Plotly is an ideal open source library for visualizing data. On this blog post, I’m going to indicate you learn how 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 out there for download here: https://public.opendatasoft.com/explore/dataset/significant-volcanic-eruption-database/information/
You’re going to see the next five visualizations:
- Global distribution of great volcanic eruptions
- Volcano types in North America
- Volcanic eruptions related to tsunamis
- Most damaging volcanic eruptions
- Funny map projections
For readers excited about using plotly for data evaluation, please confer with my recent post on visualizing data from the Women’s World Cup:
Preparing the information
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 latest columns for the latitude and longitude of an eruption.
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