python 生成器

# 1.next 函数*************************************************************

# 斐波那契方法

def fibonacci():
    numbers = []
    while True:
        if len(numbers) <2:
            numbers.append(1)
        else:
            numbers.append(sum(numbers))
            numbers.pop(0)
        yield numbers[-1]


gen = fibonacci()
# print(next(gen))
# print(next(gen))
# print(next(gen))
# print(next(gen))
# print(next(gen))
# 2.StopIteration *************************************************************

def my_generator():
    yield 1
    yield 2
    raise StopIteration #后续的代码不会继续执行
    yield 3

# 2.生成器之间的交互*************************************************************
#普通的生成器
def sqare():
    "求平方"
    cursor = 1
    while True:
        yield cursor**2
        cursor +=1

s = sqare()
# print(next(s))
# print(next(s))
# print(next(s))
# print(next(s))

# 有交互生成器

def sqare_vi():
    "求平方"
    cursor = 1
    response = None
    while True:
        if response:
            response = yield response**2
            continue
        response = yield cursor **2
        cursor +=1

s = sqare_vi()
print(next(s))
print(next(s))
print(s.send(7))
print(s.send(9))
print(next(s))
print(next(s))

# *************************************************************
# 标准库的生成器
# python2      python3
# xrange       range
# iterkeys     keys
# itervalues   values
# iteritems    items
#  zip          zip
#  map          map
#  文件对象     文件对象
z = zip([1,2,3,4,4],[6,7,8,9])
# print(next(z))
# print(next(z))
# print(next(z))
# print(next(z))
# print(next(z))

m = map(lambda x,y :max([x,y]),[4,1,7],[3,4,5])
print(next(m))

# 文件对象
f = open(‘test.txt‘,‘r‘)
# print(next(f))
# print(next(f))
# print(next(f))
# print(next(f))
# next(f) 与 readline 的区别： 当文件读到最后一行，readline捕获StopIteration异常，并返回‘‘字符串

# 何时需要生成器*************************************************************
# 1，分块读取数据
# 2.分块计算数据

# 生成器内部的生成器*************************************************************
def gen1():
    yield ‘foo‘
    yield ‘bar‘

def gen2():
    yield ‘spam‘
    yield ‘eggs‘

def full_gen():
    for i in gen1():
        yield i

    for j in gen2():
        yield j

# for m in full_gen():
#     print(m)

# m = full_gen()
# print(next(m))

import itertools
def full_gen1():

    for word in itertools.chain(gen1(),gen2()):
        yield word

full1 = full_gen1()
# print(next(full1))
# print(next(full1))
# print(next(full1))
# print(next(full1))

# 从python3.3后提供了一个更干净的语法

def gen_full2():
    yield from gen1()
    yield from gen2()

full2 = gen_full2()

# print(next(full2))
# print(next(full2))
# print(next(full2))
# print(next(full2))

迭代器:
只要定义了 __next__方法,就是一个迭代器

生成器也是一种迭代器,但是只能迭代一次,因为只保存一次值
yield a

next(yield 对象) 进行遍历

可迭代对象:
只要定义了 __iter__ 方法就是一个可迭代对象

列表,字符串,元组,字典和集合都是可迭代对象

使用 iter(可迭代对象) 可以转换为 迭代器

python 生成器

原文：https://www.cnblogs.com/xwyjh/p/13398666.html