


A brief explanation of python inheritance and multiple inheritance (code example)
The content of this article is a simple explanation (code example) of Python inheritance and multiple inheritance. It has certain reference value. Friends in need can refer to it. I hope it will be helpful to you.
Remember the following points:
Directly subclassing built-in types (such as dict, list or str) is prone to errors, because methods of built-in types usually ignore user-overridden methods, do not subclass Classifying built-in types, user-defined classes should inherit the collections module.
def __setitem__(self, key, value): super().__setitem__(key, [value] * 2) # 错误案例 class AnswerDict(dict): def __getitem__(self, item): # 错误案例 return 42 import collections class DoppelDict2(collections.UserDict): # 正确案例 def __setitem__(self, key, value): super().__setitem__(key, [value] * 2) class AnswerDict2(collections.UserDict): # 正确案例 def __getitem__(self, item): return 42
Another problem related to multiple inheritance is: if a superclass of the same level defines an attribute with the same name. How does Python determine which one to use?
class DoppelDict(dict): def __setitem__(self, key, value): super().__setitem__(key, [value] * 2) class AnswerDict(dict): def __getitem__(self, item): return 42 import collections class DoppelDict2(collections.UserDict): def __setitem__(self, key, value): super().__setitem__(key, [value] * 2) class AnswerDict2(collections.UserDict): def __getitem__(self, item): return 42 class A: def ping(self): print('Ping:', self) class B(A): def pong(self): print('pong:', self) class C(A): def pong(self): print('PONG:', self) class D(B, C): def ping(self): super().ping() print('post-ping:', self) def pingpong(self): self.ping() super().ping() self.pong() super().pong() C.pong(self) if __name__ == '__main__': d = D() print(d.pong()) # 输出来源于B print(C.pong(d)) #输出来源于C 超类的方法都可以直接调用,此时要把实例作为显示参数传入.
Python can distinguish which method is called, by Method parsing order
>>> D.mro()
[
If you want to delegate method calls to the super class, the recommended way is to use the built-in super( ) function.
The following is the interpretation of the d.pingpong() method
>>> self.ping()
Ping: <__main__.d object at> post-ping: <__main__.d object at> The first call is self.ping(), which runs the ping method of class D.
The second call The one is super().ping(), skip the ping method of class D and find the ping method of class A. Ping: <__main__.d object at>
The third call is self .pong() method, based on __mro__, finds the pong method implemented by class B. pong: <__main__.d object at>
The fourth call is super().pong(), which is also based on __mro__, find the pong method implemented by class B. pong: <__main__.d object at>
The fifth call is C.pong(self), __mro__ is ignored, and C is found. The pong method implemented by the class. PONG: <__main__.d object at>
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Single inheritance and multiple inheritance in Python
Python classes and inheritance explanation
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

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