Interactive Visualizations In Jupyter Notebook

This entry is a non-exhaustive introduction on how to create interactive content directly from your Jupyter notebook. Content mostly refers to data visualization artifacts, but we’ll see that we can easily expand beyond the usual plots and graphs, providing worthy interactive bits for all kind of scenarios, from data-exploration to animations.

I am going to start with a brief introduction to Data Visualization and better define the scope and meaning of interactiveness as intended in this article.
I will then provide a quick overview of the tools involved (Plotly and ipywidgets) plus some generic suggestions around the Jupyter ecosystem.
Finally, I will showcase some concrete examples for all I have blabbered about, mostly referring to personal projects of mine, and improvements I obtained when relying on these interactive bits. This final part is exactly to demonstrate the capabilities of such tools on an already more than impressive framework like Jupyter. It is all about pushing you to try out for yourself on your projects and spread the word.

Intro

Data Visualization is one of the core skill required to be a good data scientist — or any other role involving data for that matter. It is both about allowing you (or other people in the team) to better understand the nature of a dataset, as well as the ability to convey the proper message to an external audience (technical and non-technical).