kmodes
Python implementations of the k-modes and k-prototypes clustering algorithms for clustering categorical data.
Decision gist · record as of 2026-08-14
Yes, if you need to cluster categorical or mixed-type data and want a scikit-learn-compatible interface. The low install friction and permissive license make it a straightforward choice. However, the dormant maintenance status (last release 2022-09-06) warrants checking compatibility with your current numpy and scikit-learn versions before committing to production use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Input data must have consistent data types within columns; NaN values are not accepted and must be handled before clustering.
- Low friction install with standard dependencies (numpy, scikit-learn, scipy, joblib).
- Maintenance is dormant—last release was 2022-09-06, though the repository remains active with recent commits and no archived status.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions; you must include the license text in distributions.
last release 2022-09-06 (1438 days) · last repo commit 2024-06-19 · 1,285 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 561,891 downloads/mo, #5,992 on PyPI
Alternatives
Verify before relying
pip install kmodes
import numpy as np
from kmodes.kmodes import KModes
data = np.random.choice(20, (100, 10))
km = KModes(n_clusters=4, init='Huang', n_init=5, verbose=1)
clusters = km.fit_predict(data)- Whether the dormant maintenance status affects compatibility with current numpy/scikit-learn versions.
- Performance characteristics and scalability limits for large datasets relative to alternatives.
What it is and what it does
kmodes provides Python implementations of k-modes and k-prototypes clustering, specialized for categorical and mixed data types. Unlike k-means, which clusters numerical data by Euclidean distance, k-modes clusters categorical variables by counting matching categories between points. k-prototypes extends this to handle datasets with both numerical and categorical features. The library mirrors scikit-learn's clustering API, making it familiar to users of that ecosystem.
The package relies on numpy for computation and supports parallel execution via joblib for multiple initialization runs. It includes initialization strategies such as Huang's method and density-based approaches. All runtime dependencies (numpy, scikit-learn, scipy, joblib) are standard data-science libraries with low installation friction.
Use it for
- Cluster customer records with mixed demographic (categorical) and behavioral (numerical) attributes.
- Segment product categories or survey responses where most features are discrete or nominal.
- Analyze categorical genomic or medical data where Euclidean distance is not meaningful.
- Reduce dimensionality or find patterns in text-encoded or one-hot-encoded feature sets.
- Benchmark or compare k-modes results against k-means on datasets with predominantly categorical features.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to cluster categorical or mixed-type data and want a scikit-learn-compatible interface.
The low install friction and permissive license make it a straightforward choice. However, the dormant maintenance status (last release 2022-09-06) warrants checking compatibility with your current numpy and scikit-learn versions before committing to production use.
Install
kmodes on PyPI
Before you install
Low friction install with standard dependencies (numpy, scikit-learn, scipy, joblib). Maintenance is dormant—last release was 2022-09-06, though the repository remains active with recent commits and no archived status.
Input data must have consistent data types within columns; NaN values are not accepted and must be handled before clustering.
License in practice
MIT license permits commercial and private use with minimal restrictions; you must include the license text in distributions.
Quickstart
pip install kmodes
import numpy as np
from kmodes.kmodes import KModes
data = np.random.choice(20, (100, 10))
km = KModes(n_clusters=4, init='Huang', n_init=5, verbose=1)
clusters = km.fit_predict(data)Verify before relying
- Whether the dormant maintenance status affects compatibility with current numpy/scikit-learn versions.
- Performance characteristics and scalability limits for large datasets relative to alternatives.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumpyscikit-learnscipyjoblib |
| Maintenance | Dormant 1,438 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 561,891 / month, #5,992 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering |
Evidence: kmodes-0.12.2-py2.py3-none-any.whl
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