---
id: scikit-misc
version: "0.5.2"
license: Copyright (c) 2016, Hassan Kibirige All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:… (full text in the JSON record)
license_treatment: permissive
maintenance: aging
---

# scikit-misc — Miscellaneous tools for scientific computing.

License: permissive · Maintenance: aging · Downloads: 167.6K/mo

## What it is and what it does

scikit-misc is a collection of utility functions for scientific computing and data analysis, written in Python with performance-critical sections implemented in C and Fortran. It depends only on numpy and targets researchers and engineers who need supplementary tools beyond what numpy alone provides. The package is distributed as pre-compiled wheels for modern Python versions (3.10–3.14) on macOS, Windows, and Linux, reducing installation friction for most users.

The package has been in development since 2016 and maintains broad platform support. Its aging maintenance status (last release 284 days ago) suggests it is stable but not actively evolving; it is suitable for projects that need a stable set of scientific utilities rather than rapid feature development.

Use it for:

- Add specialized scientific computing routines to numpy-based data analysis pipelines.
- Access compiled C/Fortran implementations of mathematical operations for performance-critical sections.
- Supplement numpy in research workflows that require miscellaneous utility functions.
- Build scientific applications on Linux, macOS, or Windows with pre-built binary wheels.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

scikit-misc provides miscellaneous tools for data analysis and scientific computing, built on numpy with compiled C and Fortran components.

Yes, if you need miscellaneous scientific computing utilities beyond numpy and are comfortable with a stable, aging package. The BSD license is permissive, wheels are widely available, and there are no known vulnerabilities. The 284-day gap since the last release suggests the package is mature but not under active development; install it for stable functionality, not for ongoing feature additions.

## Install

pip install scikit-misc
uv add scikit-misc
poetry add scikit-misc

## Installing scikit-misc

Before you install: Medium install friction due to compiled components (C and Fortran); wheels are available for Python 3.10–3.14 on macOS, Windows, and Linux. Last release was 284 days ago; the repository is active but maintenance appears to be slowing.

License in practice: BSD license (permissive); you may use, modify, and distribute the package freely in commercial and private projects, provided you retain the copyright notice and disclaimer.

Quickstart:

pip install scikit-misc
import numpy as np
# Use scikit-misc functions alongside numpy for scientific tasks

Requires Python >=3.10; compiled wheels are provided for common platforms, but source builds require a C and Fortran compiler.

Verify before relying:

- What specific scientific computing functions does scikit-misc provide beyond its general description?
- Are there known limitations or edge cases in the compiled routines?
- What is the typical performance profile compared to other scientific libraries?

## Package facts

- License: Copyright (c) 2016, Hassan Kibirige All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:… (full text in the JSON record) (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: aging
- Downloads: 167.6K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

scientific computing utilities, data analysis tools, miscellaneous scientific functions, numpy-based scientific library, computational tools for research, scientific-computing, numpy-based

[View on SkillFed](https://skillfed.io/packages/scikit-misc) · [View on PyPI](https://pypi.org/project/scikit-misc/)