我想为一个老问题提供一个不同的解决方案

class IterableAdapter:
    def __init__(self, iterator_factory):
        self.iterator_factory = iterator_factory

    def __iter__(self):
        return self.iterator_factory()

squares = IterableAdapter(lambda: (x * x for x in range(5)))

for x in squares: print(x)
for x in squares: print(x)

与list(iterator)相比，这样做的好处是它的空间复杂度是O(1)，而list(iterator)是O(n)。缺点是，如果你只能访问迭代器，而不能访问产生迭代器的函数，那么你就不能使用这个方法。例如，这样做似乎是合理的，但它不会起作用。

g = (x * x for x in range(5))

squares = IterableAdapter(lambda: g)

for x in squares: print(x)
for x in squares: print(x)

我不知道你说的昂贵的准备是什么意思，但我猜你确实有

data = ... # Expensive computation
y = FunctionWithYield(data)
for x in y: print(x)
#here must be something to reset 'y'
# this is expensive - data = ... # Expensive computation
# y = FunctionWithYield(data)
for x in y: print(x)

如果是这样的话，为什么不重用数据呢?

另一种选择是使用itertools.tee()函数创建生成器的第二个版本:

import itertools
y = FunctionWithYield()
y, y_backup = itertools.tee(y)
for x in y:
    print(x)
for x in y_backup:
    print(x)

从内存使用的角度来看，如果原始迭代可能不处理所有的项，这可能是有益的。

>>> def gen():
...     def init():
...         return 0
...     i = init()
...     while True:
...         val = (yield i)
...         if val=='restart':
...             i = init()
...         else:
...             i += 1

>>> g = gen()
>>> g.next()
0
>>> g.next()
1
>>> g.next()
2
>>> g.next()
3
>>> g.send('restart')
0
>>> g.next()
1
>>> g.next()
2

现在可以使用more_itertools。Seekable(第三方工具)，允许重置迭代器。

通过> pip Install more_itertools安装

import more_itertools as mit


y = mit.seekable(FunctionWithYield())
for x in y:
    print(x)

y.seek(0)                                              # reset iterator
for x in y:
    print(x)

注意:内存消耗会随着迭代器的增加而增加，所以要警惕大型迭代对象。

好吧，你说你想多次调用一个生成器，但初始化是昂贵的…像这样的东西怎么样?

class InitializedFunctionWithYield(object):
    def __init__(self):
        # do expensive initialization
        self.start = 5

    def __call__(self, *args, **kwargs):
        # do cheap iteration
        for i in xrange(5):
            yield self.start + i

y = InitializedFunctionWithYield()

for x in y():
    print x

for x in y():
    print x

或者，你也可以创建自己的类，遵循迭代器协议，并定义某种“reset”函数。

class MyIterator(object):
    def __init__(self):
        self.reset()

    def reset(self):
        self.i = 5

    def __iter__(self):
        return self

    def next(self):
        i = self.i
        if i > 0:
            self.i -= 1
            return i
        else:
            raise StopIteration()

my_iterator = MyIterator()

for x in my_iterator:
    print x

print 'resetting...'
my_iterator.reset()

for x in my_iterator:
    print x

https://docs.python.org/2/library/stdtypes.html#iterator-types http://anandology.com/python-practice-book/iterators.html