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You know that feeling when you finally get your Python code working, only to discover bugs in production? Or when you’re reviewing someone else’s code and struggling to understand their style choices?
Writing code that works is the “easy” part. The real challenge is writing code that you—and others—can confidently modify, maintain, and scale. We’ve all been there, and that’s exactly why having the right tools makes such a big difference.
In this article, we’ll explore tools that handle the heavy lifting of code quality, so you can focus on building useful applications. You’ll understand what these tools do and how you can use them in your development workflow.
1. Pylint or Ruff – Code Style and Error Detection
When you’re writing code, it’s easy to miss small details like unused variables or inconsistent spacing. Pylint is a static code analyzer that catches these issues before they become problems.
Pylint reads through your Python code—without running it (that’s what static analyzers do)—and points out style issues, potential errors, and places where your code could be improved.
Key features:
- Checks if your code follows Python’s style guide (PEP 8)
- Spots code that’s written but never used
- Identifies complex code that might need simplifying
- Finds common programming mistakes
- Can write custom checkers to match preferences and requirements
Note: 🔖You may also want to check out Ruff and use it alongside or in place of Pylint.
2. Mypy – Static Type Checking
Have you ever run your code only to find out that somewhere, you accidentally tried to use a string where you needed a number? Mypy catches these kinds of mistakes before you run your code—saving you time and headaches in debugging.
Mypy is a static type checker that looks at your code’s type hints (those little annotations that tell you what kind of data you’re working with) and makes sure everything adds up.
Key features:
- Works with Python’s built-in type hints
- Lets you mix typed and untyped code (perfect when you want to gradually adding types)
- Catches type-related bugs early
- Helps document your code’s expected inputs and outputs
- Makes it safer to make significant changes to your code
To get started, see the mypy cheat sheet and how to use mypy with an existing project.
3. Pytest – Testing Framework
How do you know your code works? Really works, not just “seems to work”. That’s where testing comes in.
But writing tests doesn’t (always) have to be complicated. Pytest makes it almost fun – seriously! It is a testing tool that helps you write everything from simple checks to complex test suites.
Key features:
- Provides detailed information when tests fail
- Lets you run specific groups of tests
- Shares setup code between tests efficiently
Here are a couple of tutorials from the KDnuggets Python library to help you get started with Pytest:
4. Black – Code Formatter
Ever spent time wondering if you should put a space after that comma? Black makes these decisions for you.
So instead of spending time debating about formatting or manually fixing indentation, you can focus on what your code actually does. Given your Python code, Black gives a cleanly formatted version that follows consistent rules.
Key features:
- Auto-formats your code
- Requires minimal configuration
- Works with most editors
- Makes code reviews faster (yeah, no more formatting discussions!)
Learn to use Black in your project and with other tools.
5. Bandit – Security Linter
Security issues in code can be subtle and easy to miss. Bandit helps catch common security mistakes early, before they make it into your running application where they could cause real problems.
Bandit scans your code for common security problems, like using unsafe functions or hardcoded passwords and other sensitive info.
Key features:
- Identifies common security issues
- Adjusts warnings based on how serious the issue is
- Provides clear explanations of potential problems
- Can use pre-commit to integrates with your version control system
The getting started guide will help you learn to use Bandit for your projects, create reports, compare them to a baseline, and more.
6. Coverage.py – Code Coverage Tool
How much of your code are your tests actually testing? Coverage.py gives you the answer. Because only having tests isn’t enough – you need to know they’re testing the code right.
Coverage.py watches your tests run and keeps track of which lines of code they execute. It helps you find code that isn’t being tested, so you can add tests where they’re needed most.
Key features:
- Shows which lines of code were run during tests
- Creates easy-to-read reports
- Identifies branches in your code that weren’t tested
- Works well with PyTest and other testing frameworks
This starter guide contains all you need to get started with Coverage.py.
7. isort – Import Optimizer
Let’s wrap up with a tool that helps keep your code’s “front matter” tidy – isort that stands for “import sort”.
Well-organized imports make your code easier to read and understand. When you’re working with a team, having a consistent way to organize imports helps everyone follow along.
It sorts imports alphabetically and also by type. So it essentially sorts imports into neat, logical groups and makes sure they follow a consistent pattern.
Key features:
- Automatically sorts your imports
- Works with different import styling preferences
- Works super smoothly with code editors
- Lets you create custom import sections
You can check out this quickstart guide to start using the isort Python API.
Conclusion
Now that you have a handful of tools, where do you go from here?
Try installing one of these tools and running it on a small project. Make sure you have a recent Python version that’s compatible with the tools you’re planning to use.
Start with one tool (I recommend Black or Ruff) and gradually start using the others as you get comfortable. So yeah, keep coding!
Bala Priya C is a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her areas of interest and expertise include DevOps, data science, and natural language processing. She enjoys reading, writing, coding, and coffee! Currently, she’s working on learning and sharing her knowledge with the developer community by authoring tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource overviews and coding tutorials.