# `functools` --- 高阶函数和可调用对象上的操作

源代码: Lib/functools.py

`functools` 模块应用于高阶函数，即参数或（和）返回值为其他函数的函数。 通常来说，此模块的功能适用于所有可调用对象。

`functools` 模块定义了以下函数:

`@``functools.``cache`(user_function)

  Simple lightweight unbounded function cache. Sometimes called "memoize".

  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 `lru_cache()` with a size limit.

  例如:

      @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

  3.9 版新加入.

`@``functools.``cached_property`(func)

  将一个类方法转换为特征属性，一次性计算该特征属性的值，然后将其缓存为实例生命周期内的普通属性。 类似于 `property()` 但增加了缓存功能。 对于在其他情况下实际不可变的高计算资源消耗的实例特征属性来说该函数非常有用。

  示例:

      class DataSet:
          def __init__(self, sequence_of_numbers):
              self._data = sequence_of_numbers

          @cached_property
          def stdev(self):
              return statistics.stdev(self._data)

          @cached_property
          def variance(self):
              return statistics.variance(self._data)

  3.8 版新加入.

  備註

  此装饰器要求每个实例上的 `__dict__` 属性是可变的映射。 这意味着它将不适用于某些类型，例如元类（因为类型实例上的 `__dict__` 属性是类命名空间的只读代理），以及那些指定了 `__slots__` 但未包含 `__dict__` 作为所定义的空位之一的类（因为这样的类根本没有提供 `__dict__` 属性）。

`functools.``cmp_to_key`(func)

  将(旧式的)比较函数转换为新式的 key function . 在类似于 `sorted()` ， `min()` ， `max()` ， `heapq.nlargest()` ， `heapq.nsmallest()` ， `itertools.groupby()` 等函数的 key 参数中使用。此函数主要用作将 Python 2 程序转换至新版的转换工具，以保持对比较函数的兼容。

  比较函数意为一个可调用对象，该对象接受两个参数并比较它们，结果为小于则返回一个负数，相等则返回零，大于则返回一个正数。key function则是一个接受一个参数，并返回另一个用以排序的值的可调用对象。

  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 如何排序 。

  3.2 版新加入.

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

  一个为函数提供缓存功能的装饰器，缓存 maxsize 组传入参数，在下次以相同参数调用时直接返回上一次的结果。用以节约高开销或I/O函数的调用时间。

  由于使用了字典存储缓存，所以该函数的固定参数和关键字参数必须是可哈希的。

  不同模式的参数可能被视为不同从而产生多个缓存项，例如, f(a=1, b=2) 和 f(b=2, a=1) 因其参数顺序不同，可能会被缓存两次。

  如果指定了 user_function，它必须是一个可调用对象。 这允许 lru_cache 装饰器被直接应用于一个用户自定义函数，让 maxsize 保持其默认值 128:

      @lru_cache
      def count_vowels(sentence):
          sentence = sentence.casefold()
          return sum(sentence.count(vowel) for vowel in 'aeiou')

  如果 maxsize 设为 `None`，LRU 特性将被禁用且缓存可无限增长。

  如果 typed 设置为true，不同类型的函数参数将被分别缓存。例如， `f(3)` 和 `f(3.0)` 将被视为不同而分别缓存。

  The wrapped function is instrumented with a `cache_parameters()` function that returns a new `dict` showing the values for maxsize and typed. This is for information purposes only. Mutating the values has no effect.

  为了衡量缓存的有效性以便调整 maxsize 形参，被装饰的函数带有一个 `cache_info()` 函数。当调用 `cache_info()` 函数时，返回一个具名元组，包含命中次数 hits，未命中次数 misses ，最大缓存数量 maxsize 和 当前缓存大小 currsize。在多线程环境中，命中数与未命中数是不完全准确的。

  该装饰器也提供了一个用于清理/使缓存失效的函数 `cache_clear()` 。

  原始的未经装饰的函数可以通过 `__wrapped__` 属性访问。它可以用于检查、绕过缓存，或使用不同的缓存再次装饰原始函数。

  An LRU (least recently used) cache 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.

  一般来说，LRU缓存只在当你想要重用之前计算的结果时使用。因此，用它缓存具有副作用的函数、需要在每次调用时创建不同、易变的对象的函数或者诸如time（）或random（）之类的不纯函数是没有意义的。

  静态 Web 内容的 LRU 缓存示例:

      @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)

  以下是使用缓存通过 动态规划 计算 斐波那契数列 的例子。

      @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)

  3.2 版新加入.

  3.3 版更變: 添加 typed 选项。

  3.8 版更變: 添加了 user_function 选项。

  3.9 版新加入: Added the function `cache_parameters()`

`@``functools.``total_ordering`

  给定一个声明一个或多个全比较排序方法的类，这个类装饰器实现剩余的方法。这减轻了指定所有可能的全比较操作的工作。

  此类必须包含以下方法之一：`__lt__()` 、`__le__()`、`__gt__()` 或 `__ge__()`。另外，此类必须支持 `__eq__()` 方法。

  例如:

      @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()))

  備註

  虽然此装饰器使得创建具有良好行为的完全有序类型变得非常容易，但它 确实 是以执行速度更缓慢和派生比较方法的堆栈回溯更复杂为代价的。 如果性能基准测试表明这是特定应用的瓶颈所在，则改为实现全部六个富比较方法应该会轻松提升速度。

  3.2 版新加入.

  3.4 版更變: 现在已支持从未识别类型的下层比较函数返回 NotImplemented 异常。

`functools.``partial`(func, /, *args, **keywords)

  返回一个新的 部分对象，当被调用时其行为类似于 func 附带位置参数 args 和关键字参数 keywords 被调用。 如果为调用提供了更多的参数，它们会被附加到 args。 如果提供了额外的关键字参数，它们会扩展并重载 keywords。 大致等价于:

      def partial(func, /, *args, **keywords):
          def newfunc(*fargs, **fkeywords):
              newkeywords = {**keywords, **fkeywords}
              return func(*args, *fargs, **newkeywords)
          newfunc.func = func
          newfunc.args = args
          newfunc.keywords = keywords
          return newfunc

  `partial()` 会被“冻结了”一部分函数参数和/或关键字的部分函数应用所使用，从而得到一个具有简化签名的新对象。 例如，`partial()` 可用来创建一个行为类似于 `int()` 函数的可调用对象，其中 base 参数默认为二：

      >>> 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)

  返回一个新的 `partialmethod` 描述器，其行为类似 `partial` 但它被设计用作方法定义而非直接用作可调用对象。

  func 必须是一个 descriptor 或可调用对象（同属两者的对象例如普通函数会被当作描述器来处理）。

  当 func 是一个描述器（例如普通 Python 函数, `classmethod()`, `staticmethod()`, `abstractmethod()` 或其他 `partialmethod` 的实例）时, 对 `__get__` 的调用会被委托给底层的描述器，并会返回一个适当的 部分对象 作为结果。

  当 func 是一个非描述器类可调用对象时，则会动态创建一个适当的绑定方法。 当用作方法时其行为类似普通 Python 函数：将会插入 self 参数作为第一个位置参数，其位置甚至会处于提供给 `partialmethod` 构造器的 args 和 keywords 之前。

  示例:

      >>> 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

  3.4 版新加入.

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

  将两个参数的 function 从左至右积累地应用到 iterable 的条目，以便将该可迭代对象缩减为单一的值。 例如，`reduce(lambda x, y: x+y, [1, 2, 3, 4, 5])` 是计算 `((((1+2)+3)+4)+5)` 的值。 左边的参数 x 是积累值而右边的参数 y 则是来自 iterable 的更新值。 如果存在可选项 initializer，它会被放在参与计算的可迭代对象的条目之前，并在可迭代对象为空时作为默认值。 如果没有给出 initializer 并且 iterable 仅包含一个条目，则将返回第一项。

  大致相当于：

      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

  请参阅 `itertools.accumulate()` 了解有关可产生所有中间值的迭代器。

`@``functools.``singledispatch`

  将一个函数转换为 单分派 generic function。

  要定义一个泛型函数，应使用 `@singledispatch` 装饰器进行装饰。 请注意分派是作用于第一个参数的类型，要相应地创建你的函数:

      >>> from functools import singledispatch
      >>> @singledispatch
      ... def fun(arg, verbose=False):
      ...     if verbose:
      ...         print("Let me just say,", end=" ")
      ...     print(arg)

  要将重载的实现添加到函数中，请使用泛型函数的 `register()` 属性。 它是一个装饰器。 对于带有类型标注的函数，该装饰器将自动推断第一个参数的类型:

      >>> @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)

  对于不使用类型标注的代码，可以将适当的类型参数显式地传给装饰器本身:

      >>> @fun.register(complex)
      ... def _(arg, verbose=False):
      ...     if verbose:
      ...         print("Better than complicated.", end=" ")
      ...     print(arg.real, arg.imag)
      ...

  要启用注册 lambda 和现有函数，可以使用函数形式的 `register()` 属性:

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

  `register()` 属性将返回启用了装饰器堆栈、封存的未装饰函数，并会为每个变量单独创建单元测试:

      >>> @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

  在调用时，泛型函数会根据第一个参数的类型进行分派:

      >>> 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

  在没有用于特定类型的已注册实现的情况下，则会使用其方法解析顺序来查找更通用的实现。 以 `@singledispatch` 装饰的原始函数将为最基本的 `object` 类型进行注册，这意味着它将在找不到更好的实现时被使用。

  If an implementation registered to abstract base class, virtual subclasses 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

  要检查泛型函数将为给定类型选择哪个实现，请使用 `dispatch()` 属性:

      >>> fun.dispatch(float)
      <function fun_num at 0x1035a2840>
      >>> fun.dispatch(dict)    # note: default implementation
      <function fun at 0x103fe0000>

  要访问所有忆注册实现，请使用只读的 `registry` 属性:

      >>> 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>

  3.4 版新加入.

  3.7 版更變: `register()` 属性支持使用类型标注。

class `functools.``singledispatchmethod`(func)

  将一个方法转换为 单分派 generic function。

  要定义一个泛型方法，应使用 `@singledispatchmethod` 装饰器进行装饰。 请注意分派是作用于第一个非 self 或非 cls 参数的类型，要相应地创建你的函数:

      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` 支持与其他装饰器如 `@classmethod` 相嵌套。 请注意如果要允许 `dispatcher.register`，则 `singledispatchmethod` 必须是 最外层 的装饰器。 下面的示例定义了 `Negator` 类，其中包含绑定到类的 `neg` 方法:

      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

  同样的模式也被用于其他类似的装饰器: `staticmethod`, `abstractmethod` 等等。

  3.8 版新加入.

class `functools.``TopologicalSorter`(graph=None)

  Provides functionality to topologically sort a graph of hashable nodes.

  A topological order is a linear ordering of the vertices in a graph such that for every directed edge u -> v from vertex u to vertex v, vertex u comes before vertex v in the ordering. For instance, the vertices of the graph may represent tasks to be performed, and the edges may represent constraints that one task must be performed before another; in this example, a topological ordering is just a valid sequence for the tasks. A complete topological ordering is possible if and only if the graph has no directed cycles, that is, if it is a directed acyclic graph.

  If the optional graph argument is provided it must be a dictionary representing a directed acyclic graph where the keys are nodes and the values are iterables of all predecessors of that node in the graph (the nodes that have edges that point to the value in the key). Additional nodes can be added to the graph using the `add()` method.

  In the general case, the steps required to perform the sorting of a given graph are as follows:

  > - Create an instance of the `TopologicalSorter` with an optional initial graph.

  > - Add additional nodes to the graph.

  > - Call `prepare()` on the graph.

  > - While `is_active()` is `True`, iterate over the nodes returned by `get_ready()` and process them. Call `done()` on each node as it finishes processing.

  In case just an immediate sorting of the nodes in the graph is required and no parallelism is involved, the convenience method `TopologicalSorter.static_order()` can be used directly:

      >>> graph = {"D": {"B", "C"}, "C": {"A"}, "B": {"A"}}
      >>> ts = TopologicalSorter(graph)
      >>> tuple(ts.static_order())
      ('A', 'C', 'B', 'D')

  The class is designed to easily support parallel processing of the nodes as they become ready. For instance:

      topological_sorter = TopologicalSorter()

      # Add nodes to 'topological_sorter'...

      topological_sorter.prepare()
      while topological_sorter.is_active():
          for node in topological_sorter.get_ready():
              # Worker threads or processes take nodes to work on off the
              # 'task_queue' queue.
              task_queue.put(node)

          # When the work for a node is done, workers put the node in
          # 'finalized_tasks_queue' so we can get more nodes to work on.
          # The definition of 'is_active()' guarantees that, at this point, at
          # least one node has been placed on 'task_queue' that hasn't yet
          # been passed to 'done()', so this blocking 'get()' must (eventually)
          # succeed.  After calling 'done()', we loop back to call 'get_ready()'
          # again, so put newly freed nodes on 'task_queue' as soon as
          # logically possible.
          node = finalized_tasks_queue.get()
          topological_sorter.done(node)

  `add`(node, *predecessors)

    Add a new node and its predecessors to the graph. Both the node and all elements in predecessors must be hashable.

    If called multiple times with the same node argument, the set of dependencies will be the union of all dependencies passed in.

    It is possible to add a node with no dependencies (predecessors is not provided) or to provide a dependency twice. If a node that has not been provided before is included among predecessors it will be automatically added to the graph with no predecessors of its own.

    Raises `ValueError` if called after `prepare()`.

  `prepare`()

    Mark the graph as finished and check for cycles in the graph. If any cycle is detected, `CycleError` will be raised, but `get_ready()` can still be used to obtain as many nodes as possible until cycles block more progress. After a call to this function, the graph cannot be modified, and therefore no more nodes can be added using `add()`.

  `is_active`()

    Returns `True` if more progress can be made and `False` otherwise. Progress can be made if cycles do not block the resolution and either there are still nodes ready that haven't yet been returned by `TopologicalSorter.get_ready()` or the number of nodes marked `TopologicalSorter.done()` is less than the number that have been returned by `TopologicalSorter.get_ready()`.

    The `__bool__()` method of this class defers to this function, so instead of:

        if ts.is_active():
            ...

    if possible to simply do:

        if ts:
            ...

    Raises `ValueError` if called without calling `prepare()` previously.

  `done`(*nodes)

    Marks a set of nodes returned by `TopologicalSorter.get_ready()` as processed, unblocking any successor of each node in nodes for being returned in the future by a call to `TopologicalSorter.get_ready()`.

    Raises `ValueError` if any node in nodes has already been marked as processed by a previous call to this method or if a node was not added to the graph by using `TopologicalSorter.add()`, if called without calling `prepare()` or if node has not yet been returned by `get_ready()`.

  `get_ready`()

    Returns a `tuple` with all the nodes that are ready. Initially it returns all nodes with no predecessors, and once those are marked as processed by calling `TopologicalSorter.done()`, further calls will return all new nodes that have all their predecessors already processed. Once no more progress can be made, empty tuples are returned.

    Raises `ValueError` if called without calling `prepare()` previously.

  `static_order`()

    Returns an iterable of nodes in a topological order. Using this method does not require to call `TopologicalSorter.prepare()` or `TopologicalSorter.done()`. This method is equivalent to:

        def static_order(self):
            self.prepare()
            while self.is_active():
                node_group = self.get_ready()
                yield from node_group
                self.done(*node_group)

    The particular order that is returned may depend on the specific order in which the items were inserted in the graph. For example:

        >>> ts = TopologicalSorter()
        >>> ts.add(3, 2, 1)
        >>> ts.add(1, 0)
        >>> print([*ts.static_order()])
        [2, 0, 1, 3]

        >>> ts2 = TopologicalSorter()
        >>> ts2.add(1, 0)
        >>> ts2.add(3, 2, 1)
        >>> print([*ts2.static_order()])
        [0, 2, 1, 3]

    This is due to the fact that "0" and "2" are in the same level in the graph (they would have been returned in the same call to `get_ready()`) and the order between them is determined by the order of insertion.

    If any cycle is detected, `CycleError` will be raised.

  3.9 版新加入.

`functools.``update_wrapper`(wrapper, wrapped, assigned=WRAPPER_ASSIGNMENTS, updated=WRAPPER_UPDATES)

  更新一个 wrapper 函数以使其类似于 wrapped 函数。 可选参数为指明原函数的哪些属性要直接被赋值给 wrapper 函数的匹配属性的元组，并且这些 wrapper 函数的属性将使用原函数的对应属性来更新。 这些参数的默认值是模块级常量 `WRAPPER_ASSIGNMENTS` (它将被赋值给 wrapper 函数的 `__module__`, `__name__`, `__qualname__`, `__annotations__` 和 `__doc__` 即文档字符串) 以及 `WRAPPER_UPDATES` (它将更新 wrapper 函数的 `__dict__` 即实例字典)。

  为了允许出于内省和其他目的访问原始函数（例如绕过 `lru_cache()` 之类的缓存装饰器），此函数会自动为 wrapper 添加一个指向被包装函数的 `__wrapped__` 属性。

  此函数的主要目的是在 decorator 函数中用来包装被装饰的函数并返回包装器。 如果包装器函数未被更新，则被返回函数的元数据将反映包装器定义而不是原始函数定义，这通常没有什么用处。

  `update_wrapper()` 可以与函数之外的可调用对象一同使用。 在 assigned 或 updated 中命名的任何属性如果不存在于被包装对象则会被忽略（即该函数将不会尝试在包装器函数上设置它们）。 如果包装器函数自身缺少在 updated 中命名的任何属性则仍将引发 `AttributeError`。

  3.2 版新加入: 自动添加 `__wrapped__` 属性。

  3.2 版新加入: 默认拷贝 `__annotations__` 属性。

  3.2 版更變: 不存在的属性将不再触发 `AttributeError`。

  3.4 版更變: `__wrapped__` 属性现在总是指向被包装的函数，即使该函数定义了 `__wrapped__` 属性。 (参见 bpo-17482)

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

  这是一个便捷函数，用于在定义包装器函数时发起调用 `update_wrapper()` 作为函数装饰器。 它等价于 `partial(update_wrapper, wrapped=wrapped, assigned=assigned, updated=updated)`。 例如:

      >>> 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'

  如果不使用这个装饰器工厂函数，则 example 函数的名称将变为 `'wrapper'`，并且 `example()` 原本的文档字符串将会丢失。

## `partial` 对象

`partial` 对象是由 `partial()` 创建的可调用对象。 它们具有三个只读属性：

`partial.``func`

  一个可调用对象或函数。 对 `partial` 对象的调用将被转发给 `func` 并附带新的参数和关键字。

`partial.``args`

  最左边的位置参数将放置在提供给 `partial` 对象调用的位置参数之前。

`partial.``keywords`

  当调用 `partial` 对象时将要提供的关键字参数。

`partial` 对象与 `function` 对象的类似之处在于它们都是可调用、可弱引用的对象并可拥有属性。 但两者也存在一些重要的区别。 例如前者不会自动创建 `__name__` 和 `__doc__` 属性。 而且，在类中定义的 `partial` 对象的行为类似于静态方法，并且不会在实例属性查找期间转换为绑定方法。

## Exceptions

The `functools` module defines the following exception classes:

exception `functools.``CycleError`

  Subclass of `ValueError` raised by `TopologicalSorter.prepare()` if cycles exist in the working graph. If multiple cycles exist, only one undefined choice among them will be reported and included in the exception.

  The detected cycle can be accessed via the second element in the `args` attribute of the exception instance and consists in a list of nodes, such that each node is, in the graph, an immediate predecessor of the next node in the list. In the reported list, the first and the last node will be the same, to make it clear that it is cyclic.