https://towardsdatascience.com/reviewing-python-visualization-packages-fa7fe12e622b

Aaron FrederickBlockedUnblockFollowFollowingApr 10There are so many ways to create a graph using Python, but which way is best? When we make visualizations, it is important to ask some questions as to the figure’s objective: Are you trying to get an initial feel for how your data looks? Maybe you are trying to impress someone at a presentation? Perhaps you want to show someone a figure internally and want a middle-of-the-road figure? In this post, I will be walking through a number of popular Python visualization packages, their pros and cons, and situations where they can each shine. I will scope this review to 2D plots, leaving room for 3D figures and dashboards for another time, though many of these packages support both quite well.

I’m going to group these together for a few reasons, first of which being that Seaborn and Pandas plotting were built on top of Matplotlib — when you use Seaborn or df.plot() in Pandas, you are actually utilizing code that people have written using Matplotlib. The resulting aesthetic from each of these is therefore similar and the ways of customizing your plots will use eerily similar syntax.