# 10.2. `functools` — Funções e operações de ordem superior em objetos chamáveis

** Código fonte: **: source:Lib/functools.py

O módulo: mod: functools é para funções de ordem superior: funções que atuam ou retornam outras funções. Em geral, qualquer objeto invocável pode ser tratado como uma função para os propósitos deste módulo.

O módulo: mod:functools define as seguintes funções:

`functools.``cmp_to_key`(func)

  Transforma uma função de comparação de estilo antigo para um: termo: função de chave ‘. Usado com ferramentas que aceitam funções-chave (como: func: `sorted,: func:` min`,: func: max,: func:` heapq.nlargest`,: func: heapq.nsmallest,: Func: itertools.groupby). Esta função é usada principalmente como uma ferramenta de transição para programas que estão sendo convertidos a partir do Python 2, que suportou o uso de funções de comparação.

  Uma função de comparação é qualquer chamada que aceita dois argumentos, os compara e retorna um número negativo por menos de zero, igual a igualdade ou um número positivo por maior que. Uma função de chave é escalável que aceita um argumento e retorna outro valor para ser usado como a chave de classificação.

  Exemplo:

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

  Para selecionar exemplos e um breve tutorial de classificação, veja: ref: sortinghowto.

  Novo na versão 3.2.

`@``functools.``lru_cache`(maxsize=128, typed=False)

  Decorador para embrulhar uma função com memorizável é chamada e que economiza até as chamadas mais recentes * maxsize *. Pode economizar tempo quando uma função cara ou I/O é periodicamente chamada com os mesmos argumentos.

  dicionárioUma vez que um dicionário é usado para armazenar resultados em cache, os argumentos posicionais e de palavras-chave para a função devem ser hashable.

  Se * maxsize * estiver configurado para `` None``, o recurso LRU está desabilitado e o cache pode crescer sem ligação. O recurso LRU funciona melhor quando * maxsize * é um poder de dois.

  Se tipo for definido como verdadeiro, os argumentos de função de diferentes tipos serão armazenados em cache separadamente. Por exemplo, `` f (3) `` e `` f (3.0) `` serão tratados como chamadas distintas com resultados distintos.

  Para ajudar a medir a eficácia do cache e ajustar o parâmetro maxsize, a função envolvida é instrumentada com uma função: func: cache_info que retorna um: termo:` nomeado tuple` mostrando * hits *, * misses *, * Maxsize * e * currsize *. Em um ambiente multi-threaded, os hits e erros são aproximados.

  O decorador também fornece uma função: func: cache_clear para limpar ou invalidar o cache.

  A função subjacente original é acessível através do atributo: attr: __wrapped__. Isso é útil para introspecção, para ignorar o cache, ou para reinstalar a função com um cache diferente.

  Um cache LRU (least recently used - em português - menos usado recentemente) <https://en.wikipedia.org/wiki/Cache_algorithms#Examples> _ funciona melhor quando as chamadas mais recentes são os melhores preditores de chamadas futuras (por exemplo, os artigos mais populares em um servidor de notícias tendem a mudar a cada dia). O limite de tamanho do cache garante que o cache não cresça sem está ligado a processos de longa duração, como servidores web.

  Em geral, o cache LRU deve ser usado somente quando você deseja reutilizar valores calculados anteriormente. Da mesma forma, não faz sentido armazenar em cache funções com efeitos colaterais, funções que precisam criar objetos mutáveis distintos em cada chamada ou funções impuras, como time() ou random().

  Exemplo de um cache LRU para conteúdo web estático

      @lru_cache(maxsize=32)
      def get_pep(num):
          'Retrieve text of a Python Enhancement Proposal'
          resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
          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)

  Exemplo de computação eficiente dos números Fibonacci <https://en.wikipedia.org/wiki/Fibonacci_number> _ usando um cache para implementar uma” programação dinâmica “<https://en.wikipedia.org/wiki/Dynamic_programming> _ técnica:

      @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)

  Novo na versão 3.2.

  Alterado na versão 3.3: Adicionado a opção * digitado *.

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

  Por exemplo:

      @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()))

  Nota

  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.

  Novo na versão 3.2.

  Alterado na versão 3.4: Returning NotImplemented from the underlying comparison function for unrecognised types is now supported.

`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

class `functools.``partialmethod`(func, *args, **keywords)

  Return a new `partialmethod` descriptor which behaves like `partial` except that it is designed to be used as a method definition rather than being directly callable.

  func must be a 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, `classmethod()`, `staticmethod()`, `abstractmethod()` or another instance of `partialmethod`), calls to `__get__` are delegated to the underlying descriptor, and an appropriate partial object 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 `partialmethod` constructor.

  Exemplo:

      >>> class Cell(object):
      ...     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

  Novo na versão 3.4.

`functools.``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.

  Aproximadamente equivalente a:

      def reduce(function, iterable, initializer=None):
          it = iter(iterable)
          if initializer is None:
              value = next(it)
          else:
              value = initializer
          for element in it:
              value = function(value, element)
          return value

`@``functools.``singledispatch`

  Transform a function into a single-dispatch generic function.

  To define a generic function, decorate it with the `@singledispatch` decorator. Note that the dispatch happens on the type of the first argument, create your function accordingly:

      >>> 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 `register()` attribute of the generic function. It is a decorator, taking a type parameter and decorating a function implementing the operation for that type:

      >>> @fun.register(int)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Strength in numbers, eh?", end=" ")
      ...     print(arg)
      ...
      >>> @fun.register(list)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Enumerate this:")
      ...     for i, elem in enumerate(arg):
      ...         print(i, elem)

  To enable registering lambdas and pre-existing functions, the `register()` attribute can be used in a functional form:

      >>> def nothing(arg, verbose=False):
      ...     print("Nothing.")
      ...
      >>> fun.register(type(None), nothing)

  The `register()` attribute returns the undecorated function which enables decorator stacking, pickling, as well as creating 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 `object` type, which means it is used if no better implementation is found.

  To check which implementation will the generic function 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>

  Novo na versão 3.4.

`functools.``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 `__module__`, `__name__`, `__qualname__`, `__annotations__` and `__doc__`, the documentation string) and `WRAPPER_UPDATES` (which updates the wrapper function’s `__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 `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 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.

  `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). `AttributeError` is still raised if the wrapper function itself is missing any attributes named in updated.

  Novo na versão 3.2: Automatic addition of the `__wrapped__` attribute.

  Novo na versão 3.2: Copying of the `__annotations__` attribute by default.

  Alterado na versão 3.2: Missing attributes no longer trigger an `AttributeError`.

  Alterado na versão 3.4: The `__wrapped__` attribute now always refers to the wrapped function, even if that function defined a `__wrapped__` attribute. (see bpo-17482)

`@``functools.``wraps`(wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

  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.

## 10.2.1. Objetos `partial`

`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.