Doc/library/functools.rst

:mod:`functools` --- Higher order functions and operations on callable objects
==============================================================================

.. module:: functools
   :synopsis: Higher order functions and operations on callable objects.
.. moduleauthor:: Peter Harris <scav@blueyonder.co.uk>
.. moduleauthor:: Raymond Hettinger <python@rcn.com>
.. moduleauthor:: Nick Coghlan <ncoghlan@gmail.com>
.. sectionauthor:: Peter Harris <scav@blueyonder.co.uk>


The :mod:`functools` module is for higher-order functions: functions that act on
or return other functions. In general, any callable object can be treated as a
function for the purposes of this module.

The :mod:`functools` module defines the following functions:

..  function:: cmp_to_key(func)

    Transform an old-style comparison function to a key-function.  Used with
    tools that accept key functions (such as :func:`sorted`, :func:`min`,
    :func:`max`, :func:`heapq.nlargest`, :func:`heapq.nsmallest`,
    :func:`itertools.groupby`).
    This function is primarily used as a transition tool for programs
    being converted from Py2.x which supported the use of comparison
    functions.

    A compare function is any callable that accept two arguments, compares
    them, and returns a negative number for less-than, zero for equality,
    or a positive number for greater-than.  A key function is a callable
    that accepts one argument and returns another value that indicates
    the position in the desired collation sequence.

    Example::

        sorted(iterable, key=cmp_to_key(locale.strcoll))  # locale-aware sort order

   .. versionadded:: 3.2

.. decorator:: lfu_cache(maxsize)

   Decorator to wrap a function with a memoizing callable that saves up to the
   *maxsize* most frequent calls.  It can save time when an expensive or I/O
   bound function is periodically called with the same arguments.

   The *maxsize* parameter defaults to 100.  Since a dictionary is used to cache
   results, the positional and keyword arguments to the function must be
   hashable.

   The wrapped function is instrumented with two attributes, :attr:`hits`
   and :attr:`misses` which count the number of successful or unsuccessful
   cache lookups.  These statistics are helpful for tuning the *maxsize*
   parameter and for measuring the cache's effectiveness.

   The wrapped function also has a :attr:`clear` attribute which can be
   called (with no arguments) to clear the cache.

   A `LFU (least frequently used) cache
   <http://en.wikipedia.org/wiki/Cache_algorithms#Least-Frequently_Used>`_
   is indicated when the pattern of calls does not change over time, when
   more the most common calls already seen are the best predictors of the
   most common upcoming calls (for example, a phonelist of popular
   help-lines may have access patterns that persist over time).

   .. versionadded:: 3.2

.. decorator:: lru_cache(maxsize)

   Decorator to wrap a function with a memoizing callable that saves up to the
   *maxsize* most recent calls.  It can save time when an expensive or I/O bound
   function is periodically called with the same arguments.

   The *maxsize* parameter defaults to 100.  Since a dictionary is used to cache
   results, the positional and keyword arguments to the function must be
   hashable.

   The wrapped function is instrumented with two attributes, :attr:`hits`
   and :attr:`misses` which count the number of successful or unsuccessful
   cache lookups.  These statistics are helpful for tuning the *maxsize*
   parameter and for measuring the cache's effectiveness.

   The wrapped function also has a :attr:`clear` attribute which can be
   called (with no arguments) to clear the cache.

   A `LRU (least recently used) cache
   <http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_
   is indicated when the pattern of calls changes over time, such as
   when more recent calls are the best predictors of upcoming calls
   (for example, the most popular articles on a news server tend to
   change every day).

   .. versionadded:: 3.2

.. decorator:: total_ordering

   Given a class defining one or more rich comparison ordering methods, this
   class decorator supplies the rest.  This simplifies the effort involved
   in specifying all of the possible rich comparison operations:

   The class must define one of :meth:`__lt__`, :meth:`__le__`,
   :meth:`__gt__`, or :meth:`__ge__`.
   In addition, the class should supply an :meth:`__eq__` method.

   For example::

       @total_ordering
       class Student:
           def __eq__(self, other):
               return ((self.lastname.lower(), self.firstname.lower()) ==
                       (other.lastname.lower(), other.firstname.lower()))
           def __lt__(self, other):
               return ((self.lastname.lower(), self.firstname.lower()) <
                       (other.lastname.lower(), other.firstname.lower()))

   .. versionadded:: 3.2

.. function:: partial(func, *args, **keywords)

   Return a new :class:`partial` object which when called will behave like *func*
   called with the positional arguments *args* and keyword arguments *keywords*. If
   more arguments are supplied to the call, they are appended to *args*. If
   additional keyword arguments are supplied, they extend and override *keywords*.
   Roughly equivalent to::

      def partial(func, *args, **keywords):
          def newfunc(*fargs, **fkeywords):
              newkeywords = keywords.copy()
              newkeywords.update(fkeywords)
              return func(*(args + fargs), **newkeywords)
          newfunc.func = func
          newfunc.args = args
          newfunc.keywords = keywords
          return newfunc

   The :func:`partial` is used for partial function application which "freezes"
   some portion of a function's arguments and/or keywords resulting in a new object
   with a simplified signature.  For example, :func:`partial` can be used to create
   a callable that behaves like the :func:`int` function where the *base* argument
   defaults to two:

      >>> from functools import partial
      >>> basetwo = partial(int, base=2)
      >>> basetwo.__doc__ = 'Convert base 2 string to an int.'
      >>> basetwo('10010')
      18


.. function:: reduce(function, iterable[, initializer])

   Apply *function* of two arguments cumulatively to the items of *sequence*, from
   left to right, so as to reduce the sequence to a single value.  For example,
   ``reduce(lambda x, y: x+y, [1, 2, 3, 4, 5])`` calculates ``((((1+2)+3)+4)+5)``.
   The left argument, *x*, is the accumulated value and the right argument, *y*, is
   the update value from the *sequence*.  If the optional *initializer* is present,
   it is placed before the items of the sequence in the calculation, and serves as
   a default when the sequence is empty.  If *initializer* is not given and
   *sequence* contains only one item, the first item is returned.


.. function:: update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

   Update a *wrapper* function to look like the *wrapped* function. The optional
   arguments are tuples to specify which attributes of the original function are
   assigned directly to the matching attributes on the wrapper function and which
   attributes of the wrapper function are updated with the corresponding attributes
   from the original function. The default values for these arguments are the
   module level constants *WRAPPER_ASSIGNMENTS* (which assigns to the wrapper
   function's *__name__*, *__module__* and *__doc__*, the documentation string) and
   *WRAPPER_UPDATES* (which updates the wrapper function's *__dict__*, i.e. the
   instance dictionary).

   The main intended use for this function is in :term:`decorator` functions which
   wrap the decorated function and return the wrapper. If the wrapper function is
   not updated, the metadata of the returned function will reflect the wrapper
   definition rather than the original function definition, which is typically less
   than helpful.


.. decorator:: wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

   This is a convenience function for invoking ``partial(update_wrapper,
   wrapped=wrapped, assigned=assigned, updated=updated)`` as a function decorator
   when defining a wrapper function. For example:

      >>> from functools import wraps
      >>> def my_decorator(f):
      ...     @wraps(f)
      ...     def wrapper(*args, **kwds):
      ...         print('Calling decorated function')
      ...         return f(*args, **kwds)
      ...     return wrapper
      ...
      >>> @my_decorator
      ... def example():
      ...     """Docstring"""
      ...     print('Called example function')
      ...
      >>> example()
      Calling decorated function
      Called example function
      >>> example.__name__
      'example'
      >>> example.__doc__
      'Docstring'

   Without the use of this decorator factory, the name of the example function
   would have been ``'wrapper'``, and the docstring of the original :func:`example`
   would have been lost.


.. _partial-objects:

:class:`partial` Objects
------------------------

:class:`partial` objects are callable objects created by :func:`partial`. They
have three read-only attributes:


.. attribute:: partial.func

   A callable object or function.  Calls to the :class:`partial` object will be
   forwarded to :attr:`func` with new arguments and keywords.


.. attribute:: partial.args

   The leftmost positional arguments that will be prepended to the positional
   arguments provided to a :class:`partial` object call.


.. attribute:: partial.keywords

   The keyword arguments that will be supplied when the :class:`partial` object is
   called.

:class:`partial` objects are like :class:`function` objects in that they are
callable, weak referencable, and can have attributes.  There are some important
differences.  For instance, the :attr:`__name__` and :attr:`__doc__` attributes
are not created automatically.  Also, :class:`partial` objects defined in
classes behave like static methods and do not transform into bound methods
during instance attribute look-up.