通常，复制一个对象应该创建一个精确的克隆：如果它有缓存的值，也应该复制它们。如果不这样做，这通常是对深度复制的速度优化，不会有明显的副作用。在

如果您正在制作某个内容的副本，并且希望清除该副本中的缓存值，则应显式清除该缓存。在

如果您确实希望对象的副本不复制缓存，那么定义__copy__或{}方法来控制复制。(请注意，这通常用于复制底层资源，如文件描述符和句柄)。在

这里有两个例子。在class memoized(object):

"""

Decorator that caches a function's return value each time it is called.

If called later with the same arguments, the cached value is returned, and

not re-evaluated.

"""

def __init__(self, func):

self.func = func

self.cache = {}

def __copy__(self):

"""

Don't copy the cache in a copy.

"""

return memoized(self.func)

def __deepcopy__(self, memo):

"""

Don't copy the cache in a deep copy.

"""

return memoized(self.func)

def __call__(self, *args):

try:

return self.cache[args]

except KeyError:

value = self.func(*args)

self.cache[args] = value

return value

except TypeError:

# uncachable for instance, passing a list as an argument.

# Better to not cache than to blow up entirely.

return self.func(*args)

def __repr__(self):

"""Return the function's docstring."""

return self.func.__doc__

def __get__(self, obj, objtype):

"""Support instance methods."""

return functools.partial(self.__call__, obj)

def clear_cache(self):

self.cache = {}

@memoized

def fibonacci(n):

"Return the nth fibonacci number."

if n in (0, 1):

return n

return fibonacci(n-1) + fibonacci(n-2)

fibonacci(12)

print fibonacci.cache

fibonacci.clear_cache()

print fibonacci.cache

fibonacci(12)

print fibonacci.cache

import copy

f = copy.deepcopy(fibonacci)

print f.cache