mirror of https://github.com/python/cpython
293 lines
12 KiB
ReStructuredText
293 lines
12 KiB
ReStructuredText
:mod:`functools` --- Higher-order functions and operations on callable objects
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==============================================================================
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.. module:: functools
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:synopsis: Higher-order functions and operations on callable objects.
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.. moduleauthor:: Peter Harris <scav@blueyonder.co.uk>
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.. moduleauthor:: Raymond Hettinger <python@rcn.com>
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.. moduleauthor:: Nick Coghlan <ncoghlan@gmail.com>
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.. sectionauthor:: Peter Harris <scav@blueyonder.co.uk>
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**Source code:** :source:`Lib/functools.py`
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--------------
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The :mod:`functools` module is for higher-order functions: functions that act on
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or return other functions. In general, any callable object can be treated as a
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function for the purposes of this module.
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The :mod:`functools` module defines the following functions:
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.. function:: cmp_to_key(func)
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Transform an old-style comparison function to a key function. Used with
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tools that accept key functions (such as :func:`sorted`, :func:`min`,
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:func:`max`, :func:`heapq.nlargest`, :func:`heapq.nsmallest`,
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:func:`itertools.groupby`). This function is primarily used as a transition
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tool for programs being converted from Python 2 which supported the use of
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comparison functions.
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A comparison function is any callable that accept two arguments, compares them,
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and returns a negative number for less-than, zero for equality, or a positive
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number for greater-than. A key function is a callable that accepts one
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argument and returns another value indicating the position in the desired
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collation sequence.
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Example::
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sorted(iterable, key=cmp_to_key(locale.strcoll)) # locale-aware sort order
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.. versionadded:: 3.2
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.. decorator:: lru_cache(maxsize=128, typed=False)
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Decorator to wrap a function with a memoizing callable that saves up to the
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*maxsize* most recent calls. It can save time when an expensive or I/O bound
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function is periodically called with the same arguments.
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Since a dictionary is used to cache results, the positional and keyword
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arguments to the function must be hashable.
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If *maxsize* is set to None, the LRU feature is disabled and the cache can
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grow without bound. The LRU feature performs best when *maxsize* is a
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power-of-two.
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If *typed* is set to True, function arguments of different types will be
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cached separately. For example, ``f(3)`` and ``f(3.0)`` will be treated
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as distinct calls with distinct results.
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To help measure the effectiveness of the cache and tune the *maxsize*
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parameter, the wrapped function is instrumented with a :func:`cache_info`
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function that returns a :term:`named tuple` showing *hits*, *misses*,
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*maxsize* and *currsize*. In a multi-threaded environment, the hits
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and misses are approximate.
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The decorator also provides a :func:`cache_clear` function for clearing or
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invalidating the cache.
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The original underlying function is accessible through the
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:attr:`__wrapped__` attribute. This is useful for introspection, for
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bypassing the cache, or for rewrapping the function with a different cache.
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An `LRU (least recently used) cache
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<http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used>`_ works
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best when the most recent calls are the best predictors of upcoming calls (for
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example, the most popular articles on a news server tend to change each day).
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The cache's size limit assures that the cache does not grow without bound on
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long-running processes such as web servers.
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Example of an LRU cache for static web content::
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@lru_cache(maxsize=20)
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def get_pep(num):
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'Retrieve text of a Python Enhancement Proposal'
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resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
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try:
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with urllib.request.urlopen(resource) as s:
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return s.read()
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except urllib.error.HTTPError:
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return 'Not Found'
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>>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
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... pep = get_pep(n)
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... print(n, len(pep))
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>>> print(get_pep.cache_info())
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CacheInfo(hits=3, misses=8, maxsize=20, currsize=8)
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Example of efficiently computing
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`Fibonacci numbers <http://en.wikipedia.org/wiki/Fibonacci_number>`_
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using a cache to implement a
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`dynamic programming <http://en.wikipedia.org/wiki/Dynamic_programming>`_
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technique::
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@lru_cache(maxsize=None)
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def fib(n):
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if n < 2:
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return n
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return fib(n-1) + fib(n-2)
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>>> print([fib(n) for n in range(16)])
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[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]
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>>> print(fib.cache_info())
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CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)
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.. versionadded:: 3.2
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.. versionchanged:: 3.3
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Added the *typed* option.
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.. decorator:: total_ordering
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Given a class defining one or more rich comparison ordering methods, this
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class decorator supplies the rest. This simplifies the effort involved
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in specifying all of the possible rich comparison operations:
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The class must define one of :meth:`__lt__`, :meth:`__le__`,
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:meth:`__gt__`, or :meth:`__ge__`.
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In addition, the class should supply an :meth:`__eq__` method.
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For example::
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@total_ordering
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class Student:
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def __eq__(self, other):
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return ((self.lastname.lower(), self.firstname.lower()) ==
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(other.lastname.lower(), other.firstname.lower()))
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def __lt__(self, other):
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return ((self.lastname.lower(), self.firstname.lower()) <
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(other.lastname.lower(), other.firstname.lower()))
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.. versionadded:: 3.2
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.. function:: partial(func, *args, **keywords)
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Return a new :class:`partial` object which when called will behave like *func*
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called with the positional arguments *args* and keyword arguments *keywords*. If
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more arguments are supplied to the call, they are appended to *args*. If
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additional keyword arguments are supplied, they extend and override *keywords*.
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Roughly equivalent to::
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def partial(func, *args, **keywords):
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def newfunc(*fargs, **fkeywords):
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newkeywords = keywords.copy()
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newkeywords.update(fkeywords)
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return func(*(args + fargs), **newkeywords)
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newfunc.func = func
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newfunc.args = args
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newfunc.keywords = keywords
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return newfunc
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The :func:`partial` is used for partial function application which "freezes"
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some portion of a function's arguments and/or keywords resulting in a new object
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with a simplified signature. For example, :func:`partial` can be used to create
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a callable that behaves like the :func:`int` function where the *base* argument
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defaults to two:
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>>> from functools import partial
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>>> basetwo = partial(int, base=2)
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>>> basetwo.__doc__ = 'Convert base 2 string to an int.'
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>>> basetwo('10010')
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18
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.. function:: reduce(function, iterable[, initializer])
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Apply *function* of two arguments cumulatively to the items of *sequence*, from
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left to right, so as to reduce the sequence to a single value. For example,
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``reduce(lambda x, y: x+y, [1, 2, 3, 4, 5])`` calculates ``((((1+2)+3)+4)+5)``.
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The left argument, *x*, is the accumulated value and the right argument, *y*, is
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the update value from the *sequence*. If the optional *initializer* is present,
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it is placed before the items of the sequence in the calculation, and serves as
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a default when the sequence is empty. If *initializer* is not given and
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*sequence* contains only one item, the first item is returned.
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.. function:: update_wrapper(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
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Update a *wrapper* function to look like the *wrapped* function. The optional
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arguments are tuples to specify which attributes of the original function are
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assigned directly to the matching attributes on the wrapper function and which
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attributes of the wrapper function are updated with the corresponding attributes
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from the original function. The default values for these arguments are the
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module level constants *WRAPPER_ASSIGNMENTS* (which assigns to the wrapper
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function's *__name__*, *__module__*, *__annotations__* and *__doc__*, the
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documentation string) and *WRAPPER_UPDATES* (which updates the wrapper
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function's *__dict__*, i.e. the instance dictionary).
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To allow access to the original function for introspection and other purposes
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(e.g. bypassing a caching decorator such as :func:`lru_cache`), this function
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automatically adds a __wrapped__ attribute to the wrapper that refers to
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the original function.
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The main intended use for this function is in :term:`decorator` functions which
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wrap the decorated function and return the wrapper. If the wrapper function is
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not updated, the metadata of the returned function will reflect the wrapper
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definition rather than the original function definition, which is typically less
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than helpful.
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:func:`update_wrapper` may be used with callables other than functions. Any
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attributes named in *assigned* or *updated* that are missing from the object
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being wrapped are ignored (i.e. this function will not attempt to set them
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on the wrapper function). :exc:`AttributeError` is still raised if the
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wrapper function itself is missing any attributes named in *updated*.
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.. versionadded:: 3.2
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Automatic addition of the ``__wrapped__`` attribute.
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.. versionadded:: 3.2
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Copying of the ``__annotations__`` attribute by default.
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.. versionchanged:: 3.2
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Missing attributes no longer trigger an :exc:`AttributeError`.
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.. decorator:: wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
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This is a convenience function for invoking ``partial(update_wrapper,
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wrapped=wrapped, assigned=assigned, updated=updated)`` as a function decorator
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when defining a wrapper function. For example:
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>>> from functools import wraps
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>>> def my_decorator(f):
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... @wraps(f)
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... def wrapper(*args, **kwds):
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... print('Calling decorated function')
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... return f(*args, **kwds)
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... return wrapper
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...
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>>> @my_decorator
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... def example():
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... """Docstring"""
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... print('Called example function')
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...
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>>> example()
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Calling decorated function
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Called example function
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>>> example.__name__
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'example'
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>>> example.__doc__
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'Docstring'
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Without the use of this decorator factory, the name of the example function
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would have been ``'wrapper'``, and the docstring of the original :func:`example`
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would have been lost.
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.. _partial-objects:
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:class:`partial` Objects
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------------------------
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:class:`partial` objects are callable objects created by :func:`partial`. They
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have three read-only attributes:
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.. attribute:: partial.func
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A callable object or function. Calls to the :class:`partial` object will be
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forwarded to :attr:`func` with new arguments and keywords.
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.. attribute:: partial.args
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The leftmost positional arguments that will be prepended to the positional
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arguments provided to a :class:`partial` object call.
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.. attribute:: partial.keywords
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The keyword arguments that will be supplied when the :class:`partial` object is
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called.
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:class:`partial` objects are like :class:`function` objects in that they are
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callable, weak referencable, and can have attributes. There are some important
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differences. For instance, the :attr:`__name__` and :attr:`__doc__` attributes
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are not created automatically. Also, :class:`partial` objects defined in
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classes behave like static methods and do not transform into bound methods
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during instance attribute look-up.
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