Choosing the best IDE for Python depends on your experience level, project type, and specific workflow needs. In this blog, we’ll explore the top Python IDEs, compare their features, and help you decide which one is best for your use case.
What Makes a Good Python IDE?
Before diving into the options, let's define what you should look for in a Python IDE:
- Code Autocompletion & IntelliSense: Helps speed up coding with suggestions and documentation hints.
- Debugging Tools: Step-through debugging, breakpoints, variable inspection, and call stacks.
- Version Control Integration: Git support built into the IDE.
- Code Navigation: Easy jump to definitions, references, and symbol searches.
- Plugin Ecosystem: Ability to extend the IDE for frameworks like Django, Flask, NumPy, etc.
- Performance: Lightweight enough not to slow down your machine.
- Testing Support: Built-in support or plugins for unit testing and automation tools.
With that in mind, here are some of the best IDEs for Python in 2025:
- PyCharm – Best for Professional Python Development
PyCharm, developed by JetBrains, is a full-featured, Python-specific IDE built for professionals. It offers everything from advanced debugging and refactoring tools to intelligent code assistance and seamless integration with frameworks like Django and Flask.
Key Features:
- Smart code completion and real-time error detection
- Powerful debugger and test runner
- Git, Docker, and database integration
- Excellent support for web frameworks
- Built-in support for Jupyter Notebooks
Best for: Backend developers, data engineers, and full-stack Python developers
Downside: Heavier on system resources; the full-featured version (Professional) is paid
- Visual Studio Code (VS Code) – Best Free, Lightweight IDE
VS Code is technically a text editor, but with the right extensions, it transforms into a powerful IDE for Python. It’s free, open-source, and highly customizable.
Key Features:
- Python extension with IntelliSense, linting, and formatting
- Integrated terminal and debugger
- Git and version control built-in
- Massive extension marketplace
- Lightweight and fast
Best for: Beginners, hobbyists, and developers working across multiple languages
Downside: Requires some setup (installing Python extension, setting up interpreters)
- JupyterLab – Best for Data Science and Machine Learning
JupyterLab (the next generation of Jupyter Notebook) is ideal for those working in data-heavy environments. It supports code, markdown, visualizations, and interactive widgets—all in the browser.
Key Features:
- Interactive cells for code execution
- Integration with pandas, NumPy, and Matplotlib
- Built-in support for plotting and data visualization
- Easy sharing and exporting to HTML/Markdown
Best for: Data scientists, analysts, and researchers
Downside: Not ideal for building full-scale Python applications or APIs
- Thonny – Best for Beginners
Thonny is a simple IDE aimed at students and beginners who are just learning Python. It’s minimal, clean, and doesn’t overwhelm users with features.
Key Features:
- Simple interface with fewer distractions
- Built-in debugger and variable explorer
- No need to install Python separately
- Supports packages and modules
Best for: Absolute beginners and students
Downside: Lacks advanced features for large projects
- Spyder – Best for Scientific Computing
Spyder is tailored for scientific computing and comes with powerful tools for data analysis, visualization, and debugging.
Key Features:
- Variable explorer
- IPython terminal integration
- NumPy, SciPy, and Matplotlib support out of the box
- Integrated debugger and profiler
Best for: Researchers, engineers, and scientists
Downside: Not suited for web or general-purpose Python development
Bonus: IDEs That Aren’t Python-Specific but Still Great
- Sublime Text: Fast, minimal, and with Python plugins available
- Atom: Open-source and highly customizable
- Eclipse + PyDev: A classic IDE for Java developers using Python
Testing Python Applications with Keploy
Once you've chosen your IDE and started writing code, the next step is to ensure it's well-tested. While most IDEs support unit test execution, test coverage, and linting, manual test creation can be time-consuming.
That's where Keploy comes in.
Keploy is an open-source testing platform that captures API calls from your Python backend and automatically generates unit tests and mocks. This means you can write and test your Python applications—especially web services—without spending hours writing boilerplate tests.
Use case: If you’re building a Flask or Django app, Keploy can intercept your API traffic, record real-world behavior, and generate repeatable test cases that integrate into your CI/CD pipeline.
Final Thoughts
The best IDE for Python depends entirely on your background, goals, and the kind of projects you’re building. If you're a professional developer, PyCharm offers unmatched depth. If you're just getting started, Thonny or VS Code might be a better fit. And for data scientists, JupyterLab and Spyder are excellent choices.
No matter which IDE you choose, pairing it with modern testing tools like Keploy can make your development process more robust, efficient, and scalable.
Want to boost your Python development workflow?
Pick the right IDE and let Keploy automate your testing. Explore more at https://keploy.io.
Read more- https://keploy.io/blog/community/top-5-best-ides-to-use-for-python-in-2024