Exploring Geospatial Data Using GeoPandas, Geoplot and Contextily

GeoPandas is a Python library for working with geographic data represented as coordinate geometries: points, lines, and polygons. It extends the familiar pandas DataFrame into a GIS-aware structure, making it one of the most practical entry points into spatial data analysis in Python. This project teaches you how to work with geospatial datasets hands-on, using three libraries that cover the full visualization pipeline: GeoPandas, Geoplot, and Contextily.

You'll start by reading geographical data in multiple formats and exploring what makes geospatial data different from standard tabular data. Geometry columns, coordinate reference systems, and location-aware operations all change how you query and visualize information.

From there, you'll build primary and stacked maps using GeoPandas and Matplotlib by layering geometries, styling by attribute, and composing multi-layer visualizations. You'll then work with Geoplot, a library built specifically for GeoPandas, to create more expressive map types, including choropleth and kernel density visualizations that Matplotlib alone doesn't offer.

Finally, you'll use Contextily to add real-world basemap tiles to your maps. This is the technique that separates a raw geometry plot from a presentation-ready GIS map. By the end, you'll have hands-on experience with the full spatial data pipeline in Python: reading and processing geographic data, performing spatial analysis, and building polished geospatial visualizations with GeoPandas, Geoplot, and Contextily.