This article mainly introduces the differences between functions and methods in Python from several dimensions:
First, let’s analyze it from the perspective of classification.
(1) Classification of functions:
Built-in functions: Some functions embedded in python. Anonymous function: One line of code implements a function. Recursive function custom function: define functions according to your own needs.
(2) Classification of methods:
Ordinary method: method called directly with self. Private method: __function name, a method that can only be called in the class. Property method: @property, disguise the method as a property to make the code look more reasonable. Special method (double underline method): Taking __init__ as an example, it is used to encapsulate the attributes of the instantiated object. As long as the object is instantiated, the __init method will be executed. If there is no subclass of the object, the parent class will be searched ( Super class), if there is no parent class (super class), directly inherit the object (python 3.x) class and execute the __init__ method in the class. Class method: operate the properties and methods in the public template by calling the class name. Static method: There is no need to pass in the class space or object method. The function is to ensure the consistency and standardization of the code. It can be a completely independent method outside the class, but for the consistency of the code, it is uniformly placed in a certain module (py file) middle.
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Secondly, analyze from the perspective of scope:
(1 ) Function scope: From the beginning of the function call to the completion of function execution, after returning to the caller, the space opened during the execution process will be automatically released. That is to say, after the function execution is completed, the value of the variable is modified inside the function body through assignment, etc. It will not be retained, and the opened space will be automatically released after it is returned to the caller.
(2) Method scope: When a method is called through an instantiated object, the space opened after the call will not be released, which means that the modified value of the variable in the calling method will always be retained.
Finally, the calling method is different.
(1) Function: Called through "function name ()".
(2) Method: Called through "object.method name".
class Foo(object): def func(self): pass #实例化 obj = Foo() # 执行方式一:调用的func是方法 obj.func() #func 方法 # 执行方式二:调用的func是函数 Foo.func(123) # 函数
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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.

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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