geopandas
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.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill geopandas --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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, r
What does the geopandas skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill geopandas --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.