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Python Linter: Effortlessly Improve Code Quality


Python Code Quality: Tools & Best Practices

by Alexander VanTol

Table of Contents

What is Code Quality?

To define code quality, we need to consider several factors. High-quality code can be identified by the following criteria:

  • It accomplishes its intended purpose.
  • It contains no defects or issues.
  • It is easy to read, maintain, and extend.

These three criteria are generally agreed upon and help to establish a foundation of code quality.

Why Does Code Quality Matter?

Ensuring high-quality code is important for several reasons. Let’s delve into each criterion to understand their significance:

It does not do what it is supposed to do

Meeting requirements is the core function of any software. If the code fails to accomplish its intended purpose, it cannot be considered high quality. In fact, it may not even meet the minimum standards.

It does contain defects and problems

If software has issues or causes problems, it cannot be considered high quality. Users would not perceive it positively and may even stop using it. For example, a vacuum cleaner that breaks when faced with an atypical situation cannot be deemed high quality.

It is difficult to read, maintain, or extend

Code that is hard to understand, maintain, or extend presents numerous challenges. Suppose a new feature needs to be added and the original code author is no longer available. If the code is easy to comprehend, the new developer will be able to analyze the existing code quickly and implement the new feature effectively. However, complex and convoluted code will slow down the process and may lead to incorrect assumptions. Additionally, code that is not easily extendable can cause other features to break when new functionality is added. Working with low-quality code creates headaches and additional work for everyone involved.

How to Improve Python Code Quality

There are several practices and tools you can utilize to enhance the quality of your Python code. Two key approaches are discussed here:

  • Style Guides
  • Linters

Style Guides

Style guides establish coding conventions and standards to ensure consistent and readable code. Popular style guides for Python code include:

  • PEP 8: The official style guide for Python code, providing guidelines for code layout, naming conventions, and more.
  • Google Python Style Guide: A guide that outlines a set of best practices for Python code based on Google’s internal coding practices.

By following a style guide, you can improve code readability and maintainability, and make it easier for others to understand and work with your code.


Linters are tools that analyze your code for potential errors, style violations, and code quality issues. They provide automated checks to ensure your code adheres to certain guidelines and standards. Linters can help detect issues such as unused variables, incorrect indentation, missing docstrings, and more. Some popular Python linters include:

  • Pylint: A widely used tool that checks for coding errors, adherence to coding standards, and more.
  • Flake8: A combination of several other tools, including PyFlakes, pycodestyle, and McCabe, offering comprehensive code analysis.

Using linters as part of your development workflow helps catch errors and maintain code quality early on, preventing issues from escalating.

When Can I Check My Code Quality?

Ensuring code quality should be a continuous process throughout the development lifecycle. Here are some key moments to check your code quality:

  • As You Write: Follow style guides and use linters while writing code. This helps catch issues early and ensures code follows the specified guidelines.
  • Before You Check In Code: Before committing your code to version control, run linters to identify any potential errors or code quality issues. This helps maintain a high standard of code across the codebase.
  • When Running Tests: Incorporate code quality checks as part of your test suite. This ensures that code quality is assessed alongside functional and unit tests.

By integrating code quality checks at different stages, you can catch issues early, maintain a high standard of code, and prevent problems from escalating.


Code quality is crucial for any software project. By adhering to style guides, utilizing linters, and performing code quality checks at various stages, you can improve the readability, maintainability, and functionality of your Python code. Ultimately, investing in code quality practices and tools pays off in terms of robustness, scalability, and ease of collaboration in your development projects.