jenkspy

library0.4.1pypypi✓ verified 82d ago

Jenkspy is a Python library providing a fast implementation of the Fisher-Jenks algorithm for computing 'natural breaks'. It's designed for 1-dimensional clustering on lists, tuples, arrays, or NumPy ndarrays of integers/floats to determine optimal class boundaries. Widely used in cartography and data analysis, the library is currently at version 0.4.1 and is actively maintained with recent updates.

data

pip install jenkspy

github ↗package ↗changelog ↗

INSTALL

IMPORT

SIG · JENKSPY

jenkspy

datapythonv0.4.1

Install

3.7s avg

Import

270ms

Disk

89MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.4.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.920 runs

build_error

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 3.7s · import 0.270s · 88MB

89MB installed

● package 89MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

jenks_breaks

✓ from jenkspy import jenks_breaks

✗ import jenkspy

This quickstart demonstrates both the functional (`jenks_breaks`) and object-oriented (`JenksNaturalBreaks`) ways to compute natural breaks. The functional approach directly returns the break points, while the class-based API provides a scikit-learn-like interface with `fit` and `groups_` methods for more complex workflows.

import jenkspy import random # Generate some sample data data = [random.uniform(0, 100) for _ in range(100)] # Compute natural breaks with 5 classes using the function API breaks_func = jenkspy.jenks_breaks(data, n_classes=5) print(f"Jenks breaks (function API): {breaks_func}") # Alternatively, use the scikit-learn inspired class API from jenkspy import JenksNaturalBreaks classifier = JenksNaturalBreaks(n_classes=5) classifier.fit(data) # Get the breaks and group labels breaks_class = classifier.breaks_ groups = classifier.groups_ # Groups elements into corresponding class indices print(f"Jenks breaks (class API): {breaks_class}") print(f"First 10 group labels: {groups[:10]}")

Debug

Known issues

breakingThe `nb_class` parameter for `jenks_breaks` was renamed to `n_classes` in version 0.3.0 to align with scikit-learn conventions. Using `nb_class` in newer versions will raise an error.

fix

Replace `nb_class` with `n_classes` in your function calls. E.g., `jenkspy.jenks_breaks(data, n_classes=5)`.

affects: >=0.3.0

breakingNumPy became a mandatory dependency starting from version 0.3.0. Installations without NumPy will fail or raise import errors.

fix

Ensure `numpy` is installed: `pip install numpy`.

affects: >=0.3.0

breakingAttempting to compute breaks on data containing non-finite values (NaN, Inf) or a non-one-dimensional NumPy array will now raise an error instead of a warning (since 0.2.3).

fix

Filter out or handle non-finite values in your data, and ensure NumPy arrays are 1-dimensional before passing them to `jenkspy` functions or classes.

affects: >=0.2.3

gotchaIf the requested `n_classes` is greater than the number of unique values in the input data, `jenkspy` will raise an exception (since 0.4.1).

fix

Ensure that `n_classes` is less than or equal to the number of unique values in your dataset. Consider pre-processing your data to count unique elements.

affects: >=0.4.1

Upgrade

Version history

0.4.1latest on PyPI · released Jun 3, 2024

Audit

Dependencies

numpyrequiredMandatory dependency for numerical operations, required since version 0.3.0.

Agent activity

4 hits · last 30 days

node

Resources

githubhttps://github.com/mthh/jenkspy ↗

packagehttps://pypi.org/project/jenkspy/ ↗

changeloghttps://github.com/mthh/jenkspy/blob/master/CHANGES.rst ↗