What is Monkey Patching?
Monkey patching is a technique in programming that involves dynamically altering the attributes of a class or module at runtime. It is not the same as method or operator overloading or delegation.
How it Works
In Python, classes are mutable, and methods are attributes of the class. Monkey patching involves dynamically replacing these attributes with modified versions, allowing you to alter the behavior of the class or module.
Example
Consider a class with a get_data method that retrieves data from an external source. In a unit test, you may want to replace the get_data method with a stub that returns fixed data without relying on the external source.
import unittest class MyTest(unittest.TestCase): def test_data(self): # Monkey patch the original get_data method original_data = my_module.get_data_orig my_module.get_data = my_module.get_data_stub # Now, calling get_data will use the test stub my_data = my_module.get_data() # Restore the original get_data method my_module.get_data = original_data
Cautions
While monkey patching can be useful for testing and debugging, it is important to use it cautiously:
- It can affect the behavior of other code that relies on the original attributes.
- It may not affect alias variables that point to the original attribute before it was patched.
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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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