class A(object): # A must be new-style class def __init__(self): print "enter A" print "leave A" class B(C): # A --> C def __init__(self): print "enter B" super(B, self).__init__() print "leave B"
In our impression, super(B, self).__init__() is understood like this: super(B, self) first finds the parent class of B (that is, class A), and then puts class B into The object self is converted to an object of class A, and then the "converted" class A object calls its own __init__ function.
One day a colleague designed a relatively complex class architecture (let’s not worry about whether the class architecture is reasonably designed, just study this example as a topic), the code is as follows
Code segment 4:
class A(object): def __init__(self): print "enter A" print "leave A" class B(object): def __init__(self): print "enter B" print "leave B" class C(A): def __init__(self): print "enter C" super(C, self).__init__() print "leave C" class D(A): def __init__(self): print "enter D" super(D, self).__init__() print "leave D" class E(B, C): def __init__(self): print "enter E" B.__init__(self) C.__init__(self) print "leave E" class F(E, D): def __init__(self): print "enter F" E.__init__(self) D.__init__(self) print "leave F"
f = F() , the result is as follows:
enter F enter E enter B leave B enter C enter D enter A leave A leave D leave C leave E enter D enter A leave A leave D leave F
Obviously, the initialization functions of class A and class D were called twice, which is not the result we expected! The expected result is that the initialization function of class A is called at most twice - in fact, this is a problem that multiple inheritance class systems must face. We draw the class system of code segment 4, as shown below:
object
|
| A
| It can be seen from the figure that when the initialization function of class C is called, the initialization function of class A should be called, but in fact the initialization function of class D is called. What a weird question!
In other words, mro records the class type sequence of all base classes of a class. Looking at the record of mro, we found that it contains 7 elements and the 7 class names are:
F E B C D A object
class A(object): def __init__(self): print "enter A" super(A, self).__init__() # new print "leave A" class B(object): def __init__(self): print "enter B" super(B, self).__init__() # new print "leave B" class C(A): def __init__(self): print "enter C" super(C, self).__init__() print "leave C" class D(A): def __init__(self): print "enter D" super(D, self).__init__() print "leave D" class E(B, C): def __init__(self): print "enter E" super(E, self).__init__() # change print "leave E" class F(E, D): def __init__(self): print "enter F" super(F, self).__init__() # change print "leave F"f = F(), execution result: enter F enter E enter B enter C enter D enter A leave A leave D leave C leave B leave E leave FIt can be seen that the initialization of F not only completes the calls of all parent classes, but also ensures that the initialization function of each parent class is only called once.
Summary
1. super is not a function, but a class name. The form super(B, self) actually calls the initialization function of the super class,
and generates a super object;
2. The super class The initialization function does not do any special operations, it simply records the class type and specific instances;
3. The call to super(B, self).func is not used to call the func function of the parent class of the current class;
5. Mix super classes and non- Binding functions is a dangerous behavior, which may result in the parent class function that should be called not being called or a parent class function being called multiple times.
Some more in-depth questions: As you can see, when printing F.__mro__, it is found that the order of the elements inside is F E B C D A object. This is the base class search order of F. As for why it is in this order, and python’s built-in multiple inheritance How the order is implemented, this involves the implementation of mro order,

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.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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