Python进阶:生成器 懒人版本的迭代器详解

从容器、可迭代对象谈起

所有的容器都是可迭代的（iterable）,迭代器提供了一个next方法。iter()返回一个迭代器，通过next()函数可以实现遍历。

def is_iterable(param):
try:
iter(param)
return True
except TypeError:
return False
params = [
1234,
'1234',
[1, 2, 3, 4],
set([1, 2, 3, 4]),
{1:1, 2:2, 3:3, 4:4},
(1, 2, 3, 4)
]
for param in params:
print('{} is iterable? {}'.format(param, is_iterable(param)))
########## 输出 ##########
# 1234 is iterable? False
# 1234 is iterable? True
# [1, 2, 3, 4] is iterable? True
# {1, 2, 3, 4} is iterable? True
# {1: 1, 2: 2, 3: 3, 4: 4} is iterable? True
# (1, 2, 3, 4) is iterable? True

除了数字外,其他数据结构都是可迭代的。

生成器是什么

生成器是懒人版本的迭代器。例:

import os
import psutil

#显示当前 python 程序占用的内存大小
def show_memory_info(hint):
pid = os.getpid()
p = psutil.Process(pid)

info = p.memory_full_info()
memory = info.uss / 1024. / 1024
print('{} memory used: {} MB'.format(hint, memory))

def test_iterator():
show_memory_info('initing iterator')
list_1 = [i for i in range(100000000)]
show_memory_info('after iterator initiated')
print(sum(list_1))
show_memory_info('after sum called')

def test_generator():
show_memory_info('initing generator')
list_2 = (i for i in range(100000000))
show_memory_info('after generator initiated')
print(sum(list_2))
show_memory_info('after sum called')

test_iterator()
test_generator()
%time test_iterator()
%time test_generator()

######### 输出 ##########

initing iterator memory used: 48.9765625 MB
after iterator initiated memory used: 3920.30078125 MB
4999999950000000
after sum called memory used: 3920.3046875 MB
Wall time: 17 s
initing generator memory used: 50.359375 MB
after generator initiated memory used: 50.359375 MB
4999999950000000
after sum called memory used: 50.109375 MB
Wall time: 12.5 s

[i for i in range(100000000)] 声明了一个迭代器，每个元素在生成后都会保存到内存中，占用了巨量的内存。(i for i in range(100000000)) 初始化了一个生成器，可以看到，生成器并不会像迭代器一样占用大量的内存，相比于 test_iterator()，test_generator()函数节省了一次生成一亿个元素的过程。在调用next()的时候，才会生成下一个变量.

生成器能玩啥花样

数学中有一个恒等式，(1 + 2 + 3 + ... + n)^2 = 1^3 + 2^3 + 3^3 + ... + n^3，用以下代码表达

def generator(k):
i = 1
while True:
yield i ** k
i += 1

gen_1 = generator(1)
gen_3 = generator(3)
print(gen_1)
print(gen_3)

def get_sum(n):
sum_1, sum_3 = 0, 0
for i in range(n):
next_1 = next(gen_1)
next_3 = next(gen_3)
print('next_1 = {}, next_3 = {}'.format(next_1, next_3))
sum_1 += next_1
sum_3 += next_3
print(sum_1 * sum_1, sum_3)

get_sum(8)

########## 输出 ##########

# <generator object generator at 0x000001E70651C4F8>
# <generator object generator at 0x000001E70651C390>
# next_1 = 1, next_3 = 1
# next_1 = 2, next_3 = 8
# next_1 = 3, next_3 = 27
# next_1 = 4, next_3 = 64
# next_1 = 5, next_3 = 125
# next_1 = 6, next_3 = 216
# next_1 = 7, next_3 = 343
# next_1 = 8, next_3 = 512
# 1296 1296

generator()这个函数，它返回了一个生成器，当运行到yield i ** k时，暂停并把i ** k作为next()的返回值。每次调用next(gen)时，暂停的程序会启动并往下执行，而且i的值也会被记住，继续累加，最后next_1为8，next_3为512.

仔细查看这个示例，发现迭代器是一个有限集合，生成器则可以成为一个无限集。调用next()，生成器根据运算会自动生成新的元素，然后返回给你，非常便捷。

再来看一个问题：给定一个list和一个指定数字，求这个数字在list中的位置:

#常规写法
def index_normal(L, target):
result = []
for i, num in enumerate(L):
if num == target:
result.append(i)
return result
print(index_normal([1, 6, 2, 4, 5, 2, 8, 6, 3, 2], 2))
########## 输出 ##########
[2, 5, 9]
#生成器写法
def index_generator(L, target):
for i, num in enumerate(L):
if num == target:
yield i
print(list(index_generator([1, 6, 2, 4, 5, 2, 8, 6, 3, 2], 2)))
######### 输出 ##########
[2, 5, 9]

再看一例子:

查找子序列：给定两个字符串a,b,查找字符串a是否字符串b的子序列,所谓子序列,即一个序列包含在另一个序列中并且顺序一

算法:分别用两个指针指向两个字符串的头，然后往后移动找出相同的值，如果其中一个指针走完了整个字符串也没有相同的值，则不是子序列

def is_subsequence(a, b):
b = iter(b)
return all(i in b for i in a)
print(is_subsequence([1, 3, 5], [1, 2, 3, 4, 5]))
print(is_subsequence([1, 4, 3], [1, 2, 3, 4, 5]))
######### 输出 ##########
True
False

下面代码为上面代码的演化版本

def is_subsequence(a, b):
b = iter(b)
print(b)

gen = (i for i in a)
print(gen)

for i in gen:
print(i)

gen = ((i in b) for i in a)
print(gen)

for i in gen:
print(i)

return all(((i in b) for i in a))

print(is_subsequence([1, 3, 5], [1, 2, 3, 4, 5]))
print(is_subsequence([1, 4, 3], [1, 2, 3, 4, 5]))

########## 输出 ##########

# <list_iterator object at 0x000001E7063D0E80>
# <generator object is_subsequence.<locals>.<genexpr> at 0x000001E70651C570>
# 1
# 3
# 5
# <generator object is_subsequence.<locals>.<genexpr> at 0x000001E70651C5E8>
# True
# True
# True
# False
# <list_iterator object at 0x000001E7063D0D30>
# <generator object is_subsequence.<locals>.<genexpr> at 0x000001E70651C5E8>
# 1
# 4
# 3
# <generator object is_subsequence.<locals>.<genexpr> at 0x000001E70651C570>
# True
# True
# False
# False

首先iter(b)把b转为迭代器。目的是内部实现next函数，(i for i in a) 会产生一个生成器 ，同样((i in b) for i in a)也是。然后(i in b)等阶于:

while True:
val = next(b)
if val == i:
yield True

这里非常巧妙地利用生成器的特性，next()函数运行的时候，保存了当前的指针。比如下面这个示例

b = (i for i in range(5))
print(2 in b)
print(4 in b)
print(3 in b)
########## 输出 ##########
True
True
False

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