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: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>
.. moduleauthor:: Łukasz Langa <lukasz@langa.pl>
.. moduleauthor:: Pablo Galindo <pablogsal@gmail.com>
.. sectionauthor:: Peter Harris <scav@blueyonder.co.uk>
**Source code:** :source:`Lib/functools.py`
.. testsetup:: default
import functools
from functools import *
--------------
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:
.. decorator:: cache(user_function)
Simple lightweight unbounded function cache. Sometimes called
`"memoize" <https://en.wikipedia.org/wiki/Memoization>`_.
Returns the same as ``lru_cache(maxsize=None)``, creating a thin
wrapper around a dictionary lookup for the function arguments. Because it
never needs to evict old values, this is smaller and faster than
:func:`lru_cache` with a size limit.
For example::
@cache
def factorial(n):
return n * factorial(n-1) if n else 1
>>> factorial(10) # no previously cached result, makes 11 recursive calls
3628800
>>> factorial(5) # just looks up cached value result
120
>>> factorial(12) # makes two new recursive calls, the other 10 are cached
479001600
The cache is threadsafe so that the wrapped function can be used in
multiple threads. This means that the underlying data structure will
remain coherent during concurrent updates.
It is possible for the wrapped function to be called more than once if
another thread makes an additional call before the initial call has been
completed and cached.
.. versionadded:: 3.9
.. decorator:: cached_property(func)
Transform a method of a class into a property whose value is computed once
and then cached as a normal attribute for the life of the instance. Similar
to :func:`property`, with the addition of caching. Useful for expensive
computed properties of instances that are otherwise effectively immutable.
Example::
class DataSet:
def __init__(self, sequence_of_numbers):
self._data = tuple(sequence_of_numbers)
@cached_property
def stdev(self):
return statistics.stdev(self._data)
The mechanics of :func:`cached_property` are somewhat different from
:func:`property`. A regular property blocks attribute writes unless a
setter is defined. In contrast, a *cached_property* allows writes.
The *cached_property* decorator only runs on lookups and only when an
attribute of the same name doesn't exist. When it does run, the
*cached_property* writes to the attribute with the same name. Subsequent
attribute reads and writes take precedence over the *cached_property*
method and it works like a normal attribute.
The cached value can be cleared by deleting the attribute. This
allows the *cached_property* method to run again.
The *cached_property* does not prevent a possible race condition in
multi-threaded usage. The getter function could run more than once on the
same instance, with the latest run setting the cached value. If the cached
property is idempotent or otherwise not harmful to run more than once on an
instance, this is fine. If synchronization is needed, implement the necessary
locking inside the decorated getter function or around the cached property
access.
Note, this decorator interferes with the operation of :pep:`412`
key-sharing dictionaries. This means that instance dictionaries
can take more space than usual.
Also, this decorator requires that the ``__dict__`` attribute on each instance
be a mutable mapping. This means it will not work with some types, such as
metaclasses (since the ``__dict__`` attributes on type instances are
read-only proxies for the class namespace), and those that specify
``__slots__`` without including ``__dict__`` as one of the defined slots
(as such classes don't provide a ``__dict__`` attribute at all).
If a mutable mapping is not available or if space-efficient key sharing is
desired, an effect similar to :func:`cached_property` can also be achieved by
stacking :func:`property` on top of :func:`lru_cache`. See
:ref:`faq-cache-method-calls` for more details on how this differs from :func:`cached_property`.
.. versionadded:: 3.8
.. versionchanged:: 3.12
Prior to Python 3.12, ``cached_property`` included an undocumented lock to
ensure that in multi-threaded usage the getter function was guaranteed to
run only once per instance. However, the lock was per-property, not
per-instance, which could result in unacceptably high lock contention. In
Python 3.12+ this locking is removed.
.. function:: cmp_to_key(func)
Transform an old-style comparison function to a :term:`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 Python 2 which supported the use of
comparison functions.
A comparison function is any callable that accepts 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.
Example::
sorted(iterable, key=cmp_to_key(locale.strcoll)) # locale-aware sort order
For sorting examples and a brief sorting tutorial, see :ref:`sortinghowto`.
.. versionadded:: 3.2
.. decorator:: lru_cache(user_function)
lru_cache(maxsize=128, typed=False)
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 cache is threadsafe so that the wrapped function can be used in
multiple threads. This means that the underlying data structure will
remain coherent during concurrent updates.
It is possible for the wrapped function to be called more than once if
another thread makes an additional call before the initial call has been
completed and cached.
Since a dictionary is used to cache results, the positional and keyword
arguments to the function must be :term:`hashable`.
Distinct argument patterns may be considered to be distinct calls with
separate cache entries. For example, ``f(a=1, b=2)`` and ``f(b=2, a=1)``
differ in their keyword argument order and may have two separate cache
entries.
If *user_function* is specified, it must be a callable. This allows the
*lru_cache* decorator to be applied directly to a user function, leaving
the *maxsize* at its default value of 128::
@lru_cache
def count_vowels(sentence):
return sum(sentence.count(vowel) for vowel in 'AEIOUaeiou')
If *maxsize* is set to ``None``, the LRU feature is disabled and the cache can
grow without bound.
If *typed* is set to true, function arguments of different types will be
cached separately. If *typed* is false, the implementation will usually
regard them as equivalent calls and only cache a single result. (Some
types such as *str* and *int* may be cached separately even when *typed*
is false.)
Note, type specificity applies only to the function's immediate arguments
rather than their contents. The scalar arguments, ``Decimal(42)`` and
``Fraction(42)`` are be treated as distinct calls with distinct results.
In contrast, the tuple arguments ``('answer', Decimal(42))`` and
``('answer', Fraction(42))`` are treated as equivalent.
The wrapped function is instrumented with a :func:`!cache_parameters`
function that returns a new :class:`dict` showing the values for *maxsize*
and *typed*. This is for information purposes only. Mutating the values
has no effect.
To help measure the effectiveness of the cache and tune the *maxsize*
parameter, the wrapped function is instrumented with a :func:`cache_info`
function that returns a :term:`named tuple` showing *hits*, *misses*,
*maxsize* and *currsize*.
The decorator also provides a :func:`cache_clear` function for clearing or
invalidating the cache.
The original underlying function is accessible through the
:attr:`__wrapped__` attribute. This is useful for introspection, for
bypassing the cache, or for rewrapping the function with a different cache.
The cache keeps references to the arguments and return values until they age
out of the cache or until the cache is cleared.
If a method is cached, the ``self`` instance argument is included in the
cache. See :ref:`faq-cache-method-calls`
An `LRU (least recently used) cache
<https://en.wikipedia.org/wiki/Cache_replacement_policies#Least_Recently_Used_(LRU)>`_
works best when the most recent calls are the best predictors of upcoming
calls (for example, the most popular articles on a news server tend to
change each day). The cache's size limit assures that the cache does not
grow without bound on long-running processes such as web servers.
In general, the LRU cache should only be used when you want to reuse
previously computed values. Accordingly, it doesn't make sense to cache
functions with side-effects, functions that need to create
distinct mutable objects on each call (such as generators and async functions),
or impure functions such as time() or random().
Example of an LRU cache for static web content::
@lru_cache(maxsize=32)
def get_pep(num):
'Retrieve text of a Python Enhancement Proposal'
resource = f'https://peps.python.org/pep-{num:04d}'
try:
with urllib.request.urlopen(resource) as s:
return s.read()
except urllib.error.HTTPError:
return 'Not Found'
>>> for n in 8, 290, 308, 320, 8, 218, 320, 279, 289, 320, 9991:
... pep = get_pep(n)
... print(n, len(pep))
>>> get_pep.cache_info()
CacheInfo(hits=3, misses=8, maxsize=32, currsize=8)
Example of efficiently computing
`Fibonacci numbers <https://en.wikipedia.org/wiki/Fibonacci_number>`_
using a cache to implement a
`dynamic programming <https://en.wikipedia.org/wiki/Dynamic_programming>`_
technique::
@lru_cache(maxsize=None)
def fib(n):
if n < 2:
return n
return fib(n-1) + fib(n-2)
>>> [fib(n) for n in range(16)]
[0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610]
>>> fib.cache_info()
CacheInfo(hits=28, misses=16, maxsize=None, currsize=16)
.. versionadded:: 3.2
.. versionchanged:: 3.3
Added the *typed* option.
.. versionchanged:: 3.8
Added the *user_function* option.
.. versionchanged:: 3.9
Added the function :func:`!cache_parameters`
.. 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 _is_valid_operand(self, other):
return (hasattr(other, "lastname") and
hasattr(other, "firstname"))
def __eq__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) ==
(other.lastname.lower(), other.firstname.lower()))
def __lt__(self, other):
if not self._is_valid_operand(other):
return NotImplemented
return ((self.lastname.lower(), self.firstname.lower()) <
(other.lastname.lower(), other.firstname.lower()))
.. note::
While this decorator makes it easy to create well behaved totally
ordered types, it *does* come at the cost of slower execution and
more complex stack traces for the derived comparison methods. If
performance benchmarking indicates this is a bottleneck for a given
application, implementing all six rich comparison methods instead is
likely to provide an easy speed boost.
.. note::
This decorator makes no attempt to override methods that have been
declared in the class *or its superclasses*. Meaning that if a
superclass defines a comparison operator, *total_ordering* will not
implement it again, even if the original method is abstract.
.. versionadded:: 3.2
.. versionchanged:: 3.4
Returning ``NotImplemented`` from the underlying comparison function for
unrecognised types is now supported.
.. data:: Placeholder
A singleton object used as a sentinel to reserve a place
for positional arguments when calling :func:`partial`
and :func:`partialmethod`.
.. versionadded:: 3.14
.. function:: partial(func, /, *args, **keywords)
Return a new :ref:`partial object<partial-objects>` 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(*more_args, **more_keywords):
return func(*args, *more_args, **(keywords | more_keywords))
newfunc.func = func
newfunc.args = args
newfunc.keywords = keywords
return newfunc
The :func:`!partial` function 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 ``2``:
.. doctest::
>>> basetwo = partial(int, base=2)
>>> basetwo.__doc__ = 'Convert base 2 string to an int.'
>>> basetwo('10010')
18
If :data:`Placeholder` sentinels are present in *args*, they will be filled first
when :func:`!partial` is called. This makes it possible to pre-fill any positional
argument with a call to :func:`!partial`; without :data:`!Placeholder`, only the
first positional argument can be pre-filled.
If any :data:`!Placeholder` sentinels are present, all must be filled at call time:
.. doctest::
>>> say_to_world = partial(print, Placeholder, Placeholder, "world!")
>>> say_to_world('Hello', 'dear')
Hello dear world!
Calling ``say_to_world('Hello')`` raises a :exc:`TypeError`, because
only one positional argument is provided, but there are two placeholders
that must be filled in.
If :func:`!partial` is applied to an existing :func:`!partial` object,
:data:`!Placeholder` sentinels of the input object are filled in with
new positional arguments.
A placeholder can be retained by inserting a new
:data:`!Placeholder` sentinel to the place held by a previous :data:`!Placeholder`:
.. doctest::
>>> from functools import partial, Placeholder as _
>>> remove = partial(str.replace, _, _, '')
>>> message = 'Hello, dear dear world!'
>>> remove(message, ' dear')
'Hello, world!'
>>> remove_dear = partial(remove, _, ' dear')
>>> remove_dear(message)
'Hello, world!'
>>> remove_first_dear = partial(remove_dear, _, 1)
>>> remove_first_dear(message)
'Hello, dear world!'
:data:`!Placeholder` has no special treatment when used in a keyword
argument to :func:`!partial`.
.. versionchanged:: 3.14
Added support for :data:`Placeholder` in positional arguments.
.. class:: partialmethod(func, /, *args, **keywords)
Return a new :class:`partialmethod` descriptor which behaves
like :class:`partial` except that it is designed to be used as a method
definition rather than being directly callable.
*func* must be a :term:`descriptor` or a callable (objects which are both,
like normal functions, are handled as descriptors).
When *func* is a descriptor (such as a normal Python function,
:func:`classmethod`, :func:`staticmethod`, :func:`abstractmethod` or
another instance of :class:`partialmethod`), calls to ``__get__`` are
delegated to the underlying descriptor, and an appropriate
:ref:`partial object<partial-objects>` returned as the result.
When *func* is a non-descriptor callable, an appropriate bound method is
created dynamically. This behaves like a normal Python function when
used as a method: the *self* argument will be inserted as the first
positional argument, even before the *args* and *keywords* supplied to
the :class:`partialmethod` constructor.
Example::
>>> class Cell:
... def __init__(self):
... self._alive = False
... @property
... def alive(self):
... return self._alive
... def set_state(self, state):
... self._alive = bool(state)
... set_alive = partialmethod(set_state, True)
... set_dead = partialmethod(set_state, False)
...
>>> c = Cell()
>>> c.alive
False
>>> c.set_alive()
>>> c.alive
True
.. versionadded:: 3.4
.. function:: reduce(function, iterable, /[, initial])
Apply *function* of two arguments cumulatively to the items of *iterable*, from
left to right, so as to reduce the iterable 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 *iterable*. If the optional *initial* is present,
it is placed before the items of the iterable in the calculation, and serves as
a default when the iterable is empty. If *initial* is not given and
*iterable* contains only one item, the first item is returned.
Roughly equivalent to::
initial_missing = object()
def reduce(function, iterable, /, initial=initial_missing):
it = iter(iterable)
if initial is initial_missing:
value = next(it)
else:
value = initial
for element in it:
value = function(value, element)
return value
See :func:`itertools.accumulate` for an iterator that yields all intermediate
values.
.. versionchanged:: 3.14
*initial* is now supported as a keyword argument.
.. decorator:: singledispatch
Transform a function into a :term:`single-dispatch <single
dispatch>` :term:`generic function`.
To define a generic function, decorate it with the ``@singledispatch``
decorator. When defining a function using ``@singledispatch``, note that the
dispatch happens on the type of the first argument::
>>> from functools import singledispatch
>>> @singledispatch
... def fun(arg, verbose=False):
... if verbose:
... print("Let me just say,", end=" ")
... print(arg)
To add overloaded implementations to the function, use the :func:`register`
attribute of the generic function, which can be used as a decorator. For
functions annotated with types, the decorator will infer the type of the
first argument automatically::
>>> @fun.register
... def _(arg: int, verbose=False):
... if verbose:
... print("Strength in numbers, eh?", end=" ")
... print(arg)
...
>>> @fun.register
... def _(arg: list, verbose=False):
... if verbose:
... print("Enumerate this:")
... for i, elem in enumerate(arg):
... print(i, elem)
:data:`types.UnionType` and :data:`typing.Union` can also be used::
>>> @fun.register
... def _(arg: int | float, verbose=False):
... if verbose:
... print("Strength in numbers, eh?", end=" ")
... print(arg)
...
>>> from typing import Union
>>> @fun.register
... def _(arg: Union[list, set], verbose=False):
... if verbose:
... print("Enumerate this:")
... for i, elem in enumerate(arg):
... print(i, elem)
...
For code which doesn't use type annotations, the appropriate type
argument can be passed explicitly to the decorator itself::
>>> @fun.register(complex)
... def _(arg, verbose=False):
... if verbose:
... print("Better than complicated.", end=" ")
... print(arg.real, arg.imag)
...
For code that dispatches on a collections type (e.g., ``list``), but wants
to typehint the items of the collection (e.g., ``list[int]``), the
dispatch type should be passed explicitly to the decorator itself with the
typehint going into the function definition::
>>> @fun.register(list)
... def _(arg: list[int], verbose=False):
... if verbose:
... print("Enumerate this:")
... for i, elem in enumerate(arg):
... print(i, elem)
.. note::
At runtime the function will dispatch on an instance of a list regardless
of the type contained within the list i.e. ``[1,2,3]`` will be
dispatched the same as ``["foo", "bar", "baz"]``. The annotation
provided in this example is for static type checkers only and has no
runtime impact.
To enable registering :term:`lambdas<lambda>` and pre-existing functions,
the :func:`register` attribute can also be used in a functional form::
>>> def nothing(arg, verbose=False):
... print("Nothing.")
...
>>> fun.register(type(None), nothing)
The :func:`register` attribute returns the undecorated function. This
enables decorator stacking, :mod:`pickling<pickle>`, and the creation
of unit tests for each variant independently::
>>> @fun.register(float)
... @fun.register(Decimal)
... def fun_num(arg, verbose=False):
... if verbose:
... print("Half of your number:", end=" ")
... print(arg / 2)
...
>>> fun_num is fun
False
When called, the generic function dispatches on the type of the first
argument::
>>> fun("Hello, world.")
Hello, world.
>>> fun("test.", verbose=True)
Let me just say, test.
>>> fun(42, verbose=True)
Strength in numbers, eh? 42
>>> fun(['spam', 'spam', 'eggs', 'spam'], verbose=True)
Enumerate this:
0 spam
1 spam
2 eggs
3 spam
>>> fun(None)
Nothing.
>>> fun(1.23)
0.615
Where there is no registered implementation for a specific type, its
method resolution order is used to find a more generic implementation.
The original function decorated with ``@singledispatch`` is registered
for the base :class:`object` type, which means it is used if no better
implementation is found.
If an implementation is registered to an :term:`abstract base class`,
virtual subclasses of the base class will be dispatched to that
implementation::
>>> from collections.abc import Mapping
>>> @fun.register
... def _(arg: Mapping, verbose=False):
... if verbose:
... print("Keys & Values")
... for key, value in arg.items():
... print(key, "=>", value)
...
>>> fun({"a": "b"})
a => b
To check which implementation the generic function will choose for
a given type, use the ``dispatch()`` attribute::
>>> fun.dispatch(float)
<function fun_num at 0x1035a2840>
>>> fun.dispatch(dict) # note: default implementation
<function fun at 0x103fe0000>
To access all registered implementations, use the read-only ``registry``
attribute::
>>> fun.registry.keys()
dict_keys([<class 'NoneType'>, <class 'int'>, <class 'object'>,
<class 'decimal.Decimal'>, <class 'list'>,
<class 'float'>])
>>> fun.registry[float]
<function fun_num at 0x1035a2840>
>>> fun.registry[object]
<function fun at 0x103fe0000>
.. versionadded:: 3.4
.. versionchanged:: 3.7
The :func:`register` attribute now supports using type annotations.
.. versionchanged:: 3.11
The :func:`register` attribute now supports :data:`types.UnionType`
and :data:`typing.Union` as type annotations.
.. class:: singledispatchmethod(func)
Transform a method into a :term:`single-dispatch <single
dispatch>` :term:`generic function`.
To define a generic method, decorate it with the ``@singledispatchmethod``
decorator. When defining a function using ``@singledispatchmethod``, note
that the dispatch happens on the type of the first non-*self* or non-*cls*
argument::
class Negator:
@singledispatchmethod
def neg(self, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
def _(self, arg: int):
return -arg
@neg.register
def _(self, arg: bool):
return not arg
``@singledispatchmethod`` supports nesting with other decorators such as
:func:`@classmethod<classmethod>`. Note that to allow for
``dispatcher.register``, ``singledispatchmethod`` must be the *outer most*
decorator. Here is the ``Negator`` class with the ``neg`` methods bound to
the class, rather than an instance of the class::
class Negator:
@singledispatchmethod
@classmethod
def neg(cls, arg):
raise NotImplementedError("Cannot negate a")
@neg.register
@classmethod
def _(cls, arg: int):
return -arg
@neg.register
@classmethod
def _(cls, arg: bool):
return not arg
The same pattern can be used for other similar decorators:
:func:`@staticmethod<staticmethod>`,
:func:`@abstractmethod<abc.abstractmethod>`, and others.
.. versionadded:: 3.8
.. 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 :attr:`~function.__module__`, :attr:`~function.__name__`,
:attr:`~function.__qualname__`, :attr:`~function.__annotations__`,
:attr:`~function.__type_params__`, and :attr:`~function.__doc__`, the
documentation string) and ``WRAPPER_UPDATES`` (which updates the wrapper
function's :attr:`~function.__dict__`, i.e. the instance dictionary).
To allow access to the original function for introspection and other purposes
(e.g. bypassing a caching decorator such as :func:`lru_cache`), this function
automatically adds a ``__wrapped__`` attribute to the wrapper that refers to
the function being wrapped.
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.
:func:`update_wrapper` may be used with callables other than functions. Any
attributes named in *assigned* or *updated* that are missing from the object
being wrapped are ignored (i.e. this function will not attempt to set them
on the wrapper function). :exc:`AttributeError` is still raised if the
wrapper function itself is missing any attributes named in *updated*.
.. versionchanged:: 3.2
The ``__wrapped__`` attribute is now automatically added.
The :attr:`~function.__annotations__` attribute is now copied by default.
Missing attributes no longer trigger an :exc:`AttributeError`.
.. versionchanged:: 3.4
The ``__wrapped__`` attribute now always refers to the wrapped
function, even if that function defined a ``__wrapped__`` attribute.
(see :issue:`17482`)
.. versionchanged:: 3.12
The :attr:`~function.__type_params__` attribute is now copied by default.
.. decorator:: wraps(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)
This is a convenience function for invoking :func:`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 :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 :ref:`function objects <user-defined-funcs>` in that they are
callable, weak referenceable, and can have attributes. There are some important
differences. For instance, the :attr:`~definition.__name__` and :attr:`~definition.__doc__` attributes
are not created automatically.
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