jenkspy
Compute Natural Breaks (Fisher-Jenks algorithm)
Decision gist · record as of 2026-08-14
Yes. Jenkspy is a stable, production-ready library with no known vulnerabilities, permissive MIT licensing, and solid adoption. Install friction is moderate due to C compilation, but wheels eliminate this for most users. Use it if you need deterministic, optimal class boundaries for numerical data.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy as a runtime dependency; C compiler needed only when building from source.
- Medium install friction due to C extension compilation, but wheels are provided for Windows, macOS, and Linux across multiple Python versions.
- Repository is aging but stable and actively maintained with recent commit activity.
License · maintenance · safety
permissive license (permissive) — MIT License permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
last release 2024-06-03 (802 days) · last repo commit 2026-02-13 · 239 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 207,474 downloads/mo, #9,550 on PyPI
Alternatives
Verify before relying
import jenkspy
import numpy as np
data = [0.0, 1.5, 2.3, 5.1, 6.8, 9.2]
breaks = jenkspy.jenks_breaks(data, n_classes=3)
print(breaks) # [0.0, 2.3, 6.8, 9.2]- Performance characteristics (speed relative to other implementations) not quantified in fact sheet.
- Exact support status for Python 3.7–3.9—wheels shown for 3.10+ only, though classifiers list earlier versions.
- Whether the package remains actively developed or is in maintenance-only mode given 802-day release gap.
What it is and what it does
Jenkspy implements the Fisher-Jenks algorithm, a deterministic optimization method that finds the best way to partition numerical data into a specified number of classes by minimizing variance within each class. It accepts lists, tuples, arrays, or numpy arrays of integers or floats and returns the break points that define class boundaries. The package provides two interfaces: a simple `jenks_breaks()` function for direct break computation, and a scikit-learn-style `JenksNaturalBreaks` class for fitting, prediction, and grouping workflows.
The library is built as a C extension and ships with pre-compiled wheels for major platforms and Python versions, eliminating compilation for most users. It depends only on numpy and is marked Production/Stable with MIT licensing, making it suitable for integration into data analysis and geospatial classification pipelines.
Use it for
- Classify geographic or demographic data into natural groups for choropleth mapping or statistical analysis.
- Discretize continuous variables into optimal bins for machine learning feature engineering.
- Identify natural thresholds in sensor or time-series data for alerting or segmentation.
- Group numerical measurements into categories with minimal within-group variation for reporting.
- Partition income, age, or other population data into statistically meaningful brackets.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Jenkspy is a stable, production-ready library with no known vulnerabilities, permissive MIT licensing, and solid adoption. Install friction is moderate due to C compilation, but wheels eliminate this for most users. Use it if you need deterministic, optimal class boundaries for numerical data.
Install
jenkspy on PyPI
Before you install
Medium install friction due to C extension compilation, but wheels are provided for Windows, macOS, and Linux across multiple Python versions. Repository is aging but stable and actively maintained with recent commit activity.
Requires numpy as a runtime dependency; C compiler needed only when building from source.
License in practice
MIT License permits unrestricted use, modification, and distribution with minimal restrictions—suitable for commercial and open-source projects alike.
Quickstart
import jenkspy
import numpy as np
data = [0.0, 1.5, 2.3, 5.1, 6.8, 9.2]
breaks = jenkspy.jenks_breaks(data, n_classes=3)
print(breaks) # [0.0, 2.3, 6.8, 9.2]Verify before relying
- Performance characteristics (speed relative to other implementations) not quantified in fact sheet.
- Exact support status for Python 3.7–3.9—wheels shown for 3.10+ only, though classifiers list earlier versions.
- Whether the package remains actively developed or is in maintenance-only mode given 802-day release gap.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagenumpy |
| Maintenance | Aging 802 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 207,474 / month, #9,550 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/StableLicense :: OSI Approved :: MIT LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTyping :: Typed |
Evidence: jenkspy-0.4.1-cp310-cp310-macosx_10_9_x86_64.whl; jenkspy-0.4.1-cp310-cp310-macosx_11_0_arm64.whl; jenkspy-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; jenkspy-0.4.1-cp310-cp310-win_amd64.whl; jenkspy-0.4.1-cp311-cp311-macosx_10_9_x86_64.whl; jenkspy-0.4.1-cp311-cp311-macosx_11_0_arm64.whl; jenkspy-0.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; jenkspy-0.4.1-cp311-cp311-win_amd64.whl; jenkspy-0.4.1-cp312-cp312-macosx_10_9_x86_64.whl; jenkspy-0.4.1-cp312-cp312-macosx_11_0_arm64.whl; jenkspy-0.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; jenkspy-0.4.1-cp312-cp312-win_amd64.whl; jenkspy-0.4.1-cp313-cp313-macosx_10_13_x86_64.whl; jenkspy-0.4.1-cp313-cp313-macosx_11_0_arm64.whl; jenkspy-0.4.1-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl; jenkspy-0.4.1-cp313-cp313-win_amd64.whl; jenkspy-0.4.1-cp314-cp314-macosx_10_15_x86_64.whl; jenkspy-0.4.1-cp314-cp314-macosx_11_0_arm64.whl; jenkspy-0.4.1-cp314-cp314-manylinux2014_x86_64.manylinux_2_17_x86_64.whl; jenkspy-0.4.1-cp314-cp314t-macosx_10_15_x86_64.whl
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