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生成器和迭代器
生成器
通過列表生成式，我們可以直接創建一個列表。但是，受到內存限制，列表容量肯定是有限的。而且，創建一個包含100萬個元素的列表，不僅佔用很大的存儲空間，如果我們僅僅需要訪問前面幾個元素，那後面絕大多數元素佔用的空間都白白浪費了。
所以，如果列表元素可以按照某種算法推算出來，那我們是否可以在循環的過程中不斷推算出後續的元素呢？這樣就不必創建完整的list，從而節省大量的空間。在Python中，這種一邊循環一邊計算的機制，稱爲生成器：generator。
要創建一個generator，有很多種方法。第一種方法很簡單，只要把一個列表生成式的[]改成()，就創建了一個generator：
>>> L = [x*x for x in range(10)]
>>> L
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> g=(x*x for x in range(10))
>>> g
<generator object <genexpr> at 0x036B28D0>
>>> 創建L和g的區別僅在於最外層的[]和()，L是一個list，而g是一個generator。可以通過next()函數獲得generator的下一個返回值：
>>> next(g)
0
>>> next(g)
1
>>> next(g)
4
>>> next(g)
9
>>> next(g)
16
>>> next(g)
25
>>> next(g)
36
>>> next(g)
49
>>> next(g)
64
>>> next(g)
81
>>> next(g)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
StopIterationgenerator保存的是算法，每次調用next(g)，就計算出g的下一個元素的值，直到計算到最後一個元素，沒有更多的元素時，拋出StopIteration的錯誤。
yield
The yield statement is only used when defining a generator function, and is only used in the body of the generator function. Using a yield statement in a function definition is sufficient to cause that definition to create a generator function instead of a normal function.
When a generator function is called, it returns an iterator known as a generator iterator, or more commonly, a generator. The body of the generator function is executed by calling the generator’s next() method repeatedly until it raises an exception.
When a yield statement is executed, the state of the generator is frozen and the value of expression_list is returned to next()’s caller. By “frozen” we mean that all local state is retained, including the current bindings of local variables, the instruction pointer, and the internal evaluation stack: enough information is saved so that the next time next() is invoked, the function can proceed exactly as if the yield statement were just another external call.
The yield statement is not allowed in the try clause of a try … finally construct. The difficulty is that there’s no guarantee the generator will ever be resumed, hence no guarantee that the finally block will ever get executed.
generator和函數的執行流程不一樣。函數是順序執行，遇到return語句或者最後一行函數語句就返回。而變成generator的函數，在每次調用next()的時候執行，遇到yield語句返回，再次執行時從上次返回的yield語句處繼續執行。
還可通過yield實現在單線程的情況下實現併發運算的效果
#_*_coding:utf-8_*_
__author__ = 'Alex Li'
import time
def consumer(name):
print("%s 準備吃包子啦!" %name)
while True:
baozi = yield
print("包子[%s]來了,被[%s]吃了!" %(baozi,name))
def producer(name):
c = consumer('A')
c2 = consumer('B')
c.__next__()
c2.__next__()
print("老子開始準備做包子啦!")
for i in range(10):
time.sleep(1)
print("做了2個包子!")
c.send(i)
c2.send(i)
producer("alex")yield from
python 3新加內容
yield from expression, allowing a generator to delegate part of its operations to another generator. This allows a section of code containing yield to be factored out and placed in another generator. Additionally, the subgenerator is allowed to return with a value, and the value is made available to the delegating generator.
While designed primarily for use in delegating to a subgenerator, the yield from expression actually allows delegation to arbitrary subiterators.
For simple iterators, yield from iterable is essentially just a shortened form of for item in iterable: yield item
>>> def g(x):
yield from range(x, 0, -1)
yield from range(x)
... ... ...
>>> list(g(5))
[5, 4, 3, 2, 1, 0, 1, 2, 3, 4]However, unlike an ordinary loop, yield from allows subgenerators to receive sent and thrown values directly from the calling scope, and return a final value to the outer generator
>>> def accumulate():
... tally = 0
... while 1:
... next = yield
... if next is None:
... return tally
... tally += next
...
>>> def gather_tallies(tallies):
... while 1:
... tally = yield from accumulate()
... tallies.append(tally)
...
>>> tallies = []
>>> acc = gather_tallies(tallies)
>>> next(acc) # Ensure the accumulator is ready to accept values
>>> for i in range(4):
... acc.send(i)
...
>>> acc.send(None) # Finish the first tally
>>> for i in range(5):
... acc.send(i)
...
>>> acc.send(None) # Finish the second tally
>>> tallies
[6, 10]The main principle driving this change is to allow even generators that are designed to be used with the send and throw methods to be split into multiple subgenerators as easily as a single large function can be split into multiple subfunctions.
迭代器
可以直接作用於for循環的數據類型有以下幾種：
一類是集合數據類型，如list、tuple、dict、set、str等；
一類是generator，包括生成器和帶yield的generator function。
這些可以直接作用於for循環的對象統稱爲可迭代對象：Iterable。
可以使用isinstance()判斷一個對象是否是Iterable對象：
>>> from collections import Iterable
>>> isinstance([], Iterable)
True
>>> isinstance({}, Iterable)
True
>>> isinstance('abc', Iterable)
True
>>> isinstance((x for x in range(10)), Iterable)
True
>>> isinstance(100, Iterable)
False而生成器不但可以作用於for循環，還可以被next()函數不斷調用並返回下一個值，直到最後拋出StopIteration錯誤表示無法繼續返回下一個值了。
*可以被next()函數調用並不斷返回下一個值的對象稱爲迭代器：Iterator。
>>> from collections import Iterator
>>> isinstance((x for x in range(10)), Iterator)
True
>>> isinstance([], Iterator)
False
>>> isinstance({}, Iterator)
False
>>> isinstance('abc', Iterator)
False生成器都是Iterator對象，但list、dict、str雖然是Iterable，卻不是Iterator。
把list、dict、str等Iterable變成Iterator可以使用iter()函數：
>>> isinstance(iter([]), Iterator)
True
>>> isinstance(iter('abc'), Iterator)
True爲什麼list、dict、str等數據類型不是Iterator？
這是因爲Python的Iterator對象表示的是一個數據流，Iterator對象可以被next()函數調用並不斷返回下一個數據，直到沒有數據時拋出StopIteration錯誤。可以把這個數據流看做是一個有序序列，但我們卻不能提前知道序列的長度，只能不斷通過next()函數實現按需計算下一個數據，所以Iterator的計算是惰性的，只有在需要返回下一個數據時它纔會計算。
Iterator甚至可以表示一個無限大的數據流，例如全體自然數。而使用list是永遠不可能存儲全體自然數的。
小結
凡是可作用於for循環的對象都是Iterable類型；
凡是可作用於next()函數的對象都是Iterator類型，它們表示一個惰性計算的序列；
集合數據類型如list、dict、str等是Iterable但不是Iterator，不過可以通過iter()函數獲得一個Iterator對象。
Python的for循環本質上就是通過不斷調用next()函數實現的，例如：
for x in [1, 2, 3, 4, 5]:
pass實際上完全等價於：
# 首先獲得Iterator對象:
it = iter([1, 2, 3, 4, 5])
# 循環:
while True:
try:
# 獲得下一個值:
x = next(it)
except StopIteration:
# 遇到StopIteration就退出循環
break參考資料
1. http://www.cnblogs.com/alex3714/articles/5765046.html
2. https://docs.python.org/3/whatsnew/3.3.html#pep-380
3. http://www.cnblogs.com/wupeiqi/articles/4980620.html