本节大纲:
面向对象特性:封装、继承、多态。
一:多态:python本身是多态,他的参数可以多种类型。可以是字符串、数字、列表等。当传入参数的时候,python可以判断参数的数据类型。
而在java C#中不是。需要指定参数的类型。实现多态,需要指定类型为父类、参数类型可以是父类和子类的类型来实现多态特性。
由于python本身的多态导致,由于类型的不确定,到时候在读源码的时候增加难度。这也算是python本身的一个缺点。
二:类的成员
字段:分普通字段(动态字段)和静态字段。
 class test:
def __init__(self,name,age):
self.name=name
self.age=age
def show(self):
print(self.name,self.age)
evil

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 class test:
job='IT'
def __init__(self,name,age):
self.name=name
self.age=age
def show(self):
print(self.name,self.age) obj=test('evil',)
obj.show()
print(test.job)

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" alt="" />

a:访问方式:

默认是自己访问自己字段的。

普通字段属于对象,需要通过对象去访问。

静态字段属于类的(万不得已情况下,可以用对象通过类对象指针间接访问静态字段)。

b:

静态字段在程序加载的时候,就创建。

而普通字段(动态字段)在对象未创建的时候,该字段不会被创建和加载到内存中。

普通字段只能通过对象访问。

 class province:
country='China'
def __init__(self,name):
self.name=name
def show(self):
print(self.name) print(province.country)
China

说明:普通字段是属于对象的,每个对象的创建的时候,自动创建这个字段。

静态字段是属于类的,在类的创建的时候只需一份,每次调用都是调用类的中的静态字段。

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" alt="" />

也就是普通字段在对象的创建的时候生成,创建多少个对象,就创建多少普通字段,并保存在对象中。

而静态字段无论对象创建多少份,他只调用类中一份静态字段。

练习:

 class province:
country='China'
def __init__(self,name):
self.name=name
def show(self):
print(self.name) obj=province('Liaoning')
obj.show()
print(province.country)
Liaoning
China

c:使用场景

当对象有一个共同默认值的时候,可以使用静态字段来代替普通字段(动态字段)避免重复创建字段,浪费内存。

三:方法

静态方法、普通方法、类方法

 class co:
name='ack'
def __init__(self,name):#普通方法必须要有self参数
self.name=name
def show(self):#普通方法
print(self.name)
@staticmethod##静态方法,参数可以自己指定没有必要参数
def look(name,age):
print(name,age)
@classmethod#类方法,必需参数cls
def cat(cls):
print(cls.name)
co.look('evil',)
obj=co('tom')
obj.show()
co.cat()
evil
tom
ack

注意:

普通方法:必需参数:self  对象调用  obj. methodname()

静态方法:无必需参数。直接用类调用 类名.方法()

类方法:必需参数:cls 属于静态方法的特殊一种。也是直接用类调用。

 总结:

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" alt="" />

也就是说,对象想调用非自己的字段和方法,需要通过类对象指针去类或者父类中找相应的方法和字段。

而类的字段(静态字段)和方法(静态方法、类方法)可以用类本身去调用,但是对象的普通字段无法调用。

三:属性。

个人理解:把方法用字段的形式进行访问。也就是说把方法去掉括号。

也就是说方法通过属性的方式伪造成字段访问形式。

通过类似字段的设置:重新赋值、删除动作触发执行相应属性的setter、deleter函数,至于函数的内容我们自定义,当用户执行这样的操作的时候,我们执行相应的动作。

方法一:

 class Get_page:
def __init__(self,page_count):
self.page_count=page_count
@property
def get_page(self):
a,b=divmod(self.page_count,)
if b==:
return a
else:
return a+
obj=Get_page()
res=obj.get_page
print(res)

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" alt="" />

 class Get_page:
def __init__(self,page_count):
self.page_count=page_count
@property
def get_page(self):
a,b=divmod(self.page_count,)
if b==:
return a
else:
return a+
@get_page.setter
def get_page(self,value):
print(value)
@get_page.deleter
def get_page(self):
print('del') obj=Get_page()
res=obj.get_page
print(res)
obj.get_page=
del obj.get_page del

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" alt="" />

方法二:

通过方法property来实现属性。这种方式在源码里出现较多。

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" alt="" />

 class Get_page:
def __init__(self,page_count):
self.page_count=page_count
def f1(self):
print('f1')
def f2(self,value):
print('f2')
def f3(self):
print('f3')
a=property(fget=f1,fset=f2,fdel=f3) obj=Get_page()
obj.a
obj.a=
del obj.a
f1
f2
f3

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" alt="" />

四:成员修饰符

只有(public)和私有(private)两种。

a:作用域:共有在类中、类外都可以进行调用。

私有只能类中调用,外部想引用必须通过内部方法进行调用。

b:语法:

私有:两个下划线:__

字段:

 class  Test:
def __init__(self,name):
self.__name=name#私有属性
def show(self):
print(self.__name)#只能内部方法进行调用
obj=Test('tom')
obj.show()
tom

不可以这样:

class  Test:
def __init__(self,name):
self.__name=name
def show(self):
print(self.__name)
obj=Test('tom')
print(obj.__name)
会报:AttributeError: 'Test' object has no attribute '__name'

共有:

 class  Test:
def __init__(self,name):
self.name=name#就是共有字段。
def show(self):
print(self.name)
obj=Test('tom')
print(obj.name)
tom

方法:

 class  Test:
def __init__(self,name):
self.name=name
def __show(self):##私有方法
print(self.name)
def cat(self):
self.__show() obj=Test('evil')
obj.cat()

外部想调用内部属性只能通过内部函数的间接调用实现外部的调用。

 class  Test:
def __init__(self,name):
self.name=name
4 @staticmethod
5 def __show(user):##私有静态方法。
print(user)
def cat(self):
Test.__show('tom')
tom

在其他语言中私有无法访问,但是python可以强制访问,最好不要这么操作,不符合python代码规范。方法:单下划线,对象._类名__属性名双下划线,没有属性. 。

 class  Test:
def __init__(self,name):
self.__name=name
@staticmethod
def __show(user):
print(user)
def cat(self):
Test.__show('tom') obj=Test('evil')
obj._Test__show('op')
print(obj._Test__name)
op
evil

五:python特殊成员:

a:__del__ 析构方法。在类的对象回收之前,会执行该函数,如果该函数如果存在的话。

 class  Test:
def __init__(self,name):##构造方法。
self.__name=name
@staticmethod
def __show(user):
print(user)
def cat(self):
Test.__show('tom')
def __del__(self):###析构方法,在对象回收之前会执行该函数。
pass

b: __call__方法,执行方式:object()即对象后面加个括号。会自动调用该函数。

 class  Test:
def __init__(self,name):
self.name=name
def __call__(self, *args, **kwargs):
print('call')
obj=Test('OK')
obj()##执行方式 或者Test()()也是调用__call__方法。

c:__str__方法。执行方法是比如:print(obj)或者str(obj)会自动调用__strr__方法,如果没有该方法对象将返回对象在内存在地址。

注意:__str__返回的是字符串表达式。不是字符串形式,用str()转换。

 class  Test:
def __init__(self,name):
self.name=name
def __call__(self, *args, **kwargs):
print('call')
obj=Test('OK')
print(obj)
<s4.Test object at 0x00000000019B9D68>
 class  Test:
def __init__(self,name):
self.name=name
def __call__(self, *args, **kwargs):
print('call')
def __str__(self):
return self.name obj=Test('evil')
print(obj)
evil

根据这个特性可以不用直接使用obj.name访问obj的name普通字段。可以用__str__方法,直接返回相应的属性。或者返回多个数据用字符串的拼接。

 class  Test:
def __init__(self,name,age,job):
self.name=name
self.age=age
self.job=job
def __call__(self, *args, **kwargs):
print('call')
def __str__(self):
return '%s_%s_%s'%(self.name,self.age,self.job) obj=Test('evil',,'IT')
print(obj)
evil_22_IT

d:__class__获取类名字:

 class Test:
def show(self):
print('OK')
obj=Test()
print(obj.__class__)
<class '__main__.Test'>##main表示调用是在本程序内。

e:__dict__方法:如果是对象,获取对象的封装的数据以字典形式展示,如果是类获取类有的方法自带和自定义的方法,该方法比较重要,以后再form认证的时候会用到该方法。

 class Test:
def __init__(self,name,job):
self.name=name
self.job=job
def show(self):
print('OK')
obj=Test('tom','IT')
print(obj.__dict__)
print(Test.__dict__)
{'job': 'IT', 'name': 'tom'}
{'__dict__': <attribute '__dict__' of 'Test' objects>, 'show': <function Test.show at 0x0127F468>, '__init__': <function Test.__init__ at 0x0127F420>, '__module__': 's2', '__doc__': None, '__weakref__': <attribute '__weakref__' of 'Test' objects>}
<class 's2.Test'>

f:__setitem__、__getitem__、__delitem__:操作对象,不同的动作会触发相应的方法,在写session的时候会用到这几个方法。

 class Test:
def __init__(self,name,job):
self.name=name
self.job=job
def show(self):
print('OK')
def __setitem__(self, key, value):
print(key,value)
def __getitem__(self, item):
print(item)
def __delitem__(self, key):
print(key)
obj=Test('tom','IT')
obj['get']
obj['tom']=
del obj['del']
get
tom
del

字典就是用上述的方法进行相应的操作的。来实现赋值、查看、删除等操作。

在python3中,当对象操作切片的时候也会调用__setitem__、__getitem__、__delitem__。如果是切片动作传进去实参是切片对象。

 class Test:
def __getitem__(self, item):
print(type(item))
print('get')
def __setitem__(self, key, value):
print(type(key),type(value)) def __delitem__(self, key):
print(type(key)) obj=Test()
obj[:]
obj[:]=[,,,]
del obj[:]
<class 'slice'>
get
<class 'slice'> <class 'list'>
<class 'slice'>

可以根据传进去的对象类型进行相应的操作。当传进去是slice的时候,可以获取起起始、步长、结束。

class Test:
def __getitem__(self, item):
print(item.start)
print(item.stop)
print(item.step)
def __setitem__(self, key, value):
print(type(key),type(value)) def __delitem__(self, key):
print(type(key)) obj=Test()
obj[::]
obj[:]=[,,,]
del obj[:] <class 'slice'> <class 'list'>
<class 'slice'>

g:__iter__方法:当一个对象被迭代:比如放在for循环里的时候,会自动执行对应类中的__iter__方法,元组、字符串、列表等在被迭代的时候,自动执行元组、字符串等类中的__iter__方法。

 class Test:
def __iter__(self):
yield
yield
obj=Test()
for i in obj:
print(i)

__iter__方法 需要返回一个迭代器,而不是一个列表或者元组。。

 class Test:
def __iter__(self):
return iter('avcc')
obj=Test()
for i in obj:
print(i)
 h: isinstance判断对象是否是一个类的类型或者是否是该类的父类的类型。issubclass是判断一个类是否是另一个类的父类。
 class Test_old:
def f1(self):
print('Test_old.f1') class Test(Test_old):
def f1(self):
print('Test.f1') obj=Test()
print(isinstance(obj,Test))
print(isinstance(obj,Test_old))
True
True
 class Test_old:
def f1(self):
print('Test_old.f1') class Test(Test_old):
def f1(self):
print('Test.f1') print(issubclass(Test,Test_old))
True
六:super()函数的应用以及源码的修改方式
a:super(),在执行子类的方法同时,也执行父类中相同名字的方法。
 class Test_old:
def f1(self):
print('Test_old.f1') class Test(Test_old):
def f1(self):
super(Test,self).f1()#执行自己类的父类中的f1方法。
print('Test.f1') obj=Test()
obj.f1()
Test_old.f1
Test.f1
super参数可以省略自动去执行该类的父类中相同的方法。参考官网: https://docs.python.org/3.1/library/functions.html#super
 class C(B):
def method(self, arg):
super().method(arg) # This does the same thing as:
# super(C, self).method(arg)
 class Test_old:
def f1(self):
print('Test_old.f1') class Test(Test_old):
def f1(self):
super().f1()
print('Test.f1')
Test_old.f1
Test.f1
使用场景:当我们接触别人代码或者源码的时候,需要对功能的改进的时候,需要遵循不修改源码的规则,使用类的继承以及super()的方法,可以在完全执行源码的功能的基础上,执行我们自己的代码。
如下例子是将无序字典转化成有序字典: 核心思想,在不修改源代码的情况下,进行代码的重构。
 class SorDic(dict):
def __init__(self):
super().__init__()
self.lis=[]
def __setitem__(self, key, value):
super(SorDic,self).__setitem__(key,value)
self.lis.append(key) def __str__(self):
tem_lis=[]
for key in self.lis:
value=self.get(key)
tem_lis.append('%s:%s'%(key,value))
return '{'+','.join(tem_lis)+'}' obj=SorDic()
obj['m']=
obj['n']=
print(obj)

七:设计模式:单例模式。

单例模式:单个实例模式,也就是创建单个对象实例。应用场景:数据库连接池,只需要创建一个连接池既可,无须创建多个。

 class Singal:
instance=None
def __init__(self,name):
self.name=name
@classmethod
def get_instance(cls):
if cls.instance:
return cls.instance
else:
obj=cls('tom')###注意这个是常量问题。
cls.instance=obj
return cls.instance obj1=Singal.get_instance()
obj2=Singal.get_instance()
print(id(obj1),id(obj2))
注意:1:需要使用类方法,因为在对象未创建的时候可以调用类方法。
2:返回值是一个对象。
   3:初始化__init__参数直接写入一个实参。
八:异常处理:
简单的异常处理:
 while True:
try:
num1=input('Entre a number:')
num2=input('Entre a number:')
res=int(num1)+int(num2)
print(res)
except Exception as e:
print(e)

1:try:存放正常的代码。

2:except存放 Execption类的对象e  e存储的是错误信息。并把错误信息打印输出。

except代码块 可以写Exception 捕捉所有错误,或者多个except来显式的捕获具体的错误。

 while True:
try:
num1=input('Entre a number:')
num2=input('Entre a number:')
res=int(num1)+int(num2)
print(res)
lis=[,]
print(lis[])
except ValueError as e:
print(e)
except IndexError as e:
print(e)
a:通常做法是:通过except捕获多个具体的异常,最后用Execption捕获我们不需要的异常。注意顺序!
 while True:
try:
num1=input('Entre a number:')
num2=input('Entre a number:')
res=int(num1)+int(num2)
print(res)
lis=[,]
print(lis[])
except ValueError as e:
print(e)
except IndexError as e:
print(e)
except Exception as e:
print(e)
b:常见的异常:
AttributeError 试图访问一个对象没有的树形,比如foo.x,但是foo没有属性x
IOError 输入/输出异常;基本上是无法打开文件
ImportError 无法引入模块或包;基本上是路径问题或名称错误
IndentationError 语法错误(的子类) ;代码没有正确对齐
IndexError 下标索引超出序列边界,比如当x只有三个元素,却试图访问x[]
KeyError 试图访问字典里不存在的键
KeyboardInterrupt Ctrl+C被按下
NameError 使用一个还未被赋予对象的变量
SyntaxError Python代码非法,代码不能编译(个人认为这是语法错误,写错了)
TypeError 传入对象类型与要求的不符合
UnboundLocalError 试图访问一个还未被设置的局部变量,基本上是由于另有一个同名的全局变量,
导致你以为正在访问它
ValueError 传入一个调用者不期望的值,即使值的类型是正确的

c:完整的异常处理块:

 try:
pass
except Exception as e:
print(e)
else:
pass
finally:
pass
注意:
try代码块中,在执行过程中如果出现错误,则直接跳到execpt代码块中。
当执行try代码块的时候无报错,则执行else代码块,最后执行finally代码块。
如果执行try代码块的出现错误则直接跳到execpt代码块,最后执行finally代码块。

d:主动错误异常:raise 语句

 try:
raise Exception('Error')
except Exception as e:
print(e)
finally:
pass

 class Lmd(Exception):

     def __init__(self, msg):
self.message = msg def __str__(self):
return self.message try:
raise Lmd('my error')
except Lmd as e:
print(e)
e:assert断言,assert  condition   条件成立不报错,不成立报错。
 assert  =
AssertionError
 
 
												

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