函数进阶篇
1.1 装饰器
1.1.1 开放封闭原则：
对扩展开放，对修改是封闭
1.1.2 装饰器：
装饰它人的，器指的是任意可调用对象，现在的场景装饰器-》函数，被装饰的对象也是-》函数
1.1.3 原则：
1、不修改被装饰对象的源代码
2、不修改被装饰对象的调用方式
1.1.4 装饰器的目的：
在遵循1,2的前提下为被装饰对象添加上新功能
错误的示范
import time
def index():
time.sleep(3)
print('welecome to index')
def timmer(func):
start=time.time()
func()
stop=time.time()
print('run time is %s' %(stop-start))
timmer(index)
1.1.5 正确的示范
import time
def index():
time.sleep(3)
print('welecome to index')
def timmer(func):
# func=index #最原始的index
def inner():
start=time.time()
func() #最原始的index
stop=time.time()
print('run time is %s' %(stop-start))
return inner
index=timmer(index) #index=inner
# print(f)
index() #inner()
1.2 装饰器的修订
1.2.1 装饰器语法：
在被装饰对象正上方单独一行写上，@装饰器名
@deco1
@deco2
@deco3
def foo():
pass
foo=deco1(deco2(deco3(foo)))
1.2.2 改进一：
import time
def timmer(func):
def inner():
start=time.time()
res=func()
stop=time.time()
print('run time is %s' %(stop-start))
return res
return inner
@timmer #index=timmer(index)
def index():
time.sleep(1)
print('welecome to index')
return 1111
res=index() #res=inner()
print(res)
1.2.3 改进二：
import time
def timmer(func):
def inner(*args,**kwargs):
start=time.time()
res=func(*args,**kwargs)
stop=time.time()
print('run time is %s' %(stop-start))
return res
return inner
@timmer #index=timmer(index)
def index(name):
time.sleep(1)
print('welecome %s to index' %name)
return 1111
res=index('egon') #res=inner('egon')
print(res)
@timmer #home=timmer(home)
def home(name):
print('welcome %s to home page' %name)
home('egon') #inner('egon')
1.3 有参装饰器
import time def auth(func): # func=index def inner(*args,**kwargs): name=input('name>>: ').strip() password=input('password>>: ').strip() if name == 'egon' and password == '123': print('login successful') return func(*args,**kwargs) else: print('login err') return inner @auth def index(name): time.sleep(1) print('welecome %s to index' %name) return 1111 res=index('egon') print(res)
#有参装饰器
import time def auth2(engine='file'): def auth(func): # func=index def inner(*args,**kwargs): if engine == 'file': name=input('name>>: ').strip() password=input('password>>: ').strip() if name == 'egon' and password == '123': print('login successful') return func(*args,**kwargs) else: print('login err') elif engine == 'mysql': print('mysql auth') elif engine == 'ldap': print('ldap auth') else: print('engin not exists') return inner return auth @auth2(engine='mysql') #@auth #index=auth(index) #index=inner def index(name): time.sleep(1) print('welecome %s to index' %name) return 1111 res=index('egon') #res=inner('egon') print(res)
1.4 并列多个装饰器
import time def timmer(func): def inner(*args,**kwargs): start=time.time() res=func(*args,**kwargs) stop=time.time() print('run time is %s' %(stop-start)) return res return inner def auth2(engine='file'): def auth(func): # func=index def inner(*args,**kwargs): if engine == 'file': name=input('name>>: ').strip() password=input('password>>: ').strip() if name == 'egon' and password == '123': print('login successful') return func(*args,**kwargs) else: print('login err') elif engine == 'mysql': print('mysql auth') elif engine == 'ldap': print('ldap auth') else: print('engin not exists') return inner return auth @auth2(engine='file') @timmer def index(name): time.sleep(1) print('welecome %s to index' %name) return 1111 res=index('egon') print(res)
1.5 wraps补充
from functools import wraps import time def timmer(func): @wraps(func) def inner(*args,**kwargs): start=time.time() res=func(*args,**kwargs) stop=time.time() print('run time is %s' %(stop-start)) return res # inner.__doc__=func.__doc__ # inner.__name__=func.__name__ return inner @timmer def index(name): #index=inner '''index 函数。。。。。''' time.sleep(1) print('welecome %s to index' %name) return 1111 # res=index('egon') # print(res) print(help(index))
1.6 迭代器：
迭代的工具
1.6.1 什么是迭代：
指的是一个重复的过程，每一次重复称为一次迭代，并且每一次重复的结果是下一次重复的初始值
while True:
print('=====>')
l=['a','b','c']
count=0
while count < len(l):
print(l[count])
count+=1
1.6.2 为什么要有迭代器？
对于序列类型：str，list，tuple，可以依赖索引来迭代取值，
但是对于dict，set，文件,python必须为我们提供一种不依赖于索引的迭代取值的方式-》迭代器
1.6.3 可迭代的对象(下列都是)：obj.__iter__
name='egon'
l=[1,2,3]
t=(1,2,3)
d={'name':'egon','age':18,'sex':'male'}
s={'a','b','c'}
f=open('a.txt','w',encoding='utf-8')
name.__iter__ l.__iter__ t.__iter__ d.__iter__ s.__iter__ f.__iter__
1.6.4 迭代器对象（文件是）：obj.__iter__,obj.__next__
f.__iter__ f.__next__
1.6.5 总结：
1 可迭代对象不一定是迭代器对象
2 迭代器对象一定是可迭代的对象
3 调用obj.__iter__()方法,得到的是迭代器对象(对于迭代器对象，执行__iter__得到的仍然是它本身)
d={'name':'egon','age':18,'sex':'male'}
d_iter=d.__iter__()
f=open('a.txt','w',encoding='utf-8')
f_iter=f.__iter__().__iter__().__iter__().__iter__()
print(f_iter is f)
d={'name':'egon','age':18,'sex':'male'}
d_iter=d.__iter__()
print(d_iter.__next__())
print(d_iter.__next__())
print(d_iter.__next__())
print(d_iter.__next__())
迭代器d_iter没有值了，就会抛出异常StopIteration
f=open('a.txt','r',encoding='utf-8')
print(f.__next__())
print(f.__next__())
print(f.__next__())
print(f.__next__())
f.close()
l=['a','b','c'] l_iter=l.__iter__() print(l_iter.__next__()) print(l_iter.__next__()) print(l_iter.__next__()) print(l_iter.__next__())
d={'name':'egon','age':18,'sex':'male'}
d_iter=iter(d) #d_iter=d.__iter__()
len(obj) 等同于obj.__len__()
1.6.6 for循环的本质
while True:
try:
print(next(d_iter)) #print(d_iter.__next__())
except StopIteration:
break
print('=>>>')
print('=>>>')
print('=>>>')
print('=>>>')
for循环详解:
1、调用in后的obj_iter=obj.__iter__()
2、k=obj_iter.__next__()
3、捕捉StopIteration异常，结束迭代
d={'name':'egon','age':18,'sex':'male'}
for k in d:
print(k)
1.6.7 总结迭代器的优缺点：
1.6.71 优点:
1、提供一种统一的、不依赖于索引的取值方式，为for循环的实现提供了依据
2、迭代器同一时间在内存中只有一个值——》更节省内存，
1.6.72 缺点：
1、只能往后取，并且是一次性的
2、不能统计值的个数，即长度
l=[1,2,3,4,5,6] l[0] l[1] l[2] l[0] l_iter=l.__iter__() # print(l_iter) print(next(l_iter)) print(next(l_iter)) print(next(l_iter)) print(next(l_iter)) print(next(l_iter)) print(next(l_iter)) print(next(l_iter))
l_iter=l.__iter__() print(next(l_iter)) print(next(l_iter)) print(next(l_iter)) print(len(l_iter))
1.7 生成器
1.7.1 什么是生成器：
只要在函数体内出现yield关键字，那么再执行函数就不会执行函数代码，会得到一个结果，该结果就是生成器
def func():
print('=====>1')
yield 1
print('=====>2')
yield 2
print('=====>3')
yield 3
生成器就是迭代器
g=func()
res1=next(g)
print(res1)
res2=next(g)
print(res2)
res3=next(g)
# print(res3)
1.7.2 yield的功能：
1、yield为我们提供了一种自定义迭代器对象的方法
2、yield与return的区别1：yield可以返回多次值 #2：函数暂停与再继续的状态是由yield帮我们保存的
obj=range(1,1000000000000000000000000000000000000000000000000000000000000000,2) obj_iter=obj.__iter__() print(next(obj_iter)) print(next(obj_iter)) print(next(obj_iter)) print(next(obj_iter)) print(next(obj_iter))
def my_range(start,stop,step=1):
while start < stop:
yield start #start=1
start+=step #start=3
g=my_range(1,5,2)
print(g)
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
for i in my_range(1,5,2):
print(i)
1.7.3 小练习：:tail -f access.log | grep '404'
import time
def tail(filepath):
with open(filepath,'rb') as f:
f.seek(0,2)
while True:
line=f.readline()
if line:
yield line
else:
time.sleep(0.05)
def grep(lines,pattern):
for line in lines:
line=line.decode('utf-8')
if pattern in line:
yield line
lines=grep(tail('access.log'),'404')
for line in lines:
print(line)
1.7.4 yield表达式形式的用法（了解知识点）
def eater(name):
print('%s ready to eat' %name)
food_list=[]
while True:
food=yield food_list#food=yield='一盆骨头'
food_list.append(food)
print('%s start to eat %s' %(name,food))
e=eater('alex')
#首先初始化：
print(e.send(None)) # next(e)
#然后e.send:1 从暂停的位置将值传给yield 2、与next一样
print(e.send('一桶泔水'))
print(e.send('一盆骨头'))
1.8 追加文件
with open('access.log','a',encoding='utf-8') as f:
f.write('bbbbb 404\n')
f.flush()
1.9 面向过程编程
grep -rl 'python' /etc
补充：os.walk
import os
g=os.walk(r'D:\video\python20期\day4\a')
# print(next(g))
# print(next(g))
# print(next(g))
# print(next(g))
for pardir,_,files in g:
for file in files:
abs_path=r'%s\\%s' %(pardir,file)
print(abs_path)
1.9.1 分析一：
1.9.11 第一步：拿到一个文件夹下所有的文件的绝对路径
import os
def search(target): #r'D:\video\python20期\day4\a'
while True:
filepath=yield #fllepath=yield=r'D:\video\python20期\day4\a'
g=os.walk(filepath)
for pardir, _, files in g:
for file in files:
abs_path = r'%s\%s' % (pardir, file)
# print(abs_path)
target.send(abs_path)
search(r'D:\video\python20期\day4\a')
search(r'D:\video\python20期\day4')
1.9.12 第二步：打开文件拿到文件对象f
def opener():
while True:
abs_path=yield
print('opener func--->',abs_path)
target=opener()
next(target) #target.send('xxxx')
g=search(target)
next(g)
g.send(r'D:\video\python20期\day4\a')
1.9.2 分析二：
1.9.21 第一步：拿到一个文件夹下所有的文件的绝对路径
import os
def init(func):
def inner(*args,**kwargs):
g=func(*args,**kwargs)
next(g)
return g
return inner
@init
def search(target): # r'D:\video\python20期\day4\a'
while True:
filepath = yield
g = os.walk(filepath)
for pardir, _, files in g:
for file in files:
abs_path = r'%s\%s' % (pardir, file)
#把abs_path传给下一个阶段
target.send(abs_path)
1.9.22 第二步：打开文件拿到文件对象f
@init
def opener(target):
while True:
abs_path = yield
with open(abs_path,'rb') as f:
#把(abs_path,f)传给下一个阶段
target.send((abs_path,f))
1.9.23 第三步：读取f的每一行内容
@init
def cat(target):
while True:
abs_path,f=yield
for line in f:
#把(abs_path,line)传给下一个阶段
res=target.send((abs_path,line))
#满足某种条件，break掉for循环
if res:
break
1.9.24 第四步：判断'python' in line
@init
def grep(target,pattern):
pattern = pattern.encode('utf-8')
res=False
while True:
abs_path,line=yield res
res=False
if pattern in line:
#把abs_path传给下一个阶段
res=True
target.send(abs_path)
1.9.25 第五步：打印文件路径
@init
def printer():
while True:
abs_path=yield
print('<%s>' %abs_path)
g=search(opener(cat(grep(printer(),'python')))) #'python' in b'xxxxx'
g.send(r'D:\video\python20期\day4\a')
面向过程编程：核心是过程二字，过程指的就是解决问题的步骤，即先干什么后干什么，基于该思路编写程序就好比设计一条流水线，是一种机械式的思维方式
1.9.3 面向过程编程优缺点
优点：
复杂的问题流程化、进而简单化
缺点：
可扩展性差
1.10 三元表达式
def my_max(x,y):
if x >= y:
return x
else:
return y
x=10
y=20
# res=x if x >= y else y
# print(res)
name=input('>>: ').strip()
res='Sb' if name == 'alex' else 'NB'
print(res)
1.11 列表推导式与生成器表达式
1.11.1 列表推导式
l=[]
for i in range(1,11):
res='egg'+str(i)
l.append(res)
print(l)
l=['egg'+str(i) for i in range(1,11)]
print(l)
l1=['egg'+str(i) for i in range(1,11) if i >= 6]
print(l1)
l1=[]
for i in range(1,11):
if i >= 6:
l1.append('egg'+str(i))
1.11.2 生成器表达式
g=('egg'+str(i) for i in range(0,1000000000000000000000000000000000))
print(g)
print(next(g))
print(next(g))
print(next(g))
1.11.3 练习
names=['egon','alex_sb','wupeiqi','yuanhao']
names=[name.upper() for name in names]
print(names)
sbs=[name for name in names if name.endswith('sb')]
print(sbs)
obj=list('abcdef')
print(obj)
print(max([1,2,3,4,5]))
g=(i for i in range(10))
print(max(g))
print(max(g))
with open('a.txt','r',encoding='utf-8') as f:
l=[]
for line in f:
# print(len(line))
l.append(len(line))
g=(len(line) for line in f)
res=max(g)
print(res)
print(max(len(line) for line in f))
print(sum(len(line) for line in f))
1.12 递归调用：
在调用一个函数的过程中，直接或者间接又调用该函数本身，称之为递归调用
1.12.1 递归必备的两个阶段：
1、递推
2、回溯
import sys
print(sys.getrecursionlimit())
sys.setrecursionlimit(2000)
print(sys.getrecursionlimit())
def func(n):
print('---->',n)
func(n+1)
func(0)
def bar():
print('from bar')
func()
def func():
print('from func')
bar()
func()
age(5) = age(4) + 2
age(4) = age(3) + 2
age(3) = age(2) + 2
age(2) = age(1) + 2
age(1) = 18
age(n)=age(n-1)+2 # n > 1
age(1) = 18 #n = 1
def age(n):
if n == 1:
return 18
return age(n-1) + 2
res=age(5)
print(res)
l=[1,[2,[3,[4,[5,[6,[7,]]]]]]]
def func(l):
for item in l:
if type(item) is list:
func(item)
else:
print(item)
def func():
print('===>')
func()
func()
1.13 二分法（了解的知识点）
l=[1,2,10,30,33,99,101,200,301,402] #从小到大排列的数字列表
def binary_search(l,num):
print(l)
if len(l) == 0:
print('not exists')
return
mid_index=len(l) // 2
if num > l[mid_index]:
#往右找
binary_search(l[mid_index+1:],num)
elif num < l[mid_index]:
#往左找
binary_search(l[0:mid_index],num)
else:
print('find it')
# binary_search(l,301)
binary_search(l,302)
1.14 匿名函数
def func(): #func=内存地址
print('from func')
func()
func()
def my_sum(x,y):
return x+y
print(lambda x,y:x+y) print((lambda x,y:x+y)(1,2))
func=lambda x,y:x+y # print(func) print(func(1,2))
max，min，sorted,map,reduce,filter
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
print(max(salaries))
s='hello' l=[1,2,3] g=zip(s,l) # print(g) print(list(g))
g=zip(salaries.values(),salaries.keys())
# print(list(g))
print(max(g))
def func(k):
return salaries[k]
print(max(salaries,key=func)) #key=func('egon')
print(max(salaries,key=lambda k:salaries[k])) #key=func('egon')
print(min(salaries,key=lambda k:salaries[k])) #key=func('egon')
sorted
salaries={
'egon':3000,
'alex':100000000,
'wupeiqi':10000,
'yuanhao':2000
}
print(sorted(salaries,key=lambda k:salaries[k]))
print(sorted(salaries,key=lambda k:salaries[k],reverse=True))
map,reduce,filter
names=['alex','wupeiqi','yuanhao']
l=[]
for name in names:
res='%s_SB' %name
l.append(res)
print(l)
g=map(lambda name:'%s_SB' %name,names)
# print(g)
print(list(g))
names=['alex_sb','wupeiqi_sb','yuanhao_sb','egon']
g=filter(lambda x:x.endswith('sb'),names)
print(g)
print(list(g))
from functools import reduce print(reduce(lambda x,y:x+y,range(1,101),100))
1.15 内置函数（了解）
print(abs(-1))
print(all([1,'a','b',0])) print(all([]))
print(any([None,False,0,1])) print(any([]))
print(bin(11)) print(hex(11)) print(oct(11))
print('xxx'.encode('utf-8'))
print(bytes('xxx',encoding='utf-8'))
print(callable(max))
print(chr(65))
# print(chr(90))
# print(chr(39))
print(ord('A'))
print(ord('@'))
import os
print(dir(os)) s=set({1,2,3}) s.add(4) print(s)
s=frozenset({1,2,3}) #不可变集合
print(hash('xxx'))
l=[1,2,'a',4] print(list(reversed(l)))
s=slice(1,5,2) l=['a','b','c','d','e']
# print(l[1:5:2]) # print(l[1:5:2]) print(l[s])
print(vars() is locals())
obj.__dict__() #vars(obj)
1.16 面向对象
classmethod
staticmethod
property
hasattr
getattr
setattr
delattr
isinstance
issubclass
object
super
import
__import__
choice=input('>>: ')
print(choice,type(choice))
# import 'time'
m=__import__(choice)
m.sleep(10)
1.17 掌握：
1.17.1 divmod
# print(divmod(10011,25))
1.17.2 enumerate
l=['a','b','c']
for i in l:
print(l.index(i),i,)
for i,v in enumerate(l):
print(i,v)
1.17.3 eval:
res=eval('[1,2,3]')
print(res,type(res))
res=exec('[1,2,3]')
print(res)
1.17.4 pow
res=pow(2,3,3) # (2 ** 3 )%3 print(res)
1.17.5 round
print(round(3.5))
