:mod:`functools` --- Higher order functions and operations on callable objects ============================================================================== .. module:: functools :synopsis: Higher order functions and operations on callable objects. .. moduleauthor:: Peter Harris .. moduleauthor:: Raymond Hettinger .. moduleauthor:: Nick Coghlan .. sectionauthor:: Peter Harris 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:: 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 `_ 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__*, *__annotations__* 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.