Python的迭代器和生成器
先说迭代器，对于string、list、dict、tuple等这类容器对象，使用for循环遍历是很方便的。在后台for语句对容器对象调用iter()函数，iter()是python的内置函数。iter()会返回一个定义了next()方法的迭代器对象，它在容器中逐个访问容器内元素，next()也是python的内置函数。在没有后续元素时，next()会抛出一个StopIteration异常，通知for语句循环结束。比如：
>>> s = 'abc' >>> it = iter(s) >>> it <str_iterator object at 0x7f71fefe9d68> >>> next(it) 'a' >>> next(it) 'b' >>> next(it) 'c' >>> next(it) Traceback (most recent call last): File "<stdin>", line 1, in <module> StopIteration
上面说的都是python自带的容器对象，它们都实现了相应的迭代器方法，那如果是自定义类需要遍历怎么办？方法很简单，对这个类AClass，实现一个__iter__(self)方法，使其返回一个带有__next__(self)方法的对象就可以了。如果你在AClass刚好也定义了__next__(self)方法（一般使用迭代器都会定义），那在__iter__里只要返回self就可以。废话少说，先上代码：
class Fib(object):
def __init__(self, max):
super(Fib, self).__init__()
self.max = max
def __iter__(self):
self.a = 0
self.b = 1
return self
def __next__(self):
fib = self.a
if fib > self.max:
raise StopIteration
self.a, self.b = self.b, self.a + self.b
return fib
def main():
fib = Fib(100)
for i in fib:
print(i)
if __name__ == '__main__':
main()
简单讲下代码会干什么，定义了一个Fib类，用于生成fibonacci序列。用for遍历时会逐个打印生成的fibonacci数，max是生成的fibonacci序列中数字大小的上限。
在类的实现中，定义了一个__iter__(self)方法，这个方法是在遍历时被iter()调用，返回一个迭代器。因为在遍历的时候，是直接调用的python内置函数iter()，由iter()通过调用__iter__(self)获得对象的迭代器。有了迭代器，就可以逐个遍历元素了。而逐个遍历的时候，也是使用内置的next(）函数通过调用对象的__next__(self)方法对迭代器对象进行遍历。所以要实现__iter__(self)和__next__(self)。而且因为实现了__next__(self)，所以在实现__iter__(self)的时候，直接返回self就可以。
为了更好理解，我再简单重复下上面说的那一段：在循环遍历自定义容器对象时,会使用python内置函数iter()调用遍历对象的__iter__(self)获得一个迭代器,之后再循环对这个迭代器使用next()调用迭代器对象的__next__(self)。__iter__只会被调用一次,而__next__会被调用 n 次。
下面说生成器。
生成器（Generator）是创建迭代器的简单而强大的工具。它们写起来就像是正规的函数，只是在需要返回数据的时候使用yield语句。每次next()被调用时，生成器会返回它脱离的位置（它记忆语句最后一次执行的位置和所有的数据值）。以下示例演示了生成器可以很简单的创建出来:
>>> def reverse(data):
... for index in range(len(data)-1, -1, -1):
... yield data[index]
...
>>> for char in reverse('hello'):
... print(char)
...
o
l
l
e
h
关于迭代器和生成器的区别，生成器能做到迭代器能做的所有事,而且因为自动创建了__iter__()和 next()方法,生成器显得特别简洁,而且生成器也是高效的。除了创建和保存程序状态的自动方法,当发生器终结时,还会自动抛出StopIteration异常。一个带有yield的函数就是一个 生成器,它和普通函数不同,生成一个 generator 看起来像函数调用,但不会执行任何函数代码,直到对其调用next()(在 for 循环中会自动调用next())才开始执行。虽然执行流程仍按函数的流程执行,但每执行到一个yield语句就会中断,并返回一个迭代值,下次执行时从yield的下一个语句继续执行。看起来就好像一个函数在正常执行的过程中被yield中断了数次,每次中断都会通过yield返回当前的迭代值（yield暂停一个函数，next()从其暂停处恢复其运行）。
另外对于生成器，python还提供了一个生成器表达式：类似与一个yield值的匿名函数。表达式本身看起来像列表推到, 但不是用方括号而是用圆括号包围起来：
>>> unique_characters = {'E', 'D', 'M', 'O', 'N', 'S', 'R', 'Y'}
>>> gen = (ord(c) for c in unique_characters)
>>> gen
<generator object <genexpr> at 0x7f2be4668678>
>>> for i in gen:
... print(i)
...
69
79
83
77
82
78
89
68
>>>
如果需要，可以将生成器表达式传给tuple、list或是set来迭代所有的值并且返回元组、列表或是集合。在这种情况下，不需要一对额外的括号 ———— 直接将生成器表达式 ord(c) for c in unique_characters传给tuple()等函数就可以了, Python 会推断出它是一个生成器表达式。
最后，为什么要使用生成器？因为效率。使用生成器表达式取代列表解析可以同时节省 cpu 和 内存(ram)。如果你构造一个列表的目的仅仅是传递给别的函数,(比如 传递给tuple()或者set()), 那就用生成器表达式替代吧!
以上所述就是本文的全部内容了，希望大家能够喜欢。
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