Visualizing geographic data using Plotly in Python

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In 2019, IEEE ranked Python as the top programming language in the world (Link: https://spectrum.ieee.org/static/interactive-the-top-programming-languages-2019). Many data scientists and business analytics professions still felt that R was better, specifically in the areas of statistical analysis and visualization.

Python has a lot of libraries for visualizations including matplotlib and seaborn. Earlier, visualizing geographic data was a challenge. Geopandas was not simple to use. With Plotly this has changed.

We share a colab file which visualizes geographic data using Plotly in Python. Link to the file:

https://colab.research.google.com/drive/1qefpxRj52tH4D380MdPVc9RG9FZjY3Ht

Please feel free to copy it to your drive and run the file that will show how COVID-19 has moved from a local health concern to a global pandemic.

We took the raw time-series data from the widely used John Hopkins repo, processed it and then using Plotly to graphically show the spread of worldwide spread of coronavirus over time. We have four charts which are automated using choropleth maps in Python with Plotly for global confirmed cases, deaths, recovered and existing cases. 

Using the play button, you can see how the cases spread over time. Using Plotly, we showcase how simple it is to build an automated geographical visualisation. 

Hope you enjoy it!

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