Beyond Matplotlib: Building Interactive Climate Data Visualizations with Bokeh and Friends

Date and Time: 
Thursday 2018 Apr 5th
CG Auditorium
Anderson Banihirwe

Visualization represents a major bottleneck in scientific research, engineering, data science, and data analytics. The tools in the Python scientific ecosystem make it very simple to do many of the tasks required, but building visualizations to help understand complex patterns and relationships in your data still typically involves a large amount of custom coding for every new type of plot. For the last few years, the Python data visualization ecosystem has expanded tremendously. Matplotlib library has emerged as the main data visualization library, but there are also libraries such as Bokeh, HoloViews, GeoViews, and Datashader that either build on Matplotlib or have functionality that it does not support. In this tutorial, we will use global weather data sets to build fully interactive data visualization using Bokeh, Holoviews, Geoviews, and Datashader. As we do that, we will learn: - How to approach the problem of interactive visualization declaratively. - How to use these libraries to create everything from basic graphs to advanced interactive plots, dashboards, and data applications. - How to incorporate these libraries within your Jupyter/IPython notebooks. - How to serve up even more impressive real time visualizations. - How to build a pipeline system for creating meaningful representations of large data sets quickly and flexibly. - What areas each library is best in, and how to leverage the Python data visualization ecosystem effectively. Overall, the main theme of this tutorial is “stop plotting your data - annotate your data and let it visualize itself”.

Speaker Description: 

Anderson is a graduating senior in Systems Engineering department at the University of Arkansas at Little Rock, and a former research intern at CISL/NCAR. He currently works at First Orion as a Data Scientist Intern in Little Rock, AR. He is interested in parallel and distributed computing, and data analytics infrastructures for Python's open source ecosystem.

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