Visualizing Positive Covid-19 Cases in Colorado

While completing the Data Science Career Path, I discovered that I really enjoy visualizing data with Python, Pandas, Matplotlib, and Seaborn. As I have perused various off-platform communities, I became interested in the visualizations that combine heat maps with geographic maps. This lead me to digging into GeoPandas documentation so that I could try my hand at creating my own geographic heat maps.

For this project, I cleaned data from the Colorado Department of Health and Environment to normalize daily positive Covid-19 cases by county. These normalized values were used to create a GeoPandas heat map for each day’s case numbers. Once each plot was made, they were saved as JPG files and loaded into a video editor. The result is a one minute and thirty-eight seconds flipbook-style video that displays the changes in positive cases in Colorado from June 1, 2020 to October 14, 2021.

I am including two links related to this project. The first is a link to my GitHub repository for the Jupyter Notebook that I used to generate the GeoPandas heat maps. The second is a link to the YouTube video of the sequentially-ordered JPGs.

Let me know what you think and how I could improve the project!
GitHub Repository of Jupyter Notebook Project
YouTube Video of Plots


After receiving some feedback on the method used to normalize the data, I added a column to the DataFrame that displays the log-normalized values of the counts since some counties’ populations were so much larger than others.

Updated Jupyter Notebook
Updated Video Link