Iterable:-可迭代的东西是可迭代的;比如序列，比如列表，字符串等等。 它也有__getitem__方法或__iter__方法。现在如果我们对该对象使用iter()函数，我们将得到一个迭代器。

迭代器:-当我们从iter()函数获得迭代器对象;我们调用__next__()方法(在python3中)或简单地调用next()(在python2中)来逐个获取元素。该类或该类的实例称为迭代器。

从文档:

迭代器的使用遍及并统一了Python。在后台，for语句在容器对象上调用iter()。该函数返回一个迭代器对象，该对象定义了__next__()方法，该方法每次访问容器中的一个元素。当没有更多的元素时，__next__()会引发一个StopIteration异常，告诉for循环终止。你可以使用next()内置函数调用__next__()方法;这个例子展示了它是如何工作的:

>>> s = 'abc'
>>> it = iter(s)
>>> it
<iterator object at 0x00A1DB50>
>>> next(it)
'a'
>>> next(it)
'b'
>>> next(it)
'c'
>>> next(it)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
    next(it)
StopIteration

一个类的前:-

class Reverse:
    """Iterator for looping over a sequence backwards."""
    def __init__(self, data):
        self.data = data
        self.index = len(data)
    def __iter__(self):
        return self
    def __next__(self):
        if self.index == 0:
            raise StopIteration
        self.index = self.index - 1
        return self.data[self.index]


>>> rev = Reverse('spam')
>>> iter(rev)
<__main__.Reverse object at 0x00A1DB50>
>>> for char in rev:
...     print(char)
...
m
a
p
s

以下是我在教授Python课程时使用的解释:

ITERABLE是:

任何可以循环的东西(例如，你可以循环一个字符串或文件)或 任何可以出现在for循环右边的东西:for x in iterable:…或 任何你可以用iter()调用并返回ITERATOR: iter(obj)或 一个定义__iter__的对象，该对象返回一个新的ITERATOR， 或者它可能有一个适合索引查找的__getitem__方法。

ITERATOR是一个对象:

在迭代过程中，state会记住它的位置， 使用__next__方法: 返回迭代中的下一个值 更新状态以指向下一个值 信号，当它完成时，引发StopIteration 并且它是可自迭代的(意味着它有一个返回self的__iter__方法)。

注:

Python 3中的__next__方法在Python 2中拼写为next，并且 内置函数next()在传递给它的对象上调用该方法。

例如:

>>> s = 'cat'      # s is an ITERABLE
                   # s is a str object that is immutable
                   # s has no state
                   # s has a __getitem__() method 

>>> t = iter(s)    # t is an ITERATOR
                   # t has state (it starts by pointing at the "c"
                   # t has a next() method and an __iter__() method

>>> next(t)        # the next() function returns the next value and advances the state
'c'
>>> next(t)        # the next() function returns the next value and advances
'a'
>>> next(t)        # the next() function returns the next value and advances
't'
>>> next(t)        # next() raises StopIteration to signal that iteration is complete
Traceback (most recent call last):
...
StopIteration

>>> iter(t) is t   # the iterator is self-iterable

我不知道这是否对任何人都有帮助，但我总是喜欢在脑子里把概念形象化，以便更好地理解它们。所以，就像我有一个小儿子一样，我用砖块和白纸来想象迭代器/迭代器的概念。

Suppose we are in the dark room and on the floor we have bricks for my son. Bricks of different size, color, does not matter now. Suppose we have 5 bricks like those. Those 5 bricks can be described as an object – let’s say bricks kit. We can do many things with this bricks kit – can take one and then take second and then third, can change places of bricks, put first brick above the second. We can do many sorts of things with those. Therefore this bricks kit is an iterable object or sequence as we can go through each brick and do something with it. We can only do it like my little son – we can play with one brick at a time. So again I imagine myself this bricks kit to be an iterable.

现在请记住，我们是在一个黑暗的房间里。或者几乎是黑暗的。问题是我们看不清这些砖，它们是什么颜色，什么形状等等。所以即使我们想对它们做些什么——也就是迭代它们——我们也不知道要做什么，怎么做，因为太暗了。

我们能做的是靠近第一块砖-作为一个砖套件的元素-我们可以放一张白色荧光纸，以便我们看到第一块砖元素在哪里。每次我们从工具箱中取出一块砖，我们就把这张白纸换成下一块砖，这样就能在黑暗的房间里看到它。这张白纸只不过是一个迭代器。它也是一个对象。而是一个我们可以使用可迭代对象中的元素的对象——bricks工具包。

顺便说一下，这解释了我早期的错误，当我在IDLE中尝试以下操作时，得到了一个TypeError:

 >>> X = [1,2,3,4,5]
 >>> next(X)
 Traceback (most recent call last):
    File "<pyshell#19>", line 1, in <module>
      next(X)
 TypeError: 'list' object is not an iterator

这里的列表X是我们的砖块工具包，但不是一张白纸。我需要先找到一个迭代器:

>>> X = [1,2,3,4,5]
>>> bricks_kit = [1,2,3,4,5]
>>> white_piece_of_paper = iter(bricks_kit)
>>> next(white_piece_of_paper)
1
>>> next(white_piece_of_paper)
2
>>>

不知道有没有帮助，但对我有帮助。如果有人能确认/纠正这个概念的可视化，我会很感激。这会帮助我了解更多。

其他人已经全面地解释了什么是iterable和iterator，所以我将尝试对生成器做同样的事情。

恕我直言，理解生成器的主要问题是“生成器”这个词的混淆用法，因为这个词有两种不同的含义:

作为创建(生成)迭代器的工具， 以返回迭代器的函数形式(即在函数体中包含yield语句)， 以生成器表达式的形式 作为使用该工具的结果，即结果迭代器。 (在这个意思中，生成器是迭代器的一种特殊形式——“generator”这个词指出了这个迭代器是如何创建的。)

Generator作为第一种工具:

In[2]: def my_generator():
  ...:     yield 100
  ...:     yield 200

In[3]: my_generator

输出[3]:<function __main__.my_generator()> .my_generator(

In[4]: type(my_generator)

[4]:函数

生成器作为使用此工具的结果(即迭代器):

In[5]: my_iterator = my_generator()
In[6]: my_iterator

输出[6]:<生成器对象my_generator at 0x00000000053EAE48>

In[7]: type(my_iterator)

[7]:发电机

Generator作为第二种类型的工具-与该工具的结果迭代器难以区分:

In[8]: my_gen_expression = (2 * i for i in (10, 20))
In[9]: my_gen_expression

Out[9]: <generator object <genexpr> at 0x000000000542C048>

In[10]: type(my_gen_expression)

[10]:发电机

下面是我的小抄:

 sequence
  +
  |
  v
   def __getitem__(self, index: int):
  +    ...
  |    raise IndexError
  |
  |
  |              def __iter__(self):
  |             +     ...
  |             |     return <iterator>
  |             |
  |             |
  +--> or <-----+        def __next__(self):
       +        |       +    ...
       |        |       |    raise StopIteration
       v        |       |
    iterable    |       |
           +    |       |
           |    |       v
           |    +----> and +-------> iterator
           |                               ^
           v                               |
   iter(<iterable>) +----------------------+
                                           |
   def generator():                        |
  +    yield 1                             |
  |                 generator_expression +-+
  |                                        |
  +-> generator() +-> generator_iterator +-+

小测验:你看到…

每个迭代器都是可迭代对象? 容器对象的__iter__()方法可以实现为生成器? 具有__next__方法的迭代器不一定是迭代器?

答案:

Every iterator must have an __iter__ method. Having __iter__ is enough to be an iterable. Therefore every iterator is an iterable. When __iter__ is called it should return an iterator (return <iterator> in the diagram above). Calling a generator returns a generator iterator which is a type of iterator. class Iterable1: def __iter__(self): # a method (which is a function defined inside a class body) # calling iter() converts iterable (tuple) to iterator return iter((1,2,3)) class Iterable2: def __iter__(self): # a generator for i in (1, 2, 3): yield i class Iterable3: def __iter__(self): # with PEP 380 syntax yield from (1, 2, 3) # passes assert list(Iterable1()) == list(Iterable2()) == list(Iterable3()) == [1, 2, 3] Here is an example: class MyIterable: def __init__(self): self.n = 0 def __getitem__(self, index: int): return (1, 2, 3)[index] def __next__(self): n = self.n = self.n + 1 if n > 3: raise StopIteration return n # if you can iter it without raising a TypeError, then it's an iterable. iter(MyIterable()) # but obviously `MyIterable()` is not an iterator since it does not have # an `__iter__` method. from collections.abc import Iterator assert isinstance(MyIterable(), Iterator) # AssertionError

这是另一个使用collections.abc的视图。这个视图在第二次或以后可能会有用。

从集合。ABC我们可以看到下面的层次结构:

builtins.object
    Iterable
        Iterator
            Generator

例如，Generator是由Iterator派生的Iterable是由基对象派生的。

因此,

Every iterator is an iterable, but not every iterable is an iterator. For example, [1, 2, 3] and range(10) are iterables, but not iterators. x = iter([1, 2, 3]) is an iterator and an iterable. A similar relationship exists between Iterator and Generator. Calling iter() on an iterator or a generator returns itself. Thus, if it is an iterator, then iter(it) is it is True. Under the hood, a list comprehension like [2 * x for x in nums] or a for loop like for x in nums:, acts as though iter() is called on the iterable (nums) and then iterates over nums using that iterator. Hence, all of the following are functionally equivalent (with, say, nums=[1, 2, 3]): for x in nums: for x in iter(nums): for x in iter(iter(nums)): for x in iter(iter(iter(iter(iter(nums))))):