基础补充练习

基础补充1——collections模块

命名元组另一个用途就是作为字典的替代,因为字典存储需要更多的内存空间。如果你需要构建一个非常大的包含字典的数据结构,那么使用命名元组会更加高效。但是需要注意的是,不像字典那样,一个命名元组是不可更改的。

from collections import namedtuple

records=[

('mac',2,20000),

('lenovo',1,3000),

('apple',0,10),

('tesla',10,10000000)

]

cost=0.0

for re in records:

cost+=re[1]*re[2]

print('商品:%s 购买个数:%s 总价:%s'%(re[0],re[2],cost))

sk=namedtuple('Stock',['name','count','price'])

for re in records:

s=sk(*re)

print(s.count,s.price)

p=namedtuple('People',['name','gender','age'])

l=['alex','female',18]

p1=p(*l)

print(p1)

print(p1.name)

print(p1.gender)

print(p1.age)

#p1.name='karla'#报错，不可修改

p1=p1._replace(name='karla')#需要重新赋值给p1

print(p1.name)

#重新设置一个函数，弥补只能使用_replace修改的缺点

p=namedtuple('People',['name','gender','age'])

p1=p('','',None)

def dic_to_stock(s):

return p1._replace(**s)

print(dic_to_stock({'name':'karla','gender':'female','age':18}))

print(dic_to_stock({'name':'lily','gender':'male','age':30}))

基础补充2——从字典中提取子集

prices={

'shiqun':48.78,

'guanyuqiao':47.5,

'liyijun':55.3,

'zhangyaqian':66

}

prices_new={key:val for key,val in prices.items() if val>50}

print(prices_new)

基础补充4——带参数的装饰器

name='karla'

pwd='root'

def auth(auth_type):

def inner_auth(func):

def wrapper(*args,**kwargs):

username=input('username:').strip()

password=input('password:').strip()

if auth_type=='local':

if username==name and password==pwd:

print('user login successful')

return func(*args,**kwargs)

else:

exit('log err')

elif auth_type=='ldap':

print('ladp是啥呀')

return wrapper

return inner_auth

def index():

print('welcome to index page')

@auth(auth_type='local')

def home():

print('welcome to home page')

@auth(auth_type='ladp')

def bbs():

print('welcome to bbs page')

index()

home()

bbs()

基础补充5——生成器并行运算

import time

def consumer(name):

print('[%s]准备吃包子啦'%name)

while True:

baozi=yield

print('包子[%s]来啦,被[%s]吃啦 '%(baozi,name))

def producter():

c1=consumer('alex')

c2=consumer('wupeiqi')

c1.__next__()

c2.__next__()

print('开始做包子啦')

for i in range(10):

time.sleep(1)

c1.send(i)

c2.send(i)

producter()

f=open('xx.log')

f.__next__()

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