PyCanopy
A declarative spatial query layer for Polars. Rust core, Python API.
What is PyCanopy
PyCanopy brings spatial queries — range, kNN, joins, polygon containment — into the Polars ecosystem without leaving Python. You declare operations in any order; the query planner reorders, fuses, and pushes them down before execution. The index type (KD-tree, R-tree, grid, or brute force) is selected automatically by a cost model calibrated to your hardware.
Why PyCanopy
| PyCanopy | GeoPandas | DuckDB | SedonaDB | Spatial Polars | |
|---|---|---|---|---|---|
| Polars-native, no SQL or conversion | ✓ | ✗ | ✗ (SQL) | ✗ (SQL) | ✓ |
| Spatial query planner (reorder, fuse, pushdown) | ✓ | ✗ | ✗ | ✓ (SQL) | ✗ |
| Index vs scan decided by cost model | ✓ | ✗ | ✗ | ✗ | ✗ |
| Adaptive index (KD-tree / R-tree / grid) | ✓ | ✗ | ✗ | ✗ | ✗ |
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.
SF1 (~6M trips): PyCanopy wins 7/12 queries.
SF10 (~60M trips): PyCanopy wins 5/12 queries.
Full results tables with per-query times are on the Benchmarks page.
Accepted input formats
| Format | Example |
|---|---|
numpy (N, 2) array |
np.array([[x, y], ...]) |
| GeoArrow PyArrow array | pa.StructArray or FixedSizeList<2> |
geopandas GeoSeries |
gdf.geometry |
| shapely Points / Polygons / MultiPolygons | [Point(x, y), ...] |
list of (x, y) tuples |
[(x, y), ...] |
| Separate coordinate sequences | Engine.from_coords(xs, ys) |
| WKB point column (Binary) | SpatialFrame.from_wkb_points(df, "geom") |
| WKB polygon column (Binary) | SpatialFrame.from_wkb_polygons(df, "geom") |