Python's object-oriented programming organizes code through classes and objects, emphasizing the combination of data and operations. 1. The class is a template, the object is an instance, and the attributes are initialized with init; 2. Inherit the reusable class function and use super() to call the parent class; 3. Encapsulate the control of access rights through underscore or double underscore to protect the internal state; 4. Polymorphism allows different classes to implement the same-name method and unify the different behaviors of the interface. These features make the program structure clear and easy to maintain.

How to understand Python's object-oriented programming (OOP)? In fact, to put it bluntly, it is to organize the code in the form of "classes" and "objects". Compared with procedural programming, it emphasizes the combination of data and operations, and is suitable for building programs with clear structure and easy to maintain.

Let’s take a look at how OOP is implemented in Python from several common perspectives.

Classes and Objects: The basic unit of OOP

In Python, a class is like a template, and an object is a specific instance created based on this template. For example, you can define a Car class and then create multiple different car objects.

 class Car:
    def __init__(self, brand, color):
        self.brand = brand
        self.color = color

my_car = Car("Tesla", "Red")

In the above example, __init__ is a constructor that initializes the properties of an object. Each object has its own brand and color attributes. This step may seem simple, but it is the foundation of the entire OOP.

What should be noted is:

• Class names are usually nomenclature by large camels (such as ElectricCar )
• Instance attributes are more common in __init__
• Class attributes can be written in class bodies and outside methods

Inheritance: Reuse existing class functions

Inheritance allows you to derive new classes from an existing class, so that you can reuse existing code. For example, you can let ElectricCar inherit Car , so that it has all its functions and add new features on this basis.

 class ElectricCar(Car):
    def __init__(self, brand, color, battery_capacity):
        super().__init__(brand, color)
        self.battery_capacity = battery_capacity

Here super() is used to call the constructor of the parent class to avoid duplicate code. This is a common practice in Python.

The benefits of inheritance are obvious:

• Reduce duplicate code
• Improve code readability
• Easy to expand and maintain

However, you should also be careful not to overuse inheritance, otherwise it will easily lead to too deep class levels and will be difficult to understand.

Encapsulation and access control: Protect internal state

Encapsulation means wrapping data and behavior in a class and hiding implementation details from the outside. Although Python does not have a strict private variable mechanism, it can be simulated by naming conventions:

• Single _variable means protected member
• Double underscore __variable will trigger name mangling to prevent subclass overwriting

For example:

 class BankAccount:
    def __init__(self, balance):
        self.__balance = balance # private property def deposit(self, amount):
        self.__balance = amount

    def get_balance(self):
        return self.__balance

In this way, the balance cannot be changed directly from the external to the outside world, and can only be operated indirectly through methods such as deposit or get_balance . This approach improves security and makes it easier to perform logical verification.

Polymorphism: unified interface, different implementations

Polymorphism refers to the different behaviors of the same method on different objects. For example, both classes implement the draw() method, you don’t have to care about which class it is, just call draw() .

Let's give a simple example:

 class Rectangle:
    def area(self):
        return self.width * self.height

class Circle:
    def area(self):
        return 3.14 * self.radius ** 2

def print_area(shape):
    print(f"Area: {shape.area()}")

print_area here can be processed regardless of whether it is a rectangle or a circle. This is the charm of polymorphism - unified interface, flexible expansion.

In general, Python's OOP is not complicated, but to really use it well, you must understand the core concepts of classes, objects, inheritance, encapsulation and polymorphism. Some places look similar, but the difference is reflected when designing large projects.

Basically that's it.

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