geopandas

name	geopandas
description	Spatial data: GeoDataFrames, spatial joins, CRS/projections, choropleth/interactive maps, spatial autocorrelation, PySAL. Use for geographic data, spatial files (Shapefile, GeoPackage, GeoParquet), or spatial stats. For charts without GIS use plotly. R equivalent: sf-terra (use when execution language is R).
metadata	{"audience":"research-coders","domain":"python-library","library-version":"1.1.3","skill-last-updated":"2026-03-28"}

GeoPandas Skill

geopandas spatial data library for Python: manipulation, analysis, and visualization of geographic data. Covers GeoDataFrames, spatial joins, CRS/projections, vector operations, raster integration (rasterio, xarray), choropleth mapping, interactive maps (folium), basemap tiles (contextily), spatial autocorrelation, and the PySAL ecosystem. Use when working with geographic data, reading/writing spatial files (Shapefile, GeoPackage, GeoParquet), making maps, or running spatial statistics. For interactive web-based geographic charts without spatial analysis, use plotly.

Comprehensive skill for spatial data analysis with geopandas and the broader Python geospatial stack. Use the decision trees below to find the right guidance, then load detailed references as needed.

Version Notes

This skill targets geopandas 1.x (tested with 1.1.3). Key changes from earlier versions:

• Shapely >= 2.0 required (PyGEOS backend removed, vectorized ops built-in)
• pyogrio is the default I/O engine (replacing fiona, 5-10x faster)
• cascaded_union removed — use union_all() instead
• GeoSeries.unary_union property renamed to GeoSeries.union_all() method

What is GeoPandas?

GeoPandas extends pandas with spatial data types and operations:

• GeoDataFrame: A pandas DataFrame with a geometry column — tabular data meets spatial operations
• Spatial operations: Joins, overlays, dissolve, clip, buffer, and distance calculations on vector geometries
• CRS handling: Coordinate reference system management via pyproj for correct spatial computations
• Visualization: Static maps (matplotlib), interactive maps (folium via .explore()), and GPU-accelerated rendering (lonboard)
• Ecosystem hub: Integrates with PySAL (spatial statistics), rasterio (rasters), contextily (basemaps), and mapclassify (classification schemes)

How to Use This Skill

Reference File Structure

File	Purpose	When to Read
quickstart.md	Installation, GeoDataFrame creation, basic I/O and plotting	Starting with geopandas
data-io.md	File formats, pyogrio, web data, spatial databases	Loading/saving spatial data
crs-projections.md	CRS fundamentals, reprojecting, choosing projections	CRS errors or projection decisions
spatial-operations.md	Spatial joins, overlays, dissolve, clip, buffer, distance	Combining or transforming spatial data
raster-integration.md	rasterio, xarray/rioxarray, zonal statistics	Working with raster data
visualization.md	Static maps, interactive maps, basemaps, classification	Making maps and figures
pysal-spatial-stats.md	Spatial weights, autocorrelation, LISA, spatial regression	Spatial statistics and modeling
gotchas.md	CRS mismatches, invalid geometries, common errors	Debugging spatial issues

Reading Order

1. New to geopandas? Start with quickstart.md then spatial-operations.md
2. Making maps? Read visualization.md (relies on crs-projections.md for projection choices)
3. Spatial statistics? Read pysal-spatial-stats.md (for methodology context, also load data-scientist skill's geospatial-analysis.md)
4. Having issues? Check gotchas.md first

The reference-file routing in this skill applies to advisory and brainstorming turns as much as implementation. Recommending an approach, reviewing a plan, or answering a question that touches a routed topic calls for reading the routed reference file just as much as writing code does — the reference files carry curated caveats and environment-specific constraints that this overview and general knowledge lack.

Related Skills

• data-scientist (geospatial-analysis.md, geospatial-operations.md): Spatial methodology — when/why to use spatial methods, interpretation guidance, MAUP, ecological fallacy. Load alongside this skill for research workflows.
• polars: If spatial data is combined with large tabular datasets, use polars for non-spatial transformations before converting to GeoDataFrame.
• plotnine / plotly: For non-map visualizations of spatial analysis results (coefficient plots, distributions).

Quick Decision Trees

"I need to read or write spatial data"

Loading/saving spatial data?
├─ Read vector file (Shapefile, GeoPackage, GeoJSON) → ./references/data-io.md
├─ Read GeoParquet → ./references/data-io.md
├─ Read from PostGIS / DuckDB Spatial → ./references/data-io.md
├─ Download boundaries (Census, OSM) → ./references/data-io.md
├─ Create GeoDataFrame from lat/lon columns → ./references/quickstart.md
├─ Write to file → ./references/data-io.md
└─ Read raster data (GeoTIFF) → ./references/raster-integration.md

"I need to combine or transform spatial data"

Spatial operations?
├─ Join by location (point-in-polygon, etc.) → ./references/spatial-operations.md
├─ Join by nearest feature → ./references/spatial-operations.md
├─ Overlay (intersection, union, difference) → ./references/spatial-operations.md
├─ Dissolve (merge polygons by attribute) → ./references/spatial-operations.md
├─ Clip to boundary → ./references/spatial-operations.md
├─ Buffer features → ./references/spatial-operations.md
├─ Compute distances → ./references/spatial-operations.md
├─ Compute centroids or areas → ./references/spatial-operations.md
└─ Areal interpolation (mismatched boundaries) → ./references/spatial-operations.md

"I need to fix CRS or projection issues"

CRS/projection issues?
├─ Check current CRS → ./references/crs-projections.md
├─ Reproject to different CRS → ./references/crs-projections.md
├─ Choose a projection for analysis → ./references/crs-projections.md
├─ Data has no CRS (set it) → ./references/crs-projections.md
├─ CRS mismatch error → ./references/gotchas.md
└─ Area/distance calculations wrong → ./references/crs-projections.md

"I need to make a map"

Making maps?
├─ Quick static choropleth → ./references/visualization.md
├─ Classification schemes (quantiles, Fisher-Jenks) → ./references/visualization.md
├─ Add basemap tiles → ./references/visualization.md
├─ Interactive map (pan/zoom/hover) → ./references/visualization.md
├─ Large dataset (millions of features) → ./references/visualization.md
├─ Multi-panel / faceted maps → ./references/visualization.md
├─ LISA cluster map → ./references/pysal-spatial-stats.md
└─ Export to PNG/SVG/HTML → ./references/visualization.md

"I need spatial statistics"

Spatial statistics?
├─ Build spatial weights matrix → ./references/pysal-spatial-stats.md
├─ Test for spatial autocorrelation (Moran's I) → ./references/pysal-spatial-stats.md
├─ Find hot spots / cold spots (LISA) → ./references/pysal-spatial-stats.md
├─ Spatial regression (lag, error, Durbin) → ./references/pysal-spatial-stats.md
├─ Point pattern analysis → ./references/pysal-spatial-stats.md
└─ Methodology guidance (interpretation, MAUP) → data-scientist skill: geospatial-analysis.md

"I need to work with rasters"

Raster operations?
├─ Read GeoTIFF → ./references/raster-integration.md
├─ Zonal statistics (summarize raster by polygons) → ./references/raster-integration.md
├─ Clip/mask raster by polygon → ./references/raster-integration.md
├─ Extract raster values at points → ./references/raster-integration.md
├─ Multidimensional raster (xarray) → ./references/raster-integration.md
└─ Rasterize vectors / vectorize rasters → ./references/raster-integration.md

"Something isn't working"

Having issues?
├─ CRS mismatch errors → ./references/gotchas.md
├─ Invalid geometry errors → ./references/gotchas.md
├─ Spatial join produced wrong row count → ./references/gotchas.md
├─ Memory issues with large files → ./references/gotchas.md
├─ Shapely version confusion → ./references/gotchas.md
├─ Coordinate order (lon/lat vs lat/lon) → ./references/gotchas.md
└─ General troubleshooting → ./references/gotchas.md

File-First Execution in Research Workflows

Important: In data research pipelines (see CLAUDE.md), spatial operations are executed through script files, not interactively. This ensures auditability and reproducibility.

The pattern:

1. Write spatial analysis code to scripts/stage{N}_{type}/{step}_{task-name}.py
2. Execute via Bash with automatic output capture wrapper script
3. Validation results get automatically embedded in scripts as comments
4. If failed, create versioned copy for fixes

Closely read agent_reference/SCRIPT_EXECUTION_REFERENCE.md for the mandatory file-first execution protocol covering complete code file writing, output capture, and file versioning rules.

See:

• agent_reference/SCRIPT_EXECUTION_REFERENCE.md — Script execution protocol and format with validation

The examples in reference files show geopandas syntax. In research workflows, wrap them in scripts following the file-first pattern.

---

Quick Reference

Essential Imports

import geopandas as gpd
from shapely.geometry import Point, Polygon, LineString, MultiPolygon

Core Operations

Operation	Code
Read file	gpd.read_file("data.gpkg")
Read Parquet	gpd.read_parquet("data.parquet")
From lat/lon	gpd.GeoDataFrame(df, geometry=gpd.points_from_xy(df.lon, df.lat), crs="EPSG:4326")
Check CRS	gdf.crs
Reproject	gdf.to_crs(epsg=5070)
Spatial join	gpd.sjoin(points, polygons, predicate="within")
Nearest join	gpd.sjoin_nearest(gdf1, gdf2, max_distance=1000)
Overlay	gpd.overlay(gdf1, gdf2, how="intersection")
Dissolve	gdf.dissolve(by="state")
Buffer	gdf.buffer(1000) (in CRS units)
Centroid	gdf.centroid
Area	gdf.area (project first)
Distance	gdf1.distance(gdf2)
Clip	gpd.clip(gdf, mask)
Plot	gdf.plot(column="value", legend=True)
Interactive map	gdf.explore(column="value")
Write file	gdf.to_file("out.gpkg")
Write Parquet	gdf.to_parquet("out.parquet")

Common CRS Codes

EPSG	Name	Use For
4326	WGS84	Storage, exchange, web (not analysis)
5070	NAD83 Conus Albers	US thematic maps (equal-area)
3857	Web Mercator	Web tiles display only
32617	UTM Zone 17N	US East Coast local analysis

Topic Index

Topic	Reference File
Installation	./references/quickstart.md
GeoDataFrame creation	./references/quickstart.md
Basic plotting	./references/quickstart.md
File formats (GeoPackage, Shapefile, GeoJSON)	./references/data-io.md
GeoParquet	./references/data-io.md
pyogrio engine	./references/data-io.md
Web data (OSM, Census, WFS)	./references/data-io.md
Spatial databases (PostGIS, DuckDB)	./references/data-io.md
CRS fundamentals	./references/crs-projections.md
Reprojection	./references/crs-projections.md
Choosing projections	./references/crs-projections.md
Common US projections	./references/crs-projections.md
Spatial joins	./references/spatial-operations.md
Nearest-neighbor joins	./references/spatial-operations.md
Overlay operations	./references/spatial-operations.md
Dissolve and aggregation	./references/spatial-operations.md
Buffering	./references/spatial-operations.md
Clipping	./references/spatial-operations.md
Areal interpolation	./references/spatial-operations.md
rasterio basics	./references/raster-integration.md
xarray / rioxarray	./references/raster-integration.md
Zonal statistics	./references/raster-integration.md
Raster-vector conversion

Citation

When this library is used as a primary analytical tool, include in the report's Software & Tools references:

Jordahl, K. et al. geopandas: Python tools for geographic data [Computer software]. https://geopandas.org/

Cite when: geopandas is used for spatial operations, spatial joins, or map visualization central to the analysis. Do not cite when: Only used to read a shapefile for a simple reference lookup.

If PySAL spatial analysis functions are also used (spatial weights, Moran's I, etc.), additionally cite:

Rey, S.J. et al. (2022). "The PySAL Ecosystem of Open-Source Python Packages for the Analysis of Spatial Data." Geographical Analysis, 54(3), 467-487.

For method-specific citations (e.g., spatial statistics techniques), consult the reference files in this skill and agent_reference/CITATION_REFERENCE.md.