The Pros and Cons of Using Jupyter Notebooks as Your Editor for Data Science Work

TL;DR: PyCharm’s probably better

Steffen Sjursen

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Mar 2, 2020

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Jupyter notebooks have three particularly strong benefits:

• They’re great for showcasing your work. You can see both the code and the results. The notebooks at Kaggle is a particularly great example of this.
• It’s easy to use other people’s work as a starting point. You can run cell by cell to better get an understanding of what the code does.
• Very easy to host server side, which is useful for security purposes. A lot of data is sensitive and should be protected, and one of the steps toward that is no data is stored on local machines. A server-side Jupyter Notebook setup gives you that for free.

When prototyping, the cell-based approach of Jupyter notebooks is great. But you quickly end up programming several steps — instead of looking at object-oriented programming.

Downsides of Jupyter notebooks

When we’re writing code in cells instead of functions/classes/objects, you quickly end up with duplicate code that does the same thing, which is very hard to maintain.

Don’t get the support from a powerful IDE.