython') . (bpo-1115886) A new function, os.path.relpath(path, start='.') , returns a relative path from the start path, if it’s supplied, or from the current working directory to the destination path . (Contributed by Richard Barran; bpo-1339796.) On Windows, os.path.expandvars() will now expand environment variables given in the form “%var%”, and “~user” will be expanded into the user’s home directory path. (Contributed by Josiah Carlson; bpo-957650.)

The Python debugger provided by the pdb module gained a new command: “run” restarts the Python program being debugged and can optionally take new command-line arguments for the program. (Contributed by Rocky Bernstein; bpo-1393667.)

The pdb.post_mortem() function, used to begin debugging a traceback, will now use the traceback returned by sys.exc_info() if no traceback is supplied. (Contributed by Facundo Batista; bpo-1106316.)

The pickletools module now has an optimize() function that takes a string containing a pickle and removes some unused opcodes, returning a shorter pickle that contains the same data structure. (Contributed by Raymond Hettinger.)

A get_data() function was added to the pkgutil module that returns the contents of resource files included with an installed Python package. For example: >>> import pkgutil >>> print pkgutil . get_data ( 'test' , 'exception_hierarchy.txt' ) BaseException +-- SystemExit +-- KeyboardInterrupt +-- GeneratorExit +-- Exception +-- StopIteration +-- StandardError ... (Contributed by Paul Moore; bpo-2439.)

The pyexpat module’s Parser objects now allow setting their buffer_size attribute to change the size of the buffer used to hold character data. (Contributed by Achim Gaedke; bpo-1137.)

The Queue module now provides queue variants that retrieve entries in different orders. The PriorityQueue class stores queued items in a heap and retrieves them in priority order, and LifoQueue retrieves the most recently added entries first, meaning that it behaves like a stack. (Contributed by Raymond Hettinger.)

The random module’s Random objects can now be pickled on a 32-bit system and unpickled on a 64-bit system, and vice versa. Unfortunately, this change also means that Python 2.6’s Random objects can’t be unpickled correctly on earlier versions of Python. (Contributed by Shawn Ligocki; bpo-1727780.) The new triangular(low, high, mode) function returns random numbers following a triangular distribution. The returned values are between low and high, not including high itself, and with mode as the most frequently occurring value in the distribution. (Contributed by Wladmir van der Laan and Raymond Hettinger; bpo-1681432.)

Long regular expression searches carried out by the re module will check for signals being delivered, so time-consuming searches can now be interrupted. (Contributed by Josh Hoyt and Ralf Schmitt; bpo-846388.) The regular expression module is implemented by compiling bytecodes for a tiny regex-specific virtual machine. Untrusted code could create malicious strings of bytecode directly and cause crashes, so Python 2.6 includes a verifier for the regex bytecode. (Contributed by Guido van Rossum from work for Google App Engine; bpo-3487.)

The rlcompleter module’s Completer.complete() method will now ignore exceptions triggered while evaluating a name. (Fixed by Lorenz Quack; bpo-2250.)

The sched module’s scheduler instances now have a read-only queue attribute that returns the contents of the scheduler’s queue, represented as a list of named tuples with the fields (time, priority, action, argument) . (Contributed by Raymond Hettinger; bpo-1861.)

The select module now has wrapper functions for the Linux epoll() and BSD kqueue() system calls. modify() method was added to the existing poll objects; pollobj.modify(fd, eventmask) takes a file descriptor or file object and an event mask, modifying the recorded event mask for that file. (Contributed by Christian Heimes; bpo-1657.)

The shutil.copytree() function now has an optional ignore argument that takes a callable object. This callable will receive each directory path and a list of the directory’s contents, and returns a list of names that will be ignored, not copied. The shutil module also provides an ignore_patterns() function for use with this new parameter. ignore_patterns() takes an arbitrary number of glob-style patterns and returns a callable that will ignore any files and directories that match any of these patterns. The following example copies a directory tree, but skips both .svn directories and Emacs backup files, which have names ending with ‘~’: shutil . copytree ( 'Doc/library' , '/tmp/library' , ignore = shutil . ignore_patterns ( '*~' , '.svn' )) (Contributed by Tarek Ziadé; bpo-2663.)

Integrating signal handling with GUI handling event loops like those used by Tkinter or GTk+ has long been a problem; most software ends up polling, waking up every fraction of a second to check if any GUI events have occurred. The signal module can now make this more efficient. Calling signal.set_wakeup_fd(fd) sets a file descriptor to be used; when a signal is received, a byte is written to that file descriptor. There’s also a C-level function, PySignal_SetWakeupFd() , for setting the descriptor. Event loops will use this by opening a pipe to create two descriptors, one for reading and one for writing. The writable descriptor will be passed to set_wakeup_fd() , and the readable descriptor will be added to the list of descriptors monitored by the event loop via select() or poll() . On receiving a signal, a byte will be written and the main event loop will be woken up, avoiding the need to poll. (Contributed by Adam Olsen; bpo-1583.) The siginterrupt() function is now available from Python code, and allows changing whether signals can interrupt system calls or not. (Contributed by Ralf Schmitt.) The setitimer() and getitimer() functions have also been added (where they’re available). setitimer() allows setting interval timers that will cause a signal to be delivered to the process after a specified time, measured in wall-clock time, consumed process time, or combined process+system time. (Contributed by Guilherme Polo; bpo-2240.)

The smtplib module now supports SMTP over SSL thanks to the addition of the SMTP_SSL class. This class supports an interface identical to the existing SMTP class. (Contributed by Monty Taylor.) Both class constructors also have an optional timeout parameter that specifies a timeout for the initial connection attempt, measured in seconds. (Contributed by Facundo Batista.) An implementation of the LMTP protocol ( RFC 2033 ) was also added to the module. LMTP is used in place of SMTP when transferring e-mail between agents that don’t manage a mail queue. (LMTP implemented by Leif Hedstrom; bpo-957003.) SMTP.starttls() now complies with RFC 3207 and forgets any knowledge obtained from the server not obtained from the TLS negotiation itself. (Patch contributed by Bill Fenner; bpo-829951.)

The socket module now supports TIPC (http://tipc.sourceforge.net/), a high-performance non-IP-based protocol designed for use in clustered environments. TIPC addresses are 4- or 5-tuples. (Contributed by Alberto Bertogli; bpo-1646.) A new function, create_connection() , takes an address and connects to it using an optional timeout value, returning the connected socket object. This function also looks up the address’s type and connects to it using IPv4 or IPv6 as appropriate. Changing your code to use create_connection() instead of socket(socket.AF_INET, ...) may be all that’s required to make your code work with IPv6.

The base classes in the SocketServer module now support calling a handle_timeout() method after a span of inactivity specified by the server’s timeout attribute. (Contributed by Michael Pomraning.) The serve_forever() method now takes an optional poll interval measured in seconds, controlling how often the server will check for a shutdown request. (Contributed by Pedro Werneck and Jeffrey Yasskin; bpo-742598, bpo-1193577.)

The sqlite3 module, maintained by Gerhard Häring, has been updated from version 2.3.2 in Python 2.5 to version 2.4.1.

The struct module now supports the C99 _Bool type, using the format character '?' . (Contributed by David Remahl.)

The Popen objects provided by the subprocess module now have terminate() , kill() , and send_signal() methods. On Windows, send_signal() only supports the SIGTERM signal, and all these methods are aliases for the Win32 API function TerminateProcess() . (Contributed by Christian Heimes.)

A new variable in the sys module, float_info , is an object containing information derived from the float.h file about the platform’s floating-point support. Attributes of this object include mant_dig (number of digits in the mantissa), epsilon (smallest difference between 1.0 and the next largest value representable), and several others. (Contributed by Christian Heimes; bpo-1534.) Another new variable, dont_write_bytecode , controls whether Python writes any .pyc or .pyo files on importing a module. If this variable is true, the compiled files are not written. The variable is initially set on start-up by supplying the -B switch to the Python interpreter, or by setting the PYTHONDONTWRITEBYTECODE environment variable before running the interpreter. Python code can subsequently change the value of this variable to control whether bytecode files are written or not. (Contributed by Neal Norwitz and Georg Brandl.) Information about the command-line arguments supplied to the Python interpreter is available by reading attributes of a named tuple available as sys.flags . For example, the verbose attribute is true if Python was executed in verbose mode, debug is true in debugging mode, etc. These attributes are all read-only. (Contributed by Christian Heimes.) A new function, getsizeof() , takes a Python object and returns the amount of memory used by the object, measured in bytes. Built-in objects return correct results; third-party extensions may not, but can define a __sizeof__() method to return the object’s size. (Contributed by Robert Schuppenies; bpo-2898.) It’s now possible to determine the current profiler and tracer functions by calling sys.getprofile() and sys.gettrace() . (Contributed by Georg Brandl; bpo-1648.)

The tarfile module now supports POSIX.1-2001 (pax) tarfiles in addition to the POSIX.1-1988 (ustar) and GNU tar formats that were already supported. The default format is GNU tar; specify the format parameter to open a file using a different format: tar = tarfile . open ( "output.tar" , "w" , format = tarfile . PAX_FORMAT ) The new encoding and errors parameters specify an encoding and an error handling scheme for character conversions. 'strict' , 'ignore' , and 'replace' are the three standard ways Python can handle errors,; 'utf-8' is a special value that replaces bad characters with their UTF-8 representation. (Character conversions occur because the PAX format supports Unicode filenames, defaulting to UTF-8 encoding.) The TarFile.add() method now accepts an exclude argument that’s a function that can be used to exclude certain filenames from an archive. The function must take a filename and return true if the file should be excluded or false if it should be archived. The function is applied to both the name initially passed to add() and to the names of files in recursively-added directories. (All changes contributed by Lars Gustäbel).

An optional timeout parameter was added to the telnetlib.Telnet class constructor, specifying a timeout measured in seconds. (Added by Facundo Batista.)

The tempfile.NamedTemporaryFile class usually deletes the temporary file it created when the file is closed. This behaviour can now be changed by passing delete=False to the constructor. (Contributed by Damien Miller; bpo-1537850.) A new class, SpooledTemporaryFile , behaves like a temporary file but stores its data in memory until a maximum size is exceeded. On reaching that limit, the contents will be written to an on-disk temporary file. (Contributed by Dustin J. Mitchell.) The NamedTemporaryFile and SpooledTemporaryFile classes both work as context managers, so you can write with tempfile.NamedTemporaryFile() as tmp: ... . (Contributed by Alexander Belopolsky; bpo-2021.)

The test.test_support module gained a number of context managers useful for writing tests. EnvironmentVarGuard() is a context manager that temporarily changes environment variables and automatically restores them to their old values. Another context manager, TransientResource , can surround calls to resources that may or may not be available; it will catch and ignore a specified list of exceptions. For example, a network test may ignore certain failures when connecting to an external web site: with test_support . TransientResource ( IOError , errno = errno . ETIMEDOUT ): f = urllib . urlopen ( 'https://sf.net' ) ... Finally, check_warnings() resets the warning module’s warning filters and returns an object that will record all warning messages triggered (bpo-3781): with test_support . check_warnings () as wrec : warnings . simplefilter ( "always" ) # ... code that triggers a warning ... assert str ( wrec . message ) == "function is outdated" assert len ( wrec . warnings ) == 1 , "Multiple warnings raised" (Contributed by Brett Cannon.)

The textwrap module can now preserve existing whitespace at the beginnings and ends of the newly-created lines by specifying drop_whitespace=False as an argument: >>> S = """This sentence has a bunch of ... extra whitespace.""" >>> print textwrap . fill ( S , width = 15 ) This sentence has a bunch of extra whitespace. >>> print textwrap . fill ( S , drop_whitespace = False , width = 15 ) This sentence has a bunch of extra whitespace. >>> (Contributed by Dwayne Bailey; bpo-1581073.)

The threading module API is being changed to use properties such as daemon instead of setDaemon() and isDaemon() methods, and some methods have been renamed to use underscores instead of camel-case; for example, the activeCount() method is renamed to active_count() . Both the 2.6 and 3.0 versions of the module support the same properties and renamed methods, but don’t remove the old methods. No date has been set for the deprecation of the old APIs in Python 3.x; the old APIs won’t be removed in any 2.x version. (Carried out by several people, most notably Benjamin Peterson.) The threading module’s Thread objects gained an ident property that returns the thread’s identifier, a nonzero integer. (Contributed by Gregory P. Smith; bpo-2871.)

The timeit module now accepts callables as well as strings for the statement being timed and for the setup code. Two convenience functions were added for creating Timer instances: repeat(stmt, setup, time, repeat, number) and timeit(stmt, setup, time, number) create an instance and call the corresponding method. (Contributed by Erik Demaine; bpo-1533909.)

The Tkinter module now accepts lists and tuples for options, separating the elements by spaces before passing the resulting value to Tcl/Tk. (Contributed by Guilherme Polo; bpo-2906.)

The turtle module for turtle graphics was greatly enhanced by Gregor Lingl. New features in the module include: Better animation of turtle movement and rotation. Control over turtle movement using the new delay() , tracer() , and speed() methods. The ability to set new shapes for the turtle, and to define a new coordinate system. Turtles now have an undo() method that can roll back actions. Simple support for reacting to input events such as mouse and keyboard activity, making it possible to write simple games. A turtle.cfg file can be used to customize the starting appearance of the turtle’s screen. The module’s docstrings can be replaced by new docstrings that have been translated into another language. (bpo-1513695)

An optional timeout parameter was added to the urllib.urlopen() function and the urllib.ftpwrapper class constructor, as well as the urllib2.urlopen() function. The parameter specifies a timeout measured in seconds. For example: >>> u = urllib2 . urlopen ( "http://slow.example.com" , timeout=3) Traceback (most recent call last): ... urllib2.URLError : <urlopen error timed out> >>> (Added by Facundo Batista.)

The Unicode database provided by the unicodedata module has been updated to version 5.1.0. (Updated by Martin von Löwis; bpo-3811.)

The warnings module’s formatwarning() and showwarning() gained an optional line argument that can be used to supply the line of source code. (Added as part of bpo-1631171, which re-implemented part of the warnings module in C code.) A new function, catch_warnings() , is a context manager intended for testing purposes that lets you temporarily modify the warning filters and then restore their original values (bpo-3781).

The XML-RPC SimpleXMLRPCServer and DocXMLRPCServer classes can now be prevented from immediately opening and binding to their socket by passing False as the bind_and_activate constructor parameter. This can be used to modify the instance’s allow_reuse_address attribute before calling the server_bind() and server_activate() methods to open the socket and begin listening for connections. (Contributed by Peter Parente; bpo-1599845.) SimpleXMLRPCServer also has a _send_traceback_header attribute; if true, the exception and formatted traceback are returned as HTTP headers “X-Exception” and “X-Traceback”. This feature is for debugging purposes only and should not be used on production servers because the tracebacks might reveal passwords or other sensitive information. (Contributed by Alan McIntyre as part of his project for Google’s Summer of Code 2007.)

The xmlrpclib module no longer automatically converts datetime.date and datetime.time to the xmlrpclib.DateTime type; the conversion semantics were not necessarily correct for all applications. Code using xmlrpclib should convert date and time instances. (bpo-1330538) The code can also handle dates before 1900 (contributed by Ralf Schmitt; bpo-2014) and 64-bit integers represented by using <i8> in XML-RPC responses (contributed by Riku Lindblad; bpo-2985).

The zipfile module’s ZipFile class now has extract() and extractall() methods that will unpack a single file or all the files in the archive to the current directory, or to a specified directory: z = zipfile . ZipFile ( 'python-251.zip' ) # Unpack a single file, writing it relative # to the /tmp directory. z . extract ( 'Python/sysmodule.c' , '/tmp' ) # Unpack all the files in the archive. z . extractall () (Contributed by Alan McIntyre; bpo-467924.) The open() , read() and extract() methods can now take either a filename or a ZipInfo object. This is useful when an archive accidentally contains a duplicated filename. (Contributed by Graham Horler; bpo-1775025.) Finally, zipfile now supports using Unicode filenames for archived files. (Contributed by Alexey Borzenkov; bpo-1734346.) The ast module¶ The ast module provides an Abstract Syntax Tree representation of Python code, and Armin Ronacher contributed a set of helper functions that perform a variety of common tasks. These will be useful for HTML templating packages, code analyzers, and similar tools that process Python code. The parse() function takes an expression and returns an AST. The dump() function outputs a representation of a tree, suitable for debugging: import ast t = ast . parse ( """ d = {} for i in 'abcdefghijklm': d[i + i] = ord(i) - ord('a') + 1 print d """ ) print ast . dump ( t ) This outputs a deeply nested tree: Module ( body = [ Assign ( targets = [ Name ( id = 'd' , ctx = Store ()) ], value = Dict ( keys = [], values = [])) For ( target = Name ( id = 'i' , ctx = Store ()), iter = Str ( s = 'abcdefghijklm' ), body = [ Assign ( targets = [ Subscript ( value = Name ( id = 'd' , ctx = Load ()), slice = Index ( value = BinOp ( left = Name ( id = 'i' , ctx = Load ()), op = Add (), right = Name ( id = 'i' , ctx = Load ()))), ctx = Store ()) ], value = BinOp ( left = BinOp ( left = Call ( func = Name ( id = 'ord' , ctx = Load ()), args = [ Name ( id = 'i' , ctx = Load ()) ], keywords = [], starargs = None , kwargs = None ), op = Sub (), right = Call ( func = Name ( id = 'ord' , ctx = Load ()), args = [ Str ( s = 'a' ) ], keywords = [], starargs = None , kwargs = None )), op = Add (), right = Num ( n = 1 ))) ], orelse = []) Print ( dest = None , values = [ Name ( id = 'd' , ctx = Load ()) ], nl = True ) ]) The literal_eval() method takes a string or an AST representing a literal expression, parses and evaluates it, and returns the resulting value. A literal expression is a Python expression containing only strings, numbers, dictionaries, etc. but no statements or function calls. If you need to evaluate an expression but cannot accept the security risk of using an eval() call, literal_eval() will handle it safely: >>> literal = '("a", "b", {2:4, 3:8, 1:2})' >>> print ast . literal_eval ( literal ) ('a', 'b', {1: 2, 2: 4, 3: 8}) >>> print ast . literal_eval ( '"a" + "b"' ) Traceback (most recent call last): ... ValueError : malformed string The module also includes NodeVisitor and NodeTransformer classes for traversing and modifying an AST, and functions for common transformations such as changing line numbers. The future_builtins module¶ Python 3.0 makes many changes to the repertoire of built-in functions, and most of the changes can’t be introduced in the Python 2.x series because they would break compatibility. The future_builtins module provides versions of these built-in functions that can be imported when writing 3.0-compatible code. The functions in this module currently include: ascii(obj) : equivalent to repr() . In Python 3.0, repr() will return a Unicode string, while ascii() will return a pure ASCII bytestring.

filter(predicate, iterable) , map(func, iterable1, ...) : the 3.0 versions return iterators, unlike the 2.x builtins which return lists.

hex(value) , oct(value) : instead of calling the __hex__() or __oct__() methods, these versions will call the __index__() method and convert the result to hexadecimal or octal. oct() will use the new 0o notation for its result. The json module: JavaScript Object Notation¶ The new json module supports the encoding and decoding of Python types in JSON (Javascript Object Notation). JSON is a lightweight interchange format often used in web applications. For more information about JSON, see http://www.json.org. json comes with support for decoding and encoding most built-in Python types. The following example encodes and decodes a dictionary: >>> import json >>> data = { "spam" : "foo" , "parrot" : 42 } >>> in_json = json . dumps ( data ) # Encode the data >>> in_json '{"parrot": 42, "spam": "foo"}' >>> json . loads ( in_json ) # Decode into a Python object {"spam": "foo", "parrot": 42} It’s also possible to write your own decoders and encoders to support more types. Pretty-printing of the JSON strings is also supported. json (originally called simplejson) was written by Bob Ippolito. The plistlib module: A Property-List Parser¶ The .plist format is commonly used on Mac OS X to store basic data types (numbers, strings, lists, and dictionaries) by serializing them into an XML-based format. It resembles the XML-RPC serialization of data types. Despite being primarily used on Mac OS X, the format has nothing Mac-specific about it and the Python implementation works on any platform that Python supports, so the plistlib module has been promoted to the standard library. Using the module is simple: import sys import plistlib import datetime # Create data structure data_struct = dict ( lastAccessed = datetime . datetime . now (), version = 1 , categories = ( 'Personal' , 'Shared' , 'Private' )) # Create string containing XML. plist_str = plistlib . writePlistToString ( data_struct ) new_struct = plistlib . readPlistFromString ( plist_str ) print data_struct print new_struct # Write data structure to a file and read it back. plistlib . writePlist ( data_struct , '/tmp/customizations.plist' ) new_struct = plistlib . readPlist ( '/tmp/customizations.plist' ) # read/writePlist accepts file-like objects as well as paths. plistlib . writePlist ( data_struct , sys . stdout ) ctypes Enhancements¶ Thomas Heller continued to maintain and enhance the ctypes module. ctypes now supports a c_bool datatype that represents the C99 bool type. (Contributed by David Remahl; bpo-1649190.) The ctypes string, buffer and array types have improved support for extended slicing syntax, where various combinations of (start, stop, step) are supplied. (Implemented by Thomas Wouters.) All ctypes data types now support from_buffer() and from_buffer_copy() methods that create a ctypes instance based on a provided buffer object. from_buffer_copy() copies the contents of the object, while from_buffer() will share the same memory area. A new calling convention tells ctypes to clear the errno or Win32 LastError variables at the outset of each wrapped call. (Implemented by Thomas Heller; bpo-1798.) You can now retrieve the Unix errno variable after a function call. When creating a wrapped function, you can supply use_errno=True as a keyword parameter to the DLL() function and then call the module-level methods set_errno() and get_errno() to set and retrieve the error value. The Win32 LastError variable is similarly supported by the DLL() , OleDLL() , and WinDLL() functions. You supply use_last_error=True as a keyword parameter and then call the module-level methods set_last_error() and get_last_error() . The byref() function, used to retrieve a pointer to a ctypes instance, now has an optional offset parameter that is a byte count that will be added to the returned pointer. Improved SSL Support¶ Bill Janssen made extensive improvements to Python 2.6’s support for the Secure Sockets Layer by adding a new module, ssl , that’s built atop the OpenSSL library. This new module provides more control over the protocol negotiated, the X.509 certificates used, and has better support for writing SSL servers (as opposed to clients) in Python. The existing SSL support in the socket module hasn’t been removed and continues to work, though it will be removed in Python 3.0. To use the new module, you must first create a TCP connection in the usual way and then pass it to the ssl.wrap_socket() function. It’s possible to specify whether a certificate is required, and to obtain certificate info by calling the getpeercert() method. See also The documentation for the ssl module.

Deprecations and Removals¶ String exceptions have been removed. Attempting to use them raises a TypeError .

Changes to the Exception interface as dictated by PEP 352 continue to be made. For 2.6, the message attribute is being deprecated in favor of the args attribute.

(3.0-warning mode) Python 3.0 will feature a reorganized standard library that will drop many outdated modules and rename others. Python 2.6 running in 3.0-warning mode will warn about these modules when they are imported. The list of deprecated modules is: audiodev , bgenlocations , buildtools , bundlebuilder , Canvas , compiler , dircache , dl , fpformat , gensuitemodule , ihooks , imageop , imgfile , linuxaudiodev , mhlib , mimetools , multifile , new , pure , st