Concepts

Understand the core concepts behind spatial data in Python before diving into tasks and workflows.

Every practical GIS workflow rests on a small set of foundational ideas. What is a GeoDataFrame? What does a CRS actually do? How do raster and vector data differ, and when should you use each? These guides answer those questions directly — without padding, without assumed expertise.

If you're just starting with Python GIS, work through these pages first. They'll give you the vocabulary and mental models that make all the how-to guides easier to follow. If you're already working in the field, these pages make useful references when a concept needs refreshing.

The collection now runs the length of the learning paths. Alongside the starting points — GeoPandas, Shapely, CRS, EPSG codes, Rasterio, vector versus raster — you will find the concepts that decide whether a workflow survives production: what makes a geometry valid, why slivers and gaps appear, how doubles store coordinates, what GDAL actually is, how a spatial index makes a join fast, what the GIL does and does not block, what a pipeline is made of, and how the QGIS Processing framework and its data providers fit together.

101 Concepts
198 How-to guides
90 Fixes

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The fastest way in. Each topic page pairs these concepts with the tasks that use them and the errors they cause.

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Or follow a learning path

Curated routes in the order that builds on itself — concepts first, then each task, then the errors it produces.

Every concept, A–Z

101 guides in total. Showing 1–30, page 1 of 4.

Ready to apply these concepts?

Once you are comfortable with the concepts, the how-to guides walk through specific tasks step by step — from reading a shapefile to running a spatial join.

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