Last Updated: April 19, 2024


Ifrah Khan


IDE stands for Integrated Development Environment. It is used for software development. An IDE has a debugger, compiler, and all the other things needed for software development. It helps in combining different aspects of a computer program. 

An IDE is also used for the development of Machine learning and Data scene due to its cast libraries. Different aspects of writing code can be implemented via IDEs like debugging, compiling, editing source code, building executables, and so on. 

Python development is a widely-used programming language by coders, and its IDEs assist in coding and compiling efficiently. There are tons of IDEs that are used by python web development companies a lot nowadays. Let's discuss the best Python IDEs for Data science projects.


List of Python IDEs


Spyder refers to the Scientific python development environment. It is an open-source and free python IDE. It is an excellent and lightweight IDE for data science and machine learning. Spyder is used by a lot of developers and data analysts for real-time analysis of codes.

Spyder comes with an interactive code interactive pattern that allows developers to compile any section of the code, any single line in one go. You can also find the errors, redundant variables, and any other issues in your code without compiling it through the static code analysis feature. 

Pros of Spyder

  • Attractive UI and interface
  • It's free of cost
  • Run python codes


It is an amazing Python IDE for data science that will run on Linux, Windows, and Mac. Thonny debugger assists in debugging source codes line by line, which helps a lot for newbie programmers. The GUI of Thonny makes the process of installation of third-party packages convenient. 

Pros Of Thonny

  • A simple debugger
  • Free open-source IDE
  • Dedicated python community support

While Thonny excels as a learning tool, Python itself is a powerful language widely used in data science. If you're interested in leveraging Python's capabilities for data analysis and manipulation, consider exploring data integration tools. These tools can help you seamlessly combine data from various sources, a crucial step in many data science workflows.


JupyterLab is a web-based IDE for Data science professionals. With the help of the interactive output system of this IDE, you can test your source code in real-time. The interface of JupyterLab offers a simultaneous view of the text editor, terminal, file directory, and console. Autosave, auto-formatting, and code completion are some of the notable features of JupyterLab.

Pros Of JupiterLab

  • Flexible layouts
  • Run code from a text file
  • Code consoles


Pycharm is an excellent python web development IDE that has features such as code indentation and auto code completion. It also has a smart debugger that analyzes the code and bold errors. Machine learning and data science professionals who are into web application development companies prefer Pycharm because of its smooth navigation facility. 

Pros Of Pycharm

  • Excellent community support
  • Availability of a wIDE range of plugins
  • Easy to use

Visual Code

Another most used Python IDE by data science professionals. It works on Linux, Mac, and Windows. It supports different languages besIDEs Python-like C#, C, HTML, javascript, and so on. VC is an open-source, lightweight Python IDE that comes in both free and paid versions.

Pros Of Visual Code

  • Cross-platform support
  • Robust architecture
  • Lightweight

Final Words

Data science is transforming the process of web development. A good IDE is needed by data science and machine learning professionals to compile, debug and test their code to make it error-free. The above listed are some of the best python IDEs used in the market currently.

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