PyCanopy

A declarative spatial query layer for Polars.

What is PyCanopy

PyCanopy lets you filter, search, join, and aggregate spatial data stored in Polars DataFrames. It automatically chooses how to execute each query and whether a spatial index would help.

Why PyCanopy

Capability	PyCanopy	GeoPandas	DuckDB	SedonaDB	Spatial Polars
Uses Polars DataFrames directly	✓	✗	✗	✗	✓
Spatial-aware query planning	✓	✗	✓	✓	✗
Automatically accelerates spatial joins with an index	✓	✓	✓	✓	✗
Explicit cost-based choice between scanning and building an index	✓	✗	✗	✗	✗
Selects among multiple spatial index types by workload	✓	✗	✗	✗	✗

Benchmarks

Apache SpatialBench is the industry-standard single-node spatial query benchmark, maintained by the Apache Sedona project. Results below are from a single m7i.2xlarge (8 vCPU, 32 GB), the same instance type used in the published baseline.

PyCanopy is fastest on 11/24 testcases (there is some variance among benchmark runs).

SF1 (~6M trips)

SF10 (~60M trips)

Full results tables with per-query times are on the Benchmarks page.

Data sources

Source	Entry point
Point coordinate columns in a Polars DataFrame	SpatialFrame(df, x_col="x", y_col="y")
Point WKB in a Polars DataFrame	SpatialFrame.from_wkb_points(df, "geometry")
Polygon or MultiPolygon WKB in a Polars DataFrame	SpatialFrame.from_wkb_polygons(df, "geometry")
Shapely or GeoArrow polygon geometry	SpatialFrame.from_polygons(df, "geometry")
Point or polygon WKB in a Polars LazyFrame	SpatialFrame.from_lazy(lf, "geometry", "point")
Local, cloud, or GeoParquet data	SpatialFrame.scan_parquet(path)

The lower-level Engine also accepts NumPy arrays, coordinate sequences, GeoArrow arrays, GeoPandas geometry, and Shapely geometry directly.