迭代是Python最强大的功能之一，是访问集合元素的一种方式。
迭代器是一个可以记住遍历的位置的对象。
迭代器对象从集合的第一个元素开始访问，直到所有的元素被访问完结束。迭代器只能往前不会后退。
迭代器有两个基本的方法：iter() 和 next()。
字符串，列表，元组，集合、字典、range()、文件句柄等可迭代对象（iterable）都可用于创建迭代器：
>>> list = [1,2,3,4] >>> it = iter(list) # 创建迭代器对象 >>> next(it) # 输出迭代器的下一个元素 1 >>> next(it) 2 >>>
迭代器对象可以使用常规for语句进行遍历：
>>> list = [‘a‘, ‘b‘, ‘c‘, ‘d‘] >>> it = iter(list) # 创建迭代器对象 >>> for x in it: print(x, end=" ") a b c d >>>
也可以使用 next() 函数：
>>> lst = [2,6,8,9] >>> it = iter(lst) # 创建迭代器对象 >>> >>> while True: try: print(next(it)) except StopIteration: break 2 6 8 9 >>>
把一个类作为一个迭代器使用需要在类中实现两个方法 __iter__() 与 __next__() 。
如果你已经了解的面向对象编程，就知道类都有一个构造函数，Python 的构造函数为 __init__(), 它会在对象初始化的时候执行。
__iter__() 方法返回一个特殊的迭代器对象， 这个迭代器对象实现了 __next__() 方法并通过 StopIteration 异常标识迭代的完成。 __next__() 方法（Python 2 里是 next()）会返回下一个迭代器对象。
创建一个返回数字的迭代器（计数器），初始值为 1，逐步递增 1：
class Counter:
def __iter__(self):
self.a = 1
return self
def __next__(self):
x = self.a
self.a += 1
return x
myclass = Counter()
myiter = iter(myclass)
print(next(myiter))
print(next(myiter))
print(next(myiter))
print(next(myiter))
print(next(myiter))
# 执行输出结果为： 1 2 3 4 5
StopIteration 异常用于标识迭代的完成，防止出现无限循环的情况，在 __next__() 方法中我们可以设置在完成指定循环次数后触发 StopIteration 异常来结束迭代。
>>> str1 = "Python"
>>> strObj = str1.__iter__()
>>> strObj.__next__()
‘P‘
>>> strObj.__next__()
‘y‘
>>> strObj.__next__()
‘t‘
>>> strObj.__next__()
‘h‘
>>> strObj.__next__()
‘o‘
>>> strObj.__next__()
‘n‘
>>> strObj.__next__()
Traceback (most recent call last):
File "<pyshell#33>", line 1, in <module>
strObj.__next__()
StopIteration
>>>
那么如何判断一个对象是否是可迭代对象？
>>> tup = (1,2,3) >>> type(tup) <class ‘tuple‘> >>> dir(tup) # 带参数时，返回参数的属性、方法列表。 [‘__add__‘, ‘__class__‘, ‘__contains__‘, ‘__delattr__‘, ‘__dir__‘, ‘__doc__‘, ‘__eq__‘, ‘__format__‘, ‘__ge__‘, ‘__getattribute__‘, ‘__getitem__‘,
‘__getnewargs__‘, ‘__gt__‘, ‘__hash__‘, ‘__init__‘, ‘__init_subclass__‘, ‘__iter__‘, ‘__le__‘, ‘__len__‘, ‘__lt__‘, ‘__mul__‘, ‘__ne__‘, ‘__new__‘,
‘__reduce__‘, ‘__reduce_ex__‘, ‘__repr__‘, ‘__rmul__‘, ‘__setattr__‘, ‘__sizeof__‘, ‘__str__‘, ‘__subclasshook__‘, ‘count‘, ‘index‘] >>> print(‘__iter__‘ in dir(tup)) True >>>
>>> dic = {1:‘dict‘, 2:‘str‘, 3:‘list‘, 4:‘tuple‘, 5:‘set‘, 6:‘range()‘,7:‘flie handler‘}
>>> isinstance(dic, Iterable)
True
>>> isinstance(dic, Iterator)
False
>>>
>>> ran = range(6)
>>> type(ran)
<class ‘range‘>
>>> isinstance(ran, Iterable)
True
>>> isinstance(ran, Iterator)
False
>>>
在 Python 中，使用了 yield 的函数被称为生成器（generator）。
跟普通函数不同的是，生成器是一个返回迭代器的函数，只能用于迭代操作，更简单点理解生成器就是一个迭代器。
在调用生成器运行的过程中，每次遇到 yield 时函数会暂停并保存当前所有的运行信息，返回 yield 的值, 并在下一次执行 next() 方法时从当前位置继续运行。
调用一个生成器函数，返回的是一个迭代器对象。
yield Vs return：
return返回后，函数状态终止，而yield会保存当前函数的执行状态，在返回后，函数又回到之前保存的状态继续执行。
以下实例使用 yield 实现斐波那契数列：
>>> def fib(max): # 生成器函数 - 斐波那契
a, b, n = 0, 1, 0
while n < max:
yield b # 使用 yield
a, b = b, a + b
n = n + 1
>>> f = fib(6) # 调用 fab(5) 不会执行 fab 函数，而是返回一个 iterable 对象！
>>> f # Python 解释器会将其视为一个 generator
<generator object fib at 0x000001C6CB627780>
>>>
>>> for n in fib(5):
print(n)
1
1
2
3
5
>>>
>>> f = fib(5)
>>> next(f) # 使用next函数从生成器中取值，使用next可以推动生成器的执行
1
>>> next(f)
1
>>> next(f)
2
>>> next(f)
3
>>> next(f)
5
>>> next(f) # 当函数中已经没有更多的yield时继续执行next(g)，遇到StopIteration
Traceback (most recent call last):
File "<pyshell#37>", line 1, in <module>
next(f)
StopIteration
>>>
>>> fwrong = fib(6)
>>> fwrong.next() # Python2 中的语法，Python3 会报错
Traceback (most recent call last):
File "<pyshell#40>", line 1, in <module>
fwrong.next() # Python2 中的语法，Python3 会报错
AttributeError: ‘generator‘ object has no attribute ‘next‘
>>>
send向生成器中发送数据。send的作用相当于next，只是在驱动生成器继续执行的同时还可以向生成器中传递数据。
>>> import numbers
>>> def gen_sum():
total = 0
while True:
num = yield
if isinstance(num, numbers.Integral):
total += num
print(‘total: ‘, total)
elif num is None:
break
return total
>>> g = gen_sum()
>>> g
<generator object gen_sum at 0x0000026A6703D3B8>
>>> g.send(None) # 相当于next(g),预激活生成器
>>> g.send(2)
total: 2
>>> g.send(6)
total: 8
>>> g.send(12)
total: 20
>>> g.send(None) # 停止生成器
Traceback (most recent call last):
File "<pyshell#40>", line 1, in <module>
g.send(None)
StopIteration: 20
>>>
>>> try:
g.send(None) # 停止生成器
except StopIteration as e:
print(e.value)
None
>>>
yield from 将一个可迭代对象变成一个迭代器返回，也可以说，yield from关键字可以直接返回一个生成器
>>> def func(): lst = [‘str‘, ‘tuple‘, ‘list‘, ‘dict‘, ‘set‘] yield lst >>> gen = func() >>> next(gen) [‘str‘, ‘tuple‘, ‘list‘, ‘dict‘, ‘set‘] >>> for i in gen: print(i) >>> # yield from 将一个可迭代对象变成一个迭代器返回 >>> def func2(): lst = [‘str‘, ‘tuple‘, ‘list‘, ‘dict‘, ‘set‘] yield from lst >>> gen2 = func2() >>> next(gen2) ‘str‘ >>> next(gen2) ‘tuple‘ >>> for i in gen2: print(i) list dict set >>>
>>> lst = [‘H‘,‘e‘,‘l‘]
>>> dic = {‘l‘:‘vvvvv‘,‘o‘:‘eeeee‘}
>>> str1 = ‘Python‘
>>>
>>> def yield_gen():
for i in lst:
yield i
for j in dic:
yield j
for k in str1:
yield k
>>> for item in yield_gen():
print(item, end=‘‘)
HelloPython
>>>
>>> l = [‘H‘,‘e‘,‘l‘]
>>> d = {‘l‘:‘xxxxx‘,‘o‘:‘ooooo‘}
>>> s = ‘Java‘
>>>
>>> def yield_from_gen():
yield from l
yield from d
yield from s
>>> for item in yield_from_gen():
print(item, end=‘‘)
HelloJava
>>>
更容易使用，代码量较小内存使用更加高效。比如：
根据维基百科给出的定义，“协程 是为非抢占式多任务产生子程序的计算机程序组件，协程允许不同入口点在不同位置暂停或开始执行程序”。从技术的角度来说，“协程就是你可以暂停执行的函数”。如果你把它理解成“就像生成器一样”，那么你就想对了。
#基于yield实现异步
def consumer():
‘‘‘任务1:接收数据,处理数据‘‘‘
while True:
x=yield
def producer():
‘‘‘任务2:生产数据‘‘‘
g=consumer()
next(g)
for i in range(10000000):
g.send(i)
producer()
import datetime
import heapq # 堆模块
import time
class Task:
def __init__(self, wait_until, coro):
self.coro = coro
self.waiting_until = wait_until
def __eq__(self, other):
return self.waiting_until == other.waiting_until
def __lt__(self, other):
return self.waiting_until < other.waiting_until
class SleepingLoop:
def __init__(self, *coros):
self._new = coros
self._waiting = []
def run_until_complete(self):
for coro in self._new:
wait_for = coro.send(None)
heapq.heappush(self._waiting, Task(wait_for, coro))
while self._waiting:
now = datetime.datetime.now()
task = heapq.heappop(self._waiting)
if now < task.waiting_until:
delta = task.waiting_until - now
time.sleep(delta.total_seconds())
now = datetime.datetime.now()
try:
print(‘*‘*50)
wait_until = task.coro.send(now)
print(‘-‘*50)
heapq.heappush(self._waiting, Task(wait_until, task.coro))
except StopIteration:
pass
def sleep(seconds):
now = datetime.datetime.now()
wait_until = now + datetime.timedelta(seconds=seconds)
print(‘before yield wait_until‘)
actual = yield wait_until # 返回一个datetime数据类型的时间
print(‘after yield wait_until‘)
return actual - now
def countdown(label, length, *, delay=0):
print(label, ‘waiting‘, delay, ‘seconds before starting countdown‘)
delta = yield from sleep(delay)
print(label, ‘starting after waiting‘, delta)
while length:
print(label, ‘T-minus‘, length)
waited = yield from sleep(1)
length -= 1
print(label, ‘lift-off!‘)
def main():
loop = SleepingLoop(countdown(‘A‘, 5), countdown(‘B‘, 3, delay=2),
countdown(‘C‘, 4, delay=1))
start = datetime.datetime.now()
loop.run_until_complete()
print(‘Total elapsed time is‘, datetime.datetime.now() - start)
if __name__ == ‘__main__‘:
main()
执行结果：
A waiting 0 seconds before starting countdown before yield wait_until B waiting 2 seconds before starting countdown before yield wait_until C waiting 1 seconds before starting countdown before yield wait_until ************************************************** after yield wait_until A starting after waiting 0:00:00 A T-minus 5 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until C starting after waiting 0:00:01.001511 C T-minus 4 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until A T-minus 4 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until B starting after waiting 0:00:02.000894 B T-minus 3 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until C T-minus 3 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until A T-minus 3 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until B T-minus 2 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until C T-minus 2 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until A T-minus 2 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until B T-minus 1 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until C T-minus 1 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until A T-minus 1 before yield wait_until -------------------------------------------------- ************************************************** after yield wait_until B lift-off! ************************************************** after yield wait_until C lift-off! ************************************************** after yield wait_until A lift-off! Total elapsed time is 0:00:05.005168
asyncio是Python 3.4版本引入的标准库，直接内置了对异步IO的支持。
用asyncio提供的@asyncio.coroutine可以把一个generator标记为coroutine类型，然后在coroutine内部用yield from调用另一个coroutine实现异步操作。
asyncio的编程模型就是一个消息循环。我们从asyncio模块中直接获取一个EventLoop的引用，然后把需要执行的协程扔到EventLoop中执行，就实现了异步IO。
coroutine+yield from
import asyncio
@asyncio.coroutine
def hello():
print("Nice to learn asyncio.coroutine!")
# 异步调用asyncio.sleep(1):
r = yield from asyncio.sleep(1)
print("Nice to learn asyncio.coroutine again !")
# 获取EventLoop:
loop = asyncio.get_event_loop()
# 执行coroutine
loop.run_until_complete(hello())
loop.close()
Nice to learn asyncio.coroutine ! Nice to learn asyncio.coroutine again !
为了简化并更好地标识异步IO，从Python 3.5开始引入了新的语法async和await，可以让coroutine的代码更简洁易读。
请注意，async和 await是针对coroutine的新语法，要使用新的语法，只需要做两步简单的替换：
@asyncio.coroutine替换为async；yield from替换为await。async+await
在协程函数中，可以通过await语法来挂起自身的协程，并等待另一个协程完成直到返回结果：
import asyncio async def hello(): print("Nice to learn asyncio.coroutine!") # 异步调用asyncio.sleep(1): await asyncio.sleep(1) print("Nice to learn asyncio.coroutine again !") # 获取EventLoop: loop = asyncio.get_event_loop() # 执行coroutine loop.run_until_complete(hello()) loop.close()
执行多个任务
import threading
import asyncio
async def hello():
print(‘Hello Python! (%s)‘ % threading.currentThread())
await asyncio.sleep(1)
print(‘Hello Python again! (%s)‘ % threading.currentThread())
loop = asyncio.get_event_loop()
tasks = [hello(), hello()]
loop.run_until_complete(asyncio.wait(tasks))
loop.close()
结果：
Hello Python! (<_MainThread(MainThread, started 4536)>) Hello Python! (<_MainThread(MainThread, started 4536)>) Hello Python again! (<_MainThread(MainThread, started 4536)>) Hello Python again! (<_MainThread(MainThread, started 4536)>)
获取返回值
import threading
import asyncio
async def hello():
print(‘Hello Python! (%s)‘ % threading.currentThread())
await asyncio.sleep(1)
print(‘Hello Python again! (%s)‘ % threading.currentThread())
return "It‘s done"
loop = asyncio.get_event_loop()
task = loop.create_task(hello())
loop.run_until_complete(task)
ret = task.result()
print(ret)
结果：
Hello Python! (<_MainThread(MainThread, started 6136)>) Hello Python again! (<_MainThread(MainThread, started 6136)>) It‘s done
执行多个任务获取返回值
import threading
import asyncio
async def hello(seq):
print(‘Hello Python! (%s)‘ % threading.currentThread())
await asyncio.sleep(1)
print(‘Hello Python again! (%s)‘ % threading.currentThread())
return "It‘s done", seq
loop = asyncio.get_event_loop()
task1 = loop.create_task(hello(2))
task2 = loop.create_task(hello(1))
task_list = [task1, task2]
tasks = asyncio.wait(task_list)
loop.run_until_complete(tasks)
for t in task_list:
print(t.result())
结果：
Hello Python! (<_MainThread(MainThread, started 12956)>)
Hello Python! (<_MainThread(MainThread, started 12956)>)
Hello Python again! (<_MainThread(MainThread, started 12956)>)
Hello Python again! (<_MainThread(MainThread, started 12956)>)
("It‘s done", 2)
("It‘s done", 1)
原文：https://www.cnblogs.com/51try-again/p/11074621.html