pycanopy 0.2.2

Declarative spatial query layer for Polars

A spatial query layer for Polars. Rust core, Python API.

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[!NOTE] Up to 155x on range queries · up to 1,949x on kNN · up to 1,521x on polygon contains · up to 8,522x on within joins · Full benchmarks

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Installation

pip install pycanopy

Pre-built wheels for Linux, macOS, and Windows. No Rust toolchain required.

import polars as pl
from pycanopy import SpatialFrame

sf = SpatialFrame(pl.read_parquet("cities.parquet"), x_col="lon", y_col="lat")
result = sf.lazy().filter(pl.col("population") > 100_000).range_query(-10.0, 35.0, 40.0, 70.0).collect()

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Why PyCanopy

Polars has no native spatial query support. Getting bounding-box filters, k-nearest neighbours, or point-in-polygon tests typically means converting to GeoPandas, managing an index manually, or scanning every row in Python. GeoPandas applies linear scans by default for containment and range tests; its STRtree requires explicit opt-in via .sindex and is the only available index type regardless of data distribution. KNN has no built-in path at all.

PyCanopy adds a declarative lazy query layer directly on Polars DataFrames. You describe the operations you want, and PyCanopy decides which index to build, in what order to run each operation, and what to hand off to Polars to execute. It is designed for in-memory workloads at the moment.

	PyCanopy	GeoPandas	Manual STRtree
Works natively in Polars	✓	✗	✗
Lazy / declarative API	✓	✗	✗
Auto index selection	✓	✗ (STR only)	✗
KNN join built-in	✓	✗	✗
Delta buffer (live append)	✓	✗	✗

• Lazy planning -- declare ops, the optimizer reorders and fuses them before any index is built
• Auto index selection -- KD-tree, R-tree, uniform grid, or brute force chosen per query
• Native Polars -- results are pl.DataFrame, no round-trip through GeoPandas
• Rust hot paths -- zero-copy at the Python boundary, loop-level parallelism via Rayon
• Delta buffer -- append new points and query immediately without rebuilding the index

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Usage

Point dataset: range and KNN

import polars as pl
from pycanopy import SpatialFrame

df = pl.read_parquet("cities.parquet")
sf = SpatialFrame(df, x_col="lon", y_col="lat")

# Bounding-box filter combined with a scalar predicate.
# Optimizer places the scalar filter first, then runs the range query
# on the reduced row set.
result = (
    sf.lazy()
    .filter(pl.col("population") > 100_000)
    .range_query(min_x=-10.0, min_y=35.0, max_x=40.0, max_y=70.0)
    .collect()
)

# k-nearest neighbours
nearest = sf.lazy().knn(x=2.35, y=48.85, k=5).collect()

More examples -- KNN join, polygon contains, within-distance join, branching, delta buffer

Chaining multiple spatial predicates

# Two range predicates are fused into a single index build on large datasets.
result = (
    sf.lazy()
    .range_query(0.0, 0.0, 50.0, 50.0)
    .range_query(10.0, 10.0, 40.0, 40.0)
    .collect()
)

Inspecting the optimizer plan

# Declare ops in any order — explain() shows what the optimizer will actually run.
lf = (
    sf.lazy()
    .range_query(min_x=-10.0, min_y=35.0, max_x=40.0, max_y=70.0)
    .filter(pl.col("population") > 100_000)
)

print(lf.explain())
# RANGE_QUERY [(-10, 35) → (40, 70)]
# FROM
#   FILTER [(col("population")) > (dyn int: 100000)]
#   FROM
#     DF [N=100,000; path: EXPR]

print(lf.explain(optimized=False))
# FILTER [(col("population")) > (dyn int: 100000)]
# FROM
#   RANGE_QUERY [(-10, 35) → (40, 70)]
#   FROM
#     DF [N=100,000]

Follows Polars' FROM-chain convention: bottom = runs first, top = outermost result. In the optimized plan, FILTER appears below RANGE_QUERY — the scalar filter runs first on raw data, and RANGE_QUERY receives the already-filtered subset. explain(optimized=False) shows declaration order for comparison.

KNN join

query_df = pl.DataFrame({"qx": [2.35, 13.4], "qy": [48.85, 52.5]})

# For each row in query_df, find the 3 nearest rows in sf.
result = sf.lazy().knn_join(query_df, x_col="qx", y_col="qy", k=3).collect()

Polygon dataset: contains and range

from shapely.geometry import box
from pycanopy import SpatialFrame

polygons = [box(i, 0, i + 0.9, 0.9) for i in range(100_000)]
df = pl.DataFrame({"id": list(range(100_000)), "geom": polygons})
sf = SpatialFrame.from_polygons(df, geometry_col="geom")

# Which polygons contain this point?
containing = sf.lazy().contains(x=5.5, y=0.5).collect()

# Which polygon MBRs intersect this bbox?
intersecting = sf.lazy().range_query(0.0, 0.0, 10.0, 1.0).collect()

Polygon holes

from shapely.geometry import Polygon

# Interior rings (holes) are fully supported.
outer = [(0, 0), (10, 0), (10, 10), (0, 10)]
hole  = [(2, 2), (8, 2),  (8, 8),  (2, 8)]
donut = Polygon(outer, [hole])

sf = SpatialFrame.from_polygons(pl.DataFrame({"id": [0], "geom": [donut]}), geometry_col="geom")

# Point inside the hole is NOT contained.
sf.lazy().contains(x=5.0, y=5.0).collect()   # empty

# Point outside the hole but inside the outer ring IS contained.
sf.lazy().contains(x=1.0, y=1.0).collect()   # returns the polygon row

Within join

# For each query point, find which polygons in sf contain it.
query_df = pl.DataFrame({"qx": [5.5, 12.3], "qy": [0.5, 0.5]})
result = sf.lazy().within_join(query_df, x_col="qx", y_col="qy").collect()

Within-distance join

# For each query point, find all sf points within 50 km.
query_df = pl.DataFrame({"qx": [2.35, 13.4], "qy": [48.85, 52.5]})
result = sf.lazy().within_distance_join(query_df, x_col="qx", y_col="qy", distance=50.0).collect()

Branching from a shared base

from pycanopy import SpatialFrame, SpatialLazyFrame

# Expensive filter applied once; two queries branch from the result.
base = sf.lazy().filter(pl.col("population") > 100_000).range_query(-10.0, 35.0, 40.0, 70.0)

major = base.filter(pl.col("population") > 1_000_000)
minor = base.filter(pl.col("population") <= 1_000_000)

# collect_all detects the shared prefix, caches it in Polars,
# and executes both branches in a single pass.
results = SpatialLazyFrame.collect_all([major, minor])
df_major, df_minor = results

Live updates via delta buffer

# Append new points -- visible to queries immediately, no index rebuild yet.
import numpy as np
sf.engine.append_delta(np.array([2.5]), np.array([48.9]))

# Queries probe the main index and scan the delta in parallel.
result = sf.lazy().range_query(-10.0, 35.0, 40.0, 70.0).collect()

# The buffer flushes automatically when accumulated query cost exceeds
# the estimated index rebuild cost, or when it exceeds 10% of N.
# Force a flush manually if needed.
sf.engine.flush()

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Benchmarks

Apple M-series used for benchmarking. Warm = cached index, second call. Index build = one-time cost, amortised across queries. Uniform distribution; clustered note below.

Single-query operations

Operation	N	Index build	Warm	Naive	Speedup	Idx mem
Range query (points)	100,000	1.3 ms	29 µs	4.4 ms	155x	783 KB
kNN k=10	100,000	9.3 ms	3 µs	5.4 ms	1,949x	1.9 MB
Polygon contains	100,000	6.2 ms	5 µs	7.0 ms	1,521x	3.7 MB
Polygon range	100,000	5.6 ms	8 µs	3.3 ms	391x	3.7 MB
kNN join k=5	10,000	7.3 ms	2.1 ms	5.4 s	2,601x	180 KB
Within-distance join	10,000	0.5 ms	12.6 ms	1.3 s	102x	—
Within join (polygons)	10,000	1.6 ms	0.52 ms	4.4 s	8,522x	354 KB

Chained lazy queries (N = 100,000, uniform)

The optimizer reorders scalars before spatial ops regardless of declared order, and fuses consecutive wide spatial predicates into one index pass.

Chain	Optimizer action	Index build	Warm	GeoPandas	Speedup
circ_scalar → range³	scalar first	2.5 ms	0.19 ms	9.2 ms	50x
range² → 3× scalar (spatial declared first)	scalars first	1.0 ms	0.23 ms	6.0 ms	26x
range⁴ at 10% selectivity	fused	1.0 ms	0.92 ms	13 ms	14x
wide_scalar (95%) → tight_range (1%)	scalar first	4.1 ms	0.30 ms	3.1 ms	11x
circ_scalar + diag_scalar → kNN k=50	scalar first	15 ms	1.25 ms	3.6 ms	3x

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How It Works

Query flow

  sf.lazy().filter(...).range_query(...).knn_join(...).collect()
                            |
               +------------+------------+
               |     SpatialOptimizer    |
               |  * reorder ops by cost  |
               |  * fuse spatial preds   |
               |  * select index type    |
               |  * spatial join order   |
               +------------+------------+
                            |
               +------------+------------+
               |      Polars executes    |
               |  scalar filters first   |
               |  then spatial queries   |
               +------------+------------+
                            |
                      pl.DataFrame

Optimizer decisions

• Predicate pushdown: scalar predicates are placed before spatial ones and sorted cheapest-first using AST cost estimation. They cost nothing extra and shrink the row count before any index is touched.
• Fusion: consecutive spatial predicates on large datasets are merged into a single index build and one pass over the data.
• Index type: selected per query based on geometry type, data distribution, and selectivity.
• Join order: for symmetric joins (within_join, within_distance_join), the optimizer indexes the smaller side when it is less than half the size of the other. knn_join is asymmetric and always indexes the engine side.

Index management

Indexes are built lazily. Nothing is constructed at load time; stats (extent, point distribution, a 32x32 histogram) are computed eagerly and drive selection at the first query. The selected index is then cached for all subsequent queries.

Condition	Index
N < 500, selectivity > 50%, or k/N > 10%	Brute force
Point range, uniform distribution	Uniform grid
Point range, clustered distribution	KD-tree
Point KNN or contains	KD-tree
Polygons, any query	R-tree

All index types share the same underlying coordinate arrays with no duplication.

Why Rust

The hot paths need packed immutable index structures, zero-copy array slices at the Python boundary, and loop-level parallelism. C++ would require a separate FFI layer and loses the native Polars plugin integration that PyO3/Maturin provides for free.

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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
list of shapely Points or Polygons	[Point(x, y), ...]
list of (x, y) tuples	[(x, y), ...]
Separate coordinate sequences	Engine.from_coords(xs, ys)

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License

MIT