geopandas

A spatial join merges point data and census polygons by appending polygon attributes to matching points. You can achieve this by loading both datasets, ensuring they share the same CRS, and running the spatial join operation.

Reprojecting vector data to a projected CRS ensures accurate area calculations. You can set missing coordinate systems and reproject datasets so that spatial operations use compatible, distance-preserving reference systems.

Yes, you can read and write PostGIS tables, GeoJSON, Shapefiles, GeoPackage, Parquet, and Feather formats. This requires Psycopg or GeoAlchemy2 for database integration alongside standard geospatial libraries.

To create buffers and calculate distances, load your geospatial vector data and apply buffer or distance operations. Ensure datasets are correctly reprojected to a projected CRS first so measurements return accurate distance values.

PyArrow, Fiona, or Pyogrio are optional dependencies for faster spatial data I/O. Standard operations run with GeoPandas, Shapely, and PyProj, but installing these libraries accelerates reading and writing large vector formats.

Overlay and clipping operations return empty results if datasets use different coordinate reference systems. You must reproject both layers to the same CRS before performing spatial overlays, dissolves, or clipping operations.