scikit-fuzzy
Fuzzy logic toolkit for SciPy
Decision gist · record as of 2026-08-14
Yes, if you need fuzzy logic algorithms and can tolerate dormant maintenance. The package is stable, has no known vulnerabilities, installs with low friction, and serves a specific niche well. However, verify the license terms first, confirm that its dependencies meet your requirements, and be aware that long-term compatibility with future Python versions is uncertain given the maintenance gap.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Matplotlib >= 3.1, NumPy >= 1.6, SciPy >= 0.9, and NetworkX >= 1.9; these are documented as dependencies but not declared in package metadata.
- Installation is straightforward with low friction—the package is a pure Python wheel with no compiled dependencies.
- However, maintenance is dormant: the last release was 722 days ago, and while the repository remains active with recent commits, the long gap between releases suggests limited ongoing development.
License · maintenance · safety
(unclear) — License treatment is unclear—the package metadata does not specify an SPDX identifier or license string, only a reference to LICENSE.txt in the repository. You should verify the actual license terms before adopting this package in a commercial or restricted context.
last release 2024-08-22 (722 days) · last repo commit 2024-08-25 · 876 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 211,123 downloads/mo, #9,483 on PyPI
Alternatives
Verify before relying
pip install scikit-fuzzy
import scikit_fuzzy
# Fuzzy logic operations available through the package- Whether the package's documented dependencies (NumPy, SciPy, Matplotlib, NetworkX) are enforced by pip or must be installed manually.
- What the actual license is—LICENSE.txt exists in the repository but is not reflected in package metadata.
- Whether the dormant maintenance status will affect compatibility with future Python or dependency versions.
- What specific fuzzy logic algorithms and operations the package provides beyond the general toolkit description.
What it is and what it does
scikit-fuzzy is a toolkit for fuzzy logic operations in Python, providing algorithms for fuzzy sets, membership functions, and fuzzy inference systems. It is designed as an extension to the SciPy ecosystem, offering researchers and engineers a way to implement fuzzy logic approaches in scientific computing without relying on closed-source alternatives.
The package installs as a pure Python wheel with no compiled dependencies, making setup simple. However, the package is in dormant maintenance—the last release was 722 days ago—so while it remains functional, you should expect limited support for new Python versions or dependency updates.
Use it for
- Implement fuzzy control systems for applications like temperature regulation or motor speed control.
- Perform fuzzy clustering and pattern recognition on datasets where membership is gradual rather than binary.
- Build fuzzy inference engines for decision-making systems that reason under uncertainty.
- Prototype fuzzy logic algorithms for research or academic work in computational intelligence.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need fuzzy logic algorithms and can tolerate dormant maintenance.
The package is stable, has no known vulnerabilities, installs with low friction, and serves a specific niche well. However, verify the license terms first, confirm that its dependencies meet your requirements, and be aware that long-term compatibility with future Python versions is uncertain given the maintenance gap.
Install
scikit-fuzzy on PyPI
Before you install
Installation is straightforward with low friction—the package is a pure Python wheel with no compiled dependencies. However, maintenance is dormant: the last release was 722 days ago, and while the repository remains active with recent commits, the long gap between releases suggests limited ongoing development.
Requires Matplotlib >= 3.1, NumPy >= 1.6, SciPy >= 0.9, and NetworkX >= 1.9; these are documented as dependencies but not declared in package metadata.
License in practice
License treatment is unclear—the package metadata does not specify an SPDX identifier or license string, only a reference to LICENSE.txt in the repository. You should verify the actual license terms before adopting this package in a commercial or restricted context.
Quickstart
pip install scikit-fuzzy
import scikit_fuzzy
# Fuzzy logic operations available through the packageVerify before relying
- Whether the package's documented dependencies (NumPy, SciPy, Matplotlib, NetworkX) are enforced by pip or must be installed manually.
- What the actual license is—LICENSE.txt exists in the repository but is not reflected in package metadata.
- Whether the dormant maintenance status will affect compatibility with future Python or dependency versions.
- What specific fuzzy logic algorithms and operations the package provides beyond the general toolkit description.
Package facts
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Dormant 722 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 211,123 / month, #9,483 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: scikit_fuzzy-0.5.0-py2.py3-none-any.whl
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