GeoPandas

GeoPandas extends pandas to enable spatial operations on geometric types. It combines the capabilities of pandas and shapely for geospatial data analysis.

When to Use This Skill

Use this skill when the user wants to:

• Read, write, or convert vector data (Shapefile, GeoJSON, GeoPackage, GeoParquet, PostGIS)
• Run spatial joins, overlays, buffers, dissolves, clipping, or nearest-neighbour joins
• Reproject between coordinate reference systems and measure areas/lengths/distances correctly
• Make static or interactive (folium) choropleth maps from tabular vector data

Does NOT Trigger

Installation

uv pip install geopandas   # 1.x (current 1.1.x as of 2026-09); shapely 2 + pyogrio

GeoPandas 1.x requires shapely ≥ 2 and reads/writes through pyogrio by default (fiona is optional). Removed or deprecated in the 1.x line: sjoin(op=...) (use predicate=), unary_union (use union_all()), and the bundled geopandas.datasets files (use the geodatasets package).

Optional Dependencies

# For interactive maps
uv pip install folium

# For classification schemes in mapping
uv pip install mapclassify

# For faster I/O operations (2-4x speedup)
uv pip install pyarrow

# For PostGIS database support (SQLAlchemy engine + driver; geoalchemy2 for to_postgis)
uv pip install sqlalchemy "psycopg[binary]" geoalchemy2

# For basemaps
uv pip install contextily

# For cartographic projections
uv pip install cartopy

Quick Start

import geopandas as gpd

# Read spatial data
gdf = gpd.read_file("data.geojson")

# Basic exploration
print(gdf.head())
print(gdf.crs)
print(gdf.geometry.geom_type)

# Simple plot
gdf.plot()

# Reproject to a metric CRS suited to the data (local UTM zone)
gdf_projected = gdf.to_crs(gdf.estimate_utm_crs())

# Calculate area in m² (use an equal-area CRS such as EPSG:6933 for continental extents;
# never Web Mercator EPSG:3857 — it inflates areas ~4x at 60° latitude)
gdf_projected['area'] = gdf_projected.geometry.area

# Save to file
gdf.to_file("output.gpkg")

Core Concepts

Data Structures

• GeoSeries: Vector of geometries with spatial operations
• GeoDataFrame: Tabular data structure with geometry column

See data-structures.md for details.

Reading and Writing Data

GeoPandas reads/writes multiple formats: Shapefile, GeoJSON, GeoPackage, PostGIS, Parquet.

# Read with filtering
gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))

# Write with Arrow acceleration
gdf.to_file("output.gpkg", use_arrow=True)

See data-io.md for comprehensive I/O operations.

Coordinate Reference Systems

Always check and manage CRS for accurate spatial operations:

# Check CRS
print(gdf.crs)

# Reproject (transforms coordinates). EPSG:3857 (Web Mercator) is for web tiles only;
# measure in a local UTM (gdf.estimate_utm_crs()) or an equal-area CRS
gdf_projected = gdf.to_crs("EPSG:3857")

# Set CRS (only when metadata missing)
gdf = gdf.set_crs("EPSG:4326")

See crs-management.md for CRS operations.

Common Operations

Geometric Operations

Buffer, simplify, centroid, convex hull, affine transformations:

# Buffer by 10 units
buffered = gdf.geometry.buffer(10)

# Simplify with tolerance
simplified = gdf.geometry.simplify(tolerance=5, preserve_topology=True)

# Get centroids
centroids = gdf.geometry.centroid

See geometric-operations.md for all operations.

Spatial Analysis

Spatial joins, overlay operations, dissolve:

# Spatial join (intersects)
joined = gpd.sjoin(gdf1, gdf2, predicate='intersects')

# Nearest neighbor join
nearest = gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)

# Overlay intersection
intersection = gpd.overlay(gdf1, gdf2, how='intersection')

# Dissolve by attribute
dissolved = gdf.dissolve(by='region', aggfunc='sum')

See spatial-analysis.md for analysis operations.

Visualization

Create static and interactive maps:

# Choropleth map
gdf.plot(column='population', cmap='YlOrRd', legend=True)

# Interactive map
gdf.explore(column='population', legend=True).save('map.html')

# Multi-layer map
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
gdf1.plot(ax=ax, color='blue')
gdf2.plot(ax=ax, color='red')

See visualization.md for mapping techniques.

Detailed Documentation

• [Data Structures](references/data-structures.md) - GeoSeries and GeoDataFrame fundamentals
• [Data I/O](references/data-io.md) - Reading/writing files, PostGIS, Parquet
• [Geometric Operations](references/geometric-operations.md) - Buffer, simplify, affine transforms
• [Spatial Analysis](references/spatial-analysis.md) - Joins, overlay, dissolve, clipping
• [Visualization](references/visualization.md) - Plotting, choropleth maps, interactive maps
• [CRS Management](references/crs-management.md) - Coordinate reference systems and projections

Common Workflows

Load, Transform, Analyze, Export

# 1. Load data
gdf = gpd.read_file("data.shp")

# 2. Check and transform CRS (metric, local UTM zone)
print(gdf.crs)
gdf = gdf.to_crs(gdf.estimate_utm_crs())

# 3. Perform analysis
gdf['area'] = gdf.geometry.area
buffered = gdf.copy()
buffered['geometry'] = gdf.geometry.buffer(100)

# 4. Export results
gdf.to_file("results.gpkg", layer='original')
buffered.to_file("results.gpkg", layer='buffered')

Spatial Join and Aggregate

# Join points to polygons
points_in_polygons = gpd.sjoin(points_gdf, polygons_gdf, predicate='within')

# Aggregate by polygon (named aggregation: total value and number of points)
aggregated = points_in_polygons.groupby('index_right').agg(
    value_sum=('value', 'sum'),
    n_points=('value', 'size'),
)

# Merge back to polygons
result = polygons_gdf.merge(aggregated, left_index=True, right_index=True)

Multi-Source Data Integration

# Read from different sources
roads = gpd.read_file("roads.shp")
buildings = gpd.read_file("buildings.geojson")
parcels = gpd.read_postgis("SELECT * FROM parcels", con=engine, geom_col='geom')

# Ensure matching CRS
buildings = buildings.to_crs(roads.crs)
parcels = parcels.to_crs(roads.crs)

# Perform spatial operations
buildings_near_roads = buildings[buildings.geometry.distance(roads.union_all()) < 50]

Performance Tips

1. Use spatial indexing: GeoPandas creates spatial indexes automatically for most operations
2. Filter during read: Use bbox, mask, or where parameters to load only needed data
3. Use Arrow for I/O: Add use_arrow=True for 2-4x faster reading/writing
4. Simplify geometries: Use .simplify() to reduce complexity when precision isn't critical
5. Batch operations: Vectorized operations are much faster than iterating rows
6. Use appropriate CRS: Equal-area CRS for areas, local UTM for distances/buffers, geographic for storage

Best Practices

1. Always check CRS before spatial operations
2. Use a suitable projected CRS for measurements — equal-area (e.g. EPSG:6933, or a regional Albers) for areas, estimate_utm_crs() for distances; never EPSG:3857
3. Match CRS before spatial joins or overlays
4. Validate geometries with .is_valid before operations
5. Use `.copy()` when modifying geometry columns to avoid side effects
6. Preserve topology when simplifying for analysis
7. Use GeoPackage format for modern workflows (better than Shapefile)
8. Set max_distance in sjoin_nearest for better performance

Part of the AlterLab Academic Skills suite.