9.8. "functools" — Higher-order functions and operations on callable objects
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2.5 新版功能.

**Source code:** Lib/functools.py

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The "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 "functools" module defines the following functions:

functools.cmp_to_key(func)

   Transform an old-style comparison function to a *key function*.
   Used with tools that accept key functions (such as "sorted()",
   "min()", "max()", "heapq.nlargest()", "heapq.nsmallest()",
   "itertools.groupby()").  This function is primarily used as a
   transition tool for programs being converted to Python 3 where
   comparison functions are no longer supported.

   A comparison 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 to be used as the sort key.

   示例:

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

   有关排序示例和简要排序教程，请参阅 排序指南 。

   2.7 新版功能.

functools.total_ordering(cls)

   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 "__lt__()", "__le__()", "__gt__()", or
   "__ge__()". In addition, the class should supply an "__eq__()"
   method.

   例如:

      @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()))

   2.7 新版功能.

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

   This is the same function as "reduce()".  It is made available in
   this module to allow writing code more forward-compatible with
   Python 3.

   2.6 新版功能.

functools.partial(func[,*args][, **keywords])

   Return a new 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 "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, "partial()" can be used to create a callable that behaves
   like the "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

functools.update_wrapper(wrapper, wrapped[, assigned][, updated])

   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 *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.

functools.wraps(wrapped[, assigned][, updated])

   This is a convenience function for invoking "update_wrapper()" as a
   function decorator when defining a wrapper function.  It is
   equivalent to "partial(update_wrapper, wrapped=wrapped,
   assigned=assigned, updated=updated)". 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 "example()" would have been lost.


9.8.1. "partial" Objects
========================

"partial" objects are callable objects created by "partial()". They
have three read-only attributes:

partial.func

   A callable object or function.  Calls to the "partial" object will
   be forwarded to "func" with new arguments and keywords.

partial.args

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

partial.keywords

   The keyword arguments that will be supplied when the "partial"
   object is called.

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