spb/tools-spboucher-ai
Public
1{2 "slug": "geopandas",3 "name": "GeoPandas",4 "aliases": [],5 "vendor": {6 "name": "GeoPandas developers (NumFOCUS affiliated)",7 "slug": "geopandas",8 "website": "https://geopandas.org"9 },10 "homepage_url": "https://geopandas.org/en/stable/",11 "docs_url": "https://geopandas.org/en/stable/docs.html",12 "repo_url": "https://github.com/geopandas/geopandas",13 "license_url": "https://github.com/geopandas/geopandas/blob/main/LICENSE.txt",14 "product_type": "library",15 "product_status": "active",16 "summary": "Python library extending pandas with geometry columns, spatial operations, reprojection, spatial joins and file I/O for vector geodata",17 "description": "GeoPandas is an open-source Python library that adds geospatial capabilities to pandas, and is the standard entry point for scripted vector GIS in the Python data stack. A GeoDataFrame is a pandas DataFrame with a geometry column backed by Shapely objects; it reads and writes Shapefile, GeoPackage, GeoJSON, GeoParquet and other formats through pyogrio or Fiona (GDAL), reprojects with pyproj, and performs geometric operations (buffer, intersection, area, distance), spatial joins, overlays, dissolves and nearest-neighbour searches with an STRtree index.\n\nIt plots with Matplotlib, exports to interactive Folium or Leaflet maps, and interoperates with PostGIS through SQLAlchemy, with raster libraries such as rasterio and xarray, and with Dask-GeoPandas for larger-than-memory partitions. In a research pipeline it is the tool that turns an assessment roll with coordinates into a GeoDataFrame, joins it to census tracts or OSM amenities, computes distances, and hands a plain DataFrame to statsmodels or scikit-learn.\n\nIt is BSD-licensed and maintained by a community of developers with NumFOCUS affiliation, with releases tracking pandas and Shapely 2 vectorized geometry operations.\n\nLimits are those of an in-memory single-process library: datasets must fit in RAM unless partitioned, operations on millions of complex polygons are slower than in PostGIS, raster handling is delegated to other libraries, there is no interactive editing, and GDAL installation can still be a stumbling block on some platforms despite wheels.",18 "capabilities": [19 "GeoDataFrame with Shapely geometry columns",20 "Read and write vector formats via GDAL (pyogrio/Fiona) and GeoParquet",21 "Reprojection with pyproj",22 "Spatial joins, overlays, dissolve and nearest-neighbour queries",23 "Static plotting with Matplotlib and interactive maps via Folium",24 "PostGIS read/write through SQLAlchemy"25 ],26 "workflows": [27 "Geocode a sample, build a GeoDataFrame and spatially join census geographies",28 "Compute distances from properties to OSM amenities for a hedonic model",29 "Convert Shapefiles to GeoParquet for DuckDB or Polars pipelines"30 ],31 "advantages": [32 "Familiar pandas idioms",33 "Permissive BSD-3-Clause license",34 "Interoperates with the scientific Python stack and PostGIS"35 ],36 "limitations": [37 "In-memory; large datasets need Dask-GeoPandas or a database",38 "Slower than PostGIS on very large polygon operations",39 "No raster processing or interactive editing"40 ],41 "prerequisites": [42 "Python with pandas, Shapely, pyproj and GDAL-based I/O"43 ],44 "audiences": [45 "Data scientists working with spatial data",46 "Urban and real estate researchers",47 "GIS analysts automating workflows in Python"48 ],49 "alternatives": [50 "postgis",51 "qgis",52 "pandas",53 "duckdb"54 ],55 "comparison_notes": "PostGIS handles the same operations server-side in SQL with indexing for large data; QGIS offers a GUI and cartography. pandas lacks geometry support; GeoPandas is its spatial extension. DuckDB's spatial extension provides in-process spatial SQL on GeoParquet and is increasingly combined with GeoPandas through Arrow.",56 "categories": [57 {58 "slug": "gis-mapping",59 "primary": true60 },61 {62 "slug": "dataframes-arrays"63 }64 ],65 "tags": [66 "gis",67 "python",68 "pandas",69 "open-source",70 "vector",71 "parquet"72 ],73 "use_cases": [74 "map-geodata",75 "value-real-estate",76 "query-parquet-files"77 ],78 "operating_systems": [79 "macos",80 "linux",81 "windows"82 ],83 "execution_modes": [84 "local"85 ],86 "interfaces": [87 "library"88 ],89 "integrations": [90 "pandas",91 "Shapely",92 "pyproj",93 "GDAL",94 "Matplotlib",95 "Folium",96 "PostGIS",97 "Dask",98 "rasterio"99 ],100 "data_formats": [101 "Shapefile",102 "GeoPackage",103 "GeoJSON",104 "GeoParquet",105 "CSV/WKT"106 ],107 "languages": [108 "Python"109 ],110 "source_model": "open-source",111 "license_spdx": "BSD-3-Clause",112 "pricing_model": [113 "open-source",114 "free"115 ],116 "self_hostable": true,117 "sources": [118 {119 "url": "https://geopandas.org/en/stable/",120 "type": "official_site",121 "supports": [122 "name",123 "summary",124 "product_type"125 ],126 "checked_at": "2026-11-02",127 "title": "GeoPandas"128 },129 {130 "url": "https://geopandas.org/en/stable/docs.html",131 "type": "official_docs",132 "supports": [133 "capabilities",134 "data_formats",135 "integrations"136 ],137 "checked_at": "2026-11-02",138 "title": "GeoPandas documentation"139 },140 {141 "url": "https://github.com/geopandas/geopandas/blob/main/LICENSE.txt",142 "type": "license_page",143 "supports": [144 "license_spdx",145 "source_model"146 ],147 "checked_at": "2026-11-02",148 "title": "LICENSE.txt (BSD 3-Clause)"149 },150 {151 "url": "https://pypi.org/project/geopandas/",152 "type": "package_registry",153 "supports": [154 "prerequisites"155 ],156 "checked_at": "2026-11-02",157 "title": "geopandas on PyPI"158 }159 ],160 "confidence": "high",161 "depth": "deep",162 "deployment_models": [],163 "featured": false,164 "discovery_method": "editorial-research",165 "editorial_status": "reviewed",166 "visibility": "public",167 "author": "batch-F"168}169