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]