I just published a new tutorial on RealPython: GeoPandas Basics: Maps, Projections and Spatial Joins.
If you’re interested in using Python to make maps or analyze spatial data, then I recommend checking out the tutorial. It walks you through common geospatial tasks using GeoPandas, one of the most widely used geospatial libraries in Python.
Map Projections: A Side-by-Side Comparison
My favorite part of the tutorial is walking readers through map projections.
You start with a world map that uses longitude and latitude. This map stretches areas near the poles, which makes Antarctica and Greenland look much larger than they are. Then you switch to the Mollweide Equal-Area projection, which preserves relative area and gives a more accurate sense of landmass size.
The final image shows both maps side by side:
The contrast is striking: the left map distorts area, while the right map preserves it. It’s a simple, visual way to understand why projections matter.
Interactive Maps and Spatial Joins
The tutorial also walks you through reading geographic data, creating interactive maps and doing a spatial join.
In one example, you load the boundaries of New York City boroughs. You’re given the coordinates of the Empire State Building and do a spatial join to see which borough it’s in. It’s a straightforward demonstration of how spatial joins work and why they’re useful.
Reflections on Technical Writing
Over the years I’ve written a lot of technical content, but this was my first time doing it as a paid engagement. I really enjoyed the collaboration and the chance to learn more about a library that I had previously only used in passing.
If your team is looking for someone to create tutorials, workshops, or educational content around Python or R, feel free to reach out. This is work I love doing.
