This was made in python using a variety of packages like Selenium, GeoPandas, and Shapely. It took me many many hours to make this. I saw a nice data source on USAToday’s site (Source 2) so I built a scraper in Selenium that assembled a CSV. I then used Pandas to comb through the CSV and connect the county names to a shapefile of the counties that i found in Source 1. This connection was facilitated by converting the county names to FIPS ID’s which the shapefile used, Source 4. And it was displayed in matplotlib. I now have an automated data collection and data displaying system.
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This was made in python using a variety of packages like Selenium, GeoPandas, and Shapely. It took me many many hours to make this. I saw a nice data source on USAToday’s site (Source 2) so I built a scraper in Selenium that assembled a CSV. I then used Pandas to comb through the CSV and connect the county names to a shapefile of the counties that i found in Source 1. This connection was facilitated by converting the county names to FIPS ID’s which the shapefile used, Source 4. And it was displayed in matplotlib. I now have an automated data collection and data displaying system.
Source:
1.) [https://simplemaps.com/data/us-counties](https://simplemaps.com/data/us-counties)
2.) [https://data.usatoday.com/national-power-outage-map-tracker/](https://data.usatoday.com/national-power-outage-map-tracker/)
3.) [https://github.com/hadley/data-counties/blob/master/county-fips.csv](https://github.com/hadley/data-counties/blob/master/county-fips.csv)
4.) [https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html](https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html)