Python IDE for Data Science: an Overview by Data Blogger

With the rise of Data Science, Python is more popular than ever. Therefore, it is important to use a great IDE (Integrated Development Environment) that suits your needs. This blog post gives an overview of the most popular IDEs used in Data Science.


If your code is written in Python and you are looking for a great IDE, then this article is perfect for you. The IDEs (Integrated Development Environments) discussed in this post are cross platforms thus working on Windows, Linux and Mac OS and have free versions and are purely Python oriented.

Jetbrains PyCharm

Jetbrains PyCharm.

Jetbrains PyCharm.

Jetbrains has IDEs for many programming languages and therefore they have a lot of knowledge in the world of code editing. This makes PyCharm a perfect choice for editing your Python code. For Data Science there is one big drawback. It has lack of good support for interactive Python code editing. Interactive editing allows you to run snippets of code and to keep data into the memory. That makes code development really easy. It has some support for it: you can run a Jupyter notebook server, but in my opinion that does not work really well inside the editor. The user interface (UI) is great by the way. It has excellent support for code inspection and by using Ctrl + Click, you can jump straight to any function definition. It also has great support for code testing and debugging. Version control is an integrated feature and you can just click on any branch you’d like to use.

Conclusion: perfect Python IDE for code editing and debugging, less support for interactive Python code editing.

Jupyter notebook

Jupyter notebook.

Jupyter notebook.

Ladies and gentlemens, this is one of the most perfect IDEs for editing your Python code! At least in my opinion. Jupyter notebook is a web based code editor and can quickly generate visualizations. You can mix up code and text containing no, simple or complex mathematics. One thing I am missing here, is the support for code completion, but there are tons of plugins available so this should be no problem. It is also easy to turn your notebook into a presentation. For collaboration with non-technical teams, this is a great tool.

Conclusion: perfect Python IDE for data science! Less support for code inspection.




Spyder is the scientifically based IDE. I like it, because it makes use of Jupyter notebook under the hood. However, the support for mixing up text and code is not available in the IDE. What you get here, is that it has integrated support for code version control and code inspection. It also shows you code context, which is great for developing code.

Conclusion: great editor, but it is missing the support for mixing text and code.

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Conclusion (TL;DR)

There are a lot of different Python IDEs available for different use cases. If you liking mixing code and text, then you should really go for Jupyter notebook.

Interested in other Python related articles? See this great tutorial on web scraping using Python. I wrote it using the Jetbrains PyCharm IDE.

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Kevin Jacobs

I'm Kevin, a Data Scientist, PhD student in NLP and Law and blog writer for Data Blogger. You can reach me via Twitter (@kmjjacobs) or LinkedIn.