Geospatial Data with GeoPandas

Reviewed & published by Brayan K

GeoPandas extends pandas with a geometry column so you can work with maps, shapes, and locations. A GeoDataFrame behaves like a normal DataFrame but can read shapefiles and GeoJSON, reproject coordinates, run spatial joins, and plot maps.

Part of the free Pandas course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

Learn what a GeoDataFrame and geometry column are, how to read spatial files, what CRS and projections mean, how spatial joins work, and how to draw a quick map.

What You'll Learn in This Lesson

• GeoDataFrames and the geometry column
• Reading shapefiles and GeoJSON
• CRS and projections (EPSG:4326)
• Reprojecting with to_crs
• Spatial joins with sjoin
• Plotting maps with plot()

1 GeoDataFrames and the Geometry Column

A GeoDataFrame is just a pandas DataFrame with one special geometry column holding Shapely shapes — points, lines, or polygons. Every other column behaves like ordinary pandas, so all your filtering and grouping skills carry over directly.

• GeoDataFrame: a DataFrame + a geometry column
• Geometry: Shapely Point, LineString, Polygon
• CRS: the coordinate reference system tying numbers to the Earth

The runnable example below uses plain pandas: storing latitude and longitude as ordinary columns. GeoPandas would wrap these into real Point geometries instead.

import pandas as pd

# Plain pandas: lat/lon as ordinary numeric columns
cities = pd.DataFrame({
    "city": ["Paris", "Berlin", "Madrid"],
    "lat": [48.85, 52.52, 40.42],
    "lon": [2.35, 13.40, -3.70],
})

# Normal pandas works fine on the attribute columns
print(cities.sort_values("lat", ascending=False))

# In GeoPandas, lat/lon would become a geometry column of Points
print(cities[["city", "lat", "lon"]])

# ✅ Expected output:
#      city    lat    lon
# 1  Berlin  52.52  13.40
# 0   Paris  48.85   2.35
# 2  Madrid  40.42  -3.70
#      city    lat    lon
# 0   Paris  48.85   2.35
# 1  Berlin  52.52  13.40
# 2  Madrid  40.42  -3.70

2 Reading Files, CRS, and Projections

GeoPandas reads many vector formats with one call, gpd.read_file, including shapefiles and GeoJSON. Each layer carries a CRS (Coordinate Reference System); EPSG:4326 is WGS84 longitude/latitude. Use to_crs to reproject so layers share the same system before you measure or join.

3 Spatial Joins and Plotting Maps

A spatial join (gpd.sjoin) combines two layers by how their geometries relate in space — for example, which region polygon each city point falls within — rather than by a shared key. Both layers must share the same CRS. Finally, gdf.plot() draws a quick map with matplotlib.

🎯 Your Turn: Sort the Cities

Before mapping, work with the attribute columns in plain pandas. Fill in the blank to sort the cities from northernmost (highest latitude) to southernmost.

import pandas as pd

cities = pd.DataFrame({
    "city": ["Paris", "Berlin", "Madrid"],
    "lat": [48.85, 52.52, 40.42],
    "lon": [2.35, 13.40, -3.70],
})

# 🎯 YOUR TURN: sort by 'lat' from highest to lowest
north_to_south = cities.sort_values("___", ascending=False)
print(north_to_south)

# Expected order: Berlin, Paris, Madrid

📋 Quick Reference

❓ Frequently Asked Questions

You've completed the Pandas course!

Congratulations — that was the final lesson. You started with Series and DataFrames and finished with scaling tools and geospatial analysis. Along the way you mastered selection, cleaning, merging, group-by, dates, aggregation, and the capstone, then explored Dask, Polars, Spark, and GeoPandas for going bigger and further.

🚀 Keep practicing on your own datasets — real data is the best teacher. Well done!

Practice quiz

Continue this course

• Previous: Pandas & Spark
• Quick reference: Pandas cheat sheet

Frequently asked questions

What is a GeoDataFrame?

A GeoDataFrame is a pandas DataFrame with one special geometry column that holds Shapely geometry objects such as points, lines, and polygons. Every other column works exactly like normal pandas, so you can filter, group, and merge while also running spatial operations on the geometry.

What is a CRS and why does it matter?

A CRS, or Coordinate Reference System, defines how the numbers in the geometry column map to real positions on Earth. EPSG:4326 is WGS84 longitude/latitude. Before measuring distances or joining layers you usually reproject with to_crs so both datasets share the same CRS and their coordinates line up.

What is a spatial join?

A spatial join (geopandas.sjoin) combines two layers based on how their geometries relate in space rather than on a shared key. For example, you can attach the name of the region polygon that each point falls within, using a predicate such as intersects or within. Both layers must share the same CRS.