**python中的类和他的成员

面向对象是一种编程方式，此编程方式的实现基于对 类 和 对象 的使用。

Python从设计之初就已经是一门面向对象的语言，正因为如此，在Python中创建一个类和对象是很容易的。

在这里，为文章中使用的词汇做一下说明：

函数：指的是第一个参数不是self的函数，不在类中定义的函数；

方法：指是的第一个参数是self的函数，在在类中定义的函数；

实例：类的对象（对类进行实例化即为对象），instance；

 

特性：

封装

封装最好理解了。封装是面向对象的特征之一，是对象和类概念的主要特性。

封装，也就是把客观事物封装成抽象的类，并且类可以把自己的数据和方法只让可信的类或者对象操作，对不可信的进行信息隐藏。

__init__ 是封装必不可少的，看列子：

class Init():

    def __init__(self,name,age,gender):
        self.Name = name
        self.Age = age
        self.Gender = gender

obj1 = Init('xiao',15,'男')
obj2 = Init('yao',16,'女')

print(obj1.Name)
print(obj2.Name)

这样实现了数据封装

xiao
yao

继承

继承是指这样一种能力：它可以使用现有类的所有功能，并在无需重新编写原来的类的情况下对这些功能进行扩展。(此处我们只讨论python3的继承)

通过继承创建的新类称为“子类”或“派生类”。

被继承的类称为“基类”、“父类”或“超类”。

继承的过程，就是从一般到特殊的过程。

要实现继承，可以通过“继承”（Inheritance）和“组合”（Composition）来实现。

在某些 OOP 语言中，一个子类可以继承多个基类。但是一般情况下，一个子类只能有一个基类，要实现多重继承，可以通过多级继承来实现。

class C1():
    def c1(self):
        print('C1')
class C2(C1):
    def c2(self):
        print('C2')
class C3():
    def c3(self):
        print('C3')
    def c1(self):
        print('C3')
class C4(C2,C3):
    def c4(self):
        print('C4')
obj = C4()
obj.c1()
obj1 = C3()
obj1.c1()

输出结果：

C1
C3

可见，C4继承C2，C3，C2又继承C1，所以obj会有c1方法。

类的继承可以总结为：

1.左侧优先

　 class C4(C2,C3) 这里C2比C3优先

2.深度优先

上面例子C1和C3都有c1方法，但是最后C4继承的是C1的c1

3.有共同的父类，最后找父类

看图红色箭头表示继承关系，黑色箭头表示查找关系

图一：                                                                                                       图二：

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" 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" alt="" />

3.多态

多态性（polymorphisn）是允许你将父对象设置成为和一个或更多的他的子对象相等的技术，赋值之后，父对象就可以根据当前赋值给它的子对象的特性以不同的方式运作。简单的说，就是一句话：允许将子类类型的指针赋值给父类类型的指针。

那么，多态的作用是什么呢？我们知道，封装可以隐藏实现细节，使得代码模块化；继承可以扩展已存在的代码模块（类）；它们的目的都是为了——代码重用。而多态则是为了实现另一个目的——接口重用！多态的作用，就是为了类在继承和派生的时候，保证使用“家谱”中任一类的实例的某一属性时的正确调用。

 

Pyhon不支持多态并且也用不到多态，多态的概念是应用于Java和C#这一类强类型语言中

 

 

方法：

 普通方法：由对象调用；至少一个self参数；执行普通方法时，自动将调用该方法的对象赋值给self；
 类方法：由类调用； 至少一个cls参数；执行类方法时，自动将调用该方法的类复制给cls；
 静态方法：由类调用；无默认参数；

 属性方法：Python中的属性其实是普通方法的变种；  

 内置方法： '__dict__', '__doc__', '__init__', '__module__', '__new__', '__repr__', '__str__' 等；

class Foo:
    def __init__(self, name):
        """ 定义内置方法，至少有一个self参数 """
        self.name = name

    def ord_func(self):
        """ 定义普通方法，至少有一个self参数 """
        print('普通方法')

    @classmethod
    def class_func(cls):
        """ 定义类方法，至少有一个cls参数 """
        print('类方法')

    @staticmethod
    def static_func():
        """ 定义静态方法 ，无默认参数"""
        print('静态方法')

    @property
    def property_fuc(self):
        """ 定义属性方法，至少有一个self参数 """
        print('属性方法')

# 调用普通方法
f = Foo('那么')
f.ord_func()
# 调用类方法
Foo.class_func()
f.class_func()
# 调用静态方法
Foo.static_func()
f.static_func()
# 调用属性方法
f.property_fuc

　　实例是可以访问类方法和静态方法的，但不建议这样做，容易混淆。　

变量

变量包括：类变量和实例变量，他们在定义和使用中有所区别，而最本质的区别是内存中保存的位置不同。实例变量属于对象,类变量属于类.

类变量在内存中只保存一份,实例变量在每个对象中都要保存一份。

class Province:
    # 类变量
    country = '中国'

    def __init__(self, param1):
        # 实例变量
        self.name = param1
# 直接访问普通字段
obj = Province('北京')
print(obj.name)
# 直接访问静态字段
print(Province.country)
print(obj.country)

　　我们可看到同方法相似，实例也是可访问类变量的。

对于每一个类的变量而言都有两种形式：

• 公有变量，在任何地方都能访问
• 私有变量，只有在类的内部才能方法

class A:
    name = "公有类变量"
    def func(self):
        print(A.name)
class B(A):
    def show(self):
        print(A.name)
A.name         # 类访问
obj = A()
obj.func()     # 类内部可以访问
obj_son = B()
obj_son.show() # 派生类中可以访问

　　

class C:
    __name = "私有类字段"
    def func(self):
        print(C.__name)
class D(C):
    def show(self):
        print(C.__name)
C.__name       # 类访问            ==> 错误
obj = C()
obj.func()     # 类内部可以访问     ==> 正确
obj_son = D()
obj_son.show() # 派生类中可以访问   ==> 错误

　　

class C:

    def __init__(self):
        self.foo = "公有实例变量"

    def func(self):
        print(self.foo)  # 类内部访问

class D(C):
    def show(self):
        print(self.foo)  #　派生类中访问

obj = C()
obj.foo  # 通过对象访问
obj.func()  # 类内部访问
obj_son = D()
obj_son.show()  # 派生类中访问

　　

class C:

    def __init__(self):
        self.__foo = "私有实例字段"

    def func(self):

        print(self.__foo) # 类内部访问

class D(C):

    def show(self):
        print(self.__foo)  # 派生类中访问

obj = C()
obj.__foo  # 通过对象访问 ==> 错误
obj.func()  # 类内部访问 ==> 正确
obj_son = D()
obj_son.show()  # 派生类中访问 ==> 错误

　　

注：

非要访问私有属性的话，可以通过 对象._类__属性名  （  print(obj._C__foo)  ）

新式类&经典类

新式类很早在2.2就出现了,所以经典类（旧式类）完全是兼容的问题,Python3里的类全部都是新式类.

python2.X中

#新式类
class C(object):
    pass
#经典类
class B:
    pass

python3.X中 由于没有经典类，所以上述两种写法都表示新式类。