day3-Python集合、函数、文件操作，python包的概念

本节大纲：

1 python程序由包(package)、模块(module)和函数组成。包是由一系列模块组成的集合。模块是处理某一类问题的函数和类的集合。

2 包就是一个完成特定任务的工具箱。

3 包必须含有一个__init__.py文件，它用于标识当前文件夹是一个包。

4 python的程序是由一个个模块组成的。模块把一组相关的函数或代码组织到一个文件中，一个文件即是一个模块。模块由代码、函数和类组成。导入模块使用import语句。

5 包的作用是实现程序的重用

一：在执行list()函数或者dict()函数首先是调用list()函数或者dict()方法会自动调用该函数的__init__方法进行数据的初始化，list()函数通过一个可迭代的对象，用for循环进行遍历插值。

 list_test=list((,,,))
 print(list_test)
 [, , ]

上述的过程：首先list(）函数调用本身的__init__方法进行数据的初始化，通过for循环，把元组(1,2,3,) 遍历插入列表[1,2,3,]

二：集合

集合是无序数据类型，元素不可以重合。如果集合内元素重复会自动去重。用大括号表示和字典表示一样，字典是无序的数据类型，但是key-value的映射。

 set_a=set([1,2,2,3,])
 print(set_a)

 {1, 2, 3}

可以通过内部函数set()对集合进行转换。字典、字符串、列表都可以转换成集合。

 set_b=set({'name':'evil',})
 set_c=set('evil')
 set_d=set([1,2,3,])
 print(set_b,set_c,set_d)

 {'name'} {'i', 'l', 'e', 'v'} {1, 2, 3}

1、操作集合

　1）add()函数。添加元素给集合。

 s_tets=set()
 s_tets.add(456)
 s_tets.add(456)
 s_tets.add(456)
 print(s_tets)
 {456}

应用场景：爬虫。在返回url保存的时候可以用集合操作，默认去除重复的元素。

2）difference()函数。比如a.difference(b)表示a存在元素b不存在元素。但是a和b本身的集合不发生改变。

 s1_testa={1,2,3,}
 s2_testb={2,5,6,}
 print(s1_testa.difference(s2_testb))
 print(s2_testb.difference(s1_testa))
 print(s2_testb,s1_testa)

 {1, 3}
 {5, 6}
 {2, 5, 6} {1, 2, 3}

3、symmetric_difference()函数，比如a.symmetric_difference(b)表示a和b集合去掉交集部分的结合。并不能改变集合a和b的值。

s1_testa={1,2,3,}
s2_testb={2,5,6,}
print(s1_testa.symmetric_difference(s2_testb))
print(s2_testb,s1_testa)
{1, 3, 5, 6}
{2, 5, 6} {1, 2, 3}

3）上述的操作不能修改原集合的值。如果想修改原集合的值。用如下函数：a.difference_update(b）函数。会修改集合a的值。b值不变。a值会变成。a存在的元素且b不存在的元素的值。

 set_a={1,2,3,4,}
 set_b={'c',6,3,5,}
 set_a.difference_update(set_b)
 print(set_a)
 print(set_b)
 {1, 2, 4}
 {'c', 3, 5, 6}

4）同样a.symmetric_difference_update(b)函数也会修改a的值，使a的值变为，a、b集合的去除公共元素的集合。

 set_a={1,2,3,4,}
 set_b={'c',6,3,5,}
 set_a.symmetric_difference_update(set_b)
 print(set_a)
 print(set_b)
 {1, 2, 4, 5, 6, 'c'}
 {3, 5, 6, 'c'}

5）删除集合指定元素。discard()函数。如果该元素不存在不会报错。

 set_a={1,2,3,4,}
 set_a.discard(1)
 print(set_a)
 set_a.discard(111)
 print(set_a)
 {2, 3, 4}
 {2, 3, 4}

同样也可以用remove()函数，但是不存在元素会报错。KeyError: 111

 set_a={1,2,3,4,}
 set_a.remove(111)
 print(set_a)
    set_a.remove(111)
 KeyError: 111

 set_a={1,2,3,4,}
 set_a.remove(4)
 print(set_a)
 {1, 2, 3}

也可以用pop()函数删除元素。但是随即删除一个元素。

 set_a={1,2,3,4,5,6,7,8}
 set_a.pop()
 print(set_a)

 {2, 3, 4, 5, 6, 7, 8}

6)批量添加元素。用update()函数。通过for循环对迭代的对象（比如说列表，字符串，字典的keys以及字典的value值），进行add()操作添加。

 1 set_a=set()
 2 list_a=[1,2,3,4,5,4]
 3 set_a.update(list_a)
 4 print(set_a)
 5 {1, 2, 3, 4, 5}

 set_a=set()
 set_b=set()
 list_a={'name':'evil','age':22}
 list_b={'name':'evil','age':22}
 set_a.update(list_a)##默认对key值进行迭代。
 print(set_a)
 set_b.update(list_b.values())
 print(set_b)
 {'name', 'age'}
 {'evil', 22}

7）当我们在执行一些列表或者其他操作的时候，会调用list()函数的一些方法。比如：

 list_a=[1,2,3] ##调用list的__init__方法
 print(list_a[0])##调用list的__getitem__方法
 list_a[0]=44##调用list的__setitem__方法
 del list_a[2]##调用list的__delitem__方法

8）在写列表或者字典、集合、元组的时候，进行最后元素加个逗号，在以后django学习的过程中不加逗号有时候会报错。

 list_a=[1,2,3,]
 dict_a={'a':2,'b':3,}
 tuple_a=(1,2,3,)

9）集合的并集、交集。

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交集函数：a.intersection(b)和a.intersection_update(b)前一个函数不改变原先集合(a,b)的值，后一个更新被交集的集合(a)。

 s1_testa={1,2,3,}
 s2_testb={2,5,6,}
 s3=s1_testa.intersection(s2_testb)
 print(s3,s1_testa,s2_testb)
 {2} {1, 2, 3} {2, 5, 6}
 s1_testa={1,2,3,}
 s2_testb={2,5,6,}
 s1_testa.intersection_update(s2_testb)
 print(s1_testa,s2_testb)
 {2} {2, 5, 6}

并集函数：a.union(b)

 s1_testa={1,2,3,}
 s2_testb={2,5,6,}
 s3=s1_testa.union(s2_testb)
 print(s3,s1_testa,s2_testb)
 {1, 2, 3, 5, 6} {1, 2, 3} {2, 5, 6}

10）练习：

在cmdb日常资产管理的时候，日常服务器检测硬件信息收集中，如果一个服务器4个内存插槽，#1、#2、#4插槽分别插，4G、8G、16G。如果现在变化#1插槽变为16G，#2不变，3#插槽插4G的，#4不插。该如何更新信息呢？

1、该删除那个元素？

2、该更新那个元素？

3、那个元素变了，需要更新？

 dict_old={
     '#1':4,
     '#2':8,
     '#4':16,
 }
 dict_new={
     '#1':4,
     '#2':8,
     '#3':4,
 }

答案：

 1 set_old=set(dict_old.keys())##生成keys值得集合
 2 set_new=set(dict_new.keys())
 3 del_item=set_old.difference(set_new)##old有的new没有的
 4 print('delete item %s'%del_item)
 5 up_item=set_new.difference(set_old)##new有的old没有
 6 print('add item %s'%up_item)
 7 a=set_old.intersection(set_new)## old和new的集合
 8 for i in a:
 9     if dict_new[i]!=dict_old[i]:
10         print('update item: %s'%i)##交集的元素value值不相当，需要更新的。

三：函数：函数组成：

def functionname(parameter):###由关键字def进行定义，然后函数名字（functionname）和函数参数。

#function description ###函数的描述，作用；参数作用；

function body ##函数体，执行代码

 return value## 函数返回值。缺省值是None。

以下是发邮件代码：

 def sendmail():
     try:
         import smtplib
         from email.mime.text import MIMEText
         from email.utils import formataddr
         msg = MIMEText('邮件内容', 'plain', 'utf-8')
         msg['From'] = formataddr(["xxx",'[e-mail]'])
         msg['To'] = formataddr(["走人",'[e-mail]'])
         msg['Subject'] = "主题"

         server = smtplib.SMTP("smtp.126.com", 25)
         server.login("username", "password")
         server.sendmail('[e-mail]', ['[e-mail]',], msg.as_string())
         server.quit()
     except:
         # 发送失败
         return "失败"
     else:
         # 发送成功
         return "cc"

 ret = sendmail()
 print(ret)
 if ret == "cc":
     print('发送成功')
 else:
     print("发送失败")

执行顺序：

aaarticlea/png;base64,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" alt="" />

 python在执行py文件的时候从上到下依次执行。上面的sendmail.py文件，首先执行1:定义一个函数sendmail() 并不执行函数体。而是往下执行，遇到函数依然这样执行，首先让解释器知道定义这个函数，到第2部分的时候，也就是函数的调用部分的时候，
解释器会跳到该程序sendmail()函数部分，并给sendmail的形参传入相应的参数值。执行函数体，也就是标记的3部分。当函数体执行完之后，返回函数值赋值给ret值。然后程序往下执行，第4部分。
1)函数一旦执行return语句的时候，函数立即终止。不会执行函数体下面的代码。

 def  test():
     print(1+1)
     return True
     print(222)

 if __name__=='__main__':
     test()
 2

2)函数的参数：
1、普通参数（严格按照顺序，讲实际参数赋值给形式参数）
2、默认参数（必须放置在参数列表的最后）
3、指定参数（将实际参数赋值给指定形式参数）
4、动态参数：
　　* 默认传入的参数。全部放在元组中。
　　** 默认传入的参数。全部放在字典中。
1、有限的形参。

 def mon(x,y):###其中x,y是形参。
     a=x+y
     return a
 res=mon(1,2)##给函数mon()传入实参。并执行函数体a=1+2 并返回a的值。赋值给res
 print(res)
 3

执行顺序：
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" alt="" />
1）首先读取程序定义函数mon(）部分。
2）然后执行mon(1,2)函数调用部分并给函数mon(x,y)的形参传入实参（1,2）再执行3部分的函数体，执行函数体并函数返回a的值 。
3）返回a的值 并赋值给res变量，这是执行第4步。
4）然后执行第5部分打印输出res变量。

需要注意的是：

1）在有限的形参的函数，在给传入实参的时候，实参的个数和形参的个数必须一致。

2）实参和形参 是一一对应的。如果没有默认参数的情况下，数目必须一致。

否则会报错：TypeError: mon() missing 1 required positional argument: 'y'。

2、默认参数：

在函数的定义的过程中，可以给形参定义缺省值。如果该形参没有传入实参的时候会用缺省值。如果传入的实参个数和形参的个数一致的时候，不会使用缺省值。

 def mon(x,y=9):
     a=x+y
     return a
 res=mon(1)
 res1=mon(1,4)
 print(res)
 print(res1)
 10
 5

默认情况未指定实参数位置，实参传入会从左到右 一次匹配形参。如果你指定默认参数位置的时候，未指定参数按默认形式匹配，指定的参数会按指定匹配。如下所示。

 def mn(a,b=2,c=2):
     SUM=a+b+c
     return SUM

 res=mn(1,2)
 res1=mn(1)
 res2=mn(1,c=3)####默认是从形参的开头从左到右一次匹配。
 print(res,res1,res2)

 5 5 6

需要注意：

默认参数的位置必须在最后，比如上面的 不可以这么写：def mon(y=9,x)这样会报错！！

3）指定参数：

当我们用形参的时候，在传入实参的时候不指定位置的时候，会依次对应。如果在传入实参的指定参数的时候，实参的位置的可以无序的。比如：

 def mon(x,y=9):
     a=x+y
     return a
 res=mon(x=1)
 res1=mon(x=1,y=4)
 res2=mon(y=2,x=4)
 print(res)
 print(res1)
 print(res2)
 10
 5
 6

4）不固定参数。*args

 def mn(*args):
     print(args,type(args))
 mn(1,2,3,4,)
 mn([1,2,3,4,])

 def mn_1(args):
     print(args,type(args))
 mn_1([1,2,3,])

 (1, 2, 3, 4) <class 'tuple'>
 ([1, 2, 3, 4],) <class 'tuple'>
 [1, 2, 3] <class 'list'>

如果想传入的参数是字典。该怎么办呢？？？

 def mn(*args):
     return args

 dic={'a':2,'b':3}
 res=mn(*dic)
 print(res)

 ('b', 'a') ###这种情况 只能传入字典的key值 形式元组。

同样如果这么处理的话，传入的列表。不在是由传入列表组成的元组。而是把列表中的元素 变为元组的元素。

 def mn(*args):
     return args

4 list_1=[1,2,3,4,]####传入的是列表的实参 直接转成元组的。而不是([1,2,3,4,])
5 res=mn(*list_1)
 print(res)

 (1, 2, 3, 4)

也就是说，在形参不带*和不带*区别：

　　1）args：不带*的参数，在传入实参的时候 ，传入的实参的个数也是一个。需要数目一致。传入的实参的数据类型是什么，赋值之后数据类型就不会改变。

　　2）*args：带*的参数，在传入实参的时候，实参的个数可以是一个，多个。没有个数的限制。但是传入的实参，最后赋值之后，会形式一个元组形式的元素组成，，无论你传入是字符串、数字、列表还是字典。元素保持传入实参的数据类型，比如是列表，传入时候元组是由列表元素组成。

 def mn(*args):
     print(args,type(args))
 mn(1,2,3,4,)
 mn([1,2,3,4,],2)
 mn({
     'name':'evil',
     'age':22,
 })

 (1, 2, 3, 4) <class 'tuple'>
 ([1, 2, 3, 4], 2) <class 'tuple'>
 ({'age': 22, 'name': 'evil'},) <class 'tuple'>

5）带**形参形式。

这种形式的参数必须要传入这种形式的：key=value。否则报错。形参赋值之后是由元素的组成的字典。

 def mk(**args):
     return args

 res=mk(a=2,)
 res1=mk(op={
     'age':21,
     'name':'tom',
 })
 print(res,type(res))
 print(res1,type(res1))
 {'a': 2} <class 'dict'>
 {'op': {'name': 'tom', 'age': 21}} <class 'dict'>

如果传入的实参是字典，不是key=value  该如何处理呢？？？

 def mn(**kwargs):
     return kwargs

 dic={'a':2,'b':3}
 res=mn(**dic)#####需要注意进行调用的时候进行**形式进行形参的赋值。
 print(res)

 {'a': 2, 'b': 3}

6）万能参数：（格式*在**前面）

 def mn(*args,**kwargs):
     return args
 def mn1(*args,**kwargs):
     return kwargs

 res=mn(1,2,3,)
 print(res,type(res))
 res_1=mn1(a=2)
 print(res_1,type(res_1))

 (1, 2, 3) <class 'tuple'>
 {'a': 2} <class 'dict'>

也就说，当形参：(*args,**kwargs)形式的时候，当传递参数不固定的字符串、列表、字典等，则赋值给形参args并形成一个元组。而传递实参是key=value形式的时候，则赋值给：kwargs 形成一个字典。

我们称这种形参方式叫做万能参数（自己称呼）。

 def mn(*args,**kwargs):
     print(args)
     print(kwargs)

 res=mn(1,2,3,a=2,c=3,)

 (1, 2, 3)
 {'c': 3, 'a': 2}

也就是说：

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" alt="" />

按上图的所示进行实参的赋值。

3）format()函数字--符串的初始化：

2种形式：

1、

 name='evil'
 age=''
 id=1318
 pre='''
 This is %s information:
 NAME:%s
 AGE:%s
 ID:%s
 '''%(name,name,age,id)
 print(pre)

 this is evil information:
 NAME:evil
 AGE:22
 ID:1318

2:用formate()函数进行初始化。如上的例子修改为：

 name='evil'
 age=''
 id=1318
 pre='''
 This is {0} information:
 NAME:{1}
 AGE:{2}
 ID:{3}
 '''.format(name,name,age,id)
 print(pre)

 This is evil information:
 NAME:evil
 AGE:22
 ID:1318

其中有{num}比如上面的{0}，{1}....是占位符。与后面的变量的依次对应。

需要注意的是其中0,1,2,3的数字对应后面的变量（name,name,age,id）所形成的元组的索引或者是字典的key值。不能随便修改成下面。会报错的或者显示的效果不是我们想要的。

 name='evil'
 age=''
 id=1318
 pre='''
 This is {1} information:
 NAME:{2}
 AGE:{2}
 ID:{3}
 '''.format(name,name,age,id)
 print(pre)

 This is evil information:
 NAME:22
 AGE:22
 ID:1318

或者报错：

name='evil'
age=''
id=1318
pre='''
This is {1} information:
NAME:{2}
AGE:{2}
ID:{4}
'''.format(name,name,age,id)
print(pre)

Traceback (most recent call last):
  File "C:/xx", line 9, in <module>
    '''.format(name,name,age,id)
IndexError: tuple index out of range

format()函数结构：

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vMpBil6OQrth7aLbfZbM6iuRdssQAAUCP2ijPfq97VhNNeSTL9VtiZheRlULPZdMeaYPx33P+1wg9b7fHRdMrJidTUkcQooCzriQdCGdrxPdfNpNzf3Y3F2d5vfiOcvZWVKHHcjaMU+p90IBPMOrtqUYkKHY2GcxKehyUaBzn0P7nsbP9f/CF2XFpHM49VuzXLQa7Q+bIVJPVZ12J1sQIAgIrYK846vudOM8wgnY5pt9LTQILRjLEsCM+KBw/C3ihUatEf6ZVhocsKB4oMQ0hHV9ot12156aPJPQH97W1pwSUlyoxPjserpM7UPBgpKlGho2M/2q3MiNqokxD1wdsgFmf/Zy364XNtHc0yVrMVZ7EbxSWa1mdNi5XFCgAApsBecTYapcZOys5pStbTjwQf53toSR+a7KYnf8k623xHV2T5jmzmK3Yp59tkw+bDh+J8C5Qo8UnqNEkU60BeIu3ReOgrf12mamJxdvrVqdiHG41VFXGWHwYWFjxOv5Q80visbbE3oTYBAJYOu8VZgrLfycVjPEItlTtVeu24o4pPSA0V5KaBsokUGHeqsLQu+M1fxfpMNkUozrndEsy7ZXOfTOSJJx9NEOoVYaDGNRINbJ3fP4/12eCngb6OZhqrGUqZdisxglXhwmot1sJlnQAAC4CV4kzwfb10LyAWcyJxJhoRkYxdRdcK/spsXxA6XGQRe1lx1vG9N79+OPkRp+M/CAOlK1Eid1WYLemLIzeEUicUZ+EZvtf6efMoFmd//vc/aOtoNMtYzUibhVK1joSzPmtbLNOYAACzwEpxFq/oiv8vmKxRDhVIe85M/yhKWTKV00lPaoYfxlovo/pSC5smHyayypYx4XhJcfY//sNeLM7+9G/+leBy1fK7hAvZVX6Crlo8puRWWYdenUlxBB51fM/zvPGMqNf6bSsWZ/9v7b9r62iGsZrBVs1qOwASV2t81rRY3rIBADAbbBRn7ZYb74WUzhCFr3cSKavs6hzRWynEKefmvOJr43U/nt8JT3M9P+1DKlnPy+7jy6QvHUIpPbrS/s+//W0szv7413+dyldeIpFLnpfrjFPXivwKA35jyiy52j3ecit75Uoo0WNxdn7/z6o6mnGsrFudVaR+ZS1WGysAAJgCG8XZclPlNfK9b75J7QmonnVJ+RDKU7uHT5LLzupLtWSsZvH6WQAAWFAQZ9ZRQZ0FD6NlZ7179yrnq1tEJSD7VhIruXp6VfuraMvFqkJkAQBgiUGcLQL9588newJOTopfmHxNQ/mhHetm6oT0XvVicXb97LpyOtPFCgAAoCiIs0VgcHAwpz/idAMMvwxjcXb56NK0OwAAABoQZ4vA8ORk8jsBz5+bdsc6zr49i/SZU+OyMwAAgJmAOFsQenfvRvpsc9O0L9Zx2byMB8+GX4am3QEAAFCBOFsQgo2NaE/A6qppX6wjueysrj0BAAAAMwJxtiAk9wSY9sU6ksvOrp5emXYHAABABeJsQRi8e8eeAAXxsrPz++emfQEAAFCBOFscJuJsb8+0L9YRLzs7/erUtC8AAAAqEGeLQ291Ndqwub1t2hfr6L2YLDvrH/ZNuwMAACAFcbY4BJubdfyI02Iy+GkQi7Pei55pdwAAAKQgzhaHwd4eewIUdL8ev4q2yatoAQDAXhBni8Pw6GjyI07Hx6bdsY7LR9Gys7Pvzkz7AgAAIAVxtlBM9gQcHJj2xTqSy85M+wIAACAFcbZQBA8f8iNOMpLLzngVLQAAWAvibKHob2+zJ0BBvOzs+tm1aV8AAADEIM4WisHBQfQjTvfumfbFRi7WLkJxdrF2YdoXAAAAMYizhWJ4fDzZE3ByYtod67h+dh3NbDosOwMAAEtBnC0avW++4UecZPQP+/Gys+GXoWl3AAAABCDOFg32BKg5/eVp9CraV7yKFgAAbARxtmj0nz+PZjY3N037YiPxsrOrp1emfQEAABCAOFs0Bu/eRXsCVldN+2Ij8bKz8/vnpn0BAAAQgDhbNIYnJ/yIk4LksjPTvgAAAAhAnC0gvbt3ow2bR0emfbGReNlZ/7Bv2hcAAIAsiLMFJNjYiDZs7u2Z9sVGzu+fR3sCXrAnAAAArANxtoD0d3ejDZvb26Z9sZGrp1ehOLt8dGnaFwAAgCyIswUk3hPAjzgJCd4GoTg7++7MtC8AAABZEGeLSbRh884d045YCnsCAADAWhBni0lvdTXaE3B8bNoXG4mXnQVvA9O+AAAApECcLSb97e1oT8DBgWlfbCRedsaraAEAwDYQZ4vJYG+PH3FSEC87u1i7MO0LAABACsTZYjI8Oor2BGxsmPbFUkJxdvrVqWlHAAAAUiDOFpZoT8C9e6YdsZSzb89CfTb4aWDaFwAAgAmIs4UlePgw2hNwcmLaFxu5bF5Gr6J9xatoAQDAIhBnC0v/+fNoT8C7d6Z9sZHeq170Ktomr6IFAACLQJwtLIODg2hPwO6uaV9sZPhlGIqz8/vnpn0BAACYgDhbWIbHx9GegM1N075YSrzszLQjAAAAExBni0zvm28Cx+mtrpp2xFLiZWf9w75pXwAAACIQZ4tMsLERDp6ZdsRS4mVn18+uTfsCAAAQgTibMzq+10zQaqtOjvcEDI+ObsrBmdBuuc1m0/X8epONl51dPqpzT0C75brqijFBqZYzF9gZ5zxh5GtvvRlMRcPOWli81m4zpZ7Pvuc2m03P78zaq3nHVnHW8T3XTd5drluyNju+N+On4c3T8b1Sj/jBu3fRhs29vRn6pHCpvlro+N4s7ufu193ure7Zd2f1JdlulW6sM6dsy5nQbk3uQc+fTSVUw8Y4y5i29ervI3k0prkHp8nXHNVbO1SlVAtvtyzU89Zhqzgb5Z4LJb+gzagvN0vZNj08OYk2bG5vz8gldZxrrIUZ3c+Xj6JlZ8Mvw/pTt4Zq0ev4XvKmo8+rzJStd5r7yNS1BqHvv3nKxLzdsk7P24jF4ixb2+n/J77QZ8dI04cyJ6THVNstNzvEGo3Quq32+GjcP3V8z3U930+MJZR8BiQH20tdGrqdGkpMXx+6nY9G7+7dwHE+/OpX+RJFJW21wg9b7fHRdMpSn5Vx1hzNnZC7WSNnYm+LdBNJVz2/k0okVhXpfP/L7/5vKM6Ct8FIWfvCikj5lBjuFVaurI6mb1eylPMOl0rc92bSx2nvwVHVOBeIZKJJtNplJxxlXmlLFLbe+PL890xpebX3kaLVTfMknCbfcYrCC4u0dlXtK1G3du3dLfS54HNSjeLJr/NK02Irx0rhVbn7qPDzeZK65+dTiJtcvnTJwVk7IzkL7BVn6S9t6XGz3Cia72WluKKtZA4JJP/4uRN+nvTE99xE1uW+AYQNPlm8oldqLmm33FQ8ktEINjcDx+l98xfCEoWtOVRq0R8d30ukpfVZfU9Kj6prMHYmPtnzin4xyzmZqt9cvv/t3/4+FGdXT68mKchrP6PjRT4JP1bV0WiqdqVJOS5DweQy181ozkp9D04RZ2Uk0yOB7ZZbqnRqr1Ql6vie56Wm/9qtTK+gLm+Bnk86cjHNk7BqvlO19mK1L0e70EJ8d6t81j4n1eif/DKvdC12mlipvVLV0TTP54SX4Yx48khH9HUw+ZmdkZwR9oqzjIxNf//JxS1TT6r7M3u54AEknUJtt7Idf9Wuq3zly54Ggo8Txe/v7oYzm1u/fSbqySZNd/KXzLG8zxUXnClrUFTOMk/C1HMkfaE438//7M+pV9HKJ9ALeSGWsKo6mqZd6VKOPqj+pEkMkNQ3p6m5ByvHWR3J/J1eamhQ6ZWyRJnOLHeCprxFtLXsnGmehFXznbK1T9NaR9rWLrm71T6Xe06qkapZoVeaFjtlrOReyeto2ufzZOhL0gmkw6t4FNoZyfqwVpxNAp9Z+CIcb8/2HArpU0DGSa/O3pDl5tkzDpdtCuLMhM+IxKnxnoC3//FAcO247PEVqQFLrc/qAMiOKmtQWKCyq02Tj1Jtvn/81f/u3uqefnVaoEyTgXFpDQolrLKOqrcrbcrlktNQ2wIk/T1YKc4jZSQFT/lsD6RD7pWyRCJPM58py1uk/uT3WvUnYbV8a2jtBWq/jEf6o2qftc9JnUPaJ7/Yq0Ittmqs1F7J62j653M0Uym69cbpRANbo4yEsjOSM8NWcdbJVEpqRLLIULvsnGwzElWmYjIuu+qi2MM9qy8rdZdir0T3SubMUJz9+PfPs9dOvBD8VcRn9fSb9KiyBoX3ebmvNWNPsylJ8u29iN52NvhpILhKm432U20dVW1XRWpf+EmhlAUPr3oeWUXuQW2+hZ7CyVLkLphqQDGdnLpE+XyUnVl+EEM/+arQZpWfhBXzrbm1l251VRZaqH1WPifVFHzyi70q3WKLxkrvlbyOpn4+R3kJR7jCxMMzfK/V7vitcV52RnKmWCrOsi0gFUvB993ctIh0TiH1xGm3mvl1f/KHRf7rRMFOL3NhauK8INLWlGnk7fzO9mD9XwaOE2xs5JPMjzDFnhbxWT13Iz+qrMF0gcIx8JJvUmq33JborQ/ifP/nH/qhOOu96BWfipVNfoqeDpo6qtyuitR+RQ2SrU1RyuPPBTeRzmn5PThNnNWRTKdcbj2dxitdiVKrmdJLbfTl1c6QZs8pdq32SVg132lae9Hal6Ju7aq7W+qz+jmpdUf/5C/4zMm22Oqx0nqlqqMpn8+Twgr87fie50UV2G55XqJ1mY5ku5VYynYzWCjOJqOLyQj53mQFXyf9gkFB35B8OVPuO0HykOclIp4bNU3U4MSreGBJnLWuUM1m0/NK7BHLFzbXp8jHY9utZrP55te/Dhyn85d/mbw2XtLn+Z3wNNfzw1Y7PqeAz4o4K49qajD9bq20V4WQyV9Zvqe/PO3e6r7+7rU0zvm2kbudc9WU+Z4nrKMp25Ui5SItR5VovCstn3Imi9LjcqXuwaJxLhDJVL6pR39xh0VeSUsUdV2tdnIFbaZdaFLOJS5rVKITqj4Jp823amsvEg0JmtauerarfC7wnFSjfIpqvVK02ClipfSq5H1UJhrJDcLtlqCX7yRe1pOT2UYjWb4Pmh4LxRnUzGBvL/qdgJMT075YysXaRWpPABShE31jMu1HNayYuQAoDC22LuYjkoizxWd4dBT9TsC7d6Z9sZTrZ9fhzKZpR+aJdubdA3PFdAvOAG4aWmxdzEskEWdLQe+bbwLH6e/umnbEUvqH/eSraKEApZcDGSc1tzivA36wRNBi62IeI4k4WwqChw8Dxwk2N007Yi/hsrPrZ9emHQEAgGUHcbYU9J8/DxwnePjQtCP2Ei47u1i7MO0IAAAsO4izpWBwcBAuOzPtiL1Ey84clp0BAIBhEGdLwfD4ONqweXRk2hdLCd4G4bKz4ZehaV8AAGCpQZwtC727dwPHGeztmXbEXqJX0b7qmXYEAACWGsTZshBsbASO03+e+xEnGHN+/7x7q3v19Mq0IwAAsNQgzpYF9gRouXp6xatoAQDAOIizZWHw7l3gOL07d0w7Yi/hsrPTr05NOwIAAEsN4myJiPYEHB+bdsRewmVn/cO+aUcAAGB5QZwtEb3VVX7ESU247Kz3gj0BAABgDMTZEhFsbrInQE247Ozy0aVpRwAAYHlBnC0R/d3dwHGCjQ3TjthL71Wve6t79t2ZaUcAAGB5mU9x1vE917Xt50s7vncDP6o6zc9ND4+OAsfp3bt3w/nOEcMvw3DZmfq0ItFot9wb/pFdtVdh+4zJnDh9/RYv7+zulJu5Bw3T8b18VYWPRNfzO9Lr2i1XXMcFrp0pVe4Upc+axiy/tt0yFwWANPaKs+TPyE9+Tz6+5zq+Z59W6PjeVPd2x/c0T6h2S/ZA0l87Go33BNSZ78Jx9u1Z91Y3eBvITykajWnbQy4t5Rkqrzq+p+z86qnf4uWtMzI3lbIdSPRDu6XRN+2WVK9or509pWtN57OiuKpr260FV/YwP9gqzpJdUXyfpT+08BGseiIUYJp+peC1wcOH+T0Bi96flSBSNwUAAAq8SURBVOOyeVnXq2inbA9JpqyjGj2pJZfZ+XMzJTWF70kHi5rNpqKFqJqP7toboHStzai8HWmAAW4YW8VZ4maN/0x+ZwwHzuLRtez91G4lx9vK3m2ZQbv48vDz8X/bLdfNJB4+EeLL82PrspQzDgvc7vie6wonpNTXZnxu//rXgeMc/LvnNeQ7joLwwo7vua7n+5P0809faTQKkJyky8/QjYMf1VGqInRtI1x2drF2Ic5VFY0ouzjHVA8hz1cTq+nqKD8CnUpcXSJNrJTlVaK4U8IP44G8uIEVHNJQ34OatqFsV5mAuG6r1SpUv8lyKU7QIx/XiZqQNEmV+NFdK0UWqwL3fvWWU8znyuVdkuUbMAfYKs4SCO/bnE5KPSUz91epb0OZkzN3ecaZ1NGO73leanoo/TBVpywraRrpQ0dxbfLQYG8vcJz/9dvNOvLNLmLJFND3koez0zHaaCgIn67Rf4TzfWPNkRhyHXf1urYRLTtzFMvORM62W6kuqt1yPS/1DUOZrzpWo+naRrEpUUGJVD6ry6tGfafk1lSV6DB19+A4fVHb0LWrdB2lW3+B+q3c2mWJZMukqGJlA9BNeUudKRyr7PO5essp6HP18iLOwBasF2fi26zdyj4L4ptf9Nwrth5rnJTi5swmnh8aUegtZcrFnJSdo7o25fPw+DhwnD+vJ37EqWq+guKkTsvWUWYiWhON4kh1o/DTQm0jXHY2+GkgzjF/iWiNduIzbb6aWE3VNiQryHXXKn3WlFeH/k5RfdeaJuUSCaYDIJhWngRN365qaO0VZ9zarRtY7J8NgLw9T9lyivtTcekY4gxswXZxJl5qk90NkJoEzc8BFZ8TGac2mYDIznkk00n/VzL4kJ6gkqUsuV7gmmxyUXZtzuezf/4XqT0B1fIVbshInqaoo8kn8mgonclUbjEVVqJthMvOeq8kr6LNpS4MRnK4TpOvNlbTtI0iV4sqR+Gzprw6tHfKJKmO75XpLAvcg/JoKNuVav9RoXZVtbWPqTa+Nc66bnGmvgfl7XnKllOccu0mBm0G1mC7OBM+SbM3c/KOr3mjgKTPEDmX//qnfOgI+ozKE1eKa/M+f/ir3wSOMzw6mipf0VM2M+CRrSPNbsFCz8RcZy2+UBz4wm2j96Kn2BMgVKrCNUbxwJkmX12spmkbowJTooJrlT5ryqtDf6eMI1B2sKjIPSj7uqdsV9mhoBSlnzlVZjWnEGelV3TpPVHfg/L2PGXLKUjlVf1oM7AHy8WZbAQku6Y5Iwoy51f+Sp+5V1N9ZHbLTzh9kF6Skpy30i2j0c6QKgqiuDbv8x//7u8Dxxns7U2Xbz42qQdi/stzOlYVFxVlkk0tfEmeJO7GiraNwU+D7q3u+f1zoQ+CS9IFiN62lVxzo8xXGatcjiXbRpFJI3H9KnzWlFeN+k6JP/NarbJypEDKkrahbVd59ZioBXX9Vm/tGQ8rirMyizoKpqeOlao9T9VyCvtXOVCaTQZutVFPgApYKs466RdmpteWpjaX5TdsZq5tFt8YlRurV8z1uG7L86J7NXrEtNrJDVmZ75aalHOJyyZDRScorhX4/J/+5m9SP+JUPV/ZTE22juIakU3zlXk4pzL1vPRLR/Mpu4I+NYmsbXS/zryKVheNZBg9PxxeiHOX56uLVT7xwnWUzzQdDU2JNLFSlleG/k5JOldm+KPKPSiPRrZdjVLbWrOH1LGaqrVnIlLpymq7D3SuSGJVoD1XajnlvKsaJ9WFtfsJoMRScQazY3hyEjhO8PCh/tQl5vLRZfdWt3/YN+3IksL7pnJUXDo2ixVdNiNc1qan+h4CgJmAOFtGenfv9u7cMe2F1YTLzq6fXZt2ZBlh6Y+Qqgv7s0sOFpZO1Z/1q3+/KMC0IM6WkWBjI3Cc4fGxaUfsJVx2dvno0rQjS0S7lZpmZSQDAJYWxNky0t/dzf+IE2Toft09++7MtBcAALB0IM6WkcG7d6k9ASDiYu2ie6s7/DI07QgAACwXiLMlJXCcYGPDtBdWc/3sunurG7wNTDsCAADLBeJsSemtrvbu3TPthdX0D/uKV9ECAADMCMTZkhJsbqZ+xAlEnP7y9GLtwrQXAACwXCDOlpTB3h57ArRcrF2cfnVq2gsAAFguEGdLyvDoKHCc/u6uaUesJlx2NvhpYNoRAABYIhBny0vgOP3tbdNeWE247Kz3omfaEQAAWCIQZ8tL8PAhP+Kk5fSXp5dNXkULAAA3B+Jseelvb7MnQMv5/fPz++emvQAAgCUCcba8DA4OAscZHh2ZdsRqrp5edW91TXsBAABLBOJseRkeHweOMzg4MO2I1QRvA15FCwAAN4nV4sz33Gaz6fmd+I+6jkJI75tv+BEnLd1b3etn16a9AACAZcF2cea6nt+Z/FHXUQgJNjbYE6Dl/P755SP2BAAAwA1hszhrt1zX9fzEH3UdhYj+8+e9O3dMe2E7V0+vug7LzgAA4IawXZx5fifxR11HIWLw7l3gOMOTE9OOWE247Gz4ZWjaEQAAWAosFmcd3wtlVfxHXUdhzPDkhB9xKkL3Vrf3ilfRAgDATWCxOIMboXf3LnsCtJx9e3b19Mq0FwAAsBQgzpadYHMz2Ngw7YXtXDYveRUtAADcDIizZae/u9tbXTXthe30XvVOvzo17QUAACwFiLNlJ9wTYNoL2xl+GXZvdfuHfdOOAADA4oM4gxF7Aopw9u1Z7wV7AgAAYOYgzmDUW13t7+6a9sJ2LpuXl01eRQsAADMHcQaj/vZ2f3vbtBe203vVO/vuzLQXAACw+CDOYDTY2+NHnLSEy85MewEAAIsP4gxGw6Mj9gQUoft1N3gbmPYCAAAWHMQZjEajUeA4w6Mj017YzuWjy+tn16a9AACABQdxBqPRaBQ8fDg4ODDthe30XvQu1i5MewEAAAsO4gxGo9Go//w5P+KkZfDToOuw7AwAAGYL4gxGo9FocHDAngAAAAAbQJzBaDQaDY+Pe3fumPYCAAAAEGcAAAAANoE4AwAAALAIxBkAAACARSDOAAAAACwCcQYAAABgEYgzAAAAAItAnAEAAABYBOIMAAAAwCIQZwAAAAAWYZk4++CvNNxGo9lY8T+M2uvh343mylZncsqWF344ttZ+JpH9VvqE1OWaowAAAABGsUycjUaj0Wh/3W2stNZXxqorVGzrbenJSX2232o0muvx/z/4Kw13Ir/URwEAAABMY6s4a3hbH5IfpUVVgg9bXurkxMCYQHWpjwIAAACYxlZxtuIntVl6iKuzteKmpyZTSm5/vfpRAAAAALPYKs6kI2ft9UZKumVHztJEC9QyUq/YUQAAAICbx1ZxttJaXx9rpnDNWSihRGvIkuIsN+rWXk+sV1MfBQAAADCOteLM/xDv3FRst1zx9+PNm+vtkWDWMnWt+igAAACAcSwWZzefMQAAAIBpEGcAAAAAFmGZOEtMZYpfMAsAAACw0FgmzgAAAACWG8QZAAAAgEVoxBkAAAAA3CSIMwAAAACLQJwBAAAAWATiDAAAAMAiEGcAAAAAFoE4AwAAALAIxBkAAACARSDOAAAAACwCcQYAAABgEYgzAAAAAItAnAEAAABYBOIMAAAAwCIQZwAAAAAWgTgDAAAAsAjEGQAAAIBFIM4AAAAALAJxBgAAAGARiDMAAAAAi/j/othbSKJ6+LsAAAAASUVORK5CYII=" alt="" />

所以根据可以传递列表、key=value形式：

 info=['evil',22]
 print('my name is {0} and age is {1}'.format(*info))

 my name is evil and age is 22

用*info形式的进行传入实参。列表info会转换成('evil',22)的元组。然后进行索引的对应。

 info={'name':'evil','age':22}
 print('my name is {name} and age is {age}'.format(**info))

 my name is evil and age is 22

注意这种形式实参传入，其中的占位符是字典的key值，而不是索引!!! 传入的是字典不是元组了。也就是用format()函数的**kwargs参数。

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" 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4）关于函数的返回值。

一个函数的返回值是任意的可以是一个列表、一个字典、一个布尔值、数字、或者没有返回值。没有返回值的情况，缺省值是None。

返回值布尔值：如下简单登陆验证。

 dic_a={
     'name':'evil',
     'password':'',
 }
 def login(username,password):
     if username==dic_a['name'] and password==dic_a['password']:
         return True
     else:
         return False
 if __name__=='__main__':
     user=input('Entre your username:')
     paswd=input('password:')
     res=login(user,paswd)
     if res:
         print('Welcome to python')
     else:
         print('sorry login fail bye!')

 Entre your username:evil
 password:22
 Welcome to python

返回值列表：

 def chek():
     user=input('entre your username:')
     age=input('entre your age:')
     addr=input('entre your adrr:')
     return [user,age,addr]
 if __name__=='__main__':
     res=chek()
     print('name:{0} age:{1},addr:{2}'.format(*res))

 entre your username:tom
 entre your age:22
 entre your adrr:LianoNing
 name:tom age:22,addr:LianoNing

返回值字典：

 def chek():
     user=input('entre your username:')
     age=input('entre your age:')
     addr=input('entre your adrr:')
     return {
         'name':user,
         'age':age,
         'addr':addr
     }
 if __name__=='__main__':
     res=chek()
     print('name:{name} age:{age},addr:{addr}'.format(**res))

 entre your username:evil
 entre your age:22
 entre your adrr:;LiaoNing
 name:evil age:22,addr:;LiaoNing

没有返回值：

 def mn(x,y):
     a=x+y

 res=mn(1,2)
 print(res)
 None

5）相同函数名字执行顺序：

 def mn(x,y):
     a=x+y
     return a
 def mn(x,y):
     a=x*y
     return a
 res=mn(1,2)
 print(res)
 2

相同的名字的函数，执行 临近函数调用最近的函数定义，如上。

6）函数传入给给形参的引用，形参引用实参，而不是内存中重新开辟一个值，赋给形参。

 def mn(x):
     x.append(999)
 list_1=[1,2,3,4]
 mn(list_1)
 print(list_1)

 [1, 2, 3, 4, 999]

当x进行操作的时候，添加元素999的时候，会更新list_1.

aaarticlea/png;base64,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" alt="" />

7）函数的变量：

aaarticlea/png;base64,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" alt="" />

全局变量：所有作用域都可读。

对全局变量进行重新赋值，需要global

全局变量要大写。比如：NAME .....

私有变量：在函数里的变量是私有的，作用域只在函数体内。

全局变量作用域全局，如上所示。

私有变量：

 def sum(x,y):
     a=2
     b=a+x+y
     print(b)
     return  b
 def a(x,y):
     print(b)
 sum(1,2)
 a(1,2)

 NameError: global name 'b' is not defined
 5

如果私有变量超出函数体的时候，别的函数调用这个变量会出现错误。如上所示。

可以把私有变量变为全局变量，如下所示：

 def sum(x,y):
     a=2
     global b
     b=a+x+y
     print(b)
     return  b
 def a(x,y):
     print(b)
 sum(1,2)
 a(1,2)
 5
 5

注意：global 变量名，变量名赋值。这种写法，不要：先变量名，然后global 变量。这样会抛出一个warning 异常。

 def sum(x,y):
     a=2
     b=a+x+y
     global b
     print(b)
     return  b
 def a(x,y):
     print(b)
 sum(1,2)
 a(1,2)

 SyntaxWarning: name 'b' is assigned to before global declaration global b
 5
 5

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" alt="" />

8）函数的注释：

注释：包括函数的功能。参数的说明。以及返回值的说明。好的description帮助别人能快速了解你的代码。

 USER_INFO={
     'name':'evil',
     'age':'',
 }
 def login(username,password):
     '''
     用户登录
     :param username:用户名字
     :param password:用户密码
     :return:True表示登录成功，False表示登录失败。
     '''
     if username==USER_INFO['name'] and password==USER_INFO['age']:
         return True
     else:
         return False

 if __name__=='__main__':
     user=input('Entre your login:')
     paswd=input('Entre your password:')
     res=login(user,paswd)
     if res:
         print('Welcome to Python!!')
     else:
         print('sorry login fail!!')
 Entre your login:evil
 Entre your password:22
 Welcome to Python!!

9）三元运算（是针对if else来说）

我们常用的：

 if 1==1:
     a=2
     print(a)
 else:
     a=3
     print(a)
 2

用三元运算可以这么写：

 a=2 if 1==1 else 3
 print(a)
 2

lamdba表达式：

 sum=lambda x,y:x+y
 print(sum(1,2))
 3

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Gni64jGc+hEy7JmIW6oLFMCExjCDXGqjWarqd5lJblSKRWbMINe1QRMm9uOxAynqtZG/ac+o0OOuFiI0l1FbLDckhg0cF2HaO3vaulxiikZXvqcX1M379FKjM2FCELJvj/OczpVmZPvSbPFV4nXKrUxQc72c9OZv+kMhk5KrDoEGqMSx7UUsui62qRlmUmUig2FCELOTR6Ls864VKrrmiBFZTDdCCwcLBjDqHGGK/DjCyej7Qsa/9M0WKrWjPr7zahCImIDpkvz1WtETrnljxI6acTLh3qkgiscByWFoGZiyoNNe1pPW6hxrh6JdIyvYTQcbKnPkmxoQiJUAkVz1GVz2e9E46d6c2MwArEYfoUWPMSouiV8FPmdKuWGmMK0jJxu0I4pgwEZ6gZSnhvjikDwREER8htbl1OGGoUpav+qr4iJWJDERKRUMhOhiIUMGvXpakTjp3pzaTACsFhuVRCdOw1FGmYdSlYbyTmmR0yk1rxg/I11RiXPsRf6RI4vNh2m30EwRFE0FzjS1hgjikDwRmb+ziynwuOGmMd5njFQCyxBhXm7lJ/NS8iebGhCIlIJqRfEt/2Li88Zk89/z8zoU443l4QWGpDfwJz1cyTSYNigu218zXGOF9eKZ5Pla9i9tTHLSOotducsMBcNUHRJo4pQ9GUZJ3CScISihRkbChCIlIXYc+Fv13Ajjn4nmCO7Gd77WzXsbC9ILDUhkhg1uWVBFFlqGl31WwkiCo+RMXA5np+obG5L/yYCFsneNxIVMpsFdmw3urYawitI+Qf4g0joSAzc5HchdvxiiHyO1ZLjMsJfueumnkEQfz09pVaJv4g772zdu7cn3zpS5EDi9uucHaJZ2AxeyCWj8YsXEKopFnqrxZeRIuNH8amZeQqipCIDES2BJaEw4YDc9xBZ/bfOvWQZmCumo2Goo3R8ojt0OI9RxAbhXpa83HJZdRVs1GmD0ywVGgrx15D+DEfWjIwxyuG2PphsN5IEBHrNC8hop86XjGEMie+xnj29h+O3a12zz7yA8PAanFaJttuOGT1oz2Co0bCJ6Mi9UbVX0WEQrhB5r9ym6DYREXIPfUoQiJmEdH2gsBSGzICk6Y+weNGoj768mpdXkksP66yUxWBRSc35qLEBSabdij3inGkSGChdueZHX1sr53Z+vih2++JU2Nc+hC9vprZ9CvVZCgJgUV3hknfMbGYE3oVES+SEVtkMBuKkIh4kXsCGw7McQf3jfnmeL1zJihhidc7x+ud4w3s6+fIweAC4al3zgQlkln4sWQnw4E57uCmCX4r36bBzLy3chlYjHskprEur1S/bioLTCTClAlMWB5BVOtTEFj0S2zXsc/vuensLxcnlpZFWp+twBxTBiKoqF4ILBsxa7Hx30MKFyF1+gVbRGpDYq/MC0zOYcOBOV7vgjFGeDoYXBBSjnPMF1FarLT6xQKL3slwYI7X+/SweCdpD7kMTJJdyWVgWROYlrqZ5MquQWBc1JgI5sMVO+b+Ylz1mhX+EU5hn8oCsy4nCIKQH/3R7CNU7BX3ZFFCzGzMTmwoQhZ60FOSoM97E4hOj/ZgrrllI+aoxOVBkW8Ggwv4JEyLwKJrjErrpzfkBEZURTss1kZpFFh4Bb7DTK5QGTuYwlUzj1heL1lHJLDwhT5YbyQIglATWHiEutbJhctX0S/Xz32kU/atMBdFNSc65iAR3e8lNwoRgzh0ESKxaf9JNhQhCyRyX2D7JkLVwugqom4FZqhpDw3TkI4GFC3X8mrYheEhi/zADYXxioqjH0VHKB3Ozg81VCwhilcwNveZiwiCIIoevye0bJ7Z0SekSsvrQwMaRdZhu44NGX/QdLva0A/X90tq55rLn/kk0jS/K7Fc+TAXCV9hjgSG0eddJPpboyhC5mHoQ2CxFT89Cyz7/+oqoT5kI/3tapr4o3wVvX1Ld9U82fRLUxT8F5nzOBISG4qQ+o7cF1h0HxjZz2yaCHdxBfZFVuAfM5vcXggs2ZCd0inj7WqZXNj142JRb5nmQPpVgJFYxoYipC5CBwLrjx6FGB6XwWxye8PDFPeFhxeO5W4GJqoBqg6UR0giPLmwSocZuWIZvX1L8GJbMg25nO1BXy9H9gd9ve0Xz8U+RuRfaBcbipA5F7kosPyJXJpKKl+COd3qf+dN78IH1K4yVWuYg7tmUQsK+nqdn7d7Jrpl/2b93BGZDJHYlH+SDUXILEdWBZb9009nQGBpCT4fYsccwX07R559hrxPefRH2TJ6+5aEJi5Syr2QgSH40PpboyhCZiYgsLQFwYGMwA4O0g0NVHk5OX++QlpWQlVXM21trNud7YMF+QnrdDI2G93QQG/dSpWXU2Vlsf8byZISymSizWbaYmHa2hi7PdtHrX+yK7C8BgLLAqzTSW/dSpWVKaZlJhPd2Mg6ndk+UlAQxBUbWVxMmUxURQVtsTAtLYzdjtusxMiSwLJ92mkHAssmrNvNHDlC19aSJSVIy0CuEV9spaWUyURbLHRjI2O345ZLkWwILNvnnAkgsFyBHRxkWlqo6mrFGmNpKW2x4BoBso5IbNXVVFlZ9B0YipDyQGBpAALLRVink25oUKoxksXFVHU109KCtAzkFOzgIGOzMS0tsWJDETLDAsv22WYICCzXEWqMS5ciLQM6RU1sBVWEzIjAsn2SGQUC0w2RGqNchxnSMqA7lMSWz0XINAss26eXaSAwXSLUGMvLkZaB/ENWbPlRhGRJmg2yCQStNbJ9ZtkBAtM9jM2mVGMki4upigqmpYUdHMz2YQKQLNFio2trqbIyculS3RUhIbAUAoHlD/ygfMUaY2kpbTbnVTUGAI7j+P/5Nhtz5EhYbFR5eS4XIVMusGyfUNaAwPITlYk/kJaBAkEqtvJyurY2F4qQEFiqgMDyH8Zmo7dula8xIi0DhYdEbEJYLLTFkrEiJASWEiCwAkJl4g+kZQCIxNbQIPxNWxEyVQJL+YHpCAisQBEG5cvWGPm0rK0t28cIQE4QLTZ+ChJ+LAljtyd5w5e8wFJ1jjoFAgOqkwtXVNCNjUjLAJCFsdl4twkPbLZEi5DJCCx956UXIDAgQmniD7KkBGkZABphbDZ+0L/w1+1WKULOTmCZPJ2cBQID8qhNLoy0DIA0oEVg2T7G3AICA/FRmlwYaRkAIItAYCAxFCf+4NOynJ8HAQCQN0BgYJawbrfs5ML4EU4AQFohwmT7SEA+oDi5sMmEtAwAHu8cb6Kradyk0AibCwIDKUZ24g+kZaDAiVWR0pLYv0ACBAbSjuLkwkjLQIHhneNVCtmVZR+DMBAYyCiykwsjLQOFhoqQZN2mrrqCBQIDWUN+4g+TCT/CCfISlfQr1kzRS2IfAB4IDGQf2cmFyeJiqrqaaWlBWgbyidjCoEofmERdEJgEjEIEuYXs5MJkaSnSMpAHRAtJqYtLVloQmDoQGMg5Yif+QFoG9EtsXsUplwdln0JgSkBgIKeJnVwYaRnQNSqDD2M7xiAwdSAwoA8ikwuHOswiaRmmFQa5jcqQDdmnGEmvEQgM6I/YiT+EH+FMw8/mApBy1L8EpmW4B+CBwIC+kUwuTBYXUxUVSMtAThH3y8tcvMGHcJgsEBjIE2InF0ZaBkB+A4GBPEQy8QfSMgDyEggM5DmSyYWRlgGQN0BgoFCQmfiD/xFOpGUA6BMIDBQikok/yJIS2mxm2tqyfVwAgASAwEChI51cGGkZADoBAgNAIFJjXLoUaRkAuQ8EBoAMkYk/+MmFkZYBkHtAYADEIXpyYfwIJwC5AwQGQAKIJhc2mejGRkwrDEC2gMAAmA3RE38gLQMgK0BgACSLaHJhpGUAZAoIDIBUEp74A2kZAOkGAgMgLfCD8oXJhZGWAZAGIDAA0k5kcmE+LWtpQVoGQPJAYABkFL7GSJWVkaWltMWCtAyAWQOBAZAdIhN/PPww0jIAZgEEBkD2CU8uTJlMSMsA0AgEBkBuERmUj7QMAFUgMAByF77GSK1dix/hBCAWCAwAHcDXGOkNG4S0DNMKAwCBAaA7+BojbbEgLQMFDgQGgI5hbDbaYqEtFqRloACBwADIB/jJhemGBrqhAWkZKBAgMADyDdbpZFpa+EBaBvIYCAyAfIax2ZiWFqa5mWlrS2hD7xyvbCitLPtYfU0AkgQCA6Ag4Cf+YI4eZY4enV1aJusefmHs34R2AsDsgMAAAFJiNaOeY2nMwLSndABoAQIDAEiR5FKSB9FPY22kJCeVBA6A2QGBAQCkyBor1mfhp0qqk+xQi9IA0A4EBgCQom6a2IJhrMPUh3ugeAhSAgQGAJCiMQNTWU1FYFAXSBUQGABAisY+MMmrKutwyuPy4TMwayAwAIAUjSVEWQ/FCix6fEfctgDQDgQGAJAiqx+lV1UWquw27soAxAUCAwBISejLyBLDqSdbKCGCFAKBAQCkqPRXSaqF6pXD2N3KtpWWcwAFAAQGAABAl0BgAAAAdAkEBgAAQJdAYAAAAHQJBAYAAECXQGAAAAB0CQQGAABAl0BgAAAAdAkEBgAAQJdAYAAAAHQJBAYAAECXQGAAAAB0CQQGAABAl0BgAAAAdAkEBgAAQJdAYAAAAHQJBAYAAECXQGAAAAB0CQQGAABAl0BgAAAAdAkEBgAAQJdAYAAAAHQJBAZAKiHkyPZBAZBXhD9Z/w/Qw5izovD7bAAAAABJRU5ErkJggg==" alt="" />

之前的写法：

 def sum(x,y):
     return x+y
 print(sum(1,2))

 3

lamdba 是适用于简单的运算和判断，不适用于复杂的判断逻辑。

10）常用函数：

1：all()当所有元素为真才为真。

2：any()至少其中一个元素为真才为真。

 m=all([1,2,3,4,])
 print(m)
 n=any([None,False])
 print(n)

 True
 False

 m=all([1,2,3,0,])
 print(m)
 n=any([None,True])
 print(n)
 False
 True

3：abs() 求绝对值：

 print(abs(-))
 

3：bool() 求布尔值。

 print(bool())
 True

4：bytes()字符串转换字节。需要指定转换编码比如:utf-8 ,gbk.

 a='evil'
 print(bytes(a,encoding='utf-8'))
 b='汉武帝'
 print(bytes(b,encoding='utf-8'))
 b'evil'
 b'\xe6\xb1\x89\xe6\xad\xa6\xe5\xb8\x9d'

那字节怎么转换成字符串呢？同样用str()函数。但是必须指定相应的编码才可以哦。不正确的编码会报错哦。

 print(str(b'\xe6\xb1\x89\xe6\xad\xa6\xe5\xb8\x9d',encoding='utf8'))

 汉武帝

11）文件操作：

 操作文件时候需要2个步骤：1）打开文件。2）操作文件。
1：打开文件模式有:

 f=open('haproxy','r',encoding='utf-8')##以只读模式，以utf-8编码打开文件
 f1=open('haproxy','w',encoding='utf-8')##以写模式，以utf-8编码打开文件，如果文件存在则直接清空，如果没有则创建。
 f2=open('haproxy','x',encoding='utf-8')##在python3中新加的，文件存在报错，不存在则创建并写入内容。
 f3=open('haproxy','a',encoding='utf-8')###文件追加内容。

r+,w+,a+表示既可读也可以写。
'b'模式以二进制形式打开文件。rb,wb,ab.
如果平时open打开文件是乱码，一般是编码有问题。现在一般使用编码utf-8编码。
注意：在使用open()函数打开文件的时候，在文件操作完毕的时候需要把文件句柄关闭即：

 f=open('xxx','mod',encoding='xx')
 xxxxx
 f.close()

 由于这么操作的时候容易忘记关闭文件句柄。用with   as  函数来管理上下文件。文件操作完毕自动关闭文件。

 with open('haproxy.txt','r',encoding='utf-8') as file_obj:
     pass

在python2.7以后with函数可以同时操作多个文件。最常用的操作就是：一个文件在读，一个文件在写。

 with open(1.txt','r',encoding='utf-8') as file_obj,open('1.back','w',encoding='utf-8')as file_obj1:
     for i in file_obj.readlines():
         i.replace('evil','tom')
         file_obj1.write(i)

2:操作文件：
文件在硬盘以2进制存储，默认情况当python在以(r+,w+,a+等模式)读的时候，在不指定编码的时候，以pytho解释器默认编码，编码成字符串，供程序员进行读写。
如果以rb,wb,ab方式是不用python默认编码，直接以字节方式进行访问，存储的方式2进制，所以要把字符串写入文件，需要把字符串转化成字节才能写入。
1）常用的操作文件的函数。
file.fulsh()，强制把内存的内容刷进硬盘。
实验：
用input()交互方式，在等待用输入的时候，看文件是否内容。

 with open('test.txt','a') as f1:
     f1.write('this is test!')
     dengdai=input('entre your NUM:')

运行该程序，查看文件。文件缓存区的内容没有写入磁盘内，加上file.fulsh()，我们在看下。

 with open('test.txt','a') as f1:
     f1.write('this is test!')
     f1.flush()
     dengdai=input('entre your NUM:')

发现文件句柄没释放，已经把文件内容写入文件里。
2) file.read()在指定字节大小，可以读指定字节，在没指定，读取文件整个内容。

 with open('test.txt','r') as f1:
     for i in f1.read():
         print(i)

 t

文件内容：

1111this is test!

3）file.readline()仅读取文件一个行。

 with open('test.txt','r') as f1:
     for i  in f1.readline():
         print(i)

4）file.readlines()读取文件，一行为单位，并根据换行来保存值列表，这个函数很常用。

 with open('test.txt','r') as f1:
     for i  in f1.readlines():
         print(i)

 1111this is test!

5）file.seek()指定文件指定到指定位置（字节）。

 with open('test.txt','r') as f1:
     f1.seek()
     for i  in f1.readlines():
         print(i)

 this is test!

文件内容：1111this is test!

将文件指针移到第四个字节。然后操作读。

6）file.tell()获取文件当前指针。

 with open('test.txt','r') as f1:
     f1.seek()
     for i  in f1.readlines():
         print(i)
     print(f1.tell())

 this is test!
 

因为文件读完，文件指针在文件的末尾。
7）file.truncate (),根据文件指针位置，在文件指针位置以后被清空。

 with open('test.txt','r+') as f1:
     f1.seek()
     f1.truncate()
 

上述文件内容，指定文件指针位置，文件指针位置以后都清除。
8）file.write()写数据，有b按字节写入，没指定按字符串写入。

 with open('test.txt','r+') as f1:
     f1.seek()
     f1.write('goodbye!')
 1111goodbye!

9）需要注意，在读文件的时候，最好用如下方式进行读，这种方式是生成迭代器，效率较高。相比read(),readline(),readlines()

 with open('test.txt','r',encoding='utf-8') as f1:
     for i in f1:
         print(i)