Supercharge your Python plots with zero extra code

Graphs have a very important role during the data science workflow. We use plots to understand the distribution and nature of variables in the data and we use visualizations to describe our findings in reports or presentations. Needless to say that the importance of plotting for a data scientist can not be overstated.

But what about the libraries we use as interfaces to create these visualizations? Since the plots are so important, shouldn't the libraries provide us effective and efficient tools to create expressive visualizations?

Let's explore the two most widely used plotting libraries for Python, and see where and how we can do better with a third option.

This is a companion discussion topic for the original entry at