5 examples from the Significant Volcanic Eruption database

Plotly is a fantastic open source library for visualizing data. On this blog post, I’m going to point out you the right way to generate cartographic plots with plotly, working with the Python backend.

For illustration purposes, I’ll use the Significant Volcanic Eruption Database, published by the US National Centers for Environmental Information under the U.S. Government Work License. The dataset is obtainable for download here: https://public.opendatasoft.com/explore/dataset/significant-volcanic-eruption-database/information/

You will see the next five visualizations:

1. Global distribution of great volcanic eruptions
2. Volcano types in North America
3. Volcanic eruptions related to tsunamis
4. Most damaging volcanic eruptions
5. Funny map projections

For readers involved in using plotly for data evaluation, please discuss with my recent post on visualizing data from the Women’s World Cup:

Preparing the info

After downloading the volcanic eruption database, we load it as a pandas DataFrame. DataFrames integrate naturally with Plotly and are convenient for data evaluation. We transform the columns that encode whether a volcanic eruption is related to a volcano or an earthquake to True/False values and add recent columns for the latitude and longitude of an eruption.