Python map libraries
Making maps with the popular Python libraries: Plotly choropleths, scatter, density and animated maps, Folium heatmaps, clusters and tooltips, GeoPandas explore, Cartopy projections, pydeck 3D layers and lonboard for millions of points, with the errors each one throws.
Python has a map library for every purpose, and choosing the wrong one is the commonest reason a map is hard to read, slow to open or impossible to print. The guides here make real maps with each of them: Plotly choropleths, point, density and animated maps; Folium heatmaps, marker clusters, choropleths with tooltips and PNG export; GeoPandas explore(); Cartopy projections; pydeck 3D columns and hexagons; and lonboard for a million points, with the failures each one is known for.
Every number comes from a run on open data: the USGS earthquake catalogue, Census county estimates, OurAirports and the GNIS names. Folium writes 187.5 bytes of HTML per extra point and Plotly 21.8; 25,081 plain markers become 50,162 images in the browser; a Plotly choropleth matched 0 of 3,144 counties with the wrong featureidkey; and on 2 October 2026 every CARTO raster basemap returned an API KEY REQUIRED tile.
🧭 Start with the concepts
The ideas the how-to guides below assume you already have.
⚙️ Then do the task
Step-by-step walkthroughs with code you can copy and adapt.
- How to Make a Choropleth Map With Plotly in Python
- How to Plot Points on an Interactive Map With Plotly in Python
- How to Make a Density Heatmap Map With Plotly in Python
- How to Animate Points on a Map With Plotly in Python
- How to Make a Heatmap With Folium in Python
- How to Cluster Thousands of Markers With Folium in Python
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🔧 When it breaks
The errors this topic produces, with the cause explained rather than just the command.
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Subjects that keep coming up alongside this one, most overlap first.