Python Data Visualization 2018: Why So Many Libraries?

This post is the first in a three-part series on the state of Python data visualization tools and the trends that emerged from SciPy 2018.By James A. BednarAt a special session of SciPy 2018 in Austin, representatives of a wide range of open-source Python visualization tools shared their visions for the future of data visualization in Python. We heard updates on Matplotlib, Plotly, VisPy, and many more. I attended SciPy 2018 as a representative of PyViz, GeoViews, Datashader, Panel, hvPlot and Bokeh, and my Anaconda colleague Jean-Luc Stevens attended representing HoloViews.

This first post surveys the packages currently available and shows how they are linked, and subsequent posts will discuss how these tools have been evolving in recent years, and how they will go forward from here. The Current LandscapeTo set the stage, I showed Jake VanderPlas’s overview of how the many different visualization libraries in Python currently relate to each other:

Source: anaconda.com