How to solve Python's poor code refactoring error?

Python is a high-level programming language that has become the language of choice for many developers because of its features such as ease of learning, ease of use, and high development efficiency. However, Python's own flexibility and dynamics also bring some troubles to programmers. One of the most prominent problems is code refactoring.

In the process of Python development, the flaws of poor code refactoring often affect the maintainability, scalability and readability of the program, and also cause some problems that are difficult to discover and troubleshoot. mistake. Below we’ll explore some practical tips for solving poor refactorability errors in Python code.

1. Use appropriate naming rules

Naming in the code is of great significance for code reconstruction and maintenance. If the naming is not standardized, it will make the code extremely difficult to read and will also affect subsequent modifications and optimizations. A good naming rule should have the following characteristics:

1. Be able to express the purpose and function of the variable, function or object.

2. The name should be readable, easy to dictate and spell, and as concise as possible.

3. Can reflect type information.

4. Be able to distinguish each other.

It is recommended that you do not use simple names, such as naming with a single character or number, because this will easily be confused with other names and cause ambiguity. If you need to define variables, you can use underline or camel case naming, for example:

total_score = 90
first_name = 'Tom'

2. Build unit test

Test It is a necessary process to refactor and optimize code, and unit testing is one of the very important testing methods. Using unit tests can help check in real time that your code is running as expected, and catch errors. Some code editors (such as PyCharm) and IDEs have integrated unit testing tools that can automatically create and analyze unit test cases and annotate them in the code. This makes it easy to test code and get output without breaking the code structure or test framework.

3. Variable code

The flexibility of Python is one of its strengths, but this may also lead to reduced readability of the code. Excessive context transfers, such as the use of temporary variables and expressions to define consecutive sections of a given code, will reduce the readability of the code and make it difficult to refactor and optimize the code in the future. It is recommended to integrate the code into a clearer and more readable format by combining expressions and assignment operators:

startDate = datetime.now().date()
endDate = datetime.now(). date() timedelta(days=5)

four, use appropriate data structures and function libraries

Python’s data structures and function libraries are very rich, developers can use these tools to simplify code, Reduce development time and improve readability and maintainability. For example, using data structures such as python collections or numpy lists can greatly simplify the code:

import numpy as np
l = [1,2,3,4]
a = np.array (l)
b = a*2

5. Follow Python code specifications

The Python language itself has a very clear code specification, namely PEP8. These specifications are designed to improve code readability and consistency and are useful for avoiding refactoring errors and improving maintainability. These specifications include aspects such as indentation, comments, and function specifications, which are especially useful when writing large projects. By complying with the PEP8 specification, the code can be made more readable and maintainable, and problems caused by code refactoring can also be reduced.

Summary:

If you want to avoid poor refactoring errors in Python code, you need to use good naming rules, build unit tests, variableize the code, use appropriate data structures and function libraries, and Following Python coding standards, these techniques have become necessary tools for Python development. Improving code refactoring errors through these methods can improve code readability, maintainability, and scalability, and can also reduce the time and resource costs required for code iterative development.

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