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 https://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.

https://bugs.jython.org:

The Jython bug tracker.

https://roundup.sourceforge.io/

Roundup downloads and documentation.

https://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 https://www.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 __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 module.__package__ attribute. When this attribute is present, relative imports will be relative to the value of this attribute instead of the __name__ attribute.

PEP 302-style importers can then set __package__ as necessary. The runpy module that implements the -m switch now does this, so relative imports will now work correctly in scripts running from inside a package.

PEP 370: Per-user site-packages Directory

When you run Python, the module search path sys.path usually includes a directory whose path ends in "site-packages". This directory is intended to hold locally installed packages available to all users using a machine or a particular site installation.

Python 2.6 introduces a convention for user-specific site directories. The directory varies depending on the platform:

• Unix and Mac OS X: ~/.local/

• Windows: %APPDATA%/Python

Within this directory, there will be version-specific subdirectories, such as lib/python2.6/site-packages on Unix/Mac OS and Python26/site-packages on Windows.

If you don’t like the default directory, it can be overridden by an environment variable. PYTHONUSERBASE sets the root directory used for all Python versions supporting this feature. On Windows, the directory for application-specific data can be changed by setting the APPDATA environment variable. You can also modify the site.py file for your Python installation.

The feature can be disabled entirely by running Python with the -s option or setting the PYTHONNOUSERSITE environment variable.

See also

PEP 370 - Per-user site-packages Directory

PEP written and implemented by Christian Heimes.

PEP 371: The multiprocessing Package

The new multiprocessing package lets Python programs create new processes that will perform a computation and return a result to the parent. The parent and child processes can communicate using queues and pipes, synchronize their operations using locks and semaphores, and can share simple arrays of data.

The multiprocessing module started out as an exact emulation of the threading module using processes instead of threads. That goal was discarded along the path to Python 2.6, but the general approach of the module is still similar. The fundamental class is the Process, which is passed a callable object and a collection of arguments. The start() method sets the callable running in a subprocess, after which you can call the is_alive() method to check whether the subprocess is still running and the join() method to wait for the process to exit.

Here’s a simple example where the subprocess will calculate a factorial. The function doing the calculation is written strangely so that it takes significantly longer when the input argument is a multiple of 4.

import time
from multiprocessing import Process, Queue


def factorial(queue, N):
    "Compute a factorial."
    # If N is a multiple of 4, this function will take much longer.
    if (N % 4) == 0:
        time.sleep(.05 * N/4)

    # Calculate the result
    fact = 1L
    for i in range(1, N+1):
        fact = fact * i

    # Put the result on the queue
    queue.put(fact)

if __name__ == '__main__':
    queue = Queue()

    N = 5

    p = Process(target=factorial, args=(queue, N))
    p.start()
    p.join()

    result = queue.get()
    print 'Factorial', N, '=', result

A Queue is used to communicate the result of the factorial. The Queue object is stored in a global variable. The child process will use the value of the variable when the child was created; because it’s a Queue, parent and child can use the object to communicate. (If the parent were to change the value of the global variable, the child’s value would be unaffected, and vice versa.)

Two other classes, Pool and Manager, provide higher-level interfaces. Pool