geopandas

foryourhealth111-pixel/Vibe-SkillsLocation ServicesAudit passedSnapshot 158be777c08d

Summary

Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.

SKILL.md

GeoPandas

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

Installation

uv pip install geopandas

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
uv pip install psycopg2
uv pip install 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 different CRS
gdf_projected = gdf.to_crs("EPSG:3857")

# Calculate area (use projected CRS for accuracy)
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)

# Check CRS
print(gdf.crs)

# Reproject (transforms coordinates)
gdf_projected = gdf.to_crs("EPSG:3857")

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

# 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

# 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')

# 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')

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

# 2. Check and transform CRS
print(gdf.crs)
gdf = gdf.to_crs("EPSG:3857")

# 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')

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

# Aggregate by polygon
aggregated = points_in_polygons.groupby('index_right').agg({
    'value': 'sum',
    'count': 'size'
})

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

# 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]

geopandas

Summary

SKILL.md

GeoPandas

Installation

Optional Dependencies

Quick Start

Core Concepts

Data Structures

Reading and Writing Data

Coordinate Reference Systems

Common Operations

Geometric Operations

Spatial Analysis

Visualization

Detailed Documentation

Common Workflows

Load, Transform, Analyze, Export

Spatial Join and Aggregate

Multi-Source Data Integration

Performance Tips

Best Practices

Related skills