一、迭代器
1.迭代器即用来迭代取值的工具,而迭代是重复反馈过程的活动,其目的通常是为了逼近所需的目标或结果,每一次对过程的重复称为一次“迭代”,而每一次迭代得到的结果会作为下一次迭代的初始值,单纯的重复并不是迭代
items = ["egon","tom","lili"] def func(items): i = 0 while i < len(items): print(items[i]) i += 1
2.用迭代器的两个原因:
(1).为了找到一种新的、统一的取值方式(可以不依赖与索引以及Key的)
(2).惰性计算,不耗费内存
3.用法
(1).可迭代对象 iterable
内置有__iter__方法 (可迭代对象.__iter__() 返回迭代器对象)
(2).迭代器对象 iterator
内置有__iter__方法
内置有__next__方法 (迭代器对象.__next__() 返回的是下一个值)
# "abc".__iter__() #可迭代对象
# [1,2,3].__iter__() #...
# (1,2,3).__iter__() #...
# {'k1':111}.__iter__() #...
# {111,22}.__iter__() #...
# open('b.txt',mode='wb').__iter__() #不变
# open('b.txt',mode='wb').__next__() #文件属于迭代器对象
# dic = {'name':'egon','age':18,'gender':'male'}
# dic = "hello"
# iter_dic = dic.__iter__()
# print(iter_dic)
# print(iter_dic.__iter__().__iter__().__iter__() is iter_dic)
# print(iter_dic)
# print(iter_dic.__next__())
# print(iter_dic.__next__())
# print(iter_dic.__next__())
# print(iter_dic.__next__()) # StopIteration 取完
for循环原理:
dic = {'name':'egon','age':18,'gender':'male'}
# len("hello") # "hello".__len__()
# iter_dic = iter(dic) # dic.__iter__()
#
# while True:
# try:
# print(next(iter_dic))
# except StopIteration:
# break
# for k in dic: #dic 即为可迭代对象
# print(k)
####迭代器对象每次取完值,意味着原值不被取出,不能重复取迭代器对象,必须重新生成一个迭代器对象
dic = {'name':'egon','age':18,'gender':'male'}
iter_dic = iter(dic)
for k in iter_dic:
print(k)
print('='*50)
for k in iter_dic: #该迭代器对象已经取完了
print(k)
for k in dic:
print(k)
print('='*50)
for k in dic:
print(k)
4.总结迭代器对象优缺点
#优点
(1).提供了一种新的、统一的取值方式(可以不依赖与索引以及Key的)
(2).惰性计算,不耗费内存
#缺点
(1)取值不够灵活
(2)一次性的,只能往后取,无法预知数据的个数
二、生成器
1.函数内但凡出现 yield 关键字,再调用函数不会执行函数体代码,会得到一个生成器对象
生成器就是一种自定义的迭代器
2.yield 与 return
相同点:返回值层面都一样
不同点:return只能返回一次值函数就立即结束,而yield可以返回多次值
# def func():
# print('hello1')
# yield 1
# print('hello2')
# yield 2
# print('hello3')
# yield 3
# print('hello4')
#
# g = func()
# # print(g)
# # g.__iter__()
# # g.__next__()
#
# res1 = next(g)
# print(res1)
#
# res2 = next(g)
# print(res2)
#
# res3 = next(g)
# print(res3)
#
# next(g)
3.列表生成式
例题一
# nums = []
# for i in range(1,6):
# nums.append(i**2)
# nums = [i**2 for i in range(1,6)]
# nums = ['ok' for i in range(1,6)]
# print(nums)
例题二
# names = ['egon_nb',"lxx_sb","hxx_sb"]
# new_l = []
# for name in names:
# if name.endswith("_sb"):
# new_l.append(name)
# new_l = [name for name in names if name.endswith("_sb")]
# print(new_l)
4.字典生成式
一
# res = {i:i**2 for i in range(5)}
# print(res,type(res))
二
# items = [('k1',111),('k2',222)]
# print({k:v for k,v in items})
5.集合生成式
# res = {i for i in range(5)}
# print(res,type(res))
6.生成器表达式
# res = (i for i in range(5))
# print(res,type(res))
# print(next(res))
# print(next(res))
# print(next(res))
# for i in res:
# print(i)
# for i in res:
# print(i)
# for i in res:
# print(i)
# print(list(res))
# print(list(res))
# print(sum([1,2,3,4,5]))
with open('a.txt',mode='rt',encoding='utf-8') as f:
# data = f.read()
# print(len(data)) # 24
#
# res = 0
# for line in f:
# res += len(line)
# lines_size = (len(line) for line in f)
# res = sum(lines_size)
res = sum(len(line) for line in f)
print(res)
三、面向过程编程
面向过程编程是一种编写思想or编程范式
面向过程核心是“过程”二字,过程就是流程,流程指的就是先干什么、再干什么、后干什么
基于面向过程编写程序就好比在设计一条条流水线
优点:复杂的问题流程化、进而简单化
缺点:牵一发而动全身,扩展性差