虽然生成器对象的使用方法与迭代器对象类似，但是内部原理是不同的
构建迭代器时，不是把所有元素一次性加载到内存，而是一种延迟计算的方式返回元素。
生成器就是一特殊迭代器，它不会把所有内容放在内存里，每次调用 next()函数时，返回的都是本次计算出来的那个元素，用完之后立刻销毁。
邯城往事
#生成器函数
def generator():
print(1)
return 'a'
ret = generator()
print(ret)
#只要含有yield关键字的函数都是生成器函数
#yield不能和return共用且需要写在函数内
def generator():
print(1)
yield 'a'
#生成器函数 ： 执行之后会得到一个生成器作为返回值
ret = generator()
print(ret)
print(ret.__next__())
例如：
列表推导式：
#列表推导式
print([i*i for i in range(10)])
#生成器表达式
g = (i for i in range(10))
print(g)
for i in g:
print(i)
列表推导式和生成器表达式不同：
def check_file(filename,app):
with open(filename,encoding='utf-8') as f: #句柄 : handler,文件操作符，文件句柄
for i in f:
if app in i:
yield i
g = check_file('pwd','ding')
for i in g:
print(i.strip())
def check_file(filename):
with open(filename,encoding='utf-8') as f: #句柄 : handler,文件操作符，文件句柄
for i in f:
yield '***'+i
for i in check_file('pwd'):
print(i.strip())
def add(n,i):
return n+i
def test():
for i in range(4):
yield i
g = test()
for n in [1,10,5]:
g = (add(n,i) for i in g)
print(list(g))
此函数拆解步骤：
def add(n,i):
return n+i
def test():
for i in range(4):
yield i
g = test()
# for n in [1,10,5]:
# g = (add(n,i) for i in g)
# print(list(g))
n = 1
g = (add(n,i) for i in test())
n = 10
g = (add(n,i) for i in (add(n,i) for i in test()))
n = 5
g = (add(n,i) for i in (add(n,i) for i in (add(n,i) for i in test())))
print(list(g))
将如下进行拆解，g=test()
def add(n,i):
return n+i
def test():
for i in range(4):
yield i
g = test()
# for n in [1,10,5]:
# g = (add(n,i) for i in g)
# print(list(g))
n = 1
g = (add(n,i) for i in g)
n = 10
g = (add(n,i) for i in (add(n,i) for i in g))
n = 5
g = (add(n,i) for i in (add(n,i) for i in (add(n,i) for i in g)))
print(list(g))
n = 5
g = (add(n,i) for i in (add(n,i) for i in (add(n,i) for i in test())))
①
n = 5
g = (add(n,i) for i in (add(n,i) for i in (add(n,i) for i in (0,1,2,3))))
②
n = 5
g = (add(n,i) for i in (add(n,i) for i in (5,6,7,8)))
③
n = 5
g = (add(n,i) for i in (10,11,12,13))
④ 最终
n = 5
g = (15,16,17,18)