kdtree
A Python implemntation of a kd-tree
Decision gist · record as of 2026-08-14
Yes, if you need a lightweight, dependency-free kd-tree for nearest-neighbor queries in Python. The package is stable (ISC-licensed, no known vulnerabilities) and suitable for small to medium datasets. However, the last release was 2017-10-19 and maintenance is aging; for production use at scale or with modern Python versions, verify compatibility and consider whether a more actively maintained alternative (such as scipy.spatial.KDTree) better fits your needs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction; pure Python wheel.
- Maintenance is aging—last release was 2017-10-19 and last commit 2025-05-27, so the codebase is stable but not actively developed.
- No runtime dependencies.
License · maintenance · safety
ISC license (permissive) — ISC license is permissive; you can use, modify, and distribute this package with minimal restrictions, including in commercial software.
last release 2017-10-19 (3221 days) · last repo commit 2025-05-27 · 381 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 78,179 downloads/mo, #14,464 on PyPI
Alternatives
Verify before relying
import kdtree
# Create tree from list of points (tuples, lists, or indexable objects)
tree = kdtree.create([(2, 3, 4), (4, 5, 6), (5, 3, 2)])
# Find nearest neighbor to point (1, 2, 3)
nearest = tree.search_nn((1, 2, 3))
# Add and remove points
tree.add((5, 4, 3))
tree = tree.remove((5, 4, 3))- Whether the package is actively maintained or if aging status poses a risk for future Python versions
- Performance characteristics (insertion/search time complexity) for large datasets
- Whether rebalancing is automatic or must be called manually to maintain tree efficiency
What it is and what it does
kdtree is a pure-Python implementation of kd-trees, a data structure that partitions points in k-dimensional space to accelerate spatial queries. It lets you build a tree from any collection of indexable objects (tuples, lists, namedtuples, or custom classes that support indexing), then search for the nearest neighbor to any query point in logarithmic time. The tree supports standard operations: insertion, deletion, traversal (inorder and level-order), and rebalancing.
You'd use this when you need to find the closest point(s) in a dataset to a given location—common in machine learning, computational geometry, clustering, and spatial analysis. The package is straightforward: create a tree, add or remove points as needed, and query for nearest neighbors. It handles any number of dimensions and works with any objects that look like tuples to the tree, so you can attach metadata (a payload) to each point without needing a separate index.
Use it for
- Find the nearest landmark, location, or object in a spatial dataset given a query coordinate
- Implement k-nearest-neighbor search for machine learning or clustering algorithms
- Build a spatial index for collision detection or proximity queries in games or simulations
- Store and query multi-dimensional data (e.g., feature vectors) with fast nearest-neighbor lookup
- Attach metadata to spatial points and retrieve both the point and its associated data in one query
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a lightweight, dependency-free kd-tree for nearest-neighbor queries in Python.
The package is stable (ISC-licensed, no known vulnerabilities) and suitable for small to medium datasets. However, the last release was 2017-10-19 and maintenance is aging; for production use at scale or with modern Python versions, verify compatibility and consider whether a more actively maintained alternative (such as scipy.spatial.KDTree) better fits your needs.
Install
kdtree on PyPI
Before you install
Low install friction; pure Python wheel. Maintenance is aging—last release was 2017-10-19 and last commit 2025-05-27, so the codebase is stable but not actively developed. No runtime dependencies.
License in practice
ISC license is permissive; you can use, modify, and distribute this package with minimal restrictions, including in commercial software.
Quickstart
import kdtree
# Create tree from list of points (tuples, lists, or indexable objects)
tree = kdtree.create([(2, 3, 4), (4, 5, 6), (5, 3, 2)])
# Find nearest neighbor to point (1, 2, 3)
nearest = tree.search_nn((1, 2, 3))
# Add and remove points
tree.add((5, 4, 3))
tree = tree.remove((5, 4, 3))Verify before relying
- Whether the package is actively maintained or if aging status poses a risk for future Python versions
- Performance characteristics (insertion/search time complexity) for large datasets
- Whether rebalancing is automatic or must be called manually to maintain tree efficiency
Package facts
| License | ISC license permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Aging 3,221 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 78,179 / month, #14,464 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersLicense :: OSI Approved :: ISC License (ISCL)Operating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 2Programming Language :: Python :: 2.6Programming Language :: Python :: 2.7Programming Language :: Python :: 3Programming Language :: Python :: 3.3Programming Language :: Python :: 3.4Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: LibrariesTopic :: Utilities |
Evidence: kdtree-0.16-py2.py3-none-any.whl
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See also rtree · nutree · pynndescent · voyager · spatial_image · nmslib · annoy · pyspark-hnsw · sortedcollections